SHAPAge_PhenoAge_BioAge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None \n",
    "import numpy as np\n",
    "import xgboost\n",
    "import pickle\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "import shap\n",
    "from sklearn.linear_model import ElasticNet, LogisticRegression\n",
    "import matplotlib.pylab as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_all(permth, mortstat, time):\n",
    "    if permth > time:\n",
    "        return 0\n",
    "    else:\n",
    "        if mortstat == 1:\n",
    "            return 1\n",
    "        else:\n",
    "            return 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def val_mortality_model(age_train, age_val, age_test, train_label, val_label, test_label):\n",
    "def val_mortality_model(age_train, age_test, train_label, test_label):\n",
    "    age_train = np.array(age_train)\n",
    "    # age_val = np.array(age_val)\n",
    "    age_test = np.array(age_test)\n",
    "    age_train = age_train[train_label!=2]\n",
    "    train_label = train_label[train_label!=2]\n",
    "    # age_val = age_val[val_label!=2]\n",
    "    # val_label = val_label[val_label!=2]\n",
    "    age_test = age_test[test_label!=2]\n",
    "    test_label = test_label[test_label!=2]\n",
    "    # ### linear model\n",
    "    model_mort_linear = None\n",
    "    # print('linear model')\n",
    "    # model_mort_linear = LogisticRegression().fit(age_train.reshape(len(age_train), -1), train_label)\n",
    "    # pre = model_mort_linear.predict_proba(age_test.reshape(len(age_test), -1))\n",
    "    # print('AUC on test set: ', roc_auc_score(test_label, pre[:,1]))\n",
    "    print('xgboost')\n",
    "    model_mort_xgb = xgboost.XGBClassifier().fit(age_train.reshape(len(age_train), -1), train_label)\n",
    "    # model_mort_xgb = xgboost.XGBClassifier(learning_rate=0.01, n_estimators=1000).fit(age_train, train_label, eval_set = [(age_val, val_label)], early_stopping_rounds=100, verbose=False)\n",
    "    pre = model_mort_xgb.predict_proba(age_test.reshape(len(age_test), -1))\n",
    "    print('AUC on test set: ', roc_auc_score(test_label, pre[:,1]))\n",
    "    return model_mort_linear, model_mort_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_ci(y_pre, y_label, sample_size, repetitions = 1000, alpha = 0.05, metric='AUC'): \n",
    "    y_pre = np.array(y_pre)\n",
    "    y_label = np.array(y_label)\n",
    "    \n",
    "    res = []\n",
    "    y_pre = y_pre[y_label!=2]\n",
    "    y_label = y_label[y_label!=2]\n",
    "    for i in range(repetitions):\n",
    "        np.random.seed(i)\n",
    "        idx = list(np.random.choice(len(y_pre), replace = True, size = sample_size))\n",
    "        y_pre_bootstrap = y_pre[idx]\n",
    "        y_label_bootstrap = y_label[idx]\n",
    "        if metric == 'AUC':\n",
    "            res.append(roc_auc_score(y_label_bootstrap, y_pre_bootstrap))\n",
    "        elif metric == 'AP':\n",
    "            res.append(average_precision_score(y_label_bootstrap, y_pre_bootstrap))\n",
    "    # confidence interval\n",
    "    left_res = np.percentile(res, alpha/2*100)\n",
    "    right_res = np.percentile(res, 100-alpha/2*100)\n",
    "    # point estimate\n",
    "    print('average '+metric, np.mean(res))\n",
    "    print((1-alpha)*100,'%','confidence interval for the '+metric+':', (round(left_res,4), round(right_res,4)))\n",
    "    return res, left_res, right_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Original unit:\n",
    "# Albumin: g/L\n",
    "# Creatinine: umol/L\n",
    "# Glucose, serum: mmol/L\n",
    "# C-reactive protein (log): mg/L \n",
    "# Lymphocyte percent: %\n",
    "# Mean cell volume: fL\n",
    "# Red cell distribution width: %\n",
    "# Alkaline phosphatase: U/L\n",
    "# White blood cell count: 10^9 cells/L\n",
    "def calculate_PhenoAge(data):\n",
    "    change_name_dict = {\n",
    "    '30600-0.0': 'Albumin',  # g/L\n",
    "    '30700-0.0': 'Creatinine',  # umol/L\n",
    "    '30740-0.0': 'Glucose, serum',  # mmol/L\n",
    "    '30710-0.0': 'C-reactive protein (log)',  #mg/L\n",
    "    '30180-0.0': 'Lymphocyte percent',  # %\n",
    "    '30040-0.0': 'Mean cell volume',  # fL\n",
    "    '30070-0.0': 'Red cell distribution width',  # %\n",
    "    '30610-0.0': 'Alkaline phosphatase',  # U/L\n",
    "    '30000-0.0': 'White blood cell count',  # 10^9 cells/L\n",
    "                       }\n",
    "    data['flag'] = 1\n",
    "    for fea in change_name_dict:\n",
    "        data.loc[data[fea]<0, 'flag'] = 0\n",
    "    data = data.rename(change_name_dict, axis=1)\n",
    "    data['C-reactive protein (log)'] = np.log(data['C-reactive protein (log)']/10)\n",
    "    weights = np.array([-0.0336, 0.0095, 0.1953, 0.0954, -0.0120, 0.0268, 0.3306, 0.0019, 0.0554, 0.0804])\n",
    "    constant = -19.9067\n",
    "    gamma = 0.0077\n",
    "    # for fea in ['Albumin', 'Creatinine', 'Glucose, serum',\n",
    "    #        'C-reactive protein (log)', 'Lymphocyte percent', 'Mean cell volume',\n",
    "    #        'Red cell distribution width', 'Alkaline phosphatase',\n",
    "    #        'White blood cell count']:\n",
    "    #     data.loc[data[fea]<np.percentile(data[fea], 1), fea] = np.percentile(data[fea], 1)\n",
    "    #     data.loc[data[fea]>np.percentile(data[fea], 99), fea] = np.percentile(data[fea], 99)                 \n",
    "    features = np.array(data[['Albumin', 'Creatinine', 'Glucose, serum',\n",
    "           'C-reactive protein (log)', 'Lymphocyte percent', 'Mean cell volume',\n",
    "           'Red cell distribution width', 'Alkaline phosphatase',\n",
    "           'White blood cell count', 'Age']])\n",
    "    mortality_risk = 1-np.exp(-np.exp(np.sum(weights*features, axis=1)+constant)*(np.exp(120*gamma)-1)/gamma)\n",
    "    data['PhenoAge'] = 141.50225 + (np.log(-0.00553 * np.log(1-mortality_risk)))/0.090165\n",
    "    data['PhenoAge_acc'] = data['PhenoAge']-data['Age']\n",
    "    return data['PhenoAge'], data['PhenoAge_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Original unit:\n",
    "# Albumin: g/dL\n",
    "# Alkaline phosphatase: U/L\n",
    "# Creatinine: mg/dL\n",
    "# C-reactive protein: mg/dL\n",
    "# Hba1c: %\n",
    "# Systolic BP: mmHg\n",
    "# Total Cholesterol: mg/dL\n",
    "def calculate_BioAge(data):\n",
    "    change_name_dict = {\n",
    "    '30600-0.0': 'Albumin',  # g/L\n",
    "    '30610-0.0': 'Alkaline phosphatase',  # U/L\n",
    "    '30700-0.0': 'Creatinine',  # umol/L\n",
    "    '30710-0.0': 'C-reactive protein',  # mg/L\n",
    "    '30750-0.0': 'Hba1c',  # mmol/mol\n",
    "    '93-0.0': 'Systolic BP',  # mmHg\n",
    "    '30690-0.0': 'Total Cholesterol'  # mmol/L\n",
    "                       }\n",
    "    data['flag'] = 1\n",
    "    for fea in change_name_dict:\n",
    "        data.loc[data[fea]<0, 'flag'] = 0\n",
    "    data = data.rename(change_name_dict, axis=1)\n",
    "    data['Albumin'] = data['Albumin']/10\n",
    "    data['Creatinine'] = data['Creatinine']*0.0113\n",
    "    data['C-reactive protein'] = data['C-reactive protein']/10\n",
    "    data['Hba1c'] = (0.09148*data['Hba1c']) + 2.152\n",
    "    data['Total Cholesterol'] = data['Total Cholesterol']*38.665\n",
    "    # biological age\n",
    "    S = [0.3372441, 28.91255, 0.206529, 0.6014597, 0.9468073, 14.64641, 39.93671]\n",
    "    K = [-0.0057658, 0.4822007, 0.0029099, 0.0058188, 0.0196594, 0.6784407, 0.972077]\n",
    "    Q = [4.437681, 58.87995, 0.9299351, 0.1414078, 4.488046, 90.98659, 163.2156]\n",
    "    # for fea in ['Albumin', 'Alkaline phosphatase', 'Creatinine',\n",
    "    #        'C-reactive protein', 'Hba1c', 'Systolic BP',\n",
    "    #        'Total Cholesterol']:\n",
    "    #     data.loc[data[fea]<np.percentile(data[fea], 1), fea] = np.percentile(data[fea], 1)\n",
    "    #     data.loc[data[fea]>np.percentile(data[fea], 99), fea] = np.percentile(data[fea], 99)  \n",
    "    features = np.array(data[['Albumin', 'Alkaline phosphatase', 'Creatinine',\n",
    "           'C-reactive protein', 'Hba1c', 'Systolic BP',\n",
    "           'Total Cholesterol']])\n",
    "    k_to_s2 = [k/(s**2) for (k,s) in zip(K, S)]\n",
    "    k_to_w_sum = np.sum([(k/s)**2 for (k,s) in zip(K, S)])\n",
    "    data['sum']=np.sum((features-Q)*k_to_s2, axis=1)\n",
    "    data['BioAge'] = (data['sum']+data['Age']/31.63)/(k_to_w_sum+1/31.63)\n",
    "    data['BioAge_acc'] = data['BioAge']-data['Age']\n",
    "    return data['BioAge'], data['BioAge_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_EXPECTAge(data, path, gender, gender_feature, gender_index):\n",
    "    model_path = path+'/model.pickle.dat'\n",
    "    model_train = pickle.load(open(model_path, \"rb\"))\n",
    "    data = data.loc[data[gender_feature]==gender_index]\n",
    "    shap_age_obj = pickle.load(open(path+'/different_age_background/'+gender+'/'+'SHAP_age_exponential'+'/'+'shap_age_object.pkl', 'rb'))\n",
    "    fore_prediction = model_train.predict(data.loc[:, model_train.get_booster().feature_names], output_margin=True)\n",
    "    data.loc[:, 'shap_age'] = np.array([shap_age_obj.get_shap_age(fore_prediction[i]) for i in range(len(fore_prediction))])\n",
    "    data.loc[:, 'shap_age_acc'] = data['shap_age']-data['Age']\n",
    "    return data['shap_age'], data['shap_age_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mortality_prediction(bio_age_feature, X_train, train_label_5, train_label_10, X_test, test_label_5, test_label_10, X_valid, valid_label_5, valid_label_10):\n",
    "    if bio_age_feature == 'Age':\n",
    "        feature_list = ['Age', '31-0.0']\n",
    "    else:\n",
    "        feature_list = [bio_age_feature, 'Age', '31-0.0']\n",
    "    train_label_5_temp = train_label_5[(X_train[bio_age_feature]!=np.inf) & (X_train[bio_age_feature]!=-np.inf) & (X_train[bio_age_feature].isnull()==False)]\n",
    "    train_label_10_temp = train_label_10[(X_train[bio_age_feature]!=np.inf) & (X_train[bio_age_feature]!=-np.inf) & (X_train[bio_age_feature].isnull()==False)]\n",
    "    X_train_temp = X_train[(X_train[bio_age_feature]!=np.inf) & (X_train[bio_age_feature]!=-np.inf) & (X_train[bio_age_feature].isnull()==False)]\n",
    "    year = 5\n",
    "    print('5-year')\n",
    "    # val_label_5_temp = val_label_5[X_val['shap_age']!=np.inf]\n",
    "    # X_val = X_val[X_val['shap_age']!=np.inf]\n",
    "    # 5-year mortality, chronological age + biological age acc + Gender\n",
    "    # _, xgb_model = val_mortality_model(X_train_temp[feature_list], X_val_temp[feature_list], X_test_temp[feature_list], train_label_5_temp, val_label_5_temp, test_label_5_temp)\n",
    "    _, xgb_model = val_mortality_model(X_train_temp[feature_list], X_test[feature_list], train_label_5_temp, test_label_5)\n",
    "    print('Test set bootstrap:')\n",
    "    res, left_res, right_res = bootstrap_ci(xgb_model.predict_proba(np.array(X_test[feature_list]))[:, 1], test_label_5, len(X_test), repetitions = 1000, alpha = 0.05, metric='AUC')\n",
    "    res_test_5_year = {'res': res, 'left_res': left_res, 'right_res': right_res}\n",
    "    print('GeoValidation set bootstrap:')\n",
    "    res, left_res, right_res = bootstrap_ci(xgb_model.predict_proba(np.array(X_valid[feature_list]))[:, 1], valid_label_5, len(X_valid), repetitions = 1000, alpha = 0.05, metric='AUC')\n",
    "    res_valid_5_year = {'res': res, 'left_res': left_res, 'right_res': right_res}\n",
    "\n",
    "    year = 10\n",
    "    print('10-year')\n",
    "    # val_label_5_temp = val_label_5[X_val['shap_age']!=np.inf]\n",
    "    # X_val = X_val[X_val['shap_age']!=np.inf]\n",
    "    # 5-year mortality, chronological age + biological age acc + Gender\n",
    "    # _, xgb_model = val_mortality_model(X_train_temp[feature_list], X_val_temp[feature_list], X_test_temp[feature_list], train_label_5_temp, val_label_5_temp, test_label_5_temp)\n",
    "    _, xgb_model = val_mortality_model(X_train_temp[feature_list], X_test[feature_list], train_label_10_temp, test_label_10)\n",
    "    print('Test set bootstrap:')\n",
    "    res, left_res, right_res = bootstrap_ci(xgb_model.predict_proba(np.array(X_test[feature_list]))[:, 1], test_label_10, len(X_test), repetitions = 1000, alpha = 0.05, metric='AUC')\n",
    "    res_test_10_year = {'res': res, 'left_res': left_res, 'right_res': right_res}\n",
    "    print('GeoValidation set bootstrap:')\n",
    "    res, left_res, right_res = bootstrap_ci(xgb_model.predict_proba(np.array(X_valid[feature_list]))[:, 1], valid_label_10, len(X_valid), repetitions = 1000, alpha = 0.05, metric='AUC')\n",
    "    res_valid_10_year = {'res': res, 'left_res': left_res, 'right_res': right_res}\n",
    "    return res_test_5_year, res_valid_5_year, res_test_10_year, res_valid_10_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortality = 'all-cause'\n",
    "age_feature = 'Age'\n",
    "gender_feature = '31-0.0'\n",
    "random_state = 528"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('/projects/leelab/nobackup/wqiu/UK_Biobank_genetic_data/pheno_data/features_initial_preprocessing_missforest_imputed_no_missing_lancet_and_meaningful_adjusted_assays_remove20002and20004_AgeAdjusted_CancerAdjusted_geo.csv')\n",
    "target = pd.read_csv('/projects/leelab/nobackup/wqiu/UK_Biobank_genetic_data/pheno_data/death_label_new.csv')\n",
    "data = pd.merge(data, target[['eid', 'alive_year', 'all-cause', 'neoplasms', 'circulatory', 'respiratory', 'digestive', 'external',\n",
    "       'other']], how='right', on='eid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.copy()\n",
    "X = X[(X[age_feature]>=39.5) & (X[age_feature]<=70.5)].reset_index(drop=True)  \n",
    "X = X[(X['external']!=1)]\n",
    "X = X[(X['all-cause']==0) | (X[mortality]==1)]\n",
    "X_geo = X.loc[(X['54-0.0']==11004) | (X['54-0.0']==11005)].reset_index(drop=True)\n",
    "X = X.loc[(X['54-0.0']!=11004) & (X['54-0.0']!=11005)].reset_index(drop=True)\n",
    "mortstat = X['all-cause'].reset_index(drop=True)\n",
    "permth_int = X['alive_year'].reset_index(drop=True)\n",
    "mortstat_geo = X_geo[mortality].reset_index(drop=True)\n",
    "permth_int_geo = X_geo['alive_year'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Calculate age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## PhenoAge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in log\n",
      "divide by zero encountered in log\n"
     ]
    }
   ],
   "source": [
    "X['PhenoAge'], X['PhenoAge_acc'] = calculate_PhenoAge(X)\n",
    "X_geo['PhenoAge'], X_geo['PhenoAge_acc'] = calculate_PhenoAge(X_geo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_idx = random.sample([i for i in range(len(X))], 50000)\n",
    "data_no_missing_temp = X.loc[selected_idx]\n",
    "data_no_missing_temp = data_no_missing_temp[data_no_missing_temp['flag']==1]\n",
    "data_no_missing_temp = data_no_missing_temp[data_no_missing_temp['PhenoAge']!=np.inf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jrr32Wt577z3GjBm6a4hVgDhI9amw7jG2ePFi8vPzKSkpYevWrWzdupWdO3fyiU98gqVLl/a6lvBgOoPA7qOIjzzySFfw2Gnjxo1IKZk4cSL5+fk9HjfeeCMA9fX1R/gOFWVf3esHVrbqCCGRwPgc66D4vVQUZeCtWbOG2bNn89prr/GHP/yBhx9+GLfbffATBzFV72CQGmzbYu3YsYM33ngDKSXjx4/vtc2jjz66T1LJwUydOpUZM2bw6KOP8tOf/pREIsFTTz3FBRdcQFFRUVc7KSVCCF544YX9ruOYPHnyId1bUfrCzEA2g8Dd7QalPo3xOVby3ObPodquTlFObH/+85/52te+Rn5+Pu+88w6nnHLKQHepX6gAcZDqnNbyOz4OEgdyW6yHH34YKSUPPfQQwWBwn9fvvPNOFi9efMgBIpijiLfffjtvvPEGtbW1tLe395heBhg3bhwvvvgiZWVlTJo06TDfhaIcnu71A+MZid/x8eSL2q5OUU5MyWSSW2+9lYceeojzzjuPxx9/nPz8/IHuVr9RU8yD1GDaFsswDP7yl78wdepUvvSlL7Fo0aJ9HldeeSXr16+nvLz8kK//hS98AavVyiOPPMIjjzxCIBDgM5/5TI8211xzDQDf+973yGaz+1yjoaHh8N6cohyCwfR7qSjKwNm1axenn346Dz30EHfccQcvvfTScRUcghpBHLS6T2vVx80RirNKbQOyC8LLL7/M7t27u9b69eayyy7jhz/8IYsXL2bu3LmHdP2CggIuuuginnzySZLJJDfeeOM+2w/NnTuXH/3oR/zgBz9gxowZfO5zn6O4uJja2lpWr17N888/TzqdPqz3pyh9NZh+LxVFGRgvv/wyV155Jbqu88wzz+wzoHG8UH/VBrHBsi3W4sWLAVi4cOF+20yZMoXx48fzj3/8g1/96leHfI/rrruOZ599FoBrr7221zZ33XUXs2fP5re//S2//vWvicViFBQUMGXKFH7zm98c8j0V5XAMlt9LRVGOLcMw+NnPfsZdd93F5MmTefrppxk3btxAd+uoEWpv3P2bM2eOrKioOGCbjRs3qjVxylGnfs4URVEGTmtrK9deey3/+c9/uOqqq/jjH//YVQ94MBFCrJZS9stefuq/wYqiKIqiKPvxwQcfsHDhQqqqqnjggQf42te+hhDHf2KaSlJRFEVRFEXpxSOPPMK8efNIJpO8/fbbfP3rXz8hgkNQAaKiKIqiKEoPqVSKr33ta1x33XXMmzePNWvWMH/+/IHu1jGlAkRFURRFUZQOu3fv5swzz+T//u//+Pa3v80rr7xCYWHhQHfrmFNrEBVFURRFUYDXXnuNK664glQqxVNPPXXA6h3HOzWCqCiKoijKCc0wDH7+859zwQUXUFBQQHl5+QkdHIIaQVQURVEU5QQWDoe57rrr+Pe//83nP/95/vSnP+H1ege6WwNOBYiKoiiKopyQ1q1bx8KFC9m5cye//vWvufXWW0+YLOWDUQGioiiKoignnMcee4wvfelLBAIB3njjDU4//fSB7tKgogJERVGUwaK6GSq2QUMYCgIwZwyUhAa6V4pyXEmn03zzm9/kgQce4IwzzuCf//wnw4YNG+huDToqSUVRFGUwqG6GpeUQS0Jh0HxeWm4eVxSlX+zZs4ezzz6bBx54gG984xu89tprKjjcDzWCqCiKMhhUbIOAG/xu8/PO54ptahRRUfrBm2++yec//3lisRj//Oc/ufzyywe6S4OaGkFUlAHw5ptvIoTgL3/5y0B3RRksGsLgdfU85nWZxxVFOWxSSn7xi19w/vnnk5ubS3l5uQoO+0AFiEqfdQY1nQ+LxUJOTg5Tpkzhuuuu48UXX0RKOdDdVJShqSAA0UTPY9GEeVxRlMMSiURYtGgR3/72t7n00ktZtWoVkyZNGuhuDQlqilk5ZFdeeSUXX3wxUkra29vZvHkzzzzzDI888gjnn38+TzzxBMFgcKC7qShDy5wx5ppDMEcOowkIx+GsyQPbL0UZojZs2MBll13G1q1b+eUvf8k3vvENVcLmEKgAUTlks2bN4uqrr+5x7P777+fb3/42999/P1deeSUvvPDCAPVOUfZjsGcIl4RgwVyzj/VtZh/Pmjy4+qgoQ8Q///lPbrzxRjweD6+99hpnnXXWQHdpyFFTzEq/sFgs3HfffZx++um8+OKLLFu2rOu1cDjMd77zHcaOHYvD4SA/P58rr7yS7du373OddDrNvffey4wZM3C73QQCAebMmcMDDzzQo11fr9ne3s6dd97JKaecQl5eHg6Hg7Fjx/Ld736XeDzeo62Ukl//+tdMmzYNn8+H3+9nwoQJ3HjjjWQymR5tKyoquPTSS7uuOWHCBH7605+i6/o+7+nf//43M2fOxOl0Ulpayl133bXP9ZSjbJBlCFdHdJZUJnjwgzhLKhNURzp+bkpC8NmT4aZPmM8qOFSUQ5LJZLj99tu54oormD59OmvXrlXB4WFSI4hKv7rxxhtZtmwZzz33HKeffjrhcJhTTz2VqqoqbrjhBiZPnkxtbS2///3vOeWUU6ioqGDEiBGAGRxeeOGFvPnmm1xwwQVcffXVOJ1O1q1bx9NPP83NN98McEjX3LNnD3/605+47LLL+MIXvoDVauWtt97i3nvvZe3atbz00ktdfb/77ru56667+PSnP81Xv/pVLBYLO3bsYOnSpaRSKWw2GwDPP/88l156KWPHjuWb3/wmubm5LF++nLvuuov333+fJ554ouuaS5Ys4bLLLmPkyJHcddddWK1WHn74Yf7zn/8cq2+JAoMqQ7g6orN0a5qAAwrdgmhasnRrmgVjocSv/iQryuGqra3l8ssvZ9myZdxyyy388pe/xG63D3S3hi4ppXrs5zF79mx5MB999NFB2xy23U1SLlkp5R9fNp93Nx29e/XBG2+8IQH5i1/8Yr9tVq9eLQG5cOFCKaWUt956q3Q6nfL999/v0W7nzp3S5/PJ6667ruvY//f//X8SkHfcccc+181ms10fH8o1U6mUTKfT+1zvzjvvlIBcuXJl17GZM2fKSZMm7fe9SSllIpGQhYWF8owzzpCZTKbHa/fff78E5BtvvCGllFLXdVlaWipDoZBsbGzsatfW1ibLysokIB9++OED3q/TUf05OxH88WUpn1kl5dLyjx/PrDKPH2NPb47LR9fH5NItia7Ho+tj8unN8WPeF0U5Xrz99tuyqKhIut1u+dhjjw10dwYMUCH7KQZSU8yD1SCbEusrv98PmJljUkr+/ve/c+aZZzJ8+HCampq6Hh6Ph3nz5vHyyy93nfv3v/+dnJwc7rrrrn2uq2nmj+qhXtNut3eN/Om6TmtrK01NTZx//vkArFy5sqttIBBgz549PabH9/bKK69QX1/PF7/4Rdra2nrc/+KLLwbouv/q1avZvXs3X/ziF8nLy+txn69+9auH9oVVjswgyhBuTEi89p4L5b12QWNCVQBQlEMlpeRXv/oV55xzDj6fj5UrV3LllVcOdLeOC2o+Y7AaRFNihyISiQBmoNjY2EhzczMvv/wy+fn5vbbvDPwAtmzZwowZM3A6nfu9/qFeE+D3v/89f/jDH9iwYQOGYfR4rbW1tevjn/3sZ3z2s5/ljDPOoLi4mLPPPptLLrmERYsWdU1TbNy4EYAbbrhhv32sr68H6FoPOXHixH3anHTSSfs9XzkKBlGGcL7LnFb2Oz4OEqNpSb5LZVcqyqGIRqPceOON/Otf/+LSSy/l4YcfJhBQZaH6iwoQB6uGsDly2J3XZWY3DmIffvghABMmTOiqiXj++efzne98p0/nH6wEwaFe8/777+eb3/wmF1xwAbfeeivFxcXY7Xb27NnD9ddf3yNgnD9/Ptu2beOll17ijTfe4I033uCxxx7j7rvvZtmyZeTm5nbd/xe/+AUzZszo9Z7FxcU9+trbe+p8TTlGBlGG8NwiG0u3pgEDr90MFsMpOKvUdsz7Uh3RKa/L0JgwA9S5RTa1DlIZEjZt2sTChQvZvHkz99xzD9/+9rdVCZt+pv4SDFadU2KdI4cwJIrmLl68GIBLLrmE/Px8gsEgkUika0r3QMaPH8/GjRtJpVI4HI5e2xzqNf/2t78xcuRIXnjhhR4jiy+++GKv7b1eL5dddhmXXXYZYI4+fv3rX2fx4sX8z//8D+PGjQPA4/Ec9P5jxowBPh517K63Y8pRVhIaFKPvJX4rC8ZCeV2G+rgZmJ1VeuwDM5UsowxVTz31FNdffz0ul4tXXnmFc889d6C7dFxSaxAHqzljzCmwSBwMaT6H4+bxQSibzfKtb32LZcuWcfHFF3PaaaehaRpXXXUVq1at4sknn+z1vIaGhq6Pr7rqKlpbW7n77rv3adc54nao17RYLAgheozY6brOPffcs895TU1N+xybNWsWAC0tLQBceOGFFBQUcM8993Qd6y6RSNDe3g7A7NmzKSkp4eGHH+5x7Ugkwh/+8Ide+66cGEr8Vi4d7+Km6W4uHe8akICsvC5DwAF+h4YmBH6HRsBhHleUwUjXdf7nf/6HRYsWMXnyZNasWaOCw6NI/TdxsBpEU2J7W7NmDY8++ihAj51Udu3axQUXXMBjjz3W1fanP/0p7777LpdffjmXX3458+bNw263s2vXLp5//nlmz57dtR/xf//3f/Pss89y9913U15ezgUXXIDT6WTDhg1s3ryZV1999ZCvuWjRIu644w4uuugiFi5cSCQS4bHHHutKXOlu0qRJzJs3j1NOOYXi4mJqa2t58MEHsdvtXHHFFYA5cvjII4/w2c9+lgkTJnDDDTcwduxY2tra2LRpE08//TRLlizh7LPPxmKx8Ktf/YrLL7+ck08+mS9/+ctYrVb+/Oc/EwqFqKqqOorfJUU5sMaEpNC9b7JMfVwtf1AGn/r6ej7/+c/z1ltv8bWvfY37779/vzNNSj/pr3To4/Ex4GVuBpnOMjedD03TpN/vlyeddJK89tpr5QsvvNDrebFYTP74xz+WU6ZMkU6nU3q9Xjlx4kT5pS99Sa5YsaJH20QiIe+++2550kknSYfDIQOBgJwzZ4783e9+d1jX1HVd/uxnP5NjxoyRdrtdlpWVyf/5n/+RH330kQTkD37wg662P//5z+UZZ5wh8/Pzpd1ulyUlJXLRokVy9erV+7yndevWyauuukoWFxdLm80mCwoK5Pz58+WPf/xj2dzc3KPtU089JadPn951zTvvvFO+/PLLqsyNMqBUuR1lqHj33XdlcXGxdLlc8pFHHhno7gxq9GOZGyHVYvn9mjNnjqyoqDhgm40bN6qNv5WjTv2cKf2t+xrE7skyC8ba1RpEZVCQUvLAAw/wjW98gxEjRvD0008zbdq0ge7WoCaEWC2lnNMf11J/BRRFUY6hwZI5PFiSZRSlN7FYjC9/+cs8/vjjfPrTn+aRRx4hGAwOdLdOKOovgaIoyjEy2DKHS/xWFRAqg05lZSWXXXYZH330ET/96U/57ne/u099W+XoU38ZFEVRjpHumcNAR7Fsg/K6zOEHatXNZjJbQ9hMZpszZlAksynK4ViyZAnXX389drudl156qU/lzJSjQ4XkiqIox0i/b7M3RLfkVJS96brOHXfcwcKFC5kwYQKrV69WweEAUwGioijKMdK5zV53R7TNXvctOTVhPgfc5nFFGSIaGhq48MILueeee/jKV77CO++8Q1lZ2UB364SnppgVRVGOkX7fZm/zHmiLQ3vHrkvjiiDXN+i35FSUTitWrGDRokU0Nzfz8MMPc/311w90l5QOagRRURTlGDEzh+24bWZBardNHH5Zmepm2NFg7rIU9EAqA6u2wq6GQb8lp6JIKfm///s/zjzzTOx2O8uXL1fB4SCjRhD7gZRSbRKuHDWqVunxpd8yhyu2weRS+Kgakmlw2c3nDbth4bwjv76iHCXxeJz/+q//4pFHHuHiiy/m0UcfJScnZ6C7pexFjSAeIZvNRiKRGOhuKMexRCKhtpRS9tUQhrJ8OGUcOOzQFoOAB0YXqSxmZdDatm0b8+fP529/+xs/+tGPePbZZ1VwOEipEcQjVFBQwJ49exg+fDgul0uNJCr9QkqJruu0t7fT1NREYWHhQHdJOZoOp1RNQQCiCQj5zAeY080e59Hvr6IchmeffZZrrrkGi8XCCy+8wIUXXjjQXVIOQAWIR8jv9wNQU1NDJpMZ4N4oxxOr1YrT6aSsrAynU/2jf9zqLFUTcJulaqIJ8/MFcw8cJM4ZY7YD8LrM88JxOGvyMem2ovRVNpvlBz/4AT/96U+ZNWsWTz31FCNHjhzobikHoQLEfuD3+7sCRUVRlEPSvVQNfPxcse3AAWJJyAwiK7aZWcsFATM4VNPLyiDS1NTEF77wBV555RVuvPFGHnjgAfUf3iFCBYiKoigDqSFsjhx253X1rVRNSUgFhMqgtWrVKhYtWkRDQwMPPfQQX/rSlwa6S8ohUEkqiqIoA6lzLWF30YQqVaMMWVJKHnzwQc444ww0TePdd99VweEQNKABohDiDiHEE0KI7UIIKYTYeZD2E4QQzwghWoUQMSHEO0KIc/fTVhNC3C6E2CSESAohdgsh7hNCeI7Km1EURTkcc8aYawcjcTCk+RyOUzNhBEsqEzz4QZwllQmqI/pA91RRDiqRSHDDDTfwla98hXPOOYfVq1cze/bsge6WchgGegTxZ8C5wDag9UANhRBjgPeA+cC9wP8AXuAlIURvGzb+Crgf+Ai4BXgCuBV4Vggx0O9bURTF1LmW0OM0p5U9TmrOmckzCR/xjKTQLYhnJEu3plWQqAxq27dv59RTT+Uvf/kLd911F8899xyhkFoCMVQN9BrEMVLK7QBCiPWYAd/+/BwIArOllO93nPMIsAH4nRBiouyoKCyEmIwZFD4tpbys8wJCiB3Ab4ErgMf6/d0oiqIcjr3WEq6sTBBwSPwO8/+yfocADMrrMn0vsn04pXMU5TA9//zzXHXVVQD85z//4ZJLLhngHilHakBH0jqDw4PpmBZeALzZGRx2nB8F/gSMB+Z2O+VKQAC/3utSDwFx4OrD7rSiKMpR1piQeO09a6p67YLGRMeuOtXN8MwqePAV87m6uecFOkvnxJJmAkwsaX6+dztFOUKdJWwuueQSRowYwerVq1VweJwYKlOt0wAHsLyX11Z0PHcPEOcCBrCqe0MpZRJ4f6+2iqIog0q+SxBN99xiMZqW5LtE34K/7qVzNGE+B9zmcUXpJy0tLXzqU5/ixz/+Mddddx3Lly9n9OjRA90tpZ8M9BRzXxV3PO/p5bXOY8P3at8kpUztp/2pQgi7lDK994tCiJuAmwDKysoOv8eKoiiHaW6RjaVb04CB124Gi+EUnFVqg9f7UDfxYKVz1PSzcoRWr17NZZddRm1tLX/4wx+46aab1E5ix5mhEiB2/AWkt4AvuVebzo97a7t3+30CRCnlg8CDAHPmzJF7v64oitKb6ohOeV2GxoQ50je3yNb39YJ7KfFbWTAWyusy1MfN651V2nG9/QV/m6rN6eaGMOyoh2QaRhVCcztsqYO6VijwQ/lW83GoO7coSofFixfz9a9/nYKCAt555x1OPvnkge6SchQMlQAx3vHs6OU1515tOj8u2M+1emuvKIpy2KojOku3pgk4oNBtjvgt3ZpmwViOKEjs9dzOuon+bv8n3tUAOxrMAK8wCKkMvLsJ2mJQ0woWDWwWWnP8tN33IntGlmIZ5Wec1SCvrzu3KCe8ZDLJzTffzOLFizn//PN5/PHHycvLG+huKUfJUFmDWNPxPLyX1zqPdZ9+rgHyhBC9BZTDMaef9xk9VBRFORzldRkCDvA7NDQh8Ds0Ag7zeL/rrW7iht0wufTjNYcjC+C0ifDBLshkIeChZdpo3rOHyGYk+W1tpHTJylqdpnjWHIFsCPd/X5Xjxs6dOzn99NNZvHgx3/ve93jxxRdVcHicGyojiOswp4zn9/LavI7nim7HyoELgJOBdzoPCiGcwAzg7aPSS0VRTkiNCbNeIYCzrpnAuh2UNkeo9/jAfVL/jsz1tgfz6CIoy+/Zriwf3A6zrSbYXJPGY5MY+QGczWHcNg0w2NKWJS/VBLWtZla0WpOo7OWll17iC1/4Arqu8+9//5sFCxYMdJeUY2BIjCB2lLN5FjhbCDG987gQwgt8CdhCz4zlfwISuG2vS30Zc+3h349mfxVFObF0Zh0765opfG0t1kSauKEx+oPN8O1H4U+v9m+JmZIQfPZkqq84hyUnTeUle4jyrRFzNLBTNAEluV3b+IVTEqdVkCoIIq0WrNEETg2y2xvgvc1QnKtK4ig9GIbBT37yEy666CKGDx/O6tWrVXB4AhnQEUQhxDXAiI5P8wG7EOLOjs93SSn/1q35HcB5wMtCiF8BEcyAbzhwSWeRbAAp5TohxO+Am4UQTwPPA5Mwd1J5C1UkW1GUftSZdVyyZjtpr4t0PIN/3XYKi1xgC5hTwGn90BJBDpJp3H3do3vOaOTLq1mTkMwa4yXPSEM4Tt0Fc9j6SiWVRpwqw8Zw0kyxQevnzsRZ34aobyOvtc2cjh7ZsWxbrUlUgNbWVq655hqee+45rr76av74xz/idrsPfqJy3BDd4qpjf3Mh3gTO2s/Lb0kpz96r/STgno5z7MAa4IdSyld7ubYFcwTxJmAk0IQ5snhXx4jkQc2ZM0dWVFQcvKGiKCe26maa3q4k9s8VNOfl4k8nKQja8QddIKWZLDJ/vLmd3mf7kPHZWesw4DbXB0YT5rrDbgHmksoE8czHu60465rxvvYBBZurGBPQaDlpJI+WTmBHOMvYrTtJ14fZZPFQO2kkZ5xcSLHPRjgFVy57h5wRuebaxU6GNKevb/rEUfhiKYPd+++/z2WXXcbu3bv51a9+xde+9jVVwmaIEEKsllLO6Y9rDegI4t4BYB/abwQ+08e2WeC+joeiKMrR0RHM5QXc5J1UwIhIHLbWQE5HHdVE2hyV616H8GAqDl7rsPu6x05usuycMpYxkwNs2xph2BtrqJ4znf9Mm47DIkhkDBoSBku36Vw72cKCsU5yqnL2zYqOJsxRS+WE89e//pWvfvWrhEIh3nrrLebP723pv3IiGBJrEBVFUQat7sHc+GHm6JvbDjsbIJ4yH+OKDi3oagibAWV3e2Ua773bSmDdDqJOF+6QmcncbHMSdbkIfbQdh0XgsAqCTo1ij4XxuRp5botZRqe3rOhw3DyunDBSqRT/9V//xfXXX8/8+fNZs2aNCg5PcCpAVBRFORLdg7k8P5w8FsYUQV0YDMMMtOzWQwu6OmsddrdXgDm3yJwijqQMDCmR9WHabE7GBS0ABBwC3e3E3hzFYR4ilQWLBQrc2sf7OndmRXuc5ginx6mKZp9gqqqqOPPMM/nDH/7Ad77zHV5++WUKCvZXSlg5UQyVMjeKoiiD096Fq/P8MH8czBhhftwQNoOusyb3PeiaM8Zcgwg91yCeNbmryd67rRTkB5hnzxBym3sBjAtaqK9NU+X1EklJ7BZJNAPFXo1hHs3c17nrYiEVEJ6gXn31Va644grS6TRPP/00l1566UB3SRkkVICoKIpyJPYXzB3JKFxvtQ57CTBL/FZKImH4aBtEG83dVCaXQlk+eXqKs/w6e2ZOIhoFhyGZmGthQq4Vq2ZhbpHtyN63MqQZhsE999zD97//fSZNmsRTTz3FhAkTBrpbyiCiAkRFUYa0/twD+bB0BHNNb1dS9WEjjV4fxuypTPUHKNlvpw9cwqbrugcJMGs21tPwz1U02124cwqYONpCzvoqiKdhQjE5l0/m6yUhPtPxNaps0amJGuQ4P97l5Zh+rZRBoa2tjeuuu46lS5dy5ZVX8uCDD+L1ege6W8ogM6BlbgY7VeZGUQa37rUAvXYzaSOcggVj7cc08Nm7H7vCOhuasowOWhifa+0ZtPahhE1f77nuoeX49RSWoJukLtEb2zm5YQ/+aByG50DQAxOGw5wxVPsDg+JrpQysDz/8kIULF7Jr1y7uu+8+brnlFlXC5jhy3JS5URRFORLd90AG8DsEYFBelzmmQU/3fjTFs2xsNrBo0JYyiGckS7emWTC2Y7SuDyVsOh1odLS8LkNxewRRlANCEIhG8WzcTpM08Nc1gs8JrTFw2aGmhcqCEibXtBKMtJMK+QlPHUWz088Da2KMzrENzOirckw9+uij3HTTTQSDQd58801OO+20ge6SMoipLGZFUYasxoTEa+85+uG1i48zdI+i6ojOksoED34Q5+WdKZK6AcCWtixuG+Q6NdrTZtAYcHw8pduXEjad11+6NU08Y9Y77Aw0qyM6YL53CgJY40kAPDvrER4HojECOR7I9ZnJMXVh0LMU/uMtPJk0qbwg1kQa7wurqdnYSGPC6PX6yvEjnU5z8803c8011zB37lzWrFmjgkPloFSAqCjKkLV3LUCAaFr2zNA9CvYO3hwWeKdapymeJZKSuKyChN45orlX0NqHEjbQc1RSE2KfQDPfJdgzYSTW9jjWaAJre4xMVuKNxWFkvnkRl92sa1jbhlsYRJ1O0AS610VjVOfs597gU68vY9irqylobe0ZyCrHherqas466yx+97vf8c1vfpNXX32VoqKige6WMgSoAFFRlCFr71qAkZRBOMUBM3S7j/wtqUwc1ojZ3sHb9HwbAvigUcdnh5akQVw3umoS9gha+1iY+mCjo3OLbNTl5LL19BlknHYyukFaB+fMEWDrKHzYuYvLzgYKUglCb6/DXbEF144afFv24A234yvJwZpIU/jaWvJaWo/J6KtybLzxxhvMmjWL9evX869//Ytf/vKX2Gwqe13pGxUgKooyZJm1AO24bYL6uMRtEwdMujjYtG1f7R285bktnF5iJZWVBB0aWQMm5VrIdWn7Bq19LEx9sNHRzvcuSkN8cNostn/tM4yYXYxvynCIJqGlHWJJcFphdzOeoJMRZT7s2QyBt9fjy6axDwvid1nQvS50nxvn2u1HffRVOfqklNx7772cf/755OXlsWrVKj73uc8NdLeUIUatRlYUZUgr8Vv7nFjRX0ktncFb5xQygNOqccFIB5eOd3Ull9THzYDurNK9kj/6UMJmbpGNpVvTgNEj6/is0o9HgHq+dzeMdprJLvE0tEXNLOa2GHxiGtS14Td0/MPcsC1Lvh7j3bLJWDMGTqsgYrNDfVjVRxziIpEI119/PUuWLOFzn/scixcvxufzDXS3lCFIBYiKopwwGhPmyGF3Xrs5+ngoDha87Tdo7Uv9ww5775TSa6C5z0m9BJ4PvgKFQXNEcUudGTCGfLisFmZMymFLW5a2lCSUSTHqpFzyVBbzkLVhwwYWLlzItm3buO+++7j99ttVCRvlsKm/BIqinDB6G/k71KSWztHB9nSWqohBjlNjfK714MFb9/qHhUEzMWVp+QHrHx7K6Oh+dSbF5PnNB8COeviomjw9RV5RZy3GDJw5/sjupQyYxx9/nC996Uv4fD5ef/11zjzzzIHukjLEqTWIiqKcMA4nqaW77msYJ4asTM234bNb+lY/sHv9Q02YzwG3efxo6i0pxmqBG8876DpIZfDLZDLcdtttfOELX2DmzJmsWbNGBYdKv1AjiIqinDAOa9q2myNaw9gQNkcOu/O6zADtEBzy1oIH2td57iHdWhlkampquPzyy3n33Xe57bbbuPfee1WWstJvVICoKMrQd4hr+w532vaI1jB2TvV27pwCPesf9uE9dN/Sr9BtTpf32KXlQNdRo4PHlbfffpvLL7+caDTK448/zhVXXDHQXVKOM2qKWVGUoa1zbV8saY7QxZLm59XN/X6rIyrMfaD6h318D50jmAWtrQx7dTVTlrzJ5GWrWfd+vdngGH4tlIEhpeS+++7j3HPPJRAIsHLlShUcKkeFChAVRRnaXlwLW2pheSWs3AJp/ait7TuiNYwHqn/Yx/WJjQlJXksrha+txZowt83zZNL4Xlr98cjhwa5T3QzPrDKzm59ZpYLHIaS9vZ3LL7+cb33rWyxYsIDy8nImT5480N1SjlNqillRlKGruhle3wBFAbPmXyINq7aao3KxVL/f7kjXMO53qvcA6xO7rznc3pqhuGIrus+N7nURSRnsSFixZO1Y/r2RidkYOSNy973OpmozGNxcA9vrYHIpjCjoUya1Mjhs3LiRhQsXUllZyb333su3vvUtVcJGOapUgKgoytBVsQ0KA+ZomRDgdpjH11eZiRhHQb+UntnbftYnNnm9PdYcJjMaNTvCyNFBHMksHzVnATipyIPWGGaFz8/JDVFCRd0KI+9qgB0NZgAYjkMyDc+vNYPq4pD5XLFNBYiD2BNPPMENN9yAy+Xi1Vdf5ZxzzhnoLiknABUgKooydDWEYWoZlG81P3faAQn14X32NobDyAA+2jqnhXsb2QvHKZ8yFd3IsqFZEkmZ9Runl/iJtSTY7XDisQlGBSzkZlLohQES40ey+521hNwWc+QwmoANu2FKmRl87mmGhnYzoI6lIJWBj6rNkVdl0NF1ne9+97vcd999zJs3jyeeeIKSkpKB7pZyglBrEBVFGboKAuCwwinjwGE3dwkxgHOn7DcD+Ej3YT6Q6ojOksoED34QZ0ll4sDX7kwo2d1kjuyFE/DcalixGTxOas6ZyeMRN09tTrGhMYOGJKVL1paUkZNJMsGaYbYlzsg1G8l/831sre147IL1p87suc5xdBGU5Zv3jKVBAD6XGRTqWahqgjc3qPWIg0xdXR3nnXce9913H1//+td56623VHCoHFNqBFFRlKFrzpiPdyc5eWzXyBufnLlP0/7ah3l/+lSCpruKbZA1zBE8jwPGFkGrF1qi1EwYwTMJHzXRGO1pSTgjqIoazMyz4CzMZbVzGhdWbiL43gZkQQ5N8ycjHTZyXl6N8YnZcMHJH9/nmVXmNHNdGGpbSEVTtLqSJG02XBW78NsErhGhjzOe1XrEAffuu+/yuc99jra2Nh599FGuuuqqge6ScgJSI4iKogxdB8oM3ktjQuK171vDsDFxaPsw70/3AFQTAr9DI+Awj/eqIQy1bWZw6HKYayhzvKAb7Hp9C7qRJZoBHXBooCF5rybD+sYMFdYAzW43H86Zxu7ZE0nl+og4HLQ53Mytrup5n+G58N5m2NNMSjeItybw7qrH3xom7XCwLZRPJD/n2O3souyXlJLf/OY3nH322Xg8HlasWKGCQ2XAqBFERVGGtj4Wge6PfZgP5JCLaBcEzLI8w3I+PpZIQ0GATH2Y2phBgdtCKmsQzUAiadCekRQJmJBrIfBBlDaXj3QWkllJwCGYMsZLXnt7z/vsaTHXNr67mYihoXmdJN0BbOEYer4fuxBsyctnNhzWzi5K/4hGo3z5y1/mH//4BwsWLOCvf/0rwWBwoLulnMDUCKKiKCeEI92H+WAOuYj2nDFgs0BrFKSEeMp8DAtiKwzQEJeM9GtoQiPfpeG0aRS4NCSC8TlWRGGA4aTxOQSfHOVgfrGdPCP98c4snRrCkMjAlFKqZk6kde5EUvlBhGHgbAoTnTmGRre3o8OJfc9XjrrKykrmzZvHv/71L372s5+xZMkSFRwqA04FiIqinBDMGoZ23DZzVM9tEywYaz/s9Yd7J6SUeMWhBaAlIbjxPDNRpLYV7FaYNBysFkacOw6rAF3CuKCGgaQtJQk5BdPybeS5LYSnjsKbTBBv7mVnlu4KAuaooNOOxyZIuJwkikM0zZ+C7nGRwELAxv7PV46qJUuWMGfOHOrr63nppZe444470DT1T7My8NQUs6IoJ4y+1jA8WDmc3hJSyuskc4s0qqOy70W05441p5grttFa1comi4dtY0fisfj59BiDZ7dlSElJqVcjljKI6wKJpCmeJa8oxLbTZzJs004zACwImLUf955unzPG3G2mNUqx2822uiRGOk1s/HDiVhsRq4N5mSh4cno/XzkqdF3nf//3f7n33nuZO3cuTz75JGVlZQPdLUXpIqTsnwXax6M5c+bIioqKge6GoijHUPfgz2s3g79wih6jjUsqE8QzsisjGsyRQ7dNcOl4V7/dc26RxgeNWV6vSuOySJJZCDg0DANOyrNg1SwsGGsHOHB9x/Kt8KdXQTcIB3zs8PiJGoL2C2czdUbhwc9X+lVDQwNXXHEFb7zxBl/5ylf4zW9+g8PhGOhuKccBIcRqKeWc/riW+gugKIrSTV/K4RxyQsph3rM6Kgm5NC4Z7cDv0GiKZ9nSlqU+lqUmanDZeBsvbE/yxm6dArdgap6FeEbbt7xOt5HKQEOYGQUBM7t5zx5a/7KeFRk3csYoCstCBy/PoxyRFStWsGjRIpqbm/nLX/7CddddN9BdUpReqd9+RVGUbvoS/PVXRnTnVPZTlUlKfRrjc6zkuS373LOzP3luC3luC4aUbGrWKa8zqGzVKXILhICKuiwnDxMEHGLf+o7ds707i3QH3GyyefEnEgxf9j71581EKwrRn/UhFZOUkt///vfcfvvtlJaWsnz5cmbMmDHQ3VKU/VIrYRVFUbrpSzZyf2REd9/ZJWCHtfU6f1mf5KUdSZri2a577q8/rUmDgAN0Q6Abkqr2LFvbdJ7bniKpGweu71ixzax56HcTzoAl6Eb3uQms2wH0b31IBWKxGNdeey0333wzF1xwARUVFSo4VAY99d9DRVGUbuYW2Vi6NQ0YPdYDnlX6cfBnZkSbU8N9TkiBj/debgizO+OiaOJIwvk51LYb1ESzGBJW1GSpjxrMLLRx7RRzPWNv/clxanjtAiElG1qyWDVJPCOpbpe0JAwuGm3veetuiTdz1jVRNjaXPCDgECR1ieZ24mhqA/q3PuSJbsuWLVx22WWsX7+en/zkJ3zve99TWcrKkKACREVRlG76GvwdMCO6WyBIQeDj0jGd2wIWBolvCDNm2Vr+NW4yYS1AsVcjkpK0pgyakwYWYXRdf+/+nKNF2LV8C1pjmKm42J1bwg5fDkWtLZy6q4qCWBTLOj/vWydSbgnwTnWGneEs43MtTMjRWGs4WV7RyrgRfkYGNLa0SmzRODLX3zUa2j0gVg7P0qVLueaaa7BarbzwwgtceOGFA90lRekzlcV8ACqLWVGUQ9ZtfR9e18f7Q9ut4LKbW9oBy2vSGG1xXmuCNfNn4rBqpHSJzQKlXgtNCYNLxztpTEhKw63Mra4iLxoFTUBDmOb8XN6vTqF/uBN7Y5g1xcOxZLLEh+eR8TjRw0mCySSVp0+nNieHTFbSnpJkJJwl2jm54gOkbjAq0U5Zop2YFKz59Nloc8eoLOYjlM1mueuuu/jZz37GnDlzePLJJxkxYsRAd0s5AagsZkVRlH5ysJqHh6zb+j7g4+dlm+CTM7uajQtaWBlz4g03Ig1JSpekspIRfivt6Sw7wwbxjGREpJWcV1azxuFm1hgfeas2QluMUDLDqcu3sLs1Q51h5bzNm4jm57JS5pIWGgm3k5QuKdy4k9bRkjOqq9DrIjT6vISnj6b+pFFMf/Ydgg5JS16AiVPyGRWuBvcw8KtaiIerqamJK6+8kldffZUvf/nL/Pa3v8XpdA50txTlkKkAUVGUE1ZvBa+PpMRLdUSnYV0TDT4/gWiacUEz6xivC4QwRxP97q5yNdn2OFG/j+qYZLhXMi5owarBtjaD8bkW/A6NnPU7sOZ48cbTNL1dSd6mneYWfeVbcI0opKTMTawuQVFVKx8W5DO8toFNPi8WTZD2uDipvg5/cxgt6Ga3z4clmqD49bW0OCy8UjoKR64HEJxjtzNeSxGq2KaKZR+mVatWsWjRIhoaGvjTn/7EjTfeONBdUpTDpgJERVFOWH2pedgX1RG9qx7heYaL0fEEKZublbU6pwyDPD0Fs0ZBOE5zPMvKdhvBTJIRpGg8ZxrDExr5bo2sBE2a/ZhfbK4BdDRHMCwWij7aTsRqhzwfbK+H5hiMErjtGiMCFsJeN87mMAGbhTyXoDEO7kQc2mLUFQVwhtOMrqkmmEyQsVoIxaO8PGsWgaTEYTF3Z2mRNk5NtJJzNL7YxzEpJQ8++CC33norxcXFvPvuu8yePXugu6UoR0QFiIqinLD6o+B15yhkZz3C6okjcS97n2Ih8Hid7KiOkufKwIK5UNtK40PLmFTbgj48l7oL5lAwoZDz2zLURA1G59jIdwkmhyw4rWbQmgr5CZVvJmm34/Q4IScXNuwGl8NMgim2kIuBfU4p4v09bHYU4LcJiuwptkVTNDrc+NCZuHMXzZqNJqeLkJFhREsrU2r24NR1xogU1HrwlOSwqSTE/H79Kh/fEokE//Vf/8Vf//pXLrzwQv7+978TCqkRWGXoU7n2iqIMTdXN8MwqePAV87m6+ZAv0ZeahwfTOQqpGwK3TaAX51F5+gyqDCvBcIQWzW4GhwDlW6kcXsw782bzXnAYrcu2Et/RiMcmCHfrx4wCa1edxW1jymjd1cKONp1E2iBiCCgMQkkOtMXAMGBcEd6Ai/zJRdimlDEhHSXhsFNzzgzaygoZt6eGsMWGB8nMmmrO2LKZUGuYBe+tpCTcAgE3WluMYesqqQkGD/nreKLavn07p556Ko888gg/+MEPeO6551RwqBw31AiioihDT/dM4cKgubZvabkZiB3C+rm+1Dw8mM5RSL9DkNDBbYPMsBArc3PRim24bQJKXPDMKpptDip1QXs8iybsNBlZ9Fc3s2b+TIp91q51kOV1krlFGu836LyR8vOJqRMo211DS12UWo+TEfOm4ancTTxUiFbop6S1Ha/biudbn2b8hJGU12V4qWN3loBHMKx8Lc0WGyPq6wlEY2CxkHHb8cSTDG9tI+G00hLKYUVuEc1bGlhSmVCZzAfx3HPPcfXVVwPwn//8h4svvniAe6Qo/Uv99iuKMvTsL1P4EBMsDrvgdTedo5DjghZW1WUADSkNrJroGWw2hFkdd5E1dLJSYNcg5XSRrmllT7vBRaOtaMIMNF11TejLdzK7JcIwl4cPR5di17M02ZzsxgaNSS7OKyBYlkMmY7DDN5rpF4+neFIhJdXNlHy0jRHrmqhxeXjcMxzblPHMq/gQbyxB3G6nPeCnsLWNVp+PlNtFbTDAxtGjcFpgjhFlV0btx7w/2WyWH/3oR/zkJz9hxowZPPXUU4wePXqgu6Uo/U795iuKMvQ0hM2Rw+68LqhvO+RLHbDgdR90jkIGHII5hVbWNek0xCXnltn45Cj7x9cuCFC9ooWQ10WOS6MlaWBpT9Do92G30LUHs7OumbJla2myuQj7/ESbYoxoa2HF8DKK29rIawyz2+Vl8ZSTmT27kKakZHurTnqjZMHyDzjzpXcpcEjG5vvZXBNnTqqJuokjSG+oJO1xYpWSwtY2vIkEicJcNGFga0+QNiTBdJIdQS96Vva+n/MJrrm5mauuuoqXXnqJ66+/nt///ve4XK6B7paiHBXqN19RlKGnINBVMqZLNGEeP8Y6RyFf2J5kTYMOwLlldj45ytEzuJozBtdre9CFQPhd5GhpnNY0G04ah6Z9vOYxsG4HUacLd9B8b5VWJ9JmUNzWxvunzWJzs47bChYNmnemKfNrtCUNPI2tjHr+Ldo9FprtPiakdSY01BPNKcDV0MbO0SWcv2cPFiQxmw23zUFxYz2pHC+7QiFGkSaQTbF27CTq6jLMKbQS09Uy9U4VFRUsWrSI2tpa/vCHP3DTTTchhNqOUDl+qd9+RVGGnjljzN1JInEwpPkcjn+8pd0AyBgapw83A0OXVfDau3U0Pbb84yQawPjUbFotNhyNYdIOO2tOnk68IJcit0YkZWBIiawP02ZzMi5oYVzQgkVAvWYnt72dlC4RQuKwCHQJWQmtKYnQBKfs2Y1DGjS7PDitghrDhjfoZHo2ylg9jjPPR3PZMNrdLmzZLC6PHXxukkmDYW1t2NMpNp02E704hNuqsa5JV/sxd/jTn/7EaaedhmEYLFu2jK985SsqOFSOe2oEUVGUoackZCakVGwzp5ULAnDW5AEr8Lx3PcWC1hYC761hR9BD3thgVxLNOefM5OGL57I9kSWdBbsFprssXDDSRnVUUh+XFOQHmGfPEHKbu298YqSdp8tjVDm92DSYlGNld8wAAwJ2CCclmoCyZJRkyI+RyGDzWYllJONynfDBHiZIiVHdwjaHl+FGmDILSCFgXBG7pJu10yaxJWVlHV5GJrNYNWiIS+YWndj7MScSCW6++Wb+/Oc/84lPfILHHnuMvLy8ge6WohwTKkBUFGVoKgkNmh0/9q6nGFi3Ay3oocnmNPdO7pgKt6/dTsFJU6mOmu2m5NmYnm+hOiqpbNFpTRpkSktxrfkQgFCBl/HWDFcON3jUncvJK9fgDUdp8XpZ7/Azr6mG8Tt349IEDrugxe8jlIljxAQetwN/QzP2aJjVReNpjEn8iThBq2DduPG0u9z49RRbExbWxyyMTraT0CUfNGYZm6Nxbpn9hF5/uHPnTi677DLWrFnD//7v//KjH/0Ii8Uy0N1SlGPmxP3tVxRF6SedmczmTizm7ietAT8B+8dBY5NmZ/2GZtzTNT45ykE0LdkZzvJKzMBrhx1tWTQNPnQEcM6dxpZ1OynaXU88J4Bt3EgWbNjOKsNOLD/IlNZmLlu+gmZpQQzPJaoLvE0tFO1pYtf4kdTFdCaG20hE2nBdNAOtoAC3w4F1zTZ0h52ChhYacjTao0kaxo/Em0qxw+4lk5WM8GtYheCToxwD9eUccC+++CJXXXUV2WyWpUuX8ulPf3qgu6Qox5wKEBVFOSFVR3TK6zI0JszyNkdS92/veoptfh/pcJIp43xd+y5v3BFBulxoWYkmNPwOQWMiDQiiusBjF7htGvGM5H0RIDp1OgGn4JwyO84XKlgVt1NQ5KHQYyXYEMcvDGyaYLPVQVpAiyeIQ8RxpHUCkwqpzwvQsrWW8WNLCTdmqXd6sE8ajVfPMHz1RhyOBuodHuxGluaifJbOP4WWlCQ3I5mcZzkhRw8Nw+Duu+/mhz/8IVOnTuWpp55i7NixA90tRRkQJ95fAEVRjgtHEuB1bo8XcNBVnPpI6v7tXU/RPmM08yo+wIjEWdGx77IlEueN0WPZsSHJ1DwLswttpLMQTWfZ3GKuI3TbNIa5BVsjWSblWEkboAlBMNJOzOmiJSkp9IA1GgdNYM0aJLMwLmihwe7E0palelg+DYvOIc9twf98OTtqYgS8LipbDIotYE+maMwJUuPwYE+nKWtqIh30IYQg5BTkODXG5554/zS0trZy9dVX8/zzz3P11Vfzxz/+EbfbffATFeU4pbKYFUUZcjoDvHjGXPsX7yjsXB3R+3R+96QSszi1RsBhHu8PyaJcEhfNpjJtIy8SJumw8/LUaTTk5JDjgN2RLKtqdVoSWarbJTYLWAVkspINzVkSaQP4eMo6FfJTkE0TTpnb8UWcTsIJg+qEJJ2VZCXIRAar00om5GNLW9bsx8zRpFpijNdSWADPR7ugOcoHBcP4cNhw1o4cxfvFpRhJnQnbdtCWkliFOOGSU9auXcvs2bN55ZVXeOCBB3jkkUdUcKic8E68/yYqijLk7Z01bAZSRp8LO++dVALgtQvq43I/Z3RT3WxmTzeEzezpOWOo9gf2GZF8JuWjffZ0JoasrKzN4IlnaWk3kBJ0CUJIaqIGRR4LQadgd8RACIlEYtGgJQln5ZtJEeGpoyjcVUEsI6lvF0StHk7SBElhJSeboqEpg689SktpIQ0njSLSEUg25eZgXDibUMsezqhvYFssQ3UgSNjnxRdLUhAJk6OnwZDsdvuQsyVfmuY8oaaX//rXv/LVr36VUCjE22+/zbx58wa6S4oyKJw4fwUURTluHFGAx75JJQDRtDx43b/97AG9bspUAqGcfQLWqohBNC2JpCROi8AC7GzPomchaxi0Z2CaWwMBhR4Ne20Lk3fsxBuOEigJEmYk73mDNMQ95E2eyqL2aqq2t9Gcl8s71y8guKmKUdt2AzrbJo9l1fTJbDF8yLYMoYpmpuzczTx3Espy2HPmTLxJwfDX15K/tQl3OkWz14tuQCga5ZOb1uNb6WP4/PlQ7Oz7N2OISqVS/Pd//zd//OMfOffcc3n88ccpKCgY6G4pyqChAkRFUYaGbiN3UzMuaieORBvxcU26PgV4mNPTTfEsb+zWKXALpuZZcFq1nvsm789+9oDWVm/H+6k5XQkpkZTEZweBJJyChG6wsy2L3SrIcWhksgZxXVDkBgOQhuAiezuT9myk3e9EK8nDEU+w7OkKPpo+FUtpiLJRuZS7i2iflWViyNy3uWn+OB6ty+CyCJoSWZK6JJWQnFG7iwtefxe3kGjjcgGd0GvbCSTjeIN2PLUJMghKWlshrdMYCLA5v4CCnXv4YPEKuHEexZMKD/MbNfhVVVWxaNEiysvL+e53v8tPfvITrFb1z6GidKfWICqKMvh1jtzFklAYZLw9Q96ra8jsasKQkkjKIJzioGvnOtcuum0a55VZAclrVRkSumTB2D7U/WsIm3s+A03xLMtr0rzYqBHd08r79SlW1WVI6RB0CMIpSUsS5hZpOCyChA52DTxWCDgs2DRBoVtDYk43p1dupd3ppM3hIuTWWB61UeCUfGPlO1z5znsUvroGR20jrUlzVBLM/ZtPLrJhIGmIS4p9Vr6Sl+CL5SvJ9dhoCARYWZVg+4pdeKsb0FpipEIBnLqON5XElkgStjv4aEQp8ZwAmm6wJWtn/Yub+uGbNji98sorzJo1i02bNvH000/z85//XAWHitIL9VuhKMrgt9fIXajIx2SgctNO1uXlku8SnFV68Czm7msX/Q6N8zxWdrRl2Nqq8zyCfFdmv9nQ1RGd3RkX8Q1hsl4XbSmDArdGXjaFXhTglV1ZRgcEIyNtFHy0g4nN7QRLAzS5xzKjMIfZhVa2hQ3e25Mm3yUYG9QwEMwutFLZqpOqj6CdlMcpuVa2tGVxhtsZvquWdEpnY+lo7E1xiv72FgVBPw0G1IZ8ZGaNxjMqj/E5doZ5zJHFYa/uIpXMUCPtFNTVYU2mcFok7t2NGAbUlw0jOiyf9hTkNjaT9HswfG5cqTT43YQdDhp2hY/Gd3FAGYbBPffcw5133slJJ53E008/zfjx4we6W4oyaKkAUVGUw9aftQQPqCFsrvnrJlTgZX59G/On9z3bdO+1i03xLBuas+gGzB++/3I3nSOPRRNHMmbZWj6ozdJic1Cop7CnkzjOm0lJs8BR28KID9chgm5Co3MJ6imSL63Ge9ZMVln86AbkuTRynAKbRSO/pYXJW6s5qT5MINXKRN0G7gIq6nWG1zXQnIaM30/akLRF0kzYXEWZoZMsDpGotpDeXcP7F57GlSc5aFy+jebdbSS3VBGJZShubUL3uLBYNRxNzdjDUVpz/NisAks8gz0tkQLcyRTOdJqgkWFH6XAs0QTRPO8Rf8sGk7a2Nq699lqeffZZrrzySh566CE8Hs9Ad0tRBjUVICqKclj6u5bgARUEzIQQf7dgMJowjx+CvZNTtrRlsQgIeTrL3fSeDd058mgbkUeDYxbJVyspjkSocvhJnzeTZFGIUdkUwyuqGD8qgN4xDR3NOJEeiW3NdsJTp5PjFOQ4YF1jlmmpZhbu+IiM302bP8B4dxbeNad2A7YAmeYIGamxJT+fupjBqbuqyI3HEUKwy+oiT+qMbGhk/NoKohudxOxO6rx+RmSzlG7agTWdwbBZ8cgsBgJDE2ZNRUMnm0njjCTISHBk0uSmkxh2C2PWbyUtNLI3nHtk369B5MMPP2ThwoXs2rWL3/72t9x8880IcfC1qopyohtSaxCFEF4hxPeEEOuEEO1CiCYhxHtCiOvFXr/xQogJQohnhBCtQoiYEOIdIcTx81dPUQbY0a4l2MOcMRCOQyROU1SnvDLMso/aeCF3eJ9rH4K5RjGcgkjKwJCS+lgWwzALTXfy2gWNiZ7Z0I0Jibdj27xkUYid585mxSVnsvJkMzgEGObRCESiRGx2DCmJZwxiGUi7HZQlo5xZasNpEzisFibnWZi5u4qkx40WdHPKcBs5k4rh1AlQ08LETJRWr4/ImGE0uM0t8EY2N5OyWGj3erBbNCJWG1VWN/krP6LSsBPM9zDblkRLZ7BkMliyWchmsUXjpFI6zU43CQSZujCWVAbNY2fT6DK25+WhR5PEIhmqXF7iE4o5u363ue5ziHv00UeZN28eiUSCN998k1tuuUUFh4rSR0NmBFEIoQEvAKcCfwX+H+AGrgQeBiYB3+loOwZ4D9CBe4Ew8GXgJSHERVLKV4/5G1CU48yRlJo55KnpkhAsmEvT25Ws39CMKPCT/OREWnJz+jRq2f1+dosknpHEdI18l0axVyPP/XGA2Fs29N4jj+OCFt7abRB0SgwpiaYlVs3CvOm5tLWmaJZONMCqSbZVRfEFzZHP+cV2ABpiOo5wlNbiINXNOnvaswz3WQj5coiGBRVnncGO3FomL/+QQDqBcDlwySyGIdkcCJDKmgWyHVLSHk5TZdhINetM3FJH1OfD53XjDcdodXkwNAtJBFLTGNbSRkNeiHhuAMNhJ9ehYW3NkBOLkQgFGZaSELMhsy5z3WdJ6KDfy8EonU5z++238/vf/56zzjqLf/zjHxQVFQ10txRlSBkyASJwCnA68Gsp5e2dB4UQvwc2AV+hI0AEfg4EgdlSyvc72j0CbAB+J4SYKKXsW8E0RVF6dbi1BA97arokxDtzZhCfLj+uNwjst0B2R1mc1qpW1mXcyBmjKCwLEU1DOCVYMNaOtaWKPb9bQU5TC5niXLadM4fw+NH7lLvZe69lu0UwPsdCvtsMiLuSZAomwdJymm1a1xZ7w7NJVoyaQFutzsnDzOu9U60zx+fB1RIn7XYRzUh0Q1JRH2fUMA+FbkFyYgHvpqczeccOysJRtuQX4o+0EzUEiZSBJ5PBlYqxMreQstYWSqujDNuwle02DxksRH0e9gSCtCQTTK6ro8XrJexwoiEJ7mlkz7hSbEBIy+LOJNFcBmRiROJO2tbEyDX6PjI7mFRXV/O5z32OFStW8K1vfUtlKSvKYRpKvzX+juea7gellGkhRBPgABBCeIAFwJudwWFHu6gQ4k/Aj4G5wKpj0WlFOV7tHTRF07JPtQSPZBeU/Y1abmrWWVKZ6BqRPCUbofiNtRBws8nmxZ9IMHzZ+9SfNxOtKAQYVL68iXOffBmP18Oekjz05hhTH32BWd/8NEX+iV3X7xx9bE9nqYoYXXsVXzvF1dXfzjbPJ1yUTplKYnkl7XuaWO3wsm7MFBqFj7JMlspWSOiQ09RCTjTGpHWbSeQGqCorpq7NQpGeZP3IiRQmDOriktZQkOe802lJGERD47lsxSpC0Sj+dBxht7I1r4DNpcXctG41yYCXRrebnHAMZzLF9sJ8klgYHotT6/MTCfgIRGOEc3JoG+WnJBOn0uXFH6+lXVipy1ixpXUcDe2Q54O26CH/TAy0119/nSuuuIJEIsETTzzBokWLBrpLijJkDaUAcRXQBnxbCLETWAm4gOuB2cBXO9pNwwwWl/dyjRUdzypAVJQjVOK3smCsGfD1GEU7zCCvL1PTvY1a7grrbG/LUuKzdI1IfvBiJQ6/g5DfTbg5RTDoRo9pBNbtIFkUwmsX5DxfASEf0uOBmEEq6CVlA//ScjjHDBC7j3ZODFm7guDuU+J7j4huyfj5U/5kvMWCAo9mbq2XMNgVydKelkxIhPl05Xq2OBxsn3MSw7bvYdIHG6kaOY5dF8xgkyvAhqo04ZRBJitpjBvURg1cRTk8NudkZlVXkR+NEg96+XBkGZ9qrmbN2NGcaknQ0J7CY0myLi8fX0KnIRQg3+WkMr8Qi1WjZvIkRjY0YssY5O2qI+mN4WhrR7dYGFZdh+50kI4Imtwe2pwugof4MzFQpJT84he/4I477mDChAk8/fTTTJw48eAnKoqyX0MmQJRStgohFgB/Av7V7aV24DIp5TMdnxd3PO/p5TKdx4YflU4qygmmxG895Izlw97mjt5HLTc0ZZmcZ+kxIpnbHqHSHWQ+EHAIkrpEcztxNLV13S+3uZXIuHw2t2ZxWsBjEyQ8buoqG2mP6JT4rX0a7ezepime5cUdKWqiBlkDGuIGbrvAZRFYhGRCrpXPbKjB8LvRpJ1wFpKzJ5kJONLKrkAO9eEsmawZNDusEE5LsoDbplGdl8MreTkY0swwdFnAt2MTjfl5pMe4+GjYCGp3tZG/p4FR9XWEHS52+EMkbA42lgwnEfCAEHxi/XqiWcgakLHZsBgGUTT8iQRWYSGSF2BjXiHzD+k7OzDC4TBf/OIXWbJkCZdffjmLFy/G6z2+yvQoykAYMgFihyiwHliKmYSSC3wdeEwI8Rkp5SuYiSsAqV7OT3Y877dwmhDiJuAmgLKysn7qtqIonQ5nanr/SSaC0UELIwJ7/SkrCBBvTUCJg3FBCytrdWzRODLX37XrSnB0Hg217Th9XhxWMzj1xhPEhuV2BYCdo517b6EXdHxcAKJ7m9er0uwIG1gFJAxoS0kyhgS7oDlhbr03MRvjFVyEkwZ7ogZOi8BnszM5HWVtEiKpLHlOM2kmlQVNgM8GdXGDoEMQSUsMw7w+wDrhZk4mhSGdNCUMGt0emocNY03ZCJ6ZMp2C5lY+/dE6hM0CUjKuvY3YsDzWzp3GzJUfgEXDnU5hiUma83LQS/LIa21j27iRgz5AXL9+PQsXLmT79u3cf//93HbbbSpLWVH6yZApcyOEmIoZFL4ipfwfKeUSKeVizMSVOuAhIYQFiHec4ujlMp070Md7eQ0AKeWDUso5Uso5+fn5/fgOFEWBzqlpO26bOa3stokDbnPXOYUbz5iBmMsqyBgaF4+yc+l4F+NzrV1bz3XaM2EkoXQCInHynBrzPGk8iQRbxo7sul/gC/OhJYq7PQqGxNYWxdYWpemiOV1lbvJdgl1hfZ8t9La3ZanZWA/PrGLO0jcJvlBBfWUj4ZRhjjIKc0s9h1WQzELCEJT4LEgE2Tw/9kQSEAigOWlg7GwkVNfAzze/xwXvf4izvgVbRyJMyGXBqgn0LLhsAl1CMgtCgs8O60rLsLXH2VkVoS1pkJNJEkgmWFNahsMCiaIcXpw6lYzLTlkiir89yvaMhVDlbnJbw+gWjXa3GxsG9oxOneZgazBEsjDnKP8kHJnHHnuMU045hfb2dl5//XVuv/12FRwqSj8aSiOIt2MGeE90PyiljAshngNuBkbycRJLb9PIncd6m35WFOUY6fPUdHUzu/+9kdmNYURhgPDUUV1JJp2jfL2OSObkMu/zJ8PmXVDfRqggQOiiaczoXrZl7ljWXf1JvP9ZhX9LPYmCHCJfuJD2iaPJt5mBxtwiGy9uT2HRwGWFhC6RUnByto2Gf35I8bgAZWNzWbMtyvDNa/FPmUIqN4edEmwaeG2CdFZiBUp9GpqAB21FFNbVErfpDA+5KGtto7h6B1smjWFcSS5nZqKM2rCeHXkzyOSEGOkT1MQEeU5JUjeg49pjoq3M27qb4ckoeKxY6lq5qLqJSMrgoxGlTK2p5r/ee5vC1jaqfEGenT6d2vFlzKrejTUjCUTasRgGOe0xmj0eqouGUV1ShFtAc0kR6ZhBdcdU+2CSTqf51re+xf/7f/+P008/nX/9618MGzZsoLulKMedwfWbf2CdwZ2ll9es3Z7XYU4v9zY7Mq/juaJ/u6YoSr+rboal5cTDVjyFQWyJFIWvraX+vJkYhbldSS37S5Yp9hfCpML9Xz6i89GokWy9upQcpwAkLUkYH85y7RRX17VDLsHmFp2PmnXyXBqnFtuYUrGbZrsL/G7ygJGlHjY0pQmu20nlvCBjgxqxrMRZ28K0XVVMkXEKRwbZ6vbjb2rDHk8wMtJMe6sHVzZB7fSxNOaE2BIxmDrGRzhtULJpJ2tycijwWrmoTNCYlLxdnUEA46KtXL92FSXNLZREWtGiSYLCoPWUiVS3SM5a+yE31NazethwthQUMDwd567XX6Td4yEvnWBKQyM5Ta0IwKbreBIJJBDLD1KEjnHeePSApU+Z5cdSTU0Nl19+Oe+++y633XYb9957LzbbgbPmFUU5PIPnN//gPgIuwMxavrfzoBAiCHwGaAW2SSl1IcSzwEIhxHQp5Qcd7bzAl4AtqAxmRRnUqiM6u/+9kXjYSjV2cpMGhR3b1wXW7aAhJ6dHUsvhJMuU12UYGbBQ7NU61hdC0CnJd4seGcrNCUmhx8KkkEZCl2xpNZi+pw13iTkF2xTPsqXVYGypF9+OFj60COIZA39DC+esW481x01BaQ7GrkYWbStn86QxvJdfhNObxN6eJCkl5IQIODTCKUme28LpE/xUbW0hW2inNNzK2F072LM9zNnCxeOeYczb/BFjqvZQnI6TdjuxRWJ4EnFy315L1aixeNpjtFutTGxswG9kcKbTDIu2Y6mvp6YgH1cyhTeTobMcrM0wmFhby/jWZtqnjiTZHqbVntenzPJj5a233uLzn/880WiUxx9/nCuuuGKgu6Qox7WhFCD+GrgWuKdjPeK7mEkqXwaGAV+XUnZWdr0DOA94WQjxKyDS0W44cIkqkq0og0BHIWsawuaeynPGQEmoa83h7MYwnsIguUmDjS0GoJPvciDq2/pUb/FgOpNLNPHxTiqGlD1qKr5fn6YtlaUuDgFHlpE+C0JApcXN5dY04GBLWxaPDfyGjntsLqMDGstrDU7ZWYUn5IaAh+YUTK+rJ5BMMKV8PTXBYvYU5ZP2OnDuamZPYwznMA/1UXhxR4pQJsmoMTnMCiXg3XWUJ2y05/jwNsY598MNTN+6FWc2TavdQQYLxYYkmQUSSUp211La2krUYsGfTGLXdawYSCkJxWOEAz78sThpqwVb1gDDwAJkNIFIZYg3xRjx238T/epnyJ815ki/y0dMSsn999/Pd77zHcaOHctrr73G5MmTB7pbinLcGzIBopRylxDiZOAuzODvCiABvA98U0r5dLe2W4UQpwH3AN8F7MAa4JNqmz1FOToOtH3e3q91L2RNYRCiCVhaDgvmUh53E3CAKAyY08peF6DTkpC4Ewnc+YEDJrX0lZAGb1Tp6IbA7xCMC1poTxtdNRUtGLxfr+Oywmi/RktS8kGjzpQ8C8acMYSqN0LEQjihkZdNkWqJUT5zGvVJyYQcC3MsSSZPzgdNoDeEKdq+h3TIQzqjU+IwCO3cxftlZSR9bvKSCTbtlBQUenDEEzj0JM+dNIFL3q4kL+BmV1xQHzNw+tz48rIUfhjD0A1a3Xby9TT+TAp/NIpmGPjiMSIOBwVtYZyGjsPIIoQwS+Nks+Q3NEPWwJLNokmJObkOUmhkNI2mrIVESwrnv1cy7eKBrSXY3t7ODTfcwJNPPsnChQt5+OGH8fv9Bz9RUZQjNmQCRAAp5Tbguj623Yg59awoylF2oO3zgH1e617IGoDO54ptNI6aQqFbEJ46isLX1gKQ73LgTiQ4L6jDgklwhMFhdUSnMS5pS0KuU5LMwNu7M6SzBqcU2/A7NDY06+S7BWkDojpMK7ATzxhkJRRPLIBZXpreriRW3cg6m4f3y07C4w2itWfJSKhyeBjdGscV8hDa04Du9yDSBhGbA4/fgU2D2Q21bJ02jlfzS5lZVcX4dJRWn4+XJo2jOD9AVcWH5A134lmzk9KWOBGXi0RuHu0OByNaGihuacGOJC4s5GazpDUNZ0anxe5kpJ7BQOLM6EgBmiFJWi2E2tuRmoZFSgzoChCFlMRdDqyGQTIL1pqWI/oaH6mNGzeycOFCtmzZwi9+8Qu++c1vqixlRTmGhlSAqCjK4HSggtLAPq91L2TdxeuC+jbyTzKDSK0oRP15Mwms24Gob8OdHzCDw+6ZyIdp3fv1nPb+ds5qCrPd4WXTqBEYhbm0JERXTcVISjLCb6GyNUs4KZFSIiU0xCVzi2xUE2DpxOno47O8sj2NYUhskSxZCYaE2PTRtK39EJ9Dg7Y4eoEf7846GnP8xOqijK6pZVpLC8F4jMzodqpOncYbI/PY2pZhT7uBWJ/A3ahTun4dyYSTsNOFSKSZt249MmtQ63Yzta4Wu2HgsliIWq0YFitpzYovm6HdZsefSSGkQQaBQOLUs2hApluc1RkgArR5PGg2DYcG1cEgrQOUpPKvf/2LG264AY/Hw6uvvsrZZ599zPugKCc6FSAqinLEDrZ93t6vdS9k3SWagIJAj7I1RmEuDTk5hFOwYKz9iEcOAahuxvfSapy5HoziXMpa4/grPmDl7GlssvnZFdYZFbThdwhSOpT5LDSnDNpSEqsmmZ4nKK/L8PLOFA4LzDfaueTDLThaItS6vWwfOwJK8tBduazQphFs3oORzJK1O4ifN4uinfXYV2/HnUrRWJjLdpubEeu3Ym2KsGLKREaFI8yLRdjl8NLYHKa8UccShFBLC8Or6ihuaaHJ6WJXMJcWnw97OoPVMKtmlxeXYFisTK3bQ8zlxJ3NkLFasRgGQtexIMkIDRBkhEATgqxhYAV0oUE2S1qX6FYL750yk6mJY7tcO5PJ8J3vfIdf/epXzJ8/nyeeeILhw9XGV4oyEFSAqCgKcOA1hAfTuX1eOmt07Thi1SSTQ1by3JZ9ttbbM2Eko99ZCxG7OXIYTZjbzZ01+bD3eO7ze6nYhiPXQ9TpRM9IKjN2/E4XY7fuouHkGbxXkwVgTEDjnWodAVw8yo7TqtG8pZHi8p0UJ6NMyDjZ7PRTuXkX7S4nFp+PnHSSWas+ZJdrBh84AsQMH5tLTiJ08TAuWL+OghwfRk2EUI6XeLuFpqyFUXX1GJqGY88eCpqaeXf0OD50+PDHUkytruH9kuGM37OH6Vt3ErY7qXd7yI3HmVZfR8Ruo9Hjo9HrxZ9MEkomSVitxGx2vMkkuqahSYnNMDA0DR0whEarx03WasWWyVDj9ZMbixJIp/AnUzT4/CwfNQp7SU6ftj/sL7W1tXz+85/nnXfe4ZZbbuGXv/wldrv9mN1fUZSeVICoKMoB1xD2JTCbW2Tjr+sTbG3NkuMU2DWzpmBjXDKjQFBeJ9m7kPWpZ42DlyugugVKcmHhvK7p48MpW9Pn99IQZlSxj5X1WWqiWeyaJOVy4mgOc9pwO+1pg5qowegcG6eX2JBSkkUjt7mVgvfWUmtx8Z7NTW19lAvXlZN1OkhqFuIG2DUIyQx5jS1sPOdMgsW5NCcNVqS9VA6fyBm1e/hEdRPubJqE3YK0WLG4rWhpnak1NXxYVEyTzYlVgPQ5qfN4CTaF0eIZKkP5xJxOxjQ04DSyxCwawVQSXybDsPYwO30BvIZBVgi25+ZSHAkDoElz15WsJojZbFilxCKh2enEcLhYMXYcJc3N5MSiOCSsGT2alM3OJz5cz7S5XsB1WN+HQ7Fs2TI+97nPEYlEePTRR7nqqquO+j0VRTmwIbPVnqIoR0/3NYSaEPgdGgEHXWsID6bEb6XQoxFwmkkdTpvGWaU2RgQsVEflPlvrfdbVTtHGnTClDBbNN5/Lt5qlb47QC9uTVLamWV6TYWVthnRW9nwvBQHyjDSnDLOSMSSRtKS9Jc4el5v/bE9RUZchnJZcPMrOl6Z7+PIMLzdNdzO3uorKrIO0x0kqK7BmsxQ3tZAbiZDWNCY31jO6tp5WzU5BIsqF69aTqGqhIS4xJGxw5vDXcVP556TpRB1OYlY7ut2Gzaph1wRSCGyGgSbAaQGXTWNLyXBCkXYKI+3EbHbsuo5NZklrFlx6Gpth0Ojxmnsst7XQ4nTw2Ky5vDhpKjuDuUjNQsJqJWG1IgGLAWGHA2FkyYvFSFqs+GMxJjXVMzwRxSIMcuNRpjc1cHKhheLNu474+3EgUkp+85vfcM455+D1elmxYoUKDhVlkFAjiIqiHHQNYV8YCM4ps6N1yzQ1pKQ+LvcZEWx67H3KEzaadQuBaIZxQQd5Acy6iIeRhNI5pVzZovOf7Skm5lgo9GgkdFhVl2FOoZWY3vH/4TljYGk5eQEY49fYsSeGL5Pk+fHjiGckrQkocGf5yXtRRgctjM+1UuIVVK1ppE7z4IhJErrBmKZmoj4PWiJNsCVCzOHEpglGhdvQTyqh2e5g7Lad7PIHcVkgaoBVSN4qKuXS5Ao8qTQWBEa7Tn4kgl3XKYy2kx+PEg960Q1JUrPw5rhxXLDhIwoTMRo8Phq8fgyhUdLaggUDXzJJs8dDTjxOm8vFxRs3MKGxHgyDFODSddIWC0iJZuikbR7CbjdoGq1uDyObm0BC3Gan3eFgdCSMLMkltzUCDb1tad8/otEoX/7yl/nHP/7BZz7zGf76178SCASO2v0URTk0KkBUFKVrDWH3dYLRtDykNWh9vUZ1RGfrRy1QGCBoFyR1ycpanVMK7eQ1hA+5792nlNtSBh6rYEfYwG0zA8I90SyVLTpzi2yU1ySpjruJjZ3CmC07ob6VlMPDinHjiIWCOISgMa5TH5P4HAYfNKTJdwvakoJPWtyMFWm2ZB1E0hJXLEaNx8vwdBtFmTitTjfZjE5uMsFb7hA7DTuBtnZaExKbFYSAmA7NgRxemzqZeVW7yKlpJBSN0pAToLHIQ6gtwtk7t7JyzBikw06hnuLpqZPZMGw4t739Ok1ON+50kvxoOzZp0OJwknA4sGazONJpztu2jZ05OTjSKUKJOL50mixAx7SyLiRRm52Vo8ZQFQpxWuUWCmJRml1uInYHUYcbZ1anMBKDehucPPaQvx99sXnzZhYuXMimTZv4+c9/zre//W00TU1oKcpgogJERVF6ZA53rRM8xN1K+nqN8roMuQV+fJk0usOF2yZoT+u8uj5KTp6beGXikBJkuk+Pt6dhfI6FDc1ZPmrOYNEEQkr0LHhsgvsqkpxabGHEqBDluQGeDKYocMP2sCSYlpRGWin9cDvBSJRo0MeHZWVU5+fgsUpezSvl0k3rcbkM2jU7YamRYxhUTJ3I3B07cUdipB12tg0v4iNcOBNJGt1ekllIGpBrB12CywJvnXQSOZok3+ZgXRbcqTRjwi20eTwUtLTx+ZZVbC4sYlVxKbEMVJaN5IGzz+WKlSspDoexZLPUejy0ef3kRKP4Myl8mTSaYVDcHiYnkUDryGzWACsSi5Ela7NhzxpsDYbYlltASU4T7kyKjLBQ2h4mlkzS5PVgNMSJjs/BO6f/d1J56qmn+OIXv4jD4eDll1/mvPPO6/d7KIpy5FSAqChKv2QO9/UajQmJe+Zocl43i2DvylrYvCuGNZpgx4zxeCI6tVHJ3CKd6qg86M4sa+vTzCm04ndoHaVpBJNDsGxPhpBL4LIJpgUtJLKQ64S6uMRnN9jYbOCxmWsmS3warroWTnn/A7ZJB9VuL75okjM/WMeb06bQXpBLXSCHpZOm8pltG5m5ZRuxeAKPRWJ32nhj6hRmV25BAssKSwmlk+QYCf4+eiyaMGsNJg1zbaGehd2BHF6bNo3rd71AIJUmpy1MXX4I4bIR2BGHdJZ3i8qQNhsLN6+jYngZOW1hQPLuuHGMrm+gsD2CJ5UiNxFDk4CUZIVGKJHA0hEcZulYaN6xY0pa07DILF9cvYKw3UFpuA2HnqHZ46XN5iAvHmNkSzMtXg/vnzKN0/uh5mQnXdf53ve+xy9+8QtOOeUUnnjiCUpLS/vt+oqi9C8VICqKAhxZ5vChXCPfJWiy5qCdNxOtYhtVm1pI+LzUzhiPLxSiqiVLkTvLnz6EM0vtB92ZxWGBd6p1puVLIimDdU1ZXBYI2GFqnpVEVjKryMbqep0chyCckmxpy+K2wbgcCx80ZZmQo1G4bSdbDQctdgeGAQm3i7QuGL+9imc9QUp8GqGMwCmzfDhuLA1WO7NSbXymrYGVIsjWyeMQSNzhLFGngxfGj6PNHSTXCpks6EChR6M+auC3C2KBXN6aNIl5W7YSs1jJjScYtXs3GV3S4vEwOdJExchRpBtj3PDmmzT7fEyqr6PN7sKdSbMxr5CR4RYK2zWySLIWCxbDLIptVjo0matIBRlhwZtK4dJ1pCFpd7nIduywkh+OMEKaO7FE7Xa25uWRW9VsJg31Q5BYX1/PFVdcwZtvvsnXvvY17r//fhyOo7e+UVGUI6cCREVRjqmuqeicHN47aSpv+tKAYFTAwhhD4rZqfNCYIcep9Wlnlun5Nl7YnuQ/23RmFFgZH9DYFjaI6xBJG0zKtbClLcu2Nh0pYVzQQiQFQYcABFPzzGuNysRY7nARsAliOjisgqzTRWFDGwkdPFbBrOpduHI92K0OSpCsieaS8Hn4KGFhdWkZM3btxiEiGBKsAmxWCDg0NCGxWQXTQ1ZejKVpT0ssQufdolI+vWwFvlSSuMuBkdGxIbDpGUY1NJDJwulbNzOyxZx+duo6fkucpNVKKWFSDjtRhx1nRidttRBMptCk0bU7ioY5imgxsl0fZ4TAgSSYiBO1ObDKLG5dR0dgWKzEbXaKolHsmjzspKHuli9fzqJFi2htbeWRRx7hmmuuOaLrKYpybKgAUVGUY6pzKvrFHSnK63RcVkHIqWHVBJWtOuOCFupjkgm5PZMWvHbBpmadnZEsAgg6NcYFLeS5LeS7NZqTBq76Vmbu2MX1qSitfh8vZktYZ4TIdUKJV2N9U5a6mEGhR6MlKZHAyUU28twW/ONySVZFkX4HiYzB7oiBjCRoD/op8YAQGgXRGLG8AGTBZdUo9RjsStjJq67h/JYwFsMgty2Mb+dOzli/nqfPOI0No0eiS8FnR9qpjkKJV9CWkjSnBJmcHGr8Acpa0mi6QcLhJGq3Y8lmKWtsot7mZkRLC1Y9izuTIWa3E4rHiFrtGFaNhMuBMMCR1UlYbV1Tyt1zz62Yo4lpIGGzY1gtZMiQFQKnoWMxJEJK0DSsUpKbTGJYbQTDbdDgPuzvs5SS3/3ud3zjG9+gtLSU5cuXM3369MO+nqIox5ZKG1MUpVfVEZ0llQke/CDOksoE1RG9365d4rcScmmcOtzGrEJ7VwaroyNI9NgEwzw9/zztCutsb8visAicFkh1lLBpimdJZuF8S4TPb9nAZJeBrTiXIqEzp/wDRrS3kDYE+R4Li8bbGe7XCKckWQMm5VrIdWlEUgYflJWRm0qSbYuRycIoLcUYS5rWKSMp8Vvx2QVGnh97IgFAXcygKSkJZlKMkUlyrZIxdXVYdZ0mvx/DonH5u+8xV48wNiioqNfZ3a4zOd9Gvksj5IARkVZy9BQ5WR2sGpV5BVikJLc9RrvVQUGsHVs2S8piwZtIUtzaijVr4Ekn8SVTvFo2hl3BHBJWK95UCiFACkFKCHRA79hSLwsYmkbaopkjhUIjabNhNQxERzipSQNdmJ8FEzHiWxqg4PDKzsRiMa655hpuueUWLrzwQioqKlRwqChDjAoQFUXZR2fpmHjGrI8Yz5hrAPszSGxMSKbmWbFoUOrTsGqSWMYgpsOts5xYNQuRlIEhzbWFG5qyTM6zMD3fSkI3J1FdFsEHjRmsQjB1VxW6z43udYEmiDqdGH43F7fUcOEoB+OCFppTkM6C1yH40jQnpX4r9XFJQpe0F+QS++QsbF4nZfF2HH4n0QtmkByWz0WjHSwYa6dqwghq9sSINcfw24BInHRLnGrNgb2xjZTDDm4HNqugxe0hoxt4P9hOc1wSdGiclGuhNSWpixt4G1u45KP1RHweGkoK8VgFo1ua2OULEHY5SLjsOLJZ2m02gukUmpFFdiws9KfT7PAHaHV7qAsEqPIHEYCQEmEYZkkbi4WURSMrBFkhEIaBTUoMIYhZzWxmYRhmcW7AKiW6gHa7A6lpxOuiZs3IQ7RlyxbmzZvHY489xk9+8hP+/e9/k5OT028/N4qiHBtqillRlH10Lx0DPdcAHmkiS6d8lyCe0Rgb0HivVqclIfHYBJ8osfGpcZ6uTOXOjOjRQQsjAlY0ITh5GKyuz7C1VSeRhU+NtiMa24mUBHFKSVKXNMQNUi4HmzY1Uz4uSTglKXBbcFrMot7ldQYLxtop8VtZUpnAZRX4CwppKs1jQ1uW+phBayLLJZYwU8t342iOMDXk57HJI/HUt+JvDOPKgCedZNaWrRQ2NtNYlM+mUB4xqxOfniHs9+FuiyL3NDOsvIphiTihHB8t+cMpqayiWnPSVjicU2t2Uh3MI+OTYEhqckMk7DZKwmHc2SwZzJ1WBGAIiFltaEIwu6aaNqcbv9fHrnSaolg73nQaizQwslmsHasRDU3DJiXOTIYUAlc2i1fP9Ehk0aFjdDJNq8/DB6XDmXaI6w///e9/c+2112K1WnnxxRe54IIL+uVnRVGUY08FiIqi7KM/dlY5mLlFNh5Zn6Cy1WCET2OEz0JrUpIxBNURfZ+M6CWViR6FuLOGYHTQSsAhKPZaSeT4cLUnaPO6u6ZGRlsybA/62NaaJWVIXFawCI2Th1mxW+gKeLu/3zy3ua6xIabz/NuNeCrXsyPoJpTnJzeRZkZVA38ZOZkSv4UL3nqPwJ5G6nJyyGtppaSxCUsyzda8fITFwraCXIQGl27eQL3FwU63B38kyYW164i3pmjxeShtaSIdzVCYjhG1WhFSUhUIkh9tZ5s3yFRZBQKyQpCw25FSkrJoTGhqoCzSRgZBfSCAJiWObBbZsZONHZCd08eGWebGahjkpZIIzIDQnJOWaIAhNFxZnbTFykfFxdQU5Pf5e6nrOt///ve55557mDNnDk8++SQjRow4op8PRVEGlgoQFUXZx2HtrFLdbGa9NoTNtWtzxhwwA7bEbyXfLWhIQNoQ+B2CM/Ot2C2i15HK7oW4K1t1hDDDn/E5Zg3EzNwx5L6zlhkhB8sjFjyJFL5smrYzJkEYNANakpJLRlvJc1swpGRTs7nOcm19GofFzIjOc1toimdZVq0zZedOdmt22lN2MrvTFHg0EpqDyTt3UuSx4GuPkc3xkrLb2MhIpu+qIi/aTsTj5r1xY0kYGikD6uwOWuxOAppG2usmqcNJ4VqmttRR7/CwOxDElc6QkzATUNYWDceZzTK2uZE6r49AIoG0akTsHbumZDLomEXAR7W1EkjECWTSJK1WHLqOLZvtsX5IIs0dVTBHCztL32hSghBIaRbTjtgctHi9pCw2CqPtfSpz09jYyJVXXslrr73GTTfdxG9+8xucTucBz1EUZfA77DWIQogzhRB3CyEeEkJM7Djm7Tge7LceKopyzM0tshFO0WMNYDhlHu9VdTMsLYdYEgqD5vPScvP4AUihcU6ZgwtHOZhfbCfPbcFrFzQm9h2pNLOf7bhtgt3tBgGH6MpABrCUhVh/6kzwOMnUtmH1Oak/bybxwlzcVjOwjWU+vm5n0ks8I5lTaKUtCW/vztAQ0/mgMUMsYzAsHiNid+Fvb2fkxm0UvLOO8TW1TGppxNHSTkgz8AYc5Dg1hhW6aS8MIRC42qLUSxtLJ03FpUGDxYGehXjGIJY2qBV2vKkkmaxZesaQ4EqnKG1rYXbNbs7bsolLP1zDWds2Y3T0t93moCqYgyVrkJtKmusEbTYSNht5yQS2TIasZiHidJLWLF3ngZnFbIGu+ogCsCGxYq49tCKxSIknk8auZ3hn9Di25+TS9HblAb9/q1atYtasWSxbtow///nP/PGPf1TBoaIcJw55BFEIYQEeAxZBV7mtx4FNmLMWzwC/BH7Wb71UFOWYOuSdVSq2QcAN/o6yKJ3PB6mjd6gjld2nneMZ2bVGsvM8z8gQjC+h+aQE8YwknZWsqssQcmrUtes0J+Dh9QnG51hIZCSnFNs6dmDROKtU8EGjTkW9TjQlaYzpbLJ6KNjTyLA9taQcDmIeN6WZBGNlgld8Php0QUEmQ74wyK1pxOEWbMwPsc6XhzWdJWtAjcuLO5nCkskyprmRvEyCJBYyEt4cNZaxLU0MD7cxLBphS24+U+pqOH3HNgyLRlqzkLVoCGngSySYH2nDpuvU+gI0/v/s/XeYXOd5341/nlOnl+3Yhg6QKAQbWCVR1RJliSp+FVe5W4kTx3YSp9j5JXGc13bixM5rvVZ+tiXFtmzHdhRLVrGaZUmUKDawAEQj2mKxvU+f05/n/ePsLBYgCIIgKJDi+VzXXLN7zpk5z8wsDr5zl++dy6PLiGoqRToMkJqOa5joUbjme9hBwgXm2RfTEY2GlPS2WowsL7AysJWJMxV6LnG8Uoo/+IM/4Od//ucZGhri4Ycf5tZbb33ezzkhIeHVx9VEEP818H3APwduZN01RynlAp8G3nlNVpeQkHDdGC4YvG9Hmg/ty/C+HenLN6cs1CCXvnBbLh1vvwwvOlJ5hY/r7D+0GJDW4zq7UAl6MzqWBvNtSSuMm2I69GR03jRqsamgIxVMNRWnt27khrk5QiVwLZPuuHIPfe9G7hzUMfoKlFotrIlFAqWo1H1amQxTm4eIimlunZrg4cFRtlZXePepI9w0P83G2XlumJ6ip9Hg7nNjFF2HvO8xXuqiaaewwwjfNNEjiR2GSAQ6sJzNcqq7F9c0MZXEjEJCXcc3TTxNx7VMpICi66JzoRfilV7oDSAd+PzAk0/Qu7DEYi7/nGPa7TY//uM/zs/+7M/y1re+lSeffDIRhwkJ34VcjUD8UeATSqnfBZYusf84cO0nvCckJLxy6StC07lwW9NhKZe7rJfi+rTxfFuRMcVaZ/HleKHHdfZ7EXiRYtlT7OszuXPQ4q5Bi40Fg01Fg8NLF66n6SsqriRlQNoUzJbLzPV208qmyTTbeIbB/J7NOIM92ELQ+sE3sHTLDroDh5yhcbyvn0d27MAr5bGLGTa6TZr9ZVxNkG+0Mb2Aqp1mJl9koNVg1/wsnqZTcBy2Li/Q12ziGzpj5S6UUJhRRDoMmMvlMVA0bRvPiMVjT7PJYLXCaLWKHoY07DQKMKMIyerUFOLo4YtpLdKBDY06+088i7xtywX7zpw5wz333MOf/umf8qu/+qt8/vOfp6ur60U8e0JCwquFq2lS2QT89mX2V4HE9Coh4bXE7VvjmkOII4dNh8p8k7+98SaMVS/F9fOU1wvAFzMDumN9s+jEaejhXBwBXHTU2hi+9SLxezbZtAPFIzPB6mg9cMI4pb21qPH3EwF1T5Kz4vXVPCinNBq+4rZ+g4OLESf7+smVPOYMm7Ktc3e3wbPn6iyJNKdFgRve83pygwUMx+eZBYEKFZ4nMZoOK5kcSNg+PUvdsLA1BbpgoFEHBN3tFjuW5hlo1BECXN2iks6go1jO5ABIhwFF10VTipQfEEpJLorItAN8XcMTAkvXSXsuBc9DUwqdC4WhuOjny6EBQkq6qk3CbefLAz7/+c/zIz/yI2iaxt/+7d9y//33X9FnlpCQ8OrkaiKIDeByXxm3AYtXt5yEhIRXJcPd8MB+yKZgvgrZFI/evg9jtIuCraEJQcHWKNqsCbkXy8Xm3ZP1kN9+wmWyHj6vmXcn1WxoinagaAeSdhDPY24FipIND037fOmshxMqHthmsaPLwNQFXWmdNwxbrOzZRKbtUvI8TCEZn2qSbjtseOM2vAiOLkec3bYRb7lJpu1QcSVGw6HPdzg4OkppucK22go2EXXTIt922LU4jxGGeLpOKoyopjMspjPkAh/XNBheqVBut+lt1BmqVSl4DoGuo8uItIrnKTcsC083yErJimkRGSa6UmgoOu9ApzEFuKBp5Uroq9dYOr1MFEX8u3/373j3u9/N1q1befLJJxNxmJDwGuBqIogPAT8ihPiti3cIIcrATwJfeqkLS0hIeJUx3H1BQ8rkoTb91rXzUuyYd/uR4rHZkKfnA5SCUyuwuWRe0sy702zzxTHJ1ydD+jKC2wd0Gr7k4ZmIewYNNhaNteghxKLy0SmPx+cjgkhi1yW3ei7vPHoY0w84uWEDB995D0GqRNqTSAXfIk9u1x5GT4yzaarGnC+pC513nTxG79wS9d4SpYUKvbU6A806Wd/HCgKmikUatk3Dtsn4Ps1UCiOMsKOASNPI+hJX17GjCNc0sH1J2nPJyAhf0wiERsOysFC0dT32M9Q0UApPaNgyTjcL4pSzfoXvtQ60lWDhq0/yzl//CF/5ylf4yZ/8ST7ykY8kXcoJCa8RrkYg/jqxSPwa8Mer2/YJIbYD/wbIAv/5mqwuISHhO8bF6dv9A5fpWr4Cnq9DeaRWgb85ckV+ievX9PS8z5aCYKwGGTMWPZameGYp5NaBaM0i52IBOlww+Jmbc9y/5fxzzTQD7hnU2VyKm1rWi8v9AybFlM7WEkwcW+KdDz/KjsoSza1DTLUjulptdj55nFODXdSzJaRSzLYk+wa6ObOhm/qZRUYfPIiKQkZbDW6ZHCMMQnK1Fqkowogkjm6QDkOKnhcLQhky3KhzLJ1mY3WFpm0z1t3HnvkZHN3EDANKrSYDrRYS8BH4uk4mCDC8ODZohxFmFK6lk7VVn0hP04jQMJHI1bF6L4QAjgSL/Mvf/RnqXo2PfvSj/PRP//SL+wNISEh4VfOir/5KqSeEEO8HPg780erm/0Z8TVkA3qeUOnbtlpiQkPBy00nfFm0uWy/4YlhvbN2p8QsnVrjr+DPQn4v9EptOXLv4wP7niMSL12Trgi+eDdlW0siYBllL0PShZMOpaiwQOxY5zyd2O6/lDw+1n3dSzIG5gI1Fnb19JrOPzbDLdAl6C8wKE5GBtqGRrjbYfmaC6VtLjFUlcy2JqQVsKxvsn57EzkD61AJ1w2CuUGBgep50FFBNZTBlhKYkdTtFJgi4bXqCwDSZKHeR9j16G3XSMkIoaJoWeafFYKuFGYZEQiARGAJyQYAG6FKigJTbBlYbUzQNTSlc3cRUESYS7QrFIcBH1Rj/rP40xUI3Dz30ELfffvtV/Q0kJCS8ermqK79S6gtCiE3A2zhvdXMK+LJSqn3tlpeQkPCd4OWYvXwpL8XXVyYp9+fWfBKXDJuzTsDKJ4/R/t7bL4haXrymfb0Gj88EzLUkfVlF2daYa4UMZwRPzwXMtyIMIXj3VvOSYnf/QMhUU7HoKMYqAW6grUUQ4bz/4smVkKonafiwY7bKSs2nlctSDSIGszqNyCTvtlg+V+GbvQFOKBmtVdjw2ASpZhN7agqhCRqWQVWzcFIFBtUsZiTJ+D4L2Rzd7TYoRYRCIHAMEz0I2VGrYihoGBbZICBC0eO4KAWmiiOCOmqt40RxYdOJIh6x50iF0rS1UXtSCC5vHBTjqIh/qp7ijxnnPn0D//GD/ykRhwkJr1GuOn+klPKAz6/eEhISXsW8XLOXn9Oh/I1mHDkEltoRj82GZC2b/lqdo8H5qCXAV8Y9NKCY0theiucj39Kvc2IlouopejIab0wbfHsmRAjoy2hkDMGHn3IppwRby8ba45adgI8947O7R2e2JTlbi/j2VMD3bFbc1Geu1SDuKAu+NBaha9C9tEJ5ZonyxDzZlEWr3MW0StMV+XiawRk7i1KS/EKFu48dppFKM5PJM6IM7jo3zsGRETzLwrPTTOdKdLVa2FHIbKqIUpALfYwoFr+nu7oZrtdACKrpNHoUYcgIQypSYYAu5ZrZ9aVE4cW/p1F4MkLXdE729DJcqZKOIi7HWdXi+9XDPEWVf82NvHnLA9zqhpd9TEJCwncvySzmhISEq5u9fDV0/BILGU5VI7ImFDwfv7uwGimUfHHMJZAatg4aAi9UPD4bcscGGMjqHF6MeHY5wNQEvlRkLNhUMJhtxqnehicxBBxdVHx7yuemHgOJouYpji8LMib0ZzTmWxF/cdzlVCXi9cMmD2xLcWAuYHePzukjC+S+fpDxXB47l2PTwgL3LixSS2fQpMTtzjOUyXH/Yp29U1NYUchMusCZ3l4ObRhi/9Q4t58eY65YINAMGimbxUwWhMZSJkfO82hZFk3ToqvdZrhepeS4OKZJqGkIXSPrefQ4bcwowjcMpJTYUl4gBi/+dOK4b3wziLudu5pNcuHlO8e/pOb4oHoUCfyNuJe3iCGOKUnevMaff0JCwquGqxm1N/YChyjAASaArwAfVUq1rmJtCQkJ3yEuVS9Y8+C+kStJTL4I1vkl1hyNnsjDaDos37ETiKOWD02HvG7IYl+vyeNzARlDI21IHpr2WWxJ8hZoIvYzHK9F2ELhhbDsxP26TS9iwdHY2yso2XCuGbHUlhQtGMrrhFJxqhpRSmmEMmTFlXx9MkAIwbIj6UkLtpwaxwlCCit1dD8i5/ukZITutFm2UzSVjmXp3HF2jM1zCzw1PEq51eT90xPULBtCSdlto6EQKJqWxXSxTMVOUw5cpBb7F/Y2G+QDHy80EVFEVknqlk1fq0nG99FlFM9NlhKl1FoU8XJ09ivAikL6HImmLm1yI5Xi/+YY/0kdYy9FPinuYYvIEaLYNDsPm++5Bh96QkLCq5GriSBOAIPEfoc14Ozq9s1AkbgW0QHuAt4B/CMhxOuVUok3YkLCK5QXPXv5qk+06pf4xBn6xpZoFPNU3nID7kDcoNL045R2zhJoQuOOgbgBperCdEMylNMopTQyZiyDltoOC44i7yk0ETdvNALQxGpnry6oeIqULmj4irQhOFGR2Logmlhk+9FxultN6Cnw7T2bmC52seIE/PDJGYbmllnWLUAx1dWFrQkMz+fI6AgpU/CG6XM8nCmSzebYtjBHpOlEQjBcqxMY2qrvoEJTirQfUrMUxzYMcq6rm756jV94+BuU220CTUOLFAJJEOls8OvokUQJsSr2BJGUmFxoU9Oxr7lcjM8AhJKXPGZF+fyYeowvMscH2chHxK1khLH2OCMMoSt3NZ9yQkLCdwFXc/X/ReCrwD8GPqaUCgGEEAbwIeA/Ah8AjgA/C3wY+LXVnxMSEl6hvJiJJi/tRLFfYt+bQx5dbSbJKbUWtbyt31xLd/dk4hrCuidR+GhA2jgvd0IlEQJ8CWkDvAg0DbIGmDpUXEnR1rixrPPXp3y+NeXR9BWD1RV2PPQMjUyaxXyeYsOl56tP03r9zRykgFFrEyhoGxapIKBmpyh6LkXHQaZM2ghStSbhYA/nSmXeNHaKc8UyTdNmo7eCFIJA0/EMi3PlLswoYKRSZfviAjuX5rn77Bn6Gg0QAgUYMsBYjRaiFEpoRLpGqHQ8XScbBqDUWgq5c/9Cn5bi0hHHp1WFD6iHmcbhI+JWPsQWhDj/vgpAhBLkS6tBTUhIePVyNf8b/Dfgr5RSv79+46pQ/B9CiD3Abyul3gZ8RAhxN/C9L32pCQkJ3008X9QSuKQ9zvufHWN5sspMKsM3BjZyNldkvg15IxaHaV3ghJKsDl4IVVeiC8GuLp2JhuKmXp2KC0tRRPGZc1RTabx0ipQBiypFyoLMM2fR9+2jlcuSbbXJBD6OaZFzXUBRT6eJ2gFZE84ZGcqNJqVaHSMMKbkOec+jZqcpOm0qmQwGEiUgHYRYMmRrZZnxUplu18HXDQQKS8bRQrEaJZRAIOJ0so7ClhG+0EiruMlEWz3mUg0r6+lEFy9OS/+xOsvPqafoweYb4k3cKZ7rQSkBSyiWcjl6XvpHnZCQ8CrkagTincAnL7P/GeCD635/mDiimJCQ8ArkWhtkX4vzrReOI7UKdx1/BplP81fFPBPnWtw5cZDo9ptY1Ius+HBTCvb0WYxVQx6Z8TF0QdoQ9GcED00H9KQF92+JJ4A8OR+gfbnJYiFPWoujjpECL2VTqjRoBnCsq5cFZdDfaNAyLXKeS92ykcBtY2cxsyaH+vu5+ewkjmZwprsXS4akgoivbNvODz5zkP5Gg0jX2MQKmcBjMZ2j6LsUfI9ICALLJOfFUVGiKE4HE088AUgHQRxdXPe+dKahXKmf4frjXBXxz9RBPsoYb6aPPxd30Svs532cJiXflFnef4XnSkhI+O7iav4X8ID9wB88z/47Vo/pYAPNqzhPQkLCy8zLYZAdP/EyPHHmOdNS1p9PR/LgZMQnT3i8acTg/i2pC0bkrZ3/b47ExtqFDGK2SborQ7spuGFsAveum1l2JVMtxaArqfuK2wcs7tpgsOxB3VMIoUgZcS1j3VMUbI1qd45y26Omr46Nk2A6HnPZHEEEjw6N8vblGs/0DvD4yEZ2z0zx9lPPMl0ssTDQwwZDsnt8mnPdXTTyeVTDZaBRY6yrQLfjUrdNRtstXMukr1nHDgLaukHVtjGiiKZp0e201yafaMTiMGDVpmbV/LoTIewkeq+2p/icavH96hGeoMK/5gZ+TexBF5d/Ntcw8B47A28cet5JNwkJCd+9XM3/AJ8FfkIIcRr4cMcYWwiRAX4B+DHgT9Ydfw9w8qUuNCEh4cq50qjgtTDIvvhcd0Z1Br/+NBQzYOjw4DH434/Am3dzeMeNFLvL+JHiibmIjAkDGcHR5ZBAPo8wXahBf4mldsREQ8b2N6UMA9UaJyydIUMw04xQQCNQbCxodKV1dnTHLR2FhSXkV89yi+ZQLWT59tAox0Y28sZDz2AIqFo2luuRcRy+tWUbugZThTKf3bWXO2cmGGw3yAcBX91/G2fKXWRNwb3DNs4XDzBh5zizcwsTDUW51WL70gL3nhvjZHc/uoynnGR8n7yMGK1XeXDjFja0mqQCHysML0gRR1xYV9jZHnI+nXw1F+yvqDk+qB4jQPIpcQ8PiKEXfIwCKpksc5odC/1EICYkvOa4muvNLwG3AL8B/JoQYmZ1++Dq8x0G/iWAECIFuMBHXvpSExISroQXExV8qQbZF5/rXC1k7rNHGdI1huoOO0+PkyulYKAIRyfJn22jveM2HlR5MiZkTA2lFFUPijaXFKZLuRyHj9Z40jFwQ0UowGg6rNhZBIqqL3EjwY1dOiVbUPMUj88F3DEAw/Uqux5+hnFhM9+To7XisH/6EJU9e3n0tr0MPjtBV6XBQibHt3ZvY6m7DBKEgLlSmc92ldmQge9/6Nt4vSWsQCGVoB0oGukc3bUGZ5ab3DW3SN518DWd8WKZmWIZqWnsXpynqWtUMxm62i22Vit0N+pYClzTxIwiUqvRwgCBgVpLC0sgFIJaKk3G98lEL860WirFb3KcX1VH2U2BT4p72C7yV/RYH2hZKTwniAV6QkLCa46rmcW8IoS4A/gZ4F3E9jYC+Hvgc8Sdzf7qsS4X1iMmJCS8zFxJVLAT9Xt63sfWYV+vSU8mjri9GIPs9edaakccX5a8rt6k0ltky9g5TnkGWzWTQkpAtYXdlSV6eoz6jTehoXi2EVBzFXlb4IY6rfDC6rqpesgjUZ7Bhw9wbySZz+V5lCwpS+eZG7cx24pwAtjVrTHXVuwoGzw+GyKE4mQlZMuhMWqpNLs2ZDhRkbRTabqdFh966EGeynVzSs/y4J5djOfLSAlKrtb4ibgL2tCglNZwSwUyrkNkp6h7kifnfIrpAjfPr3D7yVMspzN4mk6X2ybnu7zu7BkKXotUKAk1nYwM8HWdnmYTJQRtwyDrRQSGSSQj9HUm2J3UskQQ6Aa6kkSaQFx+EMoFVJXPj6vH+Tyz/CCj/L64jay4ssu9AgLD5ODAIKMT0/DOZNReQsJrkaudxRwA/2P1lpCQ8ArihaKC66N+t/cbfGsq5JuTAa8bVqQM7UUZZK8/16lqnDKmt4BquGQdh2YmzZlqRC70aWgmK7pFNF3D3a4Yq4bYhkAT0G0LvjUV8rrhC897+OA8G0+OMb5xiL5aneFqnf3NBn9+992czhXxXUnO0JhpSqZbLpBie1lj0ZFMNiT5WoNtI3lsU6PswHbZYHRhnkoroDq8mU2uy/ZTR/ir7Xs4lS2jZJzSFXLVIiaKrXLYv5lNDx/ieAMKuRRW2yHQNJ4ZHGS4UccOI6p2inN2ibcuLrBleRErkri6TsH3CTWNhm3j6DqhadOWCqnrOKaJFYb0t5pIKZEIpKZhyIhA1xAoMr6Pr+mX7VhezyFV5QPqYSZo87viFv4xWy+wsHkhBOBoGggYaDbi+tGEhITXHFfaDHdFCCFMIcQ/EEJ88Vo+b0JCwpXTGZu3nvVRwfVRv76swX0jJsWU4In5kIwpeGCbdcX1h+vPVfdiI+rT2zZR9h0iQ0e1PaYXHVTLQ20biEfMlfP4YYQbQiRjU+vxumSiHrLcvjCNqj05hlbK4o70cubGrTz7+luZvGUHO906w3mToZxO05eMNyTzbcV4NeBURbIhq/F9O1LcureH27MhrQB0AcVz8/gIFnIFUroG+QxOLs1dMxOk9U7k7nzTiAb4AXwxKPB/du5hqD/FLaqFnkvx5B030c5m+fqWHRwcGqHgefxfR54hFQQ0TQuALqeNFOCZBkoIMmFA1U5RctvokSTnunS1W0RAKOJKQwlIITCjCCuM3w/PNGhpGi+U+P8zdY571d/jEvE18Ub+idj2osQhxO9B206xc2mRmf03JvWHCQmvUa6Jl4UQ4ibgp4AfArqBS891SkhIeNl5obF5F0cYezI6bxrVmG8r3rcjfdXnyluw4kpUuYR7/200Dxwh/PsjZEt5WjdvRVgm+UYb/XU3kI103rdd8PcTIYHUKNmCkqVxaEkxVQ/XBGpvs0G9VGDQEpysRIBCL6Sxxys0fUkrUKRNQehDSofjyxF7ewVHlxTv35GGzFa0//0YaTek4Rk0JqssCMEjw0MsViMsDXaEAfecPE16pcl0JsfBkVFqfWUMDfwQhAa9KcH2jf3M6AN8dTnk2ZUADcHOco7h2RVuXJhnoF4j0DTQBLaCuWyWkShCaRpCKkJdkI8U3c0GdhhirE44MaSiblkUIwcJRJrACGNT7EDTWEmlmc0XGWjWMNptUuq5MtFTEf9CHeL3OcN99PK/xF30i9RV/f0ooBB4eK7O0dv3XNVzJCQkvPq5aoEohCgSC8KfIm5akcC3gL8GPn1NVpeQkPCieaGxeZ2oX1ybGHOldYeX6o5+YJvFgbmAkq2x4kTs7tFJFXs4OngfD2/YyXurk2itFl46y/IdO9H7u+Csx3RLsbvHJJSSmZbi2UqIEyl++Zt13r8jzf4Bk9GtZZ4aa2GV02wraZyrS7SmR2pDga4U1H3oSul0pxSLjmSuJbH1kDeNxlHQKYr8/Y03senUWXbXK5zNZjmVLdLIZxERpKsttp07jZtNs1DMk215PHD8MF829jJbKmOIuGGl6kn8iWUyh8e4ealO1shxcHSUb/WP8s8PHiXSNEwZ4RsmaRmwlE5TdB2WMll62i0ykUIKQcW2KDtt8r4PSuIZBqEmKPj+WsRSjyQ60NYNKtks08USm1aWKbcdzEuIw0nV5h+oRzjACv+Cnfy62IMhXlpySAsjMkFIM0gmqSQkvFZ50QJRCPFm4CeB9wFp4PTqrg8qpf7yGq4tISHhKrnc2LwXijA+H8/fHW2tRR47ArIjTG++pY8FcwDXPi9Ymp7ktn6Tr034ZPWQyabCiyTLq5HNkysRnz/t8MkTLu/qGWbvqac5OhMxrVkMSZ+7e0KO3buXUy2TnCWp+5IlR5C1NLpSCtPQWHHPryVvQdoUHK9Lzvb20F9vIqRH005x08I0UsHRoSEyhqCSSiGBXWcnOHdTmb56hXumJ7hxZYGbmyt8rdDPeLGLVNvjHUcO85ldeznb1UvRdUCtUEmlaEUGKpKMVitYMsIKAzxdp+CGlNttQl3D03SE0BBCkA4CNCASAoVACAgRCCGQCDZWVuhptTBQz0kx/72a54fVo7hI/re4m/eL4av/o1mHpSR122bbqXEgqUFMSHgtckUCUQgxDPwE8OPAJmCR2Cj7jwCH2OfQf1lWmJCQcE15oQjj83El3dEXC9OOqLxYjD6wLZ7g8TenXKQCPxKMFOLhcMtuxJIDW0uCv1hJ8fvpndyxPMHWsAbDBb5x8252bevGeMYlbcDpSmwTo2tg6YKULtjdo3NgLqA1vszeR56maqU5YuTwhUNJKjTHp9v1KYmQx7dvxS0VEGGc8m3ZNoPNBl0rFd527DDVVBq74TDtKrY25pjXUqxks5TbLX7u4W9S9Fwc3eCr23eyY2mRnOuyd26WQNcwohAQmEqhrY7SM6MIV1N4emd2SpzWDYVA6RqGlHi6jqEkG5p1pBDoqLXReQBKKX6LE/w7dZid5PmkuIcbROGa/H3EUcyISNNgMbG4SUh4rXKlEcSzxM19XwB+EfiCUvFgUCFE8vUyIeFVxuUijOtZn1J+et7n9n5jTSDCC3smXk6MvmMzfG3CZyAjOLESYWqCiUbEQEYjVHH38BMLEd3dJR7eUOZoSqcZKN6aNwgWI7YUBZ8fUzR8RcGCjK6RMjTettFkY9Fgvq3Ye2qcOd3mTGBiGRGVVIrTxW5SoU87n2d7BNunZzkpNOqZLLYBVtNjNptj/9QE1VSaRiqF2XaZTmVJawHblxc5Beyen6XouCxlM9wxOc6+OYOHRrYwWK3Ssi3GrF4Ga1W63TZWGKJzflSeLSWmHxAKCDUNlCLSdWqmTbfbxogkga6TiQIMLuwmrKmAn1SP8xlm+ADD/KG4nby4sq7zK8UEehp1pq/QDzMhIeG7jysViDowDTwFHOqIw4SEhO9eLk4p+5Hiz4+5jBR0hvI620s6li5esHbx+cTocMHgzaMWR5bCuHMXRSklyFgCUxccXQ4YrlV46+lpctUG1oYih0ZHeXSmRDmt+N4tNj++R+d/HZNMNxTFlGB7Sacrra/VVN4Qtfi4a9EMJPlqjb7xRTbUa2ypLPLpm27lof4R3njuDK8fO8U3N24j7XvsHTtLJHQ21isc7hvgyeFRqnaKdBDgmiYl12H78iLpIKDLbTFTKvLk0Ajblxd44/hpxstdPLh1G993+CDdThtdRhirtYMdgShWX6+l4mhgBHhCw4xCIiWwlSS6RL3hYVXjH6iHOUuL3xb7+Hm2v+gu5StBAdkw7v5OSEh4bXKlAvF7iesO/z3wH4UQDwJ/TNyQkpCQ8F3IxSbYQQSBhIoj6U5pfHMyYFtZ58f2vLjO5/W8Y7ONHwm2FDWOLUWM10PqHmwrwdxchXceP0Irm6HeVWDI83jjwUN8sWuEt9hN9hxyOWtmKGWGmLAKpOaWueHYNKLaxBoqsvlt2/iGYzMx3UD5IfvGJ2ibJl1Oi7Zpc8PCPE8Oj3Lwhm3cvDDLG8ZOICNASiYLBUpum5vmZim5Lk8ODrFzeZl06FNNZehv1OlvNhgvdeGYFo5p8eTQRjY06ixlc9w6NcFgrYauFIaMTR0EYHF+vnInMtj5tp0OQ0wZ4ekGE5k8+TDEcCW2ih//F2qCf6ieoIDJ34n7eL3over3/YXQAF1GdLfbL9s5EhISXtlcUaubUuqLSqkPAEPAvwL6iOctzwH/nfPm/wkJCd8lLDqKnHXeBLs3o3FLn0EA+BKKKUF/Vrtiz8RLEaegLUYKBptLOltKOnlLkLE03rYwiZ/PULHS2IbASadpOxEf+PYjbDEjFvMFTky1efMzz/C9K5O87qnDrKy41LvypH2Xub94gs+6WXJth5tnp2mbBpoGPb7L0nA/A71pbmksspLLcWLPTsJMmuV8jqlimbZtc7arh0DX6W416Wu1OdrXjyEl9VSapWyOSiqNZxjsWJznlulJ9s7O0NYNVtJp7p4YJ+85SBV3JHd8FeH8xbJz8RXEKV1TRighkAICy6ZmW/iGTqAkvyif5oPqMW6lzAHx1pdVHK6tUdPocROBmJDwWuVFXdmVUkvA7wC/I4S4k9ji5vuJr3EfFkK8AfgU8JBSl8iPJCQkvGpYb4dT9xQlW9DwFBkjljppA5adl255eqnGlgNzAYo2j6eyDK+msJfakq2LNUq65LS0aC2HTGk2RV2x91tPcXZ0EJFN0wrhpLLpMaG4VOXJO/Zx42e/hB9CPZ3h0MAgoJNPmWSqDZwQwrpDqBQiiGil4gaaRirFkf4NbF+cZ6Re4cDoRv5mzz5mSmUGqxV+8wufiYWnYVB2XAq+x2C9xtlGjd5mA10pQk0jUGAquSYIYzvs88h120NAV1BwHewgYMWv8yPqER5lmV9gO/9Z3IT5Ei1srgQBuIaBX7j66HBCQsKrm6v+6q+Uegx4TAjxC8Qi8SeAXwB+HlgANlyTFSYkJFwXLjbBnqiHnK1LbuwyKNmCFVey4kQXGFtfDZfyVnzfjjS8boCbppr83YrGmWpEK1BsD5pkR0ocdBRnqhFpQ1E3LUpLFSpbttCvKepeXN8n0hbdzQpTpTJf3XEDtHxqdoreVos7psZZmPZYsTKkmi6+7/BQ7wi752fJBgGOZZF1XTZVljFlxHShxGShyP6pCXpPHGMxm+NsqYvNlWU21qrU7TTz2RxDtSp7fZfAMCCM0ImFlh74F6Rr1kcTdcDXNAwZeyVaUUTBdXg0nOWD6lFahPyFuIsPiJGrfo+vhjPlbhaGk8t4QsJrlZf8VVQp5Sil/lgpdR+wE/ivJJNUEhJe9XTSvxlTULI15lqKzQWNvoyGEyqUOm8nc7V0GmHaQeyB2A5ib8Wpegi3b6Xbd9nVqPA9s2f50dOH6V+u8KyjsexKQilZchStqsu5fImo4XCmGtEOFd0pQVh3qOdzTNQljwyO0uM75F2XpUyG4/0DqEBRSaXwUhaf272Xr9ywi/l0ji6nxUCzzp65GcqOw3y+SNM0+aVvfo3RaoX5XJ6M77OtusyJ7l5O9vSxlMtiKcmZnh6UELgiTiqbYUgu8NFXX6/kwuihWP3dkhKBwlAKoSS/Fx7jneqblLF4RLz1OyoOFXEkc6ZY5ODo6HfsvAkJCa8srsmovQ5KqVPAvxFC/Mq1fN6EhITrw4Xp3wZVT1L14rTznh6drrR2WZub9VwqUnhgLmCx7fP3E5IVR9KV1ripR+PAnICBIp/KbOCmI9/CdyPGCjnmhwtsPzuNLm2CUhdGyyPjOvzVrn3cMTWBH0Exl8JdcfFWWnz+hr34IUwXynzmhr3cNDnBhmaDmXKZT+3eR72njG1AxYsjeX9xx5287cQxvu/wQZQQHNwwxOHhYbYvLbGSjZtTznZ100ilmMvluW3yHPOFEkbgY4UBGBY+gkwUossQDZ4TOew0qXTo1CNGQE36/GN1gE8xzfsZ4mNiP4VrbGHzQgigrhkc2LiVZ9PF7+i5ExISXjm8lFF7byKeprJlddMY8Cml1DeUUkkEMSHhFc6lBNvlUsU7ugzagbrAB/FsNWCmKfnDQ+3LPsfFljnnaiFfGvN4dtlnoQ0DOUFvWmOqEfLYjCRtuGzI6uw/tkh9yxbOYuOGYGlxHWHPUo26MFnI5Pj6lm3MlMrMFIrcNTVBOFsn6Cvw1b1bqBTLmH4seha6y3y2WCa96jMjJQgJUQChjAXauUKZj++/F13BVDaPWrWQucWdJBQ6+2amKHsuK6k0ddNipFaj6Lv4QqfkuvTV6qTDACsMMZRCcqFA7PzeuUB2BKMETqg6H1APc4YmvyVu4p+x42WxsLkSLBURoqgl4w8SEl6zXM2oPY24g/mH6Nh5xWjAPxFC/DnwY0mTSkLCK5OpesiXznp8bcKnLyPY22PQDrTVsXlc8Yi+c7WQh2ci7hnULxq999znuNgy5/iyRNdgtiWxdEHd14ikZKoZH1/3QBMSfanBTFeetCaouQphwHxvD8vK5KP778UU4CowBOgiFnlVH9pNiV+GrCnQNfBDRaTAFBCsKjKp4ouXDgxVK9w6NUFvq0m9kCNSkPM8Wquj9yJg3+w0lXSa5VSaVBhw3/hZaukUGmAqhWua9LUa6JGkYdkUfB9TSULOdzF30redKKJYPf8n1SQ/ow6QxeDL4j7e+DJ3Kb8QKaXYMzdL8lU/IeG1y9VEEP8F8MPAJ4HfAI6tbr8R+OXVfYeA374WC0xISLh2dCJ5JyshAxmBEBpPzIfcMWBStMUFY/Mu5uKpKDNNyT2DOptLcQq0M3rvi2MuPRn9gsjk4uqcZYgtczImpA0tFm2aoHdlhcHjZ7m33aSSz/Fg3yjFbT345TxR3SPdk8XWI/wITN+lVshhabEvI8BQrcK7jh5mJZVmNpun2PZ406HDfHHPXrzuMqGCMIpFIQosE/wABqoV3nHiGG8ZO8V8Ns+hgUHSrs9Au0GvEkyUu7DCkJ2L8wzXKjiaTn+tQm/bYUtliblcngPDG+lvNNgzP40mFaaMyPkKjbiLOQJMKdfSyh1hGCGQKuJfqWf4MKe4m27+UtzNkLj+ncM6sHd+lpx1vVeSkJBwvbgagfjjwFeUUt9/0fZngB8UQpSJTbUTgZiQ8AqjE8kLJZRsbTWFqXGqGnHnBvMF6wnX1yT+4aH2mujr4IaSr0+GfO8W7YKooqUrmj4XWOY4IfRldIarVe48dJgT0qJWKlD0PB549jBHyzczvnMTN377IDQFKIuc6zEgPGY39PC+o4fI15ssZXN0O821sXgGsGKlQMGecxN8Pl8GFUcYLQO6LKgF0Fut8J5jh9m6tMBsNo/QBHdPnePRkU2cKnaTCnx6qlUeePYIva0mY6UydhRy37mznC13sZDNkQ5C7pgYJx0E5Dwfx9DRoxBdgCYVkVIYrEuzCIGn6WhSsiJb/JB6lG+zxM+xjd8S+7C+AxY2V4IANjTqbG1WgIHrvZyEhITrwNUIxC3A/7jM/s8B/+3qlpOQkPBy0onkFWyBEyoypiBtwFQj4uuBxIvg0ydfuB4RLvRJ7HB4KaQvI9bqFDtRxXagOFeTLDgBpysBIOjNaLx11CR86hx+LoWOhdAEzXSaXgO2nTnHN27dR/22vdwwPsXGep3lfI5vF3rZdWqCdjbNXD5PwfN485mTfHnLDRgCwlWNW7Fs+psNAgWWAF9BWkFqscL9R4/x3sMH0SNJ2vdxVkfoGVIyWKnw13tvZsvKMrdNTWBEEULCUL2OJSPm8gVqqQxWELKtsoz0FdkgwJASKwjwTBNNStJ00soaEolrWkRCkPc9HlWL/JB6lDoBfybu5AfEK69bWAQ+N09OESeHEhISXmtcjUBsAf2X2T+wekxCQsIrjI6o217SeXw2BCR1TzJRj6vxXj9srFnNXK4eEc7XJC47AbMtyUJbcnIl4oGtF3bd5izBVEOt2rso+jI6Uw2JGyg2lUz6zDZPGDm6gIqrGM0Luuws9niFdgjpgW6ODvfQ8BVVV/LWg89AMcOgJrlx4hxm0yHretx9bozP7r6JrlaT7UuL9DcbLGVzDFcrLJTLCKC/UuEDjz/GtpVFNBnXB26tLCEUTOULuIbJlsoS7zt8kILvkvU8yo6DHUakQx9dxh6FJcelZllULYvRWh0rCnANEw2FEUUo4hSyjsJDYgC5wEcoxYc5xb9Rz7CFLF8Sb2CPeGV2CmelYuPs7PVeRkJCwnXiagTit4CfE0L8lVLq6PodQohdwD8BvnEN1paQkHCN6Yi6oi24fUDn8FLEs5WIHSWd1w2b9GTOO/Zdrh4RYvG4fyDkY8/4hAr6MhoCwbFlRU8mWnuupq+ouJK9vSY39cXicakdcWgx4In5kB/d2MUHlc+imeLUSshUI6JVcShvLPHTe1M8sxRhCMmBuYiMqbE1aKGZGptOnKNhmtRyaebCPDdNT3O2VGakXifSINA0pgpF3n3sMJ/dtZfZUpmbJiboaTdZTmfJeD6j1QqOZpANAnJBwIph0DYsNtZWaOsGva0WVhiiCbDDADOKSAUBZiTJptOEQsMxDZQAIcA34vdLl5LA0GmhkZUhmpS4KuRn1AE+yRTvZYiPi/0Uv8MWNi8GXUbk2s71XkZCQsJ14moE4r8HHgWeFkJ8hvNNKruBdwM+8B+uzfISEhKuJesbTVqhxn0jOhuyGjd0G2jrLFVylrgif8OppuINI9ZaSvnUSsDnz3j82bGIOzYYbMhqGJpOOaWtzXUG6MnovGk09lDcOLKd0594DL0k2VFKs1XziVTItu/fzeCNBT52qMX/OuYgBJQswXI+z64jJ1jWDZRtxjY1uTTPDAxy68w0lUyG5WyBU929rGSz5F2X26cm+HJXmaF2EyuKWE7b1OwUGxp10oFPhCIVBuR9g7PlMt3tFjnPJR14WFJhhSGmjFabS8AxDbKej6kiZrIFCqGPFYUgJZkgwNdNRBiQUyEIwbOqzg+qhzlBg98Ue/kldl43C5sXw6KVud5LSEhIuE68aIGolDoshLgP+F3g+1ZvHR4GfkEpdfgare85CCG6gF8B3gsMAw3gCPDvlVLfWnfcTuC/APcBFvAU8B+UUl97udaWkPBq4OLZx58+6TynlrDpxx3IL+SVuL47eakdcaoi2VrSmGzGKecVR/HTN5lMNZ9br9g5x2MUUG+9leGT49jLNbzuAvO338BJP0f3SYcHJz2mm5K0EXctH988yt3ffoJWNk8QKlJ+QCYM+MamLbxx7BSf27V3zb8QoGnbDDQbCGA2k2NvGLJ/8hxDjRqhJpBCoCsINY1HRzZSS6fZvjhPd7OJDoSaILMqDiG2qQFBKCS2VISmzpKZJu/5FHyPUNOopCy6XIkIJP9HTfGP1OOk0fmSuI83i75r/6G+DLQtizP9r461JiQkXHuuyihbKfUEcK8QohfYTFyLPaaUWryWi7sYIcRG4vR1Dvg4cBIoAjcBQ+uO20osVkPgt4Aa8DPAl4UQ9yulvvpyrjMh4dXEen9DN5QcXopYaCv29QiOL4VsLD6/z+H6RpWOfU3D1xBIzlQjVhzJieWQd261CKRgUzGOTjZ9Rc2D+0ZMvnDWp3+0m/mNPUAsNJ+cC3j8UJvXU2fj02OMLtYZt3IcGBnldHeZkc3b2b0wS9lxaaRtHtuwgUgZTBdK5D2Peiq19vpynsdCNocj4VyhSH+jwXCtStswiEwr9ju0LMZLJXpbTQwkx3sH2Lm0wHImS1+jsWZ2HQFoGjOFEjnPpa4iNNSawKxaFgVN4BsmLd3n3wVH+D15nDvo4n+LuxkWr46IXAQ4lsXEjo3XeykJCQnXiZc0am9VEL6sovAi/ox4zTcppS5XPf2bQAm4TSl1EEAI8QngKPARIcQNiZF3QkJMJ+38v461+dwZH1uHnV06JyuSSCk25DQ0oa11JK+vTVwvLmuuxI8UBxcDlAI/ig2sZ9sRT8wFbC7otDOCVqjRmxbcNxJHI0dq82QeHKNUb7CUy3GiZ4TTVoEt9QqjTx1mUrOo5vNkmh7vPnqYr+7dy1d27sKSEeTTuGkbr+6Rbzv8nz37uHNqAkUcOcx5HiXX4cEt2+LXWq/xdztuIHv0ECXXJTRMjvX1EWoagWFR8hw+t2sPX965i62VRXbPzSFU7GdoSHl+KoqSaEqykCsyXioz1KgxWq1gRxEz6SzZ2hw/4H+Th9Qi/4it/LbYhy30S7z7r0wEMJvK0LW153ovJSEh4TrxUkbtZYBNQDcXjhYFQCn1zatf1iXP9wbgdcDPK6VmhRAmYCql2hcdlwUeAL7REYer62kKIT4G/BqwH3j8Wq4vIeHVzpmqZG+vQVdKwwnhyHLAjqLkVPV8w8nFtYmdRpW/PulxZDmk5UvShsCLIGPGlwVTB1eCp+Law/ftSK+lrr/x6BybvvEUS1YK1V1gcb7FzWcOcmrnXnafm8DNpqlKk5oH0krhhrB1bILP7NnHp3fu5a6ZCboXGlSzOf5u1zaWymVWCkX2z0ywsdVgOp3jc6uj+AarFd526lkU4JomS0IjNOOmlJZl8dXtN9C2LD6zZx8Aj45sYt/MNBrg6zqhAgOFBmysLLOYL3BwwxBZ3yPUDZqWTRvJqfYUv+h9hRo+/1PcwY+Ijbwy3A2vDAlUTYu8kgzVqkAiEhMSXotczai9DPA7wE88z+M7E6Wu9dfld67eTwghPgfcD+hCiFPAryml/mx1/02ADTxyied4dPU+EYgJCes4MBcQKuhNxebZGROKNsy3FbZ5XhB26gY7xEIv7lDeXtb5+DMui22JqSkypkaoBINZQRTFEcVFR10wl3n76XHCYgZfs/GVYFqzGSrC25cmWFqqczqdpxVCEMUXlYZts6HZWLWRAV/GgqazIkMDf6jM0c1lzsxV2HpmgnefPIavoLfVxNUNMr5H0XHpbbc4WyyRkZLuVpON1RW+sm0n7zlyiO2L8+ydmyHr+0RAKoybTQJE3IAi4KkNw2Q9l7snzxHpGlYQ8qfOYX7TfZQRsnxWews3UXxViUMAKQS2kniaIPvMGLDtei8pISHhOnA1EcTfBX4K+ALwNWD5mq7o+dm5ev9R4BTwY8RC8J8DfyqEMJVSfwQMrh43fYnn6GwbusS+hITXLIuOoi9z3jwbYFNe54m5kFvCFfpOTcNCDSNfYN87dwDxOLj1M5YLtsZt/QYPTfusuIqMBUNZDU2AQGHpoKH4vadacXNLVmPTXBVzsIu0EzHbjBtBToYm5cUax/UshbYLZmptdnEp8FjK5xhcnYJSTaVZzOfJeR7vO3aYL+zeizdYxpyt8KbDh1k00sxk87z+zEnKjsMzGwZ5+6lnqadTNC2LsuPgWiYnu3rwhODOqQmElOyfnGDzyjIlz8XRdWpWhpznoinFTLHIYj7PEyOj/KPHHyble6yYGv++/vd8KhrnfjbwcXEn3ZjX/FvydwKlYs9KPVJoS43rvZyEhITrxNUIxPcCf6GU+uFrvJYXIr963wDepJTyAYQQnwbGgN8QQvwJ0KkC9y7xHO7q/fNWigshPgR8CGB09JU33SAh4aVyqc7k3rTADTSOL0sgThOHCm4Narzt2aM0sxky/SXuMHy6v/405PfDcPdzupghTikbmsAAgkjS8BSOVExUAx6ZDjA1uKXPwAsVh0WWkYUGJwKbxbYkkBJqDuNGhieHRvmeI4dJWeDYNnnPI+86PLh5G/snJ9ZG62lAK52i1GrxM9/+JnO9PQwtLVE1TG5tzrK1ssxIpcJyKk1vq0nBdTDCCCsM0ITg8ZFRnh4aYf/0JMd6+9m9MEdfq0F/o4ZSkAsCGkLH03WUEOQCn6/3b+Dec+NoYch8sMKPNx/khKryH8UefpkbEELwaixyVgBCQ0NQctrUC4XrvaSEhITrxNVkP9JcHyPsjmPrX3TEIYBSqgJ8lniCy06gU5NoX+I5Oq2N7Uvs6zzfHyqlbldK3d7b2/vSV52Q8Aqik95tB7Gw60xNGc4JDE3nxm4NN5Q8MuPz5GzAyImzPOOZnAhN6oFCFTJQzMATZ4DzXcxL7YjH5wIsQ2NrUSNjKM7VIw4thJypRjR8Sc7S6EoJlpyIvzsXcHA+4LGhEU6ON2mvtAiloi/wKHoejw6NMlcu83c37cVJW2xoNnAti7/ds5e5cpneVpOmHf8Tl0C+0WLX/Cy9rSZnM3mGVpZ54PgRbpmewtMMAk2wc2WRXQuzZFyPwUadkusxl8nhGyZvGD9D1nPZ0KgjlCLneWSCkEDXkUKQDuNRerqMm1POlrvYPT/D441neZv7BeZx+LT+Rn5F3LjmJ6lWb68mJODqGp4myIchaqh8vZeUkJBwnbiaCOITwPZrvZArYGr1fu4S+zodzWVgZvXnS6WRO9sulX5OSPiuZ31KGM7PSp5qKvYPaPzREZ8n5kIKlqBsK8RSg5lygX6pOL4cUfMUbx626FmoAee7mE9WAtK6oOFHTLcEe3tN5tqKiiOJJGQtmGgoMkZIK4BQSQq+YKW7zB9v3sVbFyfJr9QYz+Q4uGcLC5kyXgiLXWW+3lVGAXUPLB26TVjK5sh5Ho1VO5vty4tEmmA5k0cJgSUldhQghcA3DUJNRwFZ30cpCDSBoRQDzQZjvkfG88n5LltXlrGCADMISIU+mlJEIi6rjnQTV9Nx0HjbyeP8QeWbfDg8yq108efaPWwiQ0cSCq59EfbLTQT4mo6vG2gozhZLjNQq13tZCQkJ14mrEYj/BvicEOKTSqkD13pBl+Fx4B8Rm2NfTGfbArGA9IC7L3HcXav3T1zz1SUkvApYnxLukLMEzy6HzDZ1LF3wuiGT07WI8Sq0y3m6Qp9WmKY3Lah5krMzLdiQ41snHRYdhaUrlh1JwdJYcRV3RVXyD51lz2Kd01aOZ0ZGmOoqY+uCZSe2vnEkTDUjGn6EkPEM5qIAL1RUXGiYscgarlbYOzHBQKvJbCbH40OjzJbLPD08yjuPxX78Tdumv1En1HRO9cRR/6Zlo0cKmxChwFQSX+iEGlhSspjOkPU9epw2d42P0TJNDAVFz2GgUScUApRCV2rN5kaEIZGlo9wKvzH2eR6OZvkptvA74mbSSkd71cULYzr1nZJYONdTaZazOR4b2Uhlus5UPbzsyMWEhITvTq4mxfwh4mjeI0KIbwkh/kQI8T8vun38Gq8T4G+I6w9/RAiR62wUQmwgros8pZQ6rZRqAp8D3iiE2LfuuBzw08QNLkkHc8Jrkk5KeD2dWclFG0IJGVNDSoEm4OnRUQqug9ZwsITCaDqszDf52/LIWpo6bQgyhsbOLp3uxRXKf/c0E3MOz5o5cr7HWw8fJrdQwQkUdR/aIWgq7kDuqdT4BycOY3s+U5kctH3uP3qYDZUKA5UK9z59GK3tM5bK04fPT44f4x1mg5lSmS/v2YtjWfQ3Gyxlc0wWCmxfXuJ7Th4n53vU0yl83SAbeLRNG9eyWc5mcXSdHreNhmA+m6PbcSh7LrWUjZARGmBGEiUEARBpGpqUpKKQs+0Jvsf9W56KFvgf+h38trGfjNBfdZ3K6+mMD6zZKeqpNM2UzUSpxMnePiZTOQ7MBdd7iQkJCdeBq/la+OPrfr539XYxirjT+ZqhlKoIIX4J+APgUSHE/yQeofezq/c/t+7wXwbeAnxFCPHfgTrxJJUh4HsTk+yE1wKXakZZb2y9fqJJZ1ZywY47mdMmWJrgXL7Mw7ft44axc6SW6tSLWZ7ctZONo10XpKl39+h8bdxn8OmzzOo2bTNFIGHOSJHV4abxCb5UKmPpcaTKDyAl4L6jxxicW2CzlFTsFIeLvayYad524hjblpfobzeZzRU429vLdD5LXULm4BjBjn2MF8qM7Ylr5G6dGOeXvvk1lrMZKqkMOdcl5/mMl8qc7u4jm3N565mT2E5Ay7Sw/AhTRnS1WwD4hkHJ9WinMpw1LEZrVVAgdQOpJLko4vcZ41+qpxkiw4PiTexTZVDy+ny41xgNsIIAS0mWszk8oaE0jWMbR3mDk1wuExJei7zoL75KKe0Kbi9L+Y1S6g+JZz83gf8E/FvgBHFX81fWHXeaWLg+SpwS/29AC3iHUurLL8faEhJeSTxfMwrAA9ssMmZseJ0xBQ9ss9jRZdD0FdtLOu0AyrbA0sCLFOO5IqdefwtfeMvrmXvbfvSRHnLWhWnqjUWDqqfY4jepWnZsk7J6SNWy6Wk1cQMwRbxdaLBhpcKtx07QDgUzZhqckDsnJ9jQqPKmsVN0t5pMZ/OYYcCt58bJNlo0bZt8rQnEQrPDSL3GQ5u2UE1lKHku08US/3vvzUyWukhHIVLXeXLDEDOFIhqK+WyO5XQGO4rQpcQIJb3tJr4QtGybeiqNY9nUbRsCl59SB/hF9RRvpZ8D4q3cJsoYxN+wnzMl4FVGp5nGQrGSziBQjDbqPDY8ij7SdYHvZUJCwmuHV11hiVLqU8CnruC448B7Xv4VJSS88ni+ZpQDcwHv25FmuGCsRRi/cNZHKMliW7GxqHP7gM7hJejJamwpx3YthiG4o8/g/i0pDswFa/OXOzR9hdJgYFOJ6mSbGcPCDUFXkPY9qvkchgaDOWiFMN+EG8cnmMvmAYFUgoZlEQL3nDnNmZ4+HNPEDkMcywJg6+Iix8UAC7ncc15vb6tJa/W4DjPFEouFIh+7M05y/PRj3yYQgu87/DSbKhWyvo8hJaEQlNwW2SCg1G4hlSIwLRzTZCpY4R+rr3GUGv9B7Obfcr5L+bsFsXqzpEQoRd1O8+TwKCP1Gi1bY/+Aeb2XmJCQcB14KaP2ssSNIP3AV5VS89dsVQkJCS+J52tG6YzJm6qHfOKIw4IjCSKFqQtsoXCyGhKN+0Z0fml/9jnNCVP1kFMrwdrM5qGcwNA0nEiQ0eHA8Cibxw7SDBRmyibjepiBw6e2bcOXcLoKaSOeeNLfbvJU7yC3TZ1DKmgbJqAYaDX4mz03owTcOTkBgGsYbGjUmSoU+cyW5072iIA3jJ9hOZ2lkkrT22hw18RZpoolFrM5DgyPspjNMVSt4Bgm+mrjiasblJ0WGoIIhbVqEu0qxVeDc/yc/2004LPi9dwvBq79B/UKQgIlxyEw6kwWivS2mmgpLWlQSUh4jXJV//KFED8L/CZQIM5OvA2YF0L0ApPE85L/8JqtMiEh4UXRaUa5OMrXSRd+6azHyUpEd1qQS2k4oWLeif2r3rnZ4ktnPX790TiVe+tq5BDgT444HFoIUEry7LJk4niFNy9M8K6si9eV50/SI0zdspfB4+N01+tMpnI8tXsvk9kyugCpoLba8zCfyZHyfR4b2cSO5UVKroOv6RwY3ohvGDRSKR4bGWX70iIDzQaL2Ryf2bWXmdJzvflK7RajKysM6lV8XafkOEhNp2anyPg+7zl2mMeGR3nHiaNkg4DxYokdy4vYUYASAiXl+XSxUvxmcIj/wnFupsxfirvZIrIv46f1ykAS1xzN5IuM1Ks83NNLK0jqDxMSXqtczSzm7wM+AnyGuFv4Y519SqlFIcSXiFO7iUBMSLhOPF8zyn0jcbrwyfmArlTcsQyQMQVKRTw46bHQkpysRHSlAAQPT4cstB36sxpnayEzTUkjEGxu1XjjqSNU7RQHMjneLiQ/N3WEv9i2hy/dfDM5C8JIUfMUhgPBatFgZ1j7Y8OjvOfYYRqpNI+PbCTreZRch8eGR7lzagINqGSyHOs3mCmWnlccDlYr3Do3w6mePrqcNtuX5hFKcGB4hLzvs2thjv5GnZFqhaVMjm0L8xR8Hyk0lNCIhIamgS4lC8rjx9Rj/D3z/Ki2hd8Rt5FT4AsNW8kXVW/YsY95NRAAaBpCKXqbTTavrPA7bxjlzZlXm5tjQkLCteJqIoj/Evi6Uup9Qohu1gnEVZ4g7hhOSEi4TgwXDB7YFtcizrfjyOF9I+ZF6cKL5Ytgsa2w9JAVV7LkiLibWSgemYloBIqGJ4mkIm3q7B6foJVOUzFsGm3Jw02Tu7pzfNCd4wfeNciHn3I5OB/gRXFKWQKWFtvcRMBMqcxndu1l/9QEfasRwge3bGOmVGauUGT/1AT9F23vMFitsH9qgt5Wk5tmpuhpNOgVTaqrHn51K8VIrQqaYDmbZS6XZ6DZwA4CbClZyOeZKRS4+9w4qSgE4Am1wg+oR5jH5ffFbfyItg0JyFVh+GLF3qtFHErANUwMJbFkRE+rzrN9sZ/k7u5Xs4FPQkLCS+FqBOJe4F9fZv8s0Hd1y0lISLgWXMriZr04vLXP4OHpECEUaQOcMDarzhiKUysRXSmNtAlVVzHZCOlKaeQtjaUQar5i0FCU63XO2HlkGHcmT9YjhDK4pV7lmUXJSEHj1EosCBXxFBRULA4hTmfOl8p85hJRwZnn2Q6xOHzPscNUU2kCoXHL9BSRgFDXyQYeZccBGTHQavPQpi04pkXa95nP5elt1AGwwhDPsllOZyg5bT7OWX5RPc0GUnxDvIlbRReRlEghUJqGVBBy4QXzchHCV1P0UAGakqAUodCwlMKMJPefOEb3P0jm0SckvFa5GoEYcXl7nEFiS5mEhITrQMfipmhDfyZOL3/2tM8D21gTifdvSbHQdlh0IlbcWLxtK+tMN6EdRqutrYJmKDF1gScV27IaE/WApg9j1YitVg677dFKp0BBzZOsuA6nTUn2Cwd4R7vJvkyWj5qDHMuUUdGF6+xY4USrZW6dKN2lnAXXRwy3LC8xVSjSSKW4c2KcxVwOI4qQmoZrWjQtj552m5V0mqVMlrTvkw0CpgoFbpmZQkgJUrFtZRnLafDTPMmfqrO8jX4+Ie6kV9hExAbZnmUxmS+weXmZtDq/7hCBjnpeEXi9xGGnjvBKWD8r2lAKXzdomwa+YZANAt4yfpr6q0bmJiQkXGuuRiAeAt4OfPjiHUIIDfgA8J0cwZeQkLCOy1ncdATicMHgx/aknxNl/PNjDjVPMtdStP2ImbrEVwpbg3k7jizONiUrHnxrQ1xDKIEgbZN3PUorK9SFoidn4fWWKNYd3nf6MJWtz60fVEC4qlA0oGDGk1za0YUisRMxLLWa3Dw7w77ZaZqWxWd37aHoOpwrldm+tIgZBhzaMMRkociWyjLT+SIDzQbzuTxThQI7l5cBSTOVQldwyI74Df8rPKsW+ddiF/9W240mNFyl0BAIBCEai7kiWyoV/FVTbFMpzHVj9eTq7ZXS63uxSOwIQY3z3+47si8QGrqSNGybRiqNphRL2SyuodPVatB8lY4PTEhIeOlczTXt94C/EEL8J+ATq9s0IcRO4DeA3Vw+BZ2QkPAy8kIWNx2GC8Zz/BArrmRHWcMSksfnFQhFyQSlBGfrkr60oGBDMzhfQ3jH1AT99QZuOcdiPkdNtzjbMsn6IW6k4xlp7pme4HNdZbyLwoOdFUnAV+BFz40g7p+aoNRq8vZTJ6jbNvO5HEXP5QNHDvHo8CiVbI6JYplup03ZdfA1jYdGNwHwwLNH2LEwT9r38U0dMwjZ0GzwtXCKX3C/CUrx8czbeF/US4jAM0z0KMRAcarcg9R1ep0mhpJEQmDK58Y3Na5uZum15mLx19nWEYcdFBAKgadp6JEEIdAjScb3WMzmWM5kSYURc6UiydCphITXLi9aICql/koIsZd4iskvr27+EuczRP9BKfXFa7fEhISEF8PzWdxoKD56sMlTC3FTxm39Jvt6dQ7MSUIZMduSjNcippqKoazi7Ztsmr7k+IpkyQnJ6DDbltT9uOkklLFI/NxqZFDX4EOPfRs/Z+OHilYQ4UUQGjaDrcYLJiuD8Hx9IsSRw7efOMYHnz7AcLVC07aopNOsZLKkw5B0ENDfbKAhQMAXbtiFb5hsrK7Q22zR7zSZKJbZOzfLQLNB29BJeR4f9g/yW9ERbtTK/JV2Dz2yTCWVIh94BJqOFkW0DYO0DHm2u5uC57KhUqHsuRjEAvZi0fVK4OJ+Y8n5i3Initj52dF1hNCIgJVUGs+y0JVkMZvH0w3qdoqntm/jBvFKe5UJCQnfKa4qK6KU+v8JIT4F/DBwA/F15xTwp0qpJ67h+hISEl4kl7K4eWbe50QlYqGt6EkL+jOCh6YCvj7hs6MsOF2V1D0IlcIQkifnI2wjNkl+b5fOnx2PiCS0AoUfcUHiMWJVeEioF3MMKI95UvgyPi7recxmcvgvMLZ4/e5bJ8b5N1/7MrsXF7CDgKLTxgxDhNIYL5dZzOQouC7djsPXt+xgc2WFN4+dYrpQYjGdxlIRy+kso9UKY909REKja3mKD0aP8rVomg8YW/nP9t2YwkCPInQZYfsBBekigFkjS9OwcUyTUruNEYXonBeHrwbzl44g7wjDUAhCIVDEkVBf12il0zwyuhmlaWSCgIlSGV83WMrlePLmXbw+GbOXkPCa5arLZpRSTwFPXcO1JCQkXAMutrjJzi3R/+ApBpfqtMp5Tm/bzJQqMZJTTDYilh1FIDXyliBrCExNY7IRMd2IKNg6INhe0jiyFBHGza4owCQWhx3RZABnto+y+dBhXBPmNJuC65F1HR7csu2SzSfr6UQPB6sVfuHb32DP/By6iiOf6SjEboekwpBUEDBdLHGqt5embePaNo9s2kLTtsl5Ht/77BF83WCyWCIT+jRNmxPBPL/if4lZHH7TvoufFttBwkrKwo4cyo6DLaM1UdXjOHhmg9Oql8F6lZSUeKuv+dUSUxPr7iWglMKxU6yk0/S0W9RSKeazBaZLZZYyOebzeXRgMZvj0PAoqa5yMmYvIeE1zCulrjohIeEa0qkvnDk+z+NffJK6b1HL5Bl0fe598hm+fdtNVMwypgbjNcnGgmDRiaODbT+iHcDRpYisGZI14diypOGrNbuXQJ5PX2rEAsTUwe0q87ldN3HD2DkGmw3m0jm+vnnbJQ2u4cLu5M5IvLefOMYtU5P0tps4holnmni6TiYMyQQ+Ldsm7XuUXYfD/QNUU2kaqXjSSyOVomla3DE5zu0TZ+lyHP4yOMWveI/Qi82X9LeyWx8gRNCyLHKhD0phKnWB8LOUZLhWpefZo6R9j1DXCTQda52IfKWyvikFOk00AgNFxvdwV8XzciaHoSRH+zfw5Z27LviMDOC2lEjG7CUkvIa52lF7o8A/JJ7M1c1zXR2UUuotL3FtCQkJL4GpesjhL5xkwUphZ1OolmKSFMIW3Dg2zlcKRUYLOmeqkslGRMHSCCPFXFthCiilBGjw5EJEJCUDGWj4Ai9QBJxPtRqAEKAJOFMDu1Ti2V0lUgbU/AvrCtez3s9wPpcn53n86FOPs3/iLJnAx9UNBIqS4+BYFhKBrhRCKdJRwIqdQgeatr32nFsWF9g9M83ISgVXV/yr4AB/Hp7izfTxMf1uerFRYcBSKhOHQgE7jNDWmWF3UrI2oPte/DqlxFbqBT2+rjedta9FDWE1Na5iYa/paztnikV+99438tRqQ08HAdgGOIFgqh4mIjEh4TXK1Yzaux/4NGABDWDlWi8qISHhpXNgLmCwUWexlCMfQMOPqHmKOd1iZLlOKAVbiwYVJ+LZlbihxAkVKSMWFTlLsK/X4lzNAQmO1NhU0miFIf6q02nRhkDFWqs3FdvWOCE4Elz/0p6GHfZPTTwn+tfTbKJJRduy0ZXEkhJNSexAIjWNQNf58vYbWcrlGGg22LqyTNu0mOjqpqvV5D3HDzPYbnBWtPkp75s8I5f5JXEj/17sxlIiNtSWkt52CyHgkQ3D3OFMrl0ILza4NogFrr4qJoUQKPX8/oevBDrveadO1O/sEBq6UlRTKebzOb6xZTtzhSLvOXLoggjuYrmMocFoUbvAGikhIeG1xdX8y/9NYAl4b9KQkpDwymXRUQz2FdlYczkqTAZzOpYW0VxuM5fPc/9mkx/alUbXIG0GNALFWEWS0QWGJihacaxMF7DkwUBOUEzp5MyIRaEIFXgSTA3SZiwMi8sV7jk3QXezyVQ6x+PDo8+bXu5tNZnP5S/YZkUhlWyWRc9Fl4ogDEgFAZaU1C2Tk129LBYKpAOf+XyB2VyevfMzVDJZbp2aZLBe49veJP/U/wYhik+Y9/GANoQWSRwhULqGIWNFO5XLk5PygoaTSwm/ThodQLzCbV98BKEmCAwTO4zHChpAJARtw2CuUOBUTz/dTovNy0vPieC+59hhvnHzXsyRLnb3mCw6r+zXm5CQ8PJxNdmSG4D/JxGHCQmvbHrTgumdmyj7LrvNgIIJg8rjdbmAf/qhvfyruwoMFwy6UoKUDkEElq6xISfImoolV/KFMy6zTUnFA5Tk+FLAVDOuRdQAP4KmDxUHUvMV3n74MJnAZz6fJx/4vOfYYQarlUuubzGbI+d5F2zzdYOVdIbP7NrLXD5PI51mulBiolhkpljk0PAw6cAn6/uc6u5lstzFWFcvbctiz9w0/4/zND/a/jIbRIYv2+/k++QAmSDAlPF0mEDTaZkGoOhqt9m2vIB+CW/D9ay/SK5PQ7/S8BCc7e5hulgiFAJTqbVoolAKS0mqVorBRp1yu81bzpxCSEkjlUIJQSOVIsyneXdlioGcwYasRm/SxZyQ8JrlaiKIi6zLWiQkJLwy2T9g8tlmF7zuZoZOjLNzocZEV472rXv4vJ9HHGxScSUPzwTUPcXWksZw1uDJ+YBWAG4oaQbQvVLhDeMT9DtNlnM5wtFRxrJlfGIvxNHVRpO3nD6Ooxkc3TBEYKXwUylCFaeS189V7jSmbF+cZ+vKMof7NzBZ7iLneSzlciAVi/kin7jtDvbOzTDQbPDkhmE2V1fIBQHzqTRH+gdZyWYZXVmm7LQpVOb4t/N/zaPtM7zL3sF/F7cyFKq12sJQCOxIogjwTAuUIiUjXE3HMS1E4L+oi+F6T8HrLaEk8USUM13dzOYL7FxZJBNGeAhszjffOMBAu0XbsljIZMj6AXdMjLN9aREdRTOVYn6gh0wl4N4hE0PTky7mhITXMFcjEP8U+D4uMWovISHhlcN5u5tuDvV0IZRksa3YWNTRQ8lD0yHj9YhtJY2+DEzUJQNZQTmt0fBDfCnY4VR47/hRxrGZyuQp+R4PHD/MZ2/cy4lsmQ3VCu86dphaKo1Uscfe7ZMTHBgdBQVbFxcZqccRxAPDowBrac0TfQO0TYu98zNkg4CTvX184tY7gFhUZgOfb23ZzoHVNPX6phYrDHnXkWe4Y3qCR8wWX579FCtBjd807+CdhdvItVr4wsdGEAhBzbawpEQKDU9o5JVCR5L2JaEQSAQSddmUSkRcm7l++kunGeR6Nq7UdB3ftHlieJQblhbQoohAA1sJAqHjiLh2MiMVfuhzdGCA6XyJ4doKG2sVSr7P0cENpIOAe8fPMLdvB0O9JvsHzKT+MCHhNcwL/utf7Vhezx8DbxJCfAb4XeAsl2hUVEpNXIsFJiQkXD0duxuAT590yJiKgq1xdDmkK61xuhJwZEnSndax9LgxZWeXwcmVkMGcxhvPTNHKpGkqC3yoWCn6nBY/8c1vcqarh83LS0wXirG9TDaD4Qc0TZObJycwFURCMVEsk/HjdLOr61RTacwo5I6JWYqeS8u0WMpk+MyefWvr/swl6hY7o/3efuIYbzlzksFKhS80j/BrjQcpC5v/lX8X96guvMDHlpLFXB5P17GkomVbhAqG6zW6fBclBI7QMYXElpJwVRyu9w7s0IkSdux8Ots6x19sSP2dJABMoXE2X+CrO3chBPQ36+i+TyNlU02lQSq63TahVJwt93Cip59sEDCXy5OuVclHPrYGGROsSOPuIYvbdqS/w68kISHhlcaVfD0c57klN53r4Lsu87hXw7CBhITXDJ0ZzUvtiKfnA7xQMdWMsDQYLeh4oeDwYsT2soYEDE1RqDY4k8rF01MUFFsttszMYinJ45s2s39inKLj0LJtznT3cuu5cRqmydaVJc52daMpONXTt9apfO+5MQ4MjXDH1CQt06SSSpPxfd48duoCL75L+SPOlMpr+yu6xpdnPscngme50xjkw7m3MBIZ1C2TfOATaoJMEDBZ7qJppVAoNldWaNopTvT1s7GyQt73aWkmOd/DiKJVv0DiSSOrl7yLZxs/388vtzjsRCnXn0cBvtBIhwGjjRpvOvUstVSac6Uuyo5LOggINR1f1/E0g9DUMKMIzzA4MrCB26cnOdfdQ5fTJtt2cDJpHt06yL/Mv5KNfBISEr5TXIlA/DVemTXZCQkJl2GqHnJgLuDkSkjFlUw3JLYBXqQwdVhuK1K6oBkqKq4kbQjSBjiBYjinUfUU43YGrekSWSkEsHN5kVAIltN5AiWYyxcoOS22LC1yYONmHh3ZxE2z02TCkGoqw6mePlayWWDVr1Ap9s7N0DJNHMsCQGmC+Wye/VMTHIC1COFcLs/hgcG16ONndu0FYN+zB/jdM3/GqWCaD9k38X+r3YS+wJABPYFPPZXmSG8/+xbmGa5VeXDTVjzLpG3ZnOnu4XR3Lx967CG8dpuy00Zb7fSFVdGl67QFpMJw7b0MiC+WF4u0Di935PBSjTEK0DWBKwzalsntMxNM5koUXJeWZZJ2fbKeR1lGzOQL/N2Wneg6HOvfQNO2iTQNM4j44q49eIUsaQs2SJ+op/Ayv5qEhIRXAy8oEJVSv9r5WQjRC2wBlpRSZ17GdSUkJLwEpuohnz3tE8qIsVqELgAUx5cidF2wtahxallh6oLNGY2qJ/EijT09OqMFgx+80eY/Ptziwf5R3nHkMIGCtm0z0KzjCZ0TPb0AnOnuZf9Ek/5mA6UUvq5zuqeXI/0DuKa1FjkEyHkeZ8rdPPDsYVzDpJbKsJJOozSNx4c3sn1xnsF6ja1Li8zkC4DgjqlJHhsZpZpKs39qgoWZp/iVEx9BSZ//Wrqfn/B6sMIQ4XsoTUMiqNop0A0O9w0QaRrbqis8OrKJP7rtLkbqNTK+z4GRjbzp9EnEamezUhJTxg0tZhSia3EUzRMaga6B0JFRQFrKC2oQX85YW8foGs7XPqrV7SGCSNPwDYOGZePpJlJo9DptFjM5XE3jhnCZqmWxkspQzWTp8l3+5Jb9pJRkuNXg2MAGNnlNtvWatNM6NF1uS/kcGB7l/pfxdSUkJLw6uKIKZCGEBvwP4KdZ/bIshHgEeJ9SavHlW15CQsLVcGAuoGjD0WVFztTImIK0IZhuSnShOLgYYmjx9BNDg25b560jJpNNyXg9oisluGuDwZ9Xynx+117umZlguN1gOZdjMldkJZtDAMvZLEf7NzBcr9HfbLCYzfHglm1A3IwCrM1I3lhdodBqoxRsX1rEUJKaneKv9+zDNwy6HIfpYhlLRlRTaZSI43LblxZ5bGSUuSc/wRNH/5JiYYj/2v0u7mkCTh2JIiVlPAZQgGNZnOnu4bGRTVQyGfqbDT52570MVivsm53mzWOnaBkmpoyYLBQpOw7lVhNr9b3TAKRcFX+Khp3CUoq2ZWK0mnT6el/uBhVBHLlE00BKfE1Hiti0O9J0QiGoWzb1TBY9ivA0g6LTJhUF6FLxTP8gTdteW99Dm7aQUpLP7NlHlwXtEIZrFd65PMUNjToDG0sY9+zjXOHSvpUJCQmvLa60Re3ngA8BM8AjxCP27gH+AHj/y7O0hISEq6VTb1j3FCU7FlpBpFhsS4JIYZmCG7s1nl1RTNYl3emIvz4h8STkLcVDky7zrbj7bLJU5iHg7ukJCtkWu+Zn4+2r1jRK0/i9e97wHEPsz+zay/6piTXh6AnBptoKuoK6nSLQdFJRwFtPnsA3LSrpDE3bppZK09NsUHYdsp5H5Db45NMf5lD1KDf13c6dt/8Tjmomb/vWNwh0HVtGBLqOQtDWDYYaDT7Z08tKNkvedVnM5hisVvjRJx6jp90kEIL+Rp2s52JFIXYUYii1ZgsDEGgamlRYSpEOfKSmk/P954jBa5Vajla7qDuF2+vTyRJoWhZL2SwrmTxaFGEIKDebaCoeoWcrRdZ1Kfkus7bNUi5D3bZJRZKJUonlbI7Jchf9zcbawos2uANl/m60m9Gb0uhdJkuepNe83sY9CQkJrwSuVCD+KHAcuEsp1QAQQnwU+HEhREkpVX2Z1peQkHAV9KYFTV9RsAVnqwGHlyImGpJIQtqALuDIUkTZFniR4nQFTD0ibcBSGxo+NIN4xvKGSoX7V+1ljnQNsCQutKZ5cMu2S05LmSmVL+hG/r1P/xWpIGQxl2Mlk6XLaaF7EbnQZz6bY2XVOHsxneH1Y6ep2zYTzhz/ZeqvmJFNfmzDO7l76E0MjI3zdztuYLJQZPvyEq5pspzK4BomXU6btm6weXmJkWqV/laDr23Zzvc/fYDtK4t4QicdhJRcF11KPATnil3scWcwVqfDBIaJY5poMqLoeRR9n4hL1yBeiZTq2OE837ERcR2mkrGxtaMbaCgMqZCaRlvXcFMpGlYaXUako5D/s3sf73z2KKP1KgrBdCbDtmqVCA3PMAmFQCBQQrGpusKjo5vj9zabi1+HgJIt0DVFKBXfnvbpz+rUPLhvJPE+TEhIuHKBuBP4tY44XOX/BX4K2AE8fq0XlpCQcPXsHzD57GkfNwh5aDqg5q3aCoh4JN60r8jYIJWgOyWoeYqsKdAEtEJFK+rMIIbbpyeor85M1oDprm5WMlkcy7rAmuYFUYpM4FPJZPENWCGDEUbkAp/bZqb4n7fdyZ1TE4zWKhzrG+DE9EP8/vRnyWsW/3XwBxnsuoGJdIai73HDwjxCgK4kDcsk1A0Cw6BlW3iaxuvOjTFZKLOSSXPnuXHeMH6aip3GUpL5bA5NSVYyeXpaTeYzCoFAU4pAaLiGTtZ1sTom28Tv3fro4ZV0LXeigNrqcwSrz2OtO8YHXMvCjCQmEkc3WSgW0KTECkJyvkcrleZkdy/97RZmGDGVL1DwPU7093Oyp5eBVoP+ZoNGymayUEJDYUcRhUaNhVyBxUyaQNcpuQ4PbtnGcK3C/WcnuUG2Wc7leGp4hKlUFxlTcN9I4n2YkJAQc6VXgixxenk9M+v2JSQkvILomGT//FcdLB10LZ6pHEYQrZr5CQlLjmSiARkdfA0sU6BQhDIWOBHxzOSlfB6xqnhMPa4rXEtXXiFPDo2wc2menO8SorFtZYms79I0bYarFb7n9Am+sm0nt46f4m/G/4bPV5/gNnOQfzb8AfR8H5nAw7EspgslelotFnIFSu0WlpTYUUAm9JnOFTCUJBQaShOM1qp0t5t0tVoUnTauaVFwXTxNIxICDcn26gp106CgJEIqcp6HqdSasDN5fnub56Pz3tVTaQquQwQElk0kI7TV7mgBRJqOVOAaBkJJlnJZvrTjRp4aiu1nP/Tot0iHIYFpMJfJYcuIuWKRVBRyeGCQE30D3DExjh2GjFYrmFGIqSSHB4Ywo5BI00iFIW3LWqsN/eEzh5GFLEvFAlnX5f0njzLzplt4347eF/V5JiQkfHfzYqdLXer3pGAlIeEVyHDBIGNpDOd0pFQIDVqBohoqPAWhB1kTUBBIaASKLAolz//jVsBcJkfW8fBTsdWNF7GWrnwxfHnnLrYvLbJ/+hz9tQZZ38W1TBbzeWYKRbYvLrDkVfmN0x9lrHmOe0bexu+5WylVW7Tas1RTaQZqVXpbTbauLDNW7mI2n2f30iJWGOIJQR+Cgu+xnMlQT6WwwhBUHLnLuB7VdBZDSgYbNewoom1ZREqxUCii1WsUXRdNnX/tV3KBjC5xrAJc0yTQNWq2Td7zUVJhhSGhrqNJyaJto+kmmcDH13TO9fQxXe7iqaHRNWugIwOD9DfqTHZ1I5VCCUE2CKjbKZYzWXKeR9FzqaTSzBSK7JmfpWlZuIZByXV4emgknmtdKiOB9x45hJvN0LRssqGiWMrQaGl8oDENbH5Rn2dCQsJ3Ny9GIL5TCDGw7vcM8XXwA0KImy86Viml/vtLXVxCQsJLYzincXBeEipwfHADtTYNRBJHAwerFW6amGBDu8lMJsdTI6Ms587XDh4YHuU9xw4jgPpqR3InXflimCmV+b177+PtJ47xiw99g0o6w1SpzJnuXhqpFCvTT/HXT/8xbSJ+ecP7uaH7dvSZGazQQ+GxmMny+nNjVOw0C9kcRhBw+8wMjm7gAwXfp+T71E0LBfS12+hRiCklDdvCDEOKnovt++SCAAlEmoZSgp52G0tK2raN7ftY6vJj99ajE0caO2nnzvurSRk3kRgGfiQxogghBI5h0BY6hhBIJFIIquk0Y909RLrO/SeOMp/NkQlD7DDgeO8AU+Uye+bniDRFoGlMFYpsX1pkqFZloNmgZVrMFwrM5gtU02kGmg2Wsjk+s2svs6UyhojLBYacJvfs6eF4VbLsKLpTGndvLrA9aL2ozzIhIeG7nxcjEH9o9XYx//AS2xSQCMSEhOvIVD1EQzHVUGgCMgY0JUSrETVTQNdyhbc9c5hKOs10Nk/O83jv8cP89c69nF21O+mMuNs/NcFQs8FsNseJnt540smJYxdMOnkhZkpl/ujOe7ltZgpHN2jbNkopDpz+PF8491k26UV++LZf5EbX4u5z45hhFAurKGS4VsHTDVzLYiGT5U1nT6OUJOt7mCikUjiGSSaMGGg1mVcKQynK7TapMERXCjsMsGQ8NSXU9FjAaQIrCIlWW0mEECh15bMBOmP3nFU76xSrjSlKoUcSWyk8TccUEMrYpiYjJL4EXeg0bJtaKoVj2Yx1dfH6s2N0tdscGN7ITCGuN7xhYR5DRixnC5zq7gUU/a0GjmlyvLePW2emyAcuX9y