From af030890ce9c434fc2f73c555825b27ed4fed63e Mon Sep 17 00:00:00 2001 From: Will Dampier Date: Tue, 17 Dec 2024 15:16:43 -0500 Subject: [PATCH] adding final modules --- _bblearn/Module02/Module02_lab.html | 11 + .../Module02_walkthrough_SOLUTION.html | 11 + _bblearn/Module03/Module03_lab.html | 11 + .../Module03_walkthrough_SOLUTION.html | 11 + _bblearn/Module04/Module04_lab.html | 11 + .../Module04_walkthrough_SOLUTION.html | 11 + _bblearn/Module05/Module05_lab.html | 11 + .../Module05_walkthrough_SOLUTION.html | 11 + _bblearn/Module06/Module06_lab.html | 11 + .../Module06_walkthrough_SOLUTION.html | 11 + _bblearn/Module07/Module07_lab.html | 11 + .../Module07_walkthrough_SOLUTION.html | 11 + _bblearn/Module08/Module08_lab.html | 21 + .../Module08_walkthrough_SOLUTION.html | 11 + _bblearn/Module09/Module09_lab.html | 889 ++++++ .../Module09_walkthrough_SOLUTION.html | 2421 ++++++++++++++ _bblearn/Module10/Module10_lab.html | 896 ++++++ .../Module10_walkthrough_SOLUTION.html | 1079 +++++++ ...f7ca4b28c5ebf2cdc409c3a45d03f77d0894fd.png | Bin 0 -> 28709 bytes ...c65492e76fd1439a2a3dba97a4f3c455089c66.png | Bin 0 -> 22261 bytes ...6f5ac44541346eb5280506013babc5f94e10c5.png | Bin 0 -> 15708 bytes ...ff3e293dd48346f402f17abd24a3d665de5dd4.png | Bin 0 -> 12027 bytes ...1872d87809ea377f616435b36ea039e6483d76.png | Bin 0 -> 9700 bytes ...cc9252547d77e54cd65e82ec77a20b766c9b01.png | Bin 0 -> 109275 bytes ...3fc9ceda3f8dfa0ced0b51ac6956d90ee93d8c.png | Bin 0 -> 28728 bytes ...a3b6ca2207a1e9d75c7df0ed0f4c254d2dea75.png | Bin 0 -> 50047 bytes ...28467fc3829aed099dfc6dc089c00f7179de26.png | Bin 0 -> 22890 bytes ...cf23598f86a4f9a5b5d1ce4ed57891ab0ab3c0.png | Bin 0 -> 44514 bytes ...386250feb595751b98f447d4e3e7805df7b2ae.png | Bin 0 -> 19242 bytes ...daced8362dcaec2d1f6c493ef8beb5185fb658.png | Bin 0 -> 58024 bytes ...ea8bfde335652b9009e02581d8c2473ca15dbd.png | Bin 0 -> 15336 bytes ...4701866c2026c7b55b054e58d5e8ff7dc1cdda.png | Bin 0 -> 29738 bytes _sources/_bblearn/Module09/Module09_lab.ipynb | 578 ++++ .../Module09_walkthrough_SOLUTION.ipynb | 2841 +++++++++++++++++ _sources/_bblearn/Module10/Module10_lab.ipynb | 616 ++++ .../Module10_walkthrough_SOLUTION.ipynb | 1026 ++++++ _sources/content/Module09/Module09_book.md | 3 + _sources/content/Module10/Module10_book.md | 3 + content/Module01/Module01_book.html | 11 + content/Module01/Module01_walkthrough.html | 11 + content/Module01/notebook_actions.html | 11 + content/Module02/Module02_book.html | 11 + content/Module02/dilution_calculations.html | 11 + content/Module02/nanopore_description.html | 11 + content/Module03/Module03_book.html | 11 + content/Module04/Module04_book.html | 11 + content/Module05/Module05_book.html | 11 + content/Module06/Module06_book.html | 11 + content/Module06/grammar_of_graphics.html | 11 + content/Module07/Module07_book.html | 11 + .../common_biological_distributions.html | 11 + content/Module08/Module08_book.html | 11 + content/Module09/Module09_book.html | 517 +++ content/Module10/Module10_book.html | 515 +++ content/book_index.html | 13 + content/misc/about_this_book.html | 11 + content/misc/book_intro.html | 11 + genindex.html | 11 + ...f7ca4b28c5ebf2cdc409c3a45d03f77d0894fd.png | Bin 0 -> 28709 bytes ...c65492e76fd1439a2a3dba97a4f3c455089c66.png | Bin 0 -> 22261 bytes ...6f5ac44541346eb5280506013babc5f94e10c5.png | Bin 0 -> 15708 bytes ...ff3e293dd48346f402f17abd24a3d665de5dd4.png | Bin 0 -> 12027 bytes ...1872d87809ea377f616435b36ea039e6483d76.png | Bin 0 -> 9700 bytes ...cc9252547d77e54cd65e82ec77a20b766c9b01.png | Bin 0 -> 109275 bytes ...3fc9ceda3f8dfa0ced0b51ac6956d90ee93d8c.png | Bin 0 -> 28728 bytes ...a3b6ca2207a1e9d75c7df0ed0f4c254d2dea75.png | Bin 0 -> 50047 bytes ...28467fc3829aed099dfc6dc089c00f7179de26.png | Bin 0 -> 22890 bytes .../_bblearn/Module09/Module09_lab.ipynb | 578 ++++ .../Module09_walkthrough_SOLUTION.ipynb | 2841 +++++++++++++++++ .../_bblearn/Module10/Module10_lab.ipynb | 616 ++++ .../Module10_walkthrough_SOLUTION.ipynb | 1026 ++++++ ...cf23598f86a4f9a5b5d1ce4ed57891ab0ab3c0.png | Bin 0 -> 44514 bytes ...386250feb595751b98f447d4e3e7805df7b2ae.png | Bin 0 -> 19242 bytes ...daced8362dcaec2d1f6c493ef8beb5185fb658.png | Bin 0 -> 58024 bytes ...ea8bfde335652b9009e02581d8c2473ca15dbd.png | Bin 0 -> 15336 bytes ...4701866c2026c7b55b054e58d5e8ff7dc1cdda.png | Bin 0 -> 29738 bytes objects.inv | Bin 804 -> 881 bytes search.html | 11 + searchindex.js | 2 +- 79 files changed, 16821 insertions(+), 1 deletion(-) create mode 100644 _bblearn/Module09/Module09_lab.html create mode 100644 _bblearn/Module09/Module09_walkthrough_SOLUTION.html create mode 100644 _bblearn/Module10/Module10_lab.html create mode 100644 _bblearn/Module10/Module10_walkthrough_SOLUTION.html create mode 100644 _images/2aea3ce208391390ec8f00592ef7ca4b28c5ebf2cdc409c3a45d03f77d0894fd.png create mode 100644 _images/398ace28cb7992fdeceb81ba3fc65492e76fd1439a2a3dba97a4f3c455089c66.png create mode 100644 _images/46389da22a7519abc032d7ae286f5ac44541346eb5280506013babc5f94e10c5.png create mode 100644 _images/637d07d5070fdc67fe705200fbff3e293dd48346f402f17abd24a3d665de5dd4.png create mode 100644 _images/6ea10fd9420b437a042b88ab6c1872d87809ea377f616435b36ea039e6483d76.png create mode 100644 _images/89ac3ff550cfae1dd4a04454b3cc9252547d77e54cd65e82ec77a20b766c9b01.png create mode 100644 _images/92b7b21e6c8b368939a237b44b3fc9ceda3f8dfa0ced0b51ac6956d90ee93d8c.png create mode 100644 _images/969965f6122227500606d8eb50a3b6ca2207a1e9d75c7df0ed0f4c254d2dea75.png create mode 100644 _images/9b726382c20a511fab08e520fc28467fc3829aed099dfc6dc089c00f7179de26.png create mode 100644 _images/a4cbf376070178b287ef42066dcf23598f86a4f9a5b5d1ce4ed57891ab0ab3c0.png create mode 100644 _images/b45cdc82a1a5c002e3fce8ba4f386250feb595751b98f447d4e3e7805df7b2ae.png create mode 100644 _images/c3dfa0baf557f75c479b9252f5daced8362dcaec2d1f6c493ef8beb5185fb658.png create mode 100644 _images/ca8e38a39fba588d9c549b9d9bea8bfde335652b9009e02581d8c2473ca15dbd.png create mode 100644 _images/fc6c9263b60697d1ffe4675baf4701866c2026c7b55b054e58d5e8ff7dc1cdda.png create mode 100644 _sources/_bblearn/Module09/Module09_lab.ipynb create mode 100644 _sources/_bblearn/Module09/Module09_walkthrough_SOLUTION.ipynb create mode 100644 _sources/_bblearn/Module10/Module10_lab.ipynb create mode 100644 _sources/_bblearn/Module10/Module10_walkthrough_SOLUTION.ipynb create mode 100644 _sources/content/Module09/Module09_book.md create mode 100644 _sources/content/Module10/Module10_book.md create mode 100644 content/Module09/Module09_book.html create mode 100644 content/Module10/Module10_book.html create mode 100644 jupyter_execute/2aea3ce208391390ec8f00592ef7ca4b28c5ebf2cdc409c3a45d03f77d0894fd.png create mode 100644 jupyter_execute/398ace28cb7992fdeceb81ba3fc65492e76fd1439a2a3dba97a4f3c455089c66.png create mode 100644 jupyter_execute/46389da22a7519abc032d7ae286f5ac44541346eb5280506013babc5f94e10c5.png create mode 100644 jupyter_execute/637d07d5070fdc67fe705200fbff3e293dd48346f402f17abd24a3d665de5dd4.png create mode 100644 jupyter_execute/6ea10fd9420b437a042b88ab6c1872d87809ea377f616435b36ea039e6483d76.png create mode 100644 jupyter_execute/89ac3ff550cfae1dd4a04454b3cc9252547d77e54cd65e82ec77a20b766c9b01.png create mode 100644 jupyter_execute/92b7b21e6c8b368939a237b44b3fc9ceda3f8dfa0ced0b51ac6956d90ee93d8c.png create mode 100644 jupyter_execute/969965f6122227500606d8eb50a3b6ca2207a1e9d75c7df0ed0f4c254d2dea75.png create mode 100644 jupyter_execute/9b726382c20a511fab08e520fc28467fc3829aed099dfc6dc089c00f7179de26.png create mode 100644 jupyter_execute/_bblearn/Module09/Module09_lab.ipynb create mode 100644 jupyter_execute/_bblearn/Module09/Module09_walkthrough_SOLUTION.ipynb create mode 100644 jupyter_execute/_bblearn/Module10/Module10_lab.ipynb create mode 100644 jupyter_execute/_bblearn/Module10/Module10_walkthrough_SOLUTION.ipynb create mode 100644 jupyter_execute/a4cbf376070178b287ef42066dcf23598f86a4f9a5b5d1ce4ed57891ab0ab3c0.png create mode 100644 jupyter_execute/b45cdc82a1a5c002e3fce8ba4f386250feb595751b98f447d4e3e7805df7b2ae.png create mode 100644 jupyter_execute/c3dfa0baf557f75c479b9252f5daced8362dcaec2d1f6c493ef8beb5185fb658.png create mode 100644 jupyter_execute/ca8e38a39fba588d9c549b9d9bea8bfde335652b9009e02581d8c2473ca15dbd.png create mode 100644 jupyter_execute/fc6c9263b60697d1ffe4675baf4701866c2026c7b55b054e58d5e8ff7dc1cdda.png diff --git a/_bblearn/Module02/Module02_lab.html b/_bblearn/Module02/Module02_lab.html index a0c1d40..6d1cc89 100644 --- a/_bblearn/Module02/Module02_lab.html +++ b/_bblearn/Module02/Module02_lab.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module02/Module02_walkthrough_SOLUTION.html b/_bblearn/Module02/Module02_walkthrough_SOLUTION.html index 8a4e6d6..5a55389 100644 --- a/_bblearn/Module02/Module02_walkthrough_SOLUTION.html +++ b/_bblearn/Module02/Module02_walkthrough_SOLUTION.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module03/Module03_lab.html b/_bblearn/Module03/Module03_lab.html index 323a5e8..5f5b6f5 100644 --- a/_bblearn/Module03/Module03_lab.html +++ b/_bblearn/Module03/Module03_lab.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module03/Module03_walkthrough_SOLUTION.html b/_bblearn/Module03/Module03_walkthrough_SOLUTION.html index 7602000..4d56066 100644 --- a/_bblearn/Module03/Module03_walkthrough_SOLUTION.html +++ b/_bblearn/Module03/Module03_walkthrough_SOLUTION.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module04/Module04_lab.html b/_bblearn/Module04/Module04_lab.html index d19870c..317da60 100644 --- a/_bblearn/Module04/Module04_lab.html +++ b/_bblearn/Module04/Module04_lab.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module04/Module04_walkthrough_SOLUTION.html b/_bblearn/Module04/Module04_walkthrough_SOLUTION.html index f37cb03..dedcd75 100644 --- a/_bblearn/Module04/Module04_walkthrough_SOLUTION.html +++ b/_bblearn/Module04/Module04_walkthrough_SOLUTION.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module05/Module05_lab.html b/_bblearn/Module05/Module05_lab.html index 8473608..0fb80ea 100644 --- a/_bblearn/Module05/Module05_lab.html +++ b/_bblearn/Module05/Module05_lab.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module05/Module05_walkthrough_SOLUTION.html b/_bblearn/Module05/Module05_walkthrough_SOLUTION.html index 8b19329..24a0874 100644 --- a/_bblearn/Module05/Module05_walkthrough_SOLUTION.html +++ b/_bblearn/Module05/Module05_walkthrough_SOLUTION.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module06/Module06_lab.html b/_bblearn/Module06/Module06_lab.html index 0f611a9..952ed3d 100644 --- a/_bblearn/Module06/Module06_lab.html +++ b/_bblearn/Module06/Module06_lab.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module06/Module06_walkthrough_SOLUTION.html b/_bblearn/Module06/Module06_walkthrough_SOLUTION.html index 6d4da26..c1b3af5 100644 --- a/_bblearn/Module06/Module06_walkthrough_SOLUTION.html +++ b/_bblearn/Module06/Module06_walkthrough_SOLUTION.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module07/Module07_lab.html b/_bblearn/Module07/Module07_lab.html index cdd7f7d..bd0d86e 100644 --- a/_bblearn/Module07/Module07_lab.html +++ b/_bblearn/Module07/Module07_lab.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module07/Module07_walkthrough_SOLUTION.html b/_bblearn/Module07/Module07_walkthrough_SOLUTION.html index e142bde..b1ffdc6 100644 --- a/_bblearn/Module07/Module07_walkthrough_SOLUTION.html +++ b/_bblearn/Module07/Module07_walkthrough_SOLUTION.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module08/Module08_lab.html b/_bblearn/Module08/Module08_lab.html index ecb870a..d0a19e2 100644 --- a/_bblearn/Module08/Module08_lab.html +++ b/_bblearn/Module08/Module08_lab.html @@ -62,6 +62,7 @@ + @@ -235,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • @@ -740,6 +752,15 @@

    SubmissionWalkthrough

     + +
    +

    next

    +

    Module 9: Linear Regression

    +
    + +     diff --git a/_bblearn/Module08/Module08_walkthrough_SOLUTION.html b/_bblearn/Module08/Module08_walkthrough_SOLUTION.html index 1ea90ca..ad43f3e 100644 --- a/_bblearn/Module08/Module08_walkthrough_SOLUTION.html +++ b/_bblearn/Module08/Module08_walkthrough_SOLUTION.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/_bblearn/Module09/Module09_lab.html b/_bblearn/Module09/Module09_lab.html new file mode 100644 index 0000000..a5dc354 --- /dev/null +++ b/_bblearn/Module09/Module09_lab.html @@ -0,0 +1,889 @@ + + + + + + + + + + + Lab — Quantitative Reasoning in Biology + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + +
    +
    +
    +
    + + + Ctrl+K +
    +
    + + +
    +
    Work in progress!
    +
    + + +
    +
    + + +
    +
    + + + +
    + + + +
    + + + + +
    + +
    +
    + + + +
    +
    + +
    +
    +
    + + +
    +
    + + + + +
    + +
    + + + +
    + +
    +
    + +
    +
    + +
    + +
    + +
    + + +
    + +
    + +
    + + + + + +
    + + +
    + + + + + + + + + + + + + +
    + +
    + +
    +
    + + + +
    +

    Lab

    + +
    + +
    +
    + + + + +
    + +
    +

    Lab#

    +
    +

    Learning Objectives#

    +

    At the end of this learning activity you will be able to:

    +
      +

    • Practice using robust correlation tools that account for outliers.

      • +

    • Practice using pg.qqplot and pg.normality to asses the normality of residuals.

      • +

    • Practice using regression to create covariate-controlled scores.

      • +
    +
    +
    +
    import numpy as np
+import seaborn as sns
+import pandas as pd
+import matplotlib.pyplot as plt
+
+import pingouin as pg
+
+%matplotlib inline
+
    +
    +
    +
    +
    +
    +
    data = pd.read_csv('hiv_neuro_data.csv')
+data['education'] = data['education'].astype(float)
+data.head()
+
    +
    +
    +
    +

    This lab is going to explore the inter-relationships between two cognitive domains.

    +
      +

    • Executive Function: The complex cognitive processes required for planning, organizing, problem-solving, abstract thinking, and executing strategies. This domain also encompasses decision-making and cognitive flexibility, which is the ability to switch between thinking about two different concepts or to think about multiple concepts simultaneously.

      • +
    +
      +

    • Speed of Information Processing: How quickly an individual can understand and react to the information being presented. This domain evaluates the speed at which cognitive tasks can be performed, often under time constraints.

      • +
    +

    We will explore whether these two domains are correllated after controlling for co-variates.

    +
    +

    Q1: Are Processing domain and Executive domain scores correlated?#

    + + + + + + + + + + + + + + + + + +

    Points

    5

    Public Checks

    3

    Hidden Tests

    1

    +

    Points: 5

    +
    +
    +
    # Generate a plot between processing_domain_z and exec_domain_z
+
+q1_plot = ...
+
    +
    +
    +
    +
    +
    +
    # Use pg.corr to calculate the correlation between the two variables using a `robust` correlation metric
+
+q1_corr_res = ...
+
    +
    +
    +
    +
    +
    +
    # Are the two domains significantly correlated? 'yes' or 'no'
+
+q1_is_corr = ...
+
    +
    +
    +
    +
    +
    +
    grader.check("q1_domain_corr")
+
    +
    +
    +
    +
    +
    +

    Q2: Create a regression for the processing domain that accounts for demographic covariates.#

    +
      +

    • Age

      • +

    • Race

      • +

    • Sex

      • +

    • Education

      • +

    • Years Seropositive

      • +

    • ART

      • +
    + + + + + + + + + + + + + + + + + +

    Points

    10

    Public Checks

    7

    Hidden Tests

    7

    +

    Points: 10

    +
    +
    +
    # Perform the regression using `pg.linear_regression`
+# Use the result to answer the questions below
+
    +
    +
    +
    +
    +
    +
    # Assess the normality of the residuals of the model
+
+
+q2_model_resid_normal = ...
+
    +
    +
    +
    +
    +
    +
    # Considering a p<0.01 threshold answer which of the following are significant
+
+# Age
+q2_processing_age = ...
+
+# Race
+q2_processing_race = ...
+
+# Sex
+q2_processing_sex = ...
+
+# Education
+q2_processing_edu = ...
+
+# Infection length
+q2_processing_ys = ...
+
+# ART
+q2_processing_art = ...
+
    +
    +
    +
    +
    +
    +
    grader.check("q2_exec_adj")
+
    +
    +
    +
    +
    +
    +

    Q3: Is covariate controlled EDZ still correlated with PDZ?#

    + + + + + + + + + + + + + + + + + +

    Points

    10

    Public Checks

    7

    Hidden Tests

    7

    +

    Points: 10

    +
    +
    +
    # Generate a plot between covariate controlled processing_domain_z and exec_domain_z
+
+q3_plot = ...
+
    +
    +
    +
    +
    +
    +
    # Use pg.corr to calculate the correlation between the two variables using a `pearson` correlation metric
+
+q3_corr_res = ...
+q3_corr_res
+
    +
    +
    +
    +
    +
    +
    # Are processing_domain_z and covariate controlled exec_domain_z still correlated?
+q3_corr_sig = ...
+
+
+# Correlation r-value
+# Place the r-value here rounded to 4 decimal places
+q3_corr_r = ...
+
    +
    +
    +
    +
    +
    +
    # Partial correlation r-value
+# Place the r-value here rounded to 4 decimal places
+q3_partial_corr_r = ...
+
    +
    +
    +
    +
    +
    +
    # Are the results the same between the two methods? 'yes' or 'no'
+
+q3_same_res = ...
+
    +
    +
    +
    +
    +
    +
    grader.check("q3_partial_corr")
+
    +
    +
    +
    +

    We’ve seen from above that it is important to create processing_domain_z score corrected for covariates. +We also saw in the walkthrough that it is important create an exec_domain_z score corrected for covariates. +However, pg.partial_corr only allows you to correct for covariates in x or y but not both.

    +

    Use another regression to remove the covaraites from exec_domain_z and determine if it is correlated with processing_domain_z after removing covariates.

    +
    +
    +

    Q4: Are EDZ and PDZ correlated after controlling for covariates?#

    + + + + + + + + + + + + + + + + + +

    Points

    10

    Public Checks

    7

    Hidden Tests

    7

    +

    Points: 10

    +
    +
    +
    # Find the residuals for exec_domain_z after controlling for covariates
+
    +
    +
    +
    +
    +
    +
    # Plot the two corrected values against each other
+
+q4_plot = ...
+
    +
    +
    +
    +
    +
    +
    # Test the correlation between the two sets of corrected values
+
+pg.corr(proc_res.residuals_, exec_res.residuals_)
+
    +
    +
    +
    +
    +
    +
    # After correction for covariates, are PDZ and EDZ correlated? 'yes' or 'no'
+
+q4_sig_cor = ...
+
    +
    +
    +
    +
    +
    +
    grader.check("q4_full_corr")
+
    +
    +
    +
    +
    +
    +
    +
    grader.check_all()
+
    +
    +
    +
    +
    +
    +
    +

    Submission#

    +

    Check:

    +
      +

    • That all tables and graphs are rendered properly.

      • +

    • Code completes without errors by using Restart & Run All.

      • +

    • All checks pass.

      • +
    +

    Then save the notebook and the File -> Download -> Download .ipynb. Upload this file to BBLearn.

    +
    +
    + + + + +
    + + + + + + + + +
    + + + +
    + + +
    +
    + Contents +
    +
    + +
    + + +
    +
    + +
    + +
    + +

    +By Will Dampier +

    + +
    + +
    + + +

    + + © Copyright 2023. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + +
    +
    +
    + + + + + +
    +
    + + \ No newline at end of file diff --git a/_bblearn/Module09/Module09_walkthrough_SOLUTION.html b/_bblearn/Module09/Module09_walkthrough_SOLUTION.html new file mode 100644 index 0000000..92f7985 --- /dev/null +++ b/_bblearn/Module09/Module09_walkthrough_SOLUTION.html @@ -0,0 +1,2421 @@ + + + + + + + + + + + Walkthrough — Quantitative Reasoning in Biology + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + +
    +
    +
    +
    + + + Ctrl+K +
    +
    + + +
    +
    Work in progress!
    +
    + + +
    +
    + + +
    +
    + + + +
    + + + +
    + + + + +
    + +
    +
    + + + +
    +
    + +
    +
    +
    + + +
    +
    + + + + +
    + +
    + + + +
    + +
    +
    + +
    +
    + +
    + +
    + +
    + + +
    + +
    + +
    + + + + + +
    + + +
    + + + + + + + + + + + + + +
    + +
    + +
    +
    + + + +
    +

    Walkthrough

    + +
    + +
    +
    + + + + +
    + +
    +

    Walkthrough#

    +
    +

    Learning Objectives#

    +

    At the end of this learning activity you will be able to:

    +
      +

    • Practice using pg.normality and pg.qqplot to assess normality.

      • +

    • Practice using pg.linear_regression to perform multiple regression.

      • +

    • Interpret the results of linear regression such as the coefficient, p-value, R^2, and confidence intervals.

      • +

    • Describe a residual and how to interpret it.

      • +

    • Relate the dummy variable trap and how to avoid it during regression.

      • +

    • Describe overfitting and how to avoid it.

      • +
    +

    As we discussed with Dr. Devlin in the introduction video, this week and next we are going to look at HIV neurocognitive impairment data from a cohort here at Drexel. +Each person was given a full-scale neuropsychological exam and the resulting values were aggregated and normalized into Z-scores based on demographically matched healthy individuals.

    +

    In this walkthrough we will explore the effects of antiretroviral medications on neurological impairment. +In our cohort, we have two major drug regimens, d4T (Stavudine) and the newer Emtricitabine/tenofovir (Truvada). +The older Stavudine is suspected to have neurotoxic effects that are not found in the newer Truvada. +We will use inferential statistics to understand this effect.

    +
    +
    +
    import numpy as np
+import seaborn as sns
+import pandas as pd
+import matplotlib.pyplot as plt
+
+import pingouin as pg
+
+%matplotlib inline
+
    +
    +
    +
    +
    +
    +
    data = pd.read_csv('hiv_neuro_data.csv')
+data['education'] = data['education'].astype(float)
+data.head()
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    sexageeducationraceprocessing_domain_zexec_domain_zlanguage_domain_zvisuospatial_domain_zlearningmemory_domain_zmotor_domain_zARTYearsSeropositive
    0male6210.0AA0.50.60.151646-1.0-1.152131-1.364306Stavudine13
    1male5610.0AA-0.51.2-0.255505-2.0-0.086376-0.348600Truvada19
    2female5110.0AA0.50.10.902004-0.4-1.1398920.112215Stavudine9
    3female4712.0AA-0.6-1.2-0.119866-2.10.803619-2.276768Truvada24
    4male4613.0AA-0.41.30.079129-1.3-0.533607-0.330541Truvada14
    +
    +
    +

    Before we start, we need to talk about assumptions.

    +

    Basic linear regression has a number assumptions baked into itself:

    +
      +

    • Linearity: The relationship between the independent variables (predictors) and the dependent variable (outcome) is linear. This means that changes in the predictors lead to proportional changes in the dependent variable.

      • +

    • The relationship between the independent variables and the dependent variable is additive: The effect of changes in an independent variable X on the dependent variable Y is consistent, regardless of the values of other independent variables. This assumption might not hold if there are interaction effects between independent variables that affect the dependent variable.

      • +

    • Independence: Observations are independent of each other. This means that the observations do not influence each other, an assumption that is particularly important in time-series data where time-related dependencies can violate this assumption.

      • +

    • Homoscedasticity: The variance of error terms (residuals) is constant across all levels of the independent variables. In other words, as the predictor variable increases, the spread (variance) of the residuals remains constant. This is evaluated at the end of the fit.

      • +

    • Normal Distribution of Errors: The residuals (errors) of the model are normally distributed. This assumption is especially important for hypothesis testing (e.g., t-tests of coefficients) and confidence interval construction. It’s worth noting that for large sample sizes, the Central Limit Theorem helps mitigate the violation of this assumption. This is evaluated at the end of the fit.

      • +

    • Minimal Multicollinearity: The independent variables need to be independent of each other. Multicollinearity doesn’t affect the fit of the model as much as it affects the coefficients’ estimates, making them unstable and difficult to interpret.

      • +

    • No perfect multicollinearity: Also called the dummy variable trap. It states that none of the independent variables should be a perfect linear function of other independent variables. We’ll talk more about this when we run into it.

      • +
    +

    Biology itself is highly non-linear. +That doesn’t mean we can’t use linear assumptions to explore biological questions, it just means that we need to be mindful when interpretting the results.

    +
    +
    +

    Exploration#

    +

    Let’s start by plotting the each variable against EDZ.

    +
    +
    +
    fig, (age_ax, edu_ax, ys_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))
+
+sns.regplot(data = data,
+            x = 'age',
+            y = 'exec_domain_z',
+            ax=age_ax)
+
+sns.regplot(data = data,
+            x = 'education',
+            y = 'exec_domain_z',
+            ax=edu_ax)
+
+sns.regplot(data = data,
+            x = 'YearsSeropositive',
+            y = 'exec_domain_z',
+            ax=ys_ax)
+
+fig.tight_layout()
+
    +
    +
    +
    +../../_images/89ac3ff550cfae1dd4a04454b3cc9252547d77e54cd65e82ec77a20b766c9b01.png +
    +
    +
    +

    Q1: By inspection, which variable is most correlated?#

    + + + + + + + + + + + + + + +

    Points

    5

    Public Checks

    3

    +

    Points: 5

    +
    +
    +
    # Answer: age, education, YearsSeropositive
+q1_most_correlated = 'YearsSeropositive' # SOLUTION
+
    +
    +
    +
    +
    +
    +
    grader.check("q1_initial_correlation")
+
    +
    +
    +
    +
    +
    +
    fig, (race_ax, sex_ax, art_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))
+
+sns.stripplot(data=data,
+            x = 'race',
+            y = 'exec_domain_z', ax=race_ax)
+sns.boxplot(data=data,
+            x = 'race',
+            y = 'exec_domain_z', ax=race_ax)
+
+sns.stripplot(data=data,
+            x = 'sex',
+            y = 'exec_domain_z', ax=sex_ax)
+sns.boxplot(data=data,
+            x = 'sex',
+            y = 'exec_domain_z', ax=sex_ax)
+
+sns.stripplot(data=data,
+            x = 'ART',
+            y = 'exec_domain_z', ax=art_ax)
+sns.boxplot(data=data,
+            x = 'ART',
+            y = 'exec_domain_z', ax=art_ax)
+
    +
    +
    +
    +
    <Axes: xlabel='ART', ylabel='exec_domain_z'>
+
    +
    +../../_images/969965f6122227500606d8eb50a3b6ca2207a1e9d75c7df0ed0f4c254d2dea75.png +
    +
    +
    +
    +

    Q2: By inspection, which variable has the most between class difference?#

    + + + + + + + + + + + + + + +

    Points

    5

    Public Checks

    3

    +

    Points: 5

    +
    +
    +
    # Answer: race, sex, ART
+q2_most_bcd = 'race' # SOLUTION
+
    +
    +
    +
    +
    +
    +
    grader.check("q2_initial_bcd")
+
    +
    +
    +
    +
    +
    +
    +

    Basic regression#

    +

    We’ll start by taking the simplest approach and regress the most correlated value first.

    +

    pg.linear_regression works by regressing all columns in the first parameter against the single column in the second. +By convention, we usually use the variables X and y.

    +

    You’ll often see this written as:

    +

    \(\mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\epsilon}\)

    +

    In the case of pg.linear_regression the \(\boldsymbol{\epsilon}\) is added by default and we do not need to specify it.

    +

    You do not have to use the variable names X and y, in many cases you might have multiple Xs and ys, but for simplicity, I will stick with this simple convention.

    +
    +
    +
    X = data['YearsSeropositive'] # Our independent variables
+y = data['exec_domain_z']     # Our dependent variable
+res = pg.linear_regression(X, y)
+res
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept0.7116250.1058226.7247337.994463e-110.2368150.2344530.5034370.919812
    1YearsSeropositive-0.0352580.003522-10.0113201.000644e-200.2368150.234453-0.042186-0.028329
    +
    +
    +

    This has fit the equation:

    +

    PDZ = -0.035*YS + 0.712

    +

    It tells us that the likelihood of this slope being zero is 1.0E-20 and that years-seropositive explains ~23.6% of variation in EDZ that we observe.

    +
    +
    +
    ax = sns.regplot(data = data,
+                 x = 'YearsSeropositive',
+                 y = 'exec_domain_z')
+
+# Pick "years seropositive" from 0 to 70
+x = np.arange(0, 70)
+
+# Use the coefficients from above in a linear equation
+y = res.loc[1, 'coef']*x + res.loc[0, 'coef']
+
+ax.plot(x, y, color = 'r')
+
    +
    +
    +
    +
    [<matplotlib.lines.Line2D at 0x7fafb72201f0>]
+
    +
    +../../_images/a4cbf376070178b287ef42066dcf23598f86a4f9a5b5d1ce4ed57891ab0ab3c0.png +
    +
    +
    +
    +

    Residuals#

    +

    Residuals are the difference between the observed value and the predicted value. +In the case of a simple linear regression, this is the y-distance between each point and the best-fit line. +Examining these is an import step in assessing the fit for any biases. +You can think of the residual as what is “left over” after the regression.

    +

    We could calculate these ourselves from the regression coefficients, but, pingouin conviently provides them for us. +The result DataFrame from pg.linear_regression has a special attribute .residuals_ which stores the difference between the prediction and reality for each point in the dataset.

    +
    +
    +
    print(res.residuals_[:5])
+
    +
    +
    +
    +
    [ 0.34672285  1.15826787 -0.29430717 -1.06544462  1.08198035]
+
    +
    +
    +
    +

    In order to test the Homoscedasticity we want to ensure that these residuals are not correlated with the depenendant variable.

    +

    In our case, this means that the model is equally good predicting the EDZ of people recently infected with HIV and those who have been living with HIV for a long time.

    +

    To do this, we plot the residuals vs each independent variable.

    +
    +
    +
    sns.scatterplot(x=data['YearsSeropositive'],  y=res.residuals_)
+
    +
    +
    +
    +
    <Axes: xlabel='YearsSeropositive'>
+
    +
    +../../_images/398ace28cb7992fdeceb81ba3fc65492e76fd1439a2a3dba97a4f3c455089c66.png +
    +
    +

    This is an ideal residual plot. +It should look like a random “stary-night sky” centered around 0. +This implies that the model is not better or worse for any given X value.

    +

    Let’s also test our assumption about a normal distribution of errors of the residuals.

    +
    +

    Q3: Are the residuals normally distributed?#

    + + + + + + + + + + + + + + +

    Points

    5

    Public Checks

    5

    +

    Points: 5

    +
    +
    +
    # Create a Q-Q plot of the residuals
+
+q3_plot = pg.qqplot(res.residuals_)  # SOLUTION
+
    +
    +
    +
    +../../_images/fc6c9263b60697d1ffe4675baf4701866c2026c7b55b054e58d5e8ff7dc1cdda.png +
    +
    +
    +
    +
    # Use the Jarque-Bera normal test for large sample sizes
+
+q3_norm_res = pg.normality(res.residuals_, method='jarque_bera')  # SOLUTION
+
    +
    +
    +
    +
    +
    +
    # Are the residuals normally distributed? 'yes' or 'no'
+
+q3_is_norm = 'yes' # SOLUTION
+
    +
    +
    +
    +
    +
    +
    grader.check("q3_resid_normality")
+
    +
    +
    +
    +

    You don’t need to do this test at every stage, but it is a good test to do before you are done.

    +
    +
    +
    +

    Multiple Regression#

    +

    Regression is not limited to a single independent variable, you can add as many as you’d like.

    +

    In our case, there are two others that we should consider: age and education

    +
    +
    +
    X = data[['YearsSeropositive', 'education', 'age']]
+y = data['exec_domain_z']
+res = pg.linear_regression(X, y)
+res
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept0.9774490.4047182.4151351.628781e-020.3182070.3118350.1812141.773685
    1YearsSeropositive-0.0374620.003390-11.0498542.853764e-240.3182070.311835-0.044132-0.030792
    2education-0.1026470.020406-5.0301768.170366e-070.3182070.311835-0.142794-0.062500
    3age0.0192970.0055463.4792955.721793e-040.3182070.3118350.0083850.030209
    +
    +
    +

    Now, it has fit the equation:

    +

    EDZ = -0.037*YS - 0.103*edu + 0.019*age + 0.977

    +

    The education is significant at p=8.17E-7. +Be caution when comparing coefficients, we might be tempted to compare -0.0422 and -0.0506 and say that education has a more negative effect than YS … +But, remember that education ranges from 0-12 and YS ranges from 0-60, these are not on the same scale and are not directly comparable. +We’ll talk about how to compare relative importance later.

    +

    As before, we should check the residuals of the model against each independent variable in the regression to check for homoscedasticity.

    +
    +
    +
    fig, (ys_ax, edu_ax, age_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))
+
+sns.scatterplot(x=data['YearsSeropositive'],  y=res.residuals_, ax=ys_ax)
+sns.scatterplot(x=data['education'],  y=res.residuals_, ax=edu_ax)
+sns.scatterplot(x=data['age'],  y=res.residuals_, ax=age_ax)
+
    +
    +
    +
    +
    <Axes: xlabel='age'>
+
    +
    +../../_images/c3dfa0baf557f75c479b9252f5daced8362dcaec2d1f6c493ef8beb5185fb658.png +
    +
    +

    Three more stary night skies. Perfect.

    +

    Remember, the residual is the difference between the prediction of the model and reality. +Therefore, we can also use the residual plots to see how well the regression is handling other variables we have not included in the model. +If the model has properly accounted for something, the residual plot should stay centered around 0.

    +

    This can be done for categorical or continious variables.

    +
    +
    +
    fig, (race_ax, sex_ax, art_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))
+
+race_ax.set_ylabel('residual')
+
+sns.barplot(x=data['race'],  y=res.residuals_, ax=race_ax)
+sns.barplot(x=data['sex'],  y=res.residuals_, ax=sex_ax)
+sns.barplot(x=data['ART'],  y=res.residuals_, ax=art_ax)
+
    +
    +
    +
    +
    <Axes: xlabel='ART'>
+
    +
    +../../_images/46389da22a7519abc032d7ae286f5ac44541346eb5280506013babc5f94e10c5.png +
    +
    +

    Here we see some interesting patterns:

    +
      +

    • The graph of race against residuals shows us that our model is signifacntly racially biased. AA individuals are significantly ‘under-estimated’ by the model, C individauals are significantly over-estimated, and H individuals are significantly over-estimated.

      • +

    • The graph of sex shows that there is no real difference in the residuals. It has accounted for sex already.

      • +

    • It looks like there is a real difference across ART.

      • +
    +
    +
    +

    ANCOVA#

    +

    What we have done above is create a model that accounts for the effects of age, education, and YS on EDZ. +We subtracted that effect (the predicted value) from the observed value thus creating the residual. +This is what is “left over” in the observed value after accounting for covariates or nuisance variables. +Then we plotted the residual against each of our categorical variables. +If we then took the ANOVA of these residuals we’d be testing the hypothesis: +When accounting for age, education, and YS is there a difference across race.

    +

    This process is called an Analysis of covariance or an ANCOVA.

    +
    +

    Standard first#

    +
    +
    +

    Q4: Perform an ANOVA between ART on the Executive Domain Z-score.#

    + + + + + + + + + + + + + + +

    Points

    5

    Public Checks

    4

    +

    Points: 5

    +
    +
    +
    # Create a plot showing the effect of ART on EDZ
+q4_plot = sns.barplot(data = data, x = 'ART', y = 'exec_domain_z') # SOLUTION
+
    +
    +
    +
    +../../_images/637d07d5070fdc67fe705200fbff3e293dd48346f402f17abd24a3d665de5dd4.png +
    +
    +
    +
    +
    # Perform an ANOVA testing the impact of ART on EDZ
+q4_res = pg.anova(data, dv = 'exec_domain_z', between = 'ART') # SOLUTION
+q4_res
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + +
    Sourceddof1ddof2Fp-uncnp2
    0ART13237.8096990.0055070.023608
    +
    +
    +
    +
    +
    # Does ART have a significant impact on Executive Domain? 'yes' or 'no'?
+
+q4_art_impact = 'yes' # SOLUTION
+
    +
    +
    +
    +
    +
    +
    grader.check("q4_art_test")
+
    +
    +
    +
    +
    +
    +

    With correction#

    +

    Nicely pingouin has something built right in to do this whole process.

    +
    +
    +
    sns.barplot(x=data['ART'],  y=res.residuals_)
+
+# An ANCOVA testing the impact of ART on EDZ
+# after correcting for the impace of age, education and YS
+pg.ancova(data,
+          dv = 'exec_domain_z',
+          between = 'ART',
+          covar=['YearsSeropositive', 'education', 'age'])
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    SourceSSDFFp-uncnp2
    0ART11.879147117.4700833.770731e-050.051768
    1YearsSeropositive79.8888141117.4885851.585741e-230.268552
    2education20.033725129.4626231.128191e-070.084308
    3age17.992537126.4607474.697743e-070.076374
    4Residual217.590675320NaNNaNNaN
    +
    ../../_images/6ea10fd9420b437a042b88ab6c1872d87809ea377f616435b36ea039e6483d76.png +
    +
    +

    We can notice that after correction for covaraites the F-value has increased and the p-value has decreased. +This means the analysis is attributing more difference to race after correction and is more sure this is not due to noise.

    +

    The advantage of using the pg.ancova function is that you can easily and quickly do your analysis. +The disadvantage is that you cannot examine the internal regression for Normality and Homoscedasticity.

    +

    But, what if we wanted to have a covariate that is a category like race?

    +
    +
    +
    +

    Regression with categories#

    +

    So, how do you do regression with a category like race?

    +

    Could it be as simple as adding it the X matrix?

    +
    +
    +
    # X = data[['YearsSeropositive', 'education', 'age', 'race']]
+# y = data['processing_domain_z']
+# res = pg.linear_regression(X, y)
+# res
+
    +
    +
    +
    +

    Would have been nice, but we need to get a little tricky and use dummy variables.

    +

    In their simplest terms, dummy variables are binary representations of categories. +Like so.

    +
    +
    +
    pd.get_dummies(data['race']).head()
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    AACH
    0TrueFalseFalse
    1TrueFalseFalse
    2TrueFalseFalse
    3TrueFalseFalse
    4TrueFalseFalse
    +
    +
    +
    +
    +
    # Extracting the same continious variables
+X = data[['YearsSeropositive', 'education', 'age']]
+
+# Creating new dummy variables for race
+dummy_vals = pd.get_dummies(data['race']).astype(float)
+
+
+# Adding them the end
+X = pd.concat([X, dummy_vals], axis=1)
+
+y = data['exec_domain_z']
+
+res = pg.linear_regression(X, y)
+res.round(3)
+
    +
    +
    +
    +
    /opt/tljh/user/lib/python3.9/site-packages/pingouin/regression.py:420: UserWarning: Design matrix supplied with `X` parameter is rank deficient (rank 6 with 7 columns). That means that one or more of the columns in `X` are a linear combination of one of more of the other columns.
+  warnings.warn(
+
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept-0.1940.294-0.6610.5090.4530.444-0.7720.383
    1YearsSeropositive-0.0460.003-14.1330.0000.4530.444-0.052-0.039
    2education-0.0540.019-2.7950.0060.4530.444-0.092-0.016
    3age0.0310.0055.8680.0000.4530.4440.0210.041
    4AA0.4100.1043.9410.0000.4530.4440.2050.615
    5C-0.5830.149-3.9140.0000.4530.444-0.876-0.290
    6H-0.0210.132-0.1620.8710.4530.444-0.2820.239
    +
    +
    +

    This Warning is telling us that our model has fallen into the dummy variable trap. +The dummy variable trap occurs when dummy variables created for categorical data in a regression model are perfectly collinear, meaning one variable can be predicted from the others, leading to redundancy. +This happens because the inclusion of all dummy variables for a category along with a constant term (intercept) creates a situation where the sum of the dummy variables plus the intercept equals one, introducing perfect multicollinearity. +To avoid this, one dummy variable should be dropped to serve as the reference category, ensuring the model’s design matrix is full rank and the regression coefficients are estimable and interpretable.

    +
    +
    +
    pd.get_dummies(data['race'], drop_first=True).head()
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    CH
    0FalseFalse
    1FalseFalse
    2FalseFalse
    3FalseFalse
    4FalseFalse
    +
    +
    +
    +
    +
    X = data[['YearsSeropositive', 'education', 'age']]
+dummy_vals = pd.get_dummies(data['race'], drop_first=True).astype(float)
+X = pd.concat([X, dummy_vals], axis=1)
+y = data['exec_domain_z']
+res = pg.linear_regression(X, y)
+res.round(3)
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept0.2160.3810.5670.5710.4530.444-0.5340.966
    1YearsSeropositive-0.0460.003-14.1330.0000.4530.444-0.052-0.039
    2education-0.0540.019-2.7950.0060.4530.444-0.092-0.016
    3age0.0310.0055.8680.0000.4530.4440.0210.041
    4C-0.9930.115-8.6420.0000.4530.444-1.219-0.767
    5H-0.4320.147-2.9420.0040.4530.444-0.720-0.143
    +
    +
    +

    We can notice a few things here:

    +
      +

    • AA has become the ‘reference’, the coefficients of C and H are relative to AA, which is set at 0.

      +
        +

      • C individuals have a decreased score (relative to AA), which is significant.

        • +

      • H individuals have an decreased score (relative to AA), which is significant.

        • +
      +
      • +
    +

    We can look at the residuals.

    +
    +
    +
    fig, (race_ax, sex_ax, art_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))
+
+race_ax.set_ylabel('residual')
+
+sns.barplot(x=data['race'],  y=res.residuals_, ax=race_ax)
+sns.barplot(x=data['sex'],  y=res.residuals_, ax=sex_ax)
+sns.barplot(x=data['ART'],  y=res.residuals_, ax=art_ax)
+
    +
    +
    +
    +
    <Axes: xlabel='ART'>
+
    +
    +../../_images/ca8e38a39fba588d9c549b9d9bea8bfde335652b9009e02581d8c2473ca15dbd.png +
    +
    +

    Let’s merge everything into a single analysis.

    +
    +
    +
    X = pd.concat([data[['YearsSeropositive', 'education', 'age']],
+               pd.get_dummies(data['race'], drop_first=True).astype(float),
+               pd.get_dummies(data['sex'], drop_first=True).astype(float),
+               pd.get_dummies(data['ART'], drop_first=True).astype(float),
+              ], axis=1)
+y = data['exec_domain_z']
+res = pg.linear_regression(X, y)
+res.round(3)
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept-0.3670.419-0.8770.3810.470.458-1.1910.456
    1YearsSeropositive-0.0440.003-13.7470.0000.470.458-0.051-0.038
    2education-0.0600.019-3.1070.0020.470.458-0.098-0.022
    3age0.0390.0066.7460.0000.470.4580.0280.051
    4C-0.9400.115-8.1890.0000.470.458-1.165-0.714
    5H-0.3820.146-2.6120.0090.470.458-0.670-0.094
    6male-0.0140.092-0.1580.8750.470.458-0.1950.166
    7Truvada0.3150.0983.2030.0010.470.4580.1220.508
    +
    +
    +

    Here our reference is an AA, female taking Stavudine.

    +
      +

    • Everything is signifiant except for sex.

      • +

    • We see that Truvada has a significant positive effect on EDZ relative to Stavudine.

      • +
    +

    Since this is our final model, let’s test our last normality assumption.

    +
    +
    +
    pg.qqplot(res.residuals_)
+
    +
    +
    +
    +
    <Axes: xlabel='Theoretical quantiles', ylabel='Ordered quantiles'>
+
    +
    +../../_images/2aea3ce208391390ec8f00592ef7ca4b28c5ebf2cdc409c3a45d03f77d0894fd.png +
    +
    +
    +
    +
    pg.normality(res.residuals_, method='normaltest')
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + +
    Wpvalnormal
    00.8320240.659672True
    +
    +
    +

    Perfect, now we know that our final model passes the Normal Distribution of Errors assumption.

    +

    What about understanding which parameters have the largest impact on the model? +Stated another way: which features are most important to determing EDZ?

    +

    Nicely, pingouin can do this for us.

    +
    +
    +
    res_with_imp = pg.linear_regression(X, y, relimp=True)
+res_with_imp
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]relimprelimp_perc
    0Intercept-0.3671080.418546-0.8771053.810941e-010.469840.458133-1.1905870.456370NaNNaN
    1YearsSeropositive-0.0442940.003222-13.7466884.748977e-340.469840.458133-0.050633-0.0379540.27588358.718414
    2education-0.0599100.019281-3.1072232.059458e-030.469840.458133-0.097844-0.0219750.0393588.376948
    3age0.0392150.0058136.7457787.231020e-110.469840.4581330.0277770.0506520.0396148.431478
    4C-0.9397040.114749-8.1892286.513749e-150.469840.458133-1.165470-0.7139390.07565216.101683
    5H-0.3823540.146409-2.6115389.442348e-030.469840.458133-0.670411-0.0942970.0159793.400943
    6male-0.0144460.091578-0.1577488.747561e-010.469840.458133-0.1946240.1657320.0004840.102939
    7Truvada0.3149840.0983273.2034521.495929e-030.469840.4581330.1215290.5084400.0228704.867595
    +
    +
    +
    +
    +
    # After filtering and sorting
+res_with_imp.query('pval<0.01').sort_values('relimp_perc', ascending=False)
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]relimprelimp_perc
    1YearsSeropositive-0.0442940.003222-13.7466884.748977e-340.469840.458133-0.050633-0.0379540.27588358.718414
    4C-0.9397040.114749-8.1892286.513749e-150.469840.458133-1.165470-0.7139390.07565216.101683
    3age0.0392150.0058136.7457787.231020e-110.469840.4581330.0277770.0506520.0396148.431478
    2education-0.0599100.019281-3.1072232.059458e-030.469840.458133-0.097844-0.0219750.0393588.376948
    7Truvada0.3149840.0983273.2034521.495929e-030.469840.4581330.1215290.5084400.0228704.867595
    5H-0.3823540.146409-2.6115389.442348e-030.469840.458133-0.670411-0.0942970.0159793.400943
    +
    +
    +
    +
    +

    Over fitting#

    +

    In principle we can continue to add more and more variables to the X and just let the computer figure out the p-value of each.

    +

    There are a few reasons we shouldn’t take this tack.

    +
      +

    • Overfitting : A larger model will ALWAYS fit better than a smaller model. This doesn’t mean the larger model is better at predicting all samples, it just means it fits these samples better.

      • +

    • Explainability : Large models with many parameters are difficult to explain and reason about. We are biologists, not data scientists. Our job is to reason about the result of the analysis, not create the best fitting model.

      • +

    • Statistical power : As you add more noise features you lose the power to detect real features.

      • +
    +

    So, you should limit yourself to only those features that you think are biologically meaningful.

    +

    When planning experiments there are a couple of things you can do to avoid overfitting:

    +
      +

    • Sample size : While there is no strict rule, you should plan to have at least 10 samples per feature in your model.

      • +

    • Even sampling : It is ideal to have a roughly equal representation of the entire parameter space. If you have categories, you should have an equal number of each. If you have continious data, you should have both high and low values. If you have many parameters, you should have an equal number of each of their interactions as well.

      • +
    +

    These are good guidelines for all model-fitting style analyses.

    +
    +
    +
    print('Features:', len(X.columns))
+print('Obs:', len(X.index))
+
    +
    +
    +
    +
    Features: 7
+Obs: 325
+
    +
    +
    +
    +
    +
    +

    Even more regression#

    +

    There are a number of regression based tools in pingouin that we didn’t cover that may be useful to explore.

    +
      +

    • pg.logistic_regression : This works similar to linear regression but is for binary dependent variables. +Each feature is regressed to create an equation that estimates the likelihood of the dv being True.

      • +

    • pg.partial_corr : Like the ANCOVA, this is a tool for removing the effect of covariates and then calculating a correlation coefficient.

      • +

    • pg.rm_corr : Correlation with repeated measures. This is useful if you have measured the same sample multiple times and want to account for intermeasurment variability.

      • +

    • pg.mediation_analysis : Tests the hypothesis that the independent variable X influences the dependent variable Y by a change in mediator M; like so X -> M -> Y. +This is useful to disentangle causal effects from covariation.

      • +
    +
    +
    +
    +
    grader.check_all()
+
    +
    +
    +
    +
    
+
    +
    +
    +
    +
    +
    + + + + +
    + + + + + + + + +
    + + + +
    + + +
    +
    + Contents +
    +
    + +
    + + +
    +
    + +
    + +
    + +

    +By Will Dampier +

    + +
    + +
    + + +

    + + © Copyright 2023. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + +
    +
    +
    + + + + + +
    +
    + + \ No newline at end of file diff --git a/_bblearn/Module10/Module10_lab.html b/_bblearn/Module10/Module10_lab.html new file mode 100644 index 0000000..083c3bb --- /dev/null +++ b/_bblearn/Module10/Module10_lab.html @@ -0,0 +1,896 @@ + + + + + + + + + + + Lab — Quantitative Reasoning in Biology + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + +
    +
    +
    +
    + + + Ctrl+K +
    +
    + + +
    +
    Work in progress!
    +
    + + +
    +
    + + +
    +
    + + + +
    + + + +
    + + + + +
    + +
    +
    + + + +
    +
    + +
    +
    +
    + + +
    +
    + + + + +
    + +
    + + + +
    + +
    +
    + +
    +
    + +
    + +
    + +
    + + +
    + +
    + +
    + + + + + +
    + + +
    + + + + + + + + + + + + + +
    + +
    + +
    +
    + + + +
    +

    Lab

    + +
    + +
    +
    + + + + +
    + +
    +

    Lab#

    +
    +

    Learning Objectives#

    +

    At the end of this learning activity you will be able to:

    +
      +

    • Estimate the effect size given a set of confidence intervals.

      • +

    • Calculate the effect_size, alpha, power, and sample_size when given 3 of the 4.

      • +

    • Interpret a power-plot of multiple experimental choices.

      • +

    • Calculate how changes in estimates of the experimental error impact sample size requirements.

      • +

    • Rigorously choose the appropriate experimental design for the best chance of success.

      • +
    +
    +
    +
    import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pingouin as pg
+sns.set_style('whitegrid')
+
    +
    +
    +
    +
    +
    +

    Step 1: Define the hypothesis#

    +

    For this lab we are going to investigate a similar metric. +We will imagine replicating the analysis considered in Figure 3C. +This analysis considers the different sub-values of the vigalence index. +It shows that SK609 is improving attention by reducing the number of misses.

    +

    Copying the relevant part of the caption:

    +

    “Paired t-tests revealed that SK609 (4mg/kg; i.p.) specifically affected the selection of incorrect answers, significantly reducing the average number of executed misses compared to vehicle conditions (t(6))=3.27, p=0.017; 95% CI[1.02, 7.11]).”

    +

    Since this is a paired t-test we’ll use the same strategy as the walkthrough.

    +
    +
    +

    Step 2: Define success#

    +
    +

    Q1: What is the average difference in misses between vehicle control and SK609 rodents?#

    +

    Hint: Calculate the center (average) of the confidence interval; the CI is bolded in the caption above.

    + + + + + + + + + + + +

    Total Points

    5

    Included Checks

    1

    +

    Points: 5

    +
    +
    +
    q1_change = ...
+
+print(f'On average, during an SK609 trial the rodent missed {q1_change} fewer prompts than vehicle controls.')
+
    +
    +
    +
    +
    +
    +
    grader.check("q1_change")
+
    +
    +
    +
    +
    +
    +

    Q2: Calculate the effect size.#

    +

    Hint: Use the change just defined in Q1.

    +

    Assume from our domain knowledge and inspection of the figure that there is an error of 3.5 misses.

    + + + + + + + + + + + +

    Total Points

    5

    Included Checks

    1

    +

    Points: 5

    +
    +
    +
    error = 3.5
+
+q2_effect_size = ...
+
+print(f'The normalized effect_size of SK609 is {q2_effect_size:0.3f}')
+
    +
    +
    +
    +
    +
    +
    grader.check("q2_effect_size")
+
    +
    +
    +
    +
    +
    +
    +

    Step 3: Define your tolerance for risk#

    +

    For this assignment consider that we want to have 80% chance of detecting a true effect and a 1% chance of falsely accepting an effect.

    + + + + + + + + + + + +

    Total Points

    5

    Included Checks

    2

    +

    Points: 5

    +
    +
    +
    power = ...
+alpha = ...
+
    +
    +
    +
    +
    +
    +
    grader.check("q3_tolerance")
+
    +
    +
    +
    +
    +
    +

    Step 4: Define a budget#

    +

    In the figure caption we see that the paper used a nobs of 16 mice:

    +

    “Difference in VI measurements calculated against previous day vehicle performance in rats (n=16) showed SK609 improved sustained attention performance …”

    +
    +
    +

    Step 5: Calculate#

    +
    +

    Q4: Calculate the minimum change detectable with 16 animals.#

    +

    Use alternative='two-sided' as we do not know whether the number of misses is always increasing.

    +

    Hint: Use the power-calculator, and then use that effect size to calculate the min_change.

    + + + + + + + + + + + +

    Total Points

    5

    Included Checks

    2

    +

    Points: 5

    +
    +
    +
    q4_effect_size = ...
+
+
+print('The effect size is:', q4_effect_size)
+
    +
    +
    +
    +
    +
    +
    # What is the minimum change that we can detect at this power?
+
+q4_min_change = ...
+
+print(f'with 16 animals, one could have detected as few as {q4_min_change:0.2f} min change.')
+
    +
    +
    +
    +
    +
    +
    grader.check("q4_min_effect")
+
    +
    +
    +
    +
    +
    +
    +
    +

    Step 6: Summarize#

    +

    Let’s propose a handful of different considerations for our experiment. +As before, we’ll keep the power and alpha the same, but we’ll add the following experimental changes:

    +
      +

    • A grant reviewer has commented on the proposal and believes that your estimate of the error is too optimistic. They would like you to consider a scenario in which your error is 50% larger than the current estimate.

      • +

    • A new post-doc has come from another lab that has a different attention assay. Their studies show that it has 25% less error than the current one.

      • +
    +

    Consider these two experimental changes and how they effect sample size choices.

    +
    +

    Q5: Calculate new effect sizes for these conditions.#

    +

    Hint: Refer to the bolded experimental changes above and adjust the errors then the effect sizes, keeping in mind the q1_change variable.

    +

    This can be done in two steps if needed.

    +

    Points: 5

    +
    +
    +
    q5_high_noise_effect_size = ...
+q5_new_assay_effect_size = ...
+
+print(f'Expected effect_size {q2_effect_size:0.2f}')
+print(f'High noise effect_size {q5_high_noise_effect_size:0.2f}')
+print(f'New assay effect_size {q5_new_assay_effect_size:0.2f}')
+
    +
    +
    +
    +
    +
    +
    grader.check("q5_multiple_choices")
+
    +
    +
    +
    +

    Use the power-plot below to answer the next question.

    +
    +
    +
    # Check many different nobs sizes
+nobs_sizes = np.arange(1, 31)
+
+
+names = ['Expected', 'High-Noise', 'New-Assay']
+colors = 'krb'
+effect_sizes = [q2_effect_size, q5_high_noise_effect_size, q5_new_assay_effect_size]
+
+fig, ax = plt.subplots(1,1)
+
+# Loop through each observation size
+for name, color, effect in zip(names, colors, effect_sizes):
+    # Calculate the power across the range
+    powers = pg.power_ttest(d = effect,
+                            n = nobs_sizes,
+                            power = None,
+                            alpha = alpha,
+                            contrast = 'paired')
+
+    ax.plot(nobs_sizes, powers, label = name, color = color)
+
+
+
+
+ax.legend(loc = 'lower right')
+
+ax.set_ylabel('Power')
+ax.set_xlabel('Sample Size')
+
    +
    +
    +
    +
    +
    +

    Q6 Summary Questions#

    +

    Hint: Remember, the power level is 80%, so examine the nobs at 0.8 at the specified effect size to determine sufficient power or question being asked.

    + + + + + + + + + + + + + + +

    Total Points

    5

    Included Checks

    3

    Hidden Tests

    3

    +

    Points: 5

    +
    +
    +
    # Would an experiment that had nobs=15 be sufficiently powered
+# to detect an effect under the expected assumption?
+# 'yes' or 'no'
+q6a = ...
+
+# Would an experiment that had nobs=15 be sufficiently powered
+# to detect an effect under the high-noise assumption?
+# 'yes' or 'no'
+q6b = ...
+
+# How many fewer animals could be used if the new experiment was implemented
+# vs. the expected/current one (using 80% power)?
+# Hint: Use the power calculator. Round up.
+
+
+q6c = ...
+
    +
    +
    +
    +
    +
    +
    grader.check("q6")
+
    +
    +
    +
    +
    +
    +
    +
    grader.check_all()
+
    +
    +
    +
    +
    +
    +

    Submission#

    +

    Check:

    +
      +

    • That all tables and graphs are rendered properly.

      • +

    • Code completes without errors by using Restart & Run All.

      • +

    • All checks pass.

      • +
    +

    Then save the notebook and the File -> Download -> Download .ipynb. Upload this file to BBLearn.

    +
    +
    + + + + +
    + + + + + + + + +
    + + + +
    + + +
    +
    + Contents +
    +
    + +
    + + +
    +
    + +
    + +
    + +

    +By Will Dampier +

    + +
    + +
    + + +

    + + © Copyright 2023. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + +
    +
    +
    + + + + + +
    +
    + + \ No newline at end of file diff --git a/_bblearn/Module10/Module10_walkthrough_SOLUTION.html b/_bblearn/Module10/Module10_walkthrough_SOLUTION.html new file mode 100644 index 0000000..bf50254 --- /dev/null +++ b/_bblearn/Module10/Module10_walkthrough_SOLUTION.html @@ -0,0 +1,1079 @@ + + + + + + + + + + + Walkthrough — Quantitative Reasoning in Biology + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + +
    +
    +
    +
    + + + Ctrl+K +
    +
    + + +
    +
    Work in progress!
    +
    + + +
    +
    + + +
    +
    + + + +
    + + + +
    + + + + +
    + +
    +
    + + + +
    +
    + +
    +
    +
    + + +
    +
    + + + + +
    + +
    + + + +
    + +
    +
    + +
    +
    + +
    + +
    + +
    + + +
    + +
    + +
    + + + + + +
    + + +
    + + + + + + + + + + + + + +
    + +
    + +
    +
    + + + +
    +

    Walkthrough

    + +
    + +
    +
    + + + + +
    + +
    +

    Walkthrough#

    +
    +

    Learning Objectives#

    +

    At the end of this learning activity you will be able to:

    +
      +

    • Describe a generic strategy for power calculations.

      • +

    • Define the terms effect_size, alpha, and power.

      • +

    • Describe the trade-off of effect_size, alpha, power, and sample_size.

      • +

    • Calculate the fourth value given the other three.

      • +

    • Interpret a power-plot of multiple experimental choices.

      • +

    • Rigorously choose the appropriate experimental design for the best chance of success.

      • +
    +

    For this last week, we are going to look at experimental design. +In particular, sample size calculations.

    +

    As a test-case we will imagine that we are helping Dr. Kortagere evaluate a new formulation of her SK609 compound. +It is a selective dopamine receptor activator that has been shown to improve attention in animal models. +You can review her paper Selective activation of Dopamine D3 receptors and Norepinephrine Transporter blockade enhance sustained attention +on pubmed. +We’ll be reviewing snippets through the assignment.

    +

    As part of this new testing we will have to evaluate her new formulation in the same animal model. +In this assignment we are going to determine an appropriate sample size.

    +
    +
    +

    A Power Analysis in 6 steps#

    +

    As the “biostats guy” most people know, I’m often the first person someone comes to looking for this answer. +So, over the years I’ve developed a bit of a script. +It is part art, part math, and relies on domain knowledge and assumptions.

    +

    Before you can determine a sample size you need to devise a specific, quantitative, and TESTABLE hypothesis. +Over the past few weeks we’ve covered the main ones:

    +
      +

    • Linked categories - chi2 test

      • +

    • Difference in means - t-test

      • +

    • Regression-based analysis

      • +
    +

    With enough Googling you can find a calculator for almost any type of test, and simulation strategies can be used to estimate weird or complex tests if needed.

    +

    During the signal trials, animals were trained to press a lever in response to a stimulus, which was a cue light. During the non-signal trials, the animals were trained to press the opposite lever in the absence of a cue light. [Methods] +Over a 45 minute attention assay cued at psueodorandom times, their success in this task was quantified as a Vigilance Index (VI), with larger numbers indicating improved attention.

    +

    Figure 1 shows the design.

    +

    Figure 1

    +

    Our hypothesis is that this new formulation increases the vigilance index relative to vehicle treated animals.

    +
    +
    +

    Step 2: Define success#

    +

    Next, we need to find the effect_size. +Different tests calculate this differently, but it always means the same thing: +the degree of change divided by the noise in the measurement.

    +

    These are things that rely on domain knowledge of the problem. +The amount of change should be as close to something that is clinically meaningful. +The amount of noise in the measurement is defined by your problem and your experimental setup.

    +

    If you have access to raw data, it is ideal to calculate the difference in means and the standard deviations exactly. +But often, you don’t have that data. +For this exercise I’ll teach you how to find and estimate it.

    +

    In this simple example, we’ll imagine replicating the analysis considered in Figure 3B.

    +

    Figure 3

    +

    We’ll start with B. This compares the effect of SK609 VI vs a vehicle control. Parsing through the figure caption we come to:

    +
    (B) Paired t-test indicated that 4 mg/kg SK609 significantly increased sustained attention performance as measured by average VI score relative to vehicle treatment (t(7)=3.1, p = 0.017; 95% CI[0.14, 0.19]).
+
    +
    +

    This was a paired t-test, since it is measuring the difference between vehicle and SK609 in the same animal. The p=0.017 tells use this difference is unlikely due to chance and the CI tells us that the difference in VI between control and SK609 is between 0.14 and 0.19.

    +

    If we’re testing a new formulation of SK609 we know we need to be able to detect a difference as low as 0.14. We should get a VI of ~0.8 for control and ~0.95 for SK609. If the difference is smaller than this, it probably isn’t worth the switch.

    +

    Therefore we’ll define success as:

    +
    SK609a will increase the VI of an animal by at least 0.14 units. 
+
    +
    +
    +
    +
    min_change = 0.14
+
    +
    +
    +
    +

    Then we need an estimate of the error in the measurement. +In an ideal world, we would calculate the standard deviation. +But I don’t have that. +So, I’ll make an assumption that we’ll adjust as we go.

    +

    I like to consider two pieces of evidence when I need to guess like this. +First, looking at the figure above, the error bars. +From my vision they look to be about ~0.02-0.04 units. +Or, if we considered a ~20% measurement error 0.8 x 0.2 = 0.16. +So, an estimate of 0.08 error would seem reasonable.

    +
    +
    +
    error = 0.08
+
    +
    +
    +
    +

    Our estimate of the effect_size is the ratio of the change and the error.

    +
    +
    +
    effect_size = min_change/error
+print('Effect Size', effect_size)
+
    +
    +
    +
    +
    Effect Size 1.7500000000000002
+
    +
    +
    +
    +

    You’ll notice that the effect_size is unit-less and similar to a z-scale.

    +
    +
    +

    Step 3: Define your tolerance for risk#

    +

    When doing an experiment we consider two types of failures.

    +
      +

    • False Positives - Detecting a difference when there truly isn’t one - alpha

      • +

    • False Negatives - Not detecting a true difference - power

      • +
    +

    We’ve been mostly considering rejecting false-positives (p<0.05). +The power of a test is the converse. +It is the likelihood of detecting a difference if there truly is one. +A traditional cutoff is >0.8; implying there is an 80% chance of detecting an effect if there truly is one.

    +
    +
    +

    Step 4: Define a budget#

    +

    You need to have some idea on the scale and cost of the proposed experiment. +How much for 2 samples, 20 samples, 50 samples, 200 samples.

    +

    This will be an exercise in trade-offs you need to have reasonable estimates of how much you are trading off. +This is where you should also consider things like sample dropouts. outlier rates, and other considerations.

    +
    +
    +
    # In each group
+exp_nobs = [2, 4, 8, 10]
+
    +
    +
    +
    +
    +
    +

    Step 5: Calculate#

    +

    With our 4 pieces of information:

    +
      +

    • effect_size

      • +

    • power

      • +

    • alpha

      • +

    • nobs

      • +
    +

    We can start calculating. +A power analysis is like a balancing an X with 4 different weights at each point. +At any time, 3 of the weights are fixed and we can use a calculator to determine the appropriate weight of the fourth.

    +

    Our goal is to estimate the cost and likely success of a range of different experiment choices. +Considering that we have made a lot of assumptions and so we should consider noise in our estimate.

    +

    Each type of test has a different calculator that can perform this 4-way balance.

    +

    We’ll use the pingouin Python library to do this (https://pingouin-stats.org/build/html/api.html#power-analysis). +However, a simple Google search for: “statistical power calculator” will also find similar online tools for quick checks. +Try to look for one that “draws” as well as calculates.

    +
    +
    +
    import numpy as np
+import seaborn as sns
+import pingouin as pg
+import matplotlib.pyplot as plt
+
    +
    +
    +
    +

    All Python power calculators I’ve seen work the same way. +They accept 4 parameters, one of which, must be None. +The tool will then use the other 3 parameters to estimate the 4th.

    +
    +
    +
    min_change = 0.14
+error = 0.08
+
+effect_size = min_change/error
+
+power = 0.8
+alpha = 0.05
+
+pg.power_ttest(d = effect_size,
+               n = None,
+               power = power,
+               alpha = alpha,
+               contrast = 'paired',
+               alternative = 'greater')
+
    +
    +
    +
    +
    3.7683525901861725
+
    +
    +
    +
    +

    So, in order to have an 80% likelihood of detecting an effect of 0.14 (or more) at a p<0.05 we need at least 4 animals in each group.

    +
    +

    Q1: Calculate the power if there are only two animals in each group.#

    + + + + + + + + + + + +

    Total Points

    5

    Included Checks

    1

    +

    Points: 5

    +
    +
    +
    # BEGIN SOLUTION NO PROMPT
+
+q1p = pg.power_ttest(d = effect_size,
+                     n = 2,
+                     power = None,
+                     alpha = alpha,
+                     contrast = 'paired',
+                     alternative = 'greater')
+# END SOLUTION
+
+q1_power = q1p # SOLUTION
+
+print(f'With two animals per group. The likelihood of detecting an effect drops to {q1_power*100:0.0f}%')
+
    +
    +
    +
    +
    With two animals per group. The likelihood of detecting an effect drops to 30%
+
    +
    +
    +
    +
    +
    +
    grader.check("q1_twosample_power")
+
    +
    +
    +
    +

    What if we’re worried this formulation only has a small effect or a highly noisy measurement. So, we’ve prepared 12 animals, what is the smallest difference we can detect? Assuming the same 80% power and 0.05 alpha.

    +
    +
    +

    Q2: Calculate the smallest effect size if there are 12 animals in each group.#

    + + + + + + + + + + + +

    Total Points

    5

    Included Checks

    1

    +

    Points: 5

    +
    +
    +
    # BEGIN SOLUTION NO PROMPT
+
+q2e = pg.power_ttest(n = 12,
+                     power = power,
+                     alpha = alpha,
+                     contrast = 'paired',
+                     alternative = 'greater')
+# END SOLUTION
+
+q2_effect = q2e # SOLUTION
+
+print(f'With 12 animals per group. You can detect an effect {effect_size/q2_effect:0.3f}X smaller than the minimum effect.')
+
    +
    +
    +
    +
    With 12 animals per group. You can detect an effect 2.283X smaller than the minimum effect.
+
    +
    +
    +
    +
    +
    +
    grader.check("q2_12sample_effect")
+
    +
    +
    +
    +

    The solver method is great when you have a specific calculation. +But it doesn’t tell you much beyond a cold number with little context. +How does it change as we make different assumptions about our effect size or our budget?

    +
    +
    +
    +

    Step 6: Summarize#

    +

    Let’s “propose” a number of different experiments different experiments. +We’ll keep the power and alpha the same but consider different group sizes 2, 4, 6, 10, and 15 each. +How do these choices impact our ability to detect different effect sizes? +We’ll also assume our true effect size could be 2X too high or 2X too low.

    +
    +
    +
    # I find the whitegrid style to be the best for this type of visualization
+sns.set_style('whitegrid')
+
    +
    +
    +
    +
    +
    +
    # How many animals in each proposed experiment
+nobs_sizes = np.array([2, 4, 6, 10, 15])
+
+# power_ttest accepts arrays in any parameter
+calced_power = pg.power_ttest(n = nobs_sizes,
+                              d = effect_size,
+                              power = None,
+                              alpha = alpha,
+                              contrast = 'paired',
+                              alternative = 'greater')
+
+# Then I can plot the power vs the number of animals
+plt.plot(nobs_sizes, calced_power, label = f'Cd={effect_size:0.1f}')
+plt.ylabel('Power')
+plt.xlabel('Number observations')
+plt.legend()
+
    +
    +
    +
    +
    <matplotlib.legend.Legend at 0x7fce3506bb20>
+
    +
    +../../_images/b45cdc82a1a5c002e3fce8ba4f386250feb595751b98f447d4e3e7805df7b2ae.png +
    +
    +

    Since we can plot multiple assumptions on the same graph, we can make complex reasonings about our experimental design.

    +
    +
    +
    # Pick multiple different assumptions about the effect-size
+effect_sizes = [effect_size/2, effect_size, effect_size*2]
+
+nobs_sizes = np.array([2, 4, 6, 10, 15])
+
+for ef in effect_sizes:
+    calced_power = pg.power_ttest(n = nobs_sizes,
+                                  d = ef,
+                                  power = None,
+                                  alpha = alpha,
+                                  contrast = 'paired',
+                                  alternative = 'greater')
+
+    plt.plot(nobs_sizes, calced_power, label = f'Cd={ef:0.1f}')
+
+plt.ylabel('Power')
+plt.xlabel('Number observations')
+plt.legend()
+
    +
    +
    +
    +
    <matplotlib.legend.Legend at 0x7fcdc41a08b0>
+
    +
    +../../_images/92b7b21e6c8b368939a237b44b3fc9ceda3f8dfa0ced0b51ac6956d90ee93d8c.png +
    +
    +

    With this graph we can make some decisions with better knowledge about the context.

    +

    If we’re confident our effect size estimate is correct or an ‘under-estimate’, then we should do 4-6 animals. +This will give us a >80% chance of finding an effect if it truly exists. +However, if we have any doubt that our estimate may be high, then we see that 4-6 animals would put us in the 50:50 range. +Then maybe it is better to spend the money for ~10 animals to obtain a high degree of confidence in a worst-case scenario.

    +
    +
    +

    The other use of Power Tests#

    +

    T-tests estimate whether there is a difference between two populations. +However, a p>0.05 does not mean the two distributions are the same. +It means that either they are the same or you did not have enough power to detect a difference this small. +If we want to measure whether two distributions are statistically “the same” we need a different test.

    +

    Enter, the TOST, Two one-sided test for equivelence.

    +

    This test is more algorithm than equation. +Here is the basic idea:

    +
      +

    • Specify the Equivalence Margin (bound): Before conducting the test, researchers must define an equivalence margin, which is the maximum difference between the treatments that can be considered practically equivalent. This margin should be determined based on clinical or practical relevance.

      • +

    • Conduct Two One-Sided Tests: TOST involves conducting two one-sided t-tests:

      +
        +

      • The first test checks if the upper confidence limit of the difference between treatments is less than the positive equivalence margin.

        • +

      • The second test verifies that the lower confidence limit is greater than the negative equivalence margin.

        • +
      +
      • +

    • Interpret the Results: Equivalence is concluded if both one-sided tests reject their respective null hypotheses at a predetermined significance level.

      • +
    +

    This means that the confidence interval for the difference between treatments lies entirely within the equivalence margin. +Thus, they are the same.

    +

    Imagine we were testing two different batches and wanted to ensure there was no difference between them. +A meaninful difference would be anything above 5% in the VI.

    +
    +
    +
    hyp_batchA_res = np.array([0.80, 0.76, 0.81, 0.83, 0.88, 0.78, 0.77, 0.82, 0.76, 0.72])
+hyp_batchB_res = np.array([0.81, 0.75, 0.78, 0.85, 0.88, 0.82, 0.78, 0.81, 0.79, 0.70])
+
+fig, ax = plt.subplots(1,1)
+for ctl, sk in zip(hyp_batchA_res, hyp_batchB_res):
+    ax.plot([1, 2], [ctl, sk])
+ax.set_xlim(.5, 2.5)
+ax.set_xticks([1, 2])
+ax.set_xticklabels(['Control', 'Exp'])
+ax.set_ylabel('VI')
+
    +
    +
    +
    +
    Text(0, 0.5, 'VI')
+
    +
    +../../_images/9b726382c20a511fab08e520fc28467fc3829aed099dfc6dc089c00f7179de26.png +
    +
    +

    Perform a t-test, just to see what happens.

    +
    +
    +
    pg.ttest(hyp_batchA_res, hyp_batchB_res, paired=True)
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Tdofalternativep-valCI95%cohen-dBF10power
    T-test-0.5694959two-sided0.582953[-0.02, 0.01]0.0837910.3540.056513
    +
    +
    +

    As expected, we cannot reject the hypothesis that they are the same. +But this doesn’t mean they are the same, just that they are not different.

    +

    Now, for the TOST.

    +
    +
    +
    bound = 0.05 # Should be in same units as the input
+
+pg.tost(hyp_batchA_res, hyp_batchB_res, 0.05, paired=True)
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + +
    bounddofpval
    TOST0.0590.000053
    +
    +
    +

    So, if we use a bound of 5% VI, then the likelihood that there is a difference 5% or larger is 0.000053. +Therefore we can statistically say that they are the same within this bound.

    +
    +
    +
    +
    grader.check_all()
+
    +
    +
    +
    +
    
+
    +
    +
    +
    +
    +
    + + + + +
    + + + + + + + + +
    + + + +
    + + +
    +
    + Contents +
    +
    + +
    + + +
    +
    + +
    + +
    + +

