{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b2f8545bfe2a",
   "metadata": {},
   "source": [
    "# Logistic regression analyses: linear and cubic models\n",
    "\n",
    "The same lung SUV data are analysed on both the **plain** and **log-transformed** predictor scales using three regression models:\n",
    "\n",
    "1. [Standard (linear) logistic regression](#2-standard-linear-logistic-regression)\n",
    "2. [Cubic unconstrained (non-monotonic) logistic regression](#3-cubic-unconstrained-non-monotonic-logistic-regression)\n",
    "3. [Cubic constrained (monotonic) logistic regression](#4-cubic-constrained-monotonic-logistic-regression)\n",
    "\n",
    "\n",
    "Each section discussed a separate logistic model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f6904454fdc",
   "metadata": {},
   "source": [
    "## 1. Shared setup and data\n",
    "\n",
    "The data-loading and predictor-transformation steps are common to all three models. Model-specific figures are written to separate subdirectories below `../results/logit_models/`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "737bb31e0d5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import sys\n",
    "\n",
    "project_root = Path.cwd().parent   # when notebook is in project/notebooks/\n",
    "src_dir = project_root / \"src\"\n",
    "\n",
    "if str(src_dir) not in sys.path: sys.path.insert(0, str(src_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b867066e59d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# our libs\n",
    "from irae_risk import data\n",
    "from irae_risk import logistic\n",
    "\n",
    "np.set_printoptions(precision=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86f652a20401",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = Path(\"../data/raw\")\n",
    "results_root = Path(\"../results/logit_all_models\")\n",
    "results_root.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "df = data.load_mat_data_to_dataframe(\n",
    "    suv_filename=data_path / \"suv_percentilesSLOthenUWM.mat\",\n",
    "    flags_filename=data_path / \"flags_combined.mat\",\n",
    "    nr_patients=58,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5956c35b9f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get data\n",
    "perc = 95\n",
    "organ = \"lung\"\n",
    "\n",
    "x0, y = data.get_data_from_dataframe(df, organ, perc)\n",
    "\n",
    "scales = [\"plain\", \"log\"]\n",
    "xs = [x0, np.log(x0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f8e352bec94c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Two-sided 95% confidence intervals used throughout the notebook\n",
    "alpha = 0.05\n",
    "probs = [alpha / 2, 1 - alpha / 2]\n",
    "\n",
    "# Reproducibility: pass this seed to every stochastic calculation.\n",
    "random_seed = logistic.DEFAULT_RANDOM_SEED\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c8948cbea0",
   "metadata": {},
   "source": [
    "## 2. Standard (linear) logistic regression\n",
    "\n",
    "The log-odds are linear in the selected predictor scale,\n",
    "\n",
    "$$\n",
    "\\eta(t) = \\beta_0 + \\beta_1 t,\n",
    "\\qquad\n",
    "p(t) = \\operatorname{expit}\\!\\left(\\eta(t)\\right).\n",
    "$$\n",
    "\n",
    "The model is unconstrained (`degree=1`, `mono=False`). The analysis includes the fit, asymptotic parameter covariance and confidence intervals, goodness-of-fit measures, and normal/delta confidence bands for the fitted risk curve.\n",
    "\n",
    "References: [scikit-learn logistic regression documentation](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) and [discussion of the optimized objective](https://stats.stackexchange.com/questions/186830/what-is-scikit-learns-logisticregression-minimizing).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39cbb1957ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_path = results_root / \"linear\"\n",
    "results_path.mkdir(parents=True, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e977ed6c50",
   "metadata": {},
   "source": [
    "### 2.1 Fit and parameter uncertainty\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "774797739c35",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thetas = array([[-11.68387204638564 ,   5.686965443671553],\n",
      "       [ -7.527409035255565,  10.73815986880034 ]])\n"
     ]
    }
   ],
   "source": [
    "# unconstrained linear logistic-regression model\n",
    "logit = logistic.LogisticPolyRegression(degree=1, mono=False)\n",
    "\n",
    "# fits\n",
    "fit_results = [logit.fit(x, y, method=\"local\", seed=random_seed) for x in xs]\n",
    "if not all(result[\"success\"] for result in fit_results):\n",
    "    raise RuntimeError(\"At least one linear logistic-regression fit failed.\")\n",
    "\n",
    "thetas = np.array([result[\"theta\"] for result in fit_results])\n",
    "print(f\"{thetas = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82a3535b87ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov_thetas = array([[[12.323037015518926 , -6.7129191282761775],\n",
      "        [-6.7129191282761775,  3.8187839258530176]],\n",
      "\n",
      "       [[ 5.0502362164436665, -7.934551247510305 ],\n",
      "        [-7.934551247510305 , 14.00597781508227  ]]])\n"
     ]
    }
   ],
   "source": [
    "# asymptotic covariance matrix of parameters\n",
    "cov_thetas = np.array([logit.get_cov(x, y, theta) for x, theta in zip(xs, thetas)])\n",
    "print(f\"{cov_thetas = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a4a84bad7b8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta_CIs = array([[[-18.56416558961513  ,   1.8568608502834008],\n",
      "        [ -4.803578503156151 ,   9.517070037059703 ]],\n",
      "\n",
      "       [[-11.931983300748936 ,   3.4030806585808593],\n",
      "        [ -3.1228347697621963,  18.073239079019817 ]]])\n"
     ]
    }
   ],
   "source": [
    "# CI of params (assuming asymptotic distr of parameters)\n",
    "theta_CIs = np.array([\n",
    "    logit.get_theta_quantiles_normal(probs, theta, cov_theta)\n",
    "    for theta, cov_theta in zip(thetas, cov_thetas)\n",
    "])\n",
    "\n",
    "print(f\"{theta_CIs = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2e5269ad11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "('scale', 'coef')",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "value",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "LCL",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "UCL",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "5708373e-7350-41f0-b756-30457c39467b",
       "rows": [
        [
         "('plain', 'b0')",
         "-11.68387204638564",
         "-18.56416558961513",
         "-4.803578503156151"
        ],
        [
         "('plain', 'b1')",
         "5.686965443671553",
         "1.8568608502834008",
         "9.517070037059703"
        ],
        [
         "('log', 'b0')",
         "-7.527409035255565",
         "-11.931983300748936",
         "-3.1228347697621963"
        ],
        [
         "('log', 'b1')",
         "10.73815986880034",
         "3.4030806585808593",
         "18.073239079019817"
        ]
       ],
       "shape": {
        "columns": 3,
        "rows": 4
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>LCL</th>\n",
       "      <th>UCL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scale</th>\n",
       "      <th>coef</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">plain</th>\n",
       "      <th>b0</th>\n",
       "      <td>-11.683872</td>\n",
       "      <td>-18.564166</td>\n",
       "      <td>-4.803579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1</th>\n",
       "      <td>5.686965</td>\n",
       "      <td>1.856861</td>\n",
       "      <td>9.517070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">log</th>\n",
       "      <th>b0</th>\n",
       "      <td>-7.527409</td>\n",
       "      <td>-11.931983</td>\n",
       "      <td>-3.122835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1</th>\n",
       "      <td>10.738160</td>\n",
       "      <td>3.403081</td>\n",
       "      <td>18.073239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                value        LCL        UCL\n",
       "scale coef                                 \n",
       "plain b0   -11.683872 -18.564166  -4.803579\n",
       "      b1     5.686965   1.856861   9.517070\n",
       "log   b0    -7.527409 -11.931983  -3.122835\n",
       "      b1    10.738160   3.403081  18.073239"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# present table of results\n",
    "d = thetas.shape[-1]\n",
    "index = pd.MultiIndex.from_tuples([(lab, f\"b{i}\") for lab in scales for i in range(d)], names = ['scale', 'coef'])\n",
    "pd.DataFrame({\"value\": thetas.flatten(), \"LCL\": theta_CIs[:,0].flatten(), \"UCL\": theta_CIs[:,1].flatten()}, index = index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5fc72adc9c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "LLF",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "AIC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BIC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "A",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "deviance",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "p-value(deviance_bootstrap)",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "deviance_bootstrap_samples",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "n",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "k",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "dof",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "f3cdcc5e-a60d-44ef-a144-3ef3c7c35538",
       "rows": [
        [
         "plain",
         "-7.394195051363893",
         "18.78839010272779",
         "22.909276123820625",
         "0.9655172413793104",
         "14.788390102727787",
         "0.3956043956043956",
         "1000",
         "58",
         "2",
         "56"
        ],
        [
         "log",
         "-6.795375128439737",
         "17.590750256879474",
         "21.71163627797231",
         "0.9655172413793104",
         "13.590750256879474",
         "0.39760239760239763",
         "1000",
         "58",
         "2",
         "56"
        ]
       ],
       "shape": {
        "columns": 10,
        "rows": 2
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LLF</th>\n",
       "      <th>AIC</th>\n",
       "      <th>BIC</th>\n",
       "      <th>A</th>\n",
       "      <th>deviance</th>\n",
       "      <th>p-value(deviance_bootstrap)</th>\n",
       "      <th>deviance_bootstrap_samples</th>\n",
       "      <th>n</th>\n",
       "      <th>k</th>\n",
       "      <th>dof</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scale</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>plain</th>\n",
       "      <td>-7.394195</td>\n",
       "      <td>18.78839</td>\n",
       "      <td>22.909276</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>14.78839</td>\n",
       "      <td>0.395604</td>\n",
       "      <td>1000</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log</th>\n",
       "      <td>-6.795375</td>\n",
       "      <td>17.59075</td>\n",
       "      <td>21.711636</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>13.59075</td>\n",
       "      <td>0.397602</td>\n",
       "      <td>1000</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LLF       AIC        BIC         A  deviance  \\\n",
       "scale                                                      \n",
       "plain -7.394195  18.78839  22.909276  0.965517  14.78839   \n",
       "log   -6.795375  17.59075  21.711636  0.965517  13.59075   \n",
       "\n",
       "       p-value(deviance_bootstrap)  deviance_bootstrap_samples   n  k  dof  \n",
       "scale                                                                       \n",
       "plain                     0.395604                        1000  58  2   56  \n",
       "log                       0.397602                        1000  58  2   56  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# goodness of fit measures\n",
    "index = pd.Index(scales, name = 'scale')\n",
    "pd.DataFrame(\n",
    "    [logit.goodness_of_fit(x, y, theta, bootstrap_seed=random_seed)\n",
    "     for x, theta in zip(xs, thetas)],\n",
    "    index=index,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32deb7403fd0",
   "metadata": {},
   "source": [
    "### 2.2 Fitted risk curve and confidence bands\n",
    "\n",
    "**Coverage note.** The term *confidence band* is used in its standard graphical sense. The displayed limits are pointwise confidence intervals evaluated separately at each predictor value; they do not provide simultaneous coverage of the complete fitted curve.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5630ea114f77",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABHIAAAHaCAYAAABsPG7IAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xd8W/X1+P/XlWRZ3nsmjp1BFpkEEpIACRBISEgYgbBJ2NAWSvn2x2jZtNDSwqf9lFJKS4G2KYUuyiq0rE8pUHZoITvx3lu2JGvd9++Pazl27CS2Y+lK9nny8CP4+kr3eN63zvv9PkdTSimEEEIIIYQQQgghRNSzmB2AEEIIIYQQQgghhBgcSeQIIYQQQgghhBBCxAhJ5AghhBBCCCGEEELECEnkCCGEEEIIIYQQQsQISeQIIYQQQgghhBBCxAhJ5AghhBBCCCGEEELECEnkCCGEEEIIIYQQQsQISeQIIYQQQgghhBBCxAhJ5AghhBBCCCGEEELECEnkiDFh+fLlLF++3OwwDmnTpk0kJyeP+PO++uqrzJs3D4fDgaZptLW1jfg1xrJNmzZRUlJiyrU1TePuu+8e8uPefvttNE3j7bffHvGYhBAiGslYQMYC4TTSY4G7774bTdNG7PmEEKOLJHKEGOWam5vZsGEDCQkJ/PSnP+U3v/kNSUlJEbn2o48+iqZpLFq06IDnaJp2wLdrr702InEKIYQQo5mMBYQQYnSxmR2AECK8PvroIzo6OrjvvvtYsWJFRK+9efNmSkpK+PDDD9m9ezdTpkwZ8LxTTjmFSy+9tN/xqVOnhjvEMeuEE07A4/Fgt9vNDkUIIUSYyVhACCFGF0nkCBFGXV1d2O12LBbzFr81NDQAkJ6ePmLP6XK5DjmTV1paynvvvcef//xnrrnmGjZv3sxdd9014LlTp07l4osvHrH4xKFZLBYcDofZYQghxKgnYwEZCwghxEiTrVUibKqrq7niiisoLCwkPj6eiRMnct111+Hz+XrO2bt3L+eeey6ZmZkkJiZy7LHH8vLLL/d5nlAtj+eee47vfve7jB8/HofDwcknn8zu3bv7Xffxxx9n8uTJJCQksHDhQt55553D+jyWL1/OrFmz+OSTT1iyZAkJCQlMnDiRxx57bMA4f//733P77bczbtw4EhMTcTqdAPzhD39gwYIFJCQkkJ2dzcUXX0x1dfWA19y7dy8rV64kKSmJwsJC7r33XpRSw4p948aNABxzzDFomsamTZt6Pj6YmEJ79ffs2cPq1atJSUnhoosuOuS1N2/eTEZGBmvWrOGcc85h8+bNQ46/N7fbzfbt22lqajrkubt27WL9+vXk5+fjcDgYP348559/Pu3t7X3O++1vf8vChQtJTEwkIyODE044gb///e89H//rX//KmjVren6GJ0+ezH333UcwGDxkDLqu86Mf/YgjjzwSh8NBXl4e11xzDa2trYd8bOhrPpyfg/Lycr7yla8wbdo0EhISyMrK4txzz6WsrKzPeQPVyAn9rG/dupUTTzyRxMRExo0bx4MPPnjImIUQYiAyFpCxgIwFhjcWGEggEOC+++5j8uTJxMfHU1JSwre+9S28Xm+/6959990UFhaSmJjIiSeeyNatWykpKenzvRdCxC5ZkSPCoqamhoULF9LW1sbVV1/N9OnTqa6u5o9//CNutxu73U59fT1LlizB7XZzww03kJWVxdNPP826dev44x//yFlnndXnOb/3ve9hsVj45je/SXt7Ow8++CAXXXQRH3zwQc85TzzxBNdccw1LlizhxhtvZO/evaxbt47MzEyKioqG/fm0trayevVqNmzYwAUXXMBzzz3Hddddh91u5/LLL+9z7n333Yfdbueb3/wmXq8Xu93OU089xWWXXcYxxxzDAw88QH19PT/+8Y959913+eyzz/rMkAWDQVatWsWxxx7Lgw8+yKuvvspdd91FIBDg3nvvHVLc3/72t5k2bRqPP/449957LxMnTmTy5MkAQ4opEAiwcuVKjjvuOH74wx+SmJh4yGtv3ryZs88+G7vdzgUXXMDPfvYzPvroI4455ph+53Z1dQ04KEtNTe3Z+vPhhx9y4oknctdddx20uK/P52PlypV4vV6uv/568vPzqa6u5qWXXqKtrY20tDQA7rnnHu6++26WLFnCvffei91u54MPPuDNN9/k1FNP7fkaJScnc9NNN5GcnMybb77JnXfeidPp5Ac/+MFBP/9rrrmm52t8ww03UFpayiOPPMJnn33Gu+++S1xc3EEfP9yfg48++oj33nuP888/n/Hjx1NWVsbPfvYzli9fztatWw/5vWttbWXVqlWcffbZbNiwgT/+8Y/ccsstzJ49m9NOO+2gjxVCiN5kLCBjARkLHN5YYH9XXnklTz/9NOeccw7/7//9Pz744AMeeOABtm3bxl/+8pee82677TYefPBB1q5dy8qVK/n8889ZuXIlXV1dQ7qeECKKKSHC4NJLL1UWi0V99NFH/T6m67pSSqkbb7xRAeqdd97p+VhHR4eaOHGiKikpUcFgUCml1FtvvaUANWPGDOX1envO/fGPf6wA9d///lcppZTP51O5ublq3rx5fc57/PHHFaCWLVs2rM9l2bJlClAPPfRQzzGv16vmzZuncnNzlc/n6xPnpEmTlNvt7jk3FNesWbOUx+PpOf7SSy8pQN155509xzZu3KgAdf311/f5eq1Zs0bZ7XbV2Ng45PiffPJJBfT5XgwnpltvvXXQ1/z4448VoP7xj3/0fA7jx49XX//61/udCxzw7Zlnnuk5L/T1veuuuw567c8++0wB6g9/+MMBz9m1a5eyWCzqrLPO6vk5Cwn9fCql+nwfQ6655hqVmJiourq6eo5t3LhRFRcX97z/zjvvKEBt3ry5z2NfffXVAY/vbyg/B/t/TQaK+f3331eA+vWvf91zLPT1fOutt3qOhX7We5/n9XpVfn6+Wr9+/UFjFkKI/clYQMYCMhYY/ljgrrvuUr1fqm3ZskUB6sorr+xz3je/+U0FqDfffFMppVRdXZ2y2WzqzDPP7HPe3XffrQC1cePGg15XCBEbZGuVGHG6rvP888+zdu1ajj766H4fD7VSfOWVV1i4cCHHHXdcz8eSk5O5+uqrKSsrY+vWrX0ed9lll/UpzHr88ccDxtJjgI8//piGhgauvfbaPudt2rSpZ+ZluGw2G9dcc03P+3a7nWuuuYaGhgY++eSTPudu3LiRhISEnvdDcX3lK1/pU5NkzZo1TJ8+vd/ycYCvfe1rPf+vaRpf+9rX8Pl8vP7664f1eRxOTNddd92gn3/z5s3k5eVx4oknAsbncN555/H73/9+wKXIZ5xxBv/4xz/6vYUeD8bScKXUIVtth77Xr732Gm63e8Bznn/+eXRd58477+xXs6B3q8/e38eOjg6ampo4/vjje5Z2H8gf/vAH0tLSOOWUU2hqaup5W7BgAcnJybz11lsH/RxChvNz0Dtmv99Pc3MzU6ZMIT09nU8//fSQ10xOTu5To8But7Nw4cKe3zMhhBgMGQvIWEDGAiMzFgh55ZVXALjpppv6HP9//+//AfR8v9544w0CgQBf+cpX+px3/fXXD+l6QojoJokcMeIaGxtxOp3MmjXroOeVl5czbdq0fsdnzJjR8/HeJkyY0Of9jIwMgJ59xqHzjzjiiD7nxcXFMWnSpCF8Bv0VFhb2K+gX6qKwf+2RiRMn9nk/FNdAn+v06dP7fZ4Wi6VfvAe61nANNSabzcb48eMH9dzBYJDf//73nHjiiZSWlrJ79252797NokWLqK+v54033uj3mPHjx7NixYp+b3l5eUP+3CZOnMhNN93EL3/5S7Kzs1m5ciU//elP++yJ37NnDxaLhZkzZx70ub788kvOOuss0tLSSE1NJScnpyfJsf8e+9527dpFe3s7ubm55OTk9Hnr7OzsKTp5MMP9OfB4PNx5550UFRURHx9PdnY2OTk5tLW1HTTmkPHjx/cZwILxuzbc/fxCiLFJxgIyFpCxwOGPBXorLy/HYrH06/qVn59Penp6z/cr9O/+52VmZvb8vgghYp/UyBExw2q1DnhcDaPwXzj1nrkZLeLj4wfdbePNN9+ktraW3//+9/z+97/v9/HNmzf37DsPl4ceeohNmzbx17/+lb///e/ccMMNPPDAA/z73/8e9CC0ra2NZcuWkZqayr333svkyZNxOBx8+umn3HLLLei6fsDH6rpObm7uAYs65uTkDOvzGozrr7+eJ598khtvvJHFixeTlpaGpmmcf/75B405JFZ+z4QQY1Os/I2SsYCMBcI1Fth/skUIMTZJIkeMuJycHFJTU/niiy8Oel5xcTE7duzodzy0TLW4uHhI1w2dv2vXLk466aSe436/n9LSUubOnTuk5+utpqamX5vNnTt3AlBSUjKouHbs2NEnrtCx/T9PXdfZu3dvz8zbUK41WEONaSg2b95Mbm4uP/3pT/t97M9//jN/+ctfeOyxx8I+yJ09ezazZ8/m9ttv57333mPp0qU89thjfOc732Hy5Mnous7WrVuZN2/egI9/++23aW5u5s9//jMnnHBCz/HS0tJDXnvy5Mm8/vrrLF26dNif53B/Dv74xz+yceNGHnrooZ5jXV1dtLW1DSsOIYQYDhkLDByXjAVkLDBcxcXF6LrOrl27elasAdTX19PW1tbz/Qr9u3v37j4rw5qbm2V1rRCjiGytEiPOYrFw5pln8uKLL/Lxxx/3+3ho1mz16tV8+OGHvP/++z0fc7lcPP7445SUlBxyqev+jj76aHJycnjsscf6tDV96qmnBnwRu337dioqKgb13