5m8V8kZzrrEUOIZ6Co2sgenJs1CPKgza2Ibh70IJ6G8rFF/W3lZCQ8N3PlQrEN72sq0hISLimdIyyFxzoz8CCA1UvFgmWjNOilgF3TU9Qz6SpmSnSGrQzKaSKt88Uy3irOml9R/JgtcJ7Vrua53OxqOxMOrkSkQjw5OAwrzs3hhO5/OXpP+fY4tO8MbuTnx18D5mKz61z4xTabRCClmWjI8j4Pm7GpGGYDLSb2GEIIm6sUVJiKEU6Cgk1jVDT6G82McIQG4VSipZlAyAQBAgMJbHDCN8yCDRIhREp38PkheeExmbV56OHBtBKZ5gt5Bmo1Sj4XizCo5CanaLLcTCVIhQCOwzRpUQKjYZlcba3n2cGhwl1jT0L8zw9NEwqinh0U5zyzbsuw7Uqn9u1d80b8s5zZ1lJZbBlxFd33MhTw6PcNDvNtuUlzvX0cvDGbURdZWwvLh/QBAxnIXfvNsJnj+Lbkj1bc7E4rLXhvt1X/seVkJDwmuCKBKJS6sGXeyEJCQnXjgNzAaGMOFUJ2VQ0MHVJw5e0gthMuh3E0axstcliPr/qfg9FW1CJbPrqjecUF3dSrvunJqiudjUDa/f7pyYusLW5HF/euQtt9lk+8/SHWXaW+FD3W7hn+C1UTJPvOXwIz9CRmoYdRRR9j5lcHjuSdDutuLPXis9pSomm4qicFBpK0wi12BMxE4ZIFI6mkw1D0oEfzyZGYWuCUGhIFEJJyq4Dq13Lz1dU3THFlsQ+iZ5hYsjYoFpXkqVMhkgzaKbStGwbTSnKjkuX4yCUxDUMEAIjkmhIhJIoIZgqFmmkUgil6G63OFcsx6Ky1WL78iJFp83mlWVGKitMdHUDUPRcXF2nlkoDsJLN8eDWHWxoNvjazfvY06sTuYqRvKDhK4byOv0ZQbEvy/JIiv1TE/Q0GtBXjMXhcPcVfW4JCQmvHRI/g4SE70IWHcWJlZB2qFhqK5qBisfIadDyIaWDrcN8Joe92oDiBmBoiozrMZ3J4V6UZe3U1vW2mszn8gB0tZpsX1qk5MYJ1itNNU+PP8jHD/w6lp7inW/4VTZrfRwoFPmhg0/SsixCIcgpl0hoSE1jQ6NO27JAKrLSQwGeYWAECqkipBCYKs6fC6GR81xMGaGIRSSAqUBIiSYjXNOkbqfIRCGWlKDiySmd28V0xKHH6kVTKcwwiFPHMqRpmAw26vh6PBTPlBLPMFlOpym7DukwQI8kSovlp1qtRRwrd9PbbrOQ9wFFw7LpctscKg5y5+Q4LcvC0w0mC0VeNz7GQ8QG5b6m0d1u8+CWobU1FjyPdilHTxbKKR1dk1g62IbgB25M8Y7N9qqFTR4YIiEhIeFyJAIxIeG7EA3F0/MRXbZguS1Z1SX0ZzSavsSPoB3BkyOjvOvoYTTiBhSj7pF3Hf7uxudvQFlaNbQ2o5A7JydomSaOYaApLptqHqxWuG1ijLFH//8cOf23mMO3Un7f72DUXFYW59k7P8uGeo2abWMoia8bhJpGNvDRVlPESkpKvkeoaSghmM7mGGg2yMhw1VpGYIWx1QvE0cA1c2ul0FUsGokklpR4mkGgaaQCHzOK1tLG65GALzQUinoqTcbzULqGFBpSxLWHK5kcg606ShFPZdF07DBEi0JCXaOFQTYIkEqL1yk0fF2nlk4zn8tRcB12Li3QMC02LS2xdWmJlXSauVwBpes8vGkLOc9juF7DN02O9m+gv9Uk0HWEUhQ8j27f4Zu7tpE2BJGSCAEbCwY/fVOK/YMpEhISEl4MiUBMSPguRClFyhBoGuSs2BzbDQXNQIJYnfELTBXLfHq1AWVgdSTe1zbH4vC9Rw7R02o+pwnlyeFR3nXsMFuXFhAyYttylbLrcGhgECHlJVPNg9UK7//2l/n80/8vZ+tjvH7wjbx98/9F68RZznV1c6JvgLZp8fbjR+luNbFkhCYlmSgEpfANg4Vslr52i3OFIoFpsZTOYEuF6zhkohBJLM5CEYvCi70L11v3ZGTEsmXRsFIM1qukomjNl9CX0ZqZdecxkaYxUSjiWyZCKewwwpISX9eYzRVwbJvpcpmi6zJaqyCkZD6Xp+y00CNJ2vcRQNuyCIRGpAmWsjn6m3V62i10BXOZDDqwmMuza2EOJcCOIr68fScr2VxsMG6afOzOe9fe0/1TE2xoxjOyD96yDadcpu0oNhU1fvBGe13UMCEhIeHFkVw5EhK+C1FC4/7NBp8/41PzY29DHZhrgq6fF08RFzagaMCmRoV3Hz5MLZVmIZcnf1ETyuRqV/Ovf+mzbKysUE1neHpD3GSxe36WbBA8Zz37Hvo//MnDv4UrfX5+5AO8M7WdDadO8EzfADPFIndMzDJYr9KwLbrdNhU7hRlJut02AnCEoK9Wp+h75HWXQGg829vLUjbD5uoygRC4hkEmDEkrheK5Rq0dC5po9edyu03BdckE/lonckpG+JpGJCX66nEB8P+1d99xdpV14sc/zym3l7nTM5lJLxCqkBCKNPGHBQUlVEEUF0RUXNG1rq6uunZsKxZ01QXRRRQFROkdgSQEkBBIQkgymSRTMu32ds7z++PcmcxMZtKTySTf9+t1X2fm3HPvfeaZ5OSbp3y/yYCfWLlELJlkTXU1PeEwjjJoSCVxleKwznZ6AiGixTxoF40mls8SLRQomCbaNCm7GrtUpuz3s7aqGqUUwXKZtM9HyTKpz+bo9fuJKEVbvIq8bfNaTR112Syvs23uyYHfW8iEyRGIB0zMkqY5Bu+Y6eeqY6SGgRBi90mAKMRBqC6oyJcMpsQtXA2r+xyU8na0uu7WNXVDR9aofL+wrZX+gdJ6BuRCAVyGb0LZVJVgc6yKtN9PT2hr0BIsFUnksoPfa63hiZ9z7+NfpdZfwzebL6Mh3ETBcYnmc5y1ZjWTU0lW1tYTKpYoWL7KFLMmWCrgUElH47pUF3IUTQtHKwygJZnCclx6A0ECpkWkWMSqpKfZXvZ+E8iYFqFiAdcw8LkuJbYmu/a77mCf5A2DjkiUomVTk02Ts00ShQKOZeEvlQgXixzR2U7RtLC1Q8mysdNp4sUC4O1wdkzLm+ZWivZIlDV19SgFytUkgwbRfJ5gqUxtOkWwWGB9ooaSaXobZ9BU5XNE8/ltck8O/O5CFvgtk/qgQVOdQU3IwJX6BUKIPSQBohAHoQWNNve+XiDqUzRGTByt2ZDSmIbGcbfuxh3JAWLJNJsilZ3NrlcvuDRKab2eYJCqXI5gsUjOtgmWSpiudx5A59Pwx0/AP+/mqMRRfLruHAJ2kHC+wIzebsKFPKFSiVlbOokVC2RNk4BTpmTZ9ASCxH1+LLdMNF/A5zhe+hrTRCuDTbEYPsdhdk83Kb+fSKFI0PFGLseqkzz0vKG9gC1v2VDyEmOX2Bo0q0pf4LoEyiXKpkl3IITPdbFcl7JpEssXcNEkigWSPogWit71rktZKUztbQyKlEvkDAtLudTls/zTtkgFw5QMg+pshkmpJA6Q9vkIlsrUZdJkbB8vNk3G0AxOTT82YxabqhJYbK3eYgL1IcWMuEGqBHkHJoUN6oISIAoh9owEiEIchJpjFjOqTDYkHV7pKZMtaWqCCkNrNma3/9r2wU0oDnO7u4jlcxQMk5cbGoddt7qugaztY1IqSSKfo98fYF2imo1VCXTHKrjlKuhaA2//Im9yZ/KG556lbKSpzaQrm0EUWhkEHAfTKdOcyRDI51Gmwdx0CrMSaLmGhc8pk7MslIKucJiV9Q0EC3makv1EigX8TmlwQ8pQLlsDPl353gW8RDjehhalFGg9eDMcqIqiDQPHMFEoAuUyWoNCszkawzFMQqUCQadMn99PzvZTsGya+nvJmwa2svGVyxQtk0C5TMgpeyOjrsuRnZ08MnM2r9Y1cMba1+gNBKnK5ymZJn7HxeeUUa7Lqtp6tGHw13lHsWHImk7D8OpomwYEbS/QzZYhZGlsEyzDZEGjvbt/dIQQApAAUYiDlqE0K7rLmApcrSm5iv4S+BTbpLCBrUHUkuYpXLFsMbO7OukOhcgbJtX5LA2pFE19vYObVZY0T6Ep2c+Khkmkh5Tgezz1CvzfF8AOUHvpL7mi1+KCFS8Qz+UwHAdbuwRdl7JhUrAsDNclVsjjKEXYLeO6YDsOLgq/dilpb2SwZFg4GPidMsFiian9fXQHAkxO9g/uPB6YOi9XjqXKSN7A6KG3+9nbcNKYSRMuFkApcoZBsJIORxsGDgoDjeG6RPI5UoEgwXK5EnB6Kxx95TIuBj2hCFmfn3XV1cTzWVJ+P0prpvb1DinppzAV5C2L1bW1PN/kJcMuGQZF06Av6Kc+VaKMJp7L0RWOkLNtHpk7j82JxOA6ALPyKFcGCKMWvKnFRzRg0Jf3psbPneWTjSlCiD0mdxEhDkJtyTJPbSjRnnEBTaYI3VmvvFtdr7f7tW7EDuWBmHFTVYKOcITadIqA49DvD/DYpFmUTGubdYgDJfga0ik6gkHu2vx30s/+hkTtYVx85DWc/EoH03u66QmGyNfZHLt5I1QSW+dNg5ztJZT2lR2ylj04XVw2TW/ETrsopcgD8WKerGXTGQoxta+bmkyW9lCI6myGcLk8LIfhQGobv9bkAFMZg0FV1rKoLuTRaIqmhaVdDJS33lEZWK6mbFuUlcIulwm6Lv2VILJsGszZsoWMbeMCmYCfYKlEyh8gks9TsCwCpTJF0yRn2VAuobRGaU3OtGhNVFMyLEzgznlH0dLXyxStiBVzpH0+OmtqaY/EyPpsAo6DbUFtALpyQ9aKGmC6ELQgETI4rtGmNmSSLLiEbCXBoRBir5A7iRAHoXvXFujMaSZHFNmyoi/v0O3sfJk8E3hs5pzB0m7g5REcWIc4kGJlIMi8e3I97X/9DLz+NEdOfwtHzP8gh7e1cXh7O03pJG3xKjZH42yIVzG1twef42AUimR8frI+H6FSiVixQGu8ihb6KaFIFAoYZQdLa1zDoKQ1KdtHfS5LZzjC8w2TmJbqQ5smbiVAHBgpHAgUi4CplLezWBmYriZUyHtbuV23UovZxO96ORBt7VJGodE4poWtXTYHQ2yJRAg6ZUKFPCVDESkWKZkGG31+uqMxbNch5/PzWqKGeV2dbKiK4yiYnEphaZeuQIjeSJikP0DJMOgKR9hUleDHJ5/GeSteYuaWLtw6AEW4VOKfkyZTMi2OWNfK/fEECT8kSxD3QSKgKDmaZBHObLGoDhokCy79BTi9RaaWhRB7hwSIQhyEnusoURtU5B0oul6JPb/a+TJ5XZV1iAPPw9Y0KyODzFzb8yT/8C1UOcdxb/wUc5tPxXYcFmxcTyKfxdAuzf293i7jUglTu2QsG0NBfSaNqzVJf4BguYS/7JAxLBpyGQxn615c09V0hSMsb2qmMxwi7Q8yub8fF0XJMNFKoSpTycBg2hoTQGscvN3JA1PQWaUIVzaRKNchY5qgDHzlEqVKLWelNVnLZks4TH02TVcwQsJ16QuGK5VcTCZn0rzaMIn+UIinp86gKp9jZftmJqdSJPJ5NkTjmLjkbR+OUnRGwhRsP0uapwBbR2E//8j9aKAvEGR54yR6whFMrZmUSWEoCPkUYVsT9hnUhgyiPsXRtSbzJ/npyGrqgorTW2wZPRRC7DVyNxHiYKVdXulxyZc1BQdcPbxM3oD0KDuUlzRP4bwVLw0+P7C+8LEZswaDzKTfz9pX/sIrS26i3orxo4ZLaeryc3+sj+l9vQRLRVxlkPL5iRfy1GQyaDQZ2/bWAyoDy3UJlUv4tEtHIESkVKA2l8Esl3ENA629PISGoXBRuEBjKk0HirXV1bgKmvv7sCvB3oCBXIcDuQ8Hg8UKn6txKkGlY5rkAkHKKEzLpuizyds2BdOkOpOpVCyxMHBJ235MXCxHg+F9zpGdm3l66ozBncYLYnFCxeJgpRnlODSmk4RLJQq2j18sOGnYaO2mqgQPzD6MULE4LCCPFgokYxHifgjbBgk/1IcNFs0J0l+QtYZCiH1L7i5CHISmRRUPr9NELCg5oLW3cWNLOEKsUKB/lJHBoUauL+wKRwbTrNStXMFGv8WLj3+TjWsf4ZTgLK5rehdhK4jWmouWv0gZzeZwnCnlXmqzGbTW+J0idtmhOxTEciHt9xEuFskoyPn8rKlrwHQd6jJpXMNEG97aw7JtU1SKiFMm6/NTVgYZf4ANsSrmt22gJxgiWsgDW9ceWmwdRdSVDScDHKVAgVsph4fWBEpFioZJKhCgbHpVWwytCRRL+FyXlN9PYzJJTzBEdbZAf8CPi0Hesonn8vxq/oksmzINgA3Jfv71yUdoTKcxXBfTcfA7ZVbVN7C4eSrtsfg2v6/RAvJ4Pscjh82iLmRweLXBlpz3ewzZMloohNj35A4jxEGoJmThMwvkHE3R2ZrzcGnzFN654iVcth0ZHGlohZWh1jp9LLnnK/Qn27i04c1cEj+Rsm2jXQcHRV0ySU0uQ3s4SqhcoqwUjmkTLBaxtIuloWx4u4vjxQJJy0dHOEIyECRcKtAZjuJ3SqyrriVYLBIv5DG0i8agKxLGcl2ea2rmiI52VtQ3MCnZR8kwsF2XIgoLjcnWXI9agarEhwovLYxRqZSi8XYWFy0L23HI+nz0BEM09/cTLeRxlSJl2ziGQcApU5PL0hEOESsWiRYLpGwfrVUJWpL9LMNbm3n2qlexXZeM7SORzdCQTbGqpoFHZ8yhaFmjrvkcGpA3plMkYxGWHjWL2ml1TIubNEVNFjYpWmIW754T3Jt/VIQQYlQSIApxEFrTW/LW9hUgX64kfQbatjMyOHLjydD6ywP08r/x0N/+FT8Gbzr9y1y9IUuvZVKVy4FSFCyTjM9HdSFHYzZDv89HXyCA6Wqipo+s6WC6GlNplOOQMwwc06QvEGRqbw+m66CVJlwoejWZHYeSgmipTFs8QSyX54VJk6nNZjl+Yyuv1tST8/noCkWoyWZwTAMcB10JAF3AVQpX661fK8P7HDSOYVBSJnnTxHJcIvk8gVKJaCGP4bqgFDX5POlAkA2ROFNTfcQKedK+AFuCIQKuQ1cozJyuTsBby1mbTdMXCFFNjpBTIm0H8DllZvVs4dnKKONo9ao3VSV4siHBSU0mhmEyw1IsbLKYGrdIFzXr+x22ZB1uejFLXVCxoFFGEYUQ+47cXYQ4yLQlyzzX4RC0NGUHDDW8pN5oI4M72t2snTLc9y149MfolmOJn/tdYqky+c1PEih7VU5ihQLN/RnMskNrVYJQsYjpulRncwScEo5h0h6tIlwqYbgQqaStCZZKzOtoJxkI0Bv0qpV0h8LkTQvTcQk6Lqtq67jnsCPwa0173CvxN3NLJzN7ukGDMhSv1dYRK+SJ5fPEC3lcoKQM0OAoA8cy0a5L1rYIKUXW72N1ooaafJ5osUBPIECkVGJFTR3xzW34XG8lY940aUin0EpRArpDYZQySPv9LKmrp2jbg+UF6zJpqnM56lNJ8rYNWlE0Fc3Jftr7+4Bt13wOVG8J29AcVcyr9ZEIGFQHFEopOrIaA28DTsg2iPgU6aLmrteKnDsLCRKFEPuE3FmEOMgsaS9RHVB0ZQ1c7Q6WZdue7e1u/ovlwK0fgjVPwYlXwLlfodPycyewIRbniw/fx8zOdroDQexyGUtr0pZNv8/PjN5eiraFoSBn2dRn0mR8ATqiYUoZRc7nI1Uu43MdAk6ZSCHP2qpqFIpwscDGqgSOYbCyrp6+UJi87Rts2z+mzcR2XTqiEY7duJFAuYStNTmfD8t1KZimt3lFaxxDUTRMNsfjLG9sojaVJlbI89LkZvr9AVbX1nHy2jWcun4tSX8AKul9DDR+pwyGQd4wKFk++oNhVtQ10BWNEiyViBSLg+UFu8IREpkMrlIULYu87dVszlsWkUp95qFrPk0gYHrBYSIAUZ/JUXX2YBA4sBllSXuJoKWI+b0EPjG/t/1mSXtJAkQhxD4hdxYhDjJdOc30KpO1/Q6OhrIzfARxNGPtbmb9c3DnlZDphYt+gJp/8bBrlk2ZxuNTp9PY30e8WCBn2/T4A1goWvp6KZsG/ZW1jv5yGdMFV0Eim8XvlOnwR3D9PgJlh2C5TG02Q3cgSLRUpCmVxNIOS5qmECiXOWvNKh6YddjgZ/eEwzw+fRYL2tbz0Kw5vOPVlykZCjB5tb6RWKFAezRCoFymLxAgWirx6+NPpCccZkpPN0d1bOLpKdMH12JGyiX+0TKVmlyOomljaDAdLw9j2TDI+AIkg4rXq2uoyWUp29aw8oLgbTZ573PPEM6XKZbKpCyb2kyG7lCIlO0jms8Prvm08UrlJQLgN6Howpxqc9QgsCunaQgNr68c8Xmji0IIsS9IgCjEQaYuqFi1RQ8GiM6OX7JN3kOtNf3L/o+nlt9MNFjD8W+9gdWzzmTTKK+1UDw7ZRpHd2xGa82kVJKsbWNqTWskRn0mjem6uKbJxlCQeC5HvFBEo5nZ2UHBF6A3FMRyHELFIof3bCFvmiR9foqGxcmt61lbXY3fcXj3yy9yx5HH0hMOA1C0LB6YczhLmqcQL+SpzWRo6eslUC7REwwRKJfxl4oELJuVNXX0hkJE83m0YfCLBSfTkuwfXIv50Mw5BEsljujsYEWpRHOyj4Z0igLQGYmQsQOsqq2laFmUTZMHZh82uMlnaF7Dew47koXr1zI5nUSbJk9NmYprmJRNk6zPx1MzZpGtS1BtKRIBCFoGER/ky5qTmoYnuh4IAuuC3oiiFzR60kXvvBBC7AsSIApxkLGVy92v58mUvFGpnTE0zUqfqXntse+wqu1JptYfy+Fv+gLV+Jg3ZE3iwIaWOV2dnPnaStqjEV5sbGJh63oM16tn0h0I0R2J8vKkJo7YvJFIscCU/n58rkPBMMmYNiG3TKhcJNqbxzFMioaB5TjESyW6Q2Ea0mmihQJdkTCrq2t4Y+t6rl78FM81NbOqrgFtGKysreO8FS9RMEzMcplguUygWKS/NkDKDuC3bJY2tbC6vn6bjTnLhvRBU18vVyx9FtMpY7tl/OUyOdNiUzxOezROMhDgiRmziBQKNA8JLAfea8B9c+cRcBweCwSH7RQf6LuQAQkfvKHB5Iham86sxlIwI64IWMaw38tAELig0eau14qAO2z6WSqnCCH2FQkQhTjI/PqlLFty4Oit6W12ZCDNyuErnmHZI/9Jb38rJ8y9kPoT/wWlDAa2VCxoa2UJDG5oieVztEdjzOjpZkWdjy2RMFvCIUwN982ey9zuboxymbpcFq2MwfyLPu0Sz+UwgLTPDxqClbrFOdMAZWBrF1O79Pv9NKRSmC6sj1WR99kc3tWJ33UGRwGV6xJwHBqzaXr9AXw+m2l9vWyOxng9UU2kXMSEMXdnDzIUaX+AVxomsaa6jppcBkPD2upqXmpsomRaaKPEj08+bcz32F4OSYB4AE5uspleZTG9yuaESV4ACIwZBDbHLM6d5a0vlcopQoj9QWkta1jGMn/+fL106dLxboYQO60tWWbBLVtwK5FhV37nX6tX3A//dx1KKa6dejGHJ+bRHwyxuqaOnnB4sBZzVzgyWPXj7FWv0BcIUptOU5PNECoVMctlbK15pXESDnBYx2YmJ5M4lonhavzlEgAN6RQu3jSx33HI2jahYhGrXKZg+8jbPkLFPDnbxnZc0v4AD86aTcYfZPaWTjbH4uRNi2ihQF02TdEwqclkQGkCjoPlOqysqachnWRysp+UP0DGtllfXcMPTzljMLH1gPOWv7htNZN8nkCpSE84st30PzvLBOYm4D3zwlxxZHCbAK8tWR5ccyipbIQQu0op9ZzWev7eeC+58whxEFnSXsJQUHBAGTu+HkC7Dtz/HXj4h9iN87hq9pWc3ZUiuKWTrO2jpbeHh2fNpWSadFUCpYENLf2BIIFyia5IhLJl8lqimotfeoHeQJA+f4BEPkuiUGBFQyOb4gnmt7XSmCqC9nISWmUHv1PAUYqwq3G19hJpOw4BN49jmvjLLi4uBdNgZm8vdrmL7nCYjGVx/KY2CraFVXLJh8OEnBIdoTBpQzGpv4+zV7+KoTUlwyBr2cS0Zt7mzXz3nj/z98OOZFVd/WDAN9ZGnXCpyJ1HHrPLv4uB9DWardVd6oJwXKN/1OAQvJQ1EhAKIQ4EcicS4iDSldNMiShe7NIYo8wvj0yG/Ux1lPZ7PgerH4cF7+HSye9gQVsbSV+A5q4OGt0kroJwLseWeIzXq+tI5LL4SiVaq2tYXVPHwg3rCJaK9PtDTOnvozWeoCMapaqQpy8YZlVNLbO3bCFeyNMTDOIrF5nS34/huGR9NuF8ER8OJRN6whEK+TzKMLzp5UCAtC9AtJQHZRDL5bC0w3PNU5jW082WUIT2WIwFG9aTdv10hUJM7e/DVy4TLBUxXMdLWaMgViyQsm0SxRK2donlc4SKxcF8jyM36lRnMhy9eSMBp8x5y1/c4cihhVfO0AQs5dW+LuOlsYn5YGaVRVPE4LQWqaEshDjw7eQYgxBiIqgLKqbFbWK+bTeoDCTDDhWLdESi5Da/TPrn58Hrz8Ci76IuvIGFHZ0UMagp5OiMROgPBPCVypy0sZUN8SperW+gLRbn/722kncs/yfz29ZjOg6xfJ6+YIBAucy9c+exbHIL/f4ATf19TE4mKyluNIZ2CRdLuK5LzrYpWRaZgI+CaVEyTPxOmf5QiKztw0HhKK+OsnK9esmmdtgSClM2DKoKedYlqslbFjnbYmpvLw2pFMp1ydo2Bpq8ZZM3TfKWjV12SBQKKFwKlkW8kCcVCNAXCHprK5unUJXPEc3nqUmnOf311STyWZZMbhkMJJv6esfse9uEgAG28mJSnwl+A2zDq4dtKG8sUZb1CCEmAvlvrBAHkQWNNrevzNMQUvTkhwciA8mwk34/rav+xvJnf0IwkOCMt97AYwsv8C7SmoZMarA+cdGyqE+nUVrz9ldfYWZ3Nym/H+W4NKSTtCWqQWuvmgkKVynOWvUKzekkfYEQplumZBn0hsOsr6qiMZ1GKeiKRnlk5hzCxSJvXfkKgbKXjKdoeMmtXeXdnGzXoSMQpCMaIVgsESoUaUql6O3tYU2imkDZK2G3JRxlafNU5nR1MrO7i/ZwnM5ImOk9vdRlM9hukZJpYJS9aePuYJj+gJfceqCyydDNJaesW0NvMMg/J02mp5LUeqAPR6tPbQCu61Wt8VtebsNUyTtvGV5Ow5yjsc1tfy9CCHEgkgBRiINIc8ziDfUmD6zTlEc8V5dJsyng459P3cCG1+6nrmk+x5/6GVrKisfwRhhdpTiyo50toTDJgJ/6TIZYIU/eMImU8jSmU0zq78fSLhuqEixpnsLCDa0kbYvJfT1M6e7muM0bydk2k1Q/8VyWzmiMFyZNpj1WxZ1HvYGF69dywob1lE2LeDFNW1UV0UoOQ8c06PMFaUr1k7VMCraPYLmEoTWG1qQDfl4LRwiX8jT39RIr5Ej6gyyvb6RsmKyvrqUtFmdqXy+hcpmCbZPy2UQLRXxOmbxl0xsM0BmLsbqmDhhe2WSgDOHAekSthuQdHFEib8BANZS6MFT74PWUl3tSazBNSAQNpsVMNIq436A3v7N7y4UQYvxIgCjEQUZrb0pzwMC6w+bXX+CeVb9hU6aNOcdczpxjLiNWLNEV9g1OP79a18AR7ZtJ5LM0bknS7w+QtyzSfj/9/iA5y6Il00tnyFuvN3tLFxnbJm/bnLhuDRgGOdsmUsh7tYiVgVmpkOKYJgCbozHaI1Ga+3uZ3dWBXS4TLxbZFIuxMV5FLJ/H7Ne8WlePY5pECgVqshnWVSXoCwYpWj7sgiIV0MQKWQq2TbyQZ1MszvLGSSitOfqZJynYNq3xKhpMg4FEPW3hKCsbm1jcPGUwafZAZZOhRq5HhOGB5FCWV+4ZEwj7TSa5LrOqLF7rLZMqelPKHVmHxrCF60IiICt7hBAHvgkdICqlQsDLwDTgRq31R0c8Pxf4FnA64AOWAV/SWj+8n5sqxH6zNulSE4Bkaeu6w5d7Xua/ln8fy3X5+Kwr6Zv9TorF0mBwNLQW853zjuItq18lWCqT8gfI2j6ixQJp20fRMLEcB7/j0On3ES/k6Q0ECZZLVBUKbIgnKFkmee1jQ1WCUCFPbTZLuFhkblcHlz6/hOpcjrZwhOZkEl/ZpWhZvBaKgqHYEo7ioni+qZmXmiaT8/kAWNi6jrxlY2qvqsn66mqU1rxho8m66mr6AiGenTod8FLTrKyrp98fZGZfN5tjVSxraibr85Mo5Hl0uhcMjpXoGoYnDh+a7HpkIDnAb0FfAZRyiPoM1vS5FF0veOzOaTpzmtqgZnLUYE71hL7tCiEOERP9TvUVoHa0J5RSM4F/4G0k/DbQD1wN3KeUepvW+sH91koh9rMZVSbrUg7zW9fy+Oo/s/zl/yNWPZM3L/wkLSmH2W3reWDO4YPB0ZznlxLL54gX8vQHgtw3+zBOWb+WxnSKtngVecPAp10SuSxt8QRZy6Y3GCJv+aiuJJPuDwTQaG9zSaUiinLBdMrM7u4ib5gUTJP1iQRN6TTrqxNsSNQALp3RGMrVKDR+p8yjM1qY09MNQM62yVo2tdk0q2vryNleUulAucSamlpsx6EhnUJpPRjIPT5jNnnbx4OBeVRn0izc0IqJptcf4IiOdhoyKR6eMXvMnck7SnY9lON69a5zZciXoa5O0RQxWNPv0pn1pp/nN5hMi5u83O3y1ulSHk8IceCbsAGiUuo44OPAp4EbRrnkG0AVcLzW+oXKa27GG3G8USl1mJbthOIgdFy9RWuyjJXt4bkH/p22jhdomXU2px19JYf3JvE7RVBqMDhq6utlRk8XZcOgNxgmUC4xt7uLF5ua6Onv46WGJo7o2IxrKHqDYZY3NBIrFugIR6jJZqjJZXipYRIlZXBM+yYc02RDKEysUKAp7dVlXptIEC2VcUyLvmCESakUoZJDa1UM23EoWDbxXBZDa56bNJmWZB+BUoHadIq0309nJELK76M3GCJULKINRX0qSW8wTDyXx++UmdvZzuq6hsFRvoERwDldXbhKsZ9a9wAARgVJREFUE80XQCk6Izbt4ShHdGwm4DiDJfBGGliPuCMugPJGCyM29BUh4tck/CZlx6HkaurDFlUBg8OqFW1pzYK9+QsXQoh9YEIGiEopE/gFcC9wByMCRKVUGDgXeHQgOATQWqeVUr/EG3lcACzeX20WYn85tt7iJ3e/QOmHV7Kpv4MT5n+Yw6adxYltG8jYNjnLwtAM5v9b0NbqBYGdHQRKJXK2TbBUpKW/f7CUXbhUIpHL0hMMsrEqwV+GjLwNrHEMlYok8lmUG6Ml2Y9Gs7q2rlItxUfKsEj5fDQl++kPhIjns1hOlFk9W1hbXUvRNNkYjeHXmkQuR3coTE8wTHU+y7pEDffPOYxjNm/kTa+vJmPaKBSWdugPBni5YRLaMIaNCA78bC3JXlrjCRxlULQscrYPtCaRzw2muNmZQHAsQbOyU9mGkE/REFKkiprevCZsKWZWWyya6+2YdrWmIyv/LxVCHPgmZIAIXA8cBiwa4/mjAT/w9CjPPVM5SoAoDjptyTJf/+//4cUbPgnhGmqu+C3z+2Bm+yaylgUKwsUSz7ZMo2SaLGhrZXZXB1U5r6ScN2LnY3O0ir5ggGVTprGMrUHgnK5OFrauY3ZXB6vrGgYDsjtHBItzujo5Y81K2iNRIqUy3cEQ1fkcllMiVC7SFq2iLp3kDZs30hsIkTdNarJZ6jJpXq2tJ+kPcHhnByjYGInTEY16bZkyjRcnTeZzjzxAQyZFezTGU1On01pdQzSfHxbsDW1XqFjkpNa15C1vejpYKtHvD4y5M3lHBiqkUDkGDJheZRKwDOJ+RVfO5Yhak5DFsKTY6aJXQk8IIQ50Ey5AVEpNB/4T+IrWep1SatoolzVVjhtHeW7g3OQx3v+DwAcBpkyZsmeNFWI/yufzXPq+a3jyLzdTe/TpJM+/kS2hGu7s6+XzD9+HqxT9vhDLG5oGayvP7WxnZk83ZUPRFq8iWCoRLpW8HcVDgr7zVryEcl2m9WzBNRRVuRxZ2xsNHDpFOzQoW1VXT6hYpC7Vz1tXryRYLBLPZ+kOhiCm2BiNMymbomCaTO3rI+3zMauzg2Pb2thUFaffH6QnFAalqMlmBtuysK2V3lCIV+obCDhl5mzpojcYpjcUGjXYG9hwUjRMAqUioAiXSixvnDTmzuSxGHgjhmUXtIKIBVNiBigolKE2CHUhg1kJk0lhg39scmgMKVytSRc1/QU4vcXes1+0EELsBxMx38JPgbXA97ZzTahyLIzyXH7ENcNorW/SWs/XWs+vq6vb/VYKsR+tW7eOU045hSf/cjPHXPwJGj56K6VQDeAFbQ/MOZynp87g2SnT6AmHAS9tS3Uux0sNkzA0BMsl8raNqzRHdWxiSbP3H6SBHc6T0ikyfj89oQhpn4/ZWzqZuaWTzz98H+ctf3GbKiNLmqcwtaebYzo2s64qQW8wSMm0MIEtwSDJUIi/zjmCnkiU9VUJ+v0BGrNp6rJpEtkcs3q6WLCxlepMmupcblhbOiJRAuUyOdtHxudjdnfXmMHewIaTlxsaaUolMbTL4uYWSqZFVT43+HOORgE2XnUUG68qimVC1A/zEtAcNUiVoTZocnSdSSJg4LhQ5TdoiVl8cn6AlphFR1YTshXnzpIye0KIiWFC3amUUpcDZwOnaa1L27k0Wzn6R3kuMOIaISa0e++9l8suuwzHcTjjC7eSnHEWK3uGr3MbK21LbzDEhkQ1tutwyvq11GQzdAdDrKxrGBwVHEgaHa+s2QOwHIdj2jfzXFMzrlLDahoPHU3siEapzWbwuQ4vT5rMX448lpJpkq2krzn19dXeukifj7mdHZQMk4hTIJHP0h6NESkWOKJjM6vr6oe1ZXVtHQs3tAKQtywaU0k2xeJjpqHZVJXg1wtP4b658wZrUff5fGPuTB4Qs8HRlXLO2gsO5yYMNmdcbMtkwSSb6TGD7gL05V008O8nRoYFgbIhRQgxEU2YAFEp5ccbNfwb0K6UGviXYGCqOF45twXYNOK5oQbOjTb9LMSE4bou//Vf/8WXvvQljjzySO644w6+93oNf1pZoKZn61rAgc0l3aEwgVKRcKk4mLZlQVsrLb09zNnSxbpENa/WNZDIZajNpjmudR0tyX6O3dRG3rRw8VLL5Gwf03p76PMH0IY3bT2QUHrkhg8TeGzm7GEVSZTWNKRT/G3uPC785zI2RWMoranK5zC0pjMaxee6+J0yaduPYfowKwkHBhJY94QjPNsyhdlbumhMp9gSjoy5G3mondmZbFDZmYw3Ymhq73sX8CmYXW2TCLo0hAzeMn1rIu1kwSVkKxkhFEIcFCbSnSwI1AHnVB4jXV55fAr4Gd708kmjXHdi5bh0H7RRiP2it7eX9773vdxzzz1cfvnl/PznPycUChFv6yPU3svbx1gzqA1jWCC1BHjryhWUDUXOsgmWShha0Rqr4uol/+Dx6bNZMrmF09atIVQoUrAsUgE/iXyWV2rrCReLLG/wlvyO3PDR1NfLjO4tnNC6jo6oV9quJxwenAreVJXgoZlzOKKjnap8jmQgQFcoTLDskAxYrE9UEykUsLSmJ+iNXA4dCe0NhVnRYLEpXrVTweHABpq6TJqucGTUHIgGW9cZmgYEbXA1BCxvW8rkiMXpLT5crXmotUyy4BLxKVlfKIQ46EykNYgZ4MJRHh+uPH9v5fu7tNZp4G7gDKXUMQNvoJSKAFcBq5EdzGKCev755zn++OO5//77ufHGG7n55psJhbwltRtTDseOsmYQ1+X011/jDRs38NF/PD64XnBTVYI11TU4KI7buIHDu9opKa/KiOlqUoEA3ZEoj02fxcZ4HK0g5Q+yLlFN1h/g2ZbhaxoH1gAObGxpi8UpGyZVuQwLW9cypad72Lq/++bOY01tHU9Pmc49c48gG/CTs0y6wmESuSyW1jwxdTqr6xoG23vnvKPI+nw0pFNkfb6dDg7PW/ESoWKRjkh0cEp85LrJwZFDE+I+iPi8XcmOBkMZ2KZmWXuJgGVwZotFyFayvlAIcVCaMHezyprDP448P2QX8xqt9dDnPwecBdyvlPo+kMSrpDIZOEeSZIuJ6De/+Q3XXnstNTU1PP7445x44onDnl/V63BMJk37kDWD0XyOKf29mNplZV09jenUsPWC3aEwh3d1sCGeoDqXZe6WTlr6+ni6ZevmjZ5whMdmzqEhneKXC08ZDLhKpjmsgsnAGsChpfvSfj+zt3TRkE7RnOznxyefNnzXcyVfYbhU5G+z53Hs5jZMF4qOQ9E0mJLs476584CdGwUczdD2AGNOiSu8aWWfASEb6gKQdw0MpWkMKYKmwXMdZRojJu87MigBoRDioHXQ3t201q8ppU4Bvgl8lq21mN8qZfbERFMoFPjXf/1Xfv7zn3PmmWfyf//3f9TX129zXaq0dZ1efyBIoFxiUiqJqxRpf4iAU6YjGtsmQXSoWKQl20fS7yNr+3AMg7ndW6jOpOmpjAoOHSHcUSm6gc0k4AWXz4Yjg2sPRwZ0I9cFHte6jquXPI3punREomyOxljY5m1IWThkF3OkUNhmY8xYhrZnQNrvpzmTwlagNTh4wWHIhqqAor+osQ3NiU02QdtgU8ahP6+pDioawoYEh0KIg9qEv8Nprdfh/cd/tOdeAc7brw0SYi9rbW3lggsuYMmSJXzmM5/ha1/7GpY1+l/dmA2LK+v0Nke8cnL1mRR506YnFBpcMzh0vaAJdIQj+MtlfK5L1vLxTPNU5nV1cPTmjTw2c842I4Sw/Q0fA0HqwEgdsNM5B1uS/Tw+fdaw10bzeS5Y/iIvNTbtcBRwR+0Z2IQSKxToi0YI2V4OQxPwWRD1KWJ+RXUAOrKK1/pcjqxVTIlaZIMu8xssnNFvOUIIcdCY8AGiEAezBx98kEsuuYRiscgdd9zBu9/97u1ePzth8uywadsS9ek0BcukaJoAzN/YStEwebmhEfCCp1C5zPLGpsHdxsFikecmtxAoezWOq3NeSpwFba0sgW1G7IZO/TpAVTbL8ZvbaI9EeamxiaJlbxNgjmWs0b7JyT6enjp9m/M7Uwll6OaWjN9PvFAgkc/xzFGzsBSUFFQHoD5s0Bz1bosumngAtNas7nN4Q4PBkbU2PlMRsiVAFEIc3CRAFOIA5Lou3/zmN/niF7/I4Ycfzh133MGcOXN2+Loj6nzE1pS2KX93xbLFzO7qpDsUQmmozmdpSKVo6utlSfMU3rpyBYlchp5geLCaysv1DeRsm4DjsDGeGMyfODCtCwym0pnR08VLDU1kfD5OW7cGNCyd3EJzfz9vXrOKh2fM3qmpYBh79HFjrGq3RyU3VSW4e95RzG9rpTGdIhWP8NBhswg01VBVcAlYUBMyCJgK7WrSZU1TxOTYOpPVvS4lrVk4yZbdykKIQ4YEiEIcYPr6+rjiiiu4++67ufTSS/nFL35BuLJTeEfmVFuETUgOSSO/qSpBRzhCbTpFTTZLpFgk7bOpzWZ4y8oV/HrhKfxiwUlcveQfTEol6QxHWZeoRhtekoOhmztsx2Hmli7+6967yNs+NsTjHLtpIzXZLFN6e9kUjdEdCoOCmlyOR2fNIZrPk/X5BoPDHW00GSup9x+PPGZwLeLQ8zszKmkBndUJ7q1OYChvreHkMJQBwzA4oVaxcLKfh1tLZMqaedUW0+KVBNgFl6KjebW7zJxqi9NbbFl/KIQ46MldTogDyD//+U/OP/981q9fzw9/+EOuu+46lNr56cwFjTa9xW3Pm8BLjZM5oW093eEwecsmUCpy1ppV3Dd3HsumTOOrsfg2gdvbV64g7fcKElVnMizcsI6MbXtTyVrxtldfxld26AmF6A4FWdjfy4Mz55IOBKjKe+Xxhk4DD+x+3t5Gk+1tgGmvtHG0jTHb4wJGpX5y3A9FDfOb/DRFTWbGDRwMPnhMiPfMK3PXa0XKrsPL3Q6m8srmzas1sQyTBY0SHAohDg1ypxPiAPHb3/6WD37wg1RVVfHoo49yyimn7Nb7jJa/qSsc4dTXXyPj85GzfZWzivZIdHCTx2ibToZO987u7iLj84GGWC5HS7IPRymKpoGvXGJKf5GcaTKtr5fXamvpr5TlGzoNvLPpZgbaMjDa+PaVKwaD1juPPIZdYQCWAkNBwPSmkidHDRbN9do3UAEFoDlmce4s+PGyDGUXasIGs6tMakMmyYLLkvaSBIhCiEOC3OmEGGfFYpFPfOIT3HjjjZx22mncdtttNDY27tZ7LWkvYSmvjNDIjSPTertZm6gBrQfXGS5ubqEukx77/YZM9zb19ZDI5qgqFqhPpzAdh3QwTLBcRCsD1yljuC61mRS9wSCPT59FNJ8fNg081gaU0Taa7Mxo41gsvFFDhZe+xtHgN6Eu5KW0ifsUf389j8+EuqCX03BAc8xiRsLmpMkKY8jobcTnJcUWQohDgQSIQoyjtrY2LrzwQp555hk++clP8o1vfAPb3v0NEF05jXZHD64yto9oIY/fdej3B1jeOImSadHn823zPkODy7xp0tjfy8yeHtI+Hy80NjGpvxef4xAu5sn4AuQti3g+h+VqXpjUzPOTm7G1S5/PN2waeOQGlOpMmqM3byRQLnPe8heHrUfc2dHG0ZTxgkMF+BUoBUUX0kU4a6pJdcii5GhAjZqwpi7olc+L+bc+my5q6oKye1kIcWiQAFGIcfLII49w8cUXk8vluP3227ngggv2+D0NNFkN/2+U4OrZKdM4qmMTT0+Zvt1NHqMFl3N6Wrlv9lyaU0nKlklXJIrhaqqKedL+IFmfj7TPJmf7+eabzh5zhG/oiKSvXBrc8fz49FmD5e8GRghnd3VQlcsTK+bp9wdYXVtHbyi8U2ltwJtqd/CmlRMBmBxWlLSiOmRx1lT/4HWjTR0vaLS567UiILWWhRCHpolUi1mIg4LWmm9/+9u8+c1vpra2lsWLF++V4BBgTY+3Q6Uukx7cXDJgQ6Ka16vrdljHeOjInVaKVCCA6bqES2WebZlGwbLpCQTxa4e0bdPv92O4LgYGPzz59O1O/w6tpbxg4wZ6AyEemzGb7kiEVCBAXyDIW1au4Mpnn+Idr77MsZs2UEbhL5dZuKGVlt6enUprYw05To/BYdUW8YCFZajKyOFWEZ+iKzf8nLcW0Se1loUQhyy52wmxHyWTSa688kruuOMOLrzwQv7nf/6HaDS64xfupKc3l4Gxcwmuqqsfc5PHwLTy+ctfoDWeYHVtPT2V9DodkSgN6RTPTJvOaqA6myFv2zT3J3ENg4xt88OTT+e+I47aYRsHNqAMrEfUQ9b5+cplzlqzivZIlBV1Dczq6ebIznaWN0zCVZqjOjbxlx1sUjEBwwC/hqgPWuJeoJfwgWWAbQ6fJh5r6rg5ZklAKIQ4ZMndT4j95OWXX+b8889nzZo13HDDDVx//fW7lMJmZ2xOeyNhY+USHCtn4NBp5Q2xBJP7ezh+Yyv9Pq80neWWCZbKTOnprtR2hu5IjL8dfiQ94QjRfJ6AdnepraMFsUe1b6I9EsXnunRFouRtH9N6u5m9pZMXmppJBoJsqkp4awsNQENZe2sOwdutHDS9QNAyoD5kMDdhApqePBxRbRIPGCQLMnUshBDbIwGiEPvB73//e6666iqi0SgPP/wwp5122j75HMurprfdXIKjGTqt3BkO8cZ1a9C41KXTdIdDBIplnmmZxlEdm6nJpFhV28iqujp6KtO92yt5N1Zi7NGC2IZMigdnzmFW9xaCpRKpQIDljU1U5XOsaJhEtrKhJmDA1Bh05SBbBsPxdi37TYj7vHyHUdvgHTNs+kpe0PzGZpu3Tvem3Ze0l+jIeiOHkvhaCCG2JXdFIfahUqnEpz71KX74wx9yyimn8Ic//IGmpqZ99nlTIopXeryvR8trOJah6WfqsllW1DdyZMcmosUCeZ8Pw3V4Y+ta1icSKK3I2Bazt3QR37iBfn+AzdEYG0epz/yWlSt40+ur6QhHaYvHOayjnQteep6HZ8zmvrnztgliH54xm6Jls7q2joUbWivvpCkaJlX5HE/NmEXY8pJd9xYUU6LQltbkFBTL3uhhGZgRM3jnTD+fOSk+6s8rAaEQQmyf3CWF2Ec2bdrERRddxFNPPcXHP/5xvv3tb+9RCpudURcy8Pbu7pqh071NyX7ChQL16QyG61I0DGKlErZbJJ7LYrqad69YzvKGetri1STyWWZ2b+G7p71p8P0GpqxnbumkPRwlXCryrpdfYkVDI+3hKEd0bCbgONw576hhayKHTnUvbm7hqPZNNKZTPDRzDg/MnceW6gRNQUiVvM0+6ZKiMQw9eaipghlVNme0+LAMk3NnbZu+RwghxM6RAFGIfeCxxx7j4osvJp1O8/vf/55LLrlk/3yw2r3EBEuap3DFssVM27KF+RvW45gGtlMi7Q/Q0t9H2uej3x/EVQZVpRzLGxoJl0pUFfL0BcO8UtdIS7KfZZX3G5iy9rkuvYEgLf19pAJ+qnM5usIREvkcfYHg6BVUKqOK4VKRJ2bMZknzFNqrEiT8ENTQnvXyG4ZscDVMiticO9OkjGJDyqUlZklJPCGE2ENyBxViL9Ja873vfY/PfOYzzJw5k4ceeogjjjhiv31+prR1o8jAFO/xGzeAUjzX1Mx9c+eNnYbG1TSkk/SEQsTzBTTgKrDLZSKuZnMkhqE1Wmva4lVUFfLcP+dwAJTWw9YgDkxZ9/sDBEslQuUiadtPuFQgWCrR7w+MuW5xtKlxG8iXvXJ5tuk9FBCwFFNiJo4yOLLG4oRJinfPCW7znkIIIXaNBIhC7CWpVIoPfOAD/PGPf+Td7343v/nNb4jFYvvt89uSZTozXoDY1NfLFcsWM7urk+5QCKXhjetfpyGV4v45h9GS7B+2aWRBWyvrq2toSif5ZyBItJDnxPXriBTyZHw+TNclGQzSGwhSm/OTyGfpC4QGP3tovWXYOmU9sJawjEGkmKdk2oRLJZY3TtrmNWMx8ZJdl/ASt4YsSAQMegsaDfTkHBytaY5YshtZCCH2EkmULcRe8Morr3DCCSdwxx138K1vfYs//elP+zU4BG9nblPU+yu9oK2V2nSa7nCYnM9P1u+nOxhmWs8Wrl7yNKFikY5IdLB6yeyuDnzlEg3JJCdsWEdTMsnyxkl0R6MsbpnC2po6toTDaKV4YVITNZksm6MxlNaD9ZaXNE/Z2pbmKVTlc5RMi8XNLXRGItSlM3SGwyxubqFkWtu8Znt8FlTZ4Legyq+IBwwOrzbxm4p1SYfenEuq6LCkvURbsrzjNxRCCLFdMoIoxB66/fbb+cAHPkAwGOTBBx/kzDPPHJd2dOU0tUEvQKzLpPE5Zfp8W0f5crbNvM5+NsWqtqlvfHjHZg7v6qA7GKI6k2J6dxfhUonOcBjThaVNzVhAbzDEqrp67p0zj5Zk/5gpdEauJfz74Udy08JTBkcuR9ZoHosN+EyvbF5tWOE3FLVhLzDUWlNyNa5rcMYUH1PjFumi5q7Xipw7S3YqCyHEnpA7qBC7qVQq8dnPfpbvfe97nHjiidx+++00NzePW3vqgorW/jIG3hRv0bQIlEvkbB/RfI5pPT1M6+3BRfHmVa9gAP2BIK9V1xIt5HGVAQYYSqFQoDUF2+aVxkncfNwJ2wRzy0ZtxVajrSXc0WtGMhWgYFIIpkRNsmVFc9igI+uyJaexDMWbp1lMr/KmlmN+BWxbW1kIIcSukTuoELuhvb2diy++mMcff5yPfvSj3HDDDfh845tWZUGjzecf1/iVN8V7eGc7s7s6scr9HNXRTrBc9Hb9JpMEHIflDY34yyVOW/saWsPjM2bx9pUrKJomr9fWsSkaw9Iu66uqt9ltPNRYibD3hoAFtUFwMCg4sLDRoK+kmBwzOXe2TXfO5bCa4bexiM+rnyyEEGL3SYAoxC568sknueiii+jr6+OWW27h8ssvH+8mAd6UatynKGtv9O7m407g4ueXcuVzz+IqxaqaeiKFPJMyaXq1S1N/P62JalCQ9gcoWhYd0Siv1tWDUgRLRTKWf4dVUgbyFnZEokQKBc5b8RJ3zjtqj4JEBcRtiPnBZ5k0RQxaoooTJgeG7VL+86oc6aKujBx6xqqtLIQQYufJJhUhdpLWmh/+8IeceeaZhMNhnnnmmQMmOBxwXKNNqfL1pqoEr9fV88zU6Tw8czZZv4+aQo6MZRHL5anNZShYFo9Pm8mGRDVV+RxFw6AuneaozRuZ39ZKNJ+jpbdnzN3GQ0v0aaVIBQKD+Q13ZKwQzo9XZznhB8NUNIQVMT+UXEVXbvjI4IJGm/4CJAsurtYkCy79Be+8EEKI3ScBohA7IZ1Oc+mll/Lxj3+cc845hyVLlnD00UePd7O2ceWRgWHf12XSZE2LIzvasR2Hfp8fVxmES0WW1zfy7NTpFC2bVXX13DnvKDZGYxy3aQO+cpkXGpswtcsb173OhtjoJevqMmnSfv+wc2m/n7pMeodtHe3mYwGJADSGoagVdUGD2qBBpgRtKRel3WHXN8cszp3lI2R708ohW3HuLJ+sPxRCiD0kd1EhdmDlypWcf/75vPrqq3z961/nM5/5DIZxYP7falJk+F/prnCEgFNGK2+8ricUZmZPN26l/QMpagZ2FL9e18DNx53ApFSSeCFPXyC0TZWUke8/UKJvwND8hmOtTwwYUHS9UUQF+JSXwsY2oCYIZa3IlDRxHxQcTdGBmoBCqW3HHZtjlgSEQgixl8ldVYjtuOOOO3j/+9+P3+/nvvvu481vfvN4N2m7vvB437DvlzRP4cJ/LmNNdQ3VuRyRYpGucJjXq6pJFPJkR6Sbqcuk2ZCoprW6ZvA9RlZJGfn+5614CfBGDlt6eziqYxOvV9dx5bNP0ZBKsb66ho5IlGihwLtWvMQrJx/F+mgN7WmXgAVKeYFhc8SkO+9S1IraIDSGDUzT25xyeI3JG+otnDEnpoUQQuxNEiAKMYpyucy///u/8+1vf5sTTjiBP/7xj7S0tIx3s3bob2uHJ4neVJXgoZlzOKKjnbJlsbquntU1dZRMk6zPx51HHjN4bVNfLzO6t3BC6zo6IlFW19bRUxkhHGsN4tB8h3M725nZ081LDU1sSFRz+prVJPJZ2uNx8kYAFQtQsuHETW301dWStMBvKQwFZVfjADGfYnLUZHLE5IhaczB9DXjrDEO2BIhCCLE/SIAoxAidnZ1ccsklPPLII3zoQx/iBz/4Af4R6+wOVNnStufumzuPgOPQFwiS9vuJFAqD08oDBnYjt8XixHNZqvJZTmxdz/KGRrRhDLt2pIF8h+ctf5GN8cTgdLPPdegJhJi9pYsVVRHqwia+cIh4f5KGsKLgeOXyIhb4TUWupDGV5rRmmzOn+FjS7pIsuER8inRR019ASukJIcR+IgGiEEM8/fTTXHjhhXR3d/Ob3/yG973vfePdpF3iMyDrDD83dJRvrMonQ3cjp/0BZnd30ZBK0pzs58cnn7ZTKWvqMmk6ItHB7/sDQfylIvFCHtuE0ybbxAt5ng/EuWxeiHzZ5cH1BV7sdPBbcHKDzZVHBVnQ5AWYkyJllrSX6Mh6aWtOb7FlraEQQuwncrcVAi+FzY033sgnPvEJmpubefrppzn22GPHu1m7bG614tmObZNEj1bVZKihwV1POMyz4fDg2sOdzWc4dMOKAtbU1HHG2tVkI0GmxkymqBLlXI6G048lZCsyZYOLDgvxnTNGD/xk84kQQowfufuKQ14mk+Gaa67h1ltv5ZxzzuGWW24hkdg7lUD2t1nVNs92FHf5dTvajbwzRm5YcSyTtfX1pKoiHFdIkbKqWXfycZx1UqMEfkIIcYCTu7Q4pK1evZpFixaxfPlyvvrVr/L5z3/+gE1hszOiPgMTcHZ45XAjg7vR1inuyMip7Gwswu1zTyAxI0Fnwk/Ypzi+QdYQCiHERCABojhk3XnnnVxxxRVYlsXf//533vKWt4x3k/bY1JiJ34Csu+Nrh9qZdYo7+z53VyUwgLoQnNhocvykAFPj5uBmk7teK3LuLGQUUQghDmByhxaHHMdx+I//+A++/vWvc/zxx/PHP/6RadOmjXez9oo51RZRP2Rzu/7aHa1THI0BuAxPeB3ywdyEgWEYvG1WkKCliPm9UVmvZrLLkvaSBIhCCHEAkzu0OKRs2bKFSy+9lAcffJCrr76aH/3oRwQCgR2/cIJY0GjjbLtHZa+y8IJCFwjb4GpwNJiAbUKV36A6aJIIGLgoIr7huQsjPq8snhBCiAOXBIjikLF48WIuuOACOjs7+eUvf8m//Mu/jHeT9gmbsUvc7Q0O3mihBRxWbeBqRUfWJWBCyDaYEjOoDpi8sdmmJuhNK3sjh5500UtbI4QQ4sA1cVfjC7GTtNb87Gc/49RTT8UwDJ566qmDNjhc0l5igU5y3oqXCBWLdESihIpFzlvxEk19vbv1njYQMCBseg9LefkWZ1VBddCkOmAQtDRbcpqOjEOmpGkIwlun+1nQaNNf8KqguFqTLLj0F7yRTiGEEAcuGUEUB7VcLse1117L//7v//LWt76V3/72t9TU1Oz4hRNUV07ztp42nqwkva7OZAaTXrf09Q5Ler0zo4xRCwwFJReqg94uacsAtEs8YNESNdmYKlN0IOKDExosmuM28YD3f8/mmMW5s5CE10IIMcHIXVoctF5//XUWLVrEiy++yJe+9CW++MUvYprmeDdrn6oLKmKlLPf6/VRnMizcsI6Mz0d7JEpjOsV5K17iznlHAXDeipfoCwTpiESJFAqDzw0EiT7Ab0HQhLwDNUGTo+ssoj6D3rxDydXUBBWv98G0uM07ZtjMqfEB3ojhwEYUSXgthBATj9y1xUHpnnvu4fLLLwfgr3/9K29/+9vHuUX7x4JGm5eq41St62NWdxcZn4+c7SNYmW7uCwRZ0NYKMFhaDxg8LmhrHdzJrCvLBA0TmkIGb53h56QmLwBc21diU9plepVNX0Ezv8GiPrz1diIbUYQQYmKTNYjioDKQwuYd73gH06dPZ9myZYdMcAjelK59wjSi2RwNqSR50yJYLBIulVhdW0fa76cuk6Yukybt9w977cBzA8oaUgXoy0HEgsaQGlxHaBkmHz0uzAePCXH2ND8Ba/itRDaiCCHExCYBojhodHd3c8455/DVr36VK6+8kqeeeorp06ePd7P2q7ZkmV/3hnno2KPYEo7QmE5RsCyebZlCT6WcXlc4Mlhab6iB5yy8Xcoab/1hY1hxcpOPkG3QkdWEbMW5s3yD08ayEUUIIQ4+MsUsDgrPPfccixYtYvPmzdx0001cddVVKHXojWAtaS+xPunQU5vgppNP422VdYZpv59oPj+sfN5opfUenzHLCwyBgAkRG2ZUmRzd4CNkK949J7jNZ8pGFCGEOPjIHVxMeL/85S/5yEc+QmNjI08++SQLFiwY7yaNm1U9ZXrymrgN66sT3DXvKOaPUT5voLTe5EyKdCzCfTNm0V2dABdsw9ugEvHDprQmX3bJlMeecBhrI0pbssyS9hJdOS9wXNAogaMQQkwEcqcWE1Yul+OjH/0ov/rVrzj77LO59dZbqa2tHe9mjavevEt9SNFbMIj7XTZvp3zepqoE91QlOKnJ4Ig6m3lJh2jKpb+osQxwtcJxNVGf4qUtDqe37NoO8LZkmbteKxL3Q0NI6jALIcREIndpMSGtW7eORYsWsWzZMr7whS/w5S9/+aBPYbMzEgGDyVGTsuuStV2SRSg43nrCkWy8ZNeXHRkBIO4v0VMoYirozIPf0PgtqAooOrN6l9cULmkvEfcjdZiFEGICkru0mHDuvfdeLrvsMhzH4a677uKd73zneDfpgDGn2iJoQSLgsHwLpEtl0iUoOt6uZNgaLLrA5iw82Zrj1BY/k8IGEdsg6oPGiGZj2qXoQH3IYOGkXc9l2JXTNISkDrMQQkxEsotZTBiu6/KVr3yFt7/97TQ3N7N06VIJDkdY0GhjGSYnNfl477wApzXbBEwIWuBT3u5kA2/0MGR7Xz+5qcQ9awq0JR1OajJxXUiVoC5kctFcH8c1+HnbjMAut6WuUod5KEl/I4QQE4MEiGJC6O3t5Z3vfCdf+tKXuPzyy3n66aeZNWvWeDfrgOPtKPaRK2vueq1ApgzzG0wawwYBGxpDEPWBaUDJ8QLHgKkoOLAxrWmKWLxpqo+TJtmcNcXH0fX+YSltdoWkvxFCiIlLppjFAe/5559n0aJFtLW18ZOf/IQPfehDh2QKm53VHLPQOk91wKQ6aBC0oCXm8viGPD1575qIz0thYyhF3tEUHJeAbVAbMrn62G1T2exuOyT9jRBCTExypxYHtN/85jdce+211NbW8sQTT7Bw4cLxbtKEsKyzjKU0rSmHbMlLbv2GepsnN5VoDBikChrbhLJWhExIl7y1hl25vbs+UOowCyHExCRTzOKAVCgUuOaaa7jyyis5+eSTWbZsmQSHuyBddFnT71ByNGEbSo6mI6cJmOC4mt6ipj+vCSoXQykMBZPChqwPFEIIAUiAKA5Ara2tnHrqqdx000189rOf5b777qOurm68mzWhRH0GJdf7eiDkS5c0tmlwfKPN8fUWpqnoK0HMB0fWWliGKesDhRBCADLFLA4wDzzwAJdeeimlUok///nPvOtd7xrvJk1IU2MmqYJLf9HbOWwaiqAJjVUGYZ/BsQ0WR9S6rOp1yJQ1CyfZvG3G7m1GEUIIcfCRfw3EAcF1Xb7xjW/wxS9+kXnz5nHHHXcwZ86c8W7WhDWQD7E9q0kWNDG/4vVemF5lMidhsbrPoeAo3tBgUeU3uPrYCG3JMn9elZOyeEIIISRAFOOvr6+PK664grvvvpv3vOc93HTTTYTD4fFu1oS2oNFmc1pzRI2XnDpd1HRnXSaFvZ3KtSGv6kyy4BKylZTFE0IIMYysQRTj6sUXX2T+/Pn8/e9/50c/+hG//e1vJTjcCwbyIYZsr3JJyFZcdXQAyzBHzUs4tCyeoRQxv0Hc76WoEUIIceiRoQExbm655RauueYaEokEjz32GCeffPJ4N+mgMlqKmUmR8qh5Cf+2tihl8YQQQgySAFHsd4VCgeuvv56f/vSnnH766dx22200NDSMd7MOCWPlJRwoixfzbw0SpSyeEEIcuibMFLNSao5S6itKqWeUUl1KqZRS6gWl1L8rpbaZk1RKzVVK/UUp1auUyiilnlBKvWk82i62amtr4/TTT+enP/0p//Zv/8aDDz4oweEBQMriCSGEGGoijSB+APgIcBdwK1ACzgS+BlyklDpRa50DUErNBP4BlIFvA/3A1cB9Sqm3aa0fHIf2H/IefvhhLrnkEnK5HLfffjsXXHDBeDdJVEhZPCGEEENNpLv/H4FvaK37h5z7mVJqNfDvwL8AP66c/wZQBRyvtX4BQCl1M/AycKNS6jCttSyu2k+01nz729/m85//PHPnzuWOO+7gsMMOG+9miRGkLJ4QQogBE2aKWWu9dERwOOC2yvFIgMp087nAowPBYeX1aeCXwBxgwb5trRjQ39/PokWL+OxnP8uiRYt49tlnJTgUQgghDnATJkDcjubKsaNyPBrwA0+Pcu0zlaMEiPvB8uXLWbBgAXfddRff+973uO2224hGo+PdLCGEEELswISeT1JKmcB/4K01/F3ldFPluHGUlwycm7yPm3bI+/3vf89VV11FLBbjkUce4dRTTx3vJgkhhBBiJ030EcQfACcC/6G1Xlk5F6ocC6Ncnx9xzTaUUh9USi1VSi3t6uraaw09VBSLRT72sY/xnve8h+OOO45ly5ZJcCiEEEJMMBM2QFRKfRX4KHCT1vobQ57KVo7+UV4WGHHNNrTWN2mt52ut59fV1e2dxh4iNm7cyJlnnsl///d/c/311/Pwww8zadKk8W6WEEIIIXbRhJxiVkp9GfgC8GvgQyOe3lQ5jjaNPHButOlnsQceffRRLr74YjKZDLfddhsXXXTReDdJCCGEELtpwo0gKqW+BHwJuBm4apR0NS/hTS+fNMrLT6wcl+67Fh5atNbccMMNvPnNbyaRSLB48WIJDoUQQogJbkIFiEqp/wC+DNwCXKm1dkdeU0lnczdwhlLqmCGvjQBXAauBxfulwQe5VCrFRRddxL/927/xrne9i8WLFzNv3rzxbpYQQggh9tCEmWJWSn0E+E+gFXgQeI9Sw+rEdmitH6h8/TngLOB+pdT3gSReJZXJwDmSJHvPvfLKK5x//vmsXr2a73znO3zyk59kxO9DCCGEEBPUhAkQ2Zq7cArwv6M8/xjwAIDW+jWl1CnAN4HPAj5gGfBWKbO35/7whz/wgQ98gHA4zIMPPsgZZ5wx3k0SQgghxF40YQJErfX7gffvwvWvAOftq/YcikqlEp/5zGf4/ve/z8knn8wf/vAHJk+WlJJCCCHEwWbCBIhifG3evJmLL76YJ554guuuu47vfve7+Hy+8W6WEEIIIfYBCRDFDj355JNceOGFJJNJbr31Vt7znveMd5OEEEIIsQ9NqF3MYv/SWvODH/yAM888k0gkwjPPPCPBoRBCCHEIkABRjCqdTnPppZdy/fXXc84557B06VKOOuqo8W6WEEIIIfYDCRDFNlauXMnChQu5/fbb+cY3vsEdd9xBPB4f72YJIYQQYj+RNYhimD/96U9ceeWV+P1+7r//fs4666zxbpIQQggh9jMZQRQAlMtlPv3pT3PBBRcwb948li1bJsGhEEIIcYiSEURBR0cHl1xyCY8++ijXXnst3//+9/H7/ePdLCGEEEKMEwkQD3FPP/00F1xwAb29vdx88828973vHe8mCSGEEGKcyRTzIUprzY9//GNOP/10AoEATz/9tASHQgghhAAkQDwkZTIZ3vve93Ldddfxlre8haVLl3LMMceMd7OEEEIIcYCQAPEQs3r1ak488UR+97vf8dWvfpU777yTRCIx3s0SQgghxAFE1iAeQu68806uuOIKLMvi3nvv5eyzzx7vJgkhhBDiACQjiIcAx3H4/Oc/z7ve9S7mzJnDsmXLJDgUQgghxJhkBPEg19XVxaWXXspDDz3E1VdfzY9+9CMCgcB4N0sIIYQQBzAJEA9iixcvZtGiRXR1dfGrX/2KK6+8crybJIQQQogJQKaYD0Jaa372s5/xxje+Ecuy+Mc//iHBoRBCCCF2mgSIB5lsNsuVV17Jtddey1lnncVzzz3HcccdN97NEkIIIcQEIgHiQWTNmjWcfPLJ3HzzzXzpS1/innvuobq6erybJYQQQogJRtYgHiT++te/cvnll2MYBvfccw9ve9vbxrtJQgghhJigZARxgnMchy984Qu8853vZObMmTz33HMSHAohhBBij8gI4gS2ZcsWLrvsMu6//34+8IEPcOONN0oKGyGEEELsMQkQJ6ilS5eyaNEi2tvbuemmm7j66qvHu0lCCCGEOEjIFPME9Mtf/pJTTjkFgKeeekqCQyGEEELsVRIgTiD5fJ6rrrqKq6++mjPOOIPnnnuO+fPnj3ezhBBCCHGQkQBxgli7di2nnHIK//M//8MXv/hF/va3v1FbWzvezRJCCCHEQUjWIE4Af//737nsssvQWnP33Xfzjne8Y7ybJIQQQoiDmIwgHsBc1+XLX/4y55xzDlOmTGHp0qUSHAohhBBin5MRxANUT08Pl19+OX//+9953/vex09+8hNCodB4N0sIIYQQhwAJEA9Azz//POeffz4bN27kpz/9Kddccw1KqfFulhBCCCEOETLFfID59a9/zcknn0y5XOaJJ57gQx/6kASHQgghhNivJEA8QBQKBa655ho+8IEPcMopp7Bs2TIWLlw43s0SQgghxCFIAsQDQGtrK6eeeio33XQTn/vc57jvvvuoq6sb72YJIYQQ4hAlaxDH2f3338973vMeSqUSf/nLXzjvvPPGu0lCCCGEOMTJCOI4cV2Xr33ta7z1rW9l0qRJLF26VIJDIYQQQhwQZARxHPT19fHe976Xv/71r1x22WX8/Oc/JxwOj3ezhBBCCCEACRD3uxdffJHzzz+f1tZWfvzjH/PhD39YdikLIYQQ4oAiU8z70c0338yJJ55IPp/n8ccf5yMf+YgEh0IIIYQ44EiAuB8UCgWuvfZa3ve+93HiiSeybNkyTjrppPFulhBCCCHEqCRA3Mc2bNjAaaedxs9+9jM+9alP8cADD9DQ0DDezRJCCCGEGJOsQdyHHnroIS655BIKhQJ//OMfWbRo0Xg3SQghhBBih2QEcR/QWvPNb36Ts88+m/r6epYsWSLBoRBCCCEmDBlB3Mv6+/t53/vex5133snFF1/ML3/5SyKRyHg3SwghhBBip0mAuBe99NJLLFq0iLVr1/KDH/yAj33sY7JLWQghhBATjgSIe8mtt97KBz/4QWKxGA8//DCnnnrqeDdJCCGEEGK3yBrEPVQsFrnuuuu4/PLLOf7441m2bJkEh0IIIYSY0GQEcQ9s3LiRCy+8kKeffppPfOITfPOb38S27fFulhCHrLZkmSXtJbpymrqgYkGjTXNMbnNCCLGrZARxNz366KMcd9xx/POf/+S2227jhhtukOBQiHHUlixz12tFsiVNQ0iRLWnueq1IW7I83k0TQogJRwLEXaS15jvf+Q5vfvObqa6uZvHixVx00UXj3SwhDnlL2kvE/RDzGxhKEfMbxP3eeSGEELtGAsRdkEwmueCCC/j0pz/Nu9/9bhYvXsy8efPGu1lCCKArp4n4hmcNiPgUXTk9Ti0SQoiJSwLEnbRixQpOOOEE7rzzTr773e/yhz/8gWg0Ot7NEkJU1AUV6eLwYDBd9NYiCiGE2DUSIO6EP/zhD5xwwgn09vby4IMP8slPflLyGwpxgFnQaNNfgGTBxdWaZMGlv+CdF0IIsWskQNwOrTXXX389F198MccccwzLli3jjDPOGO9mCSFG0RyzOHeWj5Ct6MhqQrbi3Fk+2cUshBC7QWkt63PGEo1GdTqd5mMf+xjf+c538Pl8490kIYQQQohRKaWe01rP3xvvJf+13o5sNsvvfvc7Lr300vFuihBCCCHEfiMjiNuhlOoC1o93OyawWmDLeDfiECV9Pz6k38eP9P34kH4fP6P1/VStdd3eeHMJEMU+o5RaureGusWukb4fH9Lv40f6fnxIv4+ffd33sklFCCGEEEIMIwGiEEIIIYQYRgJEsS/dNN4NOIRJ348P6ffxI30/PqTfx88+7XtZgyiEEEIIIYaREUQhhBBCCDGMBIhCCCGEEGIYCRDFLlNKzVFKfUUp9YxSqksplVJKvaCU+nelVHiU6+cqpf6ilOpVSmWUUk8opd40Hm0/2CilQkqptUoprZT68SjPS9/vRUqpaqXUd5VSryml8pU//48opU4dcZ30+16klIoopT6vlHqpcr/ZopT6h1Lq/UopNeJa6ftdpJT6nFLqdqXU65V7ybodXL/TfayUMpRS1yulXq38ndmglLphtH8rDkU72/fKc7lS6v8q95+sUqpVKXWXUmrhGK/Zo76XNYhilymlvgl8BLgLeAYoAWcCFwH/BE7UWucq184EFgNl4AdAP3A1cCTwNq31g/u7/QcTpdR3gWuACHCj1vqjQ56Tvt+LlFJTgUfx+vp/gFVAHDgauE9r/X+V66Tf9yKllAE8BpwM/C/ePScEXAqcAHxba/2ZyrXS97tBKaWBHmAZcDyQ1FpPG+PaXepjpdQPgY8Bfwb+DhwOXAc8AbxZa+3u/Z9o4tjZvldKBYAc8AJwD7AWmAR8CGgCrtBa/3bEa/as77XW8pDHLj2A+UB8lPNfAzTw0SHn/gA4wLFDzkXwKtSspPKfFHns1u/hOLyb9Ccq/f7jEc9L3+/d/n4C2ABM2sF10u97t99Pqvz5/v6I8z7gdaBP+n6P+3jGkK+XA+u2c+1O9zFwBOACfxrxHtdVfqfvGe+ffbwfO9v3eKWRTx/lfANeNZUOwNibfS9TzGKXaa2Xaq37R3nqtsrxSIDKMPa5wKNa6xeGvD4N/BKYAyzYt609OCmlTOAXwL3AHaM8L32/FymlTgPeiDdatVkpZSulQqNcJ/2+98Uqx01DT2qti3j/MGZA+n5PaK1f35nrdqOPLwUU3kjjUL8AssDlu93og8TO9r3Wuqy1fmyU8x14I+z1lceAPe57CRDF3tRcOXZUjkcDfuDpUa59pnKUG/buuR44DPjoGM9L3+9db68cW5VSd+NN9WSUUquUUkNvtNLve99ioA/4tFLqQqXUlMoauG/gTcl9uXKd9P2+t6t9vABvFGvx0Au11nm8qVL5fewdzUAR7+/JgD3uewkQxV5RGdH6D7wpz99VTjdVjhtHecnAucn7uGkHHaXUdOA/ga9ordeNcZn0/d41t3L8BVANvA/4F7yb8i1KqSsrz0u/72Va6168UasevOnN9cCreOugF2mtf1G5VPp+39vVPm4CtmitC2NcX6uU8u3F9h1ylFJvx1uLe1sl+Buwx31v7b1mikPcD4ATgc9rrVdWzg1MwY32BzQ/4hqx836Kt0D5e9u5Rvp+74pWjingzMr0JkqpP+Otg/u6Uup/kX7fV9J467PuAv6BF6R/BPidUuo8rfUDSN/vD7vax6Exrh15fXHPm3boUUrNBm7BC/g+OeLpPe57CRDFHlNKfRVvqvMmrfU3hjyVrRz9o7wsMOIasRMq05lnA6dprUvbuVT6fu/KVY6/HwgOwRvdUkrdBVyBN8oo/b6XKaWOwgsKr9da/2zI+d/jBY2/qOyslb7f93a1j7MMXxe3o+vFTqrMJD2Et+HkbVrrrhGX7HHfyxSz2CNKqS8DXwB+jbfdfqiBReWjTesMnBttqkKMQinlxxs1/BvQrpSapZSaBUytXBKvnKtC+n5va6sc20d5bnPlmED6fV+4Hu8ftNuHntRaZ/HSfUwFpiF9vz/sah9vwpvKHC2gnIw3BSqjh7tIKTUNeARv9/j/01q/NMple9z3EiCK3aaU+hLwJeBm4Cpd2UM/xEt4Q9wnjfLyEyvHpfuuhQedIFAHnAOsHvJ4tPL85ZXvr0L6fm8bWOjdPMpzA+c6kX7fFwYCD3OU56whR+n7fW9X+3gJXpxxwtALKzn9jkV+H7usko/1EbwcrP9Pa/38GJfucd9LgCh2i1LqP/B2D94CXKlHSbhZSX1wN3CGUuqYIa+N4AUxqxmxw0psVwa4cJTHhyvP31v5/i7p+73uL3jrDy+v9CEASqlJwLuA1Vrr16Tf94kVleP7h56sjJSfB/QCa6Tv973d6OPb8KZAPz7ira7GW/92675s78FmSLL+BHC21vq57Vy+x30vlVTELlNKfQT4MdAKfBFvK/1QHZVF41SmQBfjVVv5PpDE+wN6FHCO1vq+/dXug1VlumEt21ZSkb7fi5RSHwR+DrwM/AovUfO1eNUM3qG1vr9ynfT7XlT5R3EZ3j+KtwJP4W1SuRpvavkjWuufVK6Vvt8NSqn3snWpynV4f7ZvqHy/Xmt9y5Brd6mPlVL/jbdG/c94y2MOx6vu8RTwptEGFw4lO9v3Sqko8CIwHfhvRv/PzgOVvIgD771nfT/eWcTlMfEewG/w/mcy1uPREdcfDtyJl6MpCzyJV+Zn3H+Wg+GB94/kNpVUpO/3SV+fj5fvLYM3ong/cIr0+z7v95l4Zfba8AKTJPA4cL70/V7p30d39n6+q32MtzTgk3hVVgp4axS/B0TG++c+EB472/dD7vPbe5yxN/teRhCFEEIIIcQwsgZRCCGEEEIMIwGiEEIIIYQYRgJEIYQQQggxjASIQgghhBBiGAkQhRBCCCHEMBIgCiGEEEKIYSRAFEIIIYQQw0iAKITY55RS65RSj453O/aW/fnzKKXer5TSSqkz9vHnnFH5nPfvy88RQkwMEiAKIXaLUiqklPq4UuoJpVSPUqqklOpQSv2tEtRY491Gse8ppQ6vBJZaKfXG8W6PEGLvkABRCLHLKvVYn8erxZoHvgF8EK+Mkw38Gvj6uDXw4HILEMQrLXcg+he8soOdla+FEAcB+R++EGKXKKWCwF+BGcAirfUdIy75llJqAbBgL36mDZha6/zees+JQmvtAM54t2M0ld/Le4HbgX7gg0qpj2mtU+PbMiHEnpIRRCHErroKmAvcMEpwCIDWeonW+icjzyulDlNK3aOUSiml+pVSf1RKNY645suV6cojlFLfU0q14Y1Snlh5vlYpdaNSaoNSqlg53qiUqhnxPgNr996klPo3pdQapVRBKbVKKfW+0dqtlLpKKbVMKZWrtO/+XZk2VUq9Syn1lFIqXXk8pZQ6b4xrr1VKrVRK5Stt+uho6w3HWoOolPIppT6tlHpBKZWttHepUuqjQ65pUkrdULmmt/JZK5RSn1FKmTv7c23HO4F64H+B3wBh4OIxft5Q5fe5udLeZ5RSZymlfqOU0qNcP1spdUvl+mJl3ed3lFLhvdBuIcQOyAiiEGJXXVA53rSLr5sMPAr8GfgUcAxwDRADzh7l+luBHHADoIHNSqk48A9gFvArYBnwBuBa4E1KqRNGGb36Ot4U7c+BQuXa3yilXtNaPzVwkVLqW8CngcXA54Eo3rT5I0qp87TWf9veD6eU+jBwI/Aq8LVKm98P/EUpdY3W+qYh134G+Gal/Z8HQpU+6dreZwx5vQ+4DzgDuB/4LV4QfRRwPvDjyqVHV77/M7AGb/r/bZXPnoHX/3viX4C1wBNaa62Ueh74APDLUa69HXg78BfgQWB6pV1rR/n5jgceBvrwfm8b8f68fAw4RSl1uta6tIdtF0Jsj9ZaHvKQhzx2+gF0A8ldfM06vIDpohHnb6ycP2zIuS9Xzj0KWCOu/6/Kcx8ecf4jlfNfHXLu/ZVzzwO+Iecn4wWKvx9ybi7gAk+OuLYJL0hZhzfFPfTneXTI9wkgDbwGxIacj+EFZimgqnKuGi/w/ScQGHJtI940rQbOGOXnGHru05VzXx+lr40hXwcBNco1t+BNW08acu6Mynu+fyd/p01AGfjykHP/WnmPw0dc+/bK+V+McV6POP8iXqAdHXH+3bvSRnnIQx67/5ApZiHErooByd143Sat9R9GnHu4cpw1yvU/0FqXR5x7N94o28jRy58DWyrPj/QTrXVx4But9UZgFTB7yDXnAQr49ohrN+FNnU7FG6kcy//Dm179kdZ6sG8qX/83EAHePOTaAPBTPWRNpda6HW/UdGdcBvQCXxn5hNbaHfJ1TmutYXBKulopVYs3+mgA83fy80bz/sp73Dzk3K1ACW8Ucah3Vo7fG9HWvwGvDD2nlDoKb+Tzd4C/sqSgttLuJ4EMo484CyH2IgkQhRC7Kok3/bqrXh/lXHflWDPKc6tGOTcdWDkycKx8vxJv2nRnP3foZ06vHF8e5drlleNo7707rx+4duUo1452bjSzgVf1DjbtKKUspdQXlFKr8Kagu/EC7FsqlyR28vNGvq8CrsQbBTWUUrMqO9urgKeAK0akOZqON0L72ihvN/JnPrxy/M9KW4c+OvEC8YbdabcQYufJGkQhxK5aDpymlJqhtR4t+BrL9nbiqlHOZXetWbv8uWqMr3fHrrx+Tz9rwDYbO0bxPeA64Da86flOvBG+44BvsfuDBKezddR39RjXvANvvSFs/Zl3ps0D194A3DvGNb078T5CiD0gAaIQYlf9CTgNbzfz5/fzZ78OzFVKWUNHESujVXMYfbRwZ6ypHI8Y8vWAeUM+e2de/9AOXj+wKWMuW6fYGXJuZ6wCDldK+bXWhe1c917gca31JUNPVkb79sQH8NZxXoE3MjjSz/E2sPyl8v1avGB0NiOmlNn2Zx4IOB2t9YN72E4hxG6SKWYhxK76Jd604L9tJ4XL8ZVdvXvbX4A6vOB0qKsr5/+8m+97F97o1qcquf0AUEpNwptKXY+32WUsD+CtjbtOKTU4/V75+jq8DSwPDLm2AFyrlAoMubYRb23hzrgVb3r4CyOfqEz/DnAYMWJZSRNz/U5+zjYqO8kvAO7XWv9Ba/3HkQ+8/nxbpf8A7q4crx/xXm9n65TygOfxRqk/pJTaZlq/Mm1evbvtF0LsHBlBFELsEq11Vin1DuAevBQu9+MFPd14QdqZwFuAb++Dj/82cCFwo1LqOLxg4g14o1Urd/cztdYrlVLfwdsd/LhS6ja2prmJAJdpL2H1WK/vU0p9Gm9X9rNKqd9Unno/3lTsNVrr/sq13Uqp/8RLv/OUUuq3eGluPog3MjifHU/F/hBv48cXlJeU/H68NYZH4I3IDWyI+SNwTeXneRBv7d4H2Lr2c3dcirc7+k/bueZPeD/7+/BS6vwNb2PM1ZXNJgNpbj6It47x6IEXaq21Uuq9eKOr/1RK/QpvbWcIry/PBz6Ht3lICLGPSIAohNhlWuvXlFJvwMujtwj4d7xAqgdYihcY/G4ffG6/UuoUvA0M5+KN7nUAPwO+pPeggofW+jNKqdeAD+MFNUXgWeA9WusnduL1P1FKbcbLZ/ilyukXgXdrrf8y4tpvKKWSeGlhvgm0At/BG+2bj5cGZ3ufVVRKnQ18EngPXrCZx5ue/fWQSz+Bl2LnIryd2hvwdoAvwQvSdse/4KW3uWs71zxQ+dwrgW9Wgr5FeOsgL8XLxfhPvF3nH2b4jnK01i9U/nx9Du/3/KHK+63DCwxHTuMLIfYyVcmAIIQQYpwppf4b+CjQpLXePN7t2R+UUi8Bttb6sPFuixBiK1mDKIQQ+9nQtYdDzk3C2/Sx/GAMDpVXw3vkuXOAI9m6PlMIcYCQKWYhhNj/zqisebwDaAOm4W20iQCfHcd27Uv/UZk2fgSvYsyxbF0P+a1xbJcQYhQSIAohxP73Gl5qnKvxEnbn8dZufuMgTu3yBHAK3hrNON561T8BX9Rat41nw4QQ25I1iEIIIYQQYhhZgyiEEEIIIYaRAFEIIYQQQgwjAaIQQgghhBhGAkQhhBBCCDGMBIhCCCGEEGIYCRCFEEIIIcQw/x82If5NUQI/mgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(np.array(data_no_missing_temp['Age'])[data_no_missing_temp['all-cause'] == 0], np.array(data_no_missing_temp['PhenoAge'])[data_no_missing_temp['all-cause'] == 0], alpha=0.3, color='#018ae7', label='Alive')\n",
    "plt.scatter(np.array(data_no_missing_temp['Age'])[data_no_missing_temp['all-cause'] == 1], np.array(data_no_missing_temp['PhenoAge'])[data_no_missing_temp['all-cause'] == 1], alpha=0.3, color='#ff0255', label='Deceased')\n",
    "# plot2 = plt.plot(age_list_f, base_value_list_f, 'black')\n",
    "# plot3 = plt.plot(age_list_f, up_list_f, 'black')\n",
    "# plot3 = plt.plot(age_list_f, mid_list_f, 'orange', label='Median values')\n",
    "# plot3 = plt.plot(age_list_f, down_list_f, 'black')\n",
    "low = int(data_no_missing_temp['PhenoAge'].min())-1\n",
    "high = int(data_no_missing_temp['PhenoAge'].max()+1)\n",
    "plot3 = plt.plot(list(range(low, high)), list(range(low, high)), 'black')\n",
    "plt.ylabel('PhenoAge')\n",
    "plt.xlabel('Chronological Age')\n",
    "plt.xlim(low, high)\n",
    "plt.ylim(low, high)\n",
    "plt.legend() #指定legend的位置右下角\n",
    "plt.title('Female', fontsize=18)\n",
    "plt.tick_params(axis='both',which='major',labelsize=18)\n",
    "plt.rc('axes', labelsize=18)\n",
    "plt.rc('legend', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9955335469463055\n",
      "0.9935628596508339\n",
      "0.9933248257583194\n",
      "0.9912046644925387\n",
      "0.9922233300099701\n",
      "0.9907556270096463\n",
      "0.9896551724137931\n",
      "0.9890346382455422\n",
      "0.98611542845301\n",
      "0.9872859094252391\n",
      "0.9828115408225906\n",
      "0.9681050656660413\n",
      "0.9317507418397626\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PhenoAge: survival probability')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_num = 13\n",
    "age = np.array(data_no_missing_temp['Age'])\n",
    "shap_age = np.array(data_no_missing_temp['PhenoAge'])\n",
    "\n",
    "permth_int = np.array(data_no_missing_temp['alive_year'])\n",
    "mortstat = np.array(data_no_missing_temp['all-cause'])\n",
    "age_acc = shap_age - age\n",
    "up_value = np.quantile(age_acc, 0.75)\n",
    "down_value = np.quantile(age_acc, 0.25)\n",
    "unhealthy_index = [i for i in range(len(age)) if (age_acc[i] >= up_value)]\n",
    "healthy_index = [i for i in range(len(age)) if (age_acc[i] <= down_value)]\n",
    "# unhealthy_index = [i for i in range(len(age)) if (shap_age[i] > age[i])]\n",
    "# healthy_index = [i for i in range(len(age)) if (shap_age[i] <= age[i])]\n",
    "unhealthy_surv_prob = [1]\n",
    "for i in range(1, year_num+1):\n",
    "    label_temp = np.array([label_all(permth_int[j], mortstat[j], i) for j in unhealthy_index])\n",
    "    label_temp_pre = np.array([label_all(permth_int[j], mortstat[j], (i-1)) for j in unhealthy_index])\n",
    "    num_samples = sum(label_temp==0)+sum(label_temp==1)-sum(label_temp_pre==1)\n",
    "    unhealthy_surv_prob.append((sum(label_temp==0)/num_samples)*unhealthy_surv_prob[-1])\n",
    "    print((sum(label_temp==0)/num_samples))\n",
    "healthy_surv_prob = [1]\n",
    "for i in range(1, year_num+1):\n",
    "    label_temp = np.array([label_all(permth_int[j], mortstat[j], i) for j in healthy_index])\n",
    "    label_temp_pre = np.array([label_all(permth_int[j], mortstat[j], (i-1)) for j in healthy_index])\n",
    "    num_samples = sum(label_temp==0)+sum(label_temp==1)-sum(label_temp_pre==1)\n",
    "    healthy_surv_prob.append((sum(label_temp==0)/num_samples)*healthy_surv_prob[-1])\n",
    "#             print((sum(label_temp==0)/num_samples))\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.step([i for i in range(year_num+1)], healthy_surv_prob, 'blue',label='Healthy agers', where=\"post\")\n",
    "plt.step([i for i in range(year_num+1)], unhealthy_surv_prob, 'r',label='Unhealthy agers', where=\"post\")\n",
    "plt.xlabel('Years from screening time', fontsize=18)\n",
    "plt.ylabel('Cumulative Survival Probability', fontsize=18)\n",
    "plt.tick_params(axis='both',which='major',labelsize=18)\n",
    "plt.rc('axes', labelsize=18)\n",
    "plt.rc('legend', fontsize=18)\n",
    "plt.legend()\n",
    "plt.title('PhenoAge: survival probability', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## BioAge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['BioAge'], X['BioAge_acc'] = calculate_BioAge(X)\n",
    "X_geo['BioAge'], X_geo['BioAge_acc'] = calculate_BioAge(X_geo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_idx = random.sample([i for i in range(len(X))], 50000)\n",
    "data_no_missing_temp = X.loc[selected_idx]\n",
    "data_no_missing_temp = data_no_missing_temp[data_no_missing_temp['flag']==1]\n",
    "data_no_missing_temp = data_no_missing_temp[data_no_missing_temp['BioAge']!=np.inf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(np.array(data_no_missing_temp['Age'])[data_no_missing_temp['all-cause'] == 0], np.array(data_no_missing_temp['BioAge'])[data_no_missing_temp[mortality] == 0], alpha=0.3, color='#018ae7', label='Alive')\n",
    "plt.scatter(np.array(data_no_missing_temp['Age'])[data_no_missing_temp['all-cause'] == 1], np.array(data_no_missing_temp['BioAge'])[data_no_missing_temp[mortality] == 1], alpha=0.3, color='#ff0255', label='Deceased')\n",
    "# plot2 = plt.plot(age_list_f, base_value_list_f, 'black')\n",
    "# plot3 = plt.plot(age_list_f, up_list_f, 'black')\n",
    "# plot3 = plt.plot(age_list_f, mid_list_f, 'orange', label='Median values')\n",
    "# plot3 = plt.plot(age_list_f, down_list_f, 'black')\n",
    "low = int(data_no_missing_temp['BioAge'].min())-1\n",
    "high = int(data_no_missing_temp['BioAge'].max()+1)\n",
    "plot3 = plt.plot(list(range(low, high)), list(range(low, high)), 'black')\n",
    "plt.ylabel('BioAge')\n",
    "plt.xlabel('Chronological Age')\n",
    "plt.xlim(low, high)\n",
    "plt.ylim(low, high)\n",
    "plt.legend() #指定legend的位置右下角\n",
    "plt.title('Female', fontsize=18)\n",
    "plt.tick_params(axis='both',which='major',labelsize=18)\n",
    "plt.rc('axes', labelsize=18)\n",
    "plt.rc('legend', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9973139673696777\n",
      "0.9948129675810474\n",
      "0.9966910658778703\n",
      "0.9937625754527163\n",
      "0.9963555375582102\n",
      "0.9931924405608616\n",
      "0.9923273657289002\n",
      "0.9937113402061856\n",
      "0.9907666770411868\n",
      "0.9920418848167539\n",
      "0.9890239584562729\n",
      "0.9839889306186993\n",
      "0.9431486880466472\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PhenoAge: survival probability')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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odxhmi4TOnTszbdq0hTqGk0AzM2sSs2fPpk2bNuUOw2yR0Lp1a2bNmrVQxyhrEijpD5L+JulDSSFpbD2Ps5Ok5yVNlvRNdsxVaqjbRdJVkj6TNE3SW5J+KfdLmJk1Ov9Ta9YwGuJvqdwDQ84DvgFeAZaszwEk7Qn8HXgNOBnoApwAPCepb0R8nlO3LfAYsCFwFTAa2BG4BugBDKnfZZiZmZk1L+XuDl4tIpaOiO2Az+usnUdSG1Iy9wnws4i4JiLOB3agcFJ3BLAJcGJEnBgR10fEnsB9wKmSVl6IazEzM6tIQ4YMQRJjx45tlPrWPJU1CYyIDxfyEFsBywE3RMSknONWASOBfbNEsdpAYApwfd5xLgfaAPsuZDxmZmaMHDkSSVx88cU11pHELrvs0oRRzW/kyJEMGTKE7777rmwxWHmVuyVwYW2SPb9QYNt/gCWANQEktQI2Al6NiPzhNC8Cc3KOZ2ZmtkgbOXIkZ511lpPAFqzc9wQurOWy588KbKsuWx54C1gK6FCobkRMl/R1Vrfsnt7wBLqMqSp3GDX6YZeBbHn74HKHYWZm1ugmTpxI586dyx1Go2juLYEds+fpBbZNy6tTW93q+h0LbZA0WNIoSaMWdmLG5m6V76tY4p93ljsMM7NF1qhRo9hjjz3o1q0b7dq1o3fv3vzxj39cYDqQF198kUGDBrHmmmvSsWNHOnfuzE9+8hOGDx9e5zkGDRrEWWedBcAqq6yCJCQxZMiQ+epNnz6dU089lRVWWIF27dqxwQYbMGLEiLnbx40bR9u2bTnwwAMLnueYY46hVatWfPTRR7XGU+q1PP3002yxxRZ06NCBZZddluOPP5633nqr4DVEBH/5y1/YeOON5x5766235qmnnpqv3tixY+fuf/fdd7PxxhvToUMHjj32WAA++eQTDjvsMFZeeWXatWvHMsssw49//GOGDRtW67VVsubeEjgle25XYFv7vDq11a2uP6XQhogYCgwF6Nu3b5QeZmm2evXyxj5FvVUt2a/cIZiZNRtTpkxhwoQJRdcfMWIEe+yxB6uvvjonnXQSXbt25YUXXuCMM86gqqqKv/3tb3PrDh8+nHfeeYd99tmHlVdema+//pphw4ax5557cscddzBw4MAaz3PUUUfxww8/MHz4cC677DK6desGwPrrrz9fvUMOOYQ2bdrw29/+lhkzZnD55ZczYMAA3nvvPXr16kWPHj3YbbfduPfee/nzn//MkksuOXffadOmcdddd9G/f39WXrn2cZelXMu///1vtt9+e5Zaail+//vfs+SSS3LPPffw3HPPFTz2QQcdxF133cXee+/NoYceyvTp07njjjvYbrvtuO+++9htt93mq3///fdz5ZVX8stf/pKjjz6aJZZYglmzZrHddtvx2Wefccwxx7Dmmmvy/fff8/rrr/Pss89yyCGH1Hp9laq5J4HVI4qXJ033kqu6a7e6+/dbYCoFunwltQOWBp5uhBgXOZMmQb9+5Y6isIEDYbB7qs2sQpx55pmceeaZRdWdNm0ahx12GJttthlPPvkkrVunr+ijjjqKDTbYgBNPPJGRI0fSL/sH+PTTT+f888+f7xjHHXccG264Ieeee26tSeAWW2zB+uuvz/DhwxkwYAC9evUqWK9bt248+OCDc+ek23rrrdl000257rrr5p578ODB3Hvvvdx5550cc8wxc/e99957+e677zjiiCPqvPZSruXEE09EEs8//zyrrroqkFoc+xX4Yho+fDh33HEH1113HYNzvhyOP/54Nt98c44//nh23XXX+ebce+utt3j99ddZe+2155a9/vrrvPvuu1xwwQX87ne/q/N6movmngS+lD1vATyet21z4AfgPYCImCPpFWBDSe0iIrdbeFNS1/ioRo632VumR7kjqFlVVXp2EmjWvJxwwry/30rRpw9cfvnCH2fw4MH84he/KLhtu+22m+/1Y489xrhx4zj//PMXGKyx0047ceKJJ/Loo4/OTXY6deo0d/uUKVOYOnUqEcE222zDtddeyw8//LDQy/Qdf/zx8yVIm2yyCZ07d+b999+f7zpWWWUVbrzxxvmSwBtvvJGll16aAQMG1HmeYq9l3LhxvPTSS+yzzz5zE0CANm3acPzxx/P888/Pd9zbb7+dzp07M2DAgAVaZHfddVeGDBnC+++/z5prrjm3fOedd54vAQTo0qULAE899RSDBg1imWWWqfOamoNmkwRK6kmaCPrjiKjutn0a+AI4QtJl1dPESNoA6AfcHBEzcw5zF/ATYDBpfsFqJwCzgHsa8xoWBcv1TI+RI8sdyYIqtXXSzFquNdZYg/79+xdVd/To1KF12GGH1Vhn3Lhxc3/+6quvOP3003nggQf46quvFqj73XffLXQSmJtoVevatStff/313NeSOOKIIzjttNOoqqqiT58+fPjhh4wcOZLjjz+etm3b1nmeYq9lzJgxAPTu3XuBOoXKRo8ezcSJE+nRo+YWjHHjxs2XBOb+XG3llVfmtNNO4/zzz6dnz5706dOHbbfdll/84hdssknznVikrEmgpIOA6hsFugNtJZ2evf4oIm7LqX4+cAiwNWkOQCJipqTjgbuBZyVdT5oW5jfAeCC/Df564FDgUkm9SF3IOwF7AOdGxJgGvUAzM6tTQ7S4LQoi0i3nF110EX369ClYZ7nllptbd/vtt2f06NEcd9xxbLLJJnTp0oXFFluMm2++mTvvvJM5c+YsdEyLLbZYrbFWO+ywwzjzzDO58cYbueqqq7jpppuIiKK6gku5lvzzFnPs7t27c+edNQ9oXHfdded73bFjwTGinHvuuRx22GE89NBDPPvss9xwww1cdNFF/O53v+OCCy4oKa5KUe6WwMNJEz7nOid7fhq4jTpExN8kTQVOBy4mjf59AjglIj7LqztDUn/gXGB/0n2A/wOOBa5eiOswMzNbKGussQaQukbraj18/fXXee211zjjjDPmjvKtdsMNNxR1voZcx3nZZZdl11135Y477uBPf/oTw4YNY7PNNmOdddapc99SrqW6ZfLdd99d4DiFytZYYw3ee+89Nt98cxZffPFSLqmgVVddlWOPPZZjjz2WadOmscMOO3DhhRdy0kknNcsu4rImgRHRr4S6g4BBNWz7J/DPIo/zHfDr7GH1UVVVkX2vl1fB48sMJPX2m5k1LzvssAPLLLMMf/rTn9h3333p2rXrfNunTp3KrFmz6Ny589wWuvyWsTfffLOoKWKAuUnRN998U+PAkFIceeSRDB8+nKOPPppPP/206AExpVxLjx496Nu3Lw888AAffvjh3KRw5syZXHHFFQsc++CDD+bBBx/kD3/4A1ddddUC28eNG1drV3G177//no4dO9KmzbxFyNq3b8/aa6/NM888w7fffusk0FqAWkabldvqk6qyn5wEmlnz06lTJ2699VYGDBhA7969Oeyww1h99dX57rvveOedd7jvvvsYPnw4/fr1Y+2112adddbhwgsvZMqUKfTu3Zv33nuP6667jnXXXZdXXnmlzvNtvvnmAJxyyikccMABtG/fnnXXXXeB7tFi7bDDDqy88srcfvvtdOrUif3226+o/Uq9losvvpjtttuOH//4xxxzzDF06dKFe+65hxkzZgDzt3BWTwvz5z//mVdeeYVddtmFbt268emnn/LCCy/wwQcf8OGHda9g+9RTTzF48GD22msvevfuzeKLL87LL7/MDTfcwGabbVbwfsTmwEmglWbw4IodfvuB5zA0s2Zuhx124KWXXuJPf/oTt99+O+PHj2eppZZitdVW48QTT5w7j99iiy3GQw89xG9/+1uGDRvG5MmTWXfddRk2bBivvfZaUUngT37yEy644AKuvfZajjzySGbNmsWZZ55Z7ySwVatWHH744Zxxxhnsu+++RXe/lnotW221FQ8//DCnnnoq5513Hl26dGG//fZj4MCBbL755nTo0GG++jfddBNbb701Q4cO5fzzz2fGjBksu+yybLTRRgtMS1OTDTbYgD333JORI0dyxx13MHv2bFZaaSVOPfVUTjrppOLeoAqkUm+ybOn69u0bo0Z5JplKVLVkPyZNgtN/OrLcoRTkOQytpRs9evQCU2/YouXCCy/klFNO4fnnn2eLLbZo0nPfe++97L333tx1111Ft0I2d8X8TUl6OSL6FtrW3JeNM5trmR7QAPf9NoqqKqhlcJqZWbM3a9YsrrvuOtZbb71GTQAjgmnTps1XNnPmTC699FJat25dcNJoK8zdwbbI8ByGZmZNb8yYMbzwwgtzB2vcddddjXq+6dOns/LKK3PAAQfQu3dvvv76a+6++25ef/11TjnlFJZddtlGPf+ixEmgmZmZ1dvTTz/NoYceSrdu3TjjjDMavSu2TZs27LzzzjzwwAN88cUXRAS9e/fm6quvnm/FEqubk0AzMzOrt0GDBjFo0KAmO99iiy3GTTfd1GTnW5T5nkAzMzOzFshJoJmZmVkL5CTQzMzMrAVyEmhmZmbWAjkJNDMzM2uBnASamZmZtUBOAs3MzMxaIM8TaIuWqqqKXJ7j8iq4ecZA+vWrzMWDva6xmVnL45ZAW3QMHAh9+pQ7ioLWnlHFoW0rc/Fgr2ts1jz169ePXr16lTsMJJU0WXSp9a3xuCXQFh2DB1dsc1a7fv3og9c1NmspbrnlFg499FBuvvnmggnP2LFjWWWVVTjkkEO45ZZbmjy+xjZkyBD69OnDgAEDyh2K1cItgWZmZtagzjrrLO6///5yh2F1cBJoZmZm1sSmTp3KrFmzyhqDk0AzM7MKMHbsWCQxZMgQ/vnPf7LJJpvQvn17evbsycknn1xjwvD555+z//77s9RSS9GpUyd22GEH3nvvvQXqTZ8+nfPOO4911lmH9u3bs+SSS7Lrrrvy6quvzldvzpw5/PGPf2TLLbdk2WWXpW3btqy00kr88pe/5Ouvvy7qGgCGDRuGpLmPfC+88AJbbbUVnTp1olu3bhxxxBFMmjRp7vbjjjsOSbz//vsL7PvFF1/QunVrDj/88FrjKfVapkyZwoknnkjPnj3p2LEjm2++OU888QSDBg0qeA3vv/8+Bx10ED179qRt27b06tWLk08+mcmTJ89Xr3r/8ePHc9hhh9GjRw86derEp59+CsCtt97KpptuypJLLkmnTp1YddVVOeCAAxg/fnyt17ewfE+gmZlZBRkxYgTXXHMNRx99NIcddhgPPPAAF198MUsttRSnnnrqfHUnT57Mlltuyeabb855553HmDFjuOKKK9h999158803WWyxxQCYOXMmP//5z3n++ec56KCD+PWvf83333/P9ddfz09+8hOeeeYZ+vbtC8CMGTO46KKL2Guvvdh9993p1KkTL730EjfeeCP//ve/efnll2nbtm3B2Lt3785tt93GQQcdxM9+9jMG13CfdlVVFbvssguHHnooAwcOZOTIkdx44420atWKoUOHAnDUUUdx1VVXcdNNN3H++efPt/+wYcOYPXt2nUlgqdfyi1/8ghEjRjBgwAD69+/PmDFj2GOPPVhllVUWOPbLL7/MNttsw5JLLslRRx3F8ssvz2uvvcaVV17Jc889x9NPP02bNm3m22e77bZj2WWX5f/+7/+YPHkyiy++OLfffjuHHHIIP/vZzzj77LPp0KEDH3/8Mf/617/46quv6N69e63XuFAiwo8SHhtvvHGYlWyrrdKjAlVwaLaIefvtt8sdQpO5+eabA4ibb7654PYxY8YEEIcccsgCZR07dowxY8bMLZ8zZ06ss846seyyy853jK222iqAuOCCC+Yrv/DCCwOIhx9+eG7ZpZdeukBZRMT3338fK664YmyV84/AnDlzYsqUKQvEfMMNNwQQd99993zl+ddRU1nuNknxwgsvzFe+0047RevWrWPixIlzy7bYYovo2bNnzJw5c766a6yxRqy99toFj5+rlGt56KGHAogjjjhivrrV5Sllmmf99deP3r17xw8//DBf+X333bfA7/6QQw4JIA444IAFYtljjz2ic+fOC1xjMYr5mwJGRQ05jVsCzcysvE44Ic1VVEn69IHLLy/LqQcMGDDf1C+S2Hrrrfnzn//MpEmTWHzxxedua9WqFccdd9x8+2+zzTZA6qrcYYcdALj99ttZa6212HjjjZkwYcJ89bfbbjuGDRvG1KlT6dChA5Lo0KEDALNnz2bixInMmjVr7nH/+9//ss8++yzUNW6xxRZsvvnmC8Q9YsQIxo4dy7rrrgvA4MGDOfTQQ/nXv/7FrrvuCsAzzzzD+++/z8UXX1zneUq5lgcffBCAE088cb5j7LTTTqy99tqMHj16btkbb7zB66+/zllnncX06dOZPn363G0//elP6dSpE48++ugCI8N/+9vfLhBjly5dmDJlCg899BC77bZbwW7nxuIk0KypVPBE1o8vMxCozOl1zBZlhb7wV1111QXKll56aQC+/vrr+ZLA5ZZbjvbt29dYt9ro0aOZOnVqrV2LEyZMYMUVVwTgnnvu4ZJLLuHVV19l5syZ89X79ttv67qsOtV1jdX23XdfTjjhBG688ca5SeCNN95I27ZtOfjgg4s6V7HXMmbMGFq1asXqq6++wDF69+49XxJY/fOZZ57JmWeeWfC848aNW6BszTXXXKDs1FNP5ZlnnmHAgAEsvfTSbLXVVuy4447su+++dO7cuahrrC8ngWZNYeDAckdQo9UnVWU/OQm0MilTi1tjqm59mjJlSsHt1QMHquvlqr6Pr5DUu1d63YhgvfXW49JLL62xfnWCeN9997Hvvvuy6aabcsUVV7DiiivSvn17Zs+ezc9//nPmzJlT4zGKVWzcHTp04MADD+S6667jyy+/pGPHjvz9739nt912K+peuVKupfq8xbTEVdc96aST+PnPf16wzlJLLbVAWceOHRcoW2ONNXj77bd54okneOKJJ3j66ac58sgjOfPMM3nmmWdYbbXV6oynvpwEmjWFCp7I+oMl+5U7BLNFTvVAgtzWo1zV5YVaxBrDGmuswfjx49lmm21o1ar2iUFuu+022rdvz1NPPTVf0vLOO+80dpgFDR48mKuvvppbb711btdpXQNCqpVyLaussgpz5szh/fffZ+21155v27vvvjvf6zXWWANIyWz//v1LvaQFtGvXjp122omddtoJSIODdt55Zy699FKuvvrqhT5+TZwEmhmTJlVkTzXgdY2tedpoo41YccUV+etf/8of/vAHlltuubnbZsyYwZ///Gckze3ibGwHH3wwJ598MpdeemnB+9LGjRtHjx49gJTYSFqglezcc88t+nyLL74433zzzcIHDqy//vpsuumm3HTTTSyxxBKstNJKbL/99kXtW8q17Lrrrlx33XVcdtllc0coQ0rI8pP5DTfckHXXXZdrr72Wo446aoFkftasWfzwww907dq1zhgnTJhAt27d5ivbaKONABrsPayJk0CzFm6ZHuWOoGbVYwWcBFpz07p1a/7yl7+wxx57sN5663HEEUew2mqrMW7cOO6++27eeustTj31VHr37t0k8Rx//PE89thjnHzyyTz55JNss802LLHEEnz88cc88cQTc1vLAPbee2/uvfdettlmGw4++GBmzpzJ/fffX2PXdiGbb745jz/+OBdccAErrbQSkthvv/3qHf/gwYM54ogjgHQfXl2tmdVKuZaddtqJHXbYgeuvv54JEybMnSJm6NChrL/++rz++utz60ritttuY5tttmH99dfnsMMOY5111mHKlCl88MEH3HfffZx//vlFrZG8/fbb06VLF7bccktWXHFFvvvuO2655RYkcdBBBxX3BtVXTcOG/fAUMdZCVPAcMRUcmtVDS5oiptqLL74Ye++9d/To0SNat24dXbp0iX79+i0wzUrEvClizjzzzAW2nXnmmQHMN3XMVlttFSuvvHLRx5k5c2ZcccUV0bdv3+jYsWN07NgxVl999Rg4cGA88sgj89UdOnRorL322tGuXbtYdtll48gjj4yvv/666Olg3nvvvdhuu+2ic+fOC0yvUqh+xLxpdZ566qkFtk2aNCmWWGKJaNWqVYwdO3aB7bUp5VomTZoUxx9/fCyzzDLRvn372HTTTeOJJ56IvfbaKzp06LDAsceOHRtHHXVUrLzyytGmTZvo2rVrbLTRRvH73/8+Pv7447n1qqeIqSm+/v37R48ePaJNmzax7LLLxo477hhPPvlknde2sFPEKPJuMrXa9e3bN0aNGlXuMMwaTnU/8MiR5YyioAoOzeph9OjRC9xrZVaM6dOn07NnTzbZZBMeeeSRJj//euutx8yZM8t2X2RNivmbkvRyRPQttM3LxpmZmVlFu+OOO/j222856qijGvU8U6dOXaDsoYce4s0332S77bZr1HOXg+8JNDMzs4r04IMP8tFHHzFkyBB+9KMfsfvuuzfq+c4++2xeffVVtt56a7p06UJVVRU33XQTSy+9NKecckqjnrscnASamZlZRTr22GP5/PPP2XjjjbnhhhtqnV+wIfzsZz/jueee46KLLuL777+na9eu7LXXXpxzzjmssMIKjXrucnASaGZmZhVp7NixTXq+3Ln6WgLfE2hmZmbWAjkJNDMzM2uBnASamZmZtUBOAs3MrMl4blqzhtEQf0tOAs3MrEm0bdu24DxsZla6qVOn0qZNm4U6hkcHm1lFq6qat3JIpRk40Osal6Jbt258+umndOvWjc6dO9O6dWsklTsss2YlIpg6dSqfffYZPXos3OLvTgLNrGINHFjuCGpWVZWenQQWr0uXLrRr147x48fz9ddfM2vWrHKHZNYstWnThh49erDEEkss1HGcBJpZxTa3DQYGV2hzWwW+Xc1C+/btWXHFFcsdhpnhJNDM3NxmZtYiOQk0a+kGD67cJMvNbWZmjcajg83MzMxaoLImgZJaSfqNpHckTZP0iaRLJHUqcv82kk6VNFrSdElfS7pX0lo11F9J0nWSPpA0VdJnkh6UtGXDXpmZmZlZZSt3d/BlwHHAcOASYO3s9YaS+kfEnJp2VJpX4AFgx+z5KqA7cAzwH0k/joi3c+ovB7xMuubrgPeB5YAjgack7RYRDzX8JZqZmZlVnrIlgZLWAY4F7ouIvXLKxwBXAvsBd9ZyiN1JCeDQiDgqZ//bgDezY/TPqX8I0A0YEBEP5NS/i5QQHgk4CTQzM7MWoZzdwfsDAi7PK78emAIcWMf+W2fPN+cWRsSHwLPAtpJWytlUPZnO53nH+RKYA0wuKmozMzOzRUDRSaCknzTwuTchJV8v5hZGxDSgKttem3bZ85QC26rLNsspeyR7vkbSVpKWl7QJcBcwidQdbWZmZtYilNIS+KyktyWdJKl7A5x7OWBCREwvsO0zoJuktrXs/1b2vE1uoaSOzEv+5s5IGhEjgV8BqwAjgU9JCWhvYPOIeKWmE0kaLGmUpFHjx4+v7ZrMzMzMmoVSksDfZ88XAZ9K+rukn6v+Cz92BAolgADTcurU5HbgK+BsSUdKWiVr2fs76d6/QvuPB0YBJ5PuKTwZ6AI8JKnGKewjYmhE9I2Ivt27N0T+a2ZmZlZeRQ8MiYgLgQuzbuHDgV8AewCfSboZuDkixpZw7inAMjVsa59Tp6Z4vpXUH7gVGJqz6RngAuB04IfqQklHAtcAG0bEmznljwCvAOdT932IZtbUKnRJu8ur4PFlBpIWtzMza35KHh0cEc8Bz0k6ljS443Dg/4DTJD0J3AAMj4iZdRzqc+BHktoV6BJentRVPKOOWN4gTSezOql7+fOI+EDShVmVd3Kq/wF4JzcBrD6GpHeAreqI18yaWgUvabf6pComTYJ+/SozCazQJZfNrILUe4qYiJgM3CDpIVLL24GkKVn6A+MlXQxcGhGzazjES8D2wKak0bwASGoP9CG16BUbywfABzlFO5JaAZ/LKVse+F8Nh2hN+edMNLN8Fbyk3Q+9+7H4uHJHUZiXXDazYtQr8ZHUCtiF1Aq4Y3acf5O6ZacDvwb+BKyc/VzI3cCpwAnkJIGk+fo6AnfknK8n6d69jyOixi7irO6xwLrAWVmiWu1tYH1Jm0fEf3LqbwGsiecINLMSLNczPUaOLHckC6rA3nMzq0AlJYGS1gQOAw4GegDfkFbquD4icrte/ybpGlJ3ccEkMOuGvRr4taT7gBHMWzHkaeafKPp80mTPW5NG9lbHMwL4kJTgBallcQApoftj3inPJK1M8pika0kTRK8B/BKYAZxV/DthZmZm1rwVnQRKegb4CWmC56eBk4B7a7lv71ng6DoOewIwlnRn9c7ABFJSeUZtS8bleAHYFxiUvR5Nmgbmuvxu6Ij4h6TtSCOCDyO1LH5Lmj/wnIioKuJ8ZmZmZouEUloC1wYuJS3T9n4R9R9n3qoeBWWJ2iXUMVFzRAxiXqKXW34OcE4RsVTXfxJ4stj6ZmZmZouqUpLAnhExq9jKETGe1GJoZmZmZhWmlMmip0vav6aNkvaVVNNIYDMzMzOrIKUkgcoetW03MzMzs2aglCSwLisBExvweGZmZmbWSGq9J1DS7qQ1dqsNzpZqy9eVNEn0vxswNjMzMzNrJHUNDOnDvFG5AWyZPfJNAp6n5omhzczMzKyC1NodHBFnRUSriGhFuufvwOrXeY8lImL7bPk2MzMzM6twpUwRswowvrECMTOzhlNVVbnLxw0c6HWNzSpB0UlgRHzUmIGYmVnDGDiw3BHUrKoqPTsJNCu/GpNASTeR7gMcHBGzs9d1iYg4vMGiMzOzkg0eXLlJVqW2Tpq1RLW1BA4iJYG/BGZTYNm2AgJwEmhmZmZW4WpMArPBIDW+NjMzM7Pmq5SBIWZmlsujL8ysGXMSaGZWHx59YWbNXG0DQ86ox/EiIs5ZiHjMzJoHj74ws2autpbAIfU4XgBOAs3MzMwqXG1J4CpNFoWZmZmZNanaRgd7cmgzMzOzRZSnfTEzMzNrgWobGHJw9uNtERE5r2sVEbc2SGRmZmZm1mhquyfwFtJAj78CM3Jeq5Z9AnASaGZmZlbhaksCtwaIiBm5r83MzMys+attYMjTtb02MzOrDy+0YlYZvGKImZk1GS+0YlY5SkoCJbUHjgP2AFbNij8EhgNXRcTUhg3PzMwWJV5oxaxyFJ0ESuoOPAmsA/xASv4ErA1sBhwsaeuIGN8YgZqZmZlZwyllnsCLgB8BJwLLRMRGEbEhsAxwEikZvKjhQzQzMzOzhlZKd/CuwI0RcXluYTZ6+DJJ65C6ic3MzMyswpXSEtgWeKWW7aOyOmZmZmZW4UpJAl8CNqpl+8bAiwsXjpmZmZk1hVK6g08CnpD0BnBtRMwEkNQa+BWwJ7Btw4doZmZmZg2ttrWDnyxQ/DVwOXC2pA9Jy8StBiwB/A+4BCeCZmZmZhWvtpbAVUlJXr6Ps+eu2fN32aMN8+YONDMzM7MKVtuycb2aMA4zMzMza0JeNs7MbFHkBXrNrA5OAs3MFjVeoNfMilDq2sGrAb8hLRO3FAtOMRMRsVoDxWZmZvXhBXrNrAhFzxMoaT3SZNFHkCaFXhWYDLQHegGzmTdoxMzMzMwqWCmTRZ8NzAA2YN40MMdHxHLAUcCSpPkCzczMzKzCldId/FNgaES8K2nprEwAEXG9pJ8BfwJ2a+AYzczMmoTH01hLUkoS2Jk0ITSkFkGATjnbnwPOb4igzMzMmprH01hLU0oSOA5YFiAiJkqaDKyZs30pYLEGjM3MzKzJeDyNtTSlJIFVwCY5r58Gjpf0Iunewl8DrzVcaGZmZmbWWEoZGHInsLSkDtnr/wO6AE8BT5AGhpzaoNGZmZmZWaMoOgmMiLsjYsuImJq9fhVYhzRv4HHA+hHx71JOLqmVpN9IekfSNEmfSLpEUqe69wZJbSSdKmm0pOmSvpZ0r6S1atnnR5LulPRFts+nkoZL6lFK7GZmZmbN2UKtGBIRnwBXLsQhLiMlkMOBS4C1s9cbSuofEXNq2lGSgAeAHbPnq4DuwDHAfyT9OCLezttnB+B+0gCXK0n3OS4DbAEskb02MzMzW+TVKwmU1Js0WTTAhxHxbj2OsQ5wLHBfROyVUz6GlKDtR+qCrsnupARwaEQclbP/bcCb2TH655Qvkx1vJLBbRMwsNWYzMzOzRUUp9wQiaRtJbwFvA//MHm9LekvStrXvvYD9SfMMXp5Xfj0wBTiwjv23zp5vzi2MiA+BZ4FtJa2Us+looCvwu4iYKamjpDYlxmxmZma2SChl2bhtgIeBlUiJ2m+AE4EbgBWBf2V1irUJMAd4MbcwIqax4EjkQtplz1MKbKsu2yynbCfgB2BJSVWkJe+mSXpWUl3nMjMzM1uklNISeB7pnrm1IuLoiLgyIq7IumJ/BHwF/LGE4y0HTIiI6QW2fQZ0k9S2lv3fyp7nSzwldWRe8rdizqbepO7vh0lJ5t7A74B1gZFZ97SZmZlZi1BKErg+cF1EfJa/ISI+Ba4jrStcrI5AoQQQYFpOnZrcTko8z5Z0pKRVsha9vwPdCuzfOXs9PCIGRcS9EXEJsEdWfkZNJ5I0WNIoSaPGjx9f54WZmZmZVbpSksDvgYm1bP8B+K6E401hXpduvvY5dQqKiG9JAz/+BwwFPiR1LXcCLsiJqdrU7PmWvOOMBD4G+tVyrqER0Tci+nbv3r2mamZmZmbNRimjg/8G7C/p6oiYlbshG2Cxf1anWJ8DP5LUrkCX8