AsCpf1_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": 7,
   "id": "herbal-incidence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14584    21]\n",
      " [   27   107]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14605\n",
      "           1       0.84      0.80      0.82       134\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.92      0.90      0.91     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.8151518271038123\n",
      "No Skill PR AUC: 0.818\n",
      "Logistic ROC AUC 0.899\n",
      "SpearmanrResult(correlation=0.8153695142581562, pvalue=0.0)\n",
      "0.003256665988194586\n",
      "0.003256665988194586\n",
      "0.057067205890901876\n",
      "0.6385044990725932\n",
      "0.6385228939179488\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(123)\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import scikitplot as skplt\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.ensemble import AdaBoostClassifier\n",
    "from sys import *\n",
    "\n",
    "OT_data=pd.read_csv(\"D://PhD_related/Dataset/Pragya_Ascpf1/AsCpf1_clean_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",
    "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=88, 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()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "adopted-sphere",
   "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": "7807c9879d944e1c9a9743daa12bf441",
       "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": 22,
   "id": "union-louisville",
   "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": "dependent-maldives",
   "metadata": {},
   "source": [
    "\n",
    "# LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "moral-reaction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14557    48]\n",
      " [   25   109]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14605\n",
      "           1       0.69      0.81      0.75       134\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.85      0.91      0.87     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.7466555591476565\n",
      "No Skill PR AUC: 0.755\n",
      "Logistic ROC AUC 0.905\n",
      "SpearmanrResult(correlation=0.7490455948560197, pvalue=0.0)\n",
      "0.004952846190379266\n",
      "0.004952846190379266\n",
      "0.07037646048487567\n",
      "0.4502255923395687\n",
      "0.4504958943727101\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(123)\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import scikitplot as skplt\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.ensemble import AdaBoostClassifier\n",
    "from sys import *\n",
    "from sklearn.svm import LinearSVC\n",
    "OT_data=pd.read_csv(\"D://PhD_related/Dataset/Pragya_Ascpf1/AsCpf1_clean_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",
    "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=0.02, loss='squared_hinge', penalty='l2', dual=False, tol=0.0001, class_weight={0:0.12, 1:0.7})\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",
    "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()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "specific-promotion",
   "metadata": {},
   "source": [
    "# MLPCLassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "freelance-involvement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "Iteration 1, loss = 0.21395034\n",
      "Iteration 2, loss = 0.03788173\n",
      "Iteration 3, loss = 0.03206342\n",
      "Iteration 4, loss = 0.02791139\n",
      "Iteration 5, loss = 0.02471459\n",
      "Iteration 6, loss = 0.02190483\n",
      "Iteration 7, loss = 0.01932214\n",
      "Iteration 8, loss = 0.01719505\n",
      "Iteration 9, loss = 0.01551946\n",
      "Iteration 10, loss = 0.01405339\n",
      "Iteration 11, loss = 0.01332802\n",
      "Iteration 12, loss = 0.01231420\n",
      "Iteration 13, loss = 0.01160176\n",
      "Iteration 14, loss = 0.01113310\n",
      "Iteration 15, loss = 0.01063210\n",
      "Iteration 16, loss = 0.01016732\n",
      "Iteration 17, loss = 0.01007936\n",
      "Iteration 18, loss = 0.00970176\n",
      "Iteration 19, loss = 0.00970251\n",
      "Iteration 20, loss = 0.00921236\n",
      "Iteration 21, loss = 0.00897783\n",
      "Iteration 22, loss = 0.00875352\n",
      "Iteration 23, loss = 0.00899504\n",
      "Iteration 24, loss = 0.00849585\n",
      "Iteration 25, loss = 0.00847887\n",
      "Iteration 26, loss = 0.00821897\n",
      "Iteration 27, loss = 0.00816235\n",
      "Iteration 28, loss = 0.00809190\n",
      "Iteration 29, loss = 0.00836407\n",
      "Iteration 30, loss = 0.00807827\n",
      "Iteration 31, loss = 0.00839177\n",
      "Iteration 32, loss = 0.00796295\n",
      "Iteration 33, loss = 0.00771408\n",
      "Iteration 34, loss = 0.00770404\n",
      "Iteration 35, loss = 0.00761921\n",
      "Iteration 36, loss = 0.00758733\n",
      "Iteration 37, loss = 0.00753190\n",
      "Iteration 38, loss = 0.00719962\n",
      "Iteration 39, loss = 0.00700934\n",
      "Iteration 40, loss = 0.00721258\n",
      "Iteration 41, loss = 0.00684948\n",
      "Iteration 42, loss = 0.00761479\n",
      "Iteration 43, loss = 0.00709515\n",
      "Iteration 44, loss = 0.00697837\n",
      "Iteration 45, loss = 0.00728089\n",
      "Iteration 46, loss = 0.00702163\n",
      "Iteration 47, loss = 0.00696200\n",
      "Iteration 48, loss = 0.00684072\n",
      "Iteration 49, loss = 0.00688176\n",
      "Iteration 50, loss = 0.00710071\n",
      "Iteration 51, loss = 0.00670791\n",
      "Iteration 52, loss = 0.00670164\n",
      "Iteration 53, loss = 0.00670144\n",
      "Iteration 54, loss = 0.00655364\n",
      "Iteration 55, loss = 0.00653881\n",
      "Iteration 56, loss = 0.00662108\n",
      "Iteration 57, loss = 0.00659900\n",
      "Iteration 58, loss = 0.00633173\n",
      "Iteration 59, loss = 0.00644314\n",
      "Iteration 60, loss = 0.00653970\n",
      "Iteration 61, loss = 0.00634410\n",
      "Iteration 62, loss = 0.00687026\n",
      "Iteration 63, loss = 0.00638091\n",
      "Iteration 64, loss = 0.00634360\n",
      "Iteration 65, loss = 0.00624007\n",
      "Iteration 66, loss = 0.00655631\n",
      "Iteration 67, loss = 0.00735493\n",
      "Iteration 68, loss = 0.00615609\n",
      "Iteration 69, loss = 0.00638985\n",
      "Iteration 70, loss = 0.00642073\n",
      "Iteration 71, loss = 0.00607712\n",
      "Iteration 72, loss = 0.00608029\n",
      "Iteration 73, loss = 0.00586357\n",
      "Iteration 74, loss = 0.00609658\n",
      "Iteration 75, loss = 0.00630127\n",
      "Iteration 76, loss = 0.00594112\n",
      "Iteration 77, loss = 0.00601031\n",
      "Iteration 78, loss = 0.00596409\n",
      "Iteration 79, loss = 0.00590181\n",
      "Iteration 80, loss = 0.00574164\n",
      "Iteration 81, loss = 0.00573172\n",
      "Iteration 82, loss = 0.00595934\n",
      "Iteration 83, loss = 0.00622441\n",
      "Iteration 84, loss = 0.00590986\n",
      "Iteration 85, loss = 0.00568714\n",
      "Iteration 86, loss = 0.00588337\n",
      "Iteration 87, loss = 0.00593921\n",
      "Iteration 88, loss = 0.00564691\n",
      "Iteration 89, loss = 0.00551648\n",
      "Iteration 90, loss = 0.00585425\n",
      "Iteration 91, loss = 0.00551680\n",
      "Iteration 92, loss = 0.00605494\n",
      "Iteration 93, loss = 0.00569913\n",
      "Iteration 94, loss = 0.00574448\n",
      "Iteration 95, loss = 0.00561544\n",
      "Iteration 96, loss = 0.00542743\n",
      "Iteration 97, loss = 0.00632902\n",
      "Iteration 98, loss = 0.00555363\n",
      "Iteration 99, loss = 0.00559839\n",
      "Iteration 100, loss = 0.00525547\n",
      "Iteration 101, loss = 0.00531633\n",
      "Iteration 102, loss = 0.00517070\n",
      "Iteration 103, loss = 0.00543972\n",
      "Iteration 104, loss = 0.00533785\n",
      "Iteration 105, loss = 0.00541670\n",
      "Iteration 106, loss = 0.00538575\n",
      "Iteration 107, loss = 0.00541410\n",
      "Iteration 108, loss = 0.00518427\n",
      "Iteration 109, loss = 0.00550422\n",
      "Iteration 110, loss = 0.00514613\n",
      "Iteration 111, loss = 0.00530077\n",
      "Iteration 112, loss = 0.00527864\n",
      "Iteration 113, loss = 0.00507663\n",
      "Iteration 114, loss = 0.00542915\n",
      "Iteration 115, loss = 0.00521301\n",
      "Iteration 116, loss = 0.00517844\n",
      "Iteration 117, loss = 0.00549355\n",
      "Iteration 118, loss = 0.00533926\n",
      "Iteration 119, loss = 0.00542628\n",
      "Iteration 120, loss = 0.00513982\n",
      "Iteration 121, loss = 0.00528060\n",
      "Iteration 122, loss = 0.00506882\n",
      "Iteration 123, loss = 0.00496718\n",
      "Iteration 124, loss = 0.00516813\n",
      "Iteration 125, loss = 0.00499889\n",
      "Iteration 126, loss = 0.00516405\n",
      "Iteration 127, loss = 0.00507238\n",
      "Iteration 128, loss = 0.00502042\n",
      "Iteration 129, loss = 0.00489979\n",
      "Iteration 130, loss = 0.00489722\n",
      "Iteration 131, loss = 0.00496600\n",
      "Iteration 132, loss = 0.00483763\n",
      "Iteration 133, loss = 0.00502347\n",
      "Iteration 134, loss = 0.00474238\n",
      "Iteration 135, loss = 0.00494455\n",
      "Iteration 136, loss = 0.00456679\n",
      "Iteration 137, loss = 0.00498425\n",
      "Iteration 138, loss = 0.00464877\n",
      "Iteration 139, loss = 0.00505562\n",
      "Iteration 140, loss = 0.00506677\n",
      "Iteration 141, loss = 0.00502102\n",
      "Iteration 142, loss = 0.00486566\n",
      "Iteration 143, loss = 0.00477167\n",
      "Iteration 144, loss = 0.00484667\n",
      "Iteration 145, loss = 0.00475871\n",
      "Iteration 146, loss = 0.00487234\n",
      "Iteration 147, loss = 0.00504610\n",
      "Iteration 148, loss = 0.00482097\n",
      "Iteration 149, loss = 0.00482386\n",
      "Iteration 150, loss = 0.00471485\n",
      "Iteration 151, loss = 0.00478320\n",
      "Iteration 152, loss = 0.00469393\n",
      "Iteration 153, loss = 0.00482737\n",
      "Iteration 154, loss = 0.00482293\n",
      "Iteration 155, loss = 0.00488141\n",
      "Iteration 156, loss = 0.00478995\n",
      "Iteration 157, loss = 0.00468798\n",
      "Training loss did not improve more than tol=0.000020 for 20 consecutive epochs. Stopping.\n",
      "[[14569    36]\n",
      " [   28   106]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14605\n",
      "           1       0.75      0.79      0.77       134\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.87      0.89      0.88     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.7659261700267103\n",
      "No Skill PR AUC: 0.770\n",
      "Logistic ROC AUC 0.894\n",
      "SpearmanrResult(correlation=0.7662542363273137, pvalue=0.0)\n",
      "0.004342221317592781\n",
      "0.004342221317592781\n",
      "0.065895533366024\n",
      "0.5180059987634575\n",
      "0.5180387007107565\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(123)\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import scikitplot as skplt\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.neural_network import MLPClassifier\n",
    "from sys import *\n",
    "\n",
    "OT_data=pd.read_csv(\"D://PhD_related/Dataset/Pragya_Ascpf1/AsCpf1_clean_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",
    "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",
    "mlp= MLPClassifier(solver='adam', alpha=0.0006, activation= 'relu', hidden_layer_sizes=[10,10,10], max_iter=500, learning_rate_init=0.0003, random_state=6, tol=0.00002, learning_rate='adaptive', 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",
    "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()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "mighty-adelaide",
   "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": "c5383527d6be465f800cc597b6b3aff7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "efficient-recruitment",
   "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": []
  },
  {
   "cell_type": "markdown",
   "id": "balanced-manual",
   "metadata": {},
   "source": [
    "# DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "false-emergency",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14539    66]\n",
      " [   22   112]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14605\n",
      "           1       0.63      0.84      0.72       134\n",
      "\n",
      "    accuracy                           0.99     14739\n",
      "   macro avg       0.81      0.92      0.86     14739\n",
      "weighted avg       1.00      0.99      0.99     14739\n",
      "\n",
      "0.7149921421514684\n",
      "No Skill PR AUC: 0.733\n",
      "Logistic ROC AUC 0.916\n",
      "SpearmanrResult(correlation=0.7223644227449092, pvalue=0.0)\n",
      "0.005970554311690074\n",
      "0.005970554311690074\n",
      "0.0772693620504924\n",
      "0.33725824829975415\n",
      "0.3382474822055418\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(123)\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import scikitplot as skplt\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",
    "from sys import *\n",
    "\n",
    "OT_data=pd.read_csv(\"D://PhD_related/Dataset/Pragya_Ascpf1/AsCpf1_clean_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",
    "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",
    "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()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aerial-newsletter",
   "metadata": {},
   "source": [
    "# LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "congressional-sewing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14544    61]\n",
      " [   19   115]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14605\n",
      "           1       0.65      0.86      0.74       134\n",
      "\n",
      "    accuracy                           0.99     14739\n",
      "   macro avg       0.83      0.93      0.87     14739\n",
      "weighted avg       1.00      0.99      0.99     14739\n",
      "\n",
      "0.739243622512728\n",
      "No Skill PR AUC: 0.756\n",
      "Logistic ROC AUC 0.927\n",
      "SpearmanrResult(correlation=0.74626954602008, pvalue=0.0)\n",
      "0.005427776646990976\n",
      "0.005427776646990976\n",
      "0.07367344601001759\n",
      "0.3975074984543219\n",
      "0.3984088458767443\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(123)\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.axes import Axes\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import scikitplot as skplt\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.linear_model import LogisticRegression\n",
    "from sys import *\n",
    "\n",
    "OT_data=pd.read_csv(\"D://PhD_related/Dataset/Pragya_Ascpf1/AsCpf1_clean_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",
    "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",
    "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()))"
   ]
  },
  {
   "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": 13,
   "id": "crazy-bundle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14599    10]\n",
      " [   28   102]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14609\n",
      "           1       0.91      0.78      0.84       130\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.95      0.89      0.92     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.8416826892363948\n",
      "No Skill PR AUC: 0.849\n",
      "Logistic ROC AUC 0.892\n",
      "SpearmanrResult(correlation=0.8440594724555427, pvalue=0.0)\n",
      "0.0025781939073207137\n",
      "0.0025781939073207137\n",
      "0.05077591857682846\n",
      "0.7050911714064564\n",
      "0.7052617722478767\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(235)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_AsCpf1/AsCpf1_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=27, learning_rate=0.98, random_state=16)\n",
    "clf.fit(data_train1, target_train1)\n",
    "\n",
    "Pred_labels4 = clf.predict(data_test1)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test1,Pred_labels4))\n",
    "print(classification_report(target_test1,Pred_labels4))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test1,Pred_labels4))\n",
    "precision, recall, _ = precision_recall_curve(target_test1,Pred_labels4)\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_labels4)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)  \n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test1,Pred_labels4.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test1,Pred_labels4.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test1,Pred_labels4.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test1,Pred_labels4.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test1,Pred_labels4.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test1,Pred_labels4.round()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "composite-boundary",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "accomplished-separate",
   "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": "91ee3ab899874bb8ad0ec124ebd03a1d",
       "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": 13,
   "id": "published-swedish",
   "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": 2,
   "id": "bridal-count",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14583    26]\n",
      " [   43    87]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14609\n",
      "           1       0.77      0.67      0.72       130\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.88      0.83      0.86     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.713700844857422\n",
      "No Skill PR AUC: 0.721\n",
      "Logistic ROC AUC 0.834\n",
      "SpearmanrResult(correlation=0.7154830198791097, pvalue=0.0)\n",
      "0.004681457358029717\n",
      "0.004681457358029717\n",
      "0.06842117623974113\n",
      "0.46450765334330235\n",
      "0.46465982508148274\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(235)\n",
    "from sklearn.svm import SVC\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_AsCpf1/AsCpf1_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",
    "svc=LinearSVC(C=0.02, loss='squared_hinge', penalty='l2', dual=False, tol=0.0001, class_weight={0:0.12, 1:0.7})\n",
    "svc.fit(data_train, target_train)\n",
    "Pred_labels3 = svc.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels3))\n",
    "print(classification_report(target_test,Pred_labels3))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels3))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels3)\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_labels3)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)  \n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels3.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels3.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels3.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels3.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels3.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels3.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "precise-brass",
   "metadata": {},
   "source": [
    "# Decision tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "finite-diesel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14603     6]\n",
      " [   33    97]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14609\n",
      "           1       0.94      0.75      0.83       130\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.97      0.87      0.92     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.8313025124383439\n",
      "No Skill PR AUC: 0.845\n",
      "Logistic ROC AUC 0.873\n",
      "SpearmanrResult(correlation=0.8370306772389361, pvalue=0.0)\n",
      "0.002646041115408101\n",
      "0.002646041115408101\n",
      "0.051439684246776834\n",
      "0.6973304127592579\n",
      "0.6977142646524533\n"
     ]
    }
   ],
   "source": [
    "import warnings  \n",
    "from sklearn.tree import DecisionTreeClassifier\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(235)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_AsCpf1/AsCpf1_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 = DecisionTreeClassifier(class_weight={0:1, 1:4}, max_features=23, min_samples_leaf=15, max_depth=5, ccp_alpha=1e-4, random_state=38)\n",
    "clf.fit(data_train, target_train)\n",
    "Pred_labels = clf.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()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "global-professor",
   "metadata": {},
   "source": [
    "# MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "maritime-animation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14604     5]\n",
      " [   36    94]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14609\n",
      "           1       0.95      0.72      0.82       130\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.97      0.86      0.91     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.8195848264706488\n",
      "No Skill PR AUC: 0.838\n",
      "Logistic ROC AUC 0.861\n",
      "SpearmanrResult(correlation=0.8273183794529388, pvalue=0.0)\n",
      "0.0027817355315828754\n",
      "0.0027817355315828754\n",
      "0.05274216085431915\n",
      "0.6818088954648609\n",
      "0.6823149059852461\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(235)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_AsCpf1/AsCpf1_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=[32, 57, 39], 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=False)\n",
    "clf.fit(data_train, target_train)\n",
    "Pred_labels1 = clf.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels1))\n",
    "print(classification_report(target_test,Pred_labels1))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels1))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels1)\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_labels1)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)  \n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels1.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels1.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels1.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels1.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels1.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels1.round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defensive-frontier",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "revolutionary-conducting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14589    20]\n",
      " [   32    98]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14609\n",
      "           1       0.83      0.75      0.79       130\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.91      0.88      0.89     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.7885477868957389\n",
      "No Skill PR AUC: 0.793\n",
      "Logistic ROC AUC 0.876\n",
      "SpearmanrResult(correlation=0.7894882780660472, pvalue=0.0)\n",
      "0.0035280548205441345\n",
      "0.0035280548205441345\n",
      "0.05939743109381192\n",
      "0.5964405503456771\n",
      "0.5965163729418639\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(235)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_AsCpf1/AsCpf1_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 = LogisticRegression(class_weight={0:0.5, 1:2}, C=10, max_iter=100, solver='newton-cg')\n",
    "clf.fit(data_train, target_train)\n",
    "Pred_labels2 = clf.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels2))\n",
    "print(classification_report(target_test,Pred_labels2))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels2))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels2)\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_labels2)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)  \n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels2.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels2.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels2.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": 1,
   "id": "separate-chambers",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14588    21]\n",
      " [   22   108]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14609\n",
      "           1       0.84      0.83      0.83       130\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.92      0.91      0.92     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.8325052109472308\n",
      "No Skill PR AUC: 0.835\n",
      "Logistic ROC AUC 0.915\n",
      "SpearmanrResult(correlation=0.832511526735995, pvalue=0.0)\n",
      "0.0029174299477576497\n",
      "0.0029174299477576497\n",
      "0.0540132386342242\n",
      "0.6662873781704638\n",
      "0.6662879047162706\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 imblearn.under_sampling import NearMiss\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(235)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_AsCpF1/AsCpf1_Dataset_MM-Seq_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",
    "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",
    "clf2 = MLPClassifier(solver='adam', alpha=0.0002, activation= 'relu', hidden_layer_sizes=[10, 10, 10, 21, 13], learning_rate_init=0.001, random_state=52, 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=False)\n",
    "#MLPClassifier(solver='adam', alpha=0.002, activation= 'relu', hidden_layer_sizes=[150, 128, 109], learning_rate_init=0.0001, random_state=52, 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=False)\n",
    "#5-21, 6-13\n",
    "clf2.fit(data_train2, target_train2)\n",
    "\n",
    "Pred_labels2 = clf2.predict(data_test2)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test2,Pred_labels2))\n",
    "print(classification_report(target_test2,Pred_labels2))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test2,Pred_labels2))\n",
    "precision, recall, _ = precision_recall_curve(target_test2,Pred_labels2)\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_labels2)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)  \n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test2,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test2,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test2,Pred_labels2.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test2,Pred_labels2.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test2,Pred_labels2.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test2,Pred_labels2.round())) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "improved-passport",
   "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": "3bb0a5da3c7f401d9e37da019af58475",
       "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_test2, 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_train2, 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": 3,
