final_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cathedral-discipline",
   "metadata": {},
   "source": [
    "# Statistical and Machine Learning Analysis\n",
    "\n",
    "This notebook contains the final statistical and ML analysis investigating the relationship between the T cell data and antibody response. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "induced-judge",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package outdated is out of date. Your version is 0.2.1, the latest is 0.2.2.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.1, the latest is 0.5.3.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n"
     ]
    }
   ],
   "source": [
    "# Imports & setup\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import re\n",
    "\n",
    "import pandas as pd\n",
    "from pandas import read_csv\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.text as mtext\n",
    "CB_COLOR_CYCLE = ['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628', '#984ea3', '#999999', \n",
    "                  '#e41a1c', '#dede00']\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "import scipy.stats as stats\n",
    "from pingouin import multivariate_ttest\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, SparsePCA\n",
    "from sklearn.mixture import BayesianGaussianMixture\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import normalized_mutual_info_score as nmi\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.metrics import (balanced_accuracy_score, r2_score, confusion_matrix, recall_score, \n",
    "                             roc_auc_score, average_precision_score)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.inspection import plot_partial_dependence\n",
    "\n",
    "import xgboost as xgb\n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "from data_loader import construct_tcell_df, construct_mfi_df\n",
    "from bootstrap_utils import (run_repeated_CV_XGB, boot_train_test_XGB, boot_train_test_LR, boot_train_test_random, \n",
    "                             boot_train_test_feature, boot_feature_selection, boot_feature_selection_LR, \n",
    "                             boot_train_test_XGB_trt_vs_ab)\n",
    "from misc_utils import print_perf_statistics\n",
    "from plotting_utils import parcoord_plot, plot_errorbars, parcoord_plot_sep, plotting_setup\n",
    "from upsetplot import plot as plot_upset\n",
    "from upsetplot import UpSet\n",
    "from upsetplot import from_contents, from_memberships, from_indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "positive-entity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some extra performance metrics\n",
    "sensitivity = recall_score\n",
    "\n",
    "\n",
    "def specificity(y_true, y_pred):\n",
    "    return balanced_accuracy_score(y_true, y_pred) * 2 - recall_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "improving-archive",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LegendTitle(object):\n",
    "    def __init__(self, text_props=None):\n",
    "        self.text_props = text_props or {}\n",
    "        super(LegendTitle, self).__init__()\n",
    "\n",
    "    def legend_artist(self, legend, orig_handle, fontsize, handlebox):\n",
    "        x0, y0 = handlebox.xdescent, handlebox.ydescent\n",
    "        title = mtext.Text(x0, y0, orig_handle, usetex=False, **self.text_props)\n",
    "        handlebox.add_artist(title)\n",
    "        return title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "medieval-county",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Load raw data\n",
    "df = read_csv(filepath_or_buffer=\"data/CoV-ETH_data.csv\", low_memory=False)\n",
    "\n",
    "# Inclusion criteria: \n",
    "included = np.ones((df.shape[0], )) == 1\n",
    "\n",
    "df_included = df.loc[np.squeeze(np.argwhere(included)),]\n",
    "\n",
    "# Exclude patients with an RBD-only response from the analysis\n",
    "RBD_only_S10 = np.logical_and(df_included['RBD_IgG_S10'].values >= 50, \n",
    "                              np.logical_not(np.logical_or(df_included['N_IgG_S10'].values >= 5.0, \n",
    "                                                           np.logical_or(df_included['S1_IgG_S10'].values >=20, \n",
    "                                                                    df_included['S2_IgG_S10'].values >= 5.0))))\n",
    "RBD_only_S11 = np.logical_and(df_included['RBD_IgG_S11'].values >= 50, \n",
    "                              np.logical_not(np.logical_or(df_included['N_IgG_S11'].values >= 5.0, \n",
    "                                                           np.logical_or(df_included['S1_IgG_S11'].values >= 20, \n",
    "                                                                    df_included['S2_IgG_S11'].values >= 5.0))))\n",
    "RBD_only = np.logical_and(RBD_only_S10, RBD_only_S11)\n",
    "df_included = df_included.iloc[np.squeeze(np.argwhere(np.logical_not(RBD_only))),]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legendary-express",
   "metadata": {},
   "source": [
    "### Defining Compound Antibody Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "amazing-bowling",
   "metadata": {},
   "outputs": [],
   "source": [
    "compound_response_S10 = np.logical_and(df_included['RBD_IgG_S10'].values >= 50, \n",
    "                                       np.logical_or(df_included['N_IgG_S10'].values >= 5.0, \n",
    "                                                     np.logical_or(df_included['S1_IgG_S10'].values >= 20, \n",
    "                                                                   df_included['S2_IgG_S10'].values >= 5.0)))\n",
    "compound_response_S11 = np.logical_and(df_included['RBD_IgG_S11'].values >= 50, \n",
    "                                       np.logical_or(df_included['N_IgG_S11'].values >= 5.0, \n",
    "                                                     np.logical_or(df_included['S1_IgG_S11'].values >= 20, \n",
    "                                                                   df_included['S2_IgG_S11'].values >= 5.0)))\n",
    "compound_response_ = np.logical_or(compound_response_S10, compound_response_S11)\n",
    "compound_response = np.copy(compound_response_).astype('U32')\n",
    "compound_response[compound_response_] = 'positive'\n",
    "compound_response[np.logical_not(compound_response_)] = 'negative'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "surprised-costs",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['negative' 'positive']\n",
      "[69 65]\n",
      "[51.49253731 48.50746269]\n"
     ]
    }
   ],
   "source": [
    "# Frequencies & percentages of different response categories\n",
    "unique, counts = np.unique(compound_response, return_counts=True)\n",
    "print(unique)\n",
    "print(counts)\n",
    "print(counts / np.sum(counts) * 100.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "composite-vinyl",
   "metadata": {},
   "source": [
    "### Visualising Antibody Responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cardiovascular-transformation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on Susanne's feedback\n",
    "# RBD response \n",
    "rbd_response_s10 = df_included['RBD_IgG_S10'].values >= 50\n",
    "rbd_response_s11 = df_included['RBD_IgG_S11'].values >= 50\n",
    "rbd_response = np.copy(rbd_response_s10).astype('U32')\n",
    "rbd_response[:] = \"negative\"\n",
    "rbd_response[np.logical_or(rbd_response_s10 == True, rbd_response_s11 == True)] = 'positive'\n",
    "\n",
    "# N response\n",
    "n_response_s10 = df_included['N_IgG_S10'].values >= 5\n",
    "n_response_s11 = df_included['N_IgG_S11'].values >= 5\n",
    "n_response = np.copy(n_response_s10).astype('U32')\n",
    "n_response[:] = \"negative\"\n",
    "n_response[np.logical_or(n_response_s10 == True, n_response_s11 == True)] = 'positive'\n",
    "\n",
    "# S1 response\n",
    "s1_response_s10 = df_included['S1_IgG_S10'].values >= 20\n",
    "s1_response_s11 = df_included['S1_IgG_S11'].values >= 20\n",
    "s1_response = np.copy(s1_response_s10).astype('U32')\n",
    "s1_response[:] = \"negative\"\n",
    "s1_response[np.logical_or(s1_response_s10 == True, s1_response_s11 == True)] = 'positive'\n",
    "\n",
    "# S2 response \n",
    "s2_response_s10 = df_included['S2_IgG_S10'].values >= 5\n",
    "s2_response_s11 = df_included['S2_IgG_S11'].values >= 5\n",
    "s2_response = np.copy(s2_response_s10).astype('U32')\n",
    "s2_response[:] = \"negative\"\n",
    "s2_response[np.logical_or(s2_response_s10 == True, s2_response_s11 == True)] = 'positive'\n",
    "\n",
    "# nAB response \n",
    "nab_response_s10 = df_included['nAb_S10'].values >= 20\n",
    "nab_response_s11 = df_included['nAb_S11'].values >= 20\n",
    "nab_response = np.copy(nab_response_s10).astype('U32')\n",
    "nab_response[:] = \"negative\"\n",
    "nab_response[np.logical_or(nab_response_s10 == True, nab_response_s11 == True)] = 'positive'\n",
    "\n",
    "# Save all responses alongside IDs in an excel file\n",
    "compound_response_bin = np.zeros((compound_response.shape[0], )).astype(float)\n",
    "compound_response_bin[compound_response == 'positive'] = 1\n",
    "compound_response_bin[compound_response == 'negative'] = 0\n",
    "\n",
    "rbd_response_bin = np.zeros((rbd_response.shape[0], )).astype(float)\n",
    "rbd_response_bin[rbd_response == 'positive'] = 1\n",
    "rbd_response_bin[rbd_response == 'negative'] = 0\n",
    "\n",
    "n_response_bin = np.zeros((n_response.shape[0], )).astype(float)\n",
    "n_response_bin[n_response == 'positive'] = 1\n",
    "n_response_bin[n_response == 'negative'] = 0\n",
    "\n",
    "s1_response_bin = np.zeros((s1_response.shape[0], )).astype(float)\n",
    "s1_response_bin[s1_response == 'positive'] = 1\n",
    "s1_response_bin[s1_response == 'negative'] = 0\n",
    "\n",
    "\n",
    "s2_response_bin = np.zeros((s2_response.shape[0], )).astype(float)\n",
    "s2_response_bin[s2_response == 'positive'] = 1\n",
    "s2_response_bin[s2_response == 'negative'] = 0\n",
    "\n",
    "nab_response_bin = np.zeros((nab_response.shape[0], )).astype(float)\n",
    "nab_response_bin[nab_response == 'positive'] = 1\n",
    "nab_response_bin[nab_response == 'negative'] = 0\n",
    "\n",
    "all_responses = np.stack((compound_response_bin, \n",
    "                         rbd_response_bin,\n",
    "                         n_response_bin,\n",
    "                         s1_response_bin,\n",
    "                         s2_response_bin,\n",
    "                         nab_response_bin), axis=1)\n",
    "\n",
    "ids = df_included['ID'].values.astype(str)\n",
    "\n",
    "all_responses_dict = {'ID': ids,\n",
    "                      'Compound': all_responses[:, 0], 'RB': all_responses[:, 1],  \n",
    "                      'N': all_responses[:, 2], 'S1': all_responses[:, 3], 'S2': all_responses[:, 4], \n",
    "                      'nAb': all_responses[:, 5]}\n",
    "all_responses_df = pd.DataFrame(data=all_responses_dict)\n",
    "\n",
    "all_responses_df.to_excel('./results/all_responses.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "wireless-transparency",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1L46TDRcEp+SISFdVrQ2Drk9hs982UZvF0+5xQXDaCx0uhabTLjlRROqwvulBWFiGDiEGjtOeaMl1CI4TlRnYTJ5O2BjSdaV1x3E2TLzLyHEcxwG8y8hxHMcJuCA4juM4gAuC4ziOE3BBcBzHcQAXBMdxHCfgguA4juMALgiO4zhOwAXBcRzHAVwQnHaCiAwQkT+IyAIRWRFyUu+eUmaUiLwoItUi8rmI3JAhv7HjOGnwlcpOm0dEemBhpjcGrsZCaf8Yy2Gwq6q+IyJjsMB4y7DQFw3AqVhIjK+r6tLW9ttx2hveQnDaA2djMfkPVtULVPVaYM9w7KzwmhCBr6vqRap6CTAa+BIW5z8jEVsfr4mIptk817DTYfAWgtOmCXmFPwX+o6oHpxz7EZa16xksqdLvVfX7KWUeAvZS1QEZ7EdpfQiWdCiRGS2Zuar6UmGf0nHaBh7t1GnrDMcq59/CeoHopqqrVPXGsG+PUPbtNOd/CIwTkU1U9dM0xxOtjzGq+mKwNwVLJ3oWMCn40B34q6reU6TP5ThtDu8ycto6W4TXhSJyOTZGsFJEPhSRRIthdXjtkeb8fuF1cOqBIC7HAo8nxABAVecDZwCJJ/+tw+t7+X4Ix2kPuCC0QaL0V29AM2p6h9dfAwcCp2BP7dXAwyIyFkv+vgI4IlTygGViA/YLb7umsT0ca308E8qLiHQHUNUbVfUPoVwTQRCRbsX4YI7T1nBBaGOECm1L4GFgYsp2bSgzBngO6+r4DXA9cCTwkoj0aW2fW5gu4bU3sIeq3hFyLe+FtRYuUdU64CpgJ+BeEdlGRLYHHgQSlXd9GttRWh8AI4GVwFUishJYJSIfichRRfqMjtM2UFXf2tAGbAYocFyWMm9jT8hfStr3VaAOuCKH/deC/dTtwaQyA4A/AAuwJ+/pwO4luh/jgn+3pDl2O7AO698vwwaFG5I+0yPAz8LfX0lz/vhwbBbWyjgOE94Zwc7YUO7fiXsEHA4cj6X7VGBiqX8zvvlWrM0HldseWfurRWQ49sT6e1X9KLFfVWeKyKNYn/gZGc5Nbn00my0TyvQAXqT5rJvnRWRXVX0nr0+VP5+H14Vpji0EBOiuqquA00TkUuzJ/1NVnSsiF2OV+9w05ye3Pr6sYa1CuI8fAZdgM4t+D3RS1RsSJ4rI/cA7wOUicp+qNhT2MR2n9LggtD2a9Ver6uqk40PCaz4zaoaTe7ZMlFk3rck7wBoa70symwG1wCIRORqYp6rTsZZNgr2AN1S1Ns35ifs6VZMWrqnqMhF5BDhWRLqr6s2pJ6pqjYjcDZwPbEX678Nx2hU+htD2yNVfndeMmkCu1kfUWTetRhDDR4CDRGS9KIjIZsAhmLg1AKcB14tIeVKZA4FRwA2kJ1LrI4t7ifOylXGcdoMLQttja6yy7409jZ+ACcSfRGQi+c+oSdiGzLNlhhNt1k1rcxY24DtNRM4VkTMxcaoBfh7KXIZ9vsdE5EQR+Q3WLfYUcG8Gu1FaH51E5F0R+WWaMl8Nr7PjfyTHaYOUehCjI2zAttiA7gXh/XDSD9wmb2My2PoBcFLKvkqsT3s+0AnrplDgPmAbYHvgMSxujwJfy2D7bkxMbsVERoPdo8LxfcO+o4DLgeXh/YdY2IhS3uMRwJ8xYVgePu+WKWWOwgZ7V2NdXL8GqnLYfSB8d1sn7UuIwf3h/WfYaumeSWU2Cffy+VL//nzzrVibh64okNBF8RqwA/ArVb0gPHkfnqZ4JRZzZyGwncYIuCYiF2BCsC3wLnAlcDKNrbxHgVeAS4Gvqur7aWz8O/j5EPbU3Bub178d1hpZA0zBBGAttjq4ATgTe4reT1WfjepzeyAM0r8W3l6LicMp2HTVnVT1YxE5DPgLdt//gLXgfgxUYFNhfcGa0zEotSK11Q14MsP+Hinbr7GKVLE1AanHk7cbsAp2X6BHTH9+RMrTPzAI6yPfNLy/GJtv3zWD36cAp6f4NAB7ml4AfD9cYzH2BJwoMxT4Ahucbe3vIdv9jLzluEaU1sehwD+xlsNSTCC+WurfqW++FXPzWUaZibLqdyusf/sy4LwIZX+APZn/I10BERkCPA1MUdULUw6v76/Oc0YNwB/T7KsF7gfOAYaFfY9glWOC5cATwDFh1s2qDPbbJar6MfDtHGX+Cvy1dTxynNLgg8r50wm4CZiGdbPk4pfYIGhqRb8eVf0c6AVMFpGeif0isgm2aGqa2oyffGbUZGNReH0p5X0yi8k966alWRFzcxwnBi4I+XMaFmv/lAhltwa+BdxG0yf6dPwY66L5u4icIiK/AP6FdQWdFMrkM6NmI6yv/Gdpjn05vH6MdX9tmabMpoQ5/zn8dxynneKCkB9fxRZwnQv8L0L5/8PGDm7JVVBVHwYOw2bKXAb8FOti+rqGwUtVfQg4GltvcDWNs4LGaeYVs/OAnlhLI3kNwxDgGGx18jysa2h/GruowMTgW9j6BF+R6zgdFJ9llAEReV1Vd06zvxcWWG4VkAiANgyb035J2JLpij15T8Mq3vWo6soiu52REJLiQOBP2DqEO7Dun+8DnYF9gA+wzzI9nHYjNtvoh0AVlmimVVfkBr8TxO0GWt/tlutei0isfwRVldylHKd94S2E+JyCzf0/H+gbtt7hWGV4n1xZ7IVVvH9pPRcz8jjWmliNjWX8BOtGGouJAcAnwDeAl7HPeiYW7G0sMCebcRH5hoi8LCIrQ0juaxIL2zKU31ZE6sKUWsdxSky7nWUkIq8Bu6Q59JCqHhnKbIbN1x8Tjj0G/FRVC+kH3webfz49zbFTwzYSq1jBppjWYf37bYEnwpaNOcSMWSQie2MrnN/AutM2wQRlZxHZS1XXpZQvx1opneNcx3GclqNdCkLEqJ39sG6aCqwvvhx72t02RO2sy/PyP6exRZBgILb6909hSx443h0Ln9xq3UMl4gpMBEerag2AiHyCzXraD/hbSvlzSB8ywnGcEtEuBYFoUTtPx2brbJMYjBWRf2JPscdiK07z4c00+xLz9+fQtOVQjg3O3p7PheL0a5eyTzvEUVqEtc5qkg69EF63JUkQRGQb4BfYor5ft5afjuNkp70KQpQct0cB0zUprICqPisi74djrRGobROshfJZK1wrDnkPzqYjLITbP82h7cNrovss0VV0O5Zn4B5cEBynzdBeB5WzRu0MaSRHYP3ZqfwbaDZ7qIXoG143qEVSIrKpiBwH/A6bfZU8oP4zLIHN90vgmuM4WWivLYTknAHfAbqLyMfAuap6P41JZD5Pc+48oKeI9FLV5UXy5xPSP0W/kWF/h0VE+tI4G6ka+EkilEbIZ/BL4Meq+lkILOc4ThuhPbcQsuUMSMxdr05zbqKPOzUXgFMcEuGzJ2G5G54VkSNFpBPWVfSyli6vguM4WWiXC9NE5Ac0z3FbiXVPdAOOwObR/5+q3pZy7kXYCuONQlyg5GMnAicC9OrVa+s777zz3dRrNzQ0FE1EO3XqtC7b8Q8//HCnqLY233zzdN1j60n2e/bs2TtEtQuw2Wab/Sfxdy6fk6mtrZWTTjpp64aGBvnmN7+56OGHH974V7/61cwhQ4bUAcyfP7/zOeecs9V+++234Kijjprfq1ev+rKypre3tfyOc68h8/0+9NBDW6s70nGKTrsUhEwk5QzYHpsN9BNVvT6lzJXYDKTu2jRXcaqtTCuV06WuzItirp7NNcuotVb8prnutVjehnfJPc10M1Wdk3K+r1R2nFaivY4hZCKR47YqvG6UpszGwLJsYhCTOJVUhxxPEJGvAk8Cv1XVG1MO98C6kU6i+SK0QdhMo7uBu7CMcI7jlIh2JwhRcwaEbcc0JnYAXm85DzdIPsTCdv9ARG5NLPoTkU2x7rsXVPWF1JOSBpU/1g6Wic1x2iPtblA5Rs6Ah4Cx4ek1UWYs8BUsIYxTJFS1HouLtA3wgoicFJLS/wtrHfyklP45jhONdtdCCPwYm9v+dxFJznGbnDPgt9hMl+fCuEFXLLvZG1g3hVNEVPUeEanD1hlchQXQew6bCvxB1pMdx2kTtNtBZRE5FIsrtB02lXQ6cI6qzkwq8xUsX8Be2BTUJ4AzowS3izGonPcYwoYwqFwoPqjsOK1He20hRMpxq6rvY4ldHMdxnBy0uzEEx3Ecp2VwQXAcx3GAdtxl5LRNvC/ecdov3kJwHMdxABcEx3EcJ+CC4DiO4wAuCI7jOE7ABcFxHMcBXBAcx3GcgAuC4ziOA7ggOI7jOAEXBMdxHAdwQXAcx3ECLgiO4zgO4ILgOI7jBFwQHMdxHGADEAQR2UxEporIkrDdJSIDSu2X4zhOW6NDh78WkX7ANKACuAz7vGcC24rIrqpaV0r/HMdx2hIdWhCA04GhwDaq+h6AiPwTeAY4FvhDCX1zHMdpU3T0LqOjgOkJMQBQ1WeB98Mxx3EcJ9BhBUFE+gAjgDfSHP43sHPreuQ4jtO26bCCAAwJr5+nOTYP6CkivVrRH8dxnDZNRxaEHuG1Os2xmvDarZV8cRzHafN05EHlhNhlS/q+LvmNiJwInBjedo14nZ4x/YpMCyagb48+Q/v123HaBR1ZEFaG18o0xypTygCgqr8Hft+STjmO47RVOnKX0SfhdaM0xzYGlqnq6lb0x3Ecp03TYQVBVZcBs4Ed0xzeAXi9VR1yHMdp43RYQQg8BIwVka8mdojIWOArwP0l88pxHKcNIqrZxlzbNyFm0TtAPXAlNlB8FvAhsIeqrsnDZo/cpaKhqitzlyoOxfK7NX2G9uu347RHOnQLQVUXAXsBbwEXAqcCDwMH5CMGjuM4HZkO3UJoCbyF4C0Ex+modOgWguM4jhMdFwTHcRwHcEFwHMdxAi4IjuM4DuCC4DiO4wRcEBzHcRzABcFxHMcJuCA4juM4gAuC4ziOE3BBcBzHcQAXBMdxHCfgguA4juMALgiO4zhOwAXBcRzHAVwQHMdxnIALguM4jgO4IDiO4zgBFwTHcRwHcEFwHMdxAi4IjuM4DuCC4DiO4wRcEBzHcRzABcFxHMcJuCA4juM4gAuC4ziOE3BBcBzHcQAXBMdxHCfgguA4juMALgiOExkR6SMi/Uvth+O0FC4IjpMBEekiIj8RkWkiUgt8ASwUkToReU1ETheRLqX203GKhahqqX1oV4hIj2LZUtWVxbKVi2L53Zo+Q+n8FpHtgb8Aw4BVwBvAQqAB2AjYCegOzAIOVdX3i+Gn45SS8lI74DhtDRHZCHgKq/wnAFNUdV1KmU7AUcA1wCMispOqrmptXx2nmHiXkeM051SgB7CPqv4pVQwAVLVBVe8FxgCbAt9vVQ8dpwVwQXCc5nwLuFdV381VMJS5F2stOE67xgXBcZozDPhXjPKvA1u0kC+O02q4IDhOc8qAONNLu+LjcU4HwAXBcZozAzhSRKL+fxwK5Oxecpy2jguC4zTnFmA74I8i0jVTIRHpKiLXA3sBt7aWc47TUrggOE4KqnoX8AAwEZgtIhellhGRLwGfAz8C7sEFwekAuCA4ThpU9WjgDOx/ZESaInOB/wEnq+ok9RWeTgfAVyrHxFcqbxgrlcO1y4HdgKGAArOBf6tqQzF8cpy2hs+McJw0iMg44DosTAWYIAjwuYicpKqPlMw5x2khXBAcJwUR2RuYAnwG/AyYGQ59GfgxMFVE9lbVl0rkouO0CN5lFBPvMur4XUYi8jwwBNg59TwR6YYFuvtMVccWwzfHaSv4oLLjNGcX4NZ0IqKqq4HbsLEFx+lQuCA4TnPWYuMFjrNB4YLgOM15FfieiPRMPSAivYDJwGut7pXjtDA+qOw4zbkImA68IyI3YoPK64AtsUHlQcD3Suad47QQPqgcEx9U7viDyuG6BwM3AhvT2H2khNXJqvpYMfxynLaEC0JMXBA2DEFIuv6OWDjsCmx18uu+MM3pqLggxMQFYcMSBMfZkPAxBMdJQURmRymnqpu1tC+O05q4IDhOcz6m+bTTcmww+UtYULu/trZTjtPSuCA4Tgqq+s1Mx0RkK+Bp4M1Wc8hxWglfh+A4MVDV/wLXAGeV2BXHKTouCI4TnzJgk1I74TjFxruMHCcFETk2w6FyLOLpD4F3Ws8jx2kdXBAcpzl/xAaV083JFiws9kmt6pHjtAIuCI7TnL3T7BNgIHAYsC3wSWs65DitgS9Mi4kvTPOFaSJyH1CuquOLZdNx2gI+qOw48Xke2L/UTjhOsXFBcJwYiEgVcASwotS+OE6x8TEEx0khS+iKTtg4QgXwm9bzyHFaBxcEx2lOutAVYDkRXgCeVtW7W9clx2l5XBAcJ4VsoSscpyPjYwiO4zgO4C0Ex0FEGkjfRZQVVfUHKqdD4YLgOHAHTQVBgPHh9RHgI2wgeUtgPyyN5h9a10XHaXlcEJwNHlX9XvJ7EfkFsBjYU1U/STm2HbYOYV3reeg4rYM3eR2nOScD16eKAYCqvgVcDfy41b1ynBbGBcFxmtMJ6JLl+FqgaCFMHKet4ILgOM15CjhFREamHhCRrwCnAv9obaccp6Xx4HYx8eB2HT+4nYhsglX4gzBxmAU0AFsABwDVwOjQfeQ4HQYXhJi4IHR8QQjXHQRcgMUt6hd2rwQeBy5Q1VnF8Mtx2hIuCDFxQdgwBCHFh95AJ1X9ohi+OE5bxaedOk4GRKQvsA8wHFARmQM8q6pLSumX47QULgiOkwYRORWLaNqVpqk0a0TkZ6p6Q0kcc5wWxGcZOU4KInIkcCXwFvAdYPuwjQdmANeJyKGl8s9xWgofQ4iJjyG0mTGEG2OaqgdQ1eMjXPNV7GFpD1Vdm3KsHHgFqFfVPWL64DhtGu8yctorR5M+IJ3QtIsn9VhOQQC2AX6eKgYAqlofcipfEtVRx2kvuCA47ZVt0uzbDfgjsCewNOXYWODaiLargZ5ZjvcA1kS05TjtBhcEp73yWZp9I8Lr50DqTKA4U0anAT8UkdtVtcl1RGRT4CTgxRj2HKdd4ILgdCQ6F8nOr4B/Au+JyL3ATCy66ZbARKxL6hdFupbjtBlcEJyOxD7FMKKq74rIGOB3wOSUw68BP1bVt4txLcdpS7ggFJeolUS6/m+nME4MG1j/f2qXUU+gNqoxVX0d+LqI9AeGYgly5qrqgiL46jhtEheE4jKH5jNfyoD+WGC0z4FnWtmnjk5/7En+QOAe4HBsAPnWlHJjgYVxjavqYixZjuN0eFwQisvBWY59FXgSD5tcTCYBFwK9saBzp2JP8hcCVcDTQDfgu8A4bAZSTkSkK/BrbGrrYDIs4PScyk5HwxemxaTABV4/wyqm3cAXpkUhi99PAl8DVmOV901hf+9w7Ks0ttYUW2G8n6rmbCWIyFXAKcB72GrltFNMVfWESB/CcdoJ3kJoXRYCm5XaiQ7C7sBzWMX9adL+ZcDXgSOB7TAx+Bdwv6rWR7R9NPBXVR1XNG8dpx3ggtB6DABOIP38eSc+3wemZDi2DnggbICtMI5huwfWBeU4GxQuCMVlBunDKXQBBoa/z2g9dzo0E7ExhEiISAPWRTomQvF/ADsCt+XnmuO0T1wQissnGfY3ANOxJ9ZnW82bjs2oLMcyxTNKJ9bpOB14XkQ+Au4H5qvqupj+OU67wweVY5JjcPYN4M9Yxf9xLls+qJybLH4vw9YdPJCyf0/gMWysJnktwkHAvVFmBonIDKxFNzBbOZ9l5HQ0vIVQXPoB54TtP5g4/Jk85r9nQkTKgK6qWl0sm8nmsYQwNS1gu7XI1AqI2joAeL0YjjhOe8MFobh8GVuLMAnYC9gBmxL5MvYk+zCwKh/DItIPuApL2HIFSbF0RGQrYAI2GPoPYIqqNsQw3xfLDnYEtsjr10nHvgIcBXQHXhORO2PabitEFgSfTupsqLggFJc64KGwfRv4A9Z1tBcwGsvC9TQ2O+apqEZFpBJ4Caucp2NdU4ljBwB/wRZkKRaJc7KI7KeqdRHMdw2+bBGu8Z+kY/sA9yXZPhGYGMN2S1JP+gVjleG1S8r+KmKErnCcDRHvA205VmNPpYdjsYvOAd7E+rLvAT4UkT+IyN4RbJ2GicEkVf2mqv4FQEQqsNW3FcBPsRbC4djCt9Mj+nkSJgYnYq2bx8L+zlhWsgrg58BG2Pz8OLZbksWY36l8KbzumLJ/ZzIP+jdBRBpEZF2urQDfHadN4oPKMYkxOPst4E/ASJounOoH7IG1Go4GuucanBSRfwJLVPWAlP1HYq2N/6rqNkn7bwZ2V9XtI/j9PJZM5oiU/YcCd2Khn3dP2n95qu2WJIvft2P38EDMRzAxeITGFs3F2LqPvYCfAJeq6nkRrnkb6WNSDcAGrZcAN6rqb2N9GMdp43iXUevSDes6Ohj4ZngfJXDa5jTt109wEFZx/SVl/5vYmEIURgDpKrb9g+1HC7DdklyF+fgP4F1sMdpIbIxmL+BSLENaYgrqQ8BFUQyr6vcyHRORAdigc7P0mo7T3nFBaHn60SgCe2N92/VY1NPbsCfaXFQBy9PsHxten0vZX46tfYhCFbAizf5EV9b0Amy3JG9jonoO9tReht2HX2JRZ4/CQlcMB2aGcNYFo6qLROR6rKvt6mLYdJy2ggtCy9AJe1pVrEJNPKXOBO7FupIWx5jT/z+slbAeEdke69dfTfMIqnsCsyLankdj6skE22BRPldjCWHytd3S/BdbsZyJt8JWbGqwHAmO06HwQeXiMhQ4D4uSeW7Ytwz4PTAG64u/jvjx9R8DjhORvkn7zsCE5pHEjB8RqRSRM7DAbqkLtjLxJHAM0Cdp38nB9hM0do10Dfvj2O5QiEiFiOyKDfJ/UGp/HKfY+KByTHIMKi/BWgf1WPfFvViQtLSB1aK2EERkEBYnaTXwILA11n++BthOVWeJSDnWf14BvAKMVdU1STYy+T0AeDXYfhjLG7xPsP114KPwmeZnst2SZPH7xpim6gFU9fgI12wg+7qFdcBRqvpgTB8cp03jghCTHILwT2ze/p+IsDo5ThgIERkB3IBV1oJ1P/1AVV9KKnM7Vrn/UVXXppyfze/NsMVu3wi2P8DCSid3Rd2IhZG+KdV2Fp9vj1Iu9TRVPS7JRrbQFfnYjhK6It0sI7Cxk/nAn1T1v3lc33HaNC4IMSlWTCCI1ULYD3hNVZeGbF6Vqro0zrUi+t0FW9i1LFOBmCJWT/QVwuvLJVfaOQThHJrPgsrEWOBajz/kOJnxQeXikin8dVpERDFRHp6j6BPAGhG5AviVqjZbcSsim2IrmF8CzlLVfAZ+15AhO1g+qGrO35eIbIKFyzgEm0n18xiX+ILo+SW+iGEXABHpjrXIRmDf61zgeVWNbctx2gMuCMXl78C+2ADtf7CVsYLFONoSqAb+nVQ+zvTNMix+0a4iMi5NcDvFQmvvD7wpInurauoMoXzohM00+hQ4rAj2AAhjHqdj00QrsW6201R1UbGuUQgi8gNsfUZ3mobSrhORy1T1gpI45jgtiAtCcfkP1jWxFzZPPplvYiErpgB3Q+xQ0qdhawOOAP4qIgcmxxNS1U+Ao0RkIDaWcSEmDoXSgM2MugY4LvxdECIyCsuBvDXwPvAjVZ0W08xKbOZTVLphsaai+HcUNl7zCva5P8DiIH0JmAz8UkQWqeoNcRx2nLaO96cWl1OA62kuBmCzjm4k/4xpK7BIpw9g4vKnEAq7CSGJ/A1YAvpicQfW+rg8jGfkhYj0CwO2L2DdMOcD2+YhBgCfYy2vqGxO9DDkZ2MRavdW1amq+o6qfqiqT6nqkVgX3k/iues4bR9vIRSXvtiUxEx8gS0mywtVVRGZgHWxHA7cLSKT0oSjXoMFp4tC1NzB3YPNR0TkQlW9OOJ5AIjICcBl2D16GjhJVXMmEcrCa8B3sRbLghxlN8LyWf8tou0vA2dmCfP9OBa51nE6FN5CKC7/Av4Pm9ufSm+sUkrXeohMqKS+jT2lHg08EdYpABBmIR2PxRyKwlBgkwhbH2xM5HPsM0ZCREaKyEvArZhQHa2qBxQoBmBdOV2w1sZETLBSqQDGYwPt3bD4RlF4H1tpnoldsQkEjtOh8GmnMckxfXM7bOVvPda18yHWYtgMi63TE1vp+zzEmnbagIW+vjdpXzkW8fO72GD1w8AiLGbSCOAgVf1bUvlWT6EpIr8FTsVaoo9hq7fTxU1KvcbcJBvZ/N4NC/89FBv4nY0tDqzHBHgzTDQWAd9W1Rcj+r03FmPqN8D1ic8sIn2w1dqnAnuq6jtR7DlOe8EFISYRKtZtsCfRPVL2v4nNqFlfKRUiCEnHjsHm428Zdn2ITTv9a0y/IxFTEFK7XCJNyY24DiFBohVwEPZU3z9cZwnWGnsWuDuMrURCRD7BWkTdwq7FmLAnciwraT6Lr3Fw2jsuCDGJUbH2xiJtdsLmrzeLXxRDEIYBi1Q1Y65jEekNdMo0R75EgvDLPK9xYZKNUvidaaVyrmt46k2nXeOCEJNSrFQuBqWoWItBe/Xbcdoj3sR1HMdxABcEx3EcJ+CC4DiO4wAuCI5Tcv7xj38oNnPJt3aytZHvrOi4IDhOiVmzplVyDTlFpKN+Zy4IjuM4DuCC4DiO025YtGgRkydPZtCgQYjIChGZLiK7J5cRkf1E5CURqRaRVSLybGqZTLggOI7jtANWrlzJXnvtxQMPPMAPf/hDgPOAIcDzIjISQERGY0Ece2OhYn6FhW1/QUR2zXUNFwTHcZx2wKWXXsr777/Po48+ygUXXICqXgvsGQ6fFV6vwZJZ7aaqV6vq5cDuwGogZ4RiX6kckywrZzthqtwbE9p6LO/vMjLMCIgRuqIzFja6Z7C9FovVs1wjfoFZ/E7Y7oGFa1gDLMUS0OTtc7hmV6AfFhNIsCQzX6jqqhg2MvldGfyuCu+rsXuSNrxHTL+7B9uV2HdXDSxOl7q0GPTt21eXLl3aEqadDsCgQYOYN28em2yyCTvssAOPPro+jbgAiMiPsDrhQSzE/pWqemayDRH5C7CvqnYjC54PoTh0x0JEJ7e4KrDKagAWyyivaQkhRtEmNI2t0yVcc42IfKyqa/OxjVV6qfkZKjBxqAHmkD2/Q0ZEZDCNweASdAF6ichKYK6q5mUb2BgLPpdMBSbGy4meZ7kJIiLAppjwJtMV6Csii1X1f/nYzoaLgZONBQsWMGfOHD7//HPOOssaAqpKWVlZd1Vdpao3AohIJ+ArWGsglf7YQ2pWvMuocLrQXAyS6YxVMp3iGg5PqqlikHrtEaEii0svsifrqcSC88VGRPrTXAyS6YF9rnwYRHMxSCbX58rGJjQXg2T6hxSljtOqzJo1C4CBAwdy5pln0rt3b4CVIvKhiBwMoKoNqjor9aFFRLbFoi//Pdd1XBAKZwC572NnsldimRhE7qibXbAn47hEqdgqSZ94JiNBnKLY7hW6lOJQhnVB5aIPMVu/IhL1Pg5Il7rUcVqSZcuWAXDeeefx+OOPc+211wJMIuRCEZGx6c4LD5V3hbc5E0T5D7swysj+RJlMLEEQkQoa4/Hnom8c21hXVkXEsnGFrCfRK+O4fvcmWlhqIb5IRvWlE9YKcZxWI7EQbtmyZbzyyiscd9xxqOrdwF7YOOUlqeeISBWW6Gk74FJVfSHXdVwQCqMz0ePmR62A8ynvtgu3HTUHdT62HacgunWzZ8Nx48bRp0/jM5qqLsMq/Z1CawBYP/b4NLA3llXw3CjXcUEojDiDonEHUOOUjztVrK343ZZsx7mH+Q6GO05eDBkyBLAxhDQsxB5MuwOEca5p2LjB74H/izob0QWhMNYSffZQ5KmWgRoizAoI5MxTnMaXqJVaXNtxEtHETVoT5x7GtR3nc3qyHadVGTlyJF26dOHdd99Nd3gzbEr3ojBN+ylge+BqVf1+VDEAF4RisCRiubSpLTMRvsQWsY2JwfIobmD9k5FR1TqiVZiJdRpxqMZ++LmoI/3Uu2yswAQ+F6taaj2C42SiW7duHHLIITz22GNNREFENgMOAf6qqg3ADZgYXKuqp8e9ji9Mi0mGhVLDsKmUmViENeuakGuxVJixM4Lsg8v/U9Vm+ZrT2Er1ryzY7pLhFAU+J0U4oizwCgvpNidzv7wCs6MsUEvjdxfsiSjTNN4GbP1Ek0o7ot/dgu1MD0prgY+C6BUNEfF/QicrqsqcOXPYdVeLPnHKKafwi1/84izgFKx+2An73/gv9j97Kml6GFT1nmzXcUGISZaVs/2xmSrJlWAdsBhb+duMiJVUGTb9tC9NK8EaYKGqRnnSz+R3wnZvmlaC1ZiANXvKjrm6ejDNZwatBBaoanUBflcEvxOrq8FEZhWwgDTdeDH8rgx+J19XsX+yeQUsAsx2Tf8ndLKSqKc//vhjfvazn/HMM8+wfPnyFcBLwJmq+p6I/AC4KYedrJNgXBBiEiHpexVWcdeTIYxCgpjhFAR7EigD1qpqVttpzs/md8J2InRFxifguMnqw+rJqmC7Nu7TdQ6/y7FVxEKOMZc8/K4IthWoUdWo4zmxcUFwcpGhns5nQWpWPHRF8Yn05BuXMKYQd2A6svmWsh36NVtqELaelvO7jizCWEz69Onj4SucjAwaNKjVruWC4DglZurUqYwZM6bUbjgxmD59eof8znyWkeM4jgO4IDiO4zgBFwTHcRwHcEFwHMdxAi4IjuM4DuCC4DiO4wRcEBzHcRzABcFx2g3PP/88o0aNokePHgwZMoRTTz2VVataaq2isyHiguA47YBp06axzz77UFdXx6WXXsrEiRO55ZZb2H///Vm3ztMzOMXBVyoXlwpgRyww2hfAf4ifvMZxmnHGGWcwbNgwXnjhBSorKwEYNmwYJ510Ek899RQHHHBAiT10OgIuCMWhB3Am8D1MABQLcLcauBaLQNiQr/EQIK4Plq+4DIuxszRuwLYMJGx3D7bXYNFZC47JFCKe9qMxcF4t8EXcwHwZqMD8rgrva7D8EQXHHxKRrpjfldh3uRpYUuyw1wnGjRuXMZbRoEGDmDNnDgMGDOCII45YLwYAo0ePBmDGjBkuCE5RcEEonL7Ac8BQmucW6A78AtgHOILoGdDWIyI9sXwLyd17VUBvEakG5hQQibM3sDFNoyZWhv2rgE/Is4UjIgOwMNLJtquAviKyDPg0TianFAZh4caTqcIq8S+A+fkYDRFlh2JCk0w3YKCIzFfVZnktCiVbYLsFCxbQtWtXnnzyyWbH3nzzTcBaCo5TDHwMoXDuATYhc6KZKmA34KK4hkWkCtiUzN9TFbBZqMji0gMYQuYQut0xIYqNiPQFNspiuzdW8ebDAJqLQTL9MMHIh41pLgbJDBaRbNduFebOncsdd9zBySefzMiRIzn88MNL7ZLTQfAWQmF8BctUVJGjXBVwPPBr4qV2HETumOeVQC/ip6OMUml2x3yP3H0UxCmK7T4islBVo+akBhPGKBVyPywxUeRuupD/oF+EogNF5IsCWjcFsWTJEoYPHw5AVVUV1113HV27di2FK04HxFsIhXECmdNEprIOODSq4VBB5UrGk6BvVLuBSjK3aAq13YPo9ySu7d5E+81KKBuHbC2DZMoxAS4JIsL999/PXXfdxVZbbcXYsWN58MEHS+WO08FwQSiMLYjeyqoiXhdMrlZHMlEr93zKx/Ejru24fsfxpS35XTT69OnDd77zHSZOnMiLL77Ipptuyumnx86l7jhpcUEojDjdHQ3EmwETp0sibvdFnInrcW23pN/t1XaLUFlZyUEHHcSnn37K4sWLS+2O0wFwQSiMZ4k+JrAG+HsM2zVE7wOPO/10NdErtbhLYeOUj+t3HNtxxmri2m6plKBpmTlzJsOHD+fGG29s7sjKlYgIXbqUrNHidCBcEApjCtHv4WLg1aiGVXUdNq8+Cl9EtRtoAFZEcQNbkxAZVa0lWuXaQPyB8NVEa2XVEb/SXka0acHVRVpHEZnNN9+c5cuXc/PNN1NX1/jx586dy0MPPcTo0aPp0SPqcJPjZMYFoTBWYTOHcs3CqQFOycP+wnBuNhaESjgu88hduc4jj7UTwOc5zlNsHUI+MRc+I3uX17pw/ViEWUOfkr3l1BCu36qUl5dz3XXX8fbbbzN69GhuuOEGLrzwQnbZZRdEhOuuu661XXI6KC4IhXM9cAW2Cje1gq0J22Tg+biGVbUB+BhrKaRWgnXAZ6q6IK7dQML2cppXgnVY5RirdZAgTCX9kPStkBpgtqpGaaGkowaYTfouoWpgDnmusg4rvz/OcP5K4MM8xbdgJkyYwJQpU6irq+P000/nmmuuYfTo0bz22muMHDmyFC45HRAp0XTqdouIZGqbbwp8HzgcW9m6FLgDuIsMXTpxQk+E8BU9sCmVa1U1Vt9+Fr/Bwld0D7bryFKhxg2XEabPVgXbtXG7W3L4nbANJhQZB/nz8LsSSEzwX91SYSvCtbL+E/r/aNtj+vTpjBkzptRu5LMgNSu+MK14zAV+HraiE1oLy1rCNtZaWN4ShkNF2lKVaYvZDsLVKmMFffr0yRrLyHFaC+8ycpwSM3XqVFQ17TZ/fl5hmRwnL1wQHMdxHMAFwXEcxwm4IDiO4ziAC4LjOI4TcEFwHMdxABcEx3EcJ+CC4DiO4wAuCI7Tbnj++ecZNWoUPXr0YMiQIZx66qmsWhU3GK3jZMYFwXHaAdOmTWOfffahrq6OSy+9lIkTJ3LLLbew//77s25dPjECHac5HrrCac/0BDYLf8+hhcJvtAXOOOMMhg0bxgsvvEBlZSUAw4YN46STTuKpp57igAMOKLGHTkfABaG49AxbJ2AtFnsor8ibyYTE9b2x4HZlwfaSIsXlFyyfcDcag9stJV42uPSGLSBfHyxwHlhE2CVFCBS3BXAWlqM6YasL8DhwGfBeIcZFpDPQj6TgdpjfURMWxWLcuHFZYxnNmTOHAQMGcMQRR6wXA4DRo0cDMGPGDBcEpyi4IBSHLli+5NQ8vn2wAGlziZ79rAki0g2LpJr6XfUTkZXAJwVUVD2AIZiANbGNPW3nHftfRPoE28ndkj2BgSLyBfA/zS+M557AA1hl3YnGShvgMGB/YAKWzS42IrIR0J+mkSR7AoNF5H+qGjcZUU4yiQHAggUL6Nq1K08++WSzY2+++SZgLQXHKQY+hlA4nYHhZE7qXol1a8QOVRtCMG9GZuHuAQwPLYi4dAM2obkYJOgVjsdGRHqHczP9vvoBG+VheggmBt1I73cZFg77HmBEXONBDAaQ/rsSYEgQupIyd+5c7rjjDk4++WRGjhzJ4YcfXmqXnA6CtxAKpz+572MXrLUQNSVmgkHkFu1umDDETTgziNwi1RMTtLhdU4MjlOkvIotjdh/9ABPgXHQGTgJ+GtVw6CbqH6HoYBFZlmfrpmCWLFnC8OHDAaiqquK6666ja9eu2U9ynIh4C6EwEn37Uegby7BVUFET5faLYxvrZqnMWcqI63cPMreWCrEtwAkRbXcGjiHeA08forXiOmNCWRJEhPvvv5+77rqLrbbairFjx/Lggw+Wyh2ng+GCUBidiX4Po1aSCboQvZsp7iNilxYqC/F8iVO2R8zyZcQTnJbyu6j06dOH73znO0ycOJEXX3yRTTfdlNNPP71U7jgdDBeEwmjJboM4tttSjsWW8rueeL9XCee0hC9t4n5XVlZy0EEH8emnn7J48eJSu+N0AFwQCmNt2KKQLil8NmqAqCuO4tpeTfRKLe5S2Di+xClbDcyLUX4FNn22JXxp1eXBM2fOZPjw4dx4443Njq1cuRIRoUuXuA05x2mOC0LhRB0ojjWgrKrriF6hxZ0KWQ9ETTofp1JN5CKOsvYizudLcG1E2zXAdcR7kl9GtKnBtapa8NqSOGy++eYsX76cm2++mbq6xjH4uXPn8tBDDzF69Gh69Ig63OQ4mXFBKJwvyD0LZznRK+BkFpB7gdjiPCuo+eTuUplP9BZQMp+Ru3L9PI/1E/di9zvbeeuwyv2OOIaDAH+eo9g6ClibkS/l5eVcd911vP3224wePZobbriBCy+8kF122QUR4brrrmttl5wOigtC4SgWNmEZzZ9IG4DF5FmJqGo98BHW/ZHO9jxV/V8+trGK/mPSd5WsBf5H/JYHAKpaG2ynE6o6YK6qxm0dgHXV7AN8Svpum1VYt9K+2PcRC1Vdhn2X6US4Gvi4tVsHCSZMmMCUKVOoq6vj9NNP55prrmH06NG89tprjBw5shQuOR0QKdF06nZLmFaZiU7YlMQyrMJeTpZuC1WN3GoI01ATttcCy+PMhc/hdwUWXiIRuiKjX3F8DtetpDEsRm0e56fzuwJblXwqFsYCTICuBaZiITKakMd1u2NTcxVYXaQwIZmulfV79P/Rtsf06dMZM2ZMqd3IZ0FqVnxhWnFpIH6/eCRUdS15PrFHoI74i+YiESrSYlemddiK5QeKbHc9qrqKVho87tOnT9ZYRo7TWniXkeOUmKlTp6Kqabf58+eX2j1nA8IFwXEcxwFcEBzHcZyAC4LjOI4DuCA4juM4ARcEx3EcB3BBcBzHcQIuCI7jOA7gguA47YLJkye3hZWxTgfHBcFx2ji33XYbt956a6ndcDYAXBCKT39gM6Kn1oyEGBuJyAgRKVkKxzZGZ2CTsMXNSNfmaWho4MILL2Ty5MmldsXZQHBBKA6dgO8C/wJmAn/HopQ+BexXiGER6SoiPwJmYwHc3gIWiciTIjK6IK8b6QlsDAzBBK0oMa6CiPURkaEisomIDBCRYtjeGLgEmIvd89fC35cDwwo1LiKdRKR/8Hlo+Awt9r8ybtw4RITBgwev31dbW8uOO+7I+eefz8SJExkyZEhLXd5x1uPB7QqnC/AQsBMW1RMan1a/BtwJ3AfETnwrIr2AF7CInlUph/cF9hSR36jqxXn4TbA5FHvSTmYgFqQvToayJgTfh9D8NzZYRBaq6oI8Te8IPIrd99RWwQnAMcA44NV8jIvIQOzzJwtAX2BjEfk8hMguKonAdgsWNN6S2tpaVqxYwZQpUxg/fjzDhw8v9mUdpxkuCIVzI7ALFio5HVVY62EO8LuYth8Bvkr6RPcSbP9cRD5S1ftj2q4EhpM+hK5glaBgeRFiEbq0hmWxPUhERFXjRm4biN2TTKG8O4dtKvad5Ep409QxE4PBGQ53AoaZ27o8jt186NmzJ7NmzaK83P9FndbDu4wKYwhwCJnFIEEVcBbNn8QzIiI7ATuTXgxSbV8iInFjow8mdzz1PhGun46NItgeEHI8xGFyRH8qgB/GMRy6sqLEmt4ojt18KSsrczFwWh0XhMI4IUZZAb4Vo/zJRK+M+2PdU1HpQvMuqEz0jWE3kVgmit+JVkgcToxouwL7bjrFsN2HaAlHKnxQ3+mo+CNIYWxH9Eq7B3B38o74D/UZ6Q68UkR7GSn2NVrQ53JMcBZFLJ+rlZdMVyytqeN0KLyFUBie27DtIsC6UjvhOO0JbyEUxqvAaOyJMRergOOxqahA9jy/InIT8D2ijTtUA3uq6r+z2EseiO0MfDmCXbC0nesHf3PlJhaRKmDziLbnqWrWJ/gUv2cRrZ8f7J7ESWe6muhrR6pj2HWcdoO3EArjTqInuq4Fnolh+3dAfcSyc7OJQRrWEj1fcKxcy6paTbQcyhrXNnB9RNu1wM3EayEsi1h+Tci37DgdDheEwlgM3E7uJ8Zq4DxiVFCq+h7wN3JXgNXAT6PaTWIe0JCjzGIsoX1cPid3d9o8Vc11/VTuxIQs231U7J78IY7h4EuuKbZKzKmsjtOecEEonLOBx7Auh9RKsAGr0C8D7s3D9jHAy6R/mq/HKr6TVPVvediuw1Y/16Y51gAsCFtsQivhY2BNmsP1wGequjgP08uwld+LSS/CNVirY/9QJhaqugT4lPQtszpgtrcOnI6MjyEUzjrg/4C9gVOwMQWwSvVh4Dos3ERsVLVWRA4ADgLOpHFq6RpsxtLVqjozb8/NzkfYDJvuWPfXWqziLWjAXFVXA++Haajdgu1aYLmqFmL7Q2y18nex+71x8HUhdq/vxvzP1++lIrIM6IWNDSlQnWvspKWZM2dOKS/vbCBIYf+bGx4pg5yZ6Er6J+8mxK1kQjydCqwfO9YXF9HvnLR2xRjB73IahSwjpa7Qs9G3b19dunQpgwYNYv78uIu3nVIwffr0thCOvOhztr3LqGXIKQb5oKrrVLW2wCfsjkY9OcSgrTN16lRU1cXAKTkuCI7jOA7gguA4juMEXBAcx3EcwAXBcRzHCbggOI7jOIALguM4jhNwQXAcx3EAFwTHabfMmDGDiooKLrjgglK74nQQXBAcpx1SX1/Pcccdx9q17XpNntPG8FhG7QgR6YKFxVipqp78xegWXlcX02jIUd0di1VV3dZWh19yySW8++67pXbD6WB4C6G4dAEGYAns+1EEwRWRchEZLyJvYBE+FwLVInKXiGxbqP1AN2Ag5ndfivi7EJGeIrJR2PqEeEyF0gc4HQvM92nYZgNnYfc9b4KfF2FRU7/AAuV9KiKniUivQmxnYty4cYhI1m3w4MHry7/99ttcdNFFnHfeeS3hjrMB44JQHMqB4VimsIFYpTQYy0o2hDyDUIXsY88Bt2ERPhPB7bpg0T5fFZHvF+B31+DzcEzI+gEbAV8J7/NGRLqLyFeTbA8ANgG2FJH+BZj+CvBv4GfBZnnY+gFnhGMj8/T5a8DMYKc3llmuHPsOLwL+KyIjCvA9LUuX5k7stmCBRSKvr6/n+OOPZ+zYsUyYMKHYrjgbOC4IhVMGbEZj10UyglUsm+ZpewqwK9Z1kUonLGz1VSJycB62u2CVdZc0x8pobDHERkS6YfekIs3hTsDGIpKP4PQFngyvlWmOd8VaD09g/kdGRIZj6U17kv6eVGH348Xw+UrCZZddxqxZs7jllltK5YLTgXFBKJz+pK/4kumGxdePjIiMBL5J7nzNVcAVoc87DoOxyjkb/cj92dIRpVU0WETidqkdj33eXLYrgbgtp7PIfa/LsO/xmJi2i8K7777LhRdeyBVXXMHQoUNL4YLTwXFBKJw+Ecv1jWn3ZKzLIgpDgJ1i2O5M+lZHOmL5HZ6ec1WsYJV63HvyI9K3DFLpApxIxN+3iFQCxxLtfnfHxKPVOf744xk1ahSTJ08uxeWdDQCfZVQYFUS/h1EqsmR2jWG7G/Cv+I2E+BT7Gi3ocy+SMqcV+TqbiYi09syjt956i5dffpnFiy07aGLsobq6msWLF9O3b1/KyvwZz8kfz5gWk5QMXhXAFhFPVeC/TXZkyeIlIm8C28X1z2kV1gHlxRIEESmKndmzZzN8+PBimHJy0FEzpnkLoTDqsNzJufriIX4Wtf9gs2Wi2F4F7KOqr2YqUICQLQHmJd7kSkUZcihHnYkzX1UX5rCX7PcconczrQCGYZV3LvGtAhYTvRX3SSnWJTzzzDNN3i9YsIAJEyYwceJEJk2a1GRqquPkgwtC4SzFBpZzsSSm3WuB8dggai4WAv+MYbsOW8gVZbZMLL9VdZWIrCH9TJ0mRePaBm7G1h/kGqNYA/yRIAa5UNVqEbkPG0fI9T+xGrgyit1iM3bs2Cbv58yZA8CIESOaHXOcfPAOx8JZjFWw2agmqT87Cqr6JvB3crcsqoGf5fHEOp/cFeYSrHKNy/+wCj8bC1W1PqbdW4nW0qoDbopp+1Jyf1bFBOGumLYdp13gglA4DVhXRk2G4yuAuXnaHge8Q/qwDOswMfilqj6Yh+1azO90YqaY0M1LcywnoXtmLpCuwl+HdRUtyMP0YuBAYDnpK+86YCVwCDF9V9UPgYOw7rd0AYJqsZXLo1V1RRzbjtNe8C6j4rAW+Bjrg+6J9fuvxSquXK2HjKjqShHZA5iArcwdTuOYxRPAZdnGDSJQA8wCemDTKQWraJeF6+SNqq4Qkfew2T6Jrqk1wFJVLcT229gU2x8Ak2nsmlqLrei+ifyFbLqIbAOcBnwPe2ASTJB/B9yoqosL8L2oDB8+HJ8U4hQTn2UUk5RBzoLINUCbcl3B1jxUAktUNVOLJNP5RfE7js/FIIffZdjiOcGe3jMKTVy/RaQzNjbUAHxRoIhlpW/fvporfMWgQYOYP39+S7ngxMRnGTklJYwRxB2E7eisAxa1hGFVXUueLY24TJ06tS1ULo7jYwiO4ziO4YLgOI7jAC4IjuM4TsAFwXEcxwFcEBzHcZyAC4LjOI4DuCA4juM4ARcEx2kHTJ482dcqOC2OC4LjtHFuu+02br311lK74WwAuCA4ThuloaGBCy+80FNmOq2GC0JxKccSuPTH4g4V7f6KSIWI9BeRgSLSR0SKYbsM+BbwNBYLaDnwIXA2MLAI9hGRKhEZEPzuKcXJZVkJTATewPJRLAXeBI4nWo6HjIhILxE5RUTmiEi9iKwVkbdFZKKIRMkVHZtx48YhIk0S3NTW1rLjjjty/vnnM3HiRIYMGdISl3acJngso+JQBmyMRTpNrvAGY5VV3lHJQpC1oVhE0mQ2FpHFeYaRBqs4pwLb0rQSHYgloTkFOAp4IR/jIXH9UJpnIVsrIvNUdVk+drEsaE8BvWnq9wjgN8DPgf2Bj+IaDpFOp2E+JycmGgncCJwnIqNVtagxjhKB7RYsaPwqa2trWbFiBVOmTGH8+PGeGtNpFVwQCkeAzUifxSsRjbMz8GlswyLlwJewlJepdAIGiUi5qn4e1zYwBdiR9JnNuiaVGYvlZIhMEIMvkb6F1BkYJiJlqho3WF8PrDUziPSpRbthlflTwC6YGEf1eWNM/HqTPopkd+x7fkFEtlXVuClRY9GzZ09mzZpFebn/izqth3cZFU5/cqd07Bm2uAwivRgk0y/kBI7D7lhOgVxpLiuB82PaBhhC7t/WxiISJV90MhOwrrhs55Vh9/p7MW3/FBOUbF1a5cBGWGrTFqWsrMzFwGl1XBAKp0/EclGTwwMQKsuotvvFsQ2cTLSE8gKMwYQpEqF1EEWgyoj++RKcQjS/uwI/ImK8eBHpgiXbySW+YC2Fn0Wx6zjtDX8EKYwKrAskCnGf4rsSXbDjDqTuHMN2FyyrGgDFGRNupNj2kuiPDZK3xHW+IiKSRx5rx2nTeAuh7RKnBotb2/n3XhiJ1JqO06HwFkJhrKUxx3Eu0iWFz1VeiVbxxB3g/JDo00prgD3COTlTUYaUl5tFtL1QVbPOwEpJofkZ0cdiVgObAPWQ3W8R6Y5lXos6rXSeqq6LWNZx2g3+pFgYSlK3RA4iz3iB9SkcV0Qs/kUc28B1wKqIZWcSxCAKoeKti1KU+H7fSjRhrQPuJohBTkdUVwEPkSUncxLVwNVR7DpOe8MFoXAWkbviqSWmIAQWYHmDs7FKVaMKR4IngcXkrgBrgF/HtA3RchEvDqIXh1uwVlku1gI3xLR9KdHF5o8xbTtOu8AFoXDqgdlkfiquBuZgT8SxCHPdPyZzJbgi2I5LA3AgsJD0laBiYnA+8Gxc46q6HFt3kU7MFFiU5+KuecA4rDsonQjXY/f7KGBuHMOq+g5wdDg/nVCuxVqD38xj/YTjtAt8DKE41GEzcboDvTChrQeWYRVr3qhqtYjMpHEtQxlWOS0pcHHUp8DXsOmZJ2KzpdZhM6deBn4L/KMAv5eKyHJsamlifn9t8DtuyyCZV4GvY6upx2OVtwb/pwJXkjQrKqbPj4jI7sC5wKE0CnEZcAfwW1X9pADf82bOnDmluKyzgSE+cy4eKYOcBZFrgLaY5PC7HBiOicF8IOMTcGv6DDn9rsIGjsEGnFdnKhjXbxHpGWyvA+aoakHCno2+ffvq0qVLGTRoEPPn5x3lxGlFpk+f3hbCkRd9ppu3EByw1kzkgeM2RDXwfksYDuMy77aE7VSmTp3aFioXx/ExBMdxHMdwQXAcx3EAFwTHcRwn4ILgOI7jAC4IjuM4TsAFwXEcxwFcEBzHcZyAC4LjOI4DuCA4Trtg8uTJvnjNaXFcEBynjXPbbbdx6623ltoNZwPABaG4lGHB7foSPZFLJESkXET6iEg/Eekpxc0JWQ70xvyOm44zLSJSJiL7isgzIrJERJaLyFsiMklEoiaiyUQnLFrrM1iQvs+A57GAdAWFYxGRLiJyjIj8O/i8VESmici3RKRF/l/GjRuHiDB48OAm+xsaGrjwwguZPHlyS1zWcZrhsYyKgwCDsUo1udKox5LALM7bsEgnYONgO1kE1orIQlWNm2QmmfJgu3uK7TosNHbU5D9NCIHh/gZsS2OkU8L7G4DLRGSMquYTh2gA8AQwJPidYGfgpuD3AUTLyZDq9wjgBUzUk4PqjQn23xeRfYsd/nrpUkuVsWDBgvX7amtr2W233ZgxYwaTJk3iueeeK+YlHSct3kIoDsOxp+vU+1kODMIqr9gEMfgSFkI6tUXQGRgiIoObnRiNcmAEVvGl2q4AhmKfKRbB56eAnWguNIR9g4CX8/C9Enga87t7muPdgWFYDodYUWlFpD/wd0wg053bHdgGeE5EOsexnQ+1tbWsWLGCKVOmcOedd1Je7s9uTsvjglA4/bAwzNnoTfoKLBeDyJ3nd6CIVOZhezAmKrnKxK2JDgRGAl2ylBGsS+2smLaPAjYiu9/lQH9gUkzbp9K8hZdKBbA5cHhM27Hp2bMns2bNYvz48S19KcdZjwtC4UR9io71tB36q/tELN4vjm2sDz7KGIfE8CHBWUQTvwrg/2KOJ5xGbvEFa0mcHNVoeOI/iewilqA7cHZU2/lSVlbmrQKn1fFfXGF0xiq2KMRtIVRiFXdL2E7XlZOJHlje6KjsFKNsD6CmuOPj69kISzEKQJGvsa2IiHp2KaeD4S2EwmiRmiwP23H9aEm/o4pYe6aMlr2HjlMSXBAKYy3pE8mnoy6m7TUtVBYst3FL2Y4zu2cNMFBVJdNGYy7pXmRJkZmGGqwrrSfQM8c1ehPvcy5W1ajfu+O0G1wQCkOJPjUz1lTFkIg+ah7guFNPa4kuCktj2v4dltoyFwpMV9Wo3VEK3EVj4vts1AMPRCyLqi7HprJGqeRrgOuj2HWc9oYLQuEsAhpylKkDluVhewFWEWajmqS+8pi2c7GSaJV7MrcTrSKuAX4d0/aNEW3XYcIUh98QTSTrgVti2nacdoELQuGsBeaSuaJaA8wmetfSelS1GphDZsFZDczOc3BzFbbCN5NfK7FVwLEIi7b2D+dn8rsG+KmqvhLT/FxgYjg/3WfWcOz/gFlxDKvq68APw/npWIfd74NUNYqYOk67wwWhONQAHwCf0/hUvRyrwD7EnirzQlVXAu8l2V6NdeN8rKofqWqu1kk2lgPvA/MxgahO2AY+IXfrJJPPr2KzjR7AnrqXh60WeBE4UFVvztPnZ4Cx2EroNVjraEX4+xlMjB7L0++7gH2BaSl+rwEeAnZR1Rfz9Ntx2jziM+fiISKxVsBmI1T2rUKx/I7rs4j0AbbCpjh/rKqxWh05/O4PbBH+/ggLW5GWPPwegq0SXwe8V2CIkKz07dtXly5dyqBBg5g/f35LXcYpItOnT28L0WeLPtPN1yE4LYqqLgXidg1FZTEFxInKhqp+jrXKWpypU6e2hcrFcbzLyHEcxzFcEBzHcRzABcFxHMcJuCA4juM4gAuC4ziOE3BBcBzHcQAXBMdxHCfgguA4juMALgiO026ZMWMGFRUVXHDBBaV2xekguCA4Tjukvr6e4447jrVrI0X4dpxIuCAUj0HAOcAbWMC4vwMnYmkiC0ZEykSkp4j0FpEoeYWj8mXgWuBtYCYWIO5wsieyj4yIlAe/e4lIlJzFUfkacB/wbtgeAPYs1Gi4z/uLyNMi8omIzBGRKSKym7RQrs9x48YhIlm3wYMHNznnkksu4d13320Jd5wNGI9lVBy+D1yERQdNJI3fCPhV2I4Fns7HsIiUAYOxZPedkvbXAgtVdVmePnfCcgYciVX+id/CxsDWwG+BgzBxy8fvztg96EVSEC4RWQ3MV9U42c+S6QU8CIwEqpJsDwVGYdFlDyNmQqLg20bAc8FWspBvAhwI/ENEDivA97QsXZo7B9GCBY0Rt99++20uuugizjvvPM4777xiuuJs4HgLoXCOxyr9LjSKQYJuYbsb2COu4SAGI7Conqm5irsCw0RkQFy7geuAI4BKmj8YdAcGYq2FTeIaFpEKYHMsNWXqU3U3YESe0VcrgMeB7YOdZNuC+b0V8BT2ueL43Atr1W1O81ZdWbjeKOBvIlKyvNH19fUcf/zxjB07lgkTJpTKDaeD4oJQGFXAJeE1G5Xkl3ZxYATbG+XRFbMVJgbZbAtWMebzCDqE7F1OgolZ3N/fkVhI6myftwITsaNj2j4J6/bL5ndXYAestVASLrvsMmbNmsUtt3jSNqf4uCAUxpFETyIzGNglquHQX903YvH+Ue0GTiLaGEEnrPulV1TDQZyiPP13imM3cBr2pJ6LqlA2EuGJ/zSitSq6Az+LaruYvPvuu1x44YVcccUVDB06tBQuOB0cH0MojP2JVkERyj2XvKOYY5QtNN4J9lS8PqlNsa/Tgn5vSlKu6SJfZzcRkTxTl+bN8ccfz6hRo5g8eXJrXtbZgHBBKIzUMQNnw6AM6/ZqVUF46623ePnll1m82HICJQajq6urWbx4MX379qWszBv9Tv54Cs2YpAyGXgmcQPMB33SsBs7CBpiB7Gkdw8DsVyO6tVpVP8pWIMXvl4Fto9rG8gy/DblTUYbB2U0j2l6sqv/LYS/hdxmW+zmqCK/BxjLqIOe97gPMI/vYRDJLVTVqd15ORKQo/4SzZ89m+PDhxTDl5MBTaDrpuB2YQLS+5zLg4aiGVbVORFZhfda5iDvF8ibg8oi2FxDEICIrgHqi/bbi+L0OuB+737lsNwB/JYhBLlR1qYhMA/Yj9z/ZGqAkI7rPPPNMk/cLFixgwoQJTJw4kUmTJjVbq+A4cXFBKIx3wrY92Qdpa4A/AbESvWOVcer0ylRqgWUx7T4EXByhXDVwaRzDqqoishBbz5CNZapaG8c2NlX2O+T+3a4Brolp+2JgL3LP6qoHbohpuyiMHTu2yfs5c+YAMGLEiGbHHCcfvMOxcI7CKu5MT6M1wAysuygWYQHUJ2Tuq64FZucxuFkLHAqswp6801GNrQS+P6ZtVHUxdk8ysQL4LK5dYBY2Q6omS5ka4AxMqCOjqi8Dv8A+dyaqge+oaj6+O06bxwWhcBZhi87uwSqMFVhLYCX25H418C0idl+koqrLgfew/vNqrDJfCcwFZqlqvsFs3sKeiJ/AnqhXhC0hQmcAp+dpG1VdgK1yXoxV0omWzEeqOkdVMwlRLh7EQmu8Fmwm/K4B/g2Mx76LfHy+GmuBvE3jd7k8XOcFYG9VfTxPvx2nzeODyjHJscK2Coux0x34AvgH1p+dllwDtMUkh98DgJ2wRV2fYRVrWlrTZ8jp9whgy/D3B1gLIi1x/RaRrbE4T+uAt1R1Tpzz49C3b1