PKkruIZBfabKyLeIE0nszqpe/nziPhA0oVZlXdyqn8KrAV8WeBQXwAblRC7mZnVV1VV5a6DNnBg5a4dZ9bAamwJlLRS7gO4lpQ0PiPpF5LWk7SupH2AZ0jrBl9bwrlfys6/ad552wN9gFHFHigiPoiIZyLig6xoR1Ir4HM51aoHoKxQ4BArkLqWzcysMQ0cCH36lDuKwqqq4M7aZiYzW7TU1hI4Foi8MmXPf62h/E1SMliMu0nLzJ1AmtKl2pGke/TumHtwqSdpibqPI6LGLuKs7rGkwR5nRcTknE23AQeTpop5OKf+rqSWx+uLjNvMzOpr8ODKbWmr1NZJs0ZSWxJ4NgsmgQ0mIt6QdDXwa0n3ASOYt2LI08w/UfT5wCGkuQFHVhdKGkG6F/DtLNbtgQHAQ+SNVI6IxyXdRerSHkGa43Bl0oTVXwBDGvoazczMzCpVjUlgRAxpgvOfQGpxHAzsDEwgLf92Rm1LxuV4AdgXGJS9Hg38ijSKeXaB+gcDrwGHkSap/o40mvi0iPi8fpdgZmZm1vws1NrBCytL1C7JHrXVG8S8RC+3/BzgnBLON4s0cviCuuqamZmZLcpKSgIltSJ1y+5BztrBwH3ArUW23pmZmZlZmRWdBErqQLpvb0vS/XdfZJt2InXlHixpp2zZNzMzMzOrYKVMFn06sBWp67Z7RKwYESuSVue4mDTZ8mkNHqGZmZmZNbhSksB9gXsi4nfZah0ARMR3EXEKcA9pwmgzMzMzq3ClJIErkDM9SwFPU3giZjMzMzOrMKUMDPkOWKOW7atT2trBZmZmViSvtmcNrZSWwMeAYyTtkL9B0vbAL4FHGiowMzMzS7zanjWGUloCTwd2AEZIehV4KytfB9iQNNHzGQ0bnpmZmXm1PWsMRSeBEfGRpL6kJdx2BTbKNk0E7gJOjYiPGz5EMzMzM2toRSWBkhYDlgcmRcQBkgR0BwR8FRGNtsawmZmZmTW8Yu8JbENaGeRwgEi+iohxTgDNzMzMmp+iksBsFZAJwOTGDcfMzMzMmkIpo4NHALs0ViBmZmZm1nRKSQJ/B/SUNEzSepLaN1ZQZmZmZta4Spki5isggA2AAwHS+JD5RESUckwzMzMzK4NSErZbSUmgmZmZmTVzpcwTOKgR4zAzMzOzJlRnEiipNbA7aW3g8cA/ImJCYwdmZmZmZo2n1iRQ0lLASGBd0sTQAVwkafuIeLnxwzMzMzOzxlDX6ODTgfWAh4BjgT8DiwNDGzkuMzMzM2tEdXUH7wo8HBG7VRdIGgtcLGmFiPi0MYMzMzNrUlVV0K9fuaMobOBAGDy43FHYIqSulsAVSZNE53qQ1DW8cqNEZGZmVg4DB0KfPuWOorCqKrjzznJHYYuYuloC2wHf5JV9m7PNzMxs0TB4cOW2tFVq66Q1a6WsGJLPcwaamZmZNVPFzBN4kqT9cl63ISWAf5SUP1VMRMTuDRadmZmZmTWKYpLADbNHvs0LlLl10MzMzKwZqDUJjIiF6S42MzMzswrlJM/MzMysBXISaGZmZtYCOQk0MzMza4GcBJqZmZm1QE4CzczMzFqgYqaIMTMzM6uRl1xunpwEmpmZWb0NHFjuCGpWVZWenQQW5iTQzMzM6s1LLjdfNSaBkp6sx/EiIrZdiHjMzMzMrAnU1hK4Kl4GzszMzGyRVGMSGBG9mjAOMzMzM2tCniLGzMzMrAVyEmhmZmbWApU0OljSUsDhwGbAUiyYRHpgiJmZmVkzUHQSKGll4DlgOeB7YAngG+YlgxOAyY0Qo5mZmZk1sFJaAs8FlgS2Bd4AvgL2Bf4DnAbsB2zVwPGZmZkZeFkOa3Cl3BO4LXB9RDzFvKljFBFTIuI0UmJ4QUMHaGZm1uINHAh9+pQ7isKqquDOO8sdhdVDKS2BSwNvZj/PzJ475Gx/DDizIYIyMzOzHF6WwxpBKS2B44Gu2c8TgWlAr5ztbZk/KayTpFaSfiPpHUnTJH0i6RJJnYrcv42kUyWNljRd0teS7pW0VhH7ri9ppqSQtHcpcZuZmZk1d6UkgW8BG0AaAgy8CBwjaSVJvYDBwDslnv8y4FLgbeBY4G/AccCDkmqNTZKAB4A/Au8CvwGuBH4K/EfSj2rZtxVwPSmRNTMzM2txSukOfgA4SVKHiJgKnA08AozJtgewZ7EHk7QOKfG7LyL2yikfQ0rm9gNqu8lgd2BHYGhEHJWz/22kbusrgf417HsssA5wIXBWsTGbmZmZLSqKbgmMiGsiYrUsASQingS2AK4gteZtGRH/KOHc+wMCLs8rvx6YAhxYx/5bZ88358X5IfAssK2klfJ3krQiaaTzEODjEuI1MzMzW2SUNFl0vogYBYyq5+6bAHNI3cq5x5wmqSrbXpt22fOUAtuqyzZjwUTvGuBDUvJZV6JpZmZmtkgquiVQ0qWS1m/Acy8HTIiI6QW2fQZ0k9S2lv3fyp63yS2U1JGU/AGsmLdtX2Bn4OiImFWvqM3MzMwWAaUMDDkBeFVSlaQTJC2zkOfuCBRKAGHegI2Otex/O2nC6rMlHSlpFUmbAH8HuuXvL2lJUuvf9RHxQimBShosaZSkUePHjy9lVzMzM7OKVEoSuBbwJ9KqIZcCn0p6UNLedbTY1WQK87p087XPqVNQRHxLGvjxP2AoqYv3RaAT8yat/iFnl4tJ1/v7UgONiKER0Tci+nbv3r3U3c3MzMwqTikDQ96LiNMiohdp9ZA7gC2Be4AvJV0jaYsSzv05qcu3UCK4PKmreEYdMb0RERsCa5CWrFsjIrZiXnL5DoCkjYDDgD8DS0taXdLqQHVr5rJZWU1JqZmZmdkipZSWwLki4qmIOBRYFjiI1AJ3JGlUbrFeys6/aW6hpPZAH0oYcBIRH0TEMxHxQVa0I6kV8Lns9UqkkchnA+/nPKpbDK/KXq9XQvxmZmZmzdbCjg6eKukz4AvSfXy13cOX727gVNK9hrnJ45HZce6oLpDUE+gCfBwRNXYRZ3WPBdYFzoqIyVnxi8AvClTvB/wKuAT4D6lr2czMzGyRV68kUNKawMGkKVZWBGYD/wKGFXuMiHhD0tXAryXdB4wA1iatGPI0808UfT5wCGluwJE5cYwg3Qv4Nmmy6u2BAcBDpJVEqs/1OWnASP51LJ79+J+IWGC7mZmZ2aKq6CRQUlfSKh4Hk+bwE1BFWvrtzoioz7DZE4CxpCXndgYmkLpmz4iIOUXs/wKwLzAoez2a1LJ3XUTMrkc8ZmZmZi1CKS2BX2T1x5FGBw+LiDcX5uRZonZJ9qit3iDmJXq55ecA5yzE+W8Bbqnv/mZmZmbNVSlJ4HBSd+8jRbbSmZmZmVmFKjoJjIj9GjMQMzMzM2s69ZoixszMzMyatxpbAiWNAeYAa0XETEkfFnG8iIjVGiw6MzMzM2sUtXUHf0SadiWy1x/n/GxmZmZmzViNSWBE9KvttZmZmZk1X74n0MzMzKwFKjoJlPSKpOMkdW/MgMzMzMys8ZXSErgMcDnwqaT7Je0hqU3jhGVmZmZmjamUJHBFYAfgHmBb0lq8X0j6s6RNGiM4MzMzM2scRSeBkTwWEQcBywKHAa8BRwP/kTRa0u8bKU4zMzMza0D1GhgSEZMjYlhEbAusDJwO9ATObcjgzMzMzKxxlLJ28AIkrQocDBwILAHMbIigzMzMzKxxlZwESuoC7EtK/rYABLwOnATc0aDRmZmZWeWrqoJ+/codxQIur4LHlxkIDC53KBWp6CRQ0i6kxG8XoD3wFXAFMCwiXmuc8MzMzKyiDRxY7ghqtPqkquwnJ4GFlNIS+A9gOvAgMAx4OCJmN0pUZmZm1jwMHpweFeiDJfuVO4SKVkoSeAzw14j4rpFiMTMzM7MmUlQSKGlxYB/SwI8bGzUiMzMzswYyaVJF3q4IpJ70cjaiFjVFTERMAjwhtJmZmTUby/SAxRcvdxSFVVXBnXeWN4ZSuoOrgLUbKQ4zMzOzBrVcz/QYObLckSyoElonS5ks+kzgSElbN1YwZmZmZtY0SmkJPBD4GHhc0mvAe8CUvDoREYc3VHBmZmZm1jhKSQIH5fzcJ3vkC8BJoJmZmVmFKzoJjIh6rTNsZmZmZpXHiZ2ZmZlZC+Qk0MzMzKwFKmXt4CeLqBYRse1CxGNmZmZmTaCUgSGrkgZ+5O/fk9SiOAGY3EBxmZmZmVkjKmVgSK9C5ZLaAScChwJbNUxYZmZmZtaYFvqewIiYHhHnA/8FLl34kMzMzMyssTXkwJB/Azs04PHMzMzMrJE0ZBK4CtC2AY9nZmZmZo2klNHBK9WwqSvQHzgOGNkAMZmZmZlZIytldPBYFhwdXE3AO6RE0MzMzMwqXClJ4NksmAQG8A3wHvB4RMxpqMDMzMzMrPGUMkXMkEaMw8zMzMyakJeNMzMzM2uBak0CJXWVtJGkjnnlknSKpPckTZb0oqT+jRuqmZmZmTWUuloCTyHN/7dYXvn5wHnAasBUoC/wT0kbNHiEZmZmZtbg6koCfwqMiIiJ1QWSlgSOB8YDa0dEN9JycTNJy8eZmZmZWYWrKwlcBXg1r2xboB1weUS8BxARzwJ3Aj9r8AjNzMzMrMHVlQR2BT7PK9uUNDXMo3nlVUDPhgnLzMzMzBpTXUngeKBHXtnmwHTg9bzy6aQuYTMzMzOrcHUlgaOBfSUtBiCpJ7AZ8EJEzMqruyowruFDNDMzM7OGVlcSeBWwAfBvSRcCTwBtgJsK1N2WBVsHayWplaTfSHpH0jRJn0i6RFKnIvdvI+lUSaMlTZf0taR7Ja1VoO5Wkq6W9IakiZLGS3pO0v6SVErcZmZmZs1drSuGRMSDki4CTiK1AAJcFRF35NbLpobZDDimxPNfRlpveDhwCbB29npDSf1rW4YuS9weAHbMnq8Cumcx/EfSjyPi7ZxdLgBWyM71BtAJ2Jc0oGUb4MgSYzczMzNrtupcNi4iTpF0Mam798OIGF+g2pfAJsA7xZ5Y0jrAscB9EbFXTvkY4EpgP1KCVpPdSQng0Ig4Kmf/24A3s2PkTmB9CvDviJidU/cK4CngCElXRMSbxcZvZmZm1pwVtWxcRIyPiP/WkAASEeMi4uWImFzCufcHBFyeV349MAU4sI79t86eb86L5UPgWWBbSSvllD+dmwBmZXOAv2cv1y0hdjMzM7NmrZxrB28CzAFezC2MiGmk6WY2qWP/dtnzlALbqss2K7At3wrZswe1mJmZWYtRziRwOWBCREwvsO0zoJuktrXs/1b2vE1uYbbOcXXyt2JtAUhaDjgK+JC0PJ6ZmZlZi1DOJLAjaW7BQqbl1KnJ7cBXwNmSjpS0iqRNSN273eraP0sWh5MGiAyKiBrnOJQ0WNIoSaPGjy/YI25mZmbWrJQzCZzCvC7dfO1z6hQUEd+SBn78DxhKas17kZTUXZBV+6HQvpLaA/cDfYFDs2XvahQRQyOib0T07d69e21VzczMzJqFciaBn5O6fAslgsuTuopn1HaAiHgjIjYE1gC2AtaIiK2Yl1wuMFo5JwHsDxwZEbfX/xLMzMzMmqdyJoEvZeffNLcwS9L6AKOKPVBEfBARz0TEB1nRjqRWwOfyjt2O1AW8PTA4IgpNem1mZma2yCspCZTUWdIZkv4t6X1JW2Tl3bLyBVbqqMXdQAAn5JUfSbqXb+6E1JJ6Sloru4+vrhiPJU33clnulDVZAng/sANwdETcUEKsZmZmZouUOieLriapO2kE7arAB9lzB4CImCDpEGBJ4MRijhcRb0i6Gvi1pPuAEcxbMeRp5p8o+nzgENLcgCNzYhpBuhfwbVJCuT0wAHgI+GPeKe8Afg48DkyRlD8P4esRUdKyd2ZmZmbNVdFJIHAusCxp+pWPSSNzcz1AWj+4FCcAY4HBwM7ABNLyb2fUtmRcjhdIS78Nyl6PBn4FXJc/MTRpEAikewH7s6CzKHHtYzMzM7PmqpQkcBfgmoh4RdLSBbZ/yLxkrChZonZJ9qit3qBCx46Ic4BzijxXr1JiMzMzs0VAVRX061fuKBZweRU8vsxAUjtYeZSSBHYjdQPXZA7zpnYxMzMzK6+BA8sdQY1Wn1SV/dQ8ksAvgdVq2b4hqZvYzMzMrPwGD06PCvTBkv3KHUJJo4NHAIdL6pm/QdJmwMGk+wLNzMzMrMKVkgSeBcwCXiWN1g3gEEl3Ac+QJn++oObdzczMzKxSFJ0ERsSXwObAf4HDAAEHAfsAjwI/i4hvGiNIMzMzM2tYpdwTSER8AuwuaQmgNykR/MDJn5mZmVnzUspk0UtHxNcAEfEDadk3MzMzM2uGSrkn8HNJ90naXVJJLYhmZmZmVllKSQLvI627ex/whaQrJPWtYx8zMzMzq0ClDAzZn7Rs3GDSWr2/Bv4r6S1JJ0tarpFiNDMzM7MGVkpLIBExMSJujIitgFWBIUAb0tQwH0l6uOFDNDMzM7OGVlISmCsiPoqIcyJiTeAAYDKwXYNFZmZmZmaNpt4DPCR1Bn5BWinkp6SE8s0GisvMzMzMGlFJSaAkkQaHHAzsDnQAxgN/BoZFxKsNHqGZmZmZNbhS5gm8GBgI9ABmAg8Bw4ARETGrccIzMzMzs8ZQSkvgiaQJos8F7oqIbxsnJDMzMzNrbKUkgT+KiHcaLRIzMzMzazKlzBPoBNDMzMxsEVFjS6Ckg7Mfb4uIyHldq4i4tUEiMzMzM7NGU1t38C1AAH8FZuS8Vi37BOAk0MzMzKzC1ZYEbg0QETNyX5uZmZlZ81djEhgRT9f22szMzMyar6IHhki6SdJmtWzfVNJNDROWmZmZmTWmUtYOHgSsVsv2VYBDFioaMzMzM2sSpSSBdelEWknEzMzMzCpcrZNFS1oJ6JVTtJakLQtU7Qr8Evig4UIzMzMzs8ZS14ohhwJnkqZ+CeC07JFPwJysvpmZmZlVuLqSwPuBsaQk7yZgKPBCXp0AJgEvRcQnDRyfmZmZmTWCWpPAiHgNeA1A0srAvRHxZlMEZmZmZmaNp66WwLki4qzGDMTMzMzMmk7RSWA1ST2AvsBSFBhd7LWDzczMzCpf0UmgpFbA1cAR1D61jJNAMzMzswpXyjyBvwWOAu4iTQot4PfAr4D3gVHAdg0doJmZmZk1vFKSwEOARyLiYOBfWdnLEXEtsDHQLXs2MzMzswpXShK4KvOSvznZcxuAiJgM3EzqKjYzMzOzCldKEjiVecvCTSLND7hMzvYvgRUbKC4zMzMza0SlJIEfAasBRMRM0hJxP8/Z3h8Y13ChmZmZmVljKSUJfBLYI+f1bcD+kp6SNBL4BXBPA8ZmZmZmZo2klHkCLwYeldQuIqYD55O6gw8EZpOWlDuz4UM0MzMzs4ZWyoohXwBf5LyeDRyXPczMzMysGSmlO9jMzMzMFhE1tgRKWqk+B4yIj+sfjpmZmZk1hdq6g8eSpoEp1WL1C8XMzMzMmkptSeDZ1C8JNDMzM7MKV2MSGBFDmjAOMzMzM2tCZR0YIqmVpN9IekfSNEmfSLpEUqci928j6VRJoyVNl/S1pHslrVVD/S6SrpL0WXa+tyT9UpIa9srMzMzMKlvRU8QUO1CkxIEhl5GmmBkOXAKsnb3eUFL/iJhT045Z4vYAsGP2fBXQHTgG+I+kH0fE2zn12wKPARtmdUdn+14D9ACGlBC3mZmZWbNWymTRYynuHsGiBoZIWgc4FrgvIvbKKR8DXAnsB9xZyyF2JyVxQyPiqJz9bwPezI7RP6f+EcAmwHERcVVWdr2ke4FTJd0cER8VE7uZmZlZc1dKElhooEhr0nrCuwNvAP8q4Xj7AwIuzyu/HvgTaSWS2pLArbPnm3MLI+JDSc8C20taKadlciAwJTt+rsuBPYF9gQtLiN/MzMys2SplxZAhNW2TtCrwAjCqhHNvAswBXsw7zzRJVdn22rTLnqcU2FZdthnwsaRWwEbAKxExLa/ui1kcdZ3PzMzMbJHRIANDIuJD4DrgrBJ2Ww6YkK1DnO8zoFt2H19N3sqet8ktlNSRlPwBrJg9LwV0yI47n+z8XwPLFx+6mZmZWfPWkKODPwN+VEL9jkChBBBgWk6dmtwOfAWcLelISatI2gT4O9Atb//q59rOV+O5JA2WNErSqPHjx9cSkpmZmVnz0JBJ4ADg2xLqT2Fel26+9jl1CoqIb0kDP/4HDAU+JHXtdgIuyKr9kHec2s5X27mGRkTfiOjbvXv3mqqZmZmZNRulTBFzRg2bupK6ZNeltIEVnwM/ktSuQJfw8qSu4hm1HSAi3iBNJ7M6qXv584j4QFJ1HO9kz98CUynQ5SupHbA08HQJsZuZmZk1a6WMDh5Sy7YvgdOZ1wJXjJeA7YFNgWerCyW1B/oAzxR7oIj4APggp2hHUivgc9n2OZJeISWM+UnnpqQW0VIGtZiZmZk1a6V0B69S4NELWCIilouI8yJidgnHu5s05cwJeeVHku7Pu6O6QFJPSWtlgz5qJelYUqvkZRExOWfTXdlxB+ftcgIwC7inhNjNzMzMmrVSpohp0ImUI+INSVcDv5Z0HzCCeSuGPM38cwSeDxxCmhtwZHWhpBGkewHfJiWU25PuTXwI+GPeKa8HDgUuldSLtGLITsAewLkRMaYhr8/MzMyskpXSHdwYTiCtRDIY2BmYQFrS7YzalozL8QJpkudB2evRwK+A6/JbJSNihqT+wLmkiaqXJg0qORa4eiGvw8zMzKxZUUQxK8FllaUfk5KsNUhJlPKqRESs1nDhVZ6+ffvGqFG+fdDMzMzqr2rJfgD0+W5ko55H0ssR0bfQtlJGBx8JXAvMAN4FPq59DzMzMzOrVKV0B58KVAE7RMSExgnHzMzMzJpCKaODewA3OgE0MzMza/5KSQJHk9bgNTMzM7NmrpQk8I/AMZIWWHXDzMzMzJqXUuYJvC+brPltSfeTpnbJnxw6IuKchgvPzMzMzBpDKaOD1wTOBjoDB9VQLQAngWZmZmYVrpTRwdcAywDHk9b6/bZRIjIzMzOzRldKErg5cHFEXNVYwZiZmZlZ0yhlYMgPwPjGCsTMzMzMmk4pSeA9wJ6NFYiZmZmZNZ1SuoOvA4ZlI4OvBMaw4OhgIsLLyZmZmZlVuFKSwLdIo3/7ArvWUm+xhYrIzMzMzBpdKUng2aQk0MzMzMyauVImix7SiHGYmZmZWRMqZWCImZmZmS0iSlkxZMti6kXEM/UPx8zMzMyaQin3BI6kuHsCPTDEzMzMrMKVkgQeWsP+qwGDgLGkaWTMzMzMrMKVMjBkWE3bJF0EvNIgEZmZmZlZo2uQgSER8S1wA/C7hjiemZmZmTWuhhwd/C2wagMez8zMzMwaSYMkgZLaAwcBXzbE8czMzMyscZUyRcxNNWzqCmwBdAdOboigzMzMzKxxlTI6eFAN5d8A7wG/iYg7FzoiMzMzM2t0pYwO9uoiZmZmZosIJ3ZmZmZmLVCtSaCkxST9SdLRddT7paTzJKlhwzMzMzOzxlBXS+CBpMEeL9VR70XgFGD/hgjKzMzMzBpXXUngPsDjEfFybZWy7Y/gJNDMzMysWagrCdwYeLzIYz0F9F24cMzMzMysKdSVBHYFviryWOOz+mZmZmZW4eqaImYi0K3IYy0NTFq4cMzMzMwWfYsvXu4I6m4JfAvYvshjbZfVNzMzM7NarL56epRTXUngfUB/SbvXVknSbqQk8N6GCszMzMzMGk9dSeB1wAfAPZL+KKlX7kZJvSSdC9xDWjruukaJ0szMzMwaVK33BEbEVEk7A/8E/gD8XtJE4AegM7AEIOBdYJeImNbI8ZqZmZlZA6hz2biI+ADoAxwP/BuYBSwLzAaezco3ioj/NV6YZmZmZtaQ6hodDEDWwndV9jAzMzOzZq7OlkAzMzMzW/Q4CTQzMzNrgZwEmpmZmbVATgLNzMzMWiAngWZmZmYtkJNAMzMzsxaorEmgpFaSfiPpHUnTJH0i6RJJnYrcX5IGSnpe0gRJEyW9JekMSUsUqL+SpOskfSBpqqTPJD0oacuGvzozMzOzylXulsDLgEuBt4Fjgb8BxwEPSiomtnOBO4CpwFnAycAb2c+PSlJ1RUnLAS8D+wB/B34NXAtsADyVrYxiZmZm1iIUNVl0Y5C0Dinxuy8i9sopHwNcCewH3FnL/q2BE4BXgO0iYk626VpJs4ADSAleVVZ+CNANGBARD+Qc5y7gfeBI4KGGuDYzMzOzSlfOlsD9SesOX55Xfj0wBTiwjv3bAB2AL3MSwGqfZ8+Tc8qWyNtW7UtgTl5dMzMzs0VaOZPATUjJ14u5hdkSdVXZ9hpFxFTgGeDnkk6RtLqkXpIGAccAt0fE+zm7PJI9XyNpK0nLS9oEuAuYBFzSANdkZmZm1iyUMwlcDpgQEdMLbPsM6CapbR3HOAB4CvgTqUt3DHAT6V7Dg3MrRsRI4FfAKsBI4FNSAtob2DwiXqnpJJIGSxoladT48ePrvjIzMzOzCle2ewKBjkChBBBgWk6dGbUcYzrwISlpfBgIYC/g9OwYf8yrPx4YBTwOvAesSRpM8pCkrSLik0IniYihwFCAvn37Rq1XZWZmZtYMlDMJnAIsU8O29jl1CpLUEXgeeCUi9svZ9FdJfwXOlvT3iHg3q38kcA2wYUS8mXOcR0iDS86n7vsQzczMzBYJ5ewO/pzU5duuwLblSV3FtbUC7g2sQZpWJt/fSNf205yyPwDv5CaAABHxBvAOsFUJsZuZmZk1a+VMAl/Kzr9pbqGk9kAfUrdtbZbPnhcrsK113nN1/UJ1q+uVs1XUzMzMrEmVMwm8m3QP3wl55UeS7gW8o7pAUk9Ja2VdwNXezp4PKXDs6rKX8ur3lrR5bkVJW5DuDcyta2ZmZrZIK1vrV0S8Ielq4NeS7gNGAGuTVgx5mvknij6flNhtTRrZC/BP0ujenSQ9A9xLmndwT+BnwN/yRvyeCQwHHpN0LWk08RrAL0mDT85qhMs0MzMzq0jl7gI9ARgLDAZ2BiYAVwFnFJgAej4RMVtSf9K9fnsCF5JaFt8HTiEtR5db/x+StiONBj4M6AJ8S5o/8JyIqGqoizIzMzOrdIrwjCel6Nu3b4waVdftimZmZma16NcvPY8c2ainkfRyRPQttK2c9wSamZmZWZk4CTQzMzNrgZwEmpmZmbVATgLNzMzMWiAngWZmZmYtkJNAMzMzsxbISaCZmZlZC+Qk0MzMzKwFchJoZmZm1gI5CTQzMzNrgZwEmpmZmbVATgLNzMzMWiAngWZmZmYtkJNAMzMzsxbISaCZmZlZC+Qk0MzMzKwFchJoZmZm1gI5CTQzMzNrgZwEmpmZmbVATgLNzMzMWiAngWZmZmYtkJNAMzMzsxbISaCZmZlZC+Qk0MzMzKwFchJoZmZm1gI5CTQzMzNrgZwEmpmZmbVATgLNzMzMWiAngWZmZmYtkJNAMzMzsxbISaCZmZlZC+Qk0MzMzKwFchJoZmZm1gI5CTQzMzNrgZwEmpmZmbVArcsdgJmZmVmL06dPuSNwEmhmZmbW5C6/vNwRuDvYzMzMrCVyEmhmZmbWAjkJNDMzM2uBnASamZmZtUBOAs3MzMxaICeBZmZmZi2Qk0AzMzOzFqisSaCkVpJ+I+kdSdMkfSLpEkmditxfkgZKel7SBEkTJb0l6QxJS9Swz48k3SnpC0nTJX0qabikHg17dWZmZmaVq9wtgZcBlwJvA8cCfwOOAx6UVExs5wJ3AFOBs4CTgTeynx+VpNzKknYAXgbWB64Efgn8mfQ+FEwazczMzBZFZVsxRNI6pMTvvojYK6d8DClB2w+4s5b9WwMnAK8A20XEnGzTtZJmAQcAGwBVWf1lsuONBHaLiJkNe0VmZmZmzUc5WwL3BwRcnld+PTAFOLCO/dsAHYAvcxLAap9nz5Nzyo4GugK/i4iZkjpKalOfwM3MzMyau3ImgZsAc4AXcwsjYhqp9W6T2naOiKnAM8DPJZ0iaXVJvSQNAo4Bbo+I93N22Qn4AVhSUhUpQZwm6VlJtZ7LzMzMbFFTziRwOWBCREwvsO0zoJuktnUc4wDgKeBPwPvAGOAm0r2GB+fV7U3q/n6YlGTuDfwOWBcYmXVPm5mZmbUIZbsnEOgIFEoAAabl1JlRyzGmAx+SksaHgQD2Ak7PjvHHnLqdgcWAOyJiUHWhpJdJieQZwL6FTiJpMDAYYKWVVqolHDMzM7PmoZwtgVOAdjVsa59TpyBJHYHngSUi4pCIuCsi/hoRvwDuBs6W1Dtnl6nZ8y25x4mIkcDHQL+azhURQyOib0T07d69e81XZGZmZtZMlDMJ/JzU5VsoEVye1FVcWyvg3sAapGll8v2NdG0/zSn7NHv+skD9L4Cl6ozYzMzMbBFRziTwpez8m+YWSmoP9AFG1bH/8tnzYgW2tc57hnkDUFYoUH8F4Ks6zmdmZma2yChnEng36R6+E/LKjyTdC3hHdYGknpLWyrqAq72dPR9S4NjVZS/llN2WPR+dW1HSrqSEckQpwZuZmZk1Z4qI8p1cugr4NTCclIStTVox5Dlgm+r5/yTdQkrsts7u4UPSYqR7AjcFngXuJc07uCfwM+BvEbFP3vnuJM1P+C/gn8DKpAmrvwP6RsTn1EHSeOCj+l910boBE5rgPIsav2/14/etfvy+1Y/ft/rx+1Y/Lf19WzkiCg5oKHcSuBipJXAw0Iv0S7obOCMiJuXUu4W8JDAr7wz8gZT4rUJqWXyf1Op3aUTMyjtfa+Ak4LCs/nekUcWnRcQnDX+F9SdpVET0LXcczY3ft/rx+1Y/ft/qx+9b/fh9qx+/bzUraxJoNfOHtn78vtWP37f68ftWP37f6sfvW/34fatZOe8JNDMzM7MycRJYuYaWO4Bmyu9b/fh9qx+/b/Xj961+/L7Vj9+3Grg72MzMzKwFckugmZmZWQvkJNDMzMysBXISWCEktZL0G0nvSJom6RNJl0jqVO7YKpWkNSWdLek/ksZLmiipStJpft9KI6mjpDGSQtKfyx1PJZPUVdLFkj7I/lbHS3pK0s/KHVulkrS4pFMlvZH9nU6Q9LykQZJU7vgqgaQ/SPqbpA+zv8OxddTvLel+Sd9KmizpWUnbNFG4FaPY903JgZL+mv3tTpH0saR/SNqsicOuGK3rrmJN5DLSRNnDgUuYN3H2hpL6V0+cbfM5DPgV8A/SCjMzga2Bc4F9JG0eEVPLGF9zcjZpQlWrhaSVgZHA4sCNwHtAF2B95i1laTkktSJN0P9jYBhwFWlVqP2Bm0n/1p1StgArx3nAN8ArwJK1VZS0GmmxhFnAhcD3pNW2HpG0Y0Q83rihVpRi37d2pDmEq4C/AmOAnqRVxF6QdHBE3N6okVYgDwypAJLWAd4AhkfEXjnlxwJXAgdExJ3liq9SSeoLvB8R3+eVnwucBhwbEW7VqoOkjUhra/+O9B+QqyPi1+WNqjJJepY0sf2mEfFFmcNpFiRtQUpYLo+I3+SUtwXeAbpGxJJlCq9iSFo1Ij7Mfn4TWDwietVQ9x5gL2DjiKjKyhYH3gKmAWtFC/lyL/Z9yxaL+ElEPJ1X3oP0vs0Gera0Bhd3B1eG/UlL3l2eV349MAU4sKkDag4iYlR+Api5O3tetynjaY6yVXuuJ62cc1+Zw6lokrYEfgpcGBFfSGqTt565FbZE9jzfspwRMYO0StTkJo+oAlUnMnXJbnXZDRhZnQBm+08CbgDWBDZpjBgrUbHvW0TMyk8As/JxwNPAMtmjRXESWBk2AeaQWmPmiohppKbrFvMH3UBWyJ7HlTWK5uE3wFqkNbytdjtlzx9LehCYCkyW9J4k/0etZi+Sluj8naRfSFopu5/tfGBjYEg5g2uG1id1bb5QYNt/smd/Z5RmBWAG6XPaojgJrAzLARMiYnqBbZ8B3bKuE6tD1rJ1BuleGXeh10LSKsBZwNkRMbbM4TQHvbPn64GupPXMDyd9edwm6dByBVbJIuJbUsvVN8A9wEekbuBfAXtFxPVlDK85Wi57/qzAtuoy359aJEk7AZsCd2cNLy2KB4ZUho5AoQQQ0v0d1XVmNE04zdrlwObAqRHxbpljqXR/Id0cfWm5A2kmOmfPE4Gts+5MJA0HPgTOkzSspd1TVKRJwJukQVzPk5LoXwF3Sto9Ih4rZ3DNTPUtCIW+M6bl1bFaSFqDNFjkM+CkModTFm4JrAxTSM37hbTPqWO1kHQOqVtzaEScX+54KlnWfbk9cHREzCx3PM1E9Ujzu6oTQJjb0vUPYFnmtRZaRtJ6pMTvsYg4OSKGR8SNpPsrvwSuz1rwrTjV3wWFvjP8fVGkrCfkCSCAHSNifJlDKgsngZXhc1KXb6E/6uVJXcVuBayFpCHA6aQpJ44ubzSVLfucXQqMAL6UtLqk1YGVsypdsrIlyxVjhfo0e/6ywLbqkcJLNVEszclvSMnJ33ILI2IK8BDpc9er6cNqtqoH2BTq8q0uK9RVbBlJvYCnSFM9bRcRb5Q3ovJxElgZXiL9LjbNLZTUHugDjCpDTM2GpDOBM4FbgSNaytQIC6ED0B3YGXg/5zEy235g9vqIcgRXwaoHbq1QYFt12VdNFEtzUp2YFGrta533bHV7g9QVvEWBbZtnz/7OqEE21+dTpPk9t4uIV8scUlk5CawMd5OapE/IKz+SdG/HHU0dUHMh6QzS6MLbgEN9P1ZRJgO/KPA4Jtv+cPb6H2WJrnLdT7of8MBsTjYAJPUEBpDmrPygPKFVtLez50G5hVlL8+7At8D/mjak5iubCuZBoJ+kDarLs8/kEaT/wL1Yw+4tWs5k70sB20fEy+WNqPw8WXSFkHQV6X624aRuuuoVQ54DtnFysyBJvwL+DHwM/B9pmp1c43zDefGyLpIxeLLoGkkaDFxHmlz2JqAt8EvSygO7RMSjZQyvImVfvK+QvnjvIP2b1pX0n9xewK8i4pqyBVghJB3EvFsyjiV9ti7JXn8UEbfl1F2dlOjNJK029QPp/VwP2DkiHmmquMut2PdNUmfgNWAV0qo1hRLlx7J5A1sMJ4EVIrsx+gRgMOkfxgmkFsIzsv/5WR5Jt5Cm6ajJ0xHRr2miaf6cBBZH0p6k1VXWI/3H4wXgrIh4rqyBVbBsmbMzgG2BHqRBNlWkVUQ8STkgaSSwVQ2bF/i3TNLawJ+yfdqSEu0hLWzJuKLft5x/32qzdUSMbKjYmgMngWZmZmYtkO8JNDMzM2uBnASamZmZtUBOAs3MzMxaICeBZmZmZi2Qk0AzMzOzFshJoJmZmVkL5CTQzMzMrAVyEmhmFUfSMZLekTRdUmQTvVqFkdQr+/0MKXcsNZE0xJ8hs8KcBJo1U5L+Lmm2pJ/WsP2n2fa/N3VsC0PS1sDVwDvA0cBBwPiyBmUVTdKASk5EzSqVVwwxa6YkdQfeBCYCG0TE5JxtHUnrZC4BrBMRE8oTZekknQf8AVg6Ir4pdzxWM0kC2gGzImJWGeO4BTgkIlRgW2ugNTA9/IVnNh+3BJo1UxExHjgKWA24MG/zBcDqwFFNlQBKaiOpfQMcalmAYhJASR2yL/kWQ1LncsdQLZJp5UwA6xIRs7IYnQCa5XESaNaMRcT9wG3ALyX1B5DUD/gVcGtE3C9pDUm3SfpC0gxJYyVdJKlT7rEkrSXpGklvSZooaYqklyUdmX/enPus1pF0qaRPgWnA5tn2nSU9LWmCpKmSPpZ0n6Q1a7qW6vvLgEOz15E9Rmavb8led5d0k6RxwGRghZz9b5M0LruX8H+SzstaRQvF/iNJl2fvy2RJT0jqndXZU9IrWexjJQ0u5veh5ARJr2fv4Q+S3pV0o6Q2eXU3lPS3nHg/kXSXpNVy6kR23dtK+rekScCDOdv7Shqevc/Ts3OdVigxLuFzUP0+d5H0F0lfSZom6TlJmxX6neV2xeaWSdpF0kvZ/l9k5ysU216SXsvqfSzpTEn9s+MMquM9HwkckvN+Re5+KnBPYEN+BrI4H5X0XRb/65KOri1ms0rRov4HbbaIOg7YGrhJ0hbATcBnwHGSNgaeBL4DrsvKN8j2+YmkrSJiZnacfsCWwD+BMUAn4BfAUEndIuL8Aue+A5gKXAIE8IWkrYB/AG8A52fnXg7oT2qdfK+G6xhPuv9vMPCz7GeAcXn1HgO+BM7JYpwkaWXgRaAL8JfsHP1I3co/kbRtgdaqYcAk4DygO3AS8Iik/yO1rP4ley8PB66T9HZE/LuG2KudDpxNStSuBWYDqwC7kbpNZwJI2gW4l5TE3gB8QGoB3QFYF/hfzjH7AnsB12cxkx1jJ2B4tu8lwDfAFtn5+5B+d9V1S/kcVHuE9Ds5G1gaOBEYIalXREys430A2Ak4JnsfbgJ2B34LfEt6z6tj2xe4K7vms4BZpKRu1yLOAfBHUoNG7mcG4Pki9l2oz0CWGF4L/CeLYzKwHfAXSatFxMlFXoNZeUSEH3740cwfwPakJGx89rx9Vv4aaYBF57z6e2T1BuWUdSpw3FbASOB7oE1O+ZBs/5FA67x9Ls22LVPPa7kl/dNUuBy4vcC2O7JtO+WVX5SVH14g9gfJ7ovOyo/LyicCK+WUdye1ct5VROyvAG/XUadj9nv6Cli+0Hue83Nkj/55ddqTEuFnCrz/v8n26ZdTVsrnoPp9viav7i+y8qNyynplZUMKlE0GeuWUi3QP6xc5Za1JCek4YKmc8sWBD/NjK/Uzk/f77lWgrN6fAaBnVnZngXNeQfoPwGr1+Rvww4+merg72GwREBGPAkOBbsDQiHhU0nrA+sCdQDtJ3aofwL9JX9Lb5xwjd2BJe0lLA12BR0kDTNYqcOrLY8EWtu+z570Kdf01gItzX0hqRWppezUiRuTVPR+YQ0p28l0ZEbn3iT2bPT8QER9XF0a69/JdYI0iYvseWF41jNjO7ED6PV0SEZ/lb4yIOXlFr0XE43ll2wE9gJuBJfN+t9XvwfYApX4OclyW9/rJ7LmY9wHg/ogYm3NdATwFLCtp8ax4Y1Ir8S0R8W1O3UmkFrbGtjCfgb1Jrbs35r6n2fv6IOk/UNs2bvhmC8dJoNmi44W857Wz57NILU+5j69IXak9qneWtLikiyV9TOrinZDV/WNWZakC5yzUtftn4FXgGuAbSSMkHac0mrkh5J+zO6nl6K38ipEGl3wBrFrgOB/mva5OQsYUqPstqUu0LqeSWoeelfSZpDskDZTUNqdOdSLxahHHg8LvcfXv9iYW/N2+k23rkVe3qM9Bjvnen4j4OvuxmPdhgf0z+cdYJXt+t0DdQmUNbWE+A9Xv6+Ms+L4+lm0r9L6aVQzfE2i26KqeLuMS4OEa6nyb8/OdwC6kFsVnSPeYzSLd2/UbCv+ncUp+QUR8LWkT0j1a25HuM7wMOEvSThHxQv4+pYiI/HMuMC1IkWaXWF7neSLiBaWBHTuQ7tPcGhgInC7pp1lSWn2cYkerLvAe5xzjZKCqhv0+z6tb7OcgBRdR7/chU9P+uceo7++uoSzMZ6D654NJ/9EopFAibFYxnASaLbrez55nF+hOnI+kJUkJ4G0RcXTetv6lnjhLIEZmDyStD7xMGjixc6nHq8NXpHu41snfIGkp0r1bVQ18zhplXZn3Zg8kHUOa/Ppw0j2K1S1cGzKvxahU1b/byXX9binhc1AG1S1uvQtsK1RWk3JM/1L9vk6owPfVrCjuDjZbdL1KuhH/aEkLdIdKai2pa/ayuuVDeXV6AkeUctLsnqh875C6mLsW2LZQsnvoHgQ2lPTzvM2/J/07N7yhz1tIDdf+SvZcfe2PkrraT8re3/xjFNM69ggp+f19zu8w9xgdNG8+wVI+B01tFKkVbVCWsFfHtDhptZhiTcr2a8rruAeYTmrh7pC/UWmKnXZNGI9ZydwSaLaIioiQdBDphv7XJd1Eum+uI2mqlj1JU6jcEhETJT0KHChpKvASsDJpMuoxFH8fGMD1klYgJTsfAR2AfYHOwK0NcnELOpXU9Xy/pGtI06ZsmZ33GXKmVmlkoyX9B/gvqTu2J2nKmxnAXyF1Z0s6HPg78Kak6iliupO6kS8FHqjtJBExWdLBwP3Au9nv9gNgSdIAnj1Jg2FGlvI5aJi3oHgRMUvSb0mju1+UdCPpFoRBpPsHV6G4Vr7/AL8GrpH0EGkqnv9GRKF7+xpERHwq6ZekKX5GS7qN9HnvDqwHDAB+BIxtrBjMFpaTQLNFWERUSdqQ9CW/G6l1ZSLpi+kW4Imc6gcCfyLNz3YIqbvrNNIX6s0lnPY20pf4IaQvxB+At4G9I+Leel9MLSLiI6WJjM8mXceSwKek0cHnFhjB3FguId1DeRxpzsKvSAnK+RHxWk68/8hGEJ9K6ibuTJom5d+k+RXrFBGPZPde/p50zd1J9/b9j5RIvp5Tt5TPQZOKiDslzSLdKnAW6X24kRT/faQW5LrcRepe3480lU0r0qTjjZYEAkTEzZLeI81/eBTpczeB1OX/f6RpfMwqltcONjOziiPpJNJ0QFtExH/KHY/ZoshJoJmZlU02fc7s3NHI2T2Br5Pmp1wuImaUKz6zRZm7g83MrJxWBf4l6a+k7tuepFsJVgF+6QTQrPE4CTQzs3IaT7pv8gBgGdLAkDeA30fEPeUMzGxR5+5gMzMzsxbI8wSamZmZtUBOAs3MzMxaICeBZmZmZi2Qk0AzMzOzFshJoJmZmVkL5CTQzMzMrAX6fxBJ5mTu+DTGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_num = 13\n",
    "age = np.array(data_no_missing_temp['Age'])\n",
    "shap_age = np.array(data_no_missing_temp['BioAge'])\n",
    "\n",
    "permth_int = np.array(data_no_missing_temp['alive_year'])\n",
    "mortstat = np.array(data_no_missing_temp[mortality])\n",
    "age_acc = shap_age - age\n",
    "up_value = np.quantile(age_acc, 0.75)\n",
    "down_value = np.quantile(age_acc, 0.25)\n",
    "unhealthy_index = [i for i in range(len(age)) if (age_acc[i] >= up_value)]\n",
    "healthy_index = [i for i in range(len(age)) if (age_acc[i] <= down_value)]\n",
    "# unhealthy_index = [i for i in range(len(age)) if (shap_age[i] > age[i])]\n",
    "# healthy_index = [i for i in range(len(age)) if (shap_age[i] <= age[i])]\n",
    "unhealthy_surv_prob = [1]\n",
    "for i in range(1, year_num+1):\n",
    "    label_temp = np.array([label_all(permth_int[j], mortstat[j], i) for j in unhealthy_index])\n",
    "    label_temp_pre = np.array([label_all(permth_int[j], mortstat[j], (i-1)) for j in unhealthy_index])\n",
    "    num_samples = sum(label_temp==0)+sum(label_temp==1)-sum(label_temp_pre==1)\n",
    "    unhealthy_surv_prob.append((sum(label_temp==0)/num_samples)*unhealthy_surv_prob[-1])\n",
    "    print((sum(label_temp==0)/num_samples))\n",
    "healthy_surv_prob = [1]\n",
    "for i in range(1, year_num+1):\n",
    "    label_temp = np.array([label_all(permth_int[j], mortstat[j], i) for j in healthy_index])\n",
    "    label_temp_pre = np.array([label_all(permth_int[j], mortstat[j], (i-1)) for j in healthy_index])\n",
    "    num_samples = sum(label_temp==0)+sum(label_temp==1)-sum(label_temp_pre==1)\n",
    "    healthy_surv_prob.append((sum(label_temp==0)/num_samples)*healthy_surv_prob[-1])\n",
    "#             print((sum(label_temp==0)/num_samples))\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.step([i for i in range(year_num+1)], healthy_surv_prob, 'blue',label='Healthy agers', where=\"post\")\n",
    "plt.step([i for i in range(year_num+1)], unhealthy_surv_prob, 'r',label='Unhealthy agers', where=\"post\")\n",
    "plt.xlabel('Years from screening time', fontsize=18)\n",
    "plt.ylabel('Cumulative Survival Probability', fontsize=18)\n",
    "plt.tick_params(axis='both',which='major',labelsize=18)\n",
    "plt.rc('axes', labelsize=18)\n",
    "plt.rc('legend', fontsize=18)\n",
    "plt.legend()\n",
    "plt.title('PhenoAge: survival probability', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat((X[['eid', 'PhenoAge','BioAge']], X_geo[['eid', 'PhenoAge','BioAge']])).to_csv('PhenoAge_BioAge.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## EXPECT Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortality = 'all-cause'\n",
    "path = '/homes/gws/wqiu0528/uk_biobank_44562/result/IMPACT_Age_missforest/XGB_Cox_'+mortality+'_remove20002and20004_small_imputed_AgeAdjusted_CancerAdjusted_test_NewLabel_FloatAge_test_GeoValidation'\n",
    "X.loc[X['31-0.0']==0, 'shap_age'], X.loc[X['31-0.0']==0, 'shap_age_acc'] = calculate_EXPECTAge(X, path, 'female', '31-0.0', 0)\n",
    "X.loc[X['31-0.0']==1, 'shap_age'], X.loc[X['31-0.0']==1, 'shap_age_acc'] = calculate_EXPECTAge(X, path, 'male', '31-0.0', 1)\n",
    "X_geo.loc[X_geo['31-0.0']==0, 'shap_age'], X_geo.loc[X_geo['31-0.0']==0, 'shap_age_acc'] = calculate_EXPECTAge(X_geo, path, 'female', '31-0.0', 0)\n",
    "X_geo.loc[X_geo['31-0.0']==1, 'shap_age'], X_geo.loc[X_geo['31-0.0']==1, 'shap_age_acc'] = calculate_EXPECTAge(X_geo, path, 'male', '31-0.0', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortality = 'all-cause'\n",
    "# feature_set_list = ['PhenoAge', 'BioAge', 'blood_assay', 'Top20', 'Top10', 'CBC', 'CBC_BMP', 'CBC_CMP_LP_WBC']\n",
    "feature_set_list = ['CBC_CMP_LP_WBC', 'Ques_Top10', 'Ques_Top20']\n",
    "for feature_set in feature_set_list:\n",
    "    path = '/homes/gws/wqiu0528/uk_biobank_44562/result/IMPACT_Age_missforest/XGB_Cox_'+mortality+'_remove20002and20004_small_imputed_AgeAdjusted_CancerAdjusted_test_NewLabel_FloatAge_test_GeoValidation_'+feature_set\n",
    "    X.loc[X['31-0.0']==0, 'shap_age_'+feature_set], X.loc[X['31-0.0']==0, 'shap_age_'+feature_set+'_acc'] = calculate_EXPECTAge(X, path, 'female', '31-0.0', 0)\n",
    "    X.loc[X['31-0.0']==1, 'shap_age_'+feature_set], X.loc[X['31-0.0']==1, 'shap_age_'+feature_set+'_acc'] = calculate_EXPECTAge(X, path, 'male', '31-0.0', 1)\n",
    "    X_geo.loc[X_geo['31-0.0']==0, 'shap_age_'+feature_set], X_geo.loc[X_geo['31-0.0']==0, 'shap_age_'+feature_set+'_acc'] = calculate_EXPECTAge(X_geo, path, 'female', '31-0.0', 0)\n",
    "    X_geo.loc[X_geo['31-0.0']==1, 'shap_age_'+feature_set], X_geo.loc[X_geo['31-0.0']==1, 'shap_age_'+feature_set+'_acc'] = calculate_EXPECTAge(X_geo, path, 'male', '31-0.0', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# mortality prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([465051,  91870, 101126, 388592, 231435, 346299, 448437,  87427,\n",
      "            112084,  93829,\n",
      "            ...\n",
      "             38086,  36290, 103784,  14443, 233047, 425720, 268976,  21040,\n",
      "            299829,  40716],\n",
      "           dtype='int64', length=372504)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, _, _ = train_test_split(X, X, test_size=0.2, random_state=random_state)\n",
    "# X_train, X_val, _, _ = train_test_split(X_train, X_train, test_size=0.1, random_state=random_state)\n",
    "print(X_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 5\n",
    "train_label_5 = np.array([label_all(np.array(X_train['alive_year'])[j], np.array(X_train[mortality])[j], year) for j in range(len(X_train))])\n",
    "# val_label_5 = np.array([label_all(np.array(X_val['alive_year'])[j], np.array(X_val[mortality])[j], year) for j in range(len(X_val))])\n",
    "test_label_5 = np.array([label_all(np.array(X_test['alive_year'])[j], np.array(X_test[mortality])[j], year) for j in range(len(X_test))])\n",
    "geo_label_5 = np.array([label_all(np.array(X_geo['alive_year'])[j], np.array(X_geo[mortality])[j], year) for j in range(len(X_geo))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 10\n",
    "train_label_10 = np.array([label_all(np.array(X_train['alive_year'])[j], np.array(X_train[mortality])[j], year) for j in range(len(X_train))])\n",
    "# val_label_10 = np.array([label_all(np.array(X_val['alive_year'])[j], np.array(X_val[mortality])[j], year) for j in range(len(X_val))])\n",
    "test_label_10 = np.array([label_all(np.array(X_test['alive_year'])[j], np.array(X_test[mortality])[j], year) for j in range(len(X_test))])\n",
    "geo_label_10 = np.array([label_all(np.array(X_geo['alive_year'])[j], np.array(X_geo[mortality])[j], year) for j in range(len(X_geo))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "bio_age_list = ['Age', 'PhenoAge_acc', 'BioAge_acc', 'shap_age_acc'] + ['shap_age_'+feature_set+'_acc' for feature_set in feature_set_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age\n",
      "93127\n",
      "35735\n",
      "PhenoAge_acc\n",
      "86683\n",
      "34069\n",
      "BioAge_acc\n",
      "86683\n",
      "34069\n",
      "shap_age_acc\n",
      "86683\n",
      "34069\n",
      "shap_age_CBC_CMP_LP_WBC_acc\n",
      "86683\n",
      "34069\n",
      "shap_age_Ques_Top10_acc\n",
      "86683\n",
      "34069\n",
      "shap_age_Ques_Top20_acc\n",
      "86683\n",
      "34069\n"
     ]
    }
   ],
   "source": [
    "for fea in bio_age_list:\n",
    "    print(fea)\n",
    "    test_label_5 = test_label_5[(X_test[fea]!=np.inf) & (X_test[fea]!=-np.inf) & (X_test[fea].isnull()==False)]\n",
    "    test_label_10 = test_label_10[(X_test[fea]!=np.inf) & (X_test[fea]!=-np.inf) & (X_test[fea].isnull()==False)]\n",
    "    X_test = X_test[(X_test[fea]!=np.inf) & (X_test[fea]!=-np.inf) & (X_test[fea].isnull()==False)]\n",
    "    print(X_test.shape[0])\n",
    "    geo_label_5 = geo_label_5[(X_geo[fea]!=np.inf) & (X_geo[fea]!=-np.inf) & (X_geo[fea].isnull()==False)]\n",
    "    geo_label_10 = geo_label_10[(X_geo[fea]!=np.inf) & (X_geo[fea]!=-np.inf) & (X_geo[fea].isnull()==False)]\n",
    "    X_geo = X_geo[(X_geo[fea]!=np.inf) & (X_geo[fea]!=-np.inf) & (X_geo[fea].isnull()==False)]\n",
    "    print(X_geo.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Age\n",
      "5-year\n",
      "xgboost\n",
      "AUC on test set:  0.6963089276472083\n",
      "Test set bootstrap:\n",
      "average AUC 0.6964767254330831\n",
      "95.0 % confidence interval for the AUC: (0.684, 0.7094)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.709250748747748\n",
      "95.0 % confidence interval for the AUC: (0.6909, 0.7285)\n",
      "10-year\n",
      "xgboost\n",
      "AUC on test set:  0.7162348412562858\n",
      "Test set bootstrap:\n",
      "average AUC 0.7163175219566272\n",
      "95.0 % confidence interval for the AUC: (0.7091, 0.7239)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.7210627276067387\n",
      "95.0 % confidence interval for the AUC: (0.7095, 0.7317)\n",
      "\n",
      "\n",
      "PhenoAge_acc\n",
      "5-year\n",
      "xgboost\n",
      "AUC on test set:  0.7492107898088489\n",
      "Test set bootstrap:\n",
      "average AUC 0.7493053072910673\n",
      "95.0 % confidence interval for the AUC: (0.7366, 0.7619)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.7645691944932039\n",
      "95.0 % confidence interval for the AUC: (0.7464, 0.7821)\n",
      "10-year\n",
      "xgboost\n",
      "AUC on test set:  0.7546190049830415\n",
      "Test set bootstrap:\n",