IFAgJ///Oc97/t8Pn7+85+Tk5PDggULDhlXbm4ujz32WJ/2kH/729/Ytm0ba9as6feYRx55pOf/lVI88sgjxMXFcfLJJ/cc37NnD3v27BlU/CMR02B4PB7+/Oc/c/rpp3POOef0e/va175GR0cHL7zwwpCfe7AtR51OJ4FAoM+x2bNnY7FYej7XM888E4vFwr333ttvNi308xma9e09y+vz+Xj00UcPGeuGDRsIBoPcd999/T4WCAQGnVQZzM/B/qxWa7+Z6Z/85CeDapMqhBAjRcYC/eOSsYCMBUKGMhYIWb16NQA/+tGP+hx/+OGHAXq+XyeffDI2m42f/exnfc7r/fMkhIh9siJHhMX999/P3//+d5YtW8bVV1/NjBkzqK2t5Q9/+AP/+te/SE9P59Zbb+WZZ57htNNO44YbbiAzM5Onn36a0tJS/vSnPw16+W5IXFwc3/nOd7jmmms46aSTOO+88ygtLeXJJ58ccF/8jBkzWLZsGW+//fYhn7uwsJDvf//7lJWVMXXqVJ599lm2bNnC448/fsjWkXFxcXz/+9/nsssuY9myZVxwwQU97T1LSkr4xje+0ed8h8PBq6++ysaNG1m0aBF/+9vfePnll/nWt77VZxluaCA3nL3yQ41psF544QU6OjpYt27dgB8/9thjycnJYfPmzZx33nk9x3fu3Mlvf/vbfufn5eVxyimnAINvOfrmm2/yta99jXPPPZepU6cSCAT4zW9+g9VqZf369YCxb/zb3/429913H8cffzxnn3028fHxfPTRRxQWFvLAAw+wZMkSMjIy2LhxIzfccAOapvGb3/xmUMv3ly1bxjXXXMMDDzzAli1bOPXUU4mLi2PXrl384Q9/4Mc//jHnnHPOQZ9jsD8H+zv99NP5zW9+Q1paGjNnzuT999/n9ddfJysr65BxCyHESJKxQN+4ZCxgkLHA4McCvc2dO5eNGzfy+OOP92z5+vDDD3n66ac588wze4pC5+Xl8fWvf52HHnqIdevWsWrVKj7//HP+9re/kZ2dLVuzhBgtIt4nS4wZ5eXl6tJLL1U5OTkqPj5eTZo0SX31q1/t0w50z5496pxzzlHp6enK4XCohQsXqpdeeqnP84RaTe7fQrK0tFQB6sknn+xz/NFHH1UTJ05U8fHx6uijj1b//Oc/1bJly/q1HGWQbUiXLVumjjzySPXxxx+rxYsXK4fDoYqLi9UjjzwyqDhDnn32WTV//nwVHx+vMjMz1UUXXaSqqqr6nLNx40aVlJSk9uzZo0499VSVmJio8vLy1F133dWvNWZxcXGfNpcHMlDL0eHENBhr165VDodDuVyuA56zadMmFRcXp5qampRSB2852vv7M9iWo3v37lWXX365mjx5snI4HCozM1OdeOKJ6vXXX+937q9+9auezz8jI0MtW7asp02qUkq9++676thjj1UJCQmqsLBQ3Xzzzeq1117r17Z7/5ajIY8//rhasGCBSkhIUCkpKWr27Nnq5ptvVjU1NQf9HIbyc7D/16S1tVVddtllKjs7WyUnJ6uVK1eq7du3q+Li4j4tRw/UfvzII48cMJ7B/KwJIcT+ZCzQl4wFDDIWOPRYYP/240op5ff71T333KMmTpyo4uLiVFFRkbrtttv6tEFXSqlAIKDuuOMOlZ+frxISEtRJJ52ktm3bprKystS111570OsKIWKDplSUVYcTIsosX76cpqamQ+7zF2KkbNq0iT/+8Y90dnaaHYoQQghkLCBiX1tbGxkZGXznO9/h29/+ttnhCCEOk9TIEUIIIYQQQohRwuPx9DsWqq2zfPnyyAYjhAgLqZEjhBBCCCGEEKPEs88+y1NPPcXq1atJTk7mX//6F8888wynnnoqS5cuNTs8IcQIkESOEEIIIYQQQowSc+bMwWaz8eCDD+J0OnsKIH/nO98xOzQhxAiRGjlCCCGEEEIIIYQQMUJq5AghhBBCCCGEEELECEnkCCGEEEIIIYQQQsSIMVkjR9d1ampqSElJQdM0s8MRQgghUErR0dFBYWEhFovMsxwuudcLIYSINnKvFyNlTCZyampqKCoqMjsMIYQQop/KykrGjx9vdhgxT+71QgghopXc68XhGpOJnJSUFMD4BUpNTTU5GiGEEAKcTidFRUU99yhxeOReL4QQItrIvV6MlDGZyAktsU5NTZXBnRBCiKgi24BGhtzrhRBCRCu514vDJRvzhBBCCCGEEEIIIWKEJHKEEEIIIYQQQgghYoQkcoQQQgghhBBCCCFihCRyhBBCCCGEEEIIIWKEJHKEEEIIIYQQQgghYoQkcoQQQgghhBBCCCFihCRyhBBCCCGEEEIIIWKEJHKEEEIIIYQQQgghYoQkcoQQQgghhBBCCCFihM3sAIQICz0I5e9BZz0k50HxErBYzY5qcEKxd9SCqxGSciClYPCfQyx/7kLECvk9E0LEitH090oPQtm/oPQd0IDi42Di8TI+EuEjPzciSpmeyPnnP//JD37wAz755BNqa2v5y1/+wplnnnnQx7z99tvcdNNNfPnllxQVFXH77bezadOmiMQrYsDWF+DVW8BZs+9YaiGs+j7MXGdeXIMxUOwhg/kcYvlzFyJWyO/ZkMm9XgiTjKa/V1tfgBe/Dp6WXgd/AAkZsPZ/ZXwkRp783IgoZvrWKpfLxdy5c/npT386qPNLS0tZs2YNJ554Ilu2bOHGG2/kyiuv5LXXXgtzpCImbH0Bnru0fyLEWWsc3/qCOXENxoFiD3HWHPxziOXPXYhYIb9nwyL3eiFMMJr+Xm19AZ67ZL8kTjdPq/ExGR+JkSQ/NyLKaUopZXYQIZqmHXKW7pZbbuHll1/miy++6Dl2/vnn09bWxquvvjqo6zidTtLS0mhvbyc1NfVwwxbRQg/Cj2YdOBGCZmTRb/xv9C2JPGTsvaSO6/85xPLnLkSsCPPv2Vi5N8m9XogIGE3jgsGOkVIK4RtfyPhIHL4w/tzIvUmMFNO3Vg3V+++/z4oVK/ocW7lyJTfeeOMBH+P1evF6vT3vO53OcIUnzFT+3iFu8gqc1cZ5E4+PWFiDcsjYexnoc4jlz32MUErhDeh4/TpdgSBd/iBdfh1vIIgvoOMN6PgCOr6gjj9o/L8/qOMPKgKhf3VFUNcJ6IpA9/u6UgT1Xm9KofccN66rK0VQga6U8b5u/L+uAIx/Vff7qjtWpUDR/W/v/zce0vf97sd0f6j7ffa9v998Qf9z9vv4IaYXDvnxg394v+cawtk+F7Re3+/wt2y/4wTrf5Hfs5Ej93ohDtNoGhcMdozUUSPjo1EiqCs8/iBefxBvQKer+9/QOMnr7x4rBUNjJR1/QOHXdQJBhT+oE9RD46bu8ZK+bxwV1CGo6+iKgcdNHQ3oTWehY0GhoaOxxPIll9he745Qfm6E+WIukVNXV0deXl6fY3l5eTidTjweDwkJCf0e88ADD3DPPfdEKkRhls76kT0vkgaIKYjiafz8B73/+e9+D3b8ad/7TbuArkNfZ//HhZuuQyAAgSAEAxAM9n3T9f3eFKje//Z6U2rfv/u/Qa//78lG7Ms6hD4esi8bsZ/9j+17RykIEI+XZLwk4ut5S8CvEvDjwI+DAA78xBPAToB4AsQTJI4gcShkti/2Ffc7sq3DzgnpvQ5E49+YGCP3eiEOUyyPifbXHeNv8fHxQGOi3mJlfATGwCIYBH+g/xip3/hov3GSUsb/68F946M+YyS911io11hJD339eo2Vut/dNwYaYLzU5/BA46d9J+jK2j1OSsJHAj4Su8dICfiVo9d4KTRWsnePlewEusdLOnEj8zU+LEv7vNfmdnJJ+n6nxMLvjxi1Yi6RMxy33XYbN910U8/7TqeToqIiEyMSYZGcd+hzhnJeJA0Q09sEeZUAydD/5b+mGasDer9/qMHNQI87HMEA+Hzg9YLXBz6vMRjp6jKO+bzdA5P9kjOhAUconoMMBkAzztH2vWsco/eBXufS9/39T9lfr4/5ScBNNh4tiy7S8ZJBl5aGl3S8pOAjBV0bmYGFpoJY8GPBj5UAGoGefy0EsBDEQgCNIBaC3ecHu+eEQv/ue0MLohNERzf+1UL/r6MIojSFpox5JQv0/GsFLIruY8ZxTale72to3f+vdT9GU3QfMz4WOk73xyzdX9bQGwP8P92POzjV/Zxav+ej+/o9X8/e/6r+H9//vP3/v+852oF/ZHwuaK/s8/EMN5yaXgvpvQ5G49+YMUDu9UL0Estjov0l51GHztsE6AL6p3F7iYbxkVLGJJave2zk8xlv/kD3mMkL/u4xk673GiPtN2F10AEMGDe80NhH2+9G2es4vY6x3zkD/O+AuodrPlLoIoMu0unSMrrHS6n4tFTjX1IIaAf9Dg2ZRfm7x0aB7rFToHtMFOj1/8GefzWCWNT+46UgGsr4V1PoBFHd4yaldY+dQu+jo3Q/mr8TDR2LUlh0xaSumr73eoiN3x8xasVcIic/P5/6+r7Zz/r6elJTUwecoQOIj48nPj4+EuEJMxUvMfarOmsZODvQvZ+1eEmkIzu0/WIvR+eP+EkCJuxfkzw+FQqPAq3X8ZQCqPg3eDs54Ocen9L/cYPl9UJ7O3R2Gm+trca/Af++gYjWfWmLBlYrWG1giQerBSy93jSLcc4hRw0jTynoVGm0qWzaVBZtehZtKosOlY7v4EPDHnF4cWge4vEQr3mIpwu71oWdLuyaFzte4jQfcfiI03zY8BOHD5vmx4YfGwEs2iAGlRhbjgLodBHoefMSxEcQvdf3WQPsWLFjxYaFOKwkEEcKdpK731Kw48BGPDbsWInvPt+Olbief43HxmHB1v0WhxUrGjYsWLEMmCAZM5QOu97t+3vWoMMiO8btNIr/xsQYudcLcZhieUy0v+Il/DshlWZPF3MOlm63DzDOCff4KBg0xkNOJ7hc0NFhjJe6PN0rkgN072M2hMZIFitY44xxkdUKcd1jo9AYSTNnnATgV3G0qyxa9SzaVRZOlUGHSqdDpRPAPqjn0NCJx4ND82Cni/h+4yQvdnzY9hsv2fB3j5cC2EKTXYP8MoTGTF6C3eOlfWOmAEF6fz2N8c2+cU4cFhKII5E4Y8yk4kiq/icJPi/xOtjbdI7IsEGho+czjJnfHzFqxVwiZ/Hixbzyyit9jv3jH/9g8eLFJkUkoobFarQDfO5S9mUVQrr/eK/6XnQWs+sVexeKzfhpQDGr301cgykr+g82NAtMOQW+/AsH/NwHetyB+P3Q3GwkbBobwdkOXd59K2nibGC3gyMBkm1GsiYKX+B7VCL1+nia9HyaVT7Neu5BEzYJdJKitZOktZOkdZCkdZCodZKgdZKAiwTNjU0LjHicOgoPfjrx4caPh0BPosaGpTsBYyWDBHJIJJtEMkjoSdQkYSeJOJKwk0gcCdiIk21cI28wv2fR+jcmxsi9XojDFMtjov24g17+r2gBGTv/drA1k3DEKeEfHwUC0NYGLS3GW2uLMT7y+42PWzSIizPeEpPAZjOSNVHKq+Jp1vNpUnk06/k0qzw6VfoBz9fQSaCze4zkJEnr7BknJXaPlULJm8EmYIbKTxA3flz48eCni4CxSwzVa8xkIwk7E0ggi0Qy9xszJRJHEnE9yRsHNmy9J041ID2t++cGaAISLb0+SMz8/ojRy/RETmdnJ7t37+55v7S0lC1btpCZmcmECRO47bbbqK6u5te//jUA1157LY888gg333wzl19+OW+++SbPPfccL7/8slmfgogmM9fBhl/Dq7f0LW6XWmj8wZ25zrzYDqU79pdfvI5PPA0csf+sU3yqMdjImTbw43OmwZFnwe5/gLej1+NSDv64EJ/PSNrU10NdHbjdxt5rWxw44iEjw5g1imJdKoFqvYQ6fQL1+nicKrPfORaCpGnNpGtNpFuaSdeaSdVaSdbaidP8YY8xiI4LP068dOAj2D1LFBpUFJPOeFLJJ5ksEsgggXQcpOMgAdvBB7Ei/A70exYLf2NMJPd6IUwQy2OiXj6p+YRKm51pM86EXf+AgKfvCbYEmHZaeMdHDQ3db/XgchsJHasV4uMhKQnscUTjhNb+3CqJOr2Ien089fp42lTOgOc5cHWPkZpI1VpJ1VpJ0dpI1tqxDnJV8eFSKNzdk1yd+PBgTKRZu8dMydgpJo1CUsghiQwcZJBAGvGk4SCJuMMbM/X+uaF93/EY+/0Ro5fp7cfffvttTjzxxH7HN27cyFNPPcWmTZsoKyvj7bff7vOYb3zjG2zdupXx48dzxx13sGnTpkFfU9q+jQF60Kgk31lv7F8tXhITWfMvG77kh+8+SLynjXyr3divbU8CezKkFw1uxkjp0Fa577EHe5xSxnLgqiqoKDeWBysgIQESE6M+cQPQqmdToU+hKjiJRlVI34GUIkNrJMdSQ7ZWT5aljnStKWKDEIAAOu100UYXbvxY0EjCTjoOjiCTYtLJI4nc7reEqCjwJw4p9Hu2dTtceSGc+5XD/hszmu9Ncq8XwkQxOiYCCOpB7n/nfrY2bmVa9rTuv70V0Fph3O7TJkDGhJEfH+k6NDVBbS1UV4Or0zjucBhjJFts3Kt1ZaFeH0e1PpFqfSKtKrffOSlaK1laPVmWerK0OjItjTg0zwDPFl6h8VI7Xlz40NBIwEYydsaTyiQyySeJHJLIIZEsEvuuogkXpcMn/4KjjoDLLj7s3x+5N4mRYnoixwzyCySi1S8//SV/2/U3ZuXOQgvXmlQwEjhNTbB3L9TVGgWKExIhOSkmBncdehql+nT2Bmf0m03K0BoYZykjz1JJrqWaeM17gGcJD4WiEx8teHDiw4JGOvGMI5VZ5FJMGuNIJY8krJEYgIjw+vJLuP56OOGEw34quTeNLPl6ChH7vmz4kvvfuZ+ClAKS7cnhv2AwCDU1sGePscU8GDDGR0mJMTE+AtCVRp0+gVJ9OuXBI/bbUq7I0urJs1T1vJmRtAFjW3k7XbTShat7oiudePJJZgY5TCCtZ4Wyw+xNJNu2wcknw9VXH/ZTyb1JjBTTt1YJIfYpbysnJT4lvEkcpxN27oTKSmNPd0oypKUR7UuCdaVRoR/B9uB86vQJPcctBBhnKWO8ZQ/jrXtJ0jojHptC4cRLAy48BEgijgJSOJUpTCWTiWSQgUO2RQkhhBBD8K+Kf+ENesOfxAkEjNXJe/dAc4tR6yY1zah1EyNa9Sx2BudSGpxOF0k9x+NxM85SynhrKYWWMtMSN2DUt2nGQwsedBRpxDORdGaTxyQyKCFdxktCDJIkcoSIEh6/h0Z3I4lxieG5gM8Hu3cbs0xulzFAycgIz7VGUJdKYEdwLjsCc3ETmrlQFFgqmGjZRrF1Z8RX3YR48FNLJ534SMHOVLJYxHimkcUE0mTFjRBCCDFMtR21fFTzEXlJYWzxrJRRH3DrVqMGjs1qjI1ssfESKaCslOvT2BGYS4Ma33M8HjfF1p1MtGwnz1KFRTNvA0YQnSbcNOLGgkYWCSynhNnkMpUsckmSxI0QwxAbf6WEGAMa3Y24/W5yk/rvXz5sra3w+edGIePERMjLI9pX4HiVgy8DR7M1uKCn3aUDF1Ot/2Ga7XOStI5DPEN46CiacVOPCxsWJpHBEoqYRS4TSBvb7bmFEEKIEfJxzcc0e5qZkzsnPBfweGDHDigrBX8AsjLBGhsvjfwqjh3BuXwZOAYPxmolDZ0iy26mWv9DoaUcSwTrAe4vtFK5hg6CKLJI4BQmcRQFTCebFOJNi02I0SI2/loJMQY0uZtw+90juyJHKSgrM+p4uN2QnR31BYx9ys6XgWPYGlyAv/tGn6nVc6TtI0osO7FqQVPiCqJTSyfNuMkikRVMYjHjmUmOtPoWQgghRtju1t04rI7wbDevrYX//seY6EpJhfToX6EMxhhpW3ABWwNH4cUYLybSwTTb5xxh/Q+JmsvU+ILo1OOiCTfJ2DmKApZQxGzySMdhamxCjDaSyBEiSjS6GgGwDKbzwmD4fPDf/0JpKcTZIDeHaF6FoxSU6tP5yH9iz+xShtbAPNu7TLDsJpxlgw4mgE41TtrxUkgKlzCXJRSRTwSKLgohhBBjUFAPUt5WTpI96dAnD4Wuw65dRvHaYBBycwfX8cpkutLYFZzDZ4GlPfVvUrRWZls/YLL1y4h24xxIAJ0qnDjxkk8yZzODYxnPZDJk25QQYSKJHCGiRIOrYeSezOuFTz+FigqjkLEjumdB2vRMPgisoFYvBozByQLbPym27DQtgaOjqKWDZjxMII1zmMkSisjo0/1BCCGEECOt2dOM0+skNX4Eu/r4fPCf/xgTXA4HpKeP3HOHUU1wAh8FTqK1u0tnqtbCPNu7lFh2mFr7BozixVU46cTHBNI5jyM5lvEyVhIiAiSRI0SUKG8vJ8E2Ajc+rxc+/hiqKiEzK6o7LigFXwQX8lngOHSsWPEzx/YBR1o/xGbSFiqFogUPVTjJJ5mNzOUkJsp+biGEECJCGlwNuHwuCpILRuYJXS5jbFRXayRw4qN7gguMWoEf+E9mrz4TADse5tneY7p1i6n1b8CY7KrGSStdTCSDi5jDEopI7q5pKIQIP0nkCBEF/EE/dZ11h18fp6vLGKhUV0FWFtiiN4njVkm8419NrV4CwHjLHhbZ3iDF0m5aTF4C7KaVRGysYSqnM1W2UAkhhBARVt9ZT1AFibOOwDjG7YaPPjIaPmRnx0RB48rgJN7zr8RDMho6062fMc/2HvFal6lxKRTNeKjGSSEpXMlRLKOYJEngCBFx0f+XTIgxoMndhMvnIiPhMIrteb3GQKWmGrKyo7p1ZlVwIu/4V+MlESt+Ftne4Ajrf03bRqVQ1OOiARdzyOM8jmQ62bKvWwghhDBBbWctjMSuIY+nO4lTB9k5Ud/wwa/i+Ld/BXv0WQCkac0cF/cKOZY6kyODLgLsoYUk7KxjGmuYSi4jXMNICDFo0ftKT4gxpMndhMvvYlzquOE9ga4bhY1rqqN6tkkp+E/wWD4LHA8YxYyXxb1IuqXFtJh8BNlFCynYuZDZrOEIEojelUxCCCHEaFfaVkpC3GFuNw8lcepqYyKJ065n8Kb/TNpVNqA40voR823vYtMCpsalUN1dOz3MJ58NHMkRZMpklxAmi85Xe0KMMY3uRnSlY7MM81dy1y4o3Wu0z4zSJI6uLLwfOIVdwTkATLd+ytG2t02rhQPQRhfltDGXfC5gFtPINi0WIYQQQkBXoIvajtrD61jl8xlbzWtruie4ojuJUxGczDv+NfiJJ5EOltlfJM9SbXZYdBFgNy2k42AjcziVKTjk5aMQUUF+E4WIAo2uRpQa5hri2lqjjWZCAsRHZ0Fen7Lzlv8MavUSNHQW2t5khu0z0+JRKKrpoAMva5jK+cySAn1CCCFEFGhwNdDp6yQ3KXd4T6AUfPEF1NQYW82jdIILjFC3BJbweXApAHlaJcvsL5KouUyODJpxU00HCyjgfGYxmUyzQxJC9BK9f9mEGEMqnBU4bMPooOB0wudbIBiM2jaaHpXIa74NtKkcbPhYFvciRda9psUTRGcXLSQRx+XM5xQmY5HlwUIIIURUaHA14Pa7h98AorQU9u6FtLSorheoK433AivZHZwNwAzrJxxje9v0jlQKRTnt+AhyFtM5h5my5VyIKBS9f92EGCN0pVPtrB76gCUQgC1bjGROzjBnrcKsSyX0JHES6GSF/U9kWRpMi8dPkO00M5F0NjGPWUTn100IIYQYq+o76wGwaJahP7ipCb78AuLiwBG9LcaDysr/+U+nQp+Khs4S22scYfvC7LDwE2QnzWSTyBXM5zgmSC0cIaKUJHKEMFmLp4UObwfJ9iG2ud671yjgl5mFae2eDsKrHD1JnEQ6WGX/PamWNvPiIcAOmjmSHK7jGApJMS0WIYQQQgysylk1vCSOx2OsUu7yQk701rzzqzje9J9JrV6ChQDL415kgnW32WHhwc9OWphFDpcxn0kcRidVIUTYSSJHCJM1uZtw+91D2wvudMLOneBIiMplw14Vz99959KqcnHgYqX9WVOTOMbgpJmjKeQajiabYS7XFkIIIUTYKKUobSslKW6IhY51Hf7zH2NFTk4uROkqEr+K4+++DTSqQmz4ODnuzxRYK80Oi0587KGFYxnPVSwgk8PsGCaECLvoewUoxBjT6GrEr/uxWwdZbFcpo7ixywV50bc1KKBsvO5bT7PKJx43K+3PkmZpNS0eFz5208pSJnAVR5FG9C61FjHE54OGBqM+lRBCiBHh9Dpp8bQMvWNVZSVUVkBGBliGsZonAoLKypv+s2hUhcTjYYX9j+RY6swOi1Y8VOHkZCaxiXnS/EGIGCGJHCFM1uRuQimFNtjtUVVVUFUJGelE24yTUvCufyWNahx2PKy0P0eGpdm0eIy2ma0so5irWUCiFOsTh0PXobnZSOBoGhQUwMknw6xZZkcmhBCjQr2rnk5fJxPSJgz+QR4PbN9utBi3R2f3Tl1p/NO/hlq9GBs+TrH/gWxLvdlh0YSbejo5nalcyGzi5aWhEDFDfluFMFmls5I46yATDF1dsH2b8SIyCgcr/w0uolSfiUaQE+P+Sqal0bRYfATZQRPHMp4rOUqSOGL43G6orTVWwWVmwvLlcMwxRgInUbbpCSHESGlwNeANeom3DnKMoxTs2AFtrZAbfauUwQjx34EVlOvTsBDg5Li/REUSpwUP9XSynhmcy5FYic6VTEKIgUkiRwgTKaWobK8cfMeqXbugpSUqu1RVBCfzaeB4ABbZ3jB1z3cAne00MZ8CrmaBLBMWQxdafVNfb3Q/KSmBE06Ao46CvDyzoxNCiFEp1LFq0KuUGxuhrBRSUmE4BZIjYEtgKTuD8wDFCXEvU2CtMDsk2uiihg7OYJokcYSIUZLIEcJETq+Ttq62wRX1czqhtBSSk6Nu/3erns0//acDGtOtnzHd9rlpsegottHIDLK5lqPJkIJ9Yii8XmP1TXs7ZGXBKafA4sUwY4aR0BFCCBE2ZW1lg1+NEwgYNQP9AUiPzg5LpcFpfB5cAsCxtn9QYt1pckTgxEsl7axhKhcwW5I4QsQoSeQIYaJQx6rMhMxDn1xaauwDj7ICx6E2mgHs5FvKWWh707RYFIpdNDOBdK7jGHIZYrFEMXZ1dEB1tVG8uLgYzjkHjj4a8vPNjkwIIcaEoB6kvL188B2rSkuNVZOZgxhDmaBVz+Jd/yoAZlk/MHWSK8SFj3LaOJXJXMwcbJLEESJmSSJHCBM1uhvpCnbhsB2ik1JHB1SUQ3IS0Vbg+MPASXSoDJJoZ3ncC1g03bRYaujAgY1NzGU8qabFIWKEUsb2qbo6iI+HOXOM+jdHHQUO6W4mhBCR1ORuotPXSWr8IO7fHo+x3Tw+HmzR93LGp+y81T3JVWAp5yjbO2aHhI8gu2nhRCayiXnYsZodkhDiMETfXz4hxpAmdxMa2qH3gpeWGgVXo6w2R3nwCHYF5wCK4+2v4NC6TIulnS7a8bKJucxFVlGIg9B1o/NUQwOkp8OKFUb9m+nTo27bohBCjBWhjlUFyQWHPrmszJjkys0Je1xDpRT8y38aTpVJEk6Wxb2IRVOmxqSj2EkTc8hjI3OlO5UQo4D8FgthovrOeiyHKs7X0QHlZZCUTDStxnGrJN7znwrALOuH5FuqTIvFS4By2lnJZFYyxbQ4RJQLBo36N83NRneTs882EjgThtDmVgghRFg0uBoI6sFDd/J0u2HvXkhIiMoCx18EF1KhT8VCgOX2v+LQPKbGE9p2Po40ruQo0pAVp0KMBpLIEcJElc5KEmyHKMZbVhZ1q3GUgnf9q/CSSKZWz3zbv8yLBcVOmplHPhcxR4r2if6CQaipgdZWKCiASy6B446L2la1QggxFtW76tEGM2FVVgadnVH5N7xJz+vTwTPHUmdyRFCFkwTiuJx5FJFmdjhCiBEiiRwhTOIP+mlwNZAQd5BETmenMWCJstU424PzqdYnYcXPCXEvYzWxLk4F7eSRzGXMkzbjoq/eCZzCQli3Do4/PmoLYwohxFhW3laOI+4Qq0XcbmO7eWICDLZFeYQElZV/+U9DYaHEsp2p1v+YHRJtdOHCzxXMl23nQowyksgRwiQtnhY8fg/pjvQDn1RWBm4X5EbPahyXSuaTwAkALLD9k3RLs2mxdOLDjZ+LmSOzTGIfXd+3haqwEM44w9hClZ5udmRCCCEG4A/6qemoOXTHqtJS6OyIqnFRyGeBpbSpHBy4ODbuddPzTH6ClNPGGqZyMpPMDUYIMeIkkSOESZo9zbj9bgpTCgc+weuFigpITIyqWaeP/csJYCdHq2aG9VPT4tBR7KWVEyhmGSWmxSGiiFJGAeO6OqNt+MUXG12oZAWOEEJEtWZPMy6f6+CTWy4XlJVG3bgIoEEv4MvgMQAsjvt7lNTFaWEmOWzgSCxRtKpbCDEyJJEjhEma3c0EVODARf3q6oytVdnZkQ3sIGqDRZTqM9DQTZ9tKqON8aRyPrOwSV0c0dICVVVG0mbDBqMTVU70dTMRQgjRX6OrEZffxbjUcQc+qac2TnStxgkoG//yr0ZhYZLlS4qtu80OiVo6ScbOxcwhlXizwxFChIEkcoQwSbOnGQ7UjVIpYzWO1RI17ZB1ZeGDwAoAplk/J8vSYFosTrz4CHIOM8kn2bQ4RBRwubrrJSTCypWwejUUFZkdlRBCiCFodDeiKx2b5QAvTbxeKC+HhOhbjfNZ4DicKpNEOlgU94bZ4eDGTwseLmUOM5AJDSFGK0nkCGGS2o5abNYD/Aq2tkJTEySnRDaog9gaPIo2lU08bubb3jEtDh1FKa2cwmSOQ9pGj1l+vzE7GwzCwoWwdi1Mnx51A3whhBCH1uBqQB1wdgujcH2UrVIGaNWz2RpcAMCSuNeI17ymxqOj2E0LSyhiFUeYGosQIrwkkSOESaqcVSTaEgf+YHU1+HyQkR7RmA7ErZLYElgKwNG2/zN1oFKFk0JSWM8M2fM9Fill/H60tMARRxiFjBctAqvV7MiEEEIMU3l7OQ7rATpW6bqxGsdqjZpVymDcjj4InITCQrFlJ+OtpWaHRBVOCkjmImZjR+6LQoxmksgRwgQev4dmT/PArce9XqisNFprRkmi4hP/MgLYydZqmGL9wrQ4vARop4tzOYo82VI19rS3G4P53FzYtAlOPhmSDtHhRAghRFQL6AGqndUk2Q/w97ypyehCmBo9q5QByvWp1OnFWPFztO0ts8PBgx8nXs5nFgVE19dKCDHyJJEjhAmaPc14/B5ykgbYuxxlRY5b9Sz26DMBODbuDVN3rpTSxkxyWC5dqsYWn8+og6NpcNJJcNZZMO4gBTGFEELEjBZPC52+TlLjUwc+oaLC2EYbZ49sYAcRUDY+8i8HYJb1I1IsTlPjUd2dPOeTL2MkIcYISeQIYYJmt9F6PMG234qcKCxybGyp0ii27CTbUmdaHE68WNA4g+kkcIBOX2J0UQrq642W4tOnw9lnw1FHRc3vhhBCiMPX6GrE5XNRkFzQ/4OdnVBbA8nRtfryi+BCXKSRhJPZtg/MDodG3CRj51yOlC1VQowRksgRwgTNnmYUCqtlv5ttlBU5btZzKdenAYp5tn+ZFodCUU4byyjhKAYY6InRx+OBPXsgLQ0uvBBOO022UQkhxCjU6G4koALEWQeYpKmuBrfH2FIbJTpVKv8NLATgmLi3sGkBU+PxE6SOTjYwk6lkmRqLECJyJJEjhAma3c0Df6CmJqqKHH8WOA6ASZZtZFgOEHME1NFJBgmcyXQpcDzaKQVVVeB0GqtvNmyAKVPMjkoIIUSYNLga0Aa6twcCUF4G8fFR1ZHwE/8JBIkj31JBsWWn2eFQShtHkMlqppodihAigiSRI4QJqjqqiLPsN/MUDBozTw4H0VDkuEEvpEqfjIbOPNu7psURRKcBFxcwmwmkmRaHiAC321iFk5MDV1xh1MOxR09NBCGEECOvor0Cu3WAv/V1ddDuhMyMyAd1AK16NqX6DACOsb1len6pEx8KxdnMIJV4c4MRQkSUJHKEiDClFNXOahLj9ms93twMnR3GVpIoEFqNM8X6X1ItbabFUUMHhaSygkmmxSDCLLQKp73daCV+/vkwYYLZUQkhhAizoB6kyllFUtwAW2erqox/rdHzcuWzwFIASizbybI0mBwNlNPGQsZxNIVmhyKEiLDo+csoxBjR4evA6XX2bz3e0GCsyrGZX8i3NjiBWr0YCwHm2t43LY4gOq10cSnTyGSAVu0i9nV1wa5dxiqcK680WorHmf87IIQQIvxau1rp8HaQbE/u+wGXyxgXRVFttGY9lwp9KkbdwPfMDodWPCQQxxqmYkWaAAgx1kgiR4gIa/G04Pa7SXek7zsYDEJNNcQ7TIurt/8EjwVgqvU/JGsdpsVhrMZJYRnFpsUgwkQpY5De0GDUwrnoIigpMTsqIYQQEdToasTld5GXnNf3A/X10OWBnOgpcty7bmC6iXUDwWgCUYmTU5jEDLJNjUUIYQ5J5AgRYc3uZroCXThsvZI2zc3QER3bqpr1XGr1YjR0Ztk+NC2O0GqcdUwjQ1bjjC5+P+zeDQkJcMEFcPrp3bWhhBBCjCWN7kb8ur9v3UCljJqBVlvUFDlu1Auiom5gSAMuMklgDVMHLhQthBj1JJEjRIQ1e4xZHIvWaxlsFG2r+jJwNAAllh2mrsappoPxpLKMEtNiEGHQ3g5lZTB9urEKZ9YssyMSQghhkkZXIxoaWu+EjdNpTHBF0baqUG2cydYvTa0bCKCjqKOTc5kpTSCEGMMkkSNEhDW6G/se6NlWZX63AZdKplSfDsCRto9MiyOIThtdnMV00pGVGqOCUlBZadQ9WLnSKGicnm52VEIIIUxU4azo38Wzvh68XkiPjiRFvT6OGn0iGkHmWs2rGxhSjZNCUjmVKWaHIoQwkSRyhIiwKmdV321VLS3Q0QmpqeYF1W1b4CgUVvK0SrIt9abFEVqNc4LUxhkdfD6joHFGBlx1FZx4IlitZkclhBDCRLrSqWyvJMnea+WNrhtJf7sdomTL0H8CRt3AI6xfkGJpNzWW0LbzTUwjm8RDP0AIMWpJIkeICNKVTo2zhgRbr5ovDQ0QCJjeqcev4tgRnAuYuxpHR/WsxkmT1TixL7SVavZs2LgRJk82OyIhhBBRoK2rDafXSWJcr4REa6tx30hOPvADI6hVz6JanwQoZlnNqxsYUksnhaRwgmw7F2LMk0SOEBHU1tWGy+8iKa579ikYNAr6OczfVrUrOBs/DlK1Foose0yLowEXOSSymCLTYhAjIFSs0uk0tlJdeGFUrDoTQggRHRpdjbh8LnISc/YdrKszCuLb7eYF1svWoFE3sNiyKypq4zTj4VLmyLZzIYQkcoSIpGZ3M26/m+zE7laRLS1GtyqTX+DqSmNrcAEAM60fm9YkQqFowMWZTJclw7EsGDS2UiUlweWXwymnyFYqIYQQfYQ6Vtmt3UmbYBCqq6Kmi6FbJbEnOBMwd6VySB2d5JPE8bLtXAiBJHKEiKhmTzO+oI94a/cKnKamqNhWVaEfQadKJx43U6xfmhZHO15SsLNUVuPErq4u2LnT2EK1caN0pRJCCDGgJncTwL6OVU1N4OyImkL42wPz0bGRo9WQa6kxNRaFohE353GkTHQJIQBJ5AgRUS2eFqB70KIU1NVGxfLhHcF5AEyzfo5NC5gWRw1OjqWISWSYFoM4DG1tUFEBCxfCZZdBXp7ZEQkhhIhSNR012Cy9Xoo0NIAeBJv5L0+MuoHzgOhYjdOAi2wSWCarcYQQ3cz/SynEGNLo6tV6vLPTqB+SkHDgB0RAh55GrV4MKI6w/se0ODz4sWBhGcVoUdKpQgySUlBTY/w8n3660Vo8UWYMhRBCDEwpRUV7xb5Cx8Eg1NZAfHRsq9oTPBIvCaRobUyw7DI1FoWivnvbeQEppsYihIgeksgRIoKqnFX7Ola1tIDXC6lppsa0KzgbgEJLGSkWp2lxVNPBFDKZg6ziiCm6Dnv2GCvLLrsMTj0VLBazoxJCCBHFXH4XrZ7WfYmc1lZjgivF/KL4utL4srvI8Uzrx1g0ZWo8zXhIx8Fy6VQlhOhFEjlCREhQD1LXWUdCXHcip6kJNA3TKgtjDFZCiZypJq7GCaDjwc9JTCQOKYobMwIB2L4d8vONosZHHWV2REIIIWJAo6sRt99NgaPAONDcDH7zawYCVOmT6VAZ2OliivULs8Ohlk5WMpkJmDvxJ4SILpLIESJC2rracPvdJNuTjRfADfXgMHdbVZU+GQ/JOHBRZNltWhy1dDCOVI6h0LQYxBB5PEZR4+nT4eqrYeJEsyMSQggRI5rcTXgCHhw2R/f23Gqwm5/EAdjeXRtnqvVz4jS/qbF04MWBleOZYGocQojoI4kcISKkxdOCJ+AhJynHWELsdkOaubMrO7tX40yxfolV002JQaFopYuVTCGFeFNiEEPkdEJZGRx7LFxxBWRnmx2REEKIGNLkbgIFFs0C7e3Q7oyK2modeho1eglg7krlkBo6mEkO05D7rBCiL0nkCBEhLZ6Wfa3HW8qNwn4282afXCqZan0SgKlFjjvwkYydBRSYFoMYguZmo7DxqafCpZdGxcBbCCFEbKl31dPT16C5GXxeSDd/65Cx3Vyj0FJGqqXN1Fj8BPGjs5wSLNIEQgixH0nkCBEhPa3HAerqTE3iAOwOzkZhIU+rJM3SaloctXQwi1xpOR4LamqMmdP16+Hcc6OiloEQQojY06djVW0tWKxgcrJCV5ZedQM/NzUWgDo6GUcKR8lElxBiAJLIESJCmtxNRltttxva20xtO64U7Ax0D1Zs5q3GCaLjR2cpE6TleDRTythKpZSxCue006QzlRBCiGHxBX3UddYZiRy3G1qao2J1Z2WU1A2EfdvO1zCVJOymxiKEiE5RMRL/6U9/SklJCQ6Hg0WLFvHhhx8e9Pwf/ehHTJs2jYSEBIqKivjGN75BV1dXhKIVYngqnZXE2+KNtuNdXnCYVw+mRi/BRRp2uii27DQtjkbcZJPIPPJNi0EcglKwe7ex+ubqq2H1akniiGGRe70QAqDZ3Yzb7zYSOU1N0NUFCQ6zw2JncA4AU6xfmFY3MKSlu+X4IsaZGocQInqZPhp/9tlnuemmm7jrrrv49NNPmTt3LitXrqShoWHA83/3u99x6623ctddd7Ft2zaeeOIJnn32Wb71rW9FOHIhBi/Uerxn0AKgmffrtzc4A4BJ1m3YtIBpcTThYiHjSMf8AZwYgK4b7cVTU+ErX4HjjgNNVk6JoZN7vRAipNHduC+RE/obYOKYCKBDT6VaN7ovRkOR4zo6WUAB40g1OxQhRJQyPZHz8MMPc9VVV3HZZZcxc+ZMHnvsMRITE/nVr3414PnvvfceS5cu5cILL6SkpIRTTz2VCy644JAze0KYqd3bjtvvJsFih/p6cJiXuAgoGxX6EQBMtG4zLQ43fuKwSsvxaBUMwtatkJ8P118PRx1ldkQihsm9XggR0uRuQlc6toAODfWmbjUP2RWcA2gUREGRYzd+rFg4TlqOCyEOwtREjs/n45NPPmHFihU9xywWCytWrOD9998f8DFLlizhk08+6RnM7d27l1deeYXVq1cf8Dperxen09nnTYhIavG04PF7SHD5wO0yddBSpU/CTzxJtJOrVZsWRx2dFJPOdGmpGX0CASOJM3EifP3rMHOm2RGJGCb3eiFEb42uRpRSxlZztxsSzK2P07vI8bQoKHJcQwdTyORIcs0ORQgRxUwtdtzU1EQwGCQvL6/P8by8PLZv3z7gYy688EKampo47rjjUEoRCAS49tprD7rc+oEHHuCee+4Z0diFGIoWTwveoJf4dowXyXHm/eqVdm+rmmjdbtouGYWiEx/HM4E4rOYEIQbm98O2bTBtGnz1qzB+vNkRiRgn93ohRG89NQPrmo0tvFZzxwFV+sSoKXKso3Dj53gmYDN/44QQIorF3F+It99+m/vvv59HH32UTz/9lD//+c+8/PLL3HfffQd8zG233UZ7e3vPW2VlZQQjFqJX6/GWlu5CseZkUHzKTqU+CTDq45ilBQ8ZOJgvLTWji89nJHGOPBJuuEGSOMI0cq8XYnTSlU6Vs4okWyLU14Hd/I5Me4JHAjDZujUqihxnkiBNIIQQh2Tqipzs7GysViv19fV9jtfX15OfP/AfsDvuuINLLrmEK6+8EoDZs2fjcrm4+uqr+fa3v41lgG4q8fHxxMeb1yFIiEZXo1FzpLnZ1Po45cEj0LGRpjWRoTWaFkdDd5HjApJNi0Hsx+s1ChvPnWsUNs6VJd1iZMi9XggR0tbVRqevkyS/go4OcJhbH8er4qnUJwMwybrV1FjAGB8dxwTyZHwkhDgEU1fk2O12FixYwBtvvNFzTNd13njjDRYvXjzgY9xud78BnLV7SaZSKnzBCnEYqjuqcXiD0OUxNZFTqu/rVmXWtqogOgF0jqYQzaSVSWI/oSTOUUcZhY0liSNGkNzrhRAhTe4mo2NVR5dx7zE5+VoenIqOjXStiUxt4C56keIniEKxUFqOCyEGwdQVOQA33XQTGzdu5Oijj2bhwoX86Ec/wuVycdlllwFw6aWXMm7cOB544AEA1q5dy8MPP8z8+fNZtGgRu3fv5o477mDt2rU9gzwhoomudOo660jw+I36ODZzfu08KpFavRiAiZaB61JEQmjZsBTxixK9kzhf/SpkZpodkRiF5F4vhAAjkeML+rC3eEHTMG1WqdvengmurWaHQj0u8klmtoyPhBCDYHoi57zzzqOxsZE777yTuro65s2bx6uvvtpTFLGioqLPrNztt9+OpmncfvvtVFdXk5OTw9q1a/nud79r1qcgxEG1d7XT6esksd1tan2csuA0FBaytVpTW2s24WYxRWRjbpcKgVETR5I4IgLkXi+EgO6t5rqO1thg6gplAJdKoU43WnybWTcwpBUPJzGDJMyvGyR66ew0xkspKWZHIkQfmhqDa5SdTidpaWm0t7eTmppqdjhilNvTsoc737id8R/vICGgmXYjeNl7IY1qHMfY3uRI2yemxBBEZxtNfJ1FHE+xKTGIbqHCxpLEiRpybxpZ8vUUIvo89vFjvPWfF5ixpRJSUiEuzrRY/htYyCeBZeRplZwW/3vT4gDoxEctHdzBMqaTbWosohenE8rKYPlyuOIKSEoagaeUe5MYGaavyBFitGvxtODtaMXh8RuDFhN06Kk0qnGAYqLV/G1VM8kxLQaB0WJ8+3aYP98obCxJHCGEEGGmlKK8rZxEtx/8AYgz92XI3uBMwOhWZbZ6OplIBkcg9+Oo0doK1dWwciVs3Gj6CjIh9hdz7ceFiDXNnmZwudAC5g1aKvQjAMjTKknUXKbEAMa2qlnkkiXbqswTCBgrcWbNMpI4WVlmRySEEGIMcPvdtHhaSGxzgc2KWVvNAVr0HFpVDhYCFFt3mBYHgELhws9SirDKS7Po0NQENTWwZg1cdpkkcURUkhU5QoRZk7sJXC7QzKuPUxE0EjnF1l2mXB+MbVVBFEdRYFoMY14waCRxZswwtlNly/JtIYQQkdHkbsLtbie/rRMSzG07vjdoFDkeb9lLvOY1NZYWPGTgYB75psYhujU2Gm9nnw3nnmtakxIhDkXSvkKEWXVrBY52FzjMabHpUYk0KKOV5QTrblNiAGju6VYl26pMEUriTJlirMTpLjIrhBBCREKTu4mu9iaji6eJKxyUiq5tVQ24mEkO+SSbHYpoaDBW45xzDmzYIEkcEdUkkSNEGOlKp7ZuFwneoGmDlqrgJBQWMrV6kjWnKTGAsa1qDnlkYO4s3Jik60ZNnOJiI4kzbpzZEQkhhBhjmtxNqM5OLAqwWE2Lo16Nx00KcXQxzrLXtDgAdBQBFAsoRDNxq5kA6uqgpcVI4JxzDljN+xkVYjAkkSNEGDm9TlytDST4lWmdGcp187dVBdDRZVuVOZSCXbuMFTjXXWckc4QQQogIa+isg7Y2sJvbXrs8OBWACZbd2LSgqbGEtlXJamWT1dVBezucfz6cdRZY5CWyiH7yUypEGLV4WvC0N5Gg4jCjPo5fxVGjlwAwwWJeIqcFD1nSrSrylII9eyAtDa65xthWJYQQQpigomqrsa3KxPo4Su1L5JSYXOQYoLF7W1UOh9/WWgxTKIlzwQVwxhmSxBExQ35ShQijlo4GutqbibebM2ip1ieiYyNFayNdazIlBoBm3BxJLulI1f+IqqgwZj6vuAJmzzY7GiGEEGNUQA9QV7+bRK8OdnNqBgI0qsLubVVeCi3lpsUB+7ZVyWplE9XX71uJs3YtaLK9TcQOSeQIEUYtlTvB78fiMCeRUxE0VmBMsOwy7d6kugcqs8k1J4CxqrbWaDW+cSMcc4zZ0QghhBjDmt3NuFobSNRtpr5YDq3GGW/Zg1W2VY1t9fXQ2grnnQfr1kkSR8QcSeQIEUZN1buNF9P2yNfH0ZWFKn0yABNMrI/TjpdU7Ewly7QYxpzmZmOG6bzz4IQTzI5GCCHEGNfUWY+7rZHEuETTYlBqX93AEutO0+IIacTFDLJlW5UZGhr2JXHOOEOSOCImSSJHiDCqrt9FvLJiRn2cOr0IHw4cuMjRaiJ+/ZBm3EwgjUJSTIthTHE6oabGmF1avVoGJ0IIIUzXXLmTgNdDnMO8RE6LyqVTpWPDxzhLqWlxQN9uVSLCmpqMCa9zzpEkjohpksgRIkyUrlPbsJcEqzndGSp0Y1tVkXU3Fk2ZEgOACz9HUSBtNSPB44GyMjjlFDj3XCnYJ4QQIio0Ve6AYNDUjlVlwWkAjLOUYtMCpsUBsq3KNC0txpaqs86Cs8+WcZKIafLTK0SYOOvK6ehqIyEu8ktmlYKKoLF8eIJld8SvH+LBjwMb08g2LYYxw+eDnTthyRK49FLT2t0LIYQQ+6uq3kac0kxb/WBsqzLq4xTLtqqxqa3NWLG8dq1MdolRQX6ChQiTltKteAJdJJjQsapF5eImBRs+CkzsytCMh1ySmESGaTGMCboOO3bAnDlw5ZWQaN7SdSGEEKI3FQhQWbeDRKt5bcfbVDZOlYmFAOMte02LA2RblSk6OqCyEk47zWgzbrWaHZEQh00SOUKESUvVLroI4rBEvs1mlT4JgAJLOTYTuzK00cV88rEjN8ywUcpI4hQXw9VXQ3q62REJIYQQPToqdtHe1U6C3bxJhtBqnHGWMuyaz7Q4AFplW1Vkud1QWgorVsAll8iKZTFqSCJHiDBpqdgOFg2LCbVhqoNGIme8icX8AuhowAwZqIRXWZmRvLnyShg3zuxohBBCiD6a9vwXd7DL1I5Vobbj0bCtqgk3U8mSbVWR0NUFu3bB8cfDpk2m1mgSYqRJIkeIcPB6aa7ba0rW36scNKoCAMZZzVs+bBTyS5C24+FUW2tsq7r0Upg50+xohBBCiH6aSr+ky6KToJmzEsKpp9OqctAIUmRi3UAAhcJLkLnkmRrHmODzGSuWFy0yJrsSzNvaJ0Q4SCJHiHCoqaHG24zdFvltVTV6MQoL6VoTyVpHxK8f0oKHGWSTjsO0GEa1tjZobTUK9i1ZYnY0QgghRH/BIE17vwCbzbTulZX6ZADyLVXEa15TYgjpxEcydpnkCrdAALZvh7lzjW3nKSlmRyTEiJNEjhBhoCoqqKGDBFvkkxhV3duqxpm4rUqh8BNkjsw4hYfHAxUVsHIlrFljWhcQIYQQ4qBqa6nvrEMzsS5JVXciZ7xlj2kxhDTjoZAUJpBmdiijl64bSZxp0+C66yAz0+yIhAgLSeQIEQad5TtxWvwkENmBi1JQrU8EMLUrgxMvKcTLjFM4BAJGm/FFi+DCC6XzghBCiOhVVkaF3kqCzZxtLT5lp04fD0BRFCRyOvCygAKs8hIsPJQyxkjjxsE110CeTCiK0Uv+iggx0pSiZcfneOwWErBF9NItKpcukrDhI9dSFdFr99ZKF/kkM45U02IYlUIdqqZNg8svl/3eQgghoppv9w7qbV4SNXOKzNboJSispGnNpFraTIkhxEsAGxamkW1qHKPanj1GA4hrroGSErOjESKsJJEjxEhraaGlpYouuxbxFTm9245bNT2i1+6tEy9zyTelY9eotncv5OQYRftypBuYEEKIKBYM0rz1Y9zxFhIjPB4KqQxOAaJnW1UOSRyBbPUJi8pKo8nIFVdIAwgxJkgiR4iRVllJS1crKi4u4omM6mBoW5XZbcc1ppBhWgyjUn298e+ll8KUKebGIoQQQhxKbS1NrTW47ZopiRxdaVR1bzcvspqfyGnFwxzyIj7JNyY0NBj1Ay++GI45xuxohIgISeQIMdKqqmjRulARXoziVfE0qkLA3Lbj7XSRhoNJksgZOR0d0NgIZ51l1MYRQgghol1FBU2+VoI2KzYTXnI0qkK8JGLHQ65WHfHr9xZERwFHIqtpR1xbmzFGWr8eTjrJ7GiEiBhJ5Agx0nbtosbhx05ki9Aa+8DNbzveShclpJOJ1G8ZET6fsaVq+XJYu1Y6VAkhhIgNZWU0Wr1oJt23Ql08x1tKsWjKlBhC2ugiHYc0gRhpbrfRxXPVKjjzTBkjiTFFEjlCjCSvF7VnN9WJwcjXx4mCtuMAHvzMIQ9N6uMcPl03ui/MmQOXXGLs/RZCCCGinVLw5ZdUJQUjPrEVUhlqOx4F26qa8TCFTLJJNDuU0cPng1274Ljj4KKLpIunGHMkkSPESKqpwe1sod1BRDtWKWWsyAEYZ2LbcS8B4rDKtqqRsmcP5OcbHarS0syORgghhBic5mb0ulqqkoKm1Mfp0NNoUzlo6KZPcCkUXQSYR75Mco2UYBC2b4e5c40xksNhdkRCRJwkcoQYSdXVtPja8NhURFfktKlsPCRjw0eexbx94K10kYGDiaSbFsOoUV9vzC5deikUF5sdjRBCCDF4FRW0dzbTEa9MSeSEunjmalXEa96IX783F36SiJNtVSNFKdixAyZOhKuvNtqNCzEGSSJHiJFUVUWLzY+HQERX5NToxgv9PEsVVi0Ysevur5UuppNNEnbTYhgVOjuNwn1nnCHdF4QQQsSe8nKaLF24tWBEx0MhoW1VRSY2fwhpwUMeyUxAVtaOiNJSyMyEK6+EwkKzoxHCNJLIEWIk7dxJS6KGAqwR/PWq7U7kFFjKI3bN/SkUfoLMkI4MhycQMLZUHXccrFsnhfuEEELEnm3baEo0thQ5IpzI8as46vQiAMZbzK+P48TLHPJM6dw16tTVGStyLrkEZswwOxohTCV/UYQYKZ2dxtaqJAsQue4IurL0DFgKLBURu+7+3PhJJI7JUh9n+JQyihtPn25sqZLixkIIIWJNR4exIifFSOBEui5MnV6Ejo1krY00rSWi195fEB1AtlWNhLY2aGkx2owvWWJ2NEKYThI5QoyUmhro6KAmUccWwQ4NjaqAAHbicZOpNUTsuvtrpYtsEmXp8OGorDT2el92mbFsWAghhIg1FRXQ3k5tsh7R1ckh1fpEAMZZykxf1NqOlzTiZZLrcHk8xs/VqafC2rWyWlkIJJEjxMiprkZ5u6i2uSO6H3zftqoKU+9r7XQxm1ziTGozGvPa2sDthvPPh6lTzY5GCCGEGJ6KCvD7Kbe5TCl0HOriWWhytyqAVjxMIE3ajh+OQMBYrbxoEVx4obQZF6KbJHKEGCmVlXgsOm2aN6Idq2qCRiKn0MT6ODoKhSwdHjafD8rL4ZRTYPlys6MRQgghhm/XLtx2jWY8EU/kdOhpOFUmGkFTt5uHuPEzV9qOD19oy/nUqUab8URJiAkRIokcIUZCdyvEltQ4PPgjtiLHr+JoVAWAuYWOO/GRjJ2JsnR46EKDlLlz4bzzwCJ/loUQQsQorxd27qQpI76ndl4kVXevxsnVarBrvohee39eAtiwyLaqw1FaCllZcMUVkCPNNIToTV4xCDES2tqgoYGWFFt36/HIDFzq9fEorCRrbaRY2iNyzYG0ddfHKSDZtBhiVlkZ5ObCpk2QLF8/IYQQMayqCtraaEq1mZLIqemuj1NoLYvodQfSSheZJDBJEjnDU18Pug4XXyxbzoUYgCRyhBgJ1dXQ0UFzkgUdFbEWk9HQdhygAy9HkmNKUcOY1tICfj9ccAGUlJgdjRBCCHF4ysvB46EpPgiAJYJbinRloVafAMC4KKiP00YX08kmCbvZocSejg5oaoIzz5QOVUIcgLzqEmIk1NSA309LnJ9Ith6v0c2vj6NQ6CiZcRoqr9eYuVy5Eo47zuxohBBCiMO3dy9YLNRrrohfukEV4ieeeNxkafURv35vCoWPIDOR7UBD5vMZP0fLlsEZZ0iHKiEOQBI5QoyE8nKwWKihI2Ktxz0qkVaVC2BqQT8XfpKwU0y6aTHEHKVg1y6jLs4558ggRQghROwLBmHbNkhNpYL2iHbwBKgJlgDR0XbcGBvFMZlMcwOJNbpu1A2cMwcuuQTiIt/1TIhYIYkcIQ5X901HpSRTTUfE9oOHlg9naA04NE9ErjmQ9u494ONIMS2GmFNRYRTtu/RSSEoyOxohhBDi8NXVQXMzgbQUauk0odBxNNXH8ZBDEhNIMzuU2LJ3L+TnGx2q0uRrJ8TBRDZVLsRo1NQEzc24UxJooytiM1C1UbCtCqAdL0dTSFyEViLFvLY28HiMJM7EiWZHI4QQQoyMigro7KR5QiYufKTjiNilu1QCzSoPgEJLWcSueyDteDme4ojVTBwV6uqMzp0bN0JxsdnRRJVgMIjf7zc7DBEBcXFxWK2De00liRwhDld1NXR20lKYjBs/uURmhUVoRY6ZhY4ViiA6U2Tp8OD4/cY2vNWrjb3fQgghxGhRUQFK0WTpwo2fwgiu1K3RSwCNDK2BRBPq8/Smo9CAqWSZGkdMCRU3vvhiOPpos6OJGkop6urqaGtrMzsUEUHp6enk5+ejHWKPqCRyhDhcNTUQDNJs8+HBH5EVOZ0qlU6VjoZOnqUq7Nc7EA8BHNikPs5gKGXs+545E847z5h1EkIIIUYDpYz6OElJNOImiIroSt3qoLHCNRq6VTnxkkK8NIEYLL/f2FJ10kmwZo3UDewllMTJzc0lMTHxkC/sRWxTSuF2u2loaACgoKDgoOdLIkeIw1VaCjYbLXhQEJEW3HV6EQBZWh1xmnlLLdvpIoMEikg1LYaYUVMDqalG8b5U+XoJIYQYRdrbjRXKqak00UAkO3gqta+L57go2FbVRhcFJJMXoRXaMU0p2LHDmOSS4sZ9BIPBniROVpas7horEhISAGhoaCA3N/eg26xkSliIwxEIwO7dkJpKM+6IXTaUyMm3VEbsmgNpw8tUsoiXnPDBuVzQ2gpnnQXTp5sdjRBCCDGyKivB6YS0NCpxRnQ1TpvKwkMyVvzkWKojdt0D6cTHLPLQkNUTh1RWZjR/uPxySE83O5qoEqqJk5iYaHIkItJC3/ND1UWSRI4Qh6O+3ihem5JCDR3ERehXqj5KEjkBgrIH/FB0HfbsgaVLYeVKs6MRQgghRl5FBQQCqDgbVTgj2rEq1Pwhz1KNTQtG7LoD0btXIk2SLeeH1txsbKu68EKYPNnsaKKWbKcaewb7PZdEjhCHo7YWXC5UUiLVOEmIwMDFpVLo6K6Pk2vizFMXAexYKZbWmge3d6/RfeHCC2XJsBBCiNFp926Ii6MdL066SMIesUuHEjlmNn8IMerj2CmRRM7BeTzGVrw1a+C448yORoiYJIkcIQ5HTQ3oOi6rTjveiMxA7auPU49d84X9egfSThfpOJggiZwDa2429n9fcAHk5ZkdjRBCCDHyvF7YtQvS0mjEhQt/xFbk6ErrGRdFQyKnjS7ySCaPZLNDiV66bvy8LFwI69dLcWMhhkkSOUIcjr17IS6OFjx4CESkY1WdPh6APNPr43QxmcyIzrrFFJ8PqqqM7VTHHGN2NEIIIUR4VFcb28xTU2nCjZcA8RGqkdOk8vETjx0PmVpDRK55MB14mU0uFqmPc2C7dxsrlTduBIfD7GhEGGzatAlN0/je977X5/jzzz/fZ9uQUorHH3+cRYsWkZycTHp6OkcffTQ/+tGPcLsjV3s0VkkiR4jhCgSMRE5KCs24u1uPR2JFzgTA/Po4fnSmk21qDFFLKWOgMns2nH22zDYJIYQYvSoqjK0yiYk0dTd+iFSh333bqiqxaJHrlDUQHYWGJm3HD6a+3thmfvHFcIjWymLkBHXF+3ua+euWat7f00xQD//visPh4Pvf/z6tra0HPOeSSy7hxhtv5IwzzuCtt95iy5Yt3HHHHfz1r3/l73//e9hjjHXSakaI4aqvNzo0ZGbSQhMKwj4D41LJdKgMNHTyLFVhvdbB+AliRZNtVQdSWwspKXDRRZAsy6uFEEKMYmVlxoSFplFLJ9YIzhNHU32cju76OMVSH2dgLhc0NBhJnKOOMjuaMePVL2q558Wt1LZ39RwrSHNw19qZrJoVvmTaihUr2L17Nw888AAPPvhgv48/99xzbN68meeff54zzjij53hJSQnr1q3D6XSGLbbRQlbkCDFcNTXQ2QnJyT0zUOEW6laVqTWYWh/HiZdU4iki1bQYopbbbdTGOeMMmDbN7GiEEEKI8NF12LbNmLwAKmiPWH2cgLLRoBcC0ZHIaaOLXJLIl/o4/QWDxkrl446D00+XlcoR8uoXtVz320/7JHEA6tq7uO63n/LqF7Vhu7bVauX+++/nJz/5CVVV/SefN2/ezLRp0/okcUI0TSMtTSaLD0USOUIMV02NsYXGYqGGTuwR2A9eFyVtx514ySeZdGRvcx+hVuPHHgurVpkdjRBCCBFejY3GW1oabvw04Y5YIqdBH4eOjUScpGoH3r4RKR34pD7OgezebbQYv/hi6eAZIUFdcc+LWxloE1Xo2D0vbg3rNquzzjqLefPmcdddd/X72K5du5gmE56HRRI5QgzX3r1gt6NQ3a3HI1HoODoSOS78TCcnYnvgY0ZFBeTnG63G7VIEWgghxChXWQkdHZCSQhNuXPhIilAip6ZnW1WF6Qs89O6XxpPINDeQaFRXB/HxcMklkJNjdjRjxoelLf1W4vSmgNr2Lj4sbQlrHN///vd5+umn2bZtW9/rK3NrWo0GksgRYjj8figthZQUOvHhxBv2QsdulYRTZQKKXBPr46juwcoE2VbVl9NpFHs891wYN87saIQQQojwq6gwVqNarTTg6u7gGZlETqg+TqHV/G1Vofo4JVIfpy+XC5qa4MwzYe5cs6MZUxo6DpzEGc55w3XCCSewcuVKbrvttj7Hp06dyvbt28N67dFOEjlCDEd9PbS3d3es8nR3rArvipz67rbjmVoD8Zo3rNc6GFf351okhY73CQaNxN7xxxtvQgghxFiwfTskJADQiAtQEdla5FUOmlUeED31cXKkPk5fwaCx3XzpUlizxuxoxpzclMGVPxjseYfje9/7Hi+++CLvv/9+z7ELL7yQnTt38te//rXf+Uop2tvbwx5XrJNEjhDDUVNjzDIkJdGCJyIzUNHSdtyJlzQcFJJiahxRZe9eKCmB884Da/hrJQkhhBCm6+w0VuSkGit0a+mMYNvxCYBGmtZEouaKyDUPRurjDGD3bpg0SerimGThxEwK0hwH/InUMLpXLZwY/u2As2fP5qKLLuJ///d/e45t2LCB8847jwsuuID777+fjz/+mPLycl566SVWrFjBW2+9Ffa4Yp0kcoQYjl6Fjpu7O1aF++YdWpGTFwWJnCPIjEhx55jQ2mr8LJx3nuz9FkIIMXZUVhqrk7u7y5TTFrFCx7Xdk1sFloqIXO9gdBQKxSQyzA4letTXG7UCL75YxkYmsVo07lo7E6DfK5TQ+3etnYnVEpnk47333ouu6/ti0DR+97vf8fDDD/P888+zbNky5syZw913380ZZ5zBypUrIxJXLAt/dVYhRqPuQscAzXjCfrku5aBNZQOQZ6kO+/UOJoDOZCnmZwgEjNnI00+HRYvMjkYIIYSInMpK8PkgPp4uAjTgMiGRY/62qk58JGNngmw5N3g80NAAF1wgdXFMtmpWAT+7+CjueXFrn8LH+WkO7lo7k1WzCsJy3aeeeqrfsZKSErzevqUhLBYL1157Lddee21Y4hjtomJFzk9/+lNKSkpwOBwsWrSIDz/88KDnt7W18dWvfpWCggLi4+OZOnUqr7zySoSiFWNer0LHANU4w746paF7NU6a1oRDC3/i6EAC6FjQKJJCx4bdu2HqVDjnHExvmSFElJN7vRCjzN69PduJG3Hhwk8S4e/YaDR/yAKU6dvNwVipnEkiBbLl3Ch8vWsXLFwIa9fK2CgKrJpVwL9uOYlnrjqWH58/j2euOpZ/3XJS2JI4InIOa0WO1+slPj7+sAJ49tlnuemmm3jsscdYtGgRP/rRj1i5ciU7duwgNze33/k+n49TTjmF3Nxc/vjHPzJu3DjKy8tJT08/rDiEGLS6OqNDUXZ2d+vxjrDPQO3bVmXuahwnXlKJZ7wkcqC52djzff75PcvKhRitDvd+L/d6IUaZQMAodNxdH6cRN+4INH4AqNOLAPObP4S008UxFGKLjvlxc+3dC0VFcOmlRstxERWsFo3Fk7PMDkOMsCH9xfnb3/7Gxo0bmTRpEnFxcSQmJpKamsqyZcv47ne/S01NzZADePjhh7nqqqu47LLLmDlzJo899hiJiYn86le/GvD8X/3qV7S0tPD888+zdOlSSkpKWLZsGXNl6Z6IlF6Fjp146cQXgY5VRjvrPBPbjoORyMkjiUwSTI3DdH4/VFXBKafAvHlmRyPEiBvp+73c64UYZWprjRpx3RMZoY5V1ggkM6Kl+QOAQqGjZMs5GBNcShlbqgpktYcQ4Taov7Z/+ctfmDp1Kpdffjk2m41bbrmFP//5z7z22mv88pe/ZNmyZbz++utMmjSJa6+9lsbGxkFd3Ofz8cknn7BixYp9AVksrFixok97st5eeOEFFi9ezFe/+lXy8vKYNWsW999/P8Fg8IDX8Xq9OJ3OPm9CDFttba9Cxx7c+MO6Isev4npabOZq5iZyOvExg+yIdaWIWnv2wPTpcNZZsmxYjCrhuN/LvV6IUaiy0uhalWy0266PYMeq0IqcaCh03EWAeGyy5dzrNSa4Vq2SmoFCRMiglhE8+OCD/M///A+nnXYaFkv/3M+GDRsAqK6u5ic/+Qm//e1v+cY3vnHI521qaiIYDJKXl9fneF5eHtu3bx/wMXv37uXNN9/koosu4pVXXmH37t185Stfwe/3c9dddw34mAceeIB77rnnkPEIMSh79vQsF23CTRcBHGFckdOkF6CwkoiTZM28Fyaq+78JpJsWQ1RobDS+/xdc0FMnSYjRIhz3e7nXCzEKlZUZExndkxlltJMQgULHLpWMU2WioZNr8iplMFYqpxFP0VgudKyUURdn7lw4+2yZ4BIiQgb16vNAM2b7GzduHN/73vcOK6BD0XWd3NxcHn/8caxWKwsWLKC6upof/OAHBxzc3Xbbbdx000097zudToqKisIapxilfD5j8NI9AxVqPR7OWah6FdpWVW3qvdHY+x43tuvj+HzGiqxzzoHZs82ORogRFy33e7nXCxHFlIJt2yApCQAvAerojEjHquirj+NlHvkR69YVlaqqIDMTLrmk52dCCBF+Q15G0NXVhcPhGPBjtbW1FAxhT2R2djZWq5X6+vo+x+vr68nPzx/wMQUFBcTFxWG17usSNGPGDOrq6vD5fNjt/avlx8fHH3ZRZiGAPoWOAepxhf2S+wodm18fJ414xo3lrgx79sCMGbBuncw4iVFvpO73cq8XYpRpbTXGQ92Fjptw48JHNolhv3QokRMN9XHASGJNJ9vsMMzT0WG8XXklTJ5sdjRCjClDrkh21FFHsWXLln7H//SnPzFnzpwhPZfdbmfBggW88cYbPcd0XeeNN95g8eLFAz5m6dKl7N69G13Xe47t3LmTgoKCAQd2Qoyo2tqeQscAlTjDOgujK41GvRCIjkTOZDKJj0BHiqjU2AgOh9GlqntFlhCj2Ujd7+VeL8QoU1kJ7e39OlZFZkVOqNCx+fVxAuhYsYzd+jjBoNGl6oQT4OSTzY5GiDFnyImc5cuXc+yxx/L9738fAJfLxaZNm7jkkkv41re+NeQAbrrpJn7xi1/w9NNPs23bNq677jpcLheXXXYZAJdeeim33XZbz/nXXXcdLS0tfP3rX2fnzp28/PLL3H///Xz1q18d8rWFGLKamp5Cx36C1NMZ1o5VLSqPAHbsdJGuNYXtOoPhR+eIsdqVwe83kngrV8KsWWZHI0REjOT9Xu71QowilZXGi/g4I3HTiAsVgY5VLpVCh8pAQzd9cguMCa5U4pkwVuvj7NljrMK54ALotXpSCBEZQ34F+uijj7JmzRquvPJKXnrpJWpra0lOTubDDz9k1jBe4Jx33nk0NjZy5513UldXx7x583j11Vd7iiJWVFT0KbhYVFTEa6+9xje+8Q3mzJnDuHHj+PrXv84tt9wy5GsLMWS7d/cUOm7Bgwsf6Qy89WAkhNqO55pcHyeIjgaMG6uzTrKlSoxBI3m/l3u9EKPIzp3Qa2Wcsc08/PfG0LaqLK0eu+YL+/UOpZ0uJpBGJglmhxJ5jY1G8ubCCyEry+xohBiThrWU4LTTTuPss8/mZz/7GTabjRdffHFYSZyQr33ta3zta18b8GNvv/12v2OLFy/m3//+97CvJ8Sw+HxQXt7TqagJNx4CFIZxKXG01MfpxEcydgrHYn2cxkZjwHreebKlSow5I3m/l3u9EKNAV5cxuZG6b2KnnLawrk4Oibb6OC78zCAnYm3Xo4bXa6xSPvdcmD/f7GhEFHv//fc57rjjWLVqFS+//HLP8bKyMiZOnHjAxxx77LGRCjGmDXkN5J49e1i8eDEvvfQSr732GjfffDPr1q3j5ptvxu/3hyNGIaJDba1R6Lg7kdOMhwA6cYRnOalS0NBrRY6ZnHjJIIFcxlg3Ar/f2E63cqV0qRJjjtzvhRD9VFcb9XHSjO1EPoLURqhjVW0U1cdRKACKx9q2qlCr8Xnz4IwzZJVyrNCDUPoO/PePxr96MCKXfeKJJ7j++uv55z//SU1NTb+Pv/7669TW1vZ5W7BgQURiGw2GnD6fN28ea9as4bXXXiM9PZ1TTjmF1atXc+mll/KPf/yDzz77LBxxCmG+2lpwuyHR6MrQ1N16PFzaVSZdJGEhQLZWF9ZrHUoHPo6mEMtYm3XaswemT5ctVWJMkvu9EKKfykrweCDB2E7UjJtOfGHfXtSpUulU6VFTH6cTH0nEjb36OKFW4xdf3DMeFlFu6wvw6i3g7JVISS2EVd+HmevCdtnOzk6effZZPv74Y+rq6njqqaf61dfLyso6YPdKcWhDXpHz6KOP8vvf/5709PSeY0uWLOGzzz7jqKOOGsnYhIguvQodA9TQgS2Mhf1Cq3FytFqsWmQy5weioygh3dQYIq6lBWw22LChZxWWEGOJ3O+FEP2UlhrjoO7JjUh1rNpXH6eOOM38FYHteMkkkYKxtOW8s9NYmb5+PUyaZHY0YjC2vgDPXdo3iQPgrDWOb30hbJd+7rnnmD59OtOmTePiiy/mV7/6FUqpsF1vLBryq9BLLrlkwOMpKSk88cQThx2QEFFr926j/TTGktpK2sM6cImW+jgBdCxoY6s+TiBgzDqefLKxfFiIMUju90KIPnQdtm3rM7nRiAsdPawTWxB99XGceJlOVtg/76ih60ar8aVLpdV4rNCDxkocBkqedB979dawbbN64oknuPjiiwFYtWoV7e3t/N///V+fc5YsWUJycnKfNzF44a9MJsRo4PX2KXTswk8bXWFN5DSo6KiP04GXFOxjq2NVaSlMmQJnnSVbqoQQQgiA+nqjAUCvVXqR7lgVDfVxAHR0JpFhdhiRs3cvFBUZrcZt8vIxJpS/138lTh8KnNXGeROPH9FL79ixgw8//JC//OUvANhsNs477zyeeOIJli9f3nPes88+y4wZM0b02mOJ/CYKMRh1ddDRAbm5gFEfx42ffOLDcjmPSqRDZQCKHMvB/giHXwc+skkka6y012xrM7bQnXtun8GqEEIIMaZVVBhjoaKifYdoxxHmlxMuldKrPo65k1sAXgLEYWX8WJngam2FYNDo3pmXZ3Y0YrA660f2vCF44oknCAQCFBYW9hxTShEfH88jjzzSc6yoqIgpU6aM+PXHijGyHlCIw1RTYxQ6TjK6NjXjposACWFakROqj5OuNRGvecNyjcHqwMtUssZGe81g0Fh5tWwZHHOM2dEIIYQQ0aOiok+tQD9BqnFGsD5OfVTUx3HiJZX4sZHI8fuN7/uKFbB4sdnRiKFIHmTSbbDnDVIgEODXv/41Dz30EFu2bOl5+/zzzyksLOSZZ54Z0euNZbIiR4jBCLXM695m04QbBWHr4hQtbcfBqAc0ZroylJVBcbFRyE+2VAkhhBAGpeDLL/t0Kmrq7liVEeYVu3VRUjMwxImXI8giJUyrsqNKqHvnOefIuCjWFC8xulM5axm4To5mfLx4yYhe9qWXXqK1tZUrrriCtLS+rx/Wr1/PE088wapVqwBobm6mrq5vZ9709HQc3TVJxcHJihwhBmPXLojfd8NuxB3W9Sn13Ykcs5cQ+whixTI2Ch13dBgzT+vXQ3a22dEIIYQQ0aO9HaqrodcLs3pcdOIL+4qc+igrdOzBzzSyzA4j/BoajLHvBRf0+b6LGGGxGi3Ggf51rLrfX/U947wR9MQTT7BixYp+SRwwEjkff/wxTqcTgBUrVlBQUNDn7fnnnx/ReEazYSVy1qxZQ21tbb//F2JU8nqNZaW9ujRU0k58mBa0BZSNFmUsc8zVoqHQcTzjRnsiR9eNAsfHHgtLRnZmQohYJvd7IQRgjIPa2/u8oG/AhYKwdm5yqyScKhNQ5EbBihyFQgFFo32lstdr1IdcvRpmzzY7GjFcM9fBhl9DakHf46mFxvGZ60b8ki+++CIvv/zygB9buHAhSinmzJmDUmrAt/PPP3/EYxqthvVK9J///Ccej6ff/wsxKtXWGqs18vMBCKJTS2fYZqCaVD46VhLoJFlrD8s1BqsDH4WkkM4oX+JYVWV8fzdsAOvIzkwIEcvkfi+EAIxEjq5D3L6xTy0dYa+eV9+9rSpTazC9ZiAYXUuTsI/uCS6ljJXoc+bA2rWypSrWzVwH09cY3ak6642aOMVLRnwljog8qZEjxKGECh137wtvwYMLX9j2Rveuj2P2vdOFb/QXOna7wemE88+HXtX1hRBCCNFt584+SRyAUtrC1vQhJFToOC9KtlU58ZKOY3RvOa+tNbp2XnRRn5pIIoZZrCPeYlyYT2rkCHEo1dXGbER3VqUZD278YVuREy2FjhUKHUXRaO7KoBTs3QtHHw0nnmh2NEIIIUT08XiMore9tlV58FNPJ8nYw3rpffVxzN9WBUYiZwqZxDFKVzO43dDcDGecAUccYXY0QoiDkESOEIeye3efQsdNuPGjExeGXx+l9iVyoqHQsR0r40ZzIqeuzph12rCh30yjEEIIITC2H7e19auP04mPpDCuyOlSCbQpo/lAtHSsCqAzmQyzwwgPpYyE3cKFsHKl2dEIIQ5BEjlCHIzHY+wLT92XzGjGDRCW7UZtKgsfDmz4yNQaRvz5h6Kje/vYqF0+7PNBYyOsWQOTJpkdjRBCCBGdKiqgqwt6tQSux4WHQFi3VoW2VaVrjTg08+tzBdCxoI3eCa7KSsjNNbpU2cO70koIcfgkkSPEwdTUGIWOe3WsqqUTa5hqxoRW42Rb6rBoeliuMVgdeMknmZQwL5s2zZ49MGuWzDoJIYQQB7Nnj9EIoFfhvgZcAFjCWEMvVOg4WtqOO/GSgp3xozGR09lpjHfPPhsmTDA7GiHEIEgiR4iDqakxZqESEnoOVdIe/vo4JrcdB6Mzw1QyR2eh46YmY7vcuedKIT8hhBDiQIJB2L69z4QWQBXOsCZxYN+KnGhJ5HTgJYckskg49MmxRNeNeoFLlki9QCFiyLASOcXFxcR115Po/f9CjDqV3YOH7lkoFz6a8YRtKXGDip5CxwoYT9ohz405gYBRwHrFCmNFjhDigOR+L8QYV1trFL/tVR9HoSilNaz1cbzKQavKAaKnPk4nPqaTPfomuMrLja6d558PNmloLESsGNZv6xdffDHg/wsx6uzc2WfFRhNuXPjIJ3nEL+VRiXSoDECRY6kZ8ecfCqPQsSUsn6fpSkthyhRYtw7T+7sLEeXkfi/EGFdRYWy7KS7uOeTESytdYe1YZWyr0kjTmknQ3GG7zmCFOnkWj7YJLqcTvF64/HIoKDA7GiHEEMjWKiEOxOk0ZqJ6LSduxB224n6hbVXpWhPxmnfEn38oQoWOC0ZbIsfpNJYQr19vdKsSQgghxIGVlRmTHpZ9LxnqezpWhTuREz2rcboI4MA2uurj6Lrx/T3uOONNiBGyfPlybrzxxhF9zrvvvpt58+Yd1nNomsbzzz8/IvFEA0nkCHEgNTXGC/9eHasaw1jcr6c+jsnbqsBYPpxDIqnEH/rkWBEasCxdarTWFEIIIcSBKQVffAFJSX0ON+DCR5B4rGG7dF0UFjpOJX50dawqLYWiItiwwShmLUQU++Y3v8kbb7wxqHMPlPSpra3ltNNOG+HIzCMbIYU4kOpqo0V1rxaMNXSEbWd0KJGTFwWJHBc+jiBrdO0Dr6oylg2vX99nZlEIIYQQA2hshLq6fitYQx2rwjVG8Ck7LSoPgLwoSuTMIi9szS4irr3dqBm4YYPRclyIKJecnExy8uHtFMjPzx+haAx+v9/U2oHyakaIA6moMF7w96qjUh6mjlUBZaO5e9ASDR2rFDCOlEOeFzM8HmN11RlnyB5wIYQQYjDKyoyW1Kl9V6GU0UZcGF9CNOiFKCykaG0kaZ1hu85QdBFgGllmhzEygkHje3vCCbB4sdnRiDGgtbWVSy+9lIyMDBITEznttNPYtWtXn3N+8YtfUFRURGJiImeddRYPP/ww6b2SyPuvsnn77bdZuHAhSUlJpKens3TpUsrLy3nqqae45557+Pzzz9E0DU3TeOqpp4D+W6uqqqq44IILyMzMJCkpiaOPPpoPPvhgwM+hrKwMTdN49tlnWbZsGQ6Hg82bNwPwy1/+khkzZuBwOJg+fTqPPvpon8e+9957zJs3D4fDwdFHH83zzz+Ppmls2bJl2F9TkBU5QgxMKdixA3plfj34acQVlj3hzSoPHSsOXCRr7SP+/EPhJ4gVjYLRkshRCvbsgaOOguXLzY5GCCGEiA1lZca25F7bboLoVNAe5vo4RtvxaFmNo6PQ0CgcLeOi0lKYOBHOPVdWKMcYpRQef