/NFb5i0KBBzJ8/v6VccGLig8pOFKpJWWvQTlgEPFlqJ/Lg47AVHVVNBM1rcaZOndoWKhfH8S4jx3Ecx3BBcBzHcQAXBMdxHCfgguA4juMALgiO4zhOwAXBcRzHAVwQHMdxnIALguM4jgO4IDhOh+SNN95gn332oVu3bvTs2ZODDz6Y999/v9RuOW0cX6nsOB2M999/nzFjxlBVVcUvf/lLAK688kpGjRrFW2+9xcYb5wpE62youCAUn65Ywpx6LGhcwYhIf+B7wBFY7oXPsJwGj6tqxlhJERkM/B+WBKcCCwVxCxbMrWBEpAzzWYA1BQTjS+WrwA+BncP7N7F7EivKaTpEZBfgFGAbLJbRy8B1qvpBobbTMW7cOHLFMopCIt7RNddcw6pVq3jxxRfZYYcdAPjGN77BrrvuytVXX83ll19e8LWcjokLQvHoC/TDKtUEtVi0z+X5GhWR04DfYBVTIlb/SCzC6koR2U9V860EfwX8KPydyBa2JTAGC199KBb5NDYiUg4MAvrQ2DWpIrICWJBHLoQElcAdwcfONP6GtwSOBF4FjsFCe8f1uQ/wGLAdjcIOsDXwfyLyMHCsquYVuTYTxRADsIQ5AB9//DH9+/dfLwYAu+yyC/369ePtt+PkOnI2NHwMoTgMBTaiqRiAVSpDgf75GA1icFGwk5q4pUe45isiskUe5i8Gvo8JQXLqSMGitQ4HpgED4xoOYvAlTCCTf2MC9AI2F5FuefjcCZgK7I0JQ/IDTXnY93XgUbInLErncxXwEtbi6EbTtKidse/gEOAhESl6lMlissUWW7BkyRIWLVq0ft+SJUtYtmwZG220UQk9c9o6LgiF0wer5LIxiGhpNtcTuol+Q/YMXonK+7o4toHNgck5bHcCemNJY+IylOz5icuAYXlUrIdi2emy5VXugrUWvhPT9vexcNqpop5MFdYy2Tem7VblrLPOYujQoRx99NHMmDGDt99+m6OPPpqKigpOPvnkUrvntGFcEAqnX5HLJfgemTOlJVMGjBaRITFs/5CmT8CZ6IxVrJGf5kWkAugZ0XYuIU3ltIi+VAGnRjUaxjnOIJpodyOP7HetybBhw/j5z3/OCy+8wHbbbce2227Lc889x3333dekG8lxUvExhMLoTPYn4WSyJXpJR2IAOQpdgc9aqCejEpiXeFPsa7Rg78uXsYxnxb6OYJnm2iznnXceF110EaNHj+bEE0+koaGBm266ifHjx/PQQw9x8MEHl9pFp43iGdNikpLBqwvW/RKVJglXsmXxEpF3sMFMp+2hQHkBaUCbICJF+ydcunQpgwcPZptttuHVV1+lUydrCK5du5ZddtmF+fPnM3fuXLp0ifoc46Sjo2ZM8y6jwlhLtG4diJ9TOU4i9xXAIaoqmTasGyex/TeG7VXA6MS52a4RrtMHm6UTZRsawV7C597Eu4drsdSgOf0O5eJMEV5ZLDEoNrNmzWLNmjUcffTR68UAoHPnzhxzzDEsWLCAmTNnltBDpy3jglAY67CE91FYFtP2TTFsC/B0DNu/B1ZHLLsM+E8M2yvIkkc6hSUx7K4D/hLR9josJWikSl5VF2PTVaNQB9wesWyrk3jyb2hofpsS+9ata5Na5rQBXBAKZxG5Wwn1xKv8AB7Hns5z2a4BrlfVOE+4D2CVZi6qgStj2CU8OS/KWdCesmvi2AauJVorYQ1wdUzbFxNNJBuA38W03WpsvfXWbLzxxtxxxx3U1jYu9aitreWuu+6if//+jBw5soQeOm0ZF4TCqQU+JXPFXQ/MJfpTMwCqWg/sh7USMlXe1cBrwPlxbGNC8+1wfiaqsafsP8a0jaouxBbkZWI1+S14ewc4FxPBTNRgazdej2NYVZ8BriH7PakBJqvqx3FstyadOnXi+uuvZ+bMmey6665ce+21XHXVVey8887MnDmTa665hs6dYy3RcDYgfFA5JimDysmUY/3nvbEunAZgKdblkrZCzzaonHS9L2PrDPbC+sUTYTEEuAH4ZZRwEBn83g64HJvb35Dkdz32hH0tKUIXxeeka1Zh0227Bdu1wBdY6yDSDy+D39/CnugHp+xfBJwH/DX1hKh+i8ixmKD0ovGBSYDZwGlBOIpKMQeVE7f1+eef58ILL+Rf//oXADvuuCPnnnsu+++/f7EutUHTUQeVXRBikkUQYhOzch2KrdCtwqaBPhWnmyiH318CdsMWZX2GrVBO26KJ43MxyOH3TsBW4e/3sdZSWmLeawH2xKaurgP+rapvRj0/Ln379tVixjJyWp6OKgi+DqGdoKqfAXe3kPmPwtbeeCNsRSW0Xl4MW4szderUtlC5OI6PITiO4ziGC4LjOI4DuCA4juM4ARcEx3EcB3BBcBzHcQIuCI7jOA7gguA4juMEXBAcx3EcwAXBcdoFkydP9sVrTovjguA4bZzbbruNW2+9tdRuOBsAHrqieHQGDsaiiPbC4g3dA0wnehKdrIhIF0zE14ZoqMWkAouNspZoobFzIiIDsNzQe2P35y3gZlV9vwjmNwZOAHYN7/8N3Eq8xEJpEZHNsbzT22MxnV4Cfq+qCwq1nY5x48axdOnSZrGIGhoauPjii7ngggta4rKO0wwXhOKwL3AbFom0e9inwAFYtNNvk5I+Mw4i0g/oT2P+ZhWRlcBCVc0WrjkK/YC+mCCA+b0CixwaJ8fCekLS+kuAk4O9RG7oUcD3ReRF4Nt5Bsorx/IRfDvY7hr2fw04CYt0+iPiZ6hDRLoB9wH7hOsk4kSPAn4uIn/AIp7GCmWei0RguwULGvWmtraW3XbbjRkzZjBp0iSee+65Yl7ScdLiXUaFsy8WdK4XjWIA9rTdHRgCPAN8JR/jIrJpsJGcBDeRWvJLItIrH7uB4VgI6YqkfYJ9lhFY2Op8uAGrnLvSKAZgFWwlMAZ4UUQqm5+aFQHuBY7A7kfXpGNdwnYw8Gdi/rZFpAJ4Fvs+K2kUA8L7rlhr5/YQDbVFqa2tZcWKFUyZMoU777yT8nJ/dnNaHheEwijHuimyVWyCVay3xDUeulyyVfgCDBORfDKeDCJ7hV8GbELMELsisiswKYftLphA/iiObazFtRfZ73cl1o00LqbtE4BtaCoyqVQFu3vFtB2bnj17MmvWLMaPH9/Sl3Kc9bggFMaBWDdRLgTYEouvH4d+EW1HKZd6Tp8I5TpFLJfMT8leqSaoBH4aupeichrRWi3dgFOjGg1P/GdFtF0FnBHVdr6UlZV5q8BpdfwXVxjjgKgJcypJSetYzJ6HVujFaInrbAQ0tJDv22JjIUBR/RasW8lxOhzeQiiMQvrvnfZLRcyWjeO0C7yFUBj/w2a6RHn8rAF+AfwhsSPbLBsR6Ur0LqYaVZ2VrUBKKspu2IByFFYCnyTe5JoZJCJfYLOWolADbKmqc7PYS/jdCVhI08HebKwFhoZr5LrXA7HP2CVTmRRWqWpRpuY6TlvCn3IK4x4g6rTPMtIkf8+EqtYSKrMIxE3IuxqrMKOwLKbtu4g+5fPDbGKQQgPwN6KtkVDgBSLeP1VdiK2RiEI98KeIZR2nXeGCUBh/BxaQu5Kqw6aeLoxpP0r5tcQXBIDFEcrUkdQPH5HrsMo7F6uxtQpxuAaojVCuBrgqpu3fBJ9ysRa4OqZtx2kXuCAUzreBVWQWhTpgPjYvPxaquhxb8ZyJemB2ngullgBfZDleB8yJa1RVPwZ+TPaW02pgKnB/TPOvYxV9NtvVwE3AyzFtP4K1+LKJQjVwpqq+F9O247QLXBAKZxYwGngVe3qtxZ6QV2MrfZ8A9iS/p3hUdVG4xtJgV4Pd+cAHoWspX+YDs7FWQLLtecBHRO9WSvX5j8B3gU8xsazDxGtluNYlwHGqmk9Ij99i0z4XBXtrw7YSE7lzgV/l4bNi4SrOx7rJVgSf14bP8DlwgqrekIfPjtMukPz+JzdcUgZnU9kMm5LYHXv6fowsXTN5hm7Iixx+RyaOz2F+/2hgZ2wCw4fAI6oaOaxEFr/LgG8CW4f3M4GnydBSi+l3Z+AgbFB/HRYn6fk8BSwnffv21XSxjJy2y/Tp09tC9Nmiz9d2QYhJsSpW6PiCUAzaq99xmD59uraBysWJQUcVBO8ychzHcQAXBMdxHCfgguA4juMALgiO4zhOwAXBcRzHAVwQHMdxnIALguM4jgO4IDiO4zgBFwTHacM89dRT7LnnnlRVVdG9e3fGjh3Lq6++Wmq3nA6KC4LjtFFeeOEFDjjgAJYtW8bFF1/M+eefz0cffcTo0aN57bXXSu2e0wHx0BUxiRBKoSxsiWBxGYkaTkFEyrH8zQdjKTvnALer6swo5wcbufzuhC2Fr89WKGZMoG7AUVg8o87ADMzvyAF7svjdK9jeJbz/D3AfGYIIxvR7IHA8sB32Pb4M3NdS4S8SsYwSJGIa7bDDDixZsoT33nuPqqoqABYsWMCWW27JTjvtxDPPPNMS7jgR6KihK1wQYpKlguoO9KcxUbtiETO/IEOiligVjIgcBNwBVNCYv3ktVnG/AXw7SgWbxe8+WIazruF9A7AcC8rXLNppRJ8F+CkWdXQddm/AIsEK8GdgcpRIrWn8lmD3B8F2VdhfjQnx7cA5pAS5i+h3BXAjcAz2/VWGQ6uD7d8AFxc7yJ2INLO3ZMkS+vXrx09/+lMuv/zyJscOP/xwnn76aVavjpK+wWkJOqogeArN4jAAGJiyT7Cn2J5Y6OTlcY2KyOFYjP6qlEOdw7Yb8LqI7BDCZMdlGI0ik6ATJhC9sJZIPuG1LwJOobnfCdE5AhgmImNVNW6I7ZuAw5JsJUhc61hgcHiNjIh0Ah4FRqWxnRD5szHRPzWO7Xzo2bMn77//Pt26dWt2bPHixZSX+7+uU3x8DKFwetBcDJIRYAj2hB+Z0N1yN80r1WQ6h2vnk8FrAM3FIJlOwKbEfAoRke2A02isRNNRiYXE/r84trFw14eS/Z5UYSHID4xpeyKwRw7b3YDJIrJbTNux6dSpE1tssQUbb7xxk/0zZszglVde4etf/3pLu+BsgLggFE7/CGUE6BfTbqLbIhedgSNEpE9M+1H8KQd6x7R7evApF1XAz0L3UlRyCU2CbsGPOJwd0XZXLEFPq7Nq1SomTZoEwNlnn10KF5wOjrc7C6Oc7E+UyfQiezrMVCbR2Peei67Aknh1a34U+RqbAutayO9dSMoHXcRrlGGD+61KdXU1hxxyCG+99RbnnHMOo0ePbm0XnA0AbyEURqcWKgvxn8yd1qOLiLTa/86yZcvYd999mTZtGieccAIXX3xxa13a2cDwFkJhZJ2iWUBZsNlJUVkJfE9V/5ypQMpsne7Y03kUVgFzE29yzdYRkaVEF7NqYBtV/TiLvYTf5Vge5ajCWg8MDdfI6reIbAR8TPPB5EzUqGraVJ3FZuHChey33368+eabnHjiidx8883FbqU5znq8hVAYDdiUxCjEnWX0R6yij0I58FQM26uILlDLYtgFmEKa6aoZ+ByYHbFsPfAc0cZVAF4liEEuVHUe8EFEuw3AQxHLFsTKlSvXi8Fpp53GLbfc4mLgtCguCIWzOEKZdcR74gd4gGiV3xps0dSKnCWbEsWfOuIL2bVEE5vVwKUx5/RfQ4Y1HSlUE3/m1aVEE/c1wJUxbefFSSedxJtvvskpp5zCVVdd1RqXdDZwvMuocFZhg8UbZTi+DviM6E/NAKhqjYgcCTxM5oHrNcCn2CKwuCwGupC5e2ct8Elco6r6noj8Els8lsnvamAatuAuDi+Hc47LYfsBIO4y3vuBw7HpqplsrwZ+q6pvxrQdm/fee4+7776bXr16sf3223PPPfc0KzNhwoSWdsPZwHBBKA5LsCfXfthCNMGEYBn2JF6Xj1FVfUZEvgXcGWx3C7YTi8WeBo5T1diL3gKfY91SfWmccrkWC/+wBOseycfvK0TkC+xJupzG9Q6JFb+3Aj/Nsx/+bGA+cFZ4n5iJtQq7N1cDv83DZxWRo7HVyD/BPnvC9krs+zxHVW/Kw+fYvPDCCwAsX76c448/Pm0ZFwSn2HjoiphEiAkEVjHlvLExYhkJtoJ2P6xy/RT4k6p+HuX8YKMofseMCdQZm6K5G7Ywbybmd+TurSx+d8FWLG+D+f0u8BcKCBOSdM3uWJykrbDur9eBh1U1L2HPRaZYRk7bpaOGrnBBiEnEijUSLRUsLR3F8rs1fYb263ccpk+frm2gcnFi0FEFwQeVHcdxHMAFwXEcxwm4IDiO4ziAC4LjOI4TcEFwHMdxABcEx3EcJ+CC4DiO4wAuCI7jOE7ABcFxnGZMnjw558KrGTNmUFFRwQUXXNAqPjktjwuC4zhNuO2227j11luzlqmvr+e4445j7dpYMRudNo4Htysu2wEHYBFE52OxdeZmOyEKIZbRHljy+EQso/tV9X+F2saWv++NxUqqxPITPIgFtysKibzJMUNd56ICOAS752CxjB6mMfBf3oRYRuOxWEYNwL+Av6pqh679GhoauPjiiyM98V9yySW8++67Le+U07qoqm8xNqxCTt12xyqk1VgwNMVCU9cALwKbpzsv4vX2BD6iMeKmBrs1mOD0LsDv8Vjo7pXBrmLho2uAe4AB+fgcridAn/DZtw3bV4PNTgXe759j+ZKT/V6JRTz9VQH3ugy4JHyPybZXYBFgf9ASv6k+ffpo0rVadRs0aJCqqtbU1Oi2226rgE6aNEmHDBmio0eP1nTMmDFDKyoq9Ne//rUCev7556ct15GZNm1aqV1QbYHfoncZFc62wLPAllgc/USKxwosJePXsDj+A+MaFpFvAk8CI7BQzIlgVl3DdgDwTxHpmYff47DcAoNpDPMM1kroGo4/ET5HXL8FS9G5CU1zC1RgeSM2D5FQ8+Fi4Fysok/2uzsWwvsMLJFOLILPfwJOxnxOtt0Da/VdKSK/yMfpbCRHOm1tFixYAEBtbS0rVqxgypQp3HnnnZSXp+88qK+v5/jjj2fs2LEefrsD4oJQGIKljKwic+TBciyXwc2xDIt0BaaSOVkLWAjoTYmfwasfcBNW+WeiEtgaODWmbbBKP5tIdQGG52F3D+B7ZL8nVVjo6rExbX+H7MlxErZ/LiLbZSnTLunZsyezZs1i/PjxWctddtllzJo1i1tuuaWVPHNaExeEwhgD9CJ3GNpyrOtn4xi2xxPt++kCHBMzTPRxEctVAicRPbE9ItIJS7iT03boq4/DaWQXsQRVoWwczqYxSVA2KsgvQ12bpqysLGOrIMG7777LhRdeyBVXXMHQoUNbyTOnNXFBKIyjiVaJgPXZHhLD9vdo2m2RjXoseU5UJhKtYgUTs11i2O5J9N9V7xh2y4FvEj0G/NfI/rS/HhHZCPhKRLudgCMjlu0wNDQ0cPzxxzNq1CgmT55canecFsJnGRXGQKJXUF2x1I7r0zuGyTfFoAfw5yLaS7X9dOJNsa/RQj6D/bbXpx0r8nUqRaRM80sB2i65/PLLeeutt3j55ZdZvHgx0Dj2UV1dzeLFi+nbty9lZf6M2Z7xb68wijY102lXrNmQxADgySefpK6ujl133ZUBAwYwYMAAdtxxR8DEYsCAAXzyyScl9tIpFG8hFMaDwP5E69qpxXILz07s0CxpHUXk+8AVMWwPUdWMApUyxnAeNpumSwTbK4DNgLW5fA7XKcdmXEV5JJ8dwV6y349jYzFReB34RuJNjnstWL7nL0ewuw54NKIPHYYrr7yy2WyoBQsWMGHCBCZOnMikSZMYPHhwibxzioULQmE8iVXGuSrtBuDfJIlBBO4l2uyhtVgC+DitlVuBn0QoVwvcFq4RCVWtF5Gl5B5YXpNLDNJwNbAjucdtVoeykVBVFZFLgesi2K4l/qyuds9OO+3UbN+cOXMAGDFiBGPHxp3U5bRFvMuoMNYBE7CFXNnKrAR+EMewqq4Cjs1hey2wCDgljm1sMdq5OWyvwVZZ/zZLmUz8D6uUM7EWmJOH3Wexp/PqLGWqk8rF4W7gH2S/J6uB21T11Zi2Hadd4IJQOH8HDsMGMFdhs4nAWgXVWFfEGPKoAFX1IWxO/RJMVBLUYxXXv4CdVHVhHn7/AZs+mVjhm6AOewp+EZvVk61iz+T3OuBjTBjWpPi9CPhQVdekOzcC38d8r6WpMNSEfXcTfVrtelS1HluHcH+wkywMq8P73xJffB2n3SCqxQwv0/HJMt8/ERPoUCxkwzyscvlPJltRu0xCv/xBwMHYtM45wO2q+t8i+F0BHB58rwQ+xCrVOYX4nHLtcuz+1GvMH1wWv3tj0353Du/fxLrZ0nadxfFbRAYBJ2Cr0BuAV4B78vnsEa9X0n9CrwPiM3369JzRYFuBok/Rc0GIScwFYFlpqQomHcXyuzV9hvbrdxz69u2rpQpfMWjQIObPn5+7oNOEjioI3mXkOCVm6tSprR6kMbG5GDjJuCA4juM4gAuC4ziOE3BBcBzHcQAXBMdxHCfgguA4juMALgiO4zhOwAXBcRzHAVwQHKddMHny5LawEMrp4LggOE4b57bbbuPWW28ttRvOBoCHvy4um2KpLLsDi4HHKFISHRHZEtgXizc0D/iLqq4ohu2WIuQZGAXsiv3WPgAeV9W6YpjH4i9tE96/h0U5LThxjYh0Br6FpdVcB7wBTI8bh6lQGhoauPjii7ngggta87LOBowLQnHYDLiBxkBr5ViUzyuxpC6nAsvyMSwi22PRPbfGKsFyLBrnTSJyD3CqqmYLB52LKix3Qfdgfy0mYktpjNyaj98HY/kF+mEB9CT4vS7kHrisgAr2aODXwfdEkp9aLFLrBcCdefos2Hd1Hnafu2L3oA5YJiKnhgi0RWXcuHEsXbq0SVyh2tpadtttN2bMmMGkSZN47rnnin1Zx2mGC0LhbAE8j+UeTu6CS9zbg4CdsExfy+IYFpHdsKfebjQNZJVIyDMB2ElERqlqtjj+mRgE9E/Z1wXYCKvI5xAjOU4CETkOE8jUJPedw+svgK+KyPF5iMJPgTOz2L4Ma6ldGMdoEIMbgEk0T5JTgd3zu0RkkKreGNPnrCQC2y1YsGD9vtraWlasWMGUKVMYP348w4cPL+YlHSctPoZQOA/QXAySqcAq2BviGBWRCqx1kXhyT0cllq7y0ji2A31pLgbJVADD4xoVkc2AG2leYSfTDTgS+E5M8zsBZ+WwXQX8CNgjpu2DSS8GqbavEJGvxrQdm549ezJr1izGjx/f0pdynPW4IBTG14DB5L6PFVj//4AYtg8L5+WiEvieiGSrJNORTQwSVGD5F+LwE6BThHLdgHNi2j6FaHmgK4HTYtr+ObnTZ4K1RE6NaTs2ZWVllJd7A95pXVwQCmMC2Z9Wk1mHJc+JyvexlkcUGrDB7Kh0o7GLJRe9Y9gFe8qOImQAW4jIphHLdsIymkX5zSYGnLtGMSwiA4HtI/pRDhwTsazjtCv8EaQwhhA9SUUlcFXYALBu66LQE5haRHsZKfI1KoE5LeR3Z2B9atEiX6O7iJSFVKGO02HwFkJhtOlpn06LUedi4HREvIVQGFOBsTTO+slGDTbT6IPEjmxpHUXkFOBiovVr1wKbqurCTAVSUlEKNsc+Sl//PJLWUuRKRSkiDwBHEO1hYx4wNFvlmuL3s9iahii8TdLAco57LcDHRBtE1+CH43Q4vIVQGI9h/fe5UGAmSWIQgTuJ9v00AE9lE4MM/iyLaDtust8rMYHKRQ1wVcwn7auB1RHKrQauiWo0TH39LRBlPUc1cHlU247TnnBBKIx64P+wyi0TilVQ349jWFWXASeRvZJqwCr2n8SxHVhA9sp1HfApMRenqeo/gXtz2F4DzCLmVFzgCeAlst/vGuB1rPUWhz8C75BdzKqBh4EXYtp2nHaBC0LhPAUci40nrErar+H9PGwG0My4hlX1dmxOfXWK7XXh/YfArqr6aR5+K7bwbAG2Ejd5/3KsCyXK03g6fgDchFWuyZX32vD+RWDPPBbTKTbDZ2qwvSbp2Jqw73FsjUOUllujYdU1wDex7qDEqucEtWH7I3Bsa4ewcJzWQvy3HY+UPu1kOgOHAN/GpmrOB+4CppHhKTtXf3zSNbsBRwHjsPGKucDvgVeiVk5Z/E7QBRtbqCNLPKCoPodrDsRaUGOw+/M2cKOqRhbHLH4PAb4H7BLe/xu4DfgkXeGYfm+BCfF22L14Efi9qs6PaiMOIrL+O/T/x/bB9OnT20L02aJPz3NBiEmEijUycSqpQimW363pM7Rfv+PQt29fTY1l5LRtOqogeJeR45SYqVOnoqouBk7JcUFwHMdxABcEx3EcJ+CC4DiO4wAuCI7jOE7ABcFxHMcBXBAcx3GcgAuC4ziOA7ggOE67Z9GiRUyePJlBgwbRs2dPxowZw6uvvlpqt5x2iIe/dpx2zMqVK9lrr7343//+x2mnnUafPn24/vrr+cY3vsFrr73GyJEjS+2i045wQWgniMgQLC1kJRYw75kQkK0YbAbsjqW+/BSL5hkrOFwmRGRrLN5QORbh9KUiJpfZAUjUeO9hUU4LJuRH2APLGbEOeENVZxTDdrG59NJLef/995k+fTp77bUXAN/5zncYMWIEv/3tb7nrrrtK7KHTnnBBKB7lQB+gF9YVl8glsIwsweJyEQKt/Q4LELcWS2pTH45dD1ygqmvzNL8tFtt/h2Az4fdaLNXndcQMf53k915YToKvBpuC3YeVIvJL4I8FRA3dH/gNsHGKfwuA84BH87SLiEzEEhP1CT4rUCYiHwGnqepz+drOxLhx41i6NF7aiUGDBjFv3jzuvPNODjzwwPViADB48GCuuOIKOneOmjbbcQwPbheTDMHWegBDST8mU49FJ20WZz9C9rGRwCtYhNN0tquB14B9c4lCGr+/BvwFqMpwSjWWf+CEOD6Hax2G5UTIZHs1cLOqnhHBVqrfJwCXYC2ldNQAF5KSayGi3xcCPyWz3zXACap6fy5bcUiOdhqHjz/+mBEjRnDttddy8skno6qsXr2a7t2jJPBzCsGD2zmZ6ApsQuZ7WQ5sSrR0lesRkU7A05jYZLJdhaWUvCCObSwt55/JXPElbH+LFEHIRejayiYGiev/QEQOiWMb2JrsYkA49ktg5ziGRWQs2cUgYfuPIjIiju2WYtasWQAMHDiQM888k969e9OjRw8233xzHn0070aSswHjglA4A8it1OVA35h2D8RaBrlsVwE/FpEuMWyPJ5pAVQE5n+JT+GFE292Ac2PaPgUb58hFF+DUmLbPJbsYJOhEfhnqis6yZcsAOO+883j88ce59tprueuuu6iqquKwww7j2Wc99bMTDxeEwijDnuCj0Dum7R/FsA2wb4yy38cq5Cj0xsYYojIZq5CjsG1oUUShDEsQFEVsyoADovohIv2wLrQoVBCz1dRSrFljcwqWLVvGK6+8wnHHHcfEiRN58cUX6d27N+ecc06JPXTaGz6oXBidid6PF+XJNpmhMcr2BB6xyTFFpztJOYSLfI2uwGct5HdnYFHiTZGv0UNEyoo4WyovunUzTR83bhx9+vRZv793794ccsgh3HnnnaxatcrHFJzIeAuhMOJUCHErj+qY5Z3Wo6HUYgAwZIg1rgYOHNjs2MCBA1FVVq1a1eyY42TCBaEw1tI00Xs24qZwnErTBPXZqAU2UVXJtGGtiMT2h+B7FGqw6Z09gZ7ZrhGu8xjRp6ouBspj+B1nLcCs5HNzXKMTtrYjKi/GKNtijBw5ki5duvDuu+82OzZ79my6du3KgAEDSuCZ015xQSicL4pcLsGtROuOWge8oKqfxbB9M9EWnq0FHgDiPGZeQbTWTS3wO1WNswDuamzKai6qQ9lIhKf9K4gmwKuwtRslp1u3bhxyyCE89thjTURh9uzZPPLIIxx66KF06hRrcpuzgePrEGKSYR3CUGxBWiYWYE/DTYiwDuGn2Jz6TLNfFGt57KKqH+Swler3pcBxWWw3AEuwwdaFMXwWTES+lcX2WuATYEdVXRHD707A48CO2PhDOtYA7wL7kNQKiuB3N2xNx+ZkHu+pAaYBBxezyyjfdQiqypw5c9h1110BOOWUU6ioqODaa69l9erVvPHGG4wY0SZmyHY4fB2Ck43PgPlAXcr+2nCsmRhEQVWvxObU19L86XVluOaoXGKQgXOwrqM1NO32UuwpeC4WKmNh81Oz+qzAd7F1DrU0vSfrgu23ga/lEoM0NACHY4PcNYQV20nHqoF/AAcRvUss4fdqYE/g31grJLnCXxuu9wgwri2MHyQYPnw4r776KqNHj+byyy/noosuYvvtt+fvf/+7i4ETG28hxCRDCyGZSkxo68kxvhBl9Wy4Zn/ge8CR2FP3p8CNwBOqWp/t3CQbmfzeCPg/bNpqBTAb61J6gTRjAVF9Dtf8EjZnfzQ2o+1d4Frg1ahhK7L4vSVwEtZaAHgLuydvpysc414LFnvpFGAbTBheBq5T1fej2IhLIS0EpzR01BaCC0JMIghCZOJUroVSLL9b02dov37HoW/fvppPLKP58+e3kEdOLjqqIHiXkeOUmKlTp6KqsTYXA6clcEFwHMdxABcEx3EcJ+CC4DiO4wAuCI7jOE7ABcFxHMcBXBAcx3GcgAuC4ziOA7ggOI7TAjz11FPsueeeVFVV0b17d8aOHcurr77apMzzzz/PqFGj6NGjB0OGDOHUU0/1cN0lxgXBcZyi8sILL3DAAQewbNkyLr74Ys4//3w++ugjRo8ezWuvvQbAtGnT2Geffairq+PSSy9l4sSJ3HLLLey///6sW9dmQkVtcHjGtOJSiUUH7Y6Fu36VaGGmcyIivYCvh2vMA/5ZxCBr/bCk9BVYnKQ3i2QXERmKpeAsB2ap6jvFsg1shsU0Angf+KhYhkVkS+ArWCyjN1X1k2LZ7uiceuqpbLLJJvzzn/+kqsoC3k6aNIktt9ySc889l2eeeYYzzjiDYcOG8cILL1BZWQnAsGHDOOmkk3jqqac44IADSvkRNlziLpnf0Dcsz3HqtgnweyxK5vKwrcDCR18E9E13XsTrDQHuwiJ5LkuyPQ8LwFZWgN/bYxE8a5P8XgXMwfIu5+VzuN72wNNYlNBlYVsNvAMcVuD93hcT25okv6uB14EDC/T7W1igvNVJftcAz2Nhxov+m+rTp49igQTb9TZo0CBdsmSJioieccYZmsphhx2mVVVVWlNTo/vtt59efPHFTY6/8847Cuill17a7Ny2xrRp00rtgmpL1G8tYbQjb2kqm82w2P5rMvyjVAN/x57CY1VSwAgsL/DaDLZXY5nVcopCGr/3wISlIYvtW/KpWIFv0hhCOpPts/O835PCPc1UMVWTRswiXuvk4Fsm26uBb7XAb6rklXmxtvr6ev3ggw/0888/11RGjRqlPXv2bLY/wT333KOA3nfffRnLtBVcEHzLVEH9E4v5n+0fpRrLPRC5ksLGdz4ic4Wd2FZFqVxTrj0A69LKVGEnV4D/F6diBQZiuRpyVR6rgbEx/d6R7GKQfL+/FtPvPcguBsn3e2iRf1Mlr8iLtWXirbfeUhHR/fffv9mxOXPm6O233659+/bVkSNHak1NTUY7bYWOKgg+qFwYW2Mx8zvnKFcJHINVTlEZi1Xcub6jbsBZIhJnPGgcNl6QK3xuFZZIJw6TscxmuagCfhHT9o/Jfa/BPtspMW2fi31PuegE/Cim7Q2aVatWMWnSJADOPvvsJseWLFnC8OHDOf7446mtreW6666ja9dMyfCclsYFoTBOIHO6xVTWAYfFsP1jbHA6Cp0wAYnKD2PYHgyMjGH7JKJVrAC7icjgiGXLgKOJNhGiE3avI303ItIH+AbR4st3xbqknAhUV1dzyCGH8NZbb3H22WczevToJsdFhPvvv5+77rqLrbbairFjx/Lggw+WyFvHE+TEJCVhy1+w/nJnw0KBci3SLK98M6a1RZLrk2XLlnHQQQfxyiuvcMIJJ3DrrbdiCenSU1NTw8iRI1m7di2ffNK2J3V5ghwnHal5jp0Ng8TYi5OBhQsXsvfee/PKK69w4okn5hQDgMrKSg466CA+/fRTFi/OKw25UyAuCIXxFDYQGYVqrFunZ2JTVcm0ARdi00GjUAt8KYe9nknbPTRNUJ+NNcCwKD6H6zxH9MpyOdAlht8fRLQLNvMr6r0uB+LUQP9Sb1pnZOXKley33368+eabnHbaadxyyy1NxGDmzJkMHz6cG2+8Me25IkKXLl1a02UnQUuMVHfkjaazXgYRbWaKAh8Sb5bRxlhFn8vuOuCFmH7vTrTZOvXAlKg+h+vsR7RZRrXAb2L6/YOI93s1cGpMv3+Btfhy2V5JjHUUEX9TJZ8dVKxNVXXixIkK6CmnnKLpWLt2rfbu3Vu32WYbXbNmzfr9c+bM0Z49e+qYMWPSnteW6KizjHwMISZpkr5/D7gYmzWTiRrgCODl5J2aI/G7iFyMVWzZbK8G9lDVt3LYSvX7ZmzgNZNtxdYp7IE9bUf1uQx4EhhF5sHlBmABsK2qfhHD7wpgGraCONOA8VpgNrAnSV16EfzuhS1IG0LmgetabPHbGFUtygr0cO0O80/43//+l6222opevXpxzTXXUF7e/FZOmDCBe+65h4kTJ7L77rszYcIEvvjiC66//nrq6up4+eWXGTkyzjyG1qejjiGU/Im7vW2kXzl7Flb5pD7Rr8bmrR+Z7rwI1xLgUuxpPnXh20pgKfD1PP3uDdwdbKcufFuJVdi7xPU5XKsSeDx89voU2yuw1tKwPP3eBHgt2E5eR7Eu+P0msGmefm8MvBd8TPa5IVzvWaB7C/ymSv5kX6ztpptuitSKUFWdMmWK7rjjjlpRUaF9+vTRI488Ut9//31tD3gLwQHSPmknGIy1Fo7E1gYsAW4H/oRVMM3QHE+tSdccgU1DPZTGWEbXA1NUtbpAv7+KTRXdG5vj/ylwI/AoJhR5+RyuuQtwOhaDqRM2BnAV8DeN+ISdxe+vYyuLtwvv3wV+B7yYrnCMe12GdXudjt0bxRYfXonFjyr6P0xHaiFsKPVJR20huCDEJEsFFZs4lWuhFMvv1vQZ2q/fcejbt68uXbq01G4UzKBBg5g/f36p3WgVOqogeLRTxykxU6dObQuVi+P4tFPHcRzHcEFwHMdxABcEx3EcJ+CC4DiO4wA+y8hxSo6IvEP0MCVO26A/8cKdtASLVXX/Yhr0WUaOU3pqVXXnUjvhREdEXu+I35l3GTmO4ziAC4LjOI4TcEFwnNLz+1I74MSmQ35nPqjsOI7jAN5CcBzHcQIuCI5TIkRkMxGZKiJLwnaXiAwotV+OISKviYim2R5MKtOhvkPvMnKcEiAi/YA3sEQ/12JTwM8E5gC7qmpd6bxzxHJ+rsByYDyUcniuqr7UEb9DX4fgOKXhdGAosI2qvgcgIv8EngGOBf5QQt8cGA50B/6qqvdkKNPhvkNvIThOCRCRj4DZqjo2Zf9M4HNV/WZpPHMAROQgLEnU7qr6zwxlOtx36GMIjtPKiEgfYATW3ZDKv4EOtwK2HbJ1eE08+XdLPthRv0MXBMdpfYaE18/THJsH9BSRXq3oj9OckViO7qtEZCWwSkQ+EpGjwvEO+R36GILjtD6JtKDp8mHXhNduwPLWccdJw9bY99QbmBReTwH+JCKdgQ9DuQ71HbogOE7rk2iZZxvAW9cajjgZ+T3QSVVvSOwQkfuBd4DLgSPC7g71HbogOE7rszK8VqY5VplSxikBqnpzmn01InI3cD6wKuzuUN+hjyE4TuvzSXjdKM2xjYFlqrq6Ff1xorMwvFaF1w71HbogOE4ro6rLgNnAjmkO7wC83qoOOU0QkSEi8q6I/DLN4a+G19l0wO/QBcFxSsNDwFgRSVQwiMhY4CvA/SXzykFVPwd6AZNFpGdiv4hsAhwHTFPV+XTA79AXpjlOCQjxbt4B6oErga7AWdjslT1UdU0J3dvgEZHDgL8A72IrjnsAP8bCVOyhqu91xO/QBcFxSoSIfAW4GtgLm774BHCmqi4qqWMOACJyKPBzYDtsKul04BxVnZlUpkN9hy4IjuM4DuBjCI7jOE7ABcFxHMcBXBAcx3GcgAuC4ziOA7ggOI7jOAEXBMdxHAdwQXAcx3ECLgiO04YRkeNEREXkuDzPH5ia7au9ISIVIjIk6X3inowpnVfNEZHhwa8LSu1LvrggOE4HRUQOAN4HBpTal3wRkU2Bt4F9kna/CEwkpLdsQyzC/JpaakfyxfMhOE7HZTcs01d7ZjPgy8k7VPVj4OPSuJOZEO76nlL7UQjeQnAcx3EAFwTHaXeIyBwRuVlEJoS4/bUiMktETkoqcweW2QtgtohMTzq2lYj8RUSWiUi1iLwiIvulXGO6iDwpIheJyCoRWSgi24hIFxG5RkQ+FpE1IvKpiNwgIn1Szh8qIneJyKLg339E5Jg0n2VjEblNROaF6/wrRBoljJtMC0VvFxFN7E8dQxCRKhG5JNybuvB6qYhUJZVJnLediNwnIktFZGW4F8Mj3Pcjgn8rRWS5iDwjInskHW8yhiAiF4T3mbbhSeceF+5Rbbhnd4hIuuQ7LYp3GTlO++QAYDxwHTAf+D5wvYjMVtUngFuAnsDhwGlYGGdEZBvg5XDOb4C1wNHAEyLyXVWdknSNUcAWwJlY181/gZuB7wLXAh8BI7Gw0FsA+4ZrbAz8ExDgd8BS4FDgHhHZWFUvD+X6hnL9gOuxbqDvAlNF5HBsrOA3WMTR3wMvpbsRIlIBPAN8DbgdS06zG/AzYJSI7K2qa5NOeSR8lp8DXwJOBYYAu2a62SIyGpiCRTO9FegWPvezIrJ16MZKZSoWCjuZoeEz/Rv4X7B9PnAB8GD4nEOD7TEisrOqLs7kV9FRVd98862NblhCFgWOS9o3B0vgvm3SvsFh371J+y4I5w5P2jcdq6S6Je0rxyrf+UBFUjkFxqT4Uw1cn7Lv18C/gO7h/R3AF8BGKeXuBWqBgeH9ZeEaeySV6Rr8ey28H5Pm8yfuyZjw/gfh/akp1zsz7P9hynkPpZS7KezfIsv3cCOwghAhOuzbBhu0PzK8Hx7sXJDBRldMrBYDm4Z9I4AG4JKUsiOBOuDq1vy9eZeR47RP3lfVGYk3ahm8FmDCkBYR6QeMxp5yK0Wkv4j0xwae/wIMAnZJOqUGE4pkPgO+E7o4eodrn6equ6jqKhEpAw4L561NXCNcZyrQhcYZQwcBb6jqK0mfoxb4FnBkjHtxCFZZ35Cy/9qw/7CU/Q+kvH8zvGa8d9jn7gH8TkS2DL6+rapfUdUHI/p5K5Ze82hVnRv2HY513T+Scq/mA//B7lGr4YLgOO2TdAlY1gCdspzzpfD6k3B+8nZVODYsqfwXqrouxcYPsXrjdmCRiLwoIqeJSK9wvD+WfvKwNNdIVJyJawwHZqU6qaofqOonWT5HKpsBH2vTbiFUtQ7rhto0pXzqvUtkNst2767HRO7HwH/DGMrvRGS7KA6KyBnAMcB5qvpM0qHEd/J3mt+vXWn6fbQ4PobgOO2T1Io6CokK7wbg4Qxl3k36uyH1oKo+JyLDgIOxp9d9MTE5TUR2SrrGg9g4RjoS/e2dyO9zpCJZjpVhXS/JxL6mqq4ARovI7pjYHYAJ60kiMlFV78vonMi+wKXAX4FLUg4n7tchWIuspLggOM6Gw5zwWq+qzyYfEJGtsCft6kwni0gXYHvgM1W9H7g/dBGdDlwOHIX1x1cDndNcYxiwI7A67PoE2DzNdY7FBrRPSj2W5XN9TUQ6J7cSwmDzZmQYjI6DiHwZ6KWqrwKvAmeHe/Yi8FMgrSCIyObA/dgA/CQNAwQpvgN8qqpvppz7LWB5ob7HwbuMHKfjknjCLwNQ1XnYoOZxYSYQACLSGfgj9lSf7SGxL/AP4JzEjtCl9K/E9VS1HhujODBNd8pV2FhF//D+CWCX0LJI9uVMYOfQ5dPkM2TgUWxGVaqA/Ajr938sy7lR+R3Wz989ad9MYBlpWlIAItIDaxWUA+NCKyOVR8PrOSIiSeduj82GOrVQx+PgLQTH6bgk+srPFJG/qeojwMnA88AbInIjNhvoaGya5jmq+kUmY6o6T0TuBX4kFh/p79iU0R9jA9qJwdqzgW8AL4rIDcBcrHvpIOAWVU10S10CfBt4XkSuw6ZhHg1sCSTWRSQ+w4RQYd6ZxrVbgWOBq8K02teBnYHjsWmtt+a8U7m5Cvgb8JKI3InNljoMGwP4VYZzbgO2wmYobR0q+eTurb+r6jsi8jvse+knIg9jwvsTYCVwXhF8j05rTmnyzTff/r+du0dpKArCMPwGqxQuQbBIZ+cKQlo3kE4XkzKV27FJYWUhWASxSysJIlilisU3wvVwFGzS+D5wm5Ph/oYzlzmT/G3j57bTVSf22zjpHrojk9fzYPySvJm+k/LNI3Dd7GsFbDrHGJMJ8IXUvHekP3/SxE1IGWVbcWvye4iTJu6M/N3DjkyA98Csibkl3UIfZAL+uifTQcwpsCRlqD0p0SyAcedeTpv9d8c7135V5/dGymIPwHzw+TmDttN6HodftpuKG5HF+qd6Vq+kI+vi2N+3UZ2QJOmfcw1BkgSYECRJxYQgSQJMCJKkYkKQJAEmBElSMSFIkgATgiSpmBAkSYAJQZJUPgEF7gqvGzQXMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 384x704 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise intersections for positive groups\n",
    "plotting_setup(18)\n",
    "\n",
    "fig = plt.figure(figsize=(15, 20))\n",
    "\n",
    "all_responses_dict = {'nAb': all_responses[all_responses[:, 0] != 999, 5].astype(bool), \n",
    "                      'S2': all_responses[all_responses[:, 0] != 999, 4].astype(bool), \n",
    "                      'S1': all_responses[all_responses[:, 0] != 999, 3].astype(bool), \n",
    "                      'N': all_responses[all_responses[:, 0] != 999, 2].astype(bool),\n",
    "                      'RBD': all_responses[all_responses[:, 0] != 999, 1].astype(bool), \n",
    "                      'Compound': all_responses[all_responses[:, 0] != 999, 0].astype(bool)}\n",
    "all_responses_df = pd.DataFrame(data=all_responses_dict, dtype=\"bool\")\n",
    "_ = UpSet(from_indicators(lambda all_responses_df: all_responses_df.select_dtypes(bool),\n",
    "                            data=all_responses_df), min_subset_size=0, show_counts=True, \n",
    "          sort_categories_by=None, orientation='vertical').plot(fig=fig)\n",
    "_['totals'].set_title('Positive Antibody Response', y=1.5)\n",
    "\n",
    "plt.savefig(fname=\"figures/upset_positives.svg\", dpi=300,  bbox_inches=\"tight\")\n",
    "plt.savefig(fname=\"figures/upset_positives.pdf\", dpi=300,  bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "other-grammar",
   "metadata": {},
   "source": [
    "## Exploratory Analysis: Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ongoing-graduate",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Percentage data\n",
    "df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc', scale=True)\n",
    "# Unscaled\n",
    "df_tcell_p_unsc, feature_names_p_unsc = construct_tcell_df(df_included, feature_type='perc', scale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "lonely-gauge",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data point labels defined based on compound response\n",
    "cls = np.copy(compound_response)\n",
    "cls[compound_response == 'positive'] = 'tab:orange'\n",
    "cls[compound_response == 'negative'] = 'tab:blue'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "military-counter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PCA vs. compound response\n",
    "plotting_setup(20)\n",
    "\n",
    "xlim = [-10, 10]\n",
    "ylim = [-10, 10]\n",
    "\n",
    "pcs = PCA(n_components=2, random_state=42)\n",
    "pca = pcs.fit_transform(df_tcell_p)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(20, 10))\n",
    "\n",
    "ax[0].scatter(pca[cls == 'tab:blue', 0], pca[cls == 'tab:blue', 1], c='tab:blue', s=200, \n",
    "            alpha=0.5, label='Negative', marker='o')\n",
    "ax[0].scatter(pca[cls == 'tab:orange', 0], pca[cls == 'tab:orange', 1], c='tab:orange', s=300, \n",
    "            alpha=0.5, label='Positive', marker='P')\n",
    "\n",
    "ax[0].set_xlim(xlim)\n",
    "ax[0].set_ylim(ylim)\n",
    "ax[0].set_xlabel(\"PC 1\")\n",
    "ax[0].set_ylabel(\"PC 2\")\n",
    "\n",
    "\n",
    "# Sparse PCA vs. compound response\n",
    "xlim = [-7.5, 7.5]\n",
    "ylim = [-5, 5]\n",
    "\n",
    "pcs = SparsePCA(n_components=2, random_state=42, alpha=5)\n",
    "pca = pcs.fit_transform(df_tcell_p)\n",
    "\n",
    "ax[1].scatter(pca[cls == 'tab:blue', 0], pca[cls == 'tab:blue', 1], c='tab:blue', s=200, \n",
    "            alpha=0.5, label='Negative', marker='o')\n",
    "ax[1].scatter(pca[cls == 'tab:orange', 0], pca[cls == 'tab:orange', 1], c='tab:orange', s=300, \n",
    "            alpha=0.5, label='Positive', marker='P')\n",
    "\n",
    "ax[1].legend(title='Compound Antibody Response', loc='upper right')\n",
    "ax[1].set_xlim(xlim)\n",
    "ax[1].set_ylim(ylim)\n",
    "ax[1].set_xlabel(\"Sparse PC 1\")\n",
    "ax[1].set_ylabel(\"Sparse PC 2\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(fname=\"figures/pca_combo.svg\", dpi=300)\n",
    "plt.savefig(fname=\"figures/pca_combo.pdf\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "comfortable-times",
   "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>T2</th>\n",
       "      <th>F</th>\n",
       "      <th>df1</th>\n",
       "      <th>df2</th>\n",
       "      <th>pval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hotelling</th>\n",
       "      <td>59.540704</td>\n",
       "      <td>29.544819</td>\n",
       "      <td>2</td>\n",
       "      <td>131</td>\n",
       "      <td>2.567390e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  T2          F  df1  df2          pval\n",
       "hotelling  59.540704  29.544819    2  131  2.567390e-11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the Hotelling's T-squared test, compare PCA embeddings of the two groups\n",
    "# Vanilla PCA\n",
    "pcs = PCA(n_components=2, random_state=42)\n",
    "pca = pcs.fit_transform(df_tcell_p)\n",
    "multivariate_ttest(pca[cls == 'tab:blue', :], pca[cls == 'tab:orange', :], paired=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "insured-estonia",
   "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>T2</th>\n",
       "      <th>F</th>\n",
       "      <th>df1</th>\n",
       "      <th>df2</th>\n",
       "      <th>pval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hotelling</th>\n",
       "      <td>37.878007</td>\n",
       "      <td>18.795526</td>\n",
       "      <td>2</td>\n",
       "      <td>131</td>\n",
       "      <td>6.662063e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  T2          F  df1  df2          pval\n",
       "hotelling  37.878007  18.795526    2  131  6.662063e-08"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sparse PCA\n",
    "pcs = SparsePCA(n_components=2, random_state=42, alpha=5)\n",
    "pca = pcs.fit_transform(df_tcell_p)\n",
    "multivariate_ttest(pca[cls == 'tab:blue', :], pca[cls == 'tab:orange', :], paired=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fleet-butter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sparse PCA loadings\n",
    "\n",
    "# Visualise sparse PC loadings\n",
    "pc_loadings = pcs.components_.T\n",
    "\n",
    "fig, ax = plt.subplots(2, 1, figsize=(10, 10))\n",
    "\n",
    "ax[0].bar(x=np.arange(0, pc_loadings.shape[0]), height=pc_loadings[:, 0])\n",
    "ax[0].set_title('Sparse PC 1 Loadings')\n",
    "ax[0].set_xlabel('Feature Index')\n",
    "ax[0].set_ylabel('Loading')\n",
    "\n",
    "ax[1].bar(x=np.arange(0, pc_loadings.shape[0]), height=pc_loadings[:, 1])\n",
    "ax[1].set_title('Sparse PC 2 Loadings')\n",
    "ax[1].set_xlabel('Feature Index')\n",
    "ax[1].set_ylabel('Loading')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "attractive-surgery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CD3.IFN_pos_TNF_pos_perc_2_S10' 'CD3.IFN_neg_TNF_neg_perc_2_S10'\n",
      " 'CD3.IL2_pos_CD154_pos_perc_2_S10' 'CD3.IL2_neg_CD154_pos_perc_2_S10'\n",
      " 'CD3.IFN_pos_perc_2_S10' 'CD3.TNF_pos_perc_2_S10'\n",
      " 'CD3.CD154_pos_perc_2_S10' 'CD3.IFN_pos_TNF_pos_perc_2_S11'\n",
      " 'CD3.IFN_neg_TNF_neg_perc_2_S11' 'CD3.IL2_pos_CD154_pos_perc_2_S11'\n",
      " 'CD3.IL2_neg_CD154_pos_perc_2_S11' 'CD3.IFN_pos_perc_2_S11'\n",
      " 'CD3.TNF_pos_perc_2_S11' 'CD3.CD154_pos_perc_2_S11'\n",
      " 'CD4.IFN_pos_TNF_pos_perc_2_S10' 'CD4.IFN_neg_TNF_pos_perc_2_S10'\n",
      " 'CD4.IFN_neg_TNF_neg_perc_2_S10' 'CD4.IL2_pos_CD154_pos_perc_2_S10'\n",
      " 'CD4.IL2_neg_CD154_pos_perc_2_S10' 'CD4.IFN_pos_perc_2_S10'\n",
      " 'CD4.TNF_pos_perc_2_S10' 'CD4.CD154_pos_perc_2_S10'\n",
      " 'CD4.IFN_pos_TNF_pos_perc_2_S11' 'CD4.IFN_neg_TNF_pos_perc_2_S11'\n",
      " 'CD4.IFN_neg_TNF_neg_perc_2_S11' 'CD4.IL2_pos_CD154_pos_perc_2_S11'\n",
      " 'CD4.IL2_neg_CD154_pos_perc_2_S11' 'CD4.IFN_pos_perc_2_S11'\n",
      " 'CD4.TNF_pos_perc_2_S11' 'CD4.CD154_pos_perc_2_S11'\n",
      " 'CD3.IFN_pos_TNF_pos_perc_3_S11' 'CD3.IL2_pos_CD154_pos_perc_3_S11'\n",
      " 'CD3.CD154_pos_perc_3_S11' 'CD4.IFN_pos_TNF_pos_perc_3_S10'\n",
      " 'CD4.IFN_pos_TNF_pos_perc_3_S11' 'CD4.IFN_neg_TNF_neg_perc_3_S11'\n",
      " 'CD4.IL2_pos_CD154_pos_perc_3_S11' 'CD4.IL2_neg_CD154_pos_perc_3_S11'\n",
      " 'CD4.IFN_pos_perc_3_S11' 'CD4.TNF_pos_perc_3_S11'\n",
      " 'CD4.CD154_pos_perc_3_S11' 'CD3.IL2_pos_CD154_pos_perc_4_S11'\n",
      " 'CD4.IFN_pos_TNF_pos_perc_4_S11' 'CD4.IL2_pos_CD154_pos_perc_4_S11'\n",
      " 'CD4.CD154_pos_perc_4_S11' 'CD3.CD154_pos_perc_5_S10'\n",
      " 'CD3.IFN_pos_TNF_pos_perc_5_S11' 'CD3.IL2_neg_CD154_pos_perc_5_S11'\n",
      " 'CD3.IFN_pos_perc_5_S11' 'CD3.TNF_pos_perc_5_S11'\n",
      " 'CD3.CD154_pos_perc_5_S11' 'CD4.IFN_pos_TNF_pos_perc_5_S10'\n",
      " 'CD4.IFN_neg_TNF_neg_perc_5_S10' 'CD4.IL2_pos_CD154_pos_perc_5_S10'\n",
      " 'CD4.IL2_neg_CD154_pos_perc_5_S10' 'CD4.IFN_pos_perc_5_S10'\n",
      " 'CD4.TNF_pos_perc_5_S10' 'CD4.CD154_pos_perc_5_S10'\n",
      " 'CD4.IFN_pos_TNF_pos_perc_5_S11' 'CD4.IFN_neg_TNF_neg_perc_5_S11'\n",
      " 'CD4.IL2_pos_CD154_pos_perc_5_S11' 'CD4.IL2_neg_CD154_pos_perc_5_S11'\n",
      " 'CD4.IFN_pos_perc_5_S11' 'CD4.TNF_pos_perc_5_S11'\n",
      " 'CD4.CD154_pos_perc_5_S11' 'CD3.IFN_pos_TNF_pos_perc_6_S11'\n",
      " 'CD3.IL2_pos_CD154_pos_perc_6_S11' 'CD3.IFN_pos_perc_6_S11'\n",
      " 'CD4.IFN_pos_TNF_pos_perc_6_S10' 'CD4.IFN_pos_perc_6_S10'\n",
      " 'CD4.IFN_pos_TNF_pos_perc_6_S11' 'CD4.IFN_neg_TNF_neg_perc_6_S11'\n",
      " 'CD4.IL2_pos_CD154_pos_perc_6_S11' 'CD4.IFN_pos_perc_6_S11'\n",
      " 'CD4.TNF_pos_perc_6_S11']\n"
     ]
    }
   ],
   "source": [
    "# Important features in sparse PC 1\n",
    "print(feature_names_p[np.abs(pc_loadings[:, 0]) > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "equipped-breakfast",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CD3.IFN_pos_TNF_pos_perc_2_S10' 'CD3.IFN_neg_TNF_pos_perc_2_S10'\n",
      " 'CD3.IFN_neg_TNF_neg_perc_2_S10' 'CD3.IFN_pos_perc_2_S10'\n",
      " 'CD3.TNF_pos_perc_2_S10' 'CD8.IFN_pos_TNF_pos_perc_2_S10'\n",
      " 'CD8.IFN_neg_TNF_pos_perc_2_S10' 'CD8.IFN_neg_TNF_neg_perc_2_S10'\n",
      " 'CD8.IFN_pos_perc_2_S10' 'CD8.TNF_pos_perc_2_S10'\n",
      " 'CD8.IFN_pos_TNF_neg_perc_2_S11' 'CD8.IFN_neg_TNF_pos_perc_2_S11'\n",
      " 'CD8.IFN_neg_TNF_neg_perc_2_S11' 'CD8.IFN_pos_perc_2_S11'\n",
      " 'CD8.TNF_pos_perc_2_S11' 'CD3.IFN_pos_TNF_pos_perc_3_S10'\n",
      " 'CD3.IFN_neg_TNF_neg_perc_3_S10' 'CD3.TNF_pos_perc_3_S10'\n",
      " 'CD8.IFN_pos_TNF_pos_perc_3_S10' 'CD8.IFN_neg_TNF_pos_perc_3_S10'\n",
      " 'CD8.IFN_neg_TNF_neg_perc_3_S10' 'CD8.IFN_pos_perc_3_S10'\n",
      " 'CD8.TNF_pos_perc_3_S10' 'CD8.IFN_pos_TNF_neg_perc_3_S11'\n",
      " 'CD8.IFN_neg_TNF_neg_perc_3_S11' 'CD8.IFN_pos_perc_3_S11'\n",
      " 'CD3.IFN_pos_TNF_pos_perc_4_S10' 'CD3.IFN_neg_TNF_pos_perc_4_S10'\n",
      " 'CD3.IFN_neg_TNF_neg_perc_4_S10' 'CD3.IFN_pos_perc_4_S10'\n",
      " 'CD3.TNF_pos_perc_4_S10' 'CD3.IFN_pos_TNF_pos_perc_4_S11'\n",
      " 'CD3.IFN_neg_TNF_neg_perc_4_S11' 'CD3.IFN_pos_perc_4_S11'\n",
      " 'CD3.TNF_pos_perc_4_S11' 'CD8.IFN_pos_TNF_pos_perc_4_S10'\n",
      " 'CD8.IFN_neg_TNF_pos_perc_4_S10' 'CD8.IFN_neg_TNF_neg_perc_4_S10'\n",
      " 'CD8.IFN_pos_perc_4_S10' 'CD8.TNF_pos_perc_4_S10'\n",
      " 'CD8.IFN_pos_TNF_neg_perc_4_S11' 'CD8.IFN_pos_TNF_pos_perc_4_S11'\n",
      " 'CD8.IFN_neg_TNF_pos_perc_4_S11' 'CD8.IFN_neg_TNF_neg_perc_4_S11'\n",
      " 'CD8.IFN_pos_perc_4_S11' 'CD8.TNF_pos_perc_4_S11'\n",
      " 'CD3.IFN_pos_TNF_pos_perc_5_S10' 'CD3.IFN_pos_perc_5_S10'\n",
      " 'CD8.IFN_pos_TNF_pos_perc_5_S10' 'CD8.IFN_pos_perc_5_S10'\n",
      " 'CD8.IFN_neg_TNF_pos_perc_5_S11' 'CD8.IFN_neg_TNF_neg_perc_5_S11'\n",
      " 'CD8.TNF_pos_perc_5_S11' 'CD3.IFN_pos_TNF_pos_perc_6_S10'\n",
      " 'CD3.IFN_pos_perc_6_S10' 'CD3.IFN_pos_TNF_pos_perc_6_S11'\n",
      " 'CD3.IFN_pos_perc_6_S11' 'CD8.IFN_pos_TNF_pos_perc_6_S10'\n",
      " 'CD8.IFN_neg_TNF_pos_perc_6_S10' 'CD8.IFN_pos_perc_6_S10'\n",
      " 'CD8.TNF_pos_perc_6_S10' 'CD8.IFN_pos_TNF_pos_perc_6_S11'\n",
      " 'CD8.IFN_neg_TNF_pos_perc_6_S11' 'CD8.IFN_neg_TNF_neg_perc_6_S11'\n",
      " 'CD8.IFN_pos_perc_6_S11' 'CD8.TNF_pos_perc_6_S11']\n"
     ]
    }
   ],
   "source": [
    "# Important features in sparse PC 1\n",
    "print(feature_names_p[np.abs(pc_loadings[:, 1]) > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portable-firmware",
   "metadata": {},
   "source": [
    "## Predicting Antibody Response Using T-cell Measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continuous-bangkok",
   "metadata": {},
   "source": [
    "   ### Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "veterinary-radio",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count data: logistic regression\n",
    "if os.path.isfile('results/metrics_clf_counts_cons_lr.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_counts_cons_lr.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_counts_cons_lr.csv'):\n",
    "    metrics_clf_c_lr = np.loadtxt(fname=\"results/metrics_clf_counts_cons_lr.csv\", delimiter=\",\")\n",
    "    metrics_proba_c_lr = np.loadtxt(fname=\"results/metrics_proba_counts_cons_lr.csv\", delimiter=\",\")\n",
    "    feature_importances_c_lr = np.loadtxt(fname=\"results/feat_imps_counts_cons_lr.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_c, feature_names_c = construct_tcell_df(df_included, feature_type='count')\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == 'positive') * 1.).astype(int)\n",
    "\n",
    "    metrics_clf_c_lr, metrics_proba_c_lr, feature_importances_c_lr = boot_train_test_LR(\n",
    "        X=df_tcell_c, y=disease_status, \n",
    "        metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000)\n",
    "    np.savetxt(fname=\"results/metrics_clf_counts_cons_lr.csv\", X=metrics_clf_c_lr, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_counts_cons_lr.csv\", X=metrics_proba_c_lr, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_counts_cons_lr.csv\", X=feature_importances_c_lr, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "primary-filling",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.833; [0.619, 1.0]\n",
      "0.82; [0.454, 1.0]\n",
      "0.846; [0.5, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_c_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "alien-complexity",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.92; [0.729, 1.0]\n",
      "0.927; [0.741, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_c_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "prepared-verification",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Count data: XGBoost\n",
    "if os.path.isfile('results/metrics_clf_counts_cons.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_counts_cons.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_counts_cons.csv'):\n",
    "    metrics_clf_c = np.loadtxt(fname=\"results/metrics_clf_counts_cons.csv\", delimiter=\",\")\n",
    "    metrics_proba_c = np.loadtxt(fname=\"results/metrics_proba_counts_cons.csv\", delimiter=\",\")\n",
    "    feature_importances_c = np.loadtxt(fname=\"results/feat_imps_counts_cons.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_c, feature_names_c = construct_tcell_df(df_included, feature_type='count')\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == 'positive') * 1.).astype(int)\n",
    "\n",
    "    metrics_clf_c, metrics_proba_c, feature_importances_c = boot_train_test_XGB(\n",
    "        X=df_tcell_c, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000)\n",
    "    np.savetxt(fname=\"results/metrics_clf_counts_cons.csv\", X=metrics_clf_c, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_counts_cons.csv\", X=metrics_proba_c, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_counts_cons.csv\", X=feature_importances_c, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "departmental-shipping",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.904; [0.723, 1.0]\n",
      "0.887; [0.562, 1.0]\n",
      "0.921; [0.667, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_c, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "recovered-cornwall",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.959; [0.8, 1.0]\n",
      "0.963; [0.812, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_c, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "white-steel",
   "metadata": {},
   "source": [
    "### Percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "chinese-retreat",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Percentage data: logistic regression\n",
    "if os.path.isfile('results/metrics_clf_percs_cons_lr.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_percs_cons_lr.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_percs_cons_lr.csv'):\n",
    "    metrics_clf_p_lr = np.loadtxt(fname=\"results/metrics_clf_percs_cons_lr.csv\", delimiter=\",\")\n",
    "    metrics_proba_p_lr = np.loadtxt(fname=\"results/metrics_proba_percs_cons_lr.csv\", delimiter=\",\")\n",
    "    feature_importances_p_lr = np.loadtxt(fname=\"results/feat_imps_percs_cons_lr.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc')\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "    metrics_clf_p_lr, metrics_proba_p_lr, feature_importances_p_lr = boot_train_test_LR(\n",
    "        X=df_tcell_p, y=disease_status, \n",
    "        metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000)\n",
    "    np.savetxt(fname=\"results/metrics_clf_percs_cons_lr.csv\", X=metrics_clf_p_lr, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_percs_cons_lr.csv\", X=metrics_proba_p_lr, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_percs_cons_lr.csv\", X=feature_importances_p_lr, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "plain-camel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.836; [0.597, 1.0]\n",
      "0.856; [0.545, 1.0]\n",