    +By Will Dampier +

    + +
    + +
    + + +

    + + © Copyright 2023. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + +
    +
    +
    + + + + + +
    +
    + + \ No newline at end of file diff --git a/_images/2aea3ce208391390ec8f00592ef7ca4b28c5ebf2cdc409c3a45d03f77d0894fd.png b/_images/2aea3ce208391390ec8f00592ef7ca4b28c5ebf2cdc409c3a45d03f77d0894fd.png new file mode 100644 index 0000000000000000000000000000000000000000..59fafd0d152b405bcb3bae9678eb6a80ccca68e5 GIT binary patch literal 28709 zcmZ6ybyU^Q_dN^@FLf*^3|?k*_-=?>|*bhm(rfJjSscc;I>_vick z@vs(4yfJfTUNh(Hv-dd>%8D}RC?qH_FfizEWhGT%U|<`;FBAz8yuujIqz3-scb3w2 zRhFHFT;rhYR$mS0X8<9|L+5=c8(To zb0|n*U=U<`Ssf=B7)+C=U)W;NB1;$;yZE<~Z`9p04i?<>CnqTfPNoq)AjuvgFuqL_ zlp*GGSpE?=H&4UKO{tkcW+U@Y+hejHoy z;%_o!UaDX$%b4JFEb(wInH~isD#^5j?%xQqZ&g%O=9?Uz5fTzQ3uJzcqzT6Afln(u z*&d@g-5idL9^RWX-(j4$oo@{ zGqQZ~aKS+jQf*11-(DJ1`{fJTf6FK%#CgBC_LJr7f7bTwZVZ}&{}1AHBR(zI>*-A{Q0DNErpn9cdmw#-Uz{jm5L_IN|L>b|bX=Bj zWq-ZD>(8-z_luw6q7@S?oNh1(jtu|qM&tH}0TPvi0 zW`)<89Oxcjg<*=WFBm;PtwiWpP?P`P^6OUqaDUIxLC_EVqSP>f_uslLC9V#SJO93^ ze$VhYR>JbXHP1!h9^W_L-ekI|-mj^d{?G8LR<~Spg<@5Nyu7EEt^e=kEa8<)n~!q@ z5Y@{)A@1k@Ju?^ZWZFwLLk!zh&-2y1paEzI;4G`tsxVnDwIB$4`X}JtpU8Y__)|{x z-=wP1l2ru@we<6?&pFMzT8|2~Z^~)^_g2VuVx5tJJ(nk~O+G^%B1SM7zxvf*y!Q&- zh&khR*Hg9%={-F?_AV~8TwGtCj&=*5I1+5JUK|1{UE6Z8Vv(->n>P%CiJEm2IFLf` zF!?YXaH^o+Dhetb+K0vXd;~U7q<4{d)CO(d{(GbR^Fl=Px)BF#R(6<1S$z9D$o?6;cN9kQy&uqUZ?7Sbw8a< zZT)?)0Q4rdlxVheWQHY}bc}=ec4o`}TY3ncg1XyZ@0<4xvpEB7YtB8Hweed=v&h^B zKj|vKC3rGdmOG6rKsx&UsPg+EFG~3LUWZ)kBbnvdnMr~FZVt6Zb$y7{q`$>z*BG{a z01AHm1?RZ#|DOHC)~XbDsn_GZ+27(iqW{b-kvDr;XIGU}Gx&u;N0hMwkyKlyX*h#F zLG`O#9KVw}hs8)#ax&rGbXB8sHE=U+{jp&_cM~Kw3h8un0|gt#&zMkn!B2lQ>A*^F z7{B-R>&Y^$B9(mU3;~xPzoo!ton2oU5sBzWf4IBZGP_HuqktH1;L=+RrzIle(*61> zpX`1W78Zs}L1F7oNk9+^Jl4Am=3rT$Ea9Njjmh~qy#}#plEG7Uu)~(Vt7h$nqpN+9 z9;fTbhK7d76uhqc+WH0t=xlCoS9r`{vBXoB`%qsQ=(23nbze@Emu|SOenW0+Yjc0R z=N}mvVb*W@?0K)uueT$iRyl~R(6&2MpN>Tk!|QU_b9Pk1Xc)uuw;*Prp3G=Jqv zJa;{ue_Q>&Z&i44agoBy>kg0g-yACvGfmjKb;_KdWud7d8X@VKndDhnSukJNjIt!c zFxYMWaG4LMeOQS{rjo?DIIQTeo&8fv+c?ZD40d>jv+-kEA@PDM@R4OT-pwctv7({< z(Zjws=OhqgDV9dN&WEHHm%Y`)!;yTMSnW1BD}R~xC?nlqY{dkS`vauJ{4NJ;TOk_)8#8*Xo^zx#6SQl74Y zx5{@Fva3R`g@UrfW`C9>5Y>=1}^@oMT{P#WeMCpmb&%6BeYP}5B6+OY+({GQT8(EBT5{tEJk>felt zYK6tkG^AYqS$4Bt>>N0M>Vs(IC-n666z^ZZ31p#o^8axe`UbhmRM%^7Oc(uBQ!SLIHZ>7_>!z+zdN4n?r9F>pew zQc+Sb|AoM2OOpFby?4oef~^HcOae0mtnh}2bC_!kXVLI;d;fi8IJ2y9a`z(XS;>Xx z_6+vpEo!)J^~0;~B&2^4Ns(T$IEF^2wr6|nZD))jW~M-QAc8+iLqh(5s=2Z#Cq9== zLN350CakfPW%%{kqFO;^3kwOZo0^RCST=*u%>R+Z z`-V0{xU6ut)JW8FdTdXF{)@-igdX^86a{^^IrUR~e;tNOIMHQgXLtR0S71QHh$8Y= zzk;Z1$e5ItrJ>;STs)W+BLuZK;MuN@UVl_!HYNU7U5L{bT+Z~Yz2i3`5h9LXKM@ot zpAf>TO>js__be;x+iYDXM!Ad1C~^qRfU@WI3r+$qpSY*7;?z}CtfoH4iUg+fCiq6P zb@(s&J{fI%>8Jdck_W1AN zs6rM{Btnag@AlZB_1p*d#jhq?PaBtD@F7K^(Rt^uk$If{VH!1qR>TTDiBL2vq4bh7 zX=$l9u@tYp8a&I!9jszqlGAB7f3{a8(b>!yvgag?*qQb{jWMFZUu!QZfk;^ zz=Hz#^x1z{u!K%Y)AUb^3+I85f|{Dsa0YT4q}qo!UX`XpK2*HW?%(m!PAhDANM^P1 zZ2Y@Tpto;K$`Kf!A4(_jCPc^yS3LY1p99>F#2~ezccNP3Ic|0-?9ak68dkoL3IF^V zf;aYW9~CG>g6R~Nw^f?FXMrK;x90&n0yClMof$p`n{`3R8Xm zGkl$)=r#4zz9Z{2p!LUuP)kkZyYi%VJ3Sxf2xpZ; z@tJbW-hLbtZ&w<4+D_^}^Ub?S@2lU3rb;f#jvF5vr@Zcl?wtE~?vBUY@qym`rh5{_{Vm)`A znl83Zv3e^z5ySE2)}slcret4cuFs5T{bOy-4$uHA^@Y}}+aZH?lvcOS6(=@>zy_Pi zf?r7^-+7#X7tZ5JJDggG~AW06w=){qC^c3+8<$pSRbyZ`jC*{ z8V^L1zV@u!-NqqC-Wck|6!niB((^Odr3L3qwzKe^%IK`f^$V450;o7m)42UTMc;WK znNhK55NkBAyu;~wgvDAk>2B(uT8rbyyW@weU01XBVXq3*!0ie@w;ZG9meNe+h#)B@ z$AY0xk2WS^e^x_}w>!$4OxLzq{%vfhYgoZW2zGf8w;tzBhxcb*Hs`}~U2B^;am1TH zifA62zzRPMa@By9-BR&G^zOoG3FV?VG#Yc1iio32)11^m^a+STL(8ka6dfNC36FZFY!a(R0(Ixk zm&*KYLY|&u-_=$hVtbGGfdd0FMM02RbyOA}-p1r!n3|(NOwnM)%okkh(Rz`eICeNC z(ZCFuf^NTSEtD>gmPR*+(<7pyzD-Xj(<^0Zw|o>wvc@JTOsZ`5P-Br=+q>Izl8)`~ z6*YgTA3VBcUp_6E&cM=g-h*61nj8@t?7~7&C;fupBtD*MsTJ$uKRyr&IQyS%nOq-# zs}g^)3jOnD-tBOn%3>sAf4(_^QKR(bE*bGR7D)lOi&B)jKlSLfmz`5rOJx`HhH0mj zls;@q>^G(h3zGwVg0g~K>MleKy6P_W3|b@Wdqsph@3GuIetZR^Q){kpoN|0@-_zTR ze$<;c5DbzcdN;S3PRfkTCGY(dcIj@O$$X`}z~Kdh;mi!0uoE=e(C%4J;rq+<-TtM* zXwO5pZ7jT3FN9wVe`nOQO)DHLR-vH~a_>5vj`Ad9e-BS1({DhgtZOS?!0EJEeE7IA zM1Oc)Z8}+}%cgL#Jv{wp+|Wzz9F0c$&1I?SfVxbaM3}nu!90KS#Yeb=!wyT$VwqS9 z-2ga*x-zY*j?1R4(QND7+}zM(QnbJCj6VAjCo!&B?%vCtEi_gK`;2RNN;7jJH>hx-Pl#S~YxVY??b7FJh- z&D``f4w9VXY&+asnbOdjR6aw8@g3>W$H zIlGl>2jm94_uMRvS;Ag5_ABC=Rff@#k*K^zm0WwfyPxv&^F!q6gJm@+L^ZozA6l$* zxgpoLYxIihkB4`PQby3@lE~ zptWkFhqsps>!tbB%}<>v;;xP7?<-6#__y|l|bVLwOVg1Kji^aRyQRqSAy}?d{FHgDO*}%s%l%_bNg$=d8 ztlN)<`Q~J)98w3Ljpa3-;^x%erb4kEA%&3t%~`IeOa6kV@cvA$04u?@mjG_B7wP3g zQYY#{pdaVXTB+5Xj(t8$3n_1oCQJUxGisNopsmfO5ahO~l0epIW9@G4`XB&VD5dBX z_C*uE4vFyoz-JTVWswj#oFpsFkB24Z9)aL zpvrtLcn9Z~#$RL1hgT8x+{`(GvO=$%76gB9>>j=3nc+Bw^B>3C!ktphqM3JKBm7(b zl3L@R;mMS~%Htn=?pQvOc(&_GZBF_wEu{3l^CVgvbvY8-D)al?WIVn-3XgG$o$3zP zrD?!IvBNQtnJ8Hx_k;oW*zG91ugKlqyG}Q@^PMM35v~r2DKGhJQ>EN`z5E(4ojU}Q zzrm+7Vd@MPy)%9>+br)J2+KWUb92gD4)a14{b}(oGf>zT=YVpZu_}Yn@YH5|$H0TR zXw18e*`J%Jy>}kA2iwlW+27#x*cBbZF)e>u@u)}sCqTmScY%?a;7}Y`4zT! zfo~Y5#CYLHVor|6L0!)zrk=Ac2h6=5vV7z8qp2i}>BpDs-I}p}+T!vV9zF13xk7)< z`3OV*{9ut~`WLbvPPevvJL1pV_`mcywXF&laCUNrjiMiIZCOj)S)aM9K|c(m82KPv zIBrt+F2BnlgZg&_k;)hA@PlPY?p`?Re)bpUw{)e#VhNyU$h;0(dXF#{=$$?Gc`x&ES6)O;UoKgh>H!VUg%ft?(Ix zRy=%S>&EHhJdx|cYX5${Lw}Xjp1)}4cpnxd^0Xmu_@Xl$WGm9ev&~5?K_uKDg-hdB z&V|!&%JGcg6QFa8mPAQNda`9~Z_;W?&{b?G>D{0()nf0TGkU*Y{F>1E^;%NY2y)0X z#IEC&YQ^7NDX(D>(F5P5sxLhjDkM~~_Pk#dm_PRvrdQ0!5etAb8;tKG`+y@}pp}N$ z?$!GVUpXJ~R`hZGO315JEQ51_Q@jMY@pLotb|BOl8iNgGEhlIjf5B>-K_qE+sXNCfcph1lI3&Hx@+xjH_sJ z$apA&IEgi+FVZNLV(;9N&(HATzWX`UgA*`gIQ7^BW{yn@?Ow_AY=7UE>ovvzsH4_o zZ_?tt(-VP9|I<*6N>boha(GGhav%B;8`jHdEHC85JFY_KS`Nyc|KU51*ub|4nO(=k{u6q6C*kx9){fme97dfYoFvLRY}E=k9+Wu7;m%Cb?YlJ07GzK4;i=-oCK9 z{eE|4iecGDoo}oc*NV(yh)7!eNH5)exf-H7W3&0Le(~v$b$I`nd|QA*7d97;P4h}6 z4_>Rv@T5Yb7@F5yt9eiug)PJZ8ozSgb_-@vr~&5tVNiRUDXngsQ4 z2sK9i{=hJ7$I6Me?dlrJ7kk_47AE@WU-Ll&x-pb2j*nCFHa--N%X$jUf2-P@_25@- zkcZsMVA(s{d4JvdnO(=Ka?C^#(XCfLA*61HABR_O( z?Bl&Ejx|{U^HWY2NtYKIC1Gj!NoalX5O}WuG zyGZSYa3@C*Hi-VSMTCBo@7LO~7T_A7>X?dLk|9VaIOaSXKO9T3!_zzz61Ky;c{Q6mOv56Jd%=KCu_S~t_xDE`cfyQP{J1Q!@l*H#e8hWQaqsrXb(3F8s^AJ+#|N$jD$WXo`*X z3axFC8tVD?#;>P#-KXt1g)FR>45Q=DuENOpRg0;6NmVzI%$oZS8lC_otR#F zZI-!&*|}aXlOB)4-B#mXn(Jjb0uv4M@RuX|%)-8`b`~2{RMg(d$;4m3lmXif*;z%3 zYX}J|oBMoq)VtVjl}bEO+mOMZyZXRg{)?M7p8&b5CBwzi{?0Bhe5ZJGIj7%5Yt#*6 z&a75JQyNVwqy z1(8rx<==PKL5MRl>yi?y18pQ?{&+jmKN?&2bCfDV&@nD|7tPu|88p#N9)KAfb_H0g z(TZ8Y0xrw5oY#A7`Nomq{vkHg#Pu^go5u>TXJBkMZ((W(LYti?YXzy^eJ-{K+sG-@ zhU(=qe5EBTgHH837nLx_T*3VqW^*_w6diWb!|ePcABQJ7Q(gazrBz;7G{2R~-x1Mm zqZ^dp*isbYO>aaxvQ8*kr0G{viFLwx<ha`+baUVhDdX@OYl;d;RwoJ(oy~$DybWtyN_vOPwilU1`OA_Ey zx$Sm9c9dLLq7&t`sjgqr}9-ti_W+uzSOvpYX0`;vG~ zpYd57-&qPa8vVd;VlGS8(V1Y6m!6^V9X@w(IpaG-0?w~eIX-agkMCWO@!9)hid=W? zBSakxCeVLjHAqv;6d0?s95;Jl22)W}PyFT(bQ3Sr4CS_VJ<`ODn=nT8o8#)Dl4Buh zVbQC14+2pA+-w;q>&x;>P6CU3oin_uXxUyIa$As4Bd8A@e2c)9F0-GTomFXZw!FPM zvU|koS8cR4*qy3K-(KtO?M05Q=Qv6aWk;eiq~TD#z|-k8tOq`t(|P=88nd)Ev7RTJ zDA2~M!D^zTkwFo91C37geoM|{kANv~H7Y%n#9&Ao*H0@jpXc?~b$22HxOJUA_Xa@9 za5%2Z$`eWek#*vgb_ZFhmR2JTr@3TRm|SAl#gfgvsoD}2M6gyP*Hwt6LAv}sGRxR6 zm0%~XgDn$xMfza=)cc<{hx5&v*btbY=a_U{TocFfiy7i=A&c#~dc3`9kfG!<6&Q@W z#@dm>EEVWvOJgeG3~}aSmZ;7fHEL*n$(m;ib4BmBh@|3hShPY0gm~%wm;URu5Q%FyT2FTkqt{r3>D0oY89bKN1YPjBm1-KIIeBS7G1#6w`qihy59Ln83h5KG%Js?(S{^hFC1i zscb*-DnmerLT1Ig-teaJXmo3)8aM=2^gmQcVx1wqc*d3zNEy(={V4LMosI4BW z+n?n=m|y@5^LUN17}D-hE*yVPvB(E+7{;9bc5(Rc?X zqOJ#oi3B@&#Wb!6`DE5OCM{MJ0%qC%sD1+PXs&!X63vh@W4tYKG>$BCq8M%v&&K2)edA0fn zNl$afCT`n#KH^V6DZ_`@XcQYX!+jX4F++|bf?eGIkZ)tdv7-S+Fh9xb53&iy<=imT zJgg27@cwQ>FdqN)VhbC}bI8d@r5dOxFQbqPg}w%YE1+5G)2cni3&Jx6yGJnE+R)N( zFMAh6(Glt2>UPaHex@yBuv-(L>YC7JkxF^5HY7r2Z`IG8{;=p2#inaPgq7}_ilT-hBzwmo|<WZD2AD0+cah>tFtk8|qLE<4zSQx%(E4H^t#He@y6jq+{BGx;ZMh z!zQ-RU0}I5ttz@&>-&zo!`454>aDpI#tN5K{2%B;rZf?veSBmirZhzYpqrVSA}O4 z&&TUSP+1J9;wVy1$!#5n4ge1Q!mNV>1LgrR3HEwDLFsxc+wW&;t9bZ&JT2<_$Z*_Y z=gOaa^m^;w-mqh;=d)IN&nzBrVKADLivIMH3(=TA^vBK)xO#v8;UmD2{O-Z)cT0W9 zE|nx4*n03IE-nspe12{=UZ?;=Cg3bf$3Q?`ADOi+q{XUxTf}?fj9ZQCLj|KBfy``8MvuN4GcNmRIq7Pqy--7#9q(m8tLH@(*s9omn z@?dAaxpo%yRq*Qs>**YCkC{fyC;2g!I7l-~0#aKM6KP1hU@~0;z|S%df3uQi8kWD_ zQqsG$FL~Tp)MJaD8FP;hcgSdH-^Rxifb5-Cravg&-yGrmL^J8)k7S3Ptd%rb!2|?Y zY4Ky!E=+AD8qf8qWuO(!oz4G}|DCmUzPt6px;g^S;)h!k+A0BcMXMKKsRoeJ!f!7H z8Er|2A0Mo=bYh^wL71sDl(6>S>X|hRksBI*THy5_n+R&zpwzgtbJ^Jjc!oQ(={$EB zP3JwTzng2cv)iQ&!5ZA?epal3s+hUz-TU|@lZ0YvZL}Jv>su&t*$2n4c;r@R`yojKz^~r6h}Oqeyw6O!5-uN6e6!3o9tO8^hZT=t8LRICkG_ z=@liJN0~Rf7i^08Y%Hp%R6kcM2$?2#oY%j{1?^6jalVyyMlm7&SNw2%wNzfj8coSe z8y^MpWt~ruQ+*>-mRn=dAvs>%vo1`gcuqhdFJ&;nBYNpwo@gv1Xf0=emK<)kfpjv79iJX zmtbi!Q^Wa^-hEDkrEivYA*ucGIziS!v4%+kzeZ97emPj)A>X)`HePf`jFg~8X{&Fz zd5?IZds0;cyQdt?44@j_?F@hm>7E``lGH#3#ib7{{6PO)&?BnWq`iJM!%VB6(ahrn z%Rhm&ujKqAo5{D}=}~VfQUb@HUvVfAs(LTUldnajk;(w6LN4t2#qaM!^7rqupYRyq zx^B+4s_W~&LS}11*PI1(%nifWPZj)x4-@WR$#`L$QMrA$oY9I>+y9{*QqD#JWB;R> zD3RiT9filA_`K_`z17=(f-)lmi<*Uxn$@-T;8TnoI^mHKgUYr3MpSv# zmJ1Gf^*4aXKYelNeREoEIZg*MYTLR8pb}i)-WKjw+k%=p?e^kVNgs|B&z^TyXQN!# z3?8E$_>4g5O=)Y|mDQFrU0FX}t%*rM`A@%SaP=odGH-Vzn{b|Kmx3GewgzJr0~K`* zxO0HmJABdsPEGaXr;>bcNfPkEYwW(;j%;1Rs+PUmSa9vWLnV?0S4n$_{<BdmA>j4Q+aZGPcH=K8VgW9mPmru#G*!q>}$)g>e%qw{t z9+IqF{FQyCZcX^3fdC;%VIKQUY|>&0mjL5s6VVAMSx_3ZWIWE|UdEL*B2Qe{sDCrU%DqEs;v(~Q2ychB9` zKHt1Qb$ttdtY)9F#i$SDPrnTUR%+$m}*6kVY|WdZK)Gg(cig?Y4)-oIfeFOBsy3vVENvm>qIGN*P0oWbAcQm_p4l@ z-0lw0N5;5_IN$+g)gUsD_=SDnGxp3OaQN44*nrLFDfY3OVMs&u#O;*M}zHoPO{iJzT_XVpYT;!a+abRSFm_mKhZVT}S#ywenzrO4v9%NDJ8G3h8 z@*ZR$>?)77>oe7BPpP_t}Vmo_WkeSzmA=uQys7U^r6&Zsk@1GNguag`C zHdai~&QWR}aru3rxVtF&IxD|nR!Mb+Io>858aWD$j2tw=nN4MdUJ*F9%_2clrurQi{9;HJ2`XQ!0QF=7G$7p5MTy9*#&8O62Ig z=*o`S4%b0zdhuqzzDoAco_k?o;~j;&@PdUl>oU`g>$wEpP29C07j`w_>g|-L_q(+%b8$zkPKZusqtG|GP1Ja zCUe8-ygSoXl$1W_aXP5PL#D(1Lx1)8t_Ep0yj>0ud*}kBg5o+p*m=aaCI%y;*3&SeO1i29y ziQo60FP+zJ6A+Rg)1*QDVzEwbp-L5~Ypp)rpxXW}{DO4azDQsW{cawa?pJqu+o);a z@ZPLQFO01}v(O7plMIMpO#Io`vv91cgaTqYX94n#jt=L&sp!RaFSRnw3Ov*XVovkr zm6d>?pl2ID5>2lT7Z^^P#oMA!H*$Z(4QAvc(em5YjYgNj3%Dm7HS6|0`o-L=!IO5; zFC}(g^y|pULXl6T>B98X)$u@$BOSPMaz9_El*@9vr|Q=2}lz;<4y{ zDN!qWs=fq_@Y8g%rB$7XbJ^-pgf&HOSFCTX{v<^N+sowiGvbDoFv{VP`#B`_GvcPt zi6t0=8NhP|5qQH%l&9PG~{y0@4oVQPXg18Ve2kDXOWWmEddqGbEqH0pqArgA+I6v zFAN)W`jRUtjq=Uq=jFMdtcK{I;AaDxH*b1xFp(*F>{7qk(cx@!*nN~((3QEevNANe zwbiR8Q7L>Ti8+9!jB-432N`~DAk?^ZcRBRxEXCL>_6$s8s}G8;x<^OF>#=$~Q^{=m zA_-#~aR+KX482k-d?#d)GF?Wl#ibw*4g%RAcUz@ld)od6u6R|LV&*dPh`OoF zys^_cwO_gMnP*4rUK4B%&8K=|QCdpg<)Z9~XM$A0gHKtP_q9DpjRpYQ9r5WCH4u-S zwno_P7KNrN4bsUzz`yXi+@JaLr{D7HLWhr_qBUz;d%G~;H3l{5s8*d;_K5L3HB%G5 ztTW!A&z;#zAk1-Ii0Vv}ovu6x{lbELmFs?d;Ni=<#f>2gVC#U&-h{}azO!xS{`kwE zRz3-8yC4A0HmIy!1CRP1WZe^`8ch5@ko-YC@6)jcJ10HIYquEH*yvW{Q;9{F$`x_t zcQ~jo0L#u~u}%kyr78v2`|dn)Omx!g6GqRLLb1rmRn`!noCXtJ{X8`d4Y(+1pw`A5 zpz1-5Lime=xnw|?t8{pKfV$)qVJ{w##5x}>3il0BOOhqfz4`liAA4VC%#dT7Ec0A1 zz{2YL50|huci|Ul55+%4Q0EFcmQPRoCV*nZ7g5Vt`7e%B|MX>yk!bJvk=%5mX{Ylkye<)@2Z< zVUU5D84Y+i#WLa%%M#oTNEwo^oOtPOJ9g6{k z4bw8&^sU@!a`_K;3J<#HRl~cR5GW3)M@yg2EwEi^L8A~#0eq8cr9MAcS=1#jWaig| zJc>#Ex#u?VvOH7i?^IE=?xX}g_zt?6(iS80DGy3~h*}}=DJJH0jq?x)&S6mA_|C7r z3p<12bx;9lqG-Lfm^+)ykA%s)n3bTnKeXQ0sx&#_Om*3Og0f_R#N*$2OSaDGXGFw< z28u1ZRZ&gapEwHs`SR4@^w+2gYwyb9%=83;toE1D`p{Y(?b9DpMR?%n6z=rhLyp!^ zC_+u01>7IW_9^BA;X|vDs8%^P1cly~!AXLO`-5;*#CAp z+RqjDxsHnvF0KTG8avUablwDGFW9}e2!l_@8vMR)54cq6GPozjOEy&lHW>O!fyQe2 z1*AV_g;}cjhhi_x{{B4G>jd7AtWrPB#(hUscJHdOmxFUBE4Ys)+B=g3Wr0{bA%iPC z=0AxdD^Okp_kAQGB)J$cUv*bhiL%;%{d19$9Fhf)BI8UO4P5g(DlvuTdU8q>2V&{y zc-TMU1u$hg_+m)%x_4W``;Uer$a2fQuC{Tc{*ZZEFz=CYJ5m=*E17!Kj5Sh&(7dsr zNZWuEkqB8RM!hyU5QM?~HZFOx9Iu_?k56W%+e|l&ev^!=7{Z(gJ=>yJUf@B40S2Sg z{szw2UlA_Nv6LQtu@k``!#9|?_;TE9PLT{Em55S&q27H`1>m%^+IhLFk6;AF78(~ z&Gj8_IN?F6#Y|$z-M&kyY#jYoK#Fmzn2DGR>#Vd3>-t%)i;vEY0g^m>^9G_az${kN zl{SYpNG2ZO`<3L(hCgi?RbpL)`Vo}73AWdV@03Ph?Riakz`|FAq+l-4Y?-)Ubu)aN zCDhkv)&$Z?Ya`PZcFiS4z}_NX5o;YpQWLv5K(AFM0)ml|xvNOCbyV=o6FYbswqJ ztdQg6Kp-X;bS?NemUn+Xt^^McpJPeV_X*$hXt76cb;_Jln%YpN0P)j=$dghj7l#iXgV6*;|U(XxF}Pzl6BU!vpVi@)Sdc>K`eei8`0 zFT3-$^5gAZ)jDGW*5FWYP|;z& zz7R<$(H}*?(1Eo&{LXVTiQTl{J-ZP(ZWk2APnLGyoS~5+T~c^7J1IR}xNTy$a%vS$ zjmXQLInwdT4e)=*MXy z{*%>$qMF``RxXVE`3o)y^HS-b%B*>|l!M+9v- zQ53>yU0q_}$%X+ndF&B@*z^i}@wWQ2El_UO9${{ z$#cJ*vm85;Ka~~%=D-#cGZy}8$60{YsN;SrM2?jsTriX01IKDASg+A|SG~0Au=n!7 zDj7F+HlZ1ZGWg+nv2htebC9WI)!X6|RNFtWlcFh82a(wKf^QAPXXjX(JMPQ#U$&sbN!^AR2ga zpCbKPo?{2`u1{vc4xAT5af363aL>{ z0vB2*op}7IrE*-XpNfxZIZuK!D>WEMUGc)%X1ao2c(P~8WAq(2H4(4-Ee1p7B5a!% zfO5&k##U+6shG}V^9rzuX@w4(KZ)-j9%frym;wR<%;NqI;V0Et;&R!dd7Sm^j2l}F zjP07y4`KuAa5u|r3sj~Uyxx$E*U;Kz6 zv5?FX?B+z8FT5FxNjLWrK5cszANq9J02hN~LKo4&(rh)!NT--VL_Y)LxHU3-eX{n! zzY!YsVQ6A`H;UNg!L!&P-5-YgBA-TSc0)JhG*<@39kM5h{-B9*mb-FZbsce2XQ9xb zSZ-ilB8NovlVNOy00&3>WVH`>-Y8EJ*>2K|$y}j$aYQ9>5UYc6TFg@^unjJUf0`GzX0}HkaWIa9% ziC4de`Uu{}wgD8ge?TR%&e_np)*F@^7LfAPAYwh_ZGKA2JJ=T(qgTzO`(5?vj_Bm| zRseYxjhG`iJUpE54wO?93pjsli%dZ=aZN*XJ1ovdC6xYqeFc~5~X#f{vQBaKB-<%~>+S&@G))^XyQ3>budP>B?OLXl_x&*pj1q)XMZPoD$90s>2w1Q+lzow#P_p3)C2qbmA4FM(iy0InKTMi2(M#&J8t)3Fg_$!Fhm0TYV($#H$kA`^# zQ>f`DhffOEt35Qj)kFig+}gEtebzt;e1l_4eF5ZGxMw;ZGnoyC#1X!75E(wbnu(nE z<;G>?^>OX$92vWV2o1^!`}6tF9^n(MM@)!|0WPsPm7533AE?1~wQ+A2G485%v;56L zqm*5`;2K=ZZH>+-j0CR^os9U?MZrkQY9jyAZ`@yKeqG-DIOolC3`)h5G$bl!&|Fj? z^5%_lb4?nO$BpB;=k@(r%F_U})(VUZOQ>C(q|bMyD=c_5qFD3kpBZr^bnvK^G+fI! z<1+jO>qFhauNyocKuXCU0%C&U!omH-L*dv?e z^~K(Pm{_=w)?%wBNzFs-w}>3A&8G*HBI;F#K@}CWj5GkHg06`0aHKR|yBN^)`d{lK zO+4sGQ?AOAtI77IezBxg=l48INTrn%r@Ii0>FCD`GitM6LRz1dsm`h2lNbi+A&hl% zF?+Ck&L>#p&K-=EWWl|{1EfV5;P!BR6y4qVR#sL*uS`r#Hd&KiCx4gaj$z@#d%RD8 z*}CMiA*mIl!G?@zMwVXTVDQjWoMB!t40I~r$=f^1tpFdQiVAEjAQE&fr^1tBOs0BP zt_}P5j`CLE;znpnAeT78SdGW1ba4&=1Ruis81iePnCdP5GD}G*xUoi?*n{((obj(u zH>X50?NFJ+{n!|!Zs=8@KS8%ec2&b^vukM9_LU0U?sSpMUWx^T*uA?SQeWa~N1qc| z;X5G;DQa240kiNA7kBeKGW{l-Ywj$Qk`Dp0yi_MFP)Y+7i@Xl@j`hwL8X6nvoi=|; zv&G4MB#9!*ZF%FmFMGaRFC9JcCm)L((s4aM2MhY@URtrt6`MGhsFD>S>hV9=`p8il z<>&^8hdU4FEy1;WUgT+%X;ujr8g~aWs1e11gR;FqYW@>ONl6;r`=KmhK|sSanqf=IMGC_@rgDIW zwe=rTlJM_*o3UIG4WU$C_VNg zsFIIvodZ3og%u{89n#4+Nt8srZfK=-VQ|Bb|2{dG60v8_ErRcBm-#@E9)6q^8B?q zs6Rs8nog1sAH^&x1qB1Z2EiM&bML>3r|eHK6syV9PsLF3&WFNl*S(^fc^p*1ToPpO zp3>ICr=KB#BKis79A@ATETn^?BP`_jZZYbc{dwd>#_oFSUq!JhPfd5(J_O{DixMA; zr`AC=8``}R!YxOS(>arLPSQ)gX^E3nP=5w$@#B)xWhvy7#amrVlfUOlVOf74SnYdl z0ymRmVH2ZN>|=oo!{$u3g+Tg-Xz16ktxqrNVrrV$% zQRk4a0=?LKUZFPtO78?oLH}U371K?aQWUF3MnZWmY^0x0vH4}PF|PQ_K+qF@qfrX~ zRlfKBW;-&`Y(R6S0sZm*sc)|P(rrYk*vsn~*7$obZa};Kr1Kt>M{29Z{b&~O1GFl~ z0hS>4*uEYyJuGY&|K_NZ855c-PaED&6ok@%I1z~t8LrzX{~9p1acUpLGNInh_OK`$iAGtO; z`4K^OaR&6D*1*4CO;rmd1{&&aN%4$`7!icgH`4#%UJvZ*c}TB>`%r8Y>|WM1%;#zoZ_t@@ztKlJ^)KIWrpbeL&>eRrV?`!(M=Ba3^(wg=y$4xG9&mJ>E+6 zJ#~bs4VC%fra!PZe*KN2YaZ519ey@k&>Fb%-VhGVQCmj%M#{=O8(~0@OnTo`mfA(^ zV#J==&&sl?k!kHal+u^#@^jvG1WQdp2N;C|(0 z=fJj(b@L4&LM8Qzb z@rz-`=MT^rX#Aws=Hn8iMm8sUzP<$tDa@qIKE4<}XEhpN;TAVCPa@>N)Wpn! zlJ9z|zN7uy-?1+o#Uk%dYNpy)|H8Qvy~z$5Tg>0ddS!Vg-D*b^dVOoEMYxm{b>c>v zNsAoVn%@qMp7Af1d>)dBJ)6TTs&l|}SX7Z#uXD#z6}uTR2|^?zcR^INx0#vMs&0pj zBV%MP7^os!lR5oqDc`9Tu~YRey9Ee$;(A|U;Jh&*C=eB=I^j26?imaKJFHx((`kea+3XQ@3;#abm zTo1*H;R*aY*#n(gk$2sy$nGD0iJDaYa5Jra!Z1)V^imm)7bjLzu|T0LR`VzqqV&bY8!f9u97_u5lx^UYox(L<$x zZAP@zCodHACPRMa$F=2glGYFMlhrpLqV9IeGrqjJG5R(!@wCc$%?&Rk4oB0-U~;H5 zn7}IW^KZ}5Hr}=_+$X%0$_(fnR#_RVkwG5;iV|r;)a7as{E7`EVgLHF$_-Dm7>6Qk4#HXea}k#TPnK`OZcswByMZc5qGb&crO|m5%iNa;7O6}T1Qb#RDR=gQOQC% z+0DK-idmW!!x&`KY0lU_9MfIEd|jo;dB*iJ&FG7xm6g8h)F-#$ylch*c*1h!Ip7`S zIUlF(USb+r-XDfYAMn)^t9~IucVHkN4 ziS+Pbn#gnbz+Hxf+V}^=M&4M=Ya+u%-$yGf^&ip^#O!>*AN#2MZUOX1HKuMu!E&4H5ugJ z)p>kVqA4m^gh_Q}1xEk#fsabn`+zyvTSYAJidY#JL#$`1^l^q)MLoJ-v+(TaQS zT;te+^;mT^Kj$IzhvHf`Lnebyj%Tq6><(75Z&B0stv$5bCi1+)FmnF6NpW9#`-^zU zz?1Rk7{0y((bRClGSa`hv;^F~4(XijD|03em73>p8~^!2tG7BuII-g9T|RR$EN4RM zy+e_JAVX#R@b#qen%!0X=;KCMrVD8cCW!M3gU0K}$419{icoS%$5!>^V^E@Zvej<= zrq0-Pu5IJ8#*!tp9C6vi+S5~LPB&2|1Y0EeCIn}V4<-5i!L{7N0g;u%jlml%cb5-x zOw%^zDO4^!G7LO~Y1#biF_VUKiAFA2w9pbND&m_UH1giSIt$~#C1mnT5eH&&8R8lG z@<7MDKK9M$cLTqy5A;7CL1#(o*bg~^$aMD2!><*=wO~+@a?W?Mld=c{>jH62@k*U4 z2Y-N4kYnA{)JOhU1ct}kGr+F|Mff7oy|hI?tA2C};Q)cxk)GLaPzEvQ6L9L5onHkQ zoQZfFQ{BK9Q0&*gT&gKr8PyCk(5_H8Xa$EG1_Oc$n(ib)S(x-f>)ZsKJeck5o-8Hr z&3D`dc*SgGHcrwwO-s({*v{=x?!fO0>oa+=FQ~mG;c|#$f;wMsR;#N3d%5X@{#Pd_ zPgM(V4%!>H1XBx7fxD3G_fgerv~r(+!2GTXWMnDb(u29zzwlh6^!yrAa=Ajs`DsYI zvq0G5UghaghGI{5fb~1#-{Cm!b5)>SOrLT)C;pNc^%5O0l}xAaPeedj4#IM;4OVE< z`~m~XCIkKbX2-#yO)M9t5in(_%Ieb|WRaL84^ z8R2bC$N^JST(^xin4L>3y=?H-+1uYA0FB#j>zc67xu#`iX6B%}azI7J zv5}d~fv{AQzaj6jB`B|5r`4a5g7179Oc1}gn4u8n{dbhKA^E%@xi{cpDA#Cmk7brk z>FGBa0(opa#YH_Ze*4S>1I6B{8A7i`m!nOc&B?m6Q6u0mziJuTZ}_uUL8s8%za=;H8(f+4Dhed{H_HZjV7$|8GHei zRoNcFb?%Mt?^x*OiUn_;99Qcn9NXxn>XZF>FNUU__)=1Hw@7fL>c^B13_Bk7q2?|> zcbIF%1u>%pOzGly?5V)0fOY+MB7X&%>>+8ULZjM+fPR zR+M-=iB|_+uj$_?qs>l8*hz0CAZ`Dl(Re8MjQEe#gUfC*Ui^ZWum2j7aVtzbIoR`9 zgN;qhf_f%KMKS5e$oK87hE=aB2Mg78<;;O~DwFkXL(AM4+ZzOSb68%@+_8o+NhHk` zIIlQ%&=P8D;-6ZM6va9+73$(ndFP`4yx3R%GUpErz@lJNn`{{|)XpT_DW5_c@yjEa zgXJ9#=EYf2j->@vrxOc!-u9+zBP$Q@$6e&#@JM;r)g>Q>?XxB5%wOWLpn;8fev};l z@$X;FgHX99(M-T|!56NC}Gk`jEc*EddIfy z9ijUVAMW^RT2+qLK zXESd9F_5i%0z|4xuQRS!zhlzDW`T#7SF_rz2{uj|wYCpAzdf%mE#7Eg@m+M?p3^5| z5%G_vPgH%P^1S=Tf?Ar?!tOj&)3G{!fVD^u#{qAwJIZSZ{j+5u-=Z~L%MfvdIgPVtk<8sf#sBjWi zL%(mAy@e|LB>n_ZcCzW$uj|YF-O3W+rX4ZiKcbnQ+X=$U0$AEydHWzt^R>uL>^_b# zAQZ6L&x!%NoX2&O929Kdyv`0$(a=cldjL6Nw8FLNb_x;D8 z7}PUhW`&A@LFc9bgvr0Puh~Jtu>=AkV0J2jM+l4`Z2DMHfWSgWP5pD5&wkeL@7m{0 znz8+^?2q*9GTMER@wis&wm1a&noBRDjRzx#(85gzf~QHA_^A=FuuI34Na`qfu{&9YWIB5jEG)cUT}hjqN}4r+R@2r0Hv$9_eBEa z?3vFKHLjL0y<*U*E}d~cKi=UPu6Er@a+zuJAFfXl@*-WTg)DFRrrdQ)H+93PHMAFt zi0O;|-@4k`Qpnd9!-aaoz^*9+-Z6a5pXDz<$xDFF1I`8uqdRkLWkAuhgcBZwXiWzJ z_;9q|t7>Do#eUjP9A?L6V5IhGatEEvY_!QA^X2@Z^*_=JnV5mnws7UrbJ5zM@bK`% zL3KTIBBqx;x9;<$sR9(&sQtDBRAJX|+z2ZN^7D~a1eG|xz!nHPv_3sitTgh8R+@wP?HUQVTXP$TlUe@^)xrXEUPp`3PYi_FfwygoomP|(5cpbdZk4@< z;4fnV^CrdKVn*Fsjo+VHQxAboX_={*JOs&#gN}}FLnaTF)75#2TAjOd^BFD~ml=fG zFbsC{V6T=&M&x0yB&GSCJ9lXB_ZR93Tf;gUh3_nb5x!-z=WfvA?5yGbUn=k2PQsbA7r+O*IIF_K#|L8sB-w)bq6Hopj%Iv*_X{;q0);*S5Jut92+^pHMaB*?b0@Yv{^4ee|%fUy-+6G5b`W;0V z4#Rc{gzaRlRurv#DNrG3v<3U|{~>I}#H*dl>B6L;vbq|dlvKt9o4Mz;=g3j?u{SXn zv(E+lih`o|QxDz(-Av`EZ^vi3yy17h+XnlU>)<@Hw*ft6!EBUrLZR_QjaV3_@uTf8O}eq;^gd}vvpdKH31Wu?Rcgu zl!mKYzhfFrljEw=;Ik9PU$n7KZ1*)}LXZF4wtAsc&Biyy*wb<)`L+LTjfa5FB6gu)rmCswEqKcH5 zv^JvKT~v=!+`fJFTKkj~V=TFp8G203${m+fFz_WM2lvDl!@%cREDDYQ_RE5#cP&`N zE9%)OKZ|zQVnjYXozY$!+FnZLFt@QBB`zOnYW}9m;C6b?`%E4gy>irWw2{%A&C9O% z><1x*2M5Z{^wFejK(*E?^L@Sd;N58%tvk+GE*P#DwSDz)eP59h3_gvWtzV_WRJ$23WHYAsjfb(+1pZU83(k`0AqH61p~0hVq~DB%;M>^#Dd zw%}abj~gmBo3QEV2Ce(PG?shSR1;{HJ`TNanPiE)4|cNc;4H3LVNHobGg?7A*Z3}n zF10Eq0Tb8%)_oO>$dkSFu?A>0BXH2(ORnqZs!za98};odB{dbRLN4y>OWr*_J&1hP zT=ObUP#yrAU~YOEoWWn44Hw+!PO0fmco7UyPdL5Y%i?CY`|@8O#^k1?q>N$G`Bmk( zr19Ml4chb+==)7BLhoUgf4oi1eT(W*myb1E?;C<{1oTv-q}(=gFuNtWyF7h|f&#|4 zl`~!%8pNBMn>&AGW2hQ4%q=F{57t!MqGJ$US0vE-NyM>Lqz#si8`k(-3He+c5P^j- zCOR3kNk;b0&YZPioiiuM)M6w4Cn?`I-$sX%60C*a8yl$39CmR}P0!DZm0SJ2dFz(R z!Rlb4p!fE4qhCM(sV;OOVUBXaXk#rQJr&HxU04lQObnK=7TJo;D#M6buO})TjSj;W zg4>2i%<=}v^~Iofva+@|pQ;xutjPrjR>eiYyLG>(YZQq~h@KrMfy+4$;oG}8lbX-OGQOTpIun^l+_*L)o}R3$5fei1Ob2c(P1XPKJw5}5{WE-4}Ln1 ztWkhLu9Bk?1mZ@!ligRK*p&Dt>W@ER5n|o{=h+B9548`RsBc)9yJ~*CXEM_6{=KTx zV|ynjYM_flW$`>s>JFdlrd$v{%}A9a9k9ctt4)GSMJ#w& zK>$^E;GAOnO6`eWC|3Fu9V6pNA7{1T8>>wzIc?di0B@WcjZw{Pwg}muHA7e~>=@v7r797JSf3EUZ8o^vyKI$F8PTDf>>b$sb% zry`@dQDsAIgM*we+BC(Hw%@m#?jg;-knaSjh+W^Hu9kbM2CtL6gfY^k;7EPBaf>TTk(~x!?uOltUzu0gl4|jJB zFHR3)nAD0R>}21BhvPx3v;ZH2(#aQo?9J0;mnCA5{`0@##93&cf$in5=X3E1Y_o^@ z#61|GY~!$<{KDLD-q$D2`uG1QBthtuQX_rD!Ghf6bblp=DOeL}Pl>cA^ttke>Q&Cz zm>Pr{0$>EtCoaqO-`JeXQp8I|gQ+93p9y*YzR2lCry&&U@nCG89%4WUUE?5V<`FtL zQJ(v~l3K8~!piG_2Q~Wj={rUhs=aHaP`+zb6Pq}Lg9C04l}!#V{dZ+a>GSNLYmhUpr^WfOyVoT_ zD*%vd6gA@53eXVUxdhw#MySR?v3ZA_yb}sigbsB{rPB&Nl*a7PomDu!=u7{1Er~qv z5vaxbIZh8ZWZ|)akCb0}$exHK+KQ`*Q1oD!3qycGcDJS49qn75;w~V zn*;I4K2Wmgqkum06$r>d+0h5GR53(ktdd+l-Vcb1Eal8w(7R=V5{K}~>!wI0I?IFA zV%hkNmcO)&g^>Nog}ni?C)BKdY@06?K}F8*5(;Jc-a@Qi9~ibPBnw%u7}A6|mCef+ z>eiVJ<_ubf(`yucomoP%8wEXFppBG+*3913)fAf3>JaTORXIpBQGge4;IBdU zBo}b|2wq7MG_r)y#B_pp(qrHQ=a`qp%>q9fTqMF^Ut|Y#1$qdhkyw6L8|;|%4FF9X zA*4W41s-N_8*zYAqxdB;5ushly*yUI45&dW$Yld!ob2q128A%ZKpGB4R2;Q%&g z^TU1qks^b4=^-%!jDJ9pL_2rD+y@3O8lk$)I| zECwqbvdj8~_K0V>FH!@_VMm0}{M#PMjZCXw22Q4-(eZP`{*N>Zu89Bj)hmyX=G9bP zNY}=j|0%1A#XBziB7pa>81OM!zJUEQLm|bmh9y0Z0`^M*3ZA*fSiHlf6OkL3>vVg8Zjt6Z<~5ZN!av z(a70M^CePwUGL?A#FsMU;NfwWhHV;Lb}rp_y%tcnZUn?X|-b$GJb{dgx`LI zfEGGiXSbQEw;pMLBf~A%uCPgm-x~jl#)K`a`6~MO?2H{~c;H?r+`RR3YKoGj9zl`} zxSVpKt{_A`ASgr-@pFLO1zh}&WzGagI37N}NQ5?*#ZaF4zA0lL6A)HR0J!r5g@q$n z&LA_%+E6}NC!$?)!6``m6CJ%+zwtUGzphIAxuUW%v}wS_q8ZdbfGolR@Ji$@hvmd$ zH!YIlBMe*VD;Vv;ejWfB8OCuGCY_%RcDHx|73@uw+ymhUI72-FV(o05pn!4vZMdwT zP*Qe)Y8<3&=wAV;-d`II2i%Lyn+~B}?m4#<;OFUTOj>$c$_DvKmA7I+TI*C+?Fx{HyF8DT15s&@lhzxVq-!qVXTI@Pu z+f2QNJUKY&Qn*`WUN#&e+Eote@EM;1*zpG87hrReb8>o0df(?@RTZ}f4xV|R*Fo$? zDP(wR4YddtX>iQYM3zJ6U*Nf*G;b6maax$6m6VjssDii%X|o3j7;dI<`$u@k%M;>) z2nbRzVQ7kDH^muSUtbRuT7pL?-p>IMYUQ*6_ugTpFI_D50@0n$+Pl#@DgQ`^qE*S~&GchqC{fHhYXzCsKu96IxGV;;oypRDC=xHwE`Noos@rn@gA7XJxQs^)IK#9PlR z&Qh+IGef{w(_bJ;;%a`Ox|=_?H8w9a31x4C&zkoFi;< z%E-Wf^?D*%Qb|l)y!Y$V8{X$TZ9Hc%I05D_0ybM(7M72I*zW9g3&TyleT$suO<c0*gH13P+PO$|RvAReWH6G;lqqU&xB z2$zZtTdw}tge`9QTo(*e_s)9zmdLMesBHJk; z_rvuL;7>zDTnFdyncTwq>(fZ4K9nDx;MWB;Qy>(o;b7?!$*d;?%~tfZGn}&=uLL2JzCu=n=R=A7^F`Pv309Kh>HvMJQI2}yA6R4mP`;tX zTmjBM5({1LP8Jphfb{%)uN%e_ah#SzEbjYeQNQk7@MHd~QbC>x46nG8kaf!DRD&X% z9k1K=gIH!g6R1L+HpfZ8QwkpGF^K$mVCDtvUQk!rE&aJ4b%`OO!Te7YJW&lsuB4eJ zG^7;?xZQ#2UDk}|?H|yf(5n}a12BwyDUn%xD`eYHo5B`TD7Q*&r;;G8ynq&!l*_6e zj)8+9gB=M8nS)%dBjOPTp!MNLd<<%N)9?y|G)Q}P@HgQQyAQ1BofRggOiIQ2!q zJ_k6272G38z{ncFw*Djl8EXw!7vac$XZbTElzu3KVP2*PcO9lSETy9&W$Qpb*DSXp zL5ju3E8$%fJiTcSC>++K(Xio6!zBF!IWInE-a}|m*C%R->U0%hE2Ia9Sg+Il#=$`X znBagpW+!+DW~moa_+t|-)@;=q^`%L(Jr+ppF)y5aP9&P9{YWBI9}dda@~O!E|L#8j tc{v2U#SXmm|7m0Y=k*n`mF2y{h}fjq?DUVpfLHdRNQ*0o6^QEl{x2077nJ}2 literal 0 HcmV?d00001 diff --git a/_images/398ace28cb7992fdeceb81ba3fc65492e76fd1439a2a3dba97a4f3c455089c66.png b/_images/398ace28cb7992fdeceb81ba3fc65492e76fd1439a2a3dba97a4f3c455089c66.png new file mode 100644 index 0000000000000000000000000000000000000000..84ca84bdad31164092c6c9c10013d1f821bff6b2 GIT binary patch literal 22261 zcmeFZWl&t();3B)Ai)w`lK_ndcMU<(xCLq4rSZnyLV`4wq;V&>yVGcpK(OErL4vyz z>|5-;&-&01rVINi7#Od#H=Mv6DH9qOpsEt-Xt_l?j!bxs$V%J&c=;pN*4+%F@Nf z!C8o%-R^%1u-Q9Vu+L&X2>}|xaFEe-RNZFnD^db7) zEP9|^8y)*xLV_yh(UNKAAdO!0)KC0;fevK#!+Uc;{P zTlZ`kI+w+lc!G4Lun%69U;%M^pS2da&l4ixjF8jhT?ptJZ;x)KSm zN^4I~K_@4tFKKC?$he_Kr(5G2lV#~^7V#|FAB;FiFFRmlKwVt-kq=>Em}3(Y zCcl5gdfZ;FR~UDo4;Zap6C)f(m4NZ-g5hwu9*7W=niRX+<*rsldNoR(O&nOO!=0)j zsTng|e$TjaV9O)Y2{xoSNF3U9^TP}+;%j}}RS-?WJZ_N+ghOzWIy zt}|XqTR}{ApE0#%X+D#PKTCXCN``m&011Wr6qi=yDB?auN#7CtX`J`+As84@qZ>#EzRQnlayf=|FIUX-V!2VnNXkHpV~BJfbo6Ci+!T^w{wUJD%+K zB_#OGWR5R=T8kee(B&`i=&R~JnYZFkw}CE2Na476AGEPw!+XeflBj|8_|Ad*|3Cg- z0zj7H00H0kJ4aehmcGEm!s2w@Qr}r<)?HJ+7q)Pq0H5vDd*Qu$)O5c;uO>=#Lq_wI zS4QO7CEvdN*q=^!FJCf%&O!R^OjVdNvaoap<4|z8?M&sTKMRroq%*&$=q(IZieqEh z+SpC}yv$xaf0NLOd-UCx7h)XDzfP-rF7Y<&Cyijd33*GoTVXYQor zWEy{Ww{t`a?u*D^{=*^btKX1sMP zN!$HzF~&ojPml0&+f1+&>m!NNeU301-F6GF8ct4Jvh(t0E95?5@uqV5+=1^U8r~#k zO$7>UE7qd>LzN3c0ap;F#$p-{bX@?YEEEMj39&H)O0{MX+7yf9+ob`lq1j*oEX#HBu}Fw#Kz zY#@@a_zU-CvpIeI+itGV9;ASEa1TSomRApHa~Ga5__j-h8$2dg-T0DA%H9!7ir4i) zpBwygJw7pQn_RcNtnj5d5$}|H@dEc98>q}Q96nUOQ+s!jQTmv<0lxuI8k9?2GwCUX z+s2wdDN4FRV)a7Dy2UyfJSxuE8MeHp$WH!bbcOWvCD1GrROl7=ChwGP8@nrE-q)r; z#Y_LyHPiantDk*xrQp5pQnLG3i;dMooxqaRCXg_$(4fLoFseqic8A=MT$>TC|E=J~5K{?Zt22kDkNy zea}lpLIJ&NLlhCH%NQo0p3-xywVXO5V6gb%nHyy3qduf@pBtCDs66Nc+@uSbIk3Px z-3?GUBQZAE=R+2BE-2?Bw1=G(e(@+opfSWc=Yot7522u-af(NeTUZ8+fm<`iiue)5 zY*E{p>UANSBzX0yf(D(U0+o8Y+;J|gkSLz6>fg-;V+2~Oy($nue1)^V0uwT8Xwa#` z^wNN8$|VD3sF~=9l5xVC#-hIM(Sa_fKoZ%w zv1^K)!lDQ@)>j(BdfxcJ5RAV?Q*hXALKQqrP@yG^9ZNIOwWW^{lNFMIJ=9*xv!S#^ zx|feApN5@C>1?u2hQ<*V(Iw@}gDXs_B+^!b*G??;aiLLR<}Nk~7-9Wx?4EQg@$lu2 zQnDjLexAI(jSpX@OYr(JzgieS4USpOurO8{qbQ%=qhqJt!dJNl29>rHx8_2oJXjMnKT4sS+o25|I_0!Oc;fd)#d6So5UknKp0F07lYkqS5k6Q=5L zNr-=&SUP(1q`wJ|s3!E$R{1y|ORXu=UZi@X8T%F9vn{ZhD%r%L+7S`9h2F2L|4DSW zC5q02!sZ;(@Kk)x?a@KR z?@GwCiOH%Ofl(O`-r3r@u3%Bdsmc$WFcumf{pt~tKg*r z#qls5YLu9y8clTm6btYP&R~lTjp*mGd@IXgYzY*j(FN-fxMhh2AQFit~k>G8W~xx0s%)Ft;}T zetnENPESQ^ZyGN1VeaS=dls8S-W>L8E<39G?MfesAcuw=6eg@^ND$Nv4_tI9K0X*s zH7!4g-2E}x%yFE(8+Y8#JVhkswwzCfbzz(xjL>iebEzWYQ?*{;jpvVmU-TWAp~#^* ztjn97#3c`M*D(XVSLYA3YatJ1XGQR>eATEMlIK1KJh?Pr=`cBsV)k5# zcxF-KO2S-pS9$wn6L1|qsr)YJ%}4$kA7VQA6M?9=SDdB(@l5eW8$})lY{;3~m!(S? z%7N>cz{GEgEI1i68`I}FAHh2OsVmG0C_zz*anymlA&>|!nR$ry;V)g`!#*2yiamC+ ztGaQA&3^S&^9K8Ro@k}!&{OFiY;@35)lDc%cm#EY#^PLLBUW&1_4g*o?~7mTkfcT4 z#_}IitwiHE33ZK4<7OY19y-xoPUprIkdw718>0FlB`Ju;fBJ-7eV8$#TE-RSG2WVo zQzmes=(dy&Un^DT(QCIS8@RXMuHXbOj#BVOZ`_VIYVoJPnWca7pe1qB<95sGwc{@C zWKBeha`!ES^Vj*-z*K%RH}jVdbNyn;AKaAou|(B=3z$)vpq8ZS&;Qz`SID9`{J>wk zvYzGri=T3L{JOL_*_%ga9<6~)WlgMCp~~lOYhGZ#wNUg2lQnN^m6biOHMx!+0elMb zTB+;3VOLuZlhk|)R_RU%noX_gih=7{PB!)8I~_;X7Lw<5WOd1jLvPFubn1n%wAaaM*p?fxo`~f`!+q{h=Zo^@Pl_C4*`qlgORDv;-VQqjc zb1sGA0J;V{#3nMla{5(V9G3|6tc>7$1VZZ}O&ylIv!8&)^NG1P`1i^ruZ*&gA||Ba z=AZGPgK%(gNMCz!_#G!N_FR*hOCk#*ny3tqMcBvk;2PppHU`i+2t{h)j{GSronqk^2p2(XM=SzUbdNT4bDPDU9@mwp9hCn)MS>^kW*e$KG(*yLuMA z4m~)4AnSCiZ<3?a!emjyIeg8|Wo{6K1Ovf~p>*%PmuILKC-8b@Bn{k9q_>pF`FztY zRZXYNidoGy(ANL6Pg#j4#(mG|=!^l6HbKa>aL=Mz2OJh2uA-?KGB>A3F)^a^&&l9! zffV6nC0&kYf*h8Y-NOiIvG^Eis*T(o}1`rM-Q|Bh8IEF+*f4j z{=mzRAqxw?J%W8$)*qqtt@BqL2`I!Q-fYt`?P7^W`#ukrpnmkRJz;NA4h^&(zD}9R z)N1HbOt=fZT1s);&`vO2A>Z6ze4LBj_FLI*%q|CBTY6|vY>pqVuC=5lK^qVaA*&ZVdR)IgQXJ__4K?bj>4XG5@=l#W45`#DsPM ze?w|uVd<+(I04#f@f!wpiZIJ;z@E7)ttfr(uOln_qEMlxO{^2WaAyZxPu<#902VE| zNhUDANRDu=5n_x|d2u#3N5%d9^K3Y3p~fUF9+MtH4@EXQ==Ah-3VA-8{SEc;Q={#P zl8v7ksB&_0X16@rGONecx zu#1FL$b~WZ=?(n@yL-0<_(RO5$9QbfD>0lFwp*-htggp0TzmzX@T{!p^v?P#L{G!2 zdl;!0RuT^79K|5Ir^^{5ya7$(*5N(pt#`gtFV-tIX7bO zaT(#`7>InwU@3l?3p+xH6t30D8mfC(nn#&?de#>-{sST47QcM1>L^zCnfyrN9KRqa z0FE{nRc{Uzx#1|`XD|Ma`u77atK&%sGEkEl_~0S$eBXnON zLg4WnJj`bwa}mlohnTohl}wtS{u~RKf{=||f0CO|VKUJSJ)YxPM_7}>X?r%H*FDt% zTGehL_>XgozXp(q*)@=L=V?@*LyS)e(p2KdeQkam0#2X9v3={$>r$rqltQ3^s<|lk ze2}{y4pE*vcPY%W{V}qTrZ<7z~C*WjfxythL*eL6*cLPn3Owe$B4uFH*C=+(F)a7OU_|od#dg^2o z$qIVU_6T3=Bd(Q#ey8%u|B}7qq>#Q4ftkp`S5x;*Z=Zz0&6naH(c1B2dh`_sjmvBxy?) zKIjp}QDuV52U5Te3L{Zee3Lmn7hj`;@TYx^DnE}@CHRdTQL+vMBO;SnqEDfwN!1?oJwl^M8sDhUTXy1$1@gRP9;1yb zf2^@k^&QxW1NV?>{E87`W39-;QRx&Iu2R&NFya&AtItgLp4+*9qsHsCfH}%vmJLCP zckdSUkkX@OHwKixud3UHfS=S0zybKg_EmM(iY1|sq%=M|a9R_27;T~G9gfkfZCmkX zWzPiJ?{Mt+!6$Ca+1)EtjGf1h48@XHkq|2BqbUFe;8RY5(5Zlps5HTM0$%%mU&M3yh}{@32yt^<&TX4x*VH#=ao z5^(+bLC|~p#W91=8}S4{J%L?Zep_V9XdD0OA3G!~gFa$^Ud25%^OHkGJ=QnI_ufW7 zQ1oucqz3kTdxGFXV|>g;x+4X{U#3X-U$iAxNv+)02+!D78Pn}@+Cv)F_O&YpE*|ZR z6w=LjqoLwaWXvBGj^_~^6kA>>dK6>w73#s)IdO2H!vf|}Fb|aGL%6_te=J2?P z0{d9HcrXa=nqLCxn(TiXYG*6>kygw(S&&n>b{=qpS;z5rn?D4=%?e*f_Q>Eu?6gv+ z!@;X2FDwX}iyKLXEzukV(Q}5)e@jdWA|0xIlcQn+6Ga)jelDLOh)e!9u>!CCAL*^z zSIuFs$dwx8E4MRv*c8NB?h|2yX;n9^z*0ZI&XfyPk%WTTCPL>TN{U5Is{PH^Boba& zQ?O_GCKJhs*L`4NS*&h_oGS=Wja}6h5X`P)*t7B%HJB$)E zG0jg6d))lbj0FE3`PojDDb%MrfptPZvLe|hLdPgWYuVL%gUTDb)-Cf30N_4QSLEN$ z2v{|E9{kYiO}p;f9N3Ol{?Wnjf0mn8XFFn{i~J`$vHAIHmiLE|^_yd4YVC3JBsNv2 z((0^K?r+G5EV9t--IcepF4Ky^`eMx}`pO{X12;4sCRfww8&bH8S?!x3?BS@J6SfJd zuFRdZ5W1Pjix%c9GQY*WMtZOQ2}+fU_bJ4GH-s}_!6QB3vi*gQPDL{(6yMZ%4P#+Y zOdc2*xL`pp7laOqN-R*O{y42d1hj_Yd0cLp+CPrd&A(h_5wv%Na zWu~4IN!)}6z|I;fqtLPbRdLDs?Nhs?McGx^*@95~-NxSOc}S)zzZ+<7L@qbUEKjqo zFLOLE--c%oS*LnY;9*<#L8B~08j{lb$(Z8&`FB868~YT^UJ{Fh*eIKeD+2x>0t zd<2HFZCf#sIqpU7sg$y5Un1XK3r8&e@wZcHsra=zFA<#^JZ8+EQ?teBaPUiJXo9waTym!OQ+64EAXug@z1akQkI3%Y0e)M6dO( zC7l)jEV7D6IvSXwj8-a}bG-Ph9#zL)`+T-|2$>o`<*F>Elb(c-t_?GTrU?(POCx9! zGyXA(Gw{iN2y%h4mWx#m>qGKzI>-C?l65qz2+)#tx0>z>Zvjwb{5nmg4wA`Acvzb~ zH4j;i%hsVO)Zd>E1Ws(GGw&oa$S^N^)c_9144Q=0%g%0*?~b$eX-NSx7gEprG3%k0 z`c40IQ?y)caG*gXRB#n&;Fho9K7s_(WAe<7H*m;t=r8){a9hf((p%&r+d@n6>z;)} z4!!mPbduIdzSW|dVRx{B%+Zj_?6-J!9$j5SMZ|D|!8|KTnMXmCGht-$(Y{W*ZnOCI zY@^l2e=ANMmm~4Zy1pB-{4Q|H?8fIj*(_2Ll_ncF+ zCLW*#=0;*(+EiJ-CV?w8ka>8_h|Lv^#=!WVffz)HqS0Nt6={&tTY>69Ne+MdrxPXz zO8N&wR|t_mS}9U3I*`Aq*_j8%zyZT)MkRY&dJd9?EIy_oYqzPKrFl#4C21{U8gx*0 z2It1#US)v9;x`m5j&A9oq8xlxa;ykZASR(0ExHFL+ONlFXPfvNc^1`uG5L)ttNE$| zs)xY=Vz$cVP~f}UvpxrmGUNFp4&{|8^!Ec-ML5U={KOWq{cFW+{3_x)BG&Nu79Moz8uc^2dd8ut$HFlG=MN$5+Z)Smx<8MKG^rs{ScCK3p=qr; zq%z2LFMED_!v>=DxK7sQQ#}`mzZse;w(hIy1i-n;S6-rLIurL^`euEG!KV|X&Mp2i z;*m=8S4N%6(w9~1Ws9g*b$a`$UuXk4O2c=*zGDnD;(#|c3azfK2{jAF6#`MAQGN=( zTFn5iP+h5Q!CcJ;=p=QMb}hd4XQZ#M&)5c%p@?3Bvdl*Dx;H_Imp1C=E+3z@uDeiH zr?VFW$3Vids4j2OO9IAOrtK%h2(p zkcQs=Jn_kQ=R0V4kEg(_nN27mDYu$Gt%AKMtxmc`_ic?=;xO!h=|tSy_Ys&URAI-$ z^+TOD*VbdQ0vp>3AKFj4v|i2UUO$J#R-dQXRhDRBMo1PlTAvoDL$}p>rdl=F)w(E} zG+u(=9$RTX8ma6__$DUxAs>(I^kW1Ne!DmKZGqDxs^@fJ@90*^i0bHBik+%%^{H*T zm7T1s_LX?IlR1l8O&nMAYxq?exlsaTwj32}c=0RjVkO+j?gj0{`NIRcx1dRHWZ#u= zK-K;&szve`ai5otU{O)=4V7sFSKQ`UmyS*DR2DjSS1<$B*eDy#z*uoD49||ws7j4= z1L3JyZm$7#f9=!z1sKN;Z@XgE18&^Q$9d4?*LT0vOEA;tw_n+aCZqy*j(Jj|$wFv& z&BDo%KY!21srqvPDTA-RH1~!H;5XGOYVQXgbo#A7rWvL`3$OL>AW%q?ud(@{4fDK+ zqDE}zPuqN$e>NFvG}$jI;D{>oki*IdFE?G-{zvFqLW_;XNKWcNUFF%wX{%X+#Eh|i z!ecjOK6J73g3U9j6*1;7g{hP$|2SHzo@`u?RHHbLA2&OkXTQm}7y6hILCC|CE>0zR zcR9HCXc(;RnX2M=(U972AU$9}FgHfjEw)Oz^K!)i0Gs_M>mLOA4{ez2f=6=nX?Rl@ zk#;dOUgG zOHcjaMf0P^#J?t&-1tpPdi6PQ4sGq(R;{d^f+;%g{svb@*IdZ}C0t|$vJu^2Jzrq{ zvmDYoQp$m`v5%Ey9$cicMdwV;N>E;_0iV|4U{|306o2a?R`))|pd_L)j9fvtQQO`3 z9;$mQceH%1ncuZ@Ns%kZN-7cx%ngV+*e#1d0sDr-Mlx~lW7N)eqmwH*j!i%8p4|bk z699IHsNZRkp>cl#r`xrQ9)9S=shr3*=d3Ngsd|t#4X>F9Z(?fge1DTbs1m2WIabUx zg514qE_l)+qSM!=8tYx+z{hu+vi-&2hwprL;0 ztdHH93G2#3Mw-nLMYB19T_qvjWY_fnG*G`hg<1^}+H@)^n?OB9rlJH@JFu=671iy1 zQE4B8SXo)8D$TL>8rIV^$_%|WbEcXa>?oc7ijlXz;Kj!bGAlLEJ4vEgHyoZ+FR-X0 zv0;EEL8I8r!|Etl$jPE3r&C?X?Pv!(TY~BjQUn#gmAsr6GBaLEdXW@x;%fth6VM#` zq7$?Yp4DD+sD2!y=EW8CyA}YDsgFvjLN#8^YI#ZmPOCD>L+t-RnEep0uKWf^uSiGz zz&~rFaR6qghY}fQ#RI9Ai+w|o$iqA=$g$H%mTY(S6pB{<>xbVxUawg6{a?0MYrG}f zdy2G9{H5PUeYOsJr)MKZ!$Tp zVLVa5()rao`eJgE*pQKkY0Jh#wWo226+zBMQ=Z&>vpf30X7ir3ot%UDN8-!${Xlc* zSp8UWU|nv`Cvff+Pt*%c?-e7X7y#NOPkb&cEaWSpMY-qx03rOuBC{|RXVd8n9len} zT;E1Z9`AK?lO+n_FF3B}z-5>c4hyRMGxGT;= z_lrV&ABQi@6#yu|UU4ep3AA>^qMA(Qw4RQ0$X4|*mB4G$FK_1^YxfD$&k_=2SGM=M z^pqz^u#-sB+X6#2hWFk4o|4D7u5eg(VjPNsoz9- znZiZ2cT+TSO#(|&>v-ih{7zp#^6^U#6kz9*au}g3c&@*2 z*`825Q`YSUBt=WsyI&VldMQ#h>Q$sstq@!Atw>@Z;b$JrA2*EJP4rpUdL{SnQOh}1 ziD|}wgLVK+KjqrB&P_ho+^+S)-j54uDz2_SFO$n+5>V|(pwN-%U9^?eXudg{b-dcG zn>|z;NhtY|81rb}L=z}I+8=hOAU;BWQ{ZjBLfCF;0xJ#=YC+8D3Ic5j9fTeUeCqM3d zligo7%4J%2pRrgDQhe+ZE@|axlB)#)TpIrK+3)dNZXa3U8EcX}g)#5=lD@)@Tde0F zecNWAC{s^Ki3z;-spV4s@u_)gKD`%er$}EoZOEaExsHrppktirl!!(rwsM;%RTMI- z?x(xsDIdV08#Wi>tVqQpiyrOG{`SZf<={whv5SO=H%YQ@0w(hOoRcOzLKbH?rF=y3 zn&Sa=foy~9zPhP-^8>Q3w*Iy31pQn9df}qai|#Gep_USqmdcqSc%?)CG1b*UU8pqm zS6tk?BmPA+5La;54@Pw8xE;|)$wJMP@_7c zhJdn%c!}dw=7gxxj@)j?T{F)}#x0f00kDg+k*9SapDZh`(o!AkS4#euEeOmSgYNDK zLS^F4D?`6FJ&Cgx8*W{FgWN51P0E7aY7{MQ8P{4S5@=D6d)=4yKY~;iPIt0qJ^E(Z z>*uG`AML7E!6i6`sUh0Fy}Rfzh|q9f#2O7*SzGkWXlN=H!(3=^#wKp5(BCwrM;u4M z$G(|CUMR&JX|Qq*l|tF2@hz5wVjdznF$`!tn^Ct(P?{YkWHhy9oNGyDw9narSpdFWEfNFg zSbuS=@}6-Mom0ohsAVdo_$!K|!nP#g4#Xvs7CAAq3G7aPnw*eU z;xhw#V11wx;PnG$q}>PHU&$G@$C~jgp*A6waYdhbztm>shDKeZ$OMaRQ`JTZC ze53b5IZeA}ZnxwpxoKg*csVI-$9byniFSOuB~prS!}pp53l+qkXe}0+yZ8Eh>ZBGv z6c}6Y6YIpTdltYg>_5<~dBS_Dz7vuhcA1hj8r&Co)`CfZ`_M_Jrufa!&R$f6MYH;o z+}@XN?#_)ks0e|49vvD3DgFU8&9jaVytA|Yl1II1D+y_05T$V#lg2$Q4**dTS^@PQ zP!W>*l%q$G>&(Bke9zB72rR7cmpsC~SG{*TA#htI&5VVbUXLRS!LOw#2pW z#w+;L+6~7P4IoMo7!%hRZ|$#uIyY-#QtyM3)O(@}?<51e@Ounjp-lUDqhyRNw-=>v z4Qk2fHT*WB#;~%@(V7c^#+T0iZXCeRXdeDd!Mt65`Qi(a%y6*^brn8vi*g4i|9(UL1?Y!HzzZH5coy0{m_!e2&=yh=?txpkS5tUUdmO(#@&iSs z8a$|!UNxK8@Y8N`d+hh7F=XxBV9o{QnG)H1fL*2243jMV-cMwnaIlIF*iB>vX;bgD znSVG;eN}I;W^e{6&z_e|mkL@#+mm(ySRqYW#y(Ms#D=>M8(6=eRUD526AO4qUxwc{ zd;;x`ox?l^9c07Rou%hA3?zEX>#ykm~oEj7Z`M;sOQ z)NCw2>_&4ckX&Y0y~QJ=wQ9;UTz_^U8wOECCrOIr?(!so*o8HLNlQU6r9^=C1Fnu1 z6k%;pG|99+eFbon=G@Ii=h<+O%FsNf(HqDwLp(4Q+DJT3-ory44Aih_e6m>Hmx>`m7DvE$!2tIgr9fp8bj3S zBb(|uW4b@GE3iJw3a*V)3GrSI{?AQ+HPh+DRb-H!nVXR_&N1yQ$^3{uoJo0hIPTJ1 zvLuJ!gWZ*V6LI>esBZ4&I|X(tLlKt5X&jfEjRz$FzrNcvb1TG6U}1V~N5Ogw#2Xwg z)hKvdEzZ#Q)z72m&Cu3$`UVC#lp14((TrVxQWZhM=gAiUBAB$gVB&zgRect&7K_;q zR>EReII-trR(&dzY>WhA@!50>aQxl)0p|SC1Rte`y&MaksQvsO!_NK zLcsKB?Gf3PgiWHLN-%#?L%Z;B^J>s#XvEaK^(z|7cNLKEvF~Dm&k6P;>(yZBFVpyN z&#hzBm9udZCVT7K`?d_Y)VM^+`60j6?NmEFPz74+tPz4h@OJ4$ZUf`riIk&7Z=c>X zspdqlDgUIXNz>}7`nsFT)MX3#I&)88k%!^elZDu;YmCRsn%!*;y!5@b%qR!s^Lg@# z653Va7UxngPUC(nzc=qITQmY(QRUaOM#a7ri0N^)S@9n>}FdaS% z0%DI|W(w~t+|Df7`|a@~ixC=yv|SX39k;Fo=peJCc?RyQ^wRBgOG-Q2{Q|qGJt*`GuQ=Q*v!Bg!ifX{vZ zJEAyya8;;g_d&KuaJX^nY`a+j8~i-eM9r%yMIaIFFWfwd)Yi7_VZ`?Q@X?}bAD3qXWHArC*^5JI;X3+Ui#t2R!#WmRsILGp@@_Qc+_DAr_@y>$0#Vxs<}2g0 z?nlv|1vuKX$9GD#Q4_ERZ$JR4|DZ^#kyoAMO=69fBiDXWp|rLn&e@A1X?@eaI)0PX zY1wV^@kMAThY*x+Pw*3hWYwm{!W}Z$nxue>^B8$km_$A&}kKl>X(+Wk>JiA;YG1IMFz`&Xr?7ySyQLThP2=E*+>JH5BL5sAQL7oR@+S}4qSQ3 z1ds>Emxcx#PJGNd4!6Hz9>}F2nT{agaO>g5@p-E_}{iq4dr|x~fj?s+LxiLRn9Ebyf&;9}jgie1XTx6|9Q7u=n8$r`NWg z!LAd!4>3HSv_yBh1R+CE!;~`<(t-Gw#pP`cGUyxDdZ}T(@rZC@ zu@b3fh3<%Ti<}1J)$|Yx89xexTut&TvDj$X^;mLLirMz9YawH*Mn!&BMlPTDlLN&E zglleg=CW{jHFpsljVvfEBOw>KJ|KM$1_WnaW zv71+gkv*1~ZiUmc3U|&d$A1cz58Z8Y|RRAIc==w!@WPmRAQy6Nj$= zJr0Y6FLp=1`8!`ff_uo?S1FKP7d$t(=gF&{p)(Sv!W#KF?`a!=-rM*o0}>8+tr4$S zExD6pu`#M)cY=?IopbP2dW4D>RKQgh%%mFgwnavX2E=o%Mmgv7`4r)J;sk&sq-j|dZ4g#EshClp2pVXBtA3Hqy(35hIe zV3^e=_HM-Yg^Gu zo@LUdGyk*;dGFXPJk!E9tYcYwQ={}lk449~OHf5BQ)}Li&e;Hm74YH!a9zlI+a zlm!7j9nVA~>K6Sdbn9S}RJdYxb|wAk$Jpae)an@~(REDvbV`bRxpIf~*|fh>?LsOJ zQ()zsIhUm4+=l1Em4n=At>8n1&d=hU9JEP2XB-%EmoHItMbMAY$TYZ6aRIv4h0x}^ z>kwQiUpqrwI1CEB_Pu#W=xG!a41{y;t@Llav$|*ii^cJ87R&Iv=(TgRREyXCq02Ni zlB>5@`;SqYu8;b-{JW!@4BEP}`FTD2e3;(+m*0aTl&Z$i8U7uv5MWtIQ193~ z`Aj1#H-oOycaY)l2@B^qfq_(w;kmizm)?woP7Zb{>*L8`+Lg&oB`jieMMc?}jTr+h z$fX81xuYsYR&G2u;@uHs!~Po``-^7a1n)RE|7&a}S?W}kX1!4|%$UQMW^3X-jAF4p zY84YWEt@CP_gj5H?e2Q~w+BFgYgE4F5>Tm@-(E{gHufP4KYN2G$h^nxa=6^3@-b*g z^zL~_2*nV8hWSF1X9Bl{(mmDhNmo~wBYQ}byi{~u|3h_XTG%P<;FxV6;C*6*PoNp| z#)i55Hh(-bW`l80eQvU?mBTGy+50Oyq<*h)8(Va=+1p)>Ir?v)NNw>HGziHU*rKHKD4uFJ@robynKA|3o0p!_2iQ%zx}kvo=lB?ehK9z- z_;b*JVf(_k6ch50^}Yo($!W4z!HahBh%g}Yf<**+@~J~o^Hf@cO41!(nK}#PTA|$? z?in6bsfhd={0xMq4lj`Y?H!}&T<;lF6UGKdf&8@Vn|*(2NlCPO1g}K5?jeetygVX0 zn&vo9Uoi)2udR+*txg_kN=OI^O2R=>Z7HQyo+NMzJtZld54h*s2qVi=XLme&u@=}w zKh*Vowrsg8BVc#Axs+Hn2MBB|diA(K4qjKLWcWKn!_OISLree`+y#I-ncWs|ca1=P zL5ClfG)j5j`AYFJ?XZcN6B5ohJ5jt$62i@r*CpA+bQkZxzKKs->l z;{4lr`SdH3%LvDmrR5u<^V>+b=ea=iP+OCsHc^gMXBCI3X6ny_?=`P_La~Zc8PdX( z%L@s=hS4!%KD5K<*v7Fo2VzWLlF8u0{iO}Hg*G?EwH$oq1Nu3 zLv`N|kQ|iQ#IHRY3EB?kqAXffB740LO$DDqrl3qUmWZo@36t%|*kD4l^PurPf+0mB zRrAV9b>uK7cmLN%jl;LDqc{e!#H>!DM7p-kp90{S>WL;VF4j7o-(EVmiTEP9L!I0a3J~FV;B#Da0ygrbhlNg~gRL zvfOJ4eLqfv%ju=_ryT~@3$}*0)k{SHye&s&4G>QME!E*0KYAUucd3R+r~9u-QKCFWo6h_)X771&?B+CPGPP2ue#k7@7C9z z*5}zzhEAK@co{3w;?1&NO3{XpG}6w02zVlCnd%{^(#Wme(15zPIqPa&t0iinHBcL+ zjj->WAlzSWsUb$i<64bN7XFpS_E+@QY(}hqGJ*9BZ;;sLRaJ>u=u;~Tn=p6BMV-$9 zPsp7521Hme+Oh*A6W&w`Cn={(dRcQ7D)9(6>)6GtTG+%{Ti}|oRt30#E~Q{(&60;J zEwKPAUh;R`^#>q#?HbuF`S4;bH)}>R6Y$|spY`5n^0=K+72j=q04?qeb^@q77A%$0y~-nY`#iF1k8B=d#%0if|?OXTK0|9uv1`7t2aWa}apGA|q# zf3ypMR>_)VeqMqUH-(OOHpbg!v*QAFrR-I%>+OFVg~PukB)*_Dpxy8Zt&U3EjdD@@ zz~2>erSTLvj!mF$*B*$k$8dBCRko}(EXVC4P`|?Y1&0$d|95^x)ToOsSNifLxO~+veI^uXJ%9D~tYC?!V zq;N*KZ2Vc2O14bRH)${NVE~Le_1R7ib9I>esVZeg0-V_%>=?Ak(Ss2ciVSp))2__^4;wjc+smZiY+eZS2<`nal^R6gsNv0FI!FhKe99H=KNN zQEjCV`&C96^q@6aM|BRD5Pv$fw~*7Qwe-0L!=2Mq6*3X$VqSnYE_4IDXc8`ieUWV^ zqODx+GvmTu^*|J<6gn5l3gY|~U0Y)&+J0m;n%e)=6FT|oK0%FE%4ekZX}XbY_%v9_ zE%iPR4nP3YcGih~KG2CkANu|E4hijX1OC$-x|LLH$Mnnt2~xEy1T5N-`g!0zAc@z+ z#Jqx$*cC)JlXD>R`|U`8%qi=~X_9hV;H; zd22V{xn*4nCZyokXruE6vcs@Ft@2sZyWuGvf5fvx0A16=479@|9-va~Way@+j$aP>KCa{~#TB;X5&2*bl6~Ya4GT^G!Sh zi2wtmhf!K@$Fk9@QtB?AcrIlG7k>wm!qCcGI1zuQ{yMjCF zW5jqzL~kMywzj@BVbAWfy;-^^_%o+~tVU2&+b+fe*v)tn`#uRjIN-^@J*0G~oDP18 z&-ok=Yz${T=HliauCh>O(W!pI%6iLUudvsR(GQ2Oeu*po6W@A`W-*l3RWVgMGRdA;iq6uP1eNb?q4#sIlP<1>Hw)0Y7JLj`}{k6mV>h z)(QjKDG;qdPW|7!{n|A+kd_Oa@J}1}?}LBsMjS_Z_4QNTH8j@DaTxviyn}D#!H4Cn zxYKp^lxco{tbl0m+jH*YwSl+#`ueLwK-C=`5s+d^8;c+I0-W(BU;u@auuB06t4rzZ zEf<;N?oTP-MD(#CvH!Am8-5edo9E@_(eU%vjZ=ac93NI)aC*>WdUU213puYT=(*3k z2B&W>G<&b!Jp(Epu6#G`gyVSt(a~iC@qaJ>0!~Os`1R{osy~k(Tm!cbbX**+I1Wpa zEak)-NH}rz#eG2rGH@U*e%ETWKVje){r)QwzD#X5*1yyd!U>xb0K&+>Vl@rKfjHr6 z^B}-3s*doegoT=6K9l_Y2wG8)4{HPL(W&Y>p`)3N5uf`!F~Guop~nrXyie}8as@B= z-}&y$BT2l^_auREoNlwl3fveZ@9?l{YW#1U+eAk8r5|76W;_( z5Pere#es{67|j6kk~d91!NDm{Q{nI)Qe7}@n7NP(pFR>5ISI(A5fumz zOW5=8KgN?0V3wU#za4`rLjYblgN$btXQE$y8h553@O%zbU3S^n`@YBPU2z)P<39zu zui8L~oZRV{U;z08$dMUP8wS*Vrkf7G24H0zt*PGTsb(l;?HjyH0^q6v>~imcH@3c@ zBZgWyu4FsVR5{R8IYYB0jx&Mw^bO(|G)MnkW0+g zk@!BeM;=(Zh9~PL)Sxn;2twY0jTm$U@U_5fczt>-U@#15qK8g^1Pjj-;EaLde*rI7 zC4ptt1HC2*Cm!sQ0K@LDF8HTN$UgqRTBDL!3bR^!N|te4eZV@$;mdGmGcp^l9Y9kd@vHLIyfx@Lzc~Csj5G)J10+t_0a!S{KRqAa>Z?UEp`{bgpM|% zxA5O*yNC|j+}zZvw!|A59ZfYL%5gw$tke#7?`D3Um7=H%=WB(L6OzW?yntr^$Npg zqS)%R1^3~-qKRkI|B{#(3gp&b0hbhna5LLv0ha^(LN4C0&D7Y0eETM$kSaukbl-=a zl8M5S?|B?QzltLyZXzV}@D&C%=JUzL!ZP1~O)B7Br&SU0fT6^@9B8QW4` zA55j$zj8$e-rVaLWxpeW)%A56W@fCn!PxC_th%86E32yuHeAo$nn@1tGut_UEyBpm z+>yX(%3(D^3xwwdnyqWCEiK!AwSPQJUc8n%zPY>jeAgDO{3dkqdX4|$`gmx#!Px@1 z;)9dx?z5)dRQUtD_=z3kwbo?y+J?( z#?{S324K4zZ{MTR+E3h7*Uje|&7yvcA3x3d99& zL#YGqd8ppI@|%6rVvT2IWp%&I4x2ggwO%*C`{oEnKw;t-L{cdh&XQ&3w+1(7mnSUE! zScmj>!u3P5)h+6=sA&}vV&+{&WPW>B5ZIXX9cxbU+K3b2ozkc1^a9On?h`)ubZ-qI0^k@Q2u}~FLO6lXU_G$fh=>BJ1r>tuX-tB8_ zWf`HPgb?SB9W}J-j-Q|3LvZ}zQ%nq&!?+F8tJr%WKf}=}V~c|yTQm|9>1ePV+F|kR zL)#DDyd;%IoDyMLT^9>K8_fVF2tn79960n0EF8bQfjvp@Zt8)82Gc#qZoJ(4-t<60A!?5^Pdz2ug zhI#y_AHZP9;zv_cBl^Zon~ZWLL)BR}lpGnn70kP=x{_BNBH+pAr|=OGl_>IwSmnp< zF|YM@870tbUVVHT*|ab-)}&M{HWlR3ek`k(x^=)UwN||^7vR+l92du}ShsHdxP>M= zf%aVjnCpJ>)G6kYQnZvF*X$u^pU%dGAk!gr(X~?p@_0w+;u=uiYfm}X<%eZZ&XBWL* zYx_aQasww>WcF8E99A+`J>QW*GfqxUcJ}kT_4V!j6j=d%!K5OvB^|O>Xnq15svf}9 z(j`|958e2MX-&Sv9I<##@i-n8&{@?RfKsJ)$Hq{;F_rb3#m!2j1b7R8KIzX2%0 zQvMRa45%Xk8gsps?#?t&7p|I4YQ%^a+hpKb_2CQq{((%uOv2wr9=~h5VJcyZ;x(Qf z$0F`tQYtK|lSD!HuSxX98hYbZecFckMWhjnjuccc91IV)gkV}8Z@^@Cr?=|E5_cE^ zA0lHICG5@+Oi8BI)A}1!KE}@pWnjFOcJ11_1N5oRq9aKny7<{j)an%T0j*`=R`u{y zyNv_>9f1oe9M=}4Lp!!g%K8|$q za)}xn@NZAcv7vgSp>h*EL)0cT1OcQz{!XaLV14 zLX@FEJv=IBO0VKvPo?)5+Gmx$Lf}9w)^Wz^p0SXR7rNaQ{A|_a|F)g_Azk*iqgmpSf?*JC~mk^)+ zHEyQMC$FUB;K3Yw-!+?eAHHoHNl=r7Y<&w|?TU!cZWZ(;nxuxSI7Ean1Pt<+0fOp~ zfId$A;47QH)R(U8lP}hFR0O&-`r`V)Z;X|lZAzlepQ@nrIZEJ(LdvADE~P0&*TqqF z?|L1bWL?sQedvbJ;t&9Vm|zCwTqGcSDSHV|%W&H^eB5-0ODQKaH`n413$l5%=#$xG zoyd`O?V2G-jvWbDAZCM~9|c3I%fyWucz{^LN3OYhqR@GfP?lyX0isWA1d4;u8P*}w z6;;^0sI3v8S;IkVB|}Mh@Tj+Ua$B1lJe~OXBUW=T``d&|#ugzoK=0W3q;9ZuQj@$Y zDat3Lt-EgGp_5BjK9zwW-~q7V;h-0mm8H)e-YSLHK%H0biJ30^`k^A<)!kjE*t>L{ zy3S5)CMo7jF@N)(^R5^y2*~$Df|u6~40e^_bDK`GV$`S9(I%|F!Fylh)6uc9CRn67 zh5_Kt)K`3U8BEkAVL=?R_(0PHdugjtDQAwnG(*lSEc`)A#UP+Bb+m%1+wif{n(CN| zTft?TkYX2$MXzpJ=63~g=GY>pAhhm~ngK78?_eU8N}c25v^d$-+n3;>%`E&$q&Qp5!Jl} zCpFZeK(2LX!D}bi(j~!lUt(gSER#O3J3S%Lg@M*akQ-u2i@uSGiHQ_+s3Yc5{NgNL zxj3Z1v_Y2`rUFCCCQ`235Tc`_i%;Elh}D7U5J2{Yy*p$xa1|#vK(zvS zX`vgg^-J%x=XZL0)&uN6OV&2UU^ZEw+w0|}UB;g6ZJjMFFL$BQXvHBBAahzdhItfd z^guuE>{;V|((#^0H|;O}TpcRx9R<}J3)W}9VH$>CT=#@o*w5{mLwvf@3gNEm-fZ66 zNJ=jztlg~UIDq&QaQX5ikqlFSwko<6E;R z;&QuUKq?2q|7dM(eWQS9_REEls|OIPw)Qy`=!d0K|%SowHpDAo&#-MU8S}CE~uu$q9W$Sd3dY_mCOq7QoqEfks|@1 zN^5f`KC$fpV&A-ZgY~Tu)=_JG%b|~DY%)d;SeTtiIh1V;L4yv_S3wP>xcjxnMkr<8 zxdtER(iEVO-nZ6b8giA>CaBt^iaxQrlh*1b1G=4nj{r&UHD>H4Z^a_{e!kmw97x2n zt-ZYhFtlE|FotB7p$H0$HK6_XL7cKDG}n4Fz8>40K^R_!>Lvi2bkIUuppm4zdiCmu zR1pcOtlv0swva>rm7AL}k`ucYkH^FAG&szC)OuG5-MBQMuzRo?xa()Ax>~hD$AXmF z+)N=}lk+%<+NeiBEt{Jc-2A;IgMHfIX4R94r-`|8L4k zf2uGMMHnP@W5Oye@t15$K-ZsMvi>U?e({qUbeU?BhJHGv&k&WnsE5Q95`h@bnJknz z4?J?y0dI#u4BgxntP@%|FMFaO zq9XR(7QosxxjYVV^c8;wv@Jjd55YX3LtxwQJ6Ln(Plwk5J}!eArYSJPAqiCmS2gsT z?L_rgkka^;XDD?jxPFx3Dwt>fPL7|sanm~F*6BVDyY=NuluDYQ0CZs6w4Kx{VCKD} zhC)U(zT&%$#z=8u7+u9a>IS{r{gYnZwmmXE-g5-p^HQKbK;D*~n{hNEz#8ncaT#af zENtPV&mXTBhD5r)R#;hk=6h&6;p$+7M%;IT0i$s_PO6>+W#VivRqR2el~P z-97z_SQzO5+H~0^**6b*@0UL9)rmZ?@^glk0@9JtK+&=J7}LyL+$~vLR>in zzN`MoNrxR1m3_9rd06kI=6^U&`rcst-jow)C<;vZ-~HkL>=yrDKYdwJb#NZExY^dd9(=p?1dUyp6q+ zjn!9MUCbREt?cb2#H7Xc?cMsdlhb)eS#fdO-##E_?_eq3u=JyAxX8!nkLx=!7^}|E zp9S%Xak#Qvk@BCv)Vdha-{hTJJ3Om6*U;@M>wHh^M4-?9cBOkm+cN`CSx?T2ocMIo z_EY} zsDqnv#IxbaiwdUWoiY`*Ze1>0DmpXRTEameU_FdAvFd8;=`Rd{jdum;=b95;bk~eU ztGNsC z5exJV-H_m)VdJNQE&CI$=MS-J;X5ba3DmLmGd89yzB6W+X7*)$rmf-VwDVbfes}N% zi=$6BUj8b1KySO#^qP`5Q)%m(QpTc_TSKxuo*!J8=JKiK++#ib^r+Ws-MQqS-qpXj z>3{Jy?MXFTGa<0JxH!=zV5gcU_T9cJ3Pbz|x&g3-RM`pFb>3FYmtn zVg>#DU;MUCYV5LCFP!q;Wrm5pk=SFHzI&~p)u8l9d0>xZ{QXM`Nqck!O{;d$U9t;L z{PA7$xG(yrdQ&x~$NH<{PenWR*Gu{-&P@nUH@miY%Dle3FnzKkJWfBuqWfjE(oT6wOT!vgT%|EqkgHdLKH_x}t(R$|-|W^IX_HxJk<^!Q{>`JkMi0{x^UQw!R#N0s zhR&md3k&NlkVT5sVWD>Rx$ln!iy4R2Bpd1)HglbOOG70rIb-`gCf;f3=yd#L`%OJ_ z)|tmwV}APSryd^GS)nb>@CdxH_%f#qiQ^S7dAGp+zfBp7U4B=VK?&8{bK}lWi{gaug+JbUyUT$W|huFN!^%7O0%H@jundKV~?9Lg? zcfT~!b<1>aC?r(I(L()D{{taG_2{_*{&Yd3OM|Vl!t%zOh>)4KU5d)%15HU5MUr-n z2iLA$E9tU{j@0HAQ@hKh?UqtUnRH9(c>Z`^X_WjST;}xLyK9Q)!fi8^{pl46%W`%P z|GMeSXR5(_i#AwB+>$6i7|opgcvr-ah_%$-A%cQe$Ey=xh$>%QzQKRnk*jQ{re{|k zC<%Hx^{=P3GXzgt4|LB%F~{e3`OM?G&x12trFNNpQum)~mYJ)ri+Q`ffA(r~sAk?` zNu7$RuZnrkQZvT)SN&U1z)R{zT|f#52Wjf->+|d??7xSN~X3Y`xbYl_-qXwCnyO8gEm*(OwVPTu6PDS|^y!()gESW&hsp{=R}Ujk{cCM{DCer^l?S z<4x?}?DsJOVu1O{_nhCM0 z6cH^M_gd(l3{64)h$W+eL4C z&(?ak_hnR+=Z!b#+!R?d;q)J|!%Ip}Pyf5?hFPZ+z5e-%<51DAIQ-&1td6;jd8lz|YhnN`CK*$&~8?sm5 zADgn>(B}RxOB(>KpD$Z4#%7A^rQNyQv*7g+YwL*aOK)s_`Yex}%sh8WSfR4nzAKVq z@$4vDJl4q5LoI~C-ae=HpTUz=<859NA|Pd>m-6uK50WVr{1&G0~ z{a=35;+@nvd{ZLn?KEF-Fn`)^@$!e!iL+fnN-^^Z@iv;-FxbIK$mBPh8qO$8&(zfD zwGNl;(q}2;^qv;AX=EaBL=7`fG;y7EZ}X&F2O8H}WQf*+Nd=9i>ZY0)Jox&4rL9p|>I4{8O66N`$d$Viv^J>CGPZWR?7b*p(DcmwmrTUfSe;?)i_~ zHtaw5T&31`i~J=Y^Sn!=#f}y7S;0v}oiFp_wL?PaZ(rDM{bL%Ym+bx{eW3cb<{op^ z_53*fEH5y@D~lHNip;ZVc5Hgg_0#WXHf#4!8sAh;FuAgk{gIyO|9*BysRdBe1oa&P zero}$3%NvArFMfp>=74F?cR?9`263zQ=VbdO%O9{)CQfTK{|`4i|D|k@V$fd`FP)&uVWk5>Kfg$PRZV>wCTd1J1SVL;R;2?YnvR7gX0C1h!^C( z{u6!q9T)xRP`k5FZxlVhwcYXE&r3Rg5J`FdQb{mU-0S`I?#dX|ooZo$85IgSU9t-p zW>-*A1LWQnF67mxDTF(WzLzw9bmO(ZVUFuLZX(RAN`; zAy;*Oyl6Q$GpX$FH9OUQ_u3ZW6j|)x7eG`3gC1?_x^^sJEHa3GVVjhkS=?8b5g@c# z*6I1g`_=%V3WaH#jldcsXS%(U%aTuZNu7UPC^z%&v9iBoxy-I3*H)H+0)@2tDJl@U z-d*%-XlEQ7cba~)%3E)A^vt!*!O5BR!mD?wpSZSi^AIQ1Gdb-oUGI(*zO}!V>pB=U z(3G?Oz6iY-KU%ZfDF?Jx;lk|uXYPsfJVYQ>a%oe!OlJhM|Cj6QJ5d-^uPk1XUbHj( zqxqKSRebWxD{DH+!gc#c2XY22p5E9{=RTN6M01VtcDih}u=N^law)A<5Q!{O`t5uR zbI?X}{YbTAYme~PUBDs4qEGx<@yv8#6IJG7|YsM}-+vIp+Zg$Yy zvhUS>W&hI}@{(A7@s(8y4=d*e^Tti0+y}CkZaI7g&) zgM`JoYnxs4&V>K)!w)BJe}1<<9lTi#$Xxg~ zg`&!IHuRej2QS3jZ72rbDnz_q^3i{I{*1Q!&)+VxHUj03_>FY$9*MHE zE{TwFtW4BSS{t0jA!*mbP7%-1+He zigUM=wzvt28a&YJuct#aQ%im+-*k1AU=-G%y~s|vGS_W*X55=UZs2ywDo#Dp0m4B? z;A!5PzUHHx9Q|bI$ZysNZ za~~g2_E!$r+5Tvmc4@vro_HHO`oZ;kvPJgz= z{odJUH+|O5i*uZpI92=nNsIiUpwx5I{aF>%T-0S)?ow7)m~8XtHEa?7?Q3^C0dj{m z3%q8jo>MIFhLEZIaXC@;Uu!zn*80q^;^BqXbe@i(wqZVLEyORk{dQ6kO~N7~NoD5^ z2YjWQkAuZ3x}KstI~h)S0utVl*5S84n2~E*tP`Y zoNOBO3Q?SU??)hpwa~$}-H<4Mz79=@R1c(q%Nuodbb^P5hQh=a#xGKE?9#zq=e~Qi z%nDgekjgo=nwDJ6q*6u&UAo1?@g;{|D_nfWcc44->z6+@0{V&5yR>+YuT42q0HJE> z9*}F8*ir%@b{a@6K zf2RigzF++x^ftTZJ3B&l&px<^ULhLBv3@rm_9K``h<4C-(U@Q zGb&=Jl&!(jo14S$pM7S52;Xqv>laL0cYs7EfqyW%p5UZ;f$AQFkTls?4OSF=6x6T| zj3Gd1yVA`QR5yUNeQA$ZCN+-n=LY!>C}tLo4=>0}yuMPaT|{4K^MrY{YYZ(eNp`~L zRsF%g;(q$+GjKE`^R7&rx^!CadXtN1@<(FClWW{X|E`C9J-l{R@8N_%G$H&dwxQx4 zk))r$y{x^joROBf!J1LCit~1;64m0Kc9M?BUVC1mHp)jvalh9{v|x-$-X$4`qPo+0 zD3vza*e$v+xLiwOh2h@D+&k@ zG8YNu0I7Aja>}tMLj76L-Zscz8WEmmm;Sw!$gxRn@2wJF9?-gA?=WA)Y|Yn`;sCj}GfZ<(se+W*y)zvK=T zi-^e!}adLU=R=dW)jjz#Nk2?Ewb z7{HxK*;)M8&Wx$nCH035kav#@=`FSSV;n6Ib*rjaVgwfD5!qwQC_c=hwXF|loxLfu z6QjJ6SNG3Kxb(d`5iTW)K4~u1P+mo1R-e8y0 zFPlK;b=g-JA^ZDTx&&fPV1HJN0whhfB%S1Ru%-@-VE9kQ4baG}+pBl7e-Y#8 z-++hnnNzrgrMdx!$$2z{NricNgp5d&1+am1&f&*`bDvX=l8*Jw!X+I)9$8s*)=xgO zc$}JMo@cADuy7^jA&+h@Wc+QO;d|ANx)`Vt3hyP+;0Y<~>(?!92vLa%64OwKKoTxo zy!<&yTgEPRJJy?eT$mc|4H2-`=6WV1)T?_ThA@NnYQ>)|Q%y$pAE~y7czAv0f;5q-a=1x$Pt-=Px%qQbo(TG}f10X-iWU`LS2q z0W9&EGvN8|py=ThrgC(??8C_q;|%{<53(x+F*DzTB-K;t8Q}J&(CK5$!VhQ7_F1~2 zca(s<7IttsT~X>!I3Cr1iEl~u3zw`c1ueUsl)@E8`(928B3Q08Fhn7o0$1A$G(z}ZO|Lds zGJY_R(7$zm@g2RSLp2{YDg1(lAwbBgA=7q+?gwdSkfyGnc&Dn&s}Hs=FRVWkF}Vt4 zp#HtpSvCD4)@6m4>Ex)hcLG@!5(itnF(aTYg{vD0?Xa=(Ouu__r)Tq)m_sI ztz&^$G_BFjK@d=5>C0Ql`UQP2$zfTsCGkPu*$LeGe)XvtUq5soClAipQ%IjjCeqZ`4`)f!tJAG!CMq!M-4` z-1>t%T%yn;T`wo0&#}-ZTE<1XBzF6oMbqHJV>rB}6k<&z2|H*!yBo6Xqrk(2dcET% z-N*VoAZ{dg?~mVx+hI+}zJIdXn)~Oan$WS$nlK?Sbg0p{Y>y4MEB+6)E%iag(e=iG z7*TDjHF8vwsyi33F+Q;3ZIG@>k%@bM5TNORuCcQ=*Qwi|Iy<86tv4iwvI=ICV^u%A zY%tg!Vp@)#?pBn%dyUhcFZ@x`4v_K%6*`QDajAJ@23x!)-sD2tlQgbpBJ9-ELcUlL zChzXTt95O1))j;30$nGXso(n4H)+%~_u>Um$Aa>n=z|tqTOn#5QQSJgS2fTf?uU%H zc8Z8^=)ntLiRDkcC8cP*(XoOn9#~<6){{09q&fiAc`r8AlUjy$n$#e@nOwQcl9?@D z)9vhx^7$bkNol&bfH8ONZSNNOmCD^QgZ3EEkXHJqmTJIKriVeYX*Wu(^hf& zu^aKS1!46iEFDDz^w)WgH&sDc!hbtS5Qme)@YT5GkNxT zs3{zjI69w1SSWR;Q1gvwjJGi{n1|@LH;0-|*+HX`wKewK?%3Y^-IqUTLfZzuVot3W zVSx-tisQtaYjKtpQEmg-ot0VkE!5tU@Q!|MemH>*b2Yit9b@EDXFi$ApGs}SVv`#A zuKB}}H#SNNOE;cBV&d`smk8HpH<^KltwXQQ`XxV(KIb_ez?u20vC&!!pX&68vKV|sMgKz)BnUen|Ov}V13On8C&7Xx9 z|7K|~3zrcp!uBO9WGIu)*Ky~{%3{{sNF4c*i1_m7W+foT3l-gewk14{<;694G5hwq zcn1Q``X=}rFJBn)Lef>{$vFv_wEkpjZ<~DUg(YqnHN=Ygz1R=PD&vE9jg99 zh*i@n?CU0n=i7*NIzrrQ2lPJ=wT#NHcRrQlG2PHoPSJxtG!D~LR(*l%VDo(V;T&{C z4WTmASS@jQ9AlB==*)+wTlfm%x$h3E%a2ymd1)xlI`O248E$cFyEQYGRZs?nau{o{ zfW?W|Yz-YN8#px_QZTcF>IX^lTp<^26`M66sQMdGTN2<L|VHyM9kQL ziE7@BK0rK3C8n}BwZJ6Nw7{!dkUyd1TV2xQ2K7``Sp`{QsuuF7^ZcWacLk#67%~xe z99<-hin0pC9vEuw9)p0F^Z-Dj%Yg?#lRjbf;?9@aB^V?dt7xKd#OZMTVA}~?DL?We zaSYC$lN;fRp=C8A;xy@Mh!}PzA)qSRdcIM5boDhjyr^wPnH2m}qveqh{qF_d8<|Is zSrj>Zzj(on125g)YdAaxAdxcU*@@&*(VXfvud3qScV9_62jLon*KCwr+8*FUyyX{Q} zL%TCSxxMe~F+vsUu$28TjrO!Z-|kl`n$@Vuvz(cDpTK6bxuKS5u+S^Bn4Wt4z&U>1 z51=-7Z!-P z3L5kwztIP!GZ#O823w)(LrHVI*Ov?Z@ zsUoPbhPHJ|mW5QvWIAr2O~+7`w0SE?PX6p6xhS|^D3h4ZsOa~ZrfGL&M_wEm23PHH zEtoc*8tV^703M)Otcrbf2DwkgxLrR{<4AdX8puNfBc6gO1b7=>`yC8E;i!9N8P+a^ zd1Q5xt~hz;cd%Pf=^u&H*-q;)iJ|+u)jHh(DNaxiFi)!;3#4EN|PgI z2~B!yPfDiEO-!g=DS?+))NoGe8jU3Z$K}-6(@YBjA&oIo@~3*3AtyYlhe}4bT?0CVp!?Dwy%8&f10Fh5R9><4at2A z+^W3<3p`)MMY=0jCL3mXyuWT*itGuk(omGAK#@S!CXUho3sdoMm{^H3V9cgmEp2m}i`C!^Z012;VGj@&UjyDyi zy2v4s4JI@kBkl=ELGP6g4?`P~KAdb;imIg~_4q{Ny#R@w7`vLhSwD>L>d&_;Jk|>0 zq5;?=l_Albb)d=;oP$hBR4@|-mOt*%GwdwKszR~K&h-ToM^WurLPe{gp?u#!gO6;0 ze-*5z)k&S+{dEufku2J)cEzKfz(H$ZI}0p}!l(B^lQ=Q-M!wm^tv!h8nOVO->7 zZP2!lK#ynYIQ!dx?eL=HvYomsX;M>%w*rJBWu1#rbcqfS*DSiGyTx;+V~oXv5*AEy zk&j|!`ukTPXfTvUfHNnabU~nrj2r?8?6)YrZNuT|5X&Hjn&=F3^F;vlk05ViMc4Qp ze3~m`-_UpgKaZJ)Tlg^D1`*VbRCbvf3t zq7lN+bZNM>_?0EA)B(~QXNoP}-iL_Ows((LX++BMQV$Mb>I^Ptakt@8AGxiu=~Wao zgYGTh|HkR(iu_?$*hahyV-Az59Urh)lP!f#o`#JeGLyViuOLKUX3tQrpEd4~hKxke z2v2B`xK}T2E4fbKHdR0P<~wJP53AQ%_cl^)!@@Ri=&k$ey6AqKtr55#>tPvrZf|W@ z>L6a@v)<%3a;yW>b8km<`4(-@Z>8v%T^S*}81AEj#36zqU^;mONsff|bgObDj-QBJ zopov+_0k9Io8@TfsP7^0ddH!=g>A;T`-lp$Eh9*y`VYQ7m6A7B&+N^IGTKSK6DgHs z1j@#2B54tmnxK^kZW`Fnw@y@i(1WR=El{l8=Zzy#hxOR;=?2R-%=e+5s_h2`uif(W z(N$}3`x3C4DAK>^EJ$Ca+_ysU=Xeo#)A-MWKiyJ6bAM2M?c`plvjb3U;r!TF7A{>u z#s$oDw0!K)yljxWc%DFn$`%f-JSN1%Ky=qD@a`&1yo}If%Je^|8p5!?4b8ogbjx=J z&*hQ2G*HCUBk%C*V2^`Uwm34HPE&uh_Jgc;PcN*X6WCz5zCC?$eU?3ohT7Ct<&P%Y zo1-l+Ua(}-kJkjf4pLaCg)B)ck8r8C!W9091KiaX0IgEwroL@WL$ML^YX0JYQW~Wo zUdqUCQ+#J-u%SRF;@jDQ4i(I?&MGd_0dDwqRKB?Ok=WglI4yCT$;u zo;}u^KS$^TeIje^m^a#zPBMj~3(a~YYNRgPeJ=t8Q)X_WwJJ$B^#Q6QGq}NlXdyT% zy+jOK2E3>W#+nM}Qe&mjp5?;ljF|}i!}P3$8XdjwWu}f%WCmLNS7_`oWP?wk%-!PD^g{S>)^bE592Ry z3Tu{&R`M$+#z}-=5?-&{Ak5FK+iy#MXdc7M{u)kg21CzwZf0IO{jWY3^oPVej5*N)bl(n9lr>eqc#7$2m2Ok z<_^n9XPu~vZ@HF&}bBESS?uzyvkGMmm9F383#yb=-vtRq4=`m!Il9ije9r(vLI7wZHh_xV2A zZMcKZpIlx-@Jna+Bqq%hGk{thr~#y|@#-gU7NkW{-!*?ZRA*Rxj^i1J)B5_bc1bd| zNWegv#=xeKJvY^xlzFK8UI4r{A>=hyPQUp6Ej_kEneDr238R+V&HxZGd`R z@=>6RLJ+8g{xzTbso&Y9MRCD~Xm&v9F$uxu7S_>* zHpGg?{*6fgNqD&s23pv8rdTE>Zc3A=W!Iu#7%IAL#eq#Dg>Z-JP8vQ zSvOb+GC*NTj-scIEW&MdIIMP0ycdQUQ%uLoj})%ZBr(l_7&~VS78rlQnb%9)(D0|j zbQaodnC0R0@KH8N=7E4fAxi?lUv3Lq56P>0uIY7R-k4UZz6Mn4BI=Ih0AVBgQd!(th@ zVJ3euA~yj%ke3%1Fkc!#*XXG&_;BSQ2z^cR(*q_iacA+>M-Sr!sy`qj5b-=bX5}>4 zEKky^2~28CPZEc*{TE{z265hSBAg+PymGUwmOBSl3gb;5FnaG}l1=W@?6*INkQEG8 zlC2sFljri^`G~jiXAAgQk6yjRp(NU($Lb-Iyr2A-9^xegIl+5VDe2NIO>LzbYyw~@z#JAj;CxZW&d)jN)Wi9x1KC<@b76sdVh4#eZovUPhY4XDjrt$y(C z)tk#NhmAp$?tF7iP#w={kh7GK+l_o1ptD4z$XZebwT)!?vPVA4B*2l0ijuwtI14Tj z%h?O*S_7mil^)kBLhc06eV`sDcOO}JXYnkW%L-6kvI;V@b{zL7RhxJVwGWlH1z;lW zME;=SKX?=*douiX_hL>VIxK&(d{Zi?V-{hKmURP0z#IiSH5v~WfY?!uAyY0!##={w zJZ~qP{xM0>v(5Z~A;2Pq+Iz0k+u_BROU%i~MxaTDVLIEROAozt(qI8lQ-xh6=NJmI zI-Xh_MmcOlZ7-wI3kc^f<;APEL||NRNm7(X#bUEor{OZ`IOuO8AKEE9AmGVuaTi#{gk#gr_bqg_V?|z*IIi&l)fslbu+_e5{a~x zeDRztiL_RnL|XH5(+2z|u}wQ2{|H#0SGJZj)3>(Mw$vkC(Y7|fVP<{9Nar_OJxePi zGgDs9lbk$9e>1eUHn$Sw;xhT$2RO|v4Y)ctZ@Q0*{A_+v#fn7Qu1)-16E7NPL?Uq- zlFyxyw+|Waa#EL{E?OO*jo-u~_-K>i4#U{9w;jm03MU(R8k#dr@^_UE-L~baF37CZ zb)e=X%*+_(hicSJM9bTLu(XRD(%J4{US0j*@RMBrBX9HX$bP*yy%-g46(km|?XIny z*4LZPT)U7uYFBIEc=~2`&vaX3gkHZ?C# zb#Z1`uJg5tUb=FD#x@cueq$-O_0pV$%|PS%W2()h>D0sJ>7G7& zM)j5COvu=--M_ZuwF$>Sz-r)dN7kezKZz6}!p_dVYsZc+b3&SQB@OFC^7&d*HH1oj zA(2F4MI_ag#kP`2m%X@u`iIxrzLJK<7|E6_iqiU@eu{TnUFpcN=yh0p=I_sT=+Jp# zrx|&V-N&ZhtlQ3~8HuN3os*|mEp*Icw*S1QHu$8JTDECRi;`B#lYC@;A`eVZuo-UvkII;Ke^6Ab;59`0?Y{vs9TFeZC@A z4wHf0)^%p1EJpP1YWpIB!ooQ7yGA}Thn>`JZ`-|HGjl+s`^msKDQ;Aw4#MXUC&y5KbFu_7H2nZXMS3% zFE`klLc4Qkua^&rG`6;sTV6$F;G(MMLSuq{^EW)R;Alu6IO2LpKs>>AC5Y6(j3#s zlh??%RS*-qxiCAL=EZojwSBdkt5_b3Py5R+5vE;v1M_|1X3>|{kZy8~C2(gtP8$o4 zy;&D@(k$$?X;)%WT9N8O32%8anVhmbJJcREn3{i8Ny{aRICy`bt1_>CO@q*AK~shQ zI?~P9DjjK9ix+1VlN4<8H2@8iIlv}g9@fhYqo^vc|RYe{0*`Q_{LKeIa9GW2%eU z#x0kaglv;-Jd2BqmzI~iN*^Bu>5n1HE?YObXC_=~&-by&xTBYA_I*$|5~{DGWQ<%O z>Qn6hw%69G&h;cQtL%9*ze0`M0|fHvmKTkD>=~Q;Y5n^34(SK@4%N`$QRNQ)Uwb7g zz*N6XOl)iw3XO3Vfz##QOi5NXLHv}bAtA|+AOE6qba(rW*B26KNu;@L;XjMBv9Y~s zzfo0HC9A0T?8b>;Svk4Ey;#Ns(WNij9lt(gWk|S0B7LS1C4S)uJ3H~QiHQk5!-n6; zjVL8gA3u)x`c-EdS<`CKSJFsea*{mG&~?_)KXUSF zM0N#v`BNsSTJsYRx#cxAQyUr@7=|MwBiXsQ>;|3mu3taZdPpTj@%*`SInzt{oYA;3 zz3R(rw~p?X4DF1)63jy0?Gf|dzLeY8#O%S;ly&>CZMmYVDz$Zzz?!loihHb7cz~7va?^hckfR9`DZb*tgCAg-u}9Cr?AFh@87?(adAl@e;I@wGn-CU zI!;q#vM6nWGd_C-4g}Z60xvH4KJxI;%V_^pREP32#~Hp!-1li(pLWSl)5D!g^`fiJQLSKAL7d?^^^aNBZ`?>} zNmQ_z;h`xta;tjE7Ia+i(Z(bsM^T-DzB^Lo^sRrQM*mEfe*aBv47a?j>m;Z+vHy^EGM#&vnYXm+IQ>M#+&wp5KSS$Yr#UGw?lDhS2~kcoPc#hQfW zY@c^H%dkp)v-ob0^O6CaEp97)ZpO{=G7IIRD_^W>P=A$jrXpIcGDHc`Nz*Lkv-o2HAbU5ebK>SP!@5 zK`ona6+5Jg0EuFE?D=RcDsgLkHmAMUkx0)Ov~j_|dkvB(Z6GM&n>S*AJtxO`Bb7=u zcA4$66Ey#Pv#IbNi_zHUx4zN9zL(l9$aMSXlG;bGIk3V6j8dFkx^>d~ktn8YSj9I@ zh>=~FO~3{k`iSi!b7I&jKgiQKV351Z-FCPmCcDQ~tt;Qo&Q6$Eh^m|D~@ju*RKtVnV7IFd;h+wzP^6IUR^yz?GYYhj1Li+8}B_t#+CA;YIV{p zxM_Av<4E{dS^AC)1E2gsTxV=^K#BDA>(RfzdE|IM66M6`Tg8)!$;l>cMCy>!@^stD z9Bu^fz4RzIpXsMl;GZ0PeDb~Ty@bp}TM0nZk{#uq8k8_yni*%rMT;KAwc?B9tme<8 z`1|`Wpw1{@f62vN6<`YH(Y^i4y*M@cd%)@zazo@7vkdQ13EVy*(5(^n9HO_!b(B)+6 z$D8#Obz~a(IZpLQYL~hXU}Js&mCv;NehD$o%fEo<^RY0}?@x1EP7<^mIZAe18ZU22 z*OlnPZXh-T!HTnY6W>MVtfkpX&;4dR50AbuR=T}Q5$Z)ucOo#DlUdkNJ|Q9Dm1+w9 zT)J`vS17ORT%8(da_o=1zl)JE>4Zr~JaQ=Lw8Jax>NflH0Jp>SG2GT5%3V&Qg^wOS zl(~5E!RLy?S*$qy1@6kYo3X=<<{wYUqpk$vz5#qMD)aRkS># z#(ZE4i4>A!9XRw%&Gf@aSH4X-liiV{M{CAwk4v%rDa^m8PijeoFc8WTo~+?zIAtDz z2wHpSBT6E9({yq6z6+8i*r0YoSqsZL;NxZ6s5S5}CUgG26FadArKeRuc7f`S6g zzEchp(JK=H#Ydp%zzT)TiakAP%JW!oa}OSD`r{(nh69Z;1Y<2)nvi4?aaN|KrM;@= za!mQ97z#+83AmuI{9w$fQf{iv!x4l&h+F%1Vp39;^+2QGVr*PowQ07{>&C`LM>l3X zoRfOq<<=q>CxEnuRW1M3;l7ee(4j$_uz{vHO3B@yZDt-mdL*l@9XT`Fqt*NI?VXK) zHpRZVrZ}m=*kIGfFkz=Duv65FZ)Y&`L=wY>imoynp}x z%|HK~d#(8H-JkG%w7Yg0etvVyH+pHw8E--H!t6ff{+3B;<8&D>qoaOIkCL=m3np6~ z!=0&HzRjd7FROb?03*Agpz6U3p3tcy~%*ulb42x3T$Om+i-KuItG_xSM>E`b#!&1`=E|l z%F4=&zE}BQbs?%;cIP3o=+f97oE8VhcScFmAqFWj^wHANk|E0$I@CIskJq}#Wp)6N z*cKvSg?-L}Y>ewg5j15RX~SmDGHyu}T!dV(XQHM3p1GYwl4b;yfoC2i=Pl24GMVi5 z_-@A)fqgP|0tXP(PZ}FZBxbB8V<9x))x|dgfR>7ii*{`w_vOk*cM}{epN3BcxlisW zbjp5qROQ8s7Y6_W=OrWtiF?rt(D+fefZRS^6=UY-f|MrG>&%&3U%!5B&bKrB-q$zy zh$A(A+#K}gW^7GNU}W*qcdj4-tJpC6uMZi9w+l}_PA!~K&EAE3`D}#D$7UPw78;5F z;JP@RP3fx(-O33=DIz^*m%E@1l_j`VF{(>Hqh-O`fw*E7U=BngNYEy6WqH&M!dnJ4 z6uIO$QOU|rX+8Qk_=-d}Cz*7$mXfzNZ21@G+D={ysFY+G#St znmhN44(aA7wih0?Hbg*<0MhZ^@D-22b0Z2V)w0%!;>Zg~a~@=EB|HvE)h;oZh671j)^1=2nW+mtM;sTo$Z zvOwvA8Dh%yD*#oF%H+=MG9^NWxwpQOzhYri8^rqDXNEjPOpis+k#0e5>T| zFw6bEyKu5j=syFscHKJv&`^yiVId*4HEY)VabU{i)kbD}F;v6s6K37Xb946O;^aM! z^WOueD-QbPWQpN6r7Tf>y7FwAP+U^lG@Y*a?qXs}fwbsHDq2!*;Ui(+CS2T2^9EwK zYzS}Da?`-yLSTO{wG#*JpGs2PsT8~ic`Rn_K`n|nbDF`OrgZGv0BRmXO%y>CA`nSd9QGJL{5NV4e^jIhSfZ|~6V*^^M@vUtfC5}>4{ zgs63Y6tH`d9$ZDrV9-@HHO;S0I(O02$9J0MUx5JsmNbPWj>{={6L%%}1hH|2D7dhp zHD`~t1u$Wuci(C}r$%1?;%IRI%Q4LY<jr*LXVIwquh?20G8B!t3 z-rvTQdA;&iyH+BLkgt&jVGb8ix|#|cCPRuYiC%j{GU+Mm`r{F_4eP^%z0IC12Z7GR zIbu}bMHE;IHX1^8o(N94uqd(5CHndE=kBdA3L6a77f!STcdT(M)Nhr3$7#fY4UV8V z{!gAn2534~cJX7O?L8&6`(JmOHU;t;@Y#*1FHbc|T_Te|l+kG=$A!D9?mT?i+|G+w zvR>>U6bLw(v9J#k;DSQOj3lGJM#z9o7_KbMd#5$szkB!8?Z;iFZSNlL>{9AV+xsty zKXq@$wYO`hsV$Nw9howS1E2GpIbg>rS_Gj8#(EOg+RE5{QA2QX@7G%yPaHXMLeXh< z#74>Fs;sOmQT6@(W0I4Pw%<4b8Q3ZiE8)Yu&?mZ@Fx;8Vfeez7kwLX?L|qb=J&5cR zMtn<3&cbpTAe>4FIzEHCy^tkF5EQ84wsv0dAqml^={Ruc5DUwZ{rk@li-8qQ129f{ zOCl&^UrJk0Cp0X|Xcxj)=l4O~#lL*nFiZrp=UZdYeiY@C-S*;>KWdvYjhdPh6*yd` zK1eM1tj=w>Yhxu>)zi2wJhkA#S3uO*gd@ZhOm!Dl4~Qu7FySL{H`t2tcHji zkBImU#xU`D)neB|n4)!^q6ZI_rgK(8G#@zd;_Wn{B2h^BEWceOxB?OP= znNKU@9v!I(aa5PE^;>pV=%hF2V!_D1*VC1aJx|g^wa#cUp1AQGsf4P>r}J)8mvz$> z@WM9^i*f&oQP{#;!j{$mia%iiyQSv#g*Ekmh_dMmwKE?1lshe04`#w;uk|86{s{Pl zF)TWMZ>H1s(%RbEHr|AdnrAnf3Kqu!nH`|znoHO;Q&Ur_i}-bGWCQjvSn=eb<7Dm0L6@+{j~@$isf7AHe*9a0SL*HCYq!FL@&%G{ z;qAM3?|KySwdt=?Zlhx5=7K+l+I7oVTD}5I6VZ{#Ipw;P17rG=Y6J@^E9KpT9k8I~ zP#54D5IKT87djR;(vlPaFQ0>)n4H{9pi)7>X_wXI*^XCw9wP;lSC>JR7be43hh`X1 zz|Ab#XqIQ)RU7S`saj%z}b^M&QU0PNyrC6sc&OG z9S_Tsih@3N5o!Ep7F;+vCrA0t#_g#ex>Ggsw{+8XD7=09RyE65A=HJY)VyHZrcDhn zQclYvwGBnogZ5BAL|pRz_!#`UW9m85 zMDo;y3Mpu4q&$C456sHVKzo4XV*<5G5IH|N`_t0AW%({;7%hRp1pZ{w#xc?T(ihnp8c`xnigf9GiY)z>g6 zX~2&24+>&WYQdkx=4N@qMD6ZH#X9U!_TrG_GyCKV1hscQOcN6wU7?@B#=#+}qM`y@ z45BQh?ISpb()|29wK2xyeiCk?=sj!L6?Ai=fwS;`j9_T{M#HdW12lpRQ`zyo)`OTtL9emExV6CF4IY{>V%wSsRU5bo6%oL8TKXIabc7+ z)pVUQhsEZ@!onK1`CaM$&z|MYe8CQq8lwh9{;8xH%pua}&8>BLQ;m{LCIU6Up^bEyce@@$3XD)LdR*@3m4o?bG~e_8Tu%L z1-2IS0rx5?Egf8%*@GOQ3s)99Mh*-NeB|W?Bf=LyyXffR{5g~ar`b3;pLLne%7TEs zp__kK^LN%-R9$bM8Y@=4rux5as{Q%a{D1Li?Y~KbfmiWat8X5rq`~K})vw$rS zb@SG(3O>xj#C}<~aa(hHF^B`9{$WoU7C9fF`Q5|Hyx?SL7>^cDy^tw9FM{Ltg}bGm z_t-sv23I40n|c&7anif8qQX2`63pb$g9ql-2H4eC{FF)?Ai9i?ny(DMHflOd_C>1k zbaQ*EcGl;joyl+9d}Mm0OHKE^=Ldprfv3pDe)LO&X;1x`Rz^18O|K zcFA4A#dGJ+C%k+qy$Ey5VhH3M?Wm;LMVpaND(I=80oLZQIQM&9@9rb;=)aU+j>SD+ zO7APEswSd{|B9#7 z-Th>QPyWV@WMtP7v>j*Ck`R@xa4NY_TKDVKPSNMR>WMJTagkS2n|4|=hGO*kN4)foP0vIMf z+&?Y3L209L+|9X;)KHOg8Cd<_E;aumpEqV{-qfb|jfShmr37j~9sJZYY~@xycouUI zz2!cVz6TB*C{RZWVy@78pD1Tii>kps51H_a>pG|h9oc5##7Klk+1Fvq&)V3#*n83t z&e#Hd!EU8{p;}Yh6;SPoJ%qy@M~Y^ zX->X_*4wtkgHR>xa7QTddQ)qBcD60bM}!@TWfrjZP3_$L`}vnQ;e|eD;1x%b{ln;o z*gEV^qe|aH-Y%$~D2;8DY0_9SG3sz@BlDQ;9&_H3UxJ<@;|!sjiQ@2mbTn;j%={25 zE3toMo3=CR>FF^DSVm`8lbqd&>J*H|MYiPKpUpQK^RXc;ZKyoc#jA_O;8ejd($dgd zxGdqlcih%!`d4P=k>OuS;gBRh?1B5n!V?t-**Q6FGeMjMr$t>BGMAQ?7*a%@uj3Ur zJnqdT6gxC@LwNq19|I`)frAJ6(bb8Gi=&_=0j%+5A?&YL%16DIVYO4xP)4%JNlQOY z&7-orEDXeXPr?KQtuA`Mh}u4YMD~|oY@Wr8N63nT@9{{Pvp|l)V*| z7v9g-J;%@a66WxgkW(@N>Xr{V^ZF0Kv!+hBY1vV=(AC3*IgT75y4bBR&U+X)7vr+< zr^R3fDcIZRKto#NnX;Ex&-DIA6Ir=w^JZBDquXUx1NLmosTK4<$i4(@!_*g;kpeCd zUH@>`g#@DO|Lxm#A{>y9zR_gA!))+4^6f@#Lk0)aid#W~`HdQ*9otUoRqX;vP=d9a z+wHn&f=Pr-&EhFyx&|Oqt)Gzql1>P@x)6bbWCAdJ{rslmM3>1hA&5DhR~Z@nE(=qR zZR`2$2-8i!=6NK%JnS&7m#c&pB|--i$@_JNK|K|=q4iQt_-O~(<;B@HLjHjt7-F6W z6hi_0T(<_KYYGz65H|9{H$SON?a~b<-39Ffg;zrbV*otrP?AmgqeV$5LFPvFbbG{h zPRw+gF=8Ho8%yH}A#zYyxa;F?D3%EO3f<5chdWM z6264R62f?bWe>zU@o{70L>^F}xY@28i>MDT&Tr{O;VSyv_fwD`Hc8n_)fDe#D^YRD zXGbllv;9#%3<#Npv08zB`i?z&20n)K=(=0f^0%Fu5d}q!LeJ+t=AG&mSK2h}LV4ge zmU7edqI+}!<=G43z0_g}p}lbJ+BL5p+G0g%>1!8{19>#dSJgfmzK^q&%TA3VHM`enEd(ba~fHr$>O*FC+a zwmr>lgi2|QCU?BlzJm=1YXJova}0546JA`Oo0Un$IQURFJ^4dggE0dtSZ;0z^#K1u$#?R1#S#6q063huev5uCO>KdqwKmJB(~6THe;>@O<$soLv+0sV3qfGniaP^JEr#3&o8R6QTEDn z?za{NU>e6=C$9&gLm;FXE~KBX>^j>wKdn=5)ti)*#IzcE!XyYzf-d%8FwqsP=FA_m zE2eAMn`PAW-e~o#Xqqq4sMfG;*K_RkIyp62FG|4<%=`XKZErc}5Bx3Dx> zkNZKuhUN;#J15&>U%isW><${;jfo0LQ^;&1oag&(YH{P0eLaW8{pc{SrTlcLuP+X1 zB*@f*Z5Deu@CBo3&Y4Co%7dAauEFL$4DWpaMe#>J8~Ln)IS?iE)OzhP))rFKVhKlY z_+z>_=$Id22MpE(@}eyGp<#T44Af3d7n__Vrhy34*akCSXZqIEL?d2k@C9hod%Iox zc;hw0GGK{lm`ifzo+D~oyiC;Ia>B(x!%T3kEPA{|VEX$F$+B2U7-n&xj~bY7@r{m> z@S!5=Hb+ZVegvCY_c!bp9)GtJ*!!3TmQAH@IAa*Gc|D2wl^{y$2Sft%0r5z>{*>(Q zMmZ_r7<|d4h3|*&hr7%Kf+yNw!h&49)JSyFbQJ-0WjvD;6u-k)8X3(BBD1?4s|U0m z_*x^g`tNKPX@I>MVOBJMfzbVO4FlMxe@qVa^-&<{^MF$n=i%8|qUZM%ObCvz5}KI# zw!@7uvWQLze}93P)d}lvM_-|Mw`(wIG@&~30gST@$KBjca1~C;W|?-W z)}NmAB>E2&IOjxTQ+V;qmmPQa^X&mYV&F4;yqili8UA6C-Dr1^s9O=NL16}d(( zxz541G^KOr?qX1ngRBam!PsI1z>%m4kaSpQEw9Pd`FgijFwtq4@(UbgK5 z1`=&fqPq|CMNt18;M zWM92q^0Bqja+pSV#;K7=ga>#>v~Tq5!!)9Lz6WTfCZ*;PlOX)9o9eE?AjPb0aT&&s8cG zS?N5+&R(@8e8Wvv@Hak5VjycIe^f-zz#s|m@_rmWba{{~i~+Pkne)3Y*%Qe}X-$^F zToz`0FfZ}BIXgM|vX;xVg09;x5;MlA8iwQ*6^Sa@+-2930<(lt_K22$!5TvP%a<=y z7-Q&AiRrk8CPZ~nN${ZmA{3JSC1%uoWFaa5wG%#l{` zVp7;|0jfsz^^HK5%KG{=p6Z_(&Sg&6t|5Iktq$O7JC^bfLbu|8abwIe?5WJG9k}X{QU>7&uJzB%J z{w{9sJ#LTKcq%Ea4CS~7F3b-Rtr0=nss0Zjt2(xuxc7Tl6paRBqdh16D|-IVg#FKc bv$Ai`6E@y5w)j#ADiZnp)pMz5uigF+4Ip!9 literal 0 HcmV?d00001 diff --git a/_images/6ea10fd9420b437a042b88ab6c1872d87809ea377f616435b36ea039e6483d76.png b/_images/6ea10fd9420b437a042b88ab6c1872d87809ea377f616435b36ea039e6483d76.png new file mode 100644 index 0000000000000000000000000000000000000000..eef43055fbd4edac234fa7e5220d86d46db34280 GIT binary patch literal 9700 zcmdsd2UJt}x^L7`#)e}bLBIk^N0cI6MZ|#gHngBJNC`zs=*5mAMMG5}paP)>=^Yy& zLgMST=%qXY`IA@9d6@X56u_jTYuIS;j8JPcfJcz9iNvq5QH^SJ5g;^An2{fMWH zo4dV>^BHkjaY@l5b{-x#-Q^`Doc`|t;x2Bs5|m zLJ8=hFP=B_PGI!~7#iXR>!&D3Hf}ksd_-5|j*U=S+;!o?wBjvhF)^tRt`Usd#N3;B zO(SeP6$_)YoNOXuu=uB`^cd_5B~jjsdk?WTG_*PB-q9INHdO4V@$oXKKM3<5Txea! zH7of{uJ$X?Mh_3L5*5yHlJiA3pin=*BW9e0`_kq6upNauwQXu63Ke>x54U3_BJ>P?y6}_IG;-HcJb14%F4FY#jS&bHtTDv zArBwgjll!nq9y0$=_V?8DG$XRI4I%NQylFv-N3=YAv@)WLY=dWii#3EcI@4Z-^vv; zi^>Gw48gN!e z%*?f{5I%C`-0Rn`8$W$gWPNFhmt}^C!a39)o_f=lH8yH= zbo9BBUorBxBDtj(Br3n(xfFglgH*kwVOj2xA*p=olUiB`OTZ#n>t8VDoXRc?gXdLpFeY*xe=;wV8HYU=XR;o zgFo?Q6;Yezi&IiAY7-5dOX4SGHlp7A#9mx=Vv;Y>>GZ~i2E7Xx?oj9M-TT4D#-?NR za9LSdV`F3MlcTB*eOAWCX?Xk|(q`0E8FIeI;D@c0>oYY4*M2s)l9Bgc$ji*k+;`x> zNRg7L=#|>qT3f|pcSf_k$G0cKA|ld3YyMh^@Y zq}RE`R!CITG0$fc>efhor-;PSqemS&GL6ErL^*cxX1hv{P|H18!sxNSiW1JlXOkCa z7|a-R=M2ea8qI2LZEdG4)JaQrmoL4RLeW7s?BmDp-Grx4Uo0#v$S2k3i$+IBOV7^2 zp5Jy1%9>4(bB{_$$X;7r77-WMmZ)BMxoz7vDeJl&=#y4+i)+i1Mb~S$p<4?m%30!; z<>~4Xym;-v338;4az@2OIe8-Ew;kt|7^;t;K0U5pc=heZ{rmT4n2<1LHGx&fjvp65 ztEs683krS@CFSNC!z0d3^odwmSy_5~R`VTyT`v`|(B_Ezh)RC3`)_;b@3hwc_K!7QeIU1{YIYQ1O*a4VgP@FzF;o(fr{YEvmt_FE zW2`+R^3L76`wt!@?owVzmQ34|VF7#qf>9f!~At}e0L+$lp{47FAmwbAA0|#5SRlo}8~=ztR+4Q0Lm@TnF!6ir{6L z2F>?*?ccYr{oa{7ePc7rJy72NVSfTN$_K8LLYL_>*YfjEM@L0@i$NLC^QAGIe9GCG znZoz)-}iCawSBvRtE+2b^J}}$$D(6m9C}M#izalmw4&QaXGM4K-pzpPqgwo>q4zk0 z-(&mov$=X43LH9wo03$l0He0;+?j*B@`t{AB8T){1GPCa_};yAqby_T-2B6e-g$K$ zaaNi-I@Bm3gF;`n!%jZsmBOKw)m8fBB<;uylxR~@jtOREWn~{XcUEZV4if9op+g}N z5h^TK=-Cl8W3zfDTR-V|<>b$hnUQ%E_6oF3+1-SR`Qlr_^9|?CL@f z;kB#BXIAHF*xHc&cNW{NuOtYjD(f* zyvGLAxihwZ?Yw$M<;t2=JjDm7!Ordvr1d8{t2jsrg^D{dSG`|L={`+K-KH#X(aBSl zQFW9|B{huPjdUZ#wYM8Q`4Ek8(9eICgZ}>*>woI#s7sGxQ#E5VvDDmcKmMre=$L)_ z%o!bp;`H>(Xf%4PzpBFD-(SjaZoDq;7zl)WtSLN!klR&Yi=F5#Yiw%j`1AD=QeH@B!KPreo@9X)#9)YNq6^v^(CZt{r) zfn&#Bo^J-_^8BRv5dHhWd!Rl2Uw(H-azEbbU~P(?+hwysY2`0@8xze|@M z3=R#k{OzDmFLbTtfBpxAG->)5LZV2D$7#mjUDb1zp-nK>@iwM1lpmqGLlAZGPejxIR0;k)RYyxBfDWy>QRE=z z^+^oFd?N&XoxJFJ_SeNFX|`y~m8tyQ|4g&~iT3{kShNaG<$H|k<5=BB8#ZhR2@5m2 zabx6AebCY<5&7ZVxm)6u-!CYwugru=Y5^9YdWHm)lq?HuDV96v`HAz$LzJGm-bP8>4PPXZ}w5ChVYH^qTT(nY%Imzck6}_;}Ny)EcPJn_*KzLPBm8eT3A! zyu1NqJMBCjtESyT-Vb~?D-q4AD=r8lIaTm4E-#a5w3-b!fcURmWk$z7dGb>c=*K#v zn7bUiI!nvlG`zjNbA|!}0ytM`n6@cMKWTe+~fC8NA?r+UgZ+B10Sfgsx0s@0(`Y!#A@A?xpj8NEBZZiH==HR-Cc~Q*a@Dm9? zp^_zkk~kIfD)U0KWtJjyBF-FXoPkgjD(rS_*gtSj|7t++&l&f%G$x!^DRX%ESGh!O z_!2mIk_k&~t9Sz%PUFfI!^@W+0_0nW$0i#5fI>Zhuv=N-wf9?`zT}+;EN*CM zuvJVeEHpH+aJ|p#H!i6_1mxdA$5J27Oiv>R+SuA+zrMMRPl?6&qM^zfDHN0Wi9W2- zgkkS^PYFpo!T?+u2tSH18r@K)br^+`!WLWlAnlc)O+0DAk0>GFEpTxTdG%;5Eq0|S z-9vu=?%kiSGT>rO5l&P378cnpEiGIwmV;*mRVymH?NT+`O4b1-LiM^`*_6b|1N-6?bYN)cWPi zmxWlWIM3Oe$#vDL!JN>y+10y%JdcdZe$a#!x^Uq_v%EO?Dd!UNo?@rATN^gd4 zqkCA!paNEHq3e!tPx?Fx5AS@q`?!=-w-hXih=PK7Tbfo|y56hGe*5-U>br6*D>@6- z-tSWRNOv0rjgIH1GZX z;$PP1Oj!cZYcFxJvh-sW;ZwHmJ(2E0tM*-6WUvyD=DFvTG;<6M9NN+tEkZe4moG|U zH$OoqxRV#-q*d174FK9_{5)@{P7#jZ{A=2!yfK_6L;R z4huxW{^NAxzq>zcJH6$6XVdYKkx-2A6%EeXx;{M7(_c;PKCk!J4eW_Nb0p;WdS|GLySsxFkR&x|LaU=Uuxi&+(S$ z(%4xx)rtvjqQw0<6v2^d&&0D55<1*6j=G+n#alQJ3!;5TQ)Bmm2ZSQPP4-vSNkTC4 z8?+Mwm+W``eCixHzcH|pVb;(Iz_Frw`PM+`W$*pwk%2(4Sw(S+iOWCy? z0T@7HlSM_SX3k($7C-Qf#=={RV#g#cY zI4GYu#7GM2yt_*|Jtc*IGY1D%{)B*lnsZ4{0c0wKwn{J`GsctN&s2h{4|CQZie>eb zXyOlZs)r4Uku!12BqKvZyLb1tX6EL0!tLrPAR))ww{J1fYj$O00TlA5XJ@3Zy}NfN z{830qE2LB&04rR}oILU=z>{Zqt@QL>xDM6X!y+twdlm8H9u(@U-lX@|ty}djU;b!X z4}net%M%Md%601MEq;68Y9QI)eH-ze4TYezv@jF_Oj*CA4HBvFYH{Dp9`L@qk6*Iu zF0^lKZZ?KBPt%G|(|dKvr>YIfN6acbbNDmz^Yvx>JR*x-Xt{-jQb6t*rKP{X<%A_9 zO0b#8zWvkYMYnp<0*E+f=P0mWn@y}`+6+a7R?yDi(fG{^v)nQK82~?KWp4CC^}oc> z7u^jFqo}YKZ%9-B8S?vsfcq0v`7d^Kh>L*^Y9R;GMD|3*q;Jk9l$rA2<~p~thau2% zK+;pLLlWEo3I8XLEv_R&Mh6`#3TXw@F;E^4G+1aoLI_H}v$|KWCU=*(kOtldW1%cO zwf906@}bikt)YP@(_0}XL71$}LL4YEX(~=ryzC-HaVAuGeZ}3WztU7sPfz3W<(3z! z!Q;Op?fZODa`;{bot|M!Awc1DLar%*?&|6SvDY@ySJ6@BH!q(^{5{3wmUV7VTAL-x zM|f5AZR6xrCNe>G7o7kYmIU({}S|%K5un8b&=JipiUNt!?iVQ31#ajY{Z9#|_4 z9&L2+u|#LS4LJ&r*SF5Dsi}d)g*nd;itF_l50Z=$1GX{%bOwh#D~g7vh-4 zKz+6K^;D*$7+D{DZUB-mnjPtx_%n=aj5mb*wq}*(g6vWWEien}!VZ$PC8#TpnI_pD zX(O;KKbfmA1jNL&VB3r#T?W;^Z|~k$RE%E}_DW!2H3Yf*8IT`!SNWCHh4W+~_3`Rb zSSu7n)U#(9ni5s*%}92t`ejZ|LaUbLnPpkwDJ&{F><)4Ksf%dzqhrU8d5nLNluK+r z6Yk_n6AoBea77P_wLuwvTbLTM4U0YCMNcg+H(Q>cGyw#oEX6p+O&Vsnr|z`#Sy*0O zzW)CX6S7ikEq$h@rt}!iiSfQ%cE19UuH4*YCb$;39+3K*F3)Y%sX?C;UK0}on=;}TZuUT<(>Mi~3-X9gUZzq>j zdfia%hY#Ndg85<$muHOe}}7uJp!YU|@hyJL=ImaMQapXl>E9 z2usy=sa{Nbz{!(&>lP}Vq9m z`H@(pq}T1Syw^nbOtXRx6mVyzQC4MLECi^nDQJMp)po%5LAbO&^w{--p2iMz!P1dY5ey8jlHJ8^I93r&A~4> zmj>f0Puaep6P0C2=h!;v~sk}U*NR9v!B(A z+!@+cu$zCKNs5JMY-|%J;qzARkb-CS+$4SCJgAy9i56PCmlw zuj+B9tI7MY>Fs327#n*Sgt5!V@UgKcVS>b-q|pj;KyksY_nAh7J$f{?vbZusiRGv4 zkB4AS%&NNAy#pb*M~@yM5)xu0rlLx6>N1^HRsQ^}>r3Uu;k}^2t?*GXF=t)CWYIPjlA`RSuBWeN0zmp5i-e2WfjI z%wxIaJ<|DoR(D*Oxm5Wz0Y)_6#dkDe;|?Fe|(aTiyxt1`^V8VBulG2uVCO%0W&Vnwrs&Zoy_C2$K{r z_pCG5QUr}rx!Y1X<$PSK6t3p$d_z!9B4N|d3iH@dyctmk#nCJv=Nw~$NZyk#1i-^CE3-9{wi~ThfeDVrL>0+ ze?+$e3_1Dr=W&=|zQ-fLX<~_sHqN!k0J26*g;|wv5pU3nYZ$MR83fDsdkXC%LC@i4 zzv-b*2EEBPEvR$vI3^_2;BGS%dqO)QUAEs_HOquFw?vKQx5RZA`L3_dvti1Lh4`a< zC|sfPU5elQ1efIXdx)vXF)tB5c~aegsGT!)pn1s`kgU*#d;l5YAXc%!HUhwj2y<9w zLi6@Q4}@w#U(5ndF#*#)BSYKu6^4p*9pI`S;vS){acx?%LPw6gDIh^00mETkW8;|V z>1kvP3pu~v9LMI(_Ki_O<&)LRh4yVH0;XyYJ_kJZ_g`B#v5XbB9A+?xP(Ehs%e3`E z=RSEQFjjNQK2m1~H`kZ~7k~jxuYG)w*(gFXgh`Z?OTVI4yo?s2jZ-3HeUNZ*_wE-E zwrk)INMN#Ifkl*+F;q*hM8I$u^w)4PY8FT0al;>Xufl^M(|&5T%K9RuoK@U|nC6{_ zPG5xOXItRp=p80jMi(#Md$8lsOXOt$5Nj0|R8=XXa`nK*Z8|C)P@PgR?PRlhOlC%! zW_r0dC40g<1ZMCK0wG||ls(#}js!G7E9Ta{wFj-T$r3dd6`rh2*QB0GpP6=e@c@Z( zzqPyor9zr27>i?#1` z!$~Kf=DdjandHa!K)^TsVDgV%w)l(g`ghU_)pP-N$*jmB6^y616RpH02|_qqSNTLP zDZ3|YtCK;R@{5UbHNERh0b$|Ht%RK@1(=vWzi)!^QS<7<#mcz!XWzMVhih5Zxi=k> zRfKsrZQ0QR3WyC)Z3p2VGC2hKRqWI=B8?2Kpw&&SRraG~j$=?#Wq{39uq#VstF@P4 zb?q0Y>C$x|PxU|)jD3C;d_deHJtSmXz{*tpSj&sMpv3m`^5$wM%KL<^(q&L$98(*U z8&ZkHEU0c|7Uo>VHm(8+lJE1XGd48*mKuw}^FvdXOA@yxKl|=9wiWdTgue#>lm`A% z=+7_qp47GjUg1*qFSWhm?_UWq+AM5v2IyZ9YFx6;76w{}&g~s++{Cd*^u%8fA!1x^XIvM$EugcL?G;tsV&?fbu_mUWp^4RklE|&_Ya7f z@dFr}dQS9aeEfJhH8r)vw-??uLE>#Oi_*NOwmXvLj=-uugD_kNPTv6&J>-`pdRn}5 zD?GNg-#EPP^6DVQ`#ddZJ>Yor&*$9FAy)w7*9OBmh?cP9+;9)zy8=Otm__hNJ469F vlrC*t;grj6tKx&SBKZ)k#(!z9v%;q@qte(eX-Q6fdw@9Z*2na}bNH@~m-3=n)dk*N{ z?)|^-=Xt*Tf4}#``(e*GvuBv=I?r_-Ypvrr*5&^~S`-uQE*cyh9HzL~6InR88+~wa z@arhZ;GK=kn-1WK*Y>HBt(=9vt%H`e9-Next);1jt*MbVg}t7&jgf^pJ2N*kD3zQ(%vLX@i`-2JC2<^H! zZf*d9OEy zbIlDtqBQG)GtTV@w7QcrDv3gwW|xZ}uWF1fh=&H>CjXp=#GaQMoS)d%eH>I%@E2H- zol&p9($e|+D@UWj-AtT*wYz*OJR})d@RZQ7sQG5aLON#H5y)!RFQ)(}R$Y+p(; zu(?D9E%zEHkd?8mz1^?08h2kh<-iZS&=a{2m9j}M(>K#{Qx~J49S=j|QRJP}smCPK z_lvc zT0=#BK?G}BA&ql``*-;}@0w;6^$wwKVtPy@{C5yn8n!+wq>puqqFCK5VMwh#&taGs>eUE5ww+-g(ONNPM9vlM3VAm=&VP{d-`K{#>$&0c3^zu4V->@2bqAe$ zC;}6#ANYC_crb<^n8y#Ff(!w;It z{z$CGiuoZ&T)nM|sKFx<4^5(XO_MzYSL>&Yc(i~*sR1#=p@bG=f$NHC>2Mb>Br%AR zEB37x>L)glVt&8IvybHlqR|s$R98!`_d+-Xd;b0)&25O7$%i#Hu}&UA0q>8kH?7PW z-`FR=-4468C3It@eX%$)0;xzeV@vhZPi?h(oda=#D_o+UIRo>>!7^;G4DTq3>Wre z4-<#Aln~<7e!M?v$H8C*FE!Hdc&q1q7>JjIEGV?+E5 zhWPbgUahXT84e-IL8Z5DOMSKZ8+2*d2650eZ()qu?JtUx@LD4|OxY1i4m1(g?%p`x z-nnGf@VKxmE-R}_6r8tu*JSDFfd|l7TOj&B#srqs2b>YPBGk2h=|fdWA!KU6X{=c!9uLvL}FD>TugFj90r9xI{?QjTtv^ zmIi_$;qJ2d;nMsb4_r!7(ZEtd*fZy8TX+RF42|ocHBo+~RlT_NxUWZ!v zI_i(Kj9LQW%>W2tpHKVG>`7q5AW6|^pK{;Icg)@;}O>;8og`9PYiu+ z$nj=W7mXNUeExM4!XJ^kns8t0%{&`ra3=$6AP82TKy8~Pv&4E)NDc_lLQCSHV(2s$m0SfoxG~Sn{c_byiuq)`UIrc^9&+nmo7dsy0!aq-Y3+y(A z<;yP4SA;_y1!}+VbVM@Pb=;?fe$M?*3GcQnF8chC6t_xRYA36$!s|N9MS<21=TodQ$+!_bAxQ( z#vTNvD+3b*$A>zWO>JgWD=bjdCCokh8K6x-*`4)LyC`~_Axn_@*YzeELh_ZpSHP?8 za-;6q1H6Xs-+ACDaQstpbGw%&!)2v!$;^$m69F^;`JJBq&*UZuQKJ>d@I4W=0;LN` zqP9q{zU!hY2kLN{QLIK-IeNUWb-V|~E^N!a#BXMV_^DRgYl>f;91RT=+uWcbWC@*! z$#Fd^b2qlvwPjcc#~I7~`bsPExLx3{$bLfap^2q**x4lJ4^-C8q6w2UU^G%Z& z+CF04&&d=g*;BturORfPN9XT?lD=a zZe_Uo!(q;Q&#M%%u6AAJu0D1mN625m$uLdvI_Lvc$~~^whHr)lh^kb~OYg8P!l|E) zAOgrOtRAdOjd_Zab(5vUW$6X%pmIg5`4Q7je^*XIHLibEPC>BroW}a{X8?A;@uw+( zl=Jrij*^Vr`IgdwQQ0hUDQIH#2}i`>QI1W|RhGVsdz26Uq|dk@_@)NLYqiM=z#`bK z7;$2TEKTqiF+c|1ii;vC$*4GJAJ z0KH+$cK-=oA`q^AM?UoupE7OeMU^NhkkLVw8hlU9ieVaDo``vs#4;XVFo1UGgMBCf z`Uyim!mHf#`{Do8Xk67n0-iRH1MlC4+{G!;1V5~T-~0%qEz+w_kC`82VjWr?KdFq@ zN(k9q*jrfhpnL?;v1ES?c8Jlq4xCyseZU)VekxbTVAc1(!*3e(pWk!-zgF-#5V6~T zei4?s0m!m(#62=)^Tve748reCY$Fm8ee@K({?G@N-~|_X|ECLZ5%y<4KnCA8Nrjb} zR_(SbzFMe;)@vD~v=R@?K_!z9iz8jTL3gk@M)G)w0GK)Z{(F&r8`8G^kH0Gm>zt(L z)nM?vRvgmf>2q28klH`@;wwR?oFK3ArG8E)cn5Sv3B(eqw2b&QSoF|()A9Q4X>8D9 z;cu0Vt@~ro3zity(d)&L(DsDPt()Fe*Z%8)46|k7dACyZf6C%K`UA+I8vr22u%5=LBM9o2OoA=N z^dtfN#s!w9umtYPGO>kaKx7aiwo}r7AIYeL1fg^dt9-<9ssd}6cHqg2 zNlGZ5HpDMVnT;=83;-zwStu1r-d4oRwA%j&XxaeKUYt;@Gz$zxL@ZexS2C_ASi#F@ z=h+Nz9zDrkSQ=efs=n=#02bzGF^h=IqL@ zk3^zz-Z1bxQ2eA;^)R;UHM0q2O8Y3u@yO>IOEzR#(><~Peo&cK6@(@|p~Vcfc>|(b zOi$_%dJ&jEaC-lapcX3Vj6lIUx4lKYaXcPLc<@R_I;swTeIH!qEI8Kv+zyoO{=Eg+#45j`*VCp?g4W>JWv#OMw$@1 zzC#+o`Qo^pEYmJkCL4I%koDIlKL0=v!%_VgoIXA1gNM_>Ace;V4K*k}m=ZIctqad{ zWx*~!{KEP6qRhOZepswIu|Oe<7UFmEx$Gi)(7#AWtcUoN_9&pL5GMkxQM(f&h$K$= z@!TM75=z|)>n+H$m2XT~_v>~IT9j1IiwL&7asS%3_W(B2=A?dgvOUB3@iIq?NmxO?<7mFesjSUoO`#IeJQ9^@rbHka*!bmh zT&8*(2X9E7EB?&+7%|V~zI=@S8tqctQ>`H4K!b-7!{IU9;E|P}m*WY52c8_Y8E&1$ zWs&In$ja}+SSwb}no0spB9C)bYgjatElIsr`uqXF! zH!WB}8xMoEig^EqeNba)19|~eGk7PPtEmDN_3iuoCHbirARlaAvKGYC@=tKC^R6^6 z^fO-9A@goBNda?vgxVk$vD<3a$&BT!79Zi=+72A%*kTcIAeeD8Lel@*oqvzjb-gF| z`#SCMAjejNwasFZUzTFU)NqF$ZFl2FLGQ+?YM$DR!wi!;&r!=Okt6@apx@__cP^bI znyb+Yb&Q=F6$N3NW%-Ymbo3|_qdMm5lG&IV`IV7g4LA9CB1=W3sFJW7AwEtH0u94p z4WbUWOypusUk@(}S+xHA72Jfky#5ykmo3Y@VJTK_4}u55=*_*0+6?>y zw_Y=j?CnXgIXB>d5)||#jZ+ic!?`wQ+%hf}>z^O&1l#sF?UiFg?)s*R@z}OTRi-PB z>K{EDCzxQQ;1)GznqVAQbUgPa0-#EVfzyeZErtP*Eb`|s2+KPqL_JbqR)Bky)^a3` zvR7;EHuG#9mqp3q*P&bSFa6Y%QEwQywW&Vh^*UFr70MB@ysibBjXNSAQ2{G7iv2CX zycXV*)hEO=0Q4^4-QK0OrZLvur71cM0cI$+Y)HJ>@Ne|`7Ff0W2YT@)l}Ct14SZ<_z>e(_KVZ0W-F@sn*&?D zu^}B+Mcw25w>_bgB&+F`;i^z`3DNq!;;Cl^Z?PepS;aM`N5>^(OBP3bG-m>Uj=IdC#-r{^Hs(4F}gMgcvkWje=n4@ zR=oVq@%Pyz4e0o*m?+O?CQHiKR^dPMZw%m|lw^TwVdVF*oH!l78rR@*KC7HQNCgLU z$VUTZgh4R}EDv91_RQY8+`oS=oa~0_u^Y{D_0AR9N<~I-)z;iTY$uY@WwlV6ReL@C z}-<_G}E{AsLJxC69|tU zcu#92JU7@cmaZD2z`D2@Y0a3pg9XRk?_W8AwTH6oN<4`_1)54{wQiwrBXQMqNcYcFFsSG*?n`H{sD`n=VvN8SF!XS#M1j`0~KLD+`nls+v7iI zu!P4eJd{vJulOz2W`yf5pZuDLIu5cJ_jDRlXB#dCcDt9>ctuzdK@sW(Axr}D6)Am@S9vq8xt}L~x3AC-f zeYWjwRHq-89-O(B)hI^#umeq^W`&6ufH?x2+jslLT*Q4{rVzloT{B;bZwFwh1@J(o zY&RNlUg-G%HV%~1MmM(6hN_rrY&Yq7TWj9$Jm)a3MYU38(BwCNKh-VN3K+x+>oc$bn zaZa7K$4UuRaw%G#O^L-^vpgIE85$aAOlmw+ili{gSEkMrg*Uu#X=P6;?3 zV(%+bei%jNbleK8(h)B#la08$l2t$1QP-|t5w1purEEsGvW>(r{n?|@mFDwE6PuH% ze(#<0b!)jY@ul&pc3or}WpsF{mgO&X=^eE%T4a6>V@hb=bDRb1h$oD?$8}w4OD$Yi z&(CD4$(sLWX)l`d_A^gB|^QDK{dhVSs7# z)AhOe^9Y3pE?ReFYFNE9x9iYXS|ajhe&+kk(^7VO5fI#$distCx%AnWpTklx+KmYX z^S3s{5cd%ij!0(}r``Jd{dA`^m_c0I}q zSq|!4>jfWOar;%kt{M#$Y9>l~QE{6|(BU9Haj#M_9CD_GI?6E=i!XH;T4Pb}Z`HP4 zsC|mC0&XS8}9EIkrDv;=>asodPnJoPVDH`Q$+0i+HpFoY~ax&l1qFekZvynzb_H z8eB+q7XEmewceo#4hJtcOC^oOZjMAFU$=&R%HH26WJL7O3@jM11b^KNgBlPJUk5%n zdTszQ3^ab%t+0B0QFm75=T_FNV&;)}+Om+uDc9(Uo{g0RBWn_mQ~KJ|B5up`rJRRS z)7SuLa=>EKF}on+`eV@G9K?v}f>g<2HirMJz84wBYr8w#FDwUo)esWDVcnN6Q_81X z=kIi-*-q+|Um>b+a0ZqGE{AKew$)gL}J;oc~7V7F56z3V-JeeNk6LivR1%WMj^CN(S{^Z zNRdt-Tb38#2%zXz5`oqT;25;*K&<(ui(1(XA_@Dj9q@0SnO^WK2%xwGjG`?v2arkC zU7mMlRoZUEd{&4qX_FI#?W3Zs5KE#BtV1bSVX2=-2PYI0P zpOU=F#%0g?14eov6WZ4BPdmDAFvG6* z2hFc4{iK};`OfZI!;dFk?a!~ZN;>nVuf}=ZM{2!G9ZKS3iKa3gTPF9ypQ>>8ZAbKx z?z=1}eJ0b>?6tB*YsP?N>--Wl@fXx)0sSnij4P-mOw@UU=wCv^M}wOfzZFj&g=llK z2x~j#9D$)9qhsAC)A4)^^vQ}ny#J{1rC2FgA8C_R((QgnXBDvQ?S6sR&VEXh)>k}U zA~#z49c`QdE-XFLOrq$U9XjaMboUGtuzLu|{7<(fsJ`DK-|)?756RSI)`x%e#Ls6n zLcX!vU55S83pLBvx%KKCHNZIk@>EJN-Xj#Dz;Ou7M5XEeq=pH>da}NE8 z`vB~!OSg@|FE3K}QP^qu{KRqkG3Rhzbo`R} z5Fc&RMs@|)RBckCWZh&f8?vwY(z3}2Q%F#4F-mE?nSx-4Mt5z(G9aD$83EWjjsvnV zT+^-^FPZV{EW&_MO%Jb$fb>v0VW>ahfv)eAD?nLp-P5ZcV=2n%+hgsDHA3)eBbEsw z=4CQey0o>%D_WNG>SN3Jk#*5zuF4A3QJr%nV`8KzVl8?ix}6u=)~XeLRx!?z@CpQd z5lf~MFK|hq1H4QchGB7CJM*7d94cqG)~qw%EHI|UJp(vGOyyc8Qh2ep?l&4}gcVLf zWwuD`fBkq%kNyAjy4GLKHY%-kw#-IC*R3Z&lGo|NHZW z69~mgx!dIo#P9{RkMFTrzW5J~wFo^n8h}I;LKidc{wxCAev&PpjhxM+CK&9bQRqVK z)KE-kUX1}Pb)KHW%@&um+b7FYQ$Ei|l8Sn*f)Lk5LGgTgV(?=(y+gIJynVe!m0G6| zF1Y@TLqQdgMDrii9R|Y9>m*^Mg+ZK<-RXTHaK4!X+8&6Ot3Jl6=?J|L&3{DSk`iY} zrk;qS<@S)&$^@~;DDdGP)?TbW!Kan4@Y?N9Hy0m1DOtK+zqW#LU?_L&m_D1 z?TaP{Z_zZVFKJIH382HR27d=DkfYw4e3?tUo2aRtrz5F8BdAtUI9XnRImd-Bdzq9m zStFJEo?injhN$*5TyJy*fu82p=txfQ6IQRVac%8W7cwcvzsc?lMWwr#|I9>ds)O{o znasR|&9KKGS&>{N@THL$9nG5^Pf(8L(`=IvJkyO0@>lox!EIvh za492!inMrd+ZF0tB| zK`|q@2v;FTc7E`o^(T!?~VZ)r#18jE3iY5JEQ{HHC;VpiO> zRQ2n!D~~SDbTv==cqb;@j&Kn+|I{enH}>usr}RB$3){zT6QzP3bIyB+%$oHfzOf%w$Zr0;%}j#8&3ST3M<>-a%Q$ z3dvLmpp-r-R;h;C#@!D#?&s$9#rABnMz@Nuc6@2Q&VC!R2@|x$o&s|HRqW$3$M!md zU-Y`&RJ7cBY^L)_I*r7O$Ri869UTaYa{Jctz}BJaONNnCsl=@ibD%BS59Uneit=^8 zd&arO@D3?T(=(x9vR9)`v8JCE%Hs=)t_>bSA`<_F#by)mdieTg%R);TfBJaCnc^<$dpf>o>@O<+JCh@${QP%=(e?qs--AnI5jbN7K5!F ztvKH9Y*}?-%kReW>Ev`doiE>EtnPdGREW7^u7a@+iV<~5CD+E#_AnJGOL{>S?W=vq z+!LNCiJAQ8p#0OnG_tHK<-iz5aia+kggT0fbo;xn57M2FC12cezu3^!F*U_#4JB`F z6~43r7MkI?|L9UFwzh?58H%{>_8WU^0|F!H@Qx9Q5cwuJ0Rd1_V%gqM3j;65dx0UI zXvvI}CQHZ*UP5EXwF&dKNyB{h(&>7%Gaile^A1J4(x zt(_gwfMw@iBxeC-%Hz)7R)L6H)S+Z|Q|jR?hGE17EOEso4NyWo+v@JN5{Vd8`TRUT z+sS?)^E45WIpz7b16D#?#FWm8?&;QMXO@|m&aU;Q4ZVeiDeXq{mdVi8;neK$>9T%e zmHIIZT-)Cqf^%vp6DlgxU#nC%?^mlPiH6@<$$wFk*X>+VbtKl>&2fCqAM6J#aQ-3B z<)887MP1H6azg~baEvW0Y_Fp!f|*LF9n?qRj0kFA7+pE3gdnKp(MLZ7K?$NQgemNR zb2b~#9Npa27Q|mRvKjULO!>=RbB6}US>EZNdhC~tR(CBa8AE!~<5x7^A^@V}Q_Z9ewY$R10!G%32FVt2F7cl!6N5xMbb_$I)WsL3c3U$#YoL zXOQVZ0LL$u%dswKQZ%2$WFmytpCd{2}2OXi}{x=;_a?E=0e?1YHWQfysE;r(CXXKY1`S-8j?!2S+0d7Z>AG#o5 zW4IZCNM)e}s6@c51M*y@SPc13&z|LUn900CT6q#%jMRo-?*@1TpM5?oCwRa?(EFMu zmx|Q_JnArRODCZXoe9uIYjCbOQn5*eEU;)cb9)>k!&Jfy}L$MspGQD%_l6M#Eg5H=H*Jx};3?i1WLLDO%x5ZWV$V$Sd_M=TC*q@mT*(!Hj z1ojf+H%D2oasLoRgYCXQU>QuDl;|THc6T4b%^pu!v~t((VOF2i>^wCvpfxX_^WhYp zdB6}{_HLk2j>n$O2k1P=fR+5kh+6fAP|BV?;NSLt8L_<*lltcHj8g#VjiQ18VR z-6rNOJ9xEo&?N0_-@Es0vDwD+^CDm!bf@D^RjAS{#<;(4mi5kNuH@%_zo@C=no~_4 zQynOtGIyyNrOmo&tm7wl4LTOQ+!IX-b)vZ$=S$=L#9YO5zIAu+Y`oN|?kS zxRXF9Y_OYoV&14IT#!j)R5f+%Tp%MWGuVPn>&VNDINhsB$-N`KN>=h?pT$CA;ehY* z;^%36fVM=%p{T(MYo;n3T#5MhOBlww*y|+AwhH7H1H{a?0-c(yRSs5c(ytoJvu3%~SLV*`6EQ9_x}%3)0no2+?Rww_&ER!?EeS4&pq&j@XY@Dp>vR z{=n5&|6&$~qX;`?oc8re2M2yBkC_yKjebqk57+=>TMyo2UJAqR@0v9AO6IXc)pxHQ z(i3O7G_`&M=ml#5aA4C4p>EKyb#)1SR`$$BYs*?o?uj%7i(83|9-L?NC$VErh~Ak> zb7Rj=9zd~affTq`4Ae@hI#~z0zde--ulQQj%LQ>_ds;q*JTp4By=ot6S+!+$G%AXsEX)k}T0Ul(Oe?`iZ{-ZZg2ryHtf2`J z+D^^x+77p8Ohm5?lulcSraodrh66$An0fSjSmATbEzQ3T%hMW%87dl#9$HB}D={NX z)a=O1cIXKQ*#}J(^|N9mv@9Bp;HkRl!Q%?EfPLzyCGpQ)5sAf{87q+svgk`49+omJ z5Hos73D3a!9tJ{^jQiNYWnd|jPdsLBXx}RZsc!nWXX41~p8c!wW&X7fZWA9C$!}<0 zLMIw}0d@JUb}J;arh01$0_Z?Bzslko?<4aVm+iQ*V+NKHEu!)XM*X_!YDu+PBaBcu!&?y!@-<`v?tzY^5PFKM<7Tok`)&1j=Qsu6JCuk59l<>}5IvB+0`?|DDRclUb zO=#*$hb{z2%sXdA$ zGM~n4A}90H{@G)$dS-bbl%-(xslPTw)lHOkqc+Yh_c)eIz9qr@G29oZCsKi9=snig zB2@uYGfrbRZf5ypE|tQRX+*)6*u7N-qJ)5W@SLilOe>8r@Xa+Z^W5ZqriEw*XC8PacfJ<3hNB0B*fPXn)W}l$Vbng3 zjBE!b%?KV)hX2e@E81kIBgavlg0vQESa4tpL}<1neP7Woj{7o4DHh|NQl_x za$uMzOL-h_H&;^)((>RU<8jOE_hFU<%@>i_{c0K&0$vv9i}#q-B|b>WeJM~EiO-mw zf0EaG;5G6roRv}aY+u^L_2Oy`Cw{GA0A zFh5D|uXo~38738FMm38ofwLXRD)vW6@DCHnHO=C&;ocl9h?`=ahH;|rr#+OUwo^SD z1C_ko$&B~SBzLPJ0sN?5hSp{}`w(%hQ>lmrU=vYGl+poW6L4zvph2ron6ez-D&ooOh?ncB%n4lX(vCu#=-> zAu#f=9d{KSt#gWWFsx1KAtRQ26!vjvjN~0oCI=cF?UCfm?zoA>p98lVb~2;YQR!MY z3i1g~L3Ez&T1YXsh+^m)$^ECIc(lP$xMWU@INa%BIAl0*Afq@+PzYqdQ+#)l(y-9v8Jn06sNPM$K8%=%W*u{ zxw~9_`xn2`?>v7;ko#t(8lR&fn-?Y0d~CYCBsXu~{UV~+SW#E^rTGYR%SypKTMzSA zuV)KOKlaxPPOKAe&(pyCN3VX3@Z}sPeERJdVn2I#r}+GlQ-v9a^4sjs9>Up8Ux0x~ z>GW=RL~Hb@h@JG^zALXI0Uon=;AGbqka0sYr?N+aCYEZmil+LufX9+{ekA2NhL1d- z<7$*=2hqLD$gLPHD>WHCD<{6-SYA?v3bW?(qlE}foHpDNp^m}x$W?vlp?_MWY&^HzEGV7>X1~P_2X7FDAieXeW)l zt<~m~v8)gCRc8}emd}9csI@~8T{`jR((%Ie0`p67^Jc8r>Poo;z6)O``K{J|DZjyt z#K^q=vjnj0*CtE8B>Oo|9c2RjWm`!awu;C2oyOxP8DA!InhR7S&jQKEwpQ)a*fiE| zR(^==oHn&LHE%5q-V)Qr@YCzGKtll)LaCW)Ye@G}T~JYZy`)NO;iZS99CZcI^9e9Fi2M{*w;ifJlDT#TlEtxy^s}qY;rG z?sc$PDpzUy*2>j*Q#&5o8A1?z;?`Z*Xyr@l){Yg9%pR|jZDw)SsGCnd{Q*T3a#a>dwk}u4QQF#NdnY$7P@k%%TFz0Qu+{VtbFzJ|WNaRDw zO=!lh!H&M9!4NW(J?UQe7RJ8F^lCCxb%V~RFEy2OHr@SoO^Nkbe0USl_Cyut%=h-E zlIgb4b9xTI7Fr{&0_TRCOW9+3_7f*cW~BK8WSl#=TS9k8&F&VzjgjlLX_cNa`%Oos z)&26KEbin>W}1N|F4*}Fb7Wt&a^dmJSoP5!4`D_xM@vmx^)rp$y=*&GxwV4NBLx;M zAjQx=obwiMUT@l9_<(!z4jz@uvsu^H$HB$%o8{fI9{1mxeHG>6a;zDQr^ziWTI z)WiiumEGN3TWo@Mp8d++icNb4W|MJR*6Yd$eup>~I^!VPJst?l-AClfqFHy~ zb&=1jZtbX?NR<)F7rKCst7m1WE-8R=QE_jZ zm@j2f;FByCgysTr~vjkO{%%mqIYmzzaZaRn$fSLoUb#(2(Z$mfcOk%5fQj*kT75q)wsumm|sv}a)P#l1&WODmyvJ_ zwXL+W99pDDv*(V5%ud-E7=P;KIX%9KZn*~W4L_zyjwwx0fU7&4`a7^1Kxl5 zw3Se}Fh1!~{u}2LshYId&h4dg44(Kp0D*N!CCLzOUO>^h>ZaTzCtYn`{z9&Xf+5pI znbdcC&t$X!c!_aWP39o8t2SPKpjzVYSBQ40Y^a_1P1mjDdDh2|EQbx-c&&0fl(1an zQfSIdc@Xvl)0&;!Ag-ya708eS*=kKLn1xsx|8tgVWai7}auaUd} zTD&`ddxPqML0`SU>R}Sp^OI^4{eAJS0^6-k6Tu0*;j(LKgQG?S_!A0UH|7Q_l-Jxr z!dHOUX@HXeGhaaR4`MCOVnYzR123=P`aI-w-Y2O@m@)6mE2f3Nde))+R+9p8ofokG61`5tMaT$--XSk2vDKNXRpH6pDeLFZ%k?CFgzEf8n zWZ-Uh$Hij1X=iHKx5|7S%6NlR%EnO}@Sfe2t8!q#wGgT86uwR+fxq+2+>2`O-HQ3I z2?8aZmRZArMf_7f?xfETG=rxd81D=mP}A^|4Q!FIq+FUwrMDJpwAS|ZvO*GIN$H9$ zjPg{_hij&l@UsC{U5&Cs+dxDFyG}XwoRdAfcXCb^i+mJm3Q%mZ4VwN+RJh$Nwzn$g zWOjF7u=I8xFM^2}5>MG>$_M`N3hq_<5HrUOqa~f`4m}GRhp9n-j~{tL=Z2OT&)hGW zm_0Q$;#yc(q(1U5Hntug&Ql@Q0Js<)jThVt!tz3g>H%TG9o`gKxkCz9cEcufvcd#7 zqTrMf7?aK_b_2gD2xd-&tPKuhL+Bqn_(HE~NLs|_zi3Fy3@-y(LEt5)Kl<0vYZUr; zZ`dfSB}wZr3We&nz^yQ`A0(QYm(qef9~MUJM+cI=*VMKY(pbg_-Kj z%etTE_6grVp3Kf{PK4BAdGyWDMkt?K{M;oFI@0v;XGwM>WzkIf>TD1n{GIa?jw(s< z%`kGwm4}RPinShVeHyEf+n$uCMr9Np;no56tCDrzEqlq#QXgztRA-!;W=AU_hWy2> zI+h^dR0Jc{tEdtT%Odeo#DGnQcqFsi5E5jR@pdV6neoMQp)Eg=8M-f1@?ytnpp)+N z&NjtEPJ>6i9xwZb@`iykO7*6F4?e$>gRw9eu5g-as5aLMSTj6nM>!pAR?Jsbf9ZmA z<+{{!u?Fortfy=mK&EafaFw6%0HKpn^~x;2lNZsAi~V z4Oa`$i5H>QA5fSY-*t|zksqqESihgEcC26gLA_D9&V<<0q~ctt;W2H$3NCM|6=v)sefFvJE38 z!9c*f17sfSe0%rPn5gxS5-Ml@yl> zx!L_$C+*~Dw=1)7<+2#v1$aWp6G$6N-g0fNZwl0&?4#74u+1t`1AU#e%oJ^Qao4U( zudCU9C0>}kCG%w3bEeK@B7g%{u7qB$l1#_Nd0{_T-9aEVqrYsPHUkwQbrH>5p&NiV z$rh0-&$ZvCw>DR~b2aO-Wm8GEWzKoRC_TBt*mT&MR0lVO_d*wo?q^f2(NgW=Cq2ph z(p)QNl8=*@Fd<6LLefbawo?NY#X-NA-IW#`FfhJ~bqel8VrkCk54#b8vtz(M((Hd9 z^E4*;q;wg-*Sg^K60x!;Hq#bwVwsNNR!#bz0%$_=i;iN z$s!)7?oD%Q#rfD3xXMg1Md>FxLre&gDEZ%bV{@d42BGW8Lnz zS)6!2{%N&JH;+0Z|H}KWCO7UoVCvQ<;vG2HU&~jJDg7ce1tp1l8E59X^zur>KqvJ zbp5p)WU&2%N_ZkAM_F{(^aejOsXi!czQN(mRBO+CZL~91*amVUsLVlw4s)VNwm@fg zTnT5o3(G2>ZQSI`0vkiG>kk__v^tM`3B5qm^`~UDXrO21uDlQT)h79+9(*H0t<5r< zkW=e$bC!BJw&QXaz9sx*aRF(i^25Zy21_^TRv4#NYDOA{Hg8970%YI5jP_N-fnhOsr_0gzy7$0%BveDhtu=hf(v-I&zPv(kN~+Y${T+BG=J>P?wukye}36ihQf3A0$4s%g>kfG|C@SlnOJ52JNyUNH8emASE+-3x$0In`;Am}{nGn$;=;V`SmzeYN1N4CI#1g` z!W!>o?>!taaE+N-=6OBF)X{aY{L3FwW}CF08@o~8UdUULUzRE1@IGhCoJr%em%h*n z7#|Nr2i9TG-oQivb{jt1qQgVt;7!p2DU^3+%xQ9#MBt4@=yh*oM#l=Da>%^M>&dn%Y$m|ZcLf&7(Up31`46K zE8AIP+h#is1pvu5>76yP-nXrQE2nyz)UZ|S0$dJd113+wi zU~|D4cjaZoeEq)R>9$@nc+omwU&LfnG|4>{Bx7He^FCgW7RpiPr|Ct{N~B`o;tw$@=8{SR2~To7a2Apsewd>XR-w z3XV@1Ya27n$nmHjTc62+x~Z<7UWXyyTR%?+QXPccr~2skEmv9sM*9A^*OK{~z>q5Y z$eRc~8)MB|TN7CKe-gFsm$4DLD_pq~HQ8nC9SgAHP7|n_Hf$qwoTUL_kq&?K%pG#^ zdw2_uuz6|rZDTPKP%^N#*X(|8R&(g4z3uPKnz$}dsKQw((4sCeO6Zdu?g9&3^ylxEBz>&m2)s>Qh8OZmPDWH zbws9_c`R?FK28THe`|Eabt-lvwRYjL((`9^IOb|O0r%$G-aOFtf6RRYqw}qi6;afU zDZH@zK7f$0lm z$_P=-Un}4UE4~H+^A2TYmgA$zMFi!Dg{?Iw&=P=c1P*|h=Su_#brw6NsZXBau;SP!d|0fyQoG%mtC%irQxs#g6w`Vl$J~Ks2&+Yg=!~O2%HgRxr z=T29KUDJvkcnqR_K4X%5a@o~>{CV1J!Fi`MiX0d2{z)_#9+Kz?TMq#y z3wF1Q_q5SG@;8$&j_;fwo|gdG++l>qb%Wi7p=02$;_;a|nGhV{{zS2!?9JA$cDSlm zINgk1nJ`b<-0YN2+O%Yo!M7;tv6;+rHkMz$VXbEn%Uvr1;%h_4pBK z;h7N)hF-W?IXy66h~#GC;a{OLiTq;UBCgAFfBx0ef>JfUGxN2EEba`2^GhF0v+r{j zE#V2=6k+i+`nmQQai#6;$6ei}1#xF=6RowcJ)X99w!4T|)={PTO^qrJQB+d@KYYCf zRMhX*{yn59ponycfPl0h-KeOvO6N!_HKcTkfV7C@5Gn!!(wzgy(4};D=g>XxJ?c5X zbN$Ok!na?HwAr)RBE*k*#aFFY9#c zuxk#u>H93`ld*^a0A(ok;J`R#)u#6K)6GCjFQEQ>Y0m24jN)@v*b*^*f;YP#AHNs&%2oXjlNGRJvFP;ez#Lw7)pU z(*)@m+0-{6Pd$9)jL?E*iI^Z(5ArltJ1=@!i}Fy-0tDZS-?Opxp!R`%(|TdG=tI%< zEyv$i+=8T5LheKFrkUBkF=8Dvg^=_t8`F?MUYNZ0ToYbM8UdL4h zM@EbU;ri%tQ$AzD01A4sN&K~#;`ZfGoEm*D?us@%l7k$O-V2L@{}?ZL`18{)b}$yG zHsAS|@6UGAiZd>`?6faXd(N>@Mh)g7y{KagVwUhiZZnQ&+?ogfziQoDvgVC|*DTyT zAs0e_ZEf%7Sx>(dObIiZaS z%b}U`i8ZFib?{LcR|L+h+f8fh&2`yRhvVRyA8by9zD)I^IGps@n%&Bwie`I#Tb>AD zb^z$|9e`BWu4VZvHS6yO(m=f3FKGCw9lNw@fPMyY_`qF~arX(=5Imad7T?tgGS{;u zKA;4EON}vj?Bw4+`Io^11e-!db_dsIy$Ppc^t=viXglmHZQ1iBPqgHLb}HgqxKQNM}78g;@?bM7O(g^TdUyW*}$O zZUcP|47(*97q7s^2A?@7kb}l_^hW1~B7wwCts=MvRrhJM8;Dluu)(wo3j!M*uB*uR zWldAI`cZ01F<~TK=2SAXHKpwy0w(B8&WHJJe-H31b{B5^NgcNOz@g(3e-bkM_`nKN znW+$KuocD!KvhY#iQ4Pmcx*{2yoD<3F;2(Aw*Kr>G3k`10MvZ;7>Mmt(I|h{E&Kcv zn68nmY9jW|)?FJsVQlu3ei8%lWU*KMMNuc8j<&A;n2lFnT6~HS6OTNq@hfJw*R#>h z&C{s1vE=^cGuzLi$F~@)7DuN#rjn%8O$+{>PE<*9*x`U(@j46)DmA%<3wuKCE7d)p znqJOkT1f5J{99!Yjt^}&(dFryVdXTJEd(R_pYtQ=Ys>~k@nzYWi^f8}uT?9lA=%JIe$wRWQL`D&*`^XzVX zM`BKM*LTKS5A!5O-V~WSU7cv}3eVM}qc*Xq+j77>wjjD>N|WOSp3d(-M;{5FDj7aW zeQ*AJ;MPfF`RFz2v%3)t(h&%%l+pdB_zwu0?uh}= z*jZ?;@HDSXUmqht=E+ z7u!MhCwmq(+l_=r!!1&a4*^+*ydtaEYa`qmR-Vobxm-C!r#QoO_;IR{&Js{#42#nA zcvLMfvVsNqc- z83!4=@ZmR@JrzX|5;^OukemPK=0yB7A1|&pV6jHFojI#G@=t30=qjpCD1(Dq;j5To zaEJ3LT-ci6#abYw>@|MxNM3`M@p&Ys?1hMuw{0dt^QQ)*1Z=h|TbU8wq_zju#9=WDIQ0NK51jMgD<*%3yM~Kdh~#wR z^RIQ3wVi84QGs{=1aKK?O$_CwkN{m+bAlDaP=mryK@Kf>2;Q1_iY35Op^t9El<*=; zp{F3z<@&hE?gq=XX7!o{$XwyRo!K z8fLdl>^fIi`5)zZDVP&kj}1rA!?nB%L4(zqwqkS}!73Z==oo8Ngo~&W z@Koc$JT?obiNZ=stoK^uDby20KQvpW>P$vZ4yl=KROEUcYmE1k+kKt5GlV~41GgM_ zsmp!dh_GiY@QN1q61tzLtdVmSgIYk|f-UmR+FelOa^^gKMAD($ca!qmusd z=my4kBy$6JBJiW>%2wjjBEns=0 z{J9N#|E1US#uP-?~Qjss(O^m=|#ICpF4U*IT{5*%{<8Xvn|50 zebtG|0hFAaU%JRKkcoSG^z`Va{|wCA?wkL;)sPo$#-qruG#Rz^;br5Ri0{n{&U*Kd zy}H}R`i82WuBfgRrq;dNK%DA>(wl?zf^?8W%eEQqfa<%d|ZUDP7VgqPc&$(eO4Ev z_lq%0=)-$XeIpr*`~}es`Py&Y08SK3Wh#HadERPp!E!&pwbJiIbfQr|@0rdCoju#V#ia)<|MJ_pO>8`>6ngas$n-I{EQ`V`V_@_V zg!7_|@0)Dw(?cm*#;%ME$-Ku5USxfJeWglYR{rYzAoC^zv~L&CX{5e!G?B-cAHiJ+ zds&zzVCm%pdPn=MJY^QNGx+&v@a2JK*$)TQJ@hG{NMleg7z zUc3LD?&)^K(brf3)E02KW;p z56W%j(6IzU`06F6^W?j6J+u^{a{K`VCg>bSFgl(JV>Zz)>#K}yujMXh|3;47fD4z- zq4z%<9Q}Gc<(CZimwR3I(&`c>o;hB|ZCuUMG2rvETC*+!w}9_ko;W!B|62(7J`Q(^ z@(p<`RdNFuD)YP{HtRVPY|P^sObL8L6R?j~{@oFsnDpy(DQ9t6pIuQ;iSF1Duzkz| zON{}lywDYv{C4tP{wC&4clm~3O*B!!eb@5OH;aPBQoUf!%Kw42gq)8_Z7ar8wrmBbSUa|Hr4mk8_D4^?vMoxZYi0_yoqn z2hVQ*cPhYDZeqDA0GPrX$NC&A3XAE%IM8rsMMnITqdYv>q}-)L{eJFNCYUJ~Ha($9;W$gr=d(VB@`{)24a|p&uA;gF=qUn^;_^Jl}OE? zd0DMDk3AyE!+F$}jl7vdqg&Fgc{lWnJQ2&7V&EE8Ohws_1vY@hW%D zAK(}I#+w?)Nv;3;IBZveC>M{n#jJkhY$W`ebHrg@%B_o~YnzSz`V=-HVcbzdo|`^3 zfr>-VhpGBM(PGC=zfZtbHmgYaRp_Ngf+;^qO{VWpM{N<$0(rLT4QSTNpJHRH9d97K zUqK436!|nXsJul)0ul6yG8|TUtscFZ(=MmNb6~&<)Wr!yF@uex^)2hi{3hSujp-AF zDfRrA#ze@TG1UIj);mggr`m8nB~^S<`xG9Md9#8rI7~-_##!3r|BceF3VpYO)JxBFR1PuLLuXz>+ z-i_gnu=Sj-)0C#`|A5hG9^Vqq1#^Kv$WoB^8a!%CrRXKv-#Fw1XgvJvi?cuRgr}zX z@*gy7vcLE;i`rsbn;3xs5Jq(<6Itg}Cw)2+Eyh3qz-FDU`g0-fhh6iFm{!j=3L!K8 zrlO(|WmRL+Q2~hdiSeiF-Q;>^(KXM?9@Prb3QmP9sNFtWU9QEXl2v<@XV(WrKq3rO zs+!i~(L!KuC74tb52r|*V4F+!ibBfnCrdJZbC@G~A|Ez0m@)w66Cat9)odmu$TeY+}_g_?FWRBemklljnVqtR?86RXTB zE}N_j8--^h3n#H&$9&K{!!x@9r*He^78o>3g ztD%=`#kZ$DV9kV-`7=#cX|7&el}F`(4Cn|VRG2RtJ?4ttW)=Muh1`^GCmK?R(4@R? z_nPf~G>^ZD_ttpSTtd?7vtqDPX?Tawq-H2NN63nL6+h zx;k2RM>(gBizfVZ+#TUO4iLMC|5)z=i%;C*dwCClZF~@18l5bpO5v#T?nxIVVMBmHd>)Utt;w(k>zNw7h{Nqz7wS0R$Ef2@D?QvX^* zm-fQq>kVi4tocc0_$K^S6fNl$@SJ5~&*-0MH#SyK}o8iP*%H~h5__?wk zCb&KdYQSrBSRu4LKH%K>)PD5B=bF^;l>bZ{nM#dN2tr&q`1s9=z9DV!TxpaDl`lb$ zq-Rl$l2y9e9TRQf7=CJY85|hzRkOsT%I|y3npQu9L0Okbu-~)P&jN!BgNVaD3f}qf z!kFXi$CCsWcmnS>jlk^F@`6?cQL?l6FHSTk%HlbDw(ScHv?$gv6dDmajzfrvx^$0b zThoinz4@va@i8}{hnzRI3$RAgp6yOp15I(9_F!&uqLOV0s9y-`F5+*gwKOfXH`=}+ zfB25~-_17+o*Uq_hr}`RO~-aIuD3~NU37B;x?F!uu;AU$B?=)$GY6biEM;KsJec>b zf)}=Tt&MI7A-neU_Tz;u5uKM|sas#`vPCC5q?!5fH5bqrZQZ79VDu9#;q;kCVZ8NI2 z4p_0dT1dVdSnXw#7X&xU0fAa8P|TZCUjBBpL2q!hafYp6G10V^7K~hh0#Dv&I5O1M z0ZX4sb+*x!6pe;PLcr9H3i#{@4y;$|Q*y6cUpZbijMXiy0L2$DD^OhO_m{%+*5DBR zv^hVX;Y`-dfp3MZ$;&W9Nam~0ysl-xV<&0G)2&#?_YDBT2iL23l&U%F?FB#cX4JBL zHxjZLC{eb*C)NMO$2bmdQwd*irbYG+KJP*qZrmdR-viX5RHwEZ_eTo~a)jU7(2jL< zYov5i4cD>1Th4ISr&Ar#eR=y6JAhFbj*rqePQRi9?k$Y{Q-)W&Njz5%3?YHkKdbe^ z-j9BQ(T+1PYoSAq zg{Z^LdQhwbzmEjbRR}UNg}>f;gd(r&j~z-)E@%86zEhc1o&kJqL33o)%Vy9&ZfslE z>gy3hFWI;oFdKFDC}zXyDA_(>-f+_cBHoUI5uy{isLJxZOw{1M0C?BnL%_JZ@TC>Z zaNiHyDPz5hm24F`&n#sm=d~zoM=>Vbe-W?Y;RpXgypEROZpzL-Kay|=$~aQI7Cd6x z!&z!)fQBPHFE2HRQWhpFnOV6T@P73|tG&D%qidN|jNft`c3Hz5luYAcUMh#G95d#6 zo8}m{Enf*m&CR$R(Mf0NzD2d(MYRIH7PkWWh4Fu91$+msG zm5gWN%c_`@@l0XH$r>DApwN&3GZglL@=fcZ_nq;?11ajidbHP)p5DGCl|Q%bgWgX( z%*ff`6wddA!H&b)=q>3yI9Ts*)(CxzFho#%gov4^Kf)>&n_{Jw4A zW5>>72cp#fsWalu!`!Em5<67U{GkBVY`!DhkTXK^l=4#lrLu=46J&2eq(U;}_Dp45 z{xtfq*vQWBtT6~FJO>QeFT3!6VZvmfSprKR$b9TW#wjy@JIeF&|BDg!TQn1tKmChF zGedj-MWZSI!XJ$2*MvrhD;nL(=O0?H75Hr8lv5Bp{B8JpCmr~4&CFXSW1D_TVm76Ly98*!s5uN4kKpCvp^~m z;9Ol#{AyF&`M>`1%rCE_Els(SyR`4g)?xb+DlS}Z@=751s0L8iAy+ai{Xn$w?HHL1 z7`$CpHaeCIau|C;)Y~=A8OBTRg-%@H>g>btcG9L}oH58%kBg&NwXLFoQ^22vA3Oh6 zA!&_ByhIssADJ&YhQ=(WYBGer96ZynW=#V_P7%D$?i3P%NXZS2%N?XgO*b;5I^%my zx#}posc+?y3yxtzBi~T@KZ`&rEzo4I?^Kmhx$S z-vnx4*dRnM?1aBHvg%}Sl z-sKk$YRI{K=4w~-Dm(6ecBh@AmiSG?mUAo1VsV+GF%f#Rve?yxDR}aUUV=yFEh+mJ z%`oJWF_vM%{8+K9J2VvA}C!TAPdnB7RyDDefLDCOx zZEe+R^44Gf=Go6W3jvtspEV9PNc)%nY!Yd_acuC^qHaiUDqL4J0GL)ki-@eC zKo@nJ-~0l8>d9tluZAzT_{3D{1#baqRVjw3N-!czC^rXhR%!@1 z{3mLSh~_CdH?|1#UCGBfyq%3m9jR}Gyd6)PKb|6FtgpYN*ys$`&HCc3@S(tH1Y4){ zcXr&u0^&8Yk_t7(VWo5hg4kKxsja=hMvvFl_l=E|qqr>)bFUXKw3R%M27EYfm*R1l zRV%^ZRDEx~S^bx~wp-E8RG2_iY6k*+jfqJi^x4?3Q;hB!Ct|o6i%4N&M)RCvZfE=)} zPx5a{0`=3Cfr1Knf%M%=_qR8K*7}k5H zJB}?eG?_DmYSr%q%dkK=@#)Zx8?gbWsUR78Be*2%Z}CkRvKtuSn07g-OBKrz##LzS ztPu*Sw0NU=;bJ)kjvx@|NUOM&KC-AO-x) zXeT%!FR|LGO8w|mJOSooAmGSh|KO##A%o)RXdcD4TGl3yJ3QiP>Zi?A{+$qNqQDqE zi6Zfj@Et}>2$D;ByN*9&CDu1_3f8dG^R4J@-luDz{P^lb2#k;XcwYYB+fZk{A}I_s zEfJfyytm2>YizX+6K!*ITlFqNfuESFBj%aWwx?b#Mpf+c+0x8Q3R{@6>p*|!p`>UjdRCXQLX+2jH$}Gn|61Qt zBe>*QyF2l1f1d33d+FS31}XZJrCF;p;iWFp@k`toE9p}}C$Kf>SQ7kmv*xS7Ip!k3 zF&;td7#cuL0h&toB>Dv8NRIv4q9y3QD`y>TRWnfsPBhXY;`q19efv{#E3x(^^~4Ht zi@Qp-4yVeCX{XMP?|z`Z@m(F>?)M(raO&**fWWT=#*_M3pNxc^d6vyF`wVZY&xSxK@uuvFxfB2^ee zGY1dv8yOgC8(uYvYBpUF{TWS-15TYnligo&iD1HLGS+-Rz6%BYjsI{oHgI#i+cCXH7Y)u5R9Q9!MT`QMQv+C1z^1ERBKp0I z1o`VqGsV9H6>BK+u(f1c;nlV_yobPSHtA;k1p{DF%l-{uN&m9LyUv-w2kg=Bsr^}S z0eOPa2L^qRh7#1Wz4Ub8$`)XwPY%gSQUqsxwN{zWd^Yw`VK_Hamk!EilG=wtT&JYq z4*Ksw>w_ZE`mTU_IFNYhbacP(os5s>M}XGL_2mUSxMs}nU{}P%Y}$2Gyiiv#w)%pH zFx|KSwhRhy?`@Z|xx=kd&4b;L+n<%A0S$K7zR`Pf!dmz@Vt1)CrpN~kkO5#AXnD`> z^)d=N9m9z#@wL-p+E+Sb<>loUh6OP=oVO8{yGOkf6-2bJ0tgI;x5A zLHk^o56pRhbz%`Qh2zG(>E7u672%FpH3-2s(l_4ZgtBpj&rs(jV@Y(h-<0bT52Zd1 z1LQ5{2?T7R{F6gk+nT~(27_`r&IBBnmD7DdQ|h zeQ{r^PFWcdg{f=;;pd<2BZP+|f9OJ%9tG~QGj$@6tfsp4uS=#W`jV-GZeg4XIZ7jz z&fS7f;l@D|?%4yV*T0KFxEaZLOvgQp#~wJv%Uv&EK=D02fa%%e*nse9S}}D}Elm&c zcW5$l+1k9T2Ty?d+)FVuW^?uO12xOjwi%M4Razr zah#lH^cN}rR^`(o-P9Sdn&GIy%H#+>)WR2MaGd_6HGdjoVk(sMs`%_^VHvH^hsxpy z>k!`5#YB@=j2vh>ZfWu${<;+vZbBiL3+c61ZM$5b&)XAQlZyAWC`s6s=*6~WV@&*- z0Nd7&1dBx*0nmsl#L7ae2mkJq1o zhf_NTC$pUh9JN%#qzzzd1a?2L*hb`(K0Q!pJyhRm++zhGnLSR8xMXgkZ&v)Hf@TBG z_2JGxy4ZeFrnGa`Bp7~DKYBFa{;P+ZLT~#3>oL;SNuxbnVO+D_uMLk;MJ??@b1MXcv)scY#^Th;iF|&gQ`QM)RvSw$3NFG-AiZ zEcUN9=>AvlWvue;pWdtglk2fQP~0R?p^{ZP!j$_ z_tIAM25_)o)Xx*(7pnyleQd8*zmIIPg2E|!pbrS|jM_HHVF zXbefOL+A$%o}P*9zipPtI|G=?-Vl1vzc)Jx6!v}d6}vwXAQ~m3HXL$n^!lE#^t$5X zDd;}s4@2eD*{c?;nD%NulB41z;%$S1kl5J^i)`-K%ceDZ!EgAAG) z@sHeGk+c1pn=N1(4CXA`(q4z;k2hCVKPPI>n0m5Pvf(}aX}?2$maBi}B)%vfY%F4d z3)os4Em2Yzs7Mn~NK8yj938bt8#;^g_5`bF3jfpU)O`JI$NOWqdjS>~r|W-MNRog# zAH4pHGEM4KD^86w%)!ZDXaO4t!S=3+&U0E&c{jRZ%ccS}etpqW;2-T{2E>3oXE0te*9SfCJKg3VtV6fY26FBTMbt8M4mS%U&!Pae*84nG)2ZX`Y* zI-a7GuwQ-NhP4-v8URt^*>1{GJc>S?nte4203?i}w)3ghoOMRA@wlKz`|SZF3fde7 ze+tV~wGVpFLKa6G1dW-|GPAZG0tP9NU+i3=7!;?j&xJoYDo4;ZJ~%C(+v--H1&U3D zK&b$77!av3ai{!l{RRVJmsyPBSLpc3h2QR(ubX$T1e`&Hw}w6Oi8)x0Xymg(3r15w_XvK7 zk55U^kzVdtEyaEjN7=KSbF(_Ygs!LmVz@Mmf3v~}XR}o`)mWF#Rw;vml7qg6256Ld zN8^~RMnVF{WPk;Mzo|oJVn$GQbImesY;6-99?b>s`MC67p3Hva^%J}L4Ix?UaC2|p zS}QoG*uoB?knlx;r~s^DcmrQi8)r1Vj9h4rhG!_r%BTSxND|B#?83{<59^2lj{2j_ zt<@hO^Bp?WLV9#zW#T&1I6vFZh+Ur`oN2$6f;I6ea#iMdkn;0Y5t)DU`@z346I#{oBaPR+#zhY}l1x#rfN(7k+Y zZhv4&H>AdCtC^Qx1Sr_@Afs>_a%)|SVo80fSn!Qr)r0pei|!-9@z#N0>WNQ*@5pJ8 zY`2B>F%C3_W2m@V!I%ir9q%`}B$SoM&jC(d^P;e!&$hJU0fB|#hmRh5oby^n_I~Pv zV2H_NrH&p1tNPBpDoa%_wq?kC#04;k=DYmG0<{yR>J(E_=Ry~UKAyCP53B9AL3eW6 zOq`=qsY$lB>!|I>b4$Lm!70DAcA)7B+ni#hh|X$P>HYNC%rU+xd9ok!_p;ncykM}a zY^o_u3mtKv*O0EUCAHYOe;qG^9Be^g!a#L?Ig8$~%z&BVd;GpO+))zpH_pR|qLBp@l9;}N&>hl$rw?bdMP^6;Ypkcj14OAO_ z&T6yEXX}l)tfbU{fPwiguXYzGZWAAw9+ACVV)1!3<$QMB;}Gf@<3S6Jq5?L+iJwkb z)BkQ7i0^7o8?P+t8v9yc0l^U19qCm)+)ha6I2T_Eaz88HgNtqogqW{G1qnksHg$VS z85M~DiU|A&MDoQ6Y8kxmk9YzPL60SmZTC#pl4DZ#&}8a{94+&LyJ-ZJ^}KUv4y3BENnT%#Gw&L1NN{dWw(Opkp&K5<%8fq@w$))X_oGe&;eax~q5BhTAeyq;Si5#@UK8ST zG@@m1&#xiyuAZvb!jZOm={d_%x>iIm_y^ihBs;E2)WLM9xP&{?Ep&*Y{qr3o+V2D5 z9pq&&DG0upUts%PuPOcn>2b`8*?74Nt!bre^%I$Z&Um&l!w@ub{iaDKCHZS?6Cy8q zo8`4vKQ5hmKp=gNV&HhEOw~jH9ls<0U04HZt^DOS_@eNM>(qTU1TZc9rt%-Ki%s0s*8e3?+NWSjP4M??41`0uIZ`Ca?;Q@SAcZKVrx z_n?xl04D$Pj72*Dp+dRh4=oje4Rxl(9Y3-5@XDtOurTc;M*9qd&ARJ8ek6?X@%(Nv zC8d2{&p7YubeOcb$TK(jAUNh0^PH_GNMryF&47_X44VeTuB9}bJvXhZTPm&tv2WOC zjr9z5>O9?sK21JaV@8BOzE*n!ZM-D}vMZlIW{HCcEI(^AsZLmDH?xV|`1nA?!{(+v zeVfH{PY_yn-Pz&=>?%fiOq%^8EeYn=Gb5Ca{^P&raCb5Pk^CMb{sdVLt2exewA^~k z@_>2Hb2^3A%;_|blkCYu-N{7VV`MXS6xxGZ66B>ZwzVkVE8UJb{7FkGfgbY@#6h&} zm2w|+cA}!sIt211vZx^!Uh!g?t|m zV_r=Vve)2IamG~#am5>A9Pd&mgDcmTHav7r@34}xW^2<_rKRB@P6qM|9G6s_oSdc^ z&?tsNk3-nmsurU0{BSCAcd2KHDSs{Y$!(J0ha{0L*RKk9=Pi`Ym({N?(zOKCgTTiuHT`u& zAUJmvCyDGC?~1|wAavq&D$P=va|k{Aky-3O&S!jZ z15%E5G}{azAOrw+R)rY684%q-hBtSm&(1JRJoi(~iPrx7LdCk%MPdx?P#_7mqx9?29oc3%`?A&q#_cv^Sf)#M0}lVA zwq$4Xq}+uI+ctx;0dw8Kf>-@I&&n#g8>&$l#^Ckx;u-mW8__h00C1%D`qS%hC8JjY z&*H?mIwNn(zv_d$SXp#ql>W^T-1p)B}XeY0k7lrM{gS0667 zl;qzslox>IASn&4gzcQ2HEmvtjbr8u1oXeMvat!;<_yx^=kBt_O+Grt?^!D6_imbd z-}O2cEgOViagMZS-p%5lEX~E)8Xd%%eiTp9vRz8n9WlSC!2l{`CiJ}S=coiX%%;f} z1ob5d5GIGC8J;qLUS8P%oK_pHbijYg8t*h)1$-xF|Y=Y@IBbT8xSsTj9H1Xm_JC=6WQ+qCf%C5 z0b{Utpmw!i8P)Gvv#u>{Y&-q#U|*DYq_Y*Tr)XFamrH?sh^;RA&iizud@eoCurarM zGUTxCu=eOzT;1EOEcUYn=miTtK0Y`x7*qZ*6m=8e)?kT2H(o|xsartp=o!E!=2s@e z>}6gI*f@lK`Ci8$J|?2`*if9cE6;GhcH7BrMr}c*M^A;L?+OYU#d3$gH*gl~pwVrw zI96_n+2M6*4qKRkq|w<$@>qR9{n$`n=x+8kuqJ~$#sF2h#_nZ*RrA(i|B?796IiI% zfHB#ni&w1596V&gx%z4R!D84#N@vOwQ|mYuEA3VaqHmYfsclBvd3DFbgl$#F;MvGc z=kT|)HR9hUYxBuxizq9-Y+6Y{E{tg%T6xPtA`KWjXUt?BkmIYzS_xDp@2TtKeT}ei z^cr1H-K&-rQJ|r%Lq!X3H?=7q)o*rHxT$?Ir6CkZU}VPy@0;DK0KhK_2)upkFRVZ(B5=Xo>bz_>-B1?k_)f!FQXFw zZjHSPENAS^-;tjch?23o)Q(4fUDtLLSB|}POLHo&v%cVwyK|X+K}3zSQetxSe(hai z#VjmGUTK}lY+ccdCHx^4863!K)M2Pv!#rh{!O@6rOVU`lf-w8-lKU^1 zqJT_*LbAWe>+H-w(&smsW#Bac(}k+5kZn%V$)A+n%2=S!0bagP{9@st^Y`yPc4$~O zkpZpe?z=^qO&hvYPgCV_{xxm}3Qb-!oK}2{ej+dGX_CvOf$GCj2Yu)0xV-QM!VeMM zi<`$piuIXXc=B?(LfhqB-dCdZ?rxcQoM84N9-Rv3&79_k8*N*9+QUm=7~gzXIw#+g zFrx=D1}LEf;9Lu1oP~1u{LMS{W-QbZ+&N3S@p>_R?tbF6WGL=82a@w{@h$x`v@%?1 z=CSlilnFIc)7l?2zdc~^q!g4G^Lz(;Hv!pq?@&}c=tbr656m&tq}gdw_dXR=`>1hF zX3PnsvZp@cwNPdxB3(D=tvS`)CRU_(iEw2kIkYS5cxhGgP%n3>OV4l$br#JF^5wHy zn*E23GL-G16uDHcnP^+}FdJUZ?{4X)dZ5}A3Ht<)XWX$Pp6 zx0?&tzs)q~K>#G6K=iATM3h0&jas(#932J-1av|4J{QHpI~(T5qCXixPlPIo%Q-yz z9@BjMzi{+uy?r#w)rcE%rSjuky)F#%Tg>u@prz|&@_?BMcDuys_hkKYFWjY0WtvM_ zezd-Rd2atF?pbzh=rtxvO19{cH|%=2B9GgK**l01Hz&h9Tx^@cRP{G~Yh!ILzJo`Z z-o(xiglRw;Y*Y}>CJ`6$MMjMpM8N)~8CCt!jGRFn;Z4R}yKb;)xacFC#C+!Fhu^)E zh6XPHJ(by7wj85Az8vFXn_IG6YO=Hn7VHqz*(w?i4_?Ug_(hkktKF~cgQE2DlRhn$ z{xLjEb#i(Na@`$14O?g3Tf-+@+#l{I6%}wea#($4{?y2(*s4ocj9L1vx-{hm^l5bYjDfJzuy^*^Kk~vQ zf5LC1KmLIc;E#=owB_yr{P;9>L^3DXP-57v$8DdI>NqjmN<-M!q%JI|RNJGL*=43o z6wbPXvsMghtxna;nl~Th86c#zPG0NaPhUH*Y1x`8?K&o6)>^`Bfw9W#dd zh(4f){10vEp#Sy;-hRlvj*;-)(w!08)CLT%2zB}&8(nfIy$pG?G#I+UZuYFRf$D4e zwpOL`=o3ldIk2RLvmNk3^FAP;xrQ9M9gS;3zcOTEZg;{dSZAqCJAXmv^U1}RhzWU< zQk#IL^dlhHbi_T1s^GJ7!_UY%6FKR5@R1xftMV}A>Jc1k02VJS7#Yx5{Xj~ohk7kL zE?ky4x{ZtLYEP+-N^{RZ_hkqRTC=NiNds2OYlUpW?44CsYsqZ}4tucC;bed9_wkns z)``R?CE-Ga5QBSYKJCkew*{iW^wd>{Ha183@s zeJGk7eX)g(V169&Vq>&a@uF^$jfmUd|BZrrpC zrg90Tj8$w)7G>=8;bq>>*Q90X&{cpz6w4OUX$Pn4{2ze1_uNxK{1oO-xV==Lz7f%} z9PNn<(IzfaxjCDc`Rh2)&P=6<1>~QtyT)Pxr-*Ni)aQfIfd1?KGD?AJ^V@AXLRj@5 zhT<*m3NWdqw_Dj=vAa@Z;e}M1cv*H)zURQYBCj0t@ClWU$y1G3`VO*zt;_+L=K^$a zg0B(-*CO#q3_c3ju#YnZjZGF4Q|4M_zY8mulJK3n+Ou$8_uD8+<5sxDl=sj#J z-4*4CM+W-uh4P6>XWAAoO%?cr+ppfU8D8P@~6Vlov zh|1s_-|Z*4Z;!WO=&@>0taf$7FeF%p4(j7T;Cqw*g&LgJUUWm`v&>!U_jWyM4=TQv zQ-A#M#(}Nlo&)9vcto3CuEtmyy`lb%d&|v|pn6DXc9U)2BUl296By=?l0#@(xu}Zo{@EtRXRr9+!f8se%GviwwoVV43n=%zqUDw4mWSlwvd^eZmHls9xo#h??)OZ&=|)og6)gJ~YB{t5*F(o2-BDMbx}~L#50{aB z>RP9zqQ~QHOG}4+F=f)iM;&Hj5RK#b4DUm>UncHN+Q@_gl-rL$O<}U|&kYU!OLxw$ zQ#5?0HBUNUIYn6wmrh+9Y1^m)*Df4Wa@=^lTsl*};>V>@}!*&_j1yi31 z#$ExK9Ijjg&#Tv=7w5_Gk?n#E#hv}B0jraXEFyf=*iXHuaBJloQ5l$UvIf8LTP z|I#8!nEb)qT~+#AAjSK{VturcUipa<5Q7`5DWZx z{7m1^StM5@@tzkgMvK= zf=6a_R8%d3l&`5jWIc6T=}D)%N)#5y;=*@Xib0w`*iH#cH7YSF{lksx*f(h!`S4Wf zQX4gbMOu$V1gV=_m8cA>-}7U$GTz!A#v@W)i*C3-6xR9H-jY8|d1Es@(NaFFRlRhF zqgSi_v;Mj5%EB+kw=&ZNZ{J3F*^Ndz7C-xLKlWmdooY{x(}ai!*D$E^yhPLPBY2E1 z!AB*I(uQ38`2zjfGEk*0kq=B{^nW^Xay`=AkIz<3?hQ|c(MHl6Og_|q%^+0jwx@j{ zx3ixaNMvw0&=N6s!*&e!J^>weCL0^MsHCV(@Yl#)x^x{q?l7u8Rr8O~2yd+WA@FfX%FK*hgnVgM_H8LDh(aRfAf{q~i0vD^K-(_^NW3S8Ig z!c}jxhRM^=eYt>pZajzSWgyD^gC%Zd$2o zl4!xs$7XM56;?~gaJJXCxl-s;y=r{3eX7Z#lBHeRehkLQBrC~V>&uIdY6wcYF8L~+^)5a?IH zSI3NGm#fMqnEC#TJFekSllbuY2zothw?|tV%AMAk_lk{PL5?J7n?6D~e19H48R1VD ziA~#$Vp-6nvU6vB`$9?O_CbHG`Ny}Y@jKCeR05FWq|WGwm1w`=4~IJ~dnU1y62dob z%&@d=y{`wm`eXN%2C9c-N zUo%E7d&SYdB@wZy;;R~Ac|(e=Ge8of@sNf5nNGk*<_z~cd0#1ZAmA1%b9U{W8t_e? zkg{IeYh*M{g=lwvo6<(B8iG}M51*4nk#)+bK3pRc9mc)toI+uyF3jIe`%^XjdB={G zsI;ggtC;j z^WG;A`C_ZB_GO9vauZ?d0d~<+9{6`Y^!lMaK((00zsU6b(d>5C3&4BpvhX}Ce`%)! zy?3y++7w_#8=G{AkWThi-pr9Hgr3;hb)&Ip&BT1Q$8x{cJ>4+XtG3N%Ixahqz!#Z_GF<&`X@5A1}Y z3cd4WjJKV`HUvEE8MFl7(c$56x`xYKPE7j3zCc~8CCFEtJSbQxQ)u_ln~$bXS>1rR zyU2{cn7f)L=~J(yJVBSht-|>wND@=zZi8!{9#uw{)un*V{SZ2vCQ_UJvOD<3Jf6PYdA{wKZaF7lYc8o&nQgNjBNe;MD(oz3*2xyBxV7yKkbBJ6$rczEpAW>+y3I z{bfy-blYT>85i!<%(!e!;c_YTL*8^C~$S{jl5=i|n*q4#RW=0jZt7`7ZuJ3#zfgKciIbkV=(QviMTNOwZ00=kFCZHp!Ap_=5oV>=}(P_ z1BwI1m8RQOy?21uVR)eQY%#rgkyYmX{Z>|;Cq2(O;Q?X@3N^L|ytDeEGVCHoR(_0I zcqU4)L<2L$Q;s$v5YwJw(dCjrO%hnkWWYh_q!m}@YbN|P$4OpliYQhY(b%MD(PoTyJLgJ)#d8xO0M z7Y&{YTXS^3|2KVtNh!cAhsA$~InA~~($@lomw{m?;Pd6#Duqpu; ztghfR(I&chc0$Df@60RkVpbrsq0~e)e(MI3dVqc@IkWh)azrg%c7Vr|lV{)Y!Z6*X z?^Pn_673t7GN*-WnLkN)8DIWhO5h1DdV5eFd0z<7g#;vopyt6o9!GxRJl5mhQxvpM z=QZnR2}-zk?9#0$CW%!IUX7DKnUk@z(tDfo=^?we;drL|v$lWHhf?3j!Z5=|-;sH( zT-BH=Ey{0y z_j5BsKK=1=r_WiGC`@xx0u@__s4D?JfY!!N zJf;q;J9on-CW-KLewKqfna>)aKDZo)D~RGNyKlfHeLCbsmm4R=lqSpzqZjiZi7J)D zPQ@yt;XrS^-7h}fjW)v~*7ok@DpqC}P`=pjjdC@<^(?*1<7T3EVu5Snvn}|O?KE!gZ*^}{eja`b}jVig3b5-jObn}J$1{%b}Lw!_Ep6Tw{Qo0&0 z!{-w_aGIS0!tSjcXb==>bLgS>QcIqA-Q%VpChrfId6|V%D$+)jT%QdSi5ig9Mcr}FQeiao+@rT! zZfShQy|vYeOxc4hdIGoTb=(TwAYBRfyQ}x>_PJUL>kh+$EH$|)K8j+abnKjW9$)G3 zZbpa5Bv`5_Z)6vRa`EB^W}+yL&FsUJ?{1y;Liq}oA}pE-pkxaav8s zY@k-UFq>o!>|EZA&j|`USs~K$tWCChW*9U$sA6&GFQ9;~J6&m?)mSh@q12Z{DDL2@ zy!Vdd2<%NsOT;|LM9d9-nB6u_kJeAb(uSZT0Vtsyw5g~;Eaj$AjKwm~^#?+Y*m+hS<&g|3}bW5nKq zordE&)y1}H6#IgQdP%)Vo$vS_=pWpf?Etvo3wAZcDT(8t<%Il2C#Y zheJd}g}u+euzYZ5zw=^0uvaH#nOfO@&xx?zuVedQw*96@jW}u_g=*)M7k@=3aK_2h zDz<(oH63sqE;Y}LI)3kMlHv2%p5%4#fMS1Z2$U^C0mm=8a8%)X^zjKrk-UvL z41Cvpu)t(7z4EISv^1!H<-00@8UQ1rNubc1-{nhn&<%R_K!)Ywq{eh*&wl3@trV?P z&5cUSO?||2c|XP_K+^G%>1BOP-wRv)(MU2+qc=ifl_I|IIPxJg9O7^37BT2rL_|HN z3Mb8KKkHb5ufT!n6veVF8`{d<@l{kX1a&pF>r7!uFg-p?Zw_xX=h;iFz9B50Iz0F6 zt^@4VTJ)1Q*FAoOkAGave2;3-ox<@p9tDVuGy0O!n?~%O3oF;+q@pDax;_PM$f{t% znzjt;voEyP#9uymrq3U=sY?*c`U73xMmVmYM89smulgeqgeX1AlxwX-6gnJ-iV%@t zKodr8y=L^f-irz^USJYM4C;V*_25awGC|lY&x==-qp&p{hUE10Exu*NsJ#fezP+CN z3yQqW*h=F0XJaLKx}B*G%?D_Uko}`^)mgE$UzueqMjETp_lv~2gCCMf>u!1q&65*v zIJ1iLS3`Kt(@=U=32p+5^jyD7(aCfx=?3P4dy*GprN7KCK5OuL2m2ufctfiPV_bo2 zGWf;1TS4Z0CK5oLLOzNHRvW(J3a<> zyTa^NF_tyIr>inx-(f70DupR|F|rD_CGmt#+AP*E#YK{g`CWHYuGcL%bQg(UG9NBB z@~{TT`}vtOjue=(PqUYX6oS1^^unAz3j4cPim)2G845VL5j z*h~uQtcsAFp@ZZiqYqrfU>(A;D<5AyhGWDtz6T%B$;7?CTC}VL(Mt0tl(Cs}>EIe} zmBJ!lC+*lHz)tokF}Guc1jrLc7uI1}em`oA-{RIBK?q17DDvtwzG`aO@Q4rimFPcfY3rDP`Ek6dAho>E5)z>g-0Z zLW%w5gp_uFgjr!fu0XF^RMnw3S2oHMcU-5dZ)SEs@9jCt{AW5hX8i658gS9Ac;e~s7aY9Uwk}haf6BY{X$I2Px00I@ zUzYQ~yUQHz@&-1KFYHF_KbGg_aT2GrFx=eYy&7?rmgZJFre9+D`a)j?NLPIRs817C zR#$8CRH35(0|w=w3&=Z}^?)93AHZyCChu7*xUfNb3rnjFFAOrvj&*E@D!`bG3{oI} z$?C@XL+dM_iQ+12- zF+H zWELGf1DF2?w}5nJqdJ4Tg458Sv1C&>B@U=|b?oeW@%1i)`ya!jb^7j08ul3(gWhkX z=UYLyGAS!F&l%@m08*-R*n#u5ndC>!Aaaf-&^c_FEg%~5kJr;YR1&JD)p?ae*{QuS zPvwO7GCs&)brrjzy06L$>*WsbSH`F5b5pKGFDD|(b{EKd^C{di9~Db=VDPFS=|R?a za(9Ut!66PD6L}e?LMoa)m=kT&oi|nJ(`8D`^~o2{)5(Z)fs+9uLvr-|3Uo!5$Ga`? z{na33c5bWp(I9KKxr&}u{npe>yW38;OJ6#45Y}l+xX&>erH(e#o*=r1Ax>48J65aK zr|;AL!S-)Mrf|etp!Op(-P_Vau*Ilik_O7-8AGojJHZro6J{PwH|BiOX*h$3{KwYW zlWZ;+Ee=m={sV>e44p&L-}l=-3EA(Ps*IjEZj7SN(qrC!){RJO{8%_1nvZl!*@`ny+XAjVF*%(lIahu{)w~j3MwKUcu+r2uqFu7 z+;k!8I4A1EMRINy|L$CY!S>)7Z}w3%Hw?S~{Ea$%al}Ci%~C_Q@8+9yT1f@y_rZuo z919>~TXjWiu83}fJ9WO6E zp5Tuai}ZX#w$G}rkqgbA9zDMN$o~d?p!Zdgu@v})Da~Q7-ezhtzw@^nom=W%{i1t< zs|TC!bNI>#kXvDyFAws<6Rkl1SA2eR{B^ z`t26zgVWSHkrqoJjAeZ~jkO*6IQtJt$7k}0TO4EbKz7>$# zGGa=nttvaQIv6eFC73;*@I|F>?tf9w!Q>VnJfw1n!r!}JGck#q~H($(5?9H!FH zz0Zh}pV0*KY%od-Rc&~Yyx064M7G8`o_M0UpxM^f?q@=Rg3>x70~1T_Rn4x*46=d`sNC(DJwXLR0@8 zPOA2AxeLK}<5EiuYEl#)2N}`iS^S<$E+(}jdu;opiky~^)|$%~BkI?gc%*tFKNvsq z%kik*!QZ%ja*QJNbL*A8@BQL++Ec}RKf($T5X4vTp$uof?mXW)3ECN1yR;?!;itpM z@y8aaz2}XD2CowGMjHJq{nr!}LhWoV6X-+j(!%dO}57;do z^mtacf3#xOcCRI?qc+|4BB9QiO=jeLu&?wO1dWen;84JkuaHGnQ4SefpFU_Gb-<`G z5izt6(tPXjdrMKR5k{L^HkQusYe>bC_bCP))Oqsp$Tkp6+-kcBA_^_)zNxf!-iYHo z>hx><__8w%UXr(UO9KfVNd`gKox*WxG2;$S+aJ@uLW>ki-m*L_-_1YKDg=D6EB8ZO z@YZ11Ipak6;+P?+Dj~+Ipj5$b=-I_6GY6@o4Td`{s=`pu?=!9BHOrt4l%CCmL`-cG z#V0*b$K&Hm?^DLGwST;#$o`UIPo;~82lB$yho)>KJG=AMT`2Y+Lp_#{a6zK4Lru|c zKN!r;TBtCkdjF~pJr?5+RQE$P5M_M4UZ+3_Tz*NTT9r2#Ms9EmHqwR&h>)w$vjB=2 z@;dWRCoge0gqte_NMGPVK&W@9)T6aBwihx1qsG&b_{!dp&3NGlzLi%kZFTyc{R%$5 z=*KA@O(p3RA!o<4Ec5eRH=~iTJGwhdT7|W?BI*6$rIYV%MGkYecpdGg%(2)|U!Mz} zt^ec+2tt(${tUjNFLA$S=hr&_4sQ_(&6}DV?`u%Jsr=!%VC5$T6KJRbix#A$G9iFXl^4<1A&Wl6FF&;HeFv#(5Za)H)M$)h;AXB=o9fM+CZb zU>G*Y(Toud7C!&Y0|i8o(`pBtzT1>`^5jgEg(A7~CS&OdE zd`JPWE_j;(wNIhnP*+uaM^_W)bqFsVE3V;2{|8Ud`>a%1W@&`2ai}y0$0K_B33()D z^-;qz3w5a`q!9s8HW^ZC>_%wGrojq0?j&O-&N+zIWNM<#;_iVV*u+$<>)g z*^gUTfhFC-B4Mc41g0+!S~Qjc-(U4 zZv7e=IraW5KI?Q*PqI?jt13aa5hfY)Ri;irc{P@|>4iV*{gg0doX9dOC$t~Vc+T40 z-qN89u(eoQ_w{j1Ta9nVbmr6%)>RR}e`RJQZX;fY@pJZ08g0iEa0o@z+Z&!;MT4V< z`na4N4Ef9BZo#kHKc;a7(0U}Y;({pIrNr1n^P1|L*iEzu1tUiuuBeX0coVMpXO8ze z{~WW6#(z_H@Ua46iTZKie<2^myqY;Ws<3L5z55apg7dEu_`5Fq@gYx9p)-&I6J?-D zydy@8{aQH{16XKV-;J2t!J5mxBlLyEX;HtN(C~5^Y7tQ^kli72vOAj9NXSquzfEUKBewa-Hk^KpX*s>KiO3_E`o zgnaPrHzdzF6)rHt8gk9;pt!kVzYiQf8Z*!pY}&X-rJ=#T*)ZK1FkdX6YVn_bQCH#9 zWru|K!ppiD0i_ckPF~-P_{9<$c~?Or%KXix>vbbPLcY&% zA)cxqE> z`i%E!M||e{wN`fuquF*$AY~0Mw;?|5e6aqed2b!THxaYenWlefmLW_}jN}9(f<1tx z*PE(R7!ki43Oz5j<{7@u9o_7HUfZ$uAr{9|uMTz74Dk3rz=IJg`sLKH4|3RZgpMM+ zm8AKT_=}kAFD^v7*HzJM8hpyCW*NK{j2jg@ie^n|&Az^5$ViN5zU_WRV5wWvLMdmF zdqo<=-zOiPt9rBGd%qvgM*@A$4(eBN?E(Rm?cCW{cM2q9jy_gj3z6lj@n&Wedj+t;u#t>a8`cYbOkq_-`(s*o#H06A znG|wsQ#9(ltpM3}w$_F;Nf=B~F(#(7u*D-P%BAq>Ka8}?Qdl~HZjnxCpALpfMmY*z zg%3d8e&T@{TgjA8On+*YpWTILP|7sC;5Ii#@W_=al(?2Z@OR~S0Fs<8nU;+ z+!?>HY&bT`QHv7rY;0Jz+SPg2S`@U^WWY{39m%`KPC@gnf6JY4aRUT^5b`7o>yP|Y zebS1S#$!RYqS8B@Xl9f?e38ZS_&e@U)yGwQpwy5Y(_K!~d$!cP+}+l@YB7z)hXq9t zx*Hpra;uTT#K$If5&LkkVNQ13Ndph2xK{i8BolzhLGk}i?%-s7`jd4G7Pa%wQ$X5w zB^(=zJi1*=&2ivL6pO8dXc|uU8j-`wnSGLndM3pkOZ4*Z5I`xIx3h+1AG>%lrhaK5 zD1c~PCF9|?78JbTY`=F&srJ3uN*$a3&??YLKw#klZN$n*E9&i)FXfpC@DH`O}4S7O;Y`)SI7UN6_VM#1a)s$Xb6Q>ZKwH}=>gr&vt>v|zsi)F ztjo6K`f#9{$zmmq1WJZm@pC|=Z1~v{>}MF}uXs1TIv#9!vnnXocCw+<`IXT)s=u*j zCNm9Vt;Y;S%+nbU^rNmnovZW?KrX`mzQTq0JvHJ;xjhZ!^_{G>ww7G)zs{E2w3@bX z00SCWpa@RnrrtL&W^FmB`}#VW)OE3w?DwG~@F4=lMOXRcr@#Mo8mf}91OLltTyk#l zCwKjFtwL`Sb#RYBQA7i%qA_9H8oDBe?zFTx2I23hz{>a2lC@ILE(Z2`=aWs|CQ|Z- z%|MIH^q1yCdmGhlRl}O%@A!JmFx}Oz^U{kV6SJ4!c)IW*QDJM3?S4p8)yj<_9BD@y zxzQX+dxaK;0wT*zK$D;9U`#NF(P75+gt}@}00y#}Wz#DRZV3pW>iR+O*@-q~7sD7GRe?Rve=J8y)Dyu$@5({foR`X7wZ2Rl;EQtiZ-U!K}v zc=*Au%AF&=Mwpd?Wu&w;aFQ>dwp_Htgh)H*=1_d!6o;8uU(Rwz?;m7jhGHzr9`L@Q zlmHZus?c|tR}qRbKwOOHs4K=}%l0#7?0}8!9U9=DW&M`DNSmffU1)^C)UWwM=fbt7 z9rxiB+;;Hd4E%F#BAhrChVL2n&Rr!@^l7kCe@UtHUE?0WKK7Nkx~kvLEDD0e^-Z4Z zlJh19jdTjlObzh~Q1V{<$b@PUJ#GeWq;$ghi}x=5I(x5&ucGmlE79dH(>6WH2{7(2 zZx7ko_TvGDBj;{$XY~|%as~L8*N2v3=%C%h!yfzkrO-IP`;9_0D-(70e^lp24*M_( z=B}X;JoU-kkRX9Qf$WyU`Mm8ru^4dhhQa{CVh)!-BW?#1i+FfS%PC}r+ROvE$}PvHz1<>ZX1g|s1}A|k zOoZ7(|7Tg}`?|#~(>=easo^FDo&A-@!}k6c7lrRcq9gOI!dXk#pa&tk!bjMWtJnep zuih}Rl%e#Qwb*+hGX#M#GS49lnXOdWXt2{ki01WH74t*eOE{sXmR;3g|GClq;dI=* zK}$P~0XK38o4GC)L#`r#(JMkr7hV8rf_f8tlwa>f9c^f7Ql;_Z;%q;^pIP!PFyYJk z?;E;*^u6W>flA=$bvz-H3DT4X{s6mh_yCF`O9D>h`Ot_*Z0;OAaKr*Z)&;MiGnDOf z)iwqUA8`XsIySenQ);tuQl3%r!YK;WF(Kp0g726~?SubCF-N;8=3E4lt?^lU(Ot6o z;0IZDYluv%S^`cMcA%>FI_`B3qiBFJ7YgVEDqM(Wp-%kn1DP4m+~~HXY7MJn^oFsL zETET3=OgvLrcZ6(93^sms!`GFEB=6hMmtp<+7K=fTquHN<~2V3GDRkdh;H% zJr+j;LmWGS$^D>FS4RNZ$!%WRD7spTcdj-$={zJj-*Uh(*tBRSdK#bRhh zzrGdq1L3XH@TE9Yg|So_GJnerd}cc8Mlz0AwvRp1IgTJuGEZ^gCy0L*z#ISxsdjW= zr{JNG^^e~47tSc}qN9x;{8}u2E1Jswhr%j)tps;R`Pk^@HpF05)sG&MyU-irp&{rp zb!z}lOx7Zd8qi3<5g}KU=cF_QLL>m>1PtWN0ggq0xd8HTr?q+5hbSFaChF(JvF-0Q zs!|x8$Rn>DRY{Y?Z)O+ZRaJE)jhcDyk~{tSBIHlPE_g+jUBoae$&Xky2Y)a8H{QbB ztQp6|!;b=53zlM@e62AU84#e>J#pe%D(q*>)KrM!gz?NvY-I4MCLmQ9juDxp`66X6 zz($&MQHJF1$<0kS*vr({kNFz6YEJc@cyWDiN0ygDrxur57vZuE>i&HixPel!QUkbn zoJXI&ZORS%>%+Nwfz;6wp-L-YwDD?4Z_}HBR zyhHUPA%Wm$tDTEQO3V!N*^O{M-}1TswcHkiUhv9uI(hZO_T)s-$6h`lvHr-E_9 z^E#P3VCp^En1JkeAPob4VNL{>*xq;(K|j1afzd7g-O$mrjXsIjS}@OT4wWooEK%Sf$+a)A-*=bM*qE5)fRqlc-}~9qo(C zjp3$*)vs2%wh;ZA--R*;ym$m8vVg z;yg5rFLcv>yo&wIu4u&s?4Ve!r@6Rn>`ffc>I3rcSvL03_PJ#LpaD4mu)DlDPaz|h zvS(?ZIXi3NzJZunEJ6^j$8nXPrI6@p-B9QtMlW8=n?lklgLu{!lg_IBN) z+Z$)$vwdlpa42mDrT%2d!pcGpKbcbF`p^hbP6ZCS0`qHjPfFBLr&z(@0a3`&M8nWm2`kX0U z6CIjp^;tk{1j8~U>OGj@$G3Y@xHy%3=FV#Th0eQ%+;k`N>9;ZB(A$^45}VQnd;vDQf`b>g^|j1~WvptzPd0qx#{vfaz8z!FyU{NgNM+4i11 zQJah$%h+l1IjselH>!6%s6TTsR93N!R)UTg!c1t5z~1ZjUrtjU=feMw^$Oqn%Z`qg zOo=2FUY&Nye}J;R zQ(t|}K{Pi;$r;@>Zh0S=LX+%5+AcffFYo24_e$5cNV{E#)0 zV#@GUuoGpmq(4;gELLQZ`V@rV*U(H_V}fcA*fvd#_+&+c32mYPCholf5MX$ie+-2L zN_Y55w4PWdIUS&yj{E0GVg657v*74ENNA4y|2&n&6rnM@M%P`08c)DjEV1b4^7pw} zD9LT;*RODo@%L$6CH-80!+A0v65Y@W68~!1OaYwHKod2=ae&86ltdWnGqZ zRhR=x;nd~fsQqWMg}zxRjd2mi(pFflGIy{m=u3)sXkxCV1p*&_!u}VsZzqD>m3;I3 ztW$mGmk0-0r5v*JUeFIY_nv>?nW%rDBi;rmWGRRyy7WbT6o_5T5)v9+uS$0j<9RN% zT%c^v{60yRX-ZYX|5A$2>_06tYj;3^^E11TAfoB0CfVsq5~xlL_(;w}G6wjpLadEM zAZ*vcZ9z1CIXKc2%65^OO?mGBQNJGzpmqX=PxD19)t zB>N=-;AMz~5!7P}Jx7CoO)OKf4l=R|?TyjkhxFPd*NmkNRf#z0Fb?45I!I6SE4P~8 z9bC0cZ|QBq#s{}G584b>ZjxR}ac@m#mE^vaabq-gAl+%!Q5x^gJB`@P2*$o9qXCqs z3}ls=s*GfvTd^D;ByUo(^)sJq_63@MU=ayCm>BUb@%V)Hk6dtOcFgK>QRs782$Y6} z7`x)zx2rwUD6};=&_ABV#6IU)1<`xSyFe)=bGs9?bMeEWS2*!QJF7)?!oI1xh(ct< zV>jg^_K4V%_w>%O-_zZ5M?C;Kt+45Fp`V}h(fuL`N*!#4;s^#lh5j6yaSw?rbpn2zll}`T!j@xn%kw^bcKjtCVUueWXhfejrjU@205^sI z%y3W<@0Q3gj)8}5v@u$mfJL$mk{YFX`lMSYOl5RV*t;?IS z8x&rUHwC#A>Q{yeJ$dt-bc67qY}P7^ga~6ix_I56`p{TY+b}{sp%#ES{V*P$z3I&q z%NVP?76H)&fhx25Vk=(c-NN40QzO*9XUag58E}{V3bb&z1vSNNyh2sxi8-kCp0Rnz zo@w%qDyX`s76eKkoxJ-G`CLbFGarRsnMUb_JAr%_;Ar@(Rf=d2iUkze4fS z@8C)+-Ir4V&Pu#&21#2gWkQdsLMYS+FkB7|XTl$v-#A+v@ig#`Z1vg_!-%wyib)U( z5QWEL9;YZ26+IYgeRY1D(NFc}dM24ke-a>r-tHAuId8zCM{)p{V)l=_CSna2R*pfi zxEpOEd)Rc6mPl;n2*>vI|U!UG!=eU&Dg}?j^L; zOJ$6A#ED~#m?9D1Cv}OVgJYDf+gb_GIe7ZD-!UyMyCU+|=OQH)=*B>54V6TVp|5)4 zb!T_diou0`i(9Vi=5LJLSJ(>TG$l3qH4>5RGPvx5*t}J7L+u`TmbiQ*YDiya2f}&Y zPszy0n&Z32no30>XJN)BfrYJkjoAA-i3WLp*edatTiPLU<%>e1>DRIMDX2*%p33^z z^VN7$_M6#;2&FYJ{tjw$4{N_te44>hR>6_%nix>2QGL;?jR{Y0=fC#IPq# zr}mP9G-PC$w$LgKQxS!;8b{dA;^jzAx>Su`PqX{S)%~nLR+sbW7213;)P+`GN3#~p zw99mT<9d_Mlx!X<()txjqhv0b`okY*{2pc+_6olM_my(4CMa^ieeE;6+vabN81Z{N z2iy~NQPO$+U2AuMR$!vd=K4yS`y9KnJJ?wbwhEN#XvI+DX=)&y_EM}p3d5W|N@_;& z{S$s)uT__R>ttSEdLTa$M1+#Vx42UnhJAgqC!{*VBY|9B07Z;wGth0PY)=6~?4F>W z?fnfW`f$_c=E~~pYgX+frn`aE!6i)2agY)7-61m*jh09Uuw|SV4Iw0Wm;eIxtbB=x z&Qllp6SMycHMi=ntUn)bWZ6B=<@u*y_T)|LK0;5X+1atL4`V`2=UUrvNEd_!-cSU0 z51KE=fklKz0L%)gynfhVY&TK`?^0t8w5T@E#18%O^lG;Ee&;n3YHoLY5eNnXO>BoR z%2!=V%?xDkel#ux@&Dz6L&X}(|7HkDNAix@!~Kx*x1N|Hixuwq(OM8{eO77zgufY= zhQH;^?Eah>d+iJ;qSd#zBN*NHn_Sa!TXB%*abIV`19V<_arM7bTDqj3>!d-_tRh7I zqd!xS?`Zm=CLc6-d+_)i$HxnXvQi5O+O5n~MQpx%$Q zi3SVw^xOu(y_Q%}iWwjwgAZZHd6b!{yk$55Lbv6gCYZk5T(LYg=oI%n)Tk>ye=#06 zK3O`+JMt_qtppq~FdDM{ggH3pEm?N|vO@&esXmSldKdFbJwF>`aM@nJ~QGXddCDu<)P_nVQs$nWqB6XdT)LD6HTHqZov>^xV=Aft$E#P_nF z*m_jWto;~8_BBmh(7gLDIC)@T_znv$YGZbw6nikE}j=@#AKp&asu8UJ$uGru*T-B)quTzAscs zRqG8vb8jSevV|eZK!6gXBmNZB_OytC;*b78e^(V9ZsKs-RmLhhJ>mBpQ@U#vbl79W zi}SEcnhFVkB8!WJl*vphh3CC#*L!SasG7c-}Bp z!kXK3Ke=eVfP+H_D%F1X2|nt8C`f8mwkI6hGG_X{)<7pm34KU^U!a%WMBT|th#TwH$$KI zovrPtB8Re%wo=6m`o&q=h|CYrV~PpPt>t2^X6(yPu*+5E7pka_8*>xaS4BorT%|^G zsQhSP^kX;Evo7Bj z^&47J-6+8Eo8BS(dwp^;ibp{M02>~nI;FwD!wq(wA&Kpmth&DLCt200PfQ;Q5YzXLw`CH!t%@I#x z-J7PyQGH^6>*Ej1u4gta2epRn`EiW>mnPp%^GC01S|?I#TBl#XIhoL5tR8?q%pMhgV%)Wty&VCLK3P zzEI&TSlPN8T8^=GI@$a4?tu??mcLXrJiiEwQILK+S(HscUH*Z?Y z6u+uae0+%KzbcIWBcBfMObirrK#LsqjoG0)d@QXoOE*N00elpf+dLMrvM`liwQ&FJ zqMn@?n-XTL_T$Mz1(uwLRHOzD{xeo0)GW;fDPNdX^dWW#Xd3Kh$HJlk9F1K(Y8SkO zQWK7<3A>-=i(BTNF5b7~U&`8kDAg4Z1JBjP?Rh4}u>r(Cw>olqCEQYiPvkTep;i}z zbn1!K=bIt>J4jCZ<+p$&Id_hVl?hESqyuwBG}wqjDCg>qweIICsb8JtTq{L%-U_V8fy{uSq>-5YtLpuXV^ zO~;k!nMzxhg3?ehM5RwNL&2DZV6E1$>`K_jA%4(x@^@pe&A04J?jO}|HSvvS^sPhY z?Y3LL`&n-)iYl93vhu%Q-02@Sjr{W52<12T6uHZL1&noV&(IDPE8P)Pd^27UXWmcu zwG`$O>-q%Tqtoe%mv^oXwUg|u67y+gcOmOaNWd2ok^eFiYRhrhB~(}6eSnQoOD%`` z54q?#5K%-|aM9e`E??(U;ah3rxf!ro;;&FnesI}Zqe}sh>G+tht$IIy$0C|Y)JA-@ zrWWY@qBAWJ;5mTo;LU_UHgB}tR!fi%ebqT{_wJuTbC4=e(j8fH9DW_qf1I^&Ym5Km ztZDQ!^Vc>Ha`3PCob@BH^(gxRfEU1WYs}O_?TBYQ4sYGA#JmUqg|9|T)Gtl>oCPTua(rdy)KrAFjKa z>vu5iHCU?BG)BWQLG1wESd|9f+z)OoGjQiF^h{I)AR&}KJmG6^Q;N0Ptt>~n9WmK8 zrp|45ETCoOM6U7}34&p1K}#gO)3;ho0dK)VG;*6&TBWsqRm`@=gVh=-azI*D(+t<# z3QMfreZQbuYG7 z{YCx8%unAJ1w9IuFC`B+-i2H>&bk?a4`R%`tVz0&+ufMTFhs%<5Vav{ydrL&{zhWe!=K* zKGy8M-gZ>kI8$byZ7)Muc+>J?X1Wxn@t5RFM<--wjo_v_=RNZS#{>VKeS5=7y$4Ec zG;-(H1tI~P$N4J572vd#`@{WLHz^oK6c{Dk@t{uJIU8u%Aht&goN-)*o5BiO@+cQQ z@x;{X)xy0muE(P{M8mR`YL*56Gti;RAMvB388Gz%nC}4iW`#r}LkT+(8=C1eI1gS4 z*N#D&J)<|zGf;$IG{_K_3U*2Ow}n4Wot`&r=T=mk182Zqfz`s%%3T)4?3<6CMN3ErBgY;LNBS+ z?@cip@T4^&2EPwV3Qy%IAu&xrk~2*dsh~S21NWFC$$hxZ*LMNt;VizUaFz)XW1%I} z(fp!yGra{bR|PW0O79>%X8>$%OJ6yg&j>2Z+U2t?V3!$HzU%dU|Ws_^)T^ zFJWx)t3RCqn=@mtQF3G^CuJ~J%j%Er!J~bZ=a1-Jz8)y|&pVBSXIjoFngMooN|z2F+$x|ZSWfA7r5%B=BlBp^DYF?@1tQ<2rlE;oCtm~?_vaT! zMF3I{nDv@EJd7~9^BU*{1FVNwm7KMW^V&c#(tGpIQ-`;wNzYyWKe92fy2k=3qI)%l z8bd;43QFGjRm@x!PP^di`7$5Gc$+h2q`Vcb2)otd#RR3^zI^M@!Rei?S~0mJ9QF3PBn2V7LSPUO%rZ z{rE-EV-^7iJp(Pn41mD4z)s2fdD%hFu9Unfb@~!VLMbEJ>0B>adquhMZOypIsa4mY zaG@38ynLB&0f^^FmjjB)ERC#|m6DD{)L2@@c@s%y;jiWAK@Y<4Kxe~@@V`16 zYMk`k`2-|z&$g1kfM|?---iwcdHgX$teGE2xzzPTM~|B7ZeoC-9>f^xU>tnWzs9t{ zFsM%$Ksq}h1}0j-zb!p_Xl)#d0$PdG<>N{!(y?VtPZy(#@F;{r7;)+tXv%JpqRThBB3@r>m=#VRO0T>Emd_u2Z#@TD|;B2TApk&&5UgmuAK(`R4}@ z(ME!9^#|!sz@8Jlf*GF|dWe{X`OUz~*n^$CkZK%J_vIea;9@PY5J|58HWZR=K^Qs`HhTC9P=zcL9XM$5 zjL)MG>KdxP4VjlJ?Oj;|y#~{3AgDdVeZyMq9p9=coL z3ivkC&8gch4qiosK=T=pgJBm_1qW7boBGJx}OlM5JPlw?IL_sClajBWCM3AZ350yO)))_OO$l|HK?%T&ic_)X(d2M-g- z3cvvVq!WaDcPCUzpxI{lBVk^bO|Epw@qc_8yqJQIS_%2BDbG#~pIFtyPQ`NM9aa|K z$0Y?8(qEH2-yH&hVOP%DAu$_3E>>`%M**ViK-(KwM${p7#6VthCWzG+pXt#Aj*|m4x0BkIK ziok}huT~kpr6G*?emPP0sTY@_V{X=M+=qdp(=v4UDnJjLzOKD-E7YCQhjwRzL!I;`1S;rWXL~gK@eY+})*qj7AQwgE(B1fdbe(ln zmD?Kbmx2faN=l=2cPpWEw{%ELcb9;4NrNCrr=&c^*%#ui`U;o;Xpc#Y)XlD=H;~M60KPnryqDj zF8BzM;U*yojpD*f!mu2FNkoh^i7DX6w%419A_?2plWauo&;%iALQUYuuaHpC22W9yP6B6S~E_!}&kz3;e4UP0soMRV)Nc{{mANxPB!-h$xwNpRzdDCKs>W4DHG8}q^*5(Pge^02k4bRj((iqNG@Qk>0)s$9Ia?(YPW*;F zG&rv4mVq^2q!C->F|1p@4itqzY6w3A9f+lyq5`CJ%2lf@JNvC~Kj=gfQgP7DC(nWE^ikNr*9(xC(pq=OBm%hnz_QAIHB z@+cMKoitFilVhpw4TNU8thFg@ZJfYdwLeT-KT#ZZ(|ii33HneI1RvPhY`L^R%Z6uK zT$wDi{M?3p_0ZDh5qRTt(l&jpagsi1jO5$X7ZsSTvqgQJn!?H6QS{s0gp$tmT9v_U z#`r_r$AZh6)l|P6QYS4)za+umzjx-re|GKVQopkOiip>i z*!!YaNIlJc%VSoaW_`ca({yfPgS|_E=ZAySV_o+vc+%)cCED+s*2Qe#ShQVX?vot+ z@8$}#;HmW&C@>k9-3g(>v8y8g(G=Dl%j=b_RgGxcl?k9(&HvlQrlags8&d&BAEiCB z1n!nSDy5Y6DXcZVY6=Bra!S zzt6RoqeaZ>i54?5wA(FUAbQ00S(>8JwE3)gAN7r191)-C}#XuV3 zZF$X%L4m3+pCD3mQ;qBNZCwR78&N>Z)89{@)p__;b~5?EK#Wk;NM-Y4t#!Lc>+{Q{ zS!VQ0bzQFr7phUvv~?N6GT~(y(Vgpe9kIQ0LroJD$Fk46JDIx~X$jt&_S31JgVNJu zKH717XrXt=+1~;AtmhXB$}kBM*xgK@KYS^(pHpJw+G~X#o#K?G_B;_uVom$c%BAmPFj@u_ zE4DNrh{yO#2{?VA1euD%xcp~dg}^=hs%|XRk=_(^zu=B-I85ggA#(0^)^>BXBqq3G zd2lBa^l&1Mc}<)6UE7lfnk_$5NVvCYFXg=BY*mW?#_3A|BvrKU5{Mk3F_e$ zgX-uHZ90)M&tkPZoX2fkgS>8+a_t=#xrr(6fk$l{1!r zc0YA(UzU!;SRy*avWhRh%(N6Y6@##5dmG!-PffZxeu`N0pw^(OzMkCrEU~CVK2rQq zmPoK5YHxja%BIW%eI&oyXJERLlBE8kISG)gfbFf1@!3TkLFw04WU&XAx(r>|sULyK ziM*d;u{5{sEe^NhF>^H3|Ig!70(*S&!Q=CmVZrn<1XSYrH5>*gfNq0HB11jt`FI9V zlE&hGv*8!J%^z9^Oe2gd#Zt&$8Nxp_(ww@k)ZrMQq*vefRu0H|7hs78oIcocgfAoE zO2YKkJa*t0aZ0Rz-rUIkC@ZO+8nj5}Hh$*#ozw522 zK;Kp&A+R%KPquXu+Vgx6%TI7UvdhpJ?VYlkw#z zEz;uY(SWjZYJCwxF>O?sbHR`jr9`_^{yq?hbaWE_M<4>N<$ZL&?$wuPZ-;>A;0GrW z!k67EWWwCtJ-B?F`YwjWEot~%6H*|EhH8#3ltF-E@ts46x~P*NBH)pN-*JO(w@(;l zNbpoDdbjrIfI9n~R;-JGt*f!VFeRB~(;?QF)~`2;>T$FslepUtjpePbpP+=1;l^R= zpd^J_-u1!Ev`bA*E;E%C>R3~&X2CEE@GmLY{RVT`x9v10hOvYy(#y)VW!f2x0)wLx z!jYDfemg|Op!#&)Mr3kXDw2@HV`h7IM*Zk|`~Jhart~ffK?M$BL+q&Poop{N(r?St zh0`@UfMrJa!%3^G5)}eiU~Xp#AM~!5mC-^^nHEC=62z{84Q)(bBbDYem@1#@i6uPY z@90KMe~r{-HWh}uQ;7)}jNp%eT|z;M;0x~JQsKO(0o6V8 z;?xC+MXwc)Z78F=F*cVU*voUQCW}sHPs1v*M{$ofruQRTXTMeEWknK$l1$t!({jdYy;Y+3m6UG->hdgpc#~7IQ4~OFq1OspWab zk72(CCK)J~jvUBWd8s|O0R%A;RF&%2+!ZBeK@u8fev_Sq6`#x(6#sf=CX2|)YI}`a zmzT-i+}SWW!y_a8R=;Xx7ZQ0(`xddd)J(@1M3Et~B6v${oUEW{P?4s8AP-x=2( zoN)8KobZdX`Jk-%PPJE0?%DT6k}ciqG|PFTZO{d8^ZIJf-yZV*xL0;dnv!lCX6WaV zX?GL5+tKriMGrgp)s_nTDurIDxw$$jia(_LWTia9rNRNx+OThjMBFckKPQs+c5~g(G}@UMq5po1T=-Ru{bf0x#3o!pgxTf*4FRZnt9)YnGrI z$vR>{P-F<&qV9vYvfIw@)SFl^Qu>G$PbDKWXLYsM7!4 z4x3PZKyx@FEJeb4elaa&Q0t>&fb#ZtKZE@Omg`?Wdk7>t1NeoQv2RxP@&w54X+;kQ z#{@pq0WlZf+_#ZMkFB1$;S&`O(P^r1bQZ)PyRtT$($>&4?p29!!Yn3OL(RM+bJ&7U zZ$4k8kfVGLZG_v9vo48u6^&7nUgywUY%FO17*?w#-srk@|7P!a=FBg&cCVX9;4$or zk(MI+Sz{P?FIkE0tT!Le;cN>wo0u)jZ_>>-eQ}eQTXP=%s5yw@m%1oJA z1w;B>kkYA!wdpUaf6~^eZPvPFO3cR*Labw2?+)RdppK&tK`q_r##>S{ne_Xale)H5a9Nt+b_%Z*|X?cN(-_x-_iV zAxqkkmZi}X|L$wmZI5GxJr=-Qfg`rOTsb3B^$~Cak*mvIk4l^I7|&E!;oKhml%Uo) z$n)alrKq;Gw%%caF59bGw_OMJ;n((A3|(p#YBo8j1;nb+bJW3uxef5E=hx_PfC`^T z+pRNx^9unVXy%Xhn2@ZGYs9}Jp9nt>2qt6U1tT~JSe2Br>B-MctO)6by1#KZCA#{ZN*>?Jo(Snt8E|ESt~J_=b7QD1V!G1@#w=ZN1Xp@l7DMOlaXhZOt$&g#7CLvcH8HlB+BRCSaKe-tBgt|#~BLm zZfvwPH9>YjV8)MwZaN!o!n1)u{tp7@F#UPfb*PEzvO`dYYJ%~ z%w2%t`%hDvTTXTQkJ zFUiU#URW!=j+d1{3tjV!2Ci`GIBZQnVuhnn0iG~`9c&dlIl*J>2V*JLq1m*vW+)87 zY(P&I<53|+FPW3QVZD5=QirO-$}0ym5&WkEWo7+gA3G1W`zjWS3Qbr1Iudud6`o^F zw)~YuivPF_mg|2{&dgv-6`56mO`{aDmn&##%mJQUQBiR)Tl}P|@oe^xn-#tPY{x^~ z7g*3SRjpbkDRDB&8^$BM3U=~!D7Ym{%X_(|+gLGvLRa;ieg?gm(GqU| ziyR7>$KV0&a7JU@y_alJHC~$ZwT5}L72L! zNMEUAYPX)67;}$i>1BkXdzL zlD~${S8ZF3Hk%bb9mp~X$4LDI^{x)@<}2Wyiy8?VFbKJGChr4xa#?X#u=DOl(-c6r z8R)wG7yuw$m3=CllD0-7LQyR}HA3$Ivd))Jx<`yb!CjDJ)JuWqAm^M)7S#0#1loZ| ztSu413&A7C346rQjhAJ#NR&y3tK-zz1@I8ebDjxFa(!(4pMH=;{XSCbDOJg&;rPb} zWmkZtZ)xh}y_xHeBqOB(r5AAfm$x$~xGaFC&qTrRb^Dj-afljk9=&5P%xw>C^+RlP zO#1nuMT0ULjLRlod9*tbaZq_EjdOaT|X3O$aag}TM}>YLI|SZ{vT z2v_IwxEzQU-Lk%#NDYbe-V}m+5PtTD6R=$pT8?KB)J@w0C^-^7wcK>?P`RIl<)Uo( zW!?yrUbG;l5Z`hp6*;))a#^=(DM)CM=$H)UAiAKBlv@(s@^Z|88m! z1pI{nARRDkJkTK>`B|*6Lq~O6zx@-v>oc~o9OKi>?2Y_9d;oCuNz{^0TPZICoMbjV zr04=8icW;1m^aoF)4r!$8$aWn`lDypnLdAR#d!ia#5snZV{2G8r;a#ABLcd~Ft0&7 zmM0q(Lk5K8{ZxBq-Jnm>N5i#=gaa)zKFY+AMmiGJ`iQtVGq%jy5<{{J*xhlp*?2=2 z5E!^WHFxuLbM$Mgw)2et?5xh!?C!Nrm#-=YG)Twuz+ZL{w>8(BJ&_9@>L4x%+;Gk} z&3zeyb??G}5Dyn1qe7S#4|w9W`B5*ayS|?bbad_w7g9iiCY)+W^g03SdTr#eN((79*NA9_=DU^X?#S;l zlHx3XlzAjdzvy&#n!9eT#pW8BugIGYgMWnb&8|_~n zLx9aqb(nWVP8z`;pL-}B{x9^FlM~06Mu6TLKHz3Vh`<2#-$%scCaNFft_8OVEXod# zRr}p)Fb+nL=^SCFU(W5&e(S!KNc@6K#@hbF+v#T_%A)`ap*655`L%a5Te};TMj^j4 zf8x4RoDWcRftNAEZnP^U+k$@8&z8N;5`1l3ZhEw2@i@O#mwBr`EFPcyWZ%kJ9|l;H z@>(*zx4fK>D-<4RPVt@1qFQ&Z1=dW4`7Bf(>Isu$KlaIJU47rJ)~;m-NOx&&mrsHI z$G5*Bc2$wdK#yH zvehHPPl3go3nqy$5fbRbjPk6`sbLR;2#voG{7{8GLH(GI&wj?n6I}NO(Phqx{;!5E zsX3Eg9C|9xN)TWqw(^t<%j~?9#N&TJ@%2}LgvNwhW?!U)I~*B2_ZJFnq?-}}`W=}N zl+x|O;FANm$vowmk4&NaJn&lI^-vk5Tx3Ozu*Ev==No2=b-)Fg;7Pn+ig`_#hO*eG zgZltdWNx?7C5ee7rCEPcFlNLP6;?;we|QaO*Z@JdPzJEV2!cnw$b#ZCy$e9lm=gDb zi|Lz=#H|&WtGBMUs=K|U&Hsp*>p%hAB}4P#7NVn4b+HwcaI{sN9Cgp|@~KGRb?P7` z+OJ@K*e=A5H>DpA^+^aq$4E$VMP}k=$VyoAC4POeiV>zD;C|aooGEuEh?AOrDl+bv zDyDqw(<-uwZ#(#cFD1Rm3=|$e-?rrOb8m=or+VH$>9K$ZiiESSCT_@=UUMmb@AX}? zS8_3#8C_he_`oGw4$P4ri@)wegQP$ha&mS;c*W$gO2Y-+CxpA^Ea&!f15E?QWHvE) zxqqA{RMAFBPxSrBZav;!o?nmTT~>wT?X>R3>uH96d)SQ&q;^VUKu8X{kPaW!_Oh?7 zfWW%Z1U~YBZSLW8?i@c`X|f2VRiPj{e0;*BMwW`*xC5D&0WBECXr4<_*taV6(+x8lm9IC(-Iiqs>aw1| zws6;lY10&0+bwVT~e-mx-R)$1TkB*uVp;`Iavu6UFb7dc{;JK7OcMlUYJbtYO@bd0*r` zATKlVyaDKb6yU9+Q>6@j(-gpo13md(_8hTWkuK4TQ`<|pL3z4Pm-z1Bz_-`(qTVvmjO^@+k+gL2H*pzb!o zU8rrnQrO66+oB27wxB(6Je4$s9f*qW_GcY{8+(qWdW@D6n-v|z%nWb93O=Usfq3^; zSI-J$@AFaAvjwraAh~)=%yomk4d&k>yrZqYycyHvY%*VEApnq36fAf^pE8t(mHuFfmP0}NB^I_#vzg+ShF=85 zAG?e|%S-g2WF4J-);4CmAV3#yGXyQ>D|#tP83UML39JHPt5J;>q3(Ax!cncg38%m> ztwVSFLL{gERZEC8>eV?~Cud%2A_?5~FKIzl7A(BIm+>JaHF!-XvrKOL&N}B#RXxUa9QaOUs(9OEL1b$ zcBcm;KuL|fbGt{d=LepK{(5sduU*H_VCXK$7!35ztNlF-$&EbWa=d4cPHv&YXN9|s zAG~jmnE`1$)CNFMuQ!k+5sRmW;c`3mUXTM<2Zt`#a_bPZJ-_4lEl1a{DV81)a*!eb zA~mX&qqTh#wHmMMmBaYmAv|MK+0)RTlWvoT)Z|mj@&F*bCmhMePZSNP{4m{bSFc`O z(*p3=?HzA2wbbu6BJS?&b~4+*xe-T$Ew<3GUdoep^boql#}=)q%xW-q!ocKA-sezU zJiL1l2S>*R7)}^uO1&+3Z>$(By<~8x@N;=<{6isL1SMbhv9_fTm1}F+zZRs1;jhJA z#=&m&NdjdweUd~albkTdo3sJipSWv9WuyBNN%SCFO{Pb?Ayr_C?4gXPf_4F4js|3G`kGw_ zf8WH@OE2=tlew#$kOW<*iOy!NIoL+2pa;_hDYMY5-$|*{(LtP#lKmH;cmRjwhdjpl z^vaT%a%p4`^4EH5DJGplXhi-#P(LUIOFBDU4pcviw9KulLwL2EN3$6K)(LSROJ?(6 z$0?C!Z8lI(%lG&m3}97ZKZrfU=%32>o?>iDbl1q(9j^=yH*Q-fp}1pImMKGV|D@1H ztC=Z6cA<&R05`Rdh_>5&b^l?>E?9MsuAtz>r$ZBoPqK3-{6oh$7Hin-VcT%MTIUO3 z)v)q{A`Dv~{Dm#&nF2eh#>LHJ#Z-f@>XruCpi(VI=j%E;VrQ2TGB-Epsmgy8k3Qo8 zFDrLKV}~pu5QHh0Y@Hv0>aAVU8vGZ@)zihCOP&VM~O{$>UI!c9%wqN`*;XQc1#9cW5C9?DWt z53L{jyKikb+=~@7K#4^IM!QJu&UaF5;!OQC8)7XS?J!aC){pfSb$VUe302xkTu!0e zk|X~MNvix-U}evQ+zI&C2{ypS(%8IWz%bfCeUJvR)-RK-24y%#?V*0CiNZ9)E1 zN)jr_#X<(x#n+&RZZFpDbB^#r1N?cEI4%hcP*~If@t^in?9qRznc-yxE{KEHb^L)| zc{n=Y$U(n61hi%-DZ`j?#CXWhji0AT$z{TUVjRSTz4A_YVyQD#Ag)rt5&-bWi1%L? zPFZ!=-l=t8IN0-jeOM@=7zh-kg!dl`9%1rj6EP+q%5qb9&bJP(tl8 zCV38dEY!N)vj;$h18m@3Ez$A@Zb8^3ejUJ{6d{K&xKn9DuL7C`dCNb|s{vmq#lT(P ziUvee1w-*8jQ#rOit7!3QOyFtk+uarKID18>kW;=p32g;EHbVyC%i}oTpe*hh-&+W zyY$Ct2A~xmIIPgg8Mu+*TL0;$qYbL)3Ibx3NMM|ud6?kl96gMIs4siOptbKqf*Vlm z02&hAWZvxX@&|e2h#Yc&BIu3p&+t;~eUXKBJjponQEKD}_qR{peGxhx>+?)jm>Lm$ z)&NiTZuB%30?T^4(I#@au5B;33j^^zfCX%{MDjF!6}d9AogJXG?m;*!YhRlhOFOYm zR_k0Qh;YK#f^0rOGf-E(RSsolXOF$u>Y_^f+4uRS?Zwf$qLrzF82CRy+cO7gGJ4I$ z{#9rP0?4k=o-a!HAq_aTU~zVw^^E-RO%|YHBZ4f1zz#Uj-l`*G{%awZ zm$L?qZZ)_EI>v(0KOgbmmm==49t@;CCf*X)VTVuGW6X2J6_xwWCIm<@Vaj^ZT($3m z$NvF&2izkXkn?icx(^KI+#E@l@9NS2IA+Imrv0W$CVT<~oM;;Mn`h1n={V;Zk~Igm z`iR<%XmLB3B4rkydD&uk-)yhi_Ku}BuL4+s*Vz>!NUDiP!86c%9Ix}8x!Y`%#6NG$ zDIbvgC}95zQ@bxC=7HMOJHko*B=eCvbK$!lYx-B$&yEe|a^l078S&v#`8mP?st?3d zwv%{(xCwCYM;peyK?NTgoqr0}#C(5BLu3!#aVOL4)wcE4?{H6m^d~H%7xUta^;V#A^I%s}G8fZY#*h2+A+MwRYg2T4F|1`g`*Mc`r0y`gJ(;hH*y8 z6D%OUlcKUeg_%#0GmDIg6ocF>6U@3r{(@Dbt1821T;$VczNSMYf%%hQe2R(E4`RBX zn|#2S9bqxw9B6y4reJgSRGx4yYYW(84urvR4!$&|@@b*sXy*@}9BUDb`A;egvNI!~ z`F^1C%86jj1SV*2^;lU6QHQktK$zXU`gl}xe=yPui?@FXY`yS`s}~Pf$%o9pw>|m!wn6II!QpX#* z0=3(qgH0s#l>;4Ni>%2IBc5<<5QV@dAUsWFj7d~McGPUiWJ+Te6_B045LW)lQJiF>QrnCoA$)aUanMl(Al?D@X%b>(7hJuA zYHqvT#&?$`15A=8fhHe$Q^*TZK%5RL zArP8XgsTC=@4~iJ>kN`RZqjIa!Z~)ufC2nhuvWlixmHamLkV$Ou!v)x@>~%X_~apB z>&$6N0FWGkFR89xz-Ki!tcD&+b2daKX6wcob}Yl_v9s~DRCVCF2GjM|=??`?K7gG+ zur-S55wQ5E#g@W)Ee6$+5m0y!+J&F|Ef{^{G!p{W^@|6+4Ama?$dKn)N_!jQzPT5{ zUC(1h92GHkp+I-=$No3_6bC3c6a$Va6O4qZA4oBcF&(ov%8ilSNgx%w@-2Du3m)f- z)-BT+?5~1(-WzQ){Or*eX_@f+xsWuaS3rsr*&QU=Zhy}%c0PL6SZio1EdLcsBw&gF z>2o3ylZ#ebQuw^C7srQ7uWS6-@*3<*XUDfvb-9LOa^TD|f3jdA5RyKu3THa=u)HsE zUGI`PS!6Odj|So^TFQ_NPEs`F<(7WgeaV{Gc{z73OZdV8aqfD@c|XwmEHFqk^K6+) zAXtjA2444S5oh1o`_$POYFdUEfiWYV`uh!|4Lcb^oQUbatK+!W^N66+1$NN1eDxUD ze>*aHbA2#ok|h=JxFz?1x%2(@=l@5(Rqr|I#=gYj#@A^a(;<+tCfW9aa3GY*Y>=({ z=YWsD#3s&i(fPxgCfq?i&}&uc$ApaPPZuYYErLuSkPZ4-?)sXb2gXCTZHs^EvDAN} zoYmvDW(}lW?;oZKBx^fy8c>Zj>%MBcj-F`?K;A7P^_tZ?uU6`*oC>n zn2Dn2%aHGUyr^riL@Rf6tnmO=3X(*gBlNGQZh(6&s4FQo45ew{oH?K` zaDPuK6Y_pXRIK|8Oke#tZRQ2oXU6Lk>uy+jrTrrLv--jobjx}ShJo`camL|2fR(Zd;UyOV1`IMRr7w6W$M+$Cu6wgpe(Z8fVZPt9 zhXNMOsV}nOA+{o|nSoS$YYRvg?;K&vew(&Me8 zy4?Q46K~w5rQ;;w0*s_~5F!=S)$Y6;LV;c75!F0>KwpB=pYo~wU#dSh>bRPp5N8nJ zBlAr9A6A&(Yk`QZ|Inm$C5wN}=dd8M+uN|wIx}nlGo<1Dx7{3FoYcSK!2l}jhnolG z)5_+eh8o;E>@*0Xoye#K=T8@|5jhoyPZw4?{=Okm5`V~N^0*2n_XA4P126CE)YNE{ z^-#Sql4#bv!hDx+utf@7peQpKWcM7QG5&F#J_M{4`nqY!%shPA&rc}AHr#L%UrDPU zIQLqyU@O&wEg#^ka7;0S3qSrdHKzpMrcuR;=GR{ua#+!SFQww-qtIr44=iKYg1wZg z?e&{=r!LiYWo95a_E%a4$XAm%Pk@DH#2qgz6{Y{}RXKEi4vNdSTCbmLXyqk1h^}UU z=_9V!$%e43Cv0C(S&0h1&44tnUEJxizD0O*2xEhj$tMwK@=ioRXsw>jQx9yE=~0r8 z^x7AV*%qk40#@(_@6C@j-f-L{U<(Neot4~PFu@vK_qj{r(GuNAuEz6T3S=FLjhUdn zVRj)NIi+@cC|u?Xz)%9tC`2&o1^}Uoh)FOHfyvOE`M-4>eHIiMv;jl;%tLgb$^bYu zGH7zaKomQfo%g06&XUlaYcb_9G!T8F>Ra7CG{R)+-A_lgaqfos;|+>C6@|aq%JLZE zxMfYg@31M|Y9b%U`JfYhSIkSWt$QkFZSY^~ngFMKC-0cQa%+52`5EMjGh8HXmcLiw zU{4y+z*rBeXklGIrjBm_Qf&hSD4R`ifRg`>{gWd_u&|n#2(prw!yWApT$H-$GgvLl zWP{103<~SaB1DeOgdg_kCTP~y@NS5Jvn%X17t)eNUL{47M#u{3eiIIk3<0jye6=-5 zycYf1m!sH!_vzP!NLl#8@8HZDj`6L)UK3IHI7T`9ISsy-=K=4{Y8(qn1cIFo@V$n@ zASmZKPlJrsE*rGJmmI(jmoE{{g(!kKp}Y-z8dWlDXBtLh|_-zdRg&c51esa@_rou8jca0~*K% z1ckE=pb>#IRVC?G2 zy9orL||^#sl~qO z3@nu(tbD$zl??&_jAdVklONrysBA^9dpj%IZr3yfJr=893l_9f5L`gJ_jbWE|C`px zy95uTpX8_@7g~R$1I_LX67y?zT)Fd$^vi1L$O6f1`ipAd^ALr#e^bX*V%!cOW&knYCz zp4y}l)p+x?3blGrgX>pTeCpU9uFz8|(PT_vQG1p-7Uvtj`qtk5on0^(Px_Yi*f2qf z1Q91aoxq&eW$HU8qzOdt6lMk+W&?L_+7rF~o7d8Vdy5mUKU|!M-+PgQcxEbWB~P&g z9O63pl}TkBP=`D_E}#M?9**vQ)qsDPKkEdD#v{O7sU;g+#_USkm$pBfj&_D@f0&#f29I)FlPsc z`Cs`;YK>l=?W?e8ayNJP-jC)J16xmmDMMU7+$Ph0Of72g%Y*qZ2^(a2D>6wz^Qe%Igs%5c! zDEATAR?zJTIE)TB(ghQoo-C%NawCaoinkcdZ!J{rp5wUEL32oRoeG0eeX>NC|t^$cq43vnbtM(5*c( z%>ImeVzhnq4dd93VXi9o(i%@TtLd@8j!5Z6@Gjxz!<6t8 zAc(iQ-R)cSnfu&E;Fb(J7fg4?ceIoI2~r&*d z+{$e2w+gt=ZlMb>P61e+foL;!2o@}b7Z;S?fstF=g=#fUFF|DFxA}yQzSa8Quw{h| ztp+mKjlf3Xx9RARSB#fO;MX$XkLd|108I-il_lMg|d=<9!ysBgb=?OSeVP( zpUeCHGr;~N@3)BV8lpXeIzBQ3c_sJL&5SlSr0@pBGTh{)9M_edm09PIXuXWPqq0 z`#uwkMWj%C2C({#PsTKQRquq^uT?m}HUnsMG@_F0!IKGN3+y%)V}b|rEKRk252T~z zV@jYjRkT*PIJrFiN=yI?paHlTf)}3OVk0!S(s!hP$~y;Zb)Q-MELM>Fo~04_5Q^6# z>F@?Nbg&)Mb|!fmdJkg3NpN#wefw1RVkkixdq7&nK4<^`1x9sw4bxH0Fdh{$xChut zpkzRsarK3FV1M;5X;d!DMe%8UpFB~o2PG(>KRU_e2udbQ6`7c1c?EV{oo)0-yzXW3 zwSzzFd0BY%CfMDh$;3lkUC8n6D5CyUmXE1atIs zGZbF2SC}2c*f2;i%y1KeKV*G&l1>r6Hg{IRhfUWQKYcL(A@;b@qV1yo;{HV!V0J+K zWMe=yHYFthiyz}|lxs{!xxM0I^w{3V3=*gP;dJl5*z-aZpF|XZN&=kPf+Ui9v@n*G z&*5*G@l>xqcEIhhnUAv#>ocLs{7v0#;Nn|tx$Z+R5QzvNNQ zFJ&sDJ!^=eOytgPn%`Y5)b$i#il^`Fhe^0mra;8wmKP6811ri@i1CrGfqFWLZdBFu zhPN8j2F7$^Ldds+xcsu1f&DbKaYeGVV>6t;Zsp{$=C4%AHU5gDTf4El6a8oMeg7ev zpwg0n0lp!&NF+E`h6DIm`e|JQ4T;)Z-2akJVXua#3jeR~#eBm@hu&5MOalolU$(+3yi8L)6~z^Ea8J)-ef;dl0ywq(wt zW`F^ywh*$PSkQ%?qhv#NUI71B|32BYIbd75YV3%hXp#F=6+pY9Q>nK3Tl3U5_N@nh zNM2Dhz-PY&8MJA3sZtFuR0?-S)M8W$87YJs$sGyFEj;ycB13&rs)=vu?Z+%*^v2Ku1oDV8d^9Hm%&@U|^O@ zutyH1h1J%7NS~uwk^dLz(_V>gf`-Cz?5QX<>;x4h7tnl;ik3yEhI&l&g>wurU!$R2 zr2_#9n`*niH-OKC+gE|d zmfmCVOpcowOW-C!(W&+x5rjIOVm(#=pE~`Mju+qa5JG;h6~G)D4uAL3o7zf&$ttW9Vkpw$~}=#2nLfcGH3$uJPa;BW~)cg)v7WMv;z$R_^8 zMrDfZSz3v5y*i6ixVqS#)qv|tFs|8LFPw3aXD<~a5)sc?sOAYggrVwAF3_}_g~S@ zGD5U|G~}yf6uT6p%b9+r}@hX6opz{a`a z9m@+wZCx{bKLiA{nWld9`%%$vCL&cy z%VY%CbU}&Z<%cIUrcTckjR3}-iRcGycVZ;|B8HBg@GMru4^1Yzcm^p-%eMO$Ik9D0 zitL1Gi28t)40d0DrKV_rq{=ulkXt!A@kUo@Wa5e}t%o=JdGHnShc)k9kdKg$P*~Ym zZNUq0J}l~gxEzwTPsETI*Dc$@YXk%o?{&^|6XT^cS-3O@@!<{djmY&2&zBv(vq3CH zM&4Emi-6`cB@wfV!U<^&zPiWlzk+$ckCf~8L+NjMFaKEf+kw7&5$!CD1>h2a3A(Jl z;%0G>(PUy16#yB)w|Q9y5T1b6J741&GwJY&EEBl7Lb|_{^^lq14}Zx+Ar5>GQ|`oc zxjmt$FMyTB{D7?3w3Ijd=Tidyd^7M69ZRrIOM#VHg0&S$Tp;jCydNN9KzQ`i2F#)? z-f1QgP=ORa5NESFTl)0&1}v-U$C92=b_wR_8o%7Cd|Mw9;q-chI`=+DXf8 zJ=^BR=XdYkV2$&aYJ?O6{JPqb5LUZ-Y#kW7f|DRW%ftY0 zCd&I|8QpBY=t(fC00=$dw~2jT%-mVfmGgLuK7~Pj@{5ttq)PhaRT_XW)VoT+;y7;9TJp~Bj(@sNoZn&u z_kN~tDTKkF&$a}Z2KKWuqO%s$V2i;+zl43cMs6gDf?3jUAB&6GD_#1xNCQEr2ARiW z;tD-H$&8-F44{gET}~j|p)DrFENlT_ko!NUwgm^Hv8&Xxv}e2kmCC4wNwT`v*UjjN z!7`e(9u{?}WxT=L)yVD#V{R+mZ{87d*{Ae6mkdpsuXEUtbZ;|NrmP|nw&7QynuBw( z3s$GXwu_Q8*&}QNYQeMHn|CpQ4B`Xyrs)(9Du;1i@m7?w|At5-h z?hUl|SRzPFOJlkquIyyjk^A?kKO9)OfLG{F3S*_FWWN7MQD%az8`!~$%eM`)cq@v# zY8mA;pK9yio!)!@5B+Z7%kGXVAwWp}c=BvwD!~!e&&$wrpTij3kE}C))sAKgNNY(o z01^-mX#Cb{lw?MDmf$XK`&F@1XfhF^$I+M!&hc3BjfN?tQi?$x_x&7a4Id#wflC+4#=^3JGMQb5kN_{=<*9U;ZcI>l5auNm=6Y-FM z+sic;UdKh=U%UktV!1eA>Z=c~ydYi%HH7#6yNSe)+w=MsY5a%Vn<)Y=0 zSqd)W!+|QMs`1(qCf=?z5@te|F@QN-ErbPQlzyJb?w0R=EDYHC?}0pJw0NJ0A^4Vo zRw6eaE+Favl4vJWJMBMNp2*3(#J@hq1ONB~B<$Xu(8J^x6K)K!(gC0+O8@|{8mvm0 z!DaRv(Ig47JRlTn!i6J&FK90>p?N;Mz#;(4YWO9r;`S*BDKD>9bpa$m@)-DjOPSLP zos{pdElD+^iyt|&QqeRSm6`zL2+8oN^++{?P9Cdd2|&HigZEi#!eB8pdgC`nqWo>P zNpJ=`oTT2FZLNJva`gJ&k@mUz0Y^tBR>G>u5)7AaFV5S!|9}r|AD~Ncj7VMmk02(J ziy)}W22@C{BRK_lLkwhY$FN%S?hrLbvQx@T%X)6|J^;&I$y2S(xbg_81ak|1-_k4s zZGVv^ZW0hs|335QFGs_RdP+E3Ve$uz1c54%GxB&{?n#4;=aUWc-uXD!F_H3G%^t$O zvD`B9A${UEl!U5H3ch(oB((XR*zp2sSJFnNf%$iR9#TX~rNbp{(H~V^F=CPx6Q8raaB? za%EsA8(e>|}P zj)-NBTnL&{sy6=wn~@}4e}XG2l*E}oY3F8y=EsZQfdwo2AH^{@dT7QvFwX2@~ZUt{BaqsOn~n_RpnA z2#$Y~^}x#yF?#%a**-kzGIE44h`HH)L0AR$6Dk9slDn&>dg&XVI?^~>NG7QN!34m* zjKxdB83bbYW@3^v;o)mZKELwMx(`;#Q!jYG1ly1p7n50j5FB9#u!0QMarj5~6-&d8 z+~cNT=Lwoe!P{lBeD{p#keX8EZ++rpYz)Mj8(g2-(So3WRDd_T4sOw0S!p;3;0}LY zeQrl+!P`q~pj1~^R7>*-!CTo+fL3UkW;*yop|R!fr}{j+mm1|3`WXp=yu@P9(K^r4 z{6+X5M?PR?#}>>~>XFoX{ZF!e&$^gs_e*~CpJl;sC&qB&riwV3-O>OoDd!t(vtLeWdP~NJr2Y}xt>ztT*{caOP^Z}Bd5%Z2(V=9e$r)moi38V$^jQ%lw zWT6n`+r0uavOlH|r*|Zwi=_?~B4vp@oNtzNZi0M|fZ z28~h$0g2zx0?2joDv58+wY^vX#040g4D8X_m&FA?$(bwkDLpFAvotYS)#P=H8Q;_=vl~TV&nWd(!^o`W`%0PRv zulQBNyz=>|*~r7t??e2NYVKttDrFbu`5K-YmslEuqjev?e-jZVZ||gduN93Z*lER? zyk*(98|lxHY#1ZMTE|v*=-R0BgMFea1=^Fcn^eKJYFPYOQJg8;L+sbMhlj)tgAvVW z`qUgAF6W%^#CuyFXTul9_!QgMpYP2~TJk|r*ECNeUnKVKnc*XM(Cq2$ z!(;=^&?B;fMWl-Do8`-%FLO);45BZZcS z9k$NdMN;*0R`)$mpFG+DbVP%(sOF&AL_cKMJRBaYj$l=$4qg}FKJ zQ6YW$PgAIx8>dLX2E!xu4p+zT_>yb|# zhME^iSNi1T7oD}zX4;P5pNOQX@ij}o4(M%_o4~aje=52Z9P`ehOD>ah7M>jr^iw&KC$t2E(}`1HU)hIs)%=k7EnVQ5SSI zRBVr%d@A-^Gcqzt*zu?t1Khwr{B4O~KUu4cA9B-^# zy0y6p5ucoK$|3sny~0hk!!axP#uKS1VXYx@FGm$?-sU;Vo1af>);3Txsj&QMf8Co3 z&Fb<-=L7`ZB_V zPm)}Z4(Nk~A1FuRW+|t)>mWlb^ly5)Nj8(IHUE6>J%*-K=6=I*Cs#>v)SVUWQ`W~ z_OGjsqjU0}xltci#?6nh{T~x5Eefmq5V`vG7zj9e@E1rOE^ML-9ceDgy!uke6}Be0Oo*+fTRp_J1gQ z3#h8Mu6r0kq#LBe14@U0bV(yE-6AdBT}r1Qozie9>5`U^F6k6$5ReoEzkR@apL?JG z`@Um*W9T^df^dGj)|zY1x%RnyvxyPyBeEdvjYZv;5-4%DJM$(g3Uha+3Tf0FKb70| z9-Le{9|6nM^rJo6BhA(va>nhHk%@IF9uiOI9+mGM*$or}$=X2@j)IzvjTEIVRAFiA z(*(rr=DXb!CK?~i%cQEh@Qk%cxSDm=-QnO|KYk#d!#sZw;K%a=qn1X7Zm_PaZWOX4 zbyR)+Q{LR_gf;*dMI>fCWvt~n{T%q^noJIT&TPkADk^1fR)+j1qWV?kqx!!(=4WTk zga_--gei%)Wf!6_b}?24bR7w#u&PRmbE+5B9&KsbZJN)D)wFLtn|m2k;g*4fb$vp8 zmm0w~#2CfGvJS6rVSS&wfKe&7+W{dnPA@viq>8@YVNzV6?!jrm?u)u?jiHpnpV`@d zEo+lUd^?u)E#v0&m3QkpQR_Oxq*Wzc*-(AyUccZ$)rz${*?lGO>w>HLQjE?{(@=#b zAiJU+zCgbHOI2}0Azh55q{GdUn;O8os}nCgAb=NPi#-o4M1MbG(dA$E_R$vBq%cm% z2@X?Oz~To!D-xrkMB|>w#xpwfXRY?vsz>o#~X- z+NSV=%4yMyadqBKtP#z;&XwHqBvPA<81MqOCUwylq`kh}Z>0oRqZgz(pepdL#XTNO zR#>m}k>XD%E0Zz`XRX~-_9y@8f<~g#_`%6paB|x_W%UwVxx-xu?^jM65oB7PZz5~LUs@}( zM%H_5->_wcm7vRm5vahZszyadprqz6T0kJnk`|OYbeik68xq1!KmM4bp?z-I>Dj4> zG6^Lo2~!QVBC-jKvY^rAhM9W138mNDc@vF;pUG+#vO2>KJKpJev#unJ;gB3K;Q`qK zs);=Ap@sP2Y#t$gzI zRGd9a{cfKhJrB*Ndn2~V8!}@2momS_6KLv>N_g@=B|DJh+$`Hwa|Kde?d}ekTr)^Q zZmdD#!t2m31=aiO4~FxX-e9ue0Y4pk6sRMP%hXz3Kx zRIbC*`1K>x?Vy};<;~S%wNN_dr}cATvJhtJ#?~sNMG?0V_uoi4u1lT0G%??)&8Wa# z_JM(c2OiB{@`J{zYReb#q$AT`#RBJI*pPCt;la5yRlUEwMQSRR@URdnB36Y(?vMQ} zjcY=+Vf;c~r}Fq>-P6mTygfujT+J}XWSSWl(U|UWsCsBs!#COqqf6$ z{FP{2I_vGX-06kw<6mVwpRr+@i#UcYT&TEe$;{Ym014#i96;!OCgiV>!sf8c;jl>O z?D1(Jrn+5wo0OKB9j-_*4IdtdfI8}3(KEWbxu_TQiVdZ>nCD{}->=!pt1c3n=S{>s zU3psCT(w#Vtgr1P`^wBphK`+GI0s`+)2r`Ch>w23Yxnfz9~~tZ&*}^+T0P>pzU$!} z>L{-G1EXuRVtDf>wRPkP-&Cdbv#FM_A0_9b#gr>KlPqzB)5|-d5HdAGHRJjt{A%Z} zm%V*E!hzkBWy%Ga&wd+w1fsbbu#u+=uEdQKeZD$=(eoVHUvF;#IUw6^7GI7c5ep^L zE8)9Q+Lk7J?61?HoC?}H&(3N4_oR!9O3&;i310T@q}+xm=Y5PpA2}v&;uNhDMJL&8j>^G=+4w{K`nvrV1;LF-NK|-WJZfO!s-$_#Q&oW951VVrp*| z<&qWs9^6|=3R;zTB^&yxwePIskBe<*2qfCwT)*ONNhlcS?s{}@%NdxpZC)s7SLxtS zqv5CdcDUW8R(3+l*~FB5?voM~62vvRquXCguXhPu+UOd3vrs_d5WP}q|xET^KGD`k>0s1N5t3u~JIy+C9)KcdrPl!T3K#UK( zj2}d|gWRRB_MZA}3qhO|_4v=lC1O9+w%Bj|5?b)tQnmeD+LI==8H(cK4I;x_n!`L6 z>(fT-l{=r!Pi*P{lERPsHV2?)2W7;2exj$UIe}Xt1HI-`?tI432#+a~${SP*Cvh+P z)$T^in@f*1MwP9C4$~NA{bi9)MICc^k{Y&mp_1=hRPp)jR<+*QFC@P^h$as{_82_@#)UTV?uN(9CRuBUP|!{`KK)luf67$ z5w`I=pE|Jz;+k1~`KF{AC`K_&gqXunQW=0-ARj#cFmcOEk(yF4QeO6xW6NOx+ z(zgkkX1TFSmZ}pk2&50uqqg0nTZW^Df+JB+QL^kTM-eMweGVJB>)r%H);LRiy`VN!HjZoDrE{45I{wl8M50tJ=CSQ8 z&p;{{rY9e~n}sZgzBP|u*`l;pYH6w7xw0i8MY&~%nPOoZp{e>y2dx&69SdmjB^2qx zTBz)sRSJt7I#s@l8p#qe%>VFnujfOka36UEJx)GjozYeCJ`!AH#z^|Dy5lvR zsTI24JOO7*g|78nIC%`wk3m&DyMlLgW4Hv2vt>+h*nFvxiM7qI;Xv|v;ucTl0Oyh> zdkMl&q+*UyttIUPuKUsm>tym~a{HvT55*tIICv>IJpjK$S|@G5Df0zgvd~7vSoC;R zBxYc?+sg$7uldnPnTJ0$-A?hRt+u?M4bq(m;mgFnN!mKDh z^GUkGgfq4)r)AIC#E{}qB5%csh^zKorRuf=2t!cp!Z{bEBi?Ujs`%)$ru-%BR$*kB zcngM?G&Xt9uPeHLslE(NILStoNKi_%6*J5r&}&Eb5-eivOAa%7mR4`Fs5*bX^;xrN zk81Mc(rYXoufyS{H_9bGw!-SIb(A%DsP>04@#h2ft1tSMGL7o*I9Dxr9(RHLf~b`M zkQddJibUqc09R@G*6(gSM0kb{s zrp9;lW0cs@)ie%KVTEC?i$yh^gUiTE?5!it`5as8p7^RqU;X<{^O^OiCoZMwDg!zM z7*UT(kfqelJ&h2y(}dIMcym4_j7r~+j-nsx?5C`LR6XU+g{Ur}V5!5UtJk=D-_5f_ z^LWm+hfMfg{1ptx)lsv(PYYubi!nONJ`4I1*IzC%K*^Fo$Ie+ySo+!fYZ%xVb+jd0 ziPbK}uhCvH*gcg;Oqu-!3*qIyZg7I$V}(om5?4N~s7EOW4S0+I+r%d+#T95#1daQO zG#j=OwraJKg5XN}-OU7^(w2HWiK7O+pK#(OfMM|M$g{tm-hGWTP{`Ew1F@!2_h84h z+GuOr12lWTwXJ*qu`SB02{Dpc4+i4Fx6UrnwtBhiq}0b1#4>F7=H5IcR1nJ=M^z(0 zmO@m0a(ee77OlX-C+B>k@sYrB-{kc8e&yX%$kT*}ZU~0p3+e(jWinOOBq_}YCjm$k zsc_3XC?Jca{%EP@%aruN{6;bP)U+I-kNl%c6s}ahUGD%V<`cC*x`C?f-=+C?-?I&e zr9kDF+v2W;p89%ZIC`9}9$V$?>(w9*TTiF5floZeQCn`n@U-8DGF!-!vYGzoB~X`; zv~R)q@=!sJZb&R<{GMg(Pq)y*@`0Y4g<4$jCIUap|GxP;c{yQsLe>B$yeJC-dPT=@g$ zm@hdI?R@d(IgIjJD_aUd=dzbCt%`VQe*AHvm{n0@DUzE0N++W%X^HzPTY%B$cw60TA|>5>Mv0~t zxk#8urr(216xnNHHjr@1wtKb|2`2(QLMxAjl!dPXKC`@3ykRcOo#NtoU|X#4q|wGx z;Xd8kb6)gM7qm!M57Lyh{LETB8XrVY**%Z+P8$l8lS+6vp)fF^^78W&&P!M!Trr-2 z&E_|~qc5SrDD-OY^<9)M1yW-z$qbCmrdXGl@vwX@VUiT@l*G@V3SFK+N%>yY`WIf4 z`ACllrxk0ctmKUxOVYko;sblK%cK4UVQ<0V2DvlX3CYx~a~!#IoFj~e;|aM6b>_F9bMLW8aq*Sh z5{`~v$ZoSyhNTL+>}&u}T!bScqA$o&OW(Q|>c`}?Vg z_Qk~K?Mje*=}1zaJ;YZVC4C;_`Yn7orh8b`$ll0u>iy-j8fHa}f&Ek;!1%aOOtcz2 z)&`r}9?=D#zx3cVVDt;AKXm0qBaKXw@>X}mGj5yYjrJW%inf$9odbI*s+PeAzR1JZ zmRK2=XB26V^YCFXxkTong%-L~kH5{X-~>ipX9_s+pUR$qI?B3K4~v{bP{?#B-_eOJ(B2J;u;V=SxKNsNQ}d4!p^M~|&ji&-Asmy;toki}%ddVO-^crQ zu!ozV1i>gH)!56l#pRf(No$--^ulAbD)PEMM`wJGE@lwYX#Dc@~qgx@fmVd!}+xTZ#AwvKhK5+=NBJ2=%z9~hR^@9ksH;G^h?zAr5%+|zqX7Ap!WlKS2IJl1`F ztj{V5kVZqDo`0=w_xLtv9xQM`F#kd|P%VbaFv;2&<)OkJNsnBL(zk#J%^czB#5gH~ zu2iLO15hv_W4v#>{?`e~P>n6$a*0b*iva`4>J2^jYZUIKouEH6xt)#z-B`8*j0~r< zqIYZk0Q}`rZ^?qGAdw+A+jcX7q+1D`1!ec*h8rW*&yLAhnw8vbNSdl% zK<9?r!8TGUEYmSBE}nChw&?$AZQbwaJ83#hE;32-eg06PjD_Tg$|bAwUNO@HGJ+3S zKa8K!F}#SU!RLF~Ba4i4>A*+i50UMy(^;<6S^kjkdePvty3HWYS9F6DJ61&8+Q_s; zk)7^!BecVx1&<3GQ=jJ}FYH`eF74QsOw@95C zy>k-UmC5uhg>-lr2O}=h+OtxCa9seX-S8hReNs$2UIqVXx}B*dZd2ZqEoFZy1j~fs9VgSq)SVsA9v_?A;Slkxu^3# zgPG6!`dkl!UC+QKqj2_6>DkC>szg(WA4FWw2-B5TSKC8d?$PoXYPB zz=+?F2;FLU_N%}*o8m_Aq3|b{A7SSTyeLT>%tkTEYI05?B~jR8D;>;ovU_K0rzcLbzKm{*FDB+{ie=s37&8?Nj#Nl!MGoJpWq=o zNwbkG!^Ihgsv3IA#tYRp!=Jf=5zv<+p3MLPM@p?*PhJSoYw4Xzs?t@WOC-Edra2D! zqC91vAEm*tHSaZv|I6L+sPNKv#_oHh8YsJ0X(zBW|B=sN(*<;QSi`%T&9ctiMAkf1OZsa(YUAB!V)@)O)x*N#jD?yTCf9 zZ=JiUDAFm*(8J*UjJFTPo}S$9xyX0nMX&GLq(EtJ-tmYeDm43-OO0_lytodP;os@&=*+(GjWI;~%G@fP<01$3BK10!& zz^vxo@9MPOc)d2xpmr z@jVZgysBsezQ1t#^k$kGM-B11<_LSz%A%dWiH}Y5nM(JX(e(gB8h4JDIKr>}J>G~7 z?%l3Mu?6JtX?&J1c|Tz@tiYS^cHYYC5OH}ONh8x+M{mc=)LXU9;p+jdhz2Jj=ff@8 z84OCbit&4?5Ndoidmikd{<0T8U(ez^IR^(~WLHdz3Y5_pPt?AjbC-FKhdrUIpndDn zp^(dK;Znz!MLjEY!P3ObGfyR(Fy5S3iM-=2`OYiC7Y~{ zXC@F!M<^QGnYNG!=j(M5(?iNJ`8I9#?V|}^b6I-9e1NvkA0dW(S5z6Fr+WY0u`@u&ZZ@5ZxtRtWO?-O@eOu-^ zDzJm{sWJV$K8N((vmX@)yYyc0g0jSdr^JH3aYB+i;RR#E^@k<&Z`K=pJ@vLLzc%^W z^iF#}uWX#CjN)+qcu{aPod4#0rFf2W?>F(@ylkDcYG560MA;(p1Si&Kn6U68;r_3a zH|k`V=xIY;hVk}`5r;`2$o53CN?T-tJ$%EJVWX0B0o@|=80Glvl&rh6+*XVVKXA@4 zP%U(@`1=ko~KB&<|35^KHX{4(8Bm0}(M zAR~Q>pO+;@heVhRCfo-HIa%vuRb zAhS2pV3{DgXpYxk2f+v$ZC;9&ug1UD88bTw71QA~S}k+|y6uS8UkK~>{i*otYbp7( zccG`8#dn^O$mZb26&Fpsdn0#5UI)Mj6L?}d8}<0NkB1h%@_ITZ2=v2mSlQfZi#2vZ z?=q72LLD^0(K(2TuR$`>FuuiV|L?GT&EOc|WMh$PjZs1 z5+UF3b8r7$b?3E#hji>8Rp$GPyXk_4cgJ}&z2u7U!iST?%F~|^Th@_(N;ykCG?dl& zb@crOe(~a5wA%h&j|?8#z!g;{z;s_`eE63vO!}=nGBtr>%Y-d21T zQB8ayL!Ye0??6n_5(g_xr%S4(k3fYAltCk>ZaVEyCJ>Zb*Bs`=%zn2O;XE3a!oV%C z#==?al-0qEm84S2f!!U^2S1@2>5bVQlb>fY(@iLiEO{3UKb)!4CWcdymE8TAiEt4` zooZj=mV{DlUgGDYF*$Z`uM^$sD07wz+Iu@h#(tu7^@p2pfaEm1V9-0JVgOF95eKgxgep* zuE)J+b;nE*6d1LJg9)a)0_UGeeZZFZwI9@cz7W)$wyTO-_(3tI9^6mhr{8P)!+1 zbg4D$;4-`nPw?8$yT_EVL41gMCcb4_(W@yi{N``1^KITVo^(v1A4C>>xs%m@Or*S4 zwAf96ge&zmqnZ3CN7Bwmm&}o(4cF0u>ScTwN%;`J1qE2246P zEE(;Utsiz1Oe5{eCeY{#Hj!YpwqO91gG#18jgOLLZUwhsp#o&o?K^K)|CzLl;nlT7 zoh^g_0*X|LqF2zFQF##R4dnI1hcXrJjn95qpKlS+BKYMy7fEDDK5tox z(qT9$k|NKI)qgbHubc1e`8e(G)Eh>Pc0%JKnW)QSJ%RA>2JDdLi2_yIWimqVP zI-@p5-Tw%P(VHkxxfa|wCT8aTNmql_1(&;>-uho^pYbfC8G1 zyM=MKJ!f}PWCokj+GeOJ=hiFMSfCRQXkXv^=+9<#-=fVjCQEv^rZ-Y{nDmPZ`JZW3 z*td8z09ml^vKzyR29lJEgs3W&&v==11@Dt_wNoX!Fua6j5?}b{eeadgCc1E@6>vAa z`8CbQm?-y0U$eA`Zs5!egWj2y$F6L2OQrf%#PyjB2arrU$M+*Pg&4JF)UsaA-4TXc zK6fBA)}nAQ$Qkq(LQCc458Lb^ zLtDDN_2Vdn8UdQu-261l<)vZYWxR=n_*}F^Wpy=^&XV`%*a!hqI4=h= z&2QB&EiDP}2CO^tCJ01wHfP=J)SG#Q?bNukQ%LeHt0eRz!0=^{N`kQ_K7pQ~Axx1) zEKneF^bpss(lpLUC4TR!6BFf@1`Vj}XTMm=)_L1fDO_Q*@g8AQ%J)8eNFlsp$z15% z5Wm8Bb)?e(2M$)KgI44Bnd51V{JDD|S6%JD+dgxbQUzO+0zI7CQN3pp<8u{8bNMI{ z`&I9#v)J9&E62G@8eE{&i#JLp_78gw4k>KM?p9HmH;xH3G$PY#5N74@2WSYHcgX_% za(gGzcKqvJmx}LPVEpgt$YC;#pm0TK#{dvMAENYV)CbM~jzU0hY@5EE2wfqp;Pp44wJnX6A~d@6n3*d)^&cic%rE5Z=~SFr==I;?v`Y z3NXzwQ_xTVlm)q$$EQKq>WK4w%jA@QK*0lv!4Z}9SC@2KiV{`-zXD+I4P4k zC0`ks0L&Byz@X;WtEp#tFM(Dc)F1m#Q~5NG4RbX( zmE}W*^QpieqEL){SEL;2GxWtunw8_vc|6_@^s`>g=lw{fFsU;5S0=jC{uc6bMP2H_ z#FEQOn#C$e8ft)yVO!qw6Ai{t=jB3b^nOc=EIK;vwH=6g6_r@=hzauS-7Qc7Oal@p zj*?*V)jp}S zW~wXuZ4n?=SWo!O6++jYledf#pLuxiw9&b5r|SO~#{fxArXHm0b`ZRiEW81rf;gy+ zL9%7QjgE&Yhwy+Y8UV0H#v{(N+hb^a)3|Z*vW@_X9Rh`iViQ|19`DL-%_y)4c%m{% zNqz_vv0^`^{(UhN+5!r#l!Ar2dF3ZzFxQzHj`El3VP3C+%a^254I=$JGW*_n#nvOM z+LSkwZWB)>gG+;ga1XBL6-Th#5~PdpvX;JWGW5t}Yvvqij;$8JBkVfN8THCXCroo=Rk`uW+3}{aZU0X-t?JAH6d9aejIQ zPg6^Sqx9B{{D_e1mIvY*xkxLR*wosGDUF9mslc#CyGTau;`nR&OF{ls(#}lcGYx*; zv=jbW`=ct-6)a5M5B1)vU4WN=k1-p zA(c;Qk1f0283-nb)Cc!Ekfk^-Ydz?)zuL~t0k(#tcJ~OgaS$aQ5fIM=Vd?6>Ru-;c zNyiW;wIdHOs~W>)-Sj=5GHix#Arq5t(G6tU`Y&6Rvpc+=8e*A2!<|TGA=P?vl%uC8 z4@S1pV0vsui=j*3Pg1B=Q7$)dvih}2mFf{0ypUppU@NO>8WX#O2O#J@kSn*l^Y%cw z80!{e)mFeDRwU0-Q6KDx27MKSDgXIV;GdW87iMNzIC-w>nP*nZ_p}GKK~}(9o3NGT_jic z?rO*TBfsOxqHyh5YxIz@)$s9SGZ6jdZlZtW6K`t0+r4a-J-D0My)A! zhX=K0jfJqf^=$OJVl*Ia5Il-@6jeX|%^nQcPQESV0PHB#>A3bxvPwqwk5CaQD$dP~ zEooMKwLAU~>QJPNjy{+&EXAeVh-vq^Ugsm(xO;57VZCGfsw#%CH1? z76vNPH~3?C545L-q>X}Rc6@TzrYc*&^8p=!ZFXt;E&nw1TNRX{ulX3?qQ=%jnx z+TqcY3jkP2;u_%6UiZvZReSG~N$5O>nGIgHshUGnYd>=V7koC=Fz_IY5QMgi6EHxO z{XzW+z{&)D@K*fRPN=l4z{W|IS#tqy4~O#rH4Z^B_g)X{;%wBH)Klv0POp^Y?2X6V zj#gmv8DoB&tQi+JEfl4Kq85sxlW>fwoy|Cur;n6BzdR|P@V{aJr?PjW8R&oOm8(g> zNygBGJ{+8BTL&xs4<>2~RyGXyh+&Ae#5>;}2aao3D5B}NEt=3#XI;gs@s@~n7^Th^ zV}b(oq2)?O^Ap5TyeLsML_n1T*4A!@fC+FWRu4jRw&>YE?D+hB&a-ddAfA;>BWdm%2t2BDIb4k6wu zytHbZQrOg}`B`zP#C=Nsz5Tn`0`MaRkChwbnRuIYx|iQ5p@l3i*%Kzg;C_0JEr<;6 zD=4b{@J9Y>U^%!h^hh8{GmR%wHoT5|_06w_sZ~fH#4$g?@aJo7;NB6TB6^xC93sN@ z8n``Rj%)C(yvNR4y^MSnk{g?6nML1Zmb|cR0CUBprxy(zBR}@;1J7#=F!@yEp#evi z(N)xGThp#=Y?Y|Wrd=ms)icx3C6A;fyW?i4Y3Pdn|=*;gVJPUMJ*Y%kd7x3;Z zIC$4@_HyxX^?ffcplRw4K7~cBOWJc>l}A4W$12resoC{5H3QR~UqH0> zJKOQPUo%Yqt=>5Ppsz8GaL^|Sj4{fLIZBXs8`_~BKM@+^DXWnd?l=^yk$=ejmqlVRa~iL%B*>eZ|q04$kd=yhUX zY167R*(Mtm*L>!|O&BOw40Yt~c9}l$Y8`hBe)z;EY`o&iIP+J!yO=RO!R@hyAq7l% zmZv6S(2C(~nOEYRk1Ck}k+5<=8ub*oi{!0+@cFIZnh?8l!6|*sB>bnFn0}2O@27&q zQss#OoY;-i_`I+N4LA2e-ImU^6^wMW+rHael|`D9mMEUw;iylUTy_@IZC`qu5!slY zeTCzL>y1SZ&(1EUjR53>1J}JR_tNCQ&xz4^UFLf_)Uc!YfZ^~o=~*C3EEG5h+kj8x z3h;^Nto&;@K->X;npfxm$pR8X#75SHNe_xzayhr7K#Pd@=52P09WXJ`Fb=pec69QR zHpTVbCqS0?lEx&AgR>^oyn22;1g8r3nIbASZyId`ARP;t%#e_f*zu~&_pX#CoRyD< zC09Q%(7Ey}>3tRV@8nxXi_`t|)CuzLuTpyZeJ0W$Z!GieJLF4$pm$J60Kdl}bJ5;Q zGzO0y?#XBIx6qKm2EMG`nUkPtdv!Alg6Gi6C?(jzNXb&2kJc>XAQ`|Mte{_~fa z$v1Ct>sJLn1fr`%QEbuo9N0ks%BMhgQ!m-#E9HSyR2}yEKSR0gPl5nc~%>B5aN^3LG09?yI<@4s- z5FE<-GDiL+PP3K@1ZMCH6nVnb4X|07UW3@%#e5XBm~Z>je`1>|bvrbKZYrq=<2&h& z_Qz(>+Xb8l-Jtt~dC}iq*YdVB1GY}kOF)Mk`5R60F1}XBSNDu+Ru_wWBO96$S9bP{ z%q_DZJ_Yq|D|%0cvrk*ww42GN=8+GFXB-rA`|Z{eO;CU+2bKMyr2Ybg$vEB_Y22$; zhtGzftbtWCVU3S~O&%u-Rg(Y(WLPjP^IE7apLy~=tZ=BPk>A`#5u-ZYcK(mDJviQ< z8{h@_WcL>>Dl?{SE;8FxIy<2QUA+~+)>3Lf;i*CQ3d0Xtuuby!hAf- zXB!HX!3N$YF52TWT+ZpLzcuFpE#@b&(d+cUv(S8;ESfWM*TMKPJrcH#p#!9{#MrLh696g@KY~ucTtX z7v+KmfCu1gRMI>pfcICzN5n(}9W{u4B_#zp02zR`fG82KO+J-W6fu6ws?u=n9FIbn_o`$~3-14JBTiMO^IO8U#a(8>Y_#k6pXXmzMNSLX>E zK64)>vIKzaIoxFAD*DGH;&tW;kQo0WXd752Fg8&99ICw`uY8SK7Xfw@Xdtuej(aZl z;DW>RS#r4ClH!6EV=d9WPaVp(@1CU#x74Ee^=^9rGrIZ= z$#@TgR>Ph5N+H#W{J6@8z{DV~cIQUS{qd@dK{WsZ4|J3c497>7LV4j@(G825`^Lt9 zuat|7q^cV53_z6cG^qvMrMK+M=o7SO$rscv`Zj|;)u~j?&^m7FKW+-s;5z-WJw%O2 z<%X;0laqqXk^k<^csTC_@AnT)R&##kJoRZ;r(Y{tTPz)lcdV&?}x(7(`*l+@yay*y6^mIh%af3O#T7n}L> z>w7K|l9~h{ZnCFUWmzQe$DIy#&E}M5JaI|1gfnns{htq5|>BH5=3ONtKXv_ea1BKUyBDB5`3$2Zsyw@7B|t-`b7;p$<(-x*WmNFkGhG zI>8zz-a%0(fRbu6_Y}N{%DW_Hn1aQt4uhAQD#~z3Rvx+oVXw7{+%o!7HxGsW1GKBj zdp3YRg1;$3+xNifgi8QgOJUtlgX0QxO`94nG1rx;@ml3i`Y`|AhM2|Zz*FMz?Xyns z-n*jXfK`PjJ;eao&<1`zQM?M1f({wRs)#q-(U6~IPw*F zfD?NYMQU^#t>ea#-(!a+0)n6FsdN9wB0#LI?^pl?sN3p*&~m*iIe*e|d45qvYJb&- z0a`fq#+{zWXbZaAfq$#zDny(d^Y;jytLMYett^j>&a!NF`RCs(1UFWc4q{12YWG{; z$dCvU3D{xC;;K)$+{(J|6R6#~_YG9>H#JTXe0GJt^(;ogxlsNv0@4bT1xu4w@`-lN zZrc&Z)qqmZ=e}uD<*Z(Ie680KF(ahvRV}+8mq=2r>M6$DBRKSYKpz8W$^#TQ=%RT~ z^iBJn`0sLThos>RF+;-#AN5)1zwcgjzpaL0fAxtcuoC|RFabXNkH6`7NdLL^I)A*k zNoB^rNUyKsp8^AG>asv|v>sxSg3Z{Z}r=~Q} zblGtFcyxWXV7>g0U+Muipn*W~1$ai#o1i#5CeL(9+nPvYT(WCw(rw9{9zk&#kUcJ6*0e>G=(HXQ|WXX%T1;~ns#(JNP zx@qI&*x-$Aixho$_?SbCoR-n3HZlE7XO?4@H)hMI#O!Av_9m(%c2ps-x;~k+g#l5m zFmWwWm`@r;#!%;*fPAa*XuAlcE8v9=ORv3yCM6vk05}(bPXTmy;x&~lD$q4OcJvF0 z7`%Dp=LyE&X5d6ZeKS6vj@|mEE#z~`fn;@&uuwAKRH}Ts@>iT3IITSLk@b}1r)Bq! zIALm~k+I`1Y0Sm2Q*eTeDc=}Py0q^Bqsd>`F-LBNS42FHgrhFel!YG$XH=6dXkCb6 zbzp^x4`(a?hFQQ4gTC>hh=6|R!+n-ki@p)T9=HLAqx@=MUO%N_S6p1&sGTU~(k+4m z5Gclc|B@oqo(xvPH4;HvETQv2tgz$3mi)a&9Du!v9c-(c( zfunp%BKzBa&)4OJFumhB4PSEi1^FJV1q4u*-{zR5Q^0GoqU1=o>M0tdU!)g&--jQIN8Q7GB|3`&E)7M{@JeT&LkHAmSS`ys(Z4K z!S0tE;cG+x5NHRa|AWp$vV%Z}7S>M97_D7lJRFnO(PPT#Yctx@cVjTTh5B~}??Px( z{s%EA1ly|gi48`aCjcbxEPjKH;o%f_ZQqRmOLsUtIF@Kw(o1%l85&Z#WPX8V^}CAj zQf8|QCh3A}DZs*TnH;Drf;A|SiYb0okA;y9@YG z%XqJd1Zrzh2Ga$x;b`E1n5XM_@z|osmU-x%(=H=va`P{7ak0_jNKR3M^}pM~96OIU zb>{53YMSMcO1B+2t$&(d2lWWK((otzDF@^|BOZ?aUWi~i z%%QwOW>ha}1J{?ST$6&&`03~nb!*nIU+~(a(E*HAR9^88xRXR_x4J#Q7ht}>QatipMOtL!HH9Cb0k5<#5Vk!LWv zDlDbyPdk}+onX!duDPLgd|~yd^h5K6LhTom+O@=3Sr;bHP6l8N1TIWEXf}6lw=l=} z<4I?1@0m)Iw=@>LzNFkXa_6b*>!!MHD}nXDe)J#KlzUtEUI~NG4y9vaiuhezR;FNP zPlxfJu=3?LtlVK?x1>oHveC{rJ9Qk|T`M?#she+Qw$AlqHOfZ5uK&KN(7G{Z#9%H# zZQ|_b+Qxy}U(X#rN12GLJ-ACH>xT`xGT8fJ&vG@XV=G}~wEFh9g23UY^V7k>L7t5? zN4xc8v#;P&)yQ0aFI}J3fflq?aQTqDP%t&IO6d5#ofzB3dwBmyi9xYjtNA7#rotQG z*gs?KHtBOQwV|es|7O{!SNp$A4s#{C47@6iC&DO%7Lzu0jY#KM?qf*_x`|1VJYTEt zZ=W>BAzq%n>hTW_YtMRr9IXC!X(D%N@-CU7A^)P<085~N#w4)eU2(>JaF-9k5XOnh z6A(6!id9NTvU|^0%MI^tq|NjzZgthm&6L@a@-8wzAi581)=ZV?O=^8xTn;NUDhJ>^ z<0vBrNT-cSkR@M_0WTfs*Y##kdK3{Jw?mXF!)V1?!!ZfO=$9k6+sjGQWE!6s)ZhxK z@Bp+w*5V*vElI*_ZfGDrS|1d1c76g2O{>f8Z_KT&B`htA7lF<5-T%pXuY8r{gWaeC zYwar+Zo+YGJ`jSzbr@_1=ymw~KN}HeR4J-920(NE<(}_iKD&F!FU}qa*ODJ}La5E= zr2o6g0b<1^5j>xVwF&`{Wm^B08Iv>bgm)v(k!*MDuI9fw8PrrLjAW1v&+}A#k5ZSG zgGgFK77CipN3%LYU9fvYxxh>MIEuE@~&b78ldyp%wD~+<#_AzFR=Z` zi1!zl5e^y;*nR$9m`vFSvdf4x?rdY&50SH4fy;*I3_%whut<`^;5P4q!Xk)PKCSi! zUNOp4px5+IKkjx?71;4KrMKoTV=veejWx=h2rbb4e7O3|h3OB}-k5)^@Z{8_i>gt5 z`a!Qvvd_cNdjW9xx!55^FoCrb!U+)ze5XWk zj33Nju04tx=O-oZsh0*g7Myk&kVic-Ggxl-D(BJTp)lUj&|aRuW5{~vf1rt^xOkj? zgg-zh#^Sp<*m;zoZ_Hf$e}*K0K^+T!{1&`Kgm~j;AFn#Gh;u@2OKV{H6b;m&V4Ko5 zqly4>L}AgG4`!Ecn&(cTE(uPwJ_O(nHPu`7RAotw zjA*|h|8iE68?O3{(P^>=WXkEhL!e*u6QF)j7jF>`dca%~!{Ab`g>k$vEv8y=%) z7PAJQmDT(SoaKS5ts^PfZpnNk8HAn>u`t~(>Z{UawC%pPUe+)ik% zL2@W;{8nK2bHqir!#|wv_b~GdF{it2giK|3bNm-k{@0I+#}!Yv0FW4L4Rv4pTCo9& z9b#<|*NcILL=EXm;2(6&^qRv=2wh#p72t@vP9sC4`K?q28405W!A|J~_bmKf*Eqig z)!$!r&E%<&d~fi+V-jkJK;~Ai+oJ+6SjnY&s`g1|e6RXycvFJlZ z%B@#RHzm9`%eUA0b1*OVL@th8Zq9EfY3;Np*Ii#PHy zIAKsgjKi)=@OoNm2>)U9wzWEruhu|(tMj~|+^O33wxNWWbdHGx^)h1J09D3p z;C2xs$j!FcPLHhaU8{_YhoXYZ7G8sHq`MOFS?IosjkHa0(#`*lI9>d!Qoxn@pYpr!0S->gDbeo`y3`MY zV8wfYC<0-xzNHi8i3N;j2#5{AohaCSc0;gu5PS%ngD7)MkUnRiazh|ujDNg{{}xcF zA&_U3-NKN*M^;rpFt7*ZI7ys3nlO>~9TfJKe>Nb{exzUs5cw;JeEq5+9>*JDd%>pC z%yEsQ%w?GRM1Z471`rSGO|j82btOXSY=kS$KeRcJjp(s-*sy}&-49$pTB;eSFfkntA}$w_kCaE z{eEBfecfli2a=lK*)QLJ-3_gy*hQJ z0dr>kwzVGy?faT{R1d%jX{cr}0GW5@TnBVcf1A9vrOvaza(x#mS6QQlUX+&OiJKG4Ct!`4?n z!td?HQB|}U9%aQ}2hfw_w4>%%>F4+w7+cu5KWrb0k=LFM!YYO9b((qH6Ak)?vsZP? z?q}P}J@ljE7G4=c35`F}wmNK5Wb2{>BY6N{?KP(Y+C;#QUjPG0EPz9Bnh5UFF1sE| z!5ugU1vRm3PHt}QeJ}@G#^Zp=GMnriyFVH~550D-Vg4?cT$sLH{`N!a&y%`4S37pI z{H~BaWo7$tQvq_-Dcs`A(6YJot<11r0#3DpYVvgJqdAou569$X7=}4=Y7XVa)K+?? z&P(&jJZNUr6bap`{pee|zP#>jKq{zF8N?cBsFZn7?B08ATj%wC2W(F7sdXYGevNME zdMW;A^O6mUn6Mo&tvM3-+S`0hrbVGDN`-IFiv~@;%*w>4$jSSwJb(5h$FN^@haL24 z`i7_B0BCK5Fsi-7{}PYyhlTqejnOU~S-g?^W0u4>jJ?^QlNBQ`KLk>%181CHBaf8H z&YlY<#^oAU-5KGSd&+@Kun90gIPvD5d+i?);POKo%F|)!+aNs&fEBQn9J3 zM(|$v(DrkIB93^h9Lg5l*FuRoOR|5pa{iii^wcvZb!+hx+f0gYxKHHY^lB}nAAA0D zhj#h4$$+3t)UVUj^foNA(iKBaA2I*YT?{wgZnFwtt3R{J7S8xfL5(Xdz+3I|o^P}0 zOnU?j?caalm-v6euXPcGmO6+bBKf6@M*Y#=noD;v|bJ|rG48kxyS_P=ZbNKSz7-vovBcfoi5E+`y*h5ukCBgg$A^t)zU z)iB?AuFD^0=ea}dj})aY3F{|*g3X>{h3F4`Fe0{%`11?>mHnT&n0D9c&DI0GV&G$t zs&H(V&Tj8A*dAns8W~j>f-yMz03T6YhZf0O=3KUUFDbX_PJao z{kQIBXS5qaoY0~9$GcW4A;Vq^x}@|r3*Jsw+hNCF0LWj8)KNE~8Uk(H{v#f#Gsm6Q zWYL%CsM^c`qm;mPTIf$~VSR}C~;E!O^pqB^&e`ir2{ZMHV|e50Hy+|fT|vBX_(cfXJB|iZIa=d$$1_kbglGB z2{HIckT_4H;`Dd5-^I&4{4d2)I_P2NSpTB%ZFY4LfQS0MwfgpkKi}y3C*(0IqhcVU zqOKkQW^@0JJ`ny`H3WPjRx7}LR*E(;^w@vJux!Z7)t7(0n9^A7m@XVztKSaH6&f8u znx)R5f^9!+R3@+^7ci~NNEpBUWsPgYB%Q9uHn6m%-zK+27Gn%-b__!2U+$_&wNASI z+}wJ@UtG3N>*dgdYFUHHP`&x@X917xgsl@0B1(?E`5)G>^EU6OWs?--)%cKkFILpx z>XoOTe1b9tjDOzC-|vi6p|pOLTgL=$=+TOFTmx0pcVQD%eC`@nUHlv0dHYYk<5RRh`!G_xPtp4)yI5K=U8aC0k{D`5OqT5dC>bKv zRjM(1Tj2ecF?xmv^0$mid=}EX?$xu6R>Kj&hx#SPGG+dFE{tYhIt};f3X=WhsZ2KkHKf4Bm}VW z<+grzX!iFL*!o1gT7tzDL+vY*F=4?c-dwc261f_8_qyHF40^5g)9I1 z2w?MXFdx^L5;>m+bZ&SCXMm#{S-={b2TLuS28m*HkXYaQLF-MBvj(C2F&E?X^WQwG z{?#SgEs;Oo2AT$_|6zWT=fIPI>KG-BWaw|a^=lfOroWi9n-QWa8sarzu!y%MMM%o;kCC@!i_6?mLl3&LonHDUUS% zhT2jF&-St()L=&2w*esdxB5Ij;H>Fh{21nfefz#T`F*3oeqn+Cs9d5hqv71&W$ZVW z-;Fo>5|kmsDrFheLtxt}1f03%TE8E*U-7-1c-!0yE$jDT4ok|5DXu4i_5Xx#f>lP@ z=KB+d%;wjl&Zr~q=Si$#9f~puxmz`O%+!=Oy>$MHgp|~HoDhVKA5Xmz`nl#Kqw9Ch zx4*hdgnQ>1E?l^<*g__$mga!F{d|rzCOT7IbgbLsTusJ)c&=P98t-O1_9BvdH*M9; z+%Hj()qXzp8alH)w+;5}+2b-*IC{vuc{lA7SkWJkdiS-qy~|;|=l)1Y2>tNkB5LD| zU7Jnt?UmoT4L3T=O9E2H?QuvL83B!nIN0P(hdoLLd6sNE%Ish9wZXfFdhi|#eJ<0b z3p3po8OPOk=sNc~(kt@xk-g~T{ct$kV)>UZU(W9Ur|_e!EP$|oO)#hlM)K!_YBlJt z+uq+FGCx1hm49Tu(`3+Anv&#+FY`!FPp@5`8@B_6nRYq9(vZH=@X%Tg6!t+kMTLFq zg-$V`z2Td`cR$gbCX|z$&vhz+(vkXm5O-*N^Jzv>M-!yKB z$m6jUSjo@ldRn$Iy_|>$U8?3Q-0A{8bZW}_vZf{hMVc$>XZ|)>yS-X(-rHz z`SsSH3btM6jIkf<4EBj`uo=nTI3ipg-9Q^vOzOHaf7!%dQ`SG7Stml&j!@088r(D4 zoU$=tQX3y#C(|O>?rfG-W)igceAl6(g4hE&9B#ipx}pTS?-btc6)09868od9GxlJe z_3K7mPw~h-hn$z1be$@Chg)j1lNe)y7uq1P{d^uA2NeHvw!!VFX%qR3@_X!{4hicw z9>;vi(8HW#Ts8+@p=Mc92CwjFrx{w4ld&8-(F@+O%3L6r}O{Lm!i9a0) z{@z!~_iw(tE?E3@rn9(@UY}86t}$91`q#rBWT1o$?5YLHo@-Nt&{ZFp>`%kPM*StO zaKY3&(YnH6C`NkoC>N~G_Y9Sr#93IZ*L4I24NjgsNwROxpP!$`hl*gA8u*2T?zfIr zLp>$P8Sf#rc4S=lY8{8$#2tIxFdjkulAW0Gp7TD%^S3m37%Rc$eBVCg+Xpsy>E-## zg>te-a!QJGyF|WqpG;$d0;h(C2B^f?<<Pt~Vtu;NBioKN}c zq|I$}0ij0rXB_S`$KAhNN>Dt0oT9#=!45)}@p$_y>Yh(GyC^~g=1lX5dvZc^qa1sy zV4|7$RI2GbXEs{)7BD~&tQt$I!w!U<6ZYrqz<`q+H23ONl`q{;Yr>jD zn&!bZ7&xJdPN7=W^DEO1X6BlFqd1H4IOl$4>2d??sw1XiIMRUj&YTs?pSo^^U!MAM z;Fwfp?;4N@tk*3qtLdhx_PJJmio!wko+&x?R%+L$oDd>klr%4HSX3wWoUR>h>(w zN|oJJATPDBH4D5TfMLj4!=-1vZqudfF*>@%oibsick~v4CSeVh1Y&ch3(=S~Za~J3 zXOxGW;kno1IiK@+YAVF$VXEmC>6Z@SI(5#CGVR7+?C6eD2LuMzkXNu|)#MbI^8;vs zycW$%OG^_oGu5Ct$RsmzXvNXd(SIo9s7lG&^0@I-Zy_NDPkKqOnAzMqu(mSI$`|MG z;YmVPS#JKdw~Tf$Lf+L_$9PXXX+-LN-4H`cN_N?WYXue}ksgqWSPfRcXfb&@cbP`| zBR7#5<`>8KwWVs+VI>>p`pbL0x-yNtFni_K7Ka+I9XJv8$OH0+G0vNs011fx;rQMTwtngA`bUyHCU9 zDdn;3VAmqI@HzZh;~|S6Mn%88JV}aUAKTifxf(U*pEXLpyw*db z;vM5Tdh%_MQ|?!d4@ylh+IrM^YE#9siZ82yJ423H6Anx;mb~nB~-TDmZ2EN)1L3Y&3SS-8)Yw>vZeibRy~v$7YLa|u!23TdJYeok2DDC zPbWav_dj^v?yxaVP6@EcC}lz61{~yDv&t#>mq-IoHgI8C;evq;;gOL;-&$2UZ>7ZO z-hzc1tYI~%URzz^@~-HyDG#>l$s4>!BmC~D#Ya{C9XQyW8}m+hV}-fGicV{8gIRz& zlmjtp7cXw8&^47Bj|z+aZb&0`bgguW9UUE2!?!A}^GybE_q9uJA6Ahb+ywKr0aF{w znUINYSRYDw|7LZ{xxDJ-g_xNEFFM-AAjkC5R#k;PEwZ!Q^gZS@WamC!g8FGq>=Aq0 z&+oX}o!KXPq1i2jXmABzEdvi`8dl+ItNcD6^LSTEObgLr1-#z7xmm>>F4>`|SeAS^ zm|m)z2ZrcAcFq9zZLHNO_ljM;K+?c`k4;@<**D(OoCx)jeiOK>99hG_m(&cOp2_y3 zM{g{=J3&`zoHdZwW8Hf6ARFx7r7XvXa*_tN6wKggh=7KSD(`JBn8e)-1NQ&8Ney&YrbFXX$({h?| z{(SLN6}wHPX1~v%Y`ZbcU|>JnyPnrKS~Fc|%^aMZ2%%WgzV!07WpCPK9%bqt8ZJsw z$i*DzX?=Gn(^kVWOMZ$CPP3nMpR!K~Bx9|tgqme%&%4NS$gX~Cl^zNb$7Gh}K2JGd z*7W%Pa6{xq3|!=}LqbBruqJ@HV5(@G(392NI;2&;%q+i9*s?u}DRM4umaicEm2mpFBshnBt3t|%| z1oFXjjl$~I?y@Plo8d8OLBwa3HJPBW)(6>p9kRoGJk85v>Ckr*f>DPYPoSzdH7oD0 z8a$L~U)bH(a>thDHwn|L^yuHAU$YcI1{#6#VpJn2gnxHiFF7!;ZU0=A-{PHa(p zN)M?6@rgb5QKijt3%VaWI!s_^0}+eD)!`0ZucjC&llpLhv{?xnN-xNiN@?u|^mSI3<>JRA zdDD7Hk=?ZWcGKoGH84uXY)^`Fx}sRT4FgkqyE(sMb@&#<_5r})H7OdYGw`?3*H3S$ z6Gm@!Jrr$-Vyts!jJ}ap^KpnHTL10GGi88g0v*TO{q>5S{DrRO`Mo$7xm72k1{`d^ zH$7pC!s1=nl(Pef0pr}<9O0b7a#(U&+79^y)zR3|kg`-Mno60DZU7eo?df_4ou+yP z*5)#>{vvQ=W#W@3$0qtquToVmw|Dz&-{oCn%s?kQbEmV+qZpAl@7A#$2uuz2WH#rr z2A`@Ct=qjXLQqF+K)gNw$n#s1!zqx#_0LBgI$rYa!#U%QbUov?6ph5RwDY76>}q7c zPOGk#RwRM})c^dgKOQfV-I+z?a~D5&S@pL>g6K4M_Au?Yk6t&1Vibypf?r;NTF z|CZr7mud_z2Z7b!Zm8DZzC})|zmuBFJ0*pHKnF83GtN3cFsDWSDp=}x)KG70`2$k` zdi4i0c079Y2#o^3yB)$AK0ZE+qiHsiuXJ)w#kvsu(Jzj*CI!MM#RlyGPNLL9am=!> z$g#pa&1QDzRCj|0EHMhTJL-1Z2iZ#RHU`rt^t(+r`BQvcL+(XkSW%AFhw4Fb$BNo; zKMd$e0tG!>Nt@TN!jl~Q zuV4FRK;}wJNjWRMn^x4STY~s7fusc3-=-m5e)Fz>3K@SVL-*j6?(awW zKN#C@nSp_~5|xO5K8ZX~)%*RMztod$)|1`2z*^$s;%=L7ULVYSPQqB+cKF2)@3KuB zY`?zcqbqEh>MQ0_W|Xw)k9{D^z()N;d%1oG0Z9G>2iRv;5dN3KyW5Y5!`Hk7wBkA% z2Rl&<3@gmR?xdk#dX{-he9-7P%9<()s}5jc;C`6HhYuIbHXjv_4|OOQPcNPsiLt50 zuGMRm-8&~zDq$4!<;%_2uU`*TGpRP(PWgRXFMPwxo$V^H8yawsnQNv5iS3Yb7}|_R zTEUZR5n^4OcAo6M&wDOn;K6B*N^sg|Hy#h#y1Z$@y9{mDb!^jE7kDf?GbvEU;cywC z@K${K`elrOa3sx(7cWj&KjYsDP}?v|TP*w__t3f~*$pB69s+v6B7r#8u$IkimNC;{aFLP3CqamX_-IegHk*G=zSG z0%?#=)U(nLU#b=TNp`Cr`k)odSUIb#_)=PcQhYb>U`Ui^nXNqjp&rPyi+j<2eqL z_G;d?ii$DadALFBoN(bL7_D{wE-hWV&9u9v5ADrpkzWxEk(<|sr6HuGrYmY`b$mVu z8;fq~g9gOm`iY^}u|uyj*MilXzJOS}%MK!>F?`mE{?oM18Mhs-gt6+oP(VWR0v>9E zUAgK`BGyGIVC8|qJb=UvK!r3cf~CNqSSdZ8mSeln=WNpb`i3;X6U0mjkcwOt%R16@ zyjoR`Zm0F#xX!f87UYz;A1sbSKWcgj&QJToeq!Q>a@D0j>jmUJNByz!hBy%g|t$9#!Dg$ET)h^ULOhO2FD(W}BqA8pK!xwJ{(o zfnc;WJNgV@NsOcw4ohCZn!F9;rN-hve7FY$hVbd3YAwiqLtqymWCuYgn&>NLjns2u6E6D9jo_idN_U4Xpy?p#V3-_3 zBB5sebqx$c5w^ROq!Nr3ZV=<}_@?eYQ@O=uc`ONuWP{~v2;e?q*2R;O?x0jGg01#s_jw(llgd+#o}oo{)+_Ga=!&U zmeXMEYb5c7hzE6Wa4_Z$NDDL~CG?1&3MR8DW)k2D=ifE9p375>^z?_<6k5KnOcN0WCVO?}_(+Su$*I3t)=qNGMt={yD zkk>=o+clBPR07Wnyre3}463uRjEqcJ`@RQU)ZE;`uXA&$F^Oem`f1u(ieS?L@$vDC zqpEUq;h4RU3FWxF>G4a=&egyVySzQXCbTg0SM#l#uC+tX5v!eEpn;6no?(zMvjU)5 z9T74{w6VAb05zm>VheyBZj2vDH#A({1u%m(%6xI*GYg2{*F0B#NKnwy(C`OnBQ@+^ z3KT5y^=pZ>Sx9~-jf@8GOzyw%n8y?1;Z`nh7YKmfiTuVb@p8nLUF__eFj+=bJ}&xk zDoHa-?nJApvK4DPu)3sJDJbz|Y}`WY2bcmjejto^%8|2cL0^N4%W1Nk7vqOwA$3n% zdwT%5==LJIa=ZvKbBbf4iE4 zEd1Pk!G^@#T#el_4reg*79Ht(Uc0ZZuyhz;OA9`Qony>9a>eHK^q7=YHxG|8W3lrz ziy{>RAV+U%rfQQ=IfPi7r0_ee^N&6a@umlz+Yi~6*NE4f`3KA88et1aQ7x!jT8pY(TFfqT?mQiF67ideDZX z$)s5(;{ov&S+AEb_e)4h5`x5=&$x(9G>W(ER%f@evbqi}5(`J;V;o}8r@%G1^83$6 z-2r_YRTHEgKGPQ!7hh6I%F1GBYionzKnQsXxYjXsb#>BP>AjMh&mTX2oHSZre->;o zI2jcgRECF#QBV;zsof^1ow@hL%A2QC8Y_Q2zfNf_b*;@)V2!)p)pL(0ys zp;3ZFMxD?#>Eu4N3%}EeGh2|(FV%x}e?vpVuCLPykalz(-k;Piu;p4?DJSpZ<>g%j zL=ys@X{SUJyPaiv%Svh6%?pdVVb3WtKEiVInq+Ac0(D;A;ym)P^rnHwmRO1b3xgGQ zBqWukSiCvJIZ{(Wm%Er&XiJ)fRYB%KMd?MvTlE%*8gYVPcz~Uq-F70Y&iKp6KdH%-wD7u!061dh;637DwYh|y4O zu%8n5;#*bEd*aQ~8_{Guj)`H)0}cz4P*sE}M5@kWXB(0;P$YD;)eIE(Pk5;42G`my zJgDkRrA;a-Vn<>ZL?~pngg=ww&lXK))p5`ywX$IY$3I?Dt%0iImQNP&%6|-hI$wfo+8h50VB;~tJ!x3mZvrGV2)M{q#pT%qCwXzpHJw^w9VaKb4_PPxsUB?vF=dHL zvf$NTBLa^+F&JHsxwhntj5-ts$Y3p1!2g-Z6Y!OAvf-d0OT(RIX_X=YDQTAyg?mCeu>2$Fv920 zpDU0ehoCa(qe#^9L9|a`vjuP$%gbtNcTie$?zP(z@40*f@bF;iLVpzmI-jHjPc%77 z5YLdTW&{xoSp^Y<>HS8WfD)@Jy|xgmIM?W*;OYYFIs-z}VWSv?y1-ni5flM_y9Z$+ zbU+A`mGqhW0hK2R)Fz|NaW3;+=AeD6@{m^1go6wSiIxfUJnD?6oQ|OJ8jUnY7mPf% z$h^_IAH9Bup7Wj&V8a6%j=&#;wmEYi=`H=DhNSwEIF5F#sG4ZSE72wWKaGO z0P|X~4@$u=s|Y8QgZ7YR)g#%9C9ig78l7+-d}onfNC{9EGKie~;=sld$W7}N1*8)X z)rHoj>lM$S`>p|nDiFaghH^Xft3YWE^BnU)?f{O`S>$-IyQk-2*`JbtcA-?ZMXKLr zAIz3;;is@gbFfVgBt7ch9(&;e3!VkX4bu$DIRJj+@AyvmZBs2D>-6XEDMiHta6&}B zAvq!eU@OLW?n|2@6&Gk(SU>p3tuameRgO+d5J|HwG#J9OQ2Bsq6Ht|mL3m9}PCf&C z#vc)P5Ub^kL=S_N=srtHaryM%bTvq)-fcIT!SR?_b#3n^r=~I~Qe~Mnv5Pu>xeT?& z8guzsHX^Va86zWgE^qBD;8cni+t`;U=8|A!DyA8EdY1_iuBHL!p5JmtIn( zwjBsjSaTsl?&5K$Jthn6jsUaD3g3o+3~8FYW`mQwnrF!XOfUolj>PBBm3kdoFC;?E zjfzVNPB0<(ebUtQ1@IrF#1Nnsk`B?!tKIJE)fj{U?l%lGVya;=;$HVdWmA;m7)tLO z>xXBTs-?<^+toERc+)gf(L6)^zJN>oq||kcF%f?)KuPJwR|w?*(5>%ujPG$u*95mg zPrw5n>Qt?aT0r(G&YFJb{b>Rj)w|K8BjAca1VYT`A_Oa$wR9^feyI@k?1%@wz5%rZ z=kgUjs5`)-F+%O*2qNHr*=-RnrZW21tsdTK&Xm6u62G8;|8EuK`-lI#^gHo&yh=E7>q>56yqT#*&19tzX^#A|> literal 0 HcmV?d00001 diff --git a/_images/92b7b21e6c8b368939a237b44b3fc9ceda3f8dfa0ced0b51ac6956d90ee93d8c.png b/_images/92b7b21e6c8b368939a237b44b3fc9ceda3f8dfa0ced0b51ac6956d90ee93d8c.png new file mode 100644 index 0000000000000000000000000000000000000000..b5dd9d190a7c67c171e3894fe95f1e5dc7d7080c GIT binary patch literal 28728 zcmb5WWmJ|?*Dgv(r-ITYASK<6poDZtcSuTiO9)6yH`3kRozmTnba(Fc_Ae^h{if?A7FV-_g2K>)d_m_4=9)r5S!GLn{GjBVe7NMKsd)X=d#VEOM!qVfi5N)m zvD`A*VU@Ufsk-`d-mF^WaCExHv3B{+y!l(T_Tgd4mqO)2qA>L9E`REu)U*VKQJW7> z;_bFJ-oKJx2#oY*Vniixy06^+`hoG+edM}O9{bY=L^40j4s_EgbW%S|VMLqCtcaJw z;CD;Eghe6$yQrTt2@WWP7@c*J;)T5yQ|G-&{Jeo4DQ+S zL%X`Wr*1qCQ!XTp#;IZ?(lr0)UsEvxG;V1#y!fyV=@B%%DOnS{@6TXsC;$H+tsg7H z#5p`8VWU771qlf0mow5}5R0j7L(Nx9x_LxjQHG6>R zF%)xUjP5RWH5=^ve@gg6WLv1D#%4QJ@a4T`T|{o0XCfZ!SIuh|d3pJZ;}sHbGLau; zwp*WDp6<+Zq?5iD*9jZ_%t%jHY63$NhdGp}c5Kg~kYye~3&L~Zc@bLqL0=(@hYz_)EA9zaV=L^1S$FxJ=C&D>kBXek-}I|DFaPc+Z}23itmEi)Rm z_OU0@wVtq>&_0gOq<_fFcW;!+K2)J0E}4(bv6Z(eR4Wh>po>wc@&lGc+Iy3vt!BJ3AIfG%lyarB=+2y^mNQ6 z(nQQ!%tUAQNY7i6GqwDKA0GNFA8Z(~zsBIa{qj`HHKX}w@n?m8S9fj`ZvI{?e!zfN z%JPXy!dPHC4N;1*$8AwGGrqjN%ue!Bw(L`Ydv5%kJsGR}jrG%c+H;xbqboc*VIR=x z*=lo~5&rAs`LV7QTq$_vcSSFB?FYvqiYo>3I8CFxVJ4>wGP>h@Qdl{l0IL1 zJ&T=h4^4&9JZ*=cdqt>6{H%@QWd6WRI3!TuNFhH#`>noS>$l^(C@{K^Ola9B=HPhTG$ue7#yJf`xyjaM4Wnv1@gs(Pyj2i55tu#sn{ z#2s4qE^5r(FS8-nq(nYMDaqH|#pXAcTcl!9d!?shRfzy($CinE{vl;nac7L>hl?Cg zJ$_;G+|ooRt=-}=y^=QMY(CLfLk_?C~NkLTwTWYepQor7+^cTmO(c(|6rn1yO}i zxCA`PTWlN!OawzY+B+Zu|ER_O128U2~E@Uy^++{`VPP1xIUvV?N?A zVpJ0zp+l}3Dth9U2CQ#A5}Ktok6(jd;#iGxs|vJ&Fab?t*d1ElO9(8@lI-acPBnNo&~Ibm7)YGI;~YMPNW zv{_fI=3yt|W&32fFuoHR=@w!NPclK)R5;pp9!{896Q z0o7FC<002{HucHh=k^gsZ+)LO>hpB%ksoGMSi*rk(#Ux7Mszaq2)E122abzGOJ8IR zK2gGzeWfMjX!Uw~^yBxj&$7glCWvL=lDBs`YsW=&V!A?LI2{p@`vGGRA!^inTqFx>uD_^VQ03THAOW)Hp;mRYk+*iVg zU$$@vZqAk9HP#j9T;HO}8Y3vn?Ea|q<&i7{t5}N5N1si6HW2B}$UhV#$A)wCL?lRq z?M6i7?I23?RWYarH|#e>^HW&gRUj!m2_-HiC?OhGdTRl;d5T}09w_?n&fNg+NCz0I z6s%5lR7Kt}qnI}x4S3cklIM?CPC^H*ZS9|?RKXoc`%{WKQhY7r7Kj--&8IMaqddFZ zH4_uh3izGgzg@kSqBKA!%eFO7lwEk zAPGMQRP}wT=+s{6(KVF z7%DmZo|;kfi7)1ZUVgcIl6K*5NxPM^^=u>W#vww7dSYN>4jxYH15zzlC*Monh%i5# zP#ip$-g+)DGd*}%7j>fOA(NTK==(cyJz9}ApYMmco)e8PeIPxcZ|6>{5tjSXYuDEQ@QGOF0_l<}=L^)5 z#@*VMzL%I*i5R$tFL2p#4-q%8a)V#bmKqn`dHR)*p^Jw$^xQS_Qa&S+6;M`=_SL!# zm$Q?ltYD4TYg$mRD#7a9G%+%n#_*htdoXIdl4o+GiEs@kmHO9&mtpon~$cCg+ENvT2|!Ze#*%E#?T3X3Y)vg}hJjH9Q|?BLDW zX3g7J4&64NOf6Y+!zJ_S{dt8*lm$_{ye<<3E@y@$`re_a@$Qt7{IaEmsqf3bpDvJ3 zJ9BT|HfNHL?M^1i&nN>^rKL&guXU2MdeD4OlCeWUcZW95^)1JJ5MB#oY-9X*s05rm z18b|_H!mWGImfr5MLy%m3kz_rFR`{|kISHV;9W8&%$Pu$!WB`VpuYgbn3q1Q`e(kY z+M8%()BL@^^hgqp=LG&@g}F0$ic>Gd?GVj=QgAPt!XoF zuo#5wXx=EhAN+Aw=~E_nQ?}9bHU)o2Yk}LvMQ-PuG_6!wmAB5c z8nRiDuJv^tS7mp^7q{lxaWc{#){}$safu*4Q`fTSQ9|*`bRgQ()T2O5sgUE0Lp(!{k_VwvNl`tDVCP{rPzi51@te z%cGBFJuup4Bg$!W^D}&;R8OHOESXo1Z?U`hP_7(5OCjpvoc%<5NR1et#>%%4NbTuf z%s^i7eO!7{@1*t~9`djrA9RXj2dV*e!+xv663l{Rjg)S@k!sdkUmhH=R>(`ni$u7n zyZP3HRRJP1^^fw{GCA7`GfY2a4XzWUK8LD=HE{8o?7lfrX~7nXC@uVHy3ask_`=%g z-O5XPvGkewo8I+4T2+3y&nRuV2`+q6j~tMoG6@4m3ID{GAg|q&Ji<^^)~v>_a&FO3 z3=?R16kO>`=pdgI`rPws+WP2>yGCa=3?400M6{6T@4S@g&E@NxN3HY6H_Y2Hg<9rg z1B=3lg$mgI6T7#-AsGNO<;7y9VNM*V<=v}ZoX*g`RgK&Bw4}{>tpYTbnq<>WBXhyz zU`%z3ZALC0ZbjNBN-*KrDGB~j8!E-!&mVJ2QOV5EO<(j@0m_&ah1SCAU_%%fCv)*b zeX&0YLzwMkcWl4acb;Kgma@A|v$h|`XVUy%i7}!Q5M6O_PX($fqoDhR5tAaKBXvCe z$F3CdunB;n+l-n9T6%8fNtU$|;HxQ3TZ)q@_I~574Xx^6b5HRILi#DsD`V6<-BHjl z_X|N=dNpk1x1Jj$m$A^4&yUo~Wt}^lp~>vJctBntaN(ZFjd9u9v24Gzw)>?MaP`^K z)!jqsb4sKk*o03yFI-5<28lW<}(8GHmN?hDQrR5%3l(MWfJ>{Jh=7w`3wk&5w-{V>-O zHwwwq4S{0>0o!C8t%nie8VcgRlp+%~42iSc-o|{R8tdjl zgP4LXoyE2NxroX6apdpbyoT1qB2y^j{;|Pnhk7s2b?S-*c5V9 z4}`bJUl_wNJIuz_G%>VNW<@Egs<%jIe!u#zeSw6M?j+8Sx|_R;Jw}`&^DYM~ zMglBYEKrZ1sZa5i)JOMN*FiY>0Z&=VFN4t93S0x$q*}^*VZ>n8L~QhwK?WV<)zSK7 zm55~TX+aoSdG$vH_ZxqtsPYF>|HE&v{JU6YEqo0k238k5#h)eo z`5}UvbJ3&vdp^9ew%XqI6}EW6Swg`5InU+N%)9Siw@)Ko#U>;SGovS73OPX0cGi87 zPwraCmgA?bQDZ=5sy5lv1^RieY@Q%=Du^Vnt3f93AT>7@|C|2DdWKWEcBh*0pA9_* zBKYEE&9;xyRoeI9w0$(xMjFES8tW6Yu5V*5ntmZKXt1A_%X<0&lk9#m+VqN zNhm6*qjZL!U{qZqtk&OvaCDA5Z3)fvq-f%rbUBYBJc;{Bp39Z2)*UUgSwb!T$#JrK zNM0-)Q&7JHW`}57R)mr6shwhvg2r$!f}LtcmkG~Aqm*8+MK9J$PF}b7?d(Mp@Q9h> zAk!}u8my)F%VVzpl>HDHq3!8nzqhrHydVOzI3%Xk@epLT7o4db)_@3dgPbrq?5zRP z6*)RkD$)C0%C`M39lGXSMCyFw?VF5+(=iQ_?T75aElFsQSaqD#b}}%a^EA{JiKQ{w zosQ4;e(A2tD1iun*3J~e*mh*Kz{$w^y#39$!QZQ|=8afa*FTKztX|Ee4|Z<%gIa-u zrgXNAglb|?7Eh~>rl73Zzg6S*wP;Gx?fL+eSkY@R6S>fjyZ)qy) z`5#KVYwh08sK%Ah@Ab>$=-~h@g`K7>$?Q@Rrdv_2%Ae`6MLWF14eb|le6)04d`9Cj z#u%)o^vnANi5dc_1KT%Bj;X_0UAnX@+j$eOmO8FW;cy>KkN*A;m4Qa#CHI%2&!tAD zlPQAw!+NWJAX$*r)^q?B@yeA^51r*k7Lgr027fLxUXM7BAnmh)=m-D9@5>BbRcBQ1 z-g__j?62)nx5E2*M9EsyJ24%SQdP>X7ukI^3H}{xzOy+Zr|!MJ$!YfyzyU<`H;Tr8_88(AKxTdK=*<5Z?85~Klf$oRq=!x1)pw%?HC#iDVg4kQNNr$sZ6 zv6pr@dSOuMcl-fiBH(6i7t|dJWRsLYxYqq4EE*JE<- zD_Fi1qPG6R~fXWf=1*Sf9wmlP;O^Nkld>-Cv{MmwqaKP9*LLA0imXnc{(@p6eKDl zYEXc!d~syU@tSyY=5^C3`OVszq*Cozj3C{hRzgH(mT!P#n{avw4YD&-p}d&ONDOCjC$unmreWmRu~Z~P(hbEJ>OU{V^0iRP1MVqN3t?f5@p^q}qOUxS&>J zp_cvk+fqYJxWHW?rDSZG)rtVI+W~WqY}#n+a*Ux)WPv19Rf=R4v^a<2_Fl_E4fD7xs&?((yDUeMok?^qnL9ttzmUKDuVIF*-~Hs?az$p=iG^1Yinzv(l}QA83h1=0O+J49K@t)kdulqB62ra-vK@OKHheUz=F2x|_-uKOjXn6pe(E)? ziOa{u)NI%lSJCx#xJ$NGMwQtIB5_e6>#INV)sD|TwA9QpnnelZ7nNK{eDy?PEaD!x z>)f_sJcQwQakFq!hp)?|&iQPxvH!VMDd0lF$RS&{;O?nNyKnRF-;MQkfd5w91Ni(k za>Dqyf_k-C_rvYE=~TgMfOl`z|4k+5;)*XTD>JJmz`=3aUn{O#NjepqCfSVHUOQe5 zpZG+DuF0cYUIP#ysyZ?LierpZK_2AGifqbdEu0H??Ei9z-_xFEIOxJh}5cjpyI33S~zCQ8!S}TmU zcxD6DT1%or^`uAMB(Je3B5JPQfgi}>$Wo_XrU{`q^ zz_i`hxb4j57~H*9N` z=e^Hq(aj?S<*sB{uU-i|I&$pp?%v$qLR;@m(%`e1cn64ePbDZSD3~nNeDm+UaOFow zpVCqM_+RK;Sw-LR?ft}6=EM#z5zl-@0t4}nKOaZAf1@=Lm5j$0Uj)-fg3GG8>>utz zO?|i`{ab-NW}>Tqz0Jo@jcOdQ*W5cl&B*@YE{kOsi?vXvda_83mB(SPtFW}?v$i&I ze}Dgb^{T*f13KBo=~^otf%^ki0OR}g8?N_~co~Au6e=D|*L?XmZIHe<<6pmRV{fYn zjnOKetRUfX(R{8Ko`n9CExP?|9nJgZcvVxG41^ZMk zT!&x&O*yDM%RQpB+xX0%F8Je*!Ku%}gaT=uA*C46r84{i1|mVH*o&-@E9tJ}a(6ke(TG4&*1O6eeM)5Z zF!q}yBEeUtMAfP-Y!okvHeH1iJEzUdvBTY!hNn=S_aOOUA(Nz+&FX!g2cj zp))Tw^ZknMoGE3(3)#sJ@1i=+4vn2XI&FTUz2i&1Y+}nmBj<%5IA{{i`-qIks{&la zb=wh}y_LyaPsOj*4(L%6GK)N;g;yOtpfugwwZb_2n~w_RT6M88RDNi>d1&jaf=u>a z0(@54?7h)t{}faRT6qWQN(CiSqCKpW*I+G`ADSGt|M69d@p2-$CIiRnLHgDl*&4uY zm11!*JK)9140>yl7lgmczw>19yg;t#*qi9xtooIw0djj6M5p!B&RauyeT~T>+x=`C{Mp6yP+s|f zA3;df17kU5^gBnWWmA_oX2)GtM7bgzmU2e=Xtkb-51;S*l~sU^KMzniXS}&TSK^WN zatl14+opskKAQ8vgB3P%)F!it$7XTt0|hdV9y^qQaIb1h`-8psU8lQ7Pj8@lUfyIM zfgtf;L9b+J(!LpgryhqaNjn_}UnDY1aUj==``Z->da3T-{(zPb9`q}^53X`jqv`xN zY?bbA!m4BKn--}z+fcdVXV5odaqsvz*$ADeZe6iEi2I)N9rG+Aj!#zCxb_I?SJI_? zsLXyVWClIoUs8}*2n3B~VXm%q5g5#4O$>AdmjHY;8timi|LIdr$_l*dZk2)_2Lwa` zvG(@Lugu3!GA`kC2pTQ|K`KV15Mkn(6SzG5h=N>&^ttXBS{7eiYpUpTZY1c8UWfNn)B4- zrMabX=5b|7--R0FR82;p^5R&D9W3!r0YHe=6~+1ZeCB2xE`W_r7e}p+4#q#eamCJf z1C$^v8tp6;Ev#G3%D?rk?9+=MZ_i^sqs^Tyap z<>!QGpV?0!rMIB>L*K6fMVG2f+o(?C&ef+=(lJ~V-q>bW2tXd{Zu#7;pzpt>_U2$x z-d|Av>qw>CJZ)a%V#6LjE?})=>EFUj29xSV-8!1rt4qv=C;KR9tJ=j+At*p0TJ7}v zAQ72NwJCywB1(e2qQ_%ntx(3x(uJbonWu=#J|a`Ptl}YtzAc`}!K^g_2 zCDW#gfo8PK@i#LgR5#Z9G*ZZHC-&RLHix@->!prtWPYzD$P6~7lhsA?^P{IP)v>f0 z7kNTsrF~}1a7`TkeVWUWERL#$yRRGt7@Fv(7ianw1WlSm|8~qlV`vedLA~YcUhLRk z-H+Jo#k3MT=k6cMoQN&39NXuUCL^cMwzjd?R+u@@T&E>coe7`~07s%Nq&1{XaOc{L zH8N%0VgeaC@^0pSujFJqHcH{}&-2L&aZi2NCUyH&0kuOy3_iZ_a+ORH6;VD8Y`9Z7 zSrxi%b=%j*$-Y)5(5d9wlK!;j$s>{dB0J_fo$lgu|awnuZbehdneq|`Y zR`N(*c>%atDo_^j@S&(`cEozdwaI%ww3L#cJdfql3Ki zNfa56APZ7q0MMf6M@aSZ+PT3>(lZ*-3v`wvp*(WBRzL0)lCMj@QJ|_&F4mV$i4gOT zf7^mS89mS>`0ni9$cPy4sNo=hE2!tnf4W_;Xt7n7$TI*NfLgbY@KbR6C~Ui)G~%f zymHzAFWtr5EVg6<7+sWbPtRt|X8GC$JZLhjn?^8dCcG#$6129j)NafeUhsGqMb*mP zHz#%e3KpEwc>7R~i?`1(L^zND^3nhHmzoo?@N~PsKw z_L1!;3`irI*h~1Ad>$vmfZme>8OOH*9KZTQi|J_uF@`zsL@4JnUa(EX?^*jE>%teY^xgNhItm;g(5{JYf|&Dn`w^_d-P`GyR=+aP8&%Ylkp^ zdUY1(0>wwdl|sH;By{;^&kJ8;nhw$W6d>Fm?oJ)UqDnUj((@9C@pyRR2WbtJ8;avP-Bi?I*GCoVwfoe_BGm_Mk;jd z`{28CrP3$$Wm;8j>)}+!~_5hq00?H)M(dT5}XHtbv zC-d}$LqPOhI~KdRJht+o;0TI&n00-hccpKvF?@iX>`abHHGYU_=KM>4%xXWdb5b(8 z@4lu8W9Anj7Tn{mXn&If&fTEU{Cd?(wd6tK_WFFWp!30b^DPvSZQN;?z&L!RO9DdW9Zo)>t0kq z0=RHiguGhh@4)KZKjwsL4iStG-(K-?7gJacZDlBEtGh*8U8lm5R<-aRhaHPA2+`4( zgT4|Vm&wn`N(e@3{E{TE63U)(mio<(pNi#`%r=KeHq`EVO`&|; zwVaJL#TwYIzi=mDW-x%h*QZ=Nnab;N?JE3Y2ZY;>n8A{*s$X?T0mDYD8~d=yp9c|E zNwgAAKvY{y*`)h~sHb&SoS6}WLy)u(laJeJF#3{1>61)z3%fM-3Xg$jYf~7-xsmc?92&zE6L9IF4HF zXQZ%sZ5kKs@^y>MQy>;uC^tu-iM0fF=wT5OR4@T}Y+wYq4$5hoXn9OgR;FYW4~8xD z8wOopECL}BAM`tPd@^MQv0z0KCM)51im{skafQM^BnHydxahD4O^w}&x^M>q!CRE* zZC@2kwx4A{s5x8Mu`E_YFO`o3F@y@?iQmVMMxOjgg&Z|-AGoi9e_^FBmfQvqRccQw zBu+XEfDTY!y9x8imU8v^C;z|Wz3{96jbYJ_@G=~T=m;R)EOJ-xmxDPKOtcy_rG& z)Rb}Gk9@0@VuZ;1m*oaazs@z=yrjR5Qn#(NpQ@nDg z5M3_nUcoEhApcg1GLu1x{{(wbI_$pQ`xor%XcYQX`$=fLtn*i2Ez2xln9q+igc6`x zKOmfc1Acz^XRC7po({?Cj=v_9sG~(!)7rOF+v+n_dSI6yz-J{ub~3xFnrtrvuJRyF zE*^=YWpr|2kPLAOPa0X$Q*tv!nu?k^@x;bK_bJa2)pSM;p)PCsO|5b?x$9H=O?U>dNlde+;rA;sq28E%f4>DceJ}Y)QRS#Qs z(Qa`=Aqx5?4b@tkD5S7rc1WJQRO4?3$S#9g{Y__`^2nb<>bUou$!gFxGVlk1LkLv= zgp7y6N|Qqe@)Q0j#;mOT}h+Q?t>5KKwVOx3k?BS{gxBP-u?A`FE}F@_>Pz-&8SgbjLZ+L@hax`3?U0 zpeJ>(Oj!J$nba>Y8yvO_LjTb~?f!JEztr=3Yv3mSM0LN0zKFIl9TM>M4%@Mz=HXo{ zblt^-_uTRz?|ctPv?v4Ae{}86zCxNXOnuiK(0$w}=p~~St(OZp%H@FIkwAwIdc3$P z%AFw8CCt}SqVps$iX+P31Dp9^E>qmD%Q8A#N(6xZX$(04CmMNKN1wi|mi8?Fe!Cxj z+C5weRdwQI(0&tUk9elc84svU^3~6V8Wn~}0N9D`Qz!&B?S!-fO*^z%NUhS-TLls% zvm}0F7QBaLtF2FeV3(50J=JAt^g-`grWz$7T<{5(iVBa-mE4Wtkc@^CNu`<2HwmN0 z(|gh&!seBI7$(c;-62XaH3zQCw@&6BkE)c)1h^NjjVIkAV@;R~@Wu!!^QVAhyH}vq zcX2Qm)g6ozm6&K2#8)i}NrmZutnQ_En5I_6@IjmqE=` zqiDb(bu3XE>uDwieAbkbTL^?vd%G4u;(XkEP5X`4@jx}yufl2W)7(Sy1#9}aREzai zl|o%nvEebQC>F^#$xR)>wDie5xsjpB8PQq;-6Z(`EcK-^!sUF3)2e2*_G@oHucHO6!N#zU5&82c$cA*OAJL{u2F3YM8GN)mV86Bv7 zUA$in#!<6I_% zr@IqF@DB=OQ(Ipoc~E%x-p;9JSnA zLRiY)(6HiCQbptYQNMmcNSU&Nk8BJK3@_l|n2iRo1)d+yKaV`F2Q%)>l#*nMh6-nn zjTU^NYq{H_0~nRk(yX!_Xh`JR%B*EL>4^uE?fFqR9yC-ZD_4l&*do_E?J{h;d--)J zkS}RWZ}j3d<4#CSOS}>ee0EUGjYURAo-NaZ-RKRc0%&Y}Jnr^L+6dU6ZRZTt{{cZQ zMU~>W-@#mJaM%cJy4zWJTtJqn(zP79K3woqsoY zgoBN(L~UqmYkPAsA*)uV3vJbM`|kew_-~{bar@w4;e2i^pcEHeJ|xXqwJ@iJ=5oS} zrd|n2a$`7kr(t0>|c}Gz@+a=~^H*3>^>y4>{kmGpz zc9b$$B%q?`_1NB~ovp(-BcRVKz4OplJ{0aTWQhq6kjKs*H{e{~+xx7ir{~{0aMV~| z|HXN6Y%J3A@op?vmIR8&>F7`3OLA}tBESm(aQyeg)FK}NV3OC>)gj+M@;sS;j(+9S z?1N{wfcr%T)&zqO#am-i5YSRHrxse(TPGbj_o8&dvd<2FbY}gHNuM!`2ktD&ja^J9 zI1V>rq<>1H73@htO=~x*;@X8Ah_J!&a6po=xoffBZqQ5qd0*$5mgI#yIT;z5p|NpP zYHCZ9Bo3%V7^Yh1Y5OY-{l2$B$)h*Op(062D~6aGFnt0LwhxD=hsL z7bj+G%M$#CUjJZr;JkRzrkCj9Y>?6Y`SBWf-J_*OwMIvJa?#o4=zB4iFwC^q;hEEs zv$`?Li2^NevE39&@%VM}>&OVqBZGC-%Bd<^w#I%uKi%gJ7~y zCA_rtDY34OYdB3%))yYlWTnNUx}d`!1?OyBl5wn5M-U8Qkc)*G4n)&Hf$cI`E%W`r zAQ}EvyVR5MDHvd^WPtnJ3CshyIq-PvUQbP3MS?p58v)=oer^pJ_<9b)w}mobhtzWR zocELD%43@xY;^E$@L(=wq|T24ht2@J=k7u+BP;@Xme=zWDzUp=RPpWYZMpSYN1^3H ztCyGmeY4wjj??in=TunrX54lqs5*tR;od?cv#ecMZ7b{2AjV8!v64kb!3hOhVtVl2bwqL8_ZM5B(IJLK5v3A!|S7^o12?aaQ4LEuQ=)gpU zxXsEVC$Y4zUw*2OsGEZ%=CdJy))^IhtoJ-;u}H|)#8N;+PHqSQPQN?}U<3llpH%*&H}rc)`5`e#+ma>s@R!{)YS|8|oLw5x&GrFxtXiG#{sPy)y>u=IVdP-b8AcOZPE}O$LsUX zT@olOZ$nOoGJqfgl+Va1t9|T=;MZ*s}6;kihwPIdgkh zyW$MA+jOyxS;zC1(&O%8ZJ77C>Z3qX4X-h$OSa34283~d?K%iYZ;Et`fGbz;f7f&^ z38Qns7c2_<+>l9L+n+rJ2)N9g{0I$6NdzFkaB*?zvLt}9A@+np28*G2WI#?fW2cp- z@;WnS!uD~(GMW95f|{{t%=-&c{X;deHKB>jvnz2&0`{}z6fyZZFvc@L_K!?Xma?