   "id": "accompanied-professor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iE4HleJNAtuQelS8C40XkOeBrvG77PWg8MVRgGvB/InKz32raGThQVV+Nss4HgVtEZK5fVgpe9/QaVZ3d1DhVdbWfuL+K113/cZTxGNMqdEl3+MdJHeGkP5A/rxCAdoOyWPxVLqFgEqVB75YxecntWZtcxObfsKZxSdt147A557NudgGpHZNJ7xbfLdjGtIz4H1cYqanJw814k1DeFZFCYC7eZIoNylPVWRHH5+MlUa/UO+wOvEkCeSIyfiP1Poc3ieVjVa3tlvS7BvcHjsPrUs0D3sbrut5cF+KNqVygqrk1D7yWp5U0MAlFVVeo6uebOaHhSeD//FhPxRvf2NQvOv0XXsLzA7AQbzLPxrp+N8q/Z+OVeGM48/CS4nfwZmk3dHwYb1KRA0wRkSI/ljGbUedTeAntc36di4Eb8YYobGmcTwC74I0ZbWqLpjEx125QFu0GeS2mnbbPrtuE7jhMcfo1e52BoEOnHdpZUmjMNsJx3S3vejBtn4h8D7yvzXzz7ebWUJwi0h/vn5f+qrpkK4ViLyzTolzX5eUB4yhLWD/Wr5+7goPm/TmGURnTarVYs17YObvO+33AfT6umxDb/uht0yQichIwAa9b/hy8MaGbmuW91W0qThFJwBv/+fZWTAqNaXGO4zCocCGLMvpSmJRFz9Ll9EusP5LHGGM2jyWGpjEn4t1SJgjMA45X1bmxDalBjcYp3hTpr/DGHNpdeE2bk5PVl0OWfUlaqIywE2Tq2X9g0KZPM8Y0q7huINyAdSUb0zLshWVa3MTbp7Fw7BzaVRZQnpzCCT+fQnKWfX+xMQ1owa7kc+p1JY+N60zRWgyNMSZOjblxZ9ak5VC5rjMn3XAUiambdx9DY0xziOs8cAOWGBpjTBxLHuLdDMKSQmNMc7DE0BhjjDGmidrC9yNH2pKbIBtjjDHGmDbEEkNjjIlz47UbafeUk3JfBc/+VBnrcIzZxjj1HvHNupKNMSaOLZ6dzAdVvfjX+y9SnJTC1aVHsX9pHv336xrr0IwxcchaDI0xJo69WTaA/z33X9otz6b7olT++8qr/PrknFiHZcw2w633iHeWGBpjTBzrnFTCoqRBVAcScJ0AZVWdSeqYHOuwjDFxyhJDY4xpJQoXl7Bk0kqqSqqjPqdDcSllSRGjghyXX4/fuQWiM8Y0rG2NMbTE0BhjWoHl363itUM+5ZNzv+U/oybw+l+nEg55HVNh1+WlmWFu+CbEnHXhOucl5RRx+7EHUpiWQGFaIqXZpXw4uyoWl2CMaQPaRGIoIhNF5IaN7D9DRKZtYR2ficgt/nIfESkWkR5bUqYxxtR499XlvLLHDrwxciiBUJjCd+Yz7eiXALjk2XWc+VGYO39w2fnxCv771jpqvs60wknglVfv5eJZL3Hawrfov7yI3V+fysqStjDayZjWz8Wp84h3zTYrWUQmAnsCVUAIWADcoapvRhxzPXAHcI6qPl/v/BygLzBKVX+M2H4q8CrwlaqOaUpsqvoS8FJTzm2kvMVARnOV19aJyFnAlcB2eH8b3wO3qOoPMQ3MmFbktl5D6DdvJcf/MIs399yexZ2yOeWX7xk67jveXzAU2nvHlSclcuncAOk3zuSsO3bguB+/YeSyhQCkFhewS2g6vxQMZuoql8P6x/+HlDFm62ru29Xcrqp3iEgCcDXwmohsr6pzRCQAXACsAy4Enm/g/Fn+MT9GbLvA325akIgkqmqz9z+JyK3AFcDFwPtAKnApMFFEjlTVz5u7Tr/eFrkeYzYlv9zljTku3dPhyIFep0zBwiJW/LCaTjt14L35YV5dksAB7Yo5OH8hdzGE0JS1FAzsy2nf/sYXO/Xnqx36AXDPoUcw74cZDCzLY0X7LAC65RWR2y6Du0o68IewS1q9lsF2lUV0KC5jQFExeXNhxU9rGF+WTOrUGey4UwdGXyh8mhPm8i/C5JVDrwyoCkMXt4pLp8+iZFUxKYf04pgz+lP60TTe+3wlk3YfyRWHtGPHzpZomuaRq2vInV3I/aHOzHJSuWmvAMcO8l4veeUu108KMy/fRbo6dE2H7GSHU7dzSE1sjX+DrTGmpmuR+xiqarWIPAbcA+wIzAEOBXoCxwEfiMhwVf2t3qljgX+KyF9UtVhEBgAjgP8CozdRbScR+QAYAywCrlHV8QAicg5wg6oO8tcnAlOAfsAhwCrgalV919/vAP/AS2DS8JLY2t+8iPQDFgK9VXWp38U8GpgMnO8f9l9VvTninCOB+4A+wERgLrBLY62gIpIG3AacCGTjJcuXqeq8aK7BP+Y44EZgILACrwX3pcjnBHgCrzWvANhhY3GKyD3AUFU9NqKOA4B3gO6qWlLvGvoB1wPnqepr/uZS4Db/d/sfYKh/bA7e72hcQ8+xv+0CP9beeC3Sf1fVT/x9twD7Aj8DfwR+FpFCIFdVr4yI6U/AdcBgVbW+NtOsKkMu+74aYvoab/2u0XBJ5xLeO/5Lb0JJAO47Yk8KskI8e92N7HbVPRwwbQkH/baQw76Zhes4FKbWnVH8Zt/t2WvWInbKyaU4OZFgOERu+0xWZ6WwqhQ+7yOMWjGT5LD3f9DMrCEA5L46j+/eW0KoPER7N8zRyz6mc8VaZiw6m8M6HEnY/+tfVVpTUyKHfDefv/z4PxJfCjHrziEMmzOHs4A9O73Lodffy5wr0kkItK0PQbP1LZywjC8unwwu7JqazPsn78fxq1KYcx4Mah/gwk/CvDHH+wP9dFHN27TL8zMcvjzVvhO8pbXIGEMRScJLqqqAmrF9FwLjVfVD4FfgogZOXQ58DZzur58PjAMqoqj2POBhoB1wF/C2n1w05mzg33hJ16PA834yBnAm8BfgWKAbsAYv6diYfYHFQA/gGOA6EdkbQEQGAm8Bt/vxPejHuzFP4SVNe/gxTMZLqBOjuQYRORh4BrgK6OAf+6iIRF5HPz/ewcDuUcT5JHC4iHSP2HY+8HL9pNB3iP/zlQb2vQhs59e5SX5S+HfgDLxOteuBt0RkUMRh++IlwL3xEuongDNFJPKT9nzgaUsKTUtYWEBtUgjw/vwwK35YvX6WcRi2X7aGPRbNJTlUTXkwhYN+87qBe5es4I1Rwxi6bA3BUN0JJsmJRfzWpwv/nvAscx66jHl3XUGH0mK6psEvvXryQv9TmNB9P97ufRgTe43k3mP2orqkilB5CADXCbA01XvZFn05uzYprO/AxT+S6HrnDJuz/l6Ig9fk0mHhMortS1VMM1jyxYraG/5ll1XQf1U+LvDlYm/b3LyG/0AnLnEprmx9b91tbYxhcyeG14tIPrAUL6k6UVXn+ZM0jgKe9Y97Bu8DO7WBMp4CLvS7o8/x16Pxjqp+qqrVfquYAn/YyPGvqep3qhrGS3iy8RIkgLOAJ1R1iqpWAv8CcjdR/xxVfdyv/wfgF0D8facDk1X1FX//58C7jRUkIp382P+sqiv9GG4FugOjoryGK4GHVXWSqob9cZvj/GurUQX8Q1XLVLV0U3Gq6ny8xP1sP872wPE0/jvqDKzx469vuf+zS2PPQz1XArep6jT/ej4CvgROizhmkar+W1Ur/ev5Eljrx4iIDMP7nYyNss4mKyoqsuVtcLkdxfTOrF1lz+4OnXdqj5Ow/sOiIDWZaT36klZZyVGzlIqEIEmhCnZb+wtVwQCPHTaSUHD9W/Phs6ayMrMdx0//ieNm/gTAwLxc/v3uKywrhp/79WTeIIedSn6lf/UsPtulBzld2jPwDwNwaopxXbpUeBnrdtKNxEDDH66VgaTa5VBgfQwrM7LZZa9utEtxWsXzbMvxvdxl14616+UJQZZ0zCLouBzSz9t23rCG/wPZoUOYjCSnSfWa6DV3V/KdqnpHA9vPwxtb+IG/Pg64FziVDT+kx+N1Hd8I5KjqDBE5Poq6cxpY77WR41fULKhqiYgA1Lyl94osT1XDIrJoE/WvqLdeElFeT7zu7UiL8Fq2GtLf//mrH1eNxHrnbOwa+gP7i8jVEccHgUmR56tqZGtsNHE+AdwJ3I3XsjpLVac0ch2r8br4kxpIDmtmdK9q5Nz6+gP/EZH/i9iWgPdPSGSstVTVFZGn8FoJX/V/fqCqm0ryt1hmZqYtb4PLXTtkMuk0l6enh+mW7nDxzg7BQHsOf2E0y77OpfOIDuy6qpKflleQO+46xv0yjU8HDWD1Z2vonLOOp975L+clnc/MHj3IS88A12XE8oWMWjqP+dk9idSpuALNdXGTwtz1yUsEXZcehXlc+tMHfLzjDnTbrROHPb8Py75ZRXKXINVTyikf3IH2fz6EX/Ic/jU5TH4FpCZAfgXs1tWh9/MXMPfa50kvLSb75uNJLyph1axVLDt+DE/uk9Vqnmdbju/loadlkpiWQN6cAib16c6RGelcsatD32zvn5HLR6Zx0ECX9+a7rCkNU1Ll0CUNLt0lcbPr2jriv5UwUot/V7I/6eQ8vK7JpRGJThCvO3ls5PGqGhKRZ/ESwz9tRlX9Glj/aHPj9S2LLM8fc9i3iWXVlHdIvW19NnJ8TYIzWFVXN7HORcBYVb1vI8eE661HE+c7wCMish/e7/WJjZT/qf/zVLyu40hnAPPwxgoCFAHpEfvr3wpoEXCzqr6+kfrqXw94f1+3icgQvLGHZ2/kfGO2WN9sh9v3qTsOqvvITnQf2cnbD+xSs+OUHb0X3O3wvyHLOHLBt7z1wgNce9jZzOjamzndO/LaEUfzwOMPsV3uMn7pMIwhBQtZl9Se6hP2R7o5pFRUEHTXtwCmVVbyj5EBkhMceuzZhR57+o3yZw+rPWb7TvDikQ11GPWDKTfX2dKF6Jv1jYnWwGN6A72RRvYP6+gwrKNDG7mrXlxp8cQQOAyvxWkkXuJRY2dggojsqKrT653zEF7L1jebUc9xInIg3oSJU/C6DP/YxJhfBO4VkbeB6cA1eOP8mupV4EYROQV4E28s3HF4EyU2oKqrRORl4DERuUpVl4lIO2B/4FNVLY6izoeAsSLyA/AdXiK+I+CoqjY1TlWtEpGxeOMPBwMvNxaAqi70J6w8LCLleC3GqcAleK2NJ0WM9ZsCnC4iL/nH3FivuAeBW0RkLt641RRgN7yu6tkbiWG1iLzrX1sZ8HFjxxoTS3cccSz5H3Yj6LoMW1jMDgtnMvO2g7jrgESOyL6W6bkhssoq2ev3xVwwOpVj/zwAgH2WLGNq++HsnDeD8mAyn/QexeB2basFw5jWrC2MK4y0NRLDi/DG/9XvbswVke/9/ZdF7lDVPOCzzaznGbxb5LwLLMEb37iwaSHzAl4y+z5ekvI83ti6JvHHWZ6MN0v7Wbzk9UVg0EZOuwBv9uxEEekG5OMly59EWecn/oSN+/DuHxgGZgA3NUOcTwHXAs+rasEm4rheROYB/8RrvUvD6z4+qmZGse8GvOd5Bd4knnvx/qmoKecpEakEnsPrVq7CS1iv2Vj9vieAL/DundhQq6IxMXfiyV25KWMMfVYXsEdqBXf+tReZvb0xf1POCnLTNw6z1wU5/+xhHNZ/fSvKMRk5/NxhBFPb74TrBBiypJT/TCrn/J3SGqvKGGMa5bhu65vhsy0QkVeAIlW9MNaxbExDcYpIOl5yd7CqfreZ5Y3AS9KuUtUXmjPWjdTZH++2O/1VdcnWqJPaOXfGRG9tmUtVGLqlR98C8ca/3qTgacBZf85nZ4zklVs3NsTamG1OizXrVTiX1Hm/T3b/G9dNiFujxdAAInIMXtd4IXAk3u1UDo1pUA3YVJz+eMurgJmbmxQCqOovInI4cIiIZG+qxXFL+bPb/w68vRWTQmOapGPq5n+eZHy5lgKnU+26C9w24QW45Z91kkVjjImGJYZbz7543bMpeF2lF6vql7ENqUGNxikiXfAmi6wCTm5qBao6Ge++jC1KvJlOX+HFfFRL12dMLKRl153wvzozlcE/Kug82H1wI2cZY5pLWxtjaF3JxrQMe2GZreLDN97m+yeTScpLYFV2OumJudzz8Uvw+6MwuP7kfmO2WS2WvZU7f67zfp/iPhbXmaK1GBpjTBwLJycw6vIQHRKGEbzjdfaY9Ts8cbElhcZsNXGdB27AEkNjjGkD9j68Pxx+bazDMMbEOUsMjTHGGGOaqK2NMbRbihtjjDHGGMASQ2OMiXvl4SDfL3dZXWpznozZ+px6j/hmXcnGGBPH8qsTuWT+vpTMDZESdPnhjAR27hL/H07GmNiwFkNjjIljT+YMpQTvq/PKQw43jy+LcUTGbFtcnDqPeGeJoTHGxLHK5XXXk75o6lfEG2OMJYbGGBPXTvl2Ctkl5QD0XZXPYd9Op7oiFOOojDHxysYYGmNMHBu+dAl3vfI5eekpdCoqpW/xcsp/XU7G7r1jHZoxJg5ZYmiMMXFsaftsAmUus3p2JhxwGPb9bJzioliHZcw2oy2MK4xkieFGiMhE4DNVvaOR/WcA16rqzltQx2fAN6p6i4j0AWYCQ1R1+SZONcZsI8JVYYqWleCGXErSUyA9kZ6Z3ofRG7Ibi7OymdmzJwDTB2TxyfY9SakIsW5uId8/MYPSOavZ/4A0uv790FhehjEmDrT6xNBPzvYEqoAQsAC4Q1XfjDjmeuAO4BxVfb7e+TlAX2CUqv4Ysf1U4FXgK1Ud05TYVPUl4KWmnNtIeYuBjOYqr60TkbOAK4Ht8P42fgBuVdXv/P3FEYcn+z8rajaoqj3XplXQXJewCyMyqlnzWz7ZAzJI75pKRXmIsTfNhI8WklBeTTBcTWZVHm/suguJvdrx8F0DOH76t1x88vm1Zf3WvQ8fPTgLPluMm19JVTBASXIS//d2AgMLvmPQJXvSLR2GFK6GnFUgA5n1/TKuWtqNQGYqTx0aoFdm22oBMaZlta3XS6tPDH23q+odIpIAXA28JiLbq+ocEQkAFwDrgAuB5xs4f5Z/zI8R2y7wt5sWJCKJqlrVAuXeClwBXAy8D6QClwJfiMgxqvpJZOInIk8DCap6TnPHYrYNn+SEueGbMO2SHR4/OMCAdpv5YfDCl1Tf/S6rCxP5dbcD2PW2UXTeuQN/+bKaRyeHOOOb6fRblcfUft35eUAAN1BKWnkVV30wj0TXZUr/7gRclx0WJzIkt5DPO3diu0fLudztSd9VeeR07QDAsGVrqPpoPknVVfQqXc6KtB4khMOkVlaz6sMyzusQYpel8/nhkRtICoeoTknmvv2vYo+1OfzSrxu7rtiOV+dPpWBBMUNP78+w0/u3wLNpjGmt4iUxBEBVq0XkMeAeYEdgDnAo0BM4DvhARIar6m/1Th0L/FNE/qKqxSIyABgB/BcYvYlqO4nIB8AYYBFwjaqOBxCRc4AbVHWQvz4RmAL0Aw4BVgFXq+q7/n4H+AdeApOGl8TWfrqISD9gIdBbVZeKyC1+fJOBmiaB/6rqzRHnHAncB/QBJgJzgV0aawUVkTTgNuBEIBsvWb5MVedFcw3+MccBNwIDgRV4LbgvRT4nwBN4rXkFwA4bi1NE7gGGquqxEXUcALwDdFfVknrX0A+4HjhPVV/zN5cCt/m/2/8Agxu6fmOaoqzK5YR3w5RUAbic93GYL08NRl/AwpXwp/+QEArTHSgqhM8vhdO+OZz/TIWDf13A6NmLAei9rog5PToyu1dnAOZ278h3Q/vw4+BeAOwzaxHnfDWNs7/6hRu6HcCLo0fWJoXbLVvNpR//RELYxQ0ksCS9F44TIOBCSnWIkpQkMkvLmdprIDcdeip3j3+ZKRlD2XvOMgD6rC1kUed2/P7ZChJDYb69+Re67taRDkOymuupNKbNaWtjDOPqdjUikoSXVFUB0/zNFwLjVfVD4FfgogZOXQ58DZzur58PjCOiW3EjzgMeBtoBdwFv+4lJY84G/o2XdD0KPO8nYwBnAn8BjgW6AWuAfTdR/77AYqAHcAxwnYjsDSAiA4G3gNv9+B70492Yp4ChwB5+DJPxEurEaK5BRA4GngGuAjr4xz4qIpHX0c+PdzCwexRxPgkcLiLdI7adD7xcPyn0HeL/fKWBfS8Cg0QkpolhUVGRLbeh5bJq/KTQs6okFPW5ACVLV0EoXLueHCqnPK+CoqIi0hMhs7zuW1FmeWXt8rKOWUwZ0KN2vWY54EJqRSVze3Sq3VeQkURqZURZTt23+MTqEKXJ3kv9jZ32AGBRdtc6x2SVVpBYE6sLFXmVMX/+bdmWt3TZRC9eEsPrRSQfWIqXVJ2oqvNEpAdwFPCsf9wzwJkiktpAGU8BF/rd0ef469F4R1U/VdVqv1VMgT9s5PjXVPU7VQ3jJTzZrG+9Ogt4QlWnqGol8C8gdxP1z1HVx/36fwB+AcTfdzowWVVf8fd/DrzbWEEi0smP/c+qutKP4VagOzAqymu4EnhYVSepatgftznOv7YaVcA/VLVMVUs3FaeqzsdL3M/242wPHE/jv6POwBo//vpqJu10aex52BoyMzNtuQ0td0h1uHZ3r1UgOQi375MQ9bkA6fsMh1P2AqAikMiv7Ycj1wwnMzOTFw4PMGnH/qxLTwFgXtf2TO/t/fmmVlSyLDuNHnmFtWX1XZ0PwG+9OrO4czt65q2t3bfX4tmMXPtzbfuFA1QkeC2bLvCObEco6K0PWbmSBel9cAMJrMnw3jJzOmez3c4ZJLf3vkml9/7d6CodY/7827Itb+lyS2pr33wSL13JdzYyM/g8vLGFH/jr44B7gVPxuo8jjcfrOr4RyFHVGSJyfBR15zSw3msjx6+oWVDVEhEBqPnr7BVZnqqGRWTRJupfUW+9JKK8nnjd25EWAY3dwKxmsNCvflw1Euuds7Fr6A/sLyJXRxwfBCZFnq+qkU0g0cT5BHAncDdey+osVZ3SyHWsxuviT2ogOewRcYwxzeae/YJctZtLagK0S9nMN3/HgdeugX+vwSWRg1JTSO3ozYc6elCAA67PYtVlB1OxtoLBScmcW1xKZTBEeWk1i6oquaeonHWl5eDA/K7t+duZB1OQmsR/Dw4y6sh7eXXEnqRXVXD1Vx8QchPIv/54Vr+7nHUrQlQGXd7ZbRjT+3Th7I5l7NanmIz2yZxy/A50u/9ujigP88Z/5/M1Wex6aFdu2yeZ0IWHUp5fSXq3VBwn/j/ojDHRi5fEcAP+pJPz8Loml0YkOkG87uSxkcerakhEnsVLDP+0GVX1a2D9o82N17cssjx/zGHfJpZVU94h9bb12cjxNcnZYFVtauK0CBirqvdt5JhwvfVo4nwHeERE9sP7vT6xkfI/9X+eitd1HOkMYL6qztnI+cY0SfeMLUySenUipYHN6UkO/TsnQudEhgJeI71Hdm/H8SGX2f/LYcX3q5i/Yw+Kdu7O8UOCDGjnMJVk7hr/cm33T0FSkEFjBrDP30Yy9b+zee/NlXRPc7ju5GTG7NqxwbDOvWNHzo1YT0hNICM1bj8ejDFbIJ5f+YfhtTiNxEs8auwMTBCRHVV1er1zHsJr2fpmM+o5TkQOxJswcQpeN+4fmxjzi8C9IvI2MB24Bm+cX1O9CtwoIqcAb+KNRzwO+Lmhg1V1lYi8DDwmIlep6jIRaQfsD3yqqsUNnVfPQ8BYEfkB+A4vEd8RcFRVmxqnqlaJyFi88YeDgZcbC0BVF/oTVh4WkXK8FuNU4BK8xPC4KK7DmLgRCDpsf3p/tj+9PwfW2/fKiP0ZsWJ27XpxUirddvYmo+xyyVB2uWToVozUGBPv4mWMYUMuwhv/N0VVcyMeHwPf08AkFFXNU9XPVLV8M+p5Bu8WOQXATXjjG5v6LfUvAI/g3V5lJd44uK+bWBb+TOKT8cYJFgB/xUs+Nzap5gLgd2CiiBThJagn4w1BiqbOT/wy7sObPLMCL5lr9J6AmxHnU3izxf+nqgWbiON6v5x/+nHkAPsBB9bMGjdmm1CSwDu9jmBC9wNYktqDr3rsTrFr3b/GbC1tbYyh47pR5QMmTojIK0CRql4Y61g2pqE4RSQd7/Y4B9fcpDqO2QvLbBX/2nUCnYvKvBXX5athfXjxfdn4ScZse1osYyt0/lrn/T7L/XdcZ4fx3JVsABE5Bq9rvBA4Eu/+hK3ue682Fac/3vIqYGYbSAqN2WoyqiJvT+OwS4V9m6YxW1dc54EbsMQw/u2Ld7ueFLz7HV6sql/GNqQGNRqniHTB+6rDVXhdzsaYKA12lrLU7e7NfHZdTl89De+Wp8YYs/msK9mYlmEvLLNVTPnrQwSeXceqlM4MKlrAwBdOhRP2iHVYxrQ2LdasV+D8rc77fbZ7X1w3IVqLoTHGxLHlYwbSPTHIoWVpcNL5MHqHWIdkjIljlhgaY0ycW7F3Pzj66FiHYcw2qS3MRI4Uz7erMcYYY4wxzchaDI0xxhhjmqxttRhaYmiMMXGsy5SlZM9fC313hJ36xTocY0ycs8TQGGPi1TOfMepW76vD3dd+xZn1fzBwS75l0xizudraLShsjKExxsSpkn+9W7vsVFXz/QNN/oZNY4wBLDE0xpi4lVOZWGf9gYJuTJ5VHKNojNk2tbXvSrbE0Bhj4tSNB51MbkY2AG9vL+yxaA6TX58Z46iMMfHMxhgaY0ycCbsus9dCl9JCzjvxQlJC1Zw5dRJ//eYjfmIN3DQy1iEasw2J/1bCSJYYGmNMnKiasYQpt0zgqcT+jB2xL0ekZPL9gGHkpWWwuENnDpkzHXdV0YYnuq73XcrGGLMJlhgaY0wcGPtDCbn/+JyvBu7EsuyOpFWW8+WgHSlJSQVAew9i/NARTO41EHFdAgtXwjOfw3/GQ0kF3HQK3HhyjK/CmLanLYwrjGSJoTHGxEh12OXnFWH6zF1At37Z0K9Lnf3TVrkkB+HeH0M8Nz0RjjzD2+G3/iVVVdYeGwiHSa6oIKd9Z4qmL2fpEfdxxsmX8vvfjyejooxzvv+CFQ8s5kenPaO6wWO7lzFrURnddu1Jn/YJMHUBFJRCWjLs0h8S1388zFrrUhWGnTq3rQ9AY8yGHNdta3fgiV8iMhHYE6gCQsAC4A5VfdPffz1wB3COqj5f79wcoC8wSlV/jNh+KvAq8JWqjokihhTgeuB0oCdQCIwHblTVJSIy2l+vkQZUAtX++iRVPXwj5d8C3ACU+5tWAs8Dt6uq6x+zPXAbsD+QCiwEngEeUtWwf0zAj/MsoJsfw2zgBlX90j/mY2AnIB0oAF4H/qmqFZt6HpqBvbDMRlWHXQ5/I8Slt93LcTOUcDBAYNxVcNo+AFzxeYhHpm78z2i/udNpX1bCcTOUUYvnMnTNCqoCQZ488jguG33KRruP+65byaIOXUkMh3g993OOffDp9Tv3Hw4f3wSJCfxrcpjrJoUB+Ks43D8muMXXbkwMtNh/NWuc6+u8UDu5d8b1f1A2K7n1uV1VM4COwCvAayIyxE+ELgDWARc2cu4s/5hIF/jbN0lEgsCHwAnAGUAmsAeQBUwWkZ6qOklVM2oeeMnrxRHbGk0KI0z0z8304/sncK4fw07AZGA1MBxoB1wFXA08F1HG34E/AMeoaiZeUnw7UFbvmH6qmgUIsBtwczTPhTEt7dfVsPDnXI6boQAEQmF4+AMAKkMuj24iKQRIwOWNcQ9y9s9fM3TNCgASwyF2nv7bJscULurQFYCqQJBHi+vdFPvL3+CXhQA84P0vBsBDU1zC1phgTJtmiWErparVwGNAENgROBSvBe8sYC8RGd7AaWOBk0QkA0BEBgAjgDejrPZ0YDRwrKpOVtVqVV0InAoUA7c2+YIaoKquqn4BzAB28Tc/4O3SS1R1hapWquqnwJnAWSKyj3/cXsD7qjrLL6tYVSeo6g8R5f9Sr3UwDGzXnNfQmKKiIlu25Y0ud0uH4qwMCpNTa/fR30vWKkqL6ZlJgxx3faK2Z87vBBtI1BZ16LLBtjpcl8SqqvXVluXX3Z+cSHFWkrcve/3mPllQUrz+Pomxfg5t2ZajXTbRs67kVsTvSv5MVe8QkSTgL3itYNsD9wFBVT1GRKYBX6vq5RHn5uB10Z4MfKCqT4nIXXhdvWuAgzbVlSwiLwO9VXV0A/tuB/6kqj3rbZ+H1909NsprvAXYR1UP8ltB98drpbwQr6u3CLhIVZ9p4NwlwAuqer2I/A2vpfE+4BvgZ1UtaeCcx4Cz8Z6HPOAoVf0umli3kL2wzCZ9khPm83EzOfPDdxm6fXsS7z8L2mcAMGONy03fhimpcvkkx8XF4VT9msu//5gHxhxDSlUld374Mt1KCkgJVVMZCPJj70H83qUHofvP4pIfUhm5aA5r0zMpSElnl2ULmNazH32K87kgfxb9kqt5uN0O9B7akXt2LCXzlpdhzjLo1RGuORYO2xWARQUu/5wUpjIEd+wTYGjHuO4lM9su60qOkk0+aX2uF5Fr8MbMzQNOBEqBo/CSPvDG290qIteqalm9858CbhaR54BzgIOB46OsuzOwrJF9y4FNNENEbT8RycdrwVsB3KSqL4hIT7wW0mhiuN8/9w/AtUCaiEwALlPVJTUnqOqfReRSYAf/2KXNdA3GbLFD+gU45IbhcMOGHQA7dHJ481hvPF9BhcvERyZz5g67EXDg/95+hqTqalanZzErvRf3jjmGH/oNYV1qOsfP/JGx+2az9/YuBZ/BzjfeT3llmBX3XcjwU7rgvYSGAHBQZIXv/bPBGPtmO7x8lI0rNKYxNivZtLQ7VfWOyA0iciPe2MIP/E3jgHvxunjH1jt/PPBf4EYgR1VniEi0ieFqoHcj+3r4+5vDV6p6UAPb1+FNuunZwL6aGD4Hrxsa73kYByAiuwHPAi8B+0ae5B/7m4j8AryGN8HHmLiRnexw7DV7sKDU5Yxnd6bXbvuSVlnOB8/czUP7Hsl7O+xee2y3hBDgJZacNhxOe4R0vEHLxhizKTbGsJXzu1vPw5uEsVREcoGZeC1rF9U/XlVDeAnSjcCTm1ndBGCUiAysF0MCcAp1ZyM3O7/182u8lr06RGRfoFdjMajqFOBpvDGVjUkABm9xoMbESOc0h08u60jhFUHWXeTw6n4HkVm+vtPAccPMHLVrDCM0ZtvT1r4r2VoMW7/D8FrxRlK3i3VnYIKI7Kiq0+ud8xAwCW/s3eZ4GS8JfUdEzgOm+HXfB2QDt2xu8E3wV2CSiDyKd2uedXgTYp4DXlbVSQAicjXebOvvVLVARAbjTcyp2T8UGAp8htcVvzNwEy2c3BqzNWQmOdAxnYNn/8Jf9zuF9qVFFKSkccbPkyg+bUyswzPGxDFLDFu/i4B3/BaxSLki8r2//7LIHaqah5cQbRZVrRaRw/AmsbyK13VbiNeSODJy7F5LUdWpIrIH3n0MZwIpwCLgEbwZyzUK8VpFtxORZGAtXtJ3g7/fwRt7OBavdXUl8BbNPLPamFj6tudglmV3QJYtoHfeGnquW8WBu1hHkDFbV/y3EkayWcnGtAx7YZkWN+DOtSxMXn8/mQuyVvHkhT1iGJExrVaLZW+rnJvqvN93cW+L60zR/rU0xpg4NWxw3Zsd7rN31xhFYsy2y633iHfWlbwNEZE+eN2zDRmnqhc3Qx1nAE80svsiVX1pS+swxnheOiaRXZ7MY0llBscMCvKHYfa/vjFmy1hXsjEtw15YZqt4//33ATj66KNjHIkxrVqLde/mOjfXeb/v5t5qXcnGGGOMMSb+WVeyMcYYY0wTtYV7F0ayFkNjjIlzoXCsIzDGtBWWGBpjTJyqDrm8+HVfrvlsD47++xLWFlmGaMzW59R7xDdLDI0xJk69+spSppV1IzUUYlZJMk8/+nusQzLGxDkbY2iMMXHqpzdXMzo5HTcYpDoUoigvP9YhGbPNsTGGxhhjYq4orwpCDm4wCEBCMMiI8vIYR2WMiXeWGBpjTBzKX1TM0uy633yyrm92I0cbY1pKW/vmE0sMjTEmDrlBSArXm2wyNScmsRhj2g5LDI0xJs4UV4TJufxNuhWVEsZvpXBdeuTmxjgyY7Y9Lk6dR7yzxNAYY1qp8uJqyourqS4PUTI/n+rfV1Ly3TL+fdQnjPxqCl937cSk9ll82z6LeSnJrE1uxyf/nkXxgiJWrq2iaFkp4apN38LGrQxRvbIE+4pUY4zNSo4hEdkTuBnYE+93MRv4P1V9XkTGA6P9QxOAJKA04vTDVXVSA2UOAm4EDgLaAeuA34CnVPWtiOO6AzcBRwCdgLXAD8A9qjqlGS9zs4jILcA+qnpQM5ebA9ygquOas1xjGuP+nkvo/k+hXRrBG4/AyUqtu7+8itC/JsDidQT+vB+B3fsBkDenkN+enUvu70XMLUzCdaDcCdB1+RpCbpjEEhcpXMWa9AwKUpIBCAHL0lKY2rU/a/+3lEe+dUgIVHDitF/pUFDBk1cdw4isak78bga/FSYSAnK6t6ckHGDXvLU4c5cyZMnvhIbtwIhJp+AkWJuBMdGL/1bCSJYYxoiIHAK8B/wLOAMoA44CnhCRAap6eMSxZwJ3qGq/TZS5I/AN8BYwBlgAJPrLZ/jbEZEewI/AL3iJ4WwgFTgeOAGIWWJoTFvgVoWoOuBBWF7gbVi0loT/XVjnmNDf3yL8f18CEH77FxLn304oPZWP/jiJ8rUVrOzTFTfgfeAkuS6pFZW8v+sQ3h05FMd1uf9/b+K4Lq7jHTN09Wqu++QjQo7D6qTRXPzDd6RWVwMw+7VJ/O3ow0hfEKDDmnUAdJ+9jlmDezOnKhm6DmRdehZHTPuYxU8Np+8lw7fG02SMaYUsMYyd/wCvqOqtEdv+JyJpwNMi8pyq5mxmmQ8BP6nquRHbQsAE/1HjNqAEOF5Vq/xtxcCL0VQiIp2Bu4GD8Vol5wGnq+rvItIReBA4xD/8Y+AvqrrOPzcHeBI4EBgF5AAXqup3InIqcB0QEJFi//ydVHWBiIzGS6K3B/KAx4AHVNUVkTHAZ3jJ7114LaAfA+epapGIvA/0wXteHwe+U9Wa+FpEUVERmZmZtrytLueVrE8Kgepfl9a+2dYc4/62vHY/BWWUzF4K/ftQvrYCACfs4voNd47r4gIf7Dakdv0Z2ZfE6hCu45BeWcFNn3wMQNB1+cO0X2qTQoDtV64iJRQiqSpEpMyq9cesSe9Iu8p8copCreM5tGVbbsblltQWxhVGsv6CGBCRIcAgoKFuzZfx2qUP3swyU4H9gFeiOPwI4PWIpHBz6gngtXS2A3b3f54DFPmHvAS0B4b5j05smHD+CbgCyAY+BZ4HUNXX8BK7iaqa4T8WiMj2wEfAfUBn4EjgMuCPEWUG8ZLRnYEhwC5+Hajq0cBi4Hy/zBZNCoE6b0a2vO0tO12ycI7csXZ74nn7bHBM4Ow9wW/tc0b1J333QaR3S6XH3l0AaLc6j+TKCsoTElibnk4oECC71LtPYTDkklgV5uaXv+aRxz/mjue/ol1hZW0dxckpLGzfAYAw8MJuI6gMBKgOBgj7dVYkBFmQlVF7zo4rZpDTYRADzhnSKp5DW7bl5lw20bMWw9jo7P9cVn+HqlaKyBqgy2aW2QEvOaotU0RGABP91RRgO1Vd5Ne/Qd1REv/RSVVrmkR+9evrARwKDFHVPH/b1cBsEemuqiv8459Q1Rn+/qeBq0QkO6K8+v6Ml8i+66/PFpFHgbOAFyKO+4eqFgPFIvKOH6cxMZHwzsW4n8+GdmkERvXfYH/wrD1wdu4Fy/Jw9t8OJ8l7Oz706b1Y/t0qEp0Q2RNnMqdbMreu7Mbn23Vj9JJccjpnUVVezW7zltOxyEsUE0Ius7N60Y+VlKak8NaYUTi9szmnYhlVQzpz/KiBXJcZovfI/iwqClBVFaawdzuGryimV8f2VM5YQnbS9rT/49mkdEjeqs+TMfGurU3ZssQwNlb7P3vije+rJSJJeK1sq+ufFHHMGcATNeuqmoHXvRoCekVs/wVoJyK9gCWsHyG72q+7KfoBqxpJ4nr7PxdGbJsfsa8mMVwRsb/E/5kJNJYY9gcOEJETIrYF8K6pRkhVI5+zEr9MY2LCSQjiHLrDRo8J7NwLdu5Vd1tigF77dfNW9u3JTsCbtXu74lZUkyuPoyuScUmqfVF3LyykolsJI5ddzR6Bmq2DAa8ZHYBhA9mpTm0dvR9H1I3BGLPtssQwNubiTQz5A/B5vX2n4f0D8mljJ6vqS3hdtpHbSkXka//8pzdR/0fASSJyaxO6k3OALiKSpaqF9fbVJGr98MYdAgyot29TGrq3xiLgWVW9dDPijKZcY+KOk5xAt6mXsPOHOaw98W2CoUrasY5sClmQ0JVAoG2NdzKmtWtrYwwtMYwBf8LEZcA7IrIQbyJFGd7YuYfwbhmzcCNFNOZqYJKIPIs3Vm8hXvfy3vWOuxmYDLwhIv8A5uB1NR8D7KCqN2wsfOBnvIkclwFrgOHAGlVdLiKfAP8WkbPxWij/DYyP6EbelFygj4gkqWrNoKnHgK9EpGYSjYs3jrCzqn61GeUOjvJYY1o1JyFIj6EZhDKX0T9/Ue32JTsdwC4xjMsYE/9s8kmMqOp4vJm5++K1wq0BrgeuUdXrm1jmL3jj6hKBr/EmhMwHzsW7Fc0i/7hleBNHVgCfAIXALOA4InutGq4jDByNl8j+AuQDzwI1o9jP9Ov9Ha+bPB9vLGC0XsdrXcwVkXwR6a+qv+HdyucqP+ZVwFjWj9WMxh3AmSKS598j0pi45vTtQHZ5SZ1twcyUGEVjzLarrX3ziWN3ujemRdgLy7S4Be1uoF/BXBxChEhl8ZEHMuCDc2IdljGtUYtlbDnOv+q83/dz/xnX2aF1JRtjTJzq1C1IYWkngi6kVBfQc3h6rEMyxsQ5SwzNBup9HV8d/gxoY0wrkPHanwjJtSRWVxHq1J7Eqzfr9qfGmGYR1w2EG7DE0Gwg8uv4jDGtV2Dnvnwy9iTSlxcy+uI/QGbqpk8yxpiNsMTQGGPiWFVWCvlZKZYUGhMjbW1Auc1KNsYYY4wxgLUYGmOMMcY0WVu4RU0kazE0xpg451SHYXVj3yhpjDHRs8TQGGPiWPqyAg4673/Q5Vw4+BaoDsU6JGO2KW3tBteWGBpjTBwb+sIUUvLKvJXPfoWXvo5tQMaYuGaJoTHGxKHcEpcLPg5R9Xsxczp14/s+g3lpl72pHv9zrEMzZpvS1loMbfKJMcbEob8/sYRLH3yE1PIK+pauBXKRpQt4suex/DnWwRlj4pa1GBpjTLypDvHgjTcwcul8ilLSajcnhkOsLLe3dWO2JrfeI97ZO4gxxsSZlR9MZ2lWex7e+1A+G7QjVY73Vj67U3fKEhJwP9QYR2iMiVeO67aF/NaYVsdeWKZFfLc0zLHjSkgpKubHR66ne1F+7b6Q4xAGVl1wFD2fODdmMRrTCrXY4L+5zv113u8Hu9fE9UBDG2NojDExUF7tkpIQxedHKARriyjLzuStNxZy0YIuVCQlsffalXWSQoCg6xIEZi0t56ejnuL6oQdRlpjI6g4d2KNPIu8cUEEwM4Xk5CCVIUgMQsCJ688wY0wzs8SwFRGRicCeQBUQAhYAd6jqm/7+64E7gHNU9fl65+YAfYFRqvpjxPZTgVeBr1R1TBQxpADXA6cDPYFCYDxwo6ouEZHR/nqNNKASqPbXJ6nq4Rsp/xbgBqDc37QSeB64XVVd/5jtgduA/YFUYCHwDPCQqob9YwJ+nGcB3fwYZgM3qOqXItIFuB/YD+gI5Ppl3F1TjzGxsDDf5eA3QszPh9OGOrx0ZKDx5OzN73FPug+AipQ0pu1/PCs/e4Oi5BSuOvKPhByHYESvjwtU4JCzsopLTjqX6gTvLT6lsoKeb31FwhlPEgoEOPsPf2bc8L1onwLvHBdkdC9LDo1pqrYwEzmSjTFsfW5X1Qy8ZOYV4DURGeInQhcA64ALGzl3ln9MpAv87ZskIkHgQ+AE4AwgE9gDyAImi0hPVZ2kqhk1D7zk9eKIbY0mhREm+udm+vH9EzjXj2EnYDKwGhgOtAOuAq4Gnoso4+/AH4BjVDUTLym+HfBv6EYGMBMY49dzHHAR8JdongtjWsrdP4aZn+8tvzrb5fNFG/k/5aLHcfD6wNqVl3Lbp/9jRVY7dvrr/bw9Ys8NDneAJFxe33mP2qQQoDwxif+8/QzJoWrSqiq5983ncIF15XDtV3ZDbGPMepYYtlKqWg08BgSBHYFD8VrwzgL2EpHhDZw2FjhJRDIARGQAMAJ4M8pqTwdGA8eq6mRVrVbVhcCpQDFwa5MvqAGq6qrqF8AMYBd/8wPeLr1EVVeoaqWqfgqcCZwlIvv4x+0FvK+qs/yyilV1gqr+4K8vUNW7VXWhX89veC2nY5rzGhpTVFRky7bc4HIwXEWktESn8eOT63bqVAcCvLzLPqzOyKYyIZHrDj99g8GsqzKyuXjyZyRXR9TjOJQlJtWuliYl1y4nO+sTw9bw/NiyLbfEcktqa/cxtMSwlRKRJOBSvG7laXithONV9UPgV7zWr/qWA1/jJXgA5wPjgIooqz0CmKyq8yI3qmoV8BoQTWtg1EQkICIH4rUM/iQiqXiJ27j6x6rqRGBpRAxfA+eLyD9FZLSIpG+qLr/sac12ARuRmZlpy7bc4PLt+yVzaD+Hnhlwwx4Oe/d0Gj/+7X/gpibhAr936s45J19CYnV17XGP73EIH203gqLEZPJS0rhr/+PIqCync3Ehb4y9n35rVzJgTS57L5jFJSecz9yOXZnTuTs3nH8pPTNAusLjhyZtWK8t23IbWzbRszGGrc/1InIN3pi5ecCJQClwFHCyf8wzwK0icq2qltU7/yngZhF5DjgHOBg4Psq6OwPLGtm3HOgS7UVswn4ikg+EgRXATar6goj0xGshjSaG+/1z/wBcC6SJyATgMlVd0sC5DwDt/fOMiZmOqQ4TTgpGd/DIwTilrwKwHfBGRRUXXRSu3X37x69y5O+/ABCudqgKBHh29/2Zce2ZPNF9GUf9axxf9hrCw2OOoFvHBLo9sw+ZSQ4vN/M1GbMta2uD1i0xbH3uVNU7IjeIyI14Yws/8DeNA+7F6+IdW+/88cB/gRuBHFWdISLRJoargd6N7Ovh728OX6nqQQ1sX4c36abnRmL4HLxuaLznYRyAiOwGPAu8BOwbeZKIPIDX0nigqhY0xwUYExPJifz3zp0I/UvpsHAZsmR+7a6A69KtqICf+gziyKFJMGQwvPV39sebxWWMMdGwruRWzu8CPQ9vEsZSEcnFm1QRpIHuZFUN4SVINwJPbmZ1E4BRIjKwXgwJwCnUnY3c7PzWz6/xWgHrEJF9gV6NxaCqU4Cn8cZU1pwTEJGngEOA/VR1aQuEbcxWFejZgTPPHcqBc6czdNUy8vxvPvls0HCOmD2Vq7/+gO06RNkiaYzZYm1tjKG1GLZ+h+G14o2kbhfrzsAEEdlRVafXO+chYBLwzWbW9TJeEvqOiJwHTPHrvg/IBm7Z3OCb4K/AJBF5FO/WPOvwJsQ8B7ysqpMARORqvNnW36lqgYgMxpuYU7M/AXgRGAqMUdU1WyF2Y7aKMbu1Y/dzLqbX7/OZ2bk745+5m30XzCQpHOanDoPZvVP8fzgZY2LDEsPW7yLgHb9FLFKuiHzv778scoeq5gGfbW5FqlotIofh3WfwVbyu20K8lsSRjYzda1aqOlVE9sC7j+FMIAVYBDyCN06wRiFeq+h2IpIMrMVrTbzB3783cBrexJscEak5b6P3WTQmXrx1WVeu+rITB42dwID89f/3VI8cEsOojNn2tIVWwkj2lXjGtAx7YZmtQkfegPw0s3a9+KELyLjS/vcxpp4Wy95mOg/Veb/f3r0qrjNFG2NojDFxLO/Pwylq792tqXr7PmScve8mzjDGNCe33iPeWVfyNkRE+uB1zzZknKpe3Ax1nAE80cjui1T1pS2twxizXnnHdL5+6gSOHLEnCX06QaK9rRtjms7eQbYhqroY76viWrKOl/BuGWOM2UrCSQkwsFuswzBmm9TWxhhaV7IxxhhjjAGsxdAYY4wxpsmsxdAYY4wxxrRJlhgaY0ycqi6uoNO1S+h2Xg5L938et7wq1iEZs81pa7OSLTE0xpg49cUfJ/J5z134eOfdeTncj9UPTI51SMaYOGeJoTHGxKkfi9NxHYcwUJGUzHMzbdi4MVtbW/uuZEsMjTEmTi1LS6Fbbh5D5i0ns7CU35IzYx2SMSbO2b+XxhgTh8L5JZzyw7dkrEoGoN+i1ZDhAjvENjBjtjFtoZUwkrUYGmNMHPruL+/TPa+gdj3ouvTPL4xhRMaYtsBaDLeAiOQAN6jquI0cMwO4TVVf22qBGWPavJlzyhncIchFRx3BinYZtKuu5p/f/xDrsIzZ5rSFmciRLDFsJiLSD1gI9FbVpTXbVXWr9Os0Vn+8EREXKANvPD0wFbhGVX+pd8w6YKCq5vvbegFLgP6qmhNxbB+85+VrVd1/61yFMS0nVFjJspmFvNl1FyaOPJrK5ERwHIoTEvjXASM58NcCttspO9ZhGmPilCWGpjU6RFW/EZFM4EngHaBfvWNc4Abgmk2UdT6QD4wRkSGqOqd5QzVm49yvZ4MuhMN2wtm+Z5194aIKyl/4BScziZQzdsYJBsj7dhU5k9fx6ZBe9B2UxpAJM0j71ytklq3j/444iYLVGYQrQizo3wNcSKisptpPDo/6dRlvf1NCh1V5FGSnkHzmDlx6aU9WvZnDqq9zKVuZT9GArgw4aQADd29XG0dVeYjJd/xK4XsL6Z9aycB3jiGpe1qD11M2v5B17y0ibYf2tD+kV0s+dcbEhbY2xnCbSgz9rt+ngQOB3fFaks7AG619O9AZeB24WFWr/damB4B98BKR94G/qmpRA8VP83/+7rdo3aOqt0d2N4vIGOAzv867gE7Ax8B5NWWKyBDgKWAXP75ngYdUdVN/eY3V3xG4FzgESAG+BC5X1ZVNfE76+cdcAFwLdAUmAheo6qpNxIiInAdc55f7LuAA1ap6Tv1jVbVIRMYBp4lIJ1VdE7H7duBuEXk0soWwXl1B4DzgX8DZwIVsOpE0ptm4H02Dox4A14Wb38adejvOoK61+/MPHkv1ZK+Bv/r7JVSetDvfH/kFl15+GIvDKezwaS7PvfoO262azQ2Hnso9MgaAvivzefTRj/jbyfuSnJzA6nAKvXPzOG7CDKqDAebu1INeS1ax9ulfeOvtOWROWkpVYpCZI/tSlV/J5J9nc/TfBrLzIZ0BeO2v01kyqxQ6dSU3N5/AQf9juxnnbHA9lStKmTbqXarXVgAwZNwYupwxqGWfRGPMVrUtTj45G/gz0B4vmXob2B/YGdgROAY4VURSgC+AmUB/YHugF/BwI+Xu7P/cTlUzVPX2Ro4L4iVpOwND8BLAKwBEJAEv+ZyGl3Adj5eARWOD+kXEwWttc4HhQF+gCHi53rlRPSf1zjkL2Bfojdft2+g4yxoisi/wqH9NHYCPgFM2cnw7P7ZVeK1+kaYAbwJ3b6TKo4AuwIt4CfbZIpK8qTiNaTaf/uYlhQDF5fDt+gbrcH5ZbVIIUPHxPNZ+voLVWaks7up1BQ9YmUdqdRkAH2+3c+2xi7q2Y137dEaXlbNnYQnHrFjLaVMXApAQCtMjZx0JYZfEimr4bS0ApZnJVCWtbwtY4I3CIFQdZsmsktrt6zpkULm0of99oXjKmtqkECDv47gdtWJMM3LqPeLbtpgYPqmqs1S1Ci9BGgBcr6olqroYr/VL8JIKR1VvUtUyVc0DbgTO8FuitsQ/VLXYb7V7x68PYA+8LtO/+3UuAB7cgnp28x+XqmqBqpbitfId4I/JqxHtcxLpVlXNVdVC4G/AwSLSYxPxnAW8rqpfqGq1qr4CNPRVDeNFpBDIw3tOjlPV6gaOux44RkRGNlLfRcCH/vP8IpAFnLCJGJtFUVGRLdsyjBlau05qEowaWHuMk52Cs3OX2t3OPr1ov29XOhWU0mONd8ykob2ZkzGE0oQUxsyfWXtsz9WFuMEgwcD6D6EJew6hIDUJgKSqKkIBh/K0ZJJ2aO9VX1xBQtX6l1HfEVkAlJaV0HNYeu329nnF9DqyQ4PXlb5LRwLZibXbU/Zs3zLPmy3bcjMvm+htU13JvhURy6VASFVX19uWiddK2EdE8uud7wLdgGVNrL9+fSV+fQA9gVWqWhaxf1ET6wHvGpKBlSJ18rpyoA9Q8+9+tM9JpJwGlnsByzcST09A621r6PoO98cYDgY+wGvt/L7+Qaq6SEQeAf4NnB65T0T6AofitbqiqmtE5D28ZPGVjcTYLDIzM23ZluHY3XAnXAOaA0fshDO0R+0LyXEcOn55PmVPTyGQmUTK+bvhJATZ66MDeXPycib070PfIRkccubxfH1FN7pUwJiqPLKnr2H0zKUMrpjBd+52BBzv//tFmWl8v2N39p++hMo+qZQM64Kc1I+Rx3Rl5WsLWfnFcjqtyyO/X1cGnDqIoft0qI3z1HuH88vLC3C/mcfOJ6SReukR4DgbXFdyz3RGfH8Ma9/KIW14Bzoe27d1PM+2bMubWG5JNsZw27EImLMZs4rDzVDnMqCziKRGJId9tqD+RXiJZwdVbY74IvUD5kcsw/pEszHL8LqzI/UBFjR0sKrOFZGLgfdF5ENVbSjpvAuYh58ARjgfr0X8aRGpufY0IFNEtlPV3zcRqzHNwjl0Jzh0pwb3Bdqnkv63feps63hgdzoe2J09ardkc9hHh3EYXtN8WWlH8vIGUjBiPClFi/iu386sS0pkUXoqp6YVcviiE0jsWHfiSK+zB9Hr7MbHAianJzDqgiFwwZBNXk/asPakXd9+k8cZY+LTttiVHK0PgCQRuU5EMkXEEZGeIlI/AamxGi85G7wFdf4ALAb+JSIpItIfuCrKcxuqX/HGDP6fPwkFEeksIqdtQYw1bhSRriKSBdwDfNZI4hbpReAkEdlfRIIicipEfP41QFW/xOtuvqmR/QXAbZH7/bGa5+GNP9wJGOE/hgCz8SahGBOXUtMC9OiZRILjcO13bxNyAMdhZF4RpcG0DZJCY0zLcus94p0lho3wx+MdgDfpZDZQAHyOl2A0dHwZ3hjEV0QkX0Sub0Kd1XgTPXbFS/TewUumKqM4d4P6/VbCY/FGw04RkSK85HPM5sbWgHHAJLx7ByYBf4wixq+AK/EmguThjeN8B+9+hRtzM3CeiDTW5PG4X16No/Em0jzoj4OsfeCN2bRJKCbuVf/pGEqdJO6f8BQjVi0hJz2FeQPrN8gbY8zmcVy3LeS3bZeIXIR3i5xN9/FsBc19I20R+R54X1Xv2tKyWhl7YZkW5bouFx00maf29mYrO67LucsW8syz9l3JxjSgxQYC/uQ8Xuf9fnf34rgedGhjDFsZEdkHbzLIArxbxVxLFLeCiRcichIwAa8V9By82c5nxTImY+KR4zh0qqjgtFkLWJidwZC8QgYlb7JzwRhjNsoSw9anN94tYzrhdSe/jneDZkSkuJFzJqnq4VsnvMb5NwSf2cjucap6MXAi3g21g/iTRlR17lYK0Zg2pWN1Fdmr1zFi9ToAOvVPjXFExmx72lr3kHUlG9My7IVlWtxnl//E75PWUpyVRmJVNSc/tBO9R3WKdVjGtEYt1r37Y72u5JFx3pVsk0+MMSZOHfDAbnTcsZxeyWs56bahlhQaEwNhnDqPeGddycYYE6cCiQHST/G+iKnPwZv64iFjjNk0SwyNMcYYY5qorX3ziXUlG2OMMcYYwBJDY4yJa+VFARb+mMlvk9bFOhRjtkn2zSfGGGNahdVLSqn8awUD71/G1DMm8fELW3zPeWPMNs4SQ2OMiVPvn/4tXXOLSC6vpvuSfGY+NT/WIRmzzXFx6jzinSWGxhgTp0pX1f2a8fKKUIwiMca0FZYYGmNMnBpQsYYUygHIoIT0xLIYR2TMtqettRja7WqMMSZO9SuaxzBmESIIBJlRnhHrkIwxcc5aDI0xJg65YZcE8nFwSaCaBCr4uU+HWIdlzDanrc1KthZD0yqJyD+AvwDpwP6q+lOMQzKmVSmZmc/SrI4MyVsOQMhx+LVbv9gGZYyJe47rtoX81mxtIlIcsZrs/6wdCa+qGf5x1wN3AOeo6vNRlt0LWAwMV9WZzRPxVmcvLNPsCkrDfK7F/OPdchZ1yiK7rJgvH7+NzMIwbw7Zh8Vdsjh+WBW9r9yf/oOyYh2uMa1Jiw3++9p5ps77/b7ueXE90NBaDE2T1CR+ACLyNJCgqudEHiMiAeACYB1wIRBVYgj0A8IbSwpFJFFVqzYzbGNapdKCKt6/dx7rlpWz29HdGHli99p9RWsq+d+105m20mV8v94kuLC0eyYd15WRUO2w18V30aGojGC1d/x2373FgOevZkEwkeWZHbju4HPYflQXHr8wm+oZKyk8/13cogpm/eVgzmcQK0uhXTL8bfcAf96l7uiiJz4t5Y0fyhnULcg9Z2SSldbw6KNPplXwwAclZKc53H1GJv27rP9o+e73Su58q5ikBIc7Tstgh96Jzf8EGmOajSWGpiUdCvQEjgM+EJHhqvrbxk4QkVOBsUDQb5VcqaoDRSQHeBbYH9gdOF9E3gCuBc4BugAzgCtVVf2yEoF7gDOBMPAgXqJ6h6qObc4LNWZLfPXcEuZNzgfg0//m0G+XLLoMSAfg4/8sZGVOOZMH9MENBOibX8i6zGTSyr1MsFNeGYt7ZJJaHqLLulKmd+nDxT9/DMCA/JVcM+kNrk47nw9/LmfPS96h+kfvJtg9//w/Ftz6d6oSguRXwGWfhzmor8OQDl5jx7ScKu57rwSAhatC9OpYyvUnbDi5pbg8zFVjC6n0E9MbXi3mpSvaAeC6Lpc9U0hhmdegcs2LRYy/zsZBmralLcxEjmSTT0xLuhAYr6ofAr8CF23qBFV9DTgcCKlqhqoOjNh9AXA1kAm8C9wKHAscBnTESxwniEh7//h/AEcBewH98Voi+275ZW1aUVGRLdty1MsVJdVEylu9/piSggrcQICUkHePwvJgkGB4fc+VAwRch6LMZCoTAuy6YmGdsjIrvVvY5K4twy0or92eWlVFYnj9fQ9dYEV+Se36qnXrlwGK/OSufvyV1dQmhTXH1RwTCkNpxfpYC0tDdc61ZVveWssmejbG0GyxhrqSRaQHsAg4WVXfEZEr8BK5Hqq60ZuticgY4DNVTYjYlgM8q6q3+esOUAgcqapfRxw3HbhHVceJyFzgblV9xt+XDuQBF26FFkN7YZmorVpYyst/n0nJuiq2H9OR464fjON4rRDLZhXx2uW/sDohhR+6dmJtagKJDqxLSiUYdkmtrGKPOcv4YVgP9lg2n91WzKRX/lqOmjuFvOR0zjvmclYPHcjnN3XA/XA2Baf9DypC/HTRARw1cJ/aGM7a3uH5I4K166Gwy6VPF/LZ9Eq6tQvwwmXZDOjacCfTQx+W8OiEUtKS4NHzs9l3WFLtvrETS7nrrRKCQbj/zEyO3C2lhZ5FYzaqxZr1JjrP1nm/H+P+Ka6bEK0r2bSU8/DGFn7gr48D7gVquoqbIidiuROQAbwvIpEvykSgl7/cK/IcVS0RkVVNrNuYFtOlfxpXvLIbFSXVpGbVHYPXc1gmV47fm8JVFSSVljN54hqybnuW6nCIKeG92S43H4ATv57Ff47ehYykrgz65xiWjv4z2Z1TeagqgT6d/Lf6Y4fRed11uFUhjsxKoaDCBdel2nXokFr3sywYcHj8wmzyS8JkpDgkBBv/rLvqyHTOOyCVpASH5MS6x50zJo2T9kghGHBITYrrz0tjtgmWGJpm5086OQ9oBywVkZpdQbzu5LFNLDocsbwGKAEO2sitbJbhdR/XxJUOdG5i3ca0qEDQ2SAprBFMDNC+ZyqQygGD28MF/+KZL4vpeeR7tcckhF0O/30xZ846vs65mfXKclITcVK9erKSHTbVkNIuPboRR5mpjR+XkWKjlkzbZWMMjdm0w4DeeGP7RkQ8jgL2EJEdt7QCVXWBh4H7RWQwgIhkiMihfjc2wIvA30RkoIik4rVY2t+8aROqkhIp6pRG2P9Mqg4GCKXb//rGmC1j7yKmJVwEvKOqU+ptzxWR7/39lzVDPTcDVwDv+vc+LAF+AC739/8L6OBvC+HNSl7UDPUaE3OHDQnyyJBO9OqeTXphOYXt0/h6cPfaP35jzNbR1gaU2+QTs00RkXlsndvV2AvLtLgB16wklJxEUXIi6ZXVVAQcVt2WHeuwjGmNWqy/9wvnuTrv9we458Z137J1qxljTJwasmY1i9tnkpeWwtJ2GXRyK2MdkjHbHBenziPeWVey2epEZDQwvpHdd6nqXVszHmPiVXYwXGd9h3bWUG2M2TLWlWxMy7AXlmlxn2sxf3qjlMWdOtCpuITPL0hnp572/74xDWixprzPnOfrvN8f5J4d182G9g5ijDFx6kDJ4O+/T2ZOQTZ/OWcEfTvZW7oxZsvYu4gxxsSx3lml9M4qpW8n2fTBxphmF970IXHFJp8YY4wxxhjAWgyNMcYYY5rMDcT1kMINWGJojDFxau66MGfMOYCycCJnBKsZe4S9pRtjtoy9ixhjTJw65NEC7n7nY3oWFPLE6L34oP92HDWs4e9bNsa0DLdtNRhaYmiMMfHq1f8+yKjc3wgTYK+c33m4y4Ucde92sQ7LGBPHLDE0xpg4tG5ZITuuncPD+xyOi8O5k79BCtbGOixjtjk2xtAYY0zMZWUmcNRZ1/Dx0BEAfDBMOD8Yim1Qxpi4Z7erMcaYOBTMTOXLQcNr1ycO3B631BJDY7Y2N1D3Ee/awCUYY8y2p6QkRJ91xbXrqWVVfFIYw4CMMW2CdSWbVkdEDgMeBboCN6vqAzEOyZhW5/6rZnPmomKm9uwIOPRYV8zOC2cSrtyVQJK9tRuztbhBG2NoDAAicj1wB3COqj7fwP69gW+Asap67mYU/X/AA6r6WPNEakzThMIu3y6DDikwvHPLvfmXVrj8MLeSojIXGZhIzw5BACqqXb5b7tIr02FFkcv0nwvJ+Xwxv60LctyUuazp1Y3dl64hPymRnMwMpvcZzkN7vUvxoK6s3WkAO/cIcuyJnemYGcB1XQq+XUUgPYFFvxWx6Kc8Ug/qSeKgdnTKCrB9r837OFhXHGb2smq265FAx0zrfDKmrbDE0DSJiASAC4B1wIXABokhcJG//xQRuUpVC6IsfgDw60bqTlTVqs0M2ZjN4rouJ7wb5r35Lg7w2EEBLh7R/AlQSYXLSffnMTfXGx+YGITnL8tmRP9EDnw9xLfLwHFdei/P58n/vcHJy5fjAovohials7R7Oz7s14vKYBC6dGT7tCRmdu4OK+Cj5S7fvP4rtz6/IwV/+Y7ccQtY0C2bAbkFpADFT87k1tP3pTAjhb8dk85FB6dFFfPStSFO/Hcea4tc2mc4vH51e/p1Djb7c2NMPAi3slnJjuMcDJwGdHFd92jHcQTIcl33i2jOt8TQNNWhQE/gOOADERmuqr/V7BSR9sDJwHl4LYB/xOsebpSI9ADmAEHgExEJA7sC1wGJQBVwDPAacImIHAfcCAwEVgB3qOpLEeX9Cbge6Ay8CzhAtaqes2WXbrYFy4rhvfkuAC7wxK/hFkkMpyyoqk0KAapC8NbkCgIZiXy7zNuWVBli+xWr2WX5csD7Q+5IAUOmr2BOx2wvKfTN7tK1dtl1HFLyyvnkhxL6jlsAQHFqUu3+9Ipq+qwp5LeMFF75pizqxHDCLxWsLfKem7xilwlTK7j4kOjONca0HMdxLgeuBJ4GTvI3l+F9Du8VTRnW/m+a6kJgvKp+iNe6d1G9/WcBxcAbwEv+8RulqstVNcNfPURVM1R1jr9+MjAeL8n7q4gcDDwDXAV0AM4GHhWRfQFEZDTwH+Bif/+nwKlNu9TNV1RUZMtxvtwxBTqmuLXbB2RUt0hdfToGSKj3Ttw9u5pemZDm/+teHQywMjOdksT132pSgZfgpRaUEQyHa7enVlWuL8h1aVdcTt/eSST1TAUgLyNlff0pSeR0yQagX5dg1DEP6Fq3dbD/Zpxry7Yci+WW1MpmJV8FHOS67t1AzRvDbCDqO987rutu+ihjIvgte4uAk1X1HRG5ArgV6KGqZf4xM4BPVfUqEdkJmAbsparfR1G+C4xW1W/89bFAH1U9IOKYD4AfVfW2iG2PAKmqer6IPAWkqOofI/Z/C8zdSi2G9sJqA6audLnvpzCdUuG2vQO0S2mZLqOJMyp47JNSCktdDhuRxGWHpZMQdPhqict/fwmTneSydEUV7T6bw7E//kJaQRWhghQKO6TTbUUha7JS0cE9eHX/oZz8yzSWtOtIXkY2u69exy4XDeC0o9tTMruAhXf8SigjkY97d2P1lDyWD+1CqHMag7sncNWR6Zs1VvDVb8v4ZnYlew5J4ozRqS3yvBjTjFqsv/e97JfqvN8fU3BGzPqWHcdZBXR3XTfkOM4613U7OI6TAix0Xbd7NGVYV7JpivPwxg5+4K+PA+7Fa5Eb67fWbQ+cDqCqv4qI4rUqbjIxbEROvfX+wP4icnXEtiAwyV/uBWi9cxY2sW6zjdqlq8PLR7X82LkxOyQzZofkDbbv19thv9419SfAOTsDO1M4ZS1jj/uO4bmrCROkc2EZh0+ZT/vSMq7+5UDSkmoSvP61ZaUPzWb4uNEA7AxAvy2K+bS9Uzltb0sIjWll33zyNfAP4M6IbVcAX0ZbgCWGZrP4k07OA9oBS0WkZlcQL/Eby/pu408i9mcCO/iTUPKbUHW43voivNnO9zVy/DI2/OTrB8xrQt3GtCpZu3VkwBm9WPp8KT1yy2u3hxICEUmhMWYbdDnwvuM4FwCZjuP8DhQBR0VbgCWGZnMdBvQGRuIlXzV2BiaIyH54A14vBd6K2J8MTMWbhPJIM8TxEF7r5A/Ad3iJ6Y6Ao6oKvOjHMxb4Cm+G1igsMTRtxBF/HcL508qRX9cwZPk6ctulM/vQwbEOy5htjtuKGgxd113hOM7ueJ/RfYAlwI+u69ZvXGmUJYZmc10EvKOqU+ptzxWR74GPgDzgaVWtjDxARB73z9/ixFBVPxGRC4D78AbVhoEZwE3+/q9E5HK8mVkdgffwZjMb0yY4ndIoTc3ggeMHkl5eRWlyAn9avgzvs8AYs61yvckjk/3HZrPJJ2abISJPAwk2+cS0BaElBfz7xJ+Z370Duemp7P77cgZ1cDnt0/1jHZoxrVGLteu93emVOu/3x685PZaTT5bQyOeP67pR/ddoLYbGGBOHFlUmUpmZTJ+SUvqUlJKQHiRjh8xYh2WMia0z6613x7uv4avRFmCJodnqRKS4kV2TVPXwrRqMMXEqo1MyKZUVVCR79yUMhVwOuH5ojKMyZtsTbl1jDL+qv81xnInABODhaMqwxNBsdRE3sd7a9Z4fi3qNaQldsoPIyCRWTFhKRUKQvU/tSVrnlE2faIzZ1lQQee+qTbDE0Bhj4tSYf+/Nh3u+g+s4DD5xVKzDMWab1JruY+g4zm31NqUBR+B9c1hULDE0xpg4Fk5u+RtwG2PiRu966yXAA3i3cIuKJYbGGGOMMU3Uyu5jeO6WlmGJoTHGxLHyiiAFhSkUl4TISLfWQ2O2NY7jHBDNca7rfhHNcZYYGmNMnJoxYQUfvDWY8qQkvvt2Mffe25tOHe1t3ZityXVi3mT4TBTHuMCAaAqzdxBjjIlTn946g/LtvK/BKwkFmPhZHied2jnGURljtibXdaOecRwN+7Z1Y4yJUz1Xra2zHvh5cYwiMWbbFXbqPuKdtRgaY0yc6lSUjxMO4wYC4LpULi6MdUjGmBhyHCcLuAXYD+hExFcBRvuVeNZiaIwxcWpup+5eUgjgOOQG0mIbkDHbIDfg1HnE2GPArsBtQAfgcmAx8GC0BViLoTHGxKHyeXmsTkkG1wXHwQmH6RsuiHVYxpjYOgQY5rruWsdxQq7rvus4jgLvE2VyaC2GxhgTZ8Ihl/+eN5mbjtyHbzplkpGfT8/c1ZSlpsc6NGO2Oa5T9xFjAaDmP8Rix3GygRXAoGgLsBZDEzdEJAnv7u2HACFV7RTjkIyJCX1mIdMD2fRZU8Drz79Fu7Iy8tLTeHn0SP4Q6+CMMbE0DW984efAJLyu5WJgTrQFWGJoNouITMT7o9tPVb+O2D4PuENVx7Zg9ScBI4GeqloazQkisjfwDTBWVbf4jvDG1Fg9p5ApYxeSkp3IHpcMJiUrsVnKnaylfPtDCX37JLLdoCTeeHw5pWsqWdMxmfTcYtYlpXPQ99PZo7qKSydOIZsyANqXlDJqbg5P7DCXXWetZm7PXvy+Q08S3VKumPgcKaEqys84hKzn/8RX86r4zzcV9OsQ4NbDUklN8po5qqvCvP/4UuZOKaRb/1RO/mtfUjPsY8KYjWkF9zGMdAHrJ5xcCdwFtAPOirYAe8WbplgL3C8io1TV3Yr1DgDmR5sU+i4C1gGniMhVqmqDsMwWC1WGeffSKZTlVQJQuqaCI+7bZYvLzVlcyYP/WY3rwmSFdhXldCoqJRHoXlDOknbZ9F25lozScrKKKqn/cbQ6M41X996N7HXf8P6YXQkHvdFCHw/bh5