      "average AUC 0.7546904783618352\n",
      "95.0 % confidence interval for the AUC: (0.7468, 0.7618)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.7681659141494874\n",
      "95.0 % confidence interval for the AUC: (0.7573, 0.7786)\n",
      "\n",
      "\n",
      "BioAge_acc\n",
      "5-year\n",
      "xgboost\n",
      "AUC on test set:  0.7030273967719581\n",
      "Test set bootstrap:\n",
      "average AUC 0.7032266612194895\n",
      "95.0 % confidence interval for the AUC: (0.69, 0.7165)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.7139713465638392\n",
      "95.0 % confidence interval for the AUC: (0.6951, 0.7334)\n",
      "10-year\n",
      "xgboost\n",
      "AUC on test set:  0.7195100202242894\n",
      "Test set bootstrap:\n",
      "average AUC 0.7196233577267673\n",
      "95.0 % confidence interval for the AUC: (0.7122, 0.727)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.7253901410379576\n",
      "95.0 % confidence interval for the AUC: (0.7138, 0.7364)\n",
      "\n",
      "\n",
      "shap_age_acc\n",
      "5-year\n",
      "xgboost\n",
      "AUC on test set:  0.816021917856095\n",
      "Test set bootstrap:\n",
      "average AUC 0.8164165872163287\n",
      "95.0 % confidence interval for the AUC: (0.8051, 0.8271)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.8318940153916179\n",
      "95.0 % confidence interval for the AUC: (0.816, 0.8485)\n",
      "10-year\n",
      "xgboost\n",
      "AUC on test set:  0.8086852687770127\n",
      "Test set bootstrap:\n",
      "average AUC 0.8088529855207823\n",
      "95.0 % confidence interval for the AUC: (0.8018, 0.8158)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.8173698108361429\n",
      "95.0 % confidence interval for the AUC: (0.807, 0.8275)\n",
      "\n",
      "\n",
      "shap_age_CBC_CMP_LP_WBC_acc\n",
      "5-year\n",
      "xgboost\n",
      "AUC on test set:  0.7681386743873382\n",
      "Test set bootstrap:\n",
      "average AUC 0.7684271950386898\n",
      "95.0 % confidence interval for the AUC: (0.7556, 0.7804)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.7898572031012755\n",
      "95.0 % confidence interval for the AUC: (0.7719, 0.8073)\n",
      "10-year\n",
      "xgboost\n",
      "AUC on test set:  0.7735566177000135\n",
      "Test set bootstrap:\n",
      "average AUC 0.7737251097216055\n",
      "95.0 % confidence interval for the AUC: (0.7657, 0.7806)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.7859529434115006\n",
      "95.0 % confidence interval for the AUC: (0.7753, 0.7961)\n",
      "\n",
      "\n",
      "shap_age_Ques_Top10_acc\n",
      "5-year\n",
      "xgboost\n",
      "AUC on test set:  0.7857319122837925\n",
      "Test set bootstrap:\n",
      "average AUC 0.7860888018824642\n",
      "95.0 % confidence interval for the AUC: (0.7742, 0.7982)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.8119317879931025\n",
      "95.0 % confidence interval for the AUC: (0.7944, 0.8282)\n",
      "10-year\n",
      "xgboost\n",
      "AUC on test set:  0.7853824080349381\n",
      "Test set bootstrap:\n",
      "average AUC 0.7855411666145444\n",
      "95.0 % confidence interval for the AUC: (0.7788, 0.7927)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.795496001990997\n",
      "95.0 % confidence interval for the AUC: (0.7846, 0.8056)\n",
      "\n",
      "\n",
      "shap_age_Ques_Top20_acc\n",
      "5-year\n",
      "xgboost\n",
      "AUC on test set:  0.7962954213001885\n",
      "Test set bootstrap:\n",
      "average AUC 0.7967537300531338\n",
      "95.0 % confidence interval for the AUC: (0.785, 0.8086)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.8213428142689643\n",
      "95.0 % confidence interval for the AUC: (0.804, 0.8384)\n",
      "10-year\n",
      "xgboost\n",
      "AUC on test set:  0.7915914804493658\n",
      "Test set bootstrap:\n",
      "average AUC 0.7917409040796194\n",
      "95.0 % confidence interval for the AUC: (0.7853, 0.7986)\n",
      "GeoValidation set bootstrap:\n",
      "average AUC 0.8066067728543826\n",
      "95.0 % confidence interval for the AUC: (0.7964, 0.8164)\n"
     ]
    }
   ],
   "source": [
    "for bio_age in bio_age_list:\n",
    "    print('\\n')\n",
    "    print(bio_age)\n",
    "    result[bio_age] = {}\n",
    "    result[bio_age]['5_year_test'], result[bio_age]['5_year_geo'], result[bio_age]['10_year_test'], result[bio_age]['10_year_geo'] = mortality_prediction(\n",
    "        bio_age, X_train, train_label_5, train_label_10, X_test, test_label_5, test_label_10, X_geo, geo_label_5, geo_label_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_old = pickle.load(open('SHAPAge_PhenoAge_BioAge.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Age', 'PhenoAge_acc', 'BioAge_acc', 'shap_age_acc', 'shap_age_CBC_CMP_LP_WBC_acc', 'shap_age_Ques_Top10_acc', 'shap_age_Ques_Top20_acc'])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_old['shap_age_CBC_CMP_LP_WBC_acc'] = result['shap_age_CBC_CMP_LP_WBC_acc']\n",
    "result_old['shap_age_Ques_Top10_acc'] = result['shap_age_Ques_Top10_acc']\n",
    "result_old['shap_age_Ques_Top20_acc'] = result['shap_age_Ques_Top20_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(result, open('SHAPAge_PhenoAge_BioAge_WithValSet.pkl', 'wb'))\n",
    "pickle.dump(result_old, open('SHAPAge_PhenoAge_BioAge.pkl', 'wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Age', 'PhenoAge_acc', 'BioAge_acc', 'shap_age_acc', 'shap_age_CBC_CMP_LP_WBC_acc', 'shap_age_Ques_Top10_acc', 'shap_age_Ques_Top20_acc'])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['5_year_test', '5_year_geo', '10_year_test', '10_year_geo'])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['Age'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparison:\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'shap_age_Top10_acc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_229770/1458343434.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Comparison:'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mrepetitions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m print('p-value of AUC', sum(np.array(result['shap_age_Top10_acc']['5_year_test']['res'])\n\u001b[0m\u001b[1;32m      4\u001b[0m                             <result['PhenoAge_acc']['5_year_test']['res'])/repetitions)\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# print('p-value of AP', sum(np.array(LR_ap)<np.array(xgb_ap))/repetitions)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'shap_age_Top10_acc'"
     ]
    }
   ],
   "source": [
    "print('Comparison:')\n",
    "repetitions=1000\n",
    "print('p-value of AUC', sum(np.array(result['shap_age_Top10_acc']['5_year_test']['res'])\n",
    "                            <result['PhenoAge_acc']['5_year_test']['res'])/repetitions)\n",
    "# print('p-value of AP', sum(np.array(LR_ap)<np.array(xgb_ap))/repetitions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.mean(result['shap_age_CBC_BMP_acc']['5_year_test']['res']))\n",
    "print(np.mean(result['PhenoAge_acc']['5_year_test']['res']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(result['shap_age_CBC_CMP_LP_WBC_acc']['10_year_test']['res'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pickle.load(open('SHAPAge_PhenoAge_BioAge.pkl', 'rb'))\n",
    "year = 5\n",
    "bars = ('Chronological\\nAge', 'BioAgeAccel', 'ENABL AgeAccel\\n(BioAge)', 'PhenoAgeAccel', 'ENABL AgeAccel\\n(PheonAge)', \n",
    "        'ENABL Age-L\\nAccel', 'ENABL Age-Q\\nAccel', 'ENABL Age-20\\nAccel', 'ENABL AgeAccel')\n",
    "y_pos = np.arange(len(bars))\n",
    "y_pos_group = np.array([0, 1, 1, 2, 2, 3, 3, 3, 3])\n",
    "y_pos = y_pos+y_pos_group*0.4\n",
    "# y_pos = 0.4 * np.array([i for i in range(len(bars))])\n",
    "color = ['#bdbdbd', '#80b1d3', '#bc80bd', '#bebada', '#bc80bd', '#bc80bd', '#bc80bd', '#bc80bd', '#bc80bd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Age', 'PhenoAge_acc', 'BioAge_acc', 'shap_age_acc', 'shap_age_PhenoAge_acc', 'shap_age_BioAge_acc', 'shap_age_blood_assay_acc', 'shap_age_Top20_acc', 'shap_age_Top10_acc', 'shap_age_CBC_acc', 'shap_age_CBC_BMP_acc', 'shap_age_CBC_CMP_LP_WBC_acc', 'shap_age_Ques_Top10_acc', 'shap_age_Ques_Top20_acc'])"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_list = ['Age', 'BioAge_acc', 'shap_age_BioAge_acc', 'PhenoAge_acc', 'shap_age_PhenoAge_acc', \\\n",
    "            'shap_age_CBC_CMP_LP_WBC_acc', 'shap_age_Ques_Top20_acc', 'shap_age_Top20_acc', 'shap_age_acc']\n",
    "bincenters = []\n",
    "left_error = []\n",
    "right_error = []\n",
    "for age in age_list:\n",
    "    bincenters.append(np.mean(result[age][str(year)+'_year_test']['res']))\n",
    "    left_error.append(np.mean(result[age][str(year)+'_year_test']['res'])-result[age][str(year)+'_year_test']['left_res'])\n",
    "    right_error.append(result[age][str(year)+'_year_test']['right_res']-np.mean(result[age][str(year)+'_year_test']['res']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "print(len(age_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as plt\n",
    "\n",
    "fontsize = 18\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.bar(y_pos, bincenters, width=0.8, color=color, yerr=[left_error,right_error], error_kw=dict(lw=1, capsize=2, capthick=1))\n",
    "plt.xticks(y_pos, bars, rotation=90, fontsize=fontsize+2)\n",
    "plt.ylabel('AUROC', fontsize=fontsize+2)\n",
    "# plt.title(str(year)+'-year mortality prediction (Test set)', fontsize=fontsize)\n",
    "plt.yticks(fontsize=fontsize)\n",
    "plt.ylim([0.5, 1])\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.savefig('./result/paper_figures/UKB_'+str(year)+'_mortality.pdf', format='pdf', bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_list = ['Age', 'BioAge_acc', 'shap_age_BioAge_acc', 'PhenoAge_acc', 'shap_age_PhenoAge_acc', \\\n",
    "            'shap_age_CBC_CMP_LP_WBC_acc', 'shap_age_Ques_Top20_acc', 'shap_age_Top20_acc', 'shap_age_acc']\n",
    "bincenters = []\n",
    "left_error = []\n",
    "right_error = []\n",
    "for age in age_list:\n",
    "    bincenters.append(np.mean(result[age][str(year)+'_year_geo']['res']))\n",
    "    left_error.append(np.mean(result[age][str(year)+'_year_geo']['res'])-result[age][str(year)+'_year_geo']['left_res'])\n",
    "    right_error.append(result[age][str(year)+'_year_geo']['right_res']-np.mean(result[age][str(year)+'_year_geo']['res']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "print(len(age_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1H3bbdZ+yiyEifahdzcH8RJNCvcprd2dbHBERESlbJ0MZvdlr7j4j2+KIiIhI2TrJkLi8mW1Y/xqAmW1AdFKs4e5Xj71oIiIiUoZOgoOJ6VHPiGGO9bzD9YqIiEgfavcjfjWNmxVERERkSLUMDtx944LKISIiIn0i84mXREREZLApOBAREZEa7eZWOKjN52cCzwJ/IyZkUv+EMRoZGeHggw9+4/nkyZMZGRkpr0AiIjLutOuQOEJ0SJxluGJSHQzcb2Y7u/vULAo2Xo2MjDAyMoKZoVhLRETK0C44OLjN+7MBiwMfIOZcuMzM1nL3RzMom4iIiJSg3WiFdsHBG8xsN+BkYibHfcdUKhERESlNZh0S3f1U4Crgw1mtU4ZTpdmk8lCfChGR/pL1aIUbgLdlvE4ZMiMjI2/0p3B3BQciIn0m6+BgRg7rFBERkQJl/UO+BvB4xusUERGRAmUWHJjZpsBHaDwZk4iIiAyIdkmQdm3z+dmAxYihjJ8EXgOOyqZoIiIiUoZ2eQ5OpbNZGQ14DtjN3e/qpgBmNhvwVeALwPLAk8CvgIPc/aU2n90YuLLNJj7o7td1UyYREZHxrF1wcDqtg4OZRFDwN+Aid3++hzIcA+wDXETUOqyWnr/HzDZ395ktPns38NkGr88NnAg8RYygKNwVV1xR+jo23XTTMZdBRETGn3ZJkHbrZmWpFuAT7n5xh8u/C9gbuNDdt6l6/UHgOGBH4KwW5fsPcEaD9e5ENHmc7u6vd/M3iIiIjHeZdEg0s+XM7LvAI8CFXXx0J6JJ4ti6108CXgZ26bFIe6R/f97j50VERMatds0KTZnZ7MBWwCRgcyLQcOBPXaxmHaJpoqbq392nmdmt6f1uy7UCsAkxS+S93X5eRERkvOs6ODCztxN35rsBb0kvPwX8DPiFuz/cxeqWAp5y91cbvPcvYD0zm8vdX+tinZ8naiNUa5Czg39zR+nrmPzJ1cdcBhERqdVRcGBmcwCfJmoJNiFqCV4jmhC2AS5294N62P58QKPAAGBa1TIdBQepNmM34HngvA6Wn0T8TSy77LKdbEJERGTotexzYGYrmdkRxF38OcBmwK3EaIKl3H27MW7/ZWJkQSPzVC3TqQ8DywBnu3vbz7n7ie4+wd0nLLHEEl1sRkREZHi1qzm4l+hH8AQx5PAUd78zw+0/BrzTzOZu0LSwNNHk0E2Twu7pXzUpiIiI9KiT0QoOXAKcn3FgAHBjKsO61S+a2TzAWsDUTldkZm8GPgHc5u4df05ERERqtQsOvgM8DHwOuM7M7jKzb5jZkhlt/1wi+Ni37vU9ib4GZ1ZeMLMlzWxVM5uvybp2BeZEtQYiIiJj0jI4cPdD3f0dwEeJDIbvAA4HHjGz35vZ9mPZuLvfDhwPbG1mF5rZHmZ2FHA0cBW1CZAOIzIirjvrmoAYpTCNBkmRBslpp53GZpttBsBmm23GaaedVnKJRERkvOlotIK7XwZclqruP08MZfwoMQujA2uZ2Xvd/aYeyrAv8BAxamBLYljkj4m5FVqlTn6Dma1HpF0+y92f6aEMfWPixIlMnDix7GKIiMg41lWGRHd/wt0Pd/cVgS2A84HXgQnADWZ2i5l9pct1znD3o9x9FXef292Xdvf/cfcX65bbzd3N3ac0WMf16b2du9m2iIiIzKrn9Mnu/md334EYOvgN4O/AmsScCCIiIjKgxjy3grs/5e5HuvtqwKbA2WMvlgyzK8/+KSNbvRuAka3ezZVn/7TkEomISLWe51ZoJFX5T8lynTJ8Ntnpy2yy05fLLoaIiDSRyayMIiIiMjwUHIiIiEgNBQciIiJSQ8GBiIiI1FBwICIiIjUUHIiIiEgNBQciIiJSQ8GBiIiI1FBwICIiIjUUHIiIiEgNBQciIiJSQ8GBiIiI1FBwICIiIjUUHIiIiEgNBQciIiJSQ8GBiIiI1FBwICIiIjUUHIiIiEgNBQciIiJSQ8GBiIiI1FBwICIiIjUUHIiIiEgNBQciIiJSQ8GBiIiI1FBwICIiIjUUHIiIiEgNBQciIiJSQ8GBiIiI1FBwICIiIjUUHIiIiEgNBQciIiJSQ8GBiIiI1FBwICIiIjUUHIiIiEgNBQciIiJSQ8GBiIiI1FBwICIiIjUUHIiIiEiN0oMDM5vNzPYzs3vMbJqZPWpmR5nZ/F2sYw4z28fMbjazl8zsufT/L+RZdhERkWE0R9kFAI4B9gEuAo4CVkvP32Nmm7v7zFYfNrO5gN8AmwBnAicQf9dKwHI5lltERGQolRocmNm7gL2BC919m6rXHwSOA3YEzmqzmu8AmwNbuPuVeZVVRERkvCi7WWEnwIBj614/CXgZ2KXVh1PTw1eBi939SgsL5lFQERGR8aLs4GAdYCZwQ/WL7j4NuDW938oGwILATWb2I+B54Hkze9LMvm9m/dBsIiIiMlDK/vFcCnjK3V9t8N6/gPXMbC53f63J51dJ/+4LvAZ8A/gvsDPwTWBpYGKzjZvZJGASwLLLLttL+UVERIZO2TUH8wGNAgOAaVXLNFNpQngTsLm7/6+7/8rdtwKmALua2TubfdjdT3T3Ce4+YYklluiy6CIiIsOp7ODgZWDuJu/NU7VMM6+kf//q7vfUvXd6+nejHssmIiIyLpUdHDwGLG5mjQKEpYkmh2ZNCgD/TP/+u8F7j6d/Fx1D+URERMadsoODG1MZ1q1+0czmAdYCprb5fKUj4zIN3qu89sQYyiciIjLulB0cnAs40aGw2p5EX4MzKy+Y2ZJmtqqZvdEHwd0fBK4D1jWztauWnT2tYzpweW6lFxERGUKlBgfufjtwPLC1mV1oZnuY2VHA0cBV1CZAOgy4m7paBiKJ0svAn8xsxMz2Tp9dF/i+uz+S998hIiIyTMoeyghRa/AQMaRwS+Ap4MfAQe1SJwO4+y1mth7wvbSueYgg4nPufmouJRYRERlipQcH7j6DmFPhqDbL7Qbs1uS924BPZl02ERGR8ajsPgciIiLSZxQciIiISA0FByIiIlJDwYGIiIjUUHAgIiIiNRQciIiISA0FByIiIlJDwYGIiIjUUHAgIiIiNRQciIiISA0FByIiIlJDwYGIiIjUUHAgIiIiNRQciIiISA0FByIiIlJDwYGIiIjUUHAgIiIiNRQciIiISA0FByIiIlJDwYGIiIjUUHAgIiIiNRQciIiISA0FByIiIlJDwYGIiIjUUHAgIiIiNRQciIiISA0FByIiIlJDwYGIiIjUUHAgIiIiNRQciIiISA0FByIiIlJDwYGIiIjUUHAgIiIiNRQciIiISA0FByIiIlJDwYGIiIjUUHAgIiIiNRQciIiISA0FByIiIlJDwYGIiIjUKD04MLPZzGw/M7vHzKaZ2aNmdpSZzd/h56eYmTd5TMi7/CIiIsNmjrILABwD7ANcBBwFrJaev8fMNnf3mR2s4ylgvwavP5BZKUVERMaJUoMDM3sXsDdwobtvU/X6g8BxwI7AWR2s6iV3PyOfUoqIiIwvZTcr7AQYcGzd6ycBLwO7dLqi1DyxkJlZdsUTEREZf8oODtYBZgI3VL/o7tOAW9P7nVgaeBF4DnjRzC40s1UzLKeIiMi4Ye5e3sbNbgfe7O5vafDer4DtgLnd/bUW6zgFeAy4DZgBvA/YC3gN+KC7397is5OASenpKsC9Pf4peVic6EsxzAbhbxyEMo5Vv/+N/V6+LAzC39jvZez38kH/lfEpd/9IozfKDg7+Aczp7ss2eO904LPAou7+bJfr3QCYAlzh7ltkUNTCmdlUdx/q0RaD8DcOQhnHqt//xn4vXxYG4W/s9zL2e/lgMMpYUXazwsvA3E3em6dqma64+zXA1cAmZjZvj2UTEREZl8oODh4DFjezRgHC0kSVR9MmhTYeAmYHFu3x8yIiIuNS2cHBjakM61a/aGbzAGsBU8ew7pWA6cDTY1hHmU4suwAFGIS/cRDKOFb9/jf2e/myMAh/Y7+Xsd/LB4NRRqD8PgfvBv4GXFSX52BvIs/BZyv5C8xsSWBh4BF3fzm9tjDworvPqFvvlsDvgEvd/WOF/DEiIiJDotTgAMDMfkyMLrgIuITRDInXAZtWMiSa2anARGATd5+SXvsUcDTwWyIb4nSiFmIXosZgfXf/e3F/jYiIyODrh/TJ+xL9AyYBWxLDPH4MHNRB6uR7gZuAjwNvAeYE/gmcAHzf3f+VT5FFRESGV+k1ByIiItJfyu6QKCIy9MxsJzM7uuxyiHRKNQcyFMzsoB4/6u7+3UwLM47pODSWMrnu6u6zl7DtNwO7ARsCyxA5ZJ4kmmTPcvcbmn+6GGa2EbApsDKwCOBEOvy/E8nsriqvdK0N6zmv4ECGgpl1MrV3I17GBXtY6Tg0VlZwYGb7At8nks3VT0pXufifAexZnVPGzJYuos+Wma0DnAy8s0H5Khy4E/i8u49leHsuhvWc74cOiTKkCo6oN+lxW0PNzGZJTd4pd3+kh4/pOPSJ9P2bTIziOh/4I/Ao8SO8DLAF8ClidNfbiDt3zGyEmKcm17vaNDnelcB8wDXApcB9RI2BAQsR+Wo+BnwQuMLM1nX3e/IsVw+G8pxXzUFGzOyBHj/q7v6OTAvTJ4Y1oh4k6Rj08iV3d9fNQ0aKrjkws7WIZoOHgK3c/Y4my70L+A2wPLA3sBzw/4DJ7v69nMt4LvBpYBt3/22bZbcCzgMudPcd8yyXBH35szMbvV2Em1WlDYOhjKgHzOn0dl7KYNsbeB34uLvf3Wwhd7/TzD4O3AocS6Scv5JIQpe3jYFftQsMANz9YjM7H9gs91IJoOAgM+6+fNll6Df90InIzNYAPkMk15rf3TdPry9PJMz6o7s/U14J8+Xuu5VdBtBxKMFGwOWtAoMKd7/bzC4j8sWcAnzB3afnXUCi2eDRLpZ/OH1mIAz6Oa/gQIaWmR0CfIvRIbvVd9CzAWcTSbh+XGzJxpdhPA499KdZK49ytLAUUQ3fqTuBLd1995zK08jDpH4O7ZiZEbUGvfSDKdxQnPPurocehT6ANYDDgYuBP1W9vjywPbBoBtvYEZhJdHJaAzgUmFG3zP8R0Xvp+6Sk47Aq0eb72Ry3MZTHIf1NM9K/7R6V5WYUWL7ngKO6WP4o4LmC9+HBab+cAyzXYrnlgHPTfpxc9rHv4O8ainNeNQc5S9NRr0NMQd1oamrc/fRCC1WiAiPqfYD7ic5Yr5nZpxssczfR7jmupM5qPwfeU/XyL9N7GxEXtR28g7bgDgzrcTi47AK08SCwfhfLr090XizSYURtwPbAdmb2dyKvwXPp/YWJvAcrE32zriduKvrdUJzzCg5yZGafB44AFm22CPHjOC6CAzPbETgQuAzYH9gBOKDyvrs/YGZTgU8y9uDg3cCpXjV2u4HHiDk5xg0zWxmYQnQ8+xFx4f1o1SJXE5OWbUtMaDZWQ3kc3L3fg4PLga+Z2afd/aJWC6YJ7NYFflhEwSrcfZqZbQp8DfgisEp61HuEmC/n6DbnUb8YinNe6ZNzYmYfIe7OHieGBhlRjf5tYryxEW2Cny+rjCWojqhvAxp9ee4mxjaPlRFVe628BZiWwbYGyWRgLmBdd/8f4MbqNz3qPP9C1HZlQcehHMcCrwBnmNkeZjbLtd7C7kQSpJeIYLFQ7v6aux/m7ssR3/uPAzunx8eBldx9eXc/fEACAxiSc141B/n5GvBfYD13f8HMjgJudffDgcPTl/IE+rlDSvaKjKjvA9Zr9qaZzU4kVrkzg20Nks2IseKterE/QiTIycK4Ow6paWYjdz+krDK4+2NmthvRTPczYLKZTSFGBziwLFGtvRTxQ7ajuz9WSmETd/8H8I8yy5CRoTjnVXOQn7WB37r7C1WvvbG/3f0XwHVETcJ4UWRE/StgbTP7WpP3vwmsCJyVwbYGySLEtOatzEbULmRhPB6HjYkamlK5+/nAh4nauqWJu/H9iaa8ndNr9wEfcvcLyirnEBqKc141B/mZn2hSqJjGrGN0pzK+mhWKjKiPBbYDjjCz7UkdH83sSGADYALwV+DEDLY1SJ4gLkytvIvuxp+3ciw6DqVx9ytSmuKNiO/WkkSQ/jhwLTAlNSWVyswWJXr0P99imWWB5d396uJK1pNjGYJzXjUH+fk3sETV88eZtbPNwkTHsPGisIja3V8hMjT+kqjFWZe4KP4P8F6infUjXkyyl35yBfAJM2vU8asyEc5mRKfRMdNxKJ+HKe7+PXf/irt/2d2/6+5Xlh0YmNn7zOxvwFPAM2Z2rZm9r8ninyOyN/a1YTnnNbdCTszs98AC7r5Ren4qMf51C3e/xsxWJ5oV7nT3pnfTw8TM5iX+5jWJWhMnOr4dQ21EvVGWXxwze1PazmLEMKkb3P3JrNY/SFJQcDPwIjBCJOfZgxiPvSFRHT4PsIb3NvFSq22Pi+NgZpOBg1zzg7RkZu8g0jbPT3SenA4sSKR93sfdf1a3/MDt10E+5xUc5MTM9iKql5ZNnYPeSfQMn4cYKvYmIpr8uLtfUlpBC2ZmCxO9onemttZkJnAmsFddPw3JWBpJczajzVyVIbUGPAts6+5XlFO6wWdmE4Hd3L0v5xbphw6TqRwnAbsTeU+OIM7B7YlO2osRAcLxVcsPXHAwyBQc5MTM5iQCgGcqvfPN7P3EOP93EAlHjnX3TKpvB03eEbWZLUHkNL+lUbBhZgsRd813uftTWW13UJjZIsBE4P2MHoO/Aqe4+9MZbkfHoc/0y49smsn2X+6+Qd3rKxCJuFYCvlypQeiXcrczLOe8OiTmxN1fB/5T99pfibG74176AcozMDqQaKNcssn7M4gkPz8nhp2OK+7+LFGDk/fY9nF3HFLnugXcPatOncNqKeD8+hfd/UEz24DoX3C8mU1Po7sGxVCc8+qQKIUxsyXMbEMzW7DJ+wul9xfPYHNbELPSvdTozfT65cRQL8nPuDgOZraAmR1lZv8mOtc9WPXe+8zsEjNbu7wS9qXnaXKDmmoRNyHSKf/MzHYtsmBjNBTnvIKDnJjZZmZ2spkt1eT9pdL7GxdbslIdCPyO5rkOKhH1NzPY1tton1DlgbTcuGFmXzSzf7Q4L5dO72c1O9/QH4fUj+YvwH5EEq+7if4bFbcTHW53Kr50fe0hovd+QylA2Iw4P34BbFlMscZsKM55BQf52ZvIjtgw61h6/QNpufGiyIjaaZ/IZy7G11BSiPnlH29xXv6LSJK0S0bbGw/H4dtEbojd3H1t6qZKdveXgauIH7p+8BAxh0bZpgDrmVnTjKju/jgxrfMjxGimQTAU57yCg/ysTcwi1sq1DM4Jn4UiI+p7aRFkpPnhK9njxpNVgL+1WeY2YjrnLIyH47A1cJm3nl31YSIjYenc/bQ+GUnxa6IJpmWTgbv/k2hieLiAMmVhKM55BQf5eTNRxdjKf9Jy40WREfX5wKpm9pOUX+EN6flPiB/KczPY1iBZmBiu2MrzNJ9JtFvj4TgsQwRUrbxI7PvSmdmiZlZ6lba7X+/uS7p729kg3f0Rd1+h30cqJENxzmu0Qn6eo/0d8NuI2dDGiyIj6uOINt4vAZ8ys6uBfxF3bxsSPaX/RuSiGE8eJxIetbIGkNWw0vFwHF6gfZC/AnGXXAozWwA4mMgvsgQRqM+R3nsfkfzqQHe/uawyDpHhOOfdXY8cHkTHuueAtzZ5f6n0/iVll7XAfbI/0RnxJ8C8de/NCxxPdEr8ZkbbW4RIxTw9bbfymE6kMF2k7H1SwjE4mchA98Em72+Q9s+pGW5zqI8DcAkRTC2Ynk8m5gmovL8kUXNwTknlW5joFDmTyI55R1355iMCnB+WvS+ryrQRkdOg9LL0WP6BP+eVBCknZvYh4A9EG/vXiDbJV81sbuAjwFHE3cSW7v6H8kpanFSl9hdi6ubHiU5RjSLq9Tzyk2e13SWIhEuLEFXqN3gfJx/JU1X65NmBnxLnaOUYfJS425kBTPDW0zr3su3Mj0O6I94wPZYFFidS8T5BpOa90t3vGss2OijDh4mkPdcBk4gsfwe5++xmthpwEpFsakN3b9cPKY/yHQH8P6LD5OmNkgmZ2e+ApTw6VJZuUBIetTPQ156yo5NhfhDVeDOJi+104u5ieno+Exgpu4wl7JNFGPCIetAfxJCw56rOzRlV5+SzwMfKLmMHf8P7gdOBl6vK3ugxA7iLGBW0YI7lOahqe9PSv09Vle3rJe6r+4FLq57X1Gyk144H/lP2cW1VRj2KfajPQY7cfbKZXUdcmN5H/DA+TaSp/bG7/7HE4pXCIzPfZ8zsq+QYUQ9LCtM8uPvvzeztwG6MnpfPEuflae7+36y2lfVxMLOVgSOJAGcmcA1xx34jMRPq00QT1WLEiIsPEEPhfgR8J92R/szdm+Xa6Im7H2Jm1wD7MJqS2okmh2O83LkqlgEuaLNM33SYHHTDcu1RcJAzd7+cGLsvVTwSnOQ54dRQpDDNSwoAjipgU1kfhzuIJoMDgDM8xsE3MwU4IXV03QL4AtHfZRHgsA621RV3v5L+nFK47ztMDpmhuPZoKKMURumTx6Wsj8MBwIru/sM2gUH1NtzdL3f3bYj8I7d0uK1hcSPw8RbfuyWBjxF5V/rFQ/RHoqZeDMW1R8GBFEnpk0s26OmT3f1od5/Wa2Hc/W8+TjoAV/kR0cxxSeog+Yb0/DxiKvnjSihbQ94/iZp6MRTXHgUHGTGzmWY2PbWJVp7P6OAxveyyF0jpk8un9MkZ6/C7Pt3Mnjaz68zsa2nUUiE8poUfAdYnmmW+mcr9VHq+HjF8uPCRFM30S6KmHg3FOa8+B9m5mjgpXq57LqPeRtQMtPIA8KEMtjUUKUxzsAoNpsmtcxuwbUbbK+Q4pE5g2xAdweZ39z2qXl8BuN0zHB5b52qiM9+aRO3Xo0T207cQ5/zsxD6dA3gP0WFxJzPbqFmgnLU+7zAJDFWipuG49pQ9XEKP8fMg0vIe02aZY4AXM9hWoQmXBuVBDLM7tM0yhwLTMtpe7scB2J3INFoZNlid4Gf19PruOe7TJYmg9ixg2br3lk2v/wN4K7AgkfdgJnBw2edDvzwYwERNLf6Wobj2KAmSFMbMbiTu6t7Z5H0D7gRec/e1xritUhIu9TszexC4w90/0WKZ3wJrufuYq3XzPg5mtgWRyOk24s7yw8AXvTbBz23Aw63+5rEws9OBd7p700nUzGwqcKe7TzSz2YnzfLq7r55HmQbNICZqamZYrj3qcyBFKmxCkvSl2zit663AjsSwoR3T87OATfr5y5mTK4GPmNkHG71pZhsQmRL/nMXGCjgO+xMX4I3c/TfEMMd6twENA9KMfBhol7Pkj0RmVNx9BvGDsUKOZRo0AzWzZSvDcu1Rn4OcmNlBHSw2k6hqvxu4yt1fy7dUpSt0QhIvKOHSgPkBsAPwJzNrlj751bRcJnI+DhOIOQueb7HMP4mLcl4WBBZqs8zCabmKp/MrTi0zm0n7/k/O6LXoQuAn7v5q3mWrMlSJmobh2qPgID8j1H4hrer/9a878F8z28fdzymgbKVw91fMbGMip//2RCRdMZOIqPfKOqL2JgmX0jCuSe6+X5bb62fufq+ZbU/s632Br1a9bcQPxGc843kV0rbzOA5z0X5m00WINt683APsYGaHeoNRIGa2DBGQVe/TtwGZZaJso+87TDKkiZoG+dqjPgc5MbONiAvvx4gc8Ncy+oXcAPgs8HviIr02kWJ5XmBTd7+mjDIXqawJSdIQsu2JCXLWA/ABn9ylF2a2GAWkT26x/UyOg5ndBTzh7hun543aqm8h2vfXyaDojcqwM/BLoknjx0Q658p3/YPEd3txYFd3P9PM5iBqa65x922r1rNhr2Vw96YJg1KSo+uI43uAuz9S9d6ywOHEebA+EWgdTXTy/K67T+61TN0ws0uI68Hb3f2F+uOY/ob7gN+5+44tVtW3Bu7aU3aPyGF9ALsS0fC7m7y/JlFNtkt6/m7gNeDisste8n5bjTYjGnpc7+pEs8bTjPZqv58+7zFc4nFYM6f1ZnociDTIM4Dt0vP66ZI/l94/IOf99fX0/Z3BrJNZvVa9fSJQ+ALR6bN6HfUTYXX8aFO204GpbZaZSgSGEDUJ9xAdV4s63z7M6FwZq1Ufx/T8WmJytvWKKlOGf9tAXntUc5CTdLdys7s3zTRnZqcQF+G10/OLgPXdvV312lDJK6JOnRx3BPYk7owqTTt/A77mGY3tNrO5SFMFu/szWayzDGY2H9EnZBLwXnfPpNkxz+NgZosSQ9/eRrRZL0wk2/oqUUO3NTGM8L2ecxW5ma1AjNFfK5XjeSJV81nu/kAHnx+hx9wo7n5wi/X+BzjZ3ZtmHjWzw4DPu/tb0vMTgZ3dff5eytOL1E9rhNgHrwNzAs8AixLnzP7u/sOiyjMWRV178qQ+B/lZhfYTCz1G/ChW3EfMNjcumNnqxA/RLsTF1Ijx4r8Y43rXIr6UnyE6ihnxA3IKUe174xh/kBYkvvhbEB0pl6h6bzrRfnsFcKG7/1+v2ymKma1NHIedgAWI/fX3DNa7FjkeBwB3fyY14Z0ObFf1ViUV8DVEH4rc287d/UHge2P4/Eh2pamRSYfJvJo9qpbp+0RN7RRxzhem7KqLYX0QbY5XtFnmSqrmUCeSY/y37LLnvF/mJap6r2e0im0mcYe16RjXvQdwQ9V6Hyem931X1TIzgRN7XP/SwAlEc1Gl3E8DdxHVnjcRE8a8zmgV8U3ATmXv9wZ/ywJEQDCV0erpl4gf2Q37+Ti02O4aRHX9t4CvELUF/bCvFyRGgdxU0vZvJjryLdXk/WWIzpE3Vb12JvBI3XK5NHsMw6Oscz7Ph2oO8nMJsKuZfZ/ISPfGnYuZzU9MQrQhcTGuWJ34cRk6BUXUJxJfwAuB04BLPcaUj5mZHUyMVZ6bGLN+DnCdu88ywUqqnl+XaEfdGTgzDWma5O63ZVGeXpnZOkRQsAMwP3EcbgLeC5zp7pMy2Exux6GVtG9L3b/VzOz9xDm/PbGve2oyMLM5gc2ItvcF3P276fV5iO/SU+7ebDIziKm5fwncbGbNOkwuQnREJHWY3Jyodal2SK9/wzhQyjmfq7Kjk2F9EOOqHyQiyaeJueXPTf8+TZxIDwFLpuWXJHowf6fssme8HwqLqBm9s7mHSI6zZJNlut4WMWfG0Y3W2eZzBnyC+AE+qKRjsBDwZaJ2puFxyOoY5H0c+v1BVM/vRbQtV/b1C8DJwAd6WN9H0nWhUWro96fX29ZMkUGHST1a7t+hO+fVITFHabje4UT7dHVGwFeIQOEAd2+U0W1opAQsM4GLaBJRp2V+7mO8azWz9YiL2rbE/p5B3OWfCvza3V/rdVtmtpQ3mcmwi3W81d3/PZZ19LjdF4n98ToxZfap1B2HrI5BWldux6HBtvoi2ZiZrU/UyGxLTH9c6YD2B2B7d3+xh3VOIJqrngKOIGqjdvLaYZr3Ex2ft2+8lpr1janDZJ4GJFFTU0We80VRs0KOPBJg7G5mXyQ6KFa+kPe4++ulFq5YRgzVfBdxB/14HhvxmHL2ejPbh8gjsSdx5/Vh4Fkz6zkt81gDg7SOwgODZD5GJ4I5xmNa5tzkeRwaGKGkZGNppMRE4u9bNW3jP8D/Ej8KtwH/7CUwSL5D1FhNcPd/p7H/9W4k8qS05WPsMNlMBs0ekFGiprw7Tbb4bJHnfDHKrrrQY7gfxNDE04jObjOJu9dLiHbYudIyuVW3EcOITqa2E+GjRP+BJcrePwUdg5OIoHRG2v+XVu//vI9BnscB2Ii4i5xGtPvuSlyQd01/9zRiiOM2xGyTz6d9sMEY/54ziB/umWkb5wMfB2bPap8SNQYnVz2vyeGQXjsCeH6Mf0vPHSbJrtkjk5ktGa3eL73T5KBfe9SsUIA0yc17iE4/zxHVgNeWWqiCmdnCjEbU7ybu4p4lmle+SM7VbWn44S5EH4j3pO1PB37j7tu1+myLdc5G9Irfmbhrmt9TbgAzew/xtx7r7mMeFjhWZrYAUc49iTtNJ87Fs4ng7a8UUOWZ9XEws12JUT7rufvtDd5fk+iA90V3P8PM3k3UXl3q7luN4e+oXOyPBH7gDfJbjLUa2cymAce5+zfS88nMmv3xJ8RMhgv0sP5ZOkx6F/lFsmz2sIxmtswrV8RY5HHtKUTZ0ckwP4iL8F3Udv6p/P8uorqwl/UuQfyg/oi4+FS/vi51c4j324OSI2qiZ/6Jafs93TEQOf3/nI7lk8TkPtV3TYsQd5QHZ1HmjP/+tYghmc/WnZN/Bt5SYDmyOA63AL9os8wpxA9U5flFRMrlsZS9su8qNTE7AHPXLTPWmoN/AL+vet6o5uBa4LYu1plZh0ngYqJz9VtblO9s4N4O1vUf4LA2yxxG7dDvE4GXijpfs3hkcc4XVtayCzCsD2DFqgvI1UTb6BfSv1el158BVupyvbsTVfSNqvFWT6/vXvbf3+Hf8kZ1ZtWP1KvAeQVtf35gjx4/++1U5oOIqc9HGlwYLwf+WvZ+bvE3zJfOp/9jNEh7Lf14bllgOcZyHF4mhgq3WubQ6h8R4i73tTGWuZKv4y9V5+7TxKRi66Rlxhoc/IS4w/xgel7z40vMojkT+H4H61qf0ea96vwilxD9BHopX2bNHuk4Ht9mmZ8CL1c9P3zQgoOqsvd8zhdWxrILMKyP9EWcTsr53uD9bdP7p3Wxzi3SF/sW4JNEdWr9l/E24Ldl//097K9cI2oiBevbMlzfXcTEOZXnjS6MPwMeK3vfdvj3vDv9GFWG2eZyV5PDcSg92RjR0fY4IpFQJVC4M/170hjWu3T6AX6ZmEL73LTOLdPzF4n2/sVb7Ot9q8pSPYR19QyCl2nAEVXPG30HfgK82MG6MknU1GL9cxL9I/ajarg4MbLkzcBsWZ7nDY5DZud8UY/SCzCsj/SlPb/NMhcA/+pinX8iqq8XSs8bfRnPAP5R9t8/hv2WWURNZAE8Cvh3ujhOr3rvfcRd09o9rvsV4IdVzxsdi8OAaWXv0y7/rnmI2RqvzXCdeR6HU9I6v0/0+ag/lyoTM51S9fpV5JCtkEiQtSuRPKhyZ/468FtgK6o6K3axzrWJSXpmVj0qP/T30XxityI6TGbW7EH0h5mZzpFvAxsT/Xg2JhLG/Sf93Tun5edIr7W8xqZlM+k02S/nfFGP0gswrA+ievx7bZY5FHi1i3U+C5xQ9bzRl3FgqtrIMaIm2lZvTxeDm4E76i4K8xG1FD/scf1PU3VX2ORYnAn8u+z9XPIxzvs49GWyMWJo4zFEf5TKD3pPtUjEML5PEoHOicAPidEXc7T4zEyiZvJwYNEWy/RFs0daPvNETcAEIjj6JzFnwxkNvqf3A78alHO+qEfpBRjWRzoZL2yzTLc1By8DR1U9b/SDdAJjHNqU834pJKIm2jpnAru22Fe/o6qjWpfrvwJ4mNHhmPUXxoWJatLflL3Pm5R/jfTDcTHwp6rXlyd6rzf8Qem345A+vwQxWVdluGzl8RLR0e7NJe7nuYiU4VOAmQVu91ny7zA5pmaPJutcgagpOJ9IInRBev72HsuYWafJLraZ+zlfyDlUdgGG9UHMmTAd2LHJ+9vQfZ+Du4ApVc8bnXS3EPMUlL4PGpS/sIiauBu4tM2+Op6qtugu179T+jvOIxK9vLF+YqTCRelC+bGy93uDsh+Szr03qqmr3nt7KvfeGW0r1+NQt545ibb09Yk+FHOWva/ryrdigdvKvcNkWkdPzR4F7odCckXUra+wcz7Px2xIXg4h7lzONLNrzOwQM/uSmR1sZlcBvyIi624yll0MbGBmDcfGmtnniDvCC8ZY9rx8m+i8tZu7r038sL7B3V8m2oM3y2Bby9B+Ep4XiYCla+5+NtHevQ1RdfwleGMs9uNEG/NP3b3dtN2FMrMdiTuxPxJDGg+rft8jje5Uoho7C7keh2ru/rq73+Hu17n77Z6ykJrZbGa21VjXn0H57u9meTM70MxeN7Olm7y/lJm9ZmYHNNjWK+5+irt/gAiUjifG138R+KuZ3ZmeW/1nu+HuNxPZXz9F1Bb8nJiDZDtgNW+Qe6IXZrZgun7e1OVHFyAChFbmg0x/Cws75/Ok9Mk5cff7zWxzogZh/fSo/jLeC0x09/u6WO0RxDwNZ5vZtqSTy8z2AjYAtiai9R9n8kdkb2vgMnc/vcUyDwPrZLCtF4heyK2sQPsLR1Puvnuaf/6rRFBmxJ3UncDR7n5Kr+vO0T7Enc1WHvneP91gmbuJjmBZyP04NGNmyxGJZz5H9DXoOMFPn/gEUVPYMN21uz9mZlcSgejhzVbi7ncC+5jZ14nmhT2J6xHAbmb2VuJH/Xfew0yC6TO/SY9MZTCz5b+IG5JW1iKyM2altHM+SwoOcuTuNwKrpUk51iZ+zJ8DbnH363pY3zNmthERcFTXHhyX/r0G+IxX5RrvM8vQvlYjq4j6RuDjZragu79Q/6aZLQl8jGj765m7nwqcambzEh0sn+vj/Q9xF3mqt5586DEip30WCjkOVeubnfixnERMOzwb8YPypyzWX7AViQ50rdxFZN9ry2OSotOB081sVaJD3y5EH4GPEb3/l+q5tBmpy6a6OhF0v0TMV3FSl6u7FPiimX3QG2SlNbOPEinemwZXPSj0nM+LgoMCeJqUI6N1PQJsbGZrAB8AFiMCjr+6e7dVbkUrMqL+EXFhuMTMatLXmtlqxEVmHkYDqzFx91eI4Y39zoj24FbeQvTwzkIhx8HM3k7UEuzGaGDzFJFr4hfu/vBY1l+S+YjOfq1MI5KJdcXd7wH2M7P9iZwrk4CuJi0yswOJ9vTlG9VumNlSxEiRg9y97Y9vHjNbEs1mOwKXm9mPiQ63mNmWxN/7FaIZ8Oge1t1Modee3JTd6UGP8fMgRiI8CSyYntf38F+SqDk4J6PtHcRoB6lp6d+nGO0w9fWy90kJx6Cmw2qDYzA70eSVZZ6DXI4DcXOzHdF/otLBchrRlyXXiaQKOlZ/B65ss8yVZJTXhC47TBKZNf/YZpnLgL+0eD/XRE1pG4V3mhyGa49qDjLS4bzyjbinKU7HgaLv5g9JfQL2IZKdLEZUMV9CTF18Ra/rNrNO2ihnUjX/vLv3Q0fRXwHfM7OvuftRDd7/JlGd/aOsNpj1cTCzlYgq54nEeHcjRr+cCpzl7k+nSY/6Rpq+eC53f76Lj/0B+IqZ7eDus0z5mzqXbkSMQBgz77LDJGNs9jCzM4h+SPMwmrb7VKKn/4y0TJdFmpW732xmqxDNJzW1rcDF7j59zBuZdZu5XXuKolkZMzKGi5F7hzOhdRiAVP8gXeWt25YLl/6GEeKL8jox/OwZ4g7CgP3d/YelFbBDZvYQcedaaaOdTqR3XYzR5rrHiGGOCzB6YfiU99DpKyupb8R1wJrEqAQnOoAeQ3RqnUBcNDfK46KZhfRdc+AJItHUKR6d7uqXyX2WyU6Z2SnAZz3N2tnhZ5Ymer0vQoxU+gPRwW5pIsHQJ4nvzlru/s+sy9xB+V4hZh39ZotlDgP2c/d5GrxXuYvPbWZL6Z1qDrKzSQHbGKG2t251WF3/ugP/NbN93P2cAsrWkWGIqJM1iOrsfxB3239195lpGucPEOl85ybmw3grcCzRCemrZNu+2RV3f8XMNiFqBnZmtAf//xAX6jOAvfo1MKhSOWfOrw8M+lhXt8Hu/i8z+zDRTPIpoqNl9boeIuZuKTwwSB4lvsOtvJ8IaBp5ngievwasaWanAr/26DgpZSu7XUOPzh9EFeKFRBvWiUQe9w+nf09Kr19AjL0/lPjyvQ5sUHbZh+1BDBe9hyYpbInMePcAx6Xn8wGPkENO/zH8DW9K589niCrX3KfLzqjc3yaCskqb7l3AN0gpktMyfdXngDQHRI+fnZOofj8ife+PSM9LTfJENP/NAHZo8v6O6Tj8pMn7RcxseWC6Bi7d5P2lqEvNrEfaN2UXQI8uDlYEAS/QfLKVNYkOfbuk5+9OJ/7FZZe9hH1Vudi0ekxPF6PriLuXubtY/z+pmpGuyTI/BP5Z9fwU4IWy982wHIcU2JxPzGNSSRX8e2JM/NAEB/36IJo3/puO4YXESIMt07+VDKFPAct0sK68ZrYcc6fJHraZ67WnsONbdgGG/QEsm6LXC4A/py/RgcByPazrFmJYVqtlTqEqZ3f6kj5R9n4oYb9PSfur8qPxAHGH8kB6PjO9fzsxXGwG0QY/f4frr7S3tlrmWOCVqueHVT8fD4+8j0PaxpuBAxjtkV65AN8AvLfsfZDK2NfBAdEpcKEePjeBmPiq/gdxZjrGXe1/Mp7ZMgUbP2qzzDHAkxnuy9zP+ULOibILMMwPokf1K1VflurHNOALXa7vZeDQNsscStWsjEQV5Gtl74tUlsIiamJY5APAWcCyde8tm17/B9EfYEGiWWYmcHCH67+TaHNdsMn7C6X376p67VS6mGgrp2NwcgePSgrcPamqqu/H49Bge5sREwBNqzrfbgG+UvJ+7zo4oMAq8VS+6T1+NpdmDzKY2TJdfw9rs0ymU6sXfc7n9Si9AMP6SBepGcTsaIcQ6WhXSf8ekl6fDmzWxTr/A1zRZpkrqZrQg8ip/t+y90cqyxQKiqiJTHBT2ywzlTTxFdEx7x7gjg7X/9VU3ruIjn3LE22oyxNDt+5O5f9qWr7Sgez3JR+D6gCtPmCd2eD1VxnDmOy8j0OLdS4O/L90HGZS8l07vQUHhVWJ91K+AvddzzNbUnCuiLS+Us75zPd72QUY1gcx7OhZ4B1N3n9Hev8PXazzlHTx/n79DyaRd/yw9P4pVa9fRZ90gqPAiJoIpDq5Y6gOpE6kqtalg238lOa1ITOBE6qWfUva3qYlH4MViKatJ4BvEVniVkn/fju9fgExvHFPohPlDGIuhl62l/tx6KAMGwNnZLwf29WANXx0uY3CqsTp4+CgrpzdJmoaU6fJHstY+jmfyd9RdgGG9UFUjbfsEJV+/J7uYp1vJdr3Kr16pxBVqFPS85nE3emSafkliWFE3yl7f6TyFBZREzUPx7dZ5qfAy1XPD+/2Cwp8kKiGn0pkW7spPd+w7P3dpLz7EdW0zaqq30Z0Itu36vmLtLmDLfs4lLAfG9W6tHt0GxwUViXeS3DAAIwEIMNOk11scyjOeeU5yM+8tJ8j4Mm0XEfc/d9mti5xIu1IbS70V4g27QPc/Ym0/OPEl6NffJho027lj8DnIWZ7M7OriWr7bt0D7GBmh7r7Y/VvmtkyxAx1d1e9/DbiQtIxj8lcZpnQpY9NAs7z5jP9PWpm56Xljk3Pf0fka+hFIcchTbi0CpFMq2FSMXe/upt1tuLuRUx3P9Y8Ann7BBnMGpknLydXRCHnfN4UHOTnYWDTNstsQlTbdszdnwR2N7MvEhfDhYl8Bve4++uVuevd/eJeCp2zBYmOeq0sTO1EMk/3uK2jgF8CN6cJV64jqvveQtzt701knjsawMzmIGbxu6bH7c0izfg2yd0bTYtcluWJ1LGtPEs0P1Q8RGR57EXux8HMvkPUiCzcZtFBm7K50PTJPch01si8uPtUM1uZCGbeT5xvzxKZQH/r7q9nvMnSrz1ZUHCQn4uAb5jZT4FvufuzlTfSlKSHAOsSPXu7lk7oO6rWuZyZ9fvc9YVF1O5+ZpoV7lBiX9dsiugM+m13PzO9tggxWcr/dbutmhVHytvPA7