9CUayfEWdGG2SRk06ZN7Nq1ixdeeIHU1FRuueUWVq9ezdatW4mLi+Pdd9/l2muv5fvf/z7r1q3j9ddf54477jjg8wUCAc4880yuuuoqnnnmGXw+Hx9++CGapnHeeefxxRdf8Oqrr/L6668DkJbWv0h5Z2cny5YtY9y4cbzwwgvk5+fz6aefouv6QT+XW2+9lYceeoj58+f3JHPuvPNOHnnkEebPn89nn33GVVddRVJSEhs3bsTpdLJ27VpWr17N7373O8rLy0esftCgEzmvv/46K1asOODHdV3n/vvv5/bbbx+RwIQwVUsLNDX1K3Tswk8uSQd54PD03lZldiOlUVfouKHBmE0899w+2+SEEAOT+70QAqXgyy/71cdpwdNTLyZcehI5WnQkclz4SCJudKxUbmszVuRs2ADZ2WZHI4bI4w8y887XTLn21ntXkmgf+hqQUALn3XffZcmSJQBs3ryZoqIinn/+ec4991x+8pOfcNppp/HNb34TgKlTp/Lee+/x0ksvDficTqeT9vZ2Tj/9dCZPngzAjBkzej6enJyMzWY76Faq3/3udzQ2NvLRRx+RmZkJwJQpUw75+dx4442cffbZPe/fddddPPTQQz3HJk6cyNatW/n5z3/Oxo0b+d3vfoemafziF7/A4XAwc+ZMqqurueqqqw55rUMZdBp29erVfO1rX8Pt7t8C8IsvvuCYY47hZz/72WEHJERUqKkxlhP3SeS4cOMPy9aqBr0QiJ5Cxxk4yCTB7FAOn98P9fWwahUccYTZ0QgRE+R+L4SgpcWoFbhffZx6XD2JjXAIKBtNynjxlR8lHas68JGKg/xYn+AKBqG8HE48ERYtMjsaMUZs27YNm83Gol4/c1lZWUybNo1t27YBsGPHDhbu14hk//d7y8zMZNOmTaxcuZK1a9fy4x//mNra2iHFtWXLFubPn9+TxBmso48+uuf/XS4Xe/bs4Yorruip4ZOcnMx3vvMd9uzZAxif25w5c3A4HIP63IZi0Gm1d955h02bNjF37lyeeuopli5d2jMrd99997F+/fqe5UtCxLzqaqMIXK8CVo0YL2pGumOVUvtW5ORESSJnEeNGR6HjvXth2jQYRRXqhQg3ud8LISgvNwri7jej3YCLIIq4MHWsatQL0LGSSIfpW81DOvAyk5ywfc4Rs3cvTJoE55yD6cu/xbAkxFnZeu9K064dTZ588kluuOEGXn31VZ599lluv/12/vGPf3DssccO6vEJCcObsE7qtUqxs9Oo4fWLX/yiT6IKwBqBTnCDTuQsWrSIzz77jFtvvZUTTzyRq6++mn//+99UVlbyzDPP9FliJETMKyvr14qxjs6wpDacKgMviVgIkKXVh+EKQ6OjmED/vaQxp63NGKisX99nZZUQ4uDkfi+E6KmPY+v7UqGO8BYfruveVpVvqYyaXIOPIJPIMDuMw9PaaswcbtgAQ1yBIKKHpmnD2t5kphkzZhAIBPjggw96tlY1NzezY8cOZs6cCcC0adP46KOP+jxu//cHMn/+fObPn89tt93G4sWL+d3vfsexxx6L3W4nGDx4LaE5c+bwy1/+kpaWliGvygnJy8ujsLCQvXv3ctFFFw14zrRp0/jtb3+L1+slPt7YkjqYz20whvST4HA4+J//+R8aGhp49NFHSUpK4uOPP2batGkjEowQUUHXYdeufi/+y2gjISzbqozVONlaHVbt4AW2wi2AjgaxX+hY143ZxJUrYcECs6MRIubI/V6IMe7LL2GAGetS2nCEsVdKvRoPQF6UbKsKomPBEtv1cYJBoxPr6tUwQls6hBisI444gjPOOIOrrrqKn//856SkpHDrrbcybtw4zjjjDACuv/56TjjhBB5++GHWrl3Lm2++yd/+9rcDFlcuLS3l8ccfZ926dRQWFrJjxw527drFpZdeCkBJSQmlpaVs2bKF8ePHk5KS0pNECbngggu4//77OfPMM3nggQcoKCjgs88+o7CwkMVD6OZ2zz33cMMNN5CWlsaqVavwer18/PHHtLa2ctNNN3HhhRfy7W9/m6uvvppbb72ViooKfvjDHwIMu3h0yJBKle/Zs4cTTjiBN998k8cee4xZs2axfPly/vrXvx5WEEJElYYGY294r0SOlwD1dIZlT3iDiq76OMnYY7/QcUUFjBsHZ50ly4eFGAa53wsxhrW1GffR/erjdBGgGifJYepYFVQWGrtrBkZLxypjXBQX2x2r9u6FyZONFcoyJhImePLJJ1mwYAGnn346ixcvRinFK6+8Qlx3CYulS5fy2GOP8fDDDzN37lxeffVVvvGNb/SpK9NbYmIi27dvZ/369UydOpWrr76ar371q1xzzTUArF+/nlWrVnHiiSeSk5PDM8880+857HY7f//738nNzWX16tXMnj2b733ve0PeEnXllVfyy1/+kieffJLZs2ezbNkynnrqKSZOnAhAamoqL774Ilu2bGHevHl8+9vf5s477wQ44Oc3WJpSSg3mxEceeYRbb72VlStX8thjj5GTk4Ou6/zgBz/g7rvv5pxzzuEnP/lJn37v0crpdJKWlkZ7ezupqalmhyOizSefwPe+B9On92yvqsLJt3mDbBJJGeFODX/xXka7yubkuD9TZN0zos89VFU4SSGeH3IK1qHleaOH22201rzmGjhI5x0hok203JtGy/0+Wr6eQsSczz+H737XaBLQq1ZgOW3czpvkkxyW9uP1+jj+5rsQBy7Oi380KnIOVThJJZ4fxOq4qLUVGhvhxhtlNU6UGOy9qauri9LSUiZOnHjYL/hj0VVXXcX27dt55513zA5lxG3evJnLLruM9vb2AWv1DPZ7P+i/SHfeeSc///nP+dOf/kROTo7xYIuFW265hY8//pht27Zx5JFHDuNTESLK1NQY+4h7ZWTD1bHKqxy0K6P9Y46lZkSfezg68TGJ9NgcrIDxfduzx9hOtWyZ2dEIEZPkfi/EGFdWZmzHies75qmlEw+BsGwzB6jX922rioYkDhiFjqeQGZvjotCWqpNOgmOOMTsaIQ7qhz/8IZ9//jm7d+/mJz/5CU8//TQbN240O6wR8etf/5p//etflJaW8vzzz3PLLbewYcOGYRdcDhn0Jtcvv/ySgoKCAT925JFH8sEHH3D//fcfVjBCRIU9e/oV92vEjYIRv5GH2o6nas04NM+IPvdwBNApId3sMIavoQHS0oyODHHhGWgKMdrJ/V6IMW7rVhhgFriOThRqxLt3hvRO5ESLICp2x0WhLVVnny1bqkTU+/DDD3nwwQfp6Ohg0qRJ/O///i9XXnml2WGNiLq6Ou68807q6uooKCjg3HPP5bvf/e5hP++gEzkHGtSFWK1W7rjjjsMOSAhTeb2we7eRDOilPkxdGkJ7wXOjYDWOjrHLMmYLHfv9UF8P558PU6aYHY0QMUvu90KMYR0dxoqctP7dK8towx6mFty60noSOflRUh8ngI4FLTYLHbe1GauUzz1XulSJmPDcc8+ZHULY3Hzzzdx8880j/rwxuE5QiDCqqYH2dthvz2oZ7SSEoUtDfXfHqlzN/ELHLnwkEUd+rBY63rsXpk6F004zOxIhhBAiNpWXG0mA/WpgBdEpoy1shY5bVC4B7NjpIl1rCss1hqoDLynYY6/QcTBofB+XL5e6OEKMYoNK5FgsFqxW65Df7r333nDHL8TIqqoyiuUmJvYc8hGklo4RL+ynKwtNypj5joaOVR34SMVBHklmhzJ07e3GsuH16/u1jRdCDJ7c74UY48rKjBWu9r5jnkbctNMVtkROnV4EQK6lCos2qD4sYdeBjywSySbx0CdHk717YdIkY5u5bKkSYtQa1BKD0tLSYT15tHe0EKKf8nLjptfrxteEGzd+Mjm8glT7a1G5BIkjHg9pWsuIPvdwdOJjGlnEhWnZdNjouvF9O+UUOPpos6MRIqbJ/V6IMe7LLw9YH6cTP+MITwe4+u5ETn4U1cfpwMsSitDCVBMoLGRLlRBjxqASOcXFxeGOQwjzKQXbt0NS3xUpjbhw4WP8CA9eQtuqciw1UTFh4iXARDLMDmPoqqqgoADOOktmnoQ4THK/F2IMczqN1RwZ/ccCtXSgUNjCUJVBKaKuPg6AAorpXysoaoW2VK1cKVuqhBgDBv3XuKGh4aAfDwQCfPjhh4cdkBCmaW2Furp+9XFCHatGevDSEKqPEwXbqlR3oeOYq4/j8RiFGc84A/LzzY5GiFFB7vdCjFF79xpjoQFW2FXiDFu3qlaVgw8HNnxkavVhucZQ+QhiwxJb9XFKS6GkxNhSZZEyqEKMdoP+LS8oKOgzuJs9ezaVlfuy5s3NzSxevHhkoxMikqqqjNmo/RI59bhG/FJK7UvkREObTQ8BEmKt0LFSxqBz3jyjoJ8QYkTI/V6IMaq01FjVERfX57BCsYcWkog7wAMPz776ONVRVB/HKHQcrq1kI6693fjenXMOZGebHY0QIgIG3YZHqb5/WMvKyvD7/Qc9R4iYUllp1FvZbwBTThuOEe5Y1anS8JCMhSBZUTD71ImPZOyxVei4sRGSk41Biz08xReFGIvkfi/EGKQU/Pe/fZo9hLTRRTOesBU63lcfJ3q2VTnxMp5U0og3O5RD03WjSPUpp4Ak2ceG9najOUukJCZCWgxtMzTR3XffzfPPP8+WLVvCfq0RfXWqSX0KEcv27AFb318JH0GqcI744KVBGatxsrR6bFpgRJ97ODrxMYmMEe/MFTaBANTWGsX8pk0zOxohxhy53wsxyjQ3Q0XFgNuqjELHPnLDMNmjFNRFYX0cF36mkhUbhY7LymDCBNlSNVa0t8N990FTU+SumZ0Nd9wx5GROXV0d3/3ud3n55Zeprq4mNzeXefPmceONN3LyySdTUlLCjTfeyI033hieuEe5kV1mIESs8vlg165+26oaugsdj3Tryfooqo8D4MbP5FgqdFxaClOmwJo1ZkcihBBCxL7SUuMFYkFBvw/V0omfIPYwdLVsV1l4ScSKnyytbsSffzhU939FsVDo2OkErxfWr4fcXLOjEZHgdhtJnISEAVfQhe16bveQEjllZWUsXbqU9PR0fvCDHzB79mz8fj+vvfYaX/3qV9m+fXsYgz44v99PXFx4topG0qDTtpqm0dHRgdPppL29HU3T6OzsxOl09rwJEbNqaowBzH6JnDo6ceMf8ZUq0VToOCRmCvp1dBjLiM8+W5Z5ChEGcr8XYgzavdtYHmPtn6ypwRm2lSn76uPUYNX0sFxjqHzdSauoHxeFtlQddxwsXWp2NCLSEhMhJSX8b8NMFn3lK19B0zQ+/PBD1q9fz9SpUznyyCO56aab+Pe//z2o53jqqadIT0/ntddeY8aMGSQnJ7Nq1Spqa2t7ztF1nXvvvZfx48cTHx/PvHnzePXVV3s+XlZWhqZpPPvssyxbtgyHw8HmzZvZtGkTZ555Jvfffz95eXmkp6dz7733EggE+P/+v/+PzMxMxo8fz5NPPtknpltuuYWpU6eSmJjIpEmTuOOOO/ptP4+UIdXImTp1ap/358+f3+d9WWotYlZVlZFp3q/1eD2dKBjRTg1eFU+bMgrRRUMiJ9SZISYKHStlzBouXw6LFpkdjRCjktzvhRhjQvVxkgbeOrWHVhLDVujY2FaVF0XbqjrwkUJ89CdyysuhsNDYZj5AAk4Is7S0tPDqq6/y3e9+l6QB/q6kD7CFE2DTpk2UlZXx9ttv9xxzu9388Ic/5De/+Q0Wi4WLL76Yb37zm2zevBmAH//4xzz00EP8/Oc/Z/78+fzqV79i3bp1fPnllxxxxBE9z3Prrbfy0EMPMX/+fBwOB2+//TZvvvkm48eP55///CfvvvsuV1xxBe+99x4nnHACH3zwAc8++yzXXHMNp5xyCuPHG3+rUlJSeOqppygsLOS///0vV111FSkpKdx8880j9wUcpEEnct56661wxiGEuSoqjIHMfi9OqsLQbrNRLwQ0UrUWErQIFio7ABc+kmKlY1V1tbF0+OyzZR+4EGEi93shxpi6OqPuXEb/LdZu/NTSGZZCx0pFZ6HjDrxMII2UaK4b2NkJHg9s2gT5+WZHI0Qfu3fvRinF9OnTh/S4goICdL3vyjy/389jjz3G5MmT+f/Zu+/4OKpz4eO/2aJV78Wy3HsvFBvTi4MJhNBbICEkufdNAiFcSAIkIYRwiUlCCiQk5BIIIY0WSkIxxdiAjbGNG+64SJYlq9fVSttmzvvHaGXJ6trZIvn55qNg7c6eObMjac4+c87zANxyyy385Cc/6Xj+oYce4s477+Taa68F4Gc/+xmrVq3iN7/5DY8++mjHdrfddhuXX355l7azs7N55JFHsNlsTJ8+nZ///Oe0trby/e9/H4C7776bBx98kDVr1nS0/8Mf/rDj9RMmTOA73/kOzzzzTHwHcs4666x+t6mvrw+rM0LEhFKwe3e3O1Fmuc0G6xMdx9myqhb8ZJFEFkmx7krffD5oaICbboL2qLgQwnpyvRfiOHPwoLlseezYbk+FEh1HYnaKW2W2V/AMkqtV9P+CKIn7RMeGYZ6zM86AM8+MdW+E6GaolS2XL1/e7bHk5OSOIA6YwZ7q6moAmpubOXLkCKcds7TwtNNOY9u2bV0eO+mkk7q1PXv2bGydbgwXFBQwZ86cju/tdjs5OTkd+wN49tlneeSRRzhw4AAtLS0Eg0HSj0nNES2W3NJ+6623uPrqqykqKrKiOSGiq7HRvBt1TL6VJnw00EaKxdOJOxIda/ETyJlEluUzjyx38CDMnQtLl8a6J0Ict+R6L8QItH+/+d8eZrpW0oKXIEkRqI8Syo+Tp1Xg0HTL2x8KM80xjCE2H8wG5PBhcxbO1VfLkioRl6ZOnYqmaZYkND42KbGmaUMKFPW0xKuntnt6LDRLaN26dVx//fVceOGFvPrqq2zZsoUf/OAH+P3+QffHCkMO5Bw6dIh7772XCRMmcNVVV2Gz2Xj66aet7JsQ0VFWZmb9PyaaWtV+F8rKGTmGslGrzIoQ8TIjR8dgXLxXZqirA5fLrMqQmBjr3ghxXJHrvRAjmK6b+XF6uaNcgRsFEZmdEgrkxFN+HDPRsY3CeM2P4/GYy6ouuwwkoC7iVHZ2NsuWLePRRx/F4/F0e76xsdGS/aSnpzN69GjWrl3b5fG1a9cya9YsS/bR2Ycffsj48eP5wQ9+wEknncTUqVM5dOiQ5fsZqEGF1/1+Py+++CJ/+tOfWLt2LUuXLqWsrIwtW7Ywd+7cSPVRiMg6fBiCQTgmAltJC34MS8tt1ql8dJy4aCNDi/3SBAOFhhbf+XF03Qy2XXwxdJruKISIHLneC3GcKCuDmhrIze3x6X3UkxiBsuMAVe2JjkfZyiLS/lDEdaJjpczZyUuWmEUfhIhjjz76KKeddhqLFi3iJz/5CfPmzSMYDPL222/zhz/8gd27d3d7zd133015efmgbhZ997vf5d5772Xy5MksWLCAP//5z2zdurUjGbKVpk6dSmlpKc888wwnn3wyr732Gi+99JLl+xmoAQdyvvWtb/HPf/6TqVOncsMNN/Dss8+Sk5OD0+nELtP6xHC2b1+3IA6YgRwNa+9ChfLj5NnKj82rHBMe/CTHe6LjQ4dg/Hi45JJuyaiFENaT670Qx5GDB80ZHhMmdHvKS5BSmkjDZflu3UY6HjLQ0MmzHbG8/aFy42M8mfGZ6LisDPLyzCVVPYxbxXGmNUoFU4a4n0mTJrF582YeeOAB7rjjDioqKsjLy+PEE0/kD3/4Q4+vqaiooLS0dFD7ufXWW2lqauKOO+6gurqaWbNm8e9//7tLxSqrfP7zn+d//ud/uOWWW/D5fFx00UXcc889/PjHP7Z8XwOhqQEuMnM4HNx5553cddddpKUdjVI7nU62bdsWkelLkdLc3ExGRgZNTU0xS04k4oTXC3fcYc7IKSzs8tSDrGEH1Uwh27LdrfJ/nkPGdE50vMdcxwbL2h2qCtw4sfMrllk688gyHo8ZyLn5ZhhAAlYhhrN4uTaNlOt9vLyfQsS1Rx+F996DHn6vD9LAj1hFIamkWBzY2BeczdrgheRp5Vzk+oelbYdjJ9V8nul8kfmx7kpXbW1w4AB89avw2c/GujciDAO9Nnm9XoqLi5k4cSKJndMKNDXB/fdDbW0UetsuNxfuuadbPlERGb2e+2MMeEbOX//6V5588kkKCwu56KKL+OIXv8hn5Q+JGO4OHzaTHR9TBcmPThnNliY6NstsmvuJl/w4LfiZx6j4DOKEphCfcgock41eCBE5cr0X4jjR2gq7dkF2zzesjuDGS5Bki4s+AFQa44D4KjuuUBjEYaJjpcwgzkknwXnnxbo3ItYyMsygSrRm5AAkJ0sQJw4NOJBz3XXXcd1111FcXMxTTz3FzTffTGtrK4ZhsGvXrmFzh06ILg4dMmflHBPtrMGDGx+5JFu2K7fKxEsKNoLkaJWWtRsOH0EmkRnrbvSsshKysuDKK8FhfbUMIUTP5HovxHHiwAGzmMDEiT0+fZhmILKJjuMpkONDx4U9/vLjVFSY46Grr4aEOFzyJaIvI0MCK2LwVasmTpzIfffdR0lJCX/729+44ooruOGGGxgzZgy33nprJPooROTs32+Wbjwm90oVHjwELL0LVa3M/Dg5WlVclNlU7XUo4jI/TiBgJl/87Gd7HWAKISJLrvdCjHD79pnXW1fPOXD2UReRsuOd8+PEywxloKNSaVxVrPJ6zWDbxRfDpEmx7o0QIo4M+a+zpmksW7aMZcuWUV9fz9NPP82f//xnK/smRGQFArB7d48R7UpaAIV98LHOXoWWVRXESXWG0J2nuAzkHDgAM2fCBRfEuidCHPfkei/ECKQUbNkCKSk9Pt2Cn3KaSY9AouPQsqo8rRKnFrC8/aGKu0THSpk3HBcsgPPPj3VvhBBxxpJPqdnZ2dx2221s27bNiuaEiI6yMqiv7zGQU0YzNguDOBB/gZzQnae4C+Q0NpqzpC6/vNcBphAiNuR6L8QIUVFhjoN6yY9TTnNHKW6rHV1WNbjqNJHmIcA0siOylGxIqqogPR2uuaZbCgAhhBjQJ9UHH3yQtra2ATW4fv16XnvttbA6JURUlJaaicKOCRYoFAdpsDTRcZtKplmZg6V4mUbcgp98UkiNlztPAIZh5i0680w44YRY90aI445c74U4TuzfD263GSjowRHcHTN3raRUfObHUe3/K4qXRMc+H1RXw4UXwrRpse6NECIODSiQs2vXLsaNG8c3v/lN3njjDWpqajqeCwaDfPLJJ/z+97/n1FNP5ZprrulSrlSIuHXgANhs3fLjNOOjjlZLAxzVhpkfJ1OrwaX5LGs3HB78TCIrfu48gVlFrKgILr2023kRQkSeXO+FOE7s3m2OgWw9fxQopQkN6xMdt6gMPGRgQyfPdsTStsNhBq0c8ZHoOFSlau5cM5AjhBA9GFAg5+mnn+add94hEAjwhS98gVGjRpGQkEBaWhoul4uFCxfy5JNP8qUvfYk9e/Zw5plnDqoTjz76KBMmTCAxMZHFixezYcOGAb3umWeeQdM0Lr300kHtTwh03Sy52cOHkFCi4xQLAznxtqwKQEH83HkCaGuDlha45BIoKIh1b4Q4LkXyei/XeiHiRFsbbN8OmZk9Pq1QfEpdRGbshmbj5GoVcZcfJ42E+Ajk1NSY5Z6vucb8rxBC9GDAyY7nz5/P448/zh//+Ee2bdtGaWkpbW1t5ObmsmDBAnJzc4fUgWeffZbbb7+dxx57jMWLF/Ob3/yGZcuWsXfvXvLz83t9XUlJCd/5znc444wzhrRfcZyrqIDa2h7XhlfRgo+gpdOJ4y2QE8TAhkYBcZKDJnT36YQT4KyzYt0bIY5rkbjey7VeiDhy8KBZCWn8+B6fbsRLNZ6IJP0NJTqOt/w4bvxMJDMiOYEGJRCAykq46iqYNSu2fRFCxLVBV62y2WwsXLiQhQsXWtKBX/3qV/zXf/0XN910EwCPPfYYr732Gk8++SR33XVXj6/RdZ3rr7+e++67jw8++IDGxkZL+iKOI4cOmWvDx43r9lQFLYB104kDykm9Mj+oxEsgx4OfFJzxk+i4utqcHXXllZAQRzl7hDiOWXm9l2u9EHFk/37w+3tNoFuOm2Z85Fl8syde8+MAtBJgKj0nfo6qUNXOiy+OdU9EHGvyNtEaaI3a/pKdyWQkdi8OEwtPPfUUt91226DGBJqm8dJLL424mb0DDuTous5DDz3Ev//9b/x+P+eddx733nsvSUlJQ9653+9n06ZN3H333R2P2Ww2li5dyrp163p93U9+8hPy8/P56le/ygcffNDvfnw+Hz7f0bwkzc3NQ+6zGCFKSswcLD2sDT9IA4mDj3H2qtooQmEjVWskRWuxrN1wtOAng0RyiYMpu8GgWZnh6qsloZ8QccDq671c64WII6Gy430s2TmCGx2DBIsTHbtVJh7SsaGTH0f5ceIm0XFtrXkz6+qrITVObrSJuNPkbeL+9++ntrU2avvMTc7lnjPvGXQwp7KykgceeIDXXnuN8vJy8vPzWbBgAbfddhvnnXceEyZM4LbbbuO2226LTMd7UFJSwsSJE9myZQsLFiyI2n4jYcCfVn/605/y4x//mKVLl5KUlMTDDz9MdXU1Tz755JB3Xltbi67rFByTD6OgoIA9e/b0+Jo1a9bwxBNPsHXr1gHvZ/ny5dx3331D7qcYYZSCHTt6vEh6CXKIRkun1la1Jzou0OKjWhWYgZxZ5GO3uMT6kBw8CFOnSkI/IeKE1dd7udYLEUeqq82qnb2UHQcooTEihRBCs3HytCM4tKDl7Q9VXCQ6DgahvBwuuwzmzYtdP0Tcaw20UttaS5IjiWRn5G/IhvbXGmgdVCCnpKSE0047jczMTH7xi18wd+5cAoEAb775JjfffHOv138xcAP+FPf000/z+9//njfffJOXX36Z//znP/z973/HMIxI9q8Lt9vNF7/4RR5//PFBrdG/++67aWpq6vg6fDi+pnOKKKuuNmeAZHT/Y1SBGzd+S9eFx1t+HIAABhOIgymSTU1mYO3yy3stgSqEiK5YX+/lWi9EBH36KTQ39zgGAjBQ7KMuIrli4nVZVQt+UkmgMJaBnAMHzFnJl1wiVTvFgCQ7k0lzpUX8a6jBom9+85tomsaGDRu44oormDZtGrNnz+b222/no48+GnA7Tz31FOPGjSM5OZnLLruMurq6btu88sornHDCCSQmJjJp0iTuu+8+gsGeg8UTJ04EYOHChWiaxtlnnw3Axo0b+cxnPkNubi4ZGRmcddZZbN68efAHHkUDnpFTWlrKhZ3umC9duhRN0zhy5AhjxowZ0s5zc3Ox2+1UVVV1ebyqqopRo0Z12/7AgQOUlJRwcad1o6GBpcPhYO/evUyePLnb61wuFy5XjJOXifgRyo9TVNTtqSO4aSNAMk5LdqUrO7WqEID8OAnkhKYQF8Q6P45hmOfi3HPh5JNj2xchRAerr/dyrRcijnzySZ9lx2tppY42yxMdm/lxQomO4yuQ48bHODIiktx5QOrrzfNx9dW9BtiEGE7q6+tZsWIFDzzwACkp3XNtZfZSMe/LX/4yJSUlrF69GoD169fz1a9+leXLl3PppZeyYsUK7r333i6v+eCDD/jSl77EI488whlnnMGBAwf47//+b4Bu2wJs2LCBRYsW8c477zB79mwS2nNzut1ubrzxRn7729+ilOKXv/wlF154Ifv27SOthyrH8WDAgZxgMEjiMUnRnE4ngcDQSwcmJCRw4oknsnLlyo7kQ4ZhsHLlSm655ZZu28+YMYPt27d3eeyHP/whbrebhx9+mLFjxw65L+I4cuCAOaKwd1/7XY4bDesSHdepAnScuGglQ6u3pM1wtREkKR4SHZeVwahRcMUVvQ4ohRDRZ/X1Xq71QsSJ5maz7HhOTq+bHGmfmWz1MiO3yqSVNGwEyYuj/DgAHgJMIyciy8n6petw+DB87nNm5U4hRoD9+/ejlGLGjBmDel1hYWGX2b8PP/wwF1xwAd/73vcAmDZtGh9++CErVqzo2Oa+++7jrrvu4sYbbwRg0qRJ3H///Xzve9/rMZCTl5cHQE5OTpebSeeee26X7f7v//6PzMxM3nvvPT73uc8N6jiiZcCBHKUUX/7yl7vc7fJ6vXz961/vEml78cUXB9WB22+/nRtvvJGTTjqJRYsW8Zvf/AaPx9NR2eJLX/oSRUVFLF++nMTERObMmdPl9aGI3rGPC9Ejw4Bt23pNIrePOhItmo0DnZdVlcfNTNnQFOKYlh73es0B5TXXQGFh7PohhOgmEtd7udYLEQf27jXLjvdRWKCcZhQKh8U59CoMs9R5vOXHMecoE7tExwcPwuTJ5hLzeBkoChEmpdSQXrd8+fIu3+/evZvLLrusy2NLlizpEsjZtm0ba9eu5YEHHuh4TNd1vF4vra2tJPeR2L2zqqoqfvjDH7J69Wqqq6vRdZ3W1lZKS0uHdCzRMOBATijK1dkNN9wQdgeuueYaampq+NGPfkRlZSULFixgxYoVHUkRS0tLscndemGVigqorISsrG5PtRKgjGaL8+O0JzqOk2VVYJYen0AmKbGaQqyUOStq/nxoX5cqhIgfkbjey7VeiDiwY4d5DXb2fsNqH/WWB3EAKtqXVY22x9eHIj86Cdhik+i4sdG8wXjVVT2OS4UYrqZOnYqmaVFJaNzS0sJ9993H5Zdf3u25Y2cX9+XGG2+krq6Ohx9+mPHjx+NyuViyZAl+v9/K7lpqwIGcP//5zxHrxC233NLj9GqgY41cb5566inrOyRGroMHoaUFxo3r9lSFxdOJlTJLj0O8BXICTCKGA4aaGnNG1NVXg+SzECLuROp6L9d6IWLI64WtW/sMGHgJso86MixOdKzU0UBOoe2QpW2Hyyxw4aIw2svNdd3ME3jBBbBoUXT3LUSEZWdns2zZMh599FFuvfXWbnlyGhsbe82T09nMmTNZv359l8eOTZR8wgknsHfvXqZMmTKgvoVy4ui63uXxtWvX8vvf/74jR+Dhw4eprY1eifehkNtf4viyd685dbWHO79HcOMlSNLA45t9alS5+EnCgZ9srar/F0RRzKYQB4PmrKjzz4fp02PTByGEEOJ4s3+/WbGzj/w4ZTTThI8MBn4XeyDqVT4+knHgJ1ertLTtcLnxUUAK6RGo0tWnkhKYMMHMEyhLqsQI9Oijj6LrOosWLeJf//oX+/btY/fu3TzyyCMsWbKkx9fcfffdfOlLX+r4/tZbb2XFihU89NBD7Nu3j9/97nddllUB/OhHP+Lpp5/mvvvuY+fOnezevZtnnnmGH/7whz3uIz8/n6SkJFasWEFVVRVNTU2AOYvor3/9K7t372b9+vVcf/31JCUlWfRuRIYEcsTxw+83pxX3UhGgDDdgXaLjUJnNfFs5Nm1oa0WtFkDHjha7/DgHD5pr8y+6KDb7F0IIIY5Hu3aZ46A+lhocpok2Apbd0Aqp6FStyqYZ/WwdXR4CTCU7uomOm5shEDCDOLm50duvGFFaA624fe6If7UGWofUv0mTJrF582bOOecc7rjjDubMmcNnPvMZVq5cyR/+8IceX1NRUdElJ80pp5zC448/zsMPP8z8+fN56623ugVoli1bxquvvspbb73FySefzCmnnMKvf/1rxo8f3+M+HA4HjzzyCH/84x8ZPXo0l1xyCQBPPPEEDQ0NnHDCCXzxi1/k1ltvJT8/f0jHHi3W/qUWIp6VlkJtba/JdfdRZ+ngJRTIiacymx4CpJAQm4pVTU3m/OrLL4f0GM0IEkIIIY43wSB8/HG/196DNGBDszyoEUp0XGiLr/w4qv1/Y4hiyW/DgOJiOO88OPXU6O1XjBjJzmRyk3Opba2lLdgWlX3mJueS7BxY0uDOCgsL+d3vfsfvfve7Hp8vKSnp8n1Py6i/8pWv8JWvfKXLY3fccUeX75ctW8ayZct67cexyZe/9rWv8bWvfa3LYwsXLmTjxo1dHrvyyit7bTMeSCBHHD8OHoTWVughe7kHP0dwk2bR1Fql4jOQ04KfTBLJYfB/jMNiGOY04qVL4eSTo7tvIYQQ4nhWUgJHjkCnUrvHMlDsptaycVCIrmwdFTzjLT+OmejYHt38OIcOwdixZoJjSfAuhiAjMYN7zrxnyDNlhiLZmUxGYhQDnmJAJJAjjh+7d5uVGnpYi3wEN258jLEod0yjyo3L9eAt+JlHAbZoTiEGOHwYRo82pxHLwEUIIYSInj17zBtZKb0vq66khTpaybQ4P06tKiRIAi5aydJqLG07XKFEx1GrWOV2m0mnv/IVaK/YJ8RQZCRmSGBFSI4ccZzweMyBTHZ2j0+biY51Ei2KbXbNjxM/68ED6IyP5hRigLY2c/ByySV93g0UQgghhMWUMpdVJSf3mVT3ME0dgQ0rdV5WFW85fVvwk0dydBIdG4Y5M/zUU+H00yO/PyHEiCeBHHF8KC6GhgbopdRdGW40rE90HE/LqhQKDY2CaE4hVgoOHIATToCzz47efoUQQggB5eXmcp5+kuoewqzcYvWM3Qo9PsuOg7msfio50Ul0XFpqzky++mqw2yO/PyHEiCeBHHF8OHDArBDg6vmuyz7qSMZpya7iNT9Oa3sliqgmOq6qMpMrXn01JCREb79CCCGEMKt1NjX1WrETzBs9u6mxvFpVQDmpUaOB+Et0DKCAsRYtqe9TS4u5tO2KK3otuCFEb45N1CtGvoGecwnkiJFPKfjkE0hK6vFpNz4qcJOGNYGGzvlxcuIsP04qCdErPR4IQHU1fPazMGVKdPYphBBCCJNhwPr15vinj3VNTfgox02GxflxqowiDOyk0ESa1mhp2+HyEcSJjcJI58dRylxSdcopcOaZkd2XGFGcTvMGc2tr9JIai/gQOuehn4HeSLJjMfLV15vTirOyenzaTHTsZ5xFuWOO5sc5gj2O8uN4CDCFbJIsmnnUrwMHYOZMM5AjhBBCiOgqLTWDCPn5fW52mCaa8DKFnvMIDlUoP85o+6G4zI+TSkLkEx2XlpqJja+5BhzysUsMnN1uJzMzk+rqagCSk5PR4u0XSVhKKUVrayvV1dVkZmZi72cZpvxFESPfgQPmtOJeEu0ewY0PHRfWrFk+uqwqvqYRtxJgMj0HsyzX0GAOWK64AlKjuJRLCCGEEKYdO8xiAxMm9LlZKU3oGDgtGgeFhAI58TYeArNi1WjSyIhkomOPx1xW9YUvQFFR5PYjRqxR7Z9dQsEccXzIzMzsOPd9kUCOGPl27zantvZyJ6SUJssSHcdrfhww18BHfAoxgK6bd6AuuggWLoz8/oQQQgjRla7DRx+ZN1P6uYv/KXU4LA7ieFUS9cqcCRSP+XE8+JkWyUTHoSVVS5bAOedEZh9ixNM0jcLCQvLz8wkEArHujogCp9PZ70ycEAnkiJHN54PNm3utVmWg2EWNhflxcuIyP44fHSf26CQ6Likx7/5ddlm/g0chhBBCRMDBg+b1uJ+7ul6CHKDB8pkpR4zxgEaWVkOy5rG0bStEPNFxWRnk5cmSKmEJu90+4A/34vghyY7FyHbwINTUQE5Oj0/X4KGGVssS/FUaZpnNvDjLjxO1RMdut5nk+PLLe33PhRBCCBFh27eblZJS+r7ul9FMI17LEx0fMSYAMNpWbGm7VgglOo5YfpzWVnNJ/2WXwdixkdmHEOK4J4EcMbLt3Qteb68Vq0ppwo3Pshk58bqsqgU/WSSSTc/vgyWUguJiszLDqadGbj9CCCGE6F0gYC6rSk/vd2ZsKU20EbC09LhScESfAECRrcSydq3ixk8arsgEcpQyczOefDKce6717QshRDsJ5IiRyzBg06Y+70YdphkDsFvwq6AUVBljgPgL5HjwM4msyK0FB3MacUEBXHUVyPRPIYQQIjb27zevyf1Uq4JQfhybpeODRpVLK2nYCZBvK7OsXau48ZFHMumRSHQcWlJ17bXQT+lgIYQIhwRyxMh15AgcPgy5ub1usosay+5CNapcvKTgwE+uVmFJm1bRMRhrUXn1HrW1mdOIL7lEKjMIIYQQsbRtm5kjMDm5z818BNlJjeXLqsrbl1WNsh3GoemWtm2FVgKRSXQcWlJ16aUwfry1bQshxDEkkCNGrr17zZwt6T0ns3Pj4zBNliX4O9JeZrPAVhZX+XEMFBpa5BIdh6YRL1wo04iFEEKIWPL5YMMGyOj/5k0pTdTTSqbl+XEmAjA6DpdVKRQGyvqbW7KkSggRZRLIESPXJ5+Y01p7WR9+mGaa8Fk2tbaiPZBTaDtkSXtW8eAnGWfkAjmVleaA8ZprIMGaXENCCCGEGIKdO6G8fEDLqoppxEvQ0vw4QeWgsn2ZeVEcJjr2o5OA3fr8OOXl5gzwa6+VsZAQIiokkCNGpsZG2L0bsrN73aSUJoIYuCwYwBjK1pHoeHScBXJa2pP65UeiYpXfb1YFu/BCmDLF+vaFEEIIMXAbNoCuQ2L/s2z2UIsDu6VLjCqNMRg4SKaZDK3esnatEkp0XGjlzS1ZUiWEiAEJ5IiR6dNPoaGhz0DOAeqxWzR4qVGFBEkgEQ9ZWo0lbVqlBT9jSCeBCCQgPnAA5syBz37W+raFEEIIMXA1NWaRhwHMxmklwF5qyYrQsqoie0l/BbNiwo2PAlKsS3QcWlJ10klw3nnWtCmEEAMggRwxMu3caV5cHT3Ptgmgs5c6yxL8HdFDy6pK427g4kNnElnWN1xba97xu/rqfhMqCiGEECLCtmyB+vo+izyEHKKRetoikB9nAhCfy6oAPFYnOu5cpUqWVAkhokgCOWLk8flg61bIzOx1k3LcNOId8flxVPv/LM+PEwya68GXLoXZs61tWwghhBCDo+uwZg0kJYGt/+F9MY340Um0MD+OR6XRqHLRMCi0lVrWrlXMERGMoeciGIMmS6qEEDEkgRwx8uzfD9XVkJPT6yalNOEhQArOsHfnVwnUqNEAjLbHVyDHh44Lh/WBnAMHYNo0s9x4vE1BEkIIIY43+/aZ1+ZRowa0+Q6qLckR2Fm5PgGAHK0Sl+a1tG0r+NBJwGZNouPQkqpFi2RJlRAiJiSQI0ae7dvNWTlJSb1ucohGNLBkam2VMQaFjTStgVStOez2rNSCn1QSKLAy0XFDg3m37+qrey3tLoQQQogo2rgR2togtf8bN258HKA+gsuqSixt1yotHYmOLQjkdF5S5Qz/pqAQQgyWBHLEyOLzwfr1fS6rUih2UkMq1qxlPtK+rCreqlWBOWjJJdm6pH66DqWlcO65cMIJ1rQphBBCiKFzu+GjjwaUGwfMZVWNeC0N5BhK6xgPFdnjMz+OGx+jSCUt3PGfxwPNzXDZZTBunDWdE0KIQZJAjhhZ9u6Fioo+KzbU0EoNHjJGeH4cAA9+JpNlXVK/gwdh0iRz8CJLqoQQQojY27YNqqoGVK0KoJgGghiWVrOsUaPxk4SLNnK1CsvatZKZ6Dg7vDFR5yVV555rXeeEEGKQJJAjRpbt2yEQMKsp9aKUJprxWzJLpVWl0KjyABW3if2KrErq19RkDmCuvLLPsu5CCCGEiBKl4MMPwW7vtVJnl81RbKeaJAtyBHZ2WJ8MmNWqbJqytG0rhIo/jCEjvIZKS6GgAK67TpZUCSFiSgI5YuTwemHDBsjqu9R2CY0YKOwW/PiHZuPEY2K/IAYamjWJjnUdSkrgjDNg8eLw2xNCCCFE+A4ehJ07obBwQJs34uUQjZbnxykzJgEwxn7A0natEir+UBjOmKilxVxWdcUVMGaMdZ0TQoghkECOGDn27DGnFufl9bqJQrGNSkuqVQEc0eM3P46nPdGxJYGckhJzHfiVVw6orKkQQgghouDDD80cORkDm2lykAaa8FkayHEbGTSqPDQMimzxmx8njYShV6wyDDNotmQJnH22pX0TQoihkE9kYuTYtg2CQXD1vmSqGg/luMmyYACjVHznx2nBTwaJ5JIcXkPNzeZytauu6jNIJoQQQogoqq83Azl5eQPOW7eHWgwMHBZ+BAjNxsnXynBpPsvatZIbP6NJI22oy+pLS81ZT9dcM6AlbEIIEWkSyBEjQ2srfPxxv8uqDtJAMz4yLAjkNKg8WknDToB8W3nY7VmtBT8TyAxvsGYYUFwMp58Op55qXeeEEEIIEZ4NG8yZyAUFA9o8iMEWKkm3eFnVYcPMjzPWftDSdq3kwc80BlbVqxu32yztfuWVMHq0tR0TQoghkkCOGBl27+53WRXAAeoBsFlQxanMmAiYs3Ecmh52e1bzYzCRzPAaKSmBsWPh6qtlSZUQQggRL3w+WLUKUlMHfH0uoZFKWsghybJuBJSTSmMsAGNs8ZkfR6EAjTFDWVYVWlJ1+ulw5pmW900IIYZKPpmJkWHrVvNim5DQ6yY6BtuosqRaFUC53p7YLw7XgxsoNAgvP47bDX6/eQdqgCVNhRBCCBEF27aZN1uKigb8kn3U0UqAZAsrVh0xxmPgIE1rIEOrt6xdK7URJAnH0Kp4FhebOQKvucasDCaEEHFCAjli+Gtpgc2bISenz83KcVONx5IEfz7lolqZg6d4TOwXGqgVhpvU77TTzC8hhBBCxAelYPVq89995AXs8hIUW6kkEQeaBbOSQ8ral1WNsR0YaJqeqGvGRzquwSc6bmoycy9efbXc0BJCxB0J5Ijhb9s2c1lVbt9rnw/SQAt+0uh91s5AVRjjUdjI0GpJszWF3Z7V3O2DlgJShtZA5yVVcgdKCCGEiB/798OOHYPK11JHGwdosHRZlVJQ1j47eawtfvPjuPExgUwSGUSSYl03x0JnnWVWqhJCiDgjgRwxvCkFa9eawQZn31OFP6UOOzZL7kSFKjTE47IqMBMdjyEd12AGLSHNzeba+yuvHHACRSGEEEJEydq14PFA+sCXCu2jjka8lpYdr1MFtJGKAz8FtsOWtWs1LzpTyB7ciw4ehEmTJEegECJuyV8mMbyVlsKuXWZJyD740dlBNRkW5MfpfAdqTJzegfISZDJ9V/DqUahK1RlnmIn9hBBCCBE/KivNQE5+/oBLjgPsogYNsFtadtxcVlVkK8auGZa1a6VQzsCiwSyrqm/P9XPNNZA9yACQEEJEiQRyxPC2aZM5gyQjo8/NDtFIPW1kWTCluE4V4CUFB37ybWVht2c1szoDQ8uPE0rqJ3eghBBCiPjz3ntQWzuonC0+gmyjytLZOACH9VB+nPi8qQVm2fEUnANPdBwIwOHDsHQpnHxyZDsnhBBhkE9qYvjyes27UhkZ/d6VKqaRNgIkDWWp0THK25dVjbYdiss7UGZ1BieFg61YFUrqd9VVktRPCCGEiDc1NWaS47y8Qc3GOUADNXjItjA/jttIp06NQsNgrD0+y46Dmeg4g8SBV/E8cABmzIArrhjUeyyEENEmgRwxfO3YAWVlMGpUv5vupgYndmvy4+gTgfi9A9WCn1QSBld6PJTU78wz4dRTI9Y3IYQQQgzRBx+YS6sGMO7pbB91+NBJsrDseKkxFYACWxmJWptl7VrNjZ8pZOMYyEee6mqzCtgXvjCo/ENCCBELEsgRw9dHH5k5XfopvenBzx5qLZlS7FVJ1CizSkSRPX4DOYWkkjKY6lyS1E8IIYSIXw0NsHIl5OQM6jqtUGymghQLgzgAh/RpAIy3fWppu1YLoDNpIDkDfT4zSHbhhTBnTuQ7JoQQYZJPbGJ4qqyELVsGVFWpmEYa8JJlQSDniDEB0MjSqknRWsJuLxJaCTB5MNUZGhrMDM5XX20OEIUQQggRX9asgYqKfos7HKuCFkppIodky7rSqlKoVkUAjLPvs6xdqwUxsGHrP9GxUmZJ93nz4OKLZUmVEGJYkECOGJ42bzYDEAMIPOylFj/60EpxHyNUraooTsuOq/ZUxwOuzhAMmpW/zjsPFi2KaN+EEEIIMQTNzfDOO5CZCXb7oF66i5r2PDHhV+0MKdWnAhq52pG4vakF5gzlNBL6T3R85IiZb/GGGyDZuoCXEEJEkgRyxPATCJjrxFNT+71rYqDYRAVpg1lm1FtbykaZ0Z4fJ06XVfnRScA2uKR+06bBlVfKHSghhBAiHq1bZ+YEHD16UC9TKDZSToJFOQJDDrXnx5lgj+9lVc34yCaJ3L5mI7W2QmMjXHopTJkSra4JIUTYJJAjhp9t28ycLgOYXlxKE2U0WzKluNIYg58kXLSSr5WH3V4khBIdD6j0eG0tOJ1w3XX9lm8XQgghRAw0NcGKFZCWBo7BzSyupIV91JNHimXd8apEKo1xAIyzxe+yKgA3PqaRg623IJZhmDe0Fi2C88+PbueEECJMEsgRw4tSsGqV+e+k/sto7qW2Y2ptuEIVGsbZ92PTVNjtRYIbP3mk9D+F2u83pxIvWwbz50enc0IIIYQYnFWr4NAhGDNm0C/dRQ1NeC0p9hByWJ+CwkaWVk26rdGydiNBoZhAZu8bHDpkVgD7whcgIfxxohBCRJMEcsTwsm8ffPLJgKYXKxRbqcRlwZRipUJrwuP7DpQHP5PJ6vt4lTLfx7lzzanEsqRKCCGEiD/V1fDmm2Y+wEHOxlEoPuYITuy9z0gZgtCyqvFxvqzKj44DO6N7m6Hc3GxWqrrqKigqim7nhBDCAhLIEcPLBx+Y65nT+0lcB9TRxj7q+14bPUC1ahStpOHAT6HtUNjtRYpCMZZ+lklVVJjv3/XXQ4p1062FEEIIYaG33jKrdA6yUhVAFR4+pY58C5dV+VVCe/VOGB/HN7XAzI+TjosxPSU61nUoLoYzzzS/hBBiGJJAjhg+KivNhH8FBQOaRbKXWhotmlIcmo0zxnYQh6aH3V4kBNCx95fouLUV6urgkkvMJMdCCCGEiD+lpeayqlGjwDb44fpOqi0bA4WUGZMwcJCu1ZOp1VrWbiQ042M0aT0vNd+/30xsfO21Q3pvhRAiHshfLzF8fPihGYTIyxvQ5juoxgbYLfgxP9SRHyd+70AdTXTcSyBHqaNJ/S64ILqdE0IIIcTAKAWvvw4NDZCfP/iXt1fstHxZlW7eABpv+zTuV2W3EmA6ud2XmtfUmMvUrrsOsrNj0zkhhLCABHLE8OB2w+rV5kV3AKMHD34+oYos+k+I3J9GI5tmlYONIGNs8Vl2HMxExzl9ldk8dMiczXT99ZLUTwghhIhXe/eaN6/GjBlSHrtqPOylljwLlpaH+FUCh43JAEyw77Ws3UhQ7f8bf+xSc5/PXF7+2c/CwoWx6ZwQQlhEAjlieNiwAcrLB7xOfB/11NJqSdnxULWqQlspCZo/7PYixSyz2cPdJzDLl3q9cPXVQ6p8IYQQQogoCAbhlVfMpdBDnDGykxoa8VpyMyvkkD4NAwcZWi3ZWrVl7UZCaIZyl/w4oUIP8+ZJoQchxIgggRwR/3w+WLnSLDc+wKoNu6kliEEC9rB3f3QqcfwuqwJQwLieEh0Hg1BSAmedJUn9hBBCiHj24YewaRNMnDikl5vLqqyvVnXQmAXAJPvuuI+BNOMjk6SuFavKyyErC264AZKtm6kkhBCxIoEcEf8++si8izJ27IA2D2KwiSOk95TgbpBaVBp1ahSgGGvfH3Z7kWKW2bRR1FOZzf37YepUSeonhBBCxLPGRnM2jss15KqSVXjYY/GyqlaVQoUxDoBJtt2WtRspzfiYRjaO0MeclhZzZvIVV5hJjoUQYgSQT3Uivnm9sGKFOahxDSwwc5AGKmmxpOx4qFpVvlZOktYadnuR4sZHGgld7z4BVFeb+XC+8AXzTpQQQggh4tPrr8PBgzBhwpCb2EolDXjJtnBZVbE+E9DI18pJszVZ1m6kBDGYTPuyNF03Cz2cdhosXRrbjgkhhIUkkCPi27p15oySceMG/JJPqKKVAMk4w959x7KqOK5WBWai43xSupYZ9XrNku0XXQTz58euc0IIIYTo27598PbbZi5A+9CWhesYrKGUFJw958sbooP6TAAm2uN/No4fHSf2o/lxQoGxL3xhwMvzhRBiOJBAjohfra3mbJzExAFXWfKj8xFlZOAKexDjUWlUKXM5V7xXaPDgZxo5R49ZKTMAduKJ8PnPS1I/IYQQIl4Fg/Dii2aFzry8ITfzKXUU08AoUi3rWqORTZ0ahYbBRPsey9qNlGZ8pONiLOlQV2eOf77whSGVcRdCiHgmgRwRv9atM6fDDjA3DsBeaimnmXyGtra8s9AdqALtMCmaO+z2IkWhMFCM7Zzo+NAhczB4ww1mkmghhBBCxKc1a8wEx5MmhXXjZTMVeAmSwsBufg1EaCxUZCsmUWuzrN1IacLLaNJI92EmOL7gAjjppFh3SwghLBcXgZxHH32UCRMmkJiYyOLFi9mwYUOv2z7++OOcccYZZGVlkZWVxdKlS/vcXgxTHg+88YZZWWCAs3EAtlGJH4MkC5ZVhQYvk+y7wm4rknzouHAczY8TKjV+zTUwfnxsOyeEEO3kWi9EDyor4YUXzJsuYVRTasHPOsoszY2jFBR3qlY1HLQRYKbKQdu33yw1ftllMitZCDEixTyQ8+yzz3L77bdz7733snnzZubPn8+yZcuorq7ucfvVq1dz3XXXsWrVKtatW8fYsWM5//zzKS8vj3LPRUStXQvFxYOajdNKgPUcIbtznpghajByaFD52NCZYP807PYiKZTouIg0CATMUuPnnCOlxoUQcUOu9UL0QNfhmWfgyJGwb7x8QhWVtFgyIzmkRhXiVpk48DPWFr+VO0MMFKAxttoLOTnwpS8NufqXEELEu5gHcn71q1/xX//1X9x0003MmjWLxx57jOTkZJ588sket//73//ON7/5TRYsWMCMGTP405/+hGEYrFy5Mso9FxHT0GDOxklNBefAZ9bsooZqWsizYBBT3DGV+CAuzRt2e5Hkxk8haaSpBDMvzowZUmpcCBFX5FovRA/efx8+/NBcUhXGNVuh+Igy7Gg4GVqi5J4c1GcDMM62H6cWsKzdSPHgJyUAY5ttcNVV5vsqhBAjVEw/6fn9fjZt2sTSTuUAbTYbS5cuZd26dQNqo7W1lUAgQHZ2dq/b+Hw+mpubu3yJOLZihTmrZBCVqsBcG26gSAhzEKMUHDRCy6rifypxKwGmkgMVFeadpy9+ETIy+n+hEEJEgVzrhehBRYW5pCo52bxxFYYjuNlBNQUWJjkOKgcHdHNZ1RT7DsvajaQmo42sRh+Fp55vzkwWQogRLKaBnNraWnRdp6CgoMvjBQUFVFZWDqiNO++8k9GjR3cZIB5r+fLlZGRkdHyNHcRyHRFlxcXwzjswatSgym824WUzFeQw9PXlITVqNC0dU4kPhN1eJKn2/41tc0J9PVx+OcyaFetuCSFEB7nWC3EMXYdnnzWDOYO8adWTrVTSiJcsC5aWhxTr0wngIlVrpNB2yLJ2I0fhbqlnWsYkHNddP+QS7kIIMVwM67UXDz74IM888wwvvfQSiYm9X7zuvvtumpqaOr4OHz4cxV6KATMMePllaGwcdJnIHVRTSyu5FgRyQkmOx9v24dCCYbcXSV6CJCo7o0vq4PTTzeoMQggxgsi1Xow4775ryZIqgAA6aygllQQ0rEvqu0+fD8A0+yfDI1dwi4egXWPyOZdDHzP3hBBipHDEcue5ubnY7Xaqqqq6PF5VVcWoUaP6fO1DDz3Egw8+yDvvvMO8efP63NblcuFyucLur4iwTZtg40aYMGHQFQY+5gh2NBxhxiYNZaNYnwHEf7UqgGZ8pDd5GT1utllq3BHTX2khhOhGrvVCdLJvHzz3HKSlhb2kCmA71RTTyHisW1LdYORQrYrQMIbHsqqAH19bM86pYxkzXwo9CCGODzGdkZOQkMCJJ57YJXlhKJnhkiVLen3dz3/+c+6//35WrFjBSSedFI2uikhrazNn4yhlDm4GoYoWdlBtyWycI8Z4fCSTiGdYTCV2tzUxRqWTfMNNkJsb6+4IIUQ3cq0Xol1zMzz9tDnzeMyYsJtTKN7nEDoGSQy8OER/9ulm0HSM7QDJmseydiNCGVDfQNPoXDInzmR8ZnjVv4QQYriI+e3722+/nRtvvJGTTjqJRYsW8Zvf/AaPx8NNN90EwJe+9CWKiopYvnw5AD/72c/40Y9+xD/+8Q8mTJjQsb4+NTWVVAvubIgYefdd2LMHpk8f9Es3UUEdbcxhcMuxenKgvULDBPtebJoKu72ICvjx+luZuuASmD8/1r0RQoheybVeHPcMw8yLs2OHmcvOgvVKJTSylUpGM7gbYH0JKnvHWGia/RPL2o2Y+gbIzKBxfAELcqeSmiB/H4QQx4eYB3KuueYaampq+NGPfkRlZSULFixgxYoVHUkRS0tLsXVaP/yHP/wBv9/PlVde2aWde++9lx//+MfR7LqwyqFD8MorkJUFCQmDeqkfnfcoIZUEbGGuDfeqJA4ZUwGYat8eVlsRpwxUfR2MyaHorM/FujdCCNEnudaL495778HKlTB+PDitmT2zjsM04bN0WVWpMRUfSSTTTJGt2LJ2I8LjAZsGc+fhs9cxM3dmrHskhBBRE/NADsAtt9zCLbfc0uNzq1ev7vJ9SUlJ5DskoicQgGeegbo6mDNn0C/fThWHaGICmWF3Zb8+GwMHOVoFObbqsNuLHAX19Xiy0kieNIkx2RNj3SEhhOiXXOvFcWv/fnM2TlISZGZa0mQDbazhMHkkW5rk+NP2ZVVT7dvje2ZyMAAtLTB7Nv78HBwNTbKsSghxXBnWVavECLBqFXz8MUyePOhpxgrFhxxGR5EYZkxSKfi0vULD9HifStzSAg4HTdPGk5k5itFpo2PdIyGEEEL0pK4OnnjC/K8FpcZDNnKEKlooIMWyNpuNTCqN8YBiqiOOZyYrBXX1UFQEM2bQ5GsmIzGD8RkSyBFCHD8kkCNip6wMXnrJTG6cPPhExUdws4VKRlkwiKk0xtKssnHgZ6J9d9jtRYzfD61tMGMmzSkOpudMx2m3LsGhEEIIISzi9cKTT8LevWYOQIvqePvRWUUxyTixWziU36MvBKDIVkyq5rasXcs11ENGupkf0OGg0dvIhMwJZCRat8RMCCHinQRyRGwEg+aSqupqGDt2SE1soJxGvGSTFHZ3QlOJJ9l349QCYbcXEYZhDl7Gj4epU9GVzuTsybHulRBCCCGOFUpuvG4dTJ0KDuuyGXxCFQdpoIh0y9r0qwT26XMBmGnfbFm7lvN4zIDY3HkdVU69QS+z8mbFuGNCCBFdEsgRsfHuu7BhA0yaNKQ7VG0E+IBSMnCFvTbcTHI8DYBp9m1htRU5ypyWnZsL8+bhMwI4bA7GZVg3TVsIIYQQFnn7bXjjDfNmVVL4N5xCFIrVlKAg7GXlne3T5xLARYZWG79JjoMB8LTAtOkw2lxWHtAD2G12WVYlhDjuSCBHRN++ffD885CaCilDWxa1jSoO00yhBSU3D3QkOa4k11YVdnsR0dQMiS6YvwCSkmj2NZPhymBs+tBmMwkhhBAiQjZuNGcdp6aaFTkttJtatlJJkYUlxw2lsSt4IgCz7ZusWgFmrVBenNFFXZapNfmayHBlMCFzQmz7J4QQUSaBHBFdzc3w9NPQ2DjkJVUKxRpKsQEJ2MPqjlKwt31ZVdzOxvG2mdW9Zs8xZ+RgDlzGZowlzWXdQE4IIYQQYdq1y8yLEwiYyXgtpFC8xQG8BMkg0bJ2S42peMjARSuT7Lssa9dS9fWQmQELFnRZptbkNcdDmYmZMeuaEELEggRyRPQYhnmHascOmDZtyEn/9lHPJ1RZMhunSo2hWeXgwM+keExyrAfN2TiTJ8PEo2XGvUEvM3JnxLBjQgghhOiipAT++EdoaBhSNc7+7KWOTRxhrIW5cQB2Bk8CYIZ9Kw4taGnblmhpAbsN5s03Zzl10hpsZVbeLLS4nEYkhBCRI4EcET2rV5u5cSZMAOfQKi0pFO9SjIcAGbjC7lJoKnFcJjlW7XlxCgth9uyOAaFu6Ghosh5cCCGEiBeVlfCHP0B5eVg3q3qjULzNAdosno1TbRRSo4qwEWS6Y6tl7VrG74e2Vpgx0xwPdRI0gtg0myyrEkIclySQI6Jj/3547jkz4V/G0MtDHqKJ9ZQxmtSwkxw3G5mUGlMBmGXfFFZbEVFfD+np5jTihISOh91+N6kJqYzNkPw4QgghRMzV1cFjj5k5AGfMAJv1w+t91LORI5bmxgHY1T4bZ5J9N8max9K2wxaq1jnOrNZ5rGZfM+kJ6RLIEUIclySQIyKvthaeeMIc6IwLr8rSakpowmdJyfGd+kmAxhjbATJtdWG3Zym3G+x2M7lxetcp1M2+ZnKTc8lPyY9N34QQQghhqquDRx+FbdvMII6FZcZDQrNxPPjJtHA2jttI76jaOdv+sWXtWqN9VnJODsybZ46JjtHobWR02mhyknJi0D8hhIgtCeSIyGptNYM4e/d2qTIwFEdws5ZSRlkwG8erktivzwFgtn1jWG1ZzucFrxdmzeo2jRjA7XMzM28mNk1+fYUQQoiYCQVxtmyBmTO7zJ610gEa2EA5RaSHPf7pbLu+GIWN0bYSsmy1lrVriVC1zgULey3f7vF7mJM/R/LjCCGOS/JJUESOrsPf/w4ffWROiQ3zLtX7lFBLK3kkh921PfpCdJzkaBWMsh0Ouz3L6EGzotfEiTBlSrenlVIYymBS1qTo900IIYQQpigFcUKVqlrwk2XxbJx9+lwA5js+tKxdS4Sqdc6Z21Gt81ih/DgTsyb2+LwQQox0EsgRkaEU/Oc/8NZbZnLjXu6mDFQtrbzHIfJICftuVFA52B1cCMAcx0ar8xEOXSi58ahCcxpxD2vs24JtJDmTGJsu+XGEEEKImKiujkoQB2AH1XzIYcZYPBvnE/0UFHYKbSUU2MotazdswfZqnVOmmOPHXjR6G8lMzGRKdvebXkIIcTyQQI6IjPffhxdeMNc2h5HcOGQNpVTiYRSp/W/cj/36bHwkk6o1Mt72adjtWaM9iJORAQsX9joobPI2keHKYEz6mCj3TwghhBAcPgwPPwxbt0Y8iBNA59/sxU+QLAtyA4a4jYyO5eULHWstazdsoRtao0d3qdbZk/q2eiZmTSQrMSuKHRRCiPghgRxhvY8+gqeeMkuMjxoVdnO1tPI2B8kmEVuYd6MMpbFTPxkwE/vZNBV2/yzR3AwJTnMt+DHJjbts5mtmcvZkXI7wS68LIYQQYhD274ff/MbM+zdrVkSDOAAfUcY2qpiAtcGKbfoSFHZG24rJtx2xtO2haw/iZGaY1Tqdzj639ga9zCuYJ/lxhBDHLQnkCGtt2WImNw4Gw65QFfIW+zlCM0X0HuAYqFJjKm6VRQJtTLHvsKB3FmjrtBa8oKDPTX26j2k506LUMSGEEEIA8MknZhDn8GFzJk4EqlN11oKf/7CXBOwk03dQYzCajUwO6LMBWBBPs3Ga3WbwZv4CSOu7xHpboI1ER6IsqxJCHNciexUSx5ddu+D//g88HjO5sQV3SQ7RyEqKGUWqJbNxtgRPB2CmfTNOLRB2/8IWCIC7GabPMBMc98Gv+3HanIzPGB+lzgkhhBDHOaXgvffgb3+DlhYziBOFWSDvcpD9NDCTnpP9DtW24BIUNopsB8m3VVja9pB528DvM2clD2Amd31bPTnJOUzMlETHQojjl8zIEdbYswceewwaGiwL4igUr7OPBrzkkxJ2ewf0WTSpHFy0Mdvxcdjthc3Qob4Oxo7rdy04QENbA1lJWVKhQQghhIiGQACef968SaXrMH16VII4VbTwBvvJJgkndsvabTSyOWjMAuJoNk4wYCY3njwFJk8e0EsavY3MzZ8ry8yFEMc1mZEjwrd9uxnEqa21dJCzixo+5DDjyAi7UoOu7GwNngbAXMd6EjS/FV0cOqXM9ys/31wLPoAp2g3eBhYVLSI1IfyEz0IIIYTog9sNf/0rvPuuuew5Ly8qu1Uo/sOnVOJhLvmWtr0xeA4KG2Nt+8izVVra9pAoA+rqoagI5swZ0PjRUAYGBjNyZ0Shg0IIEb8kkCPCs2kTPP44NDbCjBmWBXGCGPyHT/Gik0li2O19qs/DQwbJuJlh32JBD8PRntAvPQNOOHFApdmVUvh1P7PyZkWhf0IIIcRxrLTULNqwZQtMmtRvzhYrbaaCVRQzjvSwl5R3VqZPpNyYhA2dkx2rLWt36NrHQtlZA0puHNLsayY9IZ3JWQObvSOEECOVBHLE0H30EfzpT2ayXounG2+knC1UMIHwS5cHlJNtwSUAzHOsw6EFw24zLE1NZqWLhQsHXJq9LdhGkiOJSVmTItw5IYQQ4jillDm2+fvfobLSvEHlit7ynWZ8PM8uDJSl5cZ1ZWND8FwAZtk3kW5rtKztIWtshMQkWHgCpA58pnFDWwNj0sdQmFYYub4JIcQwIIEcMXhKwdtvwz/+Ya4ZnzLF0iCOGx+vsBc7NlIIv7Tnbn0hXlJI0xqZZt9uQQ/D4PFAUIcTF/RboaqzRm8j2cnZkuhYCCGEiASfD156CV591RzTzJoFtuilklQoXmUve6llFtYu49qjn0CzyiYRD/Mc6yxte0g8LWAomDcPcgeXzLnF38KCUQuwaZLmUwhxfJNAjhicQABeeAH+/W9ISYEJEyzfxat8atlAxqdc7AguBmCBYw02zQi7zaF3xgutHpg1G8YPLiDT6G3k3InnSmI/IYQQwmqHD5uzcDZuNKsmRSkfTme7qOEtDjKaNEsTHLepZLYGTwXgRMf7sc8R6PNBayvMmQtjxw7qpQE9gMPmYGrO1Ah1Tgghhg8J5IiBc7vhL3+B1auhsBBycizfxW5qeJMDlg1ktgTPwE8imVoNE217LOjhEAUC0NhkVvQaZOlSpRS6oTM9Z3oEOyiEEEIcZwwD3n/frExVWWnOMB5A3jqrtRHgOXbSSoDxFiwp72xL8HQCuMjRKpli32Fp24MWDJpLqiZPHtKS/AZvA5mJmbLMXAghkECOGKjycjPx36ZNZuK/QaxnHigvQZ5lJ634LRnI1BoF7NEXALDYsRKbpsJuc0j09jLj48ab04gHOVW7xd9CSkKKDFyEEEIIq9TXw3PPwapVZvBmgFWTrBaqUrWDaqaRE3aVzs6qjdF8qs8DYJHz3Vgc3lGGYSY3Hj16SGMhgPq2ek4uOpl0V3oEOiiEEMOLBHJE35SCjz82S3CWl5t3UCKU+O9N9rOdKksGMobSWBf4DKAxybaTQvthazo5WMowy4yPKjSTGw+gzPixGrwN5KfkMyZ9TAQ6KIQQQhxHDMNMaPyvf8HBg+YS8QEWHoiELVTyKp9SQCouC4flQeVgTeCzgMZk2w4KbOWWtT1oSkFtjZkP58QTzYIPg27CrN45L39eBDoohBDDjwRyRO/8fjMXziuvmBfh2bMjlvivmAZe5VNySLZkIPOpPp86VYgTLyc537Ogh0OgFNTUQk62OXBJHFoZ9WZfM2eNPwu7zbo180IIIcRxp7raDOC8/z7Y7eYsHHvsrq3VePg7nxBEJ58US9veEjyNZpVNMm4WOd+1tO3BaS8znp5ujoWSk4fUSpOviXRXOjPzZlrcPyGEGJ4kkCN6VllpVqVauxby8wdVYWmw2gjwT3bQQBuzyQ+/PZXM5uAZAJzgWEOy5gm7zcFTUFcL6Wlw4klDXopmKDM5syT2E0IIIYYoEIA1a8yqVIcPx3wWDkAAnb/xCcU0MtviKlXVxmh26icDsMT5Fi7NZ2n7g9LYZM7AWXgCZGYOuZkaTw3Tc6dTlFZkXd+EEGIYk0CO6MowYN06ePZZKCszE9KlWHuXqDOF4iV28zFHLFsb/nHgLPwkkq1VMd2+NfxODpoy194nJZlBnKysIbfU7Gsm3ZUu+XGEEEKIodi7F158ETZvNmeDzJ0b1bLivXmdfXzIYSaThR3r+mMuqbqA0JKqsfaDlrU9aC1uc1y5cGFYNwSVUrQF2zhp9EloMU30I4QQ8UMCOeKoxkaztPi77x6dchzhwc56ynmd/YwmjUQLfhzL9IkcMOYAiiXOt2OT4LixycyFs/CEsEuYNrQ1MDZjLAUpkZsRJYQQQow4dXXw+uuwciV4PDBx4pCX9VhtG5W8zB5ySCKFweeL6Yu5pCon9kuqWlvB6zMDZ+PGhdWU2+8mNSGVWXmzLOqcEEIMfxLIEebdko0bzXXj+/bB+PFhTX8dqCO4+QfbAcgl/MFVm0puT+wHM+2byLNVhN3moLmbzdw48xealRnC1OJvYV7BPLkDJYQQQgxEayu89x689ppZpGH0aHNcEyfX0VKaeJIteAkyzuJS42X6RHbqi4AYL6nyesHTAjNnwbRpYb/3NZ4axmeOZ1xGeAEhIYQYSSSQc7yrqjKnHL//vnmhnT17SJWVBstLkKfZRhnNzLEgL45SsDZwAV5SyNRqONHxvgW9HKQWt7kOf/6CsO8+AfiCPhw2B9NzpoffNyGEEGIkCwbNm1L//jd8+qmZAyfGyYyPVU8bj7OJctzMIs/SUuNuI50PAhcBMN2+JXZLqvx+aG6CKVNh1qywgzhKKTwBD4uKFmHTYr8kTggh4oUEco5Xfr8ZvHnlFTMXThQT/xkonmcnGyhnKtnYLBjI7NEXUmZMxkaQs5yv4tB0C3o6CJ4W8Plg7jyYNMmSO3+1rbXkp+QzLWeaBR0UQgghRiBdhy1b4I03YMcO82bU9OlDKnEdSV6CPMkWdlDDLHItGfuEBJWd1YFL8JFErnaERY5VlrU9uI4EoKEeJkyEefMsWZ7vCXhIcabIsiohhDiGBHKON0rB9u3w8svwySdmNaUoJv5TKN5gH6+xjyLSSMIZdpsNRg4bg2cDcLJjNVm22rDbHJRWD7R5zTt/U6daNn27wdvAhVMvJMmZZEl7QgghxIhhGOZ45o03YNs2c3wzfnzc5MHpTMfgH2znQw4zlWycWDtLaEPwXOrUKFy0cnbCv7FH+2YWmDOi6upg7DgzubFFs7trPDUUpRcxMXOiJe0JIcRIIYGc40lZGbz6qlmCMxCAKVMgMTGqXfiIMp5lJ+m4yLEgL05AOXk/cDEGDopsB5lh32JBLwehtRU8reb04enTLQviBPQAmqYxJ3+OJe0JIYQQI0IwCFu3wjvvmDekgkFzOXNqaqx71iMDxb/YzQr2M44Mki24gdXZfn02n+oLAMWZztdI1dyWtj8gehDqaqFoDJx4oqWzodx+N58f/XnstvhZIieEEPFAAjnHg5oac8CzcqVZFnvs2LBKYg/Vbmr4C9sAGE1a2O0pBe8HLqJB5ZGIh9OcK6Kby7DVYwZxZs40vyzceV1bHTlJOczInWFZm0IIIcSw5fOZJcTffht27TIHAWPHxm0AB8wgzovs5l/sIo9kMrH25lmFPpYPA+cDsMDxIUX2EkvbHxBdh9paKBwNJ50ELpdlTbcGWklyJDE7f7ZlbQohxEghgZyRrKHBrNzw1ltQUQGjRpnLqGJQueEwTTzOZhpoYwa5lrS5OXgGh42p2AhybsLLJGseS9odEI8H2tpn4liQzO9Y9W31nD3hbNJd6Za2K4QQQgwrTU2wfr15M6q42FwKPm5cXC6h6kyh+Dd7eYFd5JBMHimWtl9v5PFu4DIMHIy37WW+/UNL2x+QUBBnVCGcfLLls7xrW2spTCtkctZkS9sVQoiRQAI5I1FdnZnIeOVKOHIEcnKimgfnWIdp4nds4BCNzCbfkioN+/XZbNdPAeA05wrybUfCbnPAPC1mTpxZsy2fiQOgGzqGMphXMM/SdoUQQohhQSkoLYWPPjLHMxUV5sybyZMtnfERKQrFq3zKs+wgi0TyLQ7iuI103vZfSQAXBVopZzhfi/49Oj3YNYiTZG0+P6UUjd5Glk1ZhtNu7XI0IYQYCSSQM5JUVsIHH8CqVea/s7PNcuIxLL1ZShO/Yz0HaGAWeZZUaagyivgwsAyAefZ1TLbvDrvNAXO7we8z39cZMyIyu6nB20BWYhYzc2da3rYQQggRt3w+M+/NmjVmAuOmJvNmVIzHMoOhY/ASe3iR3WSQSAHWLv3yqiTeDlxFG6lkaTWcm/By9Ct1Bttz4hSOjkgQB8zcOKkJqZxUeJLlbQshxEgggZzhTinYv9+8Y/XRR+bdkdxcs4JSjGbghByikd+xgWIamUUedsLvT52Rz0r/ZRjYGW/by0LHGgt6OhDKHFAaBsybbyaKjtDtr9rWWhYVLSInOSci7QshhBBxQyk4fNjMf7NmjflvpaCw0KxCFYPl4EPlI8jf2c4b7COPFMtn4nhVEm/5r6JZZZNCE59JeB6X5rN0H/0KBsyZ30VjzJw4ESqaUeGuYG7BXCZlTYpI+0IIMdxJIGe48nrNu1bvv2/+1+OBggKYNy8uBj0HqOcxPuZg+0wcq4I4b/mvxk8SeVo5pzvfiNKhKqhvMO8GLjwhogNLQxkE9AALRi2ISPtCCCFEXGhqMsuHf/QR7NwJjY2QkQGTJg2L5VPHcuPjz2zlPUoYS4bliY09KpW3/FfTpHJIxMNnEl6Ibm5AMGckNzSYJcZPPDFi5yloBAkYAU4bexpaHIxphRAiHkkgZ7iprISNG80kxqWl5mNFRTBxYlwEcAA+5gh/ZivVtFgWxKk38njLfxU+ksjVjvCZhBdwagELetsPpcw7T4mJsHCh+V5HUJO3iYzEDFlWJYQQYuTxemH3bnP2zccfm1U17XazGMO4cXEzjhmsCtz8ma1spJzJZJOKdeW3AdxGBm8GrqZFZZJCM+cnPEeGrcHSffSrrQ3czTBxEixYYGmJ8WPVeGrIT8mXm1pCCNEHCeQMB21t5l2r9evNNeP19ZCeHndJ/wwUb3OAf7KDADqzyLMksXGDkcub/qvxkUyudoTzE54nQfNb0ON+GDrU1pnv9QknQH5+xHdZ01rD7LzZjEodFfF9CSGEEBHn88HeveY4ZsMGqKoyqx3l5Ji55hzDeyi6hQqeZhuHaGQ6uSRaPLRuMHJ4238VraSRpjWwLOE5UrVmS/fRr5YWcyw6fYa5dD/C+YpqW2v5/PTPk5GYEdH9CCHEcDa8r54jWTBo5r7Ztg0+/NCs2ABmMCFGJcT74iPIv9jFv/mUVBKYQKYl7R7Rx7Mq8HkCJJKjVfCZhBeiE8QJBKC+DvILzCBORuQHE7qh4wv6WFS0SKYSCyGEGL48HjN4s2sXbNpkjmECAcjKMmcQx9FNqKEKoPM6+3iR3QTQmUOBJQUdOivVp/B+4CKCJJCp1XB+wvNRXk7Vnh9Q182x57RpEc+/6PF7SHQkcnLRyRHdjxBCDHcSyIknug6HDsGOHWbw5vBh8w5IVpaZXDeC01jDcQQ3f2UbGzjCaFLJIdmSdvcG5/NRcCkKG/laGeclvBidpH7eNmhqhnHjzenDEajG0JOa1hpyk3M5cfSJUdmfEEIIYQmlzGILn35q5rvZts38XtfNGyETJkQsKW4sVOPhGXbwPofIJZmJZFnavlKwTT+VrcHTABhlK+Vs5yskal5L99N3JwxzabnLBfMXRC3xdEVLBZOzJzM9Z3rE9yWEEMOZBHJiLRiEgwfNu1YbNpjBG48H0tJg9GhIsbbigZUUio8o45/soIxmppJNEs6w2zWUxqbgWezUzbsxk2w7Oc35JvaIl9dUZgAnEDDvOs2dG9Up3zWeGi6adhHZSdlR26cQQggxJH4/lJQcnT184ICZsFjTIDvbXP4dpzeghspAsZZSnmcXh2lmMlmW58PxqwTWBC6k1JgKwEz7Jk52rMamGZbup0/BANTVm+dxwQLIy4vKbg1l0Bpo5fRxp2O3DY9y80IIESsSyImFlhbYtw/27DET/lVUQGsrpKaaS6dSU+Nu6dSxmvHxMntYwX7saMwh35Ipxa0qhQ8CF1JhTABgoWMN8+zrIv92GO13nhJdZiWGCROieg48fg8uh4vFRYujtk8hhBBiwAwDjhyB4uKjOW9qa82Zw4mJZs6bwsKI50+JlRo8PMdOPqCUBOzMtWjc01mFPpY1gc/iIQMbQZY43maqY4el++hXWxs0N5vFHRYujOoNxdrWWnKTczmh8ISo7VMIIYYrCeREg2FAWZl512r3bnPacV2dORsnPd0c+CQnx33wBsy7URsp50V28yl1jCHdsqVUh/QpfBhYho9k7AQ4zbmCSfY9lrTdp1A5zdxcWLDQHIxGWUVLBVOypzAjd0bU9y2EEEJ0YxhmpcxDh8zZNp98AtXV5s0oTYPMTPPDflLSsBi/DJWXIO9Rwn/4lHKamUgW6Vib4yeoHGwKnslu3Vxanao1cpbzVfJsFZbup09KQWMDGAqmT4fZs8EZ/izrge9eUdVSxQVTLiA3OTdq+xVCiOFKAjmRoJRZUrO4+Ojgp6LCHPzY7cN2ynElLbzIbj7gEBoas8nHYUFp8YBysjF4Dp/q8wHI0So5w/kambb6sNvuW3sSP38AJkyEefOilg+nM93QaQu0cca4M2QqsRBCiNjw+6G83FziffCgedOppsYcu4C55Ds72ywTHuGEt/FAodhCJS+zh51Uk46LuRFIaFyhj2NdcCnNyryJNM2+jZMdq3BqAUv306dA4GhF1DlzYMyYqAfnGrwNpLnSOHfiuVHdrxBCDFcSyLGCYZjlNA8dOpqs+MgRM0gA5lKpnJyoL9exShNeVlHCm+ynCg/jySCD8JMWKgUlxnQ2Bs6hlTRAMce+gYWONdgjvRY82D5oSUmBufPMcxOjgWkoybFMJRZCCBEVoeTE5eXmjOEDB8wl301N5lJvTTM/1GdlHTeBmxCFYi91vMUB1lOGjmIaObgsHjK7jQw2Bs+m1JgGQBItnOZcwRh7saX76ZsCt9tcTjVmjHlDKy0tivs/6kjzEc6ZeA6TsibFZP9CCDHcSCAnXM3N8MtfQmmpeTFUygwOZGWZyYqH8VrxNgKsoZTX2cchmsghybJcOA1GDuuD51FpjAfMacSnOt5ktL007Lb7pqDZbVamGl1kJjSOQmnxvtR4arhw6oXkJEd/SZcQQogRzjDMGxcVFeZNpsOHzaBNba05bjEMM7F/ejqMGjVslnpbTaH4tD2As5EjtBJgLOmW3LjqzKdc7AguYqd+EgYONAym27ey0LEWVzSrUgX85rLypCSzKtWUKTEbszZ6G0lOSGbppKVox+HPnhBCDIUEcsLV0mJWbUhKgrFjR8Rdq2Z8rKeMdzjIARpIJYFZ5FmyjMptZPCJfgr79TkobNgJMNexnjn2DTgiXZXK54XGJjPQtmChubwtxoG2UJLjU8acEtN+CCGEGAF8PjOPTVWVmd+mtNRcJtXQYI5XdN0M0qSmHs3RF8XqjPEoiMF2qniPQ2ymglYCjCGdSRaXFPeqJHYGT2KPvpBAe46dQtshFjneJctWa+m++qQMc+ZVIAhjxsKsWWa+oxgqay7jjHFnMC1