OnfUziCx+x+DDhiKm9KPVCp6La5eETvC7oz1/ORT/2boFTsKaK1+7N4Zzbou6BMsbE3iLXdUMAruuuAs7f3AJsjKFpiqeAXsDpDe0Ukf1EZLKIFIjIbBG5KJpCRSRNRB4WkSUiskZE3hGRPv6+R4GbgDEiUiwiY6Morz1wMt6srDLgj1FdXTMoKiqy5Ta8vGZFXm1SCJC/uLRZyl+5sgo34l+thKrQ+hXHITEUouvaAsAhnABlJFFGEiEccjMyuPqUAyhMTWRVx6zapBBgRdb6m14vmpFXmxQCzFyxfmXFgvXxAKxeVt4s12XLthzr5ZbUyu5jmOs4zmOO4+zT1AIc192aDT4m3vldyZ8BucANwHaqWlHTlQx8BcwELgHGAQJ8BFykqq9vouwngBHACUA+8DCwO7CrqoZE5BZgH1U9KMpYr/Rj7AncB+yvqjttxuVuCXthtXEf/W0q879cBQ7sd+0wdjo5qluEbVRJSZgbbl/B8txqkpMdume6BH5fR8AFNxwiTIhgucuImTm0c9bg5GeQUl5FaUYSn++TwUP7HcXJU+dz0btf8NrB+7G6YxaBcJhrvnyG4SvmUR3MJmHGnRz0URLfLKgmIQCvnZ3BCTsnATBvaiFjb5pfm5wecUEP9j626xZflzGtQIulbC/2fb3O+/0fF50cs/TQcZxd8BptTgNCwKvAy67rTo+2DOtKNk31HHAV3hiGeyO2nw78HDHW8Ac/4TsfaDQxFJEAcDZwtKou87ddhdcNPBL4vgkxXgi8pKqVIvIMcIWI7KmqTSnLmDoOv2cEy6flk5KZQMdBmc1SZnp6gLtu7s78nEq6dUmgXXaQX6Z0YHlOOckDM2i3eAXhX1ZQEkgiScP0TfiZUMcgaSWlzFx+GE888z7tugcZ/Kee3L5XmMUVxVR360DRHmeRW7aWXmfuQLBrFp/92eWHnGp6ZgcY1DlYW/+gXbL4y5PD+P3HQvoMS6fXEJvlbEw8cV13KjAVuNZxnP3wPpO/cBxnheu6UTWMWGJomsRvwfsb8IqfdNXoDSysd/h84NhNFNkZSI48V1WLRWSVX+ZmJXMiMhrYHr+7W1V/FRHFG3NoiaHZYk7Aoecu7Zu93NTUAMOHpdSuy6hMGOUnnjIAThgAwNv9HqewbCfccIC87lkkkUT7ogKOnbt+XvL66LrXqSM5wWG/QQ1PlunYPYW9jk1pcJ8xZkOtbPJJpNnALLwbXA+O9iQbY2iaTFXHAz/hjf2rsQToV+/QAf72jVkNVESeKyIZQJcozm3Ihf7PT0QkV0Ry8RLFU0SkXRPKM6ZV2WOfTBZ16kpO354UdMgklJhAQY+sWIdljIkhx3HaOY5znuM4nwMLgDHAPXifpVGxFkOzpa4BJuMldQCvADeKyFnAy3hfzXMR3pjDRqlqWEReAG4XkZl4Ywz/jfcfz4+bE5CIdMC7tc2lwFsRu5Lxmtj/CDyyOWUa09p0++e+9Dj4c2Z0HUIoGCSzsJjuq5bFOixjtjmt4KbWkZYD3+F9/p7oum7+5hZgLYZmi6jqNLxkMMtfXwgcAVyGd1ubF4EbVfV/URT3F0DxWiEX4/V/HaOqoY2etaGzgTzgaVXNjXgsAh7HS1SNiWvODr1Z2yOLsrRUKpOTWNexHenY3ZiM2cYNdF33INd1n2lKUgg2K9mYlmIvLNPiHt7jE1Z1Wn8rmh2LfuO0r7baXZmMiSct1q733IA367zfn7vgxNbVhriZrMXQGGPiVN+cNQSrvQb1zqvyyRpq3xJpjNkyNsbQbDUich1wXSO7D1fVSZtR1hnAE43svkhVX9rc+IyJN/2GpJP15XQqkxLIKC9i1/H2rY/GbG2tbIzhFrOuZGNahr2wTIsLlVbz2dmv46512fvxI8kckh3rkIxprVosfXt2YN2u5D/Nj++uZGsxNMaYOBVMS6DyrAwASwqNiZHWdB9Dx3EcvC+UOB3o5LruTo7j7At0c103mkmgNsbQGGOMMaaNuA04D3gSqPmezqXA36MtwBJDY4wxxpgmch2nziPGzgGOcl33VdYPaVqI90UTUbHE0Bhj4tjC8kx+Ku5MWZUNazXGEASK/eWaN4WMiG2bZImhMcbEqeemh7hy0d7cvkwY8UKI8upwrEMyZpvjOnUfMTYeeMBxnGSoHXN4O/B+tAVYYmiMMXHqxm9daiZbzsmD7IfDfLXEWg6N2Yb9BegGFADZeC2FfbExhsYY07a5rktuSd1t3des5KyPNvcbJI0xW8INOHUeseI4ThA4CfgD3sSTPfC+Iu9413WLoi3HEkNjjIlDN34bhqpqTv7lO/DvR7uoY1dSclbEODJjTCy4rhsCHnBdt9x13VWu6/7kum7u5pZjiaExxsShDyYX8vB7Y7ngx88hYiZk56KCGEZlzLanlc1Kft9xnKO3pAC7wbUxxsShPX/7hVOnfUdWRRlj5v3GxEHDCYTD7Dt/BqHwDgRj2KVljImZFOANx3G+B5YQ8S1cruueFU0BlhgaY0yc+X2dy6Xj36ZTqXcHis+evJ3fO3Unu7SYbwduz+x1sEOnGAdpzDYiluMKG/Cb/2gySwyNMSbGQmGXvHLoVFECacmQnLh+Z2kFVIcgZxUFt79NZVoq7+e34y+rlpGfkkZKdRUuLsNWL8cB9lo4m2semcM+xwzkk1+KGdIzmcv3SOQfk2B1GfxzVIAD+zY8iqgmjo6p4MS+S8wYs5lc1711S8uwxDCOiMhEYE+gCggBC4A7VPVNf//1wB3AOar6fL1zc/CmrI9S1R8jtp8KvAp8papjooghBbge73sYe+JNiR8P3KCqy6I4/xbgBqDc37QSeB64XVVd/5jt8b7WZ38gFe+u7c8AD6lq2D8m4MdxFt7U/Epgth/HlyLSBbgf2A/oCOT6ZdxdU48xrcGiApcD/hdiQQEcOHcOH775CMnvXAv7DIM3v4czHoLKam494jRuGXM53fPXMv/lKzjlzL/w1k57kFFexh9//pqjZyizuvVi8MrlvJYxgNe+hE7FYf5y152ccdhpdCvKZ4eCdfxx5l788reOdEmvm/gtKXTZ/38h5ufDmN4O408MkJJgyaExm9SK/olyHOeAxva5rvtFNGXY5JP4c7uqZuAlO68Ar4nIED9RugBYB1zYyLmz/GMiXeBv3yQRCQIfAicAZwCZeIlqNjBZRLpHeQ0T/WvI9Ov/J3CuX8dOwGRgNTAcaAdcBVwNPBdRxt/xpuQfo6qZeEnv7UCZvz8DmAmM8es5DrgI7x5PxrQaD/8cZoE/X+TzwTvxXs9hcPOr3oa/vwgVVeQnp3LLmOMB6FZcQEFqGm/ttAcAxSmpLMnuSOeSIrbPXcoVx59b+0G1JiOLh/Y9krGvPsobLz7Aw++N5etHb2Te8vIN4nhkapj5+d7yxCUub8+1/5+MiUPP1Hu8B0wAno62AEsM45SqVgOP4X39zY7AoXgteGcBe4nI8AZOGwucJCIZACIyABgBvBlltacDo4FjVXWyqlar6kLgVKAUuHkzr8FV1S+AGcAu/uYHvF16iaquUNVKVf0UOBM4S0T28Y/bC3hfVWf5ZRWr6gRV/cFfX6Cqd6vqQr+e3/BaRsdsToxNVVRUZMu2HNVydnLd1obs8lKq0pP8lTQAkkLVpFZWAPBr975832cwyVWVAGSVlfK/cQ8iyxZw2Jxp/Oed52pvX1NTXnZ5We36oLUrGbhi4Qbx1I8jMVy2wTG2bMvxutySWst9DAFc1+0f+cBruLkTeDTaMiwxjFMikgRcitetPA2vlXC8qn4I/IrXOlbfcuBrvAQP4HxgHFARZbVHAJNVdV7kRlWtAl4DjtzMawiIyIF4LYM/iUgqXuI2rv6xqjoRWAoc7m/6GjhfRP4pIqNFJH1TdfllT9ucGJsqMzPTlm05quVrxOH0oQ5DgqXcOPVjDukNif/xX75jL4c9hpA2tDuvDcmlf0o1SYkOZ51yKb0K1pIQqia9spzU6qraMjuVFJJYVUWn0iJGL5zFcbOm8JqMrt1f0K0TXXcbsEE8V+/mcMYwhyHt4bpRDiftkL7BMbZsy/G6vK3y7214J3BttOfYGMP4c72IXIM3pm4ecCJea91RwMn+Mc8At4rItapaVu/8p4CbReQ54BzgYOD4KOvuDDQ2jnA50CXKcvYTkXwgDKwAblLVF0SkJ14LaDR13O+f+we8P/g0EZkAXKaqSxo49wGgvX+eMa1GepLDy0cFgSy8/60i/r/asS98fzcAR/sPgLfnBrloXDXVwQRWZHfggX2O4OpvPqI0MYlbDjqZ145wOH5Ee7w/+R29FsRxg2DpWrL/uB+kp2wQR2qiw7gjgy16rca0Ra3g3oWbcjDe521ULDGMP3eq6h2RG0TkRryxhR/4m8YB9+J18Y6td/544L/AjUCOqs4QkWgTw9VA70b29QBWRVnOV6p6UAPb1+FNqum5kTo+B68bGu86xwGIyG7As8BLwL6RJ4nIA3gtjQeqqt3918S94wcHuT2hktX++l+PPYebDjuVgOtSkpTCRyOS6p7gOPDHMVs7TGPMVuY4Tp17FwJpePc2vDTaMqwrOc75XaTn4U3SWCoiuXiTLoI00J2sqiG8BOpG4MnNrG4CMEpEBtaLIQE4Bfhkc+OvF1sZXhfxH+rvE5F9gV54iW1D507BG1w7IuKcgIg8BRwC7KeqS7ckPmNak+2X5dRZL0lOpTQxCVkyNzYBGbONcp1AnUeMnQn8MeJxGNDDdd3nN3pWBGsxjH+H4bXijaRuF+zOwAQR2VFVp9c75yFgEvDNZtb1Ml4S+o6InAdM8eu+D6/P6o6NnButvwKTRORRv7x1eBNengNeVtVJACJyNd5s6u9UtUBEBuNNvKnZnwC8CAwFxqjqmmaIzZhWY5/chbw0aPf1G1yXHx++Dic5AR65N3aBGWNiaXfXdTcYMuU4ztWu6z4QTQGWGMa/i4B3/BazSLki8r2//7LIHaqaB3y2uRWparWIHIZ3H8JX8bp8k4AfgX38GcpbRFWnisgeePcxnInXBL4IeARvnGCNQrxWz+1EJBlYi38/RX//3sBpeBNrckSk5rxJqno4xsS5P/Wr4NLqasIJ/tu44zB+2AiuKorq7lPGmGYS65nI9dxEw2Ppb6DuZ2ijHNe1e1WZphORPwH34I3f+zXW8bQi9sIyLas6RMeHKljH+vGEJ03/ntf/3BVGDYlhYMa0Si2WvT2664Q67/eX/XzYVs8UI25s/T7eZNTIGAYAN7qu2zeasqzF0GwRVX1WREqA0SIy3b5VxJitJCHI7v2S+Dhn/abR5+8Go9JiFpIx26JWMiv5Gf9nCt48ghou3jd/XR5tQZYYmloi0gev+7Yh41T14oZ2qOprEWWcATzRSBkXqepLWxalMabGuCOCbP9kMaurU5GucN6o1FiHZIyJAf9m1jiO84LrumdtSVnWlWxMy7AXltkq3nvvfYrDifzhuMNiHYoxrVmLNes9Ih/Xeb+/XA9tFU2ITWUthsYYE8ccBzKDVZs+0BjT5jmOkwXcAuwHdCIiIXZdt080ZcT8hjvGGGOMMfHKdZw6jxh7DNgV784eHfDGFi4GHoy2AGsxNMaYOFZVEcBxbOSCMQbwvtBhmOu6ax3HCbmu+67jOIo3Wzmq5NASQ2OMiVMvPLSEn7/qQ8B1SSpezpFn94h1SMZsc1rZfQwDQM1XvxY7jpMNrAAGbU4Bxhhj4kx5aTUzPl0NjkM4EOCLN1djkwmN2eZNwxtfCN43gT0G/BeYE20BlhgaY0wcmv59AVWJid7sE8chHAxStaIk1mEZs81pZWMMLwBy/OUrgTKgHd5XxkbFEkNjjIlDxYtKCAcDJFZWk1AdIqG6Gv3DxFiHZYyJIdd1F7iuO99fXuW67vmu657qum5j9yjegCWGxhgTh7IzHDqsK6QqKYHqhCBViQmU66pYh2XMNqc1tRg6ngscx/nCcZxf/W37Oo5zSrRlWGJojDFxqPv/t3ff4VGV2QPHv286KfTemwhYAD3YC4p17e2HYl17Wcu6drGCYlnr2isqYm+LXVexIx6RogIC0nsnDdLu74/3BiYxIQWSySTn8zzz5J1bz73JZM6c9713WifQaGM+GwsL6Tt1NrtOnklg/9GNaehuB84BngKK71u4ELi2shuwq5JNnSQigv++x27As6p6RXQjMqZuWXDXeLKDZhz59WTarckEIDs5nqAowNWtqySNqdeiXSUs5SxgQBAEK51zj4fT5gDdK7sBSwxNtYXJ2zBgbyAZ/0XdHwJ3q+oSEdkOuAUYDGQAy4FxwEhVnVnB5u8EPlbVa2oofGNi19I1xP84jRl/O4Ejfpy2aXJiISy9/Ava/WdwFIMzxkRRPJAVtotvU5AeMa1ClhiaahGRg/E3zHwIuERVF4lIO+BcYH8R+Q34FngbnzjOAZoBQ4EjgAcr2EV34MUt7D9RVe17wEzM+nNaNjN/zaZ7n1RWLc0nc30Bux/QlMbNEjcts2r0DHKnrmLdso0smLSKt/u0Z3Xz5nTLzmOf5G7suGAl4/p1p8fCFXRduZ7lzRsz/cvF9O/0PE327cB/Lx7Ei+M3MmDGHO7KnUrTq/dn5cfLiW+SRMtz++IStkHf82vfwswlcPI+0LPd1m+vEmZOzebP6dn02jmdbtun1so+jSlPHasYfgjc75z7J/gxh8Bw/Pt1pTi775WpDhGZCXyjqmeXM/9zIE5VD6zGttcCjYE8oAA4FtgH2A+YCJwOTFTVw0VkX2Ak0BdYg79n0/2qGoTbOgK4Fz/WYhwwC+ivqoOqGlcV2QvLlGvuHzk8fOMciopKTm/ZNonrHuxBQmIcS+5QlgybUGL+qMN3J65Ryqbn4zu0oPvcJVz68U+sbppO0/XZfNenM0uaJdF33lrOv+ioTcte9e53XDv+LRYW9qSIBFqc24cuTx+wdQfy4Fj45/O+3aox/P4wtGy8dduswB9Ts3j8tnkEAcQnOK64sxudejSq0X2aeqHGsrf79v6yxP/7f313QNQyxfC7kl8ADgcSgQ3Ap8AZQRBkVmYbNlTZVJmI9MLfRX1MOfNTgUHlza+IqjbFf7fjuaqarqqfh7P2w9/BvRNwgoj0xX86uhdoha9E/gOfOCIiPfAVyzvx93F6GH+PpxqXmZlpbWuX2/5zWs5fkkKAlUvzWLuqgMzMTLLGLf7L/IyCwhLPV6QlM/T73xk7eFc+27cf7x08kLZrsnh57wEsbZpeYtmlzdLJLUwjmRyATdvfmmPJ/3xyRDDr4fcFW73Nitqzf8+huJ5RWBAwbfKaGtuXtetPuybVhauSnXNtAYIgWB8EwXH4YsgeQI8gCI6rbFIIlhia6mkV/lxUzvxm+HEO5c2vrnmqep+q5qlqDnAx8Iaqvqeqhao6HXiEzTfyPBmYoKqjVbVAVT8F3t3GMZUpIyPD2tYut73djmnEJ/g3kMj3kTYdk2naIoGMjAwaH9qJ0tYnxJd4vte85UzbriM5qb6KmJeUyIxu7ei4KpN9f59Hh2yfBDbNyuXYCdNplJDNRtIANm1/a44l8QjZHEz75rBTl63eZkXt7XdOIy5850pIdOy4S/Ma25e160+7ASj9zSZPBEHwUxAES6u6IRtjaKpjRfizAzCtjPlrgMJw/rY0r9TzbsCBInJ8xLQ4YEHY7sjmO8AXm1MDcRlTJZ16NOKKkd2Y/Vs23fuksWpZHpnrCth13yYkJPqsp81VA0jerim5v61m3YqNLPpzAye3WMGyFmkUrC7kl0WptCxqxKL2LUtse0N8PMMTliHjjuLXHdJ4Z/RStv/qJ3Y8KZm0y4cRfLqMuCZJNB/aa+sP5KLDoHNLmLUUjt8DmqVXvM5W6t4njcvu6MbcGTlst1M67bumVLySMTWojowxLB3EoOpuyBJDU2Wq+oeIzAJOAT4vY36OiIwL5z+zDXdduvNtHvCcql5SzvKLgENLTeu6DeMxpto6dW9Ep+5+bFznnmWPkWt6TDeaHtONdkDvUvOW7PU5c9LSNj1vsj6b1qvWMfik1vS+vt+m6X+/sANcuPmzUKvtWm+zYwAgsmpYS7r2SqVrL7voxJgI22xcuyWGprouBsaKyDLgEVVdLCJtgLPxVbkrgW9E5BngDnzlrgm+ezdZVR/aBjE8BnwlIh8DH+NfGL2AVqr6FfAqcLOInAK8gf8EdSyg22DfxkRVz3O2Y97Y9RTF+wpj14XL6bBgOb2vPzjKkRnTsAR1476hCc65A9hcOSz9nCAIvqjMhmyMoakWVf0Mf6VwX2CqiGTib0/TGhinqlOAgUAj4AcgE/gFEOCDbRTDr8CRwBX4i1KWA6MIx0Cq6izgROBmYC3wT7ZtBdOYqGmelcs+P02j+7yl9P9tDjvNmM+GVOtWNaaBWo7/Uohnw8eqUs8r/d5nt6sxDYqIDAMOstvVmFj30oGfkTJzLT0W+iG/+fHxzO/SgpNmnxjlyIypk2qsrHf3oG9K/L+/dty+daKEWF3WlWyMMTFoyd5dabxiFstbNiE1N4+E/AIa7dc+2mEZY2KcJYYmKsJvRulSxqx5qrpDbcdjTKw5cu8MXh3fgvWN00jZkEdeYgLnXVbWS8oYU5PqyFXJ24wlhiYqopX8qeoIYEQ09m3MttRj+1Q2JCcBsCElCUdAj741f7sYY0z9ZhefGGNMDEru1pgrTkih26pldMhdxXV3dCEp2f6lG1Pb6sI3n2xLVjE0xpgY1f7vveneciYAbXo3qG95MMbUEEsMjTHGGGOqqT5UCSNZv4MxxhhjjAGsYmiMMTEt57uADdMd05KX0ueQttEOx5gGp75VDC0xNMaYGPXeVb9wa/5AlvZP5+An5jA8Po7tB2/j70I2xjQo1pVsjDEx6s71zZjeshlrXQJv7NabT95bFu2QjGlw7KpkY4wxdcKfTRrDxkL/pKCI3xNSoxuQMSbmWcXQGGNiVKONeZufBJC8eE30gjGmgQpcyUess8TQGGNi1EkTp+OCAIABC5Yi0+ZHOSJjTKyzrmRjjIlBRbkF9FuwjPs++Ia1jVI48udfmdK7U7TDMqbBqQ/jCiPV28RQROYCw1R19BaW+Q24XVVfq7XATNSIyDDgIFUdFO1YjNlaSyetYfTggYzbviNFwC89OnHQtOnRDssYE+PqbWIYSUS6AnOATqq6sHi6qu4Qzf0bY0xlFW0sZOOyXOLjArKbpnLvqyv4Zrse5MfH0Sgvn/yEgMQcWL9wHRvS02maFseGDUUAFOQHNGkST3x8/apsGFMXWMXQGGPMNpd16xfkPjORhL6taDLmRNb/sobZl3wHcY7v+3bi0U5deeDlj/ilaztu/r/BFLbrCXH+DenScUr7tbnkNU7m3YP/x8SOrTh+6iR6rlrK/GatWNMsIL5NE/b68ExWvP4nfzzwG43ap7LrU3uR1iW91o5x/rMzmTVyCintU+n3/D6k9bDvdzamrqkziWHY9fsMMBgYiK+wnQrsAAwHWgFvABeqaoGIdAbuB/YBAmAs8C9VzSxj85PDnzNEJADuVtXhkd3NIjII+Dzc551AS+AT4JzibYpIL+BpYEAY33PAg6pa0ceF8vbfArgHOARIAb4ELlXVZdU8J13DZc4DrgHaAOOA81R1+ZYCFJGzgGHAw+G6acDrwMWqWhguszPwYHj8a8LjH6mqhRH7PgO4HugE/ACcqapLytlnM+Ap4ED83+LC8Fi+CecfC9wE9ACWACNU9eWI9fcFRgJ9w3geA+5X1SCcfwRwL9A5PA+ztnQOjImWvPELyL5tnG8vWk/WbeOY8epqClZuoAi47tQjGfLjr3RdtY7T/nEihXFxUFQEeYWMeud5zpj4A1mJjXi390GsTWvMyRN/ps/qRQD0WbGIT5rvzP99M5av79+J1Q/PgqKA3EU5/DpsIru/tF+tHOOGxTn8eumPUBSwYWEO067+CXn7wFrZtzE1qaieVQzr2lXJZwIXA83wydQ7wAFAP2An4GhgiIikAF8AvwPd8IlBR+ChcrbbL/y5vaqmq+rwcpaLxydp/YBe+AToMgARScAnn5PxCddx+ASsMv6yfxFxwLv4pHZHoAuQCYwptW6lzkmpdc4A9sMnZ0VAueMsS+kSHlsPfCJ6EnAygIg0AT7DJ69tgSOAs4ErS21jSLjvDvjk8vYt7O9qIDXcb1P8OV0Y7u9g4FngCqA5/jw8IiL7hfP7Ah/iE79WYTz/AE4P5/cA3sYn+U3xCW9lf19bLTMz09rWrnw7N59IeetyKMotAPybTn5iPGvTUgDI2BDeoibOsee82Zwx8QcA0vNzGbhoKhnrciks9UaVlrcBgNSgAIqCTdMLcwpq7RiLNhaW2PfGzI21sl9rW9tUTV1LDJ9S1Wmqmo9PkLoDN6pqtqrOx1d9BDgScKp6s6rmquoafGXpVBGJ38oYrlPVrLBq9264P4A9gK7AteE+/wQe2Ir97Bo+LlHVdaqag6/UHSgiHSOWq+w5iXSbqi5V1fX45OtgEWlfiZhygZtVdaOqzgL+F7HtI4A8fNVuo6pOA+4Gzi1j3yvDfY8pI7ZIeUALYHv87/MPVZ0TzrsceEhVv1HVIlWdgE9wzwjnXwy8oarvqWqhqk4HHomYfzIwQVVHq2qBqn6K/33WioyMDGtbu9LtxEHdSDnNf36M796MprcMpseje+OS4khMclz17STe79eLMXvuyJ2vfUSPFWvosXINx/02k0gJuUV0WLySUQP3YHFGMwBWpmaw06qpzN5nd3YZtjs9L+0DDpJbp9D3pv61doyp3TLocfWO4CCpVTI73DmwVvZrbWvXtABX4hHr6kxXciiyyzEHKFTVFaWmZeCrhJ1FZG2p9QN8NWtRNfdfen/Z4f7AV8CWq2puxPx51dwP+GNIBpaJlMidNuC7PosvUqnsOYk0t4x2R2BxBTEtL+42DkUefydgXnE3bWh2OD1SZLyb1heRU4Eni2eoajq+2pcIvAC0E5H3gWvCpLwbcICIRFYk44FvwnY3fBJ9fMT8OGBBxPHOLRXbHPzv0Zg6xTlHk5dOoPHTR+NSEgFo3aM5LYf2xDnYKyGOm7MKWPWPA/ljaQFvZG3ks8vH8+N2/Rg/cz4dVq4DV8CvqV3p+/IhPDq4PUkJu5O/dgM9mqbAhjxISQJgx9sG0OeGnYlLisPVchfY9sMH0HNYdPZtjKmcupYYVtY84I8qXFVctA32uQhoJSKNIpLDzlux/3n4xKm5qm6L+CJ1xSdtxW3YnGhW1wKgi4i4iOSwO5sTsS0Kxwa+XGpaNnAjcKOItMVXBO/FV/3mAaNU9d5yNjkPeE5VLyln/iLg0FLTulYmVmOipTgpLBaXuLlTJyU9gQ5Ah8bxQDK/796WPb6bTfuVWQTEQxBPcpqj16GbP/skNk0JV04qsd345K3tWKm+aO7bmJpgVyXXDe8Dd4jIDcB/gCygPbCbqr5TxvIr8MnZdlQ/QRoPzAdGish1QDv8+LfKKGv/ih8z+LCI3KKqq0SkFTBYVV+tZozFbhKRX/Fdw3cDn6tqRdXCinyAv/DkBhG5F1+xu5aIKmBVichR+AtC/sD/DjcAxRXLB4FRIjIe+B5fLdwJ3+Ws+AtNvhKRj4GP8dXiXkArVf0KeBW4WUROwV+gMwg4Fn/ejYl5R17SjYmjxpeY1jwnJ0rRGGPqi7o2xrBSwvF4B+IvOpkOrMOPh+tfzvK5+DGIr4jIWhG5sRr7LMBf6LELPtF7F3gJP06uonX/sv+wSngM4ICfRSQTn3wOqmpsZRiN73JdACQRXpCxNVR1Hf7CnIOAZfgrtl/EXxleXT3wF/Ssx3f75uKTTcIxgefhK4gr8V3UDwDp4fxf8WNNrwjnLQdG4S9EIRwjeSJwM7AW+Cf+Cm9j6oUm3TJonJXNqrQ0ANalNIIWsfpZ35jYFThX4hHrXBAEFS9lyiQiF+BvkdMr2rGA3Ui7jrEXlqlx73d4nnmt2hJXVERRXByFqXDZ94dHOyxj6qIay9iGHTGxxP/7ER/sEtPZoX28rAIR2QdfnfoT3615DZW/FYwxxmxTE3v2oMW6bIrifOfPwvjUKEdkTMNTH6qEkSwxrJpO+FuwtMR3J7+Bv8EyIpJVzjrfqGrUP8KHNwT/vZzZo1X1wtqMxxiz9dZmpLK2cWMysnPJTGtE43y7b5sxZutYV7IxNcNeWKbGPXfyZ0wo7Eh+UiLNV63h9vPjaHT8rtEOy5i6qMbKejcc9UuJ//d3jh0Q0yVEqxgaY0yMOnPMQbQ69gkyZmay63m70Oj4g6IdkjEmxlliaIwxMSo+zsF5HckEMo6ypNCYaLDvSjbGGGOMMfWSVQyNMcYYY6qpvl2VbBVDY4yJUXmZ+eQPz8JdtIofz/oi2uEYY+oBSwyNMSZGTRj6GS1mroVCSH51EnPunBDtkIxpcOrbN59YYmiMMTFqzvRsZrZsy9L0Jozv0p1Fr8yMdkjGmBhniaExxsSojfGb2wlFAXNbNo1aLMY0VEXOlXjEOksMjTEmRhXG+fvqFoXvRXNaNYliNMaY+sCuSjbGmBg1r1UzZnXsSlajFDovW8WqZvZdycbUtiD2i4QlWGJo6iQR+Q9wCpACdFfV5VEOyZg6Z1GrtmSlNgJgftuWNM1eH+WIjDGxzhJDUy0ikhXxNDn8uTH82QjIjZifCuQBBeHzb1T18C1sey/gbKCrqq7YNhEbU88UFtJp7VJWNGu2adIev0+H3B7QKHkLKxpjtqWg5r6GOSosMTTVoqrpxW0ReQZIUNWzylpWRGYBI1R1VCU33x1YUl5SKCIOiFfVgrLmG1PvLFnN+ld/Jm+0kjU7m1d22psZbdrQfcMG1qQkkppfyMImjfhv/504ouNV/OuMq2ixZB37HN2Wvw9tDUVF8PkUSE2GffpUK4RFy/JZsKSAHbZLJiPNhqcbU19ZYmjqFBG5BrgdSAqrkhNU9UARCYArgNOBHYADRGRKuOwJQBNgAvAPVZ0VbisDeAQ4CsgEbgaeBQ5S1XG1eVzGVNucZRT0v4qM9dkspTeQwSnfTuKXDu24aujh/Nm6xaZFN7oiuq9exMlvf0VWUgb5P8/msTn9uHjK2/D6936hYSfC8KFVCmHKjA3c9OBK8gugTYt4HhrWmsbp8RWvaEwDUB+uRI5kH/tMnaKq9wAXAn+qarqqHhgx+xxgCJAO/AI8DfQG9gDaAj8C74tIYrj8g8B2QF9gZ+AYwN7NTGx5X0lYn00ByWwkY9Pk5KKCEkkhQMfVWSxPb07nnEWA/we/4tP5m5NCgFFfVjmErybkkh/W55etKmTKjI1bXsEYE7MsMTSx5N+qOltVC4EMYChwsaouU9U84DagHbC7iMQBpwI3qepSVV0HXFtbgWZmZlrb2tukndOtJQDx5BNH/qbpM9v4pLDt+jX0WLmUe1/5kL3mLia+qJA1SU03LZfSKQN6tN30nB07VzmGNi0KN7Xj46BT28RaOXZrW3tbtWtSffvmExcEQbRjMDFuW48xFJGzgGGq2jNiWgDsr6pfh88H4ruO15VaPQlfWfwCWAr0VNXZ4ToJQD5wQC10JdsLy2wzRaO/Zs39X7JhbiE52Y2gAEbvsQt/dk7kudcfJ7GokNVxbfm850BmtUpjbaP2tFqVRULfFpzz+C40XrkS7n7HjzEcdiI0z6h4pxGCIOC9/2Xx54J89huYiuyYUkNHakyNqbGM7fITfy/x//6hN/vGdHZoYwxNLCmKaM8Lf25X1kUqIhKPvxK6KzA7nNy1JoMzpqbEnbYfLU7br8S0W4KA1Wl/J7HIV/OaFy2lVc4ijv34WpLSE0tuIKMNPHFhtffvnOPYg6qWTBrTUNSHKmEk60o2MSm8r+EY4DER6QAgIk1F5DgRSQ+7m8cAt4lIGxFpDNwVxZCN2abinGNeszabnufHxXHbESf/NSk0xpgqsMTQxLLzgBnAOBHJBKYCJ7G5G/dyYA4wPZw3FigsYzvGxKR79jmOeekdyCWDCc13J60oL9ohGdPgFLmSj1hnYwxNgyIiBdTO7WrshWVq3PUHfsIFX07c9PzbnTpx2pTTohiRMXVWjaVs//i/aSX+3z/yep+YTg9tjKExxsSooFSnz4akpChFYkzDVd/GGFpiaGqdiJwKPFnO7AtU9eXajMeYWDW7TRte2X1nBv8+m5+7tGfmDl2iHZIxJsZZYmhqXZj4RSX5U1X7mzf1RofW6XzruvHc3gPpvXgFVw1pFe2QjGlwiurZdyXbxSfGGBOjbrqtMy3THAfOXszgPZqx/99aRzskY0yMs+qJMcbEqBZNEzjx6FkAHHVU/+gGY0wDVd/GGFrF0BhjjDHGAFYxNMYYY4yptvpw78JIlhgaY0wsCwLiNxZEOwpjTD1hiaExxsSqGYs4+NRXSMnayJz9ptH5i+uJj7cRQsbUpiIbY2iMMaYuKDx6JClZGwHo8N0kXrvhiyhHZIyJdZYYGmNMjNINqZvaSYWFLP55YRSjMaZhCpwr8Yh1lhgaY0yMenPH3Ta1s5KSeXzXwVGMxhhTH1hiaIwxMWrfuTM2tdPzNvJ/U76PYjTGNExFruQj1tnFJ8YYE4NW5QZ0Xr2c93YQVqam0y5zHYdPmwQMiXZoxpgYZomhMcbEoAsfnMukM65iVqt2m6b1XraQaVGMyRgT+ywxNMaYWLAhD36eTW6HVkxcFccHBS3IbZVaYpHpbToyb10hXZrEl7mJqSsC8otglzb1oL/LmDoioH69niwxjCEiMg7YE8gHCoE/gRGq+lY4/0ZgBHCWqr5Qat25QBdgd1WdEDF9CPAq8JWqDqpEDCnAjcApQAdgHfARMExVF1Vi/VuBYcCGcNIy4AVguKoG4TJ9gduBA4BGwBzgWeBBVS0Kl4kL4zgDaAvkAdPDOL4Ml/kE2BlIC+N8A7heVTdWFKcxtS0IAu4YH/DdooCjejguHhAxBDxnIwt2GcaTXXflkb0PZ12jNEgJytzOiHM+ZeHJB3NoN8cvf2Rz6oNPsOe8mVx52Kk8s+M+AJy1/Fee/3tL6NOxNg7NGBND7OKT2DNcVdOBFsArwGsi0itMlM4DVgPnl7PutHCZSOeF0yskIvHAB8DxwKlABj5RbQL8KCLttrB6pHHhMWSE+78e+Hu4j52BH4EVwI5AU+AK4Erg+YhtXAsMBY5W1Qx80jscyC21TFdVbQwIsCtwSyVjNKZWPfdrwE3fFfHx3IBL/lfEp3OLNs377vXf+bBZN+446ESfFAKUc1uMMQP25Zcpa/nnlwGD7h/FIRPGk7FsFQ+OeZKWWesBGNVqB4Ij7qjxYzKmIShyrsQj1lliGKNUtQB4DIgHdgIOxVfwzgD2EpEdy1htFHCiiKQDiEh3oD/wViV3ewqwL3CMqv6oqgWqOgc/2j2HKiZdqhqo6hfAb8CAcPL9fpZepKpLVDVPVT8DTgPOEJF9wuX2Asaq6rRwW1mq+rGqjo/Y/qRS1cEiYPuqxFhdmZmZ1rZ2ldozVpQsZM9Zt3mZWRktmd28DZWRk5RMm8y1AHRds2LT9LT8jbTOWrfpuZu/AoqK6sSxW9vaNd02lWeJYYwSkSTgEny38mR8lfAjVf0AmAJcUMZqi4Gv8QkewLnAaKCyXat/A35U1VmRE1U1H3gNOKKKxxAnIoPxlcGfRKQRMCiMqQRVHQcsBA4PJ30NnCsi14vIviKSVs4+HhORbGAp0A+4ryoxVldGRoa1rV2l9nkDUmjVyLe7N4HjerpNyxx8aEd+79qdjA05EBTRNCeLNmtXU5ZDp//Cr+060y4NHt77cPLi/XjDz3ruxLTWHQAYMulbuOJIiIurE8dubWvXdLsm1beKoY0xjD03ishV+DF1s4AT8NW6I4GTwmWeBW4TkWtUNbfU+k8Dt4jI88BZwMHAcZXcdyugvHGEi4HWldzO/iKyFl/BWwLcrKovikgHfAW0Mvv4d7juUOAaIFVEPgb+oaoLildQ1YtF5BJgh3BZ+2oIUydt18wx45x4Zq6Bvi0gPWnzG0z7dMeY+/vzxowicgsC3pqeRpMPp/Dl9v1Y3yjiM1EQsP3xO3H7Lons3Arm/N+ezDmnPdvPmUP3Qbtx+6J49g1Wsv+h3WGnA6JwlMaYus4Sw9hzh6qOiJwgIjfhxxa+H04aDdyD7+IdVWr9j4DHgZuAuar6m4hUNjFcAXQqZ157YHklt/OVqh5UxvTV+ItqOmxhH/8D3w2NP87RACKyK/Ac8DKwX+RK4bK/isgkfGVzz0rGaUytapbi2K2ckbqNkx3n7Oyrf//YBTov25XcDX+tTtx3eAoJcX56nxbAfl1hv670AIZ1Aahcl7QxpnLqw02tI1lXcowLLzo5B3+RxkIRWQr8jq+8/aU7WVUL8QnUTcBTVdzdx8DuItKjVAwJwP8Bn1Y1/lKx5eK7iIeWnici+wEd8YltWev+DDyDHzNZngRgu62J0Zi64uEjG5GfkBjtMIwx9YxVDGPfYfgq3m6U7ILtB3wsIjup6tRS6zwIfAN8W8V9jcEnoe+KyDnAz+G+7wWa4W+Vs7X+BXwjIo+E21uNv+DleWCMqn4DICJX4q+m/l5V14nIdvgLb4rn9wZ6A5/ju9r7ATdTTmJpTKw5drs4kvKyyUtutHmig7h6Vr0wpq4rsvsYmjrmAuDdsGIWaamI/BDO/0fkDFVdg0+YqkRVC0TkMPx9CF/Fd/kmAROAfcIrlLeKqv4iInvg72P4O5ACzAP+g79iudh6fNVzexFJBlYR3k8xnO/wYw9H4auny4C3gdu2NkZj6ooeq1cyrV3E6I4A4urB4HdjTPS4ICj7JqnGVIaInA3cDQxW1SnRjqcOsReWqXFH//0Hxu4wcNPz+MICCq5NiWJExtRZNfaJachZ80r8v39tVJeY/nRmYwzNVlHV5/AVyX1FJKZfDMbEmksmfE5SQf6m572C7ChGY4ypD6wr2WwiIp3x3bdlGa2qF5Y1Q1Vfi9jGqcCT5WzjAlV9eeuiNMYU6zuoC5MeuIbX+u1F28y1yAtnRDskYxqc+nZVsnUlG1Mz7IVlal5hIVMu/jdJK3LYfuT5uO3Lu9OTMQ1ejaVvJ/29ZFfyG8/HdleyVQyNMSZWxccz78i+APS2pNCYqKgP33YSycYYGmOMMcYYwCqGxhhjjDHVZvcxNMYYUycUFAU8ubQPczY2Ju+PQk7oFR/tkIwxMc66ko0xJkad9F4hH6zryu8bmnPifwMmLiuKdkjGNDiFruQj1lliaIwxMWrsnyWf3/SNJYbGmK1jiaExxsSog37/ZVM7sbCAeavyohiNMQ1TkXMlHrHOEkNjjIlRl3/9Pk1z/bedHPzHFLrMmRfliIwxsc4SQ2OMiUUFhTy/+2DWNkoD4MM+uxCXszHKQRnT8BS5ko9YZ4mhMcbEoMU5Ael5G0pMiy8oiFI0xpj6wm5XY4wxMejiRxZy+JwZ/Nh5OzKTG/GP7z/mmF8nwMMDIMFuW2NMbbH7GJpqEZEs4GBV/SHasVREREYBBap6brRjMabeCgJYsBJaNobU5EqtsqEg4L1Zhbw/ZQOT1jneO+l8xo66lz9ataP38sVsv3Ip590+nWvP7cLKHHAb8ui2XVNap8WxoSBgWTYkxAWkJjqapZT9ZpZXGLAkCzpkQEJc/XrDM8ZUzBLDWqKq6dHat4jMBYap6uha2Nc4YE8gHygE/gRGqOpbEcscAVwH9A8nTQbuUtX3I5ZpBdwFHAY0ATKBScDZqrpERBoBL4bb6AHcrKojyokpDZgCdFFV+5s30VdQCEfdCR//4hPD/90KO3fd4iqz1gTIS4WsywNIgZbtwTlOGXo5WSmNAHjo3ed4evhNrL03lTPOvo4fuvWGzwq5Za8inp4Ki7P8thIcvHxEHP/Xu+RoosVZAfu9WsjstdCvFXx1cjxNki05NGZLCuvBlciRbIyhqQnDw0S4BfAK8JqI9AIQkbOBN8PpHcLHy8Ab4bxio4EMYEC4rX7hOkE4PwC+B84HJlQQz13AnG1wXMZsG+N+9UkhwMr1cN9/K1zlkV+KwqQwFL4ZFSeFAO/uMBCAphtyuP7LdzctN/LHYFNSCFAQwM3f//Weh89MCZi91rcnr4BXpwd/WcYYU79Z9aSawircM8BgYCA+8TgV2AEYDrQC3gAuVNUCEQmAfVX1WxHpCjwJ7I5PcOYAp6jqjLAbNx5fcTseyAauAqYBTwO9AQVOVdXFYSyXAxfhk6w1+ERrmKoWishYoDPwjIg8AXyvqoeISCJwNXAm0B5YDlyrqm+Gh5gsIk8DJ4Ux3K6qT1blHIXH/RhwN7CTiCwG7sdXBx+LWPRxEWkD3C8ir6tqFrAXMERVl4fbWo6vEBZvewPwQHj8JUfgRxCR/YB9w2MdVJX4t0ZmZiYZGRnWtnbZ7ZaNKaFlRoXrtkp1bP5cVLZjf/tpU3tl2uZ9pCZAXqlbHLYM88nIfWXEbQCStriMta0di+2aVB+uRI5kFcOtcyZwMdAM3x36DnAAvrq1E3A0MKSM9e4E5gNtgJbAWfiErtiJwFtAc3yS+TRwO3BcuE4A3Bax/ELgcKAxcAxwNnAugKoeFe7rXFVNV9VDwnVGAKfhE7/GwP7AH6ViGBvGcCnwiIh0qeR5AUBEkoBL8EnuZHyy1wRfDSztpXDenuHzr4F7ReR8ERkgIlUeTS8iqfhzd24YQ62J/GdkbWv/pd2/Gzx6HgzoBkP3hVtPrnDdf4ljaG9HggOKimiWtR6KSlb9JnTqyS/tu/BR3wHcdtAJJBfk0T1hA5+eFM/ZOzp6NoVOGXBQF8eow+L/sq/LdmvEpQMc/VvDjXs4TugVF/1zZW1rb4O2qTyrGG6dp1R1GoCIjMFXDPdQ1WwgOxxvJ/gKXqQ8oC3QPVx/Sqn5X6jqB+F2XwQeB15S1YXhtDeB84oXjhy/B/wiIi/hK5llVvhExOETtiGqWrzvheEjMobi/q23RWQtfjxfZe6ge6OIXBUe5yzgBFWdJSK7h/MXlbHO4vBn6/DnEHxC+nfgIWBjWE29LqwWVsZIYKyqqogMquQ6xtSOiw/3j0pKSXC8fGQ8Lx/pn/e5Zj3xBKxMb7JpmQ+2788V+6Vw+EV7MbfU+gPbVbyP+DjHw4PtimZjqqLQrko2EZZEtHOAQlVdUWpaWR9ZrgZuAsaGF0a8CVwfdqGW2K6q5ohIWfvatF0ROQW4EuiO/50mAeO3EHcrII2SFcItHRv47uTKfvy6o5wLQYrPTQdgdql57SOXCc/FSGBkWHk8DF9VXA/cXFEAIrIPvorav5IxGxNTHjmlKZ/f8Cl3DT4egNSNG9ht4Szk1r2iHJkxJpZZV3IUqOoKVb1MVXsCe+PHvl1TnW2JSCd81+wIoJ2qNgEehRIfYUqPMl+BTy63q84+t8L3+MRuaBnzTg3nfV96hqrmhdXLz6l8oncQ0AmYLyIrgfeAeBFZKSJHVSN2Y+qUA/o34aVd99v0PCc5hXm9ekQxImMapkJX8hHrrGIYBSIyBH8l7VxgHb7LtbCam0vHJ/grgHwR2QM4HX+xSrGlRCSBqhqEF4XcIyLzgd/wVbzmEV3L25yqZonI1cCDIrIcGINPYE/G377miuKqqYjcj78KeSr+/OyHH785snh7IpIcrh8HJIhICr5qm4+/yOWZiN3vGW6vP7Cqpo7RmNqybiMsatKixLQmCXYVsTFm61hiGB0DgHvxt3PJxF/kcW91NqSq00TkFnxFLAn4ks0JULERwH9E5DJgvKoeDtwY7vtd/HjHpfiqZY0lhmG8T4nIUuBa4L5w8mTg5IgxjeCTvefxV1QH+HGJ/45YB2AGUHxBzL7ALcALwFmquh5fgQRARIq7qCPHURoTs5qlOPov/JOVGU1Y2LQlp078hi5phfhiuTGmthTVs/sYuiCwT5jG1AB7YZkad+Wx7/Hv/75ITmIS6XkbefWAQzj5iwujHZYxdVGNZW97X7S0xP/77x5vG9OZoo0xNMaYGDW3f1/WpaSSnreRnMQkfpX+0Q7JmAan0LkSj1hnXcmmSsKbZJ9Wzuy+qjq/NuMxpiG77Owe9M+7h73mzWBKx668c23naIdkjIlx1pVsTM2wF5apFU++9T9mb2jMFccI7dNjv1phTA2psReHXLysxP97faxNTL8QrWJojDExrH1Sjn9YUmiM2QYsMTTGGGOMqab6MK4wkiWGxhgTwwoCR16RfY2dMWbbsMTQGGNi1HszC3nztTQa5+YyNmc9o4c2jnZIxjQ4BfWrYGi3qzHGmFi14NznOfbXn+iwfjXHjnycyUvyox2SMSbGWcXQGGNi1PLkNC49/SwAmmdncv2kLPq1axbdoIxpYApq7oLnqLCKoTHGxKgJnTd9BTqr0zKYstb+pRtjto79FzHGmBgUBEGJsU1xRYX0+mpC9AIypoHKdyUfsc4SQ2OMiUGTlhfxv179Nj0viovnh6Km0QvIGFMv2BhDY4yJQV/MD6DU/dMmtu4UpWiMabjy69l9DK1iWEeISJaI7BntOCpDREaJyDPRjsOYhmz+pKV/mZaatxHW50QhGmNMfWEVwzpCVdOjtW8RmQsMU9XRtbCvccCeQD5QCPwJjFDVtyKWOQK4DugfTpoM3KWq70cs0wq4CzgMaAJkApOAs1V1SbhMGjASOAnIABYAQ1X1lxo7QGO2oZ+WBLw1s4j+rR2D2hfxxdCXSJq3jIuOP4+s5KaQGFE1DAK2W7GEzncnkZ+ygYn/uYE2GY5Rd9/Am7SjR1PHFRJHj6bVqG6sz4H/fAhBAJf+DZqkbdPjNCaW1bebRFnF0ETD8DARbgG8ArwmIr0ARORs4M1weofw8TLwRjiv2Gh8sjcg3Fa/cJ0g3I4D3gW6AruFyxwBLK7pgzNmW5i1JmD/1wq5e0LAKe8X8c5xLzD0g7E8tO8RrMxowoaklE1JYVxhIa+NfoCPR93D5AeuofWa1Vxy2OnE/bmMiS9P4qO58MikgH3GFLJ+Y1D1YI67G4aNgZte8W1jTL1lFcMaElbhngEGAwOBOcCpwA7AcKAV8AZwoaoWiEgA7Kuq34pIV+BJYHd8ojMHOEVVZ4jIKCAe/yHleCAbuAqYBjwN9AYUOFVVF4exXA5chE+y1uATrWGqWigiY4HOwDMi8gTwvaoeIiKJwNXAmUB7YDlwraq+GR5isog8ja/GZQO3q+qTVTlH4XE/BtwN7CQii4H78dXBxyIWfVxE2gD3i8jrqpoF7AUMUdXl4baWAy9GrHMIsDfQUVVXh8v8WZX4tkZmZiYZGRnWtna125OWp5FbwCY7/TkLAO3Ug9J6rF7G/00ZD0Cz3GzOmfAFj+95CABT23XZtNzSHJizDro3qlo8wQ8zNt2pLfh+c7uunCtrW7uidk3KsTGGpgrOBC4GmuG7Q98BDsBXt3YCjgaGlLHencB8oA3QEjgLn9AVOxF4C2iOTzKfBm4HjgvXCYDbIpZfCBwONAaOAc4GzgVQ1aPCfZ2rqumqeki4zgjgNHzi1xjYH/ijVAxjwxguBR4RkS5UgYgkAZfgk9zJ+GSvCb4aWNpL4bzicZhfA/eKyPkiMkBESn9Z7AH4hPo2EVkuIrNF5M4w4a1xkf+MrG3t6rT36uBo2cg/j3MwfuBAAI757ScAei1fBECbzLX849uPiTSneWvOmfAFAB3XrNw0vU9z6NWs6vG4Y3aLaA+s8WO3trW3ddtUnlUMa9ZTqjoNQETG4CuGe6hqNpAdjrcTfAUvUh7QFugerj+l1PwvVPWDcLsvAo8DL6nqwnDam8B5xQtHjt8DfhGRl/CVzDIrfGE37CX4ilzxvheGj8gY/hu23xaRtfgxgfPKPRub3SgiV4XHOQs4QVVnicju4fxFZaxT3AXcOvw5BJ+Q/h14CNgYVlOvU9UN+IS6L/ARviLaCfgQX928oxIxGhNV7dMdP58ezydzA3Zq6eh32XF8uF9ndpu4jEULZ7D9gvk89/rj7L5gFv/tK3zVrTc5iUnMbNOB8YcP5pnlX8NlVzOk3+60XRDQvSmc0juORonVqG68dDkcPdCPMfy/vbf5sRoTy3LrV8HQEsMatiSinQMUquqKUtPK+khzNXATMDa8gOJN4PqwC7XEdlU1R0TK2tem7YrIKcCVQHf87zwJGL+FuFsBaZSsEG7p2MAnXJX9eHaHqo4oY3rxuekAzC41r33kMuG5GAmMDCuPh+GriuuBm/EXoxQCN6rqRmCmiDwKDMUSQxMjOjd2nLfz5nedv/1D+Bv+xfzb8u34/shZ3HjYyWRs3MBD/x3FO30H8q+vz+EywA+p9d0SR/fcykAS4uGUfbdyI8aYWGBdyXWQqq5Q1ctUtSd+nNwg4JrqbEtEOuG7ZkcA7VS1CfAolPhyx6JSq63AJ5fbUbu+xyd2Q8uYd2o47/vSM1Q1L6xefs7mK5knlbOPaoy8N6bumZ8Vz1fd+/JVzx15fwdhj0vvoPO6lRWvaIzZpvJwJR6xziqGdZCIDAEmAHOBdfgu18Jqbi4d/wFgBZAvInsAp+MvVim2lIgkUFWD8KKQe0RkPvAbvorXPKJreZtT1SwRuRp4UESWA2PwCezJ+NvXXFFcNRWR+/FXIU/Fn5/98OMKR4abext/O5vbROTmMP6LgOdqKn5jalOfFnEsT2+y6fmK9Cb82bptFCMyxtQHVjGsmwYAXwFZ+KRsInBvdTYUjlG8BXgPWItPsF4ptdgI4DQRWSMiH4XTbgRex9/yJRMYB2xth1Rl4n0Knwiege+uXoy/iOfkcF6xOOB5/NXSa4DHgH8D94XbyQQOxVdc14TxvxIuY0zM69rEcfiMzbfkbJ6dSfNOTbawhjGmRrhSjxjngsB61oypAfbCMjXuX0e9zf37Hw1Ao40bebTtAv5+Tu8oR2VMnVRjKZv75+oS/++DB5rHdHpoXcnGGBOjPuy9y6Z2bnIyKc0bRTEaYxqoenYfQ0sMzTYV3iT7tHJm91XV+bUZjzH1WuNUXBAQOEdCYQFHtq1vX85ljKlt1pVsTM2wF5apcevnruKiO35jRXwa97RcTP8RR0U7JGPqqprrSr5yTcmu5PubxXQJ0SqGxhgToxp3bcHJR68D1tH/KEsKjTFbzxJDY4wxxpjqqmdjDO12NcYYY4wxBrCKoTHGxLSpOc1ZmpPCrlkB7dPrV+XCmJhQz152lhgaY0yMevfDhVxw1hM035DN9yP7kjJ5OM0b1bN3KWNMrbKuZGOMiVEDLrqXFhuyccBeM39n5fB3ox2SMQ1Q/frqE6sYGmNMjEpYl81tB5/Ij516ctyvP7HPt9OjHZIxJsZZYmiMMTFq5OBjeXTPwwD4qM8u3Dbtf9wc5ZiMaXBiv0hYgnUlG2NMjHpl571LPF/UpEWUIjHG1BdWMTTGmBi1NiV185MgYEJGm+gFY0xDZRVDY4wx0Za1sYi0/I2bnicWFrLzdBtjaIzZOpYY1hEikiUie0Y7jsoQkVEi8ky04zCmIVqcVcTV4wp4+MiXyUxutGl6fkICG+PjefuPItZusK/qNqb22FXJpgaoanq09i0ic4Fhqjq6FvY1DtgTyAcKgT+BEar6VsQyRwDXAf3DSZOBu1T1/YhlWgF3AYcBTYBMYBJwtqouEZGuwBwgByh+l1yrqh1r6NCMqbaflgQM/aCQdRvh34PiOL2v47QPCnl1BsQ5uHag46we+fxx2P3sNfN3ZPt+XH7UmX/5Kq7XBuzL6+/mE8TFk5YAZ+wIr0yD3quW8NYzd9N+1w7w6pXQKDlKR2qMqeusYmiiYXiYCLcAXgFeE5FeACJyNvBmOL1D+HgZeCOcV2w0kAEMCLfVL1yndKlke1VNDx+WFJo66aLPC5m1FlbkwrmfFPHOzCLGTIeiAAqK4I4fAz647lP+NlVpuiGHIZN/4IgZk/66IecI4uIByC6AxyfB2o0wPr0dt+5xJPz3J3jy09o8NGPqv/pVMLTEsKaIyFwRGSYiX4bdxFNFZGcROUVEZonIOhF5RkQSwuUDEdknbHcVkU9EZK2IrBGRiSKyfThvlIi8JCLPhfMXhdvsLyI/iUhmuM/2EbFcLiLTw3nzRWSkiMSH88YCnYFnwjg/DacnisgNIjIjXG+2iJwYcYjJIvJ0RAwXVPUcqWoB8BgQD+wkIunA/fjq4GOquj58PA7cDdwfLgOwFzBKVZeH21quqi+q6tKqxlETMjMzrW3tSrcLIz7OFAWQlb2BvygsKvk8qFp3cWFc3KbtRPt4rW3t2m6byrPEsGadCVwMNMN3h74DHICvbu0EHA0MKWO9O4H5QBugJXAWsCZi/onAW0BzYDjwNHA7cFy4TgDcFrH8QuBwoDFwDHA2cC6Aqh4V7uvcsKp2SLjOCOA04KRwvf2BP0rFMDaM4VLgERHpUsnzAoCIJAGX4LuVJ+OTvSb4amBpL4Xzisdhfg3cKyLni8iA4kS3DD+KyAoRGScig6oS39bIyMiwtrUr3X74wHjapEJKAvxncByn9U/liO5+GQdcOsBx8IhD+Gq7HSiIi+PD3gN4bec9+Isg2JQwJsfDqb0hIS5gh6wV3PzF2zBoR7jgkKgfr7WtXdvtmlW/SoY2xrBmPaWq0wBEZAxwKrCHqmYD2eF4O8F3lUbKA9oC3cP1p5Sa/4WqfhBu90XgceAlVV0YTnsTOK944cjxe8AvIvISMBh4sqygRcThE7Yhqlq874XhIzKG/4btt0VkLX5M4Lxyz8ZmN4rIVeFxzgJOUNVZIrJ7OH9RGessDn+2Dn8OwSekfwceAjaKyCjgOlXdAKzEJ5ETgUR8MvyRiOwecUzG1An7dnQsvTiBIAhw4bjB94/3z4FwWir8MZxV6/NZ9PqftJ61njnJjSBu8+f7xrnZrBnWGIC4cPpLQYBz7eCWx/8yJtEYY0qzxLBmLYlo5wCFqrqi1LSyPtJcDdwEjBWRNPyYu+tVNav0dlU1R0TK2tem7YrIKcCVQHf87zwJGL+FuFsBaZSsEG7p2ACyyzmWstyhqiPKmF58bjoAs0vNax+5THguRgIjw8rjYfiq4nrg5nB+8THmAf8RkaPxFVBLDE2d5EolbqWfA7RonMh5527PWSsy2fXOeUzt0G3TvB7rVhAX17TsbVhSaEzNqGcvLetKroNUdYWqXqaqPYG9gUHANdXZloh0wnfNjgDaqWoT4FFK/imXGrzECnxyuV119rkVvscndkPLmHdqOO/70jNUNS+sXn7O5iuZy1JEvXsJm4YqsVUGXdas4NDpvwDQdv0a9slbHuWojDGxziqGdZCIDAEmAHOBdfiKV2E1N5eO/wCwAsgXkT2A04FpEcssJSIJVNVARB4D7hGR+cBv+Cpe85rshlXVLBG5GnhQRJYDY/CJ3Mn429dcUVw1FZH78VchT8Wfn/3w4zdHhvP3ALKA6fi/8zPw4yRvqKn4jaltN3z5LnvOn8WaRmk03pDD6FPKGrJsjKlR9azcYBXDumkA8BU+sfkNP07u3upsKByjeAvwHrAWn2C9UmqxEcBp4RXQH4XTbgReB97F3yNwHNCzOjFUMd6n8IngGfju6sX4i3hODucViwOeB5bjL8x5DPg3cF84v1sY+zr8mMXTgaNU9eeaPgZjasvXPXYAoFluNvFBwMRuvaIckTEm1rmgirc8MMZUir2wTI3b55KpnDRlPLssmsOr/fYib+8+PH1Vj2iHZUxdVGN1PXd9Von/98HI9JiuIVrF0BhjYtQTH77A6zvvwbFnXEWz3CxuzPw12iEZY2KcjTE025SIPIG//2FZ+qrq/NqMx5j6rPcZwne33wJAYVIi8S88EeWIjGmAYro++FfWlWxMzbAXlqkVE258hMbz1tB7+PnQrU20wzGmrqq5ruQbSnUl3xnbXclWMTTGmBi2bI8uLNujC70tKTQmOurZPUJtjKExxhhjjAEsMTTGGGOMMSHrSjbGmBi1dFU+N+kuLEhqzMVFWQw/Jj3aIRljYpxVDI0xJkadfcciNuSm0nnlBh6b5Phudl60QzKm4XGlHjHOKobGGBOjsnIT6F6QDUDr7I3ohBT27tEyylEZY2KZVQyNMSZG7bh04aZ2fAAZq3KiGI0xDVX9KhlaYmiMMTFq0K8ziSsqAqDL8pXI+F+iHJExJtZZV7IxxsSoFY1S+apbS5KK4I+mCQydtSDaIRnT8MR+kbAESwxNnSQihwGPAG2AW1T1/iiHZEydsjazkDF79icrJQmA1akteWtxG/aNclzGmNhmiaHZKiIiwDBgbyAZWAp8CByFT+oAEvF/a7kRq1b0vckPA/er6mPbPGhj6oFfdS29F61BZq9gTse2bExKIHXF6miHZUzDYxVDYzwRORgYCzwEXKKqi0SkHXAucKOqvhouNww4SFUHVWHz3YEpW9h3oqrmVzt4Y2JA4fJscr+YR1KfFizNS2DSa/PIHb+Qj9t3Z4c/5rJzAXy0Z3/igoDEgiImd+7L/Tu/S6vOqQy9uy/xO3Rk4dJ8Zs3Lo++8GbRuVAhH7ApxlRte/sPigPnrA/7W3ZGRVM/e/YwxZbLE0GyNx4Axqnpt8QRVXQIMr+4GRaQ98AcQD3wqIkXALsAN+MpjPnA08BpwkYgcC9wE9ACWACNU9eWI7Z0N3Ai0At7Df7YrUNWzqhujMbWhcGUOi3d9nsKFmQRxjl/adGB1Wjq5SYnEty7i1N8/4O79TicjK5sDJs0gsaCQ37u2JyUPNs7IZvTR37DHwwO59oMU8vIhfUMaD713H+2O+wFeuKzC/T8zpYjzPvUXtuzcCn48NZ6UBEsOjfmr+vW6sKuSTbWISC+gJzBmW25XVReravHXNxyiqumq+kf4/CTgI3yS96+wYvkscAXQHDgTeERE9gtj3Bd4FLgwnP8ZMGRbxmtMTdnw7UIKF2YC4IoC2mT5dqO8fDqsXEOb/GWQuJ4+cxaRVFCIA3aYuxgXQACsSG3Mtx8tJy+sq2elpDGxY2949dtK7f/V6cGm9pQVMG3Vtjw6Y0xdZYmhqa5W4c9FtbjPb1X1NVUtVNUc4HLgIVX9RlWLVHUCMBo4I1z+DOBNVf1MVQtU9UVgQm0EmpmZaW1rb1U7aYeWkBy/eVpyCgAFcXH80b4t+fEJHDP9Bxrlbf62k0LnSCgKKHSO1Pw8uvXY3CkUV1RE91ULYZfulYphx2abt9ssOaBL4+ifE2tbu7rtGlW/bmOIC4Kg4qWMKSWsGM4ADlbVzytYtspjDEUkAPZV1W/D56OAIlU9O2KZ34Cu+O7lYvHAN6r6NxH5CFBVvSlindHUTleyvbDMVtvwzQKyX/udxB1a8cvGVGZ/uZzEeasZ17U77TLncejU35nRpBcARcCsDq0piHN0y1zJGWdm0OQfBzDuxxx++zWLgb8ruyWshKuPhZaNK9x3QVHAgz/7MYbn7RzHTq3qwTueachq7A/Y3ZJb4v99cFujmH6x2BhDUy2q+oeIzAJOAbaYGG5DRaWezwNGqeq95Sy/CJ84RuoKzNq2YRlTM1L27UTKvp0A2B/Y/4reAJwOfPlJC369rIjE8AbXccD8Vs145fU+OLf5fWnQ7qkM2j0V+FuV9p0Q57hqYEy/vxljqsESQ7M1LgbGisgy4BFVXSwibYCzgTnFVyXXoAeBUSIyHvgeXy3cCXCqqsBLwMdhtfEr4GRgdywxNPVAp13a8E639uw8238t3uqMVLq53BJJoTHGVJWNMTTVpqqfAfsAfYGpIpIJfAu0BsbVwv4/Bc4D7gVW4q9KfgBID+d/BVwKPAOsBg7DX81sTMzr2SqBqV3b8sARe/DkQbsyev9dSNupSbTDMqbhsTGGxsQuEXkGSLAxhqY+aDdsLUubhhfxBwH3Jszlqn9uF92gjKmbam6M4a2lxhjeGttjDK1iaIwxMappTvbmJ87RdcWa6AVjTEPlXMlHjLMxhiYqRCSrnFnfqOrhtRqMMTHqpk/e4rpj/49lGU245bM36dQ1Fdgt2mEZY2KYJYYmKiJuYl3b+z03Gvs1piYUNkti/p0XE+D7ySa9c0e0QzLGxDhLDI0xJkZ1f+o0Xh+ayfbLF/PjIftz3jG9ox2SMSbGWWJojDExau++6bx22w5oYX/OO+4gu1WNMdFQz152lhgaY0wMS40rJDWu0JJCY8w2YYmhMcYYY0y11a8PZZYYGmNMDIvbWECjVdmQXwCJ9i/dGLN17D6GxhgTq+YuZ/cL/kv6HZNZ1+dKWJ8T7YiMMTHOEkNjjIlRi64cw9s9d2VCxx5cIkczb8T70Q7JmIannn0lnvU7GGNMjPpiIVx63DnkJSbSbeVSHlwyjQeiHZQxJqZZxdAYY2LU8wP2Iy8xEYA5LdvSZt7CKEdkjIl1VjE0xpgYlVhQiD54LdutXMp/9j6MtLy8aIdkjIlxVjE0xpgYNfKjMey6aA6NN+Zy4xfv0CS3vK8gN8bUmHo2xtASQ7PVROQGERm7tcsYY6qmy9qVJZ63yV4PQRClaIwx9YF1JW+BiNwIjADOUtUXKrlOM+A24DigJbASeBu4RVXX1lColSIio4BTgY1AEbAQeFhVn9ia7arqnaX2Mw74XFVHlLfM1hKRM4ALgT5AIfATcI2qTo1YJg0YCZwEZAALgKGq+su2jMWY2vTHH+uJf+pjln76Gx2SUmiRm71p3g9derHnxiIap8RHMUJjTCyzimE5RCQOOA9YDZxfyXXSgW+AAcBhQHr4cxfg6zBRqREikljJRV9Q1XSgKTAceFxEDqypuGpQBnAL0BHoAEwEPhWRVAARccC7QFdgt/CYjwAWRyNYY7bo7rdhv2Fw48uwMQ/++Zx//uhH8M3v0OYsSDyJhw8dRf834+jf9DAOGnIVn/Xaic967siy9Ca8sfMe3HXgcdww5H3GHPwUk/rcyGeXvrtpF2s3BJz5YSGDXi3g3ZlFUTtUY0zd5gLrdiiTiBwO/Bc4Fngf2ElVf61gnWHAlUAPVV0TMb0ZMBu4R1XvEpFB+IpaQsQytwL7qOpB4fMWwD3AIUAK8CVwqaouC+fPBZ4DDgAG4pPY54C9IitiIvI18JmqDg8rhgWqem7E/JXASFW9T0ROAG7GJ1NzgVtV9Z1wua7Ak8DuQADMAU5R1RmRsYvII8BFQAGQDyxS1e3LOb4HwuMD+AT4p6qujji+p4DB4T7nAuer6vflnPsUIBfYVVUnisihwDtAx+Jt1jJ7YZnKGfsTHD1y8/MT9oS3ftj8PCkB8gooiIsj6a4xBC4OV1TE6T9/zYsDBwHQY+USZrdshwsCWmWtZdntF2xa/Zunh7Hvubtw7ieFPDvV/1kmxcPsc+PpmFEPBkQZUzk19sfu7sgr8f8+uDEppl9YVjEs3/nAR6r6ATAFuKCC5QH+BnwQmRQChM8/wFesKhRR7QqAHYEuQCYwptSi5+ET0Yxw+TeAyKSvF7AnPmEsvY94ERkKNAdURPYCXgauA1oANwCviMju4Sp3AvOBNvgu8rOANaW3q6r/wFdNh6tquqpuX85hvgw0w3cF9wm3+VKpZc4GLgOaAJ8BW+rOHwzkADPD5wfgk9fbRGS5iMwWkTurUFndKpmZmda2duXai0p9bllYctwgeQUA5CYkEjj/L7vz2pV81aPvpkVmt2xHi6x1BM6RXFBQYvUN81YBMG/t5ul5hbAiJ0rHa21rR6FtKs8SwzKISHvgSDYnVM8Cp4lIowpWbQUsKmfeYqB1JUPYNXxcoqrrVDUHuAY4UEQ6Riz3tKr+oqqBqubiK2xDw+oZwDnAx6oaGdPpIrIWWA5cBZyjql/hE723VPUjVS0IE+J38MkZQB7QFuiuqoWqOkVVl1fyeEoIz++hwJWquiZMnK8E/iYi7SIWfVJVf1PVQuAZoKeINClje72A54F/qWrxf4KWQF/8eMrO+C79k/DnscZlZGRY29qVa5+0F2zfwU/o2AJGnAqtGvvnA7rBkL39snkb2XnxXABWpDem56qlm7bTZfUKvnjidlplrePWj19jVaN0AGa260i/C/YA4No9EmkU9lEc3cPRr3UdOHZrW7uW2jWqnl2VbBeflO0c/NjC4u+XGo3v1h0CjNrCeivw493K0h6fjFVGNyAZWCYikdM34JOc4rvYzo2cqarfishi4EQReRU4k7+Oj3wpsis5Qifg51LTZuPHRwJcDdwEjA3HSr4JXK+q1bk/Rqfw55xS+yqetyRsL4mYXzzCPgNYVzxRRPriq4n/LnURTSb+opQbVXUjMFNEHgWGAndUI2ZjakaLDJh0H8xaAl1bQ3ojmPUYLFgJ27WDpES45jiYOo/J+2fwVh5MH7+ar+cVceaEL4kn4Pov3qHnqmU8+/pjtHvxAja0O4+Zs1fScUBbGmUkA3BQlzjmn+9YkQvbN4c4Vw/ewYwx25wlhqWEF52cg784Y2FEYhaP704etYXVPwYuF5GmkVcgi0hTfDfzg+GkTCBeRJLDpAV84lhsHj4Raq6qWxolXta8J8P4s/CJ0QdbWD/SAvzYwkjdw+mo6gp8t+5lItIdeA9ffbu5knGV3hfh/mZF7CtyXoVEZBf8OR+uqv8pNXtSOavZ2D9T96QkwY5dNj9vnAo7dN78fJfu/gGcANCrM/88qT0v7/845/305abFdl48ly4SlgI7/PVat5apjpapNRC/MabesMTwrw7DV612o2S3cD/gYxHZKfKWKKU8CJwCvCciFwEzgF7A48Aq4JFwuT/widu5IvI4sBdwIv7KWgAFJgMPi8gtqrpKRFoBg1X11Qrifwl/i5ZbgOfDbtjKeAH4XEReAj7HXxRyPDAIQESGABPwVcp1+K7l8ra9FOhZ3o5UdbGIfArcJyJn4ovv9+HHdC4pb71IIrI3vqJ7jao+XcYibwN34ccY3oyv5F5EGeMtjYlFqY0SaJldcgzVD5160aWc5Y0xpjJsjOFfXQC8q6o/q+rSiMcnwA9s4SIUVV0P7A1MBT7FXyX7O/6iiP2LL0oJx8H9HfgXPsm6nIgLK8Iq4TH4hOlnEckExhMmaVsS7uNNfCL7bGUPWlW/w3c9/xt/Uck9wGmqOj5cZADwFT6h/Q2fxN5bzuYeAERE1orIb+Uscxq+cjoDmA6sBc6obLz4+0s2AR4QkayIx77h8WTixzHuHR7POOCV8PiMqRcyNuSUeN5p3cpyljTG1Jh6NsbQbldTw0TkNuB0fGJY6W7Srdznrfjb1hxS0bKmxtgLy9S4BY3PoVPm5psDTGvZjj4rHo1iRMbUWTV3u5qRpW5Xc73drsZsgareAtwN7F8b+xORNvjb2DxUG/szxkTPvBatSjyf3KtXlCIxpiGrXyVDG2NYBWE35UflzL6zvK99U9Unay6qzUTkfnxX90vh7WaMMfXYe4MPYZ9n/wD8FV9Tjz6Ik6MbkjEmxllXsjE1w15YpsY9O6WICbd9wl7zZvDfvsJlI/Zi/87WEWRMGWquK/mu/JJdydclxnTZ0CqGxhgTo87ZOY4/jm/JmJztufKgrpYUGmO2miWGxhgTw/ZpvJR9Gi/l0G7dK17YGGMqYB8vjTHGGGMMYBVDY4wxxpjqi+kRhX9lFUNjjDHGGANYYmiMMcYYY0KWGBpjjDHGGMDGGBpjjDHGVJ+NMTTGGGOMMfWRJYbGGGOMMQawxNAYY4wxxoRsjKExxhhjTHW5+jXI0CqGxhhjjDE1xDk31zm3Y7TjqCyrGBpjjDHGVFf9KhhaxdAYY4wxpjY5585wzk11zk1xzr3jnGsdTv/BOTcwbD/mnPstbCc451Y659JqOjarGBpTA5xznwAtK1ouISGhZUFBwcpaCClm2TmqmJ2jitk5qlg9P0cfB0FwWE1sOLgqoUo1w7Bb+S5g1yAIljjnhgP/AYYA/wMGAz8B+wC5zrl2QFdgWhAE2dsy9rJYYmhMDajsPyARUVWVmo4nltk5qpido4rZOaqYnaNacwDwYRAES8LnTwKTw/b/gBudcy8Dq4Cv8IliN+CL2gjOupKNMcYYY+qG74FdgCPwSWJxBXFw2K5xlhgaY4wxxtSeL4G/Oefahs/PAz4DCIJgIzARuA74HBgP7A3sHLZrnHUlGxNdT0U7gBhg56hido4qZueoYnaOas7nzrmCiOfXA5855wLgT+CCiHn/AwYCPwVBUOicmwXMCYIgrzYCdUEQ1MZ+jDHGGGNMHWddycYYY4wxBrDE0BhjjDHGhGyMoTG1SERSgeeBXYEC4CpVfb+M5Y4BbgaS8ffVf05V76vNWKOlCueoAzAafwXfzPp+mw0R6QW8ALTA38biDFWdWWqZeOBh4DAgAO5S1WdqO9ZoqeQ5OgS4E9gJ+I+qXlXrgUZRJc/RTcDJQCGQD9ygqp/UdqwmOqxiaEztugpYr6o9gaOAZ0QkvYzllgJHqeqOwF7ARSKyby3GGU2VPUdZ+OR5aG0GF0VPAI+qai/gUfy9z0o7FegJbAfsCdwqIl1rLcLoq8w5+hM4F7i3NgOrQypzjiYAA1V1Z+Bs4DURaVSLMZoossTQmNo1hPAfcfgpXYHDSy+kqj+q6uKwvQ6YBnSpxTijqbLnaJ2qfgPU+DcBRJuItMZXRl8JJ70C7CIirUotOgR4WlWLVHUF8C5wUq0FGkWVPUeqOktVJ+Gr0Q1KFc7RJ6qaEz6dgu+1aFFrgZqossTQmNrVGZgX8Xw+0GlLK4hIb2APaumu93VAlc9RA9AJWKSqhQDhz8X89bw05HNX2XPUkFXnHJ0BzFbVhbUQn6kDbIyhMduQiEzEvzmXpU01ttcOeA+4uLiCGOu29TkyxtQMEdkfGA4cHO1YTO2xxNCYbUhVd9nSfBGZj+8SXhFO6oy/C35Zy7bG3/n+HlV9Y1vGGU3b8hw1IAuADiISr6qF4UUm7cPpkYrP3U/h89IVxPqssueoIav0ORKRPfEXdx2jqjNqOU4TRdaVbEzteoPwDvcish3+7vYfl15IRFrgvyLpEVV9tlYjjL5KnaOGRFWXA5OAU8JJpwC/hOMII70BnCciceG4sWOBN2srzmiqwjlqsCp7jkRkIPAacKKqTqzVIE3U2TefGFOLRCQNGAUMwN8K4hpVfS+cdzuwWFWfEJF7gX8AkZ/UH1LV52s55FpXhXMUj6+GJQNNgOXAM6p6azTirmnhWNMXgGbAGvxtRmaIyIfAzaqq4Tl5BDgkXO1uVW0wX3NWyXO0D/Aq0Bh/UcU64JyGcjuWSp6jn4CuwKKIVU9X1am1HrCpdZYYGmOMMcYYwLqSjTHGGGNMyBJDY4wxxhgDWGJojDHGGGNClhgaY4wxxhjAEkNjjDHGGBOyxNAYY0LOua7OucA517GG93Ohc+6liOcfOeeuqcl9mrI552Y5586q5LK18vdRG5xzyeGx9452LKZuscTQGFNlzrnuzrk3nHNLnXNZzrkFzrl3nHNJ4fyznHOzylivvOmnhm+4t5Qxb5xzbmO4n3XOuV+ccyfUzJHVPOdcGnA7cGvxtCAIDg+C4J6oBVWB8HezT7TjaAhq4lw75wY55woipwVBsBG4N3wYs4klhsaY6vgQWAJsD2QAewKf4G8YXB0XAKuBc5xz8WXMHx4EQTrQAngFeM0516ua+4q204CpQRDMjnYgpsF7BTjQOdcz2oGYusMSQ2NMlTjnWuATwieCIFgXeAuDIHgirEJUdXt9gH2BM4F2wOHlLRsEQQHwGBAP7FTGti5xzk0qNa2bc67QOdc1fP58WOHMdM797pwbuoXYbnXOfV5q2jjn3LCI5zs65z5xzq1wzs13zo10ziVu4ZCPxX/dYZnbjOiuPDOML9s596Fzrplz7i7n3PKwUntJxPpnhd2C1zrnloTL3BcZR0XH7Zzb2Tn3cXgcq4uP2zk3OVzk07Bq+0w55yrVOfdQuI+Vzrl3nXOdSx3jfc65t8IYZjvnjinvJEUc0z+dcwvDdf7tnGsRbmO9c256ZHXNOZfgnLvZOfenc26Nc+5/zrkdI+YnOufujziH15ax332dc9+G52C2c+5fzrlKf+Bxzp3gnJscVrcnO+eOK31MpZYfVXxOyzvXzrm54XF9G05X59zAsrYRMW2uc+4051x74CMgPlw3yzl3JkAQBOvx36t9dGWPz9R/lhgaY6okCIJVwG/AM865M5xzfavyxlmG84EpQRC8j69EXlDegs53VV8C5AOTy1hkDNDbOdc/YtpZwLggCOaGz78F+gNN8V26o5xzfasTuHOuNfAV8DbQAV85PRi4fgur7QL8XonNnwDsA3TGfz3Zj8BsoD3wd+DByMQL6BIu2z2M4yjg6oj55R63c65deBxfhftqC9wFEARBv3D9Q4IgSA+C4Nxy4n0A2CN8dAFWAmNdyQrwmcB9+K8wfAR4wTmXuoVz0CWMt3t4Li7FJzn34r/S7W0g8msirwbOAP4WHsM3wGfOucbh/OuAI4G9gG7hsXYpXjk8Hx+G228FHIH/asrTtxDjJs65vYCXw/20AG4AXnHO7V6Z9Ss41xcClwPN8d9//WHEcW1pm4vxH7YKw22mB0HwQsQiU/F/k8YAlhgaY6pnEDAOuAKYBCxzzt1UKkHs5pxbG/nAV/s2cc6l4N/Ii9/cnwUOd38d3H9juP5C4BjghCAI/jJWMQiCNcB7+MSJMJ4zgecilnk2CIJVQRAUBkHwKjAlPJ7qOAOYHATBk0EQ5AVBsAgYGU4vTzNgfSW2PTwIgtVhIv4+kB8EwdNBEBQEQfAR/ntuB0QsXwRcHQRBbthNfQ8+KQYqPO7TgVlBEIwMgiA7PJYSldItcc7F4c/zsCAIFgVBkI3/2+gD7Bax6GtBEHwfBEER8BQ+QdxuC5vOBW4L45mM/zDwUxAE44MgKARGAz2dc03C5f8O3B0EwfSwen07/vu2jwjnnxHOnxUEQS5wFRD5vbAXA28EQfBeeJ6m4xPYLf0+I50FvBUEwUfh7+kD4B3g7EquvyXPBkHwcxAEecDd+HNz5DbY7np8smkMYImhMaYagiBYGQTBDUEQ7IKv6FwD3EyYkIXmBEHQNPKBf+ONdBKQjn+DB1+tWQGUrkrdEW6jdRAEewVBMHYL4T0PDA27UQ8M43sbfALjnLvdOTcj7OpbC/TDV4eqoxuwd6nk9zl8tao8a4AKKz34MZzFcko9L56WEfF8eRAEORHP5wIdoVLH3RX4oxIxlacVkAzMKZ4QBEEWsBzoFLHckoj52WEz8hhKWx4mkcVKn4fi4y3eRqdSMRThz0NxDB3D55ExLI/YXjfglFK/z1vwQxwqo8T+Q7MpeQ6qa25xIwiCAJhP+PvdSo3x43uNASwxNMZspSAIcoIgGIWvQPWv4urn48cL/uqcW4qvCDaj/ItQKuMzYCO+K/Us4NWwOgRwCj7pPAFoFiarkyn/oplMIK3UtPYR7XnA56US4CbhhTLl+QWoVtd1BVqX6pbtij+fUPFxz2XLlbtgC/PAJ/Mbw30C4JxLB1oDCyoT/DayoFQMceHz4hgWlZqfRskPBfOA50r9PhsHQbBDdfYf6h6x/4r+nqD8cx0Zt8MPGyj+/ZbYrnMuAX/ui0Um16XtiP+bNAawxNAYU0XOXwQx0vmLLhLDAf8n4N9gvqnCdvrix40dh08oix+74Stuf6tOfGEX44vAZcDxRHQj46sjBfhEJs45dza+claen4FdnHO7hsf5D3xVqdiLgDjnznbOpYSVue7OucO2sM13gYOqfGAViwPuds41cs51x3eTFo8lq+i4RwPbO3/xSqpzLsk5FxnjUraQOIaVuReB4c659mGCeh8wHZiwjY6vMkYB1zjneoXjUW8EEoAPwvkvAVc753o45xrhu9sj3wcfA052zh0V8bfd1zm3fyX3/wJwgnPuUOdcvHPucPzfYPFQiUn4BP7I8G/lOGC/Utso71yf7ZzbJayEXw2kRhzXz8Bg5y+0SgbuACIvgFqKv/gk8m8X51wG/vX230oen2kALDE0xlRVHr4a8Ta+C2oFMAy4LAiCN6qwnQuAiUEQjA2CYGnEYwrwBlu4CKUSngf2x3dnRyYmL+Av4piFrx71ZQvJbBAE44D7gY/xXZhtgO8i5i8FDsBfaTwX3038Dr5KVJ6XgH5h8rYtzcNXkObgj/FjfOIDFRx3eIHCIPyFMwvxiUTkhSs3Arc7f6Xvk+Xs/5+A4q9ynY/vfj06TNRry734W7B8CizDDyU4JLz6Fvz4z0+A8fjzNB9/3gAIguBX/Li9K/C/7+X4ZLNSQw2CIPgOP9by3/i/hXuA04IgGB/On42/gOQp/GvnMOCtUpsp71w/BTwcbncIcEQQBOvCeS/jk7uJ+K7r+fjfc3FcfwCPAxPCLvLii2lOAb4MgmBmZY7PNAzOD1UwxhhTW5xzFwJ7B0FQqatdK7G9s/AXftj96Ooh59xc/O93dEXLVmGbycCv+OR92rbarol9CdEOwBhjGpogCJ4Anoh2HKbhCq/a3tK4UtNAWVeyMcYYY4wBrCvZGGOMMcaErGJojDHGGGMASwyNMcYYY0zIEkNjjDHGGANYYmiMMcYYY0KWGBpjjDHGGAD+H8Ob5vc4HrPzAAAAAElFTkSuQmCC\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": 21,
   "id": "residential-victory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14437   172]\n",
      " [   41    89]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99     14609\n",
      "           1       0.34      0.68      0.46       130\n",
      "\n",
      "    accuracy                           0.99     14739\n",
      "   macro avg       0.67      0.84      0.72     14739\n",
      "weighted avg       0.99      0.99      0.99     14739\n",
      "\n",
      "0.4487518983461014\n",
      "No Skill PR AUC: 0.514\n",
      "Logistic ROC AUC 0.836\n",
      "SpearmanrResult(correlation=0.4770021735727834, pvalue=0.0)\n",
      "0.014451455322613474\n",
      "0.014451455322613474\n",
      "0.12021420599335784\n",
      "-0.6530415918532839\n",
      "-0.6440055392618889\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.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(235)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_AsCpF1/AsCpf1_Dataset_MM-Seq_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",
    "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",
    "clf = DecisionTreeClassifier(class_weight={0:1, 1:9}, max_features=20, min_samples_leaf=9, max_depth=14, ccp_alpha=1e-6)\n",
    "#24\n",
    "clf.fit(data_train, target_train)\n",
    "Pred_labels2 = clf.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels2))\n",
    "print(classification_report(target_test,Pred_labels2))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels2))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels2)\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_labels2)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)  \n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels2.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels2.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels2.round())) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "protected-compiler",
   "metadata": {},
   "source": [
    "# LOgisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "comprehensive-public",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14589    20]\n",
      " [   36    94]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14609\n",
      "           1       0.82      0.72      0.77       130\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.91      0.86      0.88     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.7685845308068353\n",
      "No Skill PR AUC: 0.775\n",
      "Logistic ROC AUC 0.861\n",
      "SpearmanrResult(correlation=0.7702700633315209, pvalue=0.0)\n",
      "0.0037994436528936836\n",
      "0.0037994436528936836\n",
      "0.0616396272935981\n",
      "0.5653975157568831\n",
      "0.5655323114834372\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\utils\\optimize.py:202: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  warnings.warn(\"newton-cg failed to converge. Increase the \"\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 imblearn.under_sampling import NearMiss\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(235)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_AsCpF1/AsCpf1_Dataset_MM-Seq_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",
    "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",
    "lg1=LogisticRegression(class_weight={0:1.3, 1:2.3}, C=5, solver='newton-cg')\n",
    "lg1.fit(data_train, target_train)\n",
    "Pred_labels2 = lg1.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels2))\n",
    "print(classification_report(target_test,Pred_labels2))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels2))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels2)\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_labels2)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)  \n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels2.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels2.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels2.round())) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "closing-decision",
   "metadata": {},
   "source": [
    "# LInearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fitting-pantyhose",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14600     9]\n",
      " [   37    93]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14609\n",
      "           1       0.91      0.72      0.80       130\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.95      0.86      0.90     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.8001743626743627\n",
      "No Skill PR AUC: 0.815\n",
      "Logistic ROC AUC 0.857\n",
      "SpearmanrResult(correlation=0.8061599854361692, pvalue=0.0)\n",
      "0.0031209715720198115\n",
      "0.0031209715720198115\n",
      "0.055865656462802005\n",
      "0.6430051022288683\n",
      "0.6434179141414405\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 imblearn.under_sampling import NearMiss\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "np.random.seed(235)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_AsCpF1/AsCpf1_Dataset_MM-Seq_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",
    "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",
    "svc=LinearSVC(C=0.01, loss='squared_hinge', penalty='l2', dual=False, tol=0.0001, class_weight={0:0.26, 1:0.7})\n",
    "svc.fit(data_train, target_train)\n",
    "Pred_labels2 = svc.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels2))\n",
    "print(classification_report(target_test,Pred_labels2))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels2))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels2)\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_labels2)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)  \n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels2.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels2.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels2.round())) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aggregate-palestinian",
   "metadata": {},
   "source": [
    "# AdaBoostClassifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ruled-projector",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "[[14596    13]\n",
      " [   37    93]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14609\n",
      "           1       0.88      0.72      0.79       130\n",
      "\n",
      "    accuracy                           1.00     14739\n",
      "   macro avg       0.94      0.86      0.89     14739\n",
      "weighted avg       1.00      1.00      1.00     14739\n",
      "\n",
      "0.7864435483029659\n",
      "No Skill PR AUC: 0.798\n",
      "Logistic ROC AUC 0.857\n",
      "SpearmanrResult(correlation=0.7906082567016591, pvalue=0.0)\n",
      "0.00339236040436936\n",
      "0.00339236040436936\n",
      "0.05824397311627496\n",
      "0.6119620676400742\n",
      "0.6122653580248211\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 imblearn.under_sampling import NearMiss\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "import numpy as np\n",
    "np.random.seed(235)\n",
    "OT_data=pd.read_csv(r\"D://PhD_related/obj1_MM/mismatch_energy_AsCpF1/AsCpf1_Dataset_MM-Seq_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",
    "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",
    "clf = AdaBoostClassifier(n_estimators=90, learning_rate=0.79, random_state=16)\n",
    "clf.fit(data_train, target_train)\n",
    "Pred_labels2 = clf.predict(data_test)\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(target_test,Pred_labels2))\n",
    "print(classification_report(target_test,Pred_labels2))\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(cohen_kappa_score(target_test,Pred_labels2))\n",
    "precision, recall, _ = precision_recall_curve(target_test,Pred_labels2)\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_labels2)\n",
    "print('Logistic ROC AUC %.3f' % roc_auc)  \n",
    "from scipy.stats import spearmanr\n",
    "print(spearmanr(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_absolute_error(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(target_test,Pred_labels2.round()))\n",
    "from math import sqrt\n",
    "print(sqrt(mean_squared_error(target_test,Pred_labels2.round())))\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(target_test,Pred_labels2.round()))\n",
    "from sklearn.metrics import explained_variance_score\n",
    "print(explained_variance_score(target_test,Pred_labels2.round())) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "continental-primary",
   "metadata": {},
   "outputs": [
    {
     "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 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",
    "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(ada, 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",
    "font = font_manager.FontProperties(family='Arial Rounded MT Bold', size=50)\n",
    "#ax.legend(prop=font)\n",
    "ax.legend([\"SeqBasedClassifier (AP=0.89)\", \"MMEClassifier (AP = 0.85)\", \"CombClassifier (AP = 0.90)\"], 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_AsCpf1-saved2.png\", dpi=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "compound-potato",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off-target' :  [0 1]\n",
      "Train: [    0     1     2 ... 58950 58951 58952] Validation: [    9    10    11 ... 58946 58953 58954]\n",
      "Train: [    1     2     3 ... 58952 58953 58954] Validation: [    0    17    21 ... 58942 58944 58950]\n",
      "Train: [    0     1     3 ... 58952 58953 58954] Validation: [    2     7    28 ... 58940 58943 58948]\n",
      "Train: [    0     1     2 ... 58952 58953 58954] Validation: [    3     5     6 ... 58933 58935 58939]\n",
      "Train: [    0     2     3 ... 58950 58953 58954] Validation: [    1     4    12 ... 58949 58951 58952]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-bee6eeaad842>:77: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  f.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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sX78eCxYsyNRerFgxLF++XDFRvGXLFmGN+SlTpmics0+fPqhXr55ibPfu3aoHf6alpeHvv/9WjPXs2RNeXl4a5587dy6cnJzeasubNy+aNWumsa8+IiMj8eOPP2Zqz5cvH4KCgmBra2uSeYmIiIgo67FEBhHReyglJQVHjhzBrVu3EBsbi4SEBLi4uMDV1RXly5dH9erVjVrPVF+yLOPEiROIiorC/fv38eLFCzg5OaF8+fLw9PTMlCzJ6rVt2LABv/76K06cOGG2dehr0aJFiu0WFhb49NNPcfv2bcyZM0fxmnnz5ikm53KiadOmqe4mnjt3LqpVq6bXuAcPHsSRI0cU4/v27UPDhg11HtcUtm7dKoz5+vpq7N+kSRMULVpU8bC6oKAg4Q5nc7l16xb69euXqd3S0hIrV66Eq6urYr8dO3Yotru5uaFWrVpazf3VV18hMjIyU3vGz5MhQ4Yo9gsLC8OdO3cUY9qWISlTpgwOHjyIcePGITIyEiVLlsTEiRNRqlQprfrrIiEhAZ988oliHe758+ejUqVKRp+TiIiIiMyHCWYioveELMvYtGkTAgICsGfPHtWDnQoXLgxvb2989913Wu/cNKYnT55g4sSJWLFiBe7evat4jaWlJdq2bYsff/wRderUydL13bp1C926dcPBgwdVr2vQoIHGa8whOTkZy5YtU4w1a9YMxYoVQ7FixVCpUiVERUVlumbFihX4448/4ODgYOqlmtTjx4+xdu1aYbxVq1bo1q2bXmNbWVlh/vz58PDwQHp6eqZ4fHx8pramTZsiPDxcr/m0FRoaiqZNm77VduzYMcVrHR0dUaNGDY1jSpKERo0aISgoKFMsO+3UzvD1118rlpoYPnw4mjRpIux34cIFxXbRrmQlamUswsPDhQnm4OBgxfbixYsr1ooWqVy5suLnydhGjhypuCu+e/fu+Oyzz0w+PxERERFlLZbIICJ6D4SFhaFOnTro2LEjgoODVZPLwKtDzwIDA1GvXr0sP7AuKCgIFStWxB9//CFMLgOvHhkPDg6Gh4cHRowYIXx03RS+/PJL1cSxJEkYPny46mFv5rRp0ybExsYqxt5M/vTo0UPxmmfPnglr0eYkW7duVdxhmcHQA+pq166Nr776SjGW1W+KqDl9+rRiuy47t93d3RXbL168qNNBkqa2du1a/Pfff5naa9SogQkTJqj2ffDggWJ74cKFtZ6/VKlSwtIQak9CiBL15ngDUJPIyEhh+RG1OtRERERElHMxwUxElMvNmDEDLVu21LuMQ3BwMOrVq4fQ0FAjryyzuXPnolu3bnj06JHWfWRZxtSpU9GuXTu8ePHChKv7f2fOnBHGSpUqhV27dmHKlCmwssqeDwqJDvezs7ODn5/f63/7+/sLS6XkhhIZavWBixQpAh8fH4PnmDx5MqpUqfJWW5s2bdCxY0eDxzaG1NRUXLlyRTGmSxmDihUrKrbLsqy4C94cEhISMGjQoEztkiRh4cKFyJMnj8b+SkQHIIqIdv7funVL8bDJtLQ0nD17VrHPu/c9PDwc/fr1g5ubG/LmzQs7OzuUK1cOrVu3xrx584RvLBlLWloavv76a8Vd+zNnzoSzs7NJ5yciIiIi82CCmYgoFxs/fjwGDx6MtLQ0g8Z5/PgxvL29VWu1Gmrz5s0YMGCAaj1cNdu3b8cnn3xi5FUp8/T0zNRmaWmJAQMG4OzZs2jRokWWrEMf0dHRwsRqu3btkD9//tf/Ll26NBo3bqx47alTp7Jl+Q9dKNXCzeDl5aVz4lCJg4MDDh8+jLlz52LEiBH4559/sHnz5mxR4xx49bSCUjIQeJVk11axYsWEsZs3b+q8LlP48ccfFesY9+7dW6udwAUKFFBsV0oKi6Smpqq+gab05lVUVBQSExMVry9dujSAV/e4VatWaNq0KRYvXoyLFy/ixYsXSExMRHR0NHbu3In+/fujbNmymDRpEpKTk7Vesy7mzJmj+GZm8+bN0blzZ5PMSURERETmlz23VhERvWdEyQMlkiTBxsZG43WbNm3CL7/8onpNlSpVUL16dRQpUgQPHjxAeHi48CCp1NRU+Pv74+jRoyhfvrzW69XGgwcPFA/depOFhQWaNGkCd3d3SJKEM2fOYO/evW8lz0WP+hvbzJkzER0djePHjyNPnjzo1KkTxo0bl2mnana0ePFiYRLf398/U1uPHj2EpT7mz5+PBg0aGHV9WSU1NRWXL18WxrU9tE0b+fLlQ//+/Y02njHdv39fGCtUqJDW47i4uAhjt27d0mlNpnDlyhXFQyvz58+P33//XasxihQponi/RDWslVy8eFGY0AegmHxWe2Iib968OHr0KD766COtEt3Pnz/HqFGjsH37dgQHB7/1hpKh4uLi8NNPP2Vqt7S0xMyZM402DxERERFlP0wwE2lQ5gfT7dgk47k+qa25l2AQOzs7na+vWbMmpkyZgkaNGmWKx8fHo0+fPsJEYqVKlTB37txMO21lWcbq1asxcOBAxUep4+Li0LNnT+zfv1+n9Woybdo0YX1TAGjUqBECAgJQoUKFgTVoogABAABJREFUt9qjo6PRr18/7Nmzx6jr0aR48eI4duwYbt68iQIFCiBv3rxZOr++0tLSEBAQoBgrUKCAYkmILl264LvvvlN8E2TNmjWYMWNGjnzs/e7du6o7+3WpP5yTqSUlHR0dtR5HLVGpS8kbU/nxxx8V67QPHjxY6xrKDRo0wMmTJzO1nzhxAleuXMn080nJkiVLVONKhz+qvQlw48YNDB48GHFxcRrnflN4eDiaN2+OiIgInf//IzJlyhTFdfj7+783309ERERE7yuWyCAiyoFevnyJgwcPol27dop1QWfPno3Hjx8r9nV3d0dERIRiGQdJktC9e3eEhYWhYMGCiv0PHDiA3bt3G/YBvCExMRGLFi0Sxps3b44dO3YoJm/Kli2Lbdu2oVOnTkZbjy5KlSqVY5LLwKtD7UQHJ3bt2hXW1taZ2h0dHdGuXTvFPomJiVi6dKkxl5hlNO32VNuRm5uoPT2hqSbxm9SeqtDlCQ1TOHPmDFatWpWpPX/+/Bg8eLDW47Rs2VKxXZZljBkzRmP/06dPY/78+arXPH36NFObUtI5w6+//qpzcjnDsWPHFGtS6yMmJgazZs3K1G5hYYFx48YZZQ4iIiIiyr6YYCYiysHi4uIyPeafmpqKGTNmKF5vYWGBZcuWwdXVVXXcKlWqKD5OnmHKlCk6r1Vkx44dwmS4vb09AgMDYW9vL+yfJ08eBAQE6FQv9n0lOtwPUC6PkaFnz57CWE497O/ly5eqcWOWDtBWWFgYZFk26atp06ZvzZmUlCRcjzaleDIovTmRQdO9NrWxY8cqPs3Rp08fnXZpt23bVvhzZs2aNfj111+Ffc+ePYsOHTpovBfPnj3L1KaWYH5zV3b+/PkxfPhwHD16FI8ePUJUVBQWLlyIDz74QNj/77//FpbA0cXEiRPx/PnzTO3t27c3ekklIiIiIsp+mGAmIsrBnJ2dUbFixbfajhw5Ityd2a1bN61ry3br1g3u7u6KsbCwMMVkgj4OHDggjA0cOFD18LAM+fPnx88//2yU9eRWd+7cwbZt2xRj5cqVw4cffijs6+PjI3xTIioqCqGhoUZZY1bSdMiescoGZHdqCWYLC+1/TVQ7nFNtDlO7cOECNm/erBj79ttvdRrLxsZGdcfzuHHj4OPjg507dyIuLg7Pnj3DiRMnMHToUNSvXx/R0dEa51BK1KslmDNUqVIFx44dw5QpU1CnTh24uLjggw8+QL9+/XDixAm0b99e2Hfy5Mkax1fz+PFj4ZtXAwYMMGhsIiIiyh2ux1+H+zJ3PE5U3lhEOR8TzEREOZCjoyO8vLywdetWODg4vBULCQkR9tO1lIRoV2tycrJRdr0BQGRkpDDm5+en9Th+fn6wsuLRAiJLliwR1hz+7LPPVPtaWVmhe/fuwnhO3MWcL18+1bi5yzpkFbUkslqN6ncp1TfOYGlpqdOajGnGjBmKye8mTZpoVTP5XYMGDVJ9k27Hjh1o3bo1nJ2d4eDggNq1a+PPP//EixcvXl+j9uaG0q5xTZ8HR0dHbN26Vfjx2NvbY9WqVcKdxNu2bcP169dV51CzYMECxZ3ZZcqUQfPmzfUel4iIiHIfr9Ve5l4CmQgTzERE2YCuj7nHxcUhLCwMDRo0yDSW0iFUGVq1aqXTujw9PYWx48eP6zSWyK1btxTbXVxcUKdOHa3HcXFxUbwfBKSnp2Px4sXCuFp5jAw9evQQxjZs2KB6SGN2pKk0gr51bXMaW1tbYSw5OVnrcdSu1aWWszE9fvwYgYGBijG1si9q8uTJg5UrV+pdo7t06dL4/PPPhXGlcTXtph8zZgzKlCmjeo2dnR0mTpyoGJNlWfWNSTWpqamYO3euYszf31/jkwJERET0/rn4+KJKVPxUHGVv3OpFpMH1SW3NvQQincTGxiq2Ozo66lRvFAAqVaqk8zy6EiXySpYsqXNyonTp0oiIiDDCqnKXXbt24caNG4qx2rVro1SpUhp37Lq7u6NixYqZan4Dr5KLS5YswQ8//CDsr7aLVZedskrS09OFMdGu9uLFi8Pa2hopKSmK8Rs3bqiWDdHFy5cv8ffff+Py5cuoXLky+vbtq5g0bNq0KcLDw40yp0hoaOhbdZjV6psrHSAqolQ7OIOm3eKmsnDhQsWdtdbW1ujYsaPe41aqVAl79+5F69atcefOHa37lS9fHrt27cLMmTOF1yiVolH7HFlYWKi++fOmjz/+GHZ2dor3JDw8HP369dNqnDetW7dOeA+6du2q83hERESUs6y6uApht8Je//vp7cwHFr8r/FY4KrtUVg5apACG/WlAZsIEMxFRLiOqv1yoUCGdx1JLSBsrwfz0qfIvIfqst3DhwoYuJ1dSO9zv+PHjRqk3/Pfff2PkyJHCNwXUDowTfQ1oSy25KZrXysoK5cuXx8WLyjsozp49a9CaMqSlpaF58+Y4dOjQ67YFCxbg0KFDyJs3r1HmMITa95kuu7jVrnV2dtZhRcazaNEixXYvLy+9dyBnqFq1Kk6cOIGRI0di6dKlqjWogVdlaObMmQMnJ6e3ymW8S2knstoBpsWLF9f6gFN7e3vUrFkTBw8ezBQTPUmiiehnS4UKFYQ1/ImIiCj3uP3sNm4/u/3630kPNZ+98TT5KR6+VP6bVSQpPRnm2bJA2mKCmYjoPaHPo8pZ8XiznZ2d4k5JfebW5VCy98WDBw8QHBxs8nmio6OxY8cO+Pj4KMbz588v7GtIOYr09HTVnbZqu2c9PDyECeb9+/frvaY3LVu27K3kMvAqeT179mzVHd9ZRe0QzZiYGK3HUbvWHG/87Nu3D1euXFGMib5GdeXq6oolS5Zg+PDhWLVqFf777z9cv34djx49gpWVFSpUqIDmzZujX79+qFmz5ut+d+/eVRzP0tJSsY5yqVKlhGvQ5hDUN4k+F/q8YXjz5k3s2bNHMWase0xERES5Q8rD/39qMPB8IALPK5cx+0fQ/0XKcxRAQROsjIyFf4kTEeUyBQsq/49XnwSCWuJPnx3GSkS7G/Wp6ZvT6gBnhaVLlwrLQBjb/PnzhTG1XZbXrl3Te84rV66oHjCnVHIgg1pN8n379uH+/ft6ryvD7NmzFdvPnDlj8NjGULhwYeFO6ps3b2o9jqgEC6C8K9fUVqxYIYwZ++A5Nzc3TJgwAUeOHEFsbCxSUlKQnJyM8+fPY86cOW8ll4FXb8YoqVq1qmK96ooVKwrnVvvaVyJ6WkGtzIzIqlWrhDu3ebgfERFR7uJk42RQfzlVxtneZ3E/6D5SnmTN3yaUtZhgJiLKZUQJtbi4OJ1qqgLApUuXdJ5HV6JxdKltmuHevXuGLifXEZUJMIUtW7YIP2/lypUT9jt27JjeSfCjR4+qxtWSmz4+PrC2tlaMpaWlqSbMtREWFiY8dLNAgQIGjW0sFhYWmRKgGS5cuKD1OKKd4MCrxGlWSk9Px7p16xRjefPmRfXq1U06v1q98aSkJOEbKqKa31WrVhW+CSDaDS3y+PFjxXYnJyedxgGAoKAgYYwHrhIREeUuDYs1NMo4D7c+RNTgKJztfRZpL1lsOTdhgpmIKJepVauWYrssywgJCdFprHcf7X9TnTp1dBpLpHbt2ortsbGxOHXqlNbjJCYmGq2sQW4RFhameCifqaSlpQlrsqrVY01MTMTevXv1mnP16tXCmKurq3BHf0bc19dXGJ8+fbrOCbwM6enpGDFihDCe1UlXNfXr11dsv3jxotb1sQ8fPqzY7ubmZrSnHbR18OBB4dMMVapUUU0Aa+PMmTP48ssv0a1bN8yfP1+nN0ciIiKEB2q2adNGsd3S0hL16tVTjN27d0+n+sminfNqB7oquX37tvDNHVdXV63rQhMREVHOULVgVYzzHAcbS/G5Krq68I32mxlsLW2NNi+ZBhPMRES5TIsWLYSx9evX6zTW8uXLFdttbGzQpEkTncYSESW3AN3Wu3PnTjx//twYS8o11A73GzFiBGRZ1us1evRo4biLFi1CWlrm3Qj169cX7hYGgN9//123Dw7AyZMnsXXrVmFcm12UAwYMEMbi4+Ph7++P5ORkndc2fvx4HDlyRDFmYWGhmtjOam3btlVsT09Px/bt2zX2v337tnCntjlKJait2c3NzaCxo6Ki4OnpiYULF2LNmjX45ptv4Ofnp3X/tWvXKrbnz58fLVu2FPbr0KGDMLZy5Uqt5j516pTwDRMPDw+txsiwY8cOYczQe0xERETZU9dKXbGv+z4EfhSIJa2XKL7+aPGHSebOirOByDBMMBMR5TIeHh7CHYMrV67Uuvbr2rVrcfr0acVYixYthLU8deXt7S08nE/bHaSpqakYO3asUdaTWzx+/FhYJgAA/P399R67R48ewtidO3ewefPmTO329vaqb37s3r0bEyZM0HoNsbGx6Nmzp2IyO4MocfomLy8v1QPJQkND4efnp/VOXgCYMGECfv31V9V1Ke3wDAsL0zvpr+2radOmmeZt0qSJcKf3ggULNH68f//9t7AWb/fu3TX2B17VvJ48eTIWLFhgcC318PBwYUzXnbrvWrp0KV68ePFW2+bNm1XLRWS4ceMGAgICFGM9evRQ/ZnauXNn4c7rGTNmaPX1+ccfyn/wSZKk8xseprzHRERElH3ZWtmiZqGa8CjiofhqV7cdSpQoofV4LrYub70o52KCmYgol7GyssLQoUMVY2lpaejZsycePXqkOsbFixfRv39/YVzt0X9dlSxZUpjgS0hIQK9evfDy5UvVMUaOHJltDk3LLgIDA5GUlKQYq169umrJCk0qV66MunXrCuOipOTnn3+uOu5PP/2ETz75RPXQv4z6ug0aNFD9nDs4OKBr166q82WYNm0abGzEj/tt2bIF1atXR0BAgPBrUZZlhIWFoVGjRvjpp5+EY1laWuq1W9uUrKys8N133ynGQkNDsXHjRmHfa9euYfr06YqxevXqoVGjRhrn79evHxo3bowffvgBX3/9NSpWrIjQ0FCt1v6utLQ01brchiY/ReUwhg4diidPngj7PXv2DH5+foq74a2trTFkyBDVeYsVKyZM1t+7dw/9+vVTPahvw4YNwidS2rZtq9MfgoB6+SQmmImIiN5fkiTh77//Vjy4WElIpxCEdwt//aKciwlmIqJc6NtvvxXuYj558iS8vLxw7NixTDFZlrFmzRp4eXkhNjZWsX+TJk3g5eVl1PUOHDhQGAsJCUGLFi0UH8G/ffs2Pv30U/z5559GXU9uoHa4n9oOZG2pjbFz507Fw/46d+6MKlWqqI67atUqlC9fHjVr1kTv3r0xcuRIjB07FgMGDICvry9cXV3RuXNnXL16VXWc4cOHa31wWZUqVTB16lTVa27evIk+ffqgUKFC8Pb2xvfff49x48Zh6NCh6NKlC0qVKoVmzZpprAM+bNiwbFV/OcPAgQOFPzN69OihuCv9/Pnz8PHxwbNnzxT7qZVSyRAQEIDFixe/1RYfH4/OnTsjLi5O88LfceXKFdU3pAxNfjZu3Fix/datW2jcuLHimx5HjhyBp6en4s9cAPjmm29UD8HMMHbsWGGZmbVr18LPz0/x5/aiRYvwySefKPazsLDAuHHjNM79ppcvX+LKlSvCOBPMRERE77ePPvoIFy9exJw5czRemydPHnz66adZsCoyNUn0SCORqdWtW1dW22VkDBcuXGAtQMoWwsLC0KxZM2HcFD+LQ0JC4OPjo1pCoHbt2qhbty7y5cuHmJgYhIeH4/bt28LrnZ2dcfToUdVkiKg+lq+vr+pOSD8/P401lz09PVG1alXkyZMHly9fRlhYGFJTU1X7ZIiOjkaZMmW0utaYRPejQ4cO2LBhg0nmPH36NGrUqKEYs7CwwK1bt1CsWDGD5oiNjUXx4sWFOzpnzZqluCt237598PLyUt1taai6deviwIEDqjWflfTu3RvLli0z0aqA1q1b47///hOWhDG3oKAg1V3ftWvXhqenJ6ysrHDhwgWEhoYKv/86deqkWqIlQ9OmTYXlFpYuXYpevXppt/j/2bFjh/CJCGtrazx//lznr4s3paamonLlyqpvcDRu3BhVq1ZFeno6Tpw4IazFDQDly5fHqVOnkDdvXq3m//nnnzF+/Hhh3MbGBt7e3ihXrhwSEhIQGhqK6Oho4fUDBgzA7NmztZo7Q1RUFCpXriyMm+tnrTnx900iIjKbh5eBOQpPFhaoAHyn/OZ2VkpPT0d4eLjGczm2bNmCtm3b4oS7G2wV/rxw3R+OggWUN0OEBJxH1OH7mdpb9HZDZc+ieq07O5Ak6Zgsy+LHRrMZK3MvgIjofaD2+L1azBAtW7bEpEmTMHz4cOE1x48fx/Hjx7Uaz8rKCv/++6/GnXbW1taKSUdR/dAMc+bMwb59+1Trrx46dEj10ewePXogMDBQMWaupJ6lpaVikl/bhJI+IiMjhbFWrVoZnFwGAFdXV3To0EFYezYiIkIxwdyoUSPMnDlTWJLBUOXKlcOmTZv0SiIuWbIEsizjn3/+Mfq6PD09sXr16mybXAaALl26YPjw4cLd3Nr+vHBzc8OSJUu0mlNpp7s2MRG1Axlr1qxpUHIZePVzcOHChWjVqpXwzbuIiAhERERoHMvR0RGbNm3S6WfB6NGjERkZKTzcMikpSXG3uZJGjRph2rRpWs+dQe0eu7q6vnfJZSIiIhKzsLBAvXr1NF43ZMgQrc5Poewr+/6VQ0SUi5QuXVoYM+Uf48OGDcPixYu1roEl4uLigu3bt+Ojjz7SeG2pUqUU20WP32coWrQodu/eLTxsTJPRo0djwYIFijuGLSwsFA9VywrFixdXbNfmkXh9PX78WBj74osvjDbPt99+K4y9exDamwYMGIBFixYZ/c2V5s2bY//+/Xon0C0sLLB06VJMnjzZ4ETkm/z9/RESEgJHR0ejjWkqkydPxvfff693/6pVq2Lnzp1af6xFi4p3lajFRNRqCXt7e+s8npJmzZph0aJFGt80U+Pq6oqQkBCdy6VYWVlhzZo1qgdTaqNFixbYunWrXv9vyIp7TERERLmHtbU1rKzU97eqncFCOQMTzEREWaBYsWKoX7++YqxTp04mnbtPnz7Yu3evcH5N2rZti8jISLRo0UKr60UJhpYtW2rsW61aNezdu1f1ALl3OTo6Yvny5fjtt99gZ2eHmjVrZrqmVq1aBifZ9dW0aVPF9jZt2phsTh8fH8VEe/ny5dGhQwejzePl5QVPT0/FmK+vr2rfvn374tSpU/j4448NXoezszN+//137Nq1y+A3EiRJwogRI3D48GGDk3iVK1fG5s2bERgYaNId68YkSRJmzJiBZcuWoUCBAjr17dmzJ/bv36/TgXFdunRRbM+bN69eu1iqVKmi+AaDlZWVxkMmddG7d2+EhITo9QZh69atceLECZ1+zr3J3t4eW7duxU8//QRbW1ud+trY2ODnn3/G9u3bkT9/fr3md3Z2Rp06dRRjX375pV5jEhERUe6VJ08eNGjQQPUaY5/xQ1mPCWYioiyyePFiFC5c+K02T09PrQ7CMlT9+vVx6NAhbNy4Ee3atUO+fPlUr3d1dcVnn32GQ4cOYcuWLShfvrzWc/3444+Zdmz7+/trnUh3c3PDoUOHMHv2bFSrVk14nbOzMwYNGoSoqCh89tlnr9tnzJgBBweH1/92cHDA9OnTtV6/sf3yyy+Zdiv36tVLmJg1hurVq+Ovv/56a4ewnZ0d5s6da9CuSyWLFy/OtEv7888/R79+/TT2rVSpEjZv3oxz585hzJgxqFOnjtbrs7W1RcOGDTFnzhzcunULP/zwg1HLT9SqVQvbtm3D8ePHMWzYMFSoUEGrfnnz5n1dX/vcuXNGSaCbQ8+ePREVFYVx48ap7gi3sbFBx44dcfDgQSxbtkznXdr9+/fPdLCLnZ0d/v33X41PPYjW8+eff771dWRtbY2ZM2fq9HNMG02bNsWFCxcwY8YM1ZrEwKuv1w4dOiAkJATbt28XPtmgLQsLC4wfPx5RUVEYMmSIxjdWihUrhiFDhuDq1asYN26cxl1EmkyfPh12dnav/y1JEkaNGoUmTZoYNC4RERHlTvPnz1f93c6UfxtR1uAhf2Q2POSP3kdPnz7F+vXrce/ePbi5uaF9+/ZGT/hpIzk5GZGRkbh9+zZiY2ORkJAAFxcXuLq6oly5cqhZs6bwcDptJCQkICgoCE+ePEGdOnWEu3i1cf78eZw9exa3b99GSkoKChQoAHd3d9SuXVtYxuDmzZvYtm0bZFlGmzZthGU7skpCQgK2bNmCGzduoEaNGlqVGjGGa9euYefOnZBlGS1btkTFihVNMk9sbCzWrl2