~J z1HHD5>^SYWq@-=HZY`$!527frDbhht2CyGpKMa&8Ba@)BJPCghByMS2JjQ@!Dz=h5 zsxMAx`0j_9fu83`W_P;&DMuQEGPkDLs-p#LqPby! z_qz%@q3gEg-x-G7)o#KOFL-X%zz|fm(tY3`4Xftvjc~!?=IdpRr&-|XT<#BRZ-ECh zXG^9xKblG=93i}f!IdOicHgblEY1oGY0^K^Y6u3*Q@^Qe-BS;FF5bx@BOWkIML+d4 z!7L!qGM+sR$(ew8U<)AxJmor{oOxGjcH5%vpj|32W!D<19$)#5S@X zB%U4fAXfxo1~?Lb!g73w6UIUu=|)|inm(g_#T2&fUIcyw3md6jxHFqgXPrKe?~=N8T5qcfu>Y! zA63aa7pl!%pC}x*<7~%79uax$?NY&>Jy;rml011hQM{CPpZvES^=jmV>lLpw;E+c% z#W1wpPe0k(*`1!BM@dQ-7ay2qm$j!mDRD{?ot@Tv6aWAklvUU_z^Q0pN6G@j6dAN> z>aHM5y#=WjXg})$p}f~=A@uU{qR}ssmS3G!KR=uXcBS63;1)K+5&sw6)lr;IRE$Fz zVi1UVqGXkpOFXfRI>R79K-3U)bE^#*Z0Kq3Nv{dH>ONTOR8LGIC57j*oHvR<0gOF4 zzzwc2SsanOB|!lI|_;y-XO}^?2c0g`@fozcT}gbC*`WkOUg!{b>ax7NjvT| zQNVruvX9lbb1IDDUrwpnXY$NpSEQ|T$lQ_fj(PwBxR_A}^Cq|^1PwYWs+vKV&fuis z%5=F3{+J!HO7#))*xkRlKYr2Vfk_BTclr_W-7GhM3ZS<($5f4d4Aj{o28?f(UI+QU z=Lds|9JvDl%Bb+Lg({^Ctvc%J>T3d!#MqoVwyppmh3$`R659;r(V!D>q|p!@o%~k) zroQ~*X}FHNUtS#W>OXyXa=bb}Hvj_Ny7q8)r(K*eVFN|5`rHj}y?VbsVEpl2>{_lf zYQ1ag4BEnKlp9%+fcQVMlHUXVe?SN}>nB_n9mgU)#07ajkN6>5^)G-*mNQ;e-b=k9 z9(IkDJrD-W)%kK@5t;$X7qvKIt(?ybO`LUo78UG(w3Mpj5uX;W)gfPM)ZZ-c$9bW| z8w)}T0ccTZ6GlyZj=eaNrMfY5Vo^tX-$d@S^X76ABHavRP=X?;8LxJH7&ZAl#RTq; zC(Tl@^ZC*Bm3N$2&DK>7nvx9U!2-yGMs!-7hv=4`dtdX7Jh|+@<4hAm>xw?h9sRT{ zuzd??SP)t>e}u%z`*lFN|8sK(^%KU)>03vYC;h+6%t!>QIMm~iy5!zE)+H+!&;*S9 zJvs5ZR?uf-uKNbNjb=rMl8#K~M+9KkX`<)-DuoS()j9E{_tjla`FN7h1-ulY*W-?4V<0w7if32~MowC?1#^sWt#ku?`L_S+OAqCS2eT+jA4nH8(Hd`@7u~v7BAl8jp3%Y?C@1jZfaqk<*FxUi@mJ)yDJ1opiYKw&@YfLRX8KS)#8JJq+*_$>4#D&AE$|yxu`!$ zf<3TI#;WF`_Nf0tSYRS(I@5zmp(w#5tHGQ2q_cAf0Ly~z-QVo%QYK~>g9T1o_5w&| ztUs+y?uEe6Z%+;I!jxu-1irg&0gwhTM}{UixW&&$0vW0XVzboj?5vb1|1v!<-0I36 zl+vsT(Dw}j!!6n*6Ed;HU|Z@^KpcwdFOZV{03J)WnM6OsW=nQsAI*D8&@3id1U4rl zunx`%c>Ybr+?RCGo8OH|Ux*f@^jI3=L|Y$^Yiah;yq#SM&AxW%nUXL4C*(KaB*ipI zFBtihW$3=h>jgyu%eP};4~I`3cE8hQ6R-^XCp|sL)x8oy>MG@g^coAhblFa4aqfX@ zA&@oZPZOB+7qY_ncgbb1z_I%izR5+yT0V_t70#Plq-Ib9=|EbG>sd1uz`p&aujL^3 zhe!w1F*X{|9U#4~y|3l4R^LMYE;RogHCuh;B@Z&&E_9>-s-O(nB@^CIp_Z?dj|!Zm zm*7waeJ+$Uq@Pe#Qu@zA%z#BICoiGH?XDi64Y`72=M;0tcS!Ds!JJ&AGrhC7%=pi9 zk>I)LG-Z5b9t4NV*|L>af_9KK{DR7lJKxbURoDW)OS_hmR?_&jh}Y=RKJhh5?@ zt-kJ&rAS#S$MzMGFja`v2_xfG#}I{@&S4NPO%Cac0PO<^qpq0#JIgShSpHG}oXadP z@NOl5-_8%b39{f9ULxcGZ2#?I{*Jr4V6GiDcXuo7Z?Mh(qGbm>z`t{k!G8MWox-4{@dsOCdu zSE2vBUq>utt^f3Wpg4%@`#>`p%K3c`<+YPl$-nEWkpa6mO#KmTlRlOZN13Q)>CtX6R=r~m79o%$l605f)0V8@^g9daFWrz_W z%2O)znRk=bbmf9Mt*xkgo(YyVk$(a108kgE9Ul;czKgzGUHW*)rhMHK z-m6ys%m=!WpPW98=jVt2b1t*Y03Rr*Va@(xGAmXuyOwcQTHihOJSH!3^NPe+%WI0fG?V`-K5+SnJ~z0iAM@?)}vvz{d^}M;PC~-|VMK%gW2^b0woE z^Ms8@#TZXgKfn^V$iV*qOAv_A2M*;1r0iq6mfw12KoYIt`dEB4^Y@{&is_Bw z=5##y-(a#W?@wp5KZ_gDPYz1)4Bx~fUX8KV7l&mAVB`}?eu=$9ooC(sFT(YYdWOSN zQc{Bb=8dv*(Lqa6lE$q;cX#)ncxGSYG>=%(Q2Zs^M(~#$8n^U!jzdJr!6^Qh<%e0@yPZzCgUlio2D!X#QXeo;h<_R)3(JXvuoUKC zQ;^R7xof|zJloq#kcl(nqF(IJgr9EoPJpR#P+r0aef;>se7Z=UJa#LZ{uLfEO7+dp z6izR`;hfC4QS-ITMskyhK1oPLNoK}fND>Ks@}J~YWvK?>FXk-_0s_klk?(u|)1R7I zA1v|Wam{z1UN+Tg+L_Nt9S8z$coGiSqr<>%v0O6j5n8$pPW6Q9M+hs((+FL|qeG(iso}0*3UhR-WKEn(@jU)9<@W4G%)loI2}KT4)C4ww z>`jt6VRe@0{P5%@$ZPe${BDkWOPtRuaxIM;mQ1g_f9`2g9^~@_^;-XJ3xPx#3|2UM zUh2tc4s@<`2Khe+3YA8MLxP?|pB1Sj_uY(X_UOU~9k*$`yRWh=D~G%R%CIQMfuwDn zYX3yP2L{mJe~>Tvv#k;QuALVY=!?hUP$!#hOqM9I630Mtd7#lFS!WGpJM802_u^WS zS30zhp4MpKuNw_Vd?3>K-?V1me|a^C#|AG!KU)~yJM)n-F^?J_=fNkw&L5pPVCu{Hc^J_h!ae}0zAzLZsyF?oDI@c* z7^$^*o_nvY1^lRSfFIUam)Jkg*Yz4aR|Tn$Jlgw3mfqJ=1CENA;EGtjyq-zp50I!N z{1aUus4?$!&$LA;+P1GG6o(uf`^+S3g-Rz?pN%L-A9aD7qbdj7!d zc(9@&3;I}SVEW>dlS4=6+=qWc`v@ZAWL73%#(bN`()5@bvPA*I$HTly>(I`o9|_bs z&;F&ph&B$8=M1jA)M+klUrw~}c}si;xhx*Z>#TpN+B&F2@@eM!BPvKng$nh|a_ec} z|5s^W9hKGkZ4Cx^^e9S*lpHKdI+V5$l#&h!5ebp*62uk>DG?-;Pyq>*5Q!HMB?SQq zC6(?{kiK)nx!-qx-}s$-zcKC~j-jIa-TT?kv({X5&b8izdzX#I*-<;7EwVuHb(;Tu z%&swRB>thP0=4iM#W?w}S>Bm#-qIz3Ws2?0&WGPgP}9rBtUu*Q2$r^W{(u|m-|3Qm zb+Q8KXHq3A<6P|6+-x@J)rqp3r;hIUyZ(4=C!PO*p^KTUJ2qYE-q>gPj$`;|ry$y^ z(*$O0rrqMdA>&wfqlUu0lCp%jiT(Lc!e#>s-Mn?)u74aCnqv;NP91Q|us-_Hx7SKC z=9ib9`2l4Q272&xrQY&vj;UZrYbN^JTwL@nUv1yKGE>e#*2&Tp{Fl+9QSy0wSjWCw zH9QT`I#hnUhI2P|?M6%MYoNm#efurf3&9-+hY8*2wdTocW{Q0+qSx!WriW z%stU+_Bkn%PlXEI@C1&sS5JJ9xA|LxqK9_F2~me|7@s%_OB7dMgg;>)SJ zoifj6t&`VZHvpXD8|CHe%^LncI%pZQv?V?{bNZDEarek8RzLTBF0>l;c~_d&?i;jt z<*Rs-Gw|DRet&hLhx?RI&i;GZlmzp17GR>Or3T;t-C z*wNG!etSF9kHJB=#zh2>hiB@z&4wcu`J-r0J6V;*7(~4wz^&Co*u{SiHKOH!QN;3Q zLPEkPdP=|eb2>VAkz_xa7w(o-<>2IGf$FWGgzxm(RffM;X7 zyR(Kw{_=xuY&S*1!oon{ZrQrEw5dsR?5Jiu$JRrkf0&z_=dT31+S`|ul=!0I;_TV8 zb}mJE2OTeE+VZ@fjM(HY6B&5_Mxaj@-Mz=5Uy_kq=#F>_A8U__8g!knY@%*cPJA`l zvWd))rQ_j)g}v*0{4n;oUiS%QwnmclwfJJ0cf!URr%cvgj238(7;cd$x6%8{m--_< zzkABgSC*r-w(pHaP;%zwihg`^gmW7eyVPot1hqNkVvPDRoUmUp(pRGhX(UyfZ{NOs zgIy#UThg*9#0~AMD^=m#kUea}=i-8cOTswS@A~=e6Bmz~pBXUW-;+MS6wa;f0N#{l z*RGPAf6%lRdb(zJsXG%MYf+ZGit;vUa9$?>T87c4y^ULiA-83$H!`+kc){oMrA{ho=5Iw&-)Io1T3|F>F9Xy|)A2A`hI z&_(aY``g?1A38*F&37Z^^rs_TlO3*IQxHV8=dWtsfg6}Gafk8EI^+R+uNb|k-r}V&YmioI_`JGPq-d35bs$<#V9HGzb)FjRr8X5V=qx%o9d?oqF(DUkFfP3JmnAk3A>e7u= zlEvW8Q@@;seuEXDb6jyIJ$dp(jZMhn^I!e7G1X6w-?;1Ve+h5Ss;elbHb#PY@Cd>z zzPNE<-fKWtm{EuS>HdHM1DmBadM>`+!p=Es%qVzeWrZv;DXIRY?whl1jg5_YPD9kv z($aKhpR5~ts~SW=?V5HLxNCQ;-eL8=`=4DjeCVRla@X(w{PbjHP0b}}`%nx%=eyb> zJZN2J6{*beIU9dHG&#cE%#@id=tEYbiq5q)wD{s6KmP*^hic9+G8(!ocltDifPjD^ zpmt4-B4%z?ZwtCl&-YM0v3sny_KQxJZuj=q!jNy(_b)M#f`!wBrL3~AxR;%U==5zK3SdJ@aiwk>FH^C zWo5H<*gl(5*H!3w1UEo6P#5?7?b}DD)er3#eqKiTqN#412V!aT8mJ`FF#jLP?wj0> zPIa>E8qAvIr&R*bIp{ppaH~#w(LX^hNYH8U+`j$$2|WR=N2Zk_2ak%2PeUnyujOp6 zO^4gy(>w4)7-fGWgjR|hzR=VKyqDZOW=AU#FRF6oARwE4VkEo$CqpSh#3Rgx%k6Ms zA}vjCtB&A%Zdm9cvQV_DtjzP(H^YVfjl-(Dliv2#xu7jeQqgNktn_r2ZC9-9{oMwR zo)~5o=QRxp+^jCvpl^9`U4Xq})3qkR21(8C!7rWgIiJ+tyZ!+ZUeb36`nNmT*Z5R! zroJXI8LyQI6p{`GO$2$r3WBWSowmU(QG45Z5EX^TiV5jSh$MANZ5^5l6>Zd8!z(8= zz>o&$EtE5OOFHbLjJZuU?pM~UnYjEyD!-K58FZa8`^k%JHb*hJ%~DA zmc>VPLG6P6^N;>PTh-PQfdsJ!uxVs73=8kxz54{ERKfCW8#>~Qe$AsDFVVKEsL6(1 zNGOziBSog;K<$XM?fi7#&#|@$bfjH0G5Pw|-Xyy%We2>J=s|KDtzJ$XpleL=rQ{2+ zSSJ`=E<&I8XB6=(EZ8$Q|n)cnGr%s_&X;F&cJu_7f*gxOMwdrZfeTNIu~~FK|N* zgokN)_WTa!qs4?7g%}v;<>i(A?d1-!eF@ibJmg5ND$2&3ER3F0qo3{8P6i8<$TnD(g zQmWj^487+~5mGL96-kj13L{KRFZHthkjk{L7-@p)+=@ER|3>9kx4fg1*z!k*O!YC# zvXwS~0<5dDy#WBj&D*zMV~QS(JNm}w|KCV$?(N@u{tay^qJm5P?uZohcD(N=*&DzS=>MXetJmB7$35Uy?bG&A!7aNt8;1It&E zR~Kjd-qcC=K)`#Xc*uKcO3r1xz48Iu$-~Xhxa0`pPZ>HsVK|UiQ5lF^U1CT2?0I@; zZ^~o=D7#2}UIf5L6;=f#-~3wMnz<9WtVqXCWtLozkCusSv^I zHT%`v>-YCNf3k>&m6o1B1L9j8e)ryBIXfq(UWnmlICM@U&D&*UWPl#ZF`v-#@JSFg z@Xwe4TS(5fngBUJQ;s>Sth__%v1s27D(Sx@+Z|4Fpsn`6!Go`0AVgd7?fdt)9Y21Y zU_o;`JyO-!93iD1Qi?iN9>_xOXf7)!_Xd`teC|sS9Tn%jSlvHRz`Nd8N&a-5W!d&` z0=|t)K~)pQ*!aKpO8$B_>mx*b_J7*sv;$9XqNId}?}UvFZ)~jgvwqS@dOlZy=>GjT zU@w`e5vtS3?}&<`9rT&gZd$I@WhtM!ACWxJU*Xx)mT)|IN-^ijyJa3@%WgB5e?q&bf1)Y&hz!NNK3)t0U65>>cDrSrsxBB~C5W%nqI z#miAV6#C&?=1u%Adw=`uA3rXhRZtM~Uiz&x@qVpk_)1bOU%ruO{DKe5QM+sCfQ<&> zZ0I?b{Wyv(6PbF@NyOFp!CIT}`0d32SfN$4Y$>~^R@mBUX!K1OtW-4@lX%gQtGLiR zoM^=L#wm`LjxIPX?B;zujY61pWIP9;1`K-FpG66gw=t$^-M?{TBhUGqmtR&*WfuRn zPq;7iceJ>(AH{aY?(g4KhvvaocVp8Vf6Kb87=3i=SzFVP)9(4WZjZ%6eT^y;qrFd1 z{7#(nc{aQKIEIN)8EERJkjB>e$Tep2AnCmbqtd+3Y}sC+)r{A3cHA;XsYc!%|Iia$ zuH`ZQF@VV(@phiHx=1?Db@9s+j;(bs{r3w<#oacn#hHCoODllHc{BTkU{KA!g69yNA>kNJTj$aw@O$Y#l+*&hsp z10k(zcg)UJzim!8xRNF>G7M3IFGFH$@n}gJhPtgYQm4ia+&|r%IeMKM3|^30Lg|<_ zq?7EA zA*(#u*GTzW*lsBXe2sim()PGU{YV{C=WWq|f^}J(_vqCH;WuWV;czkl36ALT=!4dT4e8+iqz3=4^ktwmU=z?QDby4;w)5QWq@1HI#O zl0w8|n_O`aAF`)IOM8sQ*wHY?Mm|nRQfmYq3yi;>N7}1>vo@l@!>FFVT)vFvi3h~z zIrLU+m6b&xBaEm3HV4JT7!v9$P+C=BXe#09s;{r_t24m+=PQ^j)toCtEeh)U_e`CP zy{oI7DlMQsCok{EmX^7cj5Es0hu~<2(MEe-s+|iYOMR)43en(9$m)yZ^7<@E3b1)} zetplH2us^RHa1^8->U{ugx1|WdZ#X5=Da~k^$c(1u;fd*$~6Y4XUX8S~aEIEaQXr6fG#ijmpAs1u&x5w&>x{84H zH(?Z+YC~cN4<2+HZ4D+q7DXS5xJYqlE*l#g%zu9S&06agt>6((3^SZ_F-zdE({`dE z)$)(TYy(ZkMz&$&#y_@iH$^&iLUJq3f%hFg$VZayf{~Gt#3YFR zeu`q`(&{*RCME?dt5K1*)?8awR6mCyPn6Wx7kp7ZbLNj7J9a=`((qUT5y8cBm6MaR zq_T48Vdu!NioTX3$$4>1p9+2!tOf<5fsiX3ANW2Z{vaM3)~$I-TUaaZretvWs6I(uYgWea9%B|t0Ltt`2By7g@T7|3$o zuyx4*LlF2#OM-Gidp}$fd}Ug_8{4lOC6(p0wf0|fmuwnnHl7$9iBq95YLouS0eN9| zxFKm<+|^kIR`1_zu+JrBX70rPvmq_J>~-(m#mvGo|GOtB5>y8u*!#M=Ak=QJ5cRmm zy0FJEd1Mma!Uk|Ul9G}bblq1k$I9cQ{D1L4J8%dsE$w8hNlLQy8sJGRf{A|2TMYO{-1`hpNe_T;rDIV za3a{UwJGgibn%ABa5FEW6K%Lg=3d9W|MZ|b1Mb%^y5ccAY|(P8u1;>}?3_Knc|V=( z)39!-hb?F<2@16gI1f!1t?do|1zOI z=q53tp2!@QeE)ba{~tr++i!y5m?_>!g#b-Yqz*`xU|wMEQo@kSc9aw_^qwE%5M`5) zcpNEYPR+ui1Qk3I@^f->vSqni6QcfGpfqrc@z~r1$${Mu5uXl6^v^x~*I-kADC08O zeH(uGfNMuHxKC4%Oo%}e{Ml7$Y9TE`t^UdC&(+SI zD@H|FhS4AOLr!=sxiOs-fk;L4#Ox;u1FZ9_iyyXnuP(dp<}-ZhBAnWm@5)DWNUwerxHSha+&D-1S72bSa!=%r@;q)XK;7R4!GGMf?;_&wT%5+dcdj$WRY=2OQ}v?&>lcvq-`G z@$Yy;1NfUKSo@#j9go3f*n==jW%-l>6L|^owopcKNya{SaLmHO;#Rp{j#VXO=QIWt zk(S|z<2NX#Py&Q?@o{lo#OFBw@SM}Tx*^k~e24e%@00|oOc(MM?1l~e;D>cGuc3OI z!4^(tkrO!-dXM;q0?NH^*R;~~l3B{lKrHTLWv#=W*2%V{gEO-qDXk_-L;?4TZGzM# zWM0cmRA}#cASgb6Jp+)}Y|aYjQ_BrsO9TjE(>g(yR=5f1r?|$ZTpvmiJy&O)y z118G9VEOPYj7MS>H~)Qn9L*%G5H!q3n$wW;QlphwWR`!@5S;*Uu$Q3|0N#VWSAVJ& zEln$ACw$!mhlrn_AD5(-hK9y~k81cCMaAAgiSgnK{F6h^<9>{fr@I^=8fI}OFe=H@ zf&hm08m`L?+WJlFlr(h!qsYY%essSV7GNvV#95QVX+I?^OCIL43*A(8&K}*icmr4` z@DM+GdpDi)4wp4Ba9RO04^KbWuvVwP2&>3JsXq-csNpsWGW6!noA^5yD@?~?k9E6_ zH#^fCs?s@Gp=9myy>v4%*9z?TeD@71vKiJEqHYCmPb>=;dls+QK6NEAF)<=-AzDlW zQ+}D>N6}4;4+3X}GkZtMbCw@Yb%H8>et*w3gpOUbv}K463ZS3}DC z7WDKOiPVZ(0ljT2%M(5{OiV-^;lEH!*3j69NnQ*|NlBxvuWk@Lh*`~^?1bduV&9FD z3xf%gs70|?QkCfi$go!XFwSX7!nH0|O4#ztUC0|xYv}|q5HLFp6i`?gEile8I5a@o zajdG`B{|yJb(2`-`ES2F+TP`Iiy-z%sV%v&ICp2oxoH zm%5HPjJJ!GmX;oM9gpT3)7JFx5C_Gv4;YrePLcb~=dH#JNKeKCIW`?tVAQ?@vaLJ= zY%HyRR2j-%T_?X~8(d;ru%=RTlbxOuufT1l+K|4f`lCQx?LpWfuaUDIFPKp#K-N2_ zp%Xx$^AkR+)knI#P9m(uh&x{dW~7^S@2%K%I%uy_1g`<1HHkU)E6(Q-nB9H)65KG* zos3JaZ|>tZ%)q;tqc)Cm3xiI!+4X;hKn@fkzrWx&KXVo4^u zO9+3LrHl*P@D5B4$USlLWH^h23kP4}&yWTPEeE-|74SVf^AIz_&~^lbg5k(|1T#QTI<+)4fqDk^Op=_C%n z__DGxjzW)LYJgYiASyUf!fE0=Gzb!t3sU7-MMeMV**05lz-MT!eu8L|?E`<7{<#?v zW;pm0fZxmp&v#gjaLJbZB3#t^8wWPT4K+Qz6lQ5J9kg6VtSun!F;e8p!-i){Ons;bwLunz zqgGHO7LeDl;02f*F5;tHMq8ia^2}lmr-snG=zw>5eu4Q9<2uMCJF|5vOrznXVCnmCsTS zE9yLk7-cyeU;-y1Byx4xLhg8BM3Rn;jYS5k2V_S$B_p!3MSc8c=)NWB4n!pPg(8i{ z#&Xny1lB+{KQcN(FeGR)gno71FylE8mgLgpdpd_O7FFaZ99as`2sk$6xGys#JVNCJ z8bWW90vMIlnV#M8TJ@-5PreqADAWLgrnhSXT z+L^2ZdS&__l z8T)~qci1Pb-Xf70fH=2t!v?Nih9fr2MCL{gHNlrOfgakyPj3Pc(Sv10=kHENMq)Id z$8rEk4YM-0P*G(}>fj%9)B53M_T}PTlT~fY#!d`@em8|oO{^{yVYij}0W=T4Gj2Qa zY4x$eYrE!(*VT#`1yC< zC^?3Q`VB3T+p4aj0u9h}x2-o*P;BV87eyQGwY*mOGiQ<~tCz8SZ}amv_6rm`FKpDu9gvTcrP!s)3DpXK{9 zt4pB(-P5o5m_)6*2?igFmWYa(tN^{QIPgDiU!~u(N5|VKNf8JB)|@L3a9ZNCXVZXL z@Q1bv5J!T@vcRp*?_0L1GF2uYWU~w=8n^%t@I_Z6)8+8d-ie5yCn7)81dJfudh6pS zV#6TO;2;ZP4r%H8_lOoe(5C_C-dw7rM$D`D^oa;dFsde;{h)U27#Vsp`$57IW|sK) zeFT1lT^0YOug-HMEw`ew^81eu_Y3AaJ)y;sUxUlyAr?O*IM_EoUt)ZG{O!AUu+voF zsLg|b5kt2*84f>as3;Uo20}qNm$;ZDupHV{GnZbJV=3z`vvGtIIJRO&q{*-0roIpH zXd^o||5q=GNyzL^(#iriR<77W*x~1(DrzR&tcgvl3<9`fl<+-(B)DhD$;mCUzge>S z(!`4*f#22Z2f$6U8zy!yu|u{qiSA@zs6-E7E$+=WIRJo62$>kdnk-}qcqC4wHUXm6 zzBa9H&2wZ&)`&;0#WknxxiqIxSx{s1;|vZqr!nD}6a8hjwiD9>;70A>Wr6;;hiC}L z(I#{IagW{NTNWqClY@eSAqWMpE$a6nrmYDl_@Nsl6=cv}Y{CJQCK+}j^-cb)GO>VA zLI}3g4n$6TqZ%}EI%QLGr@_y@coRX0JcJx+5+wCe zW@U;BdD!cHQ8GdoN{35RKRy7rLR8=OOk(=>SFe^DH5d)@^C%b5-}0?RhWnN5`q6 z*OUGH{QPKms849bP=}#1V)uCLG?dV0v|D!78(h7Hq4WA^;yT*(l~vkoIz=v8Rd%0b z-DXE_v+a~1D(1U1zSlo`C8Jkk`Hgs2N*v<@4*pBjG#l%A*nm zR9@j*An&lXwavwT7fioLD(XYZ&CQ)C5*}`x`h-g6!9-%1rgd=ewt4oz!!a~%(6rx> zsGOFTR$x?MF#YL+2M;o;t9iU(EN_Z9q0kef)Q~Q1ai|Fer%UeVr$ez(eDR+?!KZL= zr-KciV7~s}P3}KO{dWTXKb?*L8Z=Wy)dPdB^9l1Q|%-eGistya@zvXS_>mTJQrem=s3%D@ue*LYW3p*Ys65^ohl}r)qbFw89o~m7? z$2v`G+b+P=)D*q#CA1ghYo|BkXZpK<6P5sm7}q{gXR?`;ilCy(yv!a8eV2D$(|*+5E2NeS!M{nmR^Wv_2d6zgHx9U;i@BU+D6E0EVk{vgE=| zej{JRsmw$p-HPpdCXQTUMd9Oz%>&Z>LB>?h{kkgWn3eYl$;c$!+}v7^$$fJ;mj%l2 z3mwRy6VGkf@^KhjSy{c()lI2BrKZX};?yqun4!K=G{G%+`Uzu;5?9G?rgqh0jzjAX z&+btG-D9XyJ4?;nTd&@>Q(;9Srb zb%Z5#CFHy=2Wd&xrW0^78T{T?J3o5VTa8+pjCtX~hc%Bqq39G;KNS*#yh5@Nn01 zr`7PwO1+-hotvuVD2ckcxS&9u$|uFft!8e=gW=$k;+sCE8{+s}TRSzxyfd@53b*nS zJW5lh!)TdV`~Av*;!1BlDJkh~66RB|@ra7mdR`SeYr8xcDz!B`m4HIu=ebzEvntkX z{9$ApO@qnybsx2$pkQcfD#1T1+XgvfMBovD3BPVp+V-uh8H06RpNz|DUu(IXahhwa z71`Wi`7VFPM}hVd3gv%UJfKx&bp8Y~S1m`g5JG`%Ib^$y)o@SoS0CFGts3u`{RN6O~_F&{|zmMt9v2GXsVh8xYO z@cAzsDm{1NBdxtW?Bj$HLSt{8-RJtd8NpAo6fx?5{}}m(`TzS52IMFpxDxmNPZ;tC z)>O*?5XDOTJB|O381fg4T01bgGMNw7lkJV#0v}jdS+(*u9XucWkx+ezaWZ>kP}qjtTDmSRF}jGw1j)$ZPPm>o{5^t8GiU$9SR$GiWh0d})gQbf3rq zszph7-)L*17}=y-({Hjjcb={kHeYgLHH(HqNC_P^R^T^et$n^CiNC~YsW^&@v(TAp zs*DXzDOu0nO-%|1tIf_1PYlQW5=X9%j9_Gh6Fyk<@)rMF=nfZ0mUB*hEI1OlByOY; z2GThD?M!69MER&PY4Y_(O^BvyM`Qf6o`4&Bng4Sm*s%}@r}+RHgmhOpuYLhu(Nsx> zj~lI5Sz;;W88qrmgPgm>F1V823L3G};{+~CJjmz5!h4Ws4OVYAJu7E&6_vPC!KL$+XJirM^_+&z49Y5IDeE@7;d?)ECM$0DcaUwq@avJZiOwseM^`p&{ z=rxpi9$S4qUr%;F9@oIdK-)iv@+I9EOdmYT$dT8P!nX6Y;w5fAy^DOOc$OeViiM7J z6_qtLt3jfdPc~EDoox*2`eVy61mZuBbw})BPo`vQgLvLtIneE%+=V4xF; z7k}%wbss#Z4wJ%slJ0x` zJKcD5EFZbEk0%Y%GqPkTXmi9fvftR)*%^)5rqy0-7Q*f31(2@nbj}$YVkqQCQc&Ok zaA0Y(uykM>1rL|@_4HS(38qX!(nVa{N35L2SzkP#JYIg49uFSoWQNKzv9v@9F+BXy zle85c8EN$94cF!Gp`lm3QA}z_$^uib54*-S3?iBV3jDE-^?o9`BrZ%a=MH?Ni6UH7 z%F&80f%F;?k&&4V4T7UVuX|QY-*mFXm@b8A2n;PLTjvd@bI((Zm8+-Q|Z!9&0^>7 ze1pqm!$z7{nd{EX{-C(H_?O~h`QzoBcHq{*Rv@fNVITvR8S`fp<6~onV5=sI+=pcJ zeEn}urS(|HV7om-#D4B@S}kX)jN`mHCffM(Hj)>mV0UBj^ZK8Rm-;+vKL2jNf3a0c zNBZy~0l~)it_huZ9>ehy*Y3j(Q@LN1RJ}#s1)R{jg4$ZXEfG^S6&2&32@|Xor25L!G~0 z6{Mo#c#%)w%s7ujE4r>)uf@heX)fYl4Gdcdv0ZU?Tw`-)a5Z=Fx+G4z6EV58;?!>DJT_7j zMJkIv6Ijbap{f>|1R>y(7ksScQUh|IqeVJr@NjYU+UtRHgg$I_j27nekD~)!eWf>` zsY6A@2rUXjPR$cif@wg%Ju+P^Z=pT$Br>)Q3I6g5&I)eEeea-ag01j*4fAhX*<&5& zRcrK){Lh-JQ~s+%4rUjSZjqip*Q~_=)<-m`02)S%4BKu|{Kxl!#(piK5relsuV}1) zog(=2|NMdBU2_zR|Jyw0+9NS9v7P-9F0R(Mi)e~7%u1ojzl0D>6%N7W^X)D2_JW-q zaBt612J(q(_apOzgS+oeL|uLZSJyap4cD5M-?y3j)zdPJY;Nu8|G2=d_B3Vlkh-zW zQUni)g>xPMK1~!% zKm%00&q-Bqaibp2exGTw9JLv4=0oU9 z6N|Y+CKl)U!`ZGkA|0-hIWh=oS2PtDAlBgjj#$s7xDo&^c&3EV`bW;kx*|yl7v@4Aar+kH(*QnS|)GZ`o9>t z(0>^D$ti7St6}xT&*Gc2L#rdze#Nu2B2y@|uH&cs%-t$>_orP8W)3N729>+Z#zaJ2 zLJc*G`>tR2YAEQ2oWj-7SUA6!AFh)6pNu?ueJjW-=)q2x-CxY4(3OVfzXlH~ zHF!{3I5&hSj{^pSm9=WgRN1+dNbHsT&C%0mxFIO&lLe{scK?^<9rs0hQD6xPe>wE4 zW9}^WT5W`3cnLoMc@X7@w;pT%0m{<#t%^b!_-oT6pZbP|gC8-9M&FaT0g0Es&>YXU zYKB7n&cWQmYWA2V=RRRB##e;{b0Z{E)XCt!GdMOi8*amQr#xyw*t-b2nxGXEsy16S z^Y0+#04f#DO_0~<`r*AxK3nZBmL(~(^78tL64-kZKFnQ=r0({C09Pqa_lj}Q40jGa zzLaX*{4kW4f8dJ%th&dhc-Y`Zr~q-g43=#arZ~X?HiOX{A=~#egek`My=YbJSUB}T zQMx83q|RG!?+WcN&S!kWx9LLoARJLyq@cOxV`{NWU)lUlgJ2(UH{B~3Wm;1QUov(T z_0aw#CPcxJIk?=7hzK8+$EB@yUsb@yN+Evh%>3SYpppY@pFv+Ys$SXX$tJKDYl{k0 zPm}U$nz~k>pPw7M{|qE@-~29e^Z)^YC@8i3IFR7uB_5j-9nGVER8-AWEtPr1t{pTZ z;JJUsl*%{aAc&lD5T~?VeT2nre;$AQX(Pe9O0nw1O;3-3Rrp+|_VMMT`a4(ktAzl? z;F}komqvU>9$?0@*Xt896_J|I2L z2EbyjUfTX3^))5W-l5@hTmIQH38P$WKRgxr!B<*}asC8?17BDdYe%yhOM@qYZz%ty ze;7+VAtZCVa3^#pnjZ1VccQRqn>fI?;5P(xyltyK z_3fq;t);lNf!qouRi+KEwK9CusnJA{a|@s7b(J1m7|)cV(Wgg5HZgnrdXua3h1tnB z9vd?7c;HF{yFWaDWGwkPBR68@ zJ2^pgb3`~>c6D~Yl9_-+57BN;0Hrl+&oy@!N(;y*l?$MeVUbM3x0J2+&8C`;>1Hn6 zVRUT(0n6OcnBBy^%=_9rrNj?*e{0s*k?|DGfslVQ;M5TM>$AGGP})z3A9R&5^4)fa z;%8HK+IrW&!N~H@nd-RF2F_)`(Bdd2b+KA*heHNYm%OnL~Juv_O1G=s%a`j9JWxq_6Oy1V0&2r z+u*QtXnt$G?=H#$25 z79GtPDV03w9fwKWJRN?HQMNDZdvC;OaIhv}9Jmx`cl!&L6myP=4n(HEpCAw%b~y>Q zA`2^WRx{1mKs~{VC|{4Z-6ofnmm0^;-l$TcG$uoLCbs1txAa9iy%8v5}!O^Y6dC_^+jLfiwh1 zhP5*;?78Z3C`5j2^zVIlw<*3A*zQctjbu;r$G*XO|8W3br{zULRjv>ccH%CiS+i^4 zXghzLC_b2q2_dZ@woAp0M@7I8b(TxmvYZUL7+kLPdvwAtE!S;?IKDn4hhHP2b*E1NzTO+w~ z1sRf(^ptlEUp}Z@mCxKaA$@MF@6}p@ESA(*#oLCG5 ztkMpQ$i*e6_UMN_d)`QKz23IZ7Ng87UQ;1!^65ZhhRn+XPo;YabI@CL=#HO><;=U%w3g|EihVZw)XrQz za^3wP4oB+dGpbv_!+z_v{X^lq7lEipuWs`F_ib7b4lUo40q(J*VN^79KDCoPpsWztVivHe=cS{xXW3`X zJQ>$XW~4IN$!8gr)~6g zrSDrpoK}54nuwQS>a*;lW?xI3#1YRpI*oN`d+!UD+=f~m&BWPkKi0>eHQxlDRBOQI z2MeTDYOrO8>+$bz$e6A7afp@ve72F!N!yH+cI8ZDBG2kcDt9F|wXsmOT4sOQs1}=J z$9-$;`DeG*HX8QVfJdDV+frI_f{2ZYDL{~>?=#Z+r_1Eg+@)y8~3q67VGoFq}16>7N)EB3F zCGd3f*hk;EJs(R@y;^bloUoc)xxxuJ3!W6>pAB+CCVCGgqs6bp9~T)b(TOxbfO_1v zda%?1-+A26+|{@DaCNE$gYs4h{HG4>dK_7Ja4-i8M=uO87v`dL)NT&w^A=MTy3YU~ zKn?)D>!#;8D||SDLP|pD4>4T-fj8%DY|Ph`slJSg0RNY0l|MGv5;E7yy7W1i|0<+5@{|*!{?}XDj!N77r>k z>x#)L7&EOOL_hL$d8+i_5DD#eoPSdSVd;F2!S=`T`2>B`M-Xe85%m1pu+8Ck>;l%wLe9HHMclEec;h$| zga`*f>uu&o^AYvoKc{BQPfxZ=2md_a8MWgM2-FBvMk}ksir+(BWyj1+Az(*Y^!f0s zQ3;`Tb)=ez2Hw*-YYAVq*#rZUs9^Lz>ZLjw#qglk*4&Xyu zcze~o7vA#LdNZG!ST$$qtMIC{P{fW~M08I2;Kb1`U=9bYH&x?Fia8|1hOsmZ5sXo#~*7*Sw$a9%j4s&Yuk`@Lj zlQxFCIPZrz1$@d@X%tj-A1y2btz3y);o*xa2ibena|~5Lu<9(>+GaMtOw0){_&TI&DkG5?`_g z1*?T^P4o7+_ROvR8pol9RlI!2KCtG;lsR8Pmb}q_gpvR^>bP5^38xwjie|rO7S~oI z8>&waVPZ`Wly9IShx{yQM|y_at)L8&|--u!=Lzk#j-DAUXv?cwvBzV&8mqhJ9ohb7-8;9J3HkVm8ULl zbQ+f6s<^&3MmN+?Oh(oLB;T=g-P})U2lqY&C{(>6%(ed^k|v(H{n6g#>7ydu8r7J* z+bC?V3BJ`2qf<4581OZ%KblwVl@F>2WEZ5uuV&av>Hg^SAfd^AnFsR6qd~C`i;u!_ zBLW7dR3H5B_9{Q-HKI(%8B>*qx+HM;S3UJmN3bGNT}h-OQ#4n0wK(!6%REiDL1 z%k%qqFYjRJ0p|+&{qBIgxi06I(7I00Bk43&B3Xeqg11#4Y`4XW2yyEL$!CM1((`KV zG;IHl#mcubw`o4<%YQiFyfLu+b2tpU)oZWuw9IW!UoK6E2qaVbm!fl`)ayI{kz*NU zQht&!be{Tp;cXcR-u8Wkie@46Rtdz?s zCgSvbW%r~X%35#7*x)%AoX8G4 zS)uNLtn{roRz2ct51{fx`~-JvZ|T(7eIHUXRkz{V!Suy(kVm5-%3vNCx^X#)GF+>6 zwXw8B^~*MPVz}B4=FJ@&65z%5v5~wJ6-wV=d7P{^IcYq~syKF7(}3>SbEXw|ivMUD zOP`HFu)SI>+|;z}JKi*PbyN?={H@}diOBCPofjh@b`jd&{I)+Ba*;{4C;gsU?bP|J zqFj2)#odjMw6c_aPIKumQiX1fWm&pRF8+F*VjH>Q(WkGyv0m4BMZ`y(Irn+ozWTa} z(}&+Lt%ri^D+TvgedXLlvI_Wjyu_^WE)&8%*};PF`J?~cJ{obyXlU-ZQ??sndC?WI zDO)FwIa{&wsE64@=TUEFXx)p#Mcw_|YVlrSxMUKH)Cb%60J@wdK^Pno-m_VU0H>&G8C7={xfY~$$p zWeBC1Q92PL#rGKHB5MA7PxqNp_@UD7zlaI_!^3)#l9Q9m9P`l0=uXRTzl(g1z7fLEHms7vjeeJeChC})mvN&~p^&Impjtmq5 zG&rzkk{wlQN@8Lkhxv-{gUB{0J+Ylw(d0Yu7eGLH*kpj(uXi&DLSx|dvtB+#LD@&A z5yO~w?io^;m0K6iFQa#%tZuXrddw6`nn)6zWh{@D7u4pf{97u=L$?7IJj*R zv{lR;FctO{?3Huatt5t&mJa{dZ&L5?7{vJ-8#lXC^;SY%QYK-UKQzgh_I61-8#qPo z9C>6Jd|=D9>KzWt8sTv((McL}RC)ai$afd^oYJL17jIjRqKw=&o^4W#80PXk4s9%5 zf=L~oJsKd*h@Yzj%b|1nhIj^hlrOmao{BU5Vxl~`YF2!!GpM=im zv3dz$J_qEqD<`Zx8B!z{f?y!ka5Pu{8Fkr&E2` zJ8mF-7=1oJ$m)Xdpe$W^A;f02Ljf1$B2qGuSNg#{)s^0+S8}~}mOb*6+3^IbF1=`J zT5JA^NC|u=ObY=bY`?8d8OL8ei$)4LB;ZcKjzA3Jj`9t zt`gDzgc)HsW15+;-%ZpWa?AZs;Q1U#zHFVg74YHyGJ$aZgOK;lo1m8odie5+eTT&F+D6iO^_-yZ^9ScWN|`gdWFm} z8sUPfAZ|qRGz>onZ;MxHQ;RGn-}ZE=kj}yNvWSa}{}Cg#Estv|h$Y^fMA3|^d)ycy z$@Q0FF9Egq<|)TJ!jFEh#&M?cG!RDMsE3WM*Yz`ZisEbK5qFTcSFsY%Y*WFCZns_sP)Adtu>bN)k%*wc0}S6fzW2S`8&F=$ltSv0Ky{pCnXF77mxh zu$Hj;t!sw0njTU#x+w}TYA;Tn=T%~a`{3uKjk70Cxb6;fN>kuRlT+JHbh~%j=&W6Z z!GMUAwX3U{SAVJU`)NkKuYv9u$EsyT*f7Sj4uR@3E55!zy&I=LAyi+T_`aJ=|4Fks zM0s#CLC!@GGJXrOEi9Hv_}|BhjF(_u9i*USiJZ1%h;CR$7psyrzYCC^NB=~=&(e6p zh$3*BN=jmt+Oz@>2B8!A$LS3OlXT%a=_CchE()6P0X(QAN*j4+5e5|K>mx#vFVcvJT&F+qIpOf6o5>%nH#QHP<(jZA>w^U8|xL7`Hi;`&PQ*@^L9sx52l7b z-`gEbD!T9wD8;k_nQy(L7MVL;5kv-{94{&+KIL?P3~#GrZMeFka8<&NL1`1uva7aI zdn~^Y9dsRO_tEiKOCW$Y;a3d<*d5j;+*S)>qxEA45Z7|m4kE?lsWPWqn_Ku&jeSl( zeIJj-t2=F>udw2G0_jKu=3eT#0wrzr1LWWStI|*^VxGE2iDb6oMwtq3TMEiVWO=s5 zCPC*Q6;I=r(nv@Sw#FwrBOLEYd7piYd>+gx8Zw{S#CPqO5QCCJP9723 zW*Tm5#V(K#d^geQ!S*(`d>RVIWcEM16vWQj3#IB~f~6I)3=M)oYFpPxCAYEZrc;i1pE!dcCi0R6{tU80pogS>{RcdLhE7UR&#e-me6&Z_1I(|hV zqTZDeI@=5hu>G(~gV4G_)7H}?BL*W)Uc9_>d`!^HJ3BcUB+FWd_U*Ih3zL#xAg}{r zi)7R?0IUbabbT%OrEiwf3R8aOC)i)WVs}X9HstNCgUZV}RMpkZel(6gXA?-MxQ52= z;YpO=YGLzP46!eLc4`R^P}=)Cpd1JisiMfdf-U0MUKI?XA8s99JYjweB3_uMX&cOg z@#p;3&86GkrX5ouOITf=B3Jx}U;L1UY-iSQhZ=k&ps&l=zJ$#eb>qer zOm~qToou>z6n=3@BpmstI5IlJ1^X0hwTJHJ!9w6HOXDP{WdY?a10&TvjINKC(|s>h z7Zfu8u0Zt56WsK3gC?SBHZbFuT?6gDDz_lv_hY8bLc%LR7st)3kX^hm>HFJ7uPZ#f zTEH-j5+%k|ts`}s$2yVs3VwQv==G}TinRek`>e|Q85dW8QaZ(V zPOn#MC#KtcO+or!!-&PV^r=o(k}DeIY_qCi^~auiQwwSl85tR0Q^k_?xKR-G=m0U1 zcH%$5{p=%NsPUKxV z5hq69qLgao_BapI7P`NR<*q}vKkn9{$>Lk|=JhunMA#dGD&Dip4biBYMIVyWMc?LP z^!Nt_b0TALgop?zqc{6L@$PSKafSU_0TjHZCC>*{14?bP7Pg8fK(_D$HEvmVzK3Hy z&3rLjUx@-NZL7o?xJFq-L}Rt&ic1{F8W0NI_k9)G$FEGC3lBmEew+QX>ac$@zV+#6 zA#-e9C%@u%xr?q6~Lpb|bALVOXXI$^c42{F}QQ zp^uo#%m>N64`b5Xb?h5WyV#WiBnGZDqjsi#82HNe zzrFR%p(|SHF56f7w-gGs%WW(J!kRzk(hr*zGn6ym@$MciE#*JqLqlo$j1;{kUVU_l zsfqm&dn+$+8Rvb#7Cx?NGRW>ap00Ra9=Yv(Blw^?IbA#YE<#XTdc@R@?yCP;lhm zB*IKBYF?nY!K0#trB?TNPFdFD;{jhT+LFX2=QkQ3*uVTuDeea$ z;v5Q6NeHLR6b-qtF=T(bOezzgbkt}11?R<}-&TCzE4q^v@l13!^rUBgLwV$6AD-cE z?86o}UJ?A12y>Pi({079L-m8;K7VQa8+G)(f93W3Z~q+3%8oFCwzc{*$JRRMjg{h> zh1NWgx{uJNc`(+Ckd|Rep8YXiv1*rM9id4KoxkRh*{d@0WoHl|GYB=r?#8^(Gvr=o-FxdDtft$4YwL+jE#@i5#YN! z{aDj#>w4_zv`O$3xn^r*jFD%Eqg9WvXNZ{G#gkrx^Ee(;E+y?j!(YtfYaEBv&wGn( zq)P{Cs_%ZO(a4fEou8jyzPUNy8=Jg3-&^R&Gbf-u7OA7rnPFU`qh=VC?CJ-!jJrn7 zOC16RHbF8XG&m8{WYRb$-=7h{(@jcJq>{tnXQMREi#xP)x*b}re>Lyi;kq_f=)R1g}%4Q8j^C=z@% zLk#X02D-u_#oXL4(le$X3Nkdki3;T9y4-SZ7;84^=W;}%QtFE8lFXKMy znuAOX^z0-!(a%>lB^W)2Zt$KYQ)7v#%?o5k`$x{LjK#Mo3EXvPr*l)?=m~gA>MENF zxb|r4MmmNJ0y&IhA$ceeHxG@dtGO_V7w8Kg&9c0hg=J3OTg|+A4LNRQ_)7adkbw2C zds)W3N0mi2TR1}AyoVs&Y}c>ka|`IwAejBtu@=1^+BM9YhE=mxx8nHy^a+)9EWDgU zQe;B(Ev^9$s^2$Uib|1`34n7Ef{`l@d;I;(Y;S!pnRS7i5EQAWyqx)q>BtiHV9#{q zMNEU&m{>|;uaF`P*H^6QG%$N|#b7$@#t2{LbHk6t^QHIt@Y!kN4YO`KztoR!&k?Cr z?6P_$9(SRFKnjg$p`?BCqOco802XG+&=2ai65 z41vrKI7S1{dCxurjYSw)&p*%S*_Pcpsh|WgQIFjm67cw1pJTh+K%EKXAn(LZz*3q8juKk{Ccj|G2c7v zZ6=vU;WZ`)5vo20GgqZOZBtx%vcBqRXKET3IVP@>6=8@OZO>!UO zNsL%UY1=Y7!HRA97pTh97YK>K6RnFEfqeOPX>u+|U((2VM@;PO8K z#~fBBbnH9e#$A+Mx)Ny5#hwvXt}W3g4cE@Rv^!gpfG=OExQ>QxnuYV=uH9m)V#Ve4 zuHKEh8>H?vMrBv3Lb~!Z$*2= zHMQ0OC#35#uI9dx>h|o}Ge4l12i)Ahe}4)V&XwCycgrvPFtn&%*DqWr#f#EHM1a#t zuXA$!Ehu75ipKsk+@X~@_Be${4Aqgp#;eX;>>YQduUo!Q=2uCfj7*&|`gr`%l(E){ ztO?%Eny0D(e!!53YE76{P&X^+*TocrQ}Wq4>IAhlaGGTN zPH3WM->|`Hnc%}iCuCA&6FVnRTh}&XC@4s8RyDrV&9+TOgJhzi`n`b)mE)%=T{_h8 z((h0H90`)e2U-y2T|at=i%RDz9SgF%mpCLf3-qqbJR1XF(n|>z{YkQ3StTCc0F)WC zwhLe3*-*HH@xjz;$Fb2G@jtsyz%Qx3UV{d40g6&4hk`yR)B>U`D0e0WsuL(02O8AP zwTQJ{#KO)_VkK-8htO?;$A|nfluyp~~r$?n=z)ub$k09*7fK z{2;){I-Tq^A*0r%S0S2xwxaNCb=uOr8)0?;>lnK7_9}z9OHb;xWF^49Q765p~ z5*Zuky~|2d9Z_lYJJwk@lBBU#4(X*tqgNTWv6Hbki;p!!PqR1o-=$6Sgly-8!$BeO z!MCIkN8`eOvmIL$AtbyFq*RedUQZNt-mZxX2iJ`%W`HC*GV?Khs(exyeo9;@J6xZg z7GoZZ*PHN?Xal;i+I2nhw7MEu5KT$x}H?C7-F+aeG5JV7l+hJD56PTHK>=Kzc`qGf$lCk>TTpo>uIQkhmgq(84(;ekh;nghDK05}U? zd($w|>=#T_^Sz#u$XTKdT~mL?Blx=rn#lw@kxmrxx~4vI0(!@ql*qKY_>GKO(hjSW zZ%-vGQ+4w{LHSV^UER`ul>|NmvT5GACspr>*&>_4!-mlPK~bO37z?cy85VYJbAWbm zE`J_y*sz^RX{3^Bq`1hO#xAv7B~#Pxws8gLpbSm@+l{Eh#9=~L&RI!3%0dORvd(BF z$r-Kf3l6mJwz1Xma}5W+JEt{bYvON^{e29L z-TB^RAoBCLzO1p37|asLFQ9@I8m{m3G>KfstX#ZY=hcK8-3|>6-3KCxq}#lr&tZ%- z1tCfe-{}%CiZ3vjSOzp-ltlBs7PYFHHJRk(dLRHm&8kMSas_1wFs65#b;M!N(t?r+ zp(r8W*gZfV!3LRDexN+d^M@TSd{~EfMw_w>kkw93hM)O)dTd8|&MMDJ`jPqwCsr%Q zVyW3tSjkF)(|^+EMoZk|J}0*)t-rWLMm8>FBJzhdqi2(0qEro9`Lvs>bVSk4_ll&} ziPFRxz_F|0(b0GLDtRlwcTDI;4T@C^0z`yBa~$-)?)T9Xd)+-BSbCHY;LP_96Y}B~ zuVqmd-;<2{8wZ6gE)G-}zR(~?s^BYSmdE~!9s{vcZ{(}EBxzZ_K(Te?5!Nao%gjZk zE#!*6XB*>k*FAUP8&>P>RA(pG1SQ1?#?39ZCfB7goD^e*6zAMe2bW6vDtMovFF)X1 z)aQ_h%&pAbg4Fgk5R>N@C@)=-lUT{aQ2=A|XUn zCMlBqk3-1L8oAJ~;Thj)2oV>8xHvLTya;CxYWJQVW!-P-h@Avn$_ASmldhXb&@C{X z;P8*kB~eFPKopW!ageu$L~Ih0@{=3 z&_;Jgp?rsUHy2$`E(CW@lD1PF$Kw%eXZS1&p5T5Q7m0rDQXqExU14{P6095VFlX%9 zV(CD-AsT7rD|?yZPbEA6G4r#?wd&IE@qi;OI^Cq4f z-eOJG_>SJc5!~&(cHmwHs)Y(laUY=;qsg;{9>^K8Q;F7P3B8wMk@f^kzPgsTb4Mf4 zZ)wUC9rud+JqR`d^(Qy0f0#HF6|#JDl7%sN=W$n6^UL1vl_O8DFoX#hc;>@xKhRfE zArkdHotaVa$lnO(3E*bLdv^QC=Vwlzys?U&aE&&mW($QIq&+(RUE$x6z(j7Y!}EL# zffH*Qo^7E?O(=t)LFCi&Fj|I#(@K_HqKLd z?W%GtGB0iM7S5gmN^ zUayLY=SgqBPa>;?WDXTe+)fnzRZ;dP@jN<$D<^5Z+T5kTgU2>9r_{k`m1MES;O5xC z*w7HN+8NQ7Y~K`O+!dLTpO1NSquZuxJCCa4X8TfBC z?Q`v1l2pAU(N(hf;;d&N23&qU>00Dw8|KAQp35Dor@0r3jS^fO5@AGO=e!FAq@=`` z`vnEL`gGjOFzp6qYhPD;P-mz8V=fACEhXxsCtR;_E>zG&O9&R($Nn8vF+6%<&}MX9 z>*B7tk`itJJZeByO)UeQN<|(i<#VK}udm0mkvD6X9Oem;dLr&8RSRoZ0!h$D!G5 zt}(M_fl?*zqV-ACm9N7#S7#Fzgzg7J?cn6wNNcNCY9(Omgk5bqnrqLhTh?cFAX zU5W^dSoFL{(MGuCg&A$Adc^%FjA?Q3e(1^UpRX(+5*z%1oqJ;#Tpl}J|5UvFp8xxT zzGJJ$TcnAmZplj4HGZGrDZw92E1Sw5zp5@*=Ix(gjg0ze{tMqiL(fI+teJ_4>0m^b zZQ51rO>}B>c{IX^nKSwdd=-R>-Y&?~A&V{a)i=WlSVs{?sniwQcAVNf>el?Yv|Vxr zrYxUrOmgfJiuFh-Xa`3?#fI>9cx=R6{DcqDYgv=|ZN}q`)HjO6`(!rqZ(TG=t;ElNzAMXx}mN7FhF^L1} z1-?V%@B^4gu~wi+yYrMyseb=w)PQ7!3pYG1x9zL#sH014qoI1Rc}z^TAb9Q(J}(s3O+bd_zioueOlsh?2$=ZJX)x+f3?3E!S5FcIWl}N>XtF z7N^}VPn)ijmX8hvok39SYx?ok4_voC%J}0vc`fvnFUau&O7Mp#&)XZD9zQ%sJqvf{ z1(F539%0Kr!b)kp1qFma&*%Nlx zGhaGMm2kNciA@&`1bOR>JR^~>C5KNfg)c*J1aUo-b}ZIfp)cf}@ZbDYp^sO#w+@&EAdoibYg*>RL&c zgy0P>VDw8gj6u-fexQ62i1R7ejE6lU_=o}48$wCCPYppw>&9ro49{`s#aV*lJP)1% zu?-+cGdXtv@db)`^Jife#eiGNwYgj&Qf*uG`NM+mG?uc3_zQf#k>W^hT>bdVg%t^TZ)AQJoWX?b%1}~* zFETJ3a~1o!esHvJ(F$7&`)^d*jmP&Y=Uxj*#tkX@r|lln4P>}DIaHRuo5 zF=#}ySktJ2Z~b3(TsOR0c>h$77|9t1VjvlWgd(AcXT62y$#xw4x_1Znc>u=hsy=AK-OLXTPPl#Sj zm9(+)Eq|4{Tt?fwQndKs4XdgTyt%$P3rDry_=1r2MKeGqt__ojOwCIAUWPQZqS9l$2BkUX7%79%t|%u zeEx-2!}$s56twFxB6VJh!3Cx4%@R|esfHoeQN61|`-?YPoQckDPxfFtjzWh#Gqj?u zg{M8=9gPE=#p)iU+3&m_9*m&e%H#5FFYz(wmW~{uTLrlp7F;qGg5u-MI84i|*)pBT z`$gPSA&2?OV=2KC;ZkY3S~1*I#&&l6!rx#T#TrJbpzIP#&a?Dt{tWj|SY|3#7U=44t}g(*C$At#E+DUsor&+y}Su%B(7R-@%@s zLu&84Lb0Ia1$C7#Xf~8M*s=UNc{tj5TUfm`TKro|Wm|L=Z~gOl_H?w-Of&Ggs^T4% zSCa~+n6d@a)#aXyvI?LJvylk?s_J$5C%AZ0gg{Kmb(@Eb>*16|2FA1c?IYb~e*Y^F z%*DjT8QR+7Q=fH8WW@HAV+-TsW8bxY`GUJ_Kuc*(sgM6;#v5)q?&nEGMkZoiJSAZR z=N{nmee~fslO7!m$s>d@K$sYH@{NSD_TO4+B>%J5uJvLpNqN0FrPnaLdS_y8JJ77W z4Iks8t5}WDy9!oeIxg^-na827V^-Bkvk#_p%=u|{g!SsA^h99G)W~Oa^FxMHhm(}* z*D*7$p^MLBY%9A*3qrJjYRmLRcx#t3ROi5^t<6+fcVl4 zB=dH21+OruPn*y{N=cS24huki5PXike0kWTx=VngXp`lcjjmemy!%+k8sbHajI}Hf ztKsuzC?|6N@cYhh+erWYDeXOHyQYs7(v7c@+}CV zBf+^D=MjUJY!PII<2%sputyNF zABgLFJ!EPK;iH|;WJ!hIK6z;F_wl&RE$k5F5}Za=)D}BUUF1+wGRrV5aeX^FKTQO^%LH&eZ8GuC*n{izj!1oTB;mI z@I8}>9=Rj)+#<^P9te|X9K9q%b+u%jdI^&rQo7)EIhj*~`oG@QhFY0P9~n?Y;Hqic ziD?Hkfy$n+7B1VEso;gi;mOG|_4OjP?ZyZTVi|E>&yITv5;DthJM+SJ~NRtEmgy0Da55DfSEdPl&wC%siJHraA_`Z>&rM>Ow#*IYp%hDfKIB7 z6S?$@32#MK+>GZIXF{!(CufOvg_VytQa8RRlON5n9P4!7En>+xHb>sBNV|FhS-{de z6>(0WK{v-BFtfH6v$VAAK2CeR6=Y-*ZT&kwlwqQ;-{zbgXWz z_OBV6f)u7HVuDHf1UDv9y4xZE)dia|p{2A1;w=C|@GSw{@}~Y%4e?16znB+>sgLQp zjZF+EirX6wG^O&h#@UW^)!bNb$K7?DKVd4Ly8#P`{E--`8B6!oq%Sf1!hfjcpPwIV zbW52iydWb`D3QL`^6ovXe$y!9gjQNLgUmX|x_pi(TcRA+jiU!jmdvDx8w9a1WB~+i znbcS4fcND^gKlPJ)tbm{`9@FAW_2$j?7=E7D8%@cxxCq)Fa|>moBTUOM@{(a#~J*C zjzhf-0b7xII*I11fyZLdV@da&EYHUGARUp4^CS#_Yo@>MWTVS$hfW>tC2=~D!u8w>lfAxgXxA-7@aZ$HkQYTvbM?fYoVKzzDkfc?5QFQclT}+DQ|or-A@VAnpvpAHwvi< z`4`=u9M6tS&2h61`+uevx9vI||H*acz577^liMM70ot+t_!-uOZ$g5=_heJ>kRUcY zgpy?e(1P@5_H8^brjvy#5;QB%Jn|p>O_a~7wft1YpAMpBuBo7g)%?tZ8{(Roc zFT%Q8!;g>a=2p**UsP|h?O60NUx2ttVfFgKD%Grm{CfHrILii9UifL=1n8*ctCWp* zbvp7Za#MT;5#W&l-USsyTQ8iiin5U)K6pUy)Gaxh2?f&L60C4f*blL<_#f z^#~Pv@i9OCX(^+s#FMp7H_I)_`||`rGINu)v`j8MWs#~HA({&nW}CQ@-Z~0 z?{>V4I8(0vo-G zs;a@xT>ba0@}7*?o!MGqrCd4qN)t1b|DDG6H*!R!PMI>AG|QA#H|ip-lGoG0w>S`cWU930YlemXXI;oTh@2j zTa`{;FPZhG#07f%9kRx62#@sXVQQ29RtxGOx?JTt0+qp5QN356Vys;}Bgw!<-f+3X zzFboKr@3~gA=np-SOgz+Yp$L=T;QA;k&T&xh1}_rnJH4OEFXkDoq7(dye=k65iK4M zZMRUec*>S2nUc_;pQFlfo``*+>$=k8V9rZkpq$2fovQ5H5w;=7BW^jZo@q}&^{UzQ z=bPhB|Ldf@oAo38OxOv3b_t0#&mgxqrab2fmTJ|?m6Qc|H818)8vFrRi0^p9@H^lu zK_J7q@*UoI0s*D4ZObaEx9NpMZkYmPMG}uSC)Wf>tdj&ZS6e|u6Cf|4&>uRsDQ*a7 z;jZjwy0~|rmkZV+QyfXVR?^cDr+c=mO$Iroa@}LISc=vA8#6tzCNEUqWtveFkUFU_ z@2qoQYYaS3)1`hs^?{whjTXgkyzj`PzrnK6=nrv`RnJA#2&fQ%lEv-IM?Qag*U#S{ z36h z6sX6hpF2@|X>JFWDv6hnDmzD3>wPU}-H}&p8A8>3D3_c)2102>z4^5cADiqC@}at2 zAz3dUQ{-Soh85cR*&wLN2s;x>A_f+*&K2w+8FYI(bU_gYp662n$L^1NVpfwme7R~7 z3!tkNUPq!Dy69)x1;kd*)&!M%q%Dnep|Zex+TlmByx6wfkYookG7zvffK~2z0jxV) zn7Uy^_IDs}3H=o4PMhJ6%T>k3#2IdSkuW@7{4JEBn6bJB!osnK$sF1k^(CrSyGYu3 zblAg2KUyQyY@mc*rswD z;r-U{_a?zH7QB5v;IC%oRpB564uy;o9dl{Bsq<7IwOvxTFOB}G%xq5ZdM`W{FCe3y zAiglN<~vE5UQ^s6`pvhj@-(DqeWBRhr%F7W;;iXd=vQ;n)f&pL=0R54u*8|qf-n?i zcPq0sgDWCxEn~ux3}2d&k9e=bvnZ7^+QLq)>1{Q1!*rx{?<=gZnlqH%j}lLdsAK!k83;NKo+8O+;QvK}b z^KylZ&2O%R&mriGj_N1@LjzWgucOb4s){4B%;6WPa-CuTwE8%WMrf<)phDza;Yt+IZ2y63m0 zh~8x;-o}`iFli%@mELB+t7-AD#>0|9U!vsS>qJ}>PFVNNMU@RGJ*?eXpWRKDJ#;X%0tvhmir}0B~=^ ziuPDUHl1u|Q6aI+3A=PioJO5OpYB1AMk?1HJ~Q@|5v9*0)M*@z`idlkTl((+=Hj(2 zwXUoyi82GdTCL@h2zh9rthH^`Xbx@5MLeW$K`gPUcf*TFeM-v{a`G{;u;ExM&nqsa!T z!rg@J4;Jg|5M`pY(W*eo4o}I$g>Xgw3qEs2OF^U;U~M6U%G~cqhRF4-*qsDOW11TA zRw{<8+YAkWOc8=ssqMO+?Y!T`w6|3;fD8cw?=r_tRrlXqX=O(c7AUU&BdiJb(WMn3 zqPuS}7Yy2mDfXbs@3+CCF|pQ2z_il!f>v#TW4;gZrz?96?eu_|uq|TJ7#*MmMH+Xg z5#!&dc!%|*?F5##{IZ9xY?Z5OUaX;AMBG_CA)8-GPu_@gUphxt=Ww-Rakt zWcvaBHr5FXgt*l6dTmqh@99KaPz!_co{~KwL#!BB`Nr02n%XqqK0K0{9HUnOM?--Z zEz5i~&jv4L$AgQgp?BMkYe9rz2TF6_!|%`{gbxLxV7y>K_R;>h-VFKUa7EbEHbwNR zUCAxn`eXK~%0kVq8_88G_kF7DZRQgU6~ic#D|=E#blq*YTF6hfR4Vd=m@D^TGXkB=m)$(saua=QhwlLiH-2Bq;j!2Z5(vn5EZsV9I-q^z+ z)0Me?t755VADS+@eocN3&9+|Yj#=AY5#5T^f4;98SOTx1oRnwRoZfOWIT1b{ewC&= zz^x#l*yu+}=@d_%`ZZ0VhQXnpM?l;VB$Y)R{gWxJ4V)z zl$E+7|Amyc$9^eBGA@Q>N-n>9sKcMsC5cRx-me&HrL?i0B46y$aRBvYp50Ep3K{CM zu;!@=i*XEOZvEqtStaf0F|3zWHsP*SqV14X2`ipfjGp)(~ zEnXw``UCSLZK9RT_ut%(xukQAN={6s9!+ycxHz4V!>=Ye!g8ApvIXLcxsy>O`2pRf z8XSwzKO4kac$Uitmf%>MUJ`kQ)X7r99n5~sYhBGGO`}c9@3?F7k3-KSugDmMOZ~)3 za`jp=^b7?v-@pFtnwHm~*Xkr|*@9~L~iDl?$H3}zN zw%%gaFGPmCc&H?AZ3-{ge~`8hoBus&y2yf;kHsRk5l!;M3nb#FA$E%}Upl)xZ?`N> z#L$Hx%>>o%&TlQ^L&w5o=cHB zlP?^jUIp$)Ou=`6)EcCWeWC_Lj9$>tlN`O`M_N*^d_~iQgn@((5PtaQcKJWOzhzA- zTwz20?3bv@rWmBr*XD-~gy`;!Zb({Tn%?pII(B3Ux?EA{E|NKQo#@cfF43EiR@M0x z{S`&2bL>5nq03OZEVP-lEtd|qO6SCs`70Ef(gg~XY0N#6^o zx^c{@#%d3VcoM~10fkM03>+k~ZB0ICr5jq&u`R(yu1s*OfICBd`y#;4qWOXLPo!|@~Fto~ep z3q04nY&3K**ApQR9|8S?&zqCquXDz4LXKsR4LPN)v7dP<7O`{2=HzG9-ED6d|FL`X zif%0M#y*`|%vrRCVpC^&Le)BQ2%}P?!X!N}k2%IB;^Z)rhwjzK$yS_tn8I(8m-FJ& zP=SpjlYygOxu*f$ibsbzH|W}hHJ7Fh;a~b!rG5-*#L3>YN9#qtUa6DhFQVV`z+B;# zT2EIA)&pl7o&G9`Xfp9AoXH#JsNT6ojrwnD^rCO6zea%b+YQ)sy&p=Q_B3AhhvZ|b{-$}2e=gk{9@$+N~M=?vN9TG1**G0}pN`ASYyC=ryEQx)U_&I&~} z0tAx-lGge+nKE2%Hzp_Xfv_e4FpA@y`EWegny6h*DTp+;<_E&>PUQoIAxIC=O1O`R zJx-^0;-`qMWsT4raf@W}&Y4!H_ zTDD$CBHS{Bkfs7}q7$4XKpNN8&CL#^!Ngew9XIYJ%l#&t=ra3cxaE4k8K13_4g1SI z$WzkQr*b51=GDn892&iyXU~&1r1H}psUYAn0lF0MNSg(ohe#yeCGAGBH-16j19@9u z`#|WuL3DYj?)=5)wI873bNo^oqh7OjlV9OHZx#hfhBRx-0F38x*JZdO?8Ol`q6nqH zk}O?NL>*>pe+Fvew?niV$KjdrLcrr2ua8PO?3SRArpXG^z1bZPVxAdW?_xAbA*}^z zAkL!^Z4%DEo8;cJ2b(u-DO;z0fVcdtdoiM5(aVPh+KIpL;F38OWl?14ah$(iDY2XS zoAT&EXc}!bs_wl-kg)`*(Uxh&bF-Ak#m0Ps%4X5%$DQZTR7hHl^htK4MiDhJutrUuZ7iv{7pHfFI0frXN+D-cAcX2~tNBW$88avDk2Bn)oUA@G%v#|@% z`z3nivjo{6M>%3OLkmFte`zMd*0Z}x+S7(GJVVC<{l?0OTG=_etjA69{=Y{UHo4RG zDsq>t;UiId3{X%LYSi|7-IkhntjeA$6zqTo@tMvle^Q0hUZqu~bO7=Y{D>3v_4y`; z<=7K#K!S#N)w`W!6ck`iS6NpaL9k~3r@4dsvVyoxq4cr5e^7{RSg6C@O2bi>i~Kg4 zw9mN+`90O|vxgZ^((nJE(ufKf$(XnTlgyolMThdjKQhl4g?afXLQ$?b#Xeaqy1vpP zn$j`wD$O8)k_ah4Q_!h?;`qV2SVmqAdp1tF+$1=)PKA(i?H*6tEGL+z{%C+*JbgFw z;&OKWic;X_Y#=i)uMTU=}TyyB%l8&1xLkVToTfRnD$q^T=i&BXjZcfY*1^wgja?6 zD2!9I5LPxzV4Mqu4TsgVUdv-v7}C}LhS1H&Id)UUGW|pdA6T`j7G^)px9xb|xb-G- zW6AUFp|aHK(r&D#e-M0~yTS%SnvW2S zboTesd1BNvBzI_9LKU>x8@$EKu-HyPBZc-ZXte-pVShUkoSP57Hi1%StoF32NxQPM zni_0oKo=zJg^EOI;UmD;dmshXc=*0}szfUdJffXf%+oW!XGrI42rh-bi+3}(xx65N zn%S~sB74^fD0b(=mBK23ctSN)x{9srCW@@WFk|BRgIZlHH@68mCHV07w>R1=GiJ=5S{ZcbCBMU`nG4Y$JzpfBD>9Zc--?^ zX{zqTE`clxNAq(ujnGX-I_ca_v~eeCmG8cIFB0E^D_aR0%Za4Lk>vX3R;E!b zHWHl?XxiW|Jt#X`5Ey62`38q>md!FEq$6zAzX-QxYkA(C^{_XFfL1j|H<>Jvz!ZMw z(XgwAdb6z$+jMfBO17n?q&BD{H{0J%V}TM_(OCh6bs(LpQ-cip9h?_2B_=Ga0elL8 z?zV%9dCDC7f(=d;d_b8a72}CnKAudpT7E zWr*&o23~nuw)~f3P>(58EA<=S^nKFfyjlEhcgA60F+zgsN8|NoBS=D$j&3>{pW{Oy zf^8CnMYKzQ1CKzr6+v6;=Bpmw>0HgKOLE`~P=81_+=u$>sY%L^SZQBQ!Ikk6e;{Ur zrXGq&8YZV^+yqY-X6CrdnD-VUjlOgJX9pk9`HO8}SoHMt-;c($I1Jmd`ObbtozAQZ zn4WN$Ks7W-gLK3|duqzMC=%rTb7h9hx`lt%gLh|Z#$tY{@FF;j7YKUasH(w$pxlFxU}#0up8lhy5feMI+IGaGMh*Z@zR4L zv`NGn`lR86pYL?R8vyu0MOWJS3=sT5eb5&i1^cT|db7H_yNeP=G-}=XRIP3Ni@5Nr zoX+@0zKhc00!DLQDQE$?oP~Q>O=aJV$_S%d>*=LYrhNxQ>zO!aPMud7{Jg0t9GlT> zfm2oubxaDh7T_pQ-(b?}Q7qwAq!gzf&g}AkV`;paZd^oEWVr_Th*GmAs~wo$HC{RO z#&ficp=5nV^n+GcV<3>E3(9LscGq605N7)zbX0~Silnzhm11hDPHl+>vEt$ zc7x|X7_<^2C>z=lC@s2zw^Ta!lXh`;cPgdWl3m)| zdR?Sw`3>?Fyq)-eusAJzJZ5wbIj5gRNJYSkM9^p1k9v$19GCR1Iv8&-lRrNO_7(=` zT2?pm%HaVapfNXGD_h5z_ACCa+7?79{!yJsG!P~TX_PH4L$lfU?Tyfnl~XUX!$9zi z=wE)UR?_Ok1h&QCxSMF@j; zFB_0#9to19@?9JUy*vf~7EA+NpYRg9bEJxNVoaKPy^CI5cisOBK%+zzFZ+|KxWvl!vyr z9hY_T5@|6Vo_~0BE;x95N}{7_3N`>bfb{Yl&_0UDpMHJpp>3FxqJ*dbBmsqFs^_%} zVvkU-wr5C|W773FGj<3275w5>Q17Vgi-f+x~kUYc-dN!nVNbmyX-Y^W- zO8l&@F9KLbgas}o$jWjgH8d+fpzC%LFICcgNQJtmDOufFQU)Q&=NdAdD79DmcR`7% zr9NJ>${V^)3G_S3dc^1SK4<+Z&pK`gEH{8C_PWOn6l}GC3V+UZll5}C@rqmk=zo`+ zeX-PQomfj6&Mo*3e~O-teQPj2v5O4)hn#+JL>Sky`yBLGPq|vXkI-ETA3HZWZuw_K z-tSJI>;!`eF>wmL6CjxaTn{{Ak{$tFBIrfHyzQ5`*Ygi(J>S|yEykdw8z<)^vr4A{ zh!gA5Mx~Unt zFhrF-U%pQ`(ju{2tUU@A-z@^ImcHT5`)n8N5S|SK(=#bok0*&9isTaypG*zxl~y)Y zXR_A<3c)ExNk#qb{k@u#?TzVPQ5ejzw|q|>UaWI)w({vq1Lb7LFEk8MUD%O|GauD6 z9-iey%aR{~L`)C}3JS_8u+1>uM2dXQkl+B&Isl$yXLZRS?cwfmZRBowMlov*gn58> z4Z4yT8EtcZf`SPU0!w60+_}{e|5XQr)~9DdIwbxI;8*q1{jZYnD-w;g0A{UL-9`{$ zL@)70kNluJwM+U4L4qUhrCO+XR$yx?45)+uD-sz+lOT!^fb%NRAC}3zQGHX3_AhC0 z(c^=W_qBT{Q7Q|*PZ3J zVN$oiKW1bI!rM=;XNrWd=n4IB>u&u3=M!Xfe#p^ZsfXBY788XJQ4YrU z32-g*-)n_NJJ8XO$HY5A7LCQw7l2xWk4POEi4(LgfDT!42hRu=^X`TiOQzBYBPMti z`D6oOJyoro?^BXZu zYu5u4#(=e?o3{O9kO8QQK6gJX!Js7rQk92+p_B+2Gqe5oJNB(=FL0&d3Ilclx^al< zcx6xuLv+7yT8jVjU!ZS6F+vA{6UB*(&2S2Ic!~$iLmYIwNAe;uSAu-1EE z(dc{X+*@nf9J;@_0uo7})dsTgkSu#7lbyOXN5^nI@e{;G2m$`BJGpkL8yY0iW#8BJ zv>Bfao`!s1!1MF`{V5aOcQ|a0UgcR-R06J zeo7$v=;g;)0oOfX5OyG6$>En2U>|r6XCEYbh+0S6S5axBOFzs$OR&GbujKylT>>_N z-~kNPQ9!+a9hdjEs(ovCh}K@gPn^ufl@cfya}n^DF>`Lzb9YD~|~@ z(}vpm*R0xYi4;n{y4G{<2j3}%9grnZfyIH@={`_cdR%jsRHREgmYneCV}?_vB>1Qdq_(s?mZ$w%aqqHy9^_LC%;vbt0w=ljPOaO;8O?U?rvO`t{CWm)DdnSf&J0gkR(gS{RE`;F zwAM%|j1U!_w{aWq4u<+9cxu;GG&B;vMbTTvg{x5t0?<6((b4EeDFWr^pP0C*pr=<_ zBbU{AMWC?3P(ZF}=xAJjO9Eh%I3&?&2X*oMS7vmO4{SxjuBizIlQR~T+dB&N<2OpZ zTtDef#Kyq>k0L^(c{1n54zgo-Qjt$T{Lz+)fBK`}FnH2)xY5O=n>qUqmI@A;Y9f)AhZR>l2)12)K8x3^L+2-ZMq+B14%5mDtr#h}}0 zQ(8p!jQaq8w?UjOk@5aVCsZYs{gE;_f`L%3S&*1Ez_QhQtpy5{(YAB}`XN}20!Z}g zzb}2as10l&CM3Z&5!Z@{gcKXr!KD>G5k5@cb`6WK(sOhtT;4NG%6{-L$0!Q;!4zT&FfL zD0OwckGUOH#ZMgdcSRr#UZ}zlicenilrU-l7%1X+pq>guh@eji(OB(78C>E|<%$mZ z>bN;pS*Q2>r}TodBqvQ5C$izZJ|OsG)YCjz?Dbs>x4?BlA=$rcd$M$%r(woupQHDU z;w;IP)?LY*(KRXGVN&=?dyT1J-(7(=aGM@eFbKd&UV_e@)(0o;-;etB>r5Uc4*9=} zaav4>_ZL1fG#DB9Pq#_?wYXniXnnMvc(?u9Zz#eE$tH~?giO?hkE)KOVgPKDEu|fQ z8UanJu zfZm-mk{1p1nUxkF?fc}_gMCgfaE#sHrg!tgvegZrbZuf(N>I5xyzGK&8!>(y&ch-L zB$xRmgZGMXIi5F;Kk|$RG(Y3z=W(4yTg5Yfv{hyUYU#saY^1z~=Z}c<`56^e1ac@i zC!cZ`h1B?UKr1J*0 zL;DZE(sP7?$ALb*&~ZKRn%gOpX-LK(kTgl|&my zss+&b`F87ZqTe`^5-0D+l z^E84>bW1YCa6|j}jUaI#k%~*hzC^)>Ov6KRMroSMX2&=S#`}-feK-MFYAUnA19)%D z>$sU~^DEJNvG^Nl19zA2$2lEaB+`#bufi-hzq$~2m}aq!@0bq>^Y6WUJ}6+P`5UaO zh6B>Wa)D#6q06s0r3)W0k6-#6KoF|(rFL-ynY3N@5yoIytwCrRBbZ;M9GM@{EJ~a% z=o+%D)#k0lO&8MkN^QeUZv!y-~kBhuGW;{u&jnybifGDL_pI(X%bZq~n-Ddu14w_B;M+wgZCdq7-)27P6#c_kp$>wv+y319i^h2l*A#S6aAw6iW zMp%>pil&atBI;A)Pgo3Wt8EV*gqzlBOS3tmb+z=~^z4yFs8|@5OJf)0*lsB3dfuL# zZ{?Mr2)-}3zrQcC7PSOw-yI)}q%-Wor}!ZFnB@sC%g`I@Z+u_U6A0h;Of8zFnpD(Dk3Z7X$;(NrS_ffC(`SGr?G!CiAY509{qE{?OmHlGB5Ng3e zHtvUx{eku|iY@>43f&WF9EmVq$jNo`U!Eo&lSeXoc}T9LfqRov33~ko{%reyb0%a< zQVto5M>4Sry*!@CN-p0Xeidc_>`+wbmjXvfDBULOZk(GX(HiX*I18=rlt0aEmCg0L-H?rgFMLZoSPTy-O!{D*gt@CO|>U`3HP?C=r@O z_f6$D`U~9h@cv9#MbFjAeE8$iIzo_Lfb^%l@<;r)nyw22LN;S`{*zNKuzGf!A05P~ z0kwp94X((ARd9e;#&Z{Dse3p76%PT~FV1B0meBql9RTbfSnF(k=}YI)d;1R46@qq? zmC}_glud8YjYvQLdNXql0$oJpea90YL4@>75Ryc zwRghGuYnb1YUf@&wb*V3iN`$#ia0w8iODQ5e|UAS$N&R)%0LR-?oK~NZjksde;MgS zSPTM_O9VjrP~V7Qltde1eF1-kMsLX_%4(1lF@6@hnvAK_%s?xzMyLUrmmQn`#>2!2vRG!i)PTItK#sZuIDq#+ZxSbr*?jPCa*Ct)vuXTYGCqY43f7mM8`ifBSRMZ z*^%4J&Lb`h2lley_>}XF2z3FFo&h92+@#kn<76~AfQjzW`vVCC(oWo;isI_wnTLUE2j{a>M2F=4?=in&= z&E1BG$wy?y{Lx|IY#oeB9vxU_c?xlV3r$@jq6WW!aPJIKf4s5f?^R z^mLE-BDm8hSTb5yfb)%?WeePKBduGXHQlQGzfST!w5{;vPN%M_Br;$qvpNkwnOH@HNO9QtR?4VDlLJL(4XxXKc@d_^V1->nf3fxA<~XzWxg+5}F+D zA_6Jhs2_UiNlI*~_utG<0BahxV2q%on^srRShLit7tKU;6^Gu({V$6qB`Z&;d6wf< zxX$%JS|ewPo-45LxLCa+VbA~!_+bU;eyT3(L2UP z)%q=WDUQOz=ZTj#BNDEN)@R2z;rEMc=php`84blT)|d$imX8{N=g{Gj>5+jIoOxQ& zX)=d{_Q7jE#G%(wm$;1oYimv>9pD-zOc@{ZxHnD=lQ!aw@s~$TmJv@=E4Z4?a{cx5 zsQ1XPVUIZ~`jdtkH3zGo-1d5tG%_{T{oDxqpDPr5IDH<3Pv$BWaMqO&vrU6ehv;{K zbQ4h=OX2ccsOl%30;*H&;6pdek7VHwO@$iu64zH%kAY#)PQzTclk2qRq+iG)LYD&I zuLV8e$U5rIN+3RNlEzy$zFYI|)M^@>TWd6mb;N;NpK@Hqp9r0zO1&zcsWB^fpRxYp96Fs(@TLCW%e`dqs6sHb@huL;G6G8ZE|?I>tq<%ac#RL9AJ6t9 z!jk)YH8ehv-I$ErbXY*48(dpgxVIPK*`zZ!>I`;4A?9pAE7PNS2O= z{1E*!$k8DvaH=&^&6_p1#JC^f;$@Ewk&vNKlioy%g4Y=kgdO7_h9iJz!;Mpd0(U$E zhRFJC3@y#Xr@2Eo3j2nDu`Nid^+1DI2bTa?dhkokcNA=DoG?2^+fDxUE6pop{xPDc z_MKmzYSqi~@X3rE5+EiaaBB#4< z$7WO~8SRT6Vy1_i&cK0d&JK(eY@$Eg8+8^JfE5tb^hWZu5%%L}Zuqey-qJRa_g-MK zq0)4&zr?blKCnPTIAH!@XgPE}N+XZO^=!KBef1Pz9$ga9<7)VG!BEmHJo3Z);;Bk3FX)ZRTgd&4`` zjgwc3H(G)?ZCwNx1kZXa@UZ!6g4qMhGw!$SiRb*bwCLX%mmCfl7Xik8aRtFeD|LIi zVOsT?&Q0uCmcFNNs}RH#8x0n%)UCgDsGj7pqT$0z+iOHMiz>y$=6BmVPCGM*26o7s zBJqyR{>ZzqXUmBR2+k5mH*cR!Kd$W1yNBSv3$gF9B+uy6DF{vG;Ipm$C&%>rrUZ0& zW7|rwN6{Me_=X#d?-3k@+RKy`2H0&tv!vKRc1z20Mn$VKJBAOvb_`)ecP?9&X||qZ z3$*ut_bHKe*0V@8ASm3|l~>?M{0?^d#6R}(lRLqWJx9a;oc@$)q&Oh3lK32=kn@2-Zv z;~CyC);{F&7$SchW0ut!`P>iv7o@(Gi7POhO90vXpnN!L+g8(y{Gv){|m2p zl(D*hkK4=sQvIa2H=Uu&3wF11Hufi&epK+KILVNLGkO2J=PQmlVV4y?Qe8=YZwC2 zRlm#($o0e^>4)&Oy56ohO3x|>-$lcD?n7Iv5REjvDjDjP`J4aQUiWfKDkhd)sQ+5V zA>6S(jUT5qrTe|wEAxDbPKw4+0t^$0$MgYq6|7^=>evSKom{{gHI4jw@bC7HQ0}y{ zY5_v;B0YmN^~w~n%dCZtBDLliR|r7b;s~DVZJ#6fh#h+Ct-!Gy5x*e}X;p@ClQH>k z03`eC&Z!3CMF&d^uy7RDrS5uevHhX|*2QT=NGb4>&kD`b({WBoq|AXIV`X6Ypsb6&BG1 zpQAK-oKr6`{;V7`t+RK{ySkW8f77p@_ok?Y$Mi$`t?ndq0|3`%Hf#AU5+J1Zy~N$9 zlIlBCsqiSX4vC<>iNyrFaeUUZ_d0Nt8~%*^F{(T!<90&Sx1#{C5Pt;i8MQL+y4j@9 zPMssc^m+$lkLFF0t}AI@ZVY_>_=HcjeZ8lHanLzjtc!EnFZv<_YUo%cyWFd-bOu~3 zvhQbK&dhlzZQ9HvoZj_`%q6By#!hY70u_p9?=-f4X_zGhY=2}he?gbgI~=6LBNF=& z7-%Z%G#s7m^{C9JjZ2h&oFjRp05eTtlxF{XxtsdNj{OS)F=-WYgJ~HY4___`^R_{! zRMIwWqD?N{mq1bI9o6O#Psy8I5MA^-x+k*!IP`RMd6bZQ-!!7LS0?39+w;xz}eYM>LK5(OT?%xlLxtHHj z7|umf+rLRaDQtsnoE2I@NthiT<3{xuE`6`4ky;^38BSZ(H?ja+Wt9=yA=J1IIov23_{WBhvD_zfWn0QNWt$}~5T!4TvA{0#zd4Mjqd5?`+5e1Zsz=e9dOmaPEQKrXs73<*60 zh0}{r4K!)GF#Ze>42Yyd`}4i3gr;BrKegIE&v62o;#013#y;PNFKD}wlv5|e_vQo+ zWv*%J+M*UfLVx2UY{Kyx*-4)fvH-ln0W2P`w2%V$D=44rhyu%eI}4Q{ncXM#a#UGSv`mHFUzPIy7TjM zny@q#j?V!ZV#k>pQLllAfWL$t;9ve1DLbR!$stznT)ILpu%`XTyn})c zAG^SR$c6dgabv<0M;F!Xj^k*3jnPF12N4(&`nFS0+Txx3J z4qt74s{E~2rCo;ve4HigKubfz+*y%{Xsn^`6{vL~j5vS+aHcDLHoVW1if`bPq)Q99 zB(~*vl}MwUqCMxJ2jIF#<|aKi+pC}xzo5N%nw=PN0hLDBe=u{IbI43}dbs8n1dc79 z_2Fu+c4+Z+9X{vFjg~KBw9{Ni+fs@Sk*TDv(#ZJ5kQ80mCMjq{oukZUp9c6#h*@Jq z%o-357d(nmE>9XevkOn)8by12(cPqgh1Jd<3y7{R>`{pViK5gr=g z6#gdFEn*O=3zK~o37 z{{#nR;O%;|FcpB-)lt%NkfbxxKL9pdp|dkKrRm*d#fCuZRj@mOuo&apS#8`%=D#jE zz$lZIb?krc%=JnVepi;%v+OQwNe2k0zf&LsgK0W~ob&&iZUGcle^&Cxm=PlD@=12X z8y9;Af~h~cx3@v>0I;-lduN4RI?#EzMdTvDPlY#j#pbajwwh#T*c1O$H~Jpt_&mFEF~*XvA{1!X z@P>r=j!QJ^>_3J$xFDjpJf#YR#cySd>V|^n&UKf_%w-L%2na?yaB2Pc1SJw1IK{jj zt#Sf&AD>Ly6A*TP5)1%t(CTphLoxyag#wa%L5odbT7i*FDcBQxMMR*W|5S@8+KqV( zzZ*47-yQ|zeBQ{nxdZgMNJMvy!T_+eWiiOQ&MQGdJhs-8f*=)kw}M-En<|LgXUNwu zex?ZoH?*aN+6UbAFHGB6!ACwa7b}w(qb_y?E11=Peq!_XCq5#60+{L0e%mSfZRu*) zAEE_>ZOWXAo|R+ZIV>{=5*$qT1Ww} zD;3#9TaUEcS!F_xhc-+>Y6xT><#%2L0dIxTo#1i`SCh&oqRWGWP?iWY?92~od1{vG z{#*2&taZmJvE4AOPez_C4N zzSDhg`NdgM>fu>Dg`73GJ|tGxuojoYL3Ty$MMbhja&h1`DJ)5gL@!y;nJrLcz{nbQ6_`MzgqDt8c#Y4b-JSI0aVZx%W*i?`GOK zpe+Xc1XCeWyzl|7%wL zG$;{M)rj&xlw*tw=r1QRLwFwxGGHPH;aJH>%Yl;U_x1eQ+bDdYSKi=*=~^L2!SeTb zr$Vb9JF*Dw+d1wYplHVq0M0mlL4H88ex)-XtO22VY5u|n>jEV=KMH|nI0XXaF zZ-|!!$?j#$C<9)OUTLA~-aI`wW;(mVpCNL81~N;IY%Y%5lvk^V$tE`7fds{H5KI>e zGiXxZ5m>&R17_3T<^)XGUz9x1GgVhDvCBe*VASzC4_&wT*jcCYk5VB%}~!p0dp%W5&#L z=2>RRHdD$xR+2Gg&OBzw6d4jBNrs3d!gnv{ocDdN>wT~9ukZTSb?VUGd+oKJbq~M$ zexB#|`wv3+3|->m%34IkeSRV}{`wR0{3&y(G!;@SE zeFk?B*vOaWpUp;C**{~Siq<|?Gq(;C62B_DadqHE{7G1LY8f=;e zc>6Bq$-1Oa2Jis#GRB2-_1!W$q%>y%hS2kjiRAP>N0uP;z9d?vJl&wu#Vs=)TYPxU66uxfBnBXBF$tn*x|Nx1uBcYbp-GcsipC~a0qdhz6nfQU;@ zK`|k!i0tHw04&JfHq?EQNI||tn$UxZvkIwH(s<&=Ea^vfpVOlM(M5ZE`&NnXjS>7I zzxxq-)1#H#Bc69rsamR>>)qMR$l`>7Xob!7Lw*zEU*qa54ChuL>DdQWQkvQ&Ld)T# z8U}sP^!+qk%Y%=9O&{L6LS(cq+5Bgarx07a?NvrrVGR@NZ!yqQ)~yh8l`o@!N6)Fx zFVMG(vAdd*e2KhzYZ4|4|E|zQo^QR>b|T4QxF=6VmSU~W?~9EBvTZS%XIbQ8E*a2c zL&1s<-CswMRZE>X(?6E_D+7Q3*c?i|6~Ke6lDeLGmHH130~u5P+Fxw6^wR9A??K_S zQDV_bHkTIl0u}~i9FG?Vc);@+#rlbWMfpCvq*! zakDPQ5}(t4rt}J-LQyjvy+ZJqsl=FH85vu4ZQ!?^=#p|axfXcEIQN6ekm0=e$i;TP zx&65<(18J^>w8l>pg)Dhm|7tikG5BM+Pdi(_YdQ;lyHY^Tr9IIyB#S~jB;bLC11TX z15KE2Bda)5o`?X4!Cnwo%T8-_XvxZw-G)fNl^7DISE9dC{_71#qB)e}^D;oH>L7lt z;4`C8T0!4f%9`ztB$i}qeT;WELy5`_TUeuhNI`BR^q#?LO_hF3%~Qi?4!zR(`TBv4 z7uI?kKGa+U4I#*6AKKaT$7dBjvt~wEkP{NiAGD&@aL@uv#RWt~>5e@A>}}~Dd1G!1 z^_$1=>gc@89N8w=H2K2&p5CdHOD=sy$97i@o$}VeDX#K@%?h>vgBNz53NH!k9YOG4H6>SsGw^#OhHG}WF9seV`E1z;2P%Zg>U>Qkap|uRh zvR0dCdZvM0X=MhE_iA{|-a17iZN)=$%Ie?Wx53J9wG&E?BuUp^M7JUuyqhB^zNLUdhZo8e%k!yv$Ff!iR_1ql0Av)#1 z-+6=RJS8#D&B^ieUkkF7=Oz7ra6aNQ3{MxbTgU~!8lIS-4K8cQI4|>l>R&2u7M&g> z7i2<+&$^V$2T6sN047aO689s=pxx0Ni zzXk2YcKqUhxaFs*7}>M}+~ce(vi9gEkR=w2Am-q=UOfRbe-RGv3L}qiRDtN5pKo`M zThS1xKbnhL)qt__&75Tvi;XAlH&MkLO@#f9(vNFBf{J)8x;4qX~sCZ%n3ZjW5L&p94y( z9tI^e+DBsMj)u1wyrF9W$0#TL%5wv6vB!?Emtmr^o18rPqwUS>H%teouc0d;zS^Bw zz<@i>Uta-X4R>)e8uLaUmLZer7#Y9?Q3F~Y_(v=X8`%EO ziXN%RKqAsaf(lHfPEJT+KqhZ7?UjkKlIGZN@v-Hu*Y9LvK4u5{@sKCyOs<|^WwHq$ zB_aLk&las9I<{;h!P&=NN}b~B*OE&Q1n)YqgdowlTlCA?Q<~f}Q1|jKCu1;|v|l5a z)=&u-BC@O1C(<}KL=0KxIVj8Pr3xj8NjP-b`8sAPv9lA6G$3yk|F5?iK8X3(TPGa; zuWtoKNLfs;l`W&^uA}KRnQt@eKWPZVeY40^26je%oFs{UdG>&N>Fn%xJ7j8m3?3Y^ z?iQwdK{tY~Cu<321`@>0{#9i*e4S%O7SKVZ_IT?~1@ixGwq$gQ@4&5P%Zl7m-xNYs ziL8GiHXr2_8(6s(2CD^;xt$k@+8T?8!M378L8ugQSWzd`eKiW5!=Km_;oY(t6JV-B zs?Du(0V2O&PBb^az3DRZpGo46>jvMs&mwk$_sjnTaO=Cs21$M3)Vhi(yNGfF{WFhy z2_WFXc>IJ#CQLDfBaIn|#5GpMd(f6JMHpjc&;IiC`C*I)&btT<_ncsI8V0l0EhtPr z6-jB277JgI13AGoWvHQi7U?oDYwd)>1&nPwtn7WhMiXLO6tLGxk5Qg7*%bIi;pD;*!tztEY^iYAATZoe6}H0Y&wkkf;g9~iDp~U9DkvTM z4}A5n?oaTZUG?fCqk@#44ty#2LT^>msk%jOl3&D#Ix(9PUm~?}i|8O1dnhQ1NhlLri#^Ub<)Ap-hdw24a<5NU8puCkFLZ>_&E7!^N@%Q@(u(3)=$mfe_CZ-BX zWO@N{@tBQ`jg+-JtH#e39`#<{?7YBa@0MG;B<1Mnc*A4>U)=8gEk=aQR~c--Z*d7XlK;4Hmf`*T(VL%@IPjkj{ygkh&JxpLdMe%$ zFymV5lC|0}=$&)3seEGf+tg}9jxIgduotV042!$FyZ6iQGE#~tt}l-L6*GM0)kEsa z%Av0%SQ?(I@%3Cs@0Y&}{^q|{i@_9?>n%^GPfVdtKHWMsMNddb2%k>NuCEJRt(^AI zRC3~sE6X18^6@EucsSg@?UnK=@JOn9>Et{G|NA_$d^8hN#O2QqO9foE=?U8B`qAv> zjx9cJzRM|AR^*gxTJuBcOLfu8X1ZgVQi+E&A22t9L?9{MGPESzT{T_TTRj(^QJ?{#goV`Ke_SWaXsf z6u$~}2$iGwEjPG$vou+bQBT1(X0mABTRkzdXk9bCF%jN<^!5LKS4^?ox+#O)ZQJmB zXVgbp2XNrG`4Z(2{aMa}fR(A{N(*eAhtgW_#Gz!u?eZHbjK)>}{M7zkXMxcoZqM0* z{m3C%j?1XhQb$RHZ&}KE*9Qr&2XP!+XJ;=aa*#OhXYhuMmNx2cC83*d#q4C;*1a9v zkfEYGSz(IkC+A|cmzTG{&uclPYyy*g;B?hww>x*#g6)>&; zo!7o})0M_ai~>%;-qGd6yKasZkNI}R&d!dLlhc0bm8H*@g&$=-j{QMHw%>p5IQAP$7_G80oP56XEI4{lYF-Dx{nTs2bbp-e^QW>uMqce9QM%L<6VN$f!o2v z$45p-J4$=~%xA1#H|%=^ANuNALUL4C6E~rTM(>oulI+jR%M&Yhd>=APYShU>JDX_d zE!eP8HpJdcYICOu~Z{Q2B{izCGKeqVmv@KmYcg$zL2$9`wN!<#7amzVJ6T2E% zI6fAvPo1Eob5I=t@tP-(6ylUyVPPQw0YNLQZM1N5igmHDa^HJ|TRt0za_==2ojs(t zefyz6*`-apCg2`*3kKszdY~I%2*J&OawSnO%ERrat^33B)X7=QPQ=wgg`DqCF7gX8 zC;JG7*?#Kl?@vxnKIQA{YZ5d@RuX&(dvnK-S#toyDk_=8KZ?a4U(ChU(b4&cr=}W1 zbb$!|Ks*#;vcu!=%X`b%5RI*u&tLdS|&3TR9SK%ZWnzeA6 z*_dk~H8wV$dF@Mj_3G6Il4tsiph^}U9j&aQBKJ&_WD$bU^i{4O+P8_Tl`3vEd6DIB z_4W4puc-1FGIOw2PY2Pd&fEBJR-fI^DZpkGX=fY8+l`+8jSO ztNN(_(}K8y*UG-6d#vI;jn#h6*Mpx`<%7OfqQ23@3f}v=gYd+L^yf=UV^S1U$k)~+ ztC%uIN>t9#1Qosbj~jI!@yYG`>mI*#(B0CzUGn7n&S8Mj&1H!pQ})A7up=Ga`I+V* ziSCx4q;Z;Jr^m%2v8LY~->v;cU>a#(<)|GUNv)4jo^yaf;|wIee|{cqHK(w5ZTd+AzJKcU z>C?ha?}%5T=ayZ#~h!GnkwF9b}Z6p#)g zawra(#}IUOck>?4y|hsq$P%M;Z8^NEs-|Y);!+L)_wVCP8ofH!x}op)Sp`*9A}lN{ zVXh%nRn;5X+693@$45cVccD)FiPJ+5QP_#m2?OrdHq%as60#i#efW@iZGAmCH8qMQ zcu#I|HW9?b0F;amA>ul3_`V+Fz5oJIpYyCCleDBx9_u3larHUvteZ9 z<2t{-&qbA$IXA7}6dQ!!%l4%QQOKoz)AsJB&z^1n4gHf(KpgR|F21Cs{H#J$hh3eP?Zo(#OYV4_0cEv#^l*`u0P$+bRy65SR_xPnGkSMV;zX~YuPXPR6>jKjj& z+09TlqOU+ezIQ_lyF2%>dl9LOk{sC$6O%YtI*!Z5g{L=JuQ3+ou1Nc>qaNKI z=3Kq&o~6}OS-873b56dk#^|6}XppXxxN6$c(h>_2gK1PgHAEiC{5VudLU-F_NJ19* zX6?T4;MefVTs3RSNqrDFq1R({`bIKjdYS5WNp(r_BJpvY{D}#|Ur!(v{qr-LK_JZ5 zy3eH(x6unL-4Ob*P{i9`Oo3diUad@ ze;vtq>i_iu{S%^AmT!ttb7-ib7yxZXJ(eX8#-T*y7=u| z%u;{q1+!Q*``S#sj@=fSp3;pQWYDH6SQQ|qfE3Eh!GR5d=Ica-+0ze!+iyh#u3YJ= zb6>2YqaPmwDFkG7ZI$CVk!2;_rAwCx&YV#Q2xusmf6QC7M!PQ`MIy&ZF<;SgG#UV< z;uA+;CRWza=q@*XedERr)JZ@9fJaS3W4Sh2X+Khc3nP=2mBso{)&0+&J8C~SkC&Uf z^{i`S{;Gho5}G}^y8>>_G)lR;Q+?vpaR1Z`3v6Tx3$}K#@$-{jG^#3c%UpT)%GJ^qbih0<;tE{Qn^YOK>H5Mz+0sM+sYzI>w&sxG!`Pnno$1 zcSgg*!;w)}Prr-{zUKyE8t&i>xDcYQ@uH^Y-s{&AyK7U}C~kg!B`+`GnVA`L2M0oa ze*RK}%4>^u1mU6Q)E?zQv-U+*6&iq}*imu@3@gZclKcDnlSQN5yuE`gK4=ZFYpAKo z_arb`I5SwGYh(57p@o)bv0=86AX`t7y+mETA!wVW&5@r zRKN&t-n@xw&6A5rS?1dmCYN^mBr7T|evLgjPc8Kl(yR4ofp=bmF&~p28X6+?Tpq-| zu~l{(f_qJ0Tz-4|wY&H3wU3TcxVX3gO3?bS{7UFERjuv!$x7_Dwl@387i`nDt`%9= zpCu(Hhs`$K>45iw^&&(?)|U;hwA^_@3Rak6R#Q_0qtQ2!$ReZoSTQ~+BO@j+kJZ4y zzP7$2YcdD;-XQg^djo#v3zoetx1^+1Vp6Eadn0 z_ZL36w&+dW;(B1wi6Ku?=CprbN)MZ9i)oI*v_>Eg7#JIO^!5^V+&d$0VPR1g6cR@W zK4`!CzHIZykMPt~S}>U8loT8}GTj=)_GbgPuTM@-vtPbU0XxhmK=DlvvRD_~lAuL9 zj)9@!^D@`5qU7BO$Xp6%MO^MiL||VQ5<)FS)t6&=m`SU%}d%lY~XOwCgcv zP_1PbQQ)7g9^}(mu#XJth|P_PZ2KWl3Lg}Ac3JXBa%*w^8FY>iy$crdmF7%fCE z_uI~$YfscDqH*9q3EvI0ic0kKKJcBNLfyZAA8ZFD>9>hGJowcUhK=vGF(an@R2vL~ z9RdXk0&5t|eiU<-@!x91m<9jFg>b5uz|YG|R9|1exAH6(@yAl5>SER2NFaH$f^Fn) z`uf5lf3|mYXzAkQKsp zdCPV@;RLv-cK((>>~I6f9b=*9XX-tX^Jj^9-USY~;>P9aYA5(-m~`V0YDDk50MlG} zc6PS8g#{Z94h{i?42r>AnF&>qkMrRWj0MKr(vn>=VCQROkIFuDMO0o?SL-$FSK)Vk|4szaiy8?> zFW06jRmgBGMxUs(J5@9l24=%z0b%=L**fr(r@3=2AtCb}q%z2L03?dUJHtR=TytBi zyv1`l0pk1f_Zztn)6?Ne!mC#p9S%F8GXDaD@n&G46Uc4wQ366j9SHOOnX^~9Yo}^m zS*$Oo|B!nG$nfij+KRrbPeSe?-mZZjezyZQp-H8?b+`8xzE z#>o#KIwmG=)(?z~kfTyUrp#X1o)P!mhy!DnWlu(S&i19_4!!nTpcW~rJU`ApXeg}}-`u6P^6t8oS z-C$N*TLiw|g8jR~cn>plwYFCme*=aq9&F`UWm>oA*1OE42E>Hht25PIu?4nylZdWX}r zm+k_pfb_?fto*`tPI^f!3Ca<0WCUdQ1p$PhyLIapZ>}fAA!0^RGT`aETU~txayd#@ zT7TS*kKNoHAdVxETIn()`0eKA<_*I~xw#dO?J+w&+oS{3(P-X&st{QS?GSbpZr|oZ z^b$evtYdnb0Xk^BwdW+&+|_2_U-Y__QDm`Q)&6wW*2V99a$GB@t5@t?hk#xpGB)<~ z`*PD*1V?++GfGQK`@yX2(@Kb`mRk#5j;mBHp_KF&z*Tov-r)!B@5luPO3sEH2LZ2p z?Wl1tG;NXqat%oz5Hm9~BiO^hAg%BBmD`g7NNENLqU*6zqTBlPl_fL|3Q~a99V!hl zY!CGTI^W_4hz{(lC&p!D4A)WPoZc<_Du+@G*6AuQgab|NicJ?AuI{$0`rF-t_lJK&DkNH1lro#WX#M7Q0bil*OP;&owB;*Ar|oSTcp&TUGvt^ zQ`02b#d)v>B1!e>-j9BML5ES!YK>h0zzd$x5uFwF_)GxxB^f2AYebtiqg!Degp{rW&EpGLIJ-v;!{$nAodWjv9a|{4Wfph zs8Bv6ih&FS0&Q3TzD}WLXJ>%}ssnKINVU^Vl;(GWfTOmJ@$u(6=S>>@wAM+>bwh+5 zMsY-3WC|<0Cc>6uWw@trsCm~(bOb$kA#Vcl5$+a3!j)S6v{rKu)-dMI1X|j0wGyK{s{j0 z^9R`R?m^~9>Hug=y+`1^(fs^;7&KhjE?ztXz;N$Z%W*t_1s>P1J2~Kqz+8E6a?${N z9w};}yjD_F!~^IG`SAG?2dli+P3InP_TAmxz5UgSky1k;r0x1;Ae~`O2(VVE>1!JJ z0!(ad1YoS-gLM%^4$^6&e908YI%Q*~J|bT}%5tnoYjb-$0=^D`GJwL(AxWZ8fOnrf zdj?OM8C(x(GDk(>Lp}}zNx{;zlNJt1N6QI(*BL0P5$Tkl)AEyaeU@0atp8<|7YdgCV zYIe3oYAUL`@QOw6{c{ATPj>)(th5_qhB#bwn-+i)01d18_DFas0xAHHY`D4_*SUna zIAUn_SWLaNfqL-X&{wf6MjS5{U+bA5O(2Z?e} z`pEnHi#^fi2@DJr@c&^ASXKb(oY~@< zkmKOGy1G0NG)9HO^Ma9LV`W9<=jTIcfRy!M`Y4KoDF$HcC&1i}i7ND09I3$8qRBZF zSp@{hz(V_`?1)rUQ~o~yy&TLTaNZtJBhgy~E9CLbldfHXzjA#BD+n~gRZzmMK??fx z`SX+a-INp*!>@f^8-bV-9*Epfj@113?-eF<5HL8;pI-#%AP@0s3$XFRt0C!<2Ub_{ z*zc} z(H;kim781H=5X7ryu4gol%b&53M*c@A3UgFmF%zH->p8_@`hB?j8XwqQ2aWsk}`^h zLP?(`=}!S^r7MX|-fg}e7l0@>E-o%NH@B>Wm$?$%c@L8jS>JX_2!6xK*9~MB4N3jxM zA!~9VkhMWA3eCVC0H!MNy_)LU_waj)7Hj9abLU`2{S|hS-dp{9U>UstiI%kV^m%C7?SXdzrVSsGIcB82WYP-yB3th? zWi0l0R{GXoy?nU|A@VT{ACv{6NKpiV4AK-fN_zh*2j{tS77&)}{I=L({)(KQJ{r2a ztKZ0?G0LP=R8*{zl8jJmU-R*~imaG}F_R}diLMY$y5TaDq0Ou*{Tl}qcH7`O>RYzMOlQE5VUBrbE!W%sRt3UuH3f7H#qGc{FJR1lYOQAm46NWDOuje^%{9j$u- z90BREW=Z&#k2*IWY)Bv_a;@te9*X$vRbns>(SV(z>6**wu$!C&DG`U8?)WbKfclJu zltsD|$$$s=(BH;G!W2xXDY>{@GOD@#{AIa9EDh4>fXeYJ0(} zK&ZPAy_lr}aOzD+Ac;T>!h@ci{)STJIsgd($M_$a$h5Act%#!v_Vb|9;H@BKmc$_A z2eF_XG9;9;_uwMrxgJ18M`UPdNGI-PA9>L5Z}?us7xLpXp}5bQfnAR*8ogfu#i-xm z&x!ffRitYG;E0`%j~Fhn&S#yW;PGQYh=NPI@cT)T1F%&BNv*i2Y0SIh=+$#z+I$oM zSu!+`nIwErsDXij&5hUJEdfp-X~3lxbX0({4ew^T2|^kJB7h7JKhr1YTY~GJ2F8k% z<6{V6BTx;+ofRR0Od<=~@$NwI22kNKJX#dgLWV2NpFPA-=D#*R+RrxD_lZ{;nBb4V zqLO^1dkL@|rpChHHY`Bf1JuK7USdr{?TDm|;wB|IfY@N-$UC9WQOzI$Go7^nG#;0h zsxSGH$&s1En2{Tw|joV5FkJ`no#%MF>sGiP32Gov$O< zFiX&bJjpycY(@zmVt}eWAaP!P)d#X6&s=6wQYiEnkWAp<;GnFkIy#>WmeY)&IT-X8 zu#VB%#)hW_%HG6B7meA^oqO8;3l|Ts9TFW>EMp#>=;}n6s;?RV00B=7Awaz*FYmrK zDOhUwO3@}0%HcLZ&Vqu1bRsT{48r!I5Fsp~6$5h#t?-n{&YnG8Z;}9Vh6^yN(Blb3 zv$p{V^!BobnBxTH4>}+~90A#IISZeAAJloT(m?xA5V}I@8N!6nSNB_t7lDWs4#I0l zwF8JuL0_L{VR5k%8ZgKZAd=efNpd27sGP6&Lf@YaPORy9kG2M;% zJ2HPZ<%yZZso;z^Uq{?~ljMfSko?H|>+mU9Slj_c`uyTz1b|{bqbg;avznTkYaeP! zmxpqdCdy4mz@`6ec904P-O1R#1yvSuA@(Ee5NhS2-0Wcq(M?wXFZh`lAAjZuAY^{X z@zLhaPNxz}(A}qMsSEKUv#n5nBqAMYnB{M7ZYCfmMtgW%hA!;p-j)@#nOUL91ULW{ z`t_?lIJ5cQ=6oOTFibjUi0uWUFL&`~2nA!si0dTtO1jv4GDIk;-kpL zA`Kt)g?J<5Pk4#4Yhg(O_0MIBXR{yY|2*+vmdP+FANi|>>T z_{&!T9odI36Rq@+@$m58!WVb@!%KsI*1D--FEL;LKfmN-U!W;j8&?uhCZMpSqK8n2 z-MB|ZML{21>P5`#hU=F{D0-anQXzjTJ@OWQ_FidC6pNOYwi&yrscFov?&VUX#1_l7 zhNY7eyRq1U$a8i;XYy)`T2s?Tj&_{qjyH zZ5-2n&D0i@roo0x^MB%{f=Biz)6jGJ2FwP{kn|ULc*?9v^b%>CF4@S+l9-axN8veQ zw;kGzh6|w_M}a;5g3{b@xk=JLe{*4~Kl6;oyE5{NH>ah|aGIr+Mij?Ie0@lCRV1t( z-r6c0MSIg1Re-OMbTmE3L4$gm92vriig$$;0#T(C2Z*f!zW)& z9QN;Dx}VDywY5NQJ7|*z*tDh1s%xenJP$yX=slZ^6>CP#mCENXlSf9?d>^m^YP^s7NU%gj|cxgZ2C<}F8Z1n zG?Wh%M~&)z{FUf!Ru=hbMq68(_gXknl|?^x@7J$LANk%ZMI|KkfACsHi~Sn1zHaWY zQ13W!JVUE6;Sr#pp?F=vRmnGg+H=@Y6SpZCDU7OD{v(G|2m+tr$ZMd6`>JJ0x5^am zqu+r5n{E}P{|!Y?WxyG$rHzfn{q0ro-EzQ<m$5;5@PbV-d$yGtmB(ZQ|TQ10M<#$;w_kb8Do8X;_X7&JIxA+n_p zAwkA=&nyIO)PE}VD0YLg{f}>yL&INJxy#BT$Hc@``CT~avJrCg@~+p-829wWlSS^e z*iEyvg0Aqp{laWD=D5F}=~(~yEx8fAg>a*#rDb}&{#B}ZVSRo52n^clX1Ynlq2vm-#qz;Hcm zyI8+G-UxOCR|gUj5*S1|2?>c+>o0Q<2yZ6V!~NmI8v<6HoT4IBRk1tnG9CMWj=mY1 zBm)D(Gtzi|*L6f}3c;{Zu?O;-i$kiK6{hUhAu}d{6kz(KtgIO79p*xkk{%g0yQ4u| z*1C}zmz-c{&*}DLaOOA^5t0J-Q+_^De*#s1Bvn8ZSkEb`so^JikHBF?Pk2H-H8vJ> za`H;}Y)bPPF{Zq-GHfxqf+LgW=Esj8l4+Zr%8zBB+@wX*c7Gjrw^x5YP1{A1+czw@ z?#*&KEHx8US69CiaAOR(8Zuc2M_SdwKK{KxG`Js6G$XIE?CtDu$;e_qfBxL*Ha9m1 zb#jtbRmI88%R?AlaT^uke|`Dvp#8=vDi>7(_h>zqgH}L*B!~l5;DgE{INhLjc9Nc+ zydPhmC_E-4%>DEU&Z&V+KtMp^aKnXP&BP=j9E0f1brypHX{4%3veAmiFK}~Gwqm0! zVqv*V^L6SOV2qnZreu;GOy=Se4;>ySqR^C1qlb3ko*J%LWDXNP4qJ5MBV^MJ`u))l z8-tLov)ZEnsMO>R9klhGSfJ>sugJ;C$FDP=dc4-QsX6`;h6_) z6!sssODZEAu=64_+hS_CLE5rHW5{Sa}BSDsanw zDlf-6K0by(zRbn_{AnpIBZIWRu(%ip?kGf&LY>l2<4_2MqvQ5DxQ0VzVhNGzTMXH^ z+yyPIttIouSG&V8ber96SN*w3v7SGF-se7&`{aa576xq4aVm$9yrdAa&dyw>r+c(b za9$cw72 zeoJOLjy1W3hq&C|`eX@upo8-c-Q3*#D0Af&8O)~FqALm}GQy7vqu837=eOXTMuvdR zVz$)m0Zx6IyYJG{Kk5A1jttu|Um7hXc5XO1A-68$C1^7-NoBt0q(6dMk4AL5NX;;_ z?T1#jU$c1b&xcQ!zLd(>FzboHnr-#rg5>1nK!m&wVA!VGYKSl@Hwt!4FD)&}tE%=d zxA`F;e8>*G_h~)q#<*O*Xh&&Tdb7A_*n0IV6F>nZ$lh!<0!Ql+{6X7=G1%#4#%-hk zFX8mdG16*0E^e5a$xu`x0VT(yM;y-po8(zg6u$-_j4F#jEM>ssGyV)7H|vtP;3 zR@53{Q+g7lxBuyMkB{Gb)#b6R;j((%a3a6IPe)axBbAvU=&*`sGXh}7MnJ`Fz+cC| zTpTz+q5uH?tyn6ycd750b=Kaw(LRM}n6ISwA@LdYo+UDT0wV(wJPO++eb)nibQEgR z*Lg91pViT8U+=piLM%Be?sJq6!D3v`sbyv3yvfHcy8tBrj9asS<)x{4^o}BM^|-eF z&Fh>elF83en&Vbu*c@e%r=rpDCsJh|vjDIvJsHI|lsG?+95-jX<-BC;fGDfGN($+{ z^oDXxguWO9>ql83_}O<2S?Ni*P+Yw6$3N@lR_%_$v$}%&ceC{Z`9q9X#Ke6}R~1wv z`Aw|@Y4c6NeU|arQX!3+%8ZomScC-G~3}FUalgOZ1${3oq4DPfU!l2vG!K3o90IPS8PSwl8KEC6JCO&^D z$wUiE%Lr?HB#0M|4ju>(-}Ag_u0h7qpd41*nomhFL%;!_%B==1&qd=}SKRXOa4~A` zO&yDVvIh#?itur)7oVAarz6Qnd~5V_+YmIdI|K}!tkrieQt!xf} z{R)Bonn4VHP42B?{d9wn&{B++xQUmr3CfqAiIj+X(hKX#_)oAdx#2=|B&i;I_q9H1 zeh`H{Ng4>AoQVci<0<>!#=mgKO8WY!RaVE}WYsoE_($p(7gvt*Z&*>uNTIlIvuCVB zOHQ~2OG;L48wWs-qaFuIL=9Bh4&jew3rE+R)gx7gE%TWQ;LoQB!1@hEx4@-6iEZ2z zs;V`XP*TA@)zgE$FAoR0bZaeq`;48C(A+aXRC=UMO8PUZAva)CQIBK6X#Lv@E1Lw2 zRAurx_tjVpRhfT97rt0@12nbg;07Q^v*{A;Cv0rJZ5PY28~q6=_2AlteV}o_enCx3 zD~#%P73K1`*aOL|F<@0l7=+;bqIS6f@REBiI|0;F5K)Er3E&Unc-bs|SNKS5itgXp zfo}jX$C>*Vo$|jq|5Q`+$e`XqLO}tI-KZH6@`RNYhF+Ha4n<&a1x4%Yb+Jy#i(E)mx6{P zyRx`g!oh(pDJcnl)XK&tuc?VdL_{Qt@|npHh*X63MB;25v>Zk}iNQsL<7mI6O-fFN zgS2?=qxajrq?c>#55vRNkjtxF)Fi@`W?-{x)Z(sH8*^j2sN>O-*UZCx!L) zN-w_tGwcl*!JAqq*R&)>s<4^2QvYW|^bg6u%(SgX&U zvFYjX`g-2p{(c0=cWW*%a(Dy;1hvj?ZhsOvMeXWHN#l`&4nq$XoA9XuE@{pWmSQuV z+XbpjUcA6KKU_&DH)ueHPaE2Vhisi)bAI^pB<6Pg2`Y@Nem&&FB0TAuAR!@n!ot#h zu+(B!tvfb58#QC>hegKcL|$`PqFs6fCT<;Y(<9%u%UzR)m}Dl^b?_E4MEK$M&*kZ) z>hb-}AxbzkBq}zx2Zic&P`XI~iAo_p1qH=ylN%H0YIJRFZQ^SkfX^}(yEQ}~c1kO| zH;zrucBUI2?r&Uo%Nxx9{2GP?g@nMv5Fw-rT1s=~pWL+KlQ9amVeegOyq zL%d)$!-5WAT#2|}j*aKZeVW;=c3u$$)OGRI&{N$n)>yQ3bjO3Nm0=rn_%MV{KTyLK zc|*_0s*t3ntsVZFy}KCoAM-l+JZ*QFffsDTe??Lxjd^6b^^97*@oYvvhELncNJ}FE zL=z`V7bk16Dw;VJUGVW^J!_-O4!ypuJn(=1JEy|Tn zW24E}S>w z10RHPb8}4_7RP~Y~otK45fRAQCAcuKKD{f|K#&KcVIJa^`2!xsGPxw?19T1 z=B1~`3UFHW2mhbYAG*A3e0qNV=Gst;g#Z(|r$|zB#lOvXv_eQDW%-p8K7d5>2v_tR&f0)*Trv>-C@_U+p| z(LNAn+jf6r`#pKsI+R`^ksb|zX2MvjsY51I$(&s$wk0p}3+}sbHF2zGbw%Yz@fS^o z2KcoicGmDU=>&8wRm9A(e{!t7*yHKInh7W6r}nG8)=!ir%eX!O@5pJETe z&%X&Tk%frZ<)rtv`ls=hP+grK3F{Edl$+m+bZ<|l}t-7W`&+22(o@EQ7 zxmgLcx4DQy{eH&1ztc7b-5THNb}#sB38tHy+2}9@Nu|(D$>Jk`KaDDSF6GCnlm3J7 z2@CNrY}&)-#5z=+4eZCxKbJKPu3#!xQxM+{H|N_Huvo}OyvV{s zsYw`THFjzFv8MCKWIWg^YGB7)bEx+0SE1Aw^XPxxWIqef?%s^b;^d06QRIFMU>EbU zx2m2?T}A8mGpk^>A*i!MP*X1XufbQfQ&|X>0#zckPRKRr>2zQh*uF@U%$rQO5x|Wn z0$tDDeP#?Qw7#8$l~A5mwALr0K*`7cK?DIT7qFM|ZU#4V_M=JW23Ss%9p0t9#|n0n z9e)f1?vH6T9S7mtR8Sg?T+Tduek`L`PQpo?h#z@V%l4ajTJzSNq?zB>WIm(w`Ehp3 zM1w8_t5AV0OOp^Wno_X7un%b}tEBH(b6Tx*+Jf|tB{%7($!*5{MkWnx*e7kFwVH(T zlsFmf0vlw9n(A*V>RA(IJXQ(czl)nq!IY+_m(1rg^cW2J_>uDT^fd08ywYY6KD4Vd zUmt1+HVYneT#vbg(KPcr)ZMeenz(Gize*9E;ELczIv3MU6y{BhLg{``vzh9$o zrXb1Yy?v_64TH2D4`uhNw^!ZS>ZYvX5*lzP_$O}(GLcd{J49`FcA-v^j^WPcJq6Aq zlIOk%3z*}a11v|{KKZ{lrR<6C7M1y)TXpHqH0|zIw0va75LWAAm!-8?GnXb2Kw~_E zfjptf0Oa^LvfMv-*P;9B5b+d7bRRSy-HgV^S7=uQ@uM=IJx{eIrPtO|>A61$VYtZ% z*s%_d&rLk8^|w|e1!uzBHV34n-@nB3(H;3g_%bRwT0%()6P#qj44-;-B-4lERWdQa z4`nF^VJ4XoFOi^wle-?pR^7bmm-#i9Sx5qgU>5>~aB#M77uQdLR7lZUc$bAl!q zbYe7A%<9Yzo`nisW_3C3CA4Fkn^ouO^QF=}8iY@e!?SbEd+8Zf2L!y^p8JwJxnCnF z7fy$(1iss@Z5lJ%?^v-t+n=;@>tXPzyjzEZ4?>0DsiBEysmJQ(A-l>xjgx(vOUr%# zyEcle1TP=%)J0;)R_5gUJG84sZVxckSKHIRZ1MUrx3{lBTq$8DG-HHRt4`beo0I+@A5^1TwL{{&*^sHwN4mcxjC6UndYv_!VR1 z(SOT*vhCeX9eaIGHg#RvDwshUT0i+ zle?#e7e2C3nO+%asJ^|mG$tnZy!ci$kKHpmNsLfg%Uzam@=)AyVtQiyqovcs^_J}w zBnUqPQ@hiHAnqF#6M=Y7IiYrkn@1+~{?T4X*bq~l-m)B>yetxdv7&;*?o96XEMBsm zQdV^X*GF~Qg{xcp`zu9{KB>OPpmD_8`$E`R_NI>+EX9WgB8vV8ysd~=jr|j6Q>wT@ z#63ZskG5V-Acm-9jjz1gz~SSR#d5d9mM{T&89&8c=h|lxRhwoW=;7 z+G|A~Ftx4~f5`RAoeXHuEG2Iy~NzOa^q*x+}dEt#nVaPCO%3HNhx|$puIVm3c6*A3Wbn_Ku+3!nMNKeQ8NXJbBlcfVN9KmL`jiDoQ2tHN7VGB0iI)hL$>7q82A6vNB3-Iig4W+V>(UdPH{38v}N%`;(K$A}PbE2OHM`)HL(r6UYcf77d`X4hqojkq-%j9S${VxrBvsKK`y-HYIl<~t5ZRdt&>sQ zJ^K&a*_@4T;xbd6PDN@duli*!>my52S{mB0>QdXA{MiGvvA*qNJ4}*d4(RIt+&vlO z2;t25LWZwQ2u4qH$~*nwg)zy}d1=E4A`bPr$nU0+_QH0JgL_=A+7u!7qA9;usjr5N zIoN03BiQ>G74xAuCaB0up4XUL5>@CGcNK!X!37dm^vMmLoHP!Enh^=q(V$j^KX3POFBG$k>)S@Ti6$v zGdC=3cYdubxid00>71V%?@-5mX21N}lIXhc=DU(|_N(j!`Dk!rF;;^%jz}yi_q-`> zvSYrGd>F<^t;fp!1jk~5AT5O;ZN!>m8_rlykTHu630mb5KOB}Wv!hJ%z(^z%pi;oA zj*2H1)b(yoe`O#jbluXbG=0>V73@gb21nw9^dE4jw7XIFqpY{0DQYN9#o^$hUX{A_gOy0&c%x*)Kq&lud7(18)pk|BMK2ZEs5eq6$lxSuQHuD`JOBRtv9)zA*GO;%aMF$#Y?7wJN-X+XNz5dMbla9ge zSFOV_IG$ea*u&F|s@dB5HnOQ`he=s1+!uOXO zvmJqAypHpaYyXw&Ic&dS2T5X=+e_D!l$6!H1W`#vMRZtpz-!qD#kutud17MX=wyh) z0;ERyJ@<$aCB#7|0mp+q0^$WALIaCrgY50vAKeGn4hj_qNqW({ zQ%zz_2$Pd zyPbH-FG0}dU-|S0j)DIvpFVzp(-q6n9tF}KkWRqdk0!J4xvM&4^CUo$xBcoD8K4>6 z`<`@6OiWw;=P1Xxqv` zyvgvBhRNwx@uFWb&8n7%H>YBPZ|usz4GSK_9}b{?{P##hZ?zlAivbDwVTZ7ADP+2{ z+Y-^5Hux-Um;8jVQGGNi7f8I37V5{Z%{A1Lou#(z0U#g`*n zXOa*(5V?%Ia;h~y%tUm+Lux|^-$y8Y=NjoT+%U8x`+}GMvC)dYl@h0!ba&iyjeLIi z_v+p@_B>^(Fd5gE-xj|XucDNcBB)WfkjMPb_rgBlhZ6;6|0g{`C<^EQq$WTPg~N`d z+aiwa2?j9~aY)U=e(=n`U-rO2^;5(5u!LGxyEf;~y!D2)^NMz%r=| z4BydN$&#IRoZj*{ThH1jADJ6LI)9O%*gp}LA8eqbM5xt0YcLFdxG)*tJXxsr@(KyY zIVq2XB+I919lvQ<5)#sV$w7VV%2jy-a-1Ly!x+#(koUico!zFJxw&zrvFSZJ%>tsM zK9hV-kgS`Unp!z4QuVB-dnshl(v(*-;e?8&!idk zPjT|ImuY$VzYNvVUrH}xS`x6!ffM5M({G`&d^{-blb5R(naCLb|MPP}mEl`~ca;y} z3r;Oq@87@ozS>O3qoPW%8cLJM8#mpbuLIHJp31;Gdx*H69tnu1p|pMMK#i7EW++M| z$P6!=WqTdCd;tep8aV*X-}zzpZeD52<6`~GuhApDKQ_S|zxqaG0OB|0-P4Pp=l1s} z{56{AvsGpY zK-7Z?(;sz?d=RP*xbt@#Qu&5Q*K)jgA5B z4lJE_f4v(_m`5Sxxf)Jn+zEp37!3>YAX8`olKN|x1Jc}a#73A-FPg9#@)wa;ck*wr z(m7VOc2VTp9K;nf@kmu4F4@%cB<7Ry?^!C%rHA8iX5?sdnPn5Lt^{p(-`~?KNNQ^n zoc#GSmr??0T~3Z=8`^?4hjBbyagO*Oldxe8<09YI*Vr>~GtQ5_v+Per#QDXa&F?^ zbh~n}bUX7)B8n2RZPXhHovv4~3rd88Nk?CLG06BtF=o9?8goBj`%bV4TM;%d zG{?>_r%uIFU!E+I7isbFM)VYMngevL%V{5ecqjK{sq(N`&R_I^z^jSTp|-H4aqlbj@=>m>PC3Xv6ix{JjROy{-m*c&>Ne(f!#lMbQI`FShKzgs z@nzu7en74x*o2LJUNI;1CQhIjAkml@P9d8vEKmx4onm}A7m@ke_i%5Qu24vjj)8s7 zG;DbOE2jD4BD&xSm{L;w!r_SP9Yt6W*ijpayiFw7kwsm4u>0!(i(&8{VDyZm=o{~s z8xbww+cWQtUi00xKp=%bduR4dP`byrSqsNUCj4`m<3VpLif;xRp4}MuvwyflH~xk{ zTaXYXEekpeLK$yO5dC{kDGbZ-J=&`!qpvQ%t!G!cXG2MewQ;YH69QikcLf8?aNd{& z5{%h1tZNGxzY(;J9pkYi7B;Mh(X@Pq-*=$0gpW)Of$EB#wIqr_(p|sYGj}vRs(WCw zD(};EEyr!vJ^cB{gHB$F{O&BI*hC}UxUyV=cbtAI#G~%7EVf0*r=FKkpMnelOv=_9 zc;oy4^bm&M6QRvpwk?mKXSTwB80b|4)6++Y@uoJdqT)c`Rg3_w;;5X7US8(f+A9m1 zoJOM-TP&1L{$50ABm#iY{cguS2EZXO@fQs0m;vqAC;pDAbz1+y+_2rhSlxlM450GwMRz6&F0Av-|1+Od;P|fS z+BduA`yTn8OqH2`kR+DMzoPRO4+teZ#EI3l9YaIJ6!?8y`QxMj166GsHXnL=<+c1i z4f^qsgGS+pgdbW|+yDr0nXOts``oj8rjmKcXS5K8@=4Ul$mlZR;S%Q-tdm}#lJalk z;IdUN3?$y3FE}|%Ycn>i;6mxUG5m{0DJY#(uC~K7#{zn&nh;2w>hO8Ozj6<2-nk_0 zV5kN&vz!;#x7kmn$F7{DCi{sjEn`8B*rhZ_xW0(H@PgKTz~pTFrmxA%n^k~jU5{2J zZnGc$1j5p$B7WzFGtrO6&)kG48ZWhZV24TREqvO@oBQ~XAiY1o=voH&s@J}!aC`bF zb4`}Jnc zVEu~B`?fN%VSik$+aApJLgJ=aOHf*lgJNtx8K>OySJ($4=En=PjW$zH*wCVR0o^*``MmZ=*dmi)dFvh@Tm!2H|gsa1@7{Hb$EsTXgfG&{>UqQXqbMYC*t+MVh~E*%!wH9jF;j*q zWjY4YsMbxl>LyWDZNr)KW5n-+BWT}u$g|VB4(a0+;@FYFl^C(0;e_z))%N|ri{d@2 zpSZJ6twIQ*F2WYjE4YWU^!JfW^bH3MP;E zZ(3TX1SuxmFkb(;dCUf~06(d=6g4CD^ZUBrW57ak!i}pEke>7dFMh!z*Ny(m#^a4n z*H~+|?ujr%w)kkdl0KxlfVC2aJp|UK+`LybFrHVX1% z7uk_GInT9MmIMTJj>j5BO}$v*Y9X9(E6NY-u;UWam49Qyo}Sls^Vm8U07jdAr6-NvlpL;b{?I7(`Z(8e+t4SGhNFIp#V^uRy>jO_h2ye zG%(F98mD%Q?iebqB39?x={JGIVyYjXleJ5a2H_&_Y%WXxR zvZ^4NlkrDKh#%*K+n-P4f6}?bM|5?}vXvP2_zat~8>je|pU{99g@V})Id>f_wkQ5K z4l=ncej+!snU=GUw*5YsrSfN)&)!LHV$)mgStdWC$!W!l(h9A$)6_|*SMA17J;$-t zLb^A~Ap~MA^BvueXYl=7g!mY)@<~{kSgW41$HO++IR~fAQ!lkUZFe8~Wwc7)HuH~m z#Q1UM;y;4*>M z=uG!f2mP}V=yBXvvwM1OJy6+||NRXD|5c<^XAnDwlsje(dfH8M>&eL{9^xMFh|h?I z;deVAWT2>pu92(ZqB_5HG{0B{2G>a-AvS&2CBF^H$cSRBi95NhNGXp{FOdY21U-0=RCR<@^`!?cN&H3**~eY#pT0$ zWIH&PJY;i|qG)1b7vu@Zh2JUWFsZjOY}NC4AZ)jDW&;(1SN# z%cah+SM!pRrWJE5f|PN!)9-`7Dho}K-?tg8e3FS*_)`p4#6~kfRYj0St)-z6^7%78 zOyJ|_=;(cW++Q8|5a2Lh%M9%R+8!hkQTHv}^S!yyrv~*9h)T97MeW^xW0O!?Eu_ia z{^hGUn@5LXDo$$IvWLN;?8I-!b~G<2w-=E_F(dX-cC08OE|a8*Y;Z*}4}T6s*yRU8 z)uf~l28V`@LBZXo&!~BL2wL+ScPfGI=F(!M97`ngkHw76C-O zFlyP}D>1U^)mV0mVgRX@l;Uj5EyErvB^8}`EUFJ2ts#b4?QrBLCNh>6(m61=k&TEIFDBCeYP>4Mg*7h*cD;kb;+U%7e-LOaTJaxf?Pgqzx(&`lvg`!$;rvg`*hN9lSSptPj4*L?%_q;%u$Qczr3jWEAU3tDer0DO=Z^T) z_HdOC)n#zFTfRxRd^ShFU9Oys@E@|{BG&&7F97WWkeW-3o{7APiXuLLcd_h;16(LF zl9Jttjx}r>W1pmEn?3B+RvMnffRJk7SUcEB{e!8jK;!Z@Ihkw6ZDe?O6VVTyTHz6p zoKl9M z{zCHY+qb9~QhlSfWACGejyo*Bivx=DGbKjNG03*))2B~i@n4``BSqC^Pj{x}Y7201 z^Nh+J;CK^q19!^T3Zs@P-!r=+Ef$B_ zDmbuBks*&CKQ;w5>vjZ)Kys?9@qm_oY}>u3{XhCqdN4RzgAAOSkRV5ZwN!fFU;Sp< z(!^7ytmzke<&EaW?6tzyJ@aG}o5jre7t5YBHz%TATBVovJ6qKxdM#~j8ZohK^? z$2Z~^Yer~f;R`w{hy95XR*JiTYgPRPEiLt%9jA*gQb9I*yquKF!$$- z$APkUhd)BLm%(a?;{dGbc*@`k4JuY7K@li8>*Uxvy~4wkuu13_{x%tg#;m zeGfR#Mj)76kd5zf7dDv9Vv}Ab_L#fBfg7yMJd{UANdZ*1lDul zMvT#1+;1e`mbZyf&)p>;TnL3W_$>wX!=3WpIOXMQAVhS5a>CM{Jox*wQJ=6XOLG(6 zmMd_RvQ&5MVz|k9#jzo<59Q=cacAk8A{oizDaXDe=k`~%zx#Py_XKNUVu~n35Cy47MLZ4CK(JL0za*9Z zK>W6dVrqx`$rCKw)Dy3zVl9jNQ3+}8x3HyvppGTYcqm=KdbN)zAA_W2%;z5ygxVQQo_ef&_H@9bMAg z0x*fy$g-J!b_HHepW9$a*HdNiFjNB;BHWh3h4|MV@-Axt)7> zk^~BZC}=B?R`IL7?k->>Hp}5JWTPg<2l1`kT~3(xiT~bKxR9+;F`okbAsm!Pi=O(I zgzV+=Gb#Z3qadI4W^m+%9TyFKXTqUDgbccT73;r6_91?w)ygS?#%{+ zj6~vV71~@fUMJ8d^KWTnA8JmFcwBeMH)sF?=&#H;kia)Z}n*8(9p(OJ!B&CPK5qgi(38xpP`u z$+H8lnSsz$LsPTb=Z|&k<$A1C$e4ItUEPzXPrs5mw+8q2O8?V77KtU{*+Ky`k}f!R z#Lv#^LOMADuU-hBj!Ai6Zx@<9BWC>h2ed&?0`z=hl^LU=p8u#(Sr1p})r-kQ*zOCa zlYn~DJc#8L6d(ihnLNma3a5h<)~{dIK=?&1DCj%80n7Jdk@1?&)mR1p2G=`Kgahrb zC^ruejgSzTs^}FxaOjPXjX?ma0cz%)3s-*fqRPsCU|f>(A4#G&>SxL?E;tnY}_m2WylAnhQqmsnS0)c4G?`z5g?x-t}b?BL|vwxe2 zYgo_>OlCR#3Bo6+*i@o%U~mw_!^4AT4*ZtdFQVh($R`DU|N15Qc#cVAN#UQa+IVj( zj@7MTOWUt=&Cb7TzAtW4!$udl7wPO+tqFgnUHjgR=slJR zaD5yGO!I*L5!}`yn6MG&_Pg_X(t#70DvRjyuo&Nch4y+!vy3h z)Oge*0Bpo3m=dTEWB;@1&Q{Tjl|V74wlf`v9Vb+F&$?7?PtlkQsl+t5hT6k*?ZoU6&Uy6H<3-61e=dO$aWq_+klXw0fT|W=$r$u@KGniY@Iz3*!F{igD@2Yz_gZDR%_pw zvU`>JmJlID6%~C5IKmKM1?3MI&y@kz&d%OId6?G!6bMd$?>{jmB@CZQB?mYm6U6TM zVU`5Ix{>x9oLAa&`e9ZV5W9p){{Kotvaqrq13L>1Cnw%v$Ac)$&2oD+8rZit_r>h_ z`>NMhuV88vni~h$7ZG@bwc3~cS7sKIFR2Cf^@K2g25`ujo6|x%x82RbVtmJAPYAvf z;qGDur(7qN%Vq@fmrsUG@A+Cb@Y+Td6eKpFPESoy^YRiZC@2K~{_oi-+tDIG=ppA< zw|_>*7ZzfHtp{?1^y%`_d(r3jhbmpRC+zHffSx91WeFF!rP2R&_Dp$w@BwR)>hHBE z@Ka*It`0eV4i4Ab;mceEmSmyI2T2fY0D~?2dI_vwpi_Zqae$#XoL)g*NvU_3ZzT?x zEO0@V8uZTlbX?9A*!rV#`FGrS|H&hrbGKu`mIh9r0VsF3Qv-^~49p#CK5GFp3Y8hP z5Tc=>^*Dj3bYQ|#ix*7oa@i1g!v_ia2gM8cFDM>`rb}+ILK6KuB}QZCT-&G>XRGa& zWVeDvT?i$6hvgeydh+nNv{AigoxfANx_Wtd@HZrOb!0`O)8!bZofym zw%x(^9k>kC1KSiO>>6DLZlxStM94oF8BEpF13m-XBQW16ps(O?;iburhs}9fl~U2= z|7=R(u5t$FA#l0oOm2r5=YeAW&4dye5)#t6zcro*?5(p6&Q!ppW0dTG2Xl)$$E)Mwmj@P@Nq0bOR}>8-W}ZhU)~7di!ovVZa<9wy6s(g@&fgX zruuL99tXGFAHSqSd(bj}hJQ1U{ANz|$}m?ao-GvHE&lpmhdvK`+n0iyrJfdm3TqhS z5KKk)v7-(=|DMBwkUkR|i%s;Mmx+BgtEf)Ev|hlhUTA#hWHsjDeALN)Y_}MR+DI&i z;9HSYeb-D(N|E-L(s(c;z*gCeT&@`2BrEcZ^h}ovYS6F{s^5a~3vR9G$FH!tUtvqT z;d2Lm2YKhK8?-X19cS`~&dMh!jG@?gdvTd4=ZWXZO4M?0`BbvyechHV zACAWGG+~`U3Vdmh*Mk)M^J{vW=j%D4+ET|fl~A^xAkZJmIb;frGyS5uX<2FR3K$&- zQp9~GSNWB^jj(~%8%v~cCiXhGT%MQ7ZePW)N?>d)ZEW2gl-_4!)P+#b6B2|ON(dWV z%)f`{nAEclL??k2G#7WWSnD0tr~cchPq7c4%_8j;JS~%AjNgLwGx0qHFz$&*!&BbDI<-{#>5^tmctF04tgUZG?F zrMDk_)}vL3HeD@wM3QHEc_@V%ZXcH>C$~{QC=`L2vh3e^IHUiBx~qpX>e3ZaL0aB0e@&w zcegwOp)6_0$8;+a3a4)~F_u(gF_vz*%LieTtQ%R=!EQSJi8FDUv0#CiSQ*rdv00J2 zkHO})01x!SRuLER`&0?1CRz0Q>_CoV65B>npws70UHU*>JJ06v?YAx^{SlfXT}F=Z zL66%!XLUZ$31Vr1nFY*RB1%43T0*>8BGNq+yVM%l`HSMC9c0Ye1ACe03*=j=tx)&I zu`QRi!!0bqk(@_~wx~`rgXfX?#9$i6>A$W33QjNixSD=2ib1{0jSkpOcpGd$4Zbb- zo|SQ5bWle0DIm?EZ$1>OqbQTk%_OBbjY$@oza+y8#{nBKK86}qe#|a0XL_|j!pM=b za?d29m($dk-o$>Qft_!DiL%zq&*jzFb9xqtEwZC&dHwxKAVpFg=}7$F#5l_7ojwEg z(8{`7e~u$z!egkw^P860>omG*#rAIuyZh{~7x5OStsiR#YdqXL@tWwytUsrh=2ihK z5h^KDBJ)#*{AS6m&-C`5=|!+hB^7DoEX1S~%t-WD$wxsk z!BbCwyI;IUCDif*@=7#@aYXOt*>%f&L_KX?vvA{j$DNzn%S?U9#2JGA+ce=etf``Kw(ZVC`B8LutWEPf^?nQ(u8p z@nH!q@}+G2a5QNqzPRd`E2o_+lr?F*0W-5$lDt^ACHZe92A9}F4KzYZuHZCtSRTx0S$vn&JNem;xp0NEtrJQ** z)NLEbf0h*4cOw#tL#Rx&s9{i46bjj8&sciMIw*uhmflJdqU=nIiBbqrmS;wh{V}AZ z5V8~Rb$6aq?|DzB^PKbit4Rs_xjAY$Uw*WFR}~oTKovY(~uJ91L1j6 z0v;TdLn$=ac)azR8(oW5qL#%DS`h>Zt5)@qVw@^9NnWy)^P+51Io{h-Hq6Nq7QQXH z;u4fU^3!O^rUa<%d9Q9@w}%f zIr53tLwDN=p5F1F$+ZsR2$E}eo=`sJfl`#Uhfa2l7YCnFif)YbH65}0jww!h-M#5d zs-&fQnfC6qys`?rxyD`YXBng0tUXkBp0-s?CTy6YN;`>`(hFeH+K@lUHJP84iQR&W zzZBPt&ns+sceNEVIlQW%MO`JCvU9}py7-tUPj|(>UAtYy_=V2lBh>CqzpS==U|Rb) zN$(5+1IlLQ-B zb@?56Wo_ac)Y#RE7swY*5^>V`3r1*Wm9DAyx_Y}l7q=WKS!By4i7icZCp~`Zr%YYa zlBXN&c`#==+aO1zFF^OQ{vNJmUmRIZ8R8M`dMt(AY>{_Oudg**E~8aI{I)SCmTc^f z#dqXshhJ+-qz@q&^bHTg&Sy5ZP04-irE52e zY%U;2P}df{d85&ZeP2g&UoLCrfqC1$-Lwv?!G+y;STcGi#7Unv1C-@LkfpF1cGsx%E2 z>YjODut!G%ZYgt^q$ZB)Tch37_1Fa5D}}#2{mLB_6qI=L=A~PE{iPKZ74Lcd%I`gq z3it@TpMq93>6tJjac?Xv0J$CN@)d7COwca2nRR{8jJ70RdJ`|4E52IxBZ-9Cz`r z8MtcX;FDGVft4=cYZYg~>08W2(rw)K(QasmtnV$TS>bTFV>ULI0Wl;9p4a^Rd_*F# zAy2;8q~)3kZf!sW4T3*H6Wul#jOP*a13Y0dfnPr`VF$`^$@aHKM zh`yspmZz>))oJtWi_Fak#j!=$-zkF$XBW&h%9cM6pOwORiw-oeMrLV6V;?h|dd4-c z)W&-zL^eu_E38%0X2yJcQ&F5g=`)_Th#~1_6|M{9E%y-{Sct*v&RDy^`G4X@G2iyR^)Z&G!W(3|HmiJ+DA7D@+^?Ze z^%MDdwkTCCFEo%__Y&`#@ip$erwV;&P3lu-<%KY{!1K;3jQ^bXA{mk$e3*|7 zd{C*X;hfEC#r|fb1$(d2>VKESw!I1pUFH>jO8or%;pyt4oZh>_J32wVf(yYqR;h|U z$$;;w4QG16ip3v3KAU2<&ZLdY>W9B_90WGf7TJ^ao1?|4esZUa{5mT=o>y&sKWE9l z7@G0hw*8%sVbE$_^=-_n6I7OaloKW<5g^}5U<|+y0EP+BbN^5NS$smPC;kv^f_Rx6 zbqkV&+^WY^vyQd@)eJ5Cd{b;SAczPG3E9t$_rb-}l|-s@r#y=S%Pe=r>SLc_hVLFD zbMsw}Jt1>_(TK{|PyWgUcY2$kkCL5TI6!E|(0hmS|Z-s5lC&N$xrQBc`aiuYhA=VS#k$ z1O`EeZnaKgO*0=l13_Q-w8>()z|Jv`FIZ?<%DM0i3&s`_D?YD4eo# zA60wPdL7}&9huRv_^}!2KbJ!41!4pY;?q8Q|L`x zl$Sy~28@#X+7|fv4+(EiViy+|y)&5MjwPjhU(3V-93v}pt1JEUf2#~PR9yy3D#mM& zObtU44;p*uLK_5sWrLY*$XV-#42?j3o_*82P(}CG9@f&4u>^{mg{9?fQ=V_bqfjSR zx~60)9Q80OAMCO@MU9PzG?pi71Lg;!C&66Er0U$P36D$wp)_%Q%)AQZ$G^6=h(saJAm-Zu(imBKd>VT!Q&m4GM^S>@%TM>Qr{w+gSviHlD{ zzlh*>aS#wD4uI4^OFW`V-Sm9IE}~-d^wVbnqX49wgD(|s=nXuTJ0KFv_ywi7F0D_T$KK#FqfdLncSKZMP#8sqpBK{%|VR;+qLx}N) z`e?9o4U)l}z$?Y*ZEcZ&0elBdLp6YC2u|A{f~#8U`-12x{VTI&*f|(wHarUR9;{0ua{CIPSE@NyO!txC(vWR#Va zd5tkdK7=X)Of^SjUS$U}7Jne3uvj}SB}KHH(A^=Ib&(1{mcq+?$b1|JdN10BP|czH z8dyKdy|#%Wz8%2w*!P;Hk~?=EFnOJ}+b8kLl?cM>0!{l7wG5hjdb|#rmp+(whNKD};3VRmJm6S_WJVM!fBtFs@Y6XX8Kv=~l4kl0(w51pD#^@mMffr?& zwz@z-vUE*NjeBLKVJ-_}y?X7dmX>F?(Kj?RHDv>CE2Qc>k*fJMApQxr4SogL8WH!P zS_7yX4QN1Pc3{>QgV))p019Enm&!{hg#it!WpBR;@pe#vBdp|sY=lweh8+XK7@AYO zqI<^b5!+=nO(x>~&=3;q%K+z^SS8J*S!~)*|18itkE}8r92`--=sWd)8}~;T)6DkM zlkYaJib8u?EsToUngJ!X`7T;$6v{_e504iJZ<%*|IoC*^b>TFNG?K;gAvf*eype3d|0ycd;*o{}oHOXmulFcWl1SyQ5*iJ;wBP4RuP0cH#d8?g0o; literal 0 HcmV?d00001 diff --git a/_images/a4cbf376070178b287ef42066dcf23598f86a4f9a5b5d1ce4ed57891ab0ab3c0.png b/_images/a4cbf376070178b287ef42066dcf23598f86a4f9a5b5d1ce4ed57891ab0ab3c0.png new file mode 100644 index 0000000000000000000000000000000000000000..6228a0c9be3d79f53818e4c3ede563bcd967b40d GIT binary patch literal 44514 zcmb@ubx>Tvw=O!kyF0-N1a~L6Gq?qZ;1Jw{1ouG#f#B{CNN^img9mr_ph3dhLuU$I#KGXau}$js2~ssLqT3z0|bKQ1%Y5{kr9EvfP+$; zfM0^{GJ5Ws&erZ;X0BEs6*G4i2WNK&TXSkpD_1vLXD4npel|{4Y8!WV7dIhxcE|r| zz~=1wo_z@wIRbbIii^Cy8wiAD_Hx6NikH}eKvilA(vn)Xbt#LB?(;jO2zC%IDnP3z;|rZm#uKo0o{i)#1p0~0D!>8qDd`AoUF;V)lC<_Cq4{%aro|IujuawXV^ zW`t`zTNJHz1vA|2_X@?o#t;cGev=U!I+YkXE7?bJYjp(P_Y>JNj*gBUt&-UAlGxIu zSwbGDv~uyW4|ln!cE_H=AP;p#MaAFCWdC_V_knx<_wRgZ#ht*)1rekKn6-EOLS zaU$i%vcONwY`2Y7qRhG{8@Y~Fd3@@IYUB_mEv8atbxZy30+l!WH!-EmoZWOTruv8s z!BQ$wI4glD34DzbX{r8RsZ7-W#xV$tBllSRNP6Ut6M7x-eRCQYdMH;+P}=3nua9#X zC6u^f$W<)B5|g0Xp{A&+fq;Pq$B2f>#>=Sd{5(L}Zh9~MO6n+@I`VCJ$QRh6W%Hme zPGou4(Saujda#wc6X*Y{h1Kvm?u_n&%Ou{XeCa)YrsxnhHSB9`K45+z|9fxvX;v;v zo4&td$ql{+dWYg#>Id^P<;@;Mj19ibl9ui#d6a)M#1%Vrd!&c6WHK-aynwC&>w$nj^YRru7++oe|9&I373rzL`jDshU-z%z zf!Q+!rmL1U`OFFDrB4v5T_oK?U;`YmjJZ4h{2>t7U<-8 zrO`)k3MiUx`>e@D_CC!H77RN9KeyHeiB1FRNR|>jw_9WWi6e=TM69oKZlj6T@7-0aQVh`rPf1=TSXv4gQzA1|=|wIXHSBsCC|TnAZ-fmD`9uPeciGg36%OfK zZFK)VCwWTOhW6?oeYhz;t}E-Jhqb2Rs?1b zx*3UDJ``4wiJ&ciZ^}NLeVH(y64Q_`#l;NEfe$C%XYIG=i}p2=_%f^}9pp|wgxB7Y zS#pS?K<%)lzA(NB$d0dk(XNuoC-d~!i>H=bUXI%8dGgl6!Xn#O`|%Uq3Z&BXZ7N0} zIGAZxT}%rfB6xJ`!aLgAF4m>7zXlsTmKc+swDo z$pTwT`Kf^OEHqwv2Q6(g8Su{?lzg-Fum9VdRS-mw-wV^nMt$?{J`qxp`kLAHb31gU3~6*M3@kJ zUFDt1+cmMFHOcS1}Q0|R<|ekYT4b_3lan&cRb&k67~SoKVL zG3GS5vfz{uCf=!eWlms?M1pJlcUTnOK>=6L(14E_0Y^`NFUV#qcGAV4y^2zi6rr$w zFM5ih2)s|k6PnfbIu|#FgE5g_kFi0Q*EIeP*5c@)%CY%cgvo5Sj#> zjTCEmU~xV=GG2Ex=|6kTDY|EQMgUaJU2Jls;>w55XBzy0~nx1f35iw`!i_g#4eM4$V z1jj@Qe3w3}TjQ8+>)p|lytDm&33MmYaKJ&Q4>Q4ON+O{H0&8^Dp$9gZ(;)Eq(5?DT z`vML@l1`~UXRTqr#tjf478VVxK|ORRPCH?(l3G}_P|P%Z_moB`Uk?j65%gZa79B0y z%50rPzCL8l**2JI7#O%b#lL|M{=7&Yk17%SjbI6k&|j@1hMfuOpzdaTUk+(FhfEDS zT&4!>t^Y>!^R&sguuC)RcH;sj!g#?8GpEB+y8nqmgO;F?%qIo#QT%|y2wu#{<+DnG zLGcK5`cEYLD%-rStmLE>kOqz=k?Eaa^q>Shv^sR^>qm+tKM1(hwjBrss2hMK|B9y; z(EE)wJ=+LB9Q-k!4o_Qyj|6NDgVwlK$LC{-VCwsqjbKXqFtLZx7hR|oZe8C}=0w&) z1zY+%pqzWXnp7F+tmx!uY7#2=d?@XbDh%a?4&OZQIYerm#L2U%^b2$R|0JRw*&WyABHvwXQSV==D(3w5NCL{yJZgcS*3b}9L2BXmlrR&(L? zb=J|!|L~BH1X<|cNt4u9c9Et0JiLNhZ@>8dl>oe5AHpB(Wk>^D$dRbh`Z}Q``mz#Q zvueQK)LM*HS~|40-xGX9$Cn9aq~5eF;cr{z(%0pggO}V!1p<~J@56!v+L3c80Ul?; zcODCE4Yof97NRf9uJ{JlQeRSCgL>o;Q{}bmse7tW;kkkcVxtsmEvaBF>-r4>x50nj zgew&I8XLDP!O2)p*QFWN#J+Ms1+Cv&3oP(YR7A?qJWo84Kpd}kq9{9jQj15?|HOSO zsRDze7El=WnwjccBs0lD_+N7fRj6wwObjFXn6Gf3$jeAEg#u|%)8wu6ST~tHrUHyP zlPWlK>llz}KELdLV3e1(NMGU`3jQ>Nx?@Xw} zgF|>O9Oax+EMNWLu3~TrL)hLy1le{>f$vw@&gfpt0dk`y2(?t}P%Xt?~k?##iXz2XjaH9ckiRu32L3#i`^Z>b_X3~<& zUV36`znjMkm@Xt>JQ5<)in+QsLX|U-5&Tp=b)H^emIB-|KM*>phu+$Ub^TJ=WEdP^ zEx(Yc?4oMi;J_!LF@g0MuU;D#2G;9lBjAG|mH3TZuajCB-48X6EkM0g5qu?7*+Iw^ zNl^9HvK$_R8ViYGF~Bm@1HufKO`=>VvoOy7;+%2%M8_iIh@3unSKHDXr0NCwdI~B( z>CdNtqrMP`ea#OwU+?Bn?j}x$K@K&9ndKFM_+ zgF1?1OWVhT*wyvAq#T!axx=(yHn}+@B><2U7m!odWWjzq=|<8FIbD8*c9;tv!Ib#= zS;9Tvp)M9Cu@I&(BsPx04S~b}7d}bp#ZnBtE^y8BRgi0k9J}GJFizAX2Mp~k{H2PA ziwmAB&-9+3&j0E#=2hKKt$SM_vj!M!v44*-{r6+Tz+)v~(;jiMKl@lE!Tl2Uvnp*z zyzw=?a|yy+PK;!iUn@A}J&eEA1u9s{V26?^;?D`bl9I?wk^=m=vw8?C3*guR_b$i@ zk6xw=Bq=?1+h8kx;O7S*VtCQV6FFh7K-K&Q2RB4@Gv3@~JBdt|#4F;$=`;6!vomHS z%0+kL2URbt<@mXOoxL}1H10H5V|me71YQIQ5i2b=E%t*_-6`!hx|RtO6MnqTW+&?@ zi`>RThSlp>5j2IgS3{`M3c+AgZk<~j-nC}ZP~~0iifg6UKq>ak?q72LJ?s(I7>|D*6vVaqM?ri4+CUO z>f2Ck4vK5V;^@!P|MP5nB3FzBjKXQn_^|X!@`3-J9Xh1lm~WoFtP(i{w0{>x93+3O z$ZB4uJLMBUXR{LFJ&(rxZJTn)$HYTNOfA z<3>JXWJf&dt82x8)HjOlxEgKfS6ShteobF@r!iG7DQF(3N4VNxP}oecSA*d7(=2X; z7mj3BlNC(O*7YKRV0=I#cxovg;wzki>wH=-h$!*ch+X5nkagyMFN-bxL1I8n8o!RN zj(`tNfY8R{{^5^CpGM6me0E^b$ZrMXwpcz)YYNvhv}0Uv#1q}D5B_nI4jku1S9jtq zENoG5S0f(@v7!U4I3rsVGz^v`Tv8_b#PV%hbF!u2kp;H%8@fY7zFpz39e{5a`KgM5 z!;npPlLf^B5$o6~8?-~@FMjv^DL5Z}V02?`$IyRM*eQ&CmMyEB4mKz3;zAEnSD`2_ znO(+qK2WGDd)j>K5GR@6n8;G*A*LMyD+xnZFTXTO6#_YOFZcuO3czJ)K+-dx1xXKJ(RI=_E2e24|92^g@H|f z?xGj2sBVj&l9AoF(UQwL;;qJWTE|AYkm1&EF@ z3d4>%s>rk9J$19-2q+pCl6h3%+Ro-_S5q0+SY+1rynCu;QG}Y}li~e3LjFyI?ke8=_jn`_> zQm*>ObyD>JWH7|T*s z0b5!Kzw$oHEIq}`4i)^AVt*|^^TPsgPS$%%7p68PPNf8x&e(h89X8sy_~g!B5nk&3 zItB&!N(bY%ZGLf!IK!KOA^D*fFc@JTO^qBNb;t+`?4%0p6o33SeK2a_JViTym}Erq z9y8q2Ql{JS-D*nDj2+3#%%I+B2(eE#rkYSNTLBMn$Xv|2Cg<^?CB2ju%udNJ5 z`V(L8DB>C(pYTxF&c~q?Q(BJy;i9ESHa}MfT8iE-hYFvyaldpV4Zu^Beknpphd8v* z9-G>L|WlGW>cS#0ovvEr8EGXaxvWDCNRCz9m$0@r%HvG z&0adwoW(rJwk3frEg9%a*Ft= zTK%u8Ul0APP&6mbojnM%tH~W_p5ROut7o;6`T6%7=Kn<8uY-{0P6ket4lf$}?~k$K zWw#ZZ0TXZm)-%~ae8oDF`@x@H1O^AtYzfitAds3w2qSg=2Y)LaFJ<|te8{0duQolx zER8qn+g4uXnXHy4jZs0BnZG9!fssi3lp#<&_z^A+7jythfa3K^Wnyk!RJ~~?rcoJ8 zb&aEr+$_iq`Eq*dbiD8l$?xVE21G6%NDh4H9!h4sT&iQG8A1IXxC~Q~6k5cXp>@cN zZqMlg1Z56SO>p&D6gZizEZz-e2rGd&yz~s|@R+P4U34x0)Rn))zA;&xR@qoXG({2N z%@6}S(L+;P+n=r(6MV*B61735&~Xpq1!U_m`1 z;VPj+rlCMRytCaK|EkyGZhN<<-Z2`-zZ%JyIE)=T zNz+2z&7r;w{uO<+BE!~C-9OU{6kY6m6us)1IIh2u4Kzg(6K)L@+!zNI$=@p-#y?%jr5u#80)RH>ev5TLr5XMR%v}C3?g8w}}z0iKrOCr(bgv(Jp1-bm7L?~Jl=3gZkLakAf8=r$3wZh20xqzj* zF@xaM#QOLvk8io92~&wtn=#;PBJMT-g-#w`6^zn!4VE=3w}^CCvPix?S-a?=B)2^Z z&u21I5QK7+){;)yiBm**i&^a$j7tg4lkN*z5yAi_MG{4wI`pJ0lvWW{50TiMz~zCP z>QE!TX0K7A2pu`UmPzld#C@>%RFczmtw9Leb0*1i=ahRI&qX>9(#}zm_DVNq=cG*Zl`Mt+m5&1F#wQdKNUNLEo&7lw7Q%k-h>k zWoTmM6}i3Fp3%yG^?LNuYeakw&-$NRHPcJEDj>?PMGrmNo9!`Q|2at!q?YoE1=T-4 zHvSX@pds@U6$qK;eH%ec1<;*=C{?WSR{wK^qAX$+OiQ-Gx-Os3R1pRR54C6RL@(jC zpPGlv(_eLa{_)nCJ$ic`y;#JE1ZT%?z;wT#4RX!>umyG78;$|n3SEAwXjG52kEce2 zD-0aQR?s=`^6=W?K5L7N(mUrQ_yTh(wQ zHaGpltVlNH3&!DTJfiFFTgY5=eypIl9|fiwHexmm*~UB_3m0sDh(=7%*IVPVA+XkQ z^SAB|jH^sx>f1xgs-hG93Qf%}V9cl#5cdIUGRZLj96ztt3$uD7+|#+lY0kx`W*9q8 zV9)7bP7#j9ncrjH(`08qEX;EuFtr%>U~Ct@&;@0xH6=kLANf>s_TMle`Pd9ez@^21 z;s}Hn5OIG@_#BZgYu`QMBjr1%lMDX0T5FU~Qx3K01sPe*>fRSn?z_KhH$he;=MK-g zGAUL7)^8H%RBR2b`1rd>hsqqS`;wAIb@cxl>|P{*6-=x>VSn$uQ)6{r4QVd9u{h0u zOZVFa6^(LaBpTL*6^J1w>d?tr+ay`DId}@h@M+$+h$O=F4$>keQos#c?y*T% z$0;_ZeYaQOtP~6(W>r^DewA#e=<@tVY4W~#t zrpI0}c>YI9=Y))?lJ~4(AFmCJgeov)vqMLyq|1h|SNoFa5S%H+68K86X6YG>IO|Z2 z(0#lMhlji1{^r6eBz1GL#=^pNmIfi1a$7n_Z!t@Iry)#h7U!7#bmljw-jU}ckPH|a zzxaj*^y#>wyUU8s7rb>9IoNO6iL9=i!` z8qJ^+A&(}!41|3m&uDL^mu7Vt25us0F1powKBg$Uz@IRLI7?ks1>KckMtTz^^4F>> zOvLx&-qLxK?n7Xhe|ySS2M}miD{~@rNeRuAR><#s-~ zOrFi%-5yFVt*#EAw28fMXB`m;xaX0LBTe;9aFY-V`65rg3hKChq|{3w?*Zny?q*#l z_CSazaM_AuYxd%^`8z4vRSID|h$$$SnhC}eFX7{A3$;H!!D5-9`#6gJL8m5^iOVkcr8j`=BR4M(hQ0HV>+r~L&3|cWiPQ5~PacvoZWWWwCkFzOVr84Im}JuPhFj~I zY=Ov~F%>;G7c$wV#fyw5=MC!;1N3*`jCzN%-(~}S_2Lj~*lCxD-V%;dEqu1uUV#{r{Y-L%Ix;I}#N63yB8Y3|c zUgFFl7oSjXtF`7>0!iNs@_PV+bTZ)Jd;dqOPM*grVA2^xu~FcM=rT> z=60(zwlvMuIE|x?O;?-G<#`VpyXiu`)y5iD{&ZzZloo3pP^YbcsUG4!w>go1^dHjY zI2tvp6m1}=z-o_seJkT{wpMZ1)1fzy%*4HZ};g-h9;y?(3z^eoLpnBhN-4Idpe_rTw??GRrcOCwG)+ zXgq1o0{hH&sC;kG3&CUD?6y%ktW zQBL}oKMYW$^Vs74y*7C!c(^%%2HdWc*zTbQCvIO!myn4-8CYwHCWwZec0a7{$k`;s zYzQ8c3&RQbs}8{Fu0$(|FU0Uhd^hV$^5U#~fOT5^gZpx;rs`O%0D9rLXr4tWisP9K zinoINPKOX6$Sp0q+`)n&zp(-QP)!1UqTWn%Ov}reW`J>SQg++If`zpyz6j5JG<8?o8!ZG1dZ4fEvIv^9$M`HcNjv`Sqn>c)jmLJ&|YE>f1&Xh)MMzm}JAO zIZ?DN3Q>Ma#2u5`zE(ygM2yGSn@NY~EAqsZ#QhM$>*QRaI45UJeT)7jOa1 z8@XYy5~AIlt<@}Lkyhb_(I&G#<)g=U2k9WUsu}mzmXEHeSUr{w(PMP2$n@B4xU7zpdB5!K)fx%pzmDQMvAKz zN%7{duiCsXUoawG@C5zzt;3g20DlmzFAp?mac4I6zmQ2uO%3}vzpxOc_VIrD*LO!U zOToDTjlYvzr3WlBRwP|SDH~S^W}Gffm*prZaG5UZlUr!o);KP^`B&p|(5yKs=` z4WgRz|4t-8fYIgOrS!(Cz;fs#81U?&yV)foCl^8_BPHAz{oQDz$3up8$kG#)mL#bi zr4ZCrHefb%B@92pQA;|zlBq|3V-crTNXOr>K|7=Cz_4{9gCX5+tjDsHIY#OMDg9c~R5(M?u>kN*;0pRo@hjfSZ%umZVufSS{LcAi zE_R@pInJi_o>bY81B3lcFh-57sUW@+M7MI*LG1nA#H>?#%0xfurJ4@n$Qvf)+dO93 z0^oaWE1A^nOu;NK1WkzkfPKlC#a4s?P9nBP%FHe*okK-S@2|7L^09?yL4)C$YxP=o zh-JbDNNTY7hb<#=2M?>Fd>6j$I{*SFeCI9xbo^c%>M{SWBr{i*#asalr3yraYgLeE zDnXoCiT`OKXVChgd^!#{8I)$GZin0RaSv^H_1%yUZp7*1|OJ z3IDED7jV@0A&lI#haoWDn9Cjwq)*;+p7%eS??obJ+1N<$HOMxF+3C?jf=KA*&pP0x zNr-xFZ0{y7S3VSV{>`0APA=LlO80XWeHiv0LljdqH@22kK*t1-c1QeOj*tMh2Re7@Aq|ax-g>}1yk?FGY^a|3S!S2hpd!z#Q2E` zpg)9vcJXk-+s$4~5PXkmKAsWUbawsR?N?B2wz4hTChCY&_#0IZp3T21*mAutG9{eK z$*r4xn2GzgGbhpLvsr#o%fX;Gog9d`Lmu0y$3n@cbfbp`V|W#wq(lk{@cfgGq{CtK6s7m!`+ECIgZ4e_8iNe6s^A7U- zu6syyGQ7wxC{h+(u*YEG1*&CIcphr6qwBp-EHzPcDp9O6^og%r{r?llB9F_*ni)5n=>Hbf^S`fc(gX)^#E=Oel~wCQ#B+ z$g$6FeDMH!-qrHt;vHg2(riLGD#3Lv4{1)D9o)#F%I?3w^y)v%cE`7>TO86t%(i|`r`Fpm>hmA)p?y6&&#@{o0*xDDO0=%0$84^b} z@irJ7Yq$v43QpE*&p#oFmI!P)$OuCuy0918suU|QcOf*&uxQC$1?AW9)yqE+>@gjo zz46H#dwxE&tED+HKi;#$b@~#JzK?zTq;B5{dY|iQzJfKu1%eyo;^?&-lz&;SM@ba6 z&Pc#9kSoLHglAa%cFShD#v1|ATQ{nLivb}BtPJ7}g`SiwLl%~tZKja^S-KguKJpn* znUW!rhFR9}RfrjWx({Y>HbugsD~nMFXk5rYarfXcB1U{D8g6+!xD>WsST0O$E@_v$Bp3+O;iowucbti7qi%)r= zpkmsSM1n5sZ5YILeT4d+uvDwMSQfmpvY9;&7s-|I9N}V-!#b`QnE6q1n9)ZOA*Xi2 z2@0_Ux6JRRhfA5Y_vcHcr>DJpnI=%+mKxwLYO>ti`NiYG86TSolevIxRg_ptL2WRH z2_jmCF}i{`<8&o9af~ti#ZDNomv=Dur`90S%=addCOk9@76sUBL^&CvTnC=!jj8Ac zq`EilIsmO|Y)16p^)SR%4$a52m?8b$s_9*$41lsH>8M|>`kiguAJq&*;W6G1eJ2T@ ztWtT~G4x9wyxvp)K?Y3CQZA=ya;+({HHr6&4kJ-C7CA9mjbObN zq%*L0-ib7x=kauue%o!Y=X3X!sbEaUP;gGo;FE3%TYp^(b8RMkBq}w>XwZo?4GU^Z z08L9kxxM#bS};|YSNR!RSFMKII6Buinfg}Yq3Vk9QxHHkD5=tkE-^YlqF%yE4xT6Fstr&j`q_LqH+s?xdNW6sr>B$z?NleoPgg$3|Yx8&hwU2i1;;$R`E zFkyTR07`9KD1C_IhT*+kf9nNeMFXM{3k5&%uWsITN3m!nb|vN`wL{(lcPgWz9Y`Yp ziV#hEmS5b;J#6FlR2ZhW_A~PP#@4yGirlMEg++_k(&~`O1I*m%s+RC2Wq+;iF6_D# z4hxnl4iW+HM~(jB0)On0t%G2X2aDYe^oHuicVa+ZV&Wi#43=HAD@x2qmpA538)~o4 zHV927Y7aquH=EGLMRe_p5Etw#186+N; zZg%fZ21O-e@nXy4VdK;y%jebGR4p@JhMlLBL3TwQd?qpR-GDYcdvjt(N-{3;8Xlg_ zI3QTImZp%_4<8$3+~FjM>FCgmN)dvvLN_qpf)jOX3bhpEI0>BN8W`2#i@vRT>!{Sv zDaxem8xw#CgZm<1r4^SfLF2E9@E2 z0kh;(z&Nd8{HDzWKYJQD*^5BtKo^TK`*vCawFJNf!xzzONf5}-_QQ{_O*b~M%t;+7 z%gN(BFyej*6ydl&2|Ru@Nk^&Re9}-58Cgeq{uPn1WCXau__F)DOab>6xc$?l-uL+; z!w4DPB7Fw5*)QB3j`C#)T2=i+DYxQ-kVf9=}3ci_Nexg13Dax?%jji9T zL&^wzZ?mWTo{?b(UGvX>bUrygE_FVU0n9#5$3-6Gx9$jw2h$}bZVQsokPoR%4b?P7 z-|XLH|5qjD(?VCM)vr?jQtwqYAlDM`ki0I6Mz^8KnchL!^wU*=M?=Ik0THcCyPT!< zaoo4*xtIzAt~t7IK&-bg9xuJ&#Mb}g()2G~ZcH-1a@ovjBGrLM&K-{8L}GLh(s+x@ zu|L0q?Cc}NY6eR(+^i>CjIuMbq=O4~O7n_}0R zB$IEJ2rk|L6OAqw4&0vDV>`h3)l`r0 zCQs^oa&z`qohkj#*(iiDXN$4mDm@o-_TnFueOGe@U6~(y#&Iz2_EP%xjXW^eGQ>&T z`q85_zaCEOjmZN!85t4`63YfOEB~nK5~SL++vZtDd(}jTl%oC& zXrD&Z;If_MfAhbN-R<%2L)C-ic2LJKA+WbOLG$0>wlE_(>RHQ=i(L}G#6n)z-A9M@ zb-`#$)jdSpUyS!OnhGt}-4!_Mx!I8I<~%YvG%$DG6R%S75=^KW!pGz-8y6@5IJCrdHqJtx+-l^OZ-OOO}fKzK+wf>$*!W^~bk z8p!rfut7XrP75Ul)^b>9rx$34%%nRy;k)V0f>hJ7B{!S!0{RE&*~NHzhPUs%#@)P8 zs1kb_v>~Yo?0O!oWvp7W$ln?@;8TLdhldsOs+m5A-PXQ15S;YH#*J1=W(Uoi>$PhQ zCvCN6NSE;U@myQ0$uf#V8hzX87bgM(#*5HKgAcX<+iiN}St^DN&cE}BGLX7PBZ zrbpak21?kaqpg%!^Vb>y1V@5@b3ffCc$uFG`+>jxA*ftj`(*3*X2brEpfT@aRx|$Z z#YR1WZ}T?Xsh*(5z^JN{o48l1#wr~}RR{MneTQCQV?mc}AB`HPi1rc=--9xS+WR5z zm&RbN6JA5BlizWETyrm^t-dc~N1u7Pd%FINpF;ZnVG?s0D6avwK|RG-)8Kzn_?f7C z1u{UMpQK~6YOEvqjg17_+S=f3_9wQ|tt?VhJ1s7469hUEkr7k(Fancw!x)lAE6yCe zoz<0r2j=XoB|Pj4rx675<@;B%9XF^tR5pk28~#{7k+%g5ak<#*6nyAs5@?4J`LX4$ zcmfInug`1-B8s(f=MmZ3mj{C3GSqbz;+lR^?3dvC?mi`dS+Q?3Wo5eV=tJfVY3Rdy z4TVwLH%7<~V`mz0h_DLs(@~u&@_j=&DXDUHM)IHXoCp$ckmp|`_#_PXji*9@gLy#z zbt~0-;vxnYAlLw9@sA0XB9B?fA4@D>qsmR4;9cBIVv+}3ipB=Gy;Ck2o9`bR@N-M+ zZnLJB#bAk;9V}YLgaW95fF3%zli#k?io9WR6;oC$otH)&#tU!o-758ekf0Ze4NvR5 z^zY-r^i8s*C?Bj&eCb>R z%THwQ`XaX&U-;&^WJ5gK_zYKybL}~iJ+eyIsY!dL+kz?jGyF1Y>OeKWhb$390{*L7 zX3Bk`$JvOO?)#?2Nk8m!Lc>-s*_r94;Sp3eGe@iz;pL9+{7at6z}i*)N4LAfSFo~w zgP5~7OG`L9KX-|J6N>jvVEOP9fI18M!u?75&TvGO8cTPp*Gk3;{d8IMMNOC|^`bxd zy^`ydSMgxsx_s1qKb+%+UjR@Iae1IWCx>HeU?D%Gfl5mH>$g%yWl^PBnel;=I<$Nt z%10`Kve;!1PxUR&*2$TfPsxmGG}w`8_W^stYeP*I(q=vN=nk^H?-4-$k{(C{c-T1D z7F3LMQWo8BGgj9yP{*`Tg^s?zzFD6`4#Be+6zI9s;t_gwiluT@?0v|&9qgy z*0-lCdX07jB9vImPW;0oBa@SpFfn~0<>jwY?V?lMzZa{(A|-d_0R%U7$OcNOoc3z& z%mg~G{%Z%>$C2Ztu4A&FyN~4A0zac~RGdxcZpO?C#@J65B46$~@y`~}-Y{?TRc#mL zPWa1yo%Jsy(C)s{)@fauY9>@VnH5JW?K?IVg7uJLkD2-W55NlWr5>2-79X&v$yFq`sGT>#NuuE!o}l#hO-#n zKc7`bZ5Kjkk-oTiBuwt=dbP*aocfZKD3#Y%o8kO*bfq0;%Zw0CQichkOEQ-t!c;P#Mfq-` zlID?v6D{7j*UEt27o^iq|ItCkl{Nt&P&O78=)+lO0uWUH6RG2Yjg*x1{O`lrRzI0@ zZ=O^{{p5UXp7h4mU>FV4ibc7%btZry$Qx6!IWB}XBK_DLE}-fnJP*(Dau{@}w72QK zu&Us6Iw;cPsmA5Q%TcGHyq8EL$n6C{)4D`_1b{c-i6l$dspculM>5Rv=$ODMBUL3KYUFlfuG!gH0UwRj<+QZ4@MkrAr25MB$znqH2br0a zbkoB+J>d9_onq|%jnWIj#r2Eun^$k;v}hyr)D4{^!^V)my{aQhsbuoL3DrD@r_)+Q z=lIR47apASo^!@JD0AbYzv<82-;&dwHs@!AAI5%Ib82nmVyi(dG4C(Fo=j}??MXAn&!dJ)t>9YK*TiY%`y5eHFw964j{%JrQH*3YBw zRx+uiVvz@3c|GeZO#xPaEvYsFQLd04Tu@(Ex7~K_^;Ue~$x9UkIDtV*8osb`5hJj6 z5;0IiXsWyicL1>Z?>109Zk5sq0GdR7C2;P*bfGYh>FDx7BLAi<+! zXm*hyQcL0J&UUU+|5vjcBZG2!P?6H-&5nmtDLFZ$?7(|lMRfRKfE5!pT`xYA**mT{4NYs&OPg1N*0iqkc#MmFY>hqn^LRXS;^xYg^N! zX`AZmfqv!~&cnmTbWwX6S?zSNEw5yF$b-o}Q*a$)0&vF3d+VpqD5*AL1^7vj$b#rg z!T(PAnc}ThPOagc+bTw|NG@j*NvyNUw@sv@2Nv|W%_!de zLL*voYQ@;h991U7&Csx>>r zZ4>c9vHlFJx%v@rg0O30mw&*4|DegtZq`NX4|jy|3mPN7v&r+jl$J`=zkdLfk7A97HlP-$Nq zc+g@Y@r!m^!LXWepy>KH;=Gg>M1|wKzHeB))U5w7iuW9Ui{N-Tel=M07waVw>{f{R z$>V{uJ*?=0jz)qiAbWilWp%7?j^9-m#7NyuSE^7CM8q+$;r-R$gkO?<+XS@?eI{fN zm2zQTI2`A%bOvsHH@;)@Pjm(Qnb2(Zffd)p=*6hrdh`SOb&+fNUX( z2{2bRZwM5vl>Jy5Thtx-C1Bw6feag!PC-^lZc~Wizp9ans}w1g$B)}IV*bt_QzosE zlg#P@uF%`2enyVrv4t)@a+B{T$I{d_ztpit{Wr(E?`z?{)SGRHE0AF=~!HDBrhz_wtGHcO#Mg*jE74vVw`} zBIwh@`@>klheE_=)CH8QB7Us;qU}xOM6k+LXbTAoYo2|8i2NR;eW7c9I9(Q0&fr4m z1kPyH*-YjaW>;9LbEk7X<1vHv5Scj9*7X?d$QQE$y5%`5KB$s|CLrlis(5@heW`X?Ec+V? zekAV*=xZwFzw@>$SN%Z+6jHx+<(2os<4w>ou>ec6k58Tb!AfktU{0~vd&^~YCv=Iu z+k72xtI^zIir_Zm>+`>OXN_KBar0u5=+Xf6oyww{UsQy^zvj=0NZYF4A{&lHBzdhsHT5KbYdkw%8 zmrO|!6Xr$t&qq%)`&Wk$(#A&^5MDlqpTSD6ld9Ty7wx^3Je1zgwOK)C7X~Hkt@CW8 zN{Y#XF~!wWrZgsP{02e(VbbfBy#}|tKcMGhN;XHi9IZgX*pu+!4YyjE8S41$bFg7M zD%$(l{=3(PzZBYP>PmD|UX7rJf6mDFbkRc;IQwn!`y7%o@LsPBr^tF1SCT#a(}hT0 zkj+_%WkA+uTqrEUz!L48Q4#NBI#4|aj$r{clSRVWh`0XjhX+q3XZ8v2LCy)!h9}rc z;7)$rX8MX%HRsMJzSY17$I*07+7rxTXwAnb`(^*jv8J6gLlEen0{C27i`?%Qc%OG8 zJv_CqokgsjVLcza3-KNdm<~Ux6;^_C*LxB8@e$5r6cb7?fa5j;TK*Fmt?bkhI|~>6 zjLDpFMX!?x5Pm#a5dp%OZR-OhX*-$q>|To+rVXtUG9cqt@X`F*ydSzm^r_j2oG+d% zVz0Ul20Gqg`bSPh&(DSBkS)8foOY@1aL}{q`7ehtnK;!m_nu<>#0MqyEguj3?PR87dc$O#WJwSHv!b=2*B?0VY3>vn5 zT<8Gp4xW*3edi+id~~?Rd^-xNx7r3^d@g^CWvAn3TV2kzNBQFKILtH#yw>q+!XlzaIV1%o%TnEjGsb0(}?U< zrK>CV)X~yH3e<)!!RnA5mmqmjRd@986?;W&2Qby_XkJ{8r`ucv8TY!%H}zhVwjj{AbHw~E;>Cs{IYS|ZLKk% z&HDk~@Ysy;10U+C=pqjUOk<`h;s1YcM**%ckgGOor z^GnXOKPxT|NHgeDRS%<@^C_3E0+k2xJPLIb7EJ~`kn~=kxG6D-0j}WZ^HTwU?G}=f zS;|Z*2WG`Di|GHp_?zs|d401g|8Idi?)un!OgYvU-si++FlAx9W?||6VAT08<8p7} zcBy@ck9A}nI1vAG+8a1`!#T_F<4yI10-Mx#tFLe?_F=Oi1mJ)T_BHW9{kk6SKsQke z5;;}X0MGOhXRPmxHJ?k6%60MmVt(D-0f6%DV_sc`)e{Q!?n;4xd!?K3EEuw+erluGVz{Zmohb1++S!LtJu7odn#NSBC~5;K)XG zj&6)er)Qzb!-^OVA6|qZ*^&Ea|Dj7(Clif?2tEqnC|{SGtN8Uaz(h~3*2ViWzm)TY zjbzx0qqMXB89!_PSajT+7=ZcB*7X|%;hyGT2VjAi8!%O$8EW#}w|C1Ll8ozf83XC*vdZ70KH?Eeh44*hevXK{19;JuwB*VCCP;=?6&U8dHjjDM=6b2PIp z=?GEMf1mG%fPGn4%6~yOAa}$$)_)z?xx-{I?nO&oW|y#t%JL$FenI%v!$U`SW^{hn z%1L*nlyL3gIv?xvT|0ocZ%Kul0raN$H->mt4J1D89g5}Z4{rS_(8X`lmbkbN);nJY zg?L4Iv8SswUk;)^`ft6G|FGJuC;LP+d}~h3>wK6NX7NxpfSL)j_bza(aP?qtY&xK7 zck_vHM3a3ahV!5Hs-K? zeXsvZ+21?jEtU-!+*Bh?ZranWB$1LQfnn31VmJeDrBijcyo;Xua>iy^fC$O(-OGPz zU`=@b!hxMBAG2qoX!a?tnRV!Rw!Ej*bePQq!eMQN+ZS~BB?Iky=d}`MJ6(LOgr6-I z;4Ac*B5~s5-^(VK4cNfPEvl{MX18=9kJmt)ny4j_{O0%04g~9EHREpegtiK<({~Sj z11?=Kvpo8Fmn}&MrC1T~VE-Sk-a4wPE^7NG1f;uDx+SEfC6thEkS^)&?nXK#l~%gD zr356U;RpywcYo{X{XFmceq(^c;Xkgk&)#d#HRpBxF4nJ2m%QJa@I`;#A|DOEWL>_e zC!kymjU;E_tOoPI!dHS$-VK0;d9>htpgn7@zZU1N6r@qx5WA6#vt#)jqz?>#>S=ok|l~6-mEy!=0PP5M-p6 z1W+;zU*3z4lB>pr8BIMi5Jn%f=!m>^BM&xO9?@zzWYc>XqL8<>?w)QSSQ}$FT0e^P zaNNbw({e;Nf5%JgES<)%6pI|e#UA0O=KyaGkCc@VKGQ|`t8vQ%$5BdL&F`&J)K}V% zv0QMzGGMg|g3UoICK0b_L4)Ax7@~6-{3fBB`Qvo2qejhiatQ)zHw@hPt5p0q*ts3m z@*LV>-7yJf>6|AxZ>%kh#!C-3RxmMoNsQ_|hko`p{rY1BE%@E06}nf8*aA&viaFd@ zo{Uzo^&Yz^E)_BXhX5bdIvj35|F>?dHKPq}GX8DZo!=aj$|}5rf;p1eM$b*nLAGfyWEp3fDph&(shLUAV|UYl^&mEZF|`G{>Y{ z;(j8{l$bp?q9uQpamS4oOo%UjKT8+BQF-!cMgc85Iy=irZ1vkak)6Mj_1&pb8a1!Kh8*y|F{!d9eB^+lqH-y%4z_e|Uynaw> zzQo5sOR*9V;;!@Z=w!9g_&BGrYHNb`MV&v6AM|+ge%N?k)ES-NoXD10Ox`RN4gq*j z*3pMKP+6tZbh{i`=X3O$m28Pj(41v~ZWwlg16VgVny5)3-*fmj9*XPzd*-)h?LWetNw3LO`>p)|7Zf zU^8UdCV-QAXr3_HaduL-$vSl(?^&vl;C?OQxd?GzVEArGwQ zZAY&oouo>b?q_e^s@@FT*6v=IRiZOV*uY1O;J@3rs`RlQ7 z8NLVpG!6>1c*eAgfiwbFgZ3sWlc$go1rEDRT3e&5Dx#$E$6~HltNc%*!#9y0U}i3K zPk}q zKLz3T`a>7b$nm9rIisP$hrj-F{M1<4YJEK|Ih&b}i`YKSOvxBW$yf!REbTdP@3ewb zszm7!OsmY(EL#$fF5vuR;*y7pKL}&GlJkZ4JA7a!aG~hE!_@EHl~`1oT8blHzL%E?FF>QA3ZQxRttem@8> ze~?W;Gi^gbv$4#uT0J`=`H^Yd#$fCKRRPpj`l1^=-po1vndfBHs)9l5UE6P59gx5j z4)MQj^q&Gv*nwZF*vSf%Ka`C^a!%JH6+D8usGS|gU&7ROZ6r=^3MkuI3alJbwqkW? zh^e8r#F7~UB2SlgI3`f66J;p=z%@J~i(ck|96#(jUhPL!nrsc-Lk}n{n4Q70@iaDd zo+~e(>@l$Qu_TdHd-qAYxRm3^%>+=L^s*%n{lHz4e8R)4>aqr=mjBl@J#5RGJ^z?_ zoc2YXe6hWk2m1TBoGZATS8rH~rIqpi?qZdG=PcR6-UKilK35J)K)?r;11I4IkGL?- zW@oBRq){`H)2W^?$M`>PiwK=|mp0vBZM5m!ZFia$-?%>dS%|Deex;MeA=Af3fyq%1 z#yW43^8FfV0h$UJEZNx|R6zx3xz7XS6#w*QN-S2L90@X>xAyLTWG9X~Pa=R8WEXSO z5^*KbWaZ=p0b_mTf^z1t^f{r7EWdwWSRZ&Nd-&W`CQNK4(9*;)GhKJ=tn+;0>Gx2S zbcD{>-{`Ru!gSd+S~z87bIb>m2r~w8?MN^)$9#CL1e9*);$+v40Fwp$CK)x4O6&(b zDb_$pNn^VVM5EH|I@dHqyX~0>1T^94y+)B0j90&cNNWn{?-3=5vx)k8Ag$F~N@Sp9 zj-BW5#MXHoE1{&ehWuuovp&p>+i%rGc<;FSE2t(nw(VgubDWcPK9Q6n$An4Y zr6J<@fJaQ3!Y(;=<=MoyHkfE4SiJ>GU+bTN)r8~gi-!;RtA3Z2{3@I%dklQWl7?H4f%HC5~8X$%pfcD8~2mH~{VQb^haYee)c)^>S3IM-Sh?l7J6 zE?B%tAI|Q6|Mw;It6DG}Q8o`|!j#uG`Z*wsGT(t*>b z?mu6R94okd`Ot{f3zJyTW%mo#!Qnc{kcj?|KpzJRe`;Vwh}v7LJO&n&3PcPTR6a?V z08_4w86b$C`}6rqF&^pkvJ=cL8VZiD*yWoXaBv&$5#}!lebnB?$ATi@_lz~C?YjIZ zr7lKNm(ifD|zn@qry8HPBE>0kP6b9yxce3^RkR?dnz24!e&n zuR5*4|G=)_w@KL^3)Lg8Gqifg@~(QpwT8d#dwlyY29>3BoLO?XNK1e&Q-)!$WLJ;VQ>l~dmTe!c#<>NLUzeRP9!K@b7YjSdbP^p!ZdPYlKzS&0{tUkYCDF!2r zk}w@+6b{Kc{--&wGe4K|4EkTN!aP6s$@zzGPt~l?E;4B&^gd`5IJ|$)EfvZtCh6R;D$x8B{WQDt~Ee_}=$OUWID=;|Jw#)eb%Yps*P zFSR@2bj+r|tE>SO42MBf?};4@G3DflHVQ<1pmq0M9!zpbXSN5-DlvTVZ?y~v?+EP7G9lsTf@jq$1QSfLU zt;t+u^wrUQFj37qo!)(dRUPpwQ!N`xVfZ~DU>^$`=}t}SwflvYjD6~rz=4K~;lQfB z&@E%ZhH;kt{5vK76q?I47Wb_GGzm|!TWtUUG+B3GoZo;;;WAWqQ(9Dn2<@fWwd@p? ztco7@;WphKv`)^>rUFY)mm!K3j?4=If|43i$9%yL)oF4qDieBFLr{_-^pFp_fz*To z@H~09Kuz#VvSWpGTJkIqcK8e89lf*??syfa=_r3o51hZ!!8Xg;U}OBH^=olSDg;Rc z^-1yAABWZJX7nyi(>y)gV!(r01aXhRj5C1RuEX~4wDo*J2bXLqjQ9&>I3;G4P($3u zP}d~VYTF-XC1fIa2DH6caI>BK8{$l2o%{-p`4pxA^tn+|DzGOsqf`P)A=ak z0cS;?nyHdR!dT6UCUkV{Sx-L}!mR7E`UKJTyVx{IHL(YjNkf|KU4eMBR9+xSY|6|v zL5`*Dc0rrU!)FBau$-~HjqjWgns~~qs|GE(wK;>7L6^>fgOs1>`oWW`H1WI6pWEzj zo)JF6pt6VG&R!Q{r&;yx!_Ayhi5*7$ea$0-mGJ?b+?U-~V_RBY-p%>n6Xy6{Xv2s} zNWka#oXF_8FT3uXr%%L~(q;}Ww03jJOY8GNuf+}n?oYnBO^!a2`)*BhafQu2){!0? z7;4{e<5e%W3pFQcav#_6E{K6u%3fqY;%6Km)ibJ6^>Fw^otNWwDh4Mj z0n@dGgC&hWX+l5$SPX4oSG=qtKk&c9sf^Exf6Q>Eleg9^aNK6m6P~_tJT-m zgBDo$bO7JOfO>K&X8Y`wLK=R_7L-~rl~CNIDo|2?{#|WIe3n!&Xw5)-jv>=RGe#!S z$_AeyG(bX`)CC+hC3VE>=q0dmQtk{X#d&xjXSXqzlj zX@tK;i*+uaJsGNvG-iNoct>6h@O1=&)>+}#uk2Z4Siav-P1#f%r6lFqGFhJT0rv3{H0=1QYM=|$Sx^8Ad%R^*o3 zCbJ5rlC_BhpC#4k_?J-$6A#@V#s4&*45T7CF zKhGB+hl&@|WjEiHOl@}F6`3RSo zS;ESM!rl}NsT`t%>7Q^#S`vn#650K z(z8zr&cIILZ3SD+3LnlFkwhr;!L)f*2)KHD@k-Ql&7_tE&?02{Cq$OEWUXu|g|>Uz zpYxjBPR50te%PP;l-lR*%h00vDs+H7GX|Xv*XW~^kiPtE-MI0g%>dL zudHJnN&bf zBO8{hyn2zu;W=u0fU~?uR~RlyM|7z!f6g{KH%Z=g#KpaZQQK2%#Ld2ZO!D>SN_d_o zhF6q{H0&Hb+TkpUr^MhTWi&ln=rS$svO%B;_%e4DG;m}FhDxTujEP%1wX~HT+XPFy zn7;1TockCcDS&f9rgGm@O+J)%oL*w=j9A_zjXUi?5}3aIom{_Xj=2FH*HLV-`-N&F zxU32;Exn@g?2Q*C9@B#&&1;BxK+AU%i1odcvqXRw$Y%%b(vxuOU)MlzJu9Viq|*3^ zQVY^D5U%?A?iF~q8EbsMY}@7SF-7ejwL?4@{EjZ$IY_6y_oIPg95v$VxVXs3ryd_5 zlja)^Y2o&G4cEi%jekyJ8WP;EcRn^15P-wRMqi3G$NOVFJ&~_)1QI6P0u#$~gpJ!(CmqK-5GZ zs7l^uhEP+0$p5Tgt(56TpE#`hQznkQ4k!A_jB54c=>FB@0Rho$d(da^)t(;33&Y`S zlXRyCqNCZQd0`uB{mg}kiFR;~A=yuWr*N3f!{Zdn z(%3Wisma%$^dh%(ZP&USzMO7OFeP4IrUnF#LOfMpBVt!RR9|vJI}clsMSX9n zw)~qA$}w&GKQo9%aV*@?xW#10biUPv8{D<#usERA<0`We;{k#!P@aRIbj|`i4I0d1 zMM)(UwLN4>mdp*}&jTclA7l?ri;)UGg*bNxC^>}tSqo;ke)vwdxp!ZHg{SM~`Tb+b}I8TP|_&{(u)b;jz{*>w9qMT^#q5r*R(+1c; zJ6Jjc$!X|=LxZl1FCn754-}Zb-gMG>mrO#Z=alQ!gzkoyL(%KI-;i%dIkkj3E+r}| zU%nOhchxCyL@hmQ@ik7CjW?PuY@Rr$nY$3%qxvW?ynP$Gg0Ji4_k8AdelxfZC)Adg z^kRQ;?m6rgm?ET$_`cL*PctW5 z7c89@=e0eow^Sm)?54Y-I$fykQ|_Hm&BuF-mNmi@Kj~8Xoc@YQiK8>k?ZCar0^HcF zif{Zs2n;?KGX{to8eunmniYTP{3-1cRhYTqjEfgOf3SKgvw|LlG*-Hib7Q zq@3a*wV2B^%<01u^$PNWl@KVZan9RcZ zGR$7YxssQetU8s!Hebq357pPSfNcO+;{y1ox;HU<_S5nhWknB5-|&Y?g^ooiNbt}L z`-r-^-X2dkU~+EMyJ~?k1^Rxjq4o#{=)Ax_KHekR&yM%!n!kfJ-?w7jHK`-EDsJgR zgNg|v*Imu{j)A2+uA%v8ApbO7TNg1pVQ{kHpb4^t$*hEjfR=s~Zf?41oy*v}kGk=- zoFRY7oXCOv6ZdN`&&~eXg&V1sXWQun2zs!mA&Z(DOlGIpy_=2(o zi|wu8+JBw{=ar%+qTKUCMqTUWdxVxwAG*vqx3yz7L5`bPS1o5lmR&(blg)&EEl*Ry zrN=wE6(T;&M{Wz^7;6oa2mKF-b#Tc{5i5Elx+jbo^o9FUhmp8aSBd! zia=H8h8wK%k19#9Lgg)4r1%7e(@!i?fx+TgD~j!`F}M>4J!eo~@P z+&w0?B=>QAC`m@cb#DHuk}^54w%4uM8VKz9Om zZUnIZ-^8{oS#`0Y4i214G8Y}od?N7%1_tnI0l0mjIDYzuiyh<0{A0B1%&mBV?dlCZ zQE|^t_UD-x*%EVMa}Gn^mB!$)VLWxc6tS!9yOD`@a5$Eo)}M(Oh?FQ~es%Z{)qqmW z-h{0-ZBq76+<*#mPeH0~)E|o~UFhh8KZz9KSI;DcZGt6n99 z*6*E@j3qMe`ln-t{?BLN!I6r>_e^;Sv^}m?E<3_=AI^&BJN-p>cXy9|B`I!{7U%Tn z+P9}DZ`bx4qFM#?{M?$!e8Xh!8d<%X>XpSE5N6~i^w0Q@e}8rM4hfydZRu<}uI_W$ zPd&|yVauc)0Q~!n5%K#X5)iDCZ#_1GNZ*cGtZ6l3qq?^TZu-XXDkjf>F_hd~-a(XH zwZolKayr5{VOh5D=K-@0TxY}w6-&6**nq%I#V$>0Qk@?Wwi7Dj!`#_%w# z;hho-9FCU(i%>AHrWo!|r~Ge)5KgUCx${>*t_Z+{5DU>;VkjMxacUTp8&Bm@S;b$6 zi=eim59FCwaizzs{16|9B<*$3mHBgHMHPSrjTK{QHNvJHdckWBa4alMOhhPt)d`M_ z-rhL5LD4dyB`${R<={ZIa!49wS190AA2Vo$K_8 zVJYJmB#L!hq^=fMt+rcqR)Gdo$n)$T_f2KABDWb`bYU6JXqiQ;#T}Kvx2AQ@(s@L6p4$daKeL5sTN8M)*t0T$ zGFK=l5iMLZthb!RAuO(x6_G-3T#Js`G4FGQkR&qh7284WhC zN?raxKfP*c8Np>45-}UF@(_7wp$XAyi#IBUOcBc8c$8I6ZmmqMc0Vp&bF%K~57cb2 zL49TBIf6kM+i_>y3)#Z%)aLOdTzO#mrc!~e7LCY9y^Ib-%H}=5UXIb|dP%BU*cIZ> zd$N$sA7Q(a@sX#P&dcXHjS7wJ`4l8xG!FV?8JUb=(@3uYN5kM4-)hBsr1}sitGd_Y zR$+sU4Qu&t!vO=tBMx>EvcM(_rXNtcQ{NoPUhP7-UaVr98=$~B+^Q3v?&7G5_M^g# zneOYXlu9QHX4Lv59k&%4$ZrsP`mj$JT==h7&rkIxs@IG^+CF@{tM-)ds?J@Ybg|WQBtl_pM z=IdWFG^wh*(Ahgbv45R3uQ^LeA=M&qmH5(RDNwk&*TY{ttpIutbFtoG0w4Ku!O#;PL(Y9}i+tGKrfx2fgFk#li? zQrLPd?1=`jO&Sd$3!M|>U5b?-5mSx0hM#>KXy5($Dc+12^d6JbLmYew-J^Dy;Kz;E zuVh)tI`LNgZToHq+1G|_TRBf=G<&48G|3htS>pCX9zU&MM3!{*VS+N{r~8vuQ$bD?ffH3pJV9 zf9M!aRk{U!brti-owIy<-O?57XX$4^7qHm|yD7X|>JnEx3KtGAmsO@4GY+d&Xm8mF zkFInnC4rob|AbwG%#$$3{{aW8ZGbp`4RIV}X3gfsam8+RfbZr)qo1C?xffv^dMeAs zypM=x76WW7V4ypSh#@T^5UNqLCl(F8_x+&vKCoG>cKH77NM>+bY|M2&@Whb`1U=qV zKK4NS&Cns0&Sf>@P$P*nYNC3niyKmp2zN$9?w|YL-4X1vf&CY&Y(K!WylnkuhMJ{% zOt79T*e14dc8_ndvh{;k*$?voL8|;kh8|M+FJ9F%-%PH5S+VAl8P#2HxyAU)@hp&S zTJ|o03G>ASFTa7u)5^C^c=2QHC=EQCg^t5dW0)I1EkAl7z#k1| z3bz?xEdcYcs*X<7cCJ6gO(J``_Z?xVsW{J#yUVjGkTOlG`S`31*MI zokT)IKax(>ilbsW+eKITnpe1){TXREsD`>WRb$18|4v7+J+xxTLL!y{KHi5h?X5lH z2QQCKs=8@8@BD0# ziETS-nZVDCI|F!`pYCmgtsNDfRQPlSlC%FTLY&JTj8iCvooql z4H?!WcDLeO(Wx{LX4ajK>aq$*vT^M62#Mi+Zs&uZ)^!O*C744)cyzoSlF_`}sxz$!|6CZxy@$rklQh{gD^OtN%xssH1gmanAZh};n@as_s+{-o1sE2tDaii zLOE~&>t^P6dqFP^yZIbWdeE;PFLK2%Z^oKj2CbWRzj+fK>~_t$wLJx*d4>lqKvs;k z)NVm>)zk|Mg@hN(tPlR3zl4+oAi@xFfEqiyWo=-R*M0N}gebkU3NZ_(@4jNSy(XWd#odT zxz&2{OBcP+RWL!aObzfWB$D&BZ!=aDJMdm8qp%if25z1pz?LkYME?EK+Pud$iA)z# zXZJJ@hY5f#$O;?$^z4?}b*9fAjFq6E+F6oenoaVP?OmbxH6P;L?eSd@*>=QFzWdU2 zzf2h5ZqPLtUkd6w)Jo|_4!j0_`Ygn_Ksn{LBXl7VKq3HfSIC0_u(;~^{8U4R`Si;T z?za#F+Ht&~jkpL!@OeZZjxm=UdeN^o2s^bHhZA3aj9s6_X|VD&oqfI zuD}19Yq|GXRc!e!o}w0kwsnrXBfNPM4FnEPBT5ff0NdgwKS4UHajh8KTe03NeLqq^ zDucVMY+xfLOu9|L7de}U1~h-y3yT6ii5U_lXChMMf|3}nd<$|`uNMFZVEtqPVoC!$ z(K!?>C>YWmoyZL0pCrH%!85IT2OjP&5X59A^muo=zWN*pl)%d4dG_?G6UxzUeJoC_ zm;E{pi=Ph-2PLV~F-5_pJ%0*9aq!Y(4jA-TC6xzQZojX~rydRsSQx6{ov;0XyAFFS(yWOMr6CvcuN#=Ubz+} zY;QEl(8_sz=O{1YPbO-k63aXHRwsn7h%KzqoX7_j`CQ#Crn~`TK5m&duVE?3g!~}= zTHqf9fCylE#gehH;DPF~-We3OTNFbcEghOaLt@xbp}i_L0#pn#{s6#_1cD!^YO$oe zycg8C9G)kpS3V+Rf;YL3*ST7qe!@UX4j#>B&q-#q?&<2{2+xlnx?*|nVQlmkUbLroo}yo1qMD(q!zuIj91do z2%9Qag!n&R`lIrez&fQFFvbe^_Xh(_mC)aDa=@GIxSURa?|6A#jkI zameZ*R`Ji!&@LUh=f!u$m&Zx2NQS$Ax}tgyfs6CYeGEvORG3C8!Gz)*2Ct*W>V zZgSkOExXJ%^Hs163fW!c?n{Bt5&r7UY$MmM4>j~1;dAJ{%JLhCKI4Fl(o)pmGO3i6 zzh>3U92UrWLnOYit1ZSJMPm>CcW@l40ewY}@1M60U^q~0uK2|g>7LM@UnYhB?5>#% zy{=1q%Muj?A;&Jt1imzpK5K>(9i14(RX_S#zcWVoc&Rre2fL+utV2%2Cx70^xC8fw zzfzbWRY64|x~9)`2$V~?)l9R#2VO9I@^*c?xX*)|ot}=u5t@Bkm?Rf__Zgk^moWFk*oRui?f$niB1Lq^k0Z=D*A2B| z20G6Ru>WiQ7`rGr19e|jY)|T>K~J=tXnc(`L-k1)Z^g?d{G@TpLvT=KG?ZHgWaKs? z!6McGCF9@(iy60<%ZA<1;lkU)pM=S37A|}}7pOlM`k9hl#m*8nl;BGQ*#A~s@JJY9 zsb=yyLd3kf8zhL$Ti_7b2ahTA;m{6CDj>CHO;0A3#yW>lDL8XtTkqF{O~nGE&iONK z)v<4GXH?y@5|k>TCd5y?1{RIi^2C-y#ryOI6D?j3SKan-p@nHZ0f?OFe1jY81_ZOa z>K*tPueYFJobx2n^uMF{`fw%e!Ao>3eJ^B_y%g~dJ*|i3>F<*pSkpsi@nisfH02RR z4^URZjjbQu3YQuL$ce*$ijhKR->RX z{mD>D*?71XcerYjW#?-{7H9rISUG)V|ABh+(N0*{IZ$X~muOLE-sZ1UbBlQKodZ-#kfYK0S$xyPr@1E{e4`IiL zBm03E;-ri7SZNyIo%CbqTcbCCuDdNw$0OUd1U3|oijfH>IS1x-D6EoIlppE0Pnf>3 zXt3uJJayiNTxd3B8ADgJnW`|JJd$IfLG&7(0@BO<(9w+@HaO8KpZsy0(U{iIf^4lz z4p&Kx5~)Wa_sTlPP=@=<+jQl&HyWNXAUUYlW(IRF%><7B@kh=5`AERtapVl`Y|g%G zAd}EwAKVz?A<|E6n>26v78PXIsq+o%jiU1BMkk!Uu3z;;VrFJ^^lvkhk8+1hhb;&r zsp&phf`=UB2EI;N28mIF-OlVwx5nHJl=~4k-v3W-sdw8K(CYZ2AZjCx0eo4CySk03 zbFwbNokZS?LMp@{CIIMtUhGIfu4F-uoe(T?fY+}+4tf3(xM{Mizy%q1@D1*O7ehDr z9UAe$oNm&41NeQY^YzdNZu zgzOpENRANN^&I-2&^}yrBiFS$X=?LR4gWrQ;?OV>jZ3%$EU)>C&tEAh-M-#)6*JAP zd)MMT23kRr0KS@L;75!pddJaVy9i1?K0Cc1%|c!(qS)6G&%u}fq0Ji*_+hbOWg=xs z%!#{Ls{ler;vXdM{8@r$aiB^5|BPi8Wz4`ODE&W{S;SNs{hiORgTI@;X;@8MYRx*< zmwRnLmO*Pauk31dBUD^89a04U<@eIjOLk9tXCSi;<)3fDp{cZzg!C~d>pJ_#^m3p9 z5l3`aFX#uZ-TGsHa~D9S9!Wg#IZCPYK~NMvSLP|EQWAB&ciA9OQ*NRq@DiZHE; zt|0vBLmgVH{pN<&xWLby?%8{biFP~gt$h#jk@&Xg!~ll7tH>IX3VW@PLOBhiKlrFs zmOY>m8VdI6w@>2sOQU zda>A5OQd7m17O*`_4bHmdT1rDmEoJ+8wD~ax1v`Qacyh!1ml5?kPF-28 zuVD&Xb&ki}9}(Mb`N({2XlE7e`WQ67cK7u=J6<=*YC+O%&4cr3X?~}ypCQiFvpTF( zg_|zkfVftYpou6C*^f$NsO>ByIKCz^XC8@W0Vc`WjC_g2syVPA%n+oF8w2QHTOEj?o?<-s!3jRIYZ8;Ee&{hI#7Y)6vp{Vj=K z%l4BhKVcI0&cM%Xf}CGZUv%7F)OIb7Z-~uk*`zN;aF0}WPls|lzV}5K6{vfw6ir&z zy^JuyuJBBWV^C2CAn2b=WPv=GQrzLkwZ9#%of=t7YJ{%a=;&$XE& zoD=DAC{OfLX3cv|qAXx586}kQrZK8b8H(x0{4Teqn?NFOi~we@Y;1e;+m~kqDqhKw z3b{}Hm44ja)D*0BX)`n&ieT6+voskW>RJjtJbTjI_(iyPz+@`9pTBd1WVFqOWGj{} zKI1Ww3nu0u$ zcrOxZ*A1=t@L;5KA{Qb-QZTGC`ZV(wI9d*EWxGDoDW#7fDJ3}5v@Q1sITD!8)=Z(M zO3A0-!@QkV+c|LRSD64Bu%31FjqF}chm}`}6|+^2*L~^grS)RTram4usDh&W5kJr? zp_cb_Z!6LH9BSC=7+#AQ){)2^vriTpHo0G@=x67T`ZTwCZ1Uve<(XO}jlfO%Ay>{d zlIg3;YkNQTNFmE?5vGC*`7;yN-k|O21&+CK%3$Kc;ToqcN_%-J8S$(UB#bw}`kvvo z;du-?BkLCkj&n0f#$^@kU&`MwUp^zv5d|T!7uE(EcuN;)?+|v_z?U@4iU^F@M^7aA z+lS}~is8nq)A<_6P6zbkx-8gx<3I`B(@L4|1R2q)_GdBwC>Rq8*vIRU?N9Bl?AwIR z=^unln1ZZlH)^!OfuukZ7k5lc(#yR6;^&4VK;va%xaS!)y*r=AQENQb6O6^AXDoS! zNG`+V>aw++_(mLe^B8aa5HH9K1{r3A+9XAl>pq%HHSKwN&82G1exET_N(zBhnad4% zR-+9Lrx)>RH$jPex?m#Adi&k`&Mu=Z=|h5*>oIR15tsNV8Cs{;pe zMbSOUA8Cx36 zGmZ-FP^l@aTe|PX5;Zy62_m}dgObFsoB`}5i5{vUHbSX{qzOXch7FE;b?h9OY=Ozb zV`B3eA$)P^{zlJugZeJJpwY@yN*Ior2bDsQxY1i>#DMec#LVIn5vD?#Li6p{0rjN0uIrTY z>T8Ib(8=sA(dE`lsMOV@<@-5_?Zf`FwwDhocSZ;5RbTW22TFT6TY6qP&~UyP5`Ua z@~VmWmpo@e(g|>tFK~oqAl0l)uLv;m;R2*Z+BraNDkV{;8_Hl} zIO&WcoCtx-2V`=PH&&Gm|Kt@+Pd}@~HGeSEdU{YM5x76kr0i3~q?8A;bK5b(8JsXiN=^Jal1MgB|^I)sH1wv365n>%f6yK6gD3(7b z8_Any&8rYg999k8oyL-cbt)QxCl*8FLhc&XOaK72MorBzRoRnKjKwU65lN0XfM9L?CpX_fuSQfX^7w6VP034!oK zl+NhpAUgCLiB3A~?!K4KW&U%S&1u?<+5|S5en}G;s&WXo5@i8SqN|ALHPzxCUlw50 zvgc;%Wuw3MV0|T>b$9@(f_pGg-DYEb*+Gif+~{<&l1WiGmNI8}V>ZPh8aQcAFTgXz zSOmtXJfDZ?_z6ish7CN5l~Z`Cd4N}Q&a7_)_*<3^DHAQskjp?$Y3bQ0O%UFYwn3EwC`SWj2P|Ze4qyCE7$l zn+K?y9TGa_W4V!Dja&~QG=upE=ogjU2Z(wXWmaR+5razEXz&(vL&tPEF3$o1o#9gY zZU5d>YM%EwPr9fDocZ(xiER-zyrP-=pgA6*uablSPso;~En-Rdo>}i22ljvQ&S9HB zcQh&x>bO3zML z^c!N3PU$LVywG9*$))*_)Sf+to+g%i3=Y*mDFqglEE2_=Wz}M0? zQT4i<@QH(1#vzC6>EZrn=06skM@=>PB$~WHkWmTR__Lk$6xJ9v{pnwwp)W^inq(Pf+0bM zuNQ@;c%<)dds8^|i=Ezs@LHABUTXoj=JfCnPGw-Ei2H5q;H?383K}z7tt(8hf1IUQ4ax;Kpa=%)^FjvPB_r ztoNCTMQY2;Ud-l#!@C!`ZolgL0x7lt2#$RYh(%hw^?L6GoX|0q zRjqoqpOd2MxuGh!>c>AGa4uwDKI}E!K>pvTo_o+5zq^ONY5s7rk{E=l$lXY1^*=%2 zd6Hm)7~IA?_sQgi%xPZYA#na@BBlPbhT~vinKk){yKGOSK9dwJ9E(@zy&RTijd))t z0Jld<;^_0!O1|9*R)o);R#rZkuPZ+P?|cm~tNDQ%cd$&npqUnWI}m zc!`uoIG5d=r;|m^jOR|d`C$Yqt;nDnniT`10)@p2s z9abWR8G$Ag2pI(uF9idv<&LFo=wJzX#!X&J1bP|c1;)Oe4vki-IH=kH*-l6pF(E&R>VKnYr}-@f+(OU1K<*;5t#r)SDgYOKnl#~W z!9)l?4&ZVe*Q;|oKn@?t;|L!jA1}k_jtZ?0`Yu}J@*K#joEeVp58(@?VNnU;I&aA* z7BbTwws1hhN-}3cr$8hfnaR@4_Xlz=e1@=eJ zkZ1c}6XvOOLKX-?G+{sx$t!z2m4ncn_y3axkq{0vy+>vBub;$8m1cw+sH`1Fd;AF# z;KYBSGb&+9Y3v((iTF9h_ZwltJueEYPhdngNpPk~(th>GK)q8e@c2Gad+*VKa@(Km zk}Cjzw{PEdIu3u>xq9mz2miGN7J7D z7SZ7!zsJ_be46^uX}{3L+NcTk|@uEZ7)&2t5R*tolC9K(Pk%Wbzc7;(R+ekQD5t5&%}h|WsX zGIA94kC3c1E87ME$L(%x`M(7R8$A@Fue{eKXJ^r~C21o8wjwpQu|7&K+)57IvCwGE zARW7?D2%OO?1ScqPK*{AwPZSSLm|N`WPWN98V9oJH!nd@FA3&JW>Q5y*y&~B#Q<0| zUDQB9AW-1R?g=Cm=;37l$RjW@8JRC@JJOtb-ne7(fZ)!)TYG+WpbSllzCMaXEFjZY zIQ^Z}bjwT0BUpVK$1(yddN4gsC8;X$3$o>ejK;ga+pd30T&J^s|CsG|7Opau++Q71 z=i>vT*%1)<>+2g?_$J_sW&E0Y*1d@<>E{n|_16#L{;&GRG(FxQUkl)$wwe*g04B)> z66A^Zu^W)>iBkl!oocA5BNU_LQ%X)cyF0a~-L=!QpAud#qvmGorwQ zLnYNz=axuE`|3hb`mMl;x}XRae7SvGuOCda(lc20_JLKGXSDJS$G!iz*nMj@TG*IQ<2KXAO+WODJ%*X&+jGP{vX6?wgz#a9;@hX!QO%29O9jvj zSKA1N9WF4N#6UU@w8B-PWRD^0*>oPT_{U2S-GZ*!nr@Ar|GT*9YNs8*G-?$*pm(WXC(|aaX50e)PQj zj#YTqwseY4?#vjeN|DL*T)u!r2IcgvRR}&v~W}@A7;_d$fvbP z!Q6pqQ26k>F%4t+;wb1c4aH#f#=j^czj6RTJ%O&SPBr`JDc7A3kRay5lvH?go|R4y znp7cG+PxP}wkqY$j>L#&@=PJs9~(c&PT^!L}@P62f?N=YgA^!md6GUgv?Z?S8R>U3Ekt9c-$59MNim~ zkMhi@ampaRls`LRVJUbh;DwzdiUcNQlzo>q3Xb4bkbKaueqip;=DK&-FX-Bc!@sV- zvWa(FKgU4MzMJ1ACCbp^F)@ya>rYvL5%I?L+0#tQ2W;Y8wFH1L(A1*;VM&%W_K8Xh z>w5@qU?Au$!ww;sZ+Q`?H#m{J*U}oexriL>Ab$k z!}gGzu?Gowd-hR5lJs1?%}1J2y*1AzdKyMDdt?F6g{mZ6Re4xXH++9YFoW05j-Q(4 z(PGM?sXU-)as~xn`n4f@jbYtR>%#nk7dpPkYqTpfQiZIPVe9{Wqvh~!) zAaMhSyBpmu{+s7dqeWVFr?WPyC_)(ZdhN8u-d$l5)hHYIr|4n(Caj!wl++ZKK^ooN z)(03gqkN_DbL>mkD+o`_jEo{6mT7)Sm8DA{VE_HAfz89CUx!C?c{y5lt{vua2 z*+Qx6Tgyk9M^p(i=OGp8!ciBFu!n_-+Vps_-|l1^ja_6T_|nQJI7NB?B^ByrI9M?^Ee zD5Ryy(}k<=xqyPa?ttXO9lq-o#z6yq>+T3?E9K1@yDNpo#hfcFXghVa3+_Wyp@m9$ zInd(UhGKs*)Ppp-4IZBZ1e_1b?(mwz?eVa}KOLZwi`7}Gs;RCJ09UyphCZmsy ziHT`xNiCa6&h`Ucy2eG6y+YmnWxWr1@+{hN`<$QYhFsaRwYaWcyEb=Eg1epV3P*7q zu#0oo5emAMZMvs%5oRS8)&9Jju%5nXs-^i)K?V9e>iGShkJWcJrK{1Jyg&(!e=Y5D z7w|6qhG-x>#_=}RF$EZ5*3qS8`Zt2w^(vz@)E{U(l=oGF_y9{#u;Zksb^nX?@5n*f zwYn?l;)&iF$nE;p6)j{x!u@xDARz9HoxQz%QtMefQ6;g0vcfOPKhyhagU{#fp&$!c zSKn&Ih_8BdSrvR)?(4fZwH8C6*U{&~Hm}v@&EOUI`v6Io>(-JkqpS}#hiY7mPX)?K z*eXFmP6WBrispZ+b7khBfOSOMV!zJ_vaCJ?;G)TUd!B`GR$quLe9Gzp^NW=-@fns) zRsQWW?6{R-KM`b2j;DPQKc$g=U60eLz5^E=dqlg$Gdg0`a;pQ(;{F`32le_ zj6TwsI!ZXYN3NcxmJCcs0amds>o;A(K0bDFC{!+mTZ5b)V~@{H;bUY=(HJs@*+CW^ z!hs_sO2SKq8OjUHCd3nAae0L7IBNY0oYBmwHCLr;UD@?5T$kErut)fR$CZvAW+t0xEz+kbX$+oe7YR4#fg~DgJWD!Ar}^il+_}_+oDtE3&^`f@v!|U` z@urppyZ|KS0VNYEaB#g17|GYtKo_;v&O@kY(x5FNV__DcdN6BkINv3ZaVLPFGMbRr z-&uHDD~{v3$Ks`ygTE!K!lGzd1 zCqOL@rW`89evS*YY8+=#TNm%rB+qeWWYL^8@GtN}`aH5iD4m)sWcg+^ydgcldI~JVei#!5$1YA?V1Roud zdYs9&UMPi-TuNLa!u%YDVD5WS6KaVc@Q7(MrP*1KZpYJl&cKtDS$?mek>d{@@?=5! zF)C5Ti@aub>yf?~n1$3>U7V^H+~0JBEyneTBYmZedETR|-f#G7aFSv_9J-mCJqjIn zbul=MxSuJ}1u2=(9Q~h<$RNcSB{W*jVcb<0&LcB@@LX5vA_L#)<+=yq!^$zk%Syl- z#CVqA>eZzzZ1hq_sR>stc2J24R~U9bQ-Nr&zrQ{_Zm4rXSv^up!3FqB@&ObyrAK04 z_2vu|OvVk(gmg>c>Cl7UqJo2o?5jW?D274w%*AA)&T!IBQt`>ax;q1lO{QSxaM4Sl zaLVJ$=25gay|5z4Sl%I#r-+lQ%R6#xZBm9)$V0Wbjkkw^YCQ(RY-#-4Sb|wOw;7SC zh9*~<(;ZoP`thR{l6z=eV~Oqve7CXNxv?7&;2{N+;LJ!NHKkAf3j` z%X{z7f_n4Tr)xwf#}ID?b-%l3;pF5L=W`V~U8$ep!}g-iAsH5Z;^XCp*Y$@NI>5w zT!r+a+lM$J(Q`YhnqOG)C~ULy%%a)q!H|AU*67MjzhP-`V?Id z>!%1Es)YaFJ)3T0KAKmJjgs{zs}?619Ubzs%rcQnOG}r0WGD1FLdh|cUe1xSqOGm1 zzqXG~%n9D!jNYCSaW9EaY!QXKy^ZZWet5VM*EvakKJ5Mb_qsQ4#=UwaL2_PrEsVTvX%YOT@=L<{eL7pKhDk>p2Hx8mQIRpjC zq03d!Xsj5O`E&B|6OJmTrstcRn=J*+nuyDDWV|WSXtbS!!|?XFNm7WlC6}Zcejsu% z^TX#Hnwlc5oA8}*6$flhK@pUclvM7|%*Dku3B+IHmMTY^J3C`*=U)o+qJ?Xc898wj zkVDnwkoQal&zlOgmT8bCdPU7+4^<~DAkrH#!-1H><>lp*(PL)*B)CEvE=3`STeHHi zZdD&~B4-YzF+M}87J5-l7il#@&4fY~_(-0~svHX+*{-0YG94`6KjtSSA_~faeMZg9 zP~gM&sCjzd+;6jATunnGbiy(Gjb%Hng>S(M*Q1yi5{QH+VUnWWoqi|)*aH7f?Ks-j z_TqZ;rk0nt_nVm+7Ik&?nhD1;A(H_VsgMxx&y#FlBYV0v&i8|11Zf@=mYs)_` zD~lo`B4U(uOe@*-E!$43fj%$E%fml|MxmsqT>HXI)9&d@V5ez1A}`1h~5PRF}<=UV*^ zKY#vQ3PsT5e41lDAIfkY8FI+Ha0e#+ZjPqHMctV)|)m~E{CkI zJ5PhEB;V!BKryGETBt%MSqzGR9ic==JIl9@JRHWWY|P9iFC1>CgqdHxdPY%EapLP& zY0K5L>b$BBs6KQ<5Tpo|eF6T2M(#Ratm#nGxXMxfxe6w9KZqzp#5`(SpW zw(9&*h$>BCVPTEs8|=Nq!`t89KI-Z3H$2!*!MiuNI}g1U$(8GcjURHWeM~^P-wq1L#&#X z);l1(5cK$IY;L2YqZ0@SmsLb$xImliRQu!WOQ%j?KTXKWijIpr`}XbI{POZ^w5tsc z&dw?x9%8V5xsK4J_V)IA9V|b6?(|g*b>JniwcSN)G5hurmGMhQ`oJL56H?dI^^*tq zA?OEr#fn+eOBa$2%dZ!JTEL9oZjNFev+%36mtTQmHH?Z&E5D|O9F_sfH*REns@^66 z^O%s76x{M~x1+y*Eor_!yAu`)DR~_1?Bq;Lv4ev;%_ql)uU4y?P<9WGJX%^>BBP=@ z5c`fwmvCi=>6ws|6MNy>WA6E_1U%Hj_wNXmhhSEJ+0JTgv^*w(B_nJ>%`{?MoBGq^m0|RQ9yh67+a2>zYtBcbc z7#QT&)lns1)C{hx6UolW(SGvwyw#i*+jVK5kjBcGmGJ+*JpF|T_q54M(TOe#yu z%JiIdb#;yF?I_C1$^>C;WWF#sK2di^7gogW?Fv)PWGGfKv9URA>5=hq4hadmz*afh zKbxC#8yoR3R!f}P#{&9aGOnf^rK~qM%Z!bU35$xRGBYzfIyo`@d}b~Vo};3o!Wu$Y zv3`LBk)@4Ii5tV=yV&!0g)@u|43xmssIvGO=s)F`+aA%slA$4UZ#Hmct4rk0NABD8 zxmfB|rVVj3zMG_`CML$_=KYY(maC`v>VDRPd98Qk6Tb|w@$<(LoMp6hcNZaJ zl6vdCxj6Lx{k46Pk%NOZkVaen`SSxL7=)3MlKut3v8DC(j}RWJLthlZ2a`j>Z9%Qd zylJe!g%xRG(S+30uGv}3*RNj-3k&N>zA!3>-X?0FdkJgXBw8(V4Q=f^3JNF)Dc)%O z!E91F_9kabbY$iWmY}@61%1&riQ1n>T)8^ayZ-+E*>ZlsJeY9MlP^z8D{3UXc3$bF6W~q?!h%SP$ZQQNatjEkDl6mN^!|P>TiSE| zb?dsq@2#!S`1k>vZW4N7Tr4cCdmbLl`8S9PIL5cG>R}>JBuyKfOP)UE%F4PtmR{3V@tqIfaA$XNN9iePNk%+q$CXW z%J0xaD@Q6-r6>`Rs53P+m2{bwnZ7@)3Ha#8=jXQ@AMQFQS%XJ_|DJBk*#w>zdU|?w z=*WZwa$9TbUr?Q^@+jLocQ!#ptf;9;THNuoLeeC(sA?>OGA%7_9KA?57*u^(0hIGZ zZ5V*5CxAB~ElB=oOMf(dWyM}AODxve*_lz!SHh^m_zn1~k~VOHNFE_fFPKPlR8&P~ ziBht%IaC)lbo}2b)5`Mm^BcOA!_3pxCUa@sw}l5NUj!$GMmgHsv#aMyxCX=4SAJd| z3;28qkiorfXn5olhgph+zF%sGrd{QnMBMLRpelUTHa2F+pZpb}qNJo_VHtq> zh>422j#ruYrLKyIh%Ep3;W=?>)Bk9Dd~0WCajepGVr1kk81_$A&jmR+6zOpSkB^T( zynlaNU!Mxv{{|QhXf<0Kn<3k}-`nFBKN8p!jrH^jeX`@@$pGx!t($n|f8=wPUO4o{ zix-@|08%d*Wxh12oCID7MGPk8(qNu^K<>rlM+0()eCN-fN6=BFX~Q3YMx3L{nx3BG zAcNHdMzF4>C1-7IP3GZFMOi6ZZ+-ohcU@i2gw?KHyM6!um0n^avAEb+8!IdJfWN<0 z$HvD+zzWI9$^BeeIdGXdgR#pzZAt(jSLNm9r6eVvmzG8X`#Cm1gl7!HV`D0)`U%G) zIaNp`#Nx(#bdcI*%?BZQRk(@od*hwb;0qpBB)VD9aX`?l( zy7i!J#g)bP7Zwf-k(C`_zgCi(oPRq`&U=XqCqeiSNXK!{k zBZv^kJk`q4JMi3?)&!spLOnI*<enD z$4`S5PvqR)n9+7|aWPckq-S90gE9DY#+$IfsP?G}A=9tj-EL%!7a9>U4&(<6b#a$#{1u`Ck4dx9tl$CWiu&uahk z{r%A7WK35yZKwSUc3$2yE-l-S?KSM|xE~8x!~vfPEi^sa$%!9yqpCiv0dz9xe{cVy zu%G}7Y(bi@`E1AHQmdL&{dTw9qTBr1+W!7N$g{DKQ&EK=`UhaVCGIZ`&a}S!&Sz3j z$P(~Z2sH~t4(~u=V0LwtcoxG9`2OsPkysX~!&pUQ+4wn(15%v`b`B0*eSI9z5*;d1 zEq4ZIzO=G}F*j#I^wt5ba>&Rq!ieuaCm#Uo&!psJT?2zqm>f{cn{E|dRzYCowDROC zKlIzyNq%tHS{l0N_IgY8I^Y^GfzpOwL1Kb}nYqfo=@m-y{!)Tliv7>e&k-!Lh>e;F zIFZfH&K86n!^6V^=-dTs zfZ6%^5SSUb`1s$zqI<13{-|1N)YsP+y7Q4ShF%08DhqojS5rEmWpEIFG!TEGU}TH| zSZcYs*e?-qEc0!C9t(9|%(1=Mdy|}+IuwtXroFe92oyKr8NK$urwQ!8hE|4+9%2f5 z`bW@;x4{AeI_m&WyAp6_Ne9g=({J#sycNFC}E#wFw0r`<5V?QIT&h}-U0wBtU6aixSR_v zN_n{LOwt|+_a}eZH`tHzhKGlLvb*wWU?4u4R$viMQBS&X;R2EsPfty)WMpI%M<*1F zY#RYrO1V!Q*vm{IAt6TCW`HRr2}?Y{L)=GxKZeFrvp?kkINRIvVu)42Cm`5_8LAuG zILR{wZz43VUbV8cWHmQ8M^=%f=f$}1gqml&f^nUnpN9>Bi4C49KUn!upwjqme5Y^H;L^@4LF*f{{$iZ}Jur-XvqrVFpMzB_l;s$uT5!kZ}u@k@%Zx!4@j^e*muu sZobe#(*2j_gigsE|36?1&|F6VYSK?@;K9oV4itQ;D{CoLVJw6H2UgpixBvhE literal 0 HcmV?d00001 diff --git a/_images/b45cdc82a1a5c002e3fce8ba4f386250feb595751b98f447d4e3e7805df7b2ae.png b/_images/b45cdc82a1a5c002e3fce8ba4f386250feb595751b98f447d4e3e7805df7b2ae.png new file mode 100644 index 0000000000000000000000000000000000000000..ea7ea78a644600fccdc7e92a404be2a652425b0b GIT binary patch literal 19242 zcma*PcRben|37}&BeF-58I?VfO&6k2Mn&0MB+A}Iwv0*=Qei_`9J@(+esNd>%v6YKoFz#{b)o$bT?OSP%H;c7f za}7n+KIVQDIYoC`@MR8%JNj%H35HClbY&aQy3eNB!o;ebY{gTKMb7T9b-PE07x(u` z$&?jIYBVK2B^4D_8GH5s0rDSWw1H&E|ML?b;o#tK)F%^BQc{{IVYCPh4aM9N#=tSL zLuN7T@W;|gE?1l=D?Bn)`pLsFo}LZ6?A%z`6t8 zPe4WvbK^9VL8UttVPIXx6iEO|P*BjI+B;#T`%6Z(4+$BW(zR=hUMfmT_rGFE`?AF|8H?BvJ0@9vfL_V#L?q}>sz{y4nFL5$mDD;6FejzZ<>m%6%!DJk{bb9avE znsL_5>AlgNLHZEfo2sACa6+v|gsg0NWhLmhiRF&KN_ef#!w8hv59qZ22XlYNDrebD}j9?Hn8!WPXBIhF|_iKWt!hqa)Za|n%@9i`N-U&^&iMgtc zkp>l3pL?%g3o*;MQ#wtxH@S`agt-3vELMJ)!%+rJnaj65Jnkveu_aYA+CIEN&|~;W zD~CZdtyj_Nb#?nfsijY{eE#5PDViV>1#cK^US9vyXzJf$J?LD-dvHpuX}RWH-U}V z%8$i&K`6?w+H__8C}*`I<=&dJd}4iDk!7op?~Z$0f+*41;gB5jmZ;5|{cU+Z<7#Hz ze1p4nCbi3xt#`IoZQkA*q9-9C$>=Y;*)ITp{_>+_|5t|}hnyUXvG)?ei03T7%>K^G z6M4VtnXEVjkvBWP|xL5jh_!E5hszc?rONR_KE-|$eLP6P=Jw@Ix(&UrPp zhTa@qVOXi>k5gy$+P-}Gl8uXNGHM&PRA@v*11yTKlUFx{v-9$*%ojq)js$eb?=z>V zL#liZmo-K+otTt5Og4(93c(Dt*)*{`FHG# zydKGX{3_?>bIsGf3Nrc{{8x2#@lcxAvOg=spo9=vpx3`kAZvwCSZt9s=XYozlh_(AF zb{w3X7pojzt$NBrD9-gIl#SzEZR|_qvg9GRbtxJC(3n-G5XF|xT<1GNtmDZ!&~X&) z>k=JkFMC+$1JxL>qW!vu^K2jar=cygC4ws{f)HtXr}`s%rv`8C>uhYa+&r@CnI!4N zw6QvEx|Q2wEltgP^LqZ}f&k|Atvvfo7KaDCJNx5g&HWzY0>>=Moo51I6DRUl-2RF? zU$tSBSjzSAJ>SUf@mRcZ*5~tz6H`tb!kqzZXH)wXW;orpl1}pLm*ArwJ$m%~a^@v^ zdBIbs&{|ql0RaJRQ8$VmMn_il+jH6EzK^r(6uAFpwkF>1(C-$LIx(B1>)!eakByDe|PP@8&ZsrxmqoI~zgI zzc`&1%^@U23FFrUfvGc1g`zDvY7hD=sdzu#Q)6 z&nt!;KDPK#d~^&G%ZUV_hV={c4eoCzf>k55ukx$r*v7gT1?|%&izkni?P{1uKfZ=f zDb38!&e4C|Ge**FK51}#yd^qO_L8cqg0}VpQ8mXykWQLfSp6a)elt+WETUfJCe?zsYDCm-zl|W1MQE8($ZkZhD7N1nkZ)Zsa|$PvcU0rn1VWNZ$X( zS|iuEW-m(KVW@2V zSAEWJC)WMVZdHcKMm@&oo%B}@3U%f0s_*FkcH=@!3Nu>OfrXzvwE~cpt->lH4qdO_ zqioyR{F1`;((-u+^V>Gl0}q;+`2n~{-1&y@xltVwNEeo+1-Q)MTCuXR+l)ip(kGKv zljR!DQ+khb#Dq>S!iCSFg#DN&PN+oa`>os{p(-NPF^t2e(oBy}`~sHla8&ex{}M&5~ep`2Fu zE|W&`@zS2!MVI=@**GzG=W!~7ldlvYXMAtmHg&Hty3y4ZkDN1yfpg;H86f(HM?|p4 zx3b6e?H>ZP)zjCfz!SG@+*(?uZ;X%j*UM9e5P*w^mpDPk`)jh--MPWkfahwOi<7bU z=_(4Id@Ihk2^1D&!b(c6lYrMaVxPOr_Pujo;t(*dw(E8xK>O?D1R2clc=4ANZLM>` zA~!WPy_DM~x%{DLvpcxqoo5=;&j8wiGjCWwpWdho%Q4ecR_ZzJ;T(g5a&T}^I(H78 z)-|vyQ)LwW#=3(;M5O2f%helM0OSPX#os@hq}Mh55X|{b=X&5JcHaRhPMlk~be*$D zT_*uz4=1+}9YMvYrbA>eo{?-8JYj4+%Ka<-i{`V>@hklfW^m@o>{sV3)cm|fIC1## z?6%bbx&4GGXPR+F6J%7U+!cu<9Ik)*b^Hhk@zMrW1s?}TbS;}=5AlhW55jA^oLRYD zFRUxR4~SF=;>L}b#h+7}5Jpa)o=z80-xi{|S}fJ*LR=r>Hy?-^&G3+vq>9Z!R-nJJ zqpFbkidL4G=jtqTT`0u%yvJ0I07((tAtmUzIp z@`l9d#@e9>(KTwSurPFR7E85+FWsR%7ivNr#{LTOz4hCI?n|zCN=m8bG3@0L9U=uL zT~U0*xI(9@kL`^r%CbLPE}>OY%J)YHPgB@QI+L_k%aEhobB(=nnY{#GB2zCA2!A_# zhNZ2E*K6+(%3Y+8=CMp?vN$mphodmmH#(J7@<(T@WhqeZ+>*E*6bm2VStkNupzJw` zarYJ~R@`sy6QF7XObvxjaH&qb3JsmUg#48XALnNEp5PhgN@A4z%$~iw=k0r#t5j57 ztfn#SxF$lxo3r`d1CG-osIda``MS4vla=zrVM_#~gJqtLu$e3|T;Dt`N}nMsTNn?b$XG*v+=CGFE8n8$nA+q*WhDPGhzN?^Kt zhDF)vy4Ytzp2hcV_|7-Jo^ZXcq-0^OE$phNrx)JSbEUDRg_E5fCr7s+_)_MZ^7*J^ z(n5|C=NlUvO_nF#W7^uB9g8RI1>&-VepM-Acyp9P=vBSfo#lfWIa0Y*AF+wL%^RGI zQ$-4jp;CtyC}LjQbaO|nRqyY4Aut^&mb#!un0TmR2DKFC<3|_Yt_gYp+Tq5R8;4&! zPbH*!;k!APy?2A*B`q>Gj+B{139a#hoSd9^f|ZN7w4?;}4GpnRpN2#6sFz#ewP7F0 zBFpst{ri~r7L0P<65qzhmwrv9NLSc7;_dV2Hco6DPMfykFAoxH$kbRY09nD8S@q2H$_EEy6+yA z&O(-tcS>ifT<#{%a=}dvn7lU7!Vuq)QfHZYRa1T^_dhJaz(c7^6e6nW)KFtvwMIto z?z9nE$Sv05orIG2Q@Wa#me$?6KqB+i=`Q+?g6Qe#8yJhCV1_+u&nlMd*RQ*d=2tkm z8jA=#HeHKvdEQ4d-7QRflr=iACigXn1^Q54l%7qIg3JO2$ z`{Exy41_B3^i3-{t?PI02tRo6K*;6CmF}Vo54lY@7r&la(B*z8IYI8$<2sxyy5%TT z!6;hFeC~dq|J_*r*I$?1PgWW7HhzivZS<(Uv|DBZeWYFILf^fU$i4oC@aom8nAX+? zD7=O5d{c^W5fv2`hMP=GO{JBT&?la`Ro@iBumosJ3T9q-yThpEVdAWUx?{g)<-QVY z{9%ZA6O5?_zR8at6DD5@dGv@@{po3v3l}b&zj%=V1q*yEr=S`a70DznGE*y=lhNQh33m-J}bb?gthm?enm6EGwn%(40Ab z9N)&qM#s-}XYoB${_+# zIQ}?9@Vykf%3nTbP-A6}PPQFmYJ%{tWQAN2O1}PT$q+Y+2^7kRd5|u9<6O7_9k5>< zghw1t1+bYk<%GnqjUGa*05D4i!F6mV@8fH}KIGIzp>mo>BeHj; zLb2zHz_}!zf&M`$DfR2-y6my2f{W8(7nZ(a-%=n79juhudsJxO_(b8X1&)O_$)1C@ zVC3wu4ierm&exJ%hUr(_hFNo_lK8I}uLFR*vFht!jl|4Juh zhSiE=T>HqQ5fN{pJc=zBdv+v1P6WfKWb?c#ezI;#7-f7)MU++0?&%28onrw_0#Ge{ z0di3CBY7O*WqFB!(PiE)1NIgbjrDUKu6GYE9|@OF-L&G*sP+R)IIMI?-obm-0(ahq zh7EiD?{NLE`MP~3AA%b&r*vLein7`z$NFdKd~8In{}csdm$kEOt3G&}F7ie~@yM*Bx#2FU zmqS_Q5)%CTKGI}AElf2HOts-eye?;{5X7&mR_u03fYB7{p0#Ku#P4T`JKn$)- zT2u_LN&68`~(x_}VB6BcA5Jtq_5KRoX`LP1!+ETi-dc=Y=Veh@_{dLu5MV?JYx1P5s()&34*ZDreSyr1FSjFd5p;jt?n& zq41L|(<^`I{7eUOT5K4##rO8)L+RRKfkYH$+v`7(%yQ<|XN$s-yLd#4BVG%phl2^@2keg21y6t%K5)-oKz47g_AqkcafeCL_a~ zot=HH!j<2u?TKlLy}i8vK3Zru_Ps?+>*V5cQI%blkwZbeNr|kNL%-n!t?rB0se~gU zB;V>2^;a7%Z<~>OnT5#~9#GiJkDm2xtVzmZEUc|Lp}NS^D;D90g?<)|#^K@RrJ|%X z(c7!VqcCZSI>snwOKTx=oN&{k1Ajv2vH$V8aSm)1_}sz=)si&o{jF*}>WQw1vp^ zapPM4|K!JODK2ES7bE9}sj9r(&hT%nR$puQZyw%5{J*mBPTS)Gy<*bB!oumnHz!ag z_4h~ys=UOjH@_VM5*CxO`@upar1ERj=X~DoALJ5_ON1yB&G^SU#x*C_C++|HH3EIo zQd2dJjG`Vqpzbb8N@Bhj6!Ze95UW{wTH3Q|T~GK|mX`wz1l&~fEQa4#I$r7hLHY7g zno6R7h5TJuXXqpr3#orkrU)SzEx*T@sgV*4EUNgZcOa)qlu1vPR=Yr=y?N3{x3d<+ zFSSP=`-g@$j;~%;L&ZFP91ILO2NxHCb~Y^#5-PT~JSaA9ZbDw2+}eRcb7X~KEJP5l z_V28MVPw$^{kUcSh1eu{$Wx76sxd&-qV@Ei_NE+@_x-XuYGh>eMAZ5);KT9UPtyWC zH{VLc>K=YRuN(^khS@iMQVe&9lj;j`;%VA%A+BiEgU#2%rg%p9l^}%aG#vKTyTB0}YCsOP(?0S;!uRN}He3*spgvm-QGI25 zC*w6WYM}szB|iimyw}-LDo`;$B86h#lf&+03rMSbF*e@n#~r9I92_)%9=q2o`Ed>s zdU;xs867FF?HK>VREHK5?7$Skc)2}#W=Mt#U0}oyDpg0KXeggBrt{eD!dvPBB-r#r zQK16`7jt`+i+;fQsAw2G>~G0!yee1o4x?2iJ>Qa0Rxrb{IWQYM<95|}9Exml%O&_Q zvQM!;Z9X}U9T@@0kbGDAZrtH0U`7#bQNzeVU3=)Cvs}(ec%;areC}yx9IWalsJjTd zcjMgk!a_q0m;=cY-U}8e+lP^AlXMn{I?y7od>5n zgddq;1vh9$@Uq;5fk<(Wg9_6+7#KOiBO;QQuZ01V;5#A$g&HmLB3L4d2G;JbRH?r~ zb2K3gp`5RNEXl!zApE2J|DGwh87+uWF&^1uOvyN=IMt!VYYf;6c`i|#XF`a-R_=TX zqAc6P_d>37*Z2(HQ5qS2p9GBr4?>@)euhqtu*b4Ru0h$S1TJxp(iEKs=Z|A&_KJKe zegY^_Nbw@!zR0Gks=63ovqOLYVbHeP`i9=!NgEvhIaE6Svvd>-L$Bwn2z_L2!0r!a zhzCISL`FuE?W}fdfH+4?L4j;x2(mY+EbQYG6C>ctUqK#IX?@IpYox%az(IY`?aQ;O zQr_SPuk@fy#Ye5q()MKa_dMD%6a2g0s6E<0waWG|Ir)BV?b)}sy&v8}DThlyK#-N2 zt916PfA3V@wL)AZYIu8l2d+av?|nWMR%~c3<(|%j>MSf{$T!*)%>Qhfc=3hcdh>6= z_MqJ1i6bQqs<%s6D%8s>VX}x~kqrmXj%aV>BY%YOz?6Njzd|@;$)p8NR&D3B>-2Di zep*TIKOgy^wqZ-V!lPwqc&nssbD*Q6`p4fBhw~@&&MsP3f0% zo&WLalYK$c@b)+2-$o!zHhX-vwc!5_+4Qrs&QeHS!6@yTE84osDA~kAtJ71f&wXw* z3itv@z!o%6^g`*)As}%0W?wG)>Q%bs<>lADyPoPv5?w#GrrMJgZ{6ZIGdE8*$hJO@ zgs?_8vVi);_s;;0Mx3Pi21^T_+072o`62g3poyuqFJHz%0mr@hBd0)p_WR`I3y+oW zi%UyXX+7K>?tWa@(EcU%JgkBX+h?B$c zW45BN=e`iWw(kMMlC@T6w_yiVaX}~!zUmmv`c#Q+Jc~n+N(#s;{2pW$`ltgDdyjA?i zK%eMi(!sJ`>tn#4zug@4+ut^>pgkAmCXq=Cg#}QFSkeI5r-X%)n99Aa*{H0*q&DY4 zF%4dz2#>yz#H{`tVnAs^NnBYe^Tnw{wsXdLaeckS>&}G4%pf)&eM$LfZgGW&j-KzD z|Gj(oGV258@0@3-gUOEufB9md(xY-X<{yc~RwyyBZMLB1aIxNf5S0}K;yD2?0Ve@4 zqnPstCy~&|`%H@7*4Gf$*C0=xb{Ju%4#RgWV+K*79+YHeEmleY zJ9~^qt{0V*=G(t>ih$~VnguXggyqEN|70U~eOwr82N*Zs-7V9}4{=%f`N2xs;z#F3 z_xIeo*-RX70o2sW_*C1WAN*t;Y7tmkJtyJ#cq2~|Nl8i2RVV;ZhCO-0Xkle#_O(7J zDl2-f&0R0sc#1xz)jfwFd;REGo32y>WFvqNfn8pn706%&(^!Zkz`IFr|H4JWUus6f zcQ#QA3?-uFfX2}Tp<^NRDF$u(*3-g(2M=`|aY<;=n5>iqfv65^!+fODgaz$zXOon1lKdM+fc=!B!;;!_5Q#a*8nXsLx=LBx6{<|=8@FFOB5OrgK@qpEE07YV1py6B3<4LJB??LH|N6USJcbuQ?;OK@}-v zQEJijj2-C$CG@%GNw)Y_Qz1qZA;!7u41=+3#OKA^52AI?y#a3}D-bb}OFidq7{3f<52tc3q-|GGP*J*$otzxze3wUqg#Tl_7&tWasDCT zQu^}rLv9U~3>Hh+R{kD5a1LUh?itotkhz#M)Dz4~>^?j?CE>SMIULThE!_THnoR^> z`;>#)StX^55g*aYpeDb5-D!8-)Rg_HxPyIZD#kZ*-28$2pcqoO!T@NcDVw$@Ng@L$ zU{o23M|5~>&^o0lODhd|UI`E$Fw%;q_mWw5Q2&rGhqJ7{+MNIYJ1WYtLX?Ss!v3U!Cbj*0S;bmhKfD9S$IZ zs7YFk-f>{z%|*j14gM;KN66&EG{*vX{+KAvdHVDt_U51?3k8ixQtr9ZD~*{OsXCe{ z?x3uv#{{#2#0QnWbN3$|gdAW+wp898V5*QTx0o`I74Fp3R21sYojdP*cgq{XQ=3JR zP*d@pcrh*l0gkceJ<^!->HGSnALlf0;|j0g8QEpZ7e=X z8+F!<_F!mrt0?lI+?@MT*1dWyr}wEWq5sl&%TP9-ZKP;W9M~SlruY@rx6fk1qT>@! zqJ)?j%9rWs2`oee1PQW_&mUhqj1;i{KsTK~PUQgP$Th5>XAm$rd-f~}W;q};#kaiL z((CxRO}cNg{#k)H9_@;-dP=nx*xvZ<`&RsfO<8uM_Z8h?7MV~efOaMY{pvC z8lagya9wH4T<|eY=?+wPbv*+TT-U3syxqWH5!2GrJ})Yw#=*hKn9RJCTFq`}MeeuPu*@E%SrEz!I*;W@^ z_6Xwg$Hzxlw}1Ug;NMtZS5{Reov&DsL!C{L^AWQDA~82y(f^%|^Wl5uJ_PnhsU)V zUs>);NjD1TIiqC)7HalcjD3G`fosajiW{s=L3hTRB`fSoO)B-Wy=GDOfWyM>lJPK+ z_vc~Hfg?{_TU*HH)A8e6TuA6dA-*n%$a97Z!Rx0^oicm$ zb2-wj;?g&4&B(4)X7g#`M1RFF?3s@&6f81~z{a>LU$?(ViT~1gY!|6q-rWA&&&`o} zEvP=6$-VU@knY#8@gyk#oe}mMpi3JF3&n^EVNO%cU<(ruHo^q%vpM2px9x{-kv!l~ z#8#vj4@Qfp{(p<5Zhp8(cBy0Q#%Hc{8C>(nP@Yf!_C+5GrSoKDA{YJdc&k%m%NAg* z#=rkjWBcRz!2MQ)Fz?D5B-6n@;Y_-uSizQ#tw|;60wWUv%R!EP&uH-_X=zVYCJ#!_ zXQAlk`;!NRUBZ=sJt}^z;Sn&|gN{t3PS^TqR+UK&QRK zIWyRV^4~m+UW{v1LYRc}-ee;7$CFFB*npOYY2beAiFbHvq?{jZk%j36N}-Gpu8tJN zAW7q&nw;+9K8sx@2DtD5LPoB#|H+5=FlprDP*7t-okcy{IN|S;8fp*kL3nuT5hxIy zry41K20X&1+X)dES(Nn(yh;(gJvk%M)-u9Y#!3<%VTk`{+KJ~-jXA~7e-27TP}*Vv ziGc4+F|+%+p&JmAp;1xF>gtg*9UAhx6BFT0=9T>D0@sh=lVZREg8pw@&6l8hiO|W@ z`#M$^c=FV#I^(@%2D{I1Nx)X~`q~B{i-zrmn&@hu?QqTP&BISG@!mOO43K`*$up+x zUpUFUb20B*TNTfpBY^b%OnE`KpkLhcXdfcijkKFkF1F^~t;Ulkl6E#;{}x&O?&x|W z*kX@uBZTC{DcS6WS-AH5_I70x6E?kKTT4A)z3+mT2ub8@V}9KW)|Qra9UTky7mYE_n6^(|xUPF`yhBZfIrn)%UC6J~FHiTW&o(6kjD-Bsd@bTOchw z;Q~Pvsta1uph1yw^J2YQ^cJU?$UQga??XMt&cj0rfVK|s#@N(U2#mvM#;)=|1NO)~ zMxzX+D$K@#zf>rEcrc_&GEHzk__rJVYs6-S6xkfj>fo95Ms_|LRovo{gqIjVGh1p!cbm?$?eSK%{b=U!PoM>n=1`X>4jTg$SSZ>Xm|# z5ldfzv4hJOH7IgnRRoDzx7V5oje(u1aelZ0VLz(qjCp%s^5cB>w3%S%tqj45W z4Y0PIgjMVAE@9;LQw&t@2(6OK!*YoQN*q)NEA#k2Xhz4IYTM6@Q++_bIkJ+82#h_Sc}sl|bO-_?F@M&y z53tA-!%Zf_A^jKmwtD!0eB&ssXX6z46C;ifp@AnkchN1BEc56I1$bf8$ptz;>eapdCT)O81-DIC0UGF}|~*>Kxc%5n~LD zBz6qHdNMcbZfQKNk2FEV!LXRcP@z3thwZ9;qzLPD1!%A@h?V?4C#DhL&N(7hv*TLug5v5KN5@J|L|~(EJ-4YLFWs_pv=n1D0(k9%r#VRE_Br?wntHN26UqgGiPO8y=7ufL4y4vf^!R&sGEMgqIMynJ^Nn5o+m z#VG`9c0@h{zMn=yxwsVB@TG!&PF~)ek)hqOITBPH+5^(%Ce`|`k*lPXp zDS~sq7WnPvT-=U(Z*kb8VfKw-AiDMi6z7aR#g-5E#*Rq78l8{)EoB_!rr$&&Ufujg zVDbK>9qp>XI-Dfs^1<#pNM%ca`8n9x>wuR2{D%~>v81rDcu*7CiYS%sNb&QNOC=u$ zfc)gN4K?s9F;Ab8qC&&M>U52LxRkFSc3SM~Ro#^$QWO|xz6U#K{o5+12#OM%tI!RR zBQNc{vw1AVZT^d-O5B9z3`01mrjmZJ6JiaQ7X1VN+~+Zkzs!+JJRIFz_s^Z)^HK;S zG4S)vFV*AJSnOu2$7sRa2Am>xYs>*2z5Az-f(Q>(k^rsncD-lq`+ui&p0W@gAyPd+ zd?66;DgU<7B_YZLPSECPUem&J5InWzN=lDq`MJZ;&FD{WX029!f~%(5aij-CP7D=1 zz=U6B9H>e6AUVvyMo{~b) z&FzsJy5F#Pw#7WAFb%v02OFqUJ~apn{>41tc#FToxzr38y(k2t0m_H@Yp-Q9Kk zvUu=IJ^b)#PgG18fP7ymX5(<1xd(K z$9jH`<&re;i-i$SptAMZYyN^93cCjvGlz@S{WinP%;nm6D{6Jo097?nL?AcZg0dB7p4oGYBHpkeo zv6$t;zr zbH}XyMcH4h={^mDG5FPHW@es9I&tHpYt;m+?mgz$|BS?Nhxo1gW(|pH)5=`h;5dC%%~gLwI}`UvVYhGvah6;mw!g2rIeJE-YIu4FTi3O3(*izO(E_9r--%CttZZ& z!-nzJ`5_lKtmE(Zn&SV8+HQl6zr$r?pS7!~+SGFq>a9=#096DulRCE(=IDGDm<+L< zdw&@w5T3{9wZp%&9roZ8k=tFp3_O0liIBtz}?aO0j0kzmEeLM;MJ$mb`W@ z9j_nmyY0kuKf|aL2P7wsej4bPbZ}UZ-m831bR@72m3)aP`wA(P`Pgl8g(83r%8J_7 z)b*iY324qJLGDaa3m?tS;h=8GCAS)w-5b0V`S10?_-CYQt z*+=p5;~)h+K=8%zx{>% zuRYn9Idpy)^ldE>7M!Rc|c#$MIZ^FdjfBw$d^u!NCQjAoHl%2oA72=YpI;l1_J8jmmWpOOJv zk;cZxZuZyg1(`MkvkMDT6;!P=DJUv730yplMx&pcxy6K(uS(GKyEs-iJy6IAgU0P@ zb14prd1TVkl<$k^KLgoaz_9!Ye?(+tBgkaQ*Pq>(?}?}4R31x@HxiM}&(5v`v(GeD zjoU&G4NAg$QwZt&nrw}a&`g*$iLt>)Q!X|ibr=g_^{oX!*(9KHnlKOtabNF&Zkq=7 zDYN)-5gs0%+knjw-?9n+5Q4^P!Y}IxtU-G8XFrU?NTQ$CDlSD9}AKhz`8;; zr&pbwn$QJ(oliIW3jIDM^mzp7<+Vw<)nlP*W9;Y8``MQ>!wtjLghyO` z!Q2I!oyz(1#NdpJ0R87IxZ@0xC7=;YdTywc;&O)iUFeSj6=ktaw8I3t&dlP!Q-jOg z*4DP|ac@tL`wl~#7P#G5JSM0>6R{gCB7hZT7Jr-+M(JCGpc(A;cN-&KFiOsT%s%g5 z0c>CFA5|{@({~3lQzr7XKXqL^RM~;xdNag7qVr6AT z2^m^`%@$7<H;cQ%4|N69y$c;=Po!Qz9qYj;An0{&qCjy$!N8YI}D6cerK7xQnNC$LBEBaKtjjH zsNd+gA{oki<)b*_3PiRev#cjA^o2GY9wiG)6g>YCa9u!jLUaWo=V{I8 z-FOQ#GY;5HNG+fM$q-sp_cvSQgKFhG;l)t7TIl=g-&azn2;suWe|$PXkk}k-f|ce6cb>p zpq*6_;TS2peoi<_%56<^g3TgG~yf#GUT1a@WE#6bF&h~4}eNtLO-X^o#6e5>ZjgxiT6Lwdd z3EblWnJnYr7UC#v;rufXK+gN`-v573AVep5c$S|e%2Ff!Jed0~KR?rH$gVwVs!DX7 z8@LNWT*4v+oSk(LOqq**R+M?J@xZDMhx#_WXn%_bh|*9hPE8#h!sqr@dhH5o6O53L z?B{Fm;thb)XTY-a7P4Zan%ka=MJ!Oo1`1&)%gSIQd;_yYV?oWH?b=jdlv(9kpZ*gm z7asreG;vt^bvFyDf+2~e8rS%4LONQ6lJOAu7Lb-<=xJ;#@qlJ(nbl6k>7i1=XWq*f zV7I?teS<7<7-l4^IXa4rxQ~-}A1iQ!*9c+pB!>1?aP`OlRJ7DuK58Et6%hGG#1 zSmC#W>o_LB05eKCpClw>^ygB0j3_r}hmZMBeM}4i#|7y0lp1osj)ShJ-ZAvXngTJp zLK6V;7?5d$EvWPA!>K!=nDR6 z;D82S5VgrsjIy4m5jg$E7th5l*l+qp2(l9(IGS{4T+EP86V!W44C+|}^c*1459sW$ z<(pQ&fYla}zR!a6Ghegs3rf()=b=jzlK6ml49lIV4ztSffg-QXDC7PbQIO$l4_{%^ zGJtP^08M!h{3eL{4}9md84u_Ys5g(6wbCnvpXwpZo`n8b)7ic}g^l^)pkFIfhv0(- zV03uY35HX4PEJKieDwKCm*@$}nT{vPdg)|kXHx-S<_4>UL8onT0HA5OO%OEkk%)<(i z9XMU}7=%*=;7RFVQwPCgU2~sFkB4k<4-BD~p9q@=YZ@BzL$@I*wDHh_A%TlHfDLJ2 zQ7|w_iiwS7hxlj;!>$0kGzfxI;(FqvN5>5+TrXy2Wl@TViUvKC^&*8k(ZV$$ecS^i zjbv*Y2wXw1S9Le{wil4WDk>5&1>wIQ#?PU6s&r!;*dYbTn)kr`L#&slvk7?T9y*Ya z1p0?xun`BmzWr6ZlHQ3>Wo=GVkSKnjSw z0c#^RCB;F{4?1?HKNs_YferDE7*u)EA+j#S8RGlOfiQ;+U><-TrDkv$g}}d`KI4sV z6$X72DvE~w{{Gx3SP}@9n)#GYOimtDP#}l^5Ew!T?X_=5;87^yGY%lE%WTiR87RKV z0A-=KkI$X;x$7Xz)ASb@w}7W(G1_kj4~6Y5*6`b+frr9_US0quWJg5sLBGznsR$$_ zR{#o)l9JLN7@W8E!%SWq^8_fwX#mwBYzkTAMliG{m>728?}yU@FfZY_~-|85#b?W>-w=~PF2LG3vJ`Dv+@nfa8Z!Ro1i$nGu_D!kZ24%z~B@>W@D>Hot}}0aX45cCBXQOC7K;7|$9q4uP2)skq>K3-A>S z0%%WKTrS@(=mETfv`*VyuWp3C~SuRnD2VP z_m7FAE~82pp#@QkrmNb=D;I3zg4LTGES}Ts-Hr+BgechK8J6PqUxd8!g&TWxwX|Yq zYI0t^Lc$F4r3)b0z~|Wlo78-L1@4beL^SGR44Z&JQ@ouUG^MkK1JK(hwCz~qyDJ5T z2ArPzINCgM$chJiC=8}8^~5veurV7?q<9B_5cPHOYXkA2Lk(aZSORZFLdj)KO%vBE z(;LOI(9n*ZLy*-8AdZXP_Zh5k6O`NAG`AZnAqOmuLLo0O)sa$ja{*rYrc}8ZR2$ao zTk|g=&?7DJL!*0ys7)wlB4C@054(*v)dw9~*z!)z2+e#P`ZNYH?SNf=bM8%tdMk8Z z-Gw1C?|3E)lnfZu&m3YRUjmB;o47aw^t@r6{B8?T5I-0obAg)+@*yY8H#8CyLR2^?--k+gc#s)Egd|gI3N;0~0_UBv%v)TIk#%hrEU@CEZ5@`v|rk-}NGVfIH&H z*4}|Z=cV>3*tYD%xV~F6ClE^wExnJoH>&nD@h?}zT(q~lUFf!81W^WLKf}v0k z(9C~kjZ+Ou5VbagXM*od!rW z7q`@1O9EyvBZy%T=7I`~ zAU2X&k{ZFKt%5a?(B6oAHwVg=r-QvpeaN`MK6@f@C)FmQumm}GcWbRdes76F26Q^4 z{~V?bQCIB-gw!EE&t0KMt^Vje5MvrwD;9wqiX4EXv5IPoNG( zk~lP$rU8(F`{`9}tM&ApzZV=yhH#*cI5HgngLn9=u>XGrbb0#}bWXL?8$%i=;k#9+ N3+FB=+BHll(v6gKgGh%c-QC?tr*wk~2$CXfx{=&;cL<1dZc;)JX^@tFX8S+q z+~;{dzxTKI1Dn{h=QnfB%oS^`;e(orEEYN$IsyU$mb{#l1_Hu^HUtF3E>slom*PjR zOyJvd4{1FQO&4nqZ*w;*1Z8uN*N!e8j&>H*URG}Ib}r65&jg-vJ*Bqw@ObSm#KGb8 zp9ard+-x`&(NV)eCupzb^xY8S&N4USIz8$? zCzT-&xb5}wzkA#r9>B?{^wF}4XE+x5K{PR=idWFwyVQ|4M+epysi83=?LUsD7{b?RY&Bde$nOmP#(tr$s|zb?DZM%i zct+Ycn=_VF&)UDHwDkC~}fI_Vb$e38E(!O{gBu+3@pX z8&n!8OoJ8~+pgW~m3xtn``g3yxZoQ)1hejU51a1quB+`~ti~(Z{7z(V2BL~fO7J|> zLUA=XLnUa2vxNN>I`DVM;0D=Ab%giTYjC+7xe)G!kF;IlZw^@ zArEiwSD@Fl71hFoXVpk*PEfv8w}cbRFV73v7sPF$~Zd8IMv%1gy{30exM=x z1NaDOXmjCx+fWC1D5$F9ltQ8ECML0!f%^_~^$x_p#481@ z;KP!Tk`R_er5Vj(j+djCq7?f?e|mOi?&Cwk(e@iD=ywR!s&3~M#Bp5Fna}ckZypsT zq|BQs6c+m<0rt%d{HC|6j{-ly0RKd`mx`*^7Z}?)jjh4 zZ=Cy&XY($#6 zS&wDK@Ysy5%8Fj$E&Hrv-R#+j68P%q5oy$rSQL4?YnH4oP5A1+#q|^_cz3 zSkO6-kta->7Dw@awlz8lScIbNAcLO=X#ZpNfiLHjCV$QU&r5{VpF|mn3^`6#bLyuw zgyho7R-|Omfa0_3<3Iz@M6O$sLK&6j^T%AuI&73Sxo%erp9kI@uMw`Xag=VId==>^ z4Qo+rcUVt+tl`bg|EKxmB;HT&Xh|S74^R=>e0>nS+g~Ge7bh0+(1C{N3KuU!fekvsMV-CHKzhM0N4NXNnV}ns!#um&?mTV(nw@KS^wY|+qWhN=yy~qjG8KTlShaE?K{1S>-`H~wqsV3C8m8MRXPU%zA{)*6Ppro-Hc z%AypC($?U-6sgG3PB>MlEUBqUP|tQgmL9xcpvls5EzU#0oqN;*HZ$?`=}R#x`8Sn{q8xO9sT_j(uu6M5=l*PG70T|Z<+!bk!Y zPb_zvOrq?a7Y5B$PL?Yr+=h(OAEao_``_Y!cmNNnm{?fmD;yyURKs zg@{j2Pe+LKwc$l8kf16G5|H=RMREfl#_WMcBk@mQI%_l1&Q}BRMF-5USX@|Ac-<1% z^$5k!GsX$A5^f&R%K_DK7%U7P%4@Pe^sh(suWR08he@Oe`}0GPK?3oB0uLTq@zBmN z&oF61BV;No@D_U51}MD0zfCp`B6aDwEVQV?Ko}ev0>Rd~p9Q#O7PVpruqF|qe(!N5 zGTx5A(WQ;=f`7oc)$2!$z_OUTd+oO#yu5TvA$Ml}$h1K)aIVZ$uoM%>p3GqTNxT&r zTLN)xDd>9N=k9W|+W*2?DV-lH_-ZN}0inLWzPPN6(AQRi@HyAZcP~r@-vqK~8YDIH zu2u6zaYAFq7{IdRJ@`*ox2XF+w6(P8uP@+hx;s-obtB@BfD1*FVQ`$A&t$Co1YcF) zuGv4#?HZ%4Mj}>|55gRMR8c3wjfGCNnBV6{gsM4W@<9rUFRnTB3%{HOeGpbFwC?ej z_?bhs`s8O2213KuOAAcd)892ER3bDjq^OED5*P@}FJHcSTV-kRdi!s;U|0g?;n@4+ z(W?i>!gDJx1kW1J#gH4yocl7~vua*5hT;~9iFQ3_zI}{i+=zQfN&e|JR$C}8qDT-P zRWkn_-DdwrOh!%?!b86w2=CNcipJp4P({2BTbL-)>R~HF4%8d>VsEFrL~VHb4vnT4 zZ5-z{wtZ(`;nJgt`HJr$-^)hn&b$6QC{~1J!48c?WWmA-KAaHpV5SBAeDXNMwqTZ6 zl0$VBJw?t4S#YU7{Ed(_TA_JUp`R(c_ZA*=|L@3|uBZrWy<+!+WPtUoCOZ=H7@qb5 z-T@i@`5BkoP$HqYIb>v$&p9A}_&t{!J8gc%Om zJAqIx05^*5?c?l{O2WdE2;p%z>WBShEeei?t%S(tXf_u4DLU%!zuG$#=F#hRu4twF ziZ?TdD{@b1ZF8vfVbmecY}9&le&j60tMYHQ%yB4JRmV<*xnR6xO5D&=q4r z=64wy^u~Ss#4uvgx0f;!9~|Blrd%mzLJ21)_IKUX_V)J2fkL5Mj3f|`@&{YDPr!j} zPQyAxqZwWMpS?v?P+WP;HMc>_tXca*n8po0yc_oK;U>+4pJXK5s97*2tJSBql5(pR zOh_1Yk?a1M-?Bud2#b{Uv=(QVH0sgIQ{+8q<-_B~-_J08@bVG}dgz)?!jSFq9g_cj zIx4v1Y-E64l-07~zVBST-LR=DoR~%kxap@fXb3KplwHOgPsBJ`H@4y4y$Njx?qPdH zioG$&LDe(SEv7e!|4IJu*5-T&KWa|Js-Pk6b;;-rTFNO9(grK=aTi$<~ zZ;MyXVfB)L=KoI`u^n%X_e!fiRvvWwQT~`~)aU`Nr|3R9h|W1}w0TF7OJdR8Z*@f+iKh)b^RbU`kIv_VvjVJgqHfP_`E2wr|e zIO*4{NJd{qEo=4vB!RV>2)+^EnRIcd$ls|uRhSuXeRQV-B2k}$a0ZUl&YhYgVUQ`B z&|-SEhZn`#D}rKXwr>AG$+-#7L@RQeTxJSJ_hm1~e&C_)E~X(OT>C#N zx?6s1+jZ4o{Xmu;L0cz--<2BECzBx=9}W7oSg7P_2;b?!+fE{y&*Rnp8O@N0>DzjU zm5?7!=Kt(u)e7UtGzFVv{{=)4)<(PJgVb_YA79#>`RXJmo9~O>KF-`9*>ZG{ z32;Q(DCpY~5s8j@(Rwua-iu8&TM+ZtVJ5>YJ{lW%V!?l&7^-A3E9efzE4hXaDjp5r z<=e5%b^VMyuf2|Y9FUE_qb89NqIr`e(anCW+Uv^?&X=RJFc?uE`&fI(V$dH-XWs(~ z$Cbq>EBD>YGT)3Z4FMY8WIu#z4suJy*u&Bzcao+Jn7XQ4z}9iL2bEKYHQw@{E#9<3 zh$@643%##uEpNNf(npa0{XvJhqaNu;c00`eublZ`zeKr`PUnUSJ`xW4m=-5?Pa^=# zDwR>B8Q}bhisZKf4HW~v3iA-Ix@4sQ5sba0UghmAHaOSw;WOsLuDwc3@SM=|=HZZR)ngoFlV@K`_lhmCZb2HEFJr%T zR+!hqP(LHDcWVgF>VS;pd9~aUQ;e!52)}!W6rsE#=C++CZ^7`ra~PS@3n4b(4R{S4 zjNbSAo2rP}xh{%xe6aI#)iuhwy5$1B`Ed^Y*^GI?Nn7LEWvyu=4V@5mG})`3tpoBe z@l)pgygGH{&|o%jLbfBE_sAn-z8tc3zlR{c3_Rg}c?MJb>qe`%5jrb-we;l$MG9Ml z^QxD2)xz=Du%`n;|D^b>-kFBWJXuK*rfQPD+t_`%?chgj?bA#M)r}}GbC^kwEWL17 z**Vz=MM*6a2l3lAVpOD}{Hf-Ct+u9Jn^6xt1KVXNn9ycEa;KG zrK`#@-v?0bZ)iyIp4OID=)16%W{o{B8Jqp^1MmCgI*hd_>-v90E|{O9`Bp zvvTzDT*nQb&$A-^?p0RAJY1~Mz-4-Rrzeht5P2NcrGwo!Y&=~=*m zT(Dpf#4A_%eely2%Ri&U*k-MW=HFlsKz>7+cjjPgsZ~9uqkBj#L6@JRu4!(&;uUQo z`uycP0TH2?Lc0wR-prVVB3CGGALyF!Qt%k(v=;lLxr*4=IQB_Txq>gNMq!U7iGZVl zz{qRugcWPc5JM!ZFxLBNeCAjz8NNW}Hmi^KnvU-K%~9v-dt7}MYwirvlfZPeL+DfcAx5_axo>J>HuQVXgoA%1o8tf1`|1E-vEwN zG&&na|966m`DkKtl%4g*9FzLq^VhWOwzH3I#oS*J-E`o~?mpWwk@y~x6i3~Ng^)B- zKK6VF7VU_uj!64XA}QFu9KFut_;*C;NaYjOChFW|v#z1O02h}^M|mk%b-adbiUEwD z9BJUtYVW)IUB^c}UekHOoS_C{#i(>;5T693Qp&T{^i}I)TF<-J9)yeMlu7LZ2vr+b zI{$idSXx|8-H2QgRDGU04@rtJ>?zzdb$(y^WSP*p<+d0|e!Hm~6!gQ}MX6xd?$eoNwOcu1zU!Q-6vFdzS5 zB@j2tX4ZUZW3veIN~G$Vx+8GiLUWTc{wv`S*Tn@kE$wrEbb3z?n&p1E9{2p3?j3a8 z_!LO6%&ibpqJJra8rWkpvWod-RPz&)5se~Av%V%c_Ms z-cmG6_D5C$v~OXm?FH9D&*cHiobh2RM=rAy^L`tHIF}@R>PbPY7#)95?nBR@_2Xbp z+8;yN>2xm;-2x4HQ0RVn1WA$UE>XhfvRuGsV!?EC@^RMUk9eluh|hC8R&2VNm}L$M zS0lbkwCqg%1X%h>Tv5+lOA8UG4+f6;sQyeM8MT%xx^~hM%V*#+pQ3~9PuMQOMg>jP zv4L#smvA}s+N1px3MM_!n@2*>4^-qYv+d>(-5c8$+E4o|4e&1ksYlYl!3UHUK7ksD zhIu+A=e3%rU7`c%rCb zI}p!WcU+xT!UTP)3r1ixrxnBCzT;}H>6!GVtb$qEKS%t$b^k4jmM(L@SN#JwPN8O6 zk&Bn275cmwsw-`ns)1+taKC4?Bt%d>;d;|kDKfe(l#uF>+L;SUr~^67&sdXN`$WxL zl7nSfh`$#_?|(@>&go!+91m@r?y zZ%M|b4OgH4ScVYalu|&|>i5WXMe$KlK)aG@ZLUI@ zaE?cXK@iIT)@EB6J=1B@6^r!AZ3n(%i?s+N{)Y@d)omIoeXICCBs2AVbZ_@C%RHS% zB6=a$kz4H78Tz^(IG){1Yn`s>8blwg_xSu|qWUItUtcjL7l#NOzr_U9G=`fno-jni zxIKgcnp|Z$~nwc_wV5s3y+e#Jf{O%V}o6YR(q{XOYVmoH?{qRJg+%V{m0#OVn&~v6$w0f*A71ThBaqd3#@%;I zg7*y#p|4N6bY;ptv-z9^pyJ#gzTG{h4WZ3V@l#W9XeFzMi2UQHM|U)9@^_i-*+#Zk z0NzRxE$36eN#CN8(Hbj;JkuzUzwm=0Mjo6H8n>UU{c^X+nZUKIoQ^H^ivMP~?B&G|y$R6G%W_xwb<{NnmGSSTymEdzB; zC)v2@;e}sCE++4bd9!qqGb7W~U~C^Zc1jkJvEW&pEw@N!&kmieV{SrS5n$WsOoKXfx0;RBI3atCT9Z+`+JZ+@pH3@ z6>2A%&wuxmMQM%RQUSXMK2ofX`DXY?gBx#%= zT)84;x*T)tczs@>fkzx>^TIE%DI)n!4J5QxJ0BPL?Nls_ziho{2Knt&N6+8EqBLGK zf{L`{Jfp0VyXZwnW20_^-R(sXODA=(>Rq~6#x`}pl_xB8J6EwO6@^@@m}XUBK( zO8s_f<4(hgc1!*q1K7zCF|j~3F*tsxUu`+Ci+~$JG8~N+Q)S0cQiBsmhpEy~4deqayQR1w%8*j(#ImvHUvAxog$B;V~o7b`h zM?5NbwvC|6n0FYnxc>+gXF&9C(oldf1hdnwUG z==|@jCY*`mKX739<_J+Y_O?M%ce|QO?uSGw9NKV2QaQHD_(dfM+Z_4%^Ec|h@_srI z{7fcdxO+BN|^NgvWIttqyXw>3?^(Oi~(z{?^_NS)dX-DGA15pCqOpm!>M(3-hJgfHf zt3)_pfx~cXR)n)gf8T{A;=(w6)C^WY>ap;<2)(GJF3(rSt;B^TcoOJQ<}uaF*8M^L z>N5Z)apmyZWnj8|bn4rV!_yY=R0Uru?42=`!YfJ1jTV!p*GbDR`i%d@`69OdVZo#i z+!J#_ligs?RgQ7CPW-4qq5sckD3E3kKFP;cF@-k8u$q>PSFeDP%_^(|WFVq1+JrmL zJ>w_U_~w}iKn!XGT|LTY@z`IVsLS-=ormH!()jH)%IS}}$%6+pTWE2R{^tR?q$U8d zW$ZXBcloj8z0x<3z*Oxp`^+f#_KX%s{wXa>M54?rx0<q*fc`26Dl>`RZ| zz1A019dzqgre7=bGgl5>=j`BhBsD9c6vp>}nPT@ekcKG_mqB}piGx^`@7dE0T|PV^ zVA-9I0-35@1qn>i+A~)32KlOO8L-z5uu^S6f^DwhO^=6R`B)<79~% z*|Hz>mB>Im!<-123dk`E{J0aT@fTm@`B@-5{$>YVHjGT}tp;3S(D0OrOtxNgC;7Nam zWMH%Rwo8P6t3h!ae;xqxN&+x}$cn+I(ghWG>$Dk|O7h9&R}2+KSCMI+gC3RnRU#^8 zLGDO^G)&AdXYl_lxc zP>}LGHJz+Tvy55^$M!@c_x=6<0ZI}alJg2D+bXfgN$%~fxR|=E32+OS}&)*RbK?) zx3Hy}ZYS;M3Mps8usvLkMP@K1>0)gn?C^<2|7UCdr(5bbcNd-gzMGlh&)t7TysRr4 zXCsu36`K7br(kU@leVHu+qx47Yh!f0n?&lB0Y%Ej)S!oMQ3a!qRY4SPMptc32sFyNmE9-KB$c zOys)kn`#WNRRZIOG;&{YXJE8&~>%V~O>xDH%*&M{NvR1+=C6&zMcWv$tV*Rl*I3}o! z1Z!SWR|Yyc-gy;x`cQ_DvZNLJ4x1IT2i}J}@+UtQK$hg;$ws++(osfn{8hH-XyKNF6g%eXDV*vtME#z$HJ-VG8K}FC{FFUMBeRlubOX?PJ~s% zEcCmdnQL@nN z@z0@I?qikFf2#VYh6gsMLUoqo)!oHT$NtQ;EJ=rlyA)kl=hgsi`({r;9iv)&FuTnk zNjdyS`1GM$IAUe(Pm9kaf#bs@5?*nnv;Gx2f-0I&q}v~5PDfnVp;=Rya?s4l04t&` zU(@6H;8D~|HGY%VydQX8`@fIhdb5-|mr}Ct*L17kDxUn8eCU5Psq?M;ldAI=_7haa zt$=eUj|PGK+H%sPYP7`U$N9ix2xSM7S324Xc;0zENZ8)P%~KKyV9!7Nyl`QWzy2P+ z0$6e1T!?8pCCOSQ581@MSJY(=(F0U@R{xZY#hQ}0{1v7Q z{i=uFk#x2DaMSX&dBHk8@%}RoZ7O- zk6JC?ek(k-Pk~`*Z!i=#I7|dH#*S_#hCFL^ap8e^^Jpd`CUCF)&93Dyf1Vc}=FTza zO*UElYMMW3%*W%)q>xDDV#{|stB>wTFj#3lOuH=Y*KWaPC;j|OSAQkNRCt(EB>rQj zPQY)|kVyS;YbQdeXVmh!s9Qhp+Qm3ACknEO&4k0}6yNc>SXZDNNx%Cs>x)x|aCInK z5u1>f6CdT}4X_3O92#hHqW^~`v}=`<{g+aY!MvSX-@K#_(Be0;PBY>3&7Y)1Ktyk*~t7ewZ(;4KC#B zLUJx%a^w9-T*#E(knYZ?ba3-0C3AgDYbDOf`r~d197->W8RMWJgRZT&OOKBB$%)g2 zQ~9>3jbUK`NZK08Vyh7^Q93FAjF8 z%oR(N)XE3{p#HT;_8jRzv4@y~ z2j_g05CoU4x;3r)ui=V$owtOhEyt5!f|g;WG4!LyC=n6aJ-(U9$5S;kog(nBJGx{`Z#GANRL}BEc%XysH(}v&WeD zXT!u3;DS*C43H2`UbUL;AtJyr4cKk5u&shbG5h$NGpCWcrdt0U7eAdHsfu^MEb6vh zGu*f=Hu9EaD0qbbW!0a!KN87C^0@OJv@@ricq%(P?x|+P6h{wsM}%?&J7Z_f2lGM& z`T>`LDKl)7E4NMn*tXX*#V3FiRo)jE2;rdml=<{*l`XvLw#?iy(cY9LtHYPH;l4&- z_YOWHt@Lly&`H#!RJ20Ggt6g5hhOV}2iNcJf_HFVk;jv=_syA*ROviVg`rnrufm%1 zO2s~~vY`eO4%z<;MAhv61ke~ABVOYRu_`6f(^XFlJgT7%ffb`#IUaI(G(IXD6-lD3 zCcA;=uQz{LRT_LMO$r1}Z4+xd-|eMVzbc=G8?A5Nu~_B+j8$&M`Q@?y;}0`PMYLvS zd_m1FwU(c>DDl^$=%S}0URXw+1cA#{}?q<$dEWpR_%5@i{7=>E}KX3ptci2FnAM z$~NJ+PJ3l=i|$;SNRjnJAE{WM5qAIzCT+8TJ|yt~3jv5BoP0TI;GCw6vUHLPwhV)2 zjnb%q+6f;(k-J)k#*&w2=9iCcXoHE&G8`li)H|Z3easw`r zR8IQb!VoJlH6HfH-@Q(nXa&Gb*2~tG^ykKQ#)dq!uKU}{n*Cci>xIF8>en`>fz@`_ zb7pO){P4>7Eg)&bF8WL8b^%D;@#lwYD16J%l-_3E4dU|WSqCV3&x08-WwFmKeSZ7S>) zitl;H{k^*8p;$wVTmRtWsg0Dpvs7bJB^5n6ZX{w6xbEYyX~J7$*B^m#-!%2^i!}lH z&x4WpyQ+^SmI^C8bVG>&Au*=5>NnU3z5R@q_Z=QynHH+b^$s7uG7&n(0|GfBHWunE zV2?4sk0CfgJVb=jruC7>FMm=HB*-!#zdpEuMjcg3(rHdX{*^Kp`rljY>|{*$i;qQ_ z72rW0(0m#6@VTuN`?O$3t>$QP96-t2<2+?gg@$eAM18|-0(vC(v6J;Q>Doy~ zvGeQ?>j4;7=^tinhRr75V^D>r^z1ki{f)uVC^^xzrNpUeD$Gb#rJjt=B8w}mCD3WL z6RZxezOd4jawuNRuh9(OQ zMwmYgI>%lIHfUZ6ZUy3%@mj#rhP{EvB+&3=x-$V=%EsSa=TyWuE?Ni`K%=j+rK&f( zK9q^+|HD53Qo6W=S<`bgW7)k2{ZW5}nU|mS6J*uUZY49`dA|ir-2`RChs&|IT;v;vL=EE$FX$*;i=7=v*N#3IvNe$VuWY}wjrec$|!5}f1=mvW6wL+=k{ zuB+@;$T!2yhp#MDSi(H+GDqqBidY%BEKr#cB+uqm^dG{mY4z-S?fFv2l=PQ?xL`Dq zTJN~2iu0zbl@APpk@@QIqC;3Y?ArIUVmYt!-1r zJRCzvW$(DKC%$z67ZXH+d$r)*r-?;CX)NkK7{AYvwuBU!rsodrWV6=n;r>3vb2Kt4Qc3j&U%BHQ zRAs<|KJ=%It(5SHsYB~yJV*x#IJyyfxSaT;9wisaQ0p$I=H;8u^6Jpi$v+RA4P9v% zKYkl@>~NAs$Y1~UE>%9ITMAYEi|y**HUznZkeIjxMNuI#>Z$PZ?+9LPngo``*+#zX z5kctSRo28q+WfbKd@IzfG5M%!uqA~SX(_6Px37oCg_YMr_n3J|160tT@vIiSUEqz< z<*!TU4t2+Cb5oy${^BzSG+{)H%1 zDl^tmS$`NJwZ#2b0F>#xf(z=KyJ^o4*cCzyQSrE+$oa_Zt_&9IQTwNntqm5>Xj!-N zU|Txeq`Z?;hLguBGPzrO&Q7!?P^@xTC*#+MiTCO6kZ%KJdGQ#ZaY${?Cd_Vl&p zhM1jeynR*g&Kq4%U(%VoDHkiN>#vzhK30;gYu+(WaY?a0+g_qz*@Q?m?>OTH&M{sG zk+&A948+#F2vH@(^w}2M?yH7gLNyXk>v68|sM5Ot00P*P`Vx081(A373d+A0$5aNt zH`Tt_eQ7MdPB zr<&KqG2h(wSHCec;&-X~sT4kZ@&XBKuBzrC2y*V&;svUni2d8$G7=Hj6Q6RACeZZB zy|h=VK1%!H%?s8CQBk%y(*O|lbb2K+GV=F!i^5I#*ob(!>2qq!Qy=S_W}`9bT_1J3 zm(Y^H9bxSgtPG1v^7bN?lk5HARRXw%_jC0^k$oN0!^)|=D@m}zD$I{=5wSVj9r`~DSG-lQPN+fo%mHbg=6ai0i=PHK;oVGAmc3*{z9X=+5Fxnfu` z^!>5eeOfk@5@2X8o-hN~x0-aqHHY#TI{*y(y>*6h@{FSnHVft*O0++-Y|IPW%?>e$;x-;`J z1B8xB98JBTQ2w+G*Al&^^jM?K-4S&+JJ6qXstllV_1(u;iD;j63ZQ9d2G7;Uemha<^xXht-QX@ z&4W2cx6f<&YrY@Fo`!25ZktJU$dV)UCn6Po+7+N(?;Nb|W332M6&?_Bd@Tb7TnDf%yTbaj}vCAZTsylM{3N`PifeK3PGTCTMH z0d#Ok3mLb0Fi<_J*WRzr&Y(H(NzmpT?)$v_xw`9|Jj@!6#t-8c_7~HjEJvCrHG@m( z#yv5P4ytJs#~7%5`VPG{49QU1j^&v7OBZhVSm^g7Kc(Tl$?J5m8JBdZU;DB9crBrr z-k;JngNp__T@Jpca0te2{-A}9PAg%0|`BJKI=VGwx!iU zler*^DLuy5T&(}CfKtVr{;nn+_gW3zh_v23W-i0v+)LRimBu%}P!`5`>D3_cYD!U9 zKea=iwC?P^&i6BmI4iGu{mj>_ggt6f63qPX(*`xBuLDaj>Hrm)?oE~0-T^9uyxMmC zsY)*)m7!Sf5Q2qC1hCT}M(^TGZ>X`KgAx4!IwG%aZlYO9r?3qr|+jTo9Li4_WGx={g@ zols~47)l@m&_2pC2@(%kJKF~UfM;@crQb8>Zs0FX-T-I$$w5s*D{)%MO#IXvarNKD zbte0*T*Yg^H<}q#K88l?mBS*Z@q34NPa%Xh4;#HaDEiH>;p20U|N9B+8I0+KRuMRq~;&MJWp^|rwzdP)>Q=dWqrzU2+ zZ~(+%E(nRm$K#g91>>uHjePOvp-W7x9p@0P`-w1AW= z7u3Wn*Mkg#q)+6pugezD0}k1eQwHB)#70}J1Kw;o2bg{Re*Y$;+1)#0$%1eKgC%h( zR4~NINsotr6KJOccE9%N7x)4u*BpLG;Mh&%6r-qdWk*-#w6!#pP-7rYV+swfzj&LB zGjL_yy;Zz+c6+t1%E`|~^CI{7g!pObif-Oq(LT=^L?2PGaf^u--2OU_Cg8UFa-1L6W~IY*mOkqpshj$H=t))6Qj33)imzAZC`Ms!DylxUgP9AU7RUi0qIl_;n0 zi4yb=MisrfD_ba|e=nMWI&j7L#*5{%!D|(#H*Y3ir+yqnfQ+77f4~0}kX->{SucP& zY_L#~84oXk*4%Mc^^&qey3V{y@lhVt&wxh^JFJ0!evOH0Jzbe|3xuwDk1(UQr)q)x zR}Mix1{J7{ECOf|sRcl;d8%$#Pn)Wp^mlvxE8&6iY9JH&J$n38LgYck#H$?^QBQ;M z{?Uu+eSN~uRn>HQ<@uHS9q+9wX)>YGcsUC^HQ#<` zZOGH(?BMVD@Vl7ebxG026Ajm~`lsWvF1Pm~!ARQX| zt)~eQp)c<3DJd7+nMe9+2c`^ zi4aAap8!0{L*-VB(MiKb_|G7XW3xFPkhYL42e+FVSHjZ4Q=Vz3K26m!xtz%>CTS{Y zC+!nk*zp0B#_nRex@Fm|JehMtl$2;XxNDF(*1?{>;be8rPh*82d7wYb%+D!h0jpJY7O7q{%K0kSDuVHyD-qtATqx`UrTIW!m=`aY{wh{H=)?5r7vMS>lE zB z0SG9c_53OQk&n7Yyi2FeQQ#EyDty1gP8Z)_IDF~9-~4wY!Nnht7Yj$LL*Z^SVLf#C z&Q6^Ox{af~(P@z^`e#L}sG!)O6{JP{HfoLC|JAbS6e&Tx^3Po89v|bF;wZ(4c|dH!`w*whH#hOU;s$ZQa7@M2*qvN&H?Nq0mbom60br zvy`luSGa2;QYcUYG-j1bq&LdCV*>T}s~>yepo-zVnR-t@S&KMc?! zlf^TWBUXVj_n1k2feJHCA?Xb`F4>2ztDt;IM2n4PHOc>E>Nb!eG@qsa<_In6{)|I3 zs?{Vhd-6}$fh4dL88ol~f>bKZqd>rsP|W^1g_J}Va6$jtYJjML9i!KC{Q?$x^d(^g zGXV=rpbQf(r93xU;8A$;!hpo3Gbsbr?SgodhL^TdYxtC{R_JV+HJp=b ztR(f-Bn|avPF$ig6@*eDHgmW8+_{ue6HSK(Xt;$~z2&45?1z}%CoC_-E)k3j{l-OU zP3X>ZYu2qkyLKkDf!_VYDpDz<)|nS8ulaEkphhJ?^*d!ekMZJqbf*ajrD(5(J5`tJ zFRl~)ZCEZD?Ax0V_R*5uuBXvOO3$_<71sZ}S1x#CKNdaNJ4A1P7oGY5^V17}P^mG; zUXsRq>Z=;`;<;efjMxJRJJVQ&ND=xUOKL^3>@HKI@4PTo=*fKJR81E=p8~tgY;mEW z$;_{QkE_7-Y&A=xlUu!{NJkL9^6tXFO4J-#c`6!e2n}6q`+EGBb@#(%Q1PM+(+hyc z&dAPbHcy7G2Z+}mcYLyN)Wbu-|4=4kNUrmM(7$$zMv%4}B-1wjwR@$mYOkaXFn}fU zY7NobFX&xGoP)zP2eiXKGnAsy#D40mM(11o@wmgYHO64BywyfPiqz1 z#Mmu08j&v`Cj_?qEVXA^5IPs0o?b9xcbYr7P$3-%!2#0Cc@bvtI<1?!*vXsKbyZuAFk_XdR1tjc z)aGHB{v%Kql3ZFrrVC-KJlM?z6=`S6Y-p6tjAdM#D5+bUv8Ak+V(vv2{peS=ju<=& z#+?P}wi6jEZ97-InQV$<$b1OU!PZ)LDPrSYD6{?1+h*$yzD~YRKf7M`KYMAY=td3= zY_c~Y5Mj$dx$_Z zvr<)^Uw0$l>7FB|J*5LzzKcl;#$=yoCTOaj9A)gl7y2pN+{4wq+N3Th`6Abo0!eU5 zuc=+X)Hd7+jtFG%S!jlsfZ03hFWHyUh#<_fL`$=1mJC}KUn)cqU8fTv`uz0U$jb0* zc7VNn`RBNs*LV(K2gs(}oh}n|kfkrn3B4sv-Hj7pim?fN_jNkqGna(m7@FQvJ4L#> ziEm^F&xX6KH0gPEDg+h4F3MZ-v|JU|A2GcniHse*>kz<+0SxcXr|$-xE;ZRHO76FS zYB@?hxq1KY-}9gB8esFZ*4-L|q{ggeK{vq;H19cCB0*J6lGT8mz$qWQEAzTYw45HIJ zwOAuwg>Y@8=SzT^v0%&q5Q)pl*n|ADoreV>n%%IB1my?MI>to@K>Vc;bS)U&LvJ@q z)d%3#{c-)+`nPRVP(`e#i(X6W$cF<~cA1VYvi~0|v7JpT;ZAM2P#>oNe`haBT04?` z5;iHUc%Ft4t2ThUxfTYu5j&4P>r=GXRvOwSk-#QT33$m6B6FIND!Ns(J8&zq9YOx=y1z*g)+ zNYYgxgC1`ycJPykRn=}hfbJ)us7odD!*oh75=zQFvQahKZ8#{qK0QCvoK;|u%!trU zdmQCuAXWYeB|pD3fiPr3z2|L1%b?p6D#~tvs#%}8JnyVVuT>t*;~|S8G+4|O!+WD$ z&f|Krv8f8V2D-z|>pIjUdfGju$}LVlAPV|Vz?ZRG1FpKi(Z#6DZ^aE3=XW#sbCdg) zqoR0ww9@+*hTN?i`%U-hqmU^mORu zSe48WSw2Rnb%1_B&J4&FTxX+%E1Yy`@GpGReZ?&8b0*}msQ zGTF8fS8Ge9EZ*n40|@J@07Rz^){7V303bUjdRy{!A1Ky=!(kwPTEiVd+XClqnPl(%&4Xy5L4?rA^m4E`hJ z9@tQqww&%1$&qHJHArTYEmiJaJY_b3?utRXH!Lx34l>=!;PJRw(JkHY&KoZ(H*~S% zR82ob&a5F*r%*RINC$Z`sxFNQ4YgHq%`p@kR9VgLz zkFi9LJIPsgP%M7p(-!%Vx>^cL_31~+udvrOFQRQ~5y~hX+b-U}gDZPH<2W z)}N=F2w~>0X~bxU63dtr53dA*5`1m?8T&@*>5D@EReX>_0&1nXjz;36=w(*$W6el- zIc8qOGbgh6ui7C{6^`I|$=xigyI2)1?$1@g7D9kfg4bhf^PYO%{20vok+)q3A!%&8 z23oqs=_as^ugW}psjZ#fxODlyES&+V>iIJeGB$5ruy{R-c#u`4&=!-?&i-YTu%ssg zU#X$>>`js>DOKjT<_|Y3?41bdCOZRzwm~Pq5&jZm{m`>ytL-#^06ClD3APGfdtWyG zz|+2E65vEpyXZe$34C2+cN-iAt8;F zQqtYsNJ)pZfONy5L%O>gq`SLYO1kT}kIuaByS{7v{+L;7X3Yp^Kl|)w-`9OzpWB$0 zJvX)DId>)Z7rJEy@TT=CfB7kkq)bOJFiWzB?aP$NmlUu{ff0?ae?|N8L5$S4L=16O zAYkU-E1b}PyW-Jg*YP(}mf6R_m%n#?4XPW@j(+IU7lC9}!}&ecj7TIXv4XiBDjs*! zMUJZu%Z!Gq-d{E<4fWNrU8xeukLjpZdC7^oo}_kxclXCr(I2>Nef>K^t`MnrYPn!_ zP6j2@BIew|;k-K=53&Ro<4e|GKkVCj1T{%pH#^JYb%72EG;i#xH1x^L_7w}n;1CE` z6JkcxpBiyLf&ju;;*WmuRPi93O$wh+mRq67;+a?pJ?dj#*f(!;aGwBW59E6Wuj}!P zBOM9Dxl0%gUT8P25s9V*QJ{fbX!O2Ur4H1f<(+$n{3$bcbgTca7#K1wy7Bs+T*OHTJch(Cn0~zsu%lzY$jdx_G&R^9=%Xy zw#6rDL1pR!I3?u!kWDekiA=AI!K*`$+&OQXq0;AGlKO1G7y3#0O^>_kmRJ>qSv%_$NuxlQ_vW8}W!#%sV!;Mc?6^%iW zN~QHm2M?mC&^L7k9LobcG~Fohe?GT|jVwF*hZe3^o}{JW0}P?yfF|fjf0l`9Xle=abtpEHqmJ zZO6>nMa(gc`?1N`XoJ>6lWwFHw|$ld;)Wz5IlhD$1A2|zF%7T3u%p=k@eHhdb(NOK zZj6iS`Go?_643O*I=c+u{eq;#DW4WXW5Hi9KF%=qz}pkbzUAHfY%BnJJ~OV9@=>{x zQ7MWto3TB~Chzy!BHx&C?=Eq^!I$uN`SlKX0*0Tmk^}d88Wn|+HVFGS84l8Y4;B&rPgR6p?Q+ef+)oohw=T!^Is{9c|U#; z%TxFYnrM=bg8bN8^0B&$kkUJ;L?o|H@Eh;r@a{2{dP=*&I8~rtduUQj#E}lN_BaVz zJW*L03vNl?IH?cfT>p)1B>XNVmQ4u?3fT8jkW=yD1>LoLDe(Q3NiTU`=$o43-Mn7| z15@Ucackc71Sdo^c1+s9@xl7KH0RemGi|n!aIFSKEjNF#BRUH_D|IjlGcm5}k7}D# z^$hec%%6=&OIyvBB?pUxf+f1oF(hC9xE_=c!$|1Pha8Z=&Mc3EcP8dtRv-OmY4Z!+ zjN6j`$vfV5faIgyIGz|je^M}4$&g7%Z6ca)+m+^_wY<-`eMVvjd zWk@jWQjj>VaD6zC;-2%qkpE@lH{r%!nQe#PpN-qim7X4MxZe)Qhmpn^#MlZGuwA-P za(DyTNd{^8ll#TifyTBGvMa?w8g$m6;|!mY>d735e^e6o)Qb4m%1`mNs5OxzA;B5oQdK0dG9 zEfmNhCPQ7o4K_oaNO3>pdwDcP7c+o2mgn|P)$Ove@mOV{Lr`$qtqQG^>hm^3wthNQ zI5Kx9DECCa1?Z;)_m7g1_?-VE1#dAHLXE^jgB7EJ-4iHG$e(#eu``blCKQ~E)tf#= zFMiOc>wPE3t^kQyP1579*{H1^OU#DeT`opjsRWY)b&_X-+>Z9%H#nP7_HjV-OLj)9 zHT%8?u{K;kN`^;jL~0kc-dEFdA=b05B*Ae>m93n zd#rOt5F^x!4F5C2M6nLtnWRp^7xt>B0+f?p{T^P$6*$ENw~=OqG(-;kb3oS}HRD|n3K6^r6KGRENr=#7MgvYq&Pzp$Z}pqmTV zjl4U=OJf>OWX=t#d5mh^_4~Uu)6|dCK`xLI1}iOm&Z+v5GadH}v1h32`|7MRan@xA zU>hxMhr72r7^&cCeGLr*SafcOWvG5nUw=?`b#+Q+p?jnpxDYo9`u{{{-#JGK75DWZ zfvM(pbw%?geoIP>6J69SExO~5@2)*INnGD4WeZEjYD~))UdBN^9-9xUU?<@6fT+0q z5G}S~kz&&`aaPBE7egjFPmte29gBSq61`42Aa&GAPusp3gKYRhXy!Kad(KG$qVPIevwl_;Ol#zfIvJ{%|IaMMY)Eo$IwvafY&+$!ZK%cUANZ z*n$W!4)+rsWnEB~$A~{d2T|n2RQ6ec!nLS=acCgSW(kv6;g3g}iULN3%oT-_QxGNr z&bb5w{1@@>G=sQ)TnTISZ&Z(#r%_$R{n!0LN&Sh#;IsXs^u^;2VDtji1<4yV!-rZ@ zlfREPv=JB-`g6OkgCZKc+^)ZEdxU>>wSNDqD~3LShVxJ#Xp54*11t;0l(bMIFD*`N z2Dr97e{uJtqh|#bumg;lC1^f@?8V9@4)7pYfx7EOj41h$=$LKMWZznah+Vse@^;H+ zzE)DIHT!o5LlCi>ADt{0-j5T>@1&vc|{emztatyHaja0L$d@Z8zNkdvm(ZXnRg#>k-H(35C@K6#KXg6eidBH z3$1#XbcD5;s{iqSk+%*6D;(a4iZ77VCrXFIQbq4kwIT7Oeb;DRb$J_w|8YO*=th(w zzX)wXgK&#$y=`yRQ+9dK?-uco9kTUpQtTdQ1Mm@QJ`kxbr67gmB(H5-s-GV~ONa)L zo6IFOB^{aT*LqRdGp=Fkzn3&m(3L!@QBNg_?gZ05Uob-&EJ|udtW$ z+^L(;79|Nl~)E#sBP9(MgdR_Lt__IC|LZh#iJhGUjyda{;k%d@8MmAEnRf z7woU((_LzR#L8uH%6Oo!y6suV3ud-aph;^$l7(aE+|9ou1Bn9#2=P{1GeS)hm}DSG zeY7ye)BadX6~q?WKtXxhXuwql)ihzk5rrVSJUbAPJM7M-fdC$i`&*VTHI1C9E zZMuiUkWm8>g*H+YvF85xH#ccyh7^kwY8+y;hJVBt9A!%*h2E||7E9sJG};gKLY?U8 zciO|v2af!=v(wurm;xKrM9g%0Z`aY}IP zZ@x>~)z%ryWR`L-eTFzB&^{!CnMKj2aSJ@jBR1%E`z_$zPiy1vS z3u6IS2z~x+CNM=u=$=Ity1&LlJ$}{8Z94Hji!V>75_l{G@#|fAjMS2_QXML1em6Za zR1}2}T)O(gOT#T^<8v}#K%@__O&gSrxi)vns&oa*0kH_$7C43cdMPZJY4wVoC#c!n zV$xMZb;#QAYPGIvlX*+LM+TN0o^id8{UQK{!dtv(Cr`q7T=i^#O%;}-9JU_B%Ygh4 zvu}0YoqPc`t>Z^Kgx4I4GQ$YSE9TjJj6$b=Ph{}vJT(E^VqR8AXwUalwzVsnof`Ku6ES4Z8PO}zY!(~1J{oej5J0&X#VPg6xG@{zZD{1$OWwy zeW|8S)@dAYKcAl_wx2gP#*bmkJ(d+h5-v3Qdj1vSxRW0vP8AHji{BQ;WV(`%$)J{A zhwb)OVS}fvAF%?sI73e_n3TQ%KOPC_@8tB3%^1tUUb&R~)5y*jN_x(tkgDl#G+nQ|2%%ODL#Y3O4I0zNMnMbZ58;Zbcm`iVWBEp;Lu=N#b>;gHZcv^Iu;Am883T1QTclPke}oxE)h%e~*r@@FJpb0sjH_%I6FQ zYU*?=D}9Z;#}g%o{ z&bI$gkS%V<9g;D40N^_3@NbjkQZP&AuIBWnt=ou!uQ!jfD&O*fl?gtgO=8|0OlDoSH`WYDmEonw z6^b5b@iIRGJ8N!|PEbyQ<8QX613XqJ0XY0JXZ0Ont=k)dn;GA{F~tF)r56Ied`jSC`Ni=vX-KofT5H@35WXiO;5oO&eR>GI!2ZiO!L zuY!bYto4ZPNZgr0Bx|TK1Vkvx;j$MiRBJ~Oh+H4}FDmFXg2SpqPyXZfRdR%mnl~|M zTgPphB|h(v2Ev06wP`Y0tV8zWL+^ff*l&kfGc(u!Z|ux$`H#92Mhou(EC_w( zwoHY{VCg^-IIpY!lTYDdMwq(g&7U(KTymW(<(3|Cli7>NlFY?@49P6t#pnWFiuN&jfN^&pRg>l6^Dnw4~MRFEPr|( zNv(1@Rb_?zQF>?XEcYzb-Cu6pn|m`RtEpZ|-j470Wxg;|s-kKuA)|9`MFv&Z5>7CmDD4PK%{BUDvAbdbtZD}&7)$i+T+R?BJ zQ8^dBdGC>PLX!hig|GfQKP3dn#-_B$mjcM+jPo=2K{x+Tzmv0^oJM-mO~=ddxDABM zqkFJX10PG8hNhj(D|5??nor|aK(%)^juj^_sW?6%)3P6`0_w$}(z}_)WJClrSIOFNR`&$L&_!E}m_DaMH#pY=^h_#= zSDn5GLS!5`VAIBZ`-T0J;t*m%V>@pNa|6_#{hKCUty8OEq`*=&M3O*9_qKRdzBj0@7HA-nHw_j817Og38?! zn2RP`>8c=WU|TvQEUp0o2ZTQI=R2)C9k3K<)w0fC`o*Jc2iN+&kUx zhaPY*7Mgz{FM!qTJE2b6~OP!`lq<0}mSpyn7@J+8FE?r?N@}6Ne9kv%+b`5?> z#33l%waxaF0#o3f8EpC~NO1L?m0}uY)I=XsDAEX&CT>@}?^AKkmLanpA{d zC>OSepkD<}zi2NYxlaQ*0Qki+?ixYu3+$VI@uDX_wZchmLqu+c|RFK zp?%lj_O5bUjt=#)f+9zp$HYAzb;o1Hu(-3 zEdh{b*owdB74i1c=BlsXd*sZe!w3$$fV)OUJ*3?Awyl##KbabuPLd9mqt(8&7FI7l zhq?Ui&>WcZaJzL0U;v?D#y|j11J&vK6-nwYM!XqTeAFrQ)N1M-x)_D53E#IdXq-&w z3@71|388zG`Nfg>lirT-Bi1F82W@`LAunjuM)Ar&t**ryGI}AP6pu}i8^CTpx zw>zRpLp>b%X-q~^E31|}@@&RaJ6Cw#?E%4xM#u%H@}lyJix-+h0BPCLG6>Y*TUKXh zq8%{*4&wPnB;yF|FT0blV^0YXvzlxdZn+g`Z6Snnuwf?d=-u5>t-hDs~X40ta+&R6JZkK!Gr! z?i*!vA2`gd69suc03~PON%m+iz_8i5i)MNHu&ny*py%+RRrvz)pDl_%RM4VOJ0?)95{q zUMJG+wv>DC_3b~vkN~dC#9>n$j>1=cxt<+6@oVz1fWunW3;~utiK+fiB32{vVYIIZ z+aA*s{@f)z-F+Vl=i%>tZ+L20G}i5K68G9bA#A)qkHvIGr0CD8{Nu~6QcYB^O$J)} zc4Ts5*laS5;?fOi+8$1^zVJ-# z2azBE`i=l^L|`berT5GJEhQ4deV7Td#cDBo7f^lX+-y?UQQ#*~gEq`e>wAQ%$K0_- zuwTZWUPTqP{a)hRc7%p);XF9qU5E+`6%4Hkjec1We+KI54awC#I9HCBtT{+QC!6A@g4vL_e0LSd*PY1V5$3*B+ z0yu5vS66*2@~vReR3a&%0Pc#6J*JcD;~UY#y=yV63|`Ny_~YECLD`-pm-6OhU)=8- zbIl6%waxwtuQ|t%)rF=vTn$@yxkIE~(R~JA$=vmrri@SPfo{ZUZ%^}Ff=z;5yeZO2 zsc_%1s6prtkGYVccIWBdB+K_sa6#32SZa=;Ei?tTRj^6c&6bUbWv9+w2$l7UM<`=f zaQ+9}NL5FL(H0PxwI)YC_%N*vcd_65#T$^QJY4k8`1)DC{!ZIR;@pc>w|mj^Vh48z z*ZL*XHwO? z7i0@kSS5D;&Ez{BfDWV?aN~15_&o_8K!?%fwqzVw-UU4wsN1n}sjin1P%2=;TAHFv ze?4Q?ZtI2F5kJpCGdG$x=wjdx@bhu461*%L zEQtoBrJB@Da8-$A*O|P~Ff%*i4ICxy@As*1#4+!?o)S-=i(MHjz*uf+X#_po&+7bB zwP1oI-}0aPV%28ukp6uqI37SC_Wab+;bU+-_9gDfgt{NT}kKF_qEMIUJxGH3VpCp{qQ`N`4r4+_#l~|5C=>(u{OzsTI$S* zpdSt*ejmOXzXQmJcNbhI+=BvhxROo(f`X~ zhlM0wavQdBTbue@yo9b<6oGN$9PxXX{ov)MCQTcrN%xf$n-O5Oc+qz~zqbtG>F z(}mKdqJX2l6CFW53UiHuo5G;nq0))YM5fTnZDM0{@Ijt38FNpx3~=iuzxNk@8T^Mf zH9(-kTD^#NMK?QWS@s(6Kx>KWc1W&j`#%KAq{!2TMa=Y)i9qXyW*3IOrJbGE?Mqr5 zsZLyE_zS3)~U&Pn8eobFYlBXGyt3XCsa#|EM$`37p+AXJ=`BRyAO73DB15|2(vw3*@K9B*ID^iUC@Bw~w>qpubPKePcvI#=={HvG}&AL$d z_PTn^*(*>~&Qd1?7IR*9SBf7WBjRD)ZE3l4<@~zIo%v-??}9VI34&$BE{v7C()h|m zKec2}hC~Ac<;@GIQ_fbXR$%F2YWAz&RU@2g&n zh=0xhwJoEm&T-M(PRqA$nV-rFWdSNy8?v}bvAVF$mAL8KsGOM zvn0H9G%osLVm3rEfti`tDqgt9PMi1z`gz;-&DjMMUE4oVc*~nn&b$WXh-dD% zq`)YT_Md(sk^z|@XgC1pKoF>uN1aUWe>u2bjx4znAU9f5QvBHj=}ukZ!=m0&S@y39 zlaX|GRqFd+x{D0(=I6g{drhMTqd%wYP+;~kY3ceqt=c2as}BP*Va`MX8P~X|e_T)p-&qAPs9%&)ZD+sw`oQ}b`0u(0mK&aHZ>Y1TJ5EN8pr8NpYsfg~CpsG~$5c=y z6lAzFpnR4#!K|5k%13Vp;c?k@Ak^JXyngF?mcDcb6rsYbbKXLA?$68v`aPsXbjo=C zp*k9BOlaM#@>2jds$15q@;7xriT`#Y-5uf0VhbWzQ3cn}Ea7abrQsahjsUCyr5uB< z`AfiY02lSaow2j-GsMz68nGi=AU}y017j3)EEKxGf1SHB#3 zs$gPbu0SCLW(A$D794t!5;RQEEj5xf#N2KOi?~subWzP)hBvD-zsC3h?cfQo=|`%l#}W> zbW=y}5l2OVst~Zc0kM&sC)WIl=)=Phw{0NkL4#f>;#EmbfvOmVwKjQE%imr8=IquQ!YB%7sgnW(4AHMK0N=!W)G) z4%J)4iqpe5o>u^0x|Sv@K5~=wvtk2rKIUUcRpGO>xUR8+(L+#sE?6x9 zh2O*U*m^i0l2K6%`rY(Kqw95msLD5y+WzVsW*&psrJIhgVV&d{Pn7>-vrGpFQ^48) zH_f%{gZ-giEK9v&nwA+{5AQG=_K{%+G=R!daOPu`paEG3pkfbh74!)YlCm>q;W1yk zbMhLwEXg1McOY;X5!1v@{!>KPPRU2Tp$-O@*LMk2TqwzjjG4^Fy?I7Ug&RM;F`i4B zw&|P+a5E_onnRGenyzaOZ;Iqf+e=?R21p=Mluh>pr#G6oKfGiMjskUo(R>s{J z$~SH8@FpOr&a6a|d5FMIrB36zocvj-AEY(VE5d?SqVI5bZ44p|$m!ZRS%IBaiLDLt zQ~-W+85o%Gc4!_=g9o5xs5zh5SU*`t2KHySUXRK5zj5Qjk3`9GMcQ0|hyl`Ksxw1y z>1a*i*85P0&ir}X1x9g4aAQ#fJImuR6gJ{sWBd5j17;{EpKe$hDMUE$VqUm z<$YV{uavOYnw#y}O|{aTv>$i=PDnalFdL?3nIWz$7(yDiICi94&g@c_MOO!@?N(^; zzxA|-%0-Cq6_e-e?;qZ;H<&;=P&aa=Xt6c-X{%ER4I5e$K;0;6a=kt=nlNOMxINSt zR=6~JDzZ-i#!I|?AZh0!4t`F7do3xLk$h?@{)~~6aSrG=Nz(d;52zxIsm+QHOtb>U zqTp##v3^(_u^T@vKY&>4@HK=)&RVk5?!K@66*+5>4?1d~#w)1#T(?t8IYZ}ARHv-q zK)FJiLPdDIkV6|0XBOTIhQ{!3iQZ)eZ2@ib)f2MT4dB}!*?XUe)OmRH0d8U=(-B0K zq2QQv4sK*gI!{$9v6dp=(U%{SO_}cd*RmGVd&|(wR2or01Jo-wYLW1+2pL*n9StY* z8|QEKZdCBJZQD*RrljaTm>V118Ao z#tU=5P~gU544LhfMG{=QJd`pTJs+9i^t;h~pR?m(n4F+ke#3_esQDrS_?Y%M*OJ5B zl9?4XpQ(4*%VT-~{b>xgSX-RWRe{5;u9~gzLd05Hl#(1ahq2;d=eb?Eq^Wa}-!If- zFE(Dugs`~&!|T?j(4+R?K#Bc1{}7rt0tDF=E8Rwul_tx1Y@C*7z=P|!Jo^NS{El{W z2PeOZ$SaYGQ`MyAx282}z`L;jO-TJ{hMfOAV8Y%nVZGkZ#%}EF@i|?c6LkN|m0_IS+vv?CML2!-B~n5Zs4`%NuEHqy>=dWR06n4T z?j02Mg=}$W<8lVy-1^~i-lAXwe10|1y#^C-TGmXlR+1G?P{KRQ-8lJ#S9E=4-i!`A z1pKz5RsfspXkiUlKRPx3YT1B>$c`DX^4%eH?HfHUbLB()bHd+Wb1C{3r-2UzDEZz1 zk%YJCV4(8u{i9%&5EO=5X?YdHFF#3%Se4&Hh2Fl+ZaSVKppI*|t24xbW32{}0xXo$ z*UX3l1w?gMd&3vM-Htvel3YROrH%deD!YFR1I{u^WG@8fw5&8F(2rP8lv0xkxX!b2 z3y*IENx(P)P61Zsw0Eg9BF7rubk>n><<27$Hn8*`=48Uq)u9=2*08@U8Ui_y3W8UK zgmM=Ua$5%(O&`2b9Ku46MOjqxv1(*bjQ&HaIuEsQ3tGURJscd78jg}Rai?bY z1j-W*?&~h_QozK%hwNdJXGNxzzOh7_q9bYvt8p+zr&$pWy5P&M z1C`Gb16@LeDA1scN!ey_H$Y+4s&4@hk>ADhq`HmJVopKr8-3MadtEtfIuxbss|)^( zj;dn7%V=^m1ecAn+5lWl;5}Y(eLT2>DDcb***-=ouSd1A1rp7h-@bv(3cxqeI%%vI z3lK<@mllIcVCz2&U^jjJEO1sY>LhD=I1kL1pi(T6Zwf5=cO_2oPRv@w0>xVuWU_cL zsVcjolK*EZn5Wa&0!(6vVh{z~ZaO;xx>!bUXkLj~6y!|7N+iz#3Ya3bWdxtab&N$K z%3H>A##UKIi)E~IK7xI0UmNJb$J#TnO2gR6LV5r$1_8+4Kzdu(^JO3RtO08xYkpMTEk# zrlD~qL)vLtFyZZPs=sTYeqC%I5ScR1O`1xu6gG~XWc!*!f+dxymjq|=iEYF2Pt|yw zf-%in5QVYTTXQn%bO~cMg8|$}{lY7KtIO<5L2quwsBj5c45dla&iX`%X~u<;T?dCz ztKZ|Dsx0^Ko^qaohKl6uA3Hc}T63iNY<*P0=n*qyDs5|nx(D(3aaXiE0zon`>mcW6 z1$Coe;v){IKMMZNQ6npZq;{c)yM!D~`UPwJz@g}>H*|lec}EwR=TQxfl7P!T02eAG z#b4&eKUbN{1b^JItTT&y9u%VqCL19<1^>mR==zPs&e6!95oS$u?=reW*3l?r3Q_vS z(=hg!`9H!p$d0I%rT{2bEr+_8P0 zp)E{6Qrir|fbx(S;#c@K@>;3t&hC})c+yHT(Q=lIkuGd^HhewF=vJa7-QpjjTIDTP zZ2vx7z?mEZLB@J4Kp-nfcKL@T>=tT5Gf}K%`a}aUT#iVvFBZHX|D`F`U4UvT?qzhq zOV-&ZXY5DR-){k;X5R!+4rn`<9q@O?jW7Ge0s({aPod;~ADYqy(1WrQ2XR}46MW=o zgnd`nrqsO{c4wh(Q@&f^fai)G;UUU}_aZqS3)nv&Ni?ahzv2eCihR0jswbQSEnps% zViB1A_i>2JFjE|v9<;OnOozBD!(MW|vz&FS5UpK@<2Otx8F%L_6{(*;Sf!x?66)xm zMS!;34cX*%?7d3Ee}8bhIbl!LAIfDH+>Vv0x_X&~79pVTqY4ocNsf!))@EELo!Tt} z_9LLB2?qCgM16ieEWObdt;8EIq&!`wg7(wZjcuL>s-@t^V-8T3;v8eff{L;2(=f&!tQxAs} zD%>?-xPs=)a8ZpErz%+S#~V_=k*mSIp(QDNg<1VFS~D00_H& zF8Yy8a3cS?Y)EM5F|;5CF(Q`nDKSX~{d0u!I*V8pP|0s~E-FT+E%I`lv!1-FnlBNy z;wr*Do=EAUB6F7Ppc4HgHWQ77O=YN4*Eqv*gq6lP4koYUYv%BG?s#5>A80S(eHq`3+81|&J8~_#k`E4lmoRr zw2j0fAvp?0``LXjA5bj*WyNNMjPlNLV}{kU+f>PUDhewL=Ewferyb&R03fBShmP|) z#RG&YNktAu;~lte`WJ(8x&hOw*)00{J-)D$p|9*_L9M&dtuM5Ch2KZ%imZemMbltQ(5 z?b^BFmiI3xpA^)XFx{Gs^?%^~o1SCVPK+X8&=8=6^yj>@dxr722C&c`hO}I^7ufpDnvIkDpQj|!t!;Pb@to`@uNSZWV7}sCkmg11ObJH+xV^&oHCB`oiU ziRImK9n?D|n>}`=)lh)bR@fDvce%RSB1cW}r*T_Eg0ic$%AZ-d*y4f8FWWQlpNswe zPy_a28+4f8Kw(TUx8HMOaGisMc4v>i)4KUVcvRYBj0!UDyR$LsGtrZNDuuE|{mTQN zJH4sOZ>j^pz?D7kFsKouJ|7-gs~Lq~X5&Njgi!p#-Pc6fvMu?3xcCC{lcYS55(kcL zD6GzyYSY@fYhYjyVT2#Z7qwegRs(4&#`z9lP7?bASM#=!MnwU@XWkw~QUdtiTl|vz zQ%G!`Pa3f{5k^;Xa-_-)FEm?dGGIVG^bcS;fyx|fAyKs4Aq4$Afs);e16`}l zp!~V=b5Z>&URzdpEPJ+T?j%|(>&5m-wwJPCwiy$6F*+1(TpPXEt>1e>JF6e;KeQnV z#mkAFCkDlRh@8-Gk^|yC$qfew?(!sbyBQd2kTumD${G9YmDQd_b6unvY*^YdgQRm} zAN#CzW38qFIzo39mGJ!yz&GJ7vJtWJe>!ahZemnOCuaHJz_~km@Z4Dqb{V`M&iuVu z!pHW?!1&7r=0bJ*<2NvSfIuxRFRO7IRxXv_7O}boyRkdP*+PpQr&}Eqe@yS3w4xRN z6)P#K(ez@q`ON(AM4U&FsDR6`%!~#CG#co*Cbvc59j(oG;s<-b#dWYp!o z0X#Qc<7?T81N}NgimyT#rX&OF@GbbV`D1P$qKee&TZ6NK7%vj865Jz|79P5Djzqjw z19@}bPd!UJaJ8}uPvaiN`pm%6I{H#ziEu$bvk9zW z*P?D({u_+F#`l>c&eXH4419UK4gkP6+>#1?u*%;`j$?b6o5y{>f`OP&Wa(>MxyJ!NO4B%ca_tfm!mXJht^mk(wee%I&m~{o5t?q#diE?Vm|LG1}};Kh|!4gH{hOB7t!O% zqEdDh&tz`i$I80TB+#Rze)9^y4b-K!Afh$>>r#Fw2(oqye>a4465Tch%SmhJuDNS!z*B_gG|H7+Q5nNZss3#1p!#-hIEQweX$}(Ns0M^?QL`^` zb8*1t$AT(^Pa5`2yrXAM%>i|C`u6xu18~QZbw%YHOA4hHQX`8Hfa}?_-DFp_BOApA zI49yha9%=LKfs(uLHi4$KC=MjHr9)LTsxQ9^th@!1*c{hd585cyU&80O_J{hIb6eQ zP-OHwk-hD=ZpNTj`z#XvVm%jmB0R8qTwCr^69gyc;I zNOHWIQl)l`)d{XX_g}7_{F2yaQI(hXR;dINh+j#z7rNr*_W5CbajhzB={03LDS9Ee zro1$XMC2U5-#;q~wNVndY)DU5y`WKDzQCP=wVra%;4SEDmNRWB*7xl;4{Q&XFlOBe z{dL52Jo1cZ%4o#@8z-lg8%7IEni5eIG1Q&B`kQ``|8skjULBd3kcIx&zg|2M)#a>u z3+wlChH;-vKD6zp_ruzZTr{-`PkQ$OFGy8R5D}}FrZ1~fmy)D0X(&auaH4)9_Hi!- zs+TZ{Xoe(eV+7^%p?r8r9{`)29I?1HNK(3tANu}8_+UQ= z?EhdWqd@7Y*0!UQ*!8*@9SbiBb+8d_%SJwG1D>P^N;h^gk|XS@BGEW;`+r5foy3dW z&GG4QG8-_5!o}WDBKq6yN>3OW{YScwm4HHS94EqpX>hqc@1)R0!p@lYzW4S@Es4AL z6$61I*PnUP9zBo{a1(^K$(570GnHDOd>S|X=|!@2PLh2Ic<@#?0Uu^DI>iN7N!J>} zlz99r{g8zHC#llPgZ6|6QUI6BVxdM^c+%`Iy8zJT7wrGn$s9A^&2e z%tv*A=95sQVyQ@uuLXyCHwP{JZI$e6d%;^%9A?^O|B>7c#q|$568nfyB;0K6bq#<% z`3YRQWn*SaK%30$=m4>BCHoN~W?l2`c$p=^ftOzal*sX{MP#PO4+I{)0iUZwq$!|l z?C_IM8XBO%36E^{RwgnLqQ9E3l=b|;a|mi(48`KNeH<+R?VUdYwo7VPvY$%jY#L02 zV1PvrV_0$L&zskf@m0Q!bC|B`O*05xfv2oj6@g6~2D9s$QP6 zKAgW?A)6gZvI)Uh0-feqVbam4ghb+0E6E1$Umj7vkfMi#$*;_lw@z3e`D_NW55kj(hTHW_6Hw@DjUGvHub78SVw z6wwbt(`PLi3|tP)_w%n@*1kWMlAJ_D%;5*6j@uHc@XEWNcah z4lV7ZKv!y2K#+gx;j{NXCJ;_vKwx1K`jNRkzWu8m{P}ZGNlfn1y)?Is` zSclI3#PFD`+-Y1g$(PP0*{qE6N%XW&BL)-@0eqP>rVNlifLhzc&s#;e-&h0IWfIUe zc^?+GB=)szhdMz^ZDL{r{C!N~;=tu2&ksYzuEdd=^O7ECxqs5C9p|vn*&L1TXZu)i zOiy+l>R){2n-BQa@tULI&<%Js9S`fi*X)8B-cO^9yAB+22 z{u$%IY7JazZcU9$`mN~4CimU0Q~hl_*^9EbMG*rEzk-*8BSN8@$T$pF9q4{PYRVE# z$-@)n%5ClKCG0PbbUJb>FoQR(5m;kco(l>^PnZ;dS#fqTbknIGkPvX^w7Ll7zJxjz z0VhOdZX?L9aw{FU3a_IgI{Ni`xk31#NMQ&{(d>{G__-B7)Mp)Xf90^{H1V10MePrI zOoSuqv|!4S0Wr{XpT38Pm3eN12Wde zZ{adjnUaiBSy=nW&fs9n&|yZhy0WWvK2BOAJK~pWho&lj`RfbU@FIJsq-y{9*i7e6lG; zF2xoQ+_i4~{#xVH(fVvAOc>TFV}uCl)_Au;Sq6bkbC2E*cc?t0*9%EHiav}QAyMoC z=@!+yk3j_dgUU)yJP|aCBB^g2#0$p_3r#0%njv^Y**x_oqUk`20Jmym_jKYnnnD$* z2A@twp@43UT3R~lpXcSry_CdGtXZ;|;XV7Hm+@Vgq8NHlNdNCW!Oz|PDaj65BzW%) zkmvCTa!&tu5%%w*5E+w@I^?lmh$F!&l1(-FrJ`<#mqS0rv5!61X>aOV?sA}?+?zYX zIloNo9!33|^uwT$iz-{4)O2G<2ro{;ze*>R<- zvMdclU4z;J)wL^P7+wG#PR${`*`uZk9@kbu%g01Wz(&v3@kJ#oYj+`fn;Q)mufc%& z8l~VElJ_1-0dK&f5@maR;@UMbGs83GwG8^(d>YZChq)upj=e@~i~4y0H2t5I!1#HS zTMhAp;E2%W$ZS{QLOS2^ynADs4ws&ON!Z-|r6c0@B6=6%TbMh;Eo5Hg!s6*revo!P zw>$F%4qyVLRT0FC-ad46NR$~EIfXm7g8ph>w7AfJ(q>h+-xQrjB7a%|ZdbuUO=@EQ z=WS2iCk1TXhNn29oleJb>W%AT$_)96=S6#iPi(W28c4Gi7y2b z16~V7<)GP&s4%BJ_WaL~gf>esBw_qY;+!3Q-d5|hoJ96ZbPR^x_Nyz|8_>q@m5}^~ zv9G<=7DmxtIGyxH?%BcC>5etm`513^^zS=hoN!z_51l zJM`Ppj`r(ZMklN93RWs&`pm@soG}9-*xeUpxZFOtdK$lDLBC%Y1eHKdm%)K6i%65GPL}y*wRULA zmWc0kHOlyXZ3Oay@fk|gzdFE6{)agBLEa&YYn-%%-;Y<5UyoSJ*@~ophF9zJ7dsy( z1>@3CfAGe5WFM1D3e&Pm9UNx|QT}Qrr#iFvQ))_8 zwu+)|yGdA}Z(zqdIHI$eO=eYzkab@@c;B(po!W3AEgr5n22ve+qHG4&D}7v<6>5KAwaTT+DmLERM}| zx|!Y+wq0A?s~6TUu5mbgW|joyu^@9`+u%g(b{Mu zdrlUtXsGRHTN2D}<&EM@8FevMM3gqu@f+MlIToCuzYD3B*Tk^|n2>s6G`S4=Og*#Y zy;}MpmG8om&gALG{E2gnvykV(1@0}xlLs`Ppsz~MVF4AbI2(?w?rb6W4Qgmff>GGs z$T|c_ZeusPSJS@Ckd>2p3T_<0Y6GtBF9F#Q#MYJ@o8Ew0+C=q>6WL=DC5jLfUBd78 zZmKhfT!hzVZ2>T0!rEE^A)pcxzh59*H!X=r@Ahr(hK59N)Wb+pj6)QkNuh38IJC(A zCIgPZ5&*-yTwK(gztbHWH(D6imZtym9Ho9{o3#Cf-)K^rUDB_+dUGox$bVYG1d@F} zO%R!(BXViO6TU}$`v}H~e@S-BhR-jL*`G85fE9o(hZ=;GV`R>o?q{k&h|pf?R`Uv{ zV3Tz392@uW80AXTrb5(F)#_d}pjUDw<_Zm1#cBY)3=C6SeCk!Z91T;NkR-|X9CxH{ z@TJC1R>wE-KQVj+?Y}Yn=`sZE@5$gtml9S>HIMPhpO;a(l5dTDUrW!Ug`JlkgYv}UMU&0(o3u;o zJ7+{4vDL!JZnJW^DyxMKDJ(!L2G=$A*CBFlXng9v*jY=)sSpKbobW6jBm2WRl_-}w z-y*eVpw_X%OV5em$-j$rK~kjA#{r+yaO_oZL%(9QTP%k=CkAB1BOV(MDZX`^#_&sz zFp0M~@%zlBkTUKOYngH4HZWZnI$iLNs!vhCi4bAB!A{zL z?s7ZVm;7IDk(%QSsyxUGtBRrS#DR&I_V(sT*U#09XrF?N|?l!a#@A?O+`4vQP?3~Uq(vS;1f)|CP^1x%R#GITK|rKMIz>t)rMo301wl%YPHB+t z&VSzSc)$0IGyd_NXY9Sl_7Uz_Ypyx3dCgjs&@*%HG<*N?TSiGvs$!(@M3gsL`8;kk z5Ag-5-X6}^bi-e$gtO96?YG_r%Hp$Ji5l|M&u*Cbb2RXz8ZOeds}S&fG# z2ivFcsU#LeS7xJmL_Q8a|5X2RGd`z^sP8Fn$;r-(syfefOWJagH9oYzQYUnX4Vd<`*=~d~&MXW- zt2heUBBbCf$W-{FTJ50VbF+STB8< za;~~F3uj&q)b9}ARU-~Begqw7Em_#UULbyacw9z;9{l0X*`Lv~+r%7MJso_*B)%{DvN@({B-zXHbeC-I zdv<(V8<&}?JTl>4U{zBsa@QIS=~lkn&M4@kEOQf>Nzo|-7zgLb#AeY=^H2pc+W)Ih z&f~ful7aV!Rf!r=b(>-Z$YHPUL~4Xr7Q_1m3fx&4K!(xW``Nek`pkOAenWM9%^x_EC`jT>~e zr!k3YCst_%Jio8^Ko>PAtS?lQOmu)zh0uPkRu8&!H9c!FOM7z=J&FB<>-*5T@c++-S`p}>nkMfb;am;N!ukTq3hGwz@zu0#loCxo{;EZMda8PyR#(?zAphp|(eac9A z;7$0H6b88da%&8{7xP!CL`c{_Q1RKl0^liE>m1ROZ^%;6b(6u2$tkA;e_ytK)D=n} zU|E2`8giMQ!H)KqP#Zx{3c7zey#bO{*H=utGi;3Mw&9yjAYa;-n$RT<6G_)#Ae8U zmeJ#wm_64)Cg6NU6`JZJiDh|MogxLqqN4UV@FyT2f7?H}U7D^C62e$~+P^1r#pA4Q zteu#-GW8(^k+M22M&*e^8Gn1UQ~D{Jv9iRh`(7qbD8kMov;V*x(-G4cyriY|HY5xU z%etsj>TL6jQ!u=KTJ#9*PaAH@tNjL|CulDUl-Y@t?&mzKfmDApmUh)OdHJ*h?CA01 z1HZr(f->!KGqW>ezwP;Zsm~3iIr{7O>I7u2q_F++SulC)H>}u5_8WSQD=v+VEg3|rc=Y|@8 z-uit-*1b=C{!YmDBg4?z-QIOip+>{QC*I&GSdI=MY31$iigK%<+FB2JDAg~5AAfTR z0YZVrkMgc;=7nLBV3xeI#J=vQajpi{PVg&@NVqWQCYs~d1yAT9f3p8xQU z$C43RFfLP$`PHhsXm()Zo+;+|nZN_?>>d^d2k2)fC^BC>r_Hn}H=glmmAX@v>-|&q* zr0G1JNC#5U2LJxPC0aV8uzAK^jwF zxpSY*#M;b0zNxHpu5)kvBAsnYOL^m3gGEPfXw{O% zm&Z@}hB&VZNux$uq-}In+I1%&;TNIELYZ>53C(DPz_%H=FD{n=uO z*C*3|TewW-F7w~6`M}~R(1(pya82NS|9NBR=bH~{*9&eVp9WPk6>wYG6>!UjKbg`^ z)%3qj+fnPM;zma!j~bSo_3PdX9=t5c-G!jI*9BB>e`S!9=h$C@>QTc-j9vUT_!0r) z4cgo|ue`-*d;9xQf678{jAu;W1P1|$eb!1tLPJS%vrO8X953R$2Qc^E5IcOg#9^=% zJ6r~xF+}k?OqU-n`BK}vT+%wmeE*q3;OO{U7}ovNrA@dkX3J#zMOS0MB+=->^!dk< z!ql2e&`3p@ss;}YaD1hK?}dl?#ww1X1C26aE!wg+VOt5+UGdRi_@#A6n=0?)g3|A1EPi@t9YVdQ_s)9vkc>Ex zITWAZEAApC;3^D_0<+}BMq}*4Hs9MZn?*S;esEr?OBeGN?(vVE$W9S-tnN(SUizyq z{R6(px)&M*FbOvxD`^;JLqPvIx{+}UK(rU>`tJxO+ow=N7JBg$s31Ei90{o%kS#ui zblBF5Ri4j9+Ms8|ZJM4{7X$V5>{jPAOZE0g!UNDBz~RSHhILZ#FLZN-XZB3VlqqJ| z4_Fu<^d2(GVS-2c4So+H#c{mx^{D(TPYTyl1^nUPA1 z2d%!=M6@Sgzc7e!sU;>PcF860#?qMslD{hRIC6<|rI!Dom@zl9C^`RC?W;SE&17$} zzOOrPcS&tTBpzR=J3CpM+&bX6bLZBk4TjHrW|0aP+IE89=*dqZY$ds$myqG%gKC%J z?-wcc8Nc&c{i!KW%m18VwUH$!G+B04qOKFQV=iq_90O?N#tq`^lkaiY);HTq*eUQ! zuPZdO2Jx8{U-Mi*zizkoOu%ZbX2a_NiNht`%}cqx*a!ZcSeKk+-g;A7R}&#E12(pM z6oc-lexXf%^>nfEPP%^SaN2`y=1)KmL1eW@H|g)37mD9md(v`0;{!sbkg>TrhkAYL z(MG%*m^a!LkhFi^8;lO&7LS_6M!iVWD*_e#NDoAU?E7KvgS`A{H;Oq6vm;WnRS4s@ z7Ojk*T!LGdO%jH!pcQs;X6xZ=Mz8dXo;PbU2z1I}mUKY!t+KU;iC;u!O$IWL2Ws_WP7?fG zrf;~QdInA!F4r|sn_#8}RA1yjx`?tD6FJu(y6HTmHzZ^bsUaV z8ZG;f81Oa^83y`yaiGxQ5K+j;I4k2lVLJTs$iS@D_Re^ZYqdv*`a^3gJx&9qV?9u3 z9^;@LzSn$d*IRR9?nlY@<9PQ=2Y0;{TKuRdGd&0XZe}od#G~y)5)nLeZSGHa>2ae# zpG}$8`*}69zd8*~sWw9?U%ucWui7I5$s0a80BJ1lP#!2YG1Z{=+Q@mF7u!U4_)edE zW0HZW53^-uQ$DqXk1)DWQNQSZdqDSVEh>42QVWa>QK4j8Xv9UIY4UZ-_qrV9;e8vp z)c`SV$)3{9EaEN5P%%!_|MWtBNtimk*|Ls5c?G+~|HIdL2l07t*FmD&nALpNW4TQA z>W>PImH()RIT{Oo!6!0v#5`$J2vX;jPp1>@Ks2!uBL)&ec#$8@zL)6 z63vf^Dt08>pPNcJJk;~Pn?!|1Pn2;9cjv3hg?IXmv@2i{1q|4S^!!{rKv*i3=6nr` zupk@hWvoXPO=aP)KPyp>(JUn;(5iec$a&aYe$b_lF}!ZA*koWNU^7!()V z&(FswDZU!;zMIzcTUH2x$91rsLAUL{Dfz z`0?Y5@2qQC5!*gm7pVm<5p`;cB;bA&u0A|GTyJGeOe!cKn4X^AKUwo2K*PFlE;B#> z>dX1+>GbAAm<$D#yyvCY2BIv6uTh1gsm?AeZyOi|_|K1_87C0bDB1FF^I!L#!3@ta zPnN^1?sn1^fA2RxMrKuP8JT3I>bK`dX#TLrN9h^WN21pCsyW|1`|G!m_Gu7l_Jqpb zbmw=__tYE2-m;GriR?hHvQ?mQ@~`NXLkt;4Yt2KV9#5`&|E9haDDGb#XD3dpmA^rq z*)JGg-qt(ajH%dcW0ANfxK@#+Uh%ma&-`qmy!b~W6wSaKUYA-$C4Tj2|CW_@X3VDf zt#I?KHxXw0*t9?H+P8k3??z|mxkgHEb7hFKQNApMqBmr5{S->gSalw$S>+Ez_l);I z?_$M#oj{Xa8u^|9*cqfF1IqSjTLRpsfAiAT9gwv~5Wm!TyQ{%$Ni>0wm}&jekV#@w zNv8paqDpH-P}uj7V0Z$wa;X-uavpyJH!j5ykcb17H3xDsZ^OhAr1zZd9t6~UA_1WRyyVSOiA&WBxz06V`mG+VYcGN8Eg=d=Xjbmy zkp}e2%1u1~;9?DQw;B22a~knHGykRpv}?*M<&l9*qCS#-zl^x?kkK@Ow2w(+%{Hxv zqeu&qYWtDdKS={n_{bU2%z(BO%fWb*-pZ>ZGQiJDS#H8tfuZ!hI=;6);tE^xtTe1P z%!Dx}{|uA)khn^E;`6HQoSV0=cduU+=D53ElYuM?ie|IP@-!;SnVpJ8<=3^YK`RDH z7IXg{@hO8x858FV%+0TEYOfEF_+O^LnJa#~ysf$J`t;N5r=`{(Mz|?QJzM%jqf?0) zuWN=5Zo~!k7g(|4mKu;~&HR0Jz0D@p)2Ke@PMM(XKi7pGIxym(JelAa8i_o1e{iw- zLWDK#w<`+fg_@?6E3xYH3th=XI{07r!UfQ~?7rsz){b;{c>Xv^2j4f-MUh+viwI*j zhzqry5OH-OB8nHS+e>#LidU^HUXfNsMJZj-$zQmdD)e$}t{~IrRpZ>N!^Tt!jE4Bm zBwaZkt~3EEgiKPSV}shIRD|R<_HSS7UucsS^S^mhFOulv-uAp;m8(fg6z^B(P5lgx z2_qr}%i=}H=>aBxn}?B93TU8F)JaYceo$>QCu^MTix4x?%7#|d6jkaNy&C^)%jt_E z7AxWeXEJ$gtr6PEsT?`DT;(vLrxm{v$=+tVP$TWy`_M+#()PQ;_>ig5rJ@AaK_>7z zQ2GbDtf(tEAIr+FetAO3%gcKpLM`#P*CE*qquf5Qd2qCY=g{QWmL-3A=S^7f=NZnt zHh<|FUnqRomkk8@yY>x30oMnnPr6PD)hUmj1{LVN!i~+Ar=}ZUOVOIij=!=fOvH3w zLgY6TC7d0ypOKPw=6-<+8vihP=&XNXZRgS?H2il37A`%%kqso}XSt@vkW8%+J*VPF z@DQ97$ZI8(*Yb*3{PH$o z{Ri7k@mRwTE1S{HrFB~I#!G2?;S~f`hMAeaT`f?rK$(4i8v{-Dp~5_cERiFof=v^D zo^u_ zc|FPts7tkO+PnT4Z9U2LDTh(E`;0e;1B1@P9po{q(TeQqF9jKT6N+_A^6BKH?m@DN zxY}>Y)S|dBiShS?*ynYx*5B~oox(7=w@LxiXD+ib9j`QgpS|mCI_XGG(Z`XW(p|yu zjfer$bpEoK|DJ71epHVO&5oa1oq_o8?iV*4VGI&Rg&XtRk#(<<>n|?B5RszIsfBl< zIqGFZ+&&)ew)cCK#I~Qr;F0Y7=vB{NODXFf5=;X32(r*8OK{}(n3k|1gnrOxje><6Wek_&j0QMXzbry%m z+i@$>7H-ly7vA>`bTwJ-MsGOh=uU%##vxbnV=kjNVUTU$_c=ZfiG^BAsO>=}t-*w! z&O-$#I)MpRl!6J-Ys(DdQ8O{gaE5A!e6HiX^e%{EuT;5E7v-d1YB9$Pse$#7C;pQ` zg^}G%oW1J?O-h7>a*Lf>fLJvn$|#e1E!dRW=1=zn`{81u%{Urg%D3DKkh|!x2xlL|< zvD{U&U*%AZ^KprTxMDMVO?DIWye|Iz9R`mLG-%MCh!Z#;U@s)Ut`$H^5tN~O9~!?c zn#2!|78BYSAlr3a)^~X4hri(2>?)><^G zj6V7w8S~gkj7L4XguMR}13UZo)7DcPRRuM?+c%+2&&r~k^F5s@9 zz^^!G$yl;5L+m|@09j*W+aOo9t(Tj$xOuhKW+Hi-6K$5YEVd$ePPg&WVyXe60mnQD zdG0G)&hSD+$8l|(0sq`<9@c#Q68U5N8IxPC`A|ITGmWzQh1)rGKJ9WzbAbWNT z8HD^Y+(&(kpZgU{`F{RaYM?gn&ggCe6=UIk@KAH@jdrkg2CloO%54b5<2v1o?iu_+ zfC$=^bDGXv#57ypw|G+Ieoa&nzknpY`{6FFbpXT^DaUmee*tcWmbX|;N;)G(}!4YA~3x_*%on^sp!PSICV61p##DYiCT;22u-B#ea}mk?;$kLFHAf5(?! z(_NMF^XLtc7!GAYi(QvLj%@s&)L;=$x0=F^U~rM%M|a~pFLH+ zgksgfa8>EjtCDtIvF~ADO%|rp<^BO zIUINHR=kcMu;3M_O0hOQD)x{dEOY5yQnRBN)&{czW#$xy zaF(Our)?rP)KiE?KlZ3i>UTkDXF*}Yav-UEeKvLTQN5^HoHh(IWb zpMTpG3mRQjgN*D+*dhLnx6PT1e-zZBcYkOIT7APB^8y3wH~DzD3sFM|50$I{0!ny z6AkHRl%3L4kf+k^bz4P+!ec0w;SLRy8*o$4IYGM;V06^AEemBI-q27S)r?rXuzZ+@ zbE&*VO=wcsmeeZd5=|oh^zA`p3y|3hkhn$|z-L5hOFWy!9tg@7#xp0}}ZKr(cAB$jw zu8@mO>ZAO+X@%|CXH$(v+46xIA@mGhQU-5<^X2Wne>K&Sgic+8BOfxnlo8VlHLl2z zr^cgeSaOf{I!2JnW|%CTRhNj>yxyWHQV+F3)Na;TwBq!-)>jJB&r2*Q`=#gW~s9}k3G8Zm-#Oyo#3*4u!a@BU4`?9bL2oOu3RNdX&GzIj8kMQYteTsYLe}B{RxdR8X!?4VY4#8X|Qxm zthzUIv|S!O{%2wLihRsU19n~1fK9CSr}JZ%_h^;*{G&S|v{T{eLW<15vUk+cBq%^A zB*;%wIgGN4n4xUzwQy2|yw~t8Ff|0H_7|ZURCN55(3;8*GPC5_*Q+N58c849ev>E0 zlKIImZF!EEH>lXUgk9l{`_DZ|XurPnkGzyTTQV=A6&dSIIAs~!a)5LXG8vDie%B4< zouTB&)&AC_QgJM{n2@OQcvSVbrMD|4V-3~A(hsg@pnOeGvdB|A&TF*EgF@k}YFKz7+rxZ3~L@_E!%oabU#wl|Vzxp!D-y$P#qgVyd*%jBjfaaRk_D!ws=B5A9oqj^VO8pz4i`$*CAQ#*};PoSuhz^;)mDN4|{vVznbBcJ*(-i z=z%Hq4Y&9l;v3{b_Tw*7;zqdEH&+g=C%kz8MZ!oY6itKizg*v@IA3PD(0eDc)i_3G zeASF!vHjPD#Sh?O;%{M14Hy3g$*N{U~|MO01cOdIiDCSUyPHxzme zOQUKrcNkh3(dqj&G|<^A@) z^OglnL)slr8GUz9^n`!pP7I4yU5xS``J$K@R%-|x@$gd)gqEGLf0P+lswJtSN6i25 z4Y{t>d*yp^=QTL6Xj3_oLXz+tAjCiaZ8i8&l@ZSlgY9{Y_mfSw8T1-nbc-ygFGTrI z2*vzK|NTKa8uB7X6K$nG7eg1+uxJV z>rSr^7~?S4?Q&UT?V2nOUM2Bn5_mj849fL$0<>ZC8GjWrqF?|1(;D(KA#f7n3Nk}h zN>u!jjozEkZ6>8ojca-_pA-2=_f6gy z5t6Cepi8K~XxjO#tZw{H=XsS?CHu?D0b$fy2d)M~mgxS8Ym57)5*(gM=w3EU>NozH*7tvye;Er&DApC5uP ze9pE&oPbmcGp^Q=Qb<9&shid^j~Ycp6fVI@N=)?r8-(`Fm9BD6ZEg@+Q?? zb-Z(K3EsSZ9y||z^Dc^u8?wK{4e;+U$ScW{6^%VINFKMT4{CXE+~n==zfAp{mMHvX zMIC*y2)+}M@P*T-V&dYvp_Om^TkeeN5kHQ3{ptL5)Zd?MS-8tTAGJwOQlh^#mJ%|$^diCm+je~=PjSWX~ax#o3GRz92|K|e5qbd|ZdXNDq8<5E)6 z_0;rq)Quay=XD(?4p;Pu4qg(6rq7b43y*!J48rtlD38wh&YtM+FCCCSR{i>*>g~o# z82_c72C?e(AN72u%d^H!E&lWRg#~iEfg>VnX=XKk8dg?pi@jMYRtGgMdv9W5Otz)k zB3#N_3>h)dq`K`m*0$(p#!d`kK5051h^Jb0@TRKsZ{o;btU{X@4CCKu%QqZtVJTHM z6yKcJg>X3u<)qHnHQF;E#9OnSz>*Ao9Fo8;6P2oN!7;=C-8xz za$0EW?DdR^u$9bPHLfUS{y7Te9IY3D4=z^8QXg&Hu==I%PM?kM!PEKA6Myo%wjLN7 z>MNO&wY~;VExA_dej<3dz3`g$zVDOnRGC>em($~emhazrk9DdXS0>HH)nhasY3sjq zXPE8R6(P!^e10iQO;^8_hM6%rdqZjq@1Lc1nRjuLj~66q^dl&+N$~VUYdjk$(EApl zC=y`x>$85N?}5#+0P7o(f@jegABo z&WIJB8--%XF6fzxe`QH{b;^jRW}7Kw$3c@tLQJdza7s9N{9JWa`TT~kH{`)>$bE_Jxj@*8>)6M#$6}HiucbGg) z`9VKUX4d-KVakFnH+bQ~0@25O981lzYNx~BuCrvd& z^@peBtBag$Zn~ORYI1UoG&|CvP|7rEzh9(%Qxs2i990yhDAO8pk}<%5b;&Y-xYOGlWO%NzsTY2vXw0V5w`L*#%qy&E4w2Oy$eGa0&Y83yFJ(nmzL54 z{4<>&Gh;Fd*Rcq@qfop|EE=|=C#Hi|vpxR)kA(a;XJ%)=^z>Y=tgQUGV6Pr92|9@r z6b9AHiD#H{7yTN(g(f;aBzLjm7omM^@t$>A!}8LIckkEk+tPoYiUoZy3*|UJNJva z#YZ8WOwlItJ61QN4Z~K9e_ZBT&Xf0t*Zuc5#l>Zf74mWNfB!hyrhDSf-Q?m#q(%$x z;qRa8a~oFHg8Dg$8Y2kX|J%2nAAUT#BhYKesC(#*r>i0_E*{-L1;SAlS{ayq*;-Rk zwF!jm>5l)db0=>8G-|F&ZS z6aSAV#OJ*3KSwH+`F~|9|K}~HOjppDQ7AOe>gqczQext%_-RKy-6t%Z1B66s40Gfv z9o*vLYVD)i20iC>!49CIiHeH;`9U`L6KIrDM>4)wmA?L&i=3Uu1Y3AS;YO;_<~&au zMeF8woVDxRsjpqT_S2My;N%o;v43zdIsC~S1n;W<&*I2yqDjYsH-+6j9SSkZuGINo z7Zs(ct*y1OnNVA)r)lRcE48A*m@a2y_*O78h~8{1)4`q2+|FHAEs_^vsUlJ)KQzcT ztD-XUF4o-Q5RV-b6O+fRcbR=Lb}A$hqY zQce4La;+OIw;equ15^|Dz3yMgLdZs46t^$?BKjg^L%(a{7`DzJ&@nZ)bkG&pZ%L zC-t*HPjvtAP%NeE$=>QO$yt=w{JbfA(a_gNqIhTH!jD8z>-Z<_g{EC7WGI1`pC9I8 zUydL8`HRAxJ1Fj5jg^@z22iJs^6Qbw7$71_Wc8ISH6xAJv}{HThv2LEXhN4kEV+5Bw$Xi4u3Cq(i!35&X_mTMFz<7s5%l{;t0TOg6%@G_qrR|0c1 z3e-IcjWTbYdU>6D03acRg4YY~r&((H>fJlCr3=vDMN6;eZRB9srz^bMVXQtUtHl@> zF1)kgrU<#HlKuewiN{KE8+rk?kmHlNj`vPGeHylh+t2Ro4jMS^4w+b{Z*3+Cy;x1M z94UKLVl4lDcCGe^Et*{~l-Im}0$O#O{!Bu5-dhbhK0Y=JEw$L#*m%cfgj0KV>OytL ziI9PT!D;TjiOugH)Qxw_bp0elDD*d`ngkpcuPDR|`eYP{(0*|h76joKgP zsIwy1#^g%84a?5@xcI`-K*6^UyykD-zV&)B?nZjH|4p>edB@D$PyI&P@0P%+I+r~* z;>(vehRq9TIXQ{o^}aR*k!~Dsb!yRe7pwK3ra7*Sh0wU~qr*AvyYl`ebC9U8FtxtE zzUf%??Ww6Lo89Hd)w{#yKg~Y%Q9T=C4)W;FeLPrhO;ct$;tQM9@qrh&CtbeL<8&+0 zEHsiiCA~JCfvJ+loJn?-MhoZY*DSniYS*sa`oul7`Zu)q(FXOa{BaysM<2ua2u|m< z9@C!t9&3{3-vNDyR)&q7@&=aD?!CTP?6mQ;v9zHDAS{fO z$f*AwnEaLD%}$R~6{U^ezkka`+>oD{`0~2D#6CEG7&!f9g|NiY4t8x2BN9`_aH5w~L?^AO3{Vq|a`&;UzF7Io7GGnl= zJxK2=#itf*@xs8(=Hzl#m0dR1eEasOC^~ujv?#4`+_ILO+gRGhrV!L=l?7>E{G=N6 zNb&Z*aA&K7Te2td{ zeXTR;yk@`Hi+$f0?_Q1b&d*vm7mLw~u7~;Ff^QC(wZ_YC-MWS6hfm!Aa)OR#(wWdy zWbo~0xwRgP)b4ukeofo=?>>?#N?(V((2@q0IGwjmp>;iJp5~`wM*#wff&qiIu^Q9i zQl^H+Mx_E>Vf%&dw1##*OZi8S$aa^9UMIRAVxznU2OnuyI}uGzPWI!6Fel9*GE?&( znVDmP9&aFE9Qq9xOnE4fWxC`vJ2NwJdUBMhS6AD$eYmqk2iO`}rS5l8$}~h@+AY3`i^F^I;zdP81(@h_;Dm^^N3)V#K7*wR{Q6b=mS$;7>H}zEd+1l$b%RCR4kzkgohK8pxqk5yh4=oxlewQs zG%~o!s6E=WH9g11iO~uOkZF}!+#AT(K_7HHcCNV= zFX+%9v{ro{4Os**M`56nVNgypMS}b0*1xxUGPI=6O-6rL*{!6 zDgu!Y6QA{@>vtvy`-g-`q)3L~^+YLSnwXft!A(-hPGtky2z}LSwK*bVyYCD5?td;mcym@G&Mh<`d@{G5onfKJmble2)gR) zXLR!;zF}=!e>rQc&Q(zs>nVNcvM=f}ToSa<1 zX+7!bk5xhE?VH6VC0!M^2KQGh?PhU|I}<{{hc@@qV6wKhhPyNGRvj-D)J;Wc=ziIp zZSMk9;qB}D6ch>>p9NVon~vGadM2LiDrhVUL}Qiy(O5c_EE@R}vDUR;Qe`6WdX|fH zRQV@nmzRS>sf8#KBp)t<)}0@#sftQ=wg;{T%;YO2x4^(aLK+%t?j0f`#8xAQ2i}8- zjLcLOG)<(0xXs#_u*BE#91V7QM#gCn1m8Nalln6);R4U+@BlNb7sN<6p^qYGwca8q2@~RhW4PU+poD5_tHQJN~z0I6-Q4<`D zijMXLg?v$0SGOO()gnbIOcDm-D6MYn>@ zM(7xJcv)paP9DiAX`T%#53c2prpw1Z1dVoQZ}hqEGshveKDU_T#r1`Z#K4G%2qX>G zZn6R6{q5)JfN@%9+hg*mHQ#S;ZOy__w*#Dke!V@0J*Fo^kuu?DDHu1zuEh(zkhQZb z4kqIib9Hs4XJ_wh3qPB)_Be`la(0&0){ZkYHl{dT9jh^a_pN`4!{gNTw%v@sp`oEP zyht@9nN3YhT*t(xA|x6KtGiX_@j1kQWk4UKpO}=CF@{a&?ONTL?kNmg3G(yvn{+W> z+DH_z6{Di2Hkxekc^e&lgT~{ys5keq3Y<%0NeG5rDIsAO~4Uya2jS{`y_8~kJY&JS7=<72RDk?MJxG8PuO+Mn%Z_$-I%HS^RV z2i0V@W#OI~VmiAI|23UDMMryUa-gRfxVXAgJI013qR#80P@t+v;IP{|I!1%xm3DQl z2Gpzpmqg<@pcHb<^*`Qy)R&og^BD*x7=)JTFj2PF*4F+@hcDU!sXc)MYUK#a8_;j? z#`axF1MSugLU|uk{Oc|Nf!EX+S_O@T`i)<|eUpldi~Bk-paJTc{�KZ1@?C_UE= zAFw)_;5nqe+?p%aZ@idd$I$z#Lu2T$ObvJ%E*KT|Y_y`t^phqw4$fN*J-0sNi;u4T zwTf_jXXk!`Jv8@YLq(OJy|%EdZ;#s--Jdv*jg3u|r_KU!H0oE?3oZ5uOx3F%#>VNv zl>7>=Zf+7eeX=FS-wk1$PV&G~9;LwhhXx4z?f(z_-CP;3(?gRsGs~8FU-KzQQarUy zkrG&wzdpspeZLz2rDm2X|0n(WKYhyo6|wVQ?$rO`?}W>WOG%}GwkG-U95`LD=wNcv zHJ^g>Y+`F`3*JVB+fmu4`}gkNMN`jxq`$&}I8xb{FsoZo;ujP}Pj~=K$hU!5U@9sq zYJ(IFPalHlb*0IX!a0GzJP-JzVR@(|M;Kr39k&TS$bq4gu+^vl9vMfy={4|i;UFc0 zMd_oWCL&Vv112p6yUM2R52Zyx?p6-AFJAb? z#Ax_zfxw>!SIRRb#ei1=xPY=Gkg&|hCF!3U5l%dt4;CwEV{5CiEsBNH$3u+H>V5@!L%LLa=8SPrvq4^Ud9p~DoO*j`WTp*J_T5lxif;9uRQUC25 zCRlpjNLIaCDlp!!U=6?%T;HoZ`}KwqJS7hIV<*7p6uT$jL8Coje^%|h!+f~6MgTZ; z-d+%~)%;eY905D9N@5`CcZ7vGUH5Hfe*YGAK2J;(uBPf};c&cfTWUUlubFoh0aUBM zs^qO+gIV#x2^;sfAc6yt+#E_B1h1$ML@;=%myl=&ppIk!>ZSwv)QGF;cCs8LPKIwUD=W+Kq#kX1dFZl?jLiOO)f&gkO+(oF z2xqW8nD_ngB_KotK6F4w#{>Jdv0tXwfIY&a5urjXT-Wx>NO_7@xfLP-{u7nI6Yi_4 zU#&e{WC3)U1iw7rmqY4)FdJhx67DY@f{cv?v7o(i<07c}Z@G_4IKVpu127E_M#f`G zc(}U~#BK5eiFLf=w;sFiAqfJX{@}eA$RG0lF1yS3q^12oj(+)q4z7Je<@4W#M(tOr zL|n<>+;O2Lx$LbzDtd}xFBq>sE((V}qLYvaR25p?