sTZe83TxG1AE1T3gIfonafL0L7gKKhvI+DmX0DODiV75fE3Xa/Z3fs1A+IWrOzzGwHGqdPfpqS7srJcdbIrKXr5YXpkfe2Srn2ZE1zK+TEzBYieuKvRiQu+hsx29hbgbWIL8w9wPu9u8lYqrfRdO56d//wGP+EzJnZzsQF/Akiw2CjiHpxYNf0BZuDuBhe4+7b9rjNFYgL7FqM1rLcQkzQ88CY/qDRbRij7ZgfYTQwu4pI4HJ2FtvJgpkdS6SuPosIWh+pem9ZRqe4/bG775tev5EYDrv+GLaby3Ews/uIMfprp1q1vpB+zNclvpNTeq3JM7MJRJX4csyaIv0hokr8pgafm9HL9rzDeV7SNv5ODM1tmjo+NSss6+7v6KU8g6yIa0+uyu70MMwP4oT4GdGhq7pj0ovp9UV7XO/biRELjzHaE/o/RJS6XNl/d5uyf53opNSod39N5yUiUPgCMbFM6WVv8Le8jbhrfbTqb5gJXE2TUSplP4hmnZsYHU76EHHH8hCjw0lvJiXEIWqhrge+WHbZm/w904hApoxt70GMvnkBuIJo5puL6CBcfV7PIGoSrcftNM0jQAQfWzX4TBEdJgsdCUAPiZoYgE6T/fpQzUEB0h3wqkSw8Bxwr3fZzpXW8Wni7nQT4qLwGpE5bBsGaOayfoiozWxBoi10D3d/bxefm42ozt2TqH6fnTgOvyN6fP+WPj8WZjY3MRfBRCLQrHiAGFFyhLtPK6gsPR2Hqs8/DFzi7l/KvHCtt/sRYuKnancSs0R+n6gV+z+ig+TWRFA2yd1/kdH2lyOCk88Ro5MKb0YsetbIHme2/D/geXdv2qHWzC4jgo4PjLWMHZZpTOd8YcqOTvRo/QBWIu4U/sPonchUYC/gTWmZmfRRHvl+fhAdkn5B3O11dbcEfI+4+DU8DoN4LIjmraVpkumxH49D3Xp+SKRNLjQ3PREMvkoE7Aukf18lRiD9uG7Z5dPfedUYtzk7EWj8gWi3rtRKXFbi+ZNp+uQ22+pluGXh6ZNbbCeTc76ohzok9r97GZ27/hgazF0/LPKKqFMH0M8Sd/urE+21LxFDP0/qYlXfIr7U/wv87zAcB3d/gbhYvcHMFgEmuvuPstxWhseh2kHERff8ND35gxkUtRMTiGyXF6XnF5nZ74k+QMdWL+juD5nZr4kpu7tmZm8nagl2I/rnQHQs/Rkx18rDvaw3C95mJAAwo+TRU6V2mszpnC+EgoMcmdlKRJrddWk+/tq9fWcdZ/Dmru+Ymb2f+PJsT2R6zKSty8zWJ5phtiXaKy299Qdge3d/sctVOtGcszMwu5md6u591cN4LMxsA2J/bUNMgJNJcJDDcah2J9H2vhTwMTN7jvhxqtfJ96wbixGBe7XK84caLP8w7YdavqFFM+KFxPG52N0P6q7INevPpMMkNB4JkJo9DqL80VOl5IrI+ZwvRBGJPMYlM/sAcCvwZaJtvXKC1D/aHYPvEBeWzwHXmdldZvYNM1syn5IXw8wWNrO9zOxvRPvs59JbpxIjF3pd76Jmtq+Z3Ul0DPws0afhGGCNtNg/e/xyLgd8l7jT/gJwvZndnY7HW3stc5nM7E1m9j9mdjfRkW5nogPXz8e43jyPQ7XZiCr2R9LjOXr7nnVrdiLxWLVpEMm7Giz/OqM/EE2Z2UpmdgTxY3UOMUfLrcQIk6XcfbtOC2hme5jZA2b2gpldYWarmNlcZjaFGK2yL3HzcqGZXZRG3fTMzGY3s63N7A9EGvRvE4HBn8ay3jH6A7BhCoZmUZUr4tKxbqjAc74YZbdrDOuDuNBOJ6LHOTJY38DMXd/m71gfOI2oWqvu4X8JsMAY130GUYU4k7hQnw98nKppXrPYV8QPzceB3zDaw//19DcMxLEg7kbPonbW0PuJHA3zDcJxKHn/zQQOqnttMk3akVu912C9M4hhz0cC72qyTLvU7B9h1tEItxNTW88kEu4cSbSBP5O2uXuP+6KQ0VP01uegkPTJw3jOl16AYX2kH79zclhv389d36DMixJ3KXdW/RBVLn6rZ/WlSeuZTiSFaThMNOsvKFGdfRBRlVy5CD9D5HFYq+x9X1fWJYihpPfWHYcfZrlfyjgOJezLmUQq8A2rHqek/bpB3etvvNfFvvsFkQOlp31Hzh0miSbp7Yh055XOkdOInAy5HNtO92GDz+XeaXIYz/nSCzCsj/QDcUTO2+jLuevrylhYRE20NVfu4i8lsi3OXbdMXhcuI4ZvXUS0DVeOx419cAw2B35VdZ7Mchyy3C8lH4eFiPwTXY2H72E79T809XkNGr7ewXq/TVTJV9ZzFzHsdMlu9h3wb+DCutcuTOucJQcHkZys7dTuZDh6qsl+ynRmy6ptdZ0rYlDO+bwe6pCYn+uB9+S5AXf/M/BnM1uc6Mm8O7AmkZzk+Dy33YXPEF+KI4AfuPszOW5rSSLxyiSiGeZDwHNmdg4xyuPGvDbs8e2/FLjUzN5CHIvdgbXz2mYXLic6U95C9Ok4y92fznF7hR6HlCn060SP/hWqXn+Q6DtxpLtnnVL5ajLqOFvN3Q8FDrWYLGhPYhTA4em1y4kmuU7k1WEyy9FTvfRx6Gmfe/6dJku79uSm7OhkWB/Ej/RLRNKOIre7MRnPXT/G8jxLCRE18C4iSPovo3dhlWaNkwr8+zfvg2NQqfK8jLiAzZITII9jUMRxIDISXpHWN5344ftL+nd6en0KMFfZx6HHv6+nZkTy7RMx5maPEvdn7rki+uXaM+a/o+wCDOuDiEh/m06Iq4gZuA5q8PhO2WXNeT/MS0Tmf6n6ojxNzCS3Tlomt4sJMSRvV6IDVqVPwOvp2GxFVfPGGLYxJ/Buoq15DWDOsvd7Xfl2Tj+Qlf3/TNr/76taJtcLel7HgdEOdr8BVqp77x3Ar9PfXHp6XMbYMZkumhFzDA4yafYoYd8XnnK+iGtPrvus7AIM66PqZGj36OssWRnvk1IjaiKF9TFEp6zK9h8bw/oWAk5gdORF5fFSen2Rsvd5XXlXJjqBPlG3/79R5AU9y+NApO+9DZityfuzpfdvL3G/r0jMsPh4RutbHPh/xOylDa8h5NRhsmr9fT96ihI6TbYoS6bXniIemlshJ2a2UafLuvtVeZal36Tc/jsQbaqVmf5mEFV9Pwd+543Hime1/bmI5CSTgA3dvesx8GnWzeuIgOcF4g7ucaLtcS0icLgLWM97nHUzL2Y2J5FIZxIxxrviXiKvxsWefRt9o3JkcRxeJnrf799imR8Ae7v7fD0XtvtyVfbxnkRTnxE/vnNmvJ2NiYyiu9S9PpPG7fPW6nXvco4GM3szMfx1D+LuvLLum4AveYMZI7tYd0+JmlLyuT2JuUMWJ/62m6nqb5P2T+FzoGRxzhdFwYGUysxWJRIK7UJ0onLgP+6+VEHbX9Hd7+/hc4cB+xOplL/t7s9WvbcwMQ/DV4hOmN/MqLiZM7N3EBeqiUT7thPjvk93968XWI5ej8OzwJnu/pUWy/yE6PvTSYe7MTGzVYgfpl2J89mIzn6nACd7BhMQdViOKfTQec9bTL/cwTY3I86lrYi+IE7U2vzc3Rt2kDazPYi05EsANwJfIoYdXk7UcMBoQPMbYGtv86NVFRg9QUyEdYrXdZosKzioK0NP53xRFBxIXxikiBrAzO4lhn6t12KZ64Al3H3l4krWm5Su91PE/t8MoNu7yDKY2dXEVMmru/uTDd5fHLgD+Lu7b5hTGeYiqq8nEdk9jRjOOhdRhb1jux+0YVI3emoVmtRI5DWzZfrhn0mM7DjJ3f/aZJm+nj21bH19AR4GZrasmR1oZheY2Z/N7ML0fLmyy9ZP3P01dz/L3Tcm2sb73bJEJ79WriLG3Pc9d5/u7ue7+4eINvLDyi5Th35C3HXeYGa7m9nbzWxeM1vBzD5H/LgskZbLlJm908yOJTq5nU7c6d5CpDpeOi323KAEBilAHDN3f8rdj3T31YBNgbObLLoX0VdhG+KHfxviu/814Hh338Dd/5+7704MC3+ZqJFpZyhTzhdNNQc5MrM9iQ54czHrmN7XgK+6+88KL1gfMLNliC/8IkQ+/JuLqnLNgpn9F7jI3fdoscxJRDXoYsWVbPwxs+8ToxaataUf4e4HZLzNa4EPpPX/hwbV14Nyd2pmK5KaQty9sB9QM/s3cL27b1312oVEs8TK7v6PuuV/CXys0+9TXa6IOYl+TZVcEecwAMemTEqClJPU/nYC0Vnth8RY7EqHtU2Ju4vjzex+j2RG44KZLUtkKNuiwXt/BL7o7g8VXa4e3AhsZ2Y/cPf76t9MbfnbE0M4+06acGYPIkBbmJgg5iZiCuBzyixbt9z9W2b2G6Iau/L3PEfcxZ/s7nkcg/WIqusfAAe6+8wctpGbZh0mCy5GrjNbuvtlwGV1nSY/Ssw74cBaZvbesXSaHGplD5cY1gfR8/5ZGqQqTe+/I73/h7LLWuA+eSsxhWolp/mpRPX1qYwmefkn8Nayy9rB37IZcTF9lpipcVNgNWJCo4OJXA4z6IMkSHXlNiJVbiX17etE0FqZQGoG0aO79LL28wP4e9X+upvonLpU3TJ9MaSvrkyrMDqctXIOPEjkXBnT5EM9lCWXXAxtttn3Kef75VF6AYb1kX4c2uU/Pwl4uuyyFrhPjk9fyK9TlwCEyFz2/9L7PymoPPMwhhz8xCiLaTTOo/8qMZSr9P1eV+YvpvLdmAKaytwKs6fnN6S/4YsFlmlMx6HEfbkx0Z7+SlWgdQnROXGufgkOUll2JvrAVM7Pyo/juaTm5RLKVXhwULWutrkicv7b+/6cV7NCfuYlhoS18mRabrzYErjc3X9Y/4ZHXoMjzWxzYkKgvQooz/8Sc6739D1w95+Z2aVpHfXV2We4+8NZFTRDnyeqbDd091cqL6b9f0XKz3EHUUV/QkFlGtNxADCzTxA/gKsB87v7iun11Yg25zPd/V8ZlPUN7j4FmGJmbyJ65+9BVFl/mKhRcqCw3Ar1zOydxAiKXYge/9Xj/c8mrj9ld5hc3syqR5EsD2BmGzBrP63ls9qouz9F1KAcWckVkdW6OzTmcz5vfVuwIfAwcSfWyibAIwWUpV+8lei41cpNxB1ZUXqZ/OUN7v4IcGhGZSnCO4GfVQcG1dz9FTP7NVErUqSejoOZGfFjV0kC9Aq1AfczxLA4I/oHZM5jEqujgaPTj9okoj3fgJ3MbE0iudcvPd8Jr97QoMPkMczaYbKIorQzMT2qGY1HAjVL4DQmlSAv6/V2oC8OQDMKDvJzEfANM/sp8C2fNUnOIUT2ryPKKV4pngPaDeFcNi0n+XDaX5T6+qJV58vEHdjJxBC4/YihbAC4+79TvoktySk4qObu1wDXmNnexI/eHkQWzWOI/jVF1SQMQofJXGa2lGwoOMjPYcAniTbenc3sb0THr7cS6XUXBO5hcMaTZ+FaYFsz+6m7X1//ppm9j2iv/X3hJWujqurzBnefVlcV2pK7X51TsXpxN7C1mX27Ue2Bmc1LJEO6q+iC9Wh34G/Anu7uZtbox+Y+oqq/MOlm4EfAj8zsA0RNzLYFFuF+Il/F/sCnzexUoubisQLL0JJHThPpUwoOcuLuz5vZekTNwM5E5rSKl4nOiAd4n+Xdz9mhxB3cVWme8ysZDZg2BnYi7na+X1YBW5hC3OWsRvRUrzzvRD9lGjyZmJHxajM7ALjK3aeb2ezEBDyHEbU7g1KjtQrRTNLqWDxBJEIqhcdQyr+Y2T4FbnPl1Jb+BSLYOwz4XhoufArQ0TwF/cbM5vAC5v0QBQe5cvfngC+Y2VeIWbkqHdbudffXSy1cCdz9ZjPblmgj3hn4TNXbRozw+Lz357jjQxidd6D6+aD5GZHJbyciIcxMM3saeBORMdWAX7l7UZ0Rx2o60fO7laWBFwsoS0tF3wj0e4fJblQnaiJyxUjOFBzkxMxmAOe6+2dSpHtH2WXqB+7+u5Q6eitgbWp7+P/a3V8qs3zNuPtIq+eDIt1h72xmvyNGLryHCAyqkwY1S3fbj+4CNjYza1R7YGbzEB2Db8lyo2Z2eg8fc3ev73yXu37sMNmJPknUNG4pfXJO0mxx/+t9PCPfsEkBWdd8ACYYGiRFHgcz+zIxb8JxwP8QnREPcvfZU1PJj4mq9V3dvd1ImW62200Hv0onUO+Xc83MFqG2wyTANC9wWutm+mVmy24M47VHwUFOzOxK4Hl336rssowXXV6wKzK7YJvZ3MTEMbMBd6TcAeNOkcchBQC/Bz5E9F95AViJGC30fmAp4GJ3/3QPZWq13Y06XHQZInnPivRRcFCtusOkuy9QUhkGembLsq89eVBwkJM0HelviYlC/lh2ecpgZpUZ1C5y9xeqnrfl7r1U2+Yuzey2J6Pzz5/h7jMt5qX/ATGRFETfhC+7+wWlFLQDZjY/Ud6GF6iUw6HvpdkEDwS+QtxpVjxL1Bx8t+hObGnfHkAMrZwPuB34urtfXmQ5umFmCxXdL6LDRE2aIKkECg5ykn4ItyMm+vg18UPybxp0YuvXH8KxStG0A6u5+9+rnrf8GH0aUaeZJG8E3sxoQpZTgF8BlzLaqXJBRmeBe3+/dbA0s88SQ9xWa7GYu/tA9UlKCZFWJgKE54B7iq69MbPZiB+7ycBbiOmcvwOc2s93vmUYppkth9FAffkHzKmMtjVunR5Q++NY+YEZyuCA6PDmRHUvxPzqg+wA4oL/W+CPwOZEu+hqwG1Etew/UlX33kQnsP0Yzd5XOjPbjRjOOAO4hpgIayiGhqUf3/pZ/gpjZh8nao9WBV4iAoQjm2WjzLksg9BhchASNY1bqjnIiZl1/CVz99PyLIsEM9uByEo5GzDF3bsa621mfwdedve1ql67BVgD2KQ+2VHqd/J2d2+XFbIwZnYHMRTsg+5+d0llGNNx6DdmtjaRp38j4sfuF0SnyCdKLFPfd5hM36cV0/b/TtxQ1SRqGpaag0E85xUcyFBJbf/fYrRPwJeIKWkvJ8b3w2iNzW+ArTut7jWzl4GT3P2rVa8dS9QSLOLuL9QtfxQxFWy7cfiFMbNpwGnunuvcCXkehwbbWgn4KnHxXZTGfSjc3d/Ry/pbbPdtRMKunYiL/u+Bb5QVdFUblA6TdYma5iZqtKoTNU1jQIKDIs/5IqhZQUplZksT+Q5mA6539yfHsK6PACdWvbQxcD7RlrkhcB3wf8QPyNZEeuvPE3d6nZgH+G/da88A1AcGyfNE34N+8jRxwc1NAcehelsfAP5ETLY0nWi7btRMkul8EWZ2OLAP8YN2K/C1lHSoL7j7Va3eb9ZhsoCi1RiWRE1FnvNFUc2B5M7M1gD2ZTSiPsrdXzKz7wLfYDRIfR34prsf0+N2fgdsAexI3H1sAZxD/Eif4+57Vy27PHFBvNndO7rLSlWcI+5+SNVrk0nj6hss3/S9spjZicD7gLXyumvJ+zjUbWsKMfTty8QY+EL6T1R1rr2f6JDayb50d5+ca8HaGIQOk3WJmuYh9u1d9GGipooiz/nCuLseOT2INsjfEbndXyeqzOof08suZ877YFXiCzKDaI+dQYze2DE9fwGYCvyj6v1Ne9zWv4EL6167MK3zHQ2W/yXw3y7WPxP4Tt1rk4EZTZZv+l6Jx2MxYsKvnwEL5LSNXI9D3WdfIi6+Re/HmVWPGXXPmz1KPReAjwN3pvI+TwQF85ZZpjblXYRoLrq9ah++XHa5mpS1sHO+qIeaFXJiZlsSP4KzA48QvaiHold4lw4AFiCy2F1ORNR7Ae8gJl7a2mMOCszsU8AF6f0retjWYszaW73y/KEGyz9MpG/uxn5mVj3qYhEAM3ugwbKLdLnuIpxHTPy1B/AZM7uPqL6t5+6+WY/bKOI4VLxGfL+KNjAjbxp0mDyJkjtMdsLLn9myG0We84VQcJCfEaK2YEvv48QnBdgIuM7dKzPS/S5drNYDPlcJDADc/ddmdilR7d2L2YH6YWPT0robjXd/ne7boheh8Y/+8k2W74uq2iobV/1/fmL68EbGUu4ijkPF9cT8EIXyARhh1M8dJrvlJcxs2aUiz/lCKDjIz+pEded4Dgwghs1dWPfaDURwcOesi3MXkQq3H61QdgHGyt1nK7sMGfsWcL2Zfdbdf1l2YfpFv3eY7JWPrynuS6XgID8vEj3Dx7u5iGx11Z4H8MbJYV6iSTrfDi1vZhtWP4c3OjnVR+rLd7Nid394DOUab3I5DmZ2UIOXrwBOTUPJbqJ5M8l3O91OB+XYEHjIO0wxnWY+XNOLy4b6DUY7TF4CbGpmm7b5jHuBHSYHJFFTN3K79pRBoxVyYmbnAMu6+3pll6VMRfbwb5Ge2Vq93sm2zGzeJsFMN+Ub8zoGQc7Hodcseh2tv1NpFr6D687r/Ylq+8UaLF/oyJW6/VRJctROpvuonUFI1NSpPM/5sqjmID/7AzeY2YHAoT6+o7Ci/varc9zWg2Z2GHCCu7/azQfTXeMhxKiMzO5ee5WGs30F2JlI/Ty/p3kUzOw9xMRSx7r733vcRJ7HYZOc1tutRj+289A/nVAHocNkp8eyOlFTv8rznC+Fag4yYmYnN3h5eaJD3sNEu9+zDZZxd989t4KVrMPJlmbRbxF1qgLdmWgiOZcY2/7XZjUBZvZ2IpHLrkTmvkeBXdz92mJK3FiaGvdSomPi08CrwJKV/W1mixDDsn5QZBXzoBmGnBf9bhBnthwmqjnIzm4t3lue1r3ZhzY4SLrtlVtYxGpmc3gHiXPcfVczO47o/T0pPWaY2d3ExFLPEHeOiwGrAIszOtvct4Fjuq1xyMnXiTu2EeB7wEHEeHcgho+Z2dVEYFNk+3NHx6HuM/MA7yLOlzv7ZP/KGDVJ1LQ3fZSoKQu9nPNFUnCQnYHvyZ6Hfu0db2YrEtXnuxIjKtpy96nAh1Iu/92BzYihgO+uW/RJYoTGBcAF7v56RsXOws7E0NJDAMys0cX2QeATRRSml+OQPrcXEajNn156ycy+6e7HZ1/KwTMAHSablaNvZrbMS6/nfNEUHGREPdn7n5nNSaRk3ZOoVjcig1lX3P0+oroTM5sPWJqoMXgFeMLdH2/x8bKtQIx3b+Vp4E15FWCsxyHlsT8uPX0p/bsAcJyZ3e/ul2VX2oF1JXAw0dcFaN1hkpj46CBKmj5+UBM1dSqra0+RFBxkyMzmBq4hUgJ/pNkdY1W77/zABn12Zzl0zGwVRiP1xYgv5sPEzG+N+op0zN1fBu5Lj0HwCu07zS1L4/4xY5LhcdiLaEr4fCUZkZl9nsi9vxdQVHDQz1Xc/d5hEhiuRE2N5HntyZuCg2ztDLwX+ESrH3x3f83MfkiMP96ZmMdcMpQCsO2ItssPEl/K19K/5wE7DlP7ZRduJZpG5nL31+rfNLOFif4G12exsZyOwwTgD9VZCt39ZDPbBlgni3J3aMTMRupfTMMcpY1hTdQ0LNceBQfZ2hp4wN0vabegu/8h5bXfDgUHmTGzdxJfyl2I6VENuJnYx2cT/QGeG4QvZ05OIqaRPdPMajrCppEKpxD77YSxbCTn47AY8LcGr99GzN1RlL7taDsg+j5RUzeG7dqj4CBb7yFO8k5dDXwsp7KMO2Z2LfABRkcJHAOc4u53Vi1TUun6g7ufbWabE+PgP0mMssDMphI9/+cGju8kwG2mgOMwOylvfZ1pjC27Zsf6taPtADJgJSINdkeJmihwFE2nhvHao+AgW4sTJ0an/kPcBUk21iM6M/0AONDde82mN9TcfXczu4aYDncN4oK2NjHXxdHufsoYN1HEcRiIuy9paRASNXVq6K49Cg6y9QrRa7pTC9D4Dkh6cz+RRW1/4NNmdirwS3d/rNRS9SF3P5WYj2Beogr0OXd/qfWnOlbEcaifOhtaT5/t7v6ODLc/CPo6gPIBmNmyC0N37VGGxAyZ2e3A8+6+fofLXwss5O5r5Fuy8cPMNibmff8UUUU+A/gj0ZZ+MRGM/dzdJ5VTwvEhz+PQ4/wKfZ3HPmvDkpl0kAzbtUc1B9maAnzZzCakhDlNmdl7iaqoHxdRsPEi9XaeYmZvIrJW7gF8hOiB/yxxwZyvpOKNGzkfByUc60xfd5gc1ERNzQzbtUc1BxlKY1rvJPLof6zZWF0zW5XouPg2YHV3v7e4Uo4/acrUSUQSknmIL+ldxLj4X7r7uJpa28w2ItIor0s0KTTqXOeVyZgy3K6Og7yh32e2zMIgn/MKDjJmMd/8CDGu9Xxirvl/EifFMkTK3W2IaqeD3P175ZR0/ElD9SYSEf270svT3H1govmxMrMtgV8TvfofIQLZhvnd3T2XGRDHchw0dfbwGE+TVw3itUfBQQ7M7FvEcJs5mbWqzoDXiS/FYUWXTYKZfYBoH9zW3bvpRDrQzOxG4uL0qX6Y3a7b42Bm/wbGPHW2u5c+dfZ4N56Cg2qDcu3RWN0cuPv3gZWB7xI5zu8B7k3/PwRYWYFBudz9L+6+G7BU2WUp2OrAuf0QGEBPx+Fy4GjgcTP7XzPbJI24aMjM3m5mXzKzvxAJadYkvocipRiUa486JOYkTcTUd8k6pJa7P192GQr2IjGxUl/p9Dj48EydLeNcv197FBzI0DCzXnoxu7tPzLww/evPRCa33OR9HHw4ps6WITKM1x71OZCh0eX4dyfuKMfb+PflgBuIIbSH5pHnvYzjMIBTZ4976TyZXN3/Y1D7HAzjtUfBgQyNNESvE8sQTT4r0udf0LEys0bTwi4PbERMHXsrjadndnffvcHrnWxTx0HaGqZETcN4zis4kHHDzOYHDgD2I5KR3A58vV865+Whx2yCkOOFazweB5nVeMp0OYjnvPocyNAzs9mIjmuTgbcAjwF7A6fmUa3eZ/omm+A4Pw5SZzzMbDnI57xqDmSomdnHiZnSVgVeAn4IHKkkOMXScZDxZtDPeQUHMpTMbG3gSKJtfSbwC6Iz0xOlFqxEZrYssA7Rznujuz9awDZ1HGRcGZZzXsGBDBUzexsxBn4nIsnX74lc7Q3nuRgvzOxIYF9GJ+NxYsz/13Pano6DjCvDds4rOJChYWaHA/sQ81bcCnwtzZQ2rpnZZ4AziIDgHiJAWCW9vYu7n53x9nQcZFwZxnNewYEMjaqhUfcDv6KzYVLu7kOdydLM/gxsAHzY3a9Mr20OXApc5e6bZ7w9HQcZV4bxnFdwIEOjbmhUJdFIOwM5NKobZvYkcKW7b1/3+vnAxu6+eMbb03GQcWUYz3kNZZRh8rmyC9CnFiUm/qp3D/CpHLan4yDjzdCd8woOZGi4+2lll6FPzUZME17vdTq7w+mKjoOMN8N4zg99EgoRAXpIUysi45f6HMjQMLMNgYfc/ZEOl18TWNPde5lRbWD0mMPe3b2nmkUdBxlvhvGcV82BDJMrgd2qXzCz/c3sv02W/xRwSs5l6hfW5WMs1wYdBxlvhu6cV58DGSaN2s/nARYpuBx9pYQc9joOMt4M3TmvmgMRERGpoeBAREREaig4EBERkRoKDkRERKSGggMZNhqb2x90HGS8GapzXnkOZGj0OJ6ffs5vPoh0HGS8GcZzXkMZZdh0mw5Y0XE+dBxkvBmqc141ByIiIlJDfQ5ERESkhoIDERERqaHgQERERGooOBAREZEaCg5ERESkhoIDERERqaHgQERERGr8f8WuChe6/2xmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as plt\n",
    "\n",
    "fontsize = 18\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.bar(y_pos, bincenters, width=0.8, color=color, yerr=[left_error,right_error], error_kw=dict(lw=1, capsize=2, capthick=1))\n",
    "plt.xticks(y_pos, bars, rotation=90, fontsize=fontsize+2)\n",
    "plt.ylabel('AUROC', fontsize=fontsize+2)\n",
    "# plt.title(str(year)+'-year mortality prediction (Test set)', fontsize=fontsize)\n",
    "plt.yticks(fontsize=fontsize)\n",
    "plt.ylim([0.5, 1])\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.savefig('./result/paper_figures/UKB_'+str(year)+'_mortality_GeoValidation.pdf', format='pdf', bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "### heatmap\n",
    "### 'Age', 'BioAge', 'EXPECT Age\\n(BioAge)', 'PhenoAge', 'EXPECT Age\\n(PheonAge)', 'EXPECT Age-L', 'EXPECT Age-Q', 'EXPECT Age-20', 'EXPECT Age'\n",
    "heat_map_label = np.array([\n",
    "    [0, 0, 0, 1, 1, 1, 0, 1, 1,],  # CBC\n",
    "    [0, 1, 1, 1, 1, 1, 0, 1, 1,],  # CMP\n",
    "    [0, 1, 1, 0, 0, 1, 0, 0, 1,],  # LP\n",
    "    [0, 0, 0, 1, 1, 1, 0, 1, 1,],  # WBC\n",
    "    [0, 1, 1, 1, 1, 0, 0, 1, 1,],  # Other lab tests\n",
    "    [0, 1, 1, 0, 0, 0, 1, 1, 1,],  # Physical measures\n",
    "    [1, 0, 0, 0, 0, 0, 1, 1, 1,],  # Questionnaires\n",
    "])\n",
    "# BioAge: Hba1c\n",
    "# Top20: Cystatin C, SHBG, Apolipoprotein A, Gamma glutamyltransferase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 1.4, 2.4, 3.8, 4.8, 6.2, 7.2, 8.2, 9.2])"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x448 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=18\n",
    "width = 8\n",
    "topx = 8\n",
    "width_height_ratio = np.array(heat_map_label).shape[0]/np.array(heat_map_label).shape[1]\n",
    "fig, ax = plt.subplots(1,1, figsize=(width, width*width_height_ratio)) \n",
    "pos_dict = dict(zip(np.arange(len(y_pos)), y_pos))\n",
    "for idx in range(len(heat_map_label)):\n",
    "    color_idxs = np.where(heat_map_label[idx])[0]\n",
    "    ax.scatter([pos_dict[i] for i in color_idxs], [len(heat_map_label)-1-idx]*len(color_idxs), marker=\"s\", s=(width/topx * 1100), alpha=0.7)\n",
    "ax.set_xticks([pos_dict[i] for i in range(len(y_pos))])\n",
    "# ax.set_yticks(np.arange(heat_map_label.shape[0]))\n",
    "# ax.yaxis.tick_right()\n",
    "ax.axes.yaxis.set_visible(False)\n",
    "\n",
    "# # Set ticks labels for x-axis\n",
    "# for i in np.arange(heat_map_label.shape[1]):\n",
    "#     ax.axvline(pos_dict[i] + .5, alpha=1, c=\"#eeeeee\")\n",
    "# for i in np.arange(heat_map_label.shape[0]):\n",
    "#     ax.axhline(i + .5, alpha=1, c=\"#eeeeee\")\n",
    "# ax.axvline(-.5, alpha=1, c=\"#eeeeee\")\n",
    "\n",
    "\n",
    "ax.set_xticklabels(['Chronological\\nAge', 'BioAgeAccel', 'ENABL AgeAccel\\n(BioAge)', 'PhenoAgeAccel', 'ENABL AgeAccel\\n(PheonAge)', \n",
    "        'ENABL Age-L\\nAccel', 'ENABL Age-Q\\nAccel', 'ENABL Age-20\\nAccel', 'ENABL AgeAccel'], rotation=90, fontsize=fontsize+4);\n",
    "\n",
    "# ax.set_yticklabels(['CBC', 'CMP', 'LP', 'WBC', 'Other lab tests', 'Physical\\nmeasures', 'Questionnaires'][::-1], rotation='horizontal', fontsize=20);\n",
    "ax.set_ylim(-.5, heat_map_label.shape[0]-.5)\n",
    "ax.set_xlim(-.5, max(y_pos)+0.5)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.set_facecolor('None')\n",
    "ax.xaxis.label.set_visible(False)\n",
    "ax.yaxis.label.set_visible(False)\n",
    "\n",
    "plt.savefig('./result/paper_figures/UKB_mortality_category.pdf', format='pdf', bbox_inches='tight', transparent=True)\n",
    "im = plt.show()\n",
    "# cbar = plt.colorbar(im) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_mapper = {\n",
    "    'CBC': '#64a0cb',\n",
    "    'CMP': '#ffa556', \n",
    "    'LP': '#6bbc6b', \n",
    "    'WBC': '#e26768', \n",
    "    'Other lab tests': '#b494d1', \n",
    "    'Physical\\nmeasures': '#ae8881', \n",
    "    'Questionnaires': '#eba0d4'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "custom_lines = [Line2D([0], [0], color=c, lw=5) for c in list(color_mapper.values())]\n",
    "ax = plt.subplot()  #create the axes \n",
    "ax.set_axis_off()  #turn off the axis \n",
    "num_cols = int(np.ceil((len(color_mapper.keys()) + 1)/100))\n",
    "num_cols=1\n",
    "\n",
    "# ax.legend(custom_lines,  list(color_mapper.keys()), frameon=False, ncol=num_cols, prop={'size': 20})\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "custom_lines = [Line2D([0], [0], color=c, lw=25) for c in list(color_mapper.values())]\n",
    "plt.legend(custom_lines,\n",
    "           list(color_mapper.keys()), \n",
    "           handlelength=0.065, handleheight=0.8, frameon=False, prop={'size': 30})\n",
    "plt.savefig('./result/paper_figures/mortality_category_legend.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_mapper = {\n",
    "    'Chronological\\nAge': '#bdbdbd',\n",
    "    'BioAgeAccel': '#80b1d3', \n",
    "    'PhenoAgeAccel': '#bebada', \n",
    "    'ENABL AgeAccel': '#bc80bd', \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "custom_lines = [Line2D([0], [0], color=c, lw=5) for c in list(color_mapper.values())]\n",
    "ax = plt.subplot()  #create the axes \n",
    "ax.set_axis_off()  #turn off the axis \n",
    "num_cols = int(np.ceil((len(color_mapper.keys()) + 1)/100))\n",
    "num_cols=1\n",
    "\n",
    "# ax.legend(custom_lines,  list(color_mapper.keys()), frameon=False, ncol=num_cols, prop={'size': 20})\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "custom_lines = [Line2D([0], [0], color=c, lw=25) for c in list(color_mapper.values())]\n",
    "plt.legend(custom_lines,\n",
    "           list(color_mapper.keys()), \n",
    "           handlelength=0.065, handleheight=0.8, frameon=False, prop={'size': 30})\n",
    "plt.savefig('./result/paper_figures/mortality_barplot_legend.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## EXPECT Age old version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortality = 'all-cause'\n",
    "path = '/homes/gws/wqiu0528/uk_biobank_44562/result/IMPACT_Age_missforest/XGB_Cox_'+mortality+'_remove20002and20004_small_imputed_AgeAdjusted_CancerAdjusted_FloatAge_test_GeoValidation'\n",
    "age_feature = 'Age'\n",
    "gender_feature = '31-0.0'\n",
    "random_state = 528\n",
    "model_path = path+'/model.pickle.dat'\n",
    "model_train = pickle.load(open(model_path, \"rb\"))\n",
    "shap_age_obj_female = pickle.load(open(path+'/different_age_background/'+'female'+'/'+'SHAP_age_exponential'+'/'+'shap_age_object.pkl', 'rb'))\n",
    "shap_age_obj_male = pickle.load(open(path+'/different_age_background/'+'male'+'/'+'SHAP_age_exponential'+'/'+'shap_age_object.pkl', 'rb'))\n",
    "\n",
    "features = pd.read_csv('/projects/leelab/nobackup/wqiu/UK_Biobank_genetic_data/pheno_data/features_initial_preprocessing_missforest_imputed_no_missing_lancet_and_meaningful_adjusted_assays_remove20002and20004_AgeAdjusted_CancerAdjusted_geo.csv')\n",
    "label_df = pd.read_csv('/projects/leelab/nobackup/wqiu/UK_Biobank_genetic_data/pheno_data/death_label.csv')\n",
    "data_mortality = pd.merge(features, label_df[['eid', 'alive_year', mortality, 'external']], how='left', on='eid')        \n",
    "X = data_mortality\n",
    "X = X[(X[age_feature]>=39.5) & (X[age_feature]<=70.5)].reset_index(drop=True)  \n",
    "X = X[(X['external']!=1)]\n",
    "X = X[(X['all-cause']==0) | (X[mortality]==1)]\n",
    "X_geo = X.loc[(X['54-0.0']==11004) | (X['54-0.0']==11005)].reset_index(drop=True)\n",
    "X = X.loc[(X['54-0.0']!=11004) & (X['54-0.0']!=11005)].reset_index(drop=True)\n",
    "mortstat = X['all-cause'].reset_index(drop=True)\n",
    "permth_int = X['alive_year'].reset_index(drop=True)\n",
    "mortstat_geo = X_geo[mortality].reset_index(drop=True)\n",
    "permth_int_geo = X_geo['alive_year'].reset_index(drop=True)\n",
    "print('# samples: ', X.shape[0])\n",
    "print('# positive samples: ', sum(X[mortality]==1))\n",
    "print('# negative samples: ', sum(X[mortality]==0))\n",
    "print('# features: ', X.shape[1])\n",
    "\n",
    "X_train, X_test, _, _ = train_test_split(X, X, test_size=0.2, random_state=random_state)\n",
    "# X_train, X_val, _, _ = train_test_split(X_train, X_train, test_size=0.1, random_state=random_state)\n",
    "print(X_train.index)\n",
    "\n",
    "file = open(\"./feature_names_dictionary_DateToAge.pkl\", \"rb\")\n",
    "col_dict = pickle.load(file)\n",
    "file.close()\n",
    "display_col=[]\n",
    "for col in X_train.columns:\n",
    "    if col in col_dict:\n",
    "        display_col.append(col_dict[col])\n",
    "    else:\n",
    "        display_col.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 5\n",
    "train_label_5 = np.array([label_all(np.array(X_train['alive_year'])[j], np.array(X_train[mortality])[j], year) for j in range(len(X_train))])\n",
    "# val_label_5 = np.array([label_all(np.array(X_val['alive_year'])[j], np.array(X_val[mortality])[j], year) for j in range(len(X_val))])\n",
    "test_label_5 = np.array([label_all(np.array(X_test['alive_year'])[j], np.array(X_test[mortality])[j], year) for j in range(len(X_test))])\n",
    "geo_label_5 = np.array([label_all(np.array(X_geo['alive_year'])[j], np.array(X_geo[mortality])[j], year) for j in range(len(X_geo))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 10\n",
    "train_label_10 = np.array([label_all(np.array(X_train['alive_year'])[j], np.array(X_train[mortality])[j], year) for j in range(len(X_train))])\n",
    "# val_label_10 = np.array([label_all(np.array(X_val['alive_year'])[j], np.array(X_val[mortality])[j], year) for j in range(len(X_val))])\n",
    "test_label_10 = np.array([label_all(np.array(X_test['alive_year'])[j], np.array(X_test[mortality])[j], year) for j in range(len(X_test))])\n",
    "geo_label_10 = np.array([label_all(np.array(X_geo['alive_year'])[j], np.array(X_geo[mortality])[j], year) for j in range(len(X_geo))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['shap_age'] = None\n",
    "fore_prediction_female = model_train.predict(X_train.loc[X_train[gender_feature]==0, model_train.get_booster().feature_names], output_margin=True)\n",
    "X_train.loc[X_train[gender_feature]==0, 'shap_age'] = np.array([shap_age_obj_female.get_shap_age(fore_prediction_female[i]) for i in range(len(fore_prediction_female))])\n",
    "fore_prediction_male = model_train.predict(X_train.loc[X_train[gender_feature]==1, model_train.get_booster().feature_names], output_margin=True)\n",
    "X_train.loc[X_train[gender_feature]==1, 'shap_age'] = np.array([shap_age_obj_male.get_shap_age(fore_prediction_male[i]) for i in range(len(fore_prediction_male))])\n",
    "X_train['shap_age_acc'] = X_train['shap_age']-X_train['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_val['shap_age'] = None\n",
    "# fore_prediction_female = model_train.predict(X_val.loc[X_val[gender_feature]==0, model_train.get_booster().feature_names], output_margin=True)\n",
    "# X_val.loc[X_val[gender_feature]==0, 'shap_age'] = np.array([shap_age_obj_female.get_shap_age(fore_prediction_female[i]) for i in range(len(fore_prediction_female))])\n",
    "# fore_prediction_male = model_train.predict(X_val.loc[X_val[gender_feature]==1, model_train.get_booster().feature_names], output_margin=True)\n",
    "# X_val.loc[X_val[gender_feature]==1, 'shap_age'] = np.array([shap_age_obj_male.get_shap_age(fore_prediction_male[i]) for i in range(len(fore_prediction_male))])\n",
    "# X_val['shap_age_acc'] = X_val['shap_age']-X_val['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test['shap_age'] = None\n",
    "fore_prediction_female = model_train.predict(X_test.loc[X_test[gender_feature]==0, model_train.get_booster().feature_names], output_margin=True)\n",
    "X_test.loc[X_test[gender_feature]==0, 'shap_age'] = np.array([shap_age_obj_female.get_shap_age(fore_prediction_female[i]) for i in range(len(fore_prediction_female))])\n",
    "fore_prediction_male = model_train.predict(X_test.loc[X_test[gender_feature]==1, model_train.get_booster().feature_names], output_margin=True)\n",
    "X_test.loc[X_test[gender_feature]==1, 'shap_age'] = np.array([shap_age_obj_male.get_shap_age(fore_prediction_male[i]) for i in range(len(fore_prediction_male))])\n",
    "X_test['shap_age_acc'] = X_test['shap_age']-X_test['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_geo['shap_age'] = None\n",
    "fore_prediction_female = model_train.predict(X_geo.loc[X_geo[gender_feature]==0, model_train.get_booster().feature_names], output_margin=True)\n",
    "X_geo.loc[X_geo[gender_feature]==0, 'shap_age'] = np.array([shap_age_obj_female.get_shap_age(fore_prediction_female[i]) for i in range(len(fore_prediction_female))])\n",
    "fore_prediction_male = model_train.predict(X_geo.loc[X_geo[gender_feature]==1, model_train.get_booster().feature_names], output_margin=True)\n",
    "X_geo.loc[X_geo[gender_feature]==1, 'shap_age'] = np.array([shap_age_obj_male.get_shap_age(fore_prediction_male[i]) for i in range(len(fore_prediction_male))])\n",
    "X_geo['shap_age_acc'] = X_geo['shap_age']-X_geo['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result['shap_age'] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 5\n",
    "train_label_5_temp = train_label_5[X_train['shap_age']!=np.inf]\n",
    "X_train_temp = X_train[X_train['shap_age']!=np.inf]\n",
    "# val_label_5_temp = val_label_5[X_val['shap_age']!=np.inf]\n",
    "# X_val = X_val[X_val['shap_age']!=np.inf]\n",
    "test_label_5_temp = test_label_5[X_test['shap_age']!=np.inf]\n",
    "X_test_temp = X_test[X_test['shap_age']!=np.inf]\n",
    "geo_label_5_temp = geo_label_5[X_geo['shap_age']!=np.inf]\n",
    "X_geo_temp = X_geo[X_geo['shap_age']!=np.inf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5-year mortality, chronological age + biological age acc + Gender\n",
    "feature_list = [age_feature, 'shap_age_acc', gender_feature]\n",
    "# _, xgb_model = val_mortality_model(X_train_temp[feature_list], X_val_temp[feature_list], X_test_temp[feature_list], train_label_5_temp, val_label_5_temp, test_label_5_temp)\n",
    "_, xgb_model = val_mortality_model(X_train_temp[feature_list], X_test_temp[feature_list], train_label_5_temp, test_label_5_temp)\n",
    "res, left_res, right_res = bootstrap_ci(xgb_model.predict_proba(np.array(X_test_temp[feature_list]))[:, 1], test_label_5_temp, len(X_test_temp), repetitions = 1000, alpha = 0.05, metric='AUC')\n",
    "result['shap_age']['5_year_test'] = {'res': res, 'left_res': left_res, 'right_res': right_res}\n",
    "res, left_res, right_res = bootstrap_ci(xgb_model.predict_proba(np.array(X_geo_temp[feature_list]))[:, 1], geo_label_5_temp, len(X_geo_temp), repetitions = 1000, alpha = 0.05, metric='AUC')\n",
    "result['shap_age']['5_year_geo'] = {'res': res, 'left_res': left_res, 'right_res': right_res}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 10\n",
    "train_label_10_temp = train_label_10[X_train['shap_age']!=np.inf]\n",
    "X_train_temp = X_train[X_train['shap_age']!=np.inf]\n",
    "# val_label_10_temp = val_label_10[X_val['shap_age']!=np.inf]\n",
    "# X_val = X_val[X_val['shap_age']!=np.inf]\n",
    "test_label_10_temp = test_label_10[X_test['shap_age']!=np.inf]\n",
    "X_test_temp = X_test[X_test['shap_age']!=np.inf]\n",
    "geo_label_10_temp = geo_label_10[X_geo['shap_age']!=np.inf]\n",
    "X_geo_temp = X_geo[X_geo['shap_age']!=np.inf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10-year mortality, chronological age + biological age acc + Gender\n",
    "feature_list = [age_feature, 'shap_age_acc', gender_feature]\n",
    "# _, xgb_model = val_mortality_model(X_train[feature_list], X_val[feature_list], X_test[feature_list], train_label_10, val_label_10, test_label_10)\n",
    "_, xgb_model = val_mortality_model(X_train[feature_list], X_test[feature_list], train_label_10, test_label_10)\n",
    "res, left_res, right_res = bootstrap_ci(xgb_model.predict_proba(np.array(X_test[feature_list]))[:, 1], test_label_10, len(X_test), repetitions = 1000, alpha = 0.05, metric='AUC')\n",
    "result['shap_age']['10_year_test'] = {'res': res, 'left_res': left_res, 'right_res': right_res}\n",
    "res, left_res, right_res = bootstrap_ci(xgb_model.predict_proba(np.array(X_geo[feature_list]))[:, 1], geo_label_10, len(X_geo), repetitions = 1000, alpha = 0.05, metric='AUC')\n",
    "result['shap_age']['10_year_geo'] = {'res': res, 'left_res': left_res, 'right_res': right_res}"
   ]
  }
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