Lp0+fomHDhmjcuLHeY718+RIXLlzA9evXERMTg4SEBCQlJcHGxgZOTk5wdnZG2bJlUaNGDaOWsdDG48ePcf78eVy9ehXx8fFISEiALMvImzcvihYtig8++ADu7u4GJ++yG1mWce7cORw/fhwPHjxAcnIynJycUKlSJdSrV++tN3T0tW/fPuzfvx/58uVDhw4dDE7Anj9/Hv/99x/y5cuHZs2aoVKlSgavUZPo6GgcP34cN2/efH2YYMGCBVGpUiXUrVtX593GukhPT0dUVBROnjyJmJgYPHv2DPb29ihSpAiqV6+OqlWrGvQzXcmNGzewceNGWFhYoGHDhqhdu7ZRx89J+PsmERGZTTY/5O9N8fHxaN68OY4dy7yusWPH4pdffuEhf2/IaYf8McFMZsMEMxERERHldPx9k4iIzCYHJZiBV093/vjjj5namWDOLKclmFkig4iIiIiIiIiIiIj0wgQzEREREREREREREemFCWYiIiIiIiIiIiIi0gsTzERERERERERERESkFyaYiYiIiIiIiIiIiEgvVuZeABEREREREREREWWRx9HArJr//++20wCPfmZbDuV83MFMRERERERERET0PkhLfTu5DABbhwJR28yyHModmGAmIiIiIiIiIiJ6H5xcrtwe1DtLl0G5C0tkEBERERERERER5RZP7wBLP1aOXY9Qbk9NNN16KNdjgpmIiIiIiIiIiCi3SH0pTiQTmQBLZBARERERERERERGRXphgJiIiIiIiIiIiymlsncy9AiIATDATERERERERERHlPPlcgSLVjTOWjaNxxqH3EhPMREREREREREREOVHXfwCX8oaP84G34WPQe4uH/BEREREREREREeVELmWB744BsVHAsxjN159ZA5xYbvp10XuFCWYiIiIiIiIiIqKcSpKAQpVfvTR5FsMEMxkdS2QQERERERERERERkV6YYCYiIiIiIiIiIiKz+PXXX9GkSRM8T0sz91JITyyRQURERERERERERGYTERGBxv/77/b582NS0WJmXQ/phjuYiYiIiIiIiIiIKFsIjo9HUFycuZdBOmCCmYiIiIiIiIiIiExKkiStrx0fc9+EKyFjY4KZiIiIiIiIiIiITKpmzZrmXgKZCBPMREREREREREREZFLNmzdH3bp1tbqWh8blLEwwExERERERERERkUnZ29tj586dGD9+PEqUKKF6rbeDQxatioyBCWYiIiIiIiIiIiIyOWdnZ/z000+4desWYmJi0LRpU8Xr8khMWeYk/GwRERERERERERFRlipUqBB69uxp7mWQETDBTERERERERERERER6YYKZiIiIiIiIiIiIiPTCBDMRERERERERERER6YUJZiIiIiIiIiIiIiLSCxPMRERERERERERERKQXJpiJiIiIiIiIiIiISC9MMBMRERERERERERGRXphgJiIiIiIiIiIiIiK9WJl7AURElPvExcVh3bp1iI2NRc2aNeHj45Ol81+9ehXBwcGws7NDq1atUL58+SydP6d6+fIl/vvvP1y7dg02NjaoXr06vLy8IEmSUcaXZRnbt2/HqVOn4OTkhPbt26NYsWJGGTs5ORmbNm3ClStXULRoUXTs2BGOjo5GGTtDUlISTp06hcuXL+PBgwd4/vw5bG1t4eDggLJly6Jq1aooXry4UeckIiIiIiLK7phgJiLKIrIsY968eQgICMClS5cQHx9v8Jh2dnaoWLEihg8fDn9/fyOs0nC3bt2Cp6cn7t69+7rNz88PQUFBRktUqtm/fz9atGiBpKQkAIC9vT02btyIVq1amXzuJ0+eYNeuXdixYwdOnjyJ2NhYPHz4EGlpacibNy+KFy+OSpUqoWHDhvD19UW5cuVMviZtbd26Fb169cKjR4/eaq9ZsyaCgoJQoUIFg8ZPSUnBxx9/jJ07d75uGzp0qFE+N48ePULTpk1x9uzZ120//PADdu3aBXd3d4PGfvr0KVatWoXVq1fjwIEDr7+uRCpWrIh27drhyy+/RKVKlQya29xkWca+ffuwdu1aHDlyBFevXkVcXBxsbW1RqFAhVKhQAT4+PvD19UWZMmXMvVwAQFpaGnbt2oX//vsPx44dw9WrV/H06VPIsgxnZ2c4OzvD3d0djRo1Qps2bYz25pM571VycjK2bNmC7du34/jx47h+/Tri4+NhYWEBFxcXfPDBB2jcuDH8/PxQs2ZNg+cz1z0mIiIiomxMlmW++DLLq06dOrKpnT9/3uRzEGlr7NixMgCTvbZt22buD1GWZVnu06eP4vqCgoKyZP5mzZplmrtGjRomnfPevXvy4MGDZTs7O50+Zz4+PvK5c+dMujZt7N+/X7a2thaus2zZsvLTp08NmmPGjBmKYxcpUkROSkoyaOwBAwYojl23bl29x0xISJBHjx4t58+fX6/vR0mS5Pbt28tRUVEGfWzmEhISIteoUUOrj9XKykru16+ffPv2bbOtNy0tTV64cKFcqlQpnT5HH3/8sXzkyBGD5jbXvUpOTpanT58uu7q6av0xN23aVD527Jhe85nzHmd3/H2TiIhylFOrZXl8/syvtX3NspwlS5Yo/h7RIb+jfL5SZfl8pcpy7MMYYf9dS87Jc77anel14eDdLPwojA/AUTkb5O60fbEGMxFRFkhKSsKMGTNMOsf69etNOr62QkNDFdtDQkJMPnd6ejoiIiIytZ8+fVrjzlN9bdmyBW5ubpg+fTpevnypU9/t27ejRo0aWLBggUnWpg1ZlvHNN98gJSVFeE10dDQmT55s0DzBwcGK7ffv30dkZKRBY2/ZskWx/ejRo7hz547O4+3atQuVKlXCxIkT9X7SQJZlBAcHo1q1apg4cSLS09P1GierybKM0aNHo2XLljh16pRWfVJTU7Fo0SLUqlVL8fvP1B48eICWLVviiy++wM2bN7XuJ8sytmzZggYNGuDnn3/Gq9/jtWfOe3Xjxg00atQIgwcPRmxsrNb9wsLCUL9+fUybNk2n+cx1j4mIiIgoZ2CCmYgoC1y+fBnPnj0z6RwWFtnjR/qtW7cU23VJgugrJiYGqampmdplWUZMTIzR55s/fz7at2+PuLg4vcdITU3F119/jalTpxpvYTrYtm0bTp8+rfG6GTNmGPQ5FH1daIppQy2JrEsyDACmTJkCHx+ft0q8GCIlJQVjxoxB+/btdX4Dwhy+//57/P7773r1jY2NRYsWLbBr1y4jr0osozyK6I0tbaSmpmL8+PEYNmyYTv3Mda8uX76MRo0a6f3GTGpqKoYNG6b12s15j4mIiIgoZ8ge2QgiolzuyZMnJp+jU6dOJp9DG0oJXuBV3U5TU9ulbOwdpDt37sSAAQOMtiMvo2ZwVvvrr7+0uu7FixdYvHix3vMkJycLY4buLlfbfa3L2OPHj8fIkSNNstt469ataN++vfD7IztYuHAhZs+ebdAYKSkp6NatGy5fvmykVanr27cvLly4YJSx/vzzT6xYsUKra811r548eYI2bdrg9u3bBs0NAGPHjsWhQ4c0Xmeue0xEREREOQcTzEREWcBUyVU7Ozu4u7sjMDAQ3t7eJpmDMktMTES/fv2En1cnJyeMGTMGx44dw6NHj3Dz5k0EBwfj448/Fo6Znp6OgQMHZkkiPsOdO3ewfft2ra9fvHhxrn3EfdGiRfj55581XmdhYYEPP/wQvXr1wsiRIzF06FB07doVJUqU0Ng3JCQEI0aMMMZyje7evXsYOnSoMN6uXTvs3r0b9+/fR2xsLEJCQtCmTRvFa588eYIvvvjCVEt9LSQkBJs2bRLG/fz8sGPHDsTExODx48c4efIkJkyYACcnJ2GfESNGIDExUXVec96rAQMG4MqVK4qxfPnyYcSIETh27BgeP36MS5cuYfHixXBzc1O8Pj09HWPGjFGdz1z3mIiIiIhyGHMXgebr/X3xkD96n4SGhqoehJSbiD5GX19fk88dHR0tnD86Otpo80ybNk04j7u7u3z9+nVh32XLlskWFhbC/qtWrTLaOjX5+eefdT68bs+ePXrNVbp0aeGYAQEBBn0causNDQ3V2P/s2bOyra2t6ji2trbyTz/9JN+7d09xjPT0dHnHjh1y9erVVcexsLCQDx06ZNDHawrff/+9cM3Dhw/Xq9/GjRtNuuaOHTsK554/f76w3+3bt+WKFSsK+wYHB6vOa657FR4ervpz58qVK4r9nj9/Lrdv31749RgXFyec01z3OKfh75tERJSj8JC/HAE85I+IiCh3CwwMVGx3dXXFli1bULp0aWHfnj17Yty4ccJ4Vh3WKMsylixZohhr1KgRrKysFGPz58835bLM4osvvlDdUVmxYkWcPn0a48ePR5EiRRSvkSQJ3t7eiIyMxGeffSYcKz09XfXzbw4vX77EokWLFGPVqlXDpEmThH3/+OMPVK1aVTE2ceJEo6xPiSzL2L17t2Ls008/xVdffSXsW7x4caxevVoYVytVY8579dNPPym2Fy9eHDt37kT58uUV4/b29vjnn39QtGjRTLH09HRhDXlz3WMiIiIiynmYYCYiItLBgwcPcPLkScXYqFGjUKpUKY1jDB06FA4ODoqxiIgIQ5antV27duH69euKse+//x4+Pj6KsQ0bNuDBgwcmXFnWWr9+PQ4ePCiMlytXDqGhoahYsaJW49nY2OCff/5Bhw4dhNfs2rXLKDV0jSUkJATPnz9XjA0fPlz1AFErKysMHz5cMRYZGYmoqCijrPFdsbGxiI+PV4z17t1bY/9atWrBw8NDMaZ26KS57tXFixeFh+z99ttvwjc+Mjg6OmLu3LmQJOmt9urVqwt/ZpnrHhMRERFRzsMEMxHReyglJQUHDhzA6tWrMWfOHPz+++9YsGAB1q9fj1OnTuHVEznmJ8syjh8/jpUrV2L69On47bffMHfuXGzfvl24687UoqOjFdvz5MmDnj17ajWGg4MD6tatqxiLiYnJkjrMCxcuVGx3dHREu3bt0KNHD8V4SkqKQYf9ZTd//PGHMCZJEpYvX47ixYvrNKaFhQWWLVum+mbD/v37dRrTlLZu3arYbmlpqVo3PEOnTp2EO96DgoIMWpuI2veI2hMEbypXrpxi+8uXL4V9zHWv/v33X8X2AgUKwN/fX+O8ANCxY0f8999/8PLyQqlSpdCxY0ds3rw5U9I5g7nuMRERERHlPMq/4RIRUa4jyzI2bdqEgIAA7NmzB8+ePRNeW7hwYXh7e+O7774T7kAzpSdPnmDixIlYsWIF7t69q3iNpaUl2rZtix9//BF16tTJsrWJku9+fn4oUKCA1uMUKlRIsT09PR3Jycmws7PTa33aePDggfDgrs6dO8PGxgbt27eHo6Mjnj59mumahQsXYuTIkaq7NXOCs2fPqu5e7tevHxo0aKDX2Pnz58esWbOEO5mVdoaWKVMGN27c0Gs+bUVHR6NMmTJvtR07dkzx2mrVqsHFxUXjmA4ODqhVqxaOHDmSKXbgwAG91qlJwYIFkSdPHiQnJ2eKxcTE4IMPPtA4RkxMjGK72hsK5rpXwcHBiu2tW7eGpaWlxnkz+Pj4CJ9OeJe57jERERER5Tw5+y9DIiLSSlhYGOrUqYOOHTsiODhYNbkMvEoKBAYGol69evD19cXVq1ezaKWvdvFVrFgRf/zxhzC5DLzaXRccHAwPDw+MGDECqampWbK+atWqZUokW1tbY9SoUTqNI/rY7OzsTJpcBoBly5YhJSVFMZZRP9jW1hZdunRRvCY6Oho7duww2fqyyoYNG1Tj33//vUHj+/r64qOPPlKMZeWbImrS0tJw7tw5xVi1atW0Hsfd3V2x/dChQyZ5IsLa2hr169dXjIlqpL/pxo0bwl3kTZs2VWw317169uwZzpw5o9inXr16Ws+rK3PcYyIiIiLKmZhgJiLK5WbMmIGWLVvixIkTevUPDg5GvXr1hPU/jWnu3Lno1q0bHj16pHUfWZYxdepUtGvXDi9evDDh6l7Jly8fVq5c+XoHnqOjIxYvXixMGil5+vQpIiMjFWO6jKMv0SFlJUqUeCvxIyqTAeSOw/5CQkKEsbp16woPZNPF33//nak+7tdff43atWsbPLYx3Lx5U1iuoFKlSlqPI6pR/eTJE+EuVkN9+eWXiu2LFy9WTYA+ffoUn376qeKbLEWKFIGfn59iP3PdK7WyRW+OlZKSghUrVqBTp04oXbo0bGxs4OjoCDc3N3zyySdYu3at6mGWSrL6HhMREVE2dmYt8JPj/79uHjL3iigbYYKZiCgXGz9+PAYPHmxwTd/Hjx/D29tbWH/UGDZv3owBAwbovdtx+/bt+OSTT4y8KmWtWrXC7du3ERsbiydPnqgmYpUsW7YMSUlJirHWrVsbY4lC4eHhuHTpkmLs008/fasea+PGjYW1Vrdu3ZqtDqrTVVpamrDcAQC0aNHCKPOUKFECZ86cwbRp0zBy5Ehs2LAB8+bNM8rYxqD2lICmg+PeVKxYMWHs5s2bOq1JW5999hmaN2+eqT09PR09e/ZEt27dsHPnTsTGxiIhIQEXL17EzJkz4e7uLixHMXv2bNjb2yvGzHWvTp06Jbw+4/tz7969qFKlCj777DNs2LABN2/eRHJyMuLj43Hx4kWsWrUKXbp0QeXKlbF+/Xqt15rV95iIiIiyqTvHgHV9325b0hp4lnsO/ybDsAYzEVE2oMuuMkmSYGNjo/G6TZs24ZdfflG9pkqVKqhevTqKFCmCBw8eIDw8HHfu3FG8NjU1Ff7+/jh69CjKly+v9Xq18eDBA/Tr10/1GgsLCzRp0gTu7u6QJAlnzpzB3r1730qenz592qjr0qRgwYI693n8+DF+/vlnxZiVlRV69+5t4KrUiQ73A5DpsDBJkuDv74/ffvst07VpaWlYuHAhJkyYYPQ1ZoWbN2/i+fPnwnitWrWMNlfBggUxZMgQo41nTPfv3xfGRHXClajVH75165ZJSjlIkoSgoCB89NFHik8ErFmzBmvWrNF6rKlTp6Jz587Ca8x1r0TlMQAgb968WLRoEb755hutygTduHEDfn5+GDx4MKZNmyY84C9DVt9jIiIiyqY2C0rHhU8G2k7L2rVQtsQEM5EmPzmaewWkjZ8yH0SWk+hac9fOzg41a9bElClT0KhRo0zx+Ph49OnTR7gbuFKlSpg7d26mXZqyLGP16tUYOHAgYmNjM/WLi4tDz549hXU19TVt2jQ8eCB+97tRo0YICAhAhQoV3mqPjo5Gv379sGfPHqOux1TS09Ph7+8vLAHSp08flCtXzmTzP3nyBOvWrVOMVa9eXbE8R48ePRQTzMCrx+THjRsHK6uc9+vErVu3VOO61NTNyR4+fCiMOTpq//+//PnzC2O6lLzRlYuLC8LDwzF27FjMmjVLWFtcTenSpTF//nyNh9+Z616pJbY3btyIIUOG6Pzkx/Tp05GYmIi//vpL47VZeY+JiIjIjM4EvSqDoUjwu8bF/5hgJgAskUFElCO9fPkSBw8eRLt27ZCQkJApPnv2bDx+/Fixr7u7OyIiIhRLAEiShO7duyMsLEy4O/fAgQPYvXu3YR/AGxITE4U1gQGgefPm2LFjR6bkMgCULVsW27ZtQ6dOnYy2HlORZRnffvsttm3bphgvWrQoJk2aZNI1BAYGCnfLZxzu965KlSrBw8NDMXbnzh1s3rzZaOvLSmrJQkB9l2luovb0RJ48ebQeR+2pCl3r/urK1tYWgwYNQrt27XTua21tjbFjx6JVq1YarzXXvYqPjxdeP3z4cL3LCs2bNw8rV67U6tqsusdERERkbrLgRaSOCWYiohwsLi4Oly9ffqstNTUVM2bMULzewsICy5Ytg6urq+q4VapUwZw5c4TxKVOm6LxWkR07dgiT4fb29ggMDFSt15knTx4EBAToVAM1q6WkpKB3797Cg/EsLS0RGBgIZ2dnk65DVB7DwsICn376qbBfz549hbGcetif6LC2DGq7TE3l+vXrkGXZpK8yZcq8NaeoFjigngh9l7W1tTCm6V4bIuOQzw8++ECn2sIZUlJS8MUXX8Dd3V21FAVgvnullmB+syxG2bJlMXPmTJw/fx6PHz/GqVOn8OOPP6r+XOnfv7/q+EDW3mMiIiIiypmYYCYiysGcnZ1RsWLFt9qOHDki3J3ZrVs3rWvLduvWTbFkAgCEhYWp1q/VheggKAAYOHCg6oFYGfLnzy+sa2xud+7cQZMmTfDPP/8Ir5k5c6bRDpUTOXToEM6ePasY8/LyQokSJYR9u3fvLkyK7dq1C9euXTPKGrOSptqzupatyanUkqYWFtr/mqi2i1ZtDkOkpqbik08+wYgRIwxOYl+4cAGenp7CJwwA890rTQlgAOjUqRNOnz6NgQMHws3NDc7OzqhevTomTJiAY8eO4YMPPlDsFxcXhwULFgjHzep7TERERFnASfkQb704lzHeWJSjMcFMRJQDOTo6wsvLC1u3boWDg8NbsZCQEGE/XUtJvHvoW4bk5GTs3btXp7FElA6OyuDn56f1OH5+ftmuFvCOHTtQq1YtHDp0SDFuYWGB2bNn49tvvzX5WnQ53O9dBQsWFNZOlWUZf//9t0FrM4d8+fKpxk1d1iG7UEuMvnmApiZqB8xZWlrqtCZtDRs2DKtXrxbO2bdvX+zduxdxcXF4/vw5Ll68iOnTp2d6Uy7Dixcv0K1bN+EuW3PdK01je3h4YNWqVcKv6bJly2L9+vXCN4nU6jBn9T0mIiKiLFC8DuBipHNfyjY2zjiU4zHBTESUDej6mHtcXBzCwsLQoEGDTGOdPHlSOI+uNTA9PT2FsePHj+s0lojosDUXFxfUqVNH63FcXFwU74e5zJgxA23atFE8LBEA8ubNi3Xr1mHAgAEmX0tCQoIwSWRra4vOnTtrHKNHjx7CWEBAAJKTk/VenzloOpQtLi4uaxZiZra2tsKYLp9TtWt1qU+srcOHD2PWrFmKMQcHB+zatQuLFi1C48aN4ejoCHt7e1SqVAmDBg3CqVOn0KtXL8W+CQkJ+OabbxRj5rpXmnbTz507V7XsBgBUrVoV/fr1U4xdv34dV65cydRujntMREREWcDSCui1BfjgIyCP+qYLjWyyvqwcZU/Za6sXUXb001Nzr4BIJ6KEpqOjo8ak2rsqVaqk8zy6EiXySpYsqbGMwbtKly6NiIgII6xKf7IsY/DgwZg5c6bwmpIlS2LTpk1alysx1IoVK4QlTT766CPkyZNH447dVq1aIX/+/IqP6z948ADr169H9+7dhf3VdrHqsvvzXenp6apx0a72smXLqva7ceOGVuVZtPH48WMsXLgQt2/fRu3atdGzZ0/F+1GmTBncuHHDKHOKREdHv1WHWa2+udIBoiLPnj0TxjTtFtfHlClThKUmAgIC0KxZM2FfOzs7LF68GNHR0YpPYuzfvx979uxB8+bN32o3171Sm7dKlSrCQzjf1aVLF8ybN08xFh4enukgVXPcYyIiIsoijsWBT1cBsvzqpcmO0cBh5d8jiADuYCYiynVE9ZcLFSqk81hqCWljJZifPlV+E0ef9RYuXNjQ5RgkNTUV/v7+qsnlBg0aIDIyMsuSy4B6eYwNGzbAzs5O48vZ2Vm1Fqymw/7UDkETfQ1oQy1ZpzZvyZIlVRN3onrVukpISICHhwd++OEHzJkzB3369IGPj4/GxHhWUfs+02UXt9q1xj68MjExEVu3blWM1alTR6vSOpaWlvjtt9+E8Q0bNmRqM9e9UjvAVJenPD788ENh7N0nScx1j4mIiCiLSRJgYaH5JTF9SOr4FUJE9J7QdTewvn10JXr8W5+5dTloy9iSkpLg5+eHFStWCK/p3bs3QkNDVRNGxnbixAkcO3bM5POEh4fj4sWLwnj+/OLH5wwpR6Gpr2j3rCRJqsm5/fv3672mN02dOjXTIYghISFYs2aNUcY3lNou7ZiYGK3HUbvW2G/8nD59Wnjg3scff6z1OI0aNULx4sUVY+Hh4ZnazHWvSpUqJbxel132NjY2cHJyUoy9+4ahue4xEREREeVMTDATEeUyBQsWVGzXZ8exWvJOnx3GSkS7Gx88eKDzWPr0MYYXL17g448/RnBwsGLc0tIS06ZNQ0BAgOpOXlNQ271sbGq7mNWS6u8mYHWhltQGAFdXV2FMrSZ5cHCwwXWlk5OThQcgZpdDzsqXLy+M3bx5U+tx1Ep7vFmSwxhu374tjKl9PEpEpVLu3r2r09imvFeiA/MA9QMDlYje0Ht3R7257jERERER5UxMMBMR5TKihFpcXJxOdUIB4NKlSzrPoyvROHfu3NF5rHv37hm6HJ29ePECbdu2RUhIiGI8b9682LRpE4YMGZLFK3u1NrUd1cb2zz//4OXLl4qxcuXEJ1UfPHhQ7zmPHj0qjOXLlw8FChQQxtu1ayeMPXnyxOB7t2LFCuFuVbV1ZaVChQoJd5heuHBB63FEif58+fKp7sDVx4sXL4QxUc1tEdHBfUplW8x1r+rVqyccS9ck7ZMnTxTb393ZbK57TEREREQ5ExPMRES5jKi2ryzLwiSoyKFDh4QxXWp/qqldu7Zie2xsLE6dOqX1OImJiUYra6Ctly9fol27dggLC1OMFyxYEGFhYWjbtm2WrivDmjVrsjSJ8+TJE6xevVox5u7uLux37do1XL16Va85RfMBgJubm2qplZo1a6Ju3brC+IQJEzTWeBZ5/vw5xo8fL4xXrVpVr3FNoX79+ortkZGRWvWXZVl4bePGjVUPeNSH2qGBajtvdbnewcFBsd0c96pWrVrCnceHDx/Wal4AuHz5svAwz3cPdDXnPSYiIiKinIcJZiKiXKZFixbC2Pr163Uaa/ny5YrtNjY2aNKkiU5jiYgSNoBu6925cyeeP39ujCVpJSO5vGfPHsV4RnJZLYFpamrlMf766y/IsqzX69NPPxWOKyqT0ahRI9W1Tpw4UbsP6g1btmzB6dOnhfEGDRpoHGPAgAHC2PXr1/HNN99A1uZk7Xd88803wrIJTk5OaNasmc5jmoroDZCHDx9qlcA8cOAAHj9+rBhr3ry5QWtTolZyY/fu3VqPc/PmTeFTGqIyEOa4VzY2NvDx8VGMXbt2Tevk9n///SeMeXh4vPVvc95jIiIiIsp5mGAmIsplPDw8hPWRV65cqXXt17Vr1wqTdy1atBDuqNOVt7e38HC+6dOna/UIeGpqKsaOHWuU9Wirb9++wkSLnZ0dtm7datZdqufPn8eBAwcUY9bW1ujataveY/fo0UMYO3z4ME6ePJmpvWLFiqhcubKwX0BAAJYuXar1Gq5evYpvvvlG9Rptdo77+/urfp6WL1+OL7/8Unjg2btSUlLw5ZdfIjAwUHhNjx49kCdPnkzt169f1zvpr+1LKXHYvn17WFtbK651wYIFGj9m0TWWlpbo3Lmzxv6yLGPbtm2YNGkSli5divj4eNXrq1atKtxhu2vXLq3LVcyZMydT7eEMnp6eiu3mulfdu3cXxn766SeN8yYlJWHOnDmKsQoVKmT6HjDnPSYiIiKinIcJZiKiXMbKygpDhw5VjKWlpaFnz5549OiR6hgXL15E//79hfERI0YYtMY3lSxZUrg7LyEhAb169RLW9c0wcuTILD00bcuWLVi5cqUwPnv2bNW6qVlBbffyRx99ZFAN4FatWqke2idKon3++efCPrIso0+fPvjuu+9w//594XUpKSlYtGgRGjZsqPqofpkyZVR382ewtLTEjBkzVEtpLFq0CLVr18b69euRkpKieE1aWhqCg4NRu3Zt1Xvv4OCAcePGaVxXVipYsCD8/f0VY4GBgap1rg8ePCisVe3n56fxgL/k5GS0bdsWbdq0wahRo/D555/Dzc1N9fvZ2tpaWD87PT0d3bt311jaZPfu3fjzzz+F8S5duii2m+tederUSRjftm0bZs2aJewLvPoZeeXKFcXY119/nanNnPeYiIiIiHIgU++U4Ysv0atOnTqyqZ0/f97kcxBpIzQ0VAYgfBnbs2fP5EKFCgnnq1q1qnz06NFM/dLT0+XVq1er9m3SpInq3KJ+vr6+wj7bt29XvT8NGjSQT5w4kanfrVu35E8++US1b8YrOjpax7so5uHhodWc2r4kSZKLFCki+/j4yPv37zd4fUlJSXKBAgWE8wUFBRk8x+DBg4XjOzo6yi9fvszUJz4+Xi5YsKDG+2FhYSHXq1dP7tu3rzxy5Eh59OjR8jfffCP7+PjI+fLl0+qe/vPPPzp9PEOGDNFqXBcXF7lt27byoEGD5LFjx8qDBg2S27dvL7u6umrVf968eQbfe1O4cuWKbGtrq7jmIkWKyAcPHszUZ9++fcKP28LCQj527JjGeX/88UfF/h988IGckpIi7Hfo0CHV++zm5iYfOnQoU7+XL1/KM2bMkPPkySPs6+HhkS3v1eLFi1V/howfP15OSkp6q8/z58/lAQMGCPsVKVJEjo+Pz3b3OKfh75tERJTrbRsly+PzZ37tn23w0EuWLFH8faFDfkf5fKXK8vlKleXYhzHC/ruWnJPnfLU70+vCwbsGr82cAByVs0HuTtuX9GrNRFmvbt26stpOH2O4cOEC3NzcTDoHkTbCwsJUa66a4mdxSEgIfHx8kJaWJrymdu3aqFu3LvLly4eYmBiEh4er7gp1dnbG0aNHUa5cOeE1op2gvr6+2Lhxo7Cfn5+fxprLnp6eqFq1KvLkyYPLly8jLCwMqampqn0yREdHa9xNqY2LFy+a9OeKra0tIiMjVQ/F0yQ4OBi+vr6KMUdHR8TExMDGxkbv8QHg1KlTqFmzpuoalHZArly5UrWGszG0b98emzZt0qlPSkoK2rZti127dploVcAXX3yBv//+22TjG2rq1KnCpxMkSUKjRo1Qs2ZNyLKMEydO4MCBA8KfXUOGDMG0adM0zlmmTBncuHFDMRYWFgYvLy9hX39/f/z777+q41evXh21atWCk5MTbt++jdDQUGENZODVjvaIiAiN9bvNca9kWUbLli2Fdd+BVzusvb29UbhwYdy7dw/bt29HXFyc8Pq1a9fCz89PGDfnPc5J+PsmERHlettHA4fmZm73/g34UHymiTYCAgLQp0+fTO0d8jtiYtGiAADX/eEoWEC5DGRIwHlEHc78BGSL3m6o7FnUoLWZkyRJx2RZNt+BPjqyMvcCiIjeB2rJPEMTfSItW7bEpEmTMHz4cOE1x48fx/Hjx7Uaz8rKCv/++69qchl49Wi1UhkBS0tL1X5z5szBvn378ODBA+E1hw4dwqFDh4TxHj16CGvfiuo860rtUDljSExMxOrVqw1KMKsd+tW9e3ejfM3VqFEDHh4eOHLkiGI8IiJCMcH8ySef4NixY1ol1PTh4eEhPJxSjbW1NTZt2gRfX1+TJJl9fX2FNXCzi2HDhuHYsWNYvXp1ppgsy4iIiEBERITGcby8vPD7779rNeedO3f0igGvfmacOnUKZ8+eFV5z+vRpnb5nJ02apFXi0xz3SpIkLF++HB9++CGuX7+ueM3Dhw+FZTjeNXz4cNXkMmDee0xERETvt4dabiSi7IE1mImIskDp0qWFMWPsqhUZNmwYFi9erHigmC5cXFywfft2fPTRRxqvLVWqlGK76ODBDEWLFsXu3btRsGBBvdY4evRoLFiwQHEHtYWFhWrNYF0UK1bMKOOo0fZAORG13YNffPGFQWO/6dtvvxXGXrx4IYz98ccfmDBhgtGS/hm6deuGkJAQODg46NXfzs4OW7ZswZAhQ1RrMutCkiQMGzYMa9euNfj70NQkScKyZcsMOgCySZMm2LRpk9Yfa9Gi4l0lajEAcHJywq5du+Dh4aHTGpVYWFhg8uTJGDZsmFbXm+NeAa/uyc6dO1G+fHm95wVeJZcnT56s8Tpz3mMiIiJ6v+178RxVoi7iqoF/G1HWYIKZiCgLFCtWDPXr11eMderUyaRz9+nTB3v37hXOr0nbtm0RGRmp1YFpAODt7a3Y3rJlS419q1Wrhr1796JuXe2fBHJ0dMTy5cvx22+/wc7OTrFsQ61atYyW3GvYsKFquRNDSZJk8NdEmzZtFNu9vLxQp04dg8Z+0yeffCJ8Q0FUoiPDjz/+iAMHDqBRo0YGr6NYsWJYtGgRVq1ahfz58xs0Vp48eTBt2jTs3r3b4F2W9erVw759+zB16lRYWeWMh8ZsbGywatUqTJ06Ffb29lr3s7CwwNChQ7Fz5044Ojpq3U900FuJEiXw4YcfauxfpEgRREREYPTo0XrvzK9SpQp2796t8+GlWX2vMlSsWBFHjhxBt27ddO5buHBhrFu3DlOmTNH6TRRz3mMiIiKidtejzb0E0oa5i0Dz9f6+eMgfvW/Onj0rFy5c+K2DCzw9PeWEhIQsW8PGjRvldu3aaTwozdXVVf7ss88UD3DS5N69e3Lp0qXfGs/f319OT0/XeozU1FR59uzZcrVq1YRrdHZ2lgcNGiTfv3//rb7h4eGyg4PD6+scHBzkvXv36vxxqHn+/Lk8evRo+YMPPpCtrKy0OtxN0ytPnjxy1apV5ZUrVxpljWPHjpUtLCxej+/i4qJ4UKKhwsLCZEdHx7c+lvHjx+s0RmRkpDxo0CC5atWqsiRJWt0vBwcHuWXLlvI///wjJycnG/3jyhAeHi73799fLl68uFbrcnZ2lj/77DM5JCTEZGvKKjdv3pS///572dnZWfjx5suXT+7Zs6d87tw5veZ49uyZ3Lx587fGLFCggHzgwAGdx7p165Y8btw4uVKlSho/T7a2tnLr1q3lNWvWqB4mqK2suFdKDh48KH/66aeyvb296sdbrVo1+c8//5SfP39u0HzmvMfZGX/fJCKiXM8Mh/y9+dp/IELYn4f8ZY8XD/kjs+Ehf/Q+evr0KdavX4979+7Bzc0N7du311ib2BSSk5MRGRmJ27dvIzY2FgkJCXBxcYGrqyvKlSuHmjVrGlQiICEhAUFBQXjy5Anq1KmDpk2b6j3W+fPncfbsWdy+fRspKSkoUKAA3N3dUbt2bVhbWyv2uXnzJrZt2wZZltGmTRvhLtvc7syZMwgLC4O9vT18fHxQvHhxk8xz48YNbNy4EcnJyWjVqpXq4X+aJCQk4MKFC7hx4wYePHiAZ8+eITk5GXZ2dnBycoKzszMqVqyIatWqGb3Ehib379/H+fPncePGDcTHx+PZs2eQJAn58uVD8eLFUblyZVSpUsVo5TWyi9TUVJw6dQqnTp3Cw4cPkZaWBhcXF1StWhV169aFra2tQePLsowdO3bgxIkTcHV1RadOneDi4mLQmHfv3sWZM2cQHR2N+Ph4JCUlIW/evChYsCA++OAD1KhRA3Z2dgbNocTU90okKSnpda3kx48fIykpCQ4ODihdujRq1aplkp+B5rrH2RF/3yQiolzPhIf8rVmzRuOTWf/8sxQ9evRSjPGQv+yBCWYyGyaYiYiIiCin4++bRESU65kwwXzv3j2N59yEhe+BVxPlMoVMMGcPrMFMREREREREREREWa5o0aL466+/VK/JbU8I5kZMMBMREREREREREZFZfPPNN4iO5mF+ORkTzERERERERERERGQ2ZcqUQQUbG3Mvg/TEBDMRERERERERERER6YUJZiIiIiIiIiIiIiLSCxPMRERERERERERERKQXJpiJiIiIiIiIiIiISC9MMBMRERERERERERGRXphgJiIiIiIiIiIiIiK9MMFMRERERERERERERHphgpmIiIiIiIiIiIiI9MIEMxERERERERERERHphQlmIiIiIiIiIiIiItILE8xEREREREREREREpBcmmImIiIiIiIiIiIhIL0wwExEREREREREREZFemGAmIiIiIiIiIiIiIr0wwUxEREREREREREREemGCmYiIiIiIiIiIiIj0wgQzEREREREREREREemFCWYiIiIiIiIiIiIi0gsTzERERERERERERESkFyaYiYiIiIiIiIiIiEgvTDATERERERERERFRtuTVpBlatmxp7mWQCiaYiYiIiIiIiIiIKNvavXs3hg4dau5lkAATzERERERERERERJSt/fnnn5naUuPisn4hlAkTzERERERERERERGRWNhaSzn2eh4ebYCWkKytzL4CIiIiIiIiIiIjeb7Xs7XDuZaLqNffG//T6v5OvXQPgZtpFkVaYYCYiIiIiIiIiIiKz+ty1ACITXuBScpJivIatLeJWr367sTITzNkBS2QQERERERERERGRWTlbWWFF6dLo6eysGLe3YBozu+JnhoiIiIiIiIiIiMwqyfpVErlJ3nzmXgrpiAlmIiIiIiIiIiIiMqtzJS3NvQTSExPMREREREREREREZFbLmlvjnnJ1DMrmeMgfEREZXVxcHNatW4fY2FjUrFkTPj4+WTr/1atXERwcDDs7O7Rq1Qrly5fP0vlzqpSUFOzatQsXL16EJEmoXLkyWrVqBSsr4/26EBERgQMHDsDe3h4fffQRKlSoYJRx09PTsW3bNpw5cwbOzs7w9fVFkSJFjDJ2htTUVJw5cwaXLl3CvXv38Pz5c1hbW8PBwQGlSpVClSpVULZsWaPOSURERET0vnicX8KwfpZwDJeAwMzxPOXKociYMa//nXT1GnAkCxdIQkwwExFlEVmWMW/ePAQEBODSpUuIj483eEw7OztUrFgRw4cPh7+/vxFWabhbt27B09MTd+/efd3m5+eHoKAgSJJk8vn379+PFi1aICnp1cnD9vb22LhxI1q1amXyuV+8eIH//vsP4eHhOHToEGJiYvDo0SOkp6ejQIECKFmyJD788EO0bt0arVq1ypL7oa3Dhw+jW7duuHHjxlvtZcuWxerVq+Hh4WHwHH369EFAQMDrf1tZWWHRokXo1auXQeMmJSXBx8cHYWFhr9uGDRuGTZs2oXnz5gaNnZiYiHXr1mHVqlUIDQ3F8+fPVa8vUaIE2rZti379+qFu3boGzZ0dnDhxAqtXr8ahQ4dw6dIlPHnyBJaWlihUqBDKlCkDb29vtG/fHlWqVDH3Ul+LiIjAli1bcPjwYVy+fBlPnjxBamoqnJyc4OLigsqVK6Nhw4Zo3bo1qlevbrR5Dx8+jJCQEISHhyM6OhqPHj3Cs2fP4OjoCFdXV9SpUweNGzdG165d4eTkZJQ5U1JSsHPnToSFhWH//v24e/cuHj16hJSUFDg7O6NYsWJo0KABWrRogY8//hjW1tZGmff+/fvYsWMHduzYgfPnz+Phw4d4+PAhACBfvnwoWbIk3Nzc0LhxY3To0AFFixY1yrxERESU+6VYSbheWLnggnWRInDu3v31v5OuXgWOrMmqpZEaWZb54sssrzp16simdv78eZPPQaStsWPHygBM9tq2bZu5P0RZlmW5T58+iusLCgrKkvmbNWuWae4aNWqYdM64uDj5hx9+kF1cXLT+fJUvX15evny5SdelrcuXL8uOjo7CtTo7O8vXr183aI6NGzcqjm1rayvHxMQYNPYff/yhOHaxYsXk5ORkvcZMSkqSp06dKhcqVEjv78kmTZrIR44cMehjM5djx47JTZo00erjlCRJ9vPzky9evGjWNa9bt052c3PT+XO0c+dOg+ZdvXq1XLt2ba3ntLe3lwcMGCA/ffpU7zkTExPl33//XS5evLjW8xYtWlSeMWOGnJaWpve8V69elT///HPZyspK63ktLS3lTz75RL5586be82Z3/H2TiIhyvW2jZHl8/syv/bONOk3VJbXkakuryWWGlVH8vaJly5ZvXZ945Yq81neiPOer3ZleFw7eNerashqAo3I2yN1p+2INZiKiLJCUlIQZM2aYdI7169ebdHxthYaGKraHhISYfO709HRERERkaj99+vTrHc3GFhoaCnd3d0yaNAmPHz/Wut/Vq1fh7+8PPz8/JCYmmmRt2hoyZAiePn0qjD958gSjR482aI7g4GDF9sTEROzcudOgsbds2aLYfvfuXURGRuo83vHjx1GjRg0MHz4cDx480Htde/fuRb169fD999+b7OvPFObOnQtPT0/s3btXq+tlWca6detQp04drFu3zsSry+z58+fo1q0b/Pz8cOHCBZ367t27F97e3vj222+RnJysU99Hjx6hY8eO6NatG44fP651vxcvXmDOnDmoVq0ajh07ptOcAHDq1CnUqVMHo0aNwp07d7Tud+/ePQwaNAheXl46/azKsHjxYlSpUgUBAQFITU3Vul9aWhpWrlwJNzc34c8BIiIiIsrZmGAmIsoCly9fxrNnz0w6h4VF9viRfuvWLcX22NhYk88dExOjmPiQZRkxMTFGn2/Tpk1o3bq18GPWxvr169GxY0ekpaUZcWXaO3fuHDZv3qzxupUrV+LMmTN6z6N2jwy5fwBUk2w3b97Uaax///0XH374IS5evGjQmjLIsoxZs2ahcePGePTokVHGNKVp06ZhwIABSElJ0bnv8+fP0aVLl7fKoJhaUlIS2rRpgzVrDHs08q+//oK/vz/S09O1uv7Bgwdo2rQpNm7cqPect27dgre3N86ePat1n0OHDqFJkyY4d+6c3vPu27cPrVq1QkJCgtZ9Ro0ahX79+hn0Rsnz58/RsWNHrFq1Su8xiIiIiCh7yh7ZCCKiXO7Jkycmn6NTp04mn0Mbop1tWZFAVUt+aJs40tbJkyfRtWtXvRJx79q+fTtmzZplhFXpbt68eVpdJ8sy5s6dq/c8artDDd3da6yxFy9ejB49ephkt/GRI0fQrFkznZJ6WW3nzp0YPny4QWPIsoyvv/4aBw4cMNKq1I0YMULrndaaBAUFYdKkSRqvS0tLQ4cOHXRKDIs8fvwYvXr10urn4+3bt9GmTRuj1O8/fvw4xo4dq9W1S5Ys0eq+aCM9PR19+/Y16M0qIiIiIsp+mGAmIsoCpkqu2tnZwd3dHYGBgfD29jbJHJSZLMvo1auXYmLTwsICn3/+OXbv3o179+4hPj4eFy9exLRp01CwYEHhmBMmTMjyUhmJiYlYsWKF1tevXLlS4yF3OdX27dvx1Vdf4VW5M3W1a9eGv78/hg8f/vqAzQoVKmjsd+bMGYMPNDSVly9fol+/fsKPv1GjRti8eTNu3bqFx48fY9++fejZs6fitcnJyejVq5dR3nxRExUVpfqmR/PmzbFx40bcvXsXcXFxOH/+PP744w8UL15c2GfixIm4f/++6rwzZ87EwYMHFWM1atTAsmXLcPXqVcTHx+PevXvYuHEjGjVqJBzv+PHjCAoKUp0TAPr37y98s7JDhw7YsmUL7ty5g/j4eFy7dg1///03SpcuLRxv7ty5GktsxMTE4PvvvxfGixUrhkmTJuHMmTN48uQJrl27hpUrV6p+vC9evMCQIUNU5yUiIiJScu/5PQSeD3z9+ufcPwaNl5qufdkvUmdl7gUQERG0SmpR9rF7926cPn06U7skSVixYgW6dev2VnulSpVQqVIldOnSBQ0bNlQsCfH06VNs374dHTp0MNWyMwkKCtJpd318fDzWrFmDzz//3ISrynoxMTHo0aOH6htBlpaW+PbbbzF48GCUKVNG8ZpDhw5h5MiRqjtqN2zYgKCgIHTp0sXQZRvVggULhKVKunfvjuXLl8PS0vJ1W8OGDdGwYUPUqlULgwcPztTnypUr+Ouvv1STk8ZYs+hzNnr0aPz2229vtTk6OsLNzQ19+/ZF27ZtFXdZP3/+HEFBQfjuu+8Ux5VlWVhP39fXF0FBQbC2tn7d5uDgAF9fX7Rr1w5ffPEFlixZoth31apV6P7Giejvunz5srDW+OTJkzFixIi32hwcHPDFF1+gS5cuaNasGU6ePJmpX1paGoKCgjBo0CDhvL/99puwvFOzZs0QFBSEAgUKvG5zcnJC2bJl0a1bN/z+++8YM2aMYt+QkBAcPnwY9evXF85NRERE9K7r8dcx5ciU1/8u/lDGF/hM53GO3D+CPjv6vP535w86Y3yD8UZZ4/uKO5iJiIh0tH//fsX2vn37Zkouv6lkyZKqOy5PnDhh8Np0sXDhQsX2atWqCXdbz58/35RLMotBgwbh4cOHwnihQoVw8OBBzJw5U5hcBgBPT0+EhoZmSva9a9y4cdnuTaXZs2crthcqVAgLFy58K7n8pkGDBqF169aKscmTJ5u0NM6uXbsU2xs2bJgpufwmJycnrF27Fra2tjqNC7yq6a2UiC9YsCCWLl36VnL5TRYWFpg7d65w97Sm7/0DBw4ofs20bNlS9evNyckJ//wj3tmjNm/G4XxKKlWqhA0bNryVXH6TJEkYPXq06o797HIwLREREb1fXqS8eCu5DABrL63F2ktrzbSi3IEJZiIiIh0pJXpsbGwwYcIEjX3btGkDe3t7xZimR/ONKSoqChEREYqxXr16CXdTRkZGKu6GzKmOHTuG1atXC+MFChTA7t274eHhodV4FhYWmDx5MgYOHCi8JioqKstqFGvjzJkzuHbtmmLs22+/Rb58+VT7jxo1SrH93r17CAkJMXh9IlevXlVs16YMSdGiRdGuXTvFmNqhk6I3BkaNGgUnJyfVOW1tbfHxxx8rxjR974vm1aY2sru7OypVqqTzvMeOHRO+8fL777/D0dFR49w//vij8ABa0c8fIiIieo/Jxiu0sOXqFkyKnJTpVX+F8hNUEw5q/luOxFgig4joPZSSkoIjR47g1q1biI2NRUJCAlxcXODq6ory5cujevXqkCTJ3MuELMs4ceIEoqKicP/+fbx48QJOTk4oX748PD09NSZ0TKV9+/b49ddf39qd2a9fPxQrVkxjX0tLS+TPnx8vXrzIFLOxsTHqOtUsWrRIsd3CwgKffvopbt++jTlz5iheM2/ePCxYsMCUy8sy06ZNU91NPHfuXFSrVk2vcQ8ePIgjR44oxvft24eGDRvqPK4pbN26VRjz9fXV2L9JkyYoWrQo7t27lykWFBQk3OFsKNHuaLW6w28qV66cYvvLly+FfUqXLo3atWvj+PHjr9tcXV3x9ddfazWns7OzYrum7/2WLVvCwcHhrUMi27Ztizp16phs3ujoaMX2QoUKCZPz7ypXrhxKly6tONbdu3e1GoOIiIjeH3JiaUh5LxplrIP3DuJSqvLv4mR8TDATEb0nZFnGpk2bEBAQgD179gjragJA4cKF4e3tje+++07rnZvG9OTJE0ycOBErVqwQJiEsLS3Rtm1b/Pjjj1onWYyldu3aWLt2LSZMmIDHjx+jRYsWqo/kvyk+Ph6xsbGKMdEuQ2NLTk7GsmXLFGPNmjVDsWLFUKxYMVSqVAlRUVGZrlmxYgX++OMPODg4mHqpJvX48WOsXSt+FK5Vq1aqJU/UWFlZYf78+fDw8EB6enqmeHx8fKa2pk2bIjw8XK/5tBUaGoqmTZu+1Xbs2DHFax0dHVGjRg2NY0qShEaNGikeVGfKndrFihXD9evXM7XHxMRo1V90ndohgJIkYePGjfj+++8RGRmJcuXKYeLEicKnEt515coVxXZN3/slSpTAf//9hxEjRuDWrVuoX7++sBb0u2RZFu5QV5tX9MZL3759YWWl/Z8QhQoVUkwwqyXyiYiI6P2U/sgHku0NAOK/VSl7YokMIqL3QFhYGOrUqYOOHTsiODhYNbkMvEq8BAYGol69evD19RU+im4KQUFBqFixIv744w/VHW5paWkIDg6Gh4cHRowYgdTUrD0BuEOHDjhx4gRu3LiBJUuWaPW4OAAsWbJEceelJEnw8fEx9jIVbdq0SZjk/uyz/z8ko0ePHorXPHv2DMuXLzfJ2rLS1q1bkZKSIowbekBd7dq18dVXXynGsvpNETVKB1YC0Gnntru7u2L7xYsXdTpIUheNGzdWbA8MDNTYNyEhQXho3rsJ+HeVLFkS69evx+3bt7F37140atRI43wAcOfOHWzbtk0x9tFHH2ns36hRIxw4cAC3bt3C2rVrUaJECa3m3bRpEx48eKDzvA0aNECePHneanNyclIt/6JE9HNcVL+ZiIiI3mPJxfH82vdIftJAMVw0b1H4u/m/ftUvwgODswsmmImIcrkZM2agZcuWeh8gFxwcjHr16iE0NNTIK8ts7ty56NatGx49eqR1H1mWMXXqVLRr106x7ER28t9//2HMmDGKsc6dO6N8+fJZsg7R4X52dnbw8/N7/W9/f39hqZTcUCJDrT5wkSJFjJLwnzx5MqpUqfJWW5s2bdCxY0eDxzaG1NRUvXfVvqlixYqK7bIsK+6CN4YvvvhCsX3Xrl2qTxQkJSWhd+/eivWF7ezs0KdPH4Vehnnw4AE6duyI58+fZ4o5ODjg22+/NfqcAHDkyBHhfapXrx6aN28u7Fu6dGkEBAS8Lq9RpEgRrF69GkWKFNF6/qioKGFNa9GbEkRERPR+k1OdkJpQRTFWJn8ZjKw38vVrVH3ls0Ao6zHBTESUi40fPx6DBw8W1irV1uPHj+Ht7a1aq9VQmzdvxoABA1Tr4arZvn07PvnkEyOvSn+pqal4+fIl7t+/j//++w9du3ZF27ZtFZPgJUqUwF9//ZUl64qOjhYmVtu1a4f8+fO//nfp0qWFu0RPnTqFgwcPmmSNWSUyMlIY8/LygqWlpcFzODg44PDhw5g7dy5GjBiBf/75B5s3b84WNc6BV08rKJXwAKBTIlGt/vjNmzd1Xpc2GjdujN69eyvGxo4dC29vb2zatAn37t3Ds2fPcO3aNSxevBi1atXC+vXrFfv98ssvqiUytCHLMhITExEXF4djx47hp59+gpubm7Ae98KFC1G4cGGD5gRePdWRmJiI2NhY7NmzB1988QU+/PBDxUR6/vz5tXoK4dNPP8WjR48QGxuLe/fuwdvbW6c1qb0RZara3ERERPT+MObv1D5lsuZp0tyKNZiJiLKBxMREra+VJEmrw+A2bdqEX375RfWaKlWqoHr16ihSpAgePHiA8PBw3LlzR/Ha1NRU+Pv74+jRo0bfafvgwQP069dP9RoLCws0adIE7u7ukCQJZ86cwd69e99Knose9c8qL1++xOjRo7FmzRrcu3dPq2S5o6Mj1q1bh4IFC2bBCoHFixcL1+Xv75+prUePHti7d6/i9fPnz0eDBsqPr2V3qampuHz5sjBeq1Yto82VL18+9O/f32jjGdP9+/eFsUKFCmk9jouLizAm2sFqDPPmzcPDhw8Vy13s2rULu3bt0nqsgQMHYujQoXqvJSgoCL/99hsuXryIpKQkrfqMGjVK7zrfAJCeno5ff/0VS5Yswe3bt7V6MzFPnjxYvny5cNf5uyRJ0uvn0+XLlzF37lzFmJOTEzp37qzzmERERETaalu2LTq6ZX5aa8u1LTj36FymdksLwzeXvM+YYCbSwH0ZH+HMCc70OmPuJRjEzs5O5+tr1qyJKVOmKNb/jI+PR58+fYSJxEqVKmHu3Llo0aLFW+2yLGP16tUYOHCgYo3euLg49OzZE/v379dpvZpMmzZNWCMUeFV7NCAgABUqVHirPTo6Gv369cOePXuMuh59jRw5ErNnz9b6eldXV+zYscOoyUw1aWlpCAgIUIwVKFBAsSREly5d8N133ym+CbJmzRrMmDHj9SP0Ocndu3dVk3G61B/OyZR2t2bQtq44gLd2vr9Ll5I3urK1tcXGjRsxdepU/Prrr4olKDQpWLAg/vzzT2HNcW0cOXIE3bt3F+4GV/Lzzz9j3Lhxes8JvCqBNH78eK2vt7e3x/r1602+e/jFixfo2rUrkpOTFeMjRoxQ/ZohIiIiMtSHxT9E5SpFM7U72jhi9L7RZlhR7sYSGUREOdDLly9x8OBBtGvXDgkJCZnis2fPxuPHjxX7uru7IyIiIlNyGXi1U6179+4ICwsT7lg7cOAAdu/ebdgH8IbExEQsWrRIGG/evDl27NiRKbkMAGXLlsW2bdvQqVMno63HEBERETpdb2FhIax/awpbt24VHrjVtWtXWFtbZ2p3dHREu3btFPskJiZi6dKlxlxillFLrALqO3JzE7WnJ9494E2N2lMVujyhoQ9LS0t88cUXeieIBw8ejK5duxq0hgMHDuiUXJYkCbGxsRoPXNVE1585lpaWuHbtmt6liLSRlJSE7t274+TJk4pxd3d3DBs2zGTzExEREVHWY4KZiCgHi4uLy/SYf2pqKmbMmKF4vYWFBZYtWwZXV1fVcatUqYI5c+YI41OmTNF5rSI7duwQJsPt7e0RGBgIe3t7Yf88efIgICBAp3qxplK1alWdro+JiUHXrl3Ro0cP4U4/YxId7gcol8fI0LNnT2Espx729/LlS9W4OXZXhoWFQZZlk76aNm361pxqpRy0KcWTQenNiQya7rWhli1bhooVK2L+/Pl69R8zZgwqVKiA8PBwvdeg6/e+LMuYPXs23N3dcf78+SybNyEhAf3790fr1q3x9OlTvedVG79du3bYvHmzYtze3h4rV65U/XohIiIiopyHCWYiohzM2dk5Ux3NI0eOCHdnduvWTetyDN26dYO7u3KJmLCwML0eRVdy4MABYWzgwIGqh4dlyJ8/P37++WejrMcQf/zxB3x9fXUqLQAAy5cvx0cffWTSJPOdO3ewbds2xVi5cuXw4YcfCvv6+PgI35SIiopCaGioUdaYlTQdCKJr2ZqcSi3BbGGh/a+Jajtita1HrI9hw4ahd+/eePLkiUHj3L59Gy1btsSSJUv06t+yZUv8/vvvKFmypE6HzVy/fh0NGzbE0aNH9Zp31KhR6NWrFwoUKKBTv127dqFRo0YG37c3nT9/Hh4eHsK615aWllixYoXOSXEiIiIiyv6YYCYiyoEcHR3h5eWFrVu3wsHB4a1YSEiIsJ+upSREu1qTk5OFB7/pKjIyUhjz8/PTehw/Pz9YWZn3aIEiRYpg48aNiIuLe2vXaFxcHCIjIzFixAjkzZtXse+ePXvwzTffmGxtS5YsEdYc/uyzz1T7WllZoXv37sJ4TtzFnC9fPtW4qcs6ZBdqSWRtDozLkJqaKoxZWprmwJQZM2Zg2rRpwninTp2wY8cOPHr0CC9fvsSVK1ewcOFC4Ztsqamp+Oqrr1R/hqr54YcfcPPmTaSnp7/+3k9MTMS1a9ewaNEiuLm5KfaLi4tDhw4dVA9cFMmbNy+WLl2Khw8fvvUzJyEhAadOncKvv/4qTD6fPXsW3bp106m0h0hgYCDq1auHqKgoxbitrS3WrFkDX19fg+ciIiIiouyHCWYiomxA18fc4+LiEBYWhgYNGmQaS1T3EgBatWql07o8PT2FsePHj+s0lsitW7cU211cXFCnTh2tx3FxcVG8H9mBo6MjPDw8MHnyZERGRqJ48eKK1y1ZssRoifs3paenY/HixcK4WnmMDGr1bTds2KB6SGN2pGmXeVxcXNYsxMxsbW2FMV121Ktdq0stZ23dvHkTo0aNUoxZWVlh5cqVWLduHby9veHi4gJbW1uUL18e/fr1w5EjR4R9U1NT8eWXXyIlJcUo67SxsUHZsmXRt29fnDhxQvhGzZ07d4Rr0ke+fPlQvXp1jBkzBqdOnRIeWrlr1y4EBgbqPU96ejqGDRuGnj17Cp9qcXV1xe7du7NNrXwiIiIiMj7zbvUiygHO9Dpj7iUQ6SQ2Nlax3dHRUefSDZUqVdJ5Hl2JEnm6PmoOAKVLl9b50KusVqVKFaxcuRJNmjRRjP/yyy/CR8z1tWvXLty4cUMxVrt2bZQqVUrjjl13d3dUrFgxU81v4FVyccmSJfjhhx+E/dV2seqyU1aJ2g5M0a724sWLw9raWphIvHHjhmrZEF28fPkSf//9Ny5fvozKlSujb9++iiU4mjZtalAdYG2Ehoa+VYdZrb650gGiImqH1WnaLa6PmTNnCr9mJ02apLrj3tLSEhMnTsTNmzfx77//ZopHR0cjMDAQffr0Mdp6gVfJ5qVLl+L06dOKdZcDAwMxYcIElCpVyqjzFi9eHBs3bkTVqlUVy5X8+uuv6NWrl87jJiUloUePHggKChJeU61aNWzevBllypTReXwiIiIiyjm4g5mIKJcR1V8uVKiQzmOpJaSNlWAWHTSlz3oLFy5s6HKyROPGjeHl5aUYCwsLM/rhW2qH+x0/fhx2dnZavZSSyxn+/vtv1Tq8agfGGfrxqiU3RfNaWVmhfPnywn5nz541aE0Z0tLS0Lx5cwwaNAhz587Fd999h3r16hmthrmh1L7PdNnFrXats7OzDivSzrp16xTbixYtiu+++06rMSZOnCgsEbJhwwa916bGxsYGw4cPV4ylpaVhy5YtJpm3fPnywqT7lStXdD5o8NmzZ/joo49Uk8vt27fHgQMHmFwmIiIieg8wwUxE9J7QdTewvn10JTpMTZ+5dTmUzBDJyclYtGgRBg0ahKVLl6omVkW8vb0V21NTU1XLnOjqwYMHCA4ONtp4ItHR0dixY4cwnj9/fmHMkHIU6enpqjtt1XbPenh4CGP79+/Xe01vWrZsGQ4dOvRW29mzZzF79myjjG8otUM0Y2JitB5H7Vpjv/Hz4MED4Y58b29vrUtylCpVSlhWR20n+d69ezF69Gj89NNPetVNFn3vA68OaVWSnp6OlStXYsiQIZgzZ45eByfqM6+SJ0+eoFWrVqqHe44ZMwYbN27MdEYAEREREeVOLJFBRJTLFCxYULFdnx3Haok/fXYYK3F2dlZMEOpT0zcr6gAnJSWhYcOGOHbs2Ou2c+fOYerUqTqNo/YYvC6JPU2WLl1qtHqymsyfPx8+Pj6KsSJFigj7Xbt2Te85r1y5onrAnKurqzDWqlUrYf3Zffv24f79+6rr1oYokXzmTPYov1S4cGHkzZtXcUf1zZs3tR5HlPAFYPQdrLdv3xbG1HalKylbtqzimwkJCQl49uxZpjcopk6dihEjRrz+99KlS3H+/HnVUiPvKlasGKysrBS/bpW+92VZhq+v71u7m8PDw1V3Dysxxs+c2NhYtGrVCqdOnVKM29nZYcmSJaolSoiIiIgo9+EOZiKiXEaUUIuLi9OppioAXLp0Sed5dCUa586dOzqPde/ePUOXo9GSJUveSi4DwLx58xAfH6/TOGq7no15KNqiRYuMNpYmW7ZsEX7eypUrJ+x37NgxvZPgR48eVY2rJTd9fHxgbW2tGEtLS8P8+fP1WlOGsLAw4W70AgUKGDS2sVhYWKBmzZqKsQsXLmg9zsWLF4WxqlWr6rosVS9evBDGRDW3RdQOOXy3dMvTp08xZsyYt9pu3LiBFStW6DRnenq68AkNpe/97du3ZyqdsW7dOtWSNUoM/ZkTGxuLZs2aCZPLhQoVQlhYGJPLRERERO8hJpiJiHKZWrVqKbbLsoyQkBCdxnr30f431alTR6exRGrXrq3YHhsbK0xkKElMTDRaWQM1586dy9T2/PlzLFmyRKdxrl+/LoyplS3QRVhYmM5JKEOkpaUJ6z27u7sL+yUmJmLv3r16zbl69WphzNXVVbijPyPu6+srjE+fPh13797Va13p6elv7XR9l7GTroaoX7++YvvFixe1ro99+PBhxXY3NzejPe2QQa3sidruZl2vf7e8w+XLlxXfCJk7d65Oc965c0f4horS977SzxxZlnWe15CfOQ8fPkSLFi0U1wK8egPp4MGDqFevnk5rIiIiIqLcgQlmIqJcpkWLFsLY+vXrdRpr+fLliu02NjZo0qSJTmOJiJJbgG7r3blzZ5YcnCaqJzt16lQkJiZqPc7mzZsV2/PkyQM3Nze91vYutcP9RowYAVmW9XqNHj1aOO6iRYuQlpaWqb1+/frC3cIA8Pvvv+v2wQE4efIktm7dKoyL6uu+acCAAcJYfHw8/P39kZycrPPaxo8fL6xra2FhoZrYzmpt27ZVbE9PT8f27ds19r99+7Zwp3bz5s0NWZoitV3pe/bs0XqcFy9e4MCBA4qxggULZqobLvreP3nypPD7WYlaTXSl3eSieRcuXKhTOR1d583w6NEjtGjRQljWpUKFCti7d6/qUwpERERElLsxwUxElMt4eHgIdwyuXLlS69qva9euxenTpxVjLVq0EB7Opytvb2/h4Xza7iBNTU3F2LFjjbIeTUQ1hu/evYspU6ZoNcauXbuEycfmzZsb5WCsx48fY926dcK4v7+/3mP36NFDGLtz545iss3e3l71zY/du3djwoQJWq8hNjYWPXv2VExmZxAlTt/k5eUl/JwCQGhoKPz8/LTeyQsAEyZMwK+//qq6LqXazmFhYXon/bV9NW3aNNO8TZo0Ee70XrBggcaP9++//xaWX9C2XMK+ffswefJkLFiwQGMtdScnJ3zwwQeKsYsXL6oeNvmmpUuXCkvbeHp6ZmorWbKkcOf58OHDtXoj4uXLl5gxY4ZiTJIktG/fPlN7q1atYGlpman9xYsXGDVqlMY5gVflTjZs2KAYq1y5svB+pqamomPHjsL/FxQsWBA7duxA8eLFtVoHEREREeVOTDATEeUyVlZWGDp0qGIsLS0NPXv2xKNHj1THuHjxIvr37y+Mqz36r6uSJUsKE3wJCQno1asXXr58qTrGyJEjs+zQNA8PD2FZj99++01YKiDDjRs3VJO7n3/+uUHryxAYGIikpCTFWPXq1VVLVmhSuXJl1K1bVxgXJSU1fWw//fQTPvnkE9VD/9LT07Fu3To0aNBA9XPu4OCArl27qs6XYdq0abCxsRHGt2zZgurVqyMgIED4tSjLMsLCwtCoUSP89NNPwrEsLS312q1tSlZWVvjuu+8UY6Ghodi4caOw77Vr1zB9+nTFWL169dCoUSON8/fr1w+NGzfGDz/8gK+//hoVK1ZEaGioah8/Pz9hrE+fPrh//75q/3Pnzqn+