nWkz7IYQQw8nxffW2UmrqsA/iVOPhQw7zLsWU00wKCUwjhwTCD3Y0G5l8op/CAX02qj0gNNa2j0WOVaTZmsJuv0+6bg5iNQ0mToSZM2M2dfhY5e5yJmdNZnru9Fh3RQghxHDh85kzampqzK+qKjMvXygfX1ubOUPYbjfHJykp5mybKCavjXdNeNlEBasoZj/16ChGk2Z5AKfZyGSPvpBP9XkE20uVZ2nVLHSsZaxtfxQnPynzZ8PjMWciL5gZF8vmmn3NJDoS+czkz8hsHCGEGAQJ5Bzn/OjsooaPKGMTR6iljWwSmWnBDByloNyYwF59AWXG5I4ATpHtIPMdH5If6WoMhn70rlNuDsyYad59jJOBQlugjaARZNmUZThs8qsohBCiE12HxkZzWVRdnflVWWkujaquNj+Qt7aaF1swZwanpkJ+vrk8apjfXIqEADp7qOVjjrCBcqrxkIiDcWSQhHVBLkNpHDamsFdfwBFjQsfjOVol8x3roh/AaWszl5UnJ8HsOeaM5BgUd+jJ4ebDnFJ0CjNzZ8a6K0IIMazIp8fjkIGihEZ2UM1aSjlEE0EM8klhrgU5cFpUGsX6TD7V5+FWR+9sjbEdYL5jXeTLaRq6OWDx+8xcRVOmmsve4mz6+KGmQ8zKm8XiosWx7ooQQohY8HrNGaONjeZ/GxrMgE1ZmRm0aW01v/x+c3tNM4M0SUmQm2v+exjn4ouGIAYHqGcnNaynjFKaCKCTQ7IlN61ClIIaVUixPpMSfTptpIaeochWzEz7ZopsxdEN4HhazVk4LhdMnWrmwUlPj1YH+uX2uXHanDIbRwghhiC+PtmKiAlicIhGdlPLeso4RBMt+EkjgfEW3IlqVSkc0qdRrM+gWo3peNyJl6n2HUy3byXD1hDuYfQtEDCTT+tBc9rwnDnmtOE4LPPe4m9BQ+OiqRfhtMtUdyGEGHEMw0wm3NxsfjU1mV8NDeZSqKoq8/u2NjOgEwgcnTGalASJieaSqNxc84O4fNAdsBb8HKCevdSxkXKO4KaNIGkkWDr7Rlc2qo0xlBmTOGRMpUVldjznopWp9u1Mt2+L/BLyzgwDPC3Q2mb+HE2dalbmzMqKq58hpRQljSUsGbuEOflzYt0dIYQYdiSQM0IpFPW0UUwje6llM5VU0UIrAVJwkk8KE8lEG+LsG0Np1KjRlOsTKTMmUa8Kuuy9QDvMZPsuJtr34NQC1hxUT5SCtlZzirlmM8u8T5xoVgyLwwBOSGlTKScUniAlx4UQYrhRypwl09JifrndR7+am83cNbW15pKotjYzn43PdzTRsFLm9Skx0fzKzjb/63TG1Qft4cRHkMM0U0IjO6lmN7XU04aOQTouikgn2YLgjVLQqHKoMsZSYYzniDG+I3ExgAM/42z7mWjfzWhbCXbNCHufA+yZ+TPW0gK6AakpMHs2jB8fNzkBj1XZUklWUhZXzLwCmybLAIUQYrAkkDNCGCiq8VBGM8U0sJMaymimCR8KRTouCkklGeeQgjcB5aTWKKRKFVFljKHGGN2RtC8kVzvCRPseJtj3kqK1WHVoPQgNWDzm7JukJBg33px9U1AQ93kBmrxNJNgTuGjqRdhtMiVeCCGGhRdegO3bzWVQXq+53MnvN69HnTkc5gwal8sM0GRkmP+12yVQY4HQjapy3JTTzD7q+ZQ6GmjDSxAHNrJJYgrZYRdr8CkX9UYBtWoUNUYhVcYYfCR32SYRD0W2YsbaDzDGdhCHFgxrnwOnzJ8/T/vSO1cC5BeYS8lHjTJ/5uKUX/dT01rDF+d9kYlZE2PdHSGEGJYkkBMG3VBsKaknv74Vu6OVwpQUbH0M0gylKG9ow+MPkpLgoCgrqcv2nZ9PdNg5WNNCY1sAh01jcl4qaYlO0KDJ5+eI3oLH5cWT6KMltY39Wh31tNGCHwNFMk4ySCSpzYWhQ0A3qNOD1BHEYdMIGgp/UKc9RSIuh42grvD4DbxaOipxFCSOpl4V0EgBXluOOeOlE4dqZRTFTEgoZrSthIC3mUDAIGi30QT4gjoNHj9tAR1N08hJTcCGhtsbwK8baIDNZsNp13A5bDjsNpw2G0HDwGHTaPEF8QcNdAVpThuZNp1Uw49mGObdzNxcc8BSUGBOPw9Df+fGKkopSptKOWP8GSNmKrFuKDYU11Pt9pKflsiiidnYbfJhRQgxMoT+xuW8vpqcsmKyxhVic7nM/DQul3k9svgGgpXXpN7aCugGH+yrobE1QGayk9On5FHV7O2yHdDttT09NpS+Hduv0ZlJlDe2UVbfCsCYrGTysxLY3lTPzpY66u2tBNL87DZqqVattNr9JDhsuOx20owEtDYn6cqF129QpwxanW2My0nBpkFzW5DmtgAKCBrm+CPRaSc/LZFqt5fWgEabLYs2ewGttnx8jgJabPk0q+xu/bapADmUM9pRRlZwHyl6OU6bRn2zn90+M4iTnuQkI9FJa0DHFzAAhc2mEQgaKEADEjrGPeaYzGG3kWA3f478QYMWXwB/0MBu08hNdaFpGoFAEFfQT6oKogWD5s9eejoUFZljocxMy4OFkRgfHWw4yKy8WSybssyiXsYHGQ8JIaIpLgI5jz76KL/4xS+orKxk/vz5/Pa3v2XRokW9bv/8889zzz33UFJSwtSpU/nZz37GhRdeGMUew4odFdz3n13oZeV8r7ie+qogtpIWzp6ex5T87tNY91e7Wb23hhbf0Ts1qS5Hx/bHPq9Q+OxB2px+PE4fb1T58Dh9NCS20pjYhs8RwKeC0AZOr8bE1DTGJKUyhnTs2Kj3+Cipa8Uf7D6t19CcBG0ZBOwZBBzZ+O3ZBLRs/K4c/Cn5KFvPS5IcwUaS/SUk+YtJ8pfgClajoai3aTRqENRVj68LKatv6+FRvcdtbcogIRjApftxGjptaDQ6EnCnZjB+5kSKJo8x73JaMGDp79xYqba1ltSEVC6ceuGISOwX+j2oaPJ2PFaYkci9F8/igjmFMeyZECLeDOdrfUWTl+/triLH48et+zl7egZTMiKzZMXKa1JvbaW47FQ1H51JVFoPn5R1zeOS6DRns3gDep+PDaVvnfulawZtDj+tTn/Hf90JXhoaW3G3teG36/gTgigNaAWX7iAx6MClJ2JTNgJAPQrouoy7uQ2qmn1oGujKjm5PI2DP7PgKBrPwu7Px23MJJmR0u1kVutOVqjWSrh/BaD2Eq62YxEA5Gjo+oLKX4/P4dCrw9vLsQCnshk6CHiRQG8Bh6Cg0fI4EvKlpFE2fyugpYyMSvAmJxPiooa0Bh83BFTOvINmZ3P8LhgkZDwkhoi3mgZxnn32W22+/nccee4zFixfzm9/8hmXLlrF3717y8/O7bf/hhx9y3XXXsXz5cj73uc/xj3/8g0svvZTNmzczZ050Zjis2FHBN/62GQV07mGLL8irn1TwuXl0ucDtr3bz6idHKzUZKPz2IE2qjT/vb2CiN4mtNXV40wO02f00u7y4XV589iABm07Q1j5zRlM4DDsJQQfJ/gQy9SQ0bCjNSb0nkdS8HLxJ6dS2OTjstqEnJqPbUgm2f5mDmHQMWz+zV1SQhGAtrmA1iYFyEgNHcAWO4DA8PW5uGIqhrgLXlIHD0HEYOk49iNMwBwuGZsNvd9KQlE5dcgZuVwpNian4HAlsaIDP+e1MsSiI0/nchPR2LsNR21pLtaeaS2dcytTsqZa0GUudfw86q2zy8o2/beYPN5wggxchBDD8r/WdReL6EGLlNamvtjp/MO9N52BNX4/11jcdg1YCeAjQgp8W/LjxsbexkferqmjJ9+FO8NLm9BOw6QRsOrrNAKWBpkjQHTh1e8d4x9ZeXUphQ7clotsSCdgS0bVkdFsSui0Z3ZbS8RW0p6Db0gja0tDt/c/atRleEoLVuAJVuILtX4FyxqYbVDSGG5Tpi8KmDBy6jtMI4jR07Ib5PuuaHb/DQX1SOg1J6TQlpdHsSsHnSIAm+FzAYclYqCeRGB/phk5pUykXTr2QBaMWWNTT2JPxkBAiFjSlVN/TKCJs8eLFnHzyyfzud78DwDAMxo4dy7e+9S3uuuuubttfc801eDweXn311Y7HTjnlFBYsWMBjjz02oH02NzeTkZFBU1MT6YMsw6gbitN/9m5HxD3XXc9XNjxNVVoKzYlOAnawJ9g4bXYuXhQtyuD9Q7U0qyBeh4HPofDbFUGbDcNmI6jZUJoDpdlRWgJgR1MubLjQVAIaCYALpSWgtAQMWwKG5ur40m2JoA1+DbjN8OHQG3HqDTiD9STo9Tj1ehIC1STo9WhDDs2EKDRlDk7sysBmKOxKx24Y2JWOrf3HTqERtNkJ2u20OhNpSkyjJSEJT/uXz9Hz7KBUl4OvnD4xrOm9hlI8uaa4zwGtFfsBqGqpot5bz+UzL+fKmVcO+9w4x/4eHEsDRmUksubOc2VasRADFM61Kd4N92s9wPfee4ocTyMV6XmAddeHECuvSQNpqzcKMADdBkGbImhTBGyKgM0gaIOgvf370H9tioAdcGlMG59Gi6bTTBCPMgigEQD8aPjRMJSDmhYdXdnRcGJrH+fYOsY7CSjNiaElYGjO9nGPq/37BAxbIkobWtJiTQVx6E049cb28U8jCcE6nHodCcFa7IZniOUfehMaB3UeC5n/tRs6dmWg0NBQGJqNoM2O3+6kxZWEOyGF1oREWhKSaUlIImjv+b6r1T+DIZEYH9W11lHmLmNy1mTuOv0ucpNzrepuTMl4SAzWSL7Wi+iK6Ywcv9/Ppk2buPvuuzses9lsLF26lHXr1vX4mnXr1nH77bd3eWzZsmW8/PLLve7H5/Ph65SMsLm5ech93lBc3+WPdW1aNj8/77Zu273i7/TN6CHvbuCUjl15ceHFCLZgN1pxGK3Y9RYchhuH7saht+DUm3DqTdhVW8eAResUy9NQaArMEAugzP9qSnU8p7UPTjTMAUro30dfdfT/dZsNQ7OhazaCNgdul4tWZyJtTpc5PdiRgNfhos3pQh9EcKPFF6S8oY2x2UOfllve0NbvINeK/VS4K2j2NXPt7Gu5ZMYlI6I6w7G/B8dSQEWTlw3F9SyZnBO9jgkh4s5IuNYDPHHSJdgMvcuH6qfbHDjsoZkiRx17h6yn59Qx/w7qCs/cGeYD2rGvM6+qStP4vdeGTbN1ayd0JVaAriCwcE77Nhqq/cO2ObtXa79Kt/9Xo+P7bsuLBmn9QIpUWpSDVzN82JUXu9GK3WjDZrS2j31asBseHLqnffzTjMNoxqG3dgrUqCGMgbqOfzTVfUx0LIVmjoFsNgxNQ7fZabO78DrNsZDPkdBpLGSOh9QggjJWjFF6YuX4qC3QRnFjMUmOJD4/7fNcPP3iERPEARkPCSFiJ6aBnNraWnRdp6CgoMvjBQUF7Nmzp8fXVFZW9rh9ZWVvK5Vh+fLl3HfffeF3GKh29zG9VhmAgYaOhmHOalE6SgXbZ7joaEoHgmgqiEYQ2v+rqQCaCgIBNOU3v8ff/m8fNuXr+Lem2rApL5rymv82PGiYg9cUlwOPr33qc6exgAICGgQcWsdZ7zLk0Mz/Ozow1DoNJM3BntJAtQdlDFv74ESzo9vsGJo5SDk6YAk9Z95lMl9j6zR0MgBv+9fQ7KptpDGQNOTXH2lqw6819btduPtJSUjh+rnXc9G0i0ZEXhzo5/dgCNsJIUaukXKtr0vJ6vZYA3SP2gyVjQEFOVoH0pZGZEd4Sgd0c9Sg2sc36NgwsGmGOa5pH/OYYyLze6WC6EE/5lgnNBbyd4x7IHDMeMffPtbxdox7bKoNTbV1zBzuGuzq+s8gELSDsoNZfbxzKIdjttfoGtKhS5BLAYZNax8HaR3joaNjoN7HQaHt9C7jIDDz+gSAnpeuD0S4Y5SeWDk+ctgczM2fyxWzrmBm7swRMw4KkfGQECJWYp4jJxruvvvuLnf2mpubGTt27JDayk87ZpSlgoxu/NAceHR6+OtnTWZqfhr7qt089t6BYy7cPTl2cOFo/0rulsQu1JZqv3Gm7HTcwVm6oIh/bTliPq5pHe0qwNC0jscMDdDMcJPChtEepDFsNjME1T7YCIYe6zb46J+t/csZukUY7mqtY3xt/lzmjskY8uu3lzXxw+LtEd9PakLqiBu8dPs9CHM7IYQIV0Sv9UB6634Miju+1xScNS2P/PTEo7M70Dr+1mvtC2c0TUNr36JjvoumYQs91v58ZZOXN3dWdZoporW32PX7SxcWMTYzGbvNhq1TO1r7/RdNg9J6D899fLhTtKLrnCCty2MKVNdbOZ1npBx9TafHexEa+5hLvrvPst1X7eYPqyvoEnRBA1ztX9172zEG0o6OdZTWdUaTOcuoU0+PGf+o9udV+zjI/LetfWxkBmBoD8Icfax7YGawOsZBYI6BLB4HQfhjlJ5YOT5y2pzMzp9Ngr3npfLDnYyHhBCxEtNATm5uLna7naqqqi6PV1VVMWrUqB5fM2rUqEFtD+ByuXC5XL0+PxiLJmZTmJFIZZO3/W6NgyNZZ3Y8H1oLe+115lrYEw3F70rfPbr9MUKDLsOCO3qFGYn8z1fO4eVfrOpzmudghAYhlt1xtEhhRiJfPjm89cYnFir+9K6tz3MzyoL9jETdfg+OEXrvFk3sXrpVCHF8GRHXeqA5eQowBTj6N+7hm627PuiG4r2f9T1eGJWRyF1f63+fuqF4vrr3tqx27NinNycait8dil6/rBAKScU2o2R3kRyjyPho4GQ8JISIlZgm60hISODEE09k5cqVHY8ZhsHKlStZsmRJj69ZsmRJl+0B3n777V63t5rdpnHvxbMAut2VCn1/78WzOi5sA9n+v86YiNbD84Ohte83wWHj3otnWZywL/50fo+HarDnUhwl750QYqCOh2t9vO2zr7asNpi+RbNfVvnvMycC8dXfSF9n5Ro/cPJeCSFiJeZZV2+//XYef/xx/vKXv7B7926+8Y1v4PF4uOmmmwD40pe+1CVB4re//W1WrFjBL3/5S/bs2cOPf/xjPv74Y2655Zao9fmCOYX84YYTGJXRdZrkqIzEHksM9rf93RfO6vH5gSo8Zr+h/RUOsb3BSk6wk5k8tCoSg5WV7OQxC8s4DvZciqPkvRNCDNTxcK2Pt3321lZhRiLzxvRfKSUz2dnt2p7Vw2OD7Vtv/YrU6mPHED9AZ7aPN3obo8Xyc3k0rrNyjR84ea+EELEQ8/LjAL/73e/4xS9+QWVlJQsWLOCRRx5h8eLFAJx99tlMmDCBp556qmP7559/nh/+8IeUlJQwdepUfv7zn3PhhRcOeH9WlX3TDcWG4nqq3V7y08xpk31F3PvbvvPzmYlO3t5dRUldK0lOG+fPKmB0ZjJoUO32Ud/iIzslgVEZSb3uN9ReZVMbtS1+GtvMUlrpiQ4aW4NUNLVhtFdiKMxIoqktwN7KZlr8OjNGpXPpgiI+rWrmnd1VgMY5M/Kwo7GptIE2v87csRmcPjmPU9qz8If6npvqAmUmy3tzRwUHajw4HTYunj8Kp83Ox4caqGzykmCDRJeTvNQERmclkZ3sIjslgcZWPxlJTrYebqSyqZW2gGL+mExOm5rLKZNyInJXY7DnUhwl750Q1hjpJUmPl2u9FazcZ29ttfl1fvr6LkrqWpmQk8ydF8xke3lTl+2Abq/t6bGh9O3Yfp04PouNJfWsO1AHKJZMyuXkidlsLK5n3cFaQOPkCVl8WuXmUL1ZgWrh2CwKMhIxdMX6kjqCumJ/TQttAZ1JuSl8/0JzpvJHB+tYd6AOw1A0ef1omsbEnBSuOXkcz24spaTOg1KQluTArmksmZTLKZNzeh2jhfq76VBDx5jtrV2VbCszEwSfOiWH0ybl8ml1C6X1rSilSHbZqG7yY2DeRS3MTCQ7xUV2spPGtgDZqS7yU12gQVWTl82lDVQ1t5HicnLZgiIcDhu1Lb6oX2flGj9w8l6JgRjp13oRPXERyIk2+QUSQggRb+TaZC15P4UQQsQbuTYJq8R8aZUQQgghhBBCCCGEGBgJ5AghhBBCCCGEEEIMExLIEUIIIYQQQgghhBgmJJAjhBBCCCGEEEIIMUxIIEcIIYQQQgghhBBimJBAjhBCCCGEEEIIIcQwIYEcIYQQQgghhBBCiGFCAjlCCCGEEEIIIYQQw4QEcoQQQgghhBBCCCGGCQnkCCGEEEIIIYQQQgwTjlh3IBaUUgA0NzfHuCdCCCGEKXRNCl2jRHjkWi+EECLeyLVeWOW4DOS43W4Axo4dG+OeCCGEEF253W4yMjJi3Y1hT671Qggh4pVc60W4NHUchgMNw+DIkSOkpaWhaVqsuzMgzc3NjB07lsOHD5Oenh7r7kSEHOPwN9KPD+QYR4p4PEalFG63m9GjR2OzycrncEXiWh+PPzdWk2McGY6HY4Tj4zjlGEeG0DGWlpaiaZpc60XYjssZOTabjTFjxsS6G0OSnp4+Yv/AhcgxDn8j/fhAjnGkiLdjlLtz1onktT7efm4iQY5xZDgejhGOj+OUYxwZMjIyRvwxiuiQMKAQQgghhBBCCCHEMCGBHCGEEEIIIYQQQohhQgI5w4TL5eLee+/F5XLFuisRI8c4/I304wM5xpHieDhGYb3j4edGjnFkOB6OEY6P45RjHBmOh2MU0XVcJjsWQgghhBBCCCGEGI5kRo4QQgghhBBCCCHEMCGBHCGEEEIIIYQQQohhQgI5QgghhBBCCCGEEMOEBHKEEEIIIYQQQgghhgkJ5MSB999/n4svvpjRo0ejaRovv/xyv69ZvXo1J5xwAi6XiylTpvDUU09FvJ/hGOwxrl69Gk3Tun1VVlZGp8NDsHz5ck4++WTS0tLIz8/n0ksvZe/evf2+7vnnn2fGjBkkJiYyd+5cXn/99Sj0dvCGcnxPPfVUt3OYmJgYpR4P3h/+8AfmzZtHeno66enpLFmyhDfeeKPP1wyX8xcy2GMcbuewJw8++CCapnHbbbf1ud1wO5fCevX19Vx//fWkp6eTmZnJV7/6VVpaWvrc/lvf+hbTp08nKSmJcePGceutt9LU1NRlu56uZ88880ykD6fDo48+yoQJE0hMTGTx4sVs2LChz+37+11QSvGjH/2IwsJCkpKSWLp0Kfv27YvkIfRrMMf4+OOPc8YZZ5CVlUVWVhZLly7ttv2Xv/zlbufsggsuiPRh9GkwxziQv93D/TyeffbZPf5uXXTRRR3bxNt5jNSYf7C/45E02GN88cUX+cxnPkNeXl7HuOTNN9/sss2Pf/zjbudxxowZETyKvkXqc008nUcR/ySQEwc8Hg/z58/n0UcfHdD2xcXFXHTRRZxzzjls3bqV2267ja997Wvd/ujFk8EeY8jevXupqKjo+MrPz49QD8P33nvvcfPNN/PRRx/x9ttvEwgEOP/88/F4PL2+5sMPP+S6667jq1/9Klu2bOHSSy/l0ksvZceOHVHs+cAM5fgA0tPTu5zDQ4cORanHgzdmzBgefPBBNm3axMcff8y5557LJZdcws6dO3vcfjidv5DBHiMMr3N4rI0bN/LHP/6RefPm9bndcDyXwnrXX389O3fu5O233+bVV1/l/fff57//+7973f7IkSMcOXKEhx56iB07dvDUU0+xYsUKvvrVr3bb9s9//nOX36NLL700gkdy1LPPPsvtt9/Ovffey+bNm5k/fz7Lli2jurq6x+0H8rvw85//nEceeYTHHnuM9evXk5KSwrJly/B6vVE5pmMN9hhXr17Nddddx6pVq1i3bh1jx47l/PPPp7y8vMt2F1xwQZdz9s9//jMah9OjwR4j9P+3e7ifxxdffLHL8e3YsQO73c5VV13VZbt4Oo+RGPMP5WcjkgZ7jO+//z6f+cxneP3119m0aRPnnHMOF198MVu2bOmy3ezZs7ucxzVr1kSi+wMSic818XYexTCgRFwB1EsvvdTnNt/73vfU7Nmzuzx2zTXXqGXLlkWwZ9YZyDGuWrVKAaqhoSEqfYqE6upqBaj33nuv122uvvpqddFFF3V5bPHixer//b//F+nuhW0gx/fnP/9ZZWRkRK9TEZCVlaX+9Kc/9fjccD5/nfV1jMP5HLrdbjV16lT19ttvq7POOkt9+9vf7nXbkXIuxdDt2rVLAWrjxo0dj73xxhtK0zRVXl4+4Haee+45lZCQoAKBQMdjA7nuRcqiRYvUzTff3PG9rutq9OjRavny5T1u39/vgmEYatSoUeoXv/hFx/ONjY3K5XKpf/7znxE4gv4N9hiPFQwGVVpamvrLX/7S8diNN96oLrnkEqu7OmSDPcb+/naPxPP461//WqWlpamWlpaOx+LtPHZm1Zg/3Pctkob6t2/WrFnqvvvu6/j+3nvvVfPnz7euYxay6nNNPJ9HEZ9kRs4wtG7dOpYuXdrlsWXLlrFu3boY9ShyFixYQGFhIZ/5zGdYu3ZtrLszKKGp9dnZ2b1uM5zP5UCOD6ClpYXx48czduzYfmd+xBNd13nmmWfweDwsWbKkx22G8/mDgR0jDN9zePPNN3PRRRd1O0c9Ge7nUoRv3bp1ZGZmctJJJ3U8tnTpUmw2G+vXrx9wO01NTaSnp+NwOLo8fvPNN5Obm8uiRYt48sknUUpZ1vfe+P1+Nm3a1OVn22azsXTp0l5/tvv7XSguLqaysrLLNhkZGSxevDgmvy9DOcZjtba2EggEul3PVq9eTX5+PtOnT+cb3/gGdXV1lvZ9oIZ6jH397R6J5/GJJ57g2muvJSUlpcvj8XIeh6K/30cr3rd4YxgGbre72+/jvn37GD16NJMmTeL666+ntLQ0Rj0cut4+14zE8ygiTwI5w1BlZSUFBQVdHisoKKC5uZm2trYY9cpahYWFPPbYY/zrX//iX//6F2PHjuXss89m8+bNse7agBiGwW233cZpp53GnDlzet2ut3MZz7mAYODHN336dJ588kleeeUV/va3v2EYBqeeeiplZWVR7O3gbN++ndTUVFwuF1//+td56aWXmDVrVo/bDtfzN5hjHI7nEOCZZ55h8+bNLF++fEDbD9dzKaxTWVnZbfmuw+EgOzt7wD8HtbW13H///d2WY/3kJz/hueee4+233+aKK67gm9/8Jr/97W8t63tf/dF1fVA/2/39LoT+Gy+/L0M5xmPdeeedjB49usuHqAsuuICnn36alStX8rOf/Yz33nuPz372s+i6bmn/B2Iox9jf3+6Rdh43bNjAjh07+NrXvtbl8Xg6j0PR35jfip//ePPQQw/R0tLC1Vdf3fHY4sWLO5au/uEPf6C4uJgzzjgDt9sdw54OXH+fa0bieRSR5+h/EyGib/r06UyfPr3j+1NPPZUDBw7w61//mr/+9a8x7NnA3HzzzezYsSOm63cjaaDHt2TJki4zPU499VRmzpzJH//4R+6///5Id3NIpk+fztatW2lqauKFF17gxhtv5L333us10DEcDeYYh+M5PHz4MN/+9rd5++23h11iZmG9u+66i5/97Gd9brN79+6w99Pc3MxFF13ErFmz+PGPf9zluXvuuafj3wsXLsTj8fCLX/yCW2+9Nez9ivA8+OCDPPPMM6xevbrL34trr722499z585l3rx5TJ48mdWrV3PeeefFoquDMhz/dofjiSeeYO7cuSxatKjL48P9PB5v/vGPf3DffffxyiuvdAmsf/azn+3497x581i8eDHjx4/nueee6zEnWbwZ7p9rRHySGTnD0KhRo6iqquryWFVVFenp6SQlJcWoV5G3aNEi9u/fH+tu9OuWW27h1VdfZdWqVYwZM6bPbXs7l6NGjYpkF8MymOM7ltPpZOHChXF9HhMSEpgyZQonnngiy5cvZ/78+Tz88MM9bjsczx8M7hiPNRzO4aZNm6iuruaEE07A4XDgcDh47733eOSRR3A4HD3eiR2u51L074477mD37t19fk2aNIlRo0Z1SyoZDAapr6/v9+fA7XZzwQUXkJaWxksvvYTT6exz+8WLF1NWVobP5wv7+PqSm5uL3W4f1M92f78Lof/Gy+/LUI4x5KGHHuLBBx/krbfe6jch+qRJk8jNzY3J375wjjHk2L/dI+k8ejwennnmmQF9oI/leRyK/sb8VvxsxItnnnmGr33tazz33HP9LonOzMxk2rRpw+Y89qTz55qRdB5F9EggZxhasmQJK1eu7JpAYnAAABXmSURBVPLY22+/3WeOi5Fg69atFBYWxrobvVJKccstt/DSSy/x7rvvMnHixH5fM5zO5VCO71i6rrN9+/a4Po/HMgyj1w9bw+n89aWvYzzWcDiH5513Htu3b2fr1q0dXyeddBLXX389W7duxW63d3vNSDmXoru8vDxmzJjR51dCQgJLliyhsbGRTZs2dbz23XffxTAMFi9e3Gv7zc3NnH/++SQkJPDvf/97QLPAtm7dSlZWFi6Xy5Jj7E1CQgInnnhil59twzBYuXJlrz/b/f0uTJw4kVGjRnXZprm5mfXr18fk92Uoxwhmxab777+fFStWdMmL1JuysjLq6upi8rdvqMfY2bF/u0fKeQR4/vnn8fl83HDDDf3uJ5bncSj6+3204mcjHvzzn//kpptu4p///GeX8vG9aWlp4cCBA8PmPPak8+eakXIeRZTFONmyUGZllS1btqgtW7YoQP3qV79SW7ZsUYcOHVJKKXXXXXepL37xix3bHzx4UCUnJ6vvfve7avfu3erRRx9VdrtdrVixIlaH0K/BHuOvf/1r9fLLL6t9+/ap7du3q29/+9vKZrOpd955J1aH0K9vfOMbKiMjQ61evVpVVFR0fLW2tnZs88UvflHdddddHd+vXbtWORwO9dBDD6ndu3ere++9VzmdTrV9+/ZYHEKfhnJ89913n3rzzTfVgQMH1KZNm9S1116rEhMT1c6dO2NxCP2666671HvvvaeKi4vVJ598ou666y6laZp66623lFLD+/yFDPYYh9s57M2xVatGwrkU1rvgggvUwoUL1fr169WaNWvU1KlT1XXXXdfxfFlZmZo+fbpav369UkqppqYmtXjxYjV37ly1f//+Ln8bg8GgUkqpf//73+rxxx9X27dvV/v27VO///3vVXJysvrRj34UlWN65plnlMvlUk899ZTatWuX+u///m+VmZmpKisrlVJD+1148MEHVWZmpnrllVfUJ598oi655BI1ceJE1dbWFpVjOtZgj/HBBx9UCQkJ6oUXXuhyztxut1LKHLN85zvfUevWrVPFxcXqnXfeUSeccIKaOnWq8nq9w+IYB/K3e7ifx5DTTz9dXXPNNd0ej8fzGIkxf3/vW7QN9hj//ve/K4fDoR599NEuv4+NjY0d29xxxx1q9erVqri4WK1du1YtXbpU5ebmqurq6qgfn1KR+VwTb+dRxD8J5MSBUEm6Y79uvPFGpZRZOvGss87q9poFCxaohIQENWnSJPXnP/856v0ejMEe489+9jM1efJklZiYqLKzs9XZZ5+t3n333dh0foB6Oj6gy7k566yzOo455LnnnlPTpk1TCQkJavbs2eq1116LbscHaCjHd9ttt6lx48aphIQEVVBQoC688EK1efPm6Hd+gL7yla+o8ePHq4SEBJWXl6fOO++8jgCHUsP7/IUM9hiH2znszbGBnJFwLoX16urq1HXXXadSU1NVenq6uummmzo+3CulVHFxsQLUqlWrlFK9X9sAVVxcrJQyS5gvWLBApaamqpSUFDV//nz12GOPKV3Xo3Zcv/3tbzt+jxctWqQ++uijjueG8rtgGIa65557VEFBgXK5XOq8885Te/fujcah9Gowxzh+/Pgez9m9996rlFKqtbVVnX/++SovL085nU41fvx49V//9V8x/0A1mGMcyN/u4X4elVJqz549CuhyHQuJx/MYqTF/X+9btA32GM8666w+t1fKLLleWFioEhISVFFRkbrmmmvU/v37o3tgnUTqc008nUcR/zSlolD/UgghhBBCCCGEEEKETXLkCCGEEEIIIYQQQgwTEsgRQgghhBBCCCGEGCYkkCOEEEIIIYQQQggxTEggRwghhBBCCCGEEGKYkECOEEIIIYQQQgghxDAhgRwhhBBCCCGEEEKIYUICOUIIIYQQQgghhBDDhARyhBBCCCGEEEIIIYYJCeQIIYQQQgghhBBCDBMSyBFCCCGEEEIIIYQYJiSQI4QYNurq6sjPz6ekpGRQr/t//+//cf3111u+7VBce+21/PKXv4xY+0IIIcL3ne98h0svvdTSNod6DRO9G8p7Gk9jAuh5XCBjBSFEfySQI4QYsrKyMr7+9a8zZcoUEhMTKSgo4Pzzz2f79u0AKKXIzMzkt7/9bbfXfvOb32TRokUAzJ49m3vvvbfHfSxfvpycnBzq6up44IEHuOSSS5gwYcKg+rl8+XL+7//+b0jb/s///A+XX375oPbXlx/+8Ic88MADNDU1WdamEEIIa23dupUFCxZY2uZQr2HDRbTHBDC09zSexgTQ87hAxgpCiP5IIEcIMSQlJSUsXLiQuro6/vrXv7Jnzx5eeOEFZs2ahcvlAuDAgQM0NTVx0kkndXv9pk2bOPHEEwGYO3cuO3bs6LZNRUUFP/3pT/nJT35CUlISTzzxBF/96lcH3dfs7GxSUlKGtO2GDRt67P9QzZkzh8mTJ/O3v/3NsjaFEEJYa9u2bZYGclpbW4d8DRsOoj0myMnJGfJ7Gk9jAuh5XCBjBSFEfySQI0S7f/7znyQlJVFRUdHx2E033cS8efMsuyNSUlKCpmn861//4swzzyQpKYmTTz6Z0tJSPvjgA0455RSSk5M577zzaGxs7PLae++9l7lz55KSkkJBQQHf+MY3CAQCUev7sX7729+SkpLCs88+y5IlS5gwYQJnnHEGv/nNb5g2bRpgDswcDke3wXAgEOCTTz7pGLTNmzevx0Hb97//fSZOnMjXv/51Xn/9dVwuF6ecckqXbfLz8/nTn/7U5bGNGzeSmJhIcXFxx3semnZtGAY//elPmTp1ascdwy9/+csAXbb1+/04nU4+/PBDfvCDH6BpWrd9H6uyshJN03j44YdZuHAhiYmJzJ49mzVr1nTZ7uKLL+aZZ57psy0hhBDdReN6V1ZWRm1tLfPnz+94bMeOHVx44YWkp6czatQo7rjjDvx+f8fz69ev5/TTTycpKYkFCxbw/vvvo2lax7Wtt2vYUMcFfY0JovU+dRbtMQH0/J7G05gAwhsXyFhBCNEnJYRQSillGIaaN2+euuWWW5RSSv3oRz9SY8aMUWVlZd22feCBB1RKSkqfX4cOHer2updfflkB6rzzzlMffPCB2rx5sxo7dqw644wz1IUXXqg2btyoPvroI5WTk6N+9atfdenbPffco9auXatKSkrU66+/rvLy8tTvf//7QffdKjfddJMqKChQxcXFvW7z3e9+V82bN6/b41u2bFGA2rx5s1JKqX//+9/Kbrertra2jm0+/vhjZbPZ1KpVq5RSSt16663qggsu6NbWueeeq/7nf/6ny2PnnHOOuvXWW5VS5nuemZnZ8dz//u//qrlz56p3331XlZSUqLVr16onnnii27a6rqv169crQG3dulVVVFSohoaGPt+TN954QwFq3rx5avXq1Wr37t3qggsuUOPGjVO6rnfZLiEhQXm93j7bE0II0VU0rtX/+c9/VEZGRsf3mzdvVmlpaeoHP/iB2rdvn1q1apUqLCxUP/nJT5RSSm3fvl2lpKSoH/zgB2r37t3qhRdeUPn5+crlcqlAIKCU6v0aNpRxQX9jgsG+T1aI9phAqZ7f03gaEygV3rhAxgpCiL5IIEeITv7zn/8ol8ul/vd//1dlZWWpHTt29LhdXV2d2rdvX59focFbZz/+8Y9Vdna2qq2t7XjshhtuUBMmTFAej6fjsQsuuEB973vf67Ov1113nfr2t7896L53tnLlSvXQQw8N6PkDBw6oV155peO5TZs2qXHjxilN09RJJ52k7rzzTrVz584urz/33HPVV77ylW7t/ulPf1Iul0v5/X6llFIlJSUKUFu2bOnY5vTTT1dXXXVVx/eXXHJJj23deuutatmyZR3fr1ixQqWlpamamhqllPmen3nmmR3Pn3HGGer73/9+j8d77LYvvfSSysnJ6XHbnjz44IPK6XR2Gch+/PHHClClpaUdj23btk0BqqSkZMBtCyGEMEX6Wn3//fd3uRaceOKJ6pvf/GaXbb7//e+rRYsWKaXMQMF1113X5fmLL75YLVy4sOP73q5hVo0Ljh0TKGX9uCCexgRK9fyextOYQKnwxgUyVhBC9MURg0lAQsStz33uc8yaNYuf/OQnvPXWW8yePbvH7bKzs8nOzh50+9u2beOyyy4jJyen47HS0lKuueYakpOTuzx2ySWXdHx/6NAhfv7zn/Pee+9RXl5OIBDA6/Xy4IMPDrrvnZ177rmce+65A3r+jTfewO128/nPfx6AE044gYMHD7JmzRreeustnn/+eX75y1/y4osvcvHFFwOwefNmrrzyym7tbtq0iblz5+J0OgEYP348GRkZ7NixgwULFvDss8+yadMm9uzZ0/GatrY2EhMTu7U1d+5cXnrpJcBMpHj33Xfz3e9+l9zcXKB7noPPf/7z3HnnnXz88cdcddVVXHHFFWRlZfW47ZYtW7pMre/P1q1bufzyy7skXUxPT++2XVJSEmDmTBBCCDE4kb5Wb926teNv/549e9i0aVO3XCUJCQn4fD4OHTrEqlWrui0FcrlcXa4fvV3DhjIuGMiYAKwfF8TTmAB6fk/jaUwA4Y0LZKwghOiL5MgRopMVK1awZ88edF2noKCg1+1++tOfkpqa2udXaWlpt9dt3bqVxYsXd3ls27ZtXdZZe71e9u7d2zFYqKmp4eSTT6auro5f/epXrFmzhg8//BCbzdZlQNFb39evX98xiApt98UvfhEwBzChahKPP/44J5xwAnPmzOGaa67p8vx7773HPffcwxNPPMHChQvxeDwA2O12zjrrLB544AF27txJfn4+//jHPwA4fPgwjY2NPQ4c33nnHU499dQuj82ZM4cdO3bg9Xq58847ufPOOxk3blzH87m5uTQ0NHRra86cOZSVldHS0sIzzzxDRUUFt99+e5f3vPP79J3vfIfdu3dz3nnn8etf/5opU6ZQXFzc47bHft+fnqqcrFu3jtzcXIqKijoeq6+vByAvL2/AbQshhDBF41od+lu+c+dOnE5nR56XkF27djF37ly2bt1KQkJCt2vd7t27u1w/eruGDXZcMNAxQV/v00DGBfE+JujtPY2nMUHoNUMdF8hYQQjRp1hPCRIiXmzatEmlpaWpf/zjH+r8889XV155Za/bDmW6dlNTk9I0TW3cuLHjsYMHD3abNrthwwZls9mU2+1WSin1xBNPqOzsbGUYRsc2v/3tbxWgqqur++17U1OTmjp1asf3p556qvr000+VUkpNnTpV+f1+VV9fr+bPn6+CwaBSSnWs+w49r5RSZ511Vp9r371er8rJyVHf+ta3lFLmtGtAvfbaa122e+uttxSg1q5d2+Xxb3zjG+qiiy5S999/vxo/frxqbW3t8vwvfvELNX/+/G77dbvdStM0tXbtWjV58uQuOQJC7/mmTZt67HNbW5tyOp3q1Vdf7XHbiRMnqr/85S+9HnNnra2tym63q//93//teEzXdbVw4UJ1xx13dNn2T3/6kxozZsyA2hVCCHFUpK/Vzc3NXa4Fb775prLZbF3ylBw8eFA5nU71xhtvqP/85z/KZrN1yefyzjvvKEC9++67HY/1dA0byrhgIGOC/t6n/sYF1dXVcT8mUKrn9zRexgRKhT8ukLGCEKIvEsgRQilVXFysRo0apZYvX66UUuqjjz7q82I/FO+//75yOBxdBnsvvviiys7O7rLd//3f/3UZYL388svK4XCol19+WX366afql7/8pcrNzVVFRUUD7vv48eOV3+9Xr732mrrxxhuVUuZgde7cuR3/HjdunLr99ts71tB3fl4pcwATcsMNN6if/vSn6qOPPlLFxcVq5cqV6rzzzlM5OTnqwIEDSikz0eKMGTPUvHnz1DvvvKO2bt2q/vjHP6rc3Fz15S9/udv78/vf/17l5eWplJQU9fzzz3d7/pNPPlEOh0PV19f///buJySqLg7j+M8wGhQTSqwEoaRJzSnIRZQJFblSIgYU3Yi7BhyRDAORQIQwciHBLCohogkhkhCCCIKgLJNaCcpFsBFCxo1MDi7a1fMuwqHb+CcKZ7xv389uzjkzc+7ZnIffvXMmre/gwYM6deqU/H6/K5SvrvlqAL9165YePnwox3E0Ozurrq4u7d+/X1++fEkbu7puvb29isfjSiaTad/7sw8fPig3N1cVFRV6//69HMdRY2OjDh06lHYgYltb25rnBAAA1peJvfrt27euvSCZTGrPnj26cuWKYrGYXr16pcrKSrW2tkqSFhcXtWvXLnV0dCgWi+nZs2cqKyuTmSmRSKQ+d6097E9ywWaZ4HfXaaNc4IVMsN6aStsjE0h/nwvICgA2QiEH/7xEIqHy8nKFQiFXe319vevAvL8ViURUVVXlauvr69OFCxdcbeFw2HXn7Nu3bwqFQiooKFBxcbGuXr2q9vZ2NTQ0/Pbc6+rq5DiOTp8+rU+fPkmSJicn1dLSkhqzsrKiaDSqo0ePamxszNW/sLCgM2fOpMYODQ2ppqZGRUVF8vl88vv9CofDWlhYcM0jFospGAxq79692r17t6qrqzU8PJy6y/ezd+/eycx0/vz5ddfw5MmTunv3blr7xYsXZWZ68uSJqz0SiSgQCKRe9/f368iRI/L5fCoqKtKlS5fkOM6aYyXp0aNHKikpkZmpu7tbkvTgwQOt9TDjvXv3FAgEFI1GdeDAAeXl5SkYDLoOM5R+3PErLCzU5OTkutcJAHDL5F79614wPj6u6upq+Xw+lZWV6ebNm659bGRkRKWlpcrPz1cwGFR/f78OHz6c9tm/7mF/kgs2ygTS76/TZrnAC5lgrTWVMpsJpK3JBWQFAJvJkaRM/5wLQGZ1dnba169fTZLdv3/fzH6cibO0tGS9vb02Nzdnfr/fzMza29vt7NmztrKykuqfmJiw27dv2+joaDYvw54/f27Xrl2zmZkZ27EjO0d89fX12Zs3b+z169eu9nA4bMvLy6nzANZz584dGxsbs5cvX27hLAEA2fD9+3c7d+6c1dbW2sDAgKtvO+xhqzbKBU1NTZ7IBGbbY023IheQFQBshsOOgX9AZWWlRaNRu379eqptenraAoGAmZnduHHDysvL7cSJE5aTk2NNTU2u/kAgYPPz83bs2DFzHCcr12Bm1tDQYJcvX7Z4PJ61Obx48cIGBwfT2qempuz48eObvn/nzp0WiUS2YmoAgAwbHx+3p0+f2vz8vH38+NGam5vt8+fP1t3dnTZ2O+xhqzbKBV7JBGbbY023IheQFQBshidyAOAvSbLCwkJ7/Pix1dfXZ3s6AIAMGR0dtZ6eHovH47Zv3z6rq6uzgYGBDf9NC/9/5AIAW41CDgAAAAAAgEfw0yoAAAAAAACPoJADAAAAAADgERRyAAAAAAAAPIJCDgAAAAAAgEdQyAEAAAAAAPAICjkAAAAAAAAeQSEHAAAAAADAIyjkAAAAAAAAeASFHAAAAAAAAI+gkAMAAAAAAOAR/wE2Aue5q+8r2wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting results\n",