@g^>SMO$!|Q!_FQ1`G8ix6B$MVgSiswXAD=KwG8knHU?Z zJ0D9LMtHY0SdCWDg7zbUH9>4S;?Qq)aCee@f4(xzi+(*TE6a$DPDn@#O&ZXMKLRkY z&>j~(_33g2Ne_qdxm+pvVRu`7x&T@LZEE^5LzRvSp(4~ zrlFx}G)LolKKCHyCEzzU_S*gAu2KjMNrTo38H0GAK93?`lNki&0EIhPc+YGgpY@M2 zfbdt~>({Tr>um)Ujo}F=KI(BDV!KDm@+Nl%iwx2L{Y!2|mW_bqN{G{Qaw_pqguual zEH1t}UdMikk}vX;`9Pq5%g6hE;u$^UpI}45*{(i2Ju+$r&Oj3GliX)EyT36l*#m0( zCY@yPn}e<2a=?O#7YtCgflG(sIr`Z_PW-`M4+^zbvzGu4Kc()`;bB`&L9Lmi_SxY; z-PiW^_H}0sMlM+`o0B|Cz zl2x-5Ik(|&-gp95sHm!{5ko&i@s5$3m8>idXl8w$JgKm2z9r?dAWPq892V{{F)^8rl(B+k362Jx68_;s zprFIz6NnvQ5fDh#xt|o;&B}u5Rqx%7G6TmDo;g%e;_ABPfs|IT%c7b;vJ4AMC!PPhK4F2?&!R; zsJ40s)XMy?Dt-z+3%D@>IGz{bTz&=f*>M^NEEVw7UphNGIr6}j1~U__G=_tP<%wY8 zPvCul(?pZ400h4q3AI)lM0a_3u4v}@~%ssThR5HZ5vLaE6_-S2EW3y(QXU&5o{X`wG0XMBYzO6u8~=7Un6 zoAsToDM&t$%dm#nC3q9xYTb?s4Zh*udQ^ypNpMXZTymC^xXoay+r%T~*59gMZXpgg zkf@C#Bb4>lOKo7s0Ce40V_9N)0>L&lXPFatwk{yIQn*M-NmK5qAga^fzpHdybpLB# z-=~H>y-GU_IQxKJE{r)Zu|muj6P~m2YrtiiMRexb*;vdBAy9i!fDj_Ia0!XRS90bD zvvcFtpX77>-VbTHx`D@@?MN+XUk@jZh>+0p%^SiLQGR~@(dw6%L53a`K0$|l^tsBa zIRPlon0>snlK|o=gO$%25hoTFHd@FUY^GU%?z4TPYS7ry^76a^puR8Qzz4t|^m$eQy79l%Ib4#4)wZ4MAWzX-s5{x!5)cqb0$1Mm8CZmG#|K-sh{XlY z2-b?7`v$mzqjes2qwnNu)*Eo86%-@^)ty2ak^%UozQq75ud}e=hK7dTSWyzZDJgD< zc<$J+YRGvv^q2=Bh-TG_t_(Y?`RuTS0)}e-?$jRth28b_yYTq%`>Mux84B@YGBVik zgT5fn$?!BF$Dh0I%q<@Q-U$GndkXA~?=$2KSXt%Ei~&qE%2^)F2JZrna04hwydF5; zMy0`Rp#Td=Fsn0%<==B*Al5@NnC#VMMuiE;d;snS4fst@Fxn|Vl>&X=;CqR39o(NO zz^zUTX)zs|yI?&LKNwElb>N5n2x$21$5K*KHo)4@v9aMJ?1I}N4{{mc8@5+}QKGFM zE)^Io_2>DAhBm`?!l`^J5Rr{~{`*H0uoXq_C(fy9Y5E77v#@&^t>3*wkCwSz_b2@- zz-xyD4x6d3*p2cFQlV7XfG@$(oB~%ISd}I?5gfoK_7>;?Fd=^@>LF5lv?>G~ECx8Z z^}t593kzUlVck_$CRSEa36F?SSuHpNbqCag&<%w+|F>_6x7WtUAMQeYAF=gh98b`8 z*T%!Z>z)Q}4>I;j21Soj<^~0UUnd_MPZz#(Q_IS^Mth_kfH z^MkJmYuee_iHpyGjBo))1J~p-9E&WVU4h7uLVWkqU{D4!fJvJA`qTi!0DgZA(TpcI z&)lf$Aao%JZZ0t+BjdB-(k$Rh`!cCKb9JiJL7B6{BOv%8(RDMR>kc5f&6xpBsE{)3Ii!u;HtZ0L7!igm>Q|;ws6XM@keB5I_XZqwVwO5u66iEe$@nAy%5~sA3TEMw9*h z{T4)x8-aw8BG2mJ5VgK;GxXUZr_}_!Nq?|uXm0?#5OLN%tMP9Em_BpxJCPLh-p2Ym zghG6!)zzavj>b_gUnUCY0{FkPQa&zgYMLn^BqRq4*63(=<=ovtAHgE5L;wH) literal 0 HcmV?d00001 diff --git a/_images/ca8e38a39fba588d9c549b9d9bea8bfde335652b9009e02581d8c2473ca15dbd.png b/_images/ca8e38a39fba588d9c549b9d9bea8bfde335652b9009e02581d8c2473ca15dbd.png new file mode 100644 index 0000000000000000000000000000000000000000..f6791803a65cab9b4a02ba88d760218ef5860e4b GIT binary patch literal 15336 zcmdse2UL`2+V;3cj3Sb(QA1G@MNvRds&q>%fPkTQRHQeB5u|I3kf?wTO*)96NEHNR zsA@!EXwqRQDh$nIf`62V$4rR^9`;0^vz~SYowLLXt{y~J zr*q$U+j)99dAQ3;DM}rX{Kk<;yx^rQE$#OE7o{O+*fYG$^N(7)CO+1s3ajoIdnmlX;1}@@Pu(H) zHIH@vWN)lj{-yk44Hr{?_ffS4^Xz+u;?aMC+aY|Fsd;oO{vU#nP?`|_`_-v2d}zX& zP2^SZ&!J~Oy5oB~gvgsEjE8$1QgkLea}B=#{!I&wAm^8%zc;^X(Yd6@ zK0(guy@y7vz?E|X4#svltM+uphsz%+UHJH+s5#T%fi9mFPt9?MfePOqZ>pOR`4*2u z;wn?cTe2MZuBqaHN6wez%81Tf3o+kGp`J{g1fD#e)Q=WV9KTn-G-1eQk+Wo*2wzbS z7Kz*P9eUhJh_S~je44nI$0WU_b#;4kIw}lV?UB8=%KVSJ_9V|P6 zkb35bBL4e7{kTz(Odt&Ci%095n3%Ym7#U^UyLWGu1kCsuH@Jer7nd$w5>UN-p-k=U z^B)aOOcc)?gRAB2i;cxa`G-^f+lmy}^I73t5?+c&Yh`6;OO$3mdZZ7te`T|q->=^h ziX)TB8@8)H?MvQ&_Q<2`Y@vdCIuR6YT-ks3A^*K8`t7ex(|P;z$PvxbR`0k!lK5xNg_lRW1-O!7-OV{g*ErpiV~Hl=*}&b#m8 ztAK^_!Nzcvsk`NjDt|STjW9}0=6$oHTMIH{B+W>0o#6^*Q+8=U&6QPgus;Rqwl8mN z_JzTUUT(3da7lT2bF0J0_b+Ml_m17{%sWFc|>#M$^>=XC38YV@o^+)pZgStn7@b%w~=1`VXZ(qtojB%C0q_Y?xn}Y z2}j9)0FwXFk8@zN5dtm)yaJ5Syp4>E#Kpx03wFRQbw63xe`e=t*eB1ft&OE_+I{LI zd}p=sXYg5q!oLPDe-H5g=2^IR1Eb&5&x!)D@hpGzNPP3=&3vX`5>AU^&5*nDe_+rK zLh4B=={0au|0%@%kATrXCrF5yIj8zkJ+c$NbOy+x@@7fO6`8R6pd&E%Zm(d0MejR;`70l-a3%6@Jy}n)3PF+}y^;ueK++_meX; zBX=t?8)Um|(g=sH?TOa%r5AVA=j&&+x9nO)C|Kugm`0_S4kpVstIBS*yl~jQDKRc^ zzDI2BHsa}#rlj&-_RX!*O)(bkalBGbzX6n;^vl~qaQytYlEgAHF@b0M%Sg0Xo8+{k zp(DeQdb$f!{jz?uW5kK}y(Tt+bIwaXQ+-Mri*F)4pRW_UpP6Z|X2J{8Qxn;b++$Zx?9O)>(+LMZa`l!8Fw9hgC z_TuX6>z~iBC3pZ+Ib$W;NpSsIP-iSgbm#F~uWx^QwD8q-|NA!8L1pv3F0wF9iJtX) zH3t@NHvI-;c@>R0W&x%1H^j?J>@$DzTB`g*>lerq0ee#aNgSIi>2WLV zueWKwdthg2pQm=oV9=5(Y>>$!A`YbRZJ0PwoAmBQsKck}ciC2o-XFS+Vtn`GXM{z`lel|A>%Pr}~aXPsK_Yf4ppdjEqqUi?a} zUG9<6k3+9U$&+~$py^5Wjq$X*w&8ViT{bB0>D zJJgRbl*(+&Q7=a2!fwr`gG0A0VXyaGe6`IB5Mx-|W>G(F4VUrS78&bFuM8|ynXngs z+8O>DW-tK&Bh#fTkJUG-Zen^72WpX7^_NrJNt!N|=!1lv-EH zh3D&Bj5gqJT{qyhhi=JE6a$#Xci9B1;rX&paQl_%pNT9n=GQkZICHqN%O$@uM#*dB zJlrLcC39jJj?nTj$7rlXS!L>}ZPh|0BUgqOsUAT={yI>CRzMWhr4L2Nrv8k-6ig~6 zKcIR9F%$ggPzHN@dzXjBH0rGV=|(3(>A_&8c;~F<;416#d-rx&xR=?f&t>V^wLkp+ zSUc81LRhD{l{NX?%Nu_AG)k-kKq1nk!nsb1tyZ>bCRUe>$i#6{%sz`uAI5<1+pB8_ zia06k*#}r7fH*g7RQYh*C9ljrsdFt3=OW*ho7NY!M8{I$)nQ~}2T)Vr95i1fOl&#q zJCXIEu-}`S+sc70>9A4#)7uR~3X+P00bbkv#?pZ7sNyYd)L5S8Irt~S6&(wPSFJp8 zozSftcG~*PlKG&m12OtVTlkEdL(bLOiOCVy5l;Ukzp?-Lt*;-~s#Gq|P`V!7mG5D+ zq#gB|sS^{^JRY$<2?`f&B0In7^_`t2wINP+ve=>F?j4wt2NtF7nxtyZ3d5=QiId;Ll$GmsPBUqjwv27z06{olsDr4QiFR6?R=rQnfPUOE3FJeNt?w%`?irf|7G3vu4FhhsH>Cxr^*68 z*otJAF0)2cnzQq(%uk+Wv{wZ#$+`6sqs`o!p@fPT^%zfdKOS;kQgrE*r1utE&=sPi zqY(fy9SN7${yMMr{f|tm(m@B(kB4c&X^s>H45BEF zCPu#4sFKt2ClhD5aaiRf&=12cNBQ?jr^wi8@$hf2=yhA1n~VkZRX$Ky7b)ftp*C+y zU+KeLufPLjI1ZFYu*cDdXB*pt?N$qU>FWkmqD>X;l{$uP)~dyx0Bln;LjLs%ufB zCmcIkAw`PPrl*kvu#jg}p2uvT9ZQdB(w44_(v~U{EiNt|f0KR!^kuSZ^~$^^3814P zNu{)}T02$>e(CSoY34wCpt0hFF3R$!R~k+4LPe@{ClS`qw3QgAQMKTa$2+o3zAw(X z^EX9)jxnf#B}h3BjV%J(CG2P^%&w&Et8kEaZUDD#KK+-vLZ!HnDVPlh*xA`x|08Gn zHA@47qNeqT%~xvO;m;1(*5=H+;3%n8yIqRj2zgZ80Z^^%V|P`vqu~)8W`^soZ{`-T zunw5|Yis$No?aXRic>M8wKzK-`F;9vyXm*j$`&WGdG~#RBMBxHukYUuO9L*Dx+Y15 zQ6US`tD%}bmyb$MgZ(k$kKeQoUYUF1-mk1F<2y6_mm;?~PfrM5nY6jS<#QY|tPw74 zRqoxG55H_kP?V0`ty6zx)oPL`Y}f$64Fkuk_`Q~H0C(><$F|bo+Pb4nNo^1GQf4Q+ zr0Xn-2Ec`ZAwtscqO?yAx)BA!23--zN1Nh{o49|RHXrnZ7EH()1*9^Bj+)o>NVQb2zATYI%xLI35WN;{Cs^g7vjU-nD@82!L-N8Iq8=Gn)^|F zz@J#2wBd-~=h8dJoKELV3o9$DN&$BX^cCP}F1H**nS96=_fFN)RKmrf+Gr|lT(igL zjsg8F{7}td|Md!1){B72`&X705TA?A*O3wd8hioMnSha_X`$Q2|H2#8r%#n;Xvf+E z%Vafr1VyR`E)IkHatf{w#i^hnQArj>d)Kif?g}^typ3~jOi*mzq^Ekvo8C(Un4Bj+ z$^k^ct8tM)2-oGrx z`u^n0?|=RP&-QD%nCk>LB;eB{93=Hii|iIUd?Z2Ue!bf-4m_@;HreGm_`ZM$Lr2Of{CgG|xuYrZQ21fTjG&C}@d;a6P zxn5kffP-N>hzV-`-n(@Z?>e<*(jrrq&y$AXn%dPtD>4pEiNwiHw_+-vfQ+c-4ETXL z`T9ahH*VZOXO+lh+d|RDza*;xWM^e%?OhA}t<+->l;in{_AG`VIXCQ&p4@t>BH?QX zf@#++d4f>L@r!z;Eas>~c5aEe9n09lEgF8>x@f)gM7ZRjVqRwEZWNzH4#BneuR`@& zi2GKXfQwL)2bl7Ki?gC0Ry-`-41K3!ipDLl)HHavN=a6aG$xpgu~&6J5pd|Nj9!qi zVtH2UpE0n_S=GH?Zj6>;v((!)b6q;%o!Sx+lr~J7%}k$Hb8g|(^{v(?!5Lhin^jmS z110zR)Fy(lOjC~kl*xM63GRLK@b&kWdZf(Lsns4ZON(~k_xS>VAkc}{?Tk<*UFY3u z51yE1mmR`<&Dz#IQ{$bL{tE#kvMF^1XJ5#;^*%Y3ps?>n2p{F!P+@(>x9b6-y@KYl zZKA2t)ytl+%+U{${lqnK2|&SSPcPMQEjX(Djh20ywGkpI$!h-g;DZY6KXb@$ZXz1> z&)g!gIx(Dp(qLx7;Q7*iZ;AsraiVowtER7i0Onsd67SOKLdT_dH5Z(nY2`n6b6?5p zM#aJ9obF!iS;uGfoabF}mo??>v(gyqg3aQZg+H$` z&VJMI>#43i@~@>De`9j}i$NY^(oq-KL4+Rae47F{cnb1cwVveDVgu znQa$+_u>@&t9i}8cW?g-{+b(mOuqx#^E*TwdhsRs+3{3RRvjH35GKL2Omm)Ae1-G# zd$jZS0j!G+Y^zbR>SNgKhaOoN7`T=jnpzlXSp^?MeO{pHESE+_56-N}-VR_a?%>69 zcmeTz z4`hS|A~8<14A8SR-=-RmML+qHg#tgnX6aN#NCUWO0|SkHa35ZeBzRP9e8gojrTBg3Xmc~bS8@GJZb`2={A80J zSiu@eP-0XtJwH5t1+kg}qt^qH3A2jJ7t1Og-`wN3-cP9D{aqflq{{ZAsbFYmSTX7X zoKBMjAZt`29j%pZ#n#0+#3P(z`gjNE)eispZAzZ!S!em+*0>5}n=m0KJ47Dh02~@ftQHb!F`}a>!9iQ3EKP%e zJ}{1M+exTM`opjkZHNW_3E+c7!kEXM1Dx7(FmNFQSR-S}`d9s8$-G9m!e4W6bq(2` zi^ezB?!{y%h1KAIG@#n#i6*!T8?#ldSj5I* zxoB?$9`m`}anqede-TW`N%t`Ww_I0KOY1PP(SiRQjeYYL=)`2m#gS?#=aaQdVe*@I z1^>xUPr^9oHOQp|NW&jMsGLSI6NAe{=_V=LL4FSG2>$kk`3&Rf#qO`z+q5(Etpn#e zK#VFlwf2snUfv5VE?0F~et7 z>-Q#8M@Zy(*p=W{VJ~Xg49AgY!ERbZr;c`aYiPTwCT72sX=-=KG4l!Vc9MjjEFvG* zrUc;>{~=orGc-0H0iV-YTtY&K{FVA=oN%GFobb~dQg#XQj*83as+Y@cY3c}}9p>)m z)B9z)eEbh1K;`=l#uWs_l8E~199A>%I=Lf|fZ4ETVlh{}57=L1-{^c>IoWq4jwdkq z3>`TWM7dI>65`@BAd1M(Ca&_I1C>pQb4c!Kv6Is}QmXU1J<9;&0VF-XUFztc$1AwS z8|W(8sTtRX%0u>==-unob2fCFKDhok%`K^i%N7Rwg~)Q9HRaa{3xmPIK=fwnCGv9? z;t3Bupf(7oc)JEIQ3o?iN)*)mW*x^!XCmj9na!gTHY_b*IOQ-WnE-bJ2QQr29`xy* zs8G;DBMXa!Ya5jxrcqL_auIg&ex$DR*q`^FgxKlI+-R+kM}yOYq?pP4YIT%^s8V~? z!6IaA; z6MrVOjH#{6w)OZ;<`%&?3dFsKNH6#`R1wH^6#JvLd3S${1aYczVW0ve7N1qG;CT0A z+oQ3ERzJ$k-4`5)`TM<_QuT>SUiB37VrQd#PHTpC8>Gr}#v(5dw7G5i9->IFP5QSxbj4;B4T8aLo4NZ4ghx;(s z4F9Q|#9UP{)sdb$bv!ac^OlCr7+wP_190WK1FwW5(G#iY|K;aRMX*I2n2m~h<=%9x z5yRsTQi7TnlObvr4w!xh#=0(M`{!TZt^i>^`C(}lA05P09asW)anK&fnTsHF2fD7V z1i=*(0&|5V6(3!aFgUcdX4e_x%j%b>-@>DP3#5njiPfb@s+$?NE2iU^+6zC{J1l}~^4%EF*Vf?J6h z*XhWqjLy_eiU(6v5hoA3A{NIVJjlzNS{<~|{RI(LbNNwLmL`;mvK3$)_VC2b+kE=& zz@1B@Ee)hS5t|Q0pw3Rx9cIVg(3t)ZPB`@x(!@>b5`z}U#|Nu}Ny8bj*7Z1n5M?Rg z^;nf%G^LjD3tEIxResz(>l}=is<%^W8ghElPD?g>Ul$0y6^ZA^l;g=)jj` zw;k@VNkA3u142dl_(~)N{Hyzh;vJJ-Lt%;|MJX$;K%G37KfW3b5ps7-bv83VL4*3z zKu=8`7+`h|oSnw5;O_}*$m5v_t>AoeHzjP0nU4G*`4iiH&(DJxL#~IXIAntW2{|Cb zGwhKB-1B;BbB0pnv3twGoIwtrf+4E~4mcySU|81y&?kwd1jslfT`~>_k8XKfFW5Zd zL|w?#35~_?rOJj$2*MDrG}=a}mDG+lz1(V(m;w$Yusml_j@Sqy4)E5rue6UexH9Dv z2li-Q#Z0{?P8fDmpf8F?SI((bG<3V4qcIS(-d<-Hm$vF)PQdbz2s%2l7vKM)y3psP zH1ROKb9~`5L4C-9L1HigFqOe3x3*h>>&J*fTgxeyYyvm1#JB)b@kdR56l*}0IVN@=5HGl)6RhPx*p+S2kg^Q8*NAr0Ukr%t zaJMrGqlsR+Q{X2rPncBK57$L{L7$LA?Ja@10hc<`uCm!yZ6=U1rE2gcna#oMwq1`I znZN8l3(zPTwq1=3t#n!dnfr=82CL+NqRuQrS`mw#X^V6D!B+5T##ZagY3+cDt>|g; zWW~xeU+;{FB1NHZ5+Ze-|J^T}^d#j0L%{biVst?CuTN_IVGXY!`8&rizTI#!}0FGs# zg-Y}0uvfGl5-%RF48&eG;`^V)V!#J*YD!ef5Y2$P5Z%?BtX3X%pZIdKNlr;sbjl=D z$*Kx=omh(GuDy-$87>mE7wrv%t&z7-1zi<~vLKJjP#m~C6c`c7*LC-Rtrm7v&DS%} z(ijYhK)_9LI;_uJOl6=eE+V-2m9trTfsguz%bH;;8bJSGRh$F#FvwyfMPT=I6noJ1C6q{>ydU{pu%Qk52qr4g5)F~! z6|{s(UvH!6)++~bj5bUPg#vwKw?%0JpJ5gP@oa=ElAlfMB1#Pa)(KyJ zdzEIk32KReqI=(xb28(BZiI&ZoT(m z`)khRjedlN57bf}*|t#7r5L;l#-SoXmy@CWWO+ofx(>5AJX;I>J~nhWqA}nM1wxJl zEAO6Ncy1AQN~>3L2Sz6WHY98|nu|wcz7mvb=3eB`tP2DW8!Mt>>Cj>4LR?c%4YqcL zrZP>oN>w9tKLDf3T`&^?I0me{gRK@WmLg%VsLg5em%tgPv6mRl$el$6G=c&U#h$oP z73wTx2dkJ#jQb|})=ph{R&W!t5HQR*yoHM01oDcg?BhHGCbtWxZ|a1>J7n23PkoisaOsAzG+QEr7J(06$#%oo!7NwtG# zp(ij-$;)10t_IC){6%Ufe#Gw8S=Rv9Pn*> z3ybeZF=oHPA+S3DBO(lv$Gh@v%uC&qC{pD)1G_ePl+JioVquu8eJzTa+|2&&*8#VI zYK=}#G>w@2!$WbK zDX*^GeivH?pm=#!&E9 zRD3*+re|`+ZBdlicaln ze%0I1ek%JSlwVrfKR-?#5*BSxLf8~E!xwT^Cnwu$6uA#9Ium$_Bs3N1k%phG6$xl| z^Ml^U#|`GO7ZM(nHd>J_>NCg#jaMxZqK=~^VX zm%yZLKDG|$2VSpW*@hz|16&tOdsrX{-Im zJjyNvMUx5K8Hkj2E7TNZ6$Y87xV)>U$Uzb0&*U*U?y~NEPf-_Hk%@Is+?m+ILZdJT zvMx<_D#2oS;pu{t*5l8pECNsLRFbkhV5Ab%*)HanEvB%34?u(jh322pl-~9d_@~K>0Z4J$-+}?wnzzTH$vaBu!z!(K! zoE|W$GFbQJ3E?}4hh3MTNeGFYEa*POU8tSDBh4u`8~YJ)VQCY+nv`%Fv0y=KVyd-n z0eGGnP-n|PTw^2f@Xun&ui>zegR0hr-S#KD4ZA;m7b1M2W>xlQ?ttU{=*J>90o@%F zze=rF$Bo6r%p}6oj2D-9C_*C{6%K~2Z9t!~>YaG=C4_2A;+tav;A)b67|FmnM}Rg4b){}JHs3m`3sJKt96LK;j+p6Vd~505 zW5+0i+UXhzx3q^6E8Pq=5U-nfu~q=GC^I^T`m8pUOjbr}?*;VbJe+xMyD|_eoOrl{ zAkuIyWeoce(SCruM4N>4&52s#h7ttnH!@|Uu=VT+NYN@_NBOSIb(xf1r{JDSggjK~ z-H)4Ef%tb1GOD8jk8%Wl$ z41=VX2baa+sPJ}z5LbHPaB#?50gUnJz>6c=Bp`0Pp$AJ3IC>GYNo5q8ki|U~GNc3d zR{G7=Lq~3;C{$`4css)jh?M;6i-dxA@P`zX`as~bm4W!tu83l65Ppq zaUy7z1B2W?&5S({B0C2SQNC9A!10}G`^$)|d$9~&0SzyKhL zqI5$x0n;O`=?R;A1QdDB)`nFDPUQfIZ2;P6H~`NH00fBC&wA2Z1#`xQe^G2bTa?_? z`SXp|@+A79Yds#-D}@){JqIqyv|8f6RAExR*nw7%CJ6+$0y=zP1wGXo1U3Hz-p9Z> zXhSACgS{g8)oed^AoB0||8(W}J1Dez_~Hz}H@@MJUy9X*0_oF=uvfzYnjMF!!HERv zB?bpxhlHI=c<9!UE@X&Tj+3^(mGZ;Y;n`D?#rhz8irBtqi&ly=qpi9mXSq zz3GpPve8vQK8H-ZDM>QA7M{|tb;B>S07Yo zO%m2VY|8?sbQes7)IJ7~`)1t2*l);fp@eqDcHX|!$A5;C3cUQ$@*MgBXyrj;n1LtA zJbMhO(Y}Lzd3!)vO_Fi0dF5mk&X6dppAi&`ngF^{jE(cIO{{X1M57tsjsbNZr|Rp8 z=8ffrXKQF;ui(&L!f>#xUO^%kSUJDi>}mWqZi2~TZ-RpBZ!*J};d2{0%g_&Es3nO4 zb*jf#li*k$K#~G0%>)W!2h9u4KNY6!hYsVg8ekU4_HGHvi$f=EBlaNy$d_2BVEP1D zY(<~{14-oykZjmElS5w+y01Vh&3Wu3#rlpy2%BmVity1`)tm)Ro{Tq^m5`WPTu zqtGj5fZ6~Gj@;Wjgn~5F5?8a@Frip%2*FFJ+?=HXY(z#rK0Z!(x2<_ z;=5(oDgxQ38L(G$r6CQbp#`4gGY|*-PlEeCM6H0s{a_9oQv<=p7J}m;LA;K{MP$oL94&Pqf6P)B^|F_yn&;zsHJyAr#@}k}~DW@Yl2bf5pA5R}0|1E|4Z-O33Bmd1;F;_VKmhy%< Tg~C5zdZc+w_q)6!XMXrE-wozq literal 0 HcmV?d00001 diff --git a/_images/fc6c9263b60697d1ffe4675baf4701866c2026c7b55b054e58d5e8ff7dc1cdda.png b/_images/fc6c9263b60697d1ffe4675baf4701866c2026c7b55b054e58d5e8ff7dc1cdda.png new file mode 100644 index 0000000000000000000000000000000000000000..8a3d8a165a65e78713cc06fb43a63210c849dbe9 GIT binary patch literal 29738 zcmYg%1yogC7cGc%gLJoagERsnAt4>o-5}j{2}uE^Q@Sr8ofib88!io^lt_qlz0LQJ z_r`OKqYgN+&)I9QHRoJ&e^6I_jg3K$fq;O3t*9WUfq;P63=Sw73it_IGP@@DN5tcm zo`PXF%&E|>Q< z+{+kfQQ#)%t_u3@2ne`lj|XCzRH+>Tf_s{x+)FK=>_4lXKAHUN1B%zgzh>U!HacH!%vNE~n(ZrPi?!j-HIdHl&F_bCD zj)OC`FqW*6W*z2e?~=;Hwg)&q|L2m9*T~I{pW_zuQC_ z3z@IKIY!5!mP%Bvx16og%6!!jJ!MhMNc1?|(U0sTZ!*jq64wJSpDv$MA#XHP7zbz^ z6q4q#cQQrb$v|cVcBhce4;$@x*KnK2iH6fXyJjEX7jko^d%4GQd=^om4MUL_*&=tO z6%?j};F&qEM|K6$X|w4;dz{0ui?+cRd!prTxigN3hY(8=jvc9@&jy#ae|$UwF&D(B zJ>S{Lc3pB!YAqRt^4~`kP>HuuPC?n8NW9oqP)4OtXea_7FbAi}C5l~_Ryoz@&eq{6 zg~Awk)8r-=wb{Ic1oalzj-aoNVc3t8LBH1)WHcsC&=+hJ4!N+q$ySnlhUO&{iVGFr;^%XyUB*hOf1S-*< z-wVg56H_P@Wy3~VFckY|uxi8omfdf<@tkqu?u0VYMc+(A*t9na6Z2n&<5C~)kTi_Q zd%p_+UkOB`-1LtFtQ6*QHeIZ59&kgg!9{wwbS9WcdOM#}c|qAoNZAE%c0`d3^4S~C ziat+KL2RoC4E&2lHh51$uhUwG?=wz=*_N25v^0|CW|wBqItX5`WE>6dYI|SvdZuTl zac$d;?6(k1e<^>jZ{v(jSVGW3*O6QaG0l=Uo3rJb*`l5?721{4`7e>Uj5}<9e*t6a z^*SJZla2OpL-TLn>U=MrqOZ;KBWKp$`N87c`Nd%CqA0v)JnBi2N>-{%_g(6Ej@aO? z4;cn6MB)7tB<6bs48r&S_LiSSUyi3pGqU zJb2vPwa*hVF)-+#KmUFyNlQyB8N&j>Lm$k4c|0M-<6)Z}hOPBcyN2@Y9-?l6X(2q8 zjIcA2$6_`7simc5veVyN$Pq3L3qvjx5BmFasHMG~!h~8#FF*~A&T)HR8-)9F9jt+b z#{Jh!MW#q%4yjl;INRG}S>pcZac9358eBJJaHz#et$JSRxSt*$f3#Kv6H@2RZ8>Z? zNA|GA4j;8k{aWejZ*sOjHZZK_V6}aki~c)krj$b zcXwt(1m81e>KAaaAlqXi+FR~TSKWvN*3eL@h~+P@VjYd1f?Nj&tKs}8N=1KuBBMpm1J}V7(g#p*Y@1(w01p9O0NT>)r zyJw%+bTicmY@wk!-p}!6&`#$2qCPTXa~M1&&o? zB>Hc)6ND>$o_)i}s-bt7$!#KYcMWx+IaY-RE>6AAGh@;K%LC0H1lvk$VNjDLw9~Jc zn18jMBxGTn>-KCl(F^~7YZ_{TOD)|OX+J#}e2=KBJMvDL1AcyhxQ5-Nn%Wv9{_!!n z`#&Sc__N%1cViC|R7NrEi-h_M9}B^)9~3eUc?|=Z3LYmH0)4$6h$`mQvy&%#w3gbs z{d>Wl6jo!jU)_KiJa!$*Cvm_Imwx;j2NN0mJeJW0T23eCBOW)wcLsBCuR|@D9HUEz zdCK8{Ii|yfCPZ|QKcT{bH!=!nx93?_ZFf;~@HG^}k16~+HbH6`wQU1;OG5M$Aj9>p zRp}Rol^Ha)n6N)b%M^I4l3iwc2Q$~+={Ne6_lnSx?I{5AuB3}7%e%60Un?hqJRRB(Ku%OBa{S8VU%hTbIPWV$MPrgKzg z?}&7*2Zf?%2`FRG$;sr~RScYi#AN<-wwj{-Hw4l{V$!qJDD4wpEk}SM^caZI5VIwL zxvV;QY*H~HuLU&BCP9Z;!%k!5<({ro_R^gHg4GZcTf389Lfo_;WAgV{x4Z({SEovp zz*8YOb6^%@IQGvBFqiP$@bJu@%X24%ytUg?SsAc`^%|8r=YESw*^$%W-Y@kcH`4sK zgbdMfBEwPVCK3`pvZq}w@Q~&Q`s=k4gy5k{?69bAZNct1bydX}ALq1bx?4)C#*s40 zx(RA(SoZ{suRK7U(e8iM2;#(Hf+Ka0_9|AU+F(1+g{N%o^GF&J8=UJ+dhehts$NsJ z*ME5B$8uhSQkcL?(GkA4JF51eHLVp1eTQ3 zU}9yze5nfm!^))k+||!ClaOYZkk^xbd7StYrjE`zyf!xNSs6s_in5EfwQZ6?M7NQlY9O4W}})1vYAzS2kxlrCYsaZqKOrdk^R@ulhG% zguje0zS(8iox@~S)v{_JoHhg~tg67-hzRY^vTVI2uh{Dut&g|=2#=cvgx+(!8Vn$G zav3Z;w;Cd7NE8%NoiUwZzTBp0@xg!hZk95=FS2|N^B^J>a(Q#(5VC!AB$sQO&%#L03rQ3Sff{rD(rS54bYdx%V+2_imw@;{_-U5feQ$l~Y@DElYSC*>JsU z6Bd~e%OJ05d0R$$<`uV}obM+iWSjql!%%3(qTVJdr-5tAN8jS|C*j-ysWlw>l*Dm6 z8Q=*tGjt@Bo&+lOTIx3$1P$&2Dom5vA|V(}$_(;vEpKf%`Z}qkJoccuALA)1KVtfG z?Udi7@h!`5O$KM|Gir2oRERLsKh80}uGHPr_2AlhYr`&a>r(?e@_@TW(L@l-j*Jm3 zalF(7GiwP`=k3yiPH<7(51DTY>k9QeLM)tWQ&(6_y8T|W8y9}GMOlCaSPdXFkd}t5g z*e5O{azKmttVb34BC%Wj&MK;7nIH=LF~r)d+rrL}Y!Rt|j*@cb(ZvQtBFD6-g#62Y86+pVK2`zWjv=%E zXokalSw8};c~9q!WPM<9BVo46Tk5EJ&doImpG%(Yi&Rp*K+~|#;y%aHNMFNIaR)9b z$#$3_(C7_Ql}e3QGbmU-E+6#-WMl=MHl$NRtw~T4qh0kZx8|pdHEX?_eSPF4$2rA> z8_$N8AuhIS?`E7=pvT-jLZeqW&C52<_xJY*07455TZDy$pX6+LH$}Gcpu)u zIp}XLDw(^|zmnOLW05_XDWwoLA{Wn=(PTl`Sgu2|pHuRfm>tcG?pvBuaJbyBto@oT;%?t8$-_DNd!be^I5>Fg z_k6YCGzTjrx-*_)E?Zl_!G7t_GL599fYwYTj-Z{|J0pj(R65z8GiLoi8-pLj7P>u^ zXUK)L#JVnf4ZL$q8TYM+);j$gd{3=$s6^!e4=I$G@9#$@AxZOL%fO?5+a5$^&=~fN zb>m{4LG|iHQY9Rp;SGw1+7Lg)OpIl)oHQt?%xl#5)b!HT=l&gMwS>J^c1;!>uF_(c zDSx!qxd=4_kesq-W@cvo6@d&R)5A@ky)lP2Mhr>fy`D;V62ly2yo<`1M7r6-H`ndt z-59eLlwV7|CtLd&>&Z-$HAY`3AXK_ugw7KF=a&63M2<^e(LwAVuC}9ww6}}Z)YJ%Z z z`+42$#wPDJzB%B2U0o}^H2~pWN&S`%*q5-Nu-K3(ot$}r-DEYz!7{4d%s|zm%Yj~s z>GHA%t?YciB%IQ>PrZaE#;rp!E#qgN5mGYKkSTxK>VJ8m5}goKWo?q1Vr8k7>YxL? zGe3vmQ^c<;BsZq)JxpHKj!}c*Gqxh{H@@1w9o79ue9nKD3JQ0Y)yS-S!^54LO5|<9 z&h^$nIcv+B=;VPzMRm@2;p@69*LeK$;GZ3u=;GAbk>fd2Ia0WDm4n;nim8w4#SbIh zTA5N+z&RY3&o^->wgxMA99@j|789GYtmWI&cW>%#blnxU$1wHz$;)1tleJNlo1U0o z-k$2MDNHjzBA=t-G_^C2+nidM=X zPp-G3i@Zlj_~_ke@P*lZyJc0GfO#w#a63HGGSQ@65Bu%=7@|U{plQa&*)c<1|8u5? zZ2gs1!iL0~lh<>I4ewipcVEYcthCN~80X8VhHKC1ww3X!v~VGXsBC zSFZYREqDl-1Y=A14;(NwSRpyDsLwSirqJqg3Eq5wqS>WBlVNnCqqE-}CuGT2NW#5{ z$>w{d;(s>3Dc@<^dFJo@@T?&blOoROWL&B2p*T~rGSEMrpT)Y;CzkXPaWfiaqO7)8 zw$e*IPzGJIUY|J5l>GPUSD++B!lNHf1|y98RAN0}1Wb`@6o(P9&t`w_{qi_m#f5ZU zs4e~{R-{VZa0>yG59>jd^OpX<6H!dwS}Bi)W77uuea_39cR-lvhQPw-T#?M&Kmm#_ z8X9~ZuQrl`+T+IR0+E6hR%qf)J{ppXI+i&Ll|5j6J`>VT+tdIuB<>E2(#&OP;US21CdvDpylkyl3iX&rL z8=D2xA!qwUcXbWcun@$7X3SHM(%)U_sD>!i(3n&$E2#yl)1(ob9Q&+iIxIlylAAuc zSXci7+5h`M*I*<2a6iy2_3J=>A$Yt0vQX)L%*>}Ewi|tAO)f6U4_!VE~Afy{3ZiD@afZ!f&w<&bI+Uh zDKZ7!pGd09KGS)mV+t!mjv+vyGm|AUev3VQfAV^FX7JLh;5AFz>N~u`#LIMk$9#N| zR>i~j8ADTANaoO%aws_frE)*!J4Ar8uy;{tPm?XSP_*-tJ~6?gv+-m4Q!REKX!Ljs z-Zzu#Qgx5Tjr8gz&MGC*PXNJ{_C@h>Rm|60L6Bc#qr7lMQ1!^R;afg2PsH(PZH&f6 z%^L9OGDCJkn`Y|U$@=rmNK!70GxU)JF-nf4RWdoOWpJ^_!SbzFl~Pc2w68w!DR=Q5 z8XipLsx}AFS*yf|zVjU`^tf_u_sRad<)B}6Nq_hlogYTd|5ONDW(wf@y{a)RWnQBZ zBQ}uCn^1!!dK)HpQsp>vZp*?_bE9_AkZA`i%8R;#o&<&l5O<;D>pn*5x2@f`0D@PW z-`yOkL-XAhHei!gWF3CUMi~^(Ug8pKnYsS4%VbxDdMP9pEmyiSII9U~8%vsJ! zK)I(*4^g1xGS;$KT(ma@F8_k!*v(&^3PUhzlQvAPPfr*yHT>KtQhijzSa0h7UW2Ul%aj3*s18HC zWXQEF=H^amBre*`l>$fmcF8zT2W{5$b-yGC@E=Oj0Fi5*=CTXu~Az1okY5) z*GL^5>IDj|p239t1mei5%jXb+E)m7WO_zn9zk4BhD5sb7IQ8dNv(Foa3jes0*A2exi~n6d7~JJ86Sf%WNn-dYSov#&%TWd>SxHbT$tpj8 zj(2FfwV4Neq^3v4dULKskwQ*xlUmT~iIw#2Actr+cvp>k#PyaEtS8hyTorXF#qg9% zPCVsN>4f|qB`Y%|k&!VSAr5mV*@((;-Nn1S?)}U@$>Sr)Zfj9h=WF9E*Wm9k_2shJ z-*bUs^o0&?u4DujnbwiAemLiU-516;x;4_(2DaSBuA5nN9#lrLTa7BXb_3+8-Gq)5 ze8TyAot|$|3?+mbxLT0g@Q^qROXU4x!Ezfr9GY{ZCV z?frajp_V3=nDdDiRJ%g!0|ArL#^xq06bY5XNRm!YgMjq=#!K?COc-OIy7j3iM3%fE zSNp2~%JTM`voBaes8}yLB%BHQM4C3l{m`2jVF#{;&CXN+BqEuPq;g(fU%&q~Omuzr zdoUWGfx}puPLAN@2Xdm)>_+g6KPl2)VEnOlV@k3i-C~Qtct<|5 z5~gt(^_ucLs@4^{wKzQ^Y1~sIa*i@+?hz2_N}b*FLoA!y7|xAE%6fwKV&UZDT#U=D4cGO4J9*SismGcyZBR5Nd;vm zN?w2efecWOIZ${o97q4!QfzqRph&6I zE|YjU#iTn(rQY&fa}^M_p4C+-n!9|6X)3xTRFIHgPEG%UY=Nv zq{e?@)qKA-fQ=xZKxs!wPYc1D67`7Qpp8HOvq>WQ8m(z!(VoCjjBl0?Ww|u30=XqT zZ1!skVQh85Qlv2(+(Q&y2=N>J3bShV_hcsJbf9O9kB<-hc@^`_415-1&Xt=-F4gIG z_8oLM!3@Mx2pgQ8Lhy8sN4V9?2^x|UH~M3v+oiU=0XN7fDB*>7-4Q2HN7&D;adk-q zOMZ1MFPZ*(7QcRjhn~5(+2paOu|1KemL=r$#_u&Tr@<$7-5R~Gu1A=kqg0h@f3}=(Yb*T! zW7G1W9mZQ{g;`L&jF;c~Ha6#7|5tBMr>2It3zMXd?bEtDr%}ssoh&yOs5UlQP;9a% zpkv$m9(K5jH{_kHurylJO2noW-3LT9c$e271IhbKi`Zww8q8|BaqaExC%-l~&3@CP zCkgyn44?nXT8qUsYbLhCb5~Q=j|0A1PM2t&#F8^s)AboT4W!c{%}=uo@Af3>X;$>7 zMZxB&!q<;|p2L+^S@0rjYisX1{XQ2fr`3D@)^Yg6J0FQnA^&iH7k`BGosC9zcKBhMQt+vC$MOgv+C$omZ)4-QTInhH)1K#3onci!EUB^ z_;b(J;PIl3>kr5hoXh<6GrBdF*v}Yr97n@MjikGMQ3r0-gQFQGDoJzjKH?}pMoew0 zuJhu`H-84j;$bLIQ^eh))zD1A&1oheD;qQ?EG*-lv&(O$GOLh#w56+9oc3r^V^el70 zYP#EF)BJtHWdUt%t1xqjkG)6wRnZsx0Ly>L<5@vo#3_gM?fHuJM6S~>^KYZ&nx$nL z#W5#=dNxVtf0i38hX^P>E1{Wr+%%D!1@7R`&DdxjX4NpaS3(olsU(F_mP0*0mz}A` zb855sT;gRTQNP^$>-x||J|SeO(6Jr^b#coyoL>9Bk}RV)1wh+=WugGvBkJ_p z^YU=`z@}Z6$^uIDMmLE((fc&=I3r=cCZa@Ty^D+=rQSNidG*H;wOp9>xVKN7y)z;r z;s9o#E$vrGB%Y$y@TbWNBHeq8lH-_7=5{u>9s&5YPBfn5-Hq%_SQ_m=23J@0O=dZc z{>zN4ygV8*errb0{n;eR;28nz9Mvco-qqh@V7SJU@({&Zk1KhHGBc1LLn(mX zBx}d6zBNRhV#;nvY^}A7!jvUlYy7=_k-o{?L3zGyz@zMUFB&u()(%+~H%UTlpT7&V0x;q{+!_gt*Koi%vvmy2I0lL0#mY2VE#nz*sAooENT9 z6q4XcBa_MjAl9en__fV+1kI!q>I6b1Z%Su)-~UudW>IrqvEdL;jGGkToZks%}|{&o00BRpe0yimR2SBdSYBMrFz=7yb={C2Ndt%hDq!~{mKc8}pDbmjom|*| zvyUrKMT6A%iL1a^DSnvB>*woY0V;o%$bHEJORXuBObK^aqNU(aF08aO$^V(1Uc!FHk z`UR=|0c%}5a_hjC1)fmKCHvV1)knq0y%@7rUzvZuj)+ZLLr_|{*{2IVX*Qk8*{IE9 za|0Is)oD4EL%;r;Pa^^ygevm|{y=mbBGiYk@s>kP!4EkA0_qov1F-#L*<~}!Wm5d%^sGGx9Xfgn@K6c{LP0kjgH!XoZA z0vqhQX;Jjr+SbShOHuQXsr+Z~BqMzK7=pvGsgqKx->!-pfWKZ$qL>X}*gzu9MA_Ry zpR2#cg2SLuLaTI?E0?=t)2gVt)s&oVp4Y|Z97%$Z{hk}2?ooFL)Uskm(wplQTN~kg z`t8fMki`I2jY+p^$(xMtpV>)NRaKd9CbC5=>Uz=Xek^i#_xu`}?dFTuEcKN3VOK#{ zZJ4NIbEad1tG1`*CqK{`C-?fiVLq%&vw!x3;2~f>OI(Fw@PZ2M5xS5IIod{;124y6 z^LoBAiE(@Jj#kftw9ZydqDWuRAX^cJFHTQ}7LDdfr|HOm)44|c?d{n58EUf2mVlsw zAsWTU&n*#n_M+AE)l(X!C0ac}cCyy6@fJb6amS7ht^>6%W7ir)YhsU_jGs4G<*@)@j2z=XagAH!( zuF9c(!t380P9mb?7B{VVNYe!!h#lsuHi6aTDJp8#LGa(7ANny^Akg|)l7d&5Uw;nS zW%#i;oJUNIn#8$Nx>nt)LW$z>95Y&8Hzo4BUB}^kRn#yfM2}qTWExDM8X3*tw_EEF z0Ud*XN9%b5@f2Gd8z6!$s?_D^AdRGdNi3am%{~QpMyGMl|8Ize>3-+sSnR+GB>1P+0kG<+GomqW>}+gNed4CTH9 zBpm$cO=0iA0*__I)VRN*VTS2MIepr}&Be}En6Va>4pU(a0CO{4tX#OdUSrg*MlZ3h3x0KM6<76ppG~BPpA(X6DD@p$b38PrK?X z7vEbe>#j}HAkII)q)9P#7$Dc#UCLM6i*W{Wh>0ZikxXgfpN>@QzW z(xYLidG*`@K9TlGQ+uY)Y5cb(4zaTK-tcx3$*XV77YuUm^c;U!b{bEB<|3!32pxnB z3uSj2`LEmOKrJTzi@kvfjxVt%=>hM4;gri` z888;H@mr5VwLtff*OI5FO8+=6uBwPg_>x9yX0=6%JNPup-IwpF)RW%uv5#z=+()66 z>_FZ3_=7>n+;hAs)J90hF?isL85qnbN(WK#bh=HqnV5beiei3^J1YJT#9FuJ&tk`X z+zHBQ{M)Dh{7mly1f?t^u|D>&nB&oxa6IbxW)#>lE2anAKjLgGA%U$|QBv6>NxYYI z2n!lN3&6E%3WLL+fsbcRoX9bQ4+toHr_l-mfMcBkx356yXO$?fIC6Q^gC|UG8t88zL34q zZHKM<2e%xC@n3Jkst=TDs8e$9?_igh8X^U=l1i zuJiZbp5~*75rIKO%3~VRkI4#A|NApkc;R6SLHm1uFqSzvKl+2%&JcD&%O?iSNhE}O z#oVlV?m!B1Y2p^t(|fpjW)f6<@R@uL0|g$E^419w#e=Xs))F z&G3(249f%Gr=d4CAKkiHgpb`=mACV65ZXKUgyzTz`T&uFCuqYEV|WKcaq z{gv)usorzYCO^Kta*qY9&TM#{rpfWGl-dS`eSJzfmf+z;1o!KA!(`!qKl?#ZOGcTqR^RirR^IYi)%mOyk2Io~#QSZYyTLbac zZ{_KdIl^C#QEKp=4nlPcRLjA4gU(vO#=VS{v0D zK6uk5@e|8HFR`=6z`~mK`?p)r+R|c+gM*V99v;4Za8Px&J0rcOcii-GRS7%!pp4Vu_@7Bd7z*A5D{bSUxxBSGRpA2M( z?|BgkTge{Q5kCVnGnztY-A4G~A^NU~$C+qljgYQy;}k z2Kl%RU{%*DQx6sO+;=_QR(Z6Z#_r9c-?|02!*_Wi`9DBSN5(k=l0U}_z;mSl6%&}7 z>oyR)+(e~Vi@b(GSAC>$B&A*jpucLa{~im_Aw43D_g$#brC4EVNy%tDHV!q+KWC<8!h z3n>0YJ7Cxupj;aii_7TA=Yr7*e z_ytHs2$NQ&jg1=>o-{1+LoO(IxKL-kZ_L!S;6=GfDEh%$K|GYphQ0kvXw*v zf!F_b&#uuLzk&rXS2h;m=H^BRv}$H~uwN&d=8!)QnW;?I8 zxn2FWiY4ZWYiQtSQcktJI9wI5pZ(qv`1l{&$sV>mR*0a0Edq;LMV{CRW@{@U#hk@Wue)nc;Kyfh1m3M3z=|5> zGQP}q2L~@TIVqQG6d(WnYkq%wwbJ360(=#QXYLWuPr9`mtFPf(r2TG^W~iQV%Y0i= zi4OYjyEJ6q9Wst6EiOo|3?RLJEr^p)XEDd_V%*r1l|>;F?VT>xtPAq?S73d&_dZ$^ zbUr;jodLpjjdqpZ)XGT4pFx`7+4tL{4agpEI{@ZhdXY}O`lL%bSov@c|DdC1#=9!96tDlmRN63bYv^37iDJ3cZ z)=nNR)r9>Ce$ZUKdyGPjZ0qN~ch`bkTwGp+_P})71Ox{x7vG#aEjrX@((lcqrulBc z@`vsMtt1crdzc}4%GMoW34fU#xB>!Ui&uX_3krCgDLoE3++W2o4ps1ff<#901>O48 zTaCyMe_}m3(+;M4oj^GRjO*sQ=5z$C;fRr*9fP63-g!R?@F|u!_{*Ow<2l7o*(tx) z7WW+|D3wG7j+d*t1S1&XC2d2WQ!fM^P;!XfOv$F>Eeq2#Rt-o5CW*mUR~dLa66(gf zF4>|g)irOdK17WTPaCT8?vQM)@cq8lb3+~Gm&Cnq{s)&cq=F@ZJoU16fufn@|H;@g z@WxWLt8I!&*A3;MZLw=I@8TUg$)Sf>KV9zf!^l(xmNO4iZD-5jKq!yIiO3cy za9+LS?gq*Hz_s0dN3}?TURGAt_jEh{6$TSzwcYQtZY$>76ZmX1MQ@&R6tZD%+5&LG zS%n9-WN`>r>U2ujUVD&=>s4n5ZXo;|Cdw-&={*G19RzNSN9ZKT34c!pX$*_h!Qj|+ zC)sod&dO=H73}KSsS=MBjZ-_g9}qXX+);*+X*N+>^}8qmY}h$a=3+^Gf^b7 zH?|{`4Nm>SCO_a9za}MWs<6P#Vpi|ZNSbzO(%lVgW=39^9ugQkryi0c=FMj}&8V6u zsR9)4$J#Ml_7;EMK-t#vc?h&Z>Hz{|*C~dspDf#_#csNK&|jDxC}pPXBmt1%?_m4W z@sIvx1Bx=P2VCm!2lp}^gZAMqvNAI1FWl(ChKe6RhjC*o!uv2@+xma^ZmpZ|Mrj<>tPX?A4MBE zW4PtQZ026B%vI{{^n@bSab^KuLJ|XyzYxMNc2PMB$HeUEjHeVmn@y+MP^+OYxpj(A zbTfR=Py|Xb4)8>7fu}zfb`YuQ2el^MT|gv|3-dar4Y(J1iaO)zE&<5D)5_<}Qc6Yr z)I{SlQ~!fcsefJGggZI!^?%BKV6q%8(wQ935?*t88U1bez*-SCs*K%VGzTXfhPNZ5 z9}9fzv5O6;`wOVyp>F;2{MVM3w7+M?`&q2?F*?*!$bmtc2!k}SeE$~Je@#m+jVovW z(4E%CB+$gd&a$RcLWRZ&n{O z3}w=yoTP2*4p`5DXjj#9;Of5s?0<@>92v#K^m4u4=T!#X^0hXWC!B9y7>u>BbHFjW zTCl+LY2KCOpQHo$J%Hps^EHCnU^EU03rSk(s|DXodD5#e9(OZPsOcc%DazEDPqy;6 zqaJ`{pmli~W$261+48i9uv9=Rz#YyA->=JHZ+jAu<>VERW)RH?2u;=tw=Y!~w8^=Z z6&7eXI9RSZ%46t6@$FjB&xOaQvC?`_sviWKWAUEw0nyAK>AgTf{IKu^GwIn-1%gb# z_j)UJDD7yDGT3~wKgi?!^cXxc2G~eT6UxgkxA90=9mPvT+fPb(akx}>!DbC*IB-cT zfuYiicHchvv-EO*_5kDZlS)>vcPoAme)IchzCnl~HVKDXSiIOxK)>fdn-bf1UFmx4 zG98KG(JLkcZ(sEAgp};g_(w^JNQs+ru?mQru21}!0Gvzx+fM>cz4(Td$eE@w^&;-_#;{PVP#r4%1=%NxQ>JzR9m}C- z8J9v^xKNo%!DEV0w1FI@p&cg#kNL~eUjCBpiK|}SyD$|rikK<>X26s3XZFlJU@U66 z-^!(BzKN-X2i}cn==b9X5xZ9MA2_1j z%#+D(satXOkKu3qnbhpttw*MI`LUZjrP|5(fHd8+HIwuE+AWlXG*K1#H!GSlGGBl7y=U4SO!-byP! zaM|)mx{HBLU*P!oA>~!?#ceA_!}b_#3O^DHah6ReOE{dJTV&UmRR)>^v>JkpmO?@tA`wQKTw0pC7@Dh3JU`bJ%}1maLBEm_44IP zgo}D^zt!$%Z*ZBEn%8N4*ZfpiROSxVc?a9PkEP_x7);EJ3vuAyZAY@Tg_?~^Ii_}?9^=J|zLSV|T0n$eT;!Pb$_ z?3JcB`|M!L$Ui@w>M;EVE$AOLqYSm&90V=s9M8e#i@eNtCEV+8lleyr#lq9aug^p? z1!EtU@nr(ifp`x#1%AqJzri&7aUWQwhtFjc_A-1Uilzq^8j4h)&F;D>c%!MiInxXy zE>lxg>2prlB-+#7vuLlB{in5l+c-OyOkro!Ltxg=&`B zO6l@U7u2YO(8W#SZ9on|<|Aw2BuJHwnspou4gDxCg7ACZ?BEig*zp&jfd&O)A~L>P zOdgQ>k6d3Z1#iknn~f>pqVr%QwG~Ge-g$2o4%0-gL?PIYP^6ZZ3*^TnUJLo@QZ=<& z<4&LRgN)BH&*iZ0?!{&+kYB!pdU1YrsU^k=6bAj)!dp!m1Y;4#La$w%`;FD|R{}x# zBvkrgxW<%JZ5a}hL*;K}LALWJ*vec^VNnAN=m)U$e6^d2e1ePtV0{|5i8V0L9Dq%e zG&D5R5Q!Z;ddX@7WT)k@*jVxwoleoUHG6sjbyyRNljpFM$P+-3&Db*E$tQ*ii2o8` zx}+Zys!?%`JrV=*vUFUZ(@Klm3o$WwK*ad0hM$?40Ot&uurpk^eLW~7dYKevA|vtJO~P*_Sz3WJF4*{^#5q1=zwB!I#djXNzsD*EjG z&yZXR*bw^PZDcLoV4A#USZWQ*XX+@_Wt)O!`lp68T^1O9Euk#jlS*N&LRXD*Z2LQfS8=6c@n94Jkz-!e z{diDC=DP~^)2oD_6=n*}ilbk`E+DW7!bzhHK~I5<*Ze6A2E#u`Mn-;2AodG2PE);| z4nMwq_{3?L3TR)7sHc5pV1mL%Sa^X7MYP0O?dU+t#E)o|MYnn zBnfeBv9!4-zPS}~r#bb0E%7oGuea-)Ph>KX<1{O@Sl_*Sx4YQDLKFB89|2gk%z>%o zDKfI#{w%AwxOkPuB#86Jr#Y{8jXiQK!GF@$&+FW|O)7%!mYZG{E7>D9flS`4Iz~uF z@FmR2rY%z|FaQBS2VoC(|8nQ|<@J=Cw4c#DJXn5}hwsJ`HNmja=|;`wy<4m!L0zx~ zWMrO_l2RZ?wRF!zbPNW2S*cx_3c5Ce?p#C;u_WB{fZ1Dk)^vAwqu2uZB9-$~G))SN z1G%liYFp;{-(s-oOUN~U>1pP+0p)aUVa5jg%?rS?Mi+u3{9osfRW-Aw*{fz}*mUu}4GYWeGD&u}Ub~Pu{2v4=5lB@b zv4jd1M6FW-o@2krh}6%5#I1K3w|dcXTsoW9tYBx=%hH)wH$aTjs;ZT8g!6#CYVoze z7pM>Y*SpVU5?OCG7dL1#PAc3jCN0JRuaH{8l!lIfKv`n;dOPw{NR3&v5)?HSDg4tTeP z%M(1j{IEY4Sb1-^HmLz9^DAyp`g;z-Ca^5B@zl4@Sz(drArW0LPS8iBtA%YhrpcootZVW=C@|e{NYkreBS51 z`|dsWoPGA*_vlFK>||!GxZz~vDiM1K#>t*$YmI=cp`G|7))(4T=9nr%hs8?V542wh zc3cc^nY9FNds52a(pDzB&1F;r84{R~siu4+wF_b>*U0S~8{ed`UD-5H*bziU zW3kzYa4f~iM76!a|G)$%<3-7*g&ysdB)a5iok5q;jupyu^ore3JpRqOIGB@y>zsG4 z+`QRWUx{)BxLk>dn&$?`=n7CvuR8{Wnz%D1dh%Pk+ZaA2a;SZ_4a|*%DSp*ga!Y?z zho071M%le6e)QeK+l6Pa_-X_Z&1lLeiF9!dQ}V2VfhPtX6f4Peo6m!%ZMqj7WKY-J zJ%-ftCqb)3kH9h8dxa{coCZ(wL8GBQB z)06SvwE`3Syb{angwu@~?F86;Ve*q*+TjF>_nC<%LY2nBq~n{Uw|8f{@z5lIA9QUO zG30av1NlWJ6N!Zw`KH?D~HR0}xdXohw#^S+4+s?Xt8JFlE*?cx06BoC$r%5{-P z?z?&V!Ukm#T18*__kturgL*{@-%>VDfwgt`#gM~|9pOp>q2DP2KHOXRCgGu?BZbgG z0I!jkj78egf0}@|xN_##y0Bhy*CXhR`jz)WJAYjZwMOf3yq3=ZOWQ4j%9PlG)qTc! zrL))y(k5b=I8<^um-^#@Ln4|hg^y*^#7M4fA81!PF5_ATz=xY^ufm64y(`YQ4Rx15 zdk5x<_bREM8PohO&mJ5NC7uOl_C6KC*8T{Ob*x?NWHUV;1-~$hkgmEy7V}%rq~yLO zXb66F8zym$NAJ*C(W_tWXJhWkoVL}~hF#rM1&lsCuV6`6THPIeMIMr^$b4l2oHkeq z#eC_0jQ{Mh@popvgiU7ySLkDEvy9qdGbo5i2nn{BXzKs)Zb%`Fm_)hm>Z*&|Bg`SY z!RH|1v)4|T3c)U1^E-ZIuKs?3zkZPrYzAIf#$g*=J2z*mb{WB%sdGzOal8{s90!gF zrg{(4KdvN(U~=eXo%=iU?0*S1EwZsM?)e>^7q|*ol;d%XR@JM$--n%QTlwVu)n#}+ z$p(iKp~POGv2Sw&_Udbi7wLhIHVvOLU4d=`EGrRJcWC%_JktIx(q>7|bE(fd17Fx* zTlR2cxC#HJ9Mg?iRt|Htg23@My2FmnfCU(9!zI#K2KcDc!pL|+p}sU@J?UiJ#k!sN zvnQtNFl8q6i3m-8nOt6a4kB3A&Yt8<`w`rT5`%nwQdwC6ZA(XG_m=8Q zp;_6Os=B`@Nq)J`6Rz5*$X^@a=w@801e+Ob5Z1pG55ahL5Sku@O++->K>ms%#$|M6 z2)J<1+g}xz*tmKV!dmIJp`E3p&*aF&O#ei`zT2=w!Sz-jWI65Nj$_u_iUF;T_*=6_ z+e#shTPD>XWAbc^JI7Jl-a3#Fi&P{|+fa6e2{dPOJLF=W2?5C7&*CV~5= z&7dRN;TNI*nO;Ug7htV@!DB!2(Kk-o+_y%ss-Y4OW?ZDPuF9zr_jLH*y#<`Kzjqa$ zF(%C#!49vFcN)Qf0`W}Xt+gZfSjw=oV=PctPr=2&plPu@eIe+rO;zh{dNXR7Y}P0B zn&3a557^=rmw##aHH6`FhbpPSLrZY*leI@{ewK;96+$oC_R%tqk7}lIj7GJx7LA`^ zVnA$Lj=|y7_!|4ny~ygpO~BU*>m!%2aEcgi(h5p#Onxl7@N<`IcAmQuqjzU~;NXA{ zT~qqgnj{X#?AD=s)kL3$=l;bKv+8>z+&DSq9(B3iuB*TEfNMPSEs_X3`S4jx3chIH z#({IYk_H_8WX-uceg?e1cGN_6#%mC<G zkN|tMQ4uYF5nI*rd9dzGHP8%Wib=eKc}(_P<#Cin;GxMMF#+PI;2H%kM@(71yMu|< zW0gaG){D18dQ6_XqHk0;wx^b++Ia8kpqn>-noAffJWETaJqq0CbP60XnO92U23d$E zjPNXEa?ZvA+pg`=8hL6}5oHEAsiH+jaZzF-1KY!-=;pGcxb#So^7kAV0iPo^a&A<; z_HRO*4G@{*&Q_+r-+F#}hTK(wuR*{MV36=B*MH4D1H zQ$e@p(B@M|$OtmMrOF?cnl_bI6l_ZR#E zb9CM=9?XiKG^gQV;T7m-feS%osKFx;-u*ja_dm|Xt7Hs3GTAj5tq`~*xwxb-l8hg7 zda^|?36y#F+9MsB?{3 z=A{pFPrUN?Kc9Jg`R04@S4TPuu=kx7;~3onCB;Y}5+TK6!G}jBM zW+2xwr7X^5Xnz-e#*_HN9hKocNqn&OX?_YUdZJ$@ldudhpg%?5T5U8PmT%&J!F#O{D-f1O#|L>dy zt6tDGwG;UK)BWr`MdR;pM$V;I@#W`5fV$-LX7og}saOIP$?`VzVhC%%rl|m9X`j?V z=H(UXwjLwH=3)^?0y%cFIr{1dPxpnVM zWp9Q$EwnAP)7e9bhi}YUITK(fO3+K3Q$0uvjionj3l%clQAx+}-$Of~Py3@yFEcaK z-S^`7cghVQ(~QYJn3DCiT;Na&lFPh2V(Fb+i!3Wc%^!7=bf!>)pH71(+N0}?6Z?^6 zj~e=vH`W&k=Gu}m%&lMDc*F~9UQ(c-xRp-mo-pWdRC$j%?wv~ zaTtO@f-mpdTT#`>%)RhMGvG~G?K z3NF>V6>9A6MGxHi{CW1EKzgrUXsXUpY?_BP-UL%XZf480;3>7%`*OZcT|xL#OUjG+ zb712jI3Vz~>lzy3MZKLAUNfk>xRjSfS&8?iNxx>$XcqHW$$6$Alk$So;a8_U2z{wt?SY4qy#7Al zwspK74h+VRVBxntl$7qzvh$_ssMD(Wx)AMg?s!|aG(wU&xzj~~FKr2QUmrFk1#Z(l z`C|EtE`JC2W~i2}m)&@kEh<0Xx~iio@JxUA@bG>!V1c<9sFs!>v3I}Kh#2kkb5&5- zsD+h}wvY4e^j%z1xEsN_WZ45Hc&C@xbG^1qnPBL~-jsS=GoHonhzvfZwzI$gTwppF zb>Kr4%zJ*VHVS26fjE_baHgsQcnQ?d{YH0x{mPuCLaAyyAY+oT^4@3mh5qGe4)Gt%AE+uXbX)HLo3lHb zSyzA@mZAEAOOt_;j7&~L`9UoKw)X3Y)G*SIX>R|WMf~#$8Z1A35 zt9>TUM*~W<;paBS0L$|WgU&=$KTXWH3jR($^IdpiTk^xCugbEUpVQB4XmD{h(>6F* z`?w&rBs{r__km>}PBPO#-cZ#ibAg_%Ga@_L{wx#)vB|p~n6_$&f!sjmC7@wtS$4ng zXGmbz;X?O913y>g-CZAFU*4c|fA$Z*RAL#ldi>ZPNL5(aN%!BBz2WN?6*Txgb6Ugw zFAMr~nZN}1ab#(`pch9p6$Xf~MADwnD}r8qd*!kWZXEHbdgVNHEqWWOnmU9^}V>L|KXHq z<(uapJE!Wiy4uGKF$q>}i-GFm!xh6+|w%#RWqRa=N|3Sy+-))qw+ULeV>c*b0a z#bMMW^uyY1;pK?6)8U4#{^lRl!hBAe9yP)6g_`=nFCyAp>b@{ZsnrRc0U6oq{nf9G z+hL!@#0GgVvcaUIr(1P>vYOrK2LvZxOK`V=x>R4LiC zo)QThbQp>GZ6Vu~PwfiR%{=z+KL%%pW6?-GFctF z-h3fpr5Ha;2HdgNgSMWhC~Sk8f>sksuIEuvw))?!%VTIBW=5L3;QBe7 z4Czo=_0Dcd(+38+tfTaM_YT&#+?m#QB>e@V2v+G&j9X)@OpB5({|Gx+G71vjUOB=Go6gG1Eznr9Iqzt8EG)q! zpDZ}cUJRdm37@n4YI5p6X@M6*A8CZIMcd#N=DRE8|7|uzpoS+`s7t0{>+Rs$Y9saJ zREdtsO|x0BczII#H0cEv{v)vd7%nxm2VoqTj#;st)7?*MGjG01R0Dvq%G9-M;4rmD zLtWpCO*5;;1N}^c$fjZTa^g?(OG}-=Qmgk^CI#mA>}pvHD-TcK@OM%#1=OweFX`y~ zL~Mh_UtqWSpSD3>NOUB(u7cMF&zt3^+Xc#Dc*E?K#Gl^XQl+S{=(;Qggh~jioPapp z48s}`CW}lToFuK2WK+xCN+g+W9WG2jNq<8c2A%Klo6kk8Mo`K@c9kJXd)5aIdfrb) z6+oifzw80+ft1wLI~p06*tLsa?u`OdoP>tvRRlQ$&d*d)%7+ghg1k}zPG`wR_!-0gsQ()`2Q*BDFy#w$&YT7v7dOwNl`kO12y0!$VsXSWv zt}eLg%XP9i=quowV~=`3x&I=2vw6pDs7}f@v@1UBr6TNcsO02i!GIHwp5ESvjjrhM z;WA@M^|BFrkhSm#9{|LwAAA|BidivMw~4MuyX1FDN=g+le{28{{tbNhqFkjo86f6g zWQpYi3q>D{GpoVZ2ykh`0oa90Ac(3o2AtODbwo3W>$5*8c(bs$XzefVv-M@}Fp&yE z7}xB3snk><=tYm$j#RxNRv#E--0a58{7MDGoe%F#w3zG#bP2_cYt(){S znTyWNe*IS9JwJK%;b>eI?}M9VT_gE|@7Prz=aiQK)jR=ofp`=gxL?*MY7u}Q7)`*w zxN=E-V5u(?Q>#cj*Kwjo)5spg;yD2Mj_X*c#{h_*A`yT>3D~0?%JeE(B_t%`0=1Qt za6wycvDwqOva<36TCOB$^R}mrUNOabm4HkugSNZg6|M00@Q#_H$XgbkMb?M;Tb-sb z@oG5#?L;Zx{hs5UgNaR3>J;{CM5jdITL8%cSoUuJj$C&ZdT7z+mn4qoufq#bY4$&^3%ZcF&Z?H- zFjj%2Qy$B|s|xIzz~j|$;6a*LArKMo{K%8ChX!>lwhfFP>jaxxV#eRzbjcsP=f{hT zC7?Q^$ynZhRsvtAk0e6S-b(cRiAd)%0en|ZhKAatGgn-Km9nUZ*NWP!XsQ?H1=5Z- z0}3T&6w0DzVR&|z=L;5WxLT69OXxNXu0-YFkvxX3TrNBdbXy|8otf8r<0 z!pq9a#ztN_R$9O=+`qa{VrNUlAofxt*-r-=C&E5kHxNMrOu!T&*BAGl|I92e+Ya+? zs>U&4L2Gn&yxQLC;+ovxya6|ckfacH|#BW<5X zul3)r$l3GW!8*pjc1>_K5d#z32AceR(C*Cy*0@@cL2c>6!h+S#7?T^aY$Hf%MRwO6 zUR`4h&7O6PDX~;NTEAbtwCu-Z;8EpjZqs+e-!J#Cah$N$jce5ln0(10O1hv7kcFw~ z;^iecW|XZ!xbQ2r8`f`Enz&!n+gyNt5Z;C>x_x$WQ4y3(V>OPn!tM+F47*GH%%Dwk z^55Lt>@7F_S{HCC2rZ&q;9;nx2$8U{u~o*_!_PngH3y5vqVr8KBDet+RFZ$qOVQN4 z?;c#frp3d#e&ksS-#Zh1ML_>D;ijA;)THk@qxeUgK4&^l6LE-~OgP(h zN)R_}Z(EEs7E zUr(DdG(TD`88wH#V+STA@#U+vKIS}MVl&+fcu!OFI$719#PoDsb#{K|zKNqZimvMf zWAm4y;(b%~1eiGB$vfPL0XDYGd}KEmeEhzayGd%8A6!b2{d1|jr<#`*Rd$7qjR0HA zJW3sNJMg1RnTuy4e^*;B+}`Oef&1*B(h0~8GFf@uQ0p89p`rXoGyr8^$ zm+q&bXEDg{s+3>7`K=N8eXt<3Y+EjiX|0LFt}+pD)_Mv~X==T}aeST#xiZy$UxWLIOH8dG3HD&jnJkH+tzv|`VTgQu6Y77-!*eN~a zjF^+fMVq>|&+3`JF1#6B4gQ=z*dE86`6$SKPrE`q*k5)J4DoMJFcY;8-dDTqWdNHs~1~JXZ=ZoVwzv zUq?Dy06Q+Va@E$WTK-ojmIGW9Z*6 zrhaNkhMk45G;vNJP55;YmdUKyMapyc)48Z5zk0S(=`xH_RjrlEBpsIf!oQfn%d#4Z zFyc=DqxOSV=5eQpz^go9K6q(%Qbi5|4PeZri3AR0;s#xHII8x>?5LGoU}@Fm6sk$m6sQ z($doK5$b82D_7)v9h$I{^9uWC85n?@g_OtTw;O#Iuz4&xiD4^~1}YoUgoX^`)xi;5 z1--H1d}CmuxQbuBY?gfzw&w~8N{D`14JZC zoK>gvDNB?^%^7eu(6h6_K(;b6GU7dHI-Iaai;v344*eGLJ8a`_k?M#YJ`;oKC%3kc zWB@6Oa=Ju7@h`&edGGYXq_3>0ih=($u?#}6`!YlJCBf}J{&wS%hTGh?s}E9yq(N2y z8yq1N1qj=E9`cIz>KgC$UE%Mo=*3_kf|{W7V8BFVv4L=cCS&{0ZF05s3=NS3j}AfO zKsXPIqf8)gfLA@<2i<7G3C}GSE^zlYF2}I=iR(Bnl`67g4bxuRoN`t>M~MW|0qcJI zcKh;Rl(v2Ai=$KI=D|yNlH=Bzu;K9|tmB+f#r{~o%EXonY(_?#X(J$>5i?zDYpyi^ zP4wHz`gUYYIm3Inm9-IYZ|H=Ly{dzy&;_56@Gs;)b@la`Q8^Ukun|W@;M6)zotWHz z?8kCweL;FE{^M*+(*93&o~;;EUP$geLwv@w&N9TYJTtS^T7`q8qgiK+MDypQA>RUD zy9FKe>K`s`Y%pY_*X&L>;VGC{5sR(=oCvb%;_7O;x77cMM${TUv1V-6qF66^`axrZ zSJ@M>zK`SokFov#^<=o9~o{RRq2}_)twCPf)}4Uj85CQ63wU)KD=(Wr&<7 z0JDNsNN99ezOo?lI@_Zf2R-n2cmOswj)o~h$)es|fReJPzK@0i)9q->)PS9+;sC?jx0c{KTbmYJy0-4Fs@b`*DDDyO-97lg~;8U)X&jhp# zMk*XCiHg9pXNY`%AWz-VkRz(@{{p5SxfoFPWW5Lpv4#9a{MlZ)r{2 zu3%%2H`q`BXTb}US#7|I7xG*oLy9oz5NxPE`n|$_%YoZ19Qd;A0q81##Af7hrvqZ* z;*N#{MY>HspB7*VubO>RPU5qyf-5IGoGxX*uF8~+7%z|;#dfwTBZMz{$3qzuicw4(lDf`rZW6t@eenx)!Jv$ihb03`S&%&<)PNuKxH7NE?tp7FJ(La> zaKANN@miobOogNrIkdunQeFuD(C4m_~2Nvm@+v10DZY1La)v1u6OJowiox72NHlg2P3Em z=mT=d1yFqjOY9UoCQlpv4!NMNXzlKff|D-{>OJ0@ZiusD1D{B-Y(%jj7ode3GL8>e z9Kb@r3JO>{5s%vn-Ogjzt*x!W;RzEm>0BY-9hT{ULwY!Tqf-4xLQWeeN3j`maXSSW zR6|U9Z-o!N!4K-=lR_E5VDy@Nws9!WRd3JjO!paqc?gUT1sY0`c_62kwV3T|k|Q z4Od_SrOefuVXN3i@6AW`ZkDjlTjBA2kG3*hjiEqt)aC1L_%9?UDVShNNqo7E_||#A zO+qZ6VEtJZ+fFz6-s=}*3jn*fi+{VXw?CJaaRXeAnBxHB{A>8F%4i6L6=vV@1Rcji z0sHwx0U&!UI5@?qQhI|LG#`!?0oNNu;R}=SJ3I(t^|X#>Ss94BI%*trma7H*LDgiz zl7+dsEWn*r5v;}Q^uQ5TGZO{3Q87kbGid@uZ^dV?;na~@&wjJN|A+ET>B&^*;pQ~s za!?Wc-UKXHlA-BZ@SZ{b*_(OXAmF=Dadt?YU|1toze*@Gy+zG(`lwrG=Ymq`6U(9a{wI zv#h15rEsjhN>5MUUIY=(J-fl%pn$TQTmZxc7QLe;KO9vflFBvA&H=SyYul}gph#OL zXOb3RM69X63y-$OW1t`n;TG6cC3yy5%(e2P@+PRqP0&3CLsosAmH=|(a@WUyYrY8K_ihOxWSYC(rZS^JP4;2y=kcc zfIQAoiqqu`6b5E5#2uJWw9w_S0^EyLpr8{a2%*L1k*raT12q7FZ9P3PU^_C<;Azhw z=3_D$H1h?+Lxdi>6}bRWAomToMRTDE+Z<^-sFXn7X!|b}p&uOE+skW)J=z~VtWadk7il}!iM8E!z zTI@11GM9vc&c&+jhO(f#M-+|4MxRlV=SLwBgQ$Bq0kMN6V1@(^@n>Y}1ISnUfV^%y z5s1-zuqje-J?(_4+Lg%53|Is#DrV&L9pDN^!S18k=;a7eQUUrqN#cHj5Ng_bdx?Np z48AB-nQMTz!Zg7FUiEuUgLZhJL_SNrXP+6K1A>V}6@bxyfV^;NxP%tjen8x0Fss0j${t10oYDgU5We&Lbdx3nx_&Bf zau==mv#PGeaEvNKT0t8W#rgC;^bAmWw&U=2s}YQrE@*X_lMwm#kEs#?`j}Ng7iaf> zRJ{783hbW#yZTiH+lw$!FHt3TgD|lN)(aAyw_%-d%E-#XtN9CinUILcP8VA`RaqRD zp~%i+Z!ZX=VkV#wpf0fp>%s=50DzQQX~5QuG^9@q=!$fhw{Rl~;E|$qNK1gt`XEKA zKTBR2vH+mCT0qeN!sUDI=>XRK#f{o*x8nU%2^Je*Yy~poNLY)2$YQ_Lc)xV+_pKc^ z$g!57mafg??AH>PXTTg3$)#YhH6huCXKaTyi^GAK_BTmzA|~YoAPWu0V_ehd2`~)` zJpTIuHYK(;UtzcT89@7E_fV)j>N2d4nB9$=AV3AY6X5Le(NL=Pl%W>MDv{l2>U)S~ z5!_FnQXCURi5JkpAOSizy7%Q(9GnQ;9-mFYZftDqN00{D4HtL8+V6aG6BE*#9N2hB zevnQDy&lCSB+P{wq?Rfo(AEqCoH4RG_Sx?~;Js_()lo1%c+9@x#xhFALQB0^QfCwg z;ylZ{+nO(c9S(VKn$jU9V4`o{5X0|0fJKQh6p4o0&jZ9-)94WWTC&}QXKSVv6B140 zWnM6|q#_8n>DGbt7Gbr6UYy=`eSK^8D~Qmab2lGd)-c|KQID!|*kp(14U)-@g?wM34>IJ7lI=&<}X1a`XsuWn3c69;Sjk;=%XXe zW;%N2i@T7Fg1KcZkgR!O$AH(@1s;k^(=9DymShL=s9W;!{7`SkGKi~yECflT;5N)5 zNbPM*(Zaz;yU0s#@LETD2CYaypfvWE25=u)0B=WnK5#Qa0+0#!>f`5!UR~{mIyFhe z^DdO+m`}<&DY=cx!NLG3D#3%5`Ds2Zswc&|ajfd7a=$|-|A2t8<^WL;S6#XRyI;G0 z7fjsaoo?=W@69y5mJl$;f&a7sSGEN)M!ENF&jva7%}s6Sqt-DjedJF)6amHo`{Z!< z_cnUtm~mjNVa`VY;9CwyFhCr0))sCHWCH;X60Kp>NXW?cS^>jYgL{2wGxU+1?Lo>6 z#5v@}LmV{y?}l~Idzpif3hUrHRGJJD{x-vsmqO3tU9KxXf~A7M?l4l>GGTcB|F%1# z>4bkDap0Q3u?L7o77FaSzkkE>Y{B@TC&!nQ4`xjs3=#nq&+~uByN{vlf_dXqJcej1 zpb!|ylE>$>===g=8K%#mn_9hN*uW1!Zx&?F1Po%Mx{cJrZY&^4&VU(=bS{zI4rFeX zfjFG{RY>JbK+P8l!C?nx76d|~1asJ_<-okqcBGUZcKS9rEJD{FQ>)UT!s06QI&$H{ z&@l>wwVSMU?K!*;Xh%CDc_|bGX8grB!4V7)mMbBx;DcO!`)M6d!0Ewvu#d&T!GU-9 z9ZD8t|LTPIKkdIu2#k$4(zD8;5ByrB4RPZvVyN$hgNEh@nNHgVupIyQR0=uSj5snD qv6uc|$66wG-SETzckXpgBtG`FL2Ux}5YFwqq%8kHu2jb4#eV}9$?j|b literal 0 HcmV?d00001 diff --git a/_sources/_bblearn/Module09/Module09_lab.ipynb b/_sources/_bblearn/Module09/Module09_lab.ipynb new file mode 100644 index 0000000..883066c --- /dev/null +++ b/_sources/_bblearn/Module09/Module09_lab.ipynb @@ -0,0 +1,578 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "c1305517-15b0-4538-98b3-e43cb2a6fed4", + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "# Setting up the Colab environment. DO NOT EDIT!\n", + "import os\n", + "#import warnings\n", + "#warnings.filterwarnings(\"ignore\")\n", + "\n", + "try:\n", + " import otter, pingouin\n", + "\n", + "except ImportError:\n", + " ! pip install -q otter-grader==4.0.0, pingouin\n", + " import otter\n", + "\n", + "if not os.path.exists('walkthrough-tests'):\n", + " zip_files = [f for f in os.listdir() if f.endswith('.zip')]\n", + " assert len(zip_files)>0, 'Could not find any zip files!'\n", + " assert len(zip_files)==1, 'Found multiple zip files!'\n", + " ! unzip {zip_files[0]}\n", + "\n", + "grader = otter.Notebook(colab=True,\n", + " tests_dir = 'walkthrough-tests')" + ] + }, + { + "cell_type": "markdown", + "id": "93498126", + "metadata": {}, + "source": [ + "# Lab" + ] + }, + { + "cell_type": "markdown", + "id": "aaa36b08", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "At the end of this learning activity you will be able to:\n", + " - Practice using robust correlation tools that account for outliers.\n", + " - Practice using `pg.qqplot` and `pg.normality` to asses the normality of residuals.\n", + " - Practice using regression to create covariate-controlled scores.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0120fbdb-220b-4cf4-93e6-9f61cbafeac0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pingouin as pg\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1b58e08-33dd-4abf-9f03-bf0e5adf0f68", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "data = pd.read_csv('hiv_neuro_data.csv')\n", + "data['education'] = data['education'].astype(float)\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "3c8907cb-4a06-4eae-adb9-a546165c814d", + "metadata": {}, + "source": [ + "This lab is going to explore the inter-relationships between two cognitive domains.\n", + "\n", + "* **Executive Function**: The complex cognitive processes required for planning, organizing, problem-solving, abstract thinking, and executing strategies. This domain also encompasses decision-making and cognitive flexibility, which is the ability to switch between thinking about two different concepts or to think about multiple concepts simultaneously.\n", + "- **Speed of Information Processing**: How quickly an individual can understand and react to the information being presented. This domain evaluates the speed at which cognitive tasks can be performed, often under time constraints.\n", + "\n", + "We will explore whether these two domains are correllated after controlling for co-variates." + ] + }, + { + "cell_type": "markdown", + "id": "9056e62e-2912-4f30-9a05-636b03f3c61f", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q1: Are Processing domain and Executive domain scores correlated?" + ] + }, + { + "cell_type": "markdown", + "id": "f69faf30-144e-4ac4-a3af-7abc6a378059", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 5 |\n", + "| Public Checks | 3 |\n", + "| Hidden Tests | 1 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5f244f0-7a60-4014-97b7-bd9bb50d52d4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Generate a plot between processing_domain_z and exec_domain_z\n", + "\n", + "q1_plot = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c3994fa-87bb-4d54-8a50-c51367dab36d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Use pg.corr to calculate the correlation between the two variables using a `robust` correlation metric\n", + "\n", + "q1_corr_res = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87f58703-4542-4e6b-84bd-c0f1af632a7e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Are the two domains significantly correlated? 'yes' or 'no'\n", + "\n", + "q1_is_corr = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e11a56be", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q1_domain_corr\")" + ] + }, + { + "cell_type": "markdown", + "id": "210aff4b-fc2c-4ecf-83d4-d40a9d86ca47", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q2: Create a regression for the processing domain that accounts for demographic covariates.\n", + "\n", + " - Age\n", + " - Race\n", + " - Sex\n", + " - Education\n", + " - Years Seropositive\n", + " - ART" + ] + }, + { + "cell_type": "markdown", + "id": "9163e0b1-6c31-44f6-9228-f6dd1cabb9e6", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 10 |\n", + "| Public Checks | 7 |\n", + "| Hidden Tests | 7 |\n", + "\n", + "_Points:_ 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b30cd4c0-77d3-47be-b9c1-f15f869079db", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Perform the regression using `pg.linear_regression`\n", + "# Use the result to answer the questions below\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73013a7e-1636-404a-ad88-66f34b2d2a36", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Assess the normality of the residuals of the model\n", + "\n", + "\n", + "q2_model_resid_normal = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ed0ca75-3b33-4b48-b31d-de725bd19121", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Considering a p<0.01 threshold answer which of the following are significant\n", + "\n", + "# Age\n", + "q2_processing_age = ...\n", + "\n", + "# Race\n", + "q2_processing_race = ...\n", + "\n", + "# Sex\n", + "q2_processing_sex = ...\n", + "\n", + "# Education\n", + "q2_processing_edu = ...\n", + "\n", + "# Infection length\n", + "q2_processing_ys = ...\n", + "\n", + "# ART\n", + "q2_processing_art = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "965c6839", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q2_exec_adj\")" + ] + }, + { + "cell_type": "markdown", + "id": "08ec7b71-a064-40d3-bce4-d3bd697ceac1", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q3: Is covariate controlled EDZ still correlated with PDZ?\n" + ] + }, + { + "cell_type": "markdown", + "id": "3573d869-4873-410c-91b0-a2fc985ed910", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 10 |\n", + "| Public Checks | 7 |\n", + "| Hidden Tests | 7 |\n", + "\n", + "_Points:_ 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87df2483-cc82-4199-b934-e3c47b23f609", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Generate a plot between covariate controlled processing_domain_z and exec_domain_z\n", + "\n", + "q3_plot = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b5b79b5-2c01-4383-a974-2ae15fde4837", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Use pg.corr to calculate the correlation between the two variables using a `pearson` correlation metric\n", + "\n", + "q3_corr_res = ...\n", + "q3_corr_res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b5a9705-1653-4ffe-ad1c-e1007cf304d9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Are processing_domain_z and covariate controlled exec_domain_z still correlated?\n", + "q3_corr_sig = ...\n", + "\n", + "\n", + "# Correlation r-value\n", + "# Place the r-value here rounded to 4 decimal places\n", + "q3_corr_r = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c6e993f-05b6-44df-a0bd-d2ae3965bedb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "# Partial correlation r-value\n", + "# Place the r-value here rounded to 4 decimal places\n", + "q3_partial_corr_r = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41acf0ac-a62e-4474-b8af-5e1a82eb3f87", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Are the results the same between the two methods? 'yes' or 'no'\n", + "\n", + "q3_same_res = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ea6628f", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q3_partial_corr\")" + ] + }, + { + "cell_type": "markdown", + "id": "f8f5c8cf-4fd7-4c6c-a65b-3e3471104dae", + "metadata": {}, + "source": [ + "We've seen from above that it is important to create `processing_domain_z` score corrected for covariates.\n", + "We also saw in the walkthrough that it is important create an `exec_domain_z` score corrected for covariates.\n", + "However, `pg.partial_corr` only allows you to correct for covariates in `x` or `y` but not **both**.\n", + "\n", + "Use another regression to remove the covaraites from `exec_domain_z` and determine if it is correlated with `processing_domain_z` after removing covariates." + ] + }, + { + "cell_type": "markdown", + "id": "e8f8f844-cc93-4eae-a587-f85291b0d87f", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q4: Are EDZ and PDZ correlated after controlling for covariates?" + ] + }, + { + "cell_type": "markdown", + "id": "adcd941d-767b-4014-9896-7eb8bfbd870b", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 10 |\n", + "| Public Checks | 7 |\n", + "| Hidden Tests | 7 |\n", + "\n", + "_Points:_ 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a5ce9d8-f1b0-4411-91f0-f6cc60df7c1a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Find the residuals for exec_domain_z after controlling for covariates\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48012c73-e929-40a1-90b4-d90044849bd2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Plot the two corrected values against each other\n", + "\n", + "q4_plot = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "223bddef-dc30-4eda-9c44-d171ae0e1115", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Test the correlation between the two sets of corrected values\n", + "\n", + "pg.corr(proc_res.residuals_, exec_res.residuals_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e91a69c2-fea7-45b0-9b10-3322f1c84bda", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# After correction for covariates, are PDZ and EDZ correlated? 'yes' or 'no'\n", + "\n", + "q4_sig_cor = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7372c6bb", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q4_full_corr\")" + ] + }, + { + "cell_type": "markdown", + "id": "d5653e0c", + "metadata": {}, + "source": [ + "--------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcecffa9", + "metadata": {}, + "outputs": [], + "source": [ + "grader.check_all()" + ] + }, + { + "cell_type": "markdown", + "id": "ad81e3ae", + "metadata": {}, + "source": [ + "## Submission\n", + "\n", + "Check:\n", + " - That all tables and graphs are rendered properly.\n", + " - Code completes without errors by using `Restart & Run All`.\n", + " - All checks **pass**.\n", + " \n", + "Then save the notebook and the `File` -> `Download` -> `Download .ipynb`. Upload this file to BBLearn." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "otter": { + "assignment_name": "Module09_lab" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_sources/_bblearn/Module09/Module09_walkthrough_SOLUTION.ipynb b/_sources/_bblearn/Module09/Module09_walkthrough_SOLUTION.ipynb new file mode 100644 index 0000000..b9d5a36 --- /dev/null +++ b/_sources/_bblearn/Module09/Module09_walkthrough_SOLUTION.ipynb @@ -0,0 +1,2841 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6febc445-889c-4db1-b014-6a346ab9a49f", + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "# Setting up the Colab environment. DO NOT EDIT!\n", + "import os\n", + "#import warnings\n", + "#warnings.filterwarnings(\"ignore\")\n", + "\n", + "try:\n", + " import otter, pingouin\n", + "\n", + "except ImportError:\n", + " ! pip install -q otter-grader==4.0.0, pingouin\n", + " import otter\n", + "\n", + "if not os.path.exists('walkthrough-tests'):\n", + " zip_files = [f for f in os.listdir() if f.endswith('.zip')]\n", + " assert len(zip_files)>0, 'Could not find any zip files!'\n", + " assert len(zip_files)==1, 'Found multiple zip files!'\n", + " ! unzip {zip_files[0]}\n", + "\n", + "grader = otter.Notebook(colab=True,\n", + " tests_dir = 'walkthrough-tests')" + ] + }, + { + "cell_type": "markdown", + "id": "cea3b0b0", + "metadata": {}, + "source": [ + "# Walkthrough" + ] + }, + { + "cell_type": "markdown", + "id": "71197956", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "At the end of this learning activity you will be able to:\n", + " - Practice using `pg.normality` and `pg.qqplot` to assess normality.\n", + " - Practice using `pg.linear_regression` to perform multiple regression.\n", + " - Interpret the results of linear regression such as the coefficient, p-value, R^2, and confidence intervals.\n", + " - Describe a _residual_ and how to interpret it.\n", + " - Relate the _dummy variable trap_ and how to avoid it during regression.\n", + " - Describe _overfitting_ and how to avoid it." + ] + }, + { + "cell_type": "markdown", + "id": "230f0ff0", + "metadata": {}, + "source": [ + "As we discussed with Dr. Devlin in the introduction video, this week and next we are going to look at HIV neurocognitive impairment data from a cohort here at Drexel.\n", + "Each person was given a full-scale neuropsychological exam and the resulting values were aggregated and normalized into Z-scores based on demographically matched healthy individuals.\n", + "\n", + "In this walkthrough we will explore the effects of antiretroviral medications on neurological impairment.\n", + "In our cohort, we have two major drug regimens, d4T (Stavudine) and the newer Emtricitabine/tenofovir (Truvada).\n", + "The older Stavudine is suspected to have neurotoxic effects that are not found in the newer Truvada.\n", + "We will use inferential statistics to understand this effect." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a0a08b85-58d9-4963-828b-8b515b8470f8", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pingouin as pg\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2d3c415d-aff6-401d-9ffd-61abe1112897", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    sexageeducationraceprocessing_domain_zexec_domain_zlanguage_domain_zvisuospatial_domain_zlearningmemory_domain_zmotor_domain_zARTYearsSeropositive
    0male6210.0AA0.50.60.151646-1.0-1.152131-1.364306Stavudine13
    1male5610.0AA-0.51.2-0.255505-2.0-0.086376-0.348600Truvada19
    2female5110.0AA0.50.10.902004-0.4-1.1398920.112215Stavudine9
    3female4712.0AA-0.6-1.2-0.119866-2.10.803619-2.276768Truvada24
    4male4613.0AA-0.41.30.079129-1.3-0.533607-0.330541Truvada14
    \n", + "
    " + ], + "text/plain": [ + " sex age education race processing_domain_z exec_domain_z \\\n", + "0 male 62 10.0 AA 0.5 0.6 \n", + "1 male 56 10.0 AA -0.5 1.2 \n", + "2 female 51 10.0 AA 0.5 0.1 \n", + "3 female 47 12.0 AA -0.6 -1.2 \n", + "4 male 46 13.0 AA -0.4 1.3 \n", + "\n", + " language_domain_z visuospatial_domain_z learningmemory_domain_z \\\n", + "0 0.151646 -1.0 -1.152131 \n", + "1 -0.255505 -2.0 -0.086376 \n", + "2 0.902004 -0.4 -1.139892 \n", + "3 -0.119866 -2.1 0.803619 \n", + "4 0.079129 -1.3 -0.533607 \n", + "\n", + " motor_domain_z ART YearsSeropositive \n", + "0 -1.364306 Stavudine 13 \n", + "1 -0.348600 Truvada 19 \n", + "2 0.112215 Stavudine 9 \n", + "3 -2.276768 Truvada 24 \n", + "4 -0.330541 Truvada 14 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('hiv_neuro_data.csv')\n", + "data['education'] = data['education'].astype(float)\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "ac31172e-1108-4f2c-a322-07e1f91d0942", + "metadata": {}, + "source": [ + "Before we start, we need to talk about assumptions.\n", + "\n", + "Basic linear regression has a number assumptions baked into itself:\n", + " - **Linearity**: The relationship between the independent variables (predictors) and the dependent variable (outcome) is linear. This means that changes in the predictors lead to proportional changes in the dependent variable.\n", + " - **The relationship between the independent variables and the dependent variable is additive**: The effect of changes in an independent variable X on the dependent variable Y is consistent, regardless of the values of other independent variables. This assumption might not hold if there are interaction effects between independent variables that affect the dependent variable.\n", + " - **Independence**: Observations are independent of each other. This means that the observations do not influence each other, an assumption that is particularly important in time-series data where time-related dependencies can violate this assumption.\n", + " - **Homoscedasticity**: The variance of error terms (residuals) is constant across all levels of the independent variables. In other words, as the predictor variable increases, the spread (variance) of the residuals remains constant. This is evaluated at the **end** of the fit.\n", + " - **Normal Distribution of Errors**: The residuals (errors) of the model are normally distributed. This assumption is especially important for hypothesis testing (e.g., t-tests of coefficients) and confidence interval construction. It's worth noting that for large sample sizes, the Central Limit Theorem helps mitigate the violation of this assumption. This is evaluated at the **end** of the fit.\n", + " - **Minimal Multicollinearity**: The independent variables need to be independent of each other. Multicollinearity doesn't affect the fit of the model as much as it affects the coefficients' estimates, making them unstable and difficult to interpret.\n", + " - **No perfect multicollinearity**: Also called the _dummy variable trap_. It states that none of the independent variables should be a perfect linear function of other independent variables. We'll talk more about this when we run into it.\n", + "\n", + "Biology itself is highly non-linear.\n", + "That doesn't mean we can't use linear assumptions to explore biological questions, it just means that we need to be mindful when interpretting the results." + ] + }, + { + "cell_type": "markdown", + "id": "a6ab9af5-a5ea-451c-b267-fcc0b0b1afd7", + "metadata": {}, + "source": [ + "## Exploration" + ] + }, + { + "cell_type": "markdown", + "id": "9e1954ae-3cb3-4167-8705-e9123c1e9d40", + "metadata": {}, + "source": [ + "Let's start by plotting the each variable against EDZ." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d8dd6aa8-655e-4d6b-a977-1e6d4ed91181", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (age_ax, edu_ax, ys_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))\n", + "\n", + "sns.regplot(data = data,\n", + " x = 'age',\n", + " y = 'exec_domain_z',\n", + " ax=age_ax)\n", + "\n", + "sns.regplot(data = data,\n", + " x = 'education',\n", + " y = 'exec_domain_z',\n", + " ax=edu_ax)\n", + "\n", + "sns.regplot(data = data,\n", + " x = 'YearsSeropositive',\n", + " y = 'exec_domain_z',\n", + " ax=ys_ax)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2c4b2076-e3e1-484e-bd41-31a07f419162", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q1: By inspection, which variable is most correlated?" + ] + }, + { + "cell_type": "markdown", + "id": "6e601810-0c65-4d8f-86d6-aa26184e1971", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 5 |\n", + "| Public Checks | 3 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "016c7dda-c8f7-43bd-b956-9eb418126bcc", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [], + "source": [ + "# Answer: age, education, YearsSeropositive\n", + "q1_most_correlated = 'YearsSeropositive' # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1b66cd6", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q1_initial_correlation\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a11fb13c-1794-4fad-8586-96727cd1ca88", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (race_ax, sex_ax, art_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))\n", + "\n", + "sns.stripplot(data=data,\n", + " x = 'race',\n", + " y = 'exec_domain_z', ax=race_ax)\n", + "sns.boxplot(data=data,\n", + " x = 'race',\n", + " y = 'exec_domain_z', ax=race_ax)\n", + "\n", + "sns.stripplot(data=data,\n", + " x = 'sex',\n", + " y = 'exec_domain_z', ax=sex_ax)\n", + "sns.boxplot(data=data,\n", + " x = 'sex',\n", + " y = 'exec_domain_z', ax=sex_ax)\n", + "\n", + "sns.stripplot(data=data,\n", + " x = 'ART',\n", + " y = 'exec_domain_z', ax=art_ax)\n", + "sns.boxplot(data=data,\n", + " x = 'ART',\n", + " y = 'exec_domain_z', ax=art_ax)" + ] + }, + { + "cell_type": "markdown", + "id": "a6715b3c-a00e-42e2-8633-798881ae7cbb", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q2: By inspection, which variable has the most between class difference?" + ] + }, + { + "cell_type": "markdown", + "id": "2b4bacf5-e194-4225-b3c1-3d25c2a830dd", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 5 |\n", + "| Public Checks | 3 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "737a1795-88d3-4225-b0df-e6aee178968a", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [], + "source": [ + "# Answer: race, sex, ART\n", + "q2_most_bcd = 'race' # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6016a607", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q2_initial_bcd\")" + ] + }, + { + "cell_type": "markdown", + "id": "27d11168-ead2-4651-b420-a7431b290ee4", + "metadata": {}, + "source": [ + "## Basic regression" + ] + }, + { + "cell_type": "markdown", + "id": "89603733-b40c-4d31-8ec3-d1933d2e6dd6", + "metadata": {}, + "source": [ + "We'll start by taking the simplest approach and regress the most correlated value first." + ] + }, + { + "cell_type": "markdown", + "id": "95b2c235-e31d-4198-960c-9759c8cf380a", + "metadata": {}, + "source": [ + "`pg.linear_regression` works by regressing all columns in the first parameter against the single column in the second.\n", + "By convention, we usually use the variables `X` and `y`.\n", + "\n", + "You'll often see this written as:\n", + "\n", + "$\\mathbf{y} = \\mathbf{X} \\boldsymbol{\\beta} + \\boldsymbol{\\epsilon}$\n", + "\n", + "In the case of `pg.linear_regression` the $\\boldsymbol{\\epsilon}$ is added by default and we do not need to specify it.\n", + "\n", + "You do not have to use the variable names `X` and `y`, in many cases you might have multiple `X`s and `y`s, but for simplicity, I will stick with this simple convention." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d37176f0-9513-44c9-a293-0256c7f4c08c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept0.7116250.1058226.7247337.994463e-110.2368150.2344530.5034370.919812
    1YearsSeropositive-0.0352580.003522-10.0113201.000644e-200.2368150.234453-0.042186-0.028329
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 \\\n", + "0 Intercept 0.711625 0.105822 6.724733 7.994463e-11 0.236815 \n", + "1 YearsSeropositive -0.035258 0.003522 -10.011320 1.000644e-20 0.236815 \n", + "\n", + " adj_r2 CI[2.5%] CI[97.5%] \n", + "0 0.234453 0.503437 0.919812 \n", + "1 0.234453 -0.042186 -0.028329 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = data['YearsSeropositive'] # Our independent variables\n", + "y = data['exec_domain_z'] # Our dependent variable\n", + "res = pg.linear_regression(X, y)\n", + "res" + ] + }, + { + "cell_type": "markdown", + "id": "308f2c65-40b8-4e26-93a9-2b2ac44e495f", + "metadata": {}, + "source": [ + "This has fit the equation:\n", + "\n", + "`PDZ = -0.035*YS + 0.712`\n", + "\n", + "It tells us that the likelihood of this slope being zero is 1.0E-20 and that years-seropositive explains ~23.6% of variation in EDZ that we observe." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f97f1fce-b27c-4371-bc5e-97378e170ff5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.regplot(data = data,\n", + " x = 'YearsSeropositive',\n", + " y = 'exec_domain_z')\n", + "\n", + "# Pick \"years seropositive\" from 0 to 70\n", + "x = np.arange(0, 70)\n", + "\n", + "# Use the coefficients from above in a linear equation\n", + "y = res.loc[1, 'coef']*x + res.loc[0, 'coef']\n", + "\n", + "ax.plot(x, y, color = 'r')" + ] + }, + { + "cell_type": "markdown", + "id": "7b9d1f9b-16b9-4f95-ae29-00d964a2eb3c", + "metadata": {}, + "source": [ + "## Residuals" + ] + }, + { + "cell_type": "markdown", + "id": "f9909e11-b673-4be1-9787-e4f815f04ab7", + "metadata": {}, + "source": [ + "_Residuals_ are the difference between the observed value and the predicted value.\n", + "In the case of a simple linear regression, this is the y-distance between each point and the best-fit line.\n", + "Examining these is an import step in assessing the fit for any biases.\n", + "You can think of the residual as what is \"left over\" after the regression.\n", + "\n", + "We could calculate these ourselves from the regression coefficients, but, `pingouin` conviently provides them for us.\n", + "The result `DataFrame` from `pg.linear_regression` has a special attribute `.residuals_` which stores the difference between the prediction and reality for each point in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "aff2050d-1d24-4b23-834a-dd8e9add1aa0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.34672285 1.15826787 -0.29430717 -1.06544462 1.08198035]\n" + ] + } + ], + "source": [ + "print(res.residuals_[:5])" + ] + }, + { + "cell_type": "markdown", + "id": "c2662e02-ff9b-4398-ace9-d4f05d29e098", + "metadata": {}, + "source": [ + "In order to test the **Homoscedasticity** we want to ensure that these residuals are _not correlated with the depenendant variable_.\n", + "\n", + "In our case, this means that the model is equally good predicting the EDZ of people recently infected with HIV and those who have been living with HIV for a long time.\n", + "\n", + "To do this, we plot the residuals vs each independent variable." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2eec2b7c-2bae-4b79-a740-f534751b66e9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.scatterplot(x=data['YearsSeropositive'], y=res.residuals_)" + ] + }, + { + "cell_type": "markdown", + "id": "ddc1570e-155a-4c57-ac8d-e41eb6895574", + "metadata": {}, + "source": [ + "This is an ideal residual plot.\n", + "It should look like a random \"stary-night sky\" centered around 0.\n", + "This implies that the model is not better or worse for any given X value." + ] + }, + { + "cell_type": "markdown", + "id": "6d4a62b5-c418-4222-9c87-90ecf7804f26", + "metadata": {}, + "source": [ + "Let's also test our assumption about a normal distribution of errors of the residuals." + ] + }, + { + "cell_type": "markdown", + "id": "ca391103-3c84-4fd6-9b7f-896577811ed5", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q3: Are the residuals normally distributed?" + ] + }, + { + "cell_type": "markdown", + "id": "41d6da6d-1e4c-496e-a059-85b262326bc9", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 5 |\n", + "| Public Checks | 5 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0caa835c-e80d-4ec1-ba53-de99147c41d5", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a Q-Q plot of the residuals\n", + "\n", + "q3_plot = pg.qqplot(res.residuals_) # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "753e8d3b-8d25-4ac7-81d7-8f606d9dec09", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [], + "source": [ + "# Use the Jarque-Bera normal test for large sample sizes\n", + "\n", + "q3_norm_res = pg.normality(res.residuals_, method='jarque_bera') # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5afc057b-0cf0-4df7-8d5e-734980f2fb47", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [], + "source": [ + "# Are the residuals normally distributed? 'yes' or 'no'\n", + "\n", + "q3_is_norm = 'yes' # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63e75623", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q3_resid_normality\")" + ] + }, + { + "cell_type": "markdown", + "id": "01b59934-9f51-429d-a65e-ebf77655a3dc", + "metadata": {}, + "source": [ + "You don't need to do this test at every stage, but it is a good test to do before you are _done_." + ] + }, + { + "cell_type": "markdown", + "id": "17cd99fc-7bc7-4f43-9872-50ddc5fc4a9d", + "metadata": {}, + "source": [ + "## Multiple Regression" + ] + }, + { + "cell_type": "markdown", + "id": "e0045aea-276f-4dd8-bfd2-cf9129a2cb15", + "metadata": {}, + "source": [ + "Regression is not limited to a single independent variable, you can add as many as you'd like.\n", + "\n", + "In our case, there are two others that we should consider: `age` and `education`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2c9e5a55-d612-4af6-a1b2-113e9ae5f825", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept0.9774490.4047182.4151351.628781e-020.3182070.3118350.1812141.773685
    1YearsSeropositive-0.0374620.003390-11.0498542.853764e-240.3182070.311835-0.044132-0.030792
    2education-0.1026470.020406-5.0301768.170366e-070.3182070.311835-0.142794-0.062500
    3age0.0192970.0055463.4792955.721793e-040.3182070.3118350.0083850.030209
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 \\\n", + "0 Intercept 0.977449 0.404718 2.415135 1.628781e-02 0.318207 \n", + "1 YearsSeropositive -0.037462 0.003390 -11.049854 2.853764e-24 0.318207 \n", + "2 education -0.102647 0.020406 -5.030176 8.170366e-07 0.318207 \n", + "3 age 0.019297 0.005546 3.479295 5.721793e-04 0.318207 \n", + "\n", + " adj_r2 CI[2.5%] CI[97.5%] \n", + "0 0.311835 0.181214 1.773685 \n", + "1 0.311835 -0.044132 -0.030792 \n", + "2 0.311835 -0.142794 -0.062500 \n", + "3 0.311835 0.008385 0.030209 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = data[['YearsSeropositive', 'education', 'age']]\n", + "y = data['exec_domain_z']\n", + "res = pg.linear_regression(X, y)\n", + "res" + ] + }, + { + "cell_type": "markdown", + "id": "3653f050-b236-46ff-8b0d-4db6935c6880", + "metadata": {}, + "source": [ + "Now, it has fit the equation:\n", + "\n", + "`EDZ = -0.037*YS - 0.103*edu + 0.019*age + 0.977`\n", + "\n", + "The education is significant at p=8.17E-7.\n", + "Be caution when comparing coefficients, we might be tempted to compare -0.0422 and -0.0506 and say that education has a more negative effect than YS ...\n", + "But, remember that education ranges from 0-12 and YS ranges from 0-60, these are not on the same scale and are not directly comparable.\n", + "We'll talk about how to compare relative importance later." + ] + }, + { + "cell_type": "markdown", + "id": "60eb2693-5c50-4784-889d-ac28a1faba2b", + "metadata": {}, + "source": [ + "As before, we should check the residuals of the model against _each_ independent variable in the regression to check for homoscedasticity." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d131c037-88eb-491d-a707-8526b6d2c516", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ys_ax, edu_ax, age_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))\n", + "\n", + "sns.scatterplot(x=data['YearsSeropositive'], y=res.residuals_, ax=ys_ax)\n", + "sns.scatterplot(x=data['education'], y=res.residuals_, ax=edu_ax)\n", + "sns.scatterplot(x=data['age'], y=res.residuals_, ax=age_ax)" + ] + }, + { + "cell_type": "markdown", + "id": "e162e5c1-107e-4d83-a074-8d9812b67688", + "metadata": {}, + "source": [ + "Three more stary night skies. Perfect." + ] + }, + { + "cell_type": "markdown", + "id": "6dc72fe5-e59a-434b-acba-3ceacd58ecfe", + "metadata": {}, + "source": [ + "Remember, the residual is the difference between the prediction of the model and reality.\n", + "Therefore, we can also use the residual plots to see how well the regression is handling other variables we have not included in the model.\n", + "If the model has properly accounted for something, the residual plot should stay centered around 0.\n", + "\n", + "This can be done for categorical or continious variables." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "15d2e733-b303-4aff-8451-147f222f5cd7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (race_ax, sex_ax, art_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))\n", + "\n", + "race_ax.set_ylabel('residual')\n", + "\n", + "sns.barplot(x=data['race'], y=res.residuals_, ax=race_ax)\n", + "sns.barplot(x=data['sex'], y=res.residuals_, ax=sex_ax)\n", + "sns.barplot(x=data['ART'], y=res.residuals_, ax=art_ax)" + ] + }, + { + "cell_type": "markdown", + "id": "2e0a1f0c-7df8-40f8-ab6f-bb2e70eb7493", + "metadata": {}, + "source": [ + "Here we see some interesting patterns:\n", + " - The graph of race against residuals shows us that our model is signifacntly racially biased. AA individuals are significantly 'under-estimated' by the model, C individauals are significantly over-estimated, and H individuals are significantly over-estimated.\n", + " - The graph of sex shows that there is no real difference in the residuals. It has accounted for sex already.\n", + " - It looks like there is a real difference across ART." + ] + }, + { + "cell_type": "markdown", + "id": "7bc5658b-b99f-44f1-8746-495870be08a4", + "metadata": {}, + "source": [ + "## _ANCOVA_" + ] + }, + { + "cell_type": "markdown", + "id": "2bb494a9-d773-4f50-8c7a-52535f1684f8", + "metadata": {}, + "source": [ + "What we have done above is create a model that _accounts_ for the effects of age, education, and YS on EDZ.\n", + "We **subtracted** that effect (the predicted value) from the observed value thus creating the _residual_.\n", + "This is what is \"left over\" in the observed value after accounting for covariates or nuisance variables.\n", + "Then we plotted the _residual_ against each of our categorical variables.\n", + "If we then took the ANOVA of these residuals we'd be testing the hypothesis:\n", + " _When accounting for age, education, and YS is there a difference across race._\n", + " \n", + "This process is called an _Analysis of covariance_ or an **ANCOVA**." + ] + }, + { + "cell_type": "markdown", + "id": "2b088af3-35d1-4228-a38d-0ce0edd7de10", + "metadata": {}, + "source": [ + "### Standard first" + ] + }, + { + "cell_type": "markdown", + "id": "d4c97c10-cedb-4a4a-9568-c56dfe6b737d", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q4: Perform an ANOVA between ART on the Executive Domain Z-score." + ] + }, + { + "cell_type": "markdown", + "id": "ed969ccd-12ec-41b6-b6ba-cd6d7203208a", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 5 |\n", + "| Public Checks | 4 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "0cca7821-9925-43d1-a802-62a17217125e", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a plot showing the effect of ART on EDZ\n", + "q4_plot = sns.barplot(data = data, x = 'ART', y = 'exec_domain_z') # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "07fde2af-cad6-4b78-b88d-54d027545af9", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Sourceddof1ddof2Fp-uncnp2
    0ART13237.8096990.0055070.023608
    \n", + "
    " + ], + "text/plain": [ + " Source ddof1 ddof2 F p-unc np2\n", + "0 ART 1 323 7.809699 0.005507 0.023608" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Perform an ANOVA testing the impact of ART on EDZ\n", + "q4_res = pg.anova(data, dv = 'exec_domain_z', between = 'ART') # SOLUTION\n", + "q4_res" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "46ef6bde-3ab5-43f9-bab2-5fc4dc400688", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [], + "source": [ + "# Does ART have a significant impact on Executive Domain? 'yes' or 'no'?\n", + "\n", + "q4_art_impact = 'yes' # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78303d6a", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q4_art_test\")" + ] + }, + { + "cell_type": "markdown", + "id": "8f89b18b-531d-42a1-a96a-5f5f95449fb9", + "metadata": {}, + "source": [ + "### With correction\n", + "\n", + "Nicely `pingouin` has something built right in to do this whole process." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "5377a300-35e4-472b-b960-1bc8c1d59001", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SourceSSDFFp-uncnp2
    0ART11.879147117.4700833.770731e-050.051768
    1YearsSeropositive79.8888141117.4885851.585741e-230.268552
    2education20.033725129.4626231.128191e-070.084308
    3age17.992537126.4607474.697743e-070.076374
    4Residual217.590675320NaNNaNNaN
    \n", + "
    " + ], + "text/plain": [ + " Source SS DF F p-unc np2\n", + "0 ART 11.879147 1 17.470083 3.770731e-05 0.051768\n", + "1 YearsSeropositive 79.888814 1 117.488585 1.585741e-23 0.268552\n", + "2 education 20.033725 1 29.462623 1.128191e-07 0.084308\n", + "3 age 17.992537 1 26.460747 4.697743e-07 0.076374\n", + "4 Residual 217.590675 320 NaN NaN NaN" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(x=data['ART'], y=res.residuals_)\n", + "\n", + "# An ANCOVA testing the impact of ART on EDZ\n", + "# after correcting for the impace of age, education and YS\n", + "pg.ancova(data,\n", + " dv = 'exec_domain_z',\n", + " between = 'ART',\n", + " covar=['YearsSeropositive', 'education', 'age'])" + ] + }, + { + "cell_type": "markdown", + "id": "1409e6f5-23e5-4436-a9a6-0242f4c36c7e", + "metadata": {}, + "source": [ + "We can notice that after correction for covaraites the F-value has increased and the p-value has decreased.\n", + "This means the analysis is attributing more difference to race after correction and is more sure this is not due to noise." + ] + }, + { + "cell_type": "markdown", + "id": "ff14833e-bda0-48a2-9c26-d2e530824231", + "metadata": {}, + "source": [ + "The _advantage_ of using the `pg.ancova` function is that you can easily and quickly do your analysis.\n", + "The _disadvantage_ is that you cannot examine the internal regression for Normality and Homoscedasticity." + ] + }, + { + "cell_type": "markdown", + "id": "fa572f6b-0e82-4a31-ab30-4c267bfb5be0", + "metadata": {}, + "source": [ + "But, what if we wanted to have a covariate that is a category like race?" + ] + }, + { + "cell_type": "markdown", + "id": "5f8a699c-8439-40c4-9728-a391a5785573", + "metadata": {}, + "source": [ + "## Regression with categories" + ] + }, + { + "cell_type": "markdown", + "id": "89316dac-b3db-444d-9bc1-9136c1e9970c", + "metadata": {}, + "source": [ + "So, how do you do regression with a category like race?\n", + "\n", + "Could it be as simple as adding it the `X` matrix?" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "8fbd4b6c-dbf6-4eb2-846f-ee978ab688a8", + "metadata": {}, + "outputs": [], + "source": [ + "# X = data[['YearsSeropositive', 'education', 'age', 'race']]\n", + "# y = data['processing_domain_z']\n", + "# res = pg.linear_regression(X, y)\n", + "# res" + ] + }, + { + "cell_type": "markdown", + "id": "6199f0af-45b8-43ef-946e-1ea31145f7a7", + "metadata": {}, + "source": [ + "Would have been nice, but we need to get a little tricky and use _dummy_ variables.\n", + "\n", + "In their simplest terms, dummy variables are binary representations of categories.\n", + "Like so." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "c2cd028f-1caf-4797-841d-0d508c7f9afd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    AACH
    0TrueFalseFalse
    1TrueFalseFalse
    2TrueFalseFalse
    3TrueFalseFalse
    4TrueFalseFalse
    \n", + "
    " + ], + "text/plain": [ + " AA C H\n", + "0 True False False\n", + "1 True False False\n", + "2 True False False\n", + "3 True False False\n", + "4 True False False" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.get_dummies(data['race']).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "36adb5a0-9709-402a-95e8-ec24c68524a2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/tljh/user/lib/python3.9/site-packages/pingouin/regression.py:420: UserWarning: Design matrix supplied with `X` parameter is rank deficient (rank 6 with 7 columns). That means that one or more of the columns in `X` are a linear combination of one of more of the other columns.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept-0.1940.294-0.6610.5090.4530.444-0.7720.383
    1YearsSeropositive-0.0460.003-14.1330.0000.4530.444-0.052-0.039
    2education-0.0540.019-2.7950.0060.4530.444-0.092-0.016
    3age0.0310.0055.8680.0000.4530.4440.0210.041
    4AA0.4100.1043.9410.0000.4530.4440.2050.615
    5C-0.5830.149-3.9140.0000.4530.444-0.876-0.290
    6H-0.0210.132-0.1620.8710.4530.444-0.2820.239
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 adj_r2 CI[2.5%] \\\n", + "0 Intercept -0.194 0.294 -0.661 0.509 0.453 0.444 -0.772 \n", + "1 YearsSeropositive -0.046 0.003 -14.133 0.000 0.453 0.444 -0.052 \n", + "2 education -0.054 0.019 -2.795 0.006 0.453 0.444 -0.092 \n", + "3 age 0.031 0.005 5.868 0.000 0.453 0.444 0.021 \n", + "4 AA 0.410 0.104 3.941 0.000 0.453 0.444 0.205 \n", + "5 C -0.583 0.149 -3.914 0.000 0.453 0.444 -0.876 \n", + "6 H -0.021 0.132 -0.162 0.871 0.453 0.444 -0.282 \n", + "\n", + " CI[97.5%] \n", + "0 0.383 \n", + "1 -0.039 \n", + "2 -0.016 \n", + "3 0.041 \n", + "4 0.615 \n", + "5 -0.290 \n", + "6 0.239 " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extracting the same continious variables\n", + "X = data[['YearsSeropositive', 'education', 'age']]\n", + "\n", + "# Creating new dummy variables for race\n", + "dummy_vals = pd.get_dummies(data['race']).astype(float)\n", + "\n", + "\n", + "# Adding them the end\n", + "X = pd.concat([X, dummy_vals], axis=1)\n", + "\n", + "y = data['exec_domain_z']\n", + "\n", + "res = pg.linear_regression(X, y)\n", + "res.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "be9ac92a-18be-4d29-9408-9a2ae605e8fb", + "metadata": {}, + "source": [ + "This _Warning_ is telling us that our model has fallen into the _dummy variable trap_.\n", + "The dummy variable trap occurs when dummy variables created for categorical data in a regression model are perfectly collinear, meaning one variable can be predicted from the others, leading to redundancy.\n", + "This happens because the inclusion of all dummy variables for a category along with a constant term (intercept) creates a situation where the sum of the dummy variables plus the intercept equals one, introducing perfect multicollinearity.\n", + "To avoid this, one dummy variable should be dropped to serve as the reference category, ensuring the model's design matrix is full rank and the regression coefficients are estimable and interpretable." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "635fc2b2-2c6e-4e54-afd5-0731a721840b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    CH
    0FalseFalse
    1FalseFalse
    2FalseFalse
    3FalseFalse
    4FalseFalse
    \n", + "
    " + ], + "text/plain": [ + " C H\n", + "0 False False\n", + "1 False False\n", + "2 False False\n", + "3 False False\n", + "4 False False" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.get_dummies(data['race'], drop_first=True).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "05f2d96c-2f2c-47c9-8c59-b0a068c944dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept0.2160.3810.5670.5710.4530.444-0.5340.966
    1YearsSeropositive-0.0460.003-14.1330.0000.4530.444-0.052-0.039
    2education-0.0540.019-2.7950.0060.4530.444-0.092-0.016
    3age0.0310.0055.8680.0000.4530.4440.0210.041
    4C-0.9930.115-8.6420.0000.4530.444-1.219-0.767
    5H-0.4320.147-2.9420.0040.4530.444-0.720-0.143
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 adj_r2 CI[2.5%] \\\n", + "0 Intercept 0.216 0.381 0.567 0.571 0.453 0.444 -0.534 \n", + "1 YearsSeropositive -0.046 0.003 -14.133 0.000 0.453 0.444 -0.052 \n", + "2 education -0.054 0.019 -2.795 0.006 0.453 0.444 -0.092 \n", + "3 age 0.031 0.005 5.868 0.000 0.453 0.444 0.021 \n", + "4 C -0.993 0.115 -8.642 0.000 0.453 0.444 -1.219 \n", + "5 H -0.432 0.147 -2.942 0.004 0.453 0.444 -0.720 \n", + "\n", + " CI[97.5%] \n", + "0 0.966 \n", + "1 -0.039 \n", + "2 -0.016 \n", + "3 0.041 \n", + "4 -0.767 \n", + "5 -0.143 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = data[['YearsSeropositive', 'education', 'age']]\n", + "dummy_vals = pd.get_dummies(data['race'], drop_first=True).astype(float)\n", + "X = pd.concat([X, dummy_vals], axis=1)\n", + "y = data['exec_domain_z']\n", + "res = pg.linear_regression(X, y)\n", + "res.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "72089b6c-1a01-46bc-85a7-afcc96eed850", + "metadata": {}, + "source": [ + "We can notice a few things here:\n", + " - **AA** has become the 'reference', the coefficients of C and H are relative to AA, which is set at 0.\n", + " - C individuals have a decreased score (relative to AA), which is significant.\n", + " - H individuals have an decreased score (relative to AA), which is significant." + ] + }, + { + "cell_type": "markdown", + "id": "89709ef9-443f-4583-b103-c825dceb39ff", + "metadata": {}, + "source": [ + "We can look at the residuals." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "ee1f5b5d-7fcd-4edc-9d1f-0e4a91e6934d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABN0AAAHACAYAAACWIh5pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA7VUlEQVR4nO3de5hVZf03/vdwGnAGRgQFTRQMRQwwxQg8oaWQJpHnPKAWUoZ5IjX9mWb6LSpDKUvNE0jiKbPIMg6Z9lURFYRKJerxyaAcPCLoqBz37w8fdo6gIi4YBl6v69qXc691r7U/a1h7buc991qrolQqlQIAAAAAFKZJQxcAAAAAABsboRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQMKEbAAAAABRM6AYAAAAABRO6AQAAAEDBmjV0ARu6FStW5Nlnn03r1q1TUVHR0OUANHqlUimvvvpqttlmmzRp4m8/xhmAYhlnVmWsASjWmo41Qrf38eyzz6ZTp04NXQbARmfevHnZdtttG7qMBmecAVg3jDP/ZawBWDfeb6wRur2P1q1bJ3nrG9mmTZsGrgag8Vu0aFE6depU/vm6qTPOABTLOLMqYw1AsdZ0rBG6vY+V06/btGljgAIokMtb3mKcAVg3jDP/ZawBWDfeb6xxkwMAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgzRq6AADeUiqVUldXV25XVVWloqKiASsCAABgbQndADYQdXV1GTx4cLk9YcKEVFdXN2BFAAAArC2XlwIAAABAwYRuAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQsGYNXQAAAAAAxSqVSqmrqyu3q6qqUlFR0YAVbXqEbgAAAAAbmbq6ugwePLjcnjBhQqqrqxuwok2Py0sBAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgQjcAAAAAKFijC92uuuqqdOnSJS1btkzv3r3zwAMPvGvfu+66KwceeGC23HLLtGnTJv369cukSZPWY7UAAAAAbIoaVeh2++2358wzz8wFF1yQmTNnZp999slBBx2UuXPnrrb///7v/+bAAw/MPffckxkzZmT//ffPoEGDMnPmzPVcOQAAAACbkkYVul1++eUZOnRoTj755HTv3j2jR49Op06dcvXVV6+2/+jRo3PuuefmE5/4RHbcccd897vfzY477pi77757PVcOAAAAwKakWUMXsKaWLFmSGTNm5Lzzzqu3fMCAAZk6deoa7WPFihV59dVXs8UWW7xrn8WLF2fx4sXl9qJFi9auYABYDeMMAOuasQZgw9BoZrq9+OKLWb58eTp06FBveYcOHTJ//vw12seoUaNSV1eXo4466l37jBw5MjU1NeVXp06dPlTdAPB2xhkA1jVjDcCGodGEbitVVFTUa5dKpVWWrc6tt96aiy++OLfffnu22mqrd+13/vnnZ+HCheXXvHnzPnTNALCScQaAdc1YA7BhaDSXl7Zv3z5NmzZdZVbb888/v8rst3e6/fbbM3To0PziF7/IAQcc8J59KysrU1lZ+aHrBYDVMc4AsK4ZawA2DI1mpluLFi3Su3fvTJkypd7yKVOmZM8993zX7W699dacdNJJueWWW/LZz352XZcJAAAAAI1npluSjBgxIkOGDMkee+yRfv365dprr83cuXNzyimnJHlrGvV//vOfjBs3LslbgdsJJ5yQH/3oR+nbt295llyrVq1SU1PTYMcBAAAAwMatUYVuRx99dF566aVccsklqa2tTY8ePXLPPfdk++23T5LU1tZm7ty55f4/+9nPsmzZspx66qk59dRTy8tPPPHEjB07dn2XDwAAAMAmolGFbkkyfPjwDB8+fLXr3hmk3X///eu+IAAAAAB4h0ZzTzcAAAAAaCyEbgAAAABQMKEbAAAAABRM6AYAAAAABRO6AQAAAEDBhG4AAAAAUDChGwAAAAAUTOgGAAAAAAUTugEAAABAwYRuAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQMKEbAAAAABSsWUMXAHx4pVIpdXV15XZVVVUqKioasCIAAADYtAndYCNQV1eXwYMHl9sTJkxIdXV1A1YEAAAAmzaXlwIAAABAwYRuAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQMKEbAAAAABRM6AYAAAAABRO6AQAAAEDBhG4AAAAAUDChGwAAAAAUrFlDFwAAAJuiUqmUurq6cruqqioVFRUNWBEAUCShGwAANIC6uroMHjy43J4wYUKqq6sbsCIAoEguLwUAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAoWLOGLgAAAACgKL3PGdfQJWwQKpYtSc3b2vtdeFtKzVo0WD0bihmXnbDe3stMNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgII1utDtqquuSpcuXdKyZcv07t07DzzwwLv2ra2tzbHHHptu3bqlSZMmOfPMM9dfoQBAgyiVSnnttdfKr1Kp1NAlAQCwCWpUodvtt9+eM888MxdccEFmzpyZffbZJwcddFDmzp272v6LFy/OlltumQsuuCC77rrreq4WAGgIdXV1GTx4cPlVV1fX0CUBALAJalSh2+WXX56hQ4fm5JNPTvfu3TN69Oh06tQpV1999Wr7d+7cOT/60Y9ywgknpKamZj1XCwAAAMCmqtGEbkuWLMmMGTMyYMCAessHDBiQqVOnNlBVAAAAALCqZg1dwJp68cUXs3z58nTo0KHe8g4dOmT+/PmFvc/ixYuzePHicnvRokWF7RsAjDMArGvGGoANQ6OZ6bZSRUVFvXapVFpl2YcxcuTI1NTUlF+dOnUqbN8bIjebBli/NrVxBoD1z1gDsGFoNKFb+/bt07Rp01VmtT3//POrzH77MM4///wsXLiw/Jo3b15h+94Qudk0wPq1qY0zAKx/xhqADUOjuby0RYsW6d27d6ZMmZJDDz20vHzKlCkZPHhwYe9TWVmZysrKwvYHAG9nnAFgXTPWAGwYGk3oliQjRozIkCFDsscee6Rfv3659tprM3fu3JxyyilJ3vqLzn/+85+MGzeuvM2sWbOSJK+99lpeeOGFzJo1Ky1atMguu+zSEIcAAAAAwCagUYVuRx99dF566aVccsklqa2tTY8ePXLPPfdk++23T5LU1tZm7ty59bbZbbfdyl/PmDEjt9xyS7bffvs888wz67N0AAAAADYhjSp0S5Lhw4dn+PDhq103duzYVZZ5MAAAAAAA61ujeZACAAAAADQWQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACtZsTTvutttuqaioWKO+jz/++FoXBAAAAACN3RqHbp///OfXYRkAAAAAsPFY49DtW9/61rqsAwAAAAA2Gu7pBgAAAAAFW+OZbm+3fPnyXHHFFbnjjjsyd+7cLFmypN76l19+uZDiAAAAAKAxWquZbt/+9rdz+eWX56ijjsrChQszYsSIHHbYYWnSpEkuvvjigksEAAAAgMZlrUK38ePH57rrrsvZZ5+dZs2a5Zhjjsn111+fiy66KNOmTSu6RgAAAABoVNYqdJs/f3569uyZJKmurs7ChQuTJIccckh+97vfFVcdAAAAADRCaxW6bbvttqmtrU2SdO3aNZMnT06SPPbYY6msrCyuOgAAAABohNYqdDv00ENz7733JknOOOOMXHjhhdlxxx1zwgkn5Etf+lKhBQIAAABAY7NWTy/93ve+V/76iCOOyLbbbpupU6ema9eu+dznPldYcQAAAADQGK1V6PZOffv2Td++fYvYFQAAAAA0emsVuo0bN+49159wwglrVQwAAAAAbAzWKnQ744wz6rWXLl2a119/PS1atMhmm20mdAMAAABgk7ZWD1JYsGBBvddrr72WOXPmZO+9986tt95adI0AAAAA0KisVei2OjvuuGO+973vrTILDgAAAAA2NYWFbknStGnTPPvss0XuEgAAAAAanbW6p9tvfvObeu1SqZTa2tr85Cc/yV577VVIYQAAAADQWK1V6Pb5z3++XruioiJbbrllPvWpT2XUqFFF1AUAAAAAjdZaXV66YsWKeq/ly5dn/vz5ueWWW7L11lsXXWM9V111Vbp06ZKWLVumd+/eeeCBB96z/5/+9Kf07t07LVu2zA477JBrrrlmndYHAAAAAGs1062h3H777TnzzDNz1VVXZa+99srPfvazHHTQQXnqqaey3XbbrdL/n//8Zw4++OAMGzYsN998cx566KEMHz48W265ZQ4//PAGOAIAgE1b73PGNXQJG4yKZUtS87b2fhfellKzFg1Wz4ZixmUnNHQJAFCINQ7dRowYscY7vfzyy9eqmDXZ79ChQ3PyyScnSUaPHp1Jkybl6quvzsiRI1fpf80112S77bbL6NGjkyTdu3fP9OnT88Mf/lDoBgAAAMA6s8ah28yZM+u1Z8yYkeXLl6dbt25Jkr///e9p2rRpevfuXWyF/8+SJUsyY8aMnHfeefWWDxgwIFOnTl3tNg8//HAGDBhQb9nAgQNzww03ZOnSpWnevPkq2yxevDiLFy8utxctWlRA9QDwFuMMAOuasQZgw7DGodt9991X/vryyy9P69atc9NNN6Vt27ZJkgULFuSLX/xi9tlnn+KrTPLiiy9m+fLl6dChQ73lHTp0yPz581e7zfz581fbf9myZXnxxRdXe/+5kSNH5tvf/nZxhQPA26yrccYle//lkr3V21Au2dtQ6tgQvPbaaxk8+NZy+/5Lv5Dq6uoGrIiNhbGGhrAh/XzfkGppSMaZhrdW93QbNWpUJk+eXA7ckqRt27b5n//5nwwYMCBf//rXCyvwnSoqKuq1S6XSKsver//qlq90/vnn17uUdtGiRenUqdPalgsA9ayrccb/XP6X/8EENnV+p1nHSqVULF/632bT5sl7/E4KbLrWKnRbtGhRnnvuuXzsYx+rt/z555/Pq6++Wkhh79S+ffs0bdp0lVltzz///Cqz2Vbq2LHjavs3a9Ys7dq1W+02lZWVqaysLKZoAHgH4wwA65qxZt2qWL40NX/57x93FvY6xoxqYLXWKnQ79NBD88UvfjGjRo1K3759kyTTpk3LOeeck8MOO6zQAldq0aJFevfunSlTpuTQQw8tL58yZUoGDx682m369euXu+++u96yyZMnZ4899ljt/dwAAAAANgZVVVWZMGFCvTbr11qFbtdcc03OPvvsHH/88Vm69K1ptc2aNcvQoUNz2WWXFVrg240YMSJDhgzJHnvskX79+uXaa6/N3Llzc8oppyR5axr1f/7zn4wb99a9Bk455ZT85Cc/yYgRIzJs2LA8/PDDueGGG3Lrrbe+19sAAAAANGoVFRVusdHA1ip022yzzXLVVVflsssuy9NPP51SqZSuXbuu89T06KOPzksvvZRLLrkktbW16dGjR+65555sv/32SZLa2trMnTu33L9Lly655557ctZZZ+WnP/1pttlmm/z4xz/O4Ycfvk7rBAAAAGDTtlah20pVVVXp1atXUbWskeHDh2f48OGrXTd27NhVlvXv3z+PP/74Oq4KAAAAAP5rjUO3ww47LGPHjk2bNm3e975td91114cuDAAAAAAaqzUO3WpqalLx/x6DXFNTs84KAgAAAIDGbo1DtzFjxqz2awAAAACgviZrs9Ebb7yR119/vdz+17/+ldGjR2fy5MmFFQYAAAAAjdVahW6DBw/OuHHjkiSvvPJK+vTpk1GjRmXw4MG5+uqrCy0QAAAAABqbtQrdHn/88eyzzz5JkjvvvDMdO3bMv/71r4wbNy4//vGPCy0QAAAAABqbtQrdXn/99bRu3TpJMnny5Bx22GFp0qRJ+vbtm3/961+FFggAAAAAjc1ahW5du3bNr3/968ybNy+TJk3KgAEDkiTPP/982rRpU2iBAAAAANDYrFXodtFFF+Xss89O586d06dPn/Tr1y/JW7Pedtttt0ILBAAAAIDGptnabHTEEUdk7733Tm1tbXbdddfy8k9/+tM59NBDCysOAAAAABqjtZrpliQdO3ZM69atM2XKlLzxxhtJkk984hPZeeedCysOAAAAABqjtQrdXnrppXz605/OTjvtlIMPPji1tbVJkpNPPjlf//rXCy0QAAAAABqbtQrdzjrrrDRv3jxz587NZpttVl5+9NFHZ+LEiYUVBwAAAACN0Vrd023y5MmZNGlStt1223rLd9xxx/zrX/8qpDAAAAAAaKzWaqZbXV1dvRluK7344ouprKz80EUBAAAAQGO2VqHbvvvum3HjxpXbFRUVWbFiRS677LLsv//+hRUHAAAAAI3RWl1e+sMf/jD9+/fP9OnTs2TJkpx77rl58skn8/LLL+ehhx4qukYAAAAAaFQ+8Ey3pUuXZvjw4fnNb36TPn365MADD0xdXV0OO+ywzJw5Mx/96EfXRZ0AAAAA0Gh84JluzZs3zxNPPJF27drl29/+9rqoCQAAAAAatbW6p9sJJ5yQG264oehaAAAAAGCjsFb3dFuyZEmuv/76TJkyJXvssUeqqqrqrb/88ssLKQ4AAAAAGqO1Ct2eeOKJ7L777kmSv//97/XWVVRUfPiqAAAAAKARW6vQ7b777iu6DgAAAADYaKzVPd0AAAAAgHcndAMAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIIJ3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAgjWa0G3BggUZMmRIampqUlNTkyFDhuSVV155z23uuuuuDBw4MO3bt09FRUVmzZq1XmoFAAAAYNPWrKELWFPHHnts/v3vf2fixIlJki9/+csZMmRI7r777nfdpq6uLnvttVeOPPLIDBs2bH2V2qhUVVVlwoQJ9doAAAAAfDiNInSbPXt2Jk6cmGnTpuWTn/xkkuS6665Lv379MmfOnHTr1m212w0ZMiRJ8swzz6yvUhudioqKVFdXN3QZAAAAABuVRhG6Pfzww6mpqSkHbknSt2/f1NTUZOrUqe8auq2NxYsXZ/HixeX2okWLCts3ABhnAFjXjDUAG4ZGcU+3+fPnZ6uttlpl+VZbbZX58+cX+l4jR44s3zeupqYmnTp1KnT/AGzajDMArGvGGoANQ4OGbhdffHEqKire8zV9+vQkb10G+U6lUmm1yz+M888/PwsXLiy/5s2bV+j+Adi0GWfWvZX3K135cr9SYFNjrAHYMDTo5aVf+9rX8oUvfOE9+3Tu3Dl/+ctf8txzz62y7oUXXkiHDh0KramysjKVlZWF7hMAVjLOrHvuVwps6ow1ABuGBg3d2rdvn/bt279vv379+mXhwoV59NFH06dPnyTJI488koULF2bPPfdc12UCAAAAwAfSKO7p1r1793zmM5/JsGHDMm3atEybNi3Dhg3LIYccUu8hCjvvvHN+9atfldsvv/xyZs2alaeeeipJMmfOnMyaNavw+8ABAAAAwNs1itAtScaPH5+ePXtmwIABGTBgQHr16pWf//zn9frMmTMnCxcuLLd/85vfZLfddstnP/vZJMkXvvCF7LbbbrnmmmvWa+0AAAAAbFoa9PLSD2KLLbbIzTff/J59SqVSvfZJJ52Uk046aR1WBQAAsGmYcdkJDV3CBuG1117L4MG3ltv3X/oF9xIFVqvRzHQDAAAAgMZC6AYAAAAABRO6AQAAAEDBhG4AAAAAUDChGwAAAAAUTOgGAAAAAAUTugEAAABAwYRuAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQMKEbAAAAABRM6AYAAAAABRO6AQAAAEDBhG4AAAAAUDChGwAAAAAUTOgGAAAAAAUTugEAAABAwYRuAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGaNXQBAACwKaqqqsqECRPqtQGAjYfQDQAAGkBFRUWqq6sbugwAYB1xeSkAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQMKEbAAAAABRM6AYAAAAABRO6AQAAAEDBhG4AAAAAUDChGwAAAAAUTOgGAAAAAAUTugEAAABAwYRuAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQMKEbAAAAABRM6AYAAAAABRO6AQAAAEDBhG4AAAAAUDChGwAAAAAUTOgGAAAAAAUTugEAAABAwYRuAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQMKEbAAAAABRM6AYAAAAABRO6AQAAAEDBhG4AAAAAULBmDV0A8OFVVVVlwoQJ9doAAABAwxG6wUagoqIi1dXVDV0GAAAA8P+4vBQAAAAACiZ0AwAAAICCNZrQbcGCBRkyZEhqampSU1OTIUOG5JVXXnnX/kuXLs03vvGN9OzZM1VVVdlmm21ywgkn5Nlnn11/RQMAAACwSWo0oduxxx6bWbNmZeLEiZk4cWJmzZqVIUOGvGv/119/PY8//nguvPDCPP7447nrrrvy97//PZ/73OfWY9UAAAAAbIoaxYMUZs+enYkTJ2batGn55Cc/mSS57rrr0q9fv8yZMyfdunVbZZuamppMmTKl3rIrr7wyffr0ydy5c7Pddtutl9oBAAAA2PQ0ipluDz/8cGpqasqBW5L07ds3NTU1mTp16hrvZ+HChamoqMjmm2++DqoEAAAAgLc0iplu8+fPz1ZbbbXK8q222irz589fo328+eabOe+883LsscemTZs279pv8eLFWbx4cbm9aNGiD14wALwL4wwA65qxBmDD0KAz3S6++OJUVFS852v69OlJkoqKilW2L5VKq13+TkuXLs0XvvCFrFixIlddddV79h05cmT5YQ01NTXp1KnT2h0cAKyGcQaAdc1YA7BhaNDQ7Wtf+1pmz579nq8ePXqkY8eOee6551bZ/oUXXkiHDh3e8z2WLl2ao446Kv/85z8zZcqU95zlliTnn39+Fi5cWH7NmzfvQx0jALydcQaAdc1YA7BhaNDLS9u3b5/27du/b79+/fpl4cKFefTRR9OnT58kySOPPJKFCxdmzz33fNftVgZu//jHP3LfffelXbt27/telZWVqaysXPODAIAPwDgDwLpmrAHYMDSKByl07949n/nMZzJs2LBMmzYt06ZNy7Bhw3LIIYfUe3LpzjvvnF/96ldJkmXLluWII47I9OnTM378+Cxfvjzz58/P/Pnzs2TJkoY6FAAAAAA2AY0idEuS8ePHp2fPnhkwYEAGDBiQXr165ec//3m9PnPmzMnChQuTJP/+97/zm9/8Jv/+97/z8Y9/PFtvvXX59UGeeAoAAAAAH1SjeHppkmyxxRa5+eab37NPqVQqf925c+d6bQAAAABYXxrNTDcAAAAAaCyEbgAAAABQMKEbAAAAABRM6AYAAAAABWs0D1IAAACAhlZVVZUJEybUawOsjtANAAAA1lBFRUWqq6sbugygEXB5KQAAAAAUTOgGAAAAAAUTugEAAABAwYRuAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQMKEbAAAAABRM6AYAAAAABRO6AQAAAEDBhG4AAAAAUDChGwAAAAAUTOgGAAAAAAUTugEAAABAwYRuAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQsGYNXQAAb6mqqsqECRPqtQEAAGichG4AG4iKiopUV1c3dBkAAAAUwOWlAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQMKEbAAAAABRM6AYAAAAABRO6AQAAAEDBhG4AAAAAUDChGwAAAAAUTOgGAAAAAAUTugEAAABAwYRuAAAAAFCwZg1dwIauVColSRYtWtTAlQBsHFb+PF3583VTZ5wBKJZxZlXGGoBirelYI3R7H6+++mqSpFOnTg1cCcDG5dVXX01NTU1Dl9HgjDMA64Zx5r+MNQDrxvuNNRUlfwJ6TytWrMizzz6b1q1bp6KioqHLWScWLVqUTp06Zd68eWnTpk1Dl8Na8u+4cdgU/h1LpVJeffXVbLPNNmnSxF0ONoVxpiFsCp8lNg7O1eIZZ1ZlrCmezy6NhXN13VjTscZMt/fRpEmTbLvttg1dxnrRpk0bH8KNgH/HjcPG/u9o5sF/bUrjTEPY2D9LbDycq8UyztRnrFl3fHZpLJyrxVuTscaffgAAAACgYEI3AAAAACiY0I1UVlbmW9/6ViorKxu6FD4E/44bB/+OUAyfJRoL5yo0Tj67NBbO1YblQQoAAAAAUDAz3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwA2GSeddFI+//nPN3QZNEKlUilf/vKXs8UWW6SioiKzZs1qkDqeeeaZBn1/YO107tw5o0ePLrcrKiry61//usHqgQ9iv/32y5lnntnQZTRKQrdNyNSpU9O0adN85jOfedc+t9xyS5o2bZpTTjllPVbGhzV//vycdtpp2WGHHVJZWZlOnTpl0KBBuffeexu6NNbAu4UA999/fyoqKvLKK6+s95oAqG/ixIkZO3Zsfvvb36a2tjY9evRo6JJgk/b888/nK1/5SrbbbrtUVlamY8eOGThwYB5++OEkG36oVVtbm4MOOqihy6CBVFRUvOfrpJNOaugSKUizhi6A9efGG2/Maaedluuvvz5z587Ndtttt9o+5557bq6++upcfvnl2WyzzRqgUj6IZ555JnvttVc233zz/OAHP0ivXr2ydOnSTJo0Kaeeemr+9re/NXSJANDoPf3009l6662z5557NnQpQJLDDz88S5cuzU033ZQddtghzz33XO699968/PLLDV3aGunYsWNDl0ADqq2tLX99++2356KLLsqcOXPKy1q1alWv/9KlS9O8efP1Vh/FMdNtE1FXV5c77rgjX/3qV3PIIYdk7Nixq/R55plnMnXq1Jx33nnZeeedc+edd67/QvnAhg8fnoqKijz66KM54ogjstNOO+VjH/tYRowYkWnTpjV0eQBrbb/99stpp52WM888M23btk2HDh1y7bXXpq6uLl/84hfTunXrfPSjH83vf//7JMny5cszdOjQdOnSJa1atUq3bt3yox/96D3fo1Qq5Qc/+EF22GGHtGrVKrvuuqvxj1WcdNJJOe200zJ37txUVFSkc+fO73vurJytPGnSpOy2225p1apVPvWpT+X555/P73//+3Tv3j1t2rTJMccck9dff7283cSJE7P33ntn8803T7t27XLIIYfk6aeffs/6nnrqqRx88MGprq5Ohw4dMmTIkLz44ovr7PsBDe2VV17Jgw8+mO9///vZf//9s/3226dPnz45//zz89nPfjadO3dOkhx66KHlz2zyVng+ePDgdOjQIdXV1fnEJz6RP/zhD+X9nn/++enbt+8q79erV69861vfSrL6y+w+//nP15uZ9Pzzz2fQoEFp1apVunTpkvHjx6+yz7fPxFt52fhdd92V/fffP5tttll23XXX8qy9laZOnZp99903rVq1SqdOnXL66aenrq7uA3732BB07Nix/KqpqUlFRUW5/eabb2bzzTfPHXfckf322y8tW7bMzTffnIsvvjgf//jH6+1n9OjR5fN70qRJadmy5SpXyZx++unp379/kuSll17KMccck2233TabbbZZevbsmVtvvbVe/7q6upxwwgmprq7O1ltvnVGjRq1S/80335w99tgjrVu3TseOHXPsscfm+eefL+z7szERum0ibr/99nTr1i3dunXL8ccfnzFjxqRUKtXrc+ONN+azn/1sampqcvzxx+eGG25ooGpZUy+//HImTpyYU089NVVVVaus33zzzdd/UQAFuummm9K+ffs8+uijOe200/LVr341Rx55ZPbcc888/vjjGThwYIYMGZLXX389K1asyLbbbps77rgjTz31VC666KL8f//f/5c77rjjXff/zW9+M2PGjMnVV1+dJ598MmeddVaOP/74/OlPf1qPR8mG7kc/+lEuueSSbLvttqmtrc1jjz22xufOxRdfnJ/85CeZOnVq5s2bl6OOOiqjR4/OLbfckt/97neZMmVKrrzyynL/urq6jBgxIo899ljuvffeNGnSJIceemhWrFix2tpqa2vTv3//fPzjH8/06dMzceLEPPfccznqqKPW6fcEGlJ1dXWqq6vz61//OosXL15l/WOPPZYkGTNmTPkzmySvvfZaDj744PzhD3/IzJkzM3DgwAwaNChz585Nkhx33HF55JFH6gXdTz75ZP7617/muOOOW+P6TjrppDzzzDP54x//mDvvvDNXXXXVGgUSF1xwQc4+++zMmjUrO+20U4455pgsW7YsSfLXv/41AwcOzGGHHZa//OUvuf322/Pggw/ma1/72hrXRePyjW98I6effnpmz56dgQMHvm//Aw44IJtvvnl++ctflpctX748d9xxR/n8ffPNN9O7d+/89re/zRNPPJEvf/nLGTJkSB555JHyNuecc07uu+++/OpXv8rkyZNz//33Z8aMGfXea8mSJbn00kvz5z//Ob/+9a/zz3/+0yWx76bEJmHPPfcsjR49ulQqlUpLly4ttW/fvjRlypTy+uXLl5c6depU+vWvf10qlUqlF154odS8efPSP/7xjwaplzXzyCOPlJKU7rrrroYuhQ/hxBNPLDVt2rRUVVVV79WyZctSktKCBQsaukRoEP379y/tvffe5fayZctKVVVVpSFDhpSX1dbWlpKUHn744dXuY/jw4aXDDz+83D7xxBNLgwcPLpVKpdJrr71WatmyZWnq1Kn1thk6dGjpmGOOKfBI2BhcccUVpe23375UKq3ZuXPfffeVkpT+8Ic/lNePHDmylKT09NNPl5d95StfKQ0cOPBd3/f5558vJSn99a9/LZVKpdI///nPUpLSzJkzS6VSqXThhReWBgwYUG+befPmlZKU5syZs9bHCxu6O++8s9S2bdtSy5YtS3vuuWfp/PPPL/35z38ur09S+tWvfvW++9lll11KV155Zbndq1ev0iWXXFJun3/++aVPfOIT5Xb//v1LZ5xxRr19DB48uHTiiSeWSqVSac6cOaUkpWnTppXXz549u5SkdMUVV6y2vpWf6+uvv768/sknnywlKc2ePbtUKpVKQ4YMKX35y1+u974PPPBAqUmTJqU33njjfY+TDdeYMWNKNTU15fbK82Hl7+8rfetb3yrtuuuu9Za9fWwqlUql008/vfSpT32q3J40aVKpRYsWpZdffvld3//ggw8uff3rXy+VSqXSq6++WmrRokXptttuK69/6aWXSq1atVrlvH+7Rx99tJSk9Oqrr77HkW6azHTbBMyZMyePPvpovvCFLyRJmjVrlqOPPjo33nhjuc/kyZNTV1dXvpln+/btM2DAgHp92PCU/t9sxYqKigauhA9r//33z6xZs+q9rr/++oYuCxpcr169yl83bdo07dq1S8+ePcvLOnTokCTlGQTXXHNN9thjj2y55Zaprq7OddddV57B8E5PPfVU3nzzzRx44IHlWRPV1dUZN27c+17Ox6btg5w7bz+HO3TokM022yw77LBDvWVvnwHz9NNP59hjj80OO+yQNm3apEuXLknyrufxjBkzct9999WrY+eddy7vCzZWhx9+eJ599tn85je/ycCBA3P//fdn9913X+1tdFaqq6vLueeem1122SWbb755qqur87e//a3e5+u4444rXw5aKpVy6623fqBZbrNnz06zZs2yxx57lJftvPPOa3QFytt/Xmy99dZJ/ju+zZgxI2PHjq33WR84cGBWrFiRf/7zn2tcH43H28+hNXXcccfl/vvvz7PPPpskGT9+fA4++OC0bds2yVsz377zne+kV69eadeuXaqrqzN58uTyZ+Dpp5/OkiVL0q9fv/I+t9hii3Tr1q3e+8ycOTODBw/O9ttvn9atW2e//fZL8u5j1abMgxQ2ATfccEOWLVuWj3zkI+VlpVIpzZs3z4IFC9K2bdvceOONefnll+s9OGHFihWZOXNmLr300jRt2rQhSud97LjjjqmoqMjs2bNX+/RLGo+qqqp07dq13rJ///vfDVQNbDjeedPgioqKestW/tFhxYoVueOOO3LWWWdl1KhR6devX1q3bp3LLrus3iUTb7fycr3f/e539cbIJKmsrCzyMNjIfJBz553n6+rO6bdfOjpo0KB06tQp1113XbbZZpusWLEiPXr0yJIlS961lkGDBuX73//+KutW/tIOG6uWLVvmwAMPzIEHHpiLLrooJ598cr71rW+962Vu55xzTiZNmpQf/vCH6dq1a1q1apUjjjii3ufr2GOPzXnnnZfHH388b7zxRubNm1eevJAkTZo0WeU2PUuXLi1//WH+KP5u49vK/37lK1/J6aefvsp2q3tAHo3fO28f9H7nXpL06dMnH/3oR3Pbbbflq1/9an71q19lzJgx5fWjRo3KFVdckdGjR6dnz56pqqrKmWeeWf4MvHP/q1NXV5cBAwZkwIABufnmm7Pllltm7ty5GThw4LuOVZsyodtGbtmyZRk3blxGjRqVAQMG1Ft3+OGHZ/z48TnmmGMyYcKE3HbbbfnYxz5WXr9ixYrss88++f3vf59DDjlkfZfOGthiiy0ycODA/PSnP83pp5++yg/mV155xX3dgE3GAw88kD333DPDhw8vL3uvmT677LJLKisrM3fu3PINhmFNrKtz56WXXsrs2bPzs5/9LPvss0+S5MEHH3zPbXbffff88pe/TOfOndOsmf+1Z9O2yy67lB9O0Lx58yxfvrze+gceeCAnnXRSDj300CRv3ePtmWeeqddn2223zb777pvx48fnjTfeyAEHHFCeVZ0kW265Zb0nTy5fvjxPPPFE9t9//yRJ9+7ds2zZskyfPj19+vRJ8taVR++8uf0Htfvuu+fJJ59c5Y+0bDq23HLLzJ8/P6VSqRzKzpo1a5V+xx57bMaPH59tt902TZo0yWc/+9nyugceeCCDBw/O8ccfn+St3/n/8Y9/pHv37kmSrl27pnnz5pk2bVo5zF2wYEH+/ve/l8e7v/3tb3nxxRfzve99L506dUqSTJ8+fZ0dd2NnZN7I/fa3v82CBQsydOjQ1NTU1Ft3xBFHlGfBtWvXLkceeWSaNKl/xfEhhxySG264Qei2Abvqqquy5557pk+fPrnkkkvSq1evLFu2LFOmTMnVV1+d2bNnN3SJAOtF165dM27cuEyaNCldunTJz3/+8zz22GPly/PeqXXr1jn77LNz1llnZcWKFdl7772zaNGiTJ06NdXV1TnxxBPX8xHQWKyrc6dt27Zp165drr322my99daZO3duzjvvvPfc5tRTT811112XY445Juecc07at2+f//N//k9uu+22XHfdda5WYKP00ksv5cgjj8yXvvSl9OrVK61bt8706dPzgx/8IIMHD06SdO7cOffee2/22muvVFZWpm3btunatWvuuuuuDBo0KBUVFbnwwgtX+5CS4447LhdffHGWLFmSK664ot66T33qUxkxYkR+97vf5aMf/WiuuOKKeoFat27d8pnPfCbDhg3Ltddem2bNmuXMM89Mq1atPtQxf+Mb30jfvn1z6qmnZtiwYamqqsrs2bNXeRgLG6/99tsvL7zwQn7wgx/kiCOOyMSJE/P73/8+bdq0qdfvuOOOy7e//e185zvfyRFHHJGWLVuW13Xt2jW//OUvM3Xq1LRt2zaXX3555s+fXw7dqqurM3To0Jxzzjlp165dOnTokAsuuKBeTrDddtulRYsWufLKK3PKKafkiSeeyKWXXrp+vgmNkHu6beRuuOGGHHDAAasEbslbM91mzZqViy66KIceeugqgdvKPr/97W/z3HPPrY9yWQtdunTJ448/nv333z9f//rX06NHjxx44IG59957c/XVVzd0eQDrzSmnnJLDDjssRx99dD75yU/mpZdeqjfrbXUuvfTSXHTRRRk5cmS6d++egQMH5u67737XoA5WWhfnTpMmTXLbbbdlxowZ6dGjR84666xcdtll77nNNttsk4ceeijLly/PwIED06NHj5xxxhmpqalZ7f/bwcaguro6n/zkJ3PFFVdk3333TY8ePXLhhRdm2LBh+clPfpLkrcvopkyZkk6dOmW33XZLklxxxRVp27Zt9txzzwwaNCgDBw7M7rvvvsr+jzzyyLz00kt5/fXXV7mFy5e+9KWceOKJOeGEE9K/f/906dKlPMttpTFjxqRTp07p379/DjvssHz5y1/OVltt9aGOuVevXvnTn/6Uf/zjH9lnn32y22675cILL3QZ+Sake/fuueqqq/LTn/40u+66ax599NGcffbZq/Tbcccd84lPfCJ/+ctfVrkf4YUXXpjdd989AwcOzH777ZeOHTuuco5fdtll2XffffO5z30uBxxwQPbee+/07t27vH7LLbfM2LFj84tf/CK77LJLvve97+WHP/zhOjnmjUFFaU0u2gUAAAAA1pg/fwEAAABAwYRuAAAAAFAwoRsAAAAAFEzoBgAAAAAFE7oBAAAAQMGEbgAAAABQMKEbAAAAABRM6AYAAAAABRO6AQAAAOvM1KlT07Rp03zmM5+pt/yZZ55JRUVF+VVTU5O+ffvm7rvvTpLst99+9da/89W5c+cGOBpYc0I3aASWLFnS0CUAAACslRtvvDGnnXZaHnzwwcydO3eV9X/4wx9SW1ubRx55JH369Mnhhx+eJ554InfddVdqa2tTW1ubRx99tF7f2traPPbYY+v7UOADEbrBBmi//fbL1772tYwYMSLt27fPgQcemMsvvzw9e/ZMVVVVOnXqlOHDh+e1116rt91DDz2U/v37Z7PNNkvbtm0zcODALFiwIElSKpXygx/8IDvssENatWqVXXfdNXfeeWdDHB4AG5g777wzPXv2TKtWrdKuXbsccMABqaurS5KMGTMm3bt3T8uWLbPzzjvnqquuKm/3pS99Kb169crixYuTJEuXLk3v3r1z3HHHNchxALDhqauryx133JGvfvWrOeSQQzJ27NhV+rRr1y4dO3bMzjvvnO985ztZunRp7rvvvmyxxRbp2LFjOnbsmC233LJe37cvgw2V0A02UDfddFOaNWuWhx56KD/72c/SpEmT/PjHP84TTzyRm266KX/84x9z7rnnlvvPmjUrn/70p/Oxj30sDz/8cB588MEMGjQoy5cvT5J885vfzJgxY3L11VfnySefzFlnnZXjjz8+f/rTnxrqEAHYANTW1uaYY47Jl770pcyePTv3339/DjvssJRKpVx33XW54IIL8p3vfCezZ8/Od7/73Vx44YW56aabkiQ//vGPU1dXl/POOy9JcuGFF+bFF1+sF8wBsGm7/fbb061bt3Tr1i3HH398xowZk1KptNq+S5cuzXXXXZckad68+fosE9aJitK7ne1Ag9lvv/2ycOHCzJw58137/OIXv8hXv/rVvPjii0mSY489NnPnzs2DDz64St+6urq0b98+f/zjH9OvX7/y8pNPPjmvv/56brnlluIPAoBG4fHHH0/v3r3zzDPPZPvtt6+3brvttsv3v//9HHPMMeVl//M//5N77rknU6dOTZI8/PDD6d+/f84777yMHDky9957b/bdd9/1egwAbLj22muvHHXUUTnjjDOybNmybL311rn11ltzwAEH5JlnnkmXLl3SqlWrNGnSJG+88UZWrFiRzp07Z8aMGdliiy3K+1nZd+bMmfn4xz/ecAcEH0Czhi4AWL099tijXvu+++7Ld7/73Tz11FNZtGhRli1bljfffDN1dXWpqqrKrFmzcuSRR652X0899VTefPPNHHjggfWWL1myJLvttts6OwYANny77rprPv3pT6dnz54ZOHBgBgwYkCOOOCLLli3LvHnzMnTo0AwbNqzcf9myZampqSm3+/Xrl7PPPjuXXnppvvGNbwjcACibM2dOHn300dx1111JkmbNmuXoo4/OjTfemAMOOKDc7/bbb8/OO++cv//97znzzDNzzTXX1AvcoLESusEGqqqqqvz1v/71rxx88ME55ZRTcumll2aLLbbIgw8+mKFDh2bp0qVJklatWr3rvlasWJEk+d3vfpePfOQj9dZVVlaug+oBaCyaNm2aKVOmZOrUqZk8eXKuvPLKXHDBBeUnx1133XX55Cc/uco2K61YsSIPPfRQmjZtmn/84x/rtXYANmw33HBDli1bVu93kFKplObNm5fvPZ0knTp1yo477pgdd9wx1dXVOfzww/PUU09lq622aoiyoTDu6QaNwPTp07Ns2bKMGjUqffv2zU477ZRnn322Xp9evXrl3nvvXe32u+yySyorKzN37tx07dq13qtTp07r4xAA2IBVVFRkr732yre//e3MnDkzLVq0yEMPPZSPfOQj+b//9/+uMnZ06dKlvO1ll12W2bNn509/+lMmTZqUMWPGNOCRALChWLZsWcaNG5dRo0Zl1qxZ5def//znbL/99hk/fvxqt+vfv3969OiR73znO+u5YiiemW7QCHz0ox/NsmXLcuWVV2bQoEF56KGHcs0119Trc/7556dnz54ZPnx4TjnllLRo0SL33XdfjjzyyLRv3z5nn312zjrrrKxYsSJ77713Fi1alKlTp6a6ujonnnhiAx0ZAA3tkUceyb333psBAwZkq622yiOPPJIXXngh3bt3z8UXX5zTTz89bdq0yUEHHZTFixdn+vTpWbBgQUaMGJFZs2bloosuyp133pm99torP/rRj3LGGWekf//+2WGHHRr60ABoQL/97W+zYMGCDB06tN5tCZLkiCOOyA033JBDDjlktdt+/etfz5FHHplzzz13lSt1oDEx0w0agY9//OO5/PLL8/3vfz89evTI+PHjM3LkyHp9dtppp0yePDl//vOf06dPn/Tr1y8TJkxIs2ZvZeuXXnppLrrooowcOTLdu3fPwIEDc/fdd9ebrQDApqdNmzb53//93xx88MHZaaed8s1vfjOjRo3KQQcdlJNPPjnXX399xo4dm549e6Z///4ZO3ZsunTpkjfffDPHHXdcTjrppAwaNChJMnTo0BxwwAEZMmRI+enZAGyabrjhhhxwwAGrBG5Jcvjhh2fWrFl5+eWXV7vtIYccks6dO5vtRqPn6aUAAAAAUDAz3QAAAACgYEI3AAAAACiY0A0AAAAACiZ0AwAAAICCCd0AAAAAoGBCNwAAAAAomNANAAAAAAomdAMAAACAggndAAAAAKBgQjcAAAAAKJjQDQAAAAAKJnQDAAAAgIL9/91cNVSsshyCAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (race_ax, sex_ax, art_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))\n", + "\n", + "race_ax.set_ylabel('residual')\n", + "\n", + "sns.barplot(x=data['race'], y=res.residuals_, ax=race_ax)\n", + "sns.barplot(x=data['sex'], y=res.residuals_, ax=sex_ax)\n", + "sns.barplot(x=data['ART'], y=res.residuals_, ax=art_ax)" + ] + }, + { + "cell_type": "markdown", + "id": "870e03a3-8c9d-4083-92bd-752aabd00bbc", + "metadata": {}, + "source": [ + "Let's merge everything into a single analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "40753763-7426-47a7-87c0-8fc7bf64184d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept-0.3670.419-0.8770.3810.470.458-1.1910.456
    1YearsSeropositive-0.0440.003-13.7470.0000.470.458-0.051-0.038
    2education-0.0600.019-3.1070.0020.470.458-0.098-0.022
    3age0.0390.0066.7460.0000.470.4580.0280.051
    4C-0.9400.115-8.1890.0000.470.458-1.165-0.714
    5H-0.3820.146-2.6120.0090.470.458-0.670-0.094
    6male-0.0140.092-0.1580.8750.470.458-0.1950.166
    7Truvada0.3150.0983.2030.0010.470.4580.1220.508
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 adj_r2 CI[2.5%] \\\n", + "0 Intercept -0.367 0.419 -0.877 0.381 0.47 0.458 -1.191 \n", + "1 YearsSeropositive -0.044 0.003 -13.747 0.000 0.47 0.458 -0.051 \n", + "2 education -0.060 0.019 -3.107 0.002 0.47 0.458 -0.098 \n", + "3 age 0.039 0.006 6.746 0.000 0.47 0.458 0.028 \n", + "4 C -0.940 0.115 -8.189 0.000 0.47 0.458 -1.165 \n", + "5 H -0.382 0.146 -2.612 0.009 0.47 0.458 -0.670 \n", + "6 male -0.014 0.092 -0.158 0.875 0.47 0.458 -0.195 \n", + "7 Truvada 0.315 0.098 3.203 0.001 0.47 0.458 0.122 \n", + "\n", + " CI[97.5%] \n", + "0 0.456 \n", + "1 -0.038 \n", + "2 -0.022 \n", + "3 0.051 \n", + "4 -0.714 \n", + "5 -0.094 \n", + "6 0.166 \n", + "7 0.508 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pd.concat([data[['YearsSeropositive', 'education', 'age']],\n", + " pd.get_dummies(data['race'], drop_first=True).astype(float),\n", + " pd.get_dummies(data['sex'], drop_first=True).astype(float),\n", + " pd.get_dummies(data['ART'], drop_first=True).astype(float),\n", + " ], axis=1)\n", + "y = data['exec_domain_z']\n", + "res = pg.linear_regression(X, y)\n", + "res.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "fe67da49-98ed-43fb-b15d-c511b64757f2", + "metadata": {}, + "source": [ + "Here our _reference_ is an AA, female taking Stavudine.\n", + " - Everything is signifiant except for sex.\n", + " - We see that Truvada has a _significant positive_ effect on EDZ relative to Stavudine.\n", + "\n", + "Since this is our final model, let's test our last normality assumption." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "46cdd616-d777-4517-979a-d51996f7f1c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pg.qqplot(res.residuals_)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "bb8dcb61-82af-49a9-a923-e4c58a0a220b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Wpvalnormal
    00.8320240.659672True
    \n", + "
    " + ], + "text/plain": [ + " W pval normal\n", + "0 0.832024 0.659672 True" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pg.normality(res.residuals_, method='normaltest')" + ] + }, + { + "cell_type": "markdown", + "id": "77d9739b-d623-40f1-ade2-3ab1b755d7b2", + "metadata": {}, + "source": [ + "Perfect, now we know that our final model passes the _Normal Distribution of Errors_ assumption." + ] + }, + { + "cell_type": "markdown", + "id": "63741a0f-627f-4981-b5c0-ef8b302d3335", + "metadata": {}, + "source": [ + "What about understanding which parameters have the largest impact on the model?\n", + "Stated another way: which features are most important to determing EDZ?\n", + "\n", + "Nicely, `pingouin` can do this for us." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "871beb97-cdcc-44ae-bb13-4ed78f36d495", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]relimprelimp_perc
    0Intercept-0.3671080.418546-0.8771053.810941e-010.469840.458133-1.1905870.456370NaNNaN
    1YearsSeropositive-0.0442940.003222-13.7466884.748977e-340.469840.458133-0.050633-0.0379540.27588358.718414
    2education-0.0599100.019281-3.1072232.059458e-030.469840.458133-0.097844-0.0219750.0393588.376948
    3age0.0392150.0058136.7457787.231020e-110.469840.4581330.0277770.0506520.0396148.431478
    4C-0.9397040.114749-8.1892286.513749e-150.469840.458133-1.165470-0.7139390.07565216.101683
    5H-0.3823540.146409-2.6115389.442348e-030.469840.458133-0.670411-0.0942970.0159793.400943
    6male-0.0144460.091578-0.1577488.747561e-010.469840.458133-0.1946240.1657320.0004840.102939
    7Truvada0.3149840.0983273.2034521.495929e-030.469840.4581330.1215290.5084400.0228704.867595
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 \\\n", + "0 Intercept -0.367108 0.418546 -0.877105 3.810941e-01 0.46984 \n", + "1 YearsSeropositive -0.044294 0.003222 -13.746688 4.748977e-34 0.46984 \n", + "2 education -0.059910 0.019281 -3.107223 2.059458e-03 0.46984 \n", + "3 age 0.039215 0.005813 6.745778 7.231020e-11 0.46984 \n", + "4 C -0.939704 0.114749 -8.189228 6.513749e-15 0.46984 \n", + "5 H -0.382354 0.146409 -2.611538 9.442348e-03 0.46984 \n", + "6 male -0.014446 0.091578 -0.157748 8.747561e-01 0.46984 \n", + "7 Truvada 0.314984 0.098327 3.203452 1.495929e-03 0.46984 \n", + "\n", + " adj_r2 CI[2.5%] CI[97.5%] relimp relimp_perc \n", + "0 0.458133 -1.190587 0.456370 NaN NaN \n", + "1 0.458133 -0.050633 -0.037954 0.275883 58.718414 \n", + "2 0.458133 -0.097844 -0.021975 0.039358 8.376948 \n", + "3 0.458133 0.027777 0.050652 0.039614 8.431478 \n", + "4 0.458133 -1.165470 -0.713939 0.075652 16.101683 \n", + "5 0.458133 -0.670411 -0.094297 0.015979 3.400943 \n", + "6 0.458133 -0.194624 0.165732 0.000484 0.102939 \n", + "7 0.458133 0.121529 0.508440 0.022870 4.867595 " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_with_imp = pg.linear_regression(X, y, relimp=True)\n", + "res_with_imp" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "1a5030e3-b8b5-4918-8939-381a5bc28592", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]relimprelimp_perc
    1YearsSeropositive-0.0442940.003222-13.7466884.748977e-340.469840.458133-0.050633-0.0379540.27588358.718414
    4C-0.9397040.114749-8.1892286.513749e-150.469840.458133-1.165470-0.7139390.07565216.101683
    3age0.0392150.0058136.7457787.231020e-110.469840.4581330.0277770.0506520.0396148.431478
    2education-0.0599100.019281-3.1072232.059458e-030.469840.458133-0.097844-0.0219750.0393588.376948
    7Truvada0.3149840.0983273.2034521.495929e-030.469840.4581330.1215290.5084400.0228704.867595
    5H-0.3823540.146409-2.6115389.442348e-030.469840.458133-0.670411-0.0942970.0159793.400943
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 \\\n", + "1 YearsSeropositive -0.044294 0.003222 -13.746688 4.748977e-34 0.46984 \n", + "4 C -0.939704 0.114749 -8.189228 6.513749e-15 0.46984 \n", + "3 age 0.039215 0.005813 6.745778 7.231020e-11 0.46984 \n", + "2 education -0.059910 0.019281 -3.107223 2.059458e-03 0.46984 \n", + "7 Truvada 0.314984 0.098327 3.203452 1.495929e-03 0.46984 \n", + "5 H -0.382354 0.146409 -2.611538 9.442348e-03 0.46984 \n", + "\n", + " adj_r2 CI[2.5%] CI[97.5%] relimp relimp_perc \n", + "1 0.458133 -0.050633 -0.037954 0.275883 58.718414 \n", + "4 0.458133 -1.165470 -0.713939 0.075652 16.101683 \n", + "3 0.458133 0.027777 0.050652 0.039614 8.431478 \n", + "2 0.458133 -0.097844 -0.021975 0.039358 8.376948 \n", + "7 0.458133 0.121529 0.508440 0.022870 4.867595 \n", + "5 0.458133 -0.670411 -0.094297 0.015979 3.400943 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# After filtering and sorting\n", + "res_with_imp.query('pval<0.01').sort_values('relimp_perc', ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "dea90faa-7e62-470e-8b38-bc4ec6c4b94d", + "metadata": {}, + "source": [ + "## Over fitting" + ] + }, + { + "cell_type": "markdown", + "id": "34122ab1-a41f-40ae-8404-13952ec40432", + "metadata": {}, + "source": [ + "In principle we can continue to add more and more variables to the `X` and just let the computer figure out the p-value of each.\n", + "\n", + "There are a few reasons we shouldn't take this tack.\n", + " - **Overfitting** : A larger model will **ALWAYS** fit better than a smaller model. This doesn't mean the larger model is **better** at predicting _all samples_, it just means it fits **these** samples better.\n", + " - **Explainability** : Large models with many parameters are difficult to explain and reason about. We are biologists, not data scientists. Our job is to reason about the _result_ of the analysis, not create the best fitting model.\n", + " - **Statistical power** : As you add more noise features you lose the power to detect real features.\n", + "\n", + "So, you should limit yourself to only those features that you think are biologically meaningful." + ] + }, + { + "cell_type": "markdown", + "id": "f85001ad-e7d5-4fa1-acb4-bf831e249167", + "metadata": {}, + "source": [ + "When planning experiments there are a couple of things you can do to avoid overfitting:\n", + " - **Sample size** : While there is no strict rule, you should plan to have _at least_ 10 samples per feature in your model.\n", + " - **Even sampling** : It is ideal to have a roughly equal representation of the entire parameter space. If you have categories, you should have an equal number of each. If you have continious data, you should have both high and low values. If you have many parameters, you should have an equal number of each of their interactions as well.\n", + "\n", + "These are good guidelines for all model-fitting style analyses." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "c7b277ae-b218-400b-bf21-2dbe1d4dfd72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: 7\n", + "Obs: 325\n" + ] + } + ], + "source": [ + "print('Features:', len(X.columns))\n", + "print('Obs:', len(X.index))" + ] + }, + { + "cell_type": "markdown", + "id": "a555f8e6-5863-4b26-bff3-8cef65f03861", + "metadata": {}, + "source": [ + "## Even more regression" + ] + }, + { + "cell_type": "markdown", + "id": "877c659e-f08a-4108-bdd9-6a4c1144fed9", + "metadata": {}, + "source": [ + "There are a number of regression based tools in `pingouin` that we didn't cover that may be useful to explore.\n", + " - `pg.logistic_regression` : This works similar to linear regression but is for binary dependent variables.\n", + "Each feature is regressed to create an equation that estimates the likelihood of the `dv` being `True`.\n", + " - `pg.partial_corr` : Like the ANCOVA, this is a tool for removing the effect of covariates and then calculating a correlation coefficient.\n", + " - `pg.rm_corr` : Correlation with repeated measures. This is useful if you have measured the same _sample_ multiple times and want to account for intermeasurment variability.\n", + " - `pg.mediation_analysis` : Tests the hypothesis that the independent variable `X` influences the dependent variable `Y` by a change in mediator `M`; like so `X -> M -> Y`.\n", + "This is useful to disentangle causal effects from covariation." + ] + }, + { + "cell_type": "markdown", + "id": "01aa3342", + "metadata": {}, + "source": [ + "---------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "74b8cf4e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grader.check_all()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "otter": { + "assignment_name": "Module09_walkthrough" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_sources/_bblearn/Module10/Module10_lab.ipynb b/_sources/_bblearn/Module10/Module10_lab.ipynb new file mode 100644 index 0000000..2f3ef4e --- /dev/null +++ b/_sources/_bblearn/Module10/Module10_lab.ipynb @@ -0,0 +1,616 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "700e795e-518f-453e-befd-b521ea8ba89a", + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "# Setting up the Colab environment. DO NOT EDIT!\n", + "import os\n", + "#import warnings\n", + "#warnings.filterwarnings(\"ignore\")\n", + "\n", + "try:\n", + " import otter, pingouin\n", + "\n", + "except ImportError:\n", + " ! pip install -q otter-grader==4.0.0, pingouin\n", + " import otter\n", + "\n", + "if not os.path.exists('walkthrough-tests'):\n", + " zip_files = [f for f in os.listdir() if f.endswith('.zip')]\n", + " assert len(zip_files)>0, 'Could not find any zip files!'\n", + " assert len(zip_files)==1, 'Found multiple zip files!'\n", + " ! unzip {zip_files[0]}\n", + "\n", + "grader = otter.Notebook(colab=True,\n", + " tests_dir = 'walkthrough-tests')" + ] + }, + { + "cell_type": "markdown", + "id": "0cf501d3", + "metadata": {}, + "source": [ + "# Lab" + ] + }, + { + "cell_type": "markdown", + "id": "8f8aeebe", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "At the end of this learning activity you will be able to:\n", + " - Estimate the effect size given a set of confidence intervals.\n", + " - Calculate the `effect_size`, `alpha`, `power`, and `sample_size` when given 3 of the 4. \n", + " - Interpret a power-plot of multiple experimental choices.\n", + " - Calculate how changes in estimates of the experimental error impact sample size requirements.\n", + " - Rigorously choose the appropriate experimental design for the best chance of success. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f2ffe20", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import pingouin as pg\n", + "sns.set_style('whitegrid')" + ] + }, + { + "cell_type": "markdown", + "id": "f27e4fc1", + "metadata": {}, + "source": [ + "## Step 1: Define the hypothesis" + ] + }, + { + "cell_type": "markdown", + "id": "024f5087", + "metadata": {}, + "source": [ + "For this lab we are going to investigate a similar metric. \n", + "We will imagine replicating the analysis considered in [Figure 3C](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424628/figure/F3/).\n", + "This analysis considers the different sub-values of the vigalence index.\n", + "It shows that SK609 is improving attention by reducing the number of misses." + ] + }, + { + "cell_type": "markdown", + "id": "52e7ebd5", + "metadata": {}, + "source": [ + "Copying the relevant part of the caption:\n", + "\n", + "\"Paired t-tests revealed that SK609 (4mg/kg; i.p.) specifically affected the selection of incorrect answers, significantly reducing the average number of executed misses compared to vehicle conditions (t(6))=3.27, p=0.017; **95% CI[1.02, 7.11])**.\"" + ] + }, + { + "cell_type": "markdown", + "id": "a0b30454", + "metadata": {}, + "source": [ + "Since this is a paired t-test we'll use the same strategy as the walkthrough." + ] + }, + { + "cell_type": "markdown", + "id": "7374cd64", + "metadata": {}, + "source": [ + "## Step 2: Define success" + ] + }, + { + "cell_type": "markdown", + "id": "61b6e2ca", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q1: What is the average difference in misses between vehicle control and SK609 rodents?\n", + "\n", + "_Hint: Calculate the center (average) of the confidence interval; the CI is **bolded** in the caption above._" + ] + }, + { + "cell_type": "markdown", + "id": "08b9593e-081f-4f0d-bd27-c70613d94594", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 1 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4348fa0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "q1_change = ...\n", + "\n", + "print(f'On average, during an SK609 trial the rodent missed {q1_change} fewer prompts than vehicle controls.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f3b9b55", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q1_change\")" + ] + }, + { + "cell_type": "markdown", + "id": "50e9e11e", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q2: Calculate the effect size.\n", + "_Hint: Use the change just defined in Q1._\n", + "\n", + "Assume from our domain knowledge and inspection of the figure that there is an error of 3.5 misses." + ] + }, + { + "cell_type": "markdown", + "id": "3b9f74ab-0925-48e1-a0ba-c9725786aee1", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 1 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "382bc5bd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "error = 3.5\n", + "\n", + "q2_effect_size = ...\n", + "\n", + "print(f'The normalized effect_size of SK609 is {q2_effect_size:0.3f}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce741b7d", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q2_effect_size\")" + ] + }, + { + "cell_type": "markdown", + "id": "66e2bc2d", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Step 3: Define your tolerance for risk\n", + "\n", + "For this assignment consider that we want to have 80% chance of detecting a true effect and a 1% chance of falsely accepting an effect." + ] + }, + { + "cell_type": "markdown", + "id": "4af19207-e9ba-453a-8a80-e915bde3ec3c", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 2 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49fe7bc9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "power = ...\n", + "alpha = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12d8e8ac", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q3_tolerance\")" + ] + }, + { + "cell_type": "markdown", + "id": "619043ec", + "metadata": {}, + "source": [ + "## Step 4: Define a budget\n", + "\n", + "In the figure caption we see that the paper used a nobs of 16 mice:\n", + "\n", + "\"Difference in VI measurements calculated against previous day vehicle performance in rats (n=16) showed SK609 improved sustained attention performance ...\"" + ] + }, + { + "cell_type": "markdown", + "id": "c6f5c799", + "metadata": {}, + "source": [ + "## Step 5: Calculate" + ] + }, + { + "cell_type": "markdown", + "id": "cab114ee", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q4: Calculate the minimum **change** detectable with 16 animals.\n", + "\n", + "Use `alternative='two-sided'` as we do not know whether the number of misses is always increasing.\n", + "\n", + "_Hint: Use the power-calculator, and then use that effect size to calculate the min_change._" + ] + }, + { + "cell_type": "markdown", + "id": "7d6430c4-87a0-4690-a400-4b78e69df81c", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 2 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b6b1602-d3ef-4f0e-a13b-c117a9745269", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "q4_effect_size = ...\n", + "\n", + "\n", + "print('The effect size is:', q4_effect_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02e69c61", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# What is the minimum change that we can detect at this power?\n", + "\n", + "q4_min_change = ...\n", + "\n", + "print(f'with 16 animals, one could have detected as few as {q4_min_change:0.2f} min change.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21a6ada3", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q4_min_effect\")" + ] + }, + { + "cell_type": "markdown", + "id": "2dc9e821", + "metadata": {}, + "source": [ + "# Step 6: Summarize\n", + "\n", + "Let's propose a handful of different considerations for our experiment.\n", + "As before, we'll keep the power and alpha the same, but we'll add the following experimental changes:\n", + "\n", + " - A grant reviewer has commented on the proposal and believes that your estimate of the error is too optimistic. They would like you to consider a scenario in which your error is **50% larger** than the current estimate.\n", + " - A new post-doc has come from another lab that has a different attention assay. Their studies show that it has **25% less** error than the current one.\n", + " \n", + "Consider these two experimental changes and how they effect sample size choices." + ] + }, + { + "cell_type": "markdown", + "id": "91e770b6", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q5: Calculate new effect sizes for these conditions.\n", + "\n", + "_Hint: Refer to the bolded experimental changes above and adjust the errors then the effect sizes, keeping in mind the q1_change variable._\n", + "\n", + "_This can be done in two steps if needed._\n", + "\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af7c9ce8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "q5_high_noise_effect_size = ...\n", + "q5_new_assay_effect_size = ...\n", + "\n", + "print(f'Expected effect_size {q2_effect_size:0.2f}')\n", + "print(f'High noise effect_size {q5_high_noise_effect_size:0.2f}')\n", + "print(f'New assay effect_size {q5_new_assay_effect_size:0.2f}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46491dd3", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q5_multiple_choices\")" + ] + }, + { + "cell_type": "markdown", + "id": "55cff86a", + "metadata": {}, + "source": [ + "Use the power-plot below to answer the next question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4732a77", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Check many different nobs sizes\n", + "nobs_sizes = np.arange(1, 31)\n", + "\n", + "\n", + "names = ['Expected', 'High-Noise', 'New-Assay']\n", + "colors = 'krb'\n", + "effect_sizes = [q2_effect_size, q5_high_noise_effect_size, q5_new_assay_effect_size]\n", + "\n", + "fig, ax = plt.subplots(1,1)\n", + "\n", + "# Loop through each observation size\n", + "for name, color, effect in zip(names, colors, effect_sizes):\n", + " # Calculate the power across the range\n", + " powers = pg.power_ttest(d = effect,\n", + " n = nobs_sizes,\n", + " power = None,\n", + " alpha = alpha,\n", + " contrast = 'paired')\n", + "\n", + " ax.plot(nobs_sizes, powers, label = name, color = color)\n", + "\n", + "\n", + "\n", + "\n", + "ax.legend(loc = 'lower right')\n", + "\n", + "ax.set_ylabel('Power')\n", + "ax.set_xlabel('Sample Size')" + ] + }, + { + "cell_type": "markdown", + "id": "1429aad1", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q6 Summary Questions\n", + "\n", + "_Hint: Remember, the power level is 80%, so examine the nobs at 0.8 at the specified effect size to determine sufficient power or question being asked._" + ] + }, + { + "cell_type": "markdown", + "id": "c2c98715-cc66-4fee-9be4-9b6642977bfe", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 3 |\n", + "| Hidden Tests | 3 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aba8e06d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Would an experiment that had nobs=15 be sufficiently powered\n", + "# to detect an effect under the expected assumption?\n", + "# 'yes' or 'no'\n", + "q6a = ...\n", + "\n", + "# Would an experiment that had nobs=15 be sufficiently powered\n", + "# to detect an effect under the high-noise assumption?\n", + "# 'yes' or 'no'\n", + "q6b = ...\n", + "\n", + "# How many fewer animals could be used if the new experiment was implemented\n", + "# vs. the expected/current one (using 80% power)?\n", + "# Hint: Use the power calculator. Round up.\n", + "\n", + "\n", + "q6c = ...\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c553b96", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q6\")" + ] + }, + { + "cell_type": "markdown", + "id": "d6216ba7", + "metadata": {}, + "source": [ + "--------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52fe694f", + "metadata": {}, + "outputs": [], + "source": [ + "grader.check_all()" + ] + }, + { + "cell_type": "markdown", + "id": "369512fc", + "metadata": {}, + "source": [ + "## Submission\n", + "\n", + "Check:\n", + " - That all tables and graphs are rendered properly.\n", + " - Code completes without errors by using `Restart & Run All`.\n", + " - All checks **pass**.\n", + " \n", + "Then save the notebook and the `File` -> `Download` -> `Download .ipynb`. Upload this file to BBLearn." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "otter": { + "assignment_name": "Module10_lab" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_sources/_bblearn/Module10/Module10_walkthrough_SOLUTION.ipynb b/_sources/_bblearn/Module10/Module10_walkthrough_SOLUTION.ipynb new file mode 100644 index 0000000..98b7a3f --- /dev/null +++ b/_sources/_bblearn/Module10/Module10_walkthrough_SOLUTION.ipynb @@ -0,0 +1,1026 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "54e6b29f-438b-4124-a718-f78ed9a7534b", + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "# Setting up the Colab environment. DO NOT EDIT!\n", + "import os\n", + "#import warnings\n", + "#warnings.filterwarnings(\"ignore\")\n", + "\n", + "try:\n", + " import otter, pingouin\n", + "\n", + "except ImportError:\n", + " ! pip install -q otter-grader==4.0.0, pingouin\n", + " import otter\n", + "\n", + "if not os.path.exists('walkthrough-tests'):\n", + " zip_files = [f for f in os.listdir() if f.endswith('.zip')]\n", + " assert len(zip_files)>0, 'Could not find any zip files!'\n", + " assert len(zip_files)==1, 'Found multiple zip files!'\n", + " ! unzip {zip_files[0]}\n", + "\n", + "grader = otter.Notebook(colab=True,\n", + " tests_dir = 'walkthrough-tests')" + ] + }, + { + "cell_type": "markdown", + "id": "29a82192", + "metadata": {}, + "source": [ + "# Walkthrough" + ] + }, + { + "cell_type": "markdown", + "id": "23b1746a-7c73-46c9-ba1e-94e1b6505c86", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "At the end of this learning activity you will be able to:\n", + " - Describe a generic strategy for power calculations.\n", + " - Define the terms `effect_size`, `alpha`, and `power`.\n", + " - Describe the trade-off of `effect_size`, `alpha`, `power`, and `sample_size`.\n", + " - Calculate the fourth value given the other three.\n", + " - Interpret a power-plot of multiple experimental choices.\n", + " - Rigorously choose the appropriate experimental design for the best chance of success." + ] + }, + { + "cell_type": "markdown", + "id": "6a25df40-86e5-4912-b892-61202d1e7af2", + "metadata": {}, + "source": [ + "For this last week, we are going to look at experimental design.\n", + "In particular, sample size calculations." + ] + }, + { + "cell_type": "markdown", + "id": "03b8610c-f382-49f1-a1d9-60a6d4ff94cc", + "metadata": {}, + "source": [ + "As a test-case we will imagine that we are helping Dr. Kortagere evaluate a new formulation of her SK609 compound.\n", + "It is a selective dopamine receptor activator that has been shown to improve attention in animal models.\n", + "You can review her paper [**Selective activation of Dopamine D3 receptors and Norepinephrine Transporter blockade enhance sustained attention**](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424628/)\n", + "on pubmed.\n", + "We'll be reviewing snippets through the assignment.\n", + "\n", + "As part of this new testing we will have to evaluate her new formulation in the same animal model.\n", + "In this assignment we are going to determine an appropriate sample size.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "bce0b740-54ed-4d26-a213-9c02fea739d2", + "metadata": {}, + "source": [ + "## A Power Analysis in 6 steps\n", + "\n", + "As the \"biostats guy\" most people know, I'm often the first person someone comes to looking for this answer.\n", + "So, over the years I've developed a bit of a script.\n", + "It is part art, part math, and relies on domain knowledge and assumptions." + ] + }, + { + "cell_type": "markdown", + "id": "c9a96b45-17d1-4204-917d-5468d544cd17", + "metadata": {}, + "source": [ + "Before you can determine a sample size you need to devise a *specific*, **quantitative**, and **TESTABLE** hypothesis.\n", + "Over the past few weeks we've covered the main ones:\n", + " - Linked categories - chi2 test\n", + " - Difference in means - t-test\n", + " - Regression-based analysis\n", + "\n", + "With enough Googling you can find a calculator for almost any type of test, and simulation strategies can be used to estimate weird or complex tests if needed." + ] + }, + { + "cell_type": "markdown", + "id": "043f4d00-3149-4ec8-a4f5-a06f4bc2daf7", + "metadata": {}, + "source": [ + "During the signal trials, animals were trained to press a lever in response to a stimulus, which was a cue light. During the non-signal trials, the animals were trained to press the opposite lever in the absence of a cue light. [Methods]\n", + "Over a 45 minute attention assay cued at psueodorandom times, their success in this task was quantified as a Vigilance Index (VI), with larger numbers indicating improved attention.\n", + "\n", + "Figure 1 shows the design." + ] + }, + { + "cell_type": "markdown", + "id": "15316bc2-0be0-4ea7-bb23-ec91f197f522", + "metadata": {}, + "source": [ + "![Figure 1](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ad9/6424628/c5af74734da6/nihms-1006809-f0001.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "f6e932b2-f35b-4f14-9339-c1a56b96561e", + "metadata": {}, + "source": [ + "Our hypothesis is that this new formulation increases the vigilance index relative to vehicle treated animals." + ] + }, + { + "cell_type": "markdown", + "id": "63549657-6c54-44af-8dd7-c46a80dbb7a7", + "metadata": {}, + "source": [ + "## Step 2: Define success\n", + "\n", + "Next, we need to find the `effect_size`.\n", + "Different tests calculate this differently, but it always means the same thing: \n", + "**the degree of change divided by the noise in the measurement.**\n", + "\n", + "These are things that rely on domain knowledge of the problem.\n", + "The amount of change should be as close to something that is clinically meaningful.\n", + "The amount of noise in the measurement is defined by your problem and your experimental setup.\n", + "\n", + "If you have access to raw data, it is ideal to calculate the difference in means and the standard deviations exactly.\n", + "But often, you don't have that data.\n", + "For this exercise I'll teach you how to find and estimate it." + ] + }, + { + "cell_type": "markdown", + "id": "9b547a19-961c-42d7-8a5a-f941ac0c6f6f", + "metadata": {}, + "source": [ + "In this simple example, we'll imagine replicating the analysis considered in [Figure 3B](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424628/figure/F3/).\n", + "\n", + "![Figure 3](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ad9/6424628/98810d3bec35/nihms-1006809-f0003.jpg)\n", + "\n", + "We'll start with B. This compares the effect of SK609 VI vs a vehicle control. Parsing through the figure caption we come to:" + ] + }, + { + "cell_type": "markdown", + "id": "f35b0e89-a958-4119-aee5-b4b49ebba428", + "metadata": {}, + "source": [ + "```\n", + "(B) Paired t-test indicated that 4 mg/kg SK609 significantly increased sustained attention performance as measured by average VI score relative to vehicle treatment (t(7)=3.1, p = 0.017; 95% CI[0.14, 0.19]).\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "b703ef16-47b1-422a-a85a-526b5c465ef3", + "metadata": {}, + "source": [ + "This was a *paired* t-test, since it is measuring the difference between vehicle and SK609 in the same animal. The p=0.017 tells use this difference is unlikely due to chance and the CI tells us that the difference in VI between control and SK609 is between 0.14 and 0.19.\n", + "\n", + "If we're testing a new formulation of SK609 we know we need to be able to detect a difference as low as 0.14. We should get a VI of ~0.8 for control and ~0.95 for SK609. If the difference is smaller than this, it probably isn't worth the switch." + ] + }, + { + "cell_type": "markdown", + "id": "5594f0ae-5145-4ba0-ba90-34a0521b88df", + "metadata": {}, + "source": [ + "Therefore we'll define success as:\n", + "```\n", + "SK609a will increase the VI of an animal by at least 0.14 units. \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b5cd1215-2454-4718-afba-224c1abd820b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "min_change = 0.14" + ] + }, + { + "cell_type": "markdown", + "id": "785b9a16-e516-487e-b3ef-cb0cba7c8c14", + "metadata": {}, + "source": [ + "Then we need an estimate of the error in the measurement.\n", + "In an ideal world, we would calculate the standard deviation.\n", + "But I don't have that. \n", + "So, I'll make an assumption that we'll adjust as we go.\n", + "\n", + "I like to consider two pieces of evidence when I need to guess like this.\n", + "First, looking at the figure above, the error bars. \n", + "From my vision they look to be about ~0.02-0.04 units.\n", + "Or, if we considered a ~20% measurement error 0.8 x 0.2 = 0.16.\n", + "So, an estimate of 0.08 error would seem *reasonable*." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8896357f-51e1-4c15-8dda-a537443d6210", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "error = 0.08" + ] + }, + { + "cell_type": "markdown", + "id": "bde0a728-b4b3-4462-8be2-ad178668670e", + "metadata": {}, + "source": [ + "Our estimate of the `effect_size` is the ratio of the change and the error." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0fb71e79-69a7-4953-a116-8b2f7d1aae56", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Effect Size 1.7500000000000002\n" + ] + } + ], + "source": [ + "effect_size = min_change/error\n", + "print('Effect Size', effect_size)" + ] + }, + { + "cell_type": "markdown", + "id": "40eb9490-5397-4448-af67-7582d9a21b99", + "metadata": {}, + "source": [ + "You'll notice that the `effect_size` is unit-less and similar to a z-scale." + ] + }, + { + "cell_type": "markdown", + "id": "ca54ea97-27bf-468c-a26f-2efc285875cb", + "metadata": {}, + "source": [ + "## Step 3: Define your tolerance for risk\n", + "\n", + "When doing an experiment we consider two types of failures.\n", + " - False Positives - Detecting a difference when there truly isn't one - `alpha` \n", + " - False Negatives - Not detecting a true difference - `power`\n", + " \n", + "We've been mostly considering rejecting false-positives (p<0.05).\n", + "The power of a test is the converse.\n", + "It is the likelihood of detecting a difference if there truly is one.\n", + "A traditional cutoff is `>0.8`; implying there is an 80% chance of detecting an effect if there truly is one." + ] + }, + { + "cell_type": "markdown", + "id": "787b0f59-673c-41fa-af89-8ae247e4c3e3", + "metadata": {}, + "source": [ + "## Step 4: Define a budget\n", + "\n", + "You need to have _some_ idea on the scale and cost of the proposed experiment.\n", + "How much for 2 samples, 20 samples, 50 samples, 200 samples.\n", + "\n", + "This will be an exercise in trade-offs you need to have reasonable estimates of how much you are trading off.\n", + "This is where you should also consider things like sample dropouts. outlier rates, and other considerations." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "36166945-cd2c-483e-a32f-c3e5780a99ec", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# In each group\n", + "exp_nobs = [2, 4, 8, 10]" + ] + }, + { + "cell_type": "markdown", + "id": "b2a1f3a5-99c2-44f4-b1ba-7c7d9530540b", + "metadata": {}, + "source": [ + "## Step 5: Calculate\n", + "\n", + "With our 4 pieces of information:\n", + " - effect_size\n", + " - power\n", + " - alpha\n", + " - nobs\n", + " \n", + "We can start calculating. \n", + "A power analysis is like a balancing an __X__ with 4 different weights at each point.\n", + "At any time, 3 of the weights are fixed and we can use a calculator to determine the appropriate weight of the fourth.\n", + "\n", + "Our goal is to estimate the cost and likely success of a range of different experiment choices.\n", + "Considering that we have made a _lot_ of assumptions and so we should consider noise in our estimate." + ] + }, + { + "cell_type": "markdown", + "id": "d20bf632-f478-4be5-bbd9-0266c8cfa9eb", + "metadata": {}, + "source": [ + "Each type of test has a different calculator that can perform this 4-way balance.\n", + "\n", + "We'll use the `pingouin` Python library to do this (https://pingouin-stats.org/build/html/api.html#power-analysis).\n", + "However, a simple Google search for: \"statistical power calculator\" will also find similar online tools for quick checks.\n", + "Try to look for one that \"draws\" as well as calculates." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b0cf5b21-d403-498a-968e-029c0c0157b1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "import pingouin as pg\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "b9953b5f-5dc1-4b4f-864f-756987d7fb98", + "metadata": {}, + "source": [ + "All Python power calculators I've seen work the same way.\n", + "They accept 4 parameters, one of which, must be `None`.\n", + "The tool will then use the other 3 parameters to estimate the 4th." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "696ce526-49f4-4090-be04-f48a6cc8b9c3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3.7683525901861725" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_change = 0.14\n", + "error = 0.08\n", + "\n", + "effect_size = min_change/error\n", + "\n", + "power = 0.8\n", + "alpha = 0.05\n", + "\n", + "pg.power_ttest(d = effect_size,\n", + " n = None,\n", + " power = power,\n", + " alpha = alpha,\n", + " contrast = 'paired',\n", + " alternative = 'greater')" + ] + }, + { + "cell_type": "markdown", + "id": "c9708343-fcb6-4adc-a18e-22cf01a181a4", + "metadata": {}, + "source": [ + "So, in order to have an 80% likelihood of detecting an effect of 0.14 (or more) at a p<0.05 we need at least 4 animals in each group." + ] + }, + { + "cell_type": "markdown", + "id": "bea0e078-6dc5-410f-80d0-c2ffd473c20a", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q1: Calculate the power if there are only two animals in each group." + ] + }, + { + "cell_type": "markdown", + "id": "05951051-43f5-41e0-80a9-c65e3d8754da", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 1 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b9034f1e-0ea3-4eb4-90cf-8182bfc8a651", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "With two animals per group. The likelihood of detecting an effect drops to 30%\n" + ] + } + ], + "source": [ + "# BEGIN SOLUTION NO PROMPT\n", + "\n", + "q1p = pg.power_ttest(d = effect_size,\n", + " n = 2,\n", + " power = None,\n", + " alpha = alpha,\n", + " contrast = 'paired',\n", + " alternative = 'greater')\n", + "# END SOLUTION\n", + "\n", + "q1_power = q1p # SOLUTION\n", + "\n", + "print(f'With two animals per group. The likelihood of detecting an effect drops to {q1_power*100:0.0f}%')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d55f502e", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q1_twosample_power\")" + ] + }, + { + "cell_type": "markdown", + "id": "bff2675d-1d53-4daa-8610-1deba0cc3b0b", + "metadata": {}, + "source": [ + "What if we're worried this formulation only has a small effect or a highly noisy measurement. So, we've prepared 12 animals, what is the smallest difference we can detect? Assuming the same 80% power and 0.05 alpha." + ] + }, + { + "cell_type": "markdown", + "id": "deafd365-f8f7-4d97-bf88-7f80472030a2", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q2: Calculate the smallest effect size if there are 12 animals in each group." + ] + }, + { + "cell_type": "markdown", + "id": "c52f1c30-3ab1-4d31-b1fe-74a834278ffe", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 1 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "59c492f5-1eda-4888-87da-e09cbf3d8a3c", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "With 12 animals per group. You can detect an effect 2.283X smaller than the minimum effect.\n" + ] + } + ], + "source": [ + "# BEGIN SOLUTION NO PROMPT\n", + "\n", + "q2e = pg.power_ttest(n = 12,\n", + " power = power,\n", + " alpha = alpha,\n", + " contrast = 'paired',\n", + " alternative = 'greater')\n", + "# END SOLUTION\n", + "\n", + "q2_effect = q2e # SOLUTION\n", + "\n", + "print(f'With 12 animals per group. You can detect an effect {effect_size/q2_effect:0.3f}X smaller than the minimum effect.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cdd218c", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q2_12sample_effect\")" + ] + }, + { + "cell_type": "markdown", + "id": "9423f2ee-9324-4418-87cc-9d242c38458d", + "metadata": {}, + "source": [ + "The solver method is great when you have a specific calculation.\n", + "But it doesn't tell you much beyond a cold number with little context.\n", + "How does it change as we make different assumptions about our effect size or our budget?" + ] + }, + { + "cell_type": "markdown", + "id": "294e9a43-195d-4cf8-a0ee-08e0eb493c36", + "metadata": {}, + "source": [ + "## Step 6: Summarize\n", + "\n", + "Let's \"propose\" a number of different experiments different experiments.\n", + "We'll keep the power and alpha the same but consider different group sizes 2, 4, 6, 10, and 15 each.\n", + "How do these choices impact our ability to detect different effect sizes?\n", + "We'll also assume our true effect size could be 2X too high or 2X too low." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "03b816e0-c7bb-4249-98c5-be694a28c79d", + "metadata": {}, + "outputs": [], + "source": [ + "# I find the whitegrid style to be the best for this type of visualization\n", + "sns.set_style('whitegrid')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "36a74f64-f255-4d9d-8d14-63d58f997994", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# How many animals in each proposed experiment\n", + "nobs_sizes = np.array([2, 4, 6, 10, 15])\n", + "\n", + "# power_ttest accepts arrays in any parameter\n", + "calced_power = pg.power_ttest(n = nobs_sizes,\n", + " d = effect_size,\n", + " power = None,\n", + " alpha = alpha,\n", + " contrast = 'paired',\n", + " alternative = 'greater')\n", + "\n", + "# Then I can plot the power vs the number of animals\n", + "plt.plot(nobs_sizes, calced_power, label = f'Cd={effect_size:0.1f}')\n", + "plt.ylabel('Power')\n", + "plt.xlabel('Number observations')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "5e15a19a-a5a0-4c16-9cff-505af077e8f0", + "metadata": {}, + "source": [ + "Since we can plot multiple assumptions on the same graph, we can make complex reasonings about our experimental design." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "977edb80-8d69-454b-b01a-8eb0735cb74e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pick multiple different assumptions about the effect-size\n", + "effect_sizes = [effect_size/2, effect_size, effect_size*2]\n", + "\n", + "nobs_sizes = np.array([2, 4, 6, 10, 15])\n", + "\n", + "for ef in effect_sizes:\n", + " calced_power = pg.power_ttest(n = nobs_sizes,\n", + " d = ef,\n", + " power = None,\n", + " alpha = alpha,\n", + " contrast = 'paired',\n", + " alternative = 'greater')\n", + "\n", + " plt.plot(nobs_sizes, calced_power, label = f'Cd={ef:0.1f}')\n", + "\n", + "plt.ylabel('Power')\n", + "plt.xlabel('Number observations')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "ca4d0c36-f4d8-4665-94f1-5218d0109025", + "metadata": {}, + "source": [ + "With this graph we can make some decisions with better knowledge about the context.\n", + "\n", + "If we're confident our effect size estimate is correct or an 'under-estimate', then we should do 4-6 animals.\n", + "This will give us a >80% chance of finding an effect if it truly exists.\n", + "However, if we have any doubt that our estimate may be high, then we see that 4-6 animals would put us in the 50:50 range.\n", + "Then maybe it is better to spend the money for ~10 animals to obtain a high degree of confidence in a worst-case scenario." + ] + }, + { + "cell_type": "markdown", + "id": "d9ff4a72-2ec2-451b-98bf-6a34ab8e3153", + "metadata": {}, + "source": [ + "## The other use of Power Tests" + ] + }, + { + "cell_type": "markdown", + "id": "359406ef-2b65-4b95-a15c-bb668133a56c", + "metadata": {}, + "source": [ + "T-tests estimate whether there is a difference between two populations.\n", + "However, a p>0.05 **does not mean the two distributions are the same**.\n", + "It means that either they are the same **or** you did not have enough *power* to detect a difference this small.\n", + "If we want to measure whether two distributions are statistically \"the same\" we need a different test." + ] + }, + { + "cell_type": "markdown", + "id": "58e48e9b-566a-474c-8695-ab900f27865e", + "metadata": {}, + "source": [ + "Enter, the **TOST**, Two one-sided test for _equivelence_.\n", + "\n", + "This test is more algorithm than equation.\n", + "Here is the basic idea:\n", + "\n", + " - Specify the Equivalence Margin (`bound`): Before conducting the test, researchers must define an equivalence margin, which is the maximum difference between the treatments that can be considered practically equivalent. This margin should be determined based on clinical or practical relevance.\n", + " - Conduct Two One-Sided Tests: TOST involves conducting two one-sided t-tests:\n", + " - The first test checks if the upper confidence limit of the difference between treatments is less than the positive equivalence margin.\n", + " - The second test verifies that the lower confidence limit is greater than the negative equivalence margin.\n", + " - Interpret the Results: Equivalence is concluded if both one-sided tests reject their respective null hypotheses at a predetermined significance level.\n", + "\n", + "This means that the confidence interval for the difference between treatments lies entirely within the equivalence margin.\n", + "Thus, they are the *same*." + ] + }, + { + "cell_type": "markdown", + "id": "3316221d-1435-4ed8-8263-a49045ab5b73", + "metadata": {}, + "source": [ + "Imagine we were testing two different batches and wanted to ensure there was no difference between them.\n", + "A meaninful difference would be anything above 5% in the VI." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b7ffbe6f-666b-4b02-9bf4-702bc0a2d772", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'VI')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hyp_batchA_res = np.array([0.80, 0.76, 0.81, 0.83, 0.88, 0.78, 0.77, 0.82, 0.76, 0.72])\n", + "hyp_batchB_res = np.array([0.81, 0.75, 0.78, 0.85, 0.88, 0.82, 0.78, 0.81, 0.79, 0.70])\n", + "\n", + "fig, ax = plt.subplots(1,1)\n", + "for ctl, sk in zip(hyp_batchA_res, hyp_batchB_res):\n", + " ax.plot([1, 2], [ctl, sk])\n", + "ax.set_xlim(.5, 2.5)\n", + "ax.set_xticks([1, 2])\n", + "ax.set_xticklabels(['Control', 'Exp'])\n", + "ax.set_ylabel('VI')" + ] + }, + { + "cell_type": "markdown", + "id": "bdd86e87-cfe0-4f68-a53f-1310d6cd745a", + "metadata": {}, + "source": [ + "Perform a t-test, just to see what happens." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ca00fa32-91f1-4304-b3ed-22b252044e50", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Tdofalternativep-valCI95%cohen-dBF10power
    T-test-0.5694959two-sided0.582953[-0.02, 0.01]0.0837910.3540.056513
    \n", + "
    " + ], + "text/plain": [ + " T dof alternative p-val CI95% cohen-d BF10 \\\n", + "T-test -0.569495 9 two-sided 0.582953 [-0.02, 0.01] 0.083791 0.354 \n", + "\n", + " power \n", + "T-test 0.056513 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pg.ttest(hyp_batchA_res, hyp_batchB_res, paired=True)" + ] + }, + { + "cell_type": "markdown", + "id": "0219db7d-3a0a-49ea-bb7d-42808e43ae89", + "metadata": {}, + "source": [ + "As expected, we cannot reject the hypothesis that they are the same.\n", + "But this doesn't mean they are the same, just that they are _not different_.\n", + "\n", + "Now, for the TOST." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a2ae6f13-2368-4d95-aee6-b0d50a709ad3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    bounddofpval
    TOST0.0590.000053
    \n", + "
    " + ], + "text/plain": [ + " bound dof pval\n", + "TOST 0.05 9 0.000053" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bound = 0.05 # Should be in same units as the input\n", + "\n", + "pg.tost(hyp_batchA_res, hyp_batchB_res, 0.05, paired=True)" + ] + }, + { + "cell_type": "markdown", + "id": "3fa836a4-682d-4bef-9f2d-9bdb3857b7ea", + "metadata": {}, + "source": [ + "So, if we use a bound of 5% VI, then the likelihood that there is a difference **5% or larger** is `0.000053`.\n", + "Therefore we can statistically say that they are the same _within this bound_." + ] + }, + { + "cell_type": "markdown", + "id": "42208b6c", + "metadata": {}, + "source": [ + "---------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1c313997", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grader.check_all()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "otter": { + "assignment_name": "Module10_walkthrough" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_sources/content/Module09/Module09_book.md b/_sources/content/Module09/Module09_book.md new file mode 100644 index 0000000..285e9b8 --- /dev/null +++ b/_sources/content/Module09/Module09_book.md @@ -0,0 +1,3 @@ +# Module 9: Linear Regression + +This chapter will discuss using linear regression to consider multiple variables at once. diff --git a/_sources/content/Module10/Module10_book.md b/_sources/content/Module10/Module10_book.md new file mode 100644 index 0000000..2a3683f --- /dev/null +++ b/_sources/content/Module10/Module10_book.md @@ -0,0 +1,3 @@ +# Module 10: Power Analysis + +This chapter will discuss how to do a power analysis to rigorously design your experiments to maximize the likelihood of detecting an effect. \ No newline at end of file diff --git a/content/Module01/Module01_book.html b/content/Module01/Module01_book.html index 5ca94de..0434054 100644 --- a/content/Module01/Module01_book.html +++ b/content/Module01/Module01_book.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module01/Module01_walkthrough.html b/content/Module01/Module01_walkthrough.html index 4a79079..76fe24e 100644 --- a/content/Module01/Module01_walkthrough.html +++ b/content/Module01/Module01_walkthrough.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module01/notebook_actions.html b/content/Module01/notebook_actions.html index 948cf89..66f6539 100644 --- a/content/Module01/notebook_actions.html +++ b/content/Module01/notebook_actions.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module02/Module02_book.html b/content/Module02/Module02_book.html index 8219644..9ca6f94 100644 --- a/content/Module02/Module02_book.html +++ b/content/Module02/Module02_book.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module02/dilution_calculations.html b/content/Module02/dilution_calculations.html index b19ad39..e111d96 100644 --- a/content/Module02/dilution_calculations.html +++ b/content/Module02/dilution_calculations.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module02/nanopore_description.html b/content/Module02/nanopore_description.html index 66c8958..67b7005 100644 --- a/content/Module02/nanopore_description.html +++ b/content/Module02/nanopore_description.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module03/Module03_book.html b/content/Module03/Module03_book.html index 8475804..a9ffdd9 100644 --- a/content/Module03/Module03_book.html +++ b/content/Module03/Module03_book.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module04/Module04_book.html b/content/Module04/Module04_book.html index c1e7895..8e86c6a 100644 --- a/content/Module04/Module04_book.html +++ b/content/Module04/Module04_book.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module05/Module05_book.html b/content/Module05/Module05_book.html index 739ad80..9722b77 100644 --- a/content/Module05/Module05_book.html +++ b/content/Module05/Module05_book.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module06/Module06_book.html b/content/Module06/Module06_book.html index b54224b..67f6e14 100644 --- a/content/Module06/Module06_book.html +++ b/content/Module06/Module06_book.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module06/grammar_of_graphics.html b/content/Module06/grammar_of_graphics.html index 9932e7c..39f4f9e 100644 --- a/content/Module06/grammar_of_graphics.html +++ b/content/Module06/grammar_of_graphics.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module07/Module07_book.html b/content/Module07/Module07_book.html index 290a8c4..8a707f2 100644 --- a/content/Module07/Module07_book.html +++ b/content/Module07/Module07_book.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module07/common_biological_distributions.html b/content/Module07/common_biological_distributions.html index 7209d7f..127ae8c 100644 --- a/content/Module07/common_biological_distributions.html +++ b/content/Module07/common_biological_distributions.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module08/Module08_book.html b/content/Module08/Module08_book.html index 5d2d4fa..4dfcc06 100644 --- a/content/Module08/Module08_book.html +++ b/content/Module08/Module08_book.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/Module09/Module09_book.html b/content/Module09/Module09_book.html new file mode 100644 index 0000000..0df8f17 --- /dev/null +++ b/content/Module09/Module09_book.html @@ -0,0 +1,517 @@ + + + + + + + + + + + Module 9: Linear Regression — Quantitative Reasoning in Biology + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + +
    +
    +
    +
    + + + Ctrl+K +
    +
    + + +
    +
    Work in progress!
    +
    + + +
    +
    + + +
    +
    + + + +
    + + + +
    + + + + +
    + +
    +
    + + + +
    +
    + +
    +
    +
    + + +
    +
    + + + + +
    + +
    + + + +
    + +
    +
    + +
    +
    + +
    + +
    + +
    + + +
    + +
    + +
    + + + + + +
    + + +
    + + + + + + + + + + + + + +
    + +
    + +
    +
    + + + +
    +

