LbCpf1_allFeatureSets_AllAlgos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "sufficient-archives",
   "metadata": {},
   "source": [
    "# Sequence Associated Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lasting-palace",
   "metadata": {},
   "source": [
    "# AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "herbal-incidence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[20068    10]\n",
      " [   39    90]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20078\n",
      "           1       0.90      0.70      0.79       129\n",
      "\n",
      "    accuracy                           1.00     20207\n",
      "   macro avg       0.95      0.85      0.89     20207\n",
      "weighted avg       1.00      1.00      1.00     20207\n",
      "\n",
      "0.7848265050244447\n",
      "No Skill PR AUC: 0.800\n",
      "Logistic ROC AUC 0.849\n",
      "SpearmanrResult(correlation=0.7912688958941917, pvalue=0.0)\n",
      "0.0024249022615925177\n",
      "0.0024249022615925177\n",
      "0.04924329661580871\n",
      "0.6177145566399568\n",
      "0.6180392592918623\n",
      "0.7912688958941917\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(198)\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sys import *\n",
    "OT_data=pd.read_csv(\"D://PhD_related/Dataset/LbCpf1_dataset_features_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "features = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[features]\n",
    "target = OT_data['Y']\n",
    "data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "\n",
    "ada= AdaBoostClassifier(n_estimators=118, learning_rate=0.641, random_state=0)\n",
    "ada.fit(data_train, target_train)\n",
    "\n",
    "Pred_labels = ada.predict(data_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels.round()))\n",
    "print(classification_report(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels.round()))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels.round())\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels.round())\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "rms= sqrt(mean_squared_error(target_test,Pred_labels.round()))\n",
    "print(rms)\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dependent-maldives",
   "metadata": {},
   "source": [
    "\n",
    "# LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "moral-reaction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[20038    40]\n",
      " [   19   110]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20078\n",
      "           1       0.73      0.85      0.79       129\n",
      "\n",
      "    accuracy                           1.00     20207\n",
      "   macro avg       0.87      0.93      0.89     20207\n",
      "weighted avg       1.00      1.00      1.00     20207\n",
      "\n",
      "0.787068813243954\n",
      "No Skill PR AUC: 0.793\n",
      "Logistic ROC AUC 0.925\n",
      "SpearmanrResult(correlation=0.7893390528844778, pvalue=0.0)\n",
      "0.0029197802741624188\n",
      "0.0029197802741624188\n",
      "0.054034991201650236\n",
      "0.5396971192195398\n",
      "0.5398673854139396\n",
      "0.7893390528844779\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(198)\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sys import *\n",
    "OT_data=pd.read_csv(\"D://PhD_related/Dataset/LbCpf1_dataset_features_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "features = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[features]\n",
    "target = OT_data['Y']\n",
    "data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "\n",
    "svc=LinearSVC(C=2, loss='squared_hinge', penalty='l2', dual=False, tol=0.0002, class_weight={0:0.122, 1:0.50})\n",
    "svc.fit(data_train, target_train)\n",
    "\n",
    "Pred_labels = svc.predict(data_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels.round()))\n",
    "print(classification_report(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels.round()))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels.round())\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels.round())\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "rms= sqrt(mean_squared_error(target_test,Pred_labels.round()))\n",
    "print(rms)\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "specific-promotion",
   "metadata": {},
   "source": [
    "# MLPCLassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "freelance-involvement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "Iteration 1, loss = 0.29773447\n",
      "Iteration 2, loss = 0.02324010\n",
      "Iteration 3, loss = 0.01813579\n",
      "Iteration 4, loss = 0.01412279\n",
      "Iteration 5, loss = 0.01178855\n",
      "Iteration 6, loss = 0.01121262\n",
      "Iteration 7, loss = 0.01040835\n",
      "Iteration 8, loss = 0.01047980\n",
      "Iteration 9, loss = 0.00970622\n",
      "Iteration 10, loss = 0.00974306\n",
      "Iteration 11, loss = 0.00961077\n",
      "Iteration 12, loss = 0.00956997\n",
      "Iteration 13, loss = 0.00983505\n",
      "Iteration 14, loss = 0.00921750\n",
      "Iteration 15, loss = 0.00945404\n",
      "Iteration 16, loss = 0.01045548\n",
      "Iteration 17, loss = 0.00967594\n",
      "Iteration 18, loss = 0.00947036\n",
      "Iteration 19, loss = 0.00911089\n",
      "Iteration 20, loss = 0.00932054\n",
      "Iteration 21, loss = 0.00950500\n",
      "Iteration 22, loss = 0.00933700\n",
      "Iteration 23, loss = 0.00929816\n",
      "Iteration 24, loss = 0.00954958\n",
      "Iteration 25, loss = 0.00905613\n",
      "Iteration 26, loss = 0.00941422\n",
      "Iteration 27, loss = 0.00946568\n",
      "Iteration 28, loss = 0.00946611\n",
      "Iteration 29, loss = 0.00927137\n",
      "Iteration 30, loss = 0.00916649\n",
      "Iteration 31, loss = 0.00907821\n",
      "Iteration 32, loss = 0.00913454\n",
      "Iteration 33, loss = 0.00911102\n",
      "Iteration 34, loss = 0.00908726\n",
      "Iteration 35, loss = 0.00938164\n",
      "Iteration 36, loss = 0.00890265\n",
      "Iteration 37, loss = 0.00901123\n",
      "Iteration 38, loss = 0.00870459\n",
      "Iteration 39, loss = 0.00906789\n",
      "Iteration 40, loss = 0.00868743\n",
      "Iteration 41, loss = 0.00869672\n",
      "Iteration 42, loss = 0.00879550\n",
      "Iteration 43, loss = 0.00928050\n",
      "Iteration 44, loss = 0.00871577\n",
      "Iteration 45, loss = 0.00901443\n",
      "Iteration 46, loss = 0.00993371\n",
      "Iteration 47, loss = 0.00932292\n",
      "Iteration 48, loss = 0.00900317\n",
      "Iteration 49, loss = 0.00926099\n",
      "Iteration 50, loss = 0.00939379\n",
      "Iteration 51, loss = 0.00953510\n",
      "Iteration 52, loss = 0.00947613\n",
      "Iteration 53, loss = 0.00953665\n",
      "Iteration 54, loss = 0.00957377\n",
      "Iteration 55, loss = 0.01051458\n",
      "Iteration 56, loss = 0.00949784\n",
      "Iteration 57, loss = 0.00891850\n",
      "Iteration 58, loss = 0.00893356\n",
      "Iteration 59, loss = 0.00908770\n",
      "Training loss did not improve more than tol=0.000020 for 20 consecutive epochs. Stopping.\n",
      "[[20059    19]\n",
      " [   25   104]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20078\n",
      "           1       0.85      0.81      0.83       129\n",
      "\n",
      "    accuracy                           1.00     20207\n",
      "   macro avg       0.92      0.90      0.91     20207\n",
      "weighted avg       1.00      1.00      1.00     20207\n",
      "\n",
      "0.8243018873890163\n",
      "No Skill PR AUC: 0.826\n",
      "Logistic ROC AUC 0.903\n",
      "SpearmanrResult(correlation=0.8245385745280808, pvalue=0.0)\n",
      "0.002177463255307567\n",
      "0.002177463255307567\n",
      "0.04666329666137581\n",
      "0.6567232753501653\n",
      "0.6567371746313407\n",
      "0.8245385745280809\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(198)\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sys import *\n",
    "OT_data=pd.read_csv(\"D://PhD_related/Dataset/LbCpf1_dataset_features_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "features = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[features]\n",
    "target = OT_data['Y']\n",
    "data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "mlp= MLPClassifier(solver='adam', alpha=0.0002, activation= 'relu', hidden_layer_sizes=[20,28,12,10,14,5], learning_rate_init=0.001, random_state=6, tol=0.00002, learning_rate='adaptive', beta_1=0.9, beta_2=0.71, epsilon=1e-24, n_iter_no_change=20, validation_fraction=0.25, verbose=True)\n",
    "mlp.fit(data_train, target_train)\n",
    "\n",
    "Pred_labels = mlp.predict(data_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels.round()))\n",
    "print(classification_report(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels.round()))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels.round())\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels.round())\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "rms= sqrt(mean_squared_error(target_test,Pred_labels.round()))\n",
    "print(rms)\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cardiac-clinton",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 200 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa221fc11aac41a39da364680865c5b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "data_for_prediction = shap.sample(data_test, 200)\n",
    "#data_for_prediction = data_test.iloc[row_to_show]  # use 1 row of data here. Could use multiple rows if desired\n",
    "#data_for_prediction.head()\n",
    "#data_for_prediction_array = data_for_prediction.values.reshape(1, -1)\n",
    "#n1=model.predict_proba(data_for_prediction_array)\n",
    "#n1=model1.predict_proba(data_for_prediction)\n",
    "data_for_prediction1 = shap.sample(data_train, 200)\n",
    "#data_for_prediction=np.asarray(data_for_prediction)\n",
    "#data_for_prediction1=np.asarray(data_for_prediction1)\n",
    "#explainer1 = shap.Explainer(model1.predict_proba, data_test)\n",
    "#shap_values = explainer1(data_train)\n",
    "k_explainer = shap.KernelExplainer(mlp.predict_proba, data_for_prediction)\n",
    "k_shap_values = k_explainer.shap_values(data_for_prediction1, l1_reg=\"num_features(600)\")\n",
    "\n",
    "#explainer = shap.Explainer(model, data_train.loc[:50,:], feature_names=data_train.columns)\n",
    "#shap_values = explainer(data_test.loc[:50,:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "minor-terry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0P6Q2aGFmRj8uuGzbas5pyCfjpWmH1v4rxlF71mn4zAa8sQ4iw8oxResY3rsDzWDY57o3r17F2Rs2Ee10MaikiP4VeVh8Hix+D057GKszcmmyRfDn2R9w0fol/JCZTYXNypyMFPxGA32ra0hqrCXM6ybK52/5w6+KsPOLc89gTUoS0fXN5JTWkFZfw3U/reGiZZuwt/mHPbR6A5WJHjxGG3WmRDbF5rIpOYcfBgwkYDDgMxrxGQyUxkQS0GBnbDoydwTnrtqGse2jOMwafzpvIhgM2L2tgawxoPOr5nXknJeOJcpM6oRk+t+SS/Q/pxAwaAxpaOT6XaVcUVPJhKlx2CKMZA6OZMhLY4mYkIYh3IQxTMOaE0WPV4J3wF11ipV/9/cwvbKWURkGrr00Yb/vi2azwH9vhaQo6JXEvy+20y/RQLTdQEK4RnKkxhtXhGE2aod1nifd2Jf4S7IxRluIuzCLnncNO+R19zc9YWIUI0Q4YXYD4tQIJk9NalfmN1dHkZ5kJCbSwL3XRRNmDZ5XBqOBU585FVuSjfAe4Zz27GkMub0vlmgzicPjOPX/hh5yG9T0yTutHLpu+5ygPQdGCyFeJ9hddUubMjowXkq5OPT6BuABKWXvg9T9NSCllA+2mfc2bbrD2tYthOgDfAn8XUr58r62L4T4KzAGuJI2A6OFEJlAHnChlPKL0HofAolSyolHenwOU/c8iY6C36fz+pM72SgdJKZbuePhbKLjzfst7/7fWhpv+Aj8OpaJmfjnbiUQF877IyZQbghHNxjQ9QB/mzAEtyXYyz11dR4//0ry7dAsDDFh3LPgNaJddTw69Q52xqWTUN/A+FUb+cXUcVSFh3HFiq08sGhJS4C0Iz6Gm286h3XP3k+Sq5bvcwZwzuX38Pjsn7D6g10t9RF2dmWktTZUD/BxbgbjNhWQ7tdxhVkpNhj4h20HMa8tw5BoJ+6rK5ndGM2L79eT5nAwsLgUTYcz7+3LoHPT2JfvP61k1ttlWKwGrr+3J7nD1AVfUQ7guKRpdO3adtd6TX+r06eDuuWYoJDAwYscsWIgc495PQkGK3uRUm4TQtwOzBRCfLWfMUePA9sJds21dQvBjN4rQojd+2QHIoUQfaWUW450J5Tjx2jSuPn+LDzuAJZDuLXeevkQLBcNBF1Hs5jQXV6wGrld11j7VSlz/7mNwsiIlgAIoDIyOL7Iq2m8OHoQBVFX8d57/+LJmX9HRo4nPJQ9f/d/s5h608WsS4trlxrOra5mxT+ex3rJVD6sDHDZ6HPB6eWr7B6cv2MnutGI29ra69pkNvJZrwwcNgvrctKJKa5ic3g42cMi6HN7b0zPTgarCc2gcR5w9rjwYO+Tngs6GA/wNRuTLkxk/LnxGAwahn10fymKohyJ7hwElQEHzOgchbcIdnX9F1hIcKzRaewnCAKQUn4vhFgKPATcvo/l9UKIR0LLARBCmAgOiH4CeHqPVb4nOED6nqPbFeV4OpQAaDfN3PpsIy0smDXSgGHnpdFnXALN7gArvoMfS8Go60xdFTzdZoyLZG4MfD5Q0OO+55i2pZBffrmE4JMZIKOhkSfmLcHq82OKNOBzBAAdCx4SvrwG69m9Oe1fP2Eu9OBFY2F6CqdV1xLn8RLV3IynvoFtSfHMyU7GYbNg9Af4w5dzmHB+GtpvxtIzwYDRoEFY+0yXxbw7mDm0oMakvotMUTpY1/sA0p2DoKeA/woh6ghmbpYfq4qllAuEEL8iOEg5nuDdYZ+x+7/O/v0R+F4I8Xcp5fZ9LH8R+AWwe2DDDCAWeEpKWdG2oBDiKeBxIcT9UsqDbVfp5MLjLIQD312u81MppEdA3MSBGMyDiRkWxzi3zooynQRbHOUN0ZiNVfD+egBqwsOIb/YQkWEn8QaB477v0dCxDkvCekY2mqYRGJTJ2XN3MrNvFnFuN3Ge1gHG4c1ufCYjo0rrqbeasHv9aL8dR87NGftpraIoysmh244JOtGEEEuAmVLKxzu6LceBOok6GV3XaZyVj+704cqOw1HsImN8ErY4K+4FBQSKHVhn5GKIDHZ3NZU6+WT6t6y2RFCSGs+IiuqWwc5JQ2JoOj2bT79zAhAba+T1R5OxWrrep0ZF6SSOyx9fQLu+3bXeoL/R6f/Iu3Mm6LgSQlwCzAY8wA0E7yq7riPbpCi7aZpG5PTgHXJRQHKbex6tp2ftVT481c6MjyZifuZbymoMNDSFYfL6CIu1cNVzw9EM0CfbSnW9n8mn2VUApChKp6CCoCMQ+oqK/T3i+G0p5e3AxcArBB/AuJ3g3VvbTlATFeWYi8mJJHyykWxvAzGuETQ3+Tn1/OSWgcpnnGbv4BYqinJ8db0PNyoIOgJSyl3AAb8kSkp55QlqjqKcUAYznH5Rekc3Q1EU5aipIEhRFEVRlEPQ9TJB6p5TRVEURVG6JZUJUhRFURTloLrK94W1pTJBiqIoiqJ0SyoIUhTlgPZ8lph6tJiidFfaHj+dnwqCFEXZp8aKZt6+9ieeGfcdcx/biK7rVM0xk/eEjTcu/5G6ImdHN1FRFOWoqDFBiqLs0/I3C6jY1khtdBTf/thM01N5eBb6GVBYTmCLxl8eisY9Io2rz7Qzqp/14BUqitKpdcUksMoEKYqyX3VRkTgiwmm2WflwlU5WQSUJtU0k1TRy1v+WUjO3iF8/V8f2Yu/BK1MURTnJqCBIURSchU2ULamgYksDXo+ftfkeVlSBwxL85veA38UHg3oS3dTcsk6Ms5lffrGIfjtKeO2uFTSsq0QvqiWwtmivcUSKonQFXW9MkOoOU5RuruCdHaz69TIIgCPKxhdnj2BWVCwuUw/CB3j517yvabb68RmH8ebEodzy7aqWdQ3ApPXbOKNwE02fAYZ6LIFmDJePxPzeTWha17hQKorSNakgSFE6oUBA52efe/hko49TM4x8cIWVSGtrwOHe6SD/ktm48x0k/XoIKXcOwHvpKwRW7KLstIHI8mgmr/mO/JRI1phOISYQXC+yoZkNhjBcJiMATRYz6+OiuXb9CqxeLy+cPYomi4k75q7E4vMTAOYP6kNhTgp1EREML9jO+RvmE/jfCvQnLkDLij+i/XPn1ZN36Rw8Ox0k3zuclHuHH+0hUxTlKHXF5wSpIEhRjhGfN8A3H1RSWeph9JRY+g494NfLta5X5aLkwWX4Gzyk3D8Sl1tnxwubsSaF0f++wZgjg11Suz7IZ+snhdRgpCE3no8aE7ll8Vqya+p559N4hs1I55TrszCYDBRc8Q0BmU8UDhofKOItWc4XkSPJGpbNA3PmMs3vJZxybpvwMKeuLSWmMdjN5TUaMAcC7dqXVt/AwPJSPn7tZV4+7UzqY6J5bfIokusb2ZUYS2FSDPZaCxqwpO9AhpVuomdDFf6aZj6aU0TBvBJGFxcwMF0j/HaBf0cN7jl5WCZlEf7zUe225fcFWPbffKr/vZbXM3rhzDBx85PrmH5RDrbe0QAU5bn47vNqwiONTLsyCXu48YDHd9X/dlGypo6sMQkMPDftkN4TRVG6hxMWBAkhRgN/BEaHtrsZeEZK+YYQ4mtgfJs2WYC2999Ok1Iu2kedvYEHgTOBGKAGWA+8LKX8pE25VOAh4BwgAagGfgL+KqVccQx387AIIR4GxkkpzzzG9RYAD0gp3z6W9XZ3rk01NC4sJXx0MvYhCXst//p/lXz7aRUA65c7uP/fvYlLsuy3vqYqN3mLK3E+vYpZTXainR7OPPMLSjULhgYPzWYT7mWFDL6uJ1UbGlnzcgH5OckEjAYoKOJhdwGjNuzCZzBg2VnG8iIn1Q061bU+Bv5UgUYEzUTgjazhttyxTM7fSpU9gofHTeOBBXMIB3wGI7NG9eeMNduxenzMHp7DiNIKrD4vTr/OqXnFnL2iBAeRvDHyDMoig5kdHR2MOmbdw/QtGyhM6tGyXztjUimI6kHWWf8jyhTBYIOR3iVF7IyKpmlxLemVldh8Ppo/2sjyWjMr+uQwKNvMmSNtLH+jgE8/rOKz0aewLTEGgKU5aXz67BaG3JzD4pnlfLfCgw8LBsBR7+OGe1q3rQd06j7eQcDpI/by3mxdWMWCp7ZiCATw/G89Uauzyfj9qWhGA59u9FHn0rlssIlwy/4/4QY2lhJYtB3DmBwMgw/+xbEL1zRTvL6Boe4GsqamYs8IP+g6itI5qEzQERFCTAW+AP4CXA24gHOBl4QQOVLKaW3KXgM8KqXMOkidg4HFwCfARCAPMIemrw7NRwiRBiwDVhMMgjYDYcCFwEVAhwVBSufhWl/NllM/JuD0oVkM5C66kPBTktuV2bGyvmXa59Mp3+XabxDU3ODl/ZuXUV/u5uXTBJszEgG4ed5K7vhGogEBTcPxk5PaeaupsUSSYjGyw5jaUofN76ciKhIAs89PQNeYs9hFzq6KdpeqVXH9uWfJ9zywaC4AW6OT2EUOGh5+u/A9/jH2SuYPyWF1ZgJVkTYmbi3k6rVbcBgMTF+yGR82tptyKIqJJ7rRhdNmwWEP4733/o0fI3Vhdv529lVomoEGs4lZw8YCEOFycc63K0j1VLAtKYWV2b0A2JyUytR1qzEHAjy9IYzSYhcfznfx0wYP7k2wsHcWu6LtLe2vCQ+j6JUC3tpuYV3PVEiG1IYGMl0eKrY3tTuuhb9YRNXz64PrvbmFsnP7ATB222bS6mrhwW3U51Xwl8un8eSi4B1t/5E+fvyZbZ/jlwJrivCc9jdo9oLVhOWH32IY2XOf7ynAi1808vrsYJtSanRufvIbpsyfhi0lbL/rKIrScU5UJug54D0p5Z/azPtACGEHXhFC/FdKWXCYdf4LWC6lvLHNPD8wO/Sz2yNAE3ChlHL3fbyNwFuHshEhRCLwBDCFYLZpO3CllHKLECIeeAqYGir+DfBrKWVNaN0C4D/AZOBUoAD4mZTyRyHE5cD9gEEI0Rhaf4iUMk8IMZ5gwDgAqAWeB/4ppdSFEBOBeQQDvccJZra+AW6WUjqEEDOBngSP64vAj1LK3e1TjpBjXhEBpw8A3ROg4ZvCvYKgGG8z6AbQNCweDza/b7/1VW514Ch3Ux9mbQmAABYPz+bn30gADLpOfHPw1NieFEVmVRNWpwe33QK6jr3R3bKe12TEYbehoVGSHIfDbiPS2YzLbGJy0TacjmB3lxM7gfp4wMgu+kGjj2ff/wi/pnHfRWfzzmkj2JAaz/lrdxDrab3t3eLzc/n3K+lZ3UCT1czO5FhKSKeBaBoDJpalJIGu07ve0bJOY1gYNTFRRFc4KY6Na51vC6MhzI7R76M0Orp139e5SYmIAKdOmstNfkQwcDh9ayFhPj/5ia11lEZFkeGsRFhbA0+A+i/yW9+z74qJuHIQmq6TWlfbMt89cytfDJ3S8vqnwgCVTZC0j97LwNxNwQAIwO0jMGfjAYOgRWtb35OyuChqm3RqVlSRNr3HftdRlM6iK44JOu63yAshcoHewL66Zt4lmF+bso9lB6ozDDgdeO8Qip8DfNgmADqc7RgIZrBigFGh3zcAu6/07wCxQP/QTwJ7B1c3Ab8EooG5wBsAUsr/EQxi5kspI0I/eUKIAcAs4G9AIjAduAu4tk2dRoKB11AgFxge2gZSyhnALuCWUJ3HPQByOBxdfppBkWAIXQA0CD8tea8yucNtpJdXklxZTU9XHUm9I/ZbZ1x2OJYII5HNHuIbXS3LR2e2Hd+ik+KrBODfY0/FontJLq4jubiOtF21uCytDyist1v4dHgfANxWM5+fOZInLxzH1tR4tkemklgf/OdcRQIBjBTGR3HjnRdy9a1X88mwgRh1nf/7ZiEAmdUNAFg8rUFcTXgYPUPzw91e+u4qp4FgAGN3+0ipbQBNa7mlHsDk9RHT0IQbK6X21gjD6vUQ0ezibxdMhzbZl0E5Zob0De5/stvHZTtKeOirn/jld8G70VLqWo9jRk0Nt347l+FntHY1ORwOwsektLwOGxJPzuQUNLNGTXjr9s2jMxjTo/U45yZoWPyN7erZrXlIcsv7rmsa2mnZe5VpO92vTawT43ASaQgQPTD2pDiH1XT3mVYOnXa8n+chhBhLsNuqv5Ry8z6WlxMcG/RY6PVBu8OEEOlAEcGxQrND84YB80NFbEBfKeVOIYQX+KWU8oUjaPspwA9AgpSyfo9laUAxkCul3Baa15dgd1ualLI0lAl6Tkr5t9DygQTHLMVIKev3NSZICPEsYJdS3tRm3j2hfT0zlAn6HkiSUlaGlv8N6C2lvDD0uoATOyaoWzwUxvFdEQ3ziogYn0r0tMx9ltnwZQl1hU76nZVCfM6BB0ZX7Whky5wyPKkRfJ+QSIJd446hGsWJ/8Jd6yecRiITdEy3jmGqNgRnSROXrt5EwGBh4JBoVhfqBJrd7IiL4dsh2VTEhPN4RhNNBU2ssIZz7iuLSSsPBi7GgJ8+vu1YHSbqiOPnt57D0twMINiVtvqRf+Gw2bjtmsuYtKWQzPoG0HXcPg0fBuqibIxZt7Ol7fP69eDGH9diCL3zeanxPHHBBIrjIoj1eEhsbObM1UtIq3CyoHc/+vxuKDPqKmksbCK7vIyoMA33DYL3l/vZXuxjSI6ZK86wY7VofLrIxVtzmqit8pJdU0+v4kpO6W2k38XpzFtQj1vXOM9STeKkHtjO7t3umAZcPiqfXUfA6SPxzkGYEsIoXV9PwTdFpK7fSUK/KOx3nYLPaub5ZT5qXTq3n2IiNXL/nwf98zYT+HYzhom5GM8acMD31OfX+XiBi8IVtZzmrGHg+enEDos74DqKchwcl5SNR7u93bXeor/Y6VNDJ6I7rDL0O51ggNBCCGEhmD2p3HOlNmWuBl7a/VpKGUGwi8gPZLSZvxqIEUJkAIW0ngSVoW0fiSygYs8AKGT3Z778NvN2tFlWGpoubbN89wCGSGBfdQJkA2cIIS5qM89AcJ928+8OgNrUG7mf+pRjJPKMDCLPyDhgmcO5+yihVwQJdwT/iU9qMz/tm8tw/HYumJOwP3025oFJvFUT4K4vPXw1Op3HzrRwWmIA2982U1tkpHxCDj2Ndm7oqXHfhCg0LZihmf0BuMqDdfoNBs772W1cvHYVFy3ehiusNWPjNRkpio2jKD6ZfuX17IyNIcLrpTIinK3xcUzbuIZJRVuojYnH4NJYlZHIN4OyGFpdQ1RNE0aDnzVDenHX7J94beIQ6u1WLvt2BavumMBXtkh+N8HMhQNMtP659g0eT+CufRzOS063M3qghb+978DptnHujF6MyA2Orbrl/AMfU0OYieT/a387feqgaFIHRQMDW+ZZgLvHmDkUxjP7YTyz3yGVNRk1Lj/DDmfYOfLLjqIoJ8qJCIK2ERy0fBXw7R7LriCYRZi7v5WllO8Q7HZqO88phFgYWv+Vg2x/FnCJEOJPR9AlVgAkCSGipJQNeyzbHZRkERwnBJCzx7KDCexj3k7gNSnlnYfRzkOpV+kkLKPSiV9wQ7t5OXEGZl1nazPHyLRHBgPBP6x9GfbIcJbd8gN+p4/0e4dBbCJ/TZnKutvO4p9C4+IvAtS7YdraHWzskU15RDg1VisXL17H8kGZ7EiM4zdffcnpNYuI8jbTYIng3UGX8lNuGrcvX8marBReunAAyQ0OfrN2E8U9k3nyrXlYfH6iz8/mzruTjvgYpCeY+NddsUe8vqIox15XHBN03IOg0GDeu4DPhBD5BAf5ugiOdfkXwdvU8w9Qxf78BlgkhHiN4NiafIJjZcbuUe6PwFLgIyHEfcBWgt1l5wEDpZQPHKj5wEqCg4zvAqqAQUCVlLJECDEH+IcQ4nqCmad/AF9LKUv3W2N7ZUBPIYRFSukJzXseWCCE2D3AWyc47idRSrngMOrtc4hllS4qZUoa526/mIA3gMluYquu4/BAVOihilV3arh9UHp5HlVyJ8Ux0YxaXYCm61z/zTI+GpHLRTdeja5dzfOz3+Bnq74n2tXAxVt3opvCaDaZEHVNaAEYeXMOYXfMxeTzY+4ZQeZrkw7SOkVRlI53Qr47TEr5NcE7pCYQzK5UAX8Afiul/MMR1rkaEARvi19IcLDyDuBGgre/7wyVKyY4qLkUmAM0AJuAC4CPD7KNADCDYNC2GqgDXgN2D/a4JrTdLQS7+uqA6w5jNz4kmDUqE0LUCSGypZTrCT4+4O5QmyuA1wkOkj5UjwLXCCFqQ89gUropg9mAyR78rKNpWksABGAyaIRbNHp9dBb9v5jGGW+N4bQ3xzPu/dMZ9N4Z/HfqCHxGI36Dkd+ceRV+o5GKyCR0TQNNI7cm2KOrGwy8VR7FkBUX0+uTs+m/8jJMcbZ9tkdRFOVkctwHRivdgjqJuqDsvzspqAu+tanuRorPreTffyol4AwGUk0WMzMHBcfKnNrfwtO/UN1XinKSOC79Vm7tjnbXeqv+QqfvH1Nfm6Eoyj69f7mVO2d6COjw73MT0DKTSblkJ1XfmImJjSZyRg7rNmjERhq490o1Ll9RurquOCao22eC9vjKjnZCd6IpB9e9T6JuZObMmQDMmDGjg1uiKMoBHJdopVn7ebtrvU1/vtNHRd0+E9T2KzsURVEURdmfTh/z7OWEDIxWFEVRFEU52XT7TJCiKIqiKAfXFccEqUyQoiiKoijdksoEKYpyUA6PjkkDr9uA0aweSK4o3VPXywSpIEhRlAP669IAf/vGyfStpcS6sjDZ/aw2NzOyr4Vze6lksqIonZe6gimKsl8PLfbx5289XLZuF7Gu4De7+JxGql5ax1//sYF3N6mskKJ0Fzpau5+uQAVBiqLsZf6uABPe8/HojwESm5qxBNo/CmpIXiWLXvgjMX96Zz81KIqinPxUEKQoSjs1Lp1zPw2Qv74O3WCgItyG02RsWR5X20CfHaX855TJTPru+w5sqaIoytFRY4IURQGg8cdSivOdfJHZA3czXLSpmDeH26kLs/C/wT25bPlWpi3fQnpFLYsHZbMlqyfZCfFM6eiGK4qiHKFu/7UZyjGhTqJOSNd1NtdAnA2M725i691zuezuqyiOjUXTdc7dXEyCy83ytHgyqipYkt0TR5gFgw42j5dBVQ5OKapm4IgIprrKaZxTSNzUDFL/fAqa1jXGCyhKJ3Vc/gCbtF+1u9aH6093+j90lQnqYEIIAbwGZAOvSinv7tgWKV2Fp9RJxT9WoVmNJP/fcLQIC/O/rCJvoxOz1cD3xnDe9EVhRueFL4tozMyiODYWdB1d08iPDSOnrolL12/mt3OeI/XhlwkYDAQAt9nETz0SiHV50FY0kLFoGylV9ZQtL8faO5r4G/rhC+j8a4XOzgadW4cYGJKooeu6CpAURTlpqCDoAIQQI4H7CX7Bqh2oAlYAz0kpvwuV6Q08CJwJxAA1wHrgZSnlJ4ewmceB2VLKe4/5DijdjmNhCTs2OrjGmM2OKnhiYT7nLd/Klu+KeDWzLwFv6zDAWBoYn9DIbz5dhG4ykdbsYOnTv2dEST6fDDqFt4bNwGPQWJNsw667+c9HL/GzS3+G36DRZLUBMGh7EZcsXAu6TlFyLKARs7WBeGDce36WluhoOnzwUzO5LhelBjM3nRPJfUMD5D23ico6P5m39iUtM+yg+1Zf62XDcgeJaVb6pAbgk5+gZwKcNXy/6wR0nY+26DT74fK+GqsrYXWFztRMjeyY9sHY3IIABQ1wQW+NRLsK1BRlb13v70IFQfshhJgCzASeAX4NFAIRwFTgQuA7IcRgYDHwCTARyAPMoemrQ/MPJgd48wDtMEspvUe6H0rX59pUQ+HPF+IpasSzvYHHLh7PhtMMfPzuM1y0fhkAxSvrMcX3xmNtfy/EjLX5VCXG4rJaOGvzenJrdwBw2dqfWJs6gOfGnkFEs4eHpt6KLWDjmS9m8t+Rg1jQewAWr49LF6zBGLpzLMLp5ospo9i40sEnv61geUocaBrGQICzNhYT1xw8jbdtN7Fk9nzCav1YgfnvF2D87SlcfntaS7vmfFTJ6iUN9Oxt45JbUvG4dZ76XR67HPB9dhLhgQZuXVzAr354gVenXkrajZOpLPVQXuxm9JmxjDs7jiaHj989UkJNmYc1qbE8MyiaVZUQ0CHa7eH6XfksHNGLO76R9FtfzNepafxr+mn8NU5j9XVGIiztL/ged4CPXi6lKL+ZkeOjmXxBwvF6S5Vu6uW1AZ5fHSA3VuOlKQZibF0v6DjZqCBo/14A3t4jQ+MAPg79APwLWC6lvLFNGT8wO/RzQEKIOiAKeEUI8SJwATAOmACsBK4N/Z4mhBgP/AUYANQCzwP/lFLqobqmA38DegLzge3AMCnlxMPaa6XTKbjmW1wrK1teGwIBYhpdnL9BtsxL9pQyYkMeP43o227dhNoGdqYnURcTwYuZl3LzT14Glm0FYENyHHVhFhxWE7P6D2dcYTUVUUn0LvOxIEfHr2n8+ZIJXDd/DTkVdeihbq4yewTbo8JbthHb7GkJgABiXF7Cav0trzNKqvlkbi0DR0YyaFQkm1c3Muu9CgBKCppJTLXSI8dGXbWP7/pnsDMmHIjk7gtuZFnP3ty4/Hve/FIQF3qO0Ucvl5Ldz84P39RgynOQBEzeUcZbkWEEwiwA1FstPNOnL+fM34r4dC0A12yrZltqPF+KXDZWwymp7Y/zd59Xsez7upZ2ZfUJo9fAcBTlWNhYpXPbnAA6wWxlYliAZ880HnS9E6mrPBuoLXWL/D4IIXKBXsB7BygTBpx+oDIHI6WMAXYBt0gpI6SU80KLJgClQA/gYiHEAGAWwSAnEZgO3EUwSEII0Ytg1ulxgl1yzwC3Hmm7DpfD4VDTHTjtrXTS1q3zVvLcy7Nw6lEt85qIJregjGnzV2Hy+FrmV0dHUhcTAUDAYGR2v0nUhoXzXeYwPh8omJhXzl1LtzGqpBavBmuSY3AbDfSqbsBvMjJzVF9uv+1cqqLtLBneJ1ipppHa6GrZRlSDE4u7NQjSDRo10a3BQ2V8sJ1NDh8Oh4MmR2v7AJoafETE+rDZDbja3KoP8O6I8Xzbe9Be86vKHTQ1tAZaBiDH2vra5AtOxzQ1t1svpqmZBJtO75jg67bHua7a1a5so8O/Vxk1raaPdLqmuf0dJqUO7wHLH2haOXTq7rB9EEKMJdjN1V9KuTk07zyC3VYaYCUYJBUB06SUs0NlhhHMwgDYgL5Syp0H2VYB8ICU8u3Q64eBa6WUvdqUeRawSylvajPvntC2zxRC/AE4W0o5vs3yd4D0E5QJUidRB6p+czM7b54PvgBLh2VhaQ6Qm19KlNtJIoXoaFTSg0Ao8bs1K4WfhucCMG7ZJpaIvviNwSDC5ffzyZBMXn33ZeZkj8MUFdmyneLIMD4a1BOA03ZV0rfaQXWYlTm9U7huVR7hvt1Pjw5miaitx20ycvGSjVgDOp9PGYVuCH7uMnk8WGobyHA0sz07jfRcO3f+KQtbmBGPO8Bzfyxg5zYXsQlm7vpzFvFJFkp3NfPCXCeP1ka2fCK1eT2kNbj4maeO+moPTkeA/sMjuOX3PSnKc/Hin3fiagpg7hPJfQ9m8H0RLPhfEcNfW8YrU0eyMT2R/774BZlldbhyYpn3rxlcMTacvnF7f+KtLHHz7B8LqK/xkdPfzh0PZWK2qM+RyrHhD+hc9HmAL3boxNlgziVGRqYccebluKRsHNpv2l3rI/V/dvrUkOoO27eq0O8MYDOAlPILIEYIMQ5YRLBLyh8qQ6jM6lCZDIJjiI70BNkzcMoGzhBCXNRmniG0jd3tLNhjnXwg/Qi3r3Qi8df1I3pGFronwKCEMFaudVLzq0WEL8qjjBxiLskhtkckee/m0+CHTYlRbAoz8dDHiwhze7F6fWzplY7F7WVe/0S+eeURepU2EFUWz2dTR0Gom6vRErxcpDhcnFpcA0BMs5fpG3cxfeFaNvZOp2RyDo//KpGta5so/VMJ6XO2AbCrXyruSCuWpuCnW8vwONYMHUxyT40Hs/xExZkxGoPbsVgN/OqxbOprvETGmDCZg4FGak8bj9xs49qaAH9YHMDlg1sGWhgfayImPg6fT6fJ4Scm3oSmaWT2sfPwf/riavITHRecd0lfuOShnnhvjedam4kyo4X4/7sMU5ULc6qdseb9dz8kpll54Lk+NNb7iI43YzB0+uu/chIxGjQ+u8BAkQPiw8BuVufXiaCCoH3bSnCQ8xXAvH0VkFI6hRALQ2VeOcbb3/MLmXYCr0kp79xP+WLgrD3mZR3jNiknMVOsrWX6tOHhsPBsGheVEPAEiDwjHU3TSHnsNLZvdBLAzIR4Mz3jK6n7NI+oCDNGv5/S5FhOLypmaNlOGkggqsnFqLU72NAnA7fRwOLMRACSG9t3Cw0orSXg9/HZgB7cc0UKUbEGxOkx8N0UGn8cRKDJx/DJGVxg0Ni2rgk06DOo7ViavQMPg1EjNtGyz33tE2fgg/P2zsBYjBqWPQZ+W20GrLa9y5pTg9vvETx60DNyrzL7YrYY9tsuRTlamqbRI+rg5ZRjRwVB+yCl1IUQdwKfCyGqgWcJdn2FAae2KfobYJEQ4jWC43HyCV7Rxx7jJj0PLBBC7B5wrQO5QKKUcgHwPvCQEOJK4EOCd6ddAMh91qZ0CxHj09q9toYZGTgykoG7Zzwznh7PjMfnDWD8to786gC/qxpIXHk1eT3NjF3aRP8dxfQqKOXW28+l0WoGIK6pvl29tQlRrP/4ch5ON3BOTvuAI2JM+9HFfQargcSKopw8VIf2foTG+YwjGGysBBqBDQQDnDNCZVYDguBt8QsJ3j22A7iR4G30BxwPdBhtWQ+cC9xNcMB0BfA6wUHSSCm3A5cADwF1BG/pP9bZKaWLMpkNjDs7jmuvTmDXLyzMmH0hmxMHklgfHDRs8Qe4duFa7pi9nOf+8xW3ffs9rlDyxmfQuOH2FB4ab9orAFIUpWvpit8irwZGd1FCiAeAM9XAaOVIlDy+grI/LG15XRNpIc7haXn937MH8EO/AVyf4eVX96ihZ4pykjkuEUqDdk+7a32U/o9OHwmpj26Kouwl7f6RmBJbxxlZPe2f19mn0s+EXZUM62fbc1VFUbosbY+fzk+NCTrOhBAbgMx9LNoppRy4j/mKclIYsPFKiv4k+WFuLV8NyuGuL37E5vXhNRrwJNu58KYUJpwT19HNVBRFOWKqO0w5FtRJ1IVtXdvI1+9XUNMUIK/BgSlV57k/DVe38CrKyeu4/HHWa//X7lofrf+t018EVCZIUZQDyh0SQe6Q4FOlZ86cCYDdPKIjm6QoinJMqCBIURRFUZSD6ip3hLWlBkYriqIoitItqUyQoiiKoiiHQGWCFEVRFEVRugSVCVIUZZ8aPt1O6a3zwKCR9sZUwrLCGXLHWizVHpoeiiL896d3dBMVRTmBuuJtwOoWeeVYUCdRF7TZ/gwBVwCv0cCq4bkML9lOakl5y/K47Xdj6hXfgS1UFGU/jku/Va32u3bX+lj9r52+f0x1hymKsreNheByszM5ni9OH0VxdDT2uoZ2RfRvNnZQ4xRF6Qhd8bvDVHeYoijo/gDOvy3Gt74c29VDsUQF0C1OmmzWljIVsRHEOh0EMGKjiZ2vbGThtwGq7zqVS7M1vp1dR0SkkfMuiiMsTH2+UhTl5Ke6w5RjQZ1EndSPixooXN9A9IcriV5ZQHZ9JZgMrJk2mh4zNwPQYLcxd/RQIjwuLl/5DQlN9TRhp4wMdAx8NrIfX00YxvCd1QzfWkBqrzDOfm8ChrDWz1jzdviZs93P+EwDM/qpz16KcpwdlzRNjfb7dtf6OP0vnT4dpK5GitINeZv9vPdCCQuXuRi+ZguNHh/FCRk0ma0MqSoifOE2AApSEqiJimBHdAR96uG/p55PbKODics2tdSVU1GL1ujl4q+WkuSuxbbeS8m1TWR8NAO2lbDj4W+Zvy2K/04Yz0e6j3JvMWcMtZBzs/rqPEVROpYKghSlE3M3+chfVU9Mio2U3uEt85sbfRSsqic2zUZyr+B8X1kjZS+txpCbyPxPa1njsmKxWrGFviE+oGlsTs/AYgkQ5nWxMjeb78VgAPyaxk50MuubqI6IZGdSJJkVDgIazOufw2/+9yOVUdE015np4y1C/2ItOx+0k/a3V+jldvNn4Oy1O5grRrM+zMLXNVYe3byAzAdGM+uzCiJ6hnP2pNh97qPu9aPP3gAxYRjG9zm+B1RRlP3qKuOA2lLdYSchIYQFeAuYCvillAkd3KSDUSfRceDbXo3e4MY8Iq3dfG9xI77iRoz943j91xupLHBh97g5+6JYwpLDiOgVxUcvlFJd2AwaXPxgLn16Gigb8Cxml4dCQwIfnDGeqqQo0HWGbNhOeJOLDf1zaIgKfkdYnKMRj8mI32AgwdFAeXQMi3rG4jaZ8Wg6r//vWdZF9+G5iZPpVVLPqJpaLLqOpdnL+GXryPaW4bF5sQcqMOoBDAY/KxiD320HYMGgnkjRgymb8oiuaiSgQfWF/bn56mTiB8ZgMLWOKWo86zmqfiolzllP8y+mEPbwdCKjjO2OSSCgszPfTUSEAW+dh7BIE7FpttYC20uhwQnDc0A7sgu5Z2cD/gonthFJaMajHPO0owzqmmDEobfH3RygqNBDUoqZyEjjwVdQurPjEq1Ua/e3u9bH6493+qhIZYL2QwgxHzgdOF1KubDN/O3Ao1LK14/j5i8BTgHSpZTOQ1lBCDEWWAy8LqW88Ti2TTkBXP9ZjuP2maDr2K4bRtQbFwPQOGcnhed/gd7sp/H0bCpNSSTW1zFljeQb9yiqI6Mx+8ox+Xxgs4EOP76xk/jZ8zC7PIDOjujUYAAEmD0+tmVnkFZd0xIAATTarGSXlXHJsh+IcLupDo9gfFoi3sgGzl+3Fbs3QI+oZp4dN5mtSXHsiovinJ3F2AwGCtNiSCuu4OteozE1+wEoTkvg1B8KMIbi5VO3FPPexEFMbfQAYNAhcnYe783bRZ/B4Zz16hgMJgP1m6t5vakvjaNGENHsZMpbP/JC4yDufSCdHj2Dg7YDAZ3nnipj9YpGkhsaCG/2oBlgxr29GXxmIrw8F25/CQIBuOZ0eOtXh/1+NHyynaIrZoE3QMS0LHp8eT6a4Qiv/69/Bzc/H2zPFePgvd8cdJVGh5/HHy6ivMxLeISB3z2YTnqG9aDrKcqx1BUzQeoWjgOrBv4uhDjR73wOsONQA6CQ24Aa4DIhRPTxaZZyojj/tQRCWdrmN1cTqGoCoPb5NeihwMK0pAiT30+/kkKqomOojgy+7V6TCZetNQviKHWhlzhCrzRKo+xYXR4GrtvFJR8v47zPVxJT58bs87WsY/H5GL11MxFuNwDxTY0k1vkYmO/E7g0AsKRHbwIEMxLNJhPLE+P5qxjAZdddyM/PPx9jqJ0AiZV11MW0dtcVJUQR53Rg9Ada5nktZpqiwin5oYKazfUAbFztotEWXK/RZmdNeh+cTQF+WNB6u35FuZfVK5swBQKENweDKj0Ayz8pDRb415fBgAPg7QVQWX9Y7wVAzb9XQ2i/G78uwL2p5rDraNG2Pe8vhpKD17VmVRPlZcFuy6bGAD8sdBxkDUVRDoUKgg7sZSADuHJfC4UQpwshlgoh6oUQm4UQtx1KpUIIuxDiaSFEoRCiSgjxmRCiZ2jZs8BDwEQhRKMQ4vVDqC8WuBT4BeACrj2kvTtGHA6Hmj7G08bs1vExWrwdLcqKw+HAnN0a34YRYJKjFKPHT0SzC61N17aR1uAiMTOMtlb0T8JWXMfgdYUkUMsQtnP++h8ZlpdHpNOJz+/jldxMptx0NR8OHtSyntNkYWlKH8ojYnlh7CX8lD2yXb0l4VY8pmBQ9NWAfvgNrZeXZquFhaf3Z1PfVFb1TeXbMX24avMafCYDjig7DbGRNEVHYPT5MVoN2JNsOBwOYtPat315Vn8AEhLNLccqMsqI1abh1zQCbbqWYlKCgaCvR1zLvEBcBETZD+u9ALBkR7W+H3YTzeGBQ153z2lvm/boMeEQCg4PtO7u/d0tIdF80pyravrkm1YOnRoTtB+h7rB5QBnwANBXSune3R0GLAA2AncAbwMCmAXcJqX88CB1vwQMAy4C6oCngVHACCmlXwjxMDBOSnnmIbb1V6E2pgN/AyZJKYccxu4eLXUSHWOBikYa751DoM5F+AMTMYv04Hynl4rf/4BnRz2xdwzBOiieit8twrB6J4VRMWy1xZGQZGLQvcP56ctKbBEmJt3aE+Pn63H+4RtMJZUUhiXw79GTuGjBFob5d6ABfgxsM2bQaArjy2E5vDB1GAA2r5elz/yHCns0O6MTqI/wk6L5yY/PBGBzTBRb42JIaXRSren80CsDAJPfz/uvf06j3UZyYzmNCRbWZeTiNkYS2RzMLm1Li+K6mEaczToNkRE4S5wk23wMv6kXGeOTW47F0o9KyF9Zjx5lpdxoIyvbynkXxWFo0x21ZZOLb76qJcznI6K+icgEC5Nv7UlYlBkq6uB3b0FtE/zhYhh1+IOr/Q1uKn63GG9RI/G/HkH4GT2O4F0NqWqAe9+EagfcfzGcmntIqy38vp6VsonsHBszLoxtt/+KsofjcnJUaX9od61P0B/r9CehCoL2o00Q9BdgDfCmlPLJNkFQGjBdSjm2zTp/IRjInHWAeg2AE5ghpZwbmhdBsCvrdCnlkiMIgjYAc6WUdwshhoTaO0ZKueRw9/sIqZOoE9ArHfhue4fA9moqIjKp2eIipWYXGlBKPJW0Zp9+e+3pZDfv5Hc/fonBZ2JJwilsjU/k/K0rqE2KY3ti72CdwK6EOPxGI2GORtbbrVREhnPrD6s4f+tmbLZ6Ups2t1yRF2RNYktSDlsS46i6fjhvXGrbq52Kohy14xKcVGoPtLvWJ+qPdvogSA2MPohQZub/gPeEEK+2WdQDyN+j+A7g/INUmQhY264rpWwUQlSE6jyswEUIMR4YQKjLTkq5VgghCY4ROlFBkNIJaImRmD+5HQieaIG/r6fo/5pJoRqPZmoXyqY66nh/5nNY/cFxQj6DkcSGQRTaMxhSuoEGWxQVEQlUR0XiNwa7wFyREVy7dgunFhYSPjCWjF13Yva54NK/o+eVE0hIZEhpAYXJEay4agr/O8e8ZxMVRVFOKBUEHQIp5ddCiOUEx+rsVgics0fRnND8A6kE3EAWsB1aMkFJh7Duvvws9HuOEGL3vEhgoBDibill3RHUqXQD6bfmUvHxLjb9FEVCWDMmpx8fRjZkx1PQO6YlAAIYXFaCKzKLgsQ41kYNwlDtRzf7aLJaQdex2gx4PDq2e05h0A3T22wlApb+FQ0wArHANaEfRVE6l654d5gKgg7db4GlBAMYgPeAB4UQ1wHvAiMIZl/uOFAlUsqAEOJN4M9CiI0ExwT9A9gMLDucBgkh4gjeTn8n8EmbRVZgFcEB0v8+nDqV7sMUbWHUknPwN/txzimg6LJZBNx+Bp7dg5se7cnXs09j2vqfcJosvDz4LCavKacsOoJmiwWX2YQjKpyeVTWcfkMPxl+TgderYzZ3vYukoihdlwqCDpGUco0Q4j3ghtDrfCHEOcBfCQYaZcCDUsoPDqG6XwNPAMsJBiw/AudJKf0HXGtv1wO1wCtSSk/bBUKIFwkGZSoIUg7IaDMSeV4vcktuJdDowdwzirIqH7+YeCPpI2bQYLUT7vIxddVMAlrwjq8wr4/Jg4yMvncIkQkWABUAKUqX1/X+xtXAaOVYUCdRF1O/s5GLfl/KzvgYAEZWVmCvaWJcXlFLmYRBMZz/2Rkd1EJFUQ7guEQrFdpD7a71SfojnT4qUpkgRVH2EpFq59aanchdpYR5fZx3SRL2kZksvKcUiyOYsEwbm9TBrVQU5UTqip92VRB0HAgh7gfu38/iaVLKRYdR19XAS/tZfJuU8p3DbZ+iHIzRYuCCd8cz6LNdhCXYyJmRgaZpbL9Xo1kaEKePJGdGRkc3U1EU5aio7jDlWFAnUTcxc+ZMAGbMmNHBLVEU5QCOSzdVmfbHdtf6FP1Pnb47TH1thqIoiqIo3ZLqDlMURVEU5aC64nOCVCZIURRFUZRuSQVBiqIc1Nd5AXq/7OP2rRNYUxXf0c1RFKVDaHv8dH4qCFIU5YACus7lX/jZUQ8lejh/KR3OlrVNHd0sRVGUo6aCIEVRDkh/ZyGNntabQpxmE+8vUEGQonQ3Olq7n65ABUGKouxXQb3OwA290Q1tLhWaxly3jfytro5rmKIoyjGggiBFUfYp0Ozju4u+Y+yq4r2WiYU/YDz1PsqmPwMuzz7WVhSlq9H3+OkKVBCkKMpefNXNrMl8m8HfbWP8xkLMvuBXZcQ0NfH0p6/zyJx36FlXiOX75fjvf7eDW6soinJk1HOCOpgQQgCvAdnAq1LKuzu2RUp3sXhODflbmxl7Zgw5/ewA6LrOx1t1wh5bQVJFMwADCyv5zZxVLOvXgxc/fIrcql2hGozEuZr4Yq2T1FKdVRU6UzI1smOCYwW+3xVgex2c30sjKbxrjB9QlO6sq4wDaksFQYcgFKg8AIwFrEAZMAv4q5SyVAjRB/gjMBmIBCqA+cBfpJTbDlL948BsKeW9x6n5Sje0qlzH49cp2N5M8Tv5BGrcNCZGkt03DHezTv6mJnxVzXzdP4OK/wX4edNyps//ic/TT6PaZGfUugoirWYaw23E1jVhNRgYVNlAdk0JPkwU0Bc3YSSxi7fTh/Lx2z4MAZ3s+gbe3v4lv5p8CT85bOSU1hD10UL6Z5oJu/9U0sclwJfrIMpGUc8eLJhVS22Vl9GTYxh/Vtxe+6H7AzStrMKcaMOaFXVYx6Cu2ktdjY+MLBsmc9e7eCuKcvRUEHQQQogpwEzgaeBOKWWxECIVuAU4XQixAVgMfEIwSMoHYoGrgOnAvw6yiRzgzQNs3yyl9B7tfnRXW2p0/r48QIwVHhxtIMraBf4Zvv8D+qxVLMgUFFnjGb51JQPTfPxnxoX8UG/FUdxEzEdb+GhcfxxhNtJjU7kubweN+KipdLTWE2al3mahMtzGm+4+1PQMB03DDGzK7cmGfr3xmk3E1DrYmhJLgtvHm+IsJi/bRi3JBDSoNQ5hQ0wmiY3NnL+lBKs/wLs+wZBPNrFy4mDi/TpbMlOJ2FBA5Plf0hSop49vPf886yYqI4PjCjTgw1fKyc6143L6Wfp9PUnpFs44N44dF3/Dki1+6mLCqTotjR439eG2oQZo9sDjH7O9wMdPvUaRNDiByefHYzRqeNwBXv17EZtWB+9gy8q18cuHszCZNWoqvcz9tAqjSeOsixOIjO7El8BAAJ6eBWt2wuVjYNrwjm6R0uV1gevnHjrxFeCEeR54V0r5u90zpJSlwJ8BhBDzgBVSyhvbrFMDPHuwioUQdUAU8IoQ4kXgAmAcMAFYCVwb+j1NCDEe+AswAKgNteufUko9VNd04G9AT4JZqO3AMCnlxCPb7c7P69eZ/IGf4sbg64KGAB+eZ+zYRh2t79fDlU+zOGcUn7iiAR8yMJD/e/ElbHOrefPKn/PKs/NY3L8njjAbAMUJUSztk8bAyoZ2VVWHWWg2BYcFDimvA631Aue2hGEK+BiTt4zi6BSS3JEArMw8hcQCP9XWaH5z01Tq7TbOW7+ZSSUNGELrB0xGhpXV8ecPFlHUO4Oq5DhsK7cR5vHhJZx1xlOojAg+cLHtJTV/m5PPXq/A6w0OuXSXuajaGmDVkF7BskU+fjezmUiLjateeJOaV37ghbPuxrteh/WV+Hw651yWyAcvl7UEQAAFW5vZud1Fr/52XnhsF+XFwYHcJbvc/PLhzGPzvnSEZ2fDb0Kfn95ZDKv+CoN6dmybFKWTUQOjD0AIkQv0BvY58lMIYQcm7m/5wUgpY4BdwC1Syggp5bzQoglAKdADuFgIMYBg99vfgESCGaa7CAZJCCF6EcxEPQ7EAM8Atx5Jm46Ew+E4Kafr3LQEQABrK/wnTduOeHpDEQClUUkt8wMGI+WRCQwqDy7LKa8lyt3+jq2oPe7gqrWZ+XBgBlXhwUCpLNzcssynabhMBm5a8h5XrPycZEdVyzKjDq9PHsOz00dRFxGGbtD4fEh/css3tas/oslFz4o6AOzOZqKamlvr8GuY/AH2FPDrLQEQQEmVl/q4iJbXusFATLOXlcVu2FBIdUQcXpOlZXlhXjDwKdnVuq3dYuJMBPw6FSWtx6FkZ2u5k+b9PYxpz6q81h30+WFLyUnTNjXdsdPHi3pOUPeTGPq99z3CQbGA8QDLj9ROKeU/pJQeKaUT+DnwoZTycymlX0q5mWCm6bpQ+SuAZVLKt6WUPinlHOCzY9ym/YqMjDwppxPtGufmtP6h3jzEdMjrnrTT546AhEhGFq7D7A/2ksY21dGnIp93TjkdgHmn5TJ9xXZOK6wis66JSfnlDCsoJ7m8lpg6Byll1dQbwGtszYptj49ibVI0+THhfNk3lapwG/3KtwMgdq3G6PcF2+Bw0sPlpTEqrGVdQyDAmPxVaIEAiVX1DNlYQO6OEpYM6EkACBgM7W+n1fy4zMFt757fo5eNkeOiSckIBjUGI4yZlsj427Mw6MFS9RYTDbE2rh5sg+sn0rO2mNT6spbyoycHs0ujJ8e03RqjJ0cTn2zBYNQYNSG6df4ZreVOmvf3MKYt108Cayh4zU6CiQNPmrap6Y6dVg6d6g47sMrQ73Rg0z6W1wL+0PJjaecer7OBM4QQF7WZZwAKQ9MZQMEe6+Qfh3Z1Op9eYODbnToxNo1TU7vAJ5esJFj3D3ot3859GcmUe6xkl7kJf+RB7uqdw6RqndN+Pg7L8l48+H01P6z34W/2k7arDKfZyBfjBhGmB4jW3Jj8fnyhQCi10c2C7GB2KczrI8HpYVtSNv3Lt9OvYgcXr57JFtsw+m0v5uPpoxldWEOku47K8AgemPcZXkMkjSYjroRobD4fs84cSWNiNKuSIjDqfvoOjWHwjiYaTWa2nz+E/luKeXtkf3Tg9phGrn0wE4vVwG8ez2LHRifxyRZSMqwgIsmY7GZ7gYeKGBuPZJjIjNbguklYh2bx64IadiSlEJdhJ7WHFYDxZ8XRIzuMzWsbyR0cTk5fe8vhu/rOVEadHo3JpNGrv32vw9upTBwI6/8Bm4thbF+IjTj4OopyFLrKs4HaUkHQAUgptwohtgNXAvP2sdwphJgfWv7KMdz0nn0FO4HXpJR37qd8MXDWHvOyjmF7Oi2TQeOs7C4Q/LSVEgMzBInsTlUOA4KR8u7b0zk9jXNOT+Oc0CqOH9NwfF/MTRNsRI5PhZpGVry6hH805OAs1unrbeaqiAYa4+1cca6dXR/M5/3h5zG0dDMBNJZmjeTsb9dg8foZuWYHy4b34ext9Vzy4wdE17mpSM+l169zGZIIU9Lgx29rMRo1Xixfj7msBr6aCunBu7/OANz5DVzy/nas2VHEX9G3ZddsYUYGjmz/iTY53UpyunXv4zA0G9vQbAbu4xBl5YaRlRu213xN0+g7OPyQD/VJr3dK8EdRlCOigqCD+zkwUwhRDjwrpSwRQiQDNxHMtvwGWCSEeAV4jGBGJppgF5VVSvn0MWjD88ACIcRsYDbBgDwXSJRSLgDeBx4SQlwJfEhwnNIFgDwG21a6gMgxKUSOafPPMi6Ckf83kXcBZ5Mfi8XQ7jbyyIkD+aIoHE9Gf9alZHDewrVENQa/JiM3v5SM4gqabVa8DfFUAZm/HcL9p7X2rk86d/c3zU/YZ3us2VGk/X7EMd5LRVGOp64yDqgtNSboIKSUcwnesTUAWCeEcBC8JT4JmC+lXAuMAsKAJYADWAUI4Ktj1Ib1wLnA3QQHTFcArxNKBEgptwOXAA8BdcCvObaZKaULs4cb93qOTsbDgkB6BH8fL/iqXzqfjuxNVWwkKwbnsCMzmfpoK3+96gyeunQk2V9OJuXuIR3UekVRlCOn6XpX7OVThBAPAGeeoFvk1UnUBZU2Bkh7MdgzG+t0c+2agpYvUi21BbD0ieDNy80Y+6Z1ZDMVRdnbcUnZFGh/aXetz9J/3+lTQyoTpCjKPqVGGLhuQPAaF9PsafdN8gmxdt55KFMFQIqidGpqTNBxFnqi9L6eyLZTSrmvMZ2KctJ44xwj56V5ufbrMBrNRiK8fgLA2IG2jm6aoijKUVPdYcqxoE6iLszj1+nzip/qah8965tIoJlvH03FbFaJZEU5SR2n7rAn9ugOu6/Td4epTJCiKAdkMWosuMLIs6s0KgrKOD+2ALO52z+CSlGULkB9lFMU5aCyojX+PtHIpfF5WAx7f+WFoihdn77HT1eggiBFURRFUbol1R2mKIqiKMpBqYclKorS7QW6Sh5cUZRuTwVBiqIckjUVOjfumMjFW8/igcX+jm6OoignmI7W7qcrUEGQoigH5az3cuNnHqp9Yfgx8NhPOltr1ABpRVE6NzUmSFGUvVQ1BPhpmwdbtYtoZzNfvFPKjn59iDAZ8RnB5NNZsstKbpz6HKUo3UVXyf60pYIgRVHaqar3c86fq6lxa2i6zqSiChp1naRaF7qmga6DpvHcO07qS+wM7GFi8mBrRzdbURTlsKkgSFEUnPVefvqghNqSZpaudlKTkQGArmkM2ZnHhqTEYAAEEPpd0wz//tqB12jiiUsDXDE2rKOaryjKCdAV74lQQZCidFPfrnPz4ZJmrNEmFm9yo9VamLq1ArvDTaaplqmr8nFZTQytKGFrcspe65t9fhIam5D90njuGycTBlhIizV2wJ4oiqIcGfXdYR1MCPFv4ErABuRIKSs6uElHQp1EnUxBhY8Zj1QxsrSSD0b2xmUxA9Cvopbrf9rCsB/yMIXuhffbYfvgVH7KSKfWaiHZUUVyfQXjd2zho1PPwhaAorAwauPCuSO8nml35xCT2v4LVrcvrWXhm4WERZk55+4copNV95miHEfHZfDONu3v7a71ffTfdvpBQioTdAiEEH8AHgVukFK+0Wa+BvwMuAXoD3iAUuBL4GkpZclB6h0D3ARkSSkrj1PzlePAH9DxB8BiOv7XAL8vgKZpGIx7b8vf7MdgNaBpGi6vTpi5fZmAN8D2+1ew85nNaAGdlEmx9Fn2GSkNzdw5+DKenDaOXpUlvPfeM8Q7HTw7+jwM/riWAAjAq2sURIXTr66ByGYHd/74H4x6gM/6TyWn3glAmsPJPLOZZQXNPP1gGdiM3HtNPBedZuehd+v59yYTjT36MnprCVEDP8dmNdD/sWGk3dC7ZTsNzgD+QIB73mpkQ76HKSPCeOSyCAyG1n3SdR3dE8BgNYLPD7pOs27EZtn/+xDQdbx+QAezkZb6mn06tjbvn88TwGRRA70VpTtRQdBBCCEMwK1ADcGA5402i18DpgC/AuZJKeuFEFnANcA44IODVJ8DlO4vAAoFWUYppe+odkI5puZvcPOr/zpwe3XuvyiC604/fmNhVs0qZ/Yz+RiMGuff15t+4+Nblm362Y8Uv7wNV24sf7jnLNY1GJmapfHFBQasJo3il7ey+ec/sTMzntJxvTB5/fRdWkJyg5VEavkxNwXdpePyR/JB33Gs6JXJrAEjSHI4+WN5I7k7KtGBD8YNQuam8ud3F2E3NGLUg7fGL84cRKy3ta1hHh8yOoaqsHAA7n2nia9We1i13EFd/1QAJi7P5+FzT6M2Moxxbxfy8gUevGFmzn6shpLaAOg6E4vLOM/RxPYdkczt15uzhgWzSg0rqlk9fR6eimYGnGfCvHgB1511J9vi0pg0yMLzt0Rh3iNQXFKiM+NTPzUundj6ZrI8bp69JYqnNhn5cKtOn1iYc4mBNS/sYP28KmJSrFz15ABi09pnshRFUXeHdVdnAenABcCXQohBUsr1QojxwA3ABCnlot2FpZQFBLNGBySEuBd4BLAIIRqBZVLKM4QQOnA3cC0wEJgkhFgbKnsxEA0sA+6SUm4P1RUJPAvMABzAQ8CrwJlSyvlHuf/KHh79uIkmdzBT8tgnjVw+xobVfOwvDnpAZ86z+QR8OgGfzpznClqCoPpllRS/vA2A/2X1YF1DcCzOnAKdD7fqXN1XZ/NdS3GZTJRmxQHgs5go7BVPYFWwrZtiEjEGgnd6vXzqFMqSIgCoiLTzzlmDufGbNdQHDLw1dQg5xTWU28PoVeJiXspozij7ic3RUQypaybc56cszMaOuChSqh0td48BLNjgZdL2ElaFgiDZO5XayGDQuLh/D1bv9LK8zBsMgAA0jU1xMWQ7muhd76BuWyOEgqAdf1iJp7wZgLjPZ/HM6Glsi0sD4Pv1Huau9XDO8PbdbPct9FPtAtCoiQ4jssjNbz9v5kfsAGyrhQe/dtNvXhUAdWVufni3iHN/2xtFUbo+lfs9uJ8BX0spvwLWAreF5k8DitoGQIdDSvkkcDuQJ6WMkFKe0WbxzcDlQASwCngZ6AecBqQASwkGZOZQ+X8BfYABwBDgfOCEjVB1OBzdatpsDLSZBoPhOG1LA2Ob4MpobpmkOeBumbZ62z+92W4CDBoGmwGDrgeDkhCT30+UoQavwYTT1BowaHuMDUwuayBrRzVD8ysZur2MvPQ47rx7Gu9OHgRlSbyUfh1b45P5uFcmH/TO5OvMdPwGAyPyyolzuFor0mDwjgrGri9EC+iUxkW0205UohUj7nbzjG264kYPsrYcE0NY6yntx4jV72m3Hr7W7e4+hmFtuyt1HfQ95gHh5nYv0bXW43kynG9qWk0f7vTx0hWfGK0GRh+AECIN2AlcKqX8TAjxS+BPQBrwDDBYSnlam/LvA2cTzLC9J6W89SD13wA8IKXs3WaeDlwvpXwz9DoBqAQypZS7QvMMQC0wHfgRcALTpZTfhpb3AbYCk05QJqhbnUTrd3n53TsOnB6d+y+MYMqQ4zfId8eyWr55rgCjSWP6Pb3IGBDZsqzgyfUU/nsThtwY/v6z01lWa+CC3hrPTg6OEar6uogtv1hGcXgYu2IjMXt9mDWosZqYsGkdj06YxMa0RKzeABpQFRNGY7iFPsXVPPHyd4S7fXiNBqY/fgV6aBxNXL2T/z36KQB/uG4i1VHBjAq6zpD8cm6au4by2AieuGocFrPGvdPDWPhFJePeXomt2cuHY/uztlcK8REaN51h57Ypdnx+nWueqWNlvg80GF1dxYDmJsZckML4azJa9teV72D91YtoLnLS94oo7LPn8tt+M1iX2Ydpp0XywMXtAyyAbbU613zlJ79WJ7bORX+rn79fF8lr2zTe3KAzJFHjnekG1n1QxOqvK0joGcYF9/fBHm3eqy5F6USOS4SyRftnu2t9X/03nT4SUt1hB3YzwbFAX4Zevw08STBLUwVktC0spbwCQAjxCnA0V9GCNtPZod9rhRBty5iBHkAiYN1jnfyj2LZyEIN6mvnq93EnZFu9Tonl56fE7nNZ1r2DyLp3EABj97E8YVoGCdsz9rEEYBx/vGYxf6gwkZ8ai7k5QEKdi4Q6FwavjtkfzHaZ/QH6l1bTp7YRh9VMrR5MezktJqye4FC1AHDW6jwu/mELAL11N5v/EovRFszcXH96JtUP9+CRjxox1/l58Uw7Z7Z5uKLJqPH+r9vuY+I+WxyWHcmoH89pnfHkVF7cz97t1idWY+k1uy9zrX+Sj6fA4+Nby024rgcTrutxkNoUpXvrip92VRC0H6Fsy81ADFDUJgAxEuwS+x1wnxBi/JF2iR1A2y9l2hn63WdfA6iFEEaCd6VlATtCs7OOcXuULujU507h+YdWc3etkUJLGF5jMGgpi4/gkRsmcPeXK6iIsHHJhgIsoS4qzellZa9k+hVWM25TIR+MHxjsU7+lH6nDNPRaN5n3DmoJgHaLjzTw9I1RJ3gPFUVRDkwFQft3NsFMyylAcZv5Q4HZBLuj3gbeE0K0vTusB9CL1uDlqEgpK4QQ7wLPCyHullIWCyFigEnAXCllY2j5n4QQ6wEX8MSx2LbStZmiLQx4+hTerPPz76+byCsPkJ5u5rN1LjZkJPDF+H5sjonm4rxdLes0W830rm3C7vGRXNMEQGqkzpOXRxF5w6kdtSuKopwAXWUcUFsqCNq/24DPpJQr9phfJoRYElp+PcHBzb8H3hRCuAkGTLMIjhk6Vm4F7gfmCyFSgDpgETAntPxXwHPAZqCB4N1hFxzD7StdWEqMkceubM3S/CmnkvsfXc77/UYT5fBQHmYj2RW8Kyu5pI60mkZ0oCEnhgcvDufs4VYiw9Q9FoqidD5qYHQXJYTwceJukVcnUVdTUMH8BeX8dmMKjsYAKU4nbqMJh9FEv6JqzEadX/29L6NzLR3dUkVR9nZcUjYbtX+1u9YP0O/u9KkhlQlSFGVvWUlMzEpifnOAHeU+7p4VQdXG4NOh1+Qkc+kIkwqAFEXp9FQQdBwJIa4GXtrP4tuklO+cyPYoyuGKsBkYmmnh29vM/PINP3NXuciIbOL/Ls3q6KYpinKCdcWUv+oOU44FdRJ1EzNnzgRgxowZHdwSRVEO4Lh0U23YoztsoOoOUxRFURSlO+iKd4epWzoURVEURemWVCZIURRFUZSDUpkgRVEURVGULkIFQYqiHLJd7ghm1fZkdYUaC68o3Y2+x09XoLrDFEXZJ0+jl/Vv57GtVmfNiCyyXE6++yiBwtgoRlb6+cVwjSdPN2Axdr0UuaIo3YMKghRF2cviIp0f7lhKzPZqAJLf2YYbmBr6dnmr18/Tek8aPAFeO9t4gJoURekq1JggRVG6vC2bGznnTSdn/ziHnIbg9wDb/AEsoQAIILuiFoAPt+jsrO8qiXFFUbobFQQpitLOJ09s4a5FsxlaswWPwbzXch2IaHbz+vvPsvZPd7LqkpcgENi7IkVRuhQdrd1PV6C6wxSlO6trouLiZwlfsomXbrqeZxIH8fE3HzC0bBcAw+vWUBCVginQ+nlJA1Ic1VyyejnhfhfZ8+bQPLUSxxcPkGjvGhdGRVG6BxUEdTAhhABeA7KBV6WUd3dsi5QuzefHuyKf1bYEYqJt/OPVnSwdcjHeYQY2pGTw9EcfUG5Jx0QBz48+k19ccD0AGdUN3Pr9ShIdLhpsZqIb/bzd5zKSm0s5o+gH/mwayj+e9XHnCI1rBhoZnAB2894Bkd8boHpzPfZEGxEpYSd67xVFOQpdseNbfXfYQQghRgL3A+MBO1AFrACek1J+FyrTB/gjMBmIBCqA+cBfpJTbDlL/HGC1lPLe47UPJ4A6iToDnx/vlMeY0u8CFvQagNnn584fv+Gpme8Q0DTuvOB6wmtslKdo3CgXMP2m39Jssbasbvb5SWxwcs6qrYiCsjYV64DGzvgo/nruWAImA5mROq/oa5nY24rpzEFsqdFZXRbA/c8VuH4oxWiCqc+MIm1MMoULywmLt5IyIv6EHxJF6aKOS0p2lfZsu2v9cP2uTp/6VZmgAxBCTAFmAs8AvwYKgQhgKnAh8J0QYjCwGPgEGAvkA7HAVcB04F8H2UwO8OYB2mCWUnqPakeUbkXXdXbMKqZ6SwNmu5HUUQnUZcXx1VtbGZvfxILpAwDwmoysScnBpxkx6X5+9/1XXHnpPfzUJ4O3xbi96vWajJTERtAYZt1jSfA6mFndQEpDEyVxkeTKdZStWMSXzc28em0UsyxpBHSwZQ9iSqOdaeu24Lj2P/yYnsGmhkQAxoyGwXcPhSFZ+96xBie8uRCiwuCa8WA4uiGNK8p0FhTpjC/PZ9SWTXDOcOiXfuCVvD54axG4vXD96WDf81h0HhVNOu9t1kmPgEv6quGhysF1lXFAbakg6MBeAN7eI0vjAD4O/QA8BayQUt7YpkwN8OzBKhdC1AFRwCtCiBeBC4BxwARgJXBt6Pc0IcR44C/AAKAWeB74p5RSD9U1Hfgb0JNgFmo7MExKOfEw91np5Jb+YyNrXt3e8royys6jl09kwqYmLq2vx+Lz4jEFBzxrHgvfZExgeuH36H4z8Y0u0PZ9obN4fZy3YgtWj5ctKXH0LatptzygwanbConWKvnvh/+hIiyWP0y6jqatbgL9dTBoNFstzBT9KEizc9u/3sVQvJbw+CHIxOGsX1jH4PfuA/kkDOzZfuO6Dmc+Cst3hHZyOzx38xEfI1mmM+ZdP94AmH3pLHr+VU59+CNY8yRkJ+1/xZtehLcXBac/WgrfPnjEbehILq/O2Pf8bK8Lvv5zDTwwWgVCSvejzvr9EELkAr2A9w5Qxg5MBN49km1IKWOAXcAtUsoIKeW80KIJQCnQA7hYCDEAmEUwyEkkmGG6i2CQhBCiF8FM1ONADMHM1a1H0ial8yv6oaLd6x1JsbgDGrJHbzLqa/n4zac5Z9MqfrH4G65Yuoai8BTeGjaWmdkTmLZmOyafv3XlNt3lA4sqyS2