{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Case 9: Survival Analysis of Endometrial Cancer--PODXL, RAC2, and Tumor Stage\n",
    "\n",
    "Through modern statistical methods, we can determine survival risk based on a variety of factors.  In this tutorial, we will walk through a small example of something you could do with our data to understand what factors relate with survival in various different types of cancer.  In this use case, we will be looking at Endometrial Cancer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Import Data and Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import cptac\n",
    "import cptac.utils as ut\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "import lifelines\n",
    "from lifelines import KaplanMeierFitter\n",
    "from lifelines import CoxPHFitter\n",
    "from lifelines.statistics import proportional_hazard_test\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cptac warning: Your version of cptac (1.5.1) is out-of-date. Latest is 1.5.0. Please run 'pip install --upgrade cptac' to update it. (C:\\Users\\sabme\\anaconda3\\lib\\threading.py, line 910)\n"
     ]
    }
   ],
   "source": [
    "en = cptac.Ucec()\n",
    "clinical = en.get_clinical('mssm')\n",
    "proteomics = en.get_proteomics('umich')\n",
    "follow_up = en.get_followup('mssm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Gather Data from CPTAC API\n",
    "The Endometrial cancer dataset contains months of follow-up data, including whether a patient is still alive (Survival Status) at each follow-up period.  We will first merge the clinical and follow-up tables together for analysis. Then we will choose a few attributes to focus on, and narrow our dataframe to those attributes. While you could study a wide variety of factors related to survival, we will be focusing on tumor stage, grade and a proteins of interest listed below in *omics_genes*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will join the *clinical* and *proteomics* dataframes to contain protein data for proteins of interest, and clinical data for each patient in the same dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = list(clinical.columns)\n",
    "omics_genes = ['RAC2', 'PODXL']\n",
    "\n",
    "clinical_and_protein = en.join_metadata_to_omics(metadata_name=\"clinical\",\n",
    "                                                 metadata_source=\"mssm\",\n",
    "                                                 metadata_cols=cols,\n",
    "                                                 omics_name=\"proteomics\",\n",
    "                                                 omics_source=\"umich\",\n",
    "                                                 omics_genes=omics_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will rename the foreign key (\"PPID\" -> \"Patient_ID\") on the follow_up table to allow us to easily join that data with the dataframe of clinical and protein data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>tumor_code</th>\n",
       "      <th>discovery_study</th>\n",
       "      <th>type_of_analyzed_samples_mssm_clinical</th>\n",
       "      <th>type_of_analyzed_samples_mssm_clinical</th>\n",
       "      <th>confirmatory_study</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>race</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>ethnicity_race_ancestry_identified</th>\n",
       "      <th>...</th>\n",
       "      <th>number_of_days_from_date_of_initial_pathologic_diagnosis_to_date_of_additional_surgery_for_new_tumor_event_loco-regional</th>\n",
       "      <th>number_of_days_from_date_of_initial_pathologic_diagnosis_to_date_of_additional_surgery_for_new_tumor_event_metastasis</th>\n",
       "      <th>Recurrence-free survival, days</th>\n",
       "      <th>Recurrence-free survival from collection, days</th>\n",
       "      <th>Recurrence status (1, yes; 0, no)</th>\n",
       "      <th>Overall survival, days</th>\n",
       "      <th>Overall survival from collection, days</th>\n",
       "      <th>Survival status (1, dead; 0, alive)</th>\n",
       "      <th>RAC2_umich_proteomics</th>\n",
       "      <th>PODXL_umich_proteomics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C3L-00006</th>\n",
       "      <td>UCEC</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Tumor_and_Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.182830</td>\n",
       "      <td>0.731055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00008</th>\n",
       "      <td>UCEC</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.793159</td>\n",
       "      <td>0.451984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00032</th>\n",
       "      <td>UCEC</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.583774</td>\n",
       "      <td>1.344697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00084</th>\n",
       "      <td>UCEC</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.193889</td>\n",
       "      <td>-1.994844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00090</th>\n",
       "      <td>UCEC</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1281.0</td>\n",
       "      <td>1287.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.361299</td>\n",
       "      <td>0.154995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NX5.N</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.864272</td>\n",
       "      <td>-0.980967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NX6.N</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.841041</td>\n",
       "      <td>-0.260866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NX7.N</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.430521</td>\n",
       "      <td>-0.498802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NX8.N</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000459</td>\n",
       "      <td>-0.140857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NX9.N</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.448693</td>\n",
       "      <td>-0.370379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Name       tumor_code discovery_study type_of_analyzed_samples_mssm_clinical  \\\n",
       "Patient_ID                                                                     \n",
       "C3L-00006        UCEC             Yes                       Tumor_and_Normal   \n",
       "C3L-00008        UCEC             Yes                                  Tumor   \n",
       "C3L-00032        UCEC             Yes                                  Tumor   \n",
       "C3L-00084        UCEC             Yes                                  Tumor   \n",
       "C3L-00090        UCEC             Yes                                  Tumor   \n",
       "...               ...             ...                                    ...   \n",
       "NX5.N             NaN             NaN                                    NaN   \n",
       "NX6.N             NaN             NaN                                    NaN   \n",
       "NX7.N             NaN             NaN                                    NaN   \n",
       "NX8.N             NaN             NaN                                    NaN   \n",
       "NX9.N             NaN             NaN                                    NaN   \n",
       "\n",
       "Name       type_of_analyzed_samples_mssm_clinical confirmatory_study  age  \\\n",
       "Patient_ID                                                                  \n",
       "C3L-00006                                     NaN                NaN   64   \n",
       "C3L-00008                                     NaN                NaN   58   \n",
       "C3L-00032                                     NaN                NaN   50   \n",
       "C3L-00084                                     NaN                NaN   74   \n",
       "C3L-00090                                     NaN                NaN   75   \n",
       "...                                           ...                ...  ...   \n",
       "NX5.N                                         NaN                NaN  NaN   \n",
       "NX6.N                                         NaN                NaN  NaN   \n",
       "NX7.N                                         NaN                NaN  NaN   \n",
       "NX8.N                                         NaN                NaN  NaN   \n",
       "NX9.N                                         NaN                NaN  NaN   \n",
       "\n",
       "Name           sex   race               ethnicity  \\\n",
       "Patient_ID                                          \n",
       "C3L-00006   Female  White  Not Hispanic or Latino   \n",
       "C3L-00008   Female  White  Not Hispanic or Latino   \n",
       "C3L-00032   Female  White  Not Hispanic or Latino   \n",
       "C3L-00084   Female  White  Not Hispanic or Latino   \n",
       "C3L-00090   Female  White  Not Hispanic or Latino   \n",
       "...            ...    ...                     ...   \n",
       "NX5.N          NaN    NaN                     NaN   \n",
       "NX6.N          NaN    NaN                     NaN   \n",
       "NX7.N          NaN    NaN                     NaN   \n",
       "NX8.N          NaN    NaN                     NaN   \n",
       "NX9.N          NaN    NaN                     NaN   \n",
       "\n",
       "Name       ethnicity_race_ancestry_identified  ...  \\\n",
       "Patient_ID                                     ...   \n",
       "C3L-00006                               White  ...   \n",
       "C3L-00008                               White  ...   \n",
       "C3L-00032                               White  ...   \n",
       "C3L-00084                               White  ...   \n",
       "C3L-00090                               White  ...   \n",
       "...                                       ...  ...   \n",
       "NX5.N                                     NaN  ...   \n",
       "NX6.N                                     NaN  ...   \n",
       "NX7.N                                     NaN  ...   \n",
       "NX8.N                                     NaN  ...   \n",
       "NX9.N                                     NaN  ...   \n",
       "\n",
       "Name       number_of_days_from_date_of_initial_pathologic_diagnosis_to_date_of_additional_surgery_for_new_tumor_event_loco-regional  \\\n",
       "Patient_ID                                                                                                                            \n",
       "C3L-00006                                                 NaN                                                                         \n",
       "C3L-00008                                                 NaN                                                                         \n",
       "C3L-00032                                                 NaN                                                                         \n",
       "C3L-00084                                                 NaN                                                                         \n",
       "C3L-00090                                                 NaN                                                                         \n",
       "...                                                       ...                                                                         \n",
       "NX5.N                                                     NaN                                                                         \n",
       "NX6.N                                                     NaN                                                                         \n",
       "NX7.N                                                     NaN                                                                         \n",
       "NX8.N                                                     NaN                                                                         \n",
       "NX9.N                                                     NaN                                                                         \n",
       "\n",
       "Name       number_of_days_from_date_of_initial_pathologic_diagnosis_to_date_of_additional_surgery_for_new_tumor_event_metastasis  \\\n",
       "Patient_ID                                                                                                                         \n",
       "C3L-00006                                                 NaN                                                                      \n",
       "C3L-00008                                                 NaN                                                                      \n",
       "C3L-00032                                                 NaN                                                                      \n",
       "C3L-00084                                                 NaN                                                                      \n",
       "C3L-00090                                                 NaN                                                                      \n",
       "...                                                       ...                                                                      \n",
       "NX5.N                                                     NaN                                                                      \n",