HOvSpYti+5dffqnYHhUVheHDh6vOmZ6ejr59++LKlSuKcR8fH8U3HooUKaKYeAaAgIAAjU96xMXFoVOnTsI3Y9S+L+fPn4+IiAjFmCRJWLFiBXcuExERERETzEREudG3334r3MV88uRJeHl5ZTqoDniVJFuzZg28vLwQGxur2L9Jkybw8vIy6noHDhwojIWEhKBFixaKj+Dfvn0bn376Kf7880+jrkeTP/74Q7E9OTkZrVu3Vkz4pKenIygoCHXr1hXu0KxXr54wsaUrtcP91HYga0ttjJ07dyoe9te5c2dUqVJFddxVq1ahfPnyqFmzJnr37o2RI0di7NixGDBgAHx9feHq6orOnTvj6tWrquMMHz4cTk5OWn0sVapUwdSpU1WvuXnzJvr06YNChQrB29sb33//PcaNG4ehQ4eiS5cuKFWqFJo1a6axDviwYcOyVf3lDAMHDhT+zOjRo4firvTz58/Dx8cHz549U+ynVkolQ0BAABYvXvxWW3x8PDp37oy4uDhhv2+++UZ4MN3du3dRt25dxZ3MaWlpWL58OT788ENhSZ0SJUoId15//fXXqFChgmJs1qxZGDBggOKu6MuXL6Nly5ZYuXKlYl8LCwtMnjxZMQYAkyZNEpaY+fTTT7FgwQKkp6dniu3evRu1atUSHsJYqlQp4c/f1NRU1TdDZFmGt7c3JEnS+mVpaYmSJUuiS5cuwnrORERERCYnA30PT8HXB2e+fiFd+RBm0o6kdqI0kSnVrVtXPnr0qEnnuHDhgtFqmRIZIiwsDM2aNRPGTfGzOCQkBD4+PqolBGrXro26desiX758iImJQXh4uOqhV87Ozjh69KjqjjVJUv4fs6+vr+pOSD8/P4078Tw9PVG1alXkyZMHly9fRlhYGFJTU1X7ZIiOjlat3aqr/v37Y968ecJ4hQoV0KhRIzg7O+P+/fuIiIhQvbd58+bFvn37VGuhauv06dOoUaOGYszCwgK3bt0y+CDB2NhYFC9eXHhY2axZsxR3xe7btw9eXl6KyTBjqVu3Lg4cOKBa81lJ7969sWzZMhOtCmjdujX+++8/YUkYcwsKClLd9V27dm14enrCysoKFy5cQGhoqPD7r1OnTqolWjI0bdoU4eHhirGlS5eiV69ewr5jx47Fb7/9pjp+xYoVUa9ePbi6uuLevXvYt2+f4psfb1q9erXqfdi3bx+aN28u/NrPnz8/mjVrhjJlyuDFixc4e/YsDh48qDrnmDFjVMuqAMCUKVMwcuRIYbxEiRLw8vJC4cKFERsbi8OHD+PSpUvC6y0tLbFlyxbhEyTbt2/HRx99pLomQxQsWBCnT59G0aJFTTZHVuHvm0RElOttHw0cUjhg2Ps34EPxmSa6chu3HS9T0vAy+gQerBmXKd6yZUvs2rXr9b+Trl7FlqFrcL9I5nN9WvR2Q2XPzL9nbL66GZf/lGCTZp8p9u18458foi9Jko7Jsix+bDSbsTL3AoiI3gdqj9+rxQzRsmVLTJo0SfWx7ePHj+P48eNajWdlZYV///1X4+PQ1tbWiokXpRqib5ozZw727dunWn/10KFDOHTokDDeo0cPBAYGKsaMndSbOXMmzp8/L0yOXblyRfgo/LssLCywcuVKoySXASAyMlIYa9WqlcHJZQBwdXVFhw4dEBQUpBiPiIhQTDA3atQIM2fOFJZkMFS5cuWwadMmnZPLALBkyRLIsox//vnH6Ovy9PTE6tWrs21yGXhVFmL48OHC3dza/rxwc3PDkiVLtJpTLdmrKRE8fvx4HDhwQLWcxuXLl3H58mWt1gK8evpDU2mVRo0aYc6cOfjqq68U4/Hx8di0aZPWc3br1g2//PKLxutGjBiB06dP499//1WM3759WxhTMmvWLNX646K6y8by8OFDbN26Ff369TPpPERERPR+Or3nNq6dyPxUbuxzG9ikKadDZVkWbpgiddn3rxwiolykdOnSwpgxd9W+a9iwYVi8eLHwUXJtubi4aL2brVSpUortosfvMxQtWhS7d+8WHjamyejRo7FgwQLFXwgsLCwUa5sawtraGtu2bUOnTp0MGsfFxQVbtmxBu3btjLSyVwf8iXzxxRdGm+fbb78Vxl68eCGMDRgwAIsWLTL6myvNmzfH/v379U6gW1hYYOnSpZg8ebJeCWoRf39/hISEwNHR0WhjmsrkyZPx/fff692/atWq2Llzp9Yfq9ruVU07W62trREcHGy0XbbfffcdZs+erdW1X375Jf755x+Df7YOHDgQgYGBWv8hs2zZMtXvO23Y2dlh6dKlqnX2ARjljShNRHXiiYiIiAwVezMB0aceZnq9vCLea5v0QrunYykzJpiJiLJAsWLFUL9+5sd2ABicoNSkT58+2Lt3r3B+Tdq2bYvIyEi0aNFCq+u9vb0V21u2bKmxb7Vq1bB3717VA+Te5ejoiOXLl+O3336DnZ2d4i7gWrVqGZwIUmJnZ4egoCBMnz5d63q/b2rfvj2OHz9u9MfQfXx8FBNW5cuXR4cOHYw2j5eXFzw9PRVjvr6+qn379u2LU6dO4eOPPzZ4Hc7Ozvj999+xa9cug99IkCQJI0aMwOHDh1V3d2qjcuXK2Lx5MwIDA5E3b16DxsoqkiRhxowZWLZsGQoUKKBT3549e2L//v0oUaKE1n1ENcfz5s2Ltm3bauyfL18+bNmyBdOmTUP+/Pm1nvdNJUuWxNq1azFr1iyddqz06NFD559XGcqVK4d169Zh5syZOr2ZYWlpiTlz5iAwMFCv0hJNmjTBoUOHVEuPZOjcuTMqV66s8xzasrOzQ5s2bUw2PhERERFlHSaYiYiyyOLFi1G4cOG32jw9PbU6CMtQ9evXx6FDh7Bx40a0a9cO+fLlU73e1dUVn332GQ4dOoQtW7agfPnyWs/1448/Ztqx7e/vr3Ui3c3NDYcOHcLs2bNRrVo14XXOzs4YNGgQoqKi8Nlnn71unzFjBhwcHF7/28HBAdOnT9d6/bqysLDAoEGDcPnyZYwYMUJjgrNQoULo0aMHjh07hk2bNqnubtdX9erV8ddff721Q9jOzg5z587VWKpEV4sXL0bx4sXfavv888+1euy9UqVK2Lx5M86dO4cxY8agTp06Wq/P1tYWDRs2xJw5c3Dr1i388MMPRi0/UatWLWzbtg3Hjx/HsGHDhAe7vStv3rzo0KEDNmzYgHPnzhklgW4OPXv2RFRUFMaNG6e6k9XGxgYdO3bEwYMHsWzZMp13affv3x+ffvrpW212dnb4999/NT71kMHCwgJDhgzBtWvXMHnyZNSsWVNjotjKygqNGzfG4sWLcenSJfj5+em07gz169dHZGQkAgMD4enpqTpvnjx50LRpUyxZsgRRUVEGvbno7++PS5cu4ddff9VYtsjR0RF+fn7YvXs3wsPDUb16da3msLW1xf79+9G/f3+ULVvWaN9ftra2qFOnDoKDg1G2bFmjjElERETvh5CQEPj6+r51notV6kujjW9hyfIY+uIhf2Q2POSP3kdPnz7F+vXrce/ePbi5uaF9+/ZGT/hpIzk5GZGRkbh9+zZiY2ORkJAAFxcXuLq6oly5clolaNQkJCQgKCgIT548QZ06ddC0aVO9xzp//jzOnj2L27dvIyUlBQUKFIC7uztq164t3Pl38+ZNbNu2DbIso02bNsKyHaYgyzLOnTuH06dP4+7du0hKSoKzszNcXV1RoUIFg++tLq5du4adO3dClmW0bNkSFStWNMk8sbGxWLt2LZ4+fYqGDRuicePGeo/18uVLXLhwAdevX0dMTAwSEhKQlJQEGxsbODk5wdnZGWXLlkWNGjWMWsZCG48fP8b58+dx9epVxMfHIyEhAbIsI2/evChatCg++OADuLu7w8oqdx1xkfE1ffz4cTx48ADJyclwcnJCpUqVUK9evbfe0NHXvn37sH//fuTLlw8dOnTI9KaFrh4+fIjTp0/j6tWriIuLQ2JiIuzs7ODi4oKKFSuiRo0aeu94VvPgwQMcOXIE165dQ3x8POzs7ODq6vr6KRZNb+7p6/Llyzhx4gRu376NFy9ewNHREa6urihTpgw8PDzM8v+Z9wl/3yQiolwvmxzy9yZZlpF09SoO9xyFM+5fG2X+r2Z5wSqP+PemtPQ0RD2JgpuLm8n/pstph/wxwUxmwwQzEREREeV0/H2TiIhyvWyYYI6KikJpS0tcbfsxrpbriJulNJdk1EQtwTwodBB239z9+t/9a/bHNzW+MXhOkZyWYM5dW2yyGUmSLAF4A/AF4AGgFABHAM8BxAC4AGAbgE2yLMeYa51vkiSpDoDWAJoBKAmgIAAnAIkAngCIBnAcwG4A22VZTjHPSomIiIiIiIiIKNfRojRXQEAAfurXDxKACtc2oNStEMTnLw1ZsoRV4cIoMnasYr8j94/g4SZbnZaz+ermt5LLAPDXyb/QslRLVHQ2zVOiOQ0TzCYiSVIXAJMBKBWXc/rfqxKADgBmSJI0F8BEWZafZNES3yJJUnsA4wHUFlyS93+vEgAaA/gewENJkn4DMEeWZR61SUREREREREREBrEpXB6QLAA5XXjNu+f15ElJQMFHZ1/9d/5yKFfLVbHfuatpSJWSYSVnPgTeZ91HSLfMnN6KfRmrONasE7Mwu/ls4RrfJzzkz8gkSbKRJCkAwBooJ5eV2AEYBuCIJElVTLY4BZIkOUiStA7AJoiTyyIFAUwHcFCSpMKaLiYiIiIiIiIiIlJjYZsPjp5dVK9xcXEx+rwPXz5E7MvYTC+RS48vGX0NORUTzEb0v5IYqwD01nOI8niVrHU32qJUSJLkDOAQAP2PMX+lLl6tW7vj3omIiIiIiIiIiAQcG/vDtZN6HWbKPphgNq6f8arkhSHyAwiWJKmg4csRkyTJCsA6AMbaMV0WwBpJkvg1RUREREREREREepMkCfYV68O2bB2jjlvY3ngP4Bey5z7LDEwGGokkSTUBjBSEZQD/AGgIwBVAEbw6+O+g4PoyAKYZd4WZfIVXB/mJ7AHQGa8Sx84AqgMYB+CBSh8vAJ8Ya4FERERERERERETGUrNQTUiSZJSx6hWtZ5RxcgMmmI3nJwCWgtgAWZZ7ybJ8QJblh7Isx8iyHAygCV7tIlbi/7+ktdFJkmQH4EeVS4bKstxCluV1sixfl2U5TpblM7Is/wrAHcABlb7Kx3QSEREREREREREZQVpaml798ljmgbVF5gP+9OFk42SUcXIDK3MvIDeQJKkUgPaC8DZZlv9SCsiynCpJUh+8SjS/e7ylBYBRALoZbaH/zxuAaB//ZFmW/xR1lGX5gSRJHQGcEYxRWZIkN1mWLxhhnURERERERERElBPdOw2s6QE8uQ58OBDw/kVjlzpSFCZbL4SdlITRKf2wRnBdu3btYGVlhVPlyuu8I/nV9XKm9m2d/oNlnsx7cSdHTsbOGzt1muN9wx3MxuELQPTVPEmtoyzL8QDmCsLtJUlyNGRhAq0F7bEAxmvqLMvyAwDzVC5prM+iiIiIiIiIiIgoF7gVCSxo/Cq5DAAHZgGTSqt26SiFYp3NBFSwuIvi0iMsyzMZZaT7wutTU1PR+cZ1oy3Z1d4VhewLKb5IHXcwG0dbQfsjAPu16L8ar0psvMsWQDsAy/VbllBZQXugLMtJWo6xB+JkdDHdl0RERERERERERDlG2O/A/pnKsecKR3glxgGngwB7Z8UuEy3mZ2orY3EfF1WWcCFJ2zQWmRITzMZRW9C+T5ZljUVhZFm+KEnSAyiXnPgQxk8wK+22lgH8rcMYaof92em2HCIiIiIiIiIiylGSn7166WJ9P50ud8hjnAP5yLRYIsNAkiQVReb6yRnO6jCU6NoGuq1IK2EKbWtkWY7SYQy1XcqPdFsOERERERERERHR25qX5d7YnIAJZsNVVonpkrC9rMf4+poO4F8A6Xi1czkEwAAdx2ilEjuj57qIiIiIiIiIiCg7yW++Sqj9alujc0dfYbyIFRPQ2QETzIZT+y4TVyLP7K6g3VaSJKNWE5dlOUmWZX8A+QA4yrLcSpblh9r2lyTJDkAPQfgFgAgjLJOIiIiIiIiIiMztg9aApY1ZpraykLB02VJs3LhRMW5vwdRmdsDPguGKqMTU6hS/67FKrKQO42hNluWXsiwn6NF1OIDigthqWZZ1LMBDRERERERERETZUsGKwCcrgIIfmGV6SZLwwQfmmZu0w33khiuoEnuqwzjxKrECOoxjUpIkNQEwVhBOBPBLFi6HiIiIiIiIiIhMrUJLYEBL4MVjIC1Fuz6PrwIn/gWeRANWWuyAvrrHsDWS2TDBbDhblViyDuMk6TlHlpEkqTqAdQCsBZdMkGU5WsMYXwL4EgBKlSpl3AUSEREREREREZHp2Ltof61DYaD0h1pf/mJ8IdhLaukxyq5YIsNwam/B6PJdofb2j50O45iEJEkNAIRBvGN7D4ApmsaRZflvWZbryrJc19XV1YgrJCIiIiIiIiIioqzGBLPh1BLM6TqMI+k5h8lJkvQNXiWXnQWXRAHoKsuyLh8vERERERERERER5XAskWE4taSqpQ7jqH0u0nQYx2gkScoLYCGAT1QuOw2gtSzLj7JmVURERERERERERJRdMMFsuESVWB4dxlG7VpdazkYhSVIJAFsBVFe5bDte7VxOyJpVERERERERERERUXbCEhmGe6ESc9BhnHwqsWc6jGMwSZLcARyCenJ5FoCPmVwmIiIiIiIiIiJ6f3EHs+EeqMScdBhH7donOoxjEEmSPsSrnctOgkuSAQyQZXlhVq2JiIiIiIiIiIiIsicmmA13VyVWWIdx1K6N0WEcvUmS1BLARgB5BZc8AOAny/K+rFgPERERERERERERZW8skWG4qyqxUjqMU1rQngbgtg7j6EWSpHYAtkCcXD4FwIPJZSIiIiIiIiIiIsrABLPhTgNIFcTcdBinsqD9kizLKbotSTeSJPkCWAfARnDJVgANZVm+acp1EBERERERERERUc7CBLOBZFlOxKsks5L62owhSZITgEqCcJjuq9Le/3YurwFgLbhkMQBfWZafm3IdRERERERERERElPMwwWwcWwXttSRJKqpF/7YAJEFsj35L0kySpI8BrAWQR3DJXwC+kGU5zVRrICIiIiIiIiIiopyLCWbjWCdotwDwhRb9vxK0PwWwQ62jJElWkiT5SZI0SpKk7pIkicpcvNuvBoAgiJPLawEMkGVZ1mY8IiIiIiIiIiIiev8wwWwEsiyfAhAqCA+VJEl0gB8kSeoCoLEgvECW5QSVvs4ADuJVMngigJUATkiSVEKLZS8CYCuIRQHozeQyERERERERERERqWGC2Xh+BKCUkM0PYLskSZkO8ZMkqQOApYLxngOYqWHOGQDqvtPmBuAftU6SJLVR6PemSgCeSZIk6/B6IUnSBUmSxkuSZK9h3URERERERERERJQLWJl7AbmFLMv7JEmaB6C/QrgygDOSJO0BcAGvylJ4AqilMuQoWZbvioL/K4XRTRBuJklSKVmWbwriPVXm1ZcdXn2cPwH4AMBnJpiDiIiIiIiIiIiIshEmmI1rCAB3KJe8sALg/b+XJitkWZ6t4ZqCANTqLZcAIEowV9diDYboKklSX1mWE008DxEREREREREREZkRS2QYkSzLSQDaAwg3YJggAJ9rcd0jACkq8XsqMeHOaCOxBN+8ICIiIiIiIiIiyvWYBDQyWZbjJElqBeA3AIOh/T1Owqs6zn/IspyuxTyJkiRtBtBJIXxEluVole4TATSD6d5g2CnL8jMTjU1EOUBcXBzWrVuH2NhY1KxZEz4+Plk6/9WrVxEcHAw7Ozu0atUK5cuXz9L51Vy4cAE7duyAhYUFWrZsiSpVqph7Sa+lpKRg165duHjxIiRJQuXKldGqVStYWRnv14WIiAgcOHAA9vb2+Oijj1ChQgWjjJueno5t27bhzJkzcHZ2hq+vL4oUKWKUsTOkpqbizJkzuHTpEu7du4fnz5/D2toaDg4OKFWqFKpUqYKyZcsadU4iIiIiIqLsjglmE5BlOQXACEmSlgIYile1kvMKLn8EYBWAKSo1k0X6A6iAt0te3ICG+seyLO+RJKkxgF8A1ABQQMd5RWIB7AbwvZHGI8pVZFnGvHnzEBAQgEuXLiE+Pt7gMe3s7FCxYkUMHz4c/v7+Rlil4W7dugVPT0/cvfv/D0v4+fkhKCgIkiSZfP79+/ejRYsWSEpKAgDY29tj48aNaNWqlcnn1mTJkiX4+uuvkZLy6gEUSZLwxx9/YMiQIWZeGXD48GF069YNN27ceKu9bNmyWL16NTw8PAyeo0+fPggICHj9bysrKyxatAi9evUyaNykpCT4+PggLCzsdduwYcOwadMmNG/e3KCxExMTsW7dOqxatQqhoaF4/vy56vUlSpRA27Zt0a9fP9Stq3aebs5w4sQJrF69GocOHcKlS5fw5MkTWFpaolChQihTpgy8vb3Rvn37bPVGSUREBLZs2YLDhw/j8uXLePLkCVJTU+Hk5AQXFxdUrlwZDRs2ROvWrVG9unGrhp08eRIbNmzAvn37cOnSJTx69AgpKSnIly8fSpcujdq1a6Nt27Zo3749rK2tdRp7w4YNmD9/Pk6dOoVHjx4hNTXVoLXmyZMHRYoUgZeXF3788Ued3uy5fv06tm/fjp07d+LKlSuIjY3Fo0ePYGlpCQcHB5QuXRrVqlWDl5cXfH194ezsbNBaiYiIiCh7k2RZNvcacr3/HchXF0BVAC4AZAAPAZwGcEKWZb3/QpAkyQqvynJUBnALwDpZll8YvOgsULduXfno0aMmnePChQtwc3Mz6RxE2ho3bhx+/fVXk42/bdu2LN8prKRv375YsmRJpvagoCB07tzZ5PM3b94coaGhb7XVqFEDJ0+eNPncarZt24Z27dohLS3trXYrKytER0ejRIkSZloZcOXKFdStWxdPnz5VjDs7O+PEiRMoXbq03nNs2rQJHTp0yNRua2uLGzduoFChQnqPPW3aNAwbNixTe7FixXD9+nWdE3kAkJycjFmzZmHq1Kl48OCBXutq0qQJpk2bliMTzcePH8fgwYOxd+9ejddKkoROnTrht99+Q6VKlbJgdcrWr1+PsWPH4sKFC1r3adKkCcaOHWvwG1AHDhzAiBEjsH//fq2uL1KkCMaPH48vv/wSFhaaHyi7fPkyKleujPR0jQ+56aVIkSK4dOkSHBwcVK87deoUJkyYgI0bN0LbvyFsbGzQt29f/PzzzyhQwFh7GrIX/r5JRERkHC/GF4K9lJS5fdhN3Lh1V3FTQ7k8ebClbLm32vKUK4fy/20VzjP/uzCkpWT+veqrWV6wymOZqX1y5GQsv7A8U/sIjxHoUaWHcB5DSJJ0TJblHPOHBGswZwFZlpNkWd4vy/LfsixPkmV5sizLi2VZPmJIcvl/Y6fKsrxeluWJsiwH5pTkMtH7JikpCTNmzDDpHOvXrzfp+Np6N7mbISQkxORzp6enIyIiIlP76dOnX+9oNofr16/js88+y5RcBl6VXciKe6NmyJAhwuQyADx58gSjR482aI7g4GDF9sTEROzcudOgsbds2aLYfvfuXURGRuo83vHjx1GjRg0MHz5c7+QyAOzduxf16tXD999/b9avP13NnTsXnp6eWiWXgVdPZ6xbtw516tTBunXrTLy6zJ4/f45u3brBz89Pp+Qy8Opz5O3tjW+//RbJyck6z52eno5Ro0ahUaNGWieXAeD+/fv45ptv0KpVKzx58kTj9UePHjVZcjljPeHh4iNEZFnGr7/+itq1a2PDhg1aJ5eBV///++uvv+Dm5oZDhw4ZY7lERERElM0wwUxElAUuX76MZ89MW5pcm11wWeHWrVuK7bGxsSafOyYmRvGxcVmWERMTY/L5laSkpKBr166qSaQ3y4lktXPnzmHz5s0ar1u5ciXOnDmj9zyirwtNMW3cuXNHGLt5U7fqU//++y8+/PBDXLx40aA1ZZBlGbNmzULjxo3x6NEjo4xpStOmTcOAAQNel3HRxfPnz9GlS5e3yqCYWlJSEtq0aYM1a9YYNM5ff/0Ff39/nZK46enp8Pf3x6RJk3RKuL5pz549aNGiBV68UN8foGlnsTGIPueyLMPf3x/jxo0zKMkdGxuLZs2aaf3GBRERERHlHNkjG0FElMtps0PNUJ06KZ35mfVEdUGVdu8am9ouUVPu/lMzcuRIHDlyRPUafXZOGsu8efO0uk6WZcydO1fvedQ+RkN39xpr7MWLF6NHjx4m2W185MgRNGvWDAkJCUYf21h27tyJ4cOHGzSGLMv4+uuvceDAASOtSt2IESOMlrAMCgrCpEmTtL5++PDhWLlypcHznjhxAkOHDlW9pmnTpihatKjBc4k4OTmhRYsWirHx48djxYoVRpknMTERXbt2xb17/8fefUdVdW1vA34PHQEpir0rdiyIihV7R4xGUWOLJSZ2ry1REzWJJvbeu9h7jQUVVMTexa5YsIEiAkpnf3/4w089e+1TAcH3GeOMcbPmXuVQzsW5157ruVHGIyIiom/D9adRuPlc+zOMIt5l3L+vvlVMMBMRpYO0Sq5aW1vD1dUVvr6+aNKkSZrMQfrbu3cvZs6cmdHLEIqLi9MpcbRhwwaNh9xlVgcOHEDfvn212onq5uaGLl26YMSIER8P2NTmgLRr164ZfKBhWomNjUXv3r2F77927drYs2cPnjx5goiICAQGBqJbt26y1yYkJKB79+567YLWxe3btxVvejRo0AA7d+7Es2fPEBkZiRs3bmDatGnInz+/sM+kSZPw4sULjXP7+flhxowZwnjz5s2xZ88ePHv2DM+ePcORI0fQoUMH4fVLly7Fw4cPhXFbW1scP34cHTt2RJ48eYz+xMrff/+N7Nmzq7Vfu3YNEydOFPYrWbIk5s2bh1u3biEyMhJ37tzBsmXLFA9PfPnyJf744w+jrJuIiIi+Dd1XnsXA9Ze0vp4J5vTHBDMR0VdAkiS9Xu/fv8fVq1fRpUuXjH4L9IXQ0FD06NEjo5ehaMuWLTrtro+KijK4FMHX6OXLl+jatavijSBTU1MMGjQIISEhuHDhAnx9fTFlyhRMmTIFvr6+uHv3Lk6dOoW6desqzrVjxw5s2bLF2G/BYIsXLxaWKunYsSMCAgLQqlUrFChQAI6OjqhVqxZWr14tvIFy7949LFiwIC2XjMWLFwu/Z6NHj8aRI0fg7e2NvHnzwt7eHmXKlMGwYcNw/fp11KxZU7bfu3fvNH5/kpOT0b9/f9mYubk51q5di//++w+tWrVC3rx5kTdvXjRo0ACbNm3CmjVroFKpZMfUVKqmRIkS2LBhA54/f47k5GSd//+iZ8+esuO2aNEC/fr1k42NGTNG+PSHj48PLl26hP79+6NUqVKwt7eHi4sLevXqhfPnz+OXX34RvpdVq1YplrYhIiIiosyFCWYiIiIjS05ORqdOnb76mrtLly6VbS9fvjxy5swpG1u0aFFaLilDDBkyBK9evRLGc+XKhVOnTmH27NkoUqSI8DoPDw/4+/tj5MiRivP9/vvvetfsTStz586Vbc+VKxeWLl0KU1P107SBD1+7pk2bysYmT56cpqVx/Pz8ZNtr1aqluOvWwcEBW7duhZWVlU7jplq3bh3u3r0rG1uxYgV++OEHYd+uXbsKE68RERGK8xpi48aNWLFihVp7vnz5sGrVKtmkd0REBP777z/Z8erUqQNfX19ky5ZNNm5ubo558+ahQYMGsvGkpCThwZ9ERERElPkwwUxERGRkf/zxBwIDA9XaHRwc0n8xArdv38aJEydkY927d0fHjh1lY2fPnsXly5fTcGXp68KFC9i0aZMwniNHDhw5cgRVq1bVajwTExNMnjwZgwYNEl5z+/btdKtRrI1r167hwYMHsrH+/fvD1tZWsf9vv/0m2/78+XMcPnzY4PWJ3L9/X7ZdmzIkefPmhZeXl2xM06GTop3Z9erV0+ppkkmTJqFw4cKftZmYmKBFixYa++rj4cOH+Pnnn9XaTU1NsWHDBjg7O8v2O3z4sPAGwezZs2Fubq44r4mJCX7//XdhXPT5Q0RERN8uE/V73pRJmGX0AoiIKP0lJibi3LlzePLkCcLDwxEdHQ0nJyc4OzujePHiqFChguyOtvQmSRIuXbqE27dv48WLF3j//j0cHBxQvHhxeHh4fFUJ21SHDx+WPSjMy8sLTk5OWL16dQasSt2yZctk201MTNC5c2eEhoZi3rx5stcsXLgQixcvTsvlpZvp06cr7iaeP38+ypcvr9e4p06dEh7wGBgYiFq1auk8blrYt2+fMObt7a2xf926dZE3b17Zg9u2bNki3OFsKFHy88vkrUixYsVk22NjY4V97t+/jzNnzsjGBg8erNW89vb2CAoKwtixYxEQEABHR0eMGTNG65sYukhKSkKnTp3w9u1btdiECRMUS7qEhITItru5uaFy5cpazV+nTh2Ym5vL1uN+9uyZVmMQERHRt8PM1ARI+7PhKQ0wwUxE9I2QJAm7du3CypUrcfToUcTExAivzZ07N5o0aYKBAwemSdJDkzdv3mDSpElYv369MAlhamqKli1b4o8//kCVKlXSeYXyXr58iS5duqjVLM2XLx9WrlyJYcOGZdDKPpeQkCBMdNevXx/58uVDvnz5UKpUKdy+fVvtmvXr12PatGmws7NL66WmqYiICGzdulUYb9y4MXx8fPQa28zMDIsWLULVqlVla9hGRamfgl2vXj0cO3ZMr/m05e/vj3r16n3WduHCBdlr7e3tUbFiRY1jqlQq1K5dW7Z2cVru1M6XL5/swXgvX77Uqr/oOqVDAEVlHSwsLNC4cWOt5gU+rF2uZIWxzZgxA6dPn1Zrr1WrlnDneSrRjZe+fftqPb+pqSmcnJxkv9ZKiXwiIiL6NpmZqGQTzK757fHKLBba3p62MGPBhvTGrzgR0TcgICAAVapUwXfffYfdu3crJpeBD4kXX19fVKtWDd7e3sJH0dPCli1b4OLigmnTpinucEtOTsbu3btRtWpVjBw5EklJSem2RjkpKSno0qWLWiLFxMQEa9euRY4cOTJoZep27dqF8PBw2din9WO7du0qe01MTAzWrl2bJmtLT/v27ZPdWZlK2x2pIm5ubsJk3NdyUwQArl69Ktuuy85tV1dX2fZbt27pdJCkLurUqSPb7uvrq7FvdHQ09u7dKxv7MgH/KVHCvGzZsrCxsdE4b3p6/Pgx/vzzT7V2GxsbrF69GiYmyv8M8PT0VGsrWrSo8HNBTmJiovCz5mv6TCQiIqKv2+a+NTD/h6/n72dSxwQzEVEWN2vWLDRq1AiXLl3Sq//u3btRrVo1+Pv7G3ll6ubPnw8fHx+dDseTJAlTp06Fl5cX3r9/n4arUzZp0iTZerO//fYb6tevnwErEhMd7mdtbY127dp9/O8uXboIS6VkhRIZSvWB8+TJg2bNmhk8x+TJk1G2bNnP2lq0aIHvvvvO4LGNISkpCffu3ZONlSpVSutxXFxcZNslSZLdBW8Mffr0kW338/NTPOQvPj4ePXr0kD3Y0draGj179hT2FdUf//L9X7x4EYMGDULFihVhZ2cHS0tLFC5cGPXq1cO0adPw+PFj4RzGMnDgQLx7906tffz48ShevLjG/jVq1MC0adM+HuRXokQJbNmyBdbW1lqv4ejRo7I7+AHxTQkiIiIiynyYYCYiysLGjRuHoUOHCmuVaisiIgJNmjRRrNVqqD179mDAgAGK9XCVHDhwAJ06dTLyqrQTGBiI8ePHq7XXrFlTtj0jhYSECBOrXl5eyJ49+8f/Lly4sHCX6JUrV3Dq1Kk0WWN6OXv2rDDm6ekJU1NTg+ews7PDmTNnMH/+fIwcORJr1qzBnj17vooa58CHpxVECcA8efJoPU6+fPmEsbRKptapUwc9evSQjY0dOxZNmjTBrl278Pz5c8TExODBgwdYvnw5KleujO3bt8v2++uvv4QlMmJiYoRPc6TWfY6IiEDHjh1RpUoVzJ07F1evXkVMTAwSEhLw+PFjHDt2DCNGjECJEiUwYsQIREdH6/7GtbBr1y7Zch6lSpXSaWf+sGHDEBUVhVevXuHu3bs677xXuhGVVrW5iYiI6NvyICEBZW/fwhWW38pQrMFMRPQViIuL0/palUoFS0tLjdft2rULf/31l+I1ZcuWRYUKFZAnTx6EhYXh2LFjePr0qey1SUlJ6NKlC86fP6/V7jddhIWFoXfv3orXmJiYoG7dunB1dYVKpcK1a9dw/Pjxz5Lnokf909Lr16/RqVMntSS+g4MD1q9fDzOzr+v/apcvXy5M4nfp0kWtrWvXrjh+/Ljs9YsWLUKNGjWMur70kpSUhLt37wrj2h5ipg1bW1v069fPaOMZ04sXL4SxXLlyaT2Ok5OTMPbkyROd1qSLhQsX4tWrV7LlLvz8/ODn56f1WIMGDVKskx4cHCz83bGxscGDBw/QuHFjPHjwQONciYmJmDZtGvbv34+DBw8q1n3WVVJSEoYPHy4bmzVrFszNzXUaz9TUVK9yFseOHcOOHTtkY8WLF1csRUJERESkq06PH+FsCRfYGmGTCOnu6/pXL9FX6GbpMhm9BNJCmVs3M3oJBtHlkePU6ytVqoQpU6agdu3aavGoqCj07NlTmAwpVaoU5s+fj4YNG37WLkkSNm3ahEGDBsnWzYyMjES3bt1w8uRJndaryfTp0xEWFiaM165dGytXrkSJEiU+aw8JCUHv3r1x9OhRo65HW5IkoUePHggNDVWLLV269OOuxq9FcnIyVq5cKRvLkSOHbEmI9u3bY+DAgbI3QTZv3oxZs2bB0dHR6GtNa8+ePVPc2a9L/eHMTK5MRCp7e3utx/l05/uXdCl5oysrKyvs3LkTU6dOxd9//y1bEkKTnDlzYsaMGRprCysl46Ojo1G/fn2dd2sHBwejdu3aOHPmjE4JfSUrVqyQLXvi6elplLIv2ggLC1P8ev71118aa0ATERER6WrVmwgMyOmc0cv4zIPIB7j26hpaFGsBcxPdbvRnJvzLjogoE4qNjcWpU6fg5eUl+4j13LlzERERIdvX1dUVJ06cUEsuAx92R3fs2BEBAQHImTOnbP+goCAcOXLEsDfwibi4OCxbtkwYb9CgAQ4ePKiWXAY+HDi1f/9+tG3b1mjr0cWMGTNkd0726dMH33//fQasSNm+ffuEByd26NBBdmejvb09vLy8ZPvExcVh1apVxlxiulFKrALKO3KzEqWnJywsLLQeR+mpCl2e0NCHqakp+vTpo9Phc58aOnQoOnTooPG6qKgoYWzOnDl6lwJ5+PAhunfvrnd5oE/FxcUJn1wZN26cweNrIzIyEq1btxbuXG/atGmGlTMiIiKizEubA5UXvH6NaQobl9JTYkoiXFe7wnuXN8aeHAs3Xzfsvq9ewiyrYIKZiCgTi4yMVHvMPykpCbNmzZK93sTEBKtXr4azs/Jd3bJly2LevHnC+JQpU3Req8jBgweFyfBs2bLB19f34yFTciwsLLBy5Uqd6sUaw7lz5/Dbb7+ptZctW1b49c9oosP9APnyGKm6desmjGXWw/5iNdRoU9qRm1YCAgIgSVKavr4sSxAfHy9cjzaleFIplV3Q9LU21OrVq+Hi4oJFixbp1X/MmDEoUaIEjh07pnidUoI5KSnp4/+2tLRE3759cfLkSYSHh+PBgwfYsGGDYv3iAwcOwNfXV/fFf2HhwoWyT1RUrlw5XQ4bffHiBRo0aIAzZ87IxnPnzp1pb0oRERFRxipYsCCKFCmi8boVbyLw7/30ecL5+qvr2H1/t+zLzddN7foxgWOQIsmff5LZMcFMRJSJOTo6wsXF5bO2c+fOCXdn+vj4aF1b1sfHB66urrKxgIAAvR5FlxMUFCSMDRo0SPHwsFTZs2fHn3/+aZT1aOPt27fw8fFBYmLiZ+1WVlbYuHGjYkI8ozx9+hT79++XjRUrVgw1a9YU9m3WrJnwpsTt27fh7+9vlDWmJ02H7OlatiazUkow61LCQGn3rdIchho+fDh69OiBN2/eGDROaGgoGjVqhBUrVgivUUowp8qfPz9OnDiBRYsWoWbNmsiZMyeKFi2Kjh074syZM+jbt6+w75QpUwzaxZyUlITp06fLxvr376/3uNoKDAyEm5sbLl26JBu3sbHB7t270/1mIBEREWUNKpUKvr6+Wu1kPvTqZTqsCPgv5D+MCRwj+xLxe6T9GSGZCRPMRESZkL29PTw9PbFv3z7Y2dl9Fjt8+LCwn66lJES7WhMSEoQHv+nq7Nmzwli7du20Hqddu3bpdqBe7969ERISotY+ffp0YVI+o61YsUJYc/iHH35Q7GtmZoaOHTsK45lxF7Otra1iPK3LOnwtlJLISjWqv/TpDt4vmabRQSuzZs0SJlSBD593Bw8exOvXrxEbG4t79+5h6dKlwptsSUlJ6Nu3r/AzVNPXw9zcHDt27EDVqlVl46ampli4cCFq1aolGw8ODjboc3XLli2yh7Ta2NjAx8dH73G1MXXqVNSvXx/Pnz+XjTs6OuLgwYOoVq1amq6DiIiIsrbatWvj0aNHRju7IiM8jtKvrNrXjglmIqKvgK6PuUdGRiIgIAA1atRQG+vy5cvCeRo3bqzTujw8PISxixcv6jSWiKhOp5OTk+Ij5XLXy309jG3hwoXYunWrWnubNm3Qr1+/NJ9fHykpKVi+fLkwrlQeI5VSfdsdO3YoHtL4NdJ0gF1kZGT6LCSDWVlZCWMJCQlaj6N0rS61nLX1+PFj2RI1wIcbIhs2bMC2bdvQpEkTODk5wcrKCsWLF0fv3r2F5W2AD0nmn376Se3pBEDzrvY+ffoIk8upVCoVZsyYIYz7+em/o2X27Nmy7d99953GGyr6io+PR+fOnTFy5EjhTYbixYsjKChImFgnIiIi0kWOHDnSvSShqcp4GyZMTdJm80VGS5+tXkSZWJlb6VO7h8hYwsPDZdvt7e01JtW+VKpUKZ3n0ZUokVewYEGNZQy+VLhwYZw4ccIIq5J37do1/O9//1NrL1CggGICN6P5+fnh0aNHsjE3NzcUKlRI445dV1dXuLi4qNX8Bj4kF1esWIFff/1V2F9pF6suO2XlpKSI65iJdrXnz58f5ubmsolEAHj06JFi2RBdxMbGYsmSJbh79y5Kly6NXr16ySYr69Wrp7EOsKH8/f0/q8OsVM5F7gBRkZiYGGEsLZKbs2fPFv7M/vvvv4o77k1NTTFp0iQ8fvwY69atU4uHhITA19cXPXv2/KxdU+mbHj16aF44gGrVqqFw4cKyv5P6fv/PnDkjrHuszQGG+oiMjIS3t7firmtPT09s27YNOXLkSJM1EBER0bepbdu2aNGiBf777790mc/V2XhPqZZ2LG20sb4m3MFMRJTFiOov6/MYkVJC2lgJ5rdv38q267Pe3LlzG7ocoZSUFHTr1k0tqWViYoLly5cjW7ZsiIuLU3yJEqlJSUk67RbVldLhfhcvXoS1tbVWL7nkcqolS5Yo1o9VOjBO9DOgLaXkpmheMzMzFC9eXNjv+vXrBq0pVXJyMho0aIAhQ4Zg/vz5GDhwIKpVq2a0GuaGUvo902UXt9K1jo6OOqxIO9u2bZNtz5s3LwYOHKjVGJMmTRKWCNmxY4dam1LtYBMTE63r2wMQ3rwQPdGhieh33MbGBk2aNNFrTCUvXrxA3bp1FZPLP/30E/z8/JhcJiIiIqOztLTEjh07MPPvybJxayPvEq5boC6q5Nb+6VollmbaH6SdmXAHMxHRN0LX3cD69tGVtbW17E5JfebW5VAyXe3bt0+2/EhKSgqaNm1q0NgTJ07ExIkTYW1tjSJFiqBly5YYN26cUXZ+hoWFYffu3QaPo0lISAgOHjyIZs2aycazZ88u7GtIOYqUlBTFnbZKX8OqVavi1q1bsrGTJ0/qvaZPrV69GqdPn/6s7fr165g7d67iju/0onSI5suX2h+OonStsW/8hIWFCXfkN2nSROuSHIUKFUKNGjVkv9dyO4kLFSokHMvZ2VmnGvCir4k+N+5iY2OxefNm2Vj9+vUVb+7o4/Hjx2jUqJHwhpOZmRlmz5791ZYMIiIioqzBwsICnjXrpMtc1mbWWNBwAQ4+PIjL4ZeRmCz/FOSn9jzYkw4r+3owwUxElMXkzJlTtl2fxIVS4s9YBys4OjrKJgj1qemblnWAg4OD02zsVLGxsbh58yZu3ryJlJQUxQPMtLVq1SphGQhjW7RokTDBrLT788GDB3rPee/ePcUD5pydnYWxxo0bw9fXVzYWGBiIFy9eKK5bG3PnzpVtv3btmkHjGkvu3LlhY2Mju6P68WPtDyARJXwBoEiRIvosTSg0NFQYU9qVLqdo0aKyCebo6GjExMR8doOiRIkSUKlUsjv1lX4G5YjqOSuVexHZs2eP8CZLgwYNdB5Pyb1799CwYUPhz4aTkxO2bt2K+vXrG3VeIiIiooyWzTwbvnP5Dt+5fKfV9U9jnuJimHHOLcoMWCKDiCiLESXUIiMjdaqpCgB37tzReR5dicZ5+vSpzmM9f/7c0OUIubi4pNnYcm7eNE7992XLlhllHG3s3btX+H0rVqyYsN+FCxf0ToKfP39eMa6U3GzWrBnMzc1lY8nJyVi0aJFea0oVEBAgPHTzaykbYGJigkqVKsnGdPkZFO0EB4By5crpuixF79+/F8Z02UUMKB9y+GXpFnt7e5QsWVL22sjISMTGxmo9b0REhGy7g4OD1mOk2rJlizBmzINP7927B09PT2FyuUSJEjhz5gyTy0RERETfICaYiYiyGFEdUEmScPjwYZ3G+vLR/k9VqWKcGlRubm6y7eHh4bhy5YrW48TFxRmtrIEcb29vVK1aNc3G/1L16tUNHiMgIECxbrKxJScnC2vBurqKD8aIi4tTrOWqZNOmTcKYs7OzcEd/atzb21sYnzlzJp49e6bXulJSUjBy5Ehh3NhJV0OIftZu3bqldX1s0QFzZcqUMdrTDqmUyp4o7W7W9Xo7Ozu1NlHt5OTkZI03Oz4l2sGudLCqnPj4eBw4cEA2plKpFH/vdHH//n3Uq1dP+Pvg7u6OoKAglChRwijzEREREVHmwgQzEVEW07BhQ2Fs+/btOo21du1a2XZLS0vUrVtXp7FElBKpuqz30KFDaXpwmpmZGY4ePYoxY8agbNmyijsf9WVjY4NSpUph7NixGDNmjMHjKR3uN3LkSEiSpNdr9OjRwnGXLVsme5hh9erVhbuFAeCff/7R7c0BuHz5Mvbt2yeMa7N7c8CAAcJYVFQUunTpotcBjOPGjcO5c+dkYyYmJoqJ7fTWsmVL2faUlBRh8vJToaGhwp3axi7RACjvSj969KjW47x//x5BQUGysZw5c8rWDW/Tpo1wvA0bNmg1b3h4uDAZretNrMDAQOEhlwULFoSNjY1O48l58OAB6tWrJ3w6oXr16jhy5IjRnmohIiIiosyHCWYioiymatWqwh2DGzZs0Lr269atW3H16lXZWMOGDYU1RHXVpEkT4eF82u4gTUpKwtixY42yHiW2trb4+++/ERwcjNjYWJ0Ts927d5cdd9y4cZAkCTExMbh16xb++usvnR/1/1JERAS2bdsmjHfp0kXvsbt27SqMPX36FHv2qB9okS1bNsWbH0eOHMGECRO0XkN4eDi6desmm8xOJUqcfsrT01NYNxoA/P390a5dO6138gLAhAkT8PfffyuuS662c0BAgN5Jf21f9erVU5u3bt26wp3eixcv1vh+lyxZIluXGAA6duyosT/wIVE6efJkLF68WGMtdQcHB2Gpilu3buHgwYNazblq1SpERUXJxjw8PGTbmzZtKixjsXr1aoSEhGicd86cOcKbFkoJbDlyhxGm0nU3tJzo6Gg0b95cuNPbxcUF//33n+IhnkRERESU9THBTESUxZiZmWHYsGGyseTkZHTr1g2vX79WHOPWrVvo16+fMK706L+uChYsKEzwRUdHo3v37hprm44aNeqrOTTta+Hr64v4+HjZWIUKFQx6dL506dJwd3cXxkVJyR9//FFx3PHjx6NTp06Kh/6lpKRg27ZtqFGjhuL33M7ODh06dFCcL9X06dNhaWkpjO/duxcVKlTAypUrhT+LkiQhICAAtWvXxvjx44VjmZqa6rVbOy2ZmZlh4MCBsjF/f3/s3LlT2PfBgweYOXOmbKxatWqoXbu2xvl79+6NOnXq4Ndff8XPP/8MFxcX+Pv7K/Zp166dMNazZ0+8ePFCsX9wcLDi51j79u1l2y0tLdG/f3/Z2Pv379G1a1fFz6uzZ89iypQpsjFXV1fUqlVLYdXqlMoYGSPBPGHCBGEtfisrK2zfvh1OTk4Gz0NEREREmRsTzEREWVD//v2Fu5gvX74MT09PXLhwQS0mSRI2b94MT09PhIeHy/avW7cuPD09jbreQYMGCWOHDx9Gw4YNZR/BDw0NRefOnTFjxgyjricrUDrcT2kHsraUxjh06JDs4/Tff/89ypYtqzjuxo0bUbx4cVSqVAk9evTAqFGjMHbsWAwYMADe3t5wdnbG999/j/v37yuOM2LECK0PTCtbtiymTp2qeM3jx4/Rs2dP5MqVC02aNMHgwYPx+++/Y9iwYWjfvj0KFSqE+vXra6wDPnz48K+q/nKqQYMGCT8zunbtKrsr/caNG2jWrJmwRINSKZVUK1euxPLlyz9ri4qKwvfff4/IyEhhv19++QUWFhaysWfPnsHd3V12J3NycjLWrl2LmjVrCkvqFChQQHHn9ZAhQ+Do6CgbO3nyJJo0aSK7k3nXrl1o1qyZcPfyH3/8IZxTROkmi6EJ5oiICMyfP18Yj4uLg6urK1QqldYvc3NzFC1aFD179hQeFkhEREREmY9hz98SEdFXycbGBuvWrUOzZs1kSwgEBwfD3d0dbm5ucHd3h62tLV6+fIljx44pHnrl6OiIlStXGn29TZs2Rdu2bYU1l0+dOoXKlSvDw8MD5cqVg4WFBe7evYuAgAAkJSUZfT2Z3dWrV3H9+nXZmImJCTp37mzwHJ06dcLw4cORmJioFktJScH27dvVdsWamJhg8eLF8PT0REpKiuL4V65c0emQx0+5u7vj119/1anPwIEDceHCBaxevVrxupiYGPj5+cHPz0/ndTVt2hSTJk3SuV96cHBwwLx582R3fcfExKB169Zwc3ODh4cHzMzMcPPmTfj7+wt//9q2batVnWnR1zsiIgK7du0SlpUpWLAgRowYgYkTJ8rGnz59imbNmsHFxQXVqlWDs7Mznj9/jsDAQGEt4VTTp08XJq+BD/WZZ82aJVxbYGAgXFxc0KBBA5QuXRrx8fE4efIkgoODhWO2atUK33//veK6vhQfH6+4U7t8+fI6jfelzZs3Iy4uzqAxvpSUlISHDx9i5cqVCAwMxOXLl5EtWzajzkFERERE6Y8JZiKidKD0+L1SzBCNGjXCv//+ixEjRgivuXjxIi5evKjVeGZmZli3bh2KFSumeJ25ubls0tHU1FSx37x58xAYGKhYf/X06dOKj4R37doVvr6+sjFRnef0JFqDsdd29uxZYaxx48bIly+fwXM4OzujTZs22LJli2z8xIkTsmUXateujdmzZwtLMhiqWLFi2LVrl+KBgiIrVqyAJElYs2aN0dfl4eGBTZs2fRU/hyLt27fHiBEjhLu5tf28KFOmDFasWKHVnErJXk2J4HHjxiEoKEixnMbdu3dx9+5drdYCfHj6Q5vSKt26dUNQUJCwHExycrLWNyJKliyp18+c0uGTpqamcHNz03nMT4lq8BvL3bt3ERQUhEaNGqXpPERERESU9r7ef+UQEWUhhQsXFsaKFCmSZvMOHz4cy5cvV9yNpw0nJyccOHAAzZs313htoUKFZNtFj9+nyps3L44cOSI8bEyT0aNHY/HixVCpVGoxExMT2UPV0ptoDXnz5jXqPBEREcJYnz59jDaPqBYt8KEerciAAQOwbNkyo99cadCgAU6ePKl3At3ExASrVq3C5MmT9UpQi3Tp0gWHDx+Gvb290cZMK5MnT8bgwYP17l+uXDkcOnRI6/eq9LOv6ffC3Nwcu3fv1upzSRsDBw7E3Llztb5+/vz56NGjh0FzVqhQAf7+/sKSG0rs7OyEX2cPDw+DD94zxo0oTUR14omIiIgoc2GCmYgoHeTLlw/Vq1eXjbVt2zZN5+7ZsyeOHz8unF+Tli1b4uzZs2jYsKFW1zdp0kS2XZtdauXLl8fx48cVD5D7kr29PdauXYuJEyfC2toalSpVUrumcuXKBifZjcHDw0O2XZf3q41mzZrJJtqLFy+ONm3aGG0eT09P4XvSVB6hV69euHLlClq1amXwOhwdHfHPP//Az8/P4BsJKpUKI0eOxJkzZ4SHT2qrdOnS2LNnD3x9fWFjY2PQWOlFpVJh1qxZWL16NXLkyKFT327duuHkyZMoUKCA1n1Eh+nZ2NigZcuWGvvb2tpi7969mD59ut4J1YIFC2Lr1q2YM2eO7O+NiKmpKVauXIkFCxbofPPAxMQEAwcOxKlTpwxK5Io+V3/66Se9x/x0jNy5cxs8jkju3Lm1OgSSiIiIiL5+TDATEaWT5cuXq/1j3cPDQ6uDsAxVvXp1nD59Gjt37oSXlxdsbW0Vr3d2dsYPP/yA06dPY+/evShevLjWc/3xxx9qO7a7dOmidSK9TJkyOH36NObOnatYQ9TR0RFDhgzB7du38cMPP3xsnzVrFuzs7D7+t52dHWbOnKn1+tNS69at1ZJmvXv3RuXKlY06T4UKFbBgwYLPdghbW1tj/vz5GkuV6Gr58uXInz//Z20//vgjevfurbFvqVKlsGfPHgQHB2PMmDGoUqWK1uuzsrJCrVq1MG/ePDx58gS//vqrUctPVK5cGfv378fFixcxfPhwlChRQqt+NjY2aNOmDXbs2IHg4GCjJNAzQrdu3XD79m38/vvviglQS0tLfPfddzh16hRWr16tc6K1X79+ajXBra2tsW7dOo1PPaQyMTHB//73Pzx48ACTJ09GpUqVNCaKzczMUKdOHSxfvhx37txBu3btdFr3p3755Rfcu3cPf/zxB4oWLap4rZOTE/r06YMbN25gzpw5BtcfnjhxotqNgC5duhjlIM9cuXLh3Llz6Ny5MwoUKKBT8l2Jra0t6tSpg//++y9T7OonIiIiIs1UkiRl9BroG+Xu7i6dP38+Tee4efMmypQpk6ZzEOni7du32L59O54/f44yZcqgdevWRk/4aSMhIQFnz55FaGgowsPDER0dDScnJzg7O6NYsWJaJWiUREdHY8uWLXjz5g2qVKmCevXq6T3WjRs3cP36dYSGhiIxMRE5cuSAq6sr3NzchGUMHj9+jP3790OSJLRo0UJYtiMjpKSkYO/evbh79y5cXV2FO76N4cGDBzh06BAkSUKjRo3g4uKSJvOEh4dj69atePv2LWrVqoU6deroPVZsbCxu3ryJhw8f4uXLl4iOjkZ8fDwsLS3h4OAAR0dHFC1aFBUrVjRqGQttRERE4MaNG7h//z6ioqIQHR0NSZJgY2ODvHnzomTJknB1dYWZWdY64kKSJAQHB+PixYsICwtDQkICHBwcUKpUKVSrVu2zGzr6CgwMxMmTJ2Fra4s2bdqo3bTQ1atXr3D16lXcv38fkZGRiIuLg7W1NZycnODi4oKKFSsaXEJC5MGDB7hw4QKeP3+O6OhoWFpawtnZGa6urqhQoYLRfz5Sf//i4uLg7u5u0O8f6Yd/bxIRERnJxLxAokypvdHPAAv1JwIv+Z+CW4Oaau3WJqZ4nyw+DH7RwAAkJ6ofOt53jifMLIzz7/Pu+7vjYpj6+SWrmq1CldxVNPZXqVQXJEky7qOuaYgJZsowTDATERERUWbHvzeJiIiMhAnmjzJbgpklMoiIiIiIiIiIiIhIL0wwExEREREREREREZFemGAmIiIiIiIiIiIiIr0wwUxEREREREREREREemGCmYiIiIiIiIiIiIj0wgQzEREREREREREREemFCWYiIiIiIiIiIiIi0gsTzERERERERERERPRNSEpOyeglZDlmGb0AIiIiIiIiIiIiImP5Y9d12fZnkbEonyLBHKp0XlHWxgQzERERERERERERZRlrTj0SxsrDKh1X8m1giQwiIiIiIiIiIiIi0gsTzERERERERERERJSpWJqbZvQS6P8wwUxERERERERERESZirOtRUYvgf4PE8xERERERERERESUqahUPKjva8FD/oiIiIiIiIiIiChLUKmACa3LycZ2XHoKRMal84qyPiaYiYiIiIiIiIiIKEtQAehes4hsLLu1GR5du52u6/kWsEQGEREREREREREREemFCWYiIiIiIiIiIiIi0gsTzERERERERERERESkFyaYiYiIiIiIiIiIiEgvTDATERERERERERERkV6YYCYiIiIiIiIiIqIs4V1yMtasWQNJkjJ6Kd8MJpiJiIiIiIiIiIgoy+jevTtMTEyYZE4nTDATERERERERERFRljN79uyMXsI3gQlmIiIiIiIiIiIiynL+/vvvjF7CN4EJZiIiIiIiIiIiIspUbG1skMfMTPEaZ2fndFrNt40JZiIiIiIiIiIiIspUVCoVOjg4KF7TuHHj9FnMN44JZiIiIiIiIiIiIsp0+jrlwNhcuYVxR0fHdFzNt4sJZiIiIiIiIiIiIsp0VCoVOjs6on+OnBm9lG8aE8xEREREREREREREpBflSthEREREREREREREmVBKXBySIyM//rdJTBQskxORYmqRcYvKgphgJiIiIiIiIiIioizn6qJFuLNr98f/LgngWZ2ZGbegLIolMoiIiIiIiIiIiCjL2R0VhbK3b+FlYmJGLyVL4w5mIiIyusjISGzbtg3h4eGoVKkSmjVrlq7z379/H7t374a1tTUaN26M4sWLp+v8mVViYiL8/Pxw69YtqFQqlC5dGo0bN4aZmfH+XDhx4gSCgoKQLVs2NG/eHCVKlDDKuCkpKdi/fz+uXbsGR0dHeHt7I0+ePEYZO1VSUhKuXbuGO3fu4Pnz53j37h3Mzc1hZ2eHQoUKoWzZsihatKhR5yQiIiIiIsPVf3AfN0qVzuhlZFlMMBMRpRNJkrBw4UKsXLkSd+7cQVRUlMFjWltbw8XFBSNGjECXLl2MsErDPXnyBB4eHnj27NnHtnbt2mHLli1QqVRpPv/JkyfRsGFDxMfHAwCyZcuGnTt3onHjxmk25+LFi7FgwQLcuXMHcXFxOvd3cnJCyZIlMWzYMHz//fdpsELNzpw5Ax8fHzx69Oiz9qJFi2LTpk2oWrWqwXP07NkTK1eu/PjfZmZmWLZsGbp3727QuPHx8WjWrBkCAgI+tg0fPhy7du1CgwYNDBo7Li4O27Ztw8aNG+Hv7493794pXl+gQAG0bNkSvXv3hru7u0Fzfw0uXbqETZs24fTp07hz5w7evHkDU1NT5MqVC0WKFEGTJk3QunVrlC1bNqOX+tGJEyewd+9enDlzBnfv3sWbN2+QlJQEBwcHODk5oXTp0qhVqxaaNm2KChUqGG3eM2fO4PDhwzh27BhCQkLw+vVrxMTEwN7eHs7OzqhSpQrq1KmDDh06wMHBwShzPnnyBNu3b4e/vz9u3ryJFy9e4N27d7C1tYWjoyPy58+PGjVqoG7dumjWrBnMzc31nuv9+/f4448/sG3bNoSGhiIpKUmn/iqVCnny5IG7uzvGjx8PNzc3vddCREREBADm+fJBZW4OScvdyfEpKbA0YTGHNCFJEl98ZcirSpUqUlq7ceNGms9BpK2xY8dKANLstX///ox+i5IkSVLPnj1l17dly5Z0mb9+/fpqc1esWDHN5lu4cKHRvocqlUo6dOhQmq1V5O7du5K9vb1wXY6OjtLDhw8NmmPnzp2yY1tZWUkvX740aOxp06bJjp0vXz4pISFBrzHj4+OlqVOnSrly5dL7+1m3bl3p3LlzBr23jHLhwgWpbt26Wv/ctmvXTrp161aGrnnbtm1SmTJldP4eGfo7t2nTJsnNzU3rObNlyyYNGDBAevv2rd5z3r9/X+rQoYNkamqq9bz58uWTJk2aJMXHx+s1p4+Pj9E+65ycnKQXL17o/f6/Nvx7k4iIyEj+ziNJ47Krv+JjhF2e/vqbdKNUaenP3Hk0/g1yqoSLdKNUaWlB7/3SvL5H1F6J8UlGeyvd/usmlV9VXu11/sV5rfoDOC99Bbk7bV9M2xMRpYP4+HjMmjUrTefYvn17mo6vLX9/f9n2w4cPp/ncKSkpOHHihFr71atXP+5oNiZJkjBp0iSjjpcR38f//e9/ePv2rTD+5s0bjB492qA5du/eLdseFxeHQ4cOGTT23r17ZdufPXuGs2fP6jzexYsXUbFiRYwYMQJhYWF6r+v48eOoVq0aBg8enCY/f2ll/vz58PDwwPHjx7W6XpIkbNu2DVWqVMG2bdvSeHXq3r17Bx8fH7Rr1w43b97Uqe/x48fRpEkT9O/fHwkJCTr1ff36Nb777jv4+Pjg4sWLWvd7//495s2bh/Lly+PChQs6zQkAq1evhqurKzZv3ozk5GSt+z179gyjR49G1apVdf463b17F5s2bdJ1qUIRERGfPXFAREREpK+8f05Ajj69Ua9sWWTTsDu5xr27WPL6dTqt7NvCBDMRUTq4e/cuYmJi0nQOk6/kUZ8nT57ItoeHh6f53C9fvpR9bFuSJLx8+dLo8wUHBwvfr74+3KxOP8HBwdizZ4/G6zZs2IBr167pPY/S18nQr+HTp0+FscePH+s01rp161CzZk3cunXLoDWlkiQJc+bMQZ06dfA6E/wxO336dAwYMACJehyC8u7dO7Rv3/6zMihpLT4+Hi1atMDmzZsNGmfBggXo0qULUlJStLo+LCwM9erVw86dO/We88mTJ2jSpAmuX7+udZ8FCxagR48eeP/+vd7zXr16FfXr18e9e/e07nPw4EG95xNJ7886IiIiyppU5ubINWwY6hw9ggMBAXB1dVW8ftarcFx+GJROq/t2fB3ZCCKiLO7NmzdpPkfbtm3TfA5tiOpy6rLTTl9Ku0S1TRzp4tKlS0YdT6VSwcfHx6hjarJw4UKtrpMkCfPnz9d7HqXdoYbu7jXW2MuXL0fXrl3TZLfxuXPnUL9+fURHRxt9bGM5dOgQRowYYdAYkiTh559/RlBQ+vzRPnLkSK13WmuyZcsW/PvvvxqvS05ORps2bXRKDItERESge/fuWn0+njt3DoMHDzZ4TuDDzThvb2+t6ygb+7PO2dkZDRs2NOqYRERERHXq1MHVq1c1Xrfk8F+IS9D/hj2pY4KZiCgdpFVy1draGq6urvD19UWTJk3SZA4SM2RH76fs7e1RvXp1bN++HfXr1zfKmNqIi4vD+vXrtb5+w4YNGg+5y6wOHDiAvn37arWr0s3NDV26dMGIESM+HrBZokQJjf2uXbtm8IGGaSU2Nha9e/cWvv/atWtjz549ePLkCSIiIhAYGIhu3brJXpuQkIDu3bvrtQtaF7dv31a86dGgQQPs3LkTz549Q2RkJG7cuIFp06Yhf/78wj6TJk3CixcvFOedPXs2Tp06JRurWLEiVq9ejfv37yMqKgrPnz/Hzp07Ubt2beF4Fy9exJYtWxTnBIChQ4cKE8J58+bFjBkzcPPmTbx9+xaPHz/Gjh07FA83vXHjBhYtWqRxXsB4n3W5cuVCq1atcPjwYTg7OxtlTCIiIqIvFShQQOM1w1d64ewdv3RYzbfBLKMXQEREfFQ4sxLtYFyzZg26du2azqvR3ZYtW3TaXR8VFYXNmzfjxx9/TMNVpb+XL1+ia9euijeCTE1N0b9/fwwdOhRFihSRveb06dMYNWqU4o7aHTt2YMuWLWjfvr2hyzaqxYsXC0uVdOzYEWvXroWpqenHtlq1aqFWrVqoXLkyhg4dqtbn3r17WLBggdF23IrWLPqejR49GhMnTvyszd7eHmXKlEGvXr3QsmVL2V3W7969w5YtWzBw4EDZcSVJEtbT9/b2xpYtW2Bubv6xzc7ODt7e3vDy8kKfPn2wYsUK2b4bN25Ex44dZWPAh7IWJ0+elI1VqFBBLWGbPXt2FCxYEG3atMGUKVMwatQo2b4rV67EgAEDhPMCH97zjRs3ZGOPHz9GwYIFFfsTERERpbfGjRtrVbZtjf+/qFZSfEOetMcdzERERHoSJZgrVKiQzivRz9KlS2Xby5cvj5w5c8rGtN3xmJkMGTIEr169EsZz5cqFU6dOYfbs2cLkMgB4eHjA398fI0eOVJzv999//+puKs2dO1e2PVeuXFi6dOlnyeVPDRkyBE2bNpWNTZ48OU1L4/j5ye84qVWrllpy+VMODg7YunUrrKysdBoX+JBQlUvE58yZE6tWrfosufwpExMTzJ8/X7h7WlMJCtGaTE1NsWnTJsXdwCNHjoS3t7ds7OLFixprg4eEhMg+ueDg4MDkMhEREX2Vpk+fjmrVqml17cMw45y98q1jgpmIiEgPUVFRsokmMzMzlClTJgNWpJvbt2/jxIkTsrHu3bsLd1OePXsWly9fTsOVpa8LFy5g06ZNwniOHDlw5MgRVK1aVavxTExMMHnyZAwaNEh4ze3bt9OtRrE2rl27hgcPHsjG+vfvD1tbW8X+v/32m2z78+fPcfjwYYPXJ3L//n3Zdm3KkOTNmxdeXl6yMaVDJ0U3Bn777Tc4ODgozmllZYVWrVrJxjSV5RC91+rVq6N06dKKfQHgp59+EsZCQ0