    Module 9: Linear Regression

    + +
    +
    + +
    +
    +
    + + + + +
    + +
    +

    Module 9: Linear Regression#

    +

    This chapter will discuss using linear regression to consider multiple variables at once.

    +
    +
    +
    + + + + +
    + + + + + + + + +
    + + + +
    + + +
    +
    + +
    + +
    + +

    +By Will Dampier +

    + +
    + +
    + + +

    + + © Copyright 2023. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + +
    +
    +
    + + + + + +
    +
    + + \ No newline at end of file diff --git a/content/Module10/Module10_book.html b/content/Module10/Module10_book.html new file mode 100644 index 0000000..04e748c --- /dev/null +++ b/content/Module10/Module10_book.html @@ -0,0 +1,515 @@ + + + + + + + + + + + Module 10: Power Analysis — Quantitative Reasoning in Biology + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + +
    +
    +
    +
    + + + Ctrl+K +
    +
    + + +
    +
    Work in progress!
    +
    + + +
    +
    + + +
    +
    + + + +
    + + + +
    + + + + +
    + +
    +
    + + + +
    +
    + +
    +
    +
    + + +
    +
    + + + + +
    + +
    + + + +
    + +
    +
    + +
    +
    + +
    + +
    + +
    + + +
    + +
    + +
    + + + + + +
    + + +
    + + + + + + + + + + + + + +
    + +
    + +
    +
    + + + +
    +

    Module 10: Power Analysis

    + +
    +
    + +
    +
    +
    + + + + +
    + +
    +

    Module 10: Power Analysis#

    +

    This chapter will discuss how to do a power analysis to rigorously design your experiments to maximize the likelihood of detecting an effect.

    +
    +
    +
    + + + + +
    + + + + + + + + +
    + + + +
    + + +
    +
    + +
    + +
    + +