pZkOPJOYOzsFlbv0M1WQxYdDhrPX53LFoIW6DmS97TEbkVZJR3QCG9oHVurSeVIZHAZDRVBKsI8wAzR5YuHHvnaptag2AAL5Zc6SHB4Dvd+l4Qze0eU0mvus9EBwuWLL1wCu23e5366HtsepE8uppCYAAvilQd/cph0Lb46fzU0HQ/iWGfhfvniGEOE8IUSeEqBdCNBPs9jK2LXOM7JRS/kNK6ZFSOoGfAx9KKT+XUvqllJsJZpquC5W/AlgmpXxbSumTUs4BPjvGbdovh8Ohpk+i6dRRCbSVXVGHWdOptUfw8eBTOHfTKr567e88/PXn6LqJgoQorrv65zx5zngivE1M3FjQsq7J72f89i38asE3PDDra6KaPazumczNq+YwqmYFmsnJ5pQ4wj2+lnWqzfE0mCPwGiwA7EyMYU+5FSUkNDUA8FOPXGYPyWFM+TJ0iwnG9N17v0wBGJrZWsGkgUd1rMZnaBi1YIBn9PuZkL852LV1Su8DrztxQGsbxvUDk/Gked8PZzrB0EhmVOuuTOqhnTRtU9NHP60cOjUwej+EEH2BzcCUNhma3cvGAYuAcKABuF1K+coRbqcAeEBK+Xbo9cPABCnlGW3KfAWcAbjbrGoACqWUA4UQLwARUspr26zzKDDuBHWHqZPoJKIHdLZ8souqzfWYw4ykj06kqnciT60IsLXSz4u/u59BZUVsjcrBazTTFO7jo6GjuHjdcnpX1PJp5hQeP28cu5JiATAE/BQ8fjepDXW80+sCVvez89SXb7ds78nTLiE/qgfDdpUDsKBfT5ojm5m0uhyLV2NDegLPnnUKeptutlxPHUs9CygMj+GLsadzTk0ew9eshalDYVSffe9YTSO88i1E2+HmM8B0dE+q/qFY5/tdOqcXb2P8lg1w7ggYmnXgldxeePnb4O+fnQmRnfcOt2KHzpsbg2OCrh2goe2nG1TplI7Lm7lCe6HdtX6kfkenP2nUmKD92wrkEcyyzNtXASmlUwgxH7gSOKIgaD/2zE3vBF6TUt65n/LFwFl7zMs6hu1ROhHNoNHvksx28zKAN6YZASNrPJej3/4U/Rt2sD0uiaRaFx8PGsXX/YcxqjCPMksSdRGt/9wDBiMr07M4v34lO1KiiHPVtat78i7J6LvOZciuSm4ZaeTcMSlsq4MEq49hheXMSLdzY08T66p0UsOhtAkm9ognJupiYoDBAOTCubkH3rG4CLj3/KM9PC3GpmuMTdeAvqGfQ2A1w11nH7M2dKT0SI3fn9rp/4cpylFRQdB+SCl1IcSdwOdCiGqC3U9FQBhwapuivwEWCSFeAR4DCoBogsGTVUr59DFozvPAAiHEbGA2wcxLLpAopVwAvA88JIS4EviQ4DilCwB5DLatdDFDbxkFk/5J9bZq8jJ7MfB3L7H0uYe5+Lq7eVNM4M5vllESX8N6e1rLOjnVFWyIz+SvZ59OclM9161YRGZdNRuS0hlZUsAzn73O6b+fQP8LBrbZkhFGBgc4pwHDk9U/XEXpzLpiyl+NCToAKeVsgndr5RK8S6sR2EDwVvgzQmXWAqMIBkdLCN49tgoQwFfHqB3rgXOBuwkOmK4AXic0bklKuR24BHgIqCN4O/+xzEwpXU2vFOLPHsjU/jbSv/gVyWsfY9GVJra9+jAT8jfxy28XkFZfg6YH+OWirxlUXsTbA6bw4OeLOX1DKYPu+SuDf/0EOxKSAbjdVkL/8/p38E4piqIcHjUmqIsSQjwAnKnGBCmH65OLvqN8Yz1X7vwCu9eNxe/HZbTyZu8LQQt+bvJrGlkNuxjfuIbIa06DZ4/8dnVFUY6545J2Xa692O5aP0q/vdOnd1V3mKIo7Zz59Kl8+MQWRtsf5pUPXiWtroFZ2aNxJkcTtSt4B4pR1ymPSoK8/4Cl018HFUXpplQQdJwJITYAmftYtFNKOXAf8xWlQ0X1COeqfw3ngVf8jPvFQwBM89Zy+RCdvje9zPbIbOw+F5aJsUSqAEhRuo2umPJX3WHKsaBOoi5oR53O86sCJNk1fi00ttXCTY9t5YoVP7I9IYWL/jGFydnqc5SinISOy6eTZXt0h52iusMURemqesVo/GNS67N4BibA1WNKWT1McNPkQYzL6PTXP0VRDkOgizwlui0VBCmKcsiybQ6ybQ7GZQzu6KYoiqIcNRUEKYqiKIpyUF3xW+TVc4IURVEURemWVCZIURRFUZSD6op3wKggSFGUA3K6AvgD8BcZ4N01Y4iPdjPGpRMf1vVS44qidC8qCFIUZb/mL3Xyz//W4PNDWlklD2wuoNZu4a+pkTx5SWRHN09RlBNIjQlSFKXbWFSk86936/D5weAP0K+oHIDYJjdJ767r4NYpiqIcPZUJUhRlLz8U69z2zxIcEeFEWQIM31lOTKMruFDTyNhe3LENVBTlhOuKmSAVBCmK0k6gwc2q675i07TzWubFOpwENDCERkZmlWzhjb7vYTeYmPLgQGKuGkCTJ7gwXH2VhqIonYQKghSlG3PXe6hcV0ds70jCU8LYVqsjH1hFk9/RrlxVpJ1FQ/uRVVpJU5iVLDazoXcK69P68MmXTobFNPGHTRbQNe47VePR8cb9bFFRlM5K3R2mHHNCiBzgbWAQ8K2U8sIObpLSHeSVsf6tNTy+LY24MgcjSiux/n0CN68PZ3p1FNOcXmxeH81mExafn1SHi+qYSKpjgoOh+znS2JKUhQ7Uh9t5f64Tf7oVgMeW6gR0P49PUIGQoignN/UFqgchhGhs89Ia+u3ePUNKGSGESAUeAs4BEoBq4Cfgr1LKFQep/z9ANHCFlLKzvhmdtd1d2tI1Lr7/oZGUdYX0sPqIm9KTbwsMRC7aQdTqrSzsmYvF76N3dRVxjc0sHNCP0rhYfObgZ6O0uhI2J6Vj9wYYWJTHspy+RLt91FtNrEqNJLPeTXatk8owM5uSo3CajeiG4L0WYQE/f89w0uTSGdbfyjpZx9R5X9Lf3oTxNzPglD77b/j6nfDXzyDGDo9cCbERJ+BoKUqXclz6pBdqr7a71k/Qb+70fd8qE3QQUsqWK7AQ4hXAJKW8oc28NGAZsJpgELQZCAMuBC4CDhgEATnA/P0FQEIIs5TSexS7oBwn9Q4///xvLSUVPmZMiuC8ySfHP+vSpVXM+ssmPo3P5LR12/DVNpAP5H1VTLjRgNEfoN4QzagdJYwr2IEl4AfAjMbrk8a11FMWlcz8F35FwGDAbTSRderzZNd6iHV5GbOrjnqbGYDC2HCcZhNWrw+fScdnNOIyGHltmZekJg8/LazjqXdeRg9Y+fvY09j2zyaGn1XJHdcnYDC0v4a++XEtCz6qpU95Fne//x7r55Sw+Pe/YNtOL5Q3cuVPy8jMtLExJpGGYhf9r8xm0I298Tb5WPj7lVRvqqPXjB6M/GX/oz6OjqYA//xvDUVlPqZNCOeiqZF8Ns/BV/ObSEsycc9NsURFqGyXonRmKgg6eo8ATcCFbYKVRuCtg60ohFhDsBtsvBDiPuBXgB94AHgp9LoeGCiEGAT8AxgBuIB3gId2b1MIcQrwPNCPYEA2B7hJSpl1TPZS2cvrnzawfF0zAC/9r45h/a30TDN3cKvg218uxeXSsUZ6Sa5taJmvAWZ/AABTQMfi97UEQADJ9Q3t6smqKcKkB8AfwOL30a+ygWh361M1Ep0eaq1G4uocPPjBd1h8fgIavDtuMAsHZNFkMUGTB5fFgsOUwKqsTBb0GwRAyY9u+vV1MnlMeEt9Kzc28/7sJohIoDQigR515Zyyaw1zfnCGSlh5tdcQLluwlOLo4JyfHl9H2phE8mYVk/918I61Vf/eTOopCaSdlnhUx/Htz+tZuib4/r7yYT0JsUb+8796AIrLfbz5WQN3XRN7VNtQlM6kK94dpp4TdPTOAT48kmyNlHIosAj4s5QyQkr5amhRFpAG9AFGCSGSgAXAJ0A6MBqYAvweQAgRDXwNfATEAb8Gfn4U+3RYHA5Ht5xucgZa5uk6VFY3HvK6x2u6oaEBb5OPHpXVpNTW7/eS5deg2WSmyh4MQgLA4n69AciPCWNDUiT1Vjd+LVjDd70G0myy7VVPrNvP1T9uwOrzU5IQS0FaEhcu3YzZ6yPZEew1Hp63k3C3B5elfYDY5NLbtd/pCrRfbgnjneET281zWcz4DO0vWx6HF4+j/Z9ffUVrQHfE76+rfXK2rNLZ7nVTqL0dfR6qaTW957Ry6NSYoMOwn+4wL/BLKeULR1jnfGCelPLR0OsbgBeAGCmlOzTvt8A5Usoz2qx3McExR72FEFcDfwEyd3erCSEeA64+QZmgbnkS5RV6eOCpKuocASadaue3N8eiaR3/SWnjO3ks++NKBpaXsj0+kSazFc2kofv0dre5u40GFg7KIcPRyHnLVvPqGeP5fFgODWGWlroGlBWS3FjP6tTejCqqY1+dP713lWLx+dmYkwGA3dWM7BnPgMom6s0aT30zi6S8KpqsFp4+7yyKomPonWnmiXsSsYe1BjRen85DT1exZrObAH4abG4s/kj6J2oUlvrQAgGunf8jg3wNrEpOx1XjIWd6OpOeGkVjkZOvrltMY5GTjPFJTH1pNAbz0X3G21Xi5f5/VlJTH2DcyDB+d2ssf3u1loXLXcRGG3js14lkpXd85k9R9uG4XIjma6+1u9ZP1G/q+AveUVLdYUevkmB25lgq3R0AhWQDY4UQdW3madDyPykD2LnHuKL8Y9wmZQ85PSy8+WQqLrdOZPjJk1QdcHUOvc/vgWaAEU4vXqMRc7gZX7MPr9fPn65cSZ+CCuJcjVy1dAX9SyrYmJjMWcvXM3NIZru6NiZncL1cyIsfv0J+XE+eG3spPmP7f/wFaYmEu1pPV2eYjVsnhXH7KVGYbAaizLcQqHGhAy9EWWl0Q1SEYa+A0WzS+Ms9iTQ0+nFrGtEWaHbrREUYcTYHMGhgbjwPQ4yNfjp4Gn3YYoIBW2SPcC6bNxVPgwdbrJVjoWeamTf+mkqTK9Ay9ue+n8Vz59UB7DYNo7HTX/8VpdtTQdDRmwVcIoT40zEcwBzY4/VOgtmi6fspXwxkCiG0NoFQ1jFqi3IAJpNGpOnk+2doiQgFKnZzS6RstFiwAje8O4ZHlgSIMMMDcxahPVXKsPISAulR9E03sbw6QMAYDOr+MPtL7l3wOQC5VSUUxiTz8ZDJ7T5mpjoqqY+yt7y2Rhj49Xgzdltr3siY0Lo82sIBtR1svLsHzW4LBZnWYD0atARAuxmM2jELgHYzGrW9Bj+fTAGvopxIXXFMkAqCjt4fgaXAR6HBzVsBG3AeMFBK+cAx2MabwD1CiJuAdwEPwSAnV0o5G/gSeAb4PyHEU8Bg4Gba3MqvKLsNTND434zgP3b9rNNpHBKHv9hBxPWDua3WiveVSiZs3ERKfSM3Ll8MwMr0fpRGxmN3NdGjuJRFA3vRt7KMexZ+RH6v3oz/z+Vs39RMdZ2fM0fb2wVAiqIoJysVBB0lKWWxEGIUwWBoDsGBydXAEuCJY7SNMiHEpFB9jxO8Bb+A4B1kSCnrhBDTgWcJPq9oNcFxRTcdi+0rXZemaUTeMKTl9Y0ZkHpzLFtvaeJcuY4mYvloQG9eGzMDgHiHg19+NYdZw3O4dZsk/o/nc9pVQzCbNbLHhu9vM4qidAFdcfCnGhjdRQkhrgEeVQOjlSPhnbeO+rve4l8JZzBv+DDimn0ty+6ZOZuiJJ2rHhmG+YJRHdhKRVH247j0W32n/bfdtf4M/cZO3z+mOrcVRdmL+czBxG/8K5EuqLG3jr2JcDWTUlfHVeu+x7xpVwe2UFGUE01Ha/fTFajusONMCPE1MH5fy9o+jVpRTjaaQePUMxJoXLCKlX160l9v4vrZn9PbvR1zlAHOP6Wjm6goinJUVHeYciyok6iL0gM6RYsrMJg00sckMfe194jZVsWoO66Ankf3RGZFUY6b45Kmmae90e5af6Z+fadPB6lMkKIo+6UZNHpMSG553ZwYQVlihAqAFEXpElQQpCiKoijKQe35ALuuQA2MVhRFURSlW1KZIEVRFEVRDko3dPohQHtRmSBFUQ6PP0DAr8bCK4rS+akgSFGUQ+Nyc9pD33DuhW+wK+lunrtxJTVl6ptZFKW70LX2P12BCoIURTkkO276gMTVJWhAVk0hMz59C3PuffjOexa9sbmjm6coinLY1JggRVEOyLe+jLyfz6E8r5SNvaayLimH9cnRTN+2lMs2rEefuZa6+2cR+8xFHd1URVGOIzUmSFGUbuO/S91c8IqDR+9ehXtNGVvicymIySbSo3NqYR2/n3w5O6NjAfjyx0a8apyQoiidjHpitHIsqJOok/M0+Vj5Vj5ep59hV2WySDZy58f13LX6B/wGE5FeGwaDFY/J3LLOytQEdKuLlMYGlqUPoEeUmQnDbVx7RRwWS9f7xKgonchx+QOcZXur3bX+nOZrO/0fuuoOU5RuqjHfQeHMQiJ7RfLDmzup2NEEwLrPdxGoyWPuzjwyHXUAbInryffZo/BrGsbQB6cRpVU0Ws08Mv0MRhRU0H/RdlbsiEObk8e069NJnZLWUbumKIpySFQQdJISQliAt4CpgF9KmdDBTVI6kdI8J6UbHcQX1xKdE0FNcjQN1V5ym6sx1jRSlJzC6tt/JNDoBcBq8mKNj8FttuBv0hlduZ1MR0NLfYnOOgCMuk6jyUiEzw9AhNtLrLOZ6nArI9fl0auwFDRYPG8Hp/wyix49QZsxBC3ajvPbAlyfr2V9QhymKwRjc20n/LgoinLkdGOnT/zsRQVBByCEmA+cDpwupVzYZv524FEp5evHcfOXAKcA6VJK5wHa2PZb6k2ABWhbfpqUctFxa6XSTuPKKgIuH5FjktG0I79grCj04fbpjMk2H7ww4Cxx4tjRQNyweHbtaOb1h7YxavFmtlksGP0BCjPiSPTX03OdBKAwuSdre/TDGx2gX1Ut8Q31jKzcwacDT6HG6uOcK+/h/Q/fZ1jZTiL9TrbHZaADX2Wm4zSZuGzHzmA9sZFURNr/n737Do+iahs4/Jvd9F4JndC7FA8CKk0FsYBdULAhKvbePrvia8EXO/ZGseurgKJYQEVEOIL03kMCSSC9Z3e+P2aSbEJCEkjYEJ77uvbi7JQzZyZh8sxzzszQPKeIr0f2Y+jStThME8N0kTv5e1x5CbhaxrHjnOE0eet7ssP8aOl0suutRXwy9jQ6DGhOr/Na4Mwt4Lff0ghuH0b/HsHkbUqnYE8uYafE4fBzHrS/WUuTwYTQ/k0O+xgLIYQEQdXbD7yglOqvtT6aY1/aAVsPFQABaK3PKikrpcZjBWfx9dw2UYndT69g18NWkNHkmk50fH/IYdXzxA95PP5DHgDXDfTn7THBh1z+wPL9LLx4Aa7cYoLjQyg4rwt+2YX8cWJndrWIAdMEwyCwoIA+u7biV1zMO6efQbGvH/mGwQ+uYq5fuZaYHSnsDMzj884ncOf85ewvbsbPMc1IbBlGanQM0Qcy+Dc6kuSgQPYEBxJRWER4SABDduwnJq+QA5Gh5AT6E5aTy+C0P2hSlIwJGAku8qavQHfoxG+d+wHQYet2QmZtY8esbSx9qzm/t4rm8+6dYTPc830C5//fD+AyCRvUlO6/nIPDt+wejh33/82e51cB0OyOHrR7ceBhHWchRO24G9jdYYZhDAfGAk1M0xxlGIYCwkzT/LWmdcjdYdV7B2gJXFbZTKXUEKXU30qpDKXUBqXUDTWpVCkVpJR6WSm1WymVqpT6RinV2p73GvAoMFQpla2U+rCO9kXUo71vrC8tJ3+wCXd+8WHV88afZc/ceXdJQbV3Xe34YgeuXGtbOTuy8d+cQnaQvxUAAdgZqTx/f3R8e1a0aUuxrx8AAaZJXlAg+0JC8PHN4r+LZ7Fm+tN02J9lrQoEZLs5EBpC97WJ9NqVAsD2sFB2hQbTPSWNmFzrgYnBefn037eeHpmbaVKUXLo+FLAnOBrdpntpm/0zy45N9NokvujWqfT7h3lhYO9z5h97yV2bVm5/k6aVHWfPYy6EOH4YhnEr8AawGRhsT84DJtemHgmCqpeDFZD8Rynl7zlDKdUW+AHrBxENXA08o5S6pAb1vggMsD9tgFRgjlLKqbW+BfgPsFBrHaK1vrqO9qVeZGVlSRnw61CWsfFtHYQjwOew6ukQXVokPhJ87X74Krfb0qPLzAF9Bkdw6ur1+BYfHITpls1Z1qpZuWlFpklwYRGxuQcACHTn4fLoyuubuIn75s0kNC+P8xP2MmR/Gicn72fRx1N57evHeOuzxzl1zSo6pKbw8bCR/NW9N6bHzSmJ/k3ZHxaGT1HZO6jzA/xKyzkhATRPKxt/1Da/LPnpCPHFr3lQuf317xhSVu5QVm4IvwNSlnJDKNcX01H+42V3AGeYpvksZS+43wB0rk0lcov8Idhjgn4GngFWAtO11s+XjAkCmgPnaK1P8VjnGaCv1vrMQ9TrwBq3M0pr/ZM9LQQ4gDX+6C+l1OPAqVrrM2rRXm91h8kvEVC4L5ddj/6DO7eYVo/0IbBTxGHVszfTzaPz8sgvMnn0zEA6xB48JsaT6TbZ+Pp60lal0Wp0a1qOakXWzLWsf2k5vwU3IyUwFKfbTUt3KtN69WBds1a0259N5/1Z5OUVEpmXy0WbFzFu1U8A5Pr48XTvCfgYDhIjQwnyzeCF775kg18HPh0+HID4/Tu5bsn00jbsCI3nncFXlH6/ZOkPdMjYQXpsLHsiO7KtSxvSfIIx0/MJT0pFbVvDipiOFLSM5oTH++LYk8bTa30Iigng6auiKZ6ynIJd2TS9uRvhg8oHbQW7s9n1+D/ghlaP9yWgTehhHWchGrF66beaHT6r3Ll+dMY4r/WPGYaRDDQzTdNlGMYB0zSjDMMIALabptmsuvVLyJigGtBau5RS9wKfKKXe85jVCtheYfGtwHnVVBkL+Huuq7XOVkol23X+deStFkebX1wQHd4aVP2C1Wga5qh2HJAnw2HQ5dZu5aaFju9Ov4s60f2OubjWbMHv6r64rz6XlQvcRO43uebMMM6ODWbGy7tJWZXLtAGnE5uTQtfkJG4963K+734iRUFl2ZpzN6yiKCOSzmu2kx/oT3Fk+VNHkVF+AHeKszkxqjUt/xxHS6C/Pd29P4fcW+fgDm1Cu7t74nd+SRdZNF94VvB8f6ri3yqEju8d3ngrIcTha2BPjP4deAB42mPabcCC2lQiQVANaa3nKaWWYXWNldgNnF1h0Xb29ENJAQqAeGALlGaCmtRgXSFqxAj0JfitC8pNe3uEZ1bJwX3PtGPjG4Wc9MRyMujErzE9aJUfiI9pUuSx5ObwNsRmuwjMLyQwv5ADRijftB9E/31rSPcL40BaG9rs2UNaZBhxqRm0jzGJffvgZKgjOpiQj8fWzw4LIY4ntwJzDMO4Dgg1DGMjkAWcW5tKJAiqnXuAv7ECGIBPgEeUUlcCHwN9gRuAGw9VidbarZSaDjyllFoHpAP/xerPXFo/TReicu2vbE/Sz4mkLkmh3cBoxr/fhwcKHZz7PxdrUmHM3/8wbM1W1kTGl66zMySQSZfeWvr98U9/5u5/PmFjsw7kPziGdrd2qmRLQohjWUN6c7xpmkmGYfTDepRMa6wEwlLTNN2HXrM8CYJqQWu9Uin1CdYAaLTW25VSZwPPAa8Ce4FHtNaf16C6O4FngWVYXWOLgdFaa1d9tF2IqvgE+zL0q9MwTbP02UYt/GHFlT6Ypkn+i4WkfZ5HWEEumf5B+LqKOX/NWt4+60T2h1rddikd4ghZ9iF94YiejySEEDVlWoOa/7Y/h0UGRou6IL9EjVzxnkx2tX2DApcPTrcbB3D1xPP5s3NbYjJy+Oi3vzj795HebqYQwlIvVyL/i/mk3Ln+gtTLvDkwejdV/O0xTbN1TeuRTJAQolo+LcKIvEOxf8o/gEGx02TK9Lmsj21Dy9RMejyvvN1EIcTxZXyF782A24FPa1OJBEH1RCn1f8D/VTG7Vq+yUEqNA96qYvYNWutZtW2fELUV+fzp/LQlk4h/DpAW7kvu4BM4/6wofGMDCD1JXl8hRGPnbkA93aZp/lZxmmEYC7Ge3fdyTeuR7jBRF+SX6Dgxe/YcDmwLpEe3PpwwOBK/AO8/MU0IcZB6CVe+ii3fHXZRive6wypjGEYksMM0zfCariOZICFEjRkGRLfPQ42Irn5hIUSj0pCeE2QYxpMVJgVhPbJmXm3qkSBICCGEEMeaVhW+5wBTgRm1qUSCICGEEEJUq4E9J+iauqhHgiAhhBBCNHiGYZxWk+VM0/y1pnVKECSEqBHXnkxaTtsNBrhUFs5m8uJSIY4npvcfhPpe9YtgYr2+qkYkCBJC1MiWoZ8Qu2U/AKkXfEHckglebpEQ4nhimmbbuq5T7m8VQlRr+boC/Lalln7PW5nsxdYIIbzBbZT/NAaSCRJCVCk7rRBjbxYpqwpZ3749QzZvBuCftvGYa3OY9dx2CnOKOfv6Vgw4U26bF0IcHYZhhAGPA0OAGDyejSSvzRBCHLH5b+wg9P655GYF4sSgFbnsCQkhITaKBV26kHH3Mvz35FAY4M+yB5Poc+pw/IPllCJEY9WQnhMETANaAk8CM7Feo3Ev8FVtKpEzlhCilMttMm+LG6PAxeY31tCpKIxQCnAAvk6DhCZNSYqOZB8OvgmMYt7ofhQ7HXTZm8qo8z+nzQ9jMZzSyy6EqHcjgK6mae43DMNlmua3hmFoYA7wYk0rkSBIiOOUmVtI4fR/wN8HnzG9SJyxnQ9/zmaJbxBxyakMycjDx35jfBjZxLrSab0tmeSUcHICAnihfx+K7YBnQ9MYNn+fRIs1yfj2agqFRTB9oXWfxpVDwd/Xi3sqhKgLDek5QVhjmjPscrZhGOFAEtChNpVIENRIKaUeBs7QWg/1dltEw+BOzqbg5UWYTgcb41oS/ez3hCVYA5yzHljI/JiuBESFMpRs/Aozcfu4yPd3ElxYRCi5pfU0ycogz4BWB9JJbWG9ONXhdvNH83g+mp5Nx8Q/STEKabNrN7cvmYffM1/x/dgL+dkZx9CBMYw+u0VZmzYnU/z2Yozm4fjcOhjDx3l0D4oQ4li1Ems80C/AH1jdY9nAptpU0mCDIKXUQuBnrfVkb7dFiPpkFrnY/+QSCjccIOya7oScXfaIi+ICF8umbSJrTy7dL21DSEI6+VN/5eO4OP7scwL9h7ZkQ6pJkRseHeJDh2grM1Nc5OaHz1NI2JxLztYM/DA5+6f5RO87AECsM5Dt0c3IaN+Efjs2EJiSyoGOIaXbzQ6IIMpnDz7FAeT5+JBhBBBQVAhARkAACzq3ps+ufRjFxeyLCOOsjdtwEs7KvSazuvQFpwO6nczC+IEM3ZHAnJSOJAYGYjy9CfeHWyjs15Kf1hcTkZTAhrAW7EgNp9mdW9nVqRVGtot+5HHnEH9aRcPy97fiH+ZD/1s6ExDuV+3xXPZjKpuWZ9GuZwgDz42tyx+VEMe1BvCcIE/XUTYY+nbgP0AEcGVtKmmwQZAQx4v9k5dwYPISALK/3UL8uqvx6xAJwN8vb2DVzO0A7Fi4jwGr1rO+C9w1ynpi/Fd/AvlucJssSXCz6bYAAOZ9msIv39i3tJs+FBYXcIUdAAEEufJplZxGYmQ0f3Q8gTM2LKXAAH97fmzGPjpsMsn2gUKnD6vCWrOtcyCj1q/A7ZfBhf8uYXN4OzaEhvLUgmX4+VjB16WbdrCmq3VjRveUNE5KzSI3JJzTdycTtyOFuNRM9u4N5o2YzriDHNA+kr1+PqQ6nKwB2FUIof5sMMLIeGcPo7ZuoTCjCIDc1ALOelEd8lhu1Bn877XdAKxdnE5wuA8nDIo87J+NEKLB2mmapgvANM1kYOLhVNIggyCl1GvAIGCgUuoBYA/wF+AEioALsV6Wdg+wHngH6AJoYJzWOrGa+q8GHgZeAe4DgoHPgZu01i57mROAl4A+QBrwPvCM1tqllIoHtmNFnA9ivcjtL+AqrXVSFduMBN4GTsM67gnAJK31H/b884FHgPZY/ZqTtdazPNYfBDwDdLPbMw2YqrU27fnnAFOA1sBCYMuhjkFdysrKIjQ0VMqHWS7alFZ2MIvcZK7bS4wdBKVuzSid5Sp04zAK2RJT4e5PB+CGrQdM0jOyiAgPJSWpoGy+YZDl78ui+HacumObtRn8MIGZ3TvxR5sWDOnWirH/bOGdwadQ6HRy6cod3HPl2fTdkshIvZnozHz6rFxGjyzr16rP3i1MVhPI9e1EZpA/MYVWoBJVUIB/fiEFAX5E5xWWa6bTvrPkQEggbkfZ4Gk/l1n2xDLT/hiQazhKAyCAjJ051R7P1ESP/QZSEwu8/vOVspSPdrm+NLBnA+01DOML4GPTNBcdbiUN8jYOrfUtWH18T2mtQ7TWne1ZF2Pd/hYFPIUV/DwJXADEYZ0+n6jhZtrY67QH+gGXAGMBlFLhwE/AAqApcA4wAbirQh1jgMFAC6xA6slDbO9eIMjeboTd5gR7e8OxHgd+h71vVwGvKaUG2/O7Ad9jBTmxdntuAa6w57cHvqYsHfgKVqrwqPD8jyfl2pfDru2J4W+NhfHvHUvU6WXj+k4Y2w6Hj3Xmiesejm+bppy3aiUx2ZkAhFMELhOAG5STiHCrzgGnR+K013O6XMTlFvLsaSN4fshw9vpFkkcg33ftwNzO7cgI8Gd2514sbh/HRctX035fKm8OHMmQLVu5bcnvxLtTiC7OwHCWBTVO0010Riafv/0j/fUeIlJywTQpMg1Gr9sO2QWsDQok1x447SwqxpFvjStqm5xGXKYV0GCapAd4DJr2cYDDIKiomBNCTdoMjbOmG9D90jbVHs/uAyIIjbSu7YJCnZxwaoTXf75SlvLRLh8nRmCNAfrYMIzthmE8YxhGz9pW0iAzQYfwq9b6OwCl1HTgDWCG1rokmPiSmv/xzwMetTM/W5RSvwAKmIUVZBRiZWNMYL1S6jmsIGiKRx1PaK1T7W1/zKHTcYVANNAZWKG19hy8dTvwcklWCFiqlJqJlWn6HbgJ+EJr/a09f4OdLbsSmI4VvC3VWs+0589XSn2DFZyJBi74jDbEb7iGou0ZBPRvhiOoLChoO6wpY78ZSva+PJr2ioSC/uT9mcC/abvY2LYNvXpFsjPNpNAFA1qVXdN0OzGUB1/uQFpqEXlJObgK3dyckMK6n3bgwsDZJYycsKJy7UgJCKN/RhY9dqawrnkzrl28FAfg4y7C3yjmiaHn88b3bxFSWMDqqA6E7fchzA5sohOzaZe3hZx3r6R9+yjcSwtZsNbJn9FRXNXT4Oxe/hgZzSlIKyB6SFPODvfnn9/SiD8hlN2BQWTmufA1IMc0yMpw0dUXenVtjX9APEn/puEf6kt0x+pP8hFN/Lj9ta4k7cgjrnUAIRFyV5oQjZFpmiuAFcB9hmEMAS4DfjUMI8k0zRNqWs+xFgSVdjVprXOVUuWmAblATcPh5JKuL1uOx7qtgJ0lXU22rfb0Stvjub5SahzwlkdbQ7CCJ1/gI6CZUmoucJ/Weh/QFhimlPLMNDmxsmHY809TSl3oMd8B7LbLLYEdFdq2HQmCjhm+8eH4xodXOi+8dTDhrYOtL35OQka2I4SyH250YOU56pimfsQ09YMe9ro0pfVF7XHnFOHbJpw7EjP49VsXP+520jPOwUuvduf1px2c//UvfOzTHZfDgcPtBiApLISZfQcyt2tPIvNyCDIDuOXzJWDfNeZ2GHRL20v46REYoX6M6ulHUpoLX2cEMWGVJ5xPG9MMKHnToecyPpSNToLmfaMOdegOEhTmQ/sTjrurYiHqXQMbGO1pA9bQmF1Ax9qs2JCDILcXt70baKOUMjwCoXaUBR2HZI/lmVVhWg7wEPCQUqop1hMup2Blc3YCH2qtp1Ssy7YTeF9rfXMV8/cAZ1aYFl+TtorjizMmCGeMVfZrHs4PN0J+kUmAr3VyG/dMPy7v2Z1dWSb3XXomj367gLC8AqYMHQhAemAI6YEhdNufwdSz+3LXXE1oXiHf9uvAKWfHY4QGlG6rWaTc7i6EqB+GYUQAFwGXAwOA+cBzwOza1NOQg6C91PKhR3XoO6xB0f+nlJqClYm5H4/sTm0ppUZhDVbehNWPmQ+UZKJeAj5USi0BFmNlgXoChtZaYw2C/k0p9QPwA9bYp05ArNb6N+BT4FGl1GXAF8BQ4HysgeJCHFJJAATQpYmT5XeEkpJr0vbtbnx7YneiM3PpvikJXG4wDAzTTf+kFLq0NHhplMLH7WLiqg0ETRjmxb0QQtS3BvawxESsv5cfAxeZppl+OJU0yIHRthcBpZRKV0qtPZob1lpnYA26OgPYB/yINfZm6hFU2x7rcd6ZWF1XeViBFVrr+VhjmaYAqVjdbC8CIfb8NcC5WAOnk4Bk4EOsQdJorbdgDRp/FEgH7gTePYK2iuNcbJDBdxf5cGFHg/FDg3n/xHQG7dxLp/0ZXLIpgQGZKdz5eEdmNdnHm0YC417vi19E9c/wEUKIOtLeNM0zTNN873ADIADDNM3qlxLi0OSXqJErfPMP1t/xD2vaxeN0uRnech/Rv9zo7WYJISpXLzmbD9p9Ve5cf822ixpWbugwNOTuMCFEA+F3SW+6PPk/Wq3fgZ8jj5CXJAASQhz7GmUQpJRqDayrYvZMrfWko9keIY550aH4r3yc1S9MJ7tlOEPP6u3tFgkhjrIGNiaoTjTKIEhrvQt7PI0Qoo7EhpF0ahtvt0IIIepMQx4YLYQQQogGwjSMch9vMizXGYbxq2EYq+xpgw3DuLQ29UgQJIQQQohjzZPAtVjv5Cx5oWIC9l3XNSVBkBBCCCGq1ZAyQcDVwLmmaX5K2R3K2yl5CH0NSRAkhKiZomIchcXeboUQQoD1UOFsu1wSBIV4TKsRCYKEENUqmPknKc1uQV3zHW1nH9VnlwohGgjTKP/xsnnAVMMw/MEaIwQ8hfVQ4hprlHeHCSHqzroZW/n3mfVs6HsBqaGRtNu8G9e8RJo29SeuT7S3myeEOD7difXmhAysl5NnY70/7MraVCJBkBCiSilL9vHXE/9yxr6VXL7+e9wYvH7ypbzx2l7aJaYw9LIWnHRfD283UwhxFJgO76d/AAzDcGK9KupyIAxoA+w2TXNvbeuS7jAhRKXcOYX8fdnPmL4u2mTvAcCByQWrFqI27SQqO5d1H232ciuFEMcb0zRdwFTTNPNN00w2TXPZ4QRAIEGQEKIKSbN3sNcZyM6YJmT6B1GML/tpTaa7OX7F1gDpoNws5P2DQhwfGtjdYXMMwxh1pJVId5gQolSRy+TBP9z8mwzN90VS0LMdDr8ARk14DmdBDtf/ovErchOXlcHuyGgWdWjL5CfTccUEcf4JPjx1qgOH90+OQojGLwD40jCMv4DdeLzI2zTNGo8LkiDIy5RSI4HXgDjgMa31VC83SRyH8v/eQ/K5X/Bqn978d8SpAMTvg5u37wXDwKdDa4Lz8sn2DQRfOBAUDMDuqHAmv/s/nG6THdERrM5IJaFZHLmhQXS+NJ4TrutU7ba3fZ/A8lc2EBDtz5Bn+xLaKrhe91UIcXgaypgg2xr7c0QMSWXXjFLqIWAycLXW+qNK5p8CLAI+1FpfU4t6NwEvaa2n1Vljjz75JaoPbjccyIboUKiQXcnMdhFQVISPExyFhdYygJmUjsttYIQE4IwIKF3edJvMnpvGR/NzCQ1y8H+Tounczp89e3K4YL4PKevSmTZzNh2SD/BXh9Y8dPEZ3DzvH2Ky8gDI9/VhR7NYuuxKKteOk3duJaioqPT7xpgm7I6IKv0+5dyBZIb48/oPv3D2Ez1xntgMrn4V3wWryBvSk6cGXc2qbW7aJiVzwpbdmP5OnIE+DH20J+1HtSqtpzjfhbvIjTPYh7R8iA4E42hlnPILIb8IIiQ4E8eMevnP8Wb32eXO9ZPWjm5QUdHhkExQDSilHMB1wAHgeuCgIAi4wZ5/qVLqDq11Rg2rbwesOsS2fbXWRVXNF43UgSw47TFYuQP6toNfnoCIYNxuk+ffPYDzg+VcumQxzdmAgwIY0o2CdNi70h/T/m8ddmc/oqaegWtfDpuHf85b/U4Hh4P8Ajf3PJfCJ0ELeGV9KNMW7SAmJ69006M2LebK/3zBm53LMsq+LhdLOrSg9d4UgjwemOjjcpdrdrGj/DBDp2myLzSYkPS1BJzzMTnOQH5oNoyRxT4Ez1/OHX9uZ2WYomNSOln+/kw9dwT7IsLZ8nICT5/dEofTYMf8RBbctQxXoZuFg7swq0tHhrUy+P4iBwE+9XwO/nklXPg8ZOXBfefDc7W6+1aIxqUBdXUbhnFaVfNM0/y1pvVIEFQzZwItgPOBuUqpHlrr0jScUioSuATrPSavAFdgdXFVSSnVHNiE9dTL+UopN9AX+D+sZx4UAaOBz4AblVLnA48A7YEkYLLWepZHfROAh4BY4FusK4FirfXVR7brwis++NUKgACWb4OPFsDt57J+WyF//J3Dy0uXE8E+fCmwlvltHel0xKQsW5H54jJCb1PkTF9D0aY0OKnsBOZymYS89D8uDTmV6JyC0ukGLvx8U5nT4URWt42l1+YUME3+6dgGt68PP5/Uk74bttMyNR2AFc1b0i9hJwaQEhpMfNoBUoJDKXY6WdU6jg3NYxiybS0jtqwEINiVx4D9ywl2WUFXk5w0+uf8ywHiCS0o4IzV65k1eCCr2rTE5TJxOA2WTlmDq8AKtgb/toEv2rdlwW4fvtliMrZLPZ+UH/7YCoAAnv8GbjsHWsizkYRoAN6r8D0W8MN6f1iNX50hd4fVzPXAPK31d1hZmxsqzL8S60FNXwKz7OUPSWudqLUOsb+O0FqHaK032d8vwXoaZixwt1JqONYP/A4gCrgKeE0pNRhAKTUIeB2YZM//CRhzeLtae1lZWVKu63J4EOWEB5GVlUVQgAPTMCjw9cGNs9wiDlzl1/ExcAT54gj3J7SggACPbisDk0KfAKKzC8qtkuvjyyk3P8XoCffz5pB+vHF2f14cfSpfqw502bGHoPw8WqUcKGtrQCAL23Xit7YdOP/Wy7h3/IV8Osxa59WzTsJ0GBQZvuW2UeDwK/fdcz/y/KxlA/0hL996+r1faNn6hb5OXHa2Kdyv/n8WxcH+pWXTzwcC/eptW1KWcl2V64vpMMp9vMk0zbaeHyAceJpqEhAVyZigatgZm53AJVrrb5RStwFPAM211nn2MmuBn7TWdyilTgBWAidrrf+qQf0mMEhrvcj+/iHQWmt9mscyc4GlWusnPaa9CgRqrScqpd4BArTWV3jM/xPYfJQyQfJLVNdcLrj5Hfh1NQzvBa9OBPuP/7e/ZLHmwy2c/ctfdCpYT2BoEY5xgyjOKCLl/R0UFvrjiA0icuoZhFzWHbPQxf5JP7B6dQ7Tu/fhQGAg3Tr482j0BpKv/R3TbdWb5efP4xeezdcndihtRkRuIRmBvpiGgU+xi/9++i1N0gvI9rXi94DiPAqdfvi5innyoqFsiYsmMreQkMJi9gf7E52Tz6Dte7ll00/03buW1OJgVsT0YsjeRYSY+SRFtuDAnih8ik02N43j/dMGEdM6iJsuj6B3V2tM04FNmSx6eAUF2cX8flo35kfGcnEng6cHOal32/bChNchOQOeGAuXnFz/2xTiyNVLhPLGCXPLnetvXHVuw+kfAwzD8AESTNNsWtN1pDusetdijfWZa3+fCTyPlWn50M7CdAMuA9Bar1JKaaxsUbVBUBV2VPjeFhimlLrLY5oT+MMutwR0hXW2H+a2RUPgdMKbkyqddd7poZx3eh+gT7npPkCzSq6BDD8nMe+fwzBgWLk5scR1bEvG5D9xuApZ6+9LuH8+vsUuinxKAgyz9HkgxT5OlsS356V5b/Bv1Ak4cHNS6nLSfSPZ52hL84xUTtixjxP2HGBP95b0HBrDtReHExgQAXQBoLn9KXmyfTMgIt/Ne19mkJRSzD1DQzi5T2C5VkZ1CmP050MAK0V6VLVrCgufOtpbFaJBagDPBqrOcMBd7VIeJAg6BHtA9LVABJCglCqZ5cQKcj6krOtrvsf8UKC7PUA6/TA2XfGHuBPrrrMpVSy/B4ivMC0e2HIY2xbHkYBTWhIwz+o5PQco3Ozmg2/dBBUU0yIzj7D8Iv4J9APDwOF20/JADk8Mv4Fgcrhk5c+EFVtdVhOvu5X/dismb18RviqG3je1wT/M7xBbLhMY4OCW8ZH1tYtCiEbIMIxyzwYCgrCeHXRzbeqRIOjQRgKtgJOwAo0SvYAflFJDsN5fcjPwtcd8f2AF1gDpV+ugHS9hZZ2WAIuxgrCegKG11sAMuz0fAr8BY4H+SBAkaumCjg7mXgBL9/rSam8+8748QJqfQfMDmQzclEB6VAQJTayBwZtiW/PRJ4+xZsSpzHqhPbFBDf4qUQhxBEyjQQ0jHl/hew6wyTTNzNpUIkHQod0AfKO1/qfC9L1Kqb+A74E04F2tdaHnAkqpN+31jzgI0lrPV0pdB0wBOmNlitYCj9rzf1NK3Qq8C0QDs7HuKhOi1s5p7+Cc9mCa4XyoC0gOCuKWH/4mNL+I35vGli5X6ONH5pu3ccrV/bzYWiHEcaqfaZovVJxoGMZdpmnW+KHDMjC6kVJKvQv4yMBocSQmzC3mgw0QVFCI2rqH+PRcUqOiMA2DoACDL19t4e0mCiEOVi9p2df6/lDuXH/L8pFeS/8ahpFpmmZYJdMPmKYZVdk6lZFMkBCiSo8OcvLV1mIy8eP3rvEkpmTRMS0Hf1944Poan2eEEKJOeDwk0WkYxjDKB3ztgFo9K0CCoHqmlMquYtYfWuuzjmpjhKil+HCDzROdXPitm3X7Dbp13s/V0RsZPeocnE4ZAyTE8aSB3B1W8pDEAOB9j+kmsBe4tTaVSRBUzzweiHi0tzvRG9sVjU+TYAeLLrcGRM6ZswFAAiAhhFfYD0bEMIzptXlbfFUkCBJCCCFE9RrQtU9dBEAgQZAQQgghjjGGYYQBjwNDgBg8QjTTNFvXtJ4GddO/EEIIIRom0zDKfbxsGtZLx5/EemfmrcAu4MXaVCKZICGEEEIca0YAXU3T3G8Yhss0zW8Nw9DAHGoRCEkmSAhxSCm5Jn0/KCRgahGP7VbVryCEaJQa0lvkseKXDLucbRhGOJAEdKh6lcorEUKIKt3/6X5W7HdQ4DZYkRvLfxL6VL+SEELUr5VY44HAepn4NOANYFNtKpEgSAhRuZQMSMkgeX1qucmr94exP08eEi7E8aaBjQm6Dthhl28H8rBedl6ru8YkCBJClJNXZPLxjbNxx03A3XQCr30/nY7JiThcLsbr3/m/X78hNc/brRRCHM9M09xmmuZWu5xsmuZE0zTHmKa5rjb1SBAkhCjntl9dXPT2Z3zXZQCPDr+cbQVONsc0xe10sqZZKxymi06R3m6lEOJoa0iZIMNynWEYvxqGscqeNtgwjEtrU4/cHSaEKCf9j23M63oiF1x9GwBThp4NDut66d8WbVnTtBW9tpuc1c7r6XAhxPHrSWA48BLwpj0tAevOsM9rWolkgg6DUipeKWUqpVoeQR2PK6V+rst2CXGk1u83ycwqZlF8x9JphT6+5ZYpdvow+qsidme4j3bzhBBe1JAyQcDVwLmmaX6K9d4wgO1YL1GtMQmChBCYGbk89uEeun1QzPxWnXAbBk6XCwDDPDjYKTacvPbWRpYlmczf4abYbZ+DCovgxxWwYtvRbL4Q4vjjBEpeUF4SBIV4TKsR6Q4T4hjyv81u1qbCyHj4fQ84DLjhBINAX+uqzDRNZqwz2Z0FV3YzaBV26Ku1qctc3PubG7fpC0YcAMM3/ssrg4Zj2qcV0zj4WmnkuhU8+PHrDEh5go1xLTCw2tLyQCo/vD2TwOIi8q8fxuoJ53Ht98VkFoNhGDQJMvj6PIMt6Qab00zcJkQGgGnClnSIC4LzOzo4IRbeW23yZ4JJyzAY39VBt5iD92VpksmPO0wGNocz2hz+Nd2BPJNp/7pZmQL9m8GNvRwE+9XPle7PO938lQgj4w36NavZNszENPjwD2gWAVediuGQ61dx9DWA7I+n74GphmHcCdYYIeAprIcl1phhmo37Vlel1A7gbeB0oD/WLXXXa60XK6V8gP/DSqtFAsuB27XWa6qpMx4r7XYdcB8QBywErtNaJ3ts92Gt9cwK67TSWicopR4HTtVan2HPbwq8AwwG9gHPAe8CbbXWO+xlrsO6FbAVsA24X2s9357XB3gV6Am4gA3AOVrrtNoftVpr3L9EDcT7q91c+6OVlfExoNg+6qPaG8y+wAnAE4vdPL7YWqZVKKy7xklIFX/MU3PdxE4rn+XxLyog45Fr+LljT2646Hr2RERXuq5/URF/vfYULgz63fE4eJwcr1r2Bx98/j7rmzSjx71PY1Z466Jn2ysT4AOj2sEXHk/7CPODNVc7ywV1K5NNTprlotBlvTRo3kUOzmxb++DANE36THexMqVs2oh4gx8vdta6rurM2+bmnK/dmICfE5aNd3JC7KH/sJh5hdD9AdhuN/CBczGeGVPnbRONSr1EK/89ZUG5/7l3/znMa1GR/e6wj4CzAF8gH5gPXGmaZlZN6zleLicmALcB4cBPWAcO4F6sZwqcDTTFeuDST0qpsBrWeyVW0NIKcAMzj6CNs4BCu65TgSs8Z9oB0P3AOKyA7SHga6VUydMxX8f6BYjCCsrusuurd1lZWVI+CuWftpX9OD2DiIW7ygKZX3YUlZZ3Z8HW9Krr3FhJeOzjduPrdnH2hhXsDwo5eAFbga8vz5x2Lgs7dC0XAAF81dN6qrRvcfFBAVDFtlcmvxj+2FN+WmYhrEg2y7X/r0STQqvHDhP4aWtB6fK1ObbpBZQLgAAW7jZrtG5ty78nmKVXDIUuWLAtr9p1c9bvKguAABZuqJe2SbnxlOtLQxgTZBhGUwDTNDNN07wAaA0MANqbpnlBbQIgOH6CoLe01mu11i6s7EoHpVQ4cA3wnNZ6g9a6AGu0uQs4p4b1PqG13qu1zsQKqIYrpZrXtnH2AOvTgHu11pl2NumpCovdDjyptV6ptXZrrb8HFgBj7fmFWL8MrbTWRVrrJVrrnNq25XCEhoZK+SiUz+3oV1r28/if65n9OLt92SDm9hHQMbLqOntVkoHI8Q/k9lFX0efO58n38z9ovqfwvFzyfH0Pmh6XlUGOrx93jxpLZUnCML+DJpUT4gvDK7wDOjoAVFOjXPsHtzQItDv0HQac06msvbU5tpEBBic1Lb+9M+ONGq1b2/LweIOStw0E+sCIDoHVrhvcvQ10bubRuJ710jYpN55yI1fxidBvmqa5zDTNvYdT2fEyJijJo1wSGIRiZV22l8zQWrvtbqxWNax3RyXllkBiLdvXwv53l8e0nRWWaQu8rpR6xWOaD9YtgWAFdI8Ai5RSRVhZqSe01sW1bItooMZ1c9AkCNbuhzPj4Y8EcDrgim5lwcwD/R10jrKyQGM6GwT5Vn21FuJn8NPFDi781k1WWQKJ104966DsTkWheblEBRh8MvJMgnzAbUKx2xrTExYRQ6epb3BNLydXxxnc8EMxWYUmTZzFjO3rz93KyeJEk63pJm43RAaCyw3bMqBJEJzd1kGXaDizrcmSJJMWIXBJZwfNQ8q3qVuMwd/jnPyyy6R/M4OBzQ//yvTnS518sNrNqlTo1xSu7l4/14entXbwx1iDpXtNzmht0DW6+jYb/r6Yix6BT/6CpuEYl/Svl7YJUZ0GMiaoYiOGHkllx0sQVJXdQHzJF6WUw/6+u4brxwNbPcpQFpRkAcEeyx4qQ1SS/G+NNdanpOxpJ/CY1vqLyirQWm/H6vZDKdUTq2tsO/D+oXZAHFuGxzsYHm+Vu1Y+XIcLOtb8D/gZ8Q4yb7eW/3KTmycXu1mdWs1KQE5gEPe8PYzngirbVvnTyoWdSlI/ZZmaCzpWfzId181gXLdDL9Mz1qBnNWNqaiLUz+C2E+t+DFBlTm5hcHKL2rXZiAmFW0fUU4uEOKbU6RjU4z0I+hC4Tyn1O1Ym536sY/JdDdd/RCm1BuudJc8BP2utS7JA/wCXKaVmAYFYWZpK2QOlFwLPKqWuBQKAhyss9iLwuFJqM9aL4wKAE4FUrfUGpdRVwE/29tOBYqyuPSFq5OJODi7u5KDfjGL0Pmuaw+XC7Tw4OHh2MMRWGgAJIRqrBvDmeAAfwzCGUZYRqvgd0zR/rXFlddy4Y80UrMvT+ViDpv8FRthjfGpiJtZg6jjgd8oPZn4YawB2ElY31/PAyEPUdTnW3WEJWHeHvQgMAwoAtNbvKKUKgQ+wusaKsO5mu8de/zSsICoMSMMaaD2jhvshRKlBLQ30Putiy+10Wvevl0uDm0zoebyfOoQQXpJM+R6O/RW+m9TigYmN/hb5Y5VS6kzgWyBQa93Qf0gNvX2iFlJyTdq85SLPI4946raN7IqMJiw/n+TgYPY9Geu9BgohqlMvKZvnhv5R7lx//8JBDSI1dCTkcq6BUEr1xrrNfjVWpmcy8NkxEACJRiY2yGDn9Q7uWugmLR82rUhlwZvP4bQvmOZ37g5PPuDlVgohxJGTIKgKSqm1QJtKZu3UWnevh01GYnWHNQMygHnA3fWwHSGqFRvsYMY51pifK/5JKw2AAHrs3VPVakKIRqyB3B1WpyQIqkI9BTqH2t4CoEO1CwpxlHU+tyP7XgklLicLl2GQ0S6i9JkOQghxLJMgSAhxSHf1c3LTm8/Q85P5BIYV0uqSEKq5c10I0QhJJkgIcdwJ8jX4cHw4jL+EOXNq9W5CIYRo0CQIEkIIIUS1GmMmSJ52JoQQQojjkmSChBBCCFEtyQQJIY4/K7aBuhe6307sCrk9XgjReEgmSAhRpdV7Cpkwo5iEkfdwICiU3nt3cHvhDm83SwjhBY0xEyRBkBCichv30HTgoyxLSwMgMTSC0294lEf3nMTlXm6aEELUBekOE0IcxDRNfnxjObuCIkqnNc9K597fZrO1MJwil9t7jRNCeIVplP80BhIECSEOMvFHNyNbnsWki64rNz3+QAqnbV7DmDkSBAkhjn3SHSaEAGDTAZN3V7tpuXYz07PbgsOJbtWBB866jLt+/47w/FxO27qW07auZe7vfdgXcCFxI4/q22WEEF4kY4KEEI1SZoHJwFnFHCgwgA5gmJyxaRXX/f0zm2OaceMF1/LVzBdLlz93wwpc56yE1tHQoRk8MRZO7uK9HRBCiMNQ50GQUiobGK61/quu665rSqkPgWKt9URvt0UIb/hnr8nl37nYkwXhqQeY8/U7hOfl8MqpZzHj09cJKC4C4PWBI0gLDOK5oeezsH03zty0kifmf8HDnU9jQ2wL9nxmsmNpHln4oprCGW0M5m4zuSJxNTfN/hIjKgSiQ+CbpRAcgPu9m7mRnny2waTYhPgweLBoI+M+mAEdm8Fr10FYkJePjhDCk2SCakBrHVLXddaUUmoH8LDWeuZR2NZCYCBQBLiAbcBkrfVXHsucAzwA9LYnrQSe1VrP9VgmFngWGAmEA1nAv8AErXWSUmoA8AiggABgC/CU1vobjzqaAG8Cw4F84H3gQa21DNw4Vi1YDZPeAsOAN2+AoT2qXPTlf9y8oN20CYXWofD1FjCA63sZvHyas+ptfLmYmBs+4jP/IK4aezPZfgGcu345AH0Sd5QGQADNM9PocfdUEsOjAPi7TSfOW72Mmxf/yJ6wSD7oN5RtEbEEOBz8VhzGbwkmQYX5XPPYcxjFhQAsiu/MZTdN4a7fv+O6Uc8x69F3yPEPAGDtfhhvduKnVqfz6mfvY8xeTsjs+2Bwhe62l+eS/sp8xl0wiX/bdmB8Dyddow0e+9NNkyCYeY6TzlGN70QthKgfMjD6yDxlB33RwCfAZ0qpTgBKqQnAl/b0FvZnFvCFPa/ETCAU6GPX1ctex7TnRwGfAd2BSOAp4BOlVD+POmbZ/7YE+gMXAPfW7a6Ko+ryF2FTImzcA+NfqnKxLWkmdy5wk5AFfybCJxuhwAX5LnhlucmP26uIg/MKMMe/TJsDKfRO2slnM1/in5cfKJ0dUljA/sDg0u+pQaGlARBA/52b6Ju0g2ZZ6ag92+mSkkRyaAT7g8Nonr7fqqMgnyA7AAIILiwgISKGu0ZfxZ7wKEIK8su3yTB4/vtZhBYWEJKRhfuKl8vP35wId37As50H833TTiTmOXh+mcm1P7jZlQV6H9z6i8T9QtQXt2GU+zQG1WaC7OzKu8DpQD9gOzAO64/yU0As8AUwSWtdrJQygUFa60VKqXjgLaw/zKa97mVa6412V5QTK5NyIZAD3AOsB94BugAaGKe1TrTbcjtwI1ZAkYb1x/9hrbVLKTUHaA28q5R6E1istR6hlPLFCgiuApoDycD9Wusv7V30V0q9A1xit+FJrfVbtTmI9n5PA54DeiqlEoGpWFmfaR6LvqGUigOmKqU+11pnAycDY7TWyXZdycB0j7q/r7C5b5RSK4FBwDKlVFvgDKCD1joDyFBKPQc8bLen3mVlZREaGirluiznlQUP5BZWuXx+cVm0XJm84ip+RvhgFBaXLhd/IJkAV9n3LZFNiMvN5Pe2XemUmsiuyBgwTSszBQQWebQPCCosKC077BYlh0bw+sAR3PzXfIocTiaffmHpMh+eOIR9YREHtXdtXEuabFsHgJlTVmdWVhah+UVgmuT5+pVbxzPsyS02K99fKUv5OCqLmqtpJugq4CasTMRK4H/AMKysRU9gNDCmkvX+A+wC4oAY4Gqs4KXExcBXWNmOp7CCnyexMhlxWOf3JzyWTwDOAsKA84AJwEQArfUoe1sTtdYhWusR9jqTgfFYQU4YMATYVKENc+w23Aq8ppRqU8PjAoBSyg+4GSugW4kV2IRjZXkqmmHPG2h//x2YopS6XinVRyl1iP4LUEo1xQpAV9qTegEZWuutHostB+KVUmG12Y/D5fkfT8p1VH79OvD3tT6vTaxy+R6xBrf2MTCAZsFwYpPSRRjZFs5tb1S+bmggvHAVptNBRkAge8IjS+fn+/gyaNLjtHnwdYbc9AQd73+F104+Ex+3q3SZhe27M6PvIAC2RsXx5oAzrPYk7WLa1+8SXFQApslnvU7GBegW8cztdqK1/fxcnj39AgzTtAIrD9MGDsdlGBT7+uB8vez2/NDQUOjZBm49m3t+n0vnA3sBuLCjwSMDrO6/qAB4brCz7n8WUpbyMVauLyZGuU9jUNMxQW9rrdcDKKU+xsoEDdBa5wA59vgYRVm3TIlCoCnQzl5/VYX5v2qtv7PrnQ68AczQWifY074ESs+EnuNtgBVKqRlYGapKMzdKKQMrOBmjtS7ZdoL98WzDbLv8tVIqHWsMz84qj0aZh5RS99j7uQW4SGu9RSnV355f2YuWEu1/S/5cjcEKvq4BXgYK7CzZA1rrcv0FSqlgrKDxO631L/bkUCCjwjbS7X/DgMwa7IdoaMYNgTGnWmWfQ8bFvHK6kylDTPycYBgGeUUmhgEBPtWcpO4ajXHLWaxKMnhg6hbe++Q1QgrzuWP01eyNiildLNs/EPwrrGsYXDn2Fq67+AYKfH1ZdnEx3R5/k4DvNObJXci83Yciw8B0dMP50mf093Hw0kqTJ/6CqKggPjrL4MQ4gx3JRVzxq5MtaXBTH4OHbh6I652B+Pk6K9/vVybSaspVbPDzId9Vto8PDTDxdYKjkaTohRBHR02DoCSPci7g0lqnVJhWWRh6L9ag3jn2H/AvsQbsZlesV2udq5SqbFul9SqlLgPuAtrZbfcDlhyi3bFAMOUzP4faN7C6xGoaUj+ttZ5cyfSSY9MC2FphXnPPZexj8QzwjJ1RGomVLcoEHi1ZSSkVCnyH1Z13pUd9WViZJU8RHvPEsaqa4MeTv0fAE+hbi0DAz5dBbeDZezvRrdVLVXeteXSFeWoV68ebwx2oNr7w4S3l2+RRdgA39rU+nto19efPcu/gqME++/sCEOBx9vKvLuATQhyxxnh3WL0OjNZap2itb9NadwBOAYYC9x1OXUqpVljdS5OBZlrrcOB1KJeTqzgqMgUrkOp4ONs8AouxgpjKXrE0zp63uOIMrXWhnZX6mbI7ylBKRQO/YGWRLtFaew7IWAmEK6XaeUzrA+ywxwgJUa1BLR3MvsDBBR0NhreGg2KKSk5+Z7Qx2DzRh9PbyP0VQohjU70+LFEpNQZYCuzA6rIpxLqd/HCEYAVtKUCRfev4FVgDqUvsxSPg0Vqb9oDl55VSu4C1WNmZKI/usTqntc5WSt0LvKSUSgY+xgrWxmLdMn9HSTZMKTUV626w1VjHZzDWeKtn7PlNsYKif7Bum3dV2NZ2pdTP9j5OwLpT7X6q6CIUoirntndwbnur/M5KF9f/ZOJXVEhhhYHIHZMTGdo1kDcujqmkFiFEYyWZoNrrA/wGZGMFIMuBKYdTkT2m6DHgW6wxLw9gBQ+eJgPjlVJpSql59rSHgM+Bb7C6hxYCHQ6nDbVs79tYQc+VWF1uiVgDzMfa80o4gA+wurnSgGnAC8B/7fk3YA2Evhjrzq9s+/N/HnWMs+vZAyzDOkbP19OuiePAdb2c3NjL4MTdW/HxuGsMYOzKP3n78gicjsZ3QhRCHF8M0zzUDbZC1Ij8EjVCRS6T++/8iwu/nc3wSY+S7+tHXFY6kxJ+5/H3Lqy+AiGEt9TLFcpD564od65/em6fY/5KSN4dJoSolK/T4LlnFTsWfsfuyTeyumkrAo1Ccu85wdtNE0KIOiFBUBXsBy6Or2J2N631rqPZHiG8wTfIj47/PsX+TSn0LnaxeOPfuP3ktCHE8cg85vM+B5OzWRW01pOASd5uhxBe53AQ3SUOAPd2OWUIIRoPOaMJIYQQolqN5X1hnuQBH0IIIYQ4LkkmSAghhBDVaozPCZIgSAhxaAVF8Mc6aBHt7ZYIIUSdkiBICFG1omL2nf0832WF0WH/XnxGtaH49GbebpUQwgskEySEOK4k/7MbNfBaEiKsV2Tcs+AbogZGMsrL7RJCiLogQZAQ4iCZBW4u/NbNmq1R7IsILp3+bv8ziEsp4kEvtk0I4R2N8e4wCYKEEAe59I1UbnrvPYILC3ho5GX808p6s2qmfyABxY3vRCiEOD5JECSEAGDdj5u57leDLMOX236dzYVrlgHQd892mj76Dm6Hw/rI+waFOC41xidGy3OChDhOmabJ/jwTt2myLMlFv3+aszg2ntUxLZh08fVk+gcAEJuTRVBhgbWSYZDsCuHTNYW4Fq3H3ekWCsKvIP35OaX15haZZBdKoCSEaPi8kglSSmUDw7XWf3lj+7WhlPoQKNZaT/R2W4SoK7lFJiO+dPHnHvA1ICQrg9yQ8NL5LoeTd/sN465F83hjwHCyAwLLre8e/wpJOzbwbTfFQ1dcRoYjmGu/zKZXSz9emJdOVHYGo86L54qePnSMbISXj0Ich8z6eTm9V3klCNJah3hjuwBKqR3Aw1rrmUdhWwuBgUAR4AK2AZO11l95LHMO8ADQ2560EnhWaz3XY5lY4FlgJBAOZAH/AhO01klKqXhgO5ALlFyCp2utW9bTron6kJ0HC9ZA2ybQo02NV1uXarI1w2RoK4NQv8pPUoUuk083mOzKNGkRCsaSTYQsycG/XVeGbVvHvuBw0jyCIIDHzhzD2wOGE5ObzfCNK/m5Y09Mh5U8Hj/udgzTjdvhLF3+vR0BXPq/P9n4+TQCiouY+31fulxzHyPbO2gXDi1DDfyd4OeEpsEQ5meQWwx94wxabt4GSWkQGQK/ryOpWweWde1BnziDVmGHPvHuzzP5c49J12hDAi4hRK3ImKD695TWerJSyge4C/hMKdVNa71JKTUBeB24GzjHXn4c8IVS6mat9fv2tJlABtBHa52slGqCFRBV7HPorLVOqPc9EnUvtwBO+T9YtROcDvjsbrhoYLWrfbvFzUXfunGZ0D0aloxzElIhEHKbJkM/c/FXovX93gXf8vz3s7ga+LVdN5a3bMfrp4wE4Jy1mpaZB/jyhAHsDw4jwTC4Ri9k7L+LWdqiLZdedQ8YBqZhEFBUTJ89m1nctotVsWly8+IfCSguAuDcDcvpsXcX3zvi7ZZU/HW1vodTxF8vvEzXfdav7raoJpx021D2b3IT5gd/XuakR2zlwU1qroma6WJnJvg7Yf7FTga3kkBIiPogd4dVwc6uvAucDvTDykqMA7oDTwGxwBfAJK11sVLKBAZprRfZWYy3gP5YZ8XtwGVa6412V5QTK5NyIZAD3AOsB94BugAaGKe1TrTbcjtwI9ACSANmYWV+XEqpOUBr4F2l1JvAYq31CKWUL3AvcBXQHEgG7tdaf2nvor9S6h3gErsNT2qt36rNMbL3exrwHNBTKZUITMXK+kzzWPQNpVQcMFUp9bnWOhs4GRijtU6260oGptdm+6KBW77NCoAAXG6Y8VuNgqCZ60xcdmyxdj8s22syrHX5E1VCFqUBEMDVeiEAy1q256zrHqLQxxeAexbOZsp3VoL0zj++o/edU8jxD+SBc8bz5PCLuf3378vV233fbv544zHOu/pe5nZTgMm26DgG79gAQL6PL8kVskuVycCXb7qp0iBobtcT2R8cBkBmIXy92awyCFqw22RnplUucMEnG9wMbuWsdFkhhKioLgdGXwXcBERiden8DxgG9AJ6AqOBMZWs9x9gFxAHxABXYwUvJS4GvgKisAKqd4AngQvsdUzgCY/lE4CzgDDgPGACMBFAaz3K3tZErXWI1nqEvc5kYDxWkBMGDAE2VWjDHLsNtwKvKaVq3l8BKKX8gJuxArqVWIFNOFaWp6IZ9rySv4K/A1OUUtcrpfoopao6y/+tlEpRSi1USg2tTfuORFZWlpSPsJwdGwRB/qXf6dG6Ruv2iCkLDgJ9IM4n96BlYgMhwq8sC7OmaSsAfup0QmkABHDO+uWl5c4pSXRMSSr9nusXwDNnXAiGQbvUvVy4+m8+m/kSDtPk7PUr7KUMbj/vGt496TTmdzyBC6+8m71hkdREj727S8vd9+0uPy+m6uPQyj8XH4fnssZBy0hZysdbub6Ydha45NMY1GV32Nta6/UASqmPsTJBA7TWOUCOPT5GYWVmPBUCTYF29vqrKsz/VWv9nV3vdOANYEZJt49S6kvgupKFPcfbACuUUjOwMlSVZm6UUgZWcDJGa12y7QT749mG2Xb5a6VUOtYYnp1VHo0yDyml7rH3cwtwkdZ6i1Kqvz1/TyXrlFy3N7H/HYMVfF0DvAwU2FmyB7TW+UAqVsC0HPDFCvzmKaX6e+xTvQkNDZXyEZZDOreB+Y/Ce79Ah6Zw7/k1WvehAQb+Tgeb0kyu7u6gW/OQg5YJ9DVYdLkPt/xidRu9fcskmi2MIzMrGEwTDAPcbpJCI0rXTQiPYlt0HJWZsPQX7vrjewLtbq8/2nUF4JSt6/mrfReuu2RSueX9HBDmB0G+EOQDvk5oGw5+DoNAXzijmYtRGW1hVyi4XJy+YjufbJ3Hj2eczuBOAVzYyQFUfhwGxIcw+3w3n2806d3E4KbexhH/LKQs5WO9LGquLoOgJI9yLuDSWqdUmFbZT+le4BFgjlIqGPgSeNDuBipXr9Y6VylV2bZK61VKXYY19qYd1v75AUsO0e5YIJjymZ9D7RtYXWI1/Y17Wms9uZLpJcemBbC1wrzmnsvYx+IZ4Bk7ozQSK1uUCTxqzy/Zx0LgVaXUaKzMVr0HQaKOnNLV+tSCj8Pggf7VX5F1jzFYMKbkv3sYXHcVMftN9vztYns6ZCTnsbZJC+Z06cOGJi15/ZQzyfEPANPE6XbjclrJR7VrC+ev1Zx502OcsW4FK5q35ZueJwGwvXc3fj3HwS8rsuixchUJMXHk9+nAjb0NIgMO1UYn9Lm83JSx9qcmzmrn4Kx2NVxYCHHYGkv2x5PXB0bbgdJtwG1KqXbAt8B9wKO1rUsp1Qqre+lCYJ7WulAp9QJWBqqEu8JqKViBVEdgc+334LAtxgpiLsfq5vM0zp63uOJKWutCYLZS6mfK7iirjBsa4f2Mos50jTaYcbZ1CjiQF8bF2afyuvss0oNCrAyRnSUqCYA6Je9h4ZuPc9OFE7n7lk7sz+9CMxPaHzCJ8IfrT3DQJNhgSKtwGD3Im7smhBA14vUgSCk1BlgK7MC6A6oQ63bywxGCNc4pBShSSg0ArsAaSF1iL1bAA4DW2rQHLD+vlNoFrMXKzkTVZ1eS1jpbKXUv8JJSKhn4GCtoGYt1y/wdJdkwpdRU4BNgNdbxGYw13uoZe/4AIBvYgPUzvRJrXNP/1Vf7ReMSFWjw9T0tWbDbzUN/mKw/UD5+jszJYsmrD3Hbedcw68TBfNDBiaMRXhUKIarmboT/5RvCE6P7AL9h/RFfizWuZcrhVGSPKXoMK5uUjhVMfFJhscnAeKVUmlJqnj3tIeBz4BusZ/AsBDocThtq2d63sYKeK7G63BKxBpiPteeVcAAfYN21lgZMA14A/mvPb2u3PQNrjNEVwCit9T/1vQ+i8YgIMLigo5M11zh54CR7omkSkp/HFzNfJDI/l2v0QiJ9CyQAEkI0CoYp7wESR05+iRqhp/5y8cGvaax+4haCiwoB+LabYsqt41k0qbWXWyeEOIR6uUq5ecyGcuf61z/rcsxfDTWETJAQogF6ZKCTbQ/F8Oqdt7G8RVu+79KH2y+4hmtjN3i7aUIIUSe8PiboWGY/cHF8FbO7aa13Hc32CFEfJj42kPuH9Wd/Ptxa+CcxfgXebpIQwgvcjfBeGwmCjoDWehIwqdoFhTiGxQQZvDfSukNszpwML7dGCCHqjgRBQgghhKhWY3xOkIwJEkIIIcRxSTJBQgghhKiWPCdICCGEEKKRkEyQEOKQtqSZPL7Yja8DBhcFEOOb7+0mCSG8wC1jgoQQxxXTZP4Vs7hj0v30ePYjnk84wdstEkKIOiOZICFEldyf/cnEH77Fz+VCJWxjd3gU/Xf/BKcOg8gQbzdPCHEUyd1hQojjRkaBSZ+dXfB/9hNOv/4R8nx8ef77WcT+mwSXTfV284QQ4ohJECSEqNSTi12sckYA8GvHnnyohuLncmEA7p9WQbGLtHyTBbvc7MuR18cJ0di5jfKfxkC6w4QQ5RS7TW78ycW7q8tPTwyNZPqJg+mQupfe+3bx7go3Ty01Sc2DMD9YfLmT7jGN5MwohDguSBAkxHHs8w1uNqdDxwj4eaebdfthYxoUHsiBwOByyz5z+gW4nE4M0825a/8h/YP1pLbrBkBmIVw1z8WCMU5C/SQQEkIcG7wSBCmlsoHhWuu/vLH92lBKfQgUa60nerstQpTYmm7yonYTGQAPnOQg+DACj5d+z+XOpX74FxVy/4Jv6ZWdwaJTRpIa15LQSl6U6HJa7w8zDQfbo5pw3tplLIrvgumwetX/2Qenf+ZiacBS+Hkl7/Q7DZ3uy4UJazkzNwF+XwsuN5zfn3nXjOGbBF8GNje4uof0ygtxLDDlBap1Q2vttdtKlFI7gIe11jOPwrYWAgOBIsAFbAMma62/8ljmHOABoLc9aSXwrNZ6rscyscCzwEggHMgC/gUmaK2T7GWuBO4HWgMHgA+AJ7TWMlijkSlymQyY5SI1z/q+Kc3FZ6Nq/195wext0LQLH342jbErFwNw0eq/af/gq2QFBh1y3XVNW9F93+7SAKjEij3FnLnVj8CiXnyb3h6A95q1QL/yAL0TE8n18aO/6xSyZ+wnNTiMv/fv44mwULIjw4kK8eHc9rBuv0GHSEjJNSlwGTxwksHM9Sbb0uHG3gatwwwe+9NNkC88N9hBTCA8+IebjQfguhMMLugoQZUQomakO6z+PaW1nqyU8gHuAj5TSnXTWm9SSk0AXgfuBs6xlx8HfKGUullr/b49bSaQAfTRWicrpZpgBUQmgFKqF/A+cCEwB+gMLAD2AO8clb0UR82qVLM0AAL4eefh1dNr7RpmN+3Cf067gIDiIs5fu4yF7btT5HDicLlw25mfyrgdDuZ070ezjP0khUeXTi/28WF+597llnU5nVx4xd00y05n2tfv4XY42REdB8DKFvH2ipCaDlP/ATBhR8naJj/tNMkpsr79tNMkMgCSc63vCVkuBjQzeOkfs3T+umsMOkY2vitWIbytMT4ssU6CIDu78i5wOtAP2I71x7w78BQQC3wBTNJaFyulTGCQ1nqRUioeeAvoj/VHfTtwmdZ6o90V5cTKpFwI5AD3AOux/rh3ATQwTmudaLflduBGoAWQBszCyvy4lFJzsDIl7yql3gQWa61HKKV8gXuBq4DmQDJwv9b6S3sX/ZVS7wCX2G14Umv9Vm2Okb3f04DngJ5KqURgKlbWZ5rHom8opeKAqUqpz7XW2cDJwBitdbJdVzIw3WOd9kCy1nq2/X2DnYXqVZs2Hq6srCxCQ0OlfJTKRa7yxz/a331YP4vve/cHYHXzNowddzv6xfu5/PLbDsruGG73QdMAcv38ef3rd5gw5uZKnx/idLlwOZ043G62xzRle0xTLrrybnZHRB+07KGUBEAARe6yAAhgW7qbpsFlwVqxGxKyTJr6ZDeYn5eUpXy0y6Lm6jJvfBVwExCJ1aXzP2AY1h/insBoYEwl6/0H2AXEATHA1VjBS4mLga+AKKyA6h3gSeACex0TeMJj+QTgLCAMOA+YAEwE0FqPsrc1UWsdorUeYa8zGRiPFeSEAUOATRXaMMduw63Aa0qpNjU8LgAopfyAm7ECupVYgU04Vpanohn2vIH299+BKUqp65VSfZRSFS/RfwQSlVIXKKUcSqkewGBgNkeB5388Kdd/+cQ4g6GtrGl+Dnj1DJ8ar+tZzunYqnRaga8fn/U5pdJg5+uPXqBZxv6Dpp+1fjmdk5PKBUC+WAFZdE4mMz5+lT67t+L2WGdbdBMKfXxLvxtuz7kQ6FP+X4BR7SDA/j6kJUzqZW3PAO7u58ONvQyC7PkDm8PJzQ2v/4ykLGVvluuL2zDKfRqDuuwOe1trvR5AKfUxViZogNY6B8ixMxMKKzPjqRBoCrSz119VYf6vWuvv7HqnA28AM7TWCfa0L4HrShb2HG8DrFBKzcDKUFWauVFKGVjByRitdcm2E+yPZxtKAoqvlVLpWGN4atIR8ZBS6h57P7cAF2mttyil+tvz91SyTqL9bxP73zFYwdc1wMtAgZ0le0Brna+1zlFKfYCVHQrEyp49p7WeX4P2iWOMr9Ng/sVO/k2G5iHQIvTwTkYvDXNwwbdu8oph0ooF/N6u60HLNM04wDkbVtAxdW9Zt5dp8tiPn/HIr/9jWct25brOxnV3MKljMc137+fvM69jon8IqXnw+GITE2tQdZdIeOoUMBwGwZv3Qqg/WXExRAcYqKaw4YBBu3A4kA95xdCricGeLJPEbOjdxNr/Sb1MAn2gU5S171smGiRkQa8m4OdsHCdnIUT9q8sgKMmjnAu4tNYpFaZVFqreCzwCzFFKBQNfAg/a3UDl6tVa5yqlKttWab1Kqcuwxt60w9o/P2DJIdodCwRTPvNzqH0Dq0uspmH301rryZVMLzk2LYCtFeY191zGPhbPAM/YGaWRWNmiTOBRpdQ1wOPAGcAyoA3wiVLqKa31IzVspziG+DoN+jU7sjrObOsg5SaDnCJoktefgrOeZojPVfzduiMYBuG52ax48T583S5+fnsywyY9xp9tu4BhMPmMi3nr5DPZGxoB9hVhbABMHeYkMsAHOnSklce2bulj4jJNit0GTYPBKLmK7NT6oHadZO9XjMfY7BahBi08/sf1alI+0GkWYtBM3uIhRL1qLA9I9OT12yi01ila69u01h2AU4ChwH2HU5dSqhVW99JkoJnWOhxr4LHnj85dYbUUrECq4+Fs8wgsxgpiLq9k3jh73uKKM7TWhXZW6mfK7ig7EStb9bfW2q213o6VcRtVHw0XjUewn0GTYANiwvBf9hxLXunKK6c5iA6Aczb9S9OcTAB83S4uX/FH6XoD8/eR+EQMLwx10jYMRrSBtROcRAZUfpaMCjSIDXLQLMQoC4CEEMLLvH53mFJqDLAU636QDKxuI9dhVheCFdilAEVKqQHAFVgDqUvsxSPg0Vqb9oDl55VSu4C1WNmZKI/usTqntc5WSt0LvKSUSgY+xgrWxmLdMn9HSTZMKTUV+ARYjXV8BmONt3rGru5P4GWl1Ila63/sYHA88E99tV80UobBrSc6ufVEyB/YBtcnDpz2uJ31TVpay5gmXUKKMAyDu/sZ3N3P69dSQoijwN0InxPUEM5efYDfgGysAGQ5MOVwKrLHFD0GfAukYwUTn1RYbDIwXimVppSaZ097CPgc+AbrGTwLgQ6H04ZatvdtrKDnSqwut0SsAeZj7XklHFjP/UnGGjQ+DXgB+K9dzydYx+xTpVQWVlC5BqtbUIjDEnBiW/5+7V4SwiJxGQZNM9MYsfFfMAxGj473dvOEEOKIGaYpz9ITR0x+iRqpj9a4uGl2Hrcu+gFfVzFvnTqcGzps4anL+le/shDCW+olZTPm6p3lzvWffdjmmE8Neb07TAjRcLUJM8j1C+C5084HoENAOieFJHu3UUIIUUckCDoC9gMXx1cxu5vWetfRbI8Qde3UlgantYZfd0GAE66K2ejtJgkhvKQx3h0mQdAR0FpPAiZ5ux1C1Bcfh/VMonX7oWkwLPnlgLebJIQQdUaCICHEITkdBj1jvd0KIYS3NZanRHtqCHeHCSGEEEIcdZIJEkIIIUS15DlBQojjkjunkKx3/iX8t3RwyxMRhBCNg2SChBDVmnn+bywJiuTEBAc9/tkF53m7RUKIo83V+BJBkgkSQlRtw7YCJty4nUUBMZy6ciMxSYVE/p0EC1Z7u2lCCHHEJAgSQlTK5Tb5v6mpxG3cw4B128j1DSIlOIJ14Z355bE13m6eEOIocxtGuU9jIEGQEKJSRUUm6t8NnLArAbej7FSR6+dHUjIU7czwYuuEEOLISRAkhKjU1vfXsTYuggcuPYsNLawHBbmB/CCDzwcOZPrN2rsNFEIcVW6j/KcxkCBICAGA+dUS3M2uo7jdLfwyfSOD98cz/4Qu5Ps6CCgoAKwTRnaAP+0P7Obrpp1Yui7fu40WQogjIHeHNQBKqZHAa0Ac8JjWeqqXmySOMxtmbabjFS/gNN0UEkLspK+5p9eJrGjfliKnD/GpmaXLNjuQzm3/TueVU8ewdOFgTmgWxYYNOewPDuKUbv4E+Bi4XCYb/s0kJCWLVqfE4gj19+LeCSHqQmN8TpAEQYeglMr2+FpyFi8omaC1DrGXOxH4P2AQEASkAv8Ar2utf63Bpl4Bpmqtp9VFu4U4lLyFO9n/+gqymkcSdU8/tn+0mbSX/6az6cbEYB8dCcuDy5b8w3nLV/Hi2SPICA4kPCcPgGZ5+wAYsvUfvp4Rx/1f+vDTiV0JKsokI9SPyx378f9pJxn+AaSEhzDwnn/I6xCD49yOXDY6An9XMSHNggA4kO7C19cgNLjqpLQ7twhXcg75vr64itwExwbiG+yDKz0fM7cIn+ah9X/QjpDLbZKQBXHBEOBTd39ITLdJzt48AqL88Qlw1rw9hW5yU/IJjgvA4SMdAuL4JUHQIZQEOQBKqXcBH6311Z7LKKWGA3OwApk7gd1ACDACuACoSRDUDlhV1UyllK/Wuqi27ReiouKkbFZe9iMvnTaMzNxAely9ktNXraNr6n4SnW2Jc+3C9DgtBBUWMWDLVn7r2p6Bm1Zz8s519EzfAMDusKYs69EBE5P4dCtAapJdgHt9Ak1SMmhCBu337MMwIWhtMpszDB6aF4xatY3257Zkx6kd+eKHbHx84J4JUQzuF3RQewtXJZN4xido32jSg4IB8AlyMnx8M4oeWQAFLsLu6U/UlNOOwtE7PLlFJmd84eKvRGgZCr+NcdIu4sgDIVehmx8nLibxrxQCY/05e/qpRHYIq749qfnMvex3MnfkENk5jHNnDcI/3O+I2yMaP1cjuSPMkwRBR+4NYKbW+j6PaVnAV/anSkqp5sAmwAnMV0q5gb5YWSVfoAgYDXwG3KiUOh94BGgPJAGTtdazPOqbADwExALfAgZQXDFwE8cv154sfunQkcygQMBKbwcVFQLwY9uBmOZAIvLyODExoXSd2NxUXpr7LVF5meQQQTaxZPsG8cFJowEwTCjNkhsGW1o1pU3yAXbGRdFmX9lb50Ny89nbxMrabJ2bwC97wyEkiOJimDU7s9IgKOO/f5OS4yC9eXDptOJcF9nP/IV/gQuAzBf+Jvz+AThjDl6/IZi91eSvRKuckAWvr3Dz32E1z9pUZc+ifST+lQJAXkoBaz/cyqmT+1S73qYvd5K5IweAtI2ZbJm9m+5XtD/i9ghxLJI86BFQSnXCCkg+OZz1tdaJHtmmEVrrEK31Jvv7JcA8rIDmbjvj9B5wBxAFXAW8ppQabLdlEPA6MMme/xMw5nDaVVtZWVlSPkbKfr2aEBZddu2zOy6aXRFRFBsO9gcF4nI42R0dzUeDT2Z1qxYs6NGRUdvmEZVnjQnyoZADtKGwKJagAis56XC5wSx7lUZQfgH5vj4s79yW/WHWr7fbMNjZLIYTt1u/3obTwBFc1o6wEEelbXbGBuHrLqYit8cYIyPYFyPI1+vHtqpybGD5toc6C+ukfneAq1y9/lF+NVo3IKrC+KxAd520R8oNp1xfGuPdYYZpynuAaqKy7jCl1CnAIqCr1nqDPW00MB3r2thfax1Qg7pNYJDWepH9/UOgtdb6NI9l5gJLtdZPekx7FQjUWk9USr0DBGitr/CY/yew+ShkguSX6BiSl1XE1BeTWLUX/EN8iMrLI3LpDj4d2I27v1tCToAfP/Y/AQwDTJMzNv3NXX98DMB+WrIstjfJIaH82741G9s0o8veHYSlF7K2TRuCc/M5ed1GQoqzmH7aGTjdJlGZ2QS48ri3WLOreU+y9hXQ4+oOZHeK5cP/ZRAc6ODmcZE0b3JwYtqdVcD+G39k07/Z7PQPweVwEHdiNCdf357s//sV94F8Ip8aTOCZ7Y72YayVZ/5288VGN6qpwaunOfCvo3FBaz7YwuZvdhHVOYyTH++Nb1D1yX23y2Tps6tJWppKy0FxqLu7YTTCbo7jXL38QE+/IbHcuf6Xt5of87840h12ZFLtf1sCGwC01rOBCKXUqcAfR1D3jgrf2wLDlFJ3eUxzemyjJVDxwS3bj2D7opEKDPXloUdbl35PPVDMVc8GsS0ykgPB/kTlFNAi5QB7mkSDYfBz5wEUuRxsah5Lmz1FNE+xHpLYbUciPmYRY9asJRAXZwwMpc3oeFomZeK680fcW5x81f00fJpH8dCkaNq0OJGOFdryUtdDXyM4Qv2JnTmaWOCUCvNCfhh75AfjKHmwv4MH+9d94r3HNR3ocU2HWq3jcBoMeOiEOm+LaPxccneYqGATsA0YC/xcx3W7K3zfCXyotZ5SxfJ7gPgK0+KBLXXbLNHYxET5MO2hpnR9q4D/XDCY/psT8HGUPzXsCW9NUkRTovbvBsqeFH3lQD96/HxjhRrjcF56CuOAcfXeeiGEOHwSBB0BrbWplLoZ+FYptR/rWT8JQCDQv4439xLwoVJqCbAYKwvUEzC01hqYAfxgd6X9hhWY9UeCIFEDbcINFlzhz6y1PnQd3ZFTUlJ5anoWOYEBhGfn4nRZ40+2N29Cq+QDBBUUElxYQNtzW3m55UKIo0XeIi8OorX+ATgV6AQsB7KBtVjZ+zq7b1drPR+4DpiC1Q2XBLyIdTs+WuvfgFuBd4EDwEisu8qEqJH+zQxeOcOHG/s46TYkhsvyEjhzyUpOX7aGTknWs4FyA/2Zf1JPlvZsx7lTehHcv5mXWy2EEIdPBkY3YlU926geyC9RI1Rc4GLnd7tIu3I2e6KjeGvEsNJ5UfEBzHwoxoutE0IcQr3kbE65cW+5c/2fbzQ95nND0h0mhKiUj7+T9he25eE/zqZg7X4MtxvTfpv8VacFVrO2EEI0fBIEHQUVXr/h6Q+t9VlHtTFC1NKEu9pwy5NlQU/X1ikMH9jSiy0SQnhDY3xitHSHibogv0SN3KqN+Sz5N4+ctDV0aJHOqFGjvN0kIUTV6iVaGXDTvnLn+iXT4o75qEgyQUKIap3QOYATOgcwZ066t5sihPCSg5/dfuyTu8OEEEIIcVySTJAQQgghqtUYxwRJJkgIIYQQxyXJBAkhquRym7y72iQ1D67t2fiuAoUQNVfcCE8BEgQJIap0w09u3ltt3RDy/mp4Ic7Ax5CbAYUQjYMEQUKIKn2+ppj/fP8ZJyZs47PeJ5M21p9Y33xvN0sI4QXF8hZ5IURj99pyNy/+4yYtH9rv2sGzw84j39eP4MICkrcmcm3nbd5uohBC1AkJgoQQpdammtz6q7v0e1qr9nRITuR/0/9Lm/QUnh8ymh/junK+95oohPCSosaXCJK7w4QQZTIKPL6YJu1TkvjvnI8IKcxnT1gUT83/nD/XhrAj3c2qFBOXW8YHCSGOXZIJEkIA8O8+N++vLssCYRh0TUlkxJY1nD3hARZ07Ml1S36iyYF02r9bjBsHp7WC2/satI1w4OOArekmg1sahPk3wktGIY5zRY3wOUF1HgTZLwsdrrX+q67rrmtKqQ+BYq31RG+3RQhvmLPVTWKWSeHSLdyW2Y6KrxwKz88loLiI2/6cx4KOPXlnwHAGbluPX2ERZ2xaRdr2UM7b3QVwYWC9RO7s3N18FbGB9a3i2fjHLnxiQjj5tpNpHmKA203mZ3/z3r5QNnXowLje/pzasvGdWIUQx4Y6D4K01iF1XWdNKaV2AA9rrWcehW0tBAYCRYAL2AZM1lp/5bHMOcADQG970krgWa31XI9lYoFngZFAOJAF/AtM0FonKaUCgel2He2BR7XWkytpz1jg/+xlsoBXtdZP19kOC6/ILzZ5Zbmb3xNM2kfAzb2ddIqqXdCQkGXy1ko30YEGN/c28HUaFLlMHnhuPb2//BGn08n/nXcNBJTV63QVM2zrOqbMnQHA7oiY0nl/tesKwNzuiq8+msKBwGDWN21V+hbdtfn+jFrdhGZ/7mHy/C8p8PHl6l1BZJzSk6Yr1/FLZA9y/ANhA7y5vphzihJ5bc/vbO3amZ969mVIKwdntbN66otcJq//a7I/z+SGXg5ahkrAJIS3FHm7AfVAusOOzFNa68lKKR/gLuAzpVQ3rfUmpdQE4HXgbuAce/lxwBdKqZu11u/b02YCGUAfrXWyUqoJVkBU8jfFBBYD04BnKmuEUuoK4HlgPPAbEAjE1+2uCm+Y8IObTzaUjbv5eJ2LTROdRAbULBgodJkM+dTFtgwAk40HDN4Y7uTJWUk8+fhTBBcV8G/zNmQHBJZf0TDov3MTYfm5LGrTiQfPuvzgyg2D28+bQEJkTLnJO6OasDOqCQC/dDqBYoeD5NAI2Ac07VZuWYdp8u4zj7MtOo4RbcbgXgbPL3Pz48UwPN7Brb+6eWultf8fr3exfoITP6cEQkKIulFtEGRnV94FTgf6Adux/ph3B54CYoEvgEla62KllAkM0lovUkrFA28B/bH+mG8HLtNab7S7opxYweWFQA5wD7AeeAfoAmhgnNY60W7L7cCNQAsgDZiFlflxKaXmAK2Bd5VSbwKLtdYjlFK+wL3AVUBzIBm4X2v9pb2L/kqpd4BL7DY8qbV+qzYH0d7vacBzQE+lVCIwFSvrM81j0TeUUnHAVKXU51rrbOBkYIzWOtmuKxkr81NSdz7wor3/Bz2gRSnlwMokPaG1/sWenAWsrs0+HImsrCxCQ0OlXA/lxXtceN6/kJoPW9KgS0jN6kk3Q+wAyLI40SQrK4vsf3YQXGSNgm63P5nYrAxSQsNLl3M5nDTPSiO4qJBTd27i+e9msLBdd/aFhvN7++6lyx0ICqaigKJC8n39AEgMjzpovqfggjyaZmfwSZ9TcTus/TSBJUkwIDqLvxLLgrNtGbAvByIM7/9cpCzlhlyuL7mNcExQTe8Ouwq4CYjE6tL5HzAM6AX0BEYDYypZ7z/ALiAOiAGuxgpeSlwMfAVEYQVU7wBPAhfY65jAEx7LJwBnAWHAecAEYCKA1nqUva2JWusQrfUIe53JWBmSS+z1hgCbKrRhjt2GW4HXlFJtanhcAFBK+QE3YwV0K7ECm3CsLE9FM+x5A+3vvwNTlFLXK6X6KKWctdk20AkruGuqlNqglEpWSs1VSnWoZT2HzfM/npTrtnx+x/K/Dh0joVt0zetpHgL9mpatf157g9DQUFqc3ol9IVbQE1KQz5+vP8xpm1bhdLusafl5nLF5DQ+fOYabzp/A+WuWMePT1/j1zSe4d8G3DNq6jktW/ElsTlZp3T0Td/LEj59x+x/fl06LzcogPDebquT5+rMhtjnDN60iqNCK8f2dMDLeaud57ctOuv2aQvOQhvFzkbKUG3JZ1FxNu8Pe1lqvB1BKfYyVCRqgtc4BcuzxMQorM+OpEGgKtLPXX1Vh/q9a6+/seqcDbwAztNYJ9rQvgetKFvYcbwOsUErNwMpQVZq5UUoZWMHJGK11ybYT7I9nG2bb5a+VUulY4292Vnk0yjyklLrH3s8twEVa6y1Kqf72/D2VrJNo/9vE/ncMVvB1DfAyUGBnyR6ws0DVKemLuAgrQNwHvADMUUr11FoX16AO0UC9OMzB4JYmS5JM2obDJZ0cBPvV/GrM6TD49VInX2w0iQmCUe2t6557Rsfw09xnCHrvJ3rH+5Me05w2iw/QLmUv0XnZLGvVnhHX/R8HgkJJeXwi/q6yX6Pnv5/F8ubxfKSGcNfCuTxz2nlkBgYz95P/0nrvXvKdPszt2pd9bVszsrUfw5K34Y6NYFlkC3Znws+7oNANUUYRV4Qn8/60p7g7UaPjd/NHm84MbG7QM9baxydPddKvmZvUXLiks4HT0fiuRIU4VuQ1wv9+NQ2CkjzKuYBLa51SYVplYei9wCNYf5CDgS+BB+1uoHL1aq1zlVKVbau0XqXUZVhjb9rZbfcDlhyi3bFAMOUzP4faN7C6xGoaUj9d2SBloOTYtAC2VpjX3HMZ+1g8AzxjZ5RGYmWLMoFHa9CGkkvxl7XW2wGUUv+HlXHrBKyr2a6IhsgwDC7sZHBhp8OvI8TP4JpKXn46fEhTGHIFYPVzt5hgct0PLr7fYc3fGRVHdE5muQBoa1Qc7/cbytM/fkbf2daCg+8+lZwBTWh9yxT4ayO7Y5rx3+g4Brc0CPQNxUoYw7V2HYnZJqtSTPo2CaBJcEnSdThxQNdK2l8SuAkhRF2r17OL1jpFa32b1roDcAowFLjvcOpSSrXC6l6aDDTTWodjDTz2PLu7K6yWghVIdTycbR6BxVhBTCWjSRlnz1tccYbWutDOSv1M2R1l1dkI5FE2kNqTPMlO1FjzEIOrO5RPHO4PCuWREZfiNgyy/fwJKC4gz9eP3KbR1gLjh9D7wq6c0sKA8GAY2ZeOqhlntnUQ6Fv5ZWPzEIORbR00CW6El5VCNGKFGOU+jUG93h2mlBoDLAV2YN0BVYh1O/nhCMEK2lKAIqXUAOAKrIHUJfbiEfBorU17wPLzSqldwFqs7EyUR/dYndNaZyul7gVeUkolAx9jBWtjsW6Zv6MkG6aUmgp8gjWQuRAYjDXeqvROMKWUv72+A/BRSgVgZeOKtNb5SqkPgNuVUvOxBn4/Ze/roTJgQhxk5PY13PH7Hj7oNxTDNEkPCuGFIaOY3e1Epn/6Or327qJ5V4Ogb9+C3AIICay+UiGEaKDqO8/cB+uW7WysP8rLgSmHU5E9pugx4FsgHSuY+KTCYpOB8UqpNKXUPHvaQ8DnwDdYXUcLgXofNKy1fhsr6LkSq8stEWuA+Vh7XgkH8AFW8JKGdSv8C8B/PZYpyfYMwjoGeViDyEvcBSzCGpS9B2gDjNJaH27AKY5ToR1imfrdDNIfvYYn5n8BgGkY3P7nPAxg7WV96TzICQ6HBEBCHG+MCp9GwDBN6TERR0x+iRqTzxbB9IW4erTBL3Ysbo+z3bed5mEYMGrUKC82UAhRjXoJUYw7D5Q715sv1vLJrQ2QPCxRCFHemFNhzKk4gbO+Lua7bdbkk5tDI3xMiBCiphrhCUCCoCrYD1wcX8XsblrrXUezPUJ4w5ejnby7yqTIDdefYPDrj95ukRBC1B0JgqqgtZ4ETPJ2O4TwpgAfg1v6Nr6rPyGEgPofGC2EEEII0SBJJkgIIYQQ1WuEY4IkEySEEEKI45JkgoQQQghRvcaXCJIgSAhRtf15JmPnulmTYnJFd4NTzUaZERdCHKekO0wIUaXJS9z8vNNkby5MWWYydvPpbMiL8HazhBBe0fgeGS1BkBCiShvLPyCWPNOPt/dW9q53IYQ49kgQJISoUqT/wdO2FITzw3b30W+MEMK7Gl8iSIIgIUTV5mytZKJh8MRiCYKEEMc+CYKEEAcpXLmTSx7bRFZh5e/GXZVisjtT3psrxHFFMkFCiEatoAhG/4evrvqCr0Liq7wVzJ1bSId3XQz5tJi7FrjYn+vm1l9cnPu1i3nbDs4SbTpgcslsF2PmuNiWLsGTEKJhqPNb5JVS2cBwrfVfdV13XVNKfQgUa60nerstQnhDco7JhB9dLN4DUWYBd3/7Kdf89i95fQdhGlVfI+X7+YMb9NZ8Uv5Jpecj3zNmz2567N3NB/2GMe7sMcTGBhDiBxF+8Otuaz3DdNPlw9k8sek3uHAAPDGWRQkm1/zgIikHToiFT8910jqskVxmCtGoNL7/l4ZpNp6rMqXUDuBhrfXMGi7/IYcZBCmlFgIDgSLABWwDJmutv/JY5hzgAaC3PWkl8KzWeq7HMrHAs8BIIBzIAv4FJmitk5RSgcB0u472wKNa68lVtCkYWAW00VofzWdANZ5fokbg990mV81zke+C1053EOoH1/7oxm1C3yawaA90iwZ/p1UuKkncmCYBRYWMWbmYb3qchG9xMQ63C5ePD/uDwyrdVmh+Lp/PeJEBuzaTEB7FJePvYFtMMwqdPmVZJLP8w4UcbjfB+bm0yEonKSKK+NRk1sW1oMjpg6+rmI3P3U7bk1vDNw+Ab+W/xvN3uLn2RzemCW+PcHB2O0lqC+GhXqIV477yfeDm88f+1YqcOY7MU1rrECAa+AT4TCnVCUApNQH40p7ewv7MAr6w55WYCYQCfey6etnrlPyymcBi4HpgaTXteRbYXgf7JY5hE+e72JEJe3Pgyu/dXD3PTUIWJGbD3G2QXgCLE2HBbo8ACMAwyPfz56N+w8gIDCY1NJz49P2s+e/dDN2yptJt3fHH94zctJKI/Fx67Euge3IihT6+5bvRKnSpuR0OsoJC2BDXkgz/IFa2iKfIXqfIx5eXBp8L3y+HWb9XuY8l+7QnG66aJ4O0hTgqGuGYoGqzBXZ25V3gdKAf1h/ZcUB34CkgFvgCmKS1LlZKmcAgrfUipVQ88BbQH+uP+XbgMq31RjsL48TKpFwI5AD3AOuBd4AugAbGaa0T7bbcDtyIFVCkYQUVD2utXUqpOUBr4F2l1JvAYq31CKWUL3AvcBXQHEgG7tdaf2nvor9S6h3gErsNT2qt36rNQbT3exrwHNBTKZUITMXK+kzzWPQNpVQcMFUp9bnWOhs4GRijtU6260rGyvyU1J0PvGjvf35VbVBKDQYG2fs6tDbtP1JZWVmEhoZKuYGUXe7A0p+NG3AdQZ5uaZuOONwmCeHRlc53ut2H/H44XA7r2iw/J5cAe9pB+2iW7WOxu/JlpCzl47Usaq6mmaCrgJuASKwunf8Bw7CyFj2B0cCYStb7D7ALiANigKuxgpcSFwNfAVFYAdU7wJPABfY6JvCEx/IJwFlAGHAeMAGYCKC1HmVva6LWOkRrPcJeZzIwHivICQOGAJsqtGGO3YZbgdeUUm1qeFwAUEr5ATdjBXQrsQKbcKwsT0Uz7HkD7e+/A1OUUtcrpfoopZy12ba9/SCsYzfRbsNR5fkfT8reL087w0FUAAT7wtvDHbw13EG4P4T6weCW4DSgZwycGAeOkqs507Q+FfgVF+HvKqLAx/egeZgmL58yksVtOlHscPBVj5P4unu/0nriMg4QkpcDmPgUF4Fp4ldUWK4Kh9tNp5REDLcbTJMOKUk88Ov/YFBXAiaeWeU+luxTmB+8M8Jx1I6tlKV8LJTrT+NLBdV03MjbWuv1AEqpj7EyQQO01jlAjj0+RmFlZjwVAk2Bdvb6qyrM/1Vr/Z1d73TgDWCG1jrBnvYlcF3Jwp7jbYAVSqkZWBmqSjM3SikDKzgZo7Uu2XaC/fFsw2y7/LVSKh1r/M3OKo9GmYeUUvfY+7kFuEhrvUUp1d+ev6eSdRLtf5vY/47BCr6uAV4GCuws2QN2FqgmngHmaK21UmpoDdcRjdSZbR3sv8WBaZoYdldUesey6x3P6QXFbq6bb7JwF5zUFH5LMEnNNwguyKdZZhqTf/yU8Pw8fF3FB21n8Pb17I5qwim3TCbaz6RZmEFYNvgYcEoL+OisGEL87BPl0u2YN7xJjtvJ1bc+yHIzjLFdDP4zyBeDVqV9vwYt4dl3q31B2fkdHeX2SQghDkdNg6Akj3Iu4NJap1SYVlkYei/wCDDHHrT7JfCg3Q1Url6tda5SqrJtldarlLoMuAtoZ7fdD1hyiHbHAsGUz/wcat/A6hKraUj9dBWDlEuOTQtga4V5zT2XsY/FM8AzdkZpJFa2KBN4tLoGKKVOxcqO9a5hm8VxwqgikPCc7u/jYPrZZfNyCk3uWOBm6coCFr18P6EFVhwek5PFtpim1kKmydmbV/LRSycSE1TDq8H+nTD+nUooVur3oDbVrBYhhDc1wv+o9XoppbVO0VrfprXuAJyCNVblvsOpSynVCqt7aTLQTGsdDrxO+R9LxQEJKViBVMfD2eYRWIwVxFxeybxx9rzFFWdorQvtrNTP1DyoOQNoBexSSqUC3wJOpVSqUmrUYbRdHMeC/QzeOdPJyvui+fXth5nRdxBTBp/Lv83LeoibZaYxd93XNQ+AhBCigarX26iVUmOw7mjaAWRgdRu5DrO6EKygLQUoUkoNAK7AGkhdYi8eAY/W2rQHLD+vlNoFrMXKzkR5dI/VOa11tlLqXuAlpVQy8DFWsDYW65b5O0qyYUqpqVh3g63GOj6DscZbPVNSn1LK317fAfgopQKwsnFFWAOw3/XY/EC7vt7A/vraR9H4dT23K933daDYcJQbLzR2xSKM/17tvYYJIbyjEV731Heneh/gNyAbKwBZDkw5nIrsMUWPYWU60rGCiU8qLDYZGK+USlNKzbOnPQR8DnyD9QyehUCHw2lDLdv7NlbQcyVWl1si1gDzsfa8Eg7gA6y71tKAacALwH89ltkI5GHd/fWYXX7H3k6m1jqh5ENZN1uC1jqv/vZQNHadogzO7mCfIjy60NYM7QH9O3mpVUIIUXca1cMShdfIL1EjNXZOMZ9tLPvuwM1L8X9y68XDvNcoIUR16udhiQ9ml39Y4jMhx3xuSG6vEEJUqVt0+XPcZdFbiPfPrmJpIYQ4thzNVyscU+wHLo6vYnY3rfWuo9keIbzhjhMd/J7gZvEek/M6wsXuijc7CiGOG8d83udgEgRVQWs9CZjk7XYI4U1h/gY/X1r2/M45c7zYGCGEqGMSBAkhhBCietU8xPRYJGOChBBCCHFckiBICCGEEMcl6Q4TQhySaZp8vdkktwiC3Q58HUf+pnghhGgIJAgSQhzSDfNdvLPaKncL6Mezbf72boOEEN7R+IYESXeYEKJqqbkm760u+74uP4q8YjltCCEaBzmbCSGqNP6TrIPeSvxHdnOvtEUI4W1Ghc+xT4IgIUSl7lrg4se0oIOm/5HZlLwieVOKEOLYJ0GQEOIgC3e5ePGfCoGO/Z7BA3tM+n5QiLx3UIjjTONLBEkQJMTx7tXlbgZ9UsydC1zsz3Vz9ffFnPa5SWBhPpG5Ze8JG7BzEzNnvUREfh6OTYk897fLi60WQogj55W7w5RS2cBwrfVf3th+bSilPgSKtdYTvd0WIerabysyuO3XYAAW7THJmzafm5f8wgP5eTxw9jgeXPANj4wcQ2hBPhOX/MxDIy8jLD+XP9t1ZdafWfRuEkZkIBS5DGKdxfznzS2syQ+gz7DWvDDUyb/JJu0jTPKKHcQGmmxMgwAn9I6T6y8hjjmNJPvjyStBkNY6xBvbBVBK7QAe1lrPPArbWggMBIoAF7ANmKy1/spjmXOAB4De9qSVwLNa67key8QCzwIjgXAgC/gXmKC1TlJKjQPeqrD5QOA7rfXoOt8x0WBtPGCyfJ/JKS0MitywNMlkQDODthHlz15rN2Sw9T/zmJcZDKeeBUBYXg6zu57IW/2H0zdhG8/NnYmByfx3ngZgRbM2LH/lAT7ufQqL23ZmDaGc9bVpdZMZBhF5efz1xjRSgkO5Jf9qIte0t7dmAiaGaWIaBpgml3R283mb3Wyes56MApMeEW4C+ndgafsubM8wOTPeICKg9mfcTQdM/rH3v3VYIzxjCyHqlDwnqP49pbWerJTyAe4CPlNKddNab1JKTQBeB+4GzrGXHwd8oZS6WWv9vj1tJpAB9NFaJyulmmAFRCaA1noWMKtkg0qpcCDRXk8cJ5YlmQz+zEV+MYT5QbEbcosh1A/+Hueka7QVFPy6MpO2w+5jdFoK5xgGiRExzO7RjwIfXzIDrazQ8pbtuPbSG0kJCeX8NctICotEt2rPVfo3vu2uMA2PTI79PqH0wGBGX3Mfm2Mr3j1mzTdL3jtkGOT/bxnu6f+lo9u696zA4eS7Tj0Zde2DmBh0iQI93kmwX80DGc/9jwyAZeOdtI+QQEiIutP4/j/VSRBkZ1feBU4H+gHbsf6YdweeAmKBL4BJWutipZQJDNJaL1JKxWNlMfpj/VHfDlymtd5od0U5sTIpFwI5wD3AeuAdoAuggXFa60S7LbcDNwItgDSs4OBhrbVLKTUHaA28q5R6E1istR6hlPIF7gWuApoDycD9Wusv7V30V0q9A1xit+FJrXXFzMsh2fs9DXgO6KmUSgSmYmV9pnks+oZSKg6YqpT6XGudDZwMjNFaJ9t1JQPTD7G5K7CyRf+rTRvFse2bLW7yi61yZmHZ9KxC+H6bWRoErZm7idPSUgBwmiaXrPrLCoJ8/crVtysqFsPt5pO+g0qnvX7KyEO2YVdETI3aOn75HzjcZTff+7tdZAQEYdon2Q0HYGUKnNyiRtUB8O3Wsv1Py4f5O0xu7N34TtpCiLpTlx3zVwE3AZFYXTr/A4YBvYCewGhgTCXr/QfYBcQBMcDVWMFLiYuBr4AorIDqHeBJ4AJ7HRN4wmP5BOAsIAw4D5gATATQWo+ytzVRax2itR5hrzMZGI8V5IQBQ4BNFdowx27DrcBrSqk2NTwuACil/ICbsQK6lViBTTiVZ2tm2PMG2t9/B6Yopa5XSvVRSjmr2dwNwPta66LatPFwZWVlSbkBlPvGlf3Br/inv3NoXmk5pFssuR4Bz/IW7aiK6ajdKaJz8p5DzvdxuRi9ZilZ7Q8d3YT5QZxP2aDsmhyHrqH5pWWHYdK7iVHjdaUs5cZUrjeN8O4woy5uc7UzQa9rrafY388GvgOaaK1T7GmfA3u01ndWyAR9CEQD92mt11eo90MgVmt9jv09CCsTc6nW+gt72k3AdVrrPlW07QWgtdb6Uo+2lo4JUkoZWFmTMVrr7ypZv1wb7GkpWIHUt9Ucl4VYGa4CoBDYAjyjtZ5jj+OZCQRqrfMrrBcI5ALjtdazlFIhWMHXaKyxQwXAh8ADlax7ClbQ1EFrvf1Q7atDcq90A/HxejeL95ic3c4gtwgW7DYZ3sbg/I7lg5lvZ6yneOYfrIppwZzo9mQHBpV2Y/kWF1Hk43tQ3S3SU9njmemxxwJF5WTRITWJmJwsPp/5Ii8NOptZvU9lR/OWhAU68C8uIjQri+b7Uxi5Yy2hw3sw7tou+L77E9s/1uQVmTSJ8qPJkA68OmwUW7MdXNPDUS6oq6lP1rtZtMfk7LYG57SXwdfiuFUvIYrxWF65c735ROAxHwrV5ZigJI9yLuD6//buOzyqKn3g+PdNIEgXEFAQBRYRXFTAg4q9LIor2HAFLIDYy6r7E0QXxQKKZdF112XtDRV7WRQburgqtiMCdqUZQhFQNDSBJOf3xzkTboZMMgkzTMr7eZ48ufXc8947ybxzzrlzYwlQZFnjUvYbCVwDTDHGNASeBa4K3UAlyrXWrjPGlHas4nKNMYPxY2864uPLAT4so94tgYaUbPkpKzbwiVhpsZTmRmvtuFKWx85NW2Be3Lo20W3CuRgPjA8tSn3xrUX5wJi4fc8D3tiGCZCqQk7tmsWpXTfPn7x76dsdf0ZXOKMrA4C8lzey+skPyC4q4rtWbdhUpy4nz5rBqgaNeKvzXsX7nP3x2/xnD8NnO3dku00bGfrVB+zXrxPHHNCcHactJbdNW85oNpqmecto2KYp88+vy44NBf9nWB9ohe8hDy48ms4XHl2iXpdtZfyDu2YxuGv52ymlFFSBgdEhUboEuMQY0xF4CbiCLd/cy2WMaYdvXTkJeNVauzG0BJnIZvFPAViBT6R2A76veASVNgOfxJyK7+aLOi2smxG/k7V2I/AfY8w0Nt9RBoAxpjm+S29QGuqraqgH+uXwxX6H0LQe1MmC2csdOfnr2Xnik+w+6s7igc/PdduP4Z+8zTkfvcVJX3xMo0uOpuHAI30hZx7JLsAjhzvmrupG52ZUaFCzUkplQsaTIGPMQOBjYCH+DqiN+NvJK6MRfpzTCmCTMWZ//CDhaDfbMnzCA4C11oUBy7caY3KBL/GtM82ttXMqWY9yWWvXGGNGAn83xiwHnsA3YQ7C3zJ/Waw1zBhzOzAZ+Bx/fg7Bj7caH1fsUGAl8DJKVUC3lpsTlp0aCVzXh+uWZBUnQABftNmV/ztuGLPvuJwWB3akzpgBW5TTOEfo0XqbVFkpta3VwM81VaHTvAfwDrAGn4DMBG6rTEFhTNG1+NakX/DJxOS4zcYBpxtjVhljXg3LRgNPAy/ixwdNBzpVpg4VrO+9+KRnCL7LbQk+kRkU1sVkAQ/h71pbBUwE/gZMiCvyXOB+a61+la/aOllZDJtw5Jb/80R48KYzqPPaNVBvy3FDSilVnaRkYLSq9fRFVENN+KSQEe8UEf0IeEHTOUw8p2fmKqWUKk96BkZf/1vJgdHXVuIbTauYqtASpJSqoi7vlU2vdcuL57OKijg+b2YGa6SUUqmT8TFB1Vn4wsXTE6zew1qbuy3ro1Q6PFnvc879/kd+atCY6998mrp/rtBXZCmlVJWl3WEqFfRFVJMVFsLtU+CrRXzQMYeV3dvQv3//TNdKKZWYdoclSVuClFJly86GkScAsHLKlMzWRSmVOdU+5dmSjglSSimlVK2kLUFKKaWUSkLNawrSliClVHI2FWS6BkoplVKaBCmlyjfkTqh3Cn2GTKZR7i+Zro1SSqWEJkFKqbK9OpOXP8lntyvu5MCh19LwkbKeNayUqrEk7qcG0DFBSqkyffHlKvqfOQqy/GemgX0uYEGG66SUUqmgLUFKqYTyVjt6cVBxAoRzLNy+JXMXrMlsxZRSKgU0CVJKbWHWcsfz3xVyzE0/8BvZAAyc9T4Lxl/MJ/+4inGPLaawSL8jUylVvWkSpJQq4Zbp6zjmrhUMeKmIJvlrOO7zj2myfh2PPnkX7VetwOTNZ9jDkzh+3AIKN2zKdHWVUttKDRwTpEmQUqrYhPEzueios9hx9a8gwowOXXhj9+7UKSogp7CweLvGG9bzSoNdOOUvs1i3sSiDNVZKqcpL+cBoY8waoI+19oNUl51qxpiHgQJr7dmZrotSmTI9t4jl62Dy7A08cv0EGm3awPzmrYrX/5aTw285OYzuO4ixrz9Ffr36nH/S2WQXFvB8xx58NHYFaxs2pH+Xuuy5cB7fzl7JR/v04Pi969MoR/jDrlmYHf3HxkX5jvcWO3q0Erq0ECgqglc+BRE4dh//WymltpGUJ0HW2kapLjNZxpiFwNXW2se2wbGmA72BTUAhMB8YZ619LrLNscCVQPewaDZws7X25cg2LYGbgb5AU2A1MAsYbq1daoypDzwayvgdMMZaOy6uLrsDdwD74R9m+j5wqbV2YQpDVtXMinWOS98uYulauHJfYY8WwkXTCrE/QuuGMKZ3Fgtz19Js1IO0X7WCtnvuR5MN63HAkd/N4YW9e5co72+H9uexHgeR17QFRVlZIEL9jRtY3LQFAJPmQruft6eb5LNsxQZu/Kg+4Ljq3UL23wke7ptFz0lFrAvfudi1Odx6/z/p98G7AEzufTgjh1zApT2Fkftmlzj2f3OLGPehY4f6cOcRWezYMH3J0iVvFfLYV44W9eHlE7PYvUUKG8yLiuDqJ+C9b6DfPnDFiakrWylVYdodtnXGhqSvBTAZeMoY0xnAGDMceDYsbxt+HgeeCetiHgMaAz1CWXuHfWKjTh0wAzgX+DhBPSYDK4F2wK74ROrxFMWoqqmL3ypi8jeO6YscJ75UxGmvFDJlPixdC7OWw8ApRex8w6MM+/QdDpv/Fbe//CijjzqF3UbduUUClLNpIy8/MJ687XegKDu7uMVmfU694m06/PQjH/zrGqY+dCu3TH2ixP4fLoXTpm5OgAC+/hn+8MmHxfP9P53B4jVwxf8cbyzc3MW2eqPjuBeKeDvX8fS3jvPeSF/32wvfF/HPzxyrNsDcX6DPsyk+1v3TYPzz8O5XMGoSvGJTW75S6SRS8qcGKLclKLSu3A8cCfQCFgCnAb8HxgItgWeA8621BcYYBxxsrX3PGNMeuIfNLRQLgMHW2m9DV1Q2viXlJGAtMAL4GrgP6AJY4DRr7ZJQl0uBC/AJxSr8G/3V1tpCY8wUYBfgfmPM3cAMa+1Rxpi6wEhgKNAGWA6MstY+G0KsZ4y5D/hTqMMN1tp7KnISQ9wTgVuAPY0xS4Db8a0+EyOb/tsY0xq43RjztLV2DXAAMNBauzyUtRzf8hMr+zd8Kw/GmN8SVKETcKW1dl3YbhLwXIJtU2716tU0btxYp6vYdO6vBRDu7FpfAHlxd7VvKoL2q38qns8pLOS1rj2Zt8NOxOu1aB6vdenhW4Ai6hVsouPKZXzTemfO+vht2uavAuDo72ZTp7CAguzN/2LWbSwsrk/MZ23a0zv3ewBmtu1YvDxv9eZY8jfAmsj469xfN5eT6vO2eHXJO95Wrt88n5JjLf65RPm/zV3CdqksX6d1WlVIsi1BQ4ELgWb4Lp0XgMPxrRZ7AscBA0vZ7yYgF2gN7AAMwycvMSfj36yb4xOq+4AbgBPDPg64PrJ9HnAM0AQ4HhgOnA1gre0fjnW2tbaRtfaosM844HR8ktMEOBSIfuXtycCUUIc/A3cZY3ZN8rwAYIzJAS7CJ3Sz8YlNU3wrT7xJYV3so/b/gNuMMecaY3oYY7JL2ac8NwNDjDGNjTFN8Of5hUqUUynRPzydrjrTV+1fl3rh1TS4i3Bt76wSf/D9Ogp7jj+ODfV8a86Urj35rE17SjOjw+7cfmh/sooKAcguLOCABd/w5r1jmXP7SP43cQwrGm4+dtv8VZhF84rn62XBXX+oS5u4zvL+w0dx26H9ueuAozlx6AgAOm0PJ+0mxbG0bSyc2c1/6qybBX/tvTmxSvV5G9gli+bbFS9mZK+spPdNavrMI6Btcz/dpS3bnXF42mLR6do7nTY18O6wZMcE3Wut/RrAGPMEviVof2vtWmBtGB9j2LILZiOwI9Ax7D8nbv3b1tpXQrmPAv8GJllr88KyZ4FzYhtHx9sAn4UWjyPxrU1bMMYIPjkZaK2NHTsv/ETr8J8w/bwx5hf8+JsfEp6NzUYbY0aEOOcCA6y1c40x+4X1i0vZZ0n4HRt5OhCffJ0J3AlsCK1kV4ZWoGS8hm9N+wX/0pwDHJ3kvqqGOq5TFj+cK6z6DXZvDiJCn/ZCbr6jSY7QpQVkSXfIu4fln+QyfeL3uMiXIkabu5345UVZ2Rzx3Rzm7tCapx67gxZrVzO/Q3vWtGlF/TMP4441v2AWfservzeM+HNX9mrlmPuL0HsnYfvthLlnCfN/hWxx5GQLm4qa0eSUfjTPcezboClFRdC9tbBdnZL/YR/sm82ofR1N65HW8UAtGwh552XzTp6jfRPoksrxQADtW8E3/4SFy6HTTrBdTmrLV0pVSLJJ0NLI9Dqg0Fq7Im5ZaWnoSOAaYIoxpiF+jMxVoRuoRLnW2nXGmNKOVVyuMWYw8H9Ax1D3HOBDEmsJNKRky09ZsYHvEks2pb4xfpByEDs3bYF5cevaRLcJ52I8MD60KPXFtxblA2PKq4AxphnwFjABOBifBF0BvGuM2asCiZSqgVo3FFo33DzfppHQplFcErFDE1od040JTbPpO+JOcgtyuOCks9lUp+4W5YlzHDL/ayY/8Q9arc3nwQP7MPy9C+iMf+EuOHwoHyx1nLmjsFszf5zdmm3ev35d4fc7QImPkc1bArBvObHs3nzbfPSsX1fo2yGNx2pUH7pVqLFZKZUmaR0Yba1dYa29xFrbCTgQOAz/Bl1hxph2+O6lccBO1tqmwL8o2SgXP4pxBT6R2q0yx9wKM/BJzKmlrDstrJsRv8JauzG0Sk1j8x1l5fkdsD0wwVq7PowLmoCPuUuFa65qrwO60mfG5ZzwzkVs32jLBAige958rn3rOVqtzQfgyBM6lljfYXvh1K5ZxQmQUkpVZWl9gKoxZiD+jqaFwK/4bqPCShbXCJ+0rQA2GWP2B87AD6SOWUYk4bHWujBg+VZjTC7wJb51pnmkeyzlrLVrjDEjgb8bY5YDT+CTtUH4W+Yvi7WGGWNux9/d9Tn+/ByCH281PlaeMaZe2D8LqGOM2Q7fGrcJ+Ab4Gbg0lCX41rJ8fBedUhXSon4Wyy4SLp5WxL9nlxwo/Fm73zH66IFc8dMsGhzbg10vPypBKUqpGqcGfrZJ9y3yPYB3gDX4BGQmcFtlCgpjiq4FXsKPfbkSnzxEjQNON8asMsa8GpaNBp4GXsTfOj4dfzdVWllr78UnPUPwXW5L8APMB4V1MVnAQ/i71lYBE4G/4VtzYr4F1uO7u64N0/eF46wB+uF7I5aFnz8A/SLdjkpVSJYIE/tk8/7gbJoWbiheXqewgKO/nU3Om2Ooe90pNeY2WaVU7STO6UMQ1VbTF1EN9trBf+eX1QXMb9GaPt/O5v8GDOHdO/fKdLWUUoml5dOJjN9U4n+9u6putf8UlNbuMKVU9dd98QJ2XLCoeL7zLhszWBullEodTYISCF+4eHqC1XtYa3O3ZX2UypTmJ/SAO3wSNL9dG0743WL8N2IopWqVat/usyVNghKw1p4PnJ/peiiVaTkThsK+neCn1XzbfC1k69N2lFI1gyZBSqmyicCggwAomDIlw5VRSqnU0Y90SimllKqVtCVIKaWUUuWrgWOCtCVIKaWUUrWSJkFKKaWUqpU0CVJKKaVUraRjgpRSSilVPh0TpJRSSilVM2gSpJRSSqlaSZMgpZRSStVKOiZIKaWUUuWTmjcoSFuClFJKKZUSIrJQRLpluh7J0pYgpZRSSpWv5jUEaUuQUkoppdJHRIaIyOciMkdEXhCRVmH5ByLSK0xPFJEvw3QdEVkpIg3TXTdtCVJbTUReB3ao6H516tTZoaCgYGUaqpRxGlv1U1PjAo2tOtrKuF5zzvVNaYUAN6JOhduCQtfYzcA+zrmlIjIW+CcwEHgLOBL4BDgIWC8iOwHtga+dc2tTVfdENAlSW62yf2zGGGutNamuT1WgsVU/NTUu0NiqoxoU1+HAVOfc0jB/DzA7TL8FjBaRx4GfgHfwSVEH4O1tUTntDlNKKaVUJswAegLH4hOiWMvQkWE67TQJUkoppVS6/Bf4o4jsGObPAd4EcM5tAGYCVwLTgA+BA4G9wnTaaXeYyqR7M12BNNLYqp+aGhdobNVRdY5rmogUROavAt4UEQfMB86LrHsL6AV84pwrFJG5wALn3MZtUVFxzm2L4yillFJKVSnaHaaUUkqpWkmTIKWUUkrVSjomSKWVMaYB8BCwD1AAjLDWvlzKdm2Bx/B3Cnwff2uoMeYcYBT+O0tfBS6x1halufoJJRtX2LbUuhtjDgOmAt+FTTdYa/dLd90T1LEz8AjQAn+r6hBr7fdx22QD/wD6Ag642Vp7f3nrMi0FsV0HXAgsCZu/b629aNvUPrEk4zoKuAnYE/intXZEZF11v2ZlxXYdVfCaQdKxXQMMAgqBTcBfrbWvh3VJ/+9R5dOWIJVuI4B8a20noD9wvzGmUSnbrQHGAKfGrzDGdACuBXoDu4Wf09NW4+QkFVcSdf/KWts9/GQkAQruBv5lre0M/Av/XR7xTgM64WPoDVxnjGmfxLpM29rYAB6NXKcqw27VJQAADGBJREFU8WZKcnHNB84GbitlXXW/ZmXFBlXzmkFysX0M9LLW7gUMB54yxtQP65L9n6qSoEmQSreBhD/y8GnHAsfEb2St/dVa+y5Q2jeEngy8aK1dEVp/7gvlZlJScVE1616CMaYVvgVuclg0GehpjGkZt+lA4D5rbZG1dgXwIvCnJNZlTIpiq3KSjctaO9daOwvfYhCvSsacotiqpArE9rq1dl2YnYNvRW4R5pP936OSoEmQSrddgB8i87lAuwyUkWrJ1qm87TobY2YaYz4yxgxNfTWT0g5YbK0tBAi/l7BlPGXFUhWvEaQmNoBBxpg5xpg3jDG901nhJCUbV1mq+zUrT1W7ZlC52IYA86y1eWG+ql63aknHBKmtYoyZif+jLE3rbVmXVNpGcc0E2llrfw3dZtOMMYuttdNSVL5KjbuBG621m4wxfYCXjDFdrbU/ZbpiKqEacc2MMYcCY4E+ma5LTaVJkNoq1tqeZa03xuQCuwIrwqJd8N8gWhGxMmJ2ARZVsIwKSWFcCeturc2PHG+BMeZF/LelbuskaBHQ1hiTba0tDANm27DlOY7F8kmYj34iLWtdJm11bNbaZbGNrLVvGmMWAd3wzznKlGTjKkt1v2YJVdFrBhWILbRePQYcb639NrIqFf9TVaDdYSrdniF8O6gxZjf8N4O+VsEyngNOMMa0NMZk4b92/emU1rLiko0rYd2NMTsZYyRMNweOAmalv+olWWuXh+MODosGA5+FcSJRzwDnGGOywhiGE4Bnk1iXMamILdy5SJjujn/C9bdkUAXiKkt1v2YJVcVrBsnHZozpBTwFnGytnRlXTCr+p6pAW4JUut0GPGyMmYu/3fNca+1qAGPMDcASa+3d4RPRD0A9oKkxJg+431p7nbV2vjFmLJufJfMG/hNSJiUVVzl1HwBcYIzZhP9bfMRa+9I2jWKz84FHjDFjgFX4cQgYY6YCY6y1FpgE7AfEbue9wVq7IEyXtS7Ttja2m4wx++Cv80bgjGhLQwaVG5cx5iDgSaAJIMaYQcBZ4Xbran3Nyomtql4zSO71OBGoD9xjTPG3hZxhrf2cMv73qIrTx2YopZRSqlbS7jCllFJK1UqaBCmllFKqVtIkSCmllFK1kiZBSimllKqVNAlSSimlVK2kSZBStZSItBcRJyI7p/k454vIpMj8qyJyRTqPqUonInNFZFiS226T18e2ICL1QuxdMl0XVbVoEqRUOUSko4g8IyLLRGSNiCwSkRdEJCesHyYic0vZL9Hy08Kby7WlrJsuIhvCcX4Vkc9EZEB6Iks/EWkI3ABcF1vmnDvGOXdrxipVjnBtDsp0PWqDdJxrETlMREo8VNU5twH//TqJnjivailNgpQq31RgKbA70BjoDbyOf7JzZZwH/AycJSLZpawf65xrhH9q9GTgKRHpXMljZdrpwOfOuXmZroiq9SYDR4hIp0xXRFUdmgQpVQYRaYFPfu52zv3qvDzn3N3h02VFy+sKHAwMBXYCjkm0rXOuAP/NsdnAnqWUdZGIzIpb1kFECkWkfZh/KLRcrRaRr0Tk1DLqdp2ITItbNl1Ero7MdxOR10VkhYjkish4EalbRsgnAG8mKjPS5TI01G+tiEwVkWYicrOILA8tcBdF9h8WujZGicjSsM2EaD3Ki1tE9hKR10IcP8fiFpHZYZM3Qmvc/QnOVQMRuTMcY6WIvCgiu0TWTw91ei7UYZ6IHJ/oJEVi+ouI5IV9/iYiLUIZ+SLyTbTVRETqiMgYEZkvIqtE5C0R6RZZX1dEbo+cw1GlHPdgEXkvnIN5InK5iCSd3IvIABGZHVotZ4vIifExxW3/cOycJjrXIrIwxPVeWG5FpFdpZUSWLRSR00WkDfAqkB32XSMiQwGcc/n456Qdl2x8qubTJEipMjjnfgK+BO4XkSEiskdF3iRKcS4wxzn3Mr6F6bxEG4rvbrsI2ATMLmWTJ4AuItI9smwYMN05tzDMvwd0B7bHd0s9LCJ7VKbiItIK/wDK54G2+BaxPsBVZezWE/gqieIHAAfhHwbZHvgImId/uOSZwN+jSQb+AZK7AB1DPfoDIyPrE8YtIjuFON4Jx9oRuBnAObd32P8o51wj59zZCep7B7B/+NkVWAlMkZIte0OBCUBT4C7gERFpUMY52DXUt2M4F3/Gv6HfBjTDn/eHItuPxD9y4Y8hhneBN0WkSVh/JdAPOADoEGItfphvOB9TQ/ktgWOBi4EzyqhjMRE5AHg8HKcF8Fdgsojsl8z+5Zzr84FLgeb455lNjcRVVplL8B8sCkOZjZxzj0Q2+Rz/mlQK0CRIqWQcBkwHLsM//PBHEbkmLhnqICK/RH/wrTjFRGQ7/JtW7I3sAeAY2XLg6eiwfx5wPDDAObfF2CLn3CrgJXySQKjPUODByDYPOOd+cs4VOueeBOaEeCpjCDDbOXePc26jc24xMD4sT6QZkJ9E2WOdcz+HpPNlYJNz7j7nXIFz7lX8M5Z6RLYvAkY659aHrrZb8QkgUG7cZwBznXPjnXNrQywlWsDKIiJZ+PN8tXNusXNuLf610RXYN7LpU865Gc65IuBefDK0WxlFrweuD/WZjU98P3HOfeicK8Q/c66TiDQN258J3OKc+ya0St6Af5bUsWH9kLB+rnNuPTACiD4n6ULgGefcS+E8fYNP1sq6nlHDgOecc6+G6/QK8AIwPMn9y/KAc+5T59xG4Bb8uemXgnLz8YmVUoAmQUqVyzm30jn3V+dcT/wn9SuAMYTkI1jgnNs++oN/k4n6E9CIzQ9QnQqsAOJbG24MZbRyzh3gnJtSRvUeAk4NXUFHhPo9D/7NWkRuEJFvQ3fFL8De+E/9ldEBODAu0XsQ3wqRyCr8Ay7LszQyvS5uPrascWR+uXNuXWR+IbAzJBV3e+C7JOqUSEv8g36LHzbqnFsDLAfaRbZbGlm/NkxGY4i3PCRMMfHnIRZvrIx2cXUowp+HWB12DvPROiyPlNcBGBx3Pa/Fd9Mmo8Txg3mUPAeVtTA24fwDLnMJ13crNcGPx1MK0CRIqQpxzq1zzj2Mb1noXsHdz8WP7/lCRJbhW3qakXiAdDLeBDbgu4OGAU+GT/0Ag/EJ1gCgWUjMZpN4QPdqoGHcsjaR6R+AaXHJXtMwiDuRz4BKdb+Vo1Vc11J7/PmE8uNeSNktMuU9VXoF/py3jy0QkUZAK2BRMpVPkUVxdcgK87E6LI5b35CSCfAPwINx17OJc+73lTl+0DFy/PJeT5D4XEfrLfiuz9j1LVGuiNTBn/uYaCIZrxv+NakUoEmQUmUSP0B3vPgBwXXDYNQB+H+m71agnD3w4zxOxCdPsZ998S0pf6xM/UI3yaPAJcBJRLrC8J96C/Bv2lkiMhzfIpLIp0BPEdknxHkxvrUg5lHAiMhwEdkutLh0FJG+ZZT5IvCHCgdWvizgFhGpLyId8V09sbEf5cX9GLC7+IHVDUQkR0SidVxGGUlSaHF5FBgrIm1CMjYB+Ab4OEXxJeNh4AoR6RzGj40G6gCvhPWTgJEi8jsRqY/vMoz+z58IDBKR/pHX9h4icmiSx38EGCAiR4tItogcg38Nxrp7Z+GT1X7htXIicEhcGYnO9XAR6RlaOEcCDSJxfQocKf4mgHrAjUB0cP4y/MDo6GsXEWmM/3v7T5LxqVpAkyClyrYR/ynzeXwz+grgauAS59wzFSjnPGCmc26Kc25Z5GcO8AxlDJBOwkPAofguueib8CP4AcZz8a0Ce1BG4uacmw7cDryG74ZpDbwfWb8MOBx/x9dCfFfXC/hP/4lMAvYOiUoq/YBvGViAj/E1/Js8lBN3GDx7GH5Qdx7+TTM6qHo0cIP4O67uSXD8vwAWf7dRLr4L6biQlG4rt+Fv+34D+BHfHXpUuAsK/Hit14EP8ecpF3/eAHDOfYEfZ3MZ/novxydWSXWXOufex4+N+hv+tXArcLpz7sOwfh5+cPO9+L+dvsBzccUkOtf3Av8I5Q4EjnXO/RrWPY5PZGbiu99y8dc5Vq/vgH8DH4duvthA78HAf51z3ycTn6odxHe3KqVUeojI+cCBzrmk7jpKorxh+EHJ+n0vNZCILMRf38fK27YCZdYDvsAnql+nqlxV/dXJdAWUUjWbc+5u4O5M10PVXuHuubLGgalaSrvDlFJKKVUraXeYUkoppWolbQlSSimlVK2kSZBSSimlaiVNgpRSSilVK2kSpJRSSqlaSZMgpZRSStVK/w8rUfKZhs+gyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(k_shap_values[0], data_for_prediction, max_display=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "balanced-manual",
   "metadata": {},
   "source": [
    "# DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "false-emergency",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[20038    40]\n",
      " [   26   103]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20078\n",
      "           1       0.72      0.80      0.76       129\n",
      "\n",
      "    accuracy                           1.00     20207\n",
      "   macro avg       0.86      0.90      0.88     20207\n",
      "weighted avg       1.00      1.00      1.00     20207\n",
      "\n",
      "0.7557131631440027\n",
      "No Skill PR AUC: 0.760\n",
      "Logistic ROC AUC 0.898\n",
      "SpearmanrResult(correlation=0.756729812080893, pvalue=0.0)\n",
      "0.00326619488296135\n",
      "0.00326619488296135\n",
      "0.05715063326824428\n",
      "0.4850849130252479\n",
      "0.48516058688942565\n",
      "0.7567298120808928\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(198)\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sys import *\n",
    "OT_data=pd.read_csv(\"D://PhD_related/Dataset/LbCpf1_dataset_features_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "features = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[features]\n",
    "target = OT_data['Y']\n",
    "data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "\n",
    "dtc= DecisionTreeClassifier(class_weight={0:0.25, 1:1},max_features=239, max_depth=1000, ccp_alpha=1e-10, random_state=0)\n",
    "dtc.fit(data_train, target_train)\n",
    "\n",
    "Pred_labels = dtc.predict(data_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels.round()))\n",
    "print(classification_report(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels.round()))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels.round())\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels.round())\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "rms= sqrt(mean_squared_error(target_test,Pred_labels.round()))\n",
    "print(rms)\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aerial-newsletter",
   "metadata": {},
   "source": [
    "# LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "congressional-sewing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[20042    36]\n",
      " [   19   110]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20078\n",
      "           1       0.75      0.85      0.80       129\n",
      "\n",
      "    accuracy                           1.00     20207\n",
      "   macro avg       0.88      0.93      0.90     20207\n",
      "weighted avg       1.00      1.00      1.00     20207\n",
      "\n",
      "0.7986350336648574\n",
      "No Skill PR AUC: 0.804\n",
      "Logistic ROC AUC 0.925\n",
      "SpearmanrResult(correlation=0.8001864297297379, pvalue=0.0)\n",
      "0.0027218290691344584\n",
      "0.0027218290691344584\n",
      "0.05217115169453765\n",
      "0.5709040941877066\n",
      "0.5710156745282544\n",
      "0.800186429729738\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(198)\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sys import *\n",
    "OT_data=pd.read_csv(\"D://PhD_related/Dataset/LbCpf1_dataset_features_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "features = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[features]\n",
    "target = OT_data['Y']\n",
    "data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "\n",
    "lg1=LogisticRegression(class_weight={0:0.5, 1:2}, C=1, max_iter=300, solver='newton-cg')\n",
    "lg1.fit(data_train, target_train)\n",
    "\n",
    "Pred_labels = lg1.predict(data_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels.round()))\n",
    "print(classification_report(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels.round()))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels.round())\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels.round())\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "rms= sqrt(mean_squared_error(target_test,Pred_labels.round()))\n",
    "print(rms)\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "severe-jason",
   "metadata": {},
   "source": [
    "# Mismatch energy associated features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "whole-green",
   "metadata": {},
   "source": [
    "# AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "crazy-bundle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[20056    16]\n",
      " [   28   111]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20072\n",
      "           1       0.87      0.80      0.83       139\n",
      "\n",
      "    accuracy                           1.00     20211\n",
      "   macro avg       0.94      0.90      0.92     20211\n",
      "weighted avg       1.00      1.00      1.00     20211\n",
      "\n",
      "0.8334929842233965\n",
      "No Skill PR AUC: 0.837\n",
      "Logistic ROC AUC 0.899\n",
      "SpearmanrResult(correlation=0.8343537133321683, pvalue=0.0)\n",
      "0.0021770323091385877\n",
      "0.0021770323091385877\n",
      "0.04665867881904274\n",
      "0.6812611290003471\n",
      "0.6813127417555793\n",
      "0.8343537133321683\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(255)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "target = OT_data['Y']\n",
    "#fs_selector = VarianceThreshold(0.000019)\n",
    "#data=sfs.transform(data)\n",
    "#data = fs_selector.fit_transform(data, target)\n",
    "data_train1, data_test1, target_train1, target_test1 = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "class_names=np.array(['0','1'])\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "clf = AdaBoostClassifier(n_estimators=503, learning_rate= 0.98)\n",
    "#n_e=27, lr0.98\n",
    "clf.fit(data_train1, target_train1)\n",
    "Pred_labels = clf.predict(data_test1)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test1,Pred_labels.round()))\n",
    "print(classification_report(target_test1,Pred_labels.round()))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test1,Pred_labels.round()))\n",
    "precision, recall, _ = precision_recall_curve(target_test1,Pred_labels.round())\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test1,Pred_labels.round())\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test1,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test1,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test1,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "rms= sqrt(mean_squared_error(target_test1,Pred_labels.round()))\n",
    "print(rms)\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test1,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test1,Pred_labels.round()))\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test1,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dominican-interface",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 200 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8e706dc385c4f71b945b14c41210b50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "data_for_prediction = shap.sample(data_test, 200)\n",
    "#data_for_prediction = data_test.iloc[row_to_show]  # use 1 row of data here. Could use multiple rows if desired\n",
    "#data_for_prediction.head()\n",
    "#data_for_prediction_array = data_for_prediction.values.reshape(1, -1)\n",
    "#n1=model.predict_proba(data_for_prediction_array)\n",
    "#n1=model1.predict_proba(data_for_prediction)\n",
    "data_for_prediction1 = shap.sample(data_train, 200)\n",
    "#data_for_prediction=np.asarray(data_for_prediction)\n",
    "#data_for_prediction1=np.asarray(data_for_prediction1)\n",
    "#explainer1 = shap.Explainer(model1.predict_proba, data_test)\n",
    "#shap_values = explainer1(data_train)\n",
    "k_explainer = shap.KernelExplainer(clf.predict_proba, data_for_prediction)\n",
    "k_shap_values = k_explainer.shap_values(data_for_prediction1, l1_reg=\"num_features(600)\")\n",
    "\n",
    "#explainer = shap.Explainer(model, data_train.loc[:50,:], feature_names=data_train.columns)\n",
    "#shap_values = explainer(data_test.loc[:50,:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "finished-affair",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(k_shap_values[0], data_for_prediction, max_display=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "revised-skiing",
   "metadata": {},
   "source": [
    "# LnearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bridal-count",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[20066     6]\n",
      " [   40    99]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20072\n",
      "           1       0.94      0.71      0.81       139\n",
      "\n",
      "    accuracy                           1.00     20211\n",
      "   macro avg       0.97      0.86      0.91     20211\n",
      "weighted avg       1.00      1.00      1.00     20211\n",
      "\n",
      "0.8103528674534819\n",
      "No Skill PR AUC: 0.829\n",
      "Logistic ROC AUC 0.856\n",
      "SpearmanrResult(correlation=0.8184333497261822, pvalue=0.0)\n",
      "0.0022759883231903417\n",
      "0.0022759883231903417\n",
      "0.04770731938801783\n",
      "0.6667729985003629\n",
      "0.6671873342298662\n",
      "0.8184333497261823\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import LinearSVC\n",
    "import numpy as np\n",
    "np.random.seed(255)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "target = OT_data['Y']\n",
    "#fs_selector = VarianceThreshold(0.000019)\n",
    "#data=sfs.transform(data)\n",
    "#data = fs_selector.fit_transform(data, target)\n",
    "data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "class_names=np.array(['0','1'])\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "svc=LinearSVC(C=2, loss='squared_hinge', penalty='l2', dual=False, tol=0.0002, class_weight={0:0.122, 1:0.50})\n",
    "svc.fit(data_train, target_train)\n",
    "Pred_labels = svc.predict(data_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels.round()))\n",
    "print(classification_report(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels.round()))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels.round())\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels.round())\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "rms= sqrt(mean_squared_error(target_test,Pred_labels.round()))\n",
    "print(rms)\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "precise-brass",
   "metadata": {},
   "source": [
    "# Decision tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "finite-diesel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[20006    66]\n",
      " [   26   113]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20072\n",
      "           1       0.63      0.81      0.71       139\n",
      "\n",
      "    accuracy                           1.00     20211\n",
      "   macro avg       0.81      0.90      0.85     20211\n",
      "weighted avg       1.00      1.00      1.00     20211\n",
      "\n",
      "0.7084343682064109\n",
      "No Skill PR AUC: 0.723\n",
      "Logistic ROC AUC 0.905\n",
      "SpearmanrResult(correlation=0.7141961912309789, pvalue=0.0)\n",
      "0.0045519766463806835\n",
      "0.0045519766463806835\n",
      "0.06746833810299972\n",
      "0.33354599700072574\n",
      "0.3341194720588617\n",
      "0.714196191230979\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(255)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "target = OT_data['Y']\n",
    "#fs_selector = VarianceThreshold(0.000019)\n",
    "#data=sfs.transform(data)\n",
    "#data = fs_selector.fit_transform(data, target)\n",
    "data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "class_names=np.array(['0','1'])\n",
    "\n",
    "dtc= DecisionTreeClassifier(class_weight={0:0.25, 1:1},max_features=20, max_depth=10, ccp_alpha=1e-10, random_state=0)\n",
    "dtc.fit(data_train, target_train)\n",
    "Pred_labels = dtc.predict(data_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels.round()))\n",
    "print(classification_report(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels.round()))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels.round())\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels.round())\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "rms= sqrt(mean_squared_error(target_test,Pred_labels.round()))\n",
    "print(rms)\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "global-professor",
   "metadata": {},
   "source": [
    "# MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "maritime-animation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[20063     9]\n",
      " [   38   101]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20072\n",
      "           1       0.92      0.73      0.81       139\n",
      "\n",
      "    accuracy                           1.00     20211\n",
      "   macro avg       0.96      0.86      0.91     20211\n",
      "weighted avg       1.00      1.00      1.00     20211\n",
      "\n",
      "0.8100910063437146\n",
      "No Skill PR AUC: 0.823\n",
      "Logistic ROC AUC 0.863\n",
      "SpearmanrResult(correlation=0.8157104835685665, pvalue=0.0)\n",
      "0.002325466330216219\n",
      "0.002325466330216219\n",
      "0.048223089181596596\n",
      "0.6595289332503708\n",
      "0.6598303660778035\n",
      "0.8157104835685666\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(255)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "target = OT_data['Y']\n",
    "#fs_selector = VarianceThreshold(0.000019)\n",
    "#data=sfs.transform(data)\n",
    "#data = fs_selector.fit_transform(data, target)\n",
    "data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "class_names=np.array(['0','1'])\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "clf = MLPClassifier(solver='adam', alpha=0.0002, activation= 'relu', hidden_layer_sizes=[10, 10, 10, 17, 12], learning_rate_init=0.001, random_state=13, tol=0.2, learning_rate='adaptive', beta_1=0.9, beta_2=0.71, epsilon=1e-23, n_iter_no_change=20, validation_fraction=0.25, verbose=False)\n",
    "#n_e=27, lr0.98\n",
    "clf.fit(data_train, target_train)\n",
    "Pred_labels = clf.predict(data_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels.round()))\n",
    "print(classification_report(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels.round()))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels.round())\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels.round())\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "rms= sqrt(mean_squared_error(target_test,Pred_labels.round()))\n",
    "print(rms)\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defensive-frontier",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "revolutionary-conducting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[20055    17]\n",
      " [   37   102]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20072\n",
      "           1       0.86      0.73      0.79       139\n",
      "\n",
      "    accuracy                           1.00     20211\n",
      "   macro avg       0.93      0.87      0.89     20211\n",
      "weighted avg       1.00      1.00      1.00     20211\n",
      "\n",
      "0.7893613177708693\n",
      "No Skill PR AUC: 0.796\n",
      "Logistic ROC AUC 0.866\n",
      "SpearmanrResult(correlation=0.7917744578887825, pvalue=0.0)\n",
      "0.0026718123793973577\n",
      "0.0026718123793973577\n",
      "0.051689577086656044\n",
      "0.6088204765004259\n",
      "0.6089638452649602\n",
      "0.7917744578887825\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(255)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "target = OT_data['Y']\n",
    "#fs_selector = VarianceThreshold(0.000019)\n",
    "#data=sfs.transform(data)\n",
    "#data = fs_selector.fit_transform(data, target)\n",
    "data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "class_names=np.array(['0','1'])\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "lg1=LogisticRegression(class_weight={0:0.5, 1:2}, C=1, max_iter=300, solver='newton-cg')\n",
    "lg1.fit(data_train, target_train)\n",
    "Pred_labels = lg1.predict(data_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels.round()))\n",
    "print(classification_report(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels.round()))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels.round())\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels.round())\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "rms= sqrt(mean_squared_error(target_test,Pred_labels.round()))\n",
    "print(rms)\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accomplished-approach",
   "metadata": {},
   "source": [
    "# Sequence and mismatch energy associated features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "common-affair",
   "metadata": {},
   "source": [
    "# MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "separate-chambers",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [1 0]\n",
      "[[20044    26]\n",
      " [   23   114]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20070\n",
      "           1       0.81      0.83      0.82       137\n",
      "\n",
      "    accuracy                           1.00     20207\n",
      "   macro avg       0.91      0.92      0.91     20207\n",
      "weighted avg       1.00      1.00      1.00     20207\n",
      "\n",
      "0.8218840186300398\n",
      "No Skill PR AUC: 0.824\n",
      "Logistic ROC AUC 0.915\n",
      "SpearmanrResult(correlation=0.8219328923507336, pvalue=0.0)\n",
      "0.0024249022615925177\n",
      "0.0024249022615925177\n",
      "0.04924329661580871\n",
      "0.6398943115155349\n",
      "0.6398975847308143\n",
      "0.8219328923507336\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(47)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset_MM-SeqComb_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "target = OT_data['Y']\n",
    "#fs_selector = VarianceThreshold(0.000019)\n",
    "#data=sfs.transform(data)\n",
    "#data = fs_selector.fit_transform(data, target)\n",
    "data_train2, data_test2, target_train2, target_test2 = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "class_names=np.array(['0','1'])\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from random import *\n",
    "clf2 = MLPClassifier(solver='adam', alpha=0.0002, activation= 'relu', hidden_layer_sizes=[10, 10, 12, 25, 12], learning_rate_init=0.001, random_state=13, tol=0.0000002, learning_rate='adaptive', beta_1=0.9, beta_2=0.71, epsilon=1e-23, n_iter_no_change=20, validation_fraction=0.25, verbose=False)\n",
    "clf2.fit(data_train2, target_train2)\n",
    "Pred_labels = clf2.predict(data_test2)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test2,Pred_labels))\n",
    "print(classification_report(target_test2,Pred_labels))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test2,Pred_labels))\n",