MV+4pupGk6QIeIiIgoozg6OuL06dNanSGjSof1fAtYIoOI6BuUmJiIc+fO4cmTJwgPD0d0dDScnJzg7OyM4sWLo0KFClCpMv7/aiVJwqVLl3D79m28ePEC79+/h4ODA4oXLw4PDw+NCZ20FBwcLNtesmRJWFhYpPNqdLds2TLZdhMTE3Tu3BmhoaGYN2+e7DULFy7E4sWL03J56Wb69OmKu4nnz5+P8uXL6zXuqVOncO7cOdl4YGAgatWqpfO4aWHfvn3CmGjn66fq1q2LvHnz4vnz52qxLVu2CHc4G0q0O7pw4cJa9S9WrJhse2xsrLBP4cKF4ebmhosXL35sc3Z2xs8//6zVnI6OjrLtlpaWiv3S6r0Cyu8XEH/WMcFMREREXzOVSoVevXph1qxZuHv3rvA6C3P5p9pIN0wwExF9IyRJwq5du7By5UocPXoUMTExwmtz586NJk2aYODAgVrv3DSmN2/eYNKkSVi/fj2ePXsme42pqSlatmyJP/74A1WqVEnnFWbuXX0JCQlYvXq1bKx+/frIly8f8uXLh1KlSuH27dtq16xfvx7Tpk2DnZ1dWi81TUVERGDr1q3CeOPGjeHj46PX2GZmZli0aBGqVq2KlJQUtXhUVJRaW7169XDs2DG95tOWv78/6tWr91nbhQsXZK+1t7dHxYoVNY6pUqlQu3Zt2YPq0nKndr58+fDw4UO19pcvX2rVX3Sd0iGAKpUKO3fuxODBg3H27FkUK1YMkyZNQrZs2bSa8969e7LtpUqVUuyXL18+2XZD3yug/H6BzP1ZR0RERN82S0tLBAQEYOjQodi8eXNGLydLY4kMIqJvQEBAAKpUqYLvvvsOu3fvVkwuAx+SEb6+vqhWrRq8vb2Fj2enhS1btsDFxQXTpk0TJpeBDzv6du/ejapVq2LkyJFISkpKtzUCuidd5JKMGWXXrl0IDw+Xjf3www8f/7fooMKYmBisXbs2TdaWnvbt24fExERh3NAD6tzc3NC3b1/ZWEbcFBG5evWqbLsuO7dFP/e3bt3S6SBJXdSpU0e23dfXV2Pf6Oho7N27Vzb2ZQL+SwULFsT27dsRGhqK48ePo3bt2hrnA4CnT59i//79srHmzZsr9hW91xMnTsgm2b+0fv162fZixYpprKOcmT/riIiIiPLly4dNmzZlijKGmRkTzEREWdysWbPQqFEjjYdIiezevRvVqlWDv7+/kVembv78+fDx8dF46NSnJEnC1KlT4eXlhffv36fh6j6nKely5swZDB06FFWqVIGtrS1MTU2RI0cOVKhQAUOHDhXWP04PosP9rK2t0a5du4//3aVLF2GplKxQIkOpPnCePHnQrFkzg+eYPHkyypYt+1lbixYt8N133xk8tjEkJSXpvav2Uy4uLrLtkiTJ7oI3hj59+si2+/n5KR7yFx8fjx49esge7GhtbY2ePXsabY2pwsLC8N1338kelmdnZ4f+/fsr9q9Xr57s1zgxMRE+Pj6KSfxNmzYJS+JomjcpKUn4/StfvjwSExOxa9cudO/eHS4uLrC0tISFhQXy5cuHWrVqYdKkSbhz547iHERERESU+THBTESUhY0bNw5Dhw4V1u/UVkREBJo0aaJYq9VQe/bswYABAxTr4So5cOAAOnXqZORViYkSzA8ePICbmxs8PDwwa9YsXLx48WNSKSIiAteuXcOsWbNQt25d1KlTB2fPnk23NQNASEiIMLHq5eWF7Nmzf/zvwoULC3dOXrlyBadOnUqTNaYXpa+9p6cnTE1NDZ7Dzs4OZ86cwfz58zFy5EisWbMGe/bs+SpqnAMfnlYQ7TjNkyeP1uOISjgAwOPHj3Velzbq1KmDHj16yMbGjh2LJk2aYNeuXXj+/DliYmLw4MEDLF++HJUrV8b27dtl+/31118aS0ZoIkkS4uLiEBkZiQsXLmD8+PEoU6aMsB730qVLkTt3bsUxTUxMsGjRItmfybNnz8LV1RWzZs3CrVu3EB0djbCwMPj5+aFTp07o2LGj7PfYzc0NAwYMUJz37t27iI+PV2vPnTs3Vq5cicKFC6NNmzZYs2YN7t27h4SEBCQnJ+P58+cICgrCmDFjUKZMGfTo0QNPnz5VnIuIiIiIMi/WYCYi+grExcVpfa1KpdJ4IBTwoQzCX3/9pXhN2bJlUaFCBeTJkwdhYWE4duyYMAmQlJSELl264Pz58yhevLjW69VGWFgYevfurXiNiYkJ6tatC1dXV6hUKly7dg3Hjx//LHkuetTf2F69eoWwsDDZ2NChQ7UeJzAwEDVr1sSMGTMwaNAgYy1P0fLly4VJ/C5duqi1de3aFcePH5e9ftGiRahRo4ZR15dekpKSFA/7qFy5stHmsrW1Rb9+/Yw2njG9ePFCGMuVK5fW4zg5OQljT5480WlNuli4cCFevXolW+7Cz88Pfn5+Wo81aNAgDBs2TO+1bNmyBRMnTsStW7dkk7JyfvvtN63rfDdo0ADLly9H79691UoCPX36FEOHDtX686dMmTLYvXu3xgNJRTfSXr58qfVcKSkpWL16Nfbu3Ytt27bB09NTq35ERERElHkwwUykwfyfj2b0EkgL/Rc1yOglGMTa2lrn6ytVqoQpU6bI1v+MiopCz549hYnEUqVKYf78+WjYsOFn7ZIkYdOmTRg0aJBsjd7IyEh069YNJ0+e1Gm9mkyfPl2YsAWA2rVrY+XKlShRosRn7SEhIejduzeOHk3f31NR0kUfycnJGDx4MF6+fKn4WL+x5lq5cqVsLEeOHLIlIdq3b4+BAwfK3gTZvHkzZs2aBUdHR6OvNa09e/ZMcWe/LvWHMzO5MhGp7O3ttR7n053vX9Kl5I2urKyssHPnTkydOhV///23bAkKTXLmzIkZM2YIa45r49y5c8KdwiJ//vknfv/9d53m6d69O0qWLInevXvjxo0bui4TJiYm6NGjB2bOnKn4PUsVHBys8xwir1+/RuPGjbFjxw60bNnSaOMSERERUcZjiQwiokwoNjYWp06dgpeXF6Kjo9Xic+fORUREhGxfV1dXnDhxQi25DHzYHd2xY0cEBAQgZ86csv2DgoJw5MgRw97AJ+Li4oT1QYEPu/YOHjyollwGgKJFi2L//v1o27at0dajDWMmXVJNmjQJGzZsMPq4n9q3b5/w4MQOHTrA3Nxcrd3e3h5eXl6yfeLi4rBq1SpjLjHdKCVWAeUduVmJ0tMTmna3fkrpqQpdntDQh6mpKfr06aN3gnjo0KHo0KGDQWsICgrSKbmsUqkQHh6u8cBVOdWrV8e4ceNgZ2enc986depg7NixWiWXAeN/1iUmJqJz585pVpebiIiIiDIGE8xERJlYZGSk2mP+SUlJmDVrluz1JiYmWL16NZydnRXHLVu2LObNmyeMT5kyRee1ihw8eFCYDM+WLRt8fX2RLVs2YX8LCwusXLlSp3qxhlIqrQAAzZo1w+HDh/Hq1SskJSUhLCwMx48fR8+ePWFmJn546Oeff07T3Z6iw/0A+fIYqbp16yaMZdbD/mJjYxXj2ibgjCkgIACSJKXpq169ep/NqVTKQZtSPKnkbk6k0vS1NtTq1avh4uKCRYsW6dV/zJgxKFGiBI4dO6b3GsqVK6fT9ZIkYe7cuXB1ddVpJ/Lt27dRu3Zt+Pj4yN5c1OTYsWMoWbIkxowZo1VCXOmzztTUFAMGDMCFCxcQFRWFxMREPHnyBDt27ECjRo2E/aKiotCrVy+d105EREREXy8mmImIMjFHR0e4uLh81nbu3Dnh7kwfHx+ta8v6+PjA1dVVNhYQEKDXo+hygoKChLFBgwYpHh6WKnv27Pjzzz+Nsh5tfPk1T2Vvbw9fX1/s378fDRs2RI4cOWBqagpnZ2fUqVMHy5cvx6FDh4QlJaKiojB58uQ0WfPTp0+xf/9+2VixYsVQs2ZNYd9mzZoJb0rcvn0b/v7+RlljetJ0yJ6uZWsyK6UEs4mJ9n8mKh3OqW09Yn0MHz4cPXr0wJs3bwwaJzQ0FI0aNcKKFSv06t+oUSP8888/KFiwoE4HOD58+BC1atXC+fPnNV4bGBgIDw8Pgw/XTEpKwqRJk9CyZUskJCQoXiv6rCtVqhROnz6NuXPnws3NDXZ2djAzM0OBAgXQpk0b+Pn5YebMmcKvxcmTJ/Hff/8Z9D6IiIiI6OvBBDMRUSZkb28PT09P7Nu3T+0x6cOHDwv76VpKQrSrNSEhQXjwm67Onj0rjLVr107rcdq1a6e4O9iY+vTpo1ZipFGjRrhy5YriTmAAqF+/vmJJkKVLl6od4GUMK1asENYc/uGHHxT7mpmZoWPHjsJ4ZtzFbGtrqxhP67IOXwulJLJSjeovKf3Mmpqa6rQmbc2aNQvTp08Xxtu2bYuDBw/i9evXiI2Nxb1797B06VLhTbakpCT07dtX8TNUya+//orHjx8jJSXl447xuLg4PHjwAMuWLUOZMmVk+0VGRqJNmzaKBy4+ePAArVu3RmRkpGy8RIkSWLJkCUJCQhAXF4fnz5/Dz88PXbt2FX6PDxw4gJ9++knxPU2dOhWFChX6+N8mJiYYMGAALl68CHd3d8W+Q4YMwYABA4TxBQsWKPYnIiIiosyDCWYioq+Aro+5R0ZGIiAgADVq1FAb6/Lly8J5GjdurNO6PDw8hLGLFy/qNJbIkydPZNudnJxQpUoVrcdxcnKS/XqkBQsLC/j5+WH37t2YPHkyAgIC4Ofnh8KFC2vVv23btmjQQP5gysjISKMfopiSkoLly5cL45qS4gAU69vu2LFD8ZDGr5GmA+xEibysxsrKShjTtLtV22t1qeWsrcePH+O3336TjZmZmWHDhg3Ytm0bmjRpAicnJ1hZWaF48eLo3bs3zp07J+yblJSEn376CYmJiUZZp6WlJYoWLYpevXrh0qVLwhs1T58+Fa4JAAYMGCDcpd2+fXtcvXoVffr0QZEiRWBpaYk8efKgUaNGWLNmDfz9/YU19VevXq2YUC9SpAiCg4OxevVqTJ8+HTdv3sTcuXMVyxZ96t9//xXWij569Gial08hIiIiovSRPlu9iDKx/ovkk0BEX6vw8HDZdnt7e41JtS+VKlVK53l0JUrk6fqoOQAULlwYJ06cMMKqNFOpVPDy8hIegKdJly5dcPToUdnYiRMn4OnpacjyPuPn54dHjx7Jxtzc3FCoUCGNO3ZdXV3h4uIiW5M1ISEBK1aswK+//irsr7SLVZedsnKUasmKdrXnz58f5ubmwkTio0ePFMuG6CI2NhZLlizB3bt3Ubp0afTq1Uu2BEe9evUMqgOsDX9//8/qMCslCnWp8at0WJ2m3eL6mD17tvBn9t9//1XccW9qaopJkybh8ePHWLdunVo8JCQEvr6+6Nmzp9HWC3xINq9atQpXr16Vrbvs6+uLCRMmfLZjGACuXbsmLG/j5uaGtWvXKibx69ati3Xr1qFZs2aypUz+/PNPxZrJtra2inXYlWTLlg3fffcd1qxZoxaLjY3FhQsXULt2bb3GJiIiIqKvB3cwExFlMaL6y7ly5dJ5LKWEtLESzG/fvpVt12e9uXPnNnQ56UZpt/Xz58+NOpfS4X4XL16EtbW1Vi+lA7+WLFmiWIdX6cA40c+AtpSSm6J5zczMULx4cWG/69evG7SmVMnJyWjQoAGGDBmC+fPnY+DAgahWrZrRapgbSun3TJdd3ErXimqOG2Lbtm2y7Xnz5sXAgQO1GmPSpEnC8hE7duzQe21KLC0tMWLECNlYcnIy9u7dq9Yueq8AMGHCBK12iDdp0kT41ERgYKDRPs/lpOdnHRERERFlDCaYiYi+EbruBta3j65Eh6npM7cuh5JltLx58wpjxkz2hIWFYffu3UYbTyQkJAQHDx4UxrNnzy6MGVKOIiUlRXGnrdLu2apVqwpjxipTsnr1apw+ffqztuvXr2Pu3LlGGd9QSodovnz5UutxlK419o2fsLAw4Y78Jk2aaF2So1ChQsLkp9JO8uPHj2P06NEYP368Yt1kkSZNmghj586dU2sT1am3tLTUqeyRaFe3JElp+uRHen3WEREREVHGyTz/EiciIq2Iam3q8w95pcSfPjuM5Yh2N+pT0zc96wCHhITg8OHDeidIlJJgSnVxdbVq1Sqj1ZPVZNGiRcJYnjx5hLEHDx7oPee9e/cUD5hzdnYWxpSSc4GBgXolD78kSiRfu3bN4LGNIXfu3LCxsZGNPX78WOtxRAlf4EMdX2MKDQ0VxpR2pcspWrSobHt0dLTszvipU6fC09MT//zzDyZMmAAPDw+8f/9epznz5csnLN0il6gXvd/8+fMrPhnwJdF7BYBnz57JtkuShEuXLuHYsWN610tOr886IiIiIso4TDATEWUxooRaZGSkTjVVAeDOnTs6z6Mr0ThPnz7Veaz0etz6zz//RPHixdG4cWMUKlQIq1ev1nkMpbUa62sLAMuWLTPaWJrs3btX+H0rVqyYsN+FCxf0ToKfP39eMa6U3GzWrBnMzc1lY8nJyYoJc20EBAQID93MkSOHQWMbi4mJCSpVqiQbu3nzptbj3Lp1SxgrV66crstSpJTQFSVuRZQSnF+Wbnn79i3GjBnzWdujR4+wfv16neZMSUkRPqEhl4wVvd+0fK/Ah9rqLVu2hJubG+rVq4fixYsrHiIrkl6fdURERESUcZhgJiLKYipXrizbLkkSDh8+rNNYXz7a/6kqVaroNJaIm5ubbHt4eDiuXLmi9ThxcXFGK2ug5Pz58xg3btzHesNxcXHo06ePYjJeztWrV4UxYyXkAgICFOsmG1tycrKw3rOrq6uwX1xcHI4fP67XnJs2bRLGnJ2dhTv6U+Pe3t7C+MyZM4U7OzVJSUnByJEjhXFjJ10NUb16ddn2W7duaV0f+8yZM7LtZcqUMdrTDqmUyp4o7W7W9Xo7O7vP/vvu3buyN0Lmz5+v05xPnz4V3lCRK1kier9p+V6BD7vvPz1c8Pnz52jfvj0SEhJ0mjc9PuuIiIiIKGMxwUxElMU0bNhQGNu+fbtOY61du1a23dLSEnXr1tVpLBFRcgvQbb2HDh1Kl4PTLly4oNaWmJiIIUOG6DSO0sFdxvraKh3uN3LkSEiSpNdr9OjRwnGXLVuG5ORktfbq1asLdwsDwD///KPbmwNw+fJl7Nu3TxhXOlws1YABA4SxqKgodOnSReeEGgCMGzdOtp4u8GHXsFJiO721bNlStj0lJQUHDhzQ2D80NFS4s1V0sJwhlHalHz16VOtx3r9/j6CgINlYzpw51eqGi2pJX758GXv27NF6XqWa6HK7yUXv9/3794o3Ab/k5+cnjMmVFpF7OuDevXuYOXOm1nNKkiQ8MLFgwYKKTzYQERERUebBBDMRURZTtWpV4Y7BDRs2aF37devWrcKdZw0bNhQezqerJk2aCA/n03YHaVJSEsaOHWuU9WiSP39+2fb9+/drnRC/efMmNm/eLBurWLEiSpQooff6UkVERCgmsbt06aL32F27dhXGnj59Kptsy5Ytm+LNjyNHjmDChAlaryE8PBzdunWTTWanEiVOP+Xp6YlmzZoJ4/7+/mjXrp3WO3kBYMKECfj7778V1yVXkzogIEDvpL+2r3r16qnNW7duXeFO78WLF2t8v0uWLPm4o/9LooPlvhQYGIjJkydj8eLFGmupOzg4oGTJkrKxW7duKR42+alVq1YhKipKNubh4aHWVrBgQeGO2xEjRmh1IyI2NhazZs2SjalUKrRu3VqtXekm3OzZszXOCXyo7Sz6zDExMZE98FL0WffXX39pXZ97+fLlwmvbtm2r1RhERERE9PVjgpmIKIsxMzPDsGHDZGPJycno1q0bXr9+rTjGrVu30K9fP2Fc6dF/XRUsWFCY4IuOjkb37t01Hi41atSodDs0zdPTU5hc7969O44dO6bYPzIyEt26dRMmowYPHmzwGgHA19cX8fHxsrEKFSoolqzQpHTp0nB3dxfGRUnJH3/8UXHc8ePHo1OnToqH/qWkpGDbtm2oUaOG4vfczs4OHTp0UJwv1fTp0xUPS9u7dy8qVKiAlStXCn8WJUlCQEAAateujfHjxwvHMjU11Wu3dloyMzPDwIEDZWP+/v7YuXOnsO+DBw+EO1qrVauG2rVra5y/d+/eqFOnDn799Vf8/PPPcHFxgb+/v2Kfdu3aCWM9e/bUeEBjcHCw4udY+/btZdt/+ukn2fbbt29jxIgRinOmpKSgV69euHfvnmy8WbNmsjcelBKxGzduxJo1axTnTUxMRNeuXYU1+D09PWVvSjZv3lz2+nfv3qFp06Yab/5du3ZN+DU2MTER/swRERERUSaU1jtl+OJL9KpSpYqU1m7cuJHmcxBpw9/fXwIgfBlbTEyMlCtXLuF85cqVk86fP6/WLyUlRdq0aZNi37p16yrOLern7e0t7HPgwAHFr0+NGjWkS5cuqfV78uSJ1KlTJ8W+qa+QkBAdv4piffr0Ec5jbW0tzZ07V4qPj1frd/LkSal8+fLCvmXLlpUSEhKMskaleaZOnWrw+LNnzxaOb2JiIoWGhqr1SU5OlsqWLavV96tixYpS9+7dpZEjR0pjxoyR+vfvL7Vu3VpycnLSqv+ff/6p0/uZM2eOVuPa2tpKjRs3lgYNGiSNHTtW+t///id9//33UoECBbTqP2rUKIO/9mnhzZs3wt97W1tbaffu3Wp9goODJRcXF+F73blzp8Z5V6xYIdvXyclJevPmjbDf48ePJQsLC+Hc+fPnlw4cOKDWLykpSfL19ZWyZ88u7FugQAHZ319JkqT4+HipRIkSwr79+/eX3r59q9bvzp07Uv369RV/Z65evSp8v82aNVPs+/vvv0tRUVFq/YKDgyUPDw/Fn8l9+/bJzpmcnCyVKlVK2K9IkSLS/v37Zb/G69atk+zt7YV9+/TpI3yvmRH/3iQiIjKSv/NI0rjs6q/4GIOHLlOmjOzfJWM6LJfm9T0izet7REqMTzLCm/ig23/dpPKryqu9zr9Q/3e4HADnpa8gd6ftS/VhzUTpz93dXZKr72dMN2/eRJkyZdJ0DiJtBAQEoH79+sJ4WnwWHz58GM2aNVMsIeDm5gZ3d3fY2tri5cuXOHbsmOJBUI6Ojjh//rxi3UyVSiXb7u3trbgTsl27dhpLTHh4eKBcuXKwsLDA3bt3ERAQgKSkJMU+qUJCQhRrt+riyZMnKFeunHBHIPChhmuDBg1QoEABvHr1CteuXcOlS5eE11tYWODYsWOyj+br6urVq6hYsaJszMTEBE+ePJE9TEwX4eHhyJ8/v/Cwsjlz5sjuUAwMDISnpydSUlIMml+Ju7s7goKCFGs+y+nRowdWr16dRqsCmjZtiv/++09YEiajbdmyRXHXt5ubGzw8PGBmZoabN2/C399f+PvXtm1bxRItqerVqyfc9b9q1Sp0795d2Hfs2LGYOHGi4vguLi6oVq0anJ2d8fz5cwQGBuLp06eKfTZt2qT4dQgMDESDBg2EP/vZs2dH/fr1UaRIEbx//x7Xr1/HqVOnFOccM2aMYlmV69evw93dXfhUAvDhMMAGDRqgcOHCeP/+PYKDgzXWaG7RooViHfNdu3ahTZs2imOUKFECtWvXhqOjI0JDQ3H27Fk8evRIeH2hQoVw5coVODg4KI6bmfDvTSIiIiOZmBdIfK/ePvoZYGFj0NBly5bFzZs31drHdFiOvI5FAAB953jCzMLUoHlSdd/fHRfDLqq1r2q2ClVyV9HYX6VSXZAkSfzY6NcmozPcfH27L+5gpm9JUFCQcCeXpaVlms07depUrXZVavMyMzOT/vvvP41zmpuby/Zv27atYr9nz54p7pzW5tW1a1dh7NGjR8b6skqSJEmbN2+WTExMjPK1ValU0qpVq4y2tqVLlwrnatq0qdHmad++vXCe9u3bC/vNnTvXaD+XX76KFSsmPX36VK/3k5ycLHXr1i1N1uXh4SFFRkbq+6VONyNGjDD4vZYpU0br96q0G3jixImKfRMSEhR3Bevz6t+/v1brXrx4sdHm9PHxkVJSUjTOqfR7re/vyosXLzTOO3DgQKPNaW9vr7hTO7Pi35tERERGwh3MHyGT7WD+OrfQEBFlMYULFxbGjLWrVs7w4cOxfPlyWFhYGDSOk5MTDhw4IKzJ+alChQrJtosOHkyVN29eHDlyRHjYmCajR4/G4sWLZXdQm5iYyNY2NUT79u3h6+tr8NfWysoKGzZsUNypqauIiAhhrE+fPkabp3///sLY+/cyOw/+z4ABA7Bs2TLFusf6aNCgAU6ePKn37mwTExOsWrUKkydP1nn3s5IuXbrg8OHDsLe3N9qYaWXy5MkG1QEvV64cDh06pPV7zZs3r14xADA3N8fu3bu1+lzSxsCBAzF37lytrv3pp5+wZs0ag3//Bw0aBF9fX+GTH5/q3bs3Fi5caJSfzfLly+PIkSPInTu3xmtnzZpllHrJBQsWxIkTJwyq/05EREREXycmmImI0kG+fPlQvXp12ZjSAU7G0LNnTxw/flw4vyYtW7bE2bNn0bBhQ62ub9KkiWx7o0aNNPYtX748jh8/rniA3Jfs7e2xdu1aTJw4EdbW1qhUqZLaNZUrVzY4ESSnc+fOOH36tLAchSY1atTAxYsX4ePjY9R1NWvWTDZhVbx4cY2Pu+vC09NTWNLD29tbsW+vXr1w5coVtGrVyuB1ODo64p9//oGfn5/BNxJUKhVGjhyJM2fOCA+f1Fbp0qWxZ88e+Pr6wsbGsEf60otKpcKsWbOwevVq5MiRQ6e+3bp1w8mTJ1GgQAGt+4gO07OxsUHLli019re1tcXevXsxffp0ZM+eXet5P1WwYEFs3boVc+bM0SrRm6pr1646f16lKlasGLZt24bZs2frlDD++eefceLECVSuXFnnOYEPBzoOGTIEp0+f1vrmpomJCebMmYNNmzbB2dlZ5zlVKhW6d++Oq1evMrlMRERElFVl9BZqvr7dF0tk0Lfm+vXrUu7cudUem4+Ojk63NezcuVPy8vKSbG1tFR9jdnZ2ln744Qfp9OnTOs/x/PlzqXDhwp+N16VLF60eAU+VlJQkzZ07V/GgOkdHR2nIkCFqj3gfO3ZMsrOz+3idnZ2ddPz4cZ3fhy6Sk5OljRs3Sg0bNhSWCEl9WVlZSd7e3rKHYxnTwoULJUtLy4/zWltbyx56Zqjg4GApf/78n73HH3/8Uafvd3BwsDRmzBipSpUqkqmpqVaP2ltZWUm1atWS5s2bJ8XEGP7InMjFixel4cOHK5Zy+PRlY2MjtWnTRtqxY4eUnJycZutKD69evZJ+//13KV++fML3a2lpKX333XfSqVOn9JojKSlJ6ty582djWltba3VAoNx6J0+eLFWqVElSqVSK3yczMzOpTp060vLly6XY2Fi91p4qJSVF8vX1lTw8PBTntbCwkOrVqyetWLFCSkxMNGjO5ORkadeuXVLr1q0lGxsbjT+XRYsWlUaOHCk9ePDAoHmjo6OlqVOnShUrVtQ4p7Ozs9SnTx/p+vXrBs2ZGfDvTSIiIiNhiYyPkMlKZPCQP8owPOSPvkVv377F9u3b8fz5c5QpUwatW7eGqalxDhHQRUJCAs6ePYvQ0FCEh4cjOjoaTk5OcHZ2RrFixVCpUiWddvJ9KTo6Glu2bMGbN29QpUoV1KtXT++xbty4gevXryM0NBSJiYnIkSMHXF1d4ebmJtz59/jxY+zfvx+SJKFFixbCsh1pITo6GteuXcOdO3cQERGB9+/fw8rKCk5OTihbtiwqVKiAbNmypctaHjx4gEOHDkGSJDRq1AguLi5pMk94eDi2bt2Kt2/folatWqhTp47eY8XGxuLmzZt4+PAhXr58iejoaMTHx8PS0hIODg5wdHRE0aJFUbFiRaOWsdBGREQEbty4gfv37yMqKgrR0dGQJAk2NjbImzcvSpYsCVdXV5iZmaXrutKaJEkIDg7GxYsXERYWhoSEBDg4OKBUqVKoVq0a7OzsDJ4jMDAQJ0+ehK2tLdq0aYP8+fMbNN6rV69w9epV3L9/H5GRkYiLi4O1tTWcnJzg4uKCihUr6r3jWUlYWBjOnTuHBw8eICoqCtbW1nB2dv74FIutra3R50xISPj4mfPy5Uu8e/cOJiYmyJ49OwoXLozy5cunSSmmFy9e4Nq1ax/fa3x8PGxsbJAnTx6UL18eZcuWzZD/f8sI/HuTiIjISHjI30eZ7ZA/JpgpwzDBTERERESZHf/eJCIiMhImmD/KbAlm1mAmIiIiIiIiIiIiIr0wwUxEREREREREREREemGCmYiIiIiIiIiIiIj0wgQzEREREREREREREemFCWYiIiIiIiIiIiIi0gsTzERERERERERERESkFyaYiYiIiIiIiIiIiEgvTDATERERERERERERkV6YYCYiIiIiIiIiIiIivTDBTERERERERERERER6YYKZiIiIiIiIiIiIiPTCBDMRERERERERERER6YUJZiIiIiIiIiIiIiLSCxPMRERERERERERERKQXJpiJiIiIiIiIiIiISC9MMBMRERERERERERGRXphgJiIiIiIiIiIiIiK9MMFMRERERERERERERHphgpmIiIiIiIiIiIiI9MIEMxERERERERERERHphQlmIiIiIiIiIiIiItILE8xEREREREREREREpBcmmImIiIiIiIiIiIhIL0wwExEREREREREREZFemGAmIiIiIiIiIiIiIr0wwUxERERERERERETfnImbe+Hhy5sZvYxMjwlmIiIiIiIiIiIi+iZN2zkAD14EZ/QyMjUmmImIiIiIiIiIiOibNWPXoIxeQqbGBDMRERERERERERFlWVZWVhm9hCyNCWYiIiIiIiIiIiLKsmrVqpXRS8jSmGAmIiIiIiIiIiKiLOu3335DyZIlhfGiuctqHONsSAR+234VN59HGXNpWYJZRi+AiIiIiIiIiIiIKK3ky5cPZ8+eRcemXjhw5oRa3MrcBjP87gCmKrWYBGBhwP2P/73h7BOUzmOHA0PqpuWSMxUmmImIiIiIiIiIiChLs7e3R93KVWUTzACw5PgDJKnnl2XdehGNE3fDUcfF2YgrzLxYIoOIiIiIiIiIiIhIB7MO383oJXw1uIOZiOgrFBoailu3buHRo0eIjIxEbGwsrKyskD17duTIkQNly5aFi4sLzMz4Mf4tun//Pvbu3YvExEQ0btwYFStW1KrfgQMHcPnyZTg7O6Nt27ZwdHRM45Uqi42NxX///YcHDx7A0tISFSpUgKenJ1QqLbcNaCBJEg4cOIArV67AwcEBrVu3Rr58+YwydkJCAnbt2oV79+4hb968+O6772Bvb2+UsVPFx8fjypUruHv3LsLCwvDu3TtYWVnBzs4ORYsWRbly5ZA/f36jzklkqHPnzuHYsWOwsbFB8+bNUaRIkYxeEhEREdFHZibG+bcGAES+TzDaWJkdMxNERF+JI0eOYNOmTdi/fz9CQ0M1Xm9hYYE6derA29sbHTp0QO7cudNhlZTR9u/fD29vbyQmJgIAVCoVZs6cicGDByv269q1K9auXfvxv8eOHYtjx44pHnSRlvbt24fu3bvj9evXn7VXqlQJW7ZsQYkSJQwaPzExEa1atcKhQ4c+tg0bNgw7d+5E48aNDRr79evXqFevHq5fv/6x7ddff4Wfnx9cXV0NGvvt27fYuHEjNm3ahKCgIMTHxyte7+LiAi8vL/z0008oVaqUQXNnNEmSEBgYiK1bt+LcuXO4f/8+IiMjYWVlhVy5cqFEiRJo1qwZvL29v5qkZXJyMvz8/PDff//hwoULuH//Pt6+fQtJkuDo6AhHR0e4urqidu3aaNGiBYoXL26UeePj43H8+HEcOnQIJ0+exMuXL/Hq1Su8f/8e1tbWH79e1atXR/PmzeHh4WGUeTX5888/MX78eEiSBAAwNzfH2rVr0aFDB4PHfvr0KbZu3Qp/f3/cuHEDL168+Ph+8+XLh/Lly6Nx48bo0KEDnJycDJ6PiIiIsqY82a2MNlbVIvybI5Uq9Q9AovTm7u4unT9/Pk3nuHnzJsqUKZOmcxAZatu2bRg/fvxnySpdWVpa4scff8Rvv/2GQoUKGXF19DVJSUlB6dKlcffu549imZub49GjR8ibN69svxMnTqBuXfUDKFq2bIm9e/emyVqVBAUFoV69eh+T5F8qWrQoLl++jOzZs+s9x+zZszFkyBC19jx58uDRo0ewsLDQe+yBAwdi3rx5au3u7u44d+6cXmPGxMTgn3/+wbx58xAVpfup1CqVCl5eXpg6dWqG3TQwxJEjRzBs2DBcuXJF47VmZmbo0aMHxo8fn2E7uFNSUrBixQr89ddfePz4sVZ9VCoVWrZsiXHjxsHd3V2veePi4rBkyRJMnjwZz54907pf2bJlMWXKFLRs2VKvebWxZMkS9O3bV63d0dERT58+hbW1tV7jPn78GKNHj8bGjRuRnJys8Xpra2v069cPf/zxh0GfIbrg35tERERGMjEvkPhevX30M8DCxihTbBs/Ht9PmKDWXqZAVSR0Hqd1DWYAGNuyDHrXKSYb676/Oy6GXVRrX9VsFarkrqJxbJVKdUGSJP3+aMwArMFMRJRBXr58iWbNmuH77783KLkMfNjNtmjRIpQrVw4LFy4Ebx5mTaGhoWrJZeDDbt1jx44J+/n7+8u2Hz16NN1/ViRJwi+//CJMLgNASEgIJk+ebNA8u3fvlm1/8eIFzp49a9DYoqT8+fPn8fTpU53H8/PzQ6lSpTBp0iS9ksvAh6/r7t27Ub58eUyaNAkpKSl6jZPeJEnC6NGj0ahRI62SywCQlJSEZcuWoXLlyjhxQv6AlrQUFhaGRo0aoU+fPlonl4EP73Xv3r2oUaMG/vzzT51/9+7cuQN3d3cMHjxYp+QyANy4cQOtWrVCr169FH/39HXu3DkMHDhQNvbmzRucOXNGr3E3b96M8uXLY926dVoll4EPpXemT5+OihUr4vLly3rNS0RERJnQu9dAsmF/5wxt5IIRTUupvUSMVdovK2CJDCKiDHDlyhU0b94cz58/N+q4MTEx6NevH44fP441a9bA3NzcqONTxnry5IkwFh4ernO/2NhYxMTEwM7OzuC1aWv//v24evWqxutmzZqFIUOGwNlZv1OZlb5WSjFtKCWRHz9+rNOu2ilTpuC3334zWkI4MTERY8aMQVBQELZs2aL3rtH0MnjwYMydO1evvuHh4WjYsCH27dtncNkTbaWWR7l586beYyQlJWHcuHF4+/Ytpk+frlWfc+fOoVGjRnrfgEi1YsUKhIeHY+fOnTAxMc4+k8jISHTo0AEJCeIahLomxAFgwYIFGDBggN43wR4+fIj69esjKCiIu4uJiIgyu4cnAYts8rGIB8DuL250j4sE9Ej+9vUsDjMLU7X2VzHxWHnyoc7jfUu4g5mIKJ1dvXoV9evXN3py+VMbN26El5dXmuxUo4yj9P1U2t2nb7+0sGDBAq2ue//+PZYvX673PErJLk11jTVR+nrqMva4ceMwatSoNNltvG/fPrRu3RpJSUlGH9tYli5dqndyOVViYiJ8fHxkd/anhV69ehmUXP7UjBkzsH79eo3XPX36FN7e3gYnl1Pt2bMH48ePN8pYwIevycOHDxWvUfp9lLNr1y6DksupIiMj4ePj81X/HhAREZEW1rcHVrWUf32ZXAaABTXSf43fOCaYiYjSUXh4OLy8vPDmzRuN15qZmcHT0xM//vgjfv31VwwdOhQ+Pj4oVky+xtOXDh48KFuDliijPH36FAcOHND6+uXLl2fZci/Lli3Dn3/+qfE6ExMT1KxZE927d8eoUaMwbNgwdOjQAQUKFNDY9/Dhwxg5cqQxlmt0z58/x7Bhw4RxLy8vHDlyBC9evEB4eDgOHz6MFi1ayF775s0b9OnTJ62W+tHhw4exa9cuYbxdu3Y4ePAgXr58iYiICFy+fBkTJkyAg4ODsM/IkSMRFxenOO+QIUOENyRNTEzQs2dP+Pv749mzZwgLC0NgYCD69eun+ATLv//+i5CQEMV5tTF//nxs377d4HE+9fLlS/Ts2VP4u1++fHksX74c9+/fx+vXr3H+/HkMGzYM2bLJ72q6du0aNmzYYNQ1EhER0Vcu3DgbAkh7LJFBRJSOfvnlF401O3PmzIlx48bhhx9+gKOjo+w1ly9fxtSpU7Fx40bF3Y8LFixAw4YN0bZtW4PWTWQMK1as0GnH9L179xAQEID69eun4arSX3BwsLBebSorKyv8+uuv6Nu3L/LkyaMWlyQJfn5+GDFihGLJkdmzZ8PHxwfVq1c3eN3GNHnyZERHR8vGRowYgSlTpnzW1rBhQzRs2BBDhgzB7Nmz1focO3YMu3btgre3d5qsF1Defb9o0SK1A+4cHR1RsWJF9OrVC/Xr15fdZf306VP4+fnBy8tLdtwLFy5g69atsrHs2bNj586dar8fzs7OqFWrFnr16oXmzZsjLCxMrW9iYiKmTJmChQsXCt+TJpcvX1a8SaCv0aNHIyIiQjY2ZMgQTJky5bPkuZOTE6pUqYIffvgBTZs2lS0XtHPnTnTt2tXoayUiIiJjY03jzIo7mImI0smBAwewbds2xWu8vLxw9+5dDBgwQJhcBoBKlSph3bp18PPzQ65cuRTHHDZsmMYdckRpTZIkrFixQjZWu3ZtmJnJ3/NetGhRWi4rQ/Tp00fxd9LFxQVXr17FuHHjZJPLwIcDRZo0aYKzZ8/ihx9+EI6VkpKC33//3eA1G1NsbCyWLVsmGytfvjz+/fdfYd9p06ahXLlysrFJkyYZZX1yJEnCkSNHZGOdO3dWSy5/Kn/+/Ni0aZMw7ufnJ4ytXbtWtl2lUmHjxo2KN1/c3NwU5925c6feTwjExMSgQ4cOBpeb+VJISAhWr14tG+vVqxdmzpwp3JlduXJlzJ8/XzYmSlgTERHRVyZvhYxeAemJCWYionQyduxYxXiHDh2wfft2xcepv9SgQQMEBAQoHoT28OHDLJmko8zFz89PWKd18ODBaNasmWxsx44dsjswM6vt27fj1KlTwnixYsXg7+8PFxcXrcaztLTEmjVr0KZNG+E1fn5+CA0N1XWpaebw4cN49+6dbGzEiBGKh8+ZmZlhxIgRsrGzZ8/i9u3bRlnjl8LDw4U1kHv06KGxf+XKlVG1alXZmNKhkwcPHpRt9/LyQvPmzTXOW69ePXh6esrGXrx4oXft6p9//lm2r729vV7jpVq8eLHsUw6Ojo6YOnWqxv7t27eX/V0Q7RAnIiKir0zNQYBK/ZC9r9Vfe2+g3lR/2delx5rLYmYlLJFBRJQOgoKCcOHCBWG8ePHiWLFihXAXp5IyZcpgzZo1ismGhQsXYvDgwVDJnKT7+vVrbNu2DW/evEHNmjVRp04dndcAfDjEadu2bXj+/Dk8PDxQs2ZNncdISkrC+fPn8fjxY4SHhyMyMhLZsmWDs7MzypYtC1dXV8W6okoSExOxfft2PHjwAIULF0a7du1gaWn52TU3b97EsWPH8Pr1azg6OqJy5cqoXr26YsLrS5Ik4datW7hz5w5CQ0MRExODhIQEWFpawsbGBnnz5kXRokVRvnx5vd9LZrR06VLZdnt7e3h5eSElJQV79+5ViycmJmL58uX47bff0nqJ6WLatGnCmEqlwtq1a5E/f36dxjQxMcHq1avh6uoqLMFz8uRJ+Pj46DRuWtm3b59su6mpKVq1aqWxf9u2bdG7d2/Zg9u2bNmi8WaePpRKuxQuXFirMYoVK4Zz586ptcfGxgr7iG7K9O7dW6s5gQ83Io8dOyYbe/bsGUqWLKn1WMCHUjfr1q1Tax8wYACuXbsmnEsbokMPu3XrpvhUz6c2bdqESZMmYfPmzUhJSUHnzp15HgEREVFmUboF0G0ncHUzEPFA8/Xht4H3r9Tbc+r2940hHr5+L9tubSvJJl2TUrLmGTNMMBMRpYNVq1YpxhcuXAgbGxu9x2/WrBnatWsnLMFx584dnDhxAnXr1v2s/fz582jWrBlev379sW3BggX45ZdfdJo/KSkJDRo0wMmTJz+2jRo1SvFR90/t3bsXK1aswJEjR4S7BIEPychWrVph8ODBwt2AchITE1GvXj0EBQV9bJs7dy4CAwNhamqK2NhY9OrVS/YgqAIFCmDUqFHo16+fYqLZ398fK1aswN69exEZGalxTZaWlmjQoAF+/PFHfP/997LJ/6wiLCxMeDja999/D0tLS7Ru3Rr29vZ4+/at2jVLly7FqFGjdEr0f42uX7+uuHu5d+/eqFFDvxOvs2fPjjlz5gh3Msv9XhUpUgSPHj3Saz5thYSEoEiRIp+1iW62lS9fHk5OThrHtLOzQ+XKlWWTtZ/+jhtTzpw5YWFhgYSEBLXYy5cvtUrSvnz5UrZddENBVL6iYMGCwgMP5SiVUVJKbsu5ceOGbP3wChUqYNq0aWjatKlO433q0qVLwt3cLVu21HocCwsLjB8/HuPHj9d7LURERJSBitb98NJG+G1gfrW0XY+RnbgTjup5M3oVxpe5/6VGRJQJSJIkuzMzVbly5dC4cWOD59FUZ3X//v1qbT///PNnyWXgQykP0ePrIitXrvwsuQx8OMRL6fAxADhz5gw8PDzg5eWFHTt2KCaXAeDt27dYt24dqlWrhg4dOggTNl/asGGDWuLp9OnT2LFjB5KTk9GuXTvZ5DIAhIaGYuDAgfD29pbdMRkcHIy6deuiQYMGWLt2rVbJZQCIj4/H/v370aFDB3h4eAh3KmYFq1evRmJiomwstX6wlZUV2rdvL3tNSEiIsFRAZrJjxw7F+ODBgw0a39vbW/gkQ5UqVQwa21iSk5MRHBwsGytfvrzW47i6usq2nz59Wu+6wkrMzc2FByX6+vpq7P/o0SO1z8hU9erVk21XqVRqNwWBD4fgmZpq/+jos2fPhLEcOXJoPU5sbCw6dOiA9+8/36WTLVs2bNy4Ue2JEF0p3RyoVi1z/cORiIiIshZnO8P+zvkWMMFMRJTGbt26hefPnwvjP/30k1HmqVixIipXriyMHz58WK3t5s2bam0RERHYuHGjTnPPmjVLtl2pvqevry/q1q2LM2fO6DRXqi1btsDNzU2x9Eiqo0ePyrafOHECkyZNkk2+f2nv3r3YsmXLZ2179uxBtWrVcOLECe0WLXD27FnUq1dP64R5ZiM60K1AgQKfJde6du0qHCMr1BGX+x1M5e7uLjy8ThdLlixROxjw559/hpubm8FjG8Pjx4+Fu2ZLlSql9TiiGtVv3rxJs98j0Wf18uXLFZPMb9++RefOnWVvsuTJkwft2rUT9l24cOHHxLa5uTkGDRqkeKCgHNEhgiYmJjr9zA0cOFD25sDs2bNRpkwZndYk5/Lly7Ltzs7On9V2fvbsGf766y/UqlULOXLkgIWFBfLmzQt3d3eMGjVKq/9PICIiItJFg9K5YGpinCdOc2e3Mso4XxsmmImI0pimf+w2aNDAaHOJDkoDPjyen5KS8lmbKCmgzY68VGfPnsWNGzdkY6LkxbJly9CtWzfZx8118ezZMzRu3Fi4I/LT6+QEBQXh77//1nq+T+e5cuWK7G4+fT169AjDhg0zylhfk2PHjuHOnTuysc6dO39WGqROnTrCerb79u37qg6q01VycrLiZ0HDhg2NMk+BAgVw7do1TJ8+HaNGjcKOHTuwcOFCo4xtDEq7ab9MjCvJly+fMCaqQ22oH374QfbzOiUlBd26dYOPjw8OHTqE8PBwREdH49atW5g9ezZcXV2Fu3Pnzp2LbNmyCecsWrQoTp8+jTdv3iAmJgazZ8/WqZzOlStXcPr0adlYrVq1tC7NtGHDBixfvlytvUOHDjrVg1Zy5coV2fbUz4Tk5GT8+eefKFasGP744w8EBQUhIiICiYmJePHiBS5cuIApU6bA3d0d3t7eCAkJMcq6iIiIiErnyY55nSojd3bDdzJn1cqIrMFMRJTGbt26JYzZ29sbZddiKqWD9eLi4vDo0SMULVr0Y1ubNm1kk17Hjx/HkydPULBgQY1zrl69Wra9QoUKKF26tFr7lStXZGt4pjIzM0ONGjVQvnx5WFtbIywsDEFBQXjwQP6Qhzdv3qB9+/a4dOmS8BFtUSL7/PnzwnXI+fSx/CFDhiAuLk54beHChVGtWjXkzZsXFhYWiImJ+VgLW1QuYuPGjZg6dSry5s06RblEh/sBQJcuXT77b5VKhS5dumDixIlq1yYnJ2Pp0qWYMGGC0deYHh4/fqxYekbp6QNd5cyZE//73/+MNp4xvXjxQhhTqhX8JaVazU+ePEmTkgoqlQpbtmxB8+bNcfbsWbX45s2bsXnzZq3Hmjp1Kr7//nutrndwcNBlqR8p/Rxomxi+d++e7K7pIkWKYMmSJXqt60uSJOH69euyMRsbG8TGxqJdu3ZaPW0CALt370ZAQAD27NkjW2aEiIiISM6LkCiYmcvvxS0RZ4Juj00AWAMAitbKgzLNxYc9e29fjKx5nJ88JpiJNMjKB29lJWlRc9NYRIcWAR8eCTfmz1iJEiUU4w8fPvwswdytWzeMGzdObWezJElYv349Ro0apTheQkICNm3aJBsTPU7+448/ChOzrVq1wrx582R3sf7333/45ZdfZHcn3rx5EzNnzsSvv/6quF4lZmZmaNmyJUqVKoWYmBicO3cOFy9eRHJyMoAPyfgOHToA+PA9DQgIkB0nX758WLRoEby8vGTjT58+RevWrXHx4kW1WHJyMvbs2WO0sikZ7c2bN8KDJytUqCBbR7dr166yCWbgQymC33//HWZmme/PF6XPAUC3+sOZ2atXMqd8/59PyyBokj17dmHsy7ryxuTk5IRjx45h7NixmDNnjvBmkZLChQtj0aJFik+cGMM///wjLA9UtmzZj/XPlcTHx8PHxwfR0dGftZuZmWH9+vU6fc+UREVFCUunWFpa4vvvv9c6ufzpmE2bNsWBAwfg6elpjGUSERFRFrdr5iWtrw05+QIFCmRHhfoFZONWZqbQ7TjlzI0lMoiI0lhERIQwprQLTx+aHjH/MklQqFAhNG3aVPbadevWaZxv3759sskcGxsb2Xq6Bw8exKVL8v+n7ePjg927dwtLJLRo0QKBgYHC3b0zZ87UK9kDfNjxeenSJezcuROTJ0/G/PnzcfbsWYSFhWH79u04evQoduzY8fFmwO3bt2XHyZ8/P86cOSNMLqdeI/eoeSp9a1J/jXx9fYU3E0TJrVKlSqFq1aqysadPn2LPnj1GW196UkqsAsb/LPhaKe36t7Cw0HocpQPllOYwBisrKwwZMkTx91zE3NwcY8eONcrBrkrWrVuHsWPHysZMTEywZMkSrQ4KHD58uOzNsAkTJqBGjRoGrzOV0gGvR48exX///afXuHFxcejcubPG3z8iIiIifZzYJF8K8FvEBDMRURoT7coCAEdHR6POpameZkxMjFrbzz//LHvttWvXcPXqVcXx1qxZI9vesWNH2R2Gs2fPlr3e0dERCxcu1Libu2DBgpg0aZJsLCwsDEeOHFHsL7J48WLZHaROTk747rvvUL9+/c/aixQponatubk5du7ciQIF5O9gf6pSpUooVKiQbExUCiQzEpXHMDExQefOnYX9unXrJoxl1sP+lD4HAOUduWnl4cOHkCQpTV9f/q7Ex8cL16OUNP6Subm5MKbpa20ISZIwdepUlCxZEtu3b9e5f2JiIvr06QNXV1dcu3YtDVb44QBatYEAAGW8SURBVGDAbt26qT2Zkur3339HrVq1NI6zY8cOzJs3T629QYMGBj0tIkcpwZyUlPTZf7do0QJ79+7FkydP8Pz5c/j5+SkelPjs2TOMHDnSaGslIiIiInVMMBMRpTGlpGlq+QVjESUUlLRs2VJYa1lpF/Pr16+xb98+2Zhc0jo+Ph7+/v6y1/v4+GidbO/QoQOsra1lYydPntRqjE8VL14c3333nU59SpQogTFjxnz2vZ02bRrc3d11mldOeHi4Tmv5Wp0+fVpYU9XT01MxEd+xY0dhAtHPzy9TJuE13TwR/UxnNUoJZhMT7f8sVSqLpDSHIZKSktCpUyeMHDnS4CT2zZs34eHhoXPZByXx8fHo06cP+vXrJ/z/gg4dOmDcuHEax3r06BF69eql1p4zZ06sXbtWp++VNpQSzKksLCywatUq7Nu3Dy1btkSBAgWQJ08eNGrUCFu3bsXKlSuF5XPWrl2Lp0+fGnXNRERERPT/McFMRJTGsmXLJowplc/Qh9IhYoB8EsvU1FR42NOGDRuEiZwNGzbIlqRwc3OTTbSePn1a+Oi6LocwZcuWDWXLlpWNPXz4UOtxUrVo0UKvOth///03bt++jZ07d+Ly5csYNGiQxj4pKSm4cOECJk2aJDz8MS13X6YnXQ73+1LOnDmF9WklSTLawWLpydbWVjGe1mUdvhZKiUldbrh9uav1U9qUftDH8OHDhTXnTU1N0atXLxw/fhyRkZF49+4dbt26hZkzZ8LFxUW2z/v37+Hj42OUncz3799HjRo1sGzZMuE1nTt3xrp16zR+3qUm0t+8eaMWW7VqVZocQqrN937y5Mno3r27MN6jRw/hTuXExETFrw0RERGRPrLbfb1nQaU3JpiJiNKYs7OzMGbsBLOmHVqiOq+9e/eW3fn15MkTHDt2TLaPqDyGqOTGlStXhOvq3LkzVCqV1q8LFy7IjqNPDWYPDw+d+6RycXGBt7c3KlasKBt///49Tpw4gcmTJ6N169bIkSMH3N3dMWbMGDx//ly2j7F3tWeE6OhoYSLOysoK33//vcYx5Gp4p1q5ciUSEhL0Xl9G0HQYWmRkZPosJINZWVkJY7p8T5Wu1aWWs7bOnDmDOXPmyMbs7Ozg5+eHZcuWoU6dOrC3t0e2bNlQqlQpDBkyBFeuXBEmRqOjo/HLL78YtLZjx46hatWqwvr2ADBmzBisXbtWqwMyx4wZg1OnTqm1Dx48GC1btjRorSKadvCXK1cOgwcP1jjO77//Lvz/OT8/P73WRkRERN8Gs6T3yB4VIvsSsXz9KB1X+HXLfMewE6UzpcdwibQhKj8BfHhMOjExUbGeqC7u3bunGBeVJciXLx9atWqFnTt3qsXWrVuHevXqfdZ269YtnDt3Tu1aOzs7dOrUSXaO9Cj9ICo7oaRkyZJGmTspKQlXrlzBuXPncP78eZw7dw7BwcFZImGsq/Xr1wt30zdv3hwWFhYad+w2btwY2bNnl310PvXwxY4dOwr7K+1iNeR7oqkMjSiBV7RoUcV+jx49Qr58+fRe16ciIiKwdOlShIaGws3NDd26dZP9ehQpUgSPHqXtH8UhISGf1WFWeqLjy0NIlcjVk0+labe4PqZMmSL8e2DlypVqddo/ZW1tjeXLlyMkJATHjx9Xi588eRJHjx5FgwYNdF7Xxo0b0b17d2HC3dLSEsuWLdP41ECqgwcPYurUqWrtlStXxpQpU3Ren7aUfi4AoHv37lo9aWJlZYVWrVrJ3gA9e/Ys4uLiFG9yEBERUdZnIvi7wz7qIdwvTpONvcpRHlddZTYFMF30EXcwExGlsXLlygljsbGxuHz5stHmUqpBbG1tLXs4XSrRzuOtW7eq1TQV7V7u0qWLMLnz+vVr4dzGYGtri549e+rcT5tD+ZScOnUKnTp1Qs6cOeHu7o5ffvkFy5cvx9WrV7/J5DKgXB5jx44dsLa21vhydHRUrMuq6bA/pQPj3r59q/lNCCglNpXmLViwoGISTVSvWlfR0dGoWrUqfv31V8ybNw89e/ZEs2bN9KrPnhZy5coljOmyi1vpWmMfnhoXFyesN1+lShXFA+ZSmZqaYuLEicL4jh07dF7XvHnz0LlzZ2FyOW/evAgICNA6uRwTE4OePXuqJdKtra2xatUqpKSkIC4uTvEl+jlLTExUfMIkT548imurUqWKVu8BgPAAw8TERLx48ULrcYiIiChrsnAxzgYjADB1cDDaWJkdE8xERGlM0z+MjfnYrtKBUa6uroq7Ops0aYJixYqptUdGRn6WXElJScHatWtlx+jbt68OqzWMiYkJsmfPjooVK2LAgAG4ePGiXjuYHfT8oyA2NhY//vgjatasiY0bN2pMWpqbm8Pd3R1DhgzB3r174ePjo9e8X7tLly4JS5gY07Fjx4R1rAEge/bswpgh5Sg09RXdYFGpVIqfBfocUCln6tSpaocgHj58GJs3bzbK+IZS2qX98uVLrcdRujZ37tw6rUmTq1evCg8ObNWqldbj1K5dG/nz55eNiUoRifzzzz8YOHCgcFd11apVce7cOZ1KAK1cuRLPnj1Ta4+NjUXFihW1ujF04sQJ2bF/+uknWFhYwNbWFm5ubvjrr78+q6Pt6OgIOzs74dp02d2v9P3PKoeoEhERkf5MreWfZjr9/p3OT7CbKGxq+dYwwUxElMaKFi2q+Hj8kiVLjLLT9dSpU7hx44YwrvQIN/AhAfbTTz/Jxj5NKPv7++PJkydq13h4eAhrEQNAjhw5hLHdu3dDkiSdXsnJyXj79i0uX76MuXPnCg/SUmJmZqbX49KJiYlo0aIFVq1aJbymVKlS+PHHH7FgwQKcOXMG0dHROHfuHGbOnImWLVsqHnaWmSntXjY2pV3MSjsiv0zA6kIpqQ0o11xv3LixMLZ7926D60onJCQID0A0xkFyxqB0E+jx48daj6NU2kPpSQ19hIaGCmO63tQS/X+BXGJXZMyYMRg9erQw3qlTJxw/flyYzBYJDg7W6Xp9vHv3DpcuXcIff/yBhQsXfhYrUaKEsJ/SoY5fUqrn/LXs5CciIqKvTxKAcndu40LXLii0YvlnL/N8xj/kOKvJmv+6JSL6yrRu3VoYe/ToEbZu3WrwHBMmTFCMa7PTrmfPnrIHZP33338fd26KymNo2r2cM2dOYUyX5Iox6Vv7evz48QgICJCNNW3aFFevXsWtW7ewYsUK/PLLL6hWrZpa6QRd6s1mFu/fv8f69evTbb41a9YgNjZWNia3Gz+V3AFm2jp//rwwZmtrq3gjxcvLSxh78+aNwV+79evXC3f2Kq0rPeXKlUuY+Lx586bW44gS/ba2tihUqJBeaxN5//69MKbNoXmfEt3Q0rZsy6hRozBp0iRhfPz48Vi/fr1eN870uUlniC+/39WqVRNeq8v/RygdnqvvEytERET07eg6diyCzcxgU7Pmx5dl6dIZvayvHhPMRETpoHv37orxwYMH49WrV3qPv2XLFhw8eFAYL1GihLAu5aecnZ3Rtm1btfb4+Hhs3boV7969w/bt29XiDg4OGks+lC9fXhgzpKTC9evX9d55p2tyCPiQCJo9e7ZsrEWLFti/fz9cXV01jhMSIj6NOLPavHmzQfWNdfXmzRts2rRJNqb0PXjw4AHu37+v15yi+QCgTJkyigeRVapUCe7u7sL4hAkTNNZ4Fnn37h3GjRsnjCvVgk9v1atXl20/e/asVv0lSRJeW6dOHcVSQPpQOjRQaXezLtcrlYdI9dtvvwkP2jMxMcGyZcsUfwY06dWrl8bDKI3py5+DmjVrCq89c+aM1uOKduubm5sr3ngiIiIiSlW9enWoVCrhweWkjglmIqJ0ULlyZdSpU0cYf/nyJTp37izcjank8uXL6N27t+I1AwcOVEx8fUq0E3nt2rXYtm2bbAKsW7duio8lAx+SB3K7owH5gwQ1effuHVq0aAFXV1eUL18erVu31r1mlh5lKo4ePSr8Q+OXX37R6uscHh4u3K2py6PgXxul8hgLFizQuQxK6qtz587CcUVlMmrXrq24VqVdoCJ79+7F1atXhfEaNWpoHGPAgAHC2MOHD/HLL7/o/HMMfPjZE5WYcHBw0FgiJz21bNlStv3Vq1daJRKDgoKEu1QbNGhg0NrkKJXcOHLkiNbjPH78GHfu3JGNaSq1MWbMGPz777+yMRMTE6xduxa9evXSei1yHBwccPr0aQwcOBAlSpQQfl4bws7ODhUqVMCMGTPUbry2bNlSeNNv48aNWs/x33//ybZXrFhR76dWiIiIKOsoVaqU1tcqlb+jzzHBTESUTv766y/FuJ+fH5o0aYKwsDCtxzxw4AAaNGiAqKgo4TUFCxYU1laWU69ePZSWeQTo+PHjmD59umwfbQ73s7a2Rt26dWVjb968wbRp07ReY0REBBo3bvzZoYZ79uzRaZebvpR2vtrb22s1xrRp04S1QJUe7/6a3bhxA0FBQbIxc3NzdOjQQe+xu3btKoydOXMGly9fVmt3cXGR/TlOtXLlSsUa2l+6f/8+fvnlF8VrRInTT3Xp0kVxN/HatWvx008/aX3DJTExET/99BN8fX2F13Tt2lU2Wfjw4UO9k/7avuSSs61btxYm+hYvXqzxPYuuMTU1xffff6+xvyRJ2L9/P/7991+sWrVK8fMT+LD7W7SL2c/PT+vSHvPmzRP+3isdxrdkyRLFGyKzZ89Gp06dtFqDJrly5cKcOXNw9+5dxMfH6/z99vT0lB135cqVkCQJUVFRuHLlCoYOHap2TY4cOYR1ym/cuKHVQZVHjhyR/TwAgDZt2mjsT0RERFlf0aJFtd6UoM8GsG8VE8xEROnE09NTMVEGAIGBgShZsiSmT5+umGi8ePEiOnbsiBYtWuDNmzeKY86fP1/nepxyCWNJkmR3b9apUwdly5bVatz//e9/wtiECRPg7++vcYwLFy6gWrVqsnV0jXFYoiZKO4y3bdumsb+vry+mTp0qjMfGxgrr6H7NlHYvN2/e3KAawI0bN1Y8tE+UcPzxxx+FfSRJQs+ePTFw4EC8ePFCeF1iYiKWLVuGWrVqKZZDKFKkCBo2bCiMpzI1NcWsWbMUd7ovW7YMbm5u2L59OxITE2WvSU5Oxu7du+Hm5qb4tbezs8Pvv/+ucV3pKWfOnOjSpYtszNfXV7HO9alTp4S1qtu1a6fxgL+EhAS0bNkSLVq0wG+//YYff/wRZcqUUTwE0dzcXFg/OyUlBR07dtRY2uTIkSOYMWOGMN6+fXvZ9pcvXyp+bnbp0kVxV3xmo/ReBwwYoHiD7/Xr18KbnRYWFoqfB0RERPRt2bVrF/r166fVtaVKlUJsgm5P236LmGAmIkpHc+bM0XiQ0tu3bzF8+HDkypULderUQc+ePfHbb79h5MiR6Ny5M4oXL44qVapg06ZNGh+l79evn+LBYiLdu3fXWPIilTa7l1M1b94cVapUkY0lJiaiadOmmDZtmmwJirdv32L8+PGoWbOmbJLB2dlZOLYxKdUonTt3LmbMmCGbhL516xZ++OEHdOvWTeP37fTp0wavMz0lJCRo3EFrCFNTU8Udmhs2bEBcXJxa+y+//KJ4uKQkSZg3bx7y58+P6tWro3fv3vj1118xZswY9OvXD82bN4eTkxP69OmjMen/559/al37t1GjRrI7OD9148YNtGvXDnny5EGrVq0wdOhQ/P777xg6dCi8vb2RN29eeHt74/r164rjTJky5at8tG/MmDGyN76SkpLg5eUl+ztw8uRJeHt7y95IMjExwahRozTOO3HixM+efAA+HCD3/fffK948Gjx4sDB29epVVKtWTfYJiri4OMyePRstWrQQ3gCrWrWq8OmO6dOnK9b+W7t2LVQqlU4vR0dHVKtWTfF3NqM0atRIWE4qPDwc9erVw9GjR9ViwcHBqF+/vjAB3adPH+TLl8+oayUiIqLMy9bWFvPnz8elS5cUz9sAgDt37qDWlP/hwj1/vUrZfSt0P92IiIj05uDggN27d6NevXoaE1bJyckIDAxEYGCgXnM1b94cs2bN0quvo6MjfHx8NJYQyJEjh1aPpH9q5cqV8PDwwPv379ViiYmJGDFiBCZOnAhPT08ULlwYKSkpuHPnDo4fPy6bREw1evRonXdq66Nhw4YwNzeX3VmakpKCYcOGYd68eahevTry5MmDly9f4vr164o7JL90/PhxeHt7G3PZaerAgQN4/fq1bMze3l6vmxxf6t69O2bOnCkbe/v2Lfz8/NTmsbOzw5w5cxRrOAMfvm9nz57V+pC5L7Vu3VrnJPq///6La9euwc/PT/G6iIgI7Nu3T6919enTBz///LNefdNa8eLF8eeff2LkyJFqsRcvXqBmzZqoXbs2KlWqBEmScOnSJQQFBQn/qB8yZAjc3Nw0zrt69WrZ9jt37uDkyZPCEg/Vq1fHDz/8gHXr1snGb968CQ8PD1SoUAGVK1eGg4MDQkND4e/vr/g0iqmpqfDQUABpkgSOjIzEuXPn0K1bN8TFxaFPnz5Gn8MQS5cuRaVKlWQ/70NDQ9GwYUO4ubmhatWqsLCwwLVr13D8+HFh+ZH8+fNj4sSJab1sIiIiyoQqVaqEw4cPo0+fPhr/vbbyyN9YeeRvTOi8DjnsxE9XfquYYCYiSmelS5dGQEAAWrZsiQcPHqTJHO3atcO6desMOtBowIABGhPMXbt2haWlpU7jurq6Yvny5ejcubMwWRQZGYldu3ZpPWaLFi0UdxiKvg76fH1y5syJnj17KtaKDQkJQUhIiOI4BQoUEJZcECUdlQ7dUto9q28/bSklZjt27Kjzz4icihUromrVqjh37pxs/MSJE7KJ7E6dOuHChQvC+uGGqlq1KtauXatzP3Nzc+zatQve3t4ak8z68Pb2xrx584w+rjENHz4cFy5cwKZNm9RikiThxIkTOHHihMZxPD098c8//2g159OnT/WKAR9qKF+5ckVx1/jVq1cVD4L80r///is8HDIsLEyxfIsxrF692qgJZtHBqbocqFqqVCksX74cXbp