    +By Will Dampier +

    + +
    + +
    + + +

    + + © Copyright 2023. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + +
    +
    +
    + + + + + +
    +
    + + \ No newline at end of file diff --git a/content/book_index.html b/content/book_index.html index f13eb05..572952e 100644 --- a/content/book_index.html +++ b/content/book_index.html @@ -58,6 +58,8 @@ + + @@ -237,6 +239,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/misc/about_this_book.html b/content/misc/about_this_book.html index fc80f49..026333c 100644 --- a/content/misc/about_this_book.html +++ b/content/misc/about_this_book.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/content/misc/book_intro.html b/content/misc/book_intro.html index 454b88a..3e44af8 100644 --- a/content/misc/book_intro.html +++ b/content/misc/book_intro.html @@ -236,6 +236,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/genindex.html b/genindex.html index 707e262..692cae6 100644 --- a/genindex.html +++ b/genindex.html @@ -235,6 +235,17 @@
  • Module 8: Hypothesis Testing +
    • +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/jupyter_execute/2aea3ce208391390ec8f00592ef7ca4b28c5ebf2cdc409c3a45d03f77d0894fd.png b/jupyter_execute/2aea3ce208391390ec8f00592ef7ca4b28c5ebf2cdc409c3a45d03f77d0894fd.png new file mode 100644 index 0000000000000000000000000000000000000000..59fafd0d152b405bcb3bae9678eb6a80ccca68e5 GIT binary patch literal 28709 zcmZ6ybyU^Q_dN^@FLf*^3|?k*_-=?>|*bhm(rfJjSscc;I>_vick z@vs(4yfJfTUNh(Hv-dd>%8D}RC?qH_FfizEWhGT%U|<`;FBAz8yuujIqz3-scb3w2 zRhFHFT;rhYR$mS0X8<9|L+5=c8(To zb0|n*U=U<`Ssf=B7)+C=U)W;NB1;$;yZE<~Z`9p04i?<>CnqTfPNoq)AjuvgFuqL_ zlp*GGSpE?=H&4UKO{tkcW+U@Y+hejHoy z;%_o!UaDX$%b4JFEb(wInH~isD#^5j?%xQqZ&g%O=9?Uz5fTzQ3uJzcqzT6Afln(u z*&d@g-5idL9^RWX-(j4$oo@{ zGqQZ~aKS+jQf*11-(DJ1`{fJTf6FK%#CgBC_LJr7f7bTwZVZ}&{}1AHBR(zI>*-A{Q0DNErpn9cdmw#-Uz{jm5L_IN|L>b|bX=Bj zWq-ZD>(8-z_luw6q7@S?oNh1(jtu|qM&tH}0TPvi0 zW`)<89Oxcjg<*=WFBm;PtwiWpP?P`P^6OUqaDUIxLC_EVqSP>f_uslLC9V#SJO93^ ze$VhYR>JbXHP1!h9^W_L-ekI|-mj^d{?G8LR<~Spg<@5Nyu7EEt^e=kEa8<)n~!q@ z5Y@{)A@1k@Ju?^ZWZFwLLk!zh&-2y1paEzI;4G`tsxVnDwIB$4`X}JtpU8Y__)|{x z-=wP1l2ru@we<6?&pFMzT8|2~Z^~)^_g2VuVx5tJJ(nk~O+G^%B1SM7zxvf*y!Q&- zh&khR*Hg9%={-F?_AV~8TwGtCj&=*5I1+5JUK|1{UE6Z8Vv(->n>P%CiJEm2IFLf` zF!?YXaH^o+Dhetb+K0vXd;~U7q<4{d)CO(d{(GbR^Fl=Px)BF#R(6<1S$z9D$o?6;cN9kQy&uqUZ?7Sbw8a< zZT)?)0Q4rdlxVheWQHY}bc}=ec4o`}TY3ncg1XyZ@0<4xvpEB7YtB8Hweed=v&h^B zKj|vKC3rGdmOG6rKsx&UsPg+EFG~3LUWZ)kBbnvdnMr~FZVt6Zb$y7{q`$>z*BG{a z01AHm1?RZ#|DOHC)~XbDsn_GZ+27(iqW{b-kvDr;XIGU}Gx&u;N0hMwkyKlyX*h#F zLG`O#9KVw}hs8)#ax&rGbXB8sHE=U+{jp&_cM~Kw3h8un0|gt#&zMkn!B2lQ>A*^F z7{B-R>&Y^$B9(mU3;~xPzoo!ton2oU5sBzWf4IBZGP_HuqktH1;L=+RrzIle(*61> zpX`1W78Zs}L1F7oNk9+^Jl4Am=3rT$Ea9Njjmh~qy#}#plEG7Uu)~(Vt7h$nqpN+9 z9;fTbhK7d76uhqc+WH0t=xlCoS9r`{vBXoB`%qsQ=(23nbze@Emu|SOenW0+Yjc0R z=N}mvVb*W@?0K)uueT$iRyl~R(6&2MpN>Tk!|QU_b9Pk1Xc)uuw;*Prp3G=Jqv zJa;{ue_Q>&Z&i44agoBy>kg0g-yACvGfmjKb;_KdWud7d8X@VKndDhnSukJNjIt!c zFxYMWaG4LMeOQS{rjo?DIIQTeo&8fv+c?ZD40d>jv+-kEA@PDM@R4OT-pwctv7({< z(Zjws=OhqgDV9dN&WEHHm%Y`)!;yTMSnW1BD}R~xC?nlqY{dkS`vauJ{4NJ;TOk_)8#8*Xo^zx#6SQl74Y zx5{@Fva3R`g@UrfW`C9>5Y>=1}^@oMT{P#WeMCpmb&%6BeYP}5B6+OY+({GQT8(EBT5{tEJk>felt zYK6tkG^AYqS$4Bt>>N0M>Vs(IC-n666z^ZZ31p#o^8axe`UbhmRM%^7Oc(uBQ!SLIHZ>7_>!z+zdN4n?r9F>pew zQc+Sb|AoM2OOpFby?4oef~^HcOae0mtnh}2bC_!kXVLI;d;fi8IJ2y9a`z(XS;>Xx z_6+vpEo!)J^~0;~B&2^4Ns(T$IEF^2wr6|nZD))jW~M-QAc8+iLqh(5s=2Z#Cq9== zLN350CakfPW%%{kqFO;^3kwOZo0^RCST=*u%>R+Z z`-V0{xU6ut)JW8FdTdXF{)@-igdX^86a{^^IrUR~e;tNOIMHQgXLtR0S71QHh$8Y= zzk;Z1$e5ItrJ>;STs)W+BLuZK;MuN@UVl_!HYNU7U5L{bT+Z~Yz2i3`5h9LXKM@ot zpAf>TO>js__be;x+iYDXM!Ad1C~^qRfU@WI3r+$qpSY*7;?z}CtfoH4iUg+fCiq6P zb@(s&J{fI%>8Jdck_W1AN zs6rM{Btnag@AlZB_1p*d#jhq?PaBtD@F7K^(Rt^uk$If{VH!1qR>TTDiBL2vq4bh7 zX=$l9u@tYp8a&I!9jszqlGAB7f3{a8(b>!yvgag?*qQb{jWMFZUu!QZfk;^ zz=Hz#^x1z{u!K%Y)AUb^3+I85f|{Dsa0YT4q}qo!UX`XpK2*HW?%(m!PAhDANM^P1 zZ2Y@Tpto;K$`Kf!A4(_jCPc^yS3LY1p99>F#2~ezccNP3Ic|0-?9ak68dkoL3IF^V zf;aYW9~CG>g6R~Nw^f?FXMrK;x90&n0yClMof$p`n{`3R8Xm zGkl$)=r#4zz9Z{2p!LUuP)kkZyYi%VJ3Sxf2xpZ; z@tJbW-hLbtZ&w<4+D_^}^Ub?S@2lU3rb;f#jvF5vr@Zcl?wtE~?vBUY@qym`rh5{_{Vm)`A znl83Zv3e^z5ySE2)}slcret4cuFs5T{bOy-4$uHA^@Y}}+aZH?lvcOS6(=@>zy_Pi zf?r7^-+7#X7tZ5JJDggG~AW06w=){qC^c3+8<$pSRbyZ`jC*{ z8V^L1zV@u!-NqqC-Wck|6!niB((^Odr3L3qwzKe^%IK`f^$V450;o7m)42UTMc;WK znNhK55NkBAyu;~wgvDAk>2B(uT8rbyyW@weU01XBVXq3*!0ie@w;ZG9meNe+h#)B@ z$AY0xk2WS^e^x_}w>!$4OxLzq{%vfhYgoZW2zGf8w;tzBhxcb*Hs`}~U2B^;am1TH zifA62zzRPMa@By9-BR&G^zOoG3FV?VG#Yc1iio32)11^m^a+STL(8ka6dfNC36FZFY!a(R0(Ixk zm&*KYLY|&u-_=$hVtbGGfdd0FMM02RbyOA}-p1r!n3|(NOwnM)%okkh(Rz`eICeNC z(ZCFuf^NTSEtD>gmPR*+(<7pyzD-Xj(<^0Zw|o>wvc@JTOsZ`5P-Br=+q>Izl8)`~ z6*YgTA3VBcUp_6E&cM=g-h*61nj8@t?7~7&C;fupBtD*MsTJ$uKRyr&IQyS%nOq-# zs}g^)3jOnD-tBOn%3>sAf4(_^QKR(bE*bGR7D)lOi&B)jKlSLfmz`5rOJx`HhH0mj zls;@q>^G(h3zGwVg0g~K>MleKy6P_W3|b@Wdqsph@3GuIetZR^Q){kpoN|0@-_zTR ze$<;c5DbzcdN;S3PRfkTCGY(dcIj@O$$X`}z~Kdh;mi!0uoE=e(C%4J;rq+<-TtM* zXwO5pZ7jT3FN9wVe`nOQO)DHLR-vH~a_>5vj`Ad9e-BS1({DhgtZOS?!0EJEeE7IA zM1Oc)Z8}+}%cgL#Jv{wp+|Wzz9F0c$&1I?SfVxbaM3}nu!90KS#Yeb=!wyT$VwqS9 z-2ga*x-zY*j?1R4(QND7+}zM(QnbJCj6VAjCo!&B?%vCtEi_gK`;2RNN;7jJH>hx-Pl#S~YxVY??b7FJh- z&D``f4w9VXY&+asnbOdjR6aw8@g3>W$H zIlGl>2jm94_uMRvS;Ag5_ABC=Rff@#k*K^zm0WwfyPxv&^F!q6gJm@+L^ZozA6l$* zxgpoLYxIihkB4`PQby3@lE~ zptWkFhqsps>!tbB%}<>v;;xP7?<-6#__y|l|bVLwOVg1Kji^aRyQRqSAy}?d{FHgDO*}%s%l%_bNg$=d8 ztlN)<`Q~J)98w3Ljpa3-;^x%erb4kEA%&3t%~`IeOa6kV@cvA$04u?@mjG_B7wP3g zQYY#{pdaVXTB+5Xj(t8$3n_1oCQJUxGisNopsmfO5ahO~l0epIW9@G4`XB&VD5dBX z_C*uE4vFyoz-JTVWswj#oFpsFkB24Z9)aL zpvrtLcn9Z~#$RL1hgT8x+{`(GvO=$%76gB9>>j=3nc+Bw^B>3C!ktphqM3JKBm7(b zl3L@R;mMS~%Htn=?pQvOc(&_GZBF_wEu{3l^CVgvbvY8-D)al?WIVn-3XgG$o$3zP zrD?!IvBNQtnJ8Hx_k;oW*zG91ugKlqyG}Q@^PMM35v~r2DKGhJQ>EN`z5E(4ojU}Q zzrm+7Vd@MPy)%9>+br)J2+KWUb92gD4)a14{b}(oGf>zT=YVpZu_}Yn@YH5|$H0TR zXw18e*`J%Jy>}kA2iwlW+27#x*cBbZF)e>u@u)}sCqTmScY%?a;7}Y`4zT! zfo~Y5#CYLHVor|6L0!)zrk=Ac2h6=5vV7z8qp2i}>BpDs-I}p}+T!vV9zF13xk7)< z`3OV*{9ut~`WLbvPPevvJL1pV_`mcywXF&laCUNrjiMiIZCOj)S)aM9K|c(m82KPv zIBrt+F2BnlgZg&_k;)hA@PlPY?p`?Re)bpUw{)e#VhNyU$h;0(dXF#{=$$?Gc`x&ES6)O;UoKgh>H!VUg%ft?(Ix zRy=%S>&EHhJdx|cYX5${Lw}Xjp1)}4cpnxd^0Xmu_@Xl$WGm9ev&~5?K_uKDg-hdB z&V|!&%JGcg6QFa8mPAQNda`9~Z_;W?&{b?G>D{0()nf0TGkU*Y{F>1E^;%NY2y)0X z#IEC&YQ^7NDX(D>(F5P5sxLhjDkM~~_Pk#dm_PRvrdQ0!5etAb8;tKG`+y@}pp}N$ z?$!GVUpXJ~R`hZGO315JEQ51_Q@jMY@pLotb|BOl8iNgGEhlIjf5B>-K_qE+sXNCfcph1lI3&Hx@+xjH_sJ z$apA&IEgi+FVZNLV(;9N&(HATzWX`UgA*`gIQ7^BW{yn@?Ow_AY=7UE>ovvzsH4_o zZ_?tt(-VP9|I<*6N>boha(GGhav%B;8`jHdEHC85JFY_KS`Nyc|KU51*ub|4nO(=k{u6q6C*kx9){fme97dfYoFvLRY}E=k9+Wu7;m%Cb?YlJ07GzK4;i=-oCK9 z{eE|4iecGDoo}oc*NV(yh)7!eNH5)exf-H7W3&0Le(~v$b$I`nd|QA*7d97;P4h}6 z4_>Rv@T5Yb7@F5yt9eiug)PJZ8ozSgb_-@vr~&5tVNiRUDXngsQ4 z2sK9i{=hJ7$I6Me?dlrJ7kk_47AE@WU-Ll&x-pb2j*nCFHa--N%X$jUf2-P@_25@- zkcZsMVA(s{d4JvdnO(=Ka?C^#(XCfLA*61HABR_O( z?Bl&Ejx|{U^HWY2NtYKIC1Gj!NoalX5O}WuG zyGZSYa3@C*Hi-VSMTCBo@7LO~7T_A7>X?dLk|9VaIOaSXKO9T3!_zzz61Ky;c{Q6mOv56Jd%=KCu_S~t_xDE`cfyQP{J1Q!@l*H#e8hWQaqsrXb(3F8s^AJ+#|N$jD$WXo`*X z3axFC8tVD?#;>P#-KXt1g)FR>45Q=DuENOpRg0;6NmVzI%$oZS8lC_otR#F zZI-!&*|}aXlOB)4-B#mXn(Jjb0uv4M@RuX|%)-8`b`~2{RMg(d$;4m3lmXif*;z%3 zYX}J|oBMoq)VtVjl}bEO+mOMZyZXRg{)?M7p8&b5CBwzi{?0Bhe5ZJGIj7%5Yt#*6 z&a75JQyNVwqy z1(8rx<==PKL5MRl>yi?y18pQ?{&+jmKN?&2bCfDV&@nD|7tPu|88p#N9)KAfb_H0g z(TZ8Y0xrw5oY#A7`Nomq{vkHg#Pu^go5u>TXJBkMZ((W(LYti?YXzy^eJ-{K+sG-@ zhU(=qe5EBTgHH837nLx_T*3VqW^*_w6diWb!|ePcABQJ7Q(gazrBz;7G{2R~-x1Mm zqZ^dp*isbYO>aaxvQ8*kr0G{viFLwx<ha`+baUVhDdX@OYl;d;RwoJ(oy~$DybWtyN_vOPwilU1`OA_Ey zx$Sm9c9dLLq7&t`sjgqr}9-ti_W+uzSOvpYX0`;vG~ zpYd57-&qPa8vVd;VlGS8(V1Y6m!6^V9X@w(IpaG-0?w~eIX-agkMCWO@!9)hid=W? zBSakxCeVLjHAqv;6d0?s95;Jl22)W}PyFT(bQ3Sr4CS_VJ<`ODn=nT8o8#)Dl4Buh zVbQC14+2pA+-w;q>&x;>P6CU3oin_uXxUyIa$As4Bd8A@e2c)9F0-GTomFXZw!FPM zvU|koS8cR4*qy3K-(KtO?M05Q=Qv6aWk;eiq~TD#z|-k8tOq`t(|P=88nd)Ev7RTJ zDA2~M!D^zTkwFo91C37geoM|{kANv~H7Y%n#9&Ao*H0@jpXc?~b$22HxOJUA_Xa@9 za5%2Z$`eWek#*vgb_ZFhmR2JTr@3TRm|SAl#gfgvsoD}2M6gyP*Hwt6LAv}sGRxR6 zm0%~XgDn$xMfza=)cc<{hx5&v*btbY=a_U{TocFfiy7i=A&c#~dc3`9kfG!<6&Q@W z#@dm>EEVWvOJgeG3~}aSmZ;7fHEL*n$(m;ib4BmBh@|3hShPY0gm~%wm;URu5Q%FyT2FTkqt{r3>D0oY89bKN1YPjBm1-KIIeBS7G1#6w`qihy59Ln83h5KG%Js?(S{^hFC1i zscb*-DnmerLT1Ig-teaJXmo3)8aM=2^gmQcVx1wqc*d3zNEy(={V4LMosI4BW z+n?n=m|y@5^LUN17}D-hE*yVPvB(E+7{;9bc5(Rc?X zqOJ#oi3B@&#Wb!6`DE5OCM{MJ0%qC%sD1+PXs&!X63vh@W4tYKG>$BCq8M%v&&K2)edA0fn zNl$afCT`n#KH^V6DZ_`@XcQYX!+jX4F++|bf?eGIkZ)tdv7-S+Fh9xb53&iy<=imT zJgg27@cwQ>FdqN)VhbC}bI8d@r5dOxFQbqPg}w%YE1+5G)2cni3&Jx6yGJnE+R)N( zFMAh6(Glt2>UPaHex@yBuv-(L>YC7JkxF^5HY7r2Z`IG8{;=p2#inaPgq7}_ilT-hBzwmo|<WZD2AD0+cah>tFtk8|qLE<4zSQx%(E4H^t#He@y6jq+{BGx;ZMh z!zQ-RU0}I5ttz@&>-&zo!`454>aDpI#tN5K{2%B;rZf?veSBmirZhzYpqrVSA}O4 z&&TUSP+1J9;wVy1$!#5n4ge1Q!mNV>1LgrR3HEwDLFsxc+wW&;t9bZ&JT2<_$Z*_Y z=gOaa^m^;w-mqh;=d)IN&nzBrVKADLivIMH3(=TA^vBK)xO#v8;UmD2{O-Z)cT0W9 zE|nx4*n03IE-nspe12{=UZ?;=Cg3bf$3Q?`ADOi+q{XUxTf}?fj9ZQCLj|KBfy``8MvuN4GcNmRIq7Pqy--7#9q(m8tLH@(*s9omn z@?dAaxpo%yRq*Qs>**YCkC{fyC;2g!I7l-~0#aKM6KP1hU@~0;z|S%df3uQi8kWD_ zQqsG$FL~Tp)MJaD8FP;hcgSdH-^Rxifb5-Cravg&-yGrmL^J8)k7S3Ptd%rb!2|?Y zY4Ky!E=+AD8qf8qWuO(!oz4G}|DCmUzPt6px;g^S;)h!k+A0BcMXMKKsRoeJ!f!7H z8Er|2A0Mo=bYh^wL71sDl(6>S>X|hRksBI*THy5_n+R&zpwzgtbJ^Jjc!oQ(={$EB zP3JwTzng2cv)iQ&!5ZA?epal3s+hUz-TU|@lZ0YvZL}Jv>su&t*$2n4c;r@R`yojKz^~r6h}Oqeyw6O!5-uN6e6!3o9tO8^hZT=t8LRICkG_ z=@liJN0~Rf7i^08Y%Hp%R6kcM2$?2#oY%j{1?^6jalVyyMlm7&SNw2%wNzfj8coSe z8y^MpWt~ruQ+*>-mRn=dAvs>%vo1`gcuqhdFJ&;nBYNpwo@gv1Xf0=emK<)kfpjv79iJX zmtbi!Q^Wa^-hEDkrEivYA*ucGIziS!v4%+kzeZ97emPj)A>X)`HePf`jFg~8X{&Fz zd5?IZds0;cyQdt?44@j_?F@hm>7E``lGH#3#ib7{{6PO)&?BnWq`iJM!%VB6(ahrn z%Rhm&ujKqAo5{D}=}~VfQUb@HUvVfAs(LTUldnajk;(w6LN4t2#qaM!^7rqupYRyq zx^B+4s_W~&LS}11*PI1(%nifWPZj)x4-@WR$#`L$QMrA$oY9I>+y9{*QqD#JWB;R> zD3RiT9filA_`K_`z17=(f-)lmi<*Uxn$@-T;8TnoI^mHKgUYr3MpSv# zmJ1Gf^*4aXKYelNeREoEIZg*MYTLR8pb}i)-WKjw+k%=p?e^kVNgs|B&z^TyXQN!# z3?8E$_>4g5O=)Y|mDQFrU0FX}t%*rM`A@%SaP=odGH-Vzn{b|Kmx3GewgzJr0~K`* zxO0HmJABdsPEGaXr;>bcNfPkEYwW(;j%;1Rs+PUmSa9vWLnV?0S4n$_{<BdmA>j4Q+aZGPcH=K8VgW9mPmru#G*!q>}$)g>e%qw{t z9+IqF{FQyCZcX^3fdC;%VIKQUY|>&0mjL5s6VVAMSx_3ZWIWE|UdEL*B2Qe{sDCrU%DqEs;v(~Q2ychB9` zKHt1Qb$ttdtY)9F#i$SDPrnTUR%+$m}*6kVY|WdZK)Gg(cig?Y4)-oIfeFOBsy3vVENvm>qIGN*P0oWbAcQm_p4l@ z-0lw0N5;5_IN$+g)gUsD_=SDnGxp3OaQN44*nrLFDfY3OVMs&u#O;*M}zHoPO{iJzT_XVpYT;!a+abRSFm_mKhZVT}S#ywenzrO4v9%NDJ8G3h8 z@*ZR$>?)77>oe7BPpP_t}Vmo_WkeSzmA=uQys7U^r6&Zsk@1GNguag`C zHdai~&QWR}aru3rxVtF&IxD|nR!Mb+Io>858aWD$j2tw=nN4MdUJ*F9%_2clrurQi{9;HJ2`XQ!0QF=7G$7p5MTy9*#&8O62Ig z=*o`S4%b0zdhuqzzDoAco_k?o;~j;&@PdUl>oU`g>$wEpP29C07j`w_>g|-L_q(+%b8$zkPKZusqtG|GP1Ja zCUe8-ygSoXl$1W_aXP5PL#D(1Lx1)8t_Ep0yj>0ud*}kBg5o+p*m=aaCI%y;*3&SeO1i29y ziQo60FP+zJ6A+Rg)1*QDVzEwbp-L5~Ypp)rpxXW}{DO4azDQsW{cawa?pJqu+o);a z@ZPLQFO01}v(O7plMIMpO#Io`vv91cgaTqYX94n#jt=L&sp!RaFSRnw3Ov*XVovkr zm6d>?pl2ID5>2lT7Z^^P#oMA!H*$Z(4QAvc(em5YjYgNj3%Dm7HS6|0`o-L=!IO5; zFC}(g^y|pULXl6T>B98X)$u@$BOSPMaz9_El*@9vr|Q=2}lz;<4y{ zDN!qWs=fq_@Y8g%rB$7XbJ^-pgf&HOSFCTX{v<^N+sowiGvbDoFv{VP`#B`_GvcPt zi6t0=8NhP|5qQH%l&9PG~{y0@4oVQPXg18Ve2kDXOWWmEddqGbEqH0pqArgA+I6v zFAN)W`jRUtjq=Uq=jFMdtcK{I;AaDxH*b1xFp(*F>{7qk(cx@!*nN~((3QEevNANe zwbiR8Q7L>Ti8+9!jB-432N`~DAk?^ZcRBRxEXCL>_6$s8s}G8;x<^OF>#=$~Q^{=m zA_-#~aR+KX482k-d?#d)GF?Wl#ibw*4g%RAcUz@ld)od6u6R|LV&*dPh`OoF zys^_cwO_gMnP*4rUK4B%&8K=|QCdpg<)Z9~XM$A0gHKtP_q9DpjRpYQ9r5WCH4u-S zwno_P7KNrN4bsUzz`yXi+@JaLr{D7HLWhr_qBUz;d%G~;H3l{5s8*d;_K5L3HB%G5 ztTW!A&z;#zAk1-Ii0Vv}ovu6x{lbELmFs?d;Ni=<#f>2gVC#U&-h{}azO!xS{`kwE zRz3-8yC4A0HmIy!1CRP1WZe^`8ch5@ko-YC@6)jcJ10HIYquEH*yvW{Q;9{F$`x_t zcQ~jo0L#u~u}%kyr78v2`|dn)Omx!g6GqRLLb1rmRn`!noCXtJ{X8`d4Y(+1pw`A5 zpz1-5Lime=xnw|?t8{pKfV$)qVJ{w##5x}>3il0BOOhqfz4`liAA4VC%#dT7Ec0A1 zz{2YL50|huci|Ul55+%4Q0EFcmQPRoCV*nZ7g5Vt`7e%B|MX>yk!bJvk=%5mX{Ylkye<)@2Z< zVUU5D84Y+i#WLa%%M#oTNEwo^oOtPOJ9g6{k z4bw8&^sU@!a`_K;3J<#HRl~cR5GW3)M@yg2EwEi^L8A~#0eq8cr9MAcS=1#jWaig| zJc>#Ex#u?VvOH7i?^IE=?xX}g_zt?6(iS80DGy3~h*}}=DJJH0jq?x)&S6mA_|C7r z3p<12bx;9lqG-Lfm^+)ykA%s)n3bTnKeXQ0sx&#_Om*3Og0f_R#N*$2OSaDGXGFw< z28u1ZRZ&gapEwHs`SR4@^w+2gYwyb9%=83;toE1D`p{Y(?b9DpMR?%n6z=rhLyp!^ zC_+u01>7IW_9^BA;X|vDs8%^P1cly~!AXLO`-5;*#CAp z+RqjDxsHnvF0KTG8avUablwDGFW9}e2!l_@8vMR)54cq6GPozjOEy&lHW>O!fyQe2 z1*AV_g;}cjhhi_x{{B4G>jd7AtWrPB#(hUscJHdOmxFUBE4Ys)+B=g3Wr0{bA%iPC z=0AxdD^Okp_kAQGB)J$cUv*bhiL%;%{d19$9Fhf)BI8UO4P5g(DlvuTdU8q>2V&{y zc-TMU1u$hg_+m)%x_4W``;Uer$a2fQuC{Tc{*ZZEFz=CYJ5m=*E17!Kj5Sh&(7dsr zNZWuEkqB8RM!hyU5QM?~HZFOx9Iu_?k56W%+e|l&ev^!=7{Z(gJ=>yJUf@B40S2Sg z{szw2UlA_Nv6LQtu@k``!#9|?_;TE9PLT{Em55S&q27H`1>m%^+IhLFk6;AF78(~ z&Gj8_IN?F6#Y|$z-M&kyY#jYoK#Fmzn2DGR>#Vd3>-t%)i;vEY0g^m>^9G_az${kN zl{SYpNG2ZO`<3L(hCgi?RbpL)`Vo}73AWdV@03Ph?Riakz`|FAq+l-4Y?-)Ubu)aN zCDhkv)&$Z?Ya`PZcFiS4z}_NX5o;YpQWLv5K(AFM0)ml|xvNOCbyV=o6FYbswqJ ztdQg6Kp-X;bS?NemUn+Xt^^McpJPeV_X*$hXt76cb;_Jln%YpN0P)j=$dghj7l#iXgV6*;|U(XxF}Pzl6BU!vpVi@)Sdc>K`eei8`0 zFT3-$^5gAZ)jDGW*5FWYP|;z& zz7R<$(H}*?(1Eo&{LXVTiQTl{J-ZP(ZWk2APnLGyoS~5+T~c^7J1IR}xNTy$a%vS$ zjmXQLInwdT4e)=*MXy z{*%>$qMF``RxXVE`3o)y^HS-b%B*>|l!M+9v- zQ53>yU0q_}$%X+ndF&B@*z^i}@wWQ2El_UO9${{ z$#cJ*vm85;Ka~~%=D-#cGZy}8$60{YsN;SrM2?jsTriX01IKDASg+A|SG~0Au=n!7 zDj7F+HlZ1ZGWg+nv2htebC9WI)!X6|RNFtWlcFh82a(wKf^QAPXXjX(JMPQ#U$&sbN!^AR2ga zpCbKPo?{2`u1{vc4xAT5af363aL>{ z0vB2*op}7IrE*-XpNfxZIZuK!D>WEMUGc)%X1ao2c(P~8WAq(2H4(4-Ee1p7B5a!% zfO5&k##U+6shG}V^9rzuX@w4(KZ)-j9%frym;wR<%;NqI;V0Et;&R!dd7Sm^j2l}F zjP07y4`KuAa5u|r3sj~Uyxx$E*U;Kz6 zv5?FX?B+z8FT5FxNjLWrK5cszANq9J02hN~LKo4&(rh)!NT--VL_Y)LxHU3-eX{n! zzY!YsVQ6A`H;UNg!L!&P-5-YgBA-TSc0)JhG*<@39kM5h{-B9*mb-FZbsce2XQ9xb zSZ-ilB8NovlVNOy00&3>WVH`>-Y8EJ*>2K|$y}j$aYQ9>5UYc6TFg@^unjJUf0`GzX0}HkaWIa9% ziC4de`Uu{}wgD8ge?TR%&e_np)*F@^7LfAPAYwh_ZGKA2JJ=T(qgTzO`(5?vj_Bm| zRseYxjhG`iJUpE54wO?93pjsli%dZ=aZN*XJ1ovdC6xYqeFc~5~X#f{vQBaKB-<%~>+S&@G))^XyQ3>budP>B?OLXl_x&*pj1q)XMZPoD$90s>2w1Q+lzow#P_p3)C2qbmA4FM(iy0InKTMi2(M#&J8t)3Fg_$!Fhm0TYV($#H$kA`^# zQ>f`DhffOEt35Qj)kFig+}gEtebzt;e1l_4eF5ZGxMw;ZGnoyC#1X!75E(wbnu(nE z<;G>?^>OX$92vWV2o1^!`}6tF9^n(MM@)!|0WPsPm7533AE?1~wQ+A2G485%v;56L zqm*5`;2K=ZZH>+-j0CR^os9U?MZrkQY9jyAZ`@yKeqG-DIOolC3`)h5G$bl!&|Fj? z^5%_lb4?nO$BpB;=k@(r%F_U})(VUZOQ>C(q|bMyD=c_5qFD3kpBZr^bnvK^G+fI! z<1+jO>qFhauNyocKuXCU0%C&U!omH-L*dv?e z^~K(Pm{_=w)?%wBNzFs-w}>3A&8G*HBI;F#K@}CWj5GkHg06`0aHKR|yBN^)`d{lK zO+4sGQ?AOAtI77IezBxg=l48INTrn%r@Ii0>FCD`GitM6LRz1dsm`h2lNbi+A&hl% zF?+Ck&L>#p&K-=EWWl|{1EfV5;P!BR6y4qVR#sL*uS`r#Hd&KiCx4gaj$z@#d%RD8 z*}CMiA*mIl!G?@zMwVXTVDQjWoMB!t40I~r$=f^1tpFdQiVAEjAQE&fr^1tBOs0BP zt_}P5j`CLE;znpnAeT78SdGW1ba4&=1Ruis81iePnCdP5GD}G*xUoi?*n{((obj(u zH>X50?NFJ+{n!|!Zs=8@KS8%ec2&b^vukM9_LU0U?sSpMUWx^T*uA?SQeWa~N1qc| z;X5G;DQa240kiNA7kBeKGW{l-Ywj$Qk`Dp0yi_MFP)Y+7i@Xl@j`hwL8X6nvoi=|; zv&G4MB#9!*ZF%FmFMGaRFC9JcCm)L((s4aM2MhY@URtrt6`MGhsFD>S>hV9=`p8il z<>&^8hdU4FEy1;WUgT+%X;ujr8g~aWs1e11gR;FqYW@>ONl6;r`=KmhK|sSanqf=IMGC_@rgDIW zwe=rTlJM_*o3UIG4WU$C_VNg zsFIIvodZ3og%u{89n#4+Nt8srZfK=-VQ|Bb|2{dG60v8_ErRcBm-#@E9)6q^8B?q zs6Rs8nog1sAH^&x1qB1Z2EiM&bML>3r|eHK6syV9PsLF3&WFNl*S(^fc^p*1ToPpO zp3>ICr=KB#BKis79A@ATETn^?BP`_jZZYbc{dwd>#_oFSUq!JhPfd5(J_O{DixMA; zr`AC=8``}R!YxOS(>arLPSQ)gX^E3nP=5w$@#B)xWhvy7#amrVlfUOlVOf74SnYdl z0ymRmVH2ZN>|=oo!{$u3g+Tg-Xz16ktxqrNVrrV$% zQRk4a0=?LKUZFPtO78?oLH}U371K?aQWUF3MnZWmY^0x0vH4}PF|PQ_K+qF@qfrX~ zRlfKBW;-&`Y(R6S0sZm*sc)|P(rrYk*vsn~*7$obZa};Kr1Kt>M{29Z{b&~O1GFl~ z0hS>4*uEYyJuGY&|K_NZ855c-PaED&6ok@%I1z~t8LrzX{~9p1acUpLGNInh_OK`$iAGtO; z`4K^OaR&6D*1*4CO;rmd1{&&aN%4$`7!icgH`4#%UJvZ*c}TB>`%r8Y>|WM1%;#zoZ_t@@ztKlJ^)KIWrpbeL&>eRrV?`!(M=Ba3^(wg=y$4xG9&mJ>E+6 zJ#~bs4VC%fra!PZe*KN2YaZ519ey@k&>Fb%-VhGVQCmj%M#{=O8(~0@OnTo`mfA(^ zV#J==&&sl?k!kHal+u^#@^jvG1WQdp2N;C|(0 z=fJj(b@L4&LM8Qzb z@rz-`=MT^rX#Aws=Hn8iMm8sUzP<$tDa@qIKE4<}XEhpN;TAVCPa@>N)Wpn! zlJ9z|zN7uy-?1+o#Uk%dYNpy)|H8Qvy~z$5Tg>0ddS!Vg-D*b^dVOoEMYxm{b>c>v zNsAoVn%@qMp7Af1d>)dBJ)6TTs&l|}SX7Z#uXD#z6}uTR2|^?zcR^INx0#vMs&0pj zBV%MP7^os!lR5oqDc`9Tu~YRey9Ee$;(A|U;Jh&*C=eB=I^j26?imaKJFHx((`kea+3XQ@3;#abm zTo1*H;R*aY*#n(gk$2sy$nGD0iJDaYa5Jra!Z1)V^imm)7bjLzu|T0LR`VzqqV&bY8!f9u97_u5lx^UYox(L<$x zZAP@zCodHACPRMa$F=2glGYFMlhrpLqV9IeGrqjJG5R(!@wCc$%?&Rk4oB0-U~;H5 zn7}IW^KZ}5Hr}=_+$X%0$_(fnR#_RVkwG5;iV|r;)a7as{E7`EVgLHF$_-Dm7>6Qk4#HXea}k#TPnK`OZcswByMZc5qGb&crO|m5%iNa;7O6}T1Qb#RDR=gQOQC% z+0DK-idmW!!x&`KY0lU_9MfIEd|jo;dB*iJ&FG7xm6g8h)F-#$ylch*c*1h!Ip7`S zIUlF(USb+r-XDfYAMn)^t9~IucVHkN4 ziS+Pbn#gnbz+Hxf+V}^=M&4M=Ya+u%-$yGf^&ip^#O!>*AN#2MZUOX1HKuMu!E&4H5ugJ z)p>kVqA4m^gh_Q}1xEk#fsabn`+zyvTSYAJidY#JL#$`1^l^q)MLoJ-v+(TaQS zT;te+^;mT^Kj$IzhvHf`Lnebyj%Tq6><(75Z&B0stv$5bCi1+)FmnF6NpW9#`-^zU zz?1Rk7{0y((bRClGSa`hv;^F~4(XijD|03em73>p8~^!2tG7BuII-g9T|RR$EN4RM zy+e_JAVX#R@b#qen%!0X=;KCMrVD8cCW!M3gU0K}$419{icoS%$5!>^V^E@Zvej<= zrq0-Pu5IJ8#*!tp9C6vi+S5~LPB&2|1Y0EeCIn}V4<-5i!L{7N0g;u%jlml%cb5-x zOw%^zDO4^!G7LO~Y1#biF_VUKiAFA2w9pbND&m_UH1giSIt$~#C1mnT5eH&&8R8lG z@<7MDKK9M$cLTqy5A;7CL1#(o*bg~^$aMD2!><*=wO~+@a?W?Mld=c{>jH62@k*U4 z2Y-N4kYnA{)JOhU1ct}kGr+F|Mff7oy|hI?tA2C};Q)cxk)GLaPzEvQ6L9L5onHkQ zoQZfFQ{BK9Q0&*gT&gKr8PyCk(5_H8Xa$EG1_Oc$n(ib)S(x-f>)ZsKJeck5o-8Hr z&3D`dc*SgGHcrwwO-s({*v{=x?!fO0>oa+=FQ~mG;c|#$f;wMsR;#N3d%5X@{#Pd_ zPgM(V4%!>H1XBx7fxD3G_fgerv~r(+!2GTXWMnDb(u29zzwlh6^!yrAa=Ajs`DsYI zvq0G5UghaghGI{5fb~1#-{Cm!b5)>SOrLT)C;pNc^%5O0l}xAaPeedj4#IM;4OVE< z`~m~XCIkKbX2-#yO)M9t5in(_%Ieb|WRaL84^ z8R2bC$N^JST(^xin4L>3y=?H-+1uYA0FB#j>zc67xu#`iX6B%}azI7J zv5}d~fv{AQzaj6jB`B|5r`4a5g7179Oc1}gn4u8n{dbhKA^E%@xi{cpDA#Cmk7brk z>FGBa0(opa#YH_Ze*4S>1I6B{8A7i`m!nOc&B?m6Q6u0mziJuTZ}_uUL8s8%za=;H8(f+4Dhed{H_HZjV7$|8GHei zRoNcFb?%Mt?^x*OiUn_;99Qcn9NXxn>XZF>FNUU__)=1Hw@7fL>c^B13_Bk7q2?|> zcbIF%1u>%pOzGly?5V)0fOY+MB7X&%>>+8ULZjM+fPR zR+M-=iB|_+uj$_?qs>l8*hz0CAZ`Dl(Re8MjQEe#gUfC*Ui^ZWum2j7aVtzbIoR`9 zgN;qhf_f%KMKS5e$oK87hE=aB2Mg78<;;O~DwFkXL(AM4+ZzOSb68%@+_8o+NhHk` zIIlQ%&=P8D;-6ZM6va9+73$(ndFP`4yx3R%GUpErz@lJNn`{{|)XpT_DW5_c@yjEa zgXJ9#=EYf2j->@vrxOc!-u9+zBP$Q@$6e&#@JM;r)g>Q>?XxB5%wOWLpn;8fev};l z@$X;FgHX99(M-T|!56NC}Gk`jEc*EddIfy z9ijUVAMW^RT2+qLK zXESd9F_5i%0z|4xuQRS!zhlzDW`T#7SF_rz2{uj|wYCpAzdf%mE#7Eg@m+M?p3^5| z5%G_vPgH%P^1S=Tf?Ar?!tOj&)3G{!fVD^u#{qAwJIZSZ{j+5u-=Z~L%MfvdIgPVtk<8sf#sBjWi zL%(mAy@e|LB>n_ZcCzW$uj|YF-O3W+rX4ZiKcbnQ+X=$U0$AEydHWzt^R>uL>^_b# zAQZ6L&x!%NoX2&O929Kdyv`0$(a=cldjL6Nw8FLNb_x;D8 z7}PUhW`&A@LFc9bgvr0Puh~Jtu>=AkV0J2jM+l4`Z2DMHfWSgWP5pD5&wkeL@7m{0 znz8+^?2q*9GTMER@wis&wm1a&noBRDjRzx#(85gzf~QHA_^A=FuuI34Na`qfu{&9YWIB5jEG)cUT}hjqN}4r+R@2r0Hv$9_eBEa z?3vFKHLjL0y<*U*E}d~cKi=UPu6Er@a+zuJAFfXl@*-WTg)DFRrrdQ)H+93PHMAFt zi0O;|-@4k`Qpnd9!-aaoz^*9+-Z6a5pXDz<$xDFF1I`8uqdRkLWkAuhgcBZwXiWzJ z_;9q|t7>Do#eUjP9A?L6V5IhGatEEvY_!QA^X2@Z^*_=JnV5mnws7UrbJ5zM@bK`% zL3KTIBBqx;x9;<$sR9(&sQtDBRAJX|+z2ZN^7D~a1eG|xz!nHPv_3sitTgh8R+@wP?HUQVTXP$TlUe@^)xrXEUPp`3PYi_FfwygoomP|(5cpbdZk4@< z;4fnV^CrdKVn*Fsjo+VHQxAboX_={*JOs&#gN}}FLnaTF)75#2TAjOd^BFD~ml=fG zFbsC{V6T=&M&x0yB&GSCJ9lXB_ZR93Tf;gUh3_nb5x!-z=WfvA?5yGbUn=k2PQsbA7r+O*IIF_K#|L8sB-w)bq6Hopj%Iv*_X{;q0);*S5Jut92+^pHMaB*?b0@Yv{^4ee|%fUy-+6G5b`W;0V z4#Rc{gzaRlRurv#DNrG3v<3U|{~>I}#H*dl>B6L;vbq|dlvKt9o4Mz;=g3j?u{SXn zv(E+lih`o|QxDz(-Av`EZ^vi3yy17h+XnlU>)<@Hw*ft6!EBUrLZR_QjaV3_@uTf8O}eq;^gd}vvpdKH31Wu?Rcgu zl!mKYzhfFrljEw=;Ik9PU$n7KZ1*)}LXZF4wtAsc&Biyy*wb<)`L+LTjfa5FB6gu)rmCswEqKcH5 zv^JvKT~v=!+`fJFTKkj~V=TFp8G203${m+fFz_WM2lvDl!@%cREDDYQ_RE5#cP&`N zE9%)OKZ|zQVnjYXozY$!+FnZLFt@QBB`zOnYW}9m;C6b?`%E4gy>irWw2{%A&C9O% z><1x*2M5Z{^wFejK(*E?^L@Sd;N58%tvk+GE*P#DwSDz)eP59h3_gvWtzV_WRJ$23WHYAsjfb(+1pZU83(k`0AqH61p~0hVq~DB%;M>^#Dd zw%}abj~gmBo3QEV2Ce(PG?shSR1;{HJ`TNanPiE)4|cNc;4H3LVNHobGg?7A*Z3}n zF10Eq0Tb8%)_oO>$dkSFu?A>0BXH2(ORnqZs!za98};odB{dbRLN4y>OWr*_J&1hP zT=ObUP#yrAU~YOEoWWn44Hw+!PO0fmco7UyPdL5Y%i?CY`|@8O#^k1?q>N$G`Bmk( zr19Ml4chb+==)7BLhoUgf4oi1eT(W*myb1E?;C<{1oTv-q}(=gFuNtWyF7h|f&#|4 zl`~!%8pNBMn>&AGW2hQ4%q=F{57t!MqGJ$US0vE-NyM>Lqz#si8`k(-3He+c5P^j- zCOR3kNk;b0&YZPioiiuM)M6w4Cn?`I-$sX%60C*a8yl$39CmR}P0!DZm0SJ2dFz(R z!Rlb4p!fE4qhCM(sV;OOVUBXaXk#rQJr&HxU04lQObnK=7TJo;D#M6buO})TjSj;W zg4>2i%<=}v^~Iofva+@|pQ;xutjPrjR>eiYyLG>(YZQq~h@KrMfy+4$;oG}8lbX-OGQOTpIun^l+_*L)o}R3$5fei1Ob2c(P1XPKJw5}5{WE-4}Ln1 ztWkhLu9Bk?1mZ@!ligRK*p&Dt>W@ER5n|o{=h+B9548`RsBc)9yJ~*CXEM_6{=KTx zV|ynjYM_flW$`>s>JFdlrd$v{%}A9a9k9ctt4)GSMJ#w& zK>$^E;GAOnO6`eWC|3Fu9V6pNA7{1T8>>wzIc?di0B@WcjZw{Pwg}muHA7e~>=@v7r797JSf3EUZ8o^vyKI$F8PTDf>>b$sb% zry`@dQDsAIgM*we+BC(Hw%@m#?jg;-knaSjh+W^Hu9kbM2CtL6gfY^k;7EPBaf>TTk(~x!?uOltUzu0gl4|jJB zFHR3)nAD0R>}21BhvPx3v;ZH2(#aQo?9J0;mnCA5{`0@##93&cf$in5=X3E1Y_o^@ z#61|GY~!$<{KDLD-q$D2`uG1QBthtuQX_rD!Ghf6bblp=DOeL}Pl>cA^ttke>Q&Cz zm>Pr{0$>EtCoaqO-`JeXQp8I|gQ+93p9y*YzR2lCry&&U@nCG89%4WUUE?5V<`FtL zQJ(v~l3K8~!piG_2Q~Wj={rUhs=aHaP`+zb6Pq}Lg9C04l}!#V{dZ+a>GSNLYmhUpr^WfOyVoT_ zD*%vd6gA@53eXVUxdhw#MySR?v3ZA_yb}sigbsB{rPB&Nl*a7PomDu!=u7{1Er~qv z5vaxbIZh8ZWZ|)akCb0}$exHK+KQ`*Q1oD!3qycGcDJS49qn75;w~V zn*;I4K2Wmgqkum06$r>d+0h5GR53(ktdd+l-Vcb1Eal8w(7R=V5{K}~>!wI0I?IFA zV%hkNmcO)&g^>Nog}ni?C)BKdY@06?K}F8*5(;Jc-a@Qi9~ibPBnw%u7}A6|mCef+ z>eiVJ<_ubf(`yucomoP%8wEXFppBG+*3913)fAf3>JaTORXIpBQGge4;IBdU zBo}b|2wq7MG_r)y#B_pp(qrHQ=a`qp%>q9fTqMF^Ut|Y#1$qdhkyw6L8|;|%4FF9X zA*4W41s-N_8*zYAqxdB;5ushly*yUI45&dW$Yld!ob2q128A%ZKpGB4R2;Q%&g z^TU1qks^b4=^-%!jDJ9pL_2rD+y@3O8lk$)I| zECwqbvdj8~_K0V>FH!@_VMm0}{M#PMjZCXw22Q4-(eZP`{*N>Zu89Bj)hmyX=G9bP zNY}=j|0%1A#XBziB7pa>81OM!zJUEQLm|bmh9y0Z0`^M*3ZA*fSiHlf6OkL3>vVg8Zjt6Z<~5ZN!av z(a70M^CePwUGL?A#FsMU;NfwWhHV;Lb}rp_y%tcnZUn?X|-b$GJb{dgx`LI zfEGGiXSbQEw;pMLBf~A%uCPgm-x~jl#)K`a`6~MO?2H{~c;H?r+`RR3YKoGj9zl`} zxSVpKt{_A`ASgr-@pFLO1zh}&WzGagI37N}NQ5?*#ZaF4zA0lL6A)HR0J!r5g@q$n z&LA_%+E6}NC!$?)!6``m6CJ%+zwtUGzphIAxuUW%v}wS_q8ZdbfGolR@Ji$@hvmd$ zH!YIlBMe*VD;Vv;ejWfB8OCuGCY_%RcDHx|73@uw+ymhUI72-FV(o05pn!4vZMdwT zP*Qe)Y8<3&=wAV;-d`II2i%Lyn+~B}?m4#<;OFUTOj>$c$_DvKmA7I+TI*C+?Fx{HyF8DT15s&@lhzxVq-!qVXTI@Pu z+f2QNJUKY&Qn*`WUN#&e+Eote@EM;1*zpG87hrReb8>o0df(?@RTZ}f4xV|R*Fo$? zDP(wR4YddtX>iQYM3zJ6U*Nf*G;b6maax$6m6VjssDii%X|o3j7;dI<`$u@k%M;>) z2nbRzVQ7kDH^muSUtbRuT7pL?-p>IMYUQ*6_ugTpFI_D50@0n$+Pl#@DgQ`^qE*S~&GchqC{fHhYXzCsKu96IxGV;;oypRDC=xHwE`Noos@rn@gA7XJxQs^)IK#9PlR z&Qh+IGef{w(_bJ;;%a`Ox|=_?H8w9a31x4C&zkoFi;< z%E-Wf^?D*%Qb|l)y!Y$V8{X$TZ9Hc%I05D_0ybM(7M72I*zW9g3&TyleT$suO<c0*gH13P+PO$|RvAReWH6G;lqqU&xB z2$zZtTdw}tge`9QTo(*e_s)9zmdLMesBHJk; z_rvuL;7>zDTnFdyncTwq>(fZ4K9nDx;MWB;Qy>(o;b7?!$*d;?%~tfZGn}&=uLL2JzCu=n=R=A7^F`Pv309Kh>HvMJQI2}yA6R4mP`;tX zTmjBM5({1LP8Jphfb{%)uN%e_ah#SzEbjYeQNQk7@MHd~QbC>x46nG8kaf!DRD&X% z9k1K=gIH!g6R1L+HpfZ8QwkpGF^K$mVCDtvUQk!rE&aJ4b%`OO!Te7YJW&lsuB4eJ zG^7;?xZQ#2UDk}|?H|yf(5n}a12BwyDUn%xD`eYHo5B`TD7Q*&r;;G8ynq&!l*_6e zj)8+9gB=M8nS)%dBjOPTp!MNLd<<%N)9?y|G)Q}P@HgQQyAQ1BofRggOiIQ2!q zJ_k6272G38z{ncFw*Djl8EXw!7vac$XZbTElzu3KVP2*PcO9lSETy9&W$Qpb*DSXp zL5ju3E8$%fJiTcSC>++K(Xio6!zBF!IWInE-a}|m*C%R->U0%hE2Ia9Sg+Il#=$`X znBagpW+!+DW~moa_+t|-)@;=q^`%L(Jr+ppF)y5aP9&P9{YWBI9}dda@~O!E|L#8j tc{v2U#SXmm|7m0Y=k*n`mF2y{h}fjq?DUVpfLHdRNQ*0o6^QEl{x2077nJ}2 literal 0 HcmV?d00001 diff --git a/jupyter_execute/398ace28cb7992fdeceb81ba3fc65492e76fd1439a2a3dba97a4f3c455089c66.png b/jupyter_execute/398ace28cb7992fdeceb81ba3fc65492e76fd1439a2a3dba97a4f3c455089c66.png new file mode 100644 index 0000000000000000000000000000000000000000..84ca84bdad31164092c6c9c10013d1f821bff6b2 GIT binary patch literal 22261 zcmeFZWl&t();3B)Ai)w`lK_ndcMU<(xCLq4rSZnyLV`4wq;V&>yVGcpK(OErL4vyz z>|5-;&-&01rVINi7#Od#H=Mv6DH9qOpsEt-Xt_l?j!bxs$V%J&c=;pN*4+%F@Nf z!C8o%-R^%1u-Q9Vu+L&X2>}|xaFEe-RNZFnD^db7) zEP9|^8y)*xLV_yh(UNKAAdO!0)KC0;fevK#!+Uc;{P zTlZ`kI+w+lc!G4Lun%69U;%M^pS2da&l4ixjF8jhT?ptJZ;x)KSm zN^4I~K_@4tFKKC?$he_Kr(5G2lV#~^7V#|FAB;FiFFRmlKwVt-kq=>Em}3(Y zCcl5gdfZ;FR~UDo4;Zap6C)f(m4NZ-g5hwu9*7W=niRX+<*rsldNoR(O&nOO!=0)j zsTng|e$TjaV9O)Y2{xoSNF3U9^TP}+;%j}}RS-?WJZ_N+ghOzWIy zt}|XqTR}{ApE0#%X+D#PKTCXCN``m&011Wr6qi=yDB?auN#7CtX`J`+As84@qZ>#EzRQnlayf=|FIUX-V!2VnNXkHpV~BJfbo6Ci+!T^w{wUJD%+K zB_#OGWR5R=T8kee(B&`i=&R~JnYZFkw}CE2Na476AGEPw!+XeflBj|8_|Ad*|3Cg- z0zj7H00H0kJ4aehmcGEm!s2w@Qr}r<)?HJ+7q)Pq0H5vDd*Qu$)O5c;uO>=#Lq_wI zS4QO7CEvdN*q=^!FJCf%&O!R^OjVdNvaoap<4|z8?M&sTKMRroq%*&$=q(IZieqEh z+SpC}yv$xaf0NLOd-UCx7h)XDzfP-rF7Y<&Cyijd33*GoTVXYQor zWEy{Ww{t`a?u*D^{=*^btKX1sMP zN!$HzF~&ojPml0&+f1+&>m!NNeU301-F6GF8ct4Jvh(t0E95?5@uqV5+=1^U8r~#k zO$7>UE7qd>LzN3c0ap;F#$p-{bX@?YEEEMj39&H)O0{MX+7yf9+ob`lq1j*oEX#HBu}Fw#Kz zY#@@a_zU-CvpIeI+itGV9;ASEa1TSomRApHa~Ga5__j-h8$2dg-T0DA%H9!7ir4i) zpBwygJw7pQn_RcNtnj5d5$}|H@dEc98>q}Q96nUOQ+s!jQTmv<0lxuI8k9?2GwCUX z+s2wdDN4FRV)a7Dy2UyfJSxuE8MeHp$WH!bbcOWvCD1GrROl7=ChwGP8@nrE-q)r; z#Y_LyHPiantDk*xrQp5pQnLG3i;dMooxqaRCXg_$(4fLoFseqic8A=MT$>TC|E=J~5K{?Zt22kDkNy zea}lpLIJ&NLlhCH%NQo0p3-xywVXO5V6gb%nHyy3qduf@pBtCDs66Nc+@uSbIk3Px z-3?GUBQZAE=R+2BE-2?Bw1=G(e(@+opfSWc=Yot7522u-af(NeTUZ8+fm<`iiue)5 zY*E{p>UANSBzX0yf(D(U0+o8Y+;J|gkSLz6>fg-;V+2~Oy($nue1)^V0uwT8Xwa#` z^wNN8$|VD3sF~=9l5xVC#-hIM(Sa_fKoZ%w zv1^K)!lDQ@)>j(BdfxcJ5RAV?Q*hXALKQqrP@yG^9ZNIOwWW^{lNFMIJ=9*xv!S#^ zx|feApN5@C>1?u2hQ<*V(Iw@}gDXs_B+^!b*G??;aiLLR<}Nk~7-9Wx?4EQg@$lu2 zQnDjLexAI(jSpX@OYr(JzgieS4USpOurO8{qbQ%=qhqJt!dJNl29>rHx8_2oJXjMnKT4sS+o25|I_0!Oc;fd)#d6So5UknKp0F07lYkqS5k6Q=5L zNr-=&SUP(1q`wJ|s3!E$R{1y|ORXu=UZi@X8T%F9vn{ZhD%r%L+7S`9h2F2L|4DSW zC5q02!sZ;(@Kk)x?a@KR z?@GwCiOH%Ofl(O`-r3r@u3%Bdsmc$WFcumf{pt~tKg*r z#qls5YLu9y8clTm6btYP&R~lTjp*mGd@IXgYzY*j(FN-fxMhh2AQFit~k>G8W~xx0s%)Ft;}T zetnENPESQ^ZyGN1VeaS=dls8S-W>L8E<39G?MfesAcuw=6eg@^ND$Nv4_tI9K0X*s zH7!4g-2E}x%yFE(8+Y8#JVhkswwzCfbzz(xjL>iebEzWYQ?*{;jpvVmU-TWAp~#^* ztjn97#3c`M*D(XVSLYA3YatJ1XGQR>eATEMlIK1KJh?Pr=`cBsV)k5# zcxF-KO2S-pS9$wn6L1|qsr)YJ%}4$kA7VQA6M?9=SDdB(@l5eW8$})lY{;3~m!(S? z%7N>cz{GEgEI1i68`I}FAHh2OsVmG0C_zz*anymlA&>|!nR$ry;V)g`!#*2yiamC+ ztGaQA&3^S&^9K8Ro@k}!&{OFiY;@35)lDc%cm#EY#^PLLBUW&1_4g*o?~7mTkfcT4 z#_}IitwiHE33ZK4<7OY19y-xoPUprIkdw718>0FlB`Ju;fBJ-7eV8$#TE-RSG2WVo zQzmes=(dy&Un^DT(QCIS8@RXMuHXbOj#BVOZ`_VIYVoJPnWca7pe1qB<95sGwc{@C zWKBeha`!ES^Vj*-z*K%RH}jVdbNyn;AKaAou|(B=3z$)vpq8ZS&;Qz`SID9`{J>wk zvYzGri=T3L{JOL_*_%ga9<6~)WlgMCp~~lOYhGZ#wNUg2lQnN^m6biOHMx!+0elMb zTB+;3VOLuZlhk|)R_RU%noX_gih=7{PB!)8I~_;X7Lw<5WOd1jLvPFubn1n%wAaaM*p?fxo`~f`!+q{h=Zo^@Pl_C4*`qlgORDv;-VQqjc zb1sGA0J;V{#3nMla{5(V9G3|6tc>7$1VZZ}O&ylIv!8&)^NG1P`1i^ruZ*&gA||Ba z=AZGPgK%(gNMCz!_#G!N_FR*hOCk#*ny3tqMcBvk;2PppHU`i+2t{h)j{GSronqk^2p2(XM=SzUbdNT4bDPDU9@mwp9hCn)MS>^kW*e$KG(*yLuMA z4m~)4AnSCiZ<3?a!emjyIeg8|Wo{6K1Ovf~p>*%PmuILKC-8b@Bn{k9q_>pF`FztY zRZXYNidoGy(ANL6Pg#j4#(mG|=!^l6HbKa>aL=Mz2OJh2uA-?KGB>A3F)^a^&&l9! zffV6nC0&kYf*h8Y-NOiIvG^Eis*T(o}1`rM-Q|Bh8IEF+*f4j z{=mzRAqxw?J%W8$)*qqtt@BqL2`I!Q-fYt`?P7^W`#ukrpnmkRJz;NA4h^&(zD}9R z)N1HbOt=fZT1s);&`vO2A>Z6ze4LBj_FLI*%q|CBTY6|vY>pqVuC=5lK^qVaA*&ZVdR)IgQXJ__4K?bj>4XG5@=l#W45`#DsPM ze?w|uVd<+(I04#f@f!wpiZIJ;z@E7)ttfr(uOln_qEMlxO{^2WaAyZxPu<#902VE| zNhUDANRDu=5n_x|d2u#3N5%d9^K3Y3p~fUF9+MtH4@EXQ==Ah-3VA-8{SEc;Q={#P zl8v7ksB&_0X16@rGONecx zu#1FL$b~WZ=?(n@yL-0<_(RO5$9QbfD>0lFwp*-htggp0TzmzX@T{!p^v?P#L{G!2 zdl;!0RuT^79K|5Ir^^{5ya7$(*5N(pt#`gtFV-tIX7bO zaT(#`7>InwU@3l?3p+xH6t30D8mfC(nn#&?de#>-{sST47QcM1>L^zCnfyrN9KRqa z0FE{nRc{Uzx#1|`XD|Ma`u77atK&%sGEkEl_~0S$eBXnON zLg4WnJj`bwa}mlohnTohl}wtS{u~RKf{=||f0CO|VKUJSJ)YxPM_7}>X?r%H*FDt% zTGehL_>XgozXp(q*)@=L=V?@*LyS)e(p2KdeQkam0#2X9v3={$>r$rqltQ3^s<|lk ze2}{y4pE*vcPY%W{V}qTrZ<7z~C*WjfxythL*eL6*cLPn3Owe$B4uFH*C=+(F)a7OU_|od#dg^2o z$qIVU_6T3=Bd(Q#ey8%u|B}7qq>#Q4ftkp`S5x;*Z=Zz0&6naH(c1B2dh`_sjmvBxy?) zKIjp}QDuV52U5Te3L{Zee3Lmn7hj`;@TYx^DnE}@CHRdTQL+vMBO;SnqEDfwN!1?oJwl^M8sDhUTXy1$1@gRP9;1yb zf2^@k^&QxW1NV?>{E87`W39-;QRx&Iu2R&NFya&AtItgLp4+*9qsHsCfH}%vmJLCP zckdSUkkX@OHwKixud3UHfS=S0zybKg_EmM(iY1|sq%=M|a9R_27;T~G9gfkfZCmkX zWzPiJ?{Mt+!6$Ca+1)EtjGf1h48@XHkq|2BqbUFe;8RY5(5Zlps5HTM0$%%mU&M3yh}{@32yt^<&TX4x*VH#=ao z5^(+bLC|~p#W91=8}S4{J%L?Zep_V9XdD0OA3G!~gFa$^Ud25%^OHkGJ=QnI_ufW7 zQ1oucqz3kTdxGFXV|>g;x+4X{U#3X-U$iAxNv+)02+!D78Pn}@+Cv)F_O&YpE*|ZR z6w=LjqoLwaWXvBGj^_~^6kA>>dK6>w73#s)IdO2H!vf|}Fb|aGL%6_te=J2?P z0{d9HcrXa=nqLCxn(TiXYG*6>kygw(S&&n>b{=qpS;z5rn?D4=%?e*f_Q>Eu?6gv+ z!@;X2FDwX}iyKLXEzukV(Q}5)e@jdWA|0xIlcQn+6Ga)jelDLOh)e!9u>!CCAL*^z zSIuFs$dwx8E4MRv*c8NB?h|2yX;n9^z*0ZI&XfyPk%WTTCPL>TN{U5Is{PH^Boba& zQ?O_GCKJhs*L`4NS*&h_oGS=Wja}6h5X`P)*t7B%HJB$)E zG0jg6d))lbj0FE3`PojDDb%MrfptPZvLe|hLdPgWYuVL%gUTDb)-Cf30N_4QSLEN$ z2v{|E9{kYiO}p;f9N3Ol{?Wnjf0mn8XFFn{i~J`$vHAIHmiLE|^_yd4YVC3JBsNv2 z((0^K?r+G5EV9t--IcepF4Ky^`eMx}`pO{X12;4sCRfww8&bH8S?!x3?BS@J6SfJd zuFRdZ5W1Pjix%c9GQY*WMtZOQ2}+fU_bJ4GH-s}_!6QB3vi*gQPDL{(6yMZ%4P#+Y zOdc2*xL`pp7laOqN-R*O{y42d1hj_Yd0cLp+CPrd&A(h_5wv%Na zWu~4IN!)}6z|I;fqtLPbRdLDs?Nhs?McGx^*@95~-NxSOc}S)zzZ+<7L@qbUEKjqo zFLOLE--c%oS*LnY;9*<#L8B~08j{lb$(Z8&`FB868~YT^UJ{Fh*eIKeD+2x>0t zd<2HFZCf#sIqpU7sg$y5Un1XK3r8&e@wZcHsra=zFA<#^JZ8+EQ?teBaPUiJXo9waTym!OQ+64EAXug@z1akQkI3%Y0e)M6dO( zC7l)jEV7D6IvSXwj8-a}bG-Ph9#zL)`+T-|2$>o`<*F>Elb(c-t_?GTrU?(POCx9! zGyXA(Gw{iN2y%h4mWx#m>qGKzI>-C?l65qz2+)#tx0>z>Zvjwb{5nmg4wA`Acvzb~ zH4j;i%hsVO)Zd>E1Ws(GGw&oa$S^N^)c_9144Q=0%g%0*?~b$eX-NSx7gEprG3%k0 z`c40IQ?y)caG*gXRB#n&;Fho9K7s_(WAe<7H*m;t=r8){a9hf((p%&r+d@n6>z;)} z4!!mPbduIdzSW|dVRx{B%+Zj_?6-J!9$j5SMZ|D|!8|KTnMXmCGht-$(Y{W*ZnOCI zY@^l2e=ANMmm~4Zy1pB-{4Q|H?8fIj*(_2Ll_ncF+ zCLW*#=0;*(+EiJ-CV?w8ka>8_h|Lv^#=!WVffz)HqS0Nt6={&tTY>69Ne+MdrxPXz zO8N&wR|t_mS}9U3I*`Aq*_j8%zyZT)MkRY&dJd9?EIy_oYqzPKrFl#4C21{U8gx*0 z2It1#US)v9;x`m5j&A9oq8xlxa;ykZASR(0ExHFL+ONlFXPfvNc^1`uG5L)ttNE$| zs)xY=Vz$cVP~f}UvpxrmGUNFp4&{|8^!Ec-ML5U={KOWq{cFW+{3_x)BG&Nu79Moz8uc^2dd8ut$HFlG=MN$5+Z)Smx<8MKG^rs{ScCK3p=qr; zq%z2LFMED_!v>=DxK7sQQ#}`mzZse;w(hIy1i-n;S6-rLIurL^`euEG!KV|X&Mp2i z;*m=8S4N%6(w9~1Ws9g*b$a`$UuXk4O2c=*zGDnD;(#|c3azfK2{jAF6#`MAQGN=( zTFn5iP+h5Q!CcJ;=p=QMb}hd4XQZ#M&)5c%p@?3Bvdl*Dx;H_Imp1C=E+3z@uDeiH zr?VFW$3Vids4j2OO9IAOrtK%h2(p zkcQs=Jn_kQ=R0V4kEg(_nN27mDYu$Gt%AKMtxmc`_ic?=;xO!h=|tSy_Ys&URAI-$ z^+TOD*VbdQ0vp>3AKFj4v|i2UUO$J#R-dQXRhDRBMo1PlTAvoDL$}p>rdl=F)w(E} zG+u(=9$RTX8ma6__$DUxAs>(I^kW1Ne!DmKZGqDxs^@fJ@90*^i0bHBik+%%^{H*T zm7T1s_LX?IlR1l8O&nMAYxq?exlsaTwj32}c=0RjVkO+j?gj0{`NIRcx1dRHWZ#u= zK-K;&szve`ai5otU{O)=4V7sFSKQ`UmyS*DR2DjSS1<$B*eDy#z*uoD49||ws7j4= z1L3JyZm$7#f9=!z1sKN;Z@XgE18&^Q$9d4?*LT0vOEA;tw_n+aCZqy*j(Jj|$wFv& z&BDo%KY!21srqvPDTA-RH1~!H;5XGOYVQXgbo#A7rWvL`3$OL>AW%q?ud(@{4fDK+ zqDE}zPuqN$e>NFvG}$jI;D{>oki*IdFE?G-{zvFqLW_;XNKWcNUFF%wX{%X+#Eh|i z!ecjOK6J73g3U9j6*1;7g{hP$|2SHzo@`u?RHHbLA2&OkXTQm}7y6hILCC|CE>0zR zcR9HCXc(;RnX2M=(U972AU$9}FgHfjEw)Oz^K!)i0Gs_M>mLOA4{ez2f=6=nX?Rl@ zk#;dOUgG zOHcjaMf0P^#J?t&-1tpPdi6PQ4sGq(R;{d^f+;%g{svb@*IdZ}C0t|$vJu^2Jzrq{ zvmDYoQp$m`v5%Ey9$cicMdwV;N>E;_0iV|4U{|306o2a?R`))|pd_L)j9fvtQQO`3 z9;$mQceH%1ncuZ@Ns%kZN-7cx%ngV+*e#1d0sDr-Mlx~lW7N)eqmwH*j!i%8p4|bk z699IHsNZRkp>cl#r`xrQ9)9S=shr3*=d3Ngsd|t#4X>F9Z(?fge1DTbs1m2WIabUx zg514qE_l)+qSM!=8tYx+z{hu+vi-&2hwprL;0 ztdHH93G2#3Mw-nLMYB19T_qvjWY_fnG*G`hg<1^}+H@)^n?OB9rlJH@JFu=671iy1 zQE4B8SXo)8D$TL>8rIV^$_%|WbEcXa>?oc7ijlXz;Kj!bGAlLEJ4vEgHyoZ+FR-X0 zv0;EEL8I8r!|Etl$jPE3r&C?X?Pv!(TY~BjQUn#gmAsr6GBaLEdXW@x;%fth6VM#` zq7$?Yp4DD+sD2!y=EW8CyA}YDsgFvjLN#8^YI#ZmPOCD>L+t-RnEep0uKWf^uSiGz zz&~rFaR6qghY}fQ#RI9Ai+w|o$iqA=$g$H%mTY(S6pB{<>xbVxUawg6{a?0MYrG}f zdy2G9{H5PUeYOsJr)MKZ!$Tp zVLVa5()rao`eJgE*pQKkY0Jh#wWo226+zBMQ=Z&>vpf30X7ir3ot%UDN8-!${Xlc* zSp8UWU|nv`Cvff+Pt*%c?-e7X7y#NOPkb&cEaWSpMY-qx03rOuBC{|RXVd8n9len} zT;E1Z9`AK?lO+n_FF3B}z-5>c4hyRMGxGT;= z_lrV&ABQi@6#yu|UU4ep3AA>^qMA(Qw4RQ0$X4|*mB4G$FK_1^YxfD$&k_=2SGM=M z^pqz^u#-sB+X6#2hWFk4o|4D7u5eg(VjPNsoz9- znZiZ2cT+TSO#(|&>v-ih{7zp#^6^U#6kz9*au}g3c&@*2 z*`825Q`YSUBt=WsyI&VldMQ#h>Q$sstq@!Atw>@Z;b$JrA2*EJP4rpUdL{SnQOh}1 ziD|}wgLVK+KjqrB&P_ho+^+S)-j54uDz2_SFO$n+5>V|(pwN-%U9^?eXudg{b-dcG zn>|z;NhtY|81rb}L=z}I+8=hOAU;BWQ{ZjBLfCF;0xJ#=YC+8D3Ic5j9fTeUeCqM3d zligo7%4J%2pRrgDQhe+ZE@|axlB)#)TpIrK+3)dNZXa3U8EcX}g)#5=lD@)@Tde0F zecNWAC{s^Ki3z;-spV4s@u_)gKD`%er$}EoZOEaExsHrppktirl!!(rwsM;%RTMI- z?x(xsDIdV08#Wi>tVqQpiyrOG{`SZf<={whv5SO=H%YQ@0w(hOoRcOzLKbH?rF=y3 zn&Sa=foy~9zPhP-^8>Q3w*Iy31pQn9df}qai|#Gep_USqmdcqSc%?)CG1b*UU8pqm zS6tk?BmPA+5La;54@Pw8xE;|)$wJMP@_7c zhJdn%c!}dw=7gxxj@)j?T{F)}#x0f00kDg+k*9SapDZh`(o!AkS4#euEeOmSgYNDK zLS^F4D?`6FJ&Cgx8*W{FgWN51P0E7aY7{MQ8P{4S5@=D6d)=4yKY~;iPIt0qJ^E(Z z>*uG`AML7E!6i6`sUh0Fy}Rfzh|q9f#2O7*SzGkWXlN=H!(3=^#wKp5(BCwrM;u4M z$G(|CUMR&JX|Qq*l|tF2@hz5wVjdznF$`!tn^Ct(P?{YkWHhy9oNGyDw9narSpdFWEfNFg zSbuS=@}6-Mom0ohsAVdo_$!K|!nP#g4#Xvs7CAAq3G7aPnw*eU z;xhw#V11wx;PnG$q}>PHU&$G@$C~jgp*A6waYdhbztm>shDKeZ$OMaRQ`JTZC ze53b5IZeA}ZnxwpxoKg*csVI-$9byniFSOuB~prS!}pp53l+qkXe}0+yZ8Eh>ZBGv z6c}6Y6YIpTdltYg>_5<~dBS_Dz7vuhcA1hj8r&Co)`CfZ`_M_Jrufa!&R$f6MYH;o z+}@XN?#_)ks0e|49vvD3DgFU8&9jaVytA|Yl1II1D+y_05T$V#lg2$Q4**dTS^@PQ zP!W>*l%q$G>&(Bke9zB72rR7cmpsC~SG{*TA#htI&5VVbUXLRS!LOw#2pW z#w+;L+6~7P4IoMo7!%hRZ|$#uIyY-#QtyM3)O(@}?<51e@Ounjp-lUDqhyRNw-=>v z4Qk2fHT*WB#;~%@(V7c^#+T0iZXCeRXdeDd!Mt65`Qi(a%y6*^brn8vi*g4i|9(UL1?Y!HzzZH5coy0{m_!e2&=yh=?txpkS5tUUdmO(#@&iSs z8a$|!UNxK8@Y8N`d+hh7F=XxBV9o{QnG)H1fL*2243jMV-cMwnaIlIF*iB>vX;bgD znSVG;eN}I;W^e{6&z_e|mkL@#+mm(ySRqYW#y(Ms#D=>M8(6=eRUD526AO4qUxwc{ zd;;x`ox?l^9c07Rou%hA3?zEX>#ykm~oEj7Z`M;sOQ z)NCw2>_&4ckX&Y0y~QJ=wQ9;UTz_^U8wOECCrOIr?(!so*o8HLNlQU6r9^=C1Fnu1 z6k%;pG|99+eFbon=G@Ii=h<+O%FsNf(HqDwLp(4Q+DJT3-ory44Aih_e6m>Hmx>`m7DvE$!2tIgr9fp8bj3S zBb(|uW4b@GE3iJw3a*V)3GrSI{?AQ+HPh+DRb-H!nVXR_&N1yQ$^3{uoJo0hIPTJ1 zvLuJ!gWZ*V6LI>esBZ4&I|X(tLlKt5X&jfEjRz$FzrNcvb1TG6U}1V~N5Ogw#2Xwg z)hKvdEzZ#Q)z72m&Cu3$`UVC#lp14((TrVxQWZhM=gAiUBAB$gVB&zgRect&7K_;q zR>EReII-trR(&dzY>WhA@!50>aQxl)0p|SC1Rte`y&MaksQvsO!_NK zLcsKB?Gf3PgiWHLN-%#?L%Z;B^J>s#XvEaK^(z|7cNLKEvF~Dm&k6P;>(yZBFVpyN z&#hzBm9udZCVT7K`?d_Y)VM^+`60j6?NmEFPz74+tPz4h@OJ4$ZUf`riIk&7Z=c>X zspdqlDgUIXNz>}7`nsFT)MX3#I&)88k%!^elZDu;YmCRsn%!*;y!5@b%qR!s^Lg@# z653Va7UxngPUC(nzc=qITQmY(QRUaOM#a7ri0N^)S@9n>}FdaS% z0%DI|W(w~t+|Df7`|a@~ixC=yv|SX39k;Fo=peJCc?RyQ^wRBgOG-Q2{Q|qGJt*`GuQ=Q*v!Bg!ifX{vZ zJEAyya8;;g_d&KuaJX^nY`a+j8~i-eM9r%yMIaIFFWfwd)Yi7_VZ`?Q@X?}bAD3qXWHArC*^5JI;X3+Ui#t2R!#WmRsILGp@@_Qc+_DAr_@y>$0#Vxs<}2g0 z?nlv|1vuKX$9GD#Q4_ERZ$JR4|DZ^#kyoAMO=69fBiDXWp|rLn&e@A1X?@eaI)0PX zY1wV^@kMAThY*x+Pw*3hWYwm{!W}Z$nxue>^B8$km_$A&}kKl>X(+Wk>JiA;YG1IMFz`&Xr?7ySyQLThP2=E*+>JH5BL5sAQL7oR@+S}4qSQ3 z1ds>Emxcx#PJGNd4!6Hz9>}F2nT{agaO>g5@p-E_}{iq4dr|x~fj?s+LxiLRn9Ebyf&;9}jgie1XTx6|9Q7u=n8$r`NWg z!LAd!4>3HSv_yBh1R+CE!;~`<(t-Gw#pP`cGUyxDdZ}T(@rZC@ zu@b3fh3<%Ti<}1J)$|Yx89xexTut&TvDj$X^;mLLirMz9YawH*Mn!&BMlPTDlLN&E zglleg=CW{jHFpsljVvfEBOw>KJ|KM$1_WnaW zv71+gkv*1~ZiUmc3U|&d$A1cz58Z8Y|RRAIc==w!@WPmRAQy6Nj$= zJr0Y6FLp=1`8!`ff_uo?S1FKP7d$t(=gF&{p)(Sv!W#KF?`a!=-rM*o0}>8+tr4$S zExD6pu`#M)cY=?IopbP2dW4D>RKQgh%%mFgwnavX2E=o%Mmgv7`4r)J;sk&sq-j|dZ4g#EshClp2pVXBtA3Hqy(35hIe zV3^e=_HM-Yg^Gu zo@LUdGyk*;dGFXPJk!E9tYcYwQ={}lk449~OHf5BQ)}Li&e;Hm74YH!a9zlI+a zlm!7j9nVA~>K6Sdbn9S}RJdYxb|wAk$Jpae)an@~(REDvbV`bRxpIf~*|fh>?LsOJ zQ()zsIhUm4+=l1Em4n=At>8n1&d=hU9JEP2XB-%EmoHItMbMAY$TYZ6aRIv4h0x}^ z>kwQiUpqrwI1CEB_Pu#W=xG!a41{y;t@Llav$|*ii^cJ87R&Iv=(TgRREyXCq02Ni zlB>5@`;SqYu8;b-{JW!@4BEP}`FTD2e3;(+m*0aTl&Z$i8U7uv5MWtIQ193~ z`Aj1#H-oOycaY)l2@B^qfq_(w;kmizm)?woP7Zb{>*L8`+Lg&oB`jieMMc?}jTr+h z$fX81xuYsYR&G2u;@uHs!~Po``-^7a1n)RE|7&a}S?W}kX1!4|%$UQMW^3X-jAF4p zY84YWEt@CP_gj5H?e2Q~w+BFgYgE4F5>Tm@-(E{gHufP4KYN2G$h^nxa=6^3@-b*g z^zL~_2*nV8hWSF1X9Bl{(mmDhNmo~wBYQ}byi{~u|3h_XTG%P<;FxV6;C*6*PoNp| z#)i55Hh(-bW`l80eQvU?mBTGy+50Oyq<*h)8(Va=+1p)>Ir?v)NNw>HGziHU*rKHKD4uFJ@robynKA|3o0p!_2iQ%zx}kvo=lB?ehK9z- z_;b*JVf(_k6ch50^}Yo($!W4z!HahBh%g}Yf<**+@~J~o^Hf@cO41!(nK}#PTA|$? z?in6bsfhd={0xMq4lj`Y?H!}&T<;lF6UGKdf&8@Vn|*(2NlCPO1g}K5?jeetygVX0 zn&vo9Uoi)2udR+*txg_kN=OI^O2R=>Z7HQyo+NMzJtZld54h*s2qVi=XLme&u@=}w zKh*Vowrsg8BVc#Axs+Hn2MBB|diA(K4qjKLWcWKn!_OISLree`+y#I-ncWs|ca1=P zL5ClfG)j5j`AYFJ?XZcN6B5ohJ5jt$62i@r*CpA+bQkZxzKKs->l z;{4lr`SdH3%LvDmrR5u<^V>+b=ea=iP+OCsHc^gMXBCI3X6ny_?=`P_La~Zc8PdX( z%L@s=hS4!%KD5K<*v7Fo2VzWLlF8u0{iO}Hg*G?EwH$oq1Nu3 zLv`N|kQ|iQ#IHRY3EB?kqAXffB740LO$DDqrl3qUmWZo@36t%|*kD4l^PurPf+0mB zRrAV9b>uK7cmLN%jl;LDqc{e!#H>!DM7p-kp90{S>WL;VF4j7o-(EVmiTEP9L!I0a3J~FV;B#Da0ygrbhlNg~gRL zvfOJ4eLqfv%ju=_ryT~@3$}*0)k{SHye&s&4G>QME!E*0KYAUucd3R+r~9u-QKCFWo6h_)X771&?B+CPGPP2ue#k7@7C9z z*5}zzhEAK@co{3w;?1&NO3{XpG}6w02zVlCnd%{^(#Wme(15zPIqPa&t0iinHBcL+ zjj->WAlzSWsUb$i<64bN7XFpS_E+@QY(}hqGJ*9BZ;;sLRaJ>u=u;~Tn=p6BMV-$9 zPsp7521Hme+Oh*A6W&w`Cn={(dRcQ7D)9(6>)6GtTG+%{Ti}|oRt30#E~Q{(&60;J zEwKPAUh;R`^#>q#?HbuF`S4;bH)}>R6Y$|spY`5n^0=K+72j=q04?qeb^@q77A%$0y~-nY`#iF1k8B=d#%0if|?OXTK0|9uv1`7t2aWa}apGA|q# zf3ypMR>_)VeqMqUH-(OOHpbg!v*QAFrR-I%>+OFVg~PukB)*_Dpxy8Zt&U3EjdD@@ zz~2>erSTLvj!mF$*B*$k$8dBCRko}(EXVC4P`|?Y1&0$d|95^x)ToOsSNifLxO~+veI^uXJ%9D~tYC?!V zq;N*KZ2Vc2O14bRH)${NVE~Le_1R7ib9I>esVZeg0-V_%>=?Ak(Ss2ciVSp))2__^4;wjc+smZiY+eZS2<`nal^R6gsNv0FI!FhKe99H=KNN zQEjCV`&C96^q@6aM|BRD5Pv$fw~*7Qwe-0L!=2Mq6*3X$VqSnYE_4IDXc8`ieUWV^ zqODx+GvmTu^*|J<6gn5l3gY|~U0Y)&+J0m;n%e)=6FT|oK0%FE%4ekZX}XbY_%v9_ zE%iPR4nP3YcGih~KG2CkANu|E4hijX1OC$-x|LLH$Mnnt2~xEy1T5N-`g!0zAc@z+ z#Jqx$*cC)JlXD>R`|U`8%qi=~X_9hV;H; zd22V{xn*4nCZyokXruE6vcs@Ft@2sZyWuGvf5fvx0A16=479@|9-va~Way@+j$aP>KCa{~#TB;X5&2*bl6~Ya4GT^G!Sh zi2wtmhf!K@$Fk9@QtB?AcrIlG7k>wm!qCcGI1zuQ{yMjCF zW5jqzL~kMywzj@BVbAWfy;-^^_%o+~tVU2&+b+fe*v)tn`#uRjIN-^@J*0G~oDP18 z&-ok=Yz${T=HliauCh>O(W!pI%6iLUudvsR(GQ2Oeu*po6W@A`W-*l3RWVgMGRdA;iq6uP1eNb?q4#sIlP<1>Hw)0Y7JLj`}{k6mV>h z)(QjKDG;qdPW|7!{n|A+kd_Oa@J}1}?}LBsMjS_Z_4QNTH8j@DaTxviyn}D#!H4Cn zxYKp^lxco{tbl0m+jH*YwSl+#`ueLwK-C=`5s+d^8;c+I0-W(BU;u@auuB06t4rzZ zEf<;N?oTP-MD(#CvH!Am8-5edo9E@_(eU%vjZ=ac93NI)aC*>WdUU213puYT=(*3k z2B&W>G<&b!Jp(Epu6#G`gyVSt(a~iC@qaJ>0!~Os`1R{osy~k(Tm!cbbX**+I1Wpa zEak)-NH}rz#eG2rGH@U*e%ETWKVje){r)QwzD#X5*1yyd!U>xb0K&+>Vl@rKfjHr6 z^B}-3s*doegoT=6K9l_Y2wG8)4{HPL(W&Y>p`)3N5uf`!F~Guop~nrXyie}8as@B= z-}&y$BT2l^_auREoNlwl3fveZ@9?l{YW#1U+eAk8r5|76W;_( z5Pere#es{67|j6kk~d91!NDm{Q{nI)Qe7}@n7NP(pFR>5ISI(A5fumz zOW5=8KgN?0V3wU#za4`rLjYblgN$btXQE$y8h553@O%zbU3S^n`@YBPU2z)P<39zu zui8L~oZRV{U;z08$dMUP8wS*Vrkf7G24H0zt*PGTsb(l;?HjyH0^q6v>~imcH@3c@ zBZgWyu4FsVR5{R8IYYB0jx&Mw^bO(|G)MnkW0+g zk@!BeM;=(Zh9~PL)Sxn;2twY0jTm$U@U_5fczt>-U@#15qK8g^1Pjj-;EaLde*rI7 zC4ptt1HC2*Cm!sQ0K@LDF8HTN$UgqRTBDL!3bR^!N|te4eZV@$;mdGmGcp^l9Y9kd@vHLIyfx@Lzc~Csj5G)J10+t_0a!S{KRqAa>Z?UEp`{bgpM|% zxA5O*yNC|j+}zZvw!|A59ZfYL%5gw$tke#7?`D3Um7=H%=WB(L6OzW?yntr^$Npg zqS)%R1^3~-qKRkI|B{#(3gp&b0hbhna5LLv0ha^(LN4C0&D7Y0eETM$kSaukbl-=a zl8M5S?|B?QzltLyZXzV}@D&C%=JUzL!ZP1~O)B7Br&SU0fT6^@9B8QW4` zA55j$zj8$e-rVaLWxpeW)%A56W@fCn!PxC_th%86E32yuHeAo$nn@1tGut_UEyBpm z+>yX(%3(D^3xwwdnyqWCEiK!AwSPQJUc8n%zPY>jeAgDO{3dkqdX4|$`gmx#!Px@1 z;)9dx?z5)dRQUtD_=z3kwbo?y+J?( z#?{S324K4zZ{MTR+E3h7*Uje|&7yvcA3x3d99& zL#YGqd8ppI@|%6rVvT2IWp%&I4x2ggwO%*C`{oEnKw;t-L{cdh&XQ&3w+1(7mnSUE! zScmj>!u3P5)h+6=sA&}vV&+{&WPW>B5ZIXX9cxbU+K3b2ozkc1^a9On?h`)ubZ-qI0^k@Q2u}~FLO6lXU_G$fh=>BJ1r>tuX-tB8_ zWf`HPgb?SB9W}J-j-Q|3LvZ}zQ%nq&!?+F8tJr%WKf}=}V~c|yTQm|9>1ePV+F|kR zL)#DDyd;%IoDyMLT^9>K8_fVF2tn79960n0EF8bQfjvp@Zt8)82Gc#qZoJ(4-t<60A!?5^Pdz2ug zhI#y_AHZP9;zv_cBl^Zon~ZWLL)BR}lpGnn70kP=x{_BNBH+pAr|=OGl_>IwSmnp< zF|YM@870tbUVVHT*|ab-)}&M{HWlR3ek`k(x^=)UwN||^7vR+l92du}ShsHdxP>M= zf%aVjnCpJ>)G6kYQnZvF*X$u^pU%dGAk!gr(X~?p@_0w+;u=uiYfm}X<%eZZ&XBWL* zYx_aQasww>WcF8E99A+`J>QW*GfqxUcJ}kT_4V!j6j=d%!K5OvB^|O>Xnq15svf}9 z(j`|958e2MX-&Sv9I<##@i-n8&{@?RfKsJ)$Hq{;F_rb3#m!2j1b7R8KIzX2%0 zQvMRa45%Xk8gsps?#?t&7p|I4YQ%^a+hpKb_2CQq{((%uOv2wr9=~h5VJcyZ;x(Qf z$0F`tQYtK|lSD!HuSxX98hYbZecFckMWhjnjuccc91IV)gkV}8Z@^@Cr?=|E5_cE^ zA0lHICG5@+Oi8BI)A}1!KE}@pWnjFOcJ11_1N5oRq9aKny7<{j)an%T0j*`=R`u{y zyNv_>9f1oe9M=}4Lp!!g%K8|$q za)}xn@NZAcv7vgSp>h*EL)0cT1OcQz{!XaLV14 zLX@FEJv=IBO0VKvPo?)5+Gmx$Lf}9w)^Wz^p0SXR7rNaQ{A|_a|F)g_Azk*iqgmpSf?*JC~mk^)+ zHEyQMC$FUB;K3Yw-!+?eAHHoHNl=r7Y<&w|?TU!cZWZ(;nxuxSI7Ean1Pt<+0fOp~ zfId$A;47QH)R(U8lP}hFR0O&-`r`V)Z;X|lZAzlepQ@nrIZEJ(LdvADE~P0&*TqqF z?|L1bWL?sQedvbJ;t&9Vm|zCwTqGcSDSHV|%W&H^eB5-0ODQKaH`n413$l5%=#$xG zoyd`O?V2G-jvWbDAZCM~9|c3I%fyWucz{^LN3OYhqR@GfP?lyX0isWA1d4;u8P*}w z6;;^0sI3v8S;IkVB|}Mh@Tj+Ua$B1lJe~OXBUW=T``d&|#ugzoK=0W3q;9ZuQj@$Y zDat3Lt-EgGp_5BjK9zwW-~q7V;h-0mm8H)e-YSLHK%H0biJ30^`k^A<)!kjE*t>L{ zy3S5)CMo7jF@N)(^R5^y2*~$Df|u6~40e^_bDK`GV$`S9(I%|F!Fylh)6uc9CRn67 zh5_Kt)K`3U8BEkAVL=?R_(0PHdugjtDQAwnG(*lSEc`)A#UP+Bb+m%1+wif{n(CN| zTft?TkYX2$MXzpJ=63~g=GY>pAhhm~ngK78?_eU8N}c25v^d$-+n3;>%`E&$q&Qp5!Jl} zCpFZeK(2LX!D}bi(j~!lUt(gSER#O3J3S%Lg@M*akQ-u2i@uSGiHQ_+s3Yc5{NgNL zxj3Z1v_Y2`rUFCCCQ`235Tc`_i%;Elh}D7U5J2{Yy*p$xa1|#vK(zvS zX`vgg^-J%x=XZL0)&uN6OV&2UU^ZEw+w0|}UB;g6ZJjMFFL$BQXvHBBAahzdhItfd z^guuE>{;V|((#^0H|;O}TpcRx9R<}J3)W}9VH$>CT=#@o*w5{mLwvf@3gNEm-fZ66 zNJ=jztlg~UIDq&QaQX5ikqlFSwko<6E;R z;&QuUKq?2q|7dM(eWQS9_REEls|OIPw)Qy`=!d0K|%SowHpDAo&#-MU8S}CE~uu$q9W$Sd3dY_mCOq7QoqEfks|@1 zN^5f`KC$fpV&A-ZgY~Tu)=_JG%b|~DY%)d;SeTtiIh1V;L4yv_S3wP>xcjxnMkr<8 zxdtER(iEVO-nZ6b8giA>CaBt^iaxQrlh*1b1G=4nj{r&UHD>H4Z^a_{e!kmw97x2n zt-ZYhFtlE|FotB7p$H0$HK6_XL7cKDG}n4Fz8>40K^R_!>Lvi2bkIUuppm4zdiCmu zR1pcOtlv0swva>rm7AL}k`ucYkH^FAG&szC)OuG5-MBQMuzRo?xa()Ax>~hD$AXmF z+)N=}lk+%<+NeiBEt{Jc-2A;IgMHfIX4R94r-`|8L4k zf2uGMMHnP@W5Oye@t15$K-ZsMvi>U?e({qUbeU?BhJHGv&k&WnsE5Q95`h@bnJknz z4?J?y0dI#u4BgxntP@%|FMFaO zq9XR(7QosxxjYVV^c8;wv@Jjd55YX3LtxwQJ6Ln(Plwk5J}!eArYSJPAqiCmS2gsT z?L_rgkka^;XDD?jxPFx3Dwt>fPL7|sanm~F*6BVDyY=NuluDYQ0CZs6w4Kx{VCKD} zhC)U(zT&%$#z=8u7+u9a>IS{r{gYnZwmmXE-g5-p^HQKbK;D*~n{hNEz#8ncaT#af zENtPV&mXTBhD5r)R#;hk=6h&6;p$+7M%;IT0i$s_PO6>+W#VivRqR2el~P z-97z_SQzO5+H~0^**6b*@0UL9)rmZ?@^glk0@9JtK+&=J7}LyL+$~vLR>in zzN`MoNrxR1m3_9rd06kI=6^U&`rcst-jow)C<;vZ-~HkL>=yrDKYdwJb#NZExY^dd9(=p?1dUyp6q+ zjn!9MUCbREt?cb2#H7Xc?cMsdlhb)eS#fdO-##E_?_eq3u=JyAxX8!nkLx=!7^}|E zp9S%Xak#Qvk@BCv)Vdha-{hTJJ3Om6*U;@M>wHh^M4-?9cBOkm+cN`CSx?T2ocMIo z_EY} zsDqnv#IxbaiwdUWoiY`*Ze1>0DmpXRTEameU_FdAvFd8;=`Rd{jdum;=b95;bk~eU ztGNsC z5exJV-H_m)VdJNQE&CI$=MS-J;X5ba3DmLmGd89yzB6W+X7*)$rmf-VwDVbfes}N% zi=$6BUj8b1KySO#^qP`5Q)%m(QpTc_TSKxuo*!J8=JKiK++#ib^r+Ws-MQqS-qpXj z>3{Jy?MXFTGa<0JxH!=zV5gcU_T9cJ3Pbz|x&g3-RM`pFb>3FYmtn zVg>#DU;MUCYV5LCFP!q;Wrm5pk=SFHzI&~p)u8l9d0>xZ{QXM`Nqck!O{;d$U9t;L z{PA7$xG(yrdQ&x~$NH<{PenWR*Gu{-&P@nUH@miY%Dle3FnzKkJWfBuqWfjE(oT6wOT!vgT%|EqkgHdLKH_x}t(R$|-|W^IX_HxJk<^!Q{>`JkMi0{x^UQw!R#N0s zhR&md3k&NlkVT5sVWD>Rx$ln!iy4R2Bpd1)HglbOOG70rIb-`gCf;f3=yd#L`%OJ_ z)|tmwV}APSryd^GS)nb>@CdxH_%f#qiQ^S7dAGp+zfBp7U4B=VK?&8{bK}lWi{gaug+JbUyUT$W|huFN!^%7O0%H@jundKV~?9Lg? zcfT~!b<1>aC?r(I(L()D{{taG_2{_*{&Yd3OM|Vl!t%zOh>)4KU5d)%15HU5MUr-n z2iLA$E9tU{j@0HAQ@hKh?UqtUnRH9(c>Z`^X_WjST;}xLyK9Q)!fi8^{pl46%W`%P z|GMeSXR5(_i#AwB+>$6i7|opgcvr-ah_%$-A%cQe$Ey=xh$>%QzQKRnk*jQ{re{|k zC<%Hx^{=P3GXzgt4|LB%F~{e3`OM?G&x12trFNNpQum)~mYJ)ri+Q`ffA(r~sAk?` zNu7$RuZnrkQZvT)SN&U1z)R{zT|f#52Wjf->+|d??7xSN~X3Y`xbYl_-qXwCnyO8gEm*(OwVPTu6PDS|^y!()gESW&hsp{=R}Ujk{cCM{DCer^l?S z<4x?}?DsJOVu1O{_nhCM0 z6cH^M_gd(l3{64)h$W+eL4C z&(?ak_hnR+=Z!b#+!R?d;q)J|!%Ip}Pyf5?hFPZ+z5e-%<51DAIQ-&1td6;jd8lz|YhnN`CK*$&~8?sm5 zADgn>(B}RxOB(>KpD$Z4#%7A^rQNyQv*7g+YwL*aOK)s_`Yex}%sh8WSfR4nzAKVq z@$4vDJl4q5LoI~C-ae=HpTUz=<859NA|Pd>m-6uK50WVr{1&G0~ z{a=35;+@nvd{ZLn?KEF-Fn`)^@$!e!iL+fnN-^^Z@iv;-FxbIK$mBPh8qO$8&(zfD zwGNl;(q}2;^qv;AX=EaBL=7`fG;y7EZ}X&F2O8H}WQf*+Nd=9i>ZY0)Jox&4rL9p|>I4{8O66N`$d$Viv^J>CGPZWR?7b*p(DcmwmrTUfSe;?)i_~ zHtaw5T&31`i~J=Y^Sn!=#f}y7S;0v}oiFp_wL?PaZ(rDM{bL%Ym+bx{eW3cb<{op^ z_53*fEH5y@D~lHNip;ZVc5Hgg_0#WXHf#4!8sAh;FuAgk{gIyO|9*BysRdBe1oa&P zero}$3%NvArFMfp>=74F?cR?9`263zQ=VbdO%O9{)CQfTK{|`4i|D|k@V$fd`FP)&uVWk5>Kfg$PRZV>wCTd1J1SVL;R;2?YnvR7gX0C1h!^C( z{u6!q9T)xRP`k5FZxlVhwcYXE&r3Rg5J`FdQb{mU-0S`I?#dX|ooZo$85IgSU9t-p zW>-*A1LWQnF67mxDTF(WzLzw9bmO(ZVUFuLZX(RAN`; zAy;*Oyl6Q$GpX$FH9OUQ_u3ZW6j|)x7eG`3gC1?_x^^sJEHa3GVVjhkS=?8b5g@c# z*6I1g`_=%V3WaH#jldcsXS%(U%aTuZNu7UPC^z%&v9iBoxy-I3*H)H+0)@2tDJl@U z-d*%-XlEQ7cba~)%3E)A^vt!*!O5BR!mD?wpSZSi^AIQ1Gdb-oUGI(*zO}!V>pB=U z(3G?Oz6iY-KU%ZfDF?Jx;lk|uXYPsfJVYQ>a%oe!OlJhM|Cj6QJ5d-^uPk1XUbHj( zqxqKSRebWxD{DH+!gc#c2XY22p5E9{=RTN6M01VtcDih}u=N^law)A<5Q!{O`t5uR zbI?X}{YbTAYme~PUBDs4qEGx<@yv8#6IJG7|YsM}-+vIp+Zg$Yy zvhUS>W&hI}@{(A7@s(8y4=d*e^Tti0+y}CkZaI7g&) zgM`JoYnxs4&V>K)!w)BJe}1<<9lTi#$Xxg~ zg`&!IHuRej2QS3jZ72rbDnz_q^3i{I{*1Q!&)+VxHUj03_>FY$9*MHE zE{TwFtW4BSS{t0jA!*mbP7%-1+He zigUM=wzvt28a&YJuct#aQ%im+-*k1AU=-G%y~s|vGS_W*X55=UZs2ywDo#Dp0m4B? z;A!5PzUHHx9Q|bI$ZysNZ za~~g2_E!$r+5Tvmc4@vro_HHO`oZ;kvPJgz= z{odJUH+|O5i*uZpI92=nNsIiUpwx5I{aF>%T-0S)?ow7)m~8XtHEa?7?Q3^C0dj{m z3%q8jo>MIFhLEZIaXC@;Uu!zn*80q^;^BqXbe@i(wqZVLEyORk{dQ6kO~N7~NoD5^ z2YjWQkAuZ3x}KstI~h)S0utVl*5S84n2~E*tP`Y zoNOBO3Q?SU??)hpwa~$}-H<4Mz79=@R1c(q%Nuodbb^P5hQh=a#xGKE?9#zq=e~Qi z%nDgekjgo=nwDJ6q*6u&UAo1?@g;{|D_nfWcc44->z6+@0{V&5yR>+YuT42q0HJE> z9*}F8*ir%@b{a@6K zf2RigzF++x^ftTZJ3B&l&px<^ULhLBv3@rm_9K``h<4C-(U@Q zGb&=Jl&!(jo14S$pM7S52;Xqv>laL0cYs7EfqyW%p5UZ;f$AQFkTls?4OSF=6x6T| zj3Gd1yVA`QR5yUNeQA$ZCN+-n=LY!>C}tLo4=>0}yuMPaT|{4K^MrY{YYZ(eNp`~L zRsF%g;(q$+GjKE`^R7&rx^!CadXtN1@<(FClWW{X|E`C9J-l{R@8N_%G$H&dwxQx4 zk))r$y{x^joROBf!J1LCit~1;64m0Kc9M?BUVC1mHp)jvalh9{v|x-$-X$4`qPo+0 zD3vza*e$v+xLiwOh2h@D+&k@ zG8YNu0I7Aja>}tMLj76L-Zscz8WEmmm;Sw!$gxRn@2wJF9?-gA?=WA)Y|Yn`;sCj}GfZ<(se+W*y)zvK=T zi-^e!}adLU=R=dW)jjz#Nk2?Ewb z7{HxK*;)M8&Wx$nCH035kav#@=`FSSV;n6Ib*rjaVgwfD5!qwQC_c=hwXF|loxLfu z6QjJ6SNG3Kxb(d`5iTW)K4~u1P+mo1R-e8y0 zFPlK;b=g-JA^ZDTx&&fPV1HJN0whhfB%S1Ru%-@-VE9kQ4baG}+pBl7e-Y#8 z-++hnnNzrgrMdx!$$2z{NricNgp5d&1+am1&f&*`bDvX=l8*Jw!X+I)9$8s*)=xgO zc$}JMo@cADuy7^jA&+h@Wc+QO;d|ANx)`Vt3hyP+;0Y<~>(?!92vLa%64OwKKoTxo zy!<&yTgEPRJJy?eT$mc|4H2-`=6WV1)T?_ThA@NnYQ>)|Q%y$pAE~y7czAv0f;5q-a=1x$Pt-=Px%qQbo(TG}f10X-iWU`LS2q z0W9&EGvN8|py=ThrgC(??8C_q;|%{<53(x+F*DzTB-K;t8Q}J&(CK5$!VhQ7_F1~2 zca(s<7IttsT~X>!I3Cr1iEl~u3zw`c1ueUsl)@E8`(928B3Q08Fhn7o0$1A$G(z}ZO|Lds zGJY_R(7$zm@g2RSLp2{YDg1(lAwbBgA=7q+?gwdSkfyGnc&Dn&s}Hs=FRVWkF}Vt4 zp#HtpSvCD4)@6m4>Ex)hcLG@!5(itnF(aTYg{vD0?Xa=(Ouu__r)Tq)m_sI ztz&^$G_BFjK@d=5>C0Ql`UQP2$zfTsCGkPu*$LeGe)XvtUq5soClAipQ%IjjCeqZ`4`)f!tJAG!CMq!M-4` z-1>t%T%yn;T`wo0&#}-ZTE<1XBzF6oMbqHJV>rB}6k<&z2|H*!yBo6Xqrk(2dcET% z-N*VoAZ{dg?~mVx+hI+}zJIdXn)~Oan$WS$nlK?Sbg0p{Y>y4MEB+6)E%iag(e=iG z7*TDjHF8vwsyi33F+Q;3ZIG@>k%@bM5TNORuCcQ=*Qwi|Iy<86tv4iwvI=ICV^u%A zY%tg!Vp@)#?pBn%dyUhcFZ@x`4v_K%6*`QDajAJ@23x!)-sD2tlQgbpBJ9-ELcUlL zChzXTt95O1))j;30$nGXso(n4H)+%~_u>Um$Aa>n=z|tqTOn#5QQSJgS2fTf?uU%H zc8Z8^=)ntLiRDkcC8cP*(XoOn9#~<6){{09q&fiAc`r8AlUjy$n$#e@nOwQcl9?@D z)9vhx^7$bkNol&bfH8ONZSNNOmCD^QgZ3EEkXHJqmTJIKriVeYX*Wu(^hf& zu^aKS1!46iEFDDz^w)WgH&sDc!hbtS5Qme)@YT5GkNxT zs3{zjI69w1SSWR;Q1gvwjJGi{n1|@LH;0-|*+HX`wKewK?%3Y^-IqUTLfZzuVot3W zVSx-tisQtaYjKtpQEmg-ot0VkE!5tU@Q!|MemH>*b2Yit9b@EDXFi$ApGs}SVv`#A zuKB}}H#SNNOE;cBV&d`smk8HpH<^KltwXQQ`XxV(KIb_ez?u20vC&!!pX&68vKV|sMgKz)BnUen|Ov}V13On8C&7Xx9 z|7K|~3zrcp!uBO9WGIu)*Ky~{%3{{sNF4c*i1_m7W+foT3l-gewk14{<;694G5hwq zcn1Q``X=}rFJBn)Lef>{$vFv_wEkpjZ<~DUg(YqnHN=Ygz1R=PD&vE9jg99 zh*i@n?CU0n=i7*NIzrrQ2lPJ=wT#NHcRrQlG2PHoPSJxtG!D~LR(*l%VDo(V;T&{C z4WTmASS@jQ9AlB==*)+wTlfm%x$h3E%a2ymd1)xlI`O248E$cFyEQYGRZs?nau{o{ zfW?W|Yz-YN8#px_QZTcF>IX^lTp<^26`M66sQMdGTN2<L|VHyM9kQL ziE7@BK0rK3C8n}BwZJ6Nw7{!dkUyd1TV2xQ2K7``Sp`{QsuuF7^ZcWacLk#67%~xe z99<-hin0pC9vEuw9)p0F^Z-Dj%Yg?#lRjbf;?9@aB^V?dt7xKd#OZMTVA}~?DL?We zaSYC$lN;fRp=C8A;xy@Mh!}PzA)qSRdcIM5boDhjyr^wPnH2m}qveqh{qF_d8<|Is zSrj>Zzj(on125g)YdAaxAdxcU*@@&*(VXfvud3qScV9_62jLon*KCwr+8*FUyyX{Q} zL%TCSxxMe~F+vsUu$28TjrO!Z-|kl`n$@Vuvz(cDpTK6bxuKS5u+S^Bn4Wt4z&U>1 z51=-7Z!-P z3L5kwztIP!GZ#O823w)(LrHVI*Ov?Z@ zsUoPbhPHJ|mW5QvWIAr2O~+7`w0SE?PX6p6xhS|^D3h4ZsOa~ZrfGL&M_wEm23PHH zEtoc*8tV^703M)Otcrbf2DwkgxLrR{<4AdX8puNfBc6gO1b7=>`yC8E;i!9N8P+a^ zd1Q5xt~hz;cd%Pf=^u&H*-q;)iJ|+u)jHh(DNaxiFi)!;3#4EN|PgI z2~B!yPfDiEO-!g=DS?+))NoGe8jU3Z$K}-6(@YBjA&oIo@~3*3AtyYlhe}4bT?0CVp!?Dwy%8&f10Fh5R9><4at2A z+^W3<3p`)MMY=0jCL3mXyuWT*itGuk(omGAK#@S!CXUho3sdoMm{^H3V9cgmEp2m}i`C!^Z012;VGj@&UjyDyi zy2v4s4JI@kBkl=ELGP6g4?`P~KAdb;imIg~_4q{Ny#R@w7`vLhSwD>L>d&_;Jk|>0 zq5;?=l_Albb)d=;oP$hBR4@|-mOt*%GwdwKszR~K&h-ToM^WurLPe{gp?u#!gO6;0 ze-*5z)k&S+{dEufku2J)cEzKfz(H$ZI}0p}!l(B^lQ=Q-M!wm^tv!h8nOVO->7 zZP2!lK#ynYIQ!dx?eL=HvYomsX;M>%w*rJBWu1#rbcqfS*DSiGyTx;+V~oXv5*AEy zk&j|!`ukTPXfTvUfHNnabU~nrj2r?8?6)YrZNuT|5X&Hjn&=F3^F;vlk05ViMc4Qp ze3~m`-_UpgKaZJ)Tlg^D1`*VbRCbvf3t zq7lN+bZNM>_?0EA)B(~QXNoP}-iL_Ows((LX++BMQV$Mb>I^Ptakt@8AGxiu=~Wao zgYGTh|HkR(iu_?$*hahyV-Az59Urh)lP!f#o`#JeGLyViuOLKUX3tQrpEd4~hKxke z2v2B`xK}T2E4fbKHdR0P<~wJP53AQ%_cl^)!@@Ri=&k$ey6AqKtr55#>tPvrZf|W@ z>L6a@v)<%3a;yW>b8km<`4(-@Z>8v%T^S*}81AEj#36zqU^;mONsff|bgObDj-QBJ zopov+_0k9Io8@TfsP7^0ddH!=g>A;T`-lp$Eh9*y`VYQ7m6A7B&+N^IGTKSK6DgHs z1j@#2B54tmnxK^kZW`Fnw@y@i(1WR=El{l8=Zzy#hxOR;=?2R-%=e+5s_h2`uif(W z(N$}3`x3C4DAK>^EJ$Ca+_ysU=Xeo#)A-MWKiyJ6bAM2M?c`plvjb3U;r!TF7A{>u z#s$oDw0!K)yljxWc%DFn$`%f-JSN1%Ky=qD@a`&1yo}If%Je^|8p5!?4b8ogbjx=J z&*hQ2G*HCUBk%C*V2^`Uwm34HPE&uh_Jgc;PcN*X6WCz5zCC?$eU?3ohT7Ct<&P%Y zo1-l+Ua(}-kJkjf4pLaCg)B)ck8r8C!W9091KiaX0IgEwroL@WL$ML^YX0JYQW~Wo zUdqUCQ+#J-u%SRF;@jDQ4i(I?&MGd_0dDwqRKB?Ok=WglI4yCT$;u zo;}u^KS$^TeIje^m^a#zPBMj~3(a~YYNRgPeJ=t8Q)X_WwJJ$B^#Q6QGq}NlXdyT% zy+jOK2E3>W#+nM}Qe&mjp5?;ljF|}i!}P3$8XdjwWu}f%WCmLNS7_`oWP?wk%-!PD^g{S>)^bE592Ry z3Tu{&R`M$+#z}-=5?-&{Ak5FK+iy#MXdc7M{u)kg21CzwZf0IO{jWY3^oPVej5*N)bl(n9lr>eqc#7$2m2Ok z<_^n9XPu~vZ@HF&}bBESS?uzyvkGMmm9F383#yb=-vtRq4=`m!Il9ije9r(vLI7wZHh_xV2A zZMcKZpIlx-@Jna+Bqq%hGk{thr~#y|@#-gU7NkW{-!*?ZRA*Rxj^i1J)B5_bc1bd| zNWegv#=xeKJvY^xlzFK8UI4r{A>=hyPQUp6Ej_kEneDr238R+V&HxZGd`R z@=>6RLJ+8g{xzTbso&Y9MRCD~Xm&v9F$uxu7S_>* zHpGg?{*6fgNqD&s23pv8rdTE>Zc3A=W!Iu#7%IAL#eq#Dg>Z-JP8vQ zSvOb+GC*NTj-scIEW&MdIIMP0ycdQUQ%uLoj})%ZBr(l_7&~VS78rlQnb%9)(D0|j zbQaodnC0R0@KH8N=7E4fAxi?lUv3Lq56P>0uIY7R-k4UZz6Mn4BI=Ih0AVBgQd!(th@ zVJ3euA~yj%ke3%1Fkc!#*XXG&_;BSQ2z^cR(*q_iacA+>M-Sr!sy`qj5b-=bX5}>4 zEKky^2~28CPZEc*{TE{z265hSBAg+PymGUwmOBSl3gb;5FnaG}l1=W@?6*INkQEG8 zlC2sFljri^`G~jiXAAgQk6yjRp(NU($Lb-Iyr2A-9^xegIl+5VDe2NIO>LzbYyw~@z#JAj;CxZW&d)jN)Wi9x1KC<@b76sdVh4#eZovUPhY4XDjrt$y(C z)tk#NhmAp$?tF7iP#w={kh7GK+l_o1ptD4z$XZebwT)!?vPVA4B*2l0ijuwtI14Tj z%h?O*S_7mil^)kBLhc06eV`sDcOO}JXYnkW%L-6kvI;V@b{zL7RhxJVwGWlH1z;lW zME;=SKX?=*douiX_hL>VIxK&(d{Zi?V-{hKmURP0z#IiSH5v~WfY?!uAyY0!##={w zJZ~qP{xM0>v(5Z~A;2Pq+Iz0k+u_BROU%i~MxaTDVLIEROAozt(qI8lQ-xh6=NJmI zI-Xh_MmcOlZ7-wI3kc^f<;APEL||NRNm7(X#bUEor{OZ`IOuO8AKEE9AmGVuaTi#{gk#gr_bqg_V?|z*IIi&l)fslbu+_e5{a~x zeDRztiL_RnL|XH5(+2z|u}wQ2{|H#0SGJZj)3>(Mw$vkC(Y7|fVP<{9Nar_OJxePi zGgDs9lbk$9e>1eUHn$Sw;xhT$2RO|v4Y)ctZ@Q0*{A_+v#fn7Qu1)-16E7NPL?Uq- zlFyxyw+|Waa#EL{E?OO*jo-u~_-K>i4#U{9w;jm03MU(R8k#dr@^_UE-L~baF37CZ zb)e=X%*+_(hicSJM9bTLu(XRD(%J4{US0j*@RMBrBX9HX$bP*yy%-g46(km|?XIny z*4LZPT)U7uYFBIEc=~2`&vaX3gkHZ?C# zb#Z1`uJg5tUb=FD#x@cueq$-O_0pV$%|PS%W2()h>D0sJ>7G7& zM)j5COvu=--M_ZuwF$>Sz-r)dN7kezKZz6}!p_dVYsZc+b3&SQB@OFC^7&d*HH1oj zA(2F4MI_ag#kP`2m%X@u`iIxrzLJK<7|E6_iqiU@eu{TnUFpcN=yh0p=I_sT=+Jp# zrx|&V-N&ZhtlQ3~8HuN3os*|mEp*Icw*S1QHu$8JTDECRi;`B#lYC@;A`eVZuo-UvkII;Ke^6Ab;59`0?Y{vs9TFeZC@A z4wHf0)^%p1EJpP1YWpIB!ooQ7yGA}Thn>`JZ`-|HGjl+s`^msKDQ;Aw4#MXUC&y5KbFu_7H2nZXMS3% zFE`klLc4Qkua^&rG`6;sTV6$F;G(MMLSuq{^EW)R;Alu6IO2LpKs>>AC5Y6(j3#s zlh??%RS*-qxiCAL=EZojwSBdkt5_b3Py5R+5vE;v1M_|1X3>|{kZy8~C2(gtP8$o4 zy;&D@(k$$?X;)%WT9N8O32%8anVhmbJJcREn3{i8Ny{aRICy`bt1_>CO@q*AK~shQ zI?~P9DjjK9ix+1VlN4<8H2@8iIlv}g9@fhYqo^vc|RYe{0*`Q_{LKeIa9GW2%eU z#x0kaglv;-Jd2BqmzI~iN*^Bu>5n1HE?YObXC_=~&-by&xTBYA_I*$|5~{DGWQ<%O z>Qn6hw%69G&h;cQtL%9*ze0`M0|fHvmKTkD>=~Q;Y5n^34(SK@4%N`$QRNQ)Uwb7g zz*N6XOl)iw3XO3Vfz##QOi5NXLHv}bAtA|+AOE6qba(rW*B26KNu;@L;XjMBv9Y~s zzfo0HC9A0T?8b>;Svk4Ey;#Ns(WNij9lt(gWk|S0B7LS1C4S)uJ3H~QiHQk5!-n6; zjVL8gA3u)x`c-EdS<`CKSJFsea*{mG&~?_)KXUSF zM0N#v`BNsSTJsYRx#cxAQyUr@7=|MwBiXsQ>;|3mu3taZdPpTj@%*`SInzt{oYA;3 zz3R(rw~p?X4DF1)63jy0?Gf|dzLeY8#O%S;ly&>CZMmYVDz$Zzz?!loihHb7cz~7va?^hckfR9`DZb*tgCAg-u}9Cr?AFh@87?(adAl@e;I@wGn-CU zI!;q#vM6nWGd_C-4g}Z60xvH4KJxI;%V_^pREP32#~Hp!-1li(pLWSl)5D!g^`fiJQLSKAL7d?^^^aNBZ`?>} zNmQ_z;h`xta;tjE7Ia+i(Z(bsM^T-DzB^Lo^sRrQM*mEfe*aBv47a?j>m;Z+vHy^EGM#&vnYXm+IQ>M#+&wp5KSS$Yr#UGw?lDhS2~kcoPc#hQfW zY@c^H%dkp)v-ob0^O6CaEp97)ZpO{=G7IIRD_^W>P=A$jrXpIcGDHc`Nz*Lkv-o2HAbU5ebK>SP!@5 zK`ona6+5Jg0EuFE?D=RcDsgLkHmAMUkx0)Ov~j_|dkvB(Z6GM&n>S*AJtxO`Bb7=u zcA4$66Ey#Pv#IbNi_zHUx4zN9zL(l9$aMSXlG;bGIk3V6j8dFkx^>d~ktn8YSj9I@ zh>=~FO~3{k`iSi!b7I&jKgiQKV351Z-FCPmCcDQ~tt;Qo&Q6$Eh^m|D~@ju*RKtVnV7IFd;h+wzP^6IUR^yz?GYYhj1Li+8}B_t#+CA;YIV{p zxM_Av<4E{dS^AC)1E2gsTxV=^K#BDA>(RfzdE|IM66M6`Tg8)!$;l>cMCy>!@^stD z9Bu^fz4RzIpXsMl;GZ0PeDb~Ty@bp}TM0nZk{#uq8k8_yni*%rMT;KAwc?B9tme<8 z`1|`Wpw1{@f62vN6<`YH(Y^i4y*M@cd%)@zazo@7vkdQ13EVy*(5(^n9HO_!b(B)+6 z$D8#Obz~a(IZpLQYL~hXU}Js&mCv;NehD$o%fEo<^RY0}?@x1EP7<^mIZAe18ZU22 z*OlnPZXh-T!HTnY6W>MVtfkpX&;4dR50AbuR=T}Q5$Z)ucOo#DlUdkNJ|Q9Dm1+w9 zT)J`vS17ORT%8(da_o=1zl)JE>4Zr~JaQ=Lw8Jax>NflH0Jp>SG2GT5%3V&Qg^wOS zl(~5E!RLy?S*$qy1@6kYo3X=<<{wYUqpk$vz5#qMD)aRkS># z#(ZE4i4>A!9XRw%&Gf@aSH4X-liiV{M{CAwk4v%rDa^m8PijeoFc8WTo~+?zIAtDz z2wHpSBT6E9({yq6z6+8i*r0YoSqsZL;NxZ6s5S5}CUgG26FadArKeRuc7f`S6g zzEchp(JK=H#Ydp%zzT)TiakAP%JW!oa}OSD`r{(nh69Z;1Y<2)nvi4?aaN|KrM;@= za!mQ97z#+83AmuI{9w$fQf{iv!x4l&h+F%1Vp39;^+2QGVr*PowQ07{>&C`LM>l3X zoRfOq<<=q>CxEnuRW1M3;l7ee(4j$_uz{vHO3B@yZDt-mdL*l@9XT`Fqt*NI?VXK) zHpRZVrZ}m=*kIGfFkz=Duv65FZ)Y&`L=wY>imoynp}x z%|HK~d#(8H-JkG%w7Yg0etvVyH+pHw8E--H!t6ff{+3B;<8&D>qoaOIkCL=m3np6~ z!=0&HzRjd7FROb?03*Agpz6U3p3tcy~%*ulb42x3T$Om+i-KuItG_xSM>E`b#!&1`=E|l z%F4=&zE}BQbs?%;cIP3o=+f97oE8VhcScFmAqFWj^wHANk|E0$I@CIskJq}#Wp)6N z*cKvSg?-L}Y>ewg5j15RX~SmDGHyu}T!dV(XQHM3p1GYwl4b;yfoC2i=Pl24GMVi5 z_-@A)fqgP|0tXP(PZ}FZBxbB8V<9x))x|dgfR>7ii*{`w_vOk*cM}{epN3BcxlisW zbjp5qROQ8s7Y6_W=OrWtiF?rt(D+fefZRS^6=UY-f|MrG>&%&3U%!5B&bKrB-q$zy zh$A(A+#K}gW^7GNU}W*qcdj4-tJpC6uMZi9w+l}_PA!~K&EAE3`D}#D$7UPw78;5F z;JP@RP3fx(-O33=DIz^*m%E@1l_j`VF{(>Hqh-O`fw*E7U=BngNYEy6WqH&M!dnJ4 z6uIO$QOU|rX+8Qk_=-d}Cz*7$mXfzNZ21@G+D={ysFY+G#St znmhN44(aA7wih0?Hbg*<0MhZ^@D-22b0Z2V)w0%!;>Zg~a~@=EB|HvE)h;oZh671j)^1=2nW+mtM;sTo$Z zvOwvA8Dh%yD*#oF%H+=MG9^NWxwpQOzhYri8^rqDXNEjPOpis+k#0e5>T| zFw6bEyKu5j=syFscHKJv&`^yiVId*4HEY)VabU{i)kbD}F;v6s6K37Xb946O;^aM! z^WOueD-QbPWQpN6r7Tf>y7FwAP+U^lG@Y*a?qXs}fwbsHDq2!*;Ui(+CS2T2^9EwK zYzS}Da?`-yLSTO{wG#*JpGs2PsT8~ic`Rn_K`n|nbDF`OrgZGv0BRmXO%y>CA`nSd9QGJL{5NV4e^jIhSfZ|~6V*^^M@vUtfC5}>4{ zgs63Y6tH`d9$ZDrV9-@HHO;S0I(O02$9J0MUx5JsmNbPWj>{={6L%%}1hH|2D7dhp zHD`~t1u$Wuci(C}r$%1?;%IRI%Q4LY<jr*LXVIwquh?20G8B!t3 z-rvTQdA;&iyH+BLkgt&jVGb8ix|#|cCPRuYiC%j{GU+Mm`r{F_4eP^%z0IC12Z7GR zIbu}bMHE;IHX1^8o(N94uqd(5CHndE=kBdA3L6a77f!STcdT(M)Nhr3$7#fY4UV8V z{!gAn2534~cJX7O?L8&6`(JmOHU;t;@Y#*1FHbc|T_Te|l+kG=$A!D9?mT?i+|G+w zvR>>U6bLw(v9J#k;DSQOj3lGJM#z9o7_KbMd#5$szkB!8?Z;iFZSNlL>{9AV+xsty zKXq@$wYO`hsV$Nw9howS1E2GpIbg>rS_Gj8#(EOg+RE5{QA2QX@7G%yPaHXMLeXh< z#74>Fs;sOmQT6@(W0I4Pw%<4b8Q3ZiE8)Yu&?mZ@Fx;8Vfeez7kwLX?L|qb=J&5cR zMtn<3&cbpTAe>4FIzEHCy^tkF5EQ84wsv0dAqml^={Ruc5DUwZ{rk@li-8qQ129f{ zOCl&^UrJk0Cp0X|Xcxj)=l4O~#lL*nFiZrp=UZdYeiY@C-S*;>KWdvYjhdPh6*yd` zK1eM1tj=w>Yhxu>)zi2wJhkA#S3uO*gd@ZhOm!Dl4~Qu7FySL{H`t2tcHji zkBImU#xU`D)neB|n4)!^q6ZI_rgK(8G#@zd;_Wn{B2h^BEWceOxB?OP= znNKU@9v!I(aa5PE^;>pV=%hF2V!_D1*VC1aJx|g^wa#cUp1AQGsf4P>r}J)8mvz$> z@WM9^i*f&oQP{#;!j{$mia%iiyQSv#g*Ekmh_dMmwKE?1lshe04`#w;uk|86{s{Pl zF)TWMZ>H1s(%RbEHr|AdnrAnf3Kqu!nH`|znoHO;Q&Ur_i}-bGWCQjvSn=eb<7Dm0L6@+{j~@$isf7AHe*9a0SL*HCYq!FL@&%G{ z;qAM3?|KySwdt=?Zlhx5=7K+l+I7oVTD}5I6VZ{#Ipw;P17rG=Y6J@^E9KpT9k8I~ zP#54D5IKT87djR;(vlPaFQ0>)n4H{9pi)7>X_wXI*^XCw9wP;lSC>JR7be43hh`X1 zz|Ab#XqIQ)RU7S`saj%z}b^M&QU0PNyrC6sc&OG z9S_Tsih@3N5o!Ep7F;+vCrA0t#_g#ex>Ggsw{+8XD7=09RyE65A=HJY)VyHZrcDhn zQclYvwGBnogZ5BAL|pRz_!#`UW9m85 zMDo;y3Mpu4q&$C456sHVKzo4XV*<5G5IH|N`_t0AW%({;7%hRp1pZ{w#xc?T(ihnp8c`xnigf9GiY)z>g6 zX~2&24+>&WYQdkx=4N@qMD6ZH#X9U!_TrG_GyCKV1hscQOcN6wU7?@B#=#+}qM`y@ z45BQh?ISpb()|29wK2xyeiCk?=sj!L6?Ai=fwS;`j9_T{M#HdW12lpRQ`zyo)`OTtL9emExV6CF4IY{>V%wSsRU5bo6%oL8TKXIabc7+ z)pVUQhsEZ@!onK1`CaM$&z|MYe8CQq8lwh9{;8xH%pua}&8>BLQ;m{LCIU6Up^bEyce@@$3XD)LdR*@3m4o?bG~e_8Tu%L z1-2IS0rx5?Egf8%*@GOQ3s)99Mh*-NeB|W?Bf=LyyXffR{5g~ar`b3;pLLne%7TEs zp__kK^LN%-R9$bM8Y@=4rux5as{Q%a{D1Li?Y~KbfmiWat8X5rq`~K})vw$rS zb@SG(3O>xj#C}<~aa(hHF^B`9{$WoU7C9fF`Q5|Hyx?SL7>^cDy^tw9FM{Ltg}bGm z_t-sv23I40n|c&7anif8qQX2`63pb$g9ql-2H4eC{FF)?Ai9i?ny(DMHflOd_C>1k zbaQ*EcGl;joyl+9d}Mm0OHKE^=Ldprfv3pDe)LO&X;1x`Rz^18O|K zcFA4A#dGJ+C%k+qy$Ey5VhH3M?Wm;LMVpaND(I=80oLZQIQM&9@9rb;=)aU+j>SD+ zO7APEswSd{|B9#7 z-Th>QPyWV@WMtP7v>j*Ck`R@xa4NY_TKDVKPSNMR>WMJTagkS2n|4|=hGO*kN4)foP0vIMf z+&?Y3L209L+|9X;)KHOg8Cd<_E;aumpEqV{-qfb|jfShmr37j~9sJZYY~@xycouUI zz2!cVz6TB*C{RZWVy@78pD1Tii>kps51H_a>pG|h9oc5##7Klk+1Fvq&)V3#*n83t z&e#Hd!EU8{p;}Yh6;SPoJ%qy@M~Y^ zX->X_*4wtkgHR>xa7QTddQ)qBcD60bM}!@TWfrjZP3_$L`}vnQ;e|eD;1x%b{ln;o z*gEV^qe|aH-Y%$~D2;8DY0_9SG3sz@BlDQ;9&_H3UxJ<@;|!sjiQ@2mbTn;j%={25 zE3toMo3=CR>FF^DSVm`8lbqd&>J*H|MYiPKpUpQK^RXc;ZKyoc#jA_O;8ejd($dgd zxGdqlcih%!`d4P=k>OuS;gBRh?1B5n!V?t-**Q6FGeMjMr$t>BGMAQ?7*a%@uj3Ur zJnqdT6gxC@LwNq19|I`)frAJ6(bb8Gi=&_=0j%+5A?&YL%16DIVYO4xP)4%JNlQOY z&7-orEDXeXPr?KQtuA`Mh}u4YMD~|oY@Wr8N63nT@9{{Pvp|l)V*| z7v9g-J;%@a66WxgkW(@N>Xr{V^ZF0Kv!+hBY1vV=(AC3*IgT75y4bBR&U+X)7vr+< zr^R3fDcIZRKto#NnX;Ex&-DIA6Ir=w^JZBDquXUx1NLmosTK4<$i4(@!_*g;kpeCd zUH@>`g#@DO|Lxm#A{>y9zR_gA!))+4^6f@#Lk0)aid#W~`HdQ*9otUoRqX;vP=d9a z+wHn&f=Pr-&EhFyx&|Oqt)Gzql1>P@x)6bbWCAdJ{rslmM3>1hA&5DhR~Z@nE(=qR zZR`2$2-8i!=6NK%JnS&7m#c&pB|--i$@_JNK|K|=q4iQt_-O~(<;B@HLjHjt7-F6W z6hi_0T(<_KYYGz65H|9{H$SON?a~b<-39Ffg;zrbV*otrP?AmgqeV$5LFPvFbbG{h zPRw+gF=8Ho8%yH}A#zYyxa;F?D3%EO3f<5chdWM z6264R62f?bWe>zU@o{70L>^F}xY@28i>MDT&Tr{O;VSyv_fwD`Hc8n_)fDe#D^YRD zXGbllv;9#%3<#Npv08zB`i?z&20n)K=(=0f^0%Fu5d}q!LeJ+t=AG&mSK2h}LV4ge zmU7edqI+}!<=G43z0_g}p}lbJ+BL5p+G0g%>1!8{19>#dSJgfmzK^q&%TA3VHM`enEd(ba~fHr$>O*FC+a zwmr>lgi2|QCU?BlzJm=1YXJova}0546JA`Oo0Un$IQURFJ^4dggE0dtSZ;0z^#K1u$#?R1#S#6q063huev5uCO>KdqwKmJB(~6THe;>@O<$soLv+0sV3qfGniaP^JEr#3&o8R6QTEDn z?za{NU>e6=C$9&gLm;FXE~KBX>^j>wKdn=5)ti)*#IzcE!XyYzf-d%8FwqsP=FA_m zE2eAMn`PAW-e~o#Xqqq4sMfG;*K_RkIyp62FG|4<%=`XKZErc}5Bx3Dx> zkNZKuhUN;#J15&>U%isW><${;jfo0LQ^;&1oag&(YH{P0eLaW8{pc{SrTlcLuP+X1 zB*@f*Z5Deu@CBo3&Y4Co%7dAauEFL$4DWpaMe#>J8~Ln)IS?iE)OzhP))rFKVhKlY z_+z>_=$Id22MpE(@}eyGp<#T44Af3d7n__Vrhy34*akCSXZqIEL?d2k@C9hod%Iox zc;hw0GGK{lm`ifzo+D~oyiC;Ia>B(x!%T3kEPA{|VEX$F$+B2U7-n&xj~bY7@r{m> z@S!5=Hb+ZVegvCY_c!bp9)GtJ*!!3TmQAH@IAa*Gc|D2wl^{y$2Sft%0r5z>{*>(Q zMmZ_r7<|d4h3|*&hr7%Kf+yNw!h&49)JSyFbQJ-0WjvD;6u-k)8X3(BBD1?4s|U0m z_*x^g`tNKPX@I>MVOBJMfzbVO4FlMxe@qVa^-&<{^MF$n=i%8|qUZM%ObCvz5}KI# zw!@7uvWQLze}93P)d}lvM_-|Mw`(wIG@&~30gST@$KBjca1~C;W|?-W z)}NmAB>E2&IOjxTQ+V;qmmPQa^X&mYV&F4;yqili8UA6C-Dr1^s9O=NL16}d(( zxz541G^KOr?qX1ngRBam!PsI1z>%m4kaSpQEw9Pd`FgijFwtq4@(UbgK5 z1`=&fqPq|CMNt18;M zWM92q^0Bqja+pSV#;K7=ga>#>v~Tq5!!)9Lz6WTfCZ*;PlOX)9o9eE?AjPb0aT&&s8cG zS?N5+&R(@8e8Wvv@Hak5VjycIe^f-zz#s|m@_rmWba{{~i~+Pkne)3Y*%Qe}X-$^F zToz`0FfZ}BIXgM|vX;xVg09;x5;MlA8iwQ*6^Sa@+-2930<(lt_K22$!5TvP%a<=y z7-Q&AiRrk8CPZ~nN${ZmA{3JSC1%uoWFaa5wG%#l{` zVp7;|0jfsz^^HK5%KG{=p6Z_(&Sg&6t|5Iktq$O7JC^bfLbu|8abwIe?5WJG9k}X{QU>7&uJzB%J z{w{9sJ#LTKcq%Ea4CS~7F3b-Rtr0=nss0Zjt2(xuxc7Tl6paRBqdh16D|-IVg#FKc bv$Ai`6E@y5w)j#ADiZnp)pMz5uigF+4Ip!9 literal 0 HcmV?d00001 diff --git a/jupyter_execute/6ea10fd9420b437a042b88ab6c1872d87809ea377f616435b36ea039e6483d76.png b/jupyter_execute/6ea10fd9420b437a042b88ab6c1872d87809ea377f616435b36ea039e6483d76.png new file mode 100644 index 0000000000000000000000000000000000000000..eef43055fbd4edac234fa7e5220d86d46db34280 GIT binary patch literal 9700 zcmdsd2UJt}x^L7`#)e}bLBIk^N0cI6MZ|#gHngBJNC`zs=*5mAMMG5}paP)>=^Yy& zLgMST=%qXY`IA@9d6@X56u_jTYuIS;j8JPcfJcz9iNvq5QH^SJ5g;^An2{fMWH zo4dV>^BHkjaY@l5b{-x#-Q^`Doc`|t;x2Bs5|m zLJ8=hFP=B_PGI!~7#iXR>!&D3Hf}ksd_-5|j*U=S+;!o?wBjvhF)^tRt`Usd#N3;B zO(SeP6$_)YoNOXuu=uB`^cd_5B~jjsdk?WTG_*PB-q9INHdO4V@$oXKKM3<5Txea! zH7of{uJ$X?Mh_3L5*5yHlJiA3pin=*BW9e0`_kq6upNauwQXu63Ke>x54U3_BJ>P?y6}_IG;-HcJb14%F4FY#jS&bHtTDv zArBwgjll!nq9y0$=_V?8DG$XRI4I%NQylFv-N3=YAv@)WLY=dWii#3EcI@4Z-^vv; zi^>Gw48gN!e z%*?f{5I%C`-0Rn`8$W$gWPNFhmt}^C!a39)o_f=lH8yH= zbo9BBUorBxBDtj(Br3n(xfFglgH*kwVOj2xA*p=olUiB`OTZ#n>t8VDoXRc?gXdLpFeY*xe=;wV8HYU=XR;o zgFo?Q6;Yezi&IiAY7-5dOX4SGHlp7A#9mx=Vv;Y>>GZ~i2E7Xx?oj9M-TT4D#-?NR za9LSdV`F3MlcTB*eOAWCX?Xk|(q`0E8FIeI;D@c0>oYY4*M2s)l9Bgc$ji*k+;`x> zNRg7L=#|>qT3f|pcSf_k$G0cKA|ld3YyMh^@Y zq}RE`R!CITG0$fc>efhor-;PSqemS&GL6ErL^*cxX1hv{P|H18!sxNSiW1JlXOkCa z7|a-R=M2ea8qI2LZEdG4)JaQrmoL4RLeW7s?BmDp-Grx4Uo0#v$S2k3i$+IBOV7^2 zp5Jy1%9>4(bB{_$$X;7r77-WMmZ)BMxoz7vDeJl&=#y4+i)+i1Mb~S$p<4?m%30!; z<>~4Xym;-v338;4az@2OIe8-Ew;kt|7^;t;K0U5pc=heZ{rmT4n2<1LHGx&fjvp65 ztEs683krS@CFSNC!z0d3^odwmSy_5~R`VTyT`v`|(B_Ezh)RC3`)_;b@3hwc_K!7QeIU1{YIYQ1O*a4VgP@FzF;o(fr{YEvmt_FE zW2`+R^3L76`wt!@?owVzmQ34|VF7#qf>9f!~At}e0L+$lp{47FAmwbAA0|#5SRlo}8~=ztR+4Q0Lm@TnF!6ir{6L z2F>?*?ccYr{oa{7ePc7rJy72NVSfTN$_K8LLYL_>*YfjEM@L0@i$NLC^QAGIe9GCG znZoz)-}iCawSBvRtE+2b^J}}$$D(6m9C}M#izalmw4&QaXGM4K-pzpPqgwo>q4zk0 z-(&mov$=X43LH9wo03$l0He0;+?j*B@`t{AB8T){1GPCa_};yAqby_T-2B6e-g$K$ zaaNi-I@Bm3gF;`n!%jZsmBOKw)m8fBB<;uylxR~@jtOREWn~{XcUEZV4if9op+g}N z5h^TK=-Cl8W3zfDTR-V|<>b$hnUQ%E_6oF3+1-SR`Qlr_^9|?CL@f z;kB#BXIAHF*xHc&cNW{NuOtYjD(f* zyvGLAxihwZ?Yw$M<;t2=JjDm7!Ordvr1d8{t2jsrg^D{dSG`|L={`+K-KH#X(aBSl zQFW9|B{huPjdUZ#wYM8Q`4Ek8(9eICgZ}>*>woI#s7sGxQ#E5VvDDmcKmMre=$L)_ z%o!bp;`H>(Xf%4PzpBFD-(SjaZoDq;7zl)WtSLN!klR&Yi=F5#Yiw%j`1AD=QeH@B!KPreo@9X)#9)YNq6^v^(CZt{r) zfn&#Bo^J-_^8BRv5dHhWd!Rl2Uw(H-azEbbU~P(?+hwysY2`0@8xze|@M z3=R#k{OzDmFLbTtfBpxAG->)5LZV2D$7#mjUDb1zp-nK>@iwM1lpmqGLlAZGPejxIR0;k)RYyxBfDWy>QRE=z z^+^oFd?N&XoxJFJ_SeNFX|`y~m8tyQ|4g&~iT3{kShNaG<$H|k<5=BB8#ZhR2@5m2 zabx6AebCY<5&7ZVxm)6u-!CYwugru=Y5^9YdWHm)lq?HuDV96v`HAz$LzJGm-bP8>4PPXZ}w5ChVYH^qTT(nY%Imzck6}_;}Ny)EcPJn_*KzLPBm8eT3A! zyu1NqJMBCjtESyT-Vb~?D-q4AD=r8lIaTm4E-#a5w3-b!fcURmWk$z7dGb>c=*K#v zn7bUiI!nvlG`zjNbA|!}0ytM`n6@cMKWTe+~fC8NA?r+UgZ+B10Sfgsx0s@0(`Y!#A@A?xpj8NEBZZiH==HR-Cc~Q*a@Dm9? zp^_zkk~kIfD)U0KWtJjyBF-FXoPkgjD(rS_*gtSj|7t++&l&f%G$x!^DRX%ESGh!O z_!2mIk_k&~t9Sz%PUFfI!^@W+0_0nW$0i#5fI>Zhuv=N-wf9?`zT}+;EN*CM zuvJVeEHpH+aJ|p#H!i6_1mxdA$5J27Oiv>R+SuA+zrMMRPl?6&qM^zfDHN0Wi9W2- zgkkS^PYFpo!T?+u2tSH18r@K)br^+`!WLWlAnlc)O+0DAk0>GFEpTxTdG%;5Eq0|S z-9vu=?%kiSGT>rO5l&P378cnpEiGIwmV;*mRVymH?NT+`O4b1-LiM^`*_6b|1N-6?bYN)cWPi zmxWlWIM3Oe$#vDL!JN>y+10y%JdcdZe$a#!x^Uq_v%EO?Dd!UNo?@rATN^gd4 zqkCA!paNEHq3e!tPx?Fx5AS@q`?!=-w-hXih=PK7Tbfo|y56hGe*5-U>br6*D>@6- z-tSWRNOv0rjgIH1GZX z;$PP1Oj!cZYcFxJvh-sW;ZwHmJ(2E0tM*-6WUvyD=DFvTG;<6M9NN+tEkZe4moG|U zH$OoqxRV#-q*d174FK9_{5)@{P7#jZ{A=2!yfK_6L;R z4huxW{^NAxzq>zcJH6$6XVdYKkx-2A6%EeXx;{M7(_c;PKCk!J4eW_Nb0p;WdS|GLySsxFkR&x|LaU=Uuxi&+(S$ z(%4xx)rtvjqQw0<6v2^d&&0D55<1*6j=G+n#alQJ3!;5TQ)Bmm2ZSQPP4-vSNkTC4 z8?+Mwm+W``eCixHzcH|pVb;(Iz_Frw`PM+`W$*pwk%2(4Sw(S+iOWCy? z0T@7HlSM_SX3k($7C-Qf#=={RV#g#cY zI4GYu#7GM2yt_*|Jtc*IGY1D%{)B*lnsZ4{0c0wKwn{J`GsctN&s2h{4|CQZie>eb zXyOlZs)r4Uku!12BqKvZyLb1tX6EL0!tLrPAR))ww{J1fYj$O00TlA5XJ@3Zy}NfN z{830qE2LB&04rR}oILU=z>{Zqt@QL>xDM6X!y+twdlm8H9u(@U-lX@|ty}djU;b!X z4}net%M%Md%601MEq;68Y9QI)eH-ze4TYezv@jF_Oj*CA4HBvFYH{Dp9`L@qk6*Iu zF0^lKZZ?KBPt%G|(|dKvr>YIfN6acbbNDmz^Yvx>JR*x-Xt{-jQb6t*rKP{X<%A_9 zO0b#8zWvkYMYnp<0*E+f=P0mWn@y}`+6+a7R?yDi(fG{^v)nQK82~?KWp4CC^}oc> z7u^jFqo}YKZ%9-B8S?vsfcq0v`7d^Kh>L*^Y9R;GMD|3*q;Jk9l$rA2<~p~thau2% zK+;pLLlWEo3I8XLEv_R&Mh6`#3TXw@F;E^4G+1aoLI_H}v$|KWCU=*(kOtldW1%cO zwf906@}bikt)YP@(_0}XL71$}LL4YEX(~=ryzC-HaVAuGeZ}3WztU7sPfz3W<(3z! z!Q;Op?fZODa`;{bot|M!Awc1DLar%*?&|6SvDY@ySJ6@BH!q(^{5{3wmUV7VTAL-x zM|f5AZR6xrCNe>G7o7kYmIU({}S|%K5un8b&=JipiUNt!?iVQ31#ajY{Z9#|_4 z9&L2+u|#LS4LJ&r*SF5Dsi}d)g*nd;itF_l50Z=$1GX{%bOwh#D~g7vh-4 zKz+6K^;D*$7+D{DZUB-mnjPtx_%n=aj5mb*wq}*(g6vWWEien}!VZ$PC8#TpnI_pD zX(O;KKbfmA1jNL&VB3r#T?W;^Z|~k$RE%E}_DW!2H3Yf*8IT`!SNWCHh4W+~_3`Rb zSSu7n)U#(9ni5s*%}92t`ejZ|LaUbLnPpkwDJ&{F><)4Ksf%dzqhrU8d5nLNluK+r z6Yk_n6AoBea77P_wLuwvTbLTM4U0YCMNcg+H(Q>cGyw#oEX6p+O&Vsnr|z`#Sy*0O zzW)CX6S7ikEq$h@rt}!iiSfQ%cE19UuH4*YCb$;39+3K*F3)Y%sX?C;UK0}on=;}TZuUT<(>Mi~3-X9gUZzq>j zdfia%hY#Ndg85<$muHOe}}7uJp!YU|@hyJL=ImaMQapXl>E9 z2usy=sa{Nbz{!(&>lP}Vq9m z`H@(pq}T1Syw^nbOtXRx6mVyzQC4MLECi^nDQJMp)po%5LAbO&^w{--p2iMz!P1dY5ey8jlHJ8^I93r&A~4> zmj>f0Puaep6P0C2=h!;v~sk}U*NR9v!B(A z+!@+cu$zCKNs5JMY-|%J;qzARkb-CS+$4SCJgAy9i56PCmlw zuj+B9tI7MY>Fs327#n*Sgt5!V@UgKcVS>b-q|pj;KyksY_nAh7J$f{?vbZusiRGv4 zkB4AS%&NNAy#pb*M~@yM5)xu0rlLx6>N1^HRsQ^}>r3Uu;k}^2t?*GXF=t)CWYIPjlA`RSuBWeN0zmp5i-e2WfjI z%wxIaJ<|DoR(D*Oxm5Wz0Y)_6#dkDe;|?Fe|(aTiyxt1`^V8VBulG2uVCO%0W&Vnwrs&Zoy_C2$K{r z_pCG5QUr}rx!Y1X<$PSK6t3p$d_z!9B4N|d3iH@dyctmk#nCJv=Nw~$NZyk#1i-^CE3-9{wi~ThfeDVrL>0+ ze?+$e3_1Dr=W&=|zQ-fLX<~_sHqN!k0J26*g;|wv5pU3nYZ$MR83fDsdkXC%LC@i4 zzv-b*2EEBPEvR$vI3^_2;BGS%dqO)QUAEs_HOquFw?vKQx5RZA`L3_dvti1Lh4`a< zC|sfPU5elQ1efIXdx)vXF)tB5c~aegsGT!)pn1s`kgU*#d;l5YAXc%!HUhwj2y<9w zLi6@Q4}@w#U(5ndF#*#)BSYKu6^4p*9pI`S;vS){acx?%LPw6gDIh^00mETkW8;|V z>1kvP3pu~v9LMI(_Ki_O<&)LRh4yVH0;XyYJ_kJZ_g`B#v5XbB9A+?xP(Ehs%e3`E z=RSEQFjjNQK2m1~H`kZ~7k~jxuYG)w*(gFXgh`Z?OTVI4yo?s2jZ-3HeUNZ*_wE-E zwrk)INMN#Ifkl*+F;q*hM8I$u^w)4PY8FT0al;>Xufl^M(|&5T%K9RuoK@U|nC6{_ zPG5xOXItRp=p80jMi(#Md$8lsOXOt$5Nj0|R8=XXa`nK*Z8|C)P@PgR?PRlhOlC%! zW_r0dC40g<1ZMCK0wG||ls(#}js!G7E9Ta{wFj-T$r3dd6`rh2*QB0GpP6=e@c@Z( zzqPyor9zr27>i?#1` z!$~Kf=DdjandHa!K)^TsVDgV%w)l(g`ghU_)pP-N$*jmB6^y616RpH02|_qqSNTLP zDZ3|YtCK;R@{5UbHNERh0b$|Ht%RK@1(=vWzi)!^QS<7<#mcz!XWzMVhih5Zxi=k> zRfKsrZQ0QR3WyC)Z3p2VGC2hKRqWI=B8?2Kpw&&SRraG~j$=?#Wq{39uq#VstF@P4 zb?q0Y>C$x|PxU|)jD3C;d_deHJtSmXz{*tpSj&sMpv3m`^5$wM%KL<^(q&L$98(*U z8&ZkHEU0c|7Uo>VHm(8+lJE1XGd48*mKuw}^FvdXOA@yxKl|=9wiWdTgue#>lm`A% z=+7_qp47GjUg1*qFSWhm?_UWq+AM5v2IyZ9YFx6;76w{}&g~s++{Cd*^u%8fA!1x^XIvM$EugcL?G;tsV&?fbu_mUWp^4RklE|&_Ya7f z@dFr}dQS9aeEfJhH8r)vw-??uLE>#Oi_*NOwmXvLj=-uugD_kNPTv6&J>-`pdRn}5 zD?GNg-#EPP^6DVQ`#ddZJ>Yor&*$9FAy)w7*9OBmh?cP9+;9)zy8=Otm__hNJ469F vlrC*t;grj6tKx&SBKZ)k#(!z9v%;q@qte(eX-Q6fdw@9Z*2na}bNH@~m-3=n)dk*N{ z?)|^-=Xt*Tf4}#``(e*GvuBv=I?r_-Ypvrr*5&^~S`-uQE*cyh9HzL~6InR88+~wa z@arhZ;GK=kn-1WK*Y>HBt(=9vt%H`e9-Next);1jt*MbVg}t7&jgf^pJ2N*kD3zQ(%vLX@i`-2JC2<^H! zZf*d9OEy zbIlDtqBQG)GtTV@w7QcrDv3gwW|xZ}uWF1fh=&H>CjXp=#GaQMoS)d%eH>I%@E2H- zol&p9($e|+D@UWj-AtT*wYz*OJR})d@RZQ7sQG5aLON#H5y)!RFQ)(}R$Y+p(; zu(?D9E%zEHkd?8mz1^?08h2kh<-iZS&=a{2m9j}M(>K#{Qx~J49S=j|QRJP}smCPK z_lvc zT0=#BK?G}BA&ql``*-;}@0w;6^$wwKVtPy@{C5yn8n!+wq>puqqFCK5VMwh#&taGs>eUE5ww+-g(ONNPM9vlM3VAm=&VP{d-`K{#>$&0c3^zu4V->@2bqAe$ zC;}6#ANYC_crb<^n8y#Ff(!w;It z{z$CGiuoZ&T)nM|sKFx<4^5(XO_MzYSL>&Yc(i~*sR1#=p@bG=f$NHC>2Mb>Br%AR zEB37x>L)glVt&8IvybHlqR|s$R98!`_d+-Xd;b0)&25O7$%i#Hu}&UA0q>8kH?7PW z-`FR=-4468C3It@eX%$)0;xzeV@vhZPi?h(oda=#D_o+UIRo>>!7^;G4DTq3>Wre z4-<#Aln~<7e!M?v$H8C*FE!Hdc&q1q7>JjIEGV?+E5 zhWPbgUahXT84e-IL8Z5DOMSKZ8+2*d2650eZ()qu?JtUx@LD4|OxY1i4m1(g?%p`x z-nnGf@VKxmE-R}_6r8tu*JSDFfd|l7TOj&B#srqs2b>YPBGk2h=|fdWA!KU6X{=c!9uLvL}FD>TugFj90r9xI{?QjTtv^ zmIi_$;qJ2d;nMsb4_r!7(ZEtd*fZy8TX+RF42|ocHBo+~RlT_NxUWZ!v zI_i(Kj9LQW%>W2tpHKVG>`7q5AW6|^pK{;Icg)@;}O>;8og`9PYiu+ z$nj=W7mXNUeExM4!XJ^kns8t0%{&`ra3=$6AP82TKy8~Pv&4E)NDc_lLQCSHV(2s$m0SfoxG~Sn{c_byiuq)`UIrc^9&+nmo7dsy0!aq-Y3+y(A z<;yP4SA;_y1!}+VbVM@Pb=;?fe$M?*3GcQnF8chC6t_xRYA36$!s|N9MS<21=TodQ$+!_bAxQ( z#vTNvD+3b*$A>zWO>JgWD=bjdCCokh8K6x-*`4)LyC`~_Axn_@*YzeELh_ZpSHP?8 za-;6q1H6Xs-+ACDaQstpbGw%&!)2v!$;^$m69F^;`JJBq&*UZuQKJ>d@I4W=0;LN` zqP9q{zU!hY2kLN{QLIK-IeNUWb-V|~E^N!a#BXMV_^DRgYl>f;91RT=+uWcbWC@*! z$#Fd^b2qlvwPjcc#~I7~`bsPExLx3{$bLfap^2q**x4lJ4^-C8q6w2UU^G%Z& z+CF04&&d=g*;BturORfPN9XT?lD=a zZe_Uo!(q;Q&#M%%u6AAJu0D1mN625m$uLdvI_Lvc$~~^whHr)lh^kb~OYg8P!l|E) zAOgrOtRAdOjd_Zab(5vUW$6X%pmIg5`4Q7je^*XIHLibEPC>BroW}a{X8?A;@uw+( zl=Jrij*^Vr`IgdwQQ0hUDQIH#2}i`>QI1W|RhGVsdz26Uq|dk@_@)NLYqiM=z#`bK z7;$2TEKTqiF+c|1ii;vC$*4GJAJ z0KH+$cK-=oA`q^AM?UoupE7OeMU^NhkkLVw8hlU9ieVaDo``vs#4;XVFo1UGgMBCf z`Uyim!mHf#`{Do8Xk67n0-iRH1MlC4+{G!;1V5~T-~0%qEz+w_kC`82VjWr?KdFq@ zN(k9q*jrfhpnL?;v1ES?c8Jlq4xCyseZU)VekxbTVAc1(!*3e(pWk!-zgF-#5V6~T zei4?s0m!m(#62=)^Tve748reCY$Fm8ee@K({?G@N-~|_X|ECLZ5%y<4KnCA8Nrjb} zR_(SbzFMe;)@vD~v=R@?K_!z9iz8jTL3gk@M)G)w0GK)Z{(F&r8`8G^kH0Gm>zt(L z)nM?vRvgmf>2q28klH`@;wwR?oFK3ArG8E)cn5Sv3B(eqw2b&QSoF|()A9Q4X>8D9 z;cu0Vt@~ro3zity(d)&L(DsDPt()Fe*Z%8)46|k7dACyZf6C%K`UA+I8vr22u%5=LBM9o2OoA=N z^dtfN#s!w9umtYPGO>kaKx7aiwo}r7AIYeL1fg^dt9-<9ssd}6cHqg2 zNlGZ5HpDMVnT;=83;-zwStu1r-d4oRwA%j&XxaeKUYt;@Gz$zxL@ZexS2C_ASi#F@ z=h+Nz9zDrkSQ=efs=n=#02bzGF^h=IqL@ zk3^zz-Z1bxQ2eA;^)R;UHM0q2O8Y3u@yO>IOEzR#(><~Peo&cK6@(@|p~Vcfc>|(b zOi$_%dJ&jEaC-lapcX3Vj6lIUx4lKYaXcPLc<@R_I;swTeIH!qEI8Kv+zyoO{=Eg+#45j`*VCp?g4W>JWv#OMw$@1 zzC#+o`Qo^pEYmJkCL4I%koDIlKL0=v!%_VgoIXA1gNM_>Ace;V4K*k}m=ZIctqad{ zWx*~!{KEP6qRhOZepswIu|Oe<7UFmEx$Gi)(7#AWtcUoN_9&pL5GMkxQM(f&h$K$= z@!TM75=z|)>n+H$m2XT~_v>~IT9j1IiwL&7asS%3_W(B2=A?dgvOUB3@iIq?NmxO?<7mFesjSUoO`#IeJQ9^@rbHka*!bmh zT&8*(2X9E7EB?&+7%|V~zI=@S8tqctQ>`H4K!b-7!{IU9;E|P}m*WY52c8_Y8E&1$ zWs&In$ja}+SSwb}no0spB9C)bYgjatElIsr`uqXF! zH!WB}8xMoEig^EqeNba)19|~eGk7PPtEmDN_3iuoCHbirARlaAvKGYC@=tKC^R6^6 z^fO-9A@goBNda?vgxVk$vD<3a$&BT!79Zi=+72A%*kTcIAeeD8Lel@*oqvzjb-gF| z`#SCMAjejNwasFZUzTFU)NqF$ZFl2FLGQ+?YM$DR!wi!;&r!=Okt6@apx@__cP^bI znyb+Yb&Q=F6$N3NW%-Ymbo3|_qdMm5lG&IV`IV7g4LA9CB1=W3sFJW7AwEtH0u94p z4WbUWOypusUk@(}S+xHA72Jfky#5ykmo3Y@VJTK_4}u55=*_*0+6?>y zw_Y=j?CnXgIXB>d5)||#jZ+ic!?`wQ+%hf}>z^O&1l#sF?UiFg?)s*R@z}OTRi-PB z>K{EDCzxQQ;1)GznqVAQbUgPa0-#EVfzyeZErtP*Eb`|s2+KPqL_JbqR)Bky)^a3` zvR7;EHuG#9mqp3q*P&bSFa6Y%QEwQywW&Vh^*UFr70MB@ysibBjXNSAQ2{G7iv2CX zycXV*)hEO=0Q4^4-QK0OrZLvur71cM0cI$+Y)HJ>@Ne|`7Ff0W2YT@)l}Ct14SZ<_z>e(_KVZ0W-F@sn*&?D zu^}B+Mcw25w>_bgB&+F`;i^z`3DNq!;;Cl^Z?PepS;aM`N5>^(OBP3bG-m>Uj=IdC#-r{^Hs(4F}gMgcvkWje=n4@ zR=oVq@%Pyz4e0o*m?+O?CQHiKR^dPMZw%m|lw^TwVdVF*oH!l78rR@*KC7HQNCgLU z$VUTZgh4R}EDv91_RQY8+`oS=oa~0_u^Y{D_0AR9N<~I-)z;iTY$uY@WwlV6ReL@C z}-<_G}E{AsLJxC69|tU zcu#92JU7@cmaZD2z`D2@Y0a3pg9XRk?_W8AwTH6oN<4`_1)54{wQiwrBXQMqNcYcFFsSG*?n`H{sD`n=VvN8SF!XS#M1j`0~KLD+`nls+v7iI zu!P4eJd{vJulOz2W`yf5pZuDLIu5cJ_jDRlXB#dCcDt9>ctuzdK@sW(Axr}D6)Am@S9vq8xt}L~x3AC-f zeYWjwRHq-89-O(B)hI^#umeq^W`&6ufH?x2+jslLT*Q4{rVzloT{B;bZwFwh1@J(o zY&RNlUg-G%HV%~1MmM(6hN_rrY&Yq7TWj9$Jm)a3MYU38(BwCNKh-VN3K+x+>oc$bn zaZa7K$4UuRaw%G#O^L-^vpgIE85$aAOlmw+ili{gSEkMrg*Uu#X=P6;?3 zV(%+bei%jNbleK8(h)B#la08$l2t$1QP-|t5w1purEEsGvW>(r{n?|@mFDwE6PuH% ze(#<0b!)jY@ul&pc3or}WpsF{mgO&X=^eE%T4a6>V@hb=bDRb1h$oD?$8}w4OD$Yi z&(CD4$(sLWX)l`d_A^gB|^QDK{dhVSs7# z)AhOe^9Y3pE?ReFYFNE9x9iYXS|ajhe&+kk(^7VO5fI#$distCx%AnWpTklx+KmYX z^S3s{5cd%ij!0(}r``Jd{dA`^m_c0I}q zSq|!4>jfWOar;%kt{M#$Y9>l~QE{6|(BU9Haj#M_9CD_GI?6E=i!XH;T4Pb}Z`HP4 zsC|mC0&XS8}9EIkrDv;=>asodPnJoPVDH`Q$+0i+HpFoY~ax&l1qFekZvynzb_H z8eB+q7XEmewceo#4hJtcOC^oOZjMAFU$=&R%HH26WJL7O3@jM11b^KNgBlPJUk5%n zdTszQ3^ab%t+0B0QFm75=T_FNV&;)}+Om+uDc9(Uo{g0RBWn_mQ~KJ|B5up`rJRRS z)7SuLa=>EKF}on+`eV@G9K?v}f>g<2HirMJz84wBYr8w#FDwUo)esWDVcnN6Q_81X z=kIi-*-q+|Um>b+a0ZqGE{AKew$)gL}J;oc~7V7F56z3V-JeeNk6LivR1%WMj^CN(S{^Z zNRdt-Tb38#2%zXz5`oqT;25;*K&<(ui(1(XA_@Dj9q@0SnO^WK2%xwGjG`?v2arkC zU7mMlRoZUEd{&4qX_FI#?W3Zs5KE#BtV1bSVX2=-2PYI0P zpOU=F#%0g?14eov6WZ4BPdmDAFvG6* z2hFc4{iK};`OfZI!;dFk?a!~ZN;>nVuf}=ZM{2!G9ZKS3iKa3gTPF9ypQ>>8ZAbKx z?z=1}eJ0b>?6tB*YsP?N>--Wl@fXx)0sSnij4P-mOw@UU=wCv^M}wOfzZFj&g=llK z2x~j#9D$)9qhsAC)A4)^^vQ}ny#J{1rC2FgA8C_R((QgnXBDvQ?S6sR&VEXh)>k}U zA~#z49c`QdE-XFLOrq$U9XjaMboUGtuzLu|{7<(fsJ`DK-|)?756RSI)`x%e#Ls6n zLcX!vU55S83pLBvx%KKCHNZIk@>EJN-Xj#Dz;Ou7M5XEeq=pH>da}NE8 z`vB~!OSg@|FE3K}QP^qu{KRqkG3Rhzbo`R} z5Fc&RMs@|)RBckCWZh&f8?vwY(z3}2Q%F#4F-mE?nSx-4Mt5z(G9aD$83EWjjsvnV zT+^-^FPZV{EW&_MO%Jb$fb>v0VW>ahfv)eAD?nLp-P5ZcV=2n%+hgsDHA3)eBbEsw z=4CQey0o>%D_WNG>SN3Jk#*5zuF4A3QJr%nV`8KzVl8?ix}6u=)~XeLRx!?z@CpQd z5lf~MFK|hq1H4QchGB7CJM*7d94cqG)~qw%EHI|UJp(vGOyyc8Qh2ep?l&4}gcVLf zWwuD`fBkq%kNyAjy4GLKHY%-kw#-IC*R3Z&lGo|NHZW z69~mgx!dIo#P9{RkMFTrzW5J~wFo^n8h}I;LKidc{wxCAev&PpjhxM+CK&9bQRqVK z)KE-kUX1}Pb)KHW%@&um+b7FYQ$Ei|l8Sn*f)Lk5LGgTgV(?=(y+gIJynVe!m0G6| zF1Y@TLqQdgMDrii9R|Y9>m*^Mg+ZK<-RXTHaK4!X+8&6Ot3Jl6=?J|L&3{DSk`iY} zrk;qS<@S)&$^@~;DDdGP)?TbW!Kan4@Y?N9Hy0m1DOtK+zqW#LU?_L&m_D1 z?TaP{Z_zZVFKJIH382HR27d=DkfYw4e3?tUo2aRtrz5F8BdAtUI9XnRImd-Bdzq9m zStFJEo?injhN$*5TyJy*fu82p=txfQ6IQRVac%8W7cwcvzsc?lMWwr#|I9>ds)O{o znasR|&9KKGS&>{N@THL$9nG5^Pf(8L(`=IvJkyO0@>lox!EIvh za492!inMrd+ZF0tB| zK`|q@2v;FTc7E`o^(T!?~VZ)r#18jE3iY5JEQ{HHC;VpiO> zRQ2n!D~~SDbTv==cqb;@j&Kn+|I{enH}>usr}RB$3){zT6QzP3bIyB+%$oHfzOf%w$Zr0;%}j#8&3ST3M<>-a%Q$ z3dvLmpp-r-R;h;C#@!D#?&s$9#rABnMz@Nuc6@2Q&VC!R2@|x$o&s|HRqW$3$M!md zU-Y`&RJ7cBY^L)_I*r7O$Ri869UTaYa{Jctz}BJaONNnCsl=@ibD%BS59Uneit=^8 zd&arO@D3?T(=(x9vR9)`v8JCE%Hs=)t_>bSA`<_F#by)mdieTg%R);TfBJaCnc^<$dpf>o>@O<+JCh@${QP%=(e?qs--AnI5jbN7K5!F ztvKH9Y*}?-%kReW>Ev`doiE>EtnPdGREW7^u7a@+iV<~5CD+E#_AnJGOL{>S?W=vq z+!LNCiJAQ8p#0OnG_tHK<-iz5aia+kggT0fbo;xn57M2FC12cezu3^!F*U_#4JB`F z6~43r7MkI?|L9UFwzh?58H%{>_8WU^0|F!H@Qx9Q5cwuJ0Rd1_V%gqM3j;65dx0UI zXvvI}CQHZ*UP5EXwF&dKNyB{h(&>7%Gaile^A1J4(x zt(_gwfMw@iBxeC-%Hz)7R)L6H)S+Z|Q|jR?hGE17EOEso4NyWo+v@JN5{Vd8`TRUT z+sS?)^E45WIpz7b16D#?#FWm8?&;QMXO@|m&aU;Q4ZVeiDeXq{mdVi8;neK$>9T%e zmHIIZT-)Cqf^%vp6DlgxU#nC%?^mlPiH6@<$$wFk*X>+VbtKl>&2fCqAM6J#aQ-3B z<)887MP1H6azg~baEvW0Y_Fp!f|*LF9n?qRj0kFA7+pE3gdnKp(MLZ7K?$NQgemNR zb2b~#9Npa27Q|mRvKjULO!>=RbB6}US>EZNdhC~tR(CBa8AE!~<5x7^A^@V}Q_Z9ewY$R10!G%32FVt2F7cl!6N5xMbb_$I)WsL3c3U$#YoL zXOQVZ0LL$u%dswKQZ%2$WFmytpCd{2}2OXi}{x=;_a?E=0e?1YHWQfysE;r(CXXKY1`S-8j?!2S+0d7Z>AG#o5 zW4IZCNM)e}s6@c51M*y@SPc13&z|LUn900CT6q#%jMRo-?*@1TpM5?oCwRa?(EFMu zmx|Q_JnArRODCZXoe9uIYjCbOQn5*eEU;)cb9)>k!&Jfy}L$MspGQD%_l6M#Eg5H=H*Jx};3?i1WLLDO%x5ZWV$V$Sd_M=TC*q@mT*(!Hj z1ojf+H%D2oasLoRgYCXQU>QuDl;|THc6T4b%^pu!v~t((VOF2i>^wCvpfxX_^WhYp zdB6}{_HLk2j>n$O2k1P=fR+5kh+6fAP|BV?;NSLt8L_<*lltcHj8g#VjiQ18VR z-6rNOJ9xEo&?N0_-@Es0vDwD+^CDm!bf@D^RjAS{#<;(4mi5kNuH@%_zo@C=no~_4 zQynOtGIyyNrOmo&tm7wl4LTOQ+!IX-b)vZ$=S$=L#9YO5zIAu+Y`oN|?kS zxRXF9Y_OYoV&14IT#!j)R5f+%Tp%MWGuVPn>&VNDINhsB$-N`KN>=h?pT$CA;ehY* z;^%36fVM=%p{T(MYo;n3T#5MhOBlww*y|+AwhH7H1H{a?0-c(yRSs5c(ytoJvu3%~SLV*`6EQ9_x}%3)0no2+?Rww_&ER!?EeS4&pq&j@XY@Dp>vR z{=n5&|6&$~qX;`?oc8re2M2yBkC_yKjebqk57+=>TMyo2UJAqR@0v9AO6IXc)pxHQ z(i3O7G_`&M=ml#5aA4C4p>EKyb#)1SR`$$BYs*?o?uj%7i(83|9-L?NC$VErh~Ak> zb7Rj=9zd~affTq`4Ae@hI#~z0zde--ulQQj%LQ>_ds;q*JTp4By=ot6S+!+$G%AXsEX)k}T0Ul(Oe?`iZ{-ZZg2ryHtf2`J z+D^^x+77p8Ohm5?lulcSraodrh66$An0fSjSmATbEzQ3T%hMW%87dl#9$HB}D={NX z)a=O1cIXKQ*#}J(^|N9mv@9Bp;HkRl!Q%?EfPLzyCGpQ)5sAf{87q+svgk`49+omJ z5Hos73D3a!9tJ{^jQiNYWnd|jPdsLBXx}RZsc!nWXX41~p8c!wW&X7fZWA9C$!}<0 zLMIw}0d@JUb}J;arh01$0_Z?Bzslko?<4aVm+iQ*V+NKHEu!)XM*X_!YDu+PBaBcu!&?y!@-<`v?tzY^5PFKM<7Tok`)&1j=Qsu6JCuk59l<>}5IvB+0`?|DDRclUb zO=#*$hb{z2%sXdA$ zGM~n4A}90H{@G)$dS-bbl%-(xslPTw)lHOkqc+Yh_c)eIz9qr@G29oZCsKi9=snig zB2@uYGfrbRZf5ypE|tQRX+*)6*u7N-qJ)5W@SLilOe>8r@Xa+Z^W5ZqriEw*XC8PacfJ<3hNB0B*fPXn)W}l$Vbng3 zjBE!b%?KV)hX2e@E81kIBgavlg0vQESa4tpL}<1neP7Woj{7o4DHh|NQl_x za$uMzOL-h_H&;^)((>RU<8jOE_hFU<%@>i_{c0K&0$vv9i}#q-B|b>WeJM~EiO-mw zf0EaG;5G6roRv}aY+u^L_2Oy`Cw{GA0A zFh5D|uXo~38738FMm38ofwLXRD)vW6@DCHnHO=C&;ocl9h?`=ahH;|rr#+OUwo^SD z1C_ko$&B~SBzLPJ0sN?5hSp{}`w(%hQ>lmrU=vYGl+poW6L4zvph2ron6ez-D&ooOh?ncB%n4lX(vCu#=-> zAu#f=9d{KSt#gWWFsx1KAtRQ26!vjvjN~0oCI=cF?UCfm?zoA>p98lVb~2;YQR!MY z3i1g~L3Ez&T1YXsh+^m)$^ECIc(lP$xMWU@INa%BIAl0*Afq@+PzYqdQ+#)l(y-9v8Jn06sNPM$K8%=%W*u{ zxw~9_`xn2`?>v7;ko#t(8lR&fn-?Y0d~CYCBsXu~{UV~+SW#E^rTGYR%SypKTMzSA zuV)KOKlaxPPOKAe&(pyCN3VX3@Z}sPeERJdVn2I#r}+GlQ-v9a^4sjs9>Up8Ux0x~ z>GW=RL~Hb@h@JG^zALXI0Uon=;AGbqka0sYr?N+aCYEZmil+LufX9+{ekA2NhL1d- z<7$*=2hqLD$gLPHD>WHCD<{6-SYA?v3bW?(qlE}foHpDNp^m}x$W?vlp?_MWY&^HzEGV7>X1~P_2X7FDAieXeW)l zt<~m~v8)gCRc8}emd}9csI@~8T{`jR((%Ie0`p67^Jc8r>Poo;z6)O``K{J|DZjyt z#K^q=vjnj0*CtE8B>Oo|9c2RjWm`!awu;C2oyOxP8DA!InhR7S&jQKEwpQ)a*fiE| zR(^==oHn&LHE%5q-V)Qr@YCzGKtll)LaCW)Ye@G}T~JYZy`)NO;iZS99CZcI^9e9Fi2M{*w;ifJlDT#TlEtxy^s}qY;rG z?sc$PDpzUy*2>j*Q#&5o8A1?z;?`Z*Xyr@l){Yg9%pR|jZDw)SsGCnd{Q*T3a#a>dwk}u4QQF#NdnY$7P@k%%TFz0Qu+{VtbFzJ|WNaRDw zO=!lh!H&M9!4NW(J?UQe7RJ8F^lCCxb%V~RFEy2OHr@SoO^Nkbe0USl_Cyut%=h-E zlIgb4b9xTI7Fr{&0_TRCOW9+3_7f*cW~BK8WSl#=TS9k8&F&VzjgjlLX_cNa`%Oos z)&26KEbin>W}1N|F4*}Fb7Wt&a^dmJSoP5!4`D_xM@vmx^)rp$y=*&GxwV4NBLx;M zAjQx=obwiMUT@l9_<(!z4jz@uvsu^H$HB$%o8{fI9{1mxeHG>6a;zDQr^ziWTI z)WiiumEGN3TWo@Mp8d++icNb4W|MJR*6Yd$eup>~I^!VPJst?l-AClfqFHy~ zb&=1jZtbX?NR<)F7rKCst7m1WE-8R=QE_jZ zm@j2f;FByCgysTr~vjkO{%%mqIYmzzaZaRn$fSLoUb#(2(Z$mfcOk%5fQj*kT75q)wsumm|sv}a)P#l1&WODmyvJ_ zwXL+W99pDDv*(V5%ud-E7=P;KIX%9KZn*~W4L_zyjwwx0fU7&4`a7^1Kxl5 zw3Se}Fh1!~{u}2LshYId&h4dg44(Kp0D*N!CCLzOUO>^h>ZaTzCtYn`{z9&Xf+5pI znbdcC&t$X!c!_aWP39o8t2SPKpjzVYSBQ40Y^a_1P1mjDdDh2|EQbx-c&&0fl(1an zQfSIdc@Xvl)0&;!Ag-ya708eS*=kKLn1xsx|8tgVWai7}auaUd} zTD&`ddxPqML0`SU>R}Sp^OI^4{eAJS0^6-k6Tu0*;j(LKgQG?S_!A0UH|7Q_l-Jxr z!dHOUX@HXeGhaaR4`MCOVnYzR123=P`aI-w-Y2O@m@)6mE2f3Nde))+R+9p8ofokG61`5tMaT$--XSk2vDKNXRpH6pDeLFZ%k?CFgzEf8n zWZ-Uh$Hij1X=iHKx5|7S%6NlR%EnO}@Sfe2t8!q#wGgT86uwR+fxq+2+>2`O-HQ3I z2?8aZmRZArMf_7f?xfETG=rxd81D=mP}A^|4Q!FIq+FUwrMDJpwAS|ZvO*GIN$H9$ zjPg{_hij&l@UsC{U5&Cs+dxDFyG}XwoRdAfcXCb^i+mJm3Q%mZ4VwN+RJh$Nwzn$g zWOjF7u=I8xFM^2}5>MG>$_M`N3hq_<5HrUOqa~f`4m}GRhp9n-j~{tL=Z2OT&)hGW zm_0Q$;#yc(q(1U5Hntug&Ql@Q0Js<)jThVt!tz3g>H%TG9o`gKxkCz9cEcufvcd#7 zqTrMf7?aK_b_2gD2xd-&tPKuhL+Bqn_(HE~NLs|_zi3Fy3@-y(LEt5)Kl<0vYZUr; zZ`dfSB}wZr3We&nz^yQ`A0(QYm(qef9~MUJM+cI=*VMKY(pbg_-Kj z%etTE_6grVp3Kf{PK4BAdGyWDMkt?K{M;oFI@0v;XGwM>WzkIf>TD1n{GIa?jw(s< z%`kGwm4}RPinShVeHyEf+n$uCMr9Np;no56tCDrzEqlq#QXgztRA-!;W=AU_hWy2> zI+h^dR0Jc{tEdtT%Odeo#DGnQcqFsi5E5jR@pdV6neoMQp)Eg=8M-f1@?ytnpp)+N z&NjtEPJ>6i9xwZb@`iykO7*6F4?e$>gRw9eu5g-as5aLMSTj6nM>!pAR?Jsbf9ZmA z<+{{!u?Fortfy=mK&EafaFw6%0HKpn^~x;2lNZsAi~V z4Oa`$i5H>QA5fSY-*t|zksqqESihgEcC26gLA_D9&V<<0q~ctt;W2H$3NCM|6=v)sefFvJE38 z!9c*f17sfSe0%rPn5gxS5-Ml@yl> zx!L_$C+*~Dw=1)7<+2#v1$aWp6G$6N-g0fNZwl0&?4#74u+1t`1AU#e%oJ^Qao4U( zudCU9C0>}kCG%w3bEeK@B7g%{u7qB$l1#_Nd0{_T-9aEVqrYsPHUkwQbrH>5p&NiV z$rh0-&$ZvCw>DR~b2aO-Wm8GEWzKoRC_TBt*mT&MR0lVO_d*wo?q^f2(NgW=Cq2ph z(p)QNl8=*@Fd<6LLefbawo?NY#X-NA-IW#`FfhJ~bqel8VrkCk54#b8vtz(M((Hd9 z^E4*;q;wg-*Sg^K60x!;Hq#bwVwsNNR!#bz0%$_=i;iN z$s!)7?oD%Q#rfD3xXMg1Md>FxLre&gDEZ%bV{@d42BGW8Lnz zS)6!2{%N&JH;+0Z|H}KWCO7UoVCvQ<;vG2HU&~jJDg7ce1tp1l8E59X^zur>KqvJ zbp5p)WU&2%N_ZkAM_F{(^aejOsXi!czQN(mRBO+CZL~91*amVUsLVlw4s)VNwm@fg zTnT5o3(G2>ZQSI`0vkiG>kk__v^tM`3B5qm^`~UDXrO21uDlQT)h79+9(*H0t<5r< zkW=e$bC!BJw&QXaz9sx*aRF(i^25Zy21_^TRv4#NYDOA{Hg8970%YI5jP_N-fnhOsr_0gzy7$0%BveDhtu=hf(v-I&zPv(kN~+Y${T+BG=J>P?wukye}36ihQf3A0$4s%g>kfG|C@SlnOJ52JNyUNH8emASE+-3x$0In`;Am}{nGn$;=;V`SmzeYN1N4CI#1g` z!W!>o?>!taaE+N-=6OBF)X{aY{L3FwW}CF08@o~8UdUULUzRE1@IGhCoJr%em%h*n z7#|Nr2i9TG-oQivb{jt1qQgVt;7!p2DU^3+%xQ9#MBt4@=yh*oM#l=Da>%^M>&dn%Y$m|ZcLf&7(Up31`46K zE8AIP+h#is1pvu5>76yP-nXrQE2nyz)UZ|S0$dJd113+wi zU~|D4cjaZoeEq)R>9$@nc+omwU&LfnG|4>{Bx7He^FCgW7RpiPr|Ct{N~B`o;tw$@=8{SR2~To7a2Apsewd>XR-w z3XV@1Ya27n$nmHjTc62+x~Z<7UWXyyTR%?+QXPccr~2skEmv9sM*9A^*OK{~z>q5Y z$eRc~8)MB|TN7CKe-gFsm$4DLD_pq~HQ8nC9SgAHP7|n_Hf$qwoTUL_kq&?K%pG#^ zdw2_uuz6|rZDTPKP%^N#*X(|8R&(g4z3uPKnz$}dsKQw((4sCeO6Zdu?g9&3^ylxEBz>&m2)s>Qh8OZmPDWH zbws9_c`R?FK28THe`|Eabt-lvwRYjL((`9^IOb|O0r%$G-aOFtf6RRYqw}qi6;afU zDZH@zK7f$0lm z$_P=-Un}4UE4~H+^A2TYmgA$zMFi!Dg{?Iw&=P=c1P*|h=Su_#brw6NsZXBau;SP!d|0fyQoG%mtC%irQxs#g6w`Vl$J~Ks2&+Yg=!~O2%HgRxr z=T29KUDJvkcnqR_K4X%5a@o~>{CV1J!Fi`MiX0d2{z)_#9+Kz?TMq#y z3wF1Q_q5SG@;8$&j_;fwo|gdG++l>qb%Wi7p=02$;_;a|nGhV{{zS2!?9JA$cDSlm zINgk1nJ`b<-0YN2+O%Yo!M7;tv6;+rHkMz$VXbEn%Uvr1;%h_4pBK z;h7N)hF-W?IXy66h~#GC;a{OLiTq;UBCgAFfBx0ef>JfUGxN2EEba`2^GhF0v+r{j zE#V2=6k+i+`nmQQai#6;$6ei}1#xF=6RowcJ)X99w!4T|)={PTO^qrJQB+d@KYYCf zRMhX*{yn59ponycfPl0h-KeOvO6N!_HKcTkfV7C@5Gn!!(wzgy(4};D=g>XxJ?c5X zbN$Ok!na?HwAr)RBE*k*#aFFY9#c zuxk#u>H93`ld*^a0A(ok;J`R#)u#6K)6GCjFQEQ>Y0m24jN)@v*b*^*f;YP#AHNs&%2oXjlNGRJvFP;ez#Lw7)pU z(*)@m+0-{6Pd$9)jL?E*iI^Z(5ArltJ1=@!i}Fy-0tDZS-?Opxp!R`%(|TdG=tI%< zEyv$i+=8T5LheKFrkUBkF=8Dvg^=_t8`F?MUYNZ0ToYbM8UdL4h zM@EbU;ri%tQ$AzD01A4sN&K~#;`ZfGoEm*D?us@%l7k$O-V2L@{}?ZL`18{)b}$yG zHsAS|@6UGAiZd>`?6faXd(N>@Mh)g7y{KagVwUhiZZnQ&+?ogfziQoDvgVC|*DTyT zAs0e_ZEf%7Sx>(dObIiZaS z%b}U`i8ZFib?{LcR|L+h+f8fh&2`yRhvVRyA8by9zD)I^IGps@n%&Bwie`I#Tb>AD zb^z$|9e`BWu4VZvHS6yO(m=f3FKGCw9lNw@fPMyY_`qF~arX(=5Imad7T?tgGS{;u zKA;4EON}vj?Bw4+`Io^11e-!db_dsIy$Ppc^t=viXglmHZQ1iBPqgHLb}HgqxKQNM}78g;@?bM7O(g^TdUyW*}$O zZUcP|47(*97q7s^2A?@7kb}l_^hW1~B7wwCts=MvRrhJM8;Dluu)(wo3j!M*uB*uR zWldAI`cZ01F<~TK=2SAXHKpwy0w(B8&WHJJe-H31b{B5^NgcNOz@g(3e-bkM_`nKN znW+$KuocD!KvhY#iQ4Pmcx*{2yoD<3F;2(Aw*Kr>G3k`10MvZ;7>Mmt(I|h{E&Kcv zn68nmY9jW|)?FJsVQlu3ei8%lWU*KMMNuc8j<&A;n2lFnT6~HS6OTNq@hfJw*R#>h z&C{s1vE=^cGuzLi$F~@)7DuN#rjn%8O$+{>PE<*9*x`U(@j46)DmA%<3wuKCE7d)p znqJOkT1f5J{99!Yjt^}&(dFryVdXTJEd(R_pYtQ=Ys>~k@nzYWi^f8}uT?9lA=%JIe$wRWQL`D&*`^XzVX zM`BKM*LTKS5A!5O-V~WSU7cv}3eVM}qc*Xq+j77>wjjD>N|WOSp3d(-M;{5FDj7aW zeQ*AJ;MPfF`RFz2v%3)t(h&%%l+pdB_zwu0?uh}= z*jZ?;@HDSXUmqht=E+ z7u!MhCwmq(+l_=r!!1&a4*^+*ydtaEYa`qmR-Vobxm-C!r#QoO_;IR{&Js{#42#nA zcvLMfvVsNqc- z83!4=@ZmR@JrzX|5;^OukemPK=0yB7A1|&pV6jHFojI#G@=t30=qjpCD1(Dq;j5To zaEJ3LT-ci6#abYw>@|MxNM3`M@p&Ys?1hMuw{0dt^QQ)*1Z=h|TbU8wq_zju#9=WDIQ0NK51jMgD<*%3yM~Kdh~#wR z^RIQ3wVi84QGs{=1aKK?O$_CwkN{m+bAlDaP=mryK@Kf>2;Q1_iY35Op^t9El<*=; zp{F3z<@&hE?gq=XX7!o{$XwyRo!K z8fLdl>^fIi`5)zZDVP&kj}1rA!?nB%L4(zqwqkS}!73Z==oo8Ngo~&W z@Koc$JT?obiNZ=stoK^uDby20KQvpW>P$vZ4yl=KROEUcYmE1k+kKt5GlV~41GgM_ zsmp!dh_GiY@QN1q61tzLtdVmSgIYk|f-UmR+FelOa^^gKMAD($ca!qmusd z=my4kBy$6JBJiW>%2wjjBEns=0 z{J9N#|E1US#uP-?~Qjss(O^m=|#ICpF4U*IT{5*%{<8Xvn|50 zebtG|0hFAaU%JRKkcoSG^z`Va{|wCA?wkL;)sPo$#-qruG#Rz^;br5Ri0{n{&U*Kd zy}H}R`i82WuBfgRrq;dNK%DA>(wl?zf^?8W%eEQqfa<%d|ZUDP7VgqPc&$(eO4Ev z_lq%0=)-$XeIpr*`~}es`Py&Y08SK3Wh#HadERPp!E!&pwbJiIbfQr|@0rdCoju#V#ia)<|MJ_pO>8`>6ngas$n-I{EQ`V`V_@_V zg!7_|@0)Dw(?cm*#;%ME$-Ku5USxfJeWglYR{rYzAoC^zv~L&CX{5e!G?B-cAHiJ+ zds&zzVCm%pdPn=MJY^QNGx+&v@a2JK*$)TQJ@hG{NMleg7z zUc3LD?&)^K(brf3)E02KW;p z56W%j(6IzU`06F6^W?j6J+u^{a{K`VCg>bSFgl(JV>Zz)>#K}yujMXh|3;47fD4z- zq4z%<9Q}Gc<(CZimwR3I(&`c>o;hB|ZCuUMG2rvETC*+!w}9_ko;W!B|62(7J`Q(^ z@(p<`RdNFuD)YP{HtRVPY|P^sObL8L6R?j~{@oFsnDpy(DQ9t6pIuQ;iSF1Duzkz| zON{}lywDYv{C4tP{wC&4clm~3O*B!!eb@5OH;aPBQoUf!%Kw42gq)8_Z7ar8wrmBbSUa|Hr4mk8_D4^?vMoxZYi0_yoqn z2hVQ*cPhYDZeqDA0GPrX$NC&A3XAE%IM8rsMMnITqdYv>q}-)L{eJFNCYUJ~Ha($9;W$gr=d(VB@`{)24a|p&uA;gF=qUn^;_^Jl}OE? zd0DMDk3AyE!+F$}jl7vdqg&Fgc{lWnJQ2&7V&EE8Ohws_1vY@hW%D zAK(}I#+w?)Nv;3;IBZveC>M{n#jJkhY$W`ebHrg@%B_o~YnzSz`V=-HVcbzdo|`^3 zfr>-VhpGBM(PGC=zfZtbHmgYaRp_Ngf+;^qO{VWpM{N<$0(rLT4QSTNpJHRH9d97K zUqK436!|nXsJul)0ul6yG8|TUtscFZ(=MmNb6~&<)Wr!yF@uex^)2hi{3hSujp-AF zDfRrA#ze@TG1UIj);mggr`m8nB~^S<`xG9Md9#8rI7~-_##!3r|BceF3VpYO)JxBFR1PuLLuXz>+ z-i_gnu=Sj-)0C#`|A5hG9^Vqq1#^Kv$WoB^8a!%CrRXKv-#Fw1XgvJvi?cuRgr}zX z@*gy7vcLE;i`rsbn;3xs5Jq(<6Itg}Cw)2+Eyh3qz-FDU`g0-fhh6iFm{!j=3L!K8 zrlO(|WmRL+Q2~hdiSeiF-Q;>^(KXM?9@Prb3QmP9sNFtWU9QEXl2v<@XV(WrKq3rO zs+!i~(L!KuC74tb52r|*V4F+!ibBfnCrdJZbC@G~A|Ez0m@)w66Cat9)odmu$TeY+}_g_?FWRBemklljnVqtR?86RXTB zE}N_j8--^h3n#H&$9&K{!!x@9r*He^78o>3g ztD%=`#kZ$DV9kV-`7=#cX|7&el}F`(4Cn|VRG2RtJ?4ttW)=Muh1`^GCmK?R(4@R? z_nPf~G>^ZD_ttpSTtd?7vtqDPX?Tawq-H2NN63nL6+h zx;k2RM>(gBizfVZ+#TUO4iLMC|5)z=i%;C*dwCClZF~@18l5bpO5v#T?nxIVVMBmHd>)Utt;w(k>zNw7h{Nqz7wS0R$Ef2@D?QvX^* zm-fQq>kVi4tocc0_$K^S6fNl$@SJ5~&*-0MH#SyK}o8iP*%H~h5__?wk zCb&KdYQSrBSRu4LKH%K>)PD5B=bF^;l>bZ{nM#dN2tr&q`1s9=z9DV!TxpaDl`lb$ zq-Rl$l2y9e9TRQf7=CJY85|hzRkOsT%I|y3npQu9L0Okbu-~)P&jN!BgNVaD3f}qf z!kFXi$CCsWcmnS>jlk^F@`6?cQL?l6FHSTk%HlbDw(ScHv?$gv6dDmajzfrvx^$0b zThoinz4@va@i8}{hnzRI3$RAgp6yOp15I(9_F!&uqLOV0s9y-`F5+*gwKOfXH`=}+ zfB25~-_17+o*Uq_hr}`RO~-aIuD3~NU37B;x?F!uu;AU$B?=)$GY6biEM;KsJec>b zf)}=Tt&MI7A-neU_Tz;u5uKM|sas#`vPCC5q?!5fH5bqrZQZ79VDu9#;q;kCVZ8NI2 z4p_0dT1dVdSnXw#7X&xU0fAa8P|TZCUjBBpL2q!hafYp6G10V^7K~hh0#Dv&I5O1M z0ZX4sb+*x!6pe;PLcr9H3i#{@4y;$|Q*y6cUpZbijMXiy0L2$DD^OhO_m{%+*5DBR zv^hVX;Y`-dfp3MZ$;&W9Nam~0ysl-xV<&0G)2&#?_YDBT2iL23l&U%F?FB#cX4JBL zHxjZLC{eb*C)NMO$2bmdQwd*irbYG+KJP*qZrmdR-viX5RHwEZ_eTo~a)jU7(2jL< zYov5i4cD>1Th4ISr&Ar#eR=y6JAhFbj*rqePQRi9?k$Y{Q-)W&Njz5%3?YHkKdbe^ z-j9BQ(T+1PYoSAq zg{Z^LdQhwbzmEjbRR}UNg}>f;gd(r&j~z-)E@%86zEhc1o&kJqL33o)%Vy9&ZfslE z>gy3hFWI;oFdKFDC}zXyDA_(>-f+_cBHoUI5uy{isLJxZOw{1M0C?BnL%_JZ@TC>Z zaNiHyDPz5hm24F`&n#sm=d~zoM=>Vbe-W?Y;RpXgypEROZpzL-Kay|=$~aQI7Cd6x z!&z!)fQBPHFE2HRQWhpFnOV6T@P73|tG&D%qidN|jNft`c3Hz5luYAcUMh#G95d#6 zo8}m{Enf*m&CR$R(Mf0NzD2d(MYRIH7PkWWh4Fu91$+msG zm5gWN%c_`@@l0XH$r>DApwN&3GZglL@=fcZ_nq;?11ajidbHP)p5DGCl|Q%bgWgX( z%*ff`6wddA!H&b)=q>3yI9Ts*)(CxzFho#%gov4^Kf)>&n_{Jw4A zW5>>72cp#fsWalu!`!Em5<67U{GkBVY`!DhkTXK^l=4#lrLu=46J&2eq(U;}_Dp45 z{xtfq*vQWBtT6~FJO>QeFT3!6VZvmfSprKR$b9TW#wjy@JIeF&|BDg!TQn1tKmChF zGedj-MWZSI!XJ$2*MvrhD;nL(=O0?H75Hr8lv5Bp{B8JpCmr~4&CFXSW1D_TVm76Ly98*!s5uN4kKpCvp^~m z;9Ol#{AyF&`M>`1%rCE_Els(SyR`4g)?xb+DlS}Z@=751s0L8iAy+ai{Xn$w?HHL1 z7`$CpHaeCIau|C;)Y~=A8OBTRg-%@H>g>btcG9L}oH58%kBg&NwXLFoQ^22vA3Oh6 zA!&_ByhIssADJ&YhQ=(WYBGer96ZynW=#V_P7%D$?i3P%NXZS2%N?XgO*b;5I^%my zx#}posc+?y3yxtzBi~T@KZ`&rEzo4I?^Kmhx$S z-vnx4*dRnM?1aBHvg%}Sl z-sKk$YRI{K=4w~-Dm(6ecBh@AmiSG?mUAo1VsV+GF%f#Rve?yxDR}aUUV=yFEh+mJ z%`oJWF_vM%{8+K9J2VvA}C!TAPdnB7RyDDefLDCOx zZEe+R^44Gf=Go6W3jvtspEV9PNc)%nY!Yd_acuC^qHaiUDqL4J0GL)ki-@eC zKo@nJ-~0l8>d9tluZAzT_{3D{1#baqRVjw3N-!czC^rXhR%!@1 z{3mLSh~_CdH?|1#UCGBfyq%3m9jR}Gyd6)PKb|6FtgpYN*ys$`&HCc3@S(tH1Y4){ zcXr&u0^&8Yk_t7(VWo5hg4kKxsja=hMvvFl_l=E|qqr>)bFUXKw3R%M27EYfm*R1l zRV%^ZRDEx~S^bx~wp-E8RG2_iY6k*+jfqJi^x4?3Q;hB!Ct|o6i%4N&M)RCvZfE=)} zPx5a{0`=3Cfr1Knf%M%=_qR8K*7}k5H zJB}?eG?_DmYSr%q%dkK=@#)Zx8?gbWsUR78Be*2%Z}CkRvKtuSn07g-OBKrz##LzS ztPu*Sw0NU=;bJ)kjvx@|NUOM&KC-AO-x) zXeT%!FR|LGO8w|mJOSooAmGSh|KO##A%o)RXdcD4TGl3yJ3QiP>Zi?A{+$qNqQDqE zi6Zfj@Et}>2$D;ByN*9&CDu1_3f8dG^R4J@-luDz{P^lb2#k;XcwYYB+fZk{A}I_s zEfJfyytm2>YizX+6K!*ITlFqNfuESFBj%aWwx?b#Mpf+c+0x8Q3R{@6>p*|!p`>UjdRCXQLX+2jH$}Gn|61Qt zBe>*QyF2l1f1d33d+FS31}XZJrCF;p;iWFp@k`toE9p}}C$Kf>SQ7kmv*xS7Ip!k3 zF&;td7#cuL0h&toB>Dv8NRIv4q9y3QD`y>TRWnfsPBhXY;`q19efv{#E3x(^^~4Ht zi@Qp-4yVeCX{XMP?|z`Z@m(F>?)M(raO&**fWWT=#*_M3pNxc^d6vyF`wVZY&xSxK@uuvFxfB2^ee zGY1dv8yOgC8(uYvYBpUF{TWS-15TYnligo&iD1HLGS+-Rz6%BYjsI{oHgI#i+cCXH7Y)u5R9Q9!MT`QMQv+C1z^1ERBKp0I z1o`VqGsV9H6>BK+u(f1c;nlV_yobPSHtA;k1p{DF%l-{uN&m9LyUv-w2kg=Bsr^}S z0eOPa2L^qRh7#1Wz4Ub8$`)XwPY%gSQUqsxwN{zWd^Yw`VK_Hamk!EilG=wtT&JYq z4*Ksw>w_ZE`mTU_IFNYhbacP(os5s>M}XGL_2mUSxMs}nU{}P%Y}$2Gyiiv#w)%pH zFx|KSwhRhy?`@Z|xx=kd&4b;L+n<%A0S$K7zR`Pf!dmz@Vt1)CrpN~kkO5#AXnD`> z^)d=N9m9z#@wL-p+E+Sb<>loUh6OP=oVO8{yGOkf6-2bJ0tgI;x5A zLHk^o56pRhbz%`Qh2zG(>E7u672%FpH3-2s(l_4ZgtBpj&rs(jV@Y(h-<0bT52Zd1 z1LQ5{2?T7R{F6gk+nT~(27_`r&IBBnmD7DdQ|h zeQ{r^PFWcdg{f=;;pd<2BZP+|f9OJ%9tG~QGj$@6tfsp4uS=#W`jV-GZeg4XIZ7jz z&fS7f;l@D|?%4yV*T0KFxEaZLOvgQp#~wJv%Uv&EK=D02fa%%e*nse9S}}D}Elm&c zcW5$l+1k9T2Ty?d+)FVuW^?uO12xOjwi%M4Razr zah#lH^cN}rR^`(o-P9Sdn&GIy%H#+>)WR2MaGd_6HGdjoVk(sMs`%_^VHvH^hsxpy z>k!`5#YB@=j2vh>ZfWu${<;+vZbBiL3+c61ZM$5b&)XAQlZyAWC`s6s=*6~WV@&*- z0Nd7&1dBx*0nmsl#L7ae2mkJq1o zhf_NTC$pUh9JN%#qzzzd1a?2L*hb`(K0Q!pJyhRm++zhGnLSR8xMXgkZ&v)Hf@TBG z_2JGxy4ZeFrnGa`Bp7~DKYBFa{;P+ZLT~#3>oL;SNuxbnVO+D_uMLk;MJ??@b1MXcv)scY#^Th;iF|&gQ`QM)RvSw$3NFG-AiZ zEcUN9=>AvlWvue;pWdtglk2fQP~0R?p^{ZP!j$_ z_tIAM25_)o)Xx*(7pnyleQd8*zmIIPg2E|!pbrS|jM_HHVF zXbefOL+A$%o}P*9zipPtI|G=?-Vl1vzc)Jx6!v}d6}vwXAQ~m3HXL$n^!lE#^t$5X zDd;}s4@2eD*{c?;nD%NulB41z;%$S1kl5J^i)`-K%ceDZ!EgAAG) z@sHeGk+c1pn=N1(4CXA`(q4z;k2hCVKPPI>n0m5Pvf(}aX}?2$maBi}B)%vfY%F4d z3)os4Em2Yzs7Mn~NK8yj938bt8#;^g_5`bF3jfpU)O`JI$NOWqdjS>~r|W-MNRog# zAH4pHGEM4KD^86w%)!ZDXaO4t!S=3+&U0E&c{jRZ%ccS}etpqW;2-T{2E>3oXE0te*9SfCJKg3VtV6fY26FBTMbt8M4mS%U&!Pae*84nG)2ZX`Y* zI-a7GuwQ-NhP4-v8URt^*>1{GJc>S?nte4203?i}w)3ghoOMRA@wlKz`|SZF3fde7 ze+tV~wGVpFLKa6G1dW-|GPAZG0tP9NU+i3=7!;?j&xJoYDo4;ZJ~%C(+v--H1&U3D zK&b$77!av3ai{!l{RRVJmsyPBSLpc3h2QR(ubX$T1e`&Hw}w6Oi8)x0Xymg(3r15w_XvK7 zk55U^kzVdtEyaEjN7=KSbF(_Ygs!LmVz@Mmf3v~}XR}o`)mWF#Rw;vml7qg6256Ld zN8^~RMnVF{WPk;Mzo|oJVn$GQbImesY;6-99?b>s`MC67p3Hva^%J}L4Ix?UaC2|p zS}QoG*uoB?knlx;r~s^DcmrQi8)r1Vj9h4rhG!_r%BTSxND|B#?83{<59^2lj{2j_ zt<@hO^Bp?WLV9#zW#T&1I6vFZh+Ur`oN2$6f;I6ea#iMdkn;0Y5t)DU`@z346I#{oBaPR+#zhY}l1x#rfN(7k+Y zZhv4&H>AdCtC^Qx1Sr_@Afs>_a%)|SVo80fSn!Qr)r0pei|!-9@z#N0>WNQ*@5pJ8 zY`2B>F%C3_W2m@V!I%ir9q%`}B$SoM&jC(d^P;e!&$hJU0fB|#hmRh5oby^n_I~Pv zV2H_NrH&p1tNPBpDoa%_wq?kC#04;k=DYmG0<{yR>J(E_=Ry~UKAyCP53B9AL3eW6 zOq`=qsY$lB>!|I>b4$Lm!70DAcA)7B+ni#hh|X$P>HYNC%rU+xd9ok!_p;ncykM}a zY^o_u3mtKv*O0EUCAHYOe;qG^9Be^g!a#L?Ig8$~%z&BVd;GpO+))zpH_pR|qLBp@l9;}N&>hl$rw?bdMP^6;Ypkcj14OAO_ z&T6yEXX}l)tfbU{fPwiguXYzGZWAAw9+ACVV)1!3<$QMB;}Gf@<3S6Jq5?L+iJwkb z)BkQ7i0^7o8?P+t8v9yc0l^U19qCm)+)ha6I2T_Eaz88HgNtqogqW{G1qnksHg$VS z85M~DiU|A&MDoQ6Y8kxmk9YzPL60SmZTC#pl4DZ#&}8a{94+&LyJ-ZJ^}KUv4y3BENnT%#Gw&L1NN{dWw(Opkp&K5<%8fq@w$))X_oGe&;eax~q5BhTAeyq;Si5#@UK8ST zG@@m1&#xiyuAZvb!jZOm={d_%x>iIm_y^ihBs;E2)WLM9xP&{?Ep&*Y{qr3o+V2D5 z9pq&&DG0upUts%PuPOcn>2b`8*?74Nt!bre^%I$Z&Um&l!w@ub{iaDKCHZS?6Cy8q zo8`4vKQ5hmKp=gNV&HhEOw~jH9ls<0U04HZt^DOS_@eNM>(qTU1TZc9rt%-Ki%s0s*8e3?+NWSjP4M??41`0uIZ`Ca?;Q@SAcZKVrx z_n?xl04D$Pj72*Dp+dRh4=oje4Rxl(9Y3-5@XDtOurTc;M*9qd&ARJ8ek6?X@%(Nv zC8d2{&p7YubeOcb$TK(jAUNh0^PH_GNMryF&47_X44VeTuB9}bJvXhZTPm&tv2WOC zjr9z5>O9?sK21JaV@8BOzE*n!ZM-D}vMZlIW{HCcEI(^AsZLmDH?xV|`1nA?!{(+v zeVfH{PY_yn-Pz&=>?%fiOq%^8EeYn=Gb5Ca{^P&raCb5Pk^CMb{sdVLt2exewA^~k z@_>2Hb2^3A%;_|blkCYu-N{7VV`MXS6xxGZ66B>ZwzVkVE8UJb{7FkGfgbY@#6h&} zm2w|+cA}!sIt211vZx^!Uh!g?t|m zV_r=Vve)2IamG~#am5>A9Pd&mgDcmTHav7r@34}xW^2<_rKRB@P6qM|9G6s_oSdc^ z&?tsNk3-nmsurU0{BSCAcd2KHDSs{Y$!(J0ha{0L*RKk9=Pi`Ym({N?(zOKCgTTiuHT`u& zAUJmvCyDGC?~1|wAavq&D$P=va|k{Aky-3O&S!jZ z15%E5G}{azAOrw+R)rY684%q-hBtSm&(1JRJoi(~iPrx7LdCk%MPdx?P#_7mqx9?29oc3%`?A&q#_cv^Sf)#M0}lVA zwq$4Xq}+uI+ctx;0dw8Kf>-@I&&n#g8>&$l#^Ckx;u-mW8__h00C1%D`qS%hC8JjY z&*H?mIwNn(zv_d$SXp#ql>W^T-1p)B}XeY0k7lrM{gS0667 zl;qzslox>IASn&4gzcQ2HEmvtjbr8u1oXeMvat!;<_yx^=kBt_O+Grt?^!D6_imbd z-}O2cEgOViagMZS-p%5lEX~E)8Xd%%eiTp9vRz8n9WlSC!2l{`CiJ}S=coiX%%;f} z1ob5d5GIGC8J;qLUS8P%oK_pHbijYg8t*h)1$-xF|Y=Y@IBbT8xSsTj9H1Xm_JC=6WQ+qCf%C5 z0b{Utpmw!i8P)Gvv#u>{Y&-q#U|*DYq_Y*Tr)XFamrH?sh^;RA&iizud@eoCurarM zGUTxCu=eOzT;1EOEcUYn=miTtK0Y`x7*qZ*6m=8e)?kT2H(o|xsartp=o!E!=2s@e z>}6gI*f@lK`Ci8$J|?2`*if9cE6;GhcH7BrMr}c*M^A;L?+OYU#d3$gH*gl~pwVrw zI96_n+2M6*4qKRkq|w<$@>qR9{n$`n=x+8kuqJ~$#sF2h#_nZ*RrA(i|B?796IiI% zfHB#ni&w1596V&gx%z4R!D84#N@vOwQ|mYuEA3VaqHmYfsclBvd3DFbgl$#F;MvGc z=kT|)HR9hUYxBuxizq9-Y+6Y{E{tg%T6xPtA`KWjXUt?BkmIYzS_xDp@2TtKeT}ei z^cr1H-K&-rQJ|r%Lq!X3H?=7q)o*rHxT$?Ir6CkZU}VPy@0;DK0KhK_2)upkFRVZ(B5=Xo>bz_>-B1?k_)f!FQXFw zZjHSPENAS^-;tjch?23o)Q(4fUDtLLSB|}POLHo&v%cVwyK|X+K}3zSQetxSe(hai z#VjmGUTK}lY+ccdCHx^4863!K)M2Pv!#rh{!O@6rOVU`lf-w8-lKU^1 zqJT_*LbAWe>+H-w(&smsW#Bac(}k+5kZn%V$)A+n%2=S!0bagP{9@st^Y`yPc4$~O zkpZpe?z=^qO&hvYPgCV_{xxm}3Qb-!oK}2{ej+dGX_CvOf$GCj2Yu)0xV-QM!VeMM zi<`$piuIXXc=B?(LfhqB-dCdZ?rxcQoM84N9-Rv3&79_k8*N*9+QUm=7~gzXIw#+g zFrx=D1}LEf;9Lu1oP~1u{LMS{W-QbZ+&N3S@p>_R?tbF6WGL=82a@w{@h$x`v@%?1 z=CSlilnFIc)7l?2zdc~^q!g4G^Lz(;Hv!pq?@&}c=tbr656m&tq}gdw_dXR=`>1hF zX3PnsvZp@cwNPdxB3(D=tvS`)CRU_(iEw2kIkYS5cxhGgP%n3>OV4l$br#JF^5wHy zn*E23GL-G16uDHcnP^+}FdJUZ?{4X)dZ5}A3Ht<)XWX$Pp6 zx0?&tzs)q~K>#G6K=iATM3h0&jas(#932J-1av|4J{QHpI~(T5qCXixPlPIo%Q-yz z9@BjMzi{+uy?r#w)rcE%rSjuky)F#%Tg>u@prz|&@_?BMcDuys_hkKYFWjY0WtvM_ zezd-Rd2atF?pbzh=rtxvO19{cH|%=2B9GgK**l01Hz&h9Tx^@cRP{G~Yh!ILzJo`Z z-o(xiglRw;Y*Y}>CJ`6$MMjMpM8N)~8CCt!jGRFn;Z4R}yKb;)xacFC#C+!Fhu^)E zh6XPHJ(by7wj85Az8vFXn_IG6YO=Hn7VHqz*(w?i4_?Ug_(hkktKF~cgQE2DlRhn$ z{xLjEb#i(Na@`$14O?g3Tf-+@+#l{I6%}wea#($4{?y2(*s4ocj9L1vx-{hm^l5bYjDfJzuy^*^Kk~vQ zf5LC1KmLIc;E#=owB_yr{P;9>L^3DXP-57v$8DdI>NqjmN<-M!q%JI|RNJGL*=43o z6wbPXvsMghtxna;nl~Th86c#zPG0NaPhUH*Y1x`8?K&o6)>^`Bfw9W#dd zh(4f){10vEp#Sy;-hRlvj*;-)(w!08)CLT%2zB}&8(nfIy$pG?G#I+UZuYFRf$D4e zwpOL`=o3ldIk2RLvmNk3^FAP;xrQ9M9gS;3zcOTEZg;{dSZAqCJAXmv^U1}RhzWU< zQk#IL^dlhHbi_T1s^GJ7!_UY%6FKR5@R1xftMV}A>Jc1k02VJS7#Yx5{Xj~ohk7kL zE?ky4x{ZtLYEP+-N^{RZ_hkqRTC=NiNds2OYlUpW?44CsYsqZ}4tucC;bed9_wkns z)``R?CE-Ga5QBSYKJCkew*{iW^wd>{Ha183@s zeJGk7eX)g(V169&Vq>&a@uF^$jfmUd|BZrrpC zrg90Tj8$w)7G>=8;bq>>*Q90X&{cpz6w4OUX$Pn4{2ze1_uNxK{1oO-xV==Lz7f%} z9PNn<(IzfaxjCDc`Rh2)&P=6<1>~QtyT)Pxr-*Ni)aQfIfd1?KGD?AJ^V@AXLRj@5 zhT<*m3NWdqw_Dj=vAa@Z;e}M1cv*H)zURQYBCj0t@ClWU$y1G3`VO*zt;_+L=K^$a zg0B(-*CO#q3_c3ju#YnZjZGF4Q|4M_zY8mulJK3n+Ou$8_uD8+<5sxDl=sj#J z-4*4CM+W-uh4P6>XWAAoO%?cr+ppfU8D8P@~6Vlov zh|1s_-|Z*4Z;!WO=&@>0taf$7FeF%p4(j7T;Cqw*g&LgJUUWm`v&>!U_jWyM4=TQv zQ-A#M#(}Nlo&)9vcto3CuEtmyy`lb%d&|v|pn6DXc9U)2BUl296By=?l0#@(xu}Zo{@EtRXRr9+!f8se%GviwwoVV43n=%zqUDw4mWSlwvd^eZmHls9xo#h??)OZ&=|)og6)gJ~YB{t5*F(o2-BDMbx}~L#50{aB z>RP9zqQ~QHOG}4+F=f)iM;&Hj5RK#b4DUm>UncHN+Q@_gl-rL$O<}U|&kYU!OLxw$ zQ#5?0HBUNUIYn6wmrh+9Y1^m)*Df4Wa@=^lTsl*};>V>@}!*&_j1yi31 z#$ExK9Ijjg&#Tv=7w5_Gk?n#E#hv}B0jraXEFyf=*iXHuaBJloQ5l$UvIf8LTP z|I#8!nEb)qT~+#AAjSK{VturcUipa<5Q7`5DWZx z{7m1^StM5@@tzkgMvK= zf=6a_R8%d3l&`5jWIc6T=}D)%N)#5y;=*@Xib0w`*iH#cH7YSF{lksx*f(h!`S4Wf zQX4gbMOu$V1gV=_m8cA>-}7U$GTz!A#v@W)i*C3-6xR9H-jY8|d1Es@(NaFFRlRhF zqgSi_v;Mj5%EB+kw=&ZNZ{J3F*^Ndz7C-xLKlWmdooY{x(}ai!*D$E^yhPLPBY2E1 z!AB*I(uQ38`2zjfGEk*0kq=B{^nW^Xay`=AkIz<3?hQ|c(MHl6Og_|q%^+0jwx@j{ zx3ixaNMvw0&=N6s!*&e!J^>weCL0^MsHCV(@Yl#)x^x{q?l7u8Rr8O~2yd+WA@FfX%FK*hgnVgM_H8LDh(aRfAf{q~i0vD^K-(_^NW3S8Ig z!c}jxhRM^=eYt>pZajzSWgyD^gC%Zd$2o zl4!xs$7XM56;?~gaJJXCxl-s;y=r{3eX7Z#lBHeRehkLQBrC~V>&uIdY6wcYF8L~+^)5a?IH zSI3NGm#fMqnEC#TJFekSllbuY2zothw?|tV%AMAk_lk{PL5?J7n?6D~e19H48R1VD ziA~#$Vp-6nvU6vB`$9?O_CbHG`Ny}Y@jKCeR05FWq|WGwm1w`=4~IJ~dnU1y62dob z%&@d=y{`wm`eXN%2C9c-N zUo%E7d&SYdB@wZy;;R~Ac|(e=Ge8of@sNf5nNGk*<_z~cd0#1ZAmA1%b9U{W8t_e? zkg{IeYh*M{g=lwvo6<(B8iG}M51*4nk#)+bK3pRc9mc)toI+uyF3jIe`%^XjdB={G zsI;ggtC;j z^WG;A`C_ZB_GO9vauZ?d0d~<+9{6`Y^!lMaK((00zsU6b(d>5C3&4BpvhX}Ce`%)! zy?3y++7w_#8=G{AkWThi-pr9Hgr3;hb)&Ip&BT1Q$8x{cJ>4+XtG3N%Ixahqz!#Z_GF<&`X@5A1}Y z3cd4WjJKV`HUvEE8MFl7(c$56x`xYKPE7j3zCc~8CCFEtJSbQxQ)u_ln~$bXS>1rR zyU2{cn7f)L=~J(yJVBSht-|>wND@=zZi8!{9#uw{)un*V{SZ2vCQ_UJvOD<3Jf6PYdA{wKZaF7lYc8o&nQgNjBNe;MD(oz3*2xyBxV7yKkbBJ6$rczEpAW>+y3I z{bfy-blYT>85i!<%(!e!;c_YTL*8^C~$S{jl5=i|n*q4#RW=0jZt7`7ZuJ3#zfgKciIbkV=(QviMTNOwZ00=kFCZHp!Ap_=5oV>=}(P_ z1BwI1m8RQOy?21uVR)eQY%#rgkyYmX{Z>|;Cq2(O;Q?X@3N^L|ytDeEGVCHoR(_0I zcqU4)L<2L$Q;s$v5YwJw(dCjrO%hnkWWYh_q!m}@YbN|P$4OpliYQhY(b%MD(PoTyJLgJ)#d8xO0M z7Y&{YTXS^3|2KVtNh!cAhsA$~InA~~($@lomw{m?;Pd6#Duqpu; ztghfR(I&chc0$Df@60RkVpbrsq0~e)e(MI3dVqc@IkWh)azrg%c7Vr|lV{)Y!Z6*X z?^Pn_673t7GN*-WnLkN)8DIWhO5h1DdV5eFd0z<7g#;vopyt6o9!GxRJl5mhQxvpM z=QZnR2}-zk?9#0$CW%!IUX7DKnUk@z(tDfo=^?we;drL|v$lWHhf?3j!Z5=|-;sH( zT-BH=Ey{0y z_j5BsKK=1=r_WiGC`@xx0u@__s4D?JfY!!N zJf;q;J9on-CW-KLewKqfna>)aKDZo)D~RGNyKlfHeLCbsmm4R=lqSpzqZjiZi7J)D zPQ@yt;XrS^-7h}fjW)v~*7ok@DpqC}P`=pjjdC@<^(?*1<7T3EVu5Snvn}|O?KE!gZ*^}{eja`b}jVig3b5-jObn}J$1{%b}Lw!_Ep6Tw{Qo0&0 z!{-w_aGIS0!tSjcXb==>bLgS>QcIqA-Q%VpChrfId6|V%D$+)jT%QdSi5ig9Mcr}FQeiao+@rT! zZfShQy|vYeOxc4hdIGoTb=(TwAYBRfyQ}x>_PJUL>kh+$EH$|)K8j+abnKjW9$)G3 zZbpa5Bv`5_Z)6vRa`EB^W}+yL&FsUJ?{1y;Liq}oA}pE-pkxaav8s zY@k-UFq>o!>|EZA&j|`USs~K$tWCChW*9U$sA6&GFQ9;~J6&m?)mSh@q12Z{DDL2@ zy!Vdd2<%NsOT;|LM9d9-nB6u_kJeAb(uSZT0Vtsyw5g~;Eaj$AjKwm~^#?+Y*m+hS<&g|3}bW5nKq zordE&)y1}H6#IgQdP%)Vo$vS_=pWpf?Etvo3wAZcDT(8t<%Il2C#Y zheJd}g}u+euzYZ5zw=^0uvaH#nOfO@&xx?zuVedQw*96@jW}u_g=*)M7k@=3aK_2h zDz<(oH63sqE;Y}LI)3kMlHv2%p5%4#fMS1Z2$U^C0mm=8a8%)X^zjKrk-UvL z41Cvpu)t(7z4EISv^1!H<-00@8UQ1rNubc1-{nhn&<%R_K!)Ywq{eh*&wl3@trV?P z&5cUSO?||2c|XP_K+^G%>1BOP-wRv)(MU2+qc=ifl_I|IIPxJg9O7^37BT2rL_|HN z3Mb8KKkHb5ufT!n6veVF8`{d<@l{kX1a&pF>r7!uFg-p?Zw_xX=h;iFz9B50Iz0F6 zt^@4VTJ)1Q*FAoOkAGave2;3-ox<@p9tDVuGy0O!n?~%O3oF;+q@pDax;_PM$f{t% znzjt;voEyP#9uymrq3U=sY?*c`U73xMmVmYM89smulgeqgeX1AlxwX-6gnJ-iV%@t zKodr8y=L^f-irz^USJYM4C;V*_25awGC|lY&x==-qp&p{hUE10Exu*NsJ#fezP+CN z3yQqW*h=F0XJaLKx}B*G%?D_Uko}`^)mgE$UzueqMjETp_lv~2gCCMf>u!1q&65*v zIJ1iLS3`Kt(@=U=32p+5^jyD7(aCfx=?3P4dy*GprN7KCK5OuL2m2ufctfiPV_bo2 zGWf;1TS4Z0CK5oLLOzNHRvW(J3a<> zyTa^NF_tyIr>inx-(f70DupR|F|rD_CGmt#+AP*E#YK{g`CWHYuGcL%bQg(UG9NBB z@~{TT`}vtOjue=(PqUYX6oS1^^unAz3j4cPim)2G845VL5j z*h~uQtcsAFp@ZZiqYqrfU>(A;D<5AyhGWDtz6T%B$;7?CTC}VL(Mt0tl(Cs}>EIe} zmBJ!lC+*lHz)tokF}Guc1jrLc7uI1}em`oA-{RIBK?q17DDvtwzG`aO@Q4rimFPcfY3rDP`Ek6dAho>E5)z>g-0Z zLW%w5gp_uFgjr!fu0XF^RMnw3S2oHMcU-5dZ)SEs@9jCt{AW5hX8i658gS9Ac;e~s7aY9Uwk}haf6BY{X$I2Px00I@ zUzYQ~yUQHz@&-1KFYHF_KbGg_aT2GrFx=eYy&7?rmgZJFre9+D`a)j?NLPIRs817C zR#$8CRH35(0|w=w3&=Z}^?)93AHZyCChu7*xUfNb3rnjFFAOrvj&*E@D!`bG3{oI} z$?C@XL+dM_iQ+12- zF+H zWELGf1DF2?w}5nJqdJ4Tg458Sv1C&>B@U=|b?oeW@%1i)`ya!jb^7j08ul3(gWhkX z=UYLyGAS!F&l%@m08*-R*n#u5ndC>!Aaaf-&^c_FEg%~5kJr;YR1&JD)p?ae*{QuS zPvwO7GCs&)brrjzy06L$>*WsbSH`F5b5pKGFDD|(b{EKd^C{di9~Db=VDPFS=|R?a za(9Ut!66PD6L}e?LMoa)m=kT&oi|nJ(`8D`^~o2{)5(Z)fs+9uLvr-|3Uo!5$Ga`? z{na33c5bWp(I9KKxr&}u{npe>yW38;OJ6#45Y}l+xX&>erH(e#o*=r1Ax>48J65aK zr|;AL!S-)Mrf|etp!Op(-P_Vau*Ilik_O7-8AGojJHZro6J{PwH|BiOX*h$3{KwYW zlWZ;+Ee=m={sV>e44p&L-}l=-3EA(Ps*IjEZj7SN(qrC!){RJO{8%_1nvZl!*@`ny+XAjVF*%(lIahu{)w~j3MwKUcu+r2uqFu7 z+;k!8I4A1EMRINy|L$CY!S>)7Z}w3%Hw?S~{Ea$%al}Ci%~C_Q@8+9yT1f@y_rZuo z919>~TXjWiu83}fJ9WO6E zp5Tuai}ZX#w$G}rkqgbA9zDMN$o~d?p!Zdgu@v})Da~Q7-ezhtzw@^nom=W%{i1t< zs|TC!bNI>#kXvDyFAws<6Rkl1SA2eR{B^ z`t26zgVWSHkrqoJjAeZ~jkO*6IQtJt$7k}0TO4EbKz7>$# zGGa=nttvaQIv6eFC73;*@I|F>?tf9w!Q>VnJfw1n!r!}JGck#q~H($(5?9H!FH zz0Zh}pV0*KY%od-Rc&~Yyx064M7G8`o_M0UpxM^f?q@=Rg3>x70~1T_Rn4x*46=d`sNC(DJwXLR0@8 zPOA2AxeLK}<5EiuYEl#)2N}`iS^S<$E+(}jdu;opiky~^)|$%~BkI?gc%*tFKNvsq z%kik*!QZ%ja*QJNbL*A8@BQL++Ec}RKf($T5X4vTp$uof?mXW)3ECN1yR;?!;itpM z@y8aaz2}XD2CowGMjHJq{nr!}LhWoV6X-+j(!%dO}57;do z^mtacf3#xOcCRI?qc+|4BB9QiO=jeLu&?wO1dWen;84JkuaHGnQ4SefpFU_Gb-<`G z5izt6(tPXjdrMKR5k{L^HkQusYe>bC_bCP))Oqsp$Tkp6+-kcBA_^_)zNxf!-iYHo z>hx><__8w%UXr(UO9KfVNd`gKox*WxG2;$S+aJ@uLW>ki-m*L_-_1YKDg=D6EB8ZO z@YZ11Ipak6;+P?+Dj~+Ipj5$b=-I_6GY6@o4Td`{s=`pu?=!9BHOrt4l%CCmL`-cG z#V0*b$K&Hm?^DLGwST;#$o`UIPo;~82lB$yho)>KJG=AMT`2Y+Lp_#{a6zK4Lru|c zKN!r;TBtCkdjF~pJr?5+RQE$P5M_M4UZ+3_Tz*NTT9r2#Ms9EmHqwR&h>)w$vjB=2 z@;dWRCoge0gqte_NMGPVK&W@9)T6aBwihx1qsG&b_{!dp&3NGlzLi%kZFTyc{R%$5 z=*KA@O(p3RA!o<4Ec5eRH=~iTJGwhdT7|W?BI*6$rIYV%MGkYecpdGg%(2)|U!Mz} zt^ec+2tt(${tUjNFLA$S=hr&_4sQ_(&6}DV?`u%Jsr=!%VC5$T6KJRbix#A$G9iFXl^4<1A&Wl6FF&;HeFv#(5Za)H)M$)h;AXB=o9fM+CZb zU>G*Y(Toud7C!&Y0|i8o(`pBtzT1>`^5jgEg(A7~CS&OdE zd`JPWE_j;(wNIhnP*+uaM^_W)bqFsVE3V;2{|8Ud`>a%1W@&`2ai}y0$0K_B33()D z^-;qz3w5a`q!9s8HW^ZC>_%wGrojq0?j&O-&N+zIWNM<#;_iVV*u+$<>)g z*^gUTfhFC-B4Mc41g0+!S~Qjc-(U4 zZv7e=IraW5KI?Q*PqI?jt13aa5hfY)Ri;irc{P@|>4iV*{gg0doX9dOC$t~Vc+T40 z-qN89u(eoQ_w{j1Ta9nVbmr6%)>RR}e`RJQZX;fY@pJZ08g0iEa0o@z+Z&!;MT4V< z`na4N4Ef9BZo#kHKc;a7(0U}Y;({pIrNr1n^P1|L*iEzu1tUiuuBeX0coVMpXO8ze z{~WW6#(z_H@Ua46iTZKie<2^myqY;Ws<3L5z55apg7dEu_`5Fq@gYx9p)-&I6J?-D zydy@8{aQH{16XKV-;J2t!J5mxBlLyEX;HtN(C~5^Y7tQ^kli72vOAj9NXSquzfEUKBewa-Hk^KpX*s>KiO3_E`o zgnaPrHzdzF6)rHt8gk9;pt!kVzYiQf8Z*!pY}&X-rJ=#T*)ZK1FkdX6YVn_bQCH#9 zWru|K!ppiD0i_ckPF~-P_{9<$c~?Or%KXix>vbbPLcY&% zA)cxqE> z`i%E!M||e{wN`fuquF*$AY~0Mw;?|5e6aqed2b!THxaYenWlefmLW_}jN}9(f<1tx z*PE(R7!ki43Oz5j<{7@u9o_7HUfZ$uAr{9|uMTz74Dk3rz=IJg`sLKH4|3RZgpMM+ zm8AKT_=}kAFD^v7*HzJM8hpyCW*NK{j2jg@ie^n|&Az^5$ViN5zU_WRV5wWvLMdmF zdqo<=-zOiPt9rBGd%qvgM*@A$4(eBN?E(Rm?cCW{cM2q9jy_gj3z6lj@n&Wedj+t;u#t>a8`cYbOkq_-`(s*o#H06A znG|wsQ#9(ltpM3}w$_F;Nf=B~F(#(7u*D-P%BAq>Ka8}?Qdl~HZjnxCpALpfMmY*z zg%3d8e&T@{TgjA8On+*YpWTILP|7sC;5Ii#@W_=al(?2Z@OR~S0Fs<8nU;+ z+!?>HY&bT`QHv7rY;0Jz+SPg2S`@U^WWY{39m%`KPC@gnf6JY4aRUT^5b`7o>yP|Y zebS1S#$!RYqS8B@Xl9f?e38ZS_&e@U)yGwQpwy5Y(_K!~d$!cP+}+l@YB7z)hXq9t zx*Hpra;uTT#K$If5&LkkVNQ13Ndph2xK{i8BolzhLGk}i?%-s7`jd4G7Pa%wQ$X5w zB^(=zJi1*=&2ivL6pO8dXc|uU8j-`wnSGLndM3pkOZ4*Z5I`xIx3h+1AG>%lrhaK5 zD1c~PCF9|?78JbTY`=F&srJ3uN*$a3&??YLKw#klZN$n*E9&i)FXfpC@DH`O}4S7O;Y`)SI7UN6_VM#1a)s$Xb6Q>ZKwH}=>gr&vt>v|zsi)F ztjo6K`f#9{$zmmq1WJZm@pC|=Z1~v{>}MF}uXs1TIv#9!vnnXocCw+<`IXT)s=u*j zCNm9Vt;Y;S%+nbU^rNmnovZW?KrX`mzQTq0JvHJ;xjhZ!^_{G>ww7G)zs{E2w3@bX z00SCWpa@RnrrtL&W^FmB`}#VW)OE3w?DwG~@F4=lMOXRcr@#Mo8mf}91OLltTyk#l zCwKjFtwL`Sb#RYBQA7i%qA_9H8oDBe?zFTx2I23hz{>a2lC@ILE(Z2`=aWs|CQ|Z- z%|MIH^q1yCdmGhlRl}O%@A!JmFx}Oz^U{kV6SJ4!c)IW*QDJM3?S4p8)yj<_9BD@y zxzQX+dxaK;0wT*zK$D;9U`#NF(P75+gt}@}00y#}Wz#DRZV3pW>iR+O*@-q~7sD7GRe?Rve=J8y)Dyu$@5({foR`X7wZ2Rl;EQtiZ-U!K}v zc=*Au%AF&=Mwpd?Wu&w;aFQ>dwp_Htgh)H*=1_d!6o;8uU(Rwz?;m7jhGHzr9`L@Q zlmHZus?c|tR}qRbKwOOHs4K=}%l0#7?0}8!9U9=DW&M`DNSmffU1)^C)UWwM=fbt7 z9rxiB+;;Hd4E%F#BAhrChVL2n&Rr!@^l7kCe@UtHUE?0WKK7Nkx~kvLEDD0e^-Z4Z zlJh19jdTjlObzh~Q1V{<$b@PUJ#GeWq;$ghi}x=5I(x5&ucGmlE79dH(>6WH2{7(2 zZx7ko_TvGDBj;{$XY~|%as~L8*N2v3=%C%h!yfzkrO-IP`;9_0D-(70e^lp24*M_( z=B}X;JoU-kkRX9Qf$WyU`Mm8ru^4dhhQa{CVh)!-BW?#1i+FfS%PC}r+ROvE$}PvHz1<>ZX1g|s1}A|k zOoZ7(|7Tg}`?|#~(>=easo^FDo&A-@!}k6c7lrRcq9gOI!dXk#pa&tk!bjMWtJnep zuih}Rl%e#Qwb*+hGX#M#GS49lnXOdWXt2{ki01WH74t*eOE{sXmR;3g|GClq;dI=* zK}$P~0XK38o4GC)L#`r#(JMkr7hV8rf_f8tlwa>f9c^f7Ql;_Z;%q;^pIP!PFyYJk z?;E;*^u6W>flA=$bvz-H3DT4X{s6mh_yCF`O9D>h`Ot_*Z0;OAaKr*Z)&;MiGnDOf z)iwqUA8`XsIySenQ);tuQl3%r!YK;WF(Kp0g726~?SubCF-N;8=3E4lt?^lU(Ot6o z;0IZDYluv%S^`cMcA%>FI_`B3qiBFJ7YgVEDqM(Wp-%kn1DP4m+~~HXY7MJn^oFsL zETET3=OgvLrcZ6(93^sms!`GFEB=6hMmtp<+7K=fTquHN<~2V3GDRkdh;H% zJr+j;LmWGS$^D>FS4RNZ$!%WRD7spTcdj-$={zJj-*Uh(*tBRSdK#bRhh zzrGdq1L3XH@TE9Yg|So_GJnerd}cc8Mlz0AwvRp1IgTJuGEZ^gCy0L*z#ISxsdjW= zr{JNG^^e~47tSc}qN9x;{8}u2E1Jswhr%j)tps;R`Pk^@HpF05)sG&MyU-irp&{rp zb!z}lOx7Zd8qi3<5g}KU=cF_QLL>m>1PtWN0ggq0xd8HTr?q+5hbSFaChF(JvF-0Q zs!|x8$Rn>DRY{Y?Z)O+ZRaJE)jhcDyk~{tSBIHlPE_g+jUBoae$&Xky2Y)a8H{QbB ztQp6|!;b=53zlM@e62AU84#e>J#pe%D(q*>)KrM!gz?NvY-I4MCLmQ9juDxp`66X6 zz($&MQHJF1$<0kS*vr({kNFz6YEJc@cyWDiN0ygDrxur57vZuE>i&HixPel!QUkbn zoJXI&ZORS%>%+Nwfz;6wp-L-YwDD?4Z_}HBR zyhHUPA%Wm$tDTEQO3V!N*^O{M-}1TswcHkiUhv9uI(hZO_T)s-$6h`lvHr-E_9 z^E#P3VCp^En1JkeAPob4VNL{>*xq;(K|j1afzd7g-O$mrjXsIjS}@OT4wWooEK%Sf$+a)A-*=bM*qE5)fRqlc-}~9qo(C zjp3$*)vs2%wh;ZA--R*;ym$m8vVg z;yg5rFLcv>yo&wIu4u&s?4Ve!r@6Rn>`ffc>I3rcSvL03_PJ#LpaD4mu)DlDPaz|h zvS(?ZIXi3NzJZunEJ6^j$8nXPrI6@p-B9QtMlW8=n?lklgLu{!lg_IBN) z+Z$)$vwdlpa42mDrT%2d!pcGpKbcbF`p^hbP6ZCS0`qHjPfFBLr&z(@0a3`&M8nWm2`kX0U z6CIjp^;tk{1j8~U>OGj@$G3Y@xHy%3=FV#Th0eQ%+;k`N>9;ZB(A$^45}VQnd;vDQf`b>g^|j1~WvptzPd0qx#{vfaz8z!FyU{NgNM+4i11 zQJah$%h+l1IjselH>!6%s6TTsR93N!R)UTg!c1t5z~1ZjUrtjU=feMw^$Oqn%Z`qg zOo=2FUY&Nye}J;R zQ(t|}K{Pi;$r;@>Zh0S=LX+%5+AcffFYo24_e$5cNV{E#)0 zV#@GUuoGpmq(4;gELLQZ`V@rV*U(H_V}fcA*fvd#_+&+c32mYPCholf5MX$ie+-2L zN_Y55w4PWdIUS&yj{E0GVg657v*74ENNA4y|2&n&6rnM@M%P`08c)DjEV1b4^7pw} zD9LT;*RODo@%L$6CH-80!+A0v65Y@W68~!1OaYwHKod2=ae&86ltdWnGqZ zRhR=x;nd~fsQqWMg}zxRjd2mi(pFflGIy{m=u3)sXkxCV1p*&_!u}VsZzqD>m3;I3 ztW$mGmk0-0r5v*JUeFIY_nv>?nW%rDBi;rmWGRRyy7WbT6o_5T5)v9+uS$0j<9RN% zT%c^v{60yRX-ZYX|5A$2>_06tYj;3^^E11TAfoB0CfVsq5~xlL_(;w}G6wjpLadEM zAZ*vcZ9z1CIXKc2%65^OO?mGBQNJGzpmqX=PxD19)t zB>N=-;AMz~5!7P}Jx7CoO)OKf4l=R|?TyjkhxFPd*NmkNRf#z0Fb?45I!I6SE4P~8 z9bC0cZ|QBq#s{}G584b>ZjxR}ac@m#mE^vaabq-gAl+%!Q5x^gJB`@P2*$o9qXCqs z3}ls=s*GfvTd^D;ByUo(^)sJq_63@MU=ayCm>BUb@%V)Hk6dtOcFgK>QRs782$Y6} z7`x)zx2rwUD6};=&_ABV#6IU)1<`xSyFe)=bGs9?bMeEWS2*!QJF7)?!oI1xh(ct< zV>jg^_K4V%_w>%O-_zZ5M?C;Kt+45Fp`V}h(fuL`N*!#4;s^#lh5j6yaSw?rbpn2zll}`T!j@xn%kw^bcKjtCVUueWXhfejrjU@205^sI z%y3W<@0Q3gj)8}5v@u$mfJL$mk{YFX`lMSYOl5RV*t;?IS z8x&rUHwC#A>Q{yeJ$dt-bc67qY}P7^ga~6ix_I56`p{TY+b}{sp%#ES{V*P$z3I&q z%NVP?76H)&fhx25Vk=(c-NN40QzO*9XUag58E}{V3bb&z1vSNNyh2sxi8-kCp0Rnz zo@w%qDyX`s76eKkoxJ-G`CLbFGarRsnMUb_JAr%_;Ar@(Rf=d2iUkze4fS z@8C)+-Ir4V&Pu#&21#2gWkQdsLMYS+FkB7|XTl$v-#A+v@ig#`Z1vg_!-%wyib)U( z5QWEL9;YZ26+IYgeRY1D(NFc}dM24ke-a>r-tHAuId8zCM{)p{V)l=_CSna2R*pfi zxEpOEd)Rc6mPl;n2*>vI|U!UG!=eU&Dg}?j^L; zOJ$6A#ED~#m?9D1Cv}OVgJYDf+gb_GIe7ZD-!UyMyCU+|=OQH)=*B>54V6TVp|5)4 zb!T_diou0`i(9Vi=5LJLSJ(>TG$l3qH4>5RGPvx5*t}J7L+u`TmbiQ*YDiya2f}&Y zPszy0n&Z32no30>XJN)BfrYJkjoAA-i3WLp*edatTiPLU<%>e1>DRIMDX2*%p33^z z^VN7$_M6#;2&FYJ{tjw$4{N_te44>hR>6_%nix>2QGL;?jR{Y0=fC#IPq# zr}mP9G-PC$w$LgKQxS!;8b{dA;^jzAx>Su`PqX{S)%~nLR+sbW7213;)P+`GN3#~p zw99mT<9d_Mlx!X<()txjqhv0b`okY*{2pc+_6olM_my(4CMa^ieeE;6+vabN81Z{N z2iy~NQPO$+U2AuMR$!vd=K4yS`y9KnJJ?wbwhEN#XvI+DX=)&y_EM}p3d5W|N@_;& z{S$s)uT__R>ttSEdLTa$M1+#Vx42UnhJAgqC!{*VBY|9B07Z;wGth0PY)=6~?4F>W z?fnfW`f$_c=E~~pYgX+frn`aE!6i)2agY)7-61m*jh09Uuw|SV4Iw0Wm;eIxtbB=x z&Qllp6SMycHMi=ntUn)bWZ6B=<@u*y_T)|LK0;5X+1atL4`V`2=UUrvNEd_!-cSU0 z51KE=fklKz0L%)gynfhVY&TK`?^0t8w5T@E#18%O^lG;Ee&;n3YHoLY5eNnXO>BoR z%2!=V%?xDkel#ux@&Dz6L&X}(|7HkDNAix@!~Kx*x1N|Hixuwq(OM8{eO77zgufY= zhQH;^?Eah>d+iJ;qSd#zBN*NHn_Sa!TXB%*abIV`19V<_arM7bTDqj3>!d-_tRh7I zqd!xS?`Zm=CLc6-d+_)i$HxnXvQi5O+O5n~MQpx%$Q zi3SVw^xOu(y_Q%}iWwjwgAZZHd6b!{yk$55Lbv6gCYZk5T(LYg=oI%n)Tk>ye=#06 zK3O`+JMt_qtppq~FdDM{ggH3pEm?N|vO@&esXmSldKdFbJwF>`aM@nJ~QGXddCDu<)P_nVQs$nWqB6XdT)LD6HTHqZov>^xV=Aft$E#P_nF z*m_jWto;~8_BBmh(7gLDIC)@T_znv$YGZbw6nikE}j=@#AKp&asu8UJ$uGru*T-B)quTzAscs zRqG8vb8jSevV|eZK!6gXBmNZB_OytC;*b78e^(V9ZsKs-RmLhhJ>mBpQ@U#vbl79W zi}SEcnhFVkB8!WJl*vphh3CC#*L!SasG7c-}Bp z!kXK3Ke=eVfP+H_D%F1X2|nt8C`f8mwkI6hGG_X{)<7pm34KU^U!a%WMBT|th#TwH$$KI zovrPtB8Re%wo=6m`o&q=h|CYrV~PpPt>t2^X6(yPu*+5E7pka_8*>xaS4BorT%|^G zsQhSP^kX;Evo7Bj z^&47J-6+8Eo8BS(dwp^;ibp{M02>~nI;FwD!wq(wA&Kpmth&DLCt200PfQ;Q5YzXLw`CH!t%@I#x z-J7PyQGH^6>*Ej1u4gta2epRn`EiW>mnPp%^GC01S|?I#TBl#XIhoL5tR8?q%pMhgV%)Wty&VCLK3P zzEI&TSlPN8T8^=GI@$a4?tu??mcLXrJiiEwQILK+S(HscUH*Z?Y z6u+uae0+%KzbcIWBcBfMObirrK#LsqjoG0)d@QXoOE*N00elpf+dLMrvM`liwQ&FJ zqMn@?n-XTL_T$Mz1(uwLRHOzD{xeo0)GW;fDPNdX^dWW#Xd3Kh$HJlk9F1K(Y8SkO zQWK7<3A>-=i(BTNF5b7~U&`8kDAg4Z1JBjP?Rh4}u>r(Cw>olqCEQYiPvkTep;i}z zbn1!K=bIt>J4jCZ<+p$&Id_hVl?hESqyuwBG}wqjDCg>qweIICsb8JtTq{L%-U_V8fy{uSq>-5YtLpuXV^ zO~;k!nMzxhg3?ehM5RwNL&2DZV6E1$>`K_jA%4(x@^@pe&A04J?jO}|HSvvS^sPhY z?Y3LL`&n-)iYl93vhu%Q-02@Sjr{W52<12T6uHZL1&noV&(IDPE8P)Pd^27UXWmcu zwG`$O>-q%Tqtoe%mv^oXwUg|u67y+gcOmOaNWd2ok^eFiYRhrhB~(}6eSnQoOD%`` z54q?#5K%-|aM9e`E??(U;ah3rxf!ro;;&FnesI}Zqe}sh>G+tht$IIy$0C|Y)JA-@ zrWWY@qBAWJ;5mTo;LU_UHgB}tR!fi%ebqT{_wJuTbC4=e(j8fH9DW_qf1I^&Ym5Km ztZDQ!^Vc>Ha`3PCob@BH^(gxRfEU1WYs}O_?TBYQ4sYGA#JmUqg|9|T)Gtl>oCPTua(rdy)KrAFjKa z>vu5iHCU?BG)BWQLG1wESd|9f+z)OoGjQiF^h{I)AR&}KJmG6^Q;N0Ptt>~n9WmK8 zrp|45ETCoOM6U7}34&p1K}#gO)3;ho0dK)VG;*6&TBWsqRm`@=gVh=-azI*D(+t<# z3QMfreZQbuYG7 z{YCx8%unAJ1w9IuFC`B+-i2H>&bk?a4`R%`tVz0&+ufMTFhs%<5Vav{ydrL&{zhWe!=K* zKGy8M-gZ>kI8$byZ7)Muc+>J?X1Wxn@t5RFM<--wjo_v_=RNZS#{>VKeS5=7y$4Ec zG;-(H1tI~P$N4J572vd#`@{WLHz^oK6c{Dk@t{uJIU8u%Aht&goN-)*o5BiO@+cQQ z@x;{X)xy0muE(P{M8mR`YL*56Gti;RAMvB388Gz%nC}4iW`#r}LkT+(8=C1eI1gS4 z*N#D&J)<|zGf;$IG{_K_3U*2Ow}n4Wot`&r=T=mk182Zqfz`s%%3T)4?3<6CMN3ErBgY;LNBS+ z?@cip@T4^&2EPwV3Qy%IAu&xrk~2*dsh~S21NWFC$$hxZ*LMNt;VizUaFz)XW1%I} z(fp!yGra{bR|PW0O79>%X8>$%OJ6yg&j>2Z+U2t?V3!$HzU%dU|Ws_^)T^ zFJWx)t3RCqn=@mtQF3G^CuJ~J%j%Er!J~bZ=a1-Jz8)y|&pVBSXIjoFngMooN|z2F+$x|ZSWfA7r5%B=BlBp^DYF?@1tQ<2rlE;oCtm~?_vaT! zMF3I{nDv@EJd7~9^BU*{1FVNwm7KMW^V&c#(tGpIQ-`;wNzYyWKe92fy2k=3qI)%l z8bd;43QFGjRm@x!PP^di`7$5Gc$+h2q`Vcb2)otd#RR3^zI^M@!Rei?S~0mJ9QF3PBn2V7LSPUO%rZ z{rE-EV-^7iJp(Pn41mD4z)s2fdD%hFu9Unfb@~!VLMbEJ>0B>adquhMZOypIsa4mY zaG@38ynLB&0f^^FmjjB)ERC#|m6DD{)L2@@c@s%y;jiWAK@Y<4Kxe~@@V`16 zYMk`k`2-|z&$g1kfM|?---iwcdHgX$teGE2xzzPTM~|B7ZeoC-9>f^xU>tnWzs9t{ zFsM%$Ksq}h1}0j-zb!p_Xl)#d0$PdG<>N{!(y?VtPZy(#@F;{r7;)+tXv%JpqRThBB3@r>m=#VRO0T>Emd_u2Z#@TD|;B2TApk&&5UgmuAK(`R4}@ z(ME!9^#|!sz@8Jlf*GF|dWe{X`OUz~*n^$CkZK%J_vIea;9@PY5J|58HWZR=K^Qs`HhTC9P=zcL9XM$5 zjL)MG>KdxP4VjlJ?Oj;|y#~{3AgDdVeZyMq9p9=coL z3ivkC&8gch4qiosK=T=pgJBm_1qW7boBGJx}OlM5JPlw?IL_sClajBWCM3AZ350yO)))_OO$l|HK?%T&ic_)X(d2M-g- z3cvvVq!WaDcPCUzpxI{lBVk^bO|Epw@qc_8yqJQIS_%2BDbG#~pIFtyPQ`NM9aa|K z$0Y?8(qEH2-yH&hVOP%DAu$_3E>>`%M**ViK-(KwM${p7#6VthCWzG+pXt#Aj*|m4x0BkIK ziok}huT~kpr6G*?emPP0sTY@_V{X=M+=qdp(=v4UDnJjLzOKD-E7YCQhjwRzL!I;`1S;rWXL~gK@eY+})*qj7AQwgE(B1fdbe(ln zmD?Kbmx2faN=l=2cPpWEw{%ELcb9;4NrNCrr=&c^*%#ui`U;o;Xpc#Y)XlD=H;~M60KPnryqDj zF8BzM;U*yojpD*f!mu2FNkoh^i7DX6w%419A_?2plWauo&;%iALQUYuuaHpC22W9yP6B6S~E_!}&kz3;e4UP0soMRV)Nc{{mANxPB!-h$xwNpRzdDCKs>W4DHG8}q^*5(Pge^02k4bRj((iqNG@Qk>0)s$9Ia?(YPW*;F zG&rv4mVq^2q!C->F|1p@4itqzY6w3A9f+lyq5`CJ%2lf@JNvC~Kj=gfQgP7DC(nWE^ikNr*9(xC(pq=OBm%hnz_QAIHB z@+cMKoitFilVhpw4TNU8thFg@ZJfYdwLeT-KT#ZZ(|ii33HneI1RvPhY`L^R%Z6uK zT$wDi{M?3p_0ZDh5qRTt(l&jpagsi1jO5$X7ZsSTvqgQJn!?H6QS{s0gp$tmT9v_U z#`r_r$AZh6)l|P6QYS4)za+umzjx-re|GKVQopkOiip>i z*!!YaNIlJc%VSoaW_`ca({yfPgS|_E=ZAySV_o+vc+%)cCED+s*2Qe#ShQVX?vot+ z@8$}#;HmW&C@>k9-3g(>v8y8g(G=Dl%j=b_RgGxcl?k9(&HvlQrlags8&d&BAEiCB z1n!nSDy5Y6DXcZVY6=Bra!S zzt6RoqeaZ>i54?5wA(FUAbQ00S(>8JwE3)gAN7r191)-C}#XuV3 zZF$X%L4m3+pCD3mQ;qBNZCwR78&N>Z)89{@)p__;b~5?EK#Wk;NM-Y4t#!Lc>+{Q{ zS!VQ0bzQFr7phUvv~?N6GT~(y(Vgpe9kIQ0LroJD$Fk46JDIx~X$jt&_S31JgVNJu zKH717XrXt=+1~;AtmhXB$}kBM*xgK@KYS^(pHpJw+G~X#o#K?G_B;_uVom$c%BAmPFj@u_ zE4DNrh{yO#2{?VA1euD%xcp~dg}^=hs%|XRk=_(^zu=B-I85ggA#(0^)^>BXBqq3G zd2lBa^l&1Mc}<)6UE7lfnk_$5NVvCYFXg=BY*mW?#_3A|BvrKU5{Mk3F_e$ zgX-uHZ90)M&tkPZoX2fkgS>8+a_t=#xrr(6fk$l{1!r zc0YA(UzU!;SRy*avWhRh%(N6Y6@##5dmG!-PffZxeu`N0pw^(OzMkCrEU~CVK2rQq zmPoK5YHxja%BIW%eI&oyXJERLlBE8kISG)gfbFf1@!3TkLFw04WU&XAx(r>|sULyK ziM*d;u{5{sEe^NhF>^H3|Ig!70(*S&!Q=CmVZrn<1XSYrH5>*gfNq0HB11jt`FI9V zlE&hGv*8!J%^z9^Oe2gd#Zt&$8Nxp_(ww@k)ZrMQq*vefRu0H|7hs78oIcocgfAoE zO2YKkJa*t0aZ0Rz-rUIkC@ZO+8nj5}Hh$*#ozw522 zK;Kp&A+R%KPquXu+Vgx6%TI7UvdhpJ?VYlkw#z zEz;uY(SWjZYJCwxF>O?sbHR`jr9`_^{yq?hbaWE_M<4>N<$ZL&?$wuPZ-;>A;0GrW z!k67EWWwCtJ-B?F`YwjWEot~%6H*|EhH8#3ltF-E@ts46x~P*NBH)pN-*JO(w@(;l zNbpoDdbjrIfI9n~R;-JGt*f!VFeRB~(;?QF)~`2;>T$FslepUtjpePbpP+=1;l^R= zpd^J_-u1!Ev`bA*E;E%C>R3~&X2CEE@GmLY{RVT`x9v10hOvYy(#y)VW!f2x0)wLx z!jYDfemg|Op!#&)Mr3kXDw2@HV`h7IM*Zk|`~Jhart~ffK?M$BL+q&Poop{N(r?St zh0`@UfMrJa!%3^G5)}eiU~Xp#AM~!5mC-^^nHEC=62z{84Q)(bBbDYem@1#@i6uPY z@90KMe~r{-HWh}uQ;7)}jNp%eT|z;M;0x~JQsKO(0o6V8 z;?xC+MXwc)Z78F=F*cVU*voUQCW}sHPs1v*M{$ofruQRTXTMeEWknK$l1$t!({jdYy;Y+3m6UG->hdgpc#~7IQ4~OFq1OspWab zk72(CCK)J~jvUBWd8s|O0R%A;RF&%2+!ZBeK@u8fev_Sq6`#x(6#sf=CX2|)YI}`a zmzT-i+}SWW!y_a8R=;Xx7ZQ0(`xddd)J(@1M3Et~B6v${oUEW{P?4s8AP-x=2( zoN)8KobZdX`Jk-%PPJE0?%DT6k}ciqG|PFTZO{d8^ZIJf-yZV*xL0;dnv!lCX6WaV zX?GL5+tKriMGrgp)s_nTDurIDxw$$jia(_LWTia9rNRNx+OThjMBFckKPQs+c5~g(G}@UMq5po1T=-Ru{bf0x#3o!pgxTf*4FRZnt9)YnGrI z$vR>{P-F<&qV9vYvfIw@)SFl^Qu>G$PbDKWXLYsM7!4 z4x3PZKyx@FEJeb4elaa&Q0t>&fb#ZtKZE@Omg`?Wdk7>t1NeoQv2RxP@&w54X+;kQ z#{@pq0WlZf+_#ZMkFB1$;S&`O(P^r1bQZ)PyRtT$($>&4?p29!!Yn3OL(RM+bJ&7U zZ$4k8kfVGLZG_v9vo48u6^&7nUgywUY%FO17*?w#-srk@|7P!a=FBg&cCVX9;4$or zk(MI+Sz{P?FIkE0tT!Le;cN>wo0u)jZ_>>-eQ}eQTXP=%s5yw@m%1oJA z1w;B>kkYA!wdpUaf6~^eZPvPFO3cR*Labw2?+)RdppK&tK`q_r##>S{ne_Xale)H5a9Nt+b_%Z*|X?cN(-_x-_iV zAxqkkmZi}X|L$wmZI5GxJr=-Qfg`rOTsb3B^$~Cak*mvIk4l^I7|&E!;oKhml%Uo) z$n)alrKq;Gw%%caF59bGw_OMJ;n((A3|(p#YBo8j1;nb+bJW3uxef5E=hx_PfC`^T z+pRNx^9unVXy%Xhn2@ZGYs9}Jp9nt>2qt6U1tT~JSe2Br>B-MctO)6by1#KZCA#{ZN*>?Jo(Snt8E|ESt~J_=b7QD1V!G1@#w=ZN1Xp@l7DMOlaXhZOt$&g#7CLvcH8HlB+BRCSaKe-tBgt|#~BLm zZfvwPH9>YjV8)MwZaN!o!n1)u{tp7@F#UPfb*PEzvO`dYYJ%~ z%w2%t`%hDvTTXTQkJ zFUiU#URW!=j+d1{3tjV!2Ci`GIBZQnVuhnn0iG~`9c&dlIl*J>2V*JLq1m*vW+)87 zY(P&I<53|+FPW3QVZD5=QirO-$}0ym5&WkEWo7+gA3G1W`zjWS3Qbr1Iudud6`o^F zw)~YuivPF_mg|2{&dgv-6`56mO`{aDmn&##%mJQUQBiR)Tl}P|@oe^xn-#tPY{x^~ z7g*3SRjpbkDRDB&8^$BM3U=~!D7Ym{%X_(|+gLGvLRa;ieg?gm(GqU| ziyR7>$KV0&a7JU@y_alJHC~$ZwT5}L72L! zNMEUAYPX)67;}$i>1BkXdzL zlD~${S8ZF3Hk%bb9mp~X$4LDI^{x)@<}2Wyiy8?VFbKJGChr4xa#?X#u=DOl(-c6r z8R)wG7yuw$m3=CllD0-7LQyR}HA3$Ivd))Jx<`yb!CjDJ)JuWqAm^M)7S#0#1loZ| ztSu413&A7C346rQjhAJ#NR&y3tK-zz1@I8ebDjxFa(!(4pMH=;{XSCbDOJg&;rPb} zWmkZtZ)xh}y_xHeBqOB(r5AAfm$x$~xGaFC&qTrRb^Dj-afljk9=&5P%xw>C^+RlP zO#1nuMT0ULjLRlod9*tbaZq_EjdOaT|X3O$aag}TM}>YLI|SZ{vT z2v_IwxEzQU-Lk%#NDYbe-V}m+5PtTD6R=$pT8?KB)J@w0C^-^7wcK>?P`RIl<)Uo( zW!?yrUbG;l5Z`hp6*;))a#^=(DM)CM=$H)UAiAKBlv@(s@^Z|88m! z1pI{nARRDkJkTK>`B|*6Lq~O6zx@-v>oc~o9OKi>?2Y_9d;oCuNz{^0TPZICoMbjV zr04=8icW;1m^aoF)4r!$8$aWn`lDypnLdAR#d!ia#5snZV{2G8r;a#ABLcd~Ft0&7 zmM0q(Lk5K8{ZxBq-Jnm>N5i#=gaa)zKFY+AMmiGJ`iQtVGq%jy5<{{J*xhlp*?2=2 z5E!^WHFxuLbM$Mgw)2et?5xh!?C!Nrm#-=YG)Twuz+ZL{w>8(BJ&_9@>L4x%+;Gk} z&3zeyb??G}5Dyn1qe7S#4|w9W`B5*ayS|?bbad_w7g9iiCY)+W^g03SdTr#eN((79*NA9_=DU^X?#S;l zlHx3XlzAjdzvy&#n!9eT#pW8BugIGYgMWnb&8|_~n zLx9aqb(nWVP8z`;pL-}B{x9^FlM~06Mu6TLKHz3Vh`<2#-$%scCaNFft_8OVEXod# zRr}p)Fb+nL=^SCFU(W5&e(S!KNc@6K#@hbF+v#T_%A)`ap*655`L%a5Te};TMj^j4 zf8x4RoDWcRftNAEZnP^U+k$@8&z8N;5`1l3ZhEw2@i@O#mwBr`EFPcyWZ%kJ9|l;H z@>(*zx4fK>D-<4RPVt@1qFQ&Z1=dW4`7Bf(>Isu$KlaIJU47rJ)~;m-NOx&&mrsHI z$G5*Bc2$wdK#yH zvehHPPl3go3nqy$5fbRbjPk6`sbLR;2#voG{7{8GLH(GI&wj?n6I}NO(Phqx{;!5E zsX3Eg9C|9xN)TWqw(^t<%j~?9#N&TJ@%2}LgvNwhW?!U)I~*B2_ZJFnq?-}}`W=}N zl+x|O;FANm$vowmk4&NaJn&lI^-vk5Tx3Ozu*Ev==No2=b-)Fg;7Pn+ig`_#hO*eG zgZltdWNx?7C5ee7rCEPcFlNLP6;?;we|QaO*Z@JdPzJEV2!cnw$b#ZCy$e9lm=gDb zi|Lz=#H|&WtGBMUs=K|U&Hsp*>p%hAB}4P#7NVn4b+HwcaI{sN9Cgp|@~KGRb?P7` z+OJ@K*e=A5H>DpA^+^aq$4E$VMP}k=$VyoAC4POeiV>zD;C|aooGEuEh?AOrDl+bv zDyDqw(<-uwZ#(#cFD1Rm3=|$e-?rrOb8m=or+VH$>9K$ZiiESSCT_@=UUMmb@AX}? zS8_3#8C_he_`oGw4$P4ri@)wegQP$ha&mS;c*W$gO2Y-+CxpA^Ea&!f15E?QWHvE) zxqqA{RMAFBPxSrBZav;!o?nmTT~>wT?X>R3>uH96d)SQ&q;^VUKu8X{kPaW!_Oh?7 zfWW%Z1U~YBZSLW8?i@c`X|f2VRiPj{e0;*BMwW`*xC5D&0WBECXr4<_*taV6(+x8lm9IC(-Iiqs>aw1| zws6;lY10&0+bwVT~e-mx-R)$1TkB*uVp;`Iavu6UFb7dc{;JK7OcMlUYJbtYO@bd0*r` zATKlVyaDKb6yU9+Q>6@j(-gpo13md(_8hTWkuK4TQ`<|pL3z4Pm-z1Bz_-`(qTVvmjO^@+k+gL2H*pzb!o zU8rrnQrO66+oB27wxB(6Je4$s9f*qW_GcY{8+(qWdW@D6n-v|z%nWb93O=Usfq3^; zSI-J$@AFaAvjwraAh~)=%yomk4d&k>yrZqYycyHvY%*VEApnq36fAf^pE8t(mHuFfmP0}NB^I_#vzg+ShF=85 zAG?e|%S-g2WF4J-);4CmAV3#yGXyQ>D|#tP83UML39JHPt5J;>q3(Ax!cncg38%m> ztwVSFLL{gERZEC8>eV?~Cud%2A_?5~FKIzl7A(BIm+>JaHF!-XvrKOL&N}B#RXxUa9QaOUs(9OEL1b$ zcBcm;KuL|fbGt{d=LepK{(5sduU*H_VCXK$7!35ztNlF-$&EbWa=d4cPHv&YXN9|s zAG~jmnE`1$)CNFMuQ!k+5sRmW;c`3mUXTM<2Zt`#a_bPZJ-_4lEl1a{DV81)a*!eb zA~mX&qqTh#wHmMMmBaYmAv|MK+0)RTlWvoT)Z|mj@&F*bCmhMePZSNP{4m{bSFc`O z(*p3=?HzA2wbbu6BJS?&b~4+*xe-T$Ew<3GUdoep^boql#}=)q%xW-q!ocKA-sezU zJiL1l2S>*R7)}^uO1&+3Z>$(By<~8x@N;=<{6isL1SMbhv9_fTm1}F+zZRs1;jhJA z#=&m&NdjdweUd~albkTdo3sJipSWv9WuyBNN%SCFO{Pb?Ayr_C?4gXPf_4F4js|3G`kGw_ zf8WH@OE2=tlew#$kOW<*iOy!NIoL+2pa;_hDYMY5-$|*{(LtP#lKmH;cmRjwhdjpl z^vaT%a%p4`^4EH5DJGplXhi-#P(LUIOFBDU4pcviw9KulLwL2EN3$6K)(LSROJ?(6 z$0?C!Z8lI(%lG&m3}97ZKZrfU=%32>o?>iDbl1q(9j^=yH*Q-fp}1pImMKGV|D@1H ztC=Z6cA<&R05`Rdh_>5&b^l?>E?9MsuAtz>r$ZBoPqK3-{6oh$7Hin-VcT%MTIUO3 z)v)q{A`Dv~{Dm#&nF2eh#>LHJ#Z-f@>XruCpi(VI=j%E;VrQ2TGB-Epsmgy8k3Qo8 zFDrLKV}~pu5QHh0Y@Hv0>aAVU8vGZ@)zihCOP&VM~O{$>UI!c9%wqN`*;XQc1#9cW5C9?DWt z53L{jyKikb+=~@7K#4^IM!QJu&UaF5;!OQC8)7XS?J!aC){pfSb$VUe302xkTu!0e zk|X~MNvix-U}evQ+zI&C2{ypS(%8IWz%bfCeUJvR)-RK-24y%#?V*0CiNZ9)E1 zN)jr_#X<(x#n+&RZZFpDbB^#r1N?cEI4%hcP*~If@t^in?9qRznc-yxE{KEHb^L)| zc{n=Y$U(n61hi%-DZ`j?#CXWhji0AT$z{TUVjRSTz4A_YVyQD#Ag)rt5&-bWi1%L? zPFZ!=-l=t8IN0-jeOM@=7zh-kg!dl`9%1rj6EP+q%5qb9&bJP(tl8 zCV38dEY!N)vj;$h18m@3Ez$A@Zb8^3ejUJ{6d{K&xKn9DuL7C`dCNb|s{vmq#lT(P ziUvee1w-*8jQ#rOit7!3QOyFtk+uarKID18>kW;=p32g;EHbVyC%i}oTpe*hh-&+W zyY$Ct2A~xmIIPgg8Mu+*TL0;$qYbL)3Ibx3NMM|ud6?kl96gMIs4siOptbKqf*Vlm z02&hAWZvxX@&|e2h#Yc&BIu3p&+t;~eUXKBJjponQEKD}_qR{peGxhx>+?)jm>Lm$ z)&NiTZuB%30?T^4(I#@au5B;33j^^zfCX%{MDjF!6}d9AogJXG?m;*!YhRlhOFOYm zR_k0Qh;YK#f^0rOGf-E(RSsolXOF$u>Y_^f+4uRS?Zwf$qLrzF82CRy+cO7gGJ4I$ z{#9rP0?4k=o-a!HAq_aTU~zVw^^E-RO%|YHBZ4f1zz#Uj-l`*G{%awZ zm$L?qZZ)_EI>v(0KOgbmmm==49t@;CCf*X)VTVuGW6X2J6_xwWCIm<@Vaj^ZT($3m z$NvF&2izkXkn?icx(^KI+#E@l@9NS2IA+Imrv0W$CVT<~oM;;Mn`h1n={V;Zk~Igm z`iR<%XmLB3B4rkydD&uk-)yhi_Ku}BuL4+s*Vz>!NUDiP!86c%9Ix}8x!Y`%#6NG$ zDIbvgC}95zQ@bxC=7HMOJHko*B=eCvbK$!lYx-B$&yEe|a^l078S&v#`8mP?st?3d zwv%{(xCwCYM;peyK?NTgoqr0}#C(5BLu3!#aVOL4)wcE4?{H6m^d~H%7xUta^;V#A^I%s}G8fZY#*h2+A+MwRYg2T4F|1`g`*Mc`r0y`gJ(;hH*y8 z6D%OUlcKUeg_%#0GmDIg6ocF>6U@3r{(@Dbt1821T;$VczNSMYf%%hQe2R(E4`RBX zn|#2S9bqxw9B6y4reJgSRGx4yYYW(84urvR4!$&|@@b*sXy*@}9BUDb`A;egvNI!~ z`F^1C%86jj1SV*2^;lU6QHQktK$zXU`gl}xe=yPui?@FXY`yS`s}~Pf$%o9pw>|m!wn6II!QpX#* z0=3(qgH0s#l>;4Ni>%2IBc5<<5QV@dAUsWFj7d~McGPUiWJ+Te6_B045LW)lQJiF>QrnCoA$)aUanMl(Al?D@X%b>(7hJuA zYHqvT#&?$`15A=8fhHe$Q^*TZK%5RL zArP8XgsTC=@4~iJ>kN`RZqjIa!Z~)ufC2nhuvWlixmHamLkV$Ou!v)x@>~%X_~apB z>&$6N0FWGkFR89xz-Ki!tcD&+b2daKX6wcob}Yl_v9s~DRCVCF2GjM|=??`?K7gG+ zur-S55wQ5E#g@W)Ee6$+5m0y!+J&F|Ef{^{G!p{W^@|6+4Ama?$dKn)N_!jQzPT5{ zUC(1h92GHkp+I-=$No3_6bC3c6a$Va6O4qZA4oBcF&(ov%8ilSNgx%w@-2Du3m)f- z)-BT+?5~1(-WzQ){Or*eX_@f+xsWuaS3rsr*&QU=Zhy}%c0PL6SZio1EdLcsBw&gF z>2o3ylZ#ebQuw^C7srQ7uWS6-@*3<*XUDfvb-9LOa^TD|f3jdA5RyKu3THa=u)HsE zUGI`PS!6Odj|So^TFQ_NPEs`F<(7WgeaV{Gc{z73OZdV8aqfD@c|XwmEHFqk^K6+) zAXtjA2444S5oh1o`_$POYFdUEfiWYV`uh!|4Lcb^oQUbatK+!W^N66+1$NN1eDxUD ze>*aHbA2#ok|h=JxFz?1x%2(@=l@5(Rqr|I#=gYj#@A^a(;<+tCfW9aa3GY*Y>=({ z=YWsD#3s&i(fPxgCfq?i&}&uc$ApaPPZuYYErLuSkPZ4-?)sXb2gXCTZHs^EvDAN} zoYmvDW(}lW?;oZKBx^fy8c>Zj>%MBcj-F`?K;A7P^_tZ?uU6`*oC>n zn2Dn2%aHGUyr^riL@Rf6tnmO=3X(*gBlNGQZh(6&s4FQo45ew{oH?K` zaDPuK6Y_pXRIK|8Oke#tZRQ2oXU6Lk>uy+jrTrrLv--jobjx}ShJo`camL|2fR(Zd;UyOV1`IMRr7w6W$M+$Cu6wgpe(Z8fVZPt9 zhXNMOsV}nOA+{o|nSoS$YYRvg?;K&vew(&Me8 zy4?Q46K~w5rQ;;w0*s_~5F!=S)$Y6;LV;c75!F0>KwpB=pYo~wU#dSh>bRPp5N8nJ zBlAr9A6A&(Yk`QZ|Inm$C5wN}=dd8M+uN|wIx}nlGo<1Dx7{3FoYcSK!2l}jhnolG z)5_+eh8o;E>@*0Xoye#K=T8@|5jhoyPZw4?{=Okm5`V~N^0*2n_XA4P126CE)YNE{ z^-#Sql4#bv!hDx+utf@7peQpKWcM7QG5&F#J_M{4`nqY!%shPA&rc}AHr#L%UrDPU zIQLqyU@O&wEg#^ka7;0S3qSrdHKzpMrcuR;=GR{ua#+!SFQww-qtIr44=iKYg1wZg z?e&{=r!LiYWo95a_E%a4$XAm%Pk@DH#2qgz6{Y{}RXKEi4vNdSTCbmLXyqk1h^}UU z=_9V!$%e43Cv0C(S&0h1&44tnUEJxizD0O*2xEhj$tMwK@=ioRXsw>jQx9yE=~0r8 z^x7AV*%qk40#@(_@6C@j-f-L{U<(Neot4~PFu@vK_qj{r(GuNAuEz6T3S=FLjhUdn zVRj)NIi+@cC|u?Xz)%9tC`2&o1^}Uoh)FOHfyvOE`M-4>eHIiMv;jl;%tLgb$^bYu zGH7zaKomQfo%g06&XUlaYcb_9G!T8F>Ra7CG{R)+-A_lgaqfos;|+>C6@|aq%JLZE zxMfYg@31M|Y9b%U`JfYhSIkSWt$QkFZSY^~ngFMKC-0cQa%+52`5EMjGh8HXmcLiw zU{4y+z*rBeXklGIrjBm_Qf&hSD4R`ifRg`>{gWd_u&|n#2(prw!yWApT$H-$GgvLl zWP{103<~SaB1DeOgdg_kCTP~y@NS5Jvn%X17t)eNUL{47M#u{3eiIIk3<0jye6=-5 zycYf1m!sH!_vzP!NLl#8@8HZDj`6L)UK3IHI7T`9ISsy-=K=4{Y8(qn1cIFo@V$n@ zASmZKPlJrsE*rGJmmI(jmoE{{g(!kKp}Y-z8dWlDXBtLh|_-zdRg&c51esa@_rou8jca0~*K% z1ckE=pb>#IRVC?G2 zy9orL||^#sl~qO z3@nu(tbD$zl??&_jAdVklONrysBA^9dpj%IZr3yfJr=893l_9f5L`gJ_jbWE|C`px zy95uTpX8_@7g~R$1I_LX67y?zT)Fd$^vi1L$O6f1`ipAd^ALr#e^bX*V%!cOW&knYCz zp4y}l)p+x?3blGrgX>pTeCpU9uFz8|(PT_vQG1p-7Uvtj`qtk5on0^(Px_Yi*f2qf z1Q91aoxq&eW$HU8qzOdt6lMk+W&?L_+7rF~o7d8Vdy5mUKU|!M-+PgQcxEbWB~P&g z9O63pl}TkBP=`D_E}#M?9**vQ)qsDPKkEdD#v{O7sU;g+#_USkm$pBfj&_D@f0&#f29I)FlPsc z`Cs`;YK>l=?W?e8ayNJP-jC)J16xmmDMMU7+$Ph0Of72g%Y*qZ2^(a2D>6wz^Qe%Igs%5c! zDEATAR?zJTIE)TB(ghQoo-C%NawCaoinkcdZ!J{rp5wUEL32oRoeG0eeX>NC|t^$cq43vnbtM(5*c( z%>ImeVzhnq4dd93VXi9o(i%@TtLd@8j!5Z6@Gjxz!<6t8 zAc(iQ-R)cSnfu&E;Fb(J7fg4?ceIoI2~r&*d z+{$e2w+gt=ZlMb>P61e+foL;!2o@}b7Z;S?fstF=g=#fUFF|DFxA}yQzSa8Quw{h| ztp+mKjlf3Xx9RARSB#fO;MX$XkLd|108I-il_lMg|d=<9!ysBgb=?OSeVP( zpUeCHGr;~N@3)BV8lpXeIzBQ3c_sJL&5SlSr0@pBGTh{)9M_edm09PIXuXWPqq0 z`#uwkMWj%C2C({#PsTKQRquq^uT?m}HUnsMG@_F0!IKGN3+y%)V}b|rEKRk252T~z zV@jYjRkT*PIJrFiN=yI?paHlTf)}3OVk0!S(s!hP$~y;Zb)Q-MELM>Fo~04_5Q^6# z>F@?Nbg&)Mb|!fmdJkg3NpN#wefw1RVkkixdq7&nK4<^`1x9sw4bxH0Fdh{$xChut zpkzRsarK3FV1M;5X;d!DMe%8UpFB~o2PG(>KRU_e2udbQ6`7c1c?EV{oo)0-yzXW3 zwSzzFd0BY%CfMDh$;3lkUC8n6D5CyUmXE1atIs zGZbF2SC}2c*f2;i%y1KeKV*G&l1>r6Hg{IRhfUWQKYcL(A@;b@qV1yo;{HV!V0J+K zWMe=yHYFthiyz}|lxs{!xxM0I^w{3V3=*gP;dJl5*z-aZpF|XZN&=kPf+Ui9v@n*G z&*5*G@l>xqcEIhhnUAv#>ocLs{7v0#;Nn|tx$Z+R5QzvNNQ zFJ&sDJ!^=eOytgPn%`Y5)b$i#il^`Fhe^0mra;8wmKP6811ri@i1CrGfqFWLZdBFu zhPN8j2F7$^Ldds+xcsu1f&DbKaYeGVV>6t;Zsp{$=C4%AHU5gDTf4El6a8oMeg7ev zpwg0n0lp!&NF+E`h6DIm`e|JQ4T;)Z-2akJVXua#3jeR~#eBm@hu&5MOalolU$(+3yi8L)6~z^Ea8J)-ef;dl0ywq(wt zW`F^ywh*$PSkQ%?qhv#NUI71B|32BYIbd75YV3%hXp#F=6+pY9Q>nK3Tl3U5_N@nh zNM2Dhz-PY&8MJA3sZtFuR0?-S)M8W$87YJs$sGyFEj;ycB13&rs)=vu?Z+%*^v2Ku1oDV8d^9Hm%&@U|^O@ zutyH1h1J%7NS~uwk^dLz(_V>gf`-Cz?5QX<>;x4h7tnl;ik3yEhI&l&g>wurU!$R2 zr2_#9n`*niH-OKC+gE|d zmfmCVOpcowOW-C!(W&+x5rjIOVm(#=pE~`Mju+qa5JG;h6~G)D4uAL3o7zf&$ttW9Vkpw$~}=#2nLfcGH3$uJPa;BW~)cg)v7WMv;z$R_^8 zMrDfZSz3v5y*i6ixVqS#)qv|tFs|8LFPw3aXD<~a5)sc?sOAYggrVwAF3_}_g~S@ zGD5U|G~}yf6uT6p%b9+r}@hX6opz{a`a z9m@+wZCx{bKLiA{nWld9`%%$vCL&cy z%VY%CbU}&Z<%cIUrcTckjR3}-iRcGycVZ;|B8HBg@GMru4^1Yzcm^p-%eMO$Ik9D0 zitL1Gi28t)40d0DrKV_rq{=ulkXt!A@kUo@Wa5e}t%o=JdGHnShc)k9kdKg$P*~Ym zZNUq0J}l~gxEzwTPsETI*Dc$@YXk%o?{&^|6XT^cS-3O@@!<{djmY&2&zBv(vq3CH zM&4Emi-6`cB@wfV!U<^&zPiWlzk+$ckCf~8L+NjMFaKEf+kw7&5$!CD1>h2a3A(Jl z;%0G>(PUy16#yB)w|Q9y5T1b6J741&GwJY&EEBl7Lb|_{^^lq14}Zx+Ar5>GQ|`oc zxjmt$FMyTB{D7?3w3Ijd=Tidyd^7M69ZRrIOM#VHg0&S$Tp;jCydNN9KzQ`i2F#)? z-f1QgP=ORa5NESFTl)0&1}v-U$C92=b_wR_8o%7Cd|Mw9;q-chI`=+DXf8 zJ=^BR=XdYkV2$&aYJ?O6{JPqb5LUZ-Y#kW7f|DRW%ftY0 zCd&I|8QpBY=t(fC00=$dw~2jT%-mVfmGgLuK7~Pj@{5ttq)PhaRT_XW)VoT+;y7;9TJp~Bj(@sNoZn&u z_kN~tDTKkF&$a}Z2KKWuqO%s$V2i;+zl43cMs6gDf?3jUAB&6GD_#1xNCQEr2ARiW z;tD-H$&8-F44{gET}~j|p)DrFENlT_ko!NUwgm^Hv8&Xxv}e2kmCC4wNwT`v*UjjN z!7`e(9u{?}WxT=L)yVD#V{R+mZ{87d*{Ae6mkdpsuXEUtbZ;|NrmP|nw&7QynuBw( z3s$GXwu_Q8*&}QNYQeMHn|CpQ4B`Xyrs)(9Du;1i@m7?w|At5-h z?hUl|SRzPFOJlkquIyyjk^A?kKO9)OfLG{F3S*_FWWN7MQD%az8`!~$%eM`)cq@v# zY8mA;pK9yio!)!@5B+Z7%kGXVAwWp}c=BvwD!~!e&&$wrpTij3kE}C))sAKgNNY(o z01^-mX#Cb{lw?MDmf$XK`&F@1XfhF^$I+M!&hc3BjfN?tQi?$x_x&7a4Id#wflC+4#=^3JGMQb5kN_{=<*9U;ZcI>l5auNm=6Y-FM z+sic;UdKh=U%UktV!1eA>Z=c~ydYi%HH7#6yNSe)+w=MsY5a%Vn<)Y=0 zSqd)W!+|QMs`1(qCf=?z5@te|F@QN-ErbPQlzyJb?w0R=EDYHC?}0pJw0NJ0A^4Vo zRw6eaE+Favl4vJWJMBMNp2*3(#J@hq1ONB~B<$Xu(8J^x6K)K!(gC0+O8@|{8mvm0 z!DaRv(Ig47JRlTn!i6J&FK90>p?N;Mz#;(4YWO9r;`S*BDKD>9bpa$m@)-DjOPSLP zos{pdElD+^iyt|&QqeRSm6`zL2+8oN^++{?P9Cdd2|&HigZEi#!eB8pdgC`nqWo>P zNpJ=`oTT2FZLNJva`gJ&k@mUz0Y^tBR>G>u5)7AaFV5S!|9}r|AD~Ncj7VMmk02(J ziy)}W22@C{BRK_lLkwhY$FN%S?hrLbvQx@T%X)6|J^;&I$y2S(xbg_81ak|1-_k4s zZGVv^ZW0hs|335QFGs_RdP+E3Ve$uz1c54%GxB&{?n#4;=aUWc-uXD!F_H3G%^t$O zvD`B9A${UEl!U5H3ch(oB((XR*zp2sSJFnNf%$iR9#TX~rNbp{(H~V^F=CPx6Q8raaB? za%EsA8(e>|}P zj)-NBTnL&{sy6=wn~@}4e}XG2l*E}oY3F8y=EsZQfdwo2AH^{@dT7QvFwX2@~ZUt{BaqsOn~n_RpnA z2#$Y~^}x#yF?#%a**-kzGIE44h`HH)L0AR$6Dk9slDn&>dg&XVI?^~>NG7QN!34m* zjKxdB83bbYW@3^v;o)mZKELwMx(`;#Q!jYG1ly1p7n50j5FB9#u!0QMarj5~6-&d8 z+~cNT=Lwoe!P{lBeD{p#keX8EZ++rpYz)Mj8(g2-(So3WRDd_T4sOw0S!p;3;0}LY zeQrl+!P`q~pj1~^R7>*-!CTo+fL3UkW;*yop|R!fr}{j+mm1|3`WXp=yu@P9(K^r4 z{6+X5M?PR?#}>>~>XFoX{ZF!e&$^gs_e*~CpJl;sC&qB&riwV3-O>OoDd!t(vtLeWdP~NJr2Y}xt>ztT*{caOP^Z}Bd5%Z2(V=9e$r)moi38V$^jQ%lw zWT6n`+r0uavOlH|r*|Zwi=_?~B4vp@oNtzNZi0M|fZ z28~h$0g2zx0?2joDv58+wY^vX#040g4D8X_m&FA?$(bwkDLpFAvotYS)#P=H8Q;_=vl~TV&nWd(!^o`W`%0PRv zulQBNyz=>|*~r7t??e2NYVKttDrFbu`5K-YmslEuqjev?e-jZVZ||gduN93Z*lER? zyk*(98|lxHY#1ZMTE|v*=-R0BgMFea1=^Fcn^eKJYFPYOQJg8;L+sbMhlj)tgAvVW z`qUgAF6W%^#CuyFXTul9_!QgMpYP2~TJk|r*ECNeUnKVKnc*XM(Cq2$ z!(;=^&?B;fMWl-Do8`-%FLO);45BZZcS z9k$NdMN;*0R`)$mpFG+DbVP%(sOF&AL_cKMJRBaYj$l=$4qg}FKJ zQ6YW$PgAIx8>dLX2E!xu4p+zT_>yb|# zhME^iSNi1T7oD}zX4;P5pNOQX@ij}o4(M%_o4~aje=52Z9P`ehOD>ah7M>jr^iw&KC$t2E(}`1HU)hIs)%=k7EnVQ5SSI zRBVr%d@A-^Gcqzt*zu?t1Khwr{B4O~KUu4cA9B-^# zy0y6p5ucoK$|3sny~0hk!!axP#uKS1VXYx@FGm$?-sU;Vo1af>);3Txsj&QMf8Co3 z&Fb<-=L7`ZB_V zPm)}Z4(Nk~A1FuRW+|t)>mWlb^ly5)Nj8(IHUE6>J%*-K=6=I*Cs#>v)SVUWQ`W~ z_OGjsqjU0}xltci#?6nh{T~x5Eefmq5V`vG7zj9e@E1rOE^ML-9ceDgy!uke6}Be0Oo*+fTRp_J1gQ z3#h8Mu6r0kq#LBe14@U0bV(yE-6AdBT}r1Qozie9>5`U^F6k6$5ReoEzkR@apL?JG z`@Um*W9T^df^dGj)|zY1x%RnyvxyPyBeEdvjYZv;5-4%DJM$(g3Uha+3Tf0FKb70| z9-Le{9|6nM^rJo6BhA(va>nhHk%@IF9uiOI9+mGM*$or}$=X2@j)IzvjTEIVRAFiA z(*(rr=DXb!CK?~i%cQEh@Qk%cxSDm=-QnO|KYk#d!#sZw;K%a=qn1X7Zm_PaZWOX4 zbyR)+Q{LR_gf;*dMI>fCWvt~n{T%q^noJIT&TPkADk^1fR)+j1qWV?kqx!!(=4WTk zga_--gei%)Wf!6_b}?24bR7w#u&PRmbE+5B9&KsbZJN)D)wFLtn|m2k;g*4fb$vp8 zmm0w~#2CfGvJS6rVSS&wfKe&7+W{dnPA@viq>8@YVNzV6?!jrm?u)u?jiHpnpV`@d zEo+lUd^?u)E#v0&m3QkpQR_Oxq*Wzc*-(AyUccZ$)rz${*?lGO>w>HLQjE?{(@=#b zAiJU+zCgbHOI2}0Azh55q{GdUn;O8os}nCgAb=NPi#-o4M1MbG(dA$E_R$vBq%cm% z2@X?Oz~To!D-xrkMB|>w#xpwfXRY?vsz>o#~X- z+NSV=%4yMyadqBKtP#z;&XwHqBvPA<81MqOCUwylq`kh}Z>0oRqZgz(pepdL#XTNO zR#>m}k>XD%E0Zz`XRX~-_9y@8f<~g#_`%6paB|x_W%UwVxx-xu?^jM65oB7PZz5~LUs@}( zM%H_5->_wcm7vRm5vahZszyadprqz6T0kJnk`|OYbeik68xq1!KmM4bp?z-I>Dj4> zG6^Lo2~!QVBC-jKvY^rAhM9W138mNDc@vF;pUG+#vO2>KJKpJev#unJ;gB3K;Q`qK zs);=Ap@sP2Y#t$gzI zRGd9a{cfKhJrB*Ndn2~V8!}@2momS_6KLv>N_g@=B|DJh+$`Hwa|Kde?d}ekTr)^Q zZmdD#!t2m31=aiO4~FxX-e9ue0Y4pk6sRMP%hXz3Kx zRIbC*`1K>x?Vy};<;~S%wNN_dr}cATvJhtJ#?~sNMG?0V_uoi4u1lT0G%??)&8Wa# z_JM(c2OiB{@`J{zYReb#q$AT`#RBJI*pPCt;la5yRlUEwMQSRR@URdnB36Y(?vMQ} zjcY=+Vf;c~r}Fq>-P6mTygfujT+J}XWSSWl(U|UWsCsBs!#COqqf6$ z{FP{2I_vGX-06kw<6mVwpRr+@i#UcYT&TEe$;{Ym014#i96;!OCgiV>!sf8c;jl>O z?D1(Jrn+5wo0OKB9j-_*4IdtdfI8}3(KEWbxu_TQiVdZ>nCD{}->=!pt1c3n=S{>s zU3psCT(w#Vtgr1P`^wBphK`+GI0s`+)2r`Ch>w23Yxnfz9~~tZ&*}^+T0P>pzU$!} z>L{-G1EXuRVtDf>wRPkP-&Cdbv#FM_A0_9b#gr>KlPqzB)5|-d5HdAGHRJjt{A%Z} zm%V*E!hzkBWy%Ga&wd+w1fsbbu#u+=uEdQKeZD$=(eoVHUvF;#IUw6^7GI7c5ep^L zE8)9Q+Lk7J?61?HoC?}H&(3N4_oR!9O3&;i310T@q}+xm=Y5PpA2}v&;uNhDMJL&8j>^G=+4w{K`nvrV1;LF-NK|-WJZfO!s-$_#Q&oW951VVrp*| z<&qWs9^6|=3R;zTB^&yxwePIskBe<*2qfCwT)*ONNhlcS?s{}@%NdxpZC)s7SLxtS zqv5CdcDUW8R(3+l*~FB5?voM~62vvRquXCguXhPu+UOd3vrs_d5WP}q|xET^KGD`k>0s1N5t3u~JIy+C9)KcdrPl!T3K#UK( zj2}d|gWRRB_MZA}3qhO|_4v=lC1O9+w%Bj|5?b)tQnmeD+LI==8H(cK4I;x_n!`L6 z>(fT-l{=r!Pi*P{lERPsHV2?)2W7;2exj$UIe}Xt1HI-`?tI432#+a~${SP*Cvh+P z)$T^in@f*1MwP9C4$~NA{bi9)MICc^k{Y&mp_1=hRPp)jR<+*QFC@P^h$as{_82_@#)UTV?uN(9CRuBUP|!{`KK)luf67$ z5w`I=pE|Jz;+k1~`KF{AC`K_&gqXunQW=0-ARj#cFmcOEk(yF4QeO6xW6NOx+ z(zgkkX1TFSmZ}pk2&50uqqg0nTZW^Df+JB+QL^kTM-eMweGVJB>)r%H);LRiy`VN!HjZoDrE{45I{wl8M50tJ=CSQ8 z&p;{{rY9e~n}sZgzBP|u*`l;pYH6w7xw0i8MY&~%nPOoZp{e>y2dx&69SdmjB^2qx zTBz)sRSJt7I#s@l8p#qe%>VFnujfOka36UEJx)GjozYeCJ`!AH#z^|Dy5lvR zsTI24JOO7*g|78nIC%`wk3m&DyMlLgW4Hv2vt>+h*nFvxiM7qI;Xv|v;ucTl0Oyh> zdkMl&q+*UyttIUPuKUsm>tym~a{HvT55*tIICv>IJpjK$S|@G5Df0zgvd~7vSoC;R zBxYc?+sg$7uldnPnTJ0$-A?hRt+u?M4bq(m;mgFnN!mKDh z^GUkGgfq4)r)AIC#E{}qB5%csh^zKorRuf=2t!cp!Z{bEBi?Ujs`%)$ru-%BR$*kB zcngM?G&Xt9uPeHLslE(NILStoNKi_%6*J5r&}&Eb5-eivOAa%7mR4`Fs5*bX^;xrN zk81Mc(rYXoufyS{H_9bGw!-SIb(A%DsP>04@#h2ft1tSMGL7o*I9Dxr9(RHLf~b`M zkQddJibUqc09R@G*6(gSM0kb{s zrp9;lW0cs@)ie%KVTEC?i$yh^gUiTE?5!it`5as8p7^RqU;X<{^O^OiCoZMwDg!zM z7*UT(kfqelJ&h2y(}dIMcym4_j7r~+j-nsx?5C`LR6XU+g{Ur}V5!5UtJk=D-_5f_ z^LWm+hfMfg{1ptx)lsv(PYYubi!nONJ`4I1*IzC%K*^Fo$Ie+ySo+!fYZ%xVb+jd0 ziPbK}uhCvH*gcg;Oqu-!3*qIyZg7I$V}(om5?4N~s7EOW4S0+I+r%d+#T95#1daQO zG#j=OwraJKg5XN}-OU7^(w2HWiK7O+pK#(OfMM|M$g{tm-hGWTP{`Ew1F@!2_h84h z+GuOr12lWTwXJ*qu`SB02{Dpc4+i4Fx6UrnwtBhiq}0b1#4>F7=H5IcR1nJ=M^z(0 zmO@m0a(ee77OlX-C+B>k@sYrB-{kc8e&yX%$kT*}ZU~0p3+e(jWinOOBq_}YCjm$k zsc_3XC?Jca{%EP@%aruN{6;bP)U+I-kNl%c6s}ahUGD%V<`cC*x`C?f-=+C?-?I&e zr9kDF+v2W;p89%ZIC`9}9$V$?>(w9*TTiF5floZeQCn`n@U-8DGF!-!vYGzoB~X`; zv~R)q@=!sJZb&R<{GMg(Pq)y*@`0Y4g<4$jCIUap|GxP;c{yQsLe>B$yeJC-dPT=@g$ zm@hdI?R@d(IgIjJD_aUd=dzbCt%`VQe*AHvm{n0@DUzE0N++W%X^HzPTY%B$cw60TA|>5>Mv0~t zxk#8urr(216xnNHHjr@1wtKb|2`2(QLMxAjl!dPXKC`@3ykRcOo#NtoU|X#4q|wGx z;Xd8kb6)gM7qm!M57Lyh{LETB8XrVY**%Z+P8$l8lS+6vp)fF^^78W&&P!M!Trr-2 z&E_|~qc5SrDD-OY^<9)M1yW-z$qbCmrdXGl@vwX@VUiT@l*G@V3SFK+N%>yY`WIf4 z`ACllrxk0ctmKUxOVYko;sblK%cK4UVQ<0V2DvlX3CYx~a~!#IoFj~e;|aM6b>_F9bMLW8aq*Sh z5{`~v$ZoSyhNTL+>}&u}T!bScqA$o&OW(Q|>c`}?Vg z_Qk~K?Mje*=}1zaJ;YZVC4C;_`Yn7orh8b`$ll0u>iy-j8fHa}f&Ek;!1%aOOtcz2 z)&`r}9?=D#zx3cVVDt;AKXm0qBaKXw@>X}mGj5yYjrJW%inf$9odbI*s+PeAzR1JZ zmRK2=XB26V^YCFXxkTong%-L~kH5{X-~>ipX9_s+pUR$qI?B3K4~v{bP{?#B-_eOJ(B2J;u;V=SxKNsNQ}d4!p^M~|&ji&-Asmy;toki}%ddVO-^crQ zu!ozV1i>gH)!56l#pRf(No$--^ulAbD)PEMM`wJGE@lwYX#Dc@~qgx@fmVd!}+xTZ#AwvKhK5+=NBJ2=%z9~hR^@9ksH;G^h?zAr5%+|zqX7Ap!WlKS2IJl1`F ztj{V5kVZqDo`0=w_xLtv9xQM`F#kd|P%VbaFv;2&<)OkJNsnBL(zk#J%^czB#5gH~ zu2iLO15hv_W4v#>{?`e~P>n6$a*0b*iva`4>J2^jYZUIKouEH6xt)#z-B`8*j0~r< zqIYZk0Q}`rZ^?qGAdw+A+jcX7q+1D`1!ec*h8rW*&yLAhnw8vbNSdl% zK<9?r!8TGUEYmSBE}nChw&?$AZQbwaJ83#hE;32-eg06PjD_Tg$|bAwUNO@HGJ+3S zKa8K!F}#SU!RLF~Ba4i4>A*+i50UMy(^;<6S^kjkdePvty3HWYS9F6DJ61&8+Q_s; zk)7^!BecVx1&<3GQ=jJ}FYH`eF74QsOw@95C zy>k-UmC5uhg>-lr2O}=h+OtxCa9seX-S8hReNs$2UIqVXx}B*dZd2ZqEoFZy1j~fs9VgSq)SVsA9v_?A;Slkxu^3# zgPG6!`dkl!UC+QKqj2_6>DkC>szg(WA4FWw2-B5TSKC8d?$PoXYPB zz=+?F2;FLU_N%}*o8m_Aq3|b{A7SSTyeLT>%tkTEYI05?B~jR8D;>;ovU_K0rzcLbzKm{*FDB+{ie=s37&8?Nj#Nl!MGoJpWq=o zNwbkG!^Ihgsv3IA#tYRp!=Jf=5zv<+p3MLPM@p?*PhJSoYw4Xzs?t@WOC-Edra2D! zqC91vAEm*tHSaZv|I6L+sPNKv#_oHh8YsJ0X(zBW|B=sN(*<;QSi`%T&9ctiMAkf1OZsa(YUAB!V)@)O)x*N#jD?yTCf9 zZ=JiUDAFm*(8J*UjJFTPo}S$9xyX0nMX&GLq(EtJ-tmYeDm43-OO0_lytodP;os@&=*+(GjWI;~%G@fP<01$3BK10!& zz^vxo@9MPOc)d2xpmr z@jVZgysBsezQ1t#^k$kGM-B11<_LSz%A%dWiH}Y5nM(JX(e(gB8h4JDIKr>}J>G~7 z?%l3Mu?6JtX?&J1c|Tz@tiYS^cHYYC5OH}ONh8x+M{mc=)LXU9;p+jdhz2Jj=ff@8 z84OCbit&4?5Ndoidmikd{<0T8U(ez^IR^(~WLHdz3Y5_pPt?AjbC-FKhdrUIpndDn zp^(dK;Znz!MLjEY!P3ObGfyR(Fy5S3iM-=2`OYiC7Y~{ zXC@F!M<^QGnYNG!=j(M5(?iNJ`8I9#?V|}^b6I-9e1NvkA0dW(S5z6Fr+WY0u`@u&ZZ@5ZxtRtWO?-O@eOu-^ zDzJm{sWJV$K8N((vmX@)yYyc0g0jSdr^JH3aYB+i;RR#E^@k<&Z`K=pJ@vLLzc%^W z^iF#}uWX#CjN)+qcu{aPod4#0rFf2W?>F(@ylkDcYG560MA;(p1Si&Kn6U68;r_3a zH|k`V=xIY;hVk}`5r;`2$o53CN?T-tJ$%EJVWX0B0o@|=80Glvl&rh6+*XVVKXA@4 zP%U(@`1=ko~KB&<|35^KHX{4(8Bm0}(M zAR~Q>pO+;@heVhRCfo-HIa%vuRb zAhS2pV3{DgXpYxk2f+v$ZC;9&ug1UD88bTw71QA~S}k+|y6uS8UkK~>{i*otYbp7( zccG`8#dn^O$mZb26&Fpsdn0#5UI)Mj6L?}d8}<0NkB1h%@_ITZ2=v2mSlQfZi#2vZ z?=q72LLD^0(K(2TuR$`>FuuiV|L?GT&EOc|WMh$PjZs1 z5+UF3b8r7$b?3E#hji>8Rp$GPyXk_4cgJ}&z2u7U!iST?%F~|^Th@_(N;ykCG?dl& zb@crOe(~a5wA%h&j|?8#z!g;{z;s_`eE63vO!}=nGBtr>%Y-d21T zQB8ayL!Ye0??6n_5(g_xr%S4(k3fYAltCk>ZaVEyCJ>Zb*Bs`=%zn2O;XE3a!oV%C z#==?al-0qEm84S2f!!U^2S1@2>5bVQlb>fY(@iLiEO{3UKb)!4CWcdymE8TAiEt4` zooZj=mV{DlUgGDYF*$Z`uM^$sD07wz+Iu@h#(tu7^@p2pfaEm1V9-0JVgOF95eKgxgep* zuE)J+b;nE*6d1LJg9)a)0_UGeeZZFZwI9@cz7W)$wyTO-_(3tI9^6mhr{8P)!+1 zbg4D$;4-`nPw?8$yT_EVL41gMCcb4_(W@yi{N``1^KITVo^(v1A4C>>xs%m@Or*S4 zwAf96ge&zmqnZ3CN7Bwmm&}o(4cF0u>ScTwN%;`J1qE2246P zEE(;Utsiz1Oe5{eCeY{#Hj!YpwqO91gG#18jgOLLZUwhsp#o&o?K^K)|CzLl;nlT7 zoh^g_0*X|LqF2zFQF##R4dnI1hcXrJjn95qpKlS+BKYMy7fEDDK5tox z(qT9$k|NKI)qgbHubc1e`8e(G)Eh>Pc0%JKnW)QSJ%RA>2JDdLi2_yIWimqVP zI-@p5-Tw%P(VHkxxfa|wCT8aTNmql_1(&;>-uho^pYbfC8G1 zyM=MKJ!f}PWCokj+GeOJ=hiFMSfCRQXkXv^=+9<#-=fVjCQEv^rZ-Y{nDmPZ`JZW3 z*td8z09ml^vKzyR29lJEgs3W&&v==11@Dt_wNoX!Fua6j5?}b{eeadgCc1E@6>vAa z`8CbQm?-y0U$eA`Zs5!egWj2y$F6L2OQrf%#PyjB2arrU$M+*Pg&4JF)UsaA-4TXc zK6fBA)}nAQ$Qkq(LQCc458Lb^ zLtDDN_2Vdn8UdQu-261l<)vZYWxR=n_*}F^Wpy=^&XV`%*a!hqI4=h= z&2QB&EiDP}2CO^tCJ01wHfP=J)SG#Q?bNukQ%LeHt0eRz!0=^{N`kQ_K7pQ~Axx1) zEKneF^bpss(lpLUC4TR!6BFf@1`Vj}XTMm=)_L1fDO_Q*@g8AQ%J)8eNFlsp$z15% z5Wm8Bb)?e(2M$)KgI44Bnd51V{JDD|S6%JD+dgxbQUzO+0zI7CQN3pp<8u{8bNMI{ z`&I9#v)J9&E62G@8eE{&i#JLp_78gw4k>KM?p9HmH;xH3G$PY#5N74@2WSYHcgX_% za(gGzcKqvJmx}LPVEpgt$YC;#pm0TK#{dvMAENYV)CbM~jzU0hY@5EE2wfqp;Pp44wJnX6A~d@6n3*d)^&cic%rE5Z=~SFr==I;?v`Y z3NXzwQ_xTVlm)q$$EQKq>WK4w%jA@QK*0lv!4Z}9SC@2KiV{`-zXD+I4P4k zC0`ks0L&Byz@X;WtEp#tFM(Dc)F1m#Q~5NG4RbX( zmE}W*^QpieqEL){SEL;2GxWtunw8_vc|6_@^s`>g=lw{fFsU;5S0=jC{uc6bMP2H_ z#FEQOn#C$e8ft)yVO!qw6Ai{t=jB3b^nOc=EIK;vwH=6g6_r@=hzauS-7Qc7Oal@p zj*?*V)jp}S zW~wXuZ4n?=SWo!O6++jYledf#pLuxiw9&b5r|SO~#{fxArXHm0b`ZRiEW81rf;gy+ zL9%7QjgE&Yhwy+Y8UV0H#v{(N+hb^a)3|Z*vW@_X9Rh`iViQ|19`DL-%_y)4c%m{% zNqz_vv0^`^{(UhN+5!r#l!Ar2dF3ZzFxQzHj`El3VP3C+%a^254I=$JGW*_n#nvOM z+LSkwZWB)>gG+;ga1XBL6-Th#5~PdpvX;JWGW5t}Yvvqij;$8JBkVfN8THCXCroo=Rk`uW+3}{aZU0X-t?JAH6d9aejIQ zPg6^Sqx9B{{D_e1mIvY*xkxLR*wosGDUF9mslc#CyGTau;`nR&OF{ls(#}lcGYx*; zv=jbW`=ct-6)a5M5B1)vU4WN=k1-p zA(c;Qk1f0283-nb)Cc!Ekfk^-Ydz?)zuL~t0k(#tcJ~OgaS$aQ5fIM=Vd?6>Ru-;c zNyiW;wIdHOs~W>)-Sj=5GHix#Arq5t(G6tU`Y&6Rvpc+=8e*A2!<|TGA=P?vl%uC8 z4@S1pV0vsui=j*3Pg1B=Q7$)dvih}2mFf{0ypUppU@NO>8WX#O2O#J@kSn*l^Y%cw z80!{e)mFeDRwU0-Q6KDx27MKSDgXIV;GdW87iMNzIC-w>nP*nZ_p}GKK~}(9o3NGT_jic z?rO*TBfsOxqHyh5YxIz@)$s9SGZ6jdZlZtW6K`t0+r4a-J-D0My)A! zhX=K0jfJqf^=$OJVl*Ia5Il-@6jeX|%^nQcPQESV0PHB#>A3bxvPwqwk5CaQD$dP~ zEooMKwLAU~>QJPNjy{+&EXAeVh-vq^Ugsm(xO;57VZCGfsw#%CH1? z76vNPH~3?C545L-q>X}Rc6@TzrYc*&^8p=!ZFXt;E&nw1TNRX{ulX3?qQ=%jnx z+TqcY3jkP2;u_%6UiZvZReSG~N$5O>nGIgHshUGnYd>=V7koC=Fz_IY5QMgi6EHxO z{XzW+z{&)D@K*fRPN=l4z{W|IS#tqy4~O#rH4Z^B_g)X{;%wBH)Klv0POp^Y?2X6V zj#gmv8DoB&tQi+JEfl4Kq85sxlW>fwoy|Cur;n6BzdR|P@V{aJr?PjW8R&oOm8(g> zNygBGJ{+8BTL&xs4<>2~RyGXyh+&Ae#5>;}2aao3D5B}NEt=3#XI;gs@s@~n7^Th^ zV}b(oq2)?O^Ap5TyeLsML_n1T*4A!@fC+FWRu4jRw&>YE?D+hB&a-ddAfA;>BWdm%2t2BDIb4k6wu zytHbZQrOg}`B`zP#C=Nsz5Tn`0`MaRkChwbnRuIYx|iQ5p@l3i*%Kzg;C_0JEr<;6 zD=4b{@J9Y>U^%!h^hh8{GmR%wHoT5|_06w_sZ~fH#4$g?@aJo7;NB6TB6^xC93sN@ z8n``Rj%)C(yvNR4y^MSnk{g?6nML1Zmb|cR0CUBprxy(zBR}@;1J7#=F!@yEp#evi z(N)xGThp#=Y?Y|Wrd=ms)icx3C6A;fyW?i4Y3Pdn|=*;gVJPUMJ*Y%kd7x3;Z zIC$4@_HyxX^?ffcplRw4K7~cBOWJc>l}A4W$12resoC{5H3QR~UqH0> zJKOQPUo%Yqt=>5Ppsz8GaL^|Sj4{fLIZBXs8`_~BKM@+^DXWnd?l=^yk$=ejmqlVRa~iL%B*>eZ|q04$kd=yhUX zY167R*(Mtm*L>!|O&BOw40Yt~c9}l$Y8`hBe)z;EY`o&iIP+J!yO=RO!R@hyAq7l% zmZv6S(2C(~nOEYRk1Ck}k+5<=8ub*oi{!0+@cFIZnh?8l!6|*sB>bnFn0}2O@27&q zQss#OoY;-i_`I+N4LA2e-ImU^6^wMW+rHael|`D9mMEUw;iylUTy_@IZC`qu5!slY zeTCzL>y1SZ&(1EUjR53>1J}JR_tNCQ&xz4^UFLf_)Uc!YfZ^~o=~*C3EEG5h+kj8x z3h;^Nto&;@K->X;npfxm$pR8X#75SHNe_xzayhr7K#Pd@=52P09WXJ`Fb=pec69QR zHpTVbCqS0?lEx&AgR>^oyn22;1g8r3nIbASZyId`ARP;t%#e_f*zu~&_pX#CoRyD< zC09Q%(7Ey}>3tRV@8nxXi_`t|)CuzLuTpyZeJ0W$Z!GieJLF4$pm$J60Kdl}bJ5;Q zGzO0y?#XBIx6qKm2EMG`nUkPtdv!Alg6Gi6C?(jzNXb&2kJc>XAQ`|Mte{_~fa z$v1Ct>sJLn1fr`%QEbuo9N0ks%BMhgQ!m-#E9HSyR2}yEKSR0gPl5nc~%>B5aN^3LG09?yI<@4s- z5FE<-GDiL+PP3K@1ZMCH6nVnb4X|07UW3@%#e5XBm~Z>je`1>|bvrbKZYrq=<2&h& z_Qz(>+Xb8l-Jtt~dC}iq*YdVB1GY}kOF)Mk`5R60F1}XBSNDu+Ru_wWBO96$S9bP{ z%q_DZJ_Yq|D|%0cvrk*ww42GN=8+GFXB-rA`|Z{eO;CU+2bKMyr2Ybg$vEB_Y22$; zhtGzftbtWCVU3S~O&%u-Rg(Y(WLPjP^IE7apLy~=tZ=BPk>A`#5u-ZYcK(mDJviQ< z8{h@_WcL>>Dl?{SE;8FxIy<2QUA+~+)>3Lf;i*CQ3d0Xtuuby!hAf- zXB!HX!3N$YF52TWT+ZpLzcuFpE#@b&(d+cUv(S8;ESfWM*TMKPJrcH#p#!9{#MrLh696g@KY~ucTtX z7v+KmfCu1gRMI>pfcICzN5n(}9W{u4B_#zp02zR`fG82KO+J-W6fu6ws?u=n9FIbn_o`$~3-14JBTiMO^IO8U#a(8>Y_#k6pXXmzMNSLX>E zK64)>vIKzaIoxFAD*DGH;&tW;kQo0WXd752Fg8&99ICw`uY8SK7Xfw@Xdtuej(aZl z;DW>RS#r4ClH!6EV=d9WPaVp(@1CU#x74Ee^=^9rGrIZ= z$#@TgR>Ph5N+H#W{J6@8z{DV~cIQUS{qd@dK{WsZ4|J3c497>7LV4j@(G825`^Lt9 zuat|7q^cV53_z6cG^qvMrMK+M=o7SO$rscv`Zj|;)u~j?&^m7FKW+-s;5z-WJw%O2 z<%X;0laqqXk^k<^csTC_@AnT)R&##kJoRZ;r(Y{tTPz)lcdV&?}x(7(`*l+@yay*y6^mIh%af3O#T7n}L> z>w7K|l9~h{ZnCFUWmzQe$DIy#&E}M5JaI|1gfnns{htq5|>BH5=3ONtKXv_ea1BKUyBDB5`3$2Zsyw@7B|t-`b7;p$<(-x*WmNFkGhG zI>8zz-a%0(fRbu6_Y}N{%DW_Hn1aQt4uhAQD#~z3Rvx+oVXw7{+%o!7HxGsW1GKBj zdp3YRg1;$3+xNifgi8QgOJUtlgX0QxO`94nG1rx;@ml3i`Y`|AhM2|Zz*FMz?Xyns z-n*jXfK`PjJ;eao&<1`zQM?M1f({wRs)#q-(U6~IPw*F zfD?NYMQU^#t>ea#-(!a+0)n6FsdN9wB0#LI?^pl?sN3p*&~m*iIe*e|d45qvYJb&- z0a`fq#+{zWXbZaAfq$#zDny(d^Y;jytLMYett^j>&a!NF`RCs(1UFWc4q{12YWG{; z$dCvU3D{xC;;K)$+{(J|6R6#~_YG9>H#JTXe0GJt^(;ogxlsNv0@4bT1xu4w@`-lN zZrc&Z)qqmZ=e}uD<*Z(Ie680KF(ahvRV}+8mq=2r>M6$DBRKSYKpz8W$^#TQ=%RT~ z^iBJn`0sLThos>RF+;-#AN5)1zwcgjzpaL0fAxtcuoC|RFabXNkH6`7NdLL^I)A*k zNoB^rNUyKsp8^AG>asv|v>sxSg3Z{Z}r=~Q} zblGtFcyxWXV7>g0U+Muipn*W~1$ai#o1i#5CeL(9+nPvYT(WCw(rw9{9zk&#kUcJ6*0e>G=(HXQ|WXX%T1;~ns#(JNP zx@qI&*x-$Aixho$_?SbCoR-n3HZlE7XO?4@H)hMI#O!Av_9m(%c2ps-x;~k+g#l5m zFmWwWm`@r;#!%;*fPAa*XuAlcE8v9=ORv3yCM6vk05}(bPXTmy;x&~lD$q4OcJvF0 z7`%Dp=LyE&X5d6ZeKS6vj@|mEE#z~`fn;@&uuwAKRH}Ts@>iT3IITSLk@b}1r)Bq! zIALm~k+I`1Y0Sm2Q*eTeDc=}Py0q^Bqsd>`F-LBNS42FHgrhFel!YG$XH=6dXkCb6 zbzp^x4`(a?hFQQ4gTC>hh=6|R!+n-ki@p)T9=HLAqx@=MUO%N_S6p1&sGTU~(k+4m z5Gclc|B@oqo(xvPH4;HvETQv2tgz$3mi)a&9Du!v9c-(c( zfunp%BKzBa&)4OJFumhB4PSEi1^FJV1q4u*-{zR5Q^0GoqU1=o>M0tdU!)g&--jQIN8Q7GB|3`&E)7M{@JeT&LkHAmSS`ys(Z4K z!S0tE;cG+x5NHRa|AWp$vV%Z}7S>M97_D7lJRFnO(PPT#Yctx@cVjTTh5B~}??Px( z{s%EA1ly|gi48`aCjcbxEPjKH;o%f_ZQqRmOLsUtIF@Kw(o1%l85&Z#WPX8V^}CAj zQf8|QCh3A}DZs*TnH;Drf;A|SiYb0okA;y9@YG z%XqJd1Zrzh2Ga$x;b`E1n5XM_@z|osmU-x%(=H=va`P{7ak0_jNKR3M^}pM~96OIU zb>{53YMSMcO1B+2t$&(d2lWWK((otzDF@^|BOZ?aUWi~i z%%QwOW>ha}1J{?ST$6&&`03~nb!*nIU+~(a(E*HAR9^88xRXR_x4J#Q7ht}>QatipMOtL!HH9Cb0k5<#5Vk!LWv zDlDbyPdk}+onX!duDPLgd|~yd^h5K6LhTom+O@=3Sr;bHP6l8N1TIWEXf}6lw=l=} z<4I?1@0m)Iw=@>LzNFkXa_6b*>!!MHD}nXDe)J#KlzUtEUI~NG4y9vaiuhezR;FNP zPlxfJu=3?LtlVK?x1>oHveC{rJ9Qk|T`M?#she+Qw$AlqHOfZ5uK&KN(7G{Z#9%H# zZQ|_b+Qxy}U(X#rN12GLJ-ACH>xT`xGT8fJ&vG@XV=G}~wEFh9g23UY^V7k>L7t5? zN4xc8v#;P&)yQ0aFI}J3fflq?aQTqDP%t&IO6d5#ofzB3dwBmyi9xYjtNA7#rotQG z*gs?KHtBOQwV|es|7O{!SNp$A4s#{C47@6iC&DO%7Lzu0jY#KM?qf*_x`|1VJYTEt zZ=W>BAzq%n>hTW_YtMRr9IXC!X(D%N@-CU7A^)P<085~N#w4)eU2(>JaF-9k5XOnh z6A(6!id9NTvU|^0%MI^tq|NjzZgthm&6L@a@-8wzAi581)=ZV?O=^8xTn;NUDhJ>^ z<0vBrNT-cSkR@M_0WTfs*Y##kdK3{Jw?mXF!)V1?!!ZfO=$9k6+sjGQWE!6s)ZhxK z@Bp+w*5V*vElI*_ZfGDrS|1d1c76g2O{>f8Z_KT&B`htA7lF<5-T%pXuY8r{gWaeC zYwar+Zo+YGJ`jSzbr@_1=ymw~KN}HeR4J-920(NE<(}_iKD&F!FU}qa*ODJ}La5E= zr2o6g0b<1^5j>xVwF&`{Wm^B08Iv>bgm)v(k!*MDuI9fw8PrrLjAW1v&+}A#k5ZSG zgGgFK77CipN3%LYU9fvYxxh>MIEuE@~&b78ldyp%wD~+<#_AzFR=Z` zi1!zl5e^y;*nR$9m`vFSvdf4x?rdY&50SH4fy;*I3_%whut<`^;5P4q!Xk)PKCSi! zUNOp4px5+IKkjx?71;4KrMKoTV=veejWx=h2rbb4e7O3|h3OB}-k5)^@Z{8_i>gt5 z`a!Qvvd_cNdjW9xx!55^FoCrb!U+)ze5XWk zj33Nju04tx=O-oZsh0*g7Myk&kVic-Ggxl-D(BJTp)lUj&|aRuW5{~vf1rt^xOkj? zgg-zh#^Sp<*m;zoZ_Hf$e}*K0K^+T!{1&`Kgm~j;AFn#Gh;u@2OKV{H6b;m&V4Ko5 zqly4>L}AgG4`!Ecn&(cTE(uPwJ_O(nHPu`7RAotw zjA*|h|8iE68?O3{(P^>=WXkEhL!e*u6QF)j7jF>`dca%~!{Ab`g>k$vEv8y=%) z7PAJQmDT(SoaKS5ts^PfZpnNk8HAn>u`t~(>Z{UawC%pPUe+)ik% zL2@W;{8nK2bHqir!#|wv_b~GdF{it2giK|3bNm-k{@0I+#}!Yv0FW4L4Rv4pTCo9& z9b#<|*NcILL=EXm;2(6&^qRv=2wh#p72t@vP9sC4`K?q28405W!A|J~_bmKf*Eqig z)!$!r&E%<&d~fi+V-jkJK;~Ai+oJ+6SjnY&s`g1|e6RXycvFJlZ z%B@#RHzm9`%eUA0b1*OVL@th8Zq9EfY3;Np*Ii#PHy zIAKsgjKi)=@OoNm2>)U9wzWEruhu|(tMj~|+^O33wxNWWbdHGx^)h1J09D3p z;C2xs$j!FcPLHhaU8{_YhoXYZ7G8sHq`MOFS?IosjkHa0(#`*lI9>d!Qoxn@pYpr!0S->gDbeo`y3`MY zV8wfYC<0-xzNHi8i3N;j2#5{AohaCSc0;gu5PS%ngD7)MkUnRiazh|ujDNg{{}xcF zA&_U3-NKN*M^;rpFt7*ZI7ys3nlO>~9TfJKe>Nb{exzUs5cw;JeEq5+9>*JDd%>pC z%yEsQ%w?GRM1Z471`rSGO|j82btOXSY=kS$KeRcJjp(s-*sy}&-49$pTB;eSFfkntA}$w_kCaE z{eEBfecfli2a=lK*)QLJ-3_gy*hQJ z0dr>kwzVGy?faT{R1d%jX{cr}0GW5@TnBVcf1A9vrOvaza(x#mS6QQlUX+&OiJKG4Ct!`4?n z!td?HQB|}U9%aQ}2hfw_w4>%%>F4+w7+cu5KWrb0k=LFM!YYO9b((qH6Ak)?vsZP? z?q}P}J@ljE7G4=c35`F}wmNK5Wb2{>BY6N{?KP(Y+C;#QUjPG0EPz9Bnh5UFF1sE| z!5ugU1vRm3PHt}QeJ}@G#^Zp=GMnriyFVH~550D-Vg4?cT$sLH{`N!a&y%`4S37pI z{H~BaWo7$tQvq_-Dcs`A(6YJot<11r0#3DpYVvgJqdAou569$X7=}4=Y7XVa)K+?? z&P(&jJZNUr6bap`{pee|zP#>jKq{zF8N?cBsFZn7?B08ATj%wC2W(F7sdXYGevNME zdMW;A^O6mUn6Mo&tvM3-+S`0hrbVGDN`-IFiv~@;%*w>4$jSSwJb(5h$FN^@haL24 z`i7_B0BCK5Fsi-7{}PYyhlTqejnOU~S-g?^W0u4>jJ?^QlNBQ`KLk>%181CHBaf8H z&YlY<#^oAU-5KGSd&+@Kun90gIPvD5d+i?);POKo%F|)!+aNs&fEBQn9J3 zM(|$v(DrkIB93^h9Lg5l*FuRoOR|5pa{iii^wcvZb!+hx+f0gYxKHHY^lB}nAAA0D zhj#h4$$+3t)UVUj^foNA(iKBaA2I*YT?{wgZnFwtt3R{J7S8xfL5(Xdz+3I|o^P}0 zOnU?j?caalm-v6euXPcGmO6+bBKf6@M*Y#=noD;v|bJ|rG48kxyS_P=ZbNKSz7-vovBcfoi5E+`y*h5ukCBgg$A^t)zU z)iB?AuFD^0=ea}dj})aY3F{|*g3X>{h3F4`Fe0{%`11?>mHnT&n0D9c&DI0GV&G$t zs&H(V&Tj8A*dAns8W~j>f-yMz03T6YhZf0O=3KUUFDbX_PJao z{kQIBXS5qaoY0~9$GcW4A;Vq^x}@|r3*Jsw+hNCF0LWj8)KNE~8Uk(H{v#f#Gsm6Q zWYL%CsM^c`qm;mPTIf$~VSR}C~;E!O^pqB^&e`ir2{ZMHV|e50Hy+|fT|vBX_(cfXJB|iZIa=d$$1_kbglGB z2{HIckT_4H;`Dd5-^I&4{4d2)I_P2NSpTB%ZFY4LfQS0MwfgpkKi}y3C*(0IqhcVU zqOKkQW^@0JJ`ny`H3WPjRx7}LR*E(;^w@vJux!Z7)t7(0n9^A7m@XVztKSaH6&f8u znx)R5f^9!+R3@+^7ci~NNEpBUWsPgYB%Q9uHn6m%-zK+27Gn%-b__!2U+$_&wNASI z+}wJ@UtG3N>*dgdYFUHHP`&x@X917xgsl@0B1(?E`5)G>^EU6OWs?--)%cKkFILpx z>XoOTe1b9tjDOzC-|vi6p|pOLTgL=$=+TOFTmx0pcVQD%eC`@nUHlv0dHYYk<5RRh`!G_xPtp4)yI5K=U8aC0k{D`5OqT5dC>bKv zRjM(1Tj2ecF?xmv^0$mid=}EX?$xu6R>Kj&hx#SPGG+dFE{tYhIt};f3X=WhsZ2KkHKf4Bm}VW z<+grzX!iFL*!o1gT7tzDL+vY*F=4?c-dwc261f_8_qyHF40^5g)9I1 z2w?MXFdx^L5;>m+bZ&SCXMm#{S-={b2TLuS28m*HkXYaQLF-MBvj(C2F&E?X^WQwG z{?#SgEs;Oo2AT$_|6zWT=fIPI>KG-BWaw|a^=lfOroWi9n-QWa8sarzu!y%MMM%o;kCC@!i_6?mLl3&LonHDUUS% zhT2jF&-St()L=&2w*esdxB5Ij;H>Fh{21nfefz#T`F*3oeqn+Cs9d5hqv71&W$ZVW z-;Fo>5|kmsDrFheLtxt}1f03%TE8E*U-7-1c-!0yE$jDT4ok|5DXu4i_5Xx#f>lP@ z=KB+d%;wjl&Zr~q=Si$#9f~puxmz`O%+!=Oy>$MHgp|~HoDhVKA5Xmz`nl#Kqw9Ch zx4*hdgnQ>1E?l^<*g__$mga!F{d|rzCOT7IbgbLsTusJ)c&=P98t-O1_9BvdH*M9; z+%Hj()qXzp8alH)w+;5}+2b-*IC{vuc{lA7SkWJkdiS-qy~|;|=l)1Y2>tNkB5LD| zU7Jnt?UmoT4L3T=O9E2H?QuvL83B!nIN0P(hdoLLd6sNE%Ish9wZXfFdhi|#eJ<0b z3p3po8OPOk=sNc~(kt@xk-g~T{ct$kV)>UZU(W9Ur|_e!EP$|oO)#hlM)K!_YBlJt z+uq+FGCx1hm49Tu(`3+Anv&#+FY`!FPp@5`8@B_6nRYq9(vZH=@X%Tg6!t+kMTLFq zg-$V`z2Td`cR$gbCX|z$&vhz+(vkXm5O-*N^Jzv>M-!yKB z$m6jUSjo@ldRn$Iy_|>$U8?3Q-0A{8bZW}_vZf{hMVc$>XZ|)>yS-X(-rHz z`SsSH3btM6jIkf<4EBj`uo=nTI3ipg-9Q^vOzOHaf7!%dQ`SG7Stml&j!@088r(D4 zoU$=tQX3y#C(|O>?rfG-W)igceAl6(g4hE&9B#ipx}pTS?-btc6)09868od9GxlJe z_3K7mPw~h-hn$z1be$@Chg)j1lNe)y7uq1P{d^uA2NeHvw!!VFX%qR3@_X!{4hicw z9>;vi(8HW#Ts8+@p=Mc92CwjFrx{w4ld&8-(F@+O%3L6r}O{Lm!i9a0) z{@z!~_iw(tE?E3@rn9(@UY}86t}$91`q#rBWT1o$?5YLHo@-Nt&{ZFp>`%kPM*StO zaKY3&(YnH6C`NkoC>N~G_Y9Sr#93IZ*L4I24NjgsNwROxpP!$`hl*gA8u*2T?zfIr zLp>$P8Sf#rc4S=lY8{8$#2tIxFdjkulAW0Gp7TD%^S3m37%Rc$eBVCg+Xpsy>E-## zg>te-a!QJGyF|WqpG;$d0;h(C2B^f?<<Pt~Vtu;NBioKN}c zq|I$}0ij0rXB_S`$KAhNN>Dt0oT9#=!45)}@p$_y>Yh(GyC^~g=1lX5dvZc^qa1sy zV4|7$RI2GbXEs{)7BD~&tQt$I!w!U<6ZYrqz<`q+H23ONl`q{;Yr>jD zn&!bZ7&xJdPN7=W^DEO1X6BlFqd1H4IOl$4>2d??sw1XiIMRUj&YTs?pSo^^U!MAM z;Fwfp?;4N@tk*3qtLdhx_PJJmio!wko+&x?R%+L$oDd>klr%4HSX3wWoUR>h>(w zN|oJJATPDBH4D5TfMLj4!=-1vZqudfF*>@%oibsick~v4CSeVh1Y&ch3(=S~Za~J3 zXOxGW;kno1IiK@+YAVF$VXEmC>6Z@SI(5#CGVR7+?C6eD2LuMzkXNu|)#MbI^8;vs zycW$%OG^_oGu5Ct$RsmzXvNXd(SIo9s7lG&^0@I-Zy_NDPkKqOnAzMqu(mSI$`|MG z;YmVPS#JKdw~Tf$Lf+L_$9PXXX+-LN-4H`cN_N?WYXue}ksgqWSPfRcXfb&@cbP`| zBR7#5<`>8KwWVs+VI>>p`pbL0x-yNtFni_K7Ka+I9XJv8$OH0+G0vNs011fx;rQMTwtngA`bUyHCU9 zDdn;3VAmqI@HzZh;~|S6Mn%88JV}aUAKTifxf(U*pEXLpyw*db z;vM5Tdh%_MQ|?!d4@ylh+IrM^YE#9siZ82yJ423H6Anx;mb~nB~-TDmZ2EN)1L3Y&3SS-8)Yw>vZeibRy~v$7YLa|u!23TdJYeok2DDC zPbWav_dj^v?yxaVP6@EcC}lz61{~yDv&t#>mq-IoHgI8C;evq;;gOL;-&$2UZ>7ZO z-hzc1tYI~%URzz^@~-HyDG#>l$s4>!BmC~D#Ya{C9XQyW8}m+hV}-fGicV{8gIRz& zlmjtp7cXw8&^47Bj|z+aZb&0`bgguW9UUE2!?!A}^GybE_q9uJA6Ahb+ywKr0aF{w znUINYSRYDw|7LZ{xxDJ-g_xNEFFM-AAjkC5R#k;PEwZ!Q^gZS@WamC!g8FGq>=Aq0 z&+oX}o!KXPq1i2jXmABzEdvi`8dl+ItNcD6^LSTEObgLr1-#z7xmm>>F4>`|SeAS^ zm|m)z2ZrcAcFq9zZLHNO_ljM;K+?c`k4;@<**D(OoCx)jeiOK>99hG_m(&cOp2_y3 zM{g{=J3&`zoHdZwW8Hf6ARFx7r7XvXa*_tN6wKggh=7KSD(`JBn8e)-1NQ&8Ney&YrbFXX$({h?| z{(SLN6}wHPX1~v%Y`ZbcU|>JnyPnrKS~Fc|%^aMZ2%%WgzV!07WpCPK9%bqt8ZJsw z$i*DzX?=Gn(^kVWOMZ$CPP3nMpR!K~Bx9|tgqme%&%4NS$gX~Cl^zNb$7Gh}K2JGd z*7W%Pa6{xq3|!=}LqbBruqJ@HV5(@G(392NI;2&;%q+i9*s?u}DRM4umaicEm2mpFBshnBt3t|%| z1oFXjjl$~I?y@Plo8d8OLBwa3HJPBW)(6>p9kRoGJk85v>Ckr*f>DPYPoSzdH7oD0 z8a$L~U)bH(a>thDHwn|L^yuHAU$YcI1{#6#VpJn2gnxHiFF7!;ZU0=A-{PHa(p zN)M?6@rgb5QKijt3%VaWI!s_^0}+eD)!`0ZucjC&llpLhv{?xnN-xNiN@?u|^mSI3<>JRA zdDD7Hk=?ZWcGKoGH84uXY)^`Fx}sRT4FgkqyE(sMb@&#<_5r})H7OdYGw`?3*H3S$ z6Gm@!Jrr$-Vyts!jJ}ap^KpnHTL10GGi88g0v*TO{q>5S{DrRO`Mo$7xm72k1{`d^ zH$7pC!s1=nl(Pef0pr}<9O0b7a#(U&+79^y)zR3|kg`-Mno60DZU7eo?df_4ou+yP z*5)#>{vvQ=W#W@3$0qtquToVmw|Dz&-{oCn%s?kQbEmV+qZpAl@7A#$2uuz2WH#rr z2A`@Ct=qjXLQqF+K)gNw$n#s1!zqx#_0LBgI$rYa!#U%QbUov?6ph5RwDY76>}q7c zPOGk#RwRM})c^dgKOQfV-I+z?a~D5&S@pL>g6K4M_Au?Yk6t&1Vibypf?r;NTF z|CZr7mud_z2Z7b!Zm8DZzC})|zmuBFJ0*pHKnF83GtN3cFsDWSDp=}x)KG70`2$k` zdi4i0c079Y2#o^3yB)$AK0ZE+qiHsiuXJ)w#kvsu(Jzj*CI!MM#RlyGPNLL9am=!> z$g#pa&1QDzRCj|0EHMhTJL-1Z2iZ#RHU`rt^t(+r`BQvcL+(XkSW%AFhw4Fb$BNo; zKMd$e0tG!>Nt@TN!jl~Q zuV4FRK;}wJNjWRMn^x4STY~s7fusc3-=-m5e)Fz>3K@SVL-*j6?(awW zKN#C@nSp_~5|xO5K8ZX~)%*RMztod$)|1`2z*^$s;%=L7ULVYSPQqB+cKF2)@3KuB zY`?zcqbqEh>MQ0_W|Xw)k9{D^z()N;d%1oG0Z9G>2iRv;5dN3KyW5Y5!`Hk7wBkA% z2Rl&<3@gmR?xdk#dX{-he9-7P%9<()s}5jc;C`6HhYuIbHXjv_4|OOQPcNPsiLt50 zuGMRm-8&~zDq$4!<;%_2uU`*TGpRP(PWgRXFMPwxo$V^H8yawsnQNv5iS3Yb7}|_R zTEUZR5n^4OcAo6M&wDOn;K6B*N^sg|Hy#h#y1Z$@y9{mDb!^jE7kDf?GbvEU;cywC z@K${K`elrOa3sx(7cWj&KjYsDP}?v|TP*w__t3f~*$pB69s+v6B7r#8u$IkimNC;{aFLP3CqamX_-IegHk*G=zSG z0%?#=)U(nLU#b=TNp`Cr`k)odSUIb#_)=PcQhYb>U`Ui^nXNqjp&rPyi+j<2eqL z_G;d?ii$DadALFBoN(bL7_D{wE-hWV&9u9v5ADrpkzWxEk(<|sr6HuGrYmY`b$mVu z8;fq~g9gOm`iY^}u|uyj*MilXzJOS}%MK!>F?`mE{?oM18Mhs-gt6+oP(VWR0v>9E zUAgK`BGyGIVC8|qJb=UvK!r3cf~CNqSSdZ8mSeln=WNpb`i3;X6U0mjkcwOt%R16@ zyjoR`Zm0F#xX!f87UYz;A1sbSKWcgj&QJToeq!Q>a@D0j>jmUJNByz!hBy%g|t$9#!Dg$ET)h^ULOhO2FD(W}BqA8pK!xwJ{(o zfnc;WJNgV@NsOcw4ohCZn!F9;rN-hve7FY$hVbd3YAwiqLtqymWCuYgn&>NLjns2u6E6D9jo_idN_U4Xpy?p#V3-_3 zBB5sebqx$c5w^ROq!Nr3ZV=<}_@?eYQ@O=uc`ONuWP{~v2;e?q*2R;O?x0jGg01#s_jw(llgd+#o}oo{)+_Ga=!&U zmeXMEYb5c7hzE6Wa4_Z$NDDL~CG?1&3MR8DW)k2D=ifE9p375>^z?_<6k5KnOcN0WCVO?}_(+Su$*I3t)=qNGMt={yD zkk>=o+clBPR07Wnyre3}463uRjEqcJ`@RQU)ZE;`uXA&$F^Oem`f1u(ieS?L@$vDC zqpEUq;h4RU3FWxF>G4a=&egyVySzQXCbTg0SM#l#uC+tX5v!eEpn;6no?(zMvjU)5 z9T74{w6VAb05zm>VheyBZj2vDH#A({1u%m(%6xI*GYg2{*F0B#NKnwy(C`OnBQ@+^ z3KT5y^=pZ>Sx9~-jf@8GOzyw%n8y?1;Z`nh7YKmfiTuVb@p8nLUF__eFj+=bJ}&xk zDoHa-?nJApvK4DPu)3sJDJbz|Y}`WY2bcmjejto^%8|2cL0^N4%W1Nk7vqOwA$3n% zdwT%5==LJIa=ZvKbBbf4iE4 zEd1Pk!G^@#T#el_4reg*79Ht(Uc0ZZuyhz;OA9`Qony>9a>eHK^q7=YHxG|8W3lrz ziy{>RAV+U%rfQQ=IfPi7r0_ee^N&6a@umlz+Yi~6*NE4f`3KA88et1aQ7x!jT8pY(TFfqT?mQiF67ideDZX z$)s5(;{ov&S+AEb_e)4h5`x5=&$x(9G>W(ER%f@evbqi}5(`J;V;o}8r@%G1^83$6 z-2r_YRTHEgKGPQ!7hh6I%F1GBYionzKnQsXxYjXsb#>BP>AjMh&mTX2oHSZre->;o zI2jcgRECF#QBV;zsof^1ow@hL%A2QC8Y_Q2zfNf_b*;@)V2!)p)pL(0ys zp;3ZFMxD?#>Eu4N3%}EeGh2|(FV%x}e?vpVuCLPykalz(-k;Piu;p4?DJSpZ<>g%j zL=ys@X{SUJyPaiv%Svh6%?pdVVb3WtKEiVInq+Ac0(D;A;ym)P^rnHwmRO1b3xgGQ zBqWukSiCvJIZ{(Wm%Er&XiJ)fRYB%KMd?MvTlE%*8gYVPcz~Uq-F70Y&iKp6KdH%-wD7u!061dh;637DwYh|y4O zu%8n5;#*bEd*aQ~8_{Guj)`H)0}cz4P*sE}M5@kWXB(0;P$YD;)eIE(Pk5;42G`my zJgDkRrA;a-Vn<>ZL?~pngg=ww&lXK))p5`ywX$IY$3I?Dt%0iImQNP&%6|-hI$wfo+8h50VB;~tJ!x3mZvrGV2)M{q#pT%qCwXzpHJw^w9VaKb4_PPxsUB?vF=dHL zvf$NTBLa^+F&JHsxwhntj5-ts$Y3p1!2g-Z6Y!OAvf-d0OT(RIX_X=YDQTAyg?mCeu>2$Fv920 zpDU0ehoCa(qe#^9L9|a`vjuP$%gbtNcTie$?zP(z@40*f@bF;iLVpzmI-jHjPc%77 z5YLdTW&{xoSp^Y<>HS8WfD)@Jy|xgmIM?W*;OYYFIs-z}VWSv?y1-ni5flM_y9Z$+ zbU+A`mGqhW0hK2R)Fz|NaW3;+=AeD6@{m^1go6wSiIxfUJnD?6oQ|OJ8jUnY7mPf% z$h^_IAH9Bup7Wj&V8a6%j=&#;wmEYi=`H=DhNSwEIF5F#sG4ZSE72wWKaGO z0P|X~4@$u=s|Y8QgZ7YR)g#%9C9ig78l7+-d}onfNC{9EGKie~;=sld$W7}N1*8)X z)rHoj>lM$S`>p|nDiFaghH^Xft3YWE^BnU)?f{O`S>$-IyQk-2*`JbtcA-?ZMXKLr zAIz3;;is@gbFfVgBt7ch9(&;e3!VkX4bu$DIRJj+@AyvmZBs2D>-6XEDMiHta6&}B zAvq!eU@OLW?n|2@6&Gk(SU>p3tuameRgO+d5J|HwG#J9OQ2Bsq6Ht|mL3m9}PCf&C z#vc)P5Ub^kL=S_N=srtHaryM%bTvq)-fcIT!SR?_b#3n^r=~I~Qe~Mnv5Pu>xeT?& z8guzsHX^Va86zWgE^qBD;8cni+t`;U=8|A!DyA8EdY1_iuBHL!p5JmtIn( zwjBsjSaTsl?&5K$Jthn6jsUaD3g3o+3~8FYW`mQwnrF!XOfUolj>PBBm3kdoFC;?E zjfzVNPB0<(ebUtQ1@IrF#1Nnsk`B?!tKIJE)fj{U?l%lGVya;=;$HVdWmA;m7)tLO z>xXBTs-?<^+toERc+)gf(L6)^zJN>oq||kcF%f?)KuPJwR|w?*(5>%ujPG$u*95mg zPrw5n>Qt?aT0r(G&YFJb{b>Rj)w|K8BjAca1VYT`A_Oa$wR9^feyI@k?1%@wz5%rZ z=kgUjs5`)-F+%O*2qNHr*=-RnrZW21tsdTK&Xm6u62G8;|8EuK`-lI#^gHo&yh=E7>q>56yqT#*&19tzX^#A|> literal 0 HcmV?d00001 diff --git a/jupyter_execute/92b7b21e6c8b368939a237b44b3fc9ceda3f8dfa0ced0b51ac6956d90ee93d8c.png b/jupyter_execute/92b7b21e6c8b368939a237b44b3fc9ceda3f8dfa0ced0b51ac6956d90ee93d8c.png new file mode 100644 index 0000000000000000000000000000000000000000..b5dd9d190a7c67c171e3894fe95f1e5dc7d7080c GIT binary patch literal 28728 zcmb5WWmJ|?*Dgv(r-ITYASK<6poDZtcSuTiO9)6yH`3kRozmTnba(Fc_Ae^h{if?A7FV-_g2K>)d_m_4=9)r5S!GLn{GjBVe7NMKsd)X=d#VEOM!qVfi5N)m zvD`A*VU@Ufsk-`d-mF^WaCExHv3B{+y!l(T_Tgd4mqO)2qA>L9E`REu)U*VKQJW7> z;_bFJ-oKJx2#oY*Vniixy06^+`hoG+edM}O9{bY=L^40j4s_EgbW%S|VMLqCtcaJw z;CD;Eghe6$yQrTt2@WWP7@c*J;)T5yQ|G-&{Jeo4DQ+S zL%X`Wr*1qCQ!XTp#;IZ?(lr0)UsEvxG;V1#y!fyV=@B%%DOnS{@6TXsC;$H+tsg7H z#5p`8VWU771qlf0mow5}5R0j7L(Nx9x_LxjQHG6>R zF%)xUjP5RWH5=^ve@gg6WLv1D#%4QJ@a4T`T|{o0XCfZ!SIuh|d3pJZ;}sHbGLau; zwp*WDp6<+Zq?5iD*9jZ_%t%jHY63$NhdGp}c5Kg~kYye~3&L~Zc@bLqL0=(@hYz_)EA9zaV=L^1S$FxJ=C&D>kBXek-}I|DFaPc+Z}23itmEi)Rm z_OU0@wVtq>&_0gOq<_fFcW;!+K2)J0E}4(bv6Z(eR4Wh>po>wc@&lGc+Iy3vt!BJ3AIfG%lyarB=+2y^mNQ6 z(nQQ!%tUAQNY7i6GqwDKA0GNFA8Z(~zsBIa{qj`HHKX}w@n?m8S9fj`ZvI{?e!zfN z%JPXy!dPHC4N;1*$8AwGGrqjN%ue!Bw(L`Ydv5%kJsGR}jrG%c+H;xbqboc*VIR=x z*=lo~5&rAs`LV7QTq$_vcSSFB?FYvqiYo>3I8CFxVJ4>wGP>h@Qdl{l0IL1 zJ&T=h4^4&9JZ*=cdqt>6{H%@QWd6WRI3!TuNFhH#`>noS>$l^(C@{K^Ola9B=HPhTG$ue7#yJf`xyjaM4Wnv1@gs(Pyj2i55tu#sn{ z#2s4qE^5r(FS8-nq(nYMDaqH|#pXAcTcl!9d!?shRfzy($CinE{vl;nac7L>hl?Cg zJ$_;G+|ooRt=-}=y^=QMY(CLfLk_?C~NkLTwTWYepQor7+^cTmO(c(|6rn1yO}i zxCA`PTWlN!OawzY+B+Zu|ER_O128U2~E@Uy^++{`VPP1xIUvV?N?A zVpJ0zp+l}3Dth9U2CQ#A5}Ktok6(jd;#iGxs|vJ&Fab?t*d1ElO9(8@lI-acPBnNo&~Ibm7)YGI;~YMPNW zv{_fI=3yt|W&32fFuoHR=@w!NPclK)R5;pp9!{896Q z0o7FC<002{HucHh=k^gsZ+)LO>hpB%ksoGMSi*rk(#Ux7Mszaq2)E122abzGOJ8IR zK2gGzeWfMjX!Uw~^yBxj&$7glCWvL=lDBs`YsW=&V!A?LI2{p@`vGGRA!^inTqFx>uD_^VQ03THAOW)Hp;mRYk+*iVg zU$$@vZqAk9HP#j9T;HO}8Y3vn?Ea|q<&i7{t5}N5N1si6HW2B}$UhV#$A)wCL?lRq z?M6i7?I23?RWYarH|#e>^HW&gRUj!m2_-HiC?OhGdTRl;d5T}09w_?n&fNg+NCz0I z6s%5lR7Kt}qnI}x4S3cklIM?CPC^H*ZS9|?RKXoc`%{WKQhY7r7Kj--&8IMaqddFZ zH4_uh3izGgzg@kSqBKA!%eFO7lwEk zAPGMQRP}wT=+s{6(KVF z7%DmZo|;kfi7)1ZUVgcIl6K*5NxPM^^=u>W#vww7dSYN>4jxYH15zzlC*Monh%i5# zP#ip$-g+)DGd*}%7j>fOA(NTK==(cyJz9}ApYMmco)e8PeIPxcZ|6>{5tjSXYuDEQ@QGOF0_l<}=L^)5 z#@*VMzL%I*i5R$tFL2p#4-q%8a)V#bmKqn`dHR)*p^Jw$^xQS_Qa&S+6;M`=_SL!# zm$Q?ltYD4TYg$mRD#7a9G%+%n#_*htdoXIdl4o+GiEs@kmHO9&mtpon~$cCg+ENvT2|!Ze#*%E#?T3X3Y)vg}hJjH9Q|?BLDW zX3g7J4&64NOf6Y+!zJ_S{dt8*lm$_{ye<<3E@y@$`re_a@$Qt7{IaEmsqf3bpDvJ3 zJ9BT|HfNHL?M^1i&nN>^rKL&guXU2MdeD4OlCeWUcZW95^)1JJ5MB#oY-9X*s05rm z18b|_H!mWGImfr5MLy%m3kz_rFR`{|kISHV;9W8&%$Pu$!WB`VpuYgbn3q1Q`e(kY z+M8%()BL@^^hgqp=LG&@g}F0$ic>Gd?GVj=QgAPt!XoF zuo#5wXx=EhAN+Aw=~E_nQ?}9bHU)o2Yk}LvMQ-PuG_6!wmAB5c z8nRiDuJv^tS7mp^7q{lxaWc{#){}$safu*4Q`fTSQ9|*`bRgQ()T2O5sgUE0Lp(!{k_VwvNl`tDVCP{rPzi51@te z%cGBFJuup4Bg$!W^D}&;R8OHOESXo1Z?U`hP_7(5OCjpvoc%<5NR1et#>%%4NbTuf z%s^i7eO!7{@1*t~9`djrA9RXj2dV*e!+xv663l{Rjg)S@k!sdkUmhH=R>(`ni$u7n zyZP3HRRJP1^^fw{GCA7`GfY2a4XzWUK8LD=HE{8o?7lfrX~7nXC@uVHy3ask_`=%g z-O5XPvGkewo8I+4T2+3y&nRuV2`+q6j~tMoG6@4m3ID{GAg|q&Ji<^^)~v>_a&FO3 z3=?R16kO>`=pdgI`rPws+WP2>yGCa=3?400M6{6T@4S@g&E@NxN3HY6H_Y2Hg<9rg z1B=3lg$mgI6T7#-AsGNO<;7y9VNM*V<=v}ZoX*g`RgK&Bw4}{>tpYTbnq<>WBXhyz zU`%z3ZALC0ZbjNBN-*KrDGB~j8!E-!&mVJ2QOV5EO<(j@0m_&ah1SCAU_%%fCv)*b zeX&0YLzwMkcWl4acb;Kgma@A|v$h|`XVUy%i7}!Q5M6O_PX($fqoDhR5tAaKBXvCe z$F3CdunB;n+l-n9T6%8fNtU$|;HxQ3TZ)q@_I~574Xx^6b5HRILi#DsD`V6<-BHjl z_X|N=dNpk1x1Jj$m$A^4&yUo~Wt}^lp~>vJctBntaN(ZFjd9u9v24Gzw)>?MaP`^K z)!jqsb4sKk*o03yFI-5<28lW<}(8GHmN?hDQrR5%3l(MWfJ>{Jh=7w`3wk&5w-{V>-O zHwwwq4S{0>0o!C8t%nie8VcgRlp+%~42iSc-o|{R8tdjl zgP4LXoyE2NxroX6apdpbyoT1qB2y^j{;|Pnhk7s2b?S-*c5V9 z4}`bJUl_wNJIuz_G%>VNW<@Egs<%jIe!u#zeSw6M?j+8Sx|_R;Jw}`&^DYM~ zMglBYEKrZ1sZa5i)JOMN*FiY>0Z&=VFN4t93S0x$q*}^*VZ>n8L~QhwK?WV<)zSK7 zm55~TX+aoSdG$vH_ZxqtsPYF>|HE&v{JU6YEqo0k238k5#h)eo z`5}UvbJ3&vdp^9ew%XqI6}EW6Swg`5InU+N%)9Siw@)Ko#U>;SGovS73OPX0cGi87 zPwraCmgA?bQDZ=5sy5lv1^RieY@Q%=Du^Vnt3f93AT>7@|C|2DdWKWEcBh*0pA9_* zBKYEE&9;xyRoeI9w0$(xMjFES8tW6Yu5V*5ntmZKXt1A_%X<0&lk9#m+VqN zNhm6*qjZL!U{qZqtk&OvaCDA5Z3)fvq-f%rbUBYBJc;{Bp39Z2)*UUgSwb!T$#JrK zNM0-)Q&7JHW`}57R)mr6shwhvg2r$!f}LtcmkG~Aqm*8+MK9J$PF}b7?d(Mp@Q9h> zAk!}u8my)F%VVzpl>HDHq3!8nzqhrHydVOzI3%Xk@epLT7o4db)_@3dgPbrq?5zRP z6*)RkD$)C0%C`M39lGXSMCyFw?VF5+(=iQ_?T75aElFsQSaqD#b}}%a^EA{JiKQ{w zosQ4;e(A2tD1iun*3J~e*mh*Kz{$w^y#39$!QZQ|=8afa*FTKztX|Ee4|Z<%gIa-u zrgXNAglb|?7Eh~>rl73Zzg6S*wP;Gx?fL+eSkY@R6S>fjyZ)qy) z`5#KVYwh08sK%Ah@Ab>$=-~h@g`K7>$?Q@Rrdv_2%Ae`6MLWF14eb|le6)04d`9Cj z#u%)o^vnANi5dc_1KT%Bj;X_0UAnX@+j$eOmO8FW;cy>KkN*A;m4Qa#CHI%2&!tAD zlPQAw!+NWJAX$*r)^q?B@yeA^51r*k7Lgr027fLxUXM7BAnmh)=m-D9@5>BbRcBQ1 z-g__j?62)nx5E2*M9EsyJ24%SQdP>X7ukI^3H}{xzOy+Zr|!MJ$!YfyzyU<`H;Tr8_88(AKxTdK=*<5Z?85~Klf$oRq=!x1)pw%?HC#iDVg4kQNNr$sZ6 zv6pr@dSOuMcl-fiBH(6i7t|dJWRsLYxYqq4EE*JE<- zD_Fi1qPG6R~fXWf=1*Sf9wmlP;O^Nkld>-Cv{MmwqaKP9*LLA0imXnc{(@p6eKDl zYEXc!d~syU@tSyY=5^C3`OVszq*Cozj3C{hRzgH(mT!P#n{avw4YD&-p}d&ONDOCjC$unmreWmRu~Z~P(hbEJ>OU{V^0iRP1MVqN3t?f5@p^q}qOUxS&>J zp_cvk+fqYJxWHW?rDSZG)rtVI+W~WqY}#n+a*Ux)WPv19Rf=R4v^a<2_Fl_E4fD7xs&?((yDUeMok?^qnL9ttzmUKDuVIF*-~Hs?az$p=iG^1Yinzv(l}QA83h1=0O+J49K@t)kdulqB62ra-vK@OKHheUz=F2x|_-uKOjXn6pe(E)? ziOa{u)NI%lSJCx#xJ$NGMwQtIB5_e6>#INV)sD|TwA9QpnnelZ7nNK{eDy?PEaD!x z>)f_sJcQwQakFq!hp)?|&iQPxvH!VMDd0lF$RS&{;O?nNyKnRF-;MQkfd5w91Ni(k za>Dqyf_k-C_rvYE=~TgMfOl`z|4k+5;)*XTD>JJmz`=3aUn{O#NjepqCfSVHUOQe5 zpZG+DuF0cYUIP#ysyZ?LierpZK_2AGifqbdEu0H??Ei9z-_xFEIOxJh}5cjpyI33S~zCQ8!S}TmU zcxD6DT1%or^`uAMB(Je3B5JPQfgi}>$Wo_XrU{`q^ zz_i`hxb4j57~H*9N` z=e^Hq(aj?S<*sB{uU-i|I&$pp?%v$qLR;@m(%`e1cn64ePbDZSD3~nNeDm+UaOFow zpVCqM_+RK;Sw-LR?ft}6=EM#z5zl-@0t4}nKOaZAf1@=Lm5j$0Uj)-fg3GG8>>utz zO?|i`{ab-NW}>Tqz0Jo@jcOdQ*W5cl&B*@YE{kOsi?vXvda_83mB(SPtFW}?v$i&I ze}Dgb^{T*f13KBo=~^otf%^ki0OR}g8?N_~co~Au6e=D|*L?XmZIHe<<6pmRV{fYn zjnOKetRUfX(R{8Ko`n9CExP?|9nJgZcvVxG41^ZMk zT!&x&O*yDM%RQpB+xX0%F8Je*!Ku%}gaT=uA*C46r84{i1|mVH*o&-@E9tJ}a(6ke(TG4&*1O6eeM)5Z zF!q}yBEeUtMAfP-Y!okvHeH1iJEzUdvBTY!hNn=S_aOOUA(Nz+&FX!g2cj zp))Tw^ZknMoGE3(3)#sJ@1i=+4vn2XI&FTUz2i&1Y+}nmBj<%5IA{{i`-qIks{&la zb=wh}y_LyaPsOj*4(L%6GK)N;g;yOtpfugwwZb_2n~w_RT6M88RDNi>d1&jaf=u>a z0(@54?7h)t{}faRT6qWQN(CiSqCKpW*I+G`ADSGt|M69d@p2-$CIiRnLHgDl*&4uY zm11!*JK)9140>yl7lgmczw>19yg;t#*qi9xtooIw0djj6M5p!B&RauyeT~T>+x=`C{Mp6yP+s|f zA3;df17kU5^gBnWWmA_oX2)GtM7bgzmU2e=Xtkb-51;S*l~sU^KMzniXS}&TSK^WN zatl14+opskKAQ8vgB3P%)F!it$7XTt0|hdV9y^qQaIb1h`-8psU8lQ7Pj8@lUfyIM zfgtf;L9b+J(!LpgryhqaNjn_}UnDY1aUj==``Z->da3T-{(zPb9`q}^53X`jqv`xN zY?bbA!m4BKn--}z+fcdVXV5odaqsvz*$ADeZe6iEi2I)N9rG+Aj!#zCxb_I?SJI_? zsLXyVWClIoUs8}*2n3B~VXm%q5g5#4O$>AdmjHY;8timi|LIdr$_l*dZk2)_2Lwa` zvG(@Lugu3!GA`kC2pTQ|K`KV15Mkn(6SzG5h=N>&^ttXBS{7eiYpUpTZY1c8UWfNn)B4- zrMabX=5b|7--R0FR82;p^5R&D9W3!r0YHe=6~+1ZeCB2xE`W_r7e}p+4#q#eamCJf z1C$^v8tp6;Ev#G3%D?rk?9+=MZ_i^sqs^Tyap z<>!QGpV?0!rMIB>L*K6fMVG2f+o(?C&ef+=(lJ~V-q>bW2tXd{Zu#7;pzpt>_U2$x z-d|Av>qw>CJZ)a%V#6LjE?})=>EFUj29xSV-8!1rt4qv=C;KR9tJ=j+At*p0TJ7}v zAQ72NwJCywB1(e2qQ_%ntx(3x(uJbonWu=#J|a`Ptl}YtzAc`}!K^g_2 zCDW#gfo8PK@i#LgR5#Z9G*ZZHC-&RLHix@->!prtWPYzD$P6~7lhsA?^P{IP)v>f0 z7kNTsrF~}1a7`TkeVWUWERL#$yRRGt7@Fv(7ianw1WlSm|8~qlV`vedLA~YcUhLRk z-H+Jo#k3MT=k6cMoQN&39NXuUCL^cMwzjd?R+u@@T&E>coe7`~07s%Nq&1{XaOc{L zH8N%0VgeaC@^0pSujFJqHcH{}&-2L&aZi2NCUyH&0kuOy3_iZ_a+ORH6;VD8Y`9Z7 zSrxi%b=%j*$-Y)5(5d9wlK!;j$s>{dB0J_fo$lgu|awnuZbehdneq|`Y zR`N(*c>%atDo_^j@S&(`cEozdwaI%ww3L#cJdfql3Ki zNfa56APZ7q0MMf6M@aSZ+PT3>(lZ*-3v`wvp*(WBRzL0)lCMj@QJ|_&F4mV$i4gOT zf7^mS89mS>`0ni9$cPy4sNo=hE2!tnf4W_;Xt7n7$TI*NfLgbY@KbR6C~Ui)G~%f zymHzAFWtr5EVg6<7+sWbPtRt|X8GC$JZLhjn?^8dCcG#$6129j)NafeUhsGqMb*mP zHz#%e3KpEwc>7R~i?`1(L^zND^3nhHmzoo?@N~PsKw z_L1!;3`irI*h~1Ad>$vmfZme>8OOH*9KZTQi|J_uF@`zsL@4JnUa(EX?^*jE>%teY^xgNhItm;g(5{JYf|&Dn`w^_d-P`GyR=+aP8&%Ylkp^ zdUY1(0>wwdl|sH;By{;^&kJ8;nhw$W6d>Fm?oJ)UqDnUj((@9C@pyRR2WbtJ8;avP-Bi?I*GCoVwfoe_BGm_Mk;jd z`{28CrP3$$Wm;8j>)}+!~_5hq00?H)M(dT5}XHtbv zC-d}$LqPOhI~KdRJht+o;0TI&n00-hccpKvF?@iX>`abHHGYU_=KM>4%xXWdb5b(8 z@4lu8W9Anj7Tn{mXn&If&fTEU{Cd?(wd6tK_WFFWp!30b^DPvSZQN;?z&L!RO9DdW9Zo)>t0kq z0=RHiguGhh@4)KZKjwsL4iStG-(K-?7gJacZDlBEtGh*8U8lm5R<-aRhaHPA2+`4( zgT4|Vm&wn`N(e@3{E{TE63U)(mio<(pNi#`%r=KeHq`EVO`&|; zwVaJL#TwYIzi=mDW-x%h*QZ=Nnab;N?JE3Y2ZY;>n8A{*s$X?T0mDYD8~d=yp9c|E zNwgAAKvY{y*`)h~sHb&SoS6}WLy)u(laJeJF#3{1>61)z3%fM-3Xg$jYf~7-xsmc?92&zE6L9IF4HF zXQZ%sZ5kKs@^y>MQy>;uC^tu-iM0fF=wT5OR4@T}Y+wYq4$5hoXn9OgR;FYW4~8xD z8wOopECL}BAM`tPd@^MQv0z0KCM)51im{skafQM^BnHydxahD4O^w}&x^M>q!CRE* zZC@2kwx4A{s5x8Mu`E_YFO`o3F@y@?iQmVMMxOjgg&Z|-AGoi9e_^FBmfQvqRccQw zBu+XEfDTY!y9x8imU8v^C;z|Wz3{96jbYJ_@G=~T=m;R)EOJ-xmxDPKOtcy_rG& z)Rb}Gk9@0@VuZ;1m*oaazs@z=yrjR5Qn#(NpQ@nDg z5M3_nUcoEhApcg1GLu1x{{(wbI_$pQ`xor%XcYQX`$=fLtn*i2Ez2xln9q+igc6`x zKOmfc1Acz^XRC7po({?Cj=v_9sG~(!)7rOF+v+n_dSI6yz-J{ub~3xFnrtrvuJRyF zE*^=YWpr|2kPLAOPa0X$Q*tv!nu?k^@x;bK_bJa2)pSM;p)PCsO|5b?x$9H=O?U>dNlde+;rA;sq28E%f4>DceJ}Y)QRS#Qs z(Qa`=Aqx5?4b@tkD5S7rc1WJQRO4?3$S#9g{Y__`^2nb<>bUou$!gFxGVlk1LkLv= zgp7y6N|Qqe@)Q0j#;mOT}h+Q?t>5KKwVOx3k?BS{gxBP-u?A`FE}F@_>Pz-&8SgbjLZ+L@hax`3?U0 zpeJ>(Oj!J$nba>Y8yvO_LjTb~?f!JEztr=3Yv3mSM0LN0zKFIl9TM>M4%@Mz=HXo{ zblt^-_uTRz?|ctPv?v4Ae{}86zCxNXOnuiK(0$w}=p~~St(OZp%H@FIkwAwIdc3$P z%AFw8CCt}SqVps$iX+P31Dp9^E>qmD%Q8A#N(6xZX$(04CmMNKN1wi|mi8?Fe!Cxj z+C5weRdwQI(0&tUk9elc84svU^3~6V8Wn~}0N9D`Qz!&B?S!-fO*^z%NUhS-TLls% zvm}0F7QBaLtF2FeV3(50J=JAt^g-`grWz$7T<{5(iVBa-mE4Wtkc@^CNu`<2HwmN0 z(|gh&!seBI7$(c;-62XaH3zQCw@&6BkE)c)1h^NjjVIkAV@;R~@Wu!!^QVAhyH}vq zcX2Qm)g6ozm6&K2#8)i}NrmZutnQ_En5I_6@IjmqE=` zqiDb(bu3XE>uDwieAbkbTL^?vd%G4u;(XkEP5X`4@jx}yufl2W)7(Sy1#9}aREzai zl|o%nvEebQC>F^#$xR)>wDie5xsjpB8PQq;-6Z(`EcK-^!sUF3)2e2*_G@oHucHO6!N#zU5&82c$cA*OAJL{u2F3YM8GN)mV86Bv7 zUA$in#!<6I_% zr@IqF@DB=OQ(Ipoc~E%x-p;9JSnA zLRiY)(6HiCQbptYQNMmcNSU&Nk8BJK3@_l|n2iRo1)d+yKaV`F2Q%)>l#*nMh6-nn zjTU^NYq{H_0~nRk(yX!_Xh`JR%B*EL>4^uE?fFqR9yC-ZD_4l&*do_E?J{h;d--)J zkS}RWZ}j3d<4#CSOS}>ee0EUGjYURAo-NaZ-RKRc0%&Y}Jnr^L+6dU6ZRZTt{{cZQ zMU~>W-@#mJaM%cJy4zWJTtJqn(zP79K3woqsoY zgoBN(L~UqmYkPAsA*)uV3vJbM`|kew_-~{bar@w4;e2i^pcEHeJ|xXqwJ@iJ=5oS} zrd|n2a$`7kr(t0>|c}Gz@+a=~^H*3>^>y4>{kmGpz zc9b$$B%q?`_1NB~ovp(-BcRVKz4OplJ{0aTWQhq6kjKs*H{e{~+xx7ir{~{0aMV~| z|HXN6Y%J3A@op?vmIR8&>F7`3OLA}tBESm(aQyeg)FK}NV3OC>)gj+M@;sS;j(+9S z?1N{wfcr%T)&zqO#am-i5YSRHrxse(TPGbj_o8&dvd<2FbY}gHNuM!`2ktD&ja^J9 zI1V>rq<>1H73@htO=~x*;@X8Ah_J!&a6po=xoffBZqQ5qd0*$5mgI#yIT;z5p|NpP zYHCZ9Bo3%V7^Yh1Y5OY-{l2$B$)h*Op(062D~6aGFnt0LwhxD=hsL z7bj+G%M$#CUjJZr;JkRzrkCj9Y>?6Y`SBWf-J_*OwMIvJa?#o4=zB4iFwC^q;hEEs zv$`?Li2^NevE39&@%VM}>&OVqBZGC-%Bd<^w#I%uKi%gJ7~y zCA_rtDY34OYdB3%))yYlWTnNUx}d`!1?OyBl5wn5M-U8Qkc)*G4n)&Hf$cI`E%W`r zAQ}EvyVR5MDHvd^WPtnJ3CshyIq-PvUQbP3MS?p58v)=oer^pJ_<9b)w}mobhtzWR zocELD%43@xY;^E$@L(=wq|T24ht2@J=k7u+BP;@Xme=zWDzUp=RPpWYZMpSYN1^3H ztCyGmeY4wjj??in=TunrX54lqs5*tR;od?cv#ecMZ7b{2AjV8!v64kb!3hOhVtVl2bwqL8_ZM5B(IJLK5v3A!|S7^o12?aaQ4LEuQ=)gpU zxXsEVC$Y4zUw*2OsGEZ%=CdJy))^IhtoJ-;u}H|)#8N;+PHqSQPQN?}U<3llpH%*&H}rc)`5`e#+ma>s@R!{)YS|8|oLw5x&GrFxtXiG#{sPy)y>u=IVdP-b8AcOZPE}O$LsUX zT@olOZ$nOoGJqfgl+Va1t9|T=;MZ*s}6;kihwPIdgkh zyW$MA+jOyxS;zC1(&O%8ZJ77C>Z3qX4X-h$OSa34283~d?K%iYZ;Et`fGbz;f7f&^ z38Qns7c2_<+>l9L+n+rJ2)N9g{0I$6NdzFkaB*?zvLt}9A@+np28*G2WI#?fW2cp- z@;WnS!uD~(GMW95f|{{t%=-&c{X;deHKB>jvnz2&0`{}z6fyZZFvc@L_K!?Xma?~J z1HHD5>^SYWq@-=HZY`$!527frDbhht2CyGpKMa&8Ba@)BJPCghByMS2JjQ@!Dz=h5 zsxMAx`0j_9fu83`W_P;&DMuQEGPkDLs-p#LqPby! z_qz%@q3gEg-x-G7)o#KOFL-X%zz|fm(tY3`4Xftvjc~!?=IdpRr&-|XT<#BRZ-ECh zXG^9xKblG=93i}f!IdOicHgblEY1oGY0^K^Y6u3*Q@^Qe-BS;FF5bx@BOWkIML+d4 z!7L!qGM+sR$(ew8U<)AxJmor{oOxGjcH5%vpj|32W!D<19$)#5S@X zB%U4fAXfxo1~?Lb!g73w6UIUu=|)|inm(g_#T2&fUIcyw3md6jxHFqgXPrKe?~=N8T5qcfu>Y! zA63aa7pl!%pC}x*<7~%79uax$?NY&>Jy;rml011hQM{CPpZvES^=jmV>lLpw;E+c% z#W1wpPe0k(*`1!BM@dQ-7ay2qm$j!mDRD{?ot@Tv6aWAklvUU_z^Q0pN6G@j6dAN> z>aHM5y#=WjXg})$p}f~=A@uU{qR}ssmS3G!KR=uXcBS63;1)K+5&sw6)lr;IRE$Fz zVi1UVqGXkpOFXfRI>R79K-3U)bE^#*Z0Kq3Nv{dH>ONTOR8LGIC57j*oHvR<0gOF4 zzzwc2SsanOB|!lI|_;y-XO}^?2c0g`@fozcT}gbC*`WkOUg!{b>ax7NjvT| zQNVruvX9lbb1IDDUrwpnXY$NpSEQ|T$lQ_fj(PwBxR_A}^Cq|^1PwYWs+vKV&fuis z%5=F3{+J!HO7#))*xkRlKYr2Vfk_BTclr_W-7GhM3ZS<($5f4d4Aj{o28?f(UI+QU z=Lds|9JvDl%Bb+Lg({^Ctvc%J>T3d!#MqoVwyppmh3$`R659;r(V!D>q|p!@o%~k) zroQ~*X}FHNUtS#W>OXyXa=bb}Hvj_Ny7q8)r(K*eVFN|5`rHj}y?VbsVEpl2>{_lf zYQ1ag4BEnKlp9%+fcQVMlHUXVe?SN}>nB_n9mgU)#07ajkN6>5^)G-*mNQ;e-b=k9 z9(IkDJrD-W)%kK@5t;$X7qvKIt(?ybO`LUo78UG(w3Mpj5uX;W)gfPM)ZZ-c$9bW| z8w)}T0ccTZ6GlyZj=eaNrMfY5Vo^tX-$d@S^X76ABHavRP=X?;8LxJH7&ZAl#RTq; zC(Tl@^ZC*Bm3N$2&DK>7nvx9U!2-yGMs!-7hv=4`dtdX7Jh|+@<4hAm>xw?h9sRT{ zuzd??SP)t>e}u%z`*lFN|8sK(^%KU)>03vYC;h+6%t!>QIMm~iy5!zE)+H+!&;*S9 zJvs5ZR?uf-uKNbNjb=rMl8#K~M+9KkX`<)-DuoS()j9E{_tjla`FN7h1-ulY*W-?4V<0w7if32~MowC?1#^sWt#ku?`L_S+OAqCS2eT+jA4nH8(Hd`@7u~v7BAl8jp3%Y?C@1jZfaqk<*FxUi@mJ)yDJ1opiYKw&@YfLRX8KS)#8JJq+*_$>4#D&AE$|yxu`!$ zf<3TI#;WF`_Nf0tSYRS(I@5zmp(w#5tHGQ2q_cAf0Ly~z-QVo%QYK~>g9T1o_5w&| ztUs+y?uEe6Z%+;I!jxu-1irg&0gwhTM}{UixW&&$0vW0XVzboj?5vb1|1v!<-0I36 zl+vsT(Dw}j!!6n*6Ed;HU|Z@^KpcwdFOZV{03J)WnM6OsW=nQsAI*D8&@3id1U4rl zunx`%c>Ybr+?RCGo8OH|Ux*f@^jI3=L|Y$^Yiah;yq#SM&AxW%nUXL4C*(KaB*ipI zFBtihW$3=h>jgyu%eP};4~I`3cE8hQ6R-^XCp|sL)x8oy>MG@g^coAhblFa4aqfX@ zA&@oZPZOB+7qY_ncgbb1z_I%izR5+yT0V_t70#Plq-Ib9=|EbG>sd1uz`p&aujL^3 zhe!w1F*X{|9U#4~y|3l4R^LMYE;RogHCuh;B@Z&&E_9>-s-O(nB@^CIp_Z?dj|!Zm zm*7waeJ+$Uq@Pe#Qu@zA%z#BICoiGH?XDi64Y`72=M;0tcS!Ds!JJ&AGrhC7%=pi9 zk>I)LG-Z5b9t4NV*|L>af_9KK{DR7lJKxbURoDW)OS_hmR?_&jh}Y=RKJhh5?@ zt-kJ&rAS#S$MzMGFja`v2_xfG#}I{@&S4NPO%Cac0PO<^qpq0#JIgShSpHG}oXadP z@NOl5-_8%b39{f9ULxcGZ2#?I{*Jr4V6GiDcXuo7Z?Mh(qGbm>z`t{k!G8MWox-4{@dsOCdu zSE2vBUq>utt^f3Wpg4%@`#>`p%K3c`<+YPl$-nEWkpa6mO#KmTlRlOZN13Q)>CtX6R=r~m79o%$l605f)0V8@^g9daFWrz_W z%2O)znRk=bbmf9Mt*xkgo(YyVk$(a108kgE9Ul;czKgzGUHW*)rhMHK z-m6ys%m=!WpPW98=jVt2b1t*Y03Rr*Va@(xGAmXuyOwcQTHihOJSH!3^NPe+%WI0fG?V`-K5+SnJ~z0iAM@?)}vvz{d^}M;PC~-|VMK%gW2^b0woE z^Ms8@#TZXgKfn^V$iV*qOAv_A2M*;1r0iq6mfw12KoYIt`dEB4^Y@{&is_Bw z=5##y-(a#W?@wp5KZ_gDPYz1)4Bx~fUX8KV7l&mAVB`}?eu=$9ooC(sFT(YYdWOSN zQc{Bb=8dv*(Lqa6lE$q;cX#)ncxGSYG>=%(Q2Zs^M(~#$8n^U!jzdJr!6^Qh<%e0@yPZzCgUlio2D!X#QXeo;h<_R)3(JXvuoUKC zQ;^R7xof|zJloq#kcl(nqF(IJgr9EoPJpR#P+r0aef;>se7Z=UJa#LZ{uLfEO7+dp z6izR`;hfC4QS-ITMskyhK1oPLNoK}fND>Ks@}J~YWvK?>FXk-_0s_klk?(u|)1R7I zA1v|Wam{z1UN+Tg+L_Nt9S8z$coGiSqr<>%v0O6j5n8$pPW6Q9M+hs((+FL|qeG(iso}0*3UhR-WKEn(@jU)9<@W4G%)loI2}KT4)C4ww z>`jt6VRe@0{P5%@$ZPe${BDkWOPtRuaxIM;mQ1g_f9`2g9^~@_^;-XJ3xPx#3|2UM zUh2tc4s@<`2Khe+3YA8MLxP?|pB1Sj_uY(X_UOU~9k*$`yRWh=D~G%R%CIQMfuwDn zYX3yP2L{mJe~>Tvv#k;QuALVY=!?hUP$!#hOqM9I630Mtd7#lFS!WGpJM802_u^WS zS30zhp4MpKuNw_Vd?3>K-?V1me|a^C#|AG!KU)~yJM)n-F^?J_=fNkw&L5pPVCu{Hc^J_h!ae}0zAzLZsyF?oDI@c* z7^$^*o_nvY1^lRSfFIUam)Jkg*Yz4aR|Tn$Jlgw3mfqJ=1CENA;EGtjyq-zp50I!N z{1aUus4?$!&$LA;+P1GG6o(uf`^+S3g-Rz?pN%L-A9aD7qbdj7!d zc(9@&3;I}SVEW>dlS4=6+=qWc`v@ZAWL73%#(bN`()5@bvPA*I$HTly>(I`o9|_bs z&;F&ph&B$8=M1jA)M+klUrw~}c}si;xhx*Z>#TpN+B&F2@@eM!BPvKng$nh|a_ec} z|5s^W9hKGkZ4Cx^^e9S*lpHKdI+V5$l#&h!5ebp*62uk>DG?-;Pyq>*5Q!HMB?SQq zC6(?{kiK)nx!-qx-}s$-zcKC~j-jIa-TT?kv({X5&b8izdzX#I*-<;7EwVuHb(;Tu z%&swRB>thP0=4iM#W?w}S>Bm#-qIz3Ws2?0&WGPgP}9rBtUu*Q2$r^W{(u|m-|3Qm zb+Q8KXHq3A<6P|6+-x@J)rqp3r;hIUyZ(4=C!PO*p^KTUJ2qYE-q>gPj$`;|ry$y^ z(*$O0rrqMdA>&wfqlUu0lCp%jiT(Lc!e#>s-Mn?)u74aCnqv;NP91Q|us-_Hx7SKC z=9ib9`2l4Q272&xrQY&vj;UZrYbN^JTwL@nUv1yKGE>e#*2&Tp{Fl+9QSy0wSjWCw zH9QT`I#hnUhI2P|?M6%MYoNm#efurf3&9-+hY8*2wdTocW{Q0+qSx!WriW z%stU+_Bkn%PlXEI@C1&sS5JJ9xA|LxqK9_F2~me|7@s%_OB7dMgg;>)SJ zoifj6t&`VZHvpXD8|CHe%^LncI%pZQv?V?{bNZDEarek8RzLTBF0>l;c~_d&?i;jt z<*Rs-Gw|DRet&hLhx?RI&i;GZlmzp17GR>Or3T;t-C z*wNG!etSF9kHJB=#zh2>hiB@z&4wcu`J-r0J6V;*7(~4wz^&Co*u{SiHKOH!QN;3Q zLPEkPdP=|eb2>VAkz_xa7w(o-<>2IGf$FWGgzxm(RffM;X7 zyR(Kw{_=xuY&S*1!oon{ZrQrEw5dsR?5Jiu$JRrkf0&z_=dT31+S`|ul=!0I;_TV8 zb}mJE2OTeE+VZ@fjM(HY6B&5_Mxaj@-Mz=5Uy_kq=#F>_A8U__8g!knY@%*cPJA`l zvWd))rQ_j)g}v*0{4n;oUiS%QwnmclwfJJ0cf!URr%cvgj238(7;cd$x6%8{m--_< zzkABgSC*r-w(pHaP;%zwihg`^gmW7eyVPot1hqNkVvPDRoUmUp(pRGhX(UyfZ{NOs zgIy#UThg*9#0~AMD^=m#kUea}=i-8cOTswS@A~=e6Bmz~pBXUW-;+MS6wa;f0N#{l z*RGPAf6%lRdb(zJsXG%MYf+ZGit;vUa9$?>T87c4y^ULiA-83$H!`+kc){oMrA{ho=5Iw&-)Io1T3|F>F9Xy|)A2A`hI z&_(aY``g?1A38*F&37Z^^rs_TlO3*IQxHV8=dWtsfg6}Gafk8EI^+R+uNb|k-r}V&YmioI_`JGPq-d35bs$<#V9HGzb)FjRr8X5V=qx%o9d?oqF(DUkFfP3JmnAk3A>e7u= zlEvW8Q@@;seuEXDb6jyIJ$dp(jZMhn^I!e7G1X6w-?;1Ve+h5Ss;elbHb#PY@Cd>z zzPNE<-fKWtm{EuS>HdHM1DmBadM>`+!p=Es%qVzeWrZv;DXIRY?whl1jg5_YPD9kv z($aKhpR5~ts~SW=?V5HLxNCQ;-eL8=`=4DjeCVRla@X(w{PbjHP0b}}`%nx%=eyb> zJZN2J6{*beIU9dHG&#cE%#@id=tEYbiq5q)wD{s6KmP*^hic9+G8(!ocltDifPjD^ zpmt4-B4%z?ZwtCl&-YM0v3sny_KQxJZuj=q!jNy(_b)M#f`!wBrL3~AxR;%U==5zK3SdJ@aiwk>FH^C zWo5H<*gl(5*H!3w1UEo6P#5?7?b}DD)er3#eqKiTqN#412V!aT8mJ`FF#jLP?wj0> zPIa>E8qAvIr&R*bIp{ppaH~#w(LX^hNYH8U+`j$$2|WR=N2Zk_2ak%2PeUnyujOp6 zO^4gy(>w4)7-fGWgjR|hzR=VKyqDZOW=AU#FRF6oARwE4VkEo$CqpSh#3Rgx%k6Ms zA}vjCtB&A%Zdm9cvQV_DtjzP(H^YVfjl-(Dliv2#xu7jeQqgNktn_r2ZC9-9{oMwR zo)~5o=QRxp+^jCvpl^9`U4Xq})3qkR21(8C!7rWgIiJ+tyZ!+ZUeb36`nNmT*Z5R! zroJXI8LyQI6p{`GO$2$r3WBWSowmU(QG45Z5EX^TiV5jSh$MANZ5^5l6>Zd8!z(8= zz>o&$EtE5OOFHbLjJZuU?pM~UnYjEyD!-K58FZa8`^k%JHb*hJ%~DA zmc>VPLG6P6^N;>PTh-PQfdsJ!uxVs73=8kxz54{ERKfCW8#>~Qe$AsDFVVKEsL6(1 zNGOziBSog;K<$XM?fi7#&#|@$bfjH0G5Pw|-Xyy%We2>J=s|KDtzJ$XpleL=rQ{2+ zSSJ`=E<&I8XB6=(EZ8$Q|n)cnGr%s_&X;F&cJu_7f*gxOMwdrZfeTNIu~~FK|N* zgokN)_WTa!qs4?7g%}v;<>i(A?d1-!eF@ibJmg5ND$2&3ER3F0qo3{8P6i8<$TnD(g zQmWj^487+~5mGL96-kj13L{KRFZHthkjk{L7-@p)+=@ER|3>9kx4fg1*z!k*O!YC# zvXwS~0<5dDy#WBj&D*zMV~QS(JNm}w|KCV$?(N@u{tay^qJm5P?uZohcD(N=*&DzS=>MXetJmB7$35Uy?bG&A!7aNt8;1It&E zR~Kjd-qcC=K)`#Xc*uKcO3r1xz48Iu$-~Xhxa0`pPZ>HsVK|UiQ5lF^U1CT2?0I@; zZ^~o=D7#2}UIf5L6;=f#-~3wMnz<9WtVqXCWtLozkCusSv^I zHT%`v>-YCNf3k>&m6o1B1L9j8e)ryBIXfq(UWnmlICM@U&D&*UWPl#ZF`v-#@JSFg z@Xwe4TS(5fngBUJQ;s>Sth__%v1s27D(Sx@+Z|4Fpsn`6!Go`0AVgd7?fdt)9Y21Y zU_o;`JyO-!93iD1Qi?iN9>_xOXf7)!_Xd`teC|sS9Tn%jSlvHRz`Nd8N&a-5W!d&` z0=|t)K~)pQ*!aKpO8$B_>mx*b_J7*sv;$9XqNId}?}UvFZ)~jgvwqS@dOlZy=>GjT zU@w`e5vtS3?}&<`9rT&gZd$I@WhtM!ACWxJU*Xx)mT)|IN-^ijyJa3@%WgB5e?q&bf1)Y&hz!NNK3)t0U65>>cDrSrsxBB~C5W%nqI z#miAV6#C&?=1u%Adw=`uA3rXhRZtM~Uiz&x@qVpk_)1bOU%ruO{DKe5QM+sCfQ<&> zZ0I?b{Wyv(6PbF@NyOFp!CIT}`0d32SfN$4Y$>~^R@mBUX!K1OtW-4@lX%gQtGLiR zoM^=L#wm`LjxIPX?B;zujY61pWIP9;1`K-FpG66gw=t$^-M?{TBhUGqmtR&*WfuRn zPq;7iceJ>(AH{aY?(g4KhvvaocVp8Vf6Kb87=3i=SzFVP)9(4WZjZ%6eT^y;qrFd1 z{7#(nc{aQKIEIN)8EERJkjB>e$Tep2AnCmbqtd+3Y}sC+)r{A3cHA;XsYc!%|Iia$ zuH`ZQF@VV(@phiHx=1?Db@9s+j;(bs{r3w<#oacn#hHCoODllHc{BTkU{KA!g69yNA>kNJTj$aw@O$Y#l+*&hsp z10k(zcg)UJzim!8xRNF>G7M3IFGFH$@n}gJhPtgYQm4ia+&|r%IeMKM3|^30Lg|<_ zq?7EA zA*(#u*GTzW*lsBXe2sim()PGU{YV{C=WWq|f^}J(_vqCH;WuWV;czkl36ALT=!4dT4e8+iqz3=4^ktwmU=z?QDby4;w)5QWq@1HI#O zl0w8|n_O`aAF`)IOM8sQ*wHY?Mm|nRQfmYq3yi;>N7}1>vo@l@!>FFVT)vFvi3h~z zIrLU+m6b&xBaEm3HV4JT7!v9$P+C=BXe#09s;{r_t24m+=PQ^j)toCtEeh)U_e`CP zy{oI7DlMQsCok{EmX^7cj5Es0hu~<2(MEe-s+|iYOMR)43en(9$m)yZ^7<@E3b1)} zetplH2us^RHa1^8->U{ugx1|WdZ#X5=Da~k^$c(1u;fd*$~6Y4XUX8S~aEIEaQXr6fG#ijmpAs1u&x5w&>x{84H zH(?Z+YC~cN4<2+HZ4D+q7DXS5xJYqlE*l#g%zu9S&06agt>6((3^SZ_F-zdE({`dE z)$)(TYy(ZkMz&$&#y_@iH$^&iLUJq3f%hFg$VZayf{~Gt#3YFR zeu`q`(&{*RCME?dt5K1*)?8awR6mCyPn6Wx7kp7ZbLNj7J9a=`((qUT5y8cBm6MaR zq_T48Vdu!NioTX3$$4>1p9+2!tOf<5fsiX3ANW2Z{vaM3)~$I-TUaaZretvWs6I(uYgWea9%B|t0Ltt`2By7g@T7|3$o zuyx4*LlF2#OM-Gidp}$fd}Ug_8{4lOC6(p0wf0|fmuwnnHl7$9iBq95YLouS0eN9| zxFKm<+|^kIR`1_zu+JrBX70rPvmq_J>~-(m#mvGo|GOtB5>y8u*!#M=Ak=QJ5cRmm zy0FJEd1Mma!Uk|Ul9G}bblq1k$I9cQ{D1L4J8%dsE$w8hNlLQy8sJGRf{A|2TMYO{-1`hpNe_T;rDIV za3a{UwJGgibn%ABa5FEW6K%Lg=3d9W|MZ|b1Mb%^y5ccAY|(P8u1;>}?3_Knc|V=( z)39!-hb?F<2@16gI1f!1t?do|1zOI z=q53tp2!@QeE)ba{~tr++i!y5m?_>!g#b-Yqz*`xU|wMEQo@kSc9aw_^qwE%5M`5) zcpNEYPR+ui1Qk3I@^f->vSqni6QcfGpfqrc@z~r1$${Mu5uXl6^v^x~*I-kADC08O zeH(uGfNMuHxKC4%Oo%}e{Ml7$Y9TE`t^UdC&(+SI zD@H|FhS4AOLr!=sxiOs-fk;L4#Ox;u1FZ9_iyyXnuP(dp<}-ZhBAnWm@5)DWNUwerxHSha+&D-1S72bSa!=%r@;q)XK;7R4!GGMf?;_&wT%5+dcdj$WRY=2OQ}v?&>lcvq-`G z@$Yy;1NfUKSo@#j9go3f*n==jW%-l>6L|^owopcKNya{SaLmHO;#Rp{j#VXO=QIWt zk(S|z<2NX#Py&Q?@o{lo#OFBw@SM}Tx*^k~e24e%@00|oOc(MM?1l~e;D>cGuc3OI z!4^(tkrO!-dXM;q0?NH^*R;~~l3B{lKrHTLWv#=W*2%V{gEO-qDXk_-L;?4TZGzM# zWM0cmRA}#cASgb6Jp+)}Y|aYjQ_BrsO9TjE(>g(yR=5f1r?|$ZTpvmiJy&O)y z118G9VEOPYj7MS>H~)Qn9L*%G5H!q3n$wW;QlphwWR`!@5S;*Uu$Q3|0N#VWSAVJ& zEln$ACw$!mhlrn_AD5(-hK9y~k81cCMaAAgiSgnK{F6h^<9>{fr@I^=8fI}OFe=H@ zf&hm08m`L?+WJlFlr(h!qsYY%essSV7GNvV#95QVX+I?^OCIL43*A(8&K}*icmr4` z@DM+GdpDi)4wp4Ba9RO04^KbWuvVwP2&>3JsXq-csNpsWGW6!noA^5yD@?~?k9E6_ zH#^fCs?s@Gp=9myy>v4%*9z?TeD@71vKiJEqHYCmPb>=;dls+QK6NEAF)<=-AzDlW zQ+}D>N6}4;4+3X}GkZtMbCw@Yb%H8>et*w3gpOUbv}K463ZS3}DC z7WDKOiPVZ(0ljT2%M(5{OiV-^;lEH!*3j69NnQ*|NlBxvuWk@Lh*`~^?1bduV&9FD z3xf%gs70|?QkCfi$go!XFwSX7!nH0|O4#ztUC0|xYv}|q5HLFp6i`?gEile8I5a@o zajdG`B{|yJb(2`-`ES2F+TP`Iiy-z%sV%v&ICp2oxoH zm%5HPjJJ!GmX;oM9gpT3)7JFx5C_Gv4;YrePLcb~=dH#JNKeKCIW`?tVAQ?@vaLJ= zY%HyRR2j-%T_?X~8(d;ru%=RTlbxOuufT1l+K|4f`lCQx?LpWfuaUDIFPKp#K-N2_ zp%Xx$^AkR+)knI#P9m(uh&x{dW~7^S@2%K%I%uy_1g`<1HHkU)E6(Q-nB9H)65KG* zos3JaZ|>tZ%)q;tqc)Cm3xiI!+4X;hKn@fkzrWx&KXVo4^u zO9+3LrHl*P@D5B4$USlLWH^h23kP4}&yWTPEeE-|74SVf^AIz_&~^lbg5k(|1T#QTI<+)4fqDk^Op=_C%n z__DGxjzW)LYJgYiASyUf!fE0=Gzb!t3sU7-MMeMV**05lz-MT!eu8L|?E`<7{<#?v zW;pm0fZxmp&v#gjaLJbZB3#t^8wWPT4K+Qz6lQ5J9kg6VtSun!F;e8p!-i){Ons;bwLunz zqgGHO7LeDl;02f*F5;tHMq8ia^2}lmr-snG=zw>5eu4Q9<2uMCJF|5vOrznXVCnmCsTS zE9yLk7-cyeU;-y1Byx4xLhg8BM3Rn;jYS5k2V_S$B_p!3MSc8c=)NWB4n!pPg(8i{ z#&Xny1lB+{KQcN(FeGR)gno71FylE8mgLgpdpd_O7FFaZ99as`2sk$6xGys#JVNCJ z8bWW90vMIlnV#M8TJ@-5PreqADAWLgrnhSXT z+L^2ZdS&__l z8T)~qci1Pb-Xf70fH=2t!v?Nih9fr2MCL{gHNlrOfgakyPj3Pc(Sv10=kHENMq)Id z$8rEk4YM-0P*G(}>fj%9)B53M_T}PTlT~fY#!d`@em8|oO{^{yVYij}0W=T4Gj2Qa zY4x$eYrE!(*VT#`1yC< zC^?3Q`VB3T+p4aj0u9h}x2-o*P;BV87eyQGwY*mOGiQ<~tCz8SZ}amv_6rm`FKpDu9gvTcrP!s)3DpXK{9 zt4pB(-P5o5m_)6*2?igFmWYa(tN^{QIPgDiU!~u(N5|VKNf8JB)|@L3a9ZNCXVZXL z@Q1bv5J!T@vcRp*?_0L1GF2uYWU~w=8n^%t@I_Z6)8+8d-ie5yCn7)81dJfudh6pS zV#6TO;2;ZP4r%H8_lOoe(5C_C-dw7rM$D`D^oa;dFsde;{h)U27#Vsp`$57IW|sK) zeFT1lT^0YOug-HMEw`ew^81eu_Y3AaJ)y;sUxUlyAr?O*IM_EoUt)ZG{O!AUu+voF zsLg|b5kt2*84f>as3;Uo20}qNm$;ZDupHV{GnZbJV=3z`vvGtIIJRO&q{*-0roIpH zXd^o||5q=GNyzL^(#iriR<77W*x~1(DrzR&tcgvl3<9`fl<+-(B)DhD$;mCUzge>S z(!`4*f#22Z2f$6U8zy!yu|u{qiSA@zs6-E7E$+=WIRJo62$>kdnk-}qcqC4wHUXm6 zzBa9H&2wZ&)`&;0#WknxxiqIxSx{s1;|vZqr!nD}6a8hjwiD9>;70A>Wr6;;hiC}L z(I#{IagW{NTNWqClY@eSAqWMpE$a6nrmYDl_@Nsl6=cv}Y{CJQCK+}j^-cb)GO>VA zLI}3g4n$6TqZ%}EI%QLGr@_y@coRX0JcJx+5+wCe zW@U;BdD!cHQ8GdoN{35RKRy7rLR8=OOk(=>SFe^DH5d)@^C%b5-}0?RhWnN5`q6 z*OUGH{QPKms849bP=}#1V)uCLG?dV0v|D!78(h7Hq4WA^;yT*(l~vkoIz=v8Rd%0b z-DXE_v+a~1D(1U1zSlo`C8Jkk`Hgs2N*v<@4*pBjG#l%A*nm zR9@j*An&lXwavwT7fioLD(XYZ&CQ)C5*}`x`h-g6!9-%1rgd=ewt4oz!!a~%(6rx> zsGOFTR$x?MF#YL+2M;o;t9iU(EN_Z9q0kef)Q~Q1ai|Fer%UeVr$ez(eDR+?!KZL= zr-KciV7~s}P3}KO{dWTXKb?*L8Z=Wy)dPdB^9l1Q|%-eGistya@zvXS_>mTJQrem=s3%D@ue*LYW3p*Ys65^ohl}r)qbFw89o~m7? z$2v`G+b+P=)D*q#CA1ghYo|BkXZpK<6P5sm7}q{gXR?`;ilCy(yv!a8eV2D$(|*+5E2NeS!M{nmR^Wv_2d6zgHx9U;i@BU+D6E0EVk{vgE=| zej{JRsmw$p-HPpdCXQTUMd9Oz%>&Z>LB>?h{kkgWn3eYl$;c$!+}v7^$$fJ;mj%l2 z3mwRy6VGkf@^KhjSy{c()lI2BrKZX};?yqun4!K=G{G%+`Uzu;5?9G?rgqh0jzjAX z&+btG-D9XyJ4?;nTd&@>Q(;9Srb zb%Z5#CFHy=2Wd&xrW0^78T{T?J3o5VTa8+pjCtX~hc%Bqq39G;KNS*#yh5@Nn01 zr`7PwO1+-hotvuVD2ckcxS&9u$|uFft!8e=gW=$k;+sCE8{+s}TRSzxyfd@53b*nS zJW5lh!)TdV`~Av*;!1BlDJkh~66RB|@ra7mdR`SeYr8xcDz!B`m4HIu=ebzEvntkX z{9$ApO@qnybsx2$pkQcfD#1T1+XgvfMBovD3BPVp+V-uh8H06RpNz|DUu(IXahhwa z71`Wi`7VFPM}hVd3gv%UJfKx&bp8Y~S1m`g5JG`%Ib^$y)o@SoS0CFGts3u`{RN6O~_F&{|zmMt9v2GXsVh8xYO z@cAzsDm{1NBdxtW?Bj$HLSt{8-RJtd8NpAo6fx?5{}}m(`TzS52IMFpxDxmNPZ;tC z)>O*?5XDOTJB|O381fg4T01bgGMNw7lkJV#0v}jdS+(*u9XucWkx+ezaWZ>kP}qjtTDmSRF}jGw1j)$ZPPm>o{5^t8GiU$9SR$GiWh0d})gQbf3rq zszph7-)L*17}=y-({Hjjcb={kHeYgLHH(HqNC_P^R^T^et$n^CiNC~YsW^&@v(TAp zs*DXzDOu0nO-%|1tIf_1PYlQW5=X9%j9_Gh6Fyk<@)rMF=nfZ0mUB*hEI1OlByOY; z2GThD?M!69MER&PY4Y_(O^BvyM`Qf6o`4&Bng4Sm*s%}@r}+RHgmhOpuYLhu(Nsx> zj~lI5Sz;;W88qrmgPgm>F1V823L3G};{+~CJjmz5!h4Ws4OVYAJu7E&6_vPC!KL$+XJirM^_+&z49Y5IDeE@7;d?)ECM$0DcaUwq@avJZiOwseM^`p&{ z=rxpi9$S4qUr%;F9@oIdK-)iv@+I9EOdmYT$dT8P!nX6Y;w5fAy^DOOc$OeViiM7J z6_qtLt3jfdPc~EDoox*2`eVy61mZuBbw})BPo`vQgLvLtIneE%+=V4xF; z7k}%wbss#Z4wJ%slJ0x` zJKcD5EFZbEk0%Y%GqPkTXmi9fvftR)*%^)5rqy0-7Q*f31(2@nbj}$YVkqQCQc&Ok zaA0Y(uykM>1rL|@_4HS(38qX!(nVa{N35L2SzkP#JYIg49uFSoWQNKzv9v@9F+BXy zle85c8EN$94cF!Gp`lm3QA}z_$^uib54*-S3?iBV3jDE-^?o9`BrZ%a=MH?Ni6UH7 z%F&80f%F;?k&&4V4T7UVuX|QY-*mFXm@b8A2n;PLTjvd@bI((Zm8+-Q|Z!9&0^>7 ze1pqm!$z7{nd{EX{-C(H_?O~h`QzoBcHq{*Rv@fNVITvR8S`fp<6~onV5=sI+=pcJ zeEn}urS(|HV7om-#D4B@S}kX)jN`mHCffM(Hj)>mV0UBj^ZK8Rm-;+vKL2jNf3a0c zNBZy~0l~)it_huZ9>ehy*Y3j(Q@LN1RJ}#s1)R{jg4$ZXEfG^S6&2&32@|Xor25L!G~0 z6{Mo#c#%)w%s7ujE4r>)uf@heX)fYl4Gdcdv0ZU?Tw`-)a5Z=Fx+G4z6EV58;?!>DJT_7j zMJkIv6Ijbap{f>|1R>y(7ksScQUh|IqeVJr@NjYU+UtRHgg$I_j27nekD~)!eWf>` zsY6A@2rUXjPR$cif@wg%Ju+P^Z=pT$Br>)Q3I6g5&I)eEeea-ag01j*4fAhX*<&5& zRcrK){Lh-JQ~s+%4rUjSZjqip*Q~_=)<-m`02)S%4BKu|{Kxl!#(piK5relsuV}1) zog(=2|NMdBU2_zR|Jyw0+9NS9v7P-9F0R(Mi)e~7%u1ojzl0D>6%N7W^X)D2_JW-q zaBt612J(q(_apOzgS+oeL|uLZSJyap4cD5M-?y3j)zdPJY;Nu8|G2=d_B3Vlkh-zW zQUni)g>xPMK1~!% zKm%00&q-Bqaibp2exGTw9JLv4=0oU9 z6N|Y+CKl)U!`ZGkA|0-hIWh=oS2PtDAlBgjj#$s7xDo&^c&3EV`bW;kx*|yl7v@4Aar+kH(*QnS|)GZ`o9>t z(0>^D$ti7St6}xT&*Gc2L#rdze#Nu2B2y@|uH&cs%-t$>_orP8W)3N729>+Z#zaJ2 zLJc*G`>tR2YAEQ2oWj-7SUA6!AFh)6pNu?ueJjW-=)q2x-CxY4(3OVfzXlH~ zHF!{3I5&hSj{^pSm9=WgRN1+dNbHsT&C%0mxFIO&lLe{scK?^<9rs0hQD6xPe>wE4 zW9}^WT5W`3cnLoMc@X7@w;pT%0m{<#t%^b!_-oT6pZbP|gC8-9M&FaT0g0Es&>YXU zYKB7n&cWQmYWA2V=RRRB##e;{b0Z{E)XCt!GdMOi8*amQr#xyw*t-b2nxGXEsy16S z^Y0+#04f#DO_0~<`r*AxK3nZBmL(~(^78tL64-kZKFnQ=r0({C09Pqa_lj}Q40jGa zzLaX*{4kW4f8dJ%th&dhc-Y`Zr~q-g43=#arZ~X?HiOX{A=~#egek`My=YbJSUB}T zQMx83q|RG!?+WcN&S!kWx9LLoARJLyq@cOxV`{NWU)lUlgJ2(UH{B~3Wm;1QUov(T z_0aw#CPcxJIk?=7hzK8+$EB@yUsb@yN+Evh%>3SYpppY@pFv+Ys$SXX$tJKDYl{k0 zPm}U$nz~k>pPw7M{|qE@-~29e^Z)^YC@8i3IFR7uB_5j-9nGVER8-AWEtPr1t{pTZ z;JJUsl*%{aAc&lD5T~?VeT2nre;$AQX(Pe9O0nw1O;3-3Rrp+|_VMMT`a4(ktAzl? z;F}komqvU>9$?0@*Xt896_J|I2L z2EbyjUfTX3^))5W-l5@hTmIQH38P$WKRgxr!B<*}asC8?17BDdYe%yhOM@qYZz%ty ze;7+VAtZCVa3^#pnjZ1VccQRqn>fI?;5P(xyltyK z_3fq;t);lNf!qouRi+KEwK9CusnJA{a|@s7b(J1m7|)cV(Wgg5HZgnrdXua3h1tnB z9vd?7c;HF{yFWaDWGwkPBR68@ zJ2^pgb3`~>c6D~Yl9_-+57BN;0Hrl+&oy@!N(;y*l?$MeVUbM3x0J2+&8C`;>1Hn6 zVRUT(0n6OcnBBy^%=_9rrNj?*e{0s*k?|DGfslVQ;M5TM>$AGGP})z3A9R&5^4)fa z;%8HK+IrW&!N~H@nd-RF2F_)`(Bdd2b+KA*heHNYm%OnL~Juv_O1G=s%a`j9JWxq_6Oy1V0&2r z+u*QtXnt$G?=H#$25 z79GtPDV03w9fwKWJRN?HQMNDZdvC;OaIhv}9Jmx`cl!&L6myP=4n(HEpCAw%b~y>Q zA`2^WRx{1mKs~{VC|{4Z-6ofnmm0^;-l$TcG$uoLCbs1txAa9iy%8v5}!O^Y6dC_^+jLfiwh1 zhP5*;?78Z3C`5j2^zVIlw<*3A*zQctjbu;r$G*XO|8W3br{zULRjv>ccH%CiS+i^4 zXghzLC_b2q2_dZ@woAp0M@7I8b(TxmvYZUL7+kLPdvwAtE!S;?IKDn4hhHP2b*E1NzTO+w~ z1sRf(^ptlEUp}Z@mCxKaA$@MF@6}p@ESA(*#oLCG5 ztkMpQ$i*e6_UMN_d)`QKz23IZ7Ng87UQ;1!^65ZhhRn+XPo;YabI@CL=#HO><;=U%w3g|EihVZw)XrQz za^3wP4oB+dGpbv_!+z_v{X^lq7lEipuWs`F_ib7b4lUo40q(J*VN^79KDCoPpsWztVivHe=cS{xXW3`X zJQ>$XW~4IN$!8gr)~6g zrSDrpoK}54nuwQS>a*;lW?xI3#1YRpI*oN`d+!UD+=f~m&BWPkKi0>eHQxlDRBOQI z2MeTDYOrO8>+$bz$e6A7afp@ve72F!N!yH+cI8ZDBG2kcDt9F|wXsmOT4sOQs1}=J z$9-$;`DeG*HX8QVfJdDV+frI_f{2ZYDL{~>?=#Z+r_1Eg+@)y8~3q67VGoFq}16>7N)EB3F zCGd3f*hk;EJs(R@y;^bloUoc)xxxuJ3!W6>pAB+CCVCGgqs6bp9~T)b(TOxbfO_1v zda%?1-+A26+|{@DaCNE$gYs4h{HG4>dK_7Ja4-i8M=uO87v`dL)NT&w^A=MTy3YU~ zKn?)D>!#;8D||SDLP|pD4>4T-fj8%DY|Ph`slJSg0RNY0l|MGv5;E7yy7W1i|0<+5@{|*!{?}XDj!N77r>k z>x#)L7&EOOL_hL$d8+i_5DD#eoPSdSVd;F2!S=`T`2>B`M-Xe85%m1pu+8Ck>;l%wLe9HHMclEec;h$| zga`*f>uu&o^AYvoKc{BQPfxZ=2md_a8MWgM2-FBvMk}ksir+(BWyj1+Az(*Y^!f0s zQ3;`Tb)=ez2Hw*-YYAVq*#rZUs9^Lz>ZLjw#qglk*4&Xyu zcze~o7vA#LdNZG!ST$$qtMIC{P{fW~M08I2;Kb1`U=9bYH&x?Fia8|1hOsmZ5sXo#~*7*Sw$a9%j4s&Yuk`@Lj zlQxFCIPZrz1$@d@X%tj-A1y2btz3y);o*xa2ibena|~5Lu<9(>+GaMtOw0){_&TI&DkG5?`_g z1*?T^P4o7+_ROvR8pol9RlI!2KCtG;lsR8Pmb}q_gpvR^>bP5^38xwjie|rO7S~oI z8>&waVPZ`Wly9IShx{yQM|y_at)L8&|--u!=Lzk#j-DAUXv?cwvBzV&8mqhJ9ohb7-8;9J3HkVm8ULl zbQ+f6s<^&3MmN+?Oh(oLB;T=g-P})U2lqY&C{(>6%(ed^k|v(H{n6g#>7ydu8r7J* z+bC?V3BJ`2qf<4581OZ%KblwVl@F>2WEZ5uuV&av>Hg^SAfd^AnFsR6qd~C`i;u!_ zBLW7dR3H5B_9{Q-HKI(%8B>*qx+HM;S3UJmN3bGNT}h-OQ#4n0wK(!6%REiDL1 z%k%qqFYjRJ0p|+&{qBIgxi06I(7I00Bk43&B3Xeqg11#4Y`4XW2yyEL$!CM1((`KV zG;IHl#mcubw`o4<%YQiFyfLu+b2tpU)oZWuw9IW!UoK6E2qaVbm!fl`)ayI{kz*NU zQht&!be{Tp;cXcR-u8Wkie@46Rtdz?s zCgSvbW%r~X%35#7*x)%AoX8G4 zS)uNLtn{roRz2ct51{fx`~-JvZ|T(7eIHUXRkz{V!Suy(kVm5-%3vNCx^X#)GF+>6 zwXw8B^~*MPVz}B4=FJ@&65z%5v5~wJ6-wV=d7P{^IcYq~syKF7(}3>SbEXw|ivMUD zOP`HFu)SI>+|;z}JKi*PbyN?={H@}diOBCPofjh@b`jd&{I)+Ba*;{4C;gsU?bP|J zqFj2)#odjMw6c_aPIKumQiX1fWm&pRF8+F*VjH>Q(WkGyv0m4BMZ`y(Irn+ozWTa} z(}&+Lt%ri^D+TvgedXLlvI_Wjyu_^WE)&8%*};PF`J?~cJ{obyXlU-ZQ??sndC?WI zDO)FwIa{&wsE64@=TUEFXx)p#Mcw_|YVlrSxMUKH)Cb%60J@wdK^Pno-m_VU0H>&G8C7={xfY~$$p zWeBC1Q92PL#rGKHB5MA7PxqNp_@UD7zlaI_!^3)#l9Q9m9P`l0=uXRTzl(g1z7fLEHms7vjeeJeChC})mvN&~p^&Impjtmq5 zG&rzkk{wlQN@8Lkhxv-{gUB{0J+Ylw(d0Yu7eGLH*kpj(uXi&DLSx|dvtB+#LD@&A z5yO~w?io^;m0K6iFQa#%tZuXrddw6`nn)6zWh{@D7u4pf{97u=L$?7IJj*R zv{lR;FctO{?3Huatt5t&mJa{dZ&L5?7{vJ-8#lXC^;SY%QYK-UKQzgh_I61-8#qPo z9C>6Jd|=D9>KzWt8sTv((McL}RC)ai$afd^oYJL17jIjRqKw=&o^4W#80PXk4s9%5 zf=L~oJsKd*h@Yzj%b|1nhIj^hlrOmao{BU5Vxl~`YF2!!GpM=im zv3dz$J_qEqD<`Zx8B!z{f?y!ka5Pu{8Fkr&E2` zJ8mF-7=1oJ$m)Xdpe$W^A;f02Ljf1$B2qGuSNg#{)s^0+S8}~}mOb*6+3^IbF1=`J zT5JA^NC|u=ObY=bY`?8d8OL8ei$)4LB;ZcKjzA3Jj`9t zt`gDzgc)HsW15+;-%ZpWa?AZs;Q1U#zHFVg74YHyGJ$aZgOK;lo1m8odie5+eTT&F+D6iO^_-yZ^9ScWN|`gdWFm} z8sUPfAZ|qRGz>onZ;MxHQ;RGn-}ZE=kj}yNvWSa}{}Cg#Estv|h$Y^fMA3|^d)ycy z$@Q0FF9Egq<|)TJ!jFEh#&M?cG!RDMsE3WM*Yz`ZisEbK5qFTcSFsY%Y*WFCZns_sP)Adtu>bN)k%*wc0}S6fzW2S`8&F=$ltSv0Ky{pCnXF77mxh zu$Hj;t!sw0njTU#x+w}TYA;Tn=T%~a`{3uKjk70Cxb6;fN>kuRlT+JHbh~%j=&W6Z z!GMUAwX3U{SAVJU`)NkKuYv9u$EsyT*f7Sj4uR@3E55!zy&I=LAyi+T_`aJ=|4Fks zM0s#CLC!@GGJXrOEi9Hv_}|BhjF(_u9i*USiJZ1%h;CR$7psyrzYCC^NB=~=&(e6p zh$3*BN=jmt+Oz@>2B8!A$LS3OlXT%a=_CchE()6P0X(QAN*j4+5e5|K>mx#vFVcvJT&F+qIpOf6o5>%nH#QHP<(jZA>w^U8|xL7`Hi;`&PQ*@^L9sx52l7b z-`gEbD!T9wD8;k_nQy(L7MVL;5kv-{94{&+KIL?P3~#GrZMeFka8<&NL1`1uva7aI zdn~^Y9dsRO_tEiKOCW$Y;a3d<*d5j;+*S)>qxEA45Z7|m4kE?lsWPWqn_Ku&jeSl( zeIJj-t2=F>udw2G0_jKu=3eT#0wrzr1LWWStI|*^VxGE2iDb6oMwtq3TMEiVWO=s5 zCPC*Q6;I=r(nv@Sw#FwrBOLEYd7piYd>+gx8Zw{S#CPqO5QCCJP9723 zW*Tm5#V(K#d^geQ!S*(`d>RVIWcEM16vWQj3#IB~f~6I)3=M)oYFpPxCAYEZrc;i1pE!dcCi0R6{tU80pogS>{RcdLhE7UR&#e-me6&Z_1I(|hV zqTZDeI@=5hu>G(~gV4G_)7H}?BL*W)Uc9_>d`!^HJ3BcUB+FWd_U*Ih3zL#xAg}{r zi)7R?0IUbabbT%OrEiwf3R8aOC)i)WVs}X9HstNCgUZV}RMpkZel(6gXA?-MxQ52= z;YpO=YGLzP46!eLc4`R^P}=)Cpd1JisiMfdf-U0MUKI?XA8s99JYjweB3_uMX&cOg z@#p;3&86GkrX5ouOITf=B3Jx}U;L1UY-iSQhZ=k&ps&l=zJ$#eb>qer zOm~qToou>z6n=3@BpmstI5IlJ1^X0hwTJHJ!9w6HOXDP{WdY?a10&TvjINKC(|s>h z7Zfu8u0Zt56WsK3gC?SBHZbFuT?6gDDz_lv_hY8bLc%LR7st)3kX^hm>HFJ7uPZ#f zTEH-j5+%k|ts`}s$2yVs3VwQv==G}TinRek`>e|Q85dW8QaZ(V zPOn#MC#KtcO+or!!-&PV^r=o(k}DeIY_qCi^~auiQwwSl85tR0Q^k_?xKR-G=m0U1 zcH%$5{p=%NsPUKxV z5hq69qLgao_BapI7P`NR<*q}vKkn9{$>Lk|=JhunMA#dGD&Dip4biBYMIVyWMc?LP z^!Nt_b0TALgop?zqc{6L@$PSKafSU_0TjHZCC>*{14?bP7Pg8fK(_D$HEvmVzK3Hy z&3rLjUx@-NZL7o?xJFq-L}Rt&ic1{F8W0NI_k9)G$FEGC3lBmEew+QX>ac$@zV+#6 zA#-e9C%@u%xr?q6~Lpb|bALVOXXI$^c42{F}QQ zp^uo#%m>N64`b5Xb?h5WyV#WiBnGZDqjsi#82HNe zzrFR%p(|SHF56f7w-gGs%WW(J!kRzk(hr*zGn6ym@$MciE#*JqLqlo$j1;{kUVU_l zsfqm&dn+$+8Rvb#7Cx?NGRW>ap00Ra9=Yv(Blw^?IbA#YE<#XTdc@R@?yCP;lhm zB*IKBYF?nY!K0#trB?TNPFdFD;{jhT+LFX2=QkQ3*uVTuDeea$ z;v5Q6NeHLR6b-qtF=T(bOezzgbkt}11?R<}-&TCzE4q^v@l13!^rUBgLwV$6AD-cE z?86o}UJ?A12y>Pi({079L-m8;K7VQa8+G)(f93W3Z~q+3%8oFCwzc{*$JRRMjg{h> zh1NWgx{uJNc`(+Ckd|Rep8YXiv1*rM9id4KoxkRh*{d@0WoHl|GYB=r?#8^(Gvr=o-FxdDtft$4YwL+jE#@i5#YN! z{aDj#>w4_zv`O$3xn^r*jFD%Eqg9WvXNZ{G#gkrx^Ee(;E+y?j!(YtfYaEBv&wGn( zq)P{Cs_%ZO(a4fEou8jyzPUNy8=Jg3-&^R&Gbf-u7OA7rnPFU`qh=VC?CJ-!jJrn7 zOC16RHbF8XG&m8{WYRb$-=7h{(@jcJq>{tnXQMREi#xP)x*b}re>Lyi;kq_f=)R1g}%4Q8j^C=z@% zLk#X02D-u_#oXL4(le$X3Nkdki3;T9y4-SZ7;84^=W;}%QtFE8lFXKMy znuAOX^z0-!(a%>lB^W)2Zt$KYQ)7v#%?o5k`$x{LjK#Mo3EXvPr*l)?=m~gA>MENF zxb|r4MmmNJ0y&IhA$ceeHxG@dtGO_V7w8Kg&9c0hg=J3OTg|+A4LNRQ_)7adkbw2C zds)W3N0mi2TR1}AyoVs&Y}c>ka|`IwAejBtu@=1^+BM9YhE=mxx8nHy^a+)9EWDgU zQe;B(Ev^9$s^2$Uib|1`34n7Ef{`l@d;I;(Y;S!pnRS7i5EQAWyqx)q>BtiHV9#{q zMNEU&m{>|;uaF`P*H^6QG%$N|#b7$@#t2{LbHk6t^QHIt@Y!kN4YO`KztoR!&k?Cr z?6P_$9(SRFKnjg$p`?BCqOco802XG+&=2ai65 z41vrKI7S1{dCxurjYSw)&p*%S*_Pcpsh|WgQIFjm67cw1pJTh+K%EKXAn(LZz*3q8juKk{Ccj|G2c7v zZ6=vU;WZ`)5vo20GgqZOZBtx%vcBqRXKET3IVP@>6=8@OZO>!UO zNsL%UY1=Y7!HRA97pTh97YK>K6RnFEfqeOPX>u+|U((2VM@;PO8K z#~fBBbnH9e#$A+Mx)Ny5#hwvXt}W3g4cE@Rv^!gpfG=OExQ>QxnuYV=uH9m)V#Ve4 zuHKEh8>H?vMrBv3Lb~!Z$*2= zHMQ0OC#35#uI9dx>h|o}Ge4l12i)Ahe}4)V&XwCycgrvPFtn&%*DqWr#f#EHM1a#t zuXA$!Ehu75ipKsk+@X~@_Be${4Aqgp#;eX;>>YQduUo!Q=2uCfj7*&|`gr`%l(E){ ztO?%Eny0D(e!!53YE76{P&X^+*TocrQ}Wq4>IAhlaGGTN zPH3WM->|`Hnc%}iCuCA&6FVnRTh}&XC@4s8RyDrV&9+TOgJhzi`n`b)mE)%=T{_h8 z((h0H90`)e2U-y2T|at=i%RDz9SgF%mpCLf3-qqbJR1XF(n|>z{YkQ3StTCc0F)WC zwhLe3*-*HH@xjz;$Fb2G@jtsyz%Qx3UV{d40g6&4hk`yR)B>U`D0e0WsuL(02O8AP zwTQJ{#KO)_VkK-8htO?;$A|nfluyp~~r$?n=z)ub$k09*7fK z{2;){I-Tq^A*0r%S0S2xwxaNCb=uOr8)0?;>lnK7_9}z9OHb;xWF^49Q765p~ z5*Zuky~|2d9Z_lYJJwk@lBBU#4(X*tqgNTWv6Hbki;p!!PqR1o-=$6Sgly-8!$BeO z!MCIkN8`eOvmIL$AtbyFq*RedUQZNt-mZxX2iJ`%W`HC*GV?Khs(exyeo9;@J6xZg z7GoZZ*PHN?Xal;i+I2nhw7MEu5KT$x}H?C7-F+aeG5JV7l+hJD56PTHK>=Kzc`qGf$lCk>TTpo>uIQkhmgq(84(;ekh;nghDK05}U? zd($w|>=#T_^Sz#u$XTKdT~mL?Blx=rn#lw@kxmrxx~4vI0(!@ql*qKY_>GKO(hjSW zZ%-vGQ+4w{LHSV^UER`ul>|NmvT5GACspr>*&>_4!-mlPK~bO37z?cy85VYJbAWbm zE`J_y*sz^RX{3^Bq`1hO#xAv7B~#Pxws8gLpbSm@+l{Eh#9=~L&RI!3%0dORvd(BF z$r-Kf3l6mJwz1Xma}5W+JEt{bYvON^{e29L z-TB^RAoBCLzO1p37|asLFQ9@I8m{m3G>KfstX#ZY=hcK8-3|>6-3KCxq}#lr&tZ%- z1tCfe-{}%CiZ3vjSOzp-ltlBs7PYFHHJRk(dLRHm&8kMSas_1wFs65#b;M!N(t?r+ zp(r8W*gZfV!3LRDexN+d^M@TSd{~EfMw_w>kkw93hM)O)dTd8|&MMDJ`jPqwCsr%Q zVyW3tSjkF)(|^+EMoZk|J}0*)t-rWLMm8>FBJzhdqi2(0qEro9`Lvs>bVSk4_ll&} ziPFRxz_F|0(b0GLDtRlwcTDI;4T@C^0z`yBa~$-)?)T9Xd)+-BSbCHY;LP_96Y}B~ zuVqmd-;<2{8wZ6gE)G-}zR(~?s^BYSmdE~!9s{vcZ{(}EBxzZ_K(Te?5!Nao%gjZk zE#!*6XB*>k*FAUP8&>P>RA(pG1SQ1?#?39ZCfB7goD^e*6zAMe2bW6vDtMovFF)X1 z)aQ_h%&pAbg4Fgk5R>N@C@)=-lUT{aQ2=A|XUn zCMlBqk3-1L8oAJ~;Thj)2oV>8xHvLTya;CxYWJQVW!-P-h@Avn$_ASmldhXb&@C{X z;P8*kB~eFPKopW!ageu$L~Ih0@{=3 z&_;Jgp?rsUHy2$`E(CW@lD1PF$Kw%eXZS1&p5T5Q7m0rDQXqExU14{P6095VFlX%9 zV(CD-AsT7rD|?yZPbEA6G4r#?wd&IE@qi;OI^Cq4f z-eOJG_>SJc5!~&(cHmwHs)Y(laUY=;qsg;{9>^K8Q;F7P3B8wMk@f^kzPgsTb4Mf4 zZ)wUC9rud+JqR`d^(Qy0f0#HF6|#JDl7%sN=W$n6^UL1vl_O8DFoX#hc;>@xKhRfE zArkdHotaVa$lnO(3E*bLdv^QC=Vwlzys?U&aE&&mW($QIq&+(RUE$x6z(j7Y!}EL# zffH*Qo^7E?O(=t)LFCi&Fj|I#(@K_HqKLd z?W%GtGB0iM7S5gmN^ zUayLY=SgqBPa>;?WDXTe+)fnzRZ;dP@jN<$D<^5Z+T5kTgU2>9r_{k`m1MES;O5xC z*w7HN+8NQ7Y~K`O+!dLTpO1NSquZuxJCCa4X8TfBC z?Q`v1l2pAU(N(hf;;d&N23&qU>00Dw8|KAQp35Dor@0r3jS^fO5@AGO=e!FAq@=`` z`vnEL`gGjOFzp6qYhPD;P-mz8V=fACEhXxsCtR;_E>zG&O9&R($Nn8vF+6%<&}MX9 z>*B7tk`itJJZeByO)UeQN<|(i<#VK}udm0mkvD6X9Oem;dLr&8RSRoZ0!h$D!G5 zt}(M_fl?*zqV-ACm9N7#S7#Fzgzg7J?cn6wNNcNCY9(Omgk5bqnrqLhTh?cFAX zU5W^dSoFL{(MGuCg&A$Adc^%FjA?Q3e(1^UpRX(+5*z%1oqJ;#Tpl}J|5UvFp8xxT zzGJJ$TcnAmZplj4HGZGrDZw92E1Sw5zp5@*=Ix(gjg0ze{tMqiL(fI+teJ_4>0m^b zZQ51rO>}B>c{IX^nKSwdd=-R>-Y&?~A&V{a)i=WlSVs{?sniwQcAVNf>el?Yv|Vxr zrYxUrOmgfJiuFh-Xa`3?#fI>9cx=R6{DcqDYgv=|ZN}q`)HjO6`(!rqZ(TG=t;ElNzAMXx}mN7FhF^L1} z1-?V%@B^4gu~wi+yYrMyseb=w)PQ7!3pYG1x9zL#sH014qoI1Rc}z^TAb9Q(J}(s3O+bd_zioueOlsh?2$=ZJX)x+f3?3E!S5FcIWl}N>XtF z7N^}VPn)ijmX8hvok39SYx?ok4_voC%J}0vc`fvnFUau&O7Mp#&)XZD9zQ%sJqvf{ z1(F539%0Kr!b)kp1qFma&*%Nlx zGhaGMm2kNciA@&`1bOR>JR^~>C5KNfg)c*J1aUo-b}ZIfp)cf}@ZbDYp^sO#w+@&EAdoibYg*>RL&c zgy0P>VDw8gj6u-fexQ62i1R7ejE6lU_=o}48$wCCPYppw>&9ro49{`s#aV*lJP)1% zu?-+cGdXtv@db)`^Jife#eiGNwYgj&Qf*uG`NM+mG?uc3_zQf#k>W^hT>bdVg%t^TZ)AQJoWX?b%1}~* zFETJ3a~1o!esHvJ(F$7&`)^d*jmP&Y=Uxj*#tkX@r|lln4P>}DIaHRuo5 zF=#}ySktJ2Z~b3(TsOR0c>h$77|9t1VjvlWgd(AcXT62y$#xw4x_1Znc>u=hsy=AK-OLXTPPl#Sj zm9(+)Eq|4{Tt?fwQndKs4XdgTyt%$P3rDry_=1r2MKeGqt__ojOwCIAUWPQZqS9l$2BkUX7%79%t|%u zeEx-2!}$s56twFxB6VJh!3Cx4%@R|esfHoeQN61|`-?YPoQckDPxfFtjzWh#Gqj?u zg{M8=9gPE=#p)iU+3&m_9*m&e%H#5FFYz(wmW~{uTLrlp7F;qGg5u-MI84i|*)pBT z`$gPSA&2?OV=2KC;ZkY3S~1*I#&&l6!rx#T#TrJbpzIP#&a?Dt{tWj|SY|3#7U=44t}g(*C$At#E+DUsor&+y}Su%B(7R-@%@s zLu&84Lb0Ia1$C7#Xf~8M*s=UNc{tj5TUfm`TKro|Wm|L=Z~gOl_H?w-Of&Ggs^T4% zSCa~+n6d@a)#aXyvI?LJvylk?s_J$5C%AZ0gg{Kmb(@Eb>*16|2FA1c?IYb~e*Y^F z%*DjT8QR+7Q=fH8WW@HAV+-TsW8bxY`GUJ_Kuc*(sgM6;#v5)q?&nEGMkZoiJSAZR z=N{nmee~fslO7!m$s>d@K$sYH@{NSD_TO4+B>%J5uJvLpNqN0FrPnaLdS_y8JJ77W z4Iks8t5}WDy9!oeIxg^-na827V^-Bkvk#_p%=u|{g!SsA^h99G)W~Oa^FxMHhm(}* z*D*7$p^MLBY%9A*3qrJjYRmLRcx#t3ROi5^t<6+fcVl4 zB=dH21+OruPn*y{N=cS24huki5PXike0kWTx=VngXp`lcjjmemy!%+k8sbHajI}Hf ztKsuzC?|6N@cYhh+erWYDeXOHyQYs7(v7c@+}CV zBf+^D=MjUJY!PII<2%sputyNF zABgLFJ!EPK;iH|;WJ!hIK6z;F_wl&RE$k5F5}Za=)D}BUUF1+wGRrV5aeX^FKTQO^%LH&eZ8GuC*n{izj!1oTB;mI z@I8}>9=Rj)+#<^P9te|X9K9q%b+u%jdI^&rQo7)EIhj*~`oG@QhFY0P9~n?Y;Hqic ziD?Hkfy$n+7B1VEso;gi;mOG|_4OjP?ZyZTVi|E>&yITv5;DthJM+SJ~NRtEmgy0Da55DfSEdPl&wC%siJHraA_`Z>&rM>Ow#*IYp%hDfKIB7 z6S?$@32#MK+>GZIXF{!(CufOvg_VytQa8RRlON5n9P4!7En>+xHb>sBNV|FhS-{de z6>(0WK{v-BFtfH6v$VAAK2CeR6=Y-*ZT&kwlwqQ;-{zbgXWz z_OBV6f)u7HVuDHf1UDv9y4xZE)dia|p{2A1;w=C|@GSw{@}~Y%4e?16znB+>sgLQp zjZF+EirX6wG^O&h#@UW^)!bNb$K7?DKVd4Ly8#P`{E--`8B6!oq%Sf1!hfjcpPwIV zbW52iydWb`D3QL`^6ovXe$y!9gjQNLgUmX|x_pi(TcRA+jiU!jmdvDx8w9a1WB~+i znbcS4fcND^gKlPJ)tbm{`9@FAW_2$j?7=E7D8%@cxxCq)Fa|>moBTUOM@{(a#~J*C zjzhf-0b7xII*I11fyZLdV@da&EYHUGARUp4^CS#_Yo@>MWTVS$hfW>tC2=~D!u8w>lfAxgXxA-7@aZ$HkQYTvbM?fYoVKzzDkfc?5QFQclT}+DQ|or-A@VAnpvpAHwvi< z`4`=u9M6tS&2h61`+uevx9vI||H*acz577^liMM70ot+t_!-uOZ$g5=_heJ>kRUcY zgpy?e(1P@5_H8^brjvy#5;QB%Jn|p>O_a~7wft1YpAMpBuBo7g)%?tZ8{(Roc zFT%Q8!;g>a=2p**UsP|h?O60NUx2ttVfFgKD%Grm{CfHrILii9UifL=1n8*ctCWp* zbvp7Za#MT;5#W&l-USsyTQ8iiin5U)K6pUy)Gaxh2?f&L60C4f*blL<_#f z^#~Pv@i9OCX(^+s#FMp7H_I)_`||`rGINu)v`j8MWs#~HA({&nW}CQ@-Z~0 z?{>V4I8(0vo-G zs;a@xT>ba0@}7*?o!MGqrCd4qN)t1b|DDG6H*!R!PMI>AG|QA#H|ip-lGoG0w>S`cWU930YlemXXI;oTh@2j zTa`{;FPZhG#07f%9kRx62#@sXVQQ29RtxGOx?JTt0+qp5QN356Vys;}Bgw!<-f+3X zzFboKr@3~gA=np-SOgz+Yp$L=T;QA;k&T&xh1}_rnJH4OEFXkDoq7(dye=k65iK4M zZMRUec*>S2nUc_;pQFlfo``*+>$=k8V9rZkpq$2fovQ5H5w;=7BW^jZo@q}&^{UzQ z=bPhB|Ldf@oAo38OxOv3b_t0#&mgxqrab2fmTJ|?m6Qc|H818)8vFrRi0^p9@H^lu zK_J7q@*UoI0s*D4ZObaEx9NpMZkYmPMG}uSC)Wf>tdj&ZS6e|u6Cf|4&>uRsDQ*a7 z;jZjwy0~|rmkZV+QyfXVR?^cDr+c=mO$Iroa@}LISc=vA8#6tzCNEUqWtveFkUFU_ z@2qoQYYaS3)1`hs^?{whjTXgkyzj`PzrnK6=nrv`RnJA#2&fQ%lEv-IM?Qag*U#S{ z36h z6sX6hpF2@|X>JFWDv6hnDmzD3>wPU}-H}&p8A8>3D3_c)2102>z4^5cADiqC@}at2 zAz3dUQ{-Soh85cR*&wLN2s;x>A_f+*&K2w+8FYI(bU_gYp662n$L^1NVpfwme7R~7 z3!tkNUPq!Dy69)x1;kd*)&!M%q%Dnep|Zex+TlmByx6wfkYookG7zvffK~2z0jxV) zn7Uy^_IDs}3H=o4PMhJ6%T>k3#2IdSkuW@7{4JEBn6bJB!osnK$sF1k^(CrSyGYu3 zblAg2KUyQyY@mc*rswD z;r-U{_a?zH7QB5v;IC%oRpB564uy;o9dl{Bsq<7IwOvxTFOB}G%xq5ZdM`W{FCe3y zAiglN<~vE5UQ^s6`pvhj@-(DqeWBRhr%F7W;;iXd=vQ;n)f&pL=0R54u*8|qf-n?i zcPq0sgDWCxEn~ux3}2d&k9e=bvnZ7^+QLq)>1{Q1!*rx{?<=gZnlqH%j}lLdsAK!k83;NKo+8O+;QvK}b z^KylZ&2O%R&mriGj_N1@LjzWgucOb4s){4B%;6WPa-CuTwE8%WMrf<)phDza;Yt+IZ2y63m0 zh~8x;-o}`iFli%@mELB+t7-AD#>0|9U!vsS>qJ}>PFVNNMU@RGJ*?eXpWRKDJ#;X%0tvhmir}0B~=^ ziuPDUHl1u|Q6aI+3A=PioJO5OpYB1AMk?1HJ~Q@|5v9*0)M*@z`idlkTl((+=Hj(2 zwXUoyi82GdTCL@h2zh9rthH^`Xbx@5MLeW$K`gPUcf*TFeM-v{a`G{;u;ExM&nqsa!T z!rg@J4;Jg|5M`pY(W*eo4o}I$g>Xgw3qEs2OF^U;U~M6U%G~cqhRF4-*qsDOW11TA zRw{<8+YAkWOc8=ssqMO+?Y!T`w6|3;fD8cw?=r_tRrlXqX=O(c7AUU&BdiJb(WMn3 zqPuS}7Yy2mDfXbs@3+CCF|pQ2z_il!f>v#TW4;gZrz?96?eu_|uq|TJ7#*MmMH+Xg z5#!&dc!%|*?F5##{IZ9xY?Z5OUaX;AMBG_CA)8-GPu_@gUphxt=Ww-Rakt zWcvaBHr5FXgt*l6dTmqh@99KaPz!_co{~KwL#!BB`Nr02n%XqqK0K0{9HUnOM?--Z zEz5i~&jv4L$AgQgp?BMkYe9rz2TF6_!|%`{gbxLxV7y>K_R;>h-VFKUa7EbEHbwNR zUCAxn`eXK~%0kVq8_88G_kF7DZRQgU6~ic#D|=E#blq*YTF6hfR4Vd=m@D^TGXkB=m)$(saua=QhwlLiH-2Bq;j!2Z5(vn5EZsV9I-q^z+ z)0Me?t755VADS+@eocN3&9+|Yj#=AY5#5T^f4;98SOTx1oRnwRoZfOWIT1b{ewC&= zz^x#l*yu+}=@d_%`ZZ0VhQXnpM?l;VB$Y)R{gWxJ4V)z zl$E+7|Amyc$9^eBGA@Q>N-n>9sKcMsC5cRx-me&HrL?i0B46y$aRBvYp50Ep3K{CM zu;!@=i*XEOZvEqtStaf0F|3zWHsP*SqV14X2`ipfjGp)(~ zEnXw``UCSLZK9RT_ut%(xukQAN={6s9!+ycxHz4V!>=Ye!g8ApvIXLcxsy>O`2pRf z8XSwzKO4kac$Uitmf%>MUJ`kQ)X7r99n5~sYhBGGO`}c9@3?F7k3-KSugDmMOZ~)3 za`jp=^b7?v-@pFtnwHm~*Xkr|*@9~L~iDl?$H3}zN zw%%gaFGPmCc&H?AZ3-{ge~`8hoBus&y2yf;kHsRk5l!;M3nb#FA$E%}Upl)xZ?`N> z#L$Hx%>>o%&TlQ^L&w5o=cHB zlP?^jUIp$)Ou=`6)EcCWeWC_Lj9$>tlN`O`M_N*^d_~iQgn@((5PtaQcKJWOzhzA- zTwz20?3bv@rWmBr*XD-~gy`;!Zb({Tn%?pII(B3Ux?EA{E|NKQo#@cfF43EiR@M0x z{S`&2bL>5nq03OZEVP-lEtd|qO6SCs`70Ef(gg~XY0N#6^o zx^c{@#%d3VcoM~10fkM03>+k~ZB0ICr5jq&u`R(yu1s*OfICBd`y#;4qWOXLPo!|@~Fto~ep z3q04nY&3K**ApQR9|8S?&zqCquXDz4LXKsR4LPN)v7dP<7O`{2=HzG9-ED6d|FL`X zif%0M#y*`|%vrRCVpC^&Le)BQ2%}P?!X!N}k2%IB;^Z)rhwjzK$yS_tn8I(8m-FJ& zP=SpjlYygOxu*f$ibsbzH|W}hHJ7Fh;a~b!rG5-*#L3>YN9#qtUa6DhFQVV`z+B;# zT2EIA)&pl7o&G9`Xfp9AoXH#JsNT6ojrwnD^rCO6zea%b+YQ)sy&p=Q_B3AhhvZ|b{-$}2e=gk{9@$+N~M=?vN9TG1**G0}pN`ASYyC=ryEQx)U_&I&~} z0tAx-lGge+nKE2%Hzp_Xfv_e4FpA@y`EWegny6h*DTp+;<_E&>PUQoIAxIC=O1O`R zJx-^0;-`qMWsT4raf@W}&Y4!H_ zTDD$CBHS{Bkfs7}q7$4XKpNN8&CL#^!Ngew9XIYJ%l#&t=ra3cxaE4k8K13_4g1SI z$WzkQr*b51=GDn892&iyXU~&1r1H}psUYAn0lF0MNSg(ohe#yeCGAGBH-16j19@9u z`#|WuL3DYj?)=5)wI873bNo^oqh7OjlV9OHZx#hfhBRx-0F38x*JZdO?8Ol`q6nqH zk}O?NL>*>pe+Fvew?niV$KjdrLcrr2ua8PO?3SRArpXG^z1bZPVxAdW?_xAbA*}^z zAkL!^Z4%DEo8;cJ2b(u-DO;z0fVcdtdoiM5(aVPh+KIpL;F38OWl?14ah$(iDY2XS zoAT&EXc}!bs_wl-kg)`*(Uxh&bF-Ak#m0Ps%4X5%$DQZTR7hHl^htK4MiDhJutrUuZ7iv{7pHfFI0frXN+D-cAcX2~tNBW$88avDk2Bn)oUA@G%v#|@% z`z3nivjo{6M>%3OLkmFte`zMd*0Z}x+S7(GJVVC<{l?0OTG=_etjA69{=Y{UHo4RG zDsq>t;UiId3{X%LYSi|7-IkhntjeA$6zqTo@tMvle^Q0hUZqu~bO7=Y{D>3v_4y`; z<=7K#K!S#N)w`W!6ck`iS6NpaL9k~3r@4dsvVyoxq4cr5e^7{RSg6C@O2bi>i~Kg4 zw9mN+`90O|vxgZ^((nJE(ufKf$(XnTlgyolMThdjKQhl4g?afXLQ$?b#Xeaqy1vpP zn$j`wD$O8)k_ah4Q_!h?;`qV2SVmqAdp1tF+$1=)PKA(i?H*6tEGL+z{%C+*JbgFw z;&OKWic;X_Y#=i)uMTU=}TyyB%l8&1xLkVToTfRnD$q^T=i&BXjZcfY*1^wgja?6 zD2!9I5LPxzV4Mqu4TsgVUdv-v7}C}LhS1H&Id)UUGW|pdA6T`j7G^)px9xb|xb-G- zW6AUFp|aHK(r&D#e-M0~yTS%SnvW2S zboTesd1BNvBzI_9LKU>x8@$EKu-HyPBZc-ZXte-pVShUkoSP57Hi1%StoF32NxQPM zni_0oKo=zJg^EOI;UmD;dmshXc=*0}szfUdJffXf%+oW!XGrI42rh-bi+3}(xx65N zn%S~sB74^fD0b(=mBK23ctSN)x{9srCW@@WFk|BRgIZlHH@68mCHV07w>R1=GiJ=5S{ZcbCBMU`nG4Y$JzpfBD>9Zc--?^ zX{zqTE`clxNAq(ujnGX-I_ca_v~eeCmG8cIFB0E^D_aR0%Za4Lk>vX3R;E!b zHWHl?XxiW|Jt#X`5Ey62`38q>md!FEq$6zAzX-QxYkA(C^{_XFfL1j|H<>Jvz!ZMw z(XgwAdb6z$+jMfBO17n?q&BD{H{0J%V}TM_(OCh6bs(LpQ-cip9h?_2B_=Ga0elL8 z?zV%9dCDC7f(=d;d_b8a72}CnKAudpT7E zWr*&o23~nuw)~f3P>(58EA<=S^nKFfyjlEhcgA60F+zgsN8|NoBS=D$j&3>{pW{Oy zf^8CnMYKzQ1CKzr6+v6;=Bpmw>0HgKOLE`~P=81_+=u$>sY%L^SZQBQ!Ikk6e;{Ur zrXGq&8YZV^+yqY-X6CrdnD-VUjlOgJX9pk9`HO8}SoHMt-;c($I1Jmd`ObbtozAQZ zn4WN$Ks7W-gLK3|duqzMC=%rTb7h9hx`lt%gLh|Z#$tY{@FF;j7YKUasH(w$pxlFxU}#0up8lhy5feMI+IGaGMh*Z@zR4L zv`NGn`lR86pYL?R8vyu0MOWJS3=sT5eb5&i1^cT|db7H_yNeP=G-}=XRIP3Ni@5Nr zoX+@0zKhc00!DLQDQE$?oP~Q>O=aJV$_S%d>*=LYrhNxQ>zO!aPMud7{Jg0t9GlT> zfm2oubxaDh7T_pQ-(b?}Q7qwAq!gzf&g}AkV`;paZd^oEWVr_Th*GmAs~wo$HC{RO z#&ficp=5nV^n+GcV<3>E3(9LscGq605N7)zbX0~Silnzhm11hDPHl+>vEt$ zc7x|X7_<^2C>z=lC@s2zw^Ta!lXh`;cPgdWl3m)| zdR?Sw`3>?Fyq)-eusAJzJZ5wbIj5gRNJYSkM9^p1k9v$19GCR1Iv8&-lRrNO_7(=` zT2?pm%HaVapfNXGD_h5z_ACCa+7?79{!yJsG!P~TX_PH4L$lfU?Tyfnl~XUX!$9zi z=wE)UR?_Ok1h&QCxSMF@j; zFB_0#9to19@?9JUy*vf~7EA+NpYRg9bEJxNVoaKPy^CI5cisOBK%+zzFZ+|KxWvl!vyr z9hY_T5@|6Vo_~0BE;x95N}{7_3N`>bfb{Yl&_0UDpMHJpp>3FxqJ*dbBmsqFs^_%} zVvkU-wr5C|W773FGj<3275w5>Q17Vgi-f+x~kUYc-dN!nVNbmyX-Y^W- zO8l&@F9KLbgas}o$jWjgH8d+fpzC%LFICcgNQJtmDOufFQU)Q&=NdAdD79DmcR`7% zr9NJ>${V^)3G_S3dc^1SK4<+Z&pK`gEH{8C_PWOn6l}GC3V+UZll5}C@rqmk=zo`+ zeX-PQomfj6&Mo*3e~O-teQPj2v5O4)hn#+JL>Sky`yBLGPq|vXkI-ETA3HZWZuw_K z-tSJI>;!`eF>wmL6CjxaTn{{Ak{$tFBIrfHyzQ5`*Ygi(J>S|yEykdw8z<)^vr4A{ zh!gA5Mx~Unt zFhrF-U%pQ`(ju{2tUU@A-z@^ImcHT5`)n8N5S|SK(=#bok0*&9isTaypG*zxl~y)Y zXR_A<3c)ExNk#qb{k@u#?TzVPQ5ejzw|q|>UaWI)w({vq1Lb7LFEk8MUD%O|GauD6 z9-iey%aR{~L`)C}3JS_8u+1>uM2dXQkl+B&Isl$yXLZRS?cwfmZRBowMlov*gn58> z4Z4yT8EtcZf`SPU0!w60+_}{e|5XQr)~9DdIwbxI;8*q1{jZYnD-w;g0A{UL-9`{$ zL@)70kNluJwM+U4L4qUhrCO+XR$yx?45)+uD-sz+lOT!^fb%NRAC}3zQGHX3_AhC0 z(c^=W_qBT{Q7Q|*PZ3J zVN$oiKW1bI!rM=;XNrWd=n4IB>u&u3=M!Xfe#p^ZsfXBY788XJQ4YrU z32-g*-)n_NJJ8XO$HY5A7LCQw7l2xWk4POEi4(LgfDT!42hRu=^X`TiOQzBYBPMti z`D6oOJyoro?^BXZu zYu5u4#(=e?o3{O9kO8QQK6gJX!Js7rQk92+p_B+2Gqe5oJNB(=FL0&d3Ilclx^al< zcx6xuLv+7yT8jVjU!ZS6F+vA{6UB*(&2S2Ic!~$iLmYIwNAe;uSAu-1EE z(dc{X+*@nf9J;@_0uo7})dsTgkSu#7lbyOXN5^nI@e{;G2m$`BJGpkL8yY0iW#8BJ zv>Bfao`!s1!1MF`{V5aOcQ|a0UgcR-R06J zeo7$v=;g;)0oOfX5OyG6$>En2U>|r6XCEYbh+0S6S5axBOFzs$OR&GbujKylT>>_N z-~kNPQ9!+a9hdjEs(ovCh}K@gPn^ufl@cfya}n^DF>`Lzb9YD~|~@ z(}vpm*R0xYi4;n{y4G{<2j3}%9grnZfyIH@={`_cdR%jsRHREgmYneCV}?_vB>1Qdq_(s?mZ$w%aqqHy9^_LC%;vbt0w=ljPOaO;8O?U?rvO`t{CWm)DdnSf&J0gkR(gS{RE`;F zwAM%|j1U!_w{aWq4u<+9cxu;GG&B;vMbTTvg{x5t0?<6((b4EeDFWr^pP0C*pr=<_ zBbU{AMWC?3P(ZF}=xAJjO9Eh%I3&?&2X*oMS7vmO4{SxjuBizIlQR~T+dB&N<2OpZ zTtDef#Kyq>k0L^(c{1n54zgo-Qjt$T{Lz+)fBK`}FnH2)xY5O=n>qUqmI@A;Y9f)AhZR>l2)12)K8x3^L+2-ZMq+B14%5mDtr#h}}0 zQ(8p!jQaq8w?UjOk@5aVCsZYs{gE;_f`L%3S&*1Ez_QhQtpy5{(YAB}`XN}20!Z}g zzb}2as10l&CM3Z&5!Z@{gcKXr!KD>G5k5@cb`6WK(sOhtT;4NG%6{-L$0!Q;!4zT&FfL zD0OwckGUOH#ZMgdcSRr#UZ}zlicenilrU-l7%1X+pq>guh@eji(OB(78C>E|<%$mZ z>bN;pS*Q2>r}TodBqvQ5C$izZJ|OsG)YCjz?Dbs>x4?BlA=$rcd$M$%r(woupQHDU z;w;IP)?LY*(KRXGVN&=?dyT1J-(7(=aGM@eFbKd&UV_e@)(0o;-;etB>r5Uc4*9=} zaav4>_ZL1fG#DB9Pq#_?wYXniXnnMvc(?u9Zz#eE$tH~?giO?hkE)KOVgPKDEu|fQ z8UanJu zfZm-mk{1p1nUxkF?fc}_gMCgfaE#sHrg!tgvegZrbZuf(N>I5xyzGK&8!>(y&ch-L zB$xRmgZGMXIi5F;Kk|$RG(Y3z=W(4yTg5Yfv{hyUYU#saY^1z~=Z}c<`56^e1ac@i zC!cZ`h1B?UKr1J*0 zL;DZE(sP7?$ALb*&~ZKRn%gOpX-LK(kTgl|&my zss+&b`F87ZqTe`^5-0D+l z^E84>bW1YCa6|j}jUaI#k%~*hzC^)>Ov6KRMroSMX2&=S#`}-feK-MFYAUnA19)%D z>$sU~^DEJNvG^Nl19zA2$2lEaB+`#bufi-hzq$~2m}aq!@0bq>^Y6WUJ}6+P`5UaO zh6B>Wa)D#6q06s0r3)W0k6-#6KoF|(rFL-ynY3N@5yoIytwCrRBbZ;M9GM@{EJ~a% z=o+%D)#k0lO&8MkN^QeUZv!y-~kBhuGW;{u&jnybifGDL_pI(X%bZq~n-Ddu14w_B;M+wgZCdq7-)27P6#c_kp$>wv+y319i^h2l*A#S6aAw6iW zMp%>pil&atBI;A)Pgo3Wt8EV*gqzlBOS3tmb+z=~^z4yFs8|@5OJf)0*lsB3dfuL# zZ{?Mr2)-}3zrQcC7PSOw-yI)}q%-Wor}!ZFnB@sC%g`I@Z+u_U6A0h;Of8zFnpD(Dk3Z7X$;(NrS_ffC(`SGr?G!CiAY509{qE{?OmHlGB5Ng3e zHtvUx{eku|iY@>43f&WF9EmVq$jNo`U!Eo&lSeXoc}T9LfqRov33~ko{%reyb0%a< zQVto5M>4Sry*!@CN-p0Xeidc_>`+wbmjXvfDBULOZk(GX(HiX*I18=rlt0aEmCg0L-H?rgFMLZoSPTy-O!{D*gt@CO|>U`3HP?C=r@O z_f6$D`U~9h@cv9#MbFjAeE8$iIzo_Lfb^%l@<;r)nyw22LN;S`{*zNKuzGf!A05P~ z0kwp94X((ARd9e;#&Z{Dse3p76%PT~FV1B0meBql9RTbfSnF(k=}YI)d;1R46@qq? zmC}_glud8YjYvQLdNXql0$oJpea90YL4@>75Ryc zwRghGuYnb1YUf@&wb*V3iN`$#ia0w8iODQ5e|UAS$N&R)%0LR-?oK~NZjksde;MgS zSPTM_O9VjrP~V7Qltde1eF1-kMsLX_%4(1lF@6@hnvAK_%s?xzMyLUrmmQn`#>2!2vRG!i)PTItK#sZuIDq#+ZxSbr*?jPCa*Ct)vuXTYGCqY43f7mM8`ifBSRMZ z*^%4J&Lb`h2lley_>}XF2z3FFo&h92+@#kn<76~AfQjzW`vVCC(oWo;isI_wnTLUE2j{a>M2F=4?=in&= z&E1BG$wy?y{Lx|IY#oeB9vxU_c?xlV3r$@jq6WW!aPJIKf4s5f?^R z^mLE-BDm8hSTb5yfb)%?WeePKBduGXHQlQGzfST!w5{;vPN%M_Br;$qvpNkwnOH@HNO9QtR?4VDlLJL(4XxXKc@d_^V1->nf3fxA<~XzWxg+5}F+D zA_6Jhs2_UiNlI*~_utG<0BahxV2q%on^srRShLit7tKU;6^Gu({V$6qB`Z&;d6wf< zxX$%JS|ewPo-45LxLCa+VbA~!_+bU;eyT3(L2UP z)%q=WDUQOz=ZTj#BNDEN)@R2z;rEMc=php`84blT)|d$imX8{N=g{Gj>5+jIoOxQ& zX)=d{_Q7jE#G%(wm$;1oYimv>9pD-zOc@{ZxHnD=lQ!aw@s~$TmJv@=E4Z4?a{cx5 zsQ1XPVUIZ~`jdtkH3zGo-1d5tG%_{T{oDxqpDPr5IDH<3Pv$BWaMqO&vrU6ehv;{K zbQ4h=OX2ccsOl%30;*H&;6pdek7VHwO@$iu64zH%kAY#)PQzTclk2qRq+iG)LYD&I zuLV8e$U5rIN+3RNlEzy$zFYI|)M^@>TWd6mb;N;NpK@Hqp9r0zO1&zcsWB^fpRxYp96Fs(@TLCW%e`dqs6sHb@huL;G6G8ZE|?I>tq<%ac#RL9AJ6t9 z!jk)YH8ehv-I$ErbXY*48(dpgxVIPK*`zZ!>I`;4A?9pAE7PNS2O= z{1E*!$k8DvaH=&^&6_p1#JC^f;$@Ewk&vNKlioy%g4Y=kgdO7_h9iJz!;Mpd0(U$E zhRFJC3@y#Xr@2Eo3j2nDu`Nid^+1DI2bTa?dhkokcNA=DoG?2^+fDxUE6pop{xPDc z_MKmzYSqi~@X3rE5+EiaaBB#4< z$7WO~8SRT6Vy1_i&cK0d&JK(eY@$Eg8+8^JfE5tb^hWZu5%%L}Zuqey-qJRa_g-MK zq0)4&zr?blKCnPTIAH!@XgPE}N+XZO^=!KBef1Pz9$ga9<7)VG!BEmHJo3Z);;Bk3FX)ZRTgd&4`` zjgwc3H(G)?ZCwNx1kZXa@UZ!6g4qMhGw!$SiRb*bwCLX%mmCfl7Xik8aRtFeD|LIi zVOsT?&Q0uCmcFNNs}RH#8x0n%)UCgDsGj7pqT$0z+iOHMiz>y$=6BmVPCGM*26o7s zBJqyR{>ZzqXUmBR2+k5mH*cR!Kd$W1yNBSv3$gF9B+uy6DF{vG;Ipm$C&%>rrUZ0& zW7|rwN6{Me_=X#d?-3k@+RKy`2H0&tv!vKRc1z20Mn$VKJBAOvb_`)ecP?9&X||qZ z3$*ut_bHKe*0V@8ASm3|l~>?M{0?^d#6R}(lRLqWJx9a;oc@$)q&Oh3lK32=kn@2-Zv z;~CyC);{F&7$SchW0ut!`P>iv7o@(Gi7POhO90vXpnN!L+g8(y{Gv){|m2p zl(D*hkK4=sQvIa2H=Uu&3wF11Hufi&epK+KILVNLGkO2J=PQmlVV4y?Qe8=YZwC2 zRlm#($o0e^>4)&Oy56ohO3x|>-$lcD?n7Iv5REjvDjDjP`J4aQUiWfKDkhd)sQ+5V zA>6S(jUT5qrTe|wEAxDbPKw4+0t^$0$MgYq6|7^=>evSKom{{gHI4jw@bC7HQ0}y{ zY5_v;B0YmN^~w~n%dCZtBDLliR|r7b;s~DVZJ#6fh#h+Ct-!Gy5x*e}X;p@ClQH>k z03`eC&Z!3CMF&d^uy7RDrS5uevHhX|*2QT=NGb4>&kD`b({WBoq|AXIV`X6Ypsb6&BG1 zpQAK-oKr6`{;V7`t+RK{ySkW8f77p@_ok?Y$Mi$`t?ndq0|3`%Hf#AU5+J1Zy~N$9 zlIlBCsqiSX4vC<>iNyrFaeUUZ_d0Nt8~%*^F{(T!<90&Sx1#{C5Pt;i8MQL+y4j@9 zPMssc^m+$lkLFF0t}AI@ZVY_>_=HcjeZ8lHanLzjtc!EnFZv<_YUo%cyWFd-bOu~3 zvhQbK&dhlzZQ9HvoZj_`%q6By#!hY70u_p9?=-f4X_zGhY=2}he?gbgI~=6LBNF=& z7-%Z%G#s7m^{C9JjZ2h&oFjRp05eTtlxF{XxtsdNj{OS)F=-WYgJ~HY4___`^R_{! zRMIwWqD?N{mq1bI9o6O#Psy8I5MA^-x+k*!IP`RMd6bZQ-!!7LS0?39+w;xz}eYM>LK5(OT?%xlLxtHHj z7|umf+rLRaDQtsnoE2I@NthiT<3{xuE`6`4ky;^38BSZ(H?ja+Wt9=yA=J1IIov23_{WBhvD_zfWn0QNWt$}~5T!4TvA{0#zd4Mjqd5?`+5e1Zsz=e9dOmaPEQKrXs73<*60 zh0}{r4K!)GF#Ze>42Yyd`}4i3gr;BrKegIE&v62o;#013#y;PNFKD}wlv5|e_vQo+ zWv*%J+M*UfLVx2UY{Kyx*-4)fvH-ln0W2P`w2%V$D=44rhyu%eI}4Q{ncXM#a#UGSv`mHFUzPIy7TjM zny@q#j?V!ZV#k>pQLllAfWL$t;9ve1DLbR!$stznT)ILpu%`XTyn})c zAG^SR$c6dgabv<0M;F!Xj^k*3jnPF12N4(&`nFS0+Txx3J z4qt74s{E~2rCo;ve4HigKubfz+*y%{Xsn^`6{vL~j5vS+aHcDLHoVW1if`bPq)Q99 zB(~*vl}MwUqCMxJ2jIF#<|aKi+pC}xzo5N%nw=PN0hLDBe=u{IbI43}dbs8n1dc79 z_2Fu+c4+Z+9X{vFjg~KBw9{Ni+fs@Sk*TDv(#ZJ5kQ80mCMjq{oukZUp9c6#h*@Jq z%o-357d(nmE>9XevkOn)8by12(cPqgh1Jd<3y7{R>`{pViK5gr=g z6#gdFEn*O=3zK~o37 z{{#nR;O%;|FcpB-)lt%NkfbxxKL9pdp|dkKrRm*d#fCuZRj@mOuo&apS#8`%=D#jE zz$lZIb?krc%=JnVepi;%v+OQwNe2k0zf&LsgK0W~ob&&iZUGcle^&Cxm=PlD@=12X z8y9;Af~h~cx3@v>0I;-lduN4RI?#EzMdTvDPlY#j#pbajwwh#T*c1O$H~Jpt_&mFEF~*XvA{1!X z@P>r=j!QJ^>_3J$xFDjpJf#YR#cySd>V|^n&UKf_%w-L%2na?yaB2Pc1SJw1IK{jj zt#Sf&AD>Ly6A*TP5)1%t(CTphLoxyag#wa%L5odbT7i*FDcBQxMMR*W|5S@8+KqV( zzZ*47-yQ|zeBQ{nxdZgMNJMvy!T_+eWiiOQ&MQGdJhs-8f*=)kw}M-En<|LgXUNwu zex?ZoH?*aN+6UbAFHGB6!ACwa7b}w(qb_y?E11=Peq!_XCq5#60+{L0e%mSfZRu*) zAEE_>ZOWXAo|R+ZIV>{=5*$qT1Ww} zD;3#9TaUEcS!F_xhc-+>Y6xT><#%2L0dIxTo#1i`SCh&oqRWGWP?iWY?92~od1{vG z{#*2&taZmJvE4AOPez_C4N zzSDhg`NdgM>fu>Dg`73GJ|tGxuojoYL3Ty$MMbhja&h1`DJ)5gL@!y;nJrLcz{nbQ6_`MzgqDt8c#Y4b-JSI0aVZx%W*i?`GOK zpe+Xc1XCeWyzl|7%wL zG$;{M)rj&xlw*tw=r1QRLwFwxGGHPH;aJH>%Yl;U_x1eQ+bDdYSKi=*=~^L2!SeTb zr$Vb9JF*Dw+d1wYplHVq0M0mlL4H88ex)-XtO22VY5u|n>jEV=KMH|nI0XXaF zZ-|!!$?j#$C<9)OUTLA~-aI`wW;(mVpCNL81~N;IY%Y%5lvk^V$tE`7fds{H5KI>e zGiXxZ5m>&R17_3T<^)XGUz9x1GgVhDvCBe*VASzC4_&wT*jcCYk5VB%}~!p0dp%W5&#L z=2>RRHdD$xR+2Gg&OBzw6d4jBNrs3d!gnv{ocDdN>wT~9ukZTSb?VUGd+oKJbq~M$ zexB#|`wv3+3|->m%34IkeSRV}{`wR0{3&y(G!;@SE zeFk?B*vOaWpUp;C**{~Siq<|?Gq(;C62B_DadqHE{7G1LY8f=;e zc>6Bq$-1Oa2Jis#GRB2-_1!W$q%>y%hS2kjiRAP>N0uP;z9d?vJl&wu#Vs=)TYPxU66uxfBnBXBF$tn*x|Nx1uBcYbp-GcsipC~a0qdhz6nfQU;@ zK`|k!i0tHw04&JfHq?EQNI||tn$UxZvkIwH(s<&=Ea^vfpVOlM(M5ZE`&NnXjS>7I zzxxq-)1#H#Bc69rsamR>>)qMR$l`>7Xob!7Lw*zEU*qa54ChuL>DdQWQkvQ&Ld)T# z8U}sP^!+qk%Y%=9O&{L6LS(cq+5Bgarx07a?NvrrVGR@NZ!yqQ)~yh8l`o@!N6)Fx zFVMG(vAdd*e2KhzYZ4|4|E|zQo^QR>b|T4QxF=6VmSU~W?~9EBvTZS%XIbQ8E*a2c zL&1s<-CswMRZE>X(?6E_D+7Q3*c?i|6~Ke6lDeLGmHH130~u5P+Fxw6^wR9A??K_S zQDV_bHkTIl0u}~i9FG?Vc);@+#rlbWMfpCvq*! zakDPQ5}(t4rt}J-LQyjvy+ZJqsl=FH85vu4ZQ!?^=#p|axfXcEIQN6ekm0=e$i;TP zx&65<(18J^>w8l>pg)Dhm|7tikG5BM+Pdi(_YdQ;lyHY^Tr9IIyB#S~jB;bLC11TX z15KE2Bda)5o`?X4!Cnwo%T8-_XvxZw-G)fNl^7DISE9dC{_71#qB)e}^D;oH>L7lt z;4`C8T0!4f%9`ztB$i}qeT;WELy5`_TUeuhNI`BR^q#?LO_hF3%~Qi?4!zR(`TBv4 z7uI?kKGa+U4I#*6AKKaT$7dBjvt~wEkP{NiAGD&@aL@uv#RWt~>5e@A>}}~Dd1G!1 z^_$1=>gc@89N8w=H2K2&p5CdHOD=sy$97i@o$}VeDX#K@%?h>vgBNz53NH!k9YOG4H6>SsGw^#OhHG}WF9seV`E1z;2P%Zg>U>Qkap|uRh zvR0dCdZvM0X=MhE_iA{|-a17iZN)=$%Ie?Wx53J9wG&E?BuUp^M7JUuyqhB^zNLUdhZo8e%k!yv$Ff!iR_1ql0Av)#1 z-+6=RJS8#D&B^ieUkkF7=Oz7ra6aNQ3{MxbTgU~!8lIS-4K8cQI4|>l>R&2u7M&g> z7i2<+&$^V$2T6sN047aO689s=pxx0Ni zzXk2YcKqUhxaFs*7}>M}+~ce(vi9gEkR=w2Am-q=UOfRbe-RGv3L}qiRDtN5pKo`M zThS1xKbnhL)qt__&75Tvi;XAlH&MkLO@#f9(vNFBf{J)8x;4qX~sCZ%n3ZjW5L&p94y( z9tI^e+DBsMj)u1wyrF9W$0#TL%5wv6vB!?Emtmr^o18rPqwUS>H%teouc0d;zS^Bw zz<@i>Uta-X4R>)e8uLaUmLZer7#Y9?Q3F~Y_(v=X8`%EO ziXN%RKqAsaf(lHfPEJT+KqhZ7?UjkKlIGZN@v-Hu*Y9LvK4u5{@sKCyOs<|^WwHq$ zB_aLk&las9I<{;h!P&=NN}b~B*OE&Q1n)YqgdowlTlCA?Q<~f}Q1|jKCu1;|v|l5a z)=&u-BC@O1C(<}KL=0KxIVj8Pr3xj8NjP-b`8sAPv9lA6G$3yk|F5?iK8X3(TPGa; zuWtoKNLfs;l`W&^uA}KRnQt@eKWPZVeY40^26je%oFs{UdG>&N>Fn%xJ7j8m3?3Y^ z?iQwdK{tY~Cu<321`@>0{#9i*e4S%O7SKVZ_IT?~1@ixGwq$gQ@4&5P%Zl7m-xNYs ziL8GiHXr2_8(6s(2CD^;xt$k@+8T?8!M378L8ugQSWzd`eKiW5!=Km_;oY(t6JV-B zs?Du(0V2O&PBb^az3DRZpGo46>jvMs&mwk$_sjnTaO=Cs21$M3)Vhi(yNGfF{WFhy z2_WFXc>IJ#CQLDfBaIn|#5GpMd(f6JMHpjc&;IiC`C*I)&btT<_ncsI8V0l0EhtPr z6-jB277JgI13AGoWvHQi7U?oDYwd)>1&nPwtn7WhMiXLO6tLGxk5Qg7*%bIi;pD;*!tztEY^iYAATZoe6}H0Y&wkkf;g9~iDp~U9DkvTM z4}A5n?oaTZUG?fCqk@#44ty#2LT^>msk%jOl3&D#Ix(9PUm~?}i|8O1dnhQ1NhlLri#^Ub<)Ap-hdw24a<5NU8puCkFLZ>_&E7!^N@%Q@(u(3)=$mfe_CZ-BX zWO@N{@tBQ`jg+-JtH#e39`#<{?7YBa@0MG;B<1Mnc*A4>U)=8gEk=aQR~c--Z*d7XlK;4Hmf`*T(VL%@IPjkj{ygkh&JxpLdMe%$ zFymV5lC|0}=$&)3seEGf+tg}9jxIgduotV042!$FyZ6iQGE#~tt}l-L6*GM0)kEsa z%Av0%SQ?(I@%3Cs@0Y&}{^q|{i@_9?>n%^GPfVdtKHWMsMNddb2%k>NuCEJRt(^AI zRC3~sE6X18^6@EucsSg@?UnK=@JOn9>Et{G|NA_$d^8hN#O2QqO9foE=?U8B`qAv> zjx9cJzRM|AR^*gxTJuBcOLfu8X1ZgVQi+E&A22t9L?9{MGPESzT{T_TTRj(^QJ?{#goV`Ke_SWaXsf z6u$~}2$iGwEjPG$vou+bQBT1(X0mABTRkzdXk9bCF%jN<^!5LKS4^?ox+#O)ZQJmB zXVgbp2XNrG`4Z(2{aMa}fR(A{N(*eAhtgW_#Gz!u?eZHbjK)>}{M7zkXMxcoZqM0* z{m3C%j?1XhQb$RHZ&}KE*9Qr&2XP!+XJ;=aa*#OhXYhuMmNx2cC83*d#q4C;*1a9v zkfEYGSz(IkC+A|cmzTG{&uclPYyy*g;B?hww>x*#g6)>&; zo!7o})0M_ai~>%;-qGd6yKasZkNI}R&d!dLlhc0bm8H*@g&$=-j{QMHw%>p5IQAP$7_G80oP56XEI4{lYF-Dx{nTs2bbp-e^QW>uMqce9QM%L<6VN$f!o2v z$45p-J4$=~%xA1#H|%=^ANuNALUL4C6E~rTM(>oulI+jR%M&Yhd>=APYShU>JDX_d zE!eP8HpJdcYICOu~Z{Q2B{izCGKeqVmv@KmYcg$zL2$9`wN!<#7amzVJ6T2E% zI6fAvPo1Eob5I=t@tP-(6ylUyVPPQw0YNLQZM1N5igmHDa^HJ|TRt0za_==2ojs(t zefyz6*`-apCg2`*3kKszdY~I%2*J&OawSnO%ERrat^33B)X7=QPQ=wgg`DqCF7gX8 zC;JG7*?#Kl?@vxnKIQA{YZ5d@RuX&(dvnK-S#toyDk_=8KZ?a4U(ChU(b4&cr=}W1 zbb$!|Ks*#;vcu!=%X`b%5RI*u&tLdS|&3TR9SK%ZWnzeA6 z*_dk~H8wV$dF@Mj_3G6Il4tsiph^}U9j&aQBKJ&_WD$bU^i{4O+P8_Tl`3vEd6DIB z_4W4puc-1FGIOw2PY2Pd&fEBJR-fI^DZpkGX=fY8+l`+8jSO ztNN(_(}K8y*UG-6d#vI;jn#h6*Mpx`<%7OfqQ23@3f}v=gYd+L^yf=UV^S1U$k)~+ ztC%uIN>t9#1Qosbj~jI!@yYG`>mI*#(B0CzUGn7n&S8Mj&1H!pQ})A7up=Ga`I+V* ziSCx4q;Z;Jr^m%2v8LY~->v;cU>a#(<)|GUNv)4jo^yaf;|wIee|{cqHK(w5ZTd+AzJKcU z>C?ha?}%5T=ayZ#~h!GnkwF9b}Z6p#)g zawra(#}IUOck>?4y|hsq$P%M;Z8^NEs-|Y);!+L)_wVCP8ofH!x}op)Sp`*9A}lN{ zVXh%nRn;5X+693@$45cVccD)FiPJ+5QP_#m2?OrdHq%as60#i#efW@iZGAmCH8qMQ zcu#I|HW9?b0F;amA>ul3_`V+Fz5oJIpYyCCleDBx9_u3larHUvteZ9 z<2t{-&qbA$IXA7}6dQ!!%l4%QQOKoz)AsJB&z^1n4gHf(KpgR|F21Cs{H#J$hh3eP?Zo(#OYV4_0cEv#^l*`u0P$+bRy65SR_xPnGkSMV;zX~YuPXPR6>jKjj& z+09TlqOU+ezIQ_lyF2%>dl9LOk{sC$6O%YtI*!Z5g{L=JuQ3+ou1Nc>qaNKI z=3Kq&o~6}OS-873b56dk#^|6}XppXxxN6$c(h>_2gK1PgHAEiC{5VudLU-F_NJ19* zX6?T4;MefVTs3RSNqrDFq1R({`bIKjdYS5WNp(r_BJpvY{D}#|Ur!(v{qr-LK_JZ5 zy3eH(x6unL-4Ob*P{i9`Oo3diUad@ ze;vtq>i_iu{S%^AmT!ttb7-ib7yxZXJ(eX8#-T*y7=u| z%u;{q1+!Q*``S#sj@=fSp3;pQWYDH6SQQ|qfE3Eh!GR5d=Ica-+0ze!+iyh#u3YJ= zb6>2YqaPmwDFkG7ZI$CVk!2;_rAwCx&YV#Q2xusmf6QC7M!PQ`MIy&ZF<;SgG#UV< z;uA+;CRWza=q@*XedERr)JZ@9fJaS3W4Sh2X+Khc3nP=2mBso{)&0+&J8C~SkC&Uf z^{i`S{;Gho5}G}^y8>>_G)lR;Q+?vpaR1Z`3v6Tx3$}K#@$-{jG^#3c%UpT)%GJ^qbih0<;tE{Qn^YOK>H5Mz+0sM+sYzI>w&sxG!`Pnno$1 zcSgg*!;w)}Prr-{zUKyE8t&i>xDcYQ@uH^Y-s{&AyK7U}C~kg!B`+`GnVA`L2M0oa ze*RK}%4>^u1mU6Q)E?zQv-U+*6&iq}*imu@3@gZclKcDnlSQN5yuE`gK4=ZFYpAKo z_arb`I5SwGYh(57p@o)bv0=86AX`t7y+mETA!wVW&5@r zRKN&t-n@xw&6A5rS?1dmCYN^mBr7T|evLgjPc8Kl(yR4ofp=bmF&~p28X6+?Tpq-| zu~l{(f_qJ0Tz-4|wY&H3wU3TcxVX3gO3?bS{7UFERjuv!$x7_Dwl@387i`nDt`%9= zpCu(Hhs`$K>45iw^&&(?)|U;hwA^_@3Rak6R#Q_0qtQ2!$ReZoSTQ~+BO@j+kJZ4y zzP7$2YcdD;-XQg^djo#v3zoetx1^+1Vp6Eadn0 z_ZL36w&+dW;(B1wi6Ku?=CprbN)MZ9i)oI*v_>Eg7#JIO^!5^V+&d$0VPR1g6cR@W zK4`!CzHIZykMPt~S}>U8loT8}GTj=)_GbgPuTM@-vtPbU0XxhmK=DlvvRD_~lAuL9 zj)9@!^D@`5qU7BO$Xp6%MO^MiL||VQ5<)FS)t6&=m`SU%}d%lY~XOwCgcv zP_1PbQQ)7g9^}(mu#XJth|P_PZ2KWl3Lg}Ac3JXBa%*w^8FY>iy$crdmF7%fCE z_uI~$YfscDqH*9q3EvI0ic0kKKJcBNLfyZAA8ZFD>9>hGJowcUhK=vGF(an@R2vL~ z9RdXk0&5t|eiU<-@!x91m<9jFg>b5uz|YG|R9|1exAH6(@yAl5>SER2NFaH$f^Fn) z`uf5lf3|mYXzAkQKsp zdCPV@;RLv-cK((>>~I6f9b=*9XX-tX^Jj^9-USY~;>P9aYA5(-m~`V0YDDk50MlG} zc6PS8g#{Z94h{i?42r>AnF&>qkMrRWj0MKr(vn>=VCQROkIFuDMO0o?SL-$FSK)Vk|4szaiy8?> zFW06jRmgBGMxUs(J5@9l24=%z0b%=L**fr(r@3=2AtCb}q%z2L03?dUJHtR=TytBi zyv1`l0pk1f_Zztn)6?Ne!mC#p9S%F8GXDaD@n&G46Uc4wQ366j9SHOOnX^~9Yo}^m zS*$Oo|B!nG$nfij+KRrbPeSe?-mZZjezyZQp-H8?b+`8xzE z#>o#KIwmG=)(?z~kfTyUrp#X1o)P!mhy!DnWlu(S&i19_4!!nTpcW~rJU`ApXeg}}-`u6P^6t8oS z-C$N*TLiw|g8jR~cn>plwYFCme*=aq9&F`UWm>oA*1OE42E>Hht25PIu?4nylZdWX}r zm+k_pfb_?fto*`tPI^f!3Ca<0WCUdQ1p$PhyLIapZ>}fAA!0^RGT`aETU~txayd#@ zT7TS*kKNoHAdVxETIn()`0eKA<_*I~xw#dO?J+w&+oS{3(P-X&st{QS?GSbpZr|oZ z^b$evtYdnb0Xk^BwdW+&+|_2_U-Y__QDm`Q)&6wW*2V99a$GB@t5@t?hk#xpGB)<~ z`*PD*1V?++GfGQK`@yX2(@Kb`mRk#5j;mBHp_KF&z*Tov-r)!B@5luPO3sEH2LZ2p z?Wl1tG;NXqat%oz5Hm9~BiO^hAg%BBmD`g7NNENLqU*6zqTBlPl_fL|3Q~a99V!hl zY!CGTI^W_4hz{(lC&p!D4A)WPoZc<_Du+@G*6AuQgab|NicJ?AuI{$0`rF-t_lJK&DkNH1lro#WX#M7Q0bil*OP;&owB;*Ar|oSTcp&TUGvt^ zQ`02b#d)v>B1!e>-j9BML5ES!YK>h0zzd$x5uFwF_)GxxB^f2AYebtiqg!Degp{rW&EpGLIJ-v;!{$nAodWjv9a|{4Wfph zs8Bv6ih&FS0&Q3TzD}WLXJ>%}ssnKINVU^Vl;(GWfTOmJ@$u(6=S>>@wAM+>bwh+5 zMsY-3WC|<0Cc>6uWw@trsCm~(bOb$kA#Vcl5$+a3!j)S6v{rKu)-dMI1X|j0wGyK{s{j0 z^9R`R?m^~9>Hug=y+`1^(fs^;7&KhjE?ztXz;N$Z%W*t_1s>P1J2~Kqz+8E6a?${N z9w};}yjD_F!~^IG`SAG?2dli+P3InP_TAmxz5UgSky1k;r0x1;Ae~`O2(VVE>1!JJ z0!(ad1YoS-gLM%^4$^6&e908YI%Q*~J|bT}%5tnoYjb-$0=^D`GJwL(AxWZ8fOnrf zdj?OM8C(x(GDk(>Lp}}zNx{;zlNJt1N6QI(*BL0P5$Tkl)AEyaeU@0atp8<|7YdgCV zYIe3oYAUL`@QOw6{c{ATPj>)(th5_qhB#bwn-+i)01d18_DFas0xAHHY`D4_*SUna zIAUn_SWLaNfqL-X&{wf6MjS5{U+bA5O(2Z?e} z`pEnHi#^fi2@DJr@c&^ASXKb(oY~@< zkmKOGy1G0NG)9HO^Ma9LV`W9<=jTIcfRy!M`Y4KoDF$HcC&1i}i7ND09I3$8qRBZF zSp@{hz(V_`?1)rUQ~o~yy&TLTaNZtJBhgy~E9CLbldfHXzjA#BD+n~gRZzmMK??fx z`SX+a-INp*!>@f^8-bV-9*Epfj@113?-eF<5HL8;pI-#%AP@0s3$XFRt0C!<2Ub_{ z*zc} z(H;kim781H=5X7ryu4gol%b&53M*c@A3UgFmF%zH->p8_@`hB?j8XwqQ2aWsk}`^h zLP?(`=}!S^r7MX|-fg}e7l0@>E-o%NH@B>Wm$?$%c@L8jS>JX_2!6xK*9~MB4N3jxM zA!~9VkhMWA3eCVC0H!MNy_)LU_waj)7Hj9abLU`2{S|hS-dp{9U>UstiI%kV^m%C7?SXdzrVSsGIcB82WYP-yB3th? zWi0l0R{GXoy?nU|A@VT{ACv{6NKpiV4AK-fN_zh*2j{tS77&)}{I=L({)(KQJ{r2a ztKZ0?G0LP=R8*{zl8jJmU-R*~imaG}F_R}diLMY$y5TaDq0Ou*{Tl}qcH7`O>RYzMOlQE5VUBrbE!W%sRt3UuH3f7H#qGc{FJR1lYOQAm46NWDOuje^%{9j$u- z90BREW=Z&#k2*IWY)Bv_a;@te9*X$vRbns>(SV(z>6**wu$!C&DG`U8?)WbKfclJu zltsD|$$$s=(BH;G!W2xXDY>{@GOD@#{AIa9EDh4>fXeYJ0(} zK&ZPAy_lr}aOzD+Ac;T>!h@ci{)STJIsgd($M_$a$h5Act%#!v_Vb|9;H@BKmc$_A z2eF_XG9;9;_uwMrxgJ18M`UPdNGI-PA9>L5Z}?us7xLpXp}5bQfnAR*8ogfu#i-xm z&x!ffRitYG;E0`%j~Fhn&S#yW;PGQYh=NPI@cT)T1F%&BNv*i2Y0SIh=+$#z+I$oM zSu!+`nIwErsDXij&5hUJEdfp-X~3lxbX0({4ew^T2|^kJB7h7JKhr1YTY~GJ2F8k% z<6{V6BTx;+ofRR0Od<=~@$NwI22kNKJX#dgLWV2NpFPA-=D#*R+RrxD_lZ{;nBb4V zqLO^1dkL@|rpChHHY`Bf1JuK7USdr{?TDm|;wB|IfY@N-$UC9WQOzI$Go7^nG#;0h zsxSGH$&s1En2{Tw|joV5FkJ`no#%MF>sGiP32Gov$O< zFiX&bJjpycY(@zmVt}eWAaP!P)d#X6&s=6wQYiEnkWAp<;GnFkIy#>WmeY)&IT-X8 zu#VB%#)hW_%HG6B7meA^oqO8;3l|Ts9TFW>EMp#>=;}n6s;?RV00B=7Awaz*FYmrK zDOhUwO3@}0%HcLZ&Vqu1bRsT{48r!I5Fsp~6$5h#t?-n{&YnG8Z;}9Vh6^yN(Blb3 zv$p{V^!BobnBxTH4>}+~90A#IISZeAAJloT(m?xA5V}I@8N!6nSNB_t7lDWs4#I0l zwF8JuL0_L{VR5k%8ZgKZAd=efNpd27sGP6&Lf@YaPORy9kG2M;% zJ2HPZ<%yZZso;z^Uq{?~ljMfSko?H|>+mU9Slj_c`uyTz1b|{bqbg;avznTkYaeP! zmxpqdCdy4mz@`6ec904P-O1R#1yvSuA@(Ee5NhS2-0Wcq(M?wXFZh`lAAjZuAY^{X z@zLhaPNxz}(A}qMsSEKUv#n5nBqAMYnB{M7ZYCfmMtgW%hA!;p-j)@#nOUL91ULW{ z`t_?lIJ5cQ=6oOTFibjUi0uWUFL&`~2nA!si0dTtO1jv4GDIk;-kpL zA`Kt)g?J<5Pk4#4Yhg(O_0MIBXR{yY|2*+vmdP+FANi|>>T z_{&!T9odI36Rq@+@$m58!WVb@!%KsI*1D--FEL;LKfmN-U!W;j8&?uhCZMpSqK8n2 z-MB|ZML{21>P5`#hU=F{D0-anQXzjTJ@OWQ_FidC6pNOYwi&yrscFov?&VUX#1_l7 zhNY7eyRq1U$a8i;XYy)`T2s?Tj&_{qjyH zZ5-2n&D0i@roo0x^MB%{f=Biz)6jGJ2FwP{kn|ULc*?9v^b%>CF4@S+l9-axN8veQ zw;kGzh6|w_M}a;5g3{b@xk=JLe{*4~Kl6;oyE5{NH>ah|aGIr+Mij?Ie0@lCRV1t( z-r6c0MSIg1Re-OMbTmE3L4$gm92vriig$$;0#T(C2Z*f!zW)& z9QN;Dx}VDywY5NQJ7|*z*tDh1s%xenJP$yX=slZ^6>CP#mCENXlSf9?d>^m^YP^s7NU%gj|cxgZ2C<}F8Z1n zG?Wh%M~&)z{FUf!Ru=hbMq68(_gXknl|?^x@7J$LANk%ZMI|KkfACsHi~Sn1zHaWY zQ13W!JVUE6;Sr#pp?F=vRmnGg+H=@Y6SpZCDU7OD{v(G|2m+tr$ZMd6`>JJ0x5^am zqu+r5n{E}P{|!Y?WxyG$rHzfn{q0ro-EzQ<m$5;5@PbV-d$yGtmB(ZQ|TQ10M<#$;w_kb8Do8X;_X7&JIxA+n_p zAwkA=&nyIO)PE}VD0YLg{f}>yL&INJxy#BT$Hc@``CT~avJrCg@~+p-829wWlSS^e z*iEyvg0Aqp{laWD=D5F}=~(~yEx8fAg>a*#rDb}&{#B}ZVSRo52n^clX1Ynlq2vm-#qz;Hcm zyI8+G-UxOCR|gUj5*S1|2?>c+>o0Q<2yZ6V!~NmI8v<6HoT4IBRk1tnG9CMWj=mY1 zBm)D(Gtzi|*L6f}3c;{Zu?O;-i$kiK6{hUhAu}d{6kz(KtgIO79p*xkk{%g0yQ4u| z*1C}zmz-c{&*}DLaOOA^5t0J-Q+_^De*#s1Bvn8ZSkEb`so^JikHBF?Pk2H-H8vJ> za`H;}Y)bPPF{Zq-GHfxqf+LgW=Esj8l4+Zr%8zBB+@wX*c7Gjrw^x5YP1{A1+czw@ z?#*&KEHx8US69CiaAOR(8Zuc2M_SdwKK{KxG`Js6G$XIE?CtDu$;e_qfBxL*Ha9m1 zb#jtbRmI88%R?AlaT^uke|`Dvp#8=vDi>7(_h>zqgH}L*B!~l5;DgE{INhLjc9Nc+ zydPhmC_E-4%>DEU&Z&V+KtMp^aKnXP&BP=j9E0f1brypHX{4%3veAmiFK}~Gwqm0! zVqv*V^L6SOV2qnZreu;GOy=Se4;>ySqR^C1qlb3ko*J%LWDXNP4qJ5MBV^MJ`u))l z8-tLov)ZEnsMO>R9klhGSfJ>sugJ;C$FDP=dc4-QsX6`;h6_) z6!sssODZEAu=64_+hS_CLE5rHW5{Sa}BSDsanw zDlf-6K0by(zRbn_{AnpIBZIWRu(%ip?kGf&LY>l2<4_2MqvQ5DxQ0VzVhNGzTMXH^ z+yyPIttIouSG&V8ber96SN*w3v7SGF-se7&`{aa576xq4aVm$9yrdAa&dyw>r+c(b za9$cw72 zeoJOLjy1W3hq&C|`eX@upo8-c-Q3*#D0Af&8O)~FqALm}GQy7vqu837=eOXTMuvdR zVz$)m0Zx6IyYJG{Kk5A1jttu|Um7hXc5XO1A-68$C1^7-NoBt0q(6dMk4AL5NX;;_ z?T1#jU$c1b&xcQ!zLd(>FzboHnr-#rg5>1nK!m&wVA!VGYKSl@Hwt!4FD)&}tE%=d zxA`F;e8>*G_h~)q#<*O*Xh&&Tdb7A_*n0IV6F>nZ$lh!<0!Ql+{6X7=G1%#4#%-hk zFX8mdG16*0E^e5a$xu`x0VT(yM;y-po8(zg6u$-_j4F#jEM>ssGyV)7H|vtP;3 zR@53{Q+g7lxBuyMkB{Gb)#b6R;j((%a3a6IPe)axBbAvU=&*`sGXh}7MnJ`Fz+cC| zTpTz+q5uH?tyn6ycd750b=Kaw(LRM}n6ISwA@LdYo+UDT0wV(wJPO++eb)nibQEgR z*Lg91pViT8U+=piLM%Be?sJq6!D3v`sbyv3yvfHcy8tBrj9asS<)x{4^o}BM^|-eF z&Fh>elF83en&Vbu*c@e%r=rpDCsJh|vjDIvJsHI|lsG?+95-jX<-BC;fGDfGN($+{ z^oDXxguWO9>ql83_}O<2S?Ni*P+Yw6$3N@lR_%_$v$}%&ceC{Z`9q9X#Ke6}R~1wv z`Aw|@Y4c6NeU|arQX!3+%8ZomScC-G~3}FUalgOZ1${3oq4DPfU!l2vG!K3o90IPS8PSwl8KEC6JCO&^D z$wUiE%Lr?HB#0M|4ju>(-}Ag_u0h7qpd41*nomhFL%;!_%B==1&qd=}SKRXOa4~A` zO&yDVvIh#?itur)7oVAarz6Qnd~5V_+YmIdI|K}!tkrieQt!xf} z{R)Bonn4VHP42B?{d9wn&{B++xQUmr3CfqAiIj+X(hKX#_)oAdx#2=|B&i;I_q9H1 zeh`H{Ng4>AoQVci<0<>!#=mgKO8WY!RaVE}WYsoE_($p(7gvt*Z&*>uNTIlIvuCVB zOHQ~2OG;L48wWs-qaFuIL=9Bh4&jew3rE+R)gx7gE%TWQ;LoQB!1@hEx4@-6iEZ2z zs;V`XP*TA@)zgE$FAoR0bZaeq`;48C(A+aXRC=UMO8PUZAva)CQIBK6X#Lv@E1Lw2 zRAurx_tjVpRhfT97rt0@12nbg;07Q^v*{A;Cv0rJZ5PY28~q6=_2AlteV}o_enCx3 zD~#%P73K1`*aOL|F<@0l7=+;bqIS6f@REBiI|0;F5K)Er3E&Unc-bs|SNKS5itgXp zfo}jX$C>*Vo$|jq|5Q`+$e`XqLO}tI-KZH6@`RNYhF+Ha4n<&a1x4%Yb+Jy#i(E)mx6{P zyRx`g!oh(pDJcnl)XK&tuc?VdL_{Qt@|npHh*X63MB;25v>Zk}iNQsL<7mI6O-fFN zgS2?=qxajrq?c>#55vRNkjtxF)Fi@`W?-{x)Z(sH8*^j2sN>O-*UZCx!L) zN-w_tGwcl*!JAqq*R&)>s<4^2QvYW|^bg6u%(SgX&U zvFYjX`g-2p{(c0=cWW*%a(Dy;1hvj?ZhsOvMeXWHN#l`&4nq$XoA9XuE@{pWmSQuV z+XbpjUcA6KKU_&DH)ueHPaE2Vhisi)bAI^pB<6Pg2`Y@Nem&&FB0TAuAR!@n!ot#h zu+(B!tvfb58#QC>hegKcL|$`PqFs6fCT<;Y(<9%u%UzR)m}Dl^b?_E4MEK$M&*kZ) z>hb-}AxbzkBq}zx2Zic&P`XI~iAo_p1qH=ylN%H0YIJRFZQ^SkfX^}(yEQ}~c1kO| zH;zrucBUI2?r&Uo%Nxx9{2GP?g@nMv5Fw-rT1s=~pWL+KlQ9amVeegOyq zL%d)$!-5WAT#2|}j*aKZeVW;=c3u$$)OGRI&{N$n)>yQ3bjO3Nm0=rn_%MV{KTyLK zc|*_0s*t3ntsVZFy}KCoAM-l+JZ*QFffsDTe??Lxjd^6b^^97*@oYvvhELncNJ}FE zL=z`V7bk16Dw;VJUGVW^J!_-O4!ypuJn(=1JEy|Tn zW24E}S>w z10RHPb8}4_7RP~Y~otK45fRAQCAcuKKD{f|K#&KcVIJa^`2!xsGPxw?19T1 z=B1~`3UFHW2mhbYAG*A3e0qNV=Gst;g#Z(|r$|zB#lOvXv_eQDW%-p8K7d5>2v_tR&f0)*Trv>-C@_U+p| z(LNAn+jf6r`#pKsI+R`^ksb|zX2MvjsY51I$(&s$wk0p}3+}sbHF2zGbw%Yz@fS^o z2KcoicGmDU=>&8wRm9A(e{!t7*yHKInh7W6r}nG8)=!ir%eX!O@5pJETe z&%X&Tk%frZ<)rtv`ls=hP+grK3F{Edl$+m+bZ<|l}t-7W`&+22(o@EQ7 zxmgLcx4DQy{eH&1ztc7b-5THNb}#sB38tHy+2}9@Nu|(D$>Jk`KaDDSF6GCnlm3J7 z2@CNrY}&)-#5z=+4eZCxKbJKPu3#!xQxM+{H|N_Huvo}OyvV{s zsYw`THFjzFv8MCKWIWg^YGB7)bEx+0SE1Aw^XPxxWIqef?%s^b;^d06QRIFMU>EbU zx2m2?T}A8mGpk^>A*i!MP*X1XufbQfQ&|X>0#zckPRKRr>2zQh*uF@U%$rQO5x|Wn z0$tDDeP#?Qw7#8$l~A5mwALr0K*`7cK?DIT7qFM|ZU#4V_M=JW23Ss%9p0t9#|n0n z9e)f1?vH6T9S7mtR8Sg?T+Tduek`L`PQpo?h#z@V%l4ajTJzSNq?zB>WIm(w`Ehp3 zM1w8_t5AV0OOp^Wno_X7un%b}tEBH(b6Tx*+Jf|tB{%7($!*5{MkWnx*e7kFwVH(T zlsFmf0vlw9n(A*V>RA(IJXQ(czl)nq!IY+_m(1rg^cW2J_>uDT^fd08ywYY6KD4Vd zUmt1+HVYneT#vbg(KPcr)ZMeenz(Gize*9E;ELczIv3MU6y{BhLg{``vzh9$o zrXb1Yy?v_64TH2D4`uhNw^!ZS>ZYvX5*lzP_$O}(GLcd{J49`FcA-v^j^WPcJq6Aq zlIOk%3z*}a11v|{KKZ{lrR<6C7M1y)TXpHqH0|zIw0va75LWAAm!-8?GnXb2Kw~_E zfjptf0Oa^LvfMv-*P;9B5b+d7bRRSy-HgV^S7=uQ@uM=IJx{eIrPtO|>A61$VYtZ% z*s%_d&rLk8^|w|e1!uzBHV34n-@nB3(H;3g_%bRwT0%()6P#qj44-;-B-4lERWdQa z4`nF^VJ4XoFOi^wle-?pR^7bmm-#i9Sx5qgU>5>~aB#M77uQdLR7lZUc$bAl!q zbYe7A%<9Yzo`nisW_3C3CA4Fkn^ouO^QF=}8iY@e!?SbEd+8Zf2L!y^p8JwJxnCnF z7fy$(1iss@Z5lJ%?^v-t+n=;@>tXPzyjzEZ4?>0DsiBEysmJQ(A-l>xjgx(vOUr%# zyEcle1TP=%)J0;)R_5gUJG84sZVxckSKHIRZ1MUrx3{lBTq$8DG-HHRt4`beo0I+@A5^1TwL{{&*^sHwN4mcxjC6UndYv_!VR1 z(SOT*vhCeX9eaIGHg#RvDwshUT0i+ zle?#e7e2C3nO+%asJ^|mG$tnZy!ci$kKHpmNsLfg%Uzam@=)AyVtQiyqovcs^_J}w zBnUqPQ@hiHAnqF#6M=Y7IiYrkn@1+~{?T4X*bq~l-m)B>yetxdv7&;*?o96XEMBsm zQdV^X*GF~Qg{xcp`zu9{KB>OPpmD_8`$E`R_NI>+EX9WgB8vV8ysd~=jr|j6Q>wT@ z#63ZskG5V-Acm-9jjz1gz~SSR#d5d9mM{T&89&8c=h|lxRhwoW=;7 z+G|A~Ftx4~f5`RAoeXHuEG2Iy~NzOa^q*x+}dEt#nVaPCO%3HNhx|$puIVm3c6*A3Wbn_Ku+3!nMNKeQ8NXJbBlcfVN9KmL`jiDoQ2tHN7VGB0iI)hL$>7q82A6vNB3-Iig4W+V>(UdPH{38v}N%`;(K$A}PbE2OHM`)HL(r6UYcf77d`X4hqojkq-%j9S${VxrBvsKK`y-HYIl<~t5ZRdt&>sQ zJ^K&a*_@4T;xbd6PDN@duli*!>my52S{mB0>QdXA{MiGvvA*qNJ4}*d4(RIt+&vlO z2;t25LWZwQ2u4qH$~*nwg)zy}d1=E4A`bPr$nU0+_QH0JgL_=A+7u!7qA9;usjr5N zIoN03BiQ>G74xAuCaB0up4XUL5>@CGcNK!X!37dm^vMmLoHP!Enh^=q(V$j^KX3POFBG$k>)S@Ti6$v zGdC=3cYdubxid00>71V%?@-5mX21N}lIXhc=DU(|_N(j!`Dk!rF;;^%jz}yi_q-`> zvSYrGd>F<^t;fp!1jk~5AT5O;ZN!>m8_rlykTHu630mb5KOB}Wv!hJ%z(^z%pi;oA zj*2H1)b(yoe`O#jbluXbG=0>V73@gb21nw9^dE4jw7XIFqpY{0DQYN9#o^$hUX{A_gOy0&c%x*)Kq&lud7(18)pk|BMK2ZEs5eq6$lxSuQHuD`JOBRtv9)zA*GO;%aMF$#Y?7wJN-X+XNz5dMbla9ge zSFOV_IG$ea*u&F|s@dB5HnOQ`he=s1+!uOXO zvmJqAypHpaYyXw&Ic&dS2T5X=+e_D!l$6!H1W`#vMRZtpz-!qD#kutud17MX=wyh) z0;ERyJ@<$aCB#7|0mp+q0^$WALIaCrgY50vAKeGn4hj_qNqW({ zQ%zz_2$Pd zyPbH-FG0}dU-|S0j)DIvpFVzp(-q6n9tF}KkWRqdk0!J4xvM&4^CUo$xBcoD8K4>6 z`<`@6OiWw;=P1Xxqv` zyvgvBhRNwx@uFWb&8n7%H>YBPZ|usz4GSK_9}b{?{P##hZ?zlAivbDwVTZ7ADP+2{ z+Y-^5Hux-Um;8jVQGGNi7f8I37V5{Z%{A1Lou#(z0U#g`*n zXOa*(5V?%Ia;h~y%tUm+Lux|^-$y8Y=NjoT+%U8x`+}GMvC)dYl@h0!ba&iyjeLIi z_v+p@_B>^(Fd5gE-xj|XucDNcBB)WfkjMPb_rgBlhZ6;6|0g{`C<^EQq$WTPg~N`d z+aiwa2?j9~aY)U=e(=n`U-rO2^;5(5u!LGxyEf;~y!D2)^NMz%r=| z4BydN$&#IRoZj*{ThH1jADJ6LI)9O%*gp}LA8eqbM5xt0YcLFdxG)*tJXxsr@(KyY zIVq2XB+I919lvQ<5)#sV$w7VV%2jy-a-1Ly!x+#(koUico!zFJxw&zrvFSZJ%>tsM zK9hV-kgS`Unp!z4QuVB-dnshl(v(*-;e?8&!idk zPjT|ImuY$VzYNvVUrH}xS`x6!ffM5M({G`&d^{-blb5R(naCLb|MPP}mEl`~ca;y} z3r;Oq@87@ozS>O3qoPW%8cLJM8#mpbuLIHJp31;Gdx*H69tnu1p|pMMK#i7EW++M| z$P6!=WqTdCd;tep8aV*X-}zzpZeD52<6`~GuhApDKQ_S|zxqaG0OB|0-P4Pp=l1s} z{56{AvsGpY zK-7Z?(;sz?d=RP*xbt@#Qu&5Q*K)jgA5B z4lJE_f4v(_m`5Sxxf)Jn+zEp37!3>YAX8`olKN|x1Jc}a#73A-FPg9#@)wa;ck*wr z(m7VOc2VTp9K;nf@kmu4F4@%cB<7Ry?^!C%rHA8iX5?sdnPn5Lt^{p(-`~?KNNQ^n zoc#GSmr??0T~3Z=8`^?4hjBbyagO*Oldxe8<09YI*Vr>~GtQ5_v+Per#QDXa&F?^ zbh~n}bUX7)B8n2RZPXhHovv4~3rd88Nk?CLG06BtF=o9?8goBj`%bV4TM;%d zG{?>_r%uIFU!E+I7isbFM)VYMngevL%V{5ecqjK{sq(N`&R_I^z^jSTp|-H4aqlbj@=>m>PC3Xv6ix{JjROy{-m*c&>Ne(f!#lMbQI`FShKzgs z@nzu7en74x*o2LJUNI;1CQhIjAkml@P9d8vEKmx4onm}A7m@ke_i%5Qu24vjj)8s7 zG;DbOE2jD4BD&xSm{L;w!r_SP9Yt6W*ijpayiFw7kwsm4u>0!(i(&8{VDyZm=o{~s z8xbww+cWQtUi00xKp=%bduR4dP`byrSqsNUCj4`m<3VpLif;xRp4}MuvwyflH~xk{ zTaXYXEekpeLK$yO5dC{kDGbZ-J=&`!qpvQ%t!G!cXG2MewQ;YH69QikcLf8?aNd{& z5{%h1tZNGxzY(;J9pkYi7B;Mh(X@Pq-*=$0gpW)Of$EB#wIqr_(p|sYGj}vRs(WCw zD(};EEyr!vJ^cB{gHB$F{O&BI*hC}UxUyV=cbtAI#G~%7EVf0*r=FKkpMnelOv=_9 zc;oy4^bm&M6QRvpwk?mKXSTwB80b|4)6++Y@uoJdqT)c`Rg3_w;;5X7US8(f+A9m1 zoJOM-TP&1L{$50ABm#iY{cguS2EZXO@fQs0m;vqAC;pDAbz1+y+_2rhSlxlM450GwMRz6&F0Av-|1+Od;P|fS z+BduA`yTn8OqH2`kR+DMzoPRO4+teZ#EI3l9YaIJ6!?8y`QxMj166GsHXnL=<+c1i z4f^qsgGS+pgdbW|+yDr0nXOts``oj8rjmKcXS5K8@=4Ul$mlZR;S%Q-tdm}#lJalk z;IdUN3?$y3FE}|%Ycn>i;6mxUG5m{0DJY#(uC~K7#{zn&nh;2w>hO8Ozj6<2-nk_0 zV5kN&vz!;#x7kmn$F7{DCi{sjEn`8B*rhZ_xW0(H@PgKTz~pTFrmxA%n^k~jU5{2J zZnGc$1j5p$B7WzFGtrO6&)kG48ZWhZV24TREqvO@oBQ~XAiY1o=voH&s@J}!aC`bF zb4`}Jnc zVEu~B`?fN%VSik$+aApJLgJ=aOHf*lgJNtx8K>OySJ($4=En=PjW$zH*wCVR0o^*``MmZ=*dmi)dFvh@Tm!2H|gsa1@7{Hb$EsTXgfG&{>UqQXqbMYC*t+MVh~E*%!wH9jF;j*q zWjY4YsMbxl>LyWDZNr)KW5n-+BWT}u$g|VB4(a0+;@FYFl^C(0;e_z))%N|ri{d@2 zpSZJ6twIQ*F2WYjE4YWU^!JfW^bH3MP;E zZ(3TX1SuxmFkb(;dCUf~06(d=6g4CD^ZUBrW57ak!i}pEke>7dFMh!z*Ny(m#^a4n z*H~+|?ujr%w)kkdl0KxlfVC2aJp|UK+`LybFrHVX1% z7uk_GInT9MmIMTJj>j5BO}$v*Y9X9(E6NY-u;UWam49Qyo}Sls^Vm8U07jdAr6-NvlpL;b{?I7(`Z(8e+t4SGhNFIp#V^uRy>jO_h2ye zG%(F98mD%Q?iebqB39?x={JGIVyYjXleJ5a2H_&_Y%WXxR zvZ^4NlkrDKh#%*K+n-P4f6}?bM|5?}vXvP2_zat~8>je|pU{99g@V})Id>f_wkQ5K z4l=ncej+!snU=GUw*5YsrSfN)&)!LHV$)mgStdWC$!W!l(h9A$)6_|*SMA17J;$-t zLb^A~Ap~MA^BvueXYl=7g!mY)@<~{kSgW41$HO++IR~fAQ!lkUZFe8~Wwc7)HuH~m z#Q1UM;y;4*>M z=uG!f2mP}V=yBXvvwM1OJy6+||NRXD|5c<^XAnDwlsje(dfH8M>&eL{9^xMFh|h?I z;deVAWT2>pu92(ZqB_5HG{0B{2G>a-AvS&2CBF^H$cSRBi95NhNGXp{FOdY21U-0=RCR<@^`!?cN&H3**~eY#pT0$ zWIH&PJY;i|qG)1b7vu@Zh2JUWFsZjOY}NC4AZ)jDW&;(1SN# z%cah+SM!pRrWJE5f|PN!)9-`7Dho}K-?tg8e3FS*_)`p4#6~kfRYj0St)-z6^7%78 zOyJ|_=;(cW++Q8|5a2Lh%M9%R+8!hkQTHv}^S!yyrv~*9h)T97MeW^xW0O!?Eu_ia z{^hGUn@5LXDo$$IvWLN;?8I-!b~G<2w-=E_F(dX-cC08OE|a8*Y;Z*}4}T6s*yRU8 z)uf~l28V`@LBZXo&!~BL2wL+ScPfGI=F(!M97`ngkHw76C-O zFlyP}D>1U^)mV0mVgRX@l;Uj5EyErvB^8}`EUFJ2ts#b4?QrBLCNh>6(m61=k&TEIFDBCeYP>4Mg*7h*cD;kb;+U%7e-LOaTJaxf?Pgqzx(&`lvg`!$;rvg`*hN9lSSptPj4*L?%_q;%u$Qczr3jWEAU3tDer0DO=Z^T) z_HdOC)n#zFTfRxRd^ShFU9Oys@E@|{BG&&7F97WWkeW-3o{7APiXuLLcd_h;16(LF zl9Jttjx}r>W1pmEn?3B+RvMnffRJk7SUcEB{e!8jK;!Z@Ihkw6ZDe?O6VVTyTHz6p zoKl9M z{zCHY+qb9~QhlSfWACGejyo*Bivx=DGbKjNG03*))2B~i@n4``BSqC^Pj{x}Y7201 z^Nh+J;CK^q19!^T3Zs@P-!r=+Ef$B_ zDmbuBks*&CKQ;w5>vjZ)Kys?9@qm_oY}>u3{XhCqdN4RzgAAOSkRV5ZwN!fFU;Sp< z(!^7ytmzke<&EaW?6tzyJ@aG}o5jre7t5YBHz%TATBVovJ6qKxdM#~j8ZohK^? z$2Z~^Yer~f;R`w{hy95XR*JiTYgPRPEiLt%9jA*gQb9I*yquKF!$$- z$APkUhd)BLm%(a?;{dGbc*@`k4JuY7K@li8>*Uxvy~4wkuu13_{x%tg#;m zeGfR#Mj)76kd5zf7dDv9Vv}Ab_L#fBfg7yMJd{UANdZ*1lDul zMvT#1+;1e`mbZyf&)p>;TnL3W_$>wX!=3WpIOXMQAVhS5a>CM{Jox*wQJ=6XOLG(6 zmMd_RvQ&5MVz|k9#jzo<59Q=cacAk8A{oizDaXDe=k`~%zx#Py_XKNUVu~n35Cy47MLZ4CK(JL0za*9Z zK>W6dVrqx`$rCKw)Dy3zVl9jNQ3+}8x3HyvppGTYcqm=KdbN)zAA_W2%;z5ygxVQQo_ef&_H@9bMAg z0x*fy$g-J!b_HHepW9$a*HdNiFjNB;BHWh3h4|MV@-Axt)7> zk^~BZC}=B?R`IL7?k->>Hp}5JWTPg<2l1`kT~3(xiT~bKxR9+;F`okbAsm!Pi=O(I zgzV+=Gb#Z3qadI4W^m+%9TyFKXTqUDgbccT73;r6_91?w)ygS?#%{+ zj6~vV71~@fUMJ8d^KWTnA8JmFcwBeMH)sF?=&#H;kia)Z}n*8(9p(OJ!B&CPK5qgi(38xpP`u z$+H8lnSsz$LsPTb=Z|&k<$A1C$e4ItUEPzXPrs5mw+8q2O8?V77KtU{*+Ky`k}f!R z#Lv#^LOMADuU-hBj!Ai6Zx@<9BWC>h2ed&?0`z=hl^LU=p8u#(Sr1p})r-kQ*zOCa zlYn~DJc#8L6d(ihnLNma3a5h<)~{dIK=?&1DCj%80n7Jdk@1?&)mR1p2G=`Kgahrb zC^ruejgSzTs^}FxaOjPXjX?ma0cz%)3s-*fqRPsCU|f>(A4#G&>SxL?E;tnY}_m2WylAnhQqmsnS0)c4G?`z5g?x-t}b?BL|vwxe2 zYgo_>OlCR#3Bo6+*i@o%U~mw_!^4AT4*ZtdFQVh($R`DU|N15Qc#cVAN#UQa+IVj( zj@7MTOWUt=&Cb7TzAtW4!$udl7wPO+tqFgnUHjgR=slJR zaD5yGO!I*L5!}`yn6MG&_Pg_X(t#70DvRjyuo&Nch4y+!vy3h z)Oge*0Bpo3m=dTEWB;@1&Q{Tjl|V74wlf`v9Vb+F&$?7?PtlkQsl+t5hT6k*?ZoU6&Uy6H<3-61e=dO$aWq_+klXw0fT|W=$r$u@KGniY@Iz3*!F{igD@2Yz_gZDR%_pw zvU`>JmJlID6%~C5IKmKM1?3MI&y@kz&d%OId6?G!6bMd$?>{jmB@CZQB?mYm6U6TM zVU`5Ix{>x9oLAa&`e9ZV5W9p){{Kotvaqrq13L>1Cnw%v$Ac)$&2oD+8rZit_r>h_ z`>NMhuV88vni~h$7ZG@bwc3~cS7sKIFR2Cf^@K2g25`ujo6|x%x82RbVtmJAPYAvf z;qGDur(7qN%Vq@fmrsUG@A+Cb@Y+Td6eKpFPESoy^YRiZC@2K~{_oi-+tDIG=ppA< zw|_>*7ZzfHtp{?1^y%`_d(r3jhbmpRC+zHffSx91WeFF!rP2R&_Dp$w@BwR)>hHBE z@Ka*It`0eV4i4Ab;mceEmSmyI2T2fY0D~?2dI_vwpi_Zqae$#XoL)g*NvU_3ZzT?x zEO0@V8uZTlbX?9A*!rV#`FGrS|H&hrbGKu`mIh9r0VsF3Qv-^~49p#CK5GFp3Y8hP z5Tc=>^*Dj3bYQ|#ix*7oa@i1g!v_ia2gM8cFDM>`rb}+ILK6KuB}QZCT-&G>XRGa& zWVeDvT?i$6hvgeydh+nNv{AigoxfANx_Wtd@HZrOb!0`O)8!bZofym zw%x(^9k>kC1KSiO>>6DLZlxStM94oF8BEpF13m-XBQW16ps(O?;iburhs}9fl~U2= z|7=R(u5t$FA#l0oOm2r5=YeAW&4dye5)#t6zcro*?5(p6&Q!ppW0dTG2Xl)$$E)Mwmj@P@Nq0bOR}>8-W}ZhU)~7di!ovVZa<9wy6s(g@&fgX zruuL99tXGFAHSqSd(bj}hJQ1U{ANz|$}m?ao-GvHE&lpmhdvK`+n0iyrJfdm3TqhS z5KKk)v7-(=|DMBwkUkR|i%s;Mmx+BgtEf)Ev|hlhUTA#hWHsjDeALN)Y_}MR+DI&i z;9HSYeb-D(N|E-L(s(c;z*gCeT&@`2BrEcZ^h}ovYS6F{s^5a~3vR9G$FH!tUtvqT z;d2Lm2YKhK8?-X19cS`~&dMh!jG@?gdvTd4=ZWXZO4M?0`BbvyechHV zACAWGG+~`U3Vdmh*Mk)M^J{vW=j%D4+ET|fl~A^xAkZJmIb;frGyS5uX<2FR3K$&- zQp9~GSNWB^jj(~%8%v~cCiXhGT%MQ7ZePW)N?>d)ZEW2gl-_4!)P+#b6B2|ON(dWV z%)f`{nAEclL??k2G#7WWSnD0tr~cchPq7c4%_8j;JS~%AjNgLwGx0qHFz$&*!&BbDI<-{#>5^tmctF04tgUZG?F zrMDk_)}vL3HeD@wM3QHEc_@V%ZXcH>C$~{QC=`L2vh3e^IHUiBx~qpX>e3ZaL0aB0e@&w zcegwOp)6_0$8;+a3a4)~F_u(gF_vz*%LieTtQ%R=!EQSJi8FDUv0#CiSQ*rdv00J2 zkHO})01x!SRuLER`&0?1CRz0Q>_CoV65B>npws70UHU*>JJ06v?YAx^{SlfXT}F=Z zL66%!XLUZ$31Vr1nFY*RB1%43T0*>8BGNq+yVM%l`HSMC9c0Ye1ACe03*=j=tx)&I zu`QRi!!0bqk(@_~wx~`rgXfX?#9$i6>A$W33QjNixSD=2ib1{0jSkpOcpGd$4Zbb- zo|SQ5bWle0DIm?EZ$1>OqbQTk%_OBbjY$@oza+y8#{nBKK86}qe#|a0XL_|j!pM=b za?d29m($dk-o$>Qft_!DiL%zq&*jzFb9xqtEwZC&dHwxKAVpFg=}7$F#5l_7ojwEg z(8{`7e~u$z!egkw^P860>omG*#rAIuyZh{~7x5OStsiR#YdqXL@tWwytUsrh=2ihK z5h^KDBJ)#*{AS6m&-C`5=|!+hB^7DoEX1S~%t-WD$wxsk z!BbCwyI;IUCDif*@=7#@aYXOt*>%f&L_KX?vvA{j$DNzn%S?U9#2JGA+ce=etf``Kw(ZVC`B8LutWEPf^?nQ(u8p z@nH!q@}+G2a5QNqzPRd`E2o_+lr?F*0W-5$lDt^ACHZe92A9}F4KzYZuHZCtSRTx0S$vn&JNem;xp0NEtrJQ** z)NLEbf0h*4cOw#tL#Rx&s9{i46bjj8&sciMIw*uhmflJdqU=nIiBbqrmS;wh{V}AZ z5V8~Rb$6aq?|DzB^PKbit4Rs_xjAY$Uw*WFR}~oTKovY(~uJ91L1j6 z0v;TdLn$=ac)azR8(oW5qL#%DS`h>Zt5)@qVw@^9NnWy)^P+51Io{h-Hq6Nq7QQXH z;u4fU^3!O^rUa<%d9Q9@w}%f zIr53tLwDN=p5F1F$+ZsR2$E}eo=`sJfl`#Uhfa2l7YCnFif)YbH65}0jww!h-M#5d zs-&fQnfC6qys`?rxyD`YXBng0tUXkBp0-s?CTy6YN;`>`(hFeH+K@lUHJP84iQR&W zzZBPt&ns+sceNEVIlQW%MO`JCvU9}py7-tUPj|(>UAtYy_=V2lBh>CqzpS==U|Rb) zN$(5+1IlLQ-B zb@?56Wo_ac)Y#RE7swY*5^>V`3r1*Wm9DAyx_Y}l7q=WKS!By4i7icZCp~`Zr%YYa zlBXN&c`#==+aO1zFF^OQ{vNJmUmRIZ8R8M`dMt(AY>{_Oudg**E~8aI{I)SCmTc^f z#dqXshhJ+-qz@q&^bHTg&Sy5ZP04-irE52e zY%U;2P}df{d85&ZeP2g&UoLCrfqC1$-Lwv?!G+y;STcGi#7Unv1C-@LkfpF1cGsx%E2 z>YjODut!G%ZYgt^q$ZB)Tch37_1Fa5D}}#2{mLB_6qI=L=A~PE{iPKZ74Lcd%I`gq z3it@TpMq93>6tJjac?Xv0J$CN@)d7COwca2nRR{8jJ70RdJ`|4E52IxBZ-9Cz`r z8MtcX;FDGVft4=cYZYg~>08W2(rw)K(QasmtnV$TS>bTFV>ULI0Wl;9p4a^Rd_*F# zAy2;8q~)3kZf!sW4T3*H6Wul#jOP*a13Y0dfnPr`VF$`^$@aHKM zh`yspmZz>))oJtWi_Fak#j!=$-zkF$XBW&h%9cM6pOwORiw-oeMrLV6V;?h|dd4-c z)W&-zL^eu_E38%0X2yJcQ&F5g=`)_Th#~1_6|M{9E%y-{Sct*v&RDy^`G4X@G2iyR^)Z&G!W(3|HmiJ+DA7D@+^?Ze z^%MDdwkTCCFEo%__Y&`#@ip$erwV;&P3lu-<%KY{!1K;3jQ^bXA{mk$e3*|7 zd{C*X;hfEC#r|fb1$(d2>VKESw!I1pUFH>jO8or%;pyt4oZh>_J32wVf(yYqR;h|U z$$;;w4QG16ip3v3KAU2<&ZLdY>W9B_90WGf7TJ^ao1?|4esZUa{5mT=o>y&sKWE9l z7@G0hw*8%sVbE$_^=-_n6I7OaloKW<5g^}5U<|+y0EP+BbN^5NS$smPC;kv^f_Rx6 zbqkV&+^WY^vyQd@)eJ5Cd{b;SAczPG3E9t$_rb-}l|-s@r#y=S%Pe=r>SLc_hVLFD zbMsw}Jt1>_(TK{|PyWgUcY2$kkCL5TI6!E|(0hmS|Z-s5lC&N$xrQBc`aiuYhA=VS#k$ z1O`EeZnaKgO*0=l13_Q-w8>()z|Jv`FIZ?<%DM0i3&s`_D?YD4eo# zA60wPdL7}&9huRv_^}!2KbJ!41!4pY;?q8Q|L`x zl$Sy~28@#X+7|fv4+(EiViy+|y)&5MjwPjhU(3V-93v}pt1JEUf2#~PR9yy3D#mM& zObtU44;p*uLK_5sWrLY*$XV-#42?j3o_*82P(}CG9@f&4u>^{mg{9?fQ=V_bqfjSR zx~60)9Q80OAMCO@MU9PzG?pi71Lg;!C&66Er0U$P36D$wp)_%Q%)AQZ$G^6=h(saJAm-Zu(imBKd>VT!Q&m4GM^S>@%TM>Qr{w+gSviHlD{ zzlh*>aS#wD4uI4^OFW`V-Sm9IE}~-d^wVbnqX49wgD(|s=nXuTJ0KFv_ywi7F0D_T$KK#FqfdLncSKZMP#8sqpBK{%|VR;+qLx}N) z`e?9o4U)l}z$?Y*ZEcZ&0elBdLp6YC2u|A{f~#8U`-12x{VTI&*f|(wHarUR9;{0ua{CIPSE@NyO!txC(vWR#Va zd5tkdK7=X)Of^SjUS$U}7Jne3uvj}SB}KHH(A^=Ib&(1{mcq+?$b1|JdN10BP|czH z8dyKdy|#%Wz8%2w*!P;Hk~?=EFnOJ}+b8kLl?cM>0!{l7wG5hjdb|#rmp+(whNKD};3VRmJm6S_WJVM!fBtFs@Y6XX8Kv=~l4kl0(w51pD#^@mMffr?& zwz@z-vUE*NjeBLKVJ-_}y?X7dmX>F?(Kj?RHDv>CE2Qc>k*fJMApQxr4SogL8WH!P zS_7yX4QN1Pc3{>QgV))p019Enm&!{hg#it!WpBR;@pe#vBdp|sY=lweh8+XK7@AYO zqI<^b5!+=nO(x>~&=3;q%K+z^SS8J*S!~)*|18itkE}8r92`--=sWd)8}~;T)6DkM zlkYaJib8u?EsToUngJ!X`7T;$6v{_e504iJZ<%*|IoC*^b>TFNG?K;gAvf*eype3d|0ycd;*o{}oHOXmulFcWl1SyQ5*iJ;wBP4RuP0cH#d8?g0o; literal 0 HcmV?d00001 diff --git a/jupyter_execute/_bblearn/Module09/Module09_lab.ipynb b/jupyter_execute/_bblearn/Module09/Module09_lab.ipynb new file mode 100644 index 0000000..ce5ffb7 --- /dev/null +++ b/jupyter_execute/_bblearn/Module09/Module09_lab.ipynb @@ -0,0 +1,578 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "c1305517-15b0-4538-98b3-e43cb2a6fed4", + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "# Setting up the Colab environment. DO NOT EDIT!\n", + "import os\n", + "#import warnings\n", + "#warnings.filterwarnings(\"ignore\")\n", + "\n", + "try:\n", + " import otter, pingouin\n", + "\n", + "except ImportError:\n", + " ! pip install -q otter-grader==4.0.0, pingouin\n", + " import otter\n", + "\n", + "if not os.path.exists('walkthrough-tests'):\n", + " zip_files = [f for f in os.listdir() if f.endswith('.zip')]\n", + " assert len(zip_files)>0, 'Could not find any zip files!'\n", + " assert len(zip_files)==1, 'Found multiple zip files!'\n", + " ! unzip {zip_files[0]}\n", + "\n", + "grader = otter.Notebook(colab=True,\n", + " tests_dir = 'walkthrough-tests')" + ] + }, + { + "cell_type": "markdown", + "id": "93498126", + "metadata": {}, + "source": [ + "# Lab" + ] + }, + { + "cell_type": "markdown", + "id": "aaa36b08", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "At the end of this learning activity you will be able to:\n", + " - Practice using robust correlation tools that account for outliers.\n", + " - Practice using `pg.qqplot` and `pg.normality` to asses the normality of residuals.\n", + " - Practice using regression to create covariate-controlled scores.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0120fbdb-220b-4cf4-93e6-9f61cbafeac0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pingouin as pg\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1b58e08-33dd-4abf-9f03-bf0e5adf0f68", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "data = pd.read_csv('hiv_neuro_data.csv')\n", + "data['education'] = data['education'].astype(float)\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "3c8907cb-4a06-4eae-adb9-a546165c814d", + "metadata": {}, + "source": [ + "This lab is going to explore the inter-relationships between two cognitive domains.\n", + "\n", + "* **Executive Function**: The complex cognitive processes required for planning, organizing, problem-solving, abstract thinking, and executing strategies. This domain also encompasses decision-making and cognitive flexibility, which is the ability to switch between thinking about two different concepts or to think about multiple concepts simultaneously.\n", + "- **Speed of Information Processing**: How quickly an individual can understand and react to the information being presented. This domain evaluates the speed at which cognitive tasks can be performed, often under time constraints.\n", + "\n", + "We will explore whether these two domains are correllated after controlling for co-variates." + ] + }, + { + "cell_type": "markdown", + "id": "9056e62e-2912-4f30-9a05-636b03f3c61f", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q1: Are Processing domain and Executive domain scores correlated?" + ] + }, + { + "cell_type": "markdown", + "id": "f69faf30-144e-4ac4-a3af-7abc6a378059", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 5 |\n", + "| Public Checks | 3 |\n", + "| Hidden Tests | 1 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5f244f0-7a60-4014-97b7-bd9bb50d52d4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Generate a plot between processing_domain_z and exec_domain_z\n", + "\n", + "q1_plot = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c3994fa-87bb-4d54-8a50-c51367dab36d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Use pg.corr to calculate the correlation between the two variables using a `robust` correlation metric\n", + "\n", + "q1_corr_res = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87f58703-4542-4e6b-84bd-c0f1af632a7e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Are the two domains significantly correlated? 'yes' or 'no'\n", + "\n", + "q1_is_corr = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e11a56be", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q1_domain_corr\")" + ] + }, + { + "cell_type": "markdown", + "id": "210aff4b-fc2c-4ecf-83d4-d40a9d86ca47", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q2: Create a regression for the processing domain that accounts for demographic covariates.\n", + "\n", + " - Age\n", + " - Race\n", + " - Sex\n", + " - Education\n", + " - Years Seropositive\n", + " - ART" + ] + }, + { + "cell_type": "markdown", + "id": "9163e0b1-6c31-44f6-9228-f6dd1cabb9e6", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 10 |\n", + "| Public Checks | 7 |\n", + "| Hidden Tests | 7 |\n", + "\n", + "_Points:_ 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b30cd4c0-77d3-47be-b9c1-f15f869079db", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Perform the regression using `pg.linear_regression`\n", + "# Use the result to answer the questions below\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73013a7e-1636-404a-ad88-66f34b2d2a36", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Assess the normality of the residuals of the model\n", + "\n", + "\n", + "q2_model_resid_normal = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ed0ca75-3b33-4b48-b31d-de725bd19121", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Considering a p<0.01 threshold answer which of the following are significant\n", + "\n", + "# Age\n", + "q2_processing_age = ...\n", + "\n", + "# Race\n", + "q2_processing_race = ...\n", + "\n", + "# Sex\n", + "q2_processing_sex = ...\n", + "\n", + "# Education\n", + "q2_processing_edu = ...\n", + "\n", + "# Infection length\n", + "q2_processing_ys = ...\n", + "\n", + "# ART\n", + "q2_processing_art = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "965c6839", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q2_exec_adj\")" + ] + }, + { + "cell_type": "markdown", + "id": "08ec7b71-a064-40d3-bce4-d3bd697ceac1", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q3: Is covariate controlled EDZ still correlated with PDZ?\n" + ] + }, + { + "cell_type": "markdown", + "id": "3573d869-4873-410c-91b0-a2fc985ed910", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 10 |\n", + "| Public Checks | 7 |\n", + "| Hidden Tests | 7 |\n", + "\n", + "_Points:_ 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87df2483-cc82-4199-b934-e3c47b23f609", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Generate a plot between covariate controlled processing_domain_z and exec_domain_z\n", + "\n", + "q3_plot = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b5b79b5-2c01-4383-a974-2ae15fde4837", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Use pg.corr to calculate the correlation between the two variables using a `pearson` correlation metric\n", + "\n", + "q3_corr_res = ...\n", + "q3_corr_res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b5a9705-1653-4ffe-ad1c-e1007cf304d9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Are processing_domain_z and covariate controlled exec_domain_z still correlated?\n", + "q3_corr_sig = ...\n", + "\n", + "\n", + "# Correlation r-value\n", + "# Place the r-value here rounded to 4 decimal places\n", + "q3_corr_r = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c6e993f-05b6-44df-a0bd-d2ae3965bedb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "# Partial correlation r-value\n", + "# Place the r-value here rounded to 4 decimal places\n", + "q3_partial_corr_r = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41acf0ac-a62e-4474-b8af-5e1a82eb3f87", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Are the results the same between the two methods? 'yes' or 'no'\n", + "\n", + "q3_same_res = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ea6628f", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q3_partial_corr\")" + ] + }, + { + "cell_type": "markdown", + "id": "f8f5c8cf-4fd7-4c6c-a65b-3e3471104dae", + "metadata": {}, + "source": [ + "We've seen from above that it is important to create `processing_domain_z` score corrected for covariates.\n", + "We also saw in the walkthrough that it is important create an `exec_domain_z` score corrected for covariates.\n", + "However, `pg.partial_corr` only allows you to correct for covariates in `x` or `y` but not **both**.\n", + "\n", + "Use another regression to remove the covaraites from `exec_domain_z` and determine if it is correlated with `processing_domain_z` after removing covariates." + ] + }, + { + "cell_type": "markdown", + "id": "e8f8f844-cc93-4eae-a587-f85291b0d87f", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q4: Are EDZ and PDZ correlated after controlling for covariates?" + ] + }, + { + "cell_type": "markdown", + "id": "adcd941d-767b-4014-9896-7eb8bfbd870b", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 10 |\n", + "| Public Checks | 7 |\n", + "| Hidden Tests | 7 |\n", + "\n", + "_Points:_ 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a5ce9d8-f1b0-4411-91f0-f6cc60df7c1a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Find the residuals for exec_domain_z after controlling for covariates\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48012c73-e929-40a1-90b4-d90044849bd2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Plot the two corrected values against each other\n", + "\n", + "q4_plot = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "223bddef-dc30-4eda-9c44-d171ae0e1115", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Test the correlation between the two sets of corrected values\n", + "\n", + "pg.corr(proc_res.residuals_, exec_res.residuals_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e91a69c2-fea7-45b0-9b10-3322f1c84bda", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# After correction for covariates, are PDZ and EDZ correlated? 'yes' or 'no'\n", + "\n", + "q4_sig_cor = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7372c6bb", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q4_full_corr\")" + ] + }, + { + "cell_type": "markdown", + "id": "d5653e0c", + "metadata": {}, + "source": [ + "--------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcecffa9", + "metadata": {}, + "outputs": [], + "source": [ + "grader.check_all()" + ] + }, + { + "cell_type": "markdown", + "id": "ad81e3ae", + "metadata": {}, + "source": [ + "## Submission\n", + "\n", + "Check:\n", + " - That all tables and graphs are rendered properly.\n", + " - Code completes without errors by using `Restart & Run All`.\n", + " - All checks **pass**.\n", + " \n", + "Then save the notebook and the `File` -> `Download` -> `Download .ipynb`. Upload this file to BBLearn." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "otter": { + "assignment_name": "Module09_lab" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/jupyter_execute/_bblearn/Module09/Module09_walkthrough_SOLUTION.ipynb b/jupyter_execute/_bblearn/Module09/Module09_walkthrough_SOLUTION.ipynb new file mode 100644 index 0000000..207691c --- /dev/null +++ b/jupyter_execute/_bblearn/Module09/Module09_walkthrough_SOLUTION.ipynb @@ -0,0 +1,2841 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6febc445-889c-4db1-b014-6a346ab9a49f", + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "# Setting up the Colab environment. DO NOT EDIT!\n", + "import os\n", + "#import warnings\n", + "#warnings.filterwarnings(\"ignore\")\n", + "\n", + "try:\n", + " import otter, pingouin\n", + "\n", + "except ImportError:\n", + " ! pip install -q otter-grader==4.0.0, pingouin\n", + " import otter\n", + "\n", + "if not os.path.exists('walkthrough-tests'):\n", + " zip_files = [f for f in os.listdir() if f.endswith('.zip')]\n", + " assert len(zip_files)>0, 'Could not find any zip files!'\n", + " assert len(zip_files)==1, 'Found multiple zip files!'\n", + " ! unzip {zip_files[0]}\n", + "\n", + "grader = otter.Notebook(colab=True,\n", + " tests_dir = 'walkthrough-tests')" + ] + }, + { + "cell_type": "markdown", + "id": "cea3b0b0", + "metadata": {}, + "source": [ + "# Walkthrough" + ] + }, + { + "cell_type": "markdown", + "id": "71197956", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "At the end of this learning activity you will be able to:\n", + " - Practice using `pg.normality` and `pg.qqplot` to assess normality.\n", + " - Practice using `pg.linear_regression` to perform multiple regression.\n", + " - Interpret the results of linear regression such as the coefficient, p-value, R^2, and confidence intervals.\n", + " - Describe a _residual_ and how to interpret it.\n", + " - Relate the _dummy variable trap_ and how to avoid it during regression.\n", + " - Describe _overfitting_ and how to avoid it." + ] + }, + { + "cell_type": "markdown", + "id": "230f0ff0", + "metadata": {}, + "source": [ + "As we discussed with Dr. Devlin in the introduction video, this week and next we are going to look at HIV neurocognitive impairment data from a cohort here at Drexel.\n", + "Each person was given a full-scale neuropsychological exam and the resulting values were aggregated and normalized into Z-scores based on demographically matched healthy individuals.\n", + "\n", + "In this walkthrough we will explore the effects of antiretroviral medications on neurological impairment.\n", + "In our cohort, we have two major drug regimens, d4T (Stavudine) and the newer Emtricitabine/tenofovir (Truvada).\n", + "The older Stavudine is suspected to have neurotoxic effects that are not found in the newer Truvada.\n", + "We will use inferential statistics to understand this effect." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a0a08b85-58d9-4963-828b-8b515b8470f8", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pingouin as pg\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2d3c415d-aff6-401d-9ffd-61abe1112897", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    sexageeducationraceprocessing_domain_zexec_domain_zlanguage_domain_zvisuospatial_domain_zlearningmemory_domain_zmotor_domain_zARTYearsSeropositive