      "0.817; [0.444, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_p_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "retired-spouse",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.908; [0.688, 1.0]\n",
      "0.919; [0.705, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_p_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "practical-airfare",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Percentage data: XGBoost\n",
    "if os.path.isfile('results/metrics_clf_percs_cons.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_percs_cons.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_percs_cons.csv'):\n",
    "    metrics_clf_p = np.loadtxt(fname=\"results/metrics_clf_percs_cons.csv\", delimiter=\",\")\n",
    "    metrics_proba_p = np.loadtxt(fname=\"results/metrics_proba_percs_cons.csv\", delimiter=\",\")\n",
    "    feature_importances_p = np.loadtxt(fname=\"results/feat_imps_percs_cons.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc')\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "    metrics_clf_p, metrics_proba_p, feature_importances_p = boot_train_test_XGB(\n",
    "        X=df_tcell_p, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000, verbose=True)\n",
    "    np.savetxt(fname=\"results/metrics_clf_percs_cons.csv\", X=metrics_clf_p, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_percs_cons.csv\", X=metrics_proba_p, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_percs_cons.csv\", X=feature_importances_p, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "compact-liver",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.895; [0.702, 1.0]\n",
      "0.886; [0.571, 1.0]\n",
      "0.905; [0.615, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_p, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "danish-hardwood",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.951; [0.762, 1.0]\n",
      "0.954; [0.75, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_p, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unnecessary-feeding",
   "metadata": {},
   "source": [
    "### Counts & Percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "innovative-burst",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count + percentage data: logistic regression\n",
    "if os.path.isfile('results/metrics_clf_pc_cons_lr.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_pc_cons_lr.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_pc_cons_lr.csv'):\n",
    "    metrics_clf_pc_lr = np.loadtxt(fname=\"results/metrics_clf_pc_cons_lr.csv\", delimiter=\",\")\n",
    "    metrics_proba_pc_lr = np.loadtxt(fname=\"results/metrics_proba_pc_cons_lr.csv\", delimiter=\",\")\n",
    "    feature_importances_pc_lr = np.loadtxt(fname=\"results/feat_imps_pc_cons_lr.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_c, feature_names_c = construct_tcell_df(df_included, feature_type='count')\n",
    "    df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc')\n",
    "    df_tcell_pc = np.concatenate((df_tcell_p, df_tcell_c), axis=1)\n",
    "    feature_names_pc = np.concatenate((feature_names_p, feature_names_c))\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "    metrics_clf_pc_lr, metrics_proba_pc_lr, feature_importances_pc_lr = boot_train_test_LR(\n",
    "        X=df_tcell_pc, y=disease_status, \n",
    "        metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000)\n",
    "    np.savetxt(fname=\"results/metrics_clf_pc_cons_lr.csv\", X=metrics_clf_pc_lr, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_pc_cons_lr.csv\", X=metrics_proba_pc_lr, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_pc_cons_lr.csv\", X=feature_importances_pc_lr, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "golden-briefs",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.872; [0.688, 1.0]\n",
      "0.838; [0.5, 1.0]\n",
      "0.906; [0.667, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_pc_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "stainless-filling",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.945; [0.786, 1.0]\n",
      "0.949; [0.771, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_pc_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "conventional-anatomy",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count + percentage data: XGBoost\n",
    "if os.path.isfile('results/metrics_clf_pc_cons.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_pc_cons.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_pc_cons.csv'):\n",
    "    metrics_clf_pc = np.loadtxt(fname=\"results/metrics_clf_pc_cons.csv\", delimiter=\",\")\n",
    "    metrics_proba_pc = np.loadtxt(fname=\"results/metrics_proba_pc_cons.csv\", delimiter=\",\")\n",
    "    feature_importances_pc = np.loadtxt(fname=\"results/feat_imps_pc_cons.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_c, feature_names_c = construct_tcell_df(df_included, feature_type='count')\n",
    "    df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc')\n",
    "    df_tcell_pc = np.concatenate((df_tcell_p, df_tcell_c), axis=1)\n",
    "    feature_names_pc = np.concatenate((feature_names_p, feature_names_c))\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "    metrics_clf_pc, metrics_proba_pc, feature_importances_pc = boot_train_test_XGB(\n",
    "        X=df_tcell_pc, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity],\n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000,)\n",
    "    np.savetxt(fname=\"results/metrics_clf_pc_cons.csv\", X=metrics_clf_pc, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_pc_cons.csv\", X=metrics_proba_pc, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_pc_cons.csv\", X=feature_importances_pc, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "working-arrow",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.903; [0.7, 1.0]\n",
      "0.884; [0.545, 1.0]\n",
      "0.921; [0.666, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_pc, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ordered-mechanics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.956; [0.799, 1.0]\n",
      "0.959; [0.764, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_pc, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prospective-trash",
   "metadata": {},
   "source": [
    "### MFIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "interracial-seafood",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MFI data: LR\n",
    "if os.path.isfile('results/metrics_clf_mfi_cons_lr.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_mfi_cons_lr.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_mfi_cons_lr.csv'):\n",
    "    metrics_clf_mfi_lr = np.loadtxt(fname=\"results/metrics_clf_mfi_cons_lr.csv\", delimiter=\",\")\n",
    "    metrics_proba_mfi_lr = np.loadtxt(fname=\"results/metrics_proba_mfi_cons_lr.csv\", delimiter=\",\")\n",
    "    feature_importances_mfi_lr = np.loadtxt(fname=\"results/feat_imps_mfi_cons_lr.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_mfi, feature_names_mfi = construct_mfi_df(df_included)\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "    metrics_clf_mfi_lr, metrics_proba_mfi_lr, feature_importances_mfi_lr = boot_train_test_LR(\n",
    "        X=df_tcell_mfi, y=disease_status, \n",
    "        metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000)\n",
    "    np.savetxt(fname=\"results/metrics_clf_mfi_cons_lr.csv\", X=metrics_clf_mfi_lr, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_mfi_cons_lr.csv\", X=metrics_proba_mfi_lr, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_mfi_cons_lr.csv\", X=feature_importances_mfi_lr, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "attended-diagram",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.79; [0.558, 0.971]\n",
      "0.773; [0.4, 1.0]\n",
      "0.806; [0.444, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_mfi_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "catholic-report",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.871; [0.636, 1.0]\n",
      "0.881; [0.61, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_mfi_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "removed-secondary",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MFI data: XGBoost\n",
    "if os.path.isfile('results/metrics_clf_mfi_cons.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_mfi_cons.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_mfi_cons.csv'):\n",
    "    metrics_clf_mfi = np.loadtxt(fname=\"results/metrics_clf_mfi_cons.csv\", delimiter=\",\")\n",
    "    metrics_proba_mfi = np.loadtxt(fname=\"results/metrics_proba_mfi_cons.csv\", delimiter=\",\")\n",
    "    feature_importances_mfi = np.loadtxt(fname=\"results/feat_imps_mfi_cons.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_mfi, feature_names_mfi = construct_mfi_df(df_included)\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "    metrics_clf_mfi, metrics_proba_mfi, feature_importances_mfi = boot_train_test_XGB(\n",
    "        X=df_tcell_mfi, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000)\n",
    "    np.savetxt(fname=\"results/metrics_clf_mfi_cons.csv\", X=metrics_clf_mfi, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_mfi_cons.csv\", X=metrics_proba_mfi, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_mfi_cons.csv\", X=feature_importances_mfi, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "russian-virus",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.856; [0.636, 1.0]\n",
      "0.85; [0.533, 1.0]\n",
      "0.861; [0.5, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_mfi, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "honest-earthquake",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.927; [0.731, 1.0]\n",
      "0.927; [0.689, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_mfi, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weekly-athens",
   "metadata": {},
   "source": [
    "### Random Guess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "literary-athens",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random guess\n",
    "\n",
    "if os.path.isfile('results/metrics_clf_cons_rand.csv') and os.path.isfile('results/metrics_proba_cons_rand.csv'):\n",
    "    metrics_clf_r = np.loadtxt(fname=\"results/metrics_clf_cons_rand.csv\", delimiter=\",\")\n",
    "    metrics_proba_r = np.loadtxt(fname=\"results/metrics_proba_cons_rand.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc')\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "    metrics_clf_r, metrics_proba_r = boot_train_test_random(X=df_tcell_p, \n",
    "                                                            y=disease_status, \n",
    "                                                            metrics_clf=[balanced_accuracy_score, sensitivity, \n",
    "                                                                         specificity], \n",
    "                                                            metrics_proba=[roc_auc_score, \n",
    "                                                                           average_precision_score], \n",
    "                                                            B=10000)\n",
    "    np.savetxt(fname=\"results/metrics_clf_cons_rand.csv\", X=metrics_clf_r, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_cons_rand.csv\", X=metrics_proba_r, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "surprised-geology",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.499; [0.306, 0.7]\n",
      "0.485; [0.125, 0.778]\n",
      "0.514; [0.143, 0.8]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_r, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "choice-mathematics",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.499; [0.306, 0.7]\n",
      "0.496; [0.236, 0.76]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_r, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equipped-removal",
   "metadata": {},
   "source": [
    "## Examining Important Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "earned-bulgarian",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting setup\n",
    "plt.rcParams.update({'font.size': 10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "convinced-newfoundland",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top 10 most important features among percentages in the GBM model\n",
    "df_tcell_c, feature_names_c = construct_tcell_df(df_included, feature_type='count')\n",
    "df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc', scale=True)\n",
    "mean_feature_importances_p = np.mean(feature_importances_p, axis=0)\n",
    "feature_importance_sd_p = np.std(feature_importances_p, axis=1)\n",
    "top_10_features_p = feature_names_p[mean_feature_importances_p.argsort()[-10:][::-1]]\n",
    "top_10_importances_p = feature_importances_p[:, mean_feature_importances_p.argsort()[-10:][::-1]]\n",
    "fig = plt.figure(figsize=(10,7.5))\n",
    "ax = plt.gca()\n",
    "g0 = ax.axhline(c='grey', lw=1)\n",
    "bx = plt.boxplot(top_10_importances_p, positions=np.arange(0, 10), showmeans=True, showfliers=False,\n",
    "                labels=top_10_features_p)\n",
    "xt = plt.xticks(rotation=80)\n",
    "ylab = plt.ylabel(\"Bootstrapped XGBoost Variable Importance\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(fname=\"figures/xgb_varimp_percs_cons.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bright-premiere",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top 10 most important features among percentages in the LR model\n",
    "mean_feature_importances_p_lr = np.mean(feature_importances_p_lr, axis=0)\n",
    "feature_importance_sd_p_lr = np.std(feature_importances_p_lr, axis=1)\n",
    "top_10_features_p_lr = feature_names_p[mean_feature_importances_p_lr.argsort()[-10:][::-1]]\n",
    "top_10_importances_p_lr = feature_importances_p_lr[:, mean_feature_importances_p_lr.argsort()[-10:][::-1]]\n",
    "fig = plt.figure(figsize=(10,7.5))\n",
    "ax = plt.gca()\n",
    "g0 = ax.axhline(c='grey', lw=1)\n",
    "bx = plt.boxplot(top_10_importances_p_lr, positions=np.arange(0, 10), showmeans=True, showfliers=False,\n",
    "                labels=top_10_features_p_lr)\n",
    "xt = plt.xticks(rotation=80)\n",
    "ylab = plt.ylabel(\"Bootstrapped LR Absolute Coefficient Value\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(fname=\"figures/lr_varimp_percs_cons.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "verbal-marriage",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importance plot for the final paper\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "plotting_setup(24)\n",
    "\n",
    "\n",
    "# Custom grid layout\n",
    "plt.figure(figsize=(17, 20))\n",
    "ax1 = plt.subplot(2,2,1)\n",
    "ax2 = plt.subplot(2,2,2)\n",
    "ax3 = plt.subplot(2,1,2)\n",
    "ax = [ax1, ax2, ax3]\n",
    "\n",
    "\n",
    "# XGBoost importances\n",
    "med_feature_importances_p = np.median(feature_importances_p, axis=0)\n",
    "feature_importance_sd_p = np.std(feature_importances_p, axis=1)\n",
    "top_features_p = feature_names_p[med_feature_importances_p.argsort()[-5:][::-1]]\n",
    "top_importances_p = feature_importances_p[:, med_feature_importances_p.argsort()[-5:][::-1]]\n",
    "\n",
    "top_features_p = [s.replace('.', ' ').replace('_S10', ' $t_1$').replace('_S11', ' $t_2$').replace(\n",
    "    '_perc', '').replace('_pos_', '$^+$ ').replace(' 2 ', '; CoV-Mix, ').replace(\n",
    "    ' 3 ', '; N, ').replace(' 4 ', '; S1, ').replace('8_2', '8; CoV-Mix,').replace(\n",
    "    '4_2', '4; CoV-Mix,').replace('IFN', 'IFN-$\\gamma$').replace('IL2',  'IL-2').replace(\n",
    "    'IL-2$^+$', 'IL-2$^+$,').replace('IFN-$\\gamma$$^+$', 'IFN-$\\gamma$$^+$,') for s in top_features_p]\n",
    "\n",
    "flierprops = dict(marker='o', markerfacecolor='green', markersize=12,\n",
    "                  markeredgecolor='none')\n",
    "medianprops = dict(linestyle='-', linewidth=8, color='black')\n",
    "meanpointprops = dict(marker='X', markeredgecolor='black',\n",
    "                      markerfacecolor='black', markersize=20)\n",
    "\n",
    "bx = ax[0].boxplot(top_importances_p, positions=np.arange(0, 5), showmeans=True, showfliers=False,\n",
    "                   labels=top_features_p, notch=True, flierprops=flierprops, medianprops=medianprops, \n",
    "                   meanprops=meanpointprops)\n",
    "ylab = ax[0].set_ylabel(\"Bootstrapped GB Variable Importance\")\n",
    "ax[0].tick_params(axis='x', rotation=80)\n",
    "\n",
    "\n",
    "# Legend\n",
    "legend_elements = [Line2D([0], [0], marker='X', color='white', label='Mean', \n",
    "                          markerfacecolor='black', markersize=20), \n",
    "                   Line2D([0], [0], color='black', lw=8, label='Median')]\n",
    "ax[1].legend(loc='upper right', handles=legend_elements)\n",
    "\n",
    "\n",
    "# LR importances\n",
    "med_feature_importances_p_lr = np.median(feature_importances_p_lr, axis=0)\n",
    "feature_importance_sd_p_lr = np.std(feature_importances_p_lr, axis=1)\n",
    "top_features_p_lr = feature_names_p[med_feature_importances_p_lr.argsort()[-5:][::-1]]\n",
    "top_importances_p_lr = feature_importances_p_lr[:, med_feature_importances_p_lr.argsort()[-5:][::-1]]\n",
    "\n",
    "top_features_p_lr = [s.replace('.', ' ').replace('_S10', ' $t_1$').replace('_S11', ' $t_2$').replace(\n",
    "    '_perc', '').replace('_pos_', '$^+$ ').replace('_neg_', '− ').replace(\n",
    "    ' 2 ', '; CoV-Mix, ').replace(' 3 ', '; N, ').replace(' 4 ', '; S1, ').replace('8_2', '8; CoV-Mix,').replace(\n",
    "    '4_2', '4; CoV-Mix,').replace('IFN', 'IFN-$\\gamma$').replace('IL2',  'IL-2').replace(\n",
    "    'IL-2$^+$', 'IL-2$^+$,').replace('IFN-$\\gamma$$^+$', 'IFN-$\\gamma$$^+$,') for s in top_features_p_lr]\n",
    "\n",
    "bx = ax[1].boxplot(top_importances_p_lr, positions=np.arange(0, 5), showmeans=True, showfliers=False, \n",
    "                   labels=top_features_p_lr, notch=True, flierprops=flierprops, medianprops=medianprops, \n",
    "                   meanprops=meanpointprops)\n",
    "ylab = ax[1].set_ylabel(\"Bootstrapped LR Absolute Coefficient\")\n",
    "ax[1].tick_params(axis='x', rotation=80)\n",
    "\n",
    "\n",
    "# Parallel coordinate plot for CD4 IL2+ CD154+ Cov-mix\n",
    "cls = np.copy(compound_response)\n",
    "cls[cls == 'positive'] = 'tab:orange'\n",
    "cls[cls == 'negative'] = 'tab:blue'\n",
    "\n",
    "x1=df_tcell_p[:, med_feature_importances_p.argsort()[-10:][::-1]][:, 1]\n",
    "x2=df_tcell_p[:, med_feature_importances_p.argsort()[-10:][::-1]][:, 0]\n",
    "tick1='$t_1$' \n",
    "tick2='$t_2$'\n",
    "colors=cls\n",
    "labels={'tab:blue': 'Negative', 'tab:orange': 'Positive'} \n",
    "\n",
    "cnt = 1\n",
    "ticks_x = []\n",
    "for c in np.unique(colors):\n",
    "    label_c = labels[c]\n",
    "    x1_c = x1[colors == c]\n",
    "    x2_c = x2[colors == c]\n",
    "    ax[2].scatter(np.ones_like(x1_c) * cnt, x1_c, c=c, label=label_c, s=25, alpha=0.15)\n",
    "    ax[2].scatter(np.ones_like(x2_c) * (cnt + 1), x2_c, c=c, s=25, alpha=0.15)\n",
    "    for i in range(len(x1_c)):\n",
    "        ax[2].plot([cnt, cnt + 1], [x1_c[i], x2_c[i]], c=c, alpha=0.15)\n",
    "    ticks_x.append(cnt)\n",
    "    ticks_x.append(cnt + 1)\n",
    "    cnt = cnt + 1.5\n",
    "ax[2].set_ylim([min(np.min(x1), np.min(x2)) - 1, max(np.max(x1), np.max(x2)) + 1])\n",
    "ax[2].set_xticks(ticks=ticks_x)\n",
    "ax[2].set_xticklabels(labels=[tick1, tick2] * len(np.unique(colors)))\n",
    "ax[2].set_ylabel(\"Normalized & Rescaled Percentage\")\n",
    "legend_elements = [Line2D([0], [0], color='tab:blue', lw=8, label='Negative'),\n",
    "                  Line2D([0], [0], color='tab:orange', lw=8, label='Positive')]\n",
    "ax[2].legend(loc='upper left', handles=legend_elements, title='Compound Antibody Response')\n",
    "ax[2].set_title('CD4 IL-2$^+$, CD154$^+$; CoV-Mix % at $t_1$ & $t_2$')\n",
    "\n",
    "plt.tight_layout(pad=2.0)\n",
    "\n",
    "\n",
    "plt.savefig(fname=\"figures/xgb_varimp_cons.svg\", dpi=300, bbox_inches='tight')\n",
    "plt.savefig(fname=\"figures/xgb_varimp_cons.pdf\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "desirable-induction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P4BxnXQHwtGM0DXAD9vOz3hhTnOP5srpeY0ylMeYsY8x0Y8xCYyuzVRtj3jfG/NwRtxVF6cWosKMoirJlmed5nk2Z0gTOxCWf39teH4kvnXMcBFyI/WX/FBGpzuP5tgSjsOkUmZaMVXScX5PvxqYS+Jz9v+sYnvZmevSY25I46Q+uuDEQ+sSYz4WuGAtlzmNNmm0tcVPxSjJsfxFwK279oMU21zR5LcnIGi8d/ux7eBlrTg1womf94dg0tgX5qpImthLfm55VteRuhp0L12PTgH7r8SZyzZsvF5G3RGSziPwZ69NTio3iyQljzLeMMR9jRYZbgQuwkUA7Yr1//h9d6wOzM8lIoDaFHazh9/lYs+dcKvC1xfdJRrE9ICK/c8U6EbkP+KOzbQjwjJMC6u7/bgdMubO93uOAu7DGzbOxUWKPAqOx1dNezVL8VBSlh6LCjqIoypbldWy1EEia8mbLnsBGY8x/jTGHtbt3Gzgh+NOcl6tFZLHz/GTsr6h+4G3nF8yUxdPvUZ7193WmP92NY8o6g+TkcSn2V9YOe2n0IHr6mMsbxqQtpd0Sd/LielxsTWO+K8aCK+iUZtGGKwLVptvYwkT5UOdziTGmiGQZ8nuyiMzqEI7Z7ePOS2/Zc7caVmdMk1MwxuxNasW9EXSu9HVb5zoE+36/JyL/dtaVkIz0+KDFIe7raeSAMWYyttraJGxk3H+AX2OFsfFAuYgcQXbRXR0lFyPhC7CG4mXY6KV88D3P86tbbhSRS0kade+KTdNyhZn7OnC+bK93ITbtb6iInOAITudgBd452M/3TzpwfkVReggq7CiKomxBHB8Jt2T2rsaYXXI4/FTsr8ZHkPkX72z5nqeNXCvr9Fgco1zTxnJ6y2OMMaOBd0mmB7wL7OZ4gvR6toYxZ4x53BizHlhnjPG3sd8QbJoPdH+FsC1OF40Ft8LTDq2OaM1OzuPiNva5Gxs9FSTpdXMsdvItwJ3pDurIZz8D7qR7vDFmqjGmH8nqTXlJw3I8WB7A/h++mKTR8MnGmOMzHdfBc/mx0TpgK2K59Pc8bxlt5UaNVOZ4uj9j04ziwD4icoyIXCciz4rIVyLiiopdmRI62XlsAtr8DheR10TkC0+/8oH32tZk2OdMbFQU2PQ3sCbLT3TgfFldr4i8KiJPt0z1cyIUr3Ne7teB8yuK0kNQYUdRFGXLc63zaIB/ZJPqYoyZRDI1YQnwdEdP7uTS/955KaSmKJyL/eW9rcWNYlnqWZe2fHRPx5nov0YyRehRYD8RyfQPeW+lJ4+5fLAJOwmtxP7ynAlves+zzuNWNebJ/1hwhaKJzn5ttTHReflWpv1EZCVJE+XjnEdX4HlJRL5ur7+d5G3s+wzwf8DR2Civt0VkUcajcuPvJH2GzsZGtbhC423GmGF5Oo/b/o7A4yLyrme9V8xpGW3lVk/LNS3I/ex9JCLvpNvBiRRyRfR0Y6+zIsv2zuMXnpSzLcliz/P90+3g9Oskkn5fAO93UGDKx/W6EXC5lpVXFKUHocKOoijKFkZEnicZsbAH1kQxYxqDMWYH7ETHrRLzy47+A+ekw9xGMnz7bhH52NO3RhGpbWshWVlHPOu74x/oTuH8ku16DIAVG07qjdfSHj15zOWJ+z3Pb0nnFWGM2RW41Hn5tlOZaKsa89AlY+GfJCuc3e6kTbVso5ikMXKcDFE3Htx993DOf5DbfjvHdRpncu0a6H6fpKiUr2id47DRTwB3iK3o1ogtB9+MjeC4P8u0wvbOVYqtshXFVrtKICJVwErn5TdbHLq78ziP3HCrPo3JMA4C2PewNLnKBDK00dGqbW7bLdvdUnjNl//iCFkpOFFg/yS1j+cZYy7owPnycb1nOY8vdKINRVG6GRV2FEVRuoczANeE83BgoTHmKsd4chtjzCBjzF7GmJuxIdojnH2vEJEnMzVqWleCKTPGDDXGTDHG/ASbS+/+E/cB8PMuubr8UWKMOTubpQNtnwns4zx/DzvxKU5zD71Lt5QLN8acb4z53FmO7mAzfXbMicjb2NQWsGaiM40xhzvXNcYYcyE2sqQYG91zVoamejw9bSyILTv+J+flN4H3jDHHGmMGO8sx2PTG3T1ttCfsvYCNmvEB92LTslYCz3TwenPFrY41BvgOVmz4d2cbNcYMxU7oAZYBv3K3OREubrWqA8jP5+R32NLit2SIdHL9hK40xhxsjBlmjLkS2A0bOfNImmPa4nnnsRJrDLynMWag8xk8ETsOTmpxTEtR0S2TPsQYc6Sx1ZxySQN1r3NbY8y1zpjeyVmGt3lkHhCRV0net/HADGPMd4wxA4wx440xP8WmTB2Gvcd/IWnYfYMx5q9pxK626NT1GlsF8hDs9/Q9OZxXUZSehojooosuuujSDQvWp+JO7C/Y0s6yAfhBhnZmZHF8y+UhoKSD/X7BaWNxJ679dE9fTk+zfXGu19SBPnzegft2eTeNlcvbul99ecx5zvVlO/uFsBOqtvqwDJjaHWO+r44Fpy2DFXea22gjhk3HM1n27w8tjr8yz/fR/Y55JcP2zzznfjrDPu53yIwW60d7jr3Ec49e9Kw/OE17RcBXzvZ6YMdOXN8Ip40NQP8M+1R4ztfp7zpgODZVr62xtBQrYLmvd2vRxv5pjrk0hz7sDjRmOPdf2jhuX2efR/Mwtgpo/7toPXC8s/8uzvskzmeo1djI9/U6x56K/fyvAEbn8/Oliy66bPlFI3YURVG6CbHpHD/Emo7+EXgDa7YYdZZV2AnlT4FxInJ/prbaoQFYjS2tew2wi4icLDbFZKvEGFNJ0pugtyEdPrAPjzkRiYrIicChwHTsZCWGjdB5HxuRNVHyVK66h9AjxoJYLsZW+bkD+AJbdawa+BS4AdhZRK4WkWz77Joog53s3pXTBXYeb7RKPqph/RQb/QM2HfHFljuISB3WE0ew4sC/OhEl6BoZXykim9PtICIbsX43dwPrsALBR8CpInJ5ricUkeXYlMtrscJYA/YzuBY7vn6JNdD+E8n39v9atPEqNppyntOfKpKG59n04X1gb2y64WrPeSAZpdaliEiD8110MDYqajn2M7UJmAlcDGwvIo85+3+MFWg+xqY6thobbZyrQ9drjDkDW4VrFbCvdEGVQkVRtiwm+7+viqIoiqJ0F8aY8dgJ87HSRmqU0vfRsaAo+cUYsy/WSP8xETmh7b27rA8BEelyA2NjzI+Bf2Cjp/aXrjckVxRlC6ARO4qiKIrSO3DNh7/o1l4oPQEdC4rSx9hCos4vgVuxKXh7q6ijKH0HFXYURVEUpYdjjNkZm97wjojM7e7+KN2HjgVFUTqCMeZ3wF+B+VhRZ2k3d0lRlDzSXaUAFUVRFEXJntHAAuAH3dwPpfsZjY4FRek0xpijgKOcl0Ocx92NMfe5+4jI6Vu0U12EMeYHwNVY/6bXgHONMS132ywiN23hrimKkifUY0dRFEVRFEVRlK0KY8zlwGVt7SMirdSP3kg21wosEZHRXd8bRVG6AhV2FEVRFEVRFEVRFEVReinqsaMoiqIoiqIoiqIoitJLUY+dPsSAAQNk9OjR3d0NRVEURVEURVEURVHyyIcffrheRAam26bCTh9i9OjRzJo1q7u7oSiKoiiKoiiKoihKHjHGLMm0TVOxFEVRFEVRFEVRFEVReikq7CiKoiiKoiiKoiiKovRSVNhRFEVRFEVRFEVRFEXppaiwoyiKoiiKoiiKoiiK0ktRYUdRFEVRFEVRFEVRFKWXosKOoiiKoiiKoiiKoihKL0WFHUVRFEVRFEVRFEVRlF6KCjuKoiiKoiiKoiiKoii9FBV2FEVRFEVRFEVRFEVReikq7CiKoiiKoiiKoiiKovRSAt3dAaV7ERFqamqorq6mrq6OeDze3V1SFEXpM/j9foqKiigrK6O0tBRjTHd3SVEURVEUReljqLCzFSMirF27lkgkQkVFBUOGDMHv9+vEQ1EUJQ+ICPF4nNraWtavX099fT2DBg3S71hFURRFURQlr6iwsxVTU1NDJBJh1KhR+P3+7u6OoihKn8IYQyAQoH///pSWlrJkyRJqamooKyvr7q4piqIoiqIofQj12NmKqa6upqKiQkUdRVGULsbv91NRUUF1dXV3d0VRlI4SjUDdRvuoKIqiKD0IjdjZiqmrq2PIkCHd3Q1FUZStgpKSEtauXdvd3VAUpSNsWgq1q8H4QJqhZAiUj+zuXimKoigKoBE7WzXxeFyjdRRFUbYQfr9fDeoVpTcSjVhRJx4H44dQqX2tkTuKoihKD0EjdrZy1MRTURRly6Dft4rSS2lqBBFojkFdnV0Xb4TaddAvDH79d1pRFEXpXvQvkaIoiqIoiqJkIhAGY6B0G2hugmgt1KyGxmrYtBiCBRAqtpE8KvIoiqIo3YD+9VEURVEURVGUTISKraeO67GDwDa7QOkQK/JEayGywS6BMIRLVORRFEVRtij6F0dRFEVRFEVR2qJ8JBRX2rSsQNiKPQCBCiiqgKZoGyJPCfiD3dt/RVEUpU+j5slKzqxYsYLLLruM3XffnX79+hEMBhk8eDCHHHIIDzzwgJqD9jD+/ve/Y4xh3333zflYEWHs2LEYY6isrKSxsTH/HXRYs2YNmzdvTll3+umnY4zhV7/6VWLdjBkzMMYwYMCALutLtkydOhVjDPfdd1+XtH/fffdhjMm4+P1+ysrK2GmnnTj//PNZtWpVl/RDURRFwYo5RRVJUcdLIGS39R8J5aPsc8QKPJuWwOZlUL8J4rEt3m1FURSl76MRO0pO3HXXXfz0pz+loaEBn8/HsGHDGDduHIsXL+bFF1/kxRdf5G9/+xtPPfUUw4cP7+7uKp3krbfeYtGiRQBs3LiRJ598khNPPDHv57npppu47LLLePvtt+nfv3/e2+/thMNhpk6d2mp9U1MTK1asYN68ecybN4+HH36Y9957j+22264beqkoitLHiUZaR+ykwx+0wk5RhRVyGmvssd5InlAxhEs1kkdRFEXJCxqxo2TNb37zG374wx8Sj8f5zW9+w5o1a1i6dCmzZ89m48aNPPvss4wfP54PP/yQAw44gKqqqu7ustJJHnjgAQAOPvhgwAp7XcEFF1xAdXV1q/V//vOfmT9/Pr/5zW+65Ly9hSFDhvDWW2+1Wt577z2WLVvGjBkzGDBgAJs2beKMM87o7u4qiqL0PTYthdVzYePXsGaefZ0NrsjTf4SN5CmutOvrNjqRPEvtc43kURRFUTqBCjtKVrz00ktcd911+P1+Hn74Ya655ppWqTCHHXYYr7/+OgMGDGDhwoVcfvnl3dNZJS80NDTw+OOPA3DFFVfg9/t57bXX+Prrr7dYH7bZZhsmTJjAwIEDt9g5eyP77LMPN9xwAwDvvPMO8+fP7+YeKYqi9CGiEahZBY0RaKy1fjrrPofqVRCrh+YsU9D9QSgsbyHymNYiT1O0Sy9HURRF6XuosKO0S3NzM+eddx4iwumnn873v//9jPsOHTqU3/72twDceeedRCKRLdVNJc/897//paqqirFjx7L77ruzzz77ICLcfffd3d01JQ2HHnpo4rkKO4qiKHmkqdGWOy/sD4ECQCBWB9UroGoFbFxkl6oVULsOGqraF3xSRJ7RVuQxPivsbF6qIo+iKIqSEyrsKO3y+uuv89VXXwFw4YUXtrv/6aefzgMPPMBnn31GcXFqDvrmzZu5/PLLmTRpEkVFRZSUlDBlyhSuu+466uvrW7W17777Yoxhzpw5vPzyy+y///6UlZVRXl7OwQcfzKxZswBYt24dP/rRjxg6dCjhcJjtt9+eG2+8ERFp1TdjDI8++ijvvPMO+++/P6WlpQwcOJCDDz6Y//3vfxmv64svvuDcc89lzJgxhMNhKioqOOiggxJRLV4WL16cMLitra1ttf3TTz9NbE/XvyeeeIKPP/6YY489loEDB1JQUMCOO+7I1VdfndHAeM6cORx33HFss802FBcXs+eee/LCCy9kvJ72cNOwjjjiCACOO+44wBr6ZjLIzrX/l19+eco92HnnnTHGMGPGjJT2vObJXqqqqvj5z3/OsGHDEuf405/+lHYsgY1CuuGGG9htt90oKSmhqKiInXbaiUsvvZRNmzalPUZEuOeee9htt90oLS1l0KBBnHHGGWmNip955hmMMVRUVBCNpv9n/IEHHsAYw4EHHph2e0fx3seW4x5g7dq1/OpXv2L77bensLCQ8vJyDjjgAJ544om07bki3n777cfQoUMpKChg5MiRnHTSSXzwwQet9h89ejTGGFavXs19993HpEmTKCwsZOTIkfzgBz9g4cKFGfv+2muvcfTRRzN48GBCoRBDhw7lhBNOSHy+vbjm2YcffjiRSITf//73jB8/noKCAoYMGcIpp5yS9ly5Xg9YUfu+++5j7733pn///hQWFrLDDjtw6aWXaqqpomxNBML2MVwGxQNs6fOyoVA5Hsq2saJMsAikGRqrrbiTEHy+bl/w8QesyNNvuIo8iqIoSscQEV36yDJlyhTJhc8++yyr/S655BIBZJtttsmp/ZYsXLhQRo4cKYD4/X7ZZZddZKeddhJjjAAyadIkWbVqVcox++yzjwDyox/9SIwxUl5eLpMnT5aioiIBpKioSF5//XUZPny4BAIB2XHHHWX48OECCCBXXXVVSns/+MEPBJAf/vCHEgwGJRgMyje+8Q0ZOnSoAGKMkRtvvLFV3//zn/9IYWGhAFJSUiJTp06VUaNGJc5z4oknSlNTU2L/RYsWJbbV1NS0am/u3LmJ7en69+Mf/1hCoZAEg0HZaaedEvcNkCOPPLJVe08++aSEw2EBZMCAATJ16lQpKSkRQPbcc08BZJ999sn6vVqzZo0EAgEB5O233xYRkXXr1iXW/fe//017XK79v/vuu2WPPfZIbJs8ebLsscceMnv27JT2LrzwwsQxr732mgBSWloqu+yyiwAyduxYmTRpkvh8PgHkG9/4hmzevDmlb2vXrpVJkyYl3ueJEyfKrrvumrimkSNHyvz581OOicfjcsIJJyT6N2HChMSYHTFihIwePVoAuffee0VEJBaLycCBA9u8RwcddJAAct9997X7Ptx7770CyKhRo9rd9/bbb09c2+LFi1O2ffjhhzJo0CABJBwOy6RJk2TcuHGJ6zrjjDOkubk55Zhzzz030d52220nU6ZMkfLy8sTn96mnnkrZ3/08/OQnPxFA+vXrJ1OnTpWysjIBpKysTN54441W/f7tb3+b6MfgwYNl2rRpUllZKYD4fD655ZZbUvZ33/+9995bdt11VwFk+PDhsuuuu4rf7xdAysvL5auvvurU9TQ0NMh3v/vdRN9GjRolkydPllAolBhzLc+RDdl+7yqK0sPYuERk6fsiyz6wjxuXZN63KSrSWCtSt1GkerXIpqUi678UWfdFctnwlcjm5SI1a0XqNolE60TiTS3aidltm5clj9u4WCSyQSTW0JVXqyiKovRAgFmSQQvodjFCl54v7Hz/+98XQA466KCc2vcSjUZl++23TwgMy5YtS2xbsGBBYsK91157pRznCjuA/PSnP5WGBvuPzIoVKxICjs/nk8mTJycmWc3NzfKzn/1MAKmoqEiZsLpCgTv5X7JkSeKY66+/PtHeRx99lNK/goKCRB8ikUhi2/PPPy/9+/cXQC655JLE+s4KO4B897vfldWrVye23XzzzYltM2fOTKxfs2ZNYvL8m9/8RmKxmIiI1NTUpIgSuQg7N954Y2Iy671/hxxySEZxqaP9F5HE+rlz56ZtL52wA0hhYWHKhPzTTz9NCAznnntuSlv777+/ALLTTjuljP0VK1Yktm277bZSX1+f2HbbbbclhILXX389sf6jjz5KERBdYUdEEmPvhBNOaHV/Vq1aJX6/X4qKiqS6ujrtPfSSjbATj8flP//5T2IMnHjiiSnbN2/eLCNGjBBAzj777JTzvvXWWwlR0ytofvrppwLIwIED5dNPP02sb2hokPPOOy8hbHjxCp2nnXZa4nPiHYfDhw9P+fzcf//9AkgoFJK77747MdaamprkL3/5ixhjxBgjL730UuIY7/s/ZMgQeeWVVxLb5s+fL0OGDBFAzjvvvE5dz09/+lMBZOLEiQmhUURk/fr1cswxxyS+Q+LxeMb3Jh0q7ChKL6ax1ooqjbUdO74plhR8atZkJ/g0RqzgkxB5lqvIoyiKspWiws5WsnSVsHPggQdmnKhmywMPPJD4Rb5lJIWIyLJly6S4uFgAee655xLrXWFnu+22azWB+v3vf5+Y4LWMtFi2bFlimyveiCSFgpKSElm+fHmrfpx22mmtJsdnnnmmAHLIIYekvbb//Oc/CZFh/fr1ItJ5YaeiokLq6upaHedGWdx8882JdVdccYUAsu+++7bav7GxUcaPH5+zsDN58mQB5Ne//nXKenciHggEZOXKla2O60j/RaTDws7f//73Vud48803E2LBunXrRETkjTfeEEAKCgpaRbOIiFRXVyeEmltvvTWxfsyYMa2EG5dXX301rbAza9YsARtNVlub+s//DTfcIICcdNJJrdpLhyvshMNh2WOPPVotu+66a0JYBOTggw9uJRhde+21ife/ZVSOiMhzzz0ngAwaNEii0aiIiDzyyCMCyPe+971W+0ciETnooIPk7LPPThnbrrAzderUVp/VxsZG2XbbbQWQ22+/PbF+7NixAsg111yT9vp//vOfCyC77bZbYp33/X/00UdbHXPVVVcJ2Ogvl1yvZ8WKFRIMBiUUCrX6bhERqa+vT0ShtYz0aQ8VdhRFaUXWgs8yK/hENohUrbRRQ16Rp3a9ijyKoih9mLaEHfXYUdqlqKgIgKampg638eyzzwJw8skn069fv1bbhw8fzjHHHAPA//t//6/V9oMPPhifL3W4jho1CoARI0YwYcKElG2DBw9OPK+pqWnV3rHHHsuwYcNarf/hD38IwPPPP09zczMAzz33HADnnXde2ms76qijGDlyJPX19bz66qtp98mVfffdl8LCwlbrt99+e4CU0uCuj87JJ5/cav9QKMSpp56a07nnzZvHRx99BMCJJ56Ysu2oo46ioKCApqYm7rvvvrz0v6OEw+G0pb333HNPxo8fTzQa5bXXXgOS4++www5LjBsvpaWlibbc8bdw4UIWLVpEMBjk+OOPb3XMfvvtx5gxY1qtnzJlCjvuuCN1dXU8/fTTKdseeughgJzfk8bGRt5+++1Wy5w5cwgGg5x55pk8//zzvPDCC5SWlqYc+9///heAE044oZWnE8AhhxxCeXk5a9eu5cMPPwRg3LhxgL1v1113HStXrkzsX1RUxEsvvcSdd95JSUlJq/Z+8pOftPqshkIhTjvtNCB5fz///HO+/vprfD4f5557btrr/tnPfgbAzJkzWbt2bco2Y0yKYbRLujGW6/U8//zzxGIxJk+e3Oq7BaCgoICjjz46sa+iKEqn8AcgVGx9dkoGWUPlynHWb6dsqPXcCZWAiPXwqdtoK3XFHc+d5pj179m8FNYthA1fQWSDNX1WFEVRtgpU2FHaZciQIQCsX7++w224ZqaTJ0/OuM83vvGNlH29pBNhQqEQQNpS2MFgMPHcipupTJ06NW0fdtppJ8CaPK9fv57q6mpWr17dbt/dbW0ZxOZCuusFEmKJ17z4iy++AGCHHXZIe8ykSZNyOrdrmrz99tuz6667pmwrKyvju9/9LgB333132nsLufW/o2y33XYJ0bElO+64IwALFiwAOjb+3Ps6evTotCIVZL63rnDzyCOPJNYtWLCADz/8kMGDB3PQQQdl7Ec6Ro0alaLIV1VVccstt1BcXMzGjRvp378/3/nOd9Ie+9lnnwHwt7/9jT333LPVstdeeyWMnt37NW3aNI4++miampr49a9/zbBhw9hll1349a9/zRtvvJEQPdPR3mfryy+/BJL3ecyYMfTv3z/tMWPHjk0Iwe774VJSUkJZWVmrY9KNsVyvx71nX375Zdp7tueee/LMM88AyXumKIqSd/wBCBW1LfgUlEG41ApDoSKQJqhZDas/gWUfwPIPYcMiKwbFO/4DnaIoitKzCXR3B5Sez3bbbQckJzvZMHfuXCZMmJAQWNyomZbRBF7cX8vTRdi0rK7lJV0UQnuUl5enXe/tX1VVFbFYLO22lrTV947gilaZ8AoqbnWeTPco06Q5Hc3NzYmokgULFrR5b7/66itmzJjBfvvt12pbLv3vKOmiRVpuq6urAzo2/tq7r5D53p5yyin8/ve/58UXX2Tjxo1UVFTw4IMPAnDSSSfh9/sztpkNZWVlnH/++UyYMIFDDjmEG264gVgsxt/+9rdW+7qRK9mUQPdWenr88ce57bbbuOuuu/j444/55JNP+OSTT7juuusYNWoUN998M9/73vdatdHeZ8s9RzbvCdj3paqqqtVnK9cxlsv1uPdsw4YNvP32222eR6tjKYqyxfEH7EKLHzfiTTaKJ94IsQZo2Az1m2HTItgo4PdDqNSWbQ+Xgj/kWXRKoCiK0pvRiB2lXdwIjbVr1zJ37tx296+trWXq1KlUVFTw4osvAslJc1spOO4Eqa0Je75wJ/wt8fZvwIABKX3paN/TiRiZzt8R3Il0urLqQMbS3+n43//+x4oVKwAbdZNpcSfVd911Vyd733EikUjGba4I4AovHRl/7d1XyHxvhw0bxv77708sFuPJJ58E4NFHHwVyT8NqiwMPPJDf/e53ANxyyy089thjrfZxhalZs2a169Plpj4B+P1+zj//fObMmcPSpUu59957OfHEEykrK2PJkiUce+yxidQtL+19tgYMGABk955A/r4Xcrke956df/757d6z2bNnd6pfiqIoecMb4VO2DQyaCKO+BaP3gCE72TLtEofqVbBuAaz/wpZk37TYKcu+HGrXQv0miNZphI+iKEovQoUdpV0mTJiQSPO54YYb2t3/vvvuIxqNEovFmDJlCpD0vXC9W9LhTqrGjx/f2S63S6boo48//hiw6Wfl5eX069cvkYqWqe8iktjm9j0QSP7y1djYOsd91apVHe98C9x7O2fOnLTbs4nUcHGjSnbbbTeWL1+ecXH9hp588kk2bdrUuQvoIF9//XVG3yf3XrgpWR0Zf+4xixYtyig+tHVvXU+Zp556is8++4yvvvqKHXfcsc10sI7whz/8IZHmdN5557VKmdx2223b7euMGTP4/PPPEylZ1dXVzJw5kyVLlgDWx+r000/n4YcfZunSpey0007E4/GEWOWlvc+W+13i3t/FixezefPmtMcsXLgwIax15nsh1+vJ5p7Nnz+fWbNmddv4VxRFyZpAGEoHw6AJMGwKbDPJpnQVlIE0QywCTVHryROttf481Sut4LPhKxV8FEVRegEq7ChZ8Yc//AGw/iuuGWs6vv76ay677DLAGhG7v867UT8PP/xw2tSF5cuXJ4xmM3mF5JPHH388bbSFG4HiGqOCNdwFuO2229K29dRTT7FixQoCgUAiLcmbjpLOg6Ote5grbvrIPffc08orpLm5OSHWtEckEklEl7iiRCbOPvtsABoaGvjXv/6Va5db4aZ85ZKiVVtby3/+859W65977jmWLl1KaWkpe++9N5Acf88991xicu+lpqYm4S3kjr+xY8cmJvz33ntvq2Nmz57dZgTbMcccQ0lJCf/73//497//DeQ3WsclGAxy5513Yoxh48aN/OpXv0rZ7l77HXfckfb+vvnmm+y3337ssMMOiXvz61//mt13350///nPrfbv168f06ZNA9J7Jd1///2t1jU2NibGofvZmjBhAuPGjaO5uZnbb7897bX94x//AGDnnXdOCKwdIdfrOfTQQ/H5fMyYMSPt57epqYmjjjqKadOmZSV2K4qi9Bh8fivolA2Fym2hfBQUDwR/0JozY2yaVlE5FA+AsONllk7w2bzMI/hEVPBRFEXpTtoLM9el9yxdVe7c5aSTThJA/H6/XHrppYlS0iIi8Xhcpk+fLttss02iPLm37HJjY6Nsv/32ibLcy5YtS2xbsGCBTJo0SQD51re+JU1NTYltbrnzW265pVV/3FLQma6bNCW03fLZgBx11FGJ0uvxeFz+/Oc/J8pUe0tiz58/XwoLCwWQn/3sZxKJRBLbnn/+eSkvLxdALrroopTzu9e09957J84Ti8Xk5ptvFp/P12a5c295by/HHnusAHLZZZcl1lVVVcmwYcMEkLPOOitRZry+vl7OOeecxHnaK3fuljIPhUKJsu1t8c1vflMAmTRpUqf6LyJSWloqgDzzzDMp69srd15ZWSlvv/12Ytt7770ngwYNSnuO/fbbTwDZaaedUkpYr1ixQvbff38BZOzYsSnjdvr06YlS9t6y1p9//nmifDcZyqGLiJx22mkCSHFxsfh8Plm6dGna/TLhjvFRo0a1u+9ZZ52V6M/rr7+eWL9mzRoZMGCAAHLGGWckxqKIyAcffCBDhw5NfB5cXn/99cRn/cEHH0wpkz5jxgwpKSkRQF566aXEerfcOSBXXXVV4nNcVVWVeN932WWXlM+3d8zdc889ifM0NTXJtddeK8aYVuPCff8rKyvT3odnnnmm1T3ryPWceuqpAsj48ePlww8/TKzftGmTHH/88Yn3dfny5W28K63RcueKovRI4nGR+ipbRt0ttb7ha1tePVrn7NMk0hgRqdtk129eZsuwe8uyr//SlmuvWWPLtzfW2nLuiqIoSqehjXLn3S5G6NJ7hJ3GxsaUyWMgEJDx48fL1KlTE+IGILvttpusWLGi1fHz58+XkSNHJo7dZZddZKeddkpM3iZNmtRq4ttVws7EiRPFGCPFxcUydepUGTx4sAASDofliSeeaNXW9OnTpaCgQAApKSmRadOmpUxk/+///k+i0WjKMU899VRCwCktLZUpU6bIwIEDBZBf/OIX0q9fv7wIOyIib731VqK9/v37y7Rp06R///4CyBFHHJGVsHPAAQcIIMccc0yb+7ncddddieufOXNmp/q/xx57JCbKU6dOlRdffDFje+7Efscdd0yIKxMnTpQdd9wxRbSLxVL/kVy1apXsvPPOAogxRnbYYQfZddddJRAIJISAjz/+uFWff/WrXyXaHTdunOyyyy7i8/mksrJSvvWtb7Up7LzyyiuJY/fff/+s7quXXISddevWSUVFhQCyww47pIzH119/PTEeCgoKZMqUKTJ+/PhE3yZNmiSbNm1Kae+Xv/xlYvugQYNk6tSpMnr06MS6c845J2V/9/Pgvg+DBg2SadOmSXFxsQAyYsSItN85F110UaLNIUOGyLRp06SysjIhxPz1r39N2b8jwk5Hrqe6ulr23nvvxPZtt91WJk+eLEVFRQJIMBiUF154ob23pRUq7CiK0uOJx0UaqtsWeVL2b7LrE4LP8syCT/VqFXwURVE6SFvCjqZiKVkTCoW46667ePfddzn77LMZP348q1atYs6cOYRCIQ477DAeeugh3nnnHYYOHdrq+AkTJjBnzhwuueQStt9+exYuXMiyZcuYNm0aN910E++//z4jRozYItdy2GGH8eKLL7LLLrswb948/H4/J510Eh988AHHHntsq/2POeYY5syZw1lnnUVFRQUff/wxDQ0NHHLIIUyfPp3HHnsspcQ62BSpV199lUMOOQSfz8f8+fMZM2YMDz74IDfeeGNer2ePPfbgww8/5IwzzqCkpIS5c+cyevRoHnrooYQfTlusWLGC1157DWg/DcvlhBNOSFQ06qyJ8j333MN+++2HiLBw4cJWpa3T0b9/f95++23OOOMM1q1bx1dffcWuu+7KbbfdxvTp01N8jsD6Jr333ntce+21TJ48mSVLlvDll1+yww47cNVVV/HRRx+lLV9+3XXX8eSTT7LXXnuxfv16lixZwpFHHsl7773HyJEj2+zjfvvtlyj93hVpWF4GDBiQSDX67LPPuP766xPb9t57b+bOncvPf/5zRowYwbx581i+fDk777wzV1xxBW+//XarCl/XX3899913H/vuuy9NTU3MmTOH2tpaDjnkEJ544omM6VN//vOfueWWW6ioqOCTTz5hyJAhXHTRRcyaNYuJEye22v/aa6/l5Zdf5ogjjqCpqYmPP/6YkpISTjvtNN5//31++ctf5uX+5Ho9paWlvPLKK9x+++3sueeeCfP48vJyTj75ZGbNmsXBBx+cl74piqL0KHw+m45Vtg2Uj7H+PIECaKyGqhXWaLl2nfXbAZveFSy01bZKBkK/YVAxFirG2OduSpfxWT+fyAZr4OxN6apZ40npirXVO0VRFCUNxgo/Sl9g6tSpMmvWrKz3nz9/ftqJVl/m9NNP5/777+fCCy9MmfgqSlcQiUQYPHgwIsLq1avbLe3dmxk9ejRLlizhmWee4fDDD+/u7vRYtsbvXUVR+gjNjtFy1FlErAgUKrFLsBAcz7y224k7Zdmj1rTZfd7s8W0zJlmKPeAtyx7M3K6iKEofxxjzoYhMTbctkG6loiiK0nn+/e9/E4lE+MEPftCnRR1FURRlK8CN5AmXpoo8jTXQUJ29yOPzg6/Q7uMlneATq7Ptu3gFH3/QVvxSwUdRFEWFHUVRlHzy5ZdfEggE+Pzzz7nooosA+OlPf9rNvVIURVGUPOIVeUScKJ7aNCJPMQSLsovkyUbwicdsWfZYHTRmiPBRwUdRlK0QFXYURVHyyJ133sm1116beH3mmWcyZcqUbuyRoiiKonQhxkC4xC75Enm8dFrwCYI/7DyGHNFHBR9FUfoWKuwoiqLkkSlTplBWVkYgEOCkk07ir3/9a3d3SVEURVG2DGlFHk+6ljFW5AmXdEzk8ZJR8Gl2xJ5GFXwURdlqUPPkPoSaJyuKovRs9HtXUZStEhFHXKm10Twi+RV5sqGl4BOPWtGnlWlz0JPW5TFwVhRF6WbUPFlRFEVRFEVRlO7BGJuKFSoGGZQq8jTWJEUed5+uEHl8PvAVQLAgdX06wSdWb/vn7X86wccf7HpBSlEUJQtU2FEURVEURVEUZcuQTuTx+vIktpd0ncjjpV3Bx7M0NajgoyhKj0SFHUVRFEVRFEVRtjwpIs9AGykTdSN5are8yONFBR9FUXoRKuwoiqIoiqIoitK9GAOhIru0KfIUQ7DYCi/dQVaCT8ymdrUr+HgMnFXwURSlE6iwoyiKoiiKoihKz6G3iDxeOiv4+AJOZS4VfBRFyR0VdhRFURRFURRF6Zl4RR4GQdT15KlJijzBIqe6Vg8RebxkEnxEkpW5VPBRFKWTqLCjKIqiKIqiKErvICHyDExWr4rWWrGnp4s8Xoyxgk0gnLq+leCTwcPHF3BKsYdT07tU8FGUrRIVdhRFURRFURRF6X0EC+3Slsjjmi/3ZJHHSy6CTzxqr9WLK/Ko4KMoWxUq7CiKoiiKoiiK0rtpKfJEI7Z8ejQCZp3dFirpXSKPl/YEn3gUmqLtCz7+EARCKvgoSh9DhR1FURRFURRFUfoOrshTPABiDY7pco3150kReYrB5+/u3nYOr+Dj1XxU8FGUrQoVdhRFURRFURRF6ZsEHeNir8gTrYXatU66Vh8SebxkI/jEY05qVzrBJ+AYNTumza6Bswo+itIj6YVxiIqi9DUWL16MMQZjDBdffHG7+++7774YY/j73/++BXqXP5qbm1m4cGHKuhkzZmCMYcCAAd3UK0VRFEXZSnAFnvLR0G84FPSzokbtWti0GKpXQkMVNMe7u6ddhyv4hEuhqALKtoHyUVA5DvqPgNLBdn2gwN6bhs32/mxeBhu+cu7TKohsgIZqKwyJdPdVKcpWjwo7iqL0KK677jo++eST7u5G3pk1axbTpk3jjjvu6O6uKIqiKIrSSuTp74g862DjIqha0fdFHi8tBZ/SIVbwqRgL/UdmKfisTAo+sQYVfBRlC6KpWIqi9ChisRhnn3027733Hr7eaG6Ygb///e/Mnj2b/fbbL2X9brvtxvz58wkE9OtYURRFUbqFRLpWpY1AaayFaI0VeXA8ecKu8XIfStfKBmOs904glLpexKnO5S3L3miNq72Cjt8py+5vUaWrD/2Ppyg9AZ1JKIrSozDG8MEHH3DzzTdzwQUXdHd3upyioiImTJjQ3d1QFEVRFAWSvjQpIk9tqsgTKraRLVubyOOlXcEnmrpkFHxaLCr4KEqHUGFHUZQexbnnnss///lPLr30Uo4++mhGjx7d3V1SFEVRFGVrpKXI45ZQj6y3S7AgWULdr9MqQAUfRekm9BOiKEqP4oorrmDs2LFEIhF+9KMf5XRsY2MjN910E9OmTaO0tJTi4mK+8Y1vcP3119PQ0JD2mHg8zl133cVuu+1GWVkZFRUVHHPMMcybN4/LL78cYwyXX355q+PeeustTjnlFMaMGUNRURFFRUWMGzeOc845hy+//DKxn2sMff/99wPw17/+FWMMp59+OtDaPLm2tpbi4mKMMXz44Ydp+7xo0SKMMRQUFLB58+bE+ubmZu677z723ntv+vfvT2FhITvssAOXXnopVVVVOd1LRVEURVE8BMLWY6Z8lPWcKaqw/juR9dZfpmo51G+GeFN397Rn4go+4ZKkh0//kR4PnyFJD5/mJutvVLvW3teNX1vfo+qV9n67Hj7Nzd19VYrSY1BpWVG6mNG/fba7u9ClLL7mu3ltr6ioiNtvv52DDjqIF198kQcffJBTTz213eM2btzIoYceysyZM/H5fIwdO5aioiI++eQTPvroIx599FFefPFFKisrE8fEYjFOPPFEpk+fDsCECRMIBAI89dRTvPDCC+y5555pz/XHP/6RSy+9FIBtttmGHXfckfXr17No0SLuvPNOHnvsMWbOnMn2229PQUEBe+yxB1988QVr165l2LBhjB49mu222y5t2yUlJRx99NE89NBDPProo0yZMqXVPo888ggAhx9+OP379wesqHXsscfy7LN2vI0aNYqKigrmzZvHH//4Rx5++GFefvllxo4d2+69VBRFURSlDQIhCFRYIaIpmiyhnhLJUwyhUo3kaY/ORvj4/MlS7P5gskS7RvgoWxk64hVF6XEceOCB/OAHPwDgggsuYN26de0ec/rppzNz5ky+/e1vs3DhQr744gs+/vhjFi1axF577cWHH37IWWedlXLMzTffzPTp0xkwYABvvfUW8+fPZ+7cuXzyyScMHTqUl19+udV5PvnkEy677DL8fj8PP/wwK1eu5IMPPmDRokV89NFHDB8+nOrqam6++WYAhgwZwltvvcWhhx4KwAknnMBbb73F73//+4zX4gpZ//73v5E0FSVcYeeUU05JrLvooot49tlnmThxIrNnz2bx4sXMnj2blStXcswxx/D1119z3HHH0ay/bimKoihK/giErMDTf2QykkeabXWoTYtt1aj6TRrJkyuZInwqx6VG+ASLPBE+6zJE+FRphI/S51FhR1GUHskNN9zAoEGD2LBhA7/4xS/a3HfWrFk888wzVFZW8tRTTzFu3LjEthEjRvDEE09QWlrK008/zccffwxAU1MTf/7znwG4++672WOPPRLH7LTTTjz55JMYY1qd6+WXXyYYDHLcccdx4oknpmzbZZddOPfccwH47LPPOnTdYIWtbbbZhqVLl/LOO++kbJs7dy6ffvopFRUVHHbYYQCsXLmSf/7zn4RCIZ588kkmT56c2L+yspKHHnqIkSNHMnv2bJ555pkO90tRFGWrJhqBuo32UVHSkU7kQdKIPLHu7mnvJkXwGZwUfMpHZSf4VK3wCD71KvgofQIVdhRF6ZFUVFRw0003AfDwww/z/PPPZ9z36aefBqwgMnDgwFbbBw0axP777w+QaOedd95h48aNDBkyhCOOOKLVMZMmTUqbinXhhRdSX1/Pfffdl7YvRUVFANTV1WW+uHbw+/0J0ejRRx9N2eZG6xx//PGEQjZs+fnnnycWizF58uS0FbYKCgo4+uijE/sqiqIoObJpKayZZyeGa+bZ14rSFm2KPEtU5OkK/MHMgk/ZNknBR+IewWeFCj5Kn0CTPhVF6bGceOKJPPTQQzz77LP86Ec/Yt68eZSUlLTaz42OefPNNzP64ixatAiABQsWADB//nwAdt5557SROQCTJ0/mzTffbLXeGIMxhldffZV58+bx9ddf88UXX/DRRx+xcuVKgE6nPJ122mnccMMNPP7449x00034/bakqiv0eNOw3Ov/8ssvM17/qlWrgOT1K4qiKFkSjUDtavCFrXeK8dnXxZXWR0VR2sPryROP2cpa0YgVeSIbrEeMW0LdH+zu3vY9/EG7tPy8uh4+TY3J5w1VrT183MpcAW+Vrq241L3SI1FhR1GUHs1tt93GDjvswNKlS7n44osT3jVeqqurAZuS5AormXCrQ23YsAGA4uLM/5SXlpamXX/HHXdw5ZVXsmLFisS6YDDI5MmTmTRpEi+88ELbF5UFu+yyCzvvvDNz585lxowZHHDAAbz77rssWrSIsWPH8u1vfzuxr3v9GzZs4O23326zXa2OpSiKkiNNjXaiV7fe+n4EC+0v/k2NKuwoueMPWoHHFXmitdBYa9P86jZa8SBUoiLPliAXwaexGhpU8FF6LpqKpShKj2bEiBFcffXVAPz973/n/fffb7WPK85cf/31iEiby5NPPplyTE1NTcZzp9t25513cu6557JixQpOPfVUHnnkEebOnUtNTQ3vv/9+IuUpH7gmyo899hiQ3jQZktdy/vnnt3v9s2fPzlv/FEVRtgoCYSvolA6xE+5Ygy3DXLvGTsTVFFfpKP4gFJZD/xE2Xai4EjB2XG1aApuX2udN0e7u6daFK/YkUrpGpKZ0FVc6KV3NVvBJpHQtsmldVSvsukRKV7y7r0jZCtCIHUVRejw/+clPePjhh3nvvfc4++yzKSsrS9m+7bbbAsn0qnR89NFHGGMYN24cpaWl7LDDDgB8+umniEjadKxPP/201brrrrsOgMsvv5zLLrus1fbly5dnf2HtcPLJJ/Pb3/6Wp59+mltvvZUnnngCaC3sZHP98+fPJxKJMG7cOMrLy/PWR0VRlD5PqBhKhjjpWD4IFVmRJ1yWjLIIFdkIi1CJFYEUJVdckaew3IqF0Zr0kTyhktalwZUtgxvhQ4YIn3jUinBpI3x8yVLs/mCyRLtG+Ch5QoUdReliFl/z3e7uQq/H5/Nx1113MXnyZD799NNWIsx3v/tdrr/+eqZPn84111zDgAEDUrZXVVVxwAEHsGnTJu666y7OOuss9tprLyoqKlizZg3PP/98osKUy1dffcXrr7/eqi+LFy8GSKk85VJfX5+IqmlqSv0F1+ezAZLpypdnYujQoRxwwAG8/PLL3HTTTaxatYpvfvObCSHH5dBDD8Xn8zFjxgwWLFjA9ttvn7K9qamJo446ioULF3LJJZdw1VVXZd0HRVEUBSgfaX+lb2pM+qFA0i+loQqideBbZwWfcJlOvpWO4w+0Fnncqmwq8vQ8Mgo+TRBvdESfmP3+aKxu4eHTQvDxh+x3jAo+So5oKpaiKL2CHXfckd/+9rdAa3Fk3333Ze+992bz5s1897vf5csvv0xsW7lyJUcddRSbNm1im2224aSTTgJspaiLLroIgLPOOislxeurr77i6KOPJh5vHTq73XbbAXDLLbckvG3cY4444ojEuRsaGlKOc02flyxZktN1u+lYV1xxBdA6Wgdg7NixnHzyycTjcQ4//PCUdKvNmzdzyimnsHDhQoqLi/nRj36U0/kVRVEUBzc1w+vH4fqlVIyxKRrBIivybF5qqx41VGllHaVzuCJPv+FQPhqKB1gD77qNdpxtWuKkazV2d0+VlvgD9vuisBxKBnlSukYnU7pCJcmUrsh6qF7pSelablO66jdb4VhTupQ20IgdRVF6DRdffDFPPPFE2pSjRx55hO985zvMnDmT7bbbjh122AG/38/nn39ONBqlrKyM5557jsLCwsQxv/rVr3jzzTd57rnn+OY3v8nEiRMJhUJ8+umnlJSUMH78eL788ksCgeRX5ZVXXsn3v/99XnnlFYYNG8Z2221HTU0NX375JSLC/vvvz6uvvsrq1atTUrwmTZoEwPTp09l5553Zd999ueWWW9q95mOOOYYf//jH1NbWEgwGOf7449Pu949//IMlS5bwxhtvMGXKFLbddltKSkpYsGABdXV1BINBpk+fzrBhw3K654qiKEqWhIrt0hx30jAc7w2z3qZphctsVS1F6Sj+ABT2t0u8yRovRz3pWv6gkxJYbKM+lJ6JP2CXtBE+USfKp60In1DqohE+Cj00YscYU26MuckYs8QY02iMWW6MudsYM2pLtmeMOc0YM9MYU2+M2WyMmWGMOTKH8w41xlQZY55oYx9jjKk2xkgby79yuV5F6auEw2HuvPPOtH44Q4cOZebMmVx77bVMmTKFJUuWMH/+fIYOHcq5557Lxx9/zK677ppyTCAQ4Omnn+bGG29k0qRJLF68mKVLl3LMMcfwwQcfMHbsWIAUMeiYY47hzTff5OCDD6a0tJRPPvmEqqoqDjroIKZPn87//vc/hg4dSlVVFW+88UbiuNNPP50LLriAQYMG8cUXX/Dxxx9ndc3FxcUJQ+ZDDjmkVZqZS2lpKa+88gq33347e+65J2vXrmXu3LmUl5dz8sknM2vWLA4++OCszqkoiqJ0Ap/f/kJfPspGWYRLbbpW1XIbXVG/SX95VzqPK/J4I3l8ASeSZ5kda5ENGsnTm/AHrF9X2gifockIH7DfKWkjfNYmI3zU2H2rwuTi97AlMMaUA28DE4EaYCEwFigHNgP7iMgnXd2eMeYa4DeAAPOAAmC8s/kPItKmSYUxJgw8D+wHTBeR72fYbzSwCIgAczI092J75wOYOnWqzJo1q73dEsyfP5+JEydmvb+ibG3svvvuzJw5k3/961+cfPLJ3d0dpQ+g37uKspXS3Gx9Uhqq7UTbGJu2VVCmJdOV/NIcT5ZQj9Xbdf4AhEohXKKRPH2JlhE+bon29iJ8/CEnYkjpbRhjPhSRqem29cR39E6sCPMccIKI1BhjCoDbgNOBR40xO4tItj915NyeMeZwrKizEThERD5w1n8PeAy4whjzqoi8ne6ExpgS4FGsqNMek5zH10TkiCyvSVGUTlJbW8v222/Pdtttx7PPPktRUVHK9tWrVzNnzhwgvVGyoiiKomSNzwcF/ezS5FTMaayG6oiN8CnoZyN7/MHu7qnS23HHU0G/VJGnfpNdXJEnVKypgb2dREpX6v+wScHHs0RrU/2+jElW5vIHPQbOPVEeULKhR6ViGWMmAMcAtcCpIlIDICINwNnAfKxIc3QXt/d75/G3rqjjHPc0cAVggN9lOOcUYCaQbSmknZ3HeVnuryhKHigpKaG8vJwZM2ZwySWXpJgdL1++nOOPP55oNMpee+2VKI2uKIqiKJ0mELJpM+VjoHSwnVzVbbSpM1UrbIpFD4uoV3oprsjTb5g1+C4ZaCfvDZud1MDFNp0n1tBeS0pvIpHS1d+mdPUbDhVjHZP3ofb7J1xq943W2pS96pV2PGz4ypPStUlTunoRPUrYAU7BiibPiMhG7wYnouZe52V699A8tGeMGQ98C4gBD6dp827n8TvGmP7eDcaYi7GizkTgfeCfWfRRhR1F6SZuuukmwuEwN954I0OGDGHKlClMnDiRMWPG8MYbbzB+/HgeeOCB7u6moiiK0hcxxk6uyoZaD42iCmiOQc0a2LTIGi+rP4qSL1yRxx1vCZGnSkWerQWfPwvBp8zum07w2bzMI/hEVPDpYfS0WKvdncd3Mmx/z3ncqwvbc4/5REQiLQ8QkbXGmK+xPj3fBF7wbJ4GNABXA38BLsmij24qlgo7irKFOfDAA5k3bx5//etfef3111mwYAGBQICddtqJY489lp/+9Kf069evu7upKIqi9HX8ASvsFFXYX8jdVK2GKhvRU1Bm02d8Pe03WaVX0ipdK2In8g1V1njX53eqa5VoutbWgCv4tEzpao5bcTkedTx8GtOndLmVufxBJ50rrCld3UBPu+OuOfGiDNuXOI+DjTElIlLbBe21d4x73FjPvi4PAD8WkVVA2so9XhyD5W2BZiBijPkDVlgKAp8D94nI7DYbURSlU4wbN45bb721u7uhKIqiKJZQkV3csumNNcmy6aESK/IEC9tvR1Gywee3Y6qgzDH5rs0g8hTruNvaaEvwcY2aXdPmTIKPP2TTT1Xw6XJ62p0d6DxuyLDdm041AOudk+/22jvGe1xK3WERebKd/rRkIvY9aMJWxPJK4gcB5xtjrhaRbCJ/FEVRFEVRlL6CWza9sNymx7giT2ON/WU8XGrTJnSipOQLny+NyBNJFXlCJba6loo8Wy8+P/gKW4+BdIJPLGK/s1zSCj4hNY7PAz3tL4E7OuozbPeuz+bbpCPttXeMd1tnv9HcNKwA8BTwJ6yh82Dgh1gT54uNMatE5B/pGjDGnAOcAzBy5MhOdkdRFEVRFEXpcQQL7FI0wKlyVG0Nl+s22kiKgjJbPr2daHFFyZqWIk8sYqtruSmCKvIoLWlP8IlHbVXAeBRidZkFH38wtWKXkhV5EXayTIvKhjhtGzp7t2VTLqAj7TWn2zHDcZ0tWfA18A9gg4hc5lm/FLjUGLMZuB64yhhzj4i0EptE5A7gDoCpU6dqCQVFURRFUZS+ineyna5setjZppMhJZ/4fE6EWGnbIk+o2EndURQPOQs+8eQ+KvhkTYeEHWPMcOAC4FCsR4wBAsaYocAjwPUi8kwHmo4A/UlNSfIS9jxvK6KmM+25AlVbTmHucdn0ISMi8hbwVhu73AJcBpQDewIvd+Z8iqIoiqIoSh8hEILAACiqtOkyjdW2Wk39JjuBcifiGsWj5JN0Io87/hqq7PZQiWO8XKjjT8lMNoJPPGZTu9IKPkHHtydHwScasW0GwlaM7CPkLOwYYw4GHgXKsIIOJCNXxmArTO1pjLlCRK7MsfkNWCGmIsP2Ss/zdV3Unuutk+kY73HZ9KHDiEjUGDMf2A0Y1ZXnUhRFURRFUXohxth0mHCJLT/sRvHUroXIumQUTyDcfluKkgsZRZ4aaKhWkUfpGBkFn2ZbmSsXwcdbsQtg01KoXQ3GB9IMJUOgvG/YmeQk7BhjxgHTsdbY/8ZG5/wB2NXZ5QvgPuB04DJjzIci8mwOp/gcGAeMzrDdFTdWiUhdF7X3ufOY6RjvcV9m0Yc2Mcb4AZ+IxDLt4jxGO3suRVEURVEUpQ/TZtn0kBV5wmVaNl3JP16RRyRZQl1FHiVf+HxtCD5RR/SJZU7pam6CjYusV1mg0Io+tauhuLJPRO7k+q3+O6yoc4mInCAiTwMJgUVE1orImcBvsILEeTm2P8t5/GaG7e7697uwPfeYXYwxrdKxjDGDsJFJzcAHWfYjLcaYt4EY8KsM20PYyllgTZUVRVEURVEUpX1CRVA6BMpHQ7FTyDWyHjYtgpo1VvhRlK7AjSIrHQIVY+1jsMiKPNUr7RisXWvFH1GLUKWT+HzWXL6gn/2uKxsKFWPs2Os3HEoG2m1uildDlRV+jLGRO02N3X0FeSFXYec7wCbgL+3sdwM2pWm3HNt3y4UfbYxJSYVyIltOd17+q6vaE5HFwEdYH51T0rR5tvP4nIhsyrIfmZiLFcBOMsakSwg8FyjBmix/2MlzKYqiKIqiKFsbPj8U9of+I6H/CBuxE611JtiLbWWteFN391Lpq6QVeYodkWeVR2hUkUfJMy0Fn/JRdvz1G26/B0VsOlYfSVPNVdgZBHwlIvG2dnK2L8L68GSNiHwCPAeUAk8YYyoBnMiZu7DRKwuA/3iPM8YMMMZMcFLFOt0e8Gfn8a/GmH095zkSm3omtC9uZcPN2IidnYA7jTElznmMMeZU4Dpnv9+ISDbVuhRFURRFUZSuIBqxIkg00t096TiBsP31umIslAwCX8Be06bFVuhprNXJtdJ1JESewXYMlm1jRZ5orRV5Nn6tIo/SdYSKradOYxVEa6Bhk33dB9KwIHfz5M1kb+I7DNiYY/tgo1TeAvYDljrmwWOxlaGqgGPSiBznY6tHLaG1N07O7YnI48aYe4AzgdeMMZ8BQWwFMICLnYpWnUJE5htjzgbuBn4AHGeMWQAMBoZ6zvVEZ8+lKIqiKIqidJC+ZrhpTLJsejxmPVAaqyG62vFK6We9UgKh7u6p0lcxximPXgwyyPFEqU368hjjKaFerJ48Sn4oH2k9dfpgVaxcI3beBQYYY77f1k7GmOOBbYD3cu2QiCwHpgB/w1admgQ0YY2ap4nIZ1uovbOxws5MrFg0HHv9J4rI1bldVZv9ewCbsvYIVmjaCSsiPQ3sm89zKYqiKIqiKDkSjVhRRwBfEEJl9nVvjtzx4g/aiU75aBtBESi0JdM3L4Wq5Vb00egJpStxRR5vJE+41FbZqlntRPKs1ogyJT+Eiq3BfB8SdQCM5PDhMMbsDczAChA/x1bGehn4toj4jTEB4DRsilERcLCIvJLvTivpmTp1qsyaNav9HR3mz5/PxIkT299RUbqYxYsXM2bMmLTbjDGEw2EqKyuZMmUKZ555Jt/73ve6rX81NTWUlJSkbP/888+ZMGFC1vsrWy/6vasovZC6jbD+C2s27AZ5xxtg4ARr0hko6HvRBImy6TU2oscYp+JRmfWsUJQtgYiN5HErbDU3eyJ9SjSSR9nqcKqOT023LadULBF5wxjzB+BK4F5sClGzc5KPsSlORVhD4BtU1FEUJVemTp1KOJw0MRMRGhoaWLRoEf/973/573//y3nnncc//vGPbuyl5YsvvuDHP/4x/fv354knNGNSURSlTxII28lj2TBojtmJZm29nWxWrbDbgkW2ClWwyEbA9Ha8ZdNj9U6qllO2OlE2vdQaMytKV5GSrjXQjsWom65Vm7o9VKIij7JVk6vHDiLyR8dz5iqs+bD7jb6z87gYuEpE7s1LDxVF2ap4/PHHGT16dKv1sViMyy+/nKuvvppbb72VQw45hCOOOGKL9GnYsGHMnz8fgOLiZNjmQw89xP/+9z+OPfbYrPZXFEVReiGu4abrsYPAkEnQb5idaCYiCpzULH8wKfQECq1nTW8mWGiX5oGO4Wi1LZtet8Hem3CZvVZF6UqMseMsVJRB5FmbFHmCxb3/c6coOZKzsAMgIk8CTxpjxgI7YKtfRYAvcvXAURRFyYZgMMif/vQnXnvtNd59911uvfXWLSbsBIPBlFSrfO+vKIqi9HAyGW6GS+wC0BS1Ik+szqYxNVTZyWigwAojoeLeXVbX57Nlgwv62fvQUG2FnsZaG+ETLrOLv0PTC0XJHq/IwyCbJhmNJMejG0UXLlGRR9lq6NQ3r4h8DXydp74oiqK0yxFHHMG7777LBx980N1dURRFUbYm3GiATARCdins73iD1CeFnrqNdvH57ETTFXp6ayqTWzZdBtiIiYbq5DWGipwoHvU/UbYQCZHHieRxq2tFIyryKFsNOY1sY8zIHJahxpjyruq4oihbJ/369QOsKbGX2bNnc/LJJzNs2DBCoRADBw7kyCOP5JVX0lt91dTUcNlllzFt2jTKy8spLi5m4sSJ/OIXv2DFihUp+y5evBhjDMYYamtrAWvqfMUVVwAwffp0jDHsu+++afcXEUaPHo0xhunTp6ftTyQSobi4GGNMIo3L5emnn+bggw+msrKSgoICxo0bx89//nNWrVqV281TFEVRtgxuREHxAOg/0lacKhlkJ5ixCNSuhY2LbOWpyAbHmLkXVvtxTZX7DYPyUVBYbqN5albDpkU2Zasp2t29VLYmgoVWdKwYY8dluAyaGqBmjR2T1ausENnc3N09VZS8kmvEzmJsscesMcZEgTnAP0Xk/hzPpyi9n8v7dXcPupbLq7bo6b788ksARowYkVh366238rOf/Yx4PE55eTm77rory5cv55lnnuGZZ57hoosu4tprr03sX19fzz777MNHH31EQUEB48ePx+/3s2DBAm6++WYefvhh3nvvPcaOHZuxH3vssQdLly5l2bJlVFZWMmHCBHbeeee0+xpjOOWUU/jTn/7Eo48+2sqTB6x4U1dXx5QpUxJVk0SEc889lzvvvBOAbbbZhjFjxvD555/zt7/9jYcffpjnn3+eqVPTmuMriqIoPQV/APxlUFBmXzc12miCWB00bLblxY1x/GwcE+ZAqFu7nDNu2fTiSnttDU46Wv1mW0krXAqhUo2YULYcrj+UG8kTjVgT8GgEzDoncq7ELjoulV5OriN4KbAWW/XKXWqAlUB1i/XuEgZ2B+4xxvwzP91WFGVrZPPmzfzrX/8C4LDDDgNgxowZnH/++TQ3N3P11Vezdu1aZs6cyYoVK7j//vsJh8Ncd9113H333Yl27rnnHj766CP23HNPVqxYwdy5c5kzZw5Llizhm9/8JuvWreNPf/pTm3156623OPPMMwHYd999eeutt7jlllsy7n/qqacC8Oyzzyaifrw8/PDDAJxyyimJdTfeeCN33nknQ4cO5ZVXXmHlypXMmjWLNWvWcN5557F+/XqOOeaYVtFLiqIoSg8nELYVp/oNh/IxULaNjSyIR22Uy+alsGmxjexprO190QWhYntN5aOt0NMch9p1NmKidi3EGrq7h8rWRrDQRtBVjLGfO9crqnatE8mzUiN5lF5NrsLOBGzUTjPwV2CciPQXkREiUg6MxJZCjwIfAGOw5soXYs2Vf2iMOTJPfVcUZStARNi8eTMvvPACBx98MOvWraNfv35cdNFFAFx11VWJyJbf/e53BAI2ENEYw2mnncY111wDwB/+8Afi8TgAc+fOBeDYY4+loqIica5BgwZxww03cNhhh6WtzNUZtt9+e6ZNm0Z9fT1PP/10yraNGzfy0ksv4ff7OfHEEwFoaGjg6quvBuBf//oXBxxwQGL/4uJi/vGPf7D77ruzbNky7rnnnrz2VVEURdmC+HxO5a2BVggpH2Wf+8M2uqBmNWz8GqqWWw+b3iSK+Pw2Pat8lE2LCZXYa6paDpuW2Eil5nh391LZ2ggWtBZ54tE0Io+OTaX3kKuwczGwG3C+iFwkIou8G0VkuYhcDpwFTAVOFpHPReRG4HhsBM8PO99tRVH6KmPGjEn40xhj8Pl8lJeXc+ihhzJz5kwGDBjA008/zYgRI6itreWNN94A4Lzzzkvb3jnnnEM4HGblypXMnj0bgHHjxgFw7bXX8u9//zslguZb3/oWzz77LJdeemner82N2nn00UdT1j/xxBPEYjG+853vMHjwYADefvttNmzYwODBg9lvv/3StnfCCScA8Pzzz+e9r4qiKEo34Q/aiWbZNlAx1goiheXWg6duoxVFNn5tBZ+GKog3dXePsyNYCKWDbYRSyUBbOj6ywUYm1axOlotXlC2JK/KUj04j8ix2RJ4qFXmUHk+uHjsnAqtEpM2UKhF5yBhzNXA6cLWz7nljzEpgckc6qijK1sHUqVMJh5PlYH0+HyUlJQwbNoxvf/vb/N///R/FxbYqyddff01TUxPBYJAdd9wxbXtFRUVMmDCBjz/+mIULFzJt2jTOPvtsbr/9dr766iuOP/54gsEge+yxBwcffDBHHnkkO+ywQ5dc24knnsiFF17ISy+9xKZNmygvt/7y6dKwPvvsMwBqa2vZc88907a3ceNGABYsWNAl/VUURVG6mYTvTiHgpDTF6qzZcqzOpmmxzopBoeKkR09PrkbVsmx6Y40tD99YayN8CvpZPx5/sLt7qmxtBAuSQk+sIVlCvbYOcDx5wq4nTy+taKf0WXIVdoYBc7Pcdw3Q0kl0ZZp1iqIoCR5//PGs06Bcb5ni4mJ8bZjelZSUpOxfXl7OBx98wNVXX80jjzzCihUrmDFjBjNmzOB3v/sd3/72t7nzzjvzLvAMGDCAQw45hGeeeYYnn3ySs846ixUrVvDmm29SUlLCUUcdldi3uroasNWy3n777TbbrarasgbWiqIoSjfh81vRI1xqXzc1JoUe16jYGAgU2KpcwSLr59NTCYQdv6HK1mXTg4XWbDpU0rOFKqVvkhB5Kh0BstYRedahIo/SE8k1FWs5sIMxpn9bOxlj+mG9dda32DQQWJ3jORVFUdLiCjaRSITmNszuXOHD3R+suHPdddexfPly5syZww033MB3vvMdAoEA77zzDgcddBB1dXV577ObjvXYY48lHpubmznmmGMoKipK7OdGJR1++OGISJuLG7mjKIqibGUEwjZNq98wm7ZVto2NeGlusmlOm5fZsuo1a2xkTE9NJ2lZNr2owqbDuCWqI+vt5FpRuoNA2Ao85aOh/wj7mWtusiLPxkVQtcKKqj3186VsFeQq7DwHFAH3G2MK0+1gjAkD9wKFwAue9fsCo4DPOtJRRVGUlowbN45AIEAsFuPTTz9Nu08kEkmkKo0fPx6A1atXM2PGDCIRm8+/yy67cMEFF/Diiy8yZ84cCgsLWblyJa+99lre+3zkkUfSv39/XnvtNTZv3pwQeFzBx2XbbbcFYP78+RnbWrx4Me+99x5r167Nez8VRVGUXoYxNh2reIAVR8pHWy+bYCHEIlYk2bjIij2RDbb8s0h397o1/qAVdirGQNlQG3XUUGX7vXmZ43eilYuUbiIh8oyyIk9RhSOkrk8VeXqL95XSZ8g1Fesa4DjgcOBzY8xDwCfYilel2DSrE7ACzgbgcgBjzMXAbwABbs1HxxWl13C5psl0FSUlJey99968+uqr3Hbbbdx2222t9rnjjjuIxWJUVlYyZcoUwJYnX7BgAY888kjCgNhlxx13ZMiQISxatChRRSsTbvqX5PCPcTgc5rjjjuPOO+/kzjvvZObMmQwdOpT9998/Zb+9996bkpISvvrqK1555RUOPPDAVm2dddZZvPrqq5xyyik8+OCDWfdBURRF2QrwB8DveNmA9QyJOd489ZvsYowVToKFVhTqab42oSK7NMetD09DtY2SMOttCkxBmeM/pCjdQCKVsMJGlEUjNjIust4uwQI7TkMl9vOoKF1IThE7IrIKOAjrszMCK9Y8BDwFPAj8GivqzAP2FZEVzqEnAyXA3SLybF56riiKAlx66aX4fD5uv/12rrnmGpqa7C8kIsKDDz7I7373OwCuvPJKgkH7D+tJJ50EwC9/+UtmzZqVaKu5uZmbb76ZRYsWUVJSktG02MVN7Vq6dGlOfXajc6688spEf1p6BJWWlnLBBRcAcPLJJ/PKK68kttXX13PBBRfw6quv4vf7+cUvfpHT+RVFUZStkGCBnYD2G27TtkqH2PSneKOdhG5aYqsA1a6zfiI9KSompWz6cNvvaK2NjtCy6UpPwBV4ykdB/5FOJE/c+WwtttXs6jdpJI/SZZhcfmlOHGSMAb4HHAFMBCqxUTufAU8DT4pI3LP/OcCHIvJhPjqtpGfq1KninaS2x/z585k4cWIX9khRsmPx4sWMGTMGgEWLFmVtnuzy97//nZ///Oc0NzdTUVHB+PHjWbZsGatWrQLgF7/4BTfeeGNi/8bGRvbbbz/effddwJZYr6ioYOnSpaxbtw6fz8cDDzzAySef3Kp/NTU1CUHnlVde4aCDDgJs6tSkSZN44oknMu7vIiKMHTuWxYsXA/Dxxx8zadKkVtfV1NTESSedxOOPPw7AqFGjGDBgAF988UXCXPmuu+7irLPOyul+Kd2Hfu8qSi8mGrG/ygfCNrqlL9EUTUbzuClaxthrDTomzMGC7u5lKs3N1sy2scZGI7nRRwVlfe/9UXovTVErQkZr7XNwInmKIVSqkTxKThhjPhSRqem25eqxA4BYnhKRs0Tk2yKyvYh8Q0ROEZHHvaKOs/8dKuooitJVnH/++bz33nuccMIJhMNhPvroI3w+H8ceeyz/+9//UkQdsOlQL730EldeeSWTJ09m7dq1fPLJJ4TDYU4++WQ++OCDhKjTFgceeCBXX301w4cPZ8mSJcyePbtNE2cXY0yitPnOO++cVtQBCAQCPPbYYzz66KMcdNBB1NTU8PHHH1NQUMDRRx/NG2+8oaKOoijKlmDTUlj9KWz8GtbMs6/7EoEQFPa3njYVY+1jQX+QZluhqmq5vfaa1TYdqidEHbhl0/sNtxESBf2gqR6qV1mvk8gGiMe6u5fK1k4gZKN3+o9MRvJIsx2fmxZb36j6TTpWlU7ToYidrBs3ZpSILOmyEygpaMSOoihKz0a/dxWlFxKNWFEnWueUQC6BeD0M3nHriAyJNyUjeWKRZIpWIOSJ5insGSXJRRyfk2r7foGWTVd6JukieQLhZAn1nuZ3pfQI2orYyTn2yxhTDpyFLWdeROuonwBQAAwFdgR0VCqKoiiKoii9E7fMdrjEmYjVQVMdFA+0v8D7/N3bv67GHwB/mRVHIGkSG6u3FarqNztpUIXOUmxFn+7AGPs+hUusINVYbZeaNeBbZ1NfCsrsBFpRupNACAIVNoInHrMphdGIjeSJbEimfIZLVeRRsiInYccYMxj4ABgGuJK3eJ67r3HWaUyZoiiKoiiK0nsJhK1gUNDfLtFaqK63E7FNi+3kq6Df1lOdya0EBDZ6p6neil2xOidKZoMVu0LFjtBT1D3ilz9gJ81FFbZfrsjTUGX7X1BmhR5fh5wpFCV/+IPJsRqP2e+YxlqbBlm30YpAoRIVeZQ2yTVi59fAcKAWeAxrmPwz4E3gLWfb4UA58CrWYFlRFEVRFEVReiehYigZArWrwfisP8aQSbaqVEOVIxjU2glXQRmEy/p+FI+Lz+eYwDopafFYUuBprLF+PJCMPuguE+aUsuk19j3TsulKT8QftBXgCsvbFnlCJd0XGaf0SHIVdg7FRuR8V0TeBDDGnAQ0i8jFzutBwIvAfsAU4I38dVdRFEVRFEVRtjDlI6G4snVVrJKBUDzAETGqbApF3UZHLOjX8ypJdTX+IPj72WsXgaYGm7IVjdj7wkYb/ZSI5ineslWBfH5rEl3Y31bSaqx2hJ4a2/dwqRXmtFKR0hNIEXmanCpwKvIo6cn1W2sEsNwVdRxmA3sZY3wi0iwia40xZzjrf4YKO4qiKIqiKEpvxxuZ4sUYG/FRUGaFn4aqpFgQCFmhIFy29aX8eH13iipstIxrwhyN2Akq6+zk1Zu2taUMjoMFdikemIzicSfMrrdJqFgNl5WegT/QWuRxBVMVeRRyF3YCwOoW6xYCBwHjgC8ARGSOMWYRsHune6goiqIoiqIovYFAGEoGQdEAO/FqqILIeqjbkIwG2dqieFx8fucelNrXTVFbZaulCXOgwKZNBYu2jMlxijAXdUSeKjtp9vkdYa5UJ8tKz6GVyONU13JFHjf6TEWerYpchZ11wKAW6xY5jzvhCDsOG4GdO9gvRVEURVEURemd+Hw2HamgX2rKT0O1Gve6BEJ2KSy3aVuxeieip86mtLkmzMGipNDT1d5FgRAEKh3DZadsev0muwQLkiKPRvEoPQV/IJle2KbIU6zV4Po4uQo7HwBHGWP2FhE3xeozbAWsfYD/ABhjQsBYYHOe+qkoiqIoiqIovQ835adoQLIyk2vcGy7T8tvg+O44Ag7YCaor8sQiVhQDe59coSdQ0HUCS6ay6bVrIbJO3zelZ5JW5ImkijwhZ1zr2O1z5Crs3AscDfw/Y8zfgMuxFbE2Aj82xswC5gC/wlbGej1vPVUURVEURVGU3orP5zHurbfROynlt/tpNIiLPwB+RzwBG/XkCj1uBI0xTpWtQhuN0FVloFuVTa/xvG+hZBTP1lIJTekdeEWe5niyupb7+VGRp8+Rk7AjIv/PGHMPcCZWvLlUROqMMTcCVwH3e3cHrstbT5UuQUQw+g+EoihKlyMi3d0FRVF6Cq6pcLMTxdPgiQYp6GfFAvXGSOJGPVEBzc2eaJ46G5EQWW8nskGPCXNXpLklyqYPTEbxuB5KoWL7vrlRR4rSU/D5k6mhaUWegE0NDRVvvR5gfYCca/mJyNnGmKeBAyT5X+rVQBG2ClYxNgXrShF5Ll8dVfKP3+8nHo8TCGhJR0VRlK4mHo/j9+svuoqiePD5kyao0bpkJEj9ZjvBKuhnf1XXH+GS+HzJNClwTJgdkce9f8Yk07aCRfmfrHqjr5oak9FXjbV2kuxWQtOy6UpPI53IE41Aw2YVeXo5Jp+/IBpjAsBAYK2IxPPWsJIVU6dOlVmzZmW9//LlyykpKaF///5d1ylFURQFgM2bN1NbW8vw4cO7uyuKovRkmuNOyfRq65Ph8yWFAo3iaZuECXO99eZpitr1Pp9H5CnqGsFFJFnmPlZv14WKnCgeLZuu9HC8Ik+s3o5nfyBZQl1Fnh6BMeZDEZmabltO32rGmD8AS0XkvnTbRaQJWOXs+xtgJxE5NbfuKluKsrIy1q9fT2lpqf6KrCiK0oXE43E2btzIgAEDursriqL0dHz+VE8XN4KnfrNNMyoo0yieTKSYMFc6k9VIUuhprLX7BUIeoacwP/fSWzY9HktG8URXqzin9HxaRfJErNDjfv/4/MkS6iry9EhyitgxxjQDb4nI3lnsOxvYXkSKO9E/JQdyjdgREdauXUskEqGiooKSkhL8fr967iiKouQBESEej1NbW8vGjRspLi5m0KBB+h2rKErueCszJaJ4+lkRoatMg/siTY1JoaepwUYlGJP0PAoW51d4EbEpYg3V9lEkWTY9VLJ1l7tXegfNzckS6m4kT0LkcTytlC1GhyJ2jDGjgcPSbNrGGHNeW+cDRgE7A1U59FPZwhhjGDRoEDU1NVRXV7N27Vricc2gUxRFyRd+v5+ioiIGDBhAaWmpijqKonSMlMpMESsUuManmu6TPYFwsgJQczM01duoqFidfWSDnbSGvCbMnYhqN8a2FSr2iHM11ijbrLOT43CZRkAoPRefLxmJlhB5IqmRPG51LRV5upWMETvGmBDwKTCuo20D94nImR08XsmRXCN2FEVRFEVRlF6KKxQ0VNnUCTeVQk17O0Y8lhR43OgasEKQK/QECvIjnrnl7qO19jyBUFLk0bLpSm+guTmZ3uh+XlTk6XI6FLEjIlFjzE+A33tW74ONwpnTxvmagVrgE+CanHurKIqiKIqiKErbuFE8heX2F/TGaqjbaJdQkVNRSx0RssYfBL/jMSJiU7Vi9fbe1m20+xhjo3hCrglzB9PgEuXuB0K0xoo8kQ3Oe6dl05VegM/niJGlqSKPKza7Ik+oWMfyFqJNOV9EXgZedl87HjtzRWS/ru6YoiiKoiiKoijtYEyy/HfCtLcKqus0iqejeH13iipsRFSsLin0RCN2P38wKfQECnP3zPH5koa1btn0aI2WTVd6F+lEHldsbqiy2xPVtfJkVq60Itdvif1Q3xxFURRFURRlayMasZNvNzWnJ+IPQnGl48VTa4WCuo3WiyfoRvHor+c545rFhkvt66aonbzG6pOTV2Nsqlaw0I4P18snWwJhKBkIMiD1vXMjsNyKRDopVnoyGUUeJzJNRZ4uIydhR0Re76qOKIqiKIqiKEqPZNNSqF0NxgfSDCVDoHxkd/cqM8YkJ1dN0WRFrepIMhKkoJ/6uXSUQMguheVO5at6J6KnLinG+Hy2ypYr9GR7r73vXTzmTIirrPePb52WTVd6D16RRyRZQl1Fni4h57g+Y0wZ8EPgW0AZ4McaJadDROSAjndPURRFURRFUbqRaMSKOhjwh8Efsq+LK3tu5I6XQAgCA6Co0okEqUpG8aifS+cxxt4/9x7Gm5IiT8yJVAD7PrhCT7aTWH+wdTU0txpRIGwrFYVKtWy60vPxpoy2KfIU2+hCFXlyJidhxxgzBHgHW848m7udvuSWoiiKoiiKovQGmhqdiYiTHmMMxBuhZg30G5Z7yk13kS6Kp6HK8XMJWpFAqzJ1Hn8A/E55aIBYQ1LoadhsBbWEh49jwpxN9I1bNr057rx31VC7Dsx6LZuu9C7Sijwt0rWCxU51LRV5siXXiJ0/AKOBOuARYAFQn+c+KYqiKIqiKErPIBC2E4vCSmts2xy3URnRWti8zJmEFCWX3mB0643icVN9ElWZSqwooeWK80OwwBFcKhzPEceEORax6VVgx4x3DLUVgePz2xSwwnIrGjVWJyfEKtApvY0UkWeQ/Xw01iajeYzxlFBXkactcv3Lczg2CudAEXmvC/qjKIqiKIqiKD2HUDHE47BmNvgC0NwEAybAoB1SJ+mNtXZ/f9BJJyjs+RMRY6wQUFCWrMrkCgWBUNLPRVN98oPPl5zEMtB66ERdE2ZHnAFHDHKFnjaicFzRqGhA67LpwSInVasXpAsqCjgijhOZ1pbI4+7Tk79bu4FchZ1BwDwVdRRFURRFUZStgmgE/H7oN8KZWJRaQ4J4Y1IUASuMxOpsFIbrg9LZSklbErcqU1GlRyRYD3UbNNWnq/AHobC/XUSgqcGOH9eEmY3ZRYSllE1vYZadKHlfas+nKL2BTCKP61ulIk8rchV21gDqrqYoiqIoiqJsHbgeO4EC+7yxxk4uCvpB2bDkpCIQtkuiUlJdslpSukpJPTVtyysStEz1CYSckulq2Jt3Er47hUClTflzhUJ3UgueiLCi9CbMLc2yG2uS4y9Y6ETxaNl0pReRIvI4361e8+XE9pKtWuTJ9a/JM8CPjDG7iMjHXdEhRVEURVEURekxuB47JYNtqfNYHdRGbRpNzerkhDxUbEUbfyB1IgLtVEoqyjxJ7268qT5uFIjXsLegX8+OQurN+PxJs2vIHBHmikHB4lQT5pSy6U3J969mjS2bHiq1Io++f0pvIkXkGWjF86ibrlWbun0rEzCNSPaFq4wxlcBsrHnyqSIyq6s6puTO1KlTZdYsfUsURVEURVHyyqaltsS58Vlxp2QI9B+RjMiJ1trJM9jJdaikbX+Upsakt0pTg/0V2k3bCrmVknrohDvWYIWFaK0TyaRlt7c43oiwaMSKjGDFIK+/UzoD5WidFXiiEX3/lL6DSKrI09ycKvIEi5PjOxqx38GBcK/zoDLGfCgiU9Nuy1HY+RswDDgaa6K8EVgBRDMcIiKye27dVTqKCjuKoiiKoihdRHuTgaaonVDE6qz4AZ6JdhvVjpqboak+/STdjeQJFfe8KkfN8WRFrXjMiRAp0yiQ7iDeZCPB3DHkzu/csRostKKhN3rBLZveWGPHrutZohXRlN5OQuSJOJUMHZEnWGQj3RqrrBG+K9KXj+zuHmdNPoWdZqygk21Mk4hID/sr1HdRYUdRFEVRFKUH0Bx3InLqkhPtdClb6UhJ26qzkxLo2WlbsXonise51mCBU1GrtGf1c2tAxJO25YiRkJzYuhFhXiNlr5eSiN3mGmb3RB8oRcmFWL1N04qsg3WfQ7hf0kesYRMM3rHXRO60Jezk+kk9Iw/9URRFURRFUZS+i8+frJjl/nrspmzV1gHrMqds+QPg91TbijUk027Seqv0gLQtty9uFEhDNdSuhbr1yZLpXv8XpeswxuONVJE0YU5EMETsfv5gUugJFELJIKdseq19D13D5VCxE8VTpCKd0jtxv598fqjfBL6gHf/G2PTapsZeI+y0RU7Cjojc31UdURRFURRFUZQ+hzF28hwqguIBqSlbiZLWbaRsuZN0SKZtuZWSonXAhmTalhuN0V1pWz6/rQpWWJ70cnHFKK3I1D20MmGOJtO23PfH9XdyI8oKhqcvm+6m2mnZdKU3EgjbsVtQbse8iE3H6m5hPE9obJ2iKIqiKIqitEftOjvJDZdBycCOtxMIQaACqEhN2XJLireVsuXzpam25UzSW1XbKk7+Ut0dQoorZqWryBTupwJBdxEI2aWwPDWazBUa6zbaceaOn34jrcF3Y7WNdqjfZNe7YpGKdEpvIVRsPXVaGuH3gWgdyNFjJ3GQMcXAmcChwPZAmYgMNMYMBK4HbhSROfnsqNI+6rGjKIqiKIrSBSx+F9Z/bg03m5tgwAQY/a38nqNlylYuVbZcEmlbdTa9wOvtk64k9pYmGrHilZsOFCy0PhehYhUIegJt+jsVWyEuHrOGtPEmNcxWeid9tCpWzhE7xphvAE8CI0iaKLvq0FjgVOBEY8w5InJf7t1VFEVRFEVRlB5C7TpYN9/+uusPQ7gY1nwCleOgdFD+ztPZlC3wpG1VtJ221V5J7K7CjTZyo3gaqqBmtab59BRa+js1NSYjyho2pwqF/jA0x5LvYyCU9FPSsulKT8Yb9diHyEnYMcYMAV4ABgDvA48C5wATnF1WA68B+wF3G2MWisg7+euuoiiKoiiKomxBGp30qGijrawCtlzuytm2moobSRMozO+EtjMpW5AmbSuWNNF12wD7q7VbaWtLpW35A9bYt7DcEQ2qkmk+oaJkFI/SvQTCTiSOIxS64ycWSUaUGR/Q7AiIDVC3wUaYhUvte6koyhYh14id32JFnX+IyE8BjDHfdzeKyBLgAGPMjcDPgQsAFXYURVEURVGU3knYiV6oGOOkodTaxfhg81IbbRJwhJ1QoX10hZ58lYruTJUtF38Q/E6JX0hN23JFlUTalhMR1NVpW8Z4onhiVmxqrIbqVc4199OS2z0Fnw/CJXZhoPNZiCTHos9APGpf12+2n4lwcTKKR99DRelScvLYMcZ8CQwCBotIvbPuTeDbIuL37FeAjd6pF5Ft8ttlJRPqsaMoiqIoitIFtPTYqdwOhu7iRNDU2glu3PW08TlCT9hOZr1CT1cIJd6UrViDXddeylZLMkVjdEfalojjxVNl+wPJktsaxdMzEbEGy27aX1NDMsIMp+JWcSUUVqifkqJ0gnx67AwD5rqiTiZEpMEY8wUwKcf2FUVRFEVRFKVnMfpbMGC8TWEKlyarYoVLoGRQ0oskGrGTWldsMT4wNdaPxPiswJIi9IQ7P8nNNmUrWGQjetJFTqSLxnB9edKlbYWK7GS9KyboxiT7kojiqbIlt/0Bx4unX/eVdFda4zXoptKOQ3f8NFTZZcPXYBY7HlKD7ecmqIbLipIvchV2IsDQLPcdAFTn2L6iKIqiKIqi9DxKBmYuc+56kRS1EFfcykLNMZA4iB+aa5NVoYwTzRAsdKJ8Cjrn09NWyla0DiLrs6uy5U3bErHCVdq0raKkaNQV0Uj+oI30KKqw19BQbY2k6zfZyI9wmfq49ER8/mQ59NLBjvBZB3Xr7RhcvxA2fGHHackgKB7UfsU3RVHaJFdh50PgQGPMviIyI9NOxpgDgFHAS53om6IoiqIoiqL0LlqKK4kUlYiN5AHAWAHH+G1qV93G5PGBcFLoCRZ2PDIlH1W23HZaVtvylsR2RSp/INlOZ/qd6VpcoaApan14GqttGpw/aO91uEyjeHoqCeGzHCrHW4EustYu676A9V/a97B4kN1nS1drU5Q+QK7Czq3AQcCDxpiTReSNljsYY/YHHsKWQL+7811UFEVRFEVRlG4mGrGRB4Fw9l4vLVNU4k1W4HG9SJrjyagdn9+mazU32dQV2Wzb8AdbRPV0MDImHylb0CJti1QT3ZZpW64/Tz7TtgIhCAyAokp7vsZqiGywYlWo2EYZBQvzcy4l/xgDhf3sMmBb+x7WrLHRPBu/gs1+O/6KKu1jV6b9KUofIifzZABjzB3A2VjhZg1QAhQD/w+YCIwDDPBvETkhr71V2kTNkxVFURRFUbqATUth0yIruvgCUD4Gykd2rk2RpA+J17DYH3QiFgKAkwbVVG8jZSD/Pj0tU7bcfmSTspWuraYG254rhEFq2lao2F5jPmlqTFbUEnGiePrZCB+N/OgdNDdDtAZq19mqWvFGK3S64k5Xjh9F6SW0ZZ6cs7DjNPhL4HdAZZrNtcCNwJUiEs+5caXDqLCjKIqiKIqSZ6IR+PJ/0FCbnFAGwzD+gPxWaWqKJqN5mhqcClsmmSrlC0Jz1Fa+aqpPCjAtfXqChZ0TevJRZculOZ4UjWJ1HvHKm7aVQ3vtns8RBxqqrdhjjBUGCvqph0tvIiHUVUFjnfWo8gWsiOkLOOPHW60tT+NHUXo4eRd2nEbDwB7ADkAZ1lj5C+ANEantYF+VTqDCjqIoiqIoSp7ZvBy+fBkaI1bYMQFo2Ahj9oH+w63g4g/YCacvmJx4dgavj000YgUS8KQ3ORE9TfVJocf17zEG/KH8+PR4U7aikaTYlE3KVjqaop6y6nW2PXA8fByRJx+VwsARB6psqo+IjUAKu148KgT0CkSSptmxepv2Z4wznp0xYkyyWlsu0WWK0gvJZ7lzL80i8irwqudEE4AwNmpHURRFURRFUXo5YieY4WIbcdLseMrUb7KRO8bvCDoe8cSdfPqCVgzyBZKL+7ot8aKlj423nHrC+NhnoxZCRdaPBFKFnpY+PV6hJ9tUlmyrbAWLHcGpnUl1IGSXwv4tjKU9hs5u2pabftPRtJtA2FZcKhrgRPFU2f7WbXCMmMtUBOjpeE2z47Fkup3rTeUP2c9Sc6zF58ITDdZZkVVRegkd8dgpAW4Avg+MEJGIZ9t/gMOAB4ALRUTLnW9BNGJHURRFURQlz0Qj8OWrEI8CkkxPGjHNijoA0uyIOUE7sfQF7et4zPryNKdxJ/D5PaJP0PPaXZchqiRTFE2gwBFDiq144vW7cR/d//t9/lShJxDO/b7kM2XLvS43mscbpZTPtJtYg1NNy43iCVvRKlSqUTy9iWjEieJxor6Cbgqi34qgsUjSkyoQSq3WpibMSi8mb6lYxpgy4E1gZ2fVZBH52LP9aeAIrLHyh8CeIhJt1ZDSJaiwoyiKoiiK0gUkzJPjVrxwzZObm61o4l1STI4LnMXxBmmOO0JPk0f0idn1Lf8n9/lap3clnjtCUKZy6q4Y4ka9uJPZpsZUoccVT9z0Kq9XTy4T4HynbIEnbasuVZTypm11NOKmuTlZMr0p6kSGOJFJHRG5lO6hOW7fw4bqZJqWG41lTLJam9ezyq1S5wqgitKLyKewcw3wa+BT4AwR+TDNPtsDjwC7AJeIyJ871GslZ1TYURRFURRF6SKyLXfeFHU8bxqTviAugZCnolVBapqRm+aVEH3i9rUrALX8n92Y1uldYPdvanQijGhbYInHUoUet6+ub4m3r9n69LRVZSvblK2MbTrteqtthTzRPB1J24rVW2EgWuuJ4nEqaml0R++h5fvoD1qhLlwGGPuZdNP+3HGeiDBzo8G0gprSs8mnsLMAGAlsJyLL2thvNLAAWCgiO2faT8kvKuwoiqIoiqL0MJrjjnDiiepplRIVbr90uRvtk4j0aRn105y6v4iz3hGHRKyoEyhyfHP6tU5N8fY1VmfFIbevKaJUYfYROPlO2XL7mSgVX+dJ2wom/XkChbmngrWM/ijoZ4UBjezoPaStjFZshTpXkI3HUsdPYox7zMnVf0npgeRT2GkAPhORb2Sx7yfAtiJSmPUJlE6hwo6iKIqiKEoPR8QKJm6kTFNDi9LlHpEnl4pWIqlCT7zJ8zzmiDX1yWgiEfD7rXARKk2mIaUYPPva9+lxU7eySWHqipQtSJaKd6N6Er5DHayWFK2zIo/bx2CBFXlCJRrF05toarR+So3VVvDx+ZPRWG50l9eLyo3KgxbRYMVqwqz0CPIp7KwBqkVk2yz2/QwYKiL9sz5B8thy4DLgaGAIsA54EbhSRJZsqfaMMacB52M9hRqBOcANIvLfLM87FJgPvCwi329jv0Lgt8CJwChgMzAD+KOIzM3mXKDCjqIoiqIoSq8k3pQUeWL1qZEy/kBqSlRHPWBEkuldiQmvE9kQb7Tb/EHnXJ7ooYTQ4/r6NNt947GkPxCkegpl49PTFSlbKe3Wp/oOJaol5TBRb447JdOrbf98vmTJdI3i6T20LJsOdhwUlLUW6zKaeAdT07ZU4FO6gXwKO68C+wB7icg7bez3DWAW8LaI7JVjZ8uBt4GJQA2wEBgLlGMFj31E5JOubs/xE/oN1gh6HlAAjHc2/0FErmrnvGHgeWA/YHomYccYEwJeBvbGikefAsOBwUADcKSIvJzNtaqwoyiKoiiK0gdwowgSKVz1yVQrtwJWIlKmoPMVndzUq8YaaKy1kT4Gp1x7yIog0tw63cuY5PrmJpC4UyHM75kIFyXTt9rqpxt1E43kL2UL8pe2Fa2zIk/UKQicSRhQejbxmCNoVjlm6K5YV5peNE2My7pUE+ZEFboiNdxWthj5FHZOBh4E1gA/EJGX0uyzL3A/Vpw4W0TuzbGzTwDHAs8BJ4hIjTGmALgNOB0bAbOziKSpG5mf9owxhwPPABuBQ0TkA2f994DHgBBW3Ho7wzlLgEeB7zqr2hJ2rgcuBD7AijirjTF+4ArgYmA9MC6b0vEq7CiKoiiKovRRvEbHTQ3JSBRw/G880TIdMRF2aauceqDQnitRyj2exvA57pg3NzpVwpxKYj5Ppa5wKYRLMgsqXZWyBY6pdV2atK2CZPttRQrFmxwvHq8w0M+KPJ2578qWJxqxIo87xgJhR6wrTT8uU6LMIqkmzK5IqCbMSheSN2HHaewp4EhsJMsqbDRLLVAM7AAMw2r8z2KFiqxPYIyZAHwGRIBRIrLRs80PzMVG3hwnIk90VXvGmHeAbwHniMidLdr8HXA18KyIHJ7mnFOw4tdEz+q0wo4xpgJYChQCO4rI5y22vwh8B7hIRK5v73pV2FEURVEURdlK8JZa95Z0BictyuPTk2v5cpdcy6mLpDF4jtoooISBcqON6nHbCRRYkSdU4lS2CiTLvPuD1uenK1K23P66bcfq0qRtFSX7lI5oxIniqbOvQ0WOZ1GxRvH0JtIZZ4dKrMgTbMMuNt6UHDuxumREm+vtFCrq+GdPUdLQlrDTEan7OGwkyQXAUGfxUgf8Hbg0F1HH4RSsKPSMV4QBEJG4MeZe4FrgeKBdYacj7RljxmNFnRjwcJo278YKO98xxvQXkc3uBmPMxcCVgA94H/gI+FEb/TsGK4i921LUcbgLK+wcD7Qr7CiKoiiKoihbCT6fnTiGipLrmhpTK3C5aUPG2JQqbwWubCJeElEyhUClp5pQJBm14u4TKna8a4KtI1dKPc+bYlYkctO+YhFo2Ax1G6zg4ws7fQzbPvt8ybLuwSIINFuxKB61/agPOClbJbmnbBmTeg8TE3XHn6ex1q73+qt4o4xCjrDkjeKJrvaY9Jap6W5vwOeHwnK7xBrse+l6UHnLpreMxPEHwF9mt0MypTFWB/Wb7GJM0tspVKxRXUqXkfM3jYjEgMuNMVdjfWHGAZVYQWch8FY2aUMZ2N15zOTf857zmK1vT0fac4/5REQiLQ8QkbXGmK+xPj3fBF7wbJ6G9cW5GvgLcEme+vcNY0xxuv4oiqIoiqIoWwC3Yo5bErkn4goiBf3s6+a4E83TaH16GqpamDJ707dC7UcW+IPg72fbF/F410Sgtg5Y54mkyRCtEAhCoD8U9k+ua4ra/sWc6KCmeisixaPJCB7jt4JKys/GPiclJgo1q+xrf9BJ9SqFgv65eRC1nKi7aVuuv0795tS0rVCxI0AFoKjCCgOu6FW30S6hYie9p4eOGSWVYIFdigbYsumNNRDZYN/LYFHb76V7LBU2escbzRONQGR9MtrNTfvrrD+WojjkJOwYY+4DFgPXi0gt8Iqz5AvXnHhRhu1uBavBxpgSpw/5bq+9Y9zjxnr2dXkA+LGIrAIw7YfdtXeuFUAT9n0agzVWVhRFURRFUbYkm5ZC7WqbFiTNUDIEykd2d6/ax+e3aU7hEvtaJCnyNDVa0ceNSkn46BQkK3C15RXiloMOFQMDU01mGzYnoxVc8+NQceb2AiG7uIJUvCkp9DTVJ1OkxNnXuJW6nPZcEShaayfQNath83K7zR+CcJH1wXEjJnz+ZKqXL9BGvxyhrLC8ddqWK9y0TNty73c8ZlN7GqugOmIn9G5FLY3i6fn4fHY8FvSz46+x2i7VEedzVda2r5LPl/rZa4omx4432s1N22rP20lR2iHXb5UjsSlKbVaE6gQDnccNGbZ706kGYL198t1ee8d4jxvgXSkiT7bTn5z6JyLNxpgqbETUgHT7KIqiKIqiKF1INGJFHV8YgmEbOVK7Goore18UhjGeqAKHFBGlwUkhcbb5g6npW22V+HbFmcLyZLRCNJKa0uRGO7mRLpnwB8DvRN1AGj8hR2Dx9rGgH5QMtn2INzkpXtXJSlaRNVAjdrvfuQfGl7wvibLugRbPg7Y/2aZtBULJ1JuiCru4pbbrNtr7Gyyy/fWm0Sk9l0AIAgOgqDIZkeWmWmVbHS3x+ejvEQmdsVO3EUgjEqoAqORArqMlBCzKtiJVB3DdqeozbPeub8PJqlPttXeMd1s2fWiLLXkuRVEURVEUJVeaGu1ErG69fe0PQXPUTtR7m7CTjrZElKaGpCgBjilzQeqSLpWkZbSC6z0SjXgiXXIoZd7ST8gbeRRL18dCK9wUD4R+w22UlbfKllua3Rgr4AQKHOPnmFPJK01Zd7eyVyLSx4n6KSyHkkFJz59Yfeu0rVARFA8ATGrkhxvFU9BPKyn1BozxRGQ1Jd/LmjXgW2eraRWUtV/+PEUkrPRUgcskEjpCoZowK22Qq7Dz/4BjjDG7icjMLuhPHGs8nAnvtmyMmTvSXnO6HTMcl6s5dEs6fS5jzDnAOQAjR/aCkGBFURRFUZTeRCBsJ1Sl21ghwfVbiayzIkC24kRvIa0pczQ1fcutAgVOJEJhMn0rXWpKwrekIrWUeWONFWS8AkiwuO3IIEiNPHJ/+vT69DTVpxpHu5444TIoHuRE/niqbMVrU6ts+UNOVS+3lLvneawOGtP8xu3zJUWfcJndP95k71U0YqODfD57jsJKKza50Rr1m+x5w2UaxdNbcH2Viirse+yKPA1V7ZdNb4nPb/f3ejulEwldI/NsPiPKVkeuws7vgZHADGPMk8C72JLnDZkOEJHncmg/AvQHMiUYeuXPtqJcOtOem97VVpKje1w2fWiLTp9LRO4A7gBb7ryT/VEURVEURVG8hIqtp87a+VYQCBTANpNtKlZLccI1RA2V9K00CjeNxKU5nlp9y53Qgp2kpqRvhVMjDbyTWG859WitNallQ+Zy6tn0MZNPT53jpOCtEFY0wKbWxZ0KYm56jc+XrLKVbnLulnVvVdo9ZiflzU1Jk2pjnPtVb9fXrgWcSl8hpxQ9OCk+tW1XYVJ6Jq4Q2hx3qmlVQ+06MOuzK5veEtfbCZwIOkdMdY282eCJeHNNmHWsbO3k+hfnC+fRACc6S1tIjufYgBViKjJsr/Q8X9dF7bl+N5mO8R6XTR/aos1zGWP8QL88nUtRFEVRFEXpCFUrYM08G2XhD9lJd+WYpDjhmupGa+3EK7I+NQKkr5miupNKNxVNxKYixeqTKVwppswekSdYmJyEeqMQip1y6olonkzl1LOcWqRNMfMIPQ1VIJudfYPJczQ322ibtgQ7N4XLPS4dzXFH9PFG/TiRP9FIMi3NTfXzilf+YNKHp7DcjjNfQFNxejo+v/XQKeyfvmx6uDR382yfL/WzFo8lBR53jELSvypYmL4andLnyVXYWUbn04/a4nNs+fTRGbaPch5XiUhdhn06297nzmOmY7zHfZlFH9ric+CANs41DPseNQNfd/JciqIoiqIoSq7UroPFb0HVMvCH7bLgeSgdDP1H2kma+4t98QBPdagMESB9JWXLS0K88QTDuxEzbvpWw+YWpdY96Vvucf5gcmLc0XLqmWg5QXajhVwxqrEm2T+f375PYMW8eDR3wc7ndwSsDPu4AlJTNFlWu7HGEQkbbPSSLHaExFKnQpPj3+J6/fj8jt+Pa/Tcx8ZVb8ZNFSwemIzicf2lQsVOFE+W0Whe/EHwO9W6vGPYFQrBESOLkhFvmSp3KX2KnIQdERndRf1wmQV8F/gmcFua7d90Ht/vwvZmOY+7GGMKRCQlzcwYMwhberwZ+CDLfrTVP28/MvXvk5b9UBRFURRFUbYAtWts9Emo1BEAqmyFpU+fggHj7C/whU5kRag06ZVSWJ7ZTyYRAVLcdyddLSNm3Emom8IVi9h7Aqk+OF5T5vbKqXurCLVVTj0d3mghF1eEcifLzfHkvr4g4Ig8scbOC3Y+n620FghDQWlyvVvRy73Ous1OyfRaKzIWOpN6fzApRKW0GUgKPX7v86Cm63QHxiRTD5uijshTlVo2PVzaMc8c7xh2/avcSm1uVBgkI8BCRVZQVQGwT9LTkn+fBC4DjjbGXCAiiXLkTlrS6c7Lf3VVeyKy2BjzETAZOAW4q0WbZzuPz4nIpiz7kYlngCZgT2PM9iKyoMX2s1r2T1EURVEURdmC+J0JV/8R9jEeBXxQMdZOlho22fLn8SZHzAjbCVTYETW8Ik48Zo+v32wjMlyfFVcQ6mspW168k1BXS4nHUtO36jYm9w+EkiJPsDBNOfVIMpon13LqmWgVdRRLFXrirmmyOKlWzbYPbkn0fHgs+QPJiIyybZIVxeo3Qd0Ge63RiBWDiiqhoNweE4/ZPjU7YyxW11r4ccu6py3t7gg/msLTdQRCEKh0DJdblk0vSIo8HX0PfP7k9w4kxdBYfWpqo/uZ6ujnROmRGGn5gc/2QGMGAPsD2wP9RORXxpgC4Fsi8lqHO2TMs8BhwGvAcSKywWn3NqwQswDYQUSaPccMAAYAMRH5Kg/tHQf8G6gGviciM5z1RzrrQ8DeIvJWO9dyOVZYmi4i38+wzz+A84C5wBEissQRnS4HLsH68IwVkeq2zgXWPHnWrFnt7aYoiqIoiqJkSzQCcx6DyFpnAt1kKyvterydGIk4k7QaO/FuqILGiE1DQsAEIBC0v5SHCsHnCBQCNNU5ni+NTlpNwIo84VL76A8kJ99uue2+PPH2llp3BZVEepSvRfqWJw3LW069qdHZP4dy6ln3L57sW6zeCijSbM8vzSBNzvsdTlb4yqdg5wpaNWvteIzW2WpbBaVQPBiK+qca6TbH0xs8Jyp+pSnQmxhzGaJ++vL46w68ZdPjTU5J9bLsyqbngtcLLFZnRR9IVmpzhR6N6urRGGM+FJGpabflKuwYY4LAX4AfYwUOAETEb4zZEfgE+AgriKzoQGeHA29hfWzqgPnAWKAcqAK+LSKftTjmcqyAsqRlulhH2nOOuxs403n5GRAEtnVeXywiV2dxLW6/2hJ2+mFFp8lADCvwDAMGO6+/4wpL7aHCjqIoiqIoShewaWlqVaxBE6F8ZPp9Ray40FQPDU7ahSveSMxO/P1B8Dvlvf1BwCSjLKK1dvItkjQbDhY6qUB4oi5cocef9HNxRSB3XV+YhDc1eipw1dvJL6RWt3LNmf2B1PS3aCRpTJxLOfVsaenTE6t3yq67E2eTfA+LKvLvsdRYAzWrraAYq7fvfbjUSQssSY6dTONAJFXo8Ro8uxFALXHLuqeN+gmoMNAZXEPkaK3z+Q8lo3jyfV/jTUmRJ1aXFPlcD6n2xo7SLbQl7OQUI2iM8QFPAYdgf2f4BBhOsqpTKdZ75hvA28aYKSKyIU1TGRGR5caYKcAfgO8Bk4DNwCPAZSLyRRuH57O9s7GC0I+AnbCVwN4F/iYij+bSh3b6V2WM2Qv4DXC8c64I8F/gKhFRpUZRFEVRFKU7KR9pqzY1NSbTfTJhTNI4tbDcrmuKOiWLnZSIaATijVBbYyMuAmErPBT0g5LB9ph4EzQ32mPjTXbiFSiwYoY/aCfgLUtrt8T1XDF+jxjk97z2rOupuOlRbhnz5rgjoLjiWVULU2Ynmqew3EZWxRuTfiOtyqkXd27y2p5PjxvJVe+k6/lCdlwU9Lf9C5d2PGULkmk3zWOTJbYbq6F6uT1XqMSZqBcmRSWvqGWM8zqD0CXiSe9qEfXTXrqXN73La/Dcmevt6yTKpg9MRvFE1lvhzvXuChXl51z+APidyCBIRr3FPB5WXj+wlmNH6XHkFLFjjDkbuAMb9XKciHxmjHkTG/Xid/YZCzwN7ABcJyK/zX+3lXRoxI6iKIqiKEoX4U6aw2VQMrBzbbkVoxImwrVWEIjHALETYFfs8QVsmk9zHGh2Jsi+ZGWoYLGdgDU3JVNvJJ583nJduv/9jbECU1aRQD3MeNUbIeWKKl7T44RPT4Htf5M7ga1PRvMkKgjlUE49W1yfnli99VZqrLbveXOzFekKSqGgwgqH+Zi0xxyxq6HGXic4gkrIEV38SUHLm7bVURJl3dNE/TQ3tU73cvvQVtSPRokkaWq0puuN1Xa8+gNOFE+OZdNzobk5+RmJRZJRcv5AUuTpi9X9egF5S8UyxryNrdS0o4h87qxLEXacddti05e+EJEdOtN5JXtU2FEURVEURekCFr8Lyz+wk1VfEIZPg9Hfyl/7Lb1bYnW28lK8ERskb1IjdaTZTr58fitahEucX/tL2q+y1dzsEXpc4Seefl06EhPzNJFALdd11wQ9HktN33L9RMCpEFRo76eIvV7v5LWj5dSzxY04aqiGho1W7Ik5vkDBMBRW2KWoonMREs3xpGFu3Bkzfuf98UZ5BcKeikldcb3NrdO7XNEnY7qXP0PUj+v3sxUKCiJORa0aO37AMWkvs0JdV37W4jEnvbE+NUorWOARevqw8XsPIm+pWNg0oc9dUef/s/ffUbJt6V0g+NvHhkl3zTNV9V69crIlA7JIIGjQYobGm1ZjhsY1bs0AA0wPzQADNKJpuqcHhmYYXNPTUgOiWbAAYQYzoAFkEBLIoJJKUvlX9dx16cIdt+eP7/v2/s6JE5EReTPvTbN/a+XNzLAn9jknbn6/+JlVsNb+pDHmJ0BZNgEBAQEBAQEBAQHXE6cPgB//h8DxW2y5MWSrufdBYPfFi3kOUVGIxUuCTnV+S1UQ0dPUTK5ENKwvjoHpAxrYE7bfDO6QEqRvUI8isulsglWqH7msZotZXwivPFeL/EmUMkhddtFWsDilL2kH0qHM1ZwyTGSbo8ireoSEkKaip6lTX4UoJiIu3wH23+0DkaeP2bL1NnD8Jofo7pBta3Sfft6G6IlisnsN73B2C1sAm5KG8JiPjWrmX+9lWG/c8bbO7rUi4LmaA82k3+7VtXcJ6XNTa911bXpdehVP8Ratsah4LsMuFafA8IC+JFeqYNvW9DGAx+1zJR0Fy91zwLYrnoAydDZBAWAF1R8QEBAQEBAQEBBwDfDkkxRQWxdwLVdvfwr46D8AXv1qaq9KhxfbIGSMz9sA2nYjFyJcE9EjIcJNzfXGD4Cjz/n67cE+MLxHQ9m2A++m+Tsui2WNEqiar7aC6edqqX4UCaQv2xZR1F5PwOceiX2rLv11ccotVw0HYF9QnfqqbZOsnDuvEdEjGSfTR8Dx54DD16lZLd0hJc9wn8Kik3yzY0xeu25gKg/5ufeB0QtE+Ij1pmALl7PeXJBtqw9C0qxTmvWFOjclb/O2te5yPF1ju1ecknVvdJczcViZNTv0tenZzuUom1q5UvdYgTb1RI+cK3GqWulCCPOzwLbEzscBfIEx5gVr7YNVNzLGvATgw6AsnoCAgICAgICAgIDrCctfyYharWzpVRZHn/MKFLEjSC4O4BuE9EDZsphsOHi1Apn5MiEmdFuUDLzyqXq1oG188rr/xH94h0iefP/iPlU3hsNYN3i8pumQP/y9ddmCiZUVeUCtAGht/eqqg9YMk0nWVjc4O5xS9kgGT1MBsMBiTkN0ktHjX3SdOkCPM2Ir1r0P0n6eP2FFzxFw+GngSUPETjbiBqyxVx2t24448Y9dTOi1iFpHSMDxfbanTX1L0/yY1yz3+TyXYdtat91xAmCF3adplgOeRfmj1VkaXXtXt+b9Oti9jPFEoyPtToDTdwDzgAnDvcu1SUWxJyYBJkr52BGySZNBF9lKF9DCtu/mfxtU3/0XjTG/0lpbdm9gjMkA/BUAMahBKyAgICAgICAgIOB64u77gb130cDUJPSJ9M6LwN0PAaN7ROzUC98og4jVFaJ0iLm9ap2lZMVwuY4oEWJC2qJ0IHM1AypmgKQ9q5zTts8OQZk9/In68A49hgzrlz3QRhGA6OwsIGB9/o8oONZZwVZWw68Iie7a4epCWeHmnjyTeujpYyb1hnzfC65TB+ixdl6iL6lyXxzTc5dzYP4ptpMNvWVM9mU6XK2y6RIC8yNSpkUxkQGDffrSle4FW8ZkbeUYz8ab7c/LQhQBUb5aRdWtdXeBz+Vqu9dSrXvcIWmvmN1Lk3aS4SSkXJJ5kueyt1ta7IZ32FI69cdOMQXwyJ9rl6kEu4XYNjx5B8APgLJzfhxE3PynAN4P4FeAmrB+PYDPA/A6gC+31h5e6BYHrEQITw4ICAgICAgIuARIeHJd0RDy0oeBFz+fQ29ZPZEMiGRYHHHA6UK1Lg09eZKNyc7lmqxUkGyvpaQ7UG7YINQNZK4LHtJLoJ5xOPOc1EiAD0LNd73VKBlsbvd5nhAr2JlKoKeshgeTPa7SXFWbNwWAmAKQ0zEN2JdpQ5EcppLzcxYT2g5YIhvFqiVh0UL0rCJgZAifH3krVjai41bILsCHPwuReVMak+rOudjN+1lb6961eqVXI2OmaYCCyZ1q4RU+F1mbvg1qDioXokcHeD8PJdg1xIW1YvGDvQbg7wD4KfD/FbRuAuAnAfxSa22wYj1DBGInICAgICAgIOCScPqACJt819edi92gmPDQtEM2pzijQWpxQtdLm01dcgV3xm1WuzQ4xxkNN2LhWjVcrmoQ2sRS0hfI3NSd5qiGbWcpheumQ9rWOOOfmex5nuqMp4WE9V5ENbyJlPqD7UvVnMifpubGqSEFIA/v0P6+rIG/KjgfZwIUTOQ1JW2jibjuPFLqnsHqnB4dztvUdIwN9vsrtlc1Jt20YV2ODVH6aNXP2lr3TqjzRWRwnQdSm16c0LY+i9r0dZDcsHJKx0/FzXCiBMskhPkav9dcAi6U2OEHjAD8cgC/GKTS2QMwARE6/xDAt1lri9WPEHAZCMROQEBAQEBAQMBzQFUwgXNMA4sodPIdfxtrfa7J/IiyP2QwjBImTYbeyhBnnlSJcxq+1llKnEKlz1LSN1zGnE1SeHKn4OG8XtB1xtBtxdIT576RSzJdhOy5iXaKpWr4queyTjV8XbI1b87KrYnPPYqH1FY2vkeBxcMDlQ90gUO+C7SdeGVEU/FzGFIhyefzxng1j3yXbXHH7JGq2B5TVpNW8Qi6tq3bNKzrWvem6lEA9ZGyPRlcl13rbi2998yPO7Xpu0RMPy8CTo5ZOXZkveLUHzvJ8PopwS4YF07sBFxNBGInICAgICAgIOA5oqmJ3Jkd0s9xQmqNfG95IGkazsSZ0aC1mLBdqmZlhViwOK9F2q/EXhMz+bOkoHgKS4kONhaiR4gKURpIYHAUtYfVOFFkzzWxcF0kVql+6oKUXbNDYPaYvgvhEcdAtseV5vvUsObItwuqhteWreLUW6ekit5EACwrtuCPs25Oj1PxHLUVH4P91du0clhPyK7mMlZuwbDulGKr1Hgrzs3zZnBtgrr0qsKmxqXXpm8DUaCJlVRsrXJMXmQz3TXCUxM7xpgEwOcD2AXwurX2jYvdxICLQCB2AgICAgICAgKuAORTcRnijeEw2oPVw5gbguesmpFP+WvO5JFB3LZtH1GkVD2pV/usG7bXqn7UYzcVkTp1ScRTOaXbwQDZEMgPSL3hGnHmfnh3RJRS9TzvYfEqwFqqUJ8+pDrz6SMOgLY0rOcjUk6kYz/Ud/E01fDaslXO+fHYnmVYOVQv6LiVOVHn9CQDul4UH9vktlSFz+aRYR3w+U7p6PYRghrdc7FL0l5kBlcXoiYUO12SszJr9/kTb5qcLKeehIwiTxBm45upGuzg3MSOMWYM4I8C+M8B7KurfhjAH7LW/sML3M6Ap0QgdgICAgICAgICLgliLZHskE1RzoH5ITVSAWTPGhycXUFcV34ALqeeMIliJm54gEdD2yXhyAK5XVfhc9aQts5SUi24lemESCuXGxQB+RgY3gdGB2zpiFmFUIEiOOE/cReyZ11r021CMQEmj/g4OabB1US8RmM6ZiSDSfZ5Sx10zmp4KBJHBnoJ+06GdFtpjtJETBSzZSum406se3HKRN8G7Uti2ypkWL9Ftq3zQmrdnyaDy/2+otZdVIfzY58J9ixq07eBe2+c+oY6gIhjpwS7pNDy54xzETvGmBTAvwLwNXDvxi1YAL/bWvtnL2pDA54OgdgJCAgICAgICLgEPPkMcPoWDdu2AXZeBu68d7vHqCsa3OdH/hPx4cHmuRZ12SF6eJhxbURDIm6aipubCh66ewgfUfXor00/lRdLiSgLZofA5CF9zZ74YTAbkaJnuEekUiRET83qlNhnCwlxkQz4trfAmrMK0hw0fQLMnxAxKFa4ZOjzUJIBByBzvtFZ1fDushXV8IA/duqCbhdF3JS2Q4ozk1Amk7SBaXWWkIEWKhh8j47LTXBWxsptsm2dF91zs0+dty6DS+xd8nNT07G4OOGMqC2Iu2cJIZyFINQEpVOCKbXgeUn6K4DzEju/E8CfAVAA+FMAvh3AEYAPAfhdAL6Rr/tAsGZdDQRiJyAgICDgeWBaVFiUDfI0wii7AhWvAQEXiWICvP0RUhaIYqI4ocrzwZ765HvDANym4U/ED319+vAAyPe3G1pluF5qI8q49UgNwnXpa7olM6dr7YgTr+pJFOGzzafeTUPkzuRt4PQxUBxTvXoUcQ34LpANAJMCsEwSMJEA1SIk1opsh7+Pb+Sn72dCW1DmR6T6qhZE0kQ600hl4pxledu0Gr6cLatpxLKV79CXBd+/pH3ZWFJolWzJS/m24xc5KHoLMmBlxkquatWviILkOqEv1Lkp12dwmZjeM4opEdtxQtlQ47tE8lylc7NpFAE+8ZlSQoBPHgOLQzp/zkvSP0ecl9j5lwB+BoBfZq399p7r/yqAXw3g/2St/X9c3OYGnBfXkdgJw0BAQEDA9cbrj6d452SO2BjU1uLF3QFevXtGzkJAwHXC9DHw6GPtYOHZE2D/FbJUaTiri2RcqN9d/okagooJKV4kq0TCaLfNotHVwVJlrmunneKh03gkhI98VYvl4S5OeyxdG1Y1VwtSnswes7VjDsDQY4htImE1Dwxnv0yBinM0RF0in75roicdtNu+rtJweVlw1eJTyjyq5vCV5ryuYq9pBVkPzqd0EQVIVZBqozgmcqkuecBPaT9GmVd41IW3bi2OvbosHQCje8DOS8DorlcauUygNdvnCC4e1lsZK3Jsj59PbfdNQ6vWvWv1KoFyQflhxSmrumJgeIfet9LhsurneZ+b7pyZUZ7Vw5/w77ODA1LFvfTha6PcOS+x8w6AmbX2tRXXfyWA7wPw16y1/9lFbWzA+XHdiJ0wDAQEBARcb0wWJX7o9UPkSYTaAvfGGQ5nJb7oXXuBrA+4ORDFTjygoTXOiKB44QuJlHDDT+V/XtVyA8BVHOu2o6Zme8Ccfs/GNHScFUa7Crp2umtP0ERPMlgeuqxVRI8iferS30baerp2rnWElFiM5sdE9JQLUnkgpqE/G/tKY8mTqeZEJAiZUU79Wkdxuw5e7BYtUk2Hyl4h68hFQFrVpNJcbDdofNuVUWSJWN3SAa3xeUmQVsvWxB8XYqsTwq2pgJqP6/khcPo2DdZ1xY1au8DwrlIaZT2B0H0h0TGRSuXUK4pCNfazgxDCYsOcH1IYeFMptd0IrSQX/Z7XW+3+jM7NySPgwUf9e0Y2ou2/+wEiG68B1hE7687oPQCfXHP9j/L3F867YQG3F9OiwjsnczSNxayqkUQGP/nOCfIkwv4oRRZHMLfhk5eAgICAa4aqbjAta8yKGm8ezvBkWmJ3kCBP6A+z2BgUVYNRKL8JuCnIxjSgfu67PTny8peTDQsAsKZy1wURq4wL/Xs1W848mR0Bx294lcrwLrdpdVpv1g2tLl9iCOAukwCK6Jk+9reT+mCxtgj5k+TtlyaEj7ZzCfGin1erelyWD1c1x/v0Sfn+K34wXxzz8H9Eg6LUpgtRlAyB4T4P6rGyoHF9dzmj7Sqn3jYiwdFxilZw88rBMr1+ao8o8uoloJ0zIm1XAFsEI9p/i2NaZ7k8ZRJNyLRN/vaW/KRsBIzvt1u25EvairIxDcwR6wTqGpg9Ak7epOO8OKFjKJVsJVF78Neqkh+pahcioWl89s/ixNfESwBzsG1dHIxhAjej/bv/HiLrFscc/F0CsESEZ0PaF6L6kfO0t91LzseegOeLOjfTAecEHdBzWksk4Q2pTV+3ShmAxaorrbUzHrw3TMQKCPBYlA1iY5ClROAUdYN5UePNoxlOF+SFTOMIWRK571kcIY1NIHwCAgICniGstVhUDaZFjWlRoahoCE3jCPd2MkyKCi/t5oiiCNZa1NYiS8KnpAE3CMUEmDygoSVilcrjTwB3X2sTLn1/n0QR2VSwhulskT9K/TN/QgTM4WcAfIZqh/Nd/+l2H1HRsn6lnvyJIj+M457PUClnRC5NHwN47BuJhBTSA48mfLrbL81IdektYYu6fd8lO1cO7IyBnRc8KSGqpXIO1KdAYYDomG5rDJM+QxrQxi/QUNnUdP9q7r+axitYnOIj8iG/chsNXR29SvVzlf8Gdfvmrg8iLqacM1L62ziyy7aJObd/h9s1liVMwA3vqOedEOm2OGmH2GZjYOdF+irndE5NH3vroKy3KI2EGIwSeg7bLAdCV3MfCixNYuWctuH0LdrPJgaS1Oc25Xte3bWuGj5gM8QJEXiju22ydrbwtemj+36Nu6HOtSa7Nz03z1Hrno0pU+fJJ2kbohi48/5rY8M6CxdBf13hd7iAq4o8jVBbi90swThPYK1FFhu8dm+MJI5QVg2KukFRNZgw0QMAxhiksUHWIX3SOLwZBwQEBFwU6sZiWlSYFTWmRY3GWhhjMEgj3BvnGGaxIm/Mkq022LACbhSmj4HpA1IgNA0Nl49+Ati5D+y/12dIOBIg8xkTYldaN3SsIn92X6TvxZQyfRbHFEwr7Uhid5Ea8r6qYyF/9FesBiIZ3nUjkQzmsm26kaivejqKgGiwrIiQvBVt6ZJcDr19uqFrdI8tWCq41za+Ut0yibQ48c8tRI+oQwAmmOaK8Fl4QkDyZ2K2/phYEQalbx9b