    "precision, recall, _ = precision_recall_curve(target_test2,Pred_labels)\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test2,Pred_labels)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test2,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test2,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test2,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test2,Pred_labels.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test2,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test2,Pred_labels.round())) \n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test2,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fancy-soccer",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 200 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "303b808c65c94bfd886087894d688490",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "data_for_prediction = shap.sample(data_test, 200)\n",
    "#data_for_prediction = data_test.iloc[row_to_show]  # use 1 row of data here. Could use multiple rows if desired\n",
    "#data_for_prediction.head()\n",
    "#data_for_prediction_array = data_for_prediction.values.reshape(1, -1)\n",
    "#n1=model.predict_proba(data_for_prediction_array)\n",
    "#n1=model1.predict_proba(data_for_prediction)\n",
    "data_for_prediction1 = shap.sample(data_train, 200)\n",
    "#data_for_prediction=np.asarray(data_for_prediction)\n",
    "#data_for_prediction1=np.asarray(data_for_prediction1)\n",
    "#explainer1 = shap.Explainer(model1.predict_proba, data_test)\n",
    "#shap_values = explainer1(data_train)\n",
    "k_explainer = shap.KernelExplainer(clf2.predict_proba, data_for_prediction)\n",
    "k_shap_values = k_explainer.shap_values(data_for_prediction1, l1_reg=\"num_features(600)\")\n",
    "\n",
    "#explainer = shap.Explainer(model, data_train.loc[:50,:], feature_names=data_train.columns)\n",
    "#shap_values = explainer(data_test.loc[:50,:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "following-barcelona",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(k_shap_values[0], data_for_prediction, max_display=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "verified-portsmouth",
   "metadata": {},
   "source": [
    "# DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "residential-victory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [1 0]\n",
      "[[20033    37]\n",
      " [   40    97]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20070\n",
      "           1       0.72      0.71      0.72       137\n",
      "\n",
      "    accuracy                           1.00     20207\n",
      "   macro avg       0.86      0.85      0.86     20207\n",
      "weighted avg       1.00      1.00      1.00     20207\n",
      "\n",
      "0.7139492526815092\n",
      "No Skill PR AUC: 0.717\n",
      "Logistic ROC AUC 0.853\n",
      "SpearmanrResult(correlation=0.7139935956782119, pvalue=0.0)\n",
      "0.003810560696788242\n",
      "0.003810560696788242\n",
      "0.06172973916021549\n",
      "0.43411963238155493\n",
      "0.4341229055968341\n",
      "0.713993595678212\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import numpy as np\n",
    "np.random.seed(47)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset_MM-SeqComb_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "target = OT_data['Y']\n",
    "#fs_selector = VarianceThreshold(0.000019)\n",
    "#data=sfs.transform(data)\n",
    "#data = fs_selector.fit_transform(data, target)\n",
    "data_train2, data_test2, target_train2, target_test2 = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "class_names=np.array(['0','1'])\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from random import *\n",
    "dtc= DecisionTreeClassifier(class_weight={0:0.25, 1:1},max_features=240, max_depth=18, ccp_alpha=1e-10, random_state=0)\n",
    "dtc.fit(data_train, target_train)\n",
    "Pred_labels = dtc.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels))\n",
    "print(classification_report(target_test,Pred_labels))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels)\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round())) \n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "protected-compiler",
   "metadata": {},
   "source": [
    "# LOgisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "comprehensive-public",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [1 0]\n",
      "[[20040    30]\n",
      " [   21   116]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20070\n",
      "           1       0.79      0.85      0.82       137\n",
      "\n",
      "    accuracy                           1.00     20207\n",
      "   macro avg       0.90      0.92      0.91     20207\n",
      "weighted avg       1.00      1.00      1.00     20207\n",
      "\n",
      "0.8185184422083208\n",
      "No Skill PR AUC: 0.821\n",
      "Logistic ROC AUC 0.923\n",
      "SpearmanrResult(correlation=0.8189385322415085, pvalue=0.0)\n",
      "0.0025238778641064976\n",
      "0.0025238778641064976\n",
      "0.050238211195329174\n",
      "0.6251961201488221\n",
      "0.6252255790863364\n",
      "0.8189385322415087\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(47)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset_MM-SeqComb_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "target = OT_data['Y']\n",
    "#fs_selector = VarianceThreshold(0.000019)\n",
    "#data=sfs.transform(data)\n",
    "#data = fs_selector.fit_transform(data, target)\n",
    "data_train2, data_test2, target_train2, target_test2 = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "class_names=np.array(['0','1'])\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from random import *\n",
    "lg1=LogisticRegression(class_weight={0:0.5, 1:2}, C=0.1, max_iter=300, solver='newton-cg')\n",
    "lg1.fit(data_train, target_train)\n",
    "Pred_labels = lg1.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels))\n",
    "print(classification_report(target_test,Pred_labels))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels)\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round())) \n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "closing-decision",
   "metadata": {},
   "source": [
    "# LInearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fitting-pantyhose",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [1 0]\n",
      "[[20034    36]\n",
      " [   24   113]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20070\n",
      "           1       0.76      0.82      0.79       137\n",
      "\n",
      "    accuracy                           1.00     20207\n",
      "   macro avg       0.88      0.91      0.89     20207\n",
      "weighted avg       1.00      1.00      1.00     20207\n",
      "\n",
      "0.7887172259189708\n",
      "No Skill PR AUC: 0.792\n",
      "Logistic ROC AUC 0.912\n",
      "SpearmanrResult(correlation=0.7894223435784805, pvalue=0.0)\n",
      "0.002969268075419409\n",
      "0.002969268075419409\n",
      "0.05449099077296548\n",
      "0.5590542589986143\n",
      "0.5591066304430841\n",
      "0.7894223435784807\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(47)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset_MM-SeqComb_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "target = OT_data['Y']\n",
    "#fs_selector = VarianceThreshold(0.000019)\n",
    "#data=sfs.transform(data)\n",
    "#data = fs_selector.fit_transform(data, target)\n",
    "data_train2, data_test2, target_train2, target_test2 = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "class_names=np.array(['0','1'])\n",
    "svc=LinearSVC(C=0.2, loss='squared_hinge', penalty='l2', dual=False, tol=0.0002, class_weight={0:0.173, 1:0.80})\n",
    "svc.fit(data_train, target_train)\n",
    "Pred_labels = svc.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels))\n",
    "print(classification_report(target_test,Pred_labels))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels)\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round())) \n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aggregate-palestinian",
   "metadata": {},
   "source": [
    "# AdaBoostClassifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ruled-projector",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [1 0]\n",
      "[[20062     8]\n",
      " [   45    92]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     20070\n",
      "           1       0.92      0.67      0.78       137\n",
      "\n",
      "    accuracy                           1.00     20207\n",
      "   macro avg       0.96      0.84      0.89     20207\n",
      "weighted avg       1.00      1.00      1.00     20207\n",
      "\n",
      "0.7750844821101217\n",
      "No Skill PR AUC: 0.797\n",
      "Logistic ROC AUC 0.836\n",
      "SpearmanrResult(correlation=0.7848193972046549, pvalue=0.0)\n",
      "0.002622853466620478\n",
      "0.002622853466620478\n",
      "0.05121380152478898\n",
      "0.6104979287821093\n",
      "0.6109958211951598\n",
      "0.784819397204655\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(47)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset_MM-SeqComb_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "target = OT_data['Y']\n",
    "#fs_selector = VarianceThreshold(0.000019)\n",
    "#data=sfs.transform(data)\n",
    "#data = fs_selector.fit_transform(data, target)\n",
    "data_train2, data_test2, target_train2, target_test2 = train_test_split(data,target, test_size = 0.25, shuffle=True)\n",
    "class_names=np.array(['0','1'])\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from random import *\n",
    "ada= AdaBoostClassifier(n_estimators=255, learning_rate=0.741, random_state=0)\n",
    "ada.fit(data_train, target_train)\n",
    "Pred_labels = ada.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels))\n",
    "print(classification_report(target_test,Pred_labels))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels)\n",
    "auc_score = auc(recall, precision)\n",
    "print('No Skill PR AUC: %.3f' % auc_score)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc = roc_auc_score(target_test,Pred_labels)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)\n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels.round())) \n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "print(matthews_corrcoef(target_test,Pred_labels.round()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "grand-respect",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SO3h89IyU+24u683msj5t2ksG/Ideo2cnnFNctLO+uvruedvytpptvLL2FWrqE19J8vr61/nVS7+iuq6avqV92/RX1VZRVVeV1p7Xzr6Wj8z4CB+f+fGMapYkKRsMmFVQ//vgDwpdgiRJkrTLu3/cASn7l/Udxo1T35n1fd81fcSOz6viAOYnyI5PnzmWbx5x0o7vn77xRW5+YVnWa8lUTV0N/3rrX7y46kW2121POOam129Ka80N1RuyUFmD38z5DWdNPYsRfUa0P1iSpBwwYFbBrOw9iJ/sd0ahy5AkSZJ2WfWhiCdG7c2Wst5ZW3P0gAquOHZ3kt3K0L+ilEMmDWZI3/IW7bcveJsvPpa1Mjqkrr6OBRsXUF1bnbB/2dZl/Oz5n7Fy20r6lLY9dV1TX8Pm7ZtzXWanvbT6JU7oc0Khy5Ak9VAGzCqYTeV9uGfiIYUuQ5IkSeoRigKMGtArYV9dfeSAiYP45HG7U16S/FV6owf2anGdRaFE4OWyMv5TUU7ly7+HkvI2Yx5Z+ghz187t8JrVdYlD6O4g2bUckiTlgwGzCmZg71JO3W90ocuQJEmSdllFITB9dH9O2Xc0w/tXFLqchB5/+3HOv/v8Hd/frN5KrwnVlG7ZBEDFmOvp1b+Ui4r7E2IfIoEXK5oFynN/l++SJUlSMwbMKphxg3rzk7P3L3QZkiRJkgpoXdU61lWta9FW0huKy+oBKO61jJLegRcpBUoLUGHhfGCPD7DbgN12fL99we28uu7VAlYkSVJbBsySJEmSJGXZwaMOTtgeCMwYOoMPTP0A5cVtr/YAKC8up3dp23uzZ6+ZbcAsSepyDJglSZIkSTk3sHxgoUvIilMmn8K+w/ZN2FdRUsGsEbMY03dMnquSJKlwDJglSZIkSTk3a8QsepX0orK2smA1TBs8jeLQ9iWG9dQzddBUzt/rfAZWDEw4t7y4nH5l/XJcoSRJ3Y8BsyRJkiQp5/qU9uEX7/wFn3n4M6yvXp/VtQdXDOYDUz+QsK+sqIz9hu/HzOEzKS5qGy5LkqTOMWCWJEmSJOXFgSMP5OGzHuaN9W+wtWZrwjE/e/AN/v36GraveQV4kKqlH2Tb+n78pdd3KKmrajG2NEamfuJVyvoMzUP1kiQpEQNmSZIkSVLeFIUipg6emrR/UFERdZV9qa9ZBUBd1Vjqigaxf3EtpbXVbScUl+WqVEmS1AFFhS5AkiRJkiRJktQ9GTBLkiRJkiRJkjJiwCxJkiRJkiRJyogBsyRJkiRJkiQpIwbMkiRJkiRJkqSMGDBLkiRJkiRJkjJSUugCJEmSJEnqCjZV1bBqU3W74ypKixgzsBchhDxUJUlS12bALEmSJEnapVXV1DH37U3U1tUn7N+6vZZP/OVFNlfXdnjNMQN78d0z9uHwKUOzVaYkSd2SAbMkSZIkqdt6+e2NvF25LWn/dU8u4vH5a7O+77INlVz0h2d55HPHMHJARdbXlySpuzBgliRJkiR1ebV1kdIE7Wdf+xRb6J2VPf7f7Fs5eunz1IXilOO2lVbwuXd8lA0V/Xhs/hrOmDU2K/tLktQdGTBLkiRJkrqMmKS9tj5CgiuPi2I9vWsrU665x4alfP2J31Ac66lPtEjTWo27/3PiIUnHDK/cwIErX2PEtnVsqOjH6s3t39mcaze8dgMPv/Vwm/a+ZX05ZNQhnDDhBO+LliTljAGzJEmSJKnLuHfuihbfh1Rtoi4UUVcfqAlFbcZ/96FfMWHTyg6t/ciY/VjWN/WdyU+O2pv5A5OfSD5g5ascuPK1Du2XL7NXz2b26tkJ+/7++t85b9p5XHXQVXmuSpLUUxgwS5IkSZK6jG3b6wA4cOWrAPzo3z8DYDkDE46fQEO4/Ou9T0m57payXjww7gBI4yTvpGF9dnxeu2U7GytrOjy3K/nzq3/m4r0vZljvYYUuRZK0CzJgliRJkiR1OUX19QDcNukdLO4/ki+XXEcFbQPer9aezxPD92ZV78EdXnvWhEEUJwmaJw3rw2n7j+GQSUNatH/r7lf59SML0/gJsq9vad+M57685mWOGX9MFquRJKmBAbMkSZIkqcvYe0x/Xl62ie/POoeTFj3JP3ZvCEW/W/4b+oW2dy0/UHXAjpf8jRpQwfTRAxKu27usmIMnDebMWeMoK2l71UZ3cPCog7np9ZsymlsX67JcjSRJDQyYJUmSJEldxq8/dACHf/tBKksrdoTLAKXFAerbjj9p71GMGzWCk/cZxaRhmZ/w7Q6On3A8Z009ixvn3VjoUiRJ2sGAWZIkSZLUZYwZ2IvHrzqWy//8PC++tYHTZ47he2fsS/G3i2F72/HfPWMfqOif/0ILoCgU8aVDvsTFe1/MnDVzEp5Kvm7udcxdO7cA1UmSeioDZkmSJElSlzJmYC9uvfzwQpeRsZq6emYv3cBb69pe6dFaWUkRB0wYxPD+FR1ef1TfUYzqOyph372L7jVgliTllQGzJEmSJEkZmvv2Rm59YdmO78s3VvGde15Le53PvWsqlx8zJZulSZKUFwbMkiRJkiRl6M7Zy7lz9vI27UX1dQyq3tLu/OriUraU9eZ7987jiN2Hss/YgTmoMrmN1RuJMaYc06u0F+XF5XmqSJLU3RgwS5IkSZKUpoHVWxhSuTFp/1XP/Ym9177Z7jr1BC4/5lMsGjCaJxaszVnA/ObGN1m4ceGO77fNv43fv/z7Ds0tLSrlwJEH8o13fIOhvYbmpD5JUvdlwCxJkiRJUjtKigIAtUUN/xp9zdP/16F5P9nvjKR9o7es4cz5DzOw8aTzlqraTlaZ3E9f+Ck/feGnGc2tqa/hibef4LIHLuNv7/kbIYQsVydJ6s4MmCVJkiRJase+jSeL5wyZxHdmnUtF3fZ257w8ZBJL+w1P2j997ZucOf/hLFXY4PFlj2d1veZeW/caSzYvYUL/CTnbQ5LU/RgwS5IkSZLUjiP3GMahk4bw5MK1PDxuZlpzh/Yt4/ApO6+WmLdiM6+t2JztEgGoqqvKybpNVmxdYcAsSWrBgFmSJEmSpHZUlBbz+/86kNtfWsZzi9azva6+3TkTBvfm3XuPYq/R/Vu0/+xfb+QsYJYkKd8MmCVJkiRJ6oBeZcWcdeB4zjpwfKFLSaqkqITa+szuch5QPmDH5y3bt1AX67JVliRpF2bALEmSJElSN1ddW8fLyzZyxbTv8uO5n27TP6LXuB2fS4oCpcVFAPQv789how/jIzM+Qllx2Y4xF997Mc+seCb3hUuSuj0DZkmSJEmSurC1W6pZsm5b0v57Xl7Br/+9cMf34j4X0nv8/wFQX9OPrQs+x+ZY1mLO7sP7cv3FBzNyQEVuipYk9RgGzJIkSZIkdQGL1m5l0ZqtO75X1tTxsb+8wPxVWzo0P8R6QozEzVPYOvcbO9obzirvvO6iPhTxxqotfPpvL3LDRw7JUvWSpJ7KgFmSJEmSpC7gztnLuXP28ozmDqraxG8f+A69a6vbHfvEyOl87ZALeebNdVRur6NXWXFGe0qSBAbMkiRJkiTl1dsbq9Iaf8Erd/O++Y+kHFPe+GK/1weO5clReycdd9TSFxi7ZTUAtfWRLdW1BsySpE4xYJYkSZIkKY8efaMh4B1ctRGAbz1xLWsqBiQd33/7VraU9uJf42elXLe6qJRbphzFttLk9ypP3LSc3TZmdkpakqREDJglSZIkScqjVZsbrrEYuXUdALWhiOdGTE0559kR03hi9IwO77HXqP4M6lO64/szb66jpi5mUK0kSakZMEuSJEmSlEfHTB3GvXNXcu+Eg7nolbu44IQvsq5X8hPMre07bmDC9rLiwMwJg7jo8N0Y0b/lKeYDvn4/a7Zs70zZkiQlZMAsSZIkSVIuzLsbHv8prJgNsX5H869ipKq8HsqBs+AxPsnyOJh76g/k7/0voD60vRN5zMBevHff0Zxz0Pg8/gCSJLXPgFmSJEmS1H394iAIRYWuoq1Ny5J2FQG9Q8u2yWE5lxfdzuV7j4STvpfb2iRJyiIDZkmSJElS97V5F3th3Us3wru+BcX+67okqXvogn/MK0mSJElSD1W9EbatLXQVkiR1mAGzJEmSJKnr6zey0BVIkqQEDJglSZIkSV3ftPcWugJJkpSAlzpJkiRJkrq+Y74IVRth9o2wfUuhq0nfqH3h/b+D/qN3tv14H9i2pnA1SZKUBQbMkiRJkqSur7gE3vNDOPE7sGVloatJT3l/qOjftj34S8WSpO7PgFmSJEmS1H0Ul8KAsYWuQpIkNfKPSyVJkiRJkiRJGTFgliRJkiRJkiRlxIBZkiRJkiRJkpQRA2ZJkiRJkiRJUkYMmCVJkiRJkiRJGTFgliRJkiRJkiRlxIBZkiRJkiRJkpQRA2ZJkiRJkiRJUkZKCl2AJEmSJEnKrfJYzRXFt3JIeIXSUMutZVcDMPDP34fi0HJwaW+oqCxAlZKk7siAWZIkSZKkruTez0Npr+ytV1/P43U3QCksDYOoDiVML1rW0LciyZyRw6FXRfZqkCTtsgyYJUmSJEnqSl7+R6Er2HXUVMErt8FbT0Ftdeqx5f1h0lGwx7shhNRjJUk7GDBLkiRJkqTuadnz8Oa/oXpz27667fDET9Nb7+lfwWEfgxO+np36JKkHMGCWJEmSJKkQyvrA1kIX0YVVbYTFT8DW1Yn7H/sRrFuY/X2f+Bkc+jHoNyL7a0vSLsiAWZIkSZKkQtjtCFj/ZqGrSM/KubC9Lvf7LHkK/vXV3O+TzLLnYM+TC7e/JHUjBsySJEmSJBXCO69puOJh5ct533pu/QSurrmQ3114MIN6l8FTv4KX/97+xHuugqp27jLeFdTVFLoCSeo2DJglSZIkSSqEPkPgkocbQuY183K61dfufIXN1bUcWf8CfeqruHD754FA7ahZ0K8ctqzM6f5qtGY+zH+g/b/fRcUwen+YdAyU9c5PbZKUIQNmSZIkSZIKpbgUxh/c8OTQbXfdz5q67UxiKbuxHAgAvL2hku119YxZ9GhO9+8yyvrCu78NoeHn55nfwPIX259Xux2WPgMrX8l879fvhgUPpjdn/KFw3k1Q3i/zfSUpxwyYJUmSJEnaxdXHxO2n/uJxABZVtGzvuwXGbW47qWh9MVXVO6OE0t51FJclWbwQDr0CKga2bS+taAhrx8zaGS4DvH5v+wFz5Qa4/jR4+4Xs1dlRS56EF2+Agy/N/96S1EEGzJIkSZIk7eLWbd3e4bG11UVccmMxRTHRy/wG0vy1hOUDaph04upO19chI2bA6H3btheVwJgDYK9ToGJAdvZa8wasfr3h8/1XFyZcbrLkSQNmSV2aAbMkSZIkST3EUcteAuD3932zRfv8MHzH51gfKIqBu2cFXhkfWow7ZX0JY7cXAVA6v4b6zYFn6qcCsM+YAVSUFme54gAj94b9PwijEoTLufLQ1xuerqC24384IEmFYMAsSZIkSVIPM3fIbi2+71m8tMX3R/tWcOuhpazv1zJgfmjxRdRtmwLAJ5b9jQM2zeND268G4OH3H83EoX1yWHUOLHio0BVIUrdnwCxJkiRJUg+xrrwfg6s384NZ57Ro/1jFHS2+3zdyOOt7tQyXAU7YawSPzSlhc3VtTuvMm+2bC7v//h+EQY1h/+p5MOdvha1HkjJgwCxJkiRJUg9x3olfafF99ICGt/vdVnM8p9bf3+78/zp8N155c9uuEzB3xv4fgpKK9sclMmIvmHoS9Bu5s+3VOw2YJXVLBsySJEmSJO3i5lxzAjOuua9F232fOpI9RvRr+LL9EPjm6AJU1k0d/P/gxO8UugpJ6hIMmCVJkiRJ2sX1qyhl0bdPZtGarazftp39xw9qOaCsD3zhbbjnKnjhz9B/DNSsLUyx+XTS9+Guz7ZtH7QbFCWITPoOh6knwiGX5742SeomDJglSZIkSeohJg7tw0SSvIivrA+c8rOG596LYUV6AfPJP32UoqK29zYXFwX2HTuQy4+ZwkG7Dc6k7Nw56COw6lV47nc72y79N4zat3A1SVI3Y8AsSZIkSZI6bev2uqR9j7y+mmfeXMfNHz2MaaP657GqDnjPDxueGCG0DcglSakZMEuSJEmSpIz0376Vq5/6v5Rj6oqK+cNeJ/J232HcNWd51wuYmxguS1JGDJglSZIkSVKHjR7Qi8Vrt/H88D3YfcNSRmxbn3RsSaxjwuaVvDR0Mm/3HcbC1VvzWKkkKR8MmCVJkiRJUoedvM8only4lkfH7MejY/ZLOXZA9Wb+evdX81OYJKkgDJglSZIkSVKHnXfweDZW1nD9k4tZsamq0OVIkgrMgFmSJEmSJHVYCIHLj5nCR4+ezKaq2oRj7p27gv/+++w8VyZJKgQDZkmSJEmSlLYQAgN6lSbs611WnOdqJEmFUlToAiRJkiRJkiRJ3ZMBsyRJkiRJkiQpIwbMkiRJkiRJkqSMGDBLkiRJkiRJkjJiwCxJkiRJkiRJyogBsyRJkiRJkiQpIwbMkiRJkiRJkqSMGDBLkiRJkiRJkjJSUugCJEmSJEmSlMS8f8LNl7RtLy6D8YfA3u+H0l75r0uSGhkwS5IkSZIkdWWzb0zc/sL1MOcmOPdvUFKe35okqZFXZEiSJEmSJBXa1lWZzVv4MCx4KKulSFI6DJglSZIkSZIKbdnznZj7XPbqkKQ0GTBLkiRJkiQVWlEnbjGtr81eHZKUJgNmSZIkSZKkQjvww4WuQJIy4kv+JEmSJEmSCm3k3onbD/8kDJ/W8HneXfDKbXkrSZI6woBZkiRJkiT1KAtWb+HR11ezfltNu2MH9CrlHbsPZY8R/XJf2Fc2wO0fgxeuh2F7wll/gqG77+zftMyAWVKXY8AsSZIkSZJ2KbV19Un7bn/pbT79t5fSXvNbp8/gnIPGd6as9oUAp/684ZGkbsKAWZIkSZIk7RJueWEpv35kIa+t2Nzu2FFb1zB024Z2x63rNYBlfYfx5dte5pR9R9On3ChFkprzn4qSJEmSJKnLe33lZh59Yw0bKxNfa/HwvFXMXrqxw+v95OGf0K+mst1x24tKeP97vk4NJbz01gYOmzK0w3tIUk9gwCxJkiRJkrq0vzyzhM/fPKdDY6evWcgFr95DUUx+TQZAv5pK/jV2JvdOOCjpmKOXvchJi56iuL6e2iKork29piT1RAbMkiRJkiSpYG57cRn/9/giXluxiRjb9jcPdcdsXsWQqk0p1zt+ybPMWLuQF4btnnLc88P24J+7HcarQyYmHTN1w1sp15AkGTBLkiRJkqQ8WbxuKzc8vWTH93+9upJ/vbaqQ3OL6+v45UM/pKy+tt2xW0sq+MLhl3a4rpnjB3LE7sN2fL/txWUsWrutw/MlqSczYJYkSZIkSXnx8rJNfOGWtlddzFizgDPfeIiER5gbFcdIWX0td008hIfH7p9yn9W9BracWxTajAnA9DED+MgRu/GefUa36Htp6QYDZknqIANmSZIkSZKUU4OqtzBmy+qk/ccvfpaZK+fxxqCxKdeZO3gi948/kNcGT2h3z6IAPzprP07db0za9UqSOs6AWZIkSZIkZVVJUREAtaEhdjhv3v2cN+/+lHPWl/flU0d9osN7HLzbYA6dPCRh3+A+ZbxjylAmDevb4fUkSZkxYJYkSZIkSVm1//iBAGwt68V/H/7/2n0xH8CyvsPaHQMwdUQ/Lj92CqfsO7r9wZKknDNgliRJkiRJWTWifwWXHjWJXz+ykDnDpqQ19yvv3YtzDhqftL+itLiz5UmSssiAWZIkSZIkZd1V796TQycN4YkFa9lcVdPu+CF9yjlmz+HMmjAoD9VJkrLFgFmSJEmSJGVdCIGjpw7n6KnDC12KJCmHigpdgCRJkiRJkiSpezJgliRJkiRJkiRlxIBZkiRJkiRJkpQRA2ZJkiRJkiRJUkYMmCVJkiRJkiRJGTFgliRJkiRJkiRlpKTQBUiSJEmSJClzG7bVsGHN1oR9owZWUF5SnOeKJPUkBsySJEmSJEnd2A3PLOG7TzycsK+itIjTZ47la6fuTXFRyG9hknqELhcwhxDKgV6ND0AlUBVjrCpcVZIkSZIkSbn18rKNPPTaKlZtrk7Yv/vrizk/zTWrauq54eklDO1bzqeP36PzRUpSK3kPmEMIfYAZwD6Nf50AjAHGAkNTzIvAGmAZ8DawCJgDvATMiTFuy2nhkiRJkiRJnRBjTNp35+zlfOKvL1CffAgfLa6CUqhaX8LSxwcT6xpOJB/KS1wf30g456bdj+b2yUdw39wVBsySciLnAXPjieQjgeOA42kIllv/TkZHfkcjAMMbn/1a9dWHEF4EHmh8/h1jrMm8akmSJEmSpOx4eN4qfvzAG8xeuiFlgAwwftMKxm9embBvVNFqNhVXsG11GTVbSug3rpKi0nqW1I9gdpzUZvw7ls1m+to3uX3yEaxOcipakjorJwFzCKEYeBdwNnAq0LepK8W0dv4Ru3P5BOsUA7OAmcB/A5tCCLcAfwUeiDHWd3BtSZIkSZKkDttYWcPTC9eyduv2hP1zlm3khqeXdHi9q5/+A2O3rknav4zBDR9CZOSsjZRU1HNr7UR+UvuBNmP3Wreow/tKUqayGjCHEMYAlwEfYed1F63D4FRBckcC6GTzmwfPA4ALGp/lIYRfA7+JMa5Isb4kSZIkSVKHPbdoHRf8/hm2bq9rd+yETcu5ZM7tlNanHjuicj2Pj9qbP057d5u+s4sf5OKSewAoKqunpKLhPF1xUWDCkN4A1NZFlm2oTPdHkaSMZSVgDiHsB3wBOI2G08TNg+JEgXAmry1tL3xuvU/T+NHANcAXQwj/AL4ZY5ybwf6SJEmSJKkHu/APz3LQxIYTxJHIs4vWA9C7poqB1ZtTzj387TnMXP0GcwdPpLaoOOm4uYMnct+Eg1jSf2Sbvi3FfSgvrW3TfumRk7j0uGMAWLOlmgO+/kCHfyZJ6qxOBcwhhJnAV4D3NDU1/jVZ2JsriU5Jx1b9ZTRc2XFWCOFm4Gsxxjk5rkuSJEmSJHUzS9ZuS9r3zKJ1bdp++vCPGZPiWovmrjnkIraU9e7Q2H3HDuD9s8bu+L73wqcg8bv8JKlgMgqYQwi7Ad8FTm9qavxr61C3Izp693KbMjrY1zxsbrpG4/3A6SGEPwNfiDEuy7AGSZIkSZK0i1m4ZisAQyo3AfCh1+6lurg06fhhlRv4z/A9+Ne4A1Kuu66iX4fC5akj+vHBQyfwoUMmtOyoHWjALKnLSStgDiH0A64GPkbDieB0guX2guR0A+lUdzEn+p4oaP4g8P4QwneB78YYqzpYgyRJkiRJ2sUdtewFAN4//xHqU8QWEXh81AweGjezQ+teeuQkPn/StGyUKEkF1+GAOYRwDvADYARtg+VE/5Tt6N3LVcBKYB2wFtgKbG98IlBOQ5jdGxgCDG6soVeSPZNdz5EsaO5NwzUf/xVC+GiM8Z4E60qSJEmSpB6mKDbEBx846atsLuvT4XlnHzguYXu/ihIOmzKUo/cYlpX6JKkraDdgDiEMA34HnEzqYLm9QHkd8Bwwp/F5DVgcY1yZZs1NdQ0HJgB7ADOAvYEDgeb/lE4VOLcOmicC/wwh/B/wiRjj1kzqkiRJkiRJu4bvHnAun3vuhg6FyyHA+2eO5dunz6CkuCgP1UlS15AyYA4h7A/cDoymIYRNdBVGsusx3gIeAh4Gnogxvt7ZYpuLMa4CVgHPtqp5EnAIcAxwLLBb82m0Dcdbt10IHBJCODnGuDibNUuSJEmSpK7v26fP4Kqb5/D88Kmcc9JXd7TfeMkhCceHENhjRF8G9i7LV4mS1GUkDZhDCGOB+2m4kgJahrCtTwY3tT0O3AbcEWOcl/VqOyDGuBBYCNwAOwLn9wKnAkcCTX+MmKj+2Ph5L+ChEMK+McbNeSpdkiRJkiR1AWcfNJ61W7fzvXt3RhvPfek4hvYtL2BVktQ1pTrB/N80hMvJguWmE8BzgT8AN8YYl+agxk5pDJx/AvwkhDASOBu4ANi3aQhtf8ZAw/UblwHfzWvBkiRJkiSp4C4/ZgqXHzOl0GVIUpeX6lKg42l5yrd5CFsJ/B44JMY4I8b4g64YLrcWY1wRY/xxjHF/4ADgtzS8VDDZdR9n5rlESZIkSZIkSeo2UgXM45t9Do3PIuAzwOgY44djjM/ksLacijE+H2O8BBgDfJqGazWafs6moHlCgcqTJEmSJEmSpC4vVcBcxM7A9XkarpbYPcb4oxjjpnwUlw8xxs0xxh8DU4GzgP+w80Rz30LVJUmSJEmSJEldXao7mP9Ewx3MP48xPpSnegomxlgP3ATcFEI4BrgCWFfYqiRJkiRJkiSp60oaMMcYP5LPQrqSxkB9lw/VJUmSJEnqLv658J/8bd7fWLBxQbtjh/cezvHjj+fSfS+lKKT65W1JUmelOsEsSZIkSZK0w/wN8ykvLs/7vvcuupc/vfqnDo/fWL2RN9a/wabtm7jyoCtzVld9rGdLzZZ2x/Ut7WvQLWmXZcAsSZIkSZI65NvPfLvQJaTltvm38dkDPktxUXFW162rr+N7z32Pfy78JxuqN7Q7flD5IN4z+T18ZtZnsl6LJBWaAbMkSZIkSdolba7ZzNqqtQzvPTyteZW1lbyw8gWWbF6SsP+H//khlbWVHV5vffV6rn/lesqLy/nEzE+kVYskdXUGzJIkSZIkqYVAKHQJBbN8y3Iuuvcilm5ZmvW17110rwGzpF2OAbMkSZIkSWph6uCpPL3i6UKXkRMvrHqBOxbcweJNixP2P7PimZztvXzr8ozmPbTkIe5ffD8r334WRiY4jb3633DvxQCM7j2Boorh1FeN60ypktRhBsySJEmSJKmF03c/ndsW3MbG6o2FLiWp8/c6n4/M+MiO76fddhprq9a2GVdVW7XjOosnlj3BJx/+ZL5K7LCq2irWV61P2HfjvBv53cu/29nQq6LtoO2rYcXqxi/P0HtCCZVLLsp+oZKUgAGzJEmSJElqYfLAyfzhXX/gptdv4tV1r1If6wtd0g7Dew/nmHHH8J5J7yGEnVd5NP/c3Mm3nJyv0uhX2g+ASGRLzZZ2x1fWVnL141fz0JKH2F6/PWt1hKJaSgc/nrX1JCkVA2ZJkiRJktTGlEFT+PzBny90GR22pnJNwfbuVdKLXx33K2aNmAVATV0NM/80s8242vpafjvntzu+/+T5n+SspuKKZUBRztaXpCYGzJIkSZIkSe04a+pZCdvH9h3LMeOPYUL/CR1aJ5ehcgsh5mcfST2eAbMkSZIkSVISew/Zm18e90sGVQwqdCmS1CUZMEuSJEmSpB5rz8F78tkDPpuwb3Sf0YzrPy7tNWtjbWfLSun9m7dw4patAKwtLubK4UPbjAlF26EoUFS2jtKBz1BXVszfX294aeOUgVPYe+jelBQZC0nqPP9JIkmSJEmSdlnlxeUE2r4AcGSfkZww8QQ+uu9HKS4qzuqeMXbueoqRfUYmbN+tvojTF8/m3Vu37WhbXpy49lBcSVFJLcUVm6kYdTN1wFef3Nk/Y+gM/vf4/6V/Wf9O1SpJBsySJEmSJKnb++wBn+X7z32/RdvM4TO57sTr8l5LeXF5RvMGVwzm9+/6PZMHTk484NEfwCtPdaKyneasmcOvX/o1nzvwc1lZT1LPZcAsSZIkSZK6vQumX8DQXkO56tGrAPjwjA/ziZmfKEgtqU5EX7z3xQnbJ/SfwBFjj2Bor7bXXaRSQuanpZ9d8WzGcyWpSYcD5hBCX2AS0AcS/G5J11ADbAEWxRgrC12MJEmSJEnKn5MnnczJk04udBkAPHDGAxz39+NatP37rH9n/WWBQ+vqGVXcm+V129of3Mq22vTnSFJr7QbMIYQDgO8AR9F1g+XWYghhLnA98PMYY1WhC5IkSZIkST3HiD4jmHPBHBZsWEBJUQkT+k/IyT4B+HT/GVy14TnqYl1O9pCkVFIGzCGEw4H7gXK6T7gMDbXOoCEYPyeEcGyMcWOBa5IkSZIkST1M0vuUs+jdvccy7vBP8fBbD/PWxhXc8sIyAGL9i9Rv703NpvGU9p/TZt7iTYt5fuXzbdpDCEwaMIkB5QNyXbqkXUB7J5i/A1QAsfHpbgKwH/BV4JMFrUSSJEmSJClHpg+ZzvQh01mzpZq/3v0AALF2AXVVI6lefULCgBnggnsuSNheFIo4ZfIpXHPoNSnvlJakonb6Z9IyWA7d5GkSG7+f3sG/H5IkSZIkST1efazn1vm38sdX/ljoUiR1ce0FzNubfe5uV2Q0N7wgVUiSJEmSJBVYKM78ZX6PLXssi5VI2hW1FzDfx86wtjtekdFkRaELkCRJkiRJKoTOBMzrq9dnsRJJu6L2AuYLgdvoviFzoKHmmwtdiCRJkiRJUiHE2j6FLkHSLizlS/5ijFuB94UQPgt8o3F8073GKadmp7ysmAf8T6GLkCRJkiRJKoT6qvFJ+/Yfvj8AlbWVvLbutTb9b6x/g2eWP9OmPYTA7gN3Z2DFwKzVKal7ShkwN4kxfj+E8G/g78BY2g+Zu8J9zUuBG4GvxRg3FboYSZIkSZKkQilefzJ1g/7Zou2O0+5g4oCJAMxbN48z7jgj4dyL77s4YXsgcNqU0/jKoV+huKg4q/VK6j46FDADxBifCSEcRMN1E4fQNmRu/n0+cBJQk6U601EHbDJUliRJkiRJalC8+Z3cfOEl/PyFnzOx/0Qu3PtC+pX169Sakcgt829hj0F78MG9PpilSiV1Nx0OmAFijCtCCEcD1wFn0TJUDs2+TwYujzF+KmuVSpIkSZIkKWOTBkzih0f/MGHfym0rM173sbcfM2CWerD2XvLXRoxxO3Ae8A92hspNmofMHw8hnJSNIiVJkiRJkpQ7G6s3Zj63KvO5krq/tANmgBhjPXAO8E/ahsywM2T+fQhhWKcqlCRJkiRJUk4Nrhhc6BIkdVNpXZHRXIyxNoTwAeBpYDo7Q+XmgfMw4NfA6Z2sU5IkSZIkSTly2OjDkvYdOPJAALZs38Kr615t07+6cjXXzb2uTXsgMG3INPYbth+lxaXZK1ZSl5JxwAwQY6wMIZwBvABU0DZkDsCpIYSzYow3drZYSZIkSZIkZV8IgUv3uZRfz/51i/Z73n8PY/qOAWDO6jmce9e5beau3LaS7z/3/aRrzxw+k18d9yt6l/bObtGSuoSMrshoLsb4OnA1O1/216K7sf3HIYT+nd1LkiRJkiRJuXHF/ldwyym3cNz44/jovh/lqXOf2hEud8bzq57nhtduyEKFkrqiTgfMjX4MvNL4uel6jOaB83Dgy1naS5IkSZIkSTkwZdAUfnTMj7hsv8voU9qnRV9nrrl4bsVznS1NUheVlYC58aV/nyH1KebLQwhjs7GfJEmSJEmS8mvywMltQueOqqytzHI1krqKbJ1gJsZ4L/AELV/y1zxwLgM+ka39uoMQQnEI4cQQwv+GEP4TQlgdQtgeQlgfQngthHBLCOGSEMKIQtcKEEIoDyG8P4RwbQjhmcZ6q0MIlSGEZSGE2SGE6xprHl3oeiVJkiRJUv6UFpVy1UFXERKeL5TUU3XqJX8JXAPcl6C96RTzB4HPZXnPLimEcCbwHWC3BN0DG5+pwGk03FH9C+CbMcb1eSpxhxBCOQ3h/2eBYUmGjW58ZgDnAzUhhJuAa2KMb+SlUEmSJEmSVFCnTTmNqYOm8vjbj7Oual2b/pVbV3Lf4rbR0POrnuesO89q015EEdOGTOPsPc9mj0F75KRmSbmV1YA5xvhACOFp4GB2hsrNTzQPDyFMiDEuzua+XUljWPu/wH+lMa0XDeHu+0IIp8QYX2lvQraEEKYA/wD2SXNqKXAucFoI4ZMxxt9kvThJkiRJktTlTBsyjWlDpiXse27FcwkDZoBX1iaOO15e+zL3LrqXP574RyYPnJy1OiXlR9auyGimvZf5bcnBnl1CCKEY+CvphcvNTQaeDCHMyFpRKYQQJgH/Jv1wubnewLUhhI9lpypJkiRJktRdbdy+MaN5m7Zv4rb5t2W5Gkn5kPWAOcZ4P3AvO08uN51ejsBdMca12d6zC/kfGq686Iz+wO0hhKGdLye5xjD8JmBUlpb8SQjh8CytJUmSJEmSuqEFGxZkPHfe+nlZrERSvuTiBDPApcBydl6REYDnyfxkb5cXQtgPuDJJdwT+CBxOwx3HI4FTgSeTjJ8I/CC7FbZxITAzSV898FvgSGBo43NwY01VSeYE4CdZrlGSJEmSJHUjMcb2B+VgrqTCyUnAHGNcAkwHPgx8EXgfcPAufnr5GqA4Sd8VMcYLYoxPxBjXxBhXxhhvpyHA/UeSOR9sDK1z5aNJ2muAU2OMH4kxPhpjXNv4PBNj/CxwALAmydxZ+breQ5IkSZIkdT1Hjzu60CVIyrOsvuSvuRjjBuD3uVq/KwkhjAdOSdJ9d4zxl4k6Yoy1IYSLaAiah7XqLgI+D7R9xWonhRCGAPsn6f5ejPHOZHNjjHNDCB8Bbkky5HhgTidLlCRJkiRJ3dDUwVMTth83/jg+POPDALy0+iW+9cy38lmWpBzK1RUZPc2pNFwRkci3U02MMW4CfpGk+5QQwoDOFJZEqley/qED828DVibpG5d2NZIkSZIkaZfx2NmPtfj+wWkf5EfH/IjpQ6czfeh0JvafWJjCJOVEzk4w9zAnJ2lfCzzegfk30nDFRmsVwHuBP2VWVlLJrvIAWNze5BhjDCEsAkYk6O6VaVGSJEmSJKn7G1A+gDkX+MvNUk/hCebsSPayvMdijHXtTY4xvgasStJ9WMZVJfd2ir5EoXE645alWYskSZIkSZKkbsqAuZNCCKNoe39yk5fTWCrZ2EPTq6h9McbFwFtJuj/U3vwQwqHAxCTdD2dWlSRJkiRJkqTuxoC58/ZM0TcvjXXeyGD9zrg2SfvVIYTjkk0KIYwm+T3Ns4HHkvRJkiRJkiRJ2sUYMHfe6BR9K9JYJ9m1FRUhhOFprNNRPwBeSbQfcE8I4dchhCNCCENCCP1DCPuEEL4IzAH2SDCvBvh/McaYg1olSZIkSZIkdUG+5K/zRqboS3avciLrUvSNS3OtdsUYK0MIJwP3A1NadRcDlzQ+HVEDfCjG+GQWS5QkSZIkSZLUxXmCufOGpujbmMY6m1L0DUljnQ6LMS4CDgSuAzI9efwycHiM8cZs1SVJkiRJkiSpezBg7ryKFH3b01inOsM9OiXGuAH4FpndnbwJ+FKM8dmsFiVJkiRJkiSpWzBg7rzyFH2pQuPWalL09UpjnQ4LIZSHEL4LzAWOyGCJ/sCtIYTHQwjjOrjnJSGE50IIz61evTqDLSVJkiRJkiR1FQbMnZcqYK5PY52Q4R4ZCSH0A+4BPkfDncudcRjwfAhhZnsDY4zXxhgPiDEeMGzYsE5uK0mSJEmSJKmQfMlf56UKkdMJblP936IujXU66nrg6CR9VcAvgb8A8xrbJgCnAZcBoxPMGQrcEUI4MMb4dlYrlSRJkiRJnbJ263aue2JRwr5epcUctNtgJg7tk9+iJO0SDJg7rypFX1ka66Qam85dzu0KIXwAODVJ99vAiTHG2a3aXwZeDiH8Evgz8O4Ec0cD3wU+mK1aJUmSJElSdnzl9rkp+698955cdvTkPFXT1qbtm3hh1Qtt2otCEbsP3J3epb0LUJWk9hgwd962FH390linb4q+LWms0xFXJmmvB05LEC7vEGNcF0I4E3gOmJpgyDkhhK/GGN/IQp2SJEmSJClDvWqrmbh5BRM2LU85bmtJL9b0Hsh37nmNE6aPYPKwVBFF7sxdO5fz7z4/YV9JUQkX7X0RV+x3BSGkumVUUr4ZMHfeqhR9A9NYJ9XY9Wmsk1IIYSKQ7K7kW2KMz7a3RoxxSwjh6zRcs9FaEXAK8IOMi5QkSZIkSRnZWl274/Owyo0A/O+D7f8r+gXHf4FVfQbz9MJ1BQuYU6mtr+Xa2dcyddBUTph4QqHLkdSMAXPnpbpveEQa66QauzKNddpzUIq+O9NY5+/AH0h8z/RRGDBLkiRJkpR3VTVtXxX1jQM/lHT8Huvf4sz5D9OvppJVwLbttUnHZk0nDiA/uuxRA2apizFg7rwFKfrGp7HOhCTtdcDSNNZpz9gUfal+lhZijFUhhOVJ1kv0EkBJkiRJkpRjvcvangN7bMy+ScfXFhXD/FxW1NbUQYlu3OyYDVUbsleIpKwwYO682UAtif9eTktjnT2TtL8eY6xJu6rkUt2In+4fUyZ7weGANNeRJEmSJElZMG7wzn/t/8wRl7OprM+O7+cfuvNs2zNvruO1FZvbzP/6P1/lthcT/7L2hCG9OX3mGI7dM51f2G5rSK8hnDblNG6df2un1pHUNRgwd1LjSd7ZJL7X+OCOrBFCGEjiF+YBPJxZZUmlemFgqtPNiYxJ0t72/0NJkiRJkqS8+K/DJvKHJxbxypDddrT9/Nz9ec8+O3/h+NM3vpgwYAaYs2xj0vZ/zlnOT8/en/fu27lfXr7m0GuYNngaT7z9BBur2+63cftG3tz4Zqf2kJQfBszZ8U8SB8z7hxBGxRhTv64VTib5DUQPdqqythal6HsncFNHFgkhvAPolaS7w1dtSJIkSZKk7LrmlOkcMmkwX7zlZYb2Lefb75/B/uMHtRjzwKsNr3vqXVMNwIdfvoMFA5OdI2vw5MjpzB06id8//manA+biomLOnXYu5047N2H/Q0se4uMPfbxTe0jKDwPm7PgHcHWC9iLgI8D/tDP/0iTtG4F7U00MIZQApwJ7AG8Ct8QYq1NMeQaIJA60zwkhXB1jXN1OvQCp/in/VAfmS5IkSZKkHHn33qN4996jkvZvqmq4JXPG2oYzYvutmc/U9UuSji+vq2Hc5pV8ZegkXl2+KbvFSurWDJizIMb4UgjhIeCYBN2fCSFcF2NcnGhuCOFM4IgkS/86xpj0uokQwiDgPuCAZs2vhhBOiDEmfDFgjPHtEMJTwKEJuvsD14cQ3pvq3ucQwn8BZybpjjQE7pIkSZIkqYsrihGAH+3/Ae6bcFDScT965CeExrGNf5EkoOGErbLjyzSEq631B+4JIbR5iV8I4TTgD0nW2wr8pJ09f0zLcBkaXiz4x3bmpVr3XcBDSertH0L4BvD7FPP/HmNc1M7+kiRJkiSpgHqXFQPw2OgZALzc7L5mSUqHJ5izJMb4WAjhV8BHE3TvCcwJITwIvAqUAYcA+6dY8vMxxsSvbQVCCOXAWUm6jwkhjI8xJvvdlpuAy0l+cvpwGk5CPwXMBbYBE2i4o7lPkjnQ8ALBq1L0S5IkSZKkLuDL79mLq26ew7Mj9+LE076/o/1bp89g+uj+AFTX1nPm/z5ZqBIldRMGzNn1aWAGiYPbEuCExqc9N8QYf9bOmKFAeYr+sUDCgDnGWB9C+CDwJJDqVv5DGp+OqAc+HGNc2MHxkiRJkiSpQM4+aDy3vriMpxau29F20eG7cc5B43d8r6qpK0RpkroZA+YsijFWhxBOAW4FjspwmZuACzswbi1QA5Qm6V+eanKMcUkI4VjgTmBKWhW2VQV8JMZ4YyfXkSRJkiRJefLXSw6lqqaOp99cxzumDKW4KBS6JEndUM7uYA4hHB9CuDGEMCeE8HwI4XshhGE53G+/EMJ1IYTZIYSXQgh/DiEcmav9kokxbgCOB74H1KYxtRq4Ejg7xri9A/tUAXck6X42xvhmB9aYR8MdztfScAI5E08Ch8QY/5ThfEmSJEmSVCAVpcUctccww2VJGctJwBxCuAa4BzgDmA7sR8P1EXNCCDNzsN9FwDPAB4G9abim4mwaXlZ3Zbb3a0+MsSbG+N/AvjS8EG9riuFrgV8Ae8QYvxtjTCfo/Sgwu1XbYuC8NGrdGGO8lIaXA/64cX57tgA3A+8GDo8xvtTR/SRJkiRJ0q5re209VTV17T4xxkKXKilLsn5FRgjhOODLibqA4cA/Qwj7xxhXZGm/A4FfA8UJ9gP4RgjhqRjjI9nYLx0xxleAi0MIH6XhpPB0YDAQgTU0hMMvxBjTOencfP2VIYRZwCk0vEjwLeAfMcZtGaz1OvAp4FMhhN0aax0P9KfhPydbgZXAa8CcGGNNJjVLkiRJkqRdz4tvbeB/7pjLS0s3Ulfffng8tG85p+43mi+cNM3T01I3l4s7mD/T+NdE/zRpCpmvBi7P0n5X0RAuJ9uvCPgskPeAuUmMsRp4vPHJ9tq1NJwmzuaabwLtXrEhSZIkSZJ6nvoYW4TIKzZVcd5vnmLr9o6/FHDNlmp+99ibFBcFvnDStFyUKSlPchEwH8zOsLf5H0HFxicAp5G9gPnIDux3eJb2kiRJkiRJ6tFq6iKTv3BXm/aBVZu58rk/U1FX3e4aj4zZn1unHMntL77N50/ckxA8xSx1V7m4g7lXB8YMzuJ+fTswpiM1SZIkSZIkKUMTN69gvzXzAdhc2jvpM3rLWt7xdsMrpVZsqurQlRqSuq5cnGB+E5iapK/pj6MWZHG/RcAe7ey3MIv7SZIkSZIk7fKav4dvz/VvAfC9R3+RdHyfmkoAfjv9vcwdOinpuG8+/mvK6ny1k7SryMUJ5ltoeVVFaxH4Yxb3+1sH9rspi/tJkiRJkiTt8qpr296pXBuKkz4by/ryxMjpLO4/csf48pKiHY+kXVMuTjB/FzgPGE/iF+89Bfwoy/udDkxPst8C4PtZ3E+SJEmSJKlH+vw7/l+Hxg3tW8Z9nzqKwX3KdrRN/sJdXoch7YKy/sdHMcaNwNHA0zScLG56AP4MvCvGmLXfg4gxbm3c785W+wXg38CxMcYt2dpPkiRJkiSpJ+hfUbrj81t9h/HS0Mk7vheFxE/vsmKO3GMYN3zkkBbhsqRdVy5OMBNjXAQcGkKYDkwD6oFnY4xv5Wi/tcApIYTJwP6NzXNijPNysZ8kSZIkSdKurqho542klxx3ZYu+hd86Od/lSOqichIwN4kxzgXm5nKPVvstILsvEJQkSZIkSeqxXvvau9nz6ntatC345kkFqkZSV5TTgFmSJEmSJEndV0VpMYu+fTJ19ZGiACGE9idJ6lEMmCVJkiRJkpRScZHBsqTEsv6SP0mSJEmSJElSz2DALEmSJEmSJEnKiAGzJEmSJEmSJCkj3sEsSZIkSZLUna1bCPPuyd36A8bCiOngC/4kJWDALEmSJEmS1J29clvDk0vD9oTzb4N+I3O7j6RuxysyJEmSJEmSlNrq1+C2KwpdhaQuqMMnmEMIpcBAoDRn1WRHDbAhxlhT6EIkSZIkSZKyJhT4nOCiR6GuBoq7ejQkKZ9SBswhhDLgs8B5wFSgu1y2E0MIrwLXAT+OMdYWuiBJkiRJkqROGb1/YfevrYLaagNmSS0k/aOvEEIf4FHga8C0xrGhmzxFwHTgO8CDIYSKzP8WSZIkSZIkdQHjD4Pdjix0FZLUQqoTzJ8HDmz2Pea4llwIwOHAlcBXC1yLJEmSJElS5krK4Jwb4fnrYMmTUFOVu73m3w+xPnfrS9plpAqYz6JtqNxtrsho9tdAwxUfBsySJEmSJKl7K+sNh1zW8OTSN0ZDzdbc7iFpl5DqdvhxzT43XT3RXbSudXxBqpAkSZIkSZKkXViqE8zrgBH5KiTH1ha6AEmSJEmSpG5v2XNQ2jv9eaGIXlSxhfLs1ySpoFIFzHcDF9JwzUTTVRPdTaCh9lsLXIckSZIkSVL398dTM576fGkxv6s7ie75mi9JyaS6IuNLwDJ2Bsuxmz1NFgJfSevviiRJkiRJkrKqLNRxWckdDGNjoUuRlEVJA+YY43LgIOBPQBU772HuLs824LfAITHGNZn/LZIkSZIkSephQu5+kX1Q2JyztSXlX6orMppC5vNDCBcBewCD2pvTBdTScH/06zHGukIXI0mSJEmS1O2MnAFLnszJ0qUY10i7kg6FxTHGWuCVHNciSZIkSZKkruDQy+GtpyHWF7oSSV1cVz+NLEmSJEmSpHyb9l744D9g9t9g7fzM1qjaCGtez25dkrocA2ZJkiRJkiS1NfnYhidT8/8Ffzo9e/VI6pKSvuRPkiRJkiRJkqRUDJglSZIkSZIkSRkxYJYkSZIkSZIkZcQ7mCVJkiRJkpQ3A8MWJoYVfLvkWgCK7rgbAlBcBuMOgmmnFLZASWkpWMAcQigGjgOOAPYHRgB9gQ3AHOCPMcZHU8wvAfYFyoHZMcYtua5ZkiRJkiRJnTc0bOSAkocbvrzYrOO538GLf4YjryhAVZIykfeAOYQwCvg0cCEwqHlX418jcCBwUQjhyhjj9xOsMQ64C9irsakyhPB74BsxxpU5K16SJEmSJEkdU70ps3lv/humHJbdWtrx2opN3PrC28xf1f75xZKiwL7jBnLmAWMZ2rc8D9VJXVveAubGE8dfAD4PlLEzUG4utmr/Zgjhphjj4lbjvglMb/a9N3A5cG4I4dwY433Zq1ySJEmSJElpW/5S5nPXzM9eHe14fsl6PvTbp9m6va7Dc+6Zu4K//+ct/nbpoQwxZFYPl5eAOYQwBrgT2IeWJ5UTaWoPQDFwLvCtVmMOTzA/AIOBO0MIZ8cYb+5s3ZIkSZIkScpQXU3GU19bvgF6JWhfsYkv3/Zy5jUl8McnG841jtqyhoNXvNLu+NqiYh4cN4sFq+HO2cu54LCJWa1H6m5yHjCHEKYB9wOjaAiBmwfDiU4x02rMOxL017ZaIzabUwL8OYRwTIzxqYyKliRJkiRJUuf0GZbx1GXrtyUMmJeur+SPc1r/ont2nP36A5yw5LkOja0qKeOB8Qfy0tINOalF6k5yGjCHEEbQcFfyaFqGwMmC5eaarsvYI0HfHcCnEqzXtEc58JcQwj4xxs2ZVS9JkiRJkqSMHXARPPCVhF0rqgZz15uHsmfREg4oer1N/7iSrRy8qT7BzLnUbLwxQXugfvsQ4vYhdCx2amvU1rWs7DWIjx776aRjhlZu5NcPfp/i+obaXl+5md899mbisX3LOHzKUO9p1i4v1yeYbwQmkF6w3NrwBG1fBPYHjqblvc3NT0iPB74MfC6DPSVJkiRJktQZFf1h33Pgpb+0aP5X8UwO3TKXY55+HoBlDG4z9UhWc2TShZ/Nbp3NvD5wLNtKExydbrSttrrF95eXbeLlZcmv1ehVWsz/XXggh0wakrUapa4mZwFzCOEK4EjaD5cT3aXcXO82E2KsCiG8B7gPOIy2Lwds+n55COH7McaVaZYvSZIkSZKkznrf/8Ihl8Ej3+WGVRP4ytsHE2ZFRu25BoDTih7jspI72kz7YcU+PDZ0dUZbVq06nrot0zOau7rXwB2fr/3QrB2fn1q4jt8/vvOk8vDK9UzasCz1Wr0Hspk+fOZvL/HYlccQQmYnq6WuLicBcwihH/A/pA6XU93F3PwkclGiPWKM20IIJwGP0PDywKZQufnccuAy4Jr0fgJJkiRJkiRlxah94ew/c8S6bQy/9imWbahkcf9RAGwq7ktFaW2bKa8OquOtgZkFsjWl66haPirjcvtXlPCnDx/MPmMH7mjbXlfP7x+H7UWlAJw77wHOnfdAynWW9hnKR46/imUbKlm6vpJxg9ucoZR2Cbk6wXw5MJC2J4ubNG9/A7gbeAFYSENg3PpUc0Ixxk0hhFOB54AhrdZt+vxfGDBLkiRJkiQV1LjBvbn/00fyzJvrWLx2GwDTFr8I89qOHdS/LON9po/txSkHZnaCeUT/Cg6dPIQBvUpbtO8/fhAAm8r78Kkjr2BQ1ZaU65y06EmmbFi643tVTV1G9UjdQa4C5ktIHBI3P9H8CHB1jPGx5gPS/XWBGOOSEMI5wD00nHZuCpab9hoXQtg/xvhCWgtLkiRJkiQpq3qXlXD01Gav2yoanDBgHtmnAioz22PikD5ccNjEzCYnMWZgLz569GR++fACXhvc/tozV89rETBLu7KsB8whhH2BiSS/FzkCX40xfjVbe8YY/xVC+D5wJYmD7XfScEJakiRJkiRJXdzZvSbwl8rFCfs+OO2DACzatIjHlj2WcEwufO5dUzli92E8uWANm6vbXusBcPPzy9hYWZO3mqSuIBcnmN+VoK15uPyjbIbLzXwd+DAwmLYh88wc7CdJkiRJkqQcmFTSN2H7rafeyuSBkwG4Z9E9eQ2YQwgcOnkIh04eknTMo2+sMWBWj5PwBXqdtE+r783D3sXAVTnYkxjjVuB6Er8wcI9c7ClJkiRJkqTcmH3+bM7Z8xwA9hy8J3e+784d4bKkriMXJ5gT3aLe/PRy4t8hyI57gE82+950cnp0DveUJEmSJElSloUQ+MLBX+ALB3+h0KVISiEXAfNwEt+DDHB3DvZrbk6S9gE53leSJEmSJEldwH2L7+PG125s015cVMyMoTPYY9AehND6F+AlZSoXAXP/Zp+bB821Mcb5OdivubVJ2styvK8kSZIkSZK6iK8//fWkfe+d9F6+dvjXKC4qzmNF0q4rF3cwVyRpX5+DvVpL9sdP3q4uSZIkSZK0C9m8fXNG8+5YeAf3L74/y9VIPVcuAubKJO2JX/+ZXWOTtCerSZIkSZIkSd3Qi6tezHjucyufy14hUg+Xi4B5Y7PPzU8U9wohDMvBfs29I0n7qhzvK0mSJEmSpDyqrM38POG2mm1ZrETq2XIRMC8i+VUV78rBfs2d3+p7oOEe6MU53leSJEmSJEl5VFpUWugSJJGbgPn1JO0B+FQO9mtYPIQTgGNoCJRbB9xzc7WvJEmSJEmS8u+sqWcl7fvAHh/gA3t8gINGHpTHiqSeqSQHaz4JXNjse9Mp4gDsF0L4eIzxp9ncMIQwBvh94z6JPJPN/SRJkiRJklRYM0fMTNh+yym3MGXQFABuX3A7z6wwFpJyKRcnmB9M0t4UMn8/hHBetjYLIUwFHgJGNzXRMmiuB/6Vrf0kSZIkSZLUNbz4oRc5auxRAAzvPZybT7l5R7gsKT+yfoI5xrgwhPA8MJOdoXLTlRWxcc8/hhCOAr4SY1yeyT4hhHIartz4ItCHtldjNAXNj8cY12SyhyRJkiRJkrqu4qJifv7Onxe6DKlHy8UJZoDfJmlvfl3GxcD8EMINIYQPhBDGtbdoCGFoCOFdIYSfAMuAb9AQLqfy6zTqliRJkiRJkiR1UC7uYAb4A3ANMIzEL91rausFnNX4EELYkmixEMJSoD8tw+Tmp6ITfQdYBNyUfvmSJEmSJEmSpPbkJGCOMVaFEL4I/Ia2L95LFgwD9EvQFth5v3KLbRKs2fx7BK6KMdZ2tG5JkiRJkiQp21ZvrmZA79I27cUhMLhPGSG0jrak7iNXJ5iJMf4uhHAOcCyJTzE3hcDJAugWyyXZJtnJ6AjcEmP09LIkSZIkSZLyIsbEEda5v3066ZwxA3vxyeN258wD2r09VuqScnUHc5NzgaWNnxP9Nyy0etqTanzz9efRcMezJEmSJEmSlBcLVm9Ne86yDZV87u+zeWL+mhxUJOVezk4wA8QYV4UQ3gk8Aowg8bUYzaXbvmOrZuMWASfEGDemUaokSZIkSZKUFUcufYn+Ndu49oHvpBxXW1TMz/Y9g1eHTOSeuSs4bMrQPFUoZU9OA2aAGOMbIYSDgduA/Wh5LUZnL5hpfQ/zc8B7Y4wrO7muJEmSJEmSlJH+NdsAWDhgTNIxJfW1HL78ZaauX8KrQyayfGNVvsqTsirnATNAjPGtEMIhwFeBzzTu2/r+5Y6GzYnubK4DfgBcHWOs6WS5kiRJkiRJUqd9+8APJu3rs72Sw5e/nMdqpNzI9R3MO8QYt8cYPw/sBfwZqKXlXcqxgw/N5tUDNwMzYoxXGS5LkiRJkiSp0B4YN6tN29C+5QztW06/iryc95TyJu//iY4xzgc+FEL4NHAecApwOFDawSXqgWeB24HrY4xv5aRQSZIkSZIkKQ17j+nPy8s28YNZ5/CDWefsaF/07ZN3fL537gouvf4/hShPyomC/ZFJjHE18GPgxyGEcmBfGk43jweGAL0ah1YC64G3gHnACzHG9F/JKUmSJEmSJOXQHVe8g6lfuoftdfU72h7972MKWJGUe13iTH6MsRp4pvGRJEmSJEmSup0QAq9/40Tq6iNbt9fSv6Kjv7AvdV95u4NZkiRJkiRJ6gmKi4LhsnoMA2ZJkiRJkiRJUkYMmCVJkiRJkiRJGTFgliRJkiRJkiRlpEu85C+EsCcwBZgMDAH6AL2B7UAlsApYBrwKvBpjrClQqZIkSZIkSZKkRgUJmEMIfYGzgfcARwID0pi+PYTwNHA38McY4/IclChJkiRJkiRJakder8gIIYwOIfwaWAH8GngvMBAIaTzlwBHAN4HFIYRbQgjvyufPIUmSJEmSJEnK0wnmEEIAvgRcBVTQEBQ3iZks2fjXEuAU4JQQwl3A5THGJZ2pVZIkSZIkSQX29P9CUYLYqqwPTHwHTDwCQmjbLynvch4whxCGA38HDmdnMNw6VE7nnwix1fymuScBL4cQrowx/iqTWiVJkiRJkpRHMcW5wyd/nrj9ke/AoVfACV83ZJa6gJxekRFCGAU8ws5wuXk43Pzai7SWbTWvac0A9AV+HkL4SecqlyRJkiRJUs6tmpvZvCd/DmsXZLcWSRnJWcAcQqig4UV8UxubWgfLWdmm2dM8aL4ihPDNLO0hSZIkSZKkXHjrmU7MfSp7dUjKWC5PMP8I2If0guXYzpNK8xPNAbgyhHBa2lVLkiRJkiQpP2qrCzNXUtbkJGAOIewLXELLcDmZRAFysusz2gubW4fM14YQ+ne8ckmSJEmSJOVNSUWhK5DUSbk6wfw1doa9ycLlRPcxA6wCXgSeAJ4CXgOWJxiXLGhuvt8Q4Kr0SpckSZIkSVJeHPSR5H3Hf63hGXNA/uqRlLaSbC8YQhgBnEjqKy2aThgDvA7cCNwP/CfGWJlk3X7ATOBY4AJgPC3vXU62x6UhhP+JMVal/9NIkiRJkiQpZ2b9F9z12bbtn1sAfYY2fF63EJY9l9eyJHVcLk4wvx8obvyc7IqLQEOwfEqMcc8Y41dijI8lC5cBYoybY4yPxBi/AuwGnAesZucL/pprvu9A4KRMfxhJkiRJkiTlSHEpfHEFTD624fvo/eEz83aGy5K6vKyfYAYOSdLe/KTx9cBHYozbM9kgxhiBv4QQ7gVuAY4g+UlmgBOAmzPZS5IkSZIkSTlU2gs+dEuhq5CUoVycYN43QVtT+BuBG2KMF2QaLrdYNMZ1wLuBx0h8kpnG9v06u5ckSZIkSZIkqaVcBMwjaBn0Nv+8FLg0m5s1XqtxHrApwX5Nnydlc09JkiRJkiRJUm4C5oEJ2ppOF/8gxrg12xvGGN8CfkbyKzL6Z3tPSZIkSZIkSerpchEwJ7qmosmtOdivyfUp+upzuK8kSZIkSZIk9Ui5CJg3N/vcPGzeGmNckoP9mrwB1CTYF3ZenyFJkiRJkiRJypJcBMyLSXxVRdavxmguxhiBDa2am+p4K5d7S5IkSZIkSVJPlIuA+ZUk7QNzsNcOIYQiYEiCrgi8lMu9JUmSJEmSJKknykXA/GCzz81PMpeFEGbkYL8mk9n587Q+Qf0gkiRJkiRJkqSsykXAfAdQ1fi59V3IZ+ZgvyanNfvcfN/qxpokSZIkSZIkSVmU9YA5xrgOuJ6Wp4hj4/fLQwiJrrHolBBCBXAZLYPl0Pj9/2KMmxNOlCRJkiRJkiRlLBcnmAG+DCQKdQcCv8/BftcAExO0bwK+noP9JEmSJEmSJKnHy0nAHGNcCXyGnaeYm04TB+A9IYQ/hhBKsrFXCOHjwH83W7/5fp+KMS7Pxj6SJEmSJEmSpJZydYKZGONvgb/QMlxu+ut5wNMhhJmZrh9C6BtCuBb4UfNtm/31lzHGP2S6viRJkiRJkiQptZwFzI0uAp4icci8P/BsCOH2EML7Gu9RblcIYVII4WvA68DFzdak8XMA/gB8PIs/hyRJkiRJkiSplaxcU5FMjLE6hHAi8C9gJm1D5gCc3PjUhBBeBF4DlgBbgBqgf+MzCTgAGN24fNN1GM3D5Qj8JMb46Vz+XJIkSZIkSZKkdgLmxlPFHwdOB/YE+rIz2M1E6zuSm7eVAQcBB3ZgPgnmN4XWnwghfARYAPwD+EGMcVsnapYkSZIkSZIkJZA0YA4hDAIeBvZuasry3q1PIDe1pdontvoeEnwOQB9gH2AGcHoI4cgY4+ZO1CpJkiRJkiRJaiXVHcxfpSGgbQp9YyefZJqHyh1ZI5A6iG4+PtAQNH85xf6SJEmSJEmSpAykCphPI3FAHDJ82pPNdZo0hcxndmCeJEmSJEmSJCkNqe5gHt7sc7avx8i15nc8A4wsVCGSJEmSJEmStKtKdYL57bxVkXvLC12AJEmSJEmSJO1qUgXMf6bjdyN3xadJBP6a1t8VSZIkSZIkSVK7UgXMXwfuJL37j7uSppofoeGFhZIkSZIkSZKkLEp6B3OMsRo4JYRwEnAGsDcwKNWcLiICm4HXgVuAv8YY6wtbkiRJkiRJkiTtetoNi2OMdwF35aEWSZIkSZIkSVI3kuqKDEmSJEmSJEmSkjJgliRJkiRJkiRlxIBZkiRJkiRJkpQRA2ZJkiRJkiRJUkYMmCVJkiRJkiRJGSkpdAHZFkIYDxwM9AfujzEuKXBJkiRJkiRJ6kLmb5jPG+vfaNNeUlTC+H7jKS4qLkBVUvfU7QPmEMJE4DDgcOAEYFKz7u0hhI/GGP+vELVJkiRJkiSp63l13aucfvvpCfsGlA/gM7M+w/t2f1+eq5K6p7wHzCGEkTSEwXsAE4AhQG+gAggdXKYXMBQYBvRrvnyrceXAtSGEN2KMj3WmbkmSJEmSJHUvlTWVac/ZWL2RrzzxFSYPnMw+w/bJQVXSriUvAXMIoR9wKXABsFe2lk3QFhO0FQNXA+/K0r6SJEmSJEnqBp5f9XxG8yKRR5Y+YsAsdUDOA+YQwpnAT4HhdPyEckckCpNptkfz/sOyuK8kSZIkSZK6gbVVazOfW5n5XKknyWnAHEL4LPAdWgbLyYLhjLfpwJiqLO8pSZIkSZIkST1eUa4WDiG8h53hcmz2hCw/Kcto3PPG7P1kkiRJkiRJ6g6mDZ6WtG/KwClMGTiFYb2G5bEiadeTk4A5hNAL+A07A17oWCCcLc1PSc8DvpinfSVJkiRJktRFXLz3xQnbPznzk9xy6i3ccuotXLbfZXmuStq15OoE8/8DRtDyxHK+NA+0XwCOjTFuzOP+kiRJkiRJ6gIGVgzka4d/rUXbrBGzuHhG4uBZUvpydQfzBR0Yk+gu5tZBdKr7mhOF1rFZ+2+BT8QYKztQiyRJkiRJknZBp005jdOmnMaijYsY3388RSFnN8ZKPVLWA+YQwiRgH1qGvc01D43be/lfspPPTfc5Jxq/HjgvxnhP+9VKkiRJkiSpJ5g4YGKhS0jp/ldW8rvH3sx4flGA6aMHsP/4gZQWG6Irf3JxgvnQFH3Nr69YBTwDLAA2AVc1q6fp7uZHgOuAcqAXMASYCMwCprVas8kAYHtnfgBJkiRJkiQp37525yudXuOwyUP43QUH0qusOAsVSe3LRcB8QIK25qeZ3wQ+BfwzxljfNCCEcARwNDtPJwdgfIzxukSbhBDGAZ8ErmDnzxFpuFf6+hDCjBjjus7+MJIkSZIkSVK2bK2ubfF90sa3mbRhWafXrQ9FLOk/gicWrOX6pxZxyZGTO72m1BG5CJintPre/ITxKuCIGOPyBPPupSFgbm63EMIeMcbXWw+OMb4FfCaEcBNwJzCoWfdI4Ed07C5oSZIkSZIkKS+eX7IegLL6GgCOf+s5jn/ruays/asZp3H75Hfw9MJ1BszKm1wEzKMTtDVdefGTJOEywP3AtxK0Hwe0CZibxBifCiGcCjxIw8/TdPr5gyGE38UY/51O8ZIkSZIkSVKuVG5v+IX+0rqdJ5n/56D/6tSaRbGeLz37R/rUVDbsUVPXqfWkdOQiYB5J4hfwAfwzxbwXgA003KHc3FHAL1NtGGN8PITwAxrucW5+z/MPgAPbqVeSJEmSJEndzbL/wKh927b3GgSDJ0EIbfu6oJW9BvHk6L07tUbRzltopbzLRcDcu9nn5kFzPfByskkxxhhCeBR4Ly3vYU710sDmvgVcDvRtNndmCOGkGONdHS9fkiRJkiRJXd6Lf254Ehk8Cc65EYbtkd+aOunCwyemNf7tDZXcO3dlboqROigXAXNFkvZNMcZkJ5ubNAXMzY0JIYyOMb6damKMcXMI4Ubgw7QMtj8FGDBLkiRJkiR1R289nf6cdQvhz++HT8zuNieZAb7y3ulpjX98/hoDZhVcUQ7WLE3SXtWBuU8kaT+4g3s3D5KbTjEfHUIY1cH5kiRJkiRJ6kpWvZLZvA1LYPW87NaSBSMHlAOwpazhEoC5Q3YrZDlSp+UiYK5M0p7sZHNz/wFqErQf1MG9X0zQVgSc2MH5kiRJkiRJ2lVsW1PoCtq4+B2TANhWWsF7TvkO35t1DgAH7za4kGVJGctnwDwghJDsdDMAMcZqYDYNJ4+b6+gJ5hVJ2t/RwfmSJEmSJElSzgzuU8a5B48HoK6oeMcVHtdf3NH4S+pacnEH82pgSOPnwM77kAOwO9De7zU8A8xq/Nx0zcWBIYSiGNt9JWZdkvZp7cyTJEmSJElSdzOmMUJaPQ+2bylsLWn45vtmcMUxU7j23ws5Zs/hHLn7UEI3uitaai4XJ5gX0/YEcpOjOzD/2QRtvYEDOjC39V3LTQH1hA7MlSRJkiRJUldz8g8Tt3/yZfjIgw3PqH3zW1MWjB7Yi2tOmc5RewwzXFa3losTzAuStAfgkhDCr2KMMckYgOeStJ9Dw+nmVI5J0t6/nXmSJEmSJEnqig68GF6/F964d2fbid+FgeMKV1MX98SCtUz90t0J+wb2LuWoPYZx9Xv2ol9FyttspQ7JRcD8NHB5s+9N12QEYAbwZeCrKea/QsM9zk0vBWya+18hhO/EGBPesxxCKAY+lWTN8g5XL0mSJEmSpK7lvL9BfT2sfQOGTS10Nd1CdW3im2ZXbqrmb88tZfHabdx46aF5rkq7olwEzI8naW8Kir8UQngjxnhDwkEx1ocQXgAOY+f9zdBwCvn3IYT3JLmL+WfAPs32aT53a5o/gyRJkiRJkrqSoiLD5VaWra9s8f381+5l2rpFqef0Hc6v9zmVp99cx9L12xg7qHcOK1RPkPU7mGOMbwIv0vYFf03fi4HrQwjfDCEk27/5PczNT0C/C7gnhHBICKE0hFAeQjgyhHAfcGmzca2t7OSPJUmSJEmSJHUpi9Y2nKksanYWs1/NtqTPbptWcNrCR3eMX7x2W0Hq1q4lFyeYAf4E7JegvXlYfCVwXAjhQzHGea3GPQ58Ism8dzY+ydZO1PZKmvVLkiRJkiSpB1u6ZSlvbX6rTXtpUSkjeo/oki/mqwnFfOqoTyTtP+e1+zn/tXuT9kuZyFXA/BvgS8AAEp8qbmo7APhPY8h8S7P+B4A6Gk5YN40NSdZqvWYiD6f/I0iSJEmSJKmnenr505x080kJ+0b2GcmVB17JcROOy3NVLQ3uUwZAfeMlAW8OGFXIctRDZf2KDIAY42bgeyQOfJuHxQC9gR+0mr+BhlC49fymeYme5mObn2SuB25N+4eQJEmSJEnSLm/r9vRf3bVi6wo+98jneH396zmoqOPOPmg80BAwn3/CF/nsEVcA0K+ihNe+9u4dz8G7DS5kmdrF5SRgbvQ9YG7j59ZXV0DLkHlCaPt7BT9Jsm5I8iRb//YY4+I06pYkSZIkSVIP8dzK5zKaVxtr+ffSf2e5mvT0LS/hffuPAWB170HUFDdcVvDI546horR4x1Nc1PWu89CuI1dXZBBjrA0hvA94ChhEyxf+QcvQeVuMMbaaf2cI4XHgMFJff9Fm62aftwNXpVu7JEmSJEmSeobVlaszn7st87nZ8qOz9uOCwyby4wde5+DdhnDh4ROpKC0udFnqQXIWMAPEGOeHEN4J3AWMYud1Fi2GAf9MssSHgWeBPqQXMjedXr4yxvhGunVLkiRJkiRJ3cV+4wbyhwsPKnQZ6qFyeUUGADHGl4B9gOtouA+59dUWy4HPJpk7D3gfDSeRIXFATZK+H8QYf9rZ+iVJkiRJkrTrmth/YtK+MX3HMKbvGAaUD8hfQVI3k/OAGSDGuDbGeCEwGfg08EfgZuBrwP4xxqUp5v4LOBpYSsvrNVo/NPZXAv8vxvjf2f9JJEmSJEmStCv56H4fTdj+sf0/xj3vv4d73n8Pl+17WZ6rkrqPnF6R0VqMcQnw4wzmPR1C2Au4ArgUmJhg2Brgz8B3Y4zLO1GmJEmSJEmSeogJ/SdwwV4XcN0r1+1oG9VnFJfsc0kBq5K6j7wGzJ0RY9wKfAf4TghhMrA7MBjYBiwA5sYY6wtYoiRJkiRJkrqhzx74WT4+8+P8a8m/OGz0YV6JIaWh2wTMzcUYF9AQKkuSJEmSJEmdVlZcxom7nVjoMqRuJy93MEuSJEmSJEmSdj0GzJIkSZIkSZKkjBgwS5IkSZIkSZIyYsAsSZIkSZIkScqIAbMkSZIkSZIkKSMlHR0YQigFBgKlOasmO2qADTHGmkIXIkmSJEmSJEm7spQBcwihDPgscB4wFQj5KCoLYgjhVeA64McxxtpCFyRJkiRJkiR1Jef99mn2Hz+wTXsApo3qzzkHjWfvMQPyXpe6l6QBcwihD/AgcADdJ1huEoDpwHeAU0IIJ8QYqwpckyRJkiRJkpR3b6zakrTvhSUbErY/v2QDt734Nn+95BBDZqWU6gTz54EDm32POa4lFwJwOHAl8NUC1yJJkiRJkiTl3erN1QAMr1wPwH/NvYv6kPzVbDEE7ht/IMsZyg3PLOGb75uRlzrVPaUKmM+ibajcXU4yx2Z/DTRc8WHALEmSJEmSpB7r2LeeB+D0+Y9QX5Q8YC6tr6Mo1vN/009m7tub8lWeuqlUAfO4Zp+7S7DcJNAyHB9fqEIkSZIkSZKkriDEhrjstFO+RW1R8ljwljs+v2MssTteaqB8Sv5HFbAub1Xk3tpCFyBJkiRJkiQV0tbSCgBitztLqq4s1Qnmu4ELaTgJ3HTVRHfTdJL51gLXIUmSJEmSJBXUJ4/6OIe/PYe6omIAfnHuTEYOaAid56/azJX/mFPI8tRNpTrB/CVgGTuD5djNniYLga+k9XdFkiRJkiRJ2kX8/f8dCsDKPkO4efejd7SfvM8oZk0YxKwJg5g6sn+BqlN3lzRgjjEuBw4C/gRU0RA0d6dnG/Bb4JAY45rM/xZJkiRJkiRJbb299W3e3tL2WVPZtaKoAyYO5rbLD2do3zIAzpw1lje/dVKBq9KuItUVGU0h8/khhIuAPYBB7c3pAmppuD/69RhjXaGLkSRJkiRJ0q7p4bce5uG3Hk7YN6bvGL5w8Bc4cuyR+SwpqX3HDeS5Lx1f6DK0C+pQWBxjrAVeyXEtkiRJkiRJUpezsXpj2nOWbVnGJx/6JDefcjMTB0zMflFSF5HqDmZJkiRJkiSpx3t6+dMZzaupr+HRZY9muRqpazFgliRJkiRJklJYXbk687nbMp8rdQcGzJIkSZIkSZKkjBgwS5IkSZIkSSmM6Tsmad+oPqMY1WcU/Ur75bEiqeswYJYkSZIkSZJSuGL/KxK2f2LmJ7jvjPu474z7+PA+H85zVVLXYMAsSZIkSZIkpbDvsH05ZtwxLdr6lfbjwzMMlaWSQhcgSZIkSZIkdXU/PfanrKtaxy1v3MK7Jr6Lsf3GFrokqUswYJYkSZIkSZI6YHDFYC6ecXGhy5C6FK/IkCRJkiRJkiRlxIBZkiRJkiRJkpQRA2ZJkiRJkiRJUkYMmCVJkiRJkiRJGTFgliRJkiRJkiRlxIBZkiRJkiRJkpQRA2ZJkiRJkiRJUkYMmCVJkiRJkiRJGTFgliRJkiRJkiRlxIBZkiRJkiRJkpQRA2ZJkiRJkiRJUkYMmCVJkiRJkiRJGTFgliRJkiRJkiRlxIBZkiRJkiRJkpQRA2ZJkiRJkiRJUkZKCl1ACKECmAGMAPoCG4CXY4xLC1mXJEmSJEmSJCm1ggTMIYShwPnAmcAsoDjBmAeBi2OMS5KscS5wDlAOPAn8b4xxec6KliRJkiRJkiS1kNeAOYQwGLgGuAjo1dScZPixwO0hhJkxxvpW61wI/LZZ0zuB/w4h/BD4coyxLquFS5IkSZIkSZLayFvAHEL4APAzYCgtQ+WYbAoNV2e8F7itVd9ltA2my4GrgENCCKfEGLd2umhJkiRJkiR1X384GYoSxF/9R8Nep8I7r4Higt8gK3VreXnJXwjhf4C/AMNoCIZjs4fGtuZPcyclWHJMqzWangAcDdwRQvCfDpIkSZIkST3B4seT99XXtn02LIEnfgb3XJm/GqVdVM4D5hDCd4Av0jJYhuSBcovpwP4J2t9oNaZpjaaQ+SjgxxkXLUmSJEmSpF3fnJug3ptWpc7IacAcQvgw8LnGr62D5fY0jZ+QoO/LQE2SdZtC5stCCEenV7EkSZIkSZJ6jKqNsG1doauQurWcBcwhhEk0nCLuSLDc+sqM5vq1GRzjv4GzgaY/Ymo9rylk/mFaRUuSJEmSJEmSOiyXJ5j/F+jd+Lm9YJlW45qPL084McZbgQuazW8eZDfZN4SQ6A5nSZIkSZIkSVIn5SRgDiEcBhzHzpPErTV/KV/TtRZLgAeb9bcrxvgX4LNJ9mhyaceqliRJkiRJUveUJBr67zfh6jUNT+8h+S1J6iFKcrTuf6foa37S+EHgd8C9McZ1ACGE+nQ2ijH+OIRwIHAOLQPtps8nhhAGxBg3prOuJEmSJEmSuomP/Qd+NrNl24TDoffgZg0deSVY9t2/+H7e3Phmm/ay4jL2H74/p+9+Or1LeyeYKXUPWQ+YQwj9gHeT+F5kaPhv83LggzHGh7K07YeBfYC92BksN+1XDJwA3JSlvSRJkiRJktSVDJkMn3kd7vsiLHkKjrsGZpxR6KoAWLplKUu3LE3Yd9/i+7h/8f1ce8K1lBcnvCVW6vJycYL5JKCMtqeJafy+GDgsxrg8WxvGGCtDCB8FHiHx9RqHYsAsSZIkSZK06+o3At7/20JXkbbnVz3P08uf5sixRxa6FCkjubiD+eBW35uHy3XA6dkMl3dsEuOjNATMzU8vN5mR7f0kSZIkSZKkJv3K+mU8d+6auVmsRMqvXATMeydoawp9/xJjfCEHeza5odX3plPUE3K4pyRJkiRJknq4g0ceTMjwnue6WJflaqT8yUXAPJnE11QA/DkH+zX3ZJL2YTneV5IkSZIkST3Y+P7j+Z/D/4eSkIsbaaWuKxf/iR+Qou/RHOzX3JIk7b6KU5IkSZIkSTl12pTTOH7C8by0+iWqa6vb9N+96G7ufvPuAlQm5U4uAubmF840P8m8Oca4LQf7NbclSXtmv58gSZIkSZIkpaFPaR8OG31Ywr7X1r+W52qk3MvFFRn1Sdq352Cv1pKdnq7Kw96SJEmSJEmS1KPkImDemqR9cAghF/s1l+gFgwCbcryvJEmSJEmSJPU4uQh8VzT7HFp9PigH+zX33lbfm/ZfmuN9JUmSJEmSJKnHycUdzAuBvWh5/3KTM4GncrAnIYT+wEUJ9o3Aglzs2Z4QQjFwAnAqcCAwnoZrPLYCK4FXgbuB22KMKwtRYyIhhN2B9wNHAXsCw4AK/j979xkuWVXmbfx+OtAB6Cbn0CBBQARJEhQRJZgAUYygjHFUDKOiMsZhdMxZMI0COoryihIUFAVBQBBEMkjOIDQNTTfddH7eD1VFV9epfGpXnT7n/l3Xvrpqr73Weqpl5sOfxbNLdd8HXAf8HvhVH/pqS5IkSZIkSRqhijjBfHWde0npNPG/R8SmBewJ8FVgrfLn2pf6XVXQng1FxBHAbcA5wDuBXYB1gInAGsC2wGHA94G7IuLLEbFmv+usFhE7RMTvgFuAzwMHAzOAVYHxwDRgB+D1wCnAgxHxnxGxymAqliRJkiRJkjRIRQTMl9R8rw57pwBnRMTUXm4YER8D3sryILvWpb3cr0UtkyLiJOA0YIs2p00BPgxcGRHbF1ZcExHxfuAfwEup/3dYz3Tgc8AVEbFZUbVJkiRJkiRJGpmKCJgvYvlL9SrtKqLq887ApRHRbvjaUESsHhE/ohRyVqtuk/FIZv5tuHu1Wc944BfA0V0u8QzgsojYsWdFtSEivgh8A+j2JPJOwMURsX7PipIkSZIkSZI04vU8YM7MRcCvGXoKthIyB6VA8saI+EZEbNPpHhGxTkR8hFILiqNr1q7d7xedrj8Mx1NqezEc04CzImKd4ZfTWkS8F/hID5baDDi5B+tIkiRJkiRJWkkUcYIZSqdh66k+yTwZeC9wc0TcFBE/iogP1Z0U8baI+GBEfCciLgMeotQjeD2GhsvVp5eXAd8Z1i9pU0TsDHy0wXACPwH2ofTCvA0ovfjvsgbPz6DUU7pQEbED8JUmj/wVeC2l8Hg9SvX/AFjS4PmDI+L5PS1SkiRJkiRJ0og1oYhFM/O6iDiD0mneRieLqbr/TEovvaPmfuXz92u2qA2TG52W/mlm3tFh+d36DKUX4dVzTGaeWHPvrIg4h9IJ61fVmXNkRHw9M6/pXYlDfIvGbTGOBb6amdWB/UzgrxHxK+BMSr2jax0GXNzLIiVJkiRJkiSNTEWdYAb4IDC//DlrxqoD4soVNH65XNRclTnUzKne53HguG4K71T5BXeHNBg+t064DEBmLgHeQim4rTWOAuuPiH2B/RsMfzIzv1ITLj8tM/8IfLrB3LV6UZ8kSZIkSZKkka+wgDkz7wbeQ3uhMawYGg9Zruaqnl9v3QTempkPd1x4dw5tUAvAF5pNzMw5wAkNhg+JiOnDKayJdze4fxstai77OvD3OvfP7roiSZIkSZIkSSuVIk8wk5mnAF9k6KnjWrVhc7PxRs9Vr31cZp7ZTc1delmD+7OAS9uY/8sG9ycDr+iqoiYiYlVKoXg9J5ZPVjdVfubFwDeB24EbgHdn5q97VqgkSZIkSZKkEa3QgBkgM48DPkd7J5W72oIV+zwfl5lf6uH67dilwf1LMnNpq8mZ+U/gkQbDe3ddVWMHUgqv6/ldu4tk5hOZ+YHM3Dozd8zM7/amPEmSJEmSJEkrg8IDZoDM/CTwOmA2vQuaa9tlPA4ckZlfHMaaHYuIDYF1Gwzf0MFSjZ7dq7OK2tIotJ6dmbcVsJ8kSZIkSZKkUagvATNAZp4G7AD8BFhG/Rf9NQqdGz1TWeNUYMcBtWd4ZpOxWzpYp1Gw22z9bu3cTg0RsXVEfCEiroyI2RGxKCLuj4i/RsSnIqKI2iRJkiRJkiStJPoWMANk5r8y82hge+BblE4d1+ur3Chwrn52HvC/wM6Z+cbMfLDwH1DfRk3G/tXBOo3qnxwR63WwTjt2anD/HoCImBIR3wb+CXwU2A2YDkwENqZ0qvq/gJsi4scF1CdJkiRJkiRpJTBhEJuW2zB8ICI+BOxD6WVxu1EKnjeqU9cy4CFKJ4KvAv4MXJCZi/pWdGMbNBlr1Fe5nseajG3a4VoNRcT6NG7pMS8i1gb+AOzaznLAvwEHRsSBmXlTL2qUJEmSJEmStHIYSMBcUX4B3l/KFwARMY7Sadkp5VtPAXPaeVnegKzTZOyJDtaZ02Rs7Q7WaaVZIB7AeTR+aWEjGwMXR8Q+5RcWSpIkSZIkSRoDBhow15OZyyi1znh80LW0aXKTsU5OWC/sco9OTWsy9ga6/2diLeCXEfHczFzQ5RqSJEmSJEmSViJ97cE8Sk1qMtYsNK61uMnYlCZjnWoWMFeHy8sovTzxRZROPW8GHAJc0GT+s4GPNNs8It4REX+PiL/PnDmzvYolSZIkSZIkjUgGzMPXLGBe1sE60WSs2R6dahYwVzwBvCwz35CZF2Tmw5l5X2aenZkvAj7RZO57I6JhIJ6ZP8jM3TJzt3XXbdQKWpIkSZIkqU8WPAFPzR56ZQ60LGllUUiLjHIf5bcBLwUWANcCvxml/XmbhcjjO1in2f8Wvew/3U5NR2fm7xsNZubnImJ7Si01aq0DvAY4pcv6JEmSJEmSCtAgMP7OrvXvr7oe7HIU7P9JiGbnAqWxrecBc0RMBM4GDqi6fQTw2Yi4EjgBOC0zO2kfMZI16ze8SgfrNHu2k17OrTzVYvyczDyjjXU+DLyW+oH1ARgwS5IkSZKkkWT+rM6en/cIXPzV0rXG5kPHI2CDZ8Nz/x1m7NObGqWVUBEnmI8DDmwwtgewO/CNiDgF+GFm3lxADf00v8nY6h2ss1qTsSc7WKeVZvUCnNzOIpn5UERcCuxbZ/gFnRYlSZIkSZI0Ys2+p/79x++G286DN/8WNt29ryVJI0URPZjfROm/Oah3QanX8JrA+4EbIuIvEfGGiOjktO9I8kiTsTU6WKfZs493sE4r/2oxflUHa13a4P5G5TYpkiRJkiRJo9uSBXDtzwddhTQwRYSAm1Z9jpqrOmyu3NsH+CnwYER8JSK2LaCmIj3YZGz9DtZp9uzDHazTyr0txpv9nlqN6hoHrN3BOpIkSZIkSSuvR1b2/0Bf6l4RAXOzE7KVUBmGBs1rAf8B3BQRF0bE68r9nEe6O5qMbdbBOnWa+QClF/zd38E6TWXmLJqfiO6kbUqzfs6eYJYkSZIkSSuHSdMHXYG00iqiB/P/UerDXNsWo1r196xz//nl61sRcTKlXs239b7UnrgOWEL9v8vtOljnmQ3u35qZizuuqrkrgIMajG0E3NrmOms1GZvdSUGSJEmSJEkD8ZknSn8+fjd8c6eBljISXXv/E7zyxPpdUqdNnsjztlqHo/eZwcTxnjUcq4oImD9FqZ/wv7NiWwwYGjRX36v33DrAh4APRcSFwPeB3xQQuHYtMxdExHXALnWGn9vOGhGxBtCoNciF3VXW1F9pHDA/l/YD5h0b3L8nMxd2XJUkSZIkSVJR3nQW/OSQFe8d8N/LP685o/Hc911T+vPBf8Cv3tLryka8q++d3XDsoltncs39sznhDfWiMY0FPQ+YM3Mp8J6I+B7wUeBVwKTKcNWjnZ5q3q98PRoRJ1E61dysPUU//Y76AfNzImLDzHyoxfyXUT98B7hgWJXVdwbwXw3GXk+pJ3ZT5fYlL24wfGV3ZUmSJEmSJBVkyxfA+6+DP30anpwJB/43bFwT52z7MrjldyveO+C/Ya0tSp/ntop4Vl7zFy55+vPkpYs54vYLWRbNTyU/MnUNztlib3533UMc95L5bLLm1KLL1AhUxAlmADLzeuDIiDgGeANwFMtP9FafVobGYXO9U83rAscCH46IPwPfA87MzCUMzunAJ+vcHwe8HTi+xfx3Nrj/BPCHZhMjYgJwKLANcBelE95NTw9n5nURcTP1W3gcHBF7ZublLWp+E7Beg7EzWsyVJEmSJEnqvzU3hyNObjz++p/DJd8ohdAArz4JnnV4PyobuNtnPjnk3uF3XNTw+XHLljGe5MJNdmH+xMnc9OAcA+YxqrCAuSIzZwMnAidGxFbAm4E3AjMqj9A4bG52qjmA/cvXzIj4MfCjQZxqzsxry2H3C+sMfygiTsnMe+rNjYgjKPWbruf7mTm30b4RsSZwHrBb1e2bI+LAzGz1YsCvAT+styxwSkTsk5mPNth3S+BLDdadCfy6xd6SJEmSJEkj0/M+ULrGmMyh9w455IsNnz/s9r/wzhvOIsoT60zXGNHX7tuZeXtmfjIzt6TU7uIkYC7LA2MYGjhX1Humcm89Su04bo2IP0XEYUX9hiY+Rf26pwG/j4ghL/Er13lyg/XmAd9ssec3WDFchtKp5J+0mEf5mUZh/DbAhRGxc+1ARDwP+DONX/D3pcx8qo39JUmSJEmSNEKMa9S8VWqh8BPMjWTmX4C/RMR7gFdSaqFxADC+8kjV4/VONTc6+fxC4IUR8U/gfZl5fq9rryczL4mI7wLvrjP8TOD6iLgAuBlYBdgTeE6TJY/LzAcbDUbEJOC1DYZfGBGbZea9TepdFBFvo9Tjud7/C9kB+EdEXApcV35mV2CPJjVfQyn0liRJkiRJ0krkoB024JNn3gjAhRvvzHmbL4+ATn/X3k9//uK5/+SKux/re30auQYWMFdk5gLgVODUiFifUvuMo4CdKo9QP0iubZ9R26t5O+APEfGRzPxaEbXX8UFgR+q3vJgAHFi+Wvl5Zn67xTPrsPzlifVsAjQMmAEy88KI+E/g8w0eCeB55auVR4BXDrgXtiRJkiRJkrqw3rTJT3/+4u5HPv35vw97FrtuvubT36dPndjXujTy9bVFRiuZ+XBmfi0znwM8m1Kf4H9Rvz1GvZ7MUTM+DvhSRNTrjVxE/QuBQ4DGHdBb+3/Av7Xx3CxgcZPxtl5rmplfAD7bzrNN3Avsl5l3D3MdSZIkSZIkDcjtn3sJM9Ze/qK+4w/dgaP23HyAFWllMPATzI1k5g3AhyPiI5RaZ7wZOBSYUnmkwdSoGR8HfIhS3+DCZebsiDgA+BzwH7T/d7yQUh/nr2Tmsjb2WRARZwP1XmV6ZWbe1UHNn4yIa4BvAxu2O6/sF8AxmTmrw3mSJEmSJEkaQSaMH8eFx/blnKZGkRF1grmezFyWmX/IzDcA6wNvBS6sDNP8JZWVsd2Lq7DOppmLM/MjlNp8/JjSC/samQWcAGyTmV9qJ1yu8m5K/ZGr3UOpzUhHMvN0YGvgWOCGFo8/SSlY3j0zX2+4LEmSJEmSJI1NI/YEcz2Z+SRwUkRcBBxHKWxuFjBXrFZoYQ1k5k3AWyPi3cBulF6ctxalmh+lFA5f3W3f4sx8OCJ2pdSW45nAfcDpmTm/y/XmAV8BvhIRm1B6qd+mwDRK7ThmATcB15R7Z0uSJEmSJEkaw1aagDki1gZeS+l07p7VQ21Mv6+QotpU7s18afnq9dpLgF8XsO79wP29XleSJEmSJEmjzzt/ehU7bbpG3bFt11+N1+6+2QovC9ToMaID5oiYAhxGKVQ+gFK97QTKTy9B6bTw73tenCRJkiRJkjTG3Dur8X84f+19sxveP/OaB/m/tz2X3WesVVBlGpQRFzBHxDjgQEqh8qHAqpWhqseyzj3qjAPMBL7QyxolSZIkSZKkseiWh+cCsNaCOQC85abfMW/i5IbPJ8H5m+7KvdM24KeX3WPAPAqNmIA5Ip5LKVR+DbBu5XbVI9WhcTvBclB64d1hmfmvXtUpSZIkSZIkjXXPe/A6AF569+UsHNc4Ypy0bAlTlizkxJ0O56aH5vSrPPXRQAPmiNgaOBJ4A7Bl5XbVI7Uv8KsXLNcLnh8FvgN8PTPn9qBUSZIkSZIkSWXjshTJvfmA/+SRVRufSv75uZ95+tnM2qhPo0HfA+aIWB94HaXTyrtWblc90k6oXPtc5ZkbgG8B/5eZC4ZZqiRJkiRJkqQ6Fo8bD8CyceMGXIkGrS8Bc0SsBhxOKVTeHxjH8EPlynMLgdOB72fmxcOvVpIkSZIkSVIzn33um3n7DWfz6JQ1ADjuJc9kjy1KJ5nve/wp3nfq1QOsTv1UaMAcEQcAbwNeDlS6fXfaV7nRc7cCPwBOycxZwyxVkiRJkiRJUgs/ecsevOnHV3DPtA35xN7vePr+O1/wjKc/rz55xLz2TX1QyBn2iDg4Iq4Hfg+8GphCKRgOSmFx5Yqqq1b1c5SfWQz8Etg/M5+ZmV8zXJYkSZIkSZL6Y99t1uUHR+369Pc1p07kpuMPGmBFGrSe/+uEiHg18AtWDI7bbYFR+2zluTuAHwInZebMXtQpSZIkSZIkqXMH7rABd3/hZYMuQyNEEefVP0rpZHT16WNoP1SuPLsEOJNSb+U/9bRCSZIkSZIkSdKwFREwb8+KbS2aqRdA303ptPKPM/Ph3pYmSZIkSZIkSeqVIgLmBSx/oV89jU4r/xb4PnBeZtY+I0mSJEmSJEkaYYoImC8BXsHQILneaeX7gP8F/jczHyqgFkmSJEmSJElSQYoImI8HDgImUv+08lLgXEqnlc/NzGUF1CBJkiRJkiRJKljPA+bMvCoiDgdOBtapGroZ+DlwcmY+0Ot9JUmSJEmSJEn9VcQJZjLznIjYHHghMAW4NjNvL2IvSZIkSZIkSdJgFBIwA2TmU8A5Ra0vSZIkSZIkSRqscYMuQJIkSZIkSZK0cjJgliRJkiRJkiR1xYBZkiRJkiRJktSVwnowS5IkSZIkSVLFgsXLuPa+2XXHVp00gWesuyoR0d+iNGwGzJIkSZIkSZIK98Dspzj0hEsbjq8/bRInvnEXdt18rT5WpeFq2iIjItaMiG9GxJ0R8WRE3B4RX4qIaS3mHRUR50fEIxGxMCIW9emaFxH3RcSZEXFYT/+mJEmSJEmSJBXm4TkLefOPr2T+oiWDLkUdaHiCOSKmAhcD2wGVs+lbAh8CXhwR+2TmUzVzxgG/AF5VudXzipubAGwMbAS8PCJ+lplv6nMNkiRJkiRJ0hi2PBJMgoPv+Rsvuu/vTWc8Pmka79r/gzwJXHHXY+y37XoF16headYi42PA9kCWr4oAdiqPf7pmznuBV1d9Twaj8k/xGyPi8sw8cUB1SJIkSZIkSWPKZmtNZeoq45m/aCnf3/FQtp59X9Pnt5jzELs+ciurL3qKhRMm8di8RX2qVL3QLGB+LfUD4qQU4L6OoQHzW6ueGaTK/gG8EzBgliRJkiRJkvpglQnjOHrvGZx44R38ZZOd+csmOzd9/sC7/8auj9zan+LUc80C5s2rPldOBGeD8YqtWTHcHZTqOrcZWBWSJEmSJEnSGHTsQduy6VpT+dNNDzPzyYV1n7n3sfnMnr+4z5Wp15oFzI8B6zcZf6LOvSeBkfaax7mDLkCSJEmSJEkaSyKC1++xGa/fY7OGz/zHL6/hN1c/0MeqVIRxTcb+QP1TyEHphPCf64xd0GDOIFTqPG/QhUiSJEmSJEnSaNQsYP4My08p177obxHwuTpzPlFnziCuilnlmiRJkiRJkiRJPdYwYM7Me4CDgXspnQauXDOBIzLz+jpzbgP2A66omTOI60/APpl5d8d/K5IkSZIkSZKklpr1YCYz/xYRWwP7ABsBDwN/zcwFTeZcC+wVEZsDzwamA+N7V3JTSyj1jr4mMx/q056SJEmSJEmSNCY1DZgBMnMJcFGnC5dPQN/TTVGSJEmSJEmSpJGvWQ9mSZIkSZIkSZIaGnUBc0SMi4gtI2LyoGuRJEmSJEmSpNFsVAXMEXEo8AhwG/BoRBw74JIkSZIkSZIkadQaNQFzRGwJnAqsBQQwFfhCRBw9yLokSZIkSZIkabQqNGCOiAkRsX5E9CPIPgCYDGTVFcDnI2J8H/aXJEmSJEmSpDGlsOA3Io4HZgMPAjMj4q1F7VW2rHr7qs/rAQcVvLckSZIkSZIkjTmFBMwR8SbgE5TaVASwJvC9iHhWEfuV3dJkbL8C95UkSZIkSZKkMamoE8zHlP+stKqo7HVkQfsBXN9kbOcC95UkSZIkSZKkMamogHkblgfL1XYvaD8y83FKLTmo2rvSh3nzovaVJEmSJEmSpLGqqIB5WvnPSi/kStC7UUH7VTT6PesWvK8kSZIkSZIkjTmFveSvgSlFLRwR2zE02C58X0mSJEmSJEkaq/odMBf1UsFdgJ81eWRCEftKkiRJkiRJ0ljWVvAaEQEcCOwPbELppPAqDD0p3Mo6EXFeh3PqlgRMBFYv17NO+X6lFUet+T3YU5IkSZIkSZJUpWXAHBHrAL8B9u5w7aj5E2AS8KIO12lnj1Zm93BPSZIkSZIkSRLtnWD+ObBPj/br9MRzO7LJ+lEev62AfSVJkiRJkqRh++H1P+SkG04acn/ShEnsst4ufHi3DzNj+oz+Fya1oWnAHBF7Ay9mxRC3HY2C5E7XGe5+FX8vaF9JkiRJkiRpWJblMhbloiH3Fy1axEX3X8RNs27iN4f+humTpg+gOqm5Vi/d26vqc3RwNdLJGr3Yr+LsNp6RJEmSJEmSRpyZT83k8ocuH3QZUl2tAuah/+pk5VB52V8Cf8vMSwdcjyRJkiRJksa4jVbdqOu59865t4eVSL3TKmBeGf/VSHUbjvnA2wdViCRJkiRJklSxz8b7MG2VaYMuQ+qppgFzZl4J/J7lp4HbvRou2YeLcr2PAS/LzBvb+HuQJEmSJEmSCrXOlHX44YE/ZJf1dmFCNH01mrTSaOef5NcD/we8rIN1Ky0qarXTL3m4/gX8HPhCZj7ah/0kSZIkSZKktmy/9vac8pJTWLxsMcty2ZDxE64+gZNuPGkAlUndaRkwZ+YTwCsi4jnAfsDGwKrAREonoOuFxm9mxT7IlD/PA3417KpLay4FFgJPADOBu4EbMvP2HqwvSZIkSZIkFWbiuIl1748fN77PlUjD0/ZZ/My8Gri6nWcj4k2VaTVDj2Xmv7W7pyRJkiRJkiRp5Gr1kr9uzan5XjnlPLOg/SRJkiRJkiRJfVZUwHxVnXsJ3FTQfpIkSZIkSZKkPisqYP4qQ3szLwW+VNB+kiRJkiRJkqQ+KyRgzsxzgSOBO4AlwHXAYZl5QxH7SZIkSZIkSZL6r+2X/HUqM38O/Lyo9SVJkiRJkiRJg1VUiwxJkiRJkiRJ0ihnwCxJkiRJkiRJ6ooBsyRJkiRJkiSpKwbMkiRJkiRJkqSutPWSv4jYDFgP+Fdm3t/mnInA1sB0ILqusDOLgccy844+7SdJkiRJkiRJY1bTgDkidgN+BDyr6t5VwNsy87oGc9YEvgK8Dpjcu1LbFxFzgFOAT2bm3EHUIEmSJEmSJEmjXcOAOSJmAOcDq7HiCeTdgD9HxHMy896aOWsCfwW2oX+nluuZDrwX2D8inpeZcwZYiyRJkiRJkiSNSs1OMH8SWB3I8lURwBrAp4G31pmzbZ05gxDADpRqOnbAtUiSJEmSJEld+9bV3+K8e84bcn/y+Mnsuv6uvGXHtzBtlWkDqExjXbOA+WCWh8SV08iV4DiAl9SZczhDw+hBqK7z1RgwS5IkSZIkaSUwd1Hjbq//fOyfde9fM/MaLn/ocn7ykp+wyvhViipNqmtck7G1W8ytN75h1edBtsiotmHrRyRJkiRJkqTBu/SBS7uad+OsG7n6kat7XI3UWrOA+d4mYwAP1Ln30DBqKcqDgy5AkiRJkiRJasf9T97f9dxbHrulh5VI7WkWMP+SFVtjVET5+5l15pzeYM6gJKXfIUmSJEmSJI1qOSLiOI01zQLmzwM3Uj8wfhD4bJ05/1VnziAuyjVcCfx3s78ASZIkSZIkSVJ3GgbMmTkf2A84FVhEKbBdCvwO2DczZ9WZMwfYE/gipRYbMaDrNuATwAvKv0OSJEmSJEka8Q6ecXDd+7tvsDunvfw0Tnv5abxsy5f1uSqpsQnNBssh8hsjYhVKL/V7LDMXtpgzDzgOOC4iVgemA+N7VG8rS4DHDZUlSZIkSZK0Mjp292P5/d2/H3L/6/t9nemTpgOw1uS1+l2W1FDTgLkiMxfRxQv8MnMuMLfTeZIkSZIkSdJYtN7U9TjrsLM45IxDABgf4zn38HOfDpelkaatgFmSJEmSJElSf2wxfQuuf/P1gy5Dakuzl/xJkiRJkiRJktSQAbMkSZIkSZIkqSsGzJIkSZIkSZKkrhgwS5IkSZIkSZK6YsAsSZIkSZIkSerKhEEX0K6IeCbwZmAvYH1gETALuAG4GDgrMxcOrkJJkiRJkiRJGlv6HjBHxKROguCImAx8A3gbEJXb5T8TeAHwHmBORHwN+HJmLuhdxZIkSZIkSZKkevrSIiMipkXE5yLiPmB+RDweEV+NiIkt5k0HLgLeXq41yldWHqm6pgOfAa6KiK2K+SWSJEmSJEmSpIrCTzBHxPbA2cAMlp88ng58AFgDeGuDeeOAM4Hdy7ey5pHa75TX3w64NCL2yczbh1G6JEmSJEmSJKmJQk8wl8Plv7I8XM6qK4A3RcR6Dab/J7Bv1fOw4onl2ouqZ9cFzomIKb39RZIkSZIkSZKkisIC5ohYHfg1MK18qzokrt5/xzpzZ1AKmGuD5aZb1jzzDOC/OypakiRJkiRJktS2Ik8wfxzYhtYh8aIGcydXzetU5YT0MRGxYRfzJUmSJEmSJEktFNKDOSI2AI6h/qnl6t7J84DrauauC7yJ+j2W663x9NSqPyvjE4H3AJ9oq3BJkiRJkiRpJfW1q77Go089OuT+lAlT2H2D3dlt/d2I6OYsp9RYUS/5ewcwleUniWtVQuD/yswnasaOphQMN5pbL7Su3K8NsgN4PQbMkiRJkiRJGuWW5TJOvvHkumPfvfa7HLX9URy727GGzOqpolpkHEH9U8bV976cmV+t88zrG6xZHSzfCXwEeBlwJPA3Vjy5XP1/JTMiYkifZ0mSJEmSJGks+elNP+WuOXcNugyNMj0/wRwR2wA7sOKJ4urgdxnw/sz8Tp25WwA70/g0cgKXAC/JzPlV804DTgdeUWcuwF7A9cP5XZIkSZIkSdJIMHn85NYPNXDNI9ew5fQte1iNxroiTjDvXfO9OlxeBLyhXrhcdlCde9WnnhcBR1aHywCZuQR4MzCrzhyAXVsVLUmSJEmSJK0Mdl2/+6hr0dJFPaxEKqYH88517lVOHx+bmac1mfviBvcr8/8vM++r90Bmzo6IHwIfY2jA/IymFUuSJEmSJEkrib022otXbf0qTr/t9I7nXv/o9ewwc4ch96dOnMoW07dgXBTVUVejVREB805Vn6tbW1yamd9uMfcF1O/dXPHDFvN/Rylgrt1/0xbzJEmSJEmSpJXCuBjHp/b6FEdscwRXP3I1i5ctHvLMn+79E9fNvG7I/bPuOIuz7jir7rrrTFmHL+/7ZXbbYLee16zRq4iAeQPqh8TfbzYpIp4FrE393s0A92fmFS32vqnB/XVazJMkSZIkSZJWGuNiHDusswM7rDP0NDLAA08+UDdgbubRpx7l3ee/mwuOuIDVVlmtF2VqDCjizPv0Bvf/2mLeCxvcr5yAPruNvZ9ocL/7zueSJEmSJEnSGPHUkqf4+8N/H3QZWokUETBPa3D/wRbzXtRi/Pw29l6lwf1JbcyVJEmSJEmSRoVt19q267mPL3i8h5VotCsiYG4U5g5tBlMWEeOB/VixJUb152XABW3svXaD+wvbmCtJkiRJkiSNCi/a7EVsstomgy5DY0ARPZjnA/WatKwNzGww53mUTj5X91+m6vPVmdmo/UW17Rrcn9fGXEmSJEmSJGlUWGvyWpx08En87Oafce3Ma1m8dOjZz/ufvJ/ZC2f3vziNKkUEzHOoHzDvBPypwZwjm6yXwB/b3Lu2zUYloH6kzfmSJEmSJEnSqLDBqhvwod0+1HD8k5d+kjNuP6N/BWlUKqJFxl2seAq54tX1Ho6ItYDXsGJLjFq/a7VpREwCjqqzTpZrkiRJkiRJkiT1UBEB86013yttL94cEfW6i38OWL38OarmVDwKXNbGvp8ANqpZp+LmNuZLkiRJkiRJkjpQRMB8adXn6qB3EnBOROwKEBETI+KjwDsZ2nu5MjeBX2dms9PNRMRrgeNofAr6r+2XL0mSJEmSJElqRxE9mM9nedBbHRwnsAVwRUTMBFYFprax3qnNBiPiA8CXKIXllf2qg+YlwEVt1i5JkiRJkiRJalPPTzBn5r3AX6jfh7kSAK9HKWAOhp5erg6Hb8vMv9TbJyK2iIg/Al+lFJTXrlNZ+7zMfLy7XyNJkiRJkiRJaqSIFhkA36pzr/okc/VVL4iuhMPfqLd4RBwFXA/sz9ATy7W+31bFkiRJkiRJkqSOFBIwZ+ZvgL9VvlYNBUNPGa8wterz3cCPateOiL2BUyi116gOl6sD7Mr9azPzt53/AkmSJEmSJElSK0WdYAb4N2BB+XPtCePaoLl2LIF3ZebiOuNvrFqzEiZXh8tUfX5fhzVLkiRJkiRJktpUWMCcmf8EjgSWVW7RvJVF9YsBP5+Z5zV4br2qz03ba2TmJe1XLEmSJEmSJEnqRJEnmCutMg4H5tK4B3N18BzACZn5iSbL3lJvK1YMr88Aju26cEmSJEmSJElSS4UGzACZeTbwbOD/sWJLi9prJnB0ZrZqa/E94InK8qwYTifwVeCIzGx2WlqSJEmSJEmSNEwT+rFJZt4LvDYiNgVeCexOqdXFQuBe4C/AmZm5sI217o+Ig4GTgW3Lt58CzgG+kJlX9f4XSJIkSZIkSZJq9SVgrsjM+4Bv9WCdvwHbRcQWwHjg3sxcNNx1JUmSJEmSJEnt62vA3GuZedega5AkSZIkSdIYd+9lcOLeQ++PnwCb7A57HQNrbdH/uqQ+WKkDZkmSJEmSJGlEeOTG+vcfuhZuORfefgGsvkF/a5L6oPCX/EmSJEmSJEmjwlOPdzdvzgNw89m9rUUaIQyYJUmSJEmSpHbM/Gf3cx++oXd1SCPIiGmRERHTge2BGcC6wFQggHnALOAe4KbMfGxQNUqSJEmSJGkMyxzMXGkEG2jAHBHPBw4HDga2phQot5pzB/BH4NeZeX6xFUqSJEmSJEll48YPugJpxOl7i4woeUtE3AhcCLwP2LZcS7RxbQX8O3BeRNweEe+JiBFzEluSJEmSJEmj1A6HNx7790tL174f6V890gjQ12A2InYHfkypFUb1aeVO/xuBytwtgW8B74+If8/MC4ZfpSRJkiRJklTHmpvXv//KH8AGzyp9vv/K/tUzSl1060wen7+47thW663Gc7dYi8kTPU0+UvQtYI6IDwOfK+9ZCYhrg+WWLTLKc6rnVU41nxcRX87M44ZbqyRJkiRJklTXJ2fBiXvCrNtK3w/7Huz02sHWNMqcec2DnHnNgw3Ht1pvNU59+56su/qkPlalRvoSMEfEN4FjGBostxMoD1mu6nN12DwO+EhEbAG8ITOXdVOrJEmSJEmS1ND4CfDevw+6ilFp87n/YtqieU2fuXfa+tz+yJN87Y+38PnDn92nytRM4QFzRHwOeG/563CC5brLV62b5e9HAAuAo3u0hyRJkiRJkqQemzi+FO0tnLAKAJ+97H9bzjlzy+fxvWcfxt/ufKzQ2tS+QgPmiHg1cBxDW1q0Uq8nc7N5wYoh81ERcXVmfrPdWiVJkiRJkiT1z66br8lpf7+fSzfakU/u9VYmLl3a9Pljrj2d1RbNB+Cpxc2fVf8UFjBHxHrAD2l9arndF/y16tdcGzJ/MSL+kJn/bHN9SZIkSZIkSX3yip024vR/PMAVdz3G39ffruXzb7/h7D5UpU4VeYL5f4DpLA98azU71TwPeAJYCqwBrEbj3svV96Pq/irAN4GDOi9dkiRJkiRJUpGmrjKBU/5tDy745yNcc9/jLFk29BzqvIVLOO3v9w+gOrWrkIA5IjYD3kTj08nV4fBi4GzgXOAfwA2ZubhmvXHAM4FdgQOAVwFTWPHEMlVrVu69OCL2yszLevCzJEmSJEmSJPXQlFXG87Jnb8jLnr1h3fEHZz9lwDzCjSto3fewPLyud/I4gGXA14CNM/PVmfmjzLy6NlwGyMxlmXlTZv40M98EbAh8GlhYtW4j7xvmb5EkSZIkSZIk1VFUwPxahoa+1aeWHwKem5kfzsxHO108M+dk5n8DOwG3sWJrjOr9AnhFREztdA9JkiRJkiRJUnM9D5gjYidgs8rX8p/V4fKDwAsy8x/D3SszbwP2A26ts0/FFODFw91LkiRJkiRJkrSiIk4w79PgfuWU8bsy8/ZebZaZ/wKOYnm4XK9dxt692k+SJEmSJEmSVFJEwLxTzfdKq4oELszMs3u9YWZeCZzKiieXqz2713tKkiRJkiRJ0lhXRMC8RZOxUwvYr+Lnde5Vwu0tC9xXkiRJkiRJksakIgLm9ZuMXVzAfu2svW6B+0qSJEmSJEnSmFREwLwq9fsgAzxUwH4AZOaTwJOVr3VqkiRJkiRJkiT1UBEBc7M1nypgv3bWb9SbWZIkSZIkSZLUpSIC5nlNxpq1zxiWiJgArNlguOhgW5IkSZIkSZLGnCIC5sdofGJ49wL2q9gZmFD+XLv/YwXuK0mSJEmSJEljUhEB811Nxl5XwH7N1g5K/Zib1SRJkiRJkiRJ6kIRAfONde4lpbD38IjYsdcbRsRGwDto/HLBG3q9pyRJkiRJkiSNdUUEzH+t+V7drmI88POIWK1Xm5V7L/8fUFmzXnuO2pokSZIkSZIkScNURMD8N+Dx8ufKieKo+rw98KeIWG+4G5WD6jOB/Vh+Srp6X4AlwB+Hu5ckSZIkSZIkaUU9D5gzcwml0Lf2JHElZA5gD+DGiHhb+QRyxyLi1ZRaXxzc6JHyfn/KTF/yJ0mSJEmSJEk9VsQJZoDvNLhffZJ5beD7wF0R8eWIeGFErNpowYhYJSL2iojPRMStwC+BzVgxuK7nW139AkmSJEmSJElSU12dHm4lM/8REX8CXszQ8Lc6ZA5gY+CD5Ssj4mHgAWAesAyYCmwAbMLyQLy2FUb1+pX9ErgyM//Qu18mSZIkSZIkSaooJGAu+yBwNaVQuFHIXBsQB7Bh+cqa56s1Gsuaz+/tpnBJkiRJkiRJUmtFtcggM28A/ovGrSuCFU8iV1/V49FivN66CXwhM68c3q+QJEmSJEmSJDVSWMBc9jngd6zYFqNWdZBcUS9QbvRs9ZzKn38APtl11ZIkSZIkSZKklgoNmDMzgSOAixjaFqOeaHE13Kpq/sXAq8p7S5IkSZIkSZIKUvQJZjJzAXAQ8HOGtsQY9vKsGC7/EjgoM5/qwdqSJEmSJEmSpCYKD5gBMnNRZh4JvAWYTf3ey20vx9BgeQ7wtsx8fWYu7EnRkiRJkiRJkqSm+hIwV2TmycAzgK8Ac2n+or9GF1Xz5gPfBLbKzB/35UdIkiRJkiRJkoA+B8wAmTk7Mz8CbAy8DTgHWEDr/suVazHwJ+BdwMaZ+R+ZOavfv0OSJEmSJEmSxroJg9o4M+cBPwZ+HBHjgWcDOwCbA+sCU8uPPgXMAu4BbgKuzcxF/a9YkiRJkiRJklRtYAFztcxcClxdviRJkiRJkiRJK4G+t8iQJEmSJEmSJI0OPTvBHBGTKL3AbzNgdWAiy9tb3JWZ9/VqL0mSJEmSJEnS4A0rYI6ITYE3AocAu1AKlRs9+zhwCfBr4FeZOX84e0uSJEmSJEkrjUXzYHad85cTp8Kqa/e/HqlHugqYI2Jj4PPA64Dxldstpq0FvKJ8fS0ivgp8JTMXd1ODJEmSJEmStNK44Velq551toVDT4BNd+9vTVIPdNyDOSKOBG6mdHJ5AqVgOYBs46o8uxbwWeDqiNhm2L9CkiRJkiRJWlk9egv83+Ewb9agK5E61lHAHBGfAU4BVmNoqAzLA+R6FwwNm7cH/hYRewznR0iSJEmSJEkrtYVz4K4LB12F1LG2A+aI+A/gU6wYLMPQELnhEtQPm6cDv4+IrdovW5IkSZIkSRqB1n1m93Pn/qt3dUh90lbAHBF7AV+ifrDcjdqgeQ3gtIgY33CGJEmSJEmSNNJtshtssOOgq5D6pmXAHBHjgB+w4sv8ug2Whyxf9Xkn4H09WleSJEmSJEnqv/ET4U1nwW5vLb28b9omQ69VVht0lVLPTGjjmVcDO7C8b3I7supzO3Mqa/9nRHw3Mxe0uY8kSZIkSZI0skxdC17+tcbjvz8OLj+xf/VIBWqnRca/t7lW7Qv/Wt2vqA6g1wKObHM/SZIkSZIkSdIANQ2YI2IdYF8ah8MVtX2Z5wMPAk8ytN9yK29s4xlJkiRJkiRJ0oC1OsH8vKpn6rW6qJxMDuA24J3AJpm5emZumpnTgU0p9VZ+oPxco5C5ss7zImJ6R79CkiRJkiRJktR3rQLm3ZqMVfdkPgF4Vmb+MDMfXOGhzAcy8zvA9sB51A+Zq8PrcZSCbUmSJEmSJEnSCNYqYN6qwf1KuJzAaZn53sxc0myhzHwSeCVwU9UajTQLtiVJkiRJkiRJI0CrgHnzOveqg+FlwEfb3SwznwI+RP12G9W2a3dNSZIkSZIkSdJgtAqY16b+SePK6eW/ZOa9nWyYmX8A7qh8bbD2jE7WlCRJkiRJkiT1X6uAec0W4+d3ue/vaPzSQID1u1xXkiRJkiRJktQnrQLmKS3Gr+1y38tbjLcKtiVJkiRJkiRJA9YqYJ7YYvzOLve9tcX4pC7XlSRJkiRJkiT1SbsBc6OX8s3sct8H69yr3mOVLteVJEmSJEmSJPVJq4C5lTldznt8mPtKkiRJkiRJkgZsWAFzZi7qcmq38yRJkiRJkiRJI8SEQWyamRnRqOuGJEmSJEmSpEE5565zmDR++SvSbn7sZk6+8WQApk6YymqrrNZw7uoTV2fPjfbkvc95L6tOXLXoUjUCDCRgliRJkiRJkjRYmVn3/uUPXc7lD11ed2z+kvnMXzK/4ZqP8Ah3PHEHP7v5Z7zlWW8h6rzabdqkaey14V5st/Z23RWuEcWAWZIkSZIkSRqDLrr/okLX//ENP2449nW+zn8+9z95/TNfX2gNKt5wX/InSZIkSZIkaSU0e+Hsge7/pSu+xLzF8wZag4bPgFmSJEmSJElS3y3JJVz/6PWDLkPDZMAsSZIkSZIkaSAWLV006BI0TMPqwRwRz4c6nbp7YBhrLwYeB27LzKW9rUqSJEmSJEka/V4y4yUAnHv3uXXHv7HfN9hhnR0A+OlNP+UnN/2kb7VpZOkmYI6qPy8c5v61AXIv114QEWcCn87M24a5liRJkiRJkjSqfGyPj/GFK74w5P6Fr7mQtaesDcCHd/8wL/p/LxryzIs2X37v2N2PZe6iufzm9t88fe/AzQ9ku7W3e/r7mbefyd1z7u5h9RophnWCmYJOL/do7SnAa4FXRMSBmXlZD2qSJEmSJEmSRoU3bvdGLnngEi554JKn7x2/9/FPh8sA601djz+/5s8cf9nx/Pm+P/P2Hd/Oe5/z3iFrHb/P8Ry/z/FkJhFDY72rHr7KgHmUGm7AnMOY2ypAHs7a1XusCpwaEdtm5sIerClJkiRJkiSNCt998XfJTB548gE2WX2Tus+sM2UdvrX/t9par164rNGt1Uv+lpX/bBT2xjCuVoazdmX9St2bAge3sackSZIkSZI0pkREw3BZaqXVCeY5wPR+FFKAYMVgfNtBFSJJkiRJkiRpqMxkWS4bcj8IT0OvJFoFzHcBz6E37SoG7alBFyBJkiRJkiRpuWMuOKbu/dVXWZ19NtqHt2334T5XpE61CpgvohQwjwa+5E+SJEmSJEkagKsevqqj5+cumsvv7/499855EHhDMUWpJ1r1YD4RqLwYL1fCq+KPmfn39v9aJEmSJEmSJPXKU0u6ay5w02PXERMf63E16qWmAXNm3g68BVhE+y/nGwmqX/Z3CfC6wZYjSZIkSZIkqRvjDJhHtFYtMsjMUyPiEkpB897ARsCqjNyweQkwG7gBOBM4MzNHQw9pSZIkSZIkSRpRWgbMAJl5H/BfBdciSZIkSZIkaYyJ8jnWxDOiK6NWPZglSZIkSZIkaVi2WmOruvcvff2lXPfm67juzdexxwZ79Lkq9YIBsyRJkiRJkqRC/eylPxty75BnHMK0VaYNoBr1UlstMiRJkiRJkiSpW1MnTuWao67hrDvO4rKHLuMdO76Drdasf6pZKxcDZkmSJEmSJEmFGz9uPK/c+pW8cutXDroU9ZAtMiRJkiRJkiRJXTFgliRJkiRJkiR1xYBZkiRJkiRJktSVhgFzRGwYEWv1s5iRJCLWiogNB12HJEmSJEmSJI1UzU4wXwQ8EBEnR8TOfapn4CJix4g4GXgAuHCw1UiSJEmSJEnSyNUsYJ4BTAKOAq6KiD9GxEv7UtUARMRLIuIPwDWUfvMkYIuBFiVJkiRJkiRJI1izgHkekECUr/2BsyPi1og4JiJW70eBRYqI1cu/5Rbgt8CLWf57AeYOrDhJkiRJkiRJGuGaBcy3lP9MVgyatwK+CTwYET+OiOcVW2LvRcQLIuIU4CFKv2Vrlv++LD+WwM2DqVCSJEmSJEmSRr5mAfNPWH6StxK8VofNqwJvBi6KiH9GxCciYtsiix2OiNghIo6PiNuAC4AjgakM/W3VftjfKiVJkiRJkiRp5TGhydh3gRcBr2R58Fodxla+A2wD/BfwX+UA93fAH4BLMnN+r4tuR0RMBfYFDgZeBmxZGap6rDpQjprvP8rMUwotUpIkSZIkSZJWYg0D5szMiHgtcCLwNoYGy7UnfqvD5q2BDwCLI+LvwF/L1xWZ+WAvf8DTm0dsDOwB7AXsA+zG8t/XKFSujFWfzF4GfA74dBF1SpIkSZIkSdJo0ewEM5m5BHhHRJwHnACsy4ph7NOPUj9sXoVS4LsX8CGAiHgMuI5Sj+e7y9dDwKzyNQ9YlJmLys+vUl5nVWDt8rUhMKN8PRPYEVirpvxWoXLtWAB3AW/JzIvq/HVIkiRJkiRJkqo0DZgrMvNXEXE+8AXgrZR6N1cHs+2EzRVrA/uVr4Yiaqc1Ve/hZqFy9XgAiym97O8zg2rpsbKIiInA4cAWwJ3AWZm5YLBVSZIkSZIkSRqEtgJmgMx8HHhnRJwAfAV4MfX7MTcLm6nzTC/U26PRPrX1/hr4aGbe0eOaiIjxwIHAocDuwGbAdEqntB8GbgbOBc7MzId7vX+vRcSawB+BXatu3xoR+64M9UuSJEmSJEnqrXGdTsjM6zLzQEovzruZ5UFtvTA56lzVz/bqarbX06WzvLVHAFcAz8/MVxcULh8B3AacA7wT2AVYB5gIrAFsCxwGfB+4KyK+XA5wR6SImACcwYrhMpR6bn+m3/VIkiRJkiRJGryOA+aKzDwXeDZwFHA9y4Pb2uC3Vr0guBdX3TIZGixfDhyWmXtm5qVd/fgmImJSRJwEnEapjUQ7pgAfBq6MiO17XVOPfBnYt8HYS/pZiCRJkiRJkqSRoeuAGSAzl2XmzzJzZ0onmi+i8Unlfml0svlcYL/M3Dszzypi43JLjF8AR3e5xDOAyyJix54V1QMRcRjwgSaPbNSfSiRJkiRJkiSNJMMKmKtl5rmZ+UJKrSC+AfyL1m0xhr1tgzUr+z4AfAnYITNflpl/6cGezRxPqe3FcEwDzoqIdYZfzvBFxObAj1s8NrEftUiSJEmSJEkaWXoWMFdk5jWZ+UFgU0ovuDsZeIT67Sx60XuZmrUfBH4E7J+Zm2XmxzLz5l7/zloRsTPw0QbDCfwE2AdYF9iA0ov/Lmvw/Azgq72tsHPlvsunAiO2N7QkSZIkSZKkwZlQ1MKZuQz4U/kiIp4J7Fe+9qQUQDfqndyuZcC9lPoq/xn4c2bePsw1u/UZYHyDsWMy88Sae2dFxDmUWmq8qs6cIyPi65l5Te9K7NjngL0GuL8kSZIkSZLGuBj/FIybD7EMYimMm0/GUhYtXcQq41cZdHljXmEBc63M/CfwT+B7ABExmVLP4a2BrSid6l0VWK3856rlqfOAJ6v+fBi4rXzdnpmL+vUbGomIzYBDGgyfWydcBiAzl0TEWyi9PG/dmuFxwHHAa3tWaAci4mDg2EHsLUmSJEmSJFVM2eRnAMQFS5g4bTarb3sj84C9fv5f7LnRnnx2n8+y5mT/A/xB6VvAXCszFwA3lq+V3aE0Po39hWYTM3NORJxA6QR0rUMiYnpmPjHM+joSERtRaulR+5tmA2v0sxZJkiRJkiSNDVf864qOnl+0bBF/uf8vHHPBMfzspT8rqCq10vMezGPUyxrcnwVc2sb8Xza4Pxl4RVcVdSkixgE/Y+iJ6iXAu/pZiyRJkiRJktTKdTOv41/z/jXoMsYsA+be2KXB/Usyc2mryeX2IY80GN6766q68ylKfbLr3b+8v6VIkiRJkiRJrRkwD44B8zBFxIYMPe1bcUMHSzV6tm8v2YuI/YBP1hk6H/hiv+qQJEmSJEmStHIYWA/mUeSZTcZu6WCd24D9O1y/ZyJiXeDnDP2XDjOBozJzWUSjNtOSJEmSJElScXLpFOBJGr0G7f/d+v+4+IGLh9xfa/Ja7LPRPsyYPqPQ+sYyA+bh26jJWCdn8x9scH9yRKyXmY1aaAxblJLjnwAb1gwlcHRmPlTU3pIkSZIkSRLAdmttx82P3Tzk/tybPweMJxd9nly6EFgw5Jmz7jir4brjYzxffsGXOWDzA3pYrSpskTF8GzQZ6yQUfqzJ2KYdrNONjwAH17n/zcw8p+C9JUmSJEmSJH7+sp8PuXfMsz8CjB/WuktzKcdfdjzLctmw1lF9nmAevnWajD3RwTpzmoyt3cE6HYmIvYDP1hn6B/DRovaVJEmSJEmSqk0YN4Hr33w9tz5+K9c8cg2HbnUos+Yu4/Nc8PQzmd21cJ29cDZ3PXEXz1jjGb0qV2UGzMM3ucnYog7WWdjlHl2LiDWBXzD0n4MngddnZif1S5IkSZIkScO2zZrbsM2a25S/PbXCWC5dbci9di1c2ix+U7dskTF8k5qMdfJP7eImY1M6WKcTPwY2q3P/mMy8tYgNI+IdEfH3iPj7zJkzi9hCkiRJkiRJo9Syheux9KmNB12GqniCefiaBcydNHZpdr6/2R5diYj3AYfVGfp5Zp7S6/0qMvMHwA8AdttttyxqH0mSJEmSJI1COZ7597yTdde9j7e9aBLJ0Hjpl7f8kkefenQAxY1NBszD1yxE7qQDebP/LZZ2sE5LEbEL8OU6Q3cA/97LvSRJkiRJktSmf54Da84Yen/yGrDJbjCh52cQV065ChMW7sA7d3pR3eEL7r3AgLmPDJiHb0GTsVU6WKfZsz3rhRwRqwO/rLPfYkp9l+f2ai9JkiRJkiR14J5LSlc9k6bBG34Jm+/d35qkFuzBPHzzm4yt3sE6qzUZe7KDdVr5HrBVnfsfz8wre7iPJEmSJEmS6rn38s7nLJwDp74OljZ7jZfUfwbMw/dIk7E1Olin2bOPd7BOQxHxSuANdYb+DHwnIiY3u2jSC7r8jP88SZIkSZIktfLgP7qbt+AJePCanpYiDZctMobvwSZj63ewTrNnH+5gnWY+0eD+C2l+ErsdTwHLIuIx4Argm5l53jDXlCRJkiRJUrUFTwy6AmkFnjgdvjuajG3WwTqbN7i/FLi/g3Wa2aFH6zQyDlgHeClwdkSsU/B+kiRJkiRJkgbIgHn4rgOWNBjbroN1ntng/q2Z2avmOrf1aJ12rAJs2cf9JEmSJEmSVg5T1mo8tu1LS9dUz+1p5WCLjGHKzAURcR2wS53h57azRkSsAWzbYPjC7iqr61PAr+jPv1iYC9zch30kSZIkSZJWLi/7Kvzq34bef88VsG45Ivrp4XDH+f2ta5S76uGrmL94aJfYtaeszYxpM4iIAVS18jNg7o3fUT9gfk5EbJiZD7WY/zKg0T/BFwyrsiqZ+ZuIeDHwIUrh91r0NmxeBjwKXAUcn5lze7i2JEmSJEnS6PCsw+HWP8B1v1h+78WfWR4ua1iW5tK697905ZcazpkxbQbfffF32WT1TYoqa9QyYO6N04FP1rk/Dng7cHyL+e9scP8J4A/NJkbEBOBQYBvgLuA3mbmw0fOZ+Wfgzy3qabTXjPIe9db1X/FIkiRJkiS16/Dvwyu/B4/dCWs/Y9DVjCq3Pn5rx3PunnM3H7zwg5z2itMKqGh0swdzD2TmtTQObT8UEY1e4EdEHAE8v8Hw95udAo6INYHLKLW9+B/gVODqiPBftUiSJEmSJI10EYbLI8jNj93Mo089OugyVjoGzL3zKSDr3J8G/D4ihrzELyIOA05usN484Jst9vwGsFvNve2An7SYJ0mSJEmSJKnGEwufGHQJKx1bZPRIZl4SEd8F3l1n+JnA9RFxAaUX360C7Ak8p8mSx2Xmg40GI2IS8NoGwy+MiM0y8972qpckSZIkSZKkzhkw99YHgR2p3/JiAnBg+Wrl55n57RbPrANMajK+CdDrgHlZg/v1Tm5LkiRJkiRJI8aak9Zki+lbAHDTrJtYsHTBgCsaHQyYeygzF0bEIcAZwAu6XOb/Af/WxnOzgMXAxAbjD3W5fzMPUwqTa1/oV8RekiRJkiRJUsf233R/LrjvgiH3//K6vzz9+dAzDuXOJ+7sZ1mjVl8D5oiYCOwL7AVsD2wArAFMoRSU9qIn9BLgMeBa4KeZeUkP1mxbZs6OiAOAzwH/Qft/xwsp9XH+SmY2Oilcvc+CiDgbOLzO8JWZeVe7NberHKBfw9DWHlf2ei9JkiRJkiSpG9/c/5scdsZh3PHEHU/fO+9V5w2wotGtLwFzRKwOfBh4B7Be7XABWyawO/C2iDghM99XwB6NN89cDHwkIk4GPkSpV/KqDR6fBfwC+FIXPZPfDWwFPLvq3j3AGztcpxMfBH7L8t/zGKVgXJIkSZIkSRoRzjjsDAAWL1vMxHGNGgCoFwoPmCPiZcD/UgqWG4XJve7hW73PeyLi+sz8YY/3aCkzbwLeGhHvBnYDdgDWovR7HwWuA67OzCVdrv9wROwKHELpRYL3Aadn5vxe1N9gzwsj4tnAwZRadPw2M22RIUmSJEmSpBHHcLl4hQbMEfEe4Jssb33Rr5fBVe8TlE769j1grsjMhcCl5avXay8Bft3rdVvseSdwYj/3lCRJkiRJkjTyFBYwR8ShwLcoBby1gW8/VO+5bZ/2lCRJkiRJkqQxo5CAOSLWBn7AiuFyv4LleuYNcG9JkiRJkiRJGpXGtX6kKx8F1mXw4XIl4L5gQPtLkiRJkiRJ0qjV8xPMETEFeCfthcv96Mk8F/hUH/aRJEmSJEmSpDGliBPMBwGrlz83CpeTFQPooq7rgRdl5i09+3WSJEmSJEmSJKCYHswvbDGeLA+elwI3AHcC91PqlTyf4Z1sXgw8BlyVmVcPYx1JkiRJkiRJUhNFBMzPanC/+sTy34BvAOdk5twCapAkSZIkSZIkFayIgHl9hp5ArpxaTuDzwCcysx/9lyVJkiRJkiRJBSkiYF615nt1uHxhZn68gD0lSZIkSZIkSX1WxEv+5jcZ+0EB+0mSJEmSJEmSBqCIgPkxlr/Er9ZfC9hPkiRJkiRJkjQARQTMtzYZe7iA/SRJkiRJkiRJA1BEwHxFk7FVCthPkiRJkiRJkjQARQTM5zYZ26aA/SRJkiRJkiRJA9DzgDkz7wUuoNSHOWuGX9Hr/SRJkiRJkiRJg1HECWaAL9Z8T0qB87siYlpBe0qSJEmSJEmS+qiQgDkz/wicQSlUrrYu8J0i9pQkSZIkSZIk9VdRJ5gB3g48UPW9cor5jRHxtQL3lSRJkiRJkiT1QWEBc2bOAl4OPFF9m1LI/P6IuCAiti9qf0mSJEmSJElSsSYUuXhmXhsRBwG/Bdap3KYUMu8HXB8RFwDnATcA9wKzKIXSCzNzWZH1SZIkSZIkSZK6V2jAHBG7AFsCpwAfqhqqhMwB7F++6s0HWNrhtosphdRXA6dk5q87nC9JkiRJkiRJakMhAXNE7A6cBGxXO1T1Oevcq6fTGicAmwAbAy+PiF8AR2ZmNp8mSZIkSZIkSepEz3swR8QzgD9RCpej5lrh0fKVBV2V9V8HHNPr3ylJkiRJkiRJY10RL/n7KLB6+XNt6FtPbQjdLJTuRHXQ/PZhrCNJkiRJkiRJqqOIFhl70n77i3Z0u0Z1oL11D+qQJEmSJEmSJFUp4gTzKgWsOVxzB12AJEmSJEmSJI02RQTM/6A3J5d7odLj+dxBFyJJkiRJkiRJo00RAfOXgIXlz0W9wK+dq2Im8MkCfqckSZIkSZIkjWk9D5gz8xrg5cAdtH6BX9HXH4F9MvPeXv9OSZIkSZIkSRrrinjJH5l5PrBNROwAbAusRv/aZiwBHgeuzsyH+rSnJEmSJEmSJI05hQTMFZl5I3BjkXtIkiRJkiRJkgajiB7MkiRJkiRJkqQxwIBZkiRJkiRJktQVA2ZJkiRJkiRJUlcMmCVJkiRJkiRJXTFgliRJkiRJkiR1ZcKgCwCIiAB2BHYAZgDrAlOBAOYBs4B7gBuB6zJz6WAqlSRJkiRJkiRVDCxgjojJwBHA4cCLKQXK7VgQERcCvwZ+kZnziqlQkiRJkiRJktRM31tkRMRqEXE8cD9wMnAIsCql08rtXFOAg4EfAA9ExJcjYs0+/wxJkiRJkiRJGvP6GjBHxCuB24GPA2uxPDTODq/KvGnAB4E7IuLoPv4USZIkSZIkSRrz+hIwR8l3gF8B6zE0VIb2TzDD0LB5DeBHEXFqRKzSh58kSZIkSZIkSWNe4QFzRIwHTgfexYrBMgwNjttakvphcwCvAf4YEe32c5YkSZIkSZIkdakfJ5hPAg5jebgMnYfKjdQGzQE8D/h1OdiWJEmSJEmSJBWk0IA5Io4BjmRo7+RmmvVebrgVywPsAA4APjuc2iVJkiRJkiRJzRUWMEfEFsCXWfHUcj2NQuR67TPaCZsrIfOxEfHcziuXJEmSJEmSJLVjQoFrfwWYxPLAt1ZtmFzxIPA48ASwlNIL/NYANmFo3+XaudVtOMYB3wIMmSVJkiRJkiSpAIUEzBGxHaW+y41OGleHwzOBnwLnAv/IzMcbrLka8BxK7S+OphQ4V7feePrRqnu7RcRBmfmHYfwcSZIkSZIkSVIdRbXIOIbloW+9FhcBPAm8D9g4Mz+cmec3CpcBMvPJzLw4Mz8FbA68GXiUFU8tN6pFkiRJkiRJktRjPQ+YI2Ic8CqGhr7VL/m7GdghM7+TmUs63SNLfgpsB/yV+iFzZb8DI2KNTveQJEmSJEmSJDVXxAnm3YD1yp+reyZXQuBbgP0y8/7hbpSZjwEHAX9jxZC5+tT0BEptNSRJkiRJkiRJPVREwLxPzfes+fzWzJzZq80ycx6ldhkL6+xXsXev9pMkSZIkSZIklRQRMD+rzr3K6eLfZuZfe71hZt4KnMSKJ5db1SRJkiRJkiRJGoYiAuYtmoydVsB+Fb+oc6/SmmNGgftKkiRJkiRJ0phURMC8TpOxKwrYr521m9UkSZIkSZIkSepCEQHzqtTvgwzwYAH7AZCZC4AnKl9rhqcWta8kSZIkSZIkjVVFBMzNLC14/SUFry9JkiRJkiRJKptQwJrzWP5Sv1obAXcVsCcRMQlYs8Hw/CL2lCRJkiRJkvrq0m/Aw9cPvb/6hrDlC2H19fteksa2IgLmmU3G9qKggBnYg9KJ7MqL/aoD7kcK2lOSJEmSJEnqn7svLl31TFwVjjwdNt+rvzVpTCuiRUazAPmoAvarOLLOvUrQXFSoLUmSJEmSJI0Mi+fBWe8ddBUaY4oImK+r+V4JeQM4MCL27fWGEbEtcDSNXy5YW5MkSZIkSZI0ck2Y1N28WbfBk80aDEi9VUTA/NcG9ysh888iYoNebRYRqwGnARMrtzqoSZIkSZIkSRp5Nt2j+7lLnupdHVILRQTMVwH/Kn+unCiOqu8bA5dExPbD3SgiNgEuBHZkeYBdvS/AAuBPw91LkiRJkiRJ6ptd/w02es6gq5Ba6vlL/jIzI+JXwDGsGPRWWmUksCVwVUR8FfhaZj7WyR4RsSrwLuATwOqNHivv9dvMfLKzXyFJkiRJkiQN0JQ14M2/hTvOhwevhlw29JkrfwyL5va9NKlazwPmsm8D7yl/rj5ZXB0yTwKOA/4jIs4BzgH+BtyemYuqF4uIccAWwB7AQcBhlILl6hPL9VpjAHx9+D9HkiRJkiRJ6rNJq8H2h5auem74tQGzBq6QgDkzb4uI/we8hqEv3qsOmQOYAhxevgAyIh4D5gHLgKnA2sD4mjVgaAuOyr3KHn/MzMt78ZskSZIkSZIkSSsq6gQzwEeAl1MKkGtPGFeHzNQZW6d8NVLbeqPe/UXA+zorWZIkSZIkSZLUrsIC5sy8NyL+A/g+Q08xw4qnkOuNt9KoJUYlvD42M2/tYl1JkiRJkiRJI9hDTyzgoltn1h2busp4nr3JdCZNGF93XL1V5AlmMvOHEbE78Dbqn1au971R2NwoUK6dl8DJmfmdtguVJEmSJEmStFJ584+vaDg2ZeJ4vvG6nTlohw36WNHYNK4Pe/w7cCpD+yY3Eg2uZqrD69OAt3dVqSRJkiRJkqQRKVjGxGVLmLi0+TVu2VKeWryU9/zsH8ycu3DQZY96hZ5gBsjMZcAbI+Ju4GOV2+U/WwXHLZevWieBL2bmccNcU5IkSZIkSdIIMHfBkqc/bzD/cTaY/zj7Pnhd0znzJkzmLQccx5xJq3LZnbM4ZKeNii5zTCs8YK7IzI9HxPmUejI/g6G9l9sNm2tPQAdwF/COzDx/2IVKkiRJkiRJGhGeXLh4yL2Ttn9Jw+e3eOIh9nvgGtZYOJc5k1blifmLiixP9DFgBsjMCyJiO+AdwAeArSpDdPaiv0oYfQ/wDeDEzBz6T5skSZIkSZKkldazNp7+9OfLNtiB69fZkt9s9YKGzz//gWvY74Fr+lCZKvoaMANk5hLgRODEiNgfOBw4kOVhcyv3AH8ETgf+WG7BIUmSJEmSJGmUmTRh/NOfj9/z31YYe/7W6zz9+fZHnuShJxb0rS4t1/eAuVpmXgBcABARawI7AJsD6wJTy489BcyiFCzflJkzB1CqJEmSJEmSpAG4439eym6f/SOPzy81MHjFThvx7dc/Z4VnPnHG9fzf5fcOorwxb6ABc7XMfBy4pHxJkiRJkiRJEuPHBVd/6sBBl6EGxg26AEmSJEmSJEnSysmAWZIkSZIkSZLUFQNmSZIkSZIkSVJXDJglSZIkSZIkSV0xYJYkSZIkSZIkdWVCOw9FxARgDeDxzFza7uIRMRWYDkRX1XVuMTA7Mxf3aT9JkiRJkiRJGrOaBswRMQP4JnAQMBFYFBFnAx/MzPsbzJkIfAw4GpjRw1rblRFxI3AS8O1OAnFJkiRJkiRJUvsaBswRsQ5wCbAhy08gTwJeBewREbtk5mM1cyYDfwT2pn+nlmsFsCPwVeAVEfGSzFw0oFokSZIkSZIkadRq1oP5E8BG5c9ZdQWwKfCpOnM+CuxTfiYHfAWwH3Bsm38XkiRJkiRJkqQONAuYD6UU1FZUTiRXwtvD6sx5I8sD3sqcfl8VlTqPavIbJUmSJEmSJEldahYwb1T1uV67iw3r3NusxZx+qN13xiCKkCRJkiRJkqTRrlnA/FCLuTPr3Huszr1BmzXoAiRJkiRJkiRpNGoWMJ9J/VPIlf7Kv68z9ruqOVlnvJ8qdZ4x4DokSZIkSZIkaVRqFjD/N/AgK76wr2JOebzWp6rmQHEv8Gt1VdwJfLrZX4AkSZIkSZIkqTsNA+bMfBTYF7iMFV+idwNwQGbeU2fOQ8AewC+BxfT25X2dXPOB/wX2LP8OSZIkSZIkSVKPTWg2mJl3AvtExOaUXvr3cPleszkPAq+PiFWAbYHpwPge1dvKEkp9oG/NzKV92lOSJEmSJEmSxqSmAXNF+bTykBPLLeYsAq7vpihJkiRJkiRJ0sjXrAezJEmSJEmSJEkNGTBLkiRJkiRJkrpiwCxJkiRJkiRJ6ooBsyRJkiRJkiSpKwbMkiRJkiRJkqSuGDBLkiRJkiRJkroyYdAFtCsi1gBeC+wFrA8sAmYBNwAXZ+aVg6tOkiRJkiRJksaelSJgjoiPAR8HpjZ55jbgc5n5074VJkmSJEmSJEljWF9aZETEuIh4e0RcHBEPRcQ1EfHeNuatEhFnAp8DVgWiybUNcHJEXBAR6xT2YyRJkiRJkiRJQB9OMEfEBsAZwO6VW5RaXHwjIjbKzOOaTP8p8Iry52xnO2A/4LKI2CczH+mqaEmSJEmSJElSS4WeYI6IjYErKIXLlZPGWb4C+EC5t3K9ue8Ejqh6HpqfYKbquWcAZ0fE+N7+IkmSJEmSJElSRWEBc0RMBH4FbFK+VR0UV6wC7Fxn7rrAFxgaLDfdkuUBNsBuwLGd1i1JkiRJkiRJak+RJ5g/CDyX+qePq9ULjj8KTG8y3krlhPR/NjohLUmSJEmSJEkankIC5oiYBnyEFcPliupTzIuB6+vMfSfNey5nnevpJao+r1peS5IkSZIkSZLUY0WdYH47sGb5c70TyJVWFt/KzEdrxt5AKRhuNLfRieh6gXQAR7VZsyRJkiRJkiSpAxMKWveIBverQ+BfUmqFUevIFnMDeBw4FbiRUpD9NmAGy1tjRNXn7SJiq8y8vYP6JUmSJEmSJEkt9DxgjojNgD1YHvBWVH//GnBsZmbN3A2AvRh6GrkyNymFygdm5kNV874NnMfyns+1J5+fBxgwS5IkSZIkSVIPFdEi43l17lUHxB/JzA/XhstlB7M8HK7X+mIZ8LrqcBkgM+dSOjU9r84cgF3bL1+SJEmSJEmS1I4iAuada75Xh8ufz8yvNJn74gb3K/N/nZk31nsgM+8HTqJ+3+ZtmhUsSZIkSZIkSepcEQHzTlWfq9tVXA98qsXc/aj/sr6K77eYf0bN98r+m7aYJ0mSJEmSJEnqUBEB86bU76F8QmYuazQpIrYCNqp8rZpX8Sjw5xZ7X9/g/rot5kmSJEmSJEmSOlREwDytwf2LWsx7YYP7lfYYv23Qt7naYw3uT2kxT5IkSZIkSZLUoX4GzPe1mPeiFuN/amPvCQ3uT2pjriRJkiRJkiSpA0UEzI1OCy9sMW9/mvdfPr+NvddocH9xG3MlSZIkSZIkSR0oImB+qsH96Y0mRMTuwDqVr+U/q18QeFNmPtLG3ts2uD+/jbmSJEmSJEmSpA4UETDPbXB/hyZz3tBkLIE/trn3Cxrcf7TN+ZIkSZIkSZKkNhURMN/L8pPH1Q6t93BETAHeSPP2GOe02jQixgFH1axTeUHgXa3mS5IkSZIkSZI6U0TAfHvN90qri3dExAZ1nv849dtjVMwFLmxj3/cAW9WsU3FLG/MlSZIkSZIkSR0oImD+a9Xn6qB3deCsiNjo6cGI1wIfof7p5crp47Myc0mzDSPiBcCXGqwDcFkbdUuSJEmSJEmSOjChgDUvqPleCYoBdgPuiIhrgbWBLavG67XVADi12WYRcRjwE2BS1TrVQXMCF7VfviRJkiRJkiSpHT0/wZyZtwBXsWLQWx0eTwL2AJ5B/XC5Ohx+APh9vX0iYnpE/Ag4HVitzjqVtS/OzH91+3skSZIkSZIkSfUV0SID4MQ69yqBbyVArv7c6NkTMnPIMxGxP/BP4GiGnliu9b/tlSxJkiRJkiRJ6kRRAfMplAJgWDH8rX2JX9D49PKjwHdqF46IHYBzgfWof0q6eo07adFiQ5IkSZIkSZLUnUIC5sxcBrwFWFa5VTUcDA2WqRlP4D8yc16d8bcDE6vWrbdWZY1jy7VIkiRJkiRJknqsqBPMZOblwAeof7K47pSqP0/JzJ83eG7jqs+1wXL1Gqdm5hltFStJkiRJkiRJ6lhhATNAZp4AHAMsqdxqckEpMD4TeEeTZe9ttF3VGpcDb+26cEmSJEmSJElSS4UGzACZeSLwPOBKhrbHqP6+GPgM8OrMXDJ0paf9EFhYWb7qqqxzOnBAZi6sP12SJEmSJEmS1AsT+rFJZl4J7BkR+wCvAnan9JK+hZROJP+FUluMh9tY658R8Vrgx8BaVUNXAZ+zLYYkSZIkSZIk9UdfAuaKzLwUuLQH65wVEZsCewLjgX9m5v3DXVeSJEmSJEmS1L6+Bsy9lJlPAX8edB2SJEmSJEmSNFYV3oNZkiRJkiRJkjQ6GTBLkiRJkiRJkrpiwCxJkiRJkiRJ6ooBsyRJkiRJkiSpKwbMkiRJkiRJkqSuTGjnoYiYAKwBPJ6ZS9tdPCKmAtOB6Kq6zi0GZmfm4j7tJ0mSJEmSJEljVtOAOSJmAN8EDgImAosi4mzgg5l5f4M5E4GPAUcDM3pYa7syIm4ETgK+3UkgLkmSJEmSJElqX8OAOSLWAS4BNmT5CeRJwKuAPSJil8x8rGbOZOCPwN7079RyrQB2BL4KvCIiXpKZiwZUiyRJkiRJkiSNWs16MH8C2Kj8OauuADYFPlVnzkeBfcrP5ICvAPYDjm3z70KSJEmSJEmS1IFmAfOhlILaisqJ5Ep4e1idOW9kecBbmdPvq6JS51FNfqMkSZIkSZIkqUvNAuaNqj7Xa3exYZ17m7WY0w+1+84YRBGSJEmSJEmSNNo1C5gfajF3Zp17j9W5N2izBl2AJEmSJEmSJI1GzQLmM6l/CrnSX/n3dcZ+VzUn64z3U6XOMwZchyRJkiRJkiSNSs0C5v8GHmTFF/ZVzCmP1/pU1Rwo7gV+ra6KO4FPN/sLkCRJkiRJkiR1p2HAnJmPAvsCl7HiS/RuAA7IzHvqzHkI2AP4JbCY3r68r5NrPvC/wJ7l3yFJkiRJkiRJ6rEJzQYz805gn4jYnNJL/x4u32s250Hg9RGxCrAtMB0Y36N6W1lCqQ/0rZm5tE97SpIkSZIkSdKY1DRgriifVh5yYrnFnEXA9d0UJUmSJEmSJEka+Zr1YJYkSZIkSZIkqSEDZkmSJEmSJElSVwyYJUmSJEmSJEldMWCWJEmSJEmSJHXFgFmSJEmSJEmS1BUDZkmSJEmSJElSVyYMauOImAgsyczscN4k4FBgS2AmcHFm3lpAiZIkSZIkSZKkJvoaMEfEmsD7gdcDWwGLIuJM4P2Z+XAb83cFTgc2rbl/OfCZzPxj76uWJEmSJEmSJNXTtxYZEfFS4Fbgk8DWQACTgCOAP0fEqi3mrw+cB2xWnlt97QX8PiJOjogphf0ISZIkSZIkSdLT+hIwR8QngLOAtSkFwll1BbAt8J4Wy7wZWLNmbuWivM5RwPkRMb3HP0GSJEmSJEmSVKPwgDki3g8cX96rNhCudkCLpTauXrbmqg6rnwv8NiJWGV7lkiRJkiRJkqRmCg2YI+Jg4GsMDZZrw2WAyS2Wu6XZViwPmgPYG/hGJ7VKkiRJkiRJkjpTWMAcEasB32fFMLlesFxxaYslTwEeKH+uDqxX2JblIfM7I2Lf9qqVJEmSJEmSJHWqyBPM/w1syvLAtzZcrg6IrwH+p9limTkPeBFwV9Va9ULmigC+2n65kiRJkiRJkqROFBIwR8SawNtpHABXt8v4NbBfZs5ptW5m3go8m1L7i2U1az29fdW9XSKiVW9nSZIkSZIkSVIXJhS07tuAqSw/vVxRHSwvAN6fmT/sZOHMnA98MCJ+BvwUeGadfaq9HfhjJ3tIkiRJkiRJK61Zd8DSxUPvT9sIJk7pfz0a1YoKmN/J0JPF1eHyI8DLM/Pv3W6QmVdFxO7Ar4CDqB9mB/DyiJiSmU91u5ckSZIkSZI04uSy+vd/elj9++MnwbMOh1d8CyasUlhZGlt63iIjIrYHtqx8Lf9ZHS4/TqklRtfhckW5L/OhwGWs2BqjOmieBOw/3L0kSZIkSZKkEWXOA509v3QhXHsq/OkzhZSjsamIHsz7NrhfCYCPzsx/9mqzzFwEvB6onFCu1/d5r17tJ0mSJEmSJK3UbvndoCvQKFJEwLxLzfdKq4oE/pyZZ/d6w8y8F/gRjfsw79TrPSVJkiRJkqSV0tyHB12BRpEiejDPaDJ2cgH7VZwKHFPnftC8JkmSJEmSJGnsWPIUnPTSOgMB6+8AuxwFG+zY97K0cioiYN6gydhlBexX8Q9gKaVT2ZU2GZXT081qkiRJkiRJkkaPNTaHceNLLwF8/O76z9xzaYP7l5T6NB/9W9jQpgBqrYgWGatSvw8ywIMF7AdAZi4EZjcYXrWofSVJkiRJkqSBeOHHh95bbX34wHXwvqvhXV2e9Vw4B6780fBq05hRRMDcKFwGWFbAftXGN7g/seB9JUmSJEmSpP56wUfg4C8u/779YfDhW5d/X7Kg+7X/dV33czWmFNEiYw7LX+pXayPgrgL2JCImA2uw4ksFKxYWsackSZIkSZI0UHv+e+mqJ5udA20hiz4nqtGiiBPMc5qMPaeA/Sp2azI2t8B9JUmSJEmSpJFn8vRBV6AxoIiA+e4mY68pYL9ma0f5zwcK3FeSJEmSJEkaecY3aV5w9O9K18u/0bdyNDoV0SLjhjr3Km0rDo+I52Tm1b3cMCI2A95C/bYcCdzZy/0kSZIkSZKklcJH74Evbr7ivY8/DBMnlz6vsmr/a9KoUkTAXBseV/dDngCcHBHPz8xmrTTaFhETgB8CU1keZNf6Ry/2kiRJkiRJklYqU9aAzzwBC+bAuAmwytRBV6RRpogWGX9heR/mSrBcHTI/Czg/ItYe7kYRMRH4JXAAjcNlgEuGu5ckSZIkSZK00po8zXBZheh5wJyZi4FzGRr2VkLmAHYFboqII7vdJyJeAFwLHFavjKrPjwKXdruPJEmSJEmSJKm+Ik4wA5zY4H71SeZ1gVMi4paI+GhEbNdq0YjYNCLeFhGXARcAz2TF4LreXj/PzHq9mSVJkiRJkiRJw1BED2Yy8+KI+CuwF0PD3+qQOYCtgf8B/ici5lF6SeCjlNpsLAamA2sC21EKpavXoc761WHyMuA7PfhJkiRJkiRJkqQahQTMZR+m1Pu43gnjyr3qoBlgNeC5DdarPaFcO7f22QT+NzPv6KxsSZIkSZIkSVI7imqRQWZeDnyNxi/eC1Y8hVy5osGVDZ5bYduqzw8BHx/u75AkSZIkSZIk1VdYwFz2ceDPrNgWo1Z1iAxDg+Tak871gmVqnlkMvDEzHxtm/ZIkSZIkSZKkBgoNmDNzMXAocBVD22LU0+j0cqNQ+emtquYvA/4tMy8aVvGSJEmSJEmSpKaKPsFMZj4JvAD4DUNPKfdki/KfATwFHJGZP+/R2pIkSZIkSZKkBgoPmAEyc35mvgr4D2Au9dthdLQkQ1tnXAc8NzN/M/yKJUmSJEmSJEmt9CVgrsjMbwLbAj8AFtBe7+VW/ZhnAu8HdsvMG/ryQyRJkiRJkiRJ/Q2YATLz4cz8d2Bj4EPAX4AltO6/XH0tAn4PHAVslpnfzswlff4pkiRJkiRJkjSmTRjUxpk5G/g68PWIWB3YA9iO0gnntYHVgSmUwuQngQeBO4Grgasyc/4AypYkSZIkSZIklQ0sYK6WmXOB88uXJEmSJEmSJGklMCICZkmSJEmSJEnqtU+eeSPfvuD2IfcXrD8PJg6goFHIgFmSJEmSJEnSSu2h2Qsajj0yd+GQe1PXTcYXWdAYYsAsSZIkSZIkaaV2/j8fAWDNBXMB+Mjff86909Zv+PyEmx/n4p2Wcc0zxvWlvtHMgFmSJEmSJEnSqLDjrDsBeMacB5m0dFHD59Z/6CkmLwuueUa/Khu9RkTAHBHrAusDqwGzgTszs/E/AZIkSZIkSZJUIwkAPrv7m7h042c3fO7ESz8GLO1TVaPbQM6AR8TEiDg8Ik6NiPuBfwHXApcCNwKzIuKHEbFGkzWeFxHfjogfRMRbImJyf6qXJEmSJEmSNJJsttZUAE7e/iUA/G3D7QdZzpjS1xPMETEReC/wQWDDyu06j64KvAXYCnhhnXVeDvyG5QH5W4HPR8QnMvOHva5bkiRJkiRJ0sj11dfsxBHfu4wHV1uXlxz2lafv/+mD+7L65IkAzF2wmBd/7S+DKnHU6tsJ5ojYm9Ip5S8DG1EKlgPIBlcA+0bEi+os91FgfNUaAawLfC8ifhIRvgRSkiRJkiRJGiN2n7EW33zdzivcO+d9z2er9VZn/WmTWX/aZNZd3QYIRejLCeaIeAvw3fJ+lVB5hUdqvlePHwqcXzO+ZZ01Kuu8sbzPG7qtV5IkSZIkSdLK5dCdN+bQnTcedBljTuEnmCPivcAPgYmsGC5Xnz5uZrc69+6v3qJqjcrJ59dGxCe6rVmSJEmSJEmS1FqhAXNEvAL4Oiu2wmgnVKbq2Rl1xr5Q8xwMDZk/FRE7d1y0JEmSJEmSJKkthQXMEbEu8OPyHrUhcD1J/bYX04c8mPkb4H111qv+PoFSuC1JkiRJkiRJKkCRJ5i/BaxN81PL1S/1q1b9bN3u25l5AqWX/dX2dK7+vm9EPK/jyiVJkiRJkiRJLRUSMEfEs4DXUP9EMqwYKlfC56XAXVXjLWXml4GvUv/FgRXvamctSZIkSZIkSVJnijrBXDlZDENPLlcHy7cBHwd2AaZk5lZd7nU+Q0PmysnpwyJiShfrSpIkSZIkSZKa6HnAHBGTgcMYeqK4+iV/84G3ANtn5ucz85rMXNrNfpm5DHgdcG/VPrUtNl7UzdqSJEmSJEmSpMaKOMF8ILBq+XMl6K0+tTwL2DMzTy6Hw8OWmbOA99P4JYL2YZYkSZIkSZKkHptQwJp7N7hfaWHx+sy8sdebZuaZEfEPSu02ak9PP7vX+0mSJEmSJEmj1kPXwh0XDL0/bgJsuBNMnt7/mjQiFREw71jzvdKyIoHfZeafCtiz4ieUAubavWcUuKckSZIkSZI0+vz0lfXvx3jY98Ow33EQjRoKaKwookXG1gw9QVzxowL2q3ZJg/sbFLyvJEmSJEmStPLJRjFeszlL4aIvwm3n9b4erXSKCJjXaDJ2YQH7Vbuzwf2pBe8rSZIkSZIkrXxm39v93NvP710dWmkVETBPq/pc/a9A5mfmEwXsV21Og/vjC95XkiRJkiRJWvk89fhg5mrUKKIHcyPz+rDHag3uL+jD3pIkSZIkSdLKZbX1up875wF44v6h9ydMhlXX6X5drVSKCJjnA/VeI7lWAXvVemaD+3P7sLckSZIkSZK0ctnmJY3Httyv9Ofch2HmzUPH77kUvr5D/blrbw2v+CbM2GfYJWpkK6JFxiNVn6tfIzk+InYqYL9qtf8XUdn/gYL3lSRJkiRJklY+48bBvh8Zev9Dt8Cbzixde76r83Vn3QY/OwLmPDT8GjWiFXGC+S5gG1bsv1xxGHBtAXsSEZOAt9XZN8s1SZIkSZIkSaq1/8fh2a+FCz8PG+0Mu78dJk5ePn7fFd2tu3ge3HEBPOeNPSmzHw474RJi8e1Pf588cTx7bLEWHz34mWy61tQBVjZyFREwXwscVHMvKZ0mfn9EfDszHytg308BG1ftVR00X13Afi1FxHjgQOBQYHdgM0rtQ+YBDwM3A+cCZ2bmw4OosVZE7Erpf78XApsC6wBrUOpj/TilsP4fwPnA7zNz8WAqlSRJkiRJUs+ssxW8+kf1x+YO4xTy3Ae7nzsA8xYuZdmiJU9/n7NgCb+97iGuvnc2v//A81l98sQBVjcyFREwX1rzvTrsnQ6cGhEvycxlvdowIt4AfJT6p6YBLuvVXu2KiCOALwJb1Bleo3xtS+lU9zci4gTgfzJzIK/fjIhDgE8DuzR4ZNXytQnwfOD9wKMR8TngO5m5pME8SZIkSZIkrcwmTxt0BT2RuTw63GLmEraYCR8+fekKzyyZ9xtYtvz09rwJkzlhp1fywGy47I5ZHLjDBv0qd6VRRA/mC4Cnyp8r/6tVQuYAXgz8NiLWHO5GUfJJ4BSW/5ba08tPABcPd68OapoUEScBp1E/XK5nCvBh4MqI2L6w4uqIiNUj4nTgTBqHy42sA3wduCwi1u95cZIkSZIkSRq8vd/beGzaxqVrldX6V0+X5i4Yej5yvdm5wrXBk0+wwbxZbDBvFls+8QAH3Pd3Np9bajxw56Pz+l3ySqHnJ5gzc15EnAO8ihWD3uqQ+SDgpoj4IvC/mflkJ3uUW0+8CvgksH3N2rX7/Tozlw5ZpADlun5B6VRyN55BKax9XmZe37PCGiiH/JdQ+jscjt0o1b1nZj7S8mlJkiRJkiStPDbeFcZNhGU1nVI/chdMXav0+fzj4eKv9r+2DowfF0PufeStK8aj8+44mmWLSucod//XzRx/eYO2IXpaES0yAL5FKQCuVR0Erw98Ffh8RPyZUhuLm+otFhEvotReY0tgV0qnoNdieaDcqDUGwHe6qL9bx9N9uFwxDTgrInbPzEeHX1J9ETEBOJ3hh8sVWwCnRcT+vWx/IkmSJEmSpBHgU4/CZSfCxV+B7V4BBxwPk6cPuqqObLTGlKc/n7LvNGav0dGZVzVQSMCcmRdHxF+AfWl8srjyeRKlE80H1TxT/fm8mi2qx7PmXvVL/s7OzGu6+xWdiYidKfWBrieBnwLfB24FxgPPBT4G7FXn+RmUwvc397rOKu+k9CK/Ri4ATgSuAmZTeuHfocB7gfUazHkB8HrgZz2rUpIkSZIkSSPDXu8uXSuxHTeezvUPPMFZu63G+Enzh4z/+t37cMYVSzj5r3f3v7iVVBE9mCveB1Qam9SeMK4OgyuBcLBicFz7fPWVdebW7vMU8KHuy+/YZygFx/Uck5lvzsy/ZuajmflwZp5FKYA/vcGcI8uhdc9FxBTgU00e+VBmvigzT8/MuzNzdmZen5mfBXYE/tpk7id6WqwkSZIkSZLUI2e/93m8ZrdNGo6vPnkCkyYUGZmOPoX9bWXmdZR6JLcKjWHFwLjucnWeaRRIVwLoD2bmHZ1X3rmI2Aw4pMHwuZl5Yr2BzFwCvAWYWWd4HHBcbyoc4kAan0L+YmZ+rdHEco/lVwKNei0/MyK2G2Z9kiRJkiRJUiG+9Oqd2Hq9kf9SwpVFoXF8Zn6RUruE6lPHtWpPJ9dT+0y956rXPiEzf9Bl2d04lMa1f6HZxMycA5zQYPiQiCiimc1BDe7PBD7danI5ZP5uk0ee301RkiRJkiRJklYu/Tjv/Wbg/2jvhXzdqj7V/P3MfF8BezTzsgb3ZwGXtjH/lw3uTwZe0VVFzW3R4P5PM3Nhm2tc0GRsow7rkSRJkiRJkrQSKjxgzsxlmfkmSi+0W8zQHspdL82KfZgXU+od/K7hVdyVXRrcvyQzl7aanJn/pHHLib27rqqxRifAOzn13ahegClNxiRJkiRJkiSNEn3rWJ2ZXwJ2B/5C4/7LzQLnZn2Y/wrskZlf733lzUXEhsC6DYZv6GCpRs/u1VlFbbmwzr3TMvOWDtZodkp5VmflSJIkSZIkSVoZ9fWViJl5fWbuBxwAnAUsY2hP5doguTZ4rn7+T8Ahmfm88ksFB+GZTcY6CWxv62L9bn2dUm/sZZT+bv8EHNPhGgc0Gbu+y7okSZIkSZIkrUQmDGLTzDwfOD8i1gFeCrwY2A3YGhjfaBpwF3AV8Gfg7Mx8oA/lttLsJO+/OljnwQb3J0fEeuUX6/VEuc/ykRHxdmBCZs7tZH5ETAH+P3t3HVZF9sYB/DsXEJAQFFSwCwvFwO5WDOzuXrt1XWtdXXtda+3uWrtFDGzBBCxEURFFlG44vz/44YJ3zty5Rej7eZ55dj1n5pxzL3PrnTPv6cOpjgZwTcshEkIIIYQQQgghhBBCsoFMCTCnYox9BrDj/xsEQTAAUABAHvyXxzcGwFcA7xljCZkxThXyS9SpExT+IlFXSM22ZGGMxWh46GSk/J3E7GeMRWrYboZITk5GZGQkIiIiEBMTg6SkJCQnJ2f2sAghhBBCCCGE6JhCoYCBgQFMTU1hYWEBc3NzKBQZejM3IYT88PQeYBYEQQHABkAwY0xyUb//L4gX8P8tu7CRqAtTo51wibo8arSjV4Ig1Acwg1MdC+CPDByOWhITE/Hx40dERkZ++3Jha2sLAwMDKBQKCILY2oeEEEIIIYQQQrIjxhiSk5ORlJSEqKgofP36FR8+fIC5uTny5csHQ8NMnXNHCMmifL/44kuCAgrj9xCMUpYZUxgFQzCwzuSRZV16ezcVBMEYKbl+BwDIAeCLIAgLGGN/6avPTGIiURevRjtxGvaRYQRBqAjgMAAjzi6/M8b8VbQxFMBQAChcuLBuByghMTERAQEBMDMzQ4kSJeiLBCGEEEIIIYT84ARBgIGBAQwMDJAjRw5YW1sjMTERISEhCAgIQOHChem3ISE/sWQmfjf7r9d+BQCYFQdM/n/Hu0mBAzC3F3DoQ2l0jlkHG1Op+aY/H73cFyKkTAU9AWAYAGOkLMqXB8ASQRB+00efmchYok4qaPw9qfQfphJ1GUIQhFoALoM/Y/sSgMWq2mGMbWCMOTPGnG1tbXU4Qr7U4LK5uTny5s1LXyAIIYQQQggh5CdlaGiIvHnzwtzcHAEBAUhMTMzsIRFCMsnr8NdqH/Mx7hkmX5ms+8Fkc/pKPDQcKQv3ASmL86VuAoDfBEHIpad+M4NUgFmdxL5S+Rmk+tA7QRB+QUpwmXcvwDMAXRnjXPrJZB8/foSZmRlsbW0pDQYhhBBCCCGE/OQEQYCtrS3MzMzw8ePHzB4OISSb8frkhYj4iMweRpai8wCzIAiGAKYjJaD8rTjN/xsDKKPrfjORVFDVQI12pKbVJqnRjs4IgmAmCMIeAP8gJc2JmEcAGjLGQjJuZPKlLuiXJ08eCi4TQgghhBBCCAGQEmTOkycPIiMjadF3QohaklkyQuNCM3sYWYo+ZjC3AVDg//8v/H9LG2xmAF7qod/MEitRxwvKqruvOrmcdUIQhIIAbgDoIbHbWQB1GWNBGTMq9aUu6EdpMQghhBBCCCGEpGVoaAgTExNERUVl9lAIISRb00eAuR2nPHX6qFdWne2qoWiJOgs12jGXqItUox2tCYJQAcAtABUldlsJoA1jLEvfExAREQELC3X+DIQQQgghhBBCfhaWlpYIDw/P7GEQQrKQMrnLwNqwKJJi7cD0ll34x6KPaZ3VkH7GcloMwBk99JmZPknUWanRjtS+X9VoRyuCINQGcAr88cQDGMUY25hRY9JGTEwMMmoxQUIIIYQQQggh2YuZmRlCQn6kOXCEZEGX5gHex5TLTSyB4g2BOmMBw4xffsyjuwfq7qubrmxJ/SVoWawlFpz2xfrHr8CEPwCEZfjYsht9BJgLqah310OfmSlQoi6fGu1I7Zshqw4IgtAUwFEAZpxdPgHoxBjzyIjx6EJSUhIMDNRJhU0IIYQQQggh5GdhYGCApKRMWfaIkB9PrMTdAB8fi5e/uQ689wJ67tPPmCTkMs6FR30f4XrgdfiF+qFb6W4wMTTJ8HH8CPQRYM6pot5XD31mJj+JusJqtFOEU54E4J0a7WhEEIS2AA4iZRFGMQ8BtGOMBeh7LLqUnJwMhYJuZyCEEEIIIYQQokyhUNAif4ToysuLmh33/Azw5RWQu7huxyODIAioW6Au6haoq3pnwqWPyFuoivrPeugzMz0CkMipK6tGO2U45c8ZYwnqDUk9giC4AjgMfnD5FIA62S24nEoQBNU7EUIIIYQQQgj56dDvRUJ06Ku/5sd+9NbdOEiG00eA+TH+W9BPTG499JlpGGOxSAkyi6khpw1BEKwAlOZUX1Z/VPL9f+byAQBGnF02A3BljNGyuoQQQgghhBBCCCFE9xhvOTeSHegjwHxcRX19PfSZ2U5xyisLgmAn4/jW4AflL2k2JNUEQWgD4BCAHJxd/gEwhDFGCakIIYQQQgghhBBCCF+l3pk9ApJJ9BFg3gQgNZXC95cfBAAzBEHQR+7nzHSYU64AMETG8cM45WEAzkkdKAiCoSAInQRB+FUQhO6CIMhadlMQBCek5FzmBZcPARjFGF1CIoQQQgghhBBCCCEqNJsrXj7gDDDsWspWtF7GjolkCJ0HmP+fSqEzgMjUIqQEllMDlRUBHBYEwULXfWcWxthDAO6c6omCIPAW8IMgCF0A8F5d6xljERLHWgO4iZRg8J8A9gK4LwhCQRnD3gSAtzTmMwD9KbhMCCGEEEIIIYQQQmQxywNM8AUsC6T826oIMNkPKFIbsKuYspnkytwxEr3QxwxmMMbuAWgEIAj/BZfT/rcNAF9BEEYLgvCj5GSeBeUZ2wBgCeCsIAhKi/gJgtAewDZOe1EAVqjo828Azt+VlQWwQ+ogQRBcRI5LqzSASEEQmBpbtCAIvoIgzBYEIaeKcRNCCCGEEEIIIYSQH42lPTDBB5gTBox7BJjZZPaISAbQW6oKxpjX/9MwbEFKQPn7ILM9UgKkfwmC8AApi9n5AHgBwB/AZ8ZYnL7Gp2uMMQ9BENYCGCFSXQbAY0EQLgHwRUpaipoAKks0+StjLJBX+f9UGN041Y0EQSjMGAvg1PeV6FdTpkh5nHMAOADopYc+CCGEEEIIIYQQQsiP5sNDwKqwcrl53pSgNcnS9BZgFgTBCIAjgNMA8iNlxuz36TIEAAYAqgKoItJGAlJSbcQCiAeQBPFZwmklAPgC4D6AHf+fTZ1RJgCoAPGUF4YAmv9/U2UPY2yVin1sAEjlWy6I/3Jhf6+ijDFoo6sgCIMYY7F67ocQQgghhBBCCCGEZHfXlqZsYuycgB77KNCcheklwCwIwhikzGT9PrGKkOa/DOkDzQKU5QCgSQoNBqA2gJGCICxkjP2mQRvqd8pYnCAI7QAcBdBAw2YOAhggY78QpATTjTj1HySODURKKg19MYAeL14QQgghhBBCCCGEkGzo+Vn1j/nwEDjQFxh8UffjITqh8yCgIAgDkJL6QuWu//9v2kCzToaA9MHqaYIgeDPG9uiwDy7GWKggCM0AzAcwHvKf4zik5HFeyhhLltFPrCAIJwB0FKm+yxjzlzj8T6TkyNZLDm4A5xljkap3I4SkFRwcjCdPnuDNmzf48uULoqOjYWJigty5c8Pa2hrly5eHg4NDZg+TkJ/Co0ePcO7cOeTJkwetWrWCnZ2dztpOTEzE48eP8fz5c3z48AFRUVEwMjKChYUFChcujHLlyqFYsWI66cvPzw8nT55EQkICmjVrBicnJ520S9Tj6emJa9euIU+ePHB1dYWlpaXabcTFxeH+/ft4/vw5Pn36hJiYGBgZGcHa2hoODg6oWbMmTE1N9dI3+U9QUBBOnTqFoKAglC1bFh07in0VV9/t27dx7949REZGonDhwnBxcUGuXLQIEvlxJSQkYP369YiPj0fJkiXRqlUrGBnx5k4RQn4oyYmaHffuLhD1mXI6Z1H6mGU66v///T5oLDZDWapcrA05xPodCyBDAswAwBhLADBFEIRtACYiJVeyGWf3EAD7ACyWyJnMMwJASaRPefEGKvIfM8YuCYJQD8AfAJwA5FGzX55gAG5Ieb4JITI8fvwYO3bswLFjx/DixQuV++fNmxeNGjXCsGHD0KhRowwY4Y/B19cXc+bMwdWrVxEcHIykpCTZxyoUCpiamsLMzAwFChRAsWLFULt2bbi4uKBsWX3eDEIyy8GDB9G9e3ckJ6dc782TJw/c3Ny0Cs7Gxsbi8OHD2LdvH9zd3REVFSW5f8GCBdG6dWsMHjwYzs5S6/LynTlzBq6urkhISAAACIKA5cuXY+xY+pjOSJMmTcJff/0FxlK+otrZ2eHixYsoV66crOMDAwMxe/Zs7Nu3D5GR/Ov3FhYWmDZtGqZPn66zvkl658+fR6dOnb79HSpWrKh1gDkoKAg9evTA5cuX05WbmZlhzZo16Nevn1btp/Xq1SvMnTsXly5dQlBQ0Lf3BikKhQJGRkYwMzODtbU1ChYsiDJlyqB+/fpo3749cubMnmt7f/78Gfv378elS5fw8OFDBAcHIyYmBjY2NsifP/+3z/nmzZvD0DBr3Jj59u1b/Pvvv3B3d4evry+CgoIQFRUFc3NzWFtbo0CBAqhVqxbq16+Pli1bqh2sTUpKwrJly3Dw4EG8evUKoaGh3z4HNWVqaoqiRYuibdu2mDFjBiwsLL7VhYaGYvz48UhMTAk0FS1aFOfPn0epUqW06pMQ8oOLDqEAc1bFGNPpBiAKKbmSkwAkZ+KWdgxRun6caj4nxgDqABgKYBqAqQAGAagGwFDLtg2RMot5OoA+AHJm5mNVZ6tatSrTNx8fH733QYgmXr58ydq2bZt6B4dGm5OTE7t161ZmP5Qs7927d8za2lqr51psEwSBtW3blj1//jyzHyLRoaSkJFaiRAmlv7erq6tG7cXFxbElS5awvHnzanyu1a9fn929e1ftx1GqVCmltoyMjFhgYKBGj4Wo78SJE6J/0wYNGsg6/vHjx2qfOwcPHtRJ3yQ9b29vljNnznTP5Zo1a7RqMy4ujlWuXFnyc+bo0aM6GX9QUBCztbXV6eeghYUFGz16NAsLC9PJGDNCaGgomzhxIjM2Npb1GEuVKsX27t2bqWP28/NjXbt2ZQYGBrL/Nvb29uzPP/9kcXFxsvvZvHmzzr8rpd1cXFyU+uzRo0e6fUqXLs2ioqJ0+fTJRr8bCclAsy013z49zbBh/nnKhxWZepL1mlaD+ZQuw1z/LM8ctzl+2wLCA/TaP4B7LAvE7uRu+kiRoPpSeMaLy8zOGWNxjLHrjLENjLGFjLFFjLHNjLG7jDEN7w341nYiY+xfxtifjLGdjLFoXY2bEKIfe/fuRYUKFXDixAmt2nn48CHq1KmDefPm6WhkP6YtW7bg69evOm+XMYYTJ06gSpUqOHXqlM7bJ5nj3bt38PPzUyq/c+eO2m15eXnByckJkydPxqdPnzQe09WrV1G9enWMHTsWcXHyvtK8e/dO9K6IhIQEXLlyReOxEPWcO3dOtPzWrVsq76RISkpCt27d1D537t+/r3XfJL2kpCT06dMH0dH/fc22trbWenbx6tWrv/29xDDGMH78eMTHx2vVDwBs2rQJwcHBWreTVkREBFatWoUKFSrg9u3bOm1bH7y9veHs7Ixly5bJfi998eIFevTogf79+8s+Rpe2b9+OChUq4MCBA2q9bgMDAzF9+nRUq1YNvr6+so7R99/wzJkzCA8PT1c2ZcqUdP9+9uwZfv31V72OgxCSBZTvIF5uXRTIXzFlMzDO0CER7ekjwHxPD21qKnUxwauZPRBCCAGA3bt3o1evXoiJidFJe0lJSZg5cyZ++y1D1jLNlry8vPTafmRkJDp16oSrV+mj5kfw/v170fKgoCC12tm9ezdq166Np0+f6mJYYIxh5cqVqFevHkJCQlTu//btW26droNMhM/ExES0XKFQQBCkssQBp06dgo+Pj9p91qpVS+u+SXqrV69W+iwZOnQozMx4GfBUS0hIwF9//aVyP39/f2zcuFHjflLdu6e/n2gBAQFo2bIlHj16pLc+tPXs2TM0bNgQL1++1Oj47du3o02bNt/SOWSEf/75B/379093YUNdjx49QqNGjWQ97rTpK/SBMab0/FWqVAkNGzZMV7ZmzRrJCy+EkB9A563KZU1mA2MfAsOvpWzWRTJ+XEQr+ggwz0VKagpAj7fYyNhSxSBl8TxCCMlUT58+xeDBg1PT24jKkycPWrdujZEjR2LixIkYOnQoGjduzA0UpPrzzz9pFi1HWFiY3vuIi4tD7969lWbmkOyHN0NN6nX7vc2bN6NPnz56me129+5dNGrUCBEREZL7SeVWpdmrGadu3bqi5bVr14ZCIf01/MyZM9w6IyMjdOrUCRMnTsTgwYPRpEkTVKtWDUuWLEGbNm207pv85+vXr/j999/TlRkaGmLkyJFatXvs2DHuBa3vrV69Wqu+AP1/FoaGhqJDhw46mW2ta9HR0Wjbti0+f/6sVTsXL17E+PHjdTQqaXfv3tVZvvyPHz/C1dVVZXC8W7duMDAw0EmfYho1aoTcuXMrlX//nCYlJWXY80wIySSCAMwJA8Y9BtqtBmZ8AupNyOxRES3p/NslY+wqgNYAXiBlBnFmbn4AWjHGsu7ldELIT2Py5MmIjY0VrcufPz927tyJDx8+4OTJk1i9ejWWLl2K9evXw83NDUFBQSp/aIwePVrrxVh+RFLPyW+//YaYmBilLTo6GiEhIXj16hWuXr2Kv//+Gy1atJAMyrx9+xbr1q3Tx0Mg2cjZs2cxbNgwWQHpKlWqoHfv3pg8eTImT56M3r17o2TJkiqPe/z4sU4X/iL64+rqiv79+6crs7e3lxUwfPjwIbdu165dOHToEJYuXYqNGzfi4sWLuHPnDiZNmqSTvsl/5s2bp5RmydXVFYUKFdKq3U2bNsne9+nTp7h27ZpW/an7WRgVFYWPHz/ixYsXOHXqFObOnYsyZcpI9vHq1SusWrVKq3Hqw6xZs7gLKdva2mLp0qXw8fFBaGgonj9/jqVLl4oGQoGUYL+7u7s+hwsA6Ra/+56dnR3++usv+Pr6IiwsDAEBAThy5AiaNWvGbc/Hx0fld5Rq1arh7NmzaNy4MXLlyqXTOx2MjIy4M/bbtGmj9Hq6cuUK/v33X531TwjJoqwKA1X6AIaUDuNHoJclcRljFwCUEQShIoBKAPIDMEFK0FffEgF8BfAAwE2mzpQjQgjRk4CAAJw+fVq0rmDBgrh58yYKFizIPT5Xrlz4+++/YWpqioULF4ru4+/vD3d3dzRp0kQnY/4ZGBoacmeHm5qaInfu3ChWrBjq1auHsWPH4vbt22jTpg13FtTWrVuV8gmSn8fHjx/Rp08fyRnCBgYGGDlyJMaPH4+iRYuK7nPr1i1MnTpVMu3KkSNHcPDgQXTp0kXbYRM927p1KwYPHow7d+7AxsYG7dq1Q65cuVQex5vdamlpiQ4dOLkLddQ3SfH69WvRgPyQIUO0avfNmze4cOGCWsds2rQJ9erV06pfHt5nYc6cOZE3b16ULFkSLi4u+O2337BgwQLMmDGD29b69esxceJEvYxTE0FBQVizZo1onb29Pa5cuZLuwl6uXLkwceJEtGvXDrVr1xb9vJ80aRLu3bunt1Qzjx49wvXr10XrKlasiIsXL8LW1vZbmaWlJQoVKoT27dtj8eLFmDp1quixW7duxahRoyT7btq0KZo2barRuGNiYlCtWjV4e3sr1f3xxx+oVKmS6HEKhQL9+/fHH3/8ka58xowZaN++Pd1xQQgh2YReAsyp/j9zmGYPE0J+ehcvXuTOHlq6dKlkcDmtBQsW4ObNm9xFuu7du0cBZj2qUaMGli1bxp09+vTpU3z69Al58+bN4JGRrGDcuHGSt2DnzZsXJ0+eRLVq1STbqVmzJtzd3fHrr79i8eLF3P1mzpyJzp07Uz7dbKBOnTqoU6eOWsdERUWJlhctWhRGRkZ67ZukWLJkiVLKhyJFikjOFJVj8+bNot8JDA0NUbNmTXh4eCjVHThwAH///Tesra216lsbCoUCv/32G7y9vbF3717RfV68eIEXL16gVKlSGTw6cRs3buTePbZ8+XLuXSOlSpXCqlWr0KNHD6U6Ly8vnD17Fq1atdLpWFPxLj4YGBhg//796YLL35syZQpu3LiBY8eOKdV5eXkhJCQEefLk0dlY0xo/frxocLl58+YqL74PHDgQ8+bNS3f3j6+vLw4ePIhu3brpfKyEEKILv5/wgamQ/rt/4dymcKlgh/L2P98FfbocSAghGUBqoSZ1ZyStXLmSmyNPV4sHEr5OnTpJ5iiUWlyN/Lg8PT2xf/9+bn2ePHng5uamMricSqFQYNGiRRgzZgx3n2fPnuHGjRtqj5VkD7zb462srDJ2ID+pz58/Y+tW5UWIevXqpdWMyqSkJNF2AaBFixYYN26caF1sbCy2bdumcb+6lDYVi5istEDb0aNHRcuLFy+u8g6Qbt26oXjx4qJ1O3bs0HZoXH5+fqLlNWrUUJmmBEhZgJLn3bt3Go9Lyr///ov169crldvb22PXrl0qL4QWLVpU9ELYggULdDZGQghRV2yC9Lolbr4fceJhYLptjbsfOvxzAx4vtMv7nx3pdQYzIYSQFFKL63z+/Bn29vay26pYsSKGDx8uestn/fr1NRpfKm9vb7x48QKfPn3C58+fkSNHDlhbW6N06dKoWLEiLC0ttWr/e+/evcOdO3cQEBCAqKgomJubo2TJkqhduzZ3ltbbt2+/pRtp1aoVChcurNMxqWJmZgYbGxt8/PhRtF5qcTUpiYmJ8Pb2xsuXLxEYGIioqCgkJCTAxMQE5ubmsLe3R8mSJVGmTBmdLcLz9OlTeHt74927d4iMjISRkREsLS2RL18+VKhQASVKlND57Njk5GQ8ePAA/v7++PTpE758+QJTU1PkyZMHZcqUQcWKFWFqaqqz/pKSknD79m28evUKQUFBSEhIgLW1NRwcHFCzZk3kzJlTJ/0sW7ZMMu/ymjVr4OjoqFG7N2/exN27d0XrPTw8Mmx2anBwMB49eoQ3b97g69eviImJgYGBAUxMTGBra4uCBQuiYsWK3Nyl6oqIiMC9e/fg5+eHL1++ICEhAebm5siVKxdKlSqFChUq6Ow9KTY2Fp6ennjx4gU+f/6M2NhYmJmZwdLSEsWLF0fFihW1mvV39epVeHp6In/+/GjTpg0sLCw0bkvd16Qu+wZSgl/e3t4IDg5GcHAwFAoFrKysULJkSTg5OWn1PHl6esLNzQ0mJiZo166dUhqZsLAwnD17Fv7+/jA0NESpUqXQsGFDvaT9WL16tehF265du2rV7tmzZ7lBvt69e6NNmzawsrJCaGioUv369euzxOJnlStXRs6cOREdHS1a/+nTpwwekbigoCB4eXmJ1rVr107la0kQBHTp0gWLFi1Sqjt27Bji4+ORI0cOnYw1LV6apSJFisg6nhcUB/QzEeHt27cYPHiwUrmBgQH27t0rOeM6ra5duyrN3n/48CGuXr2q9fdbQgjRxJXnwQAAw/+/LTe7n4xiH//77Ij7dB8sySzdMU9siuOtRT6scHuOuqVsMmysWQJjjDbaMmWrWrUq0zcfHx+990GIHKNHj2YARLcBAwao3V5YWBgrXbp0unZ69+6t0dgePXrEBg8ezAoUKMAdIwBmZGTEmjZtyg4dOsSSkpI06osxxpKTk9nevXtZjRo1uH0ZGhoyV1dXdu/evXTHenh4MEtLy2/7WVhYsKtXr0r216BBA24/s2fP1ugxmJubc9t89eqV7HaSk5PZ0aNHWYcOHVjOnDkln//UzdzcnHXu3JmdO3dOo7EHBQWxqVOnMnt7e5V9WVtbs/79+zN3d3eN+krLw8OD9ezZk9nY2Ej2aWpqylxdXdmFCxe06u/du3ds6NChLE+ePNy+jI2NWe/evdnz58+/Hefu7s7dnyckJIQZGRlxj2vWrJlWj8XT05MpFArRtqdPny56jNTjWL58uey+3717x2bOnMnKlCkj6/wEwMqVK8d+//139vnzZ40e77Fjx1izZs2YoaGhZD+CILDq1auzJUuWsC9fvmjU15UrV1jHjh2ZsbGxysdVvnx5Nnv2bPb+/Xu1+hg6dGi6dooVK8b8/PzS7RMSEsIGDhzI7O3tmYGBgazn2cjIiBUoUIB169aNPX36VOO+5fD392djx45lJUuWlByTQqFgtWvXZps3b2ZxcXFq9bF69Wql94K7d+9+q9+5cyezsLBQ6jNHjhysf//+LDAwUO3HxZOcnMwKFiyo1FfhwoW1btvV1VX0ubOwsGDR0dGMMeW/W9rt0qVLGvWr689COzs7bnt//vlnun23bt0q+/1D061fv35KYzx58iR3/yNHjsh6nKdOneK2cfv2bbWfNznmzJkj2l/jxo1lHX/58mXumAMCAnQ+XhcXF9G+pk2bplY7/v7+ou1069ZN52MWQ78bCcliVjkzNttSefsk/p1HH4pMPcmKTD3JFvWszHxKl5G1ba/nyopMPclKTT+tdf8A7rEsELuTu2X6AGj7eTcKMJOfybJlyyR/GI0cOfLbD0u5vnz5wpYsWcLGjh3Ldu3apXbQ9/3796x79+5MEAS1f8hVqlSJeXp6qtUfY4y9evWK1apVS3Y/CoWCzZgxgyUnJzPGGKtatarSPlWqVJHsU9c/qp88ecJtL0+ePLLb8fDwYBUqVNDqB7WLi4taQbx169YxMzMzjfpq2LAhe/nypdrPl6+vL2vRooVGfTZq1EijPleuXKnW48yRIwdbunQpY0yzAPOOHTsk2z958qTaj+F7v/zyi2jbhw8fFt1f2wBzVFQUmzRpEsuRI4fG56elpSXbu3ev7McYEBDAGjZsqFFfuXLlUitw/uXLF9a5c2eN+jIxMWG//vorS0hIUNkPL9Dj6uqabr8+ffpo9V5QtGhRjfuWEhYWxn755RfJCyi8rXjx4uzixYuy+omKihK9cNe6dWvGGGO7d+9W+VmVJ08e9uTJE9mPTcqlS5dE+xg8eLBW7QYGBnIvnPTt2/fbfh4eHtzH2bVrV4361uVnYUJCguSFkPXr16fbP7MCzH/++Sd3/xcvXsh6rAEBAdw2/v77b7WeN7nc3NxE+zMyMmL+/v4qj+ddoChevLjOx3rw4EHRvpycnNS+yMQYY6VKlVJqy8TEhIWHh+t87N+j342EZDFZKMC8uEdKgHnmkHKs7ury37bKE9ewSmP3fNv+rdmM7anThhWZepIVm6b9939kswAz5WAmhJAMoOoW9jVr1qBIkSKYMWMGAgICZLVpbW2NSZMm4e+//1Y7J+T9+/dRvXp17Nu3DymfXep58OABatWqhe3bt6t1TPXq1XHz5k3ZxyQnJ2PevHkYMmQIYmNjRW91ffDgAeLi4mS3qa2FCxdy6+QuRLNhwwY0aNAAjx8/1mosp0+fRvPmzbm3Kaf166+/Yvjw4dyFw1S5fPkynJ2d1fr7nT9/HjVr1sS5c+c06tPd3R1VqlRR6/hp06ZhzJgxaj3O+Ph4TJo0CQMGDODemizl4sWL3Lr8+fOjZcuWarf5vUWLFqFcuXLpylxcXNChQwet2/7e58+fUbduXSxdulRpgTN1hIeHo2fPntzFwNLy8/NDrVq1cPnyZY36CgsLw/jx49GzZ0/ugqqpQkJCUL9+fRw6dEijvmJjY7FgwQI0a9aMu3hYKnd3d1nl2uasff36tVLaHrl98/j7+6N27dpYu3atRql/Xr16hWbNmsnKoXrnzh1ERkYqlV+7dg3Pnj3DkCFDVH5WhYSEYMKECWqPU8zu3btFyxs3bqxVu1u3buXm1u7du/e3/69Tpw43zcGRI0cyPQXFvXv3JN8r8+XLl4Gj4Xv69KlouZGRkWQaibQKFSoEExMT0TpfX1+NxyalYcOGooskJiQkoFu3bvj69Sv32P3792PTpk2idSNHjtTZGIGUNEZiOcMFQcDGjRs1Sh/SqFEjpbLY2FhuLm1CyE/I5zjgc0x5+/AQUPEdUF0NHFJS/ByobYZPuYDjNQWEmv+3sJ8KNQABAABJREFUfTU1x1cTy29bouLnzkJMAWZCCMkANWrUQOnSpSX3CQ4Oxvz581GkSBE4Ojpi7NixOHbsmGgeRm08evQI9erVw/v377VqJz4+HgMGDOD+EE/rw4cPaNmyJT5/1myxg82bN2Pq1KmiAYbk5GRuPmRdW7t2LXbt2iValzNnTkybNk1lG2fPnsXw4cM1CmSK8fLywp9//im5z6FDh7iB8Vy5cqFr166YPHkyxowZgxYtWsDQUPzLUWhoKFxdXRESEqJyXOfOnUPr1q0l84/LER4ejnbt2skKiq1atUo0V6Zc27Zt0yhAdefOHW5dgwYNdJIz28LCArdv38aaNWswZcoU7NixAydOnNBLjuwOHTrobJEuxhhGjBgh+dpPSkpCp06duO9JNWrUwNChQzFt2jQMGDBAMu/63r17MX/+fMkx9e/fH0+ePBGtc3R0xMCBAzFt2jQMHjxYckGty5cvqwzY8HLthoeHIzw8/Nu/5S7+yGNlZaWU+1hu32Lev3+PWrVqwdvbW6txMcYwffp0le9RgYGBouXh4eEYNGiQrItoALQeb6rjx4+LltetW1fjNhlj2Lx5s2idnZ0dmjRpkq4sbcA5rYSEBG47GYExhnnz5nHrBUFAzZo1M3BEfLzzytbWVq2L8nZ2dqLlcicEqEuhUGDdunWinx137txBhQoV8Pfff+Pp06eIiIjAp0+fcOHCBfTo0QPdu3cXvchWpUoVjBo1SqfjnDVrluj7dv/+/TV+T+NNyOC9JgkhP7AETs5493nAgb7K2/r6wNraQKTuLsL+4Zqyfkq0sQKjRhgiySD99+5Zbcrh726VdNZfdqeT8LogCA4A6gAoC6AwAAsARgBiAIQA8AfwEMB1xpjqX6WEEPIDWrJkCdq1aydrX29vb3h7e2PlypVQKBSoWrUqmjRpgnbt2qFmzZoaB5UiIiLQpUsXydmdjo6OqF69OqytrREWFoYHDx7A09NTNLjLGMPgwYPh7OwsGUAfMGCAZBA49QdpxYoVYWRkhGfPnuHy5cvpZs2tXLmSe7yqGYs8iYmJ3BmIycnJiI6OxocPH/Dw4UNs376dO1M1dbZOoUKFJPtLTk7GqFGjJGfilS5dGlWqVEG+fPlgaGiIsLAweHt749atW9zH+c8//2DmzJkwNjYW7XPKlCmix02YMAFz586FmVn6xSlev36NXr164caNG0rHBAcHY+3atZgxYwb3Mbx//x69e/fmztQTBAHOzs6oVKkSLCws8OXLF9y9e5cbHIqPj0e3bt3g4+MDGxvxxTJ8fX0xdepU7pgAIEeOHGjcuDHKlCmDhIQEeHp64tatW+n2efTokWQb30tMTMSLFy+49ZUrV1arPSnm5uYYMWKEztoTs3PnTqVFlr4fQ82aNVGiRAlYWFggLi4OHz58gIeHB4KCgkSPCQ0NxZYtW7jn4Y4dO/Dw4UOl8sKFC+PAgQOoUaNGuvLk5GSsW7cO48ePF51hvXjxYkyePFl01qG7uztOnjypVG5tbY1du3bBxcVFqe7gwYMYMmSI6MWSbdu2Yfbs2dygN+81kPo4Ui1btgwJCQk4efKkWhcVzc3NUblyZUyfPl3pwpDcvr+XlJSE7t27S75nlyhRArVr14atrS2ioqLw5MkT3Lp1i3vhbMaMGahduzYaNmwoWi81U/769evcuu9VqFBB9r48qQsYfs/e3l7le7wUNzc3vHr1SrSuR48eSgHP3r17Y+7cuaL7b9y4EVOnTlUrSKoLMTExGDNmDE6dOsXdp2rVqllmBjPvPSlv3rxqtZM7d274+/srlb99+1ajccnRuHFjbN68GYMHD1Z6Lb9//x7jx4+XveBj2bJlcfz4cZ0uSPjy5UusXr1aqdzS0lLWXQs8vIsT7u7uYIzp/KIqISQLC9PgPTbYFzg1AegmPiFIXYXz5MSxkXXQ86x4fdNy+WBvZo9x+x/opL/sTuMAsyAIVgBGAOgLQPkeHnHJgiBcA7AewAGmyX3ZhGSwotP4X6JJ1vF6YevMHoJKbdu2xcSJE7Fs2TK1jktOTsbdu3dx9+5dLFy4EHZ2dujSpQt++eUXydl1YmbNmoXnz5+L1pUpUwabNm0SnT3i4+OD4cOH49q1a0p1sbGxGDt2LM6eFf/kPXPmjGSKg8qVK2Pbtm2oWLFiuvKgoCBMnDgRe/bskXpIWpk/f77K2Y6qmJiYYMOGDejZs6fKfa9fvw4/Pz/RutKlS2PLli2oXbu2aL2vry9cXFzw+vVrpbqvX7/iypUraN68uVKdh4eH6A/j6tWrc8/FokWL4sKFC2jatKloSgze7M9Uo0aN4s5YrVWrFjZu3Ijy5csr1d26dQtDhgwRbT84OBgzZszAunXrRNudM2cOYmI4Mx0AtG/fHmvXrkX+/PnTlT98+BD9+/fHgwcPJB4RX2BgoORsdEdHR43azSw7duwQLVcoFJg4cSJmzZoFc3NzpfrExETMmTOH+3o6evQoN8C8c+dO0fJ169YpBZdTxzJixAjky5cPXbt2VQqWRkZG4vXr16Lvj7y+5s+fLxpcBoAuXbqgQIECaNq0qdI5lpycDB8fH8lZ1XJYW1t/G1tcXBwYY8ifP79oULtevXo4f/48DA0NuXcbaGP16tXciwz29vZYv3492rRpo1QXEBCAcePG4ciRI0p1qTPZnzx5olVQ1MnJCXXr1oWpqSmePXuGq1evfnuO8ufPj7/++kvjtlPx0rRo+1reuHEjt05stnKpUqVQs2ZNpYtgQEr6knPnzqFVq1ZajUlKcnIywsPDERoaiidPnuD69evYvHmzaPA9LV2lKdEF3udQrly51GrH0tJStFzO3Tza6NevHxwcHDB48GD4+PiofbxCoUD//v2xfPly7mPQ1KxZs0QvYo0fP16rCwwlS5aEiYmJ0sX/kJAQPH78WOm7IiGEKHl1RafNORWyQqHcpnivnMmLfEftb3hCinEAXgP4A4ADAEHmZgCgAYA9AJ4IglBP60dACCHZyNKlS2WlUZDy4cMHrFy5EmXLlkWLFi1UBvtSffnyhfsDt3Tp0rh+/Tr31sRy5crh4sWL3PyT586dE52BCEDyB7+zszOuXLki+oMhf/782L17NyZOnMg9PrM1adIEnp6e6NOnj6z9nz17JlpeoUIF3LlzhxtcBlJmIC1fvpxbf/v2bdFysbzVgOoZXDlz5sSxY8dQrFgxpTonJyfucU+fPsWxY8dE6+rVqwd3d3fR4DKQMnPJw8ODW79161bR4EZgYCD+/fdf7ph69eqFQ4cOKQWXgZTHcvnyZZV50nlUpX3JnTu3Ru1mFt45umnTJixevFg0uAwAhoaG+OOPP1CpUiXRek9PT+6MWk3P0U6dOommRMmVKxeKFi2q075q166Nbdu2KQVHjYyMlHJja8vY2Jib8xVICRqZmJjoJbicmJjIfc/Omzcvrl+/LhpcBlJmnB8+fJj7fujr6ys6e1yuhQsX4v79+1i9ejWWLFmC48eP4/Pnz7h27RoOHjwIHx8flC1bVuP2U4kFdAGoTHMl5fPnz9wcsmXLluXe6SD12cK72KaJ33//HYIgpNsMDAxgbW2NYsWKoW3btli4cKHK4HKdOnXQo0cPnY1LW7w7lNSdySt2d5BU+7pUo0YNzJ49GxYWFmofW69ePcyYMUPnweXHjx9j3759SuWWlpayZ1XzKBQKlCxZUrRO0wvBhJCfTJx0GjCiP2oFmP8/a/ksgGUALJESNFZ3ld/UYHNZAO6CIGgXaSGEkGxmwYIFOH36tOgCLuo6f/48KleujJkzZ6pcAGnTpk3c1Bjr1q1TGQjLkSMHNmzYwJ19JraQV2BgINzc3ET3NzExwZ49e1T+aFq0aBGqV68uuU9mEQQBT548kZ2iQyzoZWVlhRMnTsj6Aeji4gIjIyPROt6t17zcpe7u7irTQdja2sLd3f1b4NvQ0BC9evWSnKG2cuVK0XPRwMAAW7du5f5QT5UrVy7R226BlNvoDx8+rFR+8OBBbvDS3t4eGzZskMyDnCtXLuzevRumpqaSYxMjNWsa4M98y6rEztHx48djwIABKo8VBIGbBig+Pp6bY5l3jq5YsUJln5MmTcLy5cu/pXkpUqQI9u/fzw3Q8vr6559/VC5k17VrV+zevftbrmMbGxts3rxZ69nLWcm///7LzSu7aNEibuA+lSAIWLlyJaysrETrNb0jpW3btpg6darS7fGGhoaoW7cuOnfuDGtra43a/h7vLh9tAszbt2/npgHh5VoGgO7du3ODoadOneLm2c4MZcuW5V7o69+/v95Xrt+2bZtSv7wFgFV9Dn2P97mr6v1fW8+ePUPdunXRrVs3REREqH38lStX4ODggN9++03jVGJiZsyYIfo5P3DgQLVnh4vhvdbUTWFFCCEkY8kOMAuCYA3ADUBTpA8sA/JnMAPpg80KAPMFQZBe+YMQQn4wrVq1go+PD3bt2qXxzMlUiYmJmDdvHvr06SN5q/6ZM2dEy8uUKcPNi/m9EiVKoF498ZtPxPJkXrx4kRv47tWrl6wgu4GBAWbNmiVrfBnt4sWL6NatG5ycnERTSXyvadOm6WakKRQKbN26FUWKFJHVX44cOVCwYEHROt7MMnt7e9HyqKgo1KlTB/Pnz5fMtVqkSBFcv34doaGhiImJwa5duyR/nPPOs2bNmqFEiRLc49Jq2LAh9zkRO8/EckWnmjVrFnLmzKmyzyJFimg080pVPkhNgtaZadmyZenyXNesWZO7QKQYqb+xuufozp07Ub9+fbi5uUleQBs3bhzCwsLw9etXvH79Gi1atODuy+vr0qVLcHZ2xtGjRyVzF3fv3h3BwcEICQlBcHCw7LsXsgve6zdXrlyyZ6ZaWVnB1dVVtE6dfMppTZo0SaPjNPHy5UvRcm0CzJs2bRItFwQBvXr14h6XO3dubuqWpKQkybQbGally5a4dOmS2rmN9Y0XYFY3TQvv/YfXvi54eHigZs2asr5bSElMTMSff/6J1q1bS+Y6l8vX1xcnTpwQrVO16KlcvNcaL8UYIeQHVYTzG7ls2/82kqXI+nQVBEEB4ACA1Pu3vg8syyUWbBYATBUEob8a7RBCSLaXOhvUw8MDr169wuLFi1GvXj3uTBlVdu/ejcWLF4vWxcXFcW/7rV+/vlr9ODs7i5aL5Qa+e/cutx05OYtTtWrVKsv9cE3ryZMnqF+/vqwf+6kLmh09ehS+vr5o3769ymMSEhLg4eGBmTNnclMy8GZSNWnShPtjOjIyEjNmzEDBggXRsGFDzJs3Dzdv3hQNsOXKlUvlLflv3rwRPQ8A/Z5nd+7cEd1XEAR06NBBdp/dunWTvW8qXsqIVBlxC7Uu1ahRAy9fvvyWO/3y5cuybicPDAzEtm3bsH37du4+vHNULHd4qmvXrqFp06YoWLAgBg0ahD179uDDhw9K+xkYGHBnzcrt69GjR+jQoQPy58+Pnj17YsuWLaLnmyAI2S71iVy8/MM1atRQa9Yn7/X7/v17yQuhYqysrLS+ECtXaGgovnz5Ilqn6WfQtWvX8PTpU9G6unXrqrzAKHURY/PmzWo/n7pUo0YN7Nu3D2fOnBFNQZTZeJ996j5nvItOUnfGaOPVq1do164dd8HPkiVLYsOGDfD390dsbCw+fPiACxcuoE+fPtzHfPbsWQwdOlTrsf3999+iAff69etzU1uoi/day0oz9gkhGWDAacCyQPqyic9TFvBL3UiWIjd520QATZA+sKyttLOgBQCrBEFwY4zpbzleQgjJoooVK4bJkydj8uTJiIqKgoeHB65evQp3d3fcvXtXckZdWnPmzEG3bt1QvHjxdOXPnj3jBro2bNiADRs2aP0YxG4vf/Hihei+CoVCrbQXCoUCdevWlcyzq4nZs2djzpw53PqEhARERETgzZs334LCJ06cEL3VNDExEcOGDYONjY3KoGbFihUlF6oJCwvD3bt3cePGDXh4eODGjRvc9CapeD+YCxUqhP79+2PLli3cYxMTE3HlyhVcuXIFM2fOhJmZGerVq4cWLVqgQ4cOsmdY8/JwA8D06dMxffp0We1IETvPeD86nZyc1AoKVaxYEYUKFcLbt/K/iqi6HZgXIMjKcuXKhZYtW3LrGWN49uwZbt68iRs3buDKlSvc13pavHN00qRJ2Llzp2QwPjAwEFu2bPl2HpcuXRpNmzZFmzZt0KRJE9kX5oYNG4a///5bMpdsSEgI9u7d+y3tT+HChdG0aVO0bt0aLVu2lDUjPjsKDw/nXiA6f/68ytn6cjDGkJCQoFZgrlq1anoL5H3v69ev3DpNcuAC6i/u973WrVvD2tpadGzv37/HiRMnZF2o1EbOnDlhYWEBOzs7VKxYEZUrV4aLiwscHBz02q+2TExMEBmpvCqTujN5efurm8tZrlGjRnHPxS5dumD79u3p7o7Jnz8/8ufPj6ZNm2Lw4MHo1KmT6MXo7du3o3fv3mjatKlG4/ry5Qt3odS+fftq1KYY3mtN7OIiIeQHN8EHYAxITgQMNJuERTKOygCzIAg2AGZDt8Hlb82naTcngEUA5E9pIyQDvF7YOrOHQH4yZmZmaNGixbfbvMPCwnD27Fls3boV586dkzw2Pj4eO3fuxOzZs9OVq1qYRxfEbo3npV7Ily+fypmf3ytTpoxG49KGkZERcufOjdy5c6Ny5cro378/Hjx4gPbt2+PNmzdK+zPGMHz4cDRt2lR2MCIuLg6enp64d+8e7t27h7t37+LZs2cqc2qrY+XKlQgICMDFixdl7R8VFYWzZ8/i7NmzmDBhAho2bIiJEyeidWvp98PMOM8iIyO5F2DkBsa/P0adAHOBAgVgZGTEzd/75s0bycUb1RETE4MNGzbgxYsXKFOmDAYNGpQhKTgCAgJw586db+fovXv3EBYWprP2HRwcsG/fPnTr1k32LefPnj3Ds2fPsGbNGtja2mLQoEEYN24c8uXLJ3lcnjx5cOzYMbRu3VoymJhWQEDAt+C2hYUFevfujcmTJ4sugJmdZcTr197eXnIBQzEZGcSUupCn7mcWkHKB6dChQ6J1RkZGaNu2ray7HDp27IjNmzeL1q1bt07rAPNvv/2GGTNmiNYZGRlpHeDftm2brDzu2ujXr59SHuacOXOKBpjVzWcs1gag2TmhyuPHj7mpaqpUqYJdu3ZJBrbr16+P3bt3o2XLlqLfI+bOnatxgHnjxo2id6IYGRmpdbeQKrznlfd3IIT84ASBgsvZhJwUGaOREvwFVAeXpRb3U3WcAKCLIAjFVexLCCHZliaLrOTKlQvdunXD2bNncffuXZV5bMV+zIaEhKjdr7rGjBmjVMb7sa7JIjBZ5Zb0SpUq4fTp09wfeJ8+fcKOHTtUtnP27Fm0a9cO1tbWqFOnDsaOHYudO3fi6dOnOg0uAykXLU6ePIlhw4ZplHvS3d0dbdq0QcuWLSWDr/o+zwwNDTFixIh0ZVKBTk1uaVcVoBQbk9Rr8smTJ2qPQUxSUhIaN26McePGYc2aNRg9ejSqV6+ucma7pj59+oTZs2fDwcEBRYoUQZcuXbBo0SK4ubnpNLicytXVFefOnVO6+0KO4OBgLFy4EA4ODtwgXFq1atXC1atX4eTkpHZfERERWLt2LcqWLYsFCxZkanoCXcuIz4mxY8eqfQwv77w+6DrAvGvXLm5qmISEBNjb28PU1FTlJnVenz9/Hv7+/mqPLS1DQ0OYmJiIbhk1e1wfeJ8B6t5ZwttfVwtLpiW2kG2q33//Xdas6ebNm6Nx48aidR4eHhpfTOLlEm/QoIFOv6PxXmvZLeUUIYT8bOT8yuwDeQFiqQX/vt8nrbRBawUA7ZNDEUJIFvPu3Tu0b98eFhYWsLS0xOTJkzUKNjs7O+Pu3buSiw3xFijSJUEQYGpqipIlS6JHjx5wc3NTK3+tJkEZXSxOoyvlypWTnM178uRJbl1ISAhat26NVq1a4cSJEypXoTc1NUWdOnUwbdo0uLm5aZyL1NjYGOvWrYOXlxfatm2rdqAZAM6dO4dKlSrpLGiqiiAIMDc3R9myZTFw4EDcvn1b6fFLzeDV5JZ+TZ6XatWqces0XdTse9u3b1fKo/7kyROsWrVKJ+2ntXfvXpQuXRpz586VlfqiSJEi6NGjB9avX8+9fVqOBg0awMfHB0uWLNEoqBgeHo7Bgwdj2LBhKvd1dHSEl5cXNm3apNEM2bi4OEyfPh3t2rXjzl4nKSkKihYtivbt2+Pw4cOYMmWK2m3Iya2tK1J/S03SIWTEInyMMaxfv17v/WRHvEU9pRa2VWd/dS9IysFbU8DY2BjNmjWT3U737t1FyxljuHbtmtrj8vDw4H6/lEqppAlevnd6ryWEkKxN8leUIAjlARRN/Sdnt7SB5VgAFwGsATAfwCoAbgDiJY5P244AoKuqQRNCSHaSmJiIdu3a4dixY4iOjkZERASWLl2KuXPnatSetbU1/vzzT259bGys0mybPHnycPefMGECGGNqbcnJyYiOjsaLFy+wZ88e7kwZ3iyUT58+qX6g31H3B6G+1apVi1vHy0UcGhqK+vXr4/Tp06L1giDAyckJw4YNw6ZNm3D//n2Eh4fDw8MDCxYsQOPGjTW6MJGWk5MTjh8/jrdv32L58uWoX7++ysX70vry5QtatGghmt9R6jxbuXKlRudZREQEfHx8sHnzZlSpUkWpXSsrK24gWZPzTJNjpH70e3h4ICgoSO02v8cLJD9+/FjrttPasGEDevXqJTljr02bNpg7dy5Onz6Njx8/4vXr19izZw+GDh2q9WKcxsbGmDRpEl6/fo3z589j6NCh3CCR1GOQeo9MpVAoMGjQIDx79gw3btzAuHHj1J5Bffr0aYwcOVKtY7Iqqddvx44d1X79MsYQExMDf39/HDlyBB07dtRoXJrmPtaEVH5tdW/Pv3PnDh49eqTtkGTZunVrlroIm1Xw7i75+vWr7DQZYWFh3Ls2ihYtqunQuHhrChQoUECthTalUvgEBgaqPa49e/Zw63jfATXFe62pm16HEEJIxlI1TUdqmlTaBfoSAcwBYMcYa84YG80Ym8kYG8sYawagAICV3x2XVtpfhkUEQSgl9wEQQkhW9+DBA9y/f1+pfMmSJRrfpqjqy/z3txHa2Nhw99Xkh4ZcvFXlw8LC1P6xntVWD5dK88G71XzkyJHw8fERrevVqxf8/Pzw4MEDrFu3DoMGDUKlSpWUgr/q5o7ksbe3x7hx43DlyhWEhITg2LFjGDVqlMoULEDKObNo0SKl8sw4zxQKBffW3Pfv36vdniaLCLVs2ZK7yFxSUhLWrVundptpXb58GQ8ePBCtkwoKqsvb2xujR48WTdFiZ2eHPXv24OPHjzhx4gRmzpyJVq1aKQWUdXV+GhgYoFmzZli/fj3ev3+PR48e4a+//kKrVq1kBVn++OMPtVI+1KpVC8uXL4efnx+eP3+OtWvXomPHjrLSImzatCnDZvXrU2Z9TqgidwFHXTAzM+PWqfuZlRGzl1N9+vRJ54vg/gjELkqmevr0qaw2pPYrX7682mNSJTo6WrRcnQvBgHQwVt00R8nJydzUHWZmZpKLFmuC9zmSEWsOEEII0ZyqALMjpzztrOVIAA0ZY3MZY+GiOzP2hTE2DsAwyFsksK6MfQghJFvgBTmio6OxevVqjdqU+sEtCIJSXkAHBwfu7b2enp4ajQFIWfzK09OTuzCXVCqP8+fPy+4nISFB9gJ1GUXq4oDYc/3ixQvs3btXdP/hw4dj165dKhcNY4zh9evXao0zVWhoKPdHpaWlJdq1a4dVq1bh5cuX8PT0xMCBAyV/0B49elSpzNGR97VBu/PsxYsXePDgATe1Ci+I4OXlhS9fvsjuJyAgAM+fP1d7fLa2tnB1deXWL1++XOMAXXJysmRaAV0GOBYsWCA6C9LS0hJXrlxBjx49VAb7NM0Fm5ycjA8fPnD/xhUqVMD48eNx+vRpBAcHY+PGjZKpLWJjY3H27FnROsYYgoKCuLdblypVCsOHD8fhw4cRHByMffv2wdnZmdsXYwzHjh2TeHTZQ65cubipSby9vTXOf/rx40fcvXtX4wW61A2saUNqtrQ644+MjMS+fft0MSTZtL2Q9SOqUaMGt+727duy2pDar2HDhuoOSSXeRS11L7JL7a/uXQE3b97k3t1Trlw5nefp5r3WdHlBlRBCiO6pCjBLTWMSkBJonsYYuymnM8bYJgA70hzLo9vLoIQQkomkbrn+559/NJrxx0vBAKQER76f4Wdqasr9ofXixQtuzj8pM2fORNGiReHs7Ixy5cqJprCQ+nGnzmyrCxcu6GVhMW24u7tz6woUKKBUdvLkSe7ifXJvsffy8uL+8EpMTBQtDwsLg4uLC6ytrWFtbY3Ro0er7KdKlSrYvHkzzp07xw3uvHnzRqmsRIkSKFSokOj+ly5dUnt2cFJSEgYNGgQHBwdUrlwZNWvWFM1ZzTvPkpKS1Ar8iQXN5Ro1ahS3Ljw8HL1799boFvbZs2fj7t27onUKhUIysK0OxhhOnTolWtesWTOUKiXv5rIrV65w63jn6IEDB5AnTx7Y29ujWLFi3NnaqSwsLDB48GA8evQIrVq14u4ndo66u7ujUKFCsLOzQ/78+VVe6DIxMUG3bt1w+/ZtDB48WK2+sqNGjRqJlkdEROD48eNqt7d+/XoUKlQI1atXR4kSJbh3cEjRJC+6pvLmzct9zwsPF51HI2rv3r3c9+rq1atrlG6EMSZ5vl65ckX2rNyfRYUKFVC4cGHROt773fdOnDghWm5paYmqVatqPDYeXtqN6OhopTz8Ui5cuMCtk3OnUlq8i3UAULZsWbXakoP3WhP7bkUIISTrUPWNTSzpXdpfx18BiC8nyzcTQGoCSV6QmT/ljRBCsplSpUpxfzB8/vwZv//+u9ptLl68mFvHS5/RokUL7jEzZszgBj+/l5ycjBEjRmDevHnfjnn16hW2bt2qtG+TJk24M1v27dsnGShPlZCQgKlTp8oaW0a5evUq3NzcuPViAU8/Pz/u/lLpNtKS+rvzZsrPmjULZ86cAZASRFy9ejV3JfjvNW7cGO3btxet46Up4J1nSUlJmDVrlqx+gZQZqJ06dcKWLVu+ld27d080YCy1wNCcOXO4txynFRYWhgULFsge3/caNGggOQ53d3d06tRJrQslv//+O+bNm8etb926NTcNjbpCQkK4eZflnp9Pnjz5dq7x+vjehw8f0Ldv3299v337Fp07d5Y1W9bY2BhLliyRrE8rPj4eXbt2/ZY65cuXL+jevbusvNsKhQJLlizhBh/VyY2alUl9Tvzxxx/cu1XELFiwAMOHD/82U/zTp09YuXKliqMyl6GhIffz+tWrV7LbkUqP0bt3b3WH9U2TJk0kc5Jn1VnM/fv31zioLnfbtm2baN+83N8XLlxQ+Td98eIF92Jyly5dZM3cDQwMxD///IPFixfLupgvdWF+xYoVKo8HUu4aOHDggGidQqGQXJhWjNSFQ6k71TTF+7vwLmATQgjJGlQFmHNDPAicOgP5FGNMrek4jLG3AC6DnypDQErOZkII+WH88ssv3Lq//voLO3fulN3Wb7/9Jjkrs3///qLlw4YN4y5gdOHCBVmLYn369AmtWrXC2rVrlerEbm23tbVF69atRdtKSkrCgAED8PXrV25/jDFMnDgxS+U3dXNzQ4cOHST36dKli1IZb/YmAG5uw7Tmz5/P/cEIpATmxP4GYj9oJ02aJHvGZcmSJUXLeUGYcePGcRfd27RpE3bt2qWyTz8/P9StW1f0PBd7jHXq1OGmiggICMAvv/zCTb0ApPxthg4dqvVifMuWLZMMNJ48eRIVK1bE1q1bRWdiAynn/OXLl1G3bl3MmTOH25aBgYFWAfHvSZ2fZ8+eVRmkf/fuHdq2bSv5PIuld3n06JFS0NLPzw/Tpk2THvD/8c5PQPkc9ff3V1qc8uvXrxg2bJisvqysrLh5ivWx2Fdm6NKlCzdNxpMnTzBmzBiVC41GRkaiT58+mD59ulKd1PmRVfBm6z979kzW8Q8fPuTedWBoaIju3btrPDaFQoGePXty63fs2KFxKpMf1ZgxY0QDwUlJSZg4caLksePGjRM9ZwVBwIQJE1T2ffbsWTg4OGDkyJGYOnUqatSogTFjxkgeI7UY5r59+7Bjxw7J4xMSEtCnTx/u3XENGjRQazHWpKQk3Lt3j1uvjwAzL1WVPnJeE0II0R1VAWZVU1ZuaNgvL5FmajBbuyXICSEkixk9ejQ3AMEYQ79+/TBixAjJPK03btxAo0aNJAPBrVu35s5+sbGxwfDhw7nHzpgxAwMGDBAdQ2JiInbt2gUnJyfuLbq82ZuTJk3i9nn//n00aNBA9Mf469ev0aVLF6xatYp7vLYSExMRGxvL3aKiovDx40fcu3cPGzZsQLNmzdC0aVPJvL6Ojo5wcXFRKpfKrzx9+nTs2rVLdBa5p6cnWrRogRkzZkg+lqioKNFAvFiwKCwsDD169FB5yzdjjDtzqX79+qLl5cuXlwzA9+3bF9OmTRN9DmNiYrBy5UpUqVJFNGdzjhw5uDP0pVJ/7NixA+3btxedRf7s2TO0atVKMngvV7ly5SRn1AIpAe+BAwcib968aN68OcaOHYuZM2di4sSJ6NKlCwoXLoxGjRrh+vXrku1MmjRJpz+2bW1tuQucBQYGolOnTqI5PcPDw7Fq1SqUL19eZX5wsdu7ecHMlStXYv/+/SrHzZtdaGBggDp10q9VbWdnJxpoOnr0qMq/GwD4+PiIpgIC+K+H7CZHjhySOb83bNiAdu3a4cWLF0p1jDGcPHkSVapU4V5Ikprln1XwcsnLDTBL3SHSokUL2NraajSuVH369OHWff36FUeOHNGq/R9NsWLFMHDgQNG6o0ePYsSIEUoX/KKjozF06FCcPn1a9DhXV1eUK1dOst+vX7+iR48eiIqKSle+atUqySBx6dKlJV8nAwYMwKxZs0QDyD4+Pqhfv75kegyp72RiXr58yb0gmjpeXeMFmJ2cnHTeFyGEEB2SutUIQDSApP9vyf/fktL8t54mtzABaPVdW8nf9ROh79uoaMv8rWrVqkzffHx89N4HIXJ5eXmxnDlzMqRcTBPdFAoFq1WrFuvduzcbP348GzVqFOvYsSMrVKiQ5HEAWO7cudmbN28kxxAREcHKli0r2Y6hoSFr0qQJGzZsGBs3bhzr2LEjs7W1lTzG1dVVst+OHTuqHH+NGjXY4MGD2bBhw1jDhg2ZgYGBymNSN39/f27fjRo1kt2OtpuBgQG7evWq6DgePHig8vhy5cqxPn36sLFjx7KuXbuykiVLqtX/ypUrlfr9999/ufuXL1+ePXjwQHS8QUFBrHfv3qLHCYLAPD09uc/5+/fvWb58+STHampqylxcXNjIkSPZmDFjWJs2bZilpaXkMWPHjuX2mZSUxKpVq6by79OwYUM2bNgwNmTIEFa3bl0mCILs51eufv366fU8a9GiBUtKSpIcg7u7O/f45cuXix7Tvn17WX+z0aNHs0GDBrGmTZsyY2Nj2eO2sbFhycnJSv06OTlxz7O5c+eymJgY0fEeP36c2djYiB7Le09q164dd3wjRoxgYWFhosddu3aNFStWTPQ4Jycn7t9B6lz4+vUr9zjGGMuVK5focQ0aNJA8Ttu+ExMTVb5vCoLA6tatywYNGsQmTJjAunfvzgoWLCh5TJUqVbjn7datW7nHHTlyRNbj1ZWTJ09yx69KUlIS95wEwPbu3auTMVaqVInbR8eOHUWPadCgAfeYOXPm6GRcWdXXr19ZgQIFuI8/b968rHfv3mzChAmsV69ekt95LC0t2du3b1X2KXVON2zYUPLYx48fq3xvNTc3Z+3atfv2flyzZk2V78EuLi5qP3dnz57ltmdkZMTi4+PVblNKRESEaF8KhULle6a26HcjIdnQbEvxTcdaHGrBHLc5Km0B4QEsMSmZFZl6khWZepIdqN2K7anThhWZepIVm3ZS634B3GNZIHYnd5OuBBIgHWAuqlGngAOkA8zxmf3E0Kb/jQLM5Gd04MABplAoVP4IUHczNzdnly9fljWGp0+fcgMXmmyFCxdmHz9+lOwzODhYZfBB1ZYnTx5unVSAWVXQTJfbihUrJJ+HFi1aaN2H1PPYtm1b0X6bNWsm2WblypXZkCFD2PTp09nYsWNZ27ZtmYmJCXf/gQMHqjzPrl69yoyMjHT23FauXJlFR0dL9vnw4UOVF3GkNkEQWK9evbh1ciUlJbG+ffvq5RyrWbMmCw0NVTmG69evc9sQuxCReow6AXexzdTUVPK1KnZh4sqVK5IXlKysrFj79u3ZxIkT2fTp01m/fv0kL5TlzJmTPX36VPQxPnv2jJmZmUke26pVKzZu3Dj222+/sUGDBrGqVaty91coFMzd3Z37dxg8eDD32KioKMm/IS9Q2bRpU8njdNH3p0+fWOHChXV23lpbWzNfX19uf7t27eIee/Kk9j/Q1BEWFiZ6PhoZGXEvdqR68eKF5Hms6ni51qxZw+3H1tZW9Bipz4ElS5boZFxZ2Z07dyRf+3I2AwMDdurUKVn9zZs3j9tOqVKlVB6/ceNGnb3+ALDixYuzoKAgtZ+348ePc9usVq2a2u2pcuXKlQzr63v0u5GQbIgCzFlqU5UiI/U+Ql6+ZPEVhVQTW00lbR+qV0wghJBsqEuXLjh37hzy5cunszaLFSuGS5cuoUGDBrL2L126NG7cuKH2KuJiSpUqhatXr6rM52djY4Pz58+rlfcvrVq1anFvVVVFH6u8f8/ExAQbN25UmVtx27Zt3JQAqhgZGWH+/Pnw8/ODs7Oz6D5eXl6i5fv370f16tW5bd+/fx8bN27En3/+iRUrVuDEiRPcPJ4NGzbE6tWrVY63Xr16cHNz0/hvnlaNGjXg5uYGU1NTyf0qVqyIkydPqtxPjIGBAf755x/MnDlTtF5qYa3vKRQKbNu2DYsWLYKRkZHaY+Hp3bs3Ll68KGvRvcKFC3PreH+T2rVrY+7cuRqPr3LlyvDy8sLmzZu5+4ido/Xr18fmzZu5C+iFhobi6NGjWLZsGf78809s374dvr6+ovsaGxtjz5493Nu2HRwc8O+//8Lc3Fy0Pjo6GmfOnMHff/+N+fPnY/PmzaLpWoCUPKyrVq1Cw4YNResB/t/BxsaGmxNf1bFy8z1r07etrS1u3LjBfa9RR968eeHm5oYyZcpw95E6X6Xq9MHS0lI01VRCQoJkLloAkumTevfuDRMTE63HB6SkybCwsBCt4+VLL1KkCLc9BwcHnYwrK6tWrRqOHz8OKysrjY7PkSMHdu/eLZoCS4ydnZ1GdakGDx6MtWvX6uQzxNHREW5ubhp995T6ztK8eXNthiVKLJUSIL0AKSGEkCxCKvoM5VnG389gNtQkqg3ASFXbmR15p03/G81gJj+zDx8+sK5du2o1m9nU1JRNmTKFhYeHazSGL1++sH79+jFDQ0ONZvGMHDmSRUZGqtXn69evWa1atWT3Y2RkxCZNmsRiY2OZv78/dz+pGcyBgYHM2tpapzOBUrccOXKwbt26sefPn8t+Dh4/fszKlCmjVj+NGjViXl5e39rw9fUVnYUudet8TEwMmzJlisazig0NDb/9LdTx5s0b5urqqtHMWBMTEzZnzhy1b8G9fv06K1WqlOx+7Ozs2Pnz5xljjMXHx4um9+jUqZNaY0jl5eXFWrZsqdV5VqZMGXbixAm1+k1OTmblypUTfU19+PBB8thff/1VrfemXLlysfnz56f7Ow0bNkx0X6nZvjdu3GCOjo4aP09ly5ZlN2/elPX8+Pj4sLp162rcV8GCBWXNZLx165bo8T169FB57NSpU0WPPXDggKzHqE3fqWJiYtj48eOZqampRs9Tt27dWHBwsMp+wsPDWe7cuZWOt7e3V/s9RxdWrlwp+njmzp0reVxcXByzt7dXOs7AwIC9fPlSp2OcNm2a6Bh79uwpuv+pU6dE97exsVF5d8iP5Pnz56xOnTpqnccODg7s1q1bavUTFBTEvaNmzZo1stu5desWq1y5skavP0NDQzZu3Di1v6ulFRsbK3pOGxoa6vycZowxFxcX0cfy6NEjnff1PfrdSEg2RDOYs9QmXanHILA+26Yte2wUYCYk5XbtadOmsfLly8v6sWBkZMTq1KnDli9fLutHuxx+fn5s1KhRsvL9FilShE2YMIG9fv1a4/6SkpLYjh07JH8wWVpasj59+qS7pVrTADNjjHl7e7POnTuzfPnyqR3UNzIyYubm5ix//vysYsWKrFWrVmzy5Mls//793HytqkRHR7NZs2ZJ5tY2MzNjrq6u3wKf37t06RKzsrL6tr+dnR3z9vZW2XdAQACbNm0aN5/s91uJEiXYlClTVD7Hqjx48IANGDBAVqqU0qVLszlz5qhMvSIlJiaG/fHHH6x48eLcfuzt7dns2bOVLtLs27eP5ciR49t++fLl46ZckMvLy4tNmjRJdl5tMzMz1r59e3bkyBGV+ZZ5Ll26lC7ViaGhoezAhoeHB2vZsqXkBaiyZcuyWbNmsS9fvigdn5CQwDp37pxuf6kc2qmSkpLYoUOHWKtWrWTldzY2NmYuLi5s//79LDExUd2niJ0/f5516dJF1q3zhoaGrGHDhmzjxo1qBT2/D7bb29vLeg8NDQ1VuhjFS4Oj676/9+HDBzZt2jTm6Oio8mJRvnz52JAhQ9iTJ0/U6mPr1q3p3p9z5szJjh07pvZYdeHjx4+i5379+vVVHnv9+nVmZ2eX7rjff/9d52OMiIhg9erVS9dPlSpVWEhICPeY4cOHp9vf1NSUnTlzRudjy+qSk5PZvn37VF5kqlChAlu3bh2Li4vTqJ/Dhw8rXZzp3bu32u/pSUlJ7NixY6xdu3ay3quKFSvGpkyZwl69eqXRuL+3b9++dGljjIyM1AqSyxUfH88sLCyUHk/58uV13pcY+t1ISDZEAeYstQkpYxYnCELy/9/Y06avSP03Y4xpnMpCn22T7MHZ2ZmputVQW76+vihbtqxe+yBEV758+QIfHx+8fPkSYWFh31YeNzMzQ968eVGiRAk4OjqqvLVZG+/evcODBw/w+fNnhISEID4+Hrlz54aNjQ2qVKmCYsWK6bS/V69e4c6dO3j//j0SEhKQP39+FClSBLVr14axsXG6fV+/fs3t39/fX/Zt41kJYwx37tzBy5cvERgYCIVCgXz58qFgwYKoVauW0nPwvffv3+Po0aMwNzdHhw4dYGlpqVb/AQEBePz4Md68eYPw8HAkJCTA2NgY1tbWKFy4MCpWrCjrVl51vXjxAj4+PggJCcHnz5/BGEOePHlga2uL6tWr67xPT09PPH/+HO/fv0dycjJsbW1RuXJlVKxYEQqFeLawp0+f4sKFCzAzM0O7du1gY2Ojs/Gkvtb9/PwQHh6OiIgIMMZgZmYGOzs7ODg4oEKFCtyUEep4/fo1jh07BkNDQzRu3Fjtz8RPnz7h9u3beP/+Pb58+YI8efIgX758qFixIooXL67y+NOnT+Pp06eoXbs2atasqVbfcXFxePDgAV68eIFPnz4hOjoaCoUCOXPmhJ2dHUqWLIkKFSogR44carUrJjExEY8ePcLz58/x4cOHb2kGcubMiXz58qF48eJwcnLSKP0KAFy8eBF3795F3rx50blzZ1lpTgAgJiYGhw4dwtu3b1GxYkW0bt0agsDLXKfbvnmCg4Nx7949BAcHIyQkBDExMbC2tkaePHlQoUIFrb53PXnyBGfPnoWlpSWaN2+eqe/rbdq0walTp9KVGRoaIjg4WGWahdDQUJw4cQKfP39G9erVUadOHb2MMT4+HocOHcLr16/h4OCA9u3bq3zfuHHjBjw8PJAzZ064urqiUKFCehlbdvHp0yfcvHkTAQEBCA8Ph6mpKQoXLgxnZ2ednH+BgYE4duwYwsLCUKdOHdSrV0+r9uLj4/H48WM8f/4cHz9+RFRUFBQKBSwtLVGkSBE4Ojrq5XXj4+OD06dPw9zcHI0aNeKmIdKGm5sbmjZtqlS+YsUKlSnIdIF+NxKSDc3hfKeZE6bTbloebon3ke+Vyk93PA17s4IoMT0lleOSa2uQKBjg17rDoRCAVwtaa9WvIAiejDHt85VlEAowk0xDAWZCiDp+xAAzIYSQrOnSpUto0qSJUvnOnTvRu3fvTBgRIT+2MWPGYNWqVenKzMzM8P79e60vjMlBvxsJyYYowJylqFrkjxBCCCGEEEJ+Ko0bNxZd5HDfvn2ZMBpCfmxJSUk4cOCAUvngwYMzJLhMCCFEexRgJoQQQgghhJDvTJs2Tans3LlzCAoKyoTREPLjOnPmDD5+/JiuzNjYGFOmTMmkERFCCFEXBZgJIYQQQggh5DsdO3ZEtWrV0pUlJiZi+/btmTQiQn5MmzZtUiobNmwY7O3tM2E0hBBCNEEBZkIIIYQQQgj5jiAIWLp0qVL5pk2bILWODSFEvg8fPigtqGllZYVZs2Zl0ogIIUR7yQx49zVadAuOiMvs4emFVsuTC4KwRVcD0WHbCQC+AngE4DRjLFRngyKEEJJpDA35H1kKBV0vJYQQonv169dHu3btcPz48W9lL1++xJkzZ+Di4pKJIyPkx7BmzRokJiamK5s5cyby5MmTSSMihBDdqLvInVtXJE9OzGvviHqlbDNwRPqlSYBZSPPfflr2L3D+rYu2ASBGEIT5jLEFOmiLEEJIJsqdOze3ztLSMgNHQggh5GeyfPlyXLx4EdHR0d/KVqxYQQFmQrQUExOD9evXpytzdHTE6NGjM2lEhBCSMd6ERGPw9nu4OKEBCuXOmdnD0QmtZjBDOUCsS7poOyeAeYIgGDPG5uigPUIIIZkkZ86cyJ8/v9LiSnZ2drCyssqcQRFCCPnhFS9eHAsXLsSYMWO+lZ0/fx5PnjyBo6NjJo6MkOxt69at+Pz587d/GxoaYsuWLTAyMsrEURFCiHxpU2Y5hvgDALaeny95TIyhMWbVHIzPOa1w+Xkw+tQsotcxZhRt7ylmWmz6bDvtJgD4VRCEQlo+VkIIIZmsbdu2SmV9+vTJhJEQQgj5mYwaNQpdunRJV/bXX39l0mgIyf6Sk5OxbNmydGXLli1TWliTEEKysmSR6OaTPMW52zvzvCgWHoSCUcEAgE/hsRk8Yv1RNYM5DkAO/Beo/V5Wn8Gc+qc2BNAcwGYdtEkIISSTzJs3D0FBQbh06RIMDAzQtWtXzJ07N7OHRQgh5AcnCAL27NmD4sWLY/fu3QgKCsKzZ88ye1iEZFufPn1CWFgYLC0tUaZMGUyYMAHdunXL7GERQohachj+N2/XvWBlxCuM8HeVrtz9y39+BedPP+b3B1UB5q8A8mXEQDLAj5M5mxBCflJ58+ZNt9ASIYQQklEMDQ2xcOFCLFy4MLOHQki2lz9//nTpMQghJLuyMDZERFwiFjv3SldewMr02/+HxyQgIi7x+0N/KKoCzI8B5Ie8lBZZXWBmD4AQQgghhBBCCCGEEPJjeDSnOeoucsf70BgAQDEbM1ya2ACC8F9ihpVuL/DXheeZNcQMoSrAfAxAs4wYiB6kDYrHATibWQMhhBBCCCGEEEIIIYT8WARBwPVpjTN7GJlO1SJ/WwA8Q0o+ZF0tupdRG/BfHueZjLFPaj0zhBBCCCGEEEIIIYQQQiRJzmBmjMUKgtAYKYHmFhkzJJ0KBjCDMbYxswdCCCGEEEIIIYQQQgjJ/lz+dYGFkYVSed6cedG0SFP84vQLDBQGmTCyzKEqRQYYYx8AtBIEoSiAWgDsAZjhv9nBWU0igFAATwDcYIwlZO5wCCGEEEIIIYQQQggh2c37yPfcuoiECOWysAj4PfJDRHwEfq3xqz6HlqWoDDCnYoy9BvBabyMhhBBCCCGEEEIIIYSQbO6E3wlMrjYZhgrZoddsTVUOZkIIIYQQQgghhBBCCCEyRSRE4Evsl8weRoahADMhhBBCCCGEEEIIIYQQjVCAmRBCCCGEEEIIIYQQQohGfo5EIIQQQgghhBBCCCGEEKKGu73uotruaunK1jdbD0cbx2//bnekHUJiQzJ6aFkKBZgJIYQQQgghhBBCCCHkOyaGJnjc7zHeRbxDUFQQnPM7K+2jEChBBAWYCSGEEEIIIYQQQgghhKOgRUEUtCiY2cPIsijETgghhBBCCCGEEEIIIUQjFGAmhBBCCCGEEEIIIYQQohEKMBNCCCGEEEIIIYQQQgjRCAWYCSGEEEIIIYQQQgghhGiEAsyEEEIIIYQQQgghhBBCNEIBZkIIIYQQQgghhBBCCCEaoQAzIYQQQgghhBBCCCGEEI1QgJkQQgghhBBCCCGEEEKIRijATAghhBBCCCGEEEIIIUQjFGAmhBBCCCGEEEIIIYQQohEKMBNCCCGEEEIIIYQQQgjRCAWYCSGEEEIIIYQQQgghhGjEMLMHQAghhPwMgoKC8PjxY/j7+yM0NBQJCQkwNzdH7ty5Ubp0aZQvXx5mZmZa95OUlISjR4/i+fPnKFSoEDp16gRTU1MdPAKijsjISJw4cQJBQUGoXbs2atSooVE7b9++xf379/H27VtEREQgMTEROXPmRIECBeDs7IwSJUrorW/yHzc3N9y/fx9JSUno27cv7OzstG4zNDQUp0+fxtu3b2Fubo7q1aujWrVqOhgtIT+3e/fu4fz587C1tUXDhg1RqlSpzB4SIYQQ8sOjADMhhGSQiIgIrF27Fvv27cPr16/x9etXAIAgCDA1NUXhwoXRtWtXTJ8+HcbGxmq1HRISgunTp+P8+fP48OED4uLivrWdN29elCpVCgMGDEC/fv1gYGAg2Za/vz/+/vtvnDt3Du/evUNUVBQAQKFQwMLCAqVLl8bo0aPRu3dvtZ+Dx48fY/r06bh37x5CQkKQkJAAADA0NIS9vT0qVaqEMWPGoEmTJirb+vjxI2bPno1z584hMDAQ8fHxao+HJ0+ePKhatSqWLFmCihUratyOt7c3du7ciSNHjuD58+eS+xoZGaFu3bro3Lkz+vbtC3Nzc7X7S05ORpMmTXDlypVvZfPmzcO1a9dga2urdntEM0+ePEHbtm3x+vXrb2VjxozBihUrZLexd+9eLFq0CA8fPpTcr1atWti2bRscHBx01jf5T2xsLLp27YoTJ058K3NwcECHDh20anfLli0YPXo0oqOj05U3btwYe/fuRd68ebVqPxVjDJs2bcLmzZvh4+ODiIgIWccZGhrC2NgYuXLlgq2tLYoXL44qVaqgXbt2Wr0n/ugYY/Dw8MChQ4dw9+5d+Pn5ITQ0FCYmJsibNy9KliyJli1bwtXVFUWLFs3s4QJIuSh54cIFnD59Gp6envDz80NYWBgYY7C2toa1tTUqVKiAunXrwsXFRfSilioPHz7EokWL4OHhgc+fPyMmJkarMRsYGCB37tyoUqUKJk2ahKZNm6ar37Vr17f3PEEQMHXqVCxYsECrPgkhhBCiAmOMNtoyZatatSrTNx8fH733QYhcdevWZQBUbuPHj1e77UaNGslqe9SoUZLtBAUFsTx58shq69ixY2qN8fPnz8zW1lZW2wcPHpRsKzo6mjk4OMhqS5stX758LDQ0VK3HyRhjDx48YC4uLhr3mytXLvbHH3+wmJgYtfrdsWOHaHsjR45U+zEQzVWtWlX07+Du7i7r+BEjRqh1vpQvX15nfZP0+vfvn+55tLe3Z/Hx8Vq1eeDAAcm/Z7Vq1VhiYqJOxj9r1iydvy9WrVqVnTt3Tifj+5FcvHiROTk5yXoODQ0N2eDBg9m7d+8ybbxJSUls48aNrHDhwrL/9oIgsDZt2rC7d+/K7ic5OVmtPtTdFAoFu3nzZro+fX19mSAI6fbbtGmTrp9C8oOh342EZEOzLcW3TNBofyPmuM1RafsY9ZExxtiKi89ZkaknmcuglcyndBnmOnA5KzL1JFt67im3TQD3WBaI3cndKAczIYRkAC8vL3h4eMjad926dbJnmQEpt4K6u7vL2nfjxo2IjY3l1u/evRshISGy2lq8eLGs/VJt3boVwcHBsvZdtWqVZL2cGcG68PHjR1y/fl32/omJiZg6dSqqVq2K06dPa9xvWFgYZs6ciXLlyuHmzZuyj+OdBxcvXtR4LEQ9ISEh8PT0FK2Tcy7t3bsX//zzj1p9ent7Iz4+Xuu+SXpHjx7Ftm3b0pWNGjUKRkZGGrcZHR2NMWPGSO5z9+5dbNmyReM+UsXHx2PZsmVat/M9T09PtGjRAr/88guSkpJ03n52wxjD9OnT0bRpU5V3HKRKTEzEpk2bULlyZVy7dk3PI1T26dMnNG3aFEOGDEFAQIDs4xhjOHnyJGrVqoW5c+ci5bevtKCgILX6UFdycjIOHTqUrqxMmTJo165durJx48bh7du3ehsHIYQQ8rOjADMhhGSAFy9eyN43JiYGZ86ckb3/4cOHZe8bFxcn+UNPnXHeuHEDQUFBsvdXZ5yqxuHl5SW7LW0pFPI+KkNDQ9G4cWMsXrxYZ0EXf39/1K9fX3awiffjWW5gn2jP2NgYgiCI1sk5l5YsWaJ2n1WrVkWOHDm07pv8JyoqCqNGjUpXZmpqimHDhmnV7rZt22S9b86ZM0frtD++vr7fUhzpw7p167R+Pn4EY8eO1Tj9QnBwMJo0aYILFy7oeFR8ISEhaNiwoewL02ISExMxe/ZsTJo0SeW+ZmZm3PclXUlNt5XW+PHj0/07MjISY8eO1es4CCGEkJ8Z/doghJAMoG6+QXUCzGfPnlWr7e9zfqalzjgZYzh37pysfb98+YI7d+7IbltqjAC+5a/Wt/z586N27doq9wsNDUXDhg31MhMtMTERgwcPxtatW1XuK/YjGwDNMsxA5ubmcHJyEq2rW7eu5LEfP37E/fv3ufUODg4YOXIkxo8fj86dO6NatWpo27Yt9u/fr3XfJL2lS5fi/fv36cr69OmD3Llza9Xu2rVrZe0XGBiIf//9V6u+wsLCtDpejs2bN2Pz5s167yer2rhxo8o7blRJSEhAt27d1LrAq41BgwbB19dXJ2399ddf2LNnj+Q+lpaWaNOmjU764+nUqZNSWYMGDVC5cuV0ZUeOHNEqsE4IISSbeHNDfIv+ktkj+6HRIn+EEJIFubm5ydovODhY9i25+nDx4kX069dP5X6XLl1CcnKyzvrVd8A0d+7ccHZ2xuLFi2FpaSm5b2JiIjp06CDr72BlZYV69eqhQIECyJ07NyIiIuDj44MbN25IBvcZYxg+fDgcHR1RrVo1tR8PyVgbN25Eq1at8Pnz529lEyZMQL169SSPkzqHnJyccPfuXZXpGTTtm/wnKChIdCb56NGjtWr31q1bePLkiez9N27ciO