       "NX6.N                                                     NaN                                                                      \n",
       "NX7.N                                                     NaN                                                                      \n",
       "NX8.N                                                     NaN                                                                      \n",
       "NX9.N                                                     NaN                                                                      \n",
       "\n",
       "Name       Recurrence-free survival, days  \\\n",
       "Patient_ID                                  \n",
       "C3L-00006                             NaN   \n",
       "C3L-00008                             NaN   \n",
       "C3L-00032                             NaN   \n",
       "C3L-00084                             NaN   \n",
       "C3L-00090                            50.0   \n",
       "...                                   ...   \n",
       "NX5.N                                 NaN   \n",
       "NX6.N                                 NaN   \n",
       "NX7.N                                 NaN   \n",
       "NX8.N                                 NaN   \n",
       "NX9.N                                 NaN   \n",
       "\n",
       "Name       Recurrence-free survival from collection, days  \\\n",
       "Patient_ID                                                  \n",
       "C3L-00006                                             NaN   \n",
       "C3L-00008                                             NaN   \n",
       "C3L-00032                                             NaN   \n",
       "C3L-00084                                             NaN   \n",
       "C3L-00090                                            56.0   \n",
       "...                                                   ...   \n",
       "NX5.N                                                 NaN   \n",
       "NX6.N                                                 NaN   \n",
       "NX7.N                                                 NaN   \n",
       "NX8.N                                                 NaN   \n",
       "NX9.N                                                 NaN   \n",
       "\n",
       "Name       Recurrence status (1, yes; 0, no) Overall survival, days  \\\n",
       "Patient_ID                                                            \n",
       "C3L-00006                                0.0                  737.0   \n",
       "C3L-00008                                0.0                  898.0   \n",
       "C3L-00032                                0.0                 1710.0   \n",
       "C3L-00084                                0.0                  335.0   \n",
       "C3L-00090                                1.0                 1281.0   \n",
       "...                                      ...                    ...   \n",
       "NX5.N                                    NaN                    NaN   \n",
       "NX6.N                                    NaN                    NaN   \n",
       "NX7.N                                    NaN                    NaN   \n",
       "NX8.N                                    NaN                    NaN   \n",
       "NX9.N                                    NaN                    NaN   \n",
       "\n",
       "Name       Overall survival from collection, days  \\\n",
       "Patient_ID                                          \n",
       "C3L-00006                                   737.0   \n",
       "C3L-00008                                   898.0   \n",
       "C3L-00032                                  1710.0   \n",
       "C3L-00084                                   335.0   \n",
       "C3L-00090                                  1287.0   \n",
       "...                                           ...   \n",
       "NX5.N                                         NaN   \n",
       "NX6.N                                         NaN   \n",
       "NX7.N                                         NaN   \n",
       "NX8.N                                         NaN   \n",
       "NX9.N                                         NaN   \n",
       "\n",
       "Name       Survival status (1, dead; 0, alive) RAC2_umich_proteomics  \\\n",
       "Patient_ID                                                             \n",
       "C3L-00006                                  0.0             -0.182830   \n",
       "C3L-00008                                  0.0             -0.793159   \n",
       "C3L-00032                                  0.0              0.583774   \n",
       "C3L-00084                                  0.0             -0.193889   \n",
       "C3L-00090                                  1.0             -0.361299   \n",
       "...                                        ...                   ...   \n",
       "NX5.N                                      NaN              0.864272   \n",
       "NX6.N                                      NaN              0.841041   \n",
       "NX7.N                                      NaN              0.430521   \n",
       "NX8.N                                      NaN             -0.000459   \n",
       "NX9.N                                      NaN              0.448693   \n",
       "\n",
       "Name       PODXL_umich_proteomics  \n",
       "Patient_ID                         \n",
       "C3L-00006                0.731055  \n",
       "C3L-00008                0.451984  \n",
       "C3L-00032                1.344697  \n",
       "C3L-00084               -1.994844  \n",
       "C3L-00090                0.154995  \n",
       "...                           ...  \n",
       "NX5.N                   -0.980967  \n",
       "NX6.N                   -0.260866  \n",
       "NX7.N                   -0.498802  \n",
       "NX8.N                   -0.140857  \n",
       "NX9.N                   -0.370379  \n",
       "\n",
       "[152 rows x 126 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "follow_up = follow_up.rename({'PPID' : 'Patient_ID'}, axis='columns')\n",
    "clin_prot_follow = pd.merge(clinical_and_protein, follow_up, on = \"Patient_ID\")\n",
    "clinical_and_protein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determine columns to focus on, and create a subset to work with\n",
    "columns_to_focus_on = ['Survival status (1, dead; 0, alive)',\n",
    "                       'number_of_days_from_date_of_collection_to_date_of_last_contact', \n",
    "                       'number_of_days_from_date_of_collection_to_date_of_death',\n",
    "                       'tumor_stage_pathological']\n",
    "\n",
    "#This adds the protein data that we got from the clinical and proteomics join\n",
    "#so that it will be present in our subset of data to work with\n",
    "for i in range(len(omics_genes)):\n",
    "    omics_genes[i] += '_umich_proteomics'\n",
    "    columns_to_focus_on.append(omics_genes[i])\n",
    "\n",
    "focus_group = clin_prot_follow[columns_to_focus_on].copy().drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Kaplan Meier Plotting\n",
    "Kaplan Meier plots show us the probability of some event occuring over a given length of time, based on some attribute(s).  Oftentimes, they are used to plot the probability of death for clinical attributes, however they could also be used in a variety of other contexts. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by finding all patients that have died during the follow-up period and update the column to contain boolean values, where True denotes that the event occurred ('Deceased'), and False denotes that it did not ('Living').  We will then combine the two columns containing timeframe data ('Days_Between_Collection_And_Last_Contact', and 'Days_Between_Collection_And_Death'), to help us with plotting survival curves. These steps are necessary to fit the requirements of the *lifelines* package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make the Survival Status column boolean\n",
    "focus_group['Survival status (1, dead; 0, alive)'] = focus_group['Survival status (1, dead; 0, alive)'].replace(0, False)\n",
    "focus_group['Survival status (1, dead; 0, alive)'] = focus_group['Survival status (1, dead; 0, alive)'].replace(1, True)\n",
    "focus_group['Survival status (1, dead; 0, alive)'] = focus_group['Survival status (1, dead; 0, alive)'].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['number_of_days_from_date_of_collection_to_date_of_last_contact', 'number_of_days_from_date_of_collection_to_date_of_death']\n",
    "\n",
    "focus_group = focus_group.assign(Days_Until_Last_Contact_Or_Death=focus_group[cols].sum(1)).drop(cols, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will create a general Kaplan Meier Plot of overall survival for our cohort, using the KaplanMeierFitter() from the *lifelines* package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='timeline'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = focus_group['Days_Until_Last_Contact_Or_Death']\n",
    "status = focus_group['Survival status (1, dead; 0, alive)']\n",
    "\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(time, event_observed = status)\n",
    "kmf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4 Prepare Data for Multivariate Kaplan Meier Plots and Cox's Proportional Hazard Test\n",
    "We will now group our columns of interest into 3-4 distinct categories each, and assign them numeric values.  It is necessary for the requirements of the *lifelines* package that the categories are assigned numeric values (other data types, including category, are not compatible with the functions we will be using)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genes = focus_group.copy()\n",
    "\n",
    "#Here, we are separating the protein abundance values for each of our proteins\n",
    "#of interest into 3 groups, based on relative abundance of the protein\n",
    "for col in omics_genes:\n",
    "    lower_25_filter = df_genes[col] <= df_genes[col].quantile(.25)\n",
    "    upper_25_filter = df_genes[col] >= df_genes[col].quantile(.75)\n",
    "\n",
    "    df_genes[col] = np.where(lower_25_filter, \"Lower_25%\", df_genes[col])\n",
    "    df_genes[col] = np.where(upper_25_filter, \"Upper_25%\", df_genes[col])\n",
    "    df_genes[col] = np.where(~lower_25_filter & ~upper_25_filter, \"Middle_50%\", df_genes[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we map numeric values to correspond with our 3 protein categories\n",
    "proteomics_map = {\"Lower_25%\" : 1, \"Middle_50%\" : 2, \"Upper_25%\" : 3}\n",
    "for gene in omics_genes:\n",
    "    df_genes[gene] = df_genes[gene].map(proteomics_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we map numeric values to corresponding tumor stages\n",
    "figo_map = {\"Stage III\" : 3, \"Stage IV\" : 4, \n",
    "            \"Not Reported/ Unknown\" : np.nan,\n",
    "            \"Stage I\" : 1, \"Stage II\" : 2}\n",
    "\n",
    "df_genes['tumor_stage_pathological'] = df_genes['tumor_stage_pathological'].map(figo_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name        Survival status (1, dead; 0, alive)  tumor_stage_pathological  \\\n",
      "Patient_ID                                                                  \n",
      "C3L-00006                                 False                       1.0   \n",
      "C3L-00008                                 False                       1.0   \n",
      "C3L-00032                                 False                       1.0   \n",
      "C3L-00084                                 False                       1.0   \n",
      "C3L-00090                                  True                       1.0   \n",
      "...                                         ...                       ...   \n",
      "C3N-01520                                  True                       1.0   \n",
      "C3N-01521                                 False                       1.0   \n",
      "C3N-01537                                 False                       2.0   \n",
      "C3N-01802                                 False                       2.0   \n",
      "C3N-01825                                 False                       1.0   \n",
      "\n",
      "Name        RAC2_umich_proteomics  PODXL_umich_proteomics  \\\n",
      "Patient_ID                                                  \n",
      "C3L-00006                       2                       3   \n",
      "C3L-00008                       1                       3   \n",
      "C3L-00032                       3                       3   \n",
      "C3L-00084                       2                       1   \n",
      "C3L-00090                       2                       2   \n",
      "...                           ...                     ...   \n",
      "C3N-01520                       2                       2   \n",
      "C3N-01521                       3                       3   \n",
      "C3N-01537                       3                       2   \n",
      "C3N-01802                       1                       1   \n",
      "C3N-01825                       3                       1   \n",
      "\n",
      "Name        Days_Until_Last_Contact_Or_Death  \n",
      "Patient_ID                                    \n",
      "C3L-00006                              737.0  \n",