9KynkERLyoIOkfa8EcVcUb1Lv5dzr3ASIsdZ/vj4rEva//NDr6iI03b71lnKBv28XctWwc1pkq0kJM/ovlcSSXaPrLFt/L4GVDB4j51MZ2DpRrC6YKJhSoqw0wfc8MRhumJRE9vXqmp4fdlVJvieN4RAltr0xQmtuXlIpJo0pa0jDR3ZLaHOEtBdXf9z8xIR/vIKeC4YZQle3B20hoGX9oY4EO2++n/DWutInrK2KKoGi6pxyh6ACB8ieAzyOEaaEPmTBMInICAgYCPM2V41LWssSvqjKYkijPMEoyzGMI0RRct/zL56d4R7OxmKqkGWhCD8gJsItvfsvEyhvo0ldUo8oEGyWHjSxCxYbdA5V7S1IE7bP5+lipBBqXqBhu7FCQ3K6ZDr0vnTZmk7kgFINx4JWbGS/In9AJTtApBg5ZKeT5MBLaJnzfkexUAkdjAFUdjo0GapU3b3jUilk+/ycF7RmrttjnhQk7aeCYBHXh0i9rLRjrdcuFBfVvUUU/94QmAkA1JhCYGxKkS2mgPNZMPq6GTzfX1ZSJmcGd31WUfF1K+7WPLyXdqvlhVd1cLXXQPt9T0rlHmdZWv2hL60ZWv/VdqX82O22FW0H8b3ae3EgqftZFrBlQw5c6mHfMS7/I9i15LzqKnYPpaonCjbb5PUr62rkBP7VysTaMOmvJsICVUe3vHqLFn3OKVjbbDff06cpXRs1bpveG6KWu/Rx4HhPWA4ouP39C1gfO9GqHbCX18Bzw2bDgPGGORJ7PIbBE3DhE/dOIXPvGhw2njCJ3KEDyl7cv457hlOAgICAm4T6sZiVtZOmVM39EfQII1xd5xhmC2/767CKEtCpk7AzcXoDg26Dz5KA7ptgN0XgHd/GQ1vzgJU+PuYiJU8EVzjU7NCCaKDiCXrRSt/BEnGCod7NNjOj4DjN+m+wwOyl8Rn5FE48qcn6LmpeJitO0ORaX9yPnkHAGecpAN63nxMhNAmWRhRzJawTjB0XS1burqEjygy6opbmTImLUZ+QNSkjbYXpQPaxsE+XSe5Q6LoKadtAiPOvEplVYW4tpC0lAXl+iyRlVk/z0ANorOOuqqayUO+DduWBvtA+jIrJWZ+vSSjCfAqGrFv9RIrcrsNLVuDfToW58e0TVFEx9n4BU/yVDNWIk36FVyrcoPEsja+3379pTSx1QAsq3nUY3SVQHIsVot+IkEgZN9ZgdA3LdxbwxGL93l/swVv9oT39x593/TYd++ZK441oP/cnLBaa/YIKE/peDIR7cNbQOy8aIz5dU9zG2vtt26/WQG3BU8zDESRwSCKMUjbb4R1Y1HWpOohlQ/ZuU7m/g03iSKn6kk5vyeLo95PowMCAgJuChZV7exVc1blxJHBMIsxyhIM0zgQ3wEBfRi9AJw+BOwcMDmRAsdv0jAgFqws82oZUcgA/pN7GZTFAuSsPzpbojMgdkNFZZjJdkhZUk68vWT6CMh5YF9FsEhOzVkEzFLlcYcMkkF4/gQ4ectvc8bkScZET9INfF5DXLjt6hI+5XJos2xXOQNOj7gli3NY8j3OUMm98mR+CMyk/r1j59HNY3XVtnAtjgH5+1FULdqa5LZ5RTDvuurori1N0Bcce1nV0V1Vja5TlzBryV3KRhTkHSdtUsypaHidWuTKoL957SzLFkD7KWPFVV347UkHvqp6yI9bFZ7okaYweX2i3upTGOnXD7CaSSyJE69UE6IrHdKx1bcPREWi83/ceSMkUB9xqrZllepn6bJr6kiIIiJxBnu0zyRw+XjCxwRft46w2RR952Y2IsVOxeq92SG9H7/4RU//fFcA6+rOGwArqMeNYa21QRX0jHDd6s6fNSqn7rFY1LWzdelzII11bo9xoc0hsDkgIOA6onGqHCJ0Kh4i8jTGKI0xzJbJ8YCAgA6mj4FPfRd92ttUAAyQ7wDv+QoaRKz6BN/a9tDm2qMWTEhwxouEvMrAnLItQNqL0JDly2WG8KfOS9Yfbn+SuvS6oIF4sM9V0pfYBKRJi2JKDUeLExqMax5mpeFKyBAY9Na9nycIVVQ9QvosjknhUU5onQFq5Rncof0V535f1QuVI9Nj59EQ0sDl9Shllnt9+WoSYx1cdXRPiKyoDLp4VtXRfXXqQDsjRx9fsj5CrtSsoBf1kyZX1hGL2rIl7V6Si+QUUY1X8Qz2l4mAumoTPXqfibJICJ916yVKrmLqA541WZSNz9eopAPT3TnedH6v+/c/0LZPnqUEuuozjLW0ryUHCWACjYm/i9z+YgJ87J8Di4kPEU9S4EPfeG0UO+vqztcRO5/C0xM7sNa+/2kfI2AzBGLnfChrr+wp2NJV1naJ8HHNXKGhKyAg4AqjqBrOyqkwL4m8joyhnBzOygn5YwEBW+D0AfDRfwDUDRCBsnUWx8AX/2JqZerChbgK2cPBvBICLGoXqaUWW5FunZIGKROrT/Jj+svcNopEqln10fB1tQ8dNoYDhe+R/aWl/LnEz12dAmNGqpRi6gdWZzXLaDB329+By6pZ99XzPiZEyeKU1ETzI/pZ2rGk7SvfBe3M2u8vcGuWVpxIuG73ObSFq5p7EgNgRZAme56iStmpQFaofpqq3+7VCo29oOrovjp1l5EzatvhALjmNb1WLTJt4AmSVQHjLcvWxBMrgLLjcQj4YM8rfJYep/HqItkeHRCtiZ5VahFr2wHjQhbJGmySO3UedFU/Db/uJXXQijwgXQ2/LhD6KljBdG16XdG+FBXP01aS1yU1DL7zowAiOuZ2XyaS9+4HfPD6Fce5iJ2A64dA7FwcrLWk6FH5PUL+CFxDl1i5OL8nNHQFBAQ8S1jbVuXI+5Rkl42yGHkSlIcBAefG9DHwI38HOPwU/V4VpIb5vG8kwsRlsWS+bWmb860qvPVDlAFCNrimrcR/d/kenUFOhj0JGl6ckIqmKoHIEJmR7QMxD3KyrUJApNIUdcE5L6L86A7ELoiXiSwTd3JMdAh0z9C6ad17UzPRc0SWLGnIEvIoHdH3hhuZHFHGBEmStQf/PlVOU7eDmTVxoC1cstYXOfy76ujS7/91Kq+LqI7u1qnL/hEyrE/JYq3PxpEgZFGk9NncukRD17Il5FZd0Pc4J4VWvn+2nUdvi6h65DUIAair3/vWw9m2+Evun2QcMM4h48/q/16dn9VV/XQvW5kHtCb/51lXw+vadGvbNstNn79plJKQm+GOXgd2380KHUME8EsfvtmKnYDrh0DsXD50Q5du6arUHxyRMa3cHrF1hU/IAwICLgpl3TgiZ1bWsFxHPkxjjHKyWYX3nICAC4IodpIRlV2ZlMI3P/SNnBXRUSOIYsIRJ4PVw2EfWqG+c0/0yGM7giGn4dwYRfYo0kfUQvNjCimdH9NglwyANCeFT1f1IYqVOKNBOR3Qd60iilPV+HOOvA9RcsiQXpd0uVPKsPJBW6K0csUpVroh0Ksav3S+T0yV8ZKxIWoqd7uU9rHLRGELnay/tCblO5QhJPatvjWQ53CqnsKvcxQvW7gua1huGrSyn3St+6p1W1kdnfYP9uWc1TwTpWThXKlsvHqNpJa+zzKllTR94dVdy5YQLBZ0/IzvUg5PNt7s3Ovm9GgrmSb1VpE1TtE0W7ZtObvlU6pOLgqrquFbhBArg1blAT2ravim5jatY9pHxpAyK99t52MJRFklrWfW+haufA84foOasExEr2/nZeDOe59uG58hArFzS3AdiZ1pUWFRNsjT612Rqxu6tK1LWmYACihNlbInNHQFBARsCmst5mWDaVFhqlQ5aRxhxMHHgzSocgICLgXTx8CnvhM4/KwfBpIMeOWrgZ0XvJJG8nVs49udtNLEVWqLsifffJhvkQQ9A7BWOqwaHss5W5NOmOAZAoMdIiuqBT1mvWB7mPw+5yFPvY6WKoYDi+OUCaCsTfq0BkD9PWpbarTFRYiGVrX6GouM4Ky69z4SQ4iFekHXm4TWQwbxmMkEy4qcxamvTW84sFlun+8Dg93V7WBOJaLJnnL1ftxW+XVetKqjS6UAqvzv62rdpcY9UmofaSaTZjDdeJWNV+/LLqlZzhQZFrXtW1o5pS1bc7byFKdk+cvHNLyP7m2nlFqV09PNDOrL6dEqNVEXAe1jWoLXrzq06qcVCt1VAj2Davhy7kkea2l/iorHCgF04hV5Gef0dLPGigmdg0l+bZQ6gkDs3BJcN2Ln9cdTvHMyR2wMamvx4u4Ar97tYV6vMerGutwel99TNWjUeacbulw1e2joCgi49ajqBtOSVTlFjYZVOYM0wihNMMxiZElQ5QQEXDqKCfD2R2jor2YADCkF7n2QhlkJc9WQgFshgnTOjoZkjWhlzybD3iaqnlW2lrpigueI7pPkFEC7KqhUBv0WISGB0AV/+i85NZYJHQ6Ilk/vxeKj0Uf6yHrVKhQZnDEkLUlCEJx3KHZWpc5XtfDV28VM5R6x4iIb034S8q4pWSky4+1lhUcU+fakfIcIn3ynf3vFwuUIn+5+VDk9yeBi2oLOA2luW5X1s67WXexSVQHAsv2NiY1N7Eo6vLqceZJEV9KLckoIViFUJg+B+WOgmNNtx/eA3ZeI5NkWLbvdrJMZdEZOz1WzbV0GhFjdSAm0aTV80nMZv08sTkiJOHlE+zpmImd8n869dUqt0wdEDuV7RM5fIwRi55bgOhE706LCj7157LJskshgVtb48Lv3cTDKbryKparbZI/YuvoCm1stXaGhKyDgxsJai0VFFqtpUaGo6A+/JIowzGKM8xiDJA6kb0DA88CTz6yX7+uck1oPwGU7WBfwFikLAI1SqEiWjm5aykkJs4nSoC7ZBrKhqsdaGm5mhz5ceMB16ZuSJhJWXBdoVbdrhYwLem4U4SOkD5M2iPxadGeTuuTXM1dET8xEz9h/dS1iEjh9HgKoriibQ9Qf5dyrAGKlUBI0tVc4iRWnnNNrMgk172Q7lMk02CMSTexuXdVWl0TTBIJWrQjZcxVUHy1lVJ/qh4kMZ73iQO0oZlUNW2sG+57AWvX37ro8o758nLokIvPkLWD6kMnMDBi/SCTPNpktGhKi7fb3rGc7lG1Sw1nHOrYtUael42Xr2U1Ctxp+ZSB0Xx6QhFjPPbEMy+cGE2SDA2B4wGqqzr791PcADz/qScf7Xwi87+uezeu+AARi55bgOhE7TyYFPv1ogsgQoVPUDY5nJV65M8TuIEUScQtVotqobkELlbZxlXWDRU9gcxIZZ+PSxE9AQMD1Q91YTIvKZeWIfXOQxq7FKk+uwB/tAQEB55fvu0rrPtKn9J901wX/XjHpY73FRZ5Tsmc2UW9so+qRHItyxk00uzQcPc1w2bX41OXy69aQ6nbX0BPBVcND1AANV6tPgGrqG7dkWNeEh1F/G0kzkJA92hLmrGFdu5j6m9PlufAwLkgyb0FrVZfzV6GyPkrOgrE1k3cjCvvNdv0QmrMiSRM+1nr7l5AZfRYure65an8vu3pvFfBcF9yAdEIWNyE4xeaUj4kMa4Vj69wftT5a8bQUyqxInigFZo+Bk7dJ7QFLzzV+gZQe62xim0BURX3bsSqA29XKTzuZU2zb6mscu02QY6c4pfDz2RH9Dvi1jDh43TXx8Tma5j5bJ06B6SHwk/+E9rfk9Bx/FvjCX3htlDuB2LkluE7Ejih2DoYpjDGw1uLRpMAHX9hBEkVY1PWSikW3UOVx7Eifm67ukcBmCWouz2joyuPYWbtCeGpAwNXDnO1V07LGoqQ/+uLIYMhZOaM0qHICAm4VNKmjiRBtPakWnvRxTVmZJ3vynf4moi7OUvUgAhp+riiloXJw0B9SehmvW5Q/q6xtcerJHxn4WwOdZPSw5UdCn+NU2eFqTxKtm4N0zbwmfWD82tVzIuEkKDjfbZMDjtCo/D4tTmnAnB9xaxm3Oom1KM64XWqowqSH3rITs8VPyB4hNTSJEHdIrquu/rCWyK/FMamkilO/j7RCSpMb3Vp3UbtFsvb6HFLB1UKEIeLne0LHDRoa9kf3/X7sZrNsC3e+zbFkIxPCqWuXrCsmASVzStu2xv54uGrk3WWgLn1uTl1ycLI6z7oQtU85ZxLoMTA/YQttRKqtBz/G6rk94OB9RPC99tOBex94xi/ufAjEzi3BdSJ2gM0ydjZpoYqjduW4/HzT1T0S2Fy28nvs6oYu9f2mk2EBAVcJTWMxLWunzBFVTp5Se9UwizFIb+kncQEBAeshjUaO7ClVE9BUET7WZ9akIw4NHQLpjs+G6bObtFQ9C1/73NQ0XNcFDcv5LrDzEilLnsXfV/p1S531KmubG/AV0SOkkTRe6WYjaScyppMLIrYxlYe0RAR1a+atJw/KKW1bFLHCapcVOKOOOkiTRNY3SskAW82AcgFAWongM0YA/1qlhj0bEUFgzHK2iSCKuOVsiCtl4VqFvjp1UToJuWditHJ/zqp1d8HatSfSwMeGrUk15OxzsW9dSseeQH3a5jJphHNEjyKckkwRPUNvv9SNX2LLu8m2rabxWVeijksHvD921++DvsDris+vpgQWU+Dj/4LI6sEYsIb2wVf8Z0GxE3C1cN2IHQAuRyJLtmvFcqHEVbNe3dMhe26DgqVubMvGJeqebkOXtnFlIbA5IOBCsahYlVPUmGtVDhM5oywJBGtAwHXDVWxSqTn019mEpjQUlVOgVgqOKAWyAdl/tKpESAKd4aNVPeWUq9JPaABKcrKs7LCV4XnUN/da25TyR882ktthOfNFSJIk5YDVc9ZQN12yhwmgasH2oilZxeoaMJbtWkwsmZ6/RYWEgGmrl+qSLhNCKTL0GoSUsirHCDHtxzjz6iajt5W/x0wSJQPOJxp5W95V/VD0rDr1dMQk3ZqA575GNDk2ZH0tWCVTeiIuG3EOT6LW7CktW4Izc3pGnuhJsvW2rWyMpw4Wf55wtsXTTuPV7uq13rSiPmHl1elbwA/+deDkHVY4DoCd+8CX/6qr855+BgKxc0twHYmdi4QEMWuFz1nqHqkdv+nqHoACm0XxJNXsfQ1dLqj5FqmfAgKeFk1jMSuJyJkVtXvfEdJ6lMW35r0mIOBG4qzw5KsIGYaLU0/2VBJyXHvlQ5xRFoUQHC2rE1dZ1wXVvk8eENFjLd1+uE/D16oGrucBF+BcdMKc+bsL3Z0poifzWRyDvYsZjptGEW4THjothQWnKgeo1ZrWIWFs4y1fFTeRyd+1kWF7kqqQrkugERWI9a9PV0tLkLU0j0kodJR4u1c24gayER8LW9SEXzbEqrRtnXorA6kn4NkdGwta79khKz0ayvYW9VvGSh4hfC7CstXaxqI/p6ev7r2Rti1uAXPqn9zvx6tM2FWFry8XdV0uFeXD9m1dlpIicty5ELXVTqtypqaPgcef4PfGKSkQowi4+wFgdPfSX+5F4FzEjjHmExfw3NZa+8ELeJyADXDbiZ1V0AoW3ULVbaDKk9un7gGAsmPnWtfQlcWRs3bdhjDrgIB1KKqGs3IqzEt6T4kMZeUMM7JZ3Zb3kYCAGw2pOy8XXu1QnAIvfymRADrI9aqjrrztanHKCoi5t6kArFRQpI80SwkRZBvKQZlziGkUA/HA5/B0G7ji7OoMlt32snLmLVDFRA3Rsc8uyvc40DhR63IOoqNkpUVx6pUWceoVIH0DuKtWV2RPOQeqCamCygnZ86RqXprBDAdP1zq7SBQroghhpZYxcO1DdUmEjwU/RsJ5P4M2UaCJIrE6dQOnnwWk2rzkQO3uum5bI96tdS8mRPCcvk3V6XVJKqgo8XYwCb0eHHDj2R4RhV0l3HmxcU7PkEkhqVWf+9vpjKbnbduSoPbFCbfcgUmyXSITZV9pNZNuDwNoXVtEzoavSd7LB3e86m3+BHjpwzdbsWOMaXqv2AwWdNhba+011IJdTwRiZzvoIOKiJ5A4Mmapmeu22JW0+qlcsT5du5tu6QoIuImwtq3KkfMhjSOM2F41SIMqJyDgxmH6GHj0MR76+GtxBOy/SsMc4BUPYoFZ1eBzFSH10aIIqeV71SZ79FeUEOGwOKUAYFiq9c52SI3iMn9YSaIbuK6CqqcPLsD3xLc1SbAx4LNp0sHy/m3Z2tZUdQuENCgm7bprbS3adI1crsjMD/6iAopTUgglrMJy7VEzr1ySsGpraV/poGnJN2okw8ZyRlDiA5qF9DKK/JMAaEf+qK/L/D+yLn2IdqtGfOTX9bxkS9MA00fA8VsUyuuIBuvPGRPTcSKqE1HzCPkVp+3cn02OlaXt6OT0CDkCtHN64tyrf5xiDM/HtqXPLVFZiUou26V9omvsy+lm+UPnwXVUXyqcl9j5BSse7/cB+AYA/wrAXwHwwwAOAQwBfBGA3wjgFwL4dgC/31r70afZ+IDNEYidp4cEEnftXM0K9YqQPreFzNBh1rqlq0v4iJ0rNHQFXHeUdeOInFlZw1oLY3RWTnxrzv+AgFuLvk95pw+BFz6fBti69IqXvpwXoB3wq4e7q0r8NHWb5JGQZgBUPd7AV5Ebun5+RAMZDCkYkgEAaYUSoiEhAigdertFOrxaqh4NyRkqZ6RSkv2rG5lMz2As1e2xGuAl+6a7r5vGK04kKBigITYbbx+Ou21Ar97XBd+vXnCWTeOVPtLwZRuye9Xyc8WEUAKAQ6EN6DpjvIJIvuLEK7ncV+qJs/OQHb3r0Hj1ilZjSUaW5NecB+XcN3hVC1qnKOOA3iMiPKuClV8Db+Pqy3IS8muJEE42e2/QAejrlC0SLK4tbLIel2XbKueshjvx7xmSm2Pi5aBjwLe6uXyhSyCCTx/Q/sv3rk1osuDCMnaMMb8GwP8C4E9ba/+LNbf7wwD+CIDfZa39c1tub8A5EYidy0PZaubywcSCbvtUntwedQ/QJsTKNQ1dWtmT888hQDbgKsFai3nZYFpUmHZUOULkDNM4qHICAm4btv2UV1t+mqqT9XIG8SOZJi31zxVQuDSNUvNw24xuI5KWoQXn+USRzyORem5Xtc65MYBXf+Q7RGLkO3S/9DkEM6+DZHwI0aMHaHkNMqRLoPNZ1e1Lap+kPyj4vNYiYPPBv2tpqSWUmkk92e9yX7GGuRauCoBhIsIookeIibpTaS/376yPkD9a/RNnqro+V2smLWMbEKPVwrfJiU0pirj1ituvtiVYm4bVXce0ryS0NxnQOs8eE/kjz5dkdGwP9ug4d8dK6ddk5XtDT627rIHGJlk0QuA0tb+dPJccY+e1bdWVV7zpivIkhyOB1wUdX1aVu9gaH38KOH3Tqwlvg2Jn1QMBeC+Ad1lrqzW3iwC8AeCxtfaLt9zegHMiEDvPFtuoe7oqn9uCbr6REGTdwGZR9aS3zPIWcDVQ1Q1mpW+xaliVM0gjjNIEwyy+VedtQEDAClzkp7xLwa2dbI+nHe6eFWSIFIVHNfdtTtJwI3Xp4xdI9RRF7YybYkKf6BfT9mvX+TP5rh80N7U7PYvX7pQxnSpqV63OaqRWXbsOc15T3S4BwK2GLPhBWexF2+77tYN/3B6w+xQmsj2i4hLiR1qpqoIGaLC9y9m2cv/Yce7tNDXfx5FIRZtIcnawLgHETWJCgsqaJUL+MAEkyrJWzTxUfktHJZVycPS2pEY5J9WatDolOTDYp30k7XLTJ0T2NI3PvhneoeDefJcDkWv/vqBJ4bW17l2lT+fnulzfHhXnoLBttkPJ9bEK1E6Hq4+1bkW5tbTuotCrF8vk0llBx0vPIS10Omy86f/qvc7Sfnj0Ma8e2n03fb/pGTsrHmgC4EestV+7wW3/LYAvsdaONn6CgKdCIHauBqoO2dNV94hVKeuQPbdJuVLVXtWzqGtn6+oGNntizISGroALxZyzcqZFhaKi8zOJ2qqcQC4GBAQ4PPkM8MYPemLn3T/lcj/lleFOf5KviYFVw51usupav54VpNmnWtAgOX1IX1VJ25HvAqN7pJKQnBYhEOqSP+3nYOfFKQ/7rAZJMq/ayEY+v6SbcfM8iC6popYwX20tEZKnW61+nup2afwCq4PynXZo73mga6PLqbctybZrRcWqv8O6ZExdeMKmmjPhUwMQRQ7bbfJdb7lZNeRbS69dSMRSSKUOGdRw/by1PlBaMqGE2JE8IGeRS9tklbQzxSoDRtQ1Ekq9Dk3tbVqiWMn3iORJMtq2xTFld00f+9DnJKX1GBzQbVflyTRN+3hp1br3KMSA9vtBlJBVru7cV5DkPkPLcn6Qtm1JPk8yaIePlzNa865FMU480Zak6rE1AcP7ayU5swFfYcyy9S/q2ADnx8BH/xEwe8T2TwCjF4AP/9Kb3Yq14oE+DWAPpNiZr7ndAYDPAng7tGI9OwRi5+pCZ9PosOa6Wa4a12HNt615qqybpVDr0NAVcBGoXR15hVlRu3NvkDKRk8XIkytgdQgICLh6KCbA9/w54K0f8zaTFz4EfP3vfH6f8raGuw7xs1Ld0PeJvrJ+XTZmh8DpO9ymVfJgP/bKFGnTEsVFMqD1rksiBgomeoqJV7sY+CE9yen1yIDniJ5s2dr2LP5mkKwbIXpkeHaKhQ3sLtrC1yKASs7DERKp9Hku+T4REoNdpVrZ8v+3mmu0N2liWreWcpxWioip5r4RScgUEzHJl3qyJ9vxiieHFXOrhBhL1pUjPGoilMq5IkUVYSDkmRwzYKulU55VniBw4d+sMomUCgiRVwZpe1hVkJKlmtNapAOuSFftT/UCmJ8A80M6Nyq2qsnzyTq4DCIhv9ashbSgOTWg/ip77mSY4KkBW9EayWuLMrbT8XoVM97eJ0yCwWf0uPeTzL+vmB4irI+YFiuntKt1A9vRc5kjcszq41Ceq6mBR58AfvjbyH6V71Pd+ZNPAF/1nwN3X+u//xXDOmJn23fxfw7g1wP488aY32ytrbs3MMYMQTk8QwB/b9uNDQi4iTDGIE+WB8eucqWoGsxmtSMyXPNUEiGPY0f63FR1j6h0NHRDlyZ9JgsvX9brpFu6Qqjt7caiInvVpKixKOm/qzgyrMpJMEzjG3suBQQEXCAefAx4/d8DJ+8ACQ9tjz4BjF8C3vVlnA0z9J+uC7nQxcoPU9d8yLr2A1jJtomAOPIECaCIHjXolnOyPNU8uLUgdo5uhbXk/awYGTbZPo3RXSLD5ke+SlqIDmOA+oFXiwCesJGAXbHJSLuTC3dm5QBquADfiIc+a9vbaTgHpjeoNqHrtn1dZ62FDKD1HFjMgeotGqIB2k7XGjbsrPWqx2SCRQiHak6k1+k7wJNPetWJEAHOxtZpqJLXvg6ipijn/dlKSQrErLYRtcdZSEdeKZPkvP3HwEQIlYJeepRwexKHSKdj+rkvsHodYj6O02E7E0jnBNkaaApW/YAsRJHxRFS98OdNnCuiZUC3k/3Sh6ZmpdGMiCNRWuV7rFJioiLfB9LSK6cWp0D9Bj1sJDlDqbe2SQj1KuJOcmWc6sUAiD3hI3lHtbKD1qysqgugLGhNqjmHLnMYN+CJ2HwPyPgxBnud/KMu+dJt14v8dUtY8/eZAcjqh857mbqPKMiqBZ13TQWcvEHrJeSysfSetExpXEtsS+z8cQC/FMCvA/B1xpi/CeBHAUwA7AD4KQB+NYBXALwO4E9e1IYGBNxEJNIWlQEA/UHWVfeUtcW8aHDaeCIjjsxSM9dNtSmRdY1eL1rqZetsbqvWqRVqrWxdoaHrZqJp2nXkEt6dpzHujDIMsxiDNKhyAgICtsTkIVkmmhIoeEAoToFPfRdw8ha8tURZgpIBkA2BZMzVwyoI1n2qr2wDzwJRDCAGYnirg/4kv1qo31fkmbSIgc7v2/wNIqSErkCOU27MGahhsyDyR/3fTmSG5KhkwGjUtn+5vBYLoFRZRLzWNgLA9pNyCjS2PUNGEWBUjpEofgw/hl+U5TXq+9k9bkKB0IMD+r0pVUbP3FtadKBxPGA11boht3OdbWgIl8cTtYxtmLyqgcT6jCODDpEnOUY9JKX8LIHMNdvtaj52iorPBVabpMM24bi88Z1tt/6xFqdcXc6Bx9MnAJ4wEchKlnzMYcS7qwnVVc+1av0AdW50vov1qDjlNiwLoAYilakUpW3LlhAaYEVQOfFZPMUJHavSfmYMgJxem4SVlzMiJiRwXBROUeSzcVzF/JAui2LeNj62TUTP7aCSUlpEC58jFT/v7BCYPKBtFbWTq7jPvdrMWn4/iICY93m+w7Xve5wFdcnvc7LNojID2DqmrIR3PgAcfY73iVjALG3nDcBWxI619hPGmF8I4K8B+HwAf7DnZgbADwD41dbad55+EwMCbhdWqXvqxvrcHlb3HJdVr7pH5/fcVBKDwm2XB/VuqDXVVVc4Uba3ODLe0uXsXDdXCXWTUVQNhR6XFeYl5TRFxrC9KsUoS8J+DQgIeDrkIxro8/v0V25dUxDq+346cO+DXjUilgeAgzoXwGICSFOQYfWJ2DnSXNmDxC7UCXkVQsUk6hNwuZ6tC6vwNEOu2FS01WtdjocjfsSK0fm+jviRzBGxoUQRML5POSOiRGjVryvVCMDKjhhID9pWLgmCLbkRSufGiJ0o4ewPNO1sm264sQVgS7KpaBtbN9vnaT5gqwrVuDXz5IlNVVPRmgDbVRD1R3Hqc39gPTEWJ538JiYU6gXdVFe3G37taUpKBxnW+wKZxT62SSDzWZC1kOrsBQf1nrwD4B3epxmTPSoXR17fZUDyoOYn3IrFrVsmbhO46GTGIPKKpXLuX0tkgGSHiAgTeULJNiA2ltdBjhFR4wjJagBvCUs4g0pqzBXh0yUsreF9z9svjVaLCT1mvgMcvAqM7hOBJs8rKrFi4sm3ogLqR15xpUnDbOTD0JMBrxOHKwvpCvQo3+zy5dZ6kkvOcasCwCWvKc5p7dztZnQOTx5TBX2SATv3adtuALbK2HF3MiYH8J8C+HkAPg/APoAnAH4cZL/6u9baZvUjBFwGQsbO7UPXpiRfuma8q+5JY6oav4nqnnVwxJjO7+lp6NJhzWlo6LpysLatypFg8iyJMMoSjLL4Vh7fAQEBl4jD14F/+keBhx/1eRx7rwA/43eSHctKACkPYjKgRBwaangQWkz8wFwt6DJjSAkSScZI5ocel9WxYpAXJYC+nSN+op7L1jzWttChv3117k2PtcG1F61o7zGGlCbzQ/puDGfF7PeTAZvUr3ftKkIQSYZKt/Zbbi8hvhLa62q6O+1W3TmqG1rdF+jcHVD9L+3LRYFQTokosWw9EWWGKGJM1zq25vGbii01E/oux3M69E1b0m7VDXV24cfqOdxxpfcrq30aIdYWQDVtHxNiPRP7lqx1CxusUzln5cuUCB8JrdYByEnuLVxi64vStoKk+/gug6cTxiykRjfY1za8v4SYY1JMLFSibkPn/rAs+lGNcoiI3Mn2iVRJOjlRUUavzT3flAllSyQNQO85osKTbTd8bMYJE5VCZvJ6SaZQFNO5MOAMmmwEFxytVT2RImMAb/sUokXWQc5LyfYxbBuNpSUtU+9RHfunqO1g6H3WBXGLWkr2seQfDXidV6jnZofA4ac5+6kmAjmKgLsfuBHhyeeiMK21C1COzv/yNBsWEBDwdFhlU+rWjBd1g+O5V/cAcASPVq3cVHUP4LNVhmj/UdtqMePvs7I/sLnV0nVDrW9XEaS6IiJnVtZOlTPMYuyPUozS+EYfuwEBAc8ZyYCbh/bJthMx4ZDz4AV4C5ME/VYLGjarwg9B2QjI7vicDD3QScVwVdD9ZBCUT//jzFu6DCt7YNoZIaJKsXUnmqU7MIuCSA9obBNpKYXUbdwA1zdsm3aGidgbXIgtK1/KBdCc+rXqPo62dcHSwHr0Bm1Hxusvn6z3DfpxRmsjFdzVgpRVmsAB2CrHailYGvLmbHNx5IMiBcRyEsXtxzGRIvV6WorksRzRFK0YXDexskVs3ZoD9eN2tbpuFlvXWtVFMvRV7dPHfnuTzCuE4ozWIkqBLFVKLnndyr63trqdX19T0WPM5/4YMMZn1shw7qxL6rHaD07fYlYOje76y6o5kajlKTBnO9fiGDhiFYmoRES1FYtNT8gX9fi9zw1FyAqJylYnE9FjCTEnCjNYT5jku161AjA5VHsibHbE6rU5PZ4jLJSlzYDIn8EObYetfOiyrVkhw0RdY4FmQY1Q80Of82NjuCBmY/zt0xHvA9m2pr0eS2sh4dL5stpQCB1nj6wos0fOFbl9NmQCjPeFEEOLCStzSn88JQNgeJfee2UdpQnwrGM/G9MaxAPanjijxz+PiuwK4qm0acaYCMB7AOxba3+ELzP2PDKggICAC0McGcTRsk2p2zi1qBqcqhDiSKrYFdlz0xUrknM06pRSFK38HlqrbmBzEhnkLcKHvgc8Hay1mJdkoZsqVU4aR9gbJBhlCQZpINYCAgKeEWZPaCDM9gHwkDN9DLz+b4E7rwKQzBf55JktUqIUqQqyKkwesnJBLAOpVyykQ/q0XdQ9ZQFUTIq4muuKrjdK6eLsLUOuEhZLEDqqAlYbuMu0CkGUCWeI7btET1+dcOuyzqClFUOihJEvUQ+I+sCRIg0wnwKHn+E1zUlFkO/6oF5X4czPFRlQ5smA7uNUNzJcsuVEK3vilOw7zg7SeLKsnMGTV7m3E8WZ39fyOBoWntSyVZv4kbySpqFBV44HIUGSzL8uOa70c7QUPTO2TbE9xTVuif1G/i5ZkwkE461EFeeUCGTgz8bqPj3Eh9uniuTSSqelJqTI73u5vrE01LcINVbX6OO0dVzrimw+hjO2II1fpHWRfJpi1s6LWZyAgsfZvpcOPKnl1FtKCdIiQjeEtSp8eELbMj8G4ikpibIRkO77xzzg+4gtqpjSa4hztlXlKhhdziHJ8RlwvtIRcPRZskqhIdIkGxMBNjjw50FTgRRCYyZKJAieX3OcdFRKtSKPlaJJLqsLoJL3lq6aLeH7GE8alVM6P47mrAYTEnzs39vyF2m/yPuHKINKVga1nkMp5hyBqtRk2ZistJ/7bk+Mvvzlz6/d8IJxLmLHGPMVoHydnwtgDHrrSowxrwH4DmPMn7LW/j8vbjMDAgIuAkLYjBUx3c2kKaoGp/OqZVHSFePyGDe9capvrSTYuqxti/A5XdHQlccx0sTceDXURaCqG0xLVuUUNRprYYzBMI2xN0wxyuIbf8wFBARcUdQFMLpDhIKoDCZvedVMU5ECwTY8sBg/hIgaY3gH2H0X/Sx5FiV/om8bGlCMhDBnRFzofBlBU9JgImoYW3PmyBFcZkXEJFPGQ6M0I4k1qDvQazj1QM8A3XSsKJDvFr2DPhpei8S/tq4trJUZpC4DlO2Jv88Pgekjbgs6QSv0tKnUUCdhyZ2hrjuMO3WV2GAWaGUHibJHqrR1jbTYv3RWz7b5N+uqzKsSgKqljiJl6Up9OO3wDtueGs63mXk7UDmDs1m5avU1yoQkowwV2Taxa5VTIhmmD31mSzruz67RoeC2ARJ1/NSs3NBKFk0wyLo2BR3jtvEqpySjTJR87LNznCUo6RCOBm3CsUtGRl4dI1ahxYnaHj7milM6l4RcknNTzqUWubcGeh/gXnttJVvK3YYJNBc+vMPH/jHddnZIr22w7/e9JviKCb2+fI/VhEyeFRzY/PjjtLbJgO4/ukcZYtKiN3lEJKSsm9StZ2NP+GxKanVJHxfYXvP2Tuh1zQ7pMcuCSVRLgc0xk2z5mF7PYMcfd3EOJEz2WQugJlIQTHhaViV129PqBfDgo3SdtOxNH9L63AByZ+uMHWPMbwLw5yEVPgRrrY2NMT8LwHeA3uG/xVr7my5sSwPORMjYCbhIlDq3RylXBK3GqYRtXTdc3bMKQo6Vrfwe28o6EjVUlyS7rcG+0mo2LWpMiwpFRWuVRBFGeYxRFmOQxLfyeAoICLhiOH0A/PtvZdtCDBfK+1P/d57s0Z+gO3tV0bHmVP5TYsklkWyRPqtJlPpWIRmY5XkAVrl0wo2FFHAWCiZd0oEnBGR4zEbe5hVnOHdrjVMCyRDXdH7u+7S/5xP91ss3nuzp5ghVbCspJ/TyUra3xKlfAyGhNIRo0/YgRwLxvqgrn3Uig78m1sTG46qWlfpJrtcqD8mP2RZNA1c9vSrQubVWnUBnE/nb6qDpKFKKlGG71ailfOmovVxIrqhduGErHXjl1CYzpa681uSLs+9V6vipfPCznFcWdPuEyU8Xljz2xOV5oW2UUutd8fo7skPsVkpFpevHJTtm0yBtaz0hU078vhV1iQsa5veJ4pTej2aPiaQwMdfAM6moQ6qjlEnfEw4Xtm3lU8UqGcneSTLaj3Xla8KFhLMN3TdOWWXEKq58h1VhCVYqmeS5RP1WzdX5kra3GcY3oc2PSC1ZTkhpJcR1xoRXOvKk5qq1FZVjY+n70ZvA698LjF8gcmx8Hzj+HPChbwT2X9n6kHkeWJexsxWxw0qd7wXpUP80gG8DkTxfy8TODoDfC1LzJAB+vbX2rz7l9gdsiEDsBFw2+tQ9RSeAuGtLym6xPUlnHWlbV91p6FparxtKkNWNxbSoqMVKqXIGaYRRmmCYxbf2WAkICLji+NT3AJ/9PhqWoxR45auB933d+vu4gOGqTb5UC9/UtJTLwradRmw6FWVOiA1jcJc/QWdSRgJxtbrF2Y5K/3xCVjTyCbciImTITzlkNh1xno+QPk8xLK9D0yWAziKDmvYQV5ecn3LK6hBl0zIRr40FkTDKdqZfv66bb6lilN1NVA9CtEgekkCydwy/Jq1g6jZwib3laaADnUXl41QwC6Wsqj1paEHX10KQNEohlvvQ7mjNtgkp4yxqnJMjqpl81+eetIK79dc5/rbRQd2LiW/FKlhl4wKgc1J5iKVIAoddffuGZIt+Xjl3dAiwrD8sXBaQ5NTo/R4lXtUjX0KkrUJVeDWPZGbVpV87WUfJkZL3o2yHbFb5Hl2+OPF2wzj1teP62NPPJfXgUcTvAXz+N5pgm6gGrFmbnHGWUn5fEjWhKAxFoSeEkCOfzlC4CfFVzcn+Oj9iO1sJl1s2vOMzz4xpv2c0ipxsaqo6/9S/BkYvsCJqDzj5HPDBbwQOrj+xs+07y+8DEAH4bdba/4kf3NHY1tpTAH/MGPMJAN8K4DcBCMROQMANQRQZDHqye6oO2dOXR9OtYb8NapVVWUeVsnMJUda1v3UbumTdrluuzJztVdOyxqKk/y6SKMI4pwarYRpUOQEBAdcA++/hSuM5DSX77zn7PlLBjDXDnM7JEDWOZJxIhoQ03xy/BTz6FICGw0ozbvvZZ+vFLoWQSquWAXyIce0JABlU68LXU0+fKMLDAlChrcmAg4svUOUD8H2j7YmjpkMGiU1rdsQD8cxnlwjZYplskXUBlApE8ocaOBJIyBlRDiHyOUpxBuTDjk2NSTiA7meFUDJcZ33qSZOuqidOlWKmmxlTn3GdbK+sKat1alG+VJTVJIMtLOemcKbRYg7gGEBCGU3ZiI6nwZ5vI1tHyjR1p+6aSQLXsrXCsrUN9HmUjQG86K+rKxr2i1NWpsyoyvr0HSbqMhUqzSohIStbKqeeY9mw0i0dqNer1CdyLrUa4IwnYGB8Jbh+TK3qccQPnwOi5EtyOp4WE2+xkxrvfI+UJpIzVZxSftejjxPpEee0/8Yv0m309mskTDQN79DrEuKmnNBaGs4Jy8bA+B4Qv9xeB7n97BCYPQImfP7VFa89q2sy3gYTexVjOeOK96RtWdUNebJekpc0ugtnhywmpFqaPiGypqnpObMxMLhD1tnBHSLFNYYHwOlbHMYc0bEz5NvfAGyr2HkTAKy171KX/WsAX2+tjTu3fQPA0Fp7M1bqGiAodgKuEiSPpmvn0moVIS80cZHG5tqRFxeFsmPnIoVPf0NXFkfOCneV1qxupI6clDmyv/M0xjiLMcxi5MkWGQQBAQEBzxvFBHjrR3wFNwypBV74AhpcuoqEJZVCN1x4SzJEGohEnTE/4oabI8rWKWW4bACT+nYhsWNIFXA6YOWIsgjpRiOX0TLzn8qXU58BIzkziVjEBpTPke3R8DbYwaWrfM7C4pSbf+b0+gb79BUlPcqgFQGwQpq4Fh/9pYg4ADTIy/fIkzBOJcQ5HxIc6+quOY8JfDwkkt2SeWuTRqSPob4Aa3P29UBb/eJeX8lBvSeeUJS/O7KhsjztebtRty5cHrvPVpTkqnFpBclwUZD8FrH9lDP/el0rGRN13b+b+nKMtG1vFeTc0aSpa3oT25bsA1aUiLKqEbWVsliKsiffZfKGCUDZT8WEn6P2DVdipxJSUgijwR7tt22yn1zYMz+X2PiESJbHkjV21tAazCbDEVy6al1IK1lLd071WKmEGFr7xcefvCfOHhO5K/lbUUTrNzgg8mZ4hy578hngM/8GmD8h8ue9Pw24897N1+c54yIVO3cA/IcNb/s6gC/f8vEDAgJuCIwxyJPlIV7UPWVlsahrqhef1Y686IYPC+lz09U9AJE2aaehy1pL6p66QakIn64iSirY27Xsz8bWtKhqZ6+asyonjij4eJQnGKbxrdh/AQEBNxTVwg/I5dxn3lRzGp6cimKL3MrWIL6ODOpcJ0qI/VfatowFZ2ksjukTfAkEtjO6TGeZ6Od3zT9sxcp3KX8i4RYabeUqp5R1sTj2zzd97O1jFmzhYOJosEukz2CflURsR3oalc9ZkMDZcs4E2BEpCrIxfVqfDv1tW20/naBo2Z9C8LSsbqW3NInyqSlIBaAboSz8YOsyjwCAFShC1kRKCSTkWzakIXqw65uKLuIDnJaKrCcsVl6XZJwsTun75CEN4GLZEiVH3EOEDO8AOy/4li2pU8djb8cRoueiP5SKYn8MAOvzXaKIiFDJWoL1OTv1aftxXY5RR+0TZ8qGxsHToirRqh5pb6oLf66I0kvO84Sr5YXYqUvKmNF2LlFQARwkzsROOgSioV9XyQGbPKK1z3aI5NHH/yrosOfRPTrfp4/pGJh9rE2aDO+SmicdL9vMJKC6nHviS5rpJA8o321nl7UIVLGSzvvJn1a2VAKM7gM7L9HaFifAnN8Pn3yavqKI1uD17wMe/DgHiX+K7n+NiJ112JbYeQjgg2fdiGvQPwDgwXk2KiAg4OZC6sVJmU6f6HXVPWVtMS8anDaevJAsGt3MdR2tSduCbGz02tHT0CXrJRa4bkOX2LkusqGrcaocInQkJDpLIhyMMgo+Trf4dCggICDgKiPJgZM3+ZNgHsTyXeDuB5abVLohtL1fK27jgmPlNhsSRZLvYSIiL7Id1a6khyLryQTL/w80BRFBsydwdcMCURu4SvUBPf7eu4h8iFOljJiSbURIn3LKj1lRxkbMn8CLyifdZTXBDpCMPPGzKemzNuyXv2R4nB0CTx4AjyrOIxnR8Lvx2gqpNuiQbkop082TkbY0Tf7I93LBOSGi8pjRICr7ydb+8aPEW+IGu0z6jFSVedx5/m7D2JZ/I8VcEZ2NiJyRtRYFx/yYVRwFMJ3xczPRmeRAN2NGbEfJgIf0go6P+bEnEC7KstWHKPJWHvdaFt6CWM2AYsG3jf32CLHZDa+uCzq2u+emNLC1iC4JFU4AcANWNfN2pCjxarpsrIjPlJZRlDz1wufL6Nyc4R3g4L18LFtPgkwf+W1KB3T+CREbp0S05nv951ofGSOvdXwf2HuPP98azuyaPgaSqSfstK1MLmutvSLaxKams6jkeNcqI8k16tpW5fduyDlA2zG+TxauxRQoT4G3PgJ84l8Buy8D+QF9f+MHgHd/BXD3te2PryuGbc+gfwngVxljfru19i+sud3/AcA9ULhyQEBAwFqsUvfUjfW5PazuOS6rXnWPzu+5DdXiq9ZMB1yLwmdW1C2SrNvQlfPP61Q1RdVwVk6FednAWovIGAyzGAdZilEa34p1DwgIuKU4eQA8/DEOBm1oYHjxC8mC1FXYdId8qfs28XZqFUdenEUWda53toyGhqq68GHNxcxbK6RdKB0BwxxA1A5lFevH7IkfpFx+CACTAGmmqrSHRAbcfR/9DMPE0QwojukT9OKUhqzJExqQYdEK2tWVz0nug32dvchsro6Sbc3GRHaVU3r+ckbD4OCAhlxpkVrab0/7AUUGYA2BJLlHogIquMZ9ceq3s+YBeHYInLzhiRRpN5P1kp+7RM4S8aSJn2j5sq51Sx5DFBzj+/DV6qzikuG/qZnYSADwYzZlO2gXgM99YkUK4Am3wcH6XJinhc7NEfFKJVlTs2WyQQhN3U4F9FfVVwuyARZTX+cu6hZpkBpxNk46Ap0fKvdqcQzMtaIu9ooxY4hU3XmRLXuJUgPN/OuL1LlfLYBmysSSAdAAiwVtX/LI15jDKiKn8M+f5LQ/+ogWYNmyNXlIX3HqA961Qqgvs0jb2MoZZ2XpUGZtIT3D4inHoCh9tHUyGQDNHtWoj+7RPnD5PinZuHD7iJ3/FsA3AfgzxpgXAfwNAG4vG2PeDeC3A/j9gGvOCggICDgX4oiIg2EWQ6t7xJokpM9Z6p40JvLipqt7AKwMuHYkmWrnmiwqnMz9H1tJFClVj0HdWFS1da1eANnF9gYJRlmCQXo71jQgIOCWY/qYhrPxizSApEMa3I7fAvZiCujsNjatQ9detao9qJuVYiJPQpy3Qlsq0sUiIwN6w20/SU6KGrF+QJFGNQ/pUj9cTomkmT5WliQdPgw/aLayWRLfXCRh0TIMTx56EklCVZOsTfjkbCkR5Y22qcSZWs8VaySBrzJISg7HWY1FFw1pChIMO7Gkop5YnNKalzNgzm1QBQdqL049QRMnZH3LlfpD8l1ESVMXQLWBdbBFUPaogEzslSawXAvOapaqBFDyID/iNibJdOqQIXXpCaKTtyn/REiFfI9DnPd82LAO1r0oSIjwYJ/XqOLXwaRJMfVrolUlUQoYVtmJsifOgUHq94fLV6r96xZbmjymnBeDA9rni2PKiik53ybm0OjBns/HSodEEkUR2+dEFVZ466CQd9KIJ9tQFZ7clIDj4Z5XtwiZddY6dwk/3bI1e0Jf0rKVjTifp0NsxwkQd2xszja38EHO+vl08Lh+PGO84mwlIuCz3w/svUK3EyJoeHeDA+XqY6vwZAAwxvwGAH8JitBhnALYUb//Xmvtn3mqrQvYCiE8OeA2Q1eLawKjGzycd8Kab7vKRGceTYoSx/MKx7MK87JyDaI7eYrdQYK9YYoRV5LfBhtcQEBAAADg8LPAD/+vQCkBwg1QT4HP/49Jyi8Dn4TfSmZHS0mjclvOshBtrEZZE5i7Sj2Ezvu2DHvSvLXggVaCfqUuWvJE3KZZLCmGmpJsRlK7LYOmqBJcMxNoO0xEqp844arqHBT+zMN7VfisIFflXXpVkjF0/0xUK0M/eGejTtW0aiASBZW0aUnYqihGxLZzVaHtMsWEXsP8GKi4laou/dokuVeIpANWVskaxP4YkbDblXXzjc+EWYcoomNEslRqVq3AcOsWq6eyHc6L4W0QokeykcTS54J7B55EEFJlKeumJ9D5ota75GypGa+1VMcnKR1vgz0K4s13lgkHjVXh1fMTVvuI1SrmQPJ9WiNb+X1gjSe4XOYR293izBMyYntbHLOF7oTskkLkoqF9JZapwT6w+y56T3taxVSrZWvKWULGq5+0ZessyHuA+yr8dUnmScx0uNlj/sjfIftVlNI6vPunAl/yy873Op8D1oUnb03s8AN+DYA/AuAb0e5wrAF8J4Bvttb+i3Nsa8BTIBA7AQHLEHVKV60iEFuSJnuyOLoVFdzWWszLBtOiwrSoW6qcNDaII4MkMj68uaehK++ENV+lhq6AgICAC8HpA+DffQtV60oVeGSAL/kVFBxqAUAycpSVQvJpZPjSQ9c6rLJe6XYsqdd2v3cam2puyXIV3ityexw5JJadiF6fDPW1IlKimFUzPJhLC5azmkXqMXpek2RjVAuviKhmTAbNVfgwK3CdXSgmPsoY2jZrebBmu4uoEJoF7wsmEiTfRIZeUQ/FKRNKqVdViK1FbDPDO75NqKuwQuf38zSdXTQkJFgIsDkP80LYNaUn/5y9RZE8AFwujg6kle+tNqMu2SNtYrZDBtXeElRKps3ck3txTJlLzsonaitRMhlWAU1IqVQXAGoi/9LckzkaS81Wib/dthk+st3d9idXaW78uSqIE1b0DM8mGjTxIjZH2TdRouxefIzLuSutWpowBh+HlslYsSxKzk8sTXZs45L9Mj8iddD0IZODM1qr4V1g/91E8jjSOj2fWmpVy9Yqy9ZZECug3j8uEDvu2Lfy/m1++8dIIbb7EvDSF23/mp4jLpzYUQ+cA/g8AHsAJgA+bq09XX+vgMtCIHYCAjaDzqFZlJ74aVbUigvp86xapi4TVd1gWlLo8ayo0VgLYwwGaYRRmmDIipw+dG1wZQ9R1pd7JG1fAQEBAdcS08f0Ke+jjxMBgYgChD/4c7yFAPDDqMha9NAn9iSnMFCDZ1fJ09vStGGYsiYhuuoco7aNmBK/bfqmffk9usrZqTDANpGhV1TEab96KIqWL9NKI6Bt03EZLjNPBtjSkwowngSAZS7H8FqBiZ+Sh2BWWkURXPi14XwNIXZkO0rOubFsBcv3SMkg6hIhPvqGxU2q7s+8jSLanhY6rLaYAosjzu6Ze6JAyC6n5hkyQdBTQy3qHq2WceTPGcSJbiArZ7QdouZw5GGkVFapv5/AWQE518cY2qcx23LSfJkU1cdZFHNOS7ZM+kQJHTOayHHHuJA1Kuuld51VILOsnQQyy/kRJT7IuOLQ5mzkK+XX7XfJZBKrVTlnFQ6TQ4upV8fJ80q2z+AO2xiZlO2rql+cUNbM0RvA6dukALMRt5zd9+eBU8GJ/XFD5Y1AW7YkH+gsy9aZjyn7jveDI4eNJ3bl6/gN4PQtPs4bYOfla9WKdWnEzhlP+r8F8JK19lsv5QkClhCInYCAp0OpcntWqXtSRVjkyfVQ98y5wWpaVCgqej1JFGGYxRhlMYZp/FSvwVqfw6Nbuqpmxdo5wsfceitcQEDANcDpA+DffytVBxt4EuBLv4kUOw1nXDjlSeHVO67m2nplDyxcooHkQkSZtxOIuqQ3e0eIku51WwYzb4o+9VBT+6G8nHo1g22UdUsyerAZISVrser1ue1gYkdquaVqvFHqpFY7Dq+7ZGnIvpDbAl69I3k+UmEvxJIxaiBOPGGg82Y0mRXHitTrWta2WYuLIIo6JJFUcZczJgSOOMNnrlQjGRMNu155Ykw7MFgUYt1tFuKnV/WzghATBZeEF8saOZtNRucHrFIBiZKHA7nFJiaBuwmrXnSTUlPRa7CVJ/xsCVR8nRCuKRMtwwP6SnfIcrUNdCBzwVlIxYTeH5KM1nb8AmXTbEqMOGKLyScXgm68ci5KfO5OMWECbeqtY3L7dODDk+V7kvt91NTUrnXyFpEgJZNF+S5nJinyTXKCRNXjFD4bKKScZWtK35/GstVaq6pj3+KA73IKPPkkMHqR29iGwPwJ8NKHlxsOrygujNgxxjQAPg3gl1trf+CM2/5rAF9vrX3aSPmADRGInYCAi4dW9zjSZ4W6p6vyeV6oG4tpUZEqp6xRN7Stg5SJnGy5Tesy0F07IX5kewAKuk7jthXurIaugICAgGeKw9eBH/qb9Ck2GrI5NQXw/p9JAaaAIjRyHgJjwHDjk2tqabzVSeq4rYFrGHfVyFADqti48udv91kFHXjatUZoC0iS+0/JbVeVpBQdSza0sxRLVg3tkgNT035yA22HZHMhz2jbh4xlBZH1hI1koiQ5qRZG94noEaVRXy6SC8VVNiZHbkRqHfq+Vljxlq7b4sP5swigpvKV9dIeVheA2M7SEb324R6Q71OmjOQUiR1QiB9dR92FW4c+1Q8f+5ro0cdSkqsmMNUA1mf1kfNL8meiWL02tgEKSSXWPQgJZntIq4jOazmOnc1S5Wp1g6brgrfpxFeXiw1Qtn2p5nvoz/NVVrAo6iiIVtiNBGKDLOdM9Jx60keau+R4TQZcD7/jFXjWsGXrMRFUtiIbX87ESMRh0U1nnzs7aq6In2w14XPRlq2lx14AJ28CD37cE0fDO5RtdPcDFBx9DXDRxA4AzAD8NmvtX11z20DsPGMEYicg4Nmh6pA9ix5LUtZR91wmYTFne9W0rLEo6T9WaRUbZQmGaXxlyJJuQ5dUs2uyLIm8qseRZtdAHRUQEHADcfhZ4OP/HEBEREEyAspT4H3fQJYsybapC0/gaIjdxSSgIdIqIkPdXlQFutJbqsulESvJ/SfiMrBeNcgQpZuFlmwtG2SQ9D3uJsHTXdJIWoMqVvfUbOty2TxsO3LqDQ6WrUu+be0tLuWcrkuHZG8Z3WV1y9gPwzq3RkiOPuKnT92if97EjtWsIoA2JYq0gqmDquQGroknBOqKORADJNzUJO1VUiHtrIDAUvaOtMe5bJjOa5RzRYgfIZ20QkvWzwUqj9rkRlWQCmP6hAgJCQOXUO3hHV9f3iWI5LjqU+FJu5QLAlfrK4QVDJM4bLOKU25d22OLEVsALfhYLGidxV7lXl9EJEg6WCZ+LrK9ra58qLLbx0zOOQVe7gmapmIF0gm9RhPTeTDYAwa7tH0uw0cCtBft4yuK2qo+eezue1ldesWREHxPa9kqJsDbH6FtFtwgxc6WKVIAqP1qDOBbjDFfAeC/sNb2vBsEBAQE3Fwk3Kg1Uv+/WtuuYS9qCibuVop3w5rPEzjcNBbTsnbKHFHB5GmMO6MMw2y58vyqwNXYd8oVW2QZf5+Vy4HNWSuw2YSGroCAgMvF6A4Ne8ef8R/u772Hchn6hoGm8SSPrnaupqtJH8k7QQY0FmQTMjRIOdWCfEXeeqFVBDok+HlCrB66WcepMObtCmMJZN5kaBXb01Ix75bokiGuJnrhyQPd6iWBz/Xc22rmj4Gj14EnnyISIhFFldhSuC0p5ypn19IlIdpgwueUyQSrbF2RVzu4PKYO6SMNUKIAuwicRQDZhki62RFZuKTJafIQzp6W5kT4tOw9K/aXhSfRmlo9vyJNnN1Mwr1V21zzmE+HhNZTFGGAX6/RfXp8IfxMwlY0WeO0bSlywc0ZgBGwSiCibWnVgmya88ecnVNyWxYTMsYwcTLzrwPwBJJYheSYkzeZVAgdITLkK0dLGSS2wPOc93FCRNfwjj8G6sJvk6i4moqOe4DWa3jgSa/JO8DxZ+mx0jGf+ztE9OR7ZDmLYv/65Eva6ARRpEgkJnzyfdq2rmVrcXI+y1Y2pkydbsbONSF1zsJ5iJ0fBPCnAPxVAP9HAF9mjPmV1tpHF7lhAQEBAdcNxhjkybLNSVeKL+qaCItZ7QgLHTicx7EjfboKm0XFqpyixoKr3CNjnL1qlCVXRpVzHvSRZQCWgpoXVYPJonLXG0PtXd2GrudphwsICLhhsA0weexVBBLgmo6WlRVRBEQDAD2VwU3Tbrpx1cdd6wqrdoyhYcdZiazP0CinQAG4Omld6+0qkLfIu7hMyLbIoNyymXSJHgmbHV2sOkHQJUPWVTvrNi9tN6oKCqydPqFP/Mu5H9rrmgNcZ8BkAhx/zpMKETcLJSkpvwZjGoIdwRC37UCuIl5IDrELMckh+9aRQDnb93hA7svZWYVNiLPhHWDv3e31kWDm+TGtSTmjfVpOmHwccp32LtsKU2VFW0MmNbVqhOJcpWrqbWPFlNZ4PmEidQEgoTDvbIcI2cEdJtfGlNMjRMrsERES4MBmyVBKRyrfac2axUIQcRB0nNC65KzMEeJOzm3JNBJys5a6cUvH+/CAj3fVnievt5hSzfrkAds54/Y5HqtzxIWUK+JH51V1yaBuNpezhuX+sqZRwdILH2aeMVlWLZS9raafiyltL0DPlQ25vn2vbYOS9zk5p2o+r+aa8In960yHtMa2oXOsmBCxOHm4uWXrznspG61a0OPeEFIHOJ8V6zuttT/TGPPlAL4dwKsAPgXgl1lrf0jdNlixnjGCFSsg4Pqgq+5ZFTjcWIuqblA1FlFkkEYGeUokziiLkSe3U60i6yfrJqTPqoauPI6RJqTuCYHNAQEBW+Hws8BP/lMarpqCMieKE+CVr6ZhTqqjMx5gk9H2YauAIn061owW6WO9VUJ+d1YuJgDc3/ZKkeCsT0rZ87zJHg0dNtubJ8JfeuC8CpDq5ckjIgtqttJlYyJyhBTSaoN6wcG9TFzYBjCqpUm+MlYySXaTJvdcjpCQTWyd0XB2L6X2cY/PJFArpLubw9MJsN7U9iKk4/zYhzNLxowM6TKgD/aUtXAFieeatCbe3iftaFLRLtteFXRuSqiy5CvB+LwqOY5EMSTnk4kp+FqOt2zUJkVEQSfbIiq7fIeUJdnI37ap2vYt2TfGsNqK94uJ2Y5Z9lv2tDXNWsBWngAR4g/WHyOtpj1RP9XLj9s9TrRaqUX8rCCDYL3KSAKK69Lb9qKEjn8Jvm4Kn2tkIlKuZSNPSsnrFCUi4FWKjWRlddZHzhdE9PiuMQ9nW7ZOHxCBlO8BOy+sP56vGC7aigUAsNb+kDHmqwD8HQBfD+C7jTG/2Vr7bXyTnsSsgICAgABgtbpnXtY4nJY4npU4XhQU1NxYZEmEQRojyej2VdNgXgKNtbeSrPDrB6D1wZIQPt7SNS8anDZe4RMZ0xt2fZ3VTgEBAZcJDtTdew9nZ0T0af9gj4acxTF9YqxtVnHig16zEZE9zkKhgnT1wBFFQJT3kxdikdAqAEcA8YAnvzvFQ8PKBlYbRYBrgHJBqSoXRtQUzwMJ2y8G+/R7XXp1g3wyD2wfHHvZiCJvBdl/hdUGh0QoRDGpRpIhXGaPED2SPSRWnpIDY6WuuTgBpg94kOZjxZFbUi0/4ArqQZt4sGzls02bHJTnEvKvqRWho45LE8PVwHftPUIWrayuV+TQ8ICUETB0zJYLzkw5JlvS/HPA4adZ1cPqk3yfj3+VxaPbn5KcVTHSfHWGfqCcMcF0wiHGai1iVvfEB3CNSdWcq8OP6PWkQ7gKeLHq2cYHOSesDJk+ImWW5OvounMhUmWbhaBrKsCwEksUajqPSL5Xc6CeKGta5MmsxtBtyjl9CYGXjdsWR8ntaj22tr2pyyQE+ayAbmePi/17h6hmRME1mdM5ku/RsZqO+FyovOomzgDLCq5i0lEu8jkW8Wty28PkZjfDSta1rIDpIVzFvdTJZ2Pg9e8HHn4UrjXt/hcC7/u69cfRNcFTUfXW2gfGmJ8N4C8C+A0A/irn7vw+ANOn37yAgICAmw1rLeYlZfFMi9opToZZjHs7GUZZgjwxqBq0FD5dsiKO2mHNaRzdSjVPFBkMouV8obqxzsYlpE83/0jWsEX4hMDmgICA0V0aIh7+OFksLIC9V4BXv8YPG9I6oyvAywmFt04f8xAN/wm2Cw3VNcVx57tuUuqxSAisVSRPJ9ennKtcC1Z2LE5okLOg76LeSIeca7FLKoSzlBSXiTgF4v0O0SPWrakneozpWLeeI9ETRb4iu5iQwmt2CJgjP9jqteyzeYm9R9d/VzN/bM2PSbURGQDStiWKLCYOpHZajh2p/hYlhChYpA1JQrurAq3g6aaij+mFyIEKRYYFIkvExLZ17umYyIa6IiJkcQQcv8F2HlZrJUw85nuUkzM8API7RMQkQ1LWbAIhw3Zfot/FAihrLGRbzFlPEoJcTIm8nTzg8wVEzO68QFXZad5W2IHzXvJdPpfYOhTF6y1nTbnZ+kUxrb8EgbeIGbXPhOitue1KVDDZyFuhhge0jZsosfqIJh1crrdDjqcoBoZ3yQZXTsiuOHkHOPoMZUnlTDpFioYQm1U68nlUUeqPQ9swwVW1t8+RkvzGLCoqmwEJr8fi1OdAFVPgc98PDF8A4hoYHBDJc/9D106504en1mBaa0sAv8kY8xEAfxLA7wXwhbiwJK+AgICAm4Wqbij4eEF15NZaGGMwTGPsDVOMshhpR4GTRaDMGPU3vSYrRJ1yPK+WwobzTljzbVP3AETaxD2ET6XtXJyFdDqv1jZ0yTreNtIsIOBWw1gaUhDREFFzuKioBuTT6sFu+36ST+EGSh7YxdJRTpTNIVIDeOf9RSwnra8OAZSNl0kNIX1crg8PhoUMttwUVUwoK8bC20Z069Bwn5USQ5WT8gzfA10GzR79Xldt61YxBfAY7bYk3tbn8V4tKp5qQeTO4phIvmxEw2TG2Uw6vLcLUSTokN5iSrkrC66sFtJnNgOmEjiceIKim1dkIm8FEshxl+/AM5ca1pM/riae6+RlkI9zRUJqWxfbdnRujpBzTUHnkYmI9JKsE5evw8Tkk08BT9huFKWs7hoSeSJklpBXLQuZUg9plVE2JgLGRMvV6mKzqgt6zLsfoNfWFJ6oO3kA1xjl7GRMVMQJvT84IofXW+xUZ9nZtqm6715flyoAvPTvN4tTIqjqwu9aZ/Vj+2g2IsJZqsl7FVlqPVfehm/XUgHx98UJkdzTx56ckve9xQnZGa0cU2m7dj0ZEPkWsc1MFDu2Znus2NLUvpbcsYibyeqSCJ6HHwfqj9AxN9infbc4CcSOhrX2/87kzrcB+PkX9bgBAQEB1x3WWiyqBtOCWqyKiv5oTuMIuwPKyhmm8dZEwSqyQleIF0z8nKqwYbEiaZLitipTKLCZFFIapbZz8TquaujK4gjpUzScBQQEXHFMn9CgsP8aDbdRQkP6mz8M7L5L5WAokiXWP2c+IBXwtqpq7tuYqkXbThBJAKrYLmJlM5iutklockgTQIazQzL53SyTPlXBOTBcMVzOvM3sCQ/1kteTjWmoHd7h4NehD319FogTIObGKcDnsIjCZfqYLhelkwTT6nrrZ4EkJ7VIfc/btI7fYNvZARMMK7YnimjYxprsGVFqCTlRnNKQujih42t+BNSPvColGRDxk+ZAzDbBTPZdT8aL25aYSRX+v1LFOLkslHK6bKNxigsmgixn2YjNafQCKTi6jWjSLCfZRNWMcnMkz6Vi0iKK4PKJotgrmFxz2BnKHrEoSVtbMSXS03JWkrzONCcC4O5rtK2irhFSRecjGeP3SV/Nva5zl3V17x+SYXNBEbXSLiah0YtTPg6P6ViRyzTJGLMtM4poOySQWfJ7uqRz35ou5TUx4bL7EjC+77N4pOVvlxVOTUktXELaTh/6fZmPlfXQAIbVQsYCYHJUCEgduu3eK/l9d37Iip0cSHdJSVQtLma9nzMuNDXNWvuPjTFfB+DvA/gglinfgICAgFuBqm4wK32LVcOqnEEa4d44xzCLL621SQibvuyZhQoa7ipTupkzYku6jUhjeu3dOvuyti3SrOhp6JIK9qzT0hUQEHBdIa1TolLhga8piARpfTptvSVBf8qtB7hWgG1KuSKjzFsz5JP3ag5UFaj6CjRUSaNMktOAbqDUFHX752qxPGgL+mxf6YBUG9G7FZHEqomCW45mhzQUzh4DJ2/RoARFnuR7wHCPrBjDO/0qosuAC7Hdod+F6BEVRpfo0Tk9m4YCPw3ihCx9wztevXP6Dg2ugwMaWLetq5bg43RAqhGNPnvg/IRsT8WEhvnm0GedxLEP1nb5LCOv5IhYeeMynXoIC8mMquZ8bkhwMA/VOldK1EMlkzatKnf1c74PjO7516TDequ5b5iqC78dUNsieTSaTBH72eKU9sPskLbBNnRfUVPB8iTL218tSK3TskVGdO7PF5yZE7Eahlu2Eg69bjjEWWxSEgANeOIDRhE/iSdShKiKNyCqlo4RttDFfH4LuSyo4re/LgAA0qdJREFUmIByZODCE3GgTSI7n4RNN35f68YtR0qZflVRUwNW2c4AsuSVUyJ4Jg+9rTLdAQb8flLO6fr5Edm5LCu38h2yeo3u0HuO7C85TsSWBd5/8h5dFXT76dtAOaTHHd65eqHs58S2xM77AczX3cBa+1FjzNcA+GMAds67YQEBAQHXDXMhcsoai5L+U0yiCOPcq3KelypmVfZMV5XSJSoiY5waJUvY1nVL1T3GGGSJWSLNdMOZ2Lq6KinjApuNq7RPY3MrbXEBAdcOkrFz+g4NL3VJuR8vfxkRLUvWCFFSdHJvygmwqNvtLRrO1pLQMB2nACL6RFqGFLHDCGEUJ6x2GLRtKS1bhCJ8hIByBBB/kq+Dn/X26ME4vQ/svMSKAvghe3FKNq7FMXD6JnD0acrSMGDiivNlBges8NnHpVewLxE9TSer5hCY8ZpKS5ME5V4m0WOMt4AUvB3Tx8DsCal3BvsXM2RqBUbXHgjAVbKLVWd+TOHCYomp32KiAmr/j3lN95i0yAAJbK7m9Bqk0cw2rPzQtdzaZiiDuPU/S6h037HoFHCpt5jlTGZJa1LF9kjJKqrmcGSJiWgbSglHPvEE7OCAVCSS3+OCufmTHRfiXHH4M9vhyjkrRvgxpA2rKWktpg/p/nJOJkNFzLAFSd4L6gVa+UbyPgLTPpedcorzr3RW13ma7iS0XAifVpbVzDfySaOZI3CY0KrK/uPOqZDyZVJN0KjXWs29ZVECwIcHwB4Tw0J2z49obefHtL6PGtqWlNU8YhddZW+MEmB0ANz9IL2PDfaB2QP/nnbNsVXd+bOCMeYOgD8C4JcBeBnAAwD/BMAfs9Z++lk9njHm1wH4HQC+FMACwA8C+FPW2m9fc58P83P9bAC7AD4N4G8A+G+ttUuB0oY0+0d821X4a9baX7vmegCh7jwg4FmjbixmJdmrZkWNuqH300EaE5GTLbdeXQeIukeHNRdV06vu6ap8Ajz0OmqFjxwnANnp0k7gdRqHhq6AgCuFYgJ87J8DR5+l3yPO1vjQz2ErTezVE2eh0eRP4S0DdUFDkiOCOA9HNxxZNQg3bGvRhJL+BF1CmiWEtkX2rMjFWMrtsD3DZs/coGu1EXkbyvyYs2BOiUSQ4NPIsC1sh0iHwR1W9wx5AMwul/SR/SDDazVrW+GSrG3d2lYlsS0qtocI2SC2PcmcedZw9eJTbjc68aRPcUpWmXLazjTJdnwL1vA+7c+Uc3eEtGlqOrZ1Xk8XunrdgklN+f/QevXH0v1UdpBT0DHBszgh0mDykNVBEdnQhgekLBO1Wcb7PMk2Cw1vahVyPVXHd8TtV2C748ITODHnAqVDuLwYCR5uhSrzeacJYrEW1YqU7VbcR1HbUiXB6I4EStsqwr73An28d5Vv2rKUZEAs2Tcq6Ni191XL7xeiQIq1CklqziO6/eKECJ5yTtuaMWmTDtuPVS2YFD0kBaFYVCWfZ7BH6h9ZDxNRe9lH/xFw8iZbEgfA3ruBD/9SIvCvAdbVna8kdowxkpPzHdbaWeeyjWGt/Ufb3J5JmO8C8EUATgD8BIAPALgD4BDAz7LW/vBlP54x5k8C+C9BbysfATAA8CG++g9ba7+55z5fCeBfAhgDeAvAGwC+BGSO/SEA32CtPenc530APglgAiKO+vBP+p6vi0DsBARcPhaVt1fNWZUTRwbDLMYoSzBM4xs7lFcdskcapgSiStHqnkBSLKNubEshJaqpbmBzmphWfs9tVUoFBDx3TB8Djz5GWTvVgofHAth/lT6pF7QGptiTHd3LXLX0BkGqWlnjhmMVqCtWGjReQVNLpgm39UhAqVNQDInwMalXA236QW+f1cKF46rhVJQS8vzgAbeYc4aPHob5ftmQMi/yMZM++0T8xNwcpu1rFw1rOwPs3K9JnLaroy+LdGpqUiTMj5gESL0K4VnYxbpw1icJqRZFjORCVUCzIGJKVDsVK0+ihAbnJKP9mu2qyumhDxt2NdxKGaKP+T7Ll9iWROkjpCTAihO2ns2eEEkgYeXZmLYj26FjTJ5fLE/GALr7x1muBv7cWZcj5QKLZ+28IZeboyxBANujOGR7k+ynvsBksdtpglh/CfmjW7dWha87clatc5fwsfDkc7Wg79b4tcrEwseKrlq9V8l7mNy/S+wJOSdkDxp6n6g4YDtOiazJ9/rJ1nLGJA/vd6mdFyXegNV7n/le4JTDpLMxsP8e4EPf+PyI1C1xXmKH/5fAF1trf0Jdto3Ex1prt3r3M8b8LQC/AsA/AvCrrLUnxpgBgD8PqlT/MQBfaq3toWsv5vGMMb8QlBP0GMDPs9Z+H1/+SwD8ryCi5hustd+l7jMA8DEA7wHwX4HUQI0x5j0A/h6ArwTwl621v7Wzfb+Yr/8H1tpftPFC9SAQOwEBF4/GqXKI0Kn4P6I8jTFKSZXTtTfdJmgbUrFClSKtUjqsOYQML6Ny6h6LRV07W1c3sNkrfExo6AoIeBYoJsDbHyFliTS+zB4BL3wBDX3O4qSydrTtaRVpchFEUKsyW2ft8O8yGEn7VVXQdkYRf2LNeSDxoG1BETJmZSvPqi8VKqvXQdbHrQsP7eWCBjDJ8SlO/XViA3JhzTuUuTLY8/klraE79cP608LaNslTzjpEj2qbumiix1pah9khE4kRv+79y1UyiYppJbk19CqmLrkm2TE1B3DPj1ixxYHHNQeFowHASg15LGk/ctk+uR/wYTxJqQkfOeZFRSJ5O5LZU5dMnOxQDsvwrj825HVKaHm9IGuaIzpSIj5bLV9K2eYI0tznEvUdc5Jf41rAmMhwOV0Vq5Iir0xJR/T9olRiUsmu3yOkYc3l6QgZ23jiR2rENTEsa9BS7lkOgJfco8K/TqmQl/MkHcA3lvF7Wet9s6E1EbWSez5L1rdiBqAmJeJgj7KX8t1+sq2pfZbU7Ik/JqoF8PHvIIVPOgbyEbD3CvDlv/LGEzufAh1uP8da+8nOZRvDWvv+LTb0CwH8KEi98pq19rG6LgbwH0DKm2+y1v6ty3o8Y8x3A/g6AL/VWvuXO4/5fwHwJwD8Q2vtL1SX/1YAfxHAd1trf3rnPq8B+EnQf1Hvtda+qa77gwD+OMiq9fvPek3rEIidgICLQVE1nJVTYV7SYB0Z4+xVwzQO2ShnoEtSSP6M/J/jQoaTyGXOZElQ9/Sh7Ch7irq9lgBaNi6xdQXyLCDgAvHkM8DpW36w2XkZuPPeze5rOTR1ifR5RkSQa7+STJ0FhyFPVdVz4Z9Hwm3jIastxj4fpNv+1fceszEBpKwmToFUcEU4EwPlCRM+U7ai8GfMYjeLcz8MS5aLC0fm7ZfhW+weay1p0erX1FKvaKIn8c+3Lt/jPBAVQjFRtpQDGpKfFmfZbC7Cjqab16o5kz5M+Ehde8UKDgMAsSKRmEga8PEXD0CqF1Gu1XSfYkYqsKqAy3jJ92ifizWrCyFX5GchpaqFJxtl/0oosA52tg3IhpYwgTTk9VKET5eI1dXqcvxIdbwF24j4fJYQawlKvywI2apVNY4E6lHVaNultn46C5fx+1leqw7QdhbRTOUt9UEI6wpA498ryxmRNSWnm8Q52wD3fTOfkLzyPhAlrBacAg8/Bnzk79JxF6fA8B7d7su+CTh45bJW+UKxjthZSftaa9+3yWUXjF8L2sN/X5Mw/Ny1Meb/DeC/A/ArAZxJ7Jzn8YwxHwKROiWAv97zmH8FROz8b4wxB9baQ7781/H3b+newVr7aWPMPwPVwP9yAH9OXf2l/P0jG7yegICAS4C1bVWO2IuyJML+MMUoi5EnQRWxDahGPOKWVvojt6vuKWuLedHgtPEhw9qCpBU+t3ntRaWjoRu6hPTpC2zWqh5P+ARSMiBga9x5L+WHVAs/dG0KY1hpsaHaYlMiqCrPRwRFMakYzH1lEwENO8WEB+UJDWjTJ8DpQ/okPU79UCa2qG61uq55l+yMp3n/luFXqrtnT/x3afEp59x6VQFG10bzwCnb4NQWrPJJRXHBhJAe9F27klYt8M8xE0oNB7qWBQ2b1nrLSDZmW8r47KyWdRCyqC69TWtxyk1YBz4cehP0BePK601yzsW54KYwY/j1Z7Qm0nAFeOuVqHykbnxxwqHOh8DxpG0rihIfRBwrEiTfoQDk4QGpm5KMM3eiZWKzRV7wzyZmImlI4RuAUvSURPaUM38sx5knAUtD4eF1AUCdZ0L2aCXOkPOkhCh0xOrcX2YbtpId0v6Q4GqxOV3k30NRROc0VhyjmpgTosWRsKrZS+9vIYDTe57k1Goh+ZvPBXznnqQF0M71Uuo/TQzXBb03zR4BiyekwJH3pW4QsnuejMLdjz8LJGNgGNO6T96hfXADcMnJZFvja/n7d6+4/t/w92+4xMeT+/ywtXbSvYO19h1jzCdAOT0/DcA/ZvXPV27wXD+fn0sTO1/G3wOxExDwDFHWjSNyZmUNy3XkoyzG/ijFKKhyLhzGGOTJcqC0y5ypGqfuOS6rXnVPpsKab/P+WdfQtVDqnj7yrNV0pmxdt3k9AwKuFJ6KCKrUz+ckghIeykzUJphcTXMDNDM/6JqYlTBKQaShszykslk3HK2zFwnJknG47d67/HVNw5/EnxLZsTihoF+xE0GCdzlYWp6nrgCrApMlgDZOVNgsE1eiMBDVBtCxovA2JgO/Potj4PhzypKSeCtKygqMPpXQWhVRzMTFXQ40PqK6+WnCOTz7KxQiishx9dqGW6V2vb3qeXx40q1rryuviJqfENEjuUy28oTHfEJEiq2ZqBv542D62DdbRUmHhOQsnWSwbNvTTXG9Qc8cWizWLVlbgPhAk3iVjmUyqTih7dHkR5q3q+RTDp2OEmWR4jyjpiKi1VrAPlLtbSOv5rnsYG9NzPWhRZqVnvhpKibn+tr/Ir5tQ0Serf17R6bUd9nO+tcnHKGo2uZHrHqKvUJPh09LFlFT0fFUNfy5owXMVoakK4urRuxIOPEnV1z/af7+kjFmx1p7egmPd9Z95H4fULd9BZ7f/dQZzyX3gTEmB/B5IMPpxBjzh0HEUgrgowD+Z2vtv1+zHQEBARvCWot52WBaVJgqVU4aR9gbJBhlCQbp7VaGPC9I+PQwi6HVPVqRUlTNEkERR8tqlNuurDLGYJAu5z51m86I2KxwsqKhy2chBXtcQACAp7NiXTZaRNAGto0ziSCd06OIoCihYUsHoFYlDUnoEBOimNC2JGnL6iOWhOyJleJGiJ9V5E8UARErWsYv+NcmQ3c5YcvPlO0+Bb2elIN7YZm0kdwQpQYoTtvbKUokyYZZstx0Xj+gFBlTVqKckg3JgMkjJpGiLcYxTQA1NbVpHb3pyZok96+BFom2PRuTpcnZ6q4AkV8VnsTQrWnGEDEzuuPbpQom7iRsOxabVcn7ly1d9QKYTkCjlfGEQZx0LHlyjMWK9OHjLZGMn85+cW1USr1STGnbygkfc+o16McCE4x1QQTE5BG9DlHSJaJckTyaIT1HnLJiiYml8hFgH3r7X777bCxbq7AJ8aOtXXoNhUCzHPxeTkh5IwoqV2u/w4HXu/0h06Jq23mJq+yP+Xyv6T67L/u12XmR3ssd6ZoCwz0iB28AVr6TGGP+7QU8vrXWfu3ZN3Pgd2U8WnG9tlPdB3AWsXOexzvrPvp+9zvPs1hDNnXvA1C+TwKgAjViacPszwXwO4wxf8Ja+4fWbEtAQMAKVHWDacmqnKJGw6qcQRphb5hjmMahovuKYpUipW6ssx1J3szx3Kt7AJ83owmK265GiSKDQbRM+HQbuoq6wem8Wmro0mHNaU9D17SosCgb5GmEUXbVPjMKCHhKFBMidazkS2TA0Wdo8Mx3n/fWbY/LIILckM4KkdmEhlFBFNMAmAy9LUIG58Z6W44QP5ogkW0Wm1PX/qXJHyEF0gGoABee7KkXPqOjOPV2krLk+/PgPzjgamUJuJV2soIsOQUTRrIWre0SciDl15r5odPVQfNQK9X1ovgRoihO6bpVuUS2oQE5Lun25YyIgmJC+yTfIcvTYI9DgFP/nPPjzpqepRTaRE20IpeoewxpNUo188N1FLEFap/tUDmt0+IEmDyg7TbGD/dZzxDuCITCZ9gUEyZcmIQspzT0G9C666Bk06nglvVpVXIre2E6XibH5DVKflDBWTDNwpNWUUbqqpjzZUoJcS6pRp4eiBVmQiIOlGKMj8n5DKgfwTWQZWMgPyBVVz6+eMvWeeFsUCtyp7rEjygDZf2KUyZ75FiJPZGV79B+iNV5N9gnBVTJ1s3FCR3zSe6ve+HzgcM3aN8kQ+Dg3fRefgOw7q+v3lCeLbGtrmnI31cZ3fTlwxW3edrHO+s++rqnuQ/gbVgJgL8L4L8GtXS9BOC3APgDAP6gMeZNa622bzlwaPNvBYD3vveKfHIUEPCcIDaUaVFjWlQoKvqPIIkijPME4zzGIIlDZfQ1RhwZxD0EhQsV5u/dvJlIqtgV2RPqw5VaCu31bFXb8/dZuRzYnCUR3jme42hWIk9oPV/cHeDVuzfj06+AAAA+VHb6gH6OMxryH/w45WW0hnqlADjLZnRdcF4iqC55sJ76Yb6UhilWAsUxDdXaMmMME0Zl2yrl3n/ECiVtPZJronOE1P6Q4Vwq1Hf5sSQsV8Kki1MiqOaHtK8t2OrDaop0TGGrJgYiA4ArrIVMkPYxWwKLBdlDAEWOxIogSLyypq6A5pG3kbiMHlYqZLv0POXUN0BJ2HU6pGH1xS+ix6wKsgDVFT1Pvsd5UBs2nPXVY296jGgCCBJIXKpabCirn8qeSQeshAO9xukjb3NKB3SOZTvrVUaaQOjmXzWNCkZWCipR+RTcWCXbHcETOHIuC8mjycZuPbcQDIN9Ilikht0dHwv/vJKbleZcvc75TRJuLrlC82PAHtL+M5En/iQnytZ0+9OHwOHnAFuQ+iQbUybY8C4dA2JNk9yhq4JNiR+xW8r32RMi/Zp6OStL2z6zXaCe+YasJKNsnoc/zha6GNh76do0Yp2Fdf/b/MZnthUeNeh0WgV93SbvNOd5vJ7o9JX3e5r7AMAnQHk7j6y1f0Rd/hkA/1djzCGA/x7ANxtj/idr7RJxZK39SwD+EkCtWBtsR0DAjULdWEyLilqsWJUDAIM0xt1xhmG2nOkScPMghI2eOcR+tKi8/airRklVZo88RggX9uHXI6WuFnucbuk6mpX41KMJdgcJbAm8+2CAd07muLeTBeVOwM1BklOeScVhs+UJDfa772KSh60Fkj+j4VpklBqg+/tV+GT9IiFEUJxQK5WGVs9UnEmja5erhVLApGqYtlhqDWtl/9SejID133W9somJSHLNObnfBzsvANG74OqhXV4Okz3ljNQeFt5+EkntNQcDS1aKbG+r8atQ2S21skoZn+UjeS7lnBQ45dTfL4p97fvwjrfgpKO2tcgYwL5A2yq5I8UpkQ2D/e0zWZYIIN4PrZ9l+J744O268PsqZpWbrHuUcAbNhFU1oq5htZYQUvkuPc/imF7DpkoiCb2W20QRELGKa7jffn0uf2WmmpymPix5ceK3yUTqHJZmJyZWhMTR6Nq80iGpqMAZM46kU7Xykn2kq9YlI6k4XW5kA+g2g13A7pBKrpoS8XH8JpEX6YDUPIMDup20RrmAc02EXjEyWogfCZ0WSBC4XpemAuYLRe7GgEk8KVfNgQc/Abz+fXTbfERE7eNPAqcP6D3gmmPlnrPWfsuz3BDGBMAB2pYkDf0xwSbx1ed5PLFSresRlPs9zX1grf1OAN+55j5/FsAfAWlJfwaAf7bmtgEBtwZztldNyxqLkqTecWQwymOMsgSjNKhyAlbbj0qV2yNqlElH3ZMqsidPgroHaNvjxvw/WhpHeOVgiGGWoGarY2wMiqppkUIBAdcfhiqVo5g/opOBfhe+eSpStp1Oi8y6MFH55L9L/Oi8mZuCllVKQfJRhOypF15pAbRVCqKkANZnBFlWDVUqMLVYAM2xJ4UAeOVP5NdbnicZeluLUyIV/NgFWWKcpYoVFQk3Jgl5lO34rBW5X7fmvZzB1b+XMwANXD17OvbqAhhSFC1O21k/oiTRiqV0SJcXE+Doc8Dp2zTcj+5t3tQlSqOOqtNtp/4O0OMOdn2AcTfPp2GCqJqTGmV+SOuaDUnlko1ozZaURDUpoTTBtCk2spJFRLoMD/xaikXItYhJdtMcaE7otURC9nCrk1jfpDVLAp+75722eWW7tL/dMVt5laA0uQ32gd2X2JaVeNWR2L2KKakI4xpIEsAyKVbPgfkpEdNHn/VWpnzPt6BJsLi0SfWS0Un75+dNRst2ZGNgdJf2kyYJhTC2DVy4tTG0TvNjIhtrVl4ePwImb99sYuc54RGIiLm74nrVkYcHl/R4kq2z6j76ft37DIwxI2vtdIP7nAlrbWGM+TEAXwPgtU3vFxBw09A0FtOydsqcmgNf8zTGnRGpcrrDe0DAKkjl91hR+91wYSF7Tub96h5N/Nxm5GmEBqD8HRPBWova2lu/LgE3DNWCAjiNoUE8Sun7k0/ToKyxKrdEhj+YzrCqLDDlHD70tZMv0x2wur8/70HraZGwAkJ/5FpXvoFI1A0LFWXp2m+EZBv0WzrOygiStpyayaW6AiqVoaPbpET5Y5jM8U8C2AgwU95fMtzb9qAvpJEFXH6PbeBqvOOM7X0JgJieQp5fbDd1yQTAwg+0Sc52ksQfp5JjIwqZ+TGpOACvMMr3PJnVyiyK24TiRTRsNTUpYKSmHuBt2CXya9tjuGmWCaB1uUTrLGdnwUSe5JNzVx+b82MVNs6GEWcN4xasOGdLVOrXtinJKrS0Dbzddc3r/YiPn9S3YyU5BwPzmrtWrYUnOOoFsFezGmnuQ6gXbxDZk+ZELAnZk6TeW2JkO+C3uUsg9pE+Z9m9Nt5v9RnXr9hvQo6LMrDmNYYhm2J5Cuy8nzJ6kiE32PUQ7tcQ5yJ2jDFfBeCLAYywbHVKQMqVdwP4j621X7DFQ38UwAcBvG/F9UJuvLmCPLmIx/sof191H32/jwGAtfazxphTADt83Y+ddR8BV6VH1tpyxXPJu1yx4vqAgBuJRVU7e9VcqXKGKbUnjbIktPUEXBhWqXuqDtmz6Kh7jGk3c+XJ7WqSGmUJXtwl+1VsDGpr8eLuINiwAm4WkhyABfZeoUEhSmhovvMaDQZnDZKbqg0M57aIqqOp4fJkHAlUw2WVaAKpVe2c+1wYyeS4Srkam8LZuVT+RdN4RY+ofAo1EhijyJ7ctyCdJyNIclCkKr5WVdd1qbZBDfQVZ+zUBee2SA4P70t6Ahp+xVqVDmmoHt3x1iqxy8hrFmWP1DXLEF+c+u+zJ54EzHa4TWjXkwmSBzR9zBklDynPRixdMnJYy6+18GozqGMsHan8n54Q4e5aSti0WK2SjPJf8r2nU6NFEdYnbmwBOW+bGoD13zchiVygtSJ7hASrZsDxUbsBysCTfdmIFFnpwCuvnCqwaROL9QKoT4l4tDXZQU3qSb18TPt7sEfrC/STo0Iiz56QYmp+TJa96C2qXx8d8HHDx4S13AA24/cxdMgXS9sCscJZtX+6JLfKwVqHJYKcFUTbhHq7wGnev9WC1D1v/hCdLw2TZrsvkSXrBmCrv7yMMRmAvwPg521yc2CjHByN7wfwCwD8NAB/vuf6n8bfv/cSH+/7+fuXG2MG1tq5voMx5kUA7we9O3+fuurfAfhZ/Jh9xM7ScxljvgvA1wH4gwD+m+4deL2/iH/te8yAgBuDprGYlUTkzIoaFecUZEmEg1GGURbf+irrgGePVVkzRcfONS3a6h5pkmpXh5sbefy+eneEezsZiqpBloRWrIAbiGxMQ9vbP0ADSVMB97/QV2yfB0ufWnc/nV4zRDZMIDSqSaZYUBZJU/Jj6wwO4/M+Yq4hTyQYVixHWc+gFHcGpStADkURtyKpgPa+3J7FMSDvyZLTIYqYVk15D3RG0CYQ+50MziXbPeanwOIQmD/hbWO7lVSQFxNq/DFi/2JVV8JETDpQIcNsa8pGQLzLgy5b/1xIccEEyik9/+yJPw5cYPEuWaWGd/y2ivVH9nnNw78QUy54Vw3kko8j69VV+9QVhxPPAMuZRIN9Uuc8j1rus+DCrS9A/d137jolDa/bgm1U1RyYPmQVFhMkrq0r9yool4eTeotnvSAFyuQduCpxwNsA8x0gGdMxk419LlSUgoi6mMi1uqA8ptkh2fUOP8X2rIyuH+yRMjGO+b1FZUVJppgQm9bCtXdFSnloRNGTklXMqPcj/ZVknoy+yL+XxAJ6933AB382cPqI1i/OgZ17N6YVy9hN5GdyY2N+LyjMFwA+DuAJqD3rkwDeAvAKgFdBhM73Avij1tp/usXjfxmAHwJwAuB91trH6roYwH8AER3/ibX2b1/W4xlj/j2Anwrgt1hr/8fOY/4BUHvVP7DW/iJ1+e8C8GcAfJe19md07vMagJ8Ercv7rbVv8OV/AcBvA/AjAL6iq9oxxvxOAP8DKGT586yVWoB+fNVXfZX9/u///nU3CQi4UiiqhrNyKszLBtZaRIZberIYozS+9TXVAdcHou4pK4tFXXPAsG+SMsa4yvA8jh3pc1vUPQEB1xbFBHj7I5yXU/NwVQAvfZgtJDq09Rn/n9UdIkUV4HJcRFVSgWqXRfVRq/ty0LBk/UjmRpS0f0b3U/RVX31WtHj5NpcNl9uz8MSPDreOU6/oSXJfK32e59E13m7ANvSYMpwnnB1TzrmG+YgG/cWJsoMtOmoRtnxJkK4QdImQPqKeGbXtMlFMhEqlgonLqVf61AVIYWFZjTTnhqEhMH4R2HuZiMvumjSND3mWL2clXLAChBUqYtMSVVIUM3nStXx1fr+BH4CshG7s0vtqftpWh1l4okTIj4SJWXmcZuHbnxan3Eg387Yld+zkvklKVDBx4uUYdQEsjoDZsc9+imIieEb3gJ2Xgd0XqbZdW0blmNUWU6veZ7QNEpaOT3lSyZDSodF9dq/W7+ck4Z58BnjySdqWKAbuvB+4897zPdZzgDHm31lrv6r3ui2JnX8D4KsB/G5r7Z9lRcljAP/EWvsr+DY/F8BfB9mxfoq19uNbbuw/BPDzAXwHgG+y1j4yxgxAipvfAODHAXyxJjmMMfcB3AdQdp/vnI/3TQD+JoBjAL/EWvv/48t/MV+eAfiZHH4s99kF8BMAXgbwpwD8l9bayhjzbgDfDuArAfyP1trfou7zRSDiKQXwLQB+h7X21NBHur8WwF8GaUa/yVr7t85au0DsBFx1WNtW5ZQ1nXZpHGHE9qpBGlQ5ATcHfeqesrJOkQaQuidNTKuZK4vDeRAQcGUwfQw8+jhw+g4NPVJ3vv/qcsYO0LYRtKwIqy7rXq6u77vtRUDsPWLr0ZkhEuzbVRDJdrVIGqOUPcBSK9gqrMoiOpMkekoVUdeaIpk6glZuj1i5VG6Pq69WFe7ymiPJYWF1TZJvvr+a2it+HBHDWTayzU3Jdp8SZBdSxIrYe6KUhvYk520YwFmLRDFSzZnMKXxobjoC0ozaxeq5b3HKdkn5oau/JTtGMqMkt0VXlKcjen69nWJv09vdN4dKIPES4aMG/6vU3CQ4y6rVp8RrkSAdq2ZdeiVYyeonqU0Xm6DkOSUc9J2NydqV8VcUs+WIA5bLCZO8nPAhShohSwA4hR8iep7ihFraZk9oX9cl3TYdENEzugtke55c7DvmJdNKrKWiUBTlohh9bA00lm7XOu/RQzpHnVyfuP37umPk8adJKTW6D9y9XjG2F0nsPAFQAXjR8h2NMd8J4AustS+o2/0yAH8bwF+w1v7vt9zYV0BNUa8BmIIsSB8ANUMdAfh6a+2Pdu7zR0HtUZ+21r7vaR+P7/dXAPwm/vVHQeTL5/Hvf9Ba+yd67vNzAfx9EBnzDoDPAvgw//4DAL7BWjvp3OfXAfgrIFvcFEQ0vQTKKFr5XH0IxE7AVURZN47ImZU1LDfn+KycONRLB9w61I31uT1nqHsyFdocFGwBAc8BxQT42D8HijkNnXVFQ8MHfhYNNzKsSc22HuBaOR3qZ3fbLVMLZGjqIz56iaGe2/aRTV0VjdiLZLiUL/17d9u1JUceT4KdhZDZNK9k01Bbed6nURFZy6+raLdzuXUo2vtWkxstIueCqwCbhrdlrtrCSr9v3JDM24+aCJvFMQ3x1Yxfh2XyJqOhP9+l71EK2Mrn9zgVF4ft1nMAEddl77MFjte7LrkdqqDHyceUUzK84wf8TciXLtnT+l2FXfehpfaJsVIBdBY2JVuexfGqa9pX3Qbw52I59URgMWXVl5B9xhNwKdv4Es5wkvweqXqXNZe8ru7zCeqSSLzpY7IYFjM6XtIBHVMShp1LtlPGhNEZ66f3ta2IwLT6OGiUkk1yxixIdSbvL0aRVKknI7t2rzd+AHjwYwBiUkG9/OXA+75us313BbCO2NmW7hwB+GHbZoN+DMDXGWPeY639HF/2d0HtT9+47cZyEPFXAvjDAH4JgC8DcAjg2wD8EWvtTz6jx/vNIELotwP4EhBX+D0A/gdr7d9Y8Vz/jIOl/zCA/wjAl4LInb8N4Ju7pA7f51uNMf8BwP+Z7/MlvH1/D8Cfttb+y21eb0DA84a1FvOSMkemHVXO7iDBKIsxTOOgRgi41Ygjbzmkzw3o3CnrtsJnXjQ4barW/TTZk8ZRyJ4KCHgmMIAtQa1DgKuTznee7mGtIgzWkkDN+tsuhb2egzQClpU0XbJIBiVH0lT+U3fJ3XDBz1bdX4ie2H+iLmG8Oj+kSy65dVCkwyYDd6MUR9uoiOS+VeEH5pqHaGm0SobUwJWN6SXqSNEouTg7XhQB0ZCOM70mouyRoXxxAme/a0ofamw4XDdKQCSQ2kfVlLeVM33SMe/Til57OiRrlRAH82Of+xKxcso2rGYydNvida7U5uvl9nFGQ37MmTHycyR2mxhrQ611mLUmfcRWWEw9OeXODT5e+o5Bd1wzIQBzNgHUpzCLYlrfJQKxazt8CoXZKoiiarDXvlzUduXMK3TEnjV74tVpceyJScnwiVlhZmv46vPIH9OGv4/ukFWvYuvX/ITUL4tTYPqAb5dSllO+R8fi+C79nI77M642acpyasKiTcQ6YlICnk89OQcmuOQUXZwCn/pXdEyO7wGjF4HPfA9w/0M3ou58W8XO2wAeWWu/WF32BwB8M4CfZ639Z+ryfwvgS6y1o+VHCrgMBMVOwPNCVTeYlqzKKWo0rMoZpBFGaYJhFof644CAc6JuLMqaGrnEzlVUDfT/30LwaCtXUPcEBFwQnBXrbbTqzvdfJYWCG/Ji9Yl7rAZcfdkFBbRuCrsJWSSXYcvbnjFDtD6JrzwpoxURzl7Gw3YsJE/GGSLdT97TfsLpLHJbtx7pYVGIAWetmvvXHCXeiiUWk0pq0dVXK7dHApBzr+bpKiD6VESRIhjWZRFJk4/UjtcFD70FEY5OwSCkBeCawiK2lJmIXl/J1h6nwqo75IUhImDBAb2n79DtkwEwvg/svYeGfMkPguHHKdv5RvL4jmjhY0lbu1r7PPOvWxRVrt1ojTpGlExuP9f+mHNEp+wrvQ94rbTFzH2p7btI0u5ZQ6yXQsZI5tIqlQ/QVtuJ3ctZ/YaeoDFgIvSECMDpI1LzzI9ZMcbHd5zQsSJhztkOK4l22taxc7/G2iuZNAnU8HlalcDbPwp8/F8AOy8C+QGw9xLw+JPAl/0K4KUvuYCFvnxcpGLnRwB8gzHmFWvtZ/myHwft0q8G8M/UbV9GqOgOCLixmHNWzrSoUFT0H2USRRjnXpUThVDYgICnRhwZxD1V7D6zh74vqganqoo9kip2RfZkcRTOy4CAbSF153fe74fG+RNg92UaVBo1QFYL+DyJNRD1iiN+VOitJopapNE5hko34F8CmbSJ7axPbSSXN7X/tF23fJVTCuB1n7p3Xo+23Ejmhm7ZibSNpENUuPYqJh5sDTd4ZiP61F5sJY5siTqvWauIGm+TcgHKbJlaHFOYsAzLJm3ng2yCplJB2AVcroqJmTgaUp7N6K4ikGL/WmVorwvKRxFCxBhaq9G9NoFUS6vYCakbignth+E94OA1uu3imKrSn3wSePxxT2BpMiQZeEIpzgE03u4ljVvlAqiO4ZrdwOoqIX1aREuqAq7VOrYyVTRJlHpiou+47QZAaytQU3nirIu+BrAl+9eK532eiCIg4uylJZVP5XOjhOQsJj5ku64AsAKqETI28sHg2YisV9kOcHAA3P98WsOSlV6Th3TslXNv4SsmoNQS4wlUE/nqdmmDyzirKU7PJn1kX0gjWB/iFHjnI8DeqxyCz2SjhFBfc2xL7PxtAD8bwP/HGPN7rLX/X5BdqQTwu4wx32at/aQx5reDGrJ+4GI3NyAg4HmhbiymReWycuqG/jgYpDHujjMMsxh58gw/hQwIuOUQwkYr2JvGOpKHcnsanM4rNB11Tyu7hy1dAQEBK5CNqQnm9C04C9LdDwL7r6y/X8PDvxA/Wi2iyaCmBqxqqloFrS7YmAy6xJYhrQq5DDRCBpQ+5LiSEGG+zKmBSq+2kddvDVyGTFPSJ/a04TToxqw0SVhlA6ga70c9r/WMjKJ0TGHDztKliRXeBoD2cwQf0BwrgqKcezWFqGlcZfmQFC1SWS+kWcVkUh9aChfriS1Rb0iWkFwn22kiVmeMiLSBYcsXq2d0Xbo0OrWaxVh9FedMwGSeiHHqNbHi8drLejriRfaxnD+NP0dkG/Vj6ZBrt88itEjA7rkSCQnVQ5yuawBrKg7OXnHORmq7rnoDWMxh1NkIGKvLl1Q+E2/xEiJofthR+wzIppjvE9mY7wIvfjGdh8XEE0fVQin/DIDKr+X8CJg+8dc7C2TGKh9ug3PEXo+NcxXuvAbcfT9w/AaJtyIAB68C+++50CV9XtiW2JFA4a8AkTsja+3bxpi/DuDXA/ioMeYEFExsQU1PAQEB1xSLiqxVk6LGoqTwOskGGWUJhmkcqpoDAq4Qoshg0KPuKTvNXEXVYNKj7hHSJ0+CuicgoIU776VPpxcnNKxskscQRQCi5YFzHVqWIU0G1Z4ochanUl1/FiF0hl2s9/rnfP6LygA5gBWfwLvgY1GlTGgfFROgPOWh0/LgusNVz6x0cUGx/HpjZYM6y6LWqnbWl63INpI1lUpzqbguJj4bpy79kJrmRBJlOxR4m2SgsFgoS1NrIZZtZmtzmcCkBkhVsTihgVsqoNMREPPta5WVI9koFgB26Zh0bUtMYkUxkaGSv5PkXNE98I8Rc6CuEDiNtCF11izZVa8Zy2sttjLZ/zrLCZE6RioAZ5CnZxFBQk51FXRdtU/353K6+hxd1QDmnv85NoBto/IR4md+RD9PHlJAsaxVNqKMneEdzibb9etUzQGr7IrS5GaMPy5sSfk5s0dkiYX19j35SgfLii33O69hNgZe+GJgMaHnjxL6PRt3X/21xFYZOwBgjNkDNVD9LPF3GWMOAPwDAF+vbvr3APwn1toVUeYBF42QsRPwtGiadh25VDLnaYwRt1h1B8aAgIDrCVH3tOrYq2aluidVKp8upkWFRdkgTyOMsitYQxsQ8LR48pm2YmfnZSJ7rgokYFYrhHSmTIsgUgqIdViyi3Uzg7pk0DntYudFb96MqFsylXHDxJqrd6/QqnrvbfdiNYcbDju/r7L4OEWHzhZq/HOJIkgUObYmS5LsIyMEACtX0PDaytCf+uG/mzG01J4kRIUKwI0SwEY+eLleMJHD1ebZmG4nOSXy1WhSxLDKKVdDsyG7zfQhMD3kx02IzDFG2d5UjpLYd8RO5tZQkWXWktpDfu5mALXyd/gfuW2kjkuxUDmLFq+PkK/SEtU9h56GCGpdFsMTWX0NYJV//j5cVAPYZaOr8pkdkm11dkzHRzX3+ywdEIkjyjXJ8DHGBznLvhKFnYQ4QxOKkhHVABHndTkiLvVquyih4/7xJwDJgxrepcd86cPXhty5sLrzDZ7o6wC8D8BPWGv/3YU9cMBGCMROwHlQVA1mRY1pWWFeUiBrZAzl5LAyJ6hyAgJuD6oO2bNgS5fAmHYz14PjOR5PC2RxhNpavLg7wKt3Q29CwA1CMQHe/ghZeWRIK45pGMj3OuTGNfv/smsXO5MMOsMuBmBlaHSrzjneft1qtr/o0F8ALstFV49vQzDVVVtFU1dU9S3ZP3UJdPODDBSRpcgUIYFM7G1P8lUXPGCyZUWUQ+lIWZREMdT4hiNRo2gSQysRnN0pWVYWibrHNqR4WBxTyK1YZ9KRzxM6a79qG1e9oPPBBWBHPlsnium6ckFKC4AVUgnbxia0ruWcHstVx0tWyy7ZeYxZJh9btdgd8qwVyM1r2LC6B/BKLJeTk3mSKeUwYGeNy7zyQ157H+lz4USQqKE6JKHtkkA9pKx7/HX2r+cYAC3n7+wJkX+zJ6TwEUub5CwhonMkFYUdW6+SgSfmhIyURi/XmrXwNi8XCK5I6PkR8MYP0vGX77AiLgNe+3qyjl0DXGR48lpYa78HVAkeEBBwRdE0FvPKq3JkYMuSCPvDFKMsDvXJAQG3GAk3ao1UlqC17Rr2om4wLSo8OKnw8Qen2B0kKOMIL+8N8M7JHPd2sqDcCbg5qBb8Q+OH2cUxVTsPDtq31aTG0vCmbRxXpGHnIuxiLetY1y5WAc1itRVF0LWLmYgex9UbL8iSIeuajoDBvrcIaeJFiItVTVhdq9LKtUmBjLNsREkhw6eQBvqxmpqOC6l7NyDyIMmA4T6RgAMmLjbNBNFoajW8cshxMQUw9WsoDVVxxjXprKQpTmlfZCNuBNqln1ft295WtK4VzfoQagmNrmZAUfn7yLbOD4nMGt8BRq/CNZKJsqM4oTalk7d47SNSUOS7RDzlO550ccoUA2hLmtjmWpXoFSl+HEHC+61cAM0RH2Pc2EYP4s/VWIizEZAxCSdWsoRJrDhrK9hECeTWax0RdA5rWJqp51H7TPaHvHaxSfU9tlOkrbF/Xcb7U5wA8S7t0wOldiznHDR+TGTP4oSI2wX/LtlUccaBzRmQMCEjuU7ZmN4PBvtE7LpjT75YKdQ0dJyN7sO9700erFZKXTOc+68uY8wrAHZBZ9VKWGt/9LzPERAQcDEo68YRObOydqqcYRZjf5RilMahGjkgIGAljDHIk+WA9AcncxzPSgyzmMsyDGJjUFRNixgKCLjWSDihfOdlb/+YjYB7H6Lrege36uyGrFWf4GslS4scuiIfuOgA520gg6drwKpVOLKyKXUtFi6Il1UWUeIHVrFptGxIatCOlYVKhnVp4OkLQF56HGV16r4WCQ2Wlp+Kw5zBrU4yJOtA4OkT+gLYLqKqtKXpaVW1tjR3YbS8HXqInT6hfKGClTFxSqTS6C61YCWD1UP7RQRiS26KqJSqOTA/ocF9fkxtW4M7tC35uF2VLoG88xM17B/SoB4npOpxbUm5Pz/kWAB85koXmqwSK15T+XO3FjJIyKopMK+A5h2vCjIAIOcmH0tiKUoHPpBb9t+qYytRmTKwzE81oCDhTi6Sy0/agghyzWSRfzzIcwCOeKsWQDNZQwB1yZ8eBdDTvi+lvHY7L/ImquNnceK/ZoekuJmdAPaQ1jdKSQkWqX0vGT6je5TpM75P+6QufcbWmz9IzzPYB+5/3tWwsV0AtiZ2jDF/AMDvAbCJXsme5zkCbg9CLsPlwFqLeUmfqE+VKieNI+wNEoyyBIM0qHICAgKeDuM8QZpE2MkTGGNgrUVtbW8OT0DAtUVfK9buu4HhwWb37+bfuGGyaQ+WmwxuLdWPsm/0kUMXia46Rdd9dxUeq740wSVDZcs+wa87TmnIS0eskkiX10QHCGvFgrudRUvV0H3OtaHRPdlBgCcpJNfHbW/iA7WTISkJutAhz03JJAL/vKpdyWXj9BA/zvIlWSU5EE39a4wHwIDVQnFG+6paUBuQrLEoe0Tlc1HHjIQn68wSURsVJxSsO3lEaox0QKSTKHKSAbD/XuDegK1llpUbR2wjO/XkVZz5QT3nrB5H4GX+XNW5O+sCsVuh2Ez+iPpKVD3lghRoxZzsekI+CFFpALISMekgbU6i7klY8aMhGTCSiYSeBrZIn9emfZy7YGlRJJXtcwJoq4lazytqPU0AyRdfJkTXymBwUSmus4BtQUzr40fsUaK6Kfn8mx3S8bA4Bmp+bxXVYTUjcvDRx/jxUiICh3foZb3xQ3Q8JRmToBPgi37RZtt2xbHVJG2M+d0A/vg2d9lqawJuFV5/PMU7J3PExoRchgtAVTeYlqzKKWo01sIYg2EaY48tVqHSOCAg4CIxyhK8uDtYei8PRH3AjcOd9wLjezRgJPl2QZuGFSeb/tmtc2808aPJoboAqg3UQG5INFgeGAEa3kQlcA67Ut9z6q+Is2OsGhBrzq0xEQ32wwMavNKRalO6ALg161piOjaZPrtYy/Y0V3k+vM2SjZONfdArwFk4dYc04nVPMgBrpIzdUGfXfFb222okg6aa03YCRB4MDoDdlyg/RA/TdcW3ZQtUOSP1jCCKPbkipMlF7QtRG2UjYOclem2zQ7LBlBNg+tiTHkLIRZEP1x3dBfbeRSSBkGvzI1L0VHPKvLIWrr5ctj3JuYVL/Xwei5FuFxMCyKl7WHFWszWunKssKM4RWhwxX8L7MEk98RMlfhtFwSWV7r2NZorUhIU7f+X8hlIKudvAE1qS4+Pupske+McxTCK5cznyj+0ymSwRK6gAKLKzaxeT/ela6FQAtFPdxepxebs1QWUin0s12Kc1rhZ0HM2YoKk4DysZANkBYDirqjgFjt+kEPy3f4TWPdsF9t4NnD6k6zZpOrzi2PYvr98OOgT+LID/G4C3rbXV+rsEBCxjWlR452SOYRLBwiA3wJtHs5DLsAWstVhUZLGaFhWKyqtydgYJhR+ncVDlBAQEXCpevTvCvZ0MRdUgS4L6MiDgqWEMYFUQqpWhp0O2yM8yXLZIgapNBulq7r7nE/uGtgaJ8sFljagq4SXbk2kPiHXZaaxSxIjkYJwn6HgbbKNcku0tZ2RhsgUPmzmpQaLUZ6tIc5OQP+VsPfnl2sXU8Gp6BloZeNMMyHu2W1Qk5ZwUCbNDX1Eep6wWGtLxcPI2gLe9ciJW+1SIyYjzg7pWrmLa3nad2yPfn/ZvyygmonR0l4bu2SFnWSkFUlOzJUqtbxT5FqXxfVLOwdI6VMoSV06BwnpiTQKm5bmXCJ8zXpNRpEkLZ3wgLRY8Vw0+8xY+d25IWPfMExhRRsdanAFpzhYvPv+cDVKpi1xWkFgchRSs1fuFyhjqU97J+4OVHCmldAL4mLf+eeX1RWASSFkbJWAccnzLugmhzCHiJkHLOin7xzXBqfOjq1YU5JzbI2s8P2br1kN+yojq2vfeQ9sd53RczZ7QtiQDOgZvALb96+t9AD5rrf3dF78pAbcJi7JBbAxOWV0CAKeLCj/59gnujnMksUESRUgi436OI4M0NreaqKgbi2lRUYuVUuUM0gj3xjmGWRwsEAEBAc8coywJmToBNxtPPgM8+SQNP1EM3Hn/ct35qoHpvHaldZBB02V2ZIAZdAiXHgUN0LFpNH74W2rg0cRR0X7+bhaQy8speMC0PISrzAtpsbkKf8cJISPDtYSnGkPbOTzYbnu7odFOIdQsX1YpK946aLuYify2lnO4VqCdF4koE7LNNorYUyRfNe8n9oxRpA9XkEeJH+ItZ0XNj5Qaw7RJHhcifI6/P43xg7lYbIoJbW82BsYv0vN0yafu9ojSaDxmYqH2yhmxPDYlHDEjJJ5+DCG9hLwUNU0ro6ajItn4OtBzZ2wvxL6/rql4e5Tap1oAi0Ov3JIKeMNkSBR74ifhbB9n1UsoWyrl7KKWFa2rAFJEjQufFjWbOqbRQwjB8trq491625o73tRzo/HqQRjAiHQI9F0IHfceBk9MxorsijMmMgc+50j2l3xZy5k8j1nRM6fnnjyk7U8y4HDG2U2D7Y/dK4htiZ1HAI4uY0MCbhfylGpxD4Yp9gYpqroBDHBvJ0cWRygbi1lRo+r5Ty+ODLW2RIbInihCHBsigfi6m4Q526umZY1FSX94JFGEce5VOVGoIw8ICAgICLgcFBOqyJ0+pEGjroFHnwBe+zoaKi7KrqTDfLtNO0tfz+j/fVH9dENmK7bxlBOvyLG2becxOV1WzogYKibLn7p384IuI8TU2nY2TksBEnsCRyqvzwOxqWyLVXYxyWUqJsD8MakQZPBOR77mWQKnBfr4krXVgdEWoEFeKzmsIn86f3c7NVfmtxeNV/ZEKrdFnkfUMPKcwArCo4cUGezRa1scAadvU0tWknMF+ohec5YAdqgyjwpan7rg18SblHBlOeAJLwk0h1UqE1Hj8G1q1ZAkxJco16KUSdRzHKdyzhpFZAjJIQ1PIyE94JUvTU2EaTn3bWj1gs85PkaaE95XqVffSSNbNvKKpShRNrDUv/e47dLWzQ3QZ1FbaqRTFe3OviYWtgIuaF4HqbscqhIoS1LgLBHg8nzKihaz2imOgIhzo5IhkVwmIuJQSE+AroOhsPEbgG2Jnb8P4DcaY16z1n76MjYo4HagL5fhA/d38MqdtqTRWouqsagbi7Ju+Dv9XlT0e9P5Y8oY40gfp/yJTYsIuspESN1YzMraKXPqhl5fnsa4O84wzJabaQICAgICAgIuCdPHwPwJq3USGpYmDym4c/Be+sS3t12pQ85chXrzbSFhraL+0MHBcQpkL3hiRNQtLdWPUrG4sOC5V8j0PqfK4ugGRLeyOeL+NW0a31Yl6gcdzJzveiLnojJkzos+u1jNg2w5oTXLdii8W1RPtmFVRENDs6v0VoHcYv+xtQ+X7RKQrfwSwCsnAKfkaiwAnUtkFQEQ+edoqdSsP/6TnK1EuSdFog3XPBkSeXj6DlC/QedZtkvWOE1upmxXArw9rql8DTv4tYkaq9UUpcglEwPDe5zzExNPV/Nxr8ODq6JNYMZs00ulhaqTaQVcDhHrAp6ZIBHSp5zR9spxMHtM+9GA1T4SxJ369zPdGtcKPlYh3X1YaVG7wNdoa3pt9YIydHROV73wa1CqMHYhhaoF2axEufb4U0RspQd0LOy9B5i8Q+fbDcC2xM4fAvALAPwtY8yvsdb+5CVsU8AtwSa5DMaQ/SqNgUHaT2Y0jUXZtEmfqmlQ1RaLssGkoXpvjcgsW7zoO/2eRM/W8rWoamevmrMqJ46ojnyUJRimMeIrTEYFBAQEBATcXBi4bAb5FN02NGxINsNGLUtR+/KrWrHb1G11S7Xw1yU5Z+QwMdL3GjYlS7r18H2WsFUWIoHhT+tlkJO2KSGHkiFZevIdIB1ziLWC7Q74Z1hr5D5n2W62tevYhpQXi1Mio6xlpcqIKr6L03MMn2zbSfg1txQP2mLDChZtHXO3F/mL8ftat0a5p5Jslait1lgcA/OG7p9mpKJIBhw8zeHT6aifFHH71xDJNTuiod1E1KQ1PNjsWHOkgHzpkF/1t7V7/axOA4f1ju7QNprYB4BXRbslTeAsQ7myDF2STzliVUqfykxa2OpCkT+lX4OmIhVQNaMsIgu/1lLlrhV0S1lNsSJ+kuXz6iJfIyLezxsG1uvw+VYIfQO88MPA4096+1VTE3G69+rlbP8zxlZ7wVr70BjzawB8B4CPGmNeB/AA7p2u7y72a59yGwNuMC4ilyGKDPIz/jiq6gZVw+qfWhNBDRaVdaoYjYQtXqlT/yxn/pwXjVPl1C3LWZZEOBhlGGXxSiIrICAgICAg4BlidAcY3QNmx17Cf+c14P6HOORVqRUcWXFGdbmgjxBqEUE9RNFFE0Irg445OHd01yty1qmOXMjqCuLjrOu06im2y4/nrBlzIjrKqd9usQgZNXDGGWCO2s8j6oIW0SZrGrXtYd1Wn22wznajv5czblKa0jIkCatzdniYXaf+WPGYXVLkrOvO+iBTq67WZQfJOVArIsGyBacuvIJqfuwHbVH+JAkRb9kOvfZ8h9Q5kqGS7xGhWM4pN2VxTN+zsc9DWoWEyZV8119Ws3JMW5vkfKu5WalqiGgznBWlbXuDfbaaGU/ySNuY2A71sSCqHmn+irPLI0PkOZMVpNIS6VO1fxZytV4ANauvNPHjlD+d9yFnAdOV5zqI/RkVK7TIoA5e+1rg878ReOfH/WUvfiVw/4PPZtsuGdvWnX8lgH8ER93ivfy1ChsajgMCLhdJHGGde8lar/Ypm6ZF/iwqIoW6qh+xfCVxO+snXRH0XFQNZ+VUmJcNrLWIjKGcnCzFMI1vXD5QQEBAQEDAtUc2BkYvAMdvwOV37H0BsPvy2fdtZU10B+QzCKGVag/4gVxbvmB85knrOvmZ/yYp5zzUSnCwCjuWXA4JxIXlHJfT9duzacbQtrC2MzQrW1UU04CdvsRBqrl/nW4NdYi12hcuDLamfWrBl1km74wn0XQga5R4NVDEbWEy5Pa1g/WhKkh9szim58l3qMo73yPlylVDFAOIt7OtdY/7bo5QOecA61MitiQraPqIj0d+HAkETga0NtmOb1KrF9T+FcVAPgaGd+l4WFLM9ewPaYDTYpdWHfzcZw5JuHFTc5OSofNEqrflK7vjn8taT/K4evlpW3Ul4eJdwueyLZubkD4NEz9d0sfdrqbLnFLL8Pm0ALDwj9V93t7zqWMDu0xkY+DLfhVlphWndDy9+6fQ5TcA21Jn/zVIB/VZAN8K4NMAirX3CAi4BjDGIEvozXiI/jcVbfGqGotKMn8ai7JocNpUrdsLWVSyWqixFkkUYZDG2B0k2M0T7AySW93yFRAQEBAQcOVRTGjYevlLaeiJEqCaAkefJYvGuVpy1ila+PpWE40ajK31pIQmKJqeEGdNjEhQqbEAOEQ1HZLVJxtxwCiTFBI2G/MgphUjK5UicvWq69bdj3+2lrNRFlQBXS0Aw3aT0Z12TfpFWVwkoLZlCdNkm7q8KleTWN1qZh0QDVDVdTnlfcANSfkuDZc37W9BY7BUS70JJOh6fkxqmeKYzr9yTtahyTu0H1xzUuQJP9REjoi6J+GsnCRTjUkrLJGtTKCRJ1fqatnGJSqf6WNSJIEVOcmAwp+zHW9V7BJ1kgGkCZ/FMTBXx1ScdAif7GIq5jeBkD7IsGR9apE+5TIBBOsJOWkZM5ETiQGx/3ldS1vX5tVnA3sa3HkvML5Ha5/kN4bUAbYndr4GwATA11hr37qE7QkIuLKII4M4ipGvOGusJXvV6bzC8bzEZFGhrC0aC6SxQZ7EyGJS+MxLytJ5OCmWgp5bmT9XPOg5ICAgICDgxqNim4YEsTY1DZwnbwGDg80sMnK9UxBsYps54zH7rnMKmykHCEuVd0MqA2n3cfXYoiLqqd62NVDVAAo/cLlhXYdDqyF+yUq2gc1nZZ5P5lUsyfDyrBzG8GMnaEs4Vm0zqziWcjwUCVQtOGOGQ5BLznVJUhoks12fdbI4aZNAfeTQTSN+VkECjtMhsPuSv9wRIkywlHNPkknjkti8FqwCynZorcuY9ouz4cn5w8ewKEW6TVdRhwAyMVezQxEbvK8rttRNn4AIJiZmBrv0HiFkTxQToYd2WYzLv2kpfDp17K6dS2f4PMPw7xbp04E0q7lQY00AVW0CR15Lyq1V3ffARtZ1dgb507F9dW1gtxDbvkPGAH48kDoBAQRrLeZlg2lRYVrUKGv6wyhPYpeVM0xjp8rRQc+k+tks6FkHOwvpo9u+guonICAgICDgkpDkACyw924mQyywGAMvfCHZaJ4npHq8kAYoJaRPR5QNtC7oWKO3ersTtusuL1YTQhqaEDKRVwLVBSkwmsYTQcmIFA/pqK2auGoQy9oqlHMia4oTHxIs303s1VWaHGoW3i7WB1FALDWDRZ3Lz6GSuQ7oI0Qku8fZpxa+HWpxRCRPyXab4T6RaTBKgaXCpOsSQEnXi53RGiDi/QRWnvTtozil+1QFEYR1SSTP7BA4/CypeqKECMp8nzKBBndom4RgFbJVQ9R2UgVeM9mzUNXcxnSCmsXO9YyPAUc89RBNjvRR9eZCYJUrSJ8oBbKBypni9wJH5FUbkD9dqxd/P3oTmD6gx7YNZVrdWZcsc32wLbHz7wB8mTEmtdaWZ946IOAGoqobTEsKPZ4VNRprYYzBII2wN8gxzGJkSf8fI08T9Fw1FgtVf66xFPTMpI8mhAICAgICAgLOgWxMf/w/+SQNEVEM3Hn/8yF1JOi4nNFQI7kX2wYd9+G8pMC6cN2CA47LQ1JY1CVc61LMFqskIaLE1jSMz4+XCaHWzz1Kik0VQpeFuiJLzeKEXqMxfNzscqPShtvlbHWrLGE1D/uz1aSaKMOWVD9dEkh+vqZ/I0YREA3adidNhpQzYPoQmDykYT5+SIqb4R3aN1Hq1WbSDiZKE0f8APQPZzHFCZYyrQyH9UpDmxz/dcmk64SVRCfA9NPAQy6VjgyTfrtEaA4OmPzrCUyPU24OY4VLU6JVA744aZMbUsfeJXyex75ukT6j5euXFD5MAJXTZdInSrxSKR1yzhWTP47wKeGasOqSlV01PUY5BR59jJSA+R4dC6dvkTXrBliytiV2/hsA/xjAfwfg91z85gQEXD1YSwHK06LGtKhQVPQfaRJFGOeJU+VclGVqm6DnVuZP05w76DmJTLB8BQQEBAQEXDXIgOqCjnlAiSKyJw32VXDwc/p/XMJ1o4SzMxZ+ey03UQ32KWxa1ENJvkwItZRBHaKoEnXLpgqhPvJnXSX9OdauaXwNuVRepwNSZGS751McrWv06aKVDaSzgLrk0BkNbUtqICGEesihq64GEgVLkhNZsvsSve7ZE+D0Hdpfp28ROZCOOD8KTIRwA1fMbVwmUkRBxa1s8nsnTFigg7WzEVmxdFZM03B1+yFt0/yIg7RPgMPXyaqXDIF0B8hHtD3RmmNBjuk4o9dpa0/4OEJDnRNJTudfi/B5hnauPqxS+gCd8GZFAK0kfVKfp5SOvD0LoLWYPKC1j2Kv9pKcpltI7LwN4C8C+F3GmJ8H4J8A+Bwod6cX1tr/1/k3LyDg+aBuLKZFRaqc0qtkBmmMu+MMwyxGvo59uUTooGdcUNAzIBlCXuGT6Ip3vi5YvgICAgICbh2KCQ+DOZDy8Pvkk9TEk+/x4HsB/z9KcKwmcnQDVDq6+ODgp4HkmkiNdaUaq+KU1BFC5PQNbudpW5Ln1WTGsyaEqgUdExXn5sQJKaXy3WefeeKygTaAXreWDayrBlqzXloNdKYl7ILOi6eFMbR/RnfpnJod0v6zDW2jqy1ftGvKo8irymKuS9fnnRBrjvgRpU/VrziRbZHnPHgViN5PayeV98WEVV9zYDJT7V079F4T59765VrdpOmNVUVC5CAGYtsmR2aPWTVn4RrzZHsk7DkeABmTPy0i9Dnsy3hNTXq3sUt+1u+bQJv0qSvg+HNAY+k1n7xNZNqLX/RsXs8lY1ti5wcBV/T4BQA+f4P7BGIn4FpgzvaqaVljUdInYnFkMMxijLIEowtU5Vw2Ngl6rhqrsn4al/lT1g1mBbV4daFzfZKezJ9g+QoICAgIuHGQQF9pcgEoPPnxJ8k+AfRkOnQDcFVVtmATYiQdXm5w8DaQPB+3vZznYwwNgYP9zfN8ngbbqFo0WoTQqhwh/r1atAmhuiS1RzGh27lWqx16rcUpDZRLhJAmip7zkLztugkB1KsGkpDoFRknglbodk8WkL78WaiBJJi5LongWXDrVjqgPKp0tJzbMz/qhBhnXhUU56zuWbE/tSVIZ8PI8dQl0LIxnfe25oDoCbWpFSfA8RteaZQOiOjJduj3rtVKq7n6bJJS7S7vO+UcWBwCtdoeV8eeMcHCFs84bh/Tcjz3HeeXuU/PIn10Y5f8rO2S+Q6tH27O7LLt/xL/Cr7ILCDgWqNpLKZl7ZQ5osrJ0xh3RqTKGaRXXHJ6ThhDhMy6l9c03uKlg57rhqxpkyIEPQcEBAQE3AIk3JS09x4aYOoKmOXAwWv0CX6jBl1brx52G/Wpstxe6sXTIQ0ZMmxcBUVOVbSJnFV5PunwaigzzsJ5iI3FCQ32TUlD9+guD9IDAHY1IWTtarIDWFa+tFQwXUIoag/QzwrbDOZdFZUjhBr/c1OuzwYCvJXpTEvYU6qB4hTYeYHInMURkTwnbxFRMDhgJR6TsK0QY6koP6HcHP14yYDryZn0ceRu7N9DunAV4iobRn6P2c41sr5qvTgFpo+AmhU6MD6YO9sl65e8fySD7XN1JPjZET6sHqwroD6lcyEy/L7FRLURgmXF86y1QPZdFp/PxqghpE86bF8+fQy868u9zS4b+31626xY1tr/6JK2IyDgmWBRsSqnoLpxgFU5aeyUOUF1QogigywyyLD6zbUb9OxIoDVBz3HH4qWDnkUJFBAQEBAQcGXQCk/mhps776fBcB3KOVcvn9In79WcB2DLZA4PfzFbQeqCBo/pY7q/Vv3Il7O8JBcz4AqsDI+KyJEBXOf5pKPnF8L6LGAtWWLmx95Kk2SUD5Ttbqec6lplurYxrRwSC9TTEELdSvpnSQidSw20xhLWVEBVbqEG0sRPDzm0iqCKIgrQHRzQeTo7pLDl6SM63gfcXCUKHQ2pKJca9nJKhI97bJ3bI0RL5/hZVyHunkdn+3DWTzHhfCfOeJpyhpAVsmfIyh5WKGVjJoDGRPysIk7ilNq6sN//WnUde616lGzD6y4tXwmTP9Hyfj0r80nWpZf4ecrjXGx38h5mmZxdRbxdM2xF7Bhj/mcAnwLw31trT9ffOiDg+aNpLGYlETmzokbFf6RkSeTqyPMkCiqSc2KToOdurbuogIqqwXSDoOeW/SuKQtBzQEBAQMDzQTknciYZLF93FjEyvKvycTpBx1IH7KwvXftLRY8tNrAuXEvUtjaweTvTx9nAEiAdX608n8uGqDAWx7Q2UURDfb57/qFPyI5t4Wq4V+QILRFCcvtNCaGuPWxFA9llEkJODbTBsaXPjxZB0LTPmXXnCNA5T/osYSkReHVJx8HskL7yHSY1O+oPnX8jaGqvrhECZFVuj7NynbEGznKk33cUqSyBzItTtpZNifSpZsDklNbJGMCokOJ04JU+6YjIHnk9fUTYmXXsRUfVxLdxdew5kEtoc07r4OxiXbWXEJz6spLfo7Y5zrvWMN7/8wnwzvd6++zLX34j1DrA9lasXwygBPDNl7AtAbcQ06LComyQpxFG2cX4x4uq4aycCvOygbUWkaGsnIMsxSiNgyrkGaFt+epngCTouW5U2xcrgeZFg2pN0HM38ycEPQcEBAQEXDiKCfDmD5GSJuY8ldf/LYVuRpknSM5LjLTqgNeg1YKkCKDWcLvKBlYrGxjfT9QN+pP8fOfGfHp9JuqKsksWJ2Q7k4ryfHe7ivKLhiOEtvy7+CxCSCuHnpoQ6qgpLpMQ2vT8ELTUPz2WMMvkS1OtVwMZEEkyeUCvLx2SuiffYwKk5/VGMduiVK1305CiR1u5zszt2aLlLoromM13AbyLLpOa73JO36s5WyvnQMP17VLBLusQJ/zc0sw1ovcurRiMUyLA5DX3KZlcCLcifBbHwLyvjj33iqhsTVZR6/F7jvNNw9OLCfDoo7Rt6ZD25fQBXX4DyJ1tJ+kMwCettWvo0ICAzfD64yneOZkjNga1tXhxd4BX747OvmMH1lrMywYTzsopOfgrjSPsDRKMsgSDNKhyriok6HkVrFUhz52gZ6p4t72WL036tOxfcQh6DggICAjYAtPHNNwVU2qrqRtg/pgGhtF9/vR7TPXE6Y7P1zCxDyq9iGG31YJ0BvlSzrlhh61gtdjAahrQUh7Y4oyG1rqk+uXZEzXMK7tXS+Wgf79m/5da6yvKiyldluRkq8t2Ns+UuYo4LyG0VJu+Jlh6W+XE2lyVvmDpC/jgdZtsINdsVS0TBU1N5Mbg/9/evwfLtmXnXeA35lwrc7/O876qVHVvlcp6q2RLqGxJ0HKZMG0TRkjYIGxst8OAMW5CEd0QQdjYYFktIIAAu4PGDe0WQRBt3DYPE0ZIGBxg2Q0SWHLb1sMuvep1q+o+z7nnnP3MzDXn4I8xxpwzc+d+n7332bnHr2rfnTtz5cr1Pjm/9Y1v3BUh5uApsPOezHt8Z/54Cc26LLaHt8ejTYDuVrfKnMPl4PS5Pach9kDUUjJAhR7rtrdX87KshCprSVLSkOHpLrD/VMQt21fmLiJb59gIPeoqslbvcXzY4TTM5Do0a5yCB09VdDGRK6pgrj/dSD6nlCgu/OZ8+DnLvmqfI5J9vPcBsPcE2HwZGN+TzKztL8vzt1DY+W8B/A4i+g3M/DcuY4Gc28HedFBRBxgyIxDw+Uc72BxH3F3rT3TUDClL8PFE2pEzM4gkK+fueo+NUUTvrpyVgEjFmGP+LVsMek6JMWuCnocTSr6sxKsVffrorh/HcRwHAEi++E93aj5oGAEvfa20LEZTKmJZG8uw7I/F7lnPQyxpg05n+7UkJQRg44GGHG8cUwa2xAFkjgfLBjpunRZLvkomUMShMrDrYLavpVbbNeNo/cHhFta3kbO2TTeOFYQuWEqz1A10CYKQrftps5MOnonQO92Rcz1oZzSK9Zw5lRtooT18pzk4zDVTh4fa/tworppjcnuWEToRokabAD9UoWdXzovZXu1wB4hQTQQwQa5tU3l9sg3sD/IcqYhIkMcmADLkdRNqyj6K2k3LnD/NtYGH6uzJUxWXGg9JcWyNmvU2kYvq9gTpcaDPwY4lqs+lGbB+X0SdwHJMAliVxlhnFXb+KIA3APwEEf1FAD8F4C0AB0e9gZl//PyL56wqk1lGJMLeNOHZwQyBCLOU8ebjPdxZ68ugu4/qulBXzzRlzHSgDogr585ah41RxHoffSB+SzlN0LOUemUt9ZoPem7zl1qWBT3HkvfjQc+O4zi3gm4MGcxoDgcYgFr5ywBG7zAbbQlIEUimQN6bHwwvo73jH1vBp6sD2zRoecdBHTDbe7s1YLReu+IYw778HDeKOWqga3fHF0tccgJmkzqoPWrg3rY/PhRs2zdi0OJdnBO+1x35vU8HcZNtKbdKgyzDaFM7j9ndea7t7E/7ucd+1zzv+45570nvu67vvs9DEDrUirsVhDJKp6hTCUJHtNw+spQsnl0QWrsrP8NE8ncswHi0IQHMbQlWu55pJj88NGHIqTp2ODfOFXOjNI/brlmllFIFsBhV7ND8rtDrfmnncwwURdycHejyHIi7xrarzbPrAVpryjqTiD+U9bozrtcoE114qK5FRt3naarT6bKONurjqG5CcN1uaSaij31Xt/WyMi5ry35SCVs3Br74N4APPiuflQZg81UReVeAswo7v6y/CcA/pT/Hwef4DOcWMO4D3nq6j4NZQgyElDP6GPHRBxvou4AhMQ5mA54dzLBzMMPuJCEzgwgYdxGb44ittR7jLiAQSR7LLHtLbedITlPytdjW3TJ/pkPGfmbkI1w/sXX+eNCz4zjOahEi8MrXA48/J4MSCsD41dp+eBmLgykKQFzoGtQOYssAcKiDppx0IDirJRKD3tVmnWc3nu98Q1EFjQxM93WAdMnYHXXoulmmRev6scHp3PouE4CaTJfFgFu05WE6cFyEs3YM2q2CTb9WuwItdh67DZz3O/F5hagT33/G94UIoCnHKYKQiqawxws/uZmOsFCuo3+rKaW0mG8dIIuuD1IRyH6bUDHZluuAdcwbbanoS/Ofd9J6FhG4yT7ioZZJcarzWwyOTjMAKrRYd6p+rGKPufXW1fnSdtSLdT3Hd+tyWRh8mojgw03e5WgD6B4CYQwEUjeWllkV0WqQ5TBx2bJ0YKVvTdZXMlfgotBNdTlN4I6hbvecgGFHOpi127gbLzh8+ipwT/ekJXyayTKsbQJbLy3fPzeQs17t30S5NeE4F4XweHeC/RkjEGNz1OPLT/YRiDAZMlJiySqLhNfujjHuA0ZRBuYZjJyBvWlCbi82SnH6hIA+VtdFHz1bxVnOZQU9Bzpc4hWbY9GFSMdxnBecbgzceQ24/zEZDIReBjGvfN3l5DKkYb671mxSB7NkuRZdvavdhsIC84MczvNiyKHuWc3jY+/sH/Paed+Xsw5c2zKwVhCywV/rqOU6kDWXD4IOLrUrGYK4cu59VDJGFkutjlzeE4Y4JzkfLmMbnfiZ533fFS3rnJiyUK5jr9lgf/H5Ni/lULZK+157HXXadnqCHmsMET9aZ5AJKK0olHC49LAp7wFE0GgdQNBypMm2ZNOECKzfBUb3gM5af3fzZUFFRGrmS83vVmha3L5zApeJwloKOuxraeaBZNikR/V9du2w3+Z2aR1MraspqBsIo1qWufd4vnSr05Kwbk0Erazi9FQzdMz9A8j8zV0TR5ofpKJwWxZqLqfhoLk2LClvs5I2cyyWfavHgeUBEcl1e9gHtr8C7G/LZ482ZR7D5PZl7DDzxy9pOZxbxmSWEQjq1knIDBxQwuPdCTZHPfqOsDHqMNLBb2ZGysD+EbZlc1KkzGAGDmYyz6wuixAIkUhuKoWAcU8YdxGjGNBFFX9UBPJBtnMUJ7l+ANRw54Wsn1k6Pug5RkLvQc+O4zgvHqNNYOtDwM7bWgZ1IH8/r4HAMJ0XcizYlEgGS5svyYCpO6FrTBsEe1Tb9DRdMmhF45RZFH1a8WdZqdQVcCjgVtdruqudfZ5qVysAnXb46sYalvoIpa1xuy5HreuqkZeIIsvCZZeG0eKM0y8TX04LqbZBOGTooQUh5LgMlcXMlVYoOfX0zfvK9lvMEcqHhRV7fbonAs9sX3N4IrC2Xl0jJp4cGyy9+PoFSv8tOHm6J9eX6V5T/pVUpFpwx7VOP9uPoatCCFEtJTMBlhOQWNxBFirfrcuym/tnaLqDZRX58qDC0Hi+rGoZOWkOj13ftDwtNQ6gdrnzDCIoqgA43QUefVbcRuv3gckusP8YeP07zr99XyC8TMq5FhJn/PK729jZHzDNGQFAPyR89cub+OiDzUMDWWZGZhFuMtuP/q3PJRYXT9YuSjZNzhlDBlLOmGXGwWyGZ/vALGfkzGAAkUiua+raGPUi+ow7+Rl1AeMY0HextNp2nGV0MZwq6DllFX3OGfQsQpAHPTuO41wJD94QgWWYaGvec4o6VuLQCjkmtFjr8TW98x1HZyujOW0Q7FECUOuUaZdr8TPmRJFufjB4GQJQu145Sevk6Y6IOf2adLYZ39Fg2Dy/Pnlonht0ILq7XHQ4Sdyi5u/T7Bc+Sfw4TizBOcWYRVHmgpwkjMyVKR0hwJxaSFk2/2v+bnOe49j2yWxfRIP9JyJkAMDojpRItaVUbHk1J+yvUwlCyxw32ip+7W6dV06az9W0YE+zuvyAuIysBMrm2QqrgcX1wuq2sflNtoHhi3WZ+w35GWnXwPUNWTabV9JrzmxPhBcAJdvHgqJLaPIIwEK3rRZz/JnYlNrHMxGXRpuQsk6SH47y/ApwbmGHiH4rgO8B8HUA7gDYAfArAP4HAH+JF0cmjtMwHTKe7Q14sj8BQQfCXPNKFiEiRHX4nJWlwg+bIATMhoxJSpgOjOmQME2MvcmAp0nKbA4tR6jBzuMuoFfxZxwDojqMIpGU0+v0RO1jH4DfZizoGQDWjyn5Wgx6FiHo+KDntsSr96Bnx3Gc58to8+yCTs414Ni6VtlX5NhLm3TLyGnzdy6TMwlACyLJ3OPzCEBLSsBOgnl5i/LNl0XQaedRBqAniB9zd/k1byNpu+fZXh0IHuV4KUG91h0oVFGiDKo7HLafnIFDosoS8SNoe/PjXCdncqm0Yo1/Xz0XdmyMt+Tn7kdqu/Q0AZClRHB8d96JsxhQPucGasvEnoMg1ApBo005j0DzYu9wUEUfWy/rxDXarKLLXFaYOXcGdQntqLPuCbD7nqyHBR6bS8fcQUU40vlMd+U3Gsddt6YZRuOa39MepyEAQUvHlu6bDnjpa+Rz01TmNdvDqgTNnFnYIaJXAfznAL7bnmpe/s0A/nkAP0lEv4uZv3zxRXRWkf1JRgzAg80RdiYDCMDbzw7wU7/6GB97aYK+CxhFwigGjLqIvlMhJQaM+nAmkSQEQgCd+WBndf5MBunEdaDiz2xImCTGdCat1ncmAzJLCRhDxF8pn1H3RrAsFRGGRKSysrDlwk8MpEKyPXaX0G3itEHPrQDUBj2bY63luKBnE4I86NlxHOeC5DTfdrztuNSNNfNl7fRtiq8TC0U+SXBqszHKXXITRmYyQJsrZWkcJYQqMLRlJxSl5GKiLZlNiLCWzYA4dw6e4mIlQIvrHDTodQyMVOiYy11pw3nbwfZQ3y8P6jJH7fxlv1uxK46qK2Kp08W58YQorrL1ByJ07D8Bdt8XN8/4nlwToolz8XRiZ8uZBKF0RkEo1PenQTqAWZaXiZlWJtpvVJF6cR3MqWiunOmOZolNAJqKGFOEI2quF1yD46cHwP4Hek1VUdPEnvLZ+vuoEsvNhwAYePIleW37XWDrZWDjwdm2+QvKmf5FIaI1iCPn1wLYBfAXAfz/IW6dewC+HcD3AfgHAPwoEX0nM0+PmJ1zi1kfS5tyANgcd5gNjLUu4MP317A+ipimhO0DaW+e8+EBqgkiXSCMutC4ZwijENF3Iv6EgCKMhOZ9p4Es9DYGYLx8mqzlNEMSZ4U9ngxpboAt+SpAgCwTqWgTSLomEeTvxcH4ImVdVPiJ5TGV9W3FIMsxcpfQatEGPa/1y78A2LGZ8ny3ryExJrOM3ZwOlXzZsVNcPx707DiOU9l5T8SE8V1g6xV5zvIrTMixcgbLx9l4WIWci+RknIUSBnuWTJQFweXU059SVKEoHW3aNuk8iEumzQFKExn4zdQJRCSuptEmMN4EZruHWyVbq/lSNmLdi04o8znOuXKef+uWOpsWMo5KB6PF7dMKW0eUuJUSG/93+EZCJM6Y8R05vg+eilCx/4E4e9buS2nheeZ7YUGoEX8sgHgxO2iZ2DJMROyxTn6AXOdiL/k6vZaVWit2CiJkbTyswk6e1swdO7f7Dc3oWZP3zJ1Hmu0z25Vy1tkesPf+fEhz14tY1FlI87o8N0zkWmF5ZesPgM1Xzr7NX1DOeqvgByCizt8B8D3LHDlE9FEAPwbg1wH4gwD+g4supLN63N8Y4atfuYOdyRTMBCLG1vgevu2NB9gYzR+WQ8qYDAmzxDgYMoaUMR30J2XMUsb+NNUcHnXP2EDUfodQg2jHHSGqQ6EIQI3QEo94fpEQCOMQMT7iTLIg3VnKc8KPDbJbGNDlDSLQoAo1Qf8Rb0vIEkupmGUKHQctEX5al1ArDhUn0RHr7NwM7Ng8jucS9KzOn1YAchzHWTk+/1PA+5/RO8/7wL2PAR/65vnBTLcugzYrEQCqMJKmEix6KmHkrHksl52rskT8KA6To8SSJc8dNT2z3snflg5ggJapqRPA2lnPZQI1HXJK6QiqQLIohhwKTL4Eka20YT/ibqCx2AreOn+1AddHlbgBKN3AFnN/DgVE+7/HLyzmbNmY1TKtyY6IGWv3xJl22fvveQtCaSLn72xfO3NpBy0TioD5jlhxNO8GLCWRA7Dzjjr6oogz5sYZb2lezxYQXm2uJxCxabon1xL7KSVhuzLtdB840Pb0XQ+QlqDdxq5YAH4XgAzg+48qs2LmLxHR9wP4ewB+D1zYcZawMerwyY/cw5sf7CIlRoyE1x9sHhJ1AAujPf4f4MUB6pBF9GkFoKytqScDY2fCpYaQVDgxF00swg7m3AnLxJEy/UKmTjufLoalrgpmKZ8ZcpbfjQg0mR0upbF59epS6jQ8t4syoM5Zs4M0S6g+tjBpFBFolnKZ5qQ4rNYlZCHT9fFhV5S7hG4OJwU92zHaBj0P2vL9NEHPbdZPH8KcyOo4jnNj2HkPePcXtKQIMpj54k/JIOzOq1pG0GuJwe7FP+8kYSQsc5gs6QJ02oDaxSDcq8K6B013ZBAXewmoHt85Xd6QdSY6FAKdmqDkJS2SgcMC0CHh5zIFIMsBOYFlIddtEHROQJ5UcXERouX5RstcQc71EHvJilp/KN3dDp4C2+8A8ZHm8Ny7OqffaTmLIJTzfDcsE31yViFnqsdhL+WP/QZK+/JB3TjTPSlfm3MEramjR3N+FrODxnfFjZOzOgOtNPQd4NmbAGkYs3XFeuN2dsX6egC/wMy/ctxEzPxLRPQLOr3jLOX1hxt4aWuE6ZAx6sJSUee0nGaAelQmiZWnZBU5TACZJi6ZONIxi8EgUAasGyMRITOOFUeKCLRMGCmBt4Rx1805ZWxZBxWkTASaDBk7k2HuMxa7JvVaQjaKAf3oeBeFZQm1ws8ycchcQjPdVie5hMIJws/SEjJ3Cb1QEBFGneyP44Ke7RyyY9WCnmfTjJ08HHqPBz07jnOjmDyTQUAPzZwZy93e8V1pez4nruDqS4BuEsNUxJzJMxmolfKUu2cvQ7Hg4pNEoKUOmYWyjuM6ZS3r+EWLz13Cv1tEpwu5Bk4uAxsOjs5VOW0Z2IueCXWTCUGEiPUH4tw5eALsPhLHy/iutOa+qnD150kIQFB3ksFcy7jaNuictTRzkHUd3xXRK47l7zSTbTNTAT1NqzPH3D+WYWUuv0XyoNfYoV5/iOaz0G4wZz1DA4AjZOFDJBwZSe04wsaow8YVHCWnzSQxN8JiaYoNWpmlPTo0KJmAkjti7dJNvABErGBgTigxQem4PB2bX+v8CSRh0uu9uIvMsW3iC2fGwMDedDgkugSi4uyxgXO/IAKdB1uPlLksS9LtlNRBxCzTTVMu05zZJdQKP0vEIgubdpfQ1WNBz0eVIx4nqkoZ5fJzoQ137hYzfzzo2XGcq2R8VwbIDz6GEiaa9oGHX12zdpyjyUnFnO06gBptaIvyKyg5Oa1Dpi2HavM8TtsqfVnHr7lyqUsSgICzl4EdWkfLODqpDOyI3J9D6+z/Rp8b66Y1O9ASLQ0JH22KwNOKJDcRouq0aUkz7SA41bIudfQZsRORZ+0esPUqgCCuH3P2JL2RyKmWcXZjEXvakPP7b0jZbErAxj0t07qytb9Uzirs/AqATxLRh5n5raMmIqKPAPhmAJ+5yMI5q8/edMBkljHuL+bYeR5YG+oRjv5Hd9GdkHItoZLnDuuebSDtKAZ0vZapNG4gBg45ZcrffPrSqdYdlCGiSmYtTRuAzAMyQ0UjlCDqMoheKO86yT0RAyFCBLOzsEz4WVx3Znk9MWM2O1kMs/U/Tvg5VEJGVUByLocLBz0PGbvT0wc9t4KQC32O4zwXtl4BXv4GydgJnQwOXv4GF3WOg1kGTJNtbSfMkpWx+ZK2eX4By39OapVsLOb9tC2aT+WOOab9exGBLksAOuU6lk5nC04gXhS6jrjfb53NljmArkLoWgV6LTdKL6nA8xR4uivn0dp9EUZX6XuOdeBr9Z40VEdPmsq51Za7Bu1iN7ojjzNL6dVsX0q4AJQg+35dHEBhJBk+IQL770t48vqDK13Vy+KsI+n/GsC3APizRPSPMfP24gREdAfA/wdA1OkdZylvPt7DO8/20YWAxIxX76zh9Ycb171Yx3KSOwE4OpB2yIzJNC0tYVpWmjIahVKa0mbWZHPJLBNC8vzzOTOIAWT7DiXzKAPoQZYng6VsW4WguXbrMWAUCOM+YtRJ9zHpQBbO7ZwoHcfO/M55l1C2wOzMh7aLlNOdziW06JBaJvyUEjJ3CT13zhP0bM66486rbrHEy4OeL40XSaR3nEvh498FvPw1IlSM77iocxSzA83N2RYBJETNCrlz+A79TeW8AtBiRs6pBaAlpVGXHZBMdLpW94AMvg+VubUC0P7JZWAnBUG/iELgVRE7EUQ3Hop7Z/8JsPMusKc5PGv3Vnf7WCliG2y8mNuTJlK6ZseXOYLiGFKSpcfi3mMVmS2MPd36rlh/CsA/DeA3AfhFIvrPAPwtANsA7gL4NgC/G8CHAHwBwJ98bkvqrBR70wHvbh9gf5qwfTDBqAt4d3uCWcrYGnfawaqW4tiA+iYEr54278dcCW1pStsi/dB8GyeCBSnbgHW9jycOUlOedwGJGHLYIWMuiWmSTmSzIeEgA7O9KVKqy2Zh030MRfCpjyNGHWHcBeny1ZSUXZTzuoSWCT+JRdA6tA1yBiec2iVUuoyF6sRaDNVuxSIPlz47Zwl6nsv8yRKgvneGoOciAt2A682LwJuP9/Du9gEi0Y0R6R3nXGy94oLOMtIgA87JtpRTEGl78jsShnpb/727iAA0l49zkjCyJPD5qgQgQLN3Opy/DEx/W2bKUd+7ljqcwrz4s8plYERVyJnuisCz91jbpd8RF093g1NQFvf7oeOg+du6ao226rTm6EkTLefarSWFBABRzqWd94Cdt4FuQ47Dbu12dsVi5qdE9A8B+FEAXwfgX1oyGQH4RQD/GDM/vfgiOqvIZJbx3vYEX3i0i2d7A9bHAS9vjTEZEvoYjh1Qt92pYiP8xAUh6EVt2d2WpuCIQNrFvJ9WBDptN6K5jBIdpI66s1leF11AVhZ2MEuYJsZ0SJjMMmZZAp2niZFSrcuupV41JLrvIkaRMO4i+hjO3Wr+rIRACDi7S8gCtQ+JYrxcLJpoC/qTQrXtOKbWCbRkWyw6idxlspw26Pmo8+qooOchMw5OCHpezPxZ5qa7LbTXhd3JgC883sWdcQciwp1Rh3e3D/DS1sidO87qMd2VAUA3XolBwIXIWbpZTbal7AGQspH1+7UswjkdpxaAFpwwrTDC6fTOmLbsa1EYWfz37LjB9dLXTzGNhYmHbvk0pQxsdrgLmLmc7O9ln01tGViobidqRC4ThU69Tosfc471Xnz9otNYuPb+U2D7bXmuX1dBdSGH51yfs2S6c8/nDK8/LygCyCL4pKmIzk++CLz/ixJ6v/EA2HoN+OCz0gJ94+HVLNclcuZvXcz8y0T0SUjr838EIvDcAbADEXR+DMBfYObZ81xQZ7VInPETn3kHj3YmiCFgyBkPNkb49Ne/glfvrCMGmhs8FLdJ466w12xwdmohqHUBvaBC0GnzfmapzSWZ70Y0LBmkWohyF06XS3LWkilzI01nGQcpSav5WcYkZUwHcQDtTwfsZhM+UIKRAwFR27nHENCFWuZ0nlbzz6tMSj4b5xJUjnIJlS5jjVg05CzfU/j0LqFSIlbKxQ5vJyJ3CRlnCXq2c6oVVg9mZw96tnPtReRI0XJZ1lWux3C7DZ7tz/B0b4aUGIEIW+MOkQjTIV9JML7jXBkffFHu8lp48taHgAdvzE9zljvOp53mNIOt5zWf0yyvtSifaYhwiDUE2cp2Js/OsSyLb3kBBs4XWpbnNc05Bs7F7dP8th+2crAjBKCSjRMxF4xMcT4359q+TzQOJWZZP27KbTgDeXq6dTWx69D6LnnuyMU5YTssfZ1OmGbJe46ahqIIEmv3gNmOCBSzPQkMXrtXw8nt/Sd91rmW9zTTnGadTvP6BZdlvAU8/pxcx+NIttX6QzmOVoBz3U5j5gHAn9Ufxzkzbz89wOO9KXYmCXuzCToivLczwf/w82/jE6/cKeG9633ULBcZ7PedDJ76GLAxinMDeYIMUoZ8eIAy5Bo+PMxOLwR1IcyVz5QSMbp+IcgGqUdhwtiwMEg1QWgyLG9ZvljmdSin5Jgw5epGCthccnmxkNwhyWDZHs9SLiKVhChnHKQm+yYACSJShEDPtdV864h53vvzubuEWnGoyVIa1CV0Ugv6RZfQMuGnlpNh7vy6Dcy76ZZzkpvuuKBnE1W79vFzCnpezJ5qXWUmJiZunjtFmWG7/7sQELr5a2Egwr31HgdDwsONEYIJ8sxndgc6zgvNdBfYfkta7RLJYPHplyTXob8FZYdpJu6c6a4MkilIV6vRppQxpEHKQi6D6x44n3aamzhwPkkAyiqaSIsPdcjMgETV6WNt0BezcVp3zLmX9xzb5aj5tKVtnJaUheUqDC2bx1G5P4sB0dd9A41ZhNeDJ1KOlKfAWMu3vF09cOfDwN0PARsvyzERIrD3vojTK4DvYeda2DkYsD8dsDeZYZozJhkYmPHW0wOsjSIAAphlYB8IHQUwcRnQRx2gdxQQI0T00VKjTkN9exModOA0Ks9bq29rRY65gfOQ81wA8WzGS0ufDBssxzhfQlRcQNckBBW3zTlySSzvZ3/J4M9KvqzEyhwK7WD1KCHAQnKPckyY+DRLeU74scGzLDPmWs1Lho+5ftS9A9m3iwLIRVrNBxM6GpfXZblhLuoSWlzvbAN7G+zzfLe107iEDrWg13NwmUuo3V6r5hI6jZvuuKDnvelwrKAaoC5yVIENBATQodD03Ig4x7G47/q+3W8XEzi3xh32pulQxo6XYTkrxTCRE3Ptbn2ONEfmpG4qL/RA/5hpctJSqx35e7QFbL4qA6DxYovy57C8K/ZvxUqxtPxr8blBBMBF2ryfQ+HIVyiInFbUKGVgS9a1ZB4d1OyWRUJYWNfuiG1wSTc/iOQ6tXZXSiT3n0gGz8ETOYfX769OiPl5WPEOh0ce5UT0LzyPD2Dm/+fzmI+zWqyPIvZnGXfXOmQEDCnhg70ZvureOj50dx2Ja44MM5AzgYgR1LWRmHXQk8E5YJoS9nLGoEpNBpABUAZAKhA1boUQZeBswkQgFYVU+CllFeoW2hxFBB1g9SCQikJWEmaPh5yRjnDCGCaMVOeIiAPFHTR3l/xyB8anySU5Tyvqo0Jp+1BFoGUDRwvIXdYW20SoIWf53YhA0+GwMGED5SL4Na3cIwEMOtxh6zm0mi9umDkRhA7t28tywphL6KwwHxZ+DrmErIRM98VZXEJHCT9LxaFrdMI9D0TsZHSZkSIj5zAnsA0pY5rkOJ4OGZMhq2vt6NJSIki3vL7mZY2jOBrXx5JXZedb27ntKtxWrz/cwPooYOdgwNZah5e31i79Mx3nSunGABhYe6COHZa/73xotbJ2mJe3KN94ILk5fsf/dmLlSSeGIx+R/1N+n9AefTH/5zoEIKLTdQNjPiz4LK6vdWA6rgxsbp2XBEFfpNtVvy4/aSYCjwWc92vaLn3r/PO+yaxwh8PjrtD/AZYWdp4JBuDCjnOIu2s9vuHVO/j8oz3NcSB8/KVNPNgaYdxHZA4y+BkyZpmRWYQEExfAAEMGSWx3tgOKYNARoSOAomX1qAMENmjVsltmMweBwXO/MwMdERDkbnnUu+gAgGbwLoMpK10Cgj62AVVsSocYADEDRHOlNiZgtbTu1SoShDIgnhMJmiDpo4SgZU8vm3bpfbYlTwYiBC1bsVKUzKz7KWNPH1vpyuKHxNBuw1CcPm0o7bLBffuMTBtBIxFpqlNCjp39aSpOCWaee28fq9jUR0LU8jPr7LVs/XMRfDDXYp65KYkq0zUt54/ZpvMCX2jEj8MOoeNcMEv375JPXj5dxZwbRIfvJh21HuYSsm2RgTmXUNLj28oATyOUAUe7hJaJZZfVgr7N+rJ1WJaTtCgOniY4OwbC1lqHezRfBhdC/R6YTdxuSysbB5uVMQLzGVptB72+cdVdhlDcdsV6tDvF/jR7VyxntRhtSqbOYsbOqog6wwQ4eNa0KA+r16LcuXwuLABZQPIZBKDrKokiqq24T+KQwLXY9n4C5N2jBaCjBJ/TrnPsRbzYeAmYPBWRZ/ttYK/T8/ze5TmIXlRWtMPhcUfjX8fZhZ0ewHfpYzrH+51bwp31Dt/00Xt446UNPN6b4sF6j7Vxh7/vY/fx0ub83V5ucyJ0kHMw02Beu/Otd70nKYPNsaMDbxv01QEri1BOYa5kxQZidtASIMIEZ7CWQWTOCBT02ssgLflhFvfKBACQJJSf5gfWDC7uBBmgm5jRODyIQRRKORBITyQd2InoRJimvDzni7GQIzMfrLs4KA6og2BecrravzHtvzW85BFwuA353HtscJxrqVfSAeokZaSc5hwKtixtOdtieVvUMqzDyzz/XstvXcxD2ZsmDCxdvBZdJ4GocRgdHeq8DCrbQtxdQCvgNSU0eV4EaIUgm/bIz7DSs0bwWHTG2Gs27YuQk7Nsuy0KFwAWtouKKiA5t5vpDjn5dXvX7VOP/yKuMpdzU3RdQtbjLVDj2MpV5D1ufRYzi5aV7RHJd6YIzag6YrlTZiQwkJatW/2McQcwh1KWVTN/JOC5DZi39xrLMrPa/J/F4+QkIXB/mvD5Rzu4t9YjhoA7ffCuWM5q8uANYPOl1emKlQYRcg6eeYty52o5rQCUWjfMogA0nE8AWiwJu4rjvKzvCeS8ZH3bv2fa+ewsZWALzqfxPXHrTHeAg6fA7iNpmW5t1E9yKjkvNEd+62Lm33SWGRHRbwDwI/YngL8H4J8795I5K83LW2uYDYy/+cUPys2h7/zEy/iqexsXvqM8aElDG8w7nWVMhoRkAgmLdGBZJl0IIC3NCjLqkzDfBC1Dyoe6xVi5ipWqmJNChqAixiewiCcqAFFgEJNIQlTDYrIujzg201wosHU+qq4EzP1NOo3kEUm5GutQMYu1SeYHlvmTiF3QfxfagOHDwdCHA6QvqzQsN6LPLGfNJpnP/lnmgih5P0d0+7KB6rL3FldEbo+X+WNn8XMzoynjq+6IPobiPpr7jGMEs+NgczwthCgPOR8OVk5VFGo/YygfVIWLeQeYlBUearveCkS2v5cs82nXbdnqLt0fS6c7/Jw5f8ShlXU/Ndk2iTFJqTwWlxAjg4/5PiTnmeU2SQkhoesaVxmpyytCHwcVi+e/Gy7dv6LXIA352OnOv01l+WtppYllEMeiion7Q0Ke1nNtWRmluXva37ZNFoXNZ/szPNsf8O6zA2wfJHzda1vYGHXeFctZTUabN1vQYa4tyqd78lw3ljvXo62LlX04zvMmdpCh6jEC0LElUSYAHZeJc0TJVyuQXFUJYggAwtnKwMp6n7UMTNcz9lJ++cHn5bm1e5IbNr7j14MbyIWPVCLaAPBvAPgBiAFgAPBvAfg3mHl60fk7q8n7Owf40gd76nRJ6GLAz33pCf7G5x7h4ea4DDBb10tbmkRkd+DlddVi1OFS/yYijGLAuAu4i764NUSsQRkYTgZJ/bf3WHectT7gThclm0UFji6G6rxgEUtECMjye0AdbDYiQRt6ujiYyixSTFBxpgsBrAM1U3+ICQkMTioczQkstX24lXW1A3Nz/2jlGQBzL8nnQT+bVQiSarFGUArzQcFW2rEYDL0sQPq0QpCF0gLA+hF5PyXHKDehzzqYn8wydvPZOhKZCNR1Ad0pwnCXhTofzObbylu+kGWelMwmfXzZ2TFtWdRROTlt2ZSdD0dxUqv5Zc+fthzq0LKU5Z1vE39cULAIDqH5e8GdtpDbE8hEJQ0mZoBJhViuYsfiNloWLt2GTrcZS12sZZElmHixhIzqcl42x4maJpLZOVWvkTX4eX6dgQAunfPurXf47372A3zpyS4CBfzclz/Axx9u4es+tBodJhxnJZjti5gz2UZpUW6Dt84VWOcGQ6csiTpWDDmtALSkK1abCXRVAtBp1xk4uQwMLOIuBbk+PP48wJ+V64K1S4/94TKw63A+OSdyoSOQiH4rgP8IwBuQ8eL/CuAPMPPffQ7L5qwwbz7axxcf7WI6Y0wTYzob8N7TCf7GZx/h17y6JeVIUAdN1NIGIoQgXWNMaJDngw6aoO12UUQMQf0rJvagDjpNAOojidCTGSmJY6YdwLcDIxus9TFgFOVO/ihW10bfASPq5oQnlPweuSOfBp7LDirtyLUkxwSnnAiJxW2AMi+9fjKDzKkDea3XbRVi0Gss2/8BZmRW5xAYqbiCmnVr5l9cTe2OozJLzQ6iuYFt2SdqDQlA3S4l9yOg76owdBYh6KQW78DxHYkkc+fwP9q2LG0WiQ1c279PCnUeMmM2yMB4mqQs5rhQ525R/HkOjigLTz6uZfeydWiDk9scmcXnp0OuOVVWvjhXSoayznq4AGAtgyqfWMTZUiZpbjlbj0YoiYHQd+GwONI4jC47KHhZC/ry94IQlVlKDBe30zIWu7AtE37arlW2vaysjDGfEYaFvxmYm67kk3F9Pls2UrNe0pWQEQmYZcZsYMyyuKCmg5xbk5TwzrN9/MybHyCnjBgDXrszxi+/t41HOxNsPPRSLMe5NtJMxZxnUtJCJIO08R1pVe44t4nzCkAmirQlUqcSgJYIIe3zV8VZysCs3Gv/A2D/MTCbSLnbaEPKM8Enl4Ed6n62GBDtAtBlcq5vXUT0EoD/O4DfDfnuvgPgjwL403xSGqbjANifDdgfMtZHhDztkEPCZG+G7cmAJ/uDCg8iRCCjZGBYGZW4S9RdowKHjeki1QyJSFQ6YJkw1JU76DJtX0SQxu1DIvb0MSKMCTlnZKZyZ3vIGQdDQmpEHxuoxmD5LFK+UMUC1MHtgvAUiDDqI8Y4LDypfaYMqIeckTMVV5B1KBpSRppUp4a5EExUMtGlXcfifEJ1mkAHlQwAWcpHAOlClnXa0GwvE2ZQ1kuFLBOMuHFvkA326+BUhIgqDJnTpbO29AvC0Cja/g1LnSHWWesomJuSrzTv/pmljP3pcneGZZP0ReypXYgkcHn55dSOl1mSwfC0cYkt6yh2eP1rqPNlCRdEsp1ZS3eIgMiERIwQTLiQDKgY6jE2JJRt2JYmth2grCzQjgc7/qooozlYVuIWrZQuHJlZc1kt1atQpX/r81nL35glv6t1xolIpRk4IPn+xASOltMDpJQxsGSBDdlcUnL9GFREzollmpSLuNtmM9XcMNSF05PXVt98S3Je65mm5xXDugrKxJlZBVyzO9p1oFxy6v7SF6k9l3W9H23PMMwyQMBkksFb4p57++kBXn94g0tWHOcmkrPk5ky2gdmBPNevA+sPtdTqlgWkOs5ZuagAVNxAx+ThEGE+EPkIEeRKBaAAhBGAkZSb3vuolGjtPxHHH2dgrG3UKR7jfEpAnpyiDOyEIGgvAzsXZxZ2iOj3APhTAF6CfA/8MQD/Z2b+0nNeNmeFee3uGh6sj7A7mYIY6IN0xfpNX/8KXn+4qQ4BHUg1YcjyvOSvzCyElzOGQctkWO4sW6bENCXkmZU/yWfXQVt1szDJoAxUS6KsKVAgCQWmCAR9TQb5cgKUO/VJMnWSll1xthwfdcIEWc9RjFWwCBHj3hxJAUziUrKRlQkvNiwOJO+XSiUTpFQEgq1f4xxALW2RbYJSUmSuDCs9qXfsZdsD0BygBVcOJCsI3JTKNU6eQLa0dckXuzMFzRhiAohVFQoMZCoOD1kXKu6hNgC3Da01Aa6LUBdVqGVQ9ndn213CkG2wO+7iXNleO7CtgbSsx5tss8lwOHcHqMKYiT2LnYk2ehEJF2nLvNp27nvTYWmos61X7XhEKlDWEX4bVm0iS9K8HhO1Muda+pTqcVy3fn3AKhBEnX/rlOkjYdyHhcyn6jixbnC2XHJ8onRQm2+lLqLA3sQcbYxhmA9OB+S4rm4UeSYUlUO7kDHp8VVVCpITVg9SKT20c7UVbLK5WkwYRXPd4MN/z20z1Mws2HTNi0UwsXPHnmMu28rE1qyv2XWPubp0ilSWqPm7nq8xiIAareSMTJDVPC2S8y5qN0HLGzO3XdeIZ8UVpaJV0XYIeLI7xePdCQjAaBTxufd25Yz3m3KOczUwS57GZFsGYsxSOrHxUAZi3qLccZ4/ZxKAlnT+Ok0g8pEC0JKSsMvAMsWGSW2XfvBUHDynaZe+dJ0b99NwoH8fJQAdIfg8j/K36e7qBOE3nHprENEbkLKr3wr5zvkugP8rM//5S1o2Z4X58P11vHxnjLeeSrvzGAjf8tG7+NY3Hj73TiomDkmZTFJBCCoEaf7NXGCuiCBZRSLrRjUnNnF1wUQKiBC7gYkEAAACUhkw57IM+7MBz/bnhmaia+hAqmtLz6IMwMQRQ9XpQ3onnvWDiMs0RHrnnqh0vSJVLLoAjGIARlGdE5YhIvM0cYYgDg1b95SrYJWhQryF0TLrXX8p2xCdJyJxVuErgFk8V2QDZt02zEDiBM4y2Ie6sQD5HFklFXyoDubBpI/VQ8QMECNzKE4E2Q7zkpIIT6x5SSKGyHYXl0800Y7E+RM1ENk6qUUKKviRulG4lOrNSknOfCZLGaCjZr10UbJhAtU8FhODyNxPkEH8VI/T6cAYtBPckLMImE3pT8lpCiYyiXBgXY/aMidz/kTNn0Hr5tD3BRXsArE4NQLV7Q9z48ix0wqwgLaCh4mFts9q9y/Wn2zFiSwihJUcFsG1cdAMdh7m+pOKAMNIAHKqJUbZ3lj2AopzhdR9EjU0PZCUeFIgxCZzypxFpWQQQIgE4qDHWO2wZ6KgCFtaHKkiE5XzkRrXWhVw7Ml67qLsy2bpq7Da7MtlYcsAalh0OUbsfMt1v6jAdjCrwu4hIVYuMsV9tOgO+tIHB5hmOUam+wPubfRyzjqOc7kMk1pqZV0oxne1q9Xaye93HOfyIRKh9VSByEtEEHsuTc8pAC2IQeehGwN3XgPyyyLsHDwBnn1Fc3juyzVn2d2c03Y/szKwozqCHbfuwHwZWCkFWyKA2TJ+8EVg523ZZpyBrQ9J18MV4FQjaCL6vwD4YQCbkO+Y/ymAf4mZP7jEZXNWmL3pgN2DGVJm7A8Z40h49+k+PvveNl7aXDsc/IvDF4zjhg5HVQSWZwnou4AeAXzMWZA5l4HzYDkaSTvNmPsgi/NBus7MB6pyZy6FKFkWrHUuGUjIyK3zqBGcuIygZXAYIiEgFMGHqbYrZ0gpB3Sg3LaPtsesK29Og5KxUTZK02YbKAPi0l2rcROAJRwaLG2iE+cSPAuI4DPIyupAkMsd/NbJQ0xmnBBBCAQ1HJS9ZQIAGBjUsVCXTZ0TzT43Madkh7AMOEGhDJwz61iVVTRSIYBCk9HE4thiSEkSleMRZSAfY+0u1VNAiEBXXCoBIFlegji5YAHGqC3WU2qEEgCcRNiQDCkGJw3XDYzA5iQjdYPZcabiHKiEeReBRI4gFYtYStgoqiBBpRucZKnU7UYQ9xiybBdmEdZo7rgURwy4CiaBqvupCBmw13Xf6N9k5yjNh52Xnm6hHiMhSBeqej0w4aPuE3NySVlfnS/n6rLJtq11PdqcGROSyqajKoa1gq1lfEVzJcV6Xpqbz9wtwdwxoZmPzXNuvtXhYs+buIPF5wEE1PJRtM/T/HxEQRLh1c55PRTLNiht5bM5urJ20tLyTuYiYlrWUs5VFFofTXBn3OFgKiHiEcDWWr/0BpzjOBckJxFyJtvAMJWTv9/Q3JzN5YMrx3FefM4lAC1piZ6m2hFryT/CR7pglpSELSNEcQKuP5Br0METYOddYO99EXjW7p1PPCplYCewzP3UBkHnJIJ3TsvfTyTZY49/FVh7CIzGcv3ceRvYfGklnDvHCjtE9ElIC/NfD/mK+FkA/zwz/49XsGzOCvPFR3v47Hs7eLRzgGkCwIxHO1P82M++hY8+0BOraAA1J8LyHmwAV+5yN3eyyyDRBn2oz7UDy/lL3vyd/UOq0cJ3pdN8d+JcSzKsPIoZpUQKQBFTLI/EsLbMiXMRfaADKRCh0xWSlsyS/TLqJMy57yLGWo5Uq7qaoFXdnoAMnLMNzJlLiYuFp7IKLZznnQG5mRasWUgqlJigbmU3uRGcShaL3v2vGSRVpDChwvY3E0tANjEIQf/NqIHRFEwA4qLcZBWCrCOUiGUo5UgMLoNeXVhxf2jeD4BSBsWw9QdmQwYTY+ABSKiuFJuGJZMohLruoh2JYGHClZWVFXOOHmQZDCrzrPlHliVEGowcQ3X8SOiuTNMRyU2JEHVTyMJZ6VPOGQeouT5tKVBH1d3Td+JqGsWA0BNGFEGhOnwWQ3wt/0aWtYoL1k7dSg2DrpBNH1Azctr3zuVAASU0vQo45m6hpkRv/v0h1OuFOV/MWWPC8aLIItvCsoYWunJxDZNunVJt97HjWNatqw0MXwwSt2nLOcS1S5eVr3G7bAvL1Iq7ixTB14Q83a5WSjgfYo0izNqzRJI7xgBmKQFEuLc+AgjYGEXszTJGnWd5OM5zgXl5i/LNl70lsePcNs4tAA3VGXNaAWipA0Z/RluStzPdE4Fn77GELo+2gPX7co26rnUHji4D230fAAF5CqQeGJHc/B0mqy3sENEPA/iXAYwg45b/CMAPAtjTFuengpn3LrqQzurx9rMDvLM9wcFEQpSl8wrwhfd3MFYrShFwqA78xG1oYZ4y6LGhmd3JN8FHBnyk5UE12FjKU1DcDyVMmUT2sbvxdUAqocukj2sLZfk8mhsE0dzv+lKdZlEUqnfYaV7oQZP9UUopGNOcMCTpTCO5LFX82U+M/WkuH9kHyZ/pI5WcGcmgIS0Xqa4T2T6HlxeYdzXIE1XoKdpI83zJUbFcJC0LSTljSCgOgYRm4JzFyTKDCCip6W41tdDqYcCMgSGl2q3JXFKwx7ksTxv8WoRCNjEpAZDlTElKeSzzJbO4FqwdtzgbVChRkcaq10SF0sfEoBDKMUKNkBChLg6y7lfyuLdyoEAYEaGLsRncAwhWcgfEGIAsbp66m0wUQRVLSErJ+ijv6QNh1HcYae5PryVlibOWjgE5Z9kWmh9EZO4u2V8lh6UJNS5lSuZImT+05ZjIjAHiUENTolME1uY7BXHz3MI+s/Pezn37Zc/NibqnvGs9d11priFzzpoFcdiOp7nn9LOLE8icc1n+8Uwpl1Kx4oSB5nI17zFnDXNGylRK1UJzHFeBW0VNEHoNgicCYrTrpV2L5g6Vcvw3eefF3WZiELdOHq4CIFDFZREmCY92p7gz7rF7MAVI3IkfvruG8XEJ5o7jnIy3KHcc57xcSABqXEDDRJ0xJwhAcSRC0bMvA9tvicCz+RIwvlfvrl0lR5WBjTYkYH7tQf3CyflyhKhr4DjHzh+D3T6X735/SH/OAp/wGc4tpSPgye4BdqfyN+tzd9Z73NsclSBTAorDggApQWnUBdI327lpA+6cGSHLYGRGqB+yANmojm2gw2XwFMhyMjDnDpJpSZ0HOpgKQTsGVTdBpDrA7xphiHR0aIKVZZzYQJEtMRWyPqQjsqh2hxgh5WNaQs86KB/UmTKZDdhPGbMZ42CW8fRghulMgqWtixcToFEiTVvpGpRaHQw62GUGRdJtreuguTogK3+S+bX7h3X0Xh0i84PEdvxdxvE6WO5iwKjTAbbtgzKgreIZTBTS46Q6FhiDiTOZwRDhgrIcJdKZS+cFFNdEF4AYonSlCtZli9F1HWyoKs6crFlDQCIug/ZZrp87ZOl0ZCV3OefixDK3Utlaxd3V5N7EgAhxLdUuUJYLU/cVWESyKmiIM22WgemQsQsA+0P5LHOEiKuLSvB0HwPG2ore9vGgpXZZS3EOplmCfE3M0p3XxSCOoUClK539LaeL2JXsXLbtWI8XKudXES9UfaDmOwHrG1vRybYbyu/qNqvOvzrvhZktHIH1JRFrUNuBazHXkKAlh1aWJPsZwJwrzXKIbK62bFnPI3O12XbIprqAinBq5YpURJ+ydLbVUC6EmsNlIh+hin0mJJZOgaXDmOYIqStLpkF9PVLjsDLHUT0O92Zj3FuPCNRjSBlbax02RxF9dzpxzXGcBm9R7jjOVXJaAejIHBz9DZZ50KZcw55+CfjgCxJuPL4rpVpdP1/yRYtlYFcgAI02JVNnMWNnBdw6wMmiy0W/mfk3O2cpOwcDmAI6kgGilf8czBIScyk3YRAi2xCHQNEyUBisAgM3o7oMKgOllDNm3Az0SrgrlYGUhaxmDZ9goAzUbJkktNd+SJdXRuU2DVhzVGwuNthUcaktY6olDVUMsQEYQUUXULmtHkzwAUrOijmazD0ig64AIMtAGjp9tReoACLLkgBwqmUl3GwnFIEHc6Uj1j0HZNkmMsAjiPsjQstNYkAXgC5CW3aLA6XTVuFdDBiFiEhA31k7ehUAGKDAoCz7OjOsWVYVAFgEjNZh0Do0qgNKEEdJKM6FOqGtLmnplw2qNYulEQSoeUsoLpXamaodBHdk5TTymSY0SQcq6YI1SQlTbX09qOvKWtbL89YhS8KZp7k6mMwFVY42fdzbsUUE0m1CTehv62OyAF1oSc8sEaZJhJ9QVBQtgQu6PjqvcU/o9QtAIHEyiQDbuLM4Y1C3kmXRjEcRo0joQixt3PtA6K1kh2uOUxVpFkWbei2AiZ4lVJyLkwZAaVEOEicWl32gYeqckZmQUi15zJw1RwvqqBHxBtwcf7AcouqiKd3qtKueiS1BxbY5B58ddxDnVRFmuJm7rpe4eEwx1VXV60Muy5XL8WDHhm2rku2kIpJ1CAt6TDAs/BoYKCFmOcc5AJkDZiCEVM+r2hWvlr7uTAa8sz3Fk90DMAi70wHTpCHtjuOcjLcodxznRee0OTil3GsGHDwD9h6J+3D3HaDXLlvzX6oqrQNoMQNoTgS64DXxwRviJrplXbG++sqWwrl1WOjp0JzbXZTrwcGsWhkyuLgCkrpakDNykGlrZo4McIurI6jzIdRsk6AlLQQut8G5jHib7J0MueZI/q0ug4WvqvuECQiynClL6UtxCDR6pt2FLwOuMtjiOi9QaWtsTgQZqOqy66AvEoHUMqIxM5I7Q6GUlESK4uroJDMlhljCXkumh96xD9rNK6gTB7ru5pYqLaiLiwBlMGcuF7trb04chrwvaUv6/ZyxPZmKqMaSeyOza9qs2/Yqg87GiaIDdXEgBMTAUgpE1bVCjdBEuh07smyXw9qyPWUCWCva2HrYEzUbqYopti62Py2Ilpr5U3MclNI9c7CQlEd1RTBr26MHjPvQfF5VtDLLZ4Osc5sJmFy6u2W2blnmVAImSUQhFDGMi+hnzrRgopYer526RCQ8nEQEZBHcrOuWuUKAGhTcqfgXVZQjbc99MGTsTIZyTAUKxe4j+yyoU0pyoqK2cB9pWZG4vjKsA1stG7MSstoSPWdp6V7Ov7kuClXkBMnymTDTxSpidh1hTIQ4rp3lQuBi4yG1ERGx5lTVrm5MXHL7THjMonypQUhzc9TxZee3desyNyAg87asINi1TIW70qFN1R6CZS3VjCO0xzNEBEpcXUjlOLHSQ9YOWQCYZ2AVkAi23mjOS3nw5uN9PN6d4GCSJNQdwPu7B3i0PcXrD1fny5LjPHem1qJ8Ry4WpUX5ndNlSDiO47xoFAFoJILJ3Q+LsHPwFJjsyEBjvCUuntAtuIAaN9CJrdCP6vzVikC3TxQ/Uthh5i9c5YI4t4sYZHCxFgEEOXdnGdgYBTxc7zSwV8sRggzmbBBmd6kTNwKEDVh1DCeDbYCRyyCcM8+5MwgEUvcgRUK0u+xddS3YID1AHHs17BSIEDeDPC+Dvy6I1ETBBIkmk0edLaWUwe7kU71zX4QI7QikxRcauIwyIAXXvBdxhIgDYyjBrvU9KWcdoMlA0lxO5rIgUGlPTF1TvqEDxCGrmJGlJXxxoGSW4NRqKNBSIXHmbI4jgma6WGhucVs1LoVA9bHNi3RgG8qA1Qb3dgTJYNNCfItTpdlnRYQiubaTtttiPT4sEDprK3a7gSAuBhNTio1CjzXMBV/b/ABgGHJxeSR1iKXMmEBK4Cyv51DQbtXN6jFnQbtamxTQZE0VQebweWViWcqNg8xCvIujRjKFZjlLZ7Zs5UO6PXIN7s6QXBgTQ8QVguKEqcoE7InGzwIznRURLQTt6EUWvI0SBJyzCh46y2xnMsv2iCrYdur2EoGPmyDk2qaeiNBHmU+koGHjIjwBpCYfEkELKhY162eunJQlwppNh2JzFHERT6qgZ86wKr6YgB00zLqW/GkLeYhYS1q2Wc9H/bxyLkC3g+0K8wVxKVtjsqPRAsxlJgwVhRHAyDI9U9k5rSAppYms55o5qQiznMGQfKyUWTv6yT763PvPsHMwEyEZwLgLeLo3wxce7+BbP/bg8EHqOLeZYdq0KJfgcYzvyCDHW5Q7jrOK9OvyszHTdukq8vRr0klrtNV82VmgDXwuItBZBaCFlu/P3pZOXnEk09y2dueO87xZ6zs8WO/weH8mYkMENgKQmfBsMjQlSuYO0YFhuaMtd9ijDtqjthi2O9yRGBQDwPUutnV3MVeC3E3PpdTDXADEVBwAdicd0EGziSwsbYFtaC+vURECAqrDw+QZmVL+KwPJXO56N2M3EZh5UaioA2VbXiudkjvoltFDZZ4gLnf/TcQprhzmUqpijpPMGZxCCW6WgS4XQaS0PNf3FOMD1TbKIqTp2jAjxlAG9lJ2Qxh1nYg/ERhFyeKQ0hApz6mCWCguptQ4h0rOCGlWDqO4qGyPLG43K9WqmUcy/xj12IE8nnPRxNp16ixYnhDPPYe553KuQs+QqlvCSoVSZgy6f1KyEkKUkkA9ipB5vvObrbv9x3KjbHtkrq4dEwNlGUQMmuVcBShzdOgyzgaZbuCE2SxjxoxZJskPUrdQ0tKlpAdJsuOoKREC1DAHFX1Iyvek7EvbzlNAREZAKCHViQlpyJhisIMNmaRUjyiU9URzzNt2Kudxc12x8zCaDUWFIiuxsutOICDEoMHO9VwNJOd1Nc3MHyfULEN7DELXuy2nbF02bdgxA0XQtvmV0kNTf+TQr/O1461Z1hK2Dd2YJuzQ/IeZwElUNhWApkV9u376czDJmA7AhBkgxsBTxEBLv2M5zq0kpxqCPEzkuZG1KD9mQOM4jrNKxF66+a0/FHH74Amw/Q4QH4nAsyxouXUAHceiALTYCt0EoOku8OhXREyPYwlRvi3tzh3nsniwMcIbL9/B65zwbJKw0QUMmfAbv/5VfMtH7gPQ8Flzn5jTQe0WibX8IqF2j2qcEItjiiJ3ENT9oXfOYXfQbYCMJkzYBnJ1yNi2FAZQs0pQ2xK3ZTtleYpYAhEqeH4ha6ZPbUM8NI9N1JhraayfgwwM5hRR8YmZMSQUcSZniCuHq7hlmSbiaiCAQsmGmdtuVAeM5kQidZJICZk6qmRFdABNmk0iAsWQGZPEOBgYOU9g4cW27p2Gs3YUSvCuiHZBc35Qy39IysiqK0f3q9oZpKxIB/qNgMAQB1BuBq42mDX3TTvObVtw15wZKaHq1VHTackQhRoebNlC5oDIOZdBvTgydDkjAVkcKPJaPXZEcKw5QkkDmAcNYZ6mBM6EgVMRhYYMKZlC280I5f1FRKTqoLLyOXN/jMQKU7ZB1mO/dp8T4SNx7ZqVwQi6vW0dU9Z8GgA55Xq+ap3fNANpyCVcOmWAc0ZScS5nbW3fON+sK1enrhLrzkWond0slFjEESkz7FQwjJHQh4gYJP9lFAmhE8GmOJDA6m6pDh5maD5X69KRUj9xHeWytew6wOWIk+2VEwCybnco57BdR5jFGZVJ3TbcbH87RnX7y/K0mUkoohmVi4ocryaQm1hTOmGVFSlSWMnjioQmUDyUkr1yPuj7zDX2eGdSBOLAwGxg5MiesePcbphlADHZri2Fu5F2ibkLb1HuOM6tJQRpib5+X5w7B0+A3UfSMn18V54/aznqaQWgnffk2jzakuuwjn9Wvt2541wm3/SRe7iz1uGX39lHIML+JOFrX7uD3/yNr2FjdLHD0hwHJYPEBpom/vB8i+1DpTFLaMUeC5E1Qai2PzeBqBGKjrkTN2gL75mG4opLg2uZjLo2kpbKDDpdGyhbB58ykC+DM+1gY2Vi1o7aSsNMzOH6Jp0vl65cScUFNINPNscMoCHTWQeOMtgHUEJdk+WgtAKVdgqaZW3TnoBpGqR1O2dMZ1L+M2TWrJgs27F0+JGRajQHE4k4krVkp1VmzMFFZGKTPg8qGS91fBvAOZdB8FwgsI78U5LA7pR0/VmWD1lFLBIBgCFt6+cdGDTv4GjdFIC4N8jK88Q1FYOU3IRQjBlSysRN4K7NoDhR5HEVc1SgyPPTlXBo2HFDTSCuOWlM9AziegsSdkwkyxZhOTgoQltZn7LeJrRQKV8ydxXD9mt1x+UMZFJnUEoaFo3S8c2EwlTUOS6fAQJ6qkIPAhDN1RLkuE3alWw6SElSF7ScEpLrQzBBKReh0FYmFhGshnWT7TwAGVEESn1PzZ9SN05z7YjqBjp0LQmSVxRJt6muS4j1b7B8BzF1sgiYdj0jOcc4qfMwZ2RocLdeB6UcjmRbJj1PUw2VFtE863vkXAaLgGyB3jmLqPhsMivuHdv/wa4DjnPbmB1obs62XNBC1DvRd1amna7jOM5zY7wlP8ME2H+iTp6nIrKs35cSrufJaEOuy91a/R55S9qdO86lsTcd8JH7G9ibzLA7TVjvA+6t9/j8+zt4uDk+VLrQDpDam83luWb66uIwMUPdEdGKLJazVBBadAwlGRS2gpDdhU+56byj77MBLKADMB2g2430mhHDJUNFcmpC04qb0EctYaIqLtk6i4hjg36ZseXumEjROj9MoLFQ15wleyexTd90BOOmdXMzUGvXaZE6ma7L3CC4URdgItUaihOBmzDgIWOaMmaZMZmZsJXLsicTTzQfSVwMMl8plUul/XhWYSZBXV6MGgxtjiau4la7HmRiio7OI0QoMBHP8l0CGJ0KRn0kFWIYARHQbmVB59Gp8CDbR0N4VZmJErBUcpvs+A3qwiB1NgUGYicOiy5ElPwd1SOCCl1Bn0M5+ufPGSloIwwpw8rcOFvGTA3YZUDFOxPXUNbH3BvS6UqWJwRpp04kN1J6CiJQqIgCNGHOjWho55SYYWRfltIwDTOfDhmzIWOaEg5mSYTBlDEZMqaJsZ+SON5yFUEJsn9jME1E9lmbpzXSAGVz83UxYNQRgjp9OpJMnIAwf33S35MhyTGc5MhKAHJOcn5lKmHwGXb8qWCSq2BqJ5jqbuLe4uq+ITSlkM1nM887ckzsMdHL9n75nFw/z+ZDuiFsStB8AHkJKLf1DoScJfje2FwLAAdMhgTHWTmmu4c7qaRBBiOTbWlXTiSvje8A/Ub90uI4juMspxsDd14D0kuawfMUeLorTse1+3I9fR7X0lve7txxLoX3tid4b/sA+7OEYWBsp4RfemcH/79feQ8fubdZRw5oHupIxvQBG3i3gyF7wIfe3eRvtDQuAwuOZWgOlz5nYbRz5VCog04ZeFmHKwttNlGHSxYHMC8+WSCz5cmAoHf824BcEw+q8yGU7I8aDFzeY2u5IHLJ4wVLRftaOz3UVQECtL06BS4tlrOOMi2U2tpbZxYHgIlEWUUaCyq2O/wWQJxTLvk+IugAVsrFuh0Xr+Hm+jHXUrb9wTV82hwVMRJGXcRGIIyihjhHQj+3DYPuo+r6AKSkyeZv+26WRSwbEiRHZsjILH9naFYOM3IOGJK6JAYg5VlxPrVGExEvCOAkgpQ5aACARdQjNRFlzUgp+7xsGJlhRFCRCKVcLpZuZ4RIDGhpUyllg4ZKy4bVzJdQBAErTSztuvVgqmHMKgLq/mY04cwmDiJrNk2YEwRMbLI29BZMbt3MOi0BMtGy60I5p2N7PBNh3EWMO0b7zxnrcTXR64u0l2dMh4TpAExTwjTNxBlkx686WpIJHmUf2XKb06Zm7sSS81VFvi6K60bWN5SQbgDF1cblP3qM27WMa35UyciCHtOxCqTBzg2iIm6H8mNONem+BqhALFqiZEuFgBCreyjqdrfyxwiUjCkTj83JxWhcSACGYcBf/6V3MRvkM/YnGWs98HDLg2CdFeODL9bBQB6A0V1g/Z50fAEkCHT9PjC6czgnwnEcxzmZ2EnJ6obm8Ow/AXbelbbpa/fk56KlrLe03fm1QUQPAPwggN8O4EMA3gPw3wP4v52nW9d550dEvw/ADwD4FgATAH8bwJ9k5v/mmPd8s37WPwjgDoAvAPjzAP5tZt474j3rAP4IgH8KwMcAPAHwEwD+dWb+udOv6c1h9yDh8e4UgYBpzogBeLY7w6NnM2z2k3ILmdmcOSLN2J1qHacucSBAxIGi/tjgkqowY113mGH5OXZH3L6L1S5OMoCzVuEhEkaaA2MDuajlTiFqO+sQdICKuXKOcpfbxIMiSrTOmMYhA+1kBQZnKvk5KTFya11Kdnu/aRWug+XQfuZcCKsNzGRDc4Z2IqsCVtF2mOcGouKAaMSzoocwOlFM0KMpzSmCExcHSuu+ar/+EpG0lrd9LqpG+ZxSUqXTMiAttSHum9ksYcbQUh7NcDEHiJa0HVh5FwGBUjl+LDskUFCBr809sgG5tvbOAI8icibkaC6IWKaXiQkpJ2Si0ilLcmVEvpklmf+Qu9L62sStIWew5ukgW9ZK3eY5L2Sv5HocFxmhCAasji7bGaQdmUJxr5Tj2wb55hSi0JQXyonRoWb0RHMr6UkZ9HGnG9jim1PKyHacQYSxPGMwp+IOswDvet6Hui66n0OoIeGBbH+pUBpUrCpCVA2NNuGy6wL6jhBJHU5zLhbN+lGB0oKtxaREpSU4c5IsIF2wzIwAVjFNzzsmdEGCwce9ik8xYtwHrPUBa31EHwNiYPQxIoaIPjJCiM0xTrqf5SKY1IEErtk7zWkJc71J5pisk623XC71MWkCENfzykoWYdcPWDi0nOsShq0CNaEcZyAJGS/dukxcilHymhxnVZjuiqgT14HZrrQqf/YV4NVvBLZe8xbljuM4zxOiKuRMd8XFs/cY2P9Arrdr98XNc15Gmysl6BgvnLCjIsz/AuAbAWwD+DkAnwDwzwD4HUT0aWb+2cueHxH9WwD+MOQ78S8AWAPwaQCfJqI/zsw/vOQ93w7grwHYBPC2vu+TAP44gO8jou9m5u2F94wA/GUAvxEiHv0cgI8C+CcBfC8RfS8z/5XTru9NYXMtYnc6w9tP96TNMTEero/wqY/dx1e/eqcMaAYtuak5ORLGamUWgzpCLJvGyqmA1hUh0kFxzehrNbulum3MnSDTcGnTbG+ywZzNe+DcCB8kA3T5MNjAzFoOi9ZE4AQw5SqmQIJTzfEAiHCVYGUktny1JTUn0twMGXyCWco+IG6T1IgxiRmmj+jYrYgxxUGhg35i6wbFxSlk4cBBO0mZw0LG+TKSC6jzLTk+MFeBlmNxHSyi7Is6+KuGmfk8Gtv2rNvdWoYDKC3dTRhL2j6qls9lcJauTRLUK2UyQ86NW8NcSPZZVeDIgHZr0mNDt784b7R8ClSOkyJG1VWUoF+IQNL3ESOoUyKyCgxS4hO7gAgJBSYtY7Is4wArM4PkGhU1jco+znqMJA0ettyaWUqyzqwt2U34AiMNQ81XAVRIktIsERiyikdSwobEmNrKMQNaRhZ0G1iL7qAdrkwYMuHMXCEEAvVV4GtdaLJucn6Yo8ocKcQBHLiUVhHJNgyhcbeQlAZ1FNDFiK4jdAT9HSRI2ZxBQVqgSx6OvjdQFUS4XnuGVHOwatmmOLZmKWM6S5ixlInZMTbTTKnJLONgloF9EVai5hWFoE4yPQb6KM+L6BO0nE1ODuniJtu01+WzQHa7NibtTJaYi6iZsoqJup/LcptIZKJazkhACb62863sVDtFCCK+6hH/q+9vl86GBGBtFNETYX9qvdwcZwUYJnJC7L4r/wCON4F+LKLOxsPrXjrHcZzVxUSYYSpBy5Nt4OCZ5O+s319Jgea8vHDCDoD/N0SE+XEAv4uZt4loDcB/COD3A/jzRPQtzHzaAv4zz4+Ivgci6jwG8A8z80/r898H4C8A+CEi+p+Y+X9p3rMG4C9BRJ0fgriBMhF9RJ//dgD/HoA/uLB8/yZE1PlpAN/LzG8TUdR5/DEAf46Ifg0zPzvl+t4IpkPG7iRhNksYOII44XGe4uffeoYn+zO5oy/jShlglLvQAMDFddOMbdVxUY0KVTSoIgehCgFWcmHZHjZWLU4Irm2/S/lPGQjZoC83ApJMR6jTyEBb756XecJsL3XwBBSXETUDKMOKuVohpC3ZMpHGBtEo26K6K0K9tQ/WttchmLBEuh2zWpqqWwoqgi0ugb0W9QNEAMqoQbR1wA4KOj4UlxTIXFLSsls+OWu4snxvrp2D6jYyD0fmZr/ZPrbOXAGqYsnAX/QHqq4mZIAIo44wpgjWcqHWOZUgbd47c3UQa9cueV+nHZm6EBA7QkfithCnAzScGegCYF2lZCmtaxKXzkYlYwYQ8SVlUCIczJLsNzuiico2tPE2URUhzLkiThkrLwNiiCANJK+BxnqAlAMC5cTJLPtfQnMThjZ0F/L6bDAHEc1l49ispAxRsqiYypPaqUucaAGybJaNRGAMxCAKiFaKpvuVAW2pnsDJ5mfHwFRFPZ0HM+bCvO08LwlMoeynohChljYya2csyyoK4jSiQAgsZYl2Jli5mbisSERnaAhx1k5XEOdV0lZ4s5Ql0Fg7x3FmsJZzlk1l1wLYSVg7f4mwYse3HB+dZi/FICJWiBF9lJygvhexSI5ZcWlJBpIeC5p/BK7HUnt02KGRmz8Cas7Pe9tdzXFK8nmUgSG7sOOsEN1YTritVyV0EwAOPliZwE3HcZwXnm4k1+ANzeE5eAo8e0vckuv3paNWO3i6hbxQwg4RfQOA3wFgB8D/ydwtzHxARH8AwHdARJrfDuC/vMT5/VH9/UdM1NH3/SUi+iGIGPOvAPie5j2/D8BHAPwkM/+J5j1fJqJ/HMAvA/iniegHmfktXb6HAP4Q5Dvy72Pmt/U9CcC/SkS/HsBvgYhB/+5J63uTeLI3xf5khp0pgzGAGRilAW892cPGSFwMsZR4oHal0m5PoRnQmihT3BKtgEF6wxmYE4BksG+BtdYNxwqzrAQBJa+k5tyo88CyLQAtZ9ELiZa2dBTkfZZ/AxNdaL5LlWXqlO44kLIOatYzcCkzIZCGy+YqbqjAlDT7BXq3vmQDqYBkwcNJQ4NzRnFDWRlQzrmIahYuPOS21XbWgX6uLgbIHf5pGjTgVgN3G6GklAgxl05WmhE85+oRF4aE7EbdnhE1h2jQY6GLdVuKeIFSElSydnSQW3JQEEp3ISufs/0YVAQiDqAoZSlBxYGUgAFJtleSzkDiDstVSOSgGgHLMWpZNlBRTBU0kXQCwAzSEq6gx5uJXUWIaPYd27Ko+6rmttSSHDvewVK6VwJRGvmrzUaRbY0yWLe/TSwi3WbjDli38kM9Nk1IzKKEaBcwOzZtmS0IWTN3kjqLVPAZNNg6DQxOGVPI8VeOmyxLKQHA6g4Cl7yfKvZpq3VKUi6k6805qXBGRURNzBh40K5R6orLwEydXSbSJp4XNm17WYkgBUIgO08B0rwaC7geRXHbjEehdrVii6kOUj4YuHSly0nL0BDAyGXdrBytvl+Ozc72T1TZj+UYtmysOfefniOsDjZkAnNCD20DT0CvGVRdR+hJAqOlREzD2YOVnMYi8pLu+0iM//7n38KBWLmQdmbYGgXc3/CyFGeFaAM30wSrFrjpOI5zYwhRnJLrD9S980TamO89Asb3pHwrvlASx5Xxoq3174V8b/5RZn7cvsDMiYj+EwD/DoDfiVMIO+eZHxF9DYDvAjAD8OeWzPM/hgg7v4WI7jPzE33+9+nv/3TxDcz8BSL6KwB+G0Ro+tP60u+AOHx+ipk/s+SzfgQi7PxOrJiw88HuDAcDY6MHplkGvykDG6MeH3mwoc4EvYNdSgFk0Ew68CvlL1b2gyZTppTG6J13qtNZ2ZAFEZfyKxOQdMBCKg4AKILBHMQIrHey1dVBqh5JYocMVAlUsmvMoTFkaSXOqWZoWPkQQdwqTNZZyz4DNpRvBn6N6QDtc5YvYkPwmv1i7hCwtdcmZFL3DkeMIOU6rC6lbAGwetue1cVkGTeWR0McipskUHVQpEZgAkyIIyRVz6xkKmVCQgZlGU1nrkKFrT2zjFKjbXI9RmC5IQQEsVdICQwADOo0ybXsxNxVgDkRGnXkBMzRklQ8k/IwGzijlOaZAEAk4lQMAbETwbKD5TShcYeI4ATYcTkvfBHPlybZsY2sv9naXouoI928TK40UakKnQltN7AaHgwGkrltGleRCYASeG3HRnMw2bGvYsic8KrHSclWgp3b8tnWmECOo1hcK5kTSPcbwlBEmhqsTSBk7fql2ywBhFzbh+vxQjGAmIuYESLQU0TsRIgNoQY2m8DKZOeuun10dW2fW9hySrkcr5aDw6gOvXp+Rtm/QBFpAAAB6Fi6aLVdwkygHTKkDXliJL0atOWDERrsTAF9pNKdjKJ2WSuStohbs5wxGYCBpUyvCK9oQsvZjmEUoVnK5kjb3otg+tn3d7A/AwZdQ0rAgToyHWelWOHATcdxnBsHEbB2V35m+xK0vP+BCD2jLXHx3DJX5Ysm7HyH/v7JI17/X/X3d1/i/Ow9P8vMu4tvYOZ3ieizkJye7wTwl7V06ttP8Vm/TT/LhJ3TLt/fR0Sby5bnprIxDtibzrA9rc91ACazGR5t7wPaurttRQxABkzNXWmrXWAZuYKC3amW3B5VV5BMAGDND8mSScPqLmENSwWaTk7mNDH3CSxY2AQKVNHF3DNloFspuTxQtwWqU0iWFEWhMUcQ15Fgceq0A68yVLSyHhXCOGcd65EO5LlxgVQVyESNuTwYG7nSwvwbd5IJDNXpWAenpXNSs3wioNkb6/qgGdyXdaw507LdYEKULJxlyFgwUStWQLehvT+GZr76P3F8WXcgUgeKlLGAze2DJnRaXRC2eYgg+TaxCGqsO5hJ9wvrcqt7KmfNiWLGkICDWZayGxNbuAbcRhXKAlDcTLWkr+6msvJlv7EKJCpzMM9tnzltTMUxEYtYBZh6npkTTkTBZhtGlHymaK20igDQuHgC9HjR9zHVdvFRH4NAbEIcicyhB0XUDzb3E/T4iaXODCi2Gdv3WlYo3blE1Bl0fc0tNgWDh0FK9GZJ9ycAHvQc4LnuXcVxFRYCyYtzT0ubTAzuo+4/XSYtBbTz0QQxUC1hs51UspMgQjFTPZb7SLq9rexNRR89KCQ3PUu790EEm+k0aekcSuYXQR1FBHHmUEDoAkYEdD2pw7AeVsnEz1SDu4cstbApZyQQkuaCfe793SLqAJJvNBuArzxZ2ivAcW42Kxq46TiOc6Pp1+UnzUTgmTwTN0+/pu3St657Ca+EF03Y+Rr9/bkjXv+C/n6NiLaYeecS5nfSe+x9n2im/SgkXBkAPn/CZ31N89xJn/VlyI3QDsBXA/j5Y5bpRrF9MENmWTFzoyQAn3u0q6NDcwrIazbgyItPKNwEFLM9kDfI7zoWrE4HoJadoBm02fOoYkNb4sNsJT115BuaacpAmFDaDovmwSBI15tk7pPGRWOrZbkcgJWwsJaw2OBahuy23bIqSxYcLH/JYD9kKfkIUbtUqShCankhFWYsRFrcD7XsSwb9KizptgmhvgbWQa86TWxwCxVPbLBo05dGPLb/VCiygX0wGYXav8owWMQ+1P3b6BVlEG9OKQtWLq/BQm8lJJiHKuaZADYvgpSFr8dKXdQ5Yarm2+hrxa2ix4eKBCMEcXEk1sOTi4so6+dnAkhtHiECPai0pu7IWpbr/tMNKdu3Ds2tvMwWiqAlflGcTCZcIYhIYM60KiHWdbfzRsQQdWqprcP2I+tnidNEW883+6nFBBuQLTPVHTk3vW6jNmcL1XFXnUjSEYtZxBkOsXGlcZk/q/oqx4e5l7hcZ0yYA6TcLidgllPV0WxDZJRtbQKRnechSmlY6xQMbMdALS2NVK9vtlzcXBNKSR0IGAapXLRzhOrng8R11I+BuFbFr+IcU0FGyigl5DlnxjQx8jBgW88ZcysGks527bUQ6uoLQTqBkV63Y9TS1XqJBSCepBSBnYPZ4Z3vODednfdkwDC+C2y9ct1L4ziO47TEXq7NGy8Bk6eSw7P9NrDXSYnW+J4MZN75e8D2W8CdDwOvfeN1L/Vz40UTduxfyUdHvN6WU70Myc553vM76T3t+15e+JzJMWLT4ntOXD4NX34K4KWF9914ZgNjLUYwp+IeAQMvba7ha1+9qwNWajpXQe/mo+RYULnbzzpYKsPVMq0JEpJXU90RIigwisIDNGpDdWMQ0LRHh7oCpKgqJy1dAYpgYC3JBxVkMksIbc5Zg5ZlZkkHXWzuIEDLsGpMMqBlOh1hRNDSM81tCXW9+iADfxMeQpDSEwta7TQlNZr7IEgqieX79FElGGsPbaINdABZxC75dCptpGXbE1eRq7iRbEc0jhToPJoN3ggI1T0CWNiwBQ23Ol4jwMDsDfbJVUSqYcUo7hrZr9yIByp+wEqoRLCQfZGbsht1daFmsLCqMDJYb9wcZTtJ62trHd6Vba/Hcqi5TV0gxCguIOtsJAG7XLq9WUmOiScEIEYLcBanS6e5LtaBjMs2qgKCHWetkChiWHWnLQpbxTlVnyrbMs9NrO+3DCftrCTHd9aW7+2+aAQI+yxzpMC2d3WpmWhkezvl5j1lvno+2vz0nMgqQGSSzmYUpOOZdN6SmVv2FogRmUShYBFi6+eL8CbB0FIWx7nmVvEg5WNzGwp6yKn9S84dbnK1QikZAzE6FTtDtJyt5pxtas5s3xIITNLHzMQycejUctNA0nEtdBIALh3MCANnzIaMKWcMAzCZMWY5lWwkgEsOUASh7wLGfcAoEsZdh1FHIEr4O1/ZLqu7M8ime/0ldzU4K8bnfwp4/zNA6IA8AC9/A/Dx77rupXIcx3EWCUEyeNbuA9MdEXh2H0nL9F/4S8A7vyBlWmkGvP7rge9Y7G10M3nRhJ11/b1/xOvt8+tHTHPR+Z30nva1i7znIu+78by8NZa2uB0gYbKSOfMbPvEQ3/UJ1bvKmL2W21iOCVAHpSYK2KDVXrQ74NwM/Dhr1xoGOEluhXS9Is150bBgns/PkEDgXAd3qJ2ykjlDCibNqNBCQOgIa9raGgBC6LXEw0KiJbjUQlXN8WCiRBc0YtecIkEGdtGcGq0FptlW7XZqB8WcpdxllhmTYbDN3LgcbHAtg99i4zBhgUyImttR5XEtSZPAWJltu5w6IG22WjuXWnlTXUP2HjN5tO8xYUmqVqg4S+z9qhcVgSqQpZzofEiWlEhzWTgWp1X5HB0kU1n/KvZlFerMDZKZMU0qEKUMppqhtKiFtFTnRM0zMQHI9k/WPJysO9bExZZOS3g6c/qoCGTZKLYdmq3aOHzsuKvbcG4ZVXws28aOC3uN23mrw6eIcc1r9XBpD6/mU1A+x8rM2kWR5aufW99ZBcb5edqHNxcQmOiq2zZZsDiX/WvXFOlkZcHiuh90PtZa3q4ZJuymbIHj4poZGJLHk2sr8hkzhpm5yXRZSrc9veblppve/O4oguj8uVRLt+yzciYkvd6ZWNnOz7q5xUAq/IZSGkp6wNj1jwCwBpC/+ehwyVUCpKub46wKO++JqDObALP3AXTAL/9lYLoNbDy47qVznItx1BeTpU8vefLI7zXLpj1q4ufw/Fnnfcz3sQvNd+nzRy3DaRfiHMtx2nmfZf8d+fxZl+Es+++oWZxyOeypNAUe/Srwiz8GbLwqmWkPvwZ486eBj3/3Sjh3XjRhJ6Hm3y6jfe00e/888ztNj1Z730Xec5H3FYjoD0JbqL/xxhunmN2Lwcdf2cLGuMNXnkwhuwm4vzHCvfURHu9NmkGyTG93463rjg2wSheerNkPLJ12cpZuRgkiyrB1f+I6v+JSAEkWhXbhiVrrYbkUJiyEJuC2p4BuJC2DuyA5LVFbYNsAqbhcqEgUQDsgDtVZIcvUjtpqWRHZcnJ7EDBYuwG1A+oi3rRjV65TiTGnlqKZu6OIH40ws3jASRC0jgU158SEtXasXgQcNkEkHBKAzJlTWXyPbit7XBe/+Zz6bm7+sLbxMtaW/WpBz/YmIkuDXlgMzO+zZumKeLDw0txyVaeTuBY6CrC+0lUkoiIW2H+r4KMdjSxDxQblaMOwUY5JUMA4opQ/ZbYjBhiGjNlMQrGDurIiieti1AWMI2HUR/QhoAsBfacD9nIYtlsbcwKNBYW3x0g95uoxWF6f11LK8XzU9HPt4cu2W5gJ1WM+l0DhOgnn3My3OT64Ht+2zEUbWzjoLYQc0PN7brI4N/3iZyys+sJ2sHXmuZdkuWt5nnUHkwBy6UiXkgpF5u4aGNMhYZYzUta28DljquV+FnoNbSVvgjJBHH/t8W/up5Rr171hyEgwkSmruEjgWcaUGY93m6A0AL0GYX/+fc/YcVaIyTNg/6l0XUkH8twwAw6eSUgngBdiAHrkvFdkAHrkLFZkAHrk02fcnqdeBudGcuiL6LETn+35pU8fNe0ZlmPptEu+gB8731PM47jnuzWAgpRjjbekbCtG+b3zrgs7l8AugPuoeTWLtNHWx7lcLjI/K6U66j3t+y7ynou8r8DMfwbAnwGAT33qUzfmqr0/S1gbBaz3hCFBBx3AW08PtCRFHTTc3D3PVewwoYf1v3Yn2bpaUWB1KMQiDhC0J02kUqIQVYTpAmlLcQ2CLe2Vq5OjzZxhrkk3okdo/oR2smIVUIpzpXGEFHRvVVFgUejQlt+NoFBKy5rH5qkoXb+ofVwdK+JGgT5fw4KLiKWdlMy9obpBEQRKF7JmEeVXU7rVrMshsaOxZyyb3v5enL7dLMteO3Yep/hHJ+fqjgBQhcNSllWPLxMDLbNHXGDN+8rr5ubRwTWa0q4kx6yJO21AcuvOqJ/HZTlF6LHSP2k3b8trbhHmhJSbskAt5zLXmc3DXm870PWREGNAH8TxI22wxVUm3aJYy8mofEec2ydFGUI5rkwAtOOUm/eYqDi3r4iKK4p0ftSEqBcHD5nANb/fLS1qznG1cE6EuWNWnVhlHvba/EFajrNmWRePcTTzxcJry8+TZrryXH3SHg9Nad6Qctn3pUQP9TpIJIIiqdhs53Ikmu9opo4hE8hyNtFGHUgZEhCkgmMgCZ4vuUQZmKWE//pvfxlffvoeeoiGOeqAySCuMcdZGagTUWd0Bwj35K7TszeBzVeArVeve+leXC488APONqg86wB0yfNXOt9rmPdl7ZMXev2ewzJc5ryPmvZMAo5zKu6/Drz/S+LUiRHiApitzHX8RRN2HkGEmIdHvP5S8/i9S5qf5d0c9Z72fYvvWSOiDWZedqty8T0nfpZ227q35H03nveeTrA3GTDqOlgA7O50wOfe20WvJQBFyNAuRZF00Eksd88pIMaaKUNUO/YEaHgp12wTc9IcLRDT3OBNfutgMDRiSglUbkpm7P3N35ZxI4NIGdK26xV0JFqEFGoEJH2/DXwPiSrt42aQWQe8ltUjzDl4lDznaLDp2oyT1tGQq8Bg02rGCbeixeK8289u3AomlJQhaSOqlMcLDg1G4+BCFVbqOpgAUtexnW9dHj70nnba6+I4cUt+SXDyOC6ZHgvTN+uVoWWEmaoTTsWllBnTlDBLjFkS0WBIjKkGS+chYToAQBI3WiBx+EQ510ZR22tb4G6wY1mOWem2JHkyXajBzEW8UDEUC2tSsoFgQg4vrB9wxMPD2+4U0x7e1mjOS/m7FYNMnGzP18Vzu8w6NNcRsmmr4Bp0/Syja0hZO1wxZnnAbGhLUQEQYxQDNnq5HnZBcrIiSe6SnE9WjFU2cy3ly/X4z6zOn5yLgG5iOtvjVlzXvwPLevVdwLe+/gB//RffE4EewMEAjCLwqY8f90+o49ww1u4AL3018ORLQO4AHoDXvgl46ROS42Bc1kDRB6CO4zgX57VvlEydN39anDqWsbMCbh3gxRN2PgPg1wD4+BGvf0x/v3WEePI85vcZ/X3Ue9r3/QoAMPOXiGgHwJa+9vdOek/zWb/5mM/6CGrjqM8eszw3joETQISRtt7NnEFM+NC9Mb72Q3fKoCcGaJteAundZxtgmVhS/9YSqCLUoHHLEMxQ0FIHPmVCeY4XJrDpGHPCQxUWrGyiyBFLyjvmQ2vtffb3IfGlERoOlbksm0crTCxOz4enf14cNzheeLh02iLg2eOFv+s0gjirqIh3QOPaaPcxmVRg4lwd6odmQUJpja6vlwE75v4Gal1k6xQJ8yvVuEYaUaCshyz33LoufCFeuj0PPd9Ov0T0o8Ovn5UhZUxmCQdDxjRlTGYZkyFhOkgOVTIHHXPZHoHE+dWp4NAFwqIDxdqHlwBpPcejTmtBwtZy3Nxi84Ic9DEvnDvzGUateLfsffY8lkxXnFxoOq5xLf20UOXBWpqrEyZlW6Yaug0tm5omCSme5oxhyBiyiWh5riuXiNSk4dihPO7VbXgUbX5SueaRHXd1+xOq8CziUijbOwRC7AAK1dkUQz1mA4mjyqTbLgDf9JG7ePvpPnICxuOAj97fwIfvrVQsnHPb6cbAa58EXvlmYLYH9BtyYd946O3PHcdxbhLf8QclU2fnXXHqrIioA7x4ws7PAPhHAHwngP9wyevfqb//t0uc38/o719HRGvMfNC+gYhehbQezwB+unnpbwL4tM5zmbBz3Gd9J5Zjz//s4nLcdB5ujrEx6pC7gKQhxH3KeOXOGBujWj41L1BIUPEAC2Qt9g2UyXk+sJNVr5HHvHRAvEi943/OaU/hFLByqXlBo/4daH5am6gOxGwwX0WtVtAA5sWOKia0pVmYm0f7vkUBpAwWTbSg+ZKTRZdEu12OKls5r+DgXA1dDOhiwLIhS8qMWZLuXUPSx5kxG3LJZ8kMTBOXbnRdc8xYN7IhM/JQBZNlWPaUiAp6/KkLRhxzGjYe5o/34lzjWlJnLqC57ltNqV3b+ey0xOZkbR1iqRF6kpbOEQWMe2ANEWEdGJkA1onrxrKOOooAqZBYzscmiHrJ+dXWtXEjEhWHXLM9W1GrBp0vCsxL3le2U/3ddxHf/sZDbE+m2J9mfOjeGPfWxlgfWSKR46wAo01g60PAztvi3uEsf7uo4ziOc/N47RtXStAxXjRh5y8C+EEAv52I/kVmLu3ItSzp9+uff/ay5sfMnyeivwXg2wD8XgA/sjDPP6C/f5yZP1j4rE8D+GcB/CftG4joYwD+IQBTAP9F89KPAhgA/B+I6OuZ+RcXPuufXVy+VeGr7m3gkx++hy8+3sWQxDXx8tYY3/xV9/HqXYkcagWAQ6LAnNvDXjz6tZPKXBYFmotkvtT3uaDhrA68IIYEIoy6gD4Ca9oWnBnqQkmYJsagbp9pytiz8q5GdLCw8RgDorbXEmGxliZJKZCGAPPhcqGTRBjpCFZzgmKQz+vMHWTOogCMijsmFBHJrg8meFonPAkrlsBiyb2RZekjwTKBYpCOZH20EjYRcHrtUrYKvHJnjEc7U+xOpsgsWTyb4xHub4yue9Ec5/ny4A3pojJMxMHjoo7jOI7zAkHPuyzjohDRjwH4bQD+KoDvZ+ZHRLQGcdz8fgC/COCbuNg1ACJ6GcDLAGbM/KvPYX7fD+A/B/AMwPcx80/o89+rz48A/EZm/p+b99wB8EsAPgTgTwL4w8w8ENFXAfhvAHw7gB9h5n9uYfn+NIB/AcDPAfhHmfkLKjr9CQD/KiSH5xPM/OykbfepT32Kf+ZnfuakyV4I9qYD/tovvot3nh5gf5aw3ke8dm8Nn/76V7ExetH0Rsd5schNuU9unCbAvBulZBZxLR1qy5fax5nrPC/iYDmKQNINrHWwWLDzXBt4deb0IUgHr0gYxYiuI4yidPSqoeAEwN5vj+t6Wec8KYvSrnr5eIeQbUPrCtW2Obft3ZaK9TFg3AWM+oC1LpYytFEMxUW06rz5eA9vfrCLlBgxEl5/sInXH25c92I5juM4juOsFET0N5n5U0tfewGFnY8C+J8hmTR7kLKmTwB4AOApgL+fmf/uwnv+BMSZ8wVm/vhF56fv+48B/DP6598F0AP4Wv37jzHzv7nkPf9HiAtnDOBdAF8C8M36998C8N3MvLvwnnsQ0enbAMwgAs9HALymf/8WE5ZO4iYJO4AMBr74eBc5S6edNx76YMC5WZxWYLHXTyOwzL2+ILDYvC5CzVlpyviabJZSikdW7lRfD1QdLPY3Lc6rlE21ocInCxyHyrtyLoHOKc+vs5QsSf7MKLZOGDq1I25IGZNBcoQmg+YHJW0dPvCcQ4hAiFF/B/n8GMXps5h5U7ODalaQ5YSFQMWlZDlDbce5m8zedMB0yBh1wcV5x3Ecx3GcS+A4YeeF+/alQcTfDuCPA/g+AL8WwBMA/18AP8jMv3xF8/sDEEHoDwH4JGQc81MA/n1m/vNHfNZfIaJP6Wf9JgDfAhF3/isAP7wo6uh7nhLRdwP4wwB+p37WLsTl88PMfHOUmjPy+sMNvLQ18sGAc2FagWXOqYLjBZZ5p8qLI7BIJ7dwLQLLdSElUBFr/eFslqNEn73pgO1jRJ9FvUS6gOXSJr6ljwF3ekIfRwtlU/NiUSkHszKxxgnUdpESR1IGpzrtSet/kwWhjVEHr75yHMdxHMe5Hl44x45zfm6aY8dZPYpzZYnAcqjcByjdhQ4JLJgXU1qBBTgs1lyERYEFqB20qBFVlgksRYw5hcAy9/oLLLDcNA5mCQfThL1Zwv50wGTI2JslHMwyUioVtiAijLuA9T5i3IuQvNYHrPcdNkYBwdqjXRJnEYSs49h5BaG2m9iiIGS/nzd70wGTWS7b1nEcx3Ecx3m+3CjHjuM4F2dRYGlzUi4isJTXr1hgETElnFpgWeqGcYHlRsKaxSOOHSnNmmknrkHzc4wQAu6sRby0NS6t1gOArJ2fBuZmHvKzfTAAeD7lXcdBRIiEM4sqZxKEmDGb8XMRhAJhTgg6ThB68/Ee3t0+QCRCYsard9a8rNZxHMdxHOcKcWHHcS6RkwSWQ3krjcDSOl6sJXE+QmBZFGsuQlvuUzt51b+7BYFlWbnPUQIL0LpaXGBxBDbBJWfMBim1Km3UG9cNIMdKH6Uj16YJMEEEmC6e3nVj5V2Dtmk/bXnXZYg+x3EZglBWEeh5CEKTIeFX393B/Y0egQh31jq8u32Al7ZG7txxHMdxHMe5Ivxbl3OtXJV938p48m0SWCAtnE8jsJTXXWBxLgnmmm8zJH3cCDgtgQh9J92mNsedtAvXblNnEW+OwzJ9AEi8fcNNEX2O46oEoe39AZMhYftAxJ6ttQ6RCNMhe+aO4ziO4zjOFeHCjnNtvPl4D2893QcBmOWMV7bW8JEH68s7Bi2E3y4TWBgSogscFmsuwmLHIOCwwHJsuc+CwLIo1rjA4qwKOc93syolU+rGaYkq0oz7gK1x1wgi4VIyYM7Cqos+x3FWQejBRo9JSri31oFBpbX8qLvczCLHcRzHcRyn4sKOcy3sTQe8u30AMGNvlsFgfObtZ5ilvLQrDnC0wGJiSACh6+jcAsu848UFFsdZxjJhw1w3y9qSd5GwNgoYxQ6dum5eBPHmvFym6NNFwiiGG3Xd2Rh1ePXO2qGMHS/DchzHcRzHuTr8m5dzLUxmGZEIm+sjbI4ZRMB6P8NLWyPc3xjNCSwEvFBtfR1n1TnUXjxlzLK4cJaJN31H2Bg1JVOae3PbztvbKvq8/nADL22NMB0yRp13xXIcx3Ecx7lq/NuXcy2M+4DEXEJQmRn7MeDOWo9xt9yx4zjO82NQkaHm3uTyeDFA18SajVF3Y0qKXjSOE32y7YcbLPpsjDrP1HEcx3Ecx7kmXNhxrgW37zvO5VNybnLGbMhzbcPb7CkiKiVSa30Vbbrg4s1VEAJhbYVFH8dxHMdxHOdy8VG0c224fd9xLgYzz4k1c4HF+WjxZr2X4OIy+A8u3ryoXIbo05bMuejjOI7jOI5z8/GRtHOtuH3fcY6HmUW0yRmzgefCipeJN1beuBGkZXivA/nn1SbceXE4r+hzVE7SRUSfvemAySxj3LtI7ziO4ziOc9X4ty/HcZxrhrnNurEBeRVwWgJJec24C9iMzUA8uHjjVE4r+rTZSucVfd58vHeorPb1hxtXsZqO4ziO4zgOXNhxHMe5EnLm4pqYK5lSN05LVJFm3AVsjbsmrPjmtgl3XhzmRJ8Fzir6zHLC59/fxcONEfoYcKcPeHf7AC9tjdy54ziO4ziOc0X4ty7HcZznRNvSuh0Uz5a0CY8WVjwKGMUOnbpuXLxxrpOzij672wOmA+PZwYAQEj5yfx2RCNMhe5mt4ziO4zjOFeHCjuM4zhlIJay4ijYzHeguczRYm3DLuekjoQ8BwcUb54axTPTZGnfYmw24u9Yhs5QVJmaMOi8LdBzHcRzHuSpc2HEcx1lgSPlQtylz32SeF28sc2Rj1HmLaefWsTHq8OqdtUMZO16G5TiO4ziOc3X4Ny/HcW4lJecmZ8yGeSGHl4g3fQxY68Vt04bJunjj3HZef7iBl7ZGmA5ZOrK5qOM4juM4jnOl+Lcvx3FWEmbWLBDrONUEFi9pE275Nut9UzKluTcu3jjO8WyMOs/UcRzHcRzHuSZc2HEc58bCzFIqlee7TB0l3vSRxFEQCH0X0GsGTu9twh3HcRzHcRzHuaG4sOM4zguNiTcm2khXnirgtASSfJtxF7CpeTcjdd10Lt44juM4juM4jrOCuLDjOM61k7Nk3ZQuU+bCGeR3S1SRZtwFbI274rjxNuGO4ziO4ziO49xGXNhxHOdKyNncNvNdpoa0XLzpY8DaKKAPHfoulAwcF28cx3Ecx3Ecx3EqLuw4jvPcSKWzlLpvcm0XnvJ8p6lO823WRxF97GpgcQgILt44juM4juM4juOcChd2HMc5E0Oabw0+pIxZZsyGjLykTXgXCRujTrJuomTguHjjOI7jOI7jOI7zfHBhx3GcQ5TW4HO5N/J4mXjTx4DxWoc+BPQdoQvivvE24Y7jOI7jOI7jOJeLCzuOcwthZs260dwbdeFMh+Vtwi3fZr2nWjKl3aZcvHEcx3Ecx3Ecx7k+XNhxnBXF2oQPmnNTcm/ScvGmj4RRF7ARCH0X0GsGTu9twh3HcRzHcRzHcV5YXNhxnBuMiTfzYcVVwGkJJPk24y5gU/NuRuq66Vy8cRzHcRzHcRzHuZG4sOM4Lzg5L2bdqAtnONwmPJC4bcZdwNa4K44bbxPuOI7jOI7jOI6zmriw4zgvADlr1k2WLlNTdd0M6bB4E9VhszYK6EOHvgslA8fFG8dxHMdxHMdxnNuFCzuOc0WkPN9dapZru/CU5ztNdZpvszYKGMWuBhZ7m3DHcRzHcRzHcRynwYUdx3mOWHepUjKVMmaZMRvyoTbhnbYG3xh1knUTJQPHxRvHcRzHcRzHcRzntLiw4zhnZFDRZj73Rh4vije9CjZbax16FXK6IO4bbxPuOI7jOI7jOI7jXBQXdhxnCSbUSNaNuHCmw/I24ZZvs95TKZly8cZxHMdxHMdxHMe5ClzYcW4l1iZ80JybtkX4UeLNqAvYCNJ1qtcMnN7bhDuO4ziO4ziO4zjXiAs7zspi4s18WHEVcFoCSb7NqAvY1PKpXl03nYs3juM4juM4juM4zguKCzvOjSbnxawbdeEMh9uEBxK3zbgL2Bx30mUqBm8T7jiO4ziO4ziO49xYXNhxrpW96YDJLGPcB2yMlh+OJt5Yl6mpum6GdFi8iUEcNmujgD50pVzKxRvHcRzHcRzHcRxnFXFhx7k23ny8h3e3DxCJME0ZDzdHeO3uWlM2JUJOyofbhHeRsDYKGMUOXQySgRO9TbjjOI7jOI7jOI5zu3Bhx7kW9qYD3t0+ADNje5KQOOOdZweYDhlrfZSuUh1hY1RLpiz3xsUbx3Ecx3Ecx3EcxxFc2HGuhcksIxIhhAAaE7rQYdxFvHJnjJe3xt4m3HEcx3Ecx3Ecx3FOgQs7zrUw7gMSM+6sdSDqwMwYMmNz3Lmo4ziO4ziO4ziO4zinxIUd51rYGHV49c5aydhJzHj1ztqRAcqO4ziO4ziO4ziO4xzGR9HOtfH6ww28tDXCdMgYdUd3xXIcx3Ecx3Ecx3EcZzk+knaulY1Rh43RdS+F4ziO4ziO4ziO49xMwnUvgOM4juM4juM4juM4jnM+XNhxHMdxHMdxHMdxHMe5obiw4ziO4ziO4ziO4ziOc0NxYcdxHMdxHMdxHMdxHOeG4sKO4ziO4ziO4ziO4zjODcWFHcdxHMdxHMdxHMdxnBuKCzuO4ziO4ziO4ziO4zg3FBd2HMdxHMdxHMdxHMdxbigu7DiO4ziO4ziO4ziO49xQXNhxHMdxHMdxHMdxHMe5obiw4ziO4ziO4ziO4ziOc0NxYcdxHMdxHMdxHMdxHOeG4sKO4ziO4ziO4ziO4zjODYWY+bqXwXlOENF7AL5w3ctxDl4G8P51L4TjOI5zIfxa7twG/Dh3HMe5+dzUa/nHmPmVZS+4sONcO0T0M8z8qeteDsdxHOf8+LXcuQ34ce44jnPzWcVruZdiOY7jOI7jOI7jOI7j3FBc2HEcx3Ecx3Ecx3Ecx7mhuLDjvAj8meteAMdxHOfC+LXcuQ34ce44jnPzWblruWfsOI7jOI7jOI7jOI7j3FDcseM4juM4juM4juM4jnNDcWHHcRzHcRzHcRzHcRznhtJd9wI4twsi+icAfBLAX2Xmv3bdy+M4juOcHb+WO6uOH+OO4zirwW25nruw41w1/xqAXwvgM9e9II7jOM658Wu5s+r4Me44jrMa3IrruZdiOVcGEa0D+Cb982euc1kcx3Gc8+HXcmfV8WPccRxnNbhN13MXdpyr5NsgLrGnAH71mpfFcRzHOR9+LXdWHT/GHcdxVoNbcz33dufOpUNE3wngp06Y7CEzf3AVy+M4juOcHb+WO6uOH+OO4zirwW28nnvGjnMVfALAOwDuAVgDsAtgp3n9nVU6qRzHcVYUv5Y7q44f447jOKvBrbueu2PHuTKI6CcAfBrAH2Lm/9c1L47jOI5zDvxa7qw6fow7juOsBrfpeu4ZO85V8mv199+51qVwHMdxLoJfy51Vx49xx3Gc1eDWXM/dseNcCUT0MQCfB5AB3GXm3etdIsdxHOes+LXcWXX8GHccx1kNbtv13B07zlXx6/T3ryw7qYhoi4h+iIh+nIjeIyImoj9yxcvoOI7jHM9J1/JfT0T/PhH9HBHtENFXiOi/JaJPXfFyOs558e8rjuM4q8Gt+s7iwo5zVdiJdZQN7mUAfxzAtwD4W1eyRI7jOM5ZOela/ocB/JMAfgLAvwjgTwH4egD/GxH9tktfOse5OP59xXEcZzW4Vd9ZvCuWc1V8q/4+6sR6C8BHmPkrRPRxAJ+7ioVyHMdxzsS36u+jruV/EsDvZuapPUFEPwLgFwD86wB+/FKXznEuzrfqb/++4jiOc7P5Vv19K76zuGPHuSpMMf3by15k5gkzf+XqFsdxHMc5Byddy3+y/YKkz30A4K8C+KbLXTTHeS749xXHcZzV4FZ9Z3Fhx7l0iGgTwCf0z5+9zmVxHMdxzscFr+VfBeD957tEjvN88e8rjuM4q8Ft/M7iwo5zFXwEAOljv8vlOI5zMznXtZyI/gEAnwbwFy5joRznOeLfVxzHcVaDW/edxYUd5yp4p3n8j1/bUjiO4zgX4czXciJ6FcCfA/AFAD90GQvlOM8R/77iOI6zGty67ywu7DiXDjM/BfBX9M+/QETPiOhtIvocEfkx6DiOcwM467WciO5AggfvAPhHmfnZFS6u45wZ/77iOI6zGtzG7yz+j5RzVfweAH8a0j1iDOA1AI+YOV/rUjmO4zhn4VTXciJaB/CjAL4RwPcw889f9YI6zjnx7yuO4zirwa36zuLtzp0rgZnfA/AD170cjuM4zvk5zbWciHoA/yWAvx/A9zLzT17FsjnO88C/rziO46wGt+07iws7juM4juM8F9Te/J8B+IcB/B5m/svXvEiO4ziO4ziHWLXvLC7sOC8MRPQDAO7rDwD8g0Rkx+j/Q2slHcdxnBeXfxfA90Pq2jsi+r3ti8z8Z69lqRznOeLfVxzHcVaClfrOQsx83cvgOAAAIvo8gI8d8fJXM/Pnr25pHMdxnLNCRD8BaRO6FGamo15znJuCf19xHMe5+azadxYXdhzHcRzHcRzHcRzHcW4o3hXLcRzHcRzHcRzHcRznhuLCjuM4juM4juM4juM4zg3FhR3HcRzHcRzHcRzHcZwbigs7juM4juM4juM4juM4NxQXdhzHcRzHcRzHcRzHcW4oLuw4juM4juM4juM4juPcUFzYcRzHcRzHcRzHcRzHuaG4sOM4juM4juM4juM4jnNDcWHHcRzHcRzHcRzHcRznhvK/AwgFLml+gXIbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importance plot for the final paper with unscaled percentages\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "plotting_setup(24)\n",
    "\n",
    "plt.figure(figsize=(17, 8))\n",
    "\n",
    "# Parallel coordinate plot for CD4 IL2+ CD154+ Cov-mix\n",
    "cls = np.copy(compound_response)\n",
    "cls[cls == 'positive'] = 'tab:orange'\n",
    "cls[cls == 'negative'] = 'tab:blue'\n",
    "\n",
    "x1=df_tcell_p_unsc[:, med_feature_importances_p.argsort()[-10:][::-1]][:, 1]\n",
    "x2=df_tcell_p_unsc[:, med_feature_importances_p.argsort()[-10:][::-1]][:, 0]\n",
    "tick1='$t_1$' \n",
    "tick2='$t_2$'\n",
    "colors=cls\n",
    "labels={'tab:blue': 'Negative', 'tab:orange': 'Positive'} \n",
    "\n",
    "cnt = 1\n",
    "ticks_x = []\n",
    "for c in np.unique(colors):\n",
    "    label_c = labels[c]\n",
    "    x1_c = x1[colors == c]\n",
    "    x2_c = x2[colors == c]\n",
    "    plt.scatter(np.ones_like(x1_c) * cnt, x1_c, c=c, label=label_c, s=25, alpha=0.15)\n",
    "    plt.scatter(np.ones_like(x2_c) * (cnt + 1), x2_c, c=c, s=25, alpha=0.15)\n",
    "    for i in range(len(x1_c)):\n",
    "        plt.plot([cnt, cnt + 1], [x1_c[i], x2_c[i]], c=c, alpha=0.15)\n",
    "    ticks_x.append(cnt)\n",
    "    ticks_x.append(cnt + 1)\n",
    "    cnt = cnt + 1.5\n",
    "plt.ylim([min(np.min(x1), np.min(x2)) - 0.0001, max(np.max(x1), np.max(x2)) + 0.0001])\n",
    "plt.xticks(ticks=ticks_x, labels=[tick1, tick2] * len(np.unique(colors)))\n",
    "plt.ylabel(\"Normalized Percentage\")\n",
    "legend_elements = [Line2D([0], [0], color='tab:blue', lw=8, label='Negative'),\n",
    "                  Line2D([0], [0], color='tab:orange', lw=8, label='Positive')]\n",
    "plt.legend(loc='upper left', handles=legend_elements, title='Compound Antibody Response')\n",
    "plt.title('CD4 IL-2$^+$, CD154$^+$; CoV-Mix % at $t_1$ & $t_2$')\n",
    "\n",
    "plt.tight_layout(pad=2.0)\n",
    "\n",
    "\n",
    "plt.savefig(fname=\"figures/xgb_varimp_cons_unscaled.svg\", dpi=300, bbox_inches='tight')\n",
    "plt.savefig(fname=\"figures/xgb_varimp_cons_unscaled.pdf\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "applicable-delhi",
   "metadata": {},
   "source": [
    "## Comparing T-cell Types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "automated-encoding",
   "metadata": {},
   "source": [
    "### CD3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "super-government",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if os.path.isfile('results/metrics_clf_cons_cd3.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_cons_cd3.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_cons_cd3.csv'):\n",
    "    metrics_clf_cd3 = np.loadtxt(fname=\"results/metrics_clf_cons_cd3.csv\", delimiter=\",\")\n",
    "    metrics_proba_cd3 = np.loadtxt(fname=\"results/metrics_proba_cons_cd3.csv\", delimiter=\",\")\n",
    "    feature_importances_cd3 = np.loadtxt(fname=\"results/feat_imps_cons_cd3.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_cd3, feature_names_cd3 = construct_tcell_df(df_included, feature_type='perc', tcell_types=['CD3'])\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "    metrics_clf_cd3, metrics_proba_cd3, feature_importances_cd3 = boot_train_test_XGB(\n",
    "        X=df_tcell_cd3, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity],\n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000)\n",
    "    np.savetxt(fname=\"results/metrics_clf_cons_cd3.csv\", X=metrics_clf_cd3, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_cons_cd3.csv\", X=metrics_proba_cd3, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_cons_cd3.csv\", X=feature_importances_cd3, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "excessive-briefing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.881; [0.667, 1.0]\n",
      "0.88; [0.571, 1.0]\n",
      "0.883; [0.556, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_cd3, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "adopted-fleece",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.939; [0.743, 1.0]\n",
      "0.941; [0.722, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_cd3, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "convenient-copyright",
   "metadata": {},
   "source": [
    "### CD4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "assigned-painting",
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.isfile('results/metrics_clf_cons_cd4.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_cons_cd4.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_cons_cd4.csv'):\n",
    "    metrics_clf_cd4 = np.loadtxt(fname=\"results/metrics_clf_cons_cd4.csv\", delimiter=\",\")\n",
    "    metrics_proba_cd4 = np.loadtxt(fname=\"results/metrics_proba_cons_cd4.csv\", delimiter=\",\")\n",
    "    feature_importances_cd4 = np.loadtxt(fname=\"results/feat_imps_cons_cd4.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_cd4, feature_names_cd4 = construct_tcell_df(df_included, feature_type='perc', tcell_types=['CD4'])\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "    metrics_clf_cd4, metrics_proba_cd4, feature_importances_cd4 = boot_train_test_XGB(\n",
    "        X=df_tcell_cd4, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity],\n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000)\n",
    "    np.savetxt(fname=\"results/metrics_clf_cons_cd4.csv\", X=metrics_clf_cd4, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_cons_cd4.csv\", X=metrics_proba_cd4, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_cons_cd4.csv\", X=feature_importances_cd4, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "phantom-canyon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.901; [0.7, 1.0]\n",
      "0.894; [0.583, 1.0]\n",
      "0.909; [0.625, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_cd4, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "opponent-enhancement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.956; [0.787, 1.0]\n",
      "0.957; [0.778, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_cd4, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charitable-proposal",
   "metadata": {},
   "source": [
    "### CD8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "gentle-rebecca",
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.isfile('results/metrics_clf_cons_cd8.csv') and \\\n",
    "   os.path.isfile('results/metrics_proba_cons_cd8.csv') and \\\n",
    "   os.path.isfile('results/feat_imps_cons_cd8.csv'):\n",
    "    metrics_clf_cd8 = np.loadtxt(fname=\"results/metrics_clf_cons_cd8.csv\", delimiter=\",\")\n",
    "    metrics_proba_cd8 = np.loadtxt(fname=\"results/metrics_proba_cons_cd8.csv\", delimiter=\",\")\n",
    "    feature_importances_cd8 = np.loadtxt(fname=\"results/feat_imps_cons_cd8.csv\", delimiter=\",\")\n",
    "else:\n",
    "    df_tcell_cd8, feature_names_cd8 = construct_tcell_df(df_included, feature_type='perc', tcell_types=['CD8'])\n",
    "    disease_status = compound_response\n",
    "    disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "    metrics_clf_cd8, metrics_proba_cd8, feature_importances_cd8 = boot_train_test_XGB(\n",
    "        X=df_tcell_cd8, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity],\n",
    "        metrics_proba=[roc_auc_score, average_precision_score], B=1000)\n",
    "    np.savetxt(fname=\"results/metrics_clf_cons_cd8.csv\", X=metrics_clf_cd8, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/metrics_proba_cons_cd8.csv\", X=metrics_proba_cd8, delimiter=\",\")\n",
    "    np.savetxt(fname=\"results/feat_imps_cons_cd8.csv\", X=feature_importances_cd8, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "tight-bulletin",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.516; [0.25, 0.773]\n",
      "0.523; [0.133, 0.934]\n",
      "0.51; [0.125, 0.9]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_cd8, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "loved-samba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.52; [0.202, 0.828]\n",
      "0.579; [0.278, 0.874]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_cd8, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immune-grounds",
   "metadata": {},