7du2vcn6r3XLH3nNjYWERGRiIoKAje3t44ffo0jh8/LplaacaMGejevTvMzMw0Hmt29OHDB0ycOJFb37ZtW4wbNw7ly5eHgYEBHj58iL/++ks0fdLXr18xZMgQXL58WY8jTvnMPnbsGLe+U6dOGDp0KCpVqgQjIyMEBATg2LFjWL58OUJDQ0WPmTJlCjp27AgTExNuu7t378bvv/+Oo0ePIiAggHsxVBOdO3dG/fr1Ret++eUXDB06NF3ZhAkT4OnpSXd1EELIj2xrK+n6aoMBhUg4VDAA7CsDpVsCxhb6GdsPjALMhBCSBb19+xbPnj1D6dKlJfdzc3OTlQNRX+QGwjMyB3C/fv2U8qbq0++//64yKODg4IDFixejVatWyJEjh1J9eHg41q5di3nz5iEyMlK0jfj4eAwePBheXl4wMDDQxdCJnjg7O+PZs2c4efIkPn/+jNq1a6NmzZoqj/t+xmxaPXr0kJX7V9O+yX9mz56tlFqiZs2acHR01KrdjRs3qrW/u7s7/Pz8UKJECa365RELCJqYmMDKygoFCxaEs7Mz+vXrh+fPn8PV1RVPnz4VbScoKAjHjh1Dz5499TLOrGrRokXc9RImT56stE5BkyZN0KRJE4wbNw4rVqxQOubKlSs4duwYXF1d9TJeAJL53cVSnlhbW8PJyQmDBg1Co0aNRGdZv3//HhcuXEDbtm25bVtYWGDp0qVYunSpRuPeuXMn+vbtq1ResGBBrFu3jntc9+7dMX78+HSv5wcPHuDAgQNaXbwhhBCSzd3dJF1foCrQ5whgkitjxvODoEu3hBCSyQwNxa/1yQnK8vbRZhEqHrFxfvjwAd7e3iqPzchxZiQfHx+VuTeHDx+OR48ewdXVVTS4DKTcQjx16lTcvn0bhQsX5rb16NGjb+kQSNaWO3du9O3bFxMmTJAd4JXKl+vg4KDXvkmK9+/fi6ajGTx4sFbtRkREcF+7jRo1Ei1njGHDhg1a9asLDg4OOHz4sOSFrVOnTmXgiDJfTEwMNm0S/3Hq6OiIhQsXco9dunQpypcvL1r3559/6mR8Yhhj3IvCPXv2lMynXaBAAcnPHn3mkH758iVGjBihVG5gYIDdu3cjT5483GMtLCzQtWtXpfJ58+Zl6sV5QgghWdx7T+DB3sweRbZDAWZCCMlkzs7OouVyZgfz9uG1qQ1Nx/nmzRv4+fkplRsZGXHzxWYX06ZNk0zXMXz4cKxduxbGxsay2itXrhwuXbokmedVTi5mkj0lJiZy66ysrDJuID+xv//+W+n2fRMTE9EglTr27t3LvYCwePFi7uzorVu3ar3Yny6UK1cOLi4u3Hqp3OE/oosXL3L/npMnT5ZMv2BoaIjJkyeL1t25cwfPnj3TyRi/FxwcjPDwcNG6/v37qzy+cuXK3BRNvAVmtZWQkIAePXqI3tnz+++/c1NjpNWrVy+lMm9vbxw9elQXQySEEPKjens7s0eQ7VCKDEIIyWQNGzbErVu3lMrd3d2RnJzM/aHq5+eH169fK5UbGRmhTp06uHnzZoaM8+LFixgzZgz3ON7sZWdnZ+6M3uwgNQ0BT4UKFbBy5Uq12y1RogS2bNmC9u3bi9Zfv35d7TY1lZiYCG9vb7x8+RKBgYGIiopCQkICTExMYG5uDnt7e5QsWRJlypTRWdqOp0+fwtvbG+/evUNkZCSMjIxgaWmJfPnyoUKFCihRogQEQdBJX/7+/nj06BECAgIQEREBhUIBS0tL2NjYwNHREaVLl9b4cQUHB+PMmTP4+vUr6tati6pVq2o1VnUes677jo2NxZ07dxAYGIhPnz4hIiIC5ubmyJ8/P8qXL49y5cppnM80MjIShw8fRmBgIMqUKQNXV1eltu7du4ebN28iPDwctra2qFatmtLiXboQHh4uOmO4ZcuWsLDQLg8fLz1GmTJl4OzsjD59+mDq1KlK9cHBwTh8+DB69OihVf+6UKdOHZw4cUK07tOnTxk8mszFm7FtYGAga0G7jh07YvDgwaIXlQ4ePIgZM2ZoPcbvSV0MLVKkiKw2ihcvjrt37yqVq7uQsVzTp0/HvXv3lMqbN2+O6dOny2qjUaNGsLW1RXBwcLryVatWoUOHDjoZJyGEkB9QUuZf4M9uKMBMCCGZrGrVqrCwsFDK5RgaGop79+6hevXqosdJBW71sdhSw4YNRW/7vXLlChITE9VO9dGwYUPcuHFDp2PMSBs3bpS8xXbdunUapwBxdXVF+/btRWdYxcbGIj4+Xm/BecYYjh8/ju3bt+PcuXOIjo5WeYy5uTlatmyJIUOGoHnz5mr3+fHjRyxfvhw7d+5EYGCg5L7W1tZwdXVFv3790LBhQ7X7Cg8Px+rVq7F161a8fPlScl8zMzO4uLigV69eaNeunewg78OHD9G8efNvQTdBEDBz5kz8/vvv6fY7fPgwFixYgOfPn3NzuaZKTaOQK1cuFCtWDAMGDBC9sCO3b1WSk5Oxd+9e7Nq1C1euXJEMINna2qJjx44YN24cypQpI7uP0NBQ1KhRA8+fP/9W1rlzZxw8eBAAEBISgu7du4u+h5QuXRqzZ8/WaeB1//79ojM8pXLLyvHgwQPRIBnw3+zKXr164ddffxVdmG/dunVZIsBsbW3NrVN1/sqxbds2DBgwQOt2pOgqR7+np6douaOjo+QdKKksLCxQuXJl0WCtvj4XbWxskCNHDtEZ8R8/fpSVhufjx4+i5QUKFNB6fN+7ffs2/vrrL6Xy3LlzY9u2bbLfjxUKBVxcXLB9+/Z05e7u7vD19UXZsmV1Ml5CCCGZoEQTwE/kbtrCtf77/wDdTroifJQigxBCMpmhoSHq1KkjWieVh1kqcKsPqYHw74WHh+POnTuixzDGcOnSJdE6fY0zoxw5coRb5+zsjNq1a2vV/vLly2FqaqpU7ujoqLfg8vXr1+Hk5IT27dvjyJEjsoLLQMos1EOHDqFFixZo3bo1QkJCZPe5fv16lChRAosWLVIZXAaAr1+/Ytu2bWjUqBEaNWokmn6F5+jRoyhRogR+++03lcFlICUn8sGDB9G+fXtUrVqVG1T63rhx49LN6GSM4Y8//oCXl9e3sufPn6Nbt27w9PRUKzgXFhaGBw8eYOzYsUoBE7l9q3LmzBk4Ojqid+/eOHv2rMrZicHBwVi/fj3Kly+PESNGyH48K1euTBdcBoBDhw7h3r17iI6ORrNmzbjvc8+ePUPPnj3xyy+/yHtQMuzatUu0vEWLFlq1K7W4X2qAuUCBAmjcuLHoPlevXoWvr69WY9AFqUUopfLg/miSkpK4aw+osxBkhQoVRMtv3bqll/zARkZGqFGjhmjdzp07VR7/5s0b7h00uv48T0pKwvDhw0UvuPzzzz+ws7NTqz3ehU/ea54QQkg20edfwHngf/92HgjMCQMGnv1v6ybyXt9wOtByYcpWqXfGjfcHRwFmQgjJAniLPPHyGzPG4O7urlZb2jI0NETdunVF63jjfPz4seit06lpPLIrf39/vHr1ilsvJ5+lKkWLFsWqVavSlZmammqUdkOODRs2oEGDBnj8+LFW7Zw+fRrNmzeXFZz+9ddfMXz4cMnF7aRcvnwZzs7OstLBrFu3Dh07dsTnz5816uv+/fuoU6eOyrydiYmJ8PDwUCpnjOHy5cvf/v348WPJW9bl+D5ljdy+pSxatAitW7fWKKCZnJyMtWvXokaNGpKvj1S8i0/Xrl3D6NGjZeX1XbdunU7SAQUEBODatWtK5Q4ODlrNzoyOjsbu3btF62rXro1ixYp9+3efPn247axbt07jMejK7dv8XIT58uXLwJFkroCAAO5Fl9KlS8tup1SpUqLlX79+5c4U1tbQoUNFyzdv3iwZZA4LC0PPnj2V8pMDQP78+dGpUyedjREAVqxYgQcPHiiVt27dGt26dVO7Pd73Ilo0lxBCfgBtlqcEleeEpfz/98q2BWYEA63/AvqdTNmv4VSg5i8pm4N2EwnIfyjATAghWQBv9s+NGzcQGxurVH7//n3RWaL6DtzyxsmbZcgrr1atml7SeGQU3oztVE2aNNFJP4MGDcKNGzcwa9asb7NQ9THz++zZsxg+fLjWAc9UXl5e+PPPPyX3OXTokGjKFSAlDUTXrl0xefJkjBkzBi1atOCmYAkNDYWrq6vkrOnbt29j1KhRorMCTU1N0b59e0yYMAHjx4+Hq6srTExMRNuJi4tDz549JRfhCgoK4i7W9+7du2//X6lSJY1TqKQqUaKERn3zzJw5E9OmTdN69qSvry8aN26MDx8+SO7Hm7F+6NAhbNmyRXZ/vNmk6jh58qTo4+ZdVJPr4MGDCAsLE63r3Tv9jJmOHTsiZ86covvu2LFDb3lu5bhz5w7OnTvHra9Vqxa37kcjdadF/vz5Zbdjb2/PrQsICFBrTHL16tVLdKZ8cnIy+vbti27duuH8+fMIDg5GREQEnj59ihUrVqBChQrc1B2rVq3inreaePfuHWbPnq1UniNHDvz9998atWlnZ5fuYk4qPz8/nbx/EEIIyeIMcwDVBgHF6mX2SH5olIOZEFXm5MrsERA55oj/gM8ueHmYY2Njce3aNTRr1ixduVTgVpc/9L7HC27eunULUVFRSkHjjE7jAQDbt28XTR0ghyAIsLOzQ/PmzbFkyRLY2NiI7ufj48Ntw8zMTFYuS7lq1aql1+BNcnIyN/iaqnTp0qhSpQry5csHQ0NDhIWFwdvbG7du3RK9hRlIuY155syZMDY2Fu1zypQposdNmDABc+fOVTqXXr9+jV69eokGOYKDg7F27VruwlhTpkwRDZ737NkTK1euVLq9//Pnzxg0aBCOHz+udExMTAyWLFmCTZs2ifbFC/ACSPdclShRAvv27cOMGTPw/Plz2cF9Q0ND2Nvbo02bNhgxYoRGfYs5e/Ys5s+fz603MTFBvXr14ODgACMjIwQGBsLDw4MbbHvz5g169+6NixcvcnOliuWCBdTPQctLNaAO3h0hNWvW1KpdXnoMIyMjdO3aNV2Zubk5OnToIDrjOTQ0FPv27dN7jmIxN2/eRLt27ST3ad26dQaNJvMFBQVx6/LmzSu7HalczW/fvuWuv6ANQRBw8OBBtGrVSvRC6YEDB3DgwAHZbS1ZsgSdO3fW6RhnzJiByMhIpfLx48ejZMmSGrdbs2ZN+Pv7K5W7ubmhfPnyGrdLCCGEkBQ0g5kQQrIAAwMD7kw5sSBtZgRuAX4e5vj4eFy9ejVdWUJCglJZqqyaf5kxhsDAQGzbtg3Dhw/n7vf27VtuXdmyZaFQZJ+P1+vXr3PzGJcuXRrXr1/H06dPsWfPHixfvhxLlizBhg0bcP36dTx58gRFixYVPfbr16+4cuWKaJ2Hh4foD/3q1atj2bJlorPbixYtigsXLnCD7U+ePBEtf/Pmjeh5aG9vj+3bt4vmjrWxscG///6LDh06qNWXujp27AgfHx/ExcV9C1zznDt3DrGxsUhISMCbN2+wZs0anV1Mio2NxcCBA7kXGQYNGoSAgACcP38eq1evxvLly7F//34EBARg165d3EDZpUuXtL4F3dTUFN26dcPUqVMxZMgQVKhQIV3AeuzYsdy8surgnavq5NT9nq+vLzdnbatWrUTPvcxOkxETE4OPHz/Cy8sL69evR6tWrVC7dm3J1DLlypXTOk91diL1XOTKJX9SgqWlJbdOnTz26sqdOzeuXLmCiRMnanwXRZEiRXD69GlMnDhRp2Pz9fUVzYtsZWWFadOmadU277UsN30QIYQQQqTRDGZCCMkiGjVqhDNnziiVf5/fOC4uTjTPamob+mRgYIB69erh9OnTSnVubm5o1arVt3+nzmr+Xo4cObJF/mWpfKNSAQapWWlZES/dQ4UKFeDh4SEZBClbtiyWL1/ODcTevn1bdHEl3oJzqmb/5cyZE8eOHUONGjWUAtROTk6ix/Dy+ObOnZubdgNIOdd3796NBg0a4O7du7L60pSBgQEMDAwkx5MjRw7R2eC6sH37dm46i8mTJ2Px4sWidQYGBujVqxccHR1Rp04d0df74sWL0b17d43GVbJkSbi7u6NgwYLpyj98+AAPDw8UKVJEJ7M8/fz8EBwcLFqnTk7d78lZ3O97TZs2hZ2dnejf486dO3jw4AEqVaqk8ZjS4s0sV7eN5cuXS567PxqxtFWp1FmAVer1LNWHLpiYmGDcuHHw9/fHv//+q9axRkZGmDFjhtKdVbowc+ZM0bs5xowZAysrK63a5r2WxXI9E0IIIXh6EvA9oVxuYAwUqgaYWmf8mLK47DPFihBCfnC8Wb3379/Hly9fvv37xo0bork4c+TIgdq1a+treN/IzcOcWWk8dEXq1nipXKhSAdmsSGwGspWVFU6cOCHrsbi4uHBnwfEWeuMtAOju7o5Hjx5J9mdrawt3d/dv57qhoSF69eqFCRMmqNWXj48PLly4INmXqakpzp07hzZt2gAAFAoFWrVqJZlKIjtasWKFaHmpUqVkPVYnJydMmjRJtO7+/ft4+vSpRuPav3+/UnAZSMmn2qVLF52lEHj+/LlouY2NjcYXjOLi4rBjxw7ROktLS27KCQMDA/Ts2ZPb7tq1azUaj76sWLFC9CKSJvr37w/GmF63bdu2aT3OuLg4bp06F4GkZg/rM982YwxLliyBg4OD2sFlIOXupNS7CbRdFDYtLy8v0fEYGxtj5MiRWrfPCzC/fv1aKT0ZIYQQAgDY31t529MFWFwcuPlPZo8uy6EAMyGEZBFVqlQRDeglJyfj0qVL3/6d2YFbXoD50aNH6WYBZlYaD21ZW1ujR48e+Ocf/pcGqZl/pqam+hiW3jRt2jTdbfkKhQJbt25FkSJFZB2fI0cO0SAgAO6sUN7iVlFRUahTpw7mz5+Pjx8/cvssUqQIrl+/jtDQUMTExGDXrl3cwA6vr+TkZLRu3RqTJk3C69evuX1ZW1vjxIkTiIiIQHR0NE6fPs3NzZ0dvXnzBr6+vqJ1gwYNkn0Lff/+/bl1vDQRUho1aoQqVaqofZwmXr58KVquzezlI0eOcNMcdOrUibuQJCCdJmPPnj1ZIhhmZWWFrVu3YvTo0Zk9lAwnFWBWJz2SVN57qT60kZiYiB49emDKlClaB7F9fX1Rs2ZN0TuvNLFo0SLR56RLly5q5bbmKVWqlOjfhzHGvRhKCCHkJ5CgwechSwbO/QoEPtD5cLIzCjATQkgWITcPc2YHbnmBcMbYt3QeERERogsIAfofZ+/evRETE6PRFhcXhy9fvmDPnj2wtbXl9mFubs6t0/etzfqwY8cOPHz4EEePHoWvry/at2+v8piEhAR4eHhg5syZ3JQhvABGkyZNuIGYyMhIzJgxAwULFkTDhg0xb9483Lx5U3QBu1y5cqm8Nb9GjRrcvKgJCQlYtmwZihcvjho1amDGjBlwd3cX/Ruam5vrLUVFZpLKP1q/fn3Z7RQtWlQ0pzAAyQA+T+qs8YzAy0GuTVBLKj1G7969JY91cnLiLlwYGRkpughgRrGyssLw4cPh6+sreVHhRyYVRJa7WCcgvSingYGBWmOSa9KkSdy86AYGBhg0aBCuXr2K0NBQREVF4enTp1i+fDlKlSolekx0dDS6deum9Uzmt2/fcmdTDxw4UKu2UxkbG3Pvynn37p1O+iCEEJINvRP/zSqLv/gaHj+rnydhGiGamhOW2SMgP5GGDRuK5jdODSqHhYXB09OTe2xGSA2E88bZvXt3XLlyRfTHc0ak8TAwMJCcHagLUgs5hYaG6rVvfalYsSIqVqzIrQ8LC8Pdu3dx48YNeHh44MaNG6I5d9PiBVsKFSqE/v37Y8uWLdxjExMTceXKFVy5cgUzZ86EmZkZ6tWrhxYtWqBDhw6yZ1ibmppi4sSJmDVrFncfxhju3LmDO3fuYP78+TA2NkatWrXQvHlzdOjQAWXKlJHVV3b08OFDbp2uXqsJCQlqHyOVokbXvn79KloutqCpHH5+fnB3dxetS71wokqfPn0wZcoU0br169dLLkKqCwYGBrCwsICVlRUcHBxQqVIl1KxZEy4uLj/khRZ1SH2+xMfHy25Hal91cjnLdfv2baxcuVK0zsLCAseOHVNax6F06dIoXbo0hg0bhl9++QXbt29XOjYiIgK//PILd20IOVavXi36naFw4cI6/W5jYWEh+hnNy0FPCCHkJ5Ak/7NbSSzFitKiGcyEEJKF8Bbp8/Pzw5s3b+Du7i4atMvohfN440ydwcybZV29evVskX9ZlWLFinHr3rx5o9O+bt68iWnTpmHq1KmSCw/qUlxcHG7cuIGVK1eib9++KFu2LKytrdGsWTPMnj0bFy5cUBlcVmXlypVo2rSp7P2joqJw9uxZjB8/HsWKFUPjxo1x6tQpWcf++uuvKmeNphUXF4fLly9j+vTpKFu2LKpVq4bdu3erNTsxu+ClMdGlEiVKqH2Mg4ODHkYijncuS92pIGXTpk3c9AedO3dGfHw8YmNjJbdOnTpxZ8o+ePAAt27d0mhsafHu5IiNjUViYiK+fv0Kf39/nDt3DosWLUKHDh30Glzetm0bBEHQ66aLWddSn2HqpC+JjIzk1ml67klZvHgx97zcunWr5CLBpqam2Lx5M/euhuvXr6dL5aWOmJgY7oz/zp0762QxylS851Xqb0EIIeQHV31YZo/gh0EBZkIIyUIqV67MnR178eJFycBtRub+5c0oev36Nfz8/DI9jYe+SeVmffXqFXdhOXUdP34c9erVw6JFi7B48WLUrl1bJ4tU8Zw9exbt2rWDtbU16tSpg7Fjx2Lnzp14+vSpZL5QTZiZmeHkyZMYNmyYWnlLgZQZx+7u7mjTpg1atmyJt2/fSu5vaGiIHTt2YObMmRoFyO7du4fevXujVq1a8Pb2Vvv4rIyXJ1hX7O3t0blzZ7WOMTY2ztA817oMMCcmJkq+Rv/++2+Ympqq3EqUKIHk5GRuO+vWrVN7bN8zMTER3X72GcqqSKVOUecOFql9ra11uzJ9bGws94Jc1apV0alTJ5VtGBgYSC76eeTIEY3GdvjwYe5dBB07dtSoTR7eazo7prYihBCiI/kdxcttywBl2qRsNpqvy/EzoQAzIYRkIaryMGeVwK1UIHzXrl3cINyPEmCuVq0aty4xMZGbf1odSUlJGDt2bLpZs8nJyRg9erRk7k5NhISEoHXr1mjVqhVOnDihcvEnU1NT1KlTB9OmTYObm5vGs+eNjY2xbt06eHl5oW3btmoHmgHg3LlzqFSpEp48eSK5nyAImDt3Lnx8fNCrVy+NbkG/e/cunJ2dNZ6p9zMwMDCAtbU1nJ2dMXXqVNy7d0/tYJmVlZV+BsfBS+GhyTly4sQJBAUFaTsklQ4cOMANyhH94i0cCkBycVJ19s2XL59aY1Ll0aNH3IUD1cl3XrduXRQoUEC07soVzfJQ8i7IWFlZ6TxVDu/iiSZpfAghhPxAZocCzoNS/t/OCRhzHxh5G+i+O2Wr2CVTh5ddUICZEEKyGF4Q9tSpU3j27Jlax+iLVCB82bJlouUZkX85oxQvXhzFixfn1h88eFDrPk6ePCm6OFpkZCT8/f21bj9VaGgo6tevL5pTG0gJzDo5OWHYsGHYtGkT7t+/j/DwcHh4eGDBggVo3Lix5ExLOZycnHD8+HG8ffsWy5cvR/369VUu3pfWly9f0KJFC+5ig2kVL14cu3btwocPH7Bhwwa0bNlSrRmbsbGxaN++Pfe1mN3wFuYDUoJSjDG1tsTERHz58gV3797FwoULYWdnp/aYNM19rCleygNNbpuXWtxPl2JiYkTz4RL9k0r5EhAQILsdqXRKRYsWVWdIKkktYqduChteiqjAwEC12gFSch/z8pXXr19f54sd8l7T+l63gRBCSBYnCECbv1LW3xp2FcjN/52XzrVlwOVFKVs8pVuiADMhhGQxvDyIvNyOmRW4VXecNWrUyNA0HvrWtm1bbt3u3bu1Xuxv+fLl3DqpoKC6Ro4cCR8fH9G6Xr16wc/PDw8ePMC6deswaNAgVKpUSSn4q07eUSn29vYYN24crly5gpCQEBw7dgyjRo2SFQAJDAzEokWLZPeVO3duDBkyBGfOnMHXr19x7tw5TJo0CY6OnNvk0oiIiMCMGTNk95WVSaWi0CRgpAtGRkYZ2p+ZmZlouboB5oCAAJw7d04XQ5Jl/fr1GdYX+U/evHm5s3h9fX1lt/P06VPRcnNzcxQuXFijsfFIpW1S52IewA/GhoWpv9DRv//+y71AWatWLbXbU4X3WfUjfTchhBCiB1KZAi//mbLF6eb3UHZGAWZCCMlipNJPiMmswK26s6Z/lPQYqYYMGcKtCwsLk8xVqcrJkye5txvnz58fuXPn1rjttF68eIG9e/eK1g0fPhy7du2SXNAQSMmHLDbTWo7Q0FBuUMLS0hLt2rXDqlWr8PLlS3h6emLgwIGSwZCjR49y6yIjI7n5hk1NTdG8eXMsWbIEjx8/xtOnTzF+/HjJ19WpU6d0nqokM0gF1D09PTVqMzk5Gffv38fLly81Ol7dgJe2eDOm1Q0wb9myRevZ/Op4+vQpLl++nGH9kf/UqFFDtFxueiTGGHffevXq6XzmrlQ+canZzersr8mdB1Lv2RUrVlS7PVV4r2ldXrQlhBDyA3qv2XdiAECk/hfUzioowEwIIVmMQqFAvXr1ZO+fWYFbdQPhP1qAuXz58pKP6a+//tJoNuPbt28xdOhQbn2HDh3UbpPn5MmT3MX7Ro4cKasNLy8v7o92XgA2LCwMLi4usLa2hrW1NUaPHq2ynypVqmDz5s04d+4cNwApdst5QkIC+vbtC0tLS9jY2KBTp04q822WLl0af/31F+7cuQNLS0vRfWJiYvDp0yeV487qeHciACkz8dX16dMn1KhRA1WqVEGpUqUwatQotdvQJBe3Nng5dcPDw2W3kZycjC1btnDrT58+rXa6kdRN6g4VXSz2R9TXunVr0fLPnz/j9u3bKo+/ceMGvnz5IlrXuHFjrcYmRirlhpubm+x2AgIC8Pz5c9E6dVNtREdH49q1a9z6smXLqtWeHLwZzLwZ6YQQQggAIOCm5sdqE5zOZijATAghWZA6wdjMCtyqEwg3NjbWy+2umW3BggXcuuTkZHTs2BEnTpyQ3d6LFy/QpEkTfPjwQbReEAQMHDhQ7XHy+Pn5cevkXjxYvHgxt443Y3jWrFk4c+YMgJSZfKtXr8amTZtk9de4cWO0b99etE4sl/Lq1auxc+fOb4H0f//9F7///rusvhwdHSWD/erkbs6qihcvjpIlS4rW+fj4qBVkfvPmDerVq4d79+59K1uzZk2GLHqnjVKlSomWv3r1SnYbZ8+exdu3b0Xr8ubNi2bNmmk0NgDo06cPt+7IkSM/xIUOAOjfv7/GQXi5G29BOXW1a9eOm8pFTuoS3j4GBgbo3LmzyuMZYzhz5gwWLlyIbdu2qbwYUr58ee4s5gsXLshO7bF69WruLH11F+S7desWd+FBY2NjFClSRK32VAkKCuKmCilUqJBO+yKEEPKDsVB/TZFvkuJ1N44sjgLMhBCSBckNGmd24FbuOKtXr/5D5jisWbMmevfuza2Pjo6Gq6sr+vTpw81zDKTMeps3bx4qV66MFy9ecPfr2rUrnJ2dtRpzWlIpHg4fPqzy+Pnz5+PAgQPc+rdv3yIpKUmpXOzW8EmTJkkuepUWLyAqNktPrK9FixalC4Jq0pelpaXOUpVktgkTJnDrRo0ahSdPnqhs4/z586hevbro7EaxcyAr4QWY/fz8ZKdBkVrcr3v37lql/ejatSty5MghWhcfH4+tW7dq3DbRjI2NDfe9f+fOnZLvLzdv3sSePXtE6zp16qRygb/4+Hi0bt0aLi4u+PXXXzFgwACULVsWjx8/5h5jZGTEXTcgOTkZ3bt3V5kSxs3NDX/99Re3vkuXLpLHf+/WrVvculKlSun8TgbezGszMzOdL6pICCHkB9N0tubHnpkC7OuF1j6TsN7oL0w23A8AGKg4haaKH2t2MwWYCSEkC6pcuTKsra1V7pfZC+fJvZVX6jZ8Xdu+fTsEQdBqMzIyQsmSJfH777+rTKewevVqyTzFjDHs2rUL5cuXR7ly5dC3b19MnToVv/32G3755RfUrVsX9vb2mDlzJqKiorjt2NraYsmSJRo/L2Kkxj19+nTs2rVLNIWGp6cnWrRooXKhu6ioKNHgZMGCBZXKwsLC0KNHD5Uz8Rhj3PzU9evXl9VXYmIievbsKWsRO3d3d9HyunXrQhAElcdnBwMHDuSmiQgNDUXt2rWxdetW0dfCx48fMXr0aLRs2VJ0Jm2FChWy/O3nZcuWFc15m5CQAH9/f5XHf/z4ESdPnuTWS81AliN37tzclAwAKMCcSX777TfRBe8SExPRtm1b0QDq9evX4erqKnrRRaFQYOrUqSr7nT9//rc7QFIFBgaic+fOkhdExo4dy6179OgRqlevLpreIzY2FitWrICLiwv3YlG1atVE33+lSAXES5curVZbcvACzBUqVPhh3ssJIYToSelWQJE6yuX1p/y3CRLrJzw9iRIhl9HC4B6qK54BAJoYPMCmHMtQ/d12PQ0642XsKiqEEEJkUSgUaNiwIY4cOSK5nz5yNaqjUqVKyJMnDzcVQipdj1PfOVoTExPh5+eHOXPmIDk5WTKlQq5cuXD48GE0btwYoaGhku36+vrKvhU5LUNDQxw8eFDnt/G2bNkS06ZNE62LiYlBnz59sGDBAlStWhW5c+fGhw8f4OXlpdbibVevXoWTk1O6sp49e+LQoUNK+968eRO1a9fG7t27lY4BUgJ5kyZNws2bynnQBEHAoEGDlMp79uyJpUuXKpW/ePECNWrUwM6dO0Vn4oeHh+OPP/7A/v37RR+X1CKP2Y2xsTG2bduGli1bit7+HhERgYEDB+LXX39F3bp1UahQIcTFxcHb2xs3btyQDGrNnTtXn0PXCQsLC1SuXFl01unTp0+5M5xT7d+/n/sclClTRid3HfTr14/7efDs2TM8fvwYFSpU0LofIl+JEiUwd+5cTJkyRakuKCgItWvXRt26dVGpUiUwxnD//n3cuHGDm/d+3LhxqFKlisp+t28X/yH6/PlzXL9+HQ0aNBCtr1GjBnr16sVNe+Pr64uaNWuiYsWKqFy5MqysrPDu3Tu4u7tz80UDKWk9VqxYoXLc35NaHFZq8VFNPXv2TLT8R0zfRQghRA8GnE5ZsO/xAcCxM2CRL3395l3QJMRa891mIHE2YJj9U+9RgJkQQjKA1IrwvFunW7RooTLA3Lx5c531p8lxgiCgWbNm2LdvH/c4c3Nz7g84Xn+qbidXZ3FBbR04cEBlzt7KlSvjwoULaNasmcogs7oMDQ2xbds2btAgLd5t9Lzn2cnJCS1atJBcjNDHx0cyvQeQMkv43bt3onUXLlxQWsSvQ4cOaNasGS5cuKC0v7e3NypVqoTKlSvD2dkZtra2iIqKwqtXr3DhwgXExsaK9jNgwADR4EzlypUxZMgQ0RQG7969Q6NGjVCmTBnUrFkTdnZ2iI+Px+vXr3HhwgXubOomTZpw80Dz8rIC/L+PnGOl6nTRd7NmzfDnn39yLzgAKQF+OalTUg0fPpz7PAH88cp5rLrWqFEj0QDz3bt3uakFUomlYUnVr18/rccGpCwqZ29vz511f+3aNaUAs9Tf3MzMTCfj+tlNmjQJnp6eoheiGGO4du2a5EJ2qRo0aCCZ0z+t9+/fa1QHpNxx8/DhQ8m0N48ePcKjR49kjQUAFi5cqFGQNj6en5OyevXqareniqen+G3ITZs21XlfhBBCflDmtkAteQuhy2WUHAsEPwPsKuq03cxAKTIIISQD2Nracuvy5s0rWt61a1fR229T5cuXDzVq1FCrTVV1vHHmzJmTG5BQFUBp1aoVN9DBG4vUGAHImuWlK7wFjb7n7OyMO3fuoGrVqjrrO2/evDhz5gx69eola//ChQtz2+HZtm2baBoJOYyMjDB//nz4+flxZ2l6eXmJlu/fv18yiHD//n1s3LgRf/75J1asWIETJ05wg8sNGzbE6tWruW2tXr0abdq04dY/ffoU27Ztw4IFC7Bs2TIcPnyYG1x2dHSUvKBiZ2fHDZCqWrSK9/cDxPNL67JvAJg6dSpWrVqlVb7gVMOGDcOaNWsk9+E9XqnnQV94i/DduHFD5bG82Z1GRkbo37+/NsP6xtDQUHLBSbHFy6T+5g4ODjoZ189OEARs374dXbt21biN+vXr49ixYyovAqWys+MvNCRVBwBWVla4cOECqlWrptYYxSgUCixatAiTJk3S6Hje546JiYnsBYTlSkpKEr2AZGJionZqD0IIIUTnWNZer0QuCjATQkgGqFWrFnLmzKlUbmNjg0qVKokeY21tLRmcmDx5MjdVRJMmTURzClaoUEEy2MibycNrD0iZRV2uXDnROoVCIfnjkxfUUTWjqFOnTpL5g3VJncBBqVKlcOPGDSxatEjyooIqRkZGGD58OHx9fdWaXcWb0S7VRv78+XHmzBmUKVNGrTE2atQIt2/fxvTp05EjRw7s3LlTdGY5b5E8a2trXLlyBVOmTNF4xqqhoSEmTZqEs2fPSuYiz5EjB44dO4bFixdrPHNTEAT069cPHh4esLGx4e6nUChEn28DAwOVuchr1qwJc3NzpfLy5ctzcyTrqu9Uo0aNgpubG/d9SZX8+fPj4MGDWLduncpUNk2aNBEtz8ic7Wn7FHtvvHHjBuLi4iSPdXFxES3v2bMn8ufPr5PxAcDIkSNhYWGhVG5gYCB6AaVAgQLcQGKPHj10Nq6fnbGxMfbt24clS5aIfs7zKBQKTJw4EefPn1frrhzeYnoFCxZE7dq1VR6fP39+XLt2DdOnT4exsWa345YrVw5ubm6i6UHk4n3+9+jRQ/Q814aXlxciIiKUyl1cXETfcwkhhBC1Gev2sytbYozRRlumbFWrVmX65uPjo/c+CJFrw4YNTKFQMAAMADM0NGQHDx6UPCYiIoI1bdr02zGp26BBg1hycrLksXPmzEl3jLm5OfPw8FA5zj59+qQ7Lm/evOzZs2eSx/j6+rKiRYumO87ExIRt2LBB8riEhATWrFmzdMeVKlWKffr0SeU43717xwYOHMgKFSrEDA0NlZ4jbTZDQ0NWokQJNnv2bBYfH69yLGKioqLY5s2bWZs2bZiJiYmsfsuUKcNmzpzJAgICNOozOTmZtWzZMl2blSpVkvV8RkdHs1mzZrFChQpxx2dmZsZcXV3Z+fPnRdu4dOkSs7Ky+ra/nZ0d8/b2Vtl3QEAAmzZtGitWrJis56lEiRJsypQpzN/fX92niH3+/JnNmzePlStXTlZf9vb2bMSIEezx48ey+/D29mbW1tbp2pk+fbqsY9euXcsEQfh2XM6cOdnVq1czpO/vHT9+nLVr147lypVL5eulVq1abNOmTSw2NlZ2+5GRkaxChQrp2mrUqJHGrzltjR49WvTxXbp0SfK4xMRENnDgwHTHFC5cmL19+1bnY9y/fz8zNjZO99xv3LiRu/+jR49Y3rx5042tRYsWmfYc/+gCAgLY2LFjlV6D338W9+3bV9Z7o5jIyEjWuHHjdG3myZOH3bhxQ+223r59y2bOnMlKly6t8r3QxMSEtWjRgh04cIAlJCRoNPa0YmNjWZUqVTT6vFLXwoULRR+Tqu9g5OdCvxsJIdpotL8Rc9zmqLR9fLCLMZ/j7MS+9ezNzBIs6hcb5lO6DIscZsPYbMuU7b2XaJsA7rEsELuTuwkpYyYk4zk7OzOx29V0ydfXF2XLltVrH4Sow9vbG2fPnoVCoUCbNm1ULh4FAIwxXL58GV5eXjA1NUWNGjVkp2G4ffs2Ll++DHNzc7Rv3x4FChSQddyFCxdw79492NjYoFOnTsidO7fKY2JjY3Hy5En4+fkhb968aNKkiaxb3ZOTk3H8+HH4+PigUKFC6NSpk1qzwLKDxMREvHz5Ek+fPsXHjx8RGRmJ6OhoGBsbw8rKCkWLFoWTkxPy5cunujEVUp/P1MXJ2rZtK/vWayDlfLtz5w5evnyJwMBAKBQK5MuXDwULFkStWrVUznh7//49jh49CnNzc3To0AGWlpZqjT8gIACPHz/GmzdvEB4ejoSEBBgbG8Pa2hqFCxdGxYoVVd4GLtenT59w//59vH79GqGhoYiLi4OxsTFy5cqFQoUKoXz58rJSU/DaTk21Ub9+fbVylD58+BDnz5+HiYkJ2rdvr/bijtr0LSYpKQkPHjyAv78/QkJC8OXLF+TIkQN58uRBgQIFULNmTY1nHMbFxeHw4cN49+4dypcvDxcXF+7dEvp279490Rm/EydOFF0o8nu3bt3CzZs3kSdPHrRp00bW+6YmfHx8cObMGRgYGKB169YqP0dCQ0Px77//4uPHj6hQoQJat26dac/xzyIxMREPHz7Ew4cP8fnzZyQlJSF37twoX748nJ2dJdNfycEYw7lz53D//n3Y2tqiY8eOWp9vgYGBePz4Mfz9/REeHo64uDiYmZnBxsYGDg4OcHJykrxTRBPx8fE4fPgwAgICULp0abRu3VovOdjr1asHDw+PdGX58uVDQECAWp+P5MdGvxsJIdpofKAxgmOClcrdurghb868WOn2Ao2udEaJz4F4c8kGhRt+hln+/69HMPQyYF9Z6VhBEDwZY9qvFp1BKMBMMg0FmAkhhBCSldSvX19pUbYSJUrg5cuXmTQiQog2Pn36BHt7eyQlpc9vOWPGDPzxxx+ZNCqSFdHvRkKINijATDmYCSGEEEIIAZCy0OH3/Pz8RBcII4RkfQcPHlQKLpuYmGDEiBGZNCJCCCHkx0QBZkIIIYQQQpCy6FeFChWUyrdu3ZoJo/kfe/cdXkW1tQH8XUmA0HuRriiKNBGkiwhSBBTFBij2XrAhdsQuekVBsGABEUUURYpKB6U3aVJEeu9CaAkp6/tjTvhCMvX0k7y/5znPvZ69Z89KOHMms2bP2kQUKLNj97777gtaqSUiIiIyMMFMRERERARARPD222/neP+7775DcnJyBCIiIn+tXLkSy5YtO+u9ggULmj6pQERERIFhgpmIiIiIyKdz58648sorz3rvyJEjGDt2bIQiIiJ/DBs2LMd7Tz/9tOsFj4mIiMg9JpiJiIiIiLJ47733ICJnvTd48OAIRUNEXh05cgRff/31We+dc845nL1MREQUIkwwExERERFl0bBhQzzwwANnvbdkyRIsWLAgQhERkReff/45Tpw4cdZ7AwcORJEiRSIUERERUe7GBDMRERERUTbvvvsuqlevftZ7AwcOjEwwROTa6dOnMWjQoLPe69q1K7p37x6hiIiIiHI/JpiJiIiIiLIpWrQoxowZg0KFCp157+eff8aWLVsiGBURORk9ejR27dp15r/PP/98fPnllxGMiIiIKPdjgpmIiIiIyETjxo0xa9YsXH755ShcuDDy5cuHNWvWRDosIrKxbt065MuXD5UrV8b999+POXPmoHTp0pEOi4iIKFdLiHQARERERETRqnHjxvjzzz8jHQYRufTOO+/gnXfeiXQYREREeQpnMBMRERERERERERGRX5hgJiIiIiIiIiIiIiK/MMFMRERERERERERERH5hgpmIiIiIiIiIiIiI/MIEMxERERERERERERH5hQlmIiIiIiIiIiIiIvILE8xERERERERERERE5BcmmImIiIiIiIiIiIjIL0wwExEREREREREREZFfmGAmIiIiIiIiIiIiIr8wwUxEREREREREREREfmGCmYiIiIiIiIiIiIj8wgQzEREREREREREREfmFCWYiIiIiIiIiIiIi8gsTzERERERERERERETkFyaYiYiIiIiIiIiIiMgvTDATERERERERERERkV+YYCYiIiIiIiIiIiIivzDBTERERERERERERER+YYKZiIiIiIiIiIiIiPzCBDMRERERERERERER+YUJZiIiIiIiIiIiIiLyCxPMREREREREREREROSXhEgHQEREOR0/fhwrVqzApk2bcPDgQZw6dQoFCxZE8eLFUaNGDdSpUwdly5aNdJh5yp9//okFCxagatWq6Ny5M4oVKxbpkExt374da9aswdatW5GUlIS0tDQULVoUZcuWxUUXXYRatWohMTEx4P2cOnUKP/30E3bs2IGaNWuia9euSEjgnxXhduDAAfz66684duwYrrzyStSpU8evcTZu3IiVK1di165dOH78ODIyMlCkSBFUrVoVTZo0QaVKlUK2bzKkp6fjt99+w7p16xAfH4/77rsvKN8z+/btw++//469e/eiRIkSaNWqFS6++OIgREyUt82YMQOLFy9GhQoV0KFDB1SsWDHSIREREUUMrwSJiKLEvn378O233+LHH3/E0qVLkZaWZtu/Xr166Nq1K+6//35Urlw5TFHmTW+88QZefvnlM/99/vnnY968eShXrlwEo/p/ixYtwqhRozBhwgRs377dtm/BggVx5ZVXonv37rjllluQP39+z/s7duwYmjRpgnXr1p15r3nz5pgxY0ZQktfkzuzZs3HTTTfh4MGDAIC4uDi8++67ePrpp11tn5GRgY8//hgffvghNm3aZNlPRNCxY0cMHz4c5cuXD8q+6WyHDx9Gp06dsGjRojPvtWnTBg0aNAho3DfeeAOvvvpqjvPJLbfcgi+//BKFCxcOaPxMqamp+OCDDzB69Gj8+++/OHHihKvt8uXLhwIFCqBEiRIoX748atSogUaNGqFbt26oUaNGUGLLjdLT0zF16lSMHz8eS5Yswfbt23H06FEULlwY5cuXR61atXD11Veja9euZ47ZSEtJScGkSZMwZcoUrFixAlu2bEFSUhLi4uJQqlQplC5dGg0aNECLFi3QpUuXoCRr09PTMX/+fEyaNAlz5szBnj17sG/fPmRkZKBkyZI4//zz0bx5c1x77bVo0aKF5/E/+ugjjB8/HoDxWf7www/x8MMPBxw3ERFRTFJVvviKyKthw4YaamvXrg35PogCtX//fn3ooYc0MTFRAXh+JSQk6B133KG7du2K9I+SKx08eFDz5cuX4/fep0+fSIems2fP1hYtWvj1uQGg5cuX16FDh2paWpqn/b722mum47333nsh+kkpu9OnT+s555xj+n3w77//Om6flpam1157rafPS+fOnYOybzpbenq6tmnT5qzfZaNGjQIe97333rP99+zWrVsQojfcfvvtfn8Pmb1ERFu3bq1LliwJWoy5xQ8//KDnnnuuq99jwYIFtU+fPnr48OGIxZucnKwDBgzQsmXLuv73z5cvn/bs2VM3bNjg937HjRuntWrVcr3P+vXr62+//eZpH1OmTDlrjLi4OJ0yZYrfMVNk8bqRiAJx5Zgrtc6IOjle+07sU1XVQdM36KqX6+mJh8ro2gsv0uMPlFF9pZjx2vWX6ZgAlmoU5O7cvliDmYgogr799lvUrFkTn3zyCZKTk/0aIy0tDV9//TUuvPBCfPXVV0GOkBYtWoTU1NQc7y9YsCAC0RhOnDiBu+66C61bt8a8efP8Hmffvn145JFHcNlll2H9+vWut5s1a5bp+9OnT/c7FvJm1apV2LNnT47309LSzpoFa+Xdd9/FhAkTPO1z+fLlQdk3nW3QoEGYOXPmWe899dRTAY25c+dO9OvXz7bPzz//jGnTpgW0n8x9ffPNNwGPk5WqYvbs2WjWrBlef/31oI4dq1JSUnDXXXfh5ptvxpYtW1xtc+rUKfzvf//DZZddhrVr14Y4wpw2btyIxo0b49lnn8WBAwdcb5eamorvvvsOl1xyCT7//HNP+zxx4gSuv/56XH/99Wc9ZeNk5cqV6NSpE+655x6cPn3a1Tbt27fHJZdccua/MzIycPfdd+PYsWOeYiYiIsoNmGAmIooAVcXjjz+O2267DUeOHAnKmMePH8c999yD+++/HxkZGUEZk4BDhw6Zvr979+4wR2LYvn07GjdujBEjRgRtzOXLl6NRo0b4/fffXfXfsWOH6fteEggUGLtSJHFx9n/epaWl4YMPPvC8z2bNmgW8bzrbrl27ciSCK1eujJtuuimgcQcPHoxTp0459nvhhRcC2g8ALFu2DMYkm+BLS0tDv3798Nprr4Vk/FiRnp6O7t27+/29v2nTJjRr1gyrV68ObmA2Nm/ejFatWmHVqlV+j3Hy5Encf//9+Oijj1z1P3ToEFq2bIlffvnF731+9dVX6Ny5M1JSUlz1f+KJJ8767127duGVV17xe/9ERESxilcBRERhpqq46667MHjw4JCM//nnn+Oee+4Jydh5UXp6uun7kUjib9u2DZdffnlIZqKdOHEC1113HaZOnerY12xGN2D9u6Lgq1mzpmkN8Hz58qFJkya22y5btsz2ZkCDBg3Qu3dv9O7dG9dddx0aNmyIHj164OOPPw5433S2l156CcePHz/rvYcffjigBTNTUlJcP82ydOlSLFy40O99AcDRo0cD2t6NV155JU8/IdGvX7+AkqYAkJSUhGuvvfZM3fRQSk9Px0033WT6pIM/Hn/8ccendTIyMtCjRw+sWLEi4P1Nnz4d999/v6u+PXr0QIUKFc56b8iQIdiwYUPAcRAREcUSJpiJiMLs5Zdfxtdff+3YL1++fGjTpg3uvvtuPP/883jqqafQtWtXlC1b1nHbESNGhCyBTZGRlJSETp06OS7iBwDly5dHt27d8Oijj+KFF17AQw89hJYtWzomrU6fPo3u3btj27ZtwQqbQiRfvnw5FmmLj4/Hu+++i/POO89225UrV1q2de7cGcuWLcOgQYMwaNAgjBs3DkuXLsV33313JqkcyL7p/61cuRIjR448670CBQq4TmxZGTdunOWTF2a8liDIzu5mW9WqVXHq1Kkcr8OHD2PHjh2YN28ehg0bhs6dOyM+Pt52P08//XSefDpnxYoVGDBggGmbiOD222/HvHnzcODAAezduxfjx48/87RBdlu3bg3LQpzDhw/HX3/9ZdoWFxeHe++9F3/++ScOHjyIgwcPYtGiRXj66actn47IfOrLzoABAyxLvuTLlw9PPfUUli9fjiNHjmDz5s0YNmwYLrroIsvxRo4ciZ9++sl2nwCQP39+3H333We9l5qaij59+jhuS0RElKtEugg0X3n3xUX+KC+aNm2aiojtQjPFixfXQYMG6X///Wc6Rmpqqo4ZM0arV69uO06hQoV0y5YtYf35cqPhw4eb/n6rVasW1jhuu+02x0WKLrvsMp0xY4amp6ebjrF//37t27ev6aKFWV+dOnWyjaVatWqWiyRReO3cuVM///xzHTRokK5evdrVNv369bP8t//+++9Dum/6f+3bt8/x++/evXvA42ZfMNDpVbhwYU1KSvJ7f1bfkV6/JxctWmS6eGTW17x58/yOM1Z17drV8vcxdOhQ021SU1P1hhtuMN0mLi5Oly9fHtKYGzRoYLl438SJEy23+/vvv7VMmTKWP++qVatMtzty5IiWKFHCdJsiRYron3/+abpdcnKy9urVy3J/5513nqakpDj+vBs3bjT9227BggXufmEUFXjdSESB4CJ/XOSPiChsUlJScN9998E4V5hr1qwZ1q9fj969e6NEiRKmfRISEnDzzTdj+fLluOqqqyzHOnnyJN56661Aw6YoMGXKFIwaNcq2z+uvv46FCxeiTZs2lnVwy5YtiwEDBmDmzJmWny8A+O233yK6iCG5V6lSJdx7773o3bs36tSp42qbEydOWLbVrFkzpPsmw5IlS0zL0dx7770Bjbtp0ybLRTivvPJK0/dPnDjh+P0SDm5qy//666/hCSZKbN++3XIxzquvvhoPP/ywaVtCQgK++uor0yeeMjIy8Pbbbwc1zqwOHTp0ZkHQ7J555hl06dLFctvatWvbzqi3mqH86aefWq5n8cknn+Dyyy83bStQoABGjBiB1q1bm7Zv3rwZP/74o2U8mWrUqGE6xquvvuq4LRERUW7BBDMRUZgMHToUW7dutWy/7LLLMHny5By1/KyUKFECEyZMsK15+t1337leqIaik6qib9++tn3efvttvPTSS64XWGvZsiV+//135M+f37KP2xquFHvS0tIs2+xuPFDwvPvuuzneq1y5Mtq0aRPQuF9++aXpTcz4+Hh8/fXXKFWqlOl2n332WUD7DZb27dujbt26lu1Wicvcavz48ZY3pZ977jnbbYsVK4ZHHnnEtG3ChAkhq529adMmy7Y777zTcfuuXbuifPnypm1WC8xa1aeuV68ebrvtNtv9xcXFYejQoZbtbkvI3HrrrTnemzx5cp77zBIRUd7l/woiRETkWnp6Oj788EPL9kKFCmHMmDEoVqyYp3ELFiyIH3/8EXXq1EFSUlKO9hMnTmDFihV+Lbx19OhRLFmyBHv37sX+/fuRmpqKsmXLoly5cqhbty6qVavmecxMqampmDBhArZu3Yo6deqgQ4cOpv3S0tIwb948bN26FXv37kWRIkVQqVIlNGrUCJUrV3bcz+rVq7FmzRrs3LkT6enpKFeuHOrXr48GDRpARPyO386OHTuwePFibNu2DSdPnjwTc8OGDf2qTztt2jSsWrXKsr1jx46OiQYzTZs2xbvvvosnnnjCtH3u3Lmex/RXSkoKVq1ahc2bN2Pv3r04efIk0tPTkZiYiGLFiqFSpUqoWbMmzj///KD8u2VkZGD16tVYt24ddu/ejZMnT57Z1znnnIP69eujatWqQfjJDOvXrz/zOTx+/Djy5cuHYsWKoXz58qhbty5q1Kjh98+1fft2TJ06FadPn8aVV16JWrVqBRSrlziCve9jx45h8eLFZ75zTp06haJFi6JSpUqoU6eOp9nV2R08eBA///wzDh8+jIYNG6Jdu3Zntasq5syZg2XLluHUqVM455xz0KxZM9sarf7avHkzfv755xzv33jjjQF9vtPS0jB8+HDTtquuugpVqlTBLbfcgk8++SRH+8qVK7FgwQLL2r3h1KJFC6xevdq0bf/+/WGOJrKsZmyXLl0aLVq0cNz+lltuQf/+/XO8n5ycjIkTJzomX/1ht9irm78bRATVq1fHvn37crSdOnUqx3v//fcflixZYjqWWdLXzMUXX4zLL78cc+bMydE2d+5cHDhwwHH9i27duuHBBx/McQPvo48+4g1bIiLKGyJdo4OvvPtiDWbKSyZOnGhbV/LNN98MaPwPP/zQcuypU6e6HufkyZM6aNAgbdmypSYkJNjGfNFFF+nTTz+tO3bs8BRrcnKyNmnS5KyxrrvuOs3IyDjTZ/v27frggw9q6dKlLWtItmvXTmfPnp1j/NTUVP3oo4/04osvtoy9cuXK+sEHH1jWKs7KbQ3m33//PcfPlf1Vt25dHTZsmKamprr+fVnV0QSg+fPn182bN7seK7v09HS99NJLTceuWLGi5XbBqMF8+vRpHTlypLZv317z589v+3vLfJUsWVJvv/12XbhwoV8/76ZNm/Thhx+2/FxlfZ1zzjn68MMP69KlS/3a1969e/XZZ5/VihUruvq57rzzTp01a5anfUydOlWLFClyVn3TYcOG5ej36aefau3atbVQoUKufs8AtFSpUtq0aVP99ttvA9q3k+TkZB06dKheccUVjrXBq1Sp4td3zubNm7V8+fJnjdWnT5+z2q2Og0aNGunkyZM9/1x2XnjhBdN9zZgxI6Bxx40bZ/m7++abb1RVdf78+ZZ9br/9dr/2G6wazJmef/55y/Fq1qzpV4xZvfLKK66PA39fr7zySsBxqqqWLVvWdPyuXbu6HqNcuXKmYzz00ENBiTG7rVu3Wv5etm/f7moMqzUmXnvttRx9Fy5caLk/L9+pr776quU4P//8s6sxrrjiihzbFixYUA8fPuw6DoocXjcSUSACqsE8/jHVJV/meCHGajBHPAC+8u6LCWbKS+6++27LC5cCBQrooUOHAho/NTVV69atm2PsfPny6d69e12N8emnnzousGQV/+OPP67Hjx93tZ+PPvrIdJzMxcWGDx+uhQsXdrVvETkrOb9161Zt1KiR69jbtWunBw4csI3XKcF85MgRvf322z39zurVq6fr1q1z/F2dOnXK9ndx4403uvqd21mwYIHp4kTXXHON5TaBJpjHjx9vOYbb15133qknT550tb/09HTt37+/YwLT6tWtWzfdv3+/q32pGseS289w9lfr1q1148aNrn6m8847zzSZsWvXrjP9ZsyYEdDvGciZpHG7bycjR47UKlWqeI4nf/782q9fP1eLb6mq3nXXXTnGiI+P1z179ujevXu1atWqjvt8++23Xf9cdjIyMkyTZ4UKFXL981jp1KmTaeyFChXSY8eOnel3/vnnm/ZLTEz0KxEW7ASz3fdps2bNPI+XXawkmHfv3m05/osvvuh6HKtFHy+55JKAY7RidVy7uZludxPEbLG+CRMmWPb3ch3w/fffW47z8ssvuxrjzTffNN3en5tvFH68biSiQLhJMG98+ULzBLPFCzGWYGYNZiKiMJg+fbpl2zXXXGNZF9OthIQEfPPNNyhcuPBZ77/++uuWtQwzJScn47bbbsODDz6IPXv2eN53SkoKBg0ahBYtWmD79u2O/a0Wj1u8eDFee+013HXXXbaLkGWlqnjxxRcxbNgwrF+/Hs2aNcPSpUtdxz5t2jR0794dqup6m6z27NmDli1bYuTIkZ62W7VqFZo1a4Zly5bZ9luwYIHt78JNPUsnTZs2xYsvvnjWe6VKlcKAAQMCHtvMyy+/jK5du2Lbtm0BjTNixAjceOONyMjIsO2nqujVqxf69++P1NRUv/b1888/o0GDBti4caNj3+effx4PPvig689wdrNnz0ajRo0cF1ncvn07Nm/enOP9U6dOYeHChWf+Oxj1PxctWuTXvq2kp6ejd+/euP322y1rqto5ffo0XnvtNbRt2xYHDx507D9z5kzTGObPn4/bbrvN1ffWSy+95Fes2WWW/MmuZcuWtjXRnezYsQOTJ082bbvuuutQpEiRM//dq1cv037JycmOi+yFmqpaljsA4Hg+y03Wr19v2XbhhRe6HueCCy7wPH6g7r//ftP3X3/9ddu/h3bv3m15XqtXrx5atmyZ4/1jx45ZjhcfH28faBZFixa1bPvrr79cjWG1kOb333/vOg4iIsq9asR5v9aOJUwwExGF2L59+2wTGG3btg3KfurXr481a9bg7bffxvPPP48ZM2bg2Weftd0mLS0NnTt3xrfffhvw/leuXInGjRs7JmusktijRo3CK6+84te+n3zySbRr186vBPmMGTPw6aefet4uKSkJLVq0wN9//+15WwA4cuQIOnfubFpnMtPixYst2+Li4kxXrffH66+/jt9//x0vvPACBgwYgJUrVwZcT9fMsGHD8MYbbwRtvN9++82xtuX777+P7777zrStQoUKuPXWW/Hss8/ikUceQatWrSxr4O7atQtdunSxTVKPHTsW77zzjmlb8eLFcfPNN+OZZ55B79690aFDByQkmC+FceTIEXTt2hWHDh2y3NfOnTtdtV122WWW/dyqUaOGX/u2cuedd+Kjjz4KOK65c+eiQ4cOOH78uG2/3bt3m74/cOBA22RXVunp6fjnn388x5jdhAkTTN83S5x58dVXX1nebMleZ9eu7m6kF/sbO3Ys1q1bZ9keDTWiw8XqcwvA9WLAAFCxYkXT95OTk0NW0/rpp5/GxRdfbLrPjh074oEHHsCcOXNw6NAhJCUlYdWqVXjzzTdRt25dbNiwIcd2+fLlw6effmr6/Vy6dGnLONzcgMpkdy62qgmeXcOGDVGgQIEc7//xxx8hW1SRiIgoWnCRPyKiEFu7dq1te4MGDYK2r2rVqnla8K1v376ms/syxcXFoVmzZqhZsyaKFi2Kbdu2YebMmZYzhvbt24cbbrgBc+fONb3IAmCZBMl+oXvRRRehSZMmKFGiBLZs2YLJkyfj9OnTptuePHkSJ0+ePOu9+vXro2nTpkhOTsacOXNMZ1xm+vjjj/HQQw9Ztpv577//8N9//531XrFixdC2bVtUr14dycnJWLJkie2M6n379uGRRx7B2LFjTdvtPjs1a9bMMWM9EB07dkTHjh2DNl52SUlJ6Nu3r2W7iKB+/fqoW7cuypQpg7i4OPz3339YsWKF7eyxgQMH4t5777Xc52uvvWa6r3fffRe9e/fOMWt01apV6N69u2mS659//sGYMWNME3QZGRmWP99TTz2F1157Lce/19atW3Hrrbdi/vz5ObY5cOAAPvnkE7z00kumY2ZfSCp7LJlatWqFTz/9FAMGDMC2bdscZ3xnyp8/P6pVq4Ybb7wR3bp182vfZj777DOMGjXKsr1o0aK44oorcO6550JEsGPHDvz555+Wyfa//voLjz32mOXidgAsbwrMmzfPNtasEhISgnLTZdasWabvN23a1O8xMzIyLG+0lCtXLseChueddx5atGhh+vP/888/mDVrluVMzFAaP3487rjjDts+nTt3DlM0kbd3717LtnLlyrkex+4JqR07dngay62CBQvi119/Rbt27XI8+ZGeno5hw4Zh2LBhrsbKly8fvvnmG8ubC3bJ9mXLlqF58+au9rNmzRrLNrubfVnlz58fl156aY4nUNLT0/HHH3/g2muvdTUOERFRLGKCmchB3a/rRjoEcmH1He5ml0SC02PVderUCVMkZ5s7dy4++OADy/bOnTvj/fffz/Eo7qlTpzBgwAC88cYbpqvFL126FK+99hrefPNNv+IqUaIERo4ciWuuueas91euXImWLVs6zlYsVqwYRo4cia5du555LyUlBbfffjt++OEH023+/vtvHD58OKBSJX379kW/fv1yJBEXLlyInj17YsuWLabb/fTTT1i8eDEaN26co83usxOpz42/xo8fbzmDq0mTJvjyyy9Ru3Zt0/b58+ejS5cuORL6ALBu3Tps2LABNWvWzNH2yy+/mN4Muemmm9CnTx/TfdWrVw+zZ89GixYtTEtiWM1Ynzt3rum/cePGjfH++++bblO9enVMmzYNV111lWlJDH9nx2f3wAMP4IEHHkBaWhrS0tLwzDPPYMiQIaZ9169fj+rVq1veIArE3r178eSTT5q2xcXFoW/fvnjhhRdyPKqekpKCzz77DM899xxOnTqVY9sRI0bggQceCChJW6JECVxzzTWoVKkS9u7di3nz5uHff/8FYNyQGDBgACpVquT3+IBxw8OqZEkgx/OUKVMsnxzp3r276Uz5Xr16WSbYP/3005AnmE+ePImjR49i27ZtWLJkCUaPHu1YFqZ9+/aW3xG5kd3s2+LFi7sep1ixYpZtbhOn/qhevTqWLFmCJ554AiNHjvSrFFWdOnXw1Vdf2T6JUbt2bRQtWtT0u/6HH37AY4895rif1NRU2xtfJ06cQHp6uquSG3Xq1DH9LM+ePZsJZiKiPG5xxoWoA/NrwtyAJTKIiELM7iIxf/78QZ2F6sWrr75q2Xbrrbdi/PjxpnUeCxYsiP79++Pbb7+1LCfw0UcfmSYD3Rg6dGiO5DJgzEh+4oknbLeNi4vDb7/9dlZyGQAKFCiAzz77DAULFrTcNjOZ5I8hQ4ZgwIABpv+WTZs2xezZs1G1alXL7T/++GPT9+0+O4HW7Q43q/ICV111FebMmWObOGrevDn69etn2Z69RnAmq5nPTjP2ypUrh19//dX0d1y/fv2g7qtQoUIYP348zj33XNf78ldCQgISExNtkyQFChQISXIZAAYNGmSaIAaM74y3337btA5qgQIF0Lt3b/zyyy+IizP/0/Xdd9/1O66mTZti8+bNGDlyJN5++20MHz4cGzZswKZNmzB69GisWrUKTz31lN/jZ1qyZInpTbmiRYvinHPO8Xvczz//3LLt1ltvNX3/5ptvtvx3HjduXNBKJ2zbtg0ikuNVuHBhVKxYEc2aNUPv3r0dk8v58+fHwIEDgxJTrEhOTrZs81Kv2+54tttHMJQoUQLPP/+8XyVgihUrhjfeeMOxzE9CQoJluai5c+fit99+c9xX//79bUtkAMYNIjes6mOvWLHC1fZERJR73Xy6H9KypWF3F6kDNLzT/BVjmGAmIgoxq4QKYD+zKJRWrFhhWXu0UqVK+Oyzzxxn6txyyy144IEHTNuOHTvmV13jKlWqoEePHpbtHTp0sN3+rrvuQosWLUzbSpQoYXuRa/c4sp0bb7wRjzzyiG2fqlWrYvDgwZbtP/30k+lj/NH42fFX9erVTd8bO3Ys8uXL57h99psGWVmVP8leNiXT2LFjsWvXLtv91axZEzNnzjyT+C5QoACefPJJdO/e3dO+Zs2ahVWrVtnuq2zZspg1a9aZR7kTEhJw6623BiWpGS1SUlIsvxNat26Nhx9+2HGM9u3bWy5Q9+uvv7pOAGVVoEAB/PTTTyhZsmSOtvPOOw/du3cP2tMCZrVlAW8LtmW3d+9eTJw40bStZs2apk9GAEDJkiXRpUsX07bU1FR8+eWXfscUbAkJCfj++++DNnu5f//+IV9FvX///gHHmZKSYtnm5SaQ3fer3TkmUCkpKejbty9q166NOXPmeN4+KSkJ1113HVq0aOH4JJhVmSQAuOOOO2yTu5k3t5w4PT2Vyep4djoPEBFRXiDocdoof/dwam9UT/4Oo+t9BVwzyPwVY5hgJiIKMatZvgBsZ9SG0u+//27ZZlbmwUr//v0tFyqbNGmS57iuvvpq29/XeeedZ7v9DTfcYNterVo1yzarBKGd+Ph4/O9//3PVt2vXrmjYsKFp2/Hjx01n4UbjZ8dft99++1kLWiYmJmLMmDGuH/WuXr265ezVAwcOmL5vtbjV3r170aBBAwwZMsR24aX69eufKZ9y8uRJDBw40PLfxGpfJ06cQIsWLfDmm2/azpCrVq0a5s2bhyNHjuDUqVMYNWpUyGYSR8LChQtx5MgR07YHH3zQ9Th33nmn6funT5/GkiVLPMfVs2dPy3+7YDMruQIElmAeMWKEZU1sq9nLmayS9YAxK9ptve5QqlixIsaPH4/rr78+0qGEnV2C2eq70IxdaQq7fQTi2LFj6NixI9577z3TWftezJ8/H5deeqltLf5rrrnG8gbEwYMH0axZM7z44otYs2YNkpOTsW/fPkycOBHt27dH7969XZXvcHMjFLA+ng8dOsSF/oiI6IyjyPnUXqxjgpmIKMSKFCli2Rbqx1OtWM1eFhFcd911rscpX758jgWkMi1evNhyMUArjRo1sm13SkY6lRSwKythtYCgnWbNmtkmrbOzq79oNsMqGj87/sqfPz+mT5+OhQsX4pdffsGGDRssZ1dmderUKUybNg3PPPOMZXLXahZe+/btLcc9cOAAHnvsMVSoUAEdO3bE+++/j+XLl5sm1UqWLOmY0Gnbtq1ln+PHj+Oll15C5cqV0bp1a7zxxhtYsGCBaWKwePHiljdtYtns2bMt21q1auV6HKubNICxaKJXVrN4Q2HTpk2m7/u7yJqq4osvvrBsN1uMMqtOnTqhdOnSpm1btmzB1KlT/YorGCpUqIBnn30W69atQ6dOnSIWRyTZfed4SdraLcrppqawP3r16mV5zCcmJuKpp57CkiVLkJSUhKSkJKxevRqvv/665c2egwcP4pprrsHu3btN20UEn3/+ueV3Z3JyMt566y3UqVMHBQsWRIUKFXDttddi2rRpOcax4vaGn93xvHPnTldjEBERxSImmImIQswuKZqUlOTXwjeBsnpc9JJLLvGc7LBaWCstLc3zI6FmZRSySkxMtGyLj493rGNqNwPJn3+Hzp07e+qfdQZvdmbJJ7vPjtVs0GjXpEkTdO3aFVWqVDFtP3jwIH799Ve8+OKLaNWqFUqUKIH27dvj/ffft0yqWL3fvHlztGnTxjae5ORkTJkyBX369MGll16K0qVLo1u3bhg2bJhjTc6sqlSpYjm7NlNaWhr++OMPvPzyy2jevDlKlCiBq6++Gh9++CG2bdvmel+xaOXKlZZtFStWNK3Ta/ayKw1jVmbGSSALA3plVZferO60G7NmzbJMWjdr1szxiY98+fLhlltusWz3p8yRV/ny5UOpUqVQo0YNdOnSBS+99BImT56MnTt34p133om5UkDBZHe+83JD1K6vl1rObv3www8YP368aVvFihWxaNEivP/++2jUqBGKFi2KokWLok6dOnjppZewevVqdOzY0XTb3bt3o2/fvpb7bdasGd566y2/437wwQdRoUIF07a4uDjXT9vY3Rjes2ePX7ERERHFgtw3RYYoyFbfsTrSIVCMM1u8K1NKSgr27t0b0AJPWW3fvh1ff/01Dh48iMsvvxw33nhjjj7p6emWiQ67heis2D3ebVW6wEr58uVt2+1mW5UsWdJ29hGAoM8MrVGjhqf+lStXtmw7fPhwjvfOPfdcy1lgwU5ITp48GdOmTUOhQoXQs2dP1KpVK6jjmzl+/DiWLFmCpUuXYunSpViyZAm2bAnuysrfffcdOnToYJvgzOrIkSMYN24cxo0bh4cffhidO3fGM88842qRqsGDB2P79u2WTwhkd+LECUyePBmTJ0/GU089hdatW+Ppp5/2fOMiFnj9LvCH1+OxSJEiYSuPARj/3lZx+MNucb+bb77Z1VMON998s+Uio5MmTcKuXbtQqVIlv+IDjHOK1QKfcXFxIUlwOunfv7/tIrfB8MorrwRch7lQoUKWbV6eDrKrHezvZ8/OgAEDTN+Pi4vDL7/8gnr16lluW6pUKfz4449o1KiR6edm9OjReOWVV3DBBReYbv/MM88gISEBTz/9tKebxvfddx+GDh2K0aNHm7aXLl3adVmS+Ph4FCxY0PTJGrd1nImIiGIRZzATEYWYU33Nv//+Oyj72bFjBxo0aIB+/fph8ODBuOmmm3D33Xfn6Hfo0CHLCy9/HtW2m9XjNalkN2PLSSTq1Xr9fZUpU8ayzawGtN1nJ1ifGwAYOnQorr76agwcOBBvvPEGGjRokOPR4WDJyMjADz/8gLZt26JUqVJo06YN+vbtix9++CHoyWXAuGnxxx9/oFu3bp63TU9Px4QJE3D55ZejV69eljdmMhUuXBiTJk3CAw884KlGKmDMoJ81axa6dOmCjh07Oi5qFWsOHToU0vHr1auH1q1be9omkMSpP4KZYD506BDGjRtn2f7kk0+iYMGCji+731l6erptEtsNEUFiYqLpKxLJ5Vhid37x8gSLXV+zxS0DsXXrVstayddffz0uu+wyxzGKFCmCl156ybQtIyMDEyZMsN3+ySefxKxZs1C3bl3HfZUoUQKff/45hg0bhri4OMtyS05PV2VndUzHWmkrIiIiL5hgJiIKsfLly1uWAwCAefPmBWU/L774Yo5ZsMOHD8fChQtdj+E0AzhY21gJJOEQibq1djPMzNjFaDaW3cX47t27/ao5m93Ro0fx3HPPnfVeSkoKHnvssYDHzm7r1q1o0aIFbrnlFsycOdOxpEHRokXRpk0b9O/fH/Pnz/drhj1g3AT56aefMGvWLFxxxRV+jTFq1Chceuml2LVrl22/AgUK4NNPP8Vff/2Fa665xnOiGQCmTJmCSy65JKg3EXKbhIQElClTBi1atMBrr72GP//80/UiXJlKlCgRmuAsWH3e/fneGzlyZMgWaMvqiy++CHiRNvKP3ex6L+V77Po6PTXk1eLFiy3bvNQ7v/HGGy2fWPrjjz8ct7/iiiuwfPlyTJw4EbfffjsuuOCCM+fY4sWLo1WrVvjf//6HLVu24N577wVglGeyKifidSFOqxve/pTxISIiihUskUFEFAbt2rXDV199Zdo2duzYgB+l3b9/P8aMGWPatnr16rPqjJYuXRoiYjqL2Z/H2O1mR/m7eFWsSEpK8tTf60yyFi1aoFChQqazmwHgxx9/xDPPPOMphuxGjBhh+tjuhg0bcPr06aDNMty2bRtatGhhuUhTfHw8GjZsiKZNm6JRo0Zo1KgRLrzwwrMStIFenLdu3RqzZ8/Gpk2b8P3332P8+PFYunSp60ept27dio4dO2Lp0qWOM+br16+PCRMmYPfu3fjhhx8wbtw4zJ8/33bBrawOHz58prSH3cz3WGG1mBxgHEf+1iEORLj3aXVDyp/H5u0W9wumXbt2YeLEiZ4Wf6XgsCv5sn37dtfjWJVTio+Pty3b5A+7Rey8lLBJTEzEOeecYzqe1Tkku/j4eHTp0uWsxHZ6erpl4truCZpLL73U1T4zWR3TgTylRUREFO04g5mIKAyuueYay7Y1a9ZY1tl16+OPP7aceZM9sRMfH2/5WKyXi9ZMGzZssGwrW7as5/Fiidc6yBs3brRsM0siFihQAO3atbPc5rPPPnOdsDSTkZGBwYMHm7YVKVIkqI+w33rrraaJgbi4ODzxxBPYuXMnFi1ahEGDBqFXr16oVatWjtm/XuqO2qlRowZefPFFLF68GPv27cP333+Pe+65x1U93r///ttTcq9ixYp44okn8Mcff+DQoUMYP348Hn30UVfJlt27d1vWM401dklytwmjYPM64zlQhQsXNn3fa4J53rx5WLt2bTBCciUci/1RTvXq1bN86mXdunWux1m/fr3p+zVr1gz6MWB1MxTw/pSRVTL26NGjnsbJym4dB7tjqnnz5p72Y3VMFyxY0NM4REREsYQJZiKiMOjSpYvtQn7PPfec348h7969GwMHDrRsr127do73GjRoYNp3+fLlnmulWpXgyJcvn+1iPrnBokWLPPW3e3zYaobUfffdZ7nNpk2b8Nlnn3mKIauhQ4di8+bNpm1mnxt/TZ8+3bIUzFtvvYUPPvgAFSpUsB3j4MGDfi+QdPDgQcv6t2XLlsUtt9yCL774Ajt37sQff/yBG264wbb0yy+//GLZduTIEcsESLFixXDttdfio48+wsaNG7Fs2TLcfffdtokXu33Fkjp16li2LVu2zK8xU1JSsHTpUr/rVYe7rI7VjGmvn+tA6yJ7NXXq1JDURyd7iYmJludQt+eeI0eOWC6y6LVmuRt29cTtZjebsSpHZPfkwfDhw3HzzTfj/vvvx4IFCzztz2rdgbJly7qqHZ0pOTnZ8sav3ZMcREREsY4JZiKiMEhISMADDzxg2b5o0SK88sornsdNTU1Fr169LGd21qpVy7R2YNu2bU37Z2RkeEpo7d+/3/KirGnTpiFZoT6a/Pzzz5Yzx82MGjXKsi1rGZOsrr76atvZrn379sXKlStdx5Bp5cqVeOGFFyzbr7/+es9jWpk4caLp+yKChx56yNUYdnU3rS7md+zYgaZNm6Js2bIoUaIE3nnnHdt9iAhatWqFsWPHYsSIEZb9zGauHz16FJ06dULJkiVRsmRJVzWsL730Unz55ZeYMmWKZbLT6yz5aHXllVdattkdF1Y2btyI2rVr47LLLkO1atXw7rvveh7Dn/rYgbCaIe+l1M7Ro0fx448/WravXbsWqur5lZ6eblkuQVUDupFF/uvcubPp+8uXL8eePXsct//1118tSwC1adMmoNjM2C2GN2PGDNfjzJ0713LBPavz4euvv467774bP/74Iz7//HNcfvnl+OGHH1zt78SJE/j9999N26677jpP3xV2x3O4FxYlIiIKJyaYiYjC5KmnnrKtSfzmm2+if//+yMjIcDXe8ePH0a1bN8ycOdOyT+biNdlZXbQCwGuvvWZ5YWfW16ourl1ZkNzi4MGDGDJkiKu+EyZMwF9//WXa1rBhQ5QqVcq0LS4uDm+++abluCdPnkT79u09zdZatGgROnToYFsn8tZbb3U9npNNmzaZvp+QkGBZNiCr9PR021n6VrPuH3300TMz/dLS0vD8889b3hDJ7vbbb0ejRo1M28zqL/fr1+9MgkJVMWTIENelNNq0aWNZ49ap1nOsaN68ueXMwylTpmDOnDmux1qxYgVatmx55nOlqp6+OyPlggsuMH3f6ikCM99++61lGYJLL70UtWrV8iu2uLg422N++PDhnm6mRbP+/fv7lYT38gp0XYVMN9xwg+n7GRkZrmayW90YKF68ODp06OC4fVpaGn766Se8/fbb+P777x0XlmzcuLHl0x+jR492vc6DVekmwPpm7Mcff3zWf6enp+Pxxx93VVrpo48+yrFIcqb777/fcfusrI7n+Ph42yfZiIiIYh0TzEREYVK0aFG89dZbtn1effVVtGzZEtOnT7dMlqSkpGDUqFGoU6cOJk2aZDlWtWrV8Mgjj5i21atXD1dffbVp2/bt2/HQQw85JmvGjh2LTz75xLStePHini/KYtVLL72EuXPn2vbZuHGj7Qx2uzIYAHDLLbfg8ssvt2zfv38/WrVqhd69e9vOeN2xYweefvppXH755di3b59lv8cffzyoM62sZhinpqZazm7OlJ6ejkceeQTz58+37LN161bT981Kktx3332ua3ief/75pu+bzdIz21efPn1cz0D2sq9YlJiYaDlbPSMjA3fccYerR+hHjRpl+vnNyMhwvVhjpFglmK1KGJixSyredtttnmPKqlevXpZt+/fvx7hx4wIan7yrX7++5ez/999/3/b75ccff7S8cfPAAw84LnL533//oVmzZrjxxhvxwgsvoEePHmjQoIHtcVqxYkXLBHBSUhJ69erluFjriBEjLGfpi4hl0t1s3L1799o+qQMYT8dYPUHWpk0byxuNVqzWpQhFzWsiIqJowgQzEVEY3XPPPejWrZttnwULFqBdu3aoXLkybrjhBvTp0wcvv/wyHn/8cVx99dUoX748evXqZXthGRcXh48//th29mP//v0tZxp9/fXXuPHGG00fwU1OTsbrr7+OHj16WCahH3/8cRQvXtz258wtTp06hQ4dOmDw4MGmidRff/0VrVq1wt69e023L1myJHr27Om4n2+++cZycUbASOJ+9NFHOPfcc3HppZfinnvuwfPPP4/nn38e9913Hy677DJUr14dAwcOtL3Ar1mzpuMFuVfnnnuuZdt9992HyZMn53hfVTF79mw0bdrU8fH8f/75B0eOHMnxvtkj/9u2bcOdd97pOBszOTnZsr54q1atXO3r6NGj6NGjh2MJBFW1LAFitq9Y9dRTT1kucrVlyxY0atQI48ePN/1e2bx5M3r27IlevXqZzrxv166d7QJe0cCqDvW2bduQnJzsuP2yZcuwYsUK07b4+Hj06NEjkPBQu3Zty/r8APDVV18FND7557XXXjM9VyclJaFjx46mi/j98ssvuPPOO03HK1y4MB5//HHH/T7xxBNYunTpWe+tW7cOt99+u+12dmNPmTIFV155pWnMSUlJePHFF3H33Xdbbn/jjTda3nSzugk7ZMgQPPbYYzm+h1NTUzFkyBBcffXVpueD+Ph4vxZZtUow169f3/NYREREsSS8q5sQERG+/PJLbNq0ybFu7p49e/Dzzz/7tY/XX38dnTp1su3TuHFjvPzyy3jttddM28eNG4dJkyahVatWuPjiiyEi2LFjB6ZPn277yGnjxo2DnqCMdidPnsTjjz+OV199FW3atEGlSpVw4sQJzJ8/33ZlegB47733HGeSAcaM9NGjR6Nr1662jymrKpYvX47ly5d7/jmKFSuG8ePHo1ixYp63tdOxY0d8+umnpm0HDx7E1VdfjYYNG6J+/fooVKgQdu/ejUWLFlku8pSdqmLOnDk5yrL07NkzR4IEMJIv7dq1w/Dhw3HeeeflaN+yZQsefPBB05nRiYmJpjNFe/bsibFjx+Z4f8GCBWjevDm+/fZb0wTDvn370KdPH9MSJyKCe+65J8f7sap8+fIYMmSI5c+0b98+XHfddahevTqaNGmCSpUq4fjx41i5ciUWL15sOUM5ISHBrxr24VarVi2ULVs2R5mAjIwM/Pvvv6hbt67t9t9++61l21VXXeW4UKYbd9xxh+V3x8yZM/Hff//Z3ujKzm6xTHKnZcuWeOihh3KUgACA9evXo27dumjTpg1q1aqF06dPY+HChbbf/2+//bZlPfBMKSkpGDNmjGnbrFmzsH37dlStWtW0/aabbsLQoUMtZ0/PmzcPtWrVQtOmTVG7dm0UKlQI27Ztw4wZMywXYwWMBQTt6ug//vjjlmtIDBkyBCNHjsRVV12FKlWq4MCBA5gxY4btkzx9+vTxPHsZsH4ioVmzZp7HIiIiiiVMMBMRhVmJEiUwY8YMtG3b1q/F2Zw8/PDDrhO8r7zyClasWIEJEyaYtqempmLGjBmuF+cpX748xo4daztzOjEx0bLNanZjVvHx8UhPT8/xvptHT+3Gt9reanGfihUrokaNGmddRB8+fNg0yWilffv2trO1suvQoQN++ukn3HDDDY61ML0qXLgwxo0bh4suusixb/78+U3ft5pB2qVLF1x88cW2yfZly5Zh2bJltvutXLmy5ePZ06ZNy5Fgfvjhh/HVV1/h77//ztH/zz//xAUXXICmTZuifv36KFWqFI4cOYJ///0XM2bMMP2MAcALL7xgWkfz+uuvR7t27UxrPK9ZswaXXHIJGjRogEaNGqFs2bI4ceIENm/ejGnTplnOXr3rrrtw6aWXmrbZfd6t/n3cbOvmOApk33fffTeWLFliecMBMEqeWJU9MfPGG2/YJoISEhJMny4I9+PqIoLWrVubPv6/ZMkSxwSzWRmWTFazVb3q1asXnnvuOdPPZFpaGhYsWJDj5qXdv7mbGuvkbODAgVi9erVp0jYtLQ1Tp07F1KlTHcfp2bOnqwVIDx48aHuO2blzp2WCOS4uDqNGjUKzZs2we/duyzEWLlxo+ZSI2ZhffPGF6Q3BTK1bt8Zdd92F4cOHm7YnJSW5vmnfoUMH27UP7Fidx6666iq/xiMiIooVLJFBRBQBpUuXxpw5c4K6kFq+fPnw/vvvY+jQoa63iYuLw08//WRZG9WLevXqYdGiRahSpYptP6vHWwsWLIjy5cs77sdqkRw3i+dUq1bNss1q9p/V+9WqVcPEiRPRuHFjx/2aadmyJX7++WfPM/w6d+6MP//8EzVq1PBrv2YyE+Vt2rRx1d8qsWC1iGV8fDx+/PFHVzO1zRQpUgRffPEF1q5da7lvswUUCxQogEmTJlnWN87IyMD8+fPxySef4M0338TQoUMxdepUy+Ryjx498OKLL1rGOWbMGNvPw/Lly/H555/jrbfewqBBgzBx4kTL5HLr1q1tF5C0+j0A9p9zu23z58/v6jgKZN8AMHToUDz//POO/ZyICN544w08++yztv2s4rX7OUKlXbt2pu/b1RjPZLUIWZkyZXD99dcHFFemUqVKoXv37pbtZgsM2v2b16xZMyhx5XUFChTAhAkTcMUVV/g9xk033WSZfM2udOnStjdgnL4nqlatipkzZ1p+93qRmJiIr7/+Grfccotj348//tjx6S0nnTp1wrhx4/wqubN//35s2bIlx/tVqlTBxRdfHFBcRERE0Y4JZiKiCClatChGjRqFsWPHonbt2gGN1b59eyxfvhxPPfWU520TEhLw8ccf45tvvnFMDpspUKAAHnvsMcybN89VcqlLly6m73fs2NHV/jp06OD39q1bt0ahQoVyvF+8eHHLhYnq169vOiO7WbNmKF68OGbMmIEbb7zRcd9Z3Xnnnfj999/9nt3XuHFjLF++HM8995zfSVvAmF34wgsvYNWqVba1V7Nr37696ft2M7QuvvhiTJw40fHR7Oyuv/56rFixAvfccw+KFi2Kb775xjTxYZXIqFatGpYsWYK7777bcja6k4IFC2LAgAH49ttvbccoWbIk/vjjD/Tt29fv2bEJCQno06cPJk+ebDvjvnLlyqYJi6JFi6JJkya2+2jbtq3pz3HVVVe5+h0Fsm/AuLH11ltv4eeff/b7Rsn555+PmTNn2ib8M7Vt29b0favF00KpW7dupp+NWbNmOW5rlTh75JFHbJ8a8eqZZ54xTa4VK1bM9HfWuHFjy+M60LrQ9P9KlCiBadOm4ZlnnkFCgvuHUAsUKIABAwbg+++/d3zCIFNiYmKOJ0IyXXbZZbZ19TNdeOGFWLp0Ke6//36/v3ubNWuGhQsXul7AMjExERMmTMDLL7/s+Tu4YMGCePPNNzFx4kRXT1OZsaqlf9NNN/k1HhERUUxRVb74isirYcOGGmpr164N+T6IgiEjI0MnTZqkd955p5YpU0YBOL7Kly+v999/vy5evDhocSQnJ+vgwYO1RYsWGh8fb7v/Cy64QJ944gndvn275/107979rLHKlSun//zzj6ttd+3apeedd95Z2zdp0kRPnDjhavvBgweriJzZNiEhQUePHm27zVtvvXXW/i688EI9ePDgWX3Gjx+vzZs3P2vsrK98+fJpp06ddObMma7idOvQoUM6ePBgvfLKKzUhIcHxcyMi2qBBA3333Xf1wIEDfu3z5MmTeskll5w1btu2bfXUqVOO2x4+fFgff/xxLVu2rGWMJUuW1J49e1p+tr/77jtNTEw8699jz549jvteu3atPvroo1qhQgVXx1idOnX01Vdf1X379nn+HW3fvl2fe+45Pffcc13tq0aNGtq3b1/dsmWL633MnDnzrN+DiOhnn33matvnnnvurP2XKVNG169fH5Z9Z5WWlqYjR47Udu3aaaFChWx/RwUKFNC2bdvqDz/8oGlpaa73sWvXLq1cufJZY912222eYw2Wa665xvTn27x5s+12x48f106dOp21Tb169fTYsWNBj3HgwIFnfZcVKlRIf/31V8v+06dP18KFC58V21133RX0uMiwZs0avfvuu3P8zrO+SpcurY888ohu27bNr33s3btX69Wrd9aY1apV0w0bNnge659//tEnnnhCq1Wr5vhdWKRIEe3WrZtOnjxZMzIy/IpdVXXDhg163333abFixWz3V6lSJe3bt6/u3LnT731levDBB033sWTJkoDHptDjdSMRBeLKMVdqnRF1crz2nTCuIwZN36DVnp2kne4ZrGsvvEi73v2BVnt2kv5vivXf3wCWahTk7ty+xIiZKPwaNWqkZosvBdO6detQq1atkO6DKBR27NiBtWvXYufOnTh27BiOHz+O+Ph4FC1aFFWrVkWtWrVwwQUXhDSGI0eOYPHixdi3bx/279+P1NRUlC1bFmXLlkW9evUsS124NXXqVCxbtgxlypTBDTfcgFKlSrne9sSJE/jpp5+wc+dOXHjhhejataunGV0rV67EtGnTkJCQgM6dO7v6XS5cuBALFixAhQoV0LVrV9OZ0ACwe/duLF68GFu2bMHJkydRtmxZVKpUCc2aNfP0M/rj9OnT+Oeff/DPP//g4MGDOHbsGJKTk5GYmIhSpUrhvPPOwyWXXOJpkS4rKSkpZ/4N6tSpg6uvvtpTuY+0tDTMnz8fW7duxZ49e1CgQAGUL18e1apVQ+PGjR3/PTds2IDff/8d5cuXx/XXX+95BufGjRuxZs0a7NixA8eOHUNaWhoSExNRpkwZVKtWDfXr10fp0qU9jWll+/btWL16NbZt24akpCSkpqaiQIECKFmyJKpWrYp69eq5Kk1hZuvWrRg/fjxSU1PRoUMHxzq+Wc2dOxdz585FyZIlceONN3r+eQPZt5nTp09j6dKl2LFjBw4dOoQjR46gUKFCKF26NKpWrYomTZrY1nC3k5SUhLFjx+K///5Dw4YN0bp164BiDcTYsWNNZzR+9NFHePTRRx23nz59OlauXInKlSvjmmuusfwuCtTixYsxa9YsFClSBNdff73j0wd79uzBL7/8gqNHj6Jp06YR/R3nFSkpKVi6dCnWrFmDw4cPQ0RQpkwZ1KtXDw0aNPB0XjSTlpaGCRMmYP369ahSpQpuuOGGgD9vW7ZswZo1a7B9+3YkJSUhLS0NhQsXRvny5XHRRRehbt26Qa2PnpaWhmXLlmHt2rU4cOAATp8+jcKFC6Ny5cqoW7euq3UH3KpatSp27Nhx1nsNGjQwLeFE0YfXjUQUiDY/tMGBUwdyvD/jphkoV6gcBs/4FwOnbUDtg5vxv7kf4/kWD2BF2QvwWJvz8XT7C03HFJFlqup9xdkIYYKZIoYJZiIiIspr0tPTccEFF+So1dq2bVtMnz49QlERUSD++usvNGzYMMf7X3zxBe65554IRERe8bqRiALBBDNrMBMRERERhU18fDyefvrpHO/Pnj0b+/bti0BERBSo0aNH53ivQoUKQV3MmYiIKJoxwUxEREREFEZ33303ypUrd9Z76enpGDlyZIQiIiJ/paWlYdSoUTnef+aZZ/wu60NERBRrmGAmIiIiIgqjggULol+/fjne/+qrryIQDREFYtKkSdi7d+9Z71WoUAEPPvhghCIiIiIKPyaYiYiIiIjC7IEHHkDNmjXPem/9+vWYO3duhCIiIn98/vnnOd574403QrYAJxERUTRigpmIiIiIKMwSEhLwzjvv5Hh/8ODBEYiGiPyxYcMG/P7772e9V79+fdx1110RioiIiCgymGAmIiIiIoqA66+/Hh07djzrvXHjxmHHjh0RioiIvBg0aBBU9cx/iwg++eQTxMXxMpuIiPIWnvmIiIiIiCJk2LBhKFGixJn/TktL4yxmohhw8OBBjBgx4qz3evfujWbNmkUmICIioghigpmIiIiIKEKqVKmCESNGID4+/sx7w4YNw7FjxyIYFRE5GTp0KE6ePHnmv5s1a4YBAwZEMCIiIqLIYYKZiIiIiCiCunbtikmTJqFhw4ZITExERkYGNm7cGOmwiMjG+vXrkT9/fpx77rno06cPpkyZggIFCkQ6LCIioohIiHQARERERER5XceOHXPUYyai6DV69OhIh0BERBQ1OIOZiIiIiIiIiIiIiPzCBDMRERERERERERER+YUJZiIiIiIiIiIiIiLyCxPMREREREREREREROQXLvJHREREREREREREFETHTh9DYkIiUjJOAHGngLgUX0tGROMKBSaYiYiIiIiIiIiIiPygUNP3rxt/3Zn/X/RCoFAho19i5W9QoNQlSNenwxFeWLBEBhEREREREREREZEfDp466Km/xJ1G/hJLseDosBBFFH5MMBMRERERERERERGF0bbkRUjLSIt0GEHBBDPleqrmjyoQERERERFR3sbrRSKKlFQ9iROpJyIdRlAwwUy5WlxcHDIycl/xdCIiIiIiIgpcRkYG4uKYGiEiCgS/RSlXi4+PR3p6eqTDICIiIiIioiiUnp6O+Pj4SIdBRDFsya1LTN8vmq8oiuYrivxSKMwRhV9CpAMgCqWCBQvixIkTyJ8/f6RDISIiIiIioihz4sQJFCxYMNJhEFEMS0xIxOo7VmP/yf04knIENUvWPKt98Ix/MWzbrQBORibAMOAMZsrVihYtimPHjkU6DCIiIiIiIopCSUlJKFasWKTDIKJcoFyhcjmSy3kFE8yUqxUpUgSnTp1CWlruWJWTiIiIiIiIgiMtLQ3JyckoXLhwpEMhIoppTDBTrhYXF4ciRYrg0KFDXB2YiIiIiIiIAACqikOHDqFIkSJc5I+IKED8FqVcr3z58jhx4gQOHDjAJDMREREREVEep6o4cOAATpw4gfLly0c6HCKimMcEM+V6CQkJqFq1Ko4fP479+/ezXAYREREREVEelZaWhv379+P48eOoWrUqEhISIh0SEVHM4zcp5QmZSeZ9+/Zh06ZNSExMRLFixVC4cGHEx8cjLi4OIhLpMImIiIiIiChIVBUZGRlIT0/HiRMnkJSUhOTkZBQpUoTJZSKiIOK3KeUZCQkJqFSpEjIyMs78cXHo0CGkp6cjIyMj0uERERERERFRkMXFxSE+Ph4FCxZEqVKlULhwYdZcJiIKMiaYKc+Ji4tD0aJFUbRo0UiHQkREREREREREFNN4246IiIiIiIiIiIiI/MIZzERERERERERERERh1vL7lsgXVyDSYQSMCWYiIiIiIiIiIiKiEJH4U5ZtqRkpYYwkNJhgJiIiIiIiIiIiIgqBTQeOn/Xf+VOBfKkaoWhCgwlmIiIiIiIiIiIiohCYt/EQUAUod8RIKj83NsNxm4tDHVSQMcFMREREREREREREFAKXX1AG05OBRRcKHp0EjGsmOFlA7Df6JzyxBQsTzEREREREREREREQh8GzHizDundeAi/rh5uddpmI/C21MwcYEcwiJSDyA9gC6ArgMQFUAxQGcALAPwDoAvwMYr6r7IhVnViJSE0APAC1gzMgvCUAAHACwA8AMAJNUdUnEgiQiIiIiIiIiIooBFYonYuIjbXDNkPyQhCRI/AkUjiuPS6qUsNzmb7QKX4BBIKq5q6h0tBCRmwAMAHCui+6nAAwF8Jaq/hfSwCyIyPkA3gNwnctNZgJ4LpBEc6NGjXTp0qX+bk5ERERERERERJTriMgyVW0U6Tjciot0ALmNiBQQkeEAfoC75DIAFATQB8ASEQl7HW8RuQHAcrhPLgNAGwDzReTRkARFREREREREREREUY8J5iDylcT4HsCdfg5RA8ACEakbtKAc+GZa/wCgiB+bJwD4SET6BzUoIiIiIiIiIiIiiglMMAfXa/A2C9hMMQATRKRM4OHYE5E6AL5B4J+DfiJyYxBCIiIiIiIiIiIiohjCBHOQiMglAJ61aFYAI2EsnFcWQAUYC/8tsOhfHcD7wY3wbCIiAIYDKGDRZTWMxf7OBVACQCMAgwCkmw0HYJiIlAp+pERERERERERERBStmGAOnv4A4i3aHlXVO1R1vqoeVNV9qjoBQCsAP1lsc5svaR0qXWEkjc3MBtBUVb9X1a2qelRVl6nqEwBuhpEwz64kgJdDEikRERERERERERFFJSaYg0BEqgK41qL5d1X92KxBVdMA3A3ggElzHIDngxOhqccs3k8BcIeqnjRrVNWfAQyz2PYhESkRhNiIiIiIiIiIiIgoBjDBHBxdYZSJMPOO3YaqmgRgqEXztSJSPJDAzIhISRizp818p6rbHYZ4B+azmAsAuCWQ2IiIiIiIiIiIiCh2MMEcHJ0t3j8EYJ6L7cdYvJ8I4Bq/IrLXAUCCRdt4p41VdSuAxRbNN/kZExEREREREREREcUYJpiD41KL9+eqqtmieGdR1fUA9ls0N/c7KmsNbdr+dDmGVb8mImJVi5qIiIiIiIiIiIhyESaYAyQi5wAoa9H8t4ehrPo28xaRK/Us3t+lqv+5HGO1xftFANTxHhIRERERERERERHFGiaYA3eRTds/Hsb514/x/WU1ZjDiBYBaHsYhIiIiIiIiIiKiGMUEc+Aq2rTt9TDObov3E0WknIdxbImIAKhg0RyMeAGgqodxiIiIiIiIiIiIKEYxwRw4q2QtYF1X2cxhm7YqHsZxUgpAfou2aIyXiIiIiIiIiIiIohQTzIErY9N21MM4STZtpT2M4yQo8arqcQAZFs3BjJeIiIiIiIiIiIiiVEKkA8gFEm3aTnsYJ8XPfXgVrHgz+5uNZ7kPEbkfwP2+/0wRES8LIRJR9CsD4GCkgyCioOJxTZT78Lgmyl14TBPlPhdGOgAvmGAOXAGbNrukcXapNm0FPYzjJFjxAkbMZslky3hVdRiAYQAgIktVtZHHfRJRFONxTZT78Lgmyn14XBPlLjymiXIfEVka6Ri8YImMwNklbK1KSJgRP/fhVbDiBaxjDma8REREREREREREFKWYYA6cXVI23sM4drPJ0z2M4yRY8QLWMQczXiIiIiIiIiIiIopSTDAHLtmmLb+Hcez6eq2NbCdY8QJAPov33cY7zOP+iCj68bgmyn14XBPlPjyuiXIXHtNEuU9MHddMMAfupE1bUQ/jFLFpO+5hHCdBiVdECsJ6xrOreH31mIkoF+FxTZT78Lgmyn14XBPlLjymiXKfWDuumWAO3H6bthIexrHr+5+HcZzEWrxEREREREREREQUpezq/pI7u23aynsYx67vPg/j2FLVQyKSAvOF+KIuXiIiIsr9RCQfgG4AzgWwGcAEVbUr60VERERERFGCCebAbbJpq+phnGoW76cD2OlhHDc2A6hl8n4w4gWArZ6iISIiItdEJB5AewBdAVwG4/xdHMAJGDd51wH4HcB4VY36m74iUhLANAANs7y9QURaxUL8RERERER5HRPMgVsFIA3mv0uzJK6Viyze36CqqZ6jsvcXzGO7UESuhosLVpt4AWBNUKO1ISI1AfQA0ALAxQBKAhAABwDsADADwCRVXRKumIiiSSwmokSkIYAOAK4EUAVAGRhleZJhlODZAuN7bAaAySH4jiSKWiJyE4ABMGb6ZlfC97oQwHUAPhSRoQDeUtWoLF8lIgkAfsHZyWUAqAmgP4CHwhwSUdjF4rkaAETkAgA3ALgCxrVBWQCJMOLeAeM6aTKAsapqtw4MUa4Ta8e1iBQA0AXG3+CXwPg7oxiADACHARwCsBzAPBjX13ZPchPlOSLyIYC7YVyvvq+qgyMbkSGcOTNR1UDHyPNEZBmAS02alqlqIxfbl4DxpS0mzZ+o6sOBRZhjf48BCOTDfgrGrOoLTNqOASilqmkBjO9IRM4H8B6MC2g3ZgJ4jolmykscElHZnQIQ0USUiFwL4BWYf59aOQjgTQBDQv29QxRJvgu/TwHc6cfmmwBcq6prgxpUEIjIBwCesGjepqrVwxcNUfjF2rkaAESkNoB3AVwN8+uX7I76+v9PVU+HMjaiaBBLx7Xv74vHAfSBcZPIjVQAPwLor6r/hio2olghIn1g5Key6qiqUyIRDxCZnBkTzEEgIq8BeNmkKQNAZVXd47D9rQBGWTTfpKpjAwwx+/7Og31pj0D8qqpdQjQ2AEBEbgAwAkARj5umAXhSVYcEPSiiKBJriSgRKQrjmO4WwDBLAXSJhhkgRMHmmwU1Fu7/QDSTBKClqq4OSlBBICLXARhn0yVVVfOHKRyisIq1c3UmEXkcRrLYn2NzJYy4twc3KqLoEGvHtS8B9ROAen4OcRLAE6r6efCiIootItIUwBzkrGowWlV7RiCkiOXM4vzZiHL4yeL9OAD3udj+AYv3jwKwveMhIgkicoOIPC8i3X0nNVuquhnAChdx+eP7EI0L4Mzd4B/g/UABjAP+IxHpH9SgiKKILxH1Pfz7wxYAagBYICJ1gxaUDV/t1YUILLkMAI1gxF0u8KiIos5rCCy5DBiPuU4QkTKBhxM4EakG4CuHbvnCEQtRuMXauTqTiAwA8CH8Sy4DQH0Ac0TEy8LiRDEh1o5r36SzP+F/chkACgEY5ntCmijPEZEKMCaBmJXMdfMEQ9BFMmfGBHMQqOpKALMsmp/2XUSZ8v3jX27R/JmqHrPZtiSABTA+0G8BGA1guYhUdhH2By76eLUTwJgQjAsAEJE6AL5B4J/bfiJyYxBCIopGMZOI8tVe/QlGLahgOBfADyLCcxvlGiJyCYBnLZoVwEgYNdXKAqgAo9bjAov+1QG8H9wIvfMd+6Nh1IAjyoti5lydyZdA6huEoarCmFVFlNvEzHHtS4b/COCcIA05SERaBGksopggIoVgPIlXyaJLwTCGAyDyOTNehAdPPxgXetkVAzBZRHIsiud7NHSExXgnAAxy2OeHMGbtZVULxsWmE6eF+BYg5wXrModt/heqxbZERAAMB2A1Q3s1jMLl58JY4KgRjN9futlwMO60lgp+pESRE4OJqAdgLORnZSaAG2Ec1yVhzLB4GcB+m22ugPFdQJRb9AcQb9H2qKreoarzVfWgqu5T1QkAWsH66arbfN8VkfQmgGYRjoEoImLwXJ1Zc/l/Nl3mA7gFRvK4HIz4h8F41NZMRxGxmmBDFHNi8Li+C9ZrnmQA+ALG3xJlfK8mvpiSLbYROOcuiHINESkCYxHbppGOJVM05MxYgzmIfCu1Wy3IlwYjWbIOxmNlTQE0sBmut6p+ZLOvAjBKaFh9eKrZ1TcTkV9gnNjsrIfx2MxJGI/stIOxMrSVhqr6l8OYfnGo0zgbQGez1alFpBuMGd5mC5B8qKpPBitGokhzOK4fUdWPTbZJgPE43w0m22TAOK5XBCvGLPstCGArjAtRM0+r6kCLbcvB+D5obrHtelWtFXCQRBEmIlVhHCdm57DfVbWTzbbFAGyE+YI9P6jqLUEJ0iMR6QjgN7hbGAyq6qofUayIpXN1lv3PANDGovkZAO+ryUWliLQDMB7ms7gGqurTwYuSKHJi7bgWkb9gnotIBdBNVSdZbFcbxrW31QzretG01gNRKPgqBoxDzsme2a1U1UtCH5EhGnJmTDAHkS/pOw3WJS/c+k5Vb3XYVyUYJSmstFDV+Rbb2l2wBiJkF6w2f9imAKjpkEz/FOZ1rlMAVFDVI0EJkiiCYi0RJSJdAfxi0TxAVZ9z2L4cjLuwVgnqi1V1nf8REkWe75H0wRbNV6jqnw7bvwJjBnR2yTDOf0cDi9AbEakIYw2I7N81R2DMpMiBCWbKTWLtXO3bbysAf1g0v6yqbzhs/wyMRQGzG6GqdwUaH1GkxdpxLSKlARy0aH5LVV902P46WCexLCeIEOUGItILRiUBNzN7w51gjnjOjCUygkhVUwBcC+s/wtz4EcYjK04OwbjDaGWPTVtXBD+5DADXikjxYA/qqzXdyqL5OxcrUb8D8/IlBWA8zkeUG9gd1+/YbaiqSQCGWjSH5LgG0MHi/QMAXnHaWFX3A/jEpgsfvaXcoLPF+4cAzHOxvdW6CIkArvErIj/5aqN/i5wX0WkAHgpnLEQRFGvnasD66cx/4RCzzwcAlpq8P9HviIiiS6wd1zVs2ka42H48gH0WbVU8R0MUA0SklYjMg1HuJupKrUZLzowJ5iDzZfbbAXgP1nXHzKTAqNvUXVVPu9hPMqz/MFuiqltsNre6YM1w2q+DUF2wdoD5qpyAcYKzpapbASy2aL7Jz5iIok2sJaKsVtX9xnezzo2ZNm0VPcZDFI2s6iPOVVWzemlnUdX1sK5ZblViJlT6AWht8f7C8IZCFDExda4WkcKwfuz/Y1V1vNbx9bkKRp3HjQD+BvCwqv4ctECJIiumjmtYr+sAANucNvaVw9lq0Rz2Rc2IQklE2orILBiTSMP9t7MXUZEzY4I5BFQ1VVX7AqgP4CsYC/ZZOQTjrmVNVX1XVb0keR8GsCrbe9sA2JbXgPUF628wVofeaLNtOowyIEcs2kNx0DW0abN9PNhFvya+VXSJYl2sJaLMZnoojEWB3LJb7I9/4FJME5FzYP7ILGAkaNyy6hu2RfZEpDWMBTqzmwFgQLjiIIoCsXaubg/r9Vd+dTuIqh5V1SdU9QJVrauqdk8gEcWaWDuud9u0lXc5hlW/XR5jIYpKIlJcRCYBmA7zCRKZ1sM6NxZOUZEzs8pwUxCo6loA94jIwzAKgNeGMZ1eYdQ9WgVguZu7/xbj7xORhjDKclwEYAeAn8wKd2dyuGBdqarvAXhPRM4H0BjGKrcFASTBSDwvVtVDNvVdQnHBWs/i/V2q+p/LMawWGygCoA6AlZ6jIooSQU5Eheu4no2cZTJ+UNV/PIxhN0v5kOeIiKLLRTZtXo6Tf2F+XNuNHzQiUhbAd8g5qeEAgF6qmmEsek2Uu8XoudoquXVEVf8Nwf6IYkosHtequk1EdsC8nEUvAG/ZbS8izQBUt2ieHVBwRNHjHVg/nZDpGxhl3tbAYi2RMIqKnBkTzGHge9x7Htw9IuN17DQAXh4xc3XBqqobYT+TOZwXrFZjer3AtlILTDBTbIvFRNQHME5UPWDMZp4B4FGPY7SzaeMK1hTr7G6g7PUwjtVMpUQRKeerZx4SYmSORwI4J1uTArhTVe3WiyDKbWLxXH2JTQxniMgFAO4B0BbABQAKwZiRuR3AZBg3kNeHID6iSIvF4xownhh83eT9l0VksapON9vIt1jvCIsxVwGYG5zwiCKurk3bYRilnsYAQJRMlIiKnBlLZOQ9Yblg9TCOLd/FaQWL5mDECwBVPYxDFI1i6rgGjBtvqnobjDuixVW1naparWidg4gUhDHLwsxJAHOCECZRJFmd+wD78jDZHbZpC/ViPH0BdDR5f5Cq/hbifRNFm5g7V8Mo92dmG2Cci0XkIxiPCD8L44nN4gDyAagEY/blqwDWishXIYiPKNJi8bgGgPcBrDXbH4DJIvKZiFwuIqVFpJiI1BORF2FM4Khpsl0qgAd99ZmJcgOr9UG+B3BxZnI5GkRTzowJ5rwn1i5YSwHIb9EWjfESRUKsHddnqOopVT3mx6bPwLh4NTNGVY8HEBZRNChj03bUwzhJNm2lPYzjie8R2jdMmv6CkYgiymti6lwtIuVh/ej/CREpDeNm7qNwvqYUAHcB+EtELg5WjERRIKaO60yqegrG4/9mTyzHA7gfRj3WgzD+5lgJ45xeyqR/KoySVwuCHSdRBL0KY+0xwFiH7DcAzVW1h6rui1xYpqImZ8YEc94TaxesQYnXl2yyWkAxZBfYRGESa8d1QESkFYCXLJqTYf7IH1GssVpYCwBOexgnxc99+E1ESsKY4ZG9FNtxAD1U1Uv8RLlFrJ2r7RJnAmAq7BcVMlMJwBwRCUsNeKIwiLXj+gxV3QrgMgBfwyhd5Y+/AbSIptmcRMGgqsdUtT2M81YZVe0cxTdRoiZnxgRz3hNrF6zBiteuf0gusInCKNaOa7+JSD0AP8F4/NbMq6q6JYwhEYVKAZs2u2M1u1SbtoIexvHiK5g/Sveoqm4I0T6Jol2snauL2bT1BHCpn+OWAjBGRKLi7wqiAMXacX0WVT0C4G34Vzs5CcBLqrokqEERRRFV3e07TqJZ1OTMmGDOe2LtgjVY8QLWMYfqApsoXGLtuPaL75H72bC+SzsTwLthC4gotOyOa6vZBWbsVh6x24dfRKQ3gOtMmr5T1a+DvT+iGBJr52q7BHPWpxMyAIyGscBfBRg3l66FcU62Ug9GjXaiWBdrx/UZIlJARN4FsAbA5X4MUQzALyIyT0RYcpIocqImZ8YEc94TaxeswYoXsI456BfYRGEWa8e1ZyLyEIzkckmLLv8AuFlVvX4vEEUru89yvIdxspepyCrdwziORORSAO+ZNG0C8GAw90UUg2LtXG2XYM50FEBnVe2pqjNVdZ+q7lDViaraFtblrADgMd+CvUSxLNaOa2NnIkUBTIaxpomXvynMNIdRX93fpxqIKDBRkzNjgjnvibUL1mDFC1jHHNQLbKIIiLXj2jURKSwi3wH4GNaLF6wC0FpVD4UvMqKQS7ZpszoWvPYNWi1k38XqGJP9pcKou+zPYp5EuUmsnavdxHSnqk62alTVNwF8Z9FcBsDN/gRGFEVi7bjO9A2A1hZtyQAGwqjPXMz3qgvgZQC7LbYpA2CiiFQMbphE5ELU5MyYYM57YuqCFcGLF7Cu2crFhijWxdpx7YqIVAYwH0APm26TAbRU1b3hiYoobE7atBX1ME4Rm7bjHsZx8imA803ef5H1GYkAxN65+pRD+2+q+ouLcfrA+sK0naeIiKJPrB3XEJGbAXS1aN4NoImqPq2qS30LnR1T1b9V9Q0YiWarm0oVwVJ1RJEQNTkzJpjznli7YA1KvL5H8Kzu3gQzXqJIiLXj2pGI1AWwEEadRiuDAXThzEjKpfbbtJXwMI5d3/88jGNJRK6HsehXdrMADBGRRLsXbB678/Xh36uUG8TaudouXgAY4WYQVd0DYJ5F8xVeAiKKQrF2XAPAsxbvZwC4TlVXWW2oqocB3ASjNJ2ZHiJyQYDxEZE3UZMz4x/seU/MXLD6xFq8RJGQq44TEWkO4E8AlSy6nAZwv6o+rqoscUO5ldVjqABQ3sM4dn33eRjHjlWd1Sth/NF7yuG13mbsUwBSReSAiPwqIu2DFDNRuMXaudrpyaBlHsaySjBX5A0kinExdVyLSHUAVrWSx7l54khVjwN4w6I5DsYin0QUPlHzPcQTet4TSxes8NVUtVr5MuriJYqQmDqu7YjIVQCmwvoEtx9AW1X9PBzxEEXQJpu2qh7GqWbxfjqAnR7GsVM7SONYiYNR37ETjBqPZUK8P6JQiLVz9XaHdrufJzuruOIAlPYwDlG0ibXjurFN2yQP44yFdekbPplAFEbRlDNjgjnviaUL1kybLd4PRrwAsNXDOETRKBaP6xxE5BoYf9wWtuiyEsBlqjo31LEQRYFVANIs2mp5GOcii/c3qGqqt5As/RukcdzID+C8MO6PKFhi6lztu2C1m7FktyhZdnb1nHk9SrEspo5rAJVt2ux+lrOoajKAPRbNXOiPKPyiImfGE3reE0sXrJn+snj/QhERl2NYxQsAazzGQxRtYvG4PouIdAXwE6xrsf4KoIWqOs2oIsoVfBdvVnUQm7gZQ0RKALjQonm296gs9YP9CtbBdAzAujDtiyiYYvFcvdimzUsSqZRN2xEP4xBFm1g7rgvZtFn9HFasFhYr7nEcIgpcVOTMmGDOY2LsgjXTIov3i8D9Y7lWP9sxWB+MRDEhRo/rrPu+BsAPsF619ksAXVX1RCjjIIpCv1q830BEznGxfWcAVn9UzvQvpJxUdRyAq2DEexDBTzZnwCiP8zuA9lzYk2JRjJ6r59u0uYrZp67F+9tU1eqxXqKoF4PHtd1CXXazm81YrZXCczRR+EVFzowJ5rwpJi5Ys7CKNzMWW74V6q+yaP5TVb3erSWKRrF2XAMARKQLjDpu+S26fAzgPi7mR3nUTxbvxwG4z8X2D1i8fxTAFLsNRSRBRG4QkedFpLuIWD1dAABQ1Vmq2kVVy6pqvKqK2xeAc23GFd945VW1k6oudPypiaJXrJ2rf7Fp6+FmABHJB+u/wx0XFCOKAbF0XG+1aWvrdhARaQmgoEWz61IbRBQ0UZEzY4I5b4qZC1YAUNXNAFZYNN8rIvEOQ/QEUNSi7Xun/RPFiJg6rn3b1QfwI6yTy2MBPKqq6mY8otxGVVcCmGXR/LSIWNZKE5GbAFxu0fyZ3SxgESkJYAGMY/AtAKMBLBcRr7ObiOhsMXWuVtVVsC5J01FEmjqNAeB2AOUs2n5xsT1RtIul43oxAKu/q3uISFmH7TP1tmnjjWCiAMR0zkxV+cqDLxh3Q9XkdRRANZvtbrLYTgEMcNhnSRgzFbJusxZAZRfx3m6z3ycc9rnTYrsdAPJF+t+CL76C9YrB4zr7dllf6wEUjvTvlC++Iv0C0BJGiQiz42QdgItMtrkOwAmLbY4DqOiwz68ttp0Zop+xutV3QaR//3zxFexXDJ6r77XZ7z8Aythsex6AQxbb7gdQMNL/HnzxFYxXLB3XMErfWO1zMhyujwHcabN9BoDqkf734IuvcL5gPBlgdjys8GOsmM6ZcQZz3tUPxgcmu2IAJotIjgLfInIdgBEW450AMMhhnx8CaJTtvVoARjpsBwDfwXoGxXsicm/2N0WkCox6jVb1of6nIV64jCjMYua4FpFOJttldSGA4yKiHl4nRWSdiLwiInaLmBDFDFWdC+ATi+aLAKwWkSki8qGIfCwifwEYB+uFfJ5X1d1W+/PNkrjFovlKEfGyGjUR5RQz52qfkbB+5L0mgNkickn2Bt8j9LNgvcDfu6p6ysX+iWJBLB3XduN2ADDLIt5iIvImgK9sth+rqlsd9k9E1j5EDOfMxJexpjxIRIYCeNiiOQ3Gndh1MB5fbwqggc1wvVX1I5t9FYBxB9dqen81Vd3uEG9TAHMBWE3vXw/gTwAnAdQA0A5AokXfJQCaKeu6Ui4TK8e1iHwP6yRWMHynqreGcHyisPEda9NgXfLCLcfjQkQqwZjFYKWFqtot/OWZL2m9zaRJVZWTISjXiZVzdZYxWvtisqoTqwDmwVjsTAA0BNDYZsgVAC5TroNCuUisHNciEgdj8UCnvykWAlgD49q6GowazYVt+h8HUF+NR/WJ8gwR2QrjGMluharaHefZx4n5nBkTzHlYLF6wisjDAIZ6jC+7fQAaOx2cRLEoVo5rEVkL425sqKQBKKrG6t5EMc+3yvwvAK7wc4gfAdymqqcd9pMIIAlAPosu56nqFj9jsNpnAQCnkDN5tVtVrWZUEMWsWDlXZxvnOQBve4zPzH4ATTjLkXKbWDqufTd2FwCo6F+IOWQA6KmqY4I0HlHMEJGNMJK12c1T1ZYexon5nBlnheRhqpoC4FoAfwQwzI8A7nLR7xAAu6n1e9zsTFU/BtAHxknMH7sAXMXkMuVWMXRcWz6iHyTxABJCvA+isFHVIzBmGbwH4waKWykAngXQ3Sm57NtPMoCJFs1Lgp1c9u0zBeYLkywJ9r6IokEMnavPUNV3ALzhpq+N7QBaM7lMuVEsHde+a+E2ADa6D81SMoA7mFymPGyXxfteZ/PHfM6MCeY8LhYvWFX1fQBdYBQc9+J3GI/j/e1xO6KYEiPH9Vvw/6TnxlRVPR7C8YnCTlVTVbUvgPowaiCesOl+CMbshZqq+q6qejneHobxqHtW2wCEsuzMUzj75zkMo6YlUa4UI+fq7GO9DOBGuLzIzeZ7AJeqqlV9SKKYF0vHtar+A6PW6zD4/zf5AgBNVXWUn9sT5QazLd7/zcsguSFnxhIZdIaIXAzgaRh1Ua3qKx2C8Qfiu17vaIhIeQBTAdTL8vY2AO1U9V8/4i0E4BEA9wM436JbOox6VwNVdbLXfRDFumg+rkWkOYDXYSTLSnvZr40DAGYAeFxV9wdpTKKo5HsctxGA2jAW0lIAB2Ekh5cHUt9URBJgzMS6CMYfpz+p6smAg7bf53kAOsKYvTFJVf1JYhHFnGg+V1uMVxjAQwDuAFDHputxAJMAvK+qS73uhyiWxdJxLSI1YRzT18O8lmxWx337HQZjQgcTSpSniUgxGEnmrPWWZwDo4HXNr1jPmTHBTDnE4gWriJwPYzGRCgAKwqgfuRHAYlU9FOj4RLEuFo9rIiKivCQWz9UiUhnGon5VABSDcYPoEIC1MBY44loIlKfF2nEtIuf6Yq0K45hOgPGE0T4YC4StVlW7x/iJ8hwRyQ9jxvAFADYBGOc1uZxlrJjNmTHBTERERERERERERER+YQ1mIiIiIiIiIiIiIvILE8xERERERERERERE5BcmmImIiIiIiIiIiIjIL0wwExEREREREREREZFfmGAmIiIiIiIiIiIiIr8wwUxEREREREREREREfmGCmYiIiIiIiIiIiIj8wgQzEREREREREREREfmFCWYiIiIiIiIiIiIi8gsTzERERERERERERETkFyaYiYiIiIiIiIiIiMgvTDATERERERERERERkV+YYCYiIiIiIiIiIiIivzDBTERERFFBROJE5HERWS0iySJySER+FpF6kRwrnEQkUUReEJEVIpIkIqki8peIFIx0bBR9RKSsiAwSkW0iclpEtvr+u2ykYzMjIvVE5EcROeg7LleLyFMiki/SsZE3InKhiPyQ5d9yjYj0FZGESMcWbCJSQkQ+EpFdIpIiIhtF5A1+LwcmL32GopWITPGdO46LyCoReUtESkc6LiKKTaKqkY6BiIhCRETKAXgEQAcANQEUR+A3FzMAnPa9TgA44HttAPAXgD9U9d8A90F5kIh8BeAuk6ZTADqo6pxIjBUuIlIcwCQALbM17QdQSVXTXI5zKYAHALQAUAVAUQDiR0ipMI7zUwAOAzgIYCuAfwEsBjBPVY/6MS4FgYjUAPAHgEomzbsBXK6qm/0YVwC0BtAAxvf9LFVdGUComeN2BTAGQAGT5j9gHJcpge6HQk9E6gKYA+NviuwmAuiqueQiU0TyA5gPoKFJ8wwA7VU1I7xRxb689BkKlIicA2AE/v9vgzkAnlbVNUEY+y8Y3/VZbQPQWlW3Bjo+EeUtTDATEeVSItIAwGQA5SKw+0UAPgHwDS+8yA0RaQPjYt3KvwAudHPBGcyxwsWX1Psdxs2g7N5Q1ZddjtMHwACE5ym1VABTAQxR1clh2B9lISLzADS36TJJVa/xOGYVAD8BuCxb02gA96nqCW9Rnhm3NIDNAIrZdOujqu/7Mz6Fl4j8CeBymy69VHVUuOIJJRF5BMAQmy49VPX7cMWTW+Slz1AgfDc45gBonK3pPwCXBpoEFpG7AXxp0vQ3gCaqejKQ8Ykob2GJDCKiXEhE4mAkBCKRXAaAJjBmWywXkdYRioFiS0+H9guQ8wIrHGOFy9MwTy6fBjDUzQAi0hTAuwjf33f5AHQG8LuIzBWRi8O03zxPRKrDPrkMAB18yQm3Y8YDmICcyWUA6AHgK9cB5nQN7JPLmX0oyolIZdgnBgHg1nDEEiY3OrR3C0sUuUge/AwFYiDM/14pCeCVIIz/LYA9Ju/XgfH3BBGRa0wwExHlTlcCuDDSQQCoB2C6bwYQkZ36QeoT7LFCTkTOB/C6RfO3qrrX5VAPwr9SGMHQAsBSEekRof3nNRVd9MkHoISHMbsCuMSm/WYRqeVhvKzcxBupG6LkTUx9vwZBHYf2i8ISRe6S1z5DfhGRbjDK3FkJ+KacryzRIIvmh0Uke8kuIiJLTDATEeVOzSIdQBbxAIaIyGuRDoSiWskg9Qn2WOEwFECiRdsHHsZxmtEaagUBfCsi90Q4jrxgn4s+p2E8Ru1Waxd92ngYLys38bq9kUKRFWvfr4Fy+lmKhCWK3CWvfYY88z0RNNyhW2kRKRGE3X0K4JhZGAA+5aKLROQWE8xERLnTOZEOwMTLInJDpIOgqOXmAiZfBMYKKRHpDKC9RfMsVV3tYTg3s0RDTQB85quDTSGiqptg1Mi0M0lVUz0Ma7ZYYHaVPYyX1WQATgv4jfNzbAqvmPl+DZJ4h3ZeT3uX1z5DnohIKQDj4VxWCAAKB7o/32K9Iyyaa8N4OoqIyBFPiEREuVOhSAdgYYSv9h5RnuerefueTRerx1atBHyhGSTxAL4RkaKRDiSXuxtAkkXbTgBPehzPzefHTcIjB1XdBSMeq4U1pwP4zJ+xiYhyCxEpBGOh1fPdbhKkXQ+B9ffzK0GaKU1EuRwTzEREuVOk6rA6KQLguUgHQRQl7gdgVdN2B4CJYYwl2CoCeDbSQeRmqroERs3k4TAWaUqD8bkZAqCBqm73OKSbGYOuFw3MTlU/gbE+wG8AjsIo4bEGwDMArlbV0/6OTUQU63w3ZSfDXbmioFLVDQBmWTSXAfByGMMhohjFejpERHnbq6ra36mTr/5aPhh1YksDqAAjMdYGwA3w9ijjvSLypqqarVpNlCeISDEAr9p0Ga6qGSHa/bmqutWswTerOj+A4gDKA6gBoAGMMh5mK9nbeURE3lHV4wHESjZUdQuMmczB4GbiSUA3L1X1DwB/BDIGEVFuIyJlAPwK7+fZYBoG6zr7j4jIQN/TKEREpjiDmYiIHKlqmqqeUtX/VHWjqs5V1c9VtQeM5NN8D8MVQBBWviaKcY8BKGvRpnBe3CckVDXdd6zvVdWVqvqzqr6sqk0A1AUw08NwJQBcG5JAKRSi9ckXIqJcS0SaAPgLkU0uA8AvAA5btBUAn0AkIgdMMBMRUUBUdQ2AqwAs9LBZhxCFQxT1RCQRQG+bLtOtZhhHkqr+DaAdgAkeNmOCmYiIKBsRKSgibwOYB6BKpONR1RQAo2263Cci0bCYMBFFKSaYiYgoYKp6CkBXWC84lV3zEIZDFO3uAlDOpv3bcAXila9sxx0A/nO5SYsQhkNERBRTxNAdwHoYs4LjIxxSVt/btBUA8FS4AiGi2MMEMxERBYWq7gfwpcvuZUWE5yDKc3w1jp+26XIaxmOqUUtVjwD41GX3yiJSJITh5GkiUlFEPheRPSKSIiL/ikh/ESkY6djMiMhlIvKriBwRkZMiskREbo90XEREoeZLLN8IYCWMmcJVIxySmXkwFou1creIFApXMEQUW7jIHxERBdMUAE+66BcPY7HAA4HsTERKwSjP0QJAbQDn+sYtDCANxozq/2DMEvkbxuJSs1Q1LZD9+hFnYRirgtcHUAfGo5DFABSFUW/3BIDdADbBqMM307d4V7jiuxjGrPLGMH6H1QCUAlAQxsKOpwEk++LcA2AXgA0AlgKYx0VfPLkGxsJ5Vv5Q1aPhCiYAvwJ43mXfqgDW+rMT34VscwDNAFwEoDqM2s6FAaTi/z+TWwAsAzBXVf/xZ19+xHYRjAWRmgM4H8bPWRTGcXMKxvdPEoyL9ZUAVvji2xak/VcCsAhApSxvnw/gFQDtRKSN75HnqCAi7QFMhLGIZKZGAL4WkfqqanfjJXOMtgBeAHAZjJrRKwC8oqpeaoM77aMIgPcAXA+gJIzv5U8ADFFVDcLYbWCcs+rD+L4tByAzYZME4AiAfwGshrG+wVTfU0J5Uiyfn3zH6LUwzv8XAagMoAiADBj/zntgnPPnA/jZd/MuXLHV8cXVFMb3RjUY31+JAE7C+CzuhfG300oAv6rqhnDF5w9f+YbnAHSB8b14FMAcAO+o6pIIxiUAboLx3XxxpOJwQ1VVRCYCeNiiS0kAt8FYEJCI6GyqyhdffPHFVy57ARgBI3Hp9Oof5P1e6HK/CqCsn/sQAJ0B/A4jweR2f5mvwwA+BlA9xP8GAiOZOAFGsslrnKsAPAigcIjiKwfjYmeDH7FlfWXAuDi+B0BCAPFsdbGvl8I9Vgh+7z87xPV4AGO7/TcL+LMPoIyH/TXxY/wrAYwBcNyPz+R6AC/Dz+8Yh7jiYZQ4WRrAMbMIQB8AxQOMZbTDfl5x2P4237F7LICf5RCAuQC6OOwrAUai3W6sKxzGuB/G90327VIAXBLEf+NfLeIbHsCYlwP4EUbizuvv+DiAUQDqBvvz7OfPcqeLmNMC3EfUnJ9c7GOryTbnwyg1kOYh1mTfv3ONEP7blQDQF8Z3pD+/zzUAHgJQINo+QzBuOh2yGgvAfRE6XurCWKPE6+96o8t+lUMQc0eHfa6MxO+SL774iv5XxAPgiy+++OIr+C9ELsFcy+V+MwDE+zF+Kxgz1gK54Mx8pQIYCqBoCH7/nWAkiIMR538A+gHIH6TYMmfn+ZPocHNB1MzPuLa6GD+mE8wwLu6THeK6KIDx3f47VQ/Cz5LgYX8tPYzbEsDiIH0eTwF4F0E6xmHM9FsbxOPlMIzZdp6PbRi1MFMcxt9is/3zQfw5Ml/32uyvtYvth9tsX9nh5/0tSP/G1zjE2NHjePUAzA7S7zcDRtKyXDB+1gB+R3e6iNWvBDOi8PzkYtytWfrGwZhh73Rs2r1OA3gNfvyNZPMz5IeRWE4K0u9yN4Du0fIZgvF9uNVpPAAXh/lYOR/eb+Cl+j5DV7rsH4oEc0EXn+E64fxd8sUXX7HxYv1LIiIKJrerS/+nquluBxWRfCLyAYwSF/X9iiynBBiPAK4WkWbBGFBEionIcBgz4OoGY0wYSclXAfwlIk0CGUhEGsNIkPWBcQERbDUA/CEiPUIwdm5wE4wLYSt7VHV9uIIJUDEPfR0f7xeRAiIyBMbjzJf5HdXZEgE8A2C9iLQOZCAReQTGTN1agYd1RkkAbwP4U0Qqe9z2QpxdasJMdbP61yKSH8CLHvfnxnM2bfVcbG/3ndkV9j/v1b6SJYF61qG9k5tBfLVWn4VRtuWKgKPyDQvgFgBrReTaII0ZNWL9/CQiJQFMBfAmnI9NO/lgPIExTUSKBiGu82E8NTEARgmMYDgHwGgRGRuMGIOgHYwSH3Yynz4Jpztg3DRxazOMJznegpHEjQg1SvIsdOjWPRyxEFFsYYKZiIiCye2F9FK3A/rqF/8O4Al/AnKhGoCZvoVX/CYi58BIjt0ZjKBM1IaRiLrJn41FpB2MBH2VoEaVUz4YNVUvD/F+YlFPh/bZ4QgiSLzUkTxk1ygipQH8CeCRgCKyVhHADF+S2DMReRjAEBgJilBoAmChr36oW8UD6FcGRg3rYLP7bnETr10fNwnHe1z0seSr9dvCoVuyi3HiAYwE8A5Cs95NaQDjRKR3CMaOiFg/P4lIFRgJubZBHPZKAJMDWSRVRJoCWALgkmAFlc0NAOZ4/O4KBbc3/sJd/7i8h75fwij1Mz9UwXg026GdCWYiyoEJZiIiCgrfRZDbC/w/XI6ZD8aiUMG8aDOTCGM2Tkd/NvYll+fB3Sy9QOQH8L2IeJqFIyI1AfwE4+cMh3wAPhERLibs45vl1dKhW7RcWLpxnct+aQB2WjX6Zv39CWMBr1CKAzBERPp42UhELgPwYUgiOlslAL94OGbcJrvF5L19MErvBJvdAmBu4jWLNdNKF9v3EJFArm3udNHHTRzDYdS3DqU4AINE5N4Q7yfkcsH5qSiMv2lqBmm8rJoD+MKfDUXkEgDTYDwFFUr1AUwVEbc3vUIhw2W/1JBGkZObxW03A+igqveq6rFQB+SB098jNUTkwrBEQkQxgwlmIiIKmG/G1nC4K5GRAWCsy6EHwZjF40UqgP1wMdMsmwQAP4jIBV42EpECMBZuO9fj/vwVB+Pi+FIP23wG74/GpsNY5X4v/r9uoxe1AXTzuE1u1hLOsxljIsHsK+fwgMvua1Q1zWKcBADjEN5ZZe+KyC0e+n8EIyFl5ziAD2A8wVHG178IjFl1DwJY7XJflyHAWbhu+MoTBbtERgqMus6h8geM7yM7lWDU6ffMl5i+1aHbaRjlj+zG6Qugl8fdpwM4AKPusFefiIjTjatoF+vnp1JwPv8nw7ix41guyMQtInKHlw18T4VMgLfyDABwAsBBuE/YZqoNoz54pCxy2W9mSKPI6SsA2y3ajsFYX6O2qk4NX0iuLYLzcXVVOAIhotjBBDMREQXEN0vmDwBuS0z8oqp2M90yx+0IY7VyN/bCqJ15EYzVzcurakEAFWAkDdwm7ooCGOayb6Z3YCz+5cYpGI9Od4ZxQZoIo5ZtHRg/q9vSIQVgzGR2SnxBRFrAWGDLjXkA7oVR37WgqpZU1XNUtbgv1sYwfl63s2w8XRTncq0d2lNgLAwZ1USkDLwlLv60aXsJ7svqpMBYvLQLjBtZBWAkc+sBeApGrVs3BMDnIuJ4Q8iXuHOqe/4XjIUZn1LVP1X1kKqmqeoJVV2vqp8BuBRGrWU3bnfZLyCq+gmMZOxPMBLgu2AkUJ2c9PXdDmA9jBmSr8NYPGtSaKIFVPU0jFidOCWJrbSF8w3S8ap6xKpRROrC+F24cdTX91IYizyWU9XCMJKV3eCQyM4iAcAXvhudMSeXn592w7jpcqGqFlTVCqpaCMbfKa/AWOTTrQG+cmFuDYH7ciOzAdwMoJSqFlHVsjBukl0C4zv6gMtxOorInR5iDBpVnQujRr6dDQA+D0M4Z6hqEoA2ABZkeXsbjHU1zlPV11XV62SIsPB91/3r0K1dGEIholgS6VUG+eKLL774Cv4LRiLGzerT/T2OmwCjZnE7GDPg5rncT+brJFys4u3bz78ux/wWQGEXY94GI8HrZswbXf4+6sIoAeBmzDkALnAYTwDc5yHOu13EOMjFOKcAPOzhc1AbRl1dp3FPAEhwOeZWF+O9FO6xgnhMLnKI568g7MPtcVjdj7HjYSwwtsPDfhRAa4vxLoCR0HQzxlwYF+NOx869MBJ3bsac4uJn/sBhjBQAVTz8Dp9xEdc+l2O1dvlzVvYQ32wX433h52ezv4uxNzqM0dTFGP/BuMnoNb5v/P0se/z9KYDpAMq4iKk93H3PKoA+gX5/ePhd3ekinjSXY0X9+cnl7z/760sARRzGLQdghocxXwzyd8NJAD1cjFcEwGiXY+6HkfwP22coy5iVAPxjMdYmADXCdYxYxFcR3s4Xbv8dXX/H+xHzWId9H4jk75QvvviKvhdnMBMR5W1PichOF699InIURvmJrTBWSn8DRn1ALx5TVTc16e4AcL6Lfl+p6q2qesKpo6qOAnANjEdrnbh91Ps9uKstOgVAW1W1nQ2ihs8BdIRx8eukh4s+lzi0pwHorKofuxgLAKCqa2DMwHFSCEADt+PmViKSH8ZMRTtuaruGnIjEi0hBEaksIk1E5A4R+QzGjNXvAVT2MNw/sJ7B/CacS08AxkzONqq62a6T79j5Asaxc9zFuO1FxOnx3ssc2leq6g4X+wIAqOp7AAY6dNvndry8RlUXAvjboVsJAJ28jOtbP+B6h27rVXW2zRht4W42/lQAHVX1oFNHNR6bvwLuzgVPxegs5ksc2mPx/PSOqt6jqrbfQ6q6H8DVAGa5HPcBlzXG3cyiTwfQRVVHO3VU1eOq2gPGxAUnZWHc6As7Vd2F/5/FvhnGDcxtMP5Oa6iqmyIRVyZV3e3lfBElnMo7lYmCBR6JKIowwUxElLcVhTHrw+lVDkYph0C8qKpfuuz7mIs+awE87CUAVZ0OI7Hl5FIRaWTXQUQuAtDBxVj/ALhejUe8XVHVPwD0hHMdxDouhnMqZdBbVf2pS/gN3C2YE4qFj2LNeXCuv/xPOALx2SIiavaCkdA5CWOm8kIYSYX74a6+enZvqGqOz7CIVAdwg4vtNwK4xeOxswDuF1hzWvCvnEN7aZf7yeoZAGNs2r/yY8y85DMXfbyWybgBgFP5AafSSW7OWfsB9FSLmuRmVPVvl2OfA+MGaqzJbeen31TVdS1y33fbzTBmXDupAoeat76SZW5qcr/qx+/1Idgv5JnJbX3+oFPVo6r6vKrWUNUCqlpdVfuqTWkbsrXeRZ9QL25NRDGECWYiIgq14wB6qepbbjqLSAMYq5I76aeqKX7E8z6Mx6id3OTQ7rY+9IOq6nlhH1WdAGCAQzc3M9vW2LRNUKMWq2eq+h/cJUWr+TN+LuNmNv7WUAcRZjN9Tw2YuQfu/gZ91M3TCdmp6ngYC286ae9Ldltx+n45T0TcPEVwhi/hfiuM76GsCbDTMGbaDfIyXh70DZwXw+siIsU9jOlU9zoFwNdWjSJSFkZtcCfvqqqbRGJ2I+Eused0zopGuen8lAJjUU9PfLPZ3cy4Bpw/Z3e5GGMfjO8fT9SoFfyGi661RaSW1/EpKm1z0aduyKMgopjBBDMREYXSDzAWwLJKNJm51kWfPXCXQMpBjUVXxrroerlDe1cXY8ywe6zahVdgvzr6OBdjvA3zRY8OwJiZGgg3j5z6M8szt7nARZ+toQ4ijHbCfhZpNxdjLFXVKQHE4OYxcYF9aYTtLsYYKSJvikghd2EBqpquqn1glBu5CsYCc+f4Ztqp23HyIlU9CvsZ4ICxAKSrRWdFpAqcF5kbq6p2C7J1gnOppNPwc4ExVU2HTYI7C6dzVjTKTeenLwMogfAljPrxTq50aHfz99MIVXW6SWNlDNyVIIrFzyLltNVFH04iIKIzmGAmIqJgOw7gUwB1VfUWX108L5zqogLGzKZAEjG/u+hTX0TErME3O8fNH9UBPe6uqqkwEiVmia75cDHrSVXXAWgBow505qJq/wC4QVUDrfea5KKP68RbLuZmBvOekEcRHjth1Bvfa9YoIpUAXOxinJGBBKGqKwD85aJrR5s2q/rRWSUAeAHANhH5UEQaW31vZKeq+1V1hqrOdEhg0tk+ddHHbZmM2+B8PeRUlsPNOWuW7+amv9ycs84RkfIB7CPsctn5ye/zvS/hO8FF19oiYlrORURqAKjuYoxf3Ed2Nl9JDzelNfL82gu5xH44l2qrFI5AiCg2MMFMRETBsAvAEBg1ICuo6kO+2pGe+BawcXNhssDr2Nksd9GnEIwa1WZauNg+A8BE1xFZUNWdMBZT/ARGTdwpMGpyXum0iFCWMVarakcAiQASVPUiVZ3jNRYRySciLUTkVRFZCHdJnESv+8mFKrjo48+j89FmNoDGqmr3OH8Tl2P9Fng4rpJydgnhr2EkvdwoA+BxGE8c7BeRMSJyv4i4mb1OHqjqYgArHLpd4buZ4aSXQ/s6F9+VtvX6fQI9Z62GUR/dSUwlmIFcc37aparLAhxjmos+AqCGRZubz2EqgEDjdPP3U8x9DiknX0knp5n1Xhb+JaJczmnBGSIiIjdKAtgN4Hff47z+qgbnxZYAY4G/QOyAcaGVz6FfEZjPgnJTX3CDqpo9+uuZbxa4pwUNLcZRGDPEXBGRogAug5HgbgUjse51xper2Zy5nNNn+rTbmwVRaieAfjAevXb6fNV2Md5BVXXzeLsTNzOYS8BYwDDHkxaquldE3gLQ3+N+y8BYuOtmABCR3TCS79MBTFbV3DJbPZI+g3HTzUocgB4A/mfVwbeQq9N3ue3sZRFJgLsSOAGds1Q1TUS2w1gw1I7TonlRK8bPT/ODMMZSl/1qAFhl8r6bJ0P+9T0ZFQg3380x+zmkHA7B+BvfStlwBUJE0Y8JZiKivO1VVe2f+R8ikgijJmEFGBdsnWHUB3VSCMBbAK4WkZutHo93oarLfotdPoUeqCMW71/oYtuVQYwj5EQkP4zVwBvBuGhvDOOClU87Bc7pYtvzQnZRIBVGwnQUgB89JC3clJbZ6HdUZ1vtsl91mCSYfd6A8VSFm5rrVioC6Ol7qYgsgPF7+85XU5i8+xbGooh2x9atsEkww3lxv2Q4l2qpBOf6ywDwQ4TPWTEtBs5P64MwxhaX/azqRrv5++liEQlHnfcjYdgHhYfT3ycFwxIFEcUEJpiJiOgM3yrhu3yvZQA+EJGmAEbAXVL1cgALRKSDw2PyVqJpQbjNNgvhuInzYDCDCTYRqQCgHYzZX5fBWAk8f0SDyr0cZzCHJQrvTgM4BeAwgH0ANgNYB6MMxAI/Z127me3ktW67lf9c9rOMSVXTReRmAMMA3BGEmATGMdccwHsi8hmAAaq6Pwhj5xmqekxERgO4z6bbJSJysarmmD0sIvkAdHfYzY+q6vQZiqZz1kkYx2jMi8Hzk5sFQW2parKIHIHxVIUdq/NJNH0W3d7co+jn9PcJE8xEdAYTzEREZEtVF4pIExgL0LRysUl1ALNEpJUfj7lH0x+qX9u0WdVmziqQBZ1CwpdUuQlGDecmYPmKcIm2BPO5qro1zPvM5OYR9mDN6HY7O9g2Jt/CVneKyHQAH8AogREMhQE8BeAeEXlOVd0sXkf/7zPYJ5gBYxbziybvXw3nmx1Oi/sB0XXOGuO7SRyTYvz8FKwSRyfhnGC2+r6Kls/iaQDfRzoIChommInItWh5rIiIiKKY7zHuznC/OExFAFNEpFToogqpnwG8Y9Pu5g/qQOscBpWINIaxOM+3AJoi8Iv3tQCWBBoXkQmnVeuDPY6rY0FVR8GogfsCgjfLGgCKA/hERH4QkQJBHDdX8y2q5nRO6mmxiKNTeYw1qjrPv8giYg6APpEOwl+54PwUrPO9m2vzaH3iBTDKytyjqv9GOhAKmnCUVCGiXIIJZiIicsX3OHxXAG4f5a4B4EffIkhunfIcWGDUt89dAObCqNfZXFVv8M1atOLmAq94EOILChHpCWAe3C2wZmULjLqx98GYAVsbwG9BCC8vcJqRG82Pfgebm2Pc60JdVtweg65nTKvqMVV9G0Yt6S4wEmJBWcwTxuzNsSLCv8/dc5plXB1GmYUzRKQkjH+7QMbNFO5zFmAk8fbBKFUzFEB7AFeo6uEIxBKwXHJ+Khakcdx8Z1l95sL9WUyH8d23AcYTbk8BqO67EUe5h9NNz0h8BxJRlGKJDCIick1Vd4nI7QB+h7sZRm0AfAjgUZe7cFsz9UI/azwHi5vHYe1W3Q4bEbkexoW3lxlhR2EkLxYBWAxgkaoeMOnH2ZbuOH1e8lKC+ZCLPhWCtK8SLvu5ieksqpoO4FcAv/oWIGsNoBOMurEXex0viy4AnoOxaCo5Gw3gfdiXLboVRgIz0y2w/+46BeAbl/t3e87qoKpTXfbNM3LR+SngBLOvRIibp6OsbvK7+SwuUNXmzt2IznD6+yRmy/IQUfAxwUxERJ6o6hTfwlQPutzkERGZoarjXPTd4XLMYM0W8tduF30CSTLlICLlANwG4FwA/wIYoaq2dZ5FpAqA4XB38b4PwFcAfgCwSlXdlBdg7T13nGbIOtVozk22uehzfpD2VctlPzcxWfI97TDV98pcoKwtgKtgJIy91mx+UUQ+jdUZqeGkqsdF5FvYn49uFpHHVTWzjIFTeYwxqnrEZQi7YDwJ4/QdG+lzVtTJZeen84IwxkUu+1mV53Hz9xM/h+SV0xNFnMFMRGfwETwiIvLHMwB2euj/qYg4LagEGImeFBf9gnExF4jNLvrU881sDJiItACwHsZMvUcBDAKwWkScfg8vwN0jt6MAXKSqL6jqCpcX70B0rVofzRxnMItIkbBEEnlrXfSpKCKVgrCvhi76JMHbd5kjVd2rqt+q6l0wZmO3AzAW7mtCF4Ixy5bccSpnURpABwAQkfMBNHPoP8ztjn03F7a46Brpc1Y0yk3np0ZBGMPN9xUArLN4/x8X255rUZOcyIrTcbQvLFEQUUxggpmIiDzz1Zg6uKAAABPySURBVGPu7WGTcnBR09I3w2yVi/HcXoiFynIXfTIfmw+IL9E2ATlLblQF8L3VxaKIJAK4w8UuRqpqLw8z9rKq7sc2eZGbC7C8kqxf7LJfhyDsq52LPkvsElYiUktEBorIWBEZJCJ1vASgqumqOl1VbwJwCdwl2AHnJCj5qOoKGOUS7Nzq+99eDv1Wq+oCjyG4Wfw20uesqJILz0+XiohdmRY32rjos93md+Hmc1gI7mdKUx7n+/uyhEO3YC54S0QxjglmIiLyi6/kxa8eNrleRK5x0W+Wiz6dPOzXkYjcLyIrRWSXiEzzzRi2M8+hPdM9gcYG4CMApSzaLgPQxKKtJZwfEVYAL/oTlG/xxvouurKMhlHSxMk5IY8iCqjqVgAbXXR1SgTaEpGaMI4BJ9NsxrgWxs2kJwHcAOOm2l8icpU/ManqahhJbze1Usv7s48AqYs+0Tr70ekG5rW+BOBtAY5jxs056ypfjd2gEJHrRWSxiOwWkbkuz63RJLednxIBdPd3YxEpBuM7xskfNm3r4O5mZtD+fhKR0iLyhYhsEZENIvKxiFj9vUKxpxyc80VuSsYRUR7BBDMREQWiN7wt8DHQRdmICS7GqSMiTT3s15KIvAsjqVAPQEUYdVOn+RJUplR1E4ySFU6uE5ELA4jtOgDXO3SzevT6Ahe72Kuq/pYHaAb7hbUyea0/mxu5STBXD3UQUeQnF31ai0jjAPbxrMt+prXhRaQQjPqw2RcKywfgWxHxaxFPVd0NY2E6J6f9GT9A6S76BC1JGmRjYCz+ZqUQjBJDdqUqTsIox+DVRDgn50vBXQLRkYg8AuMYugzGjakWAH4RkVbBGD9McuP5qbcvse2PR+Bc6xYwFlg2paoK47Po5N5glMkQkfIAZsO4kV4dxr/pQzDqZFPuUN1Fn60hjoGIYggTzERE5DdV3QzgTQ+bnA9jNqCd+QA2uBjrVQ/7zUEMH8GoJ51dQQBdHYb41sVu8sOoP+35Yk5EKgL41EXXAxbvu5lFFMjfAU+47Oem9nZu5+bzXD3UQUSRL+FutuzH/iRsfIm2u1x0/UNVrf5tmsL6GCoHYKDXuLJY46LPoQDG95eb+veBlgEICVU9CeAbh273ObR/r6p2SWqrfe8EMMNF15dFJN7r+FmJyDMAhiDnTPI4xFbd7tx4fqoD4DmvG4nIBQD6uej6HyxuiGUxwsU4FwHo4aKfJRGpDGM2tVnJoLYiwhvLuUM1F31WhzwKIooZTDATEVGg3oW7BFqmZ+1qFfpm4QxxMU57EXFTwzEHESkIY6baozbdss9czO4LuEvItIZR5sI13+zIiXB+TP40rGvannSxq/Iicq6X2ABARG4A0M1l9wt99Tbzsi0AUh365Jm6mKr6L9w9qdAQwCdexhaRi2DMoHNzU8cuSeyUCLxTRNweA9m5mf38t59jB8JNcrVyyKPwnz/lLYK1/WAXfS4G8JI/g4tIvIi8B+N8a8XpnBVNcuv5qZ9v/674Fj8eD6PEhpNRqmr7xJiqzgPwl4ux3vfNQPZMROoCmAvA7umsiJzzRaSsiHzgK9mRIiJ7RGRkIE+S5XGWT/JlsTLkURBRzGCCmYiIAqKqp2E83ulWSdgndgHjQn+ri7E+ERE3C+Oc4btgnQegp0PXhXaNqroXwMcud/uIiHzrZlaPbxGxeQAudTHuOJsZd5tcxvayy34AzpTt8PIYeX4AjbzsI7dR1TQ4L8BULxyxRJHnAaS56HeviIwRkRJOHUWkA4CZcFe/eK6q2iW53ZTAGSkirV30O8NXa/VeF13neBk3SKyehsjqEhGpEPJI/KCqf8N4AsYfK1XV7QKUZvue6HLf/UTkVudu/8933pgMoI9DV9tzVpTJreenfDAW333Kaba6iNSGUb+7lotxFcAwlzE876JPBQATRKS4yzEBACLSE8bnzG5m664ASpv4TUTOh5FcfwLGE0H5YfycvWDUzg/q2h15RF2H9gOquicskRBRTGCCmYiIAqaq02HUwHTrKbtZQx6S1gUB/CYij7u4mIsTkbsALAXQwGHcv+HukefX4X6Bk54A1ovI2yLSMGstahEpIiJtRWQEgBVwd8GZBuANm/Z5cJfAu0tEHBdSEpHyIjIMwM/wPjvJadHEvMBpIbCLA6jfGXNUdR2Ad1x2vxnAvyLSX0TqisiZv19FpKSI3Cgi42Ek4dwslpgMo1aoXXw74HxToDCAySLS203pA99j5VPgXA7lX1Vd5DReCGx10ScBRg3qqplviEgFEblKRB4WkXtFJJILVvo7CznQ2c8A8DCca2fHwbgx8ZabmbMi0hXG59BpYcmDAL5zFWV0yM3npwQY9b6Xi8ijIlI1s0yWbyZ6AxH5EMa/a22XY37mu4HiSFWnwl0d5MYAFoqIY4LdNzP4CxilwZxqRQ9yse+g8p0TfoT1ExaFAIwRkUrhiypXMCuBklUkboQSURRjgpmIKHdyU9/USz83ngSQ5LJvGRhJI0uq+hvclZYoAOBDAGtF5BkRucSXsE0QY4Xz5iLyPIwV1r+Cu9qPz/hKddhS1f9g1HrNcDEmAJSGUaNxKYAUETkoIodh/N6mA7gDzo/mZ3rH7oJTVQ8B+NXlWG+IyJ8icpOIVPT97gqLSE0R6SEi3wHYBqOGqT+LA7X3Y5vcxinBXABA/XAEEkVehfGotRtlALwCYBWAUyKyT0ROAjgMI7FwrYf9Pu0yWeOmvnwBGAmV9SLST0Su8CVcE33HUTnfzaPBMGZFu1mc9C0Xfdwk5wDn0ixZuS3L0QbANhE5LCJJAPYAmAZgKIDPYXwXN8u2jZt4vcRq5QcYyVYvTsJdTX1bqroS7maPxvn6bfTdNGkiIsV9yceSvhuQj4vIMgC/AKhqO5rhZV8d6piQR85PdWH8DbMNwGkR2Q+jrNZfAB6H+5Imu+G9tvNDvv06uQjAYhEZLyK3iEg1EckvIgV9ifFrfYnlzTAW83OyGcb3QLi1AXCJQ58iAB4MfShhF4zvzRzEKGXnVLpreij2TUSxK8/MlCEiymNOBbmfI1XdIyL9YCR73egFYKRDnycBnAegs4vxasK+PqVbb6nqZLedVXWqiDwN4AM/9lXaj20Ao35tfxf9XgVwDdzdUL7c9/LiNIyLm8IO/VqISKJD/chgJqDClczyYh6M31d+mz7N4Dxr1k463N2gCPfPbkpV03yPtM+FtxrU+WEstOePgarqqrSNqo4TkZ8AuKmpej4CXHjU508AX7vod9hFHwVwxMO+F8H4bORz2d+qlnQJGI/zZ3282k28bvrYUtVk30zWFzxsNkZV3d4cddr/QBGpCeABF90rwbhp8kqAu/1OVd0sCBsswfp+jaXzk52dMNZCsKv7nAD/FhRUAA95XXxSVQ//X3v3GitHWQZw/P8KWCqCShCRVASJaCXITS4lpggEI3wAjBBAUCAYMd4gkGAERI0kXFREolETVKIEFYQETDTIpRQkElAucgsFililQINc2lJKaR8/PFNTTs7uzO7O7rn9f8l8OTP7zrNzZnZ2n3nf5y2lHELed+pqvhfyAV0vD+nGsxI4suGDjrbv0XWj0nrdbjJY23C7FUPa/97UX5smmCW9gT2YJWl6alq24ZmW9/sjmifI5teVBIiItWRyp27m9Lb8ICJqh+OOFRGX0Mfs8X26Dji6OjZdRcS9tJN0H8/jZEL0zAbbNumd22YCaiTJrF5UP7rrhpMOOlT7xYbbvTDgflpT9WTcn2YTUw3qgog4o8fXnMDoats+CRzVZPQEObHqypptHoqIJhORAlAlWf/QdPsaO5dSNkxU39vgNW2dAz+mt+RU2z0uvziENju5CjhxRPtar5XP1yl0f1pe8/qXyRJYNzQLrSdn1tSK76gqQ3QA7X/PG89LwKHV/7SJibpHtzlqb9iaPPRaFhF194F+1Y22WVRN2CtJ/2eCWZKmp7823K7VOp9V0vNzNOudso4GpSWqBMlRZG+npj06erUK+EJEnN5vAxFxIXA0zcuE9GotWfP5Uz32tDqHZvUYm1pHlgTYLSLuAX5CDo+vs0XN+ibJpfsabNN2W22qG4b/sQHbb1Li4InJNpQ+Ip4jeyf+Yki7WA4cHxFNyhe8QfXj/UDgN61H9UZ/B/arjkWtauLIupgu7yOOc6ivI9zEkojYMMl7J5kU7yRo1nO7VkQ8TfOSFzdFxCCjBsbb/7qI+DJZpqC1UUJjrCVHsRwz5jiPQpMk4n0N25oK96du5y3kZ+pq4HCypEkb1gFfjYjvDdJIVbZlb4ZbK/dBYF5E3NbDa9o8hwAeaLhd0zJAk8Ei6h+UDbNW/wE164d9T5Q0BZlglqTpaQHZg6eb2yLi0bZ3HBH30aw379UR0ah2cUSsjYhvkT0qmtZsbep6YNeIGHiSp4i4CvgwcM3AUb3RrcBeEXFuk57LY2JaS/auuojBe+8sAD4SEaet7zVT9bY8HljS5XX/pf6hx69r1j9C1q5uos222nQN3RNO25RSPjRA+9c1jGHSiYhXIuJk4BDqEzq9uArYOSL6rrEbEasi4tPkaIrFrUWWVpJJtnkR0WtPw693iWchcGmvwVS9Hk9gsId5wZhaxNXnxMl0Pv/Pj4g2r8lzyV6V3bxGlmEaiqpsxa601yt8vduBfSLi2w17u7cqIpZSPzS+rvzV+ramwv2pbgTVtdW+VpNlMs5isIc0TwEHRkSTOShqVROWHkD2rF/WRpuV5cA3yGP+SI8xtXYOVW4GnqjZZg3De4jZuohYRX4/7eaKYey7lDIL2K9ms98OY9+SpriIcHFxcXGZhgv55XAF+aNt7PIM8P4h7/+CDvsOMsG31QBt708mjlZ22Ue35WXgMrKX07De/+7AL6t99RPjcrJH37wWY9obuKXHOF4jE5fza9qeW51XY1//Kjl0tkl8l3aIYQWwb4/vtbW2Wj4vfldzvE8foO3NgMe6tL0UeOdEvfce3sdGwDFkIm1dH9fOS2QiYZchxLYxWT9+YZ+xrV8eJhNRWw4Yz9ZkL82l1bW6iEyubjpgu/tXMfb6vhYDh3VpdzcyafISmWy+CzhuSOfRwXS+R7wGHDvCc3qP6px8sc/zZRWZ0On6OTzC97MTOZnieLFe3Webk/L+RE4O1+lauBHYaJzXfIDs4fl6D+/lafLBzOwh/t82Iye6+1uf52GQ39/OBN4+mc4h8rPluQ7trQE+O9HXTR/v6b0dztsgS7KUIe33oJpz4O6JPjYuLi6TcykRgSRpeiqlzCV7mBwIbAk8S87c/p2I+M8I9v8J4HTyi/9bgX8CvwcuioiBJyYppcwme+bsS/YU2x7YlvwRNYv8cbeSTL4sJodc3g4sjB5qkw4Y4yyy9MF+ZNJ5bIyryaTnhjHeQfYwH0qMpZSdgSPImr9zycmHZpM/tl8ka8E+RB6rP0XWyW3S7hzgQnLipiB7lH0zcphu09hOIXtafZA8LuvbeKhpG8Noqy3VxEt/7LLJLRFx0ADtb0/2Ut5jzKpHyPIqPfU0m2jVOXUwWU91Lnn9vIM8Xze8vp8kh13fASwYxfVdStmWTMTOIxNKOwBbAW8hJ8pbTSYGl5E9KBeRpTD+EhFt9tIeilLKm4CPk5Os7gbsCLwN2JRM2LxC3lOeIt/XTeRna6ORKaNQTbh3FjCf/Nx9gRwFc0G0XBqjYTybkOVg5pH3gx3Iyf42J4/rWvK4PkPeL/9RxXtzDK/Wal9KKe8DziPPkc3JUVM/B34YPY60GdPupLs/lVK2IktUHUFOzvsU2Xv0/Ijo2Fu5lLI18Engo+R3lHeT19A68iHL4+S1cwPw5xhhuZNSynbkd8O9yOO8Hfn5NZt8kLaavF7+DTxaxXljRDzcYgytnkOllG2Ar5HlSuaQD/gXkP+nUdT5b10p5T1kibhDye/xS4AryQmph1KCp5RyCXBql01OiojLh7FvSVObCWZJkqQRKaUUsg5kp1IYa4BtIqLvCY6qxOB8YE+yHNoDZK3ZJrXRJUnSDFVKWUw+/BrP88Cc6G0uEEkzxMYTHYAkSdJMERFRSrmIzhOwbULW8bxsgH2sI+t239pvG5IkaWYppexD5+QywGUmlyV14iR/kiRJo3Ul3SedOm5UgUiSJFWO7bLuFeDiUQUiaeoxwSxJkjRCVZ3N73fZZP+qNqUkSdLQVTXiuz3g/mlEPDeqeCRNPSaYJUmSRu9nwL86rCvASSOMRZIkzWyHkRM9jmcV8N0RxiJpCjLBLEmSNGJVDcOzu2xyYjVZnyRJ0rCd0mXdxRHxzMgikTQllYiY6BgkSZJmnFJKAe4G9uywyeERcf0IQ5IkSTNMKWVH4DFyBNVYS4GdImLFaKOSNNXYM0aSJGkCRD7lP6PLJqeOKhZJkjRjfYXxk8sAZ5tcltSEPZglSZImUCnlauDIDqt3iYgHRxmPJEmaGUopWwBLgC3GWX0XMC8i1o02KklTkT2YJUmSJtapwItd1kmSJA3DKYyfXH4d+LzJZUlNmWCWJEmaQBHxNJ0TyZ8ppbxrlPFIkqTpr5TyZuC0DqvPi4j7RxiOpCnOBLMkSdIEi4hfAVeMs2oW8KURhyNJkqa/44Btx/n7QuC8EcciaYqzBrMkSdIkUErZFLgWOGTMqmeBORHx+uijkiRJ01Ep5R5g9zF/vh84KCKen4CQJE1h9mCWJEmaBCLiVeBw4AzgXmA5WQNxKbDJBIYmSZKmn2XAGvJB9p3kiKl5Jpcl9cMezJIkSZIkSZKkvtiDWZIkSZIkSZLUFxPMkiRJkiRJkqS+mGCWJEmSJEmSJPXFBLMkSZIkSZIkqS8mmCVJkiRJkiRJffkfFhf9DzUyaK8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "import matplotlib.font_manager as font_manager\n",
    "\n",
    "#ax = plt.gca()\n",
    "fig, ax = plt.subplots(figsize=(20, 20))\n",
    "\n",
    "#plt.legend([\"RandomFOrestClassifier (AP=0.89)\", \"LinearSVC (AP=0.89)\", \"LogistivRegression (Ap=0.89)\", \"DecisionTreeClassifer (Ap-0.69)\", \"AdaboostClassifier (Ap=0.90)\", \"MLPClassifier (Ap=0.87)\"], prop={\"size\":25, \"family\":'Arial Rounded MT Bold'})\n",
    "\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.xticks(font=\"Arial Rounded MT Bold\", fontsize=50)\n",
    "plt.xlabel('Predicted label', font=\"Arial Rounded MT Bold\", fontsize=70)\n",
    "plt.ylabel('True label', font=\"Arial Rounded MT Bold\", fontsize=70)\n",
    "plt.yticks(font=\"Arial Rounded MT Bold\", fontsize=50)\n",
    "scv1=plot_precision_recall_curve(mlp, data_test, target_test, linewidth = 6, ax=ax) \n",
    "scv2=plot_precision_recall_curve(clf, data_test1, target_test1, linewidth = 6, ax=ax)\n",
    "scv3=plot_precision_recall_curve(clf2, data_test2, target_test2, linewidth = 6, ax=ax)\n",
    "\n",
    "scv1.plot(ax=ax)\n",
    "#ax.legend([\"MLPClassifer (Ap=0.87)\", \"RandomForestClassifier(AP = 0.89)\", \"LinearSVC (AP = 0.89)\", \"LogisticRegrgession (AP = 0.89)\", \"DecisionTreeClassifier (AP = 0.69)\", \"AdaboostClassifier (AP = 0.87)\"], prop={\"size\":50, \"family\":'Arial Rounded MT Bold'}, loc=\"lower left\")\n",
    "#plt.title('Normalized Confusion Matrix \\n', font=\"Arial Rounded MT Bold\", fontsize=18)\n",
    "font = font_manager.FontProperties(family='Arial Rounded MT Bold', size=50)\n",
    "#ax.legend(prop=font)\n",
    "ax.legend([\"SeqBasedClassifier (AP=0.87)\", \"MMEClassifier (AP = 0.87)\", \"CombClassifier (AP = 0.89)\"], prop={\"size\":50, \"family\":'Arial Rounded MT Bold'}, loc=\"lower left\")\n",
    "#plt.title('Normalized Confusion Matrix \\n', font=\"Arial Rounded MT Bold\", fontsize=18)\n",
    "#plt.show()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"D://PhD_related/manuscript_obj1/update_images/Precision-Recall_LbCpf1-saved2.png\", dpi=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "compound-potato",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [1 0]\n",
      "Train: [    2     3     4 ... 80825 80826 80827] Validation: [    0     1    11 ... 80814 80818 80821]\n",
      "Train: [    0     1     2 ... 80825 80826 80827] Validation: [    5    12    13 ... 80812 80815 80816]\n",
      "Train: [    0     1     2 ... 80824 80825 80827] Validation: [    4     7    22 ... 80810 80820 80826]\n",
      "Train: [    0     1     2 ... 80821 80825 80826] Validation: [    3     6     8 ... 80823 80824 80827]\n",
      "Train: [    0     1     3 ... 80824 80826 80827] Validation: [    2     9    18 ... 80811 80819 80825]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from numpy import interp\n",
    "import warnings  \n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import RepeatedKFold, KFold\n",
    "import matplotlib.font_manager as font_manager\n",
    "import numpy as np\n",
    "np.random.seed(235)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_LbCpf1/LbCpf1_Dataset_MM-SeqComb_clean1.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "#df = pd.DataFrame(OT_data)\n",
    "le = preprocessing.LabelEncoder()\n",
    "print(\"off-target' : \",OT_data['Y'].unique())\n",
    "OT_data['Y'] = le.fit_transform(OT_data['Y'])\n",
    "cols = [col for col in OT_data.columns if col not in ['Y']]\n",
    "data = OT_data[cols]\n",
    "data=np.asarray(data)\n",
    "target = OT_data['Y']\n",
    "target=np.asarray(target)\n",
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=29)\n",
    "i=0\n",
    "f, axes = plt.subplots(figsize=(20,20))\n",
    "'''axes[0].scatter(data[target==0,0], data[target==0,1], color='blue', s=2, label='y=0')\n",
    "axes[0].scatter(data[target!=0,0], data[target!=0,1], color='red', s=2, label='y=1')\n",
    "axes[0].set_xlabel('data[:,0]')\n",
    "axes[0].set_ylabel('data[:,1]')\n",
    "axes[0].legend(loc='lower left', fontsize='small')'''\n",
    "y_real = []\n",
    "y_proba = []\n",
    "for train_index, val_index in skf.split(data, target): \n",
    "    i=i+1\n",
    "    print(\"Train:\", train_index, \"Validation:\", val_index) \n",
    "    data_train, data_test = data[train_index], data[val_index] \n",
    "    target_train, target_test = target[train_index], target[val_index]\n",
    "    clf = clf = MLPClassifier(solver='adam', alpha=0.0002, activation= 'relu', hidden_layer_sizes=[10, 10, 12, 25, 12], learning_rate_init=0.001, random_state=13, tol=0.0000002, learning_rate='adaptive', beta_1=0.9, beta_2=0.71, epsilon=1e-23, n_iter_no_change=20, validation_fraction=0.25, verbose=False)\n",
    "    clf.fit(data_train, target_train)\n",
    "    pred_proba=clf.predict_proba(data_test)\n",
    "    precision, recall, _ = precision_recall_curve(target_test, pred_proba[:,1])\n",
    "    lab = 'Fold %d AUC=%.4f' % (i, auc(recall, precision))\n",
    "    axes.step(recall, precision, label=lab, lw=6)\n",
    "    y_real.append(target_test)\n",
    "    y_proba.append(pred_proba[:,1])\n",
    "y_real = np.concatenate(y_real)\n",
    "y_proba = np.concatenate(y_proba)\n",
    "precision, recall, _ = precision_recall_curve(y_real, y_proba)\n",
    "lab = 'Overall AUC=%.4f' % (auc(recall, precision))\n",
    "axes.step(recall, precision, label=lab, lw=6, color='black')\n",
    "axes.set_xlabel('Recall')\n",
    "axes.set_ylabel('Precision')\n",
    "axes.legend(loc='lower left', fontsize='small')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.xticks(font=\"Arial Rounded MT Bold\", fontsize=50)\n",
    "plt.xlabel('Predicted label', font=\"Arial Rounded MT Bold\", fontsize=70)\n",
    "plt.ylabel('True label', font=\"Arial Rounded MT Bold\", fontsize=70)\n",
    "plt.yticks(font=\"Arial Rounded MT Bold\", fontsize=50)\n",
    "f.tight_layout()\n",
    "#f.savefig('result.png')\n",
    "font = font_manager.FontProperties(family='Arial Rounded MT Bold', size=50)\n",
    "#ax.legend(prop=font)\n",
    "axes.legend([\"Fold 1 AUC=0.8060\", \"Fold 2 AUC=0.8622\", \"Fold 3 AUC=0.8748\", \"Fold 4 AUC=0.8253\", \"Fold 5 AUC=0.7562\", \"Overall AUC=0.8118\"], prop={\"size\":50, \"family\":'Arial Rounded MT Bold'}, loc=\"lower left\")\n",
    "#plt.title('Normalized Confusion Matrix \\n', font=\"Arial Rounded MT Bold\", fontsize=18)\n",
    "#f.show()\n",
    "plt.savefig(\"D://PhD_related/manuscript_obj1/update_images/5-foldCV_LbCpf1-saved.png\", dpi=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "numeric-organ",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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