    "fig, axs = plt.subplots(ncols = len(scales), figsize = (6*len(scales), 5))\n",
    "\n",
    "quantile_methods = {\n",
    "    \"normal\": lambda x, p, theta, cov: logit.get_model_quantiles_normal(\n",
    "        x, p, theta, cov, seed=random_seed\n",
    "    ),\n",
    "    \"delta\": logit.get_model_quantiles_delta,\n",
    "}\n",
    "\n",
    "for ax, scale, x, theta, cov_theta in zip(axs, scales, xs, thetas, cov_thetas):\n",
    "\n",
    "    ax.set_title(f\"cond. prob. for AE: scale {scale}\")\n",
    "\n",
    "    if scale == \"plain\":\n",
    "        xlab = r\"$x = max_{visit} SUV(visit, p)$\"\n",
    "    else:\n",
    "        xlab = r\"$x = log(max_{visit} SUV(visit, p))$\"\n",
    "    \n",
    "    ax.set_xlabel(xlab)\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # plotting data points\n",
    "    for i, group_label in enumerate([\"NC\", \"AE\"]):\n",
    "        ax.scatter(x[y == i], y[y == i], label=group_label)\n",
    "\n",
    "    # plotting fitted model \n",
    "    xp = np.linspace(min(x), max(x), 100)\n",
    "    yp = logit.model(xp, theta)\n",
    "\n",
    "    ax.plot(xp, yp, label = \"logistic reg\")\n",
    "\n",
    "    for ci_label, color in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        # get result of model quantiles at probs\n",
    "        # theta and cov_theta are obtained via MLE method\n",
    "        res = quantile_methods[ci_label](xp, probs, theta, cov_theta)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *res, color=color, alpha=0.5, label=f\"CI:{ci_label}\")\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(1.02, 0.5), loc='center right')\n",
    "\n",
    "fig.savefig(results_path / \"logit_linear_fit.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9fb1cb06129",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_results = {\n",
    "    \"model\": logit,\n",
    "    \"fit_results\": fit_results,\n",
    "    \"parameters\": thetas,\n",
    "    \"covariances\": cov_thetas,\n",
    "    \"parameter_ci_wald\": theta_CIs,\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4971dd5fff15",
   "metadata": {},
   "source": [
    "## 3. Cubic unconstrained (non-monotonic) logistic regression\n",
    "\n",
    "The log-odds are represented by an unconstrained cubic polynomial,\n",
    "\n",
    "$$\n",
    "\\eta(t) = \\beta_0 + \\beta_1 t + \\beta_2 t^2 + \\beta_3 t^3.\n",
    "$$\n",
    "\n",
    "Because no derivative constraint is imposed (`degree=3`, `mono=False`), the fitted probability curve may be non-monotonic. The same uncertainty and goodness-of-fit calculations used for the linear model are repeated here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98a60d53d9fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_path = results_root / \"cubic_unconstrained\"\n",
    "results_path.mkdir(parents=True, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50ae75ed9fc",
   "metadata": {},
   "source": [
    "### 3.1 Fit and parameter uncertainty\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "430ae4924260",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thetas = array([[-197.43456924470524,  248.4905000484067 ,  -98.59976324737492,\n",
      "          12.18035307606018],\n",
      "       [ -75.33868079212766,  287.5173164621367 , -339.6550928412925 ,\n",
      "         124.51434906316405]])\n"
     ]
    }
   ],
   "source": [
    "# unconstrained cubic logistic-regression model\n",
    "logit = logistic.LogisticPolyRegression(degree=3, mono=False)\n",
    "\n",
    "# fits\n",
    "fit_results = [logit.fit(x, y, method=\"local\", seed=random_seed) for x in xs]\n",
    "if not all(result[\"success\"] for result in fit_results):\n",
    "    raise RuntimeError(\"At least one cubic logistic-regression fit failed.\")\n",
    "\n",
    "thetas = np.array([result[\"theta\"] for result in fit_results])\n",
    "print(f\"{thetas = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10268adb7723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov_thetas = array([[[ 10939.684523567224   , -14010.646252568109   ,\n",
      "           5653.962064031723   ,   -708.3678994726915  ],\n",
      "        [-14010.646252568109   ,  18013.760997744266   ,\n",
      "          -7300.014221918021   ,    918.9939676648023  ],\n",
      "        [  5653.962064031723   ,  -7300.014221918021   ,\n",
      "           2973.1108397867397  ,   -376.64839161858305 ],\n",
      "        [  -708.3678994726915  ,    918.9939676648023  ,\n",
      "           -376.64839161858305 ,     48.140910244592945]],\n",
      "\n",
      "       [[  1998.703486237727   ,  -7705.433239899273   ,\n",
      "           9196.92321106423    ,  -3384.606945444059   ],\n",
      "        [ -7705.433239899273   ,  29940.973099065315   ,\n",
      "         -35991.86768534103    ,  13336.386966233815   ],\n",
      "        [  9196.92321106423    , -35991.86768534103    ,\n",
      "          43591.818688455074   , -16286.649984027337   ],\n",
      "        [ -3384.606945444059   ,  13336.386966233815   ,\n",
      "         -16286.649984027337   ,   6149.469270433855   ]]])\n"
     ]
    }
   ],
   "source": [
    "# asymptotic covariance matrix of parameters\n",
    "cov_thetas = np.array([logit.get_cov(x, y, theta) for x, theta in zip(xs, thetas)])\n",
    "print(f\"{cov_thetas = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "233e3b495e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta_CIs = array([[[-402.4329780401599  ,  -14.566758126806093,\n",
      "         -205.4692297024859  ,   -1.418592629226529],\n",
      "        [   7.56383955074935 ,  511.54775822361944 ,\n",
      "            8.269703207736043,   25.77929878134689 ]],\n",
      "\n",
      "       [[-162.96251965553296 ,  -51.62426932699947 ,\n",
      "         -748.8691888972139  ,  -29.18318823491893 ],\n",
      "        [  12.285158071277635,  626.6589022512728  ,\n",
      "           69.55900321462877 ,  278.211886361247   ]]])\n"
     ]
    }
   ],
   "source": [
    "# CI of params (assuming asymptotic distr of parameters)\n",
    "theta_CIs = np.array([\n",
    "    logit.get_theta_quantiles_normal(probs, theta, cov_theta)\n",
    "    for theta, cov_theta in zip(thetas, cov_thetas)\n",
    "])\n",
    "print(f\"{theta_CIs = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "18a1b339bcd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "('scale', 'coef')",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "value",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "LCL",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "UCL",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "1593b698-d266-4af0-96fe-6504be6d5e5a",
       "rows": [
        [
         "('plain', 'b0')",
         "-197.43456924470524",
         "-402.4329780401599",
         "7.56383955074935"
        ],
        [
         "('plain', 'b1')",
         "248.4905000484067",
         "-14.566758126806093",
         "511.54775822361944"
        ],
        [
         "('plain', 'b2')",
         "-98.59976324737492",
         "-205.4692297024859",
         "8.269703207736043"
        ],
        [
         "('plain', 'b3')",
         "12.18035307606018",
         "-1.418592629226529",
         "25.77929878134689"
        ],
        [
         "('log', 'b0')",
         "-75.33868079212766",
         "-162.96251965553296",
         "12.285158071277635"
        ],
        [
         "('log', 'b1')",
         "287.5173164621367",
         "-51.62426932699947",
         "626.6589022512728"
        ],
        [
         "('log', 'b2')",
         "-339.6550928412925",
         "-748.8691888972139",
         "69.55900321462877"
        ],
        [
         "('log', 'b3')",
         "124.51434906316405",
         "-29.18318823491893",
         "278.211886361247"
        ]
       ],
       "shape": {
        "columns": 3,
        "rows": 8
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>LCL</th>\n",
       "      <th>UCL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scale</th>\n",
       "      <th>coef</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">plain</th>\n",
       "      <th>b0</th>\n",
       "      <td>-197.434569</td>\n",
       "      <td>-402.432978</td>\n",
       "      <td>7.563840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1</th>\n",
       "      <td>248.490500</td>\n",
       "      <td>-14.566758</td>\n",
       "      <td>511.547758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2</th>\n",
       "      <td>-98.599763</td>\n",
       "      <td>-205.469230</td>\n",
       "      <td>8.269703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3</th>\n",
       "      <td>12.180353</td>\n",
       "      <td>-1.418593</td>\n",
       "      <td>25.779299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">log</th>\n",
       "      <th>b0</th>\n",
       "      <td>-75.338681</td>\n",
       "      <td>-162.962520</td>\n",
       "      <td>12.285158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1</th>\n",
       "      <td>287.517316</td>\n",
       "      <td>-51.624269</td>\n",
       "      <td>626.658902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2</th>\n",
       "      <td>-339.655093</td>\n",
       "      <td>-748.869189</td>\n",
       "      <td>69.559003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3</th>\n",
       "      <td>124.514349</td>\n",
       "      <td>-29.183188</td>\n",
       "      <td>278.211886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 value         LCL         UCL\n",
       "scale coef                                    \n",
       "plain b0   -197.434569 -402.432978    7.563840\n",
       "      b1    248.490500  -14.566758  511.547758\n",
       "      b2    -98.599763 -205.469230    8.269703\n",
       "      b3     12.180353   -1.418593   25.779299\n",
       "log   b0    -75.338681 -162.962520   12.285158\n",
       "      b1    287.517316  -51.624269  626.658902\n",
       "      b2   -339.655093 -748.869189   69.559003\n",
       "      b3    124.514349  -29.183188  278.211886"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# present table of results\n",
    "d = thetas.shape[-1]\n",
    "index = pd.MultiIndex.from_tuples([(lab, f\"b{i}\") for lab in scales for i in range(d)], names = ['scale', 'coef'])\n",
    "pd.DataFrame({\"value\": thetas.flatten(), \"LCL\": theta_CIs[:,0].flatten(), \"UCL\": theta_CIs[:,1].flatten()}, index = index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b51346c478d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "LLF",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "AIC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BIC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "A",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "deviance",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "p-value(deviance_bootstrap)",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "deviance_bootstrap_samples",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "n",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "k",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "dof",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "6b5a2895-e4c9-452b-b4a4-4d9d5d12188b",
       "rows": [
        [
         "plain",
         "-3.188295093857755",
         "14.376590187715511",
         "22.618362229901187",
         "0.9655172413793104",
         "6.37659018771551",
         "0.11188811188811189",
         "1000",
         "58",
         "4",
         "54"
        ],
        [
         "log",
         "-3.491409863193514",
         "14.982819726387028",
         "23.224591768572704",
         "0.9655172413793104",
         "6.982819726387028",
         "0.08191808191808192",
         "1000",
         "58",
         "4",
         "54"
        ]
       ],
       "shape": {
        "columns": 10,
        "rows": 2
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LLF</th>\n",
       "      <th>AIC</th>\n",
       "      <th>BIC</th>\n",
       "      <th>A</th>\n",
       "      <th>deviance</th>\n",
       "      <th>p-value(deviance_bootstrap)</th>\n",
       "      <th>deviance_bootstrap_samples</th>\n",
       "      <th>n</th>\n",
       "      <th>k</th>\n",
       "      <th>dof</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scale</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>plain</th>\n",
       "      <td>-3.188295</td>\n",
       "      <td>14.37659</td>\n",
       "      <td>22.618362</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>6.37659</td>\n",
       "      <td>0.111888</td>\n",
       "      <td>1000</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log</th>\n",
       "      <td>-3.491410</td>\n",
       "      <td>14.98282</td>\n",
       "      <td>23.224592</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>6.98282</td>\n",
       "      <td>0.081918</td>\n",
       "      <td>1000</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LLF       AIC        BIC         A  deviance  \\\n",
       "scale                                                      \n",
       "plain -3.188295  14.37659  22.618362  0.965517   6.37659   \n",
       "log   -3.491410  14.98282  23.224592  0.965517   6.98282   \n",
       "\n",
       "       p-value(deviance_bootstrap)  deviance_bootstrap_samples   n  k  dof  \n",
       "scale                                                                       \n",
       "plain                     0.111888                        1000  58  4   54  \n",
       "log                       0.081918                        1000  58  4   54  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# goodness of fit measures\n",
    "index = pd.Index(scales, name = 'scale')\n",
    "pd.DataFrame(\n",
    "    [logit.goodness_of_fit(x, y, theta, bootstrap_seed=random_seed)\n",
    "     for x, theta in zip(xs, thetas)],\n",
    "    index=index,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8029f7f521b1",
   "metadata": {},
   "source": [
    "### 3.2 Fitted risk curve and confidence bands\n",
    "\n",
    "**Coverage note.** The displayed limits are pointwise confidence intervals evaluated separately at each predictor value; they are not simultaneous confidence bands for the complete curve.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "141a540cbb5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting results\n",
    "fig, axs = plt.subplots(ncols = len(scales), figsize = (6*len(scales), 5))\n",
    "\n",
    "quantile_methods = {\n",
    "    \"normal\": lambda x, p, theta, cov: logit.get_model_quantiles_normal(\n",
    "        x, p, theta, cov, seed=random_seed\n",
    "    ),\n",
    "    \"delta\": logit.get_model_quantiles_delta,\n",
    "}\n",
    "\n",
    "for ax, scale, x, theta, cov_theta in zip(axs, scales, xs, thetas, cov_thetas):\n",
    "\n",
    "    ax.set_title(f\"cond. prob. for AE: scale {scale}\")\n",
    "\n",
    "    if scale == \"plain\":\n",
    "        xlab = r\"$x = max_{visit} SUV(visit, p)$\"\n",
    "    else:\n",
    "        xlab = r\"$x = log(max_{visit} SUV(visit, p))$\"\n",
    "    \n",
    "    ax.set_xlabel(xlab)\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # plotting data points\n",
    "    for i, group_label in enumerate([\"NC\", \"AE\"]):\n",
    "        ax.scatter(x[y == i], y[y == i], label=group_label)\n",
    "\n",
    "    # plotting fitted model \n",
    "    xp = np.linspace(min(x), max(x), 100)\n",
    "    yp = logit.model(xp, theta)\n",
    "\n",
    "    ax.plot(xp, yp, label = \"logistic reg\")\n",