   "source": [
    "## Predicting Different Antibody Type Responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "italic-blind",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Compound\n",
    "for trt in range(2, 7):\n",
    "    boot_train_test_XGB_trt_vs_ab(trt=trt, ab='cons', df_dir='data/CoV-ETH_data.csv')\n",
    "boot_train_test_XGB_trt_vs_ab(trt=-1, ab='cons', df_dir='data/CoV-ETH_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "quantitative-defendant",
   "metadata": {},
   "outputs": [],
   "source": [
    "# RBD\n",
    "for trt in range(2, 7):\n",
    "    boot_train_test_XGB_trt_vs_ab(trt=trt, ab='rbd', df_dir='data/CoV-ETH_data.csv')\n",
    "boot_train_test_XGB_trt_vs_ab(trt=-1, ab='rbd', df_dir='data/CoV-ETH_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "printable-bulgaria",
   "metadata": {},
   "outputs": [],
   "source": [
    "# N\n",
    "for trt in range(2, 7):\n",
    "    boot_train_test_XGB_trt_vs_ab(trt=trt, ab='n', df_dir='data/CoV-ETH_data.csv')\n",
    "boot_train_test_XGB_trt_vs_ab(trt=-1, ab='n', df_dir='data/CoV-ETH_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "steady-wealth",
   "metadata": {},
   "outputs": [],
   "source": [
    "# S1\n",
    "for trt in range(2, 7):\n",
    "    boot_train_test_XGB_trt_vs_ab(trt=trt, ab='s1', df_dir='data/CoV-ETH_data.csv')\n",
    "boot_train_test_XGB_trt_vs_ab(trt=-1, ab='s1', df_dir='data/CoV-ETH_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "original-spiritual",
   "metadata": {},
   "outputs": [],
   "source": [
    "# S2\n",
    "for trt in range(2, 7):\n",
    "    boot_train_test_XGB_trt_vs_ab(trt=trt, ab='s2', df_dir='data/CoV-ETH_data.csv')\n",
    "boot_train_test_XGB_trt_vs_ab(trt=-1, ab='s2', df_dir='data/CoV-ETH_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "south-incident",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nAb\n",
    "for trt in range(2, 7):\n",
    "    boot_train_test_XGB_trt_vs_ab(trt=trt, ab='nab', df_dir='data/CoV-ETH_data.csv')\n",
    "boot_train_test_XGB_trt_vs_ab(trt=-1, ab='nab', df_dir='data/CoV-ETH_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "plain-challenge",
   "metadata": {},
   "source": [
    "### Visualising Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "constitutional-ontario",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load precomputed metrics\n",
    "# RBD\n",
    "metrics_proba_cov2mix_rbd = np.loadtxt(fname=\"results/metrics_proba_rbd_cov2mix.csv\", delimiter=\",\")\n",
    "metrics_proba_n_rbd = np.loadtxt(fname=\"results/metrics_proba_rbd_protn.csv\", delimiter=\",\")\n",
    "metrics_proba_s1_rbd = np.loadtxt(fname=\"results/metrics_proba_rbd_prots1.csv\", delimiter=\",\")\n",
    "metrics_proba_s_rbd = np.loadtxt(fname=\"results/metrics_proba_rbd_prots.csv\", delimiter=\",\")\n",
    "metrics_proba_m_rbd = np.loadtxt(fname=\"results/metrics_proba_rbd_protm.csv\", delimiter=\",\")\n",
    "metrics_proba_r_rbd = np.loadtxt(fname=\"results/metrics_proba_rbd_rand.csv\", delimiter=\",\")\n",
    "# N\n",
    "metrics_proba_cov2mix_n = np.loadtxt(fname=\"results/metrics_proba_n_cov2mix.csv\", delimiter=\",\")\n",
    "metrics_proba_n_n = np.loadtxt(fname=\"results/metrics_proba_n_protn.csv\", delimiter=\",\")\n",
    "metrics_proba_s1_n = np.loadtxt(fname=\"results/metrics_proba_n_prots1.csv\", delimiter=\",\")\n",
    "metrics_proba_s_n = np.loadtxt(fname=\"results/metrics_proba_n_prots.csv\", delimiter=\",\")\n",
    "metrics_proba_m_n = np.loadtxt(fname=\"results/metrics_proba_n_protm.csv\", delimiter=\",\")\n",
    "metrics_proba_r_n = np.loadtxt(fname=\"results/metrics_proba_n_rand.csv\", delimiter=\",\")\n",
    "# S1\n",
    "metrics_proba_cov2mix_s1 = np.loadtxt(fname=\"results/metrics_proba_s1_cov2mix.csv\", delimiter=\",\")\n",
    "metrics_proba_n_s1 = np.loadtxt(fname=\"results/metrics_proba_s1_protn.csv\", delimiter=\",\")\n",
    "metrics_proba_s1_s1 = np.loadtxt(fname=\"results/metrics_proba_s1_prots1.csv\", delimiter=\",\")\n",
    "metrics_proba_s_s1 = np.loadtxt(fname=\"results/metrics_proba_s1_prots.csv\", delimiter=\",\")\n",
    "metrics_proba_m_s1 = np.loadtxt(fname=\"results/metrics_proba_s1_protm.csv\", delimiter=\",\")\n",
    "metrics_proba_r_s1 = np.loadtxt(fname=\"results/metrics_proba_s1_rand.csv\", delimiter=\",\")\n",
    "# S2\n",
    "metrics_proba_cov2mix_s2 = np.loadtxt(fname=\"results/metrics_proba_s2_cov2mix.csv\", delimiter=\",\")\n",
    "metrics_proba_n_s2 = np.loadtxt(fname=\"results/metrics_proba_s2_protn.csv\", delimiter=\",\")\n",
    "metrics_proba_s1_s2 = np.loadtxt(fname=\"results/metrics_proba_s2_prots1.csv\", delimiter=\",\")\n",
    "metrics_proba_s_s2 = np.loadtxt(fname=\"results/metrics_proba_s2_prots.csv\", delimiter=\",\")\n",
    "metrics_proba_m_s2 = np.loadtxt(fname=\"results/metrics_proba_s2_protm.csv\", delimiter=\",\")\n",
    "metrics_proba_r_s2 = np.loadtxt(fname=\"results/metrics_proba_s2_rand.csv\", delimiter=\",\")\n",
    "# nAb\n",
    "metrics_proba_cov2mix_nab = np.loadtxt(fname=\"results/metrics_proba_nab_cov2mix.csv\", delimiter=\",\")\n",
    "metrics_proba_n_nab = np.loadtxt(fname=\"results/metrics_proba_nab_protn.csv\", delimiter=\",\")\n",
    "metrics_proba_s1_nab = np.loadtxt(fname=\"results/metrics_proba_nab_prots1.csv\", delimiter=\",\")\n",
    "metrics_proba_s_nab = np.loadtxt(fname=\"results/metrics_proba_nab_prots.csv\", delimiter=\",\")\n",
    "metrics_proba_m_nab = np.loadtxt(fname=\"results/metrics_proba_nab_protm.csv\", delimiter=\",\")\n",
    "metrics_proba_r_nab = np.loadtxt(fname=\"results/metrics_proba_nab_rand.csv\", delimiter=\",\")\n",
    "# Compound\n",
    "metrics_proba_cov2mix_cons = np.loadtxt(fname=\"results/metrics_proba_cons_cov2mix.csv\", delimiter=\",\")\n",
    "metrics_proba_n_cons = np.loadtxt(fname=\"results/metrics_proba_cons_protn.csv\", delimiter=\",\")\n",
    "metrics_proba_s1_cons = np.loadtxt(fname=\"results/metrics_proba_cons_prots1.csv\", delimiter=\",\")\n",
    "metrics_proba_s_cons = np.loadtxt(fname=\"results/metrics_proba_cons_prots.csv\", delimiter=\",\")\n",
    "metrics_proba_m_cons = np.loadtxt(fname=\"results/metrics_proba_cons_protm.csv\", delimiter=\",\")\n",
    "metrics_proba_r_cons = np.loadtxt(fname=\"results/metrics_proba_cons_rand.csv\", delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "herbal-pointer",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Load precomputed AUROCs\n",
    "aurocs_rbd = np.stack((metrics_proba_cov2mix_rbd[:, 0], metrics_proba_n_rbd[:, 0], metrics_proba_s1_rbd[:, 0], \n",
    "                       metrics_proba_s_rbd[:, 0], metrics_proba_m_rbd[:, 0]), axis=1)\n",
    "\n",
    "aurocs_n = np.stack((metrics_proba_cov2mix_n[:, 0], metrics_proba_n_n[:, 0], metrics_proba_s1_n[:, 0], \n",
    "                     metrics_proba_s_n[:, 0], metrics_proba_m_n[:, 0]), axis=1)\n",
    "aurocs_n_replace = np.tile(np.nanmean(aurocs_n, axis=0), reps=(aurocs_n.shape[0], 1))\n",
    "aurocs_n[np.isnan(aurocs_n)] = aurocs_n_replace[np.isnan(aurocs_n)]\n",
    "\n",
    "aurocs_s1 = np.stack((metrics_proba_cov2mix_s1[:, 0], metrics_proba_n_s1[:, 0], metrics_proba_s1_s1[:, 0], \n",
    "                      metrics_proba_s_s1[:, 0], metrics_proba_m_s1[:, 0]), axis=1)\n",
    "\n",
    "aurocs_s2 = np.stack((metrics_proba_cov2mix_s2[:, 0], metrics_proba_n_s2[:, 0], metrics_proba_s1_s2[:, 0], \n",
    "                      metrics_proba_s_s2[:, 0], metrics_proba_m_s2[:, 0]), axis=1)\n",
    "\n",
    "aurocs_nab = np.stack((metrics_proba_cov2mix_nab[:, 0], metrics_proba_n_nab[:, 0], metrics_proba_s1_nab[:, 0], \n",
    "                       metrics_proba_s_nab[:, 0], metrics_proba_m_nab[:, 0]), axis=1)\n",
    "aurocs_nab_replace = np.tile(np.nanmean(aurocs_nab, axis=0), reps=(aurocs_nab.shape[0], 1))\n",
    "aurocs_nab[np.isnan(aurocs_nab)] = aurocs_nab_replace[np.isnan(aurocs_nab)]\n",
    "\n",
    "aurocs_cons = np.stack((metrics_proba_cov2mix_cons[:, 0], metrics_proba_n_cons[:, 0], metrics_proba_s1_cons[:, 0], \n",
    "                       metrics_proba_s_cons[:, 0], metrics_proba_m_cons[:, 0]), axis=1)\n",
    "\n",
    "\n",
    "# Load precomputed AUPRCs\n",
    "auprcs_rbd = np.stack((metrics_proba_cov2mix_rbd[:, 1], metrics_proba_n_rbd[:, 1], metrics_proba_s1_rbd[:, 1], \n",
    "                       metrics_proba_s_rbd[:, 1], metrics_proba_m_rbd[:, 1]), axis=1)\n",
    "\n",
    "auprcs_n = np.stack((metrics_proba_cov2mix_n[:, 1], metrics_proba_n_n[:, 1], metrics_proba_s1_n[:, 1], \n",
    "                     metrics_proba_s_n[:, 1], metrics_proba_m_n[:, 1]), axis=1)\n",
    "auprcs_n_replace = np.tile(np.nanmean(auprcs_n, axis=0), reps=(auprcs_n.shape[0], 1))\n",
    "auprcs_n[np.isnan(auprcs_n)] = auprcs_n_replace[np.isnan(auprcs_n)]\n",
    "\n",
    "auprcs_s1 = np.stack((metrics_proba_cov2mix_s1[:, 1], metrics_proba_n_s1[:, 1], metrics_proba_s1_s1[:, 1], \n",
    "                      metrics_proba_s_s1[:, 1], metrics_proba_m_s1[:, 1]), axis=1)\n",
    "\n",
    "auprcs_s2 = np.stack((metrics_proba_cov2mix_s2[:, 1], metrics_proba_n_s2[:, 1], metrics_proba_s1_s2[:, 1], \n",
    "                      metrics_proba_s_s2[:, 1], metrics_proba_m_s2[:, 1]), axis=1)\n",
    "\n",
    "auprcs_nab = np.stack((metrics_proba_cov2mix_nab[:, 1], metrics_proba_n_nab[:, 1], metrics_proba_s1_nab[:, 1], \n",
    "                       metrics_proba_s_nab[:, 1], metrics_proba_m_nab[:, 1]), axis=1)\n",
    "auprcs_nab_replace = np.tile(np.nanmean(auprcs_nab, axis=0), reps=(auprcs_nab.shape[0], 1))\n",
    "auprcs_nab[np.isnan(auprcs_nab)] = auprcs_nab_replace[np.isnan(auprcs_nab)]\n",
    "\n",
    "auprcs_cons = np.stack((metrics_proba_cov2mix_cons[:, 1], metrics_proba_n_cons[:, 1], metrics_proba_s1_cons[:, 1], \n",
    "                       metrics_proba_s_cons[:, 1], metrics_proba_m_cons[:, 1]), axis=1)\n",
    "\n",
    "# Plotting setup\n",
    "plotting_setup(16)\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12.5, 12))\n",
    "medianprops = dict(linestyle='-', linewidth=2, color='black')\n",
    "meanpointprops = dict(marker='x', markeredgecolor='black', markerfacecolor='black', markersize=4)\n",
    "\n",
    "\n",
    "# Plot AUROCs\n",
    "bx_rbd = ax[0].boxplot(aurocs_rbd, positions=[-4.5, -5.0, -5.5, -6.0, -6.5], showfliers=False, \n",
    "                       labels=['CoV-Mix', 'N', 'S1', 'S', 'M'], notch=True, patch_artist=True, vert=False, \n",
    "                      showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['tab:brown' for i in range(7)]\n",
    "for patch, color in zip(bx_rbd['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[0].plot([np.nanmean(metrics_proba_r_rbd[:, 0]), np.nanmean(metrics_proba_r_rbd[:, 0])], [-6.75, -4.25], \n",
    "           c='red', linewidth=4)\n",
    "\n",
    "\n",
    "bx_n = ax[0].boxplot(aurocs_n, positions=[-9.0, -9.5, -10.0, -10.5, -11.0], showfliers=False, \n",
    "                     labels=['CoV-Mix', 'N', 'S1', 'S', 'M'], notch=True, patch_artist=True, vert=False, \n",
    "                     showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['tab:green' for i in range(7)]\n",
    "for patch, color in zip(bx_n['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[0].plot([np.nanmean(metrics_proba_r_n[:, 0]), np.nanmean(metrics_proba_r_n[:, 0])], [-8.75, -11.25], \n",
    "           c='red', linewidth=4)\n",
    "    \n",
    "bx_s1 = ax[0].boxplot(aurocs_s1, positions=[-13.5, -14.0, -14.5, -15.0, -15.5], showfliers=False, \n",
    "                      labels=['CoV-Mix', 'N', 'S1', 'S', 'M'], notch=True, patch_artist=True, vert=False,\n",
    "                      showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['tab:blue' for i in range(7)]\n",
    "for patch, color in zip(bx_s1['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[0].plot([np.nanmean(metrics_proba_r_s1[:, 0]), np.nanmean(metrics_proba_r_s1[:, 0])], [-13.25, -15.75], \n",
    "           c='red', linewidth=4)\n",
    "    \n",
    "bx_s2 = ax[0].boxplot(aurocs_s2, positions=[-18.0, -18.5, -19.0, -19.5, -20.0], showfliers=False, \n",
    "                      labels=['CoV-Mix', 'N', 'S1', 'S', 'M'], notch=True, patch_artist=True, vert=False, \n",
    "                      showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['tab:purple' for i in range(7)]\n",
    "for patch, color in zip(bx_s2['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[0].plot([np.nanmean(metrics_proba_r_s2[:, 0]), np.nanmean(metrics_proba_r_s2[:, 0])], [-17.75, -20.25], \n",
    "           c='red', linewidth=4)\n",
    "    \n",
    "bx_nab = ax[0].boxplot(aurocs_nab, positions=[-22.5, -23.0, -23.5, -24.0, -24.5], showfliers=False, \n",
    "                     labels=['CoV-Mix', 'N', 'S1', 'S', 'M'], notch=True, patch_artist=True, vert=False,\n",
    "                     showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['tab:cyan' for i in range(7)]\n",
    "for patch, color in zip(bx_nab['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[0].plot([np.nanmean(metrics_proba_r_nab[:, 0]), np.nanmean(metrics_proba_r_nab[:, 0])], [-22.25, -24.75], \n",
    "         c='red', linewidth=4)\n",
    "    \n",
    "bx_cons = ax[0].boxplot(aurocs_cons, positions=[-27.0, -27.5, -28.0, -28.5, -29.0], showfliers=False, \n",
    "                      labels=['CoV-Mix', 'N', 'S1', 'S', 'M'], notch=True, patch_artist=True, vert=False, \n",
    "                      showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['white' for i in range(7)]\n",
    "for patch, color in zip(bx_cons['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[0].plot([np.nanmean(metrics_proba_r_cons[:, 0]), np.nanmean(metrics_proba_r_cons[:, 0])], [-26.75, -29.25], \n",
    "         c='red', linewidth=4)\n",
    "    \n",
    "ax[0].set_xlabel('Bootstrapped Test-set AUROC')\n",
    "\n",
    "ax[0].set_xlim([0, 1.05])\n",
    "\n",
    "\n",
    "# Plot AUPRCs\n",
    "bx_rbd = ax[1].boxplot(auprcs_rbd, positions=[-4.5, -5.0, -5.5, -6.0, -6.5], showfliers=False, \n",
    "                       labels=['', '', '', '', ''], notch=True, patch_artist=True, vert=False, \n",
    "                       showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['tab:brown' for i in range(7)]\n",
    "for patch, color in zip(bx_rbd['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[1].plot([np.nanmean(metrics_proba_r_rbd[:, 1]), np.nanmean(metrics_proba_r_rbd[:, 1])], [-6.75, -4.25], \n",
    "           c='red', linewidth=4)\n",
    "\n",
    "\n",
    "bx_n = ax[1].boxplot(auprcs_n, positions=[-9.0, -9.5, -10.0, -10.5, -11.0], showfliers=False, \n",
    "                     labels=['', '', '', '', ''], notch=True, patch_artist=True, vert=False,\n",
    "                     showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['tab:green' for i in range(7)]\n",
    "for patch, color in zip(bx_n['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[1].plot([np.nanmean(metrics_proba_r_n[:, 1]), np.nanmean(metrics_proba_r_n[:, 1])], [-8.75, -11.25], \n",
    "           c='red', linewidth=4)\n",
    "    \n",
    "bx_s1 = ax[1].boxplot(auprcs_s1, positions=[-13.5, -14.0, -14.5, -15.0, -15.5], showfliers=False, \n",
    "                      labels=['', '', '', '', ''], notch=True, patch_artist=True, vert=False,\n",
    "                      showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['tab:blue' for i in range(7)]\n",
    "for patch, color in zip(bx_s1['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[1].plot([np.nanmean(metrics_proba_r_s1[:, 1]), np.nanmean(metrics_proba_r_s1[:, 1])], [-13.25, -15.75], \n",
    "         c='red', linewidth=4)\n",
    "    \n",
    "bx_s2 = ax[1].boxplot(auprcs_s2, positions=[-18.0, -18.5, -19.0, -19.5, -20.0], showfliers=False, \n",
    "                      labels=['', '', '', '', ''], notch=True, patch_artist=True, vert=False, \n",
    "                      showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['tab:purple' for i in range(7)]\n",
    "for patch, color in zip(bx_s2['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[1].plot([np.nanmean(metrics_proba_r_s2[:, 1]), np.nanmean(metrics_proba_r_s2[:, 1])], [-17.75, -20.25], \n",
    "           c='red', linewidth=4)\n",
    "    \n",
    "bx_nab = ax[1].boxplot(auprcs_nab, positions=[-22.5, -23.0, -23.5, -24.0, -24.5], showfliers=False, \n",
    "                     labels=['', '', '', '', ''], notch=True, patch_artist=True, vert=False,\n",
    "                     showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['tab:cyan' for i in range(7)]\n",
    "for patch, color in zip(bx_nab['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[1].plot([np.nanmean(metrics_proba_r_nab[:, 1]), np.nanmean(metrics_proba_r_nab[:, 1])], [-22.25, -24.75], \n",
    "         c='red', linewidth=4)\n",
    "    \n",
    "bx_cons = ax[1].boxplot(auprcs_cons, positions=[-27.0, -27.5, -28.0, -28.5, -29.0], showfliers=False, \n",
    "                      labels=['', '', '', '', ''], notch=True, patch_artist=True, vert=False, \n",
    "                      showmeans=True, medianprops=medianprops, meanprops=meanpointprops) \n",
    "colors = ['white' for i in range(7)]\n",
    "for patch, color in zip(bx_cons['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "ax[1].plot([np.nanmean(metrics_proba_r_cons[:, 1]), np.nanmean(metrics_proba_r_cons[:, 1])], [-26.75, -29.25], \n",
    "         c='red', linewidth=4)\n",
    "    \n",
    "ax[1].legend(['Antibody Response', bx_rbd[\"boxes\"][0], bx_n[\"boxes\"][0], bx_s1[\"boxes\"][0], \n",
    "              bx_s2[\"boxes\"][0], bx_nab[\"boxes\"][0], bx_cons[\"boxes\"][0], '', \n",
    "              Line2D([0], [0], color='red', lw=4)], \n",
    "             ['', 'RBD', 'N', 'S1', 'S2', 'nAb', 'Compound', '', 'Random guess'], \n",
    "             handler_map={'Antibody Response': LegendTitle({'fontsize': 17}),\n",
    "                         '': LegendTitle({'fontsize': 17})},\n",
    "             loc='upper right',  bbox_to_anchor=(1.8, 1))\n",
    "    \n",
    "ax[1].set_xlabel('Bootstrapped Test-set AUPRC')\n",
    "\n",
    "ax[1].set_xlim([0, 1.05])\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=2.0)\n",
    "\n",
    "plt.savefig(fname=\"figures/antibody_boxplots.svg\", dpi=300, bbox_inches='tight')\n",
    "plt.savefig(fname=\"figures/antibody_boxplots.pdf\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "israeli-rotation",
   "metadata": {},
   "source": [
    "## Assessing Associations between T-cell Data and Symptoms Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "conditional-collective",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data\n",
    "rdata = df_included.values\n",
    "rdata_cols = list(df_included.columns)\n",
    "ndata, ndata_cols = construct_tcell_df(df_included, feature_type='', scale=False)\n",
    "ndata_mfi, ndata_mfi_cols = construct_mfi_df(df_included, scale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "empty-advocacy",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compund symptoms score\n",
    "symptoms_score_S10 = np.squeeze(\n",
    "    rdata[:, np.argwhere(np.array(rdata_cols) == 'symptoms_score_S10')[0]].astype(np.float64))\n",
    "symptoms_score_S10[np.isnan(symptoms_score_S10)] = 0\n",
    "symptoms_score_S11 = np.squeeze(\n",
    "    rdata[:, np.argwhere(np.array(rdata_cols) == 'symptoms_score_S11')[0]].astype(np.float64))\n",
    "symptoms_score_S11[np.isnan(symptoms_score_S11)] = 0\n",
    "symptoms_score_compound = np.maximum(symptoms_score_S10, symptoms_score_S11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "starting-communist",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some important features\n",
    "CD4_IL2_CD154_S10 = np.squeeze(\n",
    "    ndata[:, np.argwhere(ndata_cols == 'CD4.IL2_pos_CD154_pos_perc_2_S10')[0]].astype(np.float64))\n",
    "CD4_IL2_CD154_S11 = np.squeeze(\n",
    "    ndata[:, np.argwhere(ndata_cols == 'CD4.IL2_pos_CD154_pos_perc_2_S11')[0]].astype(np.float64))\n",
    "CD4_IFN_TNF_S10 = np.squeeze(\n",
    "    ndata[:, np.argwhere(ndata_cols == 'CD4.IFN_pos_TNF_pos_perc_3_S10')[0]].astype(np.float64))\n",
    "CD4_IFN_TNF_S11 = np.squeeze(\n",
    "    ndata[:, np.argwhere(ndata_cols == 'CD4.IFN_pos_TNF_pos_perc_3_S11')[0]].astype(np.float64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "subjective-shape",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x936 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Percentages vs. symptoms scores\n",
    "np.random.seed(42)\n",
    "\n",
    "plotting_setup(24)\n",
    "fig, ax = plt.subplots(2, 2, figsize=(14, 13))\n",
    "medianprops = dict(linestyle='-', linewidth=2, color='black')\n",
    "meanpointprops = dict(marker='x', markeredgecolor='black', markerfacecolor='black', markersize=8)\n",
    "\n",
    "\n",
    "scores = np.unique(symptoms_score_compound)\n",
    "cnt = 0\n",
    "for s in scores:\n",
    "    bx_s = ax[0, 0].boxplot(CD4_IL2_CD154_S10[symptoms_score_compound == s], positions=[cnt], \n",
    "                            showfliers=False, labels=['' + str(int(s))], notch=True, patch_artist=True, \n",
    "                            vert=True, showmeans=True, medianprops=medianprops, meanprops=meanpointprops)\n",
    "    colors = ['n']\n",
    "    for patch, color in zip(bx_s['boxes'], colors):\n",
    "        patch.set_facecolor((0.1, 0.2, 0.5, 0.0))\n",
    "\n",
    "    xs = np.ones_like(CD4_IL2_CD154_S10[symptoms_score_compound == s]) * cnt\n",
    "    xs = xs + np.random.normal(loc=0, scale=0.05, \n",
    "                               size=(CD4_IL2_CD154_S10[symptoms_score_compound == s].shape[0], ))\n",
    "    ax[0, 0].scatter(xs, CD4_IL2_CD154_S10[symptoms_score_compound == s], color='black', alpha=0.25, s=10)\n",
    "\n",
    "    cnt = cnt + 1\n",
    "\n",
    "ax[0, 0].set_title('CD4 IL-2$^+$ CD154$^+$ at $t_1$')\n",
    "ax[0, 0].set_ylabel('Normalized Percentage')\n",
    "\n",
    "\n",
    "scores = np.unique(symptoms_score_compound)\n",
    "cnt = 0\n",
    "for s in scores:\n",
    "    bx_s = ax[0, 1].boxplot(CD4_IL2_CD154_S11[symptoms_score_compound == s], positions=[cnt], \n",
    "                            showfliers=False, labels=['' + str(int(s))], notch=True, patch_artist=True, \n",
    "                            vert=True, showmeans=True, medianprops=medianprops, meanprops=meanpointprops)\n",
    "    colors = ['n']\n",
    "    for patch, color in zip(bx_s['boxes'], colors):\n",
    "        patch.set_facecolor((0.1, 0.2, 0.5, 0.0))\n",
    "\n",
    "    xs = np.ones_like(CD4_IL2_CD154_S11[symptoms_score_compound == s]) * cnt\n",
    "    xs = xs + np.random.normal(loc=0, scale=0.05, \n",
    "                               size=(CD4_IL2_CD154_S11[symptoms_score_compound == s].shape[0], ))\n",
    "    ax[0, 1].scatter(xs, CD4_IL2_CD154_S11[symptoms_score_compound == s], color='black', alpha=0.25, s=10)\n",
    "\n",
    "    cnt = cnt + 1\n",
    "\n",
    "ax[0, 1].set_title('CD4 IL-2$^+$ CD154$^+$ at $t_2$')\n",
    "\n",
    "\n",
    "scores = np.unique(symptoms_score_compound)\n",
    "cnt = 0\n",
    "for s in scores:\n",
    "    bx_s = ax[1, 0].boxplot(CD4_IFN_TNF_S10[symptoms_score_compound == s], positions=[cnt], \n",
    "                            showfliers=False, labels=['' + str(int(s))], notch=True, patch_artist=True, \n",
    "                            vert=True, showmeans=True, medianprops=medianprops, meanprops=meanpointprops)\n",
    "    colors = ['n']\n",
    "    for patch, color in zip(bx_s['boxes'], colors):\n",
    "        patch.set_facecolor((0.1, 0.2, 0.5, 0.0))\n",
    "\n",
    "    xs = np.ones_like(CD4_IFN_TNF_S10[symptoms_score_compound == s]) * cnt\n",
    "    xs = xs + np.random.normal(loc=0, scale=0.05, \n",
    "                               size=(CD4_IFN_TNF_S10[symptoms_score_compound == s].shape[0], ))\n",
    "    ax[1, 0].scatter(xs, CD4_IFN_TNF_S10[symptoms_score_compound == s], color='black', alpha=0.25, s=10)\n",
    "\n",
    "    cnt = cnt + 1\n",
    "\n",
    "ax[1, 0].set_title('CD4 IFN-$\\gamma^+$ TNF$^+$ at $t_1$')\n",
    "ax[1, 0].set_xlabel('Compound Symptoms Score')\n",
    "ax[1, 0].set_ylabel('Normalized Percentage')\n",
    "\n",
    "\n",
    "scores = np.unique(symptoms_score_compound)\n",
    "cnt = 0\n",
    "for s in scores:\n",
    "    bx_s = ax[1, 1].boxplot(CD4_IFN_TNF_S11[symptoms_score_compound == s], positions=[cnt], \n",
    "                            showfliers=False, labels=['' + str(int(s))], notch=True, patch_artist=True, \n",
    "                            vert=True, showmeans=True, medianprops=medianprops, meanprops=meanpointprops)\n",
    "    colors = ['n']\n",
    "    for patch, color in zip(bx_s['boxes'], colors):\n",
    "        patch.set_facecolor((0.1, 0.2, 0.5, 0.0))\n",
    "\n",
    "    xs = np.ones_like(CD4_IFN_TNF_S11[symptoms_score_compound == s]) * cnt\n",
    "    xs = xs + np.random.normal(loc=0, scale=0.05, \n",
    "                               size=(CD4_IFN_TNF_S11[symptoms_score_compound == s].shape[0], ))\n",
    "    ax[1, 1].scatter(xs, CD4_IFN_TNF_S11[symptoms_score_compound == s], color='black', alpha=0.25, s=10)\n",
    "\n",
    "    cnt = cnt + 1\n",
    "\n",
    "ax[1, 1].set_title('CD4 IFN-$\\gamma^+$ TNF$^+$ at $t_2$')\n",
    "ax[1, 1].set_xlabel('Compound Symptoms Score')\n",
    "\n",
    "plt.tight_layout(pad=3.0)\n",
    "\n",
    "plt.savefig(fname=\"figures/symptoms_score_boxplots.svg\", dpi=300, bbox_inches='tight')\n",
    "plt.savefig(fname=\"figures/symptoms_score_boxplots.pdf\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "legislative-cannon",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/local/home/anaconda3/envs/t-cells-cov/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1850: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n"
     ]
    }
   ],
   "source": [
    "# Predictive model\n",
    "metrics_clf_symp, metrics_proba_symp, feature_importances_symp = boot_train_test_XGB(\n",
    "    X=ndata, y=symptoms_score_compound, metrics_clf=[balanced_accuracy_score], \n",
    "    metrics_proba=[roc_auc_score], B=1000, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ceramic-transparency",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.358; [0.167, 0.658]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_symp, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "fifth-glory",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.521; [0.265, 0.78]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_symp, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hawaiian-preference",
   "metadata": {},
   "source": [
    "## Antibody Response Prediction: Normalising by Dividing by Background"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advised-cement",
   "metadata": {},
   "source": [
    "### Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ahead-ottawa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# LR\n",
    "df_tcell_c, feature_names_c = construct_tcell_df(df_included, feature_type='count', norm_type='ratio')\n",
    "disease_status = compound_response\n",
    "disease_status = ((disease_status == 'positive') * 1.).astype(int)\n",
    "\n",
    "metrics_clf_c_lr, metrics_proba_c_lr, feature_importances_c_lr = boot_train_test_LR(\n",
    "    X=df_tcell_c, y=disease_status, \n",
    "    metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "    metrics_proba=[roc_auc_score, average_precision_score], B=1000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "remarkable-outside",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.788; [0.537, 0.969]\n",
      "0.785; [0.429, 1.0]\n",
      "0.791; [0.444, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_c_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "french-cameroon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.861; [0.615, 1.0]\n",
      "0.854; [0.538, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_c_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "unavailable-concern",
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGBoost\n",
    "df_tcell_c, feature_names_c = construct_tcell_df(df_included, feature_type='count', norm_type='ratio')\n",
    "disease_status = compound_response\n",
    "disease_status = ((disease_status == 'positive') * 1.).astype(int)\n",
    "\n",
    "metrics_clf_c, metrics_proba_c, feature_importances_c = boot_train_test_XGB(\n",
    "    X=df_tcell_c, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "    metrics_proba=[roc_auc_score, average_precision_score], B=1000, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "furnished-investing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.84; [0.615, 1.0]\n",
      "0.842; [0.5, 1.0]\n",
      "0.838; [0.5, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_c, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "equal-cricket",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.92; [0.726, 1.0]\n",
      "0.925; [0.687, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_c, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rational-parade",
   "metadata": {},
   "source": [
    "### Percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "greenhouse-peter",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LR\n",
    "df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc', norm_type='ratio')\n",
    "disease_status = compound_response\n",
    "disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "metrics_clf_p_lr, metrics_proba_p_lr, feature_importances_p_lr = boot_train_test_LR(\n",
    "    X=df_tcell_p, y=disease_status, \n",
    "    metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "    metrics_proba=[roc_auc_score, average_precision_score], B=1000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "cutting-notebook",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.824; [0.597, 1.0]\n",
      "0.82; [0.455, 1.0]\n",
      "0.828; [0.5, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_p_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "demanding-archive",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.905; [0.673, 1.0]\n",
      "0.909; [0.668, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_p_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "unavailable-combining",
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGBoost\n",
    "df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc', norm_type='ratio')\n",
    "disease_status = compound_response\n",
    "disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "metrics_clf_p, metrics_proba_p, feature_importances_p = boot_train_test_XGB(\n",
    "    X=df_tcell_p, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "    metrics_proba=[roc_auc_score, average_precision_score], B=1000, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "serial-design",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.841; [0.634, 1.0]\n",
      "0.836; [0.533, 1.0]\n",
      "0.846; [0.5, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_p, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "injured-settlement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.915; [0.732, 1.0]\n",
      "0.918; [0.674, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_p, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "turned-lover",
   "metadata": {},
   "source": [
    "### Counts & Percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "partial-explanation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LR\n",
    "df_tcell_c, feature_names_c = construct_tcell_df(df_included, feature_type='count', norm_type='ratio')\n",
    "df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc', norm_type='ratio')\n",
    "df_tcell_pc = np.concatenate((df_tcell_p, df_tcell_c), axis=1)\n",
    "feature_names_pc = np.concatenate((feature_names_p, feature_names_c))\n",
    "disease_status = compound_response\n",
    "disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "metrics_clf_pc_lr, metrics_proba_pc_lr, feature_importances_pc_lr = boot_train_test_LR(\n",
    "    X=df_tcell_pc, y=disease_status, \n",
    "    metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "    metrics_proba=[roc_auc_score, average_precision_score], B=1000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "filled-jumping",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.81; [0.583, 1.0]\n",
      "0.821; [0.461, 1.0]\n",
      "0.799; [0.462, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_pc_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "subjective-spiritual",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.89; [0.645, 1.0]\n",
      "0.884; [0.581, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_pc_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "color-temperature",
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGBoost\n",
    "df_tcell_c, feature_names_c = construct_tcell_df(df_included, feature_type='count', norm_type='ratio')\n",
    "df_tcell_p, feature_names_p = construct_tcell_df(df_included, feature_type='perc', norm_type='ratio')\n",
    "df_tcell_pc = np.concatenate((df_tcell_p, df_tcell_c), axis=1)\n",
    "feature_names_pc = np.concatenate((feature_names_p, feature_names_c))\n",
    "disease_status = compound_response\n",
    "disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "metrics_clf_pc, metrics_proba_pc, feature_importances_pc = boot_train_test_XGB(\n",
    "    X=df_tcell_pc, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "    metrics_proba=[roc_auc_score, average_precision_score], B=1000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "rough-butter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.849; [0.623, 1.0]\n",
      "0.845; [0.5, 1.0]\n",
      "0.853; [0.5, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_pc, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "vocal-tyler",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.925; [0.733, 1.0]\n",
      "0.93; [0.718, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_pc, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pregnant-district",
   "metadata": {},
   "source": [
    "### MFIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "modified-crisis",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LR\n",
    "df_tcell_mfi, feature_names_mfi = construct_mfi_df(df_included, norm_type='ratio')\n",
    "disease_status = compound_response\n",
    "disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "metrics_clf_mfi_lr, metrics_proba_mfi_lr, feature_importances_mfi_lr = boot_train_test_LR(\n",
    "    X=df_tcell_mfi, y=disease_status, \n",
    "    metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "    metrics_proba=[roc_auc_score, average_precision_score], B=1000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "informed-piano",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.708; [0.448, 0.932]\n",
      "0.68; [0.3, 1.0]\n",
      "0.737; [0.389, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_mfi_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "tracked-charity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.776; [0.471, 0.989]\n",
      "0.792; [0.461, 0.992]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_mfi_lr, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "freelance-somewhere",
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGBoost\n",
    "df_tcell_mfi, feature_names_mfi = construct_mfi_df(df_included, norm_type='ratio')\n",
    "disease_status = compound_response\n",
    "disease_status = ((disease_status == \"positive\") * 1.0).astype(int)\n",
    "\n",
    "metrics_clf_mfi, metrics_proba_mfi, feature_importances_mfi = boot_train_test_XGB(\n",
    "    X=df_tcell_mfi, y=disease_status, metrics_clf=[balanced_accuracy_score, sensitivity, specificity], \n",
    "    metrics_proba=[roc_auc_score, average_precision_score], B=1000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "finished-ability",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.843; [0.623, 1.0]\n",
      "0.829; [0.5, 1.0]\n",
      "0.856; [0.5, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_clf_mfi, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "moral-detroit",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.916; [0.721, 1.0]\n",
      "0.92; [0.682, 1.0]\n"
     ]
    }
   ],
   "source": [
    "print_perf_statistics(metrics_proba_mfi, alpha=0.05)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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