    0male6210.0AA0.50.60.151646-1.0-1.152131-1.364306Stavudine13
    1male5610.0AA-0.51.2-0.255505-2.0-0.086376-0.348600Truvada19
    2female5110.0AA0.50.10.902004-0.4-1.1398920.112215Stavudine9
    3female4712.0AA-0.6-1.2-0.119866-2.10.803619-2.276768Truvada24
    4male4613.0AA-0.41.30.079129-1.3-0.533607-0.330541Truvada14
    \n", + "
    " + ], + "text/plain": [ + " sex age education race processing_domain_z exec_domain_z \\\n", + "0 male 62 10.0 AA 0.5 0.6 \n", + "1 male 56 10.0 AA -0.5 1.2 \n", + "2 female 51 10.0 AA 0.5 0.1 \n", + "3 female 47 12.0 AA -0.6 -1.2 \n", + "4 male 46 13.0 AA -0.4 1.3 \n", + "\n", + " language_domain_z visuospatial_domain_z learningmemory_domain_z \\\n", + "0 0.151646 -1.0 -1.152131 \n", + "1 -0.255505 -2.0 -0.086376 \n", + "2 0.902004 -0.4 -1.139892 \n", + "3 -0.119866 -2.1 0.803619 \n", + "4 0.079129 -1.3 -0.533607 \n", + "\n", + " motor_domain_z ART YearsSeropositive \n", + "0 -1.364306 Stavudine 13 \n", + "1 -0.348600 Truvada 19 \n", + "2 0.112215 Stavudine 9 \n", + "3 -2.276768 Truvada 24 \n", + "4 -0.330541 Truvada 14 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('hiv_neuro_data.csv')\n", + "data['education'] = data['education'].astype(float)\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "ac31172e-1108-4f2c-a322-07e1f91d0942", + "metadata": {}, + "source": [ + "Before we start, we need to talk about assumptions.\n", + "\n", + "Basic linear regression has a number assumptions baked into itself:\n", + " - **Linearity**: The relationship between the independent variables (predictors) and the dependent variable (outcome) is linear. This means that changes in the predictors lead to proportional changes in the dependent variable.\n", + " - **The relationship between the independent variables and the dependent variable is additive**: The effect of changes in an independent variable X on the dependent variable Y is consistent, regardless of the values of other independent variables. This assumption might not hold if there are interaction effects between independent variables that affect the dependent variable.\n", + " - **Independence**: Observations are independent of each other. This means that the observations do not influence each other, an assumption that is particularly important in time-series data where time-related dependencies can violate this assumption.\n", + " - **Homoscedasticity**: The variance of error terms (residuals) is constant across all levels of the independent variables. In other words, as the predictor variable increases, the spread (variance) of the residuals remains constant. This is evaluated at the **end** of the fit.\n", + " - **Normal Distribution of Errors**: The residuals (errors) of the model are normally distributed. This assumption is especially important for hypothesis testing (e.g., t-tests of coefficients) and confidence interval construction. It's worth noting that for large sample sizes, the Central Limit Theorem helps mitigate the violation of this assumption. This is evaluated at the **end** of the fit.\n", + " - **Minimal Multicollinearity**: The independent variables need to be independent of each other. Multicollinearity doesn't affect the fit of the model as much as it affects the coefficients' estimates, making them unstable and difficult to interpret.\n", + " - **No perfect multicollinearity**: Also called the _dummy variable trap_. It states that none of the independent variables should be a perfect linear function of other independent variables. We'll talk more about this when we run into it.\n", + "\n", + "Biology itself is highly non-linear.\n", + "That doesn't mean we can't use linear assumptions to explore biological questions, it just means that we need to be mindful when interpretting the results." + ] + }, + { + "cell_type": "markdown", + "id": "a6ab9af5-a5ea-451c-b267-fcc0b0b1afd7", + "metadata": {}, + "source": [ + "## Exploration" + ] + }, + { + "cell_type": "markdown", + "id": "9e1954ae-3cb3-4167-8705-e9123c1e9d40", + "metadata": {}, + "source": [ + "Let's start by plotting the each variable against EDZ." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d8dd6aa8-655e-4d6b-a977-1e6d4ed91181", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (age_ax, edu_ax, ys_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))\n", + "\n", + "sns.regplot(data = data,\n", + " x = 'age',\n", + " y = 'exec_domain_z',\n", + " ax=age_ax)\n", + "\n", + "sns.regplot(data = data,\n", + " x = 'education',\n", + " y = 'exec_domain_z',\n", + " ax=edu_ax)\n", + "\n", + "sns.regplot(data = data,\n", + " x = 'YearsSeropositive',\n", + " y = 'exec_domain_z',\n", + " ax=ys_ax)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2c4b2076-e3e1-484e-bd41-31a07f419162", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q1: By inspection, which variable is most correlated?" + ] + }, + { + "cell_type": "markdown", + "id": "6e601810-0c65-4d8f-86d6-aa26184e1971", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 5 |\n", + "| Public Checks | 3 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "016c7dda-c8f7-43bd-b956-9eb418126bcc", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [], + "source": [ + "# Answer: age, education, YearsSeropositive\n", + "q1_most_correlated = 'YearsSeropositive' # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1b66cd6", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q1_initial_correlation\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a11fb13c-1794-4fad-8586-96727cd1ca88", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (race_ax, sex_ax, art_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))\n", + "\n", + "sns.stripplot(data=data,\n", + " x = 'race',\n", + " y = 'exec_domain_z', ax=race_ax)\n", + "sns.boxplot(data=data,\n", + " x = 'race',\n", + " y = 'exec_domain_z', ax=race_ax)\n", + "\n", + "sns.stripplot(data=data,\n", + " x = 'sex',\n", + " y = 'exec_domain_z', ax=sex_ax)\n", + "sns.boxplot(data=data,\n", + " x = 'sex',\n", + " y = 'exec_domain_z', ax=sex_ax)\n", + "\n", + "sns.stripplot(data=data,\n", + " x = 'ART',\n", + " y = 'exec_domain_z', ax=art_ax)\n", + "sns.boxplot(data=data,\n", + " x = 'ART',\n", + " y = 'exec_domain_z', ax=art_ax)" + ] + }, + { + "cell_type": "markdown", + "id": "a6715b3c-a00e-42e2-8633-798881ae7cbb", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q2: By inspection, which variable has the most between class difference?" + ] + }, + { + "cell_type": "markdown", + "id": "2b4bacf5-e194-4225-b3c1-3d25c2a830dd", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 5 |\n", + "| Public Checks | 3 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "737a1795-88d3-4225-b0df-e6aee178968a", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [], + "source": [ + "# Answer: race, sex, ART\n", + "q2_most_bcd = 'race' # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6016a607", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q2_initial_bcd\")" + ] + }, + { + "cell_type": "markdown", + "id": "27d11168-ead2-4651-b420-a7431b290ee4", + "metadata": {}, + "source": [ + "## Basic regression" + ] + }, + { + "cell_type": "markdown", + "id": "89603733-b40c-4d31-8ec3-d1933d2e6dd6", + "metadata": {}, + "source": [ + "We'll start by taking the simplest approach and regress the most correlated value first." + ] + }, + { + "cell_type": "markdown", + "id": "95b2c235-e31d-4198-960c-9759c8cf380a", + "metadata": {}, + "source": [ + "`pg.linear_regression` works by regressing all columns in the first parameter against the single column in the second.\n", + "By convention, we usually use the variables `X` and `y`.\n", + "\n", + "You'll often see this written as:\n", + "\n", + "$\\mathbf{y} = \\mathbf{X} \\boldsymbol{\\beta} + \\boldsymbol{\\epsilon}$\n", + "\n", + "In the case of `pg.linear_regression` the $\\boldsymbol{\\epsilon}$ is added by default and we do not need to specify it.\n", + "\n", + "You do not have to use the variable names `X` and `y`, in many cases you might have multiple `X`s and `y`s, but for simplicity, I will stick with this simple convention." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d37176f0-9513-44c9-a293-0256c7f4c08c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept0.7116250.1058226.7247337.994463e-110.2368150.2344530.5034370.919812
    1YearsSeropositive-0.0352580.003522-10.0113201.000644e-200.2368150.234453-0.042186-0.028329
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 \\\n", + "0 Intercept 0.711625 0.105822 6.724733 7.994463e-11 0.236815 \n", + "1 YearsSeropositive -0.035258 0.003522 -10.011320 1.000644e-20 0.236815 \n", + "\n", + " adj_r2 CI[2.5%] CI[97.5%] \n", + "0 0.234453 0.503437 0.919812 \n", + "1 0.234453 -0.042186 -0.028329 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = data['YearsSeropositive'] # Our independent variables\n", + "y = data['exec_domain_z'] # Our dependent variable\n", + "res = pg.linear_regression(X, y)\n", + "res" + ] + }, + { + "cell_type": "markdown", + "id": "308f2c65-40b8-4e26-93a9-2b2ac44e495f", + "metadata": {}, + "source": [ + "This has fit the equation:\n", + "\n", + "`PDZ = -0.035*YS + 0.712`\n", + "\n", + "It tells us that the likelihood of this slope being zero is 1.0E-20 and that years-seropositive explains ~23.6% of variation in EDZ that we observe." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f97f1fce-b27c-4371-bc5e-97378e170ff5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.regplot(data = data,\n", + " x = 'YearsSeropositive',\n", + " y = 'exec_domain_z')\n", + "\n", + "# Pick \"years seropositive\" from 0 to 70\n", + "x = np.arange(0, 70)\n", + "\n", + "# Use the coefficients from above in a linear equation\n", + "y = res.loc[1, 'coef']*x + res.loc[0, 'coef']\n", + "\n", + "ax.plot(x, y, color = 'r')" + ] + }, + { + "cell_type": "markdown", + "id": "7b9d1f9b-16b9-4f95-ae29-00d964a2eb3c", + "metadata": {}, + "source": [ + "## Residuals" + ] + }, + { + "cell_type": "markdown", + "id": "f9909e11-b673-4be1-9787-e4f815f04ab7", + "metadata": {}, + "source": [ + "_Residuals_ are the difference between the observed value and the predicted value.\n", + "In the case of a simple linear regression, this is the y-distance between each point and the best-fit line.\n", + "Examining these is an import step in assessing the fit for any biases.\n", + "You can think of the residual as what is \"left over\" after the regression.\n", + "\n", + "We could calculate these ourselves from the regression coefficients, but, `pingouin` conviently provides them for us.\n", + "The result `DataFrame` from `pg.linear_regression` has a special attribute `.residuals_` which stores the difference between the prediction and reality for each point in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "aff2050d-1d24-4b23-834a-dd8e9add1aa0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.34672285 1.15826787 -0.29430717 -1.06544462 1.08198035]\n" + ] + } + ], + "source": [ + "print(res.residuals_[:5])" + ] + }, + { + "cell_type": "markdown", + "id": "c2662e02-ff9b-4398-ace9-d4f05d29e098", + "metadata": {}, + "source": [ + "In order to test the **Homoscedasticity** we want to ensure that these residuals are _not correlated with the depenendant variable_.\n", + "\n", + "In our case, this means that the model is equally good predicting the EDZ of people recently infected with HIV and those who have been living with HIV for a long time.\n", + "\n", + "To do this, we plot the residuals vs each independent variable." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2eec2b7c-2bae-4b79-a740-f534751b66e9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.scatterplot(x=data['YearsSeropositive'], y=res.residuals_)" + ] + }, + { + "cell_type": "markdown", + "id": "ddc1570e-155a-4c57-ac8d-e41eb6895574", + "metadata": {}, + "source": [ + "This is an ideal residual plot.\n", + "It should look like a random \"stary-night sky\" centered around 0.\n", + "This implies that the model is not better or worse for any given X value." + ] + }, + { + "cell_type": "markdown", + "id": "6d4a62b5-c418-4222-9c87-90ecf7804f26", + "metadata": {}, + "source": [ + "Let's also test our assumption about a normal distribution of errors of the residuals." + ] + }, + { + "cell_type": "markdown", + "id": "ca391103-3c84-4fd6-9b7f-896577811ed5", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q3: Are the residuals normally distributed?" + ] + }, + { + "cell_type": "markdown", + "id": "41d6da6d-1e4c-496e-a059-85b262326bc9", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 5 |\n", + "| Public Checks | 5 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0caa835c-e80d-4ec1-ba53-de99147c41d5", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a Q-Q plot of the residuals\n", + "\n", + "q3_plot = pg.qqplot(res.residuals_) # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "753e8d3b-8d25-4ac7-81d7-8f606d9dec09", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [], + "source": [ + "# Use the Jarque-Bera normal test for large sample sizes\n", + "\n", + "q3_norm_res = pg.normality(res.residuals_, method='jarque_bera') # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5afc057b-0cf0-4df7-8d5e-734980f2fb47", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [], + "source": [ + "# Are the residuals normally distributed? 'yes' or 'no'\n", + "\n", + "q3_is_norm = 'yes' # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63e75623", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q3_resid_normality\")" + ] + }, + { + "cell_type": "markdown", + "id": "01b59934-9f51-429d-a65e-ebf77655a3dc", + "metadata": {}, + "source": [ + "You don't need to do this test at every stage, but it is a good test to do before you are _done_." + ] + }, + { + "cell_type": "markdown", + "id": "17cd99fc-7bc7-4f43-9872-50ddc5fc4a9d", + "metadata": {}, + "source": [ + "## Multiple Regression" + ] + }, + { + "cell_type": "markdown", + "id": "e0045aea-276f-4dd8-bfd2-cf9129a2cb15", + "metadata": {}, + "source": [ + "Regression is not limited to a single independent variable, you can add as many as you'd like.\n", + "\n", + "In our case, there are two others that we should consider: `age` and `education`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2c9e5a55-d612-4af6-a1b2-113e9ae5f825", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept0.9774490.4047182.4151351.628781e-020.3182070.3118350.1812141.773685
    1YearsSeropositive-0.0374620.003390-11.0498542.853764e-240.3182070.311835-0.044132-0.030792
    2education-0.1026470.020406-5.0301768.170366e-070.3182070.311835-0.142794-0.062500
    3age0.0192970.0055463.4792955.721793e-040.3182070.3118350.0083850.030209
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 \\\n", + "0 Intercept 0.977449 0.404718 2.415135 1.628781e-02 0.318207 \n", + "1 YearsSeropositive -0.037462 0.003390 -11.049854 2.853764e-24 0.318207 \n", + "2 education -0.102647 0.020406 -5.030176 8.170366e-07 0.318207 \n", + "3 age 0.019297 0.005546 3.479295 5.721793e-04 0.318207 \n", + "\n", + " adj_r2 CI[2.5%] CI[97.5%] \n", + "0 0.311835 0.181214 1.773685 \n", + "1 0.311835 -0.044132 -0.030792 \n", + "2 0.311835 -0.142794 -0.062500 \n", + "3 0.311835 0.008385 0.030209 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = data[['YearsSeropositive', 'education', 'age']]\n", + "y = data['exec_domain_z']\n", + "res = pg.linear_regression(X, y)\n", + "res" + ] + }, + { + "cell_type": "markdown", + "id": "3653f050-b236-46ff-8b0d-4db6935c6880", + "metadata": {}, + "source": [ + "Now, it has fit the equation:\n", + "\n", + "`EDZ = -0.037*YS - 0.103*edu + 0.019*age + 0.977`\n", + "\n", + "The education is significant at p=8.17E-7.\n", + "Be caution when comparing coefficients, we might be tempted to compare -0.0422 and -0.0506 and say that education has a more negative effect than YS ...\n", + "But, remember that education ranges from 0-12 and YS ranges from 0-60, these are not on the same scale and are not directly comparable.\n", + "We'll talk about how to compare relative importance later." + ] + }, + { + "cell_type": "markdown", + "id": "60eb2693-5c50-4784-889d-ac28a1faba2b", + "metadata": {}, + "source": [ + "As before, we should check the residuals of the model against _each_ independent variable in the regression to check for homoscedasticity." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d131c037-88eb-491d-a707-8526b6d2c516", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ys_ax, edu_ax, age_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))\n", + "\n", + "sns.scatterplot(x=data['YearsSeropositive'], y=res.residuals_, ax=ys_ax)\n", + "sns.scatterplot(x=data['education'], y=res.residuals_, ax=edu_ax)\n", + "sns.scatterplot(x=data['age'], y=res.residuals_, ax=age_ax)" + ] + }, + { + "cell_type": "markdown", + "id": "e162e5c1-107e-4d83-a074-8d9812b67688", + "metadata": {}, + "source": [ + "Three more stary night skies. Perfect." + ] + }, + { + "cell_type": "markdown", + "id": "6dc72fe5-e59a-434b-acba-3ceacd58ecfe", + "metadata": {}, + "source": [ + "Remember, the residual is the difference between the prediction of the model and reality.\n", + "Therefore, we can also use the residual plots to see how well the regression is handling other variables we have not included in the model.\n", + "If the model has properly accounted for something, the residual plot should stay centered around 0.\n", + "\n", + "This can be done for categorical or continious variables." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "15d2e733-b303-4aff-8451-147f222f5cd7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (race_ax, sex_ax, art_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))\n", + "\n", + "race_ax.set_ylabel('residual')\n", + "\n", + "sns.barplot(x=data['race'], y=res.residuals_, ax=race_ax)\n", + "sns.barplot(x=data['sex'], y=res.residuals_, ax=sex_ax)\n", + "sns.barplot(x=data['ART'], y=res.residuals_, ax=art_ax)" + ] + }, + { + "cell_type": "markdown", + "id": "2e0a1f0c-7df8-40f8-ab6f-bb2e70eb7493", + "metadata": {}, + "source": [ + "Here we see some interesting patterns:\n", + " - The graph of race against residuals shows us that our model is signifacntly racially biased. AA individuals are significantly 'under-estimated' by the model, C individauals are significantly over-estimated, and H individuals are significantly over-estimated.\n", + " - The graph of sex shows that there is no real difference in the residuals. It has accounted for sex already.\n", + " - It looks like there is a real difference across ART." + ] + }, + { + "cell_type": "markdown", + "id": "7bc5658b-b99f-44f1-8746-495870be08a4", + "metadata": {}, + "source": [ + "## _ANCOVA_" + ] + }, + { + "cell_type": "markdown", + "id": "2bb494a9-d773-4f50-8c7a-52535f1684f8", + "metadata": {}, + "source": [ + "What we have done above is create a model that _accounts_ for the effects of age, education, and YS on EDZ.\n", + "We **subtracted** that effect (the predicted value) from the observed value thus creating the _residual_.\n", + "This is what is \"left over\" in the observed value after accounting for covariates or nuisance variables.\n", + "Then we plotted the _residual_ against each of our categorical variables.\n", + "If we then took the ANOVA of these residuals we'd be testing the hypothesis:\n", + " _When accounting for age, education, and YS is there a difference across race._\n", + " \n", + "This process is called an _Analysis of covariance_ or an **ANCOVA**." + ] + }, + { + "cell_type": "markdown", + "id": "2b088af3-35d1-4228-a38d-0ce0edd7de10", + "metadata": {}, + "source": [ + "### Standard first" + ] + }, + { + "cell_type": "markdown", + "id": "d4c97c10-cedb-4a4a-9568-c56dfe6b737d", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q4: Perform an ANOVA between ART on the Executive Domain Z-score." + ] + }, + { + "cell_type": "markdown", + "id": "ed969ccd-12ec-41b6-b6ba-cd6d7203208a", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| | |\n", + "| --------------|----|\n", + "| Points | 5 |\n", + "| Public Checks | 4 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "0cca7821-9925-43d1-a802-62a17217125e", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a plot showing the effect of ART on EDZ\n", + "q4_plot = sns.barplot(data = data, x = 'ART', y = 'exec_domain_z') # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "07fde2af-cad6-4b78-b88d-54d027545af9", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Sourceddof1ddof2Fp-uncnp2
    0ART13237.8096990.0055070.023608
    \n", + "
    " + ], + "text/plain": [ + " Source ddof1 ddof2 F p-unc np2\n", + "0 ART 1 323 7.809699 0.005507 0.023608" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Perform an ANOVA testing the impact of ART on EDZ\n", + "q4_res = pg.anova(data, dv = 'exec_domain_z', between = 'ART') # SOLUTION\n", + "q4_res" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "46ef6bde-3ab5-43f9-bab2-5fc4dc400688", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [], + "source": [ + "# Does ART have a significant impact on Executive Domain? 'yes' or 'no'?\n", + "\n", + "q4_art_impact = 'yes' # SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78303d6a", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q4_art_test\")" + ] + }, + { + "cell_type": "markdown", + "id": "8f89b18b-531d-42a1-a96a-5f5f95449fb9", + "metadata": {}, + "source": [ + "### With correction\n", + "\n", + "Nicely `pingouin` has something built right in to do this whole process." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "5377a300-35e4-472b-b960-1bc8c1d59001", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SourceSSDFFp-uncnp2
    0ART11.879147117.4700833.770731e-050.051768
    1YearsSeropositive79.8888141117.4885851.585741e-230.268552
    2education20.033725129.4626231.128191e-070.084308
    3age17.992537126.4607474.697743e-070.076374
    4Residual217.590675320NaNNaNNaN
    \n", + "
    " + ], + "text/plain": [ + " Source SS DF F p-unc np2\n", + "0 ART 11.879147 1 17.470083 3.770731e-05 0.051768\n", + "1 YearsSeropositive 79.888814 1 117.488585 1.585741e-23 0.268552\n", + "2 education 20.033725 1 29.462623 1.128191e-07 0.084308\n", + "3 age 17.992537 1 26.460747 4.697743e-07 0.076374\n", + "4 Residual 217.590675 320 NaN NaN NaN" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAGwCAYAAABhDIVPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAlUUlEQVR4nO3dfVRUdeLH8c8gAgYyPiBQK4JlKaaYYiq0prYGWpaWpYZhnlXTLXPNHlmPZXY8tJWrbWWPlmuSkaXllmuhPSweRBMZ7QHJ02pQgk/poFQ83t8fHufXBCokF/jS+3XOPXnv/d473+kc4e2dOzMOy7IsAQAAGMKnqScAAABQH8QLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADAK8QIAAIzi29QTaGjV1dXav3+/2rZtK4fD0dTTAQAAdWBZlo4fP64LLrhAPj5nvrbS4uJl//79ioiIaOppAACA36CwsFCdO3c+45gWFy9t27aVdPLJBwcHN/FsAABAXZSUlCgiIsLze/xMWly8nHqpKDg4mHgBAMAwdbnlgxt2AQCAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFEaJV6WLl2qrl27KiAgQLGxscrMzDzt2M2bN+uKK65Qx44d1aZNG/Xo0UOLFy9ujGkCAAAD2P71AOnp6Zo9e7aWLl2qK664Qi+88IJGjhypr776Sl26dKkxPjAwUDNnzlRMTIwCAwO1efNmTZ8+XYGBgbr99tvtni4AAGjmHJZlWXY+wMCBA9WvXz8999xznm3R0dEaM2aMUlNT63SOG2+8UYGBgXrttdfOOrakpEROp1Nut5vvNgIAwBD1+f1t68tG5eXlysnJUUJCgtf2hIQEZWVl1ekcubm5ysrK0pAhQ2rdX1ZWppKSEq8FAGA2y7J04sQJz2Lzv7NhGFtfNjp8+LCqqqoUFhbmtT0sLEzFxcVnPLZz5846dOiQKisrNX/+fE2dOrXWcampqXrkkUcabM4AgKZXWlqq0aNHe9bfffddBQUFNeGM0Jw0yg27v/56a8uyzvqV15mZmdq+fbuef/55LVmyRKtWrap1XEpKitxut2cpLCxssHkDAIDmx9YrLyEhIWrVqlWNqywHDx6scTXm17p27SpJ6t27tw4cOKD58+frlltuqTHO399f/v7+DTdpAADQrNl65cXPz0+xsbHKyMjw2p6RkaH4+Pg6n8eyLJWVlTX09AAAgIFsf6v0nDlzlJycrP79+ysuLk4vvviiCgoKNGPGDEknX/b5/vvvtWLFCknSs88+qy5duqhHjx6STn7uy5NPPqm77rrL7qkCAAAD2B4v48eP15EjR7RgwQIVFRWpV69eWr9+vSIjIyVJRUVFKigo8Iyvrq5WSkqK9u7dK19fX1100UV67LHHNH36dLunCgAADGD757w0Nj7nBQDMd+LECd5t9DvTbD7nBQAAoKERLwAAwCjECwAAMArxAgAAjEK8AAAAoxAvAADAKMQLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADAK8QIAAIxCvAAAAKMQLwAAwCjECwAAMArxAgAAjEK8AAAAoxAvAADAKMQLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADAK8QIAAIxCvAAAAKMQLwAAwCjECwAAMArxAgAAjEK8AAAAoxAvAADAKMQLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADAK8QIAAIxCvAAAAKMQLwAAwCjECwAAMArxAgAAjEK8AAAAoxAvAADAKMQLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADBKo8TL0qVL1bVrVwUEBCg2NlaZmZmnHbtmzRpdffXV6tSpk4KDgxUXF6cPPvigMaYJAAAMYHu8pKena/bs2Zo7d65yc3M1ePBgjRw5UgUFBbWO/+9//6urr75a69evV05OjoYNG6brrrtOubm5dk8VAAAYwGFZlmXnAwwcOFD9+vXTc88959kWHR2tMWPGKDU1tU7nuPTSSzV+/Hg99NBDZx1bUlIip9Mpt9ut4ODg3zxvAEDTOXHihEaPHu1Zf/fddxUUFNSEM4Ld6vP729YrL+Xl5crJyVFCQoLX9oSEBGVlZdXpHNXV1Tp+/Lg6dOhQ6/6ysjKVlJR4LQAAoOWyNV4OHz6sqqoqhYWFeW0PCwtTcXFxnc6xaNEilZaWaty4cbXuT01NldPp9CwRERHnPG8AANB8NcoNuw6Hw2vdsqwa22qzatUqzZ8/X+np6QoNDa11TEpKitxut2cpLCxskDkDAIDmydfOk4eEhKhVq1Y1rrIcPHiwxtWYX0tPT9eUKVO0evVqDR8+/LTj/P395e/v3yDzBQAAzZ+tV178/PwUGxurjIwMr+0ZGRmKj48/7XGrVq3S5MmT9frrr+vaa6+1c4oAAMAwtl55kaQ5c+YoOTlZ/fv3V1xcnF588UUVFBRoxowZkk6+7PP9999rxYoVkk6Gy6RJk/TUU09p0KBBnqs2bdq0kdPptHu6AACgmbM9XsaPH68jR45owYIFKioqUq9evbR+/XpFRkZKkoqKirw+8+WFF15QZWWl7rzzTt15552e7bfddpuWL19u93QBAEAzZ/vnvDQ2PucFAMzH57z8/jSbz3kBAABoaMQLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADAK8QIAAIxCvAAAAKMQLwAAwCi2fz0AAJgm9r4VTT2F3z1HZbl++W12Q+e9IcvXr8nmg5NynpjU1FOQxJUXAABgGOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABglEaJl6VLl6pr164KCAhQbGysMjMzTzu2qKhISUlJ6t69u3x8fDR79uzGmCIAADCE7fGSnp6u2bNna+7cucrNzdXgwYM1cuRIFRQU1Dq+rKxMnTp10ty5c9WnTx+7pwcAAAxje7z84x//0JQpUzR16lRFR0dryZIlioiI0HPPPVfr+KioKD311FOaNGmSnE7nWc9fVlamkpISrwUAALRctsZLeXm5cnJylJCQ4LU9ISFBWVlZDfIYqampcjqdniUiIqJBzgsAAJonW+Pl8OHDqqqqUlhYmNf2sLAwFRcXN8hjpKSkyO12e5bCwsIGOS8AAGiefBvjQRwOh9e6ZVk1tv1W/v7+8vf3b5BzAQCA5s/WKy8hISFq1apVjassBw8erHE1BgAAoC5sjRc/Pz/FxsYqIyPDa3tGRobi4+PtfGgAANBC2f6y0Zw5c5ScnKz+/fsrLi5OL774ogoKCjRjxgxJJ+9Z+f7777VixQrPMS6XS5J04sQJHTp0SC6XS35+furZs6fd0wUAAM2c7fEyfvx4HTlyRAsWLFBRUZF69eql9evXKzIyUtLJD6X79We+9O3b1/PnnJwcvf7664qMjNS+ffvsni4AAGjmGuWG3TvuuEN33HFHrfuWL19eY5tlWTbPCAAAmIrvNgIAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABgFN+mngAAAL9mtWotd8wtXuvAKcQLAKD5cThk+fo19SzQTPGyEQAAMArxAgAAjEK8AAAAoxAvAADAKMQLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADAK8QIAAIxCvAAAAKMQLwAAwCjECwAAMArxAgAAjNIo8bJ06VJ17dpVAQEBio2NVWZm5hnHf/rpp4qNjVVAQIAuvPBCPf/8840xTQAAYADb4yU9PV2zZ8/W3LlzlZubq8GDB2vkyJEqKCiodfzevXt1zTXXaPDgwcrNzdXf/vY3zZo1S2+//bbdUwUAAAZwWJZl2fkAAwcOVL9+/fTcc895tkVHR2vMmDFKTU2tMf6BBx7QunXrlJeX59k2Y8YM7dy5U1u2bKkxvqysTGVlZZ71kpISRUREyO12Kzg4uIGfDYDfg9j7VjT1FIBmKeeJSbadu6SkRE6ns06/v31tm4Wk8vJy5eTk6MEHH/TanpCQoKysrFqP2bJlixISEry2JSYmatmyZaqoqFDr1q299qWmpuqRRx5p2InXAT/cgNrZ+cOtsbSE5wC0ZLa+bHT48GFVVVUpLCzMa3tYWJiKi4trPaa4uLjW8ZWVlTp8+HCN8SkpKXK73Z6lsLCw4Z4AAABodmy98nKKw+HwWrcsq8a2s42vbbsk+fv7y9/fvwFmCQAATGDrlZeQkBC1atWqxlWWgwcP1ri6ckp4eHit4319fdWxY0fb5goAAMxga7z4+fkpNjZWGRkZXtszMjIUHx9f6zFxcXE1xn/44Yfq379/jftdAADA74/tb5WeM2eOXn75Zb3yyivKy8vT3XffrYKCAs2YMUPSyXtWJk36/5vjZsyYoW+//VZz5sxRXl6eXnnlFS1btkz33nuv3VMFAAAGsP2el/Hjx+vIkSNasGCBioqK1KtXL61fv16RkZGSpKKiIq/PfOnatavWr1+vu+++W88++6wuuOAC/fOf/9TYsWPtnioAADCA7Z/z0tjq8z7xc8FbpYHa8TZjAL9FfX5/891GAADAKMQLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADAK8QIAAIxCvAAAAKMQLwAAwCi2fz0AYBvLkqOq4v9XW7WWHI4mnBAAoDEQLzCWo6pCzl2rPOvumFtk+fo14YwAAI2Bl40AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGsTVejh49quTkZDmdTjmdTiUnJ+vYsWNnPGbNmjVKTExUSEiIHA6HXC6XnVMEAACGsTVekpKS5HK5tGHDBm3YsEEul0vJyclnPKa0tFRXXHGFHnvsMTunBgAADOVr14nz8vK0YcMGZWdna+DAgZKkl156SXFxccrPz1f37t1rPe5U3Ozbt69Oj1NWVqaysjLPeklJyblNHAAANGu2XXnZsmWLnE6nJ1wkadCgQXI6ncrKymqwx0lNTfW8LOV0OhUREdFg5wYAAM2PbfFSXFys0NDQGttDQ0NVXFzcYI+TkpIit9vtWQoLCxvs3AAAoPmpd7zMnz9fDofjjMv27dslSQ6Ho8bxlmXVuv238vf3V3BwsNcCAABarnrf8zJz5kxNmDDhjGOioqK0a9cuHThwoMa+Q4cOKSwsrL4PCwAAIOk3xEtISIhCQkLOOi4uLk5ut1vbtm3TgAEDJElbt26V2+1WfHx8/WcKAAAgG+95iY6O1ogRIzRt2jRlZ2crOztb06ZN06hRo7zeadSjRw+tXbvWs/7DDz/I5XLpq6++kiTl5+fL5XI16H0yAADAXLZ+zktaWpp69+6thIQEJSQkKCYmRq+99prXmPz8fLndbs/6unXr1LdvX1177bWSpAkTJqhv3756/vnn7ZwqAAAwhG2f8yJJHTp00MqVK884xrIsr/XJkydr8uTJNs4KAACYjO82AgAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUXybegKmynliUlNP4XfvxIkTGj16lWf9k0cnKCgoqAlnBABoDFx5AQAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwAAYBTiBQAAGIV4AQAARiFeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABjF1ng5evSokpOT5XQ65XQ6lZycrGPHjp12fEVFhR544AH17t1bgYGBuuCCCzRp0iTt37/fzmkCAACD2BovSUlJcrlc2rBhgzZs2CCXy6Xk5OTTjv/xxx+1Y8cOzZs3Tzt27NCaNWv09ddf6/rrr7dzmgAAwCC+dp04Ly9PGzZsUHZ2tgYOHChJeumllxQXF6f8/Hx17969xjFOp1MZGRle255++mkNGDBABQUF6tKlS41jysrKVFZW5lkvKSlp4GcCAACaE9uuvGzZskVOp9MTLpI0aNAgOZ1OZWVl1fk8brdbDodD7dq1q3V/amqq52Upp9OpiIiIc506AABoxmyLl+LiYoWGhtbYHhoaquLi4jqd4+eff9aDDz6opKQkBQcH1zomJSVFbrfbsxQWFp7TvAEAQPNW73iZP3++HA7HGZft27dLkhwOR43jLcuqdfuvVVRUaMKECaqurtbSpUtPO87f31/BwcFeCwAAaLnqfc/LzJkzNWHChDOOiYqK0q5du3TgwIEa+w4dOqSwsLAzHl9RUaFx48Zp7969+uijjwgSAADgUe94CQkJUUhIyFnHxcXFye12a9u2bRowYIAkaevWrXK73YqPjz/tcafCZc+ePfr444/VsWPH+k4RAAC0YLbd8xIdHa0RI0Zo2rRpys7OVnZ2tqZNm6ZRo0Z5vdOoR48eWrt2rSSpsrJSN910k7Zv3660tDRVVVWpuLhYxcXFKi8vt2uqAADAILZ+zktaWpp69+6thIQEJSQkKCYmRq+99prXmPz8fLndbknSd999p3Xr1um7777TZZddpvPPP9+z1OcdSgAAoOWy7XNeJKlDhw5auXLlGcdYluX5c1RUlNc6AADAr/HdRgAAwCjECwAAMArxAgAAjEK8AAAAoxAvAADAKMQLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADAK8QIAAIxCvAAAAKMQLwAAwCjECwAAMArxAgAAjEK8AAAAoxAvAADAKMQLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADAK8QIAAIxCvAAAAKMQLwAAwCjECwAAMArxAgAAjEK8AAAAoxAvAADAKMQLAAAwCvECAACMQrwAAACjEC8AAMAoxAsAADAK8QIAAIxCvAAAAKMQLwAAwCjECwAAMArxAgAAjEK8AAAAoxAvAADAKMQLAAAwim9TTwD4rQIDA/Xuu+96rQMAWj7iBcZyOBwKCgpq6mkAABoZLxsBAACjEC8AAMAotsbL0aNHlZycLKfTKafTqeTkZB07duyMx8yfP189evRQYGCg2rdvr+HDh2vr1q12ThMAABjE1nhJSkqSy+XShg0btGHDBrlcLiUnJ5/xmEsuuUTPPPOMPv/8c23evFlRUVFKSEjQoUOH7JwqAAAwhMOyLMuOE+fl5alnz57Kzs7WwIEDJUnZ2dmKi4vT7t271b179zqdp6SkRE6nUxs3btSf/vSnOo93u90KDg4+p+cAAAAaR31+f9t25WXLli1yOp2ecJGkQYMGyel0Kisrq07nKC8v14svviin06k+ffrUOqasrEwlJSVeCwAAaLlsi5fi4mKFhobW2B4aGqri4uIzHvvee+8pKChIAQEBWrx4sTIyMhQSElLr2NTUVM89NU6nUxEREQ0yfwAA0DzVO17mz58vh8NxxmX79u2STn4Ox69ZllXr9l8aNmyYXC6XsrKyNGLECI0bN04HDx6sdWxKSorcbrdnKSwsrO9TAgAABqn3h9TNnDlTEyZMOOOYqKgo7dq1SwcOHKix79ChQwoLCzvj8YGBgerWrZu6deumQYMG6eKLL9ayZcuUkpJSY6y/v7/8/f3r9yQAAICx6h0vISEhp30J55fi4uLkdru1bds2DRgwQJK0detWud1uxcfH1+sxLctSWVlZfacKAABaINvueYmOjtaIESM0bdo0ZWdnKzs7W9OmTdOoUaO83mnUo0cPrV27VpJUWlqqv/3tb8rOzta3336rHTt2aOrUqfruu+9088032zVVAABgEFs/5yUtLU29e/dWQkKCEhISFBMTo9dee81rTH5+vtxutySpVatW2r17t8aOHatLLrlEo0aN0qFDh5SZmalLL73UzqkCAABD2PY5L03F7XarXbt2Kiws5HNeAAAwRElJiSIiInTs2DE5nc4zjm1x3yp9/PhxSeIt0wAAGOj48eNnjZcWd+Wlurpa+/fvV9u2bc/6lmyY71Spc6UNaHn4+/37YlmWjh8/rgsuuEA+Pme+q6XFXXnx8fFR586dm3oaaGTBwcH8cANaKP5+/36c7YrLKbbesAsAANDQiBcAAGAU4gVG8/f318MPP8ynLAMtEH+/cTot7oZdAADQsnHlBQAAGIV4AQAARiFeAACAUYgX/K5ERUVpyZIlnnWHw6F33nmnyeYDwH5Dhw7V7Nmzm3oaaEDEC87JwYMHNX36dHXp0kX+/v4KDw9XYmKitmzZIqn5x0FRUZFGjhzZ1NMAjOJwOM64TJ48uamniBauxX3CLhrX2LFjVVFRoX/961+68MILdeDAAW3atEk//PBDU0+tTsLDw5t6CoBxioqKPH9OT0/XQw89pPz8fM+2Nm3aeI2vqKhQ69atG21+aPm48oLf7NixY9q8ebP+/ve/a9iwYYqMjNSAAQOUkpKia6+9VlFRUZKkG264QQ6Hw7P+zTffaPTo0QoLC1NQUJAuv/xybdy40XPelJQUDRo0qMbjxcTE6OGHH5ZU+2XgMWPGeP2L7+DBg7ruuuvUpk0bde3aVWlpaTXO+csrQ/v27ZPD4dCaNWs0bNgwnXfeeerTp4/nKtIpWVlZuvLKK9WmTRtFRERo1qxZKi0tref/PcBc4eHhnsXpdMrhcHjWf/75Z7Vr105vvvmmhg4dqoCAAK1cuVLz58/XZZdd5nWeJUuWeH4ufPDBBwoICNCxY8e8xsyaNUtDhgyRJB05ckS33HKLOnfurPPOO0+9e/fWqlWrvMaXlpZq0qRJCgoK0vnnn69FixbVmP/KlSvVv39/tW3bVuHh4UpKStLBgwcb7P8P7Ee84DcLCgpSUFCQ3nnnHZWVldXY/9lnn0mSXn31VRUVFXnWT5w4oWuuuUYbN25Ubm6uEhMTdd1116mgoECSNHHiRG3dulXffPON51xffvmlPv/8c02cOLHO85s8ebL27dunjz76SG+99ZaWLl1apx9Qc+fO1b333iuXy6VLLrlEt9xyiyorKyVJn3/+uRITE3XjjTdq165dSk9P1+bNmzVz5sw6zwv4PXjggQc0a9Ys5eXlKTEx8azjhw8frnbt2untt9/2bKuqqtKbb77p+Xv/888/KzY2Vu+9956++OIL3X777UpOTtbWrVs9x9x33336+OOPtXbtWn344Yf65JNPlJOT4/VY5eXlevTRR7Vz506988472rt3Ly91mcYCzsFbb71ltW/f3goICLDi4+OtlJQUa+fOnZ79kqy1a9ee9Tw9e/a0nn76ac96TEyMtWDBAs96SkqKdfnll3vWhwwZYv31r3/1Osfo0aOt2267zbIsy8rPz7ckWdnZ2Z79eXl5liRr8eLFtc5v7969liTr5Zdf9uz/8ssvLUlWXl6eZVmWlZycbN1+++1ej5uZmWn5+PhYP/3001mfJ9DSvPrqq5bT6fSsn/p7tGTJEq9xDz/8sNWnTx+vbYsXL7YiIyM967NmzbKuuuoqz/oHH3xg+fn5WT/88MNpH/+aa66x7rnnHsuyLOv48eOWn5+f9cYbb3j2HzlyxGrTpk2Nnxe/tG3bNkuSdfz48TM8UzQnXHnBORk7dqz279+vdevWKTExUZ988on69eun5cuXn/aY0tJS3X///erZs6fatWunoKAg7d6923PlRTp59eXUyzyWZWnVqlX1uuqSl5cnX19f9e/f37OtR48eateu3VmPjYmJ8fz5/PPPlyTPFZucnBwtX77cc9UpKChIiYmJqq6u1t69e+s8P6Cl++XfvbqaOHGiPvnkE+3fv1+SlJaWpmuuuUbt27eXdPJKzMKFCxUTE6OOHTsqKChIH374oednxzfffKPy8nLFxcV5ztmhQwd1797d63Fyc3M1evRoRUZGqm3btho6dKgkef0MQvNGvOCcBQQE6Oqrr9ZDDz2krKwsTZ482XNvSm3uu+8+vf3221q4cKEyMzPlcrnUu3dvlZeXe8YkJSXp66+/1o4dO5SVlaXCwkJNmDDBs9/Hx0fWr77ZoqKiwvPnU/scDke9n88vbyw8dXx1dbXnv9OnT5fL5fIsO3fu1J49e3TRRRfV+7GAliowMNBr/Wx/ZyVpwIABuuiii/TGG2/op59+0tq1a3Xrrbd69i9atEiLFy/W/fffr48++kgul0uJiYmenx2/Pn9tSktLlZCQoKCgIK1cuVKfffaZ1q5dK0leP4PQvPFuIzS4nj17em6Cbd26taqqqrz2Z2ZmavLkybrhhhsknbwHZt++fV5jOnfurCuvvFJpaWn66aefNHz4cIWFhXn2d+rUyesdD1VVVfriiy80bNgwSVJ0dLQqKyu1fft2DRgwQJKUn59f42bA+urXr5++/PJLdevW7ZzOA/zedOrUScXFxbIsy/OPApfLVWNcUlKS0tLS1LlzZ/n4+Ojaa6/17MvMzNTo0aM9QVNdXa09e/YoOjpaktStWze1bt1a2dnZ6tKliyTp6NGj+vrrrz03/e7evVuHDx/WY489poiICEnS9u3bbXvesAdXXvCbHTlyRFdddZVWrlypXbt2ae/evVq9erUef/xxjR49WtLJD4XbtGmTiouLdfToUUknf8CsWbPGc9UiKSnJc2XjlyZOnKg33nhDq1ev9vrXlyRdddVVev/99/X+++9r9+7duuOOO7zCpHv37hoxYoSmTZumrVu3KicnR1OnTq3xFs76euCBB7Rlyxbdeeedcrlc2rNnj9atW6e77rrrnM4LtHRDhw7VoUOH9Pjjj+ubb77Rs88+q//85z81xk2cOFE7duzQwoULddNNNykgIMCzr1u3bsrIyFBWVpby8vI0ffp0FRcXe/YHBQVpypQpuu+++7Rp0yZ98cUXmjx5snx8/v9XXZcuXeTn56enn35a//vf/7Ru3To9+uij9j55NDjiBb9ZUFCQBg4cqMWLF+vKK69Ur169NG/ePE2bNk3PPPOMpJOXeTMyMhQREaG+fftKkhYvXqz27dsrPj5e1113nRITE9WvX78a57/55pt15MgR/fjjjxozZozXvj//+c+67bbbNGnSJA0ZMkRdu3b1XHU55dVXX1VERISGDBmiG2+8UbfffrtCQ0PP6TnHxMTo008/1Z49ezR48GD17dtX8+bN89wbA6B20dHRWrp0qZ599ln16dNH27Zt07333ltj3MUXX6zLL79cu3btqnGf27x589SvXz8lJiZq6NChCg8Pr/Gz4YknntCVV16p66+/XsOHD9cf//hHxcbGevZ36tRJy5cv1+rVq9WzZ0899thjevLJJ215zrCPw6rLi4QAAADNBFdeAACAUYgXAABgFOIFAAAYhXgBAABGIV4AAIBRiBcAAGAU4gUAABiFeAEAAEYhXgAAgFGIFwDNQlZWllq1aqURI0Z4bd+3b58cDodncTqdGjRokP79739LOvmdOb/c/+slKiqqCZ4NADsRLwCahVdeeUV33XWXNm/erIKCghr7N27cqKKiIm3dulUDBgzQ2LFj9cUXX2jNmjUqKipSUVGRtm3b5jW2qKhIn332WWM/FQA2I14ANLnS0lK9+eab+stf/qJRo0Zp+fLlNcZ07NhR4eHh6tGjhxYuXKiKigp9/PHH6tChg8LDwxUeHq5OnTp5jf3lNgAtB/ECoMmlp6ere/fu6t69u2699Va9+uqrOt13xlZUVOill16SJLVu3boxpwmgmfBt6gkAwLJly3TrrbdKkkaMGKETJ05o06ZNGj58uGdMfHy8fHx89NNPP6m6ulpRUVEaN25cU00ZQBPiyguAJpWfn69t27ZpwoQJkiRfX1+NHz9er7zyite49PR05ebmat26derWrZtefvlldejQoSmmDKCJceUFQJNatmyZKisr9Yc//MGzzbIstW7dWkePHvVsi4iI0MUXX6yLL75YQUFBGjt2rL766iuFhoY2xbQBNCGuvABoMpWVlVqxYoUWLVokl8vlWXbu3KnIyEilpaXVetyQIUPUq1cvLVy4sJFnDKA5IF4ANJn33ntPR48e1ZQpU9SrVy+v5aabbtKyZctOe+w999yjF154Qd9//30jzhhAc0C8AGgyy5Yt0/Dhw+V0OmvsGzt2rFwul3744Ydajx01apSioqK4+gL8Djms070fEQAAoBniygsAADAK8QIAAIxCvAAAAKMQLwAAwCjECwAAMArxAgAAjEK8AAAAoxAvAADAKMQLAAAwCvECAACMQrwAAACj/B9SNj/K1DFTXwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(x=data['ART'], y=res.residuals_)\n", + "\n", + "# An ANCOVA testing the impact of ART on EDZ\n", + "# after correcting for the impace of age, education and YS\n", + "pg.ancova(data,\n", + " dv = 'exec_domain_z',\n", + " between = 'ART',\n", + " covar=['YearsSeropositive', 'education', 'age'])" + ] + }, + { + "cell_type": "markdown", + "id": "1409e6f5-23e5-4436-a9a6-0242f4c36c7e", + "metadata": {}, + "source": [ + "We can notice that after correction for covaraites the F-value has increased and the p-value has decreased.\n", + "This means the analysis is attributing more difference to race after correction and is more sure this is not due to noise." + ] + }, + { + "cell_type": "markdown", + "id": "ff14833e-bda0-48a2-9c26-d2e530824231", + "metadata": {}, + "source": [ + "The _advantage_ of using the `pg.ancova` function is that you can easily and quickly do your analysis.\n", + "The _disadvantage_ is that you cannot examine the internal regression for Normality and Homoscedasticity." + ] + }, + { + "cell_type": "markdown", + "id": "fa572f6b-0e82-4a31-ab30-4c267bfb5be0", + "metadata": {}, + "source": [ + "But, what if we wanted to have a covariate that is a category like race?" + ] + }, + { + "cell_type": "markdown", + "id": "5f8a699c-8439-40c4-9728-a391a5785573", + "metadata": {}, + "source": [ + "## Regression with categories" + ] + }, + { + "cell_type": "markdown", + "id": "89316dac-b3db-444d-9bc1-9136c1e9970c", + "metadata": {}, + "source": [ + "So, how do you do regression with a category like race?\n", + "\n", + "Could it be as simple as adding it the `X` matrix?" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "8fbd4b6c-dbf6-4eb2-846f-ee978ab688a8", + "metadata": {}, + "outputs": [], + "source": [ + "# X = data[['YearsSeropositive', 'education', 'age', 'race']]\n", + "# y = data['processing_domain_z']\n", + "# res = pg.linear_regression(X, y)\n", + "# res" + ] + }, + { + "cell_type": "markdown", + "id": "6199f0af-45b8-43ef-946e-1ea31145f7a7", + "metadata": {}, + "source": [ + "Would have been nice, but we need to get a little tricky and use _dummy_ variables.\n", + "\n", + "In their simplest terms, dummy variables are binary representations of categories.\n", + "Like so." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "c2cd028f-1caf-4797-841d-0d508c7f9afd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    AACH
    0TrueFalseFalse
    1TrueFalseFalse
    2TrueFalseFalse
    3TrueFalseFalse
    4TrueFalseFalse
    \n", + "
    " + ], + "text/plain": [ + " AA C H\n", + "0 True False False\n", + "1 True False False\n", + "2 True False False\n", + "3 True False False\n", + "4 True False False" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.get_dummies(data['race']).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "36adb5a0-9709-402a-95e8-ec24c68524a2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/tljh/user/lib/python3.9/site-packages/pingouin/regression.py:420: UserWarning: Design matrix supplied with `X` parameter is rank deficient (rank 6 with 7 columns). That means that one or more of the columns in `X` are a linear combination of one of more of the other columns.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept-0.1940.294-0.6610.5090.4530.444-0.7720.383
    1YearsSeropositive-0.0460.003-14.1330.0000.4530.444-0.052-0.039
    2education-0.0540.019-2.7950.0060.4530.444-0.092-0.016
    3age0.0310.0055.8680.0000.4530.4440.0210.041
    4AA0.4100.1043.9410.0000.4530.4440.2050.615
    5C-0.5830.149-3.9140.0000.4530.444-0.876-0.290
    6H-0.0210.132-0.1620.8710.4530.444-0.2820.239
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 adj_r2 CI[2.5%] \\\n", + "0 Intercept -0.194 0.294 -0.661 0.509 0.453 0.444 -0.772 \n", + "1 YearsSeropositive -0.046 0.003 -14.133 0.000 0.453 0.444 -0.052 \n", + "2 education -0.054 0.019 -2.795 0.006 0.453 0.444 -0.092 \n", + "3 age 0.031 0.005 5.868 0.000 0.453 0.444 0.021 \n", + "4 AA 0.410 0.104 3.941 0.000 0.453 0.444 0.205 \n", + "5 C -0.583 0.149 -3.914 0.000 0.453 0.444 -0.876 \n", + "6 H -0.021 0.132 -0.162 0.871 0.453 0.444 -0.282 \n", + "\n", + " CI[97.5%] \n", + "0 0.383 \n", + "1 -0.039 \n", + "2 -0.016 \n", + "3 0.041 \n", + "4 0.615 \n", + "5 -0.290 \n", + "6 0.239 " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extracting the same continious variables\n", + "X = data[['YearsSeropositive', 'education', 'age']]\n", + "\n", + "# Creating new dummy variables for race\n", + "dummy_vals = pd.get_dummies(data['race']).astype(float)\n", + "\n", + "\n", + "# Adding them the end\n", + "X = pd.concat([X, dummy_vals], axis=1)\n", + "\n", + "y = data['exec_domain_z']\n", + "\n", + "res = pg.linear_regression(X, y)\n", + "res.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "be9ac92a-18be-4d29-9408-9a2ae605e8fb", + "metadata": {}, + "source": [ + "This _Warning_ is telling us that our model has fallen into the _dummy variable trap_.\n", + "The dummy variable trap occurs when dummy variables created for categorical data in a regression model are perfectly collinear, meaning one variable can be predicted from the others, leading to redundancy.\n", + "This happens because the inclusion of all dummy variables for a category along with a constant term (intercept) creates a situation where the sum of the dummy variables plus the intercept equals one, introducing perfect multicollinearity.\n", + "To avoid this, one dummy variable should be dropped to serve as the reference category, ensuring the model's design matrix is full rank and the regression coefficients are estimable and interpretable." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "635fc2b2-2c6e-4e54-afd5-0731a721840b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    CH
    0FalseFalse
    1FalseFalse
    2FalseFalse
    3FalseFalse
    4FalseFalse
    \n", + "
    " + ], + "text/plain": [ + " C H\n", + "0 False False\n", + "1 False False\n", + "2 False False\n", + "3 False False\n", + "4 False False" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.get_dummies(data['race'], drop_first=True).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "05f2d96c-2f2c-47c9-8c59-b0a068c944dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept0.2160.3810.5670.5710.4530.444-0.5340.966
    1YearsSeropositive-0.0460.003-14.1330.0000.4530.444-0.052-0.039
    2education-0.0540.019-2.7950.0060.4530.444-0.092-0.016
    3age0.0310.0055.8680.0000.4530.4440.0210.041
    4C-0.9930.115-8.6420.0000.4530.444-1.219-0.767
    5H-0.4320.147-2.9420.0040.4530.444-0.720-0.143
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 adj_r2 CI[2.5%] \\\n", + "0 Intercept 0.216 0.381 0.567 0.571 0.453 0.444 -0.534 \n", + "1 YearsSeropositive -0.046 0.003 -14.133 0.000 0.453 0.444 -0.052 \n", + "2 education -0.054 0.019 -2.795 0.006 0.453 0.444 -0.092 \n", + "3 age 0.031 0.005 5.868 0.000 0.453 0.444 0.021 \n", + "4 C -0.993 0.115 -8.642 0.000 0.453 0.444 -1.219 \n", + "5 H -0.432 0.147 -2.942 0.004 0.453 0.444 -0.720 \n", + "\n", + " CI[97.5%] \n", + "0 0.966 \n", + "1 -0.039 \n", + "2 -0.016 \n", + "3 0.041 \n", + "4 -0.767 \n", + "5 -0.143 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = data[['YearsSeropositive', 'education', 'age']]\n", + "dummy_vals = pd.get_dummies(data['race'], drop_first=True).astype(float)\n", + "X = pd.concat([X, dummy_vals], axis=1)\n", + "y = data['exec_domain_z']\n", + "res = pg.linear_regression(X, y)\n", + "res.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "72089b6c-1a01-46bc-85a7-afcc96eed850", + "metadata": {}, + "source": [ + "We can notice a few things here:\n", + " - **AA** has become the 'reference', the coefficients of C and H are relative to AA, which is set at 0.\n", + " - C individuals have a decreased score (relative to AA), which is significant.\n", + " - H individuals have an decreased score (relative to AA), which is significant." + ] + }, + { + "cell_type": "markdown", + "id": "89709ef9-443f-4583-b103-c825dceb39ff", + "metadata": {}, + "source": [ + "We can look at the residuals." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "ee1f5b5d-7fcd-4edc-9d1f-0e4a91e6934d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (race_ax, sex_ax, art_ax) = plt.subplots(1,3, sharey=True, figsize = (15, 5))\n", + "\n", + "race_ax.set_ylabel('residual')\n", + "\n", + "sns.barplot(x=data['race'], y=res.residuals_, ax=race_ax)\n", + "sns.barplot(x=data['sex'], y=res.residuals_, ax=sex_ax)\n", + "sns.barplot(x=data['ART'], y=res.residuals_, ax=art_ax)" + ] + }, + { + "cell_type": "markdown", + "id": "870e03a3-8c9d-4083-92bd-752aabd00bbc", + "metadata": {}, + "source": [ + "Let's merge everything into a single analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "40753763-7426-47a7-87c0-8fc7bf64184d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]
    0Intercept-0.3670.419-0.8770.3810.470.458-1.1910.456
    1YearsSeropositive-0.0440.003-13.7470.0000.470.458-0.051-0.038
    2education-0.0600.019-3.1070.0020.470.458-0.098-0.022
    3age0.0390.0066.7460.0000.470.4580.0280.051
    4C-0.9400.115-8.1890.0000.470.458-1.165-0.714
    5H-0.3820.146-2.6120.0090.470.458-0.670-0.094
    6male-0.0140.092-0.1580.8750.470.458-0.1950.166
    7Truvada0.3150.0983.2030.0010.470.4580.1220.508
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 adj_r2 CI[2.5%] \\\n", + "0 Intercept -0.367 0.419 -0.877 0.381 0.47 0.458 -1.191 \n", + "1 YearsSeropositive -0.044 0.003 -13.747 0.000 0.47 0.458 -0.051 \n", + "2 education -0.060 0.019 -3.107 0.002 0.47 0.458 -0.098 \n", + "3 age 0.039 0.006 6.746 0.000 0.47 0.458 0.028 \n", + "4 C -0.940 0.115 -8.189 0.000 0.47 0.458 -1.165 \n", + "5 H -0.382 0.146 -2.612 0.009 0.47 0.458 -0.670 \n", + "6 male -0.014 0.092 -0.158 0.875 0.47 0.458 -0.195 \n", + "7 Truvada 0.315 0.098 3.203 0.001 0.47 0.458 0.122 \n", + "\n", + " CI[97.5%] \n", + "0 0.456 \n", + "1 -0.038 \n", + "2 -0.022 \n", + "3 0.051 \n", + "4 -0.714 \n", + "5 -0.094 \n", + "6 0.166 \n", + "7 0.508 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pd.concat([data[['YearsSeropositive', 'education', 'age']],\n", + " pd.get_dummies(data['race'], drop_first=True).astype(float),\n", + " pd.get_dummies(data['sex'], drop_first=True).astype(float),\n", + " pd.get_dummies(data['ART'], drop_first=True).astype(float),\n", + " ], axis=1)\n", + "y = data['exec_domain_z']\n", + "res = pg.linear_regression(X, y)\n", + "res.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "fe67da49-98ed-43fb-b15d-c511b64757f2", + "metadata": {}, + "source": [ + "Here our _reference_ is an AA, female taking Stavudine.\n", + " - Everything is signifiant except for sex.\n", + " - We see that Truvada has a _significant positive_ effect on EDZ relative to Stavudine.\n", + "\n", + "Since this is our final model, let's test our last normality assumption." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "46cdd616-d777-4517-979a-d51996f7f1c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pg.qqplot(res.residuals_)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "bb8dcb61-82af-49a9-a923-e4c58a0a220b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Wpvalnormal
    00.8320240.659672True
    \n", + "
    " + ], + "text/plain": [ + " W pval normal\n", + "0 0.832024 0.659672 True" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pg.normality(res.residuals_, method='normaltest')" + ] + }, + { + "cell_type": "markdown", + "id": "77d9739b-d623-40f1-ade2-3ab1b755d7b2", + "metadata": {}, + "source": [ + "Perfect, now we know that our final model passes the _Normal Distribution of Errors_ assumption." + ] + }, + { + "cell_type": "markdown", + "id": "63741a0f-627f-4981-b5c0-ef8b302d3335", + "metadata": {}, + "source": [ + "What about understanding which parameters have the largest impact on the model?\n", + "Stated another way: which features are most important to determing EDZ?\n", + "\n", + "Nicely, `pingouin` can do this for us." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "871beb97-cdcc-44ae-bb13-4ed78f36d495", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]relimprelimp_perc
    0Intercept-0.3671080.418546-0.8771053.810941e-010.469840.458133-1.1905870.456370NaNNaN
    1YearsSeropositive-0.0442940.003222-13.7466884.748977e-340.469840.458133-0.050633-0.0379540.27588358.718414
    2education-0.0599100.019281-3.1072232.059458e-030.469840.458133-0.097844-0.0219750.0393588.376948
    3age0.0392150.0058136.7457787.231020e-110.469840.4581330.0277770.0506520.0396148.431478
    4C-0.9397040.114749-8.1892286.513749e-150.469840.458133-1.165470-0.7139390.07565216.101683
    5H-0.3823540.146409-2.6115389.442348e-030.469840.458133-0.670411-0.0942970.0159793.400943
    6male-0.0144460.091578-0.1577488.747561e-010.469840.458133-0.1946240.1657320.0004840.102939
    7Truvada0.3149840.0983273.2034521.495929e-030.469840.4581330.1215290.5084400.0228704.867595
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 \\\n", + "0 Intercept -0.367108 0.418546 -0.877105 3.810941e-01 0.46984 \n", + "1 YearsSeropositive -0.044294 0.003222 -13.746688 4.748977e-34 0.46984 \n", + "2 education -0.059910 0.019281 -3.107223 2.059458e-03 0.46984 \n", + "3 age 0.039215 0.005813 6.745778 7.231020e-11 0.46984 \n", + "4 C -0.939704 0.114749 -8.189228 6.513749e-15 0.46984 \n", + "5 H -0.382354 0.146409 -2.611538 9.442348e-03 0.46984 \n", + "6 male -0.014446 0.091578 -0.157748 8.747561e-01 0.46984 \n", + "7 Truvada 0.314984 0.098327 3.203452 1.495929e-03 0.46984 \n", + "\n", + " adj_r2 CI[2.5%] CI[97.5%] relimp relimp_perc \n", + "0 0.458133 -1.190587 0.456370 NaN NaN \n", + "1 0.458133 -0.050633 -0.037954 0.275883 58.718414 \n", + "2 0.458133 -0.097844 -0.021975 0.039358 8.376948 \n", + "3 0.458133 0.027777 0.050652 0.039614 8.431478 \n", + "4 0.458133 -1.165470 -0.713939 0.075652 16.101683 \n", + "5 0.458133 -0.670411 -0.094297 0.015979 3.400943 \n", + "6 0.458133 -0.194624 0.165732 0.000484 0.102939 \n", + "7 0.458133 0.121529 0.508440 0.022870 4.867595 " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_with_imp = pg.linear_regression(X, y, relimp=True)\n", + "res_with_imp" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "1a5030e3-b8b5-4918-8939-381a5bc28592", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    namescoefseTpvalr2adj_r2CI[2.5%]CI[97.5%]relimprelimp_perc
    1YearsSeropositive-0.0442940.003222-13.7466884.748977e-340.469840.458133-0.050633-0.0379540.27588358.718414
    4C-0.9397040.114749-8.1892286.513749e-150.469840.458133-1.165470-0.7139390.07565216.101683
    3age0.0392150.0058136.7457787.231020e-110.469840.4581330.0277770.0506520.0396148.431478
    2education-0.0599100.019281-3.1072232.059458e-030.469840.458133-0.097844-0.0219750.0393588.376948
    7Truvada0.3149840.0983273.2034521.495929e-030.469840.4581330.1215290.5084400.0228704.867595
    5H-0.3823540.146409-2.6115389.442348e-030.469840.458133-0.670411-0.0942970.0159793.400943
    \n", + "
    " + ], + "text/plain": [ + " names coef se T pval r2 \\\n", + "1 YearsSeropositive -0.044294 0.003222 -13.746688 4.748977e-34 0.46984 \n", + "4 C -0.939704 0.114749 -8.189228 6.513749e-15 0.46984 \n", + "3 age 0.039215 0.005813 6.745778 7.231020e-11 0.46984 \n", + "2 education -0.059910 0.019281 -3.107223 2.059458e-03 0.46984 \n", + "7 Truvada 0.314984 0.098327 3.203452 1.495929e-03 0.46984 \n", + "5 H -0.382354 0.146409 -2.611538 9.442348e-03 0.46984 \n", + "\n", + " adj_r2 CI[2.5%] CI[97.5%] relimp relimp_perc \n", + "1 0.458133 -0.050633 -0.037954 0.275883 58.718414 \n", + "4 0.458133 -1.165470 -0.713939 0.075652 16.101683 \n", + "3 0.458133 0.027777 0.050652 0.039614 8.431478 \n", + "2 0.458133 -0.097844 -0.021975 0.039358 8.376948 \n", + "7 0.458133 0.121529 0.508440 0.022870 4.867595 \n", + "5 0.458133 -0.670411 -0.094297 0.015979 3.400943 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# After filtering and sorting\n", + "res_with_imp.query('pval<0.01').sort_values('relimp_perc', ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "dea90faa-7e62-470e-8b38-bc4ec6c4b94d", + "metadata": {}, + "source": [ + "## Over fitting" + ] + }, + { + "cell_type": "markdown", + "id": "34122ab1-a41f-40ae-8404-13952ec40432", + "metadata": {}, + "source": [ + "In principle we can continue to add more and more variables to the `X` and just let the computer figure out the p-value of each.\n", + "\n", + "There are a few reasons we shouldn't take this tack.\n", + " - **Overfitting** : A larger model will **ALWAYS** fit better than a smaller model. This doesn't mean the larger model is **better** at predicting _all samples_, it just means it fits **these** samples better.\n", + " - **Explainability** : Large models with many parameters are difficult to explain and reason about. We are biologists, not data scientists. Our job is to reason about the _result_ of the analysis, not create the best fitting model.\n", + " - **Statistical power** : As you add more noise features you lose the power to detect real features.\n", + "\n", + "So, you should limit yourself to only those features that you think are biologically meaningful." + ] + }, + { + "cell_type": "markdown", + "id": "f85001ad-e7d5-4fa1-acb4-bf831e249167", + "metadata": {}, + "source": [ + "When planning experiments there are a couple of things you can do to avoid overfitting:\n", + " - **Sample size** : While there is no strict rule, you should plan to have _at least_ 10 samples per feature in your model.\n", + " - **Even sampling** : It is ideal to have a roughly equal representation of the entire parameter space. If you have categories, you should have an equal number of each. If you have continious data, you should have both high and low values. If you have many parameters, you should have an equal number of each of their interactions as well.\n", + "\n", + "These are good guidelines for all model-fitting style analyses." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "c7b277ae-b218-400b-bf21-2dbe1d4dfd72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: 7\n", + "Obs: 325\n" + ] + } + ], + "source": [ + "print('Features:', len(X.columns))\n", + "print('Obs:', len(X.index))" + ] + }, + { + "cell_type": "markdown", + "id": "a555f8e6-5863-4b26-bff3-8cef65f03861", + "metadata": {}, + "source": [ + "## Even more regression" + ] + }, + { + "cell_type": "markdown", + "id": "877c659e-f08a-4108-bdd9-6a4c1144fed9", + "metadata": {}, + "source": [ + "There are a number of regression based tools in `pingouin` that we didn't cover that may be useful to explore.\n", + " - `pg.logistic_regression` : This works similar to linear regression but is for binary dependent variables.\n", + "Each feature is regressed to create an equation that estimates the likelihood of the `dv` being `True`.\n", + " - `pg.partial_corr` : Like the ANCOVA, this is a tool for removing the effect of covariates and then calculating a correlation coefficient.\n", + " - `pg.rm_corr` : Correlation with repeated measures. This is useful if you have measured the same _sample_ multiple times and want to account for intermeasurment variability.\n", + " - `pg.mediation_analysis` : Tests the hypothesis that the independent variable `X` influences the dependent variable `Y` by a change in mediator `M`; like so `X -> M -> Y`.\n", + "This is useful to disentangle causal effects from covariation." + ] + }, + { + "cell_type": "markdown", + "id": "01aa3342", + "metadata": {}, + "source": [ + "---------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "74b8cf4e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grader.check_all()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "otter": { + "assignment_name": "Module09_walkthrough" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/jupyter_execute/_bblearn/Module10/Module10_lab.ipynb b/jupyter_execute/_bblearn/Module10/Module10_lab.ipynb new file mode 100644 index 0000000..2306631 --- /dev/null +++ b/jupyter_execute/_bblearn/Module10/Module10_lab.ipynb @@ -0,0 +1,616 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "700e795e-518f-453e-befd-b521ea8ba89a", + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "# Setting up the Colab environment. DO NOT EDIT!\n", + "import os\n", + "#import warnings\n", + "#warnings.filterwarnings(\"ignore\")\n", + "\n", + "try:\n", + " import otter, pingouin\n", + "\n", + "except ImportError:\n", + " ! pip install -q otter-grader==4.0.0, pingouin\n", + " import otter\n", + "\n", + "if not os.path.exists('walkthrough-tests'):\n", + " zip_files = [f for f in os.listdir() if f.endswith('.zip')]\n", + " assert len(zip_files)>0, 'Could not find any zip files!'\n", + " assert len(zip_files)==1, 'Found multiple zip files!'\n", + " ! unzip {zip_files[0]}\n", + "\n", + "grader = otter.Notebook(colab=True,\n", + " tests_dir = 'walkthrough-tests')" + ] + }, + { + "cell_type": "markdown", + "id": "0cf501d3", + "metadata": {}, + "source": [ + "# Lab" + ] + }, + { + "cell_type": "markdown", + "id": "8f8aeebe", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "At the end of this learning activity you will be able to:\n", + " - Estimate the effect size given a set of confidence intervals.\n", + " - Calculate the `effect_size`, `alpha`, `power`, and `sample_size` when given 3 of the 4. \n", + " - Interpret a power-plot of multiple experimental choices.\n", + " - Calculate how changes in estimates of the experimental error impact sample size requirements.\n", + " - Rigorously choose the appropriate experimental design for the best chance of success. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f2ffe20", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import pingouin as pg\n", + "sns.set_style('whitegrid')" + ] + }, + { + "cell_type": "markdown", + "id": "f27e4fc1", + "metadata": {}, + "source": [ + "## Step 1: Define the hypothesis" + ] + }, + { + "cell_type": "markdown", + "id": "024f5087", + "metadata": {}, + "source": [ + "For this lab we are going to investigate a similar metric. \n", + "We will imagine replicating the analysis considered in [Figure 3C](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424628/figure/F3/).\n", + "This analysis considers the different sub-values of the vigalence index.\n", + "It shows that SK609 is improving attention by reducing the number of misses." + ] + }, + { + "cell_type": "markdown", + "id": "52e7ebd5", + "metadata": {}, + "source": [ + "Copying the relevant part of the caption:\n", + "\n", + "\"Paired t-tests revealed that SK609 (4mg/kg; i.p.) specifically affected the selection of incorrect answers, significantly reducing the average number of executed misses compared to vehicle conditions (t(6))=3.27, p=0.017; **95% CI[1.02, 7.11])**.\"" + ] + }, + { + "cell_type": "markdown", + "id": "a0b30454", + "metadata": {}, + "source": [ + "Since this is a paired t-test we'll use the same strategy as the walkthrough." + ] + }, + { + "cell_type": "markdown", + "id": "7374cd64", + "metadata": {}, + "source": [ + "## Step 2: Define success" + ] + }, + { + "cell_type": "markdown", + "id": "61b6e2ca", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q1: What is the average difference in misses between vehicle control and SK609 rodents?\n", + "\n", + "_Hint: Calculate the center (average) of the confidence interval; the CI is **bolded** in the caption above._" + ] + }, + { + "cell_type": "markdown", + "id": "08b9593e-081f-4f0d-bd27-c70613d94594", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 1 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4348fa0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "q1_change = ...\n", + "\n", + "print(f'On average, during an SK609 trial the rodent missed {q1_change} fewer prompts than vehicle controls.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f3b9b55", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q1_change\")" + ] + }, + { + "cell_type": "markdown", + "id": "50e9e11e", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q2: Calculate the effect size.\n", + "_Hint: Use the change just defined in Q1._\n", + "\n", + "Assume from our domain knowledge and inspection of the figure that there is an error of 3.5 misses." + ] + }, + { + "cell_type": "markdown", + "id": "3b9f74ab-0925-48e1-a0ba-c9725786aee1", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 1 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "382bc5bd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "error = 3.5\n", + "\n", + "q2_effect_size = ...\n", + "\n", + "print(f'The normalized effect_size of SK609 is {q2_effect_size:0.3f}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce741b7d", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q2_effect_size\")" + ] + }, + { + "cell_type": "markdown", + "id": "66e2bc2d", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Step 3: Define your tolerance for risk\n", + "\n", + "For this assignment consider that we want to have 80% chance of detecting a true effect and a 1% chance of falsely accepting an effect." + ] + }, + { + "cell_type": "markdown", + "id": "4af19207-e9ba-453a-8a80-e915bde3ec3c", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 2 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49fe7bc9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "power = ...\n", + "alpha = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12d8e8ac", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q3_tolerance\")" + ] + }, + { + "cell_type": "markdown", + "id": "619043ec", + "metadata": {}, + "source": [ + "## Step 4: Define a budget\n", + "\n", + "In the figure caption we see that the paper used a nobs of 16 mice:\n", + "\n", + "\"Difference in VI measurements calculated against previous day vehicle performance in rats (n=16) showed SK609 improved sustained attention performance ...\"" + ] + }, + { + "cell_type": "markdown", + "id": "c6f5c799", + "metadata": {}, + "source": [ + "## Step 5: Calculate" + ] + }, + { + "cell_type": "markdown", + "id": "cab114ee", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q4: Calculate the minimum **change** detectable with 16 animals.\n", + "\n", + "Use `alternative='two-sided'` as we do not know whether the number of misses is always increasing.\n", + "\n", + "_Hint: Use the power-calculator, and then use that effect size to calculate the min_change._" + ] + }, + { + "cell_type": "markdown", + "id": "7d6430c4-87a0-4690-a400-4b78e69df81c", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 2 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b6b1602-d3ef-4f0e-a13b-c117a9745269", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "q4_effect_size = ...\n", + "\n", + "\n", + "print('The effect size is:', q4_effect_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02e69c61", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# What is the minimum change that we can detect at this power?\n", + "\n", + "q4_min_change = ...\n", + "\n", + "print(f'with 16 animals, one could have detected as few as {q4_min_change:0.2f} min change.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21a6ada3", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q4_min_effect\")" + ] + }, + { + "cell_type": "markdown", + "id": "2dc9e821", + "metadata": {}, + "source": [ + "# Step 6: Summarize\n", + "\n", + "Let's propose a handful of different considerations for our experiment.\n", + "As before, we'll keep the power and alpha the same, but we'll add the following experimental changes:\n", + "\n", + " - A grant reviewer has commented on the proposal and believes that your estimate of the error is too optimistic. They would like you to consider a scenario in which your error is **50% larger** than the current estimate.\n", + " - A new post-doc has come from another lab that has a different attention assay. Their studies show that it has **25% less** error than the current one.\n", + " \n", + "Consider these two experimental changes and how they effect sample size choices." + ] + }, + { + "cell_type": "markdown", + "id": "91e770b6", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q5: Calculate new effect sizes for these conditions.\n", + "\n", + "_Hint: Refer to the bolded experimental changes above and adjust the errors then the effect sizes, keeping in mind the q1_change variable._\n", + "\n", + "_This can be done in two steps if needed._\n", + "\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af7c9ce8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "q5_high_noise_effect_size = ...\n", + "q5_new_assay_effect_size = ...\n", + "\n", + "print(f'Expected effect_size {q2_effect_size:0.2f}')\n", + "print(f'High noise effect_size {q5_high_noise_effect_size:0.2f}')\n", + "print(f'New assay effect_size {q5_new_assay_effect_size:0.2f}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46491dd3", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q5_multiple_choices\")" + ] + }, + { + "cell_type": "markdown", + "id": "55cff86a", + "metadata": {}, + "source": [ + "Use the power-plot below to answer the next question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4732a77", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Check many different nobs sizes\n", + "nobs_sizes = np.arange(1, 31)\n", + "\n", + "\n", + "names = ['Expected', 'High-Noise', 'New-Assay']\n", + "colors = 'krb'\n", + "effect_sizes = [q2_effect_size, q5_high_noise_effect_size, q5_new_assay_effect_size]\n", + "\n", + "fig, ax = plt.subplots(1,1)\n", + "\n", + "# Loop through each observation size\n", + "for name, color, effect in zip(names, colors, effect_sizes):\n", + " # Calculate the power across the range\n", + " powers = pg.power_ttest(d = effect,\n", + " n = nobs_sizes,\n", + " power = None,\n", + " alpha = alpha,\n", + " contrast = 'paired')\n", + "\n", + " ax.plot(nobs_sizes, powers, label = name, color = color)\n", + "\n", + "\n", + "\n", + "\n", + "ax.legend(loc = 'lower right')\n", + "\n", + "ax.set_ylabel('Power')\n", + "ax.set_xlabel('Sample Size')" + ] + }, + { + "cell_type": "markdown", + "id": "1429aad1", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q6 Summary Questions\n", + "\n", + "_Hint: Remember, the power level is 80%, so examine the nobs at 0.8 at the specified effect size to determine sufficient power or question being asked._" + ] + }, + { + "cell_type": "markdown", + "id": "c2c98715-cc66-4fee-9be4-9b6642977bfe", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 3 |\n", + "| Hidden Tests | 3 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aba8e06d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Would an experiment that had nobs=15 be sufficiently powered\n", + "# to detect an effect under the expected assumption?\n", + "# 'yes' or 'no'\n", + "q6a = ...\n", + "\n", + "# Would an experiment that had nobs=15 be sufficiently powered\n", + "# to detect an effect under the high-noise assumption?\n", + "# 'yes' or 'no'\n", + "q6b = ...\n", + "\n", + "# How many fewer animals could be used if the new experiment was implemented\n", + "# vs. the expected/current one (using 80% power)?\n", + "# Hint: Use the power calculator. Round up.\n", + "\n", + "\n", + "q6c = ...\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c553b96", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q6\")" + ] + }, + { + "cell_type": "markdown", + "id": "d6216ba7", + "metadata": {}, + "source": [ + "--------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52fe694f", + "metadata": {}, + "outputs": [], + "source": [ + "grader.check_all()" + ] + }, + { + "cell_type": "markdown", + "id": "369512fc", + "metadata": {}, + "source": [ + "## Submission\n", + "\n", + "Check:\n", + " - That all tables and graphs are rendered properly.\n", + " - Code completes without errors by using `Restart & Run All`.\n", + " - All checks **pass**.\n", + " \n", + "Then save the notebook and the `File` -> `Download` -> `Download .ipynb`. Upload this file to BBLearn." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "otter": { + "assignment_name": "Module10_lab" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/jupyter_execute/_bblearn/Module10/Module10_walkthrough_SOLUTION.ipynb b/jupyter_execute/_bblearn/Module10/Module10_walkthrough_SOLUTION.ipynb new file mode 100644 index 0000000..b3112fb --- /dev/null +++ b/jupyter_execute/_bblearn/Module10/Module10_walkthrough_SOLUTION.ipynb @@ -0,0 +1,1026 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "54e6b29f-438b-4124-a718-f78ed9a7534b", + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "# Setting up the Colab environment. DO NOT EDIT!\n", + "import os\n", + "#import warnings\n", + "#warnings.filterwarnings(\"ignore\")\n", + "\n", + "try:\n", + " import otter, pingouin\n", + "\n", + "except ImportError:\n", + " ! pip install -q otter-grader==4.0.0, pingouin\n", + " import otter\n", + "\n", + "if not os.path.exists('walkthrough-tests'):\n", + " zip_files = [f for f in os.listdir() if f.endswith('.zip')]\n", + " assert len(zip_files)>0, 'Could not find any zip files!'\n", + " assert len(zip_files)==1, 'Found multiple zip files!'\n", + " ! unzip {zip_files[0]}\n", + "\n", + "grader = otter.Notebook(colab=True,\n", + " tests_dir = 'walkthrough-tests')" + ] + }, + { + "cell_type": "markdown", + "id": "29a82192", + "metadata": {}, + "source": [ + "# Walkthrough" + ] + }, + { + "cell_type": "markdown", + "id": "23b1746a-7c73-46c9-ba1e-94e1b6505c86", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "At the end of this learning activity you will be able to:\n", + " - Describe a generic strategy for power calculations.\n", + " - Define the terms `effect_size`, `alpha`, and `power`.\n", + " - Describe the trade-off of `effect_size`, `alpha`, `power`, and `sample_size`.\n", + " - Calculate the fourth value given the other three.\n", + " - Interpret a power-plot of multiple experimental choices.\n", + " - Rigorously choose the appropriate experimental design for the best chance of success." + ] + }, + { + "cell_type": "markdown", + "id": "6a25df40-86e5-4912-b892-61202d1e7af2", + "metadata": {}, + "source": [ + "For this last week, we are going to look at experimental design.\n", + "In particular, sample size calculations." + ] + }, + { + "cell_type": "markdown", + "id": "03b8610c-f382-49f1-a1d9-60a6d4ff94cc", + "metadata": {}, + "source": [ + "As a test-case we will imagine that we are helping Dr. Kortagere evaluate a new formulation of her SK609 compound.\n", + "It is a selective dopamine receptor activator that has been shown to improve attention in animal models.\n", + "You can review her paper [**Selective activation of Dopamine D3 receptors and Norepinephrine Transporter blockade enhance sustained attention**](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424628/)\n", + "on pubmed.\n", + "We'll be reviewing snippets through the assignment.\n", + "\n", + "As part of this new testing we will have to evaluate her new formulation in the same animal model.\n", + "In this assignment we are going to determine an appropriate sample size.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "bce0b740-54ed-4d26-a213-9c02fea739d2", + "metadata": {}, + "source": [ + "## A Power Analysis in 6 steps\n", + "\n", + "As the \"biostats guy\" most people know, I'm often the first person someone comes to looking for this answer.\n", + "So, over the years I've developed a bit of a script.\n", + "It is part art, part math, and relies on domain knowledge and assumptions." + ] + }, + { + "cell_type": "markdown", + "id": "c9a96b45-17d1-4204-917d-5468d544cd17", + "metadata": {}, + "source": [ + "Before you can determine a sample size you need to devise a *specific*, **quantitative**, and **TESTABLE** hypothesis.\n", + "Over the past few weeks we've covered the main ones:\n", + " - Linked categories - chi2 test\n", + " - Difference in means - t-test\n", + " - Regression-based analysis\n", + "\n", + "With enough Googling you can find a calculator for almost any type of test, and simulation strategies can be used to estimate weird or complex tests if needed." + ] + }, + { + "cell_type": "markdown", + "id": "043f4d00-3149-4ec8-a4f5-a06f4bc2daf7", + "metadata": {}, + "source": [ + "During the signal trials, animals were trained to press a lever in response to a stimulus, which was a cue light. During the non-signal trials, the animals were trained to press the opposite lever in the absence of a cue light. [Methods]\n", + "Over a 45 minute attention assay cued at psueodorandom times, their success in this task was quantified as a Vigilance Index (VI), with larger numbers indicating improved attention.\n", + "\n", + "Figure 1 shows the design." + ] + }, + { + "cell_type": "markdown", + "id": "15316bc2-0be0-4ea7-bb23-ec91f197f522", + "metadata": {}, + "source": [ + "![Figure 1](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ad9/6424628/c5af74734da6/nihms-1006809-f0001.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "f6e932b2-f35b-4f14-9339-c1a56b96561e", + "metadata": {}, + "source": [ + "Our hypothesis is that this new formulation increases the vigilance index relative to vehicle treated animals." + ] + }, + { + "cell_type": "markdown", + "id": "63549657-6c54-44af-8dd7-c46a80dbb7a7", + "metadata": {}, + "source": [ + "## Step 2: Define success\n", + "\n", + "Next, we need to find the `effect_size`.\n", + "Different tests calculate this differently, but it always means the same thing: \n", + "**the degree of change divided by the noise in the measurement.**\n", + "\n", + "These are things that rely on domain knowledge of the problem.\n", + "The amount of change should be as close to something that is clinically meaningful.\n", + "The amount of noise in the measurement is defined by your problem and your experimental setup.\n", + "\n", + "If you have access to raw data, it is ideal to calculate the difference in means and the standard deviations exactly.\n", + "But often, you don't have that data.\n", + "For this exercise I'll teach you how to find and estimate it." + ] + }, + { + "cell_type": "markdown", + "id": "9b547a19-961c-42d7-8a5a-f941ac0c6f6f", + "metadata": {}, + "source": [ + "In this simple example, we'll imagine replicating the analysis considered in [Figure 3B](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424628/figure/F3/).\n", + "\n", + "![Figure 3](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ad9/6424628/98810d3bec35/nihms-1006809-f0003.jpg)\n", + "\n", + "We'll start with B. This compares the effect of SK609 VI vs a vehicle control. Parsing through the figure caption we come to:" + ] + }, + { + "cell_type": "markdown", + "id": "f35b0e89-a958-4119-aee5-b4b49ebba428", + "metadata": {}, + "source": [ + "```\n", + "(B) Paired t-test indicated that 4 mg/kg SK609 significantly increased sustained attention performance as measured by average VI score relative to vehicle treatment (t(7)=3.1, p = 0.017; 95% CI[0.14, 0.19]).\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "b703ef16-47b1-422a-a85a-526b5c465ef3", + "metadata": {}, + "source": [ + "This was a *paired* t-test, since it is measuring the difference between vehicle and SK609 in the same animal. The p=0.017 tells use this difference is unlikely due to chance and the CI tells us that the difference in VI between control and SK609 is between 0.14 and 0.19.\n", + "\n", + "If we're testing a new formulation of SK609 we know we need to be able to detect a difference as low as 0.14. We should get a VI of ~0.8 for control and ~0.95 for SK609. If the difference is smaller than this, it probably isn't worth the switch." + ] + }, + { + "cell_type": "markdown", + "id": "5594f0ae-5145-4ba0-ba90-34a0521b88df", + "metadata": {}, + "source": [ + "Therefore we'll define success as:\n", + "```\n", + "SK609a will increase the VI of an animal by at least 0.14 units. \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b5cd1215-2454-4718-afba-224c1abd820b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "min_change = 0.14" + ] + }, + { + "cell_type": "markdown", + "id": "785b9a16-e516-487e-b3ef-cb0cba7c8c14", + "metadata": {}, + "source": [ + "Then we need an estimate of the error in the measurement.\n", + "In an ideal world, we would calculate the standard deviation.\n", + "But I don't have that. \n", + "So, I'll make an assumption that we'll adjust as we go.\n", + "\n", + "I like to consider two pieces of evidence when I need to guess like this.\n", + "First, looking at the figure above, the error bars. \n", + "From my vision they look to be about ~0.02-0.04 units.\n", + "Or, if we considered a ~20% measurement error 0.8 x 0.2 = 0.16.\n", + "So, an estimate of 0.08 error would seem *reasonable*." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8896357f-51e1-4c15-8dda-a537443d6210", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "error = 0.08" + ] + }, + { + "cell_type": "markdown", + "id": "bde0a728-b4b3-4462-8be2-ad178668670e", + "metadata": {}, + "source": [ + "Our estimate of the `effect_size` is the ratio of the change and the error." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0fb71e79-69a7-4953-a116-8b2f7d1aae56", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Effect Size 1.7500000000000002\n" + ] + } + ], + "source": [ + "effect_size = min_change/error\n", + "print('Effect Size', effect_size)" + ] + }, + { + "cell_type": "markdown", + "id": "40eb9490-5397-4448-af67-7582d9a21b99", + "metadata": {}, + "source": [ + "You'll notice that the `effect_size` is unit-less and similar to a z-scale." + ] + }, + { + "cell_type": "markdown", + "id": "ca54ea97-27bf-468c-a26f-2efc285875cb", + "metadata": {}, + "source": [ + "## Step 3: Define your tolerance for risk\n", + "\n", + "When doing an experiment we consider two types of failures.\n", + " - False Positives - Detecting a difference when there truly isn't one - `alpha` \n", + " - False Negatives - Not detecting a true difference - `power`\n", + " \n", + "We've been mostly considering rejecting false-positives (p<0.05).\n", + "The power of a test is the converse.\n", + "It is the likelihood of detecting a difference if there truly is one.\n", + "A traditional cutoff is `>0.8`; implying there is an 80% chance of detecting an effect if there truly is one." + ] + }, + { + "cell_type": "markdown", + "id": "787b0f59-673c-41fa-af89-8ae247e4c3e3", + "metadata": {}, + "source": [ + "## Step 4: Define a budget\n", + "\n", + "You need to have _some_ idea on the scale and cost of the proposed experiment.\n", + "How much for 2 samples, 20 samples, 50 samples, 200 samples.\n", + "\n", + "This will be an exercise in trade-offs you need to have reasonable estimates of how much you are trading off.\n", + "This is where you should also consider things like sample dropouts. outlier rates, and other considerations." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "36166945-cd2c-483e-a32f-c3e5780a99ec", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# In each group\n", + "exp_nobs = [2, 4, 8, 10]" + ] + }, + { + "cell_type": "markdown", + "id": "b2a1f3a5-99c2-44f4-b1ba-7c7d9530540b", + "metadata": {}, + "source": [ + "## Step 5: Calculate\n", + "\n", + "With our 4 pieces of information:\n", + " - effect_size\n", + " - power\n", + " - alpha\n", + " - nobs\n", + " \n", + "We can start calculating. \n", + "A power analysis is like a balancing an __X__ with 4 different weights at each point.\n", + "At any time, 3 of the weights are fixed and we can use a calculator to determine the appropriate weight of the fourth.\n", + "\n", + "Our goal is to estimate the cost and likely success of a range of different experiment choices.\n", + "Considering that we have made a _lot_ of assumptions and so we should consider noise in our estimate." + ] + }, + { + "cell_type": "markdown", + "id": "d20bf632-f478-4be5-bbd9-0266c8cfa9eb", + "metadata": {}, + "source": [ + "Each type of test has a different calculator that can perform this 4-way balance.\n", + "\n", + "We'll use the `pingouin` Python library to do this (https://pingouin-stats.org/build/html/api.html#power-analysis).\n", + "However, a simple Google search for: \"statistical power calculator\" will also find similar online tools for quick checks.\n", + "Try to look for one that \"draws\" as well as calculates." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b0cf5b21-d403-498a-968e-029c0c0157b1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "import pingouin as pg\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "b9953b5f-5dc1-4b4f-864f-756987d7fb98", + "metadata": {}, + "source": [ + "All Python power calculators I've seen work the same way.\n", + "They accept 4 parameters, one of which, must be `None`.\n", + "The tool will then use the other 3 parameters to estimate the 4th." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "696ce526-49f4-4090-be04-f48a6cc8b9c3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3.7683525901861725" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_change = 0.14\n", + "error = 0.08\n", + "\n", + "effect_size = min_change/error\n", + "\n", + "power = 0.8\n", + "alpha = 0.05\n", + "\n", + "pg.power_ttest(d = effect_size,\n", + " n = None,\n", + " power = power,\n", + " alpha = alpha,\n", + " contrast = 'paired',\n", + " alternative = 'greater')" + ] + }, + { + "cell_type": "markdown", + "id": "c9708343-fcb6-4adc-a18e-22cf01a181a4", + "metadata": {}, + "source": [ + "So, in order to have an 80% likelihood of detecting an effect of 0.14 (or more) at a p<0.05 we need at least 4 animals in each group." + ] + }, + { + "cell_type": "markdown", + "id": "bea0e078-6dc5-410f-80d0-c2ffd473c20a", + "metadata": { + "deletable": false, + "editable": false, + "tags": [] + }, + "source": [ + "### Q1: Calculate the power if there are only two animals in each group." + ] + }, + { + "cell_type": "markdown", + "id": "05951051-43f5-41e0-80a9-c65e3d8754da", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 1 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b9034f1e-0ea3-4eb4-90cf-8182bfc8a651", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "With two animals per group. The likelihood of detecting an effect drops to 30%\n" + ] + } + ], + "source": [ + "# BEGIN SOLUTION NO PROMPT\n", + "\n", + "q1p = pg.power_ttest(d = effect_size,\n", + " n = 2,\n", + " power = None,\n", + " alpha = alpha,\n", + " contrast = 'paired',\n", + " alternative = 'greater')\n", + "# END SOLUTION\n", + "\n", + "q1_power = q1p # SOLUTION\n", + "\n", + "print(f'With two animals per group. The likelihood of detecting an effect drops to {q1_power*100:0.0f}%')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d55f502e", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q1_twosample_power\")" + ] + }, + { + "cell_type": "markdown", + "id": "bff2675d-1d53-4daa-8610-1deba0cc3b0b", + "metadata": {}, + "source": [ + "What if we're worried this formulation only has a small effect or a highly noisy measurement. So, we've prepared 12 animals, what is the smallest difference we can detect? Assuming the same 80% power and 0.05 alpha." + ] + }, + { + "cell_type": "markdown", + "id": "deafd365-f8f7-4d97-bf88-7f80472030a2", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "### Q2: Calculate the smallest effect size if there are 12 animals in each group." + ] + }, + { + "cell_type": "markdown", + "id": "c52f1c30-3ab1-4d31-b1fe-74a834278ffe", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "| **Total Points** | 5 |\n", + "|--------|----|\n", + "| Included Checks | 1 |\n", + "\n", + "_Points:_ 5" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "59c492f5-1eda-4888-87da-e09cbf3d8a3c", + "metadata": { + "tags": [ + "otter_assign_solution_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "With 12 animals per group. You can detect an effect 2.283X smaller than the minimum effect.\n" + ] + } + ], + "source": [ + "# BEGIN SOLUTION NO PROMPT\n", + "\n", + "q2e = pg.power_ttest(n = 12,\n", + " power = power,\n", + " alpha = alpha,\n", + " contrast = 'paired',\n", + " alternative = 'greater')\n", + "# END SOLUTION\n", + "\n", + "q2_effect = q2e # SOLUTION\n", + "\n", + "print(f'With 12 animals per group. You can detect an effect {effect_size/q2_effect:0.3f}X smaller than the minimum effect.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cdd218c", + "metadata": { + "deletable": false, + "editable": false + }, + "outputs": [], + "source": [ + "grader.check(\"q2_12sample_effect\")" + ] + }, + { + "cell_type": "markdown", + "id": "9423f2ee-9324-4418-87cc-9d242c38458d", + "metadata": {}, + "source": [ + "The solver method is great when you have a specific calculation.\n", + "But it doesn't tell you much beyond a cold number with little context.\n", + "How does it change as we make different assumptions about our effect size or our budget?" + ] + }, + { + "cell_type": "markdown", + "id": "294e9a43-195d-4cf8-a0ee-08e0eb493c36", + "metadata": {}, + "source": [ + "## Step 6: Summarize\n", + "\n", + "Let's \"propose\" a number of different experiments different experiments.\n", + "We'll keep the power and alpha the same but consider different group sizes 2, 4, 6, 10, and 15 each.\n", + "How do these choices impact our ability to detect different effect sizes?\n", + "We'll also assume our true effect size could be 2X too high or 2X too low." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "03b816e0-c7bb-4249-98c5-be694a28c79d", + "metadata": {}, + "outputs": [], + "source": [ + "# I find the whitegrid style to be the best for this type of visualization\n", + "sns.set_style('whitegrid')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "36a74f64-f255-4d9d-8d14-63d58f997994", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# How many animals in each proposed experiment\n", + "nobs_sizes = np.array([2, 4, 6, 10, 15])\n", + "\n", + "# power_ttest accepts arrays in any parameter\n", + "calced_power = pg.power_ttest(n = nobs_sizes,\n", + " d = effect_size,\n", + " power = None,\n", + " alpha = alpha,\n", + " contrast = 'paired',\n", + " alternative = 'greater')\n", + "\n", + "# Then I can plot the power vs the number of animals\n", + "plt.plot(nobs_sizes, calced_power, label = f'Cd={effect_size:0.1f}')\n", + "plt.ylabel('Power')\n", + "plt.xlabel('Number observations')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "5e15a19a-a5a0-4c16-9cff-505af077e8f0", + "metadata": {}, + "source": [ + "Since we can plot multiple assumptions on the same graph, we can make complex reasonings about our experimental design." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "977edb80-8d69-454b-b01a-8eb0735cb74e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pick multiple different assumptions about the effect-size\n", + "effect_sizes = [effect_size/2, effect_size, effect_size*2]\n", + "\n", + "nobs_sizes = np.array([2, 4, 6, 10, 15])\n", + "\n", + "for ef in effect_sizes:\n", + " calced_power = pg.power_ttest(n = nobs_sizes,\n", + " d = ef,\n", + " power = None,\n", + " alpha = alpha,\n", + " contrast = 'paired',\n", + " alternative = 'greater')\n", + "\n", + " plt.plot(nobs_sizes, calced_power, label = f'Cd={ef:0.1f}')\n", + "\n", + "plt.ylabel('Power')\n", + "plt.xlabel('Number observations')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "ca4d0c36-f4d8-4665-94f1-5218d0109025", + "metadata": {}, + "source": [ + "With this graph we can make some decisions with better knowledge about the context.\n", + "\n", + "If we're confident our effect size estimate is correct or an 'under-estimate', then we should do 4-6 animals.\n", + "This will give us a >80% chance of finding an effect if it truly exists.\n", + "However, if we have any doubt that our estimate may be high, then we see that 4-6 animals would put us in the 50:50 range.\n", + "Then maybe it is better to spend the money for ~10 animals to obtain a high degree of confidence in a worst-case scenario." + ] + }, + { + "cell_type": "markdown", + "id": "d9ff4a72-2ec2-451b-98bf-6a34ab8e3153", + "metadata": {}, + "source": [ + "## The other use of Power Tests" + ] + }, + { + "cell_type": "markdown", + "id": "359406ef-2b65-4b95-a15c-bb668133a56c", + "metadata": {}, + "source": [ + "T-tests estimate whether there is a difference between two populations.\n", + "However, a p>0.05 **does not mean the two distributions are the same**.\n", + "It means that either they are the same **or** you did not have enough *power* to detect a difference this small.\n", + "If we want to measure whether two distributions are statistically \"the same\" we need a different test." + ] + }, + { + "cell_type": "markdown", + "id": "58e48e9b-566a-474c-8695-ab900f27865e", + "metadata": {}, + "source": [ + "Enter, the **TOST**, Two one-sided test for _equivelence_.\n", + "\n", + "This test is more algorithm than equation.\n", + "Here is the basic idea:\n", + "\n", + " - Specify the Equivalence Margin (`bound`): Before conducting the test, researchers must define an equivalence margin, which is the maximum difference between the treatments that can be considered practically equivalent. This margin should be determined based on clinical or practical relevance.\n", + " - Conduct Two One-Sided Tests: TOST involves conducting two one-sided t-tests:\n", + " - The first test checks if the upper confidence limit of the difference between treatments is less than the positive equivalence margin.\n", + " - The second test verifies that the lower confidence limit is greater than the negative equivalence margin.\n", + " - Interpret the Results: Equivalence is concluded if both one-sided tests reject their respective null hypotheses at a predetermined significance level.\n", + "\n", + "This means that the confidence interval for the difference between treatments lies entirely within the equivalence margin.\n", + "Thus, they are the *same*." + ] + }, + { + "cell_type": "markdown", + "id": "3316221d-1435-4ed8-8263-a49045ab5b73", + "metadata": {}, + "source": [ + "Imagine we were testing two different batches and wanted to ensure there was no difference between them.\n", + "A meaninful difference would be anything above 5% in the VI." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b7ffbe6f-666b-4b02-9bf4-702bc0a2d772", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'VI')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hyp_batchA_res = np.array([0.80, 0.76, 0.81, 0.83, 0.88, 0.78, 0.77, 0.82, 0.76, 0.72])\n", + "hyp_batchB_res = np.array([0.81, 0.75, 0.78, 0.85, 0.88, 0.82, 0.78, 0.81, 0.79, 0.70])\n", + "\n", + "fig, ax = plt.subplots(1,1)\n", + "for ctl, sk in zip(hyp_batchA_res, hyp_batchB_res):\n", + " ax.plot([1, 2], [ctl, sk])\n", + "ax.set_xlim(.5, 2.5)\n", + "ax.set_xticks([1, 2])\n", + "ax.set_xticklabels(['Control', 'Exp'])\n", + "ax.set_ylabel('VI')" + ] + }, + { + "cell_type": "markdown", + "id": "bdd86e87-cfe0-4f68-a53f-1310d6cd745a", + "metadata": {}, + "source": [ + "Perform a t-test, just to see what happens." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ca00fa32-91f1-4304-b3ed-22b252044e50", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Tdofalternativep-valCI95%cohen-dBF10power
    T-test-0.5694959two-sided0.582953[-0.02, 0.01]0.0837910.3540.056513
    \n", + "
    " + ], + "text/plain": [ + " T dof alternative p-val CI95% cohen-d BF10 \\\n", + "T-test -0.569495 9 two-sided 0.582953 [-0.02, 0.01] 0.083791 0.354 \n", + "\n", + " power \n", + "T-test 0.056513 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pg.ttest(hyp_batchA_res, hyp_batchB_res, paired=True)" + ] + }, + { + "cell_type": "markdown", + "id": "0219db7d-3a0a-49ea-bb7d-42808e43ae89", + "metadata": {}, + "source": [ + "As expected, we cannot reject the hypothesis that they are the same.\n", + "But this doesn't mean they are the same, just that they are _not different_.\n", + "\n", + "Now, for the TOST." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a2ae6f13-2368-4d95-aee6-b0d50a709ad3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    bounddofpval
    TOST0.0590.000053
    \n", + "
    " + ], + "text/plain": [ + " bound dof pval\n", + "TOST 0.05 9 0.000053" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bound = 0.05 # Should be in same units as the input\n", + "\n", + "pg.tost(hyp_batchA_res, hyp_batchB_res, 0.05, paired=True)" + ] + }, + { + "cell_type": "markdown", + "id": "3fa836a4-682d-4bef-9f2d-9bdb3857b7ea", + "metadata": {}, + "source": [ + "So, if we use a bound of 5% VI, then the likelihood that there is a difference **5% or larger** is `0.000053`.\n", + "Therefore we can statistically say that they are the same _within this bound_." + ] + }, + { + "cell_type": "markdown", + "id": "42208b6c", + "metadata": {}, + "source": [ + "---------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1c313997", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grader.check_all()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "otter": { + "assignment_name": "Module10_walkthrough" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/jupyter_execute/a4cbf376070178b287ef42066dcf23598f86a4f9a5b5d1ce4ed57891ab0ab3c0.png b/jupyter_execute/a4cbf376070178b287ef42066dcf23598f86a4f9a5b5d1ce4ed57891ab0ab3c0.png new file mode 100644 index 0000000000000000000000000000000000000000..6228a0c9be3d79f53818e4c3ede563bcd967b40d GIT binary patch literal 44514 zcmb@ubx>Tvw=O!kyF0-N1a~L6Gq?qZ;1Jw{1ouG#f#B{CNN^img9mr_ph3dhLuU$I#KGXau}$js2~ssLqT3z0|bKQ1%Y5{kr9EvfP+$; zfM0^{GJ5Ws&erZ;X0BEs6*G4i2WNK&TXSkpD_1vLXD4npel|{4Y8!WV7dIhxcE|r| zz~=1wo_z@wIRbbIii^Cy8wiAD_Hx6NikH}eKvilA(vn)Xbt#LB?(;jO2zC%IDnP3z;|rZm#uKo0o{i)#1p0~0D!>8qDd`AoUF;V)lC<_Cq4{%aro|IujuawXV^ zW`t`zTNJHz1vA|2_X@?o#t;cGev=U!I+YkXE7?bJYjp(P_Y>JNj*gBUt&-UAlGxIu zSwbGDv~uyW4|ln!cE_H=AP;p#MaAFCWdC_V_knx<_wRgZ#ht*)1rekKn6-EOLS zaU$i%vcONwY`2Y7qRhG{8@Y~Fd3@@IYUB_mEv8atbxZy30+l!WH!-EmoZWOTruv8s z!BQ$wI4glD34DzbX{r8RsZ7-W#xV$tBllSRNP6Ut6M7x-eRCQYdMH;+P}=3nua9#X zC6u^f$W<)B5|g0Xp{A&+fq;Pq$B2f>#>=Sd{5(L}Zh9~MO6n+@I`VCJ$QRh6W%Hme zPGou4(Saujda#wc6X*Y{h1Kvm?u_n&%Ou{XeCa)YrsxnhHSB9`K45+z|9fxvX;v;v zo4&td$ql{+dWYg#>Id^P<;@;Mj19ibl9ui#d6a)M#1%Vrd!&c6WHK-aynwC&>w$nj^YRru7++oe|9&I373rzL`jDshU-z%z zf!Q+!rmL1U`OFFDrB4v5T_oK?U;`YmjJZ4h{2>t7U<-8 zrO`)k3MiUx`>e@D_CC!H77RN9KeyHeiB1FRNR|>jw_9WWi6e=TM69oKZlj6T@7-0aQVh`rPf1=TSXv4gQzA1|=|wIXHSBsCC|TnAZ-fmD`9uPeciGg36%OfK zZFK)VCwWTOhW6?oeYhz;t}E-Jhqb2Rs?1b zx*3UDJ``4wiJ&ciZ^}NLeVH(y64Q_`#l;NEfe$C%XYIG=i}p2=_%f^}9pp|wgxB7Y zS#pS?K<%)lzA(NB$d0dk(XNuoC-d~!i>H=bUXI%8dGgl6!Xn#O`|%Uq3Z&BXZ7N0} zIGAZxT}%rfB6xJ`!aLgAF4m>7zXlsTmKc+swDo z$pTwT`Kf^OEHqwv2Q6(g8Su{?lzg-Fum9VdRS-mw-wV^nMt$?{J`qxp`kLAHb31gU3~6*M3@kJ zUFDt1+cmMFHOcS1}Q0|R<|ekYT4b_3lan&cRb&k67~SoKVL zG3GS5vfz{uCf=!eWlms?M1pJlcUTnOK>=6L(14E_0Y^`NFUV#qcGAV4y^2zi6rr$w zFM5ih2)s|k6PnfbIu|#FgE5g_kFi0Q*EIeP*5c@)%CY%cgvo5Sj#> zjTCEmU~xV=GG2Ex=|6kTDY|EQMgUaJU2Jls;>w55XBzy0~nx1f35iw`!i_g#4eM4$V z1jj@Qe3w3}TjQ8+>)p|lytDm&33MmYaKJ&Q4>Q4ON+O{H0&8^Dp$9gZ(;)Eq(5?DT z`vML@l1`~UXRTqr#tjf478VVxK|ORRPCH?(l3G}_P|P%Z_moB`Uk?j65%gZa79B0y z%50rPzCL8l**2JI7#O%b#lL|M{=7&Yk17%SjbI6k&|j@1hMfuOpzdaTUk+(FhfEDS zT&4!>t^Y>!^R&sguuC)RcH;sj!g#?8GpEB+y8nqmgO;F?%qIo#QT%|y2wu#{<+DnG zLGcK5`cEYLD%-rStmLE>kOqz=k?Eaa^q>Shv^sR^>qm+tKM1(hwjBrss2hMK|B9y; z(EE)wJ=+LB9Q-k!4o_Qyj|6NDgVwlK$LC{-VCwsqjbKXqFtLZx7hR|oZe8C}=0w&) z1zY+%pqzWXnp7F+tmx!uY7#2=d?@XbDh%a?4&OZQIYerm#L2U%^b2$R|0JRw*&WyABHvwXQSV==D(3w5NCL{yJZgcS*3b}9L2BXmlrR&(L? zb=J|!|L~BH1X<|cNt4u9c9Et0JiLNhZ@>8dl>oe5AHpB(Wk>^D$dRbh`Z}Q``mz#Q zvueQK)LM*HS~|40-xGX9$Cn9aq~5eF;cr{z(%0pggO}V!1p<~J@56!v+L3c80Ul?; zcODCE4Yof97NRf9uJ{JlQeRSCgL>o;Q{}bmse7tW;kkkcVxtsmEvaBF>-r4>x50nj zgew&I8XLDP!O2)p*QFWN#J+Ms1+Cv&3oP(YR7A?qJWo84Kpd}kq9{9jQj15?|HOSO zsRDze7El=WnwjccBs0lD_+N7fRj6wwObjFXn6Gf3$jeAEg#u|%)8wu6ST~tHrUHyP zlPWlK>llz}KELdLV3e1(NMGU`3jQ>Nx?@Xw} zgF|>O9Oax+EMNWLu3~TrL)hLy1le{>f$vw@&gfpt0dk`y2(?t}P%Xt?~k?##iXz2XjaH9ckiRu32L3#i`^Z>b_X3~<& zUV36`znjMkm@Xt>JQ5<)in+QsLX|U-5&Tp=b)H^emIB-|KM*>phu+$Ub^TJ=WEdP^ zEx(Yc?4oMi;J_!LF@g0MuU;D#2G;9lBjAG|mH3TZuajCB-48X6EkM0g5qu?7*+Iw^ zNl^9HvK$_R8ViYGF~Bm@1HufKO`=>VvoOy7;+%2%M8_iIh@3unSKHDXr0NCwdI~B( z>CdNtqrMP`ea#OwU+?Bn?j}x$K@K&9ndKFM_+ zgF1?1OWVhT*wyvAq#T!axx=(yHn}+@B><2U7m!odWWjzq=|<8FIbD8*c9;tv!Ib#= zS;9Tvp)M9Cu@I&(BsPx04S~b}7d}bp#ZnBtE^y8BRgi0k9J}GJFizAX2Mp~k{H2PA ziwmAB&-9+3&j0E#=2hKKt$SM_vj!M!v44*-{r6+Tz+)v~(;jiMKl@lE!Tl2Uvnp*z zyzw=?a|yy+PK;!iUn@A}J&eEA1u9s{V26?^;?D`bl9I?wk^=m=vw8?C3*guR_b$i@ zk6xw=Bq=?1+h8kx;O7S*VtCQV6FFh7K-K&Q2RB4@Gv3@~JBdt|#4F;$=`;6!vomHS z%0+kL2URbt<@mXOoxL}1H10H5V|me71YQIQ5i2b=E%t*_-6`!hx|RtO6MnqTW+&?@ zi`>RThSlp>5j2IgS3{`M3c+AgZk<~j-nC}ZP~~0iifg6UKq>ak?q72LJ?s(I7>|D*6vVaqM?ri4+CUO z>f2Ck4vK5V;^@!P|MP5nB3FzBjKXQn_^|X!@`3-J9Xh1lm~WoFtP(i{w0{>x93+3O z$ZB4uJLMBUXR{LFJ&(rxZJTn)$HYTNOfA z<3>JXWJf&dt82x8)HjOlxEgKfS6ShteobF@r!iG7DQF(3N4VNxP}oecSA*d7(=2X; z7mj3BlNC(O*7YKRV0=I#cxovg;wzki>wH=-h$!*ch+X5nkagyMFN-bxL1I8n8o!RN zj(`tNfY8R{{^5^CpGM6me0E^b$ZrMXwpcz)YYNvhv}0Uv#1q}D5B_nI4jku1S9jtq zENoG5S0f(@v7!U4I3rsVGz^v`Tv8_b#PV%hbF!u2kp;H%8@fY7zFpz39e{5a`KgM5 z!;npPlLf^B5$o6~8?-~@FMjv^DL5Z}V02?`$IyRM*eQ&CmMyEB4mKz3;zAEnSD`2_ znO(+qK2WGDd)j>K5GR@6n8;G*A*LMyD+xnZFTXTO6#_YOFZcuO3czJ)K+-dx1xXKJ(RI=_E2e24|92^g@H|f z?xGj2sBVj&l9AoF(UQwL;;qJWTE|AYkm1&EF@ z3d4>%s>rk9J$19-2q+pCl6h3%+Ro-_S5q0+SY+1rynCu;QG}Y}li~e3LjFyI?ke8=_jn`_> zQm*>ObyD>JWH7|T*s z0b5!Kzw$oHEIq}`4i)^AVt*|^^TPsgPS$%%7p68PPNf8x&e(h89X8sy_~g!B5nk&3 zItB&!N(bY%ZGLf!IK!KOA^D*fFc@JTO^qBNb;t+`?4%0p6o33SeK2a_JViTym}Erq z9y8q2Ql{JS-D*nDj2+3#%%I+B2(eE#rkYSNTLBMn$Xv|2Cg<^?CB2ju%udNJ5 z`V(L8DB>C(pYTxF&c~q?Q(BJy;i9ESHa}MfT8iE-hYFvyaldpV4Zu^Beknpphd8v* z9-G>L|WlGW>cS#0ovvEr8EGXaxvWDCNRCz9m$0@r%HvG z&0adwoW(rJwk3frEg9%a*Ft= zTK%u8Ul0APP&6mbojnM%tH~W_p5ROut7o;6`T6%7=Kn<8uY-{0P6ket4lf$}?~k$K zWw#ZZ0TXZm)-%~ae8oDF`@x@H1O^AtYzfitAds3w2qSg=2Y)LaFJ<|te8{0duQolx zER8qn+g4uXnXHy4jZs0BnZG9!fssi3lp#<&_z^A+7jythfa3K^Wnyk!RJ~~?rcoJ8 zb&aEr+$_iq`Eq*dbiD8l$?xVE21G6%NDh4H9!h4sT&iQG8A1IXxC~Q~6k5cXp>@cN zZqMlg1Z56SO>p&D6gZizEZz-e2rGd&yz~s|@R+P4U34x0)Rn))zA;&xR@qoXG({2N z%@6}S(L+;P+n=r(6MV*B61735&~Xpq1!U_m`1 z;VPj+rlCMRytCaK|EkyGZhN<<-Z2`-zZ%JyIE)=T zNz+2z&7r;w{uO<+BE!~C-9OU{6kY6m6us)1IIh2u4Kzg(6K)L@+!zNI$=@p-#y?%jr5u#80)RH>ev5TLr5XMR%v}C3?g8w}}z0iKrOCr(bgv(Jp1-bm7L?~Jl=3gZkLakAf8=r$3wZh20xqzj* zF@xaM#QOLvk8io92~&wtn=#;PBJMT-g-#w`6^zn!4VE=3w}^CCvPix?S-a?=B)2^Z z&u21I5QK7+){;)yiBm**i&^a$j7tg4lkN*z5yAi_MG{4wI`pJ0lvWW{50TiMz~zCP z>QE!TX0K7A2pu`UmPzld#C@>%RFczmtw9Leb0*1i=ahRI&qX>9(#}zm_DVNq=cG*Zl`Mt+m5&1F#wQdKNUNLEo&7lw7Q%k-h>k zWoTmM6}i3Fp3%yG^?LNuYeakw&-$NRHPcJEDj>?PMGrmNo9!`Q|2at!q?YoE1=T-4 zHvSX@pds@U6$qK;eH%ec1<;*=C{?WSR{wK^qAX$+OiQ-Gx-Os3R1pRR54C6RL@(jC zpPGlv(_eLa{_)nCJ$ic`y;#JE1ZT%?z;wT#4RX!>umyG78;$|n3SEAwXjG52kEce2 zD-0aQR?s=`^6=W?K5L7N(mUrQ_yTh(wQ zHaGpltVlNH3&!DTJfiFFTgY5=eypIl9|fiwHexmm*~UB_3m0sDh(=7%*IVPVA+XkQ z^SAB|jH^sx>f1xgs-hG93Qf%}V9cl#5cdIUGRZLj96ztt3$uD7+|#+lY0kx`W*9q8 zV9)7bP7#j9ncrjH(`08qEX;EuFtr%>U~Ct@&;@0xH6=kLANf>s_TMle`Pd9ez@^21 z;s}Hn5OIG@_#BZgYu`QMBjr1%lMDX0T5FU~Qx3K01sPe*>fRSn?z_KhH$he;=MK-g zGAUL7)^8H%RBR2b`1rd>hsqqS`;wAIb@cxl>|P{*6-=x>VSn$uQ)6{r4QVd9u{h0u zOZVFa6^(LaBpTL*6^J1w>d?tr+ay`DId}@h@M+$+h$O=F4$>keQos#c?y*T% z$0;_ZeYaQOtP~6(W>r^DewA#e=<@tVY4W~#t zrpI0}c>YI9=Y))?lJ~4(AFmCJgeov)vqMLyq|1h|SNoFa5S%H+68K86X6YG>IO|Z2 z(0#lMhlji1{^r6eBz1GL#=^pNmIfi1a$7n_Z!t@Iry)#h7U!7#bmljw-jU}ckPH|a zzxaj*^y#>wyUU8s7rb>9IoNO6iL9=i!` z8qJ^+A&(}!41|3m&uDL^mu7Vt25us0F1powKBg$Uz@IRLI7?ks1>KckMtTz^^4F>> zOvLx&-qLxK?n7Xhe|ySS2M}miD{~@rNeRuAR><#s-~ zOrFi%-5yFVt*#EAw28fMXB`m;xaX0LBTe;9aFY-V`65rg3hKChq|{3w?*Zny?q*#l z_CSazaM_AuYxd%^`8z4vRSID|h$$$SnhC}eFX7{A3$;H!!D5-9`#6gJL8m5^iOVkcr8j`=BR4M(hQ0HV>+r~L&3|cWiPQ5~PacvoZWWWwCkFzOVr84Im}JuPhFj~I zY=Ov~F%>;G7c$wV#fyw5=MC!;1N3*`jCzN%-(~}S_2Lj~*lCxD-V%;dEqu1uUV#{r{Y-L%Ix;I}#N63yB8Y3|c zUgFFl7oSjXtF`7>0!iNs@_PV+bTZ)Jd;dqOPM*grVA2^xu~FcM=rT> z=60(zwlvMuIE|x?O;?-G<#`VpyXiu`)y5iD{&ZzZloo3pP^YbcsUG4!w>go1^dHjY zI2tvp6m1}=z-o_seJkT{wpMZ1)1fzy%*4HZ};g-h9;y?(3z^eoLpnBhN-4Idpe_rTw??GRrcOCwG)+ zXgq1o0{hH&sC;kG3&CUD?6y%ktW zQBL}oKMYW$^Vs74y*7C!c(^%%2HdWc*zTbQCvIO!myn4-8CYwHCWwZec0a7{$k`;s zYzQ8c3&RQbs}8{Fu0$(|FU0Uhd^hV$^5U#~fOT5^gZpx;rs`O%0D9rLXr4tWisP9K zinoINPKOX6$Sp0q+`)n&zp(-QP)!1UqTWn%Ov}reW`J>SQg++If`zpyz6j5JG<8?o8!ZG1dZ4fEvIv^9$M`HcNjv`Sqn>c)jmLJ&|YE>f1&Xh)MMzm}JAO zIZ?DN3Q>Ma#2u5`zE(ygM2yGSn@NY~EAqsZ#QhM$>*QRaI45UJeT)7jOa1 z8@XYy5~AIlt<@}Lkyhb_(I&G#<)g=U2k9WUsu}mzmXEHeSUr{w(PMP2$n@B4xU7zpdB5!K)fx%pzmDQMvAKz zN%7{duiCsXUoawG@C5zzt;3g20DlmzFAp?mac4I6zmQ2uO%3}vzpxOc_VIrD*LO!U zOToDTjlYvzr3WlBRwP|SDH~S^W}Gffm*prZaG5UZlUr!o);KP^`B&p|(5yKs=` z4WgRz|4t-8fYIgOrS!(Cz;fs#81U?&yV)foCl^8_BPHAz{oQDz$3up8$kG#)mL#bi zr4ZCrHefb%B@92pQA;|zlBq|3V-crTNXOr>K|7=Cz_4{9gCX5+tjDsHIY#OMDg9c~R5(M?u>kN*;0pRo@hjfSZ%umZVufSS{LcAi zE_R@pInJi_o>bY81B3lcFh-57sUW@+M7MI*LG1nA#H>?#%0xfurJ4@n$Qvf)+dO93 z0^oaWE1A^nOu;NK1WkzkfPKlC#a4s?P9nBP%FHe*okK-S@2|7L^09?yL4)C$YxP=o zh-JbDNNTY7hb<#=2M?>Fd>6j$I{*SFeCI9xbo^c%>M{SWBr{i*#asalr3yraYgLeE zDnXoCiT`OKXVChgd^!#{8I)$GZin0RaSv^H_1%yUZp7*1|OJ z3IDED7jV@0A&lI#haoWDn9Cjwq)*;+p7%eS??obJ+1N<$HOMxF+3C?jf=KA*&pP0x zNr-xFZ0{y7S3VSV{>`0APA=LlO80XWeHiv0LljdqH@22kK*t1-c1QeOj*tMh2Re7@Aq|ax-g>}1yk?FGY^a|3S!S2hpd!z#Q2E` zpg)9vcJXk-+s$4~5PXkmKAsWUbawsR?N?B2wz4hTChCY&_#0IZp3T21*mAutG9{eK z$*r4xn2GzgGbhpLvsr#o%fX;Gog9d`Lmu0y$3n@cbfbp`V|W#wq(lk{@cfgGq{CtK6s7m!`+ECIgZ4e_8iNe6s^A7U- zu6syyGQ7wxC{h+(u*YEG1*&CIcphr6qwBp-EHzPcDp9O6^og%r{r?llB9F_*ni)5n=>Hbf^S`fc(gX)^#E=Oel~wCQ#B+ z$g$6FeDMH!-qrHt;vHg2(riLGD#3Lv4{1)D9o)#F%I?3w^y)v%cE`7>TO86t%(i|`r`Fpm>hmA)p?y6&&#@{o0*xDDO0=%0$84^b} z@irJ7Yq$v43QpE*&p#oFmI!P)$OuCuy0918suU|QcOf*&uxQC$1?AW9)yqE+>@gjo zz46H#dwxE&tED+HKi;#$b@~#JzK?zTq;B5{dY|iQzJfKu1%eyo;^?&-lz&;SM@ba6 z&Pc#9kSoLHglAa%cFShD#v1|ATQ{nLivb}BtPJ7}g`SiwLl%~tZKja^S-KguKJpn* znUW!rhFR9}RfrjWx({Y>HbugsD~nMFXk5rYarfXcB1U{D8g6+!xD>WsST0O$E@_v$Bp3+O;iowucbti7qi%)r= zpkmsSM1n5sZ5YILeT4d+uvDwMSQfmpvY9;&7s-|I9N}V-!#b`QnE6q1n9)ZOA*Xi2 z2@0_Ux6JRRhfA5Y_vcHcr>DJpnI=%+mKxwLYO>ti`NiYG86TSolevIxRg_ptL2WRH z2_jmCF}i{`<8&o9af~ti#ZDNomv=Dur`90S%=addCOk9@76sUBL^&CvTnC=!jj8Ac zq`EilIsmO|Y)16p^)SR%4$a52m?8b$s_9*$41lsH>8M|>`kiguAJq&*;W6G1eJ2T@ ztWtT~G4x9wyxvp)K?Y3CQZA=ya;+({HHr6&4kJ-C7CA9mjbObN zq%*L0-ib7x=kauue%o!Y=X3X!sbEaUP;gGo;FE3%TYp^(b8RMkBq}w>XwZo?4GU^Z z08L9kxxM#bS};|YSNR!RSFMKII6Buinfg}Yq3Vk9QxHHkD5=tkE-^YlqF%yE4xT6Fstr&j`q_LqH+s?xdNW6sr>B$z?NleoPgg$3|Yx8&hwU2i1;;$R`E zFkyTR07`9KD1C_IhT*+kf9nNeMFXM{3k5&%uWsITN3m!nb|vN`wL{(lcPgWz9Y`Yp ziV#hEmS5b;J#6FlR2ZhW_A~PP#@4yGirlMEg++_k(&~`O1I*m%s+RC2Wq+;iF6_D# z4hxnl4iW+HM~(jB0)On0t%G2X2aDYe^oHuicVa+ZV&Wi#43=HAD@x2qmpA538)~o4 zHV927Y7aquH=EGLMRe_p5Etw#186+N; zZg%fZ21O-e@nXy4VdK;y%jebGR4p@JhMlLBL3TwQd?qpR-GDYcdvjt(N-{3;8Xlg_ zI3QTImZp%_4<8$3+~FjM>FCgmN)dvvLN_qpf)jOX3bhpEI0>BN8W`2#i@vRT>!{Sv zDaxem8xw#CgZm<1r4^SfLF2E9@E2 z0kh;(z&Nd8{HDzWKYJQD*^5BtKo^TK`*vCawFJNf!xzzONf5}-_QQ{_O*b~M%t;+7 z%gN(BFyej*6ydl&2|Ru@Nk^&Re9}-58Cgeq{uPn1WCXau__F)DOab>6xc$?l-uL+; z!w4DPB7Fw5*)QB3j`C#)T2=i+DYxQ-kVf9=}3ci_Nexg13Dax?%jji9T zL&^wzZ?mWTo{?b(UGvX>bUrygE_FVU0n9#5$3-6Gx9$jw2h$}bZVQsokPoR%4b?P7 z-|XLH|5qjD(?VCM)vr?jQtwqYAlDM`ki0I6Mz^8KnchL!^wU*=M?=Ik0THcCyPT!< zaoo4*xtIzAt~t7IK&-bg9xuJ&#Mb}g()2G~ZcH-1a@ovjBGrLM&K-{8L}GLh(s+x@ zu|L0q?Cc}NY6eR(+^i>CjIuMbq=O4~O7n_}0R zB$IEJ2rk|L6OAqw4&0vDV>`h3)l`r0 zCQs^oa&z`qohkj#*(iiDXN$4mDm@o-_TnFueOGe@U6~(y#&Iz2_EP%xjXW^eGQ>&T z`q85_zaCEOjmZN!85t4`63YfOEB~nK5~SL++vZtDd(}jTl%oC& zXrD&Z;If_MfAhbN-R<%2L)C-ic2LJKA+WbOLG$0>wlE_(>RHQ=i(L}G#6n)z-A9M@ zb-`#$)jdSpUyS!OnhGt}-4!_Mx!I8I<~%YvG%$DG6R%S75=^KW!pGz-8y6@5IJCrdHqJtx+-l^OZ-OOO}fKzK+wf>$*!W^~bk z8p!rfut7XrP75Ul)^b>9rx$34%%nRy;k)V0f>hJ7B{!S!0{RE&*~NHzhPUs%#@)P8 zs1kb_v>~Yo?0O!oWvp7W$ln?@;8TLdhldsOs+m5A-PXQ15S;YH#*J1=W(Uoi>$PhQ zCvCN6NSE;U@myQ0$uf#V8hzX87bgM(#*5HKgAcX<+iiN}St^DN&cE}BGLX7PBZ zrbpak21?kaqpg%!^Vb>y1V@5@b3ffCc$uFG`+>jxA*ftj`(*3*X2brEpfT@aRx|$Z z#YR1WZ}T?Xsh*(5z^JN{o48l1#wr~}RR{MneTQCQV?mc}AB`HPi1rc=--9xS+WR5z zm&RbN6JA5BlizWETyrm^t-dc~N1u7Pd%FINpF;ZnVG?s0D6avwK|RG-)8Kzn_?f7C z1u{UMpQK~6YOEvqjg17_+S=f3_9wQ|tt?VhJ1s7469hUEkr7k(Fancw!x)lAE6yCe zoz<0r2j=XoB|Pj4rx675<@;B%9XF^tR5pk28~#{7k+%g5ak<#*6nyAs5@?4J`LX4$ zcmfInug`1-B8s(f=MmZ3mj{C3GSqbz;+lR^?3dvC?mi`dS+Q?3Wo5eV=tJfVY3Rdy z4TVwLH%7<~V`mz0h_DLs(@~u&@_j=&DXDUHM)IHXoCp$ckmp|`_#_PXji*9@gLy#z zbt~0-;vxnYAlLw9@sA0XB9B?fA4@D>qsmR4;9cBIVv+}3ipB=Gy;Ck2o9`bR@N-M+ zZnLJB#bAk;9V}YLgaW95fF3%zli#k?io9WR6;oC$otH)&#tU!o-758ekf0Ze4NvR5 z^zY-r^i8s*C?Bj&eCb>R z%THwQ`XaX&U-;&^WJ5gK_zYKybL}~iJ+eyIsY!dL+kz?jGyF1Y>OeKWhb$390{*L7 zX3Bk`$JvOO?)#?2Nk8m!Lc>-s*_r94;Sp3eGe@iz;pL9+{7at6z}i*)N4LAfSFo~w zgP5~7OG`L9KX-|J6N>jvVEOP9fI18M!u?75&TvGO8cTPp*Gk3;{d8IMMNOC|^`bxd zy^`ydSMgxsx_s1qKb+%+UjR@Iae1IWCx>HeU?D%Gfl5mH>$g%yWl^PBnel;=I<$Nt z%10`Kve;!1PxUR&*2$TfPsxmGG}w`8_W^stYeP*I(q=vN=nk^H?-4-$k{(C{c-T1D z7F3LMQWo8BGgj9yP{*`Tg^s?zzFD6`4#Be+6zI9s;t_gwiluT@?0v|&9qgy z*0-lCdX07jB9vImPW;0oBa@SpFfn~0<>jwY?V?lMzZa{(A|-d_0R%U7$OcNOoc3z& z%mg~G{%Z%>$C2Ztu4A&FyN~4A0zac~RGdxcZpO?C#@J65B46$~@y`~}-Y{?TRc#mL zPWa1yo%Jsy(C)s{)@fauY9>@VnH5JW?K?IVg7uJLkD2-W55NlWr5>2-79X&v$yFq`sGT>#NuuE!o}l#hO-#n zKc7`bZ5Kjkk-oTiBuwt=dbP*aocfZKD3#Y%o8kO*bfq0;%Zw0CQichkOEQ-t!c;P#Mfq-` zlID?v6D{7j*UEt27o^iq|ItCkl{Nt&P&O78=)+lO0uWUH6RG2Yjg*x1{O`lrRzI0@ zZ=O^{{p5UXp7h4mU>FV4ibc7%btZry$Qx6!IWB}XBK_DLE}-fnJP*(Dau{@}w72QK zu&Us6Iw;cPsmA5Q%TcGHyq8EL$n6C{)4D`_1b{c-i6l$dspculM>5Rv=$ODMBUL3KYUFlfuG!gH0UwRj<+QZ4@MkrAr25MB$znqH2br0a zbkoB+J>d9_onq|%jnWIj#r2Eun^$k;v}hyr)D4{^!^V)my{aQhsbuoL3DrD@r_)+Q z=lIR47apASo^!@JD0AbYzv<82-;&dwHs@!AAI5%Ib82nmVyi(dG4C(Fo=j}??MXAn&!dJ)t>9YK*TiY%`y5eHFw964j{%JrQH*3YBw zRx+uiVvz@3c|GeZO#xPaEvYsFQLd04Tu@(Ex7~K_^;Ue~$x9UkIDtV*8osb`5hJj6 z5;0IiXsWyicL1>Z?>109Zk5sq0GdR7C2;P*bfGYh>FDx7BLAi<+! zXm*hyQcL0J&UUU+|5vjcBZG2!P?6H-&5nmtDLFZ$?7(|lMRfRKfE5!pT`xYA**mT{4NYs&OPg1N*0iqkc#MmFY>hqn^LRXS;^xYg^N! zX`AZmfqv!~&cnmTbWwX6S?zSNEw5yF$b-o}Q*a$)0&vF3d+VpqD5*AL1^7vj$b#rg z!T(PAnc}ThPOagc+bTw|NG@j*NvyNUw@sv@2Nv|W%_!de zLL*voYQ@;h991U7&Csx>>r zZ4>c9vHlFJx%v@rg0O30mw&*4|DegtZq`NX4|jy|3mPN7v&r+jl$J`=zkdLfk7A97HlP-$Nq zc+g@Y@r!m^!LXWepy>KH;=Gg>M1|wKzHeB))U5w7iuW9Ui{N-Tel=M07waVw>{f{R z$>V{uJ*?=0jz)qiAbWilWp%7?j^9-m#7NyuSE^7CM8q+$;r-R$gkO?<+XS@?eI{fN zm2zQTI2`A%bOvsHH@;)@Pjm(Qnb2(Zffd)p=*6hrdh`SOb&+fNUX( z2{2bRZwM5vl>Jy5Thtx-C1Bw6feag!PC-^lZc~Wizp9ans}w1g$B)}IV*bt_QzosE zlg#P@uF%`2enyVrv4t)@a+B{T$I{d_ztpit{Wr(E?`z?{)SGRHE0AF=~!HDBrhz_wtGHcO#Mg*jE74vVw`} zBIwh@`@>klheE_=)CH8QB7Us;qU}xOM6k+LXbTAoYo2|8i2NR;eW7c9I9(Q0&fr4m z1kPyH*-YjaW>;9LbEk7X<1vHv5Scj9*7X?d$QQE$y5%`5KB$s|CLrlis(5@heW`X?Ec+V? zekAV*=xZwFzw@>$SN%Z+6jHx+<(2os<4w>ou>ec6k58Tb!AfktU{0~vd&^~YCv=Iu z+k72xtI^zIir_Zm>+`>OXN_KBar0u5=+Xf6oyww{UsQy^zvj=0NZYF4A{&lHBzdhsHT5KbYdkw%8 zmrO|!6Xr$t&qq%)`&Wk$(#A&^5MDlqpTSD6ld9Ty7wx^3Je1zgwOK)C7X~Hkt@CW8 zN{Y#XF~!wWrZgsP{02e(VbbfBy#}|tKcMGhN;XHi9IZgX*pu+!4YyjE8S41$bFg7M zD%$(l{=3(PzZBYP>PmD|UX7rJf6mDFbkRc;IQwn!`y7%o@LsPBr^tF1SCT#a(}hT0 zkj+_%WkA+uTqrEUz!L48Q4#NBI#4|aj$r{clSRVWh`0XjhX+q3XZ8v2LCy)!h9}rc z;7)$rX8MX%HRsMJzSY17$I*07+7rxTXwAnb`(^*jv8J6gLlEen0{C27i`?%Qc%OG8 zJv_CqokgsjVLcza3-KNdm<~Ux6;^_C*LxB8@e$5r6cb7?fa5j;TK*Fmt?bkhI|~>6 zjLDpFMX!?x5Pm#a5dp%OZR-OhX*-$q>|To+rVXtUG9cqt@X`F*ydSzm^r_j2oG+d% zVz0Ul20Gqg`bSPh&(DSBkS)8foOY@1aL}{q`7ehtnK;!m_nu<>#0MqyEguj3?PR87dc$O#WJwSHv!b=2*B?0VY3>vn5 zT<8Gp4xW*3edi+id~~?Rd^-xNx7r3^d@g^CWvAn3TV2kzNBQFKILtH#yw>q+!XlzaIV1%o%TnEjGsb0(}?U< zrK>CV)X~yH3e<)!!RnA5mmqmjRd@986?;W&2Qby_XkJ{8r`ucv8TY!%H}zhVwjj{AbHw~E;>Cs{IYS|ZLKk% z&HDk~@Ysy;10U+C=pqjUOk<`h;s1YcM**%ckgGOor z^GnXOKPxT|NHgeDRS%<@^C_3E0+k2xJPLIb7EJ~`kn~=kxG6D-0j}WZ^HTwU?G}=f zS;|Z*2WG`Di|GHp_?zs|d401g|8Idi?)un!OgYvU-si++FlAx9W?||6VAT08<8p7} zcBy@ck9A}nI1vAG+8a1`!#T_F<4yI10-Mx#tFLe?_F=Oi1mJ)T_BHW9{kk6SKsQke z5;;}X0MGOhXRPmxHJ?k6%60MmVt(D-0f6%DV_sc`)e{Q!?n;4xd!?K3EEuw+erluGVz{Zmohb1++S!LtJu7odn#NSBC~5;K)XG zj&6)er)Qzb!-^OVA6|qZ*^&Ea|Dj7(Clif?2tEqnC|{SGtN8Uaz(h~3*2ViWzm)TY zjbzx0qqMXB89!_PSajT+7=ZcB*7X|%;hyGT2VjAi8!%O$8EW#}w|C1Ll8ozf83XC*vdZ70KH?Eeh44*hevXK{19;JuwB*VCCP;=?6&U8dHjjDM=6b2PIp z=?GEMf1mG%fPGn4%6~yOAa}$$)_)z?xx-{I?nO&oW|y#t%JL$FenI%v!$U`SW^{hn z%1L*nlyL3gIv?xvT|0ocZ%Kul0raN$H->mt4J1D89g5}Z4{rS_(8X`lmbkbN);nJY zg?L4Iv8SswUk;)^`ft6G|FGJuC;LP+d}~h3>wK6NX7NxpfSL)j_bza(aP?qtY&xK7 zck_vHM3a3ahV!5Hs-K? zeXsvZ+21?jEtU-!+*Bh?ZranWB$1LQfnn31VmJeDrBijcyo;Xua>iy^fC$O(-OGPz zU`=@b!hxMBAG2qoX!a?tnRV!Rw!Ej*bePQq!eMQN+ZS~BB?Iky=d}`MJ6(LOgr6-I z;4Ac*B5~s5-^(VK4cNfPEvl{MX18=9kJmt)ny4j_{O0%04g~9EHREpegtiK<({~Sj z11?=Kvpo8Fmn}&MrC1T~VE-Sk-a4wPE^7NG1f;uDx+SEfC6thEkS^)&?nXK#l~%gD zr356U;RpywcYo{X{XFmceq(^c;Xkgk&)#d#HRpBxF4nJ2m%QJa@I`;#A|DOEWL>_e zC!kymjU;E_tOoPI!dHS$-VK0;d9>htpgn7@zZU1N6r@qx5WA6#vt#)jqz?>#>S=ok|l~6-mEy!=0PP5M-p6 z1W+;zU*3z4lB>pr8BIMi5Jn%f=!m>^BM&xO9?@zzWYc>XqL8<>?w)QSSQ}$FT0e^P zaNNbw({e;Nf5%JgES<)%6pI|e#UA0O=KyaGkCc@VKGQ|`t8vQ%$5BdL&F`&J)K}V% zv0QMzGGMg|g3UoICK0b_L4)Ax7@~6-{3fBB`Qvo2qejhiatQ)zHw@hPt5p0q*ts3m z@*LV>-7yJf>6|AxZ>%kh#!C-3RxmMoNsQ_|hko`p{rY1BE%@E06}nf8*aA&viaFd@ zo{Uzo^&Yz^E)_BXhX5bdIvj35|F>?dHKPq}GX8DZo!=aj$|}5rf;p1eM$b*nLAGfyWEp3fDph&(shLUAV|UYl^&mEZF|`G{>Y{ z;(j8{l$bp?q9uQpamS4oOo%UjKT8+BQF-!cMgc85Iy=irZ1vkak)6Mj_1&pb8a1!Kh8*y|F{!d9eB^+lqH-y%4z_e|Uynaw> zzQo5sOR*9V;;!@Z=w!9g_&BGrYHNb`MV&v6AM|+ge%N?k)ES-NoXD10Ox`RN4gq*j z*3pMKP+6tZbh{i`=X3O$m28Pj(41v~ZWwlg16VgVny5)3-*fmj9*XPzd*-)h?LWetNw3LO`>p)|7Zf zU^8UdCV-QAXr3_HaduL-$vSl(?^&vl;C?OQxd?GzVEArGwQ zZAY&oouo>b?q_e^s@@FT*6v=IRiZOV*uY1O;J@3rs`RlQ7 z8NLVpG!6>1c*eAgfiwbFgZ3sWlc$go1rEDRT3e&5Dx#$E$6~HltNc%*!#9y0U}i3K zPk}q zKLz3T`a>7b$nm9rIisP$hrj-F{M1<4YJEK|Ih&b}i`YKSOvxBW$yf!REbTdP@3ewb zszm7!OsmY(EL#$fF5vuR;*y7pKL}&GlJkZ4JA7a!aG~hE!_@EHl~`1oT8blHzL%E?FF>QA3ZQxRttem@8> ze~?W;Gi^gbv$4#uT0J`=`H^Yd#$fCKRRPpj`l1^=-po1vndfBHs)9l5UE6P59gx5j z4)MQj^q&Gv*nwZF*vSf%Ka`C^a!%JH6+D8usGS|gU&7ROZ6r=^3MkuI3alJbwqkW? zh^e8r#F7~UB2SlgI3`f66J;p=z%@J~i(ck|96#(jUhPL!nrsc-Lk}n{n4Q70@iaDd zo+~e(>@l$Qu_TdHd-qAYxRm3^%>+=L^s*%n{lHz4e8R)4>aqr=mjBl@J#5RGJ^z?_ zoc2YXe6hWk2m1TBoGZATS8rH~rIqpi?qZdG=PcR6-UKilK35J)K)?r;11I4IkGL?- zW@oBRq){`H)2W^?$M`>PiwK=|mp0vBZM5m!ZFia$-?%>dS%|Deex;MeA=Af3fyq%1 z#yW43^8FfV0h$UJEZNx|R6zx3xz7XS6#w*QN-S2L90@X>xAyLTWG9X~Pa=R8WEXSO z5^*KbWaZ=p0b_mTf^z1t^f{r7EWdwWSRZ&Nd-&W`CQNK4(9*;)GhKJ=tn+;0>Gx2S zbcD{>-{`Ru!gSd+S~z87bIb>m2r~w8?MN^)$9#CL1e9*);$+v40Fwp$CK)x4O6&(b zDb_$pNn^VVM5EH|I@dHqyX~0>1T^94y+)B0j90&cNNWn{?-3=5vx)k8Ag$F~N@Sp9 zj-BW5#MXHoE1{&ehWuuovp&p>+i%rGc<;FSE2t(nw(VgubDWcPK9Q6n$An4Y zr6J<@fJaQ3!Y(;=<=MoyHkfE4SiJ>GU+bTN)r8~gi-!;RtA3Z2{3@I%dklQWl7?H4f%HC5~8X$%pfcD8~2mH~{VQb^haYee)c)^>S3IM-Sh?l7J6 zE?B%tAI|Q6|Mw;It6DG}Q8o`|!j#uG`Z*wsGT(t*>b z?mu6R94okd`Ot{f3zJyTW%mo#!Qnc{kcj?|KpzJRe`;Vwh}v7LJO&n&3PcPTR6a?V z08_4w86b$C`}6rqF&^pkvJ=cL8VZiD*yWoXaBv&$5#}!lebnB?$ATi@_lz~C?YjIZ zr7lKNm(ifD|zn@qry8HPBE>0kP6b9yxce3^RkR?dnz24!e&n zuR5*4|G=)_w@KL^3)Lg8Gqifg@~(QpwT8d#dwlyY29>3BoLO?XNK1e&Q-)!$WLJ;VQ>l~dmTe!c#<>NLUzeRP9!K@b7YjSdbP^p!ZdPYlKzS&0{tUkYCDF!2r zk}w@+6b{Kc{--&wGe4K|4EkTN!aP6s$@zzGPt~l?E;4B&^gd`5IJ|$)EfvZtCh6R;D$x8B{WQDt~Ee_}=$OUWID=;|Jw#)eb%Yps*P zFSR@2bj+r|tE>SO42MBf?};4@G3DflHVQ<1pmq0M9!zpbXSN5-DlvTVZ?y~v?+EP7G9lsTf@jq$1QSfLU zt;t+u^wrUQFj37qo!)(dRUPpwQ!N`xVfZ~DU>^$`=}t}SwflvYjD6~rz=4K~;lQfB z&@E%ZhH;kt{5vK76q?I47Wb_GGzm|!TWtUUG+B3GoZo;;;WAWqQ(9Dn2<@fWwd@p? ztco7@;WphKv`)^>rUFY)mm!K3j?4=If|43i$9%yL)oF4qDieBFLr{_-^pFp_fz*To z@H~09Kuz#VvSWpGTJkIqcK8e89lf*??syfa=_r3o51hZ!!8Xg;U}OBH^=olSDg;Rc z^-1yAABWZJX7nyi(>y)gV!(r01aXhRj5C1RuEX~4wDo*J2bXLqjQ9&>I3;G4P($3u zP}d~VYTF-XC1fIa2DH6caI>BK8{$l2o%{-p`4pxA^tn+|DzGOsqf`P)A=ak z0cS;?nyHdR!dT6UCUkV{Sx-L}!mR7E`UKJTyVx{IHL(YjNkf|KU4eMBR9+xSY|6|v zL5`*Dc0rrU!)FBau$-~HjqjWgns~~qs|GE(wK;>7L6^>fgOs1>`oWW`H1WI6pWEzj zo)JF6pt6VG&R!Q{r&;yx!_Ayhi5*7$ea$0-mGJ?b+?U-~V_RBY-p%>n6Xy6{Xv2s} zNWka#oXF_8FT3uXr%%L~(q;}Ww03jJOY8GNuf+}n?oYnBO^!a2`)*BhafQu2){!0? z7;4{e<5e%W3pFQcav#_6E{K6u%3fqY;%6Km)ibJ6^>Fw^otNWwDh4Mj z0n@dGgC&hWX+l5$SPX4oSG=qtKk&c9sf^Exf6Q>Eleg9^aNK6m6P~_tJT-m zgBDo$bO7JOfO>K&X8Y`wLK=R_7L-~rl~CNIDo|2?{#|WIe3n!&Xw5)-jv>=RGe#!S z$_AeyG(bX`)CC+hC3VE>=q0dmQtk{X#d&xjXSXqzlj zX@tK;i*+uaJsGNvG-iNoct>6h@O1=&)>+}#uk2Z4Siav-P1#f%r6lFqGFhJT0rv3{H0=1QYM=|$Sx^8Ad%R^*o3 zCbJ5rlC_BhpC#4k_?J-$6A#@V#s4&*45T7CF zKhGB+hl&@|WjEiHOl@}F6`3RSo zS;ESM!rl}NsT`t%>7Q^#S`vn#650K z(z8zr&cIILZ3SD+3LnlFkwhr;!L)f*2)KHD@k-Ql&7_tE&?02{Cq$OEWUXu|g|>Uz zpYxjBPR50te%PP;l-lR*%h00vDs+H7GX|Xv*XW~^kiPtE-MI0g%>dL zudHJnN&bf zBO8{hyn2zu;W=u0fU~?uR~RlyM|7z!f6g{KH%Z=g#KpaZQQK2%#Ld2ZO!D>SN_d_o zhF6q{H0&Hb+TkpUr^MhTWi&ln=rS$svO%B;_%e4DG;m}FhDxTujEP%1wX~HT+XPFy zn7;1TockCcDS&f9rgGm@O+J)%oL*w=j9A_zjXUi?5}3aIom{_Xj=2FH*HLV-`-N&F zxU32;Exn@g?2Q*C9@B#&&1;BxK+AU%i1odcvqXRw$Y%%b(vxuOU)MlzJu9Viq|*3^ zQVY^D5U%?A?iF~q8EbsMY}@7SF-7ejwL?4@{EjZ$IY_6y_oIPg95v$VxVXs3ryd_5 zlja)^Y2o&G4cEi%jekyJ8WP;EcRn^15P-wRMqi3G$NOVFJ&~_)1QI6P0u#$~gpJ!(CmqK-5GZ zs7l^uhEP+0$p5Tgt(56TpE#`hQznkQ4k!A_jB54c=>FB@0Rho$d(da^)t(;33&Y`S zlXRyCqNCZQd0`uB{mg}kiFR;~A=yuWr*N3f!{Zdn z(%3Wisma%$^dh%(ZP&USzMO7OFeP4IrUnF#LOfMpBVt!RR9|vJI}clsMSX9n zw)~qA$}w&GKQo9%aV*@?xW#10biUPv8{D<#usERA<0`We;{k#!P@aRIbj|`i4I0d1 zMM)(UwLN4>mdp*}&jTclA7l?ri;)UGg*bNxC^>}tSqo;ke)vwdxp!ZHg{SM~`Tb+b}I8TP|_&{(u)b;jz{*>w9qMT^#q5r*R(+1c; zJ6Jjc$!X|=LxZl1FCn754-}Zb-gMG>mrO#Z=alQ!gzkoyL(%KI-;i%dIkkj3E+r}| zU%nOhchxCyL@hmQ@ik7CjW?PuY@Rr$nY$3%qxvW?ynP$Gg0Ji4_k8AdelxfZC)Adg z^kRQ;?m6rgm?ET$_`cL*PctW5 z7c89@=e0eow^Sm)?54Y-I$fykQ|_Hm&BuF-mNmi@Kj~8Xoc@YQiK8>k?ZCar0^HcF zif{Zs2n;?KGX{to8eunmniYTP{3-1cRhYTqjEfgOf3SKgvw|LlG*-Hib7Q zq@3a*wV2B^%<01u^$PNWl@KVZan9RcZ zGR$7YxssQetU8s!Hebq357pPSfNcO+;{y1ox;HU<_S5nhWknB5-|&Y?g^ooiNbt}L z`-r-^-X2dkU~+EMyJ~?k1^Rxjq4o#{=)Ax_KHekR&yM%!n!kfJ-?w7jHK`-EDsJgR zgNg|v*Imu{j)A2+uA%v8ApbO7TNg1pVQ{kHpb4^t$*hEjfR=s~Zf?41oy*v}kGk=- zoFRY7oXCOv6ZdN`&&~eXg&V1sXWQun2zs!mA&Z(DOlGIpy_=2(o zi|wu8+JBw{=ar%+qTKUCMqTUWdxVxwAG*vqx3yz7L5`bPS1o5lmR&(blg)&EEl*Ry zrN=wE6(T;&M{Wz^7;6oa2mKF-b#Tc{5i5Elx+jbo^o9FUhmp8aSBd! zia=H8h8wK%k19#9Lgg)4r1%7e(@!i?fx+TgD~j!`F}M>4J!eo~@P z+&w0?B=>QAC`m@cb#DHuk}^54w%4uM8VKz9Om zZUnIZ-^8{oS#`0Y4i214G8Y}od?N7%1_tnI0l0mjIDYzuiyh<0{A0B1%&mBV?dlCZ zQE|^t_UD-x*%EVMa}Gn^mB!$)VLWxc6tS!9yOD`@a5$Eo)}M(Oh?FQ~es%Z{)qqmW z-h{0-ZBq76+<*#mPeH0~)E|o~UFhh8KZz9KSI;DcZGt6n99 z*6*E@j3qMe`ln-t{?BLN!I6r>_e^;Sv^}m?E<3_=AI^&BJN-p>cXy9|B`I!{7U%Tn z+P9}DZ`bx4qFM#?{M?$!e8Xh!8d<%X>XpSE5N6~i^w0Q@e}8rM4hfydZRu<}uI_W$ zPd&|yVauc)0Q~!n5%K#X5)iDCZ#_1GNZ*cGtZ6l3qq?^TZu-XXDkjf>F_hd~-a(XH zwZolKayr5{VOh5D=K-@0TxY}w6-&6**nq%I#V$>0Qk@?Wwi7Dj!`#_%w# z;hho-9FCU(i%>AHrWo!|r~Ge)5KgUCx${>*t_Z+{5DU>;VkjMxacUTp8&Bm@S;b$6 zi=eim59FCwaizzs{16|9B<*$3mHBgHMHPSrjTK{QHNvJHdckWBa4alMOhhPt)d`M_ z-rhL5LD4dyB`${R<={ZIa!49wS190AA2Vo$K_8 zVJYJmB#L!hq^=fMt+rcqR)Gdo$n)$T_f2KABDWb`bYU6JXqiQ;#T}Kvx2AQ@(s@L6p4$daKeL5sTN8M)*t0T$ zGFK=l5iMLZthb!RAuO(x6_G-3T#Js`G4FGQkR&qh7284WhC zN?raxKfP*c8Np>45-}UF@(_7wp$XAyi#IBUOcBc8c$8I6ZmmqMc0Vp&bF%K~57cb2 zL49TBIf6kM+i_>y3)#Z%)aLOdTzO#mrc!~e7LCY9y^Ib-%H}=5UXIb|dP%BU*cIZ> zd$N$sA7Q(a@sX#P&dcXHjS7wJ`4l8xG!FV?8JUb=(@3uYN5kM4-)hBsr1}sitGd_Y zR$+sU4Qu&t!vO=tBMx>EvcM(_rXNtcQ{NoPUhP7-UaVr98=$~B+^Q3v?&7G5_M^g# zneOYXlu9QHX4Lv59k&%4$ZrsP`mj$JT==h7&rkIxs@IG^+CF@{tM-)ds?J@Ybg|WQBtl_pM z=IdWFG^wh*(Ahgbv45R3uQ^LeA=M&qmH5(RDNwk&*TY{ttpIutbFtoG0w4Ku!O#;PL(Y9}i+tGKrfx2fgFk#li? zQrLPd?1=`jO&Sd$3!M|>U5b?-5mSx0hM#>KXy5($Dc+12^d6JbLmYew-J^Dy;Kz;E zuVh)tI`LNgZToHq+1G|_TRBf=G<&48G|3htS>pCX9zU&MM3!{*VS+N{r~8vuQ$bD?ffH3pJV9 zf9M!aRk{U!brti-owIy<-O?57XX$4^7qHm|yD7X|>JnEx3KtGAmsO@4GY+d&Xm8mF zkFInnC4rob|AbwG%#$$3{{aW8ZGbp`4RIV}X3gfsam8+RfbZr)qo1C?xffv^dMeAs zypM=x76WW7V4ypSh#@T^5UNqLCl(F8_x+&vKCoG>cKH77NM>+bY|M2&@Whb`1U=qV zKK4NS&Cns0&Sf>@P$P*nYNC3niyKmp2zN$9?w|YL-4X1vf&CY&Y(K!WylnkuhMJ{% zOt79T*e14dc8_ndvh{;k*$?voL8|;kh8|M+FJ9F%-%PH5S+VAl8P#2HxyAU)@hp&S zTJ|o03G>ASFTa7u)5^C^c=2QHC=EQCg^t5dW0)I1EkAl7z#k1| z3bz?xEdcYcs*X<7cCJ6gO(J``_Z?xVsW{J#yUVjGkTOlG`S`31*MI zokT)IKax(>ilbsW+eKITnpe1){TXREsD`>WRb$18|4v7+J+xxTLL!y{KHi5h?X5lH z2QQCKs=8@8@BD0# ziETS-nZVDCI|F!`pYCmgtsNDfRQPlSlC%FTLY&JTj8iCvooql z4H?!WcDLeO(Wx{LX4ajK>aq$*vT^M62#Mi+Zs&uZ)^!O*C744)cyzoSlF_`}sxz$!|6CZxy@$rklQh{gD^OtN%xssH1gmanAZh};n@as_s+{-o1sE2tDaii zLOE~&>t^P6dqFP^yZIbWdeE;PFLK2%Z^oKj2CbWRzj+fK>~_t$wLJx*d4>lqKvs;k z)NVm>)zk|Mg@hN(tPlR3zl4+oAi@xFfEqiyWo=-R*M0N}gebkU3NZ_(@4jNSy(XWd#odT zxz&2{OBcP+RWL!aObzfWB$D&BZ!=aDJMdm8qp%if25z1pz?LkYME?EK+Pud$iA)z# zXZJJ@hY5f#$O;?$^z4?}b*9fAjFq6E+F6oenoaVP?OmbxH6P;L?eSd@*>=QFzWdU2 zzf2h5ZqPLtUkd6w)Jo|_4!j0_`Ygn_Ksn{LBXl7VKq3HfSIC0_u(;~^{8U4R`Si;T z?za#F+Ht&~jkpL!@OeZZjxm=UdeN^o2s^bHhZA3aj9s6_X|VD&oqfI zuD}19Yq|GXRc!e!o}w0kwsnrXBfNPM4FnEPBT5ff0NdgwKS4UHajh8KTe03NeLqq^ zDucVMY+xfLOu9|L7de}U1~h-y3yT6ii5U_lXChMMf|3}nd<$|`uNMFZVEtqPVoC!$ z(K!?>C>YWmoyZL0pCrH%!85IT2OjP&5X59A^muo=zWN*pl)%d4dG_?G6UxzUeJoC_ zm;E{pi=Ph-2PLV~F-5_pJ%0*9aq!Y(4jA-TC6xzQZojX~rydRsSQx6{ov;0XyAFFS(yWOMr6CvcuN#=Ubz+} zY;QEl(8_sz=O{1YPbO-k63aXHRwsn7h%KzqoX7_j`CQ#Crn~`TK5m&duVE?3g!~}= zTHqf9fCylE#gehH;DPF~-We3OTNFbcEghOaLt@xbp}i_L0#pn#{s6#_1cD!^YO$oe zycg8C9G)kpS3V+Rf;YL3*ST7qe!@UX4j#>B&q-#q?&<2{2+xlnx?*|nVQlmkUbLroo}yo1qMD(q!zuIj91do z2%9Qag!n&R`lIrez&fQFFvbe^_Xh(_mC)aDa=@GIxSURa?|6A#jkI zameZ*R`Ji!&@LUh=f!u$m&Zx2NQS$Ax}tgyfs6CYeGEvORG3C8!Gz)*2Ct*W>V zZgSkOExXJ%^Hs163fW!c?n{Bt5&r7UY$MmM4>j~1;dAJ{%JLhCKI4Fl(o)pmGO3i6 zzh>3U92UrWLnOYit1ZSJMPm>CcW@l40ewY}@1M60U^q~0uK2|g>7LM@UnYhB?5>#% zy{=1q%Muj?A;&Jt1imzpK5K>(9i14(RX_S#zcWVoc&Rre2fL+utV2%2Cx70^xC8fw zzfzbWRY64|x~9)`2$V~?)l9R#2VO9I@^*c?xX*)|ot}=u5t@Bkm?Rf__Zgk^moWFk*oRui?f$niB1Lq^k0Z=D*A2B| z20G6Ru>WiQ7`rGr19e|jY)|T>K~J=tXnc(`L-k1)Z^g?d{G@TpLvT=KG?ZHgWaKs? z!6McGCF9@(iy60<%ZA<1;lkU)pM=S37A|}}7pOlM`k9hl#m*8nl;BGQ*#A~s@JJY9 zsb=yyLd3kf8zhL$Ti_7b2ahTA;m{6CDj>CHO;0A3#yW>lDL8XtTkqF{O~nGE&iONK z)v<4GXH?y@5|k>TCd5y?1{RIi^2C-y#ryOI6D?j3SKan-p@nHZ0f?OFe1jY81_ZOa z>K*tPueYFJobx2n^uMF{`fw%e!Ao>3eJ^B_y%g~dJ*|i3>F<*pSkpsi@nisfH02RR z4^URZjjbQu3YQuL$ce*$ijhKR->RX z{mD>D*?71XcerYjW#?-{7H9rISUG)V|ABh+(N0*{IZ$X~muOLE-sZ1UbBlQKodZ-#kfYK0S$xyPr@1E{e4`IiL zBm03E;-ri7SZNyIo%CbqTcbCCuDdNw$0OUd1U3|oijfH>IS1x-D6EoIlppE0Pnf>3 zXt3uJJayiNTxd3B8ADgJnW`|JJd$IfLG&7(0@BO<(9w+@HaO8KpZsy0(U{iIf^4lz z4p&Kx5~)Wa_sTlPP=@=<+jQl&HyWNXAUUYlW(IRF%><7B@kh=5`AERtapVl`Y|g%G zAd}EwAKVz?A<|E6n>26v78PXIsq+o%jiU1BMkk!Uu3z;;VrFJ^^lvkhk8+1hhb;&r zsp&phf`=UB2EI;N28mIF-OlVwx5nHJl=~4k-v3W-sdw8K(CYZ2AZjCx0eo4CySk03 zbFwbNokZS?LMp@{CIIMtUhGIfu4F-uoe(T?fY+}+4tf3(xM{Mizy%q1@D1*O7ehDr z9UAe$oNm&41NeQY^YzdNZu zgzOpENRANN^&I-2&^}yrBiFS$X=?LR4gWrQ;?OV>jZ3%$EU)>C&tEAh-M-#)6*JAP zd)MMT23kRr0KS@L;75!pddJaVy9i1?K0Cc1%|c!(qS)6G&%u}fq0Ji*_+hbOWg=xs z%!#{Ls{ler;vXdM{8@r$aiB^5|BPi8Wz4`ODE&W{S;SNs{hiORgTI@;X;@8MYRx*< zmwRnLmO*Pauk31dBUD^89a04U<@eIjOLk9tXCSi;<)3fDp{cZzg!C~d>pJ_#^m3p9 z5l3`aFX#uZ-TGsHa~D9S9!Wg#IZCPYK~NMvSLP|EQWAB&ciA9OQ*NRq@DiZHE; zt|0vBLmgVH{pN<&xWLby?%8{biFP~gt$h#jk@&Xg!~ll7tH>IX3VW@PLOBhiKlrFs zmOY>m8VdI6w@>2sOQU zda>A5OQd7m17O*`_4bHmdT1rDmEoJ+8wD~ax1v`Qacyh!1ml5?kPF-28 zuVD&Xb&ki}9}(Mb`N({2XlE7e`WQ67cK7u=J6<=*YC+O%&4cr3X?~}ypCQiFvpTF( zg_|zkfVftYpou6C*^f$NsO>ByIKCz^XC8@W0Vc`WjC_g2syVPA%n+oF8w2QHTOEj?o?<-s!3jRIYZ8;Ee&{hI#7Y)6vp{Vj=K z%l4BhKVcI0&cM%Xf}CGZUv%7F)OIb7Z-~uk*`zN;aF0}WPls|lzV}5K6{vfw6ir&z zy^JuyuJBBWV^C2CAn2b=WPv=GQrzLkwZ9#%of=t7YJ{%a=;&$XE& zoD=DAC{OfLX3cv|qAXx586}kQrZK8b8H(x0{4Teqn?NFOi~we@Y;1e;+m~kqDqhKw z3b{}Hm44ja)D*0BX)`n&ieT6+voskW>RJjtJbTjI_(iyPz+@`9pTBd1WVFqOWGj{} zKI1Ww3nu0u$ zcrOxZ*A1=t@L;5KA{Qb-QZTGC`ZV(wI9d*EWxGDoDW#7fDJ3}5v@Q1sITD!8)=Z(M zO3A0-!@QkV+c|LRSD64Bu%31FjqF}chm}`}6|+^2*L~^grS)RTram4usDh&W5kJr? zp_cb_Z!6LH9BSC=7+#AQ){)2^vriTpHo0G@=x67T`ZTwCZ1Uve<(XO}jlfO%Ay>{d zlIg3;YkNQTNFmE?5vGC*`7;yN-k|O21&+CK%3$Kc;ToqcN_%-J8S$(UB#bw}`kvvo z;du-?BkLCkj&n0f#$^@kU&`MwUp^zv5d|T!7uE(EcuN;)?+|v_z?U@4iU^F@M^7aA z+lS}~is8nq)A<_6P6zbkx-8gx<3I`B(@L4|1R2q)_GdBwC>Rq8*vIRU?N9Bl?AwIR z=^unln1ZZlH)^!OfuukZ7k5lc(#yR6;^&4VK;va%xaS!)y*r=AQENQb6O6^AXDoS! zNG`+V>aw++_(mLe^B8aa5HH9K1{r3A+9XAl>pq%HHSKwN&82G1exET_N(zBhnad4% zR-+9Lrx)>RH$jPex?m#Adi&k`&Mu=Z=|h5*>oIR15tsNV8Cs{;pe zMbSOUA8Cx36 zGmZ-FP^l@aTe|PX5;Zy62_m}dgObFsoB`}5i5{vUHbSX{qzOXch7FE;b?h9OY=Ozb zV`B3eA$)P^{zlJugZeJJpwY@yN*Ior2bDsQxY1i>#DMec#LVIn5vD?#Li6p{0rjN0uIrTY z>T8Ib(8=sA(dE`lsMOV@<@-5_?Zf`FwwDhocSZ;5RbTW22TFT6TY6qP&~UyP5`Ua z@~VmWmpo@e(g|>tFK~oqAl0l)uLv;m;R2*Z+BraNDkV{;8_Hl} zIO&WcoCtx-2V`=PH&&Gm|Kt@+Pd}@~HGeSEdU{YM5x76kr0i3~q?8A;bK5b(8JsXiN=^Jal1MgB|^I)sH1wv365n>%f6yK6gD3(7b z8_Any&8rYg999k8oyL-cbt)QxCl*8FLhc&XOaK72MorBzRoRnKjKwU65lN0XfM9L?CpX_fuSQfX^7w6VP034!oK zl+NhpAUgCLiB3A~?!K4KW&U%S&1u?<+5|S5en}G;s&WXo5@i8SqN|ALHPzxCUlw50 zvgc;%Wuw3MV0|T>b$9@(f_pGg-DYEb*+Gif+~{<&l1WiGmNI8}V>ZPh8aQcAFTgXz zSOmtXJfDZ?_z6ish7CN5l~Z`Cd4N}Q&a7_)_*<3^DHAQskjp?$Y3bQ0O%UFYwn3EwC`SWj2P|Ze4qyCE7$l zn+K?y9TGa_W4V!Dja&~QG=upE=ogjU2Z(wXWmaR+5razEXz&(vL&tPEF3$o1o#9gY zZU5d>YM%EwPr9fDocZ(xiER-zyrP-=pgA6*uablSPso;~En-Rdo>}i22ljvQ&S9HB zcQh&x>bO3zML z^c!N3PU$LVywG9*$))*_)Sf+to+g%i3=Y*mDFqglEE2_=Wz}M0? zQT4i<@QH(1#vzC6>EZrn=06skM@=>PB$~WHkWmTR__Lk$6xJ9v{pnwwp)W^inq(Pf+0bM zuNQ@;c%<)dds8^|i=Ezs@LHABUTXoj=JfCnPGw-Ei2H5q;H?383K}z7tt(8hf1IUQ4ax;Kpa=%)^FjvPB_r ztoNCTMQY2;Ud-l#!@C!`ZolgL0x7lt2#$RYh(%hw^?L6GoX|0q zRjqoqpOd2MxuGh!>c>AGa4uwDKI}E!K>pvTo_o+5zq^ONY5s7rk{E=l$lXY1^*=%2 zd6Hm)7~IA?_sQgi%xPZYA#na@BBlPbhT~vinKk){yKGOSK9dwJ9E(@zy&RTijd))t z0Jld<;^_0!O1|9*R)o);R#rZkuPZ+P?|cm~tNDQ%cd$&npqUnWI}m zc!`uoIG5d=r;|m^jOR|d`C$Yqt;nDnniT`10)@p2s z9abWR8G$Ag2pI(uF9idv<&LFo=wJzX#!X&J1bP|c1;)Oe4vki-IH=kH*-l6pF(E&R>VKnYr}-@f+(OU1K<*;5t#r)SDgYOKnl#~W z!9)l?4&ZVe*Q;|oKn@?t;|L!jA1}k_jtZ?0`Yu}J@*K#joEeVp58(@?VNnU;I&aA* z7BbTwws1hhN-}3cr$8hfnaR@4_Xlz=e1@=eJ zkZ1c}6XvOOLKX-?G+{sx$t!z2m4ncn_y3axkq{0vy+>vBub;$8m1cw+sH`1Fd;AF# z;KYBSGb&+9Y3v((iTF9h_ZwltJueEYPhdngNpPk~(th>GK)q8e@c2Gad+*VKa@(Km zk}Cjzw{PEdIu3u>xq9mz2miGN7J7D z7SZ7!zsJ_be46^uX}{3L+NcTk|@uEZ7)&2t5R*tolC9K(Pk%Wbzc7;(R+ekQD5t5&%}h|WsX zGIA94kC3c1E87ME$L(%x`M(7R8$A@Fue{eKXJ^r~C21o8wjwpQu|7&K+)57IvCwGE zARW7?D2%OO?1ScqPK*{AwPZSSLm|N`WPWN98V9oJH!nd@FA3&JW>Q5y*y&~B#Q<0| zUDQB9AW-1R?g=Cm=;37l$RjW@8JRC@JJOtb-ne7(fZ)!)TYG+WpbSllzCMaXEFjZY zIQ^Z}bjwT0BUpVK$1(yddN4gsC8;X$3$o>ejK;ga+pd30T&J^s|CsG|7Opau++Q71 z=i>vT*%1)<>+2g?_$J_sW&E0Y*1d@<>E{n|_16#L{;&GRG(FxQUkl)$wwe*g04B)> z66A^Zu^W)>iBkl!oocA5BNU_LQ%X)cyF0a~-L=!QpAud#qvmGorwQ zLnYNz=axuE`|3hb`mMl;x}XRae7SvGuOCda(lc20_JLKGXSDJS$G!iz*nMj@TG*IQ<2KXAO+WODJ%*X&+jGP{vX6?wgz#a9;@hX!QO%29O9jvj zSKA1N9WF4N#6UU@w8B-PWRD^0*>oPT_{U2S-GZ*!nr@Ar|GT*9YNs8*G-?$*pm(WXC(|aaX50e)PQj zj#YTqwseY4?#vjeN|DL*T)u!r2IcgvRR}&v~W}@A7;_d$fvbP z!Q6pqQ26k>F%4t+;wb1c4aH#f#=j^czj6RTJ%O&SPBr`JDc7A3kRay5lvH?go|R4y znp7cG+PxP}wkqY$j>L#&@=PJs9~(c&PT^!L}@P62f?N=YgA^!md6GUgv?Z?S8R>U3Ekt9c-$59MNim~ zkMhi@ampaRls`LRVJUbh;DwzdiUcNQlzo>q3Xb4bkbKaueqip;=DK&-FX-Bc!@sV- zvWa(FKgU4MzMJ1ACCbp^F)@ya>rYvL5%I?L+0#tQ2W;Y8wFH1L(A1*;VM&%W_K8Xh z>w5@qU?Au$!ww;sZ+Q`?H#m{J*U}oexriL>Ab$k z!}gGzu?Gowd-hR5lJs1?%}1J2y*1AzdKyMDdt?F6g{mZ6Re4xXH++9YFoW05j-Q(4 z(PGM?sXU-)as~xn`n4f@jbYtR>%#nk7dpPkYqTpfQiZIPVe9{Wqvh~!) zAaMhSyBpmu{+s7dqeWVFr?WPyC_)(ZdhN8u-d$l5)hHYIr|4n(Caj!wl++ZKK^ooN z)(03gqkN_DbL>mkD+o`_jEo{6mT7)Sm8DA{VE_HAfz89CUx!C?c{y5lt{vua2 z*+Qx6Tgyk9M^p(i=OGp8!ciBFu!n_-+Vps_-|l1^ja_6T_|nQJI7NB?B^ByrI9M?^Ee zD5Ryy(}k<=xqyPa?ttXO9lq-o#z6yq>+T3?E9K1@yDNpo#hfcFXghVa3+_Wyp@m9$ zInd(UhGKs*)Ppp-4IZBZ1e_1b?(mwz?eVa}KOLZwi`7}Gs;RCJ09UyphCZmsy ziHT`xNiCa6&h`Ucy2eG6y+YmnWxWr1@+{hN`<$QYhFsaRwYaWcyEb=Eg1epV3P*7q zu#0oo5emAMZMvs%5oRS8)&9Jju%5nXs-^i)K?V9e>iGShkJWcJrK{1Jyg&(!e=Y5D z7w|6qhG-x>#_=}RF$EZ5*3qS8`Zt2w^(vz@)E{U(l=oGF_y9{#u;Zksb^nX?@5n*f zwYn?l;)&iF$nE;p6)j{x!u@xDARz9HoxQz%QtMefQ6;g0vcfOPKhyhagU{#fp&$!c zSKn&Ih_8BdSrvR)?(4fZwH8C6*U{&~Hm}v@&EOUI`v6Io>(-JkqpS}#hiY7mPX)?K z*eXFmP6WBrispZ+b7khBfOSOMV!zJ_vaCJ?;G)TUd!B`GR$quLe9Gzp^NW=-@fns) zRsQWW?6{R-KM`b2j;DPQKc$g=U60eLz5^E=dqlg$Gdg0`a;pQ(;{F`32le_ zj6TwsI!ZXYN3NcxmJCcs0amds>o;A(K0bDFC{!+mTZ5b)V~@{H;bUY=(HJs@*+CW^ z!hs_sO2SKq8OjUHCd3nAae0L7IBNY0oYBmwHCLr;UD@?5T$kErut)fR$CZvAW+t0xEz+kbX$+oe7YR4#fg~DgJWD!Ar}^il+_}_+oDtE3&^`f@v!|U` z@urppyZ|KS0VNYEaB#g17|GYtKo_;v&O@kY(x5FNV__DcdN6BkINv3ZaVLPFGMbRr z-&uHDD~{v3$Ks`ygTE!K!lGzd1 zCqOL@rW`89evS*YY8+=#TNm%rB+qeWWYL^8@GtN}`aH5iD4m)sWcg+^ydgcldI~JVei#!5$1YA?V1Roud zdYs9&UMPi-TuNLa!u%YDVD5WS6KaVc@Q7(MrP*1KZpYJl&cKtDS$?mek>d{@@?=5! zF)C5Ti@aub>yf?~n1$3>U7V^H+~0JBEyneTBYmZedETR|-f#G7aFSv_9J-mCJqjIn zbul=MxSuJ}1u2=(9Q~h<$RNcSB{W*jVcb<0&LcB@@LX5vA_L#)<+=yq!^$zk%Syl- z#CVqA>eZzzZ1hq_sR>stc2J24R~U9bQ-Nr&zrQ{_Zm4rXSv^up!3FqB@&ObyrAK04 z_2vu|OvVk(gmg>c>Cl7UqJo2o?5jW?D274w%*AA)&T!IBQt`>ax;q1lO{QSxaM4Sl zaLVJ$=25gay|5z4Sl%I#r-+lQ%R6#xZBm9)$V0Wbjkkw^YCQ(RY-#-4Sb|wOw;7SC zh9*~<(;ZoP`thR{l6z=eV~Oqve7CXNxv?7&;2{N+;LJ!NHKkAf3j` z%X{z7f_n4Tr)xwf#}ID?b-%l3;pF5L=W`V~U8$ep!}g-iAsH5Z;^XCp*Y$@NI>5w zT!r+a+lM$J(Q`YhnqOG)C~ULy%%a)q!H|AU*67MjzhP-`V?Id z>!%1Es)YaFJ)3T0KAKmJjgs{zs}?619Ubzs%rcQnOG}r0WGD1FLdh|cUe1xSqOGm1 zzqXG~%n9D!jNYCSaW9EaY!QXKy^ZZWet5VM*EvakKJ5Mb_qsQ4#=UwaL2_PrEsVTvX%YOT@=L<{eL7pKhDk>p2Hx8mQIRpjC zq03d!Xsj5O`E&B|6OJmTrstcRn=J*+nuyDDWV|WSXtbS!!|?XFNm7WlC6}Zcejsu% z^TX#Hnwlc5oA8}*6$flhK@pUclvM7|%*Dku3B+IHmMTY^J3C`*=U)o+qJ?Xc898wj zkVDnwkoQal&zlOgmT8bCdPU7+4^<~DAkrH#!-1H><>lp*(PL)*B)CEvE=3`STeHHi zZdD&~B4-YzF+M}87J5-l7il#@&4fY~_(-0~svHX+*{-0YG94`6KjtSSA_~faeMZg9 zP~gM&sCjzd+;6jATunnGbiy(Gjb%Hng>S(M*Q1yi5{QH+VUnWWoqi|)*aH7f?Ks-j z_TqZ;rk0nt_nVm+7Ik&?nhD1;A(H_VsgMxx&y#FlBYV0v&i8|11Zf@=mYs)_` zD~lo`B4U(uOe@*-E!$43fj%$E%fml|MxmsqT>HXI)9&d@V5ez1A}`1h~5PRF}<=UV*^ zKY#vQ3PsT5e41lDAIfkY8FI+Ha0e#+ZjPqHMctV)|)m~E{CkI zJ5PhEB;V!BKryGETBt%MSqzGR9ic==JIl9@JRHWWY|P9iFC1>CgqdHxdPY%EapLP& zY0K5L>b$BBs6KQ<5Tpo|eF6T2M(#Ratm#nGxXMxfxe6w9KZqzp#5`(SpW zw(9&*h$>BCVPTEs8|=Nq!`t89KI-Z3H$2!*!MiuNI}g1U$(8GcjURHWeM~^P-wq1L#&#X z);l1(5cK$IY;L2YqZ0@SmsLb$xImliRQu!WOQ%j?KTXKWijIpr`}XbI{POZ^w5tsc z&dw?x9%8V5xsK4J_V)IA9V|b6?(|g*b>JniwcSN)G5hurmGMhQ`oJL56H?dI^^*tq zA?OEr#fn+eOBa$2%dZ!JTEL9oZjNFev+%36mtTQmHH?Z&E5D|O9F_sfH*REns@^66 z^O%s76x{M~x1+y*Eor_!yAu`)DR~_1?Bq;Lv4ev;%_ql)uU4y?P<9WGJX%^>BBP=@ z5c`fwmvCi=>6ws|6MNy>WA6E_1U%Hj_wNXmhhSEJ+0JTgv^*w(B_nJ>%`{?MoBGq^m0|RQ9yh67+a2>zYtBcbc z7#QT&)lns1)C{hx6UolW(SGvwyw#i*+jVK5kjBcGmGJ+*JpF|T_q54M(TOe#yu z%JiIdb#;yF?I_C1$^>C;WWF#sK2di^7gogW?Fv)PWGGfKv9URA>5=hq4hadmz*afh zKbxC#8yoR3R!f}P#{&9aGOnf^rK~qM%Z!bU35$xRGBYzfIyo`@d}b~Vo};3o!Wu$Y zv3`LBk)@4Ii5tV=yV&!0g)@u|43xmssIvGO=s)F`+aA%slA$4UZ#Hmct4rk0NABD8 zxmfB|rVVj3zMG_`CML$_=KYY(maC`v>VDRPd98Qk6Tb|w@$<(LoMp6hcNZaJ zl6vdCxj6Lx{k46Pk%NOZkVaen`SSxL7=)3MlKut3v8DC(j}RWJLthlZ2a`j>Z9%Qd zylJe!g%xRG(S+30uGv}3*RNj-3k&N>zA!3>-X?0FdkJgXBw8(V4Q=f^3JNF)Dc)%O z!E91F_9kabbY$iWmY}@61%1&riQ1n>T)8^ayZ-+E*>ZlsJeY9MlP^z8D{3UXc3$bF6W~q?!h%SP$ZQQNatjEkDl6mN^!|P>TiSE| zb?dsq@2#!S`1k>vZW4N7Tr4cCdmbLl`8S9PIL5cG>R}>JBuyKfOP)UE%F4PtmR{3V@tqIfaA$XNN9iePNk%+q$CXW z%J0xaD@Q6-r6>`Rs53P+m2{bwnZ7@)3Ha#8=jXQ@AMQFQS%XJ_|DJBk*#w>zdU|?w z=*WZwa$9TbUr?Q^@+jLocQ!#ptf;9;THNuoLeeC(sA?>OGA%7_9KA?57*u^(0hIGZ zZ5V*5CxAB~ElB=oOMf(dWyM}AODxve*_lz!SHh^m_zn1~k~VOHNFE_fFPKPlR8&P~ ziBht%IaC)lbo}2b)5`Mm^BcOA!_3pxCUa@sw}l5NUj!$GMmgHsv#aMyxCX=4SAJd| z3;28qkiorfXn5olhgph+zF%sGrd{QnMBMLRpelUTHa2F+pZpb}qNJo_VHtq> zh>422j#ruYrLKyIh%Ep3;W=?>)Bk9Dd~0WCajepGVr1kk81_$A&jmR+6zOpSkB^T( zynlaNU!Mxv{{|QhXf<0Kn<3k}-`nFBKN8p!jrH^jeX`@@$pGx!t($n|f8=wPUO4o{ zix-@|08%d*Wxh12oCID7MGPk8(qNu^K<>rlM+0()eCN-fN6=BFX~Q3YMx3L{nx3BG zAcNHdMzF4>C1-7IP3GZFMOi6ZZ+-ohcU@i2gw?KHyM6!um0n^avAEb+8!IdJfWN<0 z$HvD+zzWI9$^BeeIdGXdgR#pzZAt(jSLNm9r6eVvmzG8X`#Cm1gl7!HV`D0)`U%G) zIaNp`#Nx(#bdcI*%?BZQRk(@od*hwb;0qpBB)VD9aX`?l( zy7i!J#g)bP7Zwf-k(C`_zgCi(oPRq`&U=XqCqeiSNXK!{k zBZv^kJk`q4JMi3?)&!spLOnI*<enD z$4`S5PvqR)n9+7|aWPckq-S90gE9DY#+$IfsP?G}A=9tj-EL%!7a9>U4&(<6b#a$#{1u`Ck4dx9tl$CWiu&uahk z{r%A7WK35yZKwSUc3$2yE-l-S?KSM|xE~8x!~vfPEi^sa$%!9yqpCiv0dz9xe{cVy zu%G}7Y(bi@`E1AHQmdL&{dTw9qTBr1+W!7N$g{DKQ&EK=`UhaVCGIZ`&a}S!&Sz3j z$P(~Z2sH~t4(~u=V0LwtcoxG9`2OsPkysX~!&pUQ+4wn(15%v`b`B0*eSI9z5*;d1 zEq4ZIzO=G}F*j#I^wt5ba>&Rq!ieuaCm#Uo&!psJT?2zqm>f{cn{E|dRzYCowDROC zKlIzyNq%tHS{l0N_IgY8I^Y^GfzpOwL1Kb}nYqfo=@m-y{!)Tliv7>e&k-!Lh>e;F zIFZfH&K86n!^6V^=-dTs zfZ6%^5SSUb`1s$zqI<13{-|1N)YsP+y7Q4ShF%08DhqojS5rEmWpEIFG!TEGU}TH| zSZcYs*e?-qEc0!C9t(9|%(1=Mdy|}+IuwtXroFe92oyKr8NK$urwQ!8hE|4+9%2f5 z`bW@;x4{AeI_m&WyAp6_Ne9g=({J#sycNFC}E#wFw0r`<5V?QIT&h}-U0wBtU6aixSR_v zN_n{LOwt|+_a}eZH`tHzhKGlLvb*wWU?4u4R$viMQBS&X;R2EsPfty)WMpI%M<*1F zY#RYrO1V!Q*vm{IAt6TCW`HRr2}?Y{L)=GxKZeFrvp?kkINRIvVu)42Cm`5_8LAuG zILR{wZz43VUbV8cWHmQ8M^=%f=f$}1gqml&f^nUnpN9>Bi4C49KUn!upwjqme5Y^H;L^@4LF*f{{$iZ}Jur-XvqrVFpMzB_l;s$uT5!kZ}u@k@%Zx!4@j^e*muu sZobe#(*2j_gigsE|36?1&|F6VYSK?@;K9oV4itQ;D{CoLVJw6H2UgpixBvhE literal 0 HcmV?d00001 diff --git a/jupyter_execute/b45cdc82a1a5c002e3fce8ba4f386250feb595751b98f447d4e3e7805df7b2ae.png b/jupyter_execute/b45cdc82a1a5c002e3fce8ba4f386250feb595751b98f447d4e3e7805df7b2ae.png new file mode 100644 index 0000000000000000000000000000000000000000..ea7ea78a644600fccdc7e92a404be2a652425b0b GIT binary patch literal 19242 zcma*PcRben|37}&BeF-58I?VfO&6k2Mn&0MB+A}Iwv0*=Qei_`9J@(+esNd>%v6YKoFz#{b)o$bT?OSP%H;c7f za}7n+KIVQDIYoC`@MR8%JNj%H35HClbY&aQy3eNB!o;ebY{gTKMb7T9b-PE07x(u` z$&?jIYBVK2B^4D_8GH5s0rDSWw1H&E|ML?b;o#tK)F%^BQc{{IVYCPh4aM9N#=tSL zLuN7T@W;|gE?1l=D?Bn)`pLsFo}LZ6?A%z`6t8 zPe4WvbK^9VL8UttVPIXx6iEO|P*BjI+B;#T`%6Z(4+$BW(zR=hUMfmT_rGFE`?AF|8H?BvJ0@9vfL_V#L?q}>sz{y4nFL5$mDD;6FejzZ<>m%6%!DJk{bb9avE znsL_5>AlgNLHZEfo2sACa6+v|gsg0NWhLmhiRF&KN_ef#!w8hv59qZ22XlYNDrebD}j9?Hn8!WPXBIhF|_iKWt!hqa)Za|n%@9i`N-U&^&iMgtc zkp>l3pL?%g3o*;MQ#wtxH@S`agt-3vELMJ)!%+rJnaj65Jnkveu_aYA+CIEN&|~;W zD~CZdtyj_Nb#?nfsijY{eE#5PDViV>1#cK^US9vyXzJf$J?LD-dvHpuX}RWH-U}V z%8$i&K`6?w+H__8C}*`I<=&dJd}4iDk!7op?~Z$0f+*41;gB5jmZ;5|{cU+Z<7#Hz ze1p4nCbi3xt#`IoZQkA*q9-9C$>=Y;*)ITp{_>+_|5t|}hnyUXvG)?ei03T7%>K^G z6M4VtnXEVjkvBWP|xL5jh_!E5hszc?rONR_KE-|$eLP6P=Jw@Ix(&UrPp zhTa@qVOXi>k5gy$+P-}Gl8uXNGHM&PRA@v*11yTKlUFx{v-9$*%ojq)js$eb?=z>V zL#liZmo-K+otTt5Og4(93c(Dt*)*{`FHG# zydKGX{3_?>bIsGf3Nrc{{8x2#@lcxAvOg=spo9=vpx3`kAZvwCSZt9s=XYozlh_(AF zb{w3X7pojzt$NBrD9-gIl#SzEZR|_qvg9GRbtxJC(3n-G5XF|xT<1GNtmDZ!&~X&) z>k=JkFMC+$1JxL>qW!vu^K2jar=cygC4ws{f)HtXr}`s%rv`8C>uhYa+&r@CnI!4N zw6QvEx|Q2wEltgP^LqZ}f&k|Atvvfo7KaDCJNx5g&HWzY0>>=Moo51I6DRUl-2RF? zU$tSBSjzSAJ>SUf@mRcZ*5~tz6H`tb!kqzZXH)wXW;orpl1}pLm*ArwJ$m%~a^@v^ zdBIbs&{|ql0RaJRQ8$VmMn_il+jH6EzK^r(6uAFpwkF>1(C-$LIx(B1>)!eakByDe|PP@8&ZsrxmqoI~zgI zzc`&1%^@U23FFrUfvGc1g`zDvY7hD=sdzu#Q)6 z&nt!;KDPK#d~^&G%ZUV_hV={c4eoCzf>k55ukx$r*v7gT1?|%&izkni?P{1uKfZ=f zDb38!&e4C|Ge**FK51}#yd^qO_L8cqg0}VpQ8mXykWQLfSp6a)elt+WETUfJCe?zsYDCm-zl|W1MQE8($ZkZhD7N1nkZ)Zsa|$PvcU0rn1VWNZ$X( zS|iuEW-m(KVW@2V zSAEWJC)WMVZdHcKMm@&oo%B}@3U%f0s_*FkcH=@!3Nu>OfrXzvwE~cpt->lH4qdO_ zqioyR{F1`;((-u+^V>Gl0}q;+`2n~{-1&y@xltVwNEeo+1-Q)MTCuXR+l)ip(kGKv zljR!DQ+khb#Dq>S!iCSFg#DN&PN+oa`>os{p(-NPF^t2e(oBy}`~sHla8&ex{}M&5~ep`2Fu zE|W&`@zS2!MVI=@**GzG=W!~7ldlvYXMAtmHg&Hty3y4ZkDN1yfpg;H86f(HM?|p4 zx3b6e?H>ZP)zjCfz!SG@+*(?uZ;X%j*UM9e5P*w^mpDPk`)jh--MPWkfahwOi<7bU z=_(4Id@Ihk2^1D&!b(c6lYrMaVxPOr_Pujo;t(*dw(E8xK>O?D1R2clc=4ANZLM>` zA~!WPy_DM~x%{DLvpcxqoo5=;&j8wiGjCWwpWdho%Q4ecR_ZzJ;T(g5a&T}^I(H78 z)-|vyQ)LwW#=3(;M5O2f%helM0OSPX#os@hq}Mh55X|{b=X&5JcHaRhPMlk~be*$D zT_*uz4=1+}9YMvYrbA>eo{?-8JYj4+%Ka<-i{`V>@hklfW^m@o>{sV3)cm|fIC1## z?6%bbx&4GGXPR+F6J%7U+!cu<9Ik)*b^Hhk@zMrW1s?}TbS;}=5AlhW55jA^oLRYD zFRUxR4~SF=;>L}b#h+7}5Jpa)o=z80-xi{|S}fJ*LR=r>Hy?-^&G3+vq>9Z!R-nJJ zqpFbkidL4G=jtqTT`0u%yvJ0I07((tAtmUzIp z@`l9d#@e9>(KTwSurPFR7E85+FWsR%7ivNr#{LTOz4hCI?n|zCN=m8bG3@0L9U=uL zT~U0*xI(9@kL`^r%CbLPE}>OY%J)YHPgB@QI+L_k%aEhobB(=nnY{#GB2zCA2!A_# zhNZ2E*K6+(%3Y+8=CMp?vN$mphodmmH#(J7@<(T@WhqeZ+>*E*6bm2VStkNupzJw` zarYJ~R@`sy6QF7XObvxjaH&qb3JsmUg#48XALnNEp5PhgN@A4z%$~iw=k0r#t5j57 ztfn#SxF$lxo3r`d1CG-osIda``MS4vla=zrVM_#~gJqtLu$e3|T;Dt`N}nMsTNn?b$XG*v+=CGFE8n8$nA+q*WhDPGhzN?^Kt zhDF)vy4Ytzp2hcV_|7-Jo^ZXcq-0^OE$phNrx)JSbEUDRg_E5fCr7s+_)_MZ^7*J^ z(n5|C=NlUvO_nF#W7^uB9g8RI1>&-VepM-Acyp9P=vBSfo#lfWIa0Y*AF+wL%^RGI zQ$-4jp;CtyC}LjQbaO|nRqyY4Aut^&mb#!un0TmR2DKFC<3|_Yt_gYp+Tq5R8;4&! zPbH*!;k!APy?2A*B`q>Gj+B{139a#hoSd9^f|ZN7w4?;}4GpnRpN2#6sFz#ewP7F0 zBFpst{ri~r7L0P<65qzhmwrv9NLSc7;_dV2Hco6DPMfykFAoxH$kbRY09nD8S@q2H$_EEy6+yA z&O(-tcS>ifT<#{%a=}dvn7lU7!Vuq)QfHZYRa1T^_dhJaz(c7^6e6nW)KFtvwMIto z?z9nE$Sv05orIG2Q@Wa#me$?6KqB+i=`Q+?g6Qe#8yJhCV1_+u&nlMd*RQ*d=2tkm z8jA=#HeHKvdEQ4d-7QRflr=iACigXn1^Q54l%7qIg3JO2$ z`{Exy41_B3^i3-{t?PI02tRo6K*;6CmF}Vo54lY@7r&la(B*z8IYI8$<2sxyy5%TT z!6;hFeC~dq|J_*r*I$?1PgWW7HhzivZS<(Uv|DBZeWYFILf^fU$i4oC@aom8nAX+? zD7=O5d{c^W5fv2`hMP=GO{JBT&?la`Ro@iBumosJ3T9q-yThpEVdAWUx?{g)<-QVY z{9%ZA6O5?_zR8at6DD5@dGv@@{po3v3l}b&zj%=V1q*yEr=S`a70DznGE*y=lhNQh33m-J}bb?gthm?enm6EGwn%(40Ab z9N)&qM#s-}XYoB${_+# zIQ}?9@Vykf%3nTbP-A6}PPQFmYJ%{tWQAN2O1}PT$q+Y+2^7kRd5|u9<6O7_9k5>< zghw1t1+bYk<%GnqjUGa*05D4i!F6mV@8fH}KIGIzp>mo>BeHj; zLb2zHz_}!zf&M`$DfR2-y6my2f{W8(7nZ(a-%=n79juhudsJxO_(b8X1&)O_$)1C@ zVC3wu4ierm&exJ%hUr(_hFNo_lK8I}uLFR*vFht!jl|4Juh zhSiE=T>HqQ5fN{pJc=zBdv+v1P6WfKWb?c#ezI;#7-f7)MU++0?&%28onrw_0#Ge{ z0di3CBY7O*WqFB!(PiE)1NIgbjrDUKu6GYE9|@OF-L&G*sP+R)IIMI?-obm-0(ahq zh7EiD?{NLE`MP~3AA%b&r*vLein7`z$NFdKd~8In{}csdm$kEOt3G&}F7ie~@yM*Bx#2FU zmqS_Q5)%CTKGI}AElf2HOts-eye?;{5X7&mR_u03fYB7{p0#Ku#P4T`JKn$)- zT2u_LN&68`~(x_}VB6BcA5Jtq_5KRoX`LP1!+ETi-dc=Y=Veh@_{dLu5MV?JYx1P5s()&34*ZDreSyr1FSjFd5p;jt?n& zq41L|(<^`I{7eUOT5K4##rO8)L+RRKfkYH$+v`7(%yQ<|XN$s-yLd#4BVG%phl2^@2keg21y6t%K5)-oKz47g_AqkcafeCL_a~ zot=HH!j<2u?TKlLy}i8vK3Zru_Ps?+>*V5cQI%blkwZbeNr|kNL%-n!t?rB0se~gU zB;V>2^;a7%Z<~>OnT5#~9#GiJkDm2xtVzmZEUc|Lp}NS^D;D90g?<)|#^K@RrJ|%X z(c7!VqcCZSI>snwOKTx=oN&{k1Ajv2vH$V8aSm)1_}sz=)si&o{jF*}>WQw1vp^ zapPM4|K!JODK2ES7bE9}sj9r(&hT%nR$puQZyw%5{J*mBPTS)Gy<*bB!oumnHz!ag z_4h~ys=UOjH@_VM5*CxO`@upar1ERj=X~DoALJ5_ON1yB&G^SU#x*C_C++|HH3EIo zQd2dJjG`Vqpzbb8N@Bhj6!Ze95UW{wTH3Q|T~GK|mX`wz1l&~fEQa4#I$r7hLHY7g zno6R7h5TJuXXqpr3#orkrU)SzEx*T@sgV*4EUNgZcOa)qlu1vPR=Yr=y?N3{x3d<+ zFSSP=`-g@$j;~%;L&ZFP91ILO2NxHCb~Y^#5-PT~JSaA9ZbDw2+}eRcb7X~KEJP5l z_V28MVPw$^{kUcSh1eu{$Wx76sxd&-qV@Ei_NE+@_x-XuYGh>eMAZ5);KT9UPtyWC zH{VLc>K=YRuN(^khS@iMQVe&9lj;j`;%VA%A+BiEgU#2%rg%p9l^}%aG#vKTyTB0}YCsOP(?0S;!uRN}He3*spgvm-QGI25 zC*w6WYM}szB|iimyw}-LDo`;$B86h#lf&+03rMSbF*e@n#~r9I92_)%9=q2o`Ed>s zdU;xs867FF?HK>VREHK5?7$Skc)2}#W=Mt#U0}oyDpg0KXeggBrt{eD!dvPBB-r#r zQK16`7jt`+i+;fQsAw2G>~G0!yee1o4x?2iJ>Qa0Rxrb{IWQYM<95|}9Exml%O&_Q zvQM!;Z9X}U9T@@0kbGDAZrtH0U`7#bQNzeVU3=)Cvs}(ec%;areC}yx9IWalsJjTd zcjMgk!a_q0m;=cY-U}8e+lP^AlXMn{I?y7od>5n zgddq;1vh9$@Uq;5fk<(Wg9_6+7#KOiBO;QQuZ01V;5#A$g&HmLB3L4d2G;JbRH?r~ zb2K3gp`5RNEXl!zApE2J|DGwh87+uWF&^1uOvyN=IMt!VYYf;6c`i|#XF`a-R_=TX zqAc6P_d>37*Z2(HQ5qS2p9GBr4?>@)euhqtu*b4Ru0h$S1TJxp(iEKs=Z|A&_KJKe zegY^_Nbw@!zR0Gks=63ovqOLYVbHeP`i9=!NgEvhIaE6Svvd>-L$Bwn2z_L2!0r!a zhzCISL`FuE?W}fdfH+4?L4j;x2(mY+EbQYG6C>ctUqK#IX?@IpYox%az(IY`?aQ;O zQr_SPuk@fy#Ye5q()MKa_dMD%6a2g0s6E<0waWG|Ir)BV?b)}sy&v8}DThlyK#-N2 zt916PfA3V@wL)AZYIu8l2d+av?|nWMR%~c3<(|%j>MSf{$T!*)%>Qhfc=3hcdh>6= z_MqJ1i6bQqs<%s6D%8s>VX}x~kqrmXj%aV>BY%YOz?6Njzd|@;$)p8NR&D3B>-2Di zep*TIKOgy^wqZ-V!lPwqc&nssbD*Q6`p4fBhw~@&&MsP3f0% zo&WLalYK$c@b)+2-$o!zHhX-vwc!5_+4Qrs&QeHS!6@yTE84osDA~kAtJ71f&wXw* z3itv@z!o%6^g`*)As}%0W?wG)>Q%bs<>lADyPoPv5?w#GrrMJgZ{6ZIGdE8*$hJO@ zgs?_8vVi);_s;;0Mx3Pi21^T_+072o`62g3poyuqFJHz%0mr@hBd0)p_WR`I3y+oW zi%UyXX+7K>?tWa@(EcU%JgkBX+h?B$c zW45BN=e`iWw(kMMlC@T6w_yiVaX}~!zUmmv`c#Q+Jc~n+N(#s;{2pW$`ltgDdyjA?i zK%eMi(!sJ`>tn#4zug@4+ut^>pgkAmCXq=Cg#}QFSkeI5r-X%)n99Aa*{H0*q&DY4 zF%4dz2#>yz#H{`tVnAs^NnBYe^Tnw{wsXdLaeckS>&}G4%pf)&eM$LfZgGW&j-KzD z|Gj(oGV258@0@3-gUOEufB9md(xY-X<{yc~RwyyBZMLB1aIxNf5S0}K;yD2?0Ve@4 zqnPstCy~&|`%H@7*4Gf$*C0=xb{Ju%4#RgWV+K*79+YHeEmleY zJ9~^qt{0V*=G(t>ih$~VnguXggyqEN|70U~eOwr82N*Zs-7V9}4{=%f`N2xs;z#F3 z_xIeo*-RX70o2sW_*C1WAN*t;Y7tmkJtyJ#cq2~|Nl8i2RVV;ZhCO-0Xkle#_O(7J zDl2-f&0R0sc#1xz)jfwFd;REGo32y>WFvqNfn8pn706%&(^!Zkz`IFr|H4JWUus6f zcQ#QA3?-uFfX2}Tp<^NRDF$u(*3-g(2M=`|aY<;=n5>iqfv65^!+fODgaz$zXOon1lKdM+fc=!B!;;!_5Q#a*8nXsLx=LBx6{<|=8@FFOB5OrgK@qpEE07YV1py6B3<4LJB??LH|N6USJcbuQ?;OK@}-v zQEJijj2-C$CG@%GNw)Y_Qz1qZA;!7u41=+3#OKA^52AI?y#a3}D-bb}OFidq7{3f<52tc3q-|GGP*J*$otzxze3wUqg#Tl_7&tWasDCT zQu^}rLv9U~3>Hh+R{kD5a1LUh?itotkhz#M)Dz4~>^?j?CE>SMIULThE!_THnoR^> z`;>#)StX^55g*aYpeDb5-D!8-)Rg_HxPyIZD#kZ*-28$2pcqoO!T@NcDVw$@Ng@L$ zU{o23M|5~>&^o0lODhd|UI`E$Fw%;q_mWw5Q2&rGhqJ7{+MNIYJ1WYtLX?Ss!v3U!Cbj*0S;bmhKfD9S$IZ zs7YFk-f>{z%|*j14gM;KN66&EG{*vX{+KAvdHVDt_U51?3k8ixQtr9ZD~*{OsXCe{ z?x3uv#{{#2#0QnWbN3$|gdAW+wp898V5*QTx0o`I74Fp3R21sYojdP*cgq{XQ=3JR zP*d@pcrh*l0gkceJ<^!->HGSnALlf0;|j0g8QEpZ7e=X z8+F!<_F!mrt0?lI+?@MT*1dWyr}wEWq5sl&%TP9-ZKP;W9M~SlruY@rx6fk1qT>@! zqJ)?j%9rWs2`oee1PQW_&mUhqj1;i{KsTK~PUQgP$Th5>XAm$rd-f~}W;q};#kaiL z((CxRO}cNg{#k)H9_@;-dP=nx*xvZ<`&RsfO<8uM_Z8h?7MV~efOaMY{pvC z8lagya9wH4T<|eY=?+wPbv*+TT-U3syxqWH5!2GrJ})Yw#=*hKn9RJCTFq`}MeeuPu*@E%SrEz!I*;W@^ z_6Xwg$Hzxlw}1Ug;NMtZS5{Reov&DsL!C{L^AWQDA~82y(f^%|^Wl5uJ_PnhsU)V zUs>);NjD1TIiqC)7HalcjD3G`fosajiW{s=L3hTRB`fSoO)B-Wy=GDOfWyM>lJPK+ z_vc~Hfg?{_TU*HH)A8e6TuA6dA-*n%$a97Z!Rx0^oicm$ zb2-wj;?g&4&B(4)X7g#`M1RFF?3s@&6f81~z{a>LU$?(ViT~1gY!|6q-rWA&&&`o} zEvP=6$-VU@knY#8@gyk#oe}mMpi3JF3&n^EVNO%cU<(ruHo^q%vpM2px9x{-kv!l~ z#8#vj4@Qfp{(p<5Zhp8(cBy0Q#%Hc{8C>(nP@Yf!_C+5GrSoKDA{YJdc&k%m%NAg* z#=rkjWBcRz!2MQ)Fz?D5B-6n@;Y_-uSizQ#tw|;60wWUv%R!EP&uH-_X=zVYCJ#!_ zXQAlk`;!NRUBZ=sJt}^z;Sn&|gN{t3PS^TqR+UK&QRK zIWyRV^4~m+UW{v1LYRc}-ee;7$CFFB*npOYY2beAiFbHvq?{jZk%j36N}-Gpu8tJN zAW7q&nw;+9K8sx@2DtD5LPoB#|H+5=FlprDP*7t-okcy{IN|S;8fp*kL3nuT5hxIy zry41K20X&1+X)dES(Nn(yh;(gJvk%M)-u9Y#!3<%VTk`{+KJ~-jXA~7e-27TP}*Vv ziGc4+F|+%+p&JmAp;1xF>gtg*9UAhx6BFT0=9T>D0@sh=lVZREg8pw@&6l8hiO|W@ z`#M$^c=FV#I^(@%2D{I1Nx)X~`q~B{i-zrmn&@hu?QqTP&BISG@!mOO43K`*$up+x zUpUFUb20B*TNTfpBY^b%OnE`KpkLhcXdfcijkKFkF1F^~t;Ulkl6E#;{}x&O?&x|W z*kX@uBZTC{DcS6WS-AH5_I70x6E?kKTT4A)z3+mT2ub8@V}9KW)|Qra9UTky7mYE_n6^(|xUPF`yhBZfIrn)%UC6J~FHiTW&o(6kjD-Bsd@bTOchw z;Q~Pvsta1uph1yw^J2YQ^cJU?$UQga??XMt&cj0rfVK|s#@N(U2#mvM#;)=|1NO)~ zMxzX+D$K@#zf>rEcrc_&GEHzk__rJVYs6-S6xkfj>fo95Ms_|LRovo{gqIjVGh1p!cbm?$?eSK%{b=U!PoM>n=1`X>4jTg$SSZ>Xm|# z5ldfzv4hJOH7IgnRRoDzx7V5oje(u1aelZ0VLz(qjCp%s^5cB>w3%S%tqj45W z4Y0PIgjMVAE@9;LQw&t@2(6OK!*YoQN*q)NEA#k2Xhz4IYTM6@Q++_bIkJ+82#h_Sc}sl|bO-_?F@M&y z53tA-!%Zf_A^jKmwtD!0eB&ssXX6z46C;ifp@AnkchN1BEc56I1$bf8$ptz;>eapdCT)O81-DIC0UGF}|~*>Kxc%5n~LD zBz6qHdNMcbZfQKNk2FEV!LXRcP@z3thwZ9;qzLPD1!%A@h?V?4C#DhL&N(7hv*TLug5v5KN5@J|L|~(EJ-4YLFWs_pv=n1D0(k9%r#VRE_Br?wntHN26UqgGiPO8y=7ufL4y4vf^!R&sGEMgqIMynJ^Nn5o+m z#VG`9c0@h{zMn=yxwsVB@TG!&PF~)ek)hqOITBPH+5^(%Ce`|`k*lPXp zDS~sq7WnPvT-=U(Z*kb8VfKw-AiDMi6z7aR#g-5E#*Rq78l8{)EoB_!rr$&&Ufujg zVDbK>9qp>XI-Dfs^1<#pNM%ca`8n9x>wuR2{D%~>v81rDcu*7CiYS%sNb&QNOC=u$ zfc)gN4K?s9F;Ab8qC&&M>U52LxRkFSc3SM~Ro#^$QWO|xz6U#K{o5+12#OM%tI!RR zBQNc{vw1AVZT^d-O5B9z3`01mrjmZJ6JiaQ7X1VN+~+Zkzs!+JJRIFz_s^Z)^HK;S zG4S)vFV*AJSnOu2$7sRa2Am>xYs>*2z5Az-f(Q>(k^rsncD-lq`+ui&p0W@gAyPd+ zd?66;DgU<7B_YZLPSECPUem&J5InWzN=lDq`MJZ;&FD{WX029!f~%(5aij-CP7D=1 zz=U6B9H>e6AUVvyMo{~b) z&FzsJy5F#Pw#7WAFb%v02OFqUJ~apn{>41tc#FToxzr38y(k2t0m_H@Yp-Q9Kk zvUu=IJ^b)#PgG18fP7ymX5(<1xd(K z$9jH`<&re;i-i$SptAMZYyN^93cCjvGlz@S{WinP%;nm6D{6Jo097?nL?AcZg0dB7p4oGYBHpkeo zv6$t;zr zbH}XyMcH4h={^mDG5FPHW@es9I&tHpYt;m+?mgz$|BS?Nhxo1gW(|pH)5=`h;5dC%%~gLwI}`UvVYhGvah6;mw!g2rIeJE-YIu4FTi3O3(*izO(E_9r--%CttZZ& z!-nzJ`5_lKtmE(Zn&SV8+HQl6zr$r?pS7!~+SGFq>a9=#096DulRCE(=IDGDm<+L< zdw&@w5T3{9wZp%&9roZ8k=tFp3_O0liIBtz}?aO0j0kzmEeLM;MJ$mb`W@ z9j_nmyY0kuKf|aL2P7wsej4bPbZ}UZ-m831bR@72m3)aP`wA(P`Pgl8g(83r%8J_7 z)b*iY324qJLGDaa3m?tS;h=8GCAS)w-5b0V`S10?_-CYQt z*+=p5;~)h+K=8%zx{>% zuRYn9Idpy)^ldE>7M!Rc|c#$MIZ^FdjfBw$d^u!NCQjAoHl%2oA72=YpI;l1_J8jmmWpOOJv zk;cZxZuZyg1(`MkvkMDT6;!P=DJUv730yplMx&pcxy6K(uS(GKyEs-iJy6IAgU0P@ zb14prd1TVkl<$k^KLgoaz_9!Ye?(+tBgkaQ*Pq>(?}?}4R31x@HxiM}&(5v`v(GeD zjoU&G4NAg$QwZt&nrw}a&`g*$iLt>)Q!X|ibr=g_^{oX!*(9KHnlKOtabNF&Zkq=7 zDYN)-5gs0%+knjw-?9n+5Q4^P!Y}IxtU-G8XFrU?NTQ$CDlSD9}AKhz`8;; zr&pbwn$QJ(oliIW3jIDM^mzp7<+Vw<)nlP*W9;Y8``MQ>!wtjLghyO` z!Q2I!oyz(1#NdpJ0R87IxZ@0xC7=;YdTywc;&O)iUFeSj6=ktaw8I3t&dlP!Q-jOg z*4DP|ac@tL`wl~#7P#G5JSM0>6R{gCB7hZT7Jr-+M(JCGpc(A;cN-&KFiOsT%s%g5 z0c>CFA5|{@({~3lQzr7XKXqL^RM~;xdNag7qVr6AT z2^m^`%@$7<H;cQ%4|N69y$c;=Po!Qz9qYj;An0{&qCjy$!N8YI}D6cerK7xQnNC$LBEBaKtjjH zsNd+gA{oki<)b*_3PiRev#cjA^o2GY9wiG)6g>YCa9u!jLUaWo=V{I8 z-FOQ#GY;5HNG+fM$q-sp_cvSQgKFhG;l)t7TIl=g-&azn2;suWe|$PXkk}k-f|ce6cb>p zpq*6_;TS2peoi<_%56<^g3TgG~yf#GUT1a@WE#6bF&h~4}eNtLO-X^o#6e5>ZjgxiT6Lwdd z3EblWnJnYr7UC#v;rufXK+gN`-v573AVep5c$S|e%2Ff!Jed0~KR?rH$gVwVs!DX7 z8@LNWT*4v+oSk(LOqq**R+M?J@xZDMhx#_WXn%_bh|*9hPE8#h!sqr@dhH5o6O53L z?B{Fm;thb)XTY-a7P4Zan%ka=MJ!Oo1`1&)%gSIQd;_yYV?oWH?b=jdlv(9kpZ*gm z7asreG;vt^bvFyDf+2~e8rS%4LONQ6lJOAu7Lb-<=xJ;#@qlJ(nbl6k>7i1=XWq*f zV7I?teS<7<7-l4^IXa4rxQ~-}A1iQ!*9c+pB!>1?aP`OlRJ7DuK58Et6%hGG#1 zSmC#W>o_LB05eKCpClw>^ygB0j3_r}hmZMBeM}4i#|7y0lp1osj)ShJ-ZAvXngTJp zLK6V;7?5d$EvWPA!>K!=nDR6 z;D82S5VgrsjIy4m5jg$E7th5l*l+qp2(l9(IGS{4T+EP86V!W44C+|}^c*1459sW$ z<(pQ&fYla}zR!a6Ghegs3rf()=b=jzlK6ml49lIV4ztSffg-QXDC7PbQIO$l4_{%^ zGJtP^08M!h{3eL{4}9md84u_Ys5g(6wbCnvpXwpZo`n8b)7ic}g^l^)pkFIfhv0(- zV03uY35HX4PEJKieDwKCm*@$}nT{vPdg)|kXHx-S<_4>UL8onT0HA5OO%OEkk%)<(i z9XMU}7=%*=;7RFVQwPCgU2~sFkB4k<4-BD~p9q@=YZ@BzL$@I*wDHh_A%TlHfDLJ2 zQ7|w_iiwS7hxlj;!>$0kGzfxI;(FqvN5>5+TrXy2Wl@TViUvKC^&*8k(ZV$$ecS^i zjbv*Y2wXw1S9Le{wil4WDk>5&1>wIQ#?PU6s&r!;*dYbTn)kr`L#&slvk7?T9y*Ya z1p0?xun`BmzWr6ZlHQ3>Wo=GVkSKnjSw z0c#^RCB;F{4?1?HKNs_YferDE7*u)EA+j#S8RGlOfiQ;+U><-TrDkv$g}}d`KI4sV z6$X72DvE~w{{Gx3SP}@9n)#GYOimtDP#}l^5Ew!T?X_=5;87^yGY%lE%WTiR87RKV z0A-=KkI$X;x$7Xz)ASb@w}7W(G1_kj4~6Y5*6`b+frr9_US0quWJg5sLBGznsR$$_ zR{#o)l9JLN7@W8E!%SWq^8_fwX#mwBYzkTAMliG{m>728?}yU@FfZY_~-|85#b?W>-w=~PF2LG3vJ`Dv+@nfa8Z!Ro1i$nGu_D!kZ24%z~B@>W@D>Hot}}0aX45cCBXQOC7K;7|$9q4uP2)skq>K3-A>S z0%%WKTrS@(=mETfv`*VyuWp3C~SuRnD2VP z_m7FAE~82pp#@QkrmNb=D;I3zg4LTGES}Ts-Hr+BgechK8J6PqUxd8!g&TWxwX|Yq zYI0t^Lc$F4r3)b0z~|Wlo78-L1@4beL^SGR44Z&JQ@ouUG^MkK1JK(hwCz~qyDJ5T z2ArPzINCgM$chJiC=8}8^~5veurV7?q<9B_5cPHOYXkA2Lk(aZSORZFLdj)KO%vBE z(;LOI(9n*ZLy*-8AdZXP_Zh5k6O`NAG`AZnAqOmuLLo0O)sa$ja{*rYrc}8ZR2$ao zTk|g=&?7DJL!*0ys7)wlB4C@054(*v)dw9~*z!)z2+e#P`ZNYH?SNf=bM8%tdMk8Z z-Gw1C?|3E)lnfZu&m3YRUjmB;o47aw^t@r6{B8?T5I-0obAg)+@*yY8H#8CyLR2^?--k+gc#s)Egd|gI3N;0~0_UBv%v)TIk#%hrEU@CEZ5@`v|rk-}NGVfIH&H z*4}|Z=cV>3*tYD%xV~F6ClE^wExnJoH>&nD@h?}zT(q~lUFf!81W^WLKf}v0k z(9C~kjZ+Ou5VbagXM*od!rW z7q`@1O9EyvBZy%T=7I`~ zAU2X&k{ZFKt%5a?(B6oAHwVg=r-QvpeaN`MK6@f@C)FmQumm}GcWbRdes76F26Q^4 z{~V?bQCIB-gw!EE&t0KMt^Vje5MvrwD;9wqiX4EXv5IPoNG( zk~lP$rU8(F`{`9}tM&ApzZV=yhH#*cI5HgngLn9=u>XGrbb0#}bWXL?8$%i=;k#9+ N3+FB=+BHll(v6gKgGh%c-QC?tr*wk~2$CXfx{=&;cL<1dZc;)JX^@tFX8S+q z+~;{dzxTKI1Dn{h=QnfB%oS^`;e(orEEYN$IsyU$mb{#l1_Hu^HUtF3E>slom*PjR zOyJvd4{1FQO&4nqZ*w;*1Z8uN*N!e8j&>H*URG}Ib}r65&jg-vJ*Bqw@ObSm#KGb8 zp9ard+-x`&(NV)eCupzb^xY8S&N4USIz8$? zCzT-&xb5}wzkA#r9>B?{^wF}4XE+x5K{PR=idWFwyVQ|4M+epysi83=?LUsD7{b?RY&Bde$nOmP#(tr$s|zb?DZM%i zct+Ycn=_VF&)UDHwDkC~}fI_Vb$e38E(!O{gBu+3@pX z8&n!8OoJ8~+pgW~m3xtn``g3yxZoQ)1hejU51a1quB+`~ti~(Z{7z(V2BL~fO7J|> zLUA=XLnUa2vxNN>I`DVM;0D=Ab%giTYjC+7xe)G!kF;IlZw^@ zArEiwSD@Fl71hFoXVpk*PEfv8w}cbRFV73v7sPF$~Zd8IMv%1gy{30exM=x z1NaDOXmjCx+fWC1D5$F9ltQ8ECML0!f%^_~^$x_p#481@ z;KP!Tk`R_er5Vj(j+djCq7?f?e|mOi?&Cwk(e@iD=ywR!s&3~M#Bp5Fna}ckZypsT zq|BQs6c+m<0rt%d{HC|6j{-ly0RKd`mx`*^7Z}?)jjh4 zZ=Cy&XY($#6 zS&wDK@Ysy5%8Fj$E&Hrv-R#+j68P%q5oy$rSQL4?YnH4oP5A1+#q|^_cz3 zSkO6-kta->7Dw@awlz8lScIbNAcLO=X#ZpNfiLHjCV$QU&r5{VpF|mn3^`6#bLyuw zgyho7R-|Omfa0_3<3Iz@M6O$sLK&6j^T%AuI&73Sxo%erp9kI@uMw`Xag=VId==>^ z4Qo+rcUVt+tl`bg|EKxmB;HT&Xh|S74^R=>e0>nS+g~Ge7bh0+(1C{N3KuU!fekvsMV-CHKzhM0N4NXNnV}ns!#um&?mTV(nw@KS^wY|+qWhN=yy~qjG8KTlShaE?K{1S>-`H~wqsV3C8m8MRXPU%zA{)*6Ppro-Hc z%AypC($?U-6sgG3PB>MlEUBqUP|tQgmL9xcpvls5EzU#0oqN;*HZ$?`=}R#x`8Sn{q8xO9sT_j(uu6M5=l*PG70T|Z<+!bk!Y zPb_zvOrq?a7Y5B$PL?Yr+=h(OAEao_``_Y!cmNNnm{?fmD;yyURKs zg@{j2Pe+LKwc$l8kf16G5|H=RMREfl#_WMcBk@mQI%_l1&Q}BRMF-5USX@|Ac-<1% z^$5k!GsX$A5^f&R%K_DK7%U7P%4@Pe^sh(suWR08he@Oe`}0GPK?3oB0uLTq@zBmN z&oF61BV;No@D_U51}MD0zfCp`B6aDwEVQV?Ko}ev0>Rd~p9Q#O7PVpruqF|qe(!N5 zGTx5A(WQ;=f`7oc)$2!$z_OUTd+oO#yu5TvA$Ml}$h1K)aIVZ$uoM%>p3GqTNxT&r zTLN)xDd>9N=k9W|+W*2?DV-lH_-ZN}0inLWzPPN6(AQRi@HyAZcP~r@-vqK~8YDIH zu2u6zaYAFq7{IdRJ@`*ox2XF+w6(P8uP@+hx;s-obtB@BfD1*FVQ`$A&t$Co1YcF) zuGv4#?HZ%4Mj}>|55gRMR8c3wjfGCNnBV6{gsM4W@<9rUFRnTB3%{HOeGpbFwC?ej z_?bhs`s8O2213KuOAAcd)892ER3bDjq^OED5*P@}FJHcSTV-kRdi!s;U|0g?;n@4+ z(W?i>!gDJx1kW1J#gH4yocl7~vua*5hT;~9iFQ3_zI}{i+=zQfN&e|JR$C}8qDT-P zRWkn_-DdwrOh!%?!b86w2=CNcipJp4P({2BTbL-)>R~HF4%8d>VsEFrL~VHb4vnT4 zZ5-z{wtZ(`;nJgt`HJr$-^)hn&b$6QC{~1J!48c?WWmA-KAaHpV5SBAeDXNMwqTZ6 zl0$VBJw?t4S#YU7{Ed(_TA_JUp`R(c_ZA*=|L@3|uBZrWy<+!+WPtUoCOZ=H7@qb5 z-T@i@`5BkoP$HqYIb>v$&p9A}_&t{!J8gc%Om zJAqIx05^*5?c?l{O2WdE2;p%z>WBShEeei?t%S(tXf_u4DLU%!zuG$#=F#hRu4twF ziZ?TdD{@b1ZF8vfVbmecY}9&le&j60tMYHQ%yB4JRmV<*xnR6xO5D&=q4r z=64wy^u~Ss#4uvgx0f;!9~|Blrd%mzLJ21)_IKUX_V)J2fkL5Mj3f|`@&{YDPr!j} zPQyAxqZwWMpS?v?P+WP;HMc>_tXca*n8po0yc_oK;U>+4pJXK5s97*2tJSBql5(pR zOh_1Yk?a1M-?Bud2#b{Uv=(QVH0sgIQ{+8q<-_B~-_J08@bVG}dgz)?!jSFq9g_cj zIx4v1Y-E64l-07~zVBST-LR=DoR~%kxap@fXb3KplwHOgPsBJ`H@4y4y$Njx?qPdH zioG$&LDe(SEv7e!|4IJu*5-T&KWa|Js-Pk6b;;-rTFNO9(grK=aTi$<~ zZ;MyXVfB)L=KoI`u^n%X_e!fiRvvWwQT~`~)aU`Nr|3R9h|W1}w0TF7OJdR8Z*@f+iKh)b^RbU`kIv_VvjVJgqHfP_`E2wr|e zIO*4{NJd{qEo=4vB!RV>2)+^EnRIcd$ls|uRhSuXeRQV-B2k}$a0ZUl&YhYgVUQ`B z&|-SEhZn`#D}rKXwr>AG$+-#7L@RQeTxJSJ_hm1~e&C_)E~X(OT>C#N zx?6s1+jZ4o{Xmu;L0cz--<2BECzBx=9}W7oSg7P_2;b?!+fE{y&*Rnp8O@N0>DzjU zm5?7!=Kt(u)e7UtGzFVv{{=)4)<(PJgVb_YA79#>`RXJmo9~O>KF-`9*>ZG{ z32;Q(DCpY~5s8j@(Rwua-iu8&TM+ZtVJ5>YJ{lW%V!?l&7^-A3E9efzE4hXaDjp5r z<=e5%b^VMyuf2|Y9FUE_qb89NqIr`e(anCW+Uv^?&X=RJFc?uE`&fI(V$dH-XWs(~ z$Cbq>EBD>YGT)3Z4FMY8WIu#z4suJy*u&Bzcao+Jn7XQ4z}9iL2bEKYHQw@{E#9<3 zh$@643%##uEpNNf(npa0{XvJhqaNu;c00`eublZ`zeKr`PUnUSJ`xW4m=-5?Pa^=# zDwR>B8Q}bhisZKf4HW~v3iA-Ix@4sQ5sba0UghmAHaOSw;WOsLuDwc3@SM=|=HZZR)ngoFlV@K`_lhmCZb2HEFJr%T zR+!hqP(LHDcWVgF>VS;pd9~aUQ;e!52)}!W6rsE#=C++CZ^7`ra~PS@3n4b(4R{S4 zjNbSAo2rP}xh{%xe6aI#)iuhwy5$1B`Ed^Y*^GI?Nn7LEWvyu=4V@5mG})`3tpoBe z@l)pgygGH{&|o%jLbfBE_sAn-z8tc3zlR{c3_Rg}c?MJb>qe`%5jrb-we;l$MG9Ml z^QxD2)xz=Du%`n;|D^b>-kFBWJXuK*rfQPD+t_`%?chgj?bA#M)r}}GbC^kwEWL17 z**Vz=MM*6a2l3lAVpOD}{Hf-Ct+u9Jn^6xt1KVXNn9ycEa;KG zrK`#@-v?0bZ)iyIp4OID=)16%W{o{B8Jqp^1MmCgI*hd_>-v90E|{O9`Bp zvvTzDT*nQb&$A-^?p0RAJY1~Mz-4-Rrzeht5P2NcrGwo!Y&=~=*m zT(Dpf#4A_%eely2%Ri&U*k-MW=HFlsKz>7+cjjPgsZ~9uqkBj#L6@JRu4!(&;uUQo z`uycP0TH2?Lc0wR-prVVB3CGGALyF!Qt%k(v=;lLxr*4=IQB_Txq>gNMq!U7iGZVl zz{qRugcWPc5JM!ZFxLBNeCAjz8NNW}Hmi^KnvU-K%~9v-dt7}MYwirvlfZPeL+DfcAx5_axo>J>HuQVXgoA%1o8tf1`|1E-vEwN zG&&na|966m`DkKtl%4g*9FzLq^VhWOwzH3I#oS*J-E`o~?mpWwk@y~x6i3~Ng^)B- zKK6VF7VU_uj!64XA}QFu9KFut_;*C;NaYjOChFW|v#z1O02h}^M|mk%b-adbiUEwD z9BJUtYVW)IUB^c}UekHOoS_C{#i(>;5T693Qp&T{^i}I)TF<-J9)yeMlu7LZ2vr+b zI{$idSXx|8-H2QgRDGU04@rtJ>?zzdb$(y^WSP*p<+d0|e!Hm~6!gQ}MX6xd?$eoNwOcu1zU!Q-6vFdzS5 zB@j2tX4ZUZW3veIN~G$Vx+8GiLUWTc{wv`S*Tn@kE$wrEbb3z?n&p1E9{2p3?j3a8 z_!LO6%&ibpqJJra8rWkpvWod-RPz&)5se~Av%V%c_Ms z-cmG6_D5C$v~OXm?FH9D&*cHiobh2RM=rAy^L`tHIF}@R>PbPY7#)95?nBR@_2Xbp z+8;yN>2xm;-2x4HQ0RVn1WA$UE>XhfvRuGsV!?EC@^RMUk9eluh|hC8R&2VNm}L$M zS0lbkwCqg%1X%h>Tv5+lOA8UG4+f6;sQyeM8MT%xx^~hM%V*#+pQ3~9PuMQOMg>jP zv4L#smvA}s+N1px3MM_!n@2*>4^-qYv+d>(-5c8$+E4o|4e&1ksYlYl!3UHUK7ksD zhIu+A=e3%rU7`c%rCb zI}p!WcU+xT!UTP)3r1ixrxnBCzT;}H>6!GVtb$qEKS%t$b^k4jmM(L@SN#JwPN8O6 zk&Bn275cmwsw-`ns)1+taKC4?Bt%d>;d;|kDKfe(l#uF>+L;SUr~^67&sdXN`$WxL zl7nSfh`$#_?|(@>&go!+91m@r?y zZ%M|b4OgH4ScVYalu|&|>i5WXMe$KlK)aG@ZLUI@ zaE?cXK@iIT)@EB6J=1B@6^r!AZ3n(%i?s+N{)Y@d)omIoeXICCBs2AVbZ_@C%RHS% zB6=a$kz4H78Tz^(IG){1Yn`s>8blwg_xSu|qWUItUtcjL7l#NOzr_U9G=`fno-jni zxIKgcnp|Z$~nwc_wV5s3y+e#Jf{O%V}o6YR(q{XOYVmoH?{qRJg+%V{m0#OVn&~v6$w0f*A71ThBaqd3#@%;I zg7*y#p|4N6bY;ptv-z9^pyJ#gzTG{h4WZ3V@l#W9XeFzMi2UQHM|U)9@^_i-*+#Zk z0NzRxE$36eN#CN8(Hbj;JkuzUzwm=0Mjo6H8n>UU{c^X+nZUKIoQ^H^ivMP~?B&G|y$R6G%W_xwb<{NnmGSSTymEdzB; zC)v2@;e}sCE++4bd9!qqGb7W~U~C^Zc1jkJvEW&pEw@N!&kmieV{SrS5n$WsOoKXfx0;RBI3atCT9Z+`+JZ+@pH3@ z6>2A%&wuxmMQM%RQUSXMK2ofX`DXY?gBx#%= zT)84;x*T)tczs@>fkzx>^TIE%DI)n!4J5QxJ0BPL?Nls_ziho{2Knt&N6+8EqBLGK zf{L`{Jfp0VyXZwnW20_^-R(sXODA=(>Rq~6#x`}pl_xB8J6EwO6@^@@m}XUBK( zO8s_f<4(hgc1!*q1K7zCF|j~3F*tsxUu`+Ci+~$JG8~N+Q)S0cQiBsmhpEy~4deqayQR1w%8*j(#ImvHUvAxog$B;V~o7b`h zM?5NbwvC|6n0FYnxc>+gXF&9C(oldf1hdnwUG z==|@jCY*`mKX739<_J+Y_O?M%ce|QO?uSGw9NKV2QaQHD_(dfM+Z_4%^Ec|h@_srI z{7fcdxO+BN|^NgvWIttqyXw>3?^(Oi~(z{?^_NS)dX-DGA15pCqOpm!>M(3-hJgfHf zt3)_pfx~cXR)n)gf8T{A;=(w6)C^WY>ap;<2)(GJF3(rSt;B^TcoOJQ<}uaF*8M^L z>N5Z)apmyZWnj8|bn4rV!_yY=R0Uru?42=`!YfJ1jTV!p*GbDR`i%d@`69OdVZo#i z+!J#_ligs?RgQ7CPW-4qq5sckD3E3kKFP;cF@-k8u$q>PSFeDP%_^(|WFVq1+JrmL zJ>w_U_~w}iKn!XGT|LTY@z`IVsLS-=ormH!()jH)%IS}}$%6+pTWE2R{^tR?q$U8d zW$ZXBcloj8z0x<3z*Oxp`^+f#_KX%s{wXa>M54?rx0<q*fc`26Dl>`RZ| zz1A019dzqgre7=bGgl5>=j`BhBsD9c6vp>}nPT@ekcKG_mqB}piGx^`@7dE0T|PV^ zVA-9I0-35@1qn>i+A~)32KlOO8L-z5uu^S6f^DwhO^=6R`B)<79~% z*|Hz>mB>Im!<-123dk`E{J0aT@fTm@`B@-5{$>YVHjGT}tp;3S(D0OrOtxNgC;7Nam zWMH%Rwo8P6t3h!ae;xqxN&+x}$cn+I(ghWG>$Dk|O7h9&R}2+KSCMI+gC3RnRU#^8 zLGDO^G)&AdXYl_lxc zP>}LGHJz+Tvy55^$M!@c_x=6<0ZI}alJg2D+bXfgN$%~fxR|=E32+OS}&)*RbK?) zx3Hy}ZYS;M3Mps8usvLkMP@K1>0)gn?C^<2|7UCdr(5bbcNd-gzMGlh&)t7TysRr4 zXCsu36`K7br(kU@leVHu+qx47Yh!f0n?&lB0Y%Ej)S!oMQ3a!qRY4SPMptc32sFyNmE9-KB$c zOys)kn`#WNRRZIOG;&{YXJE8&~>%V~O>xDH%*&M{NvR1+=C6&zMcWv$tV*Rl*I3}o! z1Z!SWR|Yyc-gy;x`cQ_DvZNLJ4x1IT2i}J}@+UtQK$hg;$ws++(osfn{8hH-XyKNF6g%eXDV*vtME#z$HJ-VG8K}FC{FFUMBeRlubOX?PJ~s% zEcCmdnQL@nN z@z0@I?qikFf2#VYh6gsMLUoqo)!oHT$NtQ;EJ=rlyA)kl=hgsi`({r;9iv)&FuTnk zNjdyS`1GM$IAUe(Pm9kaf#bs@5?*nnv;Gx2f-0I&q}v~5PDfnVp;=Rya?s4l04t&` zU(@6H;8D~|HGY%VydQX8`@fIhdb5-|mr}Ct*L17kDxUn8eCU5Psq?M;ldAI=_7haa zt$=eUj|PGK+H%sPYP7`U$N9ix2xSM7S324Xc;0zENZ8)P%~KKyV9!7Nyl`QWzy2P+ z0$6e1T!?8pCCOSQ581@MSJY(=(F0U@R{xZY#hQ}0{1v7Q z{i=uFk#x2DaMSX&dBHk8@%}RoZ7O- zk6JC?ek(k-Pk~`*Z!i=#I7|dH#*S_#hCFL^ap8e^^Jpd`CUCF)&93Dyf1Vc}=FTza zO*UElYMMW3%*W%)q>xDDV#{|stB>wTFj#3lOuH=Y*KWaPC;j|OSAQkNRCt(EB>rQj zPQY)|kVyS;YbQdeXVmh!s9Qhp+Qm3ACknEO&4k0}6yNc>SXZDNNx%Cs>x)x|aCInK z5u1>f6CdT}4X_3O92#hHqW^~`v}=`<{g+aY!MvSX-@K#_(Be0;PBY>3&7Y)1Ktyk*~t7ewZ(;4KC#B zLUJx%a^w9-T*#E(knYZ?ba3-0C3AgDYbDOf`r~d197->W8RMWJgRZT&OOKBB$%)g2 zQ~9>3jbUK`NZK08Vyh7^Q93FAjF8 z%oR(N)XE3{p#HT;_8jRzv4@y~ z2j_g05CoU4x;3r)ui=V$owtOhEyt5!f|g;WG4!LyC=n6aJ-(U9$5S;kog(nBJGx{`Z#GANRL}BEc%XysH(}v&WeD zXT!u3;DS*C43H2`UbUL;AtJyr4cKk5u&shbG5h$NGpCWcrdt0U7eAdHsfu^MEb6vh zGu*f=Hu9EaD0qbbW!0a!KN87C^0@OJv@@ricq%(P?x|+P6h{wsM}%?&J7Z_f2lGM& z`T>`LDKl)7E4NMn*tXX*#V3FiRo)jE2;rdml=<{*l`XvLw#?iy(cY9LtHYPH;l4&- z_YOWHt@Lly&`H#!RJ20Ggt6g5hhOV}2iNcJf_HFVk;jv=_syA*ROviVg`rnrufm%1 zO2s~~vY`eO4%z<;MAhv61ke~ABVOYRu_`6f(^XFlJgT7%ffb`#IUaI(G(IXD6-lD3 zCcA;=uQz{LRT_LMO$r1}Z4+xd-|eMVzbc=G8?A5Nu~_B+j8$&M`Q@?y;}0`PMYLvS zd_m1FwU(c>DDl^$=%S}0URXw+1cA#{}?q<$dEWpR_%5@i{7=>E}KX3ptci2FnAM z$~NJ+PJ3l=i|$;SNRjnJAE{WM5qAIzCT+8TJ|yt~3jv5BoP0TI;GCw6vUHLPwhV)2 zjnb%q+6f;(k-J)k#*&w2=9iCcXoHE&G8`li)H|Z3easw`r zR8IQb!VoJlH6HfH-@Q(nXa&Gb*2~tG^ykKQ#)dq!uKU}{n*Cci>xIF8>en`>fz@`_ zb7pO){P4>7Eg)&bF8WL8b^%D;@#lwYD16J%l-_3E4dU|WSqCV3&x08-WwFmKeSZ7S>) zitl;H{k^*8p;$wVTmRtWsg0Dpvs7bJB^5n6ZX{w6xbEYyX~J7$*B^m#-!%2^i!}lH z&x4WpyQ+^SmI^C8bVG>&Au*=5>NnU3z5R@q_Z=QynHH+b^$s7uG7&n(0|GfBHWunE zV2?4sk0CfgJVb=jruC7>FMm=HB*-!#zdpEuMjcg3(rHdX{*^Kp`rljY>|{*$i;qQ_ z72rW0(0m#6@VTuN`?O$3t>$QP96-t2<2+?gg@$eAM18|-0(vC(v6J;Q>Doy~ zvGeQ?>j4;7=^tinhRr75V^D>r^z1ki{f)uVC^^xzrNpUeD$Gb#rJjt=B8w}mCD3WL z6RZxezOd4jawuNRuh9(OQ zMwmYgI>%lIHfUZ6ZUy3%@mj#rhP{EvB+&3=x-$V=%EsSa=TyWuE?Ni`K%=j+rK&f( zK9q^+|HD53Qo6W=S<`bgW7)k2{ZW5}nU|mS6J*uUZY49`dA|ir-2`RChs&|IT;v;vL=EE$FX$*;i=7=v*N#3IvNe$VuWY}wjrec$|!5}f1=mvW6wL+=k{ zuB+@;$T!2yhp#MDSi(H+GDqqBidY%BEKr#cB+uqm^dG{mY4z-S?fFv2l=PQ?xL`Dq zTJN~2iu0zbl@APpk@@QIqC;3Y?ArIUVmYt!-1r zJRCzvW$(DKC%$z67ZXH+d$r)*r-?;CX)NkK7{AYvwuBU!rsodrWV6=n;r>3vb2Kt4Qc3j&U%BHQ zRAs<|KJ=%It(5SHsYB~yJV*x#IJyyfxSaT;9wisaQ0p$I=H;8u^6Jpi$v+RA4P9v% zKYkl@>~NAs$Y1~UE>%9ITMAYEi|y**HUznZkeIjxMNuI#>Z$PZ?+9LPngo``*+#zX z5kctSRo28q+WfbKd@IzfG5M%!uqA~SX(_6Px37oCg_YMr_n3J|160tT@vIiSUEqz< z<*!TU4t2+Cb5oy${^BzSG+{)H%1 zDl^tmS$`NJwZ#2b0F>#xf(z=KyJ^o4*cCzyQSrE+$oa_Zt_&9IQTwNntqm5>Xj!-N zU|Txeq`Z?;hLguBGPzrO&Q7!?P^@xTC*#+MiTCO6kZ%KJdGQ#ZaY${?Cd_Vl&p zhM1jeynR*g&Kq4%U(%VoDHkiN>#vzhK30;gYu+(WaY?a0+g_qz*@Q?m?>OTH&M{sG zk+&A948+#F2vH@(^w}2M?yH7gLNyXk>v68|sM5Ot00P*P`Vx081(A373d+A0$5aNt zH`Tt_eQ7MdPB zr<&KqG2h(wSHCec;&-X~sT4kZ@&XBKuBzrC2y*V&;svUni2d8$G7=Hj6Q6RACeZZB zy|h=VK1%!H%?s8CQBk%y(*O|lbb2K+GV=F!i^5I#*ob(!>2qq!Qy=S_W}`9bT_1J3 zm(Y^H9bxSgtPG1v^7bN?lk5HARRXw%_jC0^k$oN0!^)|=D@m}zD$I{=5wSVj9r`~DSG-lQPN+fo%mHbg=6ai0i=PHK;oVGAmc3*{z9X=+5Fxnfu` z^!>5eeOfk@5@2X8o-hN~x0-aqHHY#TI{*y(y>*6h@{FSnHVft*O0++-Y|IPW%?>e$;x-;`J z1B8xB98JBTQ2w+G*Al&^^jM?K-4S&+JJ6qXstllV_1(u;iD;j63ZQ9d2G7;Uemha<^xXht-QX@ z&4W2cx6f<&YrY@Fo`!25ZktJU$dV)UCn6Po+7+N(?;Nb|W332M6&?_Bd@Tb7TnDf%yTbaj}vCAZTsylM{3N`PifeK3PGTCTMH z0d#Ok3mLb0Fi<_J*WRzr&Y(H(NzmpT?)$v_xw`9|Jj@!6#t-8c_7~HjEJvCrHG@m( z#yv5P4ytJs#~7%5`VPG{49QU1j^&v7OBZhVSm^g7Kc(Tl$?J5m8JBdZU;DB9crBrr z-k;JngNp__T@Jpca0te2{-A}9PAg%0|`BJKI=VGwx!iU zler*^DLuy5T&(}CfKtVr{;nn+_gW3zh_v23W-i0v+)LRimBu%}P!`5`>D3_cYD!U9 zKea=iwC?P^&i6BmI4iGu{mj>_ggt6f63qPX(*`xBuLDaj>Hrm)?oE~0-T^9uyxMmC zsY)*)m7!Sf5Q2qC1hCT}M(^TGZ>X`KgAx4!IwG%aZlYO9r?3qr|+jTo9Li4_WGx={g@ zols~47)l@m&_2pC2@(%kJKF~UfM;@crQb8>Zs0FX-T-I$$w5s*D{)%MO#IXvarNKD zbte0*T*Yg^H<}q#K88l?mBS*Z@q34NPa%Xh4;#HaDEiH>;p20U|N9B+8I0+KRuMRq~;&MJWp^|rwzdP)>Q=dWqrzU2+ zZ~(+%E(nRm$K#g91>>uHjePOvp-W7x9p@0P`-w1AW= z7u3Wn*Mkg#q)+6pugezD0}k1eQwHB)#70}J1Kw;o2bg{Re*Y$;+1)#0$%1eKgC%h( zR4~NINsotr6KJOccE9%N7x)4u*BpLG;Mh&%6r-qdWk*-#w6!#pP-7rYV+swfzj&LB zGjL_yy;Zz+c6+t1%E`|~^CI{7g!pObif-Oq(LT=^L?2PGaf^u--2OU_Cg8UFa-1L6W~IY*mOkqpshj$H=t))6Qj33)imzAZC`Ms!DylxUgP9AU7RUi0qIl_;n0 zi4yb=MisrfD_ba|e=nMWI&j7L#*5{%!D|(#H*Y3ir+yqnfQ+77f4~0}kX->{SucP& zY_L#~84oXk*4%Mc^^&qey3V{y@lhVt&wxh^JFJ0!evOH0Jzbe|3xuwDk1(UQr)q)x zR}Mix1{J7{ECOf|sRcl;d8%$#Pn)Wp^mlvxE8&6iY9JH&J$n38LgYck#H$?^QBQ;M z{?Uu+eSN~uRn>HQ<@uHS9q+9wX)>YGcsUC^HQ#<` zZOGH(?BMVD@Vl7ebxG026Ajm~`lsWvF1Pm~!ARQX| zt)~eQp)c<3DJd7+nMe9+2c`^ zi4aAap8!0{L*-VB(MiKb_|G7XW3xFPkhYL42e+FVSHjZ4Q=Vz3K26m!xtz%>CTS{Y zC+!nk*zp0B#_nRex@Fm|JehMtl$2;XxNDF(*1?{>;be8rPh*82d7wYb%+D!h0jpJY7O7q{%K0kSDuVHyD-qtATqx`UrTIW!m=`aY{wh{H=)?5r7vMS>lE zB z0SG9c_53OQk&n7Yyi2FeQQ#EyDty1gP8Z)_IDF~9-~4wY!Nnht7Yj$LL*Z^SVLf#C z&Q6^Ox{af~(P@z^`e#L}sG!)O6{JP{HfoLC|JAbS6e&Tx^3Po89v|bF;wZ(4c|dH!`w*whH#hOU;s$ZQa7@M2*qvN&H?Nq0mbom60br zvy`luSGa2;QYcUYG-j1bq&LdCV*>T}s~>yepo-zVnR-t@S&KMc?! zlf^TWBUXVj_n1k2feJHCA?Xb`F4>2ztDt;IM2n4PHOc>E>Nb!eG@qsa<_In6{)|I3 zs?{Vhd-6}$fh4dL88ol~f>bKZqd>rsP|W^1g_J}Va6$jtYJjML9i!KC{Q?$x^d(^g zGXV=rpbQf(r93xU;8A$;!hpo3Gbsbr?SgodhL^TdYxtC{R_JV+HJp=b ztR(f-Bn|avPF$ig6@*eDHgmW8+_{ue6HSK(Xt;$~z2&45?1z}%CoC_-E)k3j{l-OU zP3X>ZYu2qkyLKkDf!_VYDpDz<)|nS8ulaEkphhJ?^*d!ekMZJqbf*ajrD(5(J5`tJ zFRl~)ZCEZD?Ax0V_R*5uuBXvOO3$_<71sZ}S1x#CKNdaNJ4A1P7oGY5^V17}P^mG; zUXsRq>Z=;`;<;efjMxJRJJVQ&ND=xUOKL^3>@HKI@4PTo=*fKJR81E=p8~tgY;mEW z$;_{QkE_7-Y&A=xlUu!{NJkL9^6tXFO4J-#c`6!e2n}6q`+EGBb@#(%Q1PM+(+hyc z&dAPbHcy7G2Z+}mcYLyN)Wbu-|4=4kNUrmM(7$$zMv%4}B-1wjwR@$mYOkaXFn}fU zY7NobFX&xGoP)zP2eiXKGnAsy#D40mM(11o@wmgYHO64BywyfPiqz1 z#Mmu08j&v`Cj_?qEVXA^5IPs0o?b9xcbYr7P$3-%!2#0Cc@bvtI<1?!*vXsKbyZuAFk_XdR1tjc z)aGHB{v%Kql3ZFrrVC-KJlM?z6=`S6Y-p6tjAdM#D5+bUv8Ak+V(vv2{peS=ju<=& z#+?P}wi6jEZ97-InQV$<$b1OU!PZ)LDPrSYD6{?1+h*$yzD~YRKf7M`KYMAY=td3= zY_c~Y5Mj$dx$_Z zvr<)^Uw0$l>7FB|J*5LzzKcl;#$=yoCTOaj9A)gl7y2pN+{4wq+N3Th`6Abo0!eU5 zuc=+X)Hd7+jtFG%S!jlsfZ03hFWHyUh#<_fL`$=1mJC}KUn)cqU8fTv`uz0U$jb0* zc7VNn`RBNs*LV(K2gs(}oh}n|kfkrn3B4sv-Hj7pim?fN_jNkqGna(m7@FQvJ4L#> ziEm^F&xX6KH0gPEDg+h4F3MZ-v|JU|A2GcniHse*>kz<+0SxcXr|$-xE;ZRHO76FS zYB@?hxq1KY-}9gB8esFZ*4-L|q{ggeK{vq;H19cCB0*J6lGT8mz$qWQEAzTYw45HIJ zwOAuwg>Y@8=SzT^v0%&q5Q)pl*n|ADoreV>n%%IB1my?MI>to@K>Vc;bS)U&LvJ@q z)d%3#{c-)+`nPRVP(`e#i(X6W$cF<~cA1VYvi~0|v7JpT;ZAM2P#>oNe`haBT04?` z5;iHUc%Ft4t2ThUxfTYu5j&4P>r=GXRvOwSk-#QT33$m6B6FIND!Ns(J8&zq9YOx=y1z*g)+ zNYYgxgC1`ycJPykRn=}hfbJ)us7odD!*oh75=zQFvQahKZ8#{qK0QCvoK;|u%!trU zdmQCuAXWYeB|pD3fiPr3z2|L1%b?p6D#~tvs#%}8JnyVVuT>t*;~|S8G+4|O!+WD$ z&f|Krv8f8V2D-z|>pIjUdfGju$}LVlAPV|Vz?ZRG1FpKi(Z#6DZ^aE3=XW#sbCdg) zqoR0ww9@+*hTN?i`%U-hqmU^mORu zSe48WSw2Rnb%1_B&J4&FTxX+%E1Yy`@GpGReZ?&8b0*}msQ zGTF8fS8Ge9EZ*n40|@J@07Rz^){7V303bUjdRy{!A1Ky=!(kwPTEiVd+XClqnPl(%&4Xy5L4?rA^m4E`hJ z9@tQqww&%1$&qHJHArTYEmiJaJY_b3?utRXH!Lx34l>=!;PJRw(JkHY&KoZ(H*~S% zR82ob&a5F*r%*RINC$Z`sxFNQ4YgHq%`p@kR9VgLz zkFi9LJIPsgP%M7p(-!%Vx>^cL_31~+udvrOFQRQ~5y~hX+b-U}gDZPH<2W z)}N=F2w~>0X~bxU63dtr53dA*5`1m?8T&@*>5D@EReX>_0&1nXjz;36=w(*$W6el- zIc8qOGbgh6ui7C{6^`I|$=xigyI2)1?$1@g7D9kfg4bhf^PYO%{20vok+)q3A!%&8 z23oqs=_as^ugW}psjZ#fxODlyES&+V>iIJeGB$5ruy{R-c#u`4&=!-?&i-YTu%ssg zU#X$>>`js>DOKjT<_|Y3?41bdCOZRzwm~Pq5&jZm{m`>ytL-#^06ClD3APGfdtWyG zz|+2E65vEpyXZe$34C2+cN-iAt8;F zQqtYsNJ)pZfONy5L%O>gq`SLYO1kT}kIuaByS{7v{+L;7X3Yp^Kl|)w-`9OzpWB$0 zJvX)DId>)Z7rJEy@TT=CfB7kkq)bOJFiWzB?aP$NmlUu{ff0?ae?|N8L5$S4L=16O zAYkU-E1b}PyW-Jg*YP(}mf6R_m%n#?4XPW@j(+IU7lC9}!}&ecj7TIXv4XiBDjs*! zMUJZu%Z!Gq-d{E<4fWNrU8xeukLjpZdC7^oo}_kxclXCr(I2>Nef>K^t`MnrYPn!_ zP6j2@BIew|;k-K=53&Ro<4e|GKkVCj1T{%pH#^JYb%72EG;i#xH1x^L_7w}n;1CE` z6JkcxpBiyLf&ju;;*WmuRPi93O$wh+mRq67;+a?pJ?dj#*f(!;aGwBW59E6Wuj}!P zBOM9Dxl0%gUT8P25s9V*QJ{fbX!O2Ur4H1f<(+$n{3$bcbgTca7#K1wy7Bs+T*OHTJch(Cn0~zsu%lzY$jdx_G&R^9=%Xy zw#6rDL1pR!I3?u!kWDekiA=AI!K*`$+&OQXq0;AGlKO1G7y3#0O^>_kmRJ>qSv%_$NuxlQ_vW8}W!#%sV!;Mc?6^%iW zN~QHm2M?mC&^L7k9LobcG~Fohe?GT|jVwF*hZe3^o}{JW0}P?yfF|fjf0l`9Xle=abtpEHqmJ zZO6>nMa(gc`?1N`XoJ>6lWwFHw|$ld;)Wz5IlhD$1A2|zF%7T3u%p=k@eHhdb(NOK zZj6iS`Go?_643O*I=c+u{eq;#DW4WXW5Hi9KF%=qz}pkbzUAHfY%BnJJ~OV9@=>{x zQ7MWto3TB~Chzy!BHx&C?=Eq^!I$uN`SlKX0*0Tmk^}d88Wn|+HVFGS84l8Y4;B&rPgR6p?Q+ef+)oohw=T!^Is{9c|U#; z%TxFYnrM=bg8bN8^0B&$kkUJ;L?o|H@Eh;r@a{2{dP=*&I8~rtduUQj#E}lN_BaVz zJW*L03vNl?IH?cfT>p)1B>XNVmQ4u?3fT8jkW=yD1>LoLDe(Q3NiTU`=$o43-Mn7| z15@Ucackc71Sdo^c1+s9@xl7KH0RemGi|n!aIFSKEjNF#BRUH_D|IjlGcm5}k7}D# z^$hec%%6=&OIyvBB?pUxf+f1oF(hC9xE_=c!$|1Pha8Z=&Mc3EcP8dtRv-OmY4Z!+ zjN6j`$vfV5faIgyIGz|je^M}4$&g7%Z6ca)+m+^_wY<-`eMVvjd zWk@jWQjj>VaD6zC;-2%qkpE@lH{r%!nQe#PpN-qim7X4MxZe)Qhmpn^#MlZGuwA-P za(DyTNd{^8ll#TifyTBGvMa?w8g$m6;|!mY>d735e^e6o)Qb4m%1`mNs5OxzA;B5oQdK0dG9 zEfmNhCPQ7o4K_oaNO3>pdwDcP7c+o2mgn|P)$Ove@mOV{Lr`$qtqQG^>hm^3wthNQ zI5Kx9DECCa1?Z;)_m7g1_?-VE1#dAHLXE^jgB7EJ-4iHG$e(#eu``blCKQ~E)tf#= zFMiOc>wPE3t^kQyP1579*{H1^OU#DeT`opjsRWY)b&_X-+>Z9%H#nP7_HjV-OLj)9 zHT%8?u{K;kN`^;jL~0kc-dEFdA=b05B*Ae>m93n zd#rOt5F^x!4F5C2M6nLtnWRp^7xt>B0+f?p{T^P$6*$ENw~=OqG(-;kb3oS}HRD|n3K6^r6KGRENr=#7MgvYq&Pzp$Z}pqmTV zjl4U=OJf>OWX=t#d5mh^_4~Uu)6|dCK`xLI1}iOm&Z+v5GadH}v1h32`|7MRan@xA zU>hxMhr72r7^&cCeGLr*SafcOWvG5nUw=?`b#+Q+p?jnpxDYo9`u{{{-#JGK75DWZ zfvM(pbw%?geoIP>6J69SExO~5@2)*INnGD4WeZEjYD~))UdBN^9-9xUU?<@6fT+0q z5G}S~kz&&`aaPBE7egjFPmte29gBSq61`42Aa&GAPusp3gKYRhXy!Kad(KG$qVPIevwl_;Ol#zfIvJ{%|IaMMY)Eo$IwvafY&+$!ZK%cUANZ z*n$W!4)+rsWnEB~$A~{d2T|n2RQ6ec!nLS=acCgSW(kv6;g3g}iULN3%oT-_QxGNr z&bb5w{1@@>G=sQ)TnTISZ&Z(#r%_$R{n!0LN&Sh#;IsXs^u^;2VDtji1<4yV!-rZ@ zlfREPv=JB-`g6OkgCZKc+^)ZEdxU>>wSNDqD~3LShVxJ#Xp54*11t;0l(bMIFD*`N z2Dr97e{uJtqh|#bumg;lC1^f@?8V9@4)7pYfx7EOj41h$=$LKMWZznah+Vse@^;H+ zzE)DIHT!o5LlCi>ADt{0-j5T>@1&vc|{emztatyHaja0L$d@Z8zNkdvm(ZXnRg#>k-H(35C@K6#KXg6eidBH z3$1#XbcD5;s{iqSk+%*6D;(a4iZ77VCrXFIQbq4kwIT7Oeb;DRb$J_w|8YO*=th(w zzX)wXgK&#$y=`yRQ+9dK?-uco9kTUpQtTdQ1Mm@QJ`kxbr67gmB(H5-s-GV~ONa)L zo6IFOB^{aT*LqRdGp=Fkzn3&m(3L!@QBNg_?gZ05Uob-&EJ|udtW$ z+^L(;79|Nl~)E#sBP9(MgdR_Lt__IC|LZh#iJhGUjyda{;k%d@8MmAEnRf z7woU((_LzR#L8uH%6Oo!y6suV3ud-aph;^$l7(aE+|9ou1Bn9#2=P{1GeS)hm}DSG zeY7ye)BadX6~q?WKtXxhXuwql)ihzk5rrVSJUbAPJM7M-fdC$i`&*VTHI1C9E zZMuiUkWm8>g*H+YvF85xH#ccyh7^kwY8+y;hJVBt9A!%*h2E||7E9sJG};gKLY?U8 zciO|v2af!=v(wurm;xKrM9g%0Z`aY}IP zZ@x>~)z%ryWR`L-eTFzB&^{!CnMKj2aSJ@jBR1%E`z_$zPiy1vS z3u6IS2z~x+CNM=u=$=Ity1&LlJ$}{8Z94Hji!V>75_l{G@#|fAjMS2_QXML1em6Za zR1}2}T)O(gOT#T^<8v}#K%@__O&gSrxi)vns&oa*0kH_$7C43cdMPZJY4wVoC#c!n zV$xMZb;#QAYPGIvlX*+LM+TN0o^id8{UQK{!dtv(Cr`q7T=i^#O%;}-9JU_B%Ygh4 zvu}0YoqPc`t>Z^Kgx4I4GQ$YSE9TjJj6$b=Ph{}vJT(E^VqR8AXwUalwzVsnof`Ku6ES4Z8PO}zY!(~1J{oej5J0&X#VPg6xG@{zZD{1$OWwy zeW|8S)@dAYKcAl_wx2gP#*bmkJ(d+h5-v3Qdj1vSxRW0vP8AHji{BQ;WV(`%$)J{A zhwb)OVS}fvAF%?sI73e_n3TQ%KOPC_@8tB3%^1tUUb&R~)5y*jN_x(tkgDl#G+nQ|2%%ODL#Y3O4I0zNMnMbZ58;Zbcm`iVWBEp;Lu=N#b>;gHZcv^Iu;Am883T1QTclPke}oxE)h%e~*r@@FJpb0sjH_%I6FQ zYU*?=D}9Z;#}g%o{ z&bI$gkS%V<9g;D40N^_3@NbjkQZP&AuIBWnt=ou!uQ!jfD&O*fl?gtgO=8|0OlDoSH`WYDmEonw z6^b5b@iIRGJ8N!|PEbyQ<8QX613XqJ0XY0JXZ0Ont=k)dn;GA{F~tF)r56Ied`jSC`Ni=vX-KofT5H@35WXiO;5oO&eR>GI!2ZiO!L zuY!bYto4ZPNZgr0Bx|TK1Vkvx;j$MiRBJ~Oh+H4}FDmFXg2SpqPyXZfRdR%mnl~|M zTgPphB|h(v2Ev06wP`Y0tV8zWL+^ff*l&kfGc(u!Z|ux$`H#92Mhou(EC_w( zwoHY{VCg^-IIpY!lTYDdMwq(g&7U(KTymW(<(3|Cli7>NlFY?@49P6t#pnWFiuN&jfN^&pRg>l6^Dnw4~MRFEPr|( zNv(1@Rb_?zQF>?XEcYzb-Cu6pn|m`RtEpZ|-j470Wxg;|s-kKuA)|9`MFv&Z5>7CmDD4PK%{BUDvAbdbtZD}&7)$i+T+R?BJ zQ8^dBdGC>PLX!hig|GfQKP3dn#-_B$mjcM+jPo=2K{x+Tzmv0^oJM-mO~=ddxDABM zqkFJX10PG8hNhj(D|5??nor|aK(%)^juj^_sW?6%)3P6`0_w$}(z}_)WJClrSIOFNR`&$L&_!E}m_DaMH#pY=^h_#= zSDn5GLS!5`VAIBZ`-T0J;t*m%V>@pNa|6_#{hKCUty8OEq`*=&M3O*9_qKRdzBj0@7HA-nHw_j817Og38?! zn2RP`>8c=WU|TvQEUp0o2ZTQI=R2)C9k3K<)w0fC`o*Jc2iN+&kUx zhaPY*7Mgz{FM!qTJE2b6~OP!`lq<0}mSpyn7@J+8FE?r?N@}6Ne9kv%+b`5?> z#33l%waxaF0#o3f8EpC~NO1L?m0}uY)I=XsDAEX&CT>@}?^AKkmLanpA{d zC>OSepkD<}zi2NYxlaQ*0Qki+?ixYu3+$VI@uDX_wZchmLqu+c|RFK zp?%lj_O5bUjt=#)f+9zp$HYAzb;o1Hu(-3 zEdh{b*owdB74i1c=BlsXd*sZe!w3$$fV)OUJ*3?Awyl##KbabuPLd9mqt(8&7FI7l zhq?Ui&>WcZaJzL0U;v?D#y|j11J&vK6-nwYM!XqTeAFrQ)N1M-x)_D53E#IdXq-&w z3@71|388zG`Nfg>lirT-Bi1F82W@`LAunjuM)Ar&t**ryGI}AP6pu}i8^CTpx zw>zRpLp>b%X-q~^E31|}@@&RaJ6Cw#?E%4xM#u%H@}lyJix-+h0BPCLG6>Y*TUKXh zq8%{*4&wPnB;yF|FT0blV^0YXvzlxdZn+g`Z6Snnuwf?d=-u5>t-hDs~X40ta+&R6JZkK!Gr! z?i*!vA2`gd69suc03~PON%m+iz_8i5i)MNHu&ny*py%+RRrvz)pDl_%RM4VOJ0?)95{q zUMJG+wv>DC_3b~vkN~dC#9>n$j>1=cxt<+6@oVz1fWunW3;~utiK+fiB32{vVYIIZ z+aA*s{@f)z-F+Vl=i%>tZ+L20G}i5K68G9bA#A)qkHvIGr0CD8{Nu~6QcYB^O$J)} zc4Ts5*laS5;?fOi+8$1^zVJ-# z2azBE`i=l^L|`berT5GJEhQ4deV7Td#cDBo7f^lX+-y?UQQ#*~gEq`e>wAQ%$K0_- zuwTZWUPTqP{a)hRc7%p);XF9qU5E+`6%4Hkjec1We+KI54awC#I9HCBtT{+QC!6A@g4vL_e0LSd*PY1V5$3*B+ z0yu5vS66*2@~vReR3a&%0Pc#6J*JcD;~UY#y=yV63|`Ny_~YECLD`-pm-6OhU)=8- zbIl6%waxwtuQ|t%)rF=vTn$@yxkIE~(R~JA$=vmrri@SPfo{ZUZ%^}Ff=z;5yeZO2 zsc_%1s6prtkGYVccIWBdB+K_sa6#32SZa=;Ei?tTRj^6c&6bUbWv9+w2$l7UM<`=f zaQ+9}NL5FL(H0PxwI)YC_%N*vcd_65#T$^QJY4k8`1)DC{!ZIR;@pc>w|mj^Vh48z z*ZL*XHwO? z7i0@kSS5D;&Ez{BfDWV?aN~15_&o_8K!?%fwqzVw-UU4wsN1n}sjin1P%2=;TAHFv ze?4Q?ZtI2F5kJpCGdG$x=wjdx@bhu461*%L zEQtoBrJB@Da8-$A*O|P~Ff%*i4ICxy@As*1#4+!?o)S-=i(MHjz*uf+X#_po&+7bB zwP1oI-}0aPV%28ukp6uqI37SC_Wab+;bU+-_9gDfgt{NT}kKF_qEMIUJxGH3VpCp{qQ`N`4r4+_#l~|5C=>(u{OzsTI$S* zpdSt*ejmOXzXQmJcNbhI+=BvhxROo(f`X~ zhlM0wavQdBTbue@yo9b<6oGN$9PxXX{ov)MCQTcrN%xf$n-O5Oc+qz~zqbtG>F z(}mKdqJX2l6CFW53UiHuo5G;nq0))YM5fTnZDM0{@Ijt38FNpx3~=iuzxNk@8T^Mf zH9(-kTD^#NMK?QWS@s(6Kx>KWc1W&j`#%KAq{!2TMa=Y)i9qXyW*3IOrJbGE?Mqr5 zsZLyE_zS3)~U&Pn8eobFYlBXGyt3XCsa#|EM$`37p+AXJ=`BRyAO73DB15|2(vw3*@K9B*ID^iUC@Bw~w>qpubPKePcvI#=={HvG}&AL$d z_PTn^*(*>~&Qd1?7IR*9SBf7WBjRD)ZE3l4<@~zIo%v-??}9VI34&$BE{v7C()h|m zKec2}hC~Ac<;@GIQ_fbXR$%F2YWAz&RU@2g&n zh=0xhwJoEm&T-M(PRqA$nV-rFWdSNy8?v}bvAVF$mAL8KsGOM zvn0H9G%osLVm3rEfti`tDqgt9PMi1z`gz;-&DjMMUE4oVc*~nn&b$WXh-dD% zq`)YT_Md(sk^z|@XgC1pKoF>uN1aUWe>u2bjx4znAU9f5QvBHj=}ukZ!=m0&S@y39 zlaX|GRqFd+x{D0(=I6g{drhMTqd%wYP+;~kY3ceqt=c2as}BP*Va`MX8P~X|e_T)p-&qAPs9%&)ZD+sw`oQ}b`0u(0mK&aHZ>Y1TJ5EN8pr8NpYsfg~CpsG~$5c=y z6lAzFpnR4#!K|5k%13Vp;c?k@Ak^JXyngF?mcDcb6rsYbbKXLA?$68v`aPsXbjo=C zp*k9BOlaM#@>2jds$15q@;7xriT`#Y-5uf0VhbWzQ3cn}Ea7abrQsahjsUCyr5uB< z`AfiY02lSaow2j-GsMz68nGi=AU}y017j3)EEKxGf1SHB#3 zs$gPbu0SCLW(A$D794t!5;RQEEj5xf#N2KOi?~subWzP)hBvD-zsC3h?cfQo=|`%l#}W> zbW=y}5l2OVst~Zc0kM&sC)WIl=)=Phw{0NkL4#f>;#EmbfvOmVwKjQE%imr8=IquQ!YB%7sgnW(4AHMK0N=!W)G) z4%J)4iqpe5o>u^0x|Sv@K5~=wvtk2rKIUUcRpGO>xUR8+(L+#sE?6x9 zh2O*U*m^i0l2K6%`rY(Kqw95msLD5y+WzVsW*&psrJIhgVV&d{Pn7>-vrGpFQ^48) zH_f%{gZ-giEK9v&nwA+{5AQG=_K{%+G=R!daOPu`paEG3pkfbh74!)YlCm>q;W1yk zbMhLwEXg1McOY;X5!1v@{!>KPPRU2Tp$-O@*LMk2TqwzjjG4^Fy?I7Ug&RM;F`i4B zw&|P+a5E_onnRGenyzaOZ;Iqf+e=?R21p=Mluh>pr#G6oKfGiMjskUo(R>s{J z$~SH8@FpOr&a6a|d5FMIrB36zocvj-AEY(VE5d?SqVI5bZ44p|$m!ZRS%IBaiLDLt zQ~-W+85o%Gc4!_=g9o5xs5zh5SU*`t2KHySUXRK5zj5Qjk3`9GMcQ0|hyl`Ksxw1y z>1a*i*85P0&ir}X1x9g4aAQ#fJImuR6gJ{sWBd5j17;{EpKe$hDMUE$VqUm z<$YV{uavOYnw#y}O|{aTv>$i=PDnalFdL?3nIWz$7(yDiICi94&g@c_MOO!@?N(^; zzxA|-%0-Cq6_e-e?;qZ;H<&;=P&aa=Xt6c-X{%ER4I5e$K;0;6a=kt=nlNOMxINSt zR=6~JDzZ-i#!I|?AZh0!4t`F7do3xLk$h?@{)~~6aSrG=Nz(d;52zxIsm+QHOtb>U zqTp##v3^(_u^T@vKY&>4@HK=)&RVk5?!K@66*+5>4?1d~#w)1#T(?t8IYZ}ARHv-q zK)FJiLPdDIkV6|0XBOTIhQ{!3iQZ)eZ2@ib)f2MT4dB}!*?XUe)OmRH0d8U=(-B0K zq2QQv4sK*gI!{$9v6dp=(U%{SO_}cd*RmGVd&|(wR2or01Jo-wYLW1+2pL*n9StY* z8|QEKZdCBJZQD*RrljaTm>V118Ao z#tU=5P~gU544LhfMG{=QJd`pTJs+9i^t;h~pR?m(n4F+ke#3_esQDrS_?Y%M*OJ5B zl9?4XpQ(4*%VT-~{b>xgSX-RWRe{5;u9~gzLd05Hl#(1ahq2;d=eb?Eq^Wa}-!If- zFE(Dugs`~&!|T?j(4+R?K#Bc1{}7rt0tDF=E8Rwul_tx1Y@C*7z=P|!Jo^NS{El{W z2PeOZ$SaYGQ`MyAx282}z`L;jO-TJ{hMfOAV8Y%nVZGkZ#%}EF@i|?c6LkN|m0_IS+vv?CML2!-B~n5Zs4`%NuEHqy>=dWR06n4T z?j02Mg=}$W<8lVy-1^~i-lAXwe10|1y#^C-TGmXlR+1G?P{KRQ-8lJ#S9E=4-i!`A z1pKz5RsfspXkiUlKRPx3YT1B>$c`DX^4%eH?HfHUbLB()bHd+Wb1C{3r-2UzDEZz1 zk%YJCV4(8u{i9%&5EO=5X?YdHFF#3%Se4&Hh2Fl+ZaSVKppI*|t24xbW32{}0xXo$ z*UX3l1w?gMd&3vM-Htvel3YROrH%deD!YFR1I{u^WG@8fw5&8F(2rP8lv0xkxX!b2 z3y*IENx(P)P61Zsw0Eg9BF7rubk>n><<27$Hn8*`=48Uq)u9=2*08@U8Ui_y3W8UK zgmM=Ua$5%(O&`2b9Ku46MOjqxv1(*bjQ&HaIuEsQ3tGURJscd78jg}Rai?bY z1j-W*?&~h_QozK%hwNdJXGNxzzOh7_q9bYvt8p+zr&$pWy5P&M z1C`Gb16@LeDA1scN!ey_H$Y+4s&4@hk>ADhq`HmJVopKr8-3MadtEtfIuxbss|)^( zj;dn7%V=^m1ecAn+5lWl;5}Y(eLT2>DDcb***-=ouSd1A1rp7h-@bv(3cxqeI%%vI z3lK<@mllIcVCz2&U^jjJEO1sY>LhD=I1kL1pi(T6Zwf5=cO_2oPRv@w0>xVuWU_cL zsVcjolK*EZn5Wa&0!(6vVh{z~ZaO;xx>!bUXkLj~6y!|7N+iz#3Ya3bWdxtab&N$K z%3H>A##UKIi)E~IK7xI0UmNJb$J#TnO2gR6LV5r$1_8+4Kzdu(^JO3RtO08xYkpMTEk# zrlD~qL)vLtFyZZPs=sTYeqC%I5ScR1O`1xu6gG~XWc!*!f+dxymjq|=iEYF2Pt|yw zf-%in5QVYTTXQn%bO~cMg8|$}{lY7KtIO<5L2quwsBj5c45dla&iX`%X~u<;T?dCz ztKZ|Dsx0^Ko^qaohKl6uA3Hc}T63iNY<*P0=n*qyDs5|nx(D(3aaXiE0zon`>mcW6 z1$Coe;v){IKMMZNQ6npZq;{c)yM!D~`UPwJz@g}>H*|lec}EwR=TQxfl7P!T02eAG z#b4&eKUbN{1b^JItTT&y9u%VqCL19<1^>mR==zPs&e6!95oS$u?=reW*3l?r3Q_vS z(=hg!`9H!p$d0I%rT{2bEr+_8P0 zp)E{6Qrir|fbx(S;#c@K@>;3t&hC})c+yHT(Q=lIkuGd^HhewF=vJa7-QpjjTIDTP zZ2vx7z?mEZLB@J4Kp-nfcKL@T>=tT5Gf}K%`a}aUT#iVvFBZHX|D`F`U4UvT?qzhq zOV-&ZXY5DR-){k;X5R!+4rn`<9q@O?jW7Ge0s({aPod;~ADYqy(1WrQ2XR}46MW=o zgnd`nrqsO{c4wh(Q@&f^fai)G;UUU}_aZqS3)nv&Ni?ahzv2eCihR0jswbQSEnps% zViB1A_i>2JFjE|v9<;OnOozBD!(MW|vz&FS5UpK@<2Otx8F%L_6{(*;Sf!x?66)xm zMS!;34cX*%?7d3Ee}8bhIbl!LAIfDH+>Vv0x_X&~79pVTqY4ocNsf!))@EELo!Tt} z_9LLB2?qCgM16ieEWObdt;8EIq&!`wg7(wZjcuL>s-@t^V-8T3;v8eff{L;2(=f&!tQxAs} zD%>?-xPs=)a8ZpErz%+S#~V_=k*mSIp(QDNg<1VFS~D00_H& zF8Yy8a3cS?Y)EM5F|;5CF(Q`nDKSX~{d0u!I*V8pP|0s~E-FT+E%I`lv!1-FnlBNy z;wr*Do=EAUB6F7Ppc4HgHWQ77O=YN4*Eqv*gq6lP4koYUYv%BG?s#5>A80S(eHq`3+81|&J8~_#k`E4lmoRr zw2j0fAvp?0``LXjA5bj*WyNNMjPlNLV}{kU+f>PUDhewL=Ewferyb&R03fBShmP|) z#RG&YNktAu;~lte`WJ(8x&hOw*)00{J-)D$p|9*_L9M&dtuM5Ch2KZ%imZemMbltQ(5 z?b^BFmiI3xpA^)XFx{Gs^?%^~o1SCVPK+X8&=8=6^yj>@dxr722C&c`hO}I^7ufpDnvIkDpQj|!t!;Pb@to`@uNSZWV7}sCkmg11ObJH+xV^&oHCB`oiU ziRImK9n?D|n>}`=)lh)bR@fDvce%RSB1cW}r*T_Eg0ic$%AZ-d*y4f8FWWQlpNswe zPy_a28+4f8Kw(TUx8HMOaGisMc4v>i)4KUVcvRYBj0!UDyR$LsGtrZNDuuE|{mTQN zJH4sOZ>j^pz?D7kFsKouJ|7-gs~Lq~X5&Njgi!p#-Pc6fvMu?3xcCC{lcYS55(kcL zD6GzyYSY@fYhYjyVT2#Z7qwegRs(4&#`z9lP7?bASM#=!MnwU@XWkw~QUdtiTl|vz zQ%G!`Pa3f{5k^;Xa-_-)FEm?dGGIVG^bcS;fyx|fAyKs4Aq4$Afs);e16`}l zp!~V=b5Z>&URzdpEPJ+T?j%|(>&5m-wwJPCwiy$6F*+1(TpPXEt>1e>JF6e;KeQnV z#mkAFCkDlRh@8-Gk^|yC$qfew?(!sbyBQd2kTumD${G9YmDQd_b6unvY*^YdgQRm} zAN#CzW38qFIzo39mGJ!yz&GJ7vJtWJe>!ahZemnOCuaHJz_~km@Z4Dqb{V`M&iuVu z!pHW?!1&7r=0bJ*<2NvSfIuxRFRO7IRxXv_7O}boyRkdP*+PpQr&}Eqe@yS3w4xRN z6)P#K(ez@q`ON(AM4U&FsDR6`%!~#CG#co*Cbvc59j(oG;s<-b#dWYp!o z0X#Qc<7?T81N}NgimyT#rX&OF@GbbV`D1P$qKee&TZ6NK7%vj865Jz|79P5Djzqjw z19@}bPd!UJaJ8}uPvaiN`pm%6I{H#ziEu$bvk9zW z*P?D({u_+F#`l>c&eXH4419UK4gkP6+>#1?u*%;`j$?b6o5y{>f`OP&Wa(>MxyJ!NO4B%ca_tfm!mXJht^mk(wee%I&m~{o5t?q#diE?Vm|LG1}};Kh|!4gH{hOB7t!O% zqEdDh&tz`i$I80TB+#Rze)9^y4b-K!Afh$>>r#Fw2(oqye>a4465Tch%SmhJuDNS!z*B_gG|H7+Q5nNZss3#1p!#-hIEQweX$}(Ns0M^?QL`^` zb8*1t$AT(^Pa5`2yrXAM%>i|C`u6xu18~QZbw%YHOA4hHQX`8Hfa}?_-DFp_BOApA zI49yha9%=LKfs(uLHi4$KC=MjHr9)LTsxQ9^th@!1*c{hd585cyU&80O_J{hIb6eQ zP-OHwk-hD=ZpNTj`z#XvVm%jmB0R8qTwCr^69gyc;I zNOHWIQl)l`)d{XX_g}7_{F2yaQI(hXR;dINh+j#z7rNr*_W5CbajhzB={03LDS9Ee zro1$XMC2U5-#;q~wNVndY)DU5y`WKDzQCP=wVra%;4SEDmNRWB*7xl;4{Q&XFlOBe z{dL52Jo1cZ%4o#@8z-lg8%7IEni5eIG1Q&B`kQ``|8skjULBd3kcIx&zg|2M)#a>u z3+wlChH;-vKD6zp_ruzZTr{-`PkQ$OFGy8R5D}}FrZ1~fmy)D0X(&auaH4)9_Hi!- zs+TZ{Xoe(eV+7^%p?r8r9{`)29I?1HNK(3tANu}8_+UQ= z?EhdWqd@7Y*0!UQ*!8*@9SbiBb+8d_%SJwG1D>P^N;h^gk|XS@BGEW;`+r5foy3dW z&GG4QG8-_5!o}WDBKq6yN>3OW{YScwm4HHS94EqpX>hqc@1)R0!p@lYzW4S@Es4AL z6$61I*PnUP9zBo{a1(^K$(570GnHDOd>S|X=|!@2PLh2Ic<@#?0Uu^DI>iN7N!J>} zlz99r{g8zHC#llPgZ6|6QUI6BVxdM^c+%`Iy8zJT7wrGn$s9A^&2e z%tv*A=95sQVyQ@uuLXyCHwP{JZI$e6d%;^%9A?^O|B>7c#q|$568nfyB;0K6bq#<% z`3YRQWn*SaK%30$=m4>BCHoN~W?l2`c$p=^ftOzal*sX{MP#PO4+I{)0iUZwq$!|l z?C_IM8XBO%36E^{RwgnLqQ9E3l=b|;a|mi(48`KNeH<+R?VUdYwo7VPvY$%jY#L02 zV1PvrV_0$L&zskf@m0Q!bC|B`O*05xfv2oj6@g6~2D9s$QP6 zKAgW?A)6gZvI)Uh0-feqVbam4ghb+0E6E1$Umj7vkfMi#$*;_lw@z3e`D_NW55kj(hTHW_6Hw@DjUGvHub78SVw z6wwbt(`PLi3|tP)_w%n@*1kWMlAJ_D%;5*6j@uHc@XEWNcah z4lV7ZKv!y2K#+gx;j{NXCJ;_vKwx1K`jNRkzWu8m{P}ZGNlfn1y)?Is` zSclI3#PFD`+-Y1g$(PP0*{qE6N%XW&BL)-@0eqP>rVNlifLhzc&s#;e-&h0IWfIUe zc^?+GB=)szhdMz^ZDL{r{C!N~;=tu2&ksYzuEdd=^O7ECxqs5C9p|vn*&L1TXZu)i zOiy+l>R){2n-BQa@tULI&<%Js9S`fi*X)8B-cO^9yAB+22 z{u$%IY7JazZcU9$`mN~4CimU0Q~hl_*^9EbMG*rEzk-*8BSN8@$T$pF9q4{PYRVE# z$-@)n%5ClKCG0PbbUJb>FoQR(5m;kco(l>^PnZ;dS#fqTbknIGkPvX^w7Ll7zJxjz z0VhOdZX?L9aw{FU3a_IgI{Ni`xk31#NMQ&{(d>{G__-B7)Mp)Xf90^{H1V10MePrI zOoSuqv|!4S0Wr{XpT38Pm3eN12Wde zZ{adjnUaiBSy=nW&fs9n&|yZhy0WWvK2BOAJK~pWho&lj`RfbU@FIJsq-y{9*i7e6lG; zF2xoQ+_i4~{#xVH(fVvAOc>TFV}uCl)_Au;Sq6bkbC2E*cc?t0*9%EHiav}QAyMoC z=@!+yk3j_dgUU)yJP|aCBB^g2#0$p_3r#0%njv^Y**x_oqUk`20Jmym_jKYnnnD$* z2A@twp@43UT3R~lpXcSry_CdGtXZ;|;XV7Hm+@Vgq8NHlNdNCW!Oz|PDaj65BzW%) zkmvCTa!&tu5%%w*5E+w@I^?lmh$F!&l1(-FrJ`<#mqS0rv5!61X>aOV?sA}?+?zYX zIloNo9!33|^uwT$iz-{4)O2G<2ro{;ze*>R<- zvMdclU4z;J)wL^P7+wG#PR${`*`uZk9@kbu%g01Wz(&v3@kJ#oYj+`fn;Q)mufc%& z8l~VElJ_1-0dK&f5@maR;@UMbGs83GwG8^(d>YZChq)upj=e@~i~4y0H2t5I!1#HS zTMhAp;E2%W$ZS{QLOS2^ynADs4ws&ON!Z-|r6c0@B6=6%TbMh;Eo5Hg!s6*revo!P zw>$F%4qyVLRT0FC-ad46NR$~EIfXm7g8ph>w7AfJ(q>h+-xQrjB7a%|ZdbuUO=@EQ z=WS2iCk1TXhNn29oleJb>W%AT$_)96=S6#iPi(W28c4Gi7y2b z16~V7<)GP&s4%BJ_WaL~gf>esBw_qY;+!3Q-d5|hoJ96ZbPR^x_Nyz|8_>q@m5}^~ zv9G<=7DmxtIGyxH?%BcC>5etm`513^^zS=hoN!z_51l zJM`Ppj`r(ZMklN93RWs&`pm@soG}9-*xeUpxZFOtdK$lDLBC%Y1eHKdm%)K6i%65GPL}y*wRULA zmWc0kHOlyXZ3Oay@fk|gzdFE6{)agBLEa&YYn-%%-;Y<5UyoSJ*@~ophF9zJ7dsy( z1>@3CfAGe5WFM1D3e&Pm9UNx|QT}Qrr#iFvQ))_8 zwu+)|yGdA}Z(zqdIHI$eO=eYzkab@@c;B(po!W3AEgr5n22ve+qHG4&D}7v<6>5KAwaTT+DmLERM}| zx|!Y+wq0A?s~6TUu5mbgW|joyu^@9`+u%g(b{Mu zdrlUtXsGRHTN2D}<&EM@8FevMM3gqu@f+MlIToCuzYD3B*Tk^|n2>s6G`S4=Og*#Y zy;}MpmG8om&gALG{E2gnvykV(1@0}xlLs`Ppsz~MVF4AbI2(?w?rb6W4Qgmff>GGs z$T|c_ZeusPSJS@Ckd>2p3T_<0Y6GtBF9F#Q#MYJ@o8Ew0+C=q>6WL=DC5jLfUBd78 zZmKhfT!hzVZ2>T0!rEE^A)pcxzh59*H!X=r@Ahr(hK59N)Wb+pj6)QkNuh38IJC(A zCIgPZ5&*-yTwK(gztbHWH(D6imZtym9Ho9{o3#Cf-)K^rUDB_+dUGox$bVYG1d@F} zO%R!(BXViO6TU}$`v}H~e@S-BhR-jL*`G85fE9o(hZ=;GV`R>o?q{k&h|pf?R`Uv{ zV3Tz392@uW80AXTrb5(F)#_d}pjUDw<_Zm1#cBY)3=C6SeCk!Z91T;NkR-|X9CxH{ z@TJC1R>wE-KQVj+?Y}Yn=`sZE@5$gtml9S>HIMPhpO;a(l5dTDUrW!Ug`JlkgYv}UMU&0(o3u;o zJ7+{4vDL!JZnJW^DyxMKDJ(!L2G=$A*CBFlXng9v*jY=)sSpKbobW6jBm2WRl_-}w z-y*eVpw_X%OV5em$-j$rK~kjA#{r+yaO_oZL%(9QTP%k=CkAB1BOV(MDZX`^#_&sz zFp0M~@%zlBkTUKOYngH4HZWZnI$iLNs!vhCi4bAB!A{zL z?s7ZVm;7IDk(%QSsyxUGtBRrS#DR&I_V(sT*U#09XrF?N|?l!a#@A?O+`4vQP?3~Uq(vS;1f)|CP^1x%R#GITK|rKMIz>t)rMo301wl%YPHB+t z&VSzSc)$0IGyd_NXY9Sl_7Uz_Ypyx3dCgjs&@*%HG<*N?TSiGvs$!(@M3gsL`8;kk z5Ag-5-X6}^bi-e$gtO96?YG_r%Hp$Ji5l|M&u*Cbb2RXz8ZOeds}S&fG# z2ivFcsU#LeS7xJmL_Q8a|5X2RGd`z^sP8Fn$;r-(syfefOWJagH9oYzQYUnX4Vd<`*=~d~&MXW- zt2heUBBbCf$W-{FTJ50VbF+STB8< za;~~F3uj&q)b9}ARU-~Begqw7Em_#UULbyacw9z;9{l0X*`Lv~+r%7MJso_*B)%{DvN@({B-zXHbeC-I zdv<(V8<&}?JTl>4U{zBsa@QIS=~lkn&M4@kEOQf>Nzo|-7zgLb#AeY=^H2pc+W)Ih z&f~ful7aV!Rf!r=b(>-Z$YHPUL~4Xr7Q_1m3fx&4K!(xW``Nek`pkOAenWM9%^x_EC`jT>~e zr!k3YCst_%Jio8^Ko>PAtS?lQOmu)zh0uPkRu8&!H9c!FOM7z=J&FB<>-*5T@c++-S`p}>nkMfb;am;N!ukTq3hGwz@zu0#loCxo{;EZMda8PyR#(?zAphp|(eac9A z;7$0H6b88da%&8{7xP!CL`c{_Q1RKl0^liE>m1ROZ^%;6b(6u2$tkA;e_ytK)D=n} zU|E2`8giMQ!H)KqP#Zx{3c7zey#bO{*H=utGi;3Mw&9yjAYa;-n$RT<6G_)#Ae8U zmeJ#wm_64)Cg6NU6`JZJiDh|MogxLqqN4UV@FyT2f7?H}U7D^C62e$~+P^1r#pA4Q zteu#-GW8(^k+M22M&*e^8Gn1UQ~D{Jv9iRh`(7qbD8kMov;V*x(-G4cyriY|HY5xU z%etsj>TL6jQ!u=KTJ#9*PaAH@tNjL|CulDUl-Y@t?&mzKfmDApmUh)OdHJ*h?CA01 z1HZr(f->!KGqW>ezwP;Zsm~3iIr{7O>I7u2q_F++SulC)H>}u5_8WSQD=v+VEg3|rc=Y|@8 z-uit-*1b=C{!YmDBg4?z-QIOip+>{QC*I&GSdI=MY31$iigK%<+FB2JDAg~5AAfTR z0YZVrkMgc;=7nLBV3xeI#J=vQajpi{PVg&@NVqWQCYs~d1yAT9f3p8xQU z$C43RFfLP$`PHhsXm()Zo+;+|nZN_?>>d^d2k2)fC^BC>r_Hn}H=glmmAX@v>-|&q* zr0G1JNC#5U2LJxPC0aV8uzAK^jwF zxpSY*#M;b0zNxHpu5)kvBAsnYOL^m3gGEPfXw{O% zm&Z@}hB&VZNux$uq-}In+I1%&;TNIELYZ>53C(DPz_%H=FD{n=uO z*C*3|TewW-F7w~6`M}~R(1(pya82NS|9NBR=bH~{*9&eVp9WPk6>wYG6>!UjKbg`^ z)%3qj+fnPM;zma!j~bSo_3PdX9=t5c-G!jI*9BB>e`S!9=h$C@>QTc-j9vUT_!0r) z4cgo|ue`-*d;9xQf678{jAu;W1P1|$eb!1tLPJS%vrO8X953R$2Qc^E5IcOg#9^=% zJ6r~xF+}k?OqU-n`BK}vT+%wmeE*q3;OO{U7}ovNrA@dkX3J#zMOS0MB+=->^!dk< z!ql2e&`3p@ss;}YaD1hK?}dl?#ww1X1C26aE!wg+VOt5+UGdRi_@#A6n=0?)g3|A1EPi@t9YVdQ_s)9vkc>Ex zITWAZEAApC;3^D_0<+}BMq}*4Hs9MZn?*S;esEr?OBeGN?(vVE$W9S-tnN(SUizyq z{R6(px)&M*FbOvxD`^;JLqPvIx{+}UK(rU>`tJxO+ow=N7JBg$s31Ei90{o%kS#ui zblBF5Ri4j9+Ms8|ZJM4{7X$V5>{jPAOZE0g!UNDBz~RSHhILZ#FLZN-XZB3VlqqJ| z4_Fu<^d2(GVS-2c4So+H#c{mx^{D(TPYTyl1^nUPA1 z2d%!=M6@Sgzc7e!sU;>PcF860#?qMslD{hRIC6<|rI!Dom@zl9C^`RC?W;SE&17$} zzOOrPcS&tTBpzR=J3CpM+&bX6bLZBk4TjHrW|0aP+IE89=*dqZY$ds$myqG%gKC%J z?-wcc8Nc&c{i!KW%m18VwUH$!G+B04qOKFQV=iq_90O?N#tq`^lkaiY);HTq*eUQ! zuPZdO2Jx8{U-Mi*zizkoOu%ZbX2a_NiNht`%}cqx*a!ZcSeKk+-g;A7R}&#E12(pM z6oc-lexXf%^>nfEPP%^SaN2`y=1)KmL1eW@H|g)37mD9md(v`0;{!sbkg>TrhkAYL z(MG%*m^a!LkhFi^8;lO&7LS_6M!iVWD*_e#NDoAU?E7KvgS`A{H;Oq6vm;WnRS4s@ z7Ojk*T!LGdO%jH!pcQs;X6xZ=Mz8dXo;PbU2z1I}mUKY!t+KU;iC;u!O$IWL2Ws_WP7?fG zrf;~QdInA!F4r|sn_#8}RA1yjx`?tD6FJu(y6HTmHzZ^bsUaV z8ZG;f81Oa^83y`yaiGxQ5K+j;I4k2lVLJTs$iS@D_Re^ZYqdv*`a^3gJx&9qV?9u3 z9^;@LzSn$d*IRR9?nlY@<9PQ=2Y0;{TKuRdGd&0XZe}od#G~y)5)nLeZSGHa>2ae# zpG}$8`*}69zd8*~sWw9?U%ucWui7I5$s0a80BJ1lP#!2YG1Z{=+Q@mF7u!U4_)edE zW0HZW53^-uQ$DqXk1)DWQNQSZdqDSVEh>42QVWa>QK4j8Xv9UIY4UZ-_qrV9;e8vp z)c`SV$)3{9EaEN5P%%!_|MWtBNtimk*|Ls5c?G+~|HIdL2l07t*FmD&nALpNW4TQA z>W>PImH()RIT{Oo!6!0v#5`$J2vX;jPp1>@Ks2!uBL)&ec#$8@zL)6 z63vf^Dt08>pPNcJJk;~Pn?!|1Pn2;9cjv3hg?IXmv@2i{1q|4S^!!{rKv*i3=6nr` zupk@hWvoXPO=aP)KPyp>(JUn;(5iec$a&aYe$b_lF}!ZA*koWNU^7!()V z&(FswDZU!;zMIzcTUH2x$91rsLAUL{Dfz z`0?Y5@2qQC5!*gm7pVm<5p`;cB;bA&u0A|GTyJGeOe!cKn4X^AKUwo2K*PFlE;B#> z>dX1+>GbAAm<$D#yyvCY2BIv6uTh1gsm?AeZyOi|_|K1_87C0bDB1FF^I!L#!3@ta zPnN^1?sn1^fA2RxMrKuP8JT3I>bK`dX#TLrN9h^WN21pCsyW|1`|G!m_Gu7l_Jqpb zbmw=__tYE2-m;GriR?hHvQ?mQ@~`NXLkt;4Yt2KV9#5`&|E9haDDGb#XD3dpmA^rq z*)JGg-qt(ajH%dcW0ANfxK@#+Uh%ma&-`qmy!b~W6wSaKUYA-$C4Tj2|CW_@X3VDf zt#I?KHxXw0*t9?H+P8k3??z|mxkgHEb7hFKQNApMqBmr5{S->gSalw$S>+Ez_l);I z?_$M#oj{Xa8u^|9*cqfF1IqSjTLRpsfAiAT9gwv~5Wm!TyQ{%$Ni>0wm}&jekV#@w zNv8paqDpH-P}uj7V0Z$wa;X-uavpyJH!j5ykcb17H3xDsZ^OhAr1zZd9t6~UA_1WRyyVSOiA&WBxz06V`mG+VYcGN8Eg=d=Xjbmy zkp}e2%1u1~;9?DQw;B22a~knHGykRpv}?*M<&l9*qCS#-zl^x?kkK@Ow2w(+%{Hxv zqeu&qYWtDdKS={n_{bU2%z(BO%fWb*-pZ>ZGQiJDS#H8tfuZ!hI=;6);tE^xtTe1P z%!Dx}{|uA)khn^E;`6HQoSV0=cduU+=D53ElYuM?ie|IP@-!;SnVpJ8<=3^YK`RDH z7IXg{@hO8x858FV%+0TEYOfEF_+O^LnJa#~ysf$J`t;N5r=`{(Mz|?QJzM%jqf?0) zuWN=5Zo~!k7g(|4mKu;~&HR0Jz0D@p)2Ke@PMM(XKi7pGIxym(JelAa8i_o1e{iw- zLWDK#w<`+fg_@?6E3xYH3th=XI{07r!UfQ~?7rsz){b;{c>Xv^2j4f-MUh+viwI*j zhzqry5OH-OB8nHS+e>#LidU^HUXfNsMJZj-$zQmdD)e$}t{~IrRpZ>N!^Tt!jE4Bm zBwaZkt~3EEgiKPSV}shIRD|R<_HSS7UucsS^S^mhFOulv-uAp;m8(fg6z^B(P5lgx z2_qr}%i=}H=>aBxn}?B93TU8F)JaYceo$>QCu^MTix4x?%7#|d6jkaNy&C^)%jt_E z7AxWeXEJ$gtr6PEsT?`DT;(vLrxm{v$=+tVP$TWy`_M+#()PQ;_>ig5rJ@AaK_>7z zQ2GbDtf(tEAIr+FetAO3%gcKpLM`#P*CE*qquf5Qd2qCY=g{QWmL-3A=S^7f=NZnt zHh<|FUnqRomkk8@yY>x30oMnnPr6PD)hUmj1{LVN!i~+Ar=}ZUOVOIij=!=fOvH3w zLgY6TC7d0ypOKPw=6-<+8vihP=&XNXZRgS?H2il37A`%%kqso}XSt@vkW8%+J*VPF z@DQ97$ZI8(*Yb*3{PH$o z{Ri7k@mRwTE1S{HrFB~I#!G2?;S~f`hMAeaT`f?rK$(4i8v{-Dp~5_cERiFof=v^D zo^u_ zc|FPts7tkO+PnT4Z9U2LDTh(E`;0e;1B1@P9po{q(TeQqF9jKT6N+_A^6BKH?m@DN zxY}>Y)S|dBiShS?*ynYx*5B~oox(7=w@LxiXD+ib9j`QgpS|mCI_XGG(Z`XW(p|yu zjfer$bpEoK|DJ71epHVO&5oa1oq_o8?iV*4VGI&Rg&XtRk#(<<>n|?B5RszIsfBl< zIqGFZ+&&)ew)cCK#I~Qr;F0Y7=vB{NODXFf5=;X32(r*8OK{}(n3k|1gnrOxje><6Wek_&j0QMXzbry%m z+i@$>7H-ly7vA>`bTwJ-MsGOh=uU%##vxbnV=kjNVUTU$_c=ZfiG^BAsO>=}t-*w! z&O-$#I)MpRl!6J-Ys(DdQ8O{gaE5A!e6HiX^e%{EuT;5E7v-d1YB9$Pse$#7C;pQ` zg^}G%oW1J?O-h7>a*Lf>fLJvn$|#e1E!dRW=1=zn`{81u%{Urg%D3DKkh|!x2xlL|< zvD{U&U*%AZ^KprTxMDMVO?DIWye|Iz9R`mLG-%MCh!Z#;U@s)Ut`$H^5tN~O9~!?c zn#2!|78BYSAlr3a)^~X4hri(2>?)><^G zj6V7w8S~gkj7L4XguMR}13UZo)7DcPRRuM?+c%+2&&r~k^F5s@9 zz^^!G$yl;5L+m|@09j*W+aOo9t(Tj$xOuhKW+Hi-6K$5YEVd$ePPg&WVyXe60mnQD zdG0G)&hSD+$8l|(0sq`<9@c#Q68U5N8IxPC`A|ITGmWzQh1)rGKJ9WzbAbWNT z8HD^Y+(&(kpZgU{`F{RaYM?gn&ggCe6=UIk@KAH@jdrkg2CloO%54b5<2v1o?iu_+ zfC$=^bDGXv#57ypw|G+Ieoa&nzknpY`{6FFbpXT^DaUmee*tcWmbX|;N;)G(}!4YA~3x_*%on^sp!PSICV61p##DYiCT;22u-B#ea}mk?;$kLFHAf5(?! z(_NMF^XLtc7!GAYi(QvLj%@s&)L;=$x0=F^U~rM%M|a~pFLH+ zgksgfa8>EjtCDtIvF~ADO%|rp<^BO zIUINHR=kcMu;3M_O0hOQD)x{dEOY5yQnRBN)&{czW#$xy zaF(Our)?rP)KiE?KlZ3i>UTkDXF*}Yav-UEeKvLTQN5^HoHh(IWb zpMTpG3mRQjgN*D+*dhLnx6PT1e-zZBcYkOIT7APB^8y3wH~DzD3sFM|50$I{0!ny z6AkHRl%3L4kf+k^bz4P+!ec0w;SLRy8*o$4IYGM;V06^AEemBI-q27S)r?rXuzZ+@ zbE&*VO=wcsmeeZd5=|oh^zA`p3y|3hkhn$|z-L5hOFWy!9tg@7#xp0}}ZKr(cAB$jw zu8@mO>ZAO+X@%|CXH$(v+46xIA@mGhQU-5<^X2Wne>K&Sgic+8BOfxnlo8VlHLl2z zr^cgeSaOf{I!2JnW|%CTRhNj>yxyWHQV+F3)Na;TwBq!-)>jJB&r2*Q`=#gW~s9}k3G8Zm-#Oyo#3*4u!a@BU4`?9bL2oOu3RNdX&GzIj8kMQYteTsYLe}B{RxdR8X!?4VY4#8X|Qxm zthzUIv|S!O{%2wLihRsU19n~1fK9CSr}JZ%_h^;*{G&S|v{T{eLW<15vUk+cBq%^A zB*;%wIgGN4n4xUzwQy2|yw~t8Ff|0H_7|ZURCN55(3;8*GPC5_*Q+N58c849ev>E0 zlKIImZF!EEH>lXUgk9l{`_DZ|XurPnkGzyTTQV=A6&dSIIAs~!a)5LXG8vDie%B4< zouTB&)&AC_QgJM{n2@OQcvSVbrMD|4V-3~A(hsg@pnOeGvdB|A&TF*EgF@k}YFKz7+rxZ3~L@_E!%oabU#wl|Vzxp!D-y$P#qgVyd*%jBjfaaRk_D!ws=B5A9oqj^VO8pz4i`$*CAQ#*};PoSuhz^;)mDN4|{vVznbBcJ*(-i z=z%Hq4Y&9l;v3{b_Tw*7;zqdEH&+g=C%kz8MZ!oY6itKizg*v@IA3PD(0eDc)i_3G zeASF!vHjPD#Sh?O;%{M14Hy3g$*N{U~|MO01cOdIiDCSUyPHxzme zOQUKrcNkh3(dqj&G|<^A@) z^OglnL)slr8GUz9^n`!pP7I4yU5xS``J$K@R%-|x@$gd)gqEGLf0P+lswJtSN6i25 z4Y{t>d*yp^=QTL6Xj3_oLXz+tAjCiaZ8i8&l@ZSlgY9{Y_mfSw8T1-nbc-ygFGTrI z2*vzK|NTKa8uB7X6K$nG7eg1+uxJV z>rSr^7~?S4?Q&UT?V2nOUM2Bn5_mj849fL$0<>ZC8GjWrqF?|1(;D(KA#f7n3Nk}h zN>u!jjozEkZ6>8ojca-_pA-2=_f6gy z5t6Cepi8K~XxjO#tZw{H=XsS?CHu?D0b$fy2d)M~mgxS8Ym57)5*(gM=w3EU>NozH*7tvye;Er&DApC5uP ze9pE&oPbmcGp^Q=Qb<9&shid^j~Ycp6fVI@N=)?r8-(`Fm9BD6ZEg@+Q?? zb-Z(K3EsSZ9y||z^Dc^u8?wK{4e;+U$ScW{6^%VINFKMT4{CXE+~n==zfAp{mMHvX zMIC*y2)+}M@P*T-V&dYvp_Om^TkeeN5kHQ3{ptL5)Zd?MS-8tTAGJwOQlh^#mJ%|$^diCm+je~=PjSWX~ax#o3GRz92|K|e5qbd|ZdXNDq8<5E)6 z_0;rq)Quay=XD(?4p;Pu4qg(6rq7b43y*!J48rtlD38wh&YtM+FCCCSR{i>*>g~o# z82_c72C?e(AN72u%d^H!E&lWRg#~iEfg>VnX=XKk8dg?pi@jMYRtGgMdv9W5Otz)k zB3#N_3>h)dq`K`m*0$(p#!d`kK5051h^Jb0@TRKsZ{o;btU{X@4CCKu%QqZtVJTHM z6yKcJg>X3u<)qHnHQF;E#9OnSz>*Ao9Fo8;6P2oN!7;=C-8xz za$0EW?DdR^u$9bPHLfUS{y7Te9IY3D4=z^8QXg&Hu==I%PM?kM!PEKA6Myo%wjLN7 z>MNO&wY~;VExA_dej<3dz3`g$zVDOnRGC>em($~emhazrk9DdXS0>HH)nhasY3sjq zXPE8R6(P!^e10iQO;^8_hM6%rdqZjq@1Lc1nRjuLj~66q^dl&+N$~VUYdjk$(EApl zC=y`x>$85N?}5#+0P7o(f@jegABo z&WIJB8--%XF6fzxe`QH{b;^jRW}7Kw$3c@tLQJdza7s9N{9JWa`TT~kH{`)>$bE_Jxj@*8>)6M#$6}HiucbGg) z`9VKUX4d-KVakFnH+bQ~0@25O981lzYNx~BuCrvd& z^@peBtBag$Zn~ORYI1UoG&|CvP|7rEzh9(%Qxs2i990yhDAO8pk}<%5b;&Y-xYOGlWO%NzsTY2vXw0V5w`L*#%qy&E4w2Oy$eGa0&Y83yFJ(nmzL54 z{4<>&Gh;Fd*Rcq@qfop|EE=|=C#Hi|vpxR)kA(a;XJ%)=^z>Y=tgQUGV6Pr92|9@r z6b9AHiD#H{7yTN(g(f;aBzLjm7omM^@t$>A!}8LIckkEk+tPoYiUoZy3*|UJNJva z#YZ8WOwlItJ61QN4Z~K9e_ZBT&Xf0t*Zuc5#l>Zf74mWNfB!hyrhDSf-Q?m#q(%$x z;qRa8a~oFHg8Dg$8Y2kX|J%2nAAUT#BhYKesC(#*r>i0_E*{-L1;SAlS{ayq*;-Rk zwF!jm>5l)db0=>8G-|F&ZS z6aSAV#OJ*3KSwH+`F~|9|K}~HOjppDQ7AOe>gqczQext%_-RKy-6t%Z1B66s40Gfv z9o*vLYVD)i20iC>!49CIiHeH;`9U`L6KIrDM>4)wmA?L&i=3Uu1Y3AS;YO;_<~&au zMeF8woVDxRsjpqT_S2My;N%o;v43zdIsC~S1n;W<&*I2yqDjYsH-+6j9SSkZuGINo z7Zs(ct*y1OnNVA)r)lRcE48A*m@a2y_*O78h~8{1)4`q2+|FHAEs_^vsUlJ)KQzcT ztD-XUF4o-Q5RV-b6O+fRcbR=Lb}A$hqY zQce4La;+OIw;equ15^|Dz3yMgLdZs46t^$?BKjg^L%(a{7`DzJ&@nZ)bkG&pZ%L zC-t*HPjvtAP%NeE$=>QO$yt=w{JbfA(a_gNqIhTH!jD8z>-Z<_g{EC7WGI1`pC9I8 zUydL8`HRAxJ1Fj5jg^@z22iJs^6Qbw7$71_Wc8ISH6xAJv}{HThv2LEXhN4kEV+5Bw$Xi4u3Cq(i!35&X_mTMFz<7s5%l{;t0TOg6%@G_qrR|0c1 z3e-IcjWTbYdU>6D03acRg4YY~r&((H>fJlCr3=vDMN6;eZRB9srz^bMVXQtUtHl@> zF1)kgrU<#HlKuewiN{KE8+rk?kmHlNj`vPGeHylh+t2Ro4jMS^4w+b{Z*3+Cy;x1M z94UKLVl4lDcCGe^Et*{~l-Im}0$O#O{!Bu5-dhbhK0Y=JEw$L#*m%cfgj0KV>OytL ziI9PT!D;TjiOugH)Qxw_bp0elDD*d`ngkpcuPDR|`eYP{(0*|h76joKgP zsIwy1#^g%84a?5@xcI`-K*6^UyykD-zV&)B?nZjH|4p>edB@D$PyI&P@0P%+I+r~* z;>(vehRq9TIXQ{o^}aR*k!~Dsb!yRe7pwK3ra7*Sh0wU~qr*AvyYl`ebC9U8FtxtE zzUf%??Ww6Lo89Hd)w{#yKg~Y%Q9T=C4)W;FeLPrhO;ct$;tQM9@qrh&CtbeL<8&+0 zEHsiiCA~JCfvJ+loJn?-MhoZY*DSniYS*sa`oul7`Zu)q(FXOa{BaysM<2ua2u|m< z9@C!t9&3{3-vNDyR)&q7@&=aD?!CTP?6mQ;v9zHDAS{fO z$f*AwnEaLD%}$R~6{U^ezkka`+>oD{`0~2D#6CEG7&!f9g|NiY4t8x2BN9`_aH5w~L?^AO3{Vq|a`&;UzF7Io7GGnl= zJxK2=#itf*@xs8(=Hzl#m0dR1eEasOC^~ujv?#4`+_ILO+gRGhrV!L=l?7>E{G=N6 zNb&Z*aA&K7Te2td{ zeXTR;yk@`Hi+$f0?_Q1b&d*vm7mLw~u7~;Ff^QC(wZ_YC-MWS6hfm!Aa)OR#(wWdy zWbo~0xwRgP)b4ukeofo=?>>?#N?(V((2@q0IGwjmp>;iJp5~`wM*#wff&qiIu^Q9i zQl^H+Mx_E>Vf%&dw1##*OZi8S$aa^9UMIRAVxznU2OnuyI}uGzPWI!6Fel9*GE?&( znVDmP9&aFE9Qq9xOnE4fWxC`vJ2NwJdUBMhS6AD$eYmqk2iO`}rS5l8$}~h@+AY3`i^F^I;zdP81(@h_;Dm^^N3)V#K7*wR{Q6b=mS$;7>H}zEd+1l$b%RCR4kzkgohK8pxqk5yh4=oxlewQs zG%~o!s6E=WH9g11iO~uOkZF}!+#AT(K_7HHcCNV= zFX+%9v{ro{4Os**M`56nVNgypMS}b0*1xxUGPI=6O-6rL*{!6 zDgu!Y6QA{@>vtvy`-g-`q)3L~^+YLSnwXft!A(-hPGtky2z}LSwK*bVyYCD5?td;mcym@G&Mh<`d@{G5onfKJmble2)gR) zXLR!;zF}=!e>rQc&Q(zs>nVNcvM=f}ToSa<1 zX+7!bk5xhE?VH6VC0!M^2KQGh?PhU|I}<{{hc@@qV6wKhhPyNGRvj-D)J;Wc=ziIp zZSMk9;qB}D6ch>>p9NVon~vGadM2LiDrhVUL}Qiy(O5c_EE@R}vDUR;Qe`6WdX|fH zRQV@nmzRS>sf8#KBp)t<)}0@#sftQ=wg;{T%;YO2x4^(aLK+%t?j0f`#8xAQ2i}8- zjLcLOG)<(0xXs#_u*BE#91V7QM#gCn1m8Nalln6);R4U+@BlNb7sN<6p^qYGwca8q2@~RhW4PU+poD5_tHQJN~z0I6-Q4<`D zijMXLg?v$0SGOO()gnbIOcDm-D6MYn>@ zM(7xJcv)paP9DiAX`T%#53c2prpw1Z1dVoQZ}hqEGshveKDU_T#r1`Z#K4G%2qX>G zZn6R6{q5)JfN@%9+hg*mHQ#S;ZOy__w*#Dke!V@0J*Fo^kuu?DDHu1zuEh(zkhQZb z4kqIib9Hs4XJ_wh3qPB)_Be`la(0&0){ZkYHl{dT9jh^a_pN`4!{gNTw%v@sp`oEP zyht@9nN3YhT*t(xA|x6KtGiX_@j1kQWk4UKpO}=CF@{a&?ONTL?kNmg3G(yvn{+W> z+DH_z6{Di2Hkxekc^e&lgT~{ys5keq3Y<%0NeG5rDIsAO~4Uya2jS{`y_8~kJY&JS7=<72RDk?MJxG8PuO+Mn%Z_$-I%HS^RV z2i0V@W#OI~VmiAI|23UDMMryUa-gRfxVXAgJI013qR#80P@t+v;IP{|I!1%xm3DQl z2Gpzpmqg<@pcHb<^*`Qy)R&og^BD*x7=)JTFj2PF*4F+@hcDU!sXc)MYUK#a8_;j? z#`axF1MSugLU|uk{Oc|Nf!EX+S_O@T`i)<|eUpldi~Bk-paJTc{�KZ1@?C_UE= zAFw)_;5nqe+?p%aZ@idd$I$z#Lu2T$ObvJ%E*KT|Y_y`t^phqw4$fN*J-0sNi;u4T zwTf_jXXk!`Jv8@YLq(OJy|%EdZ;#s--Jdv*jg3u|r_KU!H0oE?3oZ5uOx3F%#>VNv zl>7>=Zf+7eeX=FS-wk1$PV&G~9;LwhhXx4z?f(z_-CP;3(?gRsGs~8FU-KzQQarUy zkrG&wzdpspeZLz2rDm2X|0n(WKYhyo6|wVQ?$rO`?}W>WOG%}GwkG-U95`LD=wNcv zHJ^g>Y+`F`3*JVB+fmu4`}gkNMN`jxq`$&}I8xb{FsoZo;ujP}Pj~=K$hU!5U@9sq zYJ(IFPalHlb*0IX!a0GzJP-JzVR@(|M;Kr39k&TS$bq4gu+^vl9vMfy={4|i;UFc0 zMd_oWCL&Vv112p6yUM2R52Zyx?p6-AFJAb? z#Ax_zfxw>!SIRRb#ei1=xPY=Gkg&|hCF!3U5l%dt4;CwEV{5CiEsBNH$3u+H>V5@!L%LLa=8SPrvq4^Ud9p~DoO*j`WTp*J_T5lxif;9uRQUC25 zCRlpjNLIaCDlp!!U=6?%T;HoZ`}KwqJS7hIV<*7p6uT$jL8Coje^%|h!+f~6MgTZ; z-d+%~)%;eY905D9N@5`CcZ7vGUH5Hfe*YGAK2J;(uBPf};c&cfTWUUlubFoh0aUBM zs^qO+gIV#x2^;sfAc6yt+#E_B1h1$ML@;=%myl=&ppIk!>ZSwv)QGF;cCs8LPKIwUD=W+Kq#kX1dFZl?jLiOO)f&gkO+(oF z2xqW8nD_ngB_KotK6F4w#{>Jdv0tXwfIY&a5urjXT-Wx>NO_7@xfLP-{u7nI6Yi_4 zU#&e{WC3)U1iw7rmqY4)FdJhx67DY@f{cv?v7o(i<07c}Z@G_4IKVpu127E_M#f`G zc(}U~#BK5eiFLf=w;sFiAqfJX{@}eA$RG0lF1yS3q^12oj(+)q4z7Je<@4W#M(tOr zL|n<>+;O2Lx$LbzDtd}xFBq>sE((V}qLYvaR25p?@g^>SMO$!|Q!_FQ1`G8ix6B$MVgSiswXAD=KwG8knHU?Z zJ0D9LMtHY0SdCWDg7zbUH9>4S;?Qq)aCee@f4(xzi+(*TE6a$DPDn@#O&ZXMKLRkY z&>j~(_33g2Ne_qdxm+pvVRu`7x&T@LZEE^5LzRvSp(4~ zrlFx}G)LolKKCHyCEzzU_S*gAu2KjMNrTo38H0GAK93?`lNki&0EIhPc+YGgpY@M2 zfbdt~>({Tr>um)Ujo}F=KI(BDV!KDm@+Nl%iwx2L{Y!2|mW_bqN{G{Qaw_pqguual zEH1t}UdMikk}vX;`9Pq5%g6hE;u$^UpI}45*{(i2Ju+$r&Oj3GliX)EyT36l*#m0( zCY@yPn}e<2a=?O#7YtCgflG(sIr`Z_PW-`M4+^zbvzGu4Kc()`;bB`&L9Lmi_SxY; z-PiW^_H}0sMlM+`o0B|Cz zl2x-5Ik(|&-gp95sHm!{5ko&i@s5$3m8>idXl8w$JgKm2z9r?dAWPq892V{{F)^8rl(B+k362Jx68_;s zprFIz6NnvQ5fDh#xt|o;&B}u5Rqx%7G6TmDo;g%e;_ABPfs|IT%c7b;vJ4AMC!PPhK4F2?&!R; zsJ40s)XMy?Dt-z+3%D@>IGz{bTz&=f*>M^NEEVw7UphNGIr6}j1~U__G=_tP<%wY8 zPvCul(?pZ400h4q3AI)lM0a_3u4v}@~%ssThR5HZ5vLaE6_-S2EW3y(QXU&5o{X`wG0XMBYzO6u8~=7Un6 zoAsToDM&t$%dm#nC3q9xYTb?s4Zh*udQ^ypNpMXZTymC^xXoay+r%T~*59gMZXpgg zkf@C#Bb4>lOKo7s0Ce40V_9N)0>L&lXPFatwk{yIQn*M-NmK5qAga^fzpHdybpLB# z-=~H>y-GU_IQxKJE{r)Zu|muj6P~m2YrtiiMRexb*;vdBAy9i!fDj_Ia0!XRS90bD zvvcFtpX77>-VbTHx`D@@?MN+XUk@jZh>+0p%^SiLQGR~@(dw6%L53a`K0$|l^tsBa zIRPlon0>snlK|o=gO$%25hoTFHd@FUY^GU%?z4TPYS7ry^76a^puR8Qzz4t|^m$eQy79l%Ib4#4)wZ4MAWzX-s5{x!5)cqb0$1Mm8CZmG#|K-sh{XlY z2-b?7`v$mzqjes2qwnNu)*Eo86%-@^)ty2ak^%UozQq75ud}e=hK7dTSWyzZDJgD< zc<$J+YRGvv^q2=Bh-TG_t_(Y?`RuTS0)}e-?$jRth28b_yYTq%`>Mux84B@YGBVik zgT5fn$?!BF$Dh0I%q<@Q-U$GndkXA~?=$2KSXt%Ei~&qE%2^)F2JZrna04hwydF5; zMy0`Rp#Td=Fsn0%<==B*Al5@NnC#VMMuiE;d;snS4fst@Fxn|Vl>&X=;CqR39o(NO zz^zUTX)zs|yI?&LKNwElb>N5n2x$21$5K*KHo)4@v9aMJ?1I}N4{{mc8@5+}QKGFM zE)^Io_2>DAhBm`?!l`^J5Rr{~{`*H0uoXq_C(fy9Y5E77v#@&^t>3*wkCwSz_b2@- zz-xyD4x6d3*p2cFQlV7XfG@$(oB~%ISd}I?5gfoK_7>;?Fd=^@>LF5lv?>G~ECx8Z z^}t593kzUlVck_$CRSEa36F?SSuHpNbqCag&<%w+|F>_6x7WtUAMQeYAF=gh98b`8 z*T%!Z>z)Q}4>I;j21Soj<^~0UUnd_MPZz#(Q_IS^Mth_kfH z^MkJmYuee_iHpyGjBo))1J~p-9E&WVU4h7uLVWkqU{D4!fJvJA`qTi!0DgZA(TpcI z&)lf$Aao%JZZ0t+BjdB-(k$Rh`!cCKb9JiJL7B6{BOv%8(RDMR>kc5f&6xpBsE{)3Ii!u;HtZ0L7!igm>Q|;ws6XM@keB5I_XZqwVwO5u66iEe$@nAy%5~sA3TEMw9*h z{T4)x8-aw8BG2mJ5VgK;GxXUZr_}_!Nq?|uXm0?#5OLN%tMP9Em_BpxJCPLh-p2Ym zghG6!)zzavj>b_gUnUCY0{FkPQa&zgYMLn^BqRq4*63(=<=ovtAHgE5L;wH) literal 0 HcmV?d00001 diff --git a/jupyter_execute/ca8e38a39fba588d9c549b9d9bea8bfde335652b9009e02581d8c2473ca15dbd.png b/jupyter_execute/ca8e38a39fba588d9c549b9d9bea8bfde335652b9009e02581d8c2473ca15dbd.png new file mode 100644 index 0000000000000000000000000000000000000000..f6791803a65cab9b4a02ba88d760218ef5860e4b GIT binary patch literal 15336 zcmdse2UL`2+V;3cj3Sb(QA1G@MNvRds&q>%fPkTQRHQeB5u|I3kf?wTO*)96NEHNR zsA@!EXwqRQDh$nIf`62V$4rR^9`;0^vz~SYowLLXt{y~J zr*q$U+j)99dAQ3;DM}rX{Kk<;yx^rQE$#OE7o{O+*fYG$^N(7)CO+1s3ajoIdnmlX;1}@@Pu(H) zHIH@vWN)lj{-yk44Hr{?_ffS4^Xz+u;?aMC+aY|Fsd;oO{vU#nP?`|_`_-v2d}zX& zP2^SZ&!J~Oy5oB~gvgsEjE8$1QgkLea}B=#{!I&wAm^8%zc;^X(Yd6@ zK0(guy@y7vz?E|X4#svltM+uphsz%+UHJH+s5#T%fi9mFPt9?MfePOqZ>pOR`4*2u z;wn?cTe2MZuBqaHN6wez%81Tf3o+kGp`J{g1fD#e)Q=WV9KTn-G-1eQk+Wo*2wzbS z7Kz*P9eUhJh_S~je44nI$0WU_b#;4kIw}lV?UB8=%KVSJ_9V|P6 zkb35bBL4e7{kTz(Odt&Ci%095n3%Ym7#U^UyLWGu1kCsuH@Jer7nd$w5>UN-p-k=U z^B)aOOcc)?gRAB2i;cxa`G-^f+lmy}^I73t5?+c&Yh`6;OO$3mdZZ7te`T|q->=^h ziX)TB8@8)H?MvQ&_Q<2`Y@vdCIuR6YT-ks3A^*K8`t7ex(|P;z$PvxbR`0k!lK5xNg_lRW1-O!7-OV{g*ErpiV~Hl=*}&b#m8 ztAK^_!Nzcvsk`NjDt|STjW9}0=6$oHTMIH{B+W>0o#6^*Q+8=U&6QPgus;Rqwl8mN z_JzTUUT(3da7lT2bF0J0_b+Ml_m17{%sWFc|>#M$^>=XC38YV@o^+)pZgStn7@b%w~=1`VXZ(qtojB%C0q_Y?xn}Y z2}j9)0FwXFk8@zN5dtm)yaJ5Syp4>E#Kpx03wFRQbw63xe`e=t*eB1ft&OE_+I{LI zd}p=sXYg5q!oLPDe-H5g=2^IR1Eb&5&x!)D@hpGzNPP3=&3vX`5>AU^&5*nDe_+rK zLh4B=={0au|0%@%kATrXCrF5yIj8zkJ+c$NbOy+x@@7fO6`8R6pd&E%Zm(d0MejR;`70l-a3%6@Jy}n)3PF+}y^;ueK++_meX; zBX=t?8)Um|(g=sH?TOa%r5AVA=j&&+x9nO)C|Kugm`0_S4kpVstIBS*yl~jQDKRc^ zzDI2BHsa}#rlj&-_RX!*O)(bkalBGbzX6n;^vl~qaQytYlEgAHF@b0M%Sg0Xo8+{k zp(DeQdb$f!{jz?uW5kK}y(Tt+bIwaXQ+-Mri*F)4pRW_UpP6Z|X2J{8Qxn;b++$Zx?9O)>(+LMZa`l!8Fw9hgC z_TuX6>z~iBC3pZ+Ib$W;NpSsIP-iSgbm#F~uWx^QwD8q-|NA!8L1pv3F0wF9iJtX) zH3t@NHvI-;c@>R0W&x%1H^j?J>@$DzTB`g*>lerq0ee#aNgSIi>2WLV zueWKwdthg2pQm=oV9=5(Y>>$!A`YbRZJ0PwoAmBQsKck}ciC2o-XFS+Vtn`GXM{z`lel|A>%Pr}~aXPsK_Yf4ppdjEqqUi?a} zUG9<6k3+9U$&+~$py^5Wjq$X*w&8ViT{bB0>D zJJgRbl*(+&Q7=a2!fwr`gG0A0VXyaGe6`IB5Mx-|W>G(F4VUrS78&bFuM8|ynXngs z+8O>DW-tK&Bh#fTkJUG-Zen^72WpX7^_NrJNt!N|=!1lv-EH zh3D&Bj5gqJT{qyhhi=JE6a$#Xci9B1;rX&paQl_%pNT9n=GQkZICHqN%O$@uM#*dB zJlrLcC39jJj?nTj$7rlXS!L>}ZPh|0BUgqOsUAT={yI>CRzMWhr4L2Nrv8k-6ig~6 zKcIR9F%$ggPzHN@dzXjBH0rGV=|(3(>A_&8c;~F<;416#d-rx&xR=?f&t>V^wLkp+ zSUc81LRhD{l{NX?%Nu_AG)k-kKq1nk!nsb1tyZ>bCRUe>$i#6{%sz`uAI5<1+pB8_ zia06k*#}r7fH*g7RQYh*C9ljrsdFt3=OW*ho7NY!M8{I$)nQ~}2T)Vr95i1fOl&#q zJCXIEu-}`S+sc70>9A4#)7uR~3X+P00bbkv#?pZ7sNyYd)L5S8Irt~S6&(wPSFJp8 zozSftcG~*PlKG&m12OtVTlkEdL(bLOiOCVy5l;Ukzp?-Lt*;-~s#Gq|P`V!7mG5D+ zq#gB|sS^{^JRY$<2?`f&B0In7^_`t2wINP+ve=>F?j4wt2NtF7nxtyZ3d5=QiId;Ll$GmsPBUqjwv27z06{olsDr4QiFR6?R=rQnfPUOE3FJeNt?w%`?irf|7G3vu4FhhsH>Cxr^*68 z*otJAF0)2cnzQq(%uk+Wv{wZ#$+`6sqs`o!p@fPT^%zfdKOS;kQgrE*r1utE&=sPi zqY(fy9SN7${yMMr{f|tm(m@B(kB4c&X^s>H45BEF zCPu#4sFKt2ClhD5aaiRf&=12cNBQ?jr^wi8@$hf2=yhA1n~VkZRX$Ky7b)ftp*C+y zU+KeLufPLjI1ZFYu*cDdXB*pt?N$qU>FWkmqD>X;l{$uP)~dyx0Bln;LjLs%ufB zCmcIkAw`PPrl*kvu#jg}p2uvT9ZQdB(w44_(v~U{EiNt|f0KR!^kuSZ^~$^^3814P zNu{)}T02$>e(CSoY34wCpt0hFF3R$!R~k+4LPe@{ClS`qw3QgAQMKTa$2+o3zAw(X z^EX9)jxnf#B}h3BjV%J(CG2P^%&w&Et8kEaZUDD#KK+-vLZ!HnDVPlh*xA`x|08Gn zHA@47qNeqT%~xvO;m;1(*5=H+;3%n8yIqRj2zgZ80Z^^%V|P`vqu~)8W`^soZ{`-T zunw5|Yis$No?aXRic>M8wKzK-`F;9vyXm*j$`&WGdG~#RBMBxHukYUuO9L*Dx+Y15 zQ6US`tD%}bmyb$MgZ(k$kKeQoUYUF1-mk1F<2y6_mm;?~PfrM5nY6jS<#QY|tPw74 zRqoxG55H_kP?V0`ty6zx)oPL`Y}f$64Fkuk_`Q~H0C(><$F|bo+Pb4nNo^1GQf4Q+ zr0Xn-2Ec`ZAwtscqO?yAx)BA!23--zN1Nh{o49|RHXrnZ7EH()1*9^Bj+)o>NVQb2zATYI%xLI35WN;{Cs^g7vjU-nD@82!L-N8Iq8=Gn)^|F zz@J#2wBd-~=h8dJoKELV3o9$DN&$BX^cCP}F1H**nS96=_fFN)RKmrf+Gr|lT(igL zjsg8F{7}td|Md!1){B72`&X705TA?A*O3wd8hioMnSha_X`$Q2|H2#8r%#n;Xvf+E z%Vafr1VyR`E)IkHatf{w#i^hnQArj>d)Kif?g}^typ3~jOi*mzq^Ekvo8C(Un4Bj+ z$^k^ct8tM)2-oGrx z`u^n0?|=RP&-QD%nCk>LB;eB{93=Hii|iIUd?Z2Ue!bf-4m_@;HreGm_`ZM$Lr2Of{CgG|xuYrZQ21fTjG&C}@d;a6P zxn5kffP-N>hzV-`-n(@Z?>e<*(jrrq&y$AXn%dPtD>4pEiNwiHw_+-vfQ+c-4ETXL z`T9ahH*VZOXO+lh+d|RDza*;xWM^e%?OhA}t<+->l;in{_AG`VIXCQ&p4@t>BH?QX zf@#++d4f>L@r!z;Eas>~c5aEe9n09lEgF8>x@f)gM7ZRjVqRwEZWNzH4#BneuR`@& zi2GKXfQwL)2bl7Ki?gC0Ry-`-41K3!ipDLl)HHavN=a6aG$xpgu~&6J5pd|Nj9!qi zVtH2UpE0n_S=GH?Zj6>;v((!)b6q;%o!Sx+lr~J7%}k$Hb8g|(^{v(?!5Lhin^jmS z110zR)Fy(lOjC~kl*xM63GRLK@b&kWdZf(Lsns4ZON(~k_xS>VAkc}{?Tk<*UFY3u z51yE1mmR`<&Dz#IQ{$bL{tE#kvMF^1XJ5#;^*%Y3ps?>n2p{F!P+@(>x9b6-y@KYl zZKA2t)ytl+%+U{${lqnK2|&SSPcPMQEjX(Djh20ywGkpI$!h-g;DZY6KXb@$ZXz1> z&)g!gIx(Dp(qLx7;Q7*iZ;AsraiVowtER7i0Onsd67SOKLdT_dH5Z(nY2`n6b6?5p zM#aJ9obF!iS;uGfoabF}mo??>v(gyqg3aQZg+H$` z&VJMI>#43i@~@>De`9j}i$NY^(oq-KL4+Rae47F{cnb1cwVveDVgu znQa$+_u>@&t9i}8cW?g-{+b(mOuqx#^E*TwdhsRs+3{3RRvjH35GKL2Omm)Ae1-G# zd$jZS0j!G+Y^zbR>SNgKhaOoN7`T=jnpzlXSp^?MeO{pHESE+_56-N}-VR_a?%>69 zcmeTz z4`hS|A~8<14A8SR-=-RmML+qHg#tgnX6aN#NCUWO0|SkHa35ZeBzRP9e8gojrTBg3Xmc~bS8@GJZb`2={A80J zSiu@eP-0XtJwH5t1+kg}qt^qH3A2jJ7t1Og-`wN3-cP9D{aqflq{{ZAsbFYmSTX7X zoKBMjAZt`29j%pZ#n#0+#3P(z`gjNE)eispZAzZ!S!em+*0>5}n=m0KJ47Dh02~@ftQHb!F`}a>!9iQ3EKP%e zJ}{1M+exTM`opjkZHNW_3E+c7!kEXM1Dx7(FmNFQSR-S}`d9s8$-G9m!e4W6bq(2` zi^ezB?!{y%h1KAIG@#n#i6*!T8?#ldSj5I* zxoB?$9`m`}anqede-TW`N%t`Ww_I0KOY1PP(SiRQjeYYL=)`2m#gS?#=aaQdVe*@I z1^>xUPr^9oHOQp|NW&jMsGLSI6NAe{=_V=LL4FSG2>$kk`3&Rf#qO`z+q5(Etpn#e zK#VFlwf2snUfv5VE?0F~et7 z>-Q#8M@Zy(*p=W{VJ~Xg49AgY!ERbZr;c`aYiPTwCT72sX=-=KG4l!Vc9MjjEFvG* zrUc;>{~=orGc-0H0iV-YTtY&K{FVA=oN%GFobb~dQg#XQj*83as+Y@cY3c}}9p>)m z)B9z)eEbh1K;`=l#uWs_l8E~199A>%I=Lf|fZ4ETVlh{}57=L1-{^c>IoWq4jwdkq z3>`TWM7dI>65`@BAd1M(Ca&_I1C>pQb4c!Kv6Is}QmXU1J<9;&0VF-XUFztc$1AwS z8|W(8sTtRX%0u>==-unob2fCFKDhok%`K^i%N7Rwg~)Q9HRaa{3xmPIK=fwnCGv9? z;t3Bupf(7oc)JEIQ3o?iN)*)mW*x^!XCmj9na!gTHY_b*IOQ-WnE-bJ2QQr29`xy* zs8G;DBMXa!Ya5jxrcqL_auIg&ex$DR*q`^FgxKlI+-R+kM}yOYq?pP4YIT%^s8V~? z!6IaA; z6MrVOjH#{6w)OZ;<`%&?3dFsKNH6#`R1wH^6#JvLd3S${1aYczVW0ve7N1qG;CT0A z+oQ3ERzJ$k-4`5)`TM<_QuT>SUiB37VrQd#PHTpC8>Gr}#v(5dw7G5i9->IFP5QSxbj4;B4T8aLo4NZ4ghx;(s z4F9Q|#9UP{)sdb$bv!ac^OlCr7+wP_190WK1FwW5(G#iY|K;aRMX*I2n2m~h<=%9x z5yRsTQi7TnlObvr4w!xh#=0(M`{!TZt^i>^`C(}lA05P09asW)anK&fnTsHF2fD7V z1i=*(0&|5V6(3!aFgUcdX4e_x%j%b>-@>DP3#5njiPfb@s+$?NE2iU^+6zC{J1l}~^4%EF*Vf?J6h z*XhWqjLy_eiU(6v5hoA3A{NIVJjlzNS{<~|{RI(LbNNwLmL`;mvK3$)_VC2b+kE=& zz@1B@Ee)hS5t|Q0pw3Rx9cIVg(3t)ZPB`@x(!@>b5`z}U#|Nu}Ny8bj*7Z1n5M?Rg z^;nf%G^LjD3tEIxResz(>l}=is<%^W8ghElPD?g>Ul$0y6^ZA^l;g=)jj` zw;k@VNkA3u142dl_(~)N{Hyzh;vJJ-Lt%;|MJX$;K%G37KfW3b5ps7-bv83VL4*3z zKu=8`7+`h|oSnw5;O_}*$m5v_t>AoeHzjP0nU4G*`4iiH&(DJxL#~IXIAntW2{|Cb zGwhKB-1B;BbB0pnv3twGoIwtrf+4E~4mcySU|81y&?kwd1jslfT`~>_k8XKfFW5Zd zL|w?#35~_?rOJj$2*MDrG}=a}mDG+lz1(V(m;w$Yusml_j@Sqy4)E5rue6UexH9Dv z2li-Q#Z0{?P8fDmpf8F?SI((bG<3V4qcIS(-d<-Hm$vF)PQdbz2s%2l7vKM)y3psP zH1ROKb9~`5L4C-9L1HigFqOe3x3*h>>&J*fTgxeyYyvm1#JB)b@kdR56l*}0IVN@=5HGl)6RhPx*p+S2kg^Q8*NAr0Ukr%t zaJMrGqlsR+Q{X2rPncBK57$L{L7$LA?Ja@10hc<`uCm!yZ6=U1rE2gcna#oMwq1`I znZN8l3(zPTwq1=3t#n!dnfr=82CL+NqRuQrS`mw#X^V6D!B+5T##ZagY3+cDt>|g; zWW~xeU+;{FB1NHZ5+Ze-|J^T}^d#j0L%{biVst?CuTN_IVGXY!`8&rizTI#!}0FGs# zg-Y}0uvfGl5-%RF48&eG;`^V)V!#J*YD!ef5Y2$P5Z%?BtX3X%pZIdKNlr;sbjl=D z$*Kx=omh(GuDy-$87>mE7wrv%t&z7-1zi<~vLKJjP#m~C6c`c7*LC-Rtrm7v&DS%} z(ijYhK)_9LI;_uJOl6=eE+V-2m9trTfsguz%bH;;8bJSGRh$F#FvwyfMPT=I6noJ1C6q{>ydU{pu%Qk52qr4g5)F~! z6|{s(UvH!6)++~bj5bUPg#vwKw?%0JpJ5gP@oa=ElAlfMB1#Pa)(KyJ zdzEIk32KReqI=(xb28(BZiI&ZoT(m z`)khRjedlN57bf}*|t#7r5L;l#-SoXmy@CWWO+ofx(>5AJX;I>J~nhWqA}nM1wxJl zEAO6Ncy1AQN~>3L2Sz6WHY98|nu|wcz7mvb=3eB`tP2DW8!Mt>>Cj>4LR?c%4YqcL zrZP>oN>w9tKLDf3T`&^?I0me{gRK@WmLg%VsLg5em%tgPv6mRl$el$6G=c&U#h$oP z73wTx2dkJ#jQb|})=ph{R&W!t5HQR*yoHM01oDcg?BhHGCbtWxZ|a1>J7n23PkoisaOsAzG+QEr7J(06$#%oo!7NwtG# zp(ij-$;)10t_IC){6%Ufe#Gw8S=Rv9Pn*> z3ybeZF=oHPA+S3DBO(lv$Gh@v%uC&qC{pD)1G_ePl+JioVquu8eJzTa+|2&&*8#VI zYK=}#G>w@2!$WbK zDX*^GeivH?pm=#!&E9 zRD3*+re|`+ZBdlicaln ze%0I1ek%JSlwVrfKR-?#5*BSxLf8~E!xwT^Cnwu$6uA#9Ium$_Bs3N1k%phG6$xl| z^Ml^U#|`GO7ZM(nHd>J_>NCg#jaMxZqK=~^VX zm%yZLKDG|$2VSpW*@hz|16&tOdsrX{-Im zJjyNvMUx5K8Hkj2E7TNZ6$Y87xV)>U$Uzb0&*U*U?y~NEPf-_Hk%@Is+?m+ILZdJT zvMx<_D#2oS;pu{t*5l8pECNsLRFbkhV5Ab%*)HanEvB%34?u(jh322pl-~9d_@~K>0Z4J$-+}?wnzzTH$vaBu!z!(K! zoE|W$GFbQJ3E?}4hh3MTNeGFYEa*POU8tSDBh4u`8~YJ)VQCY+nv`%Fv0y=KVyd-n z0eGGnP-n|PTw^2f@Xun&ui>zegR0hr-S#KD4ZA;m7b1M2W>xlQ?ttU{=*J>90o@%F zze=rF$Bo6r%p}6oj2D-9C_*C{6%K~2Z9t!~>YaG=C4_2A;+tav;A)b67|FmnM}Rg4b){}JHs3m`3sJKt96LK;j+p6Vd~505 zW5+0i+UXhzx3q^6E8Pq=5U-nfu~q=GC^I^T`m8pUOjbr}?*;VbJe+xMyD|_eoOrl{ zAkuIyWeoce(SCruM4N>4&52s#h7ttnH!@|Uu=VT+NYN@_NBOSIb(xf1r{JDSggjK~ z-H)4Ef%tb1GOD8jk8%Wl$ z41=VX2baa+sPJ}z5LbHPaB#?50gUnJz>6c=Bp`0Pp$AJ3IC>GYNo5q8ki|U~GNc3d zR{G7=Lq~3;C{$`4css)jh?M;6i-dxA@P`zX`as~bm4W!tu83l65Ppq zaUy7z1B2W?&5S({B0C2SQNC9A!10}G`^$)|d$9~&0SzyKhL zqI5$x0n;O`=?R;A1QdDB)`nFDPUQfIZ2;P6H~`NH00fBC&wA2Z1#`xQe^G2bTa?_? z`SXp|@+A79Yds#-D}@){JqIqyv|8f6RAExR*nw7%CJ6+$0y=zP1wGXo1U3Hz-p9Z> zXhSACgS{g8)oed^AoB0||8(W}J1Dez_~Hz}H@@MJUy9X*0_oF=uvfzYnjMF!!HERv zB?bpxhlHI=c<9!UE@X&Tj+3^(mGZ;Y;n`D?#rhz8irBtqi&ly=qpi9mXSq zz3GpPve8vQK8H-ZDM>QA7M{|tb;B>S07Yo zO%m2VY|8?sbQes7)IJ7~`)1t2*l);fp@eqDcHX|!$A5;C3cUQ$@*MgBXyrj;n1LtA zJbMhO(Y}Lzd3!)vO_Fi0dF5mk&X6dppAi&`ngF^{jE(cIO{{X1M57tsjsbNZr|Rp8 z=8ffrXKQF;ui(&L!f>#xUO^%kSUJDi>}mWqZi2~TZ-RpBZ!*J};d2{0%g_&Es3nO4 zb*jf#li*k$K#~G0%>)W!2h9u4KNY6!hYsVg8ekU4_HGHvi$f=EBlaNy$d_2BVEP1D zY(<~{14-oykZjmElS5w+y01Vh&3Wu3#rlpy2%BmVity1`)tm)Ro{Tq^m5`WPTu zqtGj5fZ6~Gj@;Wjgn~5F5?8a@Frip%2*FFJ+?=HXY(z#rK0Z!(x2<_ z;=5(oDgxQ38L(G$r6CQbp#`4gGY|*-PlEeCM6H0s{a_9oQv<=p7J}m;LA;K{MP$oL94&Pqf6P)B^|F_yn&;zsHJyAr#@}k}~DW@Yl2bf5pA5R}0|1E|4Z-O33Bmd1;F;_VKmhy%< Tg~C5zdZc+w_q)6!XMXrE-wozq literal 0 HcmV?d00001 diff --git a/jupyter_execute/fc6c9263b60697d1ffe4675baf4701866c2026c7b55b054e58d5e8ff7dc1cdda.png b/jupyter_execute/fc6c9263b60697d1ffe4675baf4701866c2026c7b55b054e58d5e8ff7dc1cdda.png new file mode 100644 index 0000000000000000000000000000000000000000..8a3d8a165a65e78713cc06fb43a63210c849dbe9 GIT binary patch literal 29738 zcmYg%1yogC7cGc%gLJoagERsnAt4>o-5}j{2}uE^Q@Sr8ofib88!io^lt_qlz0LQJ z_r`OKqYgN+&)I9QHRoJ&e^6I_jg3K$fq;O3t*9WUfq;P63=Sw73it_IGP@@DN5tcm zo`PXF%&E|>Q< z+{+kfQQ#)%t_u3@2ne`lj|XCzRH+>Tf_s{x+)FK=>_4lXKAHUN1B%zgzh>U!HacH!%vNE~n(ZrPi?!j-HIdHl&F_bCD zj)OC`FqW*6W*z2e?~=;Hwg)&q|L2m9*T~I{pW_zuQC_ z3z@IKIY!5!mP%Bvx16og%6!!jJ!MhMNc1?|(U0sTZ!*jq64wJSpDv$MA#XHP7zbz^ z6q4q#cQQrb$v|cVcBhce4;$@x*KnK2iH6fXyJjEX7jko^d%4GQd=^om4MUL_*&=tO z6%?j};F&qEM|K6$X|w4;dz{0ui?+cRd!prTxigN3hY(8=jvc9@&jy#ae|$UwF&D(B zJ>S{Lc3pB!YAqRt^4~`kP>HuuPC?n8NW9oqP)4OtXea_7FbAi}C5l~_Ryoz@&eq{6 zg~Awk)8r-=wb{Ic1oalzj-aoNVc3t8LBH1)WHcsC&=+hJ4!N+q$ySnlhUO&{iVGFr;^%XyUB*hOf1S-*< z-wVg56H_P@Wy3~VFckY|uxi8omfdf<@tkqu?u0VYMc+(A*t9na6Z2n&<5C~)kTi_Q zd%p_+UkOB`-1LtFtQ6*QHeIZ59&kgg!9{wwbS9WcdOM#}c|qAoNZAE%c0`d3^4S~C ziat+KL2RoC4E&2lHh51$uhUwG?=wz=*_N25v^0|CW|wBqItX5`WE>6dYI|SvdZuTl zac$d;?6(k1e<^>jZ{v(jSVGW3*O6QaG0l=Uo3rJb*`l5?721{4`7e>Uj5}<9e*t6a z^*SJZla2OpL-TLn>U=MrqOZ;KBWKp$`N87c`Nd%CqA0v)JnBi2N>-{%_g(6Ej@aO? z4;cn6MB)7tB<6bs48r&S_LiSSUyi3pGqU zJb2vPwa*hVF)-+#KmUFyNlQyB8N&j>Lm$k4c|0M-<6)Z}hOPBcyN2@Y9-?l6X(2q8 zjIcA2$6_`7simc5veVyN$Pq3L3qvjx5BmFasHMG~!h~8#FF*~A&T)HR8-)9F9jt+b z#{Jh!MW#q%4yjl;INRG}S>pcZac9358eBJJaHz#et$JSRxSt*$f3#Kv6H@2RZ8>Z? zNA|GA4j;8k{aWejZ*sOjHZZK_V6}aki~c)krj$b zcXwt(1m81e>KAaaAlqXi+FR~TSKWvN*3eL@h~+P@VjYd1f?Nj&tKs}8N=1KuBBMpm1J}V7(g#p*Y@1(w01p9O0NT>)r zyJw%+bTicmY@wk!-p}!6&`#$2qCPTXa~M1&&o? zB>Hc)6ND>$o_)i}s-bt7$!#KYcMWx+IaY-RE>6AAGh@;K%LC0H1lvk$VNjDLw9~Jc zn18jMBxGTn>-KCl(F^~7YZ_{TOD)|OX+J#}e2=KBJMvDL1AcyhxQ5-Nn%Wv9{_!!n z`#&Sc__N%1cViC|R7NrEi-h_M9}B^)9~3eUc?|=Z3LYmH0)4$6h$`mQvy&%#w3gbs z{d>Wl6jo!jU)_KiJa!$*Cvm_Imwx;j2NN0mJeJW0T23eCBOW)wcLsBCuR|@D9HUEz zdCK8{Ii|yfCPZ|QKcT{bH!=!nx93?_ZFf;~@HG^}k16~+HbH6`wQU1;OG5M$Aj9>p zRp}Rol^Ha)n6N)b%M^I4l3iwc2Q$~+={Ne6_lnSx?I{5AuB3}7%e%60Un?hqJRRB(Ku%OBa{S8VU%hTbIPWV$MPrgKzg z?}&7*2Zf?%2`FRG$;sr~RScYi#AN<-wwj{-Hw4l{V$!qJDD4wpEk}SM^caZI5VIwL zxvV;QY*H~HuLU&BCP9Z;!%k!5<({ro_R^gHg4GZcTf389Lfo_;WAgV{x4Z({SEovp zz*8YOb6^%@IQGvBFqiP$@bJu@%X24%ytUg?SsAc`^%|8r=YESw*^$%W-Y@kcH`4sK zgbdMfBEwPVCK3`pvZq}w@Q~&Q`s=k4gy5k{?69bAZNct1bydX}ALq1bx?4)C#*s40 zx(RA(SoZ{suRK7U(e8iM2;#(Hf+Ka0_9|AU+F(1+g{N%o^GF&J8=UJ+dhehts$NsJ z*ME5B$8uhSQkcL?(GkA4JF51eHLVp1eTQ3 zU}9yze5nfm!^))k+||!ClaOYZkk^xbd7StYrjE`zyf!xNSs6s_in5EfwQZ6?M7NQlY9O4W}})1vYAzS2kxlrCYsaZqKOrdk^R@ulhG% zguje0zS(8iox@~S)v{_JoHhg~tg67-hzRY^vTVI2uh{Dut&g|=2#=cvgx+(!8Vn$G zav3Z;w;Cd7NE8%NoiUwZzTBp0@xg!hZk95=FS2|N^B^J>a(Q#(5VC!AB$sQO&%#L03rQ3Sff{rD(rS54bYdx%V+2_imw@;{_-U5feQ$l~Y@DElYSC*>JsU z6Bd~e%OJ05d0R$$<`uV}obM+iWSjql!%%3(qTVJdr-5tAN8jS|C*j-ysWlw>l*Dm6 z8Q=*tGjt@Bo&+lOTIx3$1P$&2Dom5vA|V(}$_(;vEpKf%`Z}qkJoccuALA)1KVtfG z?Udi7@h!`5O$KM|Gir2oRERLsKh80}uGHPr_2AlhYr`&a>r(?e@_@TW(L@l-j*Jm3 zalF(7GiwP`=k3yiPH<7(51DTY>k9QeLM)tWQ&(6_y8T|W8y9}GMOlCaSPdXFkd}t5g z*e5O{azKmttVb34BC%Wj&MK;7nIH=LF~r)d+rrL}Y!Rt|j*@cb(ZvQtBFD6-g#62Y86+pVK2`zWjv=%E zXokalSw8};c~9q!WPM<9BVo46Tk5EJ&doImpG%(Yi&Rp*K+~|#;y%aHNMFNIaR)9b z$#$3_(C7_Ql}e3QGbmU-E+6#-WMl=MHl$NRtw~T4qh0kZx8|pdHEX?_eSPF4$2rA> z8_$N8AuhIS?`E7=pvT-jLZeqW&C52<_xJY*07455TZDy$pX6+LH$}Gcpu)u zIp}XLDw(^|zmnOLW05_XDWwoLA{Wn=(PTl`Sgu2|pHuRfm>tcG?pvBuaJbyBto@oT;%?t8$-_DNd!be^I5>Fg z_k6YCGzTjrx-*_)E?Zl_!G7t_GL599fYwYTj-Z{|J0pj(R65z8GiLoi8-pLj7P>u^ zXUK)L#JVnf4ZL$q8TYM+);j$gd{3=$s6^!e4=I$G@9#$@AxZOL%fO?5+a5$^&=~fN zb>m{4LG|iHQY9Rp;SGw1+7Lg)OpIl)oHQt?%xl#5)b!HT=l&gMwS>J^c1;!>uF_(c zDSx!qxd=4_kesq-W@cvo6@d&R)5A@ky)lP2Mhr>fy`D;V62ly2yo<`1M7r6-H`ndt z-59eLlwV7|CtLd&>&Z-$HAY`3AXK_ugw7KF=a&63M2<^e(LwAVuC}9ww6}}Z)YJ%Z z z`+42$#wPDJzB%B2U0o}^H2~pWN&S`%*q5-Nu-K3(ot$}r-DEYz!7{4d%s|zm%Yj~s z>GHA%t?YciB%IQ>PrZaE#;rp!E#qgN5mGYKkSTxK>VJ8m5}goKWo?q1Vr8k7>YxL? zGe3vmQ^c<;BsZq)JxpHKj!}c*Gqxh{H@@1w9o79ue9nKD3JQ0Y)yS-S!^54LO5|<9 z&h^$nIcv+B=;VPzMRm@2;p@69*LeK$;GZ3u=;GAbk>fd2Ia0WDm4n;nim8w4#SbIh zTA5N+z&RY3&o^->wgxMA99@j|789GYtmWI&cW>%#blnxU$1wHz$;)1tleJNlo1U0o z-k$2MDNHjzBA=t-G_^C2+nidM=X zPp-G3i@Zlj_~_ke@P*lZyJc0GfO#w#a63HGGSQ@65Bu%=7@|U{plQa&*)c<1|8u5? zZ2gs1!iL0~lh<>I4ewipcVEYcthCN~80X8VhHKC1ww3X!v~VGXsBC zSFZYREqDl-1Y=A14;(NwSRpyDsLwSirqJqg3Eq5wqS>WBlVNnCqqE-}CuGT2NW#5{ z$>w{d;(s>3Dc@<^dFJo@@T?&blOoROWL&B2p*T~rGSEMrpT)Y;CzkXPaWfiaqO7)8 zw$e*IPzGJIUY|J5l>GPUSD++B!lNHf1|y98RAN0}1Wb`@6o(P9&t`w_{qi_m#f5ZU zs4e~{R-{VZa0>yG59>jd^OpX<6H!dwS}Bi)W77uuea_39cR-lvhQPw-T#?M&Kmm#_ z8X9~ZuQrl`+T+IR0+E6hR%qf)J{ppXI+i&Ll|5j6J`>VT+tdIuB<>E2(#&OP;US21CdvDpylkyl3iX&rL z8=D2xA!qwUcXbWcun@$7X3SHM(%)U_sD>!i(3n&$E2#yl)1(ob9Q&+iIxIlylAAuc zSXci7+5h`M*I*<2a6iy2_3J=>A$Yt0vQX)L%*>}Ewi|tAO)f6U4_!VE~Afy{3ZiD@afZ!f&w<&bI+Uh zDKZ7!pGd09KGS)mV+t!mjv+vyGm|AUev3VQfAV^FX7JLh;5AFz>N~u`#LIMk$9#N| zR>i~j8ADTANaoO%aws_frE)*!J4Ar8uy;{tPm?XSP_*-tJ~6?gv+-m4Q!REKX!Ljs z-Zzu#Qgx5Tjr8gz&MGC*PXNJ{_C@h>Rm|60L6Bc#qr7lMQ1!^R;afg2PsH(PZH&f6 z%^L9OGDCJkn`Y|U$@=rmNK!70GxU)JF-nf4RWdoOWpJ^_!SbzFl~Pc2w68w!DR=Q5 z8XipLsx}AFS*yf|zVjU`^tf_u_sRad<)B}6Nq_hlogYTd|5ONDW(wf@y{a)RWnQBZ zBQ}uCn^1!!dK)HpQsp>vZp*?_bE9_AkZA`i%8R;#o&<&l5O<;D>pn*5x2@f`0D@PW z-`yOkL-XAhHei!gWF3CUMi~^(Ug8pKnYsS4%VbxDdMP9pEmyiSII9U~8%vsJ! zK)I(*4^g1xGS;$KT(ma@F8_k!*v(&^3PUhzlQvAPPfr*yHT>KtQhijzSa0h7UW2Ul%aj3*s18HC zWXQEF=H^amBre*`l>$fmcF8zT2W{5$b-yGC@E=Oj0Fi5*=CTXu~Az1okY5) z*GL^5>IDj|p239t1mei5%jXb+E)m7WO_zn9zk4BhD5sb7IQ8dNv(Foa3jes0*A2exi~n6d7~JJ86Sf%WNn-dYSov#&%TWd>SxHbT$tpj8 zj(2FfwV4Neq^3v4dULKskwQ*xlUmT~iIw#2Actr+cvp>k#PyaEtS8hyTorXF#qg9% zPCVsN>4f|qB`Y%|k&!VSAr5mV*@((;-Nn1S?)}U@$>Sr)Zfj9h=WF9E*Wm9k_2shJ z-*bUs^o0&?u4DujnbwiAemLiU-516;x;4_(2DaSBuA5nN9#lrLTa7BXb_3+8-Gq)5 ze8TyAot|$|3?+mbxLT0g@Q^qROXU4x!Ezfr9GY{ZCV z?frajp_V3=nDdDiRJ%g!0|ArL#^xq06bY5XNRm!YgMjq=#!K?COc-OIy7j3iM3%fE zSNp2~%JTM`voBaes8}yLB%BHQM4C3l{m`2jVF#{;&CXN+BqEuPq;g(fU%&q~Omuzr zdoUWGfx}puPLAN@2Xdm)>_+g6KPl2)VEnOlV@k3i-C~Qtct<|5 z5~gt(^_ucLs@4^{wKzQ^Y1~sIa*i@+?hz2_N}b*FLoA!y7|xAE%6fwKV&UZDT#U=D4cGO4J9*SismGcyZBR5Nd;vm zN?w2efecWOIZ${o97q4!QfzqRph&6I zE|YjU#iTn(rQY&fa}^M_p4C+-n!9|6X)3xTRFIHgPEG%UY=Nv zq{e?@)qKA-fQ=xZKxs!wPYc1D67`7Qpp8HOvq>WQ8m(z!(VoCjjBl0?Ww|u30=XqT zZ1!skVQh85Qlv2(+(Q&y2=N>J3bShV_hcsJbf9O9kB<-hc@^`_415-1&Xt=-F4gIG z_8oLM!3@Mx2pgQ8Lhy8sN4V9?2^x|UH~M3v+oiU=0XN7fDB*>7-4Q2HN7&D;adk-q zOMZ1MFPZ*(7QcRjhn~5(+2paOu|1KemL=r$#_u&Tr@<$7-5R~Gu1A=kqg0h@f3}=(Yb*T! zW7G1W9mZQ{g;`L&jF;c~Ha6#7|5tBMr>2It3zMXd?bEtDr%}ssoh&yOs5UlQP;9a% zpkv$m9(K5jH{_kHurylJO2noW-3LT9c$e271IhbKi`Zww8q8|BaqaExC%-l~&3@CP zCkgyn44?nXT8qUsYbLhCb5~Q=j|0A1PM2t&#F8^s)AboT4W!c{%}=uo@Af3>X;$>7 zMZxB&!q<;|p2L+^S@0rjYisX1{XQ2fr`3D@)^Yg6J0FQnA^&iH7k`BGosC9zcKBhMQt+vC$MOgv+C$omZ)4-QTInhH)1K#3onci!EUB^ z_;b(J;PIl3>kr5hoXh<6GrBdF*v}Yr97n@MjikGMQ3r0-gQFQGDoJzjKH?}pMoew0 zuJhu`H-84j;$bLIQ^eh))zD1A&1oheD;qQ?EG*-lv&(O$GOLh#w56+9oc3r^V^el70 zYP#EF)BJtHWdUt%t1xqjkG)6wRnZsx0Ly>L<5@vo#3_gM?fHuJM6S~>^KYZ&nx$nL z#W5#=dNxVtf0i38hX^P>E1{Wr+%%D!1@7R`&DdxjX4NpaS3(olsU(F_mP0*0mz}A` zb855sT;gRTQNP^$>-x||J|SeO(6Jr^b#coyoL>9Bk}RV)1wh+=WugGvBkJ_p z^YU=`z@}Z6$^uIDMmLE((fc&=I3r=cCZa@Ty^D+=rQSNidG*H;wOp9>xVKN7y)z;r z;s9o#E$vrGB%Y$y@TbWNBHeq8lH-_7=5{u>9s&5YPBfn5-Hq%_SQ_m=23J@0O=dZc z{>zN4ygV8*errb0{n;eR;28nz9Mvco-qqh@V7SJU@({&Zk1KhHGBc1LLn(mX zBx}d6zBNRhV#;nvY^}A7!jvUlYy7=_k-o{?L3zGyz@zMUFB&u()(%+~H%UTlpT7&V0x;q{+!_gt*Koi%vvmy2I0lL0#mY2VE#nz*sAooENT9 z6q4XcBa_MjAl9en__fV+1kI!q>I6b1Z%Su)-~UudW>IrqvEdL;jGGkToZks%}|{&o00BRpe0yimR2SBdSYBMrFz=7yb={C2Ndt%hDq!~{mKc8}pDbmjom|*| zvyUrKMT6A%iL1a^DSnvB>*woY0V;o%$bHEJORXuBObK^aqNU(aF08aO$^V(1Uc!FHk z`UR=|0c%}5a_hjC1)fmKCHvV1)knq0y%@7rUzvZuj)+ZLLr_|{*{2IVX*Qk8*{IE9 za|0Is)oD4EL%;r;Pa^^ygevm|{y=mbBGiYk@s>kP!4EkA0_qov1F-#L*<~}!Wm5d%^sGGx9Xfgn@K6c{LP0kjgH!XoZA z0vqhQX;Jjr+SbShOHuQXsr+Z~BqMzK7=pvGsgqKx->!-pfWKZ$qL>X}*gzu9MA_Ry zpR2#cg2SLuLaTI?E0?=t)2gVt)s&oVp4Y|Z97%$Z{hk}2?ooFL)Uskm(wplQTN~kg z`t8fMki`I2jY+p^$(xMtpV>)NRaKd9CbC5=>Uz=Xek^i#_xu`}?dFTuEcKN3VOK#{ zZJ4NIbEad1tG1`*CqK{`C-?fiVLq%&vw!x3;2~f>OI(Fw@PZ2M5xS5IIod{;124y6 z^LoBAiE(@Jj#kftw9ZydqDWuRAX^cJFHTQ}7LDdfr|HOm)44|c?d{n58EUf2mVlsw zAsWTU&n*#n_M+AE)l(X!C0ac}cCyy6@fJb6amS7ht^>6%W7ir)YhsU_jGs4G<*@)@j2z=XagAH!( zuF9c(!t380P9mb?7B{VVNYe!!h#lsuHi6aTDJp8#LGa(7ANny^Akg|)l7d&5Uw;nS zW%#i;oJUNIn#8$Nx>nt)LW$z>95Y&8Hzo4BUB}^kRn#yfM2}qTWExDM8X3*tw_EEF z0Ud*XN9%b5@f2Gd8z6!$s?_D^AdRGdNi3am%{~QpMyGMl|8Ize>3-+sSnR+GB>1P+0kG<+GomqW>}+gNed4CTH9 zBpm$cO=0iA0*__I)VRN*VTS2MIepr}&Be}En6Va>4pU(a0CO{4tX#OdUSrg*MlZ3h3x0KM6<76ppG~BPpA(X6DD@p$b38PrK?X z7vEbe>#j}HAkII)q)9P#7$Dc#UCLM6i*W{Wh>0ZikxXgfpN>@QzW z(xYLidG*`@K9TlGQ+uY)Y5cb(4zaTK-tcx3$*XV77YuUm^c;U!b{bEB<|3!32pxnB z3uSj2`LEmOKrJTzi@kvfjxVt%=>hM4;gri` z888;H@mr5VwLtff*OI5FO8+=6uBwPg_>x9yX0=6%JNPup-IwpF)RW%uv5#z=+()66 z>_FZ3_=7>n+;hAs)J90hF?isL85qnbN(WK#bh=HqnV5beiei3^J1YJT#9FuJ&tk`X z+zHBQ{M)Dh{7mly1f?t^u|D>&nB&oxa6IbxW)#>lE2anAKjLgGA%U$|QBv6>NxYYI z2n!lN3&6E%3WLL+fsbcRoX9bQ4+toHr_l-mfMcBkx356yXO$?fIC6Q^gC|UG8t88zL34q zZHKM<2e%xC@n3Jkst=TDs8e$9?_igh8X^U=l1i zuJiZbp5~*75rIKO%3~VRkI4#A|NApkc;R6SLHm1uFqSzvKl+2%&JcD&%O?iSNhE}O z#oVlV?m!B1Y2p^t(|fpjW)f6<@R@uL0|g$E^419w#e=Xs))F z&G3(249f%Gr=d4CAKkiHgpb`=mACV65ZXKUgyzTz`T&uFCuqYEV|WKcaq z{gv)usorzYCO^Kta*qY9&TM#{rpfWGl-dS`eSJzfmf+z;1o!KA!(`!qKl?#ZOGcTqR^RirR^IYi)%mOyk2Io~#QSZYyTLbac zZ{_KdIl^C#QEKp=4nlPcRLjA4gU(vO#=VS{v0D zK6uk5@e|8HFR`=6z`~mK`?p)r+R|c+gM*V99v;4Za8Px&J0rcOcii-GRS7%!pp4Vu_@7Bd7z*A5D{bSUxxBSGRpA2M( z?|BgkTge{Q5kCVnGnztY-A4G~A^NU~$C+qljgYQy;}k z2Kl%RU{%*DQx6sO+;=_QR(Z6Z#_r9c-?|02!*_Wi`9DBSN5(k=l0U}_z;mSl6%&}7 z>oyR)+(e~Vi@b(GSAC>$B&A*jpucLa{~im_Aw43D_g$#brC4EVNy%tDHV!q+KWC<8!h z3n>0YJ7Cxupj;aii_7TA=Yr7*e z_ytHs2$NQ&jg1=>o-{1+LoO(IxKL-kZ_L!S;6=GfDEh%$K|GYphQ0kvXw*v zf!F_b&#uuLzk&rXS2h;m=H^BRv}$H~uwN&d=8!)QnW;?I8 zxn2FWiY4ZWYiQtSQcktJI9wI5pZ(qv`1l{&$sV>mR*0a0Edq;LMV{CRW@{@U#hk@Wue)nc;Kyfh1m3M3z=|5> zGQP}q2L~@TIVqQG6d(WnYkq%wwbJ360(=#QXYLWuPr9`mtFPf(r2TG^W~iQV%Y0i= zi4OYjyEJ6q9Wst6EiOo|3?RLJEr^p)XEDd_V%*r1l|>;F?VT>xtPAq?S73d&_dZ$^ zbUr;jodLpjjdqpZ)XGT4pFx`7+4tL{4agpEI{@ZhdXY}O`lL%bSov@c|DdC1#=9!96tDlmRN63bYv^37iDJ3cZ z)=nNR)r9>Ce$ZUKdyGPjZ0qN~ch`bkTwGp+_P})71Ox{x7vG#aEjrX@((lcqrulBc z@`vsMtt1crdzc}4%GMoW34fU#xB>!Ui&uX_3krCgDLoE3++W2o4ps1ff<#901>O48 zTaCyMe_}m3(+;M4oj^GRjO*sQ=5z$C;fRr*9fP63-g!R?@F|u!_{*Ow<2l7o*(tx) z7WW+|D3wG7j+d*t1S1&XC2d2WQ!fM^P;!XfOv$F>Eeq2#Rt-o5CW*mUR~dLa66(gf zF4>|g)irOdK17WTPaCT8?vQM)@cq8lb3+~Gm&Cnq{s)&cq=F@ZJoU16fufn@|H;@g z@WxWLt8I!&*A3;MZLw=I@8TUg$)Sf>KV9zf!^l(xmNO4iZD-5jKq!yIiO3cy za9+LS?gq*Hz_s0dN3}?TURGAt_jEh{6$TSzwcYQtZY$>76ZmX1MQ@&R6tZD%+5&LG zS%n9-WN`>r>U2ujUVD&=>s4n5ZXo;|Cdw-&={*G19RzNSN9ZKT34c!pX$*_h!Qj|+ zC)sod&dO=H73}KSsS=MBjZ-_g9}qXX+);*+X*N+>^}8qmY}h$a=3+^Gf^b7 zH?|{`4Nm>SCO_a9za}MWs<6P#Vpi|ZNSbzO(%lVgW=39^9ugQkryi0c=FMj}&8V6u zsR9)4$J#Ml_7;EMK-t#vc?h&Z>Hz{|*C~dspDf#_#csNK&|jDxC}pPXBmt1%?_m4W z@sIvx1Bx=P2VCm!2lp}^gZAMqvNAI1FWl(ChKe6RhjC*o!uv2@+xma^ZmpZ|Mrj<>tPX?A4MBE zW4PtQZ026B%vI{{^n@bSab^KuLJ|XyzYxMNc2PMB$HeUEjHeVmn@y+MP^+OYxpj(A zbTfR=Py|Xb4)8>7fu}zfb`YuQ2el^MT|gv|3-dar4Y(J1iaO)zE&<5D)5_<}Qc6Yr z)I{SlQ~!fcsefJGggZI!^?%BKV6q%8(wQ935?*t88U1bez*-SCs*K%VGzTXfhPNZ5 z9}9fzv5O6;`wOVyp>F;2{MVM3w7+M?`&q2?F*?*!$bmtc2!k}SeE$~Je@#m+jVovW z(4E%CB+$gd&a$RcLWRZ&n{O z3}w=yoTP2*4p`5DXjj#9;Of5s?0<@>92v#K^m4u4=T!#X^0hXWC!B9y7>u>BbHFjW zTCl+LY2KCOpQHo$J%Hps^EHCnU^EU03rSk(s|DXodD5#e9(OZPsOcc%DazEDPqy;6 zqaJ`{pmli~W$261+48i9uv9=Rz#YyA->=JHZ+jAu<>VERW)RH?2u;=tw=Y!~w8^=Z z6&7eXI9RSZ%46t6@$FjB&xOaQvC?`_sviWKWAUEw0nyAK>AgTf{IKu^GwIn-1%gb# z_j)UJDD7yDGT3~wKgi?!^cXxc2G~eT6UxgkxA90=9mPvT+fPb(akx}>!DbC*IB-cT zfuYiicHchvv-EO*_5kDZlS)>vcPoAme)IchzCnl~HVKDXSiIOxK)>fdn-bf1UFmx4 zG98KG(JLkcZ(sEAgp};g_(w^JNQs+ru?mQru21}!0Gvzx+fM>cz4(Td$eE@w^&;-_#;{PVP#r4%1=%NxQ>JzR9m}C- z8J9v^xKNo%!DEV0w1FI@p&cg#kNL~eUjCBpiK|}SyD$|rikK<>X26s3XZFlJU@U66 z-^!(BzKN-X2i}cn==b9X5xZ9MA2_1j z%#+D(satXOkKu3qnbhpttw*MI`LUZjrP|5(fHd8+HIwuE+AWlXG*K1#H!GSlGGBl7y=U4SO!-byP! zaM|)mx{HBLU*P!oA>~!?#ceA_!}b_#3O^DHah6ReOE{dJTV&UmRR)>^v>JkpmO?@tA`wQKTw0pC7@Dh3JU`bJ%}1maLBEm_44IP zgo}D^zt!$%Z*ZBEn%8N4*ZfpiROSxVc?a9PkEP_x7);EJ3vuAyZAY@Tg_?~^Ii_}?9^=J|zLSV|T0n$eT;!Pb$_ z?3JcB`|M!L$Ui@w>M;EVE$AOLqYSm&90V=s9M8e#i@eNtCEV+8lleyr#lq9aug^p? z1!EtU@nr(ifp`x#1%AqJzri&7aUWQwhtFjc_A-1Uilzq^8j4h)&F;D>c%!MiInxXy zE>lxg>2prlB-+#7vuLlB{in5l+c-OyOkro!Ltxg=&`B zO6l@U7u2YO(8W#SZ9on|<|Aw2BuJHwnspou4gDxCg7ACZ?BEig*zp&jfd&O)A~L>P zOdgQ>k6d3Z1#iknn~f>pqVr%QwG~Ge-g$2o4%0-gL?PIYP^6ZZ3*^TnUJLo@QZ=<& z<4&LRgN)BH&*iZ0?!{&+kYB!pdU1YrsU^k=6bAj)!dp!m1Y;4#La$w%`;FD|R{}x# zBvkrgxW<%JZ5a}hL*;K}LALWJ*vec^VNnAN=m)U$e6^d2e1ePtV0{|5i8V0L9Dq%e zG&D5R5Q!Z;ddX@7WT)k@*jVxwoleoUHG6sjbyyRNljpFM$P+-3&Db*E$tQ*ii2o8` zx}+Zys!?%`JrV=*vUFUZ(@Klm3o$WwK*ad0hM$?40Ot&uurpk^eLW~7dYKevA|vtJO~P*_Sz3WJF4*{^#5q1=zwB!I#djXNzsD*EjG z&yZXR*bw^PZDcLoV4A#USZWQ*XX+@_Wt)O!`lp68T^1O9Euk#jlS*N&LRXD*Z2LQfS8=6c@n94Jkz-!e z{diDC=DP~^)2oD_6=n*}ilbk`E+DW7!bzhHK~I5<*Ze6A2E#u`Mn-;2AodG2PE);| z4nMwq_{3?L3TR)7sHc5pV1mL%Sa^X7MYP0O?dU+t#E)o|MYnn zBnfeBv9!4-zPS}~r#bb0E%7oGuea-)Ph>KX<1{O@Sl_*Sx4YQDLKFB89|2gk%z>%o zDKfI#{w%AwxOkPuB#86Jr#Y{8jXiQK!GF@$&+FW|O)7%!mYZG{E7>D9flS`4Iz~uF z@FmR2rY%z|FaQBS2VoC(|8nQ|<@J=Cw4c#DJXn5}hwsJ`HNmja=|;`wy<4m!L0zx~ zWMrO_l2RZ?wRF!zbPNW2S*cx_3c5Ce?p#C;u_WB{fZ1Dk)^vAwqu2uZB9-$~G))SN z1G%liYFp;{-(s-oOUN~U>1pP+0p)aUVa5jg%?rS?Mi+u3{9osfRW-Aw*{fz}*mUu}4GYWeGD&u}Ub~Pu{2v4=5lB@b zv4jd1M6FW-o@2krh}6%5#I1K3w|dcXTsoW9tYBx=%hH)wH$aTjs;ZT8g!6#CYVoze z7pM>Y*SpVU5?OCG7dL1#PAc3jCN0JRuaH{8l!lIfKv`n;dOPw{NR3&v5)?HSDg4tTeP z%M(1j{IEY4Sb1-^HmLz9^DAyp`g;z-Ca^5B@zl4@Sz(drArW0LPS8iBtA%YhrpcootZVW=C@|e{NYkreBS51 z`|dsWoPGA*_vlFK>||!GxZz~vDiM1K#>t*$YmI=cp`G|7))(4T=9nr%hs8?V542wh zc3cc^nY9FNds52a(pDzB&1F;r84{R~siu4+wF_b>*U0S~8{ed`UD-5H*bziU zW3kzYa4f~iM76!a|G)$%<3-7*g&ysdB)a5iok5q;jupyu^ore3JpRqOIGB@y>zsG4 z+`QRWUx{)BxLk>dn&$?`=n7CvuR8{Wnz%D1dh%Pk+ZaA2a;SZ_4a|*%DSp*ga!Y?z zho071M%le6e)QeK+l6Pa_-X_Z&1lLeiF9!dQ}V2VfhPtX6f4Peo6m!%ZMqj7WKY-J zJ%-ftCqb)3kH9h8dxa{coCZ(wL8GBQB z)06SvwE`3Syb{angwu@~?F86;Ve*q*+TjF>_nC<%LY2nBq~n{Uw|8f{@z5lIA9QUO zG30av1NlWJ6N!Zw`KH?D~HR0}xdXohw#^S+4+s?Xt8JFlE*?cx06BoC$r%5{-P z?z?&V!Ukm#T18*__kturgL*{@-%>VDfwgt`#gM~|9pOp>q2DP2KHOXRCgGu?BZbgG z0I!jkj78egf0}@|xN_##y0Bhy*CXhR`jz)WJAYjZwMOf3yq3=ZOWQ4j%9PlG)qTc! zrL))y(k5b=I8<^um-^#@Ln4|hg^y*^#7M4fA81!PF5_ATz=xY^ufm64y(`YQ4Rx15 zdk5x<_bREM8PohO&mJ5NC7uOl_C6KC*8T{Ob*x?NWHUV;1-~$hkgmEy7V}%rq~yLO zXb66F8zym$NAJ*C(W_tWXJhWkoVL}~hF#rM1&lsCuV6`6THPIeMIMr^$b4l2oHkeq z#eC_0jQ{Mh@popvgiU7ySLkDEvy9qdGbo5i2nn{BXzKs)Zb%`Fm_)hm>Z*&|Bg`SY z!RH|1v)4|T3c)U1^E-ZIuKs?3zkZPrYzAIf#$g*=J2z*mb{WB%sdGzOal8{s90!gF zrg{(4KdvN(U~=eXo%=iU?0*S1EwZsM?)e>^7q|*ol;d%XR@JM$--n%QTlwVu)n#}+ z$p(iKp~POGv2Sw&_Udbi7wLhIHVvOLU4d=`EGrRJcWC%_JktIx(q>7|bE(fd17Fx* zTlR2cxC#HJ9Mg?iRt|Htg23@My2FmnfCU(9!zI#K2KcDc!pL|+p}sU@J?UiJ#k!sN zvnQtNFl8q6i3m-8nOt6a4kB3A&Yt8<`w`rT5`%nwQdwC6ZA(XG_m=8Q zp;_6Os=B`@Nq)J`6Rz5*$X^@a=w@801e+Ob5Z1pG55ahL5Sku@O++->K>ms%#$|M6 z2)J<1+g}xz*tmKV!dmIJp`E3p&*aF&O#ei`zT2=w!Sz-jWI65Nj$_u_iUF;T_*=6_ z+e#shTPD>XWAbc^JI7Jl-a3#Fi&P{|+fa6e2{dPOJLF=W2?5C7&*CV~5= z&7dRN;TNI*nO;Ug7htV@!DB!2(Kk-o+_y%ss-Y4OW?ZDPuF9zr_jLH*y#<`Kzjqa$ zF(%C#!49vFcN)Qf0`W}Xt+gZfSjw=oV=PctPr=2&plPu@eIe+rO;zh{dNXR7Y}P0B zn&3a557^=rmw##aHH6`FhbpPSLrZY*leI@{ewK;96+$oC_R%tqk7}lIj7GJx7LA`^ zVnA$Lj=|y7_!|4ny~ygpO~BU*>m!%2aEcgi(h5p#Onxl7@N<`IcAmQuqjzU~;NXA{ zT~qqgnj{X#?AD=s)kL3$=l;bKv+8>z+&DSq9(B3iuB*TEfNMPSEs_X3`S4jx3chIH z#({IYk_H_8WX-uceg?e1cGN_6#%mC<G zkN|tMQ4uYF5nI*rd9dzGHP8%Wib=eKc}(_P<#Cin;GxMMF#+PI;2H%kM@(71yMu|< zW0gaG){D18dQ6_XqHk0;wx^b++Ia8kpqn>-noAffJWETaJqq0CbP60XnO92U23d$E zjPNXEa?ZvA+pg`=8hL6}5oHEAsiH+jaZzF-1KY!-=;pGcxb#So^7kAV0iPo^a&A<; z_HRO*4G@{*&Q_+r-+F#}hTK(wuR*{MV36=B*MH4D1H zQ$e@p(B@M|$OtmMrOF?cnl_bI6l_ZR#E zb9CM=9?XiKG^gQV;T7m-feS%osKFx;-u*ja_dm|Xt7Hs3GTAj5tq`~*xwxb-l8hg7 zda^|?36y#F+9MsB?{3 z=A{pFPrUN?Kc9Jg`R04@S4TPuu=kx7;~3onCB;Y}5+TK6!G}jBM zW+2xwr7X^5Xnz-e#*_HN9hKocNqn&OX?_YUdZJ$@ldudhpg%?5T5U8PmT%&J!F#O{D-f1O#|L>dy zt6tDGwG;UK)BWr`MdR;pM$V;I@#W`5fV$-LX7og}saOIP$?`VzVhC%%rl|m9X`j?V z=H(UXwjLwH=3)^?0y%cFIr{1dPxpnVM zWp9Q$EwnAP)7e9bhi}YUITK(fO3+K3Q$0uvjionj3l%clQAx+}-$Of~Py3@yFEcaK z-S^`7cghVQ(~QYJn3DCiT;Na&lFPh2V(Fb+i!3Wc%^!7=bf!>)pH71(+N0}?6Z?^6 zj~e=vH`W&k=Gu}m%&lMDc*F~9UQ(c-xRp-mo-pWdRC$j%?wv~ zaTtO@f-mpdTT#`>%)RhMGvG~G?K z3NF>V6>9A6MGxHi{CW1EKzgrUXsXUpY?_BP-UL%XZf480;3>7%`*OZcT|xL#OUjG+ zb712jI3Vz~>lzy3MZKLAUNfk>xRjSfS&8?iNxx>$XcqHW$$6$Alk$So;a8_U2z{wt?SY4qy#7Al zwspK74h+VRVBxntl$7qzvh$_ssMD(Wx)AMg?s!|aG(wU&xzj~~FKr2QUmrFk1#Z(l z`C|EtE`JC2W~i2}m)&@kEh<0Xx~iio@JxUA@bG>!V1c<9sFs!>v3I}Kh#2kkb5&5- zsD+h}wvY4e^j%z1xEsN_WZ45Hc&C@xbG^1qnPBL~-jsS=GoHonhzvfZwzI$gTwppF zb>Kr4%zJ*VHVS26fjE_baHgsQcnQ?d{YH0x{mPuCLaAyyAY+oT^4@3mh5qGe4)Gt%AE+uXbX)HLo3lHb zSyzA@mZAEAOOt_;j7&~L`9UoKw)X3Y)G*SIX>R|WMf~#$8Z1A35 zt9>TUM*~W<;paBS0L$|WgU&=$KTXWH3jR($^IdpiTk^xCugbEUpVQB4XmD{h(>6F* z`?w&rBs{r__km>}PBPO#-cZ#ibAg_%Ga@_L{wx#)vB|p~n6_$&f!sjmC7@wtS$4ng zXGmbz;X?O913y>g-CZAFU*4c|fA$Z*RAL#ldi>ZPNL5(aN%!BBz2WN?6*Txgb6Ugw zFAMr~nZN}1ab#(`pch9p6$Xf~MADwnD}r8qd*!kWZXEHbdgVNHEqWWOnmU9^}V>L|KXHq z<(uapJE!Wiy4uGKF$q>}i-GFm!xh6+|w%#RWqRa=N|3Sy+-))qw+ULeV>c*b0a z#bMMW^uyY1;pK?6)8U4#{^lRl!hBAe9yP)6g_`=nFCyAp>b@{ZsnrRc0U6oq{nf9G z+hL!@#0GgVvcaUIr(1P>vYOrK2LvZxOK`V=x>R4LiC zo)QThbQp>GZ6Vu~PwfiR%{=z+KL%%pW6?-GFctF z-h3fpr5Ha;2HdgNgSMWhC~Sk8f>sksuIEuvw))?!%VTIBW=5L3;QBe7 z4Czo=_0Dcd(+38+tfTaM_YT&#+?m#QB>e@V2v+G&j9X)@OpB5({|Gx+G71vjUOB=Go6gG1Eznr9Iqzt8EG)q! zpDZ}cUJRdm37@n4YI5p6X@M6*A8CZIMcd#N=DRE8|7|uzpoS+`s7t0{>+Rs$Y9saJ zREdtsO|x0BczII#H0cEv{v)vd7%nxm2VoqTj#;st)7?*MGjG01R0Dvq%G9-M;4rmD zLtWpCO*5;;1N}^c$fjZTa^g?(OG}-=Qmgk^CI#mA>}pvHD-TcK@OM%#1=OweFX`y~ zL~Mh_UtqWSpSD3>NOUB(u7cMF&zt3^+Xc#Dc*E?K#Gl^XQl+S{=(;Qggh~jioPapp z48s}`CW}lToFuK2WK+xCN+g+W9WG2jNq<8c2A%Klo6kk8Mo`K@c9kJXd)5aIdfrb) z6+oifzw80+ft1wLI~p06*tLsa?u`OdoP>tvRRlQ$&d*d)%7+ghg1k}zPG`wR_!-0gsQ()`2Q*BDFy#w$&YT7v7dOwNl`kO12y0!$VsXSWv zt}eLg%XP9i=quowV~=`3x&I=2vw6pDs7}f@v@1UBr6TNcsO02i!GIHwp5ESvjjrhM z;WA@M^|BFrkhSm#9{|LwAAA|BidivMw~4MuyX1FDN=g+le{28{{tbNhqFkjo86f6g zWQpYi3q>D{GpoVZ2ykh`0oa90Ac(3o2AtODbwo3W>$5*8c(bs$XzefVv-M@}Fp&yE z7}xB3snk><=tYm$j#RxNRv#E--0a58{7MDGoe%F#w3zG#bP2_cYt(){S znTyWNe*IS9JwJK%;b>eI?}M9VT_gE|@7Prz=aiQK)jR=ofp`=gxL?*MY7u}Q7)`*w zxN=E-V5u(?Q>#cj*Kwjo)5spg;yD2Mj_X*c#{h_*A`yT>3D~0?%JeE(B_t%`0=1Qt za6wycvDwqOva<36TCOB$^R}mrUNOabm4HkugSNZg6|M00@Q#_H$XgbkMb?M;Tb-sb z@oG5#?L;Zx{hs5UgNaR3>J;{CM5jdITL8%cSoUuJj$C&ZdT7z+mn4qoufq#bY4$&^3%ZcF&Z?H- zFjj%2Qy$B|s|xIzz~j|$;6a*LArKMo{K%8ChX!>lwhfFP>jaxxV#eRzbjcsP=f{hT zC7?Q^$ynZhRsvtAk0e6S-b(cRiAd)%0en|ZhKAatGgn-Km9nUZ*NWP!XsQ?H1=5Z- z0}3T&6w0DzVR&|z=L;5WxLT69OXxNXu0-YFkvxX3TrNBdbXy|8otf8r<0 z!pq9a#ztN_R$9O=+`qa{VrNUlAofxt*-r-=C&E5kHxNMrOu!T&*BAGl|I92e+Ya+? zs>U&4L2Gn&yxQLC;+ovxya6|ckfacH|#BW<5X zul3)r$l3GW!8*pjc1>_K5d#z32AceR(C*Cy*0@@cL2c>6!h+S#7?T^aY$Hf%MRwO6 zUR`4h&7O6PDX~;NTEAbtwCu-Z;8EpjZqs+e-!J#Cah$N$jce5ln0(10O1hv7kcFw~ z;^iecW|XZ!xbQ2r8`f`Enz&!n+gyNt5Z;C>x_x$WQ4y3(V>OPn!tM+F47*GH%%Dwk z^55Lt>@7F_S{HCC2rZ&q;9;nx2$8U{u~o*_!_PngH3y5vqVr8KBDet+RFZ$qOVQN4 z?;c#frp3d#e&ksS-#Zh1ML_>D;ijA;)THk@qxeUgK4&^l6LE-~OgP(h zN)R_}Z(EEs7E zUr(DdG(TD`88wH#V+STA@#U+vKIS}MVl&+fcu!OFI$719#PoDsb#{K|zKNqZimvMf zWAm4y;(b%~1eiGB$vfPL0XDYGd}KEmeEhzayGd%8A6!b2{d1|jr<#`*Rd$7qjR0HA zJW3sNJMg1RnTuy4e^*;B+}`Oef&1*B(h0~8GFf@uQ0p89p`rXoGyr8^$ zm+q&bXEDg{s+3>7`K=N8eXt<3Y+EjiX|0LFt}+pD)_Mv~X==T}aeST#xiZy$UxWLIOH8dG3HD&jnJkH+tzv|`VTgQu6Y77-!*eN~a zjF^+fMVq>|&+3`JF1#6B4gQ=z*dE86`6$SKPrE`q*k5)J4DoMJFcY;8-dDTqWdNHs~1~JXZ=ZoVwzv zUq?Dy06Q+Va@E$WTK-ojmIGW9Z*6 zrhaNkhMk45G;vNJP55;YmdUKyMapyc)48Z5zk0S(=`xH_RjrlEBpsIf!oQfn%d#4Z zFyc=DqxOSV=5eQpz^go9K6q(%Qbi5|4PeZri3AR0;s#xHII8x>?5LGoU}@Fm6sk$m6sQ z($doK5$b82D_7)v9h$I{^9uWC85n?@g_OtTw;O#Iuz4&xiD4^~1}YoUgoX^`)xi;5 z1--H1d}CmuxQbuBY?gfzw&w~8N{D`14JZC zoK>gvDNB?^%^7eu(6h6_K(;b6GU7dHI-Iaai;v344*eGLJ8a`_k?M#YJ`;oKC%3kc zWB@6Oa=Ju7@h`&edGGYXq_3>0ih=($u?#}6`!YlJCBf}J{&wS%hTGh?s}E9yq(N2y z8yq1N1qj=E9`cIz>KgC$UE%Mo=*3_kf|{W7V8BFVv4L=cCS&{0ZF05s3=NS3j}AfO zKsXPIqf8)gfLA@<2i<7G3C}GSE^zlYF2}I=iR(Bnl`67g4bxuRoN`t>M~MW|0qcJI zcKh;Rl(v2Ai=$KI=D|yNlH=Bzu;K9|tmB+f#r{~o%EXonY(_?#X(J$>5i?zDYpyi^ zP4wHz`gUYYIm3Inm9-IYZ|H=Ly{dzy&;_56@Gs;)b@la`Q8^Ukun|W@;M6)zotWHz z?8kCweL;FE{^M*+(*93&o~;;EUP$geLwv@w&N9TYJTtS^T7`q8qgiK+MDypQA>RUD zy9FKe>K`s`Y%pY_*X&L>;VGC{5sR(=oCvb%;_7O;x77cMM${TUv1V-6qF66^`axrZ zSJ@M>zK`SokFov#^<=o9~o{RRq2}_)twCPf)}4Uj85CQ63wU)KD=(Wr&<7 z0JDNsNN99ezOo?lI@_Zf2R-n2cmOswj)o~h$)es|fReJPzK@0i)9q->)PS9+;sC?jx0c{KTbmYJy0-4Fs@b`*DDDyO-97lg~;8U)X&jhp# zMk*XCiHg9pXNY`%AWz-VkRz(@{{p5SxfoFPWW5Lpv4#9a{MlZ)r{2 zu3%%2H`q`BXTb}US#7|I7xG*oLy9oz5NxPE`n|$_%YoZ19Qd;A0q81##Af7hrvqZ* z;*N#{MY>HspB7*VubO>RPU5qyf-5IGoGxX*uF8~+7%z|;#dfwTBZMz{$3qzuicw4(lDf`rZW6t@eenx)!Jv$ihb03`S&%&<)PNuKxH7NE?tp7FJ(La> zaKANN@miobOogNrIkdunQeFuD(C4m_~2Nvm@+v10DZY1La)v1u6OJowiox72NHlg2P3Em z=mT=d1yFqjOY9UoCQlpv4!NMNXzlKff|D-{>OJ0@ZiusD1D{B-Y(%jj7ode3GL8>e z9Kb@r3JO>{5s%vn-Ogjzt*x!W;RzEm>0BY-9hT{ULwY!Tqf-4xLQWeeN3j`maXSSW zR6|U9Z-o!N!4K-=lR_E5VDy@Nws9!WRd3JjO!paqc?gUT1sY0`c_62kwV3T|k|Q z4Od_SrOefuVXN3i@6AW`ZkDjlTjBA2kG3*hjiEqt)aC1L_%9?UDVShNNqo7E_||#A zO+qZ6VEtJZ+fFz6-s=}*3jn*fi+{VXw?CJaaRXeAnBxHB{A>8F%4i6L6=vV@1Rcji z0sHwx0U&!UI5@?qQhI|LG#`!?0oNNu;R}=SJ3I(t^|X#>Ss94BI%*trma7H*LDgiz zl7+dsEWn*r5v;}Q^uQ5TGZO{3Q87kbGid@uZ^dV?;na~@&wjJN|A+ET>B&^*;pQ~s za!?Wc-UKXHlA-BZ@SZ{b*_(OXAmF=Dadt?YU|1toze*@Gy+zG(`lwrG=Ymq`6U(9a{wI zv#h15rEsjhN>5MUUIY=(J-fl%pn$TQTmZxc7QLe;KO9vflFBvA&H=SyYul}gph#OL zXOb3RM69X63y-$OW1t`n;TG6cC3yy5%(e2P@+PRqP0&3CLsosAmH=|(a@WUyYrY8K_ihOxWSYC(rZS^JP4;2y=kcc zfIQAoiqqu`6b5E5#2uJWw9w_S0^EyLpr8{a2%*L1k*raT12q7FZ9P3PU^_C<;Azhw z=3_D$H1h?+Lxdi>6}bRWAomToMRTDE+Z<^-sFXn7X!|b}p&uOE+skW)J=z~VtWadk7il}!iM8E!z zTI@11GM9vc&c&+jhO(f#M-+|4MxRlV=SLwBgQ$Bq0kMN6V1@(^@n>Y}1ISnUfV^%y z5s1-zuqje-J?(_4+Lg%53|Is#DrV&L9pDN^!S18k=;a7eQUUrqN#cHj5Ng_bdx?Np z48AB-nQMTz!Zg7FUiEuUgLZhJL_SNrXP+6K1A>V}6@bxyfV^;NxP%tjen8x0Fss0j${t10oYDgU5We&Lbdx3nx_&Bf zau==mv#PGeaEvNKT0t8W#rgC;^bAmWw&U=2s}YQrE@*X_lMwm#kEs#?`j}Ng7iaf> zRJ{783hbW#yZTiH+lw$!FHt3TgD|lN)(aAyw_%-d%E-#XtN9CinUILcP8VA`RaqRD zp~%i+Z!ZX=VkV#wpf0fp>%s=50DzQQX~5QuG^9@q=!$fhw{Rl~;E|$qNK1gt`XEKA zKTBR2vH+mCT0qeN!sUDI=>XRK#f{o*x8nU%2^Je*Yy~poNLY)2$YQ_Lc)xV+_pKc^ z$g!57mafg??AH>PXTTg3$)#YhH6huCXKaTyi^GAK_BTmzA|~YoAPWu0V_ehd2`~)` zJpTIuHYK(;UtzcT89@7E_fV)j>N2d4nB9$=AV3AY6X5Le(NL=Pl%W>MDv{l2>U)S~ z5!_FnQXCURi5JkpAOSizy7%Q(9GnQ;9-mFYZftDqN00{D4HtL8+V6aG6BE*#9N2hB zevnQDy&lCSB+P{wq?Rfo(AEqCoH4RG_Sx?~;Js_()lo1%c+9@x#xhFALQB0^QfCwg z;ylZ{+nO(c9S(VKn$jU9V4`o{5X0|0fJKQh6p4o0&jZ9-)94WWTC&}QXKSVv6B140 zWnM6|q#_8n>DGbt7Gbr6UYy=`eSK^8D~Qmab2lGd)-c|KQID!|*kp(14U)-@g?wM34>IJ7lI=&<}X1a`XsuWn3c69;Sjk;=%XXe zW;%N2i@T7Fg1KcZkgR!O$AH(@1s;k^(=9DymShL=s9W;!{7`SkGKi~yECflT;5N)5 zNbPM*(Zaz;yU0s#@LETD2CYaypfvWE25=u)0B=WnK5#Qa0+0#!>f`5!UR~{mIyFhe z^DdO+m`}<&DY=cx!NLG3D#3%5`Ds2Zswc&|ajfd7a=$|-|A2t8<^WL;S6#XRyI;G0 z7fjsaoo?=W@69y5mJl$;f&a7sSGEN)M!ENF&jva7%}s6Sqt-DjedJF)6amHo`{Z!< z_cnUtm~mjNVa`VY;9CwyFhCr0))sCHWCH;X60Kp>NXW?cS^>jYgL{2wGxU+1?Lo>6 z#5v@}LmV{y?}l~Idzpif3hUrHRGJJD{x-vsmqO3tU9KxXf~A7M?l4l>GGTcB|F%1# z>4bkDap0Q3u?L7o77FaSzkkE>Y{B@TC&!nQ4`xjs3=#nq&+~uByN{vlf_dXqJcej1 zpb!|ylE>$>===g=8K%#mn_9hN*uW1!Zx&?F1Po%Mx{cJrZY&^4&VU(=bS{zI4rFeX zfjFG{RY>JbK+P8l!C?nx76d|~1asJ_<-okqcBGUZcKS9rEJD{FQ>)UT!s06QI&$H{ z&@l>wwVSMU?K!*;Xh%CDc_|bGX8grB!4V7)mMbBx;DcO!`)M6d!0Ewvu#d&T!GU-9 z9ZD8t|LTPIKkdIu2#k$4(zD8;5ByrB4RPZvVyN$hgNEh@nNHgVupIyQR0=uSj5snD qv6uc|$66wG-SETzckXpgBtG`FL2Ux}5YFwqq%8kHu2jb4#eV}9$?j|b literal 0 HcmV?d00001 diff --git a/objects.inv b/objects.inv index 4b2b2352bb5c4f28b577ce66fee7e44bcb5cbcc5..91a707c2af825ea5c732031c3376824cc5f20108 100644 GIT binary patch delta 777 zcmV+k1NQu+2Jr@vb$?dda+@#^efL-B2h!NNhqpADwwZAgw@#WzMt}mE2#F%m)cyJj z%a$c^+3>cbJx6EH!m`9k!U(SU@pq9mjLc7L5Hp-0sj?^&DLPu9cog}mP$ff4oXkdW z@0h)*q83eF#Ouf9^V9v~!v`h$s7CYUkm+cj+5mFnBZu8MxPPf`04nU}%m$DfA35yC z!A*4oP+>RcHh|pt$YD1QZmJuA3cIIR^~ zZmw(qx$%+1ZXDcHHvkoOb8Q32jgK65Gu^15vwfQo$AB zYTtx~4dO(IH-A@Z9BFPgM`#hDZ-g;{UPR5Z5e=utk4ZU%4|w51k+!%Pr-}+LM>RsQ zx&L8ziV`eoDnkutzXSaWkY2w|BDAJurK1$Hv|)H#KvyfHtZ7rR3met!Wf<+dT?)|T zn2So(B+iITYg%n7W0HM@T`zYTv?jkB!c)rgP|x|p0Dt-!AiaK`M(7SJ{H4Yv=~!~s zJ|GbwJ&Cgj-Ehn{l1h|pP_Ey=sw3s>WI!rFdQ#^RS}~!NZWnr|sz58uGu#n#Rwf`8 zAU&~*2>qh6!HoX1dbfhV(o8=v($&m$k4s#~;x*P`Rp=QRRqnwX{jVm0#A_RF6QP>3 zMgfNb(tmS!sfP`>!zPjLBl<}yMpLXvk6bFsQqN981rxaju{fhr)ii0K$pg8jq}weB zeYPMyl@2JZ$l1I=0|KPifGhoV-c&*rq4pu_~w>tiBItv~Jo%)E=Vt(7$ z`hSD4mcKvU6LdH$Mb?9?Hb?t}*J;CXvH)$hQH6fTJ+{=ZL|XVnb7nYelV9PS%>Dx# HDgvj_!YO*y delta 699 zcmV;s0!01s2BZd%b$?aca@!yfefL-J18M5qM{j8|Z8MW5Zk;xd8YHm91c540Q}yeM zge?QQto^pLd(IxtN-S}baEdE&`c0CW(~oC;5ObU$rAb6&icXd&o<)Ahb-~diP8Kt` zcg){(UdcMki#~wd_{eED z4(?bt02Ov~*$0ptA35#D!5!-cpu%ph`T%m{Bd6UsxMSS_RM^dRA3$z=Vw`Fwg__j}!M6X?Hj5IhSgJw|=M4eo6(GHNokeKPiqb?W=4s7w zcYvW5VZ3fju?w5k9A#MT`)vu(6j;boRx~D5r4=hX%ADjFVLQuh1+D4tnu?T(EYx#e zF<_nn(wpabgzmA%pDSF@fhFf@0VD#XCvg#>TY-6_n14b^gEI36mIEng?*mc+(v!N3 z(27f~&A8Az(>YpULGVD#S<8S}fb_(!BJ_)?8gurq*SnKq!-)A{bf}q`85g*U^ znVUN@t2~0Y`d@4T$xRz}g;32|)qukQ={dYMmkqak;Ai~pU5&FEvTT-5*o diff --git a/search.html b/search.html index e18cd9e..f9b4c60 100644 --- a/search.html +++ b/search.html @@ -237,6 +237,17 @@ +