      "C3L-00008                              898.0  \n",
      "C3L-00032                             1710.0  \n",
      "C3L-00084                              335.0  \n",
      "C3L-00090                             1287.0  \n",
      "...                                      ...  \n",
      "C3N-01520                              278.0  \n",
      "C3N-01521                              681.0  \n",
      "C3N-01537                              671.0  \n",
      "C3N-01802                              740.0  \n",
      "C3N-01825                              661.0  \n",
      "\n",
      "[103 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "#Then we will drop missing values, as missing values \n",
    "# will throw an error in the lifelines functions\n",
    "print(df_genes)\n",
    "df_clean = df_genes.dropna(axis=0, how='any').copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verify that your columns are the correct data types.  They may appear to be correct up front, but could actually be hidden as slightly different data types. The event of interest, in this case *Survival Status* needs to contain boolean values, and all other columns in the table must be of a numeric type (either int64 or float64)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival status (1, dead; 0, alive) : bool\n",
      "tumor_stage_pathological : float64\n",
      "RAC2_umich_proteomics : int64\n",
      "PODXL_umich_proteomics : int64\n",
      "Days_Until_Last_Contact_Or_Death : float64\n"
     ]
    }
   ],
   "source": [
    "for col in df_clean.columns:\n",
    "    print(col, \":\", df_clean[col].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>Survival status (1, dead; 0, alive)</th>\n",
       "      <th>tumor_stage_pathological</th>\n",
       "      <th>RAC2_umich_proteomics</th>\n",
       "      <th>PODXL_umich_proteomics</th>\n",
       "      <th>Days_Until_Last_Contact_Or_Death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C3L-00006</th>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>737.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00008</th>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>898.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00032</th>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00084</th>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00090</th>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1287.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Name        Survival status (1, dead; 0, alive)  tumor_stage_pathological  \\\n",
       "Patient_ID                                                                  \n",
       "C3L-00006                                 False                       1.0   \n",
       "C3L-00008                                 False                       1.0   \n",
       "C3L-00032                                 False                       1.0   \n",
       "C3L-00084                                 False                       1.0   \n",
       "C3L-00090                                  True                       1.0   \n",
       "\n",
       "Name        RAC2_umich_proteomics  PODXL_umich_proteomics  \\\n",
       "Patient_ID                                                  \n",
       "C3L-00006                       2                       3   \n",
       "C3L-00008                       1                       3   \n",
       "C3L-00032                       3                       3   \n",
       "C3L-00084                       2                       1   \n",
       "C3L-00090                       2                       2   \n",
       "\n",
       "Name        Days_Until_Last_Contact_Or_Death  \n",
       "Patient_ID                                    \n",
       "C3L-00006                              737.0  \n",
       "C3L-00008                              898.0  \n",
       "C3L-00032                             1710.0  \n",
       "C3L-00084                              335.0  \n",
       "C3L-00090                             1287.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5: Multivariate Survival Risk Plotting\n",
    "\n",
    "With the CoxPHFitter from the lifelines package we can create covariate survival plots, as shown below.  The variables we are interested in exploring are Tumor Stage, RAC2 abundance, and PODXL  abundance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will fit our model to the data we have prepared using the CoxPHFitter() class from the lifelines module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 103 total observations, 91 right-censored observations>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph = CoxPHFitter()\n",
    "cph.fit(df_clean, duration_col = \"Days_Until_Last_Contact_Or_Death\", \n",
    "        event_col = \"Survival status (1, dead; 0, alive)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we will plot each of the attributes to see how different levels of protein or different tumor stages affect survival outcomes in Endometrial Cancer patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = ['tumor_stage_pathological', 'PODXL_umich_proteomics', 'RAC2_umich_proteomics']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8s9B5nT59mk/Lfw/kcjl79OgRn57/PVTwvX39PnTv3j0mEAhY7969tc6Tsf+9N/v37y/T7hnvdJPQypUrcfToUa3HkSNHCuUbMGCA1q+1/CrnBw8eAADi4+Nx5coV+Pv7w8LCgs/30Ucfwd3dXWtfhw8fhlAoxJdffqmV/vXXX4MxVuj4HTt21PoV7uXlBQDo27ev1q+O/PT8Ml25cgX37t3D4MGD8eLFCzx//hzPnz9HRkYGOnbsiL/++qvYEQkF+fj4FDoPXfJ/bQJ51e0vXrxAvXr1YGlpiUuXLpXoWPnS0tIAQK9fiwWPn1975uPjgwcPHhSqqnd3d+ffRyCvRqlhw4b89QOAffv2oWnTpujdu3ehY+XXBuzZswdubm5wdXXlr/Hz58/x4YcfAgCOHz+utV1JryWQ18S1YsUK1KlTB/v378c333wDNzc3dOzYEXFxcSXahy516tSBn5+fVlqDBg3QrFkz7Nq1i09Tq9XYu3cvunfvrnVtXzdgwAAkJSVpVf3v3bsXGo0GAwYM4NMK7kOlUuH58+d8x2F9Px8FNWzYENbW1nB2dsawYcNQr149HDly5I21BJaWlrhx4wbu3bv3xmMcP34cfn5+6NixIyIiIgp1+gwICABjrMS1K7dv34a1tTWsra3h6uqKJUuWoEePHti8ebNWvtzcXOzatQsDBgzgP3P5zZ7bt2/n85Xm/6UgpVKJfv36QS6XY+HChaXaR0mZmZlp1dg1bNgQlpaWcHNz4+9jQOF7mi5Hjx5Feno6pk6dCplMpvXa6zV2ZmZm+PTTT/m/JRIJPD09i91/Uezt7bXuCwqFAkOHDsXly5eRkJCglXfkyJEQCoX838eOHUN2djYmTpyoVTsxcuRIKBQK/Pbbb288fknvO4cPHwYABAUFaW3/9ddfA0ChY7m7u8Pb25v/O/89+PDDD1G7du1C6cVduwMHDkCj0WD27NmFamHy35v82sRDhw4hJyfnjef9Ju90wOLp6QlfX1+th652/IJvFPC/qubk5GQAeX03AKB+/fqFtm3YsKHW348ePYK9vX2hG4ubm5vWvoo6dn5A5OjoqDM9v0z5N2F/f3/+xpj/+PHHH5GVlVXi9nZdI6l0yczMxOzZs/m+OTVq1IC1tTVSUlL0bttXKBQAoFdb+qlTp+Dr6wtTU1NYWlrC2tqarzZ//fivX1cg733Nv35AXh+IN83dcO/ePdy4caPQNW7QoAEAFOqPUNJrCQACgQBjx47FxYsX8fz5cxw8eBBdunTBn3/+WWQTTUkUVYYBAwbg1KlTfDAUHR2NpKQkraBDl/w+UgWDnV27dqFZs2b8dQCAly9fYsKECbC1tYVcLoe1tTVfltL2/QDyAsujR49ix44daN26NZKSkooNsPLNmzcPKSkpaNCgAZo0aYJJkybh33//LZRPpVKhW7duaN68OXbv3g2JRFLqsuZzdnbG0aNH8fvvv2PVqlVwcHDAs2fPCn3p/vHHH3j27Bk8PT0RExODmJgYxMbGokOHDvj555/5Hx2l+X/Jp1arMXDgQNy8eRN79+6Fvb39W59fcWrVqlUomLCwsHjjPU2X/H5KJZljRddxX/+fL6l69eoV2lf+Z/31/jWv/7/l3+Nf/26QSCRwcXEp9B2gS0nvO48ePYJAICg08tXOzg6WlpZl9n2jy/379yEQCIr9gebj44O+ffti7ty5qFGjBnr27IlNmzYV6l9TUu90H5aSKhgdF8Re6yRbkcd+U5nyb2RLlixBs2bNdOYt6YRYJbn5A8D48eOxadMmTJw4Ed7e3rCwsADHcRg4cGCJa3PyKRQK2Nvb8x023+T+/fvo2LEjXF1dsWzZMjg6OkIikeDw4cMIDQ0tdPyyek81Gg2aNGmCZcuW6Xz99X/0kl7L11WvXh09evRAjx490L59e5w4cQKPHj2Ck5NTkX0/1Gq1zvSiyjBgwABMmzYNe/bswcSJE7F7925YWFigc+fOxZZNKpWiV69e2L9/P1atWoXExEScOnUK3333nVa+/v374/Tp05g0aRKaNWsGMzMzaDQadO7cWe/PR0EffPAB3/eqe/fuaNKkCYYMGYKLFy8W277+wQcf4P79+zh48CD++OMP/PjjjwgNDcWaNWswYsQIrfPr2rUrDh48iMjISL7j6tswNTWFr68v/3fbtm3RokULTJ8+HT/88AOfnl+L0r9/f537OXHiBDp06ABXV1cA4Dvk62PkyJE4dOgQtm/fzv9C14e+n7/S3tPelqHu46X9ny+OvvedkvYPq+j3huM47N27F2fPnsWvv/6K33//HcOGDcP333+Ps2fP6j1pIwUsJZDfQVJX1fKdO3cK5T127BjS09O1allu376tta+3VbduXQB5X/wFb4y6lFVnx71798Lf3x/ff/89n6ZSqUo9idfHH3+MdevW4cyZM1rVlLr8+uuvyMrKwi+//KL1K+H1Jhl91K1b940BU926dXH16lV07NixwmZ59PDwwIkTJxAfHw8nJye+xi8lJUWrw2ZJfqkVVKdOHXh6emLXrl0YN24cIiIi0KtXrxLNeTFgwABs2bIFUVFRuHXrFhhjWjUzycnJiIqKwty5czF79mw+vSTNMfowMzNDcHAwAgMDsXv37jfWRFlZWSEwMBCBgYF49eoVPvjgA8yZM0crYOE4Dtu3b0fPnj3Rr18/HDlyBO3bty/Tcr/33nv49NNPsXbtWnzzzTeoXbs2MjIycPDgQQwYMEBrlFa+L7/8Etu3b0eHDh3QoEEDNGzYEAcPHkR4eHiJb/STJk3Cpk2bEBYWhkGDBpWq7AU/fwXp+/krjfz73PXr1yt0/qyYmBgwxrT+5+/evQsAb5zZPP8ef+fOHbi4uPDp2dnZiI2N1bpfF3VPKel9x8nJCRqNBvfu3eNr8QEgMTERKSkpZfZ9U1QZNRoNbt68WeSP5nytW7dG69atsWDBAuzYsQNDhgzBzp07tf4PS+KdbhIqKzVr1kSzZs2wZcsWrarto0eP4ubNm1p5u3btCrVajRUrVmilh4aGguM4vXurF6Vly5aoW7culi5dyg+9LOjZs2f8c1NTUwCFbzj6EgqFhSLu5cuXF/lL600mT54MU1NTjBgxAomJiYVev3//PsLDw/ljA9oRf2pqKjZt2lSqYwN5/YSuXr2qc3h6/nH69++PuLg4rF+/vlCezMxMflSAvhISEgp9doC8m1pUVJRWNW/+Tfuvv/7i82VkZGDLli16H3fAgAE4e/YsNm7ciOfPn7+xOSifr68vrKyssGvXLuzatQuenp5aVeG63h8A5TKL55AhQ1CrVi0sWrSo2HyvD6c2MzNDvXr1dFZHSyQSREREoFWrVujevTvOnz+v9frbDmsG8j7vOTk5/K/m/fv3IyMjA2PHjsUnn3xS6PHxxx9j3759fHnnzp2LFy9eYMSIEcjNzS20/z/++AOHDh3i/16yZAmWLl2K6dOna00PoC9dnz+1Wl0hEzp26tQJ5ubmCAkJgUql0nqtPGtOnj59qnVfSEtLw9atW9GsWTOtUXa6+Pr6QiKR4IcfftAq44YNG5Camopu3brxaaampjo/UyW973Tt2hVA4f+z/M9YwWOVtV69ekEgEGDevHmFalDzzzs5ObnQ+5Qf3JSmWeidrmE5cuQIX7NRUJs2bbQi35IICQlBt27d0K5dOwwbNgwvX77E8uXL0ahRI62AoXv37ujQoQNmzJiBhw8fomnTpvjjjz9w8OBBTJw4kf/nf1sCgQA//vgjunTpgkaNGiEwMBAODg6Ii4vD8ePHoVAo8OuvvwLIC24AYMaMGRg4cCDEYjG6d+/OBzIl9fHHH+Onn36ChYUF3N3dcebMGRw7dqzQMPGSqlu3Lnbs2IEBAwbAzc1Na6bb06dPY8+ePXwnx06dOkEikaB79+74/PPP8erVK6xfvx42NjaIj48v1fEnTZqEvXv3ol+/fhg2bBhatmyJly9f4pdffsGaNWvQtGlTfPbZZ9i9ezdGjx6N48ePo23btlCr1bh9+zZ2797Nz3eir//++w+enp748MMP0bFjR9jZ2SEpKQk///wzrl69iokTJ/LNIJ06dULt2rUxfPhwTJo0CUKhEBs3boS1tTUeP36s13H79++Pb775Bt988w2srKzeWDuXTywWo0+fPti5cycyMjKwdOlSrdcVCgU++OADLF68GDk5OXBwcMAff/yB2NhYvcpX0rJMmDABkyZNQmRkZJFNWu7u7mjfvj1atmwJKysr/PPPP9i7dy/GjRunM79cLsehQ4fw4YcfokuXLjhx4gTfd6IshjW7u7uja9eu+PHHHzFr1ixs374d1atXL3JYf48ePbB+/Xr89ttv6NOnDwYMGIBr165hwYIFuHz5MgYNGsTPdBsZGYmoqCh+bpL9+/dj8uTJqF+/Ptzc3LBt2zatfX/00UewtbUtUbkbNWqE1q1bY9q0aXj58iWsrKywc+dOnUFTWVMoFAgNDcWIESPQqlUrDB48GNWqVcPVq1ehVCpLFbSXRIMGDTB8+HBcuHABtra22LhxIxITE0v0A8na2hrTpk3D3Llz0blzZ/To0QN37tzBqlWr0KpVK62OwS1btsSuXbsQFBSEVq1awczMDN27dy/xfadp06bw9/fHunXrkJKSAh8fH5w/fx5btmxBr169dPbZLCv16tXDjBkz8O233+L9999Hnz59IJVKceHCBdjb2yMkJARbtmzBqlWr0Lt3b9StWxfp6elYv349FAoFH2zppUzGGhmZ4oY1o8AwuvxhzUuWLCm0D7w2tJMxxvbt28fc3NyYVCpl7u7uLCIiQudMt+np6eyrr75i9vb2TCwWs/r167MlS5ZoDcPLP8bYsWO10ooqU/4Qwj179milX758mfXp04dVr16dSaVS5uTkxPr378+ioqK08n377bfMwcGBCQQCrWGDuspQ1DVITk5mgYGBrEaNGszMzIz5+fmx27dvFxpOWtKZbvPdvXuXjRw5kjk7OzOJRMLMzc1Z27Zt2fLly7WGl/7yyy/svffeYzKZjDk7O7NFixaxjRs36hzaq2v4qI+PD/Px8dFKe/HiBRs3bhxzcHBgEomE1apVi/n7+7Pnz5/zebKzs9miRYtYo0aNmFQqZdWqVWMtW7Zkc+fOZampqVrXq6hr+bq0tDQWHh7O/Pz8WK1atZhYLGbm5ubM29ubrV+/vtBn5eLFi8zLy4tJJBJWu3ZttmzZsiKHNb9pKG/btm11DoUseB6vf/YZY+zo0aMMAOM4jj158qTQ6//99x/r3bs3s7S0ZBYWFqxfv37s6dOnhfb3tjPdMpY3zNTCwkLr/Xz9czh//nzm6enJLC0tmVwuZ66urmzBggVas8Tqmun2+fPnzN3dndnZ2bF79+5plbmkw5qLmnU1OjqaAWBffPEFE4lExQ4xViqVzMTEhPXu3VsrPSoqivXs2ZPZ2NgwkUjErK2tWffu3dnBgwf5PPnXrqhHSf83892/f5/5+voyqVTKbG1t2fTp0/nPw+vDmnWde1Gfy9f/Z4r6bPzyyy+sTZs2TC6XM4VCwTw9PdnPP//8xuOWZiby/LL+/vvv7L333mNSqZS5uroWuve+aUb1FStWMFdXVyYWi5mtrS374osvWHJyslaeV69escGDBzNLS8tCU2SU9L6Tk5PD5s6dy+rUqcPEYjFzdHRk06ZNKzQ0v6TvAWO6v4eKml5h48aNrHnz5nwZfXx82NGjRxljjF26dIkNGjSI1a5dm0mlUmZjY8M+/vhj9s8//+i8Zm/C/X+BCSGEkCrP2dkZjRs31mpeI8aB+rAQQgghxOi9031YCCGE6JadnY2XL18Wm8fCwqJchu0awrNnz4odICCRSGBlZVWBJSL6ooCFEEKqoNOnT7+xU+bbdDI2Nq1atSp2KLaPj4/WbM7E+FAfFkIIqYKSk5Nx8eLFYvM0atRI50rqldGpU6eQmZlZ5OvVqlXjR1QS40QBCyGEEEKMHnW6JYQQQojRe2f6sGg0Gjx9+hTm5uYVNoU6IYQQQt4OYwzp6emwt7cvdn2wdyZgefr0aaEFoQghhBBSOTx58gS1atUq8vV3JmDJX2jwyZMn/FLshBBCCDFuaWlpcHR01FowWJd3JmDJbwZSKBQUsBBCCCGVzJu6c1CnW0IIIYQYPQpYCCGEEGL0KGAhhBBCiNF7Z/qwEELebYwx5ObmFrseDCHE+AiFQohEoreecoQCFkKI0cvOzkZ8fDyUSqWhi0IIKQUTExPUrFkTEomk1PuggIUQYtQ0Gg1iY2MhFAphb28PiURCk0MSUkkwxpCdnY1nz54hNjYW9evXL3ZyuOJQwEIIMWrZ2dnQaDRwdHSEiYmJoYtDCNGTXC6HWCzGo0ePkJ2dDZlMVqr9UKdbQkilUNpfZYQQwyuL/1+6AxBCCCHE6FHAQgghhBCjp3fA8tdff6F79+6wt7cHx3E4cODAG7eJjo5GixYtIJVKUa9ePWzevLlQnpUrV8LZ2RkymQxeXl44f/68vkUjhBBiZAICAtCrV68S5X348CE4jsOVK1fKtUzvgjlz5qBZs2aGLkaF0jtgycjIQNOmTbFy5coS5Y+NjUW3bt3QoUMHXLlyBRMnTsSIESPw+++/83l27dqFoKAgBAcH49KlS2jatCn8/PyQlJSkb/HKlFqtRtqzVMQ/fIr4h0+RmpRCc0AQQkosICAAHMeB4ziIxWLUqVMHkydPhkql0sr333//QSKRoHHjxjr3wxjDunXr4OXlBTMzM1haWsLDwwNhYWH8UO/169fj/fffR7Vq1VCtWjX4+voaxQ+/8PBwnT9SKxtjCxC++eYbREVFGeTYX375JVq2bAmpVFqh10TvgKVLly6YP38+evfuXaL8a9asQZ06dfD999/Dzc0N48aNwyeffILQ0FA+z7JlyzBy5EgEBgbC3d0da9asgYmJCTZu3FjkfrOyspCWlqb1KGsZL1/hb3tP2NdxgH0dB/xm3xJHPfpS0EIIKbHOnTsjPj4eDx48QGhoKNauXYvg4GCtPJs3b0b//v2RlpaGc+fOFdrHZ599hokTJ6Jnz544fvw4rly5glmzZuHgwYP4448/AOTVZA8aNAjHjx/HmTNn4OjoiE6dOiEuLq5CzrMoFhYWsLS0NGgZipOdnW3oIpSKmZkZqlevbrDjDxs2DAMGDKjYg7K3AIDt37+/2Dzvv/8+mzBhglbaxo0bmUKhYIwxlpWVxYRCYaH9DB06lPXo0aPI/QYHBzMAhR6pqamlORWdUpNS2CFRA37f24Uu7JCoAUtNSimzYxBCipeZmclu3rzJMjMz+TSNRsOUmbkGeWg0mhKX3d/fn/Xs2VMrrU+fPqx58+Za5+Li4sIiIyPZlClT2MiRI7Xy79q1iwFgBw4cKLR/jUbDUlJ0349yc3OZubk527JlS4nKqut+bmFhwTZt2sQYYyw2NpYBYLt27WLt2rVjMpmMeXh4sDt37rDz58+zli1bMlNTU9a5c2eWlJRU5DVQq9Vs0aJFrG7dukwikTBHR0c2f/58rWPs27ePtW/fnsnlcvbee++x06dPl+gcNm3axCwsLNj+/ftZvXr1mFQqZZ06dWKPHz/m8wQHB7OmTZuy9evXM2dnZ8ZxHGOMsUePHrEePXowU1NTZm5uzvr168cSEhL4/b7+XZN/XZKTk9nw4cNZjRo1mLm5OevQoQO7cuWKVrlWrVrFXFxcmFgsZg0aNGBbt24tdO3XrFnDunXrxuRyOXN1dWWnT59m9+7dYz4+PszExIR5e3uzmJiYQudR0IYNG5i7uzuTSCTMzs6OjR07ljGW9zkJDg5mjo6OTCKRsJo1a7Lx48eX6JoWR1cZiqLr/zhfampqib6/y30eloSEBNja2mql2draIi0tDZmZmUhOToZardaZ5/bt20Xud9q0aQgKCuL/TktLg6OjY5mW3dTKDO8/PY+nGRnITM7AzVbdynT/hJDSUWVp8FG/kwY59tE97SCXCUu17fXr13H69Gk4OTnxacePH4dSqYSvry8cHBzQpk0bhIaGwtTUFACwfft2NGzYED179iy0P47jYGFhofNYSqUSOTk5sLKyKlVZixIcHIywsDDUrl0bw4YNw+DBg2Fubo7w8HCYmJigf//+mD17NlavXq1z+2nTpmH9+vUIDQ1Fu3btEB8fX+heP2PGDCxduhT169fHjBkzMGjQIMTExEAkevNXllKpxIIFC7B161ZIJBKMGTMGAwcOxKlTp/g8MTEx2LdvHyIiIiAUCqHRaNCzZ0+YmZnhxIkTyM3NxdixYzFgwABER0djwIABuH79OiIjI3Hs2DEA4K97v379IJfLceTIEVhYWGDt2rXo2LEj7t69CysrK+zfvx8TJkxAWFgYfH19cejQIQQGBqJWrVro0KEDX6Zvv/0Wy5Ytw7JlyzBlyhQMHjwYLi4umDZtGn+tx40bhyNHjug879WrVyMoKAgLFy5Ely5dkJqayp/zvn37EBoaip07d6JRo0ZISEjA1atX+W1Hjx6Nbdu2FXtdX7169cZrX94q7cRxUqkUUqm0XI8hFAqhsLaAwtoCz8XPsJu9BBjgVUmrEAkhFe/QoUMwMzNDbm4usrKyIBAIsGLFCv71DRs2YODAgRAKhWjcuDFcXFywZ88eBAQEAADu3buHhg0b6n3cKVOmwN7eHr6+vmV1KgDy+k74+fkBACZMmIBBgwYhKioKbdu2BQAMHz68yD4r6enpCA8Px4oVK+Dv7w8AqFu3Ltq1a1foGN265f1AnDt3Lho1aoSYmBi4urq+sXw5OTlYsWIFvLy8AABbtmyBm5sbzp8/D09PTwB5zUBbt26FtbU1AODo0aO4du0aYmNj+R++W7duRaNGjXDhwgW0atUKZmZmEIlEsLOz44918uRJnD9/HklJSfz30dKlS3HgwAHs3bsXo0aNwtKlSxEQEIAxY8YAAIKCgnD27FksXbpUK2AJDAxE//79AeS9d97e3pg1a5bWtQ4MDCzyvOfPn4+vv/4aEyZM4NNatWoFAHj8+DHs7Ozg6+sLsViM2rVr89cCAObNm4dvvvnmjdfW0Mo9YLGzs0NiYqJWWmJiIhQKBeRyOYRCIYRCoc48BT8YhpaTk4OfNC8AAItycgxcGkKqNplUgKN72r05YzkdWx8dOnTA6tWrkZGRgdDQUIhEIvTt2xcAkJKSgoiICJw8+b/aok8//RQbNmzgAxbGmN5lXLhwIXbu3Ino6OhSzypalPfee49/nl8z3qRJE620ogZM3Lp1C1lZWejYsWOJj1GzZk0AQFJSUokCFpFIxH9RA4CrqyssLS1x69Yt/kvaycmJD1byy+Xo6KhVS+/u7s5vV3B/BV29ehWvXr0q1JckMzMT9+/f5/c9atQordfbtm2L8PDwIs+5qOuqUqmQlpYGhUKhtW1SUhKePn1a5HXt168fwsLC4OLigs6dO6Nr167o3r07X2NlY2MDGxsbndsak3IPWLy9vXH48GGttKNHj8Lb2xsAIJFI0LJlS0RFRfFD3zQaDaKiojBu3LjyLl6JiUQidOIU/HNCiOFwHFfqZpmKZmpqinr16gEANm7ciKZNm2LDhg0YPnw4duzYAZVKxdcGAHkBikajwd27d9GgQQM0aNCg2Obx1y1duhQLFy7EsWPHtL4E34TjuELBUY6OH2disVhrG11pGo1G5zHkcnmJyqLrGEXtszTym9ve1qtXr1CzZk1ER0cXek3fjsYlua6A7uvwpuvq6OiIO3fu4NixYzh69CjGjBmDJUuW4MSJExCLxZWmSUjvUUKvXr3ClStX+HHysbGxuHLlCh4/fgwgr31y6NChfP7Ro0fjwYMHmDx5Mm7fvo1Vq1Zh9+7d+Oqrr/g8QUFBWL9+PbZs2YJbt27hiy++QEZGRrHVXxVNKpXiS6EdvhTalXtTFCHk3SQQCDB9+nTMnDkTmZmZ2LBhA77++mv+nnrlyhVcvXoV77//Pj9KcvDgwbh79y4OHjxYaH+MMaSmpvJ/L168GN9++y0iIyPh4eGhV9msra0RHx/P/33v3r0yXx27fv36kMvl5TocNzc3F//88w//9507d5CSkgI3N7cit3Fzc8OTJ0/w5MkTPu3mzZtISUmBu7s7gLwf16+PEG3RogUSEhIgEolQr149rUeNGjX4fRfsPwMAp06d4vdbFszNzeHs7FzsdZXL5ejevTt++OEHREdH48yZM7h27RqAvCahgp9BXQ9joHdVwT///KPV7pbf8dXf3x+bN29GfHw8H7wAQJ06dfDbb7/hq6++Qnh4OGrVqoUff/yRb5cDgAEDBuDZs2eYPXs2EhIS0KxZM0RGRhbqiEsIIZVdv379MGnSJKxcuRKXLl3C9u3bCzV1DBo0CPPmzcP8+fPRv39/7N+/H4MGDcLMmTPRqVMnWFtb49q1awgNDcX48ePRq1cvLFq0CLNnz8aOHTvg7OyMhIQEAHnDX83MzN5Yrg8//BArVqyAt7c31Go1pkyZovULvyzIZDJMmTIFkydPhkQiQdu2bfHs2TPcuHEDw4cPL5NjiMVijB8/Hj/88ANEIhHGjRuH1q1ba/XZeJ2vry+aNGmCIUOGICwsDLm5uRgzZgx8fHz4wM/Z2Zn/gV6rVi2Ym5vD19cX3t7e6NWrFxYvXowGDRrg6dOn+O2339C7d294eHhg0qRJ6N+/P5o3bw5fX1/8+uuviIiI4DvvlpU5c+Zg9OjRsLGxQZcuXZCeno5Tp05h/Pjx2Lx5M9RqNby8vGBiYoJt27ZBLpfznb/1bRKKiYnBq1evkJCQgMzMTD6gcXd3h0QiKdPz0qLvMCZjVdJhUaXe//8PcaZhzYRUrOKGQxo7XcOaGWMsJCSEiUQi5uzsrHO7+Ph4JhAI2MGDBxljeUOBV69ezVq1asVMTEyYQqFgLVu2ZOHh4UypVDLGGHNyctI51UNwcHCJyhoXF8c6derETE1NWf369dnhw4d1Dmu+fPkyv83x48cZAJacnMyn5Q8tLuoaqNVqNn/+fObk5MTEYjGrXbs2++6774o8RnJyMgPAjh8//sZzyD/2vn37mIuLC5NKpczX15c9evSIz1PUUNzihjUzxphKpWJ9+/ZllpaWWsOa09LS2Pjx45m9vT0Ti8XM0dGRDRkyRGsodUmGNRccUl6Sa63rPNasWcMaNmzIxGKx1tDl/fv3My8vL6ZQKJipqSlr3bo1O3bs2BuvZ1F8fHx0ftZiY2OL3KYshjVzjJWiR5cRSktLg4WFBVJTUwt1SCoL8Q+fok6dWgCA2Nj/UNPZvsyPQQgpTKVSITY2FnXq1CnzDqTk3bJ582ZMnDgRKSkphi4KeU1x/8cl/f6m3qN6yEJebJeVrUamqvLNdiuTCviOW4QQQkhlQgFLCcnlcmwQ1gEAjPzmJjLZAwOXSH9N3BRYtagZBS2EVDF///03unTpUuTrxjACpCS6dOmCv//+W+dr06dPh7091Xy/yyhgKSG5XARbToxcxiASZkPIBBAK/zdaSJ2bCQAQCKXguLzBVxpNLpgmB+Bez6sCwCAQSsBxwrLLq84CmAacQAyBIO+tZUwNjTob4AS4disNqixNpRkOSggpGx4eHkYz0uNt/Pjjj8jMzNT5mpWVFaysrPj5a8i7hwKWEsqvlfiNpSDyQA/06NEDoYuX8a+3atUKL5OTceTwYTRo0AAAsHPnTsyYORO+vr5Yu2YNn/cDHx/ExcVhf0QEP0/CwYMHEfT112jbti02bdnC5+3cuTPuxcRg+7ZtaN26NQDgj6NH8cUXX6BFixbYs3s3n7dX7964du0afly/nh/JdfLkSfgHBMDNzQ3LwveWahIqQkjlJpfL+blgKjMHBwdDF4EYkN7zsFRV70IzSgMXKQUshBBCKiUaJVRCuRlK/G7ZHLmMoe3dPyAxN9Pq6ZyRkQEg75eMQJAXB+bk5CA7OxtCoVArr1KpBGMMMpkMQmFe80zBdUYKzlqoT97MzExoNBpIpVJ+Nl61Wo3k5Fd4+TwB8moWsLN3gqlJ2c6tQEh5olFChFR+NErIAEQch3MN/d6c0QjJW7jD9s9dhi4GIYQQojdqEiohoYkc1dq0MHQxSiWLaTA19wkmnP8dGS9eGro4hBBCiN6ohqWEOI6Dd/QOqJW6e6gbs4THCbjuXhdA2S4gRgghhFQUClj0wHEcRKYmhi6G3kwsLTBVkLdEOy3cSAipSAEBAUhJScGBAwfemPfhw4eoU6cOLl++jGbNmpV72SqzOXPm4MCBA+/EcPWSoiahKkAkEqGdwBztBOZ8Z1xCSPkLCAgAx3HgOA5isRh16tTB5MmToVKptPL9999/kEgkaNy4sc79MMawbt06eHl5wczMDJaWlvDw8EBYWBi/ovL69evx/vvvo1q1aqhWrRp8fX1x/vz5cj/HNwkPD8fmzZsNXYy3NmfOHKMKor755ptyXfW6KFevXsWgQYPg6OgIuVwONzc3hIeHV8ixKWAhhJBy1LlzZ8THx+PBgwcIDQ3F2rVrERwcrJVn8+bN6N+/P9LS0nDu3LlC+/jss88wceJE9OzZE8ePH8eVK1cwa9YsHDx4EH/88QcAIDo6GoMGDcLx48dx5swZODo6olOnToiLi6uQ8yyKhYUFLC0tDVqG4mRnZxu6CKViZmaG6tWrV/hxL168CBsbG2zbtg03btzAjBkzMG3aNKxYsaL8D17KxRqNTnmv1lyZvYx/wRYLHdlioSOLf5jElJm5pX5oNBpDnw6pYnSt8qrRaJharTbIQ5//AV2rNffp04c1b95c61xcXFxYZGQkmzJlChs5cqRW/l27djEA7MCBA4X2r9FoWEqK7tXjc3Nzmbm5OduyZUuJyorXVgxmjOlcrXnXrl2sXbt2TCaTMQ8PD3bnzh12/vx51rJlS2Zqaso6d+7MkpKSirwGarWaLVq0iNWtW5dJJBLm6OjI5s+fr3WMffv2sfbt2zO5XM7ee+89dvr06RKdQ/5qzfv372f16tVjUqmUderUSWvl5PxVjtevX8+cnZ0Zx3GMseJXa960aVOhlYnzr0tycjIbPnw4q1GjBjM3N2cdOnRgV65c0SpXSVZrXrNmDevWrRuTy+XM1dWVnT59mt27d4/5+PgwExMT5u3tzWJiYgqdR0EbNmxg7u7uTCKRMDs7OzZ27FjGWN7nJDg4mDk6OjKJRKK1knNZGDNmDOvQoUOxecpitWZqH6gCVCoVJqufAAAix5xBlsCi1Pui9YiIMWCM4WFsrEGO7VynTqk//9evX8fp06fh5OTEpx0/fhxKpRK+vr5wcHBAmzZtEBoaClNTUwDA9u3b0bBhQ/Ts2bPQ/jiOg4WF7v9npVKJnJwcWFlZlaqsRQkODkZYWBhq166NYcOGYfDgwTA3N0d4eDhMTEzQv39/zJ49G6tXr9a5/bRp07B+/XqEhoaiXbt2iI+Px+3bt7XyzJgxA0uXLkX9+vUxY8YMDBo0CDExMSVq0lYqlViwYAG2bt0KiUSCMWPGYODAgTh16hSfJyYmBvv27UNERASEQiE0Gg169uwJMzMznDhxArm5uRg7diwGDBiA6OhoDBgwANevX0dkZCSOHTsGAPx179evH+RyOY4cOQILCwusXbsWHTt2xN27d2FlZYX9+/djwoQJCAsLg6+vLw4dOoTAwEDUqlWLn5EcAL799lssW7YMy5Ytw5QpUzB48GC4uLhg2rRp/LUeN24cjhw5ovO8V69ejaCgICxcuBBdunRBamoqf8779u1DaGgodu7ciUaNGiEhIQFXr17ltx09ejS2bdtW7HUtbr2p1NTUMv+c6UIBSxUgkwlRE/mTxb1doEHrERGin0OHDsHMzExrwseC1ecbNmzAwIEDIRQK0bhxY7i4uGDPnj38mjj37t1Dw4YN9T7ulClTYG9vD19f37I6FQB5fSf8/PLmopowYQIGDRqEqKgotG3bFgAwfPjwIvuspKenIzw8HCtWrIC/vz8AoG7dumjXrl2hY3Tr1g0AMHfuXDRq1AgxMTFwdXV9Y/lycnKwYsUKeHl5AQC2bNkCNzc3nD9/Hp6engDymoG2bt0Ka2trAMDRo0dx7do1xMbGwtHREQCwdetWNGrUCBcuXECrVq1gZmYGkUgEOzs7/lgnT57E+fPnkZSUxA9oWLp0KQ4cOIC9e/di1KhRWLp0KQICAjBmzBgAQFBQEM6ePYulS5dqBSyBgYHo378/gLz3ztvbG7NmzdK61oGBgUWe9/z58/H1119jwoQJfFqrVq0AAI8fP4adnR18fX0hFotRu3Zt/loAwLx58/DNN9+88drqcvr0aezatQu//fZbqbbXBwUsVYCpqSnWi/JWmn5/8wcQm5vpvQ+VSo3un50p66IRUiocx8G5Th2DHVsfHTp0wOrVq5GRkYHQ0FCIRCL07dsXAJCSkoKIiAicPHmSz//pp59iw4YNfMDCSjEZ+cKFC7Fz505ER0eX+ezA+eufAYCtrS0AoEmTJlppSUlJOre9desWsrKy0LFjxxIfo2bNvBGOSUlJJQpYRCIR/0UNAK6urrC0tMStW7f4L2knJyc+WMkvl6OjIx+sAIC7uzu/XcH9FXT16lW8evWqUF+SzMxM3L9/n9/3qFGjtF5v27ZtoY6qJbmuKpUKaWlphWaDTUpKwtOnT4u8rv369UNYWBhcXFzQuXNndO3aFd27d+drrGxsbGBjY6Nz2+Jcv34dPXv2RHBwMDp16qT39vqigKUKyWYa5OYoIeVkEIvzalzUajVUKhU4joOJyf+GbKtUKqjVakgkEojFYmg0HMRihkraP428Y/JH3lQGpqam/MKDGzduRNOmTbFhwwYMHz4cO3bsgEql4msDgLwARaPR4O7du2jQoAEaNGhQqMmkOEuXLsXChQtx7NgxrS/BN+E4rlBwlJOTUyhf/r0jfxtdaUXN91RwKZHi6DpGWc4hld/c9rZevXqFmjVrIjo6utBr+nY0Lsl1BXRfhzddV0dHR9y5cwfHjh3D0aNHMWbMGCxZsgQnTpyAWCwuVZPQzZs30bFjR4waNQozZ84s/uTKCI0SqkK+08SjuoM9wsPD8TA2Fg9jY/F7ZCQUCgXq1avHpz2MjcUnfftCoVBgYUgIHsbGIjH+MWaOVSPlYRBNPkdIKQkEAkyfPh0zZ85EZmYmNmzYgK+//hpXrlzhH1evXsX777+PjRs3AgAGDx6Mu3fv4uDBg4X2xxhDamoq//fixYvx7bffIjIyEh4eHnqVzdraGvHx8fzf9+7d44dMl5X69etDLpeX63Dc3Nxc/PPPP/zfd+7cQUpKCtzc3Ircxs3NDU+ePMGTJ0/4tJs3byIlJQXu7u4AAIlEArVarbVdixYtkJCQAJFIhHr16mk9atSowe+7YP8ZADh16hS/37Jgbm4OZ2fnYq+rXC5H9+7d8cMPPyA6OhpnzpzBtWvXAOQ1CRX8DOp6FHTjxg106NAB/v7+WLBgQZmdx5tQDUsVUBa/RJVKJdatW4ebNy4jIyMDpiaWb18wQqqgfv36YdKkSVi5ciUuXbqE7du3F2rqGDRoEObNm4f58+ejf//+2L9/PwYNGoSZM2eiU6dOsLa2xrVr1xAaGorx48ejV69eWLRoEWbPno0dO3bA2dkZCQkJAPKGv5qZvbkZ+MMPP8SKFSvg7e0NtVqNKVOmaP3CLwsymQxTpkzB5MmTIZFI0LZtWzx79gw3btzA8OHDy+QYYrEY48ePxw8//ACRSIRx48ahdevWWn02Xufr64smTZpgyJAhCAsLQ25uLsaMGQMfHx8+8HN2dkZsbCyuXLmCWrVqwdzcHL6+vvD29kavXr2wePFiNGjQAE+fPsVvv/2G3r17w8PDA5MmTUL//v3RvHlz+Pr64tdff0VERATfebeszJkzB6NHj4aNjQ26dOmC9PR0nDp1CuPHj8fmzZuhVqvh5eUFExMTbNu2DXK5nO/8rU+T0PXr1/Hhhx/Cz88PQUFB/OdMKBRqNbOVi9IOYzI2NKy5aDmvMtghUQMWIazHXsYnMJVKxQ/RzM7OZmlpaSw9PV1r6GZGRgZLS0vj8yYkvmRm/z/cLzEp2dCnRKqQ4oZDGjtdw5oZYywkJISJRCLm7Oysc7v4+HgmEAjYwYMHGWN5Q4FXr17NWrVqxUxMTJhCoWAtW7Zk4eHhTKlUMsYYc3JyKjT0FgALDg4uUVnj4uJYp06dmKmpKatfvz47fPiwzmHNly9f5rc5fvw4A8CSk5P5tPyhxUVdA7VazebPn8+cnJyYWCxmtWvXZt99912Rx0hOTmYA2PHjx994DvnH3rdvH3NxcWFSqZT5+vqyR48e8Xl0DQdmrPhhzYwxplKpWN++fZmlpaXWsOa0tDQ2fvx4Zm9vz8RiMXN0dGRDhgzRGkpdkmHNBYeUl+Ra6zqPNWvWsIYNGzKxWKw1dHn//v3My8uLKRQKZmpqylq3bs2OHTv2xuupS3BwsM7PmZOTU7HblcWwZo6xUvToMkIlXZ66KsrNUOJ3y+YAAL+Uy6VaXiBDmYPE+McAANuatWFqUra/vAgpSnHL0hNS0ObNmzFx4kSkpKQYuijkNcX9H5f0+5uahKoYdUbpFm9UK3OgUarAyWktIkIIIRWPApYq5phDm7faXt7CHbZ/7iqj0hBCKsLff/+NLl26FPl6cZOCGZMuXbrg77//1vna9OnTYW9vX8ElIhWJApYqQGgiR7U2LZB8+lKp95HDNFihSQIuJODn5BSYmeo/Zp8QYhgeHh7vxKq+P/74IzIzddcSW1lZwcrKip+/hrx7KGCpAjiOg3f0DqiVpWsOAoCExwmIcq8LAMhQZsFMpX7DFuVPJhVUmrk4CDEkuVzOzwVTmTk4OBi6CMSAKGCpIjiOK1Vn23wyhTkCBXnzCoz4+hpUeFBWRSs1WteIEEKqDpo4jpSIubkMfQVW6CuwgkBgHCOE8tc1IoQQ8u6jGhZSIgVrMfb+6Fmq9YjKCq1rRAghVQ8FLKRENBoNnrO8dUUkEg4yWq2ZEEJIBaKAhZRIZmYmAtSxAIAemZmQWdDkfIQQQioO9WEhJSb8/wfTaJCZmYn09HSoVCpoNBpoNBqo1Wqkp6cjPT0darWaT1epVHrnzczM5NM0Gg2fNzc3FxqNBhIJdbQlpDIICAhAr169SpT34cOH4DjunRiCXd7mzJmDZs2aGboYFYoCFlIipqamOChqgIOiBnj54iVGjRwJhUKBKZMn8ys8X/v3XygUCigUCsTcu8enjx8/HgqFAuPHj+fTYu7d4/Ne+/dfPn3K5MlQKBQYNXKk1urRVlZWUCgUOH/uHBLjH+PHxQ6Y+aU13pGVJcg7KiAgABzHgeM4iMVi1KlTB5MnT4ZKpdLK999//0EikaBx48Y698MYw7p16+Dl5QUzMzNYWlrCw8MDYWFh/IrK69evx/vvv49q1aqhWrVq8PX1xfnz58v9HN8kPDwcmzdvNnQx3pqxBQjffPNNua56XZQXL16gc+fOsLe3h1QqhaOjI8aNG4e0tLRyPzYFLKREjGnocHZ2NtatX4/oqK3IysoydHEIKVbnzp0RHx+PBw8eIDQ0FGvXrkVwcLBWns2bN6N///5IS0vDuXPnCu3js88+w8SJE9GzZ08cP34cV65cwaxZs3Dw4EH88ccfAIDo6GgMGjQIx48fx5kzZ+Do6IhOnTohLi6uQs6zKBYWFrC0tDRoGYqTnZ1t6CKUipmZGapXr17hxxUIBOjZsyd++eUX3L17F5s3b8axY8cwevTo8j94qZZrNEK0WnP5yl/x+ZCoActKS2dKpZKlpaWxzMxMfoXn3NxclpaWxtLS0lhubi6fnpmZqXdepVKptXp0ft6cnByWkPiSXyGUVo5+9+la5VWj0bCcVxkGeWg0mhKXXddqzX369GHNmzfXOhcXFxcWGRnJpkyZwkaOHKmVf9euXQwAO3DgQKH9azQalpKSovPYubm5zNzcnG3ZsqVEZcVrKwYzxnSu1rxr1y7Wrl07JpPJmIeHB7tz5w47f/48a9myJTM1NWWdO3dmSUlJRV4DtVrNFi1axOrWrcskEglzdHRk8+fP1zrGvn37WPv27ZlcLmfvvfceO336dInOIX+15v3797N69eoxqVTKOnXqpLVycv4qx+vXr2fOzs6M4zjGWPGrNW/atKnQ6sT51yU5OZkNHz6c1ahRg5mbm7MOHTqwK1euaJWrJKs1r1mzhnXr1o3J5XLm6urKTp8+ze7du8d8fHyYiYkJ8/b2ZjExMYXOo6ANGzYwd3d3JpFImJ2dHRs7dixjLO9zEhwczBwdHZlEItFaybkshIeHs1q1ahWbpyxWa6ZOt0RvAoEAErlc52vm5uaF0mQymc5VdkubVywWo0+fPgAAkYg+wlWRWpnJr0Be0Uq74jkAXL9+HadPn4aTkxOfdvz4cSiVSvj6+sLBwQFt2rRBaGgoTE1NAQDbt29Hw4YN0bNnz0L74zgOFhYWOo+lVCqRk5MDKyurUpW1KMHBwQgLC0Pt2rUxbNgwDB48GObm5ggPD4eJiQn69++P2bNnY/Xq1Tq3nzZtGtavX4/Q0FC0a9cO8fHxuH37tlaeGTNmYOnSpahfvz5mzJiBQYMGISYmpkT/70qlEgsWLMDWrVshkUgwZswYDBw4EKdOneLzxMTEYN++fYiIiIBQKIRGo0HPnj1hZmaGEydOIDc3F2PHjsWAAQMQHR2NAQMG4Pr164iMjMSxY8cAgL/u/fr1g1wux5EjR2BhYYG1a9eiY8eOuHv3LqysrLB//35MmDABYWFh8PX1xaFDhxAYGIhatWqhQ4cOfJm+/fZbLFu2DMuWLcOUKVMwePBguLi4YNq0afy1HjduHI4cOaLzvFevXo2goCAsXLgQXbp0QWpqKn/O+/btQ2hoKHbu3IlGjRohISEBV69e5bcdPXo0tm3bVux1LWq9qadPnyIiIgI+Pj5vfG/eFt3tSaUjlUqxZPFi/jkhxuzQoUMwMzNDbm4usrKyIBAIsGLFCv71DRs2YODAgRAKhWjcuDFcXFywZ88efk2ce/fuoWHDhnofd8qUKbC3t4evr29ZnQqAvL4Tfn5+AIAJEyZg0KBBiIqKQtu2bQEAw4cPL7LPSnp6OsLDw7FixQr4+/sDAOrWrYt27doVOka3bt0AAHPnzkWjRo0QExMDV1fXN5YvJycHK1asgJeXFwBgy5YtcHNzw/nz5+Hp6Qkgrxlo69atsLa2BgAcPXoU165dQ2xsLBwdHQEAW7duRaNGjXDhwgW0atUKZmZmEIlEsLOz44918uRJnD9/HklJSfy9aOnSpThw4AD27t2LUaNGYenSpQgICMCYMWMAAEFBQTh79iyWLl2qFbAEBgaif//+APLeO29vb8yaNUvrWgcGBhZ53vPnz8fXX3+NCRMm8GmtWrUCADx+/Bh2dnbw9fWFWCxG7dq1+WsBAPPmzcM333zzxmtb0KBBg3Dw4EFkZmaie/fu+PHHH/XavjQoYCGVWlaWGgKBcXTForWNKo7QRA6/lMsGO7Y+OnTogNWrVyMjIwOhoaEQiUTo27cvACAlJQURERE4efIkn//TTz/Fhg0b+ICFlaJj+cKFC7Fz505ER0frrLF8G++99x7/3NbWFgDQpEkTrbSkpCSd2966dQtZWVno2LFjiY9Rs2ZNAEBSUlKJAhaRSMR/UQOAq6srLC0tcevWLf5L2snJiQ9W8svl6OjIBysA4O7uzm9XcH8FXb16Fa9evSrUlyQzMxP379/n9z1q1Cit19u2bYvw8PAiz7mo66pSqZCWlgaFQntaiaSkJDx9+rTI69qvXz+EhYXBxcUFnTt3RteuXdG9e3e+xsrGxgY2NvotaBsaGorg4GDcvXsX06ZNQ1BQEFatWqXXPvRFAQvRmzqj9IsolsnxlTnQKFXg5FJ8MuI8srONY6QQrW1Ucd52bayKZGpqyi88uHHjRjRt2hQbNmzA8OHDsWPHDqhUKr42AMgLUDQaDe7evYsGDRqgQYMGhZpMirN06VIsXLgQx44d0/oSfBOO4woFRzk5OYXyicX/W5oj/7P+eppGo3vJDHkRTcklOUZR+yyN/Oa2t/Xq1SvUrFkT0dHRhV7Tt6NxSa4roPs6vOm6Ojo64s6dOzh27BiOHj2KMWPGYMmSJThx4gTEYnGpmoTs7OxgZ2cHV1dXWFlZ4f3338esWbP4ALM8UMBC9HbMoY1Bj69iGvirHwBCAVp2/RWAfr94y0v+2kZymgWYFEEgEGD69OkICgrC4MGDsWHDBnz99dd8bUq+MWPGYOPGjVi4cCEGDx6MgQMH4uDBg4X6sTDGkJaWxvenWLx4MRYsWIDff/8dHh4eepXN2toa8fHx/N/37t3jh0yXlfr160MulyMqKgojRowo033ny83NxT///MPXpty5cwcpKSlwc3Mrchs3Nzc8efIET5484WtZbt68iZSUFLi7uwMAJBIJ1GrtVepbtGiBhIQEiEQiODs7F7nvU6dO8U1gAHDq1Cl+v2XB3Nwczs7OiIqK0mpmKkgul6N79+7o3r07xo4dC1dXV1y7dg0tWrQoVZNQQflBVHmP2ixVwLJy5UosWbIECQkJaNq0KZYvX67VHlZQTk4OQkJCsGXLFsTFxaFhw4ZYtGgROnfuzOdRq9WYM2cOtm3bhoSEBNjb2yMgIAAzZ86kX6tGQmgiR7U2LZB8+pKhiwIAyIAGUGuw84f3YGlna9Cy0NpGRB/9+vXDpEmTsHLlSly6dAnbt28v1NQxaNAgzJs3D/Pnz0f//v2xf/9+DBo0CDNnzkSnTp1gbW2Na9euITQ0FOPHj0evXr2waNEizJ49Gzt27ICzszMSEhIA5A1/NTN789pfH374IVasWAFvb2+o1WpMmTJF6xd+WZDJZJgyZQomT54MiUSCtm3b4tmzZ7hx4waGDx9eJscQi8UYP348fvjhB4hEIowbNw6tW7cu8jsKAHx9fdGkSRMMGTIEYWFhyM3NxZgxY+Dj48MHfs7OzoiNjcWVK1dQq1YtmJubw9fXF97e3ujVqxcWL16MBg0a4OnTp/jtt9/Qu3dveHh4YNKkSejfvz+aN28OX19f/Prrr4iIiOA775aVOXPmYPTo0bCxsUGXLl2Qnp6OU6dOYfz48di8eTPUajW8vLxgYmKCbdu2QS6X852/9WkSOnz4MBITE/l+PTdu3MCkSZPQtm3bIoO2MqPv8KWdO3cyiUTCNm7cyG7cuMFGjhzJLC0tWWJios78kydPZvb29uy3335j9+/fZ6tWrWIymYxdunSJz7NgwQJWvXp1dujQIRYbG8v27NnDzMzMWHh4eInLRcOay58hh5IWfLyKS2Brhc5srdCZZaYY/v1WZuayth9Hs7YfRzNlZq6hi/POKW44pLHTNayZMcZCQkKYSCRizs7OOreLj49nAoGAHTx4kDGWNxR49erVrFWrVszExIQpFArWsmVLFh4ezpRKJWOMMScnp0JDbwGw4ODgEpU1Li6OderUiZmamrL69euzw4cP6xzWfPnyZX6b48ePMwAsOTmZT8sfWlzUNVCr1Wz+/PnMycmJicViVrt2bfbdd98VeYzk5GQGgB0/fvyN55B/7H379jEXFxcmlUqZr68ve/ToEZ9H13Bgxoof1swYYyqVivXt25dZWlpqDWtOS0tj48ePZ/b29kwsFjNHR0c2ZMgQraHUJRnWXHBIeUmuta7zWLNmDWvYsCETi8VaQ5f379/PvLy8mEKhYKampqx169bs2LFjb7yeuvz555/M29ubWVhYMJlMxurXr8+mTJmi9RnQpSyGNXOM6dejy8vLC61ateJ7uWs0Gjg6OmL8+PGYOnVqofz29vaYMWMGxo4dy6f17dsXcrmcbzP7+OOPYWtriw0bNhSZ503yq0VTU1MLdUgi75bs9Fc4atUSAPDRy4uQGHDlaADIVKnxUb+8TpNH97SjJqEyplKpEBsbizp16pR5B1Lybtm8eTMmTpyIlJQUQxeFvKa4/+OSfn/rNbwiOzsbFy9e1BomJxAI4OvrizNndFeJZ2VlFSqcXC7X6hXfpk0bREVF4e7duwDyel6fPHkSXbp0KbIsWVlZSEtL03oQQggh5N2kVx+W58+fQ61W80Ou8tna2hbZi93Pzw/Lli3DBx98gLp16yIqKgoRERFanZemTp2KtLQ0uLq6QigUQq1WY8GCBRgyZEiRZQkJCcHcuXP1KT55R+Tk5OCQJgUA0D4nBxLDFocQo/f3338X+wOwqEnBjE2XLl3w999/63xt+vTpsLe3r+ASkYpU7qOEwsPDMXLkSLi6uoLjONStWxeBgYHYuHEjn2f37t3Yvn07duzYgUaNGuHKlSuYOHEi7O3ttXpWF5Q/7jtfWlqa1hh68u7Kzs7GGk3ePA8LVCrINRoolUowxiCTySAU5jXJ5OTkIDs7G0KhUKuWT1fegpN6FRwimL9qdHF581ePNpbh1YS8zsPD451YAfnHH39EZqbuaRWsrKxgZWVVaMQVeXfoFbDUqFEDQqEQiYmJWumJiYlas/8VZG1tjQMHDkClUuHFixewt7fH1KlT4eLiwueZNGkSpk6dioEDBwLImyzn0aNHCAkJKTJgkUqlNMtpFSUUCtGWy+u3Ev80HimZSrRp2xaJiYn49Zdf+OGCe/ftw5QpU9C+fXtsKDAL44cdO+LRo0fYtWsXPFrm9YU5fPgwxn/5Jbw8PbFjxw4+78fdu+PWrVvYsnkzPxvn8ePHMWLkSDRp0gQH9u8HAPy42AF3H2TR6tHEKMnlcn4umMrMwcHB0EUgBqRXHxaJRIKWLVtqLWmt0WgQFRUFb2/vYreVyWRwcHBAbm4u9u3bpzWfgFKpLDRbaf76DoS8Ti6XY5rQHtOE9kYRtKpUKvTr3x8zpn5W5vNWEEIIyaP3KKFdu3bB398fa9euhaenJ8LCwrB7927cvn0btra2GDp0KBwcHBASEgIAOHfuHOLi4tCsWTPExcVhzpw5iI2NxaVLl/iZAAMCAnDs2DGsXbsWjRo1wuXLlzFq1CgMGzYMixYtKlG5aJRQ1ZGboeQXvvvo5UWITE0M2iT07Hkq7GzzFphLTEqGjbVlRVyGKoNGCRFS+ZXFKCG9+7AMGDAAz549w+zZs5GQkIBmzZohMjKS74j7+PFjrdoSlUqFmTNn4sGDBzAzM0PXrl3x008/aU1bvHz5csyaNQtjxoxBUlIS7O3t8fnnn2P27Nn6Fo9UMQKBAAKBQOfEWEU1G+rKK5FIIJEU7r6rawrv1/PKZLICK9OKkKnSng2T1hgihJC3p3cNi7GiGpaqo2ANi1/KZYOvKZOhzEFi/GMAwIjJcYU639IaQ2+HalgIqfwqfB4WQkhhMmnx/0b5awwRQggpPVr8kJC3pNFocPbsWQDAgc19+fVXaI0h0r59ezRr1gxhYWEGOX5AQABSUlJw4MABoygPIW+DAhZC3pJKpcKQTz8FAKSl9YCcmi2IkYqIiCjzBQ0JqSgUsBDyljiOQ/3/n+OC+qkQY2ZlZWXoIhBSatSHhVRq6oxM5GYoDfqQMOBwxAEcOXIEJiaG7QBMjE9ubi7GjRsHCwsL1KhRA7NmzeInGPzpp5/g4eEBc3Nz2NnZYfDgwUhKSuK3TU5OxpAhQ2BtbQ25XI769etj06ZN/OtPnjxB//79YWlpCSsrK/Ts2RMPHz4ssizt27fHxIkT+b+dnZ3x3XffYdiwYTA3N0ft2rWxbt06rW30PQYh5YVqWEildsyhjaGLwJO3cIfz6b2GLkaVkpGRAQAwMTHha7eys7ORk5MDkUikNaw9P69cLuenXihqrp6i8pamOWXLli0YPnw4zp8/j3/++QejRo1C7dq1MXLkSOTk5ODbb79Fw4YNkZSUhKCgIAQEBODw4cMAgFmzZuHmzZs4cuQIatSogZiYGH5q+pycHPj5+cHb2xt///03RCIR5s+fj86dO+Pff//VOUxfl++//x7ffvstpk+fjr179+KLL76Aj48PGjZsWGbHIKQsUA0LqXSEJnJUa9PC0MUoJPPSTaiVutc5IeXDzMwMZmZmeP78OZ+2ZMkSmJmZYdy4cVp5bWxsYGZmhsePH/NpK1euhJmZGYYPH66V19nZGWZmZrh16xaftnnz5lKV0dHREaGhoWjYsCGGDBmC8ePHIzQ0FAAwbNgwdOnSBS4uLmjdujV++OEHHDlyhF+M8PHjx2jevDk8PDzg7OwMX19fdO/eHUDeJJ4ajQY//vgjmjRpAjc3N2zatAmPHz9GdHR0icvXtWtXjBkzBvXq1cOUKVNQo0YNHD9+vEyPQUhZoBoWUulwHAfv6B1GExykJT1Hx7puAIB2mZmQmBeemI5UXa1bt9bq2+Tt7Y3vv/8earUaV65cwZw5c3D16lUkJyfzy5E8fvwY7u7u+OKLL9C3b19cunQJnTp1Qq9evdCmTV6t4tWrVxETEwNzc3Ot46lUKty/f7/E5Xvvvff45xzHwc7Ojm+WKqtjEFIWKGAhlRLHcQafMC4fJ5fiCstbQ4jWv6pY+TURBfsOTZo0CRMnToRIpH17y/8SLrj0wtixYzFy5Eh+2YV8+X00CuYt61WAVSoV/Pz84Ofnh+3bt8Pa2hqPHz+Gn58fsrOzAQBdunTBo0ePcPjwYRw9ehQdO3bE2LFjsXTpUrx69QotW7bE9u3bC+3b2tq6xOV4vZmL4zj+c1xWxyCkLFDAQshbkkql+FqQt1q5UCBAeno6OI4DJ5BCIuEKzXxLyk5Jlk4oLq9YLNbZL6WovKVx7tw5rb/Pnj2L+vXr4/bt23jx4gUWLlwIR0dHAMA///xTaHtra2v4+/vD398f77//PiZNmoSlS5eiRYsW2LVrF2xsbMptdu+KOAYhJUV9WAh5SyKRCB0ECnQQKHAsKgoKhQKurq5IjH+MHxc7YOaX1sjMzEWmSm30j3dkpQ6j8vjxYwQFBeHOnTv4+eefsXz5ckyYMAG1a9eGRCLB8uXL8eDBA/zyyy/49ttvtbadPXs2Dh48iJiYGNy4cQOHDh2Cm1te8+OQIUNQo0YN9OzZE3///TdiY2MRHR2NL7/8Ev/991+ZlL0ijkFISVENCyFv6U1zrzRwkeKTEecqRU0LrXtU9oYOHYrMzEx4enpCKBRiwoQJGDVqFDiOw+bNmzF9+nT88MMPaNGiBZYuXYoePXrw20okEkybNg0PHz6EXC7H+++/j507dwLIawb766+/MGXKFPTp0wfp6elwcHBAx44dy6w2pCKOQUhJ0eKHhLylgosxfvjsPHKF/1uduXfv3gCAbNPJUKsrxxDQo3vaQS4TvjljBaHFDwmp/Mpi8UOqYSGkDInFYsj/vzNweno6jh07BgBITNpTaKSFMaF1jwghxo4CFkLKiUQiwYL58wEAFgo5pFLjqbUghJDKhgIWQsqJWCzGwIED+eeEEEJKj0YJEUIIIcToUQ0LIeVEo9Hg7t27AIDaTk78mjSkdN6R8QGEVEll8f9LAQsh5SQzMxNdunYFkNcL3pg73Rqz/OY0pVKpNfMsIaTyUCrzZgN/m+ZxClgIKUdW1aoZugiVnlAohKWlJT+1fsGVmQkhxo0xBqVSiaSkJFhaWhZaBkMfFLAQUk5MTU1x4cIF/jkpPTu7vKUP8oMWQkjlYmlpyf8flxYFLISUIXXG/1aQ1mg00ChV4ORSA5bo3cBxHGrWrAkbGxvk5OQYujiEED2IxeK3qlnJRwELIWXomEObQmnyFu5wPr3XAKV59wiFwjK58RFCKh8KWAh5S0ITOaq1aYHk05e00rOZBuGaRKgvPMWh5y9gbl0DGo0GmZl5tTAFm4mysrKQm5sLsVjMrzSc3/arb96CfTyys7ORk5MDkUgEqfR/NT0ZGRlaeTUaDUSiXOTkUDBACDFOFLAQ8pY4joN39A6olZla6SmJSehTtw6sIMSjh49g9iod//33H3zat4dcLsf1a9f4vNOmT8fu3bsRFBSEsWPGAABevHgBTy8vAMD9mBg+77fz52Pz5s344osv8M3XXwPI64Hf5L33AADX/v0XJiZ5ywMs/f57rF69GgEBAZg1cya/j7r16gEAzp87h+rVqwMAWrocRuTvx6HRnANAgQshxLhQwEJIGeA4DqL/X0Mon8K6BtwhQ1uBOR9AGCulUol169bh1atXyMjIgKmJpaGLRAghWmi1ZkLKSW6GEpEWzaAG0CX5EkSmJkbbJJSckoGRI/whFouxes0GWFUzK6erQggh2mi1ZkKMAMdxEAEQCAT8Q9cEckVNiPa2eWUyWaGl3HXllclkWLpkCQBoBTaEEGIsaK5wQgghhBg9ClgIIYQQYvSoSYgQgoyMDDRu0gQA8PDRU+p0SwgxOhSwEEIAgO8MnJWlRqZKbeDSkIJkUgGtn0SqPApYCCGQy+U4ER0NABj65TXk5NCXozFp4qbAqkXNKGghVRoFLIQQmMhFqFWrFgCA4+IAvBOzHbwzrt1KgypLA7mMJvQjVRcFLIQQrV/uv/7kDYGA+uMbA5VKje6fnTF0MQgxChSwEEKQk5ODTZs2AQBmzpoFqVRs4BIRQog2ClgIIcjOzsb8BQsAAJOnTKHJ4wghRocCFkIIhEIhevTowT8nhBBjQwELIQQymQyhy5bxzwkhxNhQwEJIBVBnZBq6CMXSaDTQKFXg5NQURAgxTqUKWFauXIklS5YgISEBTZs2xfLly+Hp6akzb05ODkJCQrBlyxbExcWhYcOGWLRoETp37qyVLy4uDlOmTMGRI0egVCpRr149bNq0CR4eHqUpIiFG5ZhDG0MXoUTkLdzhfHqvoYtBCCGF6D12cdeuXQgKCkJwcDAuXbqEpk2bws/PD0lJSTrzz5w5E2vXrsXy5ctx8+ZNjB49Gr1798bly5f5PMnJyWjbti3EYjGOHDmCmzdv4vvvv0e1atVKf2aEGJjQRI5qbVoYuhglomIaDM69j97nf0Xas+eGLg4hhBTCMcb0miHKy8sLrVq1wooVKwDkVSU7Ojpi/PjxmDp1aqH89vb2mDFjBsaOHcun9e3bF3K5HNu2bQMATJ06FadOncLff/9d6hNJS0uDhYUFUlNToVAoSr0fQsoSYwxqpXE3BwFASmISrOvWAQC8jE9ANTtbA5eIAECmSo2P+p0EABzd044mjiPvpJJ+f+vVJJSdnY2LFy9i2rRpfJpAIICvry/OnNE9uVFWVlahTnxyuRwnT57k//7ll1/g5+eHfv364cSJE3BwcMCYMWMwcuTIIsuSlZWFrKws/u+0tDR9ToWQCsFxHESmJoYuxhuZVbfCSqETgLz/T0IIMTZ6NQk9f/4carUatrbav75sbW2RkJCgcxs/Pz8sW7YM9+7dg0ajwdGjRxEREYH4+Hg+z4MHD7B69WrUr18fv//+O7744gt8+eWX2LJlS5FlCQkJgYWFBf9wdHTU51QIIQUIBAI4cVI4cVKa5ZYQYpTK/c4UHh6O+vXrw9XVFRKJBOPGjUNgYKDWTVGj0aBFixb47rvv0Lx5c4waNQojR47EmjVritzvtGnTkJqayj+ePHlS3qdCCCGEEAPRK2CpUaMGhEIhEhMTtdITExNhZ2encxtra2scOHAAGRkZePToEW7fvg0zMzO4uLjweWrWrAl3d3et7dzc3PD48eMiyyKVSqFQKLQehJDSycnJQaQmBZGaFOTk5Bi6OIQQUoheAYtEIkHLli0RFRXFp2k0GkRFRcHb27vYbWUyGRwcHJCbm4t9+/ahZ8+e/Gtt27bFnTt3tPLfvXsXTk5O+hSPEFJK2dnZWKFJwgpNErKzsw1dHEIIKUTveViCgoLg7+8PDw8PeHp6IiwsDBkZGQgMDAQADB06FA4ODggJCQEAnDt3DnFxcWjWrBni4uIwZ84caDQaTJ48md/nV199hTZt2uC7775D//79cf78eaxbtw7r1q0ro9MkhBRHKBSiNWcKABBwHF69egXGGGQyGT9Vf25uLrKysiAQCLQ65iqVyhLnzczMhEajgVQqhUiUd/tRq9VQqVR65eU4DiYm/+vMrFKpoFarIZFIIBaL9c6r0WiQmZk3msvU1JTPm5WVhdzcXIjFYkgkEr3zMsagVCoBACYmJvyq2NnZ2cjJyXljXo1GA5EoFzk5NDqIELBSWL58OatduzaTSCTM09OTnT17ln/Nx8eH+fv7839HR0czNzc3JpVKWfXq1dlnn33G4uLiCu3z119/ZY0bN2ZSqZS5urqydevW6VWm1NRUBoClpqaW5pQIqdKy0tLZIVEDdkjUgN379zpzcHBgANj+iAh2PyaG3Y+JYcu+/54BYG3btuXT7sfEsPr16jEAbPu2bXza6tWrGQDWokULrbxNmjRhANiP69fzaVs2b2YAmJubm1ZeL09PBoAt/+EHPm3Xrl0MAHNyctLK2759ewaALVq0iE/79ZdfGABma2urlbdL584MAJsTHMynHTt2jAFg5ubmWnn79OnDALApU6bwaadOnmQAmEgk0sr76ZAhDAD7cvx4Pu3ypUsMAAPAbt+6xaePGDGCAWAjRozg027fusXnvXzpEp/+5fjxzL1Rc/YqI9vQHxNCykVJv79LNdPtuHHjMG7cOJ2vRUdHa/3t4+ODmzdvvnGfH3/8MT7++OPSFIcQ8pbyf/kT46JUKrFu/XqoVCpkZGTA1MTS0EUixGD0njjOWNHEcYSUXm6GEr9bNgcAfPTyIrI5UJMQDN8klJySgTFfjIBELEb48jWwqmZWkreTkEqlpN/fFLAQQrQCFr+Uy5VisruqIEOZg8T4vNGStjVrw9REbOASEVL2Svr9TTNEEUIIIcToUcBCCCGEEKNXqk63hBBCyl9GRgYauroCAOKePqNOt6RKo4CFEEKMWG5urqGLQIhRoICFEEKMlFwux6n/X9meVtEmVR0FLIQQYqQEAgG/Thutok2qOgpYCCGkEsjKUpdZ0CKTCmiyQFLpUMBCCCFGKjs7G+vWrwcAnL3zEXJzy+aW3cRNgVWLmlHQQioVClgIIcRICQVqLFq0CADwQY8OKKtb9rVbaVBlaSCX0aKKpPKggIUQQoyUWCxGnz59AADrf2z71h1vVSo1un92piyKRkiFo4CFEEKMlFQqxZLFiwEAlhYm1PGWVGn06SeEEEKI0aOAhRBCCCFGj5qECCHESGVkZKBZ87xVtOPi4mBubm7gEhFiOBSwEEKIEUtPTzd0EQgxChSwEEK0qDMyDV0E8v/EGoajv/4GTiahqflJlUcBCyFEyzGHNoYuAnmNvIU7uNMdDF0MQgyKOt0SQiA0kaNamxaGLgYpQualm1ArqeaLVG1Uw0IIAcdx8I7eQV+KRkb5MgVTnPM63bbPyYHEwOUhxJAoYCGEAMgLWkSmJoYuBilAnZ6ONZokAMB32dkwNXB5CDEkClgIIcRICYVCtOXM+OeEVGUUsBBCiJGSyWSYJrTnnxNSlVGnW0IIIYQYPQpYCCGEEGL0qEmIEEKMlFKpxNDc+wCA+0olJOZmBi4RIYZDAQshhBgpxhheQs0/J6Qqo4CFEEKMlEwmww/C2vxzQqoyClgIIcRICYVCuHAy5DKGTKUSTCLWClyUSiUYY5DJZPyw59zcXGRlZUEgEGitP5SZmYkMZQ7EYg1ycqj7Iql86FNLCCFGLpqlobqDPbp164aHsbH8o3HjxlAoFNi/fz+ftm7tWigUCnT88EOtvK1atYKtTTUE9nyMmV9aUxMTqXQoYCGEECPFcVy57LeBi5QCFlLpcOwd+dSmpaXBwsICqampUCgUhi4OIYS8tdwMJX63bI5cxvDBf39DqjB/qyahVxnZSEt5BqFQCNuatWFqIq7wcyLkdSX9/qY+LIQQYuREHAdzc3OITLTXejIzKzzMWSKRQCIpvEyiqakpctUCHPrrLwDAJ/2HAKCAhVQeFLAQQkgVkZWVhREjRwIAuvf8BIC8+A0IMSIUsBBCSBUhEAjQpEkTAEBODkOmSg2ZVFBufWUIKUsUsBBCSBUhl8txYP9+AMBn468hO5uhiZsCqxY1o6CFGD0aJUQIIVWETFr4ln/tVhpUWRoDlIYQ/VDAQgghVUTBWpS9P3oasCSE6I+ahAghpIrIzMxEv/79AQC//3HcwKUhRD8UsBBCSBWh0Whw6dIl/jkhlUmpmoRWrlwJZ2dnyGQyeHl54fz580XmzcnJwbx581C3bl3IZDI0bdoUkZGRReZfuHAhOI7DxIkTS1M0QgghRZBKpVi9ejVWr14NqVRq6OIQohe9A5Zdu3YhKCgIwcHBuHTpEpo2bQo/Pz8kJSXpzD9z5kysXbsWy5cvx82bNzF69Gj07t0bly9fLpT3woULWLt2Ld577z39z4QQQkixRCIROn30ETp99BFEIqpgJ5WL3gHLsmXLMHLkSAQGBsLd3R1r1qyBiYkJNm7cqDP/Tz/9hOnTp6Nr165wcXHBF198ga5du+L777/Xyvfq1SsMGTIE69evR7Vq1Up3NoQQQgh5J+kVsGRnZ+PixYvw9fX93w4EAvj6+uLMmTM6t8nKytJa+wLImwvg5MmTWmljx45Ft27dtPZdnKysLKSlpWk9CCGEFE2tVuPs2bM4e/Ys1Gq1oYtDiF70qhN8/vw51Go1bG1ttdJtbW1x+/Ztndv4+flh2bJl+OCDD1C3bl1ERUUhIiJC659l586duHTpEi5cuFDisoSEhGDu3Ln6FJ8QQqo0lUqFIZ9+CgBITOpm4NIQop9yn4clPDwc9evXh6urKyQSCcaNG4fAwEAIBHmHfvLkCSZMmIDt27cXqokpzrRp05Camso/njx5Ul6nQAgh7wSO41C/Xj3Ur1ePZrYllY5eNSw1atSAUChEYmKiVnpiYiLs7Ox0bmNtbY0DBw5ApVLhxYsXsLe3x9SpU+Hi4gIAuHjxIpKSktCiRQt+G7Vajb/++gsrVqxAVlYWv2x6QVKplHq5E0KIHkxMTPhRmiavrfxMiLHTq4ZFIpGgZcuWiIqK4tM0Gg2ioqLg7e1d7LYymQwODg7Izc3Fvn370LNnTwBAx44dce3aNVy5coV/eHh4YMiQIbhy5YrOYIUQQgghVYve49qCgoLg7+8PDw8PeHp6IiwsDBkZGQgMDAQADB06FA4ODggJCQEAnDt3DnFxcWjWrBni4uIwZ84caDQaTJ48GQBgbm6Oxo0bax3D1NQU1atXL5ROCCGEkKpJ74BlwIABePbsGWbPno2EhAQ0a9YMkZGRfEfcx48f8/1TgLxOXjNnzsSDBw9gZmaGrl274qeffoKlpWWZnQQhhJA3y8zMxFB/fwDAgYOHDVwaQvTDMcaYoQtRFtLS0mBhYYHU1FQoFApDF4cQQt5aboYSv1s2BwD4pVyGyPTt+p2kp6fz98fEpGT0GXYVAHB0TzvIZdT8TgyjpN/fNNUhIYRUEVKpFMv+f9JOGrRAKhsKWAghpIoQiUT8gAeamp9UNvSJJYSQSkCdkfnW+9BoNNAoVeDkVLtCKh8KWAghpBI45tDmrfehZgz3kQVpwzoYcuZIGZSKkIpT7jPdEkIIKR2hiRzV2rR4c8YSygFDkPoxxt48AeXL5DLbLyEVgWpYCCHESHEcB+/oHVAr3745CABSE5/Bpm49ft+EVCYUsBBCiBHjOO6thzPnM7eujo2ivGVRaGp+UtlQkxAhhBBCjB4FLIQQQggxetQkRAghVYRKpcJ8dRxyARzKzIREwiE7+52Y7JxUARSwEEJIFaFWq3GWZaAmxEiMf4IfFzvg7oMsvCMrtJB3HAUshBBSRZiamsIdMnwoUEAmk+Hu3bsAAKXSuVSjhmRSAY02IhWGAhZCCKkiBAIBFgkdoQZgprBCl2bvAQA+6BEJtUam9/6auCmwalEzClpIhaCAhRBCqhCO4yACIJMJYVWt2lvt69qtNKiyNLTSM6kQFLAQQkgVZGZmhgsXLgAAbGvWhkBQ8kGjKpUa3T87U15FI0QnClgIIaSKk8uEegUshBgCfUIJIYQQYvSohoUQQqoglUqFr4KCAAA///wzTdVPjB4FLIQQUgWp1Wr88ssv/HNCjB0FLIQQUgVJJBLMnDGDf06IsaOAhRBCqiCxWIzAwED+OSHGjjrdEkIIIcToUcBCCCFVkEajwX///Yf//vsPGo3G0MUh5I2oSYgQQqqgzMxM+LRvDwBIS0uDubm5YQtEyBtQwEIIIVWUXC43dBEIKTEKWAghpAoyNTXF9WvX+OeEGDvqw0IIIYQQo0cBCyGEEEKMHjUJEUJIFZSVlYVp06cDADZv3kz9WYjRoxoWQgipgnJzc7F7927s3r0bubm5hi4OIW9ENSyEEFIFicViBP3/4oc00y2pDChgIYSQKkgikWDsmDH8c0KMHTUJEUIIIcToUcBCCCFVEGMML168wIsXL8AYM3RxCHkjahIihJAqSKlUwtPLCwBNzU8qB6phIYQQQojRoxoWQgipgkxNTXE/JoZ/ToixoxoWQgghhBg9ClgIIYQQYvSoSYgQQqqgrKwsfDt/PgBg1apVNDU/MXqlqmFZuXIlnJ2dIZPJ4OXlhfPnzxeZNycnB/PmzUPdunUhk8nQtGlTREZGauUJCQlBq1atYG5uDhsbG/Tq1Qt37twpTdEIIYSUQFZaOjZv3ozNmzfT1PykUtA7YNm1axeCgoIQHByMS5cuoWnTpvDz80NSUpLO/DNnzsTatWuxfPly3Lx5E6NHj0bv3r1x+fJlPs+JEycwduxYnD17FkePHkVOTg46deqEjIyM0p8ZIYSQIp1s0An9OSsMrtkAIhFVthPjxzE9Zwzy8vJCq1atsGLFCgCARqOBo6Mjxo8fj6lTpxbKb29vjxkzZmDs2LF8Wt++fSGXy7Ft2zadx3j27BlsbGxw4sQJfPDBByUqV1paGiwsLJCamgqFQqHPKRFCSJXAGMOZ9oORfPqSVlqbxycgNjeDiYkJOI4DAGRnZyMnJwcikQhSqZTPn5GRgUyVGgM+v4qcHODonnaQy4QVfi7k3VHS72+9wurs7GxcvHgR06ZN49MEAgF8fX1x5swZndtkZWVBJpNppcnlcpw8ebLI46SmpgIArKysisyTlZWFrKws/u+0tLQSnQMhhFRVHMfBO3oH1MpM5KRn4E/HdsgCQ3UHewDAtX//hYmJCQBg6fffY/Xq1QgICMCsmTP5fdStVw8AcP7cObxINUNmZsmak2RSAR8MEVIaegUsz58/h1qthq2trVa6ra0tbt++rXMbPz8/LFu2DB988AHq1q2LqKgoREREQK1W68yv0WgwceJEtG3bFo0bNy6yLCEhIZg7d64+xSeEkCqP4ziITE3KZDr+Bi5SfDLiHLKz37yvJm4KrFrUjIIWUmrl3nAZHh6OkSNHwtXVFRzHoW7duggMDMTGjRt15h87diyuX79ebA0MAEybNo1fGh3Iq2FxdHQs07ITQsi7Kj9wkILDy/gEiExNtJqEli1bhkWLFhVqEkpLS4NGo8HzZ8/0Ot61W2lQZWmo+YiUml4BS40aNSAUCpGYmKiVnpiYCDs7O53bWFtb48CBA1CpVHjx4gXs7e0xdepUuLi4FMo7btw4HDp0CH/99Rdq1apVbFmkUqnWPxEhhBD9cRwHc3NziExNtNJlMlmh5nwAMDc3h0qlwqrVqwEA+zbM05kvn0qlRvfPdHcZIEQfeo0SkkgkaNmyJaKiovg0jUaDqKgoeHt7F7utTCaDg4MDcnNzsW/fPvTs2ZN/jTGGcePGYf/+/fjzzz9Rp04dPU+DEEJIRcnJycGyZcuwbNkyiIR5tSZFPWRUo0LKiN5NQkFBQfD394eHhwc8PT0RFhaGjIwMBAYGAgCGDh0KBwcHhISEAADOnTuHuLg4NGvWDHFxcZgzZw40Gg0mT57M73Ps2LHYsWMHDh48CHNzcyQkJAAALCwsaDIjQggxMiKRCP379+efE1IR9P6kDRgwAM+ePcPs2bORkJCAZs2aITIyku+I+/jxYwgE/6u4UalUmDlzJh48eAAzMzN07doVP/30EywtLfk8q/+/arF9+/Zax9q0aRMCAgL0PytCCCHlRiqVIuS77/jnhFSEUoXG48aNw7hx43S+Fh0drfW3j48Pbt68Wez+yqK3OiGEEELeXbT4ISGEEEKMHjU+EkII0UtGRgYaN2kCIG+UqLm5uYFLRKoCClgIIYToLTMz09BFIFUMBSyEEEL0IpfLceL/+yvSSE5SUShgIYQQoheBQMBP7llwVCgh5Yk+aYQQQggxehSwEEII0UtOTg42bdqETZs2IScnx9DFIVUENQkRQgjRS3Z2NuYvWAAAmDxlCk0eRyoEBSyEEEL0IhQK0aNHD/45IRWBAhZCCCF6kclkCF22jH9OSEWgPiyEEEIIMXoUsBBCCCHE6FGTECGEEL1kZGSgVatWAICHjx7R1PykQlDAQgghRG8vk5MNXQRSxVDAQgghRC9yuRxHDh/mnxNSEShgIYQQoheBQIAGDRrwzwmpCPRJI4QQQojRo4CFEEKIXnJycrBz507s3LmTpuYnFYaahAghhOglOzsbM2bOBACM//JLmpqfVAgKWAghhOhFKBTC19eXf05IRaCAhRBCiF5kMhnWrlnDPyekIlAfFkIIIYQYPQpYCCGEEGL0qEmIEEKIXpRKJT7w8QEA3L59G2ZmZgYuEakKKGAhhBCiF8YY4uLi+OeEVAQKWAghhOhFJpNhf0QE/5yQikABCyGEEL0IhUK89957/HNCKgIFLIQQUsWpMzL1yq/RaKBRqsDJacI4UnEoYCGEkCrumEMbvfKrGcNfLB1wssP8G6ch+f+ZbpVKJRhjkMlkfM1LdnY2hAIVctUU3JC3Q8OaCSGkChKayFGtTYtSbZsDhu81Cfg+9gru/nsdD2Nj8TA2Fi2aN4dCocCe3bv5tJ93bMFfv3RGysMgaDSaMj4LUpVQDQshhFRBHMfBO3oH1Er9moMAICc9A+41a0PCCWBiYlJsXpVKBQCQSTmkpLyCQKAoVXmJcZBJBeA4ziDH5tg7MiYtLS0NFhYWSE1NhUJB/xCEEFJecjOUiLRohmwwfJx8GSLTvKBFV5NQWroK/z2+DxMTE4yYHIfs7HfiK6fKOrqnHeSysu1oXdLvb6phIYQQojeO4yAFB4FAAIEgr3eBrgnkzM2kb6yFIaQkKGAhhBBSbgo2H/z6kzcf3JDKSSY13PtHAQshhJByk5mZiV69ewMAzpw5A7nM1MAlIpUVBSyEEELKjUajwbVr1/jnhJQWBSyEEELKjVQqxY/r1/PPCSktClgIIYSUG5FIhA4dOvDPCSkt6v1ECCGEEKNH4S4hhJByo1arcfLkSQCAY+3aNEqIlBoFLIQQQsqNSqWCf0AAAKB3nz4Qi8WGLRCptEoV6q5cuRLOzs6QyWTw8vLC+fPni8ybk5ODefPmoW7dupDJZGjatCkiIyPfap+EEEIqB4FAADc3N7i5uVHtCnkren96du3ahaCgIAQHB+PSpUto2rQp/Pz8kJSUpDP/zJkzsXbtWixfvhw3b97E6NGj0bt3b1y+fLnU+ySEEFI5yOVyHPr1Vxz69VfI5XJDF4dUYnqvJeTl5YVWrVphxYoVAPLG1Ts6OmL8+PGYOnVqofz29vaYMWMGxo4dy6f17dsXcrkc27ZtK9U+ASArKwtZWVn832lpaXB0dKS1hAghpJzlZijxu2VzAIBfyv/WEtJFo9HgYWwsAMC5Th2qZSGFlHQtIb0+OdnZ2bh48SJ8fX3/twOBAL6+vjhz5ozObbKysiCTybTS5HI53wmrNPsEgJCQEFhYWPAPR0dHfU6FEEIIIZWIXgHL8+fPoVarYWtrq5Vua2uLhIQEndv4+flh2bJluHfvHjQaDY4ePYqIiAjEx8eXep8AMG3aNKSmpvKPJ0+e6HMqhBBCKkBmZiYGDx6MwYMHIzMz09DFIZVYuY8SCg8Px8iRI+Hq6gqO41C3bl0EBgZi48aNb7VfqVRKsyYSQoiR02g0OPf/gyhoan7yNvQKWGrUqAGhUIjExESt9MTERNjZ2encxtraGgcOHIBKpcKLFy9gb2+PqVOnwsXFpdT7JIQQUjlIpVIs/+EH/jkhpaVXk5BEIkHLli0RFRXFp2k0GkRFRcHb27vYbWUyGRwcHJCbm4t9+/ahZ8+eb71PQgghxk0kEqFr167o2rUrTc1P3oren56goCD4+/vDw8MDnp6eCAsLQ0ZGBgIDAwEAQ4cOhYODA0JCQgAA586dQ1xcHJo1a4a4uDjMmTMHGo0GkydPLvE+CSGEEFK16R2wDBgwAM+ePcPs2bORkJCAZs2aITIyku80+/jxY61hayqVCjNnzsSDBw9gZmaGrl274qeffoKlpWWJ90kIIaRyUqvV+OfiRQA0NT95O3rPw2KsSjqOmxBCyNvRZx6W9PR0/p6clpYGc3PzCikjqTxK+v1NDYqEEELKDcdxcHJy4p8TUloUsBBCCCk3JiYm+PP/B1WYmBRdE0PIm1BjIiGEEEKMHgUshBBCCDF61CRECCGk3KhUKgwfMQIA8Ntvv1GzECk1ClgIIYSUG7VajejoaP45IaVFAQshhJByI5FIsGjRIv45IaVFAQshhJByIxaL8UnfvvxzQkqLOt0SQgghxOhRDQshhJByo1arcfPmTQA0NT95OxSwEEIIKTcqlQrde/QAkDcFOzULkdKigIUQQki54TiOX8iWpuYnb4MCFkIIIeXGxMQEp0+d4p8TUlrUmEgIIYQQo0cBCyGEEEKMHjUJEUIIKTcqlQrjxo0DAOzdt4+ahUipUcBCCCGk3KjVahyJjOSfE1JaFLAQQggpNxKJBHOCg/nnhJQWBSyEEELKjVgsxmeffcY/J6S0KGAhhBBSauqMzGJf12g00ChV4OTSCioReVdRwEIIIaTUjjm0KfZ1DWOIRw7g6oyAi5EQ/X8tS1ZWFnJzcyEWi/mmIo1Gg8zMvADI1NSU34euvIwxKJVKAHnzu+RPSpednY2cnBy98opEIkil/wuoMjIyAAByuZxfSkCfvDk5OcjOzoZQKIRMJuPzKpVKMMYgk8kgFAr1zpubm4usrCwIBALI5XI+b2ZmJjQajV55pVIpRKK8EECtVkOlUoHjOK1O0brychxnsAkAaVgzIYQQvQhN5KjWpkWJ8maD4XP1Q3x+Ixq3r/yLh7GxeBgbi08//RQKhQJz5szh086fOweFQgErKys+7WFsLEaNHAmFQoEpkyfzadf+/RcKhQIKhQIx9+7x6ePHj4dCocD48eP5tJh79/i81/79XxmmTJ4MhUKBUSNHah3PysoKCoUC58+d49PmzJkDhUKBTz/9VCuvg4MDFAoFTpw4wactDAmBQqHAJ337auWtV68eFAoFfo+M5NPCw8OhUCjQrVs3rbyNGzeGQqHA/v37+bR1a9dCoVCg44cfauVt1aoVFAoFft6xg0/7aetWKBQKeHt7a+X94IMPoFAosHHjRj5tz+7dUCgUaNG8uVZePz8/KBQKrFyxgk9jjJXzp6toVMNCCCFELxzHwTt6B9TK4puDACAnPQPuNWvjEbJpSDN5KxwzZLhUhtLS0mBhYYHU1FQoFApDF4cQQgiA3AwlIi2aIQsM3ZMvQ2SaF7RQkxA1CeUr6fc3BSyEEELKTW6GEr9bNgcA+KX8L2AhJF9Jv7+pDwshhBBCjB4FLIQQQggxehSwEEIIIcToUcBCCCGEEKNHAQshhBBCjB4FLIQQQggxehSwEEIIIcToUcBCCCGEEKNHAQshhBBCjB4FLIQQQggxehSwEEIIIcToUcBCCCGEEKNHAQshhBBCjB4FLIQQQggxehSwEEIIIcTolSpgWblyJZydnSGTyeDl5YXz588Xmz8sLAwNGzaEXC6Ho6MjvvrqK6hUKv51tVqNWbNmoU6dOpDL5ahbty6+/fZbMMZKUzxCCCGEvGNE+m6wa9cuBAUFYc2aNfDy8kJYWBj8/Pxw584d2NjYFMq/Y8cOTJ06FRs3bkSbNm1w9+5dBAQEgOM4LFu2DACwaNEirF69Glu2bEGjRo3wzz//IDAwEBYWFvjyyy/f/iwJIYQQUqnpXcOybNkyjBw5EoGBgXB3d8eaNWtgYmKCjRs36sx/+vRptG3bFoMHD4azszM6deqEQYMGadXKnD59Gj179kS3bt3g7OyMTz75BJ06dXpjzQ0hhBBCqga9Apbs7GxcvHgRvr6+/9uBQABfX1+cOXNG5zZt2rTBxYsX+eDjwYMHOHz4MLp27aqVJyoqCnfv3gUAXL16FSdPnkSXLl2KLEtWVhbS0tK0HoQQQgh5N+nVJPT8+XOo1WrY2tpqpdva2uL27ds6txk8eDCeP3+Odu3agTGG3NxcjB49GtOnT+fzTJ06FWlpaXB1dYVQKIRarcaCBQswZMiQIssSEhKCuXPn6lN8QgghhFRS5T5KKDo6Gt999x1WrVqFS5cuISIiAr/99hu+/fZbPs/u3buxfft27NixA5cuXcKWLVuwdOlSbNmypcj9Tps2DampqfzjyZMn5X0qhBBCCDEQvWpYatSoAaFQiMTERK30xMRE2NnZ6dxm1qxZ+OyzzzBixAgAQJMmTZCRkYFRo0ZhxowZEAgEmDRpEqZOnYqBAwfyeR49eoSQkBD4+/vr3K9UKoVUKtWn+IQQQgippPSqYZFIJGjZsiWioqL4NI1Gg6ioKHh7e+vcRqlUQiDQPoxQKAQAfthyUXk0Go0+xSOEEELIO0rvYc1BQUHw9/eHh4cHPD09ERYWhoyMDAQGBgIAhg4dCgcHB4SEhAAAunfvjmXLlqF58+bw8vJCTEwMZs2ahe7du/OBS/fu3bFgwQLUrl0bjRo1wuXLl7Fs2TIMGzasDE+VEEIIIZWV3gHLgAED8OzZM8yePRsJCQlo1qwZIiMj+Y64jx8/1qotmTlzJjiOw8yZMxEXFwdra2s+QMm3fPlyzJo1C2PGjEFSUhLs7e3x+eefY/bs2WVwioQQQgip7Dj2jkwnm5aWBgsLC6SmpkKhUBi6OIQQQgDkZijxu2VzAIBfymWITE0MXCJibEr6/U1rCRFCCCHE6FHAQgghhBCjRwELIYQQQoweBSyEEEIIMXoUsBBCCCHE6FHAQgghhBCjRwELIYQQQoweBSyEEEIIMXoUsBBCCCHE6FHAQgghhBCjRwELIYQQQoweBSyEEEIIMXoUsBBCCCHE6FHAQgghhBCjRwELIYQQQoweBSyEEEIIMXoUsBBCCCHE6FHAQgghhBCjJzJ0AQghhFQN6oxMQxeBvCWhiRwcxxnk2BSwEEIIqRDHHNoYugjkLfmlXIbI1MQgx6YmIUIIIeVGaCJHtTYtDF0M8g6gGhZCCCHlhuM4eEfvgFpJzUHvAqGJ3GDHpoCFEEJIueI4zmDNCOTdQU1ChBBCCDF6FLAQQgghxOhRwEIIIYQQo0cBCyGEEEKMHgUshBBCCDF6FLAQQgghxOhRwEIIIYQQo0cBCyGEEEKMHgUshBBCCDF6FLAQQgghxOhRwEIIIYQQo0cBCyGEEEKMHgUshBBCCDF678xqzYwxAEBaWpqBS0IIIYSQksr/3s7/Hi/KOxOwpKenAwAcHR0NXBJCCCGE6Cs9PR0WFhZFvs6xN4U0lYRGo8HTp09hbm4OjuPKbL9paWlwdHTEkydPoFAoymy/xqyqnXNVO1+g6p1zVTtfoOqdc1U7X+DdOWfGGNLT02Fvbw+BoOieKu9MDYtAIECtWrXKbf8KhaJSfyBKo6qdc1U7X6DqnXNVO1+g6p1zVTtf4N045+JqVvJRp1tCCCGEGD0KWAghhBBi9ChgeQOpVIrg4GBIpVJDF6XCVLVzrmrnC1S9c65q5wtUvXOuaucLVL1zfmc63RJCCCHk3UU1LIQQQggxehSwEEIIIcToUcBCCCGEEKNHAQshhBBCjB4FLIQQQggxehSwvMHKlSvh7OwMmUwGLy8vnD9/3tBFKpWQkBC0atUK5ubmsLGxQa9evXDnzh2tPO3btwfHcVqP0aNHa+V5/PgxunXrBhMTE9jY2GDSpEnIzc2tyFMpkTlz5hQ6F1dXV/51lUqFsWPHonr16jAzM0Pfvn2RmJiotY/Kcq75nJ2dC50zx3EYO3YsgMr//v7111/o3r077O3twXEcDhw4oPU6YwyzZ89GzZo1IZfL4evri3v37mnlefnyJYYMGQKFQgFLS0sMHz4cr1690srz77//4v3334dMJoOjoyMWL15c3qdWpOLOOScnB1OmTEGTJk1gamoKe3t7DB06FE+fPtXah67PxcKFC7XyGMs5v+k9DggIKHQunTt31srzLr3HAHT+T3MchyVLlvB5KtN7/FYYKdLOnTuZRCJhGzduZDdu3GAjR45klpaWLDEx0dBF05ufnx/btGkTu379Orty5Qrr2rUrq127Nnv16hWfx8fHh40cOZLFx8fzj9TUVP713Nxc1rhxY+br68suX77MDh8+zGrUqMGmTZtmiFMqVnBwMGvUqJHWuTx79ox/ffTo0czR0ZFFRUWxf/75h7Vu3Zq1adOGf70ynWu+pKQkrfM9evQoA8COHz/OGKv87+/hw4fZjBkzWEREBAPA9u/fr/X6woULmYWFBTtw4AC7evUq69GjB6tTpw7LzMzk83Tu3Jk1bdqUnT17lv3999+sXr16bNCgQfzrqampzNbWlg0ZMoRdv36d/fzzz0wul7O1a9dW1GlqKe6cU1JSmK+vL9u1axe7ffs2O3PmDPP09GQtW7bU2oeTkxObN2+e1vte8P/emM75Te+xv78/69y5s9a5vHz5UivPu/QeM8a0zjU+Pp5t3LiRcRzH7t+/z+epTO/x26CApRienp5s7Nix/N9qtZrZ29uzkJAQA5aqbCQlJTEA7MSJE3yaj48PmzBhQpHbHD58mAkEApaQkMCnrV69mikUCpaVlVWexdVbcHAwa9q0qc7XUlJSmFgsZnv27OHTbt26xQCwM2fOMMYq17kWZcKECaxu3bpMo9Ewxt6t9/f1G7tGo2F2dnZsyZIlfFpKSgqTSqXs559/ZowxdvPmTQaAXbhwgc9z5MgRxnEci4uLY4wxtmrVKlatWjWt850yZQpr2LBhOZ/Rm+n6Mnvd+fPnGQD26NEjPs3JyYmFhoYWuY2xnnNRAUvPnj2L3KYqvMc9e/ZkH374oVZaZX2P9UVNQkXIzs7GxYsX4evry6cJBAL4+vrizJkzBixZ2UhNTQUAWFlZaaVv374dNWrUQOPGjTFt2jQolUr+tTNnzqBJkyawtbXl0/z8/JCWloYbN25UTMH1cO/ePdjb28PFxQVDhgzB48ePAQAXL15ETk6O1nvr6uqK2rVr8+9tZTvX12VnZ2Pbtm0YNmyY1url79L7W1BsbCwSEhK03lMLCwt4eXlpvaeWlpbw8PDg8/j6+kIgEODcuXN8ng8++AASiYTP4+fnhzt37iA5ObmCzqb0UlNTwXEcLC0ttdIXLlyI6tWro3nz5liyZIlWM19lO+fo6GjY2NigYcOG+OKLL/DixQv+tXf9PU78v/buLqTJ940D+PefualUvrS5TUVRMylykUJjVJ4Y0giKPMgseqMyKonIQgo6qIPyyA46kA5Mgw6kg0ooKHylTBOULZNotDGVwBcyVoaJL/v+D37swec/W/Czctv/+oDwcN/383Bfu/Y8u3y2exsbw7Nnz3DixImAvkjK8c9EzK81/26fP3/G/Py86uINAAaDAR8+fFimWf0ePp8PFy5cwLZt27Bp0yal/eDBg8jIyEBKSgr6+/tRVVUFp9OJR48eAQBGR0cXfTz8faHEYrGgoaEBubm5GBkZwfXr17Fjxw4MDAxgdHQUGo0m4KJuMBiUOMIp1sU8efIEXq8Xx44dU9oiKb//yz+/xea/MKfJycmq/pUrVyIpKUk1JjMzM+AY/r7ExMQ/Mv/fYXp6GlVVVSgrK1P9cu/58+eRn5+PpKQkdHV14cqVKxgZGUFNTQ2A8Ip5165dKCkpQWZmJtxuN65evQqbzYbu7m5ERUVFfI7v37+P1atXo6SkRNUeSTkORgqW/0Pnzp3DwMAAOjs7Ve3l5eXKdl5eHkwmE4qKiuB2u5Gdnf23p7kkNptN2TabzbBYLMjIyMDDhw8RGxu7jDP7O+rq6mCz2ZCSkqK0RVJ+hdrs7Cz2798PkqitrVX1Xbx4Udk2m83QaDQ4ffo0bt26FXa/QXPgwAFlOy8vD2azGdnZ2ejo6EBRUdEyzuzvuHfvHg4dOoSYmBhVeyTlOBh5S+gndDodoqKiAlaOjI2NwWg0LtOslq6iogJPnz5Fe3s70tLSgo61WCwAAJfLBQAwGo2LPh7+vlCWkJCA9evXw+VywWg0YmZmBl6vVzVmYW7DOdahoSG0tLTg5MmTQcdFUn798wt2vhqNRoyPj6v65+bm8OXLl7DOu79YGRoaQnNzs+ruymIsFgvm5uYwODgIIDxj9svKyoJOp1M9hyMxxwDw6tUrOJ3OX57XQGTleCEpWH5Co9GgoKAAra2tSpvP50NrayusVusyzuzfIYmKigo8fvwYbW1tAbcHF+NwOAAAJpMJAGC1WvHu3TvVBcF/gdy4ceMfmffv8v37d7jdbphMJhQUFCA6OlqVW6fTieHhYSW34RxrfX09kpOTsXv37qDjIim/mZmZMBqNqpx++/YNPT09qpx6vV709fUpY9ra2uDz+ZTizWq14uXLl5idnVXGNDc3Izc3NyRvm/uLlY8fP6KlpQVr16795T4OhwMrVqxQ3joJt5gX+vTpEyYmJlTP4UjLsV9dXR0KCgqwefPmX46NpByrLPenfkNZY2MjtVotGxoa+P79e5aXlzMhIUG1iiJcnDlzhvHx8ezo6FAtfZuamiJJulwu3rhxg729vfR4PGxqamJWVhYLCwuVY/iXvRYXF9PhcPD58+fU6/Uhs+x1ocrKSnZ0dNDj8fD169fcuXMndTodx8fHSf6zrDk9PZ1tbW3s7e2l1Wql1WpV9g+nWBean59neno6q6qqVO2RkN/JyUna7Xba7XYCYE1NDe12u7Iiprq6mgkJCWxqamJ/fz/37t276LLmLVu2sKenh52dnczJyVEtefV6vTQYDDx8+DAHBgbY2NjIuLi4ZVv+GSzmmZkZ7tmzh2lpaXQ4HKrz2r8apKuri7dv36bD4aDb7eaDBw+o1+t55MiRkIw5WLyTk5O8dOkSu7u76fF42NLSwvz8fObk5HB6elo5RiTl2O/r16+Mi4tjbW1twP7hluOlkILlF+7cucP09HRqNBpu3bqVb968We4p/SsAFv2rr68nSQ4PD7OwsJBJSUnUarVct24dL1++rPqeDpIcHBykzWZjbGwsdTodKysrOTs7uwwRBVdaWkqTyUSNRsPU1FSWlpbS5XIp/T9+/ODZs2eZmJjIuLg47tu3jyMjI6pjhEusC7148YIA6HQ6Ve2RkN/29vZFn8NHjx4l+c/S5mvXrtFgMFCr1bKoqCjgcZiYmGBZWRlXrVrFNWvW8Pjx45ycnFSNefv2Lbdv306tVsvU1FRWV1f/rRADBIvZ4/H89Lz2f/dOX18fLRYL4+PjGRMTww0bNvDmzZuqF3gydGIOFu/U1BSLi4up1+sZHR3NjIwMnjp1KuAfyEjKsd/du3cZGxtLr9cbsH+45Xgp/kOSf/QWjhBCCCHEEslnWIQQQggR8qRgEUIIIUTIk4JFCCGEECFPChYhhBBChDwpWIQQQggR8qRgEUIIIUTIk4JFCCGEECFPChYhhBBChDwpWIQQQggR8qRgEUIIIUTIk4JFCCGEECHvv8kYtiVyySp0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for attribute in attributes:\n",
    "    plot_title = \"Endometrial Cancer Survival Risk: \" + attribute\n",
    "    cph.plot_partial_effects_on_outcome(attribute, [1,2,3], cmap='coolwarm', \n",
    "                          title=plot_title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "These different analyses tend to follow the baseline survival function, however, there are some differences in varying levels of each attribute. For example, FIGO Stage I tumors tend to have a higher survival rate over time comparatively to Stage III tumors.  We can explore these differences with the CoxPHFitter object's *print_summary* function (which prints out results for multivariate linear regression)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'Days_Until_Last_Contact_Or_Death'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'Survival status (1, dead; 0, alive)'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-45.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2023-09-13 20:43:11 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>untransformed variables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tumor_stage_pathological</th>\n",
       "      <td>0.759</td>\n",
       "      <td>2.136</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.222</td>\n",
       "      <td>1.295</td>\n",
       "      <td>1.249</td>\n",
       "      <td>3.652</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.773</td>\n",
       "      <td>0.006</td>\n",
       "      <td>7.490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAC2_umich_proteomics</th>\n",
       "      <td>0.170</td>\n",
       "      <td>1.185</td>\n",
       "      <td>0.409</td>\n",
       "      <td>-0.632</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.531</td>\n",
       "      <td>2.641</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PODXL_umich_proteomics</th>\n",
       "      <td>-0.173</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.416</td>\n",
       "      <td>-0.989</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.372</td>\n",
       "      <td>1.903</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.415</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>97.403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>8.276 on 3 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>4.621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\n",
       " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "tumor_stage_pathological & 0.759 & 2.136 & 0.274 & 0.222 & 1.295 & 1.249 & 3.652 & 0.000 & 2.773 & 0.006 & 7.490 \\\\\n",
       "RAC2_umich_proteomics & 0.170 & 1.185 & 0.409 & -0.632 & 0.971 & 0.531 & 2.641 & 0.000 & 0.415 & 0.678 & 0.560 \\\\\n",
       "PODXL_umich_proteomics & -0.173 & 0.841 & 0.416 & -0.989 & 0.643 & 0.372 & 1.903 & 0.000 & -0.415 & 0.678 & 0.560 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 103 total observations, 91 right-censored observations>\n",
       "             duration col = 'Days_Until_Last_Contact_Or_Death'\n",
       "                event col = 'Survival status (1, dead; 0, alive)'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 103\n",
       "number of events observed = 12\n",
       "   partial log-likelihood = -45.702\n",
       "         time fit was run = 2023-09-13 20:43:11 UTC\n",
       "                    model = untransformed variables\n",
       "\n",
       "---\n",
       "                           coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                                        \n",
       "tumor_stage_pathological  0.759      2.136      0.274            0.222            1.295                1.249                3.652\n",
       "RAC2_umich_proteomics     0.170      1.185      0.409           -0.632            0.971                0.531                2.641\n",
       "PODXL_umich_proteomics   -0.173      0.841      0.416           -0.989            0.643                0.372                1.903\n",
       "\n",
       "                           cmp to      z     p   -log2(p)\n",
       "covariate                                                \n",
       "tumor_stage_pathological    0.000  2.773 0.006      7.490\n",
       "RAC2_umich_proteomics       0.000  0.415 0.678      0.560\n",
       "PODXL_umich_proteomics      0.000 -0.415 0.678      0.560\n",
       "---\n",
       "Concordance = 0.754\n",
       "Partial AIC = 97.403\n",
       "log-likelihood ratio test = 8.276 on 3 df\n",
       "-log2(p) of ll-ratio test = 4.621"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.print_summary(model=\"untransformed variables\", decimals=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6 Cox's Proportional Hazard Test\n",