0Ef5/xMWLF3Hx4kWNY2XLlg07d+7Uuj4+ERERfXuqV6+O3bt3Kz6p+qlx63/AvL7aH/T8rWCJDCKiDFC6dGlcuHABHTt2NOq4VlZWmDp1KrZu3WpwUq9KlSrCR5WBDwkDbetWfaljx47YunUrbGxs9F3eR15eXti2bZtiTdtChQrp1K7J9OnTUbFiRb36qlQqDB48GHfv3hUeOhUcHIyEhAS1dqX15sqVSxgT9bO2ttb4SJg2lHZoGjN51b9/f2FMbkd8qmnTpmHChAk6Jbm04ePjg8OHD8POzk6v/tbW1ti7dy/+97//Kf786kKlUmH48OHYunWr4o2Fr4FKpcLq1asNOgCybt262LVrl9bvNW/evHrFgA9PoPj5+aFq1ao6rVGOiYkJJk+ejOHDhwuvsbe3R7Zs2QyeS4m2h0lqS1QrXdPX9kudO3fGwoULYWam/14YR0dHHDx4EO7u7nqPQURERN+GwoULo3DhwlpfP2BxQwxY3BBtJ7fC8uXL03BlmQcTzEREGcTBwQEbNmzA4cOHUatWLYPGMjU1RefOnREcHKyYsNDVhAkThImvH3/8UWM9aSVt27bFqVOnhLVHNcmePTvmz5+PXbt2aSyNITp8rX79+nrNbWNjg71798LDw0OnfpUrV8bhw4cxa9YsWFlZYfHixShYsKDadQULFpRNmOXPn1/2QEVzc3Pho/3Ah0Py5DRs2NAoic0WLVrItnt6ehq1LnanTp2EyXJNJUX++OMPBAUFoXbt2gavI1++fFi2bBk2btyI7NmzGzSWhYUFpk+fjiNHjgh3smqrWrVqCAwMxNSpUw1KzKUnS0tLbNy4EVOnTtUpmWpiYoJhw4bh0KFDOu1OFR2mV6BAAdSsWVNj/zx58uDEiRMYPXq03jfxypYtiyNHjsiWB/mUpaWlxmsM1a5dO6OOJ/eZaGlpCVdXV53H6tu3L44cOaLX/880atQIly5dMsrvOxEREWV9KpUKGzZs0GvjSO/evb/asnTpSpIkvvjKkFeVKlWktHbjxo00n4PIWM6dOycNGTJEcnFxkQBofJmZmUnVqlWTJk2aJD18+DDN1jV9+nTJ1NT0s7k9PT2lmJgYo81x7NgxycfHR3J2dlZ8zyqVSqpYsaI0Y8YM6e3bt1qPn5ycLDVq1OizsSpUqCBFREQYtO6kpCRpxowZUqlSpYRrtrCwkBo3bixt3rxZSklJURvj2rVrUoECBT5eb29vLwUEBAjnPHr0qGRlZfXZz8H8+fM1rrVv376fratIkSLS7du3DXr/nxo7dqxkYmLycXwnJyfp0qVLRhs/VUBAgGRvb//Zexk3bpxOY5w9e1YaMmSIVK5cOUmlUmn1+2ZnZyc1atRIWrNmjZSQkGD095Xq2LFjUr9+/aT8+fNrtS5HR0fphx9+kA4fPpxma0ovjx8/lgYPHiw5OjoK36+tra3UrVs3KTg4WK85YmJipAYNGnw2Zo4cOaSgoCCdx3ry5In0+++/K/7+p76srKykpk2bSps3b5YSExN1mmflypVS5cqVpWzZsmn1M6HppVKppMKFC0tjxozReS2axMbGSlWqVPlsvr/++sugMRMSEqTFixdL1atXV3xflpaWkpeXl+Tn52ekd6M9/r1JRESUQcJuSdK47Oqvue56DRcZGSktWbJEr7+xvlR1eRup/Kryai/fS0e1WguA89JXkLvT9qX6sGai9Ofu7i6dP38+Tee4efMmypQpk6ZzEKWF8PBwBAcHIyQkBBEREYiNjYWJiQlsbGyQJ08eFC1aFOXLl0/zx6dT3b17F35+foiOjkaFChXQtGlTo5cbAD7c9AwODsadO3fw+vVrvH79GqampsiRIwfy5MmD6tWrI0eOHHqNnZKSgl27duH27dtwcXGBl5eXUUsIXLlyBbdu3cLTp0+RnJyMXLlyIV++fPDw8NB4JzwiIgJbt24F8GEnbu7cuRWvf/jwIXbt2gUzMzM0aNBA68+5w4cP4/z58yhUqBC8vLz0Lu0gcu3aNQQEBCBbtmxo1qwZ8ufPb9TxUz169Ag7d+5EQkICGjdujEqVKuk9VnR0NG7evIlHjx4hLCwMMTExSEhIgLW1NRwcHODo6AgXFxeUL18+TX7mlbx48QI3btzAo0ePEBUVhZiYGKhUKtja2iJ//vwoXbo0ypYta7TyGl+LpKQkXLlyBVeuXMGrV6+QnJwMJycnlCtXDu7u7gYf5ilJEg4ePIhLly7B2dkZbdu2hZOTk0FjPnv2DNeuXUNISAiioqIQHx8PGxsb5MyZEyVLlkTFihVhbW1t0ByZRXx8PHbu3ImnT5/Cw8NDq53h2goPD8f58+cREhKCt2/fwsTEBI6OjihdujTc3NyMUvJHH/x7k4iIKIOE3wbmV1Nvz1kSGCB/dos2Hj9+jNmzZ2PGjBla94mKivrs31fVVnyHWNN7ateNqjgHXSppfpJWpVJdkCQp09T6YoKZMgwTzERERESU2fHvTSIiogySRgnmVN27d8eaNWu0ujY6Ovqzm93fWoI5cxToIyIiIiIiIiIiIkonixYtQlJSErZv3oK4pMSMXs5XjQlmIiIiIiIiIiIiok9YW1tj3bp1OFOoGg6H5sWjsFtYemhcRi/rq8QEMxEREREREREREZEMCzNzONjkhEPR2rAws0JCUlxGL+mrk76n1RARERERERERERFRlsEEMxERERERERERERHphQlmIiIiIiIiIiIiItILE8xEREREREREREREpBcmmImIiIiIiIiIiIhIL0wwExEREREREREREZFemGAmIiIiIiIiIiIiIr0wwUxEREREREREREREemGCmYiIiIiIiIiIiIj0wgQzEREREREREREREemFCWYiIiIiIiIiIiIi0gsTzERERERERERERESkF7OMXgARERERERERERFRZvXgcjhssr3/+N+FIorDJr4oqj1pBQB4mv0u9pVZmFHLS3NMMBMRERERERERERHp6fDKG7A0t/743/XR4rN4/igX/HRmBqRKUnovLV2wRAYRERERERERERGRnoataIWHYbc0Xpf0KB0WkwGYYKYsT5Ky5t0hIiIiIspY/DuTiIgo61Nped20Hf3xPOKh4jUJNwxezleJCWbK0kxMTJCSkpLRyyAiIiKiLCglJQUmJvwnFRERUVbmYBGj9bWr/f9RjJvmMHQ1XyfWYKYszdTUFMnJyTA1Nc3opRARERFRFsO/M4mIiLI+K9MkFAg9itACDZDN0hYJSXHCa0Nf3VMc66Z0HmtvPJONTT432aB1ZiQmmClLs7a2xrt372BhYZHRSyEiIiKiLObdu3ewtrbWfCERERFlai73tsH+bQgqOxWE/7tXwuty58iHYpWd8eBSuGz8YfQtHDrnm1bLzDB8nouyNDs7O0RHR2f0MoiIiIgoC4qKikL27NkzehlERESUxlQAcodfxCTLGFSwshJe18q7GZr1KoP7OW6m3+K+AkwwU5Zma2uL2NhYJCUlZfRSiIiIiCgLSUpKQlxcHGxsbDJ6KURERJRO7E1NsaFQYXR3dJSNR23bhlvlXVEr+G06ryxjMcFMWZqJiQlsbW3x+vVrnvJNREREREYhSRJev34NW1tbHvJHRET0jVGpVChqYalf3yyamuJfQ5Tl5c6dG+/evUN4eDiTzERERERkEEmSEB4ejnfv3iF37twZvRwiIiLKREo8z5p5KR7yR1memZkZChUqhMePH0OSJOTIkQNmZvzRJyIiIiLdJCUl4fXr13j37h0KFSrEvymJiIi+ARYFC2l97Za3b7Hl7Vvg9piPbYWdS+FR+O2P/52jaRXkaFpFw0jXdV1mhuJfRPRNSE0yv3z5Evfv34eVlRWyZ88OGxsbmJqawsTEBCqVKqOXSURERERfCUmSkJKSguTk5P/X3n2H21FVfRz/rvRAEkjoBEKH0BFC7x0BAQUUsACKIoigYn2VoiIiTURUQEWaSBEJCghoAOkldKSDFOmEkJCEhJT1/rEneHNzZmbPOXPK3Pv7PM95lLP37Fm5d2bOnXX2rM3UqVOZPHky06dPZ8iQIUoui4iI9CJDtt2Wt848kznvvVfX9l2TywATbrifWVNHs/Dm+8/z/rAZU7v817l17atd9FeR9Br9+vVj5MiRzJkz58ObhAkTJjB79mzmzJnT7vBEREREpMP06dOHvn37MnjwYEaMGMGCCy6omssiIiKd7u2n4ZErMjo43PgDmPJG+M91D4Ahi6f2HgCM+uRivHXN20x7uz8+x6DBSYqTbv8j/5o5b+J5wLtTPvz/azQ0euspwSy9Tp8+fRg6dChDhw5tdygiIiIiIiIiIlK2vxwS3/fhS3K7DAZGbfu//173yZlwWfGwurryPy+z//DhjQ3SIfT1u4iIiIiIiIiIiEikbZbvx4L9Gxvjx2++wbpPP5XfsQKUYBYREREREREREZFq6dO+wgwLDzKu/8wCjBzaWKmMme48/P77JUXVPiqRISIiIiIiIiIiItUyfHkYPBzen9iW3W8xqh8vf30Iz090ps50+hjMTTdf+sxuTB68MRMmv8Yf/3Vq5jgXTnyH0waPbH7ATaQZzCIiIiIiIiIiIlItffrCdse0NQQzY6URfVhnib6stXhf1kxeW6+5KqsuvR4br7YzQwYtlDnGSgMGtija5tEMZhEREREREREREameDb8Ai64CT10PU96I3+7tp+D1R2HYSFhr7zATOsu0CTD+PJg5LfRdYq3s/s8uDtOhj/XhU1sexR/+eQJzfE7NrmdNeJsDjtiK9Zdf7H9vHvxk/L+lA5i7tzsG6aXGjBnj48ePb3cYIiIiIiIiIiIipfnn+Y/z1N2vf/jfr098kRMu/3zmNldccQX77LMPAGZ2v7uPaWqQJdIMZhEREREREREREZEmWXL4cmy86k7c8/SNqX323Xdfzjp0XAujKo8SzE1kZn2BnYA9gQ2BUcBCwFTgDeAJ4O/A1e5eYB5/85jZqsD+wObAGsBwQo3yt4CXgXHANe5+X9uCFBERERERERERqZD+/fJrLR9xzvYtiKR8WuSvScxsX+AZ4DrgUGB9YFGgP7AwsBqwF3AO8B8zO8XMcgq+NI+ZrWxmVwFPAccDOwIjgQWAwYTk+ObAscC9ZjbOzDZsU7giIiIiIiIiIiKVsdKSOXWbK0wJ5pKZ2UAz+wNwObBC5GaDgW8C95nZGk0LLoWZ7Q08SEh4x9oOuNPMjmhKUCIiIiIiIiIiIhU0aEj/+d77yIpbs87ym7UhmuZTgrlESUmMS4GD6hxiJeAuM1u7tKByJDOtLweG1LF5P+CXZnZ8qUGJiIiIiIiIiIhU1HJrLTLfe/369ucLOx7PV3c/tQ0RNZcSzOX6EcVmAdcyDPirmS3aeDjZzGwt4CIaPw6ONbN9SghJRERERERERESk0pYdPYLN91mZfv3nTbn17dOX1UZ+hK3W3LNNkTWHuXu7Y+gRzGw9YDzQt0azExK55wBPJ302Br4LbJoy5IXufmD5kQZmZsC9wJiULo8CJwJ3AxOBlYHPAkdQ+984EVjZ3d+JjWHMmDE+fvz4ImGLiIiIiIiIiIhUwqyZs5nwylR8zrz511mzZvGTk4/nr9eN5dXX/ltr0/vdPS1n13GUYC6JmY0F0r5++Iq7/7rGNv0IJTX2rrHNHGADd3+orBi77Xsv4KqU5luA3dx9Wo3tPgH8GbAa253h7l+PjUEJZhERERERERER6e2mTZvG+PHjmT59OgA777yzEsy9jZmNAl6gdtL17+6+a8a2w4BngcVqNF/u7p8qJcj59zuOsFBfdzOAVd39pYxtzwYOTdl2SXd/NyYGJZhFRERERERERETmZWaVSjCrBnM59qR2chngpKwN3X0y8KuU5j3MbKFGAqvFzIYDW6U0X5KVXE6cRCj70d1AoCkJcREREREREREREek8SjCXY7eU9ycAd0Rsf1nK+4OAj9UVUbadgX4pbVfnbezuLxDqN9eyb50xiYiIiIiIiIiISMUowVyO9VPev93dZ+dt7O5PAm+mNG9Wd1TpNshouzVyjLR+G5tZrUUARUREREREREREpIdRgrlBZrYUtesnAzxWYKi0vpsWiyjKOinvv+LuEyPHeDTl/SHAWsVDEhERERERERERkapRgrlxozPaniowzjN1jF+vtDHLiBdg9QLjiIiIiIiIiIiISEUpwdy4pTPaXi8wzqsp7w8ys8ULjJPJzAxYMqW5jHgBRhUYR0RERERERERERCpKCebGpSVrIb2uci3vZLQtW2CcPCOAASltnRiviIiIiIiIiIiIdCglmBu3aEbbpALjTM5oW6TAOHlKidfdpwBzUprLjFdEREREREREREQ6VL92B9ADDMpo+6DAODPq3EdRZcU7t3+t8VL3YWZfAr6U/OcMMyuyEKKIdL5FgbfbHYSIlErntUjPo/NapGfROS3S86zW7gCKUIK5cQMz2rKSxt3NzGgbXGCcPGXFCyHmWsnk1Hjd/VzgXAAzG+/uYwruU0Q6mM5rkZ5H57VIz6PzWqRn0Tkt0vOY2fh2x1CESmQ0Lithm1ZCoharcx9FlRUvpMdcZrwiIiIiIiIiIiLSoZRgblxWUrZvgXGyZpPPLjBOnrLihfSYy4xXREREREREREREOpQSzI2bntE2oMA4WX2L1kbOUla8AP1T3o+N99yC+xORzqfzWqTn0Xkt0vPovBbpWXROi/Q8lTqvlWBu3LSMtqEFxhmS0TalwDh5SonXzAaTPuM5Kt6kHrOI9CA6r0V6Hp3XIj2PzmuRnkXntEjPU7XzWgnmxr2Z0bZwgXGy+k4sME6eqsUrIiIiIiIiIiIiHSqr7q/EeTWjbYkC42T1faPAOJncfYKZzaD2QnwdF6+IiIj0fGbWH/gEsALwPPBXd88q6yUiIiIiIh1CCebGPZfRNqrAOMulvD8b+G+BcWI8D6xe4/0y4gV4oVA0IiIiEs3M+gI7AXsCGxI+vxcCphK+5H0C+Dtwtbt3/Je+ZjYc+AewQZe3nzazraoQv4iIiIhIb6cEc+MeAWZR+2dZK4mbZnTK+0+7+8zCUWV7gNqxrWZmHyXihjUjXoB/lxptBjNbFdgf2BxYAxgOGPAW8DIwDrjG3e9rVUwinaSKiSgz2wDYGdgWWBZYlFCWZzqhBM9/CNexccD1TbhGinQsM9sX+Blhpm93Cyev1YC9gDPM7FfAie7ekeWrzKwfMJZ5k8sAqwLHA4e1OCSRlqviZzWAma0C7A1sTbg3WAwYRIj7ZcJ90vXAn909ax0YkR6naue1mQ0Edif8Db4e4e+MYcAc4B1gAvAgcAfh/jrrSW6RXsfMzgA+T7hfPc3dz2xvREErc2bm7o2O0euZ2f3A+jWa7nf3MRHbL0y4aFuN5t+4++GNRTjf/r4KNHKwv0+YVb1Kjbb3gBHuPquB8XOZ2crAKYQb6Bg3Ad9Voll6k5xEVHfvA21NRJnZHsBx1L6epnkb+AlwVrOvOyLtlNz4nQ0cVMfmzwF7uPvjpQZVAjP7OfC1lOYX3X351kUj0npV+6wGMLM1gZOBj1L7/qW7SUn/U939g2bGJtIJqnReJ39fHAV8k/AlUYyZwBXA8e7+TLNiE6kKM/smIT/V1S7ufkM74oH25MyUYC6Bmf0IOKZG0xxgGXd/LWf7TwMXpzTv6+5/bjDE7vtbkezSHo241t13b9LYAJjZ3sD5wJCCm84Cvu7uZ5UelEgHqVoiysyGEs7pTzQwzHhg906YASJStmQW1J+J/wOxlsnAFu7+aClBlcDM9gKuyugy090HtCgckZaq2mf1XGZ2FCFZXM+5+TAh7pfKjUqkM1TtvE4SUFcC69Q5xDTga+7+2/KiEqkWM9sEuI35qxr8yd0PaENIbcuZ9alnI5nPlSnv9wG+GLH9oSnvTwIyv/Ews35mtreZfc/M9ks+1DK5+/PAQxFx1ePSJo0LfPht8OUUP1EgnPC/NLPjSw1KpIMkiahLqe8PW4CVgLvMbO3SgsqQ1F69m8aSywBjCHEv3nhUIh3nRzSWXIbwmOtfzWzRxsNpnJktB5yX061/K2IRabWqfVbPZWY/A86gvuQywLrAbWZWZGFxkUqo2nmdTDq7lfqTywALAOcmT0iL9DpmtiRhEkitkrkxTzCUrp05MyWYS+DuDwM3pzQfndxE1ZT88rdMaT7H3d/L2HY4cBfhgD4R+BPwoJktExH2zyP6FPVf4LImjAuAma0FXETjx+2xZrZPCSGJdKLKJKKS2qtXEmpBlWEF4HIz02eb9Bhmth7wnZRmBy4k1FRbDFiSUOvxrpT+ywOnlRthccm5/ydCDTiR3qgyn9VzJQmkb5cw1CjCrCqRnqYy53WSDL8CWKqkIX9hZpuXNJZIJZjZAoQn8UamdBncwnCA9ufMdBNenmMJN3rdDQOuN7P5FsVLHg09P2W8qcAvcvZ5BmHWXlerE2428+QtxHcX89+w3p+zzanNWmzLzAz4A5A2Q/tRQuHyFQgLHI0h/Pxm1xqO8E3riPIjFWmfCiaiDiUs5JfmJmAfwnk9nDDD4hjgzYxttiZcC0R6iuOBviltR7j7ge5+p7u/7e5vuPtfga1If7rqM8m1op1+Amza5hhE2qKCn9Vzay6fmtHlTuBThOTx4oT4zyU8alvLLmaWNsFGpHIqeF4fTPqaJ3OA3xH+llg0eW2cxDQ9ZRsjP3ch0mOY2RDCIrabtDuWuTohZ6YazCVKVmpPW5BvFiFZ8gThsbJNgI9kDHeku/8yY18DCSU00g6e5bLqm5nZWMIHW5YnCY/NTCM8srMjYWXoNBu4+wM5Y9Ylp07jLcButVanNrNPEGZ411qA5Ax3/3pZMYq0W855/RV3/3WNbfoRHufbu8Y2cwjn9UNlxdhlv4OBFwg3orUc7e6np2y7OOF6sFnKtk+6++oNBynSZmY2inCe1PoM+7u775qx7TDgWWov2HO5u3+qlCALMrNdgOuIWxgMd4/qJ1IVVfqs7rL/ccB2Kc3fAk7zGjeVZrYjcDW1Z3Gd7u5HlxelSPtU7bw2sweonYuYCXzC3a9J2W5Nwr132gzrdTpprQeRZkgqBlzF/JM9u3vY3ddrfkRBJ+TMlGAuUZL0/QfpJS9iXeLun87Z10hCSYo0m7v7nSnbZt2wNqJpN6wZf9jOAFbNSaafTe061zOAJd393VKCFGmjqiWizGxPYGxK88/c/bs52y9O+BY2LUG9hrs/UX+EIu2XPJJ+Zkrz1u5+a872xxFmQHc3nfD5N6mxCIsxs6UJa0B0v9a8S5hJMR8lmKUnqdpndbLfrYB/pTQf4+4n5Gz/LcKigN2d7+4HNxqfSLtV7bw2s0WAt1OaT3T37+dsvxfpSazUCSIiPYGZfZZQSSBmZm+rE8xtz5mpREaJ3H0GsAfpf4TFuILwyEqeCYRvGNO8ltG2J+UnlwH2MLOFyh40qTW9VUrzJRErUZ9E7fIlAwmP84n0BFnn9UlZG7r7ZOBXKc1NOa+BnVPefws4Lm9jd38T+E1GFz16Kz3BbinvTwDuiNg+bV2EQcDH6oqoTklt9D8y/030LOCwVsYi0kZV+6yG9KcznyEn5sTPgfE13v9b3RGJdJaqndcrZbSdH7H91cAbKW3LFo5GpALMbCszu4NQ7qbjSq12Ss5MCeaSJZn9HYFTSK87VssMQt2m/dz9g4j9TCf9D7P73P0/GZun3bDOydtvjmbdsO5M7VU5IXzAZXL3F4B7U5r3rTMmkU5TtURU2qq6FyVf1sW4KaNt6YLxiHSitPqIt7t7rXpp83D3J0mvWZ5WYqZZjgW2SXn/7taGItI2lfqsNrMFSX/s/9funnuvk/TZgVDn8VngMeBwd/9LaYGKtFelzmvS13UAeDFv46QczgspzS1f1EykmcxsezO7mTCJtNV/OxfRETkzJZibwN1nuvu3gXWB8wgL9qWZQPjWclV3P9ndiyR5Dwce6fbei0BmeQ3Sb1ivI6wO/WzGtrMJZUDeTWlvxkm3QUZb5uPBEf02TlbRFam6qiWias30cMKiQLGyFvvTH7hSaWa2FLUfmYWQoImV1rdli+yZ2TaEBTq7Gwf8rFVxiHSAqn1W70T6+ivXxg7i7pPc/Wvuvoq7r+3uWU8giVRN1c7rVzPalogcI63fKwVjEelIZraQmV0D/JPaEyTmepL03FgrdUTOLC3DLSVw98eBL5jZ4YQC4GsSptM7oe7RI8CDMd/+p4z/hpltQCjLMRp4GbiyVuHuuXJuWB9291OAU8xsZWAjwiq3g4HJhMTzve4+IaO+SzNuWNdJef8Vd58YOUbaYgNDgLWAhwtHJdIhSk5Eteq8voX5y2Rc7u5PFRgja5byhMIRiXSW0RltRc6TZ6h9XmeNXxozWwy4hPknNbwFfNbd54RFr0V6top+Vqclt95192easD+RSqniee3uL5rZy9QuZ/FZ4MSs7c1sU2D5lOZbGgpOpHOcRPrTCXNdRCjz9m9S1hJpoY7ImSnB3ALJ4953EPeITNGxZwFFHjGLumF192fJnsncyhvWtDGL3mCnWR0lmKXaqpiI+jnhg2p/wmzmccARBcfYMaNNK1hL1WV9gfJ6gXHSZioNMrPFk3rmTWEhc3whsFS3JgcOcves9SJEepoqflavlxHDh8xsFeALwPbAKsAChBmZLwHXE75AfrIJ8Ym0WxXPawhPDP64xvvHmNm97v7PWhsli/WenzLmI8Dt5YQn0nZrZ7S9Qyj1dBlAh0yU6IicmUpk9D4tuWEtME6m5OZ0yZTmMuIFGFVgHJFOVKnzGsIXb+7+GcI3ogu5+47unrai9XzMbDBhlkUt04DbSghTpJ3SPvsguzxMd+9ktDV7MZ5vA7vUeP8X7n5dk/ct0mkq91lNKPdXy4sQPovN7JeER4S/Q3hicyGgPzCSMPvyh8DjZnZeE+ITabcqntcApwGP19ofcL2ZnWNmW5rZImY2zMzWMbPvEyZwrFpju5nAl5P6zCI9Qdr6IJcCa8xNLneCTsqZKcHc+1TthnUEMCClrRPjFWmHqp3XH3L39939vTo2/Rbh5rWWy9x9SgNhiXSCRTPaJhUYZ3JG2yIFxikkeYT2hBpNDxASUSK9TaU+q81sCdIf/Z9qZosQvsw9gvx7SgMOBh4wszXKilGkA1TqvJ7L3d8nPP5f64nlvsCXCPVY3yb8zfEw4TN9RI3+Mwklr+4qO06RNvohYe0xCOuQXQds5u77u/sb7Qurpo7JmSnB3PtU7Ya1lHiTZFPaAopNu8EWaZGqndcNMbOtgB+kNE+n9iN/IlWTtrAWwAcFxplR5z7qZmbDCTM8updimwLs7+5F4hfpKar2WZ2VODPgRrIXFaplJHCbmbWkBrxIC1TtvP6Qu78AbAhcQChdVY/HgM07aTanSBnc/T1334nwubWou+/WwV+idEzOTAnm3qdqN6xlxZvVvyk32CItVLXzum5mtg5wJeHx21p+6O7/aWFIIs0yMKMt61ztbmZG2+AC4xRxHrUfpTvC3Z9u0j5FOl3VPquHZbQdAKxf57gjgMvMrCP+rhBpUNXO63m4+7vAT6mvdvJk4Afufl+pQYl0EHd/NTlPOlnH5MyUYO59qnbDWla8kB5zs26wRVqlaud1XZJH7m8h/Vvam4CTWxaQSHNlnddpswtqyVp5JGsfdTGzI4G9ajRd4u4XlL0/kQqp2md1VoK569MJc4A/ERb4W5Lw5dIehM/kNOsQarSLVF3VzusPmdlAMzsZ+DewZR1DDAPGmtkdZqaSkyLt0zE5MyWYe5+q3bCWFS+kx1z6DbZIi1XtvC7MzA4jJJeHp3R5Cvikuxe9Loh0qqxjuW+BcbqXqehqdoFxcpnZ+sApNZqeA75c5r5EKqhqn9VZCea5JgG7ufsB7n6Tu7/h7i+7+9/cfXvSy1kBfDVZsFekyqp2XoedmQ0FriesaVLkb4paNiPUV6/3qQYRaUzH5MyUYO59qnbDWla8kB5zqTfYIm1QtfM6mpktaGaXAL8mffGCR4Bt3H1C6yITabrpGW1p50LRvqXVQk5uVi+rsb+ZhLrL9SzmKdKTVO2zOiamg9z9+rRGd/8JcElK86LAJ+sJTKSDVO28nusiYJuUtunA6YT6zMOS19rAMcCrKdssCvzNzJYuN0wRidAxOTMlmHufSt2wUl68kF6zVYsNSdVV7byOYmbLAHcC+2d0ux7Ywt1fb01UIi0zLaNtaIFxhmS0TSkwTp6zgZVrvP991WcUAar3Wf1+Tvt17j42Ypxvkn5jumOhiEQ6T9XOa8zsk8CeKc2vAhu7+9HuPj5Z6Ow9d3/M3U8gJJrTvlRaGpWqE2mHjsmZKcHc+1TthrWUeJNH8NK+vSkzXpF2qNp5ncvM1gbuJtRpTHMmsLtmRkoP9WZG28IFxsnqO7HAOKnM7OOERb+6uxk4y8wGZb3IeOwu6aO/V6UnqNpndVa8AOfHDOLurwF3pDRvXSQgkQ5UtfMa4Dsp788B9nL3R9I2dPd3gH0Jpelq2d/MVmkwPhEppmNyZvqDvfepzA1romrxirRDjzpPzGwz4FZgZEqXD4AvuftR7q4SN9JTpT2GCrBEgXGy+r5RYJwsaXVWtyX80ft+zuvJjLHfB2aa2Vtmdq2Z7VRSzCKtVrXP6rwng+4vMFZagnlpfYEkFVep89rMlgfSaiVfFfPEkbtPAU5Iae5DWORTRFqnY65D+kDvfap0w0pSUzVt5cuOi1ekTSp1Xmcxsx2AG0n/gHsT2N7df9uKeETa6LmMtlEFxlku5f3ZwH8LjJNlzZLGSdOHUN9xV0KNx0WbvD+RZqjaZ/VLOe1Z/57u0uLqAyxSYByRTlO183qjjLZrCozzZ9JL3+jJBJEW6qScmRLMvU+Vbljnej7l/TLiBXihwDginaiK5/V8zOxjhD9uF0zp8jCwobvf3uxYRDrAI8CslLbVC4wzOuX9p919ZrGQUj1T0jgxBgArtnB/ImWp1Gd1csOaNWMpa1Gy7rLqOet+VKqsUuc1sExGW9a/ZR7uPh14LaVZC/2JtF5H5Mz0gd77VOmGda4HUt5fzcwscoy0eAH+XTAekU5TxfN6Hma2J3Al6bVYrwU2d/e8GVUiPUJy85ZWB3HjmDHMbGFgtZTmW4pHlepYslewLtN7wBMt2pdImar4WX1vRluRJNKIjLZ3C4wj0mmqdl4vkNGW9u9Ik7aw2EIFxxGRxnVEzkwJ5l6mYjesc92T8v4Q4h/LTfu3vUf6yShSCRU9r7vu+2PA5aSvWvt7YE93n9rMOEQ60LUp73/EzJaK2H43IO2PypvqC2l+7n4VsAMh3rcpP9k8h1Ae5+/ATlrYU6qoop/Vd2a0RcWcWDvl/RfdPe2xXpGOV8HzOmuhrqzZzbWkrZWiz2iR1uuInJkSzL1TJW5Yu0iLd24smZIV6ndIab7V3Yt+WyvSiap2XgNgZrsT6rgNSOnya+CLWsxPeqkrU97vA3wxYvtDU96fBNyQtaGZ9TOzvc3se2a2n5mlPV0AgLvf7O67u/ti7t7X3S32BayQMa4l4y3h7ru6+925/2qRzlW1z+qxGW37xwxgZv1J/zs8d0ExkQqo0nn9Qkbb9rGDmNkWwOCU5uhSGyJSmo7ImSnB3DtV5oYVwN2fBx5KaT7EzPrmDHEAMDSl7dK8/YtURKXO62S7dYErSE8u/xk4wt09ZjyRnsbdHwZuTmk+2sxSa6WZ2b7AlinN52TNAjaz4cBdhHPwROBPwINmVnR2k4jMq1Kf1e7+COklaXYxs03yxgA+Byye0jY2YnuRTlel8/peIO3v6v3NbLGc7ec6MqNNXwSLNKDSOTN316sXvgjfhnqN1yRguYzt9k3ZzoGf5exzOGGmQtdtHgeWiYj3cxn7/VrOPv+bst3LQP92/y700qusVwXP6+7bdX09CSzY7p+pXnq1+wVsQSgRUes8eQIYXWObvYCpKdtMAZbO2ecFKdve1KR/4/Jp14J2//z10qvsVwU/qw/J2O9TwKIZ264ITEjZ9k1gcLt/H3rpVcarSuc1ofRN2j6vJ+f+GDgoY/s5wPLt/n3opVcrX4QnA2qdDw/VMValc2aawdx7HUs4YLobBlxvZvMV+DazvYDzU8abCvwiZ59nAGO6vbc6cGHOdgCXkD6D4hQzO6T7m2a2LKFeY1p9qFO9yQuXibRYZc5rM9u1xnZdrQZMMTMv8JpmZk+Y2XFmlrWIiUhluPvtwG9SmkcDj5rZDWZ2hpn92sweAK4ifSGf77n7q2n7S2ZJfCqleVszK7IatYjMrzKf1YkLSX/kfVXgFjNbr3tD8gj9zaQv8Heyu78fsX+RKqjSeZ017s7AzSnxDjOznwDnZWz/Z3d/IWf/IpLuDCqcM7MkYy29kJn9Cjg8pXkW4ZvYJwiPr28CfCRjuCPd/ZcZ+xpI+AY3bXr/cu7+Uk68mwC3A2nT+58EbgWmASsBOwKDUvreB2zqqusqPUxVzmszu5T0JFYZLnH3TzdxfJGWSc61f5Be8iJW7nlhZiMJsxjSbO7uWQt/FZYkrV+s0eTurskQ0uNU5bO6yxjbJDGl1Yl14A7CYmcGbABslDHkQ8CGrnVQpAepynltZn0Iiwfm/U1xN/Bvwr31coQazQtm9J8CrOvhUX2RXsPMXiCcI9095O5Z53n3cSqfM1OCuRer4g2rmR0O/KpgfN29AWyUd3KKVFFVzmsze5zwbWyzzAKGeljdW6TyklXmxwJb1znEFcBn3P2DnP0MAiYD/VO6rOju/6kzhrR9DgTeZ/7k1avunjajQqSyqvJZ3W2c7wI/LRhfLW8CG2uWo/Q0VTqvky927wKWri/E+cwBDnD3y0oaT6QyzOxZQrK2uzvcfYsC41Q+Z6ZZIb2Yu88A9gD+1cAwVwAHR/SbAGRNrX8tZmfu/mvgm4QPsXq8Auyg5LL0VBU6r1Mf0S9JX6Bfk/ch0jLu/i5hlsEphC9QYs0AvgPsl5dcTvYzHfhbSvN9ZSeXk33OoPbCJPeVvS+RTlChz+oPuftJwAkxfTO8BGyj5LL0RFU6r5N74e2AZ+NDSzUdOFDJZenFXkl5v+hs/srnzJRg7uWqeMPq7qcBuxMKjhfxd8LjeI8V3E6kUipyXp9I/R96MW509ylNHF+k5dx9prt/G1iXUANxakb3CYTZC6u6+8nuXuR8O5zwqHtXLwLNLDvzDeb997xDqGkp0iNV5LO6+1jHAPsQeZPbzaXA+u6eVh9SpPKqdF67+1OEWq/nUv/f5HcBm7j7xXVuL9IT3JLy/nVFBukJOTOVyJAPmdkawNGEuqhp9ZUmEP5APLnoNxpmtgRwI7BOl7dfBHZ092fqiHcB4CvAl4CVU7rNJtS7Ot3dry+6D5Gq6+Tz2sw2A35MSJYtUmS/Gd4CxgFHufubJY0p0pGSx3HHAGsSFtJy4G1CcvjBRuqbmlk/wkys0YQ/Tq9092kNB529zxWBXQizN65x93qSWCKV08mf1SnjLQgcBhwIrJXRdQpwDXCau48vuh+RKqvSeW1mqxLO6Y9Tu5ZsV1OS/Z5LmNChhJL0amY2jJBk7lpveRywc9E1v6qeM1OCWeZTxRtWM1uZsJjIksBgQv3IZ4F73X1Co+OLVF0Vz2sREZHepIqf1Wa2DGFRv2WBYYQviCYAjxMWONJaCNKrVe28NrMVklhHEc7pfoQnjN4gLBD2qLtnPcYv0uuY2QDCjOFVgOeAq4oml7uMVdmcmRLMIiIiIiIiIiIiIlIX1WAWERERERERERERkboowSwiIiIiIiIiIiIidVGCWURERERERERERETqogSziIiIiIiIiIiIiNRFCWYRERERERERERERqYsSzCIiIiIiIiIiIiJSFyWYRURERERERERERKQuSjCLiIiIiIiIiIiISF2UYBYRERERERERERGRuijBLCIiIiIiIiIiIiJ1UYJZREREREREREREROqiBLOIiIiIiIiIiIiI1EUJZhERERERERERERGpixLMIiIiItIUZraRmd1gZpPNbJqZ3WNm+9YxzoJm9mMze8bMZpjZq2Z2npmt0Iy4pfcxs8+Y2f1m9r6ZTTSza8zsI22KpY+ZHWVmj5rZdDObYGZ/MbN12hFPs5nZrmZ2V3KNmJRcMzZrd1xV1tuOIRERaT9z93bHICIiIh3OzAYDBwN7AWsDw4GBdQw1G5gJfABMAt4BXgWeAx4D7nD3x0oIWdrMzDYBbgYG1Wg+zN3Pjhxn0WSctWo0vwfs4O731h1ohzOzQcBuwMqE8+U6d3+lvVH1LGZ2FHBGjaZpwFbufn+L4zmPcL3t7n1gZ3e/rZXxNJOZ7QNcDli3ppnAR919XOujqr7edAyJiEhnUIJZREREMpnZ0sA4YHSLdvkCcBHwS3d/q0X7lJKZ2T3ARinN7wFLu/uUiHH+CByQ0eUxd1+7jhA7npltDVwGLNHl7RnA8e5+Unui6lnMbATwX2BwSpeb3X27FsazHeF6m+YZYDXvATdxZtYPeBlYMqXLk+6+egtD6hF60zEkIiKdQyUyREREJM/vaV1yGWB54BjgBTP7tpn1beG+pQRmtizpyWWAocA2EeMMBPbO6baWmS0fHVxFJOU//sq8yWUITw781MwOa31UPdJupCeXAbYxs2GtCobsL1MAViH73KqSzUlPLgOMNrM1WhVMD9KbjiEREekQSjCLiIhIqiTJtUubdr8A8DPghmSWoVRHzIziVSP6LEJcKZbFI/pUzdeBrMTmMfrypRR5x6oRypO0yrol9amCWmVvumvll5s9RW86hkREpEMowSwiIiJZNm13AMD2wDglmStleESfIRF93gFmRfR7PaJP1WyT074USr6VoaxjtSwx8cT0qYJO+9n3FL3pGBIRkQ6hBLOIiIhkWbrdASTWAy7TjM3K6B/RJ/fvUHefDtyQ0+0Bd38pKqpqGRnRZ5mmR9HzlXKslqhfRJ+YmKsg5t+q+9XietMxJCIiHUIf2CIiIpJlwXYH0MUOwFHtDkJa7kjg1ZS2ScAhLYyllWLOvVbWBhYRERERqUkJZhEREcli7Q6gm+NVKqN3cffnCTPYzwReJJTMeAO4AFjP3R9sX3RNFTPDcEDToxARERERyaEEs4iIiJThAne3tBdhobaFgZUItWUPBy4GJhbcz9BkW+lF3P0tdz/K3Zd39/7uvqS7H+TuL7Q7tiaK+Tu9074AEhEREZFeSAlmERERaTp3/8DdJ7n78+7+L3f/jbt/llDj+avA1ALDfa45UYqIiIiIiEhRSjCLiIhI27j7dHc/C9gJmBm52SpmNrqJYYmIiIiIiEgkJZhFRESk7dz9TuAnBTbZvFmxiIiIiIiISDwlmEVERKRTnAG8H9l39SbGIR3GzBYys9PM7EUz+yD5359rwUcRERERkfbr1+4ARERERADcfZKZ3QHsENF9VMyYZrYVcCywIdAfeBI4y93Pq9F3CPAFYE9gbcKihNOAl4H7gL8C17h7bCmPtJiGANsRZmGvC6wALA4skHSZDLwLPAM8CtwJ3Ojuscn3RmLrC2wP7AiMAVYGRhAWaZxGWJTxWeAh4Ebgn+4+u8kxDQFuB9bq8vYo4GvArma2mbtPKHmfw4FtgXWANQm1wocRFpmcBbwHvEL4Hd0PjHP318qMoROY2dKE83FTwpc6ywPDgQWBDwjH6tvAE4RjdRxwl7vPaUFsQ4Hd+d/vaQVgIaAvoab7G4Tfz13A3939gWbHVDXJz3Bzwu93TWA5wrE+BBhMmIz0PjAdmAC8CrxEOP/vA+5x91ktD5wPr1XbArsA6xOuVcMJcb9HuFY9QTg/r3H3e1sYW8de40VERJpFCWYRERHpJE8Rl2AemtfBzHYErmPev3c+AvzezIa5+xld+o4B/kxIsHQ1jJB4WRM4CHjFzE4Ezi6aRDOzLYEjgd0ISZA0iyavlYGPJu9NNbOxwM/c/dEi+42MbQHgCODrwJIp3YYmr1GE5Mk3CD+P04BfufsHZceV+D7zJpe7WhU4FTi40Z2YWT9gP8LveWvy/05er8v/dzO7C/gVcEXRLyHMbBDw7WT/KxIS+jEuMrOL5sZASADeB/zA3f9dJIYusfQD9gUOIyTI0p54HJy8liCcH/sAPwReN7MLgNPd/c16YsiJbxng/4AD+V/CrruFkteqhPPtBDN7APihu/+17Jiqxsw+ChwC7AoMyuk+JHktCqyWvHdg8r8TzOwvwGnu/lQzYu3OzPoAXwS+Q0jc1rJw8lqB8G88xsyeBX4KXNCsL8U6+RovIiLSbCqRISIiIp3k3ch+/SP6/JL0JOHxZjYQwMzWB25i/uRyLSMJScQbzCwrgfAhM1vHzG4BbiUk4aK262ZB4NPAw2Z2qZktXscYafFtC/wb+BnpyeU0I4HTgUeSJH0z7J/Tvt/c32W9zOxzwNPARYQZ3EUnYRiwGfBH4EUz+4qZWYHtxxKSs6sTn1yuFcNIYC/gPjNbu/AAZnsRfg6XAFtS373CkoTk3wtm9kMzG1DHGGnxfYXwFMJhpCeX06wPXG1m15rZEmXFVCVmtrWZPUj44u0T5CeX8yxCSPb+28zObPQ8zGNmqxO+QDmb9ORympWB3wMPmVnaF1b1xtXR13gREZFWUIJZREREOsmwyH6ZjxKb2br8b7ZdLQsBm5vZYsDfiJgR3c0OwM9zYjAz+w7hEe2tC46fOizwKeBxM9uj4cHMjgT+QSh90IjVgDvN7JBGY+rKzIaRn/gfBKxS5/hLmdk1wAUUT1ilWQo4C7jNzLKOwbkxbA7sXNK+5xoMHBXb2cyGmtmfgKso7+cwmFCeZnzMzyGLmQ0ws4sJP9cFG4xrV0ISb6MGx6kMM+tnZmcBtzDvzPuy9AW+CvwrKQ9ROjPbj3AtXb/BodYC7jWzT5YQU8df40VERFpFCWYRERHpJGtE9surubtexBirAecRao7W4+CknMB8kvqgFwIn0ZySZIsAVyUJ4rqY2dHALwjJoTL0B35rZseVNB6ELwLK7PchMxsN3EN4nL0ZNgfuSeqAZ4mZOV+PZWM6JTMlbyOU52iGtQlfPmxZz8bJOXYpYXZnWZYAbjGzXUocsyOZWX/Cl2hfacHuNgYuL3vQJIn7J+qbGVzLYOASM9u3gZg6/hovIiLSSkowi4iISEcws0WALSK7/yenPabUw+cJi4TVawChjmYtfwA+08DYMfoAv6hn1nBSCuGU0iMKjifUIS1DbPK7SDkKzGxNwsKBUUnYBixEKKeSNUP5iSbt++m8DskM8ZsIC5E10wjgOjNbr45tTwE+Xm44QEgyjgV6+kzmswgL4bXKR80sr6xNEV8lJHHL1pdQw7zeY7+jr/EiIiKtpgSziIiIdIpjiK8/+3BOe0xt0UZrBr8DvNX9TTP7NvDZgmPNTsaaVkccvzGz2MQ8ZrYUcD4Fk7KEReTeJcwe95y+Hyk4dsuY2QjgasIMwVYYRJgtOapWo7s/CFxW8j5fISx+mOdPhAX6ivgAeDP53yKGANcmXyRFSRaj+1rB/cD/zqf3cvoNJNS97pHMbBvgS3VsOo3w83uL4r9nCNfysuSVxHBgIuF6nHdd6m4gcGmyyGa0Tr/Gi4iItIMSzCIiItJ2yQyt2Fmvc4A78oZsLKIoJ7n77Hl2GhZW+3Hk9pOSvusDA9x9cXdfkDDb8xPAtZHj9AN+V2CBrVMpVlLiduCTwCLuPtzdFyXUyt6dsFhY1fweWCmy77vAbwgL/40iJKQWAjYAjgaeihxnBOFx+jQHAAcDNwDPEBLEMSYmfV8EHiXMyD0SWMvdX8za0MwOJdQjjvEcocTC8u4+0N2XcPeBhNrdXwQeixxnaeC0mI7J8fyryHEhXBf+CGwLLJicT8MINbG/UCDGnuT7kf0mE2rKb0c4z+f+/BZPfs/LEOoC511351rdzDYsHm4hYwnlbRZy9xHuvgjh3PwYoa58rNHAEbGdK3KNFxERaTklmEVERKRtzGyUmZ0P/Jb4pPBt7j7fzOESPEJIoixJKH+xNCGxeAJhEae5s+NmAMe5e60SE79Mts0zDljZ3Y919wfdfc7cBnef6O5XufvuhMXf3okYbzXCo+SZzGx1IPbx9Q+AL7r7lu5+hbtP7BLjFHe/1t13IySaJ6aO0kGSGbF7RXYfC6zu7oe7+03u/rK7f+Duk939AXc/nbBg2DGE5Gaerc1su1oN7j7H3c93913cfVV3XyYyxiPdfRl3X97d13H3j7v7L9393ayNzGw48WUHTiX8HH7dPWnt7i+6++8IJTa+RdzP4cDI5OPBxC84+Cqwhbt/xt1vcfcZXWJ83d3PI9Rl/7/IGCsvmSle83jr5g7CFxLfcPeb3X2+6427v+LulwNbAmdHhrBjfLSFvAFslxzr17n7h7PU3f09d7/G3XcizDCekTrKvL5nZrELvXb0NV5ERKRdlGAWERGRpjOzAWY23MxWM7Ndzex7ZjaOUEv5wILDnduEEK8FNnT3y939DXef6e6vJYnFY9x9DGFhsM2Apdz9R90HMLPtga0j9nUjsIu7v53X0d1vTMacGjHuNyJmuB1JXCJ/NrBXkjzM5O7XEha0y1t4sa3MzIDTI7v/AfiEu7+e1cndZ7n7CYQZyLOz+ibKrE3biG8AC0f0O87dv+XuM7M6JQnyUwkzhWN8N6LPUZFjvQZs5u53ZXVy99nu/lPCYoG9Icm8Dvn3ejcA27j7yzEDursTjp1XI7pvHjNmQa8Bm7r7zXkd3f1iYE/izssRRCxyWZFrvIiISFsowSwiIiJlONDMPO1FmEn2DvAkIZl7ImF2XdG/RZ6i/Hq1rwGfdvfMWqPu/pa739V1Jm83MbPL3gQOcPdZscG5+2ORYy9FeDy8JjMbQJihHeM4d/97ZF/c/QlC8rRoDdRW2pnwOHyem4FDkmRaFHe/jLhawWvFjtksyXFwaETXfxJfCgAAdz+f7FIgc+1pZkukNZrZGOJ+Vw58Kq8cyDwbuF9KuP70dENy2p8G9ityLQJw9/cJtbvzrFpk3AhzgL3dPW+B1w+5+w2EJ1BiHBzRp6Ov8SIiIu2kBLOIiIhUhQNf9m51j0twsrtPamQAM1uMUCoiZl/1zPS9kJAQyrNvRttmwPCIMZ4DTo4Jqit3/wdwcdHtWuiwiD4zCMdY4Rmu7n4W+Ym3mFmKzbYbsFhEv+8WSbJ3cTyQl1zrS6hBmybmXAK4yN1vi+zb1Y8Ix3lP9nhG22zgM3mlVDJkzhZPLJs8NVCWs/Nmqaf4KXEzrjfJWoCyItd4ERGRtlGCWURERKriGHe/peQx51BOUnRXQtIsyweEWtOFJUn1CyK6bpnRtm3k7k7LK4mQ4Ud04CxmMxsM7BTR9WJ3j0nypDmcUPYlzVUNjF2WPSL63OPu99czeDLDNLeEAeUcq7F1pOeRHN89ehazuz8HpJW4+Ym739fA8DHJ+YHkz6KOVffvK6nHHbNYpAHbZLRX4RovIiLSNkowi4iISBWc7O4/acK4D8XUyYywQ0Sfm919cgP7iClZsVRG6YGPRGw/E7g0PqR5ufuzwL/q3b6JtgEGRfQ7r5GdJDNCPw68W6P5KuCcRsYvScyxOrbBfcQcq1nH43oR249PSrPU6zJgSgPbV8GXCQsbzq2xPJVwjM9XQ76g2OvYAg3uZ64b3P2VBraPvaZtnNFWhWu8iIhI2yjBLCIiIp1sGqEe7neaNP4tJY0zJqJPPY93d/Uo+aUHICxGWMsaEdvenVFjOtbfGty+GWIWHHvL3e9sdEfu/jCwCWE24j2En8fnCPVjC9W7LVuSmFomomujx+qDEX1qHqdmtgwwLGL76Brhtbj7VOJmWlfW3IUN3X0U0N/dh7j7F+opM2RmI83ss2Z2MXB35GYxX+rE+GsjG7v788DzEV1XzmirwjVeRESkbfq1OwARERGRGuYAfwa+XWQBrzpk1SmNYmb9gFWavS93n2VmLwEr5nRNeyx9ZMRu7ikWVU23lzBG2VaP6FNXSYha3P0p4KCyxitRzJcM0Ph5EVNCoZHjFMo7VnvFomlFv9wws1WBTQlfzmxD3DVuvmHq2KaWO0oYYzz5186Var1ZoWu8iIhI2yjBLCIiIp3kReAK4Jyk3EKzPVPCGCPJr80JcHm5a16lerf7G2Y2lLjZhGX8PMoYo2yrRfR5uOlRtN+oyH5vtuBYfTfl/cUjt++px2rLmdnShBm6Y4CNgA2BEW0N6n/mUM7vKas2+lxpi/x1/DVeRESk3ZRgFhERkVaaDcwg1D59E3gFeAp4BLg9mfnZSjFJhzxpSYl2mEbtR8EXjNy+kTqnALj7RDObRnn1V8sQ8zsqoxZ3p+ukY/XRlPdbdqwC/y1hjEoxs76EEi7bEhLJGwJLtTWobK81sOjoPONE9Ek79jrpvEm7xouIiLSVEswiIiJShgvc/aB2B1GHMpKKg0sYoyyXufv0Gu8PiNx+WklxTKWzEsxDI/o0sjhXVXTSsXpByvutPFanljBGJZjZysCRwGeBhdsbTSFlLcQYc7ykXbM66bxJu8aLiIi0lRb5ExERkd5qpru/3+4gSnQb8M2UttgZgF5SLHNKGqcsMQmiMmZJSpyz3P3ClLaY30NPPU5LZ2YDzexE4AngqzSeXJ5KqI/fKmWdlzH3vR+UtK9mybrGi4iItJVmMIuIiEhvVdbMuHYkqacDk4AXCItXXQ38093TEm+xMzXLmnUcW+agVT4gvwb1Qq0IpM1afazOIRyrbxEW/ruTMAPzsYxtYo5VI3xp0Ogs5k47TktlZgsD1wKbNTDMNMI15jbgJuAOd59hZmUl+fMMK2mcmPM77fyowjVeRESkrZRgFhERkd6qrMeMJ0b229ndbyxpn4W4+2Qzm05+knWJRvdlZoOBIY2OU7Ip5P/bh7cikDaLPVYHunu7ZnO+GdlvCRqvoR67oGDlmNkA4AbCon2xnDDT+Z7kdS/wqLvP6jb2wLLijNDKBHPasdfx13gREZF2U4JZREREequyEmivEBIzltOvrERJvf4LrJzTZ6US9rNCCWOU7VVg0Zw+a5S5QzNbAdgPWBp4GLjI3WeUuY86vBzZbxjtW/QwNsaVaDzB3InHall+Snxy+QbgPMIM2Xci+reyJvHCZjbc3WOTvGlGR/RJWziyKtd4ERGRtlENZhEREemtZpcxSDLTMybRtWIZ+2vAExF9Ni5hPxuWMEbZno/oM6asnZnZ3sC/gROBI4DfAveY2SJl7aNOT0X2a9ux6u6vELfgYk89VhtmZssSjrs8bwC7u/su7n55ZHIZoNXHcRnn5gYRfWpeIyt0jRcREWkbJZhFRESktyqzluX9EX1iEhzN9GBEn83MrNHyFjs0uH0zxPzblzez1RrdkZmtB/yR+Wd5rguc3ej4jXD3l4krQdHuY/WhiD47N7IDMzNgu0bG6GCfBwbk9JkObOfu19Yx/vJ1bNOIrRvZ2MxWBJaL6PpwRlsVrvEiIiJtowSziIiISONujuizg5n1L2uHZvZxM7vXzF41s9vN7GM5m9wSMewCwL4NxDQE+Hi92zfRHZH9Pt/ITsysL2G2clqN2n3MbKlG9tF9l3Vsc0tEn13rGLcmM+tjZv9nZo+b2ctmNtbM1szZ7JaIobdIEof12hoY1cD2nWzHiD4Xu/vjdY4fm0gtq5TG58yskfvW2PP6XxltVbjGi4iItI0SzCIiIiKN+xv5M6JHAHuXsTMz+wpwJeER/6WAzYGxZrZVxmZ3ErdY1beTRGk9jgQWrHPbZrqVuLILh5hZXq3mLF8l/3H+2Lq/MSVc6klm/TWiz0fNbJk6xp6HmfUDLgZ+AqwOLAPsCYzLKRcSM6vWgO82EN7/NbBtp1slos99DYwfO3u8kXOpq2Wp84svM1sYOCSi69PunlVKpwrXeBERkbZRgllERESkQe7+X2BcRNdjGkjeAmBm3wLOYv7Zq32AT6Vtlywwd3HELkYDR9cR1wo0lvBrGnefCVwR0XUEcGo9+zCztQmJ1DxvRQ4ZsyDg0MixuroKmJTTpy9wbB1jf8jMBgCXAfvXaF6CjPIU7n4v8EjEbj5vZpvWEdvexM3yraoREX3qug80s3WILy2yWD37SPGLOmuYn0o43vKcl9VYhWu8iIhIOynBLCIiIlKOMyP6rAH8oJ7BzayvmZ0CnJzRLa00w1znRu7uBDOLrnFrZgsRZtvVk/BslV9F9jswSfBEM7PlCDMcF8jp+jrwbOSweUlgCDOCC3H3acDvI7p+wcy2LTo+gJmNAK4DPpHRLe9Y/W3ErvoCl5tZdKmLJEH6u9j+FTUtos8mRQc1s0EU+9mtV3QfGZYAripSI97MjgC+ENF1JnB+RL8qXONFRETaQglmERERkRK4+98IZSjyHGtmny4ydlK24Xrgmzld785qdPfHCOUi8vQHrjazL0XENhq4DfhIxLht4+4PEmbvxjjZzH5uZrnlPpJH1u8kbhGxC9w9dnHJmJnOOyeL1RX1U/JLhvQhJG/XLjJwssjheGD7nK735LRfRFxZk2WAO81s84jYPkaopbtwxLhV9lxEn/3MLKaUBvDhl0hXE0o2xNqsQN8YWxLKq4zO6pQkao8hLiEMMNbd38jrVIVrvIiISLsowSwiIiJSnsOBD3L69AEuNLMTkxmBmcxsT+B+YIecrm8Dl0TEeCQwK6LfQOAcM7vHzD7dtTaxmQ00sy3M7FxCKYNCScg2+iYwNbLv14AnzOwYM1sjqScMhLquZvYxM/sLYWGwpSPGmwT8vECsL0T0WRs4fe6szmRBvZXMbC8zO8rMPlXrGHP3t4krZ7IocJuZHZDX0cwGmNk3CQm4vDrT17n7M1kd3H0S8L2IGAFGJnFebmY7mtmHM8mT39VeZnYDof50TPmIqrslos9A4Nq8hRIt2At4CNipYBwbdz1vSrIR8IiZnWNm25nZhwsJmtlwMzuQUF/6R8QtgjkN+E6B/VfhGi8iItJyZX/gi4iIiPRa7v6wmX0POC2nax9C8uxzZvY74O/Ak8AUYBiwIrAF8Dlg/cjdH5OUP4iJ8VTi6yVvRFK72czeJTxOPpzW/B0ZkwiHEFMud3/ezL5GXPkFCIuL/Sh5zTGzCcAAYKHI7bs6OmaWZBePAXtE9PsacJSZvU04dro/Qv+4mW3p7u90fdPdf2Nmu0TsYyHgj2Z2NKG0xi2E5Pfc42BNwmzlA4kr2TGT+MTxb4BPEzcT1ggLwe0LzDaziYQSGgsTl2jsSS4Cvh7RbxXgQTM7C/gL8BQwnfB7XQ3YllBDe/U64xgCbEp4wqFM/YEvJS/MbBLhdzysjrGOd/f/xHauwjVeRESkHZRgFhERkSylJvhaKCbupsTs7qeb2arAoRHdRwLHJa9GXOLuZxfo/0Ngd2CtgvtZOKLPJMIsu8Ny+s2JGGtiRB+Ad/K7BO7+OzNbCzgqdptEH+pftOxX7h5T97irIkk5Iz22NQi/76/WaDuAMAN7g4h9rE98IizLEe4es4Af7u5mdghwLyFZGasvYfZ1nkeBl4DdcvrFHKtlafja5e4PmtlYYK+IsYYB/5e8iniHuNng25N+LMeUi/kt4UuGrPrm9XzhA+G4KvJUAVCJa3zbPv9ERKT3UokMERERyfJuZL/YRGCrxMQTnZSsw+HELyrXqMuBg4ps4O7TgV2B/5YcyyRgZ+Ie0X8vr4O7TwWezuk2mbias119ndb9fn5DKEtS1DigyIznLDXrYyc/3x2JqyvbKAe+7u6xC02GjdyfAPYhvyxBUY8Skp8vRfTNPVZLFHNdiunzFWBCg7HU4sApwErElXHZOKMt5uf6D8JikTMi+hbxPLCHu8d+idpdJ1/jyzqGREREoinBLCIiIlkei+wXNSOxhR6K6PNgs3bu7nPc/QjCLN73m7Sb2cDxwH7uXng2mru/TFg0Ky+BG+spYBN3vwd4NrJ/jAvz2t290AxTD44gzGIuO3E513TgK+5+eNH4AJLfadFZpWlSE/DuPpFQ+7XIDPii3gE+7u5n1LOxu98AfIxQXqAMfwO2cPe3yD9W50T0KdMDEX0eyuvg7q9S7s8MwrViG3f/tru/SyiLkndsZ5WtiLn2PNfl9x+z6GOMhwm//7q/wOnwa3wpx5CIiEgRSjCLiIhIljuBt3L6vA9c14JYiriL/FmteYnLhiWPNK9LSGiV6TZgY3f/obvHPGZek7u/AGxIYwtHOXAesJG7P5m89xBhhmCaicDNkeOfRli0q5bHgR9EjjMfdz+TUB4iNpaoYYGrgDXd/dcNDeR+Ho3PknwXOClnP++7+2GE2cyPNri/rmYD5wNrufvVjQzk7jcSflf3NzDMFOBbwJ7uPjdZOZbsJOk/3L2VM5gvyml/AhgfM5C73wVsTuMJ8smEesLruPutXca/Ffh+zrbXZLTdTHjqIc3zJIlQd/8HoR581L89hQPnAJu6+2sNjPO/ATvzGl/aMSQiIhJLCWYRERFJ5e4fkJ/AO7H7AmLtlswY/TzpM8t+5u73tiiWZ9x9D0Jy7A9kJ1SyTAcuA7Z2963cvZFEW9f4Jrv7p4HtgDsKbDoHuJYwa/kLXRJ2c3/+X6b27GAHvuruUbP+knIe2wEnAy8Saoe+RKidurm71/vznDv+Y+6+HbANcCXh51yPd4BfE5Jwn3D3rAR7kfiOIDyOX0+5g1sJMzWfiNzXP4H1CAv//Z3667S+DfwCWM3dDy4xmfc0Icl4KNlfYHQ3BTgXGO3up3ZN2CW/px+mbDcJ+Ead4dbF3e8AfpnSPBX4fJGEY1Lveh1CDeCix9CLhATycu5+krvPV6bC3U8CzkzZ/p9kLIaXlGg5OqX5A+DLXWf/u/tTwCaEa8sLMf+AuZsSjueN3f3Lsdee6ME77Bpf9jEkIiISw/TZIiIiInnM7FvAj4GBXd6eRZgZeWyn3qya2XrAj4CtCLE/Bpzp7nkzvJoZU39CaYpNCbVxVyAsBDUUGESY9TkNeJ2QRHkEuB0YlyRkmh3fOsCewBbA6oTF0voTZsK+TXi8/A5gbFJmI2usjQg//80Ii0vfD/zE3a9vVvyNMrMhhNq8mxISc8sDSxIWGetPqAX7HvAKYWboQ4RE7l3uPrvJce0F7ASsCYwiLHw3IIlpCvAqYeb+PcC17v7vBve5EOFnsRHhZ7Ec//tZDCQkAd9L9jv3Z3ELcGczfxZJbH0IXzzsRpiFvwphEco5hBnyrxNmvt8GXJ03C9nMPk1IJq9J+GJqHPD9JKnZcmZ2KOGLhdGE3+3NwHGN/E7NbDBhcc+dCV8kLM//SlhMBV4j/B7vAW4C7o69tpvZQcB3CLWZ/wP8Hjg9psaxmX0c+C7hGPuAcL073t3TnlyY+/vfgvD7Xx9YDRgODCZcP18jPOHwL8Lv/z8x/44ydMo1vhnHkIiISBolmEVERCSKmS0J7AIsTSibcaO7v9jeqERERERERKSdlGAWERERERERERERkbqoBrOIiIiIiIiIiIiI1EUJZhERERERERERERGpixLMIiIiIiIiIiIiIlIXJZhFREREREREREREpC5KMIuIiIiIiIiIiIhIXZRgFhEREREREREREZG6KMEsIiIiIiIiIiIiInVRgllERERERERERERE6qIEs4iIiIiIiIiIiIjURQlmEREREREREREREamLEswiIiIiIiIiIiIiUhclmEVERERERERERESkLkowi4iIiIiIiIiIiEhdlGAWERERERERERERkboowSwiIiIiIiIiIiIidVGCWURERERERERERETqogSziIiIiIiIiIiIiNRFCWYRERERERERERERqcv/A0VHZwP2/VXsAAAAAElFTkSuQmCC\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\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_AsCpF1/AsCpf1_Dataset_MM-Seq_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=53)\n",
    "#rs-9\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 = MLPClassifier(solver='adam', alpha=0.0002, activation= 'relu', hidden_layer_sizes=[10, 10, 10, 21, 13], learning_rate_init=0.001, random_state=52, 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=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",
    "\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=0.7967\", \"Fold 2 AUC=0.8068\", \"Fold 3 AUC=0.8332\", \"Fold 4 AUC=0.8672\", \"Fold 5 AUC=0.8385\", \"Overall AUC=0.8246\"], 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_AsCpf1-saved.png\", dpi=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "large-adapter",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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