  • Module 9: Linear Regression +
    • +
  • Module 10: Power Analysis
    • diff --git a/searchindex.js b/searchindex.js index 142e9c5..a382e8a 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"About this book": [[29, "about-this-book"]], "Acting on Columns": [[3, "acting-on-columns"]], "Acting on Rows": [[3, "acting-on-rows"]], "Basic Plotting": [[7, "basic-plotting"]], "Boolean Indexing": [[3, "boolean-indexing"]], "Box Plots": [[7, "box-plots"]], "Calculate a aerobic target heart rate?": [[15, "calculate-a-aerobic-target-heart-rate"]], "Categorical Comparisons": [[9, "categorical-comparisons"]], "Categorical comparisons": [[13, "categorical-comparisons"]], "Categorical with catplot": [[9, "categorical-with-catplot"]], "Cells": [[16, "cells"]], "Coding expectations": [[15, "coding-expectations"]], "Common Biological Distributions": [[26, "common-biological-distributions"]], "Comparing Distributions": [[9, "comparing-distributions"]], "Comparison of Variables": [[7, "comparison-of-variables"]], "Conclusion": [[0, "conclusion"], [1, "conclusion"], [3, "conclusion"]], "Continious comparisons": [[13, "continious-comparisons"]], "Counting with countplot": [[9, "counting-with-countplot"]], "Data": [[7, "data"]], "Dataset Reference": [[2, "dataset-reference"], [3, "dataset-reference"]], "Decoding samples": [[11, "decoding-samples"]], "Dilution calculations": [[18, "dilution-calculations"]], "Documentation": [[9, "documentation"]], "Don\u2019t be afraid to Restart & Run all": [[16, null]], "Explore the effect of cocaine use on mcp1": [[8, "explore-the-effect-of-cocaine-use-on-mcp1"]], "Exploring a single patient": [[5, "exploring-a-single-patient"]], "Figure Level Interface": [[9, "figure-level-interface"]], "Functions": [[1, "functions"]], "Grammar of Graphics": [[24, "grammar-of-graphics"]], "Histograms": [[7, "histograms"]], "How full is each cell?": [[10, "how-full-is-each-cell"]], "Hypothesis Testing": [[13, "hypothesis-testing"], [13, "id1"]], "Imports": [[3, "imports"]], "Indexing": [[3, "indexing"]], "Introduction": [[0, "introduction"], [2, "introduction"], [3, "introduction"], [4, "introduction"], [5, "introduction"], [6, "introduction"], [12, "introduction"], [13, "introduction"], [15, "introduction"], [30, "introduction"]], "I\u2019m pd.melting": [[9, "i-m-pd-melting"]], "Jupyter Notebooks": [[16, "jupyter-notebooks"]], "Lab": [[0, "lab"], [2, "lab"], [4, "lab"], [6, "lab"], [8, "lab"], [10, "lab"], [12, "lab"]], "Learning Objectives": [[0, "learning-objectives"], [1, "learning-objectives"], [2, "learning-objectives"], [3, "learning-objectives"], [5, "learning-objectives"], [6, "learning-objectives"], [7, "learning-objectives"], [8, "learning-objectives"], [9, "learning-objectives"], [10, "learning-objectives"], [11, "learning-objectives"], [12, "learning-objectives"], [13, "learning-objectives"]], "Linear model regression plots with lmplot": [[9, "linear-model-regression-plots-with-lmplot"]], "Linting through color": [[1, "linting-through-color"]], "Markdown": [[15, "markdown"]], "Matplotlib": [[7, "matplotlib"]], "Matplotlib Gotchas": [[7, "matplotlib-gotchas"]], "Measuring Correlation": [[9, "measuring-correlation"]], "Measuring Spread": [[9, "measuring-spread"]], "Measuring Uncertainty": [[9, "measuring-uncertainty"]], "Measuring phagocytosis": [[11, "measuring-phagocytosis"]], "Melting": [[5, "melting"]], "Merging data": [[5, "merging-data"]], "Module 1: Hello World": [[14, "module-1-hello-world"]], "Module 2: Simple calculations": [[17, "module-2-simple-calculations"]], "Module 3: DataFrames": [[20, "module-3-dataframes"]], "Module 4: Analysis by groups": [[21, "module-4-analysis-by-groups"]], "Module 5: Plotting with Pandas": [[22, "module-5-plotting-with-pandas"]], "Module 6: Visualizing with Confidence": [[23, "module-6-visualizing-with-confidence"]], "Module 7: Samples and Replicates": [[25, "module-7-samples-and-replicates"]], "Module 8: Hypothesis Testing": [[27, "module-8-hypothesis-testing"]], "Multi-group measurement": [[13, "multi-group-measurement"]], "Nanopore Sequencing": [[19, "nanopore-sequencing"]], "Non-parametric comparisons": [[13, "non-parametric-comparisons"]], "Notebook basics": [[16, "notebook-basics"]], "Numeric Variables": [[7, "numeric-variables"]], "Numpy": [[3, "numpy"]], "Otter Grader": [[15, "otter-grader"]], "Pandas": [[3, "pandas"]], "Pingouin": [[13, "pingouin"]], "Pivoting": [[5, "pivoting"]], "Pivoting & Melting Dataframes": [[5, "pivoting-melting-dataframes"]], "Plot Handles": [[7, "plot-handles"]], "Plotting Multiple Columns": [[9, "plotting-multiple-columns"]], "Programmatic Arithmetic in Python": [[1, "programmatic-arithmetic-in-python"]], "Protocol Evaluation": [[0, "protocol-evaluation"]], "Q1: Calculate the molarity of the sample": [[1, "q1-calculate-the-molarity-of-the-sample"]], "Q1: Count the number of participants of each sex and race.": [[13, "q1-count-the-number-of-participants-of-each-sex-and-race"]], "Q1: Create an fraction_area_covered column": [[10, "q1-create-an-fraction-area-covered-column"]], "Q1: Do cocaine users have a higher level of expression of mcp1?": [[8, "q1-do-cocaine-users-have-a-higher-level-of-expression-of-mcp1"]], "Q1: Explore the cocaine_use and cannabinoid_use columns.": [[7, "q1-explore-the-cocaine-use-and-cannabinoid-use-columns"]], "Q1: Explore the neurological function of the participants in the dataset.": [[6, "q1-explore-the-neurological-function-of-the-participants-in-the-dataset"]], "Q1: Extract the information for patient 3116": [[5, "q1-extract-the-information-for-patient-3116"]], "Q1: Extract the initial_viral_load column ?": [[3, "q1-extract-the-initial-viral-load-column"]], "Q1: Extract the relevant information from the text above": [[0, "q1-extract-the-relevant-information-from-the-text-above"]], "Q1: How many cells are in each well?": [[11, "q1-how-many-cells-are-in-each-well"]], "Q1: How many participants are suffering from impairment?": [[12, "q1-how-many-participants-are-suffering-from-impairment"]], "Q1: Load in the data from the CSV file.": [[2, "q1-load-in-the-data-from-the-csv-file"]], "Q1: Merge the biome_data table with the sample information": [[4, "q1-merge-the-biome-data-table-with-the-sample-information"]], "Q1: Using the information above, calculate the subject\u2019s heart rate reserve.": [[15, "q1-using-the-information-above-calculate-the-subject-s-heart-rate-reserve"]], "Q2: Calculate the amount of sample to add.": [[1, "q2-calculate-the-amount-of-sample-to-add"]], "Q2: Calculate the average count across regions for each phylum for patient 3116.": [[5, "q2-calculate-the-average-count-across-regions-for-each-phylum-for-patient-3116"]], "Q2: Calculate the average weeks_to_failure for the whole population?": [[3, "q2-calculate-the-average-weeks-to-failure-for-the-whole-population"]], "Q2: Calculate the length of for each row.": [[2, "q2-calculate-the-length-of-for-each-row"]], "Q2: Calculate the molecular weight of each template": [[0, "q2-calculate-the-molecular-weight-of-each-template"]], "Q2: Consider how pro-inflamatory markers are related to neurological impairment.": [[6, "q2-consider-how-pro-inflamatory-markers-are-related-to-neurological-impairment"]], "Q2: Describe the graph": [[11, "q2-describe-the-graph"]], "Q2: Determine the predomininant phylum across regions.": [[4, "q2-determine-the-predomininant-phylum-across-regions"]], "Q2: Do cocaine users or non-users have a higher average level of mcp1?": [[8, "q2-do-cocaine-users-or-non-users-have-a-higher-average-level-of-mcp1"]], "Q2: Is Visuospatial impairment linked with ART therapy?": [[12, "q2-is-visuospatial-impairment-linked-with-art-therapy"]], "Q2: Is race and education correlated in this dataset?": [[13, "q2-is-race-and-education-correlated-in-this-dataset"]], "Q2: Is the expression of infalpha or vegf different across neurological impairment status?": [[7, "q2-is-the-expression-of-infalpha-or-vegf-different-across-neurological-impairment-status"]], "Q2: Merge well_level_data with plate-map and visualize": [[10, "q2-merge-well-level-data-with-plate-map-and-visualize"]], "Q2: Using the information above, calculate the upper limit of the subject\u2019s target heart rate zone.": [[15, "q2-using-the-information-above-calculate-the-upper-limit-of-the-subject-s-target-heart-rate-zone"]], "Q3: Calculate the average counts of each phylum by body site.": [[5, "q3-calculate-the-average-counts-of-each-phylum-by-body-site"]], "Q3: Calculate the average weeks to failure for the treated population?": [[3, "q3-calculate-the-average-weeks-to-failure-for-the-treated-population"]], "Q3: Create a new DataFrame that includes only the treated individuals.": [[2, "q3-create-a-new-dataframe-that-includes-only-the-treated-individuals"]], "Q3: Describing the reaction yield": [[1, "q3-describing-the-reaction-yield"]], "Q3: Does Sex impact the effect of cocaine use on the average level of mcp1 expression?": [[8, "q3-does-sex-impact-the-effect-of-cocaine-use-on-the-average-level-of-mcp1-expression"]], "Q3: Hypothesis generation": [[6, "q3-hypothesis-generation"]], "Q3: Is Visuospatial score linked with ART therapy?": [[12, "q3-is-visuospatial-score-linked-with-art-therapy"]], "Q3: Use the appropriate non-parametric method.": [[13, "q3-use-the-appropriate-non-parametric-method"]], "Q3: What is the molarity of each Paragon sample?": [[0, "q3-what-is-the-molarity-of-each-paragon-sample"]], "Q3: Which body site has the largest increase in Actinobacteria when comparing typical and severe disease outcomes?": [[4, "q3-which-body-site-has-the-largest-increase-in-actinobacteria-when-comparing-typical-and-severe-disease-outcomes"]], "Q4: Calculate the average counts of each phylum by severe_disease.": [[5, "q4-calculate-the-average-counts-of-each-phylum-by-severe-disease"]], "Q4: Calculate the average weeks_to_failure for the treated population?": [[3, "q4-calculate-the-average-weeks-to-failure-for-the-treated-population"]], "Q4: Calculate the average weeks_to_failure for the untreated population?": [[3, "q4-calculate-the-average-weeks-to-failure-for-the-untreated-population"]], "Q4: Evaluate a potential covariate": [[12, "q4-evaluate-a-potential-covariate"]], "Q4: Exploration": [[6, "q4-exploration"]], "Q4: Is there a correlation between infection length and mcp1 expression?": [[8, "q4-is-there-a-correlation-between-infection-length-and-mcp1-expression"]], "Q4: Make two new tables that contain high and low initial viral load samples of the treated individuals.": [[2, "q4-make-two-new-tables-that-contain-high-and-low-initial-viral-load-samples-of-the-treated-individuals"]], "Q4: What is the yield of each PacBio sample?": [[0, "q4-what-is-the-yield-of-each-pacbio-sample"]], "Q4: Which tissues are \u201cswabbable\u201d?": [[4, "q4-which-tissues-are-swabbable"]], "Q4: Write a function which calculates the reaction yield": [[1, "q4-write-a-function-which-calculates-the-reaction-yield"]], "Q5: Calculate descriptive statistics on the weeks_to_failure column to compare the high and low viral load participants.": [[2, "q5-calculate-descriptive-statistics-on-the-weeks-to-failure-column-to-compare-the-high-and-low-viral-load-participants"]], "Q5: Does cocaine use impact the correlation between infection length and mcp1 expression?": [[8, "q5-does-cocaine-use-impact-the-correlation-between-infection-length-and-mcp1-expression"]], "Q5: Which samples are high?": [[4, "q5-which-samples-are-high"]], "Q5: Which samples are usable?": [[0, "q5-which-samples-are-usable"]], "Q6: Calculate the same descriptive statistics on the weeks_to_failure column to compare the treated participants with short and long infection lengths.": [[2, "q6-calculate-the-same-descriptive-statistics-on-the-weeks-to-failure-column-to-compare-the-treated-participants-with-short-and-long-infection-lengths"]], "Q6: Which swabbable region has the highest positive predictive value when predicting persistent disease?": [[4, "q6-which-swabbable-region-has-the-highest-positive-predictive-value-when-predicting-persistent-disease"]], "Q7: Context": [[4, "q7-context"]], "Quantifying the uncertainty of estimates": [[9, "quantifying-the-uncertainty-of-estimates"]], "Quantitative Reasoning in Biology": [[28, "quantitative-reasoning-in-biology"]], "Querying": [[3, "querying"]], "Questions": [[2, "questions"]], "Quick introduction on cells and blocks": [[15, "quick-introduction-on-cells-and-blocks"]], "Relational with relplot": [[9, "relational-with-relplot"]], "Seaborn": [[24, "seaborn"]], "Seaborn interface": [[24, "seaborn-interface"]], "Session": [[16, "session"]], "Submission": [[0, "submission"], [2, "submission"], [4, "submission"], [5, "submission"], [6, "submission"], [7, "submission"], [8, "submission"], [10, "submission"], [11, "submission"], [12, "submission"]], "Submissions": [[15, "submissions"]], "Sumarize by sample": [[11, "sumarize-by-sample"]], "Summarizing by grouping": [[5, "summarizing-by-grouping"]], "The Problem": [[1, "the-problem"]], "Try me": [[15, "try-me"]], "Two group measurement": [[13, "two-group-measurement"]], "Visualizing differences across categories with stripplot": [[9, "visualizing-differences-across-categories-with-stripplot"]], "Walkthrough": [[1, "walkthrough"], [1, "id1"], [3, "walkthrough"], [5, "walkthrough"], [7, "walkthrough"], [9, "walkthrough"], [11, "walkthrough"], [13, "walkthrough"], [15, "walkthrough"]], "What is the template weight?": [[1, "what-is-the-template-weight"]], "Why Google Colab": [[15, "why-google-colab"]], "Why Python": [[15, "why-python"]], "f-strings": [[1, "f-strings"]]}, "docnames": ["_bblearn/Module02/Module02_lab", "_bblearn/Module02/Module02_walkthrough_SOLUTION", "_bblearn/Module03/Module03_lab", "_bblearn/Module03/Module03_walkthrough_SOLUTION", "_bblearn/Module04/Module04_lab", "_bblearn/Module04/Module04_walkthrough_SOLUTION", "_bblearn/Module05/Module05_lab", "_bblearn/Module05/Module05_walkthrough_SOLUTION", "_bblearn/Module06/Module06_lab", "_bblearn/Module06/Module06_walkthrough_SOLUTION", "_bblearn/Module07/Module07_lab", "_bblearn/Module07/Module07_walkthrough_SOLUTION", "_bblearn/Module08/Module08_lab", "_bblearn/Module08/Module08_walkthrough_SOLUTION", "content/Module01/Module01_book", "content/Module01/Module01_walkthrough", "content/Module01/notebook_actions", "content/Module02/Module02_book", "content/Module02/dilution_calculations", "content/Module02/nanopore_description", "content/Module03/Module03_book", "content/Module04/Module04_book", "content/Module05/Module05_book", "content/Module06/Module06_book", "content/Module06/grammar_of_graphics", "content/Module07/Module07_book", "content/Module07/common_biological_distributions", "content/Module08/Module08_book", "content/book_index", "content/misc/about_this_book", "content/misc/book_intro"], "envversion": {"sphinx": 61, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["_bblearn/Module02/Module02_lab.ipynb", "_bblearn/Module02/Module02_walkthrough_SOLUTION.ipynb", "_bblearn/Module03/Module03_lab.ipynb", "_bblearn/Module03/Module03_walkthrough_SOLUTION.ipynb", "_bblearn/Module04/Module04_lab.ipynb", "_bblearn/Module04/Module04_walkthrough_SOLUTION.ipynb", "_bblearn/Module05/Module05_lab.ipynb", "_bblearn/Module05/Module05_walkthrough_SOLUTION.ipynb", "_bblearn/Module06/Module06_lab.ipynb", "_bblearn/Module06/Module06_walkthrough_SOLUTION.ipynb", "_bblearn/Module07/Module07_lab.ipynb", "_bblearn/Module07/Module07_walkthrough_SOLUTION.ipynb", "_bblearn/Module08/Module08_lab.ipynb", "_bblearn/Module08/Module08_walkthrough_SOLUTION.ipynb", "content/Module01/Module01_book.md", "content/Module01/Module01_walkthrough.ipynb", "content/Module01/notebook_actions.md", "content/Module02/Module02_book.md", "content/Module02/dilution_calculations.md", "content/Module02/nanopore_description.md", "content/Module03/Module03_book.md", "content/Module04/Module04_book.md", "content/Module05/Module05_book.md", "content/Module06/Module06_book.md", "content/Module06/grammar_of_graphics.md", "content/Module07/Module07_book.md", "content/Module07/common_biological_distributions.ipynb", "content/Module08/Module08_book.md", "content/book_index.md", "content/misc/about_this_book.md", "content/misc/book_intro.md"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [0, 1, 2, 3, 4, 5, 7, 9, 10, 13, 16, 19, 24], "0": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 28], "00": [7, 9], "000": 11, "000000": [3, 5, 7, 11, 13], "000001": 13, "000002": 13, "000003": 13, "000005": 13, "000013": 13, "000027": 13, "001359": 13, "002176": 7, "003752": 7, "005371": 13, "006672": 13, "006673": 13, "006674": 13, "008464": 7, "008714": 13, "008814": 11, "01": [7, 9], "010019": 7, "010214": 13, "013190": 7, "014468": 13, "014470": 13, "014471": 13, "014472": 13, "014475": 13, "017080": 13, "019158": 13, "020368": 7, "021198": 11, "02197802197804": 1, "022": 1, "023803": 5, "025": 13, "025250": 5, "025381": 7, "025789": 13, "026794": 7, "028181": 7, "028367": 7, "03": [7, 9, 15], "033597": 7, "037198": 7, "040962": 7, "041984": 3, "043077": 13, "043457": 7, "05": 13, "051659": 13, "051660": 13, "052308": 13, "053844": 13, "054118": 13, "054970": 7, "055406": 13, "056846": 5, "059672": 13, "061102": 5, "061257": 13, "061660": 5, "061873": 7, "062853": 5, "066149": 7, "066481": 5, "068860": 7, "069827": 13, "07": 13, "070039": 11, "070204": 3, "070455": 11, "073846": 13, "073912": 7, "076294": 7, "076717": 13, "077273": 13, "078210": 5, "078327": 7, "078642": 5, "079104": 13, "079129": 13, "08": [7, 9], "081597": 7, "085262": 7, "086376": 13, "087407": 7, "087955": 7, "088627": 11, "091752": 7, "093771": 7, "095385": 13, "097774": 7, "0f": [1, 9], "0x7f0d1d4514f0": 9, "0x7f0d1d5d6760": 9, "0x7f0d1f2f3b20": 9, "0x7f0d1f55fa60": 9, "1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15], "10": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13], "100": [1, 4, 9, 11, 13, 15], "1000": [1, 5], "100000": 5, "100214": 11, "10097": 5, "1010": 5, "101155": 7, "101533": 5, "1017": 5, "1023": 5, "1029": 5, "103": 5, "1038": 5, "103822": 5, "104": [5, 7, 9], "105": [5, 9], "106": 5, "106277": 5, "1065": 5, "106575": 5, "1066": 5, "107": 5, "107857": 11, "108": [5, 13], "108089": 13, "1089": 5, "109": 13, "11": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13], "110": [7, 9, 13], "1102": 5, "1105": 5, "1108": 5, "110912": 5, "111111": 5, "112215": 13, "1123": 5, "113": [7, 9], "113038": 11, "1136": 5, "1139": 5, "1143": 5, "1146": 5, "1149": 5, "115": 7, "1151084": 11, "115518": 7, "1158": 5, "116": 13, "1161": 5, "116276": 13, "1164": 5, "117": [7, 9, 13], "1171": 5, "118": [5, 7, 9], "119": [5, 11], "119345": 13, "119866": 13, "12": [1, 3, 5, 7, 9, 13, 15, 16], "1205": 5, "1207": 5, "1210": 5, "122": [5, 11], "1223": 5, "1224": 5, "1231231": 1, "1232": 5, "1233": 5, "123453": 7, "124": 5, "1243": 5, "1244": 5, "125": [5, 7], "125000": 5, "1265323": 11, "127": [5, 11], "1270": 11, "127249": 7, "127360": 5, "128": 9, "1286": 5, "129": 5, "13": [3, 5, 7, 9, 13, 15], "130": 5, "1301": 5, "131": [7, 9, 13], "1314": 5, "131693": 5, "132": 9, "132016": 7, "132588": 13, "1329": 5, "133": [11, 13], "1332": 5, "133333": 3, "134": 5, "1343": 5, "135298": 5, "1356": 5, "136": 7, "1362": 5, "138": 5, "1382": 5, "138601": 7, "138889": 11, "13948": 5, "139609": 5, "1397": 5, "139811": 5, "139892": 13, "14": [3, 5, 7, 9, 13], "140": [7, 9], "140076": 7, "1402": 5, "140374": 5, "142": 5, "1428": 11, "142857": 5, "14341": 11, "1435": 5, "1437": 5, "1440": 5, "1447": 5, "1449": 5, "1465": 5, "1467": 5, "14670": 11, "147": 5, "1474": 5, "148070": 7, "1483": 5, "1486": 5, "14889": 11, "149": 13, "1496": 5, "14987": 5, "1499": 5, "15": [0, 1, 3, 5, 7, 9, 11], "150": [1, 5, 6], "150825": 7, "151": [7, 9], "151646": 13, "151691": 7, "152": [5, 9], "152131": 13, "1531": 5, "1537": 5, "1538": 5, "153846": 13, "1540": [5, 13], "1543": 11, "1546": 5, "1556": 13, "156": 13, "1580": 5, "158109": 5, "1586": 13, "159311": 7, "1598": 5, "16": [3, 5, 7, 9, 11, 13], "160": 7, "1602": 5, "160208": 13, "1603": 5, "1614": 5, "1624": 5, "1625": 5, "1640": 5, "1651": 5, "1652": 5, "166": 5, "166206": 7, "166667": 5, "1679": 5, "1680": 1, "168163": 13, "168478x0": 7, "1689": 5, "169": 13, "1691": 5, "1698": 5, "17": [3, 5, 7, 9], "170": [7, 9], "1702": 5, "1704": 5, "170408": 7, "1715": 5, "1721": 5, "1723": 5, "1724": 11, "172775": 11, "174": 5, "1746": 5, "175": 5, "176": 9, "177": 13, "177314": 7, "18": [3, 5, 7, 9], "1800": 5, "1802": 5, "181": 5, "181085": 7, "1812": 5, "181818": 5, "182000": 1, "1822": 5, "1827": 5, "182900": 5, "183": 5, "184": [7, 9], "185": [7, 9], "1852": 5, "1857": 5, "1859": 5, "186": [7, 9], "1861": 5, "1863": 5, "1870": 5, "19": [0, 3, 5, 13], "190": 5, "1902": 5, "1908": 5, "1922": 5, "192388": 5, "193": 5, "193548": 13, "193861": 7, "1940": 5, "1953": 5, "1954": 5, "196152": 5, "196306": 11, "197": 11, "1971": 5, "1974": 5, "197413": 7, "1998": 5, "1999": 24, "1e": 1, "1f": [0, 1, 2, 3], "1st": 28, "2": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15], "20": [3, 7, 9, 11], "200": [0, 1, 5, 9], "2000": 7, "200000": 5, "2004": 5, "2007": 15, "200705": 11, "2010": 5, "2012": 24, "2016": 1, "2019": 5, "202": 13, "202042": 7, "2021": 5, "202663": 3, "203": 11, "203272": 11, "2037": 5, "2053": 5, "207338": 7, "2082": 5, "209": 13, "209317": 7, "21": [0, 3, 5, 7, 9, 11, 16], "210": [11, 13], "2101": 5, "210411": 7, "211": 5, "211610": 7, "211656": 11, "212": 5, "2133": 5, "215": [7, 9], "2155": 11, "2165": 5, "2168": 5, "217109": 13, "219": 5, "22": [3, 5, 7, 9, 13], "220": 15, "2200": 0, "2203": 5, "220332": 13, "221033": 7, "2218": 5, "2227": 5, "223": 5, "2235": 5, "223827": 7, "224": [5, 9], "22414": 11, "225": 13, "2253": 5, "225529": 13, "2259": 5, "226": 5, "2260": 5, "2263": 5, "227692": 13, "229345": 7, "2294": 5, "23": [1, 3, 5, 7, 9, 11, 15], "230": [7, 9], "2300": 5, "230186": 13, "2318": 5, "2319": 5, "232": [5, 7, 9], "2320": 5, "2322": 5, "232210": 5, "2324": 5, "2332": 5, "2342": 5, "2346": 11, "236207": 7, "2384": 5, "2389": 5, "24": [3, 5, 7, 9, 11, 13], "241": [7, 9], "241813": 7, "242": [7, 9], "242748": 11, "243": 5, "243742": 7, "244419": 7, "245": 5, "245435": 5, "2459": 5, "245961": 13, "246212": 7, "247486": 13, "247876": 5, "248006": 7, "248030": 11, "2494": 5, "249805": 7, "25": [1, 3, 5, 7, 9, 11, 13], "250000": [3, 5, 11], "2501": 11, "251": 5, "2516": 5, "25302": 11, "2536": 5, "2539": 5, "254068": 13, "255505": 13, "2560": 5, "256416": 11, "257": 11, "2575": 11, "258403": 5, "259496": 11, "26": [3, 5, 7, 9, 11, 13], "260": 5, "260339": 7, "2605": 5, "260844": 7, "262445": 13, "2625": 5, "263056": 13, "263505": 7, "265": 5, "265412": 7, "2655": 5, "266667": 3, "2672": 11, "267359": 7, "2690": 5, "2692": 5, "27": [3, 5, 7, 9, 13], "2714": 5, "272": 11, "2721": 11, "272383": 5, "272727": 5, "273085": 13, "2740": 5, "2753": 5, "275649": 5, "2757": 5, "275901": 13, "276": 5, "2767": 5, "276768": 13, "278": 1, "278298": 5, "2796": 5, "2798": 5, "28": 3, "280": 1, "280245": 7, "2810": 5, "2816": 5, "282": 5, "2846": 5, "285": 0, "285714": 5, "287822": 5, "288627": 7, "2892": 5, "29": [3, 5, 7, 9], "290394": 11, "291": 5, "2916": 5, "292": 5, "292877": 13, "2940": 5, "2962": 5, "2966": 5, "298258": 13, "298616": 7, "299": 5, "2992": 5, "299676": 5, "2999": 5, "2f": [1, 4], "3": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 15], "30": [3, 7, 9], "300": 7, "300000": 5, "3002": 5, "3006": 5, "300701": 7, "300991": 7, "300bp": 0, "301": 5, "3011914": 11, "301991": 11, "302": 11, "302081": 7, "303077": 5, "303950": 11, "3060": 5, "3062": 5, "307692": 13, "3079": 5, "309": 5, "3094": 5, "3095": 5, "31": [3, 5, 7, 9], "310915": 7, "311035": 11, "3115": 5, "3117": 5, "3118": 5, "3119": 5, "3120": 5, "312008": 11, "3121": 5, "3123": 5, "3124": 5, "313088": 13, "3131": 5, "313199": 5, "313846": 13, "314062": 7, "3145": 5, "314940": 7, "315": 11, "315743": 5, "315913": 7, "316228": 5, "317": 5, "318145": 11, "319": 13, "32": [3, 5, 7, 9], "320": 1, "3217": 5, "322": 13, "322395": 5, "322973": 13, "323": 5, "324": 11, "324745": 5, "325": 13, "325552": 7, "326": 5, "3265": 5, "3268": 5, "326940": 11, "3271": 5, "327174": 7, "33": [3, 7, 9], "330541": 13, "331381": 5, "332": 5, "333333": 5, "3343": 5, "334738": 7, "336091": 7, "3389": 5, "3394": 5, "34": [3, 5, 7, 9, 15], "340": 5, "3416": 5, "343": 5, "3441": 5, "345231": 11, "3463": 5, "3482": 13, "3486": 5, "348600": 13, "35": [0, 5], "350288": 5, "350467": 11, "351729": 7, "352273x0": 7, "3525": 5, "353137": 7, "353553": 5, "354507": 7, "3547": 5, "357502": 7, "358": 5, "359000": 7, "36": [3, 5, 7, 9, 11, 13], "363077": 13, "363636": 5, "364": [7, 9], "364306": 13, "366563": 5, "366667": 3, "36697977": 11, "3671": 5, "3673": 5, "368554": 7, "3686": 5, "37": [3, 5, 7, 9], "3709": 5, "371020": 7, "3724": 5, "373": 11, "3743": 5, "375000": 5, "375722": 7, "375902": 5, "376193": 13, "37776": 13, "38": [0, 3, 7, 9], "380": 5, "380507": 7, "382": [5, 11], "3821": 5, "382766": 11, "38322709": 11, "385": 5, "385047": 7, "385806": 7, "3865": 5, "3866": 5, "3877": 5, "389": 11, "389750": 7, "39": [5, 9], "390656": 5, "391024": 7, "391665": 5, "391667": 11, "3926": 5, "394": 5, "395": [7, 9], "396313": 5, "397": [7, 9], "3979": 5, "398": 11, "398808": 7, "399": 5, "4": [0, 2, 3, 5, 7, 9, 11, 13, 15, 28], "40": [3, 5, 7, 9], "400000": [3, 5, 11], "401388": 5, "403432": 7, "405": 5, "40514018": 11, "406": 5, "41": [3, 5, 9, 13], "412": 5, "412781": 7, "4138": 5, "414": 9, "4144": 5, "414560": 13, "415": 5, "416667": 5, "418": 11, "418228": 11, "4183": 5, "4186": 5, "418689": 7, "4195": 5, "42": [3, 5], "420381": 7, "4225": 5, "4252": 5, "425785": 13, "426": 5, "426076": 7, "4266": 5, "426620": 11, "427": 5, "427060": 7, "428": 5, "428571": [5, 11], "428603": 7, "429": 5, "4295": 5, "43": [3, 5, 9, 13], "430": 5, "430570": 5, "431": 5, "4316": 5, "432": 5, "4353": 5, "4358": 5, "436466": 7, "4372": 5, "4373": 5, "438": 13, "439171": 7, "44": [5, 9], "441315": 5, "442361": 7, "442948": 7, "444": 5, "444091": 5, "444444": 5, "444492": 7, "445546": 7, "447214": 5, "448": 5, "448138": 7, "448154": 5, "448692": 7, "4497": 5, "45": 3, "4513": 5, "451532": 7, "451760": 7, "452": 5, "453011": 7, "454": 5, "457": 5, "457242": 7, "46": [3, 13], "4604": 5, "461": 5, "461862": 13, "4640": 5, "464491": 11, "4648": 5, "466016": 7, "466990": 7, "467742": 7, "468": [5, 9], "469479": 7, "47": [3, 5, 13], "472136": 5, "4740": 5, "474836": 13, "477": 11, "478915": 7, "479059": 7, "48": [3, 7, 9, 13], "480000": 13, "481": [7, 9, 13], "482685": 7, "485122": 7, "4857": 5, "487": 5, "488": 11, "488694": 5, "49": [3, 5, 7, 9], "490802": 7, "491866": 7, "492": 5, "492297": 7, "4925": 5, "494296": 7, "495": 5, "498491": 13, "498605": 13, "4987": 5, "4yr": 13, "5": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15], "50": [0, 1, 2, 3, 7, 9, 11], "500": 0, "500000": [3, 5, 13], "500383": 5, "503098": 5, "503528": 7, "505447": 7, "51": [3, 7, 9, 13], "510": [7, 9], "511682": 7, "512550": 7, "5135": 5, "515": 5, "515722": 5, "517": 29, "519": 5, "5199": 13, "52": [5, 13], "520000": 13, "520928": 7, "521": [5, 11], "523510": 5, "525": [10, 11], "5253": 5, "528": 11, "529348": 7, "53": [3, 7, 9, 11, 13], "530835": 13, "531052": 7, "531102": 11, "531708": 7, "533607": 13, "538": 11, "538362": 11, "5392": 5, "54": [3, 7, 9], "540984": 11, "542014": 5, "5425": 5, "543195": 13, "544815": 7, "545455": 5, "547727": 7, "547734": 5, "5480": 5, "548527": 5, "549657": 5, "55": [3, 7, 9, 13], "5510": 5, "551650": 5, "552316": 5, "555556": 5, "5559": 5, "5562": 13, "556885": 7, "557713": 7, "56": [3, 7, 9, 13], "562916": 7, "566258": 11, "567577": 13, "569209": 3, "57": [3, 5, 9], "571429": 5, "572021": 7, "573143": 7, "573714": 5, "5748": 5, "57729816": 11, "581": 11, "582": 5, "582153": 7, "585359": 13, "587": [5, 13], "587972": 13, "5885": 5, "589321": 11, "59": [0, 2, 3, 6, 8, 9, 10, 12], "590502": 11, "591": 9, "59364634": 11, "594334": 7, "595": 11, "599343": 11, "6": [0, 1, 2, 3, 4, 5, 7, 9, 11, 12, 13], "60": [5, 7, 9, 11, 15], "600000": 5, "600322": 7, "600326": 5, "6004": 5, "601472": 7, "605978": 7, "606169": 7, "608108": 13, "608133": 5, "609": 11, "61": [5, 7, 9], "612": 11, "6125": 5, "6135": 5, "614170": 11, "615": 5, "617": 5, "62": [7, 9, 13], "620379": 5, "620481": 7, "622793": 5, "623": 5, "623116": 11, "6261": 5, "63": [5, 7, 9], "632728": 5, "634": 11, "6357": 5, "636364": 5, "638": 5, "638172": 7, "638858": 7, "64": [5, 7, 9], "641": 11, "641845": 5, "65": [7, 9, 13], "650": [0, 1], "651": 5, "656041": 7, "656465": 11, "658937": 13, "659681": 13, "66": [3, 5, 7, 9, 13], "660942": 5, "662": 5, "662020": 5, "664": [5, 11], "664964": 7, "665777": 7, "666667": [5, 13], "667": 9, "668": 9, "669": 9, "669285": 7, "669997": 7, "67": [7, 9], "670": [5, 9], "6709": 5, "670989": 5, "671": 9, "672": [5, 9], "676923": 13, "677255": 7, "68": [5, 7, 9], "688109": 7, "689055": 7, "69": [5, 7, 9], "692308": 13, "692426": 7, "692828": 11, "694809": 5, "6950": 1, "6951": 1, "697499": 13, "7": [0, 3, 5, 7, 9, 10, 11, 13, 15], "70": [7, 9, 15], "700000": 5, "700282": 5, "700800": 5, "700951": 13, "703": [5, 11], "708945": 5, "71": [5, 7, 9], "711649": 7, "713": 11, "713740": 13, "714286": 5, "715677": 5, "717": 11, "717813": 7, "717995": 7, "718": [5, 13], "718436": 7, "719207": 7, "72": [0, 1, 3, 9], "720": 11, "720370": 5, "722": 5, "7249": 5, "727": 5, "727273": 5, "729213": 7, "729756": 5, "731522": 7, "733": 5, "736155": 5, "736280": 7, "737265": 11, "737718": 7, "739": [11, 13], "739450": 5, "74": [3, 7, 9], "743": 5, "744087": 13, "747": 5, "747175": 7, "747258": 5, "75": [3, 5, 7, 11], "750000": [3, 5], "750044": 7, "750579": 11, "7514": 5, "751692": 5, "753": 13, "755459": 5, "755929": 5, "76": [3, 7], "760": 5, "761385": 5, "764736": 7, "766": 5, "766186": 5, "77": [5, 7, 11], "771": 5, "771142": 5, "774772": 11, "775x0": 7, "776097": 5, "777778": 5, "778935": 13, "778966": 5, "779431": 7, "78": [5, 7, 9], "782223": 7, "784": 5, "79": [5, 7, 9], "790": 11, "790041": 5, "792698": 5, "794": 11, "794172": 13, "796715": 13, "8": [1, 2, 3, 4, 5, 9, 11, 13], "80": [5, 9], "800": 11, "800000": 5, "802374": 7, "803619": 13, "8038": 5, "804961": 13, "805932": 13, "806312": 5, "808": 11, "809062": 7, "809495": 7, "81": [5, 7, 9], "816": 9, "816497": 5, "82": 7, "822714681440445": 15, "823276": 11, "824": 5, "826097": 13, "827": 1, "827337": 5, "83": [7, 9], "833333": 5, "834080": 5, "835926": 5, "838082": 5, "84": [7, 9], "842": 5, "843312": 13, "847": 5, "848419": 13, "85": [3, 7, 9, 15], "857143": 5, "86": 15, "861": 5, "862714": 5, "863": 5, "865958": 5, "868": 5, "87": [3, 7, 9], "871029": 5, "872043": 13, "875": 5, "875000": [5, 11], "88": 3, "880832": 13, "883080": 5, "888889": 5, "891": 5, "895666": 11, "898357": 13, "9": [1, 3, 5, 6, 11, 13, 15], "90": [7, 13], "900000": [3, 5], "901658": 5, "901854": 5, "902004": 13, "904235": 13, "904244": 13, "904249": 13, "904253": 13, "904258": 13, "904260": 13, "904706": 5, "905": 5, "91": 5, "911": 5, "913580": 11, "914": 5, "92": 5, "923": 5, "925963": 13, "926": 11, "93": [5, 7, 9], "930": 5, "930288": 5, "932883": 5, "933985": 7, "939": 11, "94": [3, 9], "940": 5, "941": 5, "95": [5, 7, 8, 9, 10, 11], "952127": 5, "956156": 11, "959729": 13, "96": 11, "961": 5, "965": 5, "966667": 11, "97": [5, 9], "971": 5, "971988": 5, "973627": 5, "975734": 13, "976": 5, "979050": 13, "9796": 5, "979960": 5, "98": [7, 9], "982": 5, "985457": 13, "985677": 5, "988": [5, 7, 9], "99": 3, "A": [0, 1, 3, 4, 7, 9, 11, 12, 13, 15, 16], "And": [2, 3, 4, 15, 18], "As": [0, 1, 6, 7, 9, 13, 16], "At": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 24], "BY": 28, "But": [1, 5, 9], "By": [0, 2, 3, 4, 7, 9, 15], "For": [2, 3, 5, 6, 7, 8, 10, 15, 16], "If": [0, 1, 3, 4, 5, 6, 7, 9, 13, 15, 18, 28], "In": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 15, 16, 24], "It": [0, 1, 2, 3, 4, 7, 8, 9, 10, 13, 15, 16, 24, 29], "Its": [7, 24], "NO": [1, 3, 7, 13], "NOT": [6, 7, 15], "No": 12, "Not": 1, "On": [0, 16], "One": [0, 5, 6, 7, 13], "Or": [1, 4, 15], "That": [0, 2, 4, 6, 7, 8, 10, 11, 12, 13, 15], "The": [0, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 15, 24, 28], "Then": [0, 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 15], "There": [0, 1, 3, 4, 5, 6, 7, 13, 15, 16], "These": [0, 1, 2, 5, 7, 9, 12, 13, 15, 16, 24], "To": [2, 4, 7, 10], "Will": 28, "With": [6, 9, 11, 13], "_df": 3, "_mask": 3, "aa": 13, "abil": [7, 13, 24], "abl": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15], "about": [1, 4, 6, 7, 8, 9, 10, 11, 13, 15, 16, 24], "abov": [1, 4, 9, 12, 13], "abreast": 15, "absorb": 1, "abstract": [13, 15, 24], "abund": 4, "academ": 24, "accent": 7, "accept": [8, 10, 13], "access": [4, 13, 16, 24], "accomplish": [13, 15], "accord": 0, "accordingli": 16, "accur": [0, 4], "acquisit": 13, "across": [2, 6, 8, 11, 13, 16], "act": 29, "actinobacteria": 5, "action": 16, "activ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15], "actual": [13, 24], "ad": [1, 4], "adapt": 1, "add": [0, 3, 4, 7, 13, 15], "addit": [1, 7, 24], "addition": 13, "address": 10, "adjust": [6, 7, 24], "administr": 15, "adopt": 7, "adult": [9, 15], "advanc": [5, 7, 23], "advantag": [6, 15], "ae": 24, "aesthet": [3, 24], "affect": [2, 13], "after": [0, 1, 3, 4, 9, 13, 15], "ag": [1, 2, 3, 6, 7, 9, 12, 13, 15], "again": 1, "against": [0, 2, 13], "age_col": 3, "age_initial_infect": [2, 3], "age_mask": 3, "age_mean": 3, "age_mean_short": 3, "aged_high_vl": 3, "aged_low_vl": 3, "aged_sampl": 3, "agg": [5, 11], "aggfunc": [4, 5, 7, 11], "aggreg": [4, 5, 9, 10, 11, 13], "aggress": 4, "agre": 13, "agreement": 13, "ahead": 3, "aim": [15, 24], "akin": 24, "algorithm": [3, 15], "alia": 3, "all": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15], "allow": [0, 1, 2, 4, 7, 10, 13, 15, 16, 24, 29], "almost": 7, "alon": 13, "along": [3, 6], "alpha": [7, 9, 11], "alphabet": 9, "alreadi": 15, "also": [1, 3, 5, 7, 9, 10, 13, 15, 24, 29], "alter": [1, 24], "altern": [3, 7, 13], "although": 13, "alwai": [13, 16], "among": 13, "amount": [0, 4, 10, 11], "amplicon_length": 1, "amplicon_weight": 1, "amplif": 0, "an": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 15, 16, 29], "anaconda": 15, "analys": 13, "analysi": [0, 1, 2, 3, 4, 7, 10, 13, 15, 16, 24], "analyst": 24, "analyz": [2, 3, 4, 15], "ani": [2, 3, 6, 7, 8, 9, 10, 12, 13, 15, 16, 24], "annot": 29, "anoth": [0, 1, 3, 7, 9, 15], "anova": [10, 11, 12, 13], "answer": [0, 1, 2, 4, 6, 8, 9, 10, 11, 12, 13, 15], "antiretrovir": [12, 13], "anwser": [10, 11], "anyth": [1, 15], "anywher": 1, "api": [9, 13], "append": 2, "appli": [0, 5, 7, 9, 11, 13, 24, 28, 29], "applic": [2, 4], "approach": [4, 9, 13], "appropri": [0, 4, 12], "approxim": [9, 13], "ar": [1, 2, 3, 5, 7, 8, 9, 10, 13, 15, 16, 24, 28], "arang": [7, 11], "arbitrari": [2, 3], "arbitrarili": 13, "arduou": 15, "area": [4, 5, 9, 10, 11], "arg1": 1, "arg2": 1, "around": [1, 3, 7, 9], "arrai": [3, 24], "art": [3, 13], "art_count": 13, "as_index": 5, "ask": [8, 10], "aspect": [0, 24], "ass": 12, "assai": [4, 21], "assert": 15, "assess": [7, 13], "assign": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 15], "associ": 13, "assoti": 12, "assum": [1, 8, 10, 13], "assumpit": 9, "assumpt": [4, 9, 13], "astyp": [11, 12, 13], "atop": 3, "attach": [1, 3], "attain": 13, "attent": 4, "attract": [13, 24], "attribut": [3, 24, 28], "audienc": 24, "auditori": 13, "autom": [1, 9, 11], "automat": [8, 10], "avail": [5, 13, 15], "averag": [0, 2, 4, 9, 10, 11, 12, 13, 15], "average_week": 3, "avgintench2": 11, "awai": [15, 24], "await": 16, "ax": [6, 7, 9, 11, 13], "ax_ser": 7, "axi": [6, 7, 8, 9, 10, 11], "axis_handl": 7, "axisgrid": 9, "b": [7, 11, 13], "b10": 11, "b11": 11, "b2": 11, "b3": 11, "b4": 11, "back": [3, 4, 7, 13, 15], "background": [13, 16, 19, 29], "background_gradi": 7, "bacteri": 5, "bacteria": 5, "bacteroidet": 5, "bake": 1, "balanc": [7, 13, 24], "bar": [5, 7, 9, 11, 12, 13, 24], "barcod": 1, "barh": 7, "barplot": [6, 7, 9, 10, 11, 13], "base": [0, 1, 2, 3, 4, 9, 12, 13, 15, 16, 24], "base_weight": 1, "basepair": [0, 1], "basic": [0, 1, 3, 5, 13, 14, 15, 17, 22, 27], "batteri": [13, 15], "bay": 13, "bblearn": [0, 2, 4, 5, 6, 7, 8, 10, 11, 12, 15], "bead": [10, 11], "beadsd": 10, "beat": [10, 15], "beauti": 24, "becaus": [0, 2, 4, 7, 13, 15], "becom": [15, 16], "been": [0, 1, 2, 3, 5, 7, 9, 11, 15, 18], "befor": [0, 1, 3, 7, 9, 13, 15, 16, 24], "begin": [0, 1, 3, 4], "beginn": 13, "being": [0, 2, 4, 7, 13, 15, 16, 24], "believ": 13, "below": [9, 12, 13, 15], "bera": 13, "berklei": 15, "best": [5, 7, 9, 13], "better": [0, 2, 7, 15], "between": [0, 1, 2, 3, 4, 5, 6, 7, 9, 12, 13, 15, 21], "beyond": 24, "bf10": 13, "bin": [7, 9, 11, 13, 24], "binar": 12, "biolog": [3, 6, 8, 10, 11, 13, 15, 21, 25, 27, 29], "biologi": [1, 13, 15], "biologist": 13, "biomark": [6, 7, 9], "biome_data": 5, "biomed": 5, "biopsi": [4, 5], "biostatist": [4, 13, 28, 29], "black": [1, 11], "block": 1, "bmi": [6, 7, 9, 15], "bog": 13, "boil": 13, "bold": 15, "book": [1, 24, 28, 30], "boolean": [2, 4], "bootstap": 9, "bootstrap": [7, 9], "both": [0, 4, 5, 9, 13, 15, 16], "boundari": 11, "box": [4, 6, 9, 11], "boxplot": [6, 7, 9], "bp": [0, 1, 15], "brace": 1, "bracket": [3, 13], "break": [1, 2, 7, 13, 24], "bridg": 13, "brief": [1, 15], "briefli": 19, "bring": 7, "broader": 9, "broken": 5, "browser": [15, 16], "build": [12, 13, 24], "built": [5, 13], "bulla": 5, "bullet": 15, "button": 16, "bypass": 15, "c": [7, 11, 13], "c2": 11, "c3": 11, "calc_molar": 1, "calc_yield": 1, "calcul": [4, 6, 10, 13, 24], "call": [0, 1, 2, 3, 4, 13, 15, 16, 24], "came": [4, 11], "can": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 18, 24], "cannabinoid": [7, 9], "cannabinoid_us": 9, "cannot": [3, 13, 15, 16], "capabl": [7, 13], "capit": 5, "caption": 6, "captur": [7, 15], "carefulli": 2, "carri": 15, "case": [3, 4, 16], "categor": [6, 7, 11, 12, 24], "categori": [6, 7, 11, 13], "caus": 5, "cbar": 9, "cc": 28, "cell": [0, 1, 2, 4, 7, 13], "cell_level_data": [10, 11], "cell_numb": 11, "cells_per_wel": 11, "center": 3, "central": 9, "certain": 4, "chain": [0, 3], "chanc": [3, 13], "chang": [1, 7, 9, 11, 13, 15, 24], "chapter": [14, 15, 17, 20, 21, 22, 23, 25, 27], "characterist": 0, "chart": 12, "check": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15], "check_al": [2, 13, 15], "chemic": 1, "chemokin": [6, 7, 9], "chi2": [12, 13], "chi2_independ": [12, 13], "choic": 7, "choos": [12, 13], "chosen": 2, "ci": [7, 9, 11], "ci95": 13, "circa": 1, "class": [15, 16], "classifi": 4, "clean": [0, 2], "click": 15, "clinic": [2, 4, 15], "clinician": 4, "close": [6, 7, 24], "cloud": 16, "cmap": 7, "cocain": [7, 9], "cocaine_us": 9, "code": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 16, 24], "coeffici": 6, "cognit": 13, "cohen": 13, "coher": 24, "cohort": [6, 7, 9, 12, 13], "coivd": 0, "col": [7, 9, 11], "col_wrap": 9, "colab": [7, 14, 16], "collaps": 11, "collect": [4, 5, 6, 7, 9, 13, 15], "collectiontyp": [4, 5], "colleg": [13, 28], "color": [7, 9, 24], "column": [4, 5, 6, 11, 12, 13], "com": [15, 19], "combin": [4, 5, 9, 24], "come": [1, 4, 5, 13, 15, 16, 26], "comma": 3, "command": [3, 16], "commens": 5, "comment": 9, "common": [1, 3, 5, 6, 7, 9, 13, 15, 28], "common_norm": 9, "commonli": [5, 13], "commun": [1, 3, 7, 24], "compact": 1, "compani": 16, "companion": [28, 29], "compar": [0, 7, 12, 13, 21, 24], "comparison": [0, 2, 3, 5, 12], "compat": 16, "complet": [0, 2, 3, 4, 6, 7, 8, 10, 11, 12, 15, 16], "complex": [1, 3, 7, 13, 15, 24], "complic": [0, 4, 13], "compon": 24, "comprehens": [13, 24], "compress": 10, "compris": 15, "comput": [1, 7, 9, 15, 16], "concaten": 11, "concentr": [0, 1, 9, 11], "concept": [13, 15, 16, 24], "conceptu": 24, "concis": 24, "condit": [4, 10, 11, 15], "conduct": 13, "confid": [7, 8, 9], "congratul": 0, "connect": 16, "consid": [2, 4, 9, 13, 15], "consider": 2, "constant": 5, "constraint": [3, 13], "construct": [7, 24], "consum": 24, "consumpt": 28, "contact": 28, "contain": [1, 3, 4, 7, 9, 10, 13, 16], "content": [11, 16, 18, 28, 29], "context": [1, 2, 21, 29], "contin": 12, "contini": [6, 7], "continu": [0, 1, 2, 7, 9], "contrast": [0, 13], "contribut": [7, 24], "control": [2, 3, 7, 13], "convei": [9, 24], "conveni": 24, "convent": 3, "convers": 18, "convert": [1, 5, 9], "coord": 24, "coordin": [13, 24], "copi": [1, 2, 3, 5], "core": [3, 24], "corner": 15, "corr": [6, 7], "correct": [0, 1, 2, 4, 8, 10, 13], "correctli": [4, 15], "correl": [6, 7, 12], "correspond": 3, "could": [6, 7, 9, 15], "count": [1, 3, 4, 7, 10, 11, 15], "counterpart": 13, "countplot": [12, 13], "coupl": 7, "cours": [1, 3, 15, 16, 28, 29], "covari": 13, "cover": [1, 3, 10, 11, 13, 15, 16], "covid": [0, 1], "cramer": 13, "creat": [0, 1, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 16, 24], "creation": 24, "creativ": 28, "cressi": 13, "critic": [1, 15], "cross": [6, 13], "cross_corr": 7, "crosstab": 13, "crucial": 4, "csv": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "ctrl": 16, "cultur": 5, "cure": 3, "curli": 1, "current": [0, 1, 2, 3], "current_yield": 1, "custom": [5, 7, 24], "customiz": 24, "cut": [2, 9, 13], "cutoff": [4, 13], "cytokin": [6, 7, 9], "cytokine_data": [6, 7, 8, 9], "d": [7, 11, 13], "d2": 11, "d4t": [12, 13], "da06": 11, "da07": 11, "da08": 11, "da09": 11, "da10": 11, "da11": 11, "da12": 11, "da13": 11, "da14": 11, "da_tx": 11, "dai": 15, "dampier": 28, "dandi": 13, "dash": 3, "data": [0, 1, 3, 4, 6, 8, 9, 11, 12, 13, 15, 16, 20, 21, 22, 24], "datafram": [3, 4, 6, 7, 9, 11, 13, 24], "dataset": [4, 5, 9, 10, 11, 15, 16, 24, 29], "date": 7, "ddof1": 13, "deal": [0, 5, 7], "debug": [1, 2, 7], "decad": 5, "decid": 7, "decis": [13, 15], "decreas": 9, "deep": [5, 13], "deeper": 13, "def": [1, 5, 9, 11], "default": [6, 7, 9, 13, 24], "defin": [0, 1, 2, 24], "definit": [2, 13], "degrad": 0, "delet": 16, "delimit": 4, "delv": [0, 4], "demograph": [6, 9, 12, 13], "densiti": 9, "depend": [5, 12, 13], "depth": 1, "deriv": [2, 3, 13], "describ": [0, 3, 4, 9, 13, 15, 19, 24], "descript": [1, 8, 10], "design": [1, 6, 7, 13, 24], "desir": [13, 15], "detail": [1, 5, 13, 24], "detect": [3, 4, 11], "determin": [0, 2, 12, 15], "develop": [7, 15, 24, 28], "deviat": [2, 4, 5, 9, 12, 13], "devic": 1, "devlin": 13, "dexter": 13, "df": [9, 11, 13], "dh20": 0, "diagnos": 4, "diagnost": 4, "dice": 13, "dictat": 24, "did": [11, 13], "didn": 15, "diff": 13, "diffent": 5, "differ": [0, 1, 2, 3, 4, 5, 11, 12, 13, 24], "difficult": [0, 8, 10, 13, 15, 16], "difficulti": 15, "digest": 5, "dilut": [0, 1], "direct": 6, "directli": 13, "disconnect": 16, "discuss": [1, 6, 7, 9, 11, 12, 13, 17, 20, 21, 22, 23, 25, 27], "diseas": [2, 5], "disease_typ": 5, "disitribut": 13, "disord": 13, "displai": [0, 1, 3, 4, 8, 24], "dist": 13, "distant": 0, "distinct": 9, "distinguish": 11, "distribut": [5, 7, 13, 24], "dive": [0, 3, 13], "divid": [9, 13], "dna": [0, 1, 18], "dna_conc": 1, "dna_molar": 1, "dna_weight": [0, 1], "dna_yield": 1, "dna_yield_descript": 1, "do": [0, 1, 2, 3, 4, 5, 6, 7, 9, 13, 15, 17, 22, 23], "doc": 4, "document": [5, 7, 13], "dodg": [9, 11], "doe": [0, 2, 5, 7, 10, 11, 13], "doesn": [3, 9], "dof": 13, "dollar": 1, "domain": [12, 13], "don": 9, "done": [1, 3, 4, 5, 6, 7, 9, 16, 18], "dopamin": [10, 11], "dot": [3, 7], "doubl": [0, 1, 2, 15], "down": [1, 2, 3, 5, 7, 13, 15, 24], "download": [0, 2, 4, 6, 8, 10, 11, 12, 15, 16], "downstream": 10, "dozen": [7, 15], "dpi": 7, "dr": [11, 13], "drastic": 1, "draw": [9, 24], "drexel": [1, 5, 6, 7, 9, 13, 28, 29], "drop": 13, "dropdown": 15, "drug": [12, 13], "dtype": [3, 7, 11, 13], "due": [0, 1, 2, 3, 6, 8, 10, 12, 13, 15], "durat": 4, "dure": [1, 2, 3, 15], "dv": 13, "dynam": [0, 1], "e": [11, 13, 24], "each": [1, 3, 4, 6, 7, 8, 9, 12, 15, 16], "ear": 5, "earli": 7, "earlier": 2, "eas": [13, 24], "easi": [4, 9, 15, 24], "easier": [1, 3, 13, 24], "easili": [3, 7], "eat": 10, "eb": 9, "ecosystem": [13, 24], "eda": 24, "edg": 7, "edit": [2, 7, 15, 16, 28], "educ": [12, 15], "education_bin": 13, "effect": [2, 3, 9, 12, 13, 16], "effici": [0, 24], "effort": [6, 7, 9], "effortlessli": [13, 24], "egf": [7, 9], "either": [2, 7, 9, 16], "electrophysiologi": 7, "element": 24, "elif": 9, "elimin": 3, "els": [5, 9], "embark": 13, "emerg": [5, 15], "emoji": 3, "emphas": 24, "emploi": [3, 4, 5, 6, 12, 13], "empow": 24, "empti": 16, "emtricitabin": [12, 13], "enabl": 24, "encod": 16, "encompass": 13, "encourag": 24, "end": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15], "endswith": 15, "enough": 0, "ensur": [0, 1, 15, 16], "enter": 16, "entir": [1, 3, 10], "enumer": 9, "environ": [7, 11, 13, 15], "enzymat": 1, "eotaxin": [7, 9], "eotaxin_hist": 7, "equal": [9, 13], "equal_var": 13, "equat": 1, "error": [0, 1, 2, 4, 6, 8, 9, 10, 11, 12, 13], "errorbar": [9, 11], "especi": 7, "essenc": 24, "estim": [11, 12, 13, 15], "etc": [7, 9, 11, 13, 24], "ethmoid": 5, "evalu": [2, 3, 13], "even": [0, 1, 3, 9, 15], "everi": [5, 9], "everyon": [3, 15], "everyth": [0, 1, 2, 3, 5, 16], "everywher": 5, "evid": [10, 11, 12], "evolv": [7, 29], "exacerb": 5, "exactli": [3, 13], "exam": [12, 13], "examin": [0, 2, 6], "exampl": [3, 5, 9, 13, 15], "exce": 0, "excel": [3, 15], "except": 15, "excercis": 7, "excess": [0, 2], "excit": 0, "exec_domain_z": 13, "execut": [12, 13, 15, 16], "exercis": [0, 15], "exist": [2, 15], "expand": [0, 29], "expect": [4, 9, 13], "experi": [1, 2, 6, 11, 15, 25, 27], "experiment": [0, 10, 11], "explain": 1, "explan": [1, 4, 13, 18], "explanatori": 1, "explicitli": [7, 24], "explor": [0, 1, 2, 3, 4, 9, 10, 12, 13, 24], "exploratori": [7, 24], "explos": [7, 15], "expos": 11, "express": [1, 3, 6, 15], "extend": [3, 24], "extens": [5, 7, 12, 13, 16, 24], "extra": 15, "extract": [2, 6, 7, 11], "extrem": 13, "f": [0, 2, 3, 4, 9, 11, 13, 15], "face": 15, "facet": [7, 9, 24], "facetgrid": 9, "facilit": 3, "fact": [2, 15], "factor": [2, 3, 13], "failur": 2, "fall": [5, 13], "fals": [2, 3, 5, 7, 9, 11, 13], "familiar": [13, 15], "fancy_pivot": 11, "far": [9, 12], "fast": 3, "featur": [7, 15], "feel": 0, "femal": [7, 9, 13], "female_edu": 13, "fempto": 1, "femptomol": 0, "femtomol": 1, "few": [3, 7, 10, 11, 13], "fewer": 13, "fgfbasic": [7, 9], "field": [5, 6, 7, 9, 11, 15], "fig": [7, 9], "figsiz": [7, 9], "figur": [6, 7, 8, 10, 12, 15], "file": [0, 3, 4, 5, 6, 8, 10, 11, 12, 15, 16], "filter": [2, 3], "filterwarn": 15, "final": [0, 2, 5], "financi": 7, "find": [3, 4, 6, 7, 13, 15], "fine": 13, "finish": 15, "firmicut": [4, 5], "first": [0, 2, 3, 4, 5, 7, 9, 11, 13, 15, 16, 24], "fit": [3, 9, 13, 24], "fix": [7, 15, 16], "flavor": 15, "flexibl": [7, 13, 24], "float": [12, 13], "float64": [3, 11, 13], "flouresc": 11, "flowchart": 13, "fluenci": 13, "fmol": [0, 1], "fmole": [0, 1], "focu": [2, 13], "focus": [18, 24], "follow": [0, 2, 4, 6, 15, 24, 28], "followup": 6, "footnot": 15, "form": [4, 7, 15], "format": [0, 1, 5, 11, 15, 16], "formul": 4, "found": [3, 5, 12, 13, 15], "foundat": [4, 24], "four": [3, 13], "fraction": [4, 10], "fragment": 0, "frame": [3, 15], "framework": 24, "free": [15, 16, 28], "freeli": 15, "freeman": 13, "frequenc": [7, 9, 13], "fresh": [0, 16], "fresher": 0, "freshli": [0, 16], "friendli": 13, "from": [1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 24], "frustrat": 7, "full": [13, 15], "function": [2, 3, 5, 7, 9, 11, 12, 13, 15, 24, 29], "function_nam": 1, "fundament": 24, "further": [1, 6], "futur": [0, 2, 3, 5, 6, 7, 9, 15], "g": [0, 1, 7, 11, 13, 24], "gain": [2, 12], "galleri": 9, "gap": 13, "gaskil": 11, "gcsf": [7, 9], "gender": [7, 8, 12, 13], "gender_race_piv": 7, "gene": 0, "gener": [0, 1, 5, 7, 8, 9, 10, 12, 13, 15, 24], "genom": 0, "geom": 24, "geometr": 24, "geometri": 24, "get": [0, 1, 2, 3, 4, 7, 13, 15, 16], "giant": [7, 11], "give": [4, 5, 7, 9, 13, 15, 16], "given": [4, 5, 9, 13], "glanc": 13, "gmcsf": [7, 9], "go": [7, 13, 15], "goal": 24, "good": 7, "googl": [14, 16], "goolg": 15, "got": [3, 9], "gotten": [9, 13], "grab": [7, 9], "grace": 15, "grade": [8, 10, 15], "grader": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "graph": [0, 2, 4, 6, 7, 8, 9, 10, 12, 24], "great": [0, 5, 7, 9], "green": [1, 7], "gross": 13, "group": [2, 3, 4, 7, 9, 11, 24], "groupbi": [4, 5, 7, 11], "grouped_pati": 5, "grow": 9, "grown": [7, 24], "guidelin": [0, 12, 13], "guru": 1, "h": [11, 13], "h0": 13, "h1": 13, "ha": [0, 1, 2, 3, 7, 8, 9, 11, 12, 13, 15, 18, 24], "had": [0, 1, 2, 4, 5, 9, 10, 11, 13], "hand": [0, 6, 7, 13, 15], "handi": 13, "handl": 24, "hash": 11, "have": [0, 1, 2, 3, 4, 5, 6, 7, 9, 12, 13, 15, 16, 19, 28], "he": 7, "head": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "header": 3, "health": 5, "healthi": [13, 15], "heard": 13, "heart_rate_reserv": 15, "heatmap": 9, "hedg": 13, "height": [9, 13, 15], "held": 5, "hello": 15, "help": [0, 1, 2, 4, 7, 11, 13, 15], "her": 15, "here": [2, 4, 5, 6, 7, 9, 11, 13, 15], "hgf": [7, 9], "hi": [7, 24], "hidden": [0, 2, 4, 6, 8, 10, 12], "high": [9, 11, 13, 24], "high_mean": 2, "high_min": 2, "high_treated_df": 2, "high_vl_mask": 3, "higher": [0, 13], "highli": [7, 9, 13], "hint": [1, 2], "hipaa": 16, "hist": 7, "histogram": 9, "histori": 7, "histplot": [9, 11], "hit": 15, "hiv": [2, 3, 6, 7, 9, 13], "hiv_neuro_data": [12, 13], "hoc": 13, "hold": [0, 15], "homoscedast": 13, "hood": 13, "horizont": 7, "hour": [0, 16], "how": [0, 1, 2, 3, 4, 5, 7, 9, 13, 15, 17, 20, 21, 22, 23, 24], "howev": [0, 1, 2, 3, 4, 10, 13, 15, 16], "hrr": 15, "html": [4, 9, 12, 13, 15], "http": [4, 9, 12, 13, 15, 19], "hue": [9, 11, 13], "hue_ord": 9, "human": 5, "hundr": [5, 9, 15], "hunter": 7, "hurdl": 15, "hyperlink": 15, "hypothes": [6, 11, 13], "hypothesi": [11, 29], "hypothet": [2, 3], "i": [2, 3, 4, 5, 6, 11, 15, 16, 18, 21, 24, 28], "id_var": [5, 9], "idea": [5, 7], "ideal": [0, 7, 15], "idxmax": 4, "ie": 13, "ifnalpha": [7, 9], "ifngamma": 7, "ignor": [9, 15], "il10": 7, "il12": 7, "il13": 7, "il15": 7, "il17": 7, "il1beta": 7, "il2": 7, "il2r": 7, "il4": 7, "il5": 7, "il6": [6, 7, 9], "il7": 7, "il8": 7, "iloc": 7, "ilra": 7, "imag": [7, 11, 16], "imbal": 5, "imbalanc": 1, "immedi": 1, "immun": [6, 7, 9], "immunologi": 29, "impact": [0, 2, 3, 4, 5, 9, 10, 12, 13], "impact_of_sample_s": 9, "impair": [9, 13], "impli": 13, "implic": [2, 4], "import": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 25], "importerror": 15, "imposs": 13, "improv": [9, 24], "incept": 24, "includ": [0, 3, 5, 6, 13, 15, 24], "inconsist": 13, "incorrect": 16, "incorrectli": 13, "increas": [8, 10, 11], "incred": 15, "incredibli": [3, 7, 9], "increment": 3, "independ": [13, 16], "indepth": 1, "index": [4, 5, 7, 9, 11], "indic": [2, 3, 4, 7, 9, 11, 13], "indivdu": 12, "individu": [1, 4, 5, 9, 10, 12, 13], "industri": 24, "inf": 13, "infect": [3, 4, 5], "infection_tim": 2, "inferenti": [10, 13, 15], "inferentialthink": 15, "inferior": 5, "inflamm": [6, 7, 9], "influenc": 5, "influenti": 7, "inform": [3, 6, 7, 10, 13, 16, 24], "ing": 9, "ingredi": 1, "inhabit": 5, "init_vl": 3, "initi": [1, 3, 4, 7, 15], "initial_viral_load": 2, "inlin": [5, 6, 7, 8, 9, 10, 11, 12, 13], "inner": 5, "input": [1, 5], "insid": 1, "insight": [2, 7], "instal": [15, 16], "instanc": [5, 13], "instead": [1, 3, 5, 6, 7, 11, 13, 15], "instruct": [0, 2, 6, 8, 10, 12, 15], "insurmount": 15, "int": [9, 11], "int64": [3, 7], "integ": 1, "integr": [7, 13, 24], "intend": 13, "intens": [11, 15], "interact": [13, 15, 16, 29], "interest": [0, 4, 7, 13], "interfac": [3, 15], "intermedi": 2, "intern": [13, 28], "interoper": 3, "interpret": [4, 13, 16], "interv": [7, 8, 9, 10], "intervent": 4, "introduc": [14, 24], "introduct": [7, 9], "intuit": 13, "investig": [0, 2], "involv": 13, "ipynb": [0, 2, 4, 6, 8, 10, 11, 12, 15], "iq": 13, "is_high": 4, "isaa": [7, 9], "isn": [5, 13, 15], "isol": [0, 4, 5], "issu": [15, 16], "italic": 15, "item": 1, "its": [0, 4, 7, 9, 13, 24], "itself": [7, 16, 24], "jarqu": 13, "jarque_bera": 13, "john": 7, "join": 11, "journei": [0, 13], "julia": 16, "jump": 1, "jupyt": [7, 13, 15], "jupyterlab": 15, "just": [0, 1, 2, 5, 7, 13, 15, 16, 24], "keep": [0, 4, 5, 13], "kei": [1, 5, 7, 24], "kendal": 7, "kernel": 15, "kg": 15, "kind": [7, 9, 11], "know": [0, 1, 3, 7, 11, 13, 16], "knowledg": 13, "kruskal": [12, 13], "krustal": 13, "kwarg": 9, "kwarg1": 1, "kwarg2": 1, "lab": [1, 3, 13], "label": [6, 7, 9], "labelrot": 7, "lai": 11, "lambda": [7, 11, 13], "languag": [12, 13, 15, 16, 24], "language_domain_z": 13, "larg": [2, 7, 10, 13, 15, 16], "larger": [5, 13, 15], "last": [3, 7, 13, 15], "lastli": [15, 29], "later": [3, 15], "launch": 15, "layout": 7, "lead": [1, 12, 13], "learn": [4, 15], "learningmemory_domain_z": 13, "least": [4, 6, 13], "leav": [5, 6], "lectur": 5, "left": [0, 11, 15], "left_on": [5, 11], "legend": [7, 8, 10, 11], "leland": 24, "len": [9, 15], "length": [0, 3, 9, 11], "less": [2, 10, 11, 13, 16, 24], "let": [0, 3, 4, 7, 9, 13, 15], "letter": 11, "level": [2, 3, 4, 6, 7, 11, 13, 24], "leven": 13, "leverag": [13, 24], "libari": 3, "librari": [2, 3, 7, 24], "licens": 28, "ligat": 1, "light": 1, "like": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 28], "likelihood": [4, 13], "limit": [1, 3, 6, 7, 9, 16, 24], "line": [2, 3, 7, 9, 15, 24], "linear": [7, 13], "link": [0, 13, 15, 16, 18], "linkag": [0, 12, 13], "linspac": 9, "list": [1, 3, 5, 15], "listdir": 15, "littl": 4, "live": [3, 6, 7, 9], "ll": [0, 1, 3, 4, 5, 7, 9, 10, 13, 15, 16], "load": [3, 4, 10, 11, 15, 16, 20], "loc": [3, 13], "locat": [0, 4, 5], "log": [13, 16], "logarithm": 24, "long": [0, 3, 5, 9], "long_mean": 2, "long_min": 2, "longer": [0, 2, 5], "look": [1, 2, 4, 5, 6, 7, 9, 11, 12, 13, 15, 18], "loop": [13, 15], "loos": 10, "loss": 13, "lot": [1, 3], "low": [9, 11, 13], "low_mean": 2, "low_min": 2, "low_treated_df": 2, "lower": [13, 24], "luminex": [6, 7, 9], "m": [13, 16], "made": 7, "mai": [0, 2, 5, 13], "main": 3, "major": [12, 13], "make": [0, 1, 3, 4, 5, 7, 9, 13, 24], "male": [7, 9, 13], "male_edu": 13, "manag": 1, "mani": [0, 1, 2, 4, 6, 7, 8, 9, 10, 13, 15, 16, 24], "manipul": [7, 13, 24], "mann": 13, "manner": 7, "manual": [1, 2], "manufactur": 0, "map": [4, 7, 11, 24], "margin": 13, "markdown": [2, 16], "marylin": 13, "mass": 1, "match": [3, 11, 12, 13], "materi": [0, 1], "math": [0, 1, 15, 17], "mathemat": 3, "matlab": 7, "matplotlib": [5, 6, 8, 9, 10, 11, 12, 13, 24], "matrix": [6, 7, 24], "matter": 9, "max": [2, 3, 5, 11], "maxillari": 5, "maximum": 15, "mayb": 16, "mayo": 15, "mcp1": [6, 9], "me": 28, "mean": [0, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 24], "mean_f": 13, "mean_m": 13, "mean_val": 5, "meaning": [6, 13, 24], "measur": [0, 1, 5, 6, 7, 8, 10, 12], "meatu": 5, "med": 9, "media": 5, "median": [2, 3, 5, 9], "medic": [4, 12, 13], "medicin": 28, "meet": 0, "melted_data": 9, "memori": [12, 13], "menu": [15, 16], "merg": 11, "merged_data": 4, "merged_info": 5, "meter": 15, "method": [0, 2, 3, 5, 6, 7, 9, 12, 15], "metric": 4, "michael": 24, "microbiologi": 29, "microbiom": [4, 5], "microbiome_phylum_data": [4, 5], "middl": [5, 7, 9], "mig": [7, 9], "might": [2, 3, 13], "miim": 29, "mild": [9, 12], "million": 1, "min": [2, 3, 11, 13], "minimum": 2, "minion": 1, "minor": 7, "minut": 15, "mip1alpha": [6, 7, 9], "mip1beta": [7, 9], "mircolit": 1, "miss": [5, 13], "mistak": [15, 16], "mod": 13, "mode": [3, 9], "model": 13, "moder": 12, "modif": 15, "modul": 1, "modular": 1, "mole": [0, 1], "molecul": 1, "molecular": 1, "monitor": 3, "monoton": 7, "more": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 18, 21, 24, 26], "morn": 15, "most": [4, 5, 6, 7, 8, 10, 11, 12, 15, 16], "motiv": 7, "motor": [1, 12, 13], "motor_domain_z": [12, 13], "move": [5, 15], "movement": 13, "mu": [5, 13], "much": [1, 9], "multi": 7, "multi_us": [7, 9], "multipl": [0, 1, 3, 7, 11, 12, 13, 15, 24], "multipli": 15, "must": [0, 7, 15], "mutat": 0, "my": [1, 7, 9], "n": 9, "n_f": 13, "n_m": 13, "name": [2, 3, 5, 7, 9, 11, 13, 15], "nan": [5, 7, 13], "nano": 1, "nanopor": [0, 1], "nasal": 5, "natur": 15, "nbin": 9, "nbsp": 7, "nc": 28, "ncov2": 0, "nd": 28, "ndf": 9, "nearest": 1, "neb": 18, "necessari": [0, 4], "necessarili": 2, "need": [0, 1, 2, 3, 4, 7, 9, 13, 14, 15, 16, 18, 21, 24], "neg": 13, "neither": 9, "neuro_screen_categori": 9, "neuro_screen_impairment_level": [6, 7, 9], "neuro_screen_ordin": 9, "neurobiologist": 7, "neurocognit": [6, 7, 9, 12, 13], "neurolog": [12, 13], "neuropsycholog": [12, 13], "neurotox": [12, 13], "never": 16, "new": [1, 3, 4, 6, 7, 9, 10, 13, 16], "new_concentr": 1, "new_paragon_molar": 1, "newer": [12, 13], "newest": 15, "next": [1, 2, 5, 7, 9, 11, 12, 13, 15], "neyman": 13, "ng": [0, 1], "nice": [3, 7, 15], "nn": 9, "noderiv": 28, "nois": [10, 11], "non": [7, 9, 11, 12], "non_us": 7, "noncommerci": 28, "none": [7, 9], "nonparametr": 9, "norm": [5, 13], "normal": [1, 5, 7, 9, 12, 13, 15], "normaltest": 13, "note": [2, 8, 10, 13], "notebook": [0, 1, 2, 4, 6, 7, 8, 10, 11, 12, 13, 15], "notepad": [15, 16], "notic": [1, 5, 7, 9, 15], "now": [0, 1, 2, 3, 5, 11, 13, 15], "np": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "np2": 13, "np_ax": 9, "nuanc": 10, "nucleotid": 1, "null": [4, 13], "num_otu": 5, "number": [1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 15], "numer": [1, 3, 11], "numpi": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 24], "nuniqu": 3, "ny": 9, "o": 15, "object": 24, "objectareach1": [10, 11], "objectavgintench1": 11, "objecttotalintench1": 11, "objectvarintench1": 11, "obs_cor": 13, "observ": [2, 5, 9, 11, 13], "obtain": [0, 1], "obviou": [8, 10], "ocassion": 16, "occur": 13, "off": [1, 2, 3, 15], "offer": [13, 24], "often": [0, 1, 3, 5, 10, 13, 16], "oftentim": 16, "okai": 16, "old": [1, 3, 13, 15], "older": [12, 13], "omnibu": 13, "onc": [1, 3, 13, 15, 16], "one": [0, 1, 2, 3, 4, 5, 6, 9, 13, 15, 16], "ones": [3, 13], "onli": [3, 4, 5, 6, 9, 10, 12, 15, 16], "onlin": [1, 15], "onto": 24, "open": [7, 9, 13, 15, 16], "oper": 1, "opportun": 4, "option": [4, 5, 13, 16], "orang": 1, "order": [0, 5, 9, 12, 13, 15, 16], "ordin": [7, 9], "org": [4, 9, 12, 13], "organ": [1, 13], "origin": [2, 7, 16], "other": [0, 1, 3, 6, 7, 9, 11, 13, 15, 16], "otherwis": [3, 24], "otiti": 5, "our": [0, 1, 2, 3, 4, 5, 7, 9, 10, 11, 15, 16], "out": [7, 10, 11, 13, 15], "outbreak": 1, "outcom": [5, 13], "outlier": 7, "output": [5, 7, 13, 15], "outsid": 7, "over": [0, 1, 3, 5, 7, 11, 13], "overal": 0, "overhang": 1, "overlap": [0, 7, 9], "overlapped_plot": 9, "overwrit": 16, "own": [5, 9, 13, 15, 16], "p": 13, "pacbio_amplicon_length": 0, "pacbio_degraded_molar": 0, "pacbio_degraded_us": 0, "pacbio_degraded_yield": 0, "pacbio_fresh_molar": 0, "pacbio_fresh_us": 0, "pacbio_fresh_yield": 0, "pacbio_template_weight": 0, "packag": [7, 13, 15], "page": 19, "pai": 4, "pair": [0, 13], "pairwise_test": 13, "pairwise_tukei": 13, "palett": 24, "panda": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 20, 21, 24], "panel": [6, 7, 9], "paper": 0, "par_ax": 9, "paragon": 1, "paragon_amplicon_length": 0, "paragon_degraded_molar": 0, "paragon_degraded_us": 0, "paragon_fresh_molar": 0, "paragon_fresh_us": 0, "paragon_molar": 1, "paragon_template_weight": 0, "paragraph": 4, "paramet": [4, 7, 13], "parametr": [9, 11], "part": [0, 1, 5, 13], "particip": [3, 7, 9], "particular": 15, "particularli": [13, 21, 24], "pass": [0, 2, 4, 6, 8, 9, 10, 11, 12], "past": [1, 5, 16, 29], "pat_3116": 5, "path": 15, "patient": [3, 4, 13], "pattern": [13, 24], "pcr": [0, 1], "pd": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13], "pearson": [7, 13], "peer": 13, "peopl": [3, 4, 6, 7, 9, 13, 15], "per": [1, 11, 15], "percent": [9, 11], "percentag": 10, "percentil": 9, "perceptu": 13, "perfect": 1, "perfectli": 15, "perfer": 9, "perform": [1, 3, 11, 12, 13, 24], "persist": 5, "person": [5, 7, 9, 12], "pg": [12, 13], "ph": 11, "phagasom": 11, "phagocytos": 11, "philadelphia": [0, 2, 6, 8, 10, 12], "philosophi": [7, 24], "phrase": 15, "phrodo": 10, "phrodo_conc_ug": [10, 11], "phrodo_dmem": [10, 11], "phylum_col": 5, "phylumn": 4, "pi": 9, "pick": [7, 9, 13], "pid": 5, "pingouin": 12, "pip": 15, "pivot": [4, 7, 11], "pivot_t": [5, 7, 11], "place": [4, 13, 29], "plai": 16, "plain": [15, 16], "plan": [13, 16], "plate": [1, 11], "plate_map": [10, 11], "platemap": 10, "plethora": 3, "plh": [6, 7], "plot": [5, 6, 8, 11, 13, 24], "plt": [5, 6, 7, 8, 9, 10, 11, 12, 13], "plu": 15, "plwh": [3, 9], "pm": [0, 2, 6, 8, 10, 12], "png": 7, "point": [0, 1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 24], "polymeras": 0, "pool": 1, "popul": [4, 13], "popular": [7, 13, 24], "pose": 15, "posit": [13, 24], "possibl": [1, 16], "post": [1, 13], "potenti": [4, 16], "power": [3, 7, 12, 13, 15, 16], "ppv": 4, "practic": [2, 3, 4, 5, 8, 10, 11, 12, 13], "pratic": 6, "pre": 13, "precis": [9, 15], "predictor": 4, "predomin": [4, 5], "prefer": 7, "preload": 15, "prep": [0, 1], "prepar": [0, 1, 13], "prescrib": 1, "present": [3, 7, 9, 13, 24], "preserv": 5, "presum": 13, "pretend": 13, "prevent": 7, "previou": [9, 15], "previous": [4, 13], "primari": [7, 24], "primer": 0, "principl": 24, "print": [0, 1, 2, 3, 4, 9, 15], "prism": 15, "probabl": [7, 9, 13], "problem": [2, 4, 5, 13, 15, 16, 29], "procedur": 9, "process": [1, 5, 11, 12, 13, 15, 19, 24], "processing_domain_z": 13, "produc": [0, 5, 13, 24], "profici": 3, "program": [13, 15, 16], "programat": 4, "progress": [3, 15], "project": [1, 6, 7, 9, 24], "promin": 13, "prompt": [1, 3, 7, 13], "prone": 1, "proper": [9, 29], "properli": [0, 2, 4, 6, 8, 10, 11, 12, 13], "properti": 7, "proport": [7, 9], "protect": 16, "protein": 1, "proteobacteria": 5, "protocol": 1, "provid": [0, 3, 4, 6, 7, 9, 13, 15, 24], "public": [0, 2, 4, 6, 7, 8, 10, 12, 13], "publish": 24, "purpos": [0, 1, 2, 3, 13, 15, 16], "put": [0, 2, 4, 9, 15], "pval": 13, "pydata": [4, 9], "pyplot": [5, 6, 7, 8, 9, 10, 11, 12, 13], "python": [0, 2, 3, 5, 7, 13, 16, 17, 20, 24], "q": [4, 15], "q1_add_outcom": 4, "q1_amp_length": 0, "q1_area_cov": 10, "q1_ax": 6, "q1_cells_per_wel": 11, "q1_cocaine_use_spread": 8, "q1_demographic_breakdown": 13, "q1_drug_use_plot": 7, "q1_extract_singl": 5, "q1_higher_level": 8, "q1_impaired_bar": 12, "q1_impairement_plot": 6, "q1_init_vl": 3, "q1_molar": 1, "q1_most_impair": 12, "q1_plot": [8, 10, 12], "q1_race_count": 13, "q1_sex_count": 13, "q1_table_load": 2, "q2_actinobacteria_mean": 5, "q2_an": 6, "q2_ax": [6, 7], "q2_bacteroidetes_mean": 5, "q2_cocaine_use_mean": 8, "q2_count_pivot": 4, "q2_cytokine_summari": 6, "q2_demographic_educ": 13, "q2_expect": 13, "q2_firmi_region": 4, "q2_firmicutes_mean": 5, "q2_graph": 11, "q2_higher_mean": 8, "q2_impaired_v_art": 12, "q2_infection_tim": 2, "q2_inter_an": 13, "q2_linkag": 12, "q2_merg": 10, "q2_mol_weight": 0, "q2_neuro_use_plot": 7, "q2_obs_cor": 13, "q2_pivot": 4, "q2_plot": [8, 10, 12], "q2_pop_weeks_to_failur": 3, "q2_pro_inflam": 6, "q2_proteobacteria_mean": 5, "q2_pval_an": 13, "q2_stat": 13, "q2_summary_v": 5, "q2_therapi": 12, "q2_volum": 1, "q2a": [10, 11], "q2b": [10, 11], "q3_an": 4, "q3_bar_ax": 6, "q3_bmi_hypothesis_gen": 6, "q3_cocaine_use_gender_mean": 8, "q3_comparison": 13, "q3_cross_cor": 6, "q3_dna_yield": 1, "q3_gender_impact": 8, "q3_is_norm": 12, "q3_mean_by_sit": 5, "q3_mean_phylum_sit": 5, "q3_mean_pivot": 4, "q3_molar": 0, "q3_nonparametr": 13, "q3_pivot": 4, "q3_plot": [8, 12], "q3_post_hoc": 13, "q3_scatter_ax": 6, "q3_sig_diff": 12, "q3_stat": 13, "q3_top5": 6, "q3_treated_indiv": 2, "q3_treated_weeks_to_failure_index": 3, "q3_visuo_v_art": 12, "q4_covari": 12, "q4_dna_yield": 0, "q4_fraction_swabb": 4, "q4_function_yield": 1, "q4_infection_length": 8, "q4_infection_length_corr": 8, "q4_is_sig": 12, "q4_plot": [8, 12], "q4_server": 5, "q4_severe_mean": 5, "q4_swababl": 4, "q4_treated_weeks_to_failur": 3, "q4_untreated_weeks_to_failur": 3, "q4_vl_select": 2, "q5_high_valu": 4, "q5_infection_length_cocain": 8, "q5_infection_length_cocaine_slop": 8, "q5_plot": 8, "q5_usable_sampl": 0, "q5_vl_comparison": 2, "q6_best_ppv": 4, "q6_highest_region": 4, "q6_length_comparison": 2, "q6_swabbable_ppv": 4, "qith": 3, "qq": 13, "qqplot": 13, "qualiti": 7, "quantif": 0, "quantifi": [5, 11], "quantil": 13, "quantit": 9, "quartil": 7, "qubit": 1, "queri": [2, 4, 5, 13], "question": [3, 4, 6, 8, 10, 11, 15], "quick": [9, 13, 18], "quickli": [13, 24], "r": [7, 11, 16], "race": 12, "racial": 7, "rais": 9, "rake": 7, "ran": 0, "randomli": [3, 9, 13], "rang": [3, 7, 9, 11, 13, 15, 24], "rank": [4, 7], "rapid": 1, "rate": 8, "rather": 24, "ratio": [4, 24], "raw": [13, 24], "rcp85jhlmni": 19, "rdbu": 7, "re": [1, 5, 7, 9, 11, 15, 16], "react": 13, "reaction": 0, "read": [1, 3, 7, 9, 13], "read_csv": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "readi": [0, 3, 16], "reagent": 1, "real": [3, 5], "realli": 1, "reason": [1, 8, 9, 10, 11], "rebound": 3, "recalcul": 9, "receiv": 3, "recent": [1, 15, 16], "recess": 5, "recommend": [1, 9], "reduc": 9, "refer": [1, 12, 29], "refin": 13, "reflect": 2, "refram": 4, "refresh": 18, "regimen": [12, 13], "regplot": 9, "regress": [10, 11, 12, 13, 24], "regularli": 3, "reject": 13, "rel": [4, 13, 24], "relaps": 4, "relat": [1, 13, 24], "relationship": [4, 7, 9, 13, 24], "relative_abund": 4, "releas": 24, "relev": [2, 16], "reliabl": 0, "rememb": [0, 1, 2, 3, 6, 8, 10, 12, 13, 15, 16], "remov": [0, 1, 2, 6, 7], "render": [0, 1, 2, 4, 6, 8, 10, 11, 12, 16], "rep1": 11, "rep2": 11, "rep3": 11, "repeat": [1, 13], "repetit": 1, "replac": [1, 7, 9], "replic": [7, 9, 11, 13], "repres": [1, 5, 7, 9, 11, 13, 24], "represent": 24, "reproduc": 1, "requir": [0, 1, 3, 4, 5, 13, 15, 24], "resampl": 9, "research": [3, 5, 13, 15, 24], "reshap": 5, "residu": 13, "resolv": 4, "resourc": [6, 7, 9], "respect": 9, "respond": 16, "respons": 11, "rest": [4, 15], "restart": [0, 2, 4, 6, 8, 10, 11, 12, 15], "resting_heart_r": 15, "result": [0, 1, 2, 3, 4, 12, 13, 15, 21], "retriev": 13, "return": [1, 5, 9, 11, 13, 15], "reusabl": 1, "revers": 0, "review": [1, 18], "revolv": 7, "right": [1, 13, 15], "right_index": 11, "right_on": 5, "rigor": [9, 13, 15], "rna": [0, 1], "rna_paragon_molar": 1, "robust": 7, "room": 0, "rotat": 7, "round": 1, "row": [4, 5, 7, 9, 11, 13], "row_cutoff": 4, "rt": 0, "rule": 24, "run": [0, 2, 4, 6, 7, 8, 9, 10, 11, 12, 15], "runtim": 16, "sai": 3, "said": 15, "same": [1, 3, 4, 5, 7, 8, 9, 13, 15], "sampl": [3, 5, 7, 9, 10, 13], "sample_concentr": 1, "sample_info": [4, 5], "sample_length": 1, "sample_level_data": [10, 11], "sample_s": 9, "sample_volum": 1, "sample_yield": 1, "savant": 13, "save": [0, 2, 4, 6, 7, 8, 10, 11, 12, 15], "savefig": 7, "saw": [3, 4], "scale": [5, 12, 13, 24], "scan": 11, "scatter": 7, "scatter_matrix": 7, "scatterplot": [6, 7, 9], "sciecn": 3, "scienc": [3, 15, 24], "scientif": 7, "scientist": [13, 24], "scipi": 13, "score": 13, "screen": 15, "sd": 9, "se": [9, 11, 13], "seaborn": [5, 7, 8, 9, 10, 11, 12, 13, 23], "seamlessli": [13, 24], "search": [3, 4], "searchabl": 29, "second": [15, 16], "secreti": 16, "section": 16, "secur": 16, "see": [0, 1, 2, 3, 5, 7, 11, 13, 15], "seem": 2, "seen": 13, "select": 3, "self": 1, "sem": 11, "semant": 24, "send": 16, "senior": 1, "sens": 3, "sensit": 16, "sensori": 13, "sent": 16, "sentenc": [1, 5], "sep": 5, "separ": 2, "seper": 9, "sequenc": [0, 1], "seri": [1, 3, 5, 6, 7, 15, 16], "servic": 16, "session": [1, 3, 4, 15], "set": [0, 7, 9, 13, 15, 24], "set_titl": 9, "set_xlabel": 7, "set_xlim": 7, "set_ylabel": 11, "setup": 15, "sever": 2, "sex": [7, 9, 12], "shadow": 9, "shape": [4, 5, 9, 13, 24], "shapiro": 13, "share": 16, "sharei": 9, "sharex": 9, "shift": [15, 16], "short": [0, 1], "short_mean": 2, "short_min": 2, "shortcut": 16, "shorter": [0, 2], "shortli": 4, "should": [1, 3, 4, 7, 8, 9, 10, 13, 15, 16, 24], "show": [6, 7, 9, 10, 11, 12, 13], "shown": 5, "shred": 0, "side": 13, "signific": [2, 5, 7, 10, 12, 13], "significantli": [7, 12], "similar": [3, 12, 13, 16], "simpl": [3, 5, 7, 9, 13, 15, 24], "simplest": 13, "simplic": [7, 24], "simplifi": 24, "simul": 9, "simultan": 13, "sinc": [13, 15, 18, 24], "singl": [1, 3, 9, 10, 11, 12, 24], "sinu": [4, 5], "sit": 3, "situat": 9, "size": [1, 7, 9, 10, 13, 24], "skeleton": 15, "skill": [13, 15], "skin": 5, "small": [2, 3, 7, 9, 10, 15, 24], "smaller": [1, 2, 13], "sn": [5, 8, 9, 10, 11, 12, 13], "so": [0, 1, 3, 7, 8, 9, 10, 12, 13, 16], "softwar": [15, 16], "solut": [1, 3, 4, 5, 7, 9, 11, 13, 15], "solv": [13, 15], "some": [0, 1, 3, 4, 5, 7, 9, 13, 15, 16, 18, 19], "somehow": 7, "someon": 3, "someth": [1, 15], "sometim": [3, 7, 9, 16], "somewher": 15, "sophist": [13, 24], "sort": [4, 9], "sortabl": 9, "sourc": [7, 13], "space": [1, 3, 4, 24], "spawn": 15, "spearman": 7, "speci": 5, "special": 16, "specif": [0, 2, 3, 13, 24], "specifi": [7, 9], "speed": [12, 13], "speedup": 1, "sphenoethmoid": 5, "sphenoid": 5, "spin": 15, "split": [3, 5, 9, 13, 24], "spot": 11, "spotavgareach2": 11, "spotavgintench2": 11, "spotcountch2": 11, "spottotalareach2": [10, 11], "spottotalintench2": 11, "spread": [8, 13], "spread_ax": 9, "spreadsheet": [2, 3, 11, 15, 20], "sqrt": [9, 13], "squar": 3, "ss": 13, "stack": [3, 7, 24], "stai": [9, 15], "standard": [2, 4, 5, 9, 12, 13, 24], "start": [1, 2, 3, 4, 11, 13, 15, 16, 24], "stat": [9, 11, 12, 13, 24], "state": [0, 4, 7, 24], "statement": [0, 1, 3], "statist": [3, 7, 8, 9, 10, 12, 13, 15, 24], "statment": [0, 2], "statsmodel": 13, "statu": [2, 5, 24], "stavudin": [12, 13], "std": [3, 5, 11, 13], "std_p": 13, "step": [2, 3, 4, 15], "still": 15, "stock": 1, "stop": 3, "store": 24, "stori": 13, "str": [11, 13], "stragei": 3, "straightforward": 24, "strand": 1, "strategi": [1, 3, 5, 7, 10, 11, 13, 27], "stratif": 13, "strength": 13, "string": [0, 4, 6, 9], "stripplot": 11, "strong": 13, "structur": [15, 24], "stuck": 0, "studi": [2, 3, 5, 6, 9, 13, 15], "stuf": 10, "stumbl": 15, "style": [3, 6, 7, 9, 20, 24], "sublist": 15, "submiss": 16, "submit": [5, 15], "subplot": [7, 9], "subset": [4, 9, 24], "substanti": 24, "subtract": [2, 15], "success": [2, 7, 14], "successfulli": 0, "suffici": 0, "suffix": 3, "suggest": [2, 15], "sugget": 13, "suit": 24, "suitabl": [0, 7, 13], "sum": [2, 3, 5, 7, 9, 13], "summar": [0, 1, 3, 7, 10, 11, 15, 20, 21, 24], "summari": [2, 3, 5, 7, 9, 24], "sundai": [0, 2, 6, 8, 10, 12], "superior": 5, "support": [7, 24], "sure": 0, "suspect": [12, 13], "swab": [4, 5], "swabbable_data": 4, "switch": 13, "symptom": 4, "synchron": [1, 3, 15], "syntax": [1, 13, 15], "system": [0, 4, 5, 15, 16, 24], "systemat": 24, "t": [1, 3, 4, 5, 9, 10, 13, 15], "tab": 9, "tabl": [0, 1, 3, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16], "tabul": 13, "tabular": 7, "tag": 16, "tailor": 13, "take": [1, 11, 15, 16], "taken": 11, "talk": [15, 16], "task": [1, 7, 13, 15, 24], "tast": 5, "tau": 7, "taught": 15, "teach": 15, "techniqu": [0, 3, 5, 6, 10, 11, 15], "technologi": [11, 15], "tediou": 1, "tell": [0, 1, 4, 13, 15], "temperatur": 0, "template_weight": 1, "tend": [3, 13, 18], "tendenc": 9, "tenofovir": [12, 13], "term": [0, 2, 13, 24], "test": [0, 1, 2, 4, 5, 6, 8, 10, 12, 15, 16], "tests_dir": 15, "testss": [4, 6, 12], "text": [1, 4, 6, 7, 11, 15, 16], "textbook": [1, 15, 28], "than": [0, 2, 4, 13, 16, 24], "thei": [0, 1, 3, 5, 8, 10, 11, 13], "them": [2, 3, 5, 7, 9, 15, 16, 24], "themselv": 16, "theoret": 13, "theori": 13, "therapi": [4, 13], "therebi": 15, "therefor": [0, 2, 12, 13], "thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 30], "thier": 13, "thing": [1, 2, 5, 7, 9, 12, 13, 15, 16], "think": [1, 9, 13, 15, 24], "those": [1, 2, 3, 4, 5, 15], "three": [4, 5, 13], "threshold": [12, 13], "through": [0, 2, 4, 5, 7, 13, 15, 24], "throughout": [4, 15], "ti": 7, "tick_param": 7, "tight_layout": [7, 9], "tightli": 7, "time": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 15, 24], "tissu": 5, "titl": 7, "tnfalpha": [6, 7, 9], "todai": 3, "too": [15, 16], "took": [3, 5, 11], "tool": [3, 7, 9, 12, 13, 14, 15, 24], "top": [6, 9, 11, 13, 15], "topic": 15, "total": [0, 1, 2, 9], "totalintench2": 11, "toward": 13, "track": [0, 1], "tradition": 13, "trail_data": 2, "tranform": 12, "transcrib": 0, "transform": [3, 5, 9, 24], "transgend": 7, "transpar": 1, "treat": [9, 11, 24], "treated_average_week": 3, "treated_df": 2, "treated_mask": 3, "treatment": [2, 3, 11], "tree": 13, "trend": 24, "trial": [2, 3], "trial_data": 3, "trial_df": [2, 3], "triplic": 11, "troubl": 15, "true": [2, 3, 4, 5, 7, 9, 11, 13, 15], "truli": [4, 13], "truvada": [12, 13], "try": [0, 1, 9], "ttest": [12, 13], "tube": 1, "tukei": 13, "turbin": 5, "tutori": [7, 9], "tweak": 24, "twice": [4, 15], "two": [0, 3, 5, 9, 11, 12, 15, 16, 21], "type": [1, 2, 3, 4, 5, 9, 13, 15, 16, 24], "typic": [0, 5], "typical_region_cutoff": 4, "typical_region_mean": 4, "typical_region_std": 4, "typical_swab_data": 4, "u": [0, 1, 2, 3, 4, 7, 10, 13, 15], "uc": 15, "ul": [0, 1, 2, 3], "unc": 13, "uncer_ax": 9, "uncertain": 9, "uncertainti": 11, "uncheck": 7, "uncin": 5, "uncontrol": [2, 3], "uncorrel": 13, "under": [13, 28], "underli": 9, "underneath": 15, "understand": [0, 1, 2, 3, 5, 6, 9, 13, 16, 24], "undo": 16, "unfiar": 13, "uniqu": [1, 4], "unit": [0, 1, 5, 13, 18], "unit_norm": 5, "unit_normed_data": 5, "univers": 28, "unknow": [6, 7, 9], "unless": 1, "unlik": 8, "unrel": 13, "unsustain": 5, "until": [2, 3], "untreat": 2, "untreated_average_week": 3, "unwieldi": 15, "unzip": 15, "up": [0, 1, 5, 7, 11, 13, 15], "upload": [0, 2, 4, 6, 7, 8, 10, 11, 12, 15, 16], "upon": 29, "upper_target_zon": 15, "uptak": 11, "us": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 28], "usaual": 13, "usb": 1, "use_axi": 7, "use_count": 7, "use_desc": 9, "user": [7, 13, 24], "usual": [3, 5, 9, 15], "util": [3, 4, 5, 9, 12, 13], "v": [4, 19], "v1": 11, "v2": 11, "v3": 11, "val": [1, 13], "valid": [3, 15], "valu": [1, 2, 3, 5, 7, 9, 10, 11, 12, 13, 15, 24], "valuabl": 24, "value_column": 11, "value_count": [7, 13], "value_nam": [5, 9], "value_var": [5, 9], "valueerror": 9, "var": 3, "var_nam": [5, 9], "varaibl": [6, 12], "varainc": 13, "vari": 9, "variabl": [0, 1, 6, 9, 12, 13, 15, 24], "varianc": 13, "varieti": 24, "variou": [12, 24], "vast": 24, "ve": [1, 4, 5, 9, 11, 13, 15, 18], "vegf": 9, "veh": 11, "verbal": 13, "verbos": 13, "veri": [9, 15], "versatil": 24, "version": [3, 5, 16], "vestibul": 5, "via": 24, "video": [1, 13, 19], "vield": 0, "view": 7, "viewpoint": 24, "vigor": 15, "viral": [0, 3], "virtual": 16, "visual": [5, 7, 11, 12, 13, 15, 22, 24], "visuospatial_domain_z": [12, 13], "vmax": 7, "vmin": 7, "vo": 13, "volum": [0, 1], "volume_to_add": 1, "w": 13, "wa": [2, 3, 4, 6, 7, 9, 13, 15, 24], "wai": [1, 4, 6, 7, 9, 10, 13, 15, 24], "walk": 13, "wallac": 13, "want": [3, 5, 7, 9, 13, 16], "wanted_dna": 1, "wanted_sampl": 3, "warn": 15, "waskom": 24, "watch": [1, 19], "we": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 21], "wealth": 13, "web": 7, "websit": 7, "week": [0, 1, 2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15], "weekli": [1, 3, 15], "weigh": [0, 1], "weight": 15, "well": [2, 7, 10, 13, 24], "well_level_data": 11, "went": 3, "were": [1, 2, 3, 12, 13], "what": [4, 5, 7, 9, 10, 13, 15], "when": [0, 1, 3, 5, 7, 9, 11, 13, 15, 16, 18], "where": [3, 9, 11, 13, 21], "wherea": 9, "whether": [0, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13], "which": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 24], "while": [0, 3, 4, 7, 9, 11, 13, 16, 18, 24], "whisker": 7, "whitnei": 13, "who": [3, 4, 13], "why": 13, "wide": [5, 7, 9, 13, 24], "widespread": 7, "width": 9, "wilk": 13, "wilkinson": 24, "within": [3, 7, 9, 13, 16, 24, 29], "without": [0, 1, 2, 4, 6, 8, 10, 11, 12, 13, 16], "woman": 15, "wonder": [9, 13], "word": [0, 13, 15, 24], "wordpad": 16, "work": [0, 1, 2, 3, 7, 9, 13, 15, 16, 24], "workflow": 24, "world": [0, 13, 15], "wors": 13, "worth": 6, "would": [1, 4, 5, 9, 13, 15, 24, 28], "write": [0, 2, 4, 8, 10, 13, 15], "written": [3, 16], "www": [15, 19], "x": [6, 7, 9, 11, 13, 15, 16], "xcentroid": 11, "xlabel": [7, 9, 11, 13], "y": [6, 7, 9, 11, 13, 15], "ycentroid": 11, "ye": [0, 8, 10, 11, 12, 13], "year": [1, 2, 3, 7, 13, 15], "years_infect": [3, 7, 9], "yearsseroposit": 13, "yearsseropositivedata": 12, "ylabel": [7, 9, 11, 13], "you": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 28, 29], "young": [9, 15], "your": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 15, 16, 28], "yourself": [3, 15, 16], "youtub": 19, "yr": 3, "ys_bin": 12, "yy": 9, "z": [12, 13, 15], "zip": 15, "zip_fil": 15}, "titles": ["Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Module 1: Hello World", "Walkthrough", "Notebook basics", "Module 2: Simple calculations", "Dilution calculations", "Nanopore Sequencing", "Module 3: DataFrames", "Module 4: Analysis by groups", "Module 5: Plotting with Pandas", "Module 6: Visualizing with Confidence", "Grammar of Graphics", "Module 7: Samples and Replicates", "Common Biological Distributions", "Module 8: Hypothesis Testing", "Quantitative Reasoning in Biology", "About this book", "Introduction"], "titleterms": {"": 15, "1": 14, "2": 17, "3": 20, "3116": 5, "4": 21, "5": 22, "6": 23, "7": 25, "8": 27, "The": 1, "about": 29, "abov": [0, 15], "across": [4, 5, 7, 9], "act": 3, "actinobacteria": 4, "add": 1, "aerob": 15, "afraid": 16, "all": 16, "amount": 1, "an": 10, "analysi": 21, "appropri": 13, "ar": [0, 4, 6, 11, 12], "arithmet": 1, "art": 12, "averag": [3, 5, 8], "basic": [7, 16], "between": 8, "biolog": 26, "biologi": 28, "biome_data": 4, "block": 15, "bodi": [4, 5], "book": 29, "boolean": 3, "box": 7, "calcul": [0, 1, 2, 3, 5, 15, 17, 18], "cannabinoid_us": 7, "categor": [9, 13], "categori": 9, "catplot": 9, "cell": [10, 11, 15, 16], "cocain": 8, "cocaine_us": 7, "code": 15, "colab": 15, "color": 1, "column": [2, 3, 7, 9, 10], "common": 26, "compar": [2, 4, 9], "comparison": [7, 9, 13], "conclus": [0, 1, 3], "confid": 23, "consid": 6, "contain": 2, "context": 4, "contini": 13, "correl": [8, 9, 13], "count": [5, 9, 13], "countplot": 9, "covari": 12, "creat": [2, 10], "csv": 2, "data": [2, 5, 7], "datafram": [2, 5, 20], "dataset": [2, 3, 6, 13], "decod": 11, "describ": [1, 11], "descript": 2, "determin": 4, "differ": [7, 9], "dilut": 18, "diseas": 4, "distribut": [9, 26], "do": 8, "document": 9, "doe": 8, "don": 16, "each": [0, 2, 5, 10, 11, 13], "educ": 13, "effect": 8, "estim": 9, "evalu": [0, 12], "expect": 15, "explor": [5, 6, 7, 8], "express": [7, 8], "extract": [0, 3, 5], "f": 1, "failur": 3, "figur": 9, "file": 2, "fraction_area_cov": 10, "from": [0, 2, 12], "full": 10, "function": [1, 6], "gener": 6, "googl": 15, "gotcha": 7, "grader": 15, "grammar": 24, "graph": 11, "graphic": 24, "group": [5, 13, 21], "ha": 4, "handl": 7, "have": 8, "heart": 15, "hello": 14, "high": [2, 4], "higher": 8, "highest": 4, "histogram": 7, "how": [6, 10, 11, 12], "hypothesi": [6, 13, 27], "i": [0, 1, 7, 8, 9, 10, 12, 13], "impact": 8, "impair": [6, 7, 12], "import": 3, "includ": 2, "increas": 4, "index": 3, "individu": 2, "infalpha": 7, "infect": [2, 8], "inflamatori": 6, "inform": [0, 4, 5, 15], "initi": 2, "initial_viral_load": 3, "interfac": [9, 24], "introduct": [0, 2, 3, 4, 5, 6, 12, 13, 15, 30], "jupyt": 16, "lab": [0, 2, 4, 6, 8, 10, 12], "largest": 4, "learn": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13], "length": [2, 8], "level": [8, 9], "limit": 15, "linear": 9, "link": 12, "lint": 1, "lmplot": 9, "load": 2, "long": 2, "low": 2, "m": 9, "make": 2, "mani": [11, 12], "map": 10, "markdown": 15, "marker": 6, "matplotlib": 7, "mcp1": 8, "me": 15, "measur": [9, 11, 13], "melt": [5, 9], "merg": [4, 5, 10], "method": 13, "model": 9, "modul": [14, 17, 20, 21, 22, 23, 25, 27], "molar": [0, 1], "molecular": 0, "multi": 13, "multipl": 9, "nanopor": 19, "neurolog": [6, 7], "new": 2, "non": [8, 13], "notebook": 16, "number": 13, "numer": 7, "numpi": 3, "object": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13], "onli": 2, "otter": 15, "outcom": 4, "pacbio": 0, "panda": [3, 22], "paragon": 0, "parametr": 13, "particip": [2, 6, 12, 13], "patient": 5, "pd": 9, "persist": 4, "phagocytosi": 11, "phylum": [4, 5], "pingouin": 13, "pivot": 5, "plate": 10, "plot": [7, 9, 22], "popul": 3, "posit": 4, "potenti": 12, "predict": 4, "predominin": 4, "pro": 6, "problem": 1, "programmat": 1, "protocol": 0, "python": [1, 15], "q1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15], "q2": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15], "q3": [0, 1, 2, 3, 4, 5, 6, 8, 12, 13], "q4": [0, 1, 2, 3, 4, 5, 6, 8, 12], "q5": [0, 2, 4, 8], "q6": [2, 4], "q7": 4, "quantifi": 9, "quantit": 28, "queri": 3, "question": 2, "quick": 15, "race": 13, "rate": 15, "reaction": 1, "reason": 28, "refer": [2, 3], "region": [4, 5], "regress": 9, "relat": [6, 9], "relev": 0, "relplot": 9, "replic": 25, "reserv": 15, "restart": 16, "row": [2, 3], "run": 16, "same": 2, "sampl": [0, 1, 2, 4, 11, 25], "score": 12, "seaborn": 24, "sequenc": 19, "session": 16, "sever": 4, "severe_diseas": 5, "sex": [8, 13], "short": 2, "simpl": 17, "singl": 5, "site": [4, 5], "spread": 9, "statist": 2, "statu": 7, "string": 1, "stripplot": 9, "subject": 15, "submiss": [0, 2, 4, 5, 6, 7, 8, 10, 11, 12, 15], "suffer": 12, "sumar": 11, "summar": 5, "swabbabl": 4, "t": 16, "tabl": [2, 4], "target": 15, "templat": [0, 1], "test": [13, 27], "text": 0, "therapi": 12, "thi": [13, 29], "through": 1, "tissu": 4, "treat": [2, 3], "try": 15, "two": [2, 13], "typic": 4, "uncertainti": 9, "untreat": 3, "upper": 15, "us": [8, 13, 15], "usabl": 0, "user": 8, "valu": 4, "variabl": 7, "vegf": 7, "viral": 2, "visual": [9, 10, 23], "visuospati": 12, "walkthrough": [1, 3, 5, 7, 9, 11, 13, 15], "week": 3, "weeks_to_failur": [2, 3], "weight": [0, 1], "well": 11, "well_level_data": 10, "what": [0, 1], "when": 4, "which": [0, 1, 4], "whole": 3, "why": 15, "world": 14, "write": 1, "yield": [0, 1], "zone": 15}}) \ No newline at end of file +Search.setIndex({"alltitles": {"A Power Analysis in 6 steps": [[17, "a-power-analysis-in-6-steps"]], "ANCOVA": [[15, "ancova"]], "About this book": [[35, "about-this-book"]], "Acting on Columns": [[3, "acting-on-columns"]], "Acting on Rows": [[3, "acting-on-rows"]], "Basic Plotting": [[7, "basic-plotting"]], "Basic regression": [[15, "basic-regression"]], "Boolean Indexing": [[3, "boolean-indexing"]], "Box Plots": [[7, "box-plots"]], "Calculate a aerobic target heart rate?": [[19, "calculate-a-aerobic-target-heart-rate"]], "Categorical Comparisons": [[9, "categorical-comparisons"]], "Categorical comparisons": [[13, "categorical-comparisons"]], "Categorical with catplot": [[9, "categorical-with-catplot"]], "Cells": [[20, "cells"]], "Coding expectations": [[19, "coding-expectations"]], "Common Biological Distributions": [[30, "common-biological-distributions"]], "Comparing Distributions": [[9, "comparing-distributions"]], "Comparison of Variables": [[7, "comparison-of-variables"]], "Conclusion": [[0, "conclusion"], [1, "conclusion"], [3, "conclusion"]], "Continious comparisons": [[13, "continious-comparisons"]], "Counting with countplot": [[9, "counting-with-countplot"]], "Data": [[7, "data"]], "Dataset Reference": [[2, "dataset-reference"], [3, "dataset-reference"]], "Decoding samples": [[11, "decoding-samples"]], "Dilution calculations": [[22, "dilution-calculations"]], "Documentation": [[9, "documentation"]], "Don\u2019t be afraid to Restart & Run all": [[20, null]], "Even more regression": [[15, "even-more-regression"]], "Exploration": [[15, "exploration"]], "Explore the effect of cocaine use on mcp1": [[8, "explore-the-effect-of-cocaine-use-on-mcp1"]], "Exploring a single patient": [[5, "exploring-a-single-patient"]], "Figure Level Interface": [[9, "figure-level-interface"]], "Functions": [[1, "functions"]], "Grammar of Graphics": [[28, "grammar-of-graphics"]], "Histograms": [[7, "histograms"]], "How full is each cell?": [[10, "how-full-is-each-cell"]], "Hypothesis Testing": [[13, "hypothesis-testing"], [13, "id1"]], "Imports": [[3, "imports"]], "Indexing": [[3, "indexing"]], "Introduction": [[0, "introduction"], [2, "introduction"], [3, "introduction"], [4, "introduction"], [5, "introduction"], [6, "introduction"], [12, "introduction"], [13, "introduction"], [19, "introduction"], [36, "introduction"]], "I\u2019m pd.melting": [[9, "i-m-pd-melting"]], "Jupyter Notebooks": [[20, "jupyter-notebooks"]], "Lab": [[0, "lab"], [2, "lab"], [4, "lab"], [6, "lab"], [8, "lab"], [10, "lab"], [12, "lab"], [14, "lab"], [16, "lab"]], "Learning Objectives": [[0, "learning-objectives"], [1, "learning-objectives"], [2, "learning-objectives"], [3, "learning-objectives"], [5, "learning-objectives"], [6, "learning-objectives"], [7, "learning-objectives"], [8, "learning-objectives"], [9, "learning-objectives"], [10, "learning-objectives"], [11, "learning-objectives"], [12, "learning-objectives"], [13, "learning-objectives"], [14, "learning-objectives"], [15, "learning-objectives"], [16, "learning-objectives"], [17, "learning-objectives"]], "Linear model regression plots with lmplot": [[9, "linear-model-regression-plots-with-lmplot"]], "Linting through color": [[1, "linting-through-color"]], "Markdown": [[19, "markdown"]], "Matplotlib": [[7, "matplotlib"]], "Matplotlib Gotchas": [[7, "matplotlib-gotchas"]], "Measuring Correlation": [[9, "measuring-correlation"]], "Measuring Spread": [[9, "measuring-spread"]], "Measuring Uncertainty": [[9, "measuring-uncertainty"]], "Measuring phagocytosis": [[11, "measuring-phagocytosis"]], "Melting": [[5, "melting"]], "Merging data": [[5, "merging-data"]], "Module 10: Power Analysis": [[33, "module-10-power-analysis"]], "Module 1: Hello World": [[18, "module-1-hello-world"]], "Module 2: Simple calculations": [[21, "module-2-simple-calculations"]], "Module 3: DataFrames": [[24, "module-3-dataframes"]], "Module 4: Analysis by groups": [[25, "module-4-analysis-by-groups"]], "Module 5: Plotting with Pandas": [[26, "module-5-plotting-with-pandas"]], "Module 6: Visualizing with Confidence": [[27, "module-6-visualizing-with-confidence"]], "Module 7: Samples and Replicates": [[29, "module-7-samples-and-replicates"]], "Module 8: Hypothesis Testing": [[31, "module-8-hypothesis-testing"]], "Module 9: Linear Regression": [[32, "module-9-linear-regression"]], "Multi-group measurement": [[13, "multi-group-measurement"]], "Multiple Regression": [[15, "multiple-regression"]], "Nanopore Sequencing": [[23, "nanopore-sequencing"]], "Non-parametric comparisons": [[13, "non-parametric-comparisons"]], "Notebook basics": [[20, "notebook-basics"]], "Numeric Variables": [[7, "numeric-variables"]], "Numpy": [[3, "numpy"]], "Otter Grader": [[19, "otter-grader"]], "Over fitting": [[15, "over-fitting"]], "Pandas": [[3, "pandas"]], "Pingouin": [[13, "pingouin"]], "Pivoting": [[5, "pivoting"]], "Pivoting & Melting Dataframes": [[5, "pivoting-melting-dataframes"]], "Plot Handles": [[7, "plot-handles"]], "Plotting Multiple Columns": [[9, "plotting-multiple-columns"]], "Programmatic Arithmetic in Python": [[1, "programmatic-arithmetic-in-python"]], "Protocol Evaluation": [[0, "protocol-evaluation"]], "Q1: Are Processing domain and Executive domain scores correlated?": [[14, "q1-are-processing-domain-and-executive-domain-scores-correlated"]], "Q1: By inspection, which variable is most correlated?": [[15, "q1-by-inspection-which-variable-is-most-correlated"]], "Q1: Calculate the molarity of the sample": [[1, "q1-calculate-the-molarity-of-the-sample"]], "Q1: Calculate the power if there are only two animals in each group.": [[17, "q1-calculate-the-power-if-there-are-only-two-animals-in-each-group"]], "Q1: Count the number of participants of each sex and race.": [[13, "q1-count-the-number-of-participants-of-each-sex-and-race"]], "Q1: Create an fraction_area_covered column": [[10, "q1-create-an-fraction-area-covered-column"]], "Q1: Do cocaine users have a higher level of expression of mcp1?": [[8, "q1-do-cocaine-users-have-a-higher-level-of-expression-of-mcp1"]], "Q1: Explore the cocaine_use and cannabinoid_use columns.": [[7, "q1-explore-the-cocaine-use-and-cannabinoid-use-columns"]], "Q1: Explore the neurological function of the participants in the dataset.": [[6, "q1-explore-the-neurological-function-of-the-participants-in-the-dataset"]], "Q1: Extract the information for patient 3116": [[5, "q1-extract-the-information-for-patient-3116"]], "Q1: Extract the initial_viral_load column ?": [[3, "q1-extract-the-initial-viral-load-column"]], "Q1: Extract the relevant information from the text above": [[0, "q1-extract-the-relevant-information-from-the-text-above"]], "Q1: How many cells are in each well?": [[11, "q1-how-many-cells-are-in-each-well"]], "Q1: How many participants are suffering from impairment?": [[12, "q1-how-many-participants-are-suffering-from-impairment"]], "Q1: Load in the data from the CSV file.": [[2, "q1-load-in-the-data-from-the-csv-file"]], "Q1: Merge the biome_data table with the sample information": [[4, "q1-merge-the-biome-data-table-with-the-sample-information"]], "Q1: Using the information above, calculate the subject\u2019s heart rate reserve.": [[19, "q1-using-the-information-above-calculate-the-subject-s-heart-rate-reserve"]], "Q1: What is the average difference in misses between vehicle control and SK609 rodents?": [[16, "q1-what-is-the-average-difference-in-misses-between-vehicle-control-and-sk609-rodents"]], "Q2: By inspection, which variable has the most between class difference?": [[15, "q2-by-inspection-which-variable-has-the-most-between-class-difference"]], "Q2: Calculate the amount of sample to add.": [[1, "q2-calculate-the-amount-of-sample-to-add"]], "Q2: Calculate the average count across regions for each phylum for patient 3116.": [[5, "q2-calculate-the-average-count-across-regions-for-each-phylum-for-patient-3116"]], "Q2: Calculate the average weeks_to_failure for the whole population?": [[3, "q2-calculate-the-average-weeks-to-failure-for-the-whole-population"]], "Q2: Calculate the effect size.": [[16, "q2-calculate-the-effect-size"]], "Q2: Calculate the length of for each row.": [[2, "q2-calculate-the-length-of-for-each-row"]], "Q2: Calculate the molecular weight of each template": [[0, "q2-calculate-the-molecular-weight-of-each-template"]], "Q2: Calculate the smallest effect size if there are 12 animals in each group.": [[17, "q2-calculate-the-smallest-effect-size-if-there-are-12-animals-in-each-group"]], "Q2: Consider how pro-inflamatory markers are related to neurological impairment.": [[6, "q2-consider-how-pro-inflamatory-markers-are-related-to-neurological-impairment"]], "Q2: Create a regression for the processing domain that accounts for demographic covariates.": [[14, "q2-create-a-regression-for-the-processing-domain-that-accounts-for-demographic-covariates"]], "Q2: Describe the graph": [[11, "q2-describe-the-graph"]], "Q2: Determine the predomininant phylum across regions.": [[4, "q2-determine-the-predomininant-phylum-across-regions"]], "Q2: Do cocaine users or non-users have a higher average level of mcp1?": [[8, "q2-do-cocaine-users-or-non-users-have-a-higher-average-level-of-mcp1"]], "Q2: Is Visuospatial impairment linked with ART therapy?": [[12, "q2-is-visuospatial-impairment-linked-with-art-therapy"]], "Q2: Is race and education correlated in this dataset?": [[13, "q2-is-race-and-education-correlated-in-this-dataset"]], "Q2: Is the expression of infalpha or vegf different across neurological impairment status?": [[7, "q2-is-the-expression-of-infalpha-or-vegf-different-across-neurological-impairment-status"]], "Q2: Merge well_level_data with plate-map and visualize": [[10, "q2-merge-well-level-data-with-plate-map-and-visualize"]], "Q2: Using the information above, calculate the upper limit of the subject\u2019s target heart rate zone.": [[19, "q2-using-the-information-above-calculate-the-upper-limit-of-the-subject-s-target-heart-rate-zone"]], "Q3: Are the residuals normally distributed?": [[15, "q3-are-the-residuals-normally-distributed"]], "Q3: Calculate the average counts of each phylum by body site.": [[5, "q3-calculate-the-average-counts-of-each-phylum-by-body-site"]], "Q3: Calculate the average weeks to failure for the treated population?": [[3, "q3-calculate-the-average-weeks-to-failure-for-the-treated-population"]], "Q3: Create a new DataFrame that includes only the treated individuals.": [[2, "q3-create-a-new-dataframe-that-includes-only-the-treated-individuals"]], "Q3: Describing the reaction yield": [[1, "q3-describing-the-reaction-yield"]], "Q3: Does Sex impact the effect of cocaine use on the average level of mcp1 expression?": [[8, "q3-does-sex-impact-the-effect-of-cocaine-use-on-the-average-level-of-mcp1-expression"]], "Q3: Hypothesis generation": [[6, "q3-hypothesis-generation"]], "Q3: Is Visuospatial score linked with ART therapy?": [[12, "q3-is-visuospatial-score-linked-with-art-therapy"]], "Q3: Is covariate controlled EDZ still correlated with PDZ?": [[14, "q3-is-covariate-controlled-edz-still-correlated-with-pdz"]], "Q3: Use the appropriate non-parametric method.": [[13, "q3-use-the-appropriate-non-parametric-method"]], "Q3: What is the molarity of each Paragon sample?": [[0, "q3-what-is-the-molarity-of-each-paragon-sample"]], "Q3: Which body site has the largest increase in Actinobacteria when comparing typical and severe disease outcomes?": [[4, "q3-which-body-site-has-the-largest-increase-in-actinobacteria-when-comparing-typical-and-severe-disease-outcomes"]], "Q4: Are EDZ and PDZ correlated after controlling for covariates?": [[14, "q4-are-edz-and-pdz-correlated-after-controlling-for-covariates"]], "Q4: Calculate the average counts of each phylum by severe_disease.": [[5, "q4-calculate-the-average-counts-of-each-phylum-by-severe-disease"]], "Q4: Calculate the average weeks_to_failure for the treated population?": [[3, "q4-calculate-the-average-weeks-to-failure-for-the-treated-population"]], "Q4: Calculate the average weeks_to_failure for the untreated population?": [[3, "q4-calculate-the-average-weeks-to-failure-for-the-untreated-population"]], "Q4: Calculate the minimum change detectable with 16 animals.": [[16, "q4-calculate-the-minimum-change-detectable-with-16-animals"]], "Q4: Evaluate a potential covariate": [[12, "q4-evaluate-a-potential-covariate"]], "Q4: Exploration": [[6, "q4-exploration"]], "Q4: Is there a correlation between infection length and mcp1 expression?": [[8, "q4-is-there-a-correlation-between-infection-length-and-mcp1-expression"]], "Q4: Make two new tables that contain high and low initial viral load samples of the treated individuals.": [[2, "q4-make-two-new-tables-that-contain-high-and-low-initial-viral-load-samples-of-the-treated-individuals"]], "Q4: Perform an ANOVA between ART on the Executive Domain Z-score.": [[15, "q4-perform-an-anova-between-art-on-the-executive-domain-z-score"]], "Q4: What is the yield of each PacBio sample?": [[0, "q4-what-is-the-yield-of-each-pacbio-sample"]], "Q4: Which tissues are \u201cswabbable\u201d?": [[4, "q4-which-tissues-are-swabbable"]], "Q4: Write a function which calculates the reaction yield": [[1, "q4-write-a-function-which-calculates-the-reaction-yield"]], "Q5: Calculate descriptive statistics on the weeks_to_failure column to compare the high and low viral load participants.": [[2, "q5-calculate-descriptive-statistics-on-the-weeks-to-failure-column-to-compare-the-high-and-low-viral-load-participants"]], "Q5: Calculate new effect sizes for these conditions.": [[16, "q5-calculate-new-effect-sizes-for-these-conditions"]], "Q5: Does cocaine use impact the correlation between infection length and mcp1 expression?": [[8, "q5-does-cocaine-use-impact-the-correlation-between-infection-length-and-mcp1-expression"]], "Q5: Which samples are high?": [[4, "q5-which-samples-are-high"]], "Q5: Which samples are usable?": [[0, "q5-which-samples-are-usable"]], "Q6 Summary Questions": [[16, "q6-summary-questions"]], "Q6: Calculate the same descriptive statistics on the weeks_to_failure column to compare the treated participants with short and long infection lengths.": [[2, "q6-calculate-the-same-descriptive-statistics-on-the-weeks-to-failure-column-to-compare-the-treated-participants-with-short-and-long-infection-lengths"]], "Q6: Which swabbable region has the highest positive predictive value when predicting persistent disease?": [[4, "q6-which-swabbable-region-has-the-highest-positive-predictive-value-when-predicting-persistent-disease"]], "Q7: Context": [[4, "q7-context"]], "Quantifying the uncertainty of estimates": [[9, "quantifying-the-uncertainty-of-estimates"]], "Quantitative Reasoning in Biology": [[34, "quantitative-reasoning-in-biology"]], "Querying": [[3, "querying"]], "Questions": [[2, "questions"]], "Quick introduction on cells and blocks": [[19, "quick-introduction-on-cells-and-blocks"]], "Regression with categories": [[15, "regression-with-categories"]], "Relational with relplot": [[9, "relational-with-relplot"]], "Residuals": [[15, "residuals"]], "Seaborn": [[28, "seaborn"]], "Seaborn interface": [[28, "seaborn-interface"]], "Session": [[20, "session"]], "Standard first": [[15, "standard-first"]], "Step 1: Define the hypothesis": [[16, "step-1-define-the-hypothesis"]], "Step 2: Define success": [[16, "step-2-define-success"], [17, "step-2-define-success"]], "Step 3: Define your tolerance for risk": [[16, "step-3-define-your-tolerance-for-risk"], [17, "step-3-define-your-tolerance-for-risk"]], "Step 4: Define a budget": [[16, "step-4-define-a-budget"], [17, "step-4-define-a-budget"]], "Step 5: Calculate": [[16, "step-5-calculate"], [17, "step-5-calculate"]], "Step 6: Summarize": [[16, "step-6-summarize"], [17, "step-6-summarize"]], "Submission": [[0, "submission"], [2, "submission"], [4, "submission"], [5, "submission"], [6, "submission"], [7, "submission"], [8, "submission"], [10, "submission"], [11, "submission"], [12, "submission"], [14, "submission"], [16, "submission"]], "Submissions": [[19, "submissions"]], "Sumarize by sample": [[11, "sumarize-by-sample"]], "Summarizing by grouping": [[5, "summarizing-by-grouping"]], "The Problem": [[1, "the-problem"]], "The other use of Power Tests": [[17, "the-other-use-of-power-tests"]], "Try me": [[19, "try-me"]], "Two group measurement": [[13, "two-group-measurement"]], "Visualizing differences across categories with stripplot": [[9, "visualizing-differences-across-categories-with-stripplot"]], "Walkthrough": [[1, "walkthrough"], [1, "id1"], [3, "walkthrough"], [5, "walkthrough"], [7, "walkthrough"], [9, "walkthrough"], [11, "walkthrough"], [13, "walkthrough"], [15, "walkthrough"], [17, "walkthrough"], [19, "walkthrough"]], "What is the template weight?": [[1, "what-is-the-template-weight"]], "Why Google Colab": [[19, "why-google-colab"]], "Why Python": [[19, "why-python"]], "With correction": [[15, "with-correction"]], "f-strings": [[1, "f-strings"]]}, "docnames": ["_bblearn/Module02/Module02_lab", "_bblearn/Module02/Module02_walkthrough_SOLUTION", "_bblearn/Module03/Module03_lab", "_bblearn/Module03/Module03_walkthrough_SOLUTION", "_bblearn/Module04/Module04_lab", "_bblearn/Module04/Module04_walkthrough_SOLUTION", "_bblearn/Module05/Module05_lab", "_bblearn/Module05/Module05_walkthrough_SOLUTION", "_bblearn/Module06/Module06_lab", "_bblearn/Module06/Module06_walkthrough_SOLUTION", "_bblearn/Module07/Module07_lab", "_bblearn/Module07/Module07_walkthrough_SOLUTION", "_bblearn/Module08/Module08_lab", "_bblearn/Module08/Module08_walkthrough_SOLUTION", "_bblearn/Module09/Module09_lab", "_bblearn/Module09/Module09_walkthrough_SOLUTION", "_bblearn/Module10/Module10_lab", "_bblearn/Module10/Module10_walkthrough_SOLUTION", "content/Module01/Module01_book", "content/Module01/Module01_walkthrough", "content/Module01/notebook_actions", "content/Module02/Module02_book", "content/Module02/dilution_calculations", "content/Module02/nanopore_description", "content/Module03/Module03_book", "content/Module04/Module04_book", "content/Module05/Module05_book", "content/Module06/Module06_book", "content/Module06/grammar_of_graphics", "content/Module07/Module07_book", "content/Module07/common_biological_distributions", "content/Module08/Module08_book", "content/Module09/Module09_book", "content/Module10/Module10_book", "content/book_index", "content/misc/about_this_book", "content/misc/book_intro"], "envversion": {"sphinx": 61, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["_bblearn/Module02/Module02_lab.ipynb", "_bblearn/Module02/Module02_walkthrough_SOLUTION.ipynb", "_bblearn/Module03/Module03_lab.ipynb", "_bblearn/Module03/Module03_walkthrough_SOLUTION.ipynb", "_bblearn/Module04/Module04_lab.ipynb", "_bblearn/Module04/Module04_walkthrough_SOLUTION.ipynb", "_bblearn/Module05/Module05_lab.ipynb", "_bblearn/Module05/Module05_walkthrough_SOLUTION.ipynb", "_bblearn/Module06/Module06_lab.ipynb", "_bblearn/Module06/Module06_walkthrough_SOLUTION.ipynb", "_bblearn/Module07/Module07_lab.ipynb", "_bblearn/Module07/Module07_walkthrough_SOLUTION.ipynb", "_bblearn/Module08/Module08_lab.ipynb", "_bblearn/Module08/Module08_walkthrough_SOLUTION.ipynb", "_bblearn/Module09/Module09_lab.ipynb", "_bblearn/Module09/Module09_walkthrough_SOLUTION.ipynb", "_bblearn/Module10/Module10_lab.ipynb", "_bblearn/Module10/Module10_walkthrough_SOLUTION.ipynb", "content/Module01/Module01_book.md", "content/Module01/Module01_walkthrough.ipynb", "content/Module01/notebook_actions.md", "content/Module02/Module02_book.md", "content/Module02/dilution_calculations.md", "content/Module02/nanopore_description.md", "content/Module03/Module03_book.md", "content/Module04/Module04_book.md", "content/Module05/Module05_book.md", "content/Module06/Module06_book.md", "content/Module06/grammar_of_graphics.md", "content/Module07/Module07_book.md", "content/Module07/common_biological_distributions.ipynb", "content/Module08/Module08_book.md", "content/Module09/Module09_book.md", "content/Module10/Module10_book.md", "content/book_index.md", "content/misc/about_this_book.md", "content/misc/book_intro.md"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [0, 1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 20, 23, 28], "0": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 34], "00": [7, 9], "000": [11, 15], "000000": [3, 5, 7, 11, 13], "000001": 13, "000002": 13, "000003": 13, "000005": 13, "000013": 13, "000027": 13, "000053": 17, "000484": 15, "000644e": 15, "001": 15, "001359": 13, "002": 15, "002176": 7, "003": 15, "003222": 15, "003390": 15, "003522": 15, "003752": 7, "004": 15, "005": 15, "005371": 13, "005507": 15, "005546": 15, "005813": 15, "006": 15, "006672": 13, "006673": 13, "006674": 13, "008385": 15, "008464": 7, "008714": 13, "008814": 11, "009": 15, "01": [7, 9, 14, 15, 17], "010019": 7, "010214": 13, "011320": 15, "013190": 7, "014": 15, "014446": 15, "014468": 13, "014470": 13, "014471": 13, "014472": 13, "014475": 13, "015979": 15, "016": 15, "017": [16, 17], "017080": 13, "019": 15, "019158": 13, "019281": 15, "019297": 15, "02": [15, 16, 17], "020368": 7, "020406": 15, "021": 15, "021198": 11, "021975": 15, "02197802197804": 1, "022": [1, 15], "022870": 15, "023608": 15, "023803": 5, "025": 13, "025250": 5, "025381": 7, "025789": 13, "026794": 7, "027777": 15, "028": 15, "028181": 7, "028329": 15, "028367": 7, "03": [7, 9, 15, 19], "030176": 15, "030209": 15, "030792": 15, "031": 15, "033597": 7, "033725": 15, "035": 15, "035258": 15, "037": 15, "037198": 7, "037462": 15, "037954": 15, "038": 15, "039": 15, "039215": 15, "039358": 15, "039614": 15, "04": [15, 17], "040962": 7, "041": 15, "041984": 3, "042186": 15, "0422": 15, "043077": 13, "043457": 7, "044": 15, "044132": 15, "044294": 15, "046": 15, "049854": 15, "05": [13, 15, 17], "0506": 15, "050633": 15, "050652": 15, "051": 15, "051659": 13, "051660": 13, "051768": 15, "052": 15, "052308": 13, "053844": 13, "054": 15, "054118": 13, "054970": 7, "055406": 13, "056513": 17, "056846": 5, "059458e": 15, "059672": 13, "059910": 15, "060": 15, "061102": 5, "061257": 13, "061660": 5, "061873": 7, "062500": 15, "062853": 5, "06544462": 15, "066149": 7, "066481": 5, "068860": 7, "069827": 13, "07": [13, 15], "070039": 11, "070204": 3, "070455": 11, "073846": 13, "073912": 7, "075652": 15, "076294": 7, "076374": 15, "076717": 13, "077273": 13, "078210": 5, "078327": 7, "078642": 5, "079104": 13, "079129": [13, 15], "08": [7, 9, 17], "081597": 7, "08198035": 15, "083791": 17, "084308": 15, "085262": 7, "086376": [13, 15], "087407": 7, "087955": 7, "088627": 11, "091578": 15, "091752": 7, "092": 15, "093771": 7, "094": 15, "094297": 15, "095385": 13, "097774": 7, "097844": 15, "098": 15, "098327": 15, "0e": 15, "0f": [1, 9, 17], "0x7f0d1d4514f0": 9, "0x7f0d1d5d6760": 9, "0x7f0d1f2f3b20": 9, "0x7f0d1f55fa60": 9, "0x7fafb72201f0": 15, "0x7fcdc41a08b0": 17, "0x7fce3506bb20": 17, "1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19], "10": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 14, 15, 17], "100": [1, 4, 9, 11, 13, 17, 19], "1000": [1, 5], "100000": 5, "100214": 11, "10097": 5, "1010": 5, "101155": 7, "101533": 5, "101683": 15, "1017": 5, "1023": 5, "102647": 15, "1029": 5, "102939": 15, "103": [5, 15], "1038": 5, "103822": 5, "104": [5, 7, 9, 15], "105": [5, 9], "105822": 15, "106": 5, "106277": 5, "1065": 5, "106575": 5, "1066": 5, "107": [5, 15], "107223": 15, "107857": 11, "108": [5, 13], "108089": 13, "1089": 5, "109": 13, "11": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16], "110": [7, 9, 13], "1102": 5, "1105": 5, "1108": 5, "110912": 5, "111111": 5, "112215": [13, 15], "1123": 5, "113": [7, 9], "113038": 11, "1136": 5, "1139": 5, "1143": 5, "1146": 5, "114749": 15, "1149": 5, "115": [7, 15], "1151084": 11, "115518": 7, "1158": 5, "116": 13, "1161": 5, "116276": 13, "1164": 5, "117": [7, 9, 13, 15], "1171": 5, "118": [5, 7, 9], "119": [5, 11], "119345": 13, "119866": [13, 15], "12": [1, 3, 5, 7, 9, 13, 15, 19, 20], "1205": 5, "1207": 5, "1210": 5, "121529": 15, "122": [5, 11, 15], "1223": 5, "1224": 5, "1231231": 1, "1232": 5, "1233": 5, "123453": 7, "124": 5, "1243": 5, "1244": 5, "125": [5, 7], "125000": 5, "1265323": 11, "127": [5, 11], "1270": 11, "127249": 7, "127360": 5, "128": 9, "128191e": 15, "1286": 5, "129": 5, "13": [3, 5, 7, 9, 13, 15, 19], "130": 5, "1301": 5, "131": [7, 9, 13], "1314": 5, "131693": 5, "132": [9, 15], "132016": 7, "132588": 13, "1329": 5, "133": [11, 13, 15], "1332": 5, "133333": 3, "134": 5, "1343": 5, "135298": 5, "1356": 5, "136": 7, "1362": 5, "138": 5, "1382": 5, "138601": 7, "138889": 11, "13948": 5, "139609": 5, "1397": 5, "139811": 5, "139892": [13, 15], "14": [3, 5, 7, 9, 13, 15, 17], "140": [7, 9], "140076": 7, "1402": 5, "140374": 5, "142": 5, "142794": 15, "1428": 11, "142857": 5, "143": 15, "14341": 11, "1435": 5, "1437": 5, "1440": 5, "1447": 5, "1449": 5, "146": 15, "146409": 15, "1465": 5, "1467": 5, "14670": 11, "147": [5, 15], "1474": 5, "148070": 7, "1483": 5, "1486": 5, "14889": 11, "149": [13, 15], "1496": 5, "14987": 5, "1499": 5, "15": [0, 1, 3, 5, 7, 9, 11, 15, 16, 17], "150": [1, 5, 6], "150825": 7, "151": [7, 9], "151646": [13, 15], "151691": 7, "152": [5, 9], "152131": [13, 15], "1531": 5, "1537": 5, "1538": 5, "153846": 13, "1540": [5, 13], "1543": 11, "1546": 5, "1556": 13, "156": 13, "157748": 15, "158": 15, "1580": 5, "158109": 5, "15826787": 15, "1586": 13, "159311": 7, "1598": 5, "16": [3, 5, 7, 9, 11, 13, 15, 17], "160": 7, "1602": 5, "160208": 13, "1603": 5, "1614": 5, "162": 15, "1624": 5, "1625": 5, "1640": 5, "165": 15, "1651": 5, "1652": 5, "165470": 15, "165732": 15, "166": [5, 15], "166206": 7, "166667": 5, "1679": 5, "1680": 1, "168163": 13, "168478x0": 7, "1689": 5, "169": 13, "1691": 5, "1698": 5, "17": [3, 5, 7, 9, 15], "170": [7, 9], "1702": 5, "170366e": 15, "1704": 5, "170408": 7, "1715": 5, "1721": 5, "1723": 5, "1724": 11, "172775": 11, "174": 5, "1746": 5, "175": 5, "176": 9, "177": 13, "177314": 7, "17e": 15, "18": [3, 5, 7, 9], "1800": 5, "1802": 5, "181": 5, "181085": 7, "1812": 5, "181214": 15, "181818": 5, "182000": 1, "1822": 5, "1827": 5, "182900": 5, "183": 5, "184": [7, 9], "185": [7, 9], "1852": 5, "1857": 5, "1859": 5, "186": [7, 9], "1861": 5, "1863": 5, "1870": 5, "189": 15, "189228": 15, "19": [0, 3, 5, 13, 15, 17], "190": 5, "1902": 5, "190587": 15, "1908": 5, "191": 15, "1922": 5, "192388": 5, "193": 5, "193548": 13, "193861": 7, "194": 15, "1940": 5, "194624": 15, "195": 15, "1953": 5, "1954": 5, "196152": 5, "196306": 11, "197": 11, "1971": 5, "1974": 5, "197413": 7, "1998": 5, "1999": 28, "1e": 1, "1f": [0, 1, 2, 3, 17], "1st": 34, "2": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 19], "20": [3, 7, 9, 11, 15, 17], "200": [0, 1, 5, 9, 17], "2000": 7, "200000": 5, "2004": 5, "2007": 19, "200705": 11, "2010": 5, "2012": 28, "2016": 1, "2019": 5, "202": 13, "202042": 7, "2021": 5, "202663": 3, "203": [11, 15], "203272": 11, "203452": 15, "2037": 5, "205": 15, "2053": 5, "207338": 7, "2082": 5, "209": 13, "209317": 7, "21": [0, 3, 5, 7, 9, 11, 20], "210": [11, 13], "2101": 5, "210411": 7, "211": 5, "211610": 7, "211656": 11, "212": 5, "2133": 5, "215": [7, 9], "2155": 11, "216": 15, "2165": 5, "2168": 5, "217": 15, "217109": 13, "219": [5, 15], "22": [3, 5, 7, 9, 13], "220": 19, "2200": 0, "2203": 5, "220332": 13, "221033": 7, "2218": 5, "2227": 5, "223": 5, "2235": 5, "223827": 7, "224": [5, 9], "22414": 11, "225": 13, "2253": 5, "225529": 13, "2259": 5, "226": 5, "2260": 5, "2263": 5, "227692": 13, "229345": 7, "2294": 5, "23": [1, 3, 5, 7, 9, 11, 15, 19], "230": [7, 9], "2300": 5, "230186": 13, "231020e": 15, "2318": 5, "2319": 5, "232": [5, 7, 9], "2320": 5, "2322": 5, "232210": 5, "2324": 5, "2332": 5, "2342": 5, "234453": 15, "2346": 11, "236207": 7, "236815": 15, "2384": 5, "2389": 5, "239": 15, "24": [3, 5, 7, 9, 11, 13, 15], "241": [7, 9], "241813": 7, "242": [7, 9], "242748": 11, "243": 5, "243742": 7, "244419": 7, "245": 5, "245435": 5, "2459": 5, "245961": 13, "246212": 7, "247486": 13, "247876": 5, "248006": 7, "248030": 11, "2494": 5, "249805": 7, "25": [1, 3, 5, 7, 9, 11, 13, 16], "250000": [3, 5, 11], "2501": 11, "251": 5, "2516": 5, "25302": 11, "2536": 5, "2539": 5, "254068": 13, "255505": [13, 15], "2560": 5, "256416": 11, "257": 11, "2575": 11, "258403": 5, "259496": 11, "26": [3, 5, 7, 9, 11, 13, 15], "260": 5, "260339": 7, "2605": 5, "260844": 7, "262445": 13, "2625": 5, "263056": 13, "263505": 7, "265": 5, "265412": 7, "2655": 5, "266667": 3, "2672": 11, "267359": 7, "268552": 15, "2690": 5, "2692": 5, "27": [3, 5, 7, 9, 13, 16], "2714": 5, "272": 11, "2721": 11, "272383": 5, "272727": 5, "273085": 13, "2740": 5, "2753": 5, "275649": 5, "2757": 5, "275883": 15, "275901": 13, "276": 5, "2767": 5, "276768": [13, 15], "278": 1, "278298": 5, "2796": 5, "2798": 5, "28": 3, "280": 1, "280245": 7, "2810": 5, "2816": 5, "282": [5, 15], "283x": 17, "2846": 5, "285": 0, "285714": 5, "287822": 5, "288627": 7, "2892": 5, "29": [3, 5, 7, 9, 15], "290": 15, "290394": 11, "291": 5, "2916": 5, "292": 5, "292877": 13, "294": 15, "2940": 5, "29430717": 15, "2962": 5, "2966": 5, "298258": 13, "298616": 7, "299": 5, "2992": 5, "299676": 5, "2999": 5, "2f": [1, 4, 16], "2x": 17, "3": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 19], "30": [3, 7, 9, 17], "300": 7, "300000": 5, "3002": 5, "3006": 5, "300701": 7, "300991": 7, "300bp": 0, "301": 5, "3011914": 11, "301991": 11, "302": 11, "302081": 7, "303077": 5, "303950": 11, "3060": 5, "3062": 5, "307692": 13, "3079": 5, "309": 5, "3094": 5, "3095": 5, "31": [3, 5, 7, 9, 16], "310915": 7, "311035": 11, "3115": 5, "3117": 5, "3118": 5, "311835": 15, "3119": 5, "3120": 5, "312008": 11, "3121": 5, "3123": 5, "3124": 5, "313088": 13, "3131": 5, "313199": 5, "313846": 13, "314062": 7, "3145": 5, "314940": 7, "314984": 15, "315": [11, 15], "315743": 5, "315913": 7, "316228": 5, "317": 5, "318145": 11, "318207": 15, "319": 13, "32": [3, 5, 7, 9], "320": [1, 15], "3217": 5, "322": 13, "322395": 5, "322973": 13, "323": [5, 15], "324": 11, "324745": 5, "325": [13, 15], "325552": 7, "326": 5, "3265": 5, "3268": 5, "326940": 11, "3271": 5, "327174": 7, "33": [3, 7, 9], "330541": [13, 15], "331381": 5, "332": 5, "333333": 5, "3343": 5, "334738": 7, "336091": 7, "3389": 5, "3394": 5, "34": [3, 5, 7, 9, 15, 19], "340": 5, "3416": 5, "343": 5, "3441": 5, "345231": 11, "3463": 5, "34672285": 15, "3482": 13, "3486": 5, "348600": [13, 15], "35": [0, 5], "350288": 5, "350467": 11, "351729": 7, "352273x0": 7, "3525": 5, "353137": 7, "353553": 5, "354": 17, "354507": 7, "3547": 5, "357502": 7, "358": 5, "359000": 7, "36": [3, 5, 7, 9, 11, 13], "363077": 13, "363636": 5, "364": [7, 9], "364306": [13, 15], "366563": 5, "366667": 3, "36697977": 11, "367": 15, "3671": 5, "367108": 15, "3673": 5, "368554": 7, "3686": 5, "37": [3, 5, 7, 9], "3709": 5, "371020": 7, "3724": 5, "373": 11, "3743": 5, "375000": 5, "375722": 7, "375902": 5, "376193": 13, "376948": 15, "37776": 13, "38": [0, 3, 7, 9], "380": 5, "380507": 7, "381": 15, "382": [5, 11, 15], "3821": 5, "382354": 15, "382766": 11, "383": 15, "38322709": 11, "385": 5, "385047": 7, "385806": 7, "3865": 5, "3866": 5, "3877": 5, "389": 11, "389750": 7, "39": [5, 9], "390656": 5, "391024": 7, "391665": 5, "391667": 11, "3926": 5, "394": 5, "395": [7, 9], "396313": 5, "397": [7, 9], "3979": 5, "398": 11, "398808": 7, "399": 5, "3b": 17, "3c": 16, "3f": [16, 17], "4": [0, 2, 3, 5, 7, 9, 11, 13, 14, 15, 19, 34], "40": [3, 5, 7, 9], "400000": [3, 5, 11], "400943": 15, "401388": 5, "403432": 7, "404718": 15, "405": 5, "40514018": 11, "406": 5, "41": [3, 5, 9, 13], "410": 15, "412": 5, "412781": 7, "4138": 5, "414": 9, "4144": 5, "414560": 13, "415": 5, "415135": 15, "416667": 5, "418": 11, "418228": 11, "4183": 5, "418546": 15, "4186": 5, "418689": 7, "419": 15, "4195": 5, "42": [3, 5], "420": 15, "420381": 7, "4225": 5, "4252": 5, "425785": 13, "426": 5, "426076": 7, "4266": 5, "426620": 11, "427": 5, "427060": 7, "428": 5, "428571": [5, 11], "428603": 7, "429": 5, "4295": 5, "43": [3, 5, 9, 13], "430": 5, "430570": 5, "431": 5, "431478": 15, "4316": 5, "432": [5, 15], "4353": 5, "4358": 5, "436466": 7, "4372": 5, "4373": 5, "438": 13, "439171": 7, "44": [5, 9], "441315": 5, "442348e": 15, "442361": 7, "442948": 7, "444": [5, 15], "444091": 5, "444444": 5, "444492": 7, "445546": 7, "447214": 5, "448": 5, "448138": 7, "448154": 5, "448692": 7, "4497": 5, "45": [3, 17], "4513": 5, "451532": 7, "451760": 7, "452": 5, "453": 15, "453011": 7, "454": 5, "456": 15, "456370": 15, "457": 5, "457242": 7, "458": 15, "458133": 15, "46": [3, 13, 15], "4604": 5, "460747": 15, "461": 5, "461862": 13, "462623": 15, "4640": 5, "464491": 11, "4648": 5, "466016": 7, "466990": 7, "467742": 7, "468": [5, 9], "469479": 7, "46984": 15, "47": [3, 5, 13, 15], "470083": 15, "472136": 5, "4740": 5, "474836": 13, "477": 11, "478915": 7, "479059": 7, "479295": 15, "48": [3, 7, 9, 13], "480000": 13, "481": [7, 9, 13], "482685": 7, "485122": 7, "4857": 5, "487": 5, "488": 11, "488585": 15, "488694": 5, "49": [3, 5, 7, 9], "490802": 7, "491866": 7, "492": 5, "492297": 7, "4925": 5, "494296": 7, "495": 5, "495929e": 15, "498491": 13, "498605": 13, "4987": 5, "4mg": 16, "4th": 17, "4yr": 13, "5": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 19], "50": [0, 1, 2, 3, 7, 9, 11, 16, 17], "500": 0, "500000": [3, 5, 13], "500383": 5, "503098": 5, "503437": 15, "503528": 7, "505447": 7, "508": 15, "508440": 15, "509": 15, "51": [3, 7, 9, 13, 15], "510": [7, 9], "511682": 7, "512550": 7, "5135": 5, "513749e": 15, "515": 5, "515722": 5, "517": 35, "519": 5, "5199": 13, "52": [5, 13], "520000": 13, "520928": 7, "521": [5, 11], "523510": 5, "525": [10, 11], "5253": 5, "528": 11, "529348": 7, "53": [3, 7, 9, 11, 13], "530835": 13, "531052": 7, "531102": 11, "531708": 7, "533607": [13, 15], "534": 15, "538": 11, "538362": 11, "5392": 5, "54": [3, 7, 9], "540984": 11, "542014": 5, "5425": 5, "543195": 13, "544815": 7, "545455": 5, "547727": 7, "547734": 5, "5480": 5, "548527": 5, "549657": 5, "55": [3, 7, 9, 13], "5510": 5, "551650": 5, "552316": 5, "555556": 5, "5559": 5, "5562": 13, "556885": 7, "557713": 7, "56": [3, 7, 9, 13, 15], "562916": 7, "566258": 11, "567": 15, "567577": 13, "569209": 3, "569495": 17, "57": [3, 5, 9], "571": 15, "571429": 5, "572021": 7, "573143": 7, "573714": 5, "5748": 5, "57729816": 11, "58": 15, "581": 11, "582": 5, "582153": 7, "582953": 17, "583": 15, "585359": 13, "585741e": 15, "587": [5, 13], "587972": 13, "5885": 5, "589321": 11, "59": [0, 2, 3, 6, 8, 9, 10, 12], "590502": 11, "590675": 15, "591": 9, "59364634": 11, "594334": 7, "595": 11, "599343": 11, "6": [0, 1, 2, 3, 4, 5, 7, 9, 11, 12, 13, 15], "60": [5, 7, 9, 11, 15, 19], "600000": 5, "600322": 7, "600326": 5, "6004": 5, "601472": 7, "605978": 7, "606169": 7, "608108": 13, "608133": 5, "609": 11, "61": [5, 7, 9], "611538": 15, "612": [11, 15], "6125": 5, "6135": 5, "614170": 11, "615": [5, 15], "617": 5, "62": [7, 9, 13, 15], "620379": 5, "620481": 7, "622793": 5, "623": 5, "623116": 11, "6261": 5, "628781e": 15, "63": [5, 7, 9], "632728": 5, "634": 11, "6357": 5, "636364": 5, "638": 5, "638172": 7, "638858": 7, "64": [5, 7, 9], "641": 11, "641845": 5, "642": 15, "65": [7, 9, 13], "650": [0, 1], "651": 5, "656041": 7, "656465": 11, "658937": 13, "659672": 15, "659681": 13, "66": [3, 5, 7, 9, 13], "660942": 5, "661": 15, "662": 5, "662020": 5, "664": [5, 11], "664964": 7, "665777": 7, "666667": [5, 13], "667": 9, "668": 9, "669": 9, "669285": 7, "669997": 7, "67": [7, 9], "670": [5, 9, 15], "670411": 15, "6709": 5, "670989": 5, "671": 9, "672": [5, 9], "676923": 13, "677255": 7, "68": [5, 7, 9], "688109": 7, "689055": 7, "69": [5, 7, 9], "692308": 13, "692426": 7, "692828": 11, "694809": 5, "6950": 1, "6951": 1, "697499": 13, "697743e": 15, "7": [0, 3, 5, 7, 9, 10, 11, 13, 14, 15, 16, 17, 19], "70": [7, 9, 15, 17, 19], "700000": 5, "700282": 5, "700800": 5, "700951": 13, "703": [5, 11], "708945": 5, "71": [5, 7, 9], "711625": 15, "711649": 7, "712": 15, "713": 11, "713740": 13, "713939": 15, "714": 15, "714286": 5, "715677": 5, "717": 11, "717813": 7, "717995": 7, "718": [5, 13], "718414": 15, "718436": 7, "719207": 7, "72": [0, 1, 3, 9, 17], "720": [11, 15], "720370": 5, "721793e": 15, "722": 5, "724733": 15, "7249": 5, "727": 5, "727273": 5, "729213": 7, "729756": 5, "731522": 7, "733": 5, "736155": 5, "736280": 7, "737265": 11, "737718": 7, "739": [11, 13], "739450": 5, "74": [3, 7, 9], "743": 5, "744087": 13, "745778": 15, "746": 15, "746688": 15, "747": [5, 15], "747175": 7, "747258": 5, "747561e": 15, "748977e": 15, "75": [3, 5, 7, 11, 17], "750000": [3, 5], "7500000000000002": 17, "750044": 7, "750579": 11, "7514": 5, "751692": 5, "753": 13, "755459": 5, "755929": 5, "76": [3, 7, 17], "760": 5, "761385": 5, "764736": 7, "766": 5, "766186": 5, "767": 15, "7683525901861725": 17, "77": [5, 7, 11, 17], "770731e": 15, "771": 5, "771142": 5, "772": 15, "773685": 15, "774772": 11, "775x0": 7, "776097": 5, "777778": 5, "778935": 13, "778966": 5, "779431": 7, "78": [5, 7, 9, 17], "782223": 7, "784": 5, "79": [5, 7, 9, 15, 17], "790": 11, "790041": 5, "792698": 5, "794": 11, "794172": 13, "795": 15, "796715": 13, "8": [1, 2, 3, 4, 5, 9, 11, 13, 15, 16, 17], "80": [5, 9, 16, 17], "800": 11, "800000": 5, "802374": 7, "803619": [13, 15], "8038": 5, "804961": 13, "805932": 13, "806312": 5, "808": 11, "809062": 7, "809495": 7, "809699": 15, "81": [5, 7, 9, 17], "810941e": 15, "816": 9, "816497": 5, "82": [7, 17], "822714681440445": 19, "823276": 11, "824": 5, "826097": 13, "827": 1, "827337": 5, "83": [7, 9, 17], "832024": 15, "833333": 5, "834080": 5, "835926": 5, "838082": 5, "84": [7, 9], "842": 5, "843312": 13, "847": 5, "848419": 13, "85": [3, 7, 9, 17, 19], "853764e": 15, "857143": 5, "86": 19, "861": 5, "862714": 5, "863": 5, "865958": 5, "867595": 15, "868": [5, 15], "87": [3, 7, 9], "871": 15, "871029": 5, "872043": 13, "875": [5, 15], "875000": [5, 11], "876": 15, "877": 15, "877105": 15, "879147": 15, "88": [3, 17], "880832": 13, "883080": 5, "888814": 15, "888889": 5, "891": 5, "895666": 11, "898357": 13, "9": [1, 3, 5, 6, 11, 13, 15, 17, 19], "90": [7, 13], "900000": [3, 5], "901658": 5, "901854": 5, "902004": [13, 15], "904235": 13, "904244": 13, "904249": 13, "904253": 13, "904258": 13, "904260": 13, "904706": 5, "905": 5, "91": 5, "911": 5, "913580": 11, "914": [5, 15], "919812": 15, "92": 5, "923": 5, "925963": 13, "926": 11, "93": [5, 7, 9], "930": 5, "930288": 5, "932883": 5, "933985": 7, "939": 11, "939704": 15, "94": [3, 9], "940": [5, 15], "941": [5, 15], "942": 15, "95": [5, 7, 8, 9, 10, 11, 16, 17], "952127": 5, "956156": 11, "959729": 13, "96": 11, "961": 5, "965": 5, "966": 15, "966667": 11, "97": [5, 9, 15], "971": 5, "971988": 5, "973627": 5, "975734": 13, "976": 5, "977": 15, "977449": 15, "979050": 13, "9796": 5, "979960": 5, "98": [7, 9], "982": 5, "985457": 13, "985677": 5, "988": [5, 7, 9], "99": 3, "992537": 15, "993": 15, "994463e": 15, "A": [0, 1, 3, 4, 7, 9, 11, 12, 13, 15, 16, 19, 20], "And": [2, 3, 4, 19, 22], "As": [0, 1, 6, 7, 9, 13, 15, 16, 17, 20], "At": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 28], "BY": 34, "Be": 15, "But": [1, 5, 9, 15, 17], "By": [0, 2, 3, 4, 7, 9, 19], "For": [2, 3, 5, 6, 7, 8, 10, 16, 17, 19, 20], "If": [0, 1, 3, 4, 5, 6, 7, 9, 13, 15, 17, 19, 22, 34], "In": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 15, 16, 17, 19, 20, 28], "It": [0, 1, 2, 3, 4, 7, 8, 9, 10, 13, 15, 16, 17, 19, 20, 28, 35], "Its": [7, 28], "NO": [1, 3, 7, 13, 17], "NOT": [6, 7, 19], "No": [12, 15], "Not": [1, 17], "On": [0, 16, 20], "One": [0, 5, 6, 7, 13, 17], "Or": [1, 4, 17, 19], "That": [0, 2, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 19], "The": [0, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 19, 28, 34], "Their": 16, "Then": [0, 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 14, 15, 16, 17, 19], "There": [0, 1, 3, 4, 5, 6, 7, 13, 15, 19, 20], "These": [0, 1, 2, 5, 7, 9, 12, 13, 15, 17, 19, 20, 28], "To": [2, 4, 7, 10, 15], "Will": 34, "With": [6, 9, 11, 13, 17], "_df": 3, "_mask": 3, "aa": [13, 15], "abil": [7, 13, 14, 17, 28], "abl": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19], "about": [1, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 19, 20, 28], "abov": [1, 4, 9, 12, 13, 14, 15, 16, 17], "abreast": 19, "absenc": 17, "absorb": 1, "abstract": [13, 14, 19, 28], "abund": 4, "academ": 28, "accent": 7, "accept": [8, 10, 13, 16, 17], "access": [4, 13, 17, 20, 28], "accomplish": [13, 19], "accord": 0, "accordingli": 20, "account": 15, "accur": [0, 4], "acquisit": 13, "across": [2, 6, 8, 11, 13, 15, 16, 20], "act": 35, "actinobacteria": 5, "action": 20, "activ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19], "actual": [13, 28], "ad": [1, 4, 15], "adapt": 1, "add": [0, 3, 4, 7, 13, 15, 16, 19], "addit": [1, 7, 15, 28], "addition": 13, "address": 10, "adj_r2": 15, "adjust": [6, 7, 16, 17, 28], "administr": 19, "adopt": 7, "adult": [9, 19], "advanc": [5, 7, 27], "advantag": [6, 15, 19], "ae": 28, "aesthet": [3, 28], "affect": [2, 13, 15, 16], "after": [0, 1, 3, 4, 9, 13, 15, 19], "ag": [1, 2, 3, 6, 7, 9, 12, 13, 14, 15, 19], "again": 1, "against": [0, 2, 13, 14, 15, 16], "age_ax": 15, "age_col": 3, "age_initial_infect": [2, 3], "age_mask": 3, "age_mean": 3, "age_mean_short": 3, "aged_high_vl": 3, "aged_low_vl": 3, "aged_sampl": 3, "agg": [5, 11], "aggfunc": [4, 5, 7, 11], "aggreg": [4, 5, 9, 10, 11, 13, 15], "aggress": 4, "agre": 13, "agreement": 13, "ahead": 3, "aim": [19, 28], "akin": 28, "algorithm": [3, 17, 19], "alia": 3, "all": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19], "allow": [0, 1, 2, 4, 7, 10, 13, 14, 19, 20, 28, 35], "almost": [7, 17], "alon": 13, "along": [3, 6, 15], "alpha": [7, 9, 11, 16, 17], "alphabet": 9, "alreadi": [15, 19], "also": [1, 3, 5, 7, 9, 10, 13, 14, 15, 17, 19, 28, 35], "alter": [1, 28], "altern": [3, 7, 13, 16, 17], "although": 13, "alwai": [13, 15, 16, 17, 20], "among": 13, "amount": [0, 4, 10, 11, 17], "amplicon_length": 1, "amplicon_weight": 1, "amplif": 0, "an": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 16, 17, 19, 20, 33, 35], "anaconda": 19, "analys": [13, 15], "analysi": [0, 1, 2, 3, 4, 7, 10, 13, 15, 16, 19, 20, 28], "analyst": 28, "analyz": [2, 3, 4, 19], "ani": [2, 3, 6, 7, 8, 9, 10, 12, 13, 15, 17, 19, 20, 28], "annot": 35, "anoth": [0, 1, 3, 7, 9, 14, 15, 16, 19], "anova": [10, 11, 12, 13], "answer": [0, 1, 2, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19], "antiretrovir": [12, 13, 15], "anwser": [10, 11], "anyth": [1, 17, 19], "anywher": 1, "api": [9, 13, 17], "append": 2, "appli": [0, 5, 7, 9, 11, 13, 28, 34, 35], "applic": [2, 4], "approach": [4, 9, 13, 15], "appropri": [0, 4, 12, 16, 17], "approxim": [9, 13], "ar": [1, 2, 3, 5, 7, 8, 9, 10, 13, 16, 19, 20, 28, 34], "arang": [7, 11, 15, 16], "arbitrari": [2, 3], "arbitrarili": 13, "arduou": 19, "area": [4, 5, 9, 10, 11], "arg1": 1, "arg2": 1, "around": [1, 3, 7, 9, 15], "arrai": [3, 17, 28], "art": [3, 13, 14, 17], "art_ax": 15, "art_count": 13, "as_index": 5, "ascend": 15, "ask": [8, 10, 16], "aspect": [0, 28], "ass": [12, 14], "assai": [4, 16, 17, 25], "assert": 19, "assess": [7, 13, 14, 15], "assign": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 16, 17, 19], "associ": 13, "assoti": 12, "assum": [1, 8, 10, 13, 16, 17], "assumpit": 9, "assumpt": [4, 9, 13, 15, 16, 17], "astyp": [11, 12, 13, 14, 15], "atop": 3, "attach": [1, 3], "attain": 13, "attent": [4, 16, 17], "attract": [13, 28], "attribut": [3, 15, 28, 34], "audienc": 28, "auditori": 13, "autom": [1, 9, 11], "automat": [8, 10], "avail": [5, 13, 19], "averag": [0, 2, 4, 9, 10, 11, 12, 13, 17, 19], "average_week": 3, "avgintench2": 11, "avoid": 15, "awai": [19, 28], "await": 20, "ax": [6, 7, 9, 11, 13, 15, 16, 17], "ax_ser": 7, "axi": [6, 7, 8, 9, 10, 11, 15], "axis_handl": 7, "axisgrid": 9, "b": [7, 11, 13, 17], "b10": 11, "b11": 11, "b2": 11, "b3": 11, "b4": 11, "back": [3, 4, 7, 13, 19], "background": [13, 20, 23, 35], "background_gradi": 7, "bacteri": 5, "bacteria": 5, "bacteroidet": 5, "bake": [1, 15], "balanc": [7, 13, 17, 28], "bar": [5, 7, 9, 11, 12, 13, 17, 28], "barcod": 1, "barh": 7, "barplot": [6, 7, 9, 10, 11, 13, 15], "base": [0, 1, 2, 3, 4, 9, 12, 13, 15, 17, 19, 20, 28], "base_weight": 1, "basepair": [0, 1], "basic": [0, 1, 3, 5, 13, 17, 18, 19, 21, 26, 31], "batch": 17, "batteri": [13, 19], "bay": 13, "bblearn": [0, 2, 4, 5, 6, 7, 8, 10, 11, 12, 14, 16, 19], "bead": [10, 11], "beadsd": 10, "beat": [10, 19], "beauti": 28, "becaus": [0, 2, 4, 7, 13, 15, 19], "becom": [15, 19, 20], "been": [0, 1, 2, 3, 5, 7, 9, 11, 15, 17, 19, 22], "befor": [0, 1, 3, 7, 9, 13, 15, 16, 17, 19, 20, 28], "begin": [0, 1, 3, 4, 17], "beginn": 13, "being": [0, 2, 4, 7, 13, 14, 15, 16, 19, 20, 28], "believ": [13, 16], "below": [9, 12, 13, 14, 16, 19], "bera": [13, 15], "berklei": 19, "best": [5, 7, 9, 13, 15, 16, 17], "beta": 15, "better": [0, 2, 7, 15, 17, 19], "between": [0, 1, 2, 3, 4, 5, 6, 7, 9, 12, 13, 14, 17, 19, 25], "beyond": [17, 28], "bf10": [13, 17], "bias": 15, "bin": [7, 9, 11, 13, 28], "binar": 12, "binari": 15, "biolog": [3, 6, 8, 10, 11, 13, 15, 19, 25, 29, 31, 35], "biologi": [1, 13, 15, 19], "biologist": [13, 15], "biomark": [6, 7, 9], "biome_data": 5, "biomed": 5, "biopsi": [4, 5], "biostat": 17, "biostatist": [4, 13, 34, 35], "bit": 17, "black": [1, 11], "block": 1, "blockad": 17, "bmi": [6, 7, 9, 19], "bog": 13, "boil": 13, "bold": [16, 19], "boldsymbol": 15, "book": [1, 28, 34, 36], "boolean": [2, 4], "bootstap": 9, "bootstrap": [7, 9], "both": [0, 4, 5, 9, 13, 14, 15, 17, 19, 20], "bound": 17, "boundari": 11, "box": [4, 6, 9, 11], "boxplot": [6, 7, 9, 15], "bp": [0, 1, 19], "brace": 1, "bracket": [3, 13], "break": [1, 2, 7, 13, 28], "bridg": 13, "brief": [1, 19], "briefli": 23, "bring": 7, "broader": 9, "broken": 5, "browser": [19, 20], "build": [12, 13, 17, 28], "built": [5, 13, 15], "bulla": 5, "bullet": 19, "button": 20, "bypass": 19, "c": [7, 11, 13, 15], "c2": 11, "c3": 11, "calc_molar": 1, "calc_yield": 1, "calced_pow": 17, "calcul": [4, 6, 10, 13, 14, 15, 28], "call": [0, 1, 2, 3, 4, 13, 15, 19, 20, 28], "came": [4, 11], "can": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 22, 28], "cannabinoid": [7, 9], "cannabinoid_us": 9, "cannot": [3, 13, 15, 17, 19, 20], "capabl": [7, 13], "capit": 5, "caption": [6, 16, 17], "captur": [7, 19], "carefulli": 2, "carri": 19, "case": [3, 4, 15, 17, 20], "categor": [6, 7, 11, 12, 15, 28], "categori": [6, 7, 11, 13, 17], "caus": 5, "causal": 15, "caution": 15, "cbar": 9, "cc": 34, "cd": 17, "cell": [0, 1, 2, 4, 7, 13], "cell_level_data": [10, 11], "cell_numb": 11, "cells_per_wel": 11, "center": [3, 15, 16], "central": [9, 15], "certain": 4, "chain": [0, 3], "chanc": [3, 13, 16, 17], "chang": [1, 7, 9, 11, 13, 15, 17, 19, 28], "chapter": [18, 19, 21, 24, 25, 26, 27, 29, 31, 32, 33], "characterist": 0, "chart": 12, "check": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19], "check_al": [2, 13, 14, 15, 16, 17, 19], "chemic": 1, "chemokin": [6, 7, 9], "chi2": [12, 13, 17], "chi2_independ": [12, 13], "choic": [7, 16, 17], "choos": [12, 13, 16, 17], "chosen": 2, "ci": [7, 9, 11, 15, 16, 17], "ci95": [13, 17], "circa": 1, "class": [19, 20], "classifi": 4, "clean": [0, 2], "click": 19, "clinic": [2, 4, 17, 19], "clinician": 4, "close": [6, 7, 17, 28], "cloud": 20, "cmap": 7, "co": 14, "cocain": [7, 9], "cocaine_us": 9, "code": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 28], "coef": 15, "coeffici": [6, 15], "cognit": [13, 14], "cohen": [13, 17], "coher": 28, "cohort": [6, 7, 9, 12, 13, 15], "coivd": 0, "col": [7, 9, 11], "col_wrap": 9, "colab": [7, 18, 20], "cold": 17, "collaps": 11, "collect": [4, 5, 6, 7, 9, 13, 19], "collectiontyp": [4, 5], "colleg": [13, 34], "collinear": 15, "color": [7, 9, 15, 16, 28], "column": [4, 5, 6, 11, 12, 13, 15], "com": [19, 23], "combin": [4, 5, 9, 15, 28], "come": [1, 4, 5, 13, 16, 17, 19, 20, 30], "comma": 3, "command": [3, 20], "commens": 5, "comment": [9, 16], "common": [1, 3, 5, 6, 7, 9, 13, 19, 34], "common_norm": 9, "commonli": [5, 13], "commun": [1, 3, 7, 28], "compact": 1, "compani": 20, "companion": [34, 35], "compar": [0, 7, 12, 13, 15, 16, 17, 25, 28], "comparison": [0, 2, 3, 5, 12], "compat": 20, "complet": [0, 2, 3, 4, 6, 7, 8, 10, 11, 12, 14, 16, 19, 20], "complex": [1, 3, 7, 13, 14, 17, 19, 28], "complic": [0, 4, 13], "compon": 28, "compound": 17, "comprehens": [13, 28], "compress": 10, "compris": 19, "comput": [1, 7, 9, 15, 19, 20], "concat": 15, "concaten": 11, "concentr": [0, 1, 9, 11], "concept": [13, 14, 19, 20, 28], "conceptu": 28, "concis": 28, "conclud": 17, "condit": [4, 10, 11, 19], "conduct": [13, 17], "confid": [7, 8, 9, 15, 16, 17], "congratul": 0, "connect": 20, "consid": [2, 4, 9, 13, 14, 15, 16, 17, 19, 32], "consider": [2, 16, 17], "consist": 15, "constant": [5, 15], "constraint": [3, 13, 14], "construct": [7, 15, 28], "consum": 28, "consumpt": 34, "contact": 34, "contain": [1, 3, 4, 7, 9, 10, 13, 20], "content": [11, 20, 22, 34, 35], "context": [1, 2, 17, 25, 35], "contin": 12, "contini": [6, 7, 15], "continu": [0, 1, 2, 7, 9, 15], "contrast": [0, 13, 16, 17], "contribut": [7, 28], "control": [2, 3, 7, 13, 17], "convei": [9, 28], "conveni": 28, "convent": [3, 15], "convers": [17, 22], "convert": [1, 5, 9], "convient": 15, "coord": 28, "coordin": [13, 28], "copi": [1, 2, 3, 5, 16], "core": [3, 28], "corner": 19, "corr": [6, 7, 14], "correct": [0, 1, 2, 4, 8, 10, 13, 14, 17], "correctli": [4, 19], "correl": [6, 7, 12], "correspond": 3, "cost": 17, "could": [6, 7, 9, 15, 16, 17, 19], "count": [1, 3, 4, 7, 10, 11, 19], "counterpart": 13, "countplot": [12, 13], "coupl": [7, 15], "cours": [1, 3, 19, 20, 34, 35], "covar": 15, "covarait": [14, 15], "covari": [13, 15], "cover": [1, 3, 10, 11, 13, 15, 17, 19, 20], "covid": [0, 1], "cramer": 13, "creat": [0, 1, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 19, 20, 28], "creation": 28, "creativ": 34, "cressi": 13, "critic": [1, 19], "cross": [6, 13], "cross_corr": 7, "crosstab": 13, "crucial": 4, "csv": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "ctl": 17, "ctrl": 20, "cu": 17, "cue": 17, "cultur": 5, "cure": 3, "curli": 1, "current": [0, 1, 2, 3, 16], "current_yield": 1, "custom": [5, 7, 28], "customiz": 28, "cut": [2, 9, 13], "cutoff": [4, 13, 17], "cytokin": [6, 7, 9], "cytokine_data": [6, 7, 8, 9], "d": [7, 11, 13, 15, 16, 17], "d2": 11, "d3": 17, "d4t": [12, 13, 15], "da06": 11, "da07": 11, "da08": 11, "da09": 11, "da10": 11, "da11": 11, "da12": 11, "da13": 11, "da14": 11, "da_tx": 11, "dai": [16, 19], "dampier": 34, "dandi": 13, "dash": 3, "data": [0, 1, 3, 4, 6, 8, 9, 11, 12, 13, 14, 15, 17, 19, 20, 24, 25, 26, 28], "datafram": [3, 4, 6, 7, 9, 11, 13, 15, 28], "dataset": [4, 5, 9, 10, 11, 15, 19, 20, 28, 35], "date": 7, "ddof1": [13, 15], "ddof2": 15, "deal": [0, 5, 7], "debug": [1, 2, 7], "decad": 5, "decid": 7, "decim": 14, "decis": [13, 14, 17, 19], "decreas": [9, 15], "deep": [5, 13], "deeper": 13, "def": [1, 5, 9, 11], "default": [6, 7, 9, 13, 15, 28], "defici": 15, "defin": [0, 1, 2, 28], "definit": [2, 13], "degrad": 0, "degre": 17, "delet": 20, "delimit": 4, "delv": [0, 4], "demograph": [6, 9, 12, 13, 15], "densiti": 9, "depend": [5, 12, 13, 15], "depenend": 15, "depth": 1, "deriv": [2, 3, 13], "describ": [0, 3, 4, 9, 13, 15, 17, 19, 23, 28], "descript": [1, 8, 10], "design": [1, 6, 7, 13, 15, 16, 17, 28, 33], "desir": [13, 19], "detail": [1, 5, 13, 28], "detect": [3, 4, 11, 15, 17, 33], "determ": 15, "determin": [0, 2, 12, 14, 16, 17, 19], "develop": [7, 17, 19, 28, 34], "deviat": [2, 4, 5, 9, 12, 13, 17], "devic": 1, "devis": 17, "devlin": [13, 15], "dexter": 13, "df": [9, 11, 13, 15], "dh20": 0, "diagnos": 4, "diagnost": 4, "dice": 13, "dictat": 28, "did": [11, 13, 17], "didn": [15, 19], "diff": 13, "diffent": 5, "differ": [0, 1, 2, 3, 4, 5, 11, 12, 13, 14, 17, 28], "difficult": [0, 8, 10, 13, 15, 19, 20], "difficulti": 19, "digest": 5, "dilut": [0, 1], "direct": 6, "directli": [13, 15], "disadvantag": 15, "disconnect": 20, "discuss": [1, 6, 7, 9, 11, 12, 13, 15, 21, 24, 25, 26, 27, 29, 31, 32, 33], "diseas": [2, 5], "disease_typ": 5, "disentangl": 15, "disitribut": 13, "disord": 13, "displai": [0, 1, 3, 4, 8, 28], "dist": 13, "distanc": 15, "distant": 0, "distinct": 9, "distinguish": 11, "distribut": [5, 7, 13, 17, 28], "dive": [0, 3, 13], "divid": [9, 13, 17], "dna": [0, 1, 22], "dna_conc": 1, "dna_molar": 1, "dna_weight": [0, 1], "dna_yield": 1, "dna_yield_descript": 1, "do": [0, 1, 2, 3, 4, 5, 6, 7, 9, 13, 15, 16, 17, 19, 21, 26, 27, 33], "doc": [4, 16], "document": [5, 7, 13], "dodg": [9, 11], "doe": [0, 2, 5, 7, 10, 11, 13, 15, 17], "doesn": [3, 9, 15, 17], "dof": [13, 17], "dollar": 1, "domain": [12, 13, 16, 17], "don": [9, 15, 17], "done": [1, 3, 4, 5, 6, 7, 9, 15, 16, 20, 22], "dopamin": [10, 11, 17], "dot": [3, 7], "doubl": [0, 1, 2, 19], "doubt": 17, "down": [1, 2, 3, 5, 7, 13, 19, 28], "download": [0, 2, 4, 6, 8, 10, 11, 12, 14, 16, 19, 20], "downstream": 10, "dozen": [7, 19], "dpi": 7, "dr": [11, 13, 15, 17], "drastic": 1, "draw": [9, 17, 28], "drexel": [1, 5, 6, 7, 9, 13, 15, 34, 35], "drop": [13, 15, 17], "drop_first": 15, "dropdown": 19, "dropout": 17, "drug": [12, 13, 15], "dtype": [3, 7, 11, 13], "due": [0, 1, 2, 3, 6, 8, 10, 12, 13, 15, 17, 19], "dummi": 15, "dummy_v": 15, "durat": 4, "dure": [1, 2, 3, 15, 16, 17, 19], "dv": [13, 15], "dynam": [0, 1], "e": [11, 13, 15, 28], "each": [1, 3, 4, 6, 7, 8, 9, 12, 14, 15, 16, 19, 20], "ear": 5, "earli": 7, "earlier": 2, "eas": [13, 28], "easi": [4, 9, 19, 28], "easier": [1, 3, 13, 28], "easili": [3, 7, 15], "eat": 10, "eb": 9, "ecosystem": [13, 28], "eda": 28, "edg": 7, "edit": [2, 7, 19, 20, 34], "edu": 15, "edu_ax": 15, "educ": [12, 14, 15, 19], "education_bin": 13, "edz": 15, "ef": 17, "effect": [2, 3, 9, 12, 13, 15, 20, 33], "effect_s": [16, 17], "effici": [0, 28], "effort": [6, 7, 9], "effortlessli": [13, 28], "egf": [7, 9], "either": [2, 7, 9, 17, 20], "electrophysiologi": 7, "element": 28, "elif": 9, "elimin": 3, "els": [5, 9], "embark": 13, "emerg": [5, 19], "emoji": 3, "emphas": 28, "emploi": [3, 4, 5, 6, 12, 13], "empow": 28, "empti": 20, "emtricitabin": [12, 13, 15], "enabl": 28, "encod": 20, "encompass": [13, 14], "encourag": 28, "end": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19], "endswith": 19, "enhanc": 17, "enough": [0, 17], "ensur": [0, 1, 15, 17, 19, 20], "enter": [17, 20], "entir": [1, 3, 10, 15, 17], "enumer": 9, "environ": [7, 11, 13, 19], "enzymat": 1, "eotaxin": [7, 9], "eotaxin_hist": 7, "epsilon": 15, "equal": [9, 13, 15], "equal_var": 13, "equat": [1, 15, 17], "equival": 17, "equivel": 17, "error": [0, 1, 2, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "errorbar": [9, 11], "especi": [7, 15], "essenc": 28, "estim": [11, 12, 13, 15, 16, 17, 19], "etc": [7, 9, 11, 13, 28], "ethmoid": 5, "evalu": [2, 3, 13, 14, 15, 17], "even": [0, 1, 3, 9, 19], "everi": [5, 9, 15], "everyon": [3, 19], "everyth": [0, 1, 2, 3, 5, 15, 20], "everywher": 5, "evid": [10, 11, 12, 17], "evolv": [7, 35], "exacerb": 5, "exactli": [3, 13, 17], "exam": [12, 13, 15], "examin": [0, 2, 6, 15, 16], "exampl": [3, 5, 9, 13, 17, 19], "exce": 0, "excel": [3, 19], "except": [15, 19], "excercis": 7, "excess": [0, 2], "excit": 0, "exec_domain_z": [13, 14, 15], "exec_r": 14, "execut": [12, 13, 16, 19, 20], "exercis": [0, 17, 19], "exist": [2, 17, 19], "exp": 17, "exp_nob": 17, "expand": [0, 35], "expect": [4, 9, 13, 16, 17], "experi": [1, 2, 6, 11, 15, 16, 17, 19, 29, 31, 33], "experiment": [0, 10, 11, 16, 17], "explain": [1, 15], "explan": [1, 4, 13, 22], "explanatori": 1, "explicitli": [7, 28], "explor": [0, 1, 2, 3, 4, 9, 10, 12, 13, 14, 28], "exploratori": [7, 28], "explos": [7, 19], "expos": 11, "express": [1, 3, 6, 19], "extend": [3, 28], "extens": [5, 7, 12, 13, 20, 28], "extra": 19, "extract": [2, 6, 7, 11, 15], "extrem": 13, "f": [0, 2, 3, 4, 9, 11, 13, 15, 16, 17, 19], "face": 19, "facet": [7, 9, 28], "facetgrid": 9, "facilit": 3, "fact": [2, 19], "factor": [2, 3, 13], "failur": [2, 17], "fall": [5, 13], "fallen": 15, "fals": [2, 3, 5, 7, 9, 11, 13, 15, 16, 17], "familiar": [13, 19], "fancy_pivot": 11, "far": [9, 12], "fast": 3, "featur": [7, 15, 19], "feel": 0, "femal": [7, 9, 13, 15], "female_edu": 13, "fempto": 1, "femptomol": 0, "femtomol": 1, "few": [3, 7, 10, 11, 13, 15, 16, 17], "fewer": [13, 16], "fgfbasic": [7, 9], "field": [5, 6, 7, 9, 11, 19], "fig": [7, 9, 15, 16, 17], "figsiz": [7, 9, 15], "figur": [6, 7, 8, 10, 12, 15, 16, 17, 19], "file": [0, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 19, 20], "filter": [2, 3, 15], "filterwarn": 19, "final": [0, 2, 5, 15], "financi": 7, "find": [3, 4, 6, 7, 13, 14, 17, 19], "fine": 13, "finish": 19, "firmicut": [4, 5], "first": [0, 2, 3, 4, 5, 7, 9, 11, 13, 17, 19, 20, 28], "fit": [3, 9, 13, 28], "fix": [7, 17, 19, 20], "flavor": 19, "flexibl": [7, 13, 14, 28], "float": [12, 13, 14, 15], "float64": [3, 11, 13], "flouresc": 11, "flowchart": 13, "fluenci": 13, "fmol": [0, 1], "fmole": [0, 1], "focu": [2, 13], "focus": [22, 28], "follow": [0, 2, 4, 6, 14, 16, 19, 28, 34], "followup": 6, "footnot": 19, "form": [4, 7, 19], "format": [0, 1, 5, 11, 19, 20], "formul": [4, 17], "found": [3, 5, 12, 13, 15, 19], "foundat": [4, 28], "four": [3, 13], "fourth": 17, "fraction": [4, 10], "fragment": 0, "frame": [3, 19], "framework": 28, "free": [19, 20, 34], "freeli": 19, "freeman": 13, "frequenc": [7, 9, 13], "fresh": [0, 20], "fresher": 0, "freshli": [0, 20], "friendli": 13, "from": [1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 19, 20, 28], "frustrat": 7, "full": [13, 15, 19], "function": [2, 3, 5, 7, 9, 11, 12, 13, 14, 15, 19, 28, 35], "function_nam": 1, "fundament": 28, "further": [1, 6], "futur": [0, 2, 3, 5, 6, 7, 9, 19], "g": [0, 1, 7, 11, 13, 15, 28], "gain": [2, 12], "galleri": 9, "gap": 13, "gaskil": 11, "gcsf": [7, 9], "gender": [7, 8, 12, 13], "gender_race_piv": 7, "gene": 0, "gener": [0, 1, 5, 7, 8, 9, 10, 12, 13, 14, 17, 19, 28], "genom": 0, "geom": 28, "geometr": 28, "geometri": 28, "get": [0, 1, 2, 3, 4, 7, 13, 15, 17, 19, 20], "get_dummi": 15, "giant": [7, 11], "give": [4, 5, 7, 9, 13, 17, 19, 20], "given": [4, 5, 9, 13, 15, 16, 17], "glanc": 13, "gmcsf": [7, 9], "go": [7, 13, 14, 15, 16, 17, 19], "goal": [17, 28], "good": [7, 15], "googl": [17, 18, 20], "goolg": 19, "got": [3, 9], "gotten": [9, 13], "grab": [7, 9], "grace": 19, "grade": [8, 10, 19], "grader": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "grant": 16, "graph": [0, 2, 4, 6, 7, 8, 9, 10, 12, 14, 15, 16, 17, 28], "great": [0, 5, 7, 9, 17], "greater": 17, "green": [1, 7], "gross": 13, "group": [2, 3, 4, 7, 9, 11, 28], "groupbi": [4, 5, 7, 11], "grouped_pati": 5, "grow": 9, "grown": [7, 28], "guess": 17, "gui": 17, "guidelin": [0, 12, 13, 15], "guru": 1, "h": [11, 13, 15], "h0": 13, "h1": 13, "ha": [0, 1, 2, 3, 7, 8, 9, 11, 12, 13, 16, 17, 19, 22, 28], "had": [0, 1, 2, 4, 5, 9, 10, 11, 13, 16], "hand": [0, 6, 7, 13, 16, 19], "handi": 13, "handl": [15, 28], "happen": [15, 17], "hash": 11, "have": [0, 1, 2, 3, 4, 5, 6, 7, 9, 12, 13, 15, 16, 17, 19, 20, 23, 34], "he": 7, "head": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "header": 3, "health": 5, "healthi": [13, 15, 19], "heard": 13, "heart_rate_reserv": 19, "heatmap": 9, "hedg": 13, "height": [9, 13, 19], "held": 5, "hello": 19, "help": [0, 1, 2, 4, 7, 11, 13, 15, 17, 19], "her": [17, 19], "here": [2, 4, 5, 6, 7, 9, 11, 13, 14, 15, 17, 19], "hgf": [7, 9], "hi": [7, 28], "hidden": [0, 2, 4, 6, 8, 10, 12, 14, 16], "high": [9, 11, 13, 15, 16, 17, 28], "high_mean": 2, "high_min": 2, "high_treated_df": 2, "high_vl_mask": 3, "higher": [0, 13], "highli": [7, 9, 13, 15, 17], "hint": [1, 2, 16], "hipaa": 20, "hist": 7, "histogram": 9, "histori": 7, "histplot": [9, 11], "hit": 19, "hiv": [2, 3, 6, 7, 9, 13, 15], "hiv_neuro_data": [12, 13, 14, 15], "hoc": 13, "hold": [0, 15, 19], "homoscedast": [13, 15], "hood": 13, "horizont": 7, "hour": [0, 20], "how": [0, 1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 16, 17, 19, 21, 24, 25, 26, 27, 28, 33], "howev": [0, 1, 2, 3, 4, 10, 13, 14, 17, 19, 20], "hrr": 19, "html": [4, 9, 12, 13, 17, 19], "http": [4, 9, 12, 13, 17, 19, 23], "hue": [9, 11, 13], "hue_ord": 9, "human": 5, "hundr": [5, 9, 19], "hunter": 7, "hurdl": 19, "hyp_batcha_r": 17, "hyp_batchb_r": 17, "hyperlink": 19, "hypothes": [6, 11, 13, 17], "hypothesi": [11, 15, 17, 35], "hypothet": [2, 3], "i": [2, 3, 4, 5, 6, 11, 17, 19, 20, 22, 25, 28, 34], "id_var": [5, 9], "idea": [5, 7, 17], "ideal": [0, 7, 15, 17, 19], "idxmax": 4, "ie": 13, "ifnalpha": [7, 9], "ifngamma": 7, "ignor": [9, 19], "il10": 7, "il12": 7, "il13": 7, "il15": 7, "il17": 7, "il1beta": 7, "il2": 7, "il2r": 7, "il4": 7, "il5": 7, "il6": [6, 7, 9], "il7": 7, "il8": 7, "iloc": 7, "ilra": 7, "imag": [7, 11, 20], "imagin": [16, 17], "imbal": 5, "imbalanc": 1, "immedi": 1, "immun": [6, 7, 9], "immunologi": 35, "impac": 15, "impact": [0, 2, 3, 4, 5, 9, 10, 12, 13, 15, 16, 17], "impact_of_sample_s": 9, "impair": [9, 13, 15], "implement": 16, "impli": [13, 15, 17], "implic": [2, 4], "import": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 29], "importerror": 19, "imposs": 13, "improv": [9, 16, 17, 28], "incept": 28, "includ": [0, 3, 5, 6, 13, 15, 16, 17, 19, 28], "inclus": 15, "inconsist": 13, "incorrect": [16, 20], "incorrectli": 13, "increas": [8, 10, 11, 15, 16, 17], "incred": 19, "incredibli": [3, 7, 9], "increment": 3, "independ": [13, 15, 20], "indepth": 1, "index": [4, 5, 7, 9, 11, 15, 16, 17], "indic": [2, 3, 4, 7, 9, 11, 13, 17], "indivdu": 12, "individau": 15, "individu": [1, 4, 5, 9, 10, 12, 13, 14, 15], "industri": 28, "inf": 13, "infect": [3, 4, 5, 14, 15], "infection_tim": 2, "inferenti": [10, 13, 15, 19], "inferentialthink": 19, "inferior": 5, "inflamm": [6, 7, 9], "influenc": [5, 15], "influenti": 7, "inform": [3, 6, 7, 10, 13, 14, 17, 20, 28], "ing": 9, "ingredi": 1, "inhabit": 5, "init_vl": 3, "initi": [1, 3, 4, 7, 19], "initial_viral_load": 2, "inlin": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "inner": 5, "input": [1, 5, 17], "insid": 1, "insight": [2, 7], "inspect": 16, "instal": [19, 20], "instanc": [5, 13], "instead": [1, 3, 5, 6, 7, 11, 13, 19], "instruct": [0, 2, 6, 8, 10, 12, 19], "insurmount": 19, "int": [9, 11], "int64": [3, 7], "integ": 1, "integr": [7, 13, 28], "intend": 13, "intens": [11, 19], "inter": 14, "interact": [13, 15, 19, 20, 35], "intercept": 15, "interest": [0, 4, 7, 13, 15], "interfac": [3, 19], "intermeasur": 15, "intermedi": 2, "intern": [13, 15, 34], "interoper": 3, "interpret": [4, 13, 15, 16, 17, 20], "interv": [7, 8, 9, 10, 15, 16, 17], "intervent": 4, "introduc": [15, 18, 28], "introduct": [7, 9, 15], "intuit": 13, "investig": [0, 2, 16], "involv": [13, 17], "ipynb": [0, 2, 4, 6, 8, 10, 11, 12, 14, 16, 19], "iq": 13, "is_high": 4, "isaa": [7, 9], "isn": [5, 13, 17, 19], "isol": [0, 4, 5], "issu": [19, 20], "italic": 19, "item": 1, "its": [0, 4, 7, 9, 13, 28], "itself": [7, 15, 20, 28], "jarqu": [13, 15], "jarque_bera": [13, 15], "job": 15, "john": 7, "join": 11, "journei": [0, 13], "julia": 20, "jump": 1, "jupyt": [7, 13, 19], "jupyterlab": 19, "just": [0, 1, 2, 5, 7, 13, 15, 16, 17, 19, 20, 28], "keep": [0, 4, 5, 13, 16, 17], "kei": [1, 5, 7, 28], "kendal": 7, "kernel": 19, "kg": [16, 17, 19], "kind": [7, 9, 11], "know": [0, 1, 3, 7, 11, 13, 15, 16, 17, 20], "knowledg": [13, 16, 17], "kortager": 17, "krb": 16, "kruskal": [12, 13], "krustal": 13, "kwarg": 9, "kwarg1": 1, "kwarg2": 1, "lab": [1, 3, 13], "label": [6, 7, 9, 16, 17], "labelrot": 7, "lai": 11, "lambda": [7, 11, 13], "languag": [12, 13, 19, 20, 28], "language_domain_z": [13, 15], "larg": [2, 7, 10, 13, 15, 19, 20], "larger": [5, 13, 15, 16, 17, 19], "largest": 15, "last": [3, 7, 13, 15, 17, 19], "lastli": [19, 35], "later": [3, 15, 19], "launch": 19, "layout": 7, "lead": [1, 12, 13, 15], "learn": [4, 19], "learningmemory_domain_z": [13, 15], "least": [4, 6, 13, 15, 17], "leav": [5, 6], "lectur": 5, "left": [0, 11, 15, 19], "left_on": [5, 11], "legend": [7, 8, 10, 11, 16, 17], "leland": 28, "len": [9, 15, 19], "length": [0, 3, 9, 11, 14], "less": [2, 10, 11, 13, 16, 17, 20, 28], "let": [0, 3, 4, 7, 9, 13, 15, 16, 17, 19], "letter": 11, "level": [2, 3, 4, 6, 7, 11, 13, 15, 16, 17, 28], "leven": 13, "lever": 17, "leverag": [13, 28], "li": 17, "lib": 15, "libari": 3, "librari": [2, 3, 7, 17, 28], "licens": 34, "ligat": 1, "light": [1, 17], "like": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 19, 20, 34], "likelihood": [4, 13, 15, 17, 33], "limit": [1, 3, 6, 7, 9, 15, 17, 20, 28], "line": [2, 3, 7, 9, 15, 19, 28], "line2d": 15, "linear": [7, 13, 15], "linear_regress": [14, 15], "link": [0, 13, 17, 19, 20, 22], "linkag": [0, 12, 13], "linspac": 9, "list": [1, 3, 5, 19], "listdir": 19, "littl": [4, 15, 17], "live": [3, 6, 7, 9, 15], "ll": [0, 1, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 20], "load": [3, 4, 10, 11, 19, 20, 24], "loc": [3, 13, 15, 16], "locat": [0, 4, 5], "log": [13, 20], "logarithm": 28, "logistic_regress": 15, "long": [0, 3, 5, 9, 15], "long_mean": 2, "long_min": 2, "longer": [0, 2, 5], "look": [1, 2, 4, 5, 6, 7, 9, 11, 12, 13, 15, 17, 19, 22], "loop": [13, 16, 19], "loos": 10, "lose": 15, "loss": 13, "lot": [1, 3, 17], "low": [9, 11, 13, 15, 17], "low_mean": 2, "low_min": 2, "low_treated_df": 2, "lower": [13, 16, 17, 28], "luminex": [6, 7, 9], "m": [13, 15, 17, 20], "made": [7, 17], "mai": [0, 2, 5, 13, 15, 17], "main": [3, 17], "major": [12, 13, 15], "make": [0, 1, 3, 4, 5, 7, 9, 13, 14, 15, 17, 28], "male": [7, 9, 13, 15], "male_edu": 13, "manag": 1, "mani": [0, 1, 2, 4, 6, 7, 8, 9, 10, 13, 15, 16, 17, 19, 20, 28], "manipul": [7, 13, 28], "mann": 13, "manner": 7, "manual": [1, 2], "manufactur": 0, "map": [4, 7, 11, 28], "margin": [13, 17], "markdown": [2, 20], "marylin": 13, "mass": 1, "match": [3, 11, 12, 13, 15], "materi": [0, 1], "math": [0, 1, 17, 19, 21], "mathbf": 15, "mathemat": 3, "matlab": 7, "matplotlib": [5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 28], "matrix": [6, 7, 15, 28], "matter": 9, "max": [2, 3, 5, 11], "maxillari": 5, "maxim": 33, "maximum": [17, 19], "mayb": [17, 20], "mayo": 19, "mcp1": [6, 9], "me": 34, "mean": [0, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 15, 17, 28], "mean_f": 13, "mean_m": 13, "mean_val": 5, "meanin": 17, "meaning": [6, 13, 15, 17, 28], "measur": [0, 1, 5, 6, 7, 8, 10, 12, 15, 16, 17], "meatu": 5, "med": 9, "media": 5, "median": [2, 3, 5, 9], "mediat": 15, "mediation_analysi": 15, "medic": [4, 12, 13, 15], "medicin": 34, "meet": 0, "melted_data": 9, "memori": [12, 13], "menu": [19, 20], "merg": [11, 15], "merged_data": 4, "merged_info": 5, "meter": 19, "method": [0, 2, 3, 5, 6, 7, 9, 12, 14, 15, 17, 19], "metric": [4, 14, 16], "mg": 17, "mice": 16, "michael": 28, "microbiologi": 35, "microbiom": [4, 5], "microbiome_phylum_data": [4, 5], "middl": [5, 7, 9], "mig": [7, 9], "might": [2, 3, 13, 15], "miim": 35, "mild": [9, 12], "million": 1, "min": [2, 3, 11, 13, 16], "min_chang": [16, 17], "mind": [15, 16], "minim": 15, "minimum": [2, 17], "minion": 1, "minor": 7, "minut": [17, 19], "mip1alpha": [6, 7, 9], "mip1beta": [7, 9], "mircolit": 1, "miss": [5, 13], "mistak": [19, 20], "mitig": 15, "mod": 13, "mode": [3, 9], "model": [13, 14, 15, 17], "moder": 12, "modif": 19, "modul": 1, "modular": 1, "mole": [0, 1], "molecul": 1, "molecular": 1, "monei": 17, "monitor": 3, "monoton": 7, "more": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 17, 19, 20, 22, 25, 28, 30], "morn": 19, "most": [4, 5, 6, 7, 8, 10, 11, 12, 17, 19, 20], "mostli": 17, "motiv": 7, "motor": [1, 12, 13], "motor_domain_z": [12, 13, 15], "move": [5, 19], "movement": 13, "mu": [5, 13], "much": [1, 9, 15, 17], "multi": 7, "multi_us": [7, 9], "multicollinear": 15, "multipl": [0, 1, 3, 7, 11, 12, 13, 14, 16, 17, 19, 28, 32], "multipli": 19, "must": [0, 7, 17, 19], "mutat": 0, "my": [1, 7, 9, 17], "n": [9, 16, 17], "n_f": 13, "n_m": 13, "name": [2, 3, 5, 7, 9, 11, 13, 15, 16, 19], "nan": [5, 7, 13, 15], "nano": 1, "nanopor": [0, 1], "nasal": 5, "natur": 19, "nbin": 9, "nbsp": 7, "nc": 34, "ncov2": 0, "nd": 34, "ndf": 9, "nearest": 1, "neb": 22, "necessari": [0, 4], "necessarili": 2, "need": [0, 1, 2, 3, 4, 7, 9, 13, 15, 16, 17, 18, 19, 20, 22, 25, 28], "neg": [13, 15, 17], "neither": 9, "neuro_screen_categori": 9, "neuro_screen_impairment_level": [6, 7, 9], "neuro_screen_ordin": 9, "neurobiologist": 7, "neurocognit": [6, 7, 9, 12, 13, 15], "neurolog": [12, 13, 15], "neuropsycholog": [12, 13, 15], "neurotox": [12, 13, 15], "never": 20, "new": [1, 3, 4, 6, 7, 9, 10, 13, 15, 17, 20], "new_concentr": 1, "new_paragon_molar": 1, "newer": [12, 13, 15], "newest": 19, "next": [1, 2, 5, 7, 9, 11, 12, 13, 15, 16, 17, 19], "neyman": 13, "ng": [0, 1], "nice": [3, 7, 15, 19], "night": 15, "nn": 9, "nob": [16, 17], "nobs_siz": [16, 17], "noderiv": 34, "nois": [10, 11, 15, 16, 17], "noisi": 17, "non": [7, 9, 11, 12, 15, 17], "non_us": 7, "noncommerci": 34, "none": [7, 9, 15, 16, 17], "nonparametr": 9, "norepinephrin": 17, "norm": [5, 13], "normal": [1, 5, 7, 9, 12, 13, 14, 16, 19], "normaltest": [13, 15], "note": [2, 8, 10, 13, 15], "notebook": [0, 1, 2, 4, 6, 7, 8, 10, 11, 12, 13, 14, 16, 19], "notepad": [19, 20], "notic": [1, 5, 7, 9, 15, 17, 19], "now": [0, 1, 2, 3, 5, 11, 13, 15, 17, 19], "np": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "np2": [13, 15], "np_ax": 9, "nuanc": 10, "nucleotid": 1, "nuisanc": 15, "null": [4, 13, 17], "num_otu": 5, "number": [1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 15, 16, 17, 19], "numer": [1, 3, 11], "numpi": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 28], "nuniqu": 3, "ny": 9, "o": 19, "ob": 15, "object": 28, "objectareach1": [10, 11], "objectavgintench1": 11, "objecttotalintench1": 11, "objectvarintench1": 11, "obs_cor": 13, "observ": [2, 5, 9, 11, 13, 15, 16, 17], "obtain": [0, 1, 17], "obviou": [8, 10], "ocassion": 20, "occur": [13, 15], "off": [1, 2, 3, 17, 19], "offer": [13, 28], "often": [0, 1, 3, 5, 10, 13, 14, 15, 17, 20], "oftentim": 20, "okai": 20, "old": [1, 3, 13, 19], "older": [12, 13, 15], "omnibu": 13, "onc": [1, 3, 13, 19, 20, 32], "one": [0, 1, 2, 3, 4, 5, 6, 9, 13, 15, 16, 17, 19, 20], "ones": [3, 13, 17], "onli": [3, 4, 5, 6, 9, 10, 12, 14, 15, 19, 20], "onlin": [1, 17, 19], "onto": 28, "open": [7, 9, 13, 19, 20], "oper": 1, "opportun": 4, "opposit": 17, "opt": 15, "optimist": 16, "option": [4, 5, 13, 20], "orang": 1, "order": [0, 5, 9, 12, 13, 15, 17, 19, 20], "ordin": [7, 9], "org": [4, 9, 12, 13, 17], "organ": [1, 13, 14], "origin": [2, 7, 20], "other": [0, 1, 3, 6, 7, 9, 11, 13, 14, 15, 19, 20], "otherwis": [3, 28], "otiti": 5, "our": [0, 1, 2, 3, 4, 5, 7, 9, 10, 11, 15, 16, 17, 19, 20], "ourselv": 15, "out": [7, 10, 11, 13, 15, 19], "outbreak": 1, "outcom": [5, 13, 15], "outlier": [7, 14, 17], "output": [5, 7, 13, 19], "outsid": 7, "over": [0, 1, 3, 5, 7, 11, 13, 17], "overal": 0, "overfit": 15, "overhang": 1, "overlap": [0, 7, 9], "overlapped_plot": 9, "overwrit": 20, "own": [5, 9, 13, 19, 20], "p": [13, 14, 15, 16, 17], "pacbio_amplicon_length": 0, "pacbio_degraded_molar": 0, "pacbio_degraded_us": 0, "pacbio_degraded_yield": 0, "pacbio_fresh_molar": 0, "pacbio_fresh_us": 0, "pacbio_fresh_yield": 0, "pacbio_template_weight": 0, "packag": [7, 13, 15, 19], "page": 23, "pai": 4, "pair": [0, 13, 16, 17], "pairwise_test": 13, "pairwise_tukei": 13, "palett": 28, "panda": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 24, 25, 28], "panel": [6, 7, 9], "paper": [0, 16, 17], "par_ax": 9, "paragon": 1, "paragon_amplicon_length": 0, "paragon_degraded_molar": 0, "paragon_degraded_us": 0, "paragon_fresh_molar": 0, "paragon_fresh_us": 0, "paragon_molar": 1, "paragon_template_weight": 0, "paragraph": 4, "paramet": [4, 7, 13, 15, 17], "parametr": [9, 11], "pars": 17, "part": [0, 1, 5, 13, 16, 17], "partial": 14, "partial_corr": [14, 15], "particip": [3, 7, 9], "particular": [17, 19], "particularli": [13, 15, 25, 28], "pass": [0, 2, 4, 6, 8, 9, 10, 11, 12, 14, 15, 16], "past": [1, 5, 17, 20, 35], "pat_3116": 5, "path": 19, "patient": [3, 4, 13], "pattern": [13, 15, 28], "pcr": [0, 1], "pd": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15], "pdz": 15, "pearson": [7, 13, 14], "peer": 13, "peopl": [3, 4, 6, 7, 9, 13, 15, 17, 19], "per": [1, 11, 15, 17, 19], "percent": [9, 11], "percentag": 10, "percentil": 9, "perceptu": 13, "perfect": [1, 15], "perfectli": [15, 19], "perfer": 9, "perform": [1, 3, 11, 12, 13, 14, 16, 17, 28], "persist": 5, "person": [5, 7, 9, 12, 15, 17], "pg": [12, 13, 14, 15, 16, 17], "ph": 11, "phagasom": 11, "phagocytos": 11, "philadelphia": [0, 2, 6, 8, 10, 12], "philosophi": [7, 28], "phrase": 19, "phrodo": 10, "phrodo_conc_ug": [10, 11], "phrodo_dmem": [10, 11], "phylum_col": 5, "phylumn": 4, "pi": 9, "pick": [7, 9, 13, 15, 17], "pid": 5, "piec": 17, "pingouin": [12, 14, 15, 16, 17], "pip": 19, "pivot": [4, 7, 11], "pivot_t": [5, 7, 11], "place": [4, 13, 14, 35], "plai": 20, "plain": [19, 20], "plan": [13, 14, 15, 20], "plate": [1, 11], "plate_map": [10, 11], "platemap": 10, "plethora": 3, "plh": [6, 7], "plot": [5, 6, 8, 11, 13, 14, 15, 16, 17, 28], "plt": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "plu": [15, 19], "plwh": [3, 9], "pm": [0, 2, 6, 8, 10, 12], "png": 7, "point": [0, 1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 28], "polymeras": 0, "pool": 1, "popul": [4, 13, 17], "popular": [7, 13, 28], "pose": 19, "posit": [13, 15, 17, 28], "possibl": [1, 20], "post": [1, 13, 16], "potenti": [4, 20], "power": [3, 7, 12, 13, 15, 16, 19, 20], "power_ttest": [16, 17], "ppv": 4, "practic": [2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 15, 17], "pratic": 6, "pre": 13, "precis": [9, 19], "predetermin": 17, "predict": 15, "predictor": [4, 15], "predomin": [4, 5], "prefer": 7, "preload": 19, "prep": [0, 1], "prepar": [0, 1, 13, 17], "prescrib": 1, "present": [3, 7, 9, 13, 14, 28], "preserv": 5, "press": 17, "presum": 13, "pretend": 13, "prevent": 7, "previou": [9, 16, 19], "previous": [4, 13], "primari": [7, 28], "primer": 0, "principl": [15, 28], "print": [0, 1, 2, 3, 4, 9, 15, 16, 17, 19], "prism": 19, "probabl": [7, 9, 13, 17], "problem": [2, 4, 5, 13, 14, 17, 19, 20, 35], "proc_r": 14, "procedur": 9, "process": [1, 5, 11, 12, 13, 15, 19, 23, 28], "processing_domain_z": [13, 14, 15], "produc": [0, 5, 13, 28], "profici": 3, "program": [13, 19, 20], "programat": 4, "progress": [3, 19], "project": [1, 6, 7, 9, 28], "promin": 13, "prompt": [1, 3, 7, 13, 16, 17], "prone": 1, "proper": [9, 35], "properli": [0, 2, 4, 6, 8, 10, 11, 12, 13, 14, 15, 16], "properti": 7, "proport": [7, 9, 15], "propos": [16, 17], "protect": 20, "protein": 1, "proteobacteria": 5, "protocol": 1, "provid": [0, 3, 4, 6, 7, 9, 13, 15, 19, 28], "psueodorandom": 17, "public": [0, 2, 4, 6, 7, 8, 10, 12, 13, 14, 15], "publish": 28, "pubm": 17, "purpos": [0, 1, 2, 3, 13, 19, 20], "put": [0, 2, 4, 9, 17, 19], "pval": [13, 15, 17], "py": 15, "pydata": [4, 9], "pyplot": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "python": [0, 2, 3, 5, 7, 13, 17, 20, 21, 24, 28], "python3": 15, "q": [4, 15, 19], "q1_add_outcom": 4, "q1_amp_length": 0, "q1_area_cov": 10, "q1_ax": 6, "q1_cells_per_wel": 11, "q1_chang": 16, "q1_cocaine_use_spread": 8, "q1_corr_r": 14, "q1_demographic_breakdown": 13, "q1_domain_corr": 14, "q1_drug_use_plot": 7, "q1_extract_singl": 5, "q1_higher_level": 8, "q1_impaired_bar": 12, "q1_impairement_plot": 6, "q1_init_vl": 3, "q1_initial_correl": 15, "q1_is_corr": 14, "q1_molar": 1, "q1_most_correl": 15, "q1_most_impair": 12, "q1_plot": [8, 10, 12, 14], "q1_power": 17, "q1_race_count": 13, "q1_sex_count": 13, "q1_table_load": 2, "q1_twosample_pow": 17, "q1p": 17, "q2_12sample_effect": 17, "q2_actinobacteria_mean": 5, "q2_an": 6, "q2_ax": [6, 7], "q2_bacteroidetes_mean": 5, "q2_cocaine_use_mean": 8, "q2_count_pivot": 4, "q2_cytokine_summari": 6, "q2_demographic_educ": 13, "q2_effect": 17, "q2_effect_s": 16, "q2_exec_adj": 14, "q2_expect": 13, "q2_firmi_region": 4, "q2_firmicutes_mean": 5, "q2_graph": 11, "q2_higher_mean": 8, "q2_impaired_v_art": 12, "q2_infection_tim": 2, "q2_initial_bcd": 15, "q2_inter_an": 13, "q2_linkag": 12, "q2_merg": 10, "q2_model_resid_norm": 14, "q2_mol_weight": 0, "q2_most_bcd": 15, "q2_neuro_use_plot": 7, "q2_obs_cor": 13, "q2_pivot": 4, "q2_plot": [8, 10, 12], "q2_pop_weeks_to_failur": 3, "q2_pro_inflam": 6, "q2_processing_ag": 14, "q2_processing_art": 14, "q2_processing_edu": 14, "q2_processing_i": 14, "q2_processing_rac": 14, "q2_processing_sex": 14, "q2_proteobacteria_mean": 5, "q2_pval_an": 13, "q2_stat": 13, "q2_summary_v": 5, "q2_therapi": 12, "q2_volum": 1, "q2a": [10, 11], "q2b": [10, 11], "q2e": 17, "q3_an": 4, "q3_bar_ax": 6, "q3_bmi_hypothesis_gen": 6, "q3_cocaine_use_gender_mean": 8, "q3_comparison": 13, "q3_corr_r": 14, "q3_corr_sig": 14, "q3_cross_cor": 6, "q3_dna_yield": 1, "q3_gender_impact": 8, "q3_is_norm": [12, 15], "q3_mean_by_sit": 5, "q3_mean_phylum_sit": 5, "q3_mean_pivot": 4, "q3_molar": 0, "q3_nonparametr": 13, "q3_norm_r": 15, "q3_partial_corr": 14, "q3_partial_corr_r": 14, "q3_pivot": 4, "q3_plot": [8, 12, 14, 15], "q3_post_hoc": 13, "q3_resid_norm": 15, "q3_same_r": 14, "q3_scatter_ax": 6, "q3_sig_diff": 12, "q3_stat": 13, "q3_toler": 16, "q3_top5": 6, "q3_treated_indiv": 2, "q3_treated_weeks_to_failure_index": 3, "q3_visuo_v_art": 12, "q4_art_impact": 15, "q4_art_test": 15, "q4_covari": 12, "q4_dna_yield": 0, "q4_effect_s": 16, "q4_fraction_swabb": 4, "q4_full_corr": 14, "q4_function_yield": 1, "q4_infection_length": 8, "q4_infection_length_corr": 8, "q4_is_sig": 12, "q4_min_chang": 16, "q4_min_effect": 16, "q4_plot": [8, 12, 14, 15], "q4_re": 15, "q4_server": 5, "q4_severe_mean": 5, "q4_sig_cor": 14, "q4_swababl": 4, "q4_treated_weeks_to_failur": 3, "q4_untreated_weeks_to_failur": 3, "q4_vl_select": 2, "q5_high_noise_effect_s": 16, "q5_high_valu": 4, "q5_infection_length_cocain": 8, "q5_infection_length_cocaine_slop": 8, "q5_multiple_choic": 16, "q5_new_assay_effect_s": 16, "q5_plot": 8, "q5_usable_sampl": 0, "q5_vl_comparison": 2, "q6_best_ppv": 4, "q6_highest_region": 4, "q6_length_comparison": 2, "q6_swabbable_ppv": 4, "q6a": 16, "q6b": 16, "q6c": 16, "qith": 3, "qq": 13, "qqplot": [13, 14, 15], "qualiti": 7, "quantif": 0, "quantifi": [5, 11, 17], "quantil": [13, 15], "quantit": [9, 17], "quartil": 7, "qubit": 1, "queri": [2, 4, 5, 13, 15], "question": [3, 4, 6, 8, 10, 11, 14, 15, 19], "quick": [9, 13, 17, 22], "quickli": [13, 14, 15, 28], "r": [7, 11, 14, 15, 20], "r2": 15, "race": [12, 14, 15], "race_ax": 15, "racial": [7, 15], "rais": 9, "rake": 7, "ran": 0, "random": 15, "randomli": [3, 9, 13], "rang": [3, 7, 9, 11, 13, 15, 16, 17, 19, 28], "rank": [4, 7, 15], "rapid": 1, "rat": 16, "rate": [8, 17], "rather": 28, "ratio": [4, 17, 28], "raw": [13, 17, 28], "rcp85jhlmni": 23, "rdbu": 7, "re": [1, 5, 7, 9, 11, 15, 17, 19, 20], "react": [13, 14], "reaction": 0, "read": [1, 3, 7, 9, 13], "read_csv": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "readi": [0, 3, 20], "reagent": 1, "real": [3, 5, 15], "realiti": 15, "realli": 1, "reason": [1, 8, 9, 10, 11, 15, 17], "rebound": 3, "recalcul": 9, "receiv": 3, "recent": [1, 15, 19, 20], "receptor": 17, "recess": 5, "recommend": [1, 9], "reduc": [9, 16], "redund": 15, "refer": [1, 12, 15, 16, 35], "refin": 13, "reflect": 2, "refram": 4, "refresh": 22, "regardless": 15, "regimen": [12, 13, 15], "regplot": [9, 15], "regress": [10, 11, 12, 13, 17, 28], "regularli": 3, "reject": [13, 17], "rel": [4, 13, 15, 17, 28], "relaps": 4, "relat": [1, 13, 15, 28], "relationship": [4, 7, 9, 13, 14, 15, 28], "relative_abund": 4, "releas": 28, "relev": [2, 16, 17, 20], "reli": 17, "reliabl": 0, "relimp": 15, "relimp_perc": 15, "remain": 15, "rememb": [0, 1, 2, 3, 6, 8, 10, 12, 13, 15, 16, 19, 20], "remov": [0, 1, 2, 6, 7, 14, 15], "render": [0, 1, 2, 4, 6, 8, 10, 11, 12, 14, 16, 20], "rep1": 11, "rep2": 11, "rep3": 11, "repeat": [1, 13, 15], "repetit": 1, "replac": [1, 7, 9], "replic": [7, 9, 11, 13, 16, 17], "repres": [1, 5, 7, 9, 11, 13, 28], "represent": [15, 28], "reproduc": 1, "requir": [0, 1, 3, 4, 5, 13, 14, 16, 19, 28], "res_with_imp": 15, "resampl": 9, "research": [3, 5, 13, 17, 19, 28], "reshap": 5, "residu": [13, 14], "residuals_": [14, 15], "resolv": 4, "resourc": [6, 7, 9], "respect": [9, 17], "respond": 20, "respons": [11, 17], "rest": [4, 19], "restart": [0, 2, 4, 6, 8, 10, 11, 12, 14, 16, 19], "resting_heart_r": 19, "result": [0, 1, 2, 3, 4, 12, 13, 14, 15, 17, 19, 25], "retriev": 13, "return": [1, 5, 9, 11, 13, 19], "reusabl": 1, "reveal": 16, "revers": 0, "review": [1, 16, 17, 22], "revolv": 7, "right": [1, 13, 15, 16, 19], "right_index": 11, "right_on": 5, "rigor": [9, 13, 16, 17, 19, 33], "rm_corr": 15, "rna": [0, 1], "rna_paragon_molar": 1, "robust": [7, 14], "room": 0, "rotat": 7, "roughli": 15, "round": [1, 14, 15, 16], "row": [4, 5, 7, 9, 11, 13], "row_cutoff": 4, "rt": 0, "rule": [15, 28], "run": [0, 2, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 19], "runtim": 20, "sai": [3, 15, 17], "said": 19, "same": [1, 3, 4, 5, 7, 8, 9, 13, 14, 15, 16, 17, 19], "sampl": [3, 5, 7, 9, 10, 13, 15, 16, 17], "sample_concentr": 1, "sample_info": [4, 5], "sample_length": 1, "sample_level_data": [10, 11], "sample_s": [9, 16, 17], "sample_volum": 1, "sample_yield": 1, "savant": 13, "save": [0, 2, 4, 6, 7, 8, 10, 11, 12, 14, 16, 19], "savefig": 7, "saw": [3, 4, 14], "scale": [5, 12, 13, 15, 17, 28], "scan": 11, "scatter": 7, "scatter_matrix": 7, "scatterplot": [6, 7, 9, 15], "scenario": [16, 17], "sciecn": 3, "scienc": [3, 19, 28], "scientif": 7, "scientist": [13, 15, 28], "scipi": 13, "score": [13, 17], "screen": 19, "script": 17, "sd": 9, "se": [9, 11, 13, 15], "seaborn": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 27], "seamlessli": [13, 28], "search": [3, 4, 17], "searchabl": 35, "second": [15, 17, 19, 20], "secreti": 20, "section": 20, "secur": 20, "see": [0, 1, 2, 3, 5, 7, 11, 13, 15, 16, 17, 19], "seem": [2, 17], "seen": [13, 14, 17], "select": [3, 16, 17], "self": 1, "sem": 11, "semant": 28, "send": 20, "senior": 1, "sens": 3, "sensit": 20, "sensori": 13, "sent": 20, "sentenc": [1, 5], "sep": 5, "separ": 2, "seper": 9, "sequenc": [0, 1], "seri": [1, 3, 5, 6, 7, 15, 19, 20], "seroposit": [14, 15], "serv": 15, "servic": 20, "session": [1, 3, 4, 19], "set": [0, 7, 9, 13, 14, 15, 16, 19, 28], "set_styl": [16, 17], "set_titl": 9, "set_xlabel": [7, 16], "set_xlim": [7, 17], "set_xtick": 17, "set_xticklabel": 17, "set_ylabel": [11, 15, 16, 17], "setup": [17, 19], "sever": 2, "sex": [7, 9, 12, 14, 15], "sex_ax": 15, "shadow": 9, "shape": [4, 5, 9, 13, 28], "shapiro": 13, "share": 20, "sharei": [9, 15], "sharex": 9, "shift": [19, 20], "short": [0, 1], "short_mean": 2, "short_min": 2, "shortcut": 20, "shorter": [0, 2], "shortli": 4, "should": [1, 3, 4, 7, 8, 9, 10, 13, 15, 17, 19, 20, 28], "shouldn": 15, "show": [6, 7, 9, 10, 11, 12, 13, 15, 16, 17], "shown": [5, 17], "shred": 0, "side": [13, 16, 17], "signal": 17, "signifacntli": 15, "signifi": 15, "signific": [2, 5, 7, 10, 12, 13, 14, 15, 17], "significantli": [7, 12, 14, 15, 16, 17], "similar": [3, 12, 13, 15, 16, 17, 20], "simpl": [3, 5, 7, 9, 13, 15, 17, 19, 28], "simplest": [13, 15], "simplic": [7, 15, 28], "simplifi": 28, "simul": [9, 17], "simultan": [13, 14], "sinc": [13, 15, 16, 17, 19, 22, 28], "singl": [1, 3, 9, 10, 11, 12, 15, 28], "sinu": [4, 5], "sit": 3, "site": 15, "situat": [9, 15], "size": [1, 7, 9, 10, 13, 15, 28], "sk": 17, "sk609": 17, "sk609a": 17, "skeleton": 19, "ski": 15, "skill": [13, 19], "skin": 5, "sky": 15, "slope": 15, "small": [2, 3, 7, 9, 10, 17, 19, 28], "smaller": [1, 2, 13, 15, 17], "sn": [5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "snippet": 17, "so": [0, 1, 3, 7, 8, 9, 10, 12, 13, 15, 16, 17, 20], "softwar": [19, 20], "solut": [1, 3, 4, 5, 7, 9, 11, 13, 15, 17, 19], "solv": [13, 14, 19], "solver": 17, "some": [0, 1, 3, 4, 5, 7, 9, 13, 15, 17, 19, 20, 22, 23], "somehow": 7, "someon": [3, 17], "someth": [1, 15, 17, 19], "sometim": [3, 7, 9, 20], "somewher": 19, "sophist": [13, 28], "sort": [4, 9, 15], "sort_valu": 15, "sortabl": 9, "sourc": [7, 13, 15], "space": [1, 3, 4, 15, 28], "spawn": 19, "spearman": 7, "speci": 5, "special": [15, 20], "specif": [0, 2, 3, 13, 16, 17, 28], "specifi": [7, 9, 15, 16, 17], "speed": [12, 13, 14], "speedup": 1, "spend": 17, "sphenoethmoid": 5, "sphenoid": 5, "spin": 19, "split": [3, 5, 9, 13, 28], "spot": 11, "spotavgareach2": 11, "spotavgintench2": 11, "spotcountch2": 11, "spottotalareach2": [10, 11], "spottotalintench2": 11, "spread": [8, 13, 15], "spread_ax": 9, "spreadsheet": [2, 3, 11, 19, 24], "sqrt": [9, 13], "squar": 3, "ss": [13, 15], "stack": [3, 7, 28], "stage": 15, "stai": [9, 15, 19], "standard": [2, 4, 5, 9, 12, 13, 17, 28], "stari": 15, "start": [1, 2, 3, 4, 11, 13, 15, 17, 19, 20, 28], "stat": [9, 11, 12, 13, 17, 28], "state": [0, 4, 7, 15, 28], "statement": [0, 1, 3], "statist": [3, 7, 8, 9, 10, 12, 13, 15, 17, 19, 28], "statment": [0, 2], "statsmodel": 13, "statu": [2, 5, 28], "stavudin": [12, 13, 15], "std": [3, 5, 11, 13], "std_p": 13, "step": [2, 3, 4, 15, 19], "stick": 15, "still": 19, "stimulu": 17, "stock": 1, "stop": 3, "store": [15, 28], "stori": 13, "str": [11, 13], "stragei": 3, "straightforward": 28, "strand": 1, "strategi": [1, 3, 5, 7, 10, 11, 13, 14, 16, 17, 31], "stratif": 13, "strength": 13, "strict": 15, "string": [0, 4, 6, 9], "stripplot": [11, 15], "strong": 13, "structur": [19, 28], "stuck": 0, "studi": [2, 3, 5, 6, 9, 13, 16, 19], "stuf": 10, "stumbl": 19, "style": [3, 6, 7, 9, 15, 17, 24, 28], "sub": 16, "sublist": 19, "submiss": 20, "submit": [5, 19], "subplot": [7, 9, 15, 16, 17], "subset": [4, 9, 28], "substanti": 28, "subtract": [2, 15, 19], "success": [2, 7, 18], "successfulli": 0, "suffici": [0, 16], "suffix": 3, "suggest": [2, 19], "sugget": 13, "suit": 28, "suitabl": [0, 7, 13], "sum": [2, 3, 5, 7, 9, 13, 15], "summar": [0, 1, 3, 7, 10, 11, 19, 24, 25, 28], "summari": [2, 3, 5, 7, 9, 28], "sundai": [0, 2, 6, 8, 10, 12], "superior": 5, "suppli": 15, "support": [7, 28], "sure": [0, 15], "suspect": [12, 13, 15], "sustain": [16, 17], "swab": [4, 5], "swabbable_data": 4, "switch": [13, 14, 17], "symptom": 4, "synchron": [1, 3, 19], "syntax": [1, 13, 19], "system": [0, 4, 5, 19, 20, 28], "systemat": 28, "t": [1, 3, 4, 5, 9, 10, 13, 15, 16, 17, 19], "tab": 9, "tabl": [0, 1, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 16, 19, 20], "tabul": 13, "tabular": 7, "tack": 15, "tag": 20, "tailor": 13, "take": [1, 11, 15, 19, 20], "taken": 11, "talk": [15, 19, 20], "task": [1, 7, 13, 14, 17, 19, 28], "tast": 5, "tau": 7, "taught": 19, "teach": [17, 19], "techniqu": [0, 3, 5, 6, 10, 11, 19], "technologi": [11, 19], "tediou": 1, "tell": [0, 1, 4, 13, 15, 17, 19], "temperatur": 0, "template_weight": 1, "tempt": 15, "tend": [3, 13, 22], "tendenc": 9, "tenofovir": [12, 13, 15], "term": [0, 2, 13, 15, 17, 28], "test": [0, 1, 2, 4, 5, 6, 8, 10, 12, 14, 15, 16, 19, 20], "testabl": 17, "tests_dir": 19, "testss": [4, 6, 12], "text": [1, 4, 6, 7, 11, 17, 19, 20], "textbook": [1, 19, 34], "than": [0, 2, 4, 13, 15, 16, 17, 20, 28], "thei": [0, 1, 3, 5, 8, 10, 11, 13, 16, 17], "them": [2, 3, 5, 7, 9, 15, 17, 19, 20, 28], "themselv": 20, "theorem": 15, "theoret": [13, 15], "theori": 13, "therapi": [4, 13], "therebi": 19, "therefor": [0, 2, 12, 13, 15, 17], "thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36], "thier": 13, "thing": [1, 2, 5, 7, 9, 12, 13, 15, 17, 19, 20], "think": [1, 9, 13, 14, 15, 19, 28], "those": [1, 2, 3, 4, 5, 15, 19], "three": [4, 5, 13, 15, 17], "threshold": [12, 13, 14], "through": [0, 2, 4, 5, 7, 13, 16, 17, 19, 28], "throughout": [4, 19], "thu": [15, 17], "ti": 7, "tick_param": 7, "tight_layout": [7, 9, 15], "tightli": 7, "time": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 28], "tissu": 5, "titl": 7, "tljh": 15, "tnfalpha": [6, 7, 9], "todai": 3, "too": [16, 17, 19, 20], "took": [3, 5, 11, 15], "tool": [3, 7, 9, 12, 13, 14, 15, 17, 18, 19, 28], "top": [6, 9, 11, 13, 19], "topic": 19, "tost": 17, "total": [0, 1, 2, 9, 16, 17], "totalintench2": 11, "toward": 13, "track": [0, 1], "trade": 17, "tradit": 17, "tradition": 13, "trail_data": 2, "train": 17, "tranform": 12, "transcrib": 0, "transform": [3, 5, 9, 28], "transgend": 7, "transpar": 1, "transport": 17, "trap": 15, "treat": [9, 11, 17, 28], "treated_average_week": 3, "treated_df": 2, "treated_mask": 3, "treatment": [2, 3, 11, 17], "tree": 13, "trend": 28, "trial": [2, 3, 16, 17], "trial_data": 3, "trial_df": [2, 3], "tricki": 15, "triplic": 11, "troubl": 19, "true": [2, 3, 4, 5, 7, 9, 11, 13, 15, 16, 17, 19], "truli": [4, 13, 17], "truvada": [12, 13, 15], "try": [0, 1, 9, 17], "ttest": [12, 13, 17], "tube": 1, "tukei": 13, "turbin": 5, "tutori": [7, 9], "tweak": 28, "twice": [4, 19], "two": [0, 3, 5, 9, 11, 12, 14, 15, 16, 19, 20, 25], "type": [1, 2, 3, 4, 5, 9, 13, 17, 19, 20, 28], "typic": [0, 5], "typical_region_cutoff": 4, "typical_region_mean": 4, "typical_region_std": 4, "typical_swab_data": 4, "u": [0, 1, 2, 3, 4, 7, 10, 13, 15, 17, 19], "uc": 19, "ul": [0, 1, 2, 3], "unc": [13, 15], "uncer_ax": 9, "uncertain": 9, "uncertainti": 11, "uncheck": 7, "uncin": 5, "uncontrol": [2, 3], "uncorrel": 13, "under": [13, 14, 15, 16, 17, 34], "underli": 9, "underneath": 19, "understand": [0, 1, 2, 3, 5, 6, 9, 13, 14, 15, 20, 28], "undo": 20, "unfiar": 13, "uniqu": [1, 4], "unit": [0, 1, 5, 13, 17, 22], "unit_norm": 5, "unit_normed_data": 5, "univers": 34, "unknow": [6, 7, 9], "unless": 1, "unlik": [8, 17], "unrel": 13, "unstabl": 15, "unsustain": 5, "until": [2, 3], "untreat": 2, "untreated_average_week": 3, "unwieldi": 19, "unzip": 19, "up": [0, 1, 5, 7, 11, 13, 16, 19], "upload": [0, 2, 4, 6, 7, 8, 10, 11, 12, 14, 16, 19, 20], "upon": 35, "upper": 17, "upper_target_zon": 19, "uptak": 11, "us": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, 15, 16, 20, 21, 22, 24, 25, 26, 27, 28, 32, 34], "usaual": 13, "usb": 1, "use_axi": 7, "use_count": 7, "use_desc": 9, "user": [7, 13, 15, 28], "userwarn": 15, "usual": [3, 5, 9, 15, 19], "util": [3, 4, 5, 9, 12, 13], "v": [4, 15, 16, 17, 23], "v1": 11, "v2": 11, "v3": 11, "val": [1, 13, 17], "valid": [3, 19], "valu": [1, 2, 3, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 28], "valuabl": 28, "value_column": 11, "value_count": [7, 13], "value_nam": [5, 9], "value_var": [5, 9], "valueerror": 9, "var": 3, "var_nam": [5, 9], "varaibl": [6, 12], "varainc": 13, "vari": 9, "variabl": [0, 1, 6, 9, 12, 13, 14, 16, 19, 28, 32], "varianc": [13, 15], "variat": [14, 15], "varieti": 28, "variou": [12, 28], "vast": 28, "ve": [1, 4, 5, 9, 11, 13, 14, 17, 19, 22], "vegf": 9, "veh": 11, "vehicl": 17, "verbal": 13, "verbos": 13, "veri": [9, 19], "verifi": 17, "versatil": 28, "version": [3, 5, 20], "vestibul": 5, "vi": [16, 17], "via": 28, "video": [1, 13, 15, 23], "vield": 0, "view": 7, "viewpoint": 28, "vigal": 16, "vigil": 17, "vigor": 19, "violat": 15, "viral": [0, 3], "virtual": 20, "vision": 17, "visual": [5, 7, 11, 12, 13, 17, 19, 26, 28], "visuospatial_domain_z": [12, 13, 15], "vmax": 7, "vmin": 7, "vo": 13, "volum": [0, 1], "volume_to_add": 1, "w": [13, 15], "wa": [2, 3, 4, 6, 7, 9, 13, 15, 16, 17, 19, 28], "wai": [1, 4, 6, 7, 9, 10, 13, 15, 17, 19, 28], "walk": 13, "walkthrough": [14, 16], "wallac": 13, "want": [3, 5, 7, 9, 13, 15, 16, 17, 20], "wanted_dna": 1, "wanted_sampl": 3, "warn": [15, 19], "waskom": 28, "watch": [1, 23], "we": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 25], "wealth": 13, "web": 7, "websit": 7, "week": [0, 1, 2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17, 19], "weekli": [1, 3, 19], "weigh": [0, 1], "weight": [17, 19], "weird": 17, "well": [2, 7, 10, 13, 15, 17, 28], "well_level_data": 11, "went": 3, "were": [1, 2, 3, 12, 13, 15, 17], "what": [4, 5, 7, 9, 10, 13, 15, 17, 19], "when": [0, 1, 3, 5, 7, 9, 11, 13, 15, 16, 17, 19, 20, 22], "where": [3, 9, 11, 13, 15, 17, 25], "wherea": 9, "whether": [0, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 17], "which": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 19, 20, 28], "while": [0, 3, 4, 7, 9, 11, 13, 15, 20, 22, 28], "whisker": 7, "whitegrid": [16, 17], "whitnei": 13, "who": [3, 4, 13, 15], "whole": 15, "why": 13, "wide": [5, 7, 9, 13, 28], "widespread": 7, "width": 9, "wilk": 13, "wilkinson": 28, "within": [3, 7, 9, 13, 17, 20, 28, 35], "without": [0, 1, 2, 4, 6, 8, 10, 11, 12, 13, 14, 16, 20], "woman": 19, "wonder": [9, 13], "word": [0, 13, 15, 19, 28], "wordpad": 20, "work": [0, 1, 2, 3, 7, 9, 13, 15, 17, 19, 20, 28], "workflow": 28, "world": [0, 13, 17, 19], "worri": 17, "wors": [13, 15], "worst": 17, "worth": [6, 15, 17], "would": [1, 4, 5, 9, 13, 15, 16, 17, 19, 28, 34], "write": [0, 2, 4, 8, 10, 13, 19], "written": [3, 15, 20], "www": [19, 23], "x": [6, 7, 9, 11, 13, 14, 15, 17, 19, 20], "xcentroid": 11, "xlabel": [7, 9, 11, 13, 15, 17], "y": [6, 7, 9, 11, 13, 14, 15, 19], "ycentroid": 11, "ye": [0, 8, 10, 11, 12, 13, 14, 15, 16], "year": [1, 2, 3, 7, 13, 14, 15, 17, 19], "years_infect": [3, 7, 9], "yearsseroposit": [13, 15], "yearsseropositivedata": 12, "ylabel": [7, 9, 11, 13, 15, 17], "you": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 34, 35], "young": [9, 19], "your": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 15, 19, 20, 33, 34], "yourself": [3, 15, 19, 20], "youtub": 23, "yr": 3, "ys_ax": 15, "ys_bin": 12, "yy": 9, "z": [12, 13, 17, 19], "zero": 15, "zip": [16, 17, 19], "zip_fil": 19}, "titles": ["Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Lab", "Walkthrough", "Module 1: Hello World", "Walkthrough", "Notebook basics", "Module 2: Simple calculations", "Dilution calculations", "Nanopore Sequencing", "Module 3: DataFrames", "Module 4: Analysis by groups", "Module 5: Plotting with Pandas", "Module 6: Visualizing with Confidence", "Grammar of Graphics", "Module 7: Samples and Replicates", "Common Biological Distributions", "Module 8: Hypothesis Testing", "Module 9: Linear Regression", "Module 10: Power Analysis", "Quantitative Reasoning in Biology", "About this book", "Introduction"], "titleterms": {"": 19, "1": [16, 18], "10": 33, "12": 17, "16": 16, "2": [16, 17, 21], "3": [16, 17, 24], "3116": 5, "4": [16, 17, 25], "5": [16, 17, 26], "6": [16, 17, 27], "7": 29, "8": 31, "9": 32, "A": 17, "By": 15, "The": [1, 17], "With": 15, "about": 35, "abov": [0, 19], "account": 14, "across": [4, 5, 7, 9], "act": 3, "actinobacteria": 4, "add": 1, "aerob": 19, "afraid": 20, "after": 14, "all": 20, "amount": 1, "an": [10, 15], "analysi": [17, 25, 33], "ancova": 15, "anim": [16, 17], "anova": 15, "appropri": 13, "ar": [0, 4, 6, 11, 12, 14, 15, 17], "arithmet": 1, "art": [12, 15], "averag": [3, 5, 8, 16], "basic": [7, 15, 20], "between": [8, 15, 16], "biolog": 30, "biologi": 34, "biome_data": 4, "block": 19, "bodi": [4, 5], "book": 35, "boolean": 3, "box": 7, "budget": [16, 17], "calcul": [0, 1, 2, 3, 5, 16, 17, 19, 21, 22], "cannabinoid_us": 7, "categor": [9, 13], "categori": [9, 15], "catplot": 9, "cell": [10, 11, 19, 20], "chang": 16, "class": 15, "cocain": 8, "cocaine_us": 7, "code": 19, "colab": 19, "color": 1, "column": [2, 3, 7, 9, 10], "common": 30, "compar": [2, 4, 9], "comparison": [7, 9, 13], "conclus": [0, 1, 3], "condit": 16, "confid": 27, "consid": 6, "contain": 2, "context": 4, "contini": 13, "control": [14, 16], "correct": 15, "correl": [8, 9, 13, 14, 15], "count": [5, 9, 13], "countplot": 9, "covari": [12, 14], "creat": [2, 10, 14], "csv": 2, "data": [2, 5, 7], "datafram": [2, 5, 24], "dataset": [2, 3, 6, 13], "decod": 11, "defin": [16, 17], "demograph": 14, "describ": [1, 11], "descript": 2, "detect": 16, "determin": 4, "differ": [7, 9, 15, 16], "dilut": 22, "diseas": 4, "distribut": [9, 15, 30], "do": 8, "document": 9, "doe": 8, "domain": [14, 15], "don": 20, "each": [0, 2, 5, 10, 11, 13, 17], "educ": 13, "edz": 14, "effect": [8, 16, 17], "estim": 9, "evalu": [0, 12], "even": 15, "execut": [14, 15], "expect": 19, "explor": [5, 6, 7, 8, 15], "express": [7, 8], "extract": [0, 3, 5], "f": 1, "failur": 3, "figur": 9, "file": 2, "first": 15, "fit": 15, "fraction_area_cov": 10, "from": [0, 2, 12], "full": 10, "function": [1, 6], "gener": 6, "googl": 19, "gotcha": 7, "grader": 19, "grammar": 28, "graph": 11, "graphic": 28, "group": [5, 13, 17, 25], "ha": [4, 15], "handl": 7, "have": 8, "heart": 19, "hello": 18, "high": [2, 4], "higher": 8, "highest": 4, "histogram": 7, "how": [6, 10, 11, 12], "hypothesi": [6, 13, 16, 31], "i": [0, 1, 7, 8, 9, 10, 12, 13, 14, 15, 16], "impact": 8, "impair": [6, 7, 12], "import": 3, "includ": 2, "increas": 4, "index": 3, "individu": 2, "infalpha": 7, "infect": [2, 8], "inflamatori": 6, "inform": [0, 4, 5, 19], "initi": 2, "initial_viral_load": 3, "inspect": 15, "interfac": [9, 28], "introduct": [0, 2, 3, 4, 5, 6, 12, 13, 19, 36], "jupyt": 20, "lab": [0, 2, 4, 6, 8, 10, 12, 14, 16], "largest": 4, "learn": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "length": [2, 8], "level": [8, 9], "limit": 19, "linear": [9, 32], "link": 12, "lint": 1, "lmplot": 9, "load": 2, "long": 2, "low": 2, "m": 9, "make": 2, "mani": [11, 12], "map": 10, "markdown": 19, "marker": 6, "matplotlib": 7, "mcp1": 8, "me": 19, "measur": [9, 11, 13], "melt": [5, 9], "merg": [4, 5, 10], "method": 13, "minimum": 16, "miss": 16, "model": 9, "modul": [18, 21, 24, 25, 26, 27, 29, 31, 32, 33], "molar": [0, 1], "molecular": 0, "more": 15, "most": 15, "multi": 13, "multipl": [9, 15], "nanopor": 23, "neurolog": [6, 7], "new": [2, 16], "non": [8, 13], "normal": 15, "notebook": 20, "number": 13, "numer": 7, "numpi": 3, "object": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "onli": [2, 17], "other": 17, "otter": 19, "outcom": 4, "over": 15, "pacbio": 0, "panda": [3, 26], "paragon": 0, "parametr": 13, "particip": [2, 6, 12, 13], "patient": 5, "pd": 9, "pdz": 14, "perform": 15, "persist": 4, "phagocytosi": 11, "phylum": [4, 5], "pingouin": 13, "pivot": 5, "plate": 10, "plot": [7, 9, 26], "popul": 3, "posit": 4, "potenti": 12, "power": [17, 33], "predict": 4, "predominin": 4, "pro": 6, "problem": 1, "process": 14, "programmat": 1, "protocol": 0, "python": [1, 19], "q1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 19], "q2": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 19], "q3": [0, 1, 2, 3, 4, 5, 6, 8, 12, 13, 14, 15], "q4": [0, 1, 2, 3, 4, 5, 6, 8, 12, 14, 15, 16], "q5": [0, 2, 4, 8, 16], "q6": [2, 4, 16], "q7": 4, "quantifi": 9, "quantit": 34, "queri": 3, "question": [2, 16], "quick": 19, "race": 13, "rate": 19, "reaction": 1, "reason": 34, "refer": [2, 3], "region": [4, 5], "regress": [9, 14, 15, 32], "relat": [6, 9], "relev": 0, "relplot": 9, "replic": 29, "reserv": 19, "residu": 15, "restart": 20, "risk": [16, 17], "rodent": 16, "row": [2, 3], "run": 20, "same": 2, "sampl": [0, 1, 2, 4, 11, 29], "score": [12, 14, 15], "seaborn": 28, "sequenc": 23, "session": 20, "sever": 4, "severe_diseas": 5, "sex": [8, 13], "short": 2, "simpl": 21, "singl": 5, "site": [4, 5], "size": [16, 17], "sk609": 16, "smallest": 17, "spread": 9, "standard": 15, "statist": 2, "statu": 7, "step": [16, 17], "still": 14, "string": 1, "stripplot": 9, "subject": 19, "submiss": [0, 2, 4, 5, 6, 7, 8, 10, 11, 12, 14, 16, 19], "success": [16, 17], "suffer": 12, "sumar": 11, "summar": [5, 16, 17], "summari": 16, "swabbabl": 4, "t": 20, "tabl": [2, 4], "target": 19, "templat": [0, 1], "test": [13, 17, 31], "text": 0, "therapi": 12, "thi": [13, 35], "through": 1, "tissu": 4, "toler": [16, 17], "treat": [2, 3], "try": 19, "two": [2, 13, 17], "typic": 4, "uncertainti": 9, "untreat": 3, "upper": 19, "us": [8, 13, 17, 19], "usabl": 0, "user": 8, "valu": 4, "variabl": [7, 15], "vegf": 7, "vehicl": 16, "viral": 2, "visual": [9, 10, 27], "visuospati": 12, "walkthrough": [1, 3, 5, 7, 9, 11, 13, 15, 17, 19], "week": 3, "weeks_to_failur": [2, 3], "weight": [0, 1], "well": 11, "well_level_data": 10, "what": [0, 1, 16], "when": 4, "which": [0, 1, 4, 15], "whole": 3, "why": 19, "world": 18, "write": 1, "yield": [0, 1], "your": [16, 17], "z": 15, "zone": 19}}) \ No newline at end of file
  • Module 8: Hypothesis Testing +