    "With the *proportional_hazard_test* function, we can now perform Cox's Proportional Hazard Test on the data to determine how each attribute contributes to our cohort's overall survival.  This is shown by the hazard ratio in the column labeled *-log2(p)* below.  In general, a hazard ratio of 1 suggests that an attribute has no effect on overall survival.  A ratio less than 1 suggests that an attribute contributes to lower survival risk.  A ratio greater than 1 suggests that an attribute contributes to higher survival risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>time_transform</th>\n",
       "      <td>rank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>&lt;lifelines.CoxPHFitter: fitted with 103 total ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>proportional_hazard_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PODXL_umich_proteomics</th>\n",
       "      <td>1.40</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAC2_umich_proteomics</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tumor_stage_pathological</th>\n",
       "      <td>1.73</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrr}\n",
       " & test_statistic & p & -log2(p) \\\\\n",
       "PODXL_umich_proteomics & 1.40 & 0.24 & 2.08 \\\\\n",
       "RAC2_umich_proteomics & 0.31 & 0.58 & 0.79 \\\\\n",
       "tumor_stage_pathological & 1.73 & 0.19 & 2.41 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: proportional_hazard_test>\n",
       "    time_transform = rank\n",
       " null_distribution = chi squared\n",
       "degrees_of_freedom = 1\n",
       "             model = <lifelines.CoxPHFitter: fitted with 103 total observations, 91 right-censored observations>\n",
       "         test_name = proportional_hazard_test\n",
       "\n",
       "---\n",
       "                          test_statistic    p  -log2(p)\n",
       "PODXL_umich_proteomics              1.40 0.24      2.08\n",
       "RAC2_umich_proteomics               0.31 0.58      0.79\n",
       "tumor_stage_pathological            1.73 0.19      2.41"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = proportional_hazard_test(cph, df_clean, time_transform='rank')\n",
    "results.print_summary(decimals=3, model=\"untransformed variables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we show confidence intervals for each of the hazard ratios.  Since both bars include the log(HR) of 1.0 and both of their p-values were greater than 0.05, there is insufficient evidence to suggest that a specific Histologic Grade or Tumor Stage is connected with negative clinical outcomes of death or the development of a new tumor *in our cohort of Endometrial cancer tumors*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AACyCYgcAPsrhcKhNmzaF/pvRAIo/y93uBABQOJGRkXrggQfMjgHAixixAwAflZmZqZ07dyozM9PsKAC8hGIHAD4qLS1No0ePVlpamtlRAHgJxQ4AAMAiKHYAAAAWQbEDAACwCIodAPgoPz8/hYeHy8/Pz+woALzEZhiGYXYIoLhyuVxyOBxyOp2y2+1mxwEA+KCifBcxYgcAAGARFDsA8FH79+/XE088of3795sdBYCXUOwAwEdlZWXp8OHDysrKMjsKAC+h2AEAAFgExQ4AAMAiKHYAAAAWQbEDAB8VFRWloUOHKioqyuwoALzE3+wAAABzBAcHq169embHAOBFjNgBgI86efKk5s+fr5MnT5odBYCXUOwAwEelp6dr/vz5Sk9PNzsKAC+h2AEAAFgExQ4AAMAiKHYAAAAWQbEDAB8VFhamZs2aKSwszOwoALzEZhiGYXYIoLhyuVxyOBxyOp2y2+1mxwEA+KCifBcxYgcAPiorK0uHDx9WVlaW2VEAeAnFDgB81P79+/XEE09o//79ZkcB4CUUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWwe1OgAJwuxMAgNm43QkAAIAPotgBgI9KS0vTqFGjlJaWZnYUAF5CsQMAH5WZmaldu3YpMzPT7CgAvIRiBwAAYBEUOwAAAIug2AEAAFgExQ4AfFTZsmXVt29flS1b1uwoALzE3+wAAABzhIWF6YYbbjA7BgAvYsQOAHyUy+XS119/LZfLZXYUAF5CsQMAH3X8+HHNnj1bx48fNzsKAC+h2AEAAFgExQ4AAMAiKHYAAAAWQbEDAB8VHBysevXqKTg42OwoALzEZhiGYXYIoLhyuVxyOBxyOp2y2+1mxwEA+KCifBcxYgcAPio3N1dnz55Vbm6u2VEAeAnFDgB81L59+9SzZ0/t27fP7CgAvIRiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAi/M0OAAAwR+XKlfXGG28oNDTU7CgAvIRiBwA+ys/Pj/szAhbDoVgA8FGHDx/WpEmTdPjwYbOjAPASih0A+KgzZ87o559/1pkzZ8yOAsBLKHYAAAAWQbEDAACwCIodAACARVDsAMBHRUZG6oEHHlBkZKTZUQB4Cbc7AQAf5XA41KZNG7NjAPAiRuwAwEedPn1a69at0+nTp82OAsBLKHYA4KOOHDmiadOm6ciRI2ZHAeAlFDsAAACLoNgBAABYBMUOAADAIih2AOCjAgICFBsbq4CAALOjAPASm2EYhtkhgOLK5XLJ4XDI6XTKbrebHQcA4IOK8l3EiB0AAIBFUOwAwEft3btXDz30kPbu3Wt2FABeQrEDAB9lGIays7PFGTmAdVDsAAAALIJiBwAAYBEUOwAAAIvwNzsAAMAcFStW1IQJE1SuXDmzowDwEoodAPiogIAAVapUyewYALyIQ7EA4KOOHTumt956S8eOHTM7CgAvodgBgI/KyMjQihUrlJGRYXYUAF5CsQMAALAIih0AAIBFUOwAAAAsgmIHAD7K4XDo7rvvlsPhMDsKAC/hdicA4KMiIyPVqVMns2MA8CJG7ADAR2VmZmrbtm3KzMw0OwoAL6HYAYCPSktL0/PPP6+0tDSzowDwEoodAACARVDsAAAALIJiBwAAYBEUOwDwUf7+/oqMjJS/PzdIAKzCZhiGYXYIoLhyuVxyOBxyOp2y2+1mxwEA+KCifBcxYgcAAGARFDsA8FGpqanq16+fUlNTzY4CwEsodgDgo7Kzs3XixAllZ2ebHQWAl1DsAAAALIJiBwAAYBEUOwAAAIug2AGAj4qOjtbIkSMVHR1tdhQAXsJdKQHARwUFBal27dpmxwDgRYzYAYCPOnHihObNm6cTJ06YHQWAl1DsAMBHOZ1OffHFF3I6nWZHAeAlFDsAAACLoNgBAABYBMUOAADAIih2AOCjwsPD1aJFC4WHh5sdBYCX2AzDMMwOARRXLpdLDodDTqdTdrvd7DgAAB9UlO8iRuwAwEedP39e+/fv1/nz582OAsBLKHYA4KMOHDigwYMH68CBA2ZHAeAlFDsAAACLoNgBAABYBMUOAADAIih2AOCjbDab/P39ZbPZzI4CwEu43QlQAG53AgAwG7c7AQAA8EEUOwDwUQcOHNDw4cO53QlgIRQ7APBR58+f1969e7lBMWAhFDsAAACLoNgBAABYBMUOAADAIih2AOCjypUrpwEDBqhcuXJmRwHgJf5mBwAAmCM0NFSJiYlmxwDgRYzYAYCPcjqdWrRokZxOp9lRAHgJxQ4AfNSJEyf0n//8RydOnDA7CgAvodgBAABYBMUOAADAIih2AAAAFkGxAwAfFRISon/9618KCQkxOwoAL7EZhmGYHQIorlwulxwOh5xOp+x2u9lxAAA+qCjfRYzYAYCPysnJkcvlUk5OjtlRAHgJxQ4AfFRKSooeeeQRpaSkmB0FgJdQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBLc7AQrA7U5gZbm5uTp37pwCAwNVogS/5wPFVVG+i/yvUCYAQDFTokQJBQcHmx0DgBfxKxoA+KhDhw5p/PjxOnTokNlRAHgJxQ4AfNTZs2f1yy+/6OzZs2ZHAeAlFDsAAACLoNgBAABYBMUOAADAIih2AOCjSpcurW7duql06dJmRwHgJdzuBAB8lN1u12233WZ2DABexIgdAPioU6dOadWqVTp16pTZUQB4CcUOAHzU0aNH9dprr+no0aNmRwHgJRQ7AAAAi6DYAQAAWATFDgAAwCIodgDgo4KCghQXF6egoCCzowDwEpthGIbZIYDiyuVyyeFwyOl0ym63mx0HAOCDivJdxIgdAACARVDsAMBHJScn6/7771dycrLZUQB4CcUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEX4mx0AAGCOSpUqacqUKYqMjDQ7CgAvodgBgI8qWbKkypcvb3YMAF7EoVgA8FFHjx7VjBkzdPToUbOjAPASih0A+KhTp05p9erVOnXqlNlRAHgJxQ4AAMAiKHYAAAAWQbEDAACwCIodAPioUqVK6b777lOpUqXMjgLAS7jdCQD4qIiICN13331mxwDgRRS7y6hbt246efKkPvvss0vOu3fvXlWtWlUbN25UgwYNLnu2q9mYMWP02WefadOmTWZHAa5qZ8+e1c6dO1WjRg0FBwd7ff3NmzdXampqgfPExMRo5cqVXn9vwFddtkOx3bp1k81mk81mU8mSJVW1alUNHjxYmZmZHvPt379fAQEBuuaaa/Jcj2EYeuutt5SYmKiwsDBFRESoUaNGmjp1qs6cOSNJevvtt3XjjTeqVKlSKlWqlJKSkrR+/frLtWmFNm3aNM2ePdvsGP/YmDFjilXZfPrpp7Vs2TKzY6AA1apVU7Vq1cyOgUs4dOiQxo8fr0OHDl2W9aempiolJSXf11NSUi5Z/GB9/HvhXZd1xO7222/XrFmzlJWVpQ0bNqhr166y2Wx66aWX3PPMnj1bHTp00Hfffad169YpMTHRYx0PPvigFixYoJEjR2r69OkqW7asNm/erKlTpyo2Nlbt2rXTihUr1LlzZzVt2lRBQUF66aWXdNttt2nr1q2qWLHi5dzEAjkcDtPeuzDOnz+vgIAAs2MUWVhYmMLCwsyOAaAQKleurD179uT5Gl/mgPdd1osnAgMDFRUVpZiYGLVr105JSUlaunSp+3XDMDRr1iw9+OCDuv/++/Xuu+96LP/xxx9r7ty5+vDDDzV8+HBdd911io2NVdu2bfXtt9+qZcuWkqS5c+eqb9++atCggWrWrKl33nlHubm5hR7VsdlsFx0ujYiIcI+27d27VzabTR9//LFuvPFGBQcH67rrrtPvv/+uH3/8UY0aNVJYWJhat27tcQf3bt26qV27du7nubm5mjBhguLi4hQYGKjKlStr3LhxHu+7Z88etWzZUiEhIapfv77Wrl1bqG2YPXu2IiIi9Nlnn6lGjRoKCgpSq1atPH4bvjDy9s4776hq1aoKCgqS9MdvzW3btlVYWJjsdrs6dOigw4cPu9c7duxYbd682T0Ce2G/nDx5Ur169VLZsmVlt9t18803a/PmzR65Xn/9dVWvXl0BAQFKSEjQ+++/f9G+f/PNN3XnnXcqJCREtWrV0tq1a7Vr1y61aNFCoaGhatq0qXbv3n3RdvzZzJkzVadOHQUGBio6Olr9+vWT9MdnbMyYMapcubICAwNVoUIFPf7444XapwAAXG2u2Dl2v/76q9asWaMqVaq4py1fvlxnzpxRUlKSKlasqKZNm2rKlCkKDQ2V9EdhS0hIUNu2bS9an81my3dE7MyZM8rKyvL6H7YePXq0pk6dqsqVK6tHjx66//77FR4ermnTpikkJEQdOnTQqFGj9Prrr+e5/LBhw/T2229rypQpuuGGG5SWlqbffvvNY54RI0Zo0qRJqlGjhkaMGKHOnTtr165d8ve/9I/qzJkzGjdunObMmaOAgAD17dtXnTp10urVq93z7Nq1S/Pnz9eCBQvk5+en3Nxcd6lbuXKlsrOz9dhjj6ljx45asWKFOnbsqF9//VWLFy/WN998I+n/RyLbt2+v4OBgffXVV3I4HHrzzTd1yy236Pfff1dkZKQWLlyoAQMGaOrUqUpKStKXX36p7t27q1KlSu5SLknPPfecJk+erMmTJ2vIkCG6//77Va1aNQ0bNsy9r/v166evvvoqz+1+/fXX9eSTT2r8+PFq3bq1nE6ne5vnz5+vKVOmaN68eapTp44OHTp0Ufn8s3PnzuncuXPu5y6X65L7HXlLTU1lRKaYy8rKUnp6ur7//nuVLFnS6+tPTU1VTEzMJefhc+LbCvM5QeFd1mL35ZdfKiwsTNnZ2Tp37pxKlCih6dOnu19/99131alTJ/n5+emaa65RtWrV9Mknn6hbt26SpJ07dyohIaHI7ztkyBBVqFBBSUlJ3toUSX+c29WqVStJ0oABA9S5c2ctW7ZMzZo1kyT17Nkz33PqMjIyNG3aNE2fPl1du3aVJFWvXl033HDDRe9xxx13SJLGjh2rOnXqaNeuXapZs+Yl82VlZWn69Onuw9nvvfeeatWqpfXr16tx48aS/jj8OmfOHJUtW1aStHTpUm3ZskXJycnu/7HmzJmjOnXq6Mcff9R1112nsLAw+fv7Kyoqyv1eq1at0vr163XkyBEFBgZKkiZNmqTPPvtMn376qfr06aNJkyapW7du6tu3ryTpySef1A8//KBJkyZ5FLvu3burQ4cOkv742TVp0kTPPPOMx77u3r17vtv9/PPP66mnntKAAQPc06677jpJf4xGRkVFKSkpSSVLllTlypXd+yIvL774osaOHXvJfQ1Ygc1mk5+fn2w2m9lRAHjJZS12LVu21Ouvv67Tp09rypQp8vf3d19af/LkSS1YsECrVq1yz//AAw/o3XffdRc7wzCK/J7jx4/XvHnztGLFCvehRm+pV6+e+7/Lly8vSapbt67HtCNHjuS57Pbt23Xu3DndcssthX6P6OhoSdKRI0cKVez8/f3dhUaSatasqYiICG3fvt1dZqpUqeIudRdyxcTEePy2VLt2bfdyf17fn23evFmnTp1S6dKlPaafPXvWfdh0+/bt6tOnj8frzZo107Rp0/Ld5vz2a2Zmplwul+x2u8eyR44c0cGDB/Pdr+3bt9fUqVNVrVo13X777WrTpo3uuuuufEdAhw0bpieffNL93OVy8Zvk3xQTE5PvuVXwDYUZieNzAkZsveuyFrvQ0FDFxcVJ+uMcqPr16+vdd99Vz5499cEHHygzM9PjYgnDMJSbm6vff/9d8fHxio+Pv+hQZUEmTZqk8ePH65tvvvEoC5dis9kuKpFZWVkXzffnQxUXfsP967Tc3Nw836OwtxLI6z3yW+ffceEw9z916tQpRUdHa8WKFRe9FhERUaR1FWa/Snnvh0vt15iYGO3YsUPffPONli5dqr59+2rixIlauXJlnoeeAgMD3SOQAABcba7YX54oUaKEhg8frpEjR+rs2bN699139dRTT2nTpk3ux+bNm3XjjTdq5syZkqT7779fv//+uz7//POL1mcYhpxOp/v5hAkT9Nxzz2nx4sVq1KhRkbKVLVtWaWlp7uc7d+5030rFWy7cJ+py3qYjOztbP/30k/v5jh07dPLkSdWqVSvfZWrVqqXU1FSPiyy2bdumkydPqnbt2pKkgIAA5eTkeCz3r3/9S4cOHZK/v7/i4uI8HmXKlHGv+8/n90nS6tWr3ev1hvDwcMXGxha4X4ODg3XXXXfplVde0YoVK7R27Vpt2bLFaxmAq1VKSooefvjhAm9J4o33uHA7i78+Luf7Ar7qit6guH379ho0aJBmzJihn3/+WXPnzr3oEGPnzp317LPP6vnnn1eHDh20cOFCde7cWSNHjtRtt92msmXLasuWLZoyZYr69++vdu3a6aWXXtKoUaP0wQcfKDY21n1PpsLeFuPmm2/W9OnT1aRJE+Xk5GjIkCFeP5E4KChIQ4YM0eDBgxUQEKBmzZrp6NGj2rp1q3r27OmV9yhZsqT69++vV155Rf7+/urXr5+uv/76As8pS0pKUt26ddWlSxdNnTpV2dnZ6tu3r5o3b+4uyLGxsUpOTtamTZtUqVIlhYeHKykpSU2aNFG7du00YcIExcfH6+DBg/rf//6ne+65R40aNdKgQYPUoUMHNWzYUElJSfrvf/+rBQsWuC/C8JYxY8bokUceUbly5dS6dWtlZGRo9erV6t+/v2bPnq2cnBwlJiYqJCRE//nPfxQcHOxxEQ+8j0NrV4ecnBxlZGRc9Iubt1zqNIbKlStzqgP498LbjMuka9euRtu2bS+a/uKLLxr+/v5GbGxsnsulpaUZJUqUMD7//HPDMAwjJyfHeP31143rrrvOCAkJMex2u3Httdca06ZNM86cOWMYhmFUqVLFkHTRY/To0YXKeuDAAeO2224zQkNDjRo1ahiLFi0yHA6HMWvWLMMwDCM5OdmQZGzcuNG9zPLlyw1JRnp6unvarFmzDIfDke8+yMnJMZ5//nmjSpUqRsmSJY3KlSsbL7zwQr7vkZ6ebkgyli9ffsltuPDe8+fPN6pVq2YEBgYaSUlJxr59+9zzjB492qhfv/5Fy+7bt8+4++67jdDQUCM8PNxo3769cejQIffrmZmZxn333WdEREQYktz7xeVyGf379zcqVKhglCxZ0oiJiTG6dOlipKSkuJd97bXXjGrVqhklS5Y04uPjjTlz5ni8tyRj4cKF7ueF2dd5bccbb7xhJCQkGCVLljSio6ON/v37G4ZhGAsXLjQSExMNu91uhIaGGtdff73xzTffXHJ/XuB0Og1JhtPpLPQywNViz549RufOnY09e/aYHQVAAYryXWQzjL9xhQKKndmzZ2vgwIE6efKk2VEsxeVyyeFwyOl0XnThBnC1S05O1ogRIzRu3DhVrVrV7DgA8lGU76Irdo4dAAAALi/LF7vvv//efa5dXo+rRevWrfPdhhdeeMHseACuQtHR0Ro7dqz71koArn6WPxR79uxZHThwIN/XL9yOpbg7cOCAzp49m+drkZGRXv8rG/gDh2IBAGYrynfRFb0q1gzBwcFXTXkrSMWKFc2OAMBiTpw4oUWLFqlNmzb8cghYhOUPxQIA8uZ0OrVo0SKPe4ICuLpR7AAAACyCYgcAAGARFDsAAACLoNgBgI8KDw/XrbfeqvDwcLOjAPASy18VCwDIW5kyZdS9e3ezYwDwIkbsAMBHnTt3TsnJyTp37pzZUQB4CcUOAHzUwYMHNWLECB08eNDsKAC8hGIHAABgERQ7AAAAi6DYAQAAWATFDgB8VIkSJRQUFKQSJfgqAKzCZhiGYXYIoLhyuVxyOBxyOp2y2+1mxwEA+KCifBfxaxoAAIBFUOwAwEcdOHBAgwYN0oEDB8yOAsBLKHYA4KPOnz+vAwcO6Pz582ZHAeAlFDsAAACLoNgBAABYBMUOAADAIih2AOCjypcvr6eeekrly5c3OwoAL/E3OwAAwBwhISG69tprzY4BwIsYsQMAH3Xy5El9/vnnOnnypNlRAHgJxQ4AfFR6ero++ugjpaenmx0FgJdQ7AAAACyCYgcAAGARFDsAAACLoNgBgI8KDQ1VYmKiQkNDzY4CwEtshmEYZocAiiuXyyWHwyGn0ym73W52HACADyrKdxEjdgDgo7Kzs3XixAllZ2ebHQWAl1DsAMBHpaamql+/fkpNTTU7CgAvodgBAABYBMUOAADAIih2AAAAFkGxAwAAsAhudwIUgNudwMoMw1B2drb8/f1ls9nMjgMgH0X5LvK/QpkAAMWMzWZTyZIlzY4BwIs4FAsAPiotLU3PPfec0tLSzI4CwEsodgDgozIzM7V9+3ZlZmaaHQWAl1DsAAAALIJiBwAAYBEUOwAAAIug2AGAjypTpox69+6tMmXKmB0FgJdwuxMA8FHh4eFq2bKl2TEAeBEjdgDgozIyMrR8+XJlZGSYHQWAl1DsAMBHHTt2TG+//baOHTtmdhQAXkKxAwAAsAiKHQAAgEVQ7AAAACyCYgcAPiooKEi1atVSUFCQ2VEAeInNMAzD7BBAceVyueRwOOR0OmW3282OAwDwQUX5LmLEDgB8lGEYysrKEr/fA9ZBsQMAH7V371517dpVe/fuNTsKAC+h2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCH+zAwAAzBETE6Pp06dzKx/AQih2AOCj/P39FRkZaXYMAF7EoVgA8FFHjhzRtGnTdOTIEbOjAPASih0A+KjTp09r3bp1On36tNlRAHgJxQ4AAMAiKHYAAAAWQbEDAACwCIodAPioUqVKqWPHjipVqpTZUQB4Cbc7AQAfFRERobZt25odA4AXMWIHAD7qzJkz2rBhg86cOWN2FABeQrEDAB91+PBhvfzyyzp8+LDZUQB4CcUOAADAIih2AAAAFkGxAwAAsAiKHQD4qICAAFWsWFEBAQFmRwHgJTbDMAyzQwDFlcvlksPhkNPplN1uNzsOAMAHFeW7iBE7AAAAi6DYAYCP2rdvn3r06KF9+/aZHQWAl1DsAMBH5ebmKjMzU7m5uWZHAeAlFDsAAACLoNgBAABYBMUOAADAIih2AOCjKlSooHHjxqlChQpmRwHgJf5mBwAAmCMwMFBVq1Y1OwYAL2LEDgB81LFjxzRr1iwdO3bM7CgAvIRiBwA+KiMjQ0uXLlVGRobZUQB4CcUOAADAIih2AAAAFkGxAwAAsAiKHQD4KIfDoTZt2sjhcJgdBYCXcLsTAPBRkZGReuCBB8yOAcCLGLEDAB+VmZmpnTt3KjMz0+woALyEYgcAPiotLU2jR49WWlqa2VEAeAnFDgAAwCIodgAAABZBsQMAALAIih0A+Cg/Pz+Fh4fLz8/P7CgAvMRmGIZhdgiguHK5XHI4HHI6nbLb7WbHAQD4oKJ8FzFiBwAAYBEUOwDwUfv379cTTzyh/fv3mx0FgJdQ7ADAR2VlZenw4cPKysoyOwoAL6HYAQAAWATFDgAAwCIodgAAABZBsQMAHxUVFaWhQ4cqKirK7CgAvMTf7AAAAHMEBwerXr16ZscA4EWM2AGAjzp58qTmz5+vkydPmh0FgJdQ7ADAR6Wnp2v+/PlKT083OwoAL6HYAQAAWATFDgAAwCIodgAAABZBsQMAHxUWFqZmzZopLCzM7CgAvMRmGIZhdgiguHK5XHI4HHI6nbLb7WbHAQD4oKJ8FzFiBwA+KisrS4cPH1ZWVpbZUQB4CcUOAHzU/v379cQTT2j//v1mRwHgJRQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABbB7U6AAnC7EwCA2bjdCQAAgA+i2AGAj0pLS9OoUaOUlpZmdhQAXkKxAwAflZmZqV27dikzM9PsKAC8hGIHAABgERQ7AAAAi6DYAQAAWATFDgB8VNmyZdW3b1+VLVvW7CgAvMTf7AAAAHOEhYXphhtuMDsGAC9ixA4AfJTL5dLXX38tl8tldhQAXkKxAwAfdfz4cc2ePVvHjx83OwoAL6HYAQAAWATFDgAAwCIodgAAABZBsQMAHxUcHKx69eopODjY7CgAvMRmGIZhdgiguHK5XHI4HHI6nbLb7WbHAQD4oKJ8FzFiBwA+Kjc3V2fPnlVubq7ZUQB4CcUOAHzUvn371LNnT+3bt8/sKAC8hGIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCL8zQ4AADBH5cqV9cYbbyg0NNTsKAC8hGIHAD7Kz8+P+zMCFsOhWADwUYcPH9akSZN0+PBhs6MA8BKKHQD4qDNnzujnn3/WmTNnzI4CwEsodgAAABZBsQMAALAIih0AAIBFUOwAwEdFRkbqgQceUGRkpNlRAHgJtzsBAB/lcDjUpk0bs2MA8CJG7ADAR50+fVrr1q3T6dOnzY4CwEsodgDgo44cOaJp06bpyJEjZkcB4CUUOwAAAIug2AEAAFgExQ4AAMAiKHYA4KMCAgIUGxurgIAAs6MA8BKbYRiG2SGA4srlcsnhcMjpdMput5sdBwDgg4ryXcSIHQAAgEVQ7ADAR+3du1cPPfSQ9u7da3YUAF5CsQMAH2UYhrKzs8UZOYB1UOwAAAAsgmIHAABgERQ7AAAAi/A3OwAAwBwVK1bUhAkTVK5cObOjAPASih0A+KiAgABVqlTJ7BgAvIhDsQDgo44dO6a33npLx44dMzsKAC+h2AGAj8rIyNCKFSuUkZFhdhQAXkKxAwAAsAiKHQAAgEVQ7AAAACyCYgcAPsrhcOjuu++Ww+EwOwoAL+F2JwDgoyIjI9WpUyezYwDwIkbsAMBHZWZmatu2bcrMzDQ7CgAvodhdJitWrJDNZtPJkycLNX+LFi00cODAy5rJCvbu3SubzaZNmzaZHQX4R5o3b65q1aoV+GjevPllzZCWlqbnn39eaWlpl/V9AFw5RSp23bp1k81mk81mU0BAgOLi4vTss88qOztbkpSTk6MpU6aobt26CgoKUqlSpdS6dWutXr3aYz2zZ892r8fPz0+lSpVSYmKinn32WTmdTvd8OTk5atq0qe69916P5Z1Op2JiYjRixAhJxfPLvmnTpkpLS7vqz10pbvs2JiZGaWlpuuaaa8yOUmQXvqwBSUpNTVVKSkq+r6ekpCg1NfUKJkJxxr8fKKwij9jdfvvtSktL086dO/XUU09pzJgxmjhxogzDUKdOnfTss89qwIAB2r59u1asWKGYmBi1aNFCn332mcd67Ha70tLStH//fq1Zs0Z9+vTRnDlz1KBBAx08eFCS5Ofnp9mzZ2vx4sWaO3eue9n+/fsrMjJSo0eP/mdbfxkFBAQoKipKNpvN7Cj5ysrKMjtCkfn5+SkqKkr+/pweiqtf5cqVtWfPnjwflStXNjsegKtQkYtdYGCgoqKiVKVKFT366KNKSkrSF198oY8//liffvqp5syZo169eqlq1aqqX7++3nrrLd19993q1auXTp8+7V6PzWZTVFSUoqOjVatWLfXs2VNr1qzRqVOnNHjwYPd88fHxGj9+vPr376+0tDR9/vnnmjdvnubMmaOAgIB/vAO6deumdu3aeUwbOHCgWrRo4X7eokUL9e/fXwMHDlSpUqVUvnx5vf322zp9+rS6d++u8PBwxcXF6auvvnIvk9eh2NWrV6tFixYKCQlRqVKl1KpVK6Wnp7tfz83N1eDBgxUZGamoqCiNGTOm0Nths9n0+uuvq3Xr1goODla1atX06aeful+/MPL20UcfqXnz5goKCtLcuXOVm5urZ599VpUqVVJgYKAaNGigxYsXu5erWrWqJKlhw4ay2Wwe++Wdd95RrVq1FBQUpJo1a+q1117zyLRlyxbdfPPNCg4OVunSpdWnTx+dOnXqon3/wgsvqHz58oqIiHCPAA8aNEiRkZGqVKmSZs2addF2/HkEcevWrbrzzjtlt9sVHh6uG2+8Ubt373b/HBo3bqzQ0FBFRESoWbNm2rdvX6H3KwAAV5N/POwRHBys48eP64MPPlB8fLzuuuuui+Z56qmntGDBAi1duvSiEvVn5cqVU5cuXTRz5kzl5OTIz89P0h8jdAsXLtSDDz6oLVu2aNSoUapfv/4/jV4k7733ngYPHqz169fro48+0qOPPqqFCxfqnnvu0fDhwzVlyhQ9+OCDSklJUUhIyEXLb9q0Sbfccot69OihadOmyd/fX8uXL1dOTo7Hezz55JNat26d1q5dq27duqlZs2a69dZbC5XxmWee0fjx4zVt2jS9//776tSpk7Zs2aJatWq55xk6dKhefvllNWzYUEFBQZo2bZpefvllvfnmm2rYsKFmzpypu+++W1u3blWNGjW0fv16NW7cWN98843q1KnjLtNz587VqFGjNH36dDVs2FAbN25U7969FRoaqq5du+r06dNq1aqVmjRpoh9//FFHjhxRr1691K9fP82ePdud59tvv1WlSpX03XffafXq1e6Cf9NNN2ndunX66KOP9PDDD+vWW2/N84+VHzhwQDfddJNatGihb7/9Vna7XatXr1Z2drays7PVrl079e7dWx9++KHOnz+v9evXFziKeu7cOZ07d8793OVyFWrfF1ZqaiqHUyDpj89CTEzMJee5nJ+X7OxsnTx5UqtWrWIUvJgrzOcFkP7BxROGYeibb77RkiVLdPPNN+v333/3KBB/dmH677//fsn11qxZUxkZGTp+/Lh72oXRqGXLlql8+fIaOnTo3439t9WvX18jR45UjRo1NGzYMAUFBalMmTLq3bu3atSooVGjRun48eP65Zdf8lx+woQJatSokV577TXVr19fderUUb9+/VSmTBn3PPXq1dPo0aNVo0YNPfTQQ2rUqJGWLVtW6Izt27dXr169FB8fr+eee06NGjXSq6++6jHPwIEDde+996pq1aqKjo7WpEmTNGTIEHXq1EkJCQl66aWX1KBBA02dOlWSVLZsWUlS6dKlFRUVpcjISEnS6NGj9fLLL7vXde+99+qJJ57Qm2++KUn64IMPlJmZqTlz5uiaa67RzTffrOnTp+v999/X4cOH3XkiIyP1yiuvKCEhQT169FBCQoLOnDmj4cOHu/d1QECAVq1alec2z5gxQw6HQ/PmzVOjRo0UHx+v7t27KyEhQS6XS06nU3feeaeqV6+uWrVqqWvXrgUe4nrxxRflcDjcD/4hhZX5+/urTJkylDrAQor8f/OXX36psLAwZWVlKTc3V/fff7/GjBmjL7/8UoZh/ONAF9bx11GVmTNnKiQkRMnJydq/f79iY2P/8XsVRb169dz/7efnp9KlS6tu3bruaeXLl5ckHTlyJM/lN23apPbt2xf6PSQpOjo63/XlpUmTJhc9/+tFD40aNXL/t8vl0sGDB9WsWTOPeZo1a6bNmzfn+z6nT5/W7t271bNnT/Xu3ds9PTs7232xyPbt21W/fn2FhoZ6rDc3N1c7duxw7686deqoRIn///2ifPnyHhdGXNjXBe3XG2+8USVLlrzotcjISHXr1k2tWrXSrbfeqqSkJHXo0EHR0dH5btuwYcP05JNPup+7XC6vlruYmBjt2bPHa+vD1aswI3F8XnABI/0orCKP2LVs2VKbNm3Szp07dfbsWb333nsKDQ1VfHy8tm/fnucyF6bHx8dfcv3bt2+X3W5X6dKl3dPWrFmjKVOm6Msvv1Tjxo3Vs2dPr5RISSpRosRF68rrooK/FgebzeYx7UIRzc3NzfN9goODL5klr/fIb31/15+L1t914Ty5t99+W5s2bXI/fv31V/3www9FWtel9uuFaX93v86aNUtr165V06ZN9dFHHyk+Pr7AjIGBgbLb7R4PwKpSU1PVr18/rr4FLKTIxS40NFRxcXGqXLmyx/B9p06dtHPnTv33v/+9aJmXX35ZpUuXvuS5YkeOHNEHH3ygdu3auUdxzpw5o27duunRRx9Vy5Yt9e6772r9+vV64403iho9T2XLlr3oHk6X49Ye9erVK9Jh1b/jr4Xlhx9+yPfwuPTHlckVKlS46HY0q1evVu3atSXJfU7dn88FLF++vCpUqKA9e/YoLi7O43HhYotatWpp8+bNHhfMrF69WiVKlFBCQsI/29A/qVevnr7//vsCr/Bt2LChhg0bpjVr1uiaa67RBx984LX3B/6JlJSUfO9hV9CtULwlOztbJ06ccN+yCsDVz2snVnTq1EmffPKJunbtqokTJ+qWW26Ry+XSjBkz9MUXX+iTTz7xGC0yDEOHDh2SYRg6efKk1q5dqxdeeEEOh0Pjx493zzds2DAZhuGeFhsbq0mTJunpp59W69atPQ7J7tix46JcderUyfMw3QU333yzJk6cqDlz5qhJkyb6z3/+o19//VUNGzb0wl75f8OGDVPdunXVt29fPfLIIwoICNDy5cvVvn17j/Ps/olPPvlEjRo10g033KC5c+dq/fr1evfddwtcZtCgQRo9erSqV6+uBg0aaNasWdq0aZP79jLlypVTcHCwFi9erEqVKikoKEgOh0Njx47V448/LofDodtvv13nzp3TTz/9pPT0dD355JPq0qWLRo8era5du2rMmDE6evSo+vfvrwcffNB9GNYb+vXrp1dffVWdOnXSsGHD5HA49MMPP6hx48YKCAhwX5VdoUIF7dixQzt37tRDDz3ktfcvCg6p4c8udYi/cuXKnOMJN/79QGF5rdjZbDZ9/PHHmjp1qqZMmaK+ffsqKChITZo00YoVKy46j8vlcik6Olo2m012u10JCQnq2rWrBgwY4D78tXLlSs2YMUMrVqzwuNL04Ycf1oIFC9SzZ09988037ul5/c3D1NTUPK+mvKBVq1Z65plnNHjwYGVmZqpHjx566KGHtGXLln+6SzzEx8fr66+/1vDhw9W4cWMFBwcrMTFRnTt39tp7jB07VvPmzVPfvn0VHR2tDz/80D3ylp/HH39cTqdTTz31lI4cOaLatWvriy++UI0aNST9cXL1K6+8omeffVajRo3SjTfeqBUrVqhXr14KCQnRxIkTNWjQIIWGhqpu3bruv54REhKiJUuWaMCAAbruuusUEhKi++67T5MnT/ba9kp/XNTx7bffatCgQWrevLn8/PzUoEEDNWvWTCEhIfrtt9/03nvv6fjx44qOjtZjjz2mhx9+2KsZgL9j5cqVZkcAYEE2w1snq8FUNptNCxcuLPB2Mig6l8slh8Mhp9PJ+XawnOTkZI0YMULjxo1zn0YBoPgpyncRfysWAHxUdHS0Ro4cWeCV4gCuLpYvdnXq1FFYWFiejz//mbLibO7cufluQ506dcyOB+AqFRQUpNq1aysoKMjsKAC8xPJ3pVy0aFG+V0x68yT+y+nuu+9WYmJinq9duDCEI+oAiurEiRP6+uuvddttt7lvPg7g6mb5YlelShWzI/xj4eHhCg8PNzsGAItxOp364osvlJiYSLEDLMLyh2IBAAB8BcUOAADAIih2AAAAFkGxAwAfFR4erhYtWnAOL2Ah3KAYKAA3KAYAmI0bFAMALun8+fPav3+/zp8/b3YUAF5CsQMAH3XgwAENHjxYBw4cMDsKAC+x/H3sgH/iwpkKLpfL5CSA92VkZCgrK0sZGRl8xoFi7ML/n4U5e45z7IAC7N+/XzExMWbHAABAqampqlSpUoHzUOyAAuTm5urgwYMKDw+XzWb7W+twuVyKiYlRamoqF2B4AfvTe9iX3sO+9C72pyfDMJSRkaEKFSqoRImCz6LjUCxQgBIlSlzyt6PCstvt/APlRexP72Ffeg/70rvYn//P4XAUaj4ungAAALAIih0AAIBFUOyAyywwMFCjR49WYGCg2VEsgf3pPexL72Ffehf78+/j4gkAAACLYMQOAADAIih2AAAAFkGxAwAAsAiKHXAZjBs3Tk2bNlVISIgiIiIKtYxhGBo1apSio6MVHByspKQk7dy58/IGvQqcOHFCXbp0kd1uV0REhHr27KlTp04VuEyLFi1ks9k8Ho888sgVSly8zJgxQ7GxsQoKClJiYqLWr19f4PyffPKJatasqaCgINWtW1eLFi26QkmLv6Lsy9mzZ1/0GQwKCrqCaYuv7777TnfddZcqVKggm82mzz777JLLrFixQv/6178UGBiouLg4zZ49+7LnvFpR7IDL4Pz582rfvr0effTRQi8zYcIEvfLKK3rjjTe0bt06hYaGqlWrVsrMzLyMSYu/Ll26aOvWrVq6dKm+/PJLfffdd+rTp88ll+vdu7fS0tLcjwkTJlyBtMXLRx99pCeffFKjR4/Wzz//rPr166tVq1Y6cuRInvOvWbNGnTt3Vs+ePbVx40a1a9dO7dq106+//nqFkxc/Rd2X0h831/3zZ3Dfvn1XMHHxdfr0adWvX18zZswo1PzJycm644471LJlS23atEkDBw5Ur169tGTJksuc9CplALhsZs2aZTgcjkvOl5uba0RFRRkTJ050Tzt58qQRGBhofPjhh5cxYfG2bds2Q5Lx448/uqd99dVXhs1mMw4cOJDvcs2bNzcGDBhwBRIWb40bNzYee+wx9/OcnByjQoUKxosvvpjn/B06dDDuuOMOj2mJiYnGww8/fFlzXg2Kui8L+/++r5NkLFy4sMB5Bg8ebNSpU8djWseOHY1WrVpdxmRXL0bsgGIgOTlZhw4dUlJSknuaw+FQYmKi1q5da2Iyc61du1YRERFq1KiRe1pSUpJKlCihdevWFbjs3LlzVaZMGV1zzTUaNmyYzpw5c7njFivnz5/Xhg0bPD5TJUqUUFJSUr6fqbVr13rML0mtWrXy6c+g9Pf2pSSdOnVKVapUUUxMjNq2bautW7deibiWw+eyaPhbsUAxcOjQIUlS+fLlPaaXL1/e/ZovOnTokMqVK+cxzd/fX5GRkQXul/vvv19VqlRRhQoV9Msvv2jIkCHasWOHFixYcLkjFxvHjh1TTk5Onp+p3377Lc9lDh06xGcwD39nXyYkJGjmzJmqV6+enE6nJk2apKZNm2rr1q1e+/vTviK/z6XL5dLZs2cVHBxsUrLiiRE7oJCGDh160cnQf33k9488PF3ufdmnTx+1atVKdevWVZcuXTRnzhwtXLhQu3fv9uJWAPlr0qSJHnroITVo0EDNmzfXggULVLZsWb355ptmR4PFMWIHFNJTTz2lbt26FThPtWrV/ta6o6KiJEmHDx9WdHS0e/rhw4fVoEGDv7XO4qyw+zIqKuqik9Ozs7N14sQJ9z4rjMTEREnSrl27VL169SLnvRqVKVNGfn5+Onz4sMf0w4cP57vvoqKiijS/r/g7+/KvSpYsqYYNG2rXrl2XI6Kl5fe5tNvtjNblgWIHFFLZsmVVtmzZy7LuqlWrKioqSsuWLXMXOZfLpXXr1hXpytqrRWH3ZZMmTXTy5Elt2LBB1157rSTp22+/VW5urrusFcamTZskyaM0W11AQICuvfZaLVu2TO3atZMk5ebmatmyZerXr1+eyzRp0kTLli3TwIED3dOWLl2qJk2aXIHExdff2Zd/lZOToy1btqhNmzaXMak1NWnS5KLb7vC5LIDZV28AVrRv3z5j48aNxtixY42wsDBj48aNxsaNG42MjAz3PAkJCcaCBQvcz8ePH29EREQYn3/+ufHLL78Ybdu2NapWrWqcPXvWjE0oNm6//XajYcOGxrp164xVq1YZNWrUMDp37ux+ff/+/UZCQoKxbt06wzAMY9euXcazzz5r/PTTT0ZycrLx+eefG9WqVTNuuukmszbBNPPmzTMCAwON2bNnG9u2bTP69OljREREGIcOHTIMwzAefPBBY+jQoe75V69ebfj7+xuTJk0ytm/fbowePdooWbKksWXLFrM2odgo6r4cO3assWTJEmP37t3Ghg0bjE6dOhlBQUHG1q1bzdqEYiMjI8P9b6IkY/LkycbGjRuNffv2GYZhGEOHDjUefPBB9/x79uwxQkJCjEGDBhnbt283ZsyYYfj5+RmLFy82axOKNYodcBl07drVkHTRY/ny5e55JBmzZs1yP8/NzTWeeeYZo3z58kZgYKBxyy23GDt27Ljy4YuZ48ePG507dzbCwsIMu91udO/e3aMgJycne+zblJQU46abbjIiIyONwMBAIy4uzhg0aJDhdDpN2gJzvfrqq0blypWNgIAAo3HjxsYPP/zgfq158+ZG165dPeb/+OOPjfj4eCMgIMCoU6eO8b///e8KJy6+irIvBw4c6J63fPnyRps2bYyff/7ZhNTFz/Lly/P89/HC/uvatavRvHnzi5Zp0KCBERAQYFSrVs3j3054shmGYZgyVAgAAACv4qpYAAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7ADBRixYtPP42qzfddNNN+uCDDy7Lus+fP6/Y2Fj99NNPhZr/mWeeUZ8+fS5LFrNcf/31mj9/vtkxAA8UOwCwoC+++EKHDx9Wp06d3NNiY2M1derUi+YdM2aMGjRo4PHcZrPJZrPJz89PMTEx6tOnj06cOOGeJyAgQE8//bSGDBlyySyHDh3StGnTNGLECPe0jIwMDRw4UFWqVFFwcLCaNm2qH3/80WO5bt26uXNceNx+++3u18+dO6cHH3xQdrtd8fHx+uabbzyWnzhxovr373/JfJLkcrk0YsQI1axZU0FBQYqKilJSUpIWLFigC3+g6a8lfOTIkRo6dKhyc3ML9R7AlUCxAwALeuWVV9S9e3eVKPH3/pmvU6eO0tLSlJKSolmzZmnx4sV69NFHPebp0qWLVq1apa1btxa4rnfeeUdNmzZVlSpV3NN69eqlpUuX6v3339eWLVt02223KSkpSQcOHPBY9vbbb1daWpr78eGHH7pfe+utt7RhwwatXbtWffr00f333+8uYcnJyXr77bc1bty4S27ryZMn1bRpU82ZM0fDhg3Tzz//rO+++04dO3bU4MGD5XQ681yudevWysjI0FdffXXJ9wCuFIodABQj6enpeuihh1SqVCmFhISodevW2rlzp8c8b7/9tmJiYhQSEqJ77rlHkydPVkREhPv1o0eP6ttvv9Vdd931t3P4+/srKipKFStWVFJSktq3b6+lS5d6zFOqVCk1a9ZM8+bNK3Bd8+bN88hy9uxZzZ8/XxMmTNBNN92kuLg4jRkzRnFxcXr99dc9lg0MDFRUVJT7UapUKfdr27dv19133606deroscce09GjR3Xs2DFJ0qOPPqqXXnpJdrv9kts6fPhw7d27V+vWrVPXrl1Vu3ZtxcfHq3fv3tq0aZPCwsLyXM7Pz09t2rS55PYDVxLFDgCKkW7duumnn37SF198obVr18owDLVp00ZZWVmSpNWrV+uRRx7RgAEDtGnTJt16660XjUqtWrVKISEhqlWrllcy7d27V0uWLFFAQMBFrzVu3Fjff/99vsueOHFC27ZtU6NGjdzTsrOzlZOTo6CgII95g4ODtWrVKo9pK1asULly5ZSQkKBHH31Ux48fd79Wv359rVq1SmfPntWSJUsUHR2tMmXKaO7cuQoKCtI999xzyW3Lzc3VvHnz1KVLF1WoUOGi18PCwuTv75/v8pfafuBKy//TCgC4onbu3KkvvvhCq1evVtOmTSVJc+fOVUxMjD777DO1b99er776qlq3bq2nn35akhQfH681a9boyy+/dK9n3759Kl++fJ6HYYcMGaKRI0d6TDt//rxq167tMW3Lli0KCwtTTk6OMjMzJUmTJ0++aH0VKlTQvn378t2mlJQUGYbhUZrCw8PVpEkTPffcc6pVq5bKly+vDz/8UGvXrlVcXJx7vttvv1333nuvqlatqt27d2v48OFq3bq11q5dKz8/P/Xo0UO//PKLateurTJlyujjjz9Wenq6Ro0apRUrVmjkyJGaN2+eqlevrpkzZ6pixYoX5Tt27JjS09NVs2bNfLehIBUqVFBqaqpyc3P/9mFvwJsodgBQTGzfvl3+/v5KTEx0TytdurQSEhK0fft2SdKOHTsuGolq3LixR7E7e/bsRaNhFwwaNEjdunXzmPbKK6/ou+++85iWkJCgL774QpmZmfrPf/6jTZs25XkhQnBwsM6cOZPvNp09e1aSLsrz/vvvq0ePHqpYsaL8/Pz0r3/9S507d9aGDRvc8/z5wo+6deuqXr16ql69ulasWKFbbrlFJUuW1IwZMzzW2717dz3++OPauHGjPvvsM23evFkTJkzQ448/nucVrBfOyfu7goODlZubq3Pnzik4OPgfrQvwBn69AACLKVOmjNLT0/N9LS4uzuMRGRl50XwBAQGKi4vTNddco/Hjx8vPz09jx469aL4TJ06obNmyBWaRdFGe6tWra+XKlTp16pRSU1O1fv16ZWVlqVq1avmuq1q1aipTpox27dqV5+vLly/X1q1b1a9fP61YsUJt2rRRaGioOnTooBUrVuS5TNmyZRUREaHffvst3/ctyIkTJxQaGkqpQ7FBsQOAYqJWrVrKzs7WunXr3NOOHz+uHTt2uA+VJiQkXHRbkL8+b9iwoQ4dOpRvufs7Ro4cqUmTJungwYMe03/99Vc1bNgw3+WqV68uu92ubdu25fl6aGiooqOjlZ6eriVLlqht27b5rmv//v06fvy4oqOjL3otMzNTjz32mN588035+fkpJyfHfV5iVlaWcnJy8lxniRIl1KlTJ82dO/eibZOkU6dOKTs7O99Ml9p+4Eqj2AFAMVGjRg21bdtWvXv31qpVq7R582Y98MADqlixorvw9O/fX4sWLdLkyZO1c+dOvfnmm/rqq69ks9nc62nYsKHKlCmj1atXey1bkyZNVK9ePb3wwgse07///nvddttt+S5XokQJJSUlXXRRxJIlS7R48WIlJydr6dKlatmypWrWrKnu3btL+qNQDRo0SD/88IP27t2rZcuWqW3btoqLi1OrVq0uep/nnntObdq0cZesZs2aacGCBfrll180ffp0NWvWLN+M48aNU0xMjBITEzVnzhxt27ZNO3fu1MyZM9WwYUOdOnUq32Uvtf3AlUaxA4BiZNasWbr22mt15513qkmTJjIMQ4sWLVLJkiUl/VFY3njjDU2ePFn169fX4sWL9cQTT3icw+bn56fu3btr7ty5Xs32xBNP6J133lFqaqokae3atXI6nfr3v/9d4HK9evXSvHnzPG7k63Q69dhjj6lmzZp66KGHdMMNN2jJkiXu7fTz89Mvv/yiu+++W/Hx8erZs6euvfZaff/99woMDPRY/6+//qqPP/7Y41Dxv//9b91xxx268cYb9csvv2jatGn55ouMjNQPP/ygBx54QM8//7waNmyoG2+8UR9++KEmTpwoh8OR53IHDhzQmjVr3GUUKA5sxj89cxQAYKrevXvrt99+87jtxqFDh1SnTh39/PPPHjcG9qaOHTuqfv36Gj58eIHzGYahxMREPfHEE+rcufNlyWKGIUOGKD09XW+99ZbZUQA3RuwA4CozadIkbd68Wbt27dKrr76q9957T127dvWYJyoqSu+++65SUlIuS4bz58+rbt26euKJJy45r81m01tvvVXguWpXo3Llyum5554zOwbggRE7ALjKXLjKMyMjQ9WqVVP//v31yCOPmB0LQDFAsQMAALAIDsUCAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYxP8Bd0toF7VOfRYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.plot()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Closing Remarks\n",
    "It is important to note that there are relatively few patients who died in our cohort (7 out of 88), which is good, but with such a small sample size of death events, it is difficult to conclude with certainty that these features are not more or less connected with survival.  Perhaps a sample of patients with more deaths might have different results.  Alternatively, studying an event with more negative outcomes (such as tumor recurrence) may also provide more data to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survival status (1, dead; 0, alive)\n",
       "False    91\n",
       "True     12\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['Survival status (1, dead; 0, alive)'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also important to note that the confidence intervals for these ratios are very large, especially since hazard ratios are standardly shown on a log-scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>95% lower-bound</th>\n",
       "      <th>95% upper-bound</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tumor_stage_pathological</th>\n",
       "      <td>0.222404</td>\n",
       "      <td>1.295220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAC2_umich_proteomics</th>\n",
       "      <td>-0.632089</td>\n",
       "      <td>0.971199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PODXL_umich_proteomics</th>\n",
       "      <td>-0.988706</td>\n",
       "      <td>0.643185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          95% lower-bound  95% upper-bound\n",
       "covariate                                                 \n",
       "tumor_stage_pathological         0.222404         1.295220\n",
       "RAC2_umich_proteomics           -0.632089         0.971199\n",
       "PODXL_umich_proteomics          -0.988706         0.643185"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.confidence_intervals_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just one example of how you might use Survival Analysis to learn more about different types of cancer, and how clinical and/or genetic attributes contribute to likelihood of survival. There are many other clinical and genetic attributes, as well as several other cancer types, that can be explored using a similar process to that above. In particular, lung cancer and ovarian cancer have a larger number of negative outcomes per cohort, and would be good to look into further. "
   ]
  }
 ],
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