    "\n",
    "    for ci_label, color in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        # get result of model quantiles at probs\n",
    "        # theta and cov_theta are obtained via MLE method\n",
    "        res = quantile_methods[ci_label](xp, probs, theta, cov_theta)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *res, color=color, alpha=0.5, label=f\"CI:{ci_label}\")\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(1.02, 0.5), loc='center right')\n",
    "\n",
    "fig.savefig(results_path / \"logit_cubic_nonmono_fit.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f57a346797fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cubic_unconstrained_results = {\n",
    "    \"model\": logit,\n",
    "    \"fit_results\": fit_results,\n",
    "    \"parameters\": thetas,\n",
    "    \"covariances\": cov_thetas,\n",
    "    \"parameter_ci_wald\": theta_CIs,\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b193fac8ac7",
   "metadata": {},
   "source": [
    "## 4. Cubic constrained (monotonic) logistic regression\n",
    "\n",
    "This model also uses cubic log-odds, but imposes the monotonicity condition\n",
    "\n",
    "$$\n",
    "\\eta'(t) \\ge 0\n",
    "$$\n",
    "\n",
    "over the fitted domain. It uses `degree=3`, `mono=True`, light L2 regularization, and differential-evolution fitting. In addition to the asymptotic analysis, this section compares confidence intervals obtained from normal parameter sampling, nonparametric bootstrap, stratified nonparametric bootstrap, and parametric bootstrap.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2bf9dda5f4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_path = results_root / \"cubic_monotonic\"\n",
    "results_path.mkdir(parents=True, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50d789015352",
   "metadata": {},
   "source": [
    "### 4.1 Fit and numerical diagnostics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53ae4440fced",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "theta",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "cost",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "success",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "message",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "nit",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "845fa1c5-31b8-4039-913b-3b4d12090eab",
       "rows": [
        [
         "plain",
         "[-1.2399205696060534e+02 -1.9006450710459519e-09  5.5826617153766653e+00\n -1.2798181418791243e+01]",
         "4.782069565533947",
         "True",
         "CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH",
         "6"
        ],
        [
         "log",
         "[-4.0708789645036539e+01  3.3991848080423114e-08  1.5072426370420516e+01\n -1.2365102187959996e+01]",
         "4.60500749717171",
         "True",
         "CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH",
         "9"
        ]
       ],
       "shape": {
        "columns": 5,
        "rows": 2
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>theta</th>\n",
       "      <th>cost</th>\n",
       "      <th>success</th>\n",
       "      <th>message</th>\n",
       "      <th>nit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scale</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>plain</th>\n",
       "      <td>[-123.99205696060534, -1.900645071045952e-09, ...</td>\n",
       "      <td>4.782070</td>\n",
       "      <td>True</td>\n",
       "      <td>CONVERGENCE: RELATIVE REDUCTION OF F &lt;= FACTR*...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log</th>\n",
       "      <td>[-40.70878964503654, 3.3991848080423114e-08, 1...</td>\n",
       "      <td>4.605007</td>\n",
       "      <td>True</td>\n",
       "      <td>CONVERGENCE: RELATIVE REDUCTION OF F &lt;= FACTR*...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   theta      cost  success  \\\n",
       "scale                                                                         \n",
       "plain  [-123.99205696060534, -1.900645071045952e-09, ...  4.782070     True   \n",
       "log    [-40.70878964503654, 3.3991848080423114e-08, 1...  4.605007     True   \n",
       "\n",
       "                                                 message  nit  \n",
       "scale                                                          \n",
       "plain  CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*...    6  \n",
       "log    CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*...    9  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# constrained cubic logistic-regression model, light L2 regularization, monotonicity constraints\n",
    "logit = logistic.LogisticPolyRegression(degree=3, mono=True, lam=(0.0, 1e-6))\n",
    "\n",
    "# fits\n",
    "ress = [logit.fit(x, y, method=\"diff_evol\", seed=random_seed) for x in xs]\n",
    "if not all(result[\"success\"] for result in ress):\n",
    "    raise RuntimeError(\"At least one monotonic cubic logistic-regression fit failed.\")\n",
    "\n",
    "pd.DataFrame(ress, index = pd.Index(scales, name = 'scale'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5cb58fb510b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta:\n"
     ]
    },
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "0",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "1",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "3",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "024810f2-a427-4c55-b823-bba055411437",
       "rows": [
        [
         "plain",
         "-123.99205696060534",
         "-1.900645071045952e-09",
         "5.582661715376665",
         "-12.798181418791243"
        ],
        [
         "log",
         "-40.70878964503654",
         "3.3991848080423114e-08",
         "15.072426370420516",
         "-12.365102187959996"
        ]
       ],
       "shape": {
        "columns": 4,
        "rows": 2
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scale</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>plain</th>\n",
       "      <td>-123.992057</td>\n",
       "      <td>-1.900645e-09</td>\n",
       "      <td>5.582662</td>\n",
       "      <td>-12.798181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log</th>\n",
       "      <td>-40.708790</td>\n",
       "      <td>3.399185e-08</td>\n",
       "      <td>15.072426</td>\n",
       "      <td>-12.365102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0             1          2          3\n",
       "scale                                                \n",
       "plain -123.992057 -1.900645e-09   5.582662 -12.798181\n",
       "log    -40.708790  3.399185e-08  15.072426 -12.365102"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thetas = np.array([r[\"theta\"] for r in ress])\n",
    "\n",
    "print(\"theta:\")\n",
    "pd.DataFrame(thetas, index = pd.Index(scales, name = 'scale'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f9e98365ccd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------\n",
      "theta=[np.float64(-123.99205696060534), np.float64(-1.900645071045952e-09), np.float64(5.582661715376665), np.float64(-12.798181418791243)]\n",
      "beta=[np.float64(-123.99205696060534), np.float64(163.79344762829342), np.float64(-71.44791743313088), np.float64(10.388703942777443)]\n",
      "nllf = 4.766500575785168, jac = [np.float64(0.0002480986204596025), np.float64(-1.0900029225996882e-09), np.float64(-1.117547887455328e-05), np.float64(2.559143479774484e-05)]\n",
      "------------------------\n",
      "theta=[np.float64(-40.70878964503654), np.float64(3.3991848080423114e-08), np.float64(15.072426370420516), np.float64(-12.365102187959996)]\n",
      "beta=[np.float64(-40.70878964503654), np.float64(152.89575211869308), np.float64(-186.37209229075268), np.float64(75.72601223058258)]\n",
      "nllf = 4.602970217828536, jac = [np.float64(8.141257051397233e-05), np.float64(8.37125477683628e-09), np.float64(-3.0165300964586986e-05), np.float64(2.4724851553036692e-05)]\n"
     ]
    }
   ],
   "source": [
    "for x, theta in zip(xs, thetas): \n",
    "    r = logit.get_nllf(x, y, theta, jac = True)\n",
    "    beta = logit.get_beta(theta)\n",
    "    print(\"------------------------\")\n",
    "    print(f\"theta={list(theta)}\")\n",
    "    print(f\"beta={list(beta)}\")\n",
    "    print(f\"nllf = {r[0]}, jac = {list(r[1])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "89a497ed6bfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov_theta = array([[ 3.1860503293978745e+03, -1.3110758079220654e-08,\n",
      "        -7.6608299205985645e+01,  1.6820674345954237e+02],\n",
      "       [-1.3110758079220654e-08,  1.3504487732902441e-18,\n",
      "         4.6096226532444669e-10, -7.9652853934233211e-10],\n",
      "       [-7.6608299205985645e+01,  4.6096226532444669e-10,\n",
      "         1.8958911656668260e+00, -4.0836429068690316e+00],\n",
      "       [ 1.6820674345954237e+02, -7.9652853934233211e-10,\n",
      "        -4.0836429068690316e+00,  8.9097428159705334e+00]])\n",
      "cov_theta = array([[ 4.1490663287791779e+02,  1.0392207260405765e-07,\n",
      "        -8.4427300204620849e+01,  6.4969664247282694e+01],\n",
      "       [ 1.0392207260405765e-07,  5.9218731521025397e-16,\n",
      "        -3.7295253931690259e-08,  2.0273762711287934e-08],\n",
      "       [-8.4427300204620849e+01, -3.7295253931690259e-08,\n",
      "         1.8371719672197418e+01, -1.3529613667583014e+01],\n",
      "       [ 6.4969664247282694e+01,  2.0273762711287934e-08,\n",
      "        -1.3529613667583014e+01,  1.0261564031999541e+01]])\n"
     ]
    }
   ],
   "source": [
    "# asymptotic covariance matrix of parameters\n",
    "cov_thetas = np.array([logit.get_cov(x, y, theta) for x, theta in zip(xs, thetas)])\n",
    "\n",
    "for cov_theta in cov_thetas: print(f\"{cov_theta = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "16a0f1a3981a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.305379415508290e-16 5.740738377854399e-04 8.237500289586379e-02\n",
      " 3.196773014302778e+03]\n",
      "[-7.9380946263703322e-15  7.2506809643686646e-03  1.2058261169576496e+00\n",
      "  4.4232683978419266e+02]\n"
     ]
    }
   ],
   "source": [
    "# check eigenvalues of the symmetric covariance matrices\n",
    "for cov_theta in cov_thetas:\n",
    "    print(np.linalg.eigvalsh(cov_theta))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2ee2e94167d",
   "metadata": {},
   "source": [
    "### 4.2 Wald parameter intervals and goodness of fit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ed2a1bcb6679",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta_CI = array([[-2.3462243769320924e+02, -4.1782959117261846e-09,\n",
      "         2.8839605756750313e+00, -1.8648515608435979e+01],\n",
      "       [-1.3361676228001471e+01,  3.7700576963428039e-10,\n",
      "         8.2813628550782994e+00, -6.9478472291465074e+00]])\n",
      "theta_CI = array([[-8.0631799860116473e+01, -1.3703677763387472e-08,\n",
      "         6.6715809018928045e+00, -1.8643587328887083e+01],\n",
      "       [-7.8577942995661232e-01,  8.1687373924233686e-08,\n",
      "         2.3473271838948225e+01, -6.0866170470329104e+00]])\n"
     ]
    }
   ],
   "source": [
    "# CI of params (assuming asymptotic distr of parameters) : Wald approximation\n",
    "theta_CIs = np.array([\n",
    "    logit.get_theta_quantiles_normal(probs, theta, cov_theta)\n",
    "    for theta, cov_theta in zip(thetas, cov_thetas)\n",
    "])\n",
    "\n",
    "for theta_CI in theta_CIs: print(f\"{theta_CI = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e74159b26dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "coef",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "LCL",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "UCL",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "eb6a83b6-5238-4043-95d8-7aae5ef834dd",
       "rows": [
        [
         "0",
         "plain",
         "p0",
         "-123.99205696060534",
         "-234.62243769320924",
         "-13.361676228001471"
        ],
        [
         "1",
         "plain",
         "p1",
         "-1.900645071045952e-09",
         "-4.178295911726185e-09",
         "3.770057696342804e-10"
        ],
        [
         "2",
         "plain",
         "p2",
         "5.582661715376665",
         "2.8839605756750313",
         "8.2813628550783"
        ],
        [
         "3",
         "plain",
         "p3",
         "-12.798181418791243",
         "-18.64851560843598",
         "-6.947847229146507"
        ],
        [
         "4",
         "log",
         "p0",
         "-40.70878964503654",
         "-80.63179986011647",
         "-0.7857794299566123"
        ],
        [
         "5",
         "log",
         "p1",
         "3.3991848080423114e-08",
         "-1.3703677763387472e-08",
         "8.168737392423369e-08"
        ],
        [
         "6",
         "log",
         "p2",
         "15.072426370420516",
         "6.6715809018928045",
         "23.473271838948225"
        ],
        [
         "7",
         "log",
         "p3",
         "-12.365102187959996",
         "-18.643587328887083",
         "-6.08661704703291"
        ]
       ],
       "shape": {
        "columns": 5,
        "rows": 8
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scale</th>\n",
       "      <th>coef</th>\n",
       "      <th>value</th>\n",
       "      <th>LCL</th>\n",
       "      <th>UCL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239921e+02</td>\n",
       "      <td>-2.346224e+02</td>\n",
       "      <td>-1.336168e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>-1.900645e-09</td>\n",
       "      <td>-4.178296e-09</td>\n",
       "      <td>3.770058e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>5.582662e+00</td>\n",
       "      <td>2.883961e+00</td>\n",
       "      <td>8.281363e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279818e+01</td>\n",
       "      <td>-1.864852e+01</td>\n",
       "      <td>-6.947847e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-4.070879e+01</td>\n",
       "      <td>-8.063180e+01</td>\n",
       "      <td>-7.857794e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>3.399185e-08</td>\n",
       "      <td>-1.370368e-08</td>\n",
       "      <td>8.168737e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507243e+01</td>\n",
       "      <td>6.671581e+00</td>\n",
       "      <td>2.347327e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236510e+01</td>\n",
       "      <td>-1.864359e+01</td>\n",
       "      <td>-6.086617e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   scale coef         value           LCL           UCL\n",
       "0  plain   p0 -1.239921e+02 -2.346224e+02 -1.336168e+01\n",
       "1  plain   p1 -1.900645e-09 -4.178296e-09  3.770058e-10\n",
       "2  plain   p2  5.582662e+00  2.883961e+00  8.281363e+00\n",
       "3  plain   p3 -1.279818e+01 -1.864852e+01 -6.947847e+00\n",
       "4    log   p0 -4.070879e+01 -8.063180e+01 -7.857794e-01\n",
       "5    log   p1  3.399185e-08 -1.370368e-08  8.168737e-08\n",
       "6    log   p2  1.507243e+01  6.671581e+00  2.347327e+01\n",
       "7    log   p3 -1.236510e+01 -1.864359e+01 -6.086617e+00"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nr of parameters\n",
    "d = thetas.shape[-1]\n",
    "\n",
    "# present table of results\n",
    "df_theta_CI_wald = pd.DataFrame({\n",
    "    \"scale\" :[lab for lab in scales for _ in range(d)],\n",
    "    \"coef\" : [ f\"p{i}\" for _ in scales for i in range(d)],\n",
    "    \"value\" : thetas.flatten(), \n",
    "    \"LCL\" : theta_CIs[:,0].flatten(), \n",
    "    \"UCL\": theta_CIs[:,1].flatten()\n",
    "})\n",
    "\n",
    "df_theta_CI_wald"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2f192e86182d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "LLF",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "AIC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BIC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "A",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "deviance",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "p-value(deviance_bootstrap)",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "deviance_bootstrap_samples",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "n",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "k",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "dof",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "aeec77cf-8d56-45d0-aaca-361b47ae6b71",
       "rows": [
        [
         "plain",
         "-4.766500575785168",
         "17.533001151570335",
         "25.774773193756012",
         "0.9655172413793104",
         "9.533001151570335",
         "0.22577422577422576",
         "1000",
         "58",
         "4",
         "54"
        ],
        [
         "log",
         "-4.602970217828536",
         "17.20594043565707",
         "25.44771247784275",
         "0.9482758620689655",
         "9.205940435657071",
         "0.18681318681318682",
         "1000",
         "58",
         "4",
         "54"
        ]
       ],
       "shape": {
        "columns": 10,
        "rows": 2
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LLF</th>\n",
       "      <th>AIC</th>\n",
       "      <th>BIC</th>\n",
       "      <th>A</th>\n",
       "      <th>deviance</th>\n",
       "      <th>p-value(deviance_bootstrap)</th>\n",
       "      <th>deviance_bootstrap_samples</th>\n",
       "      <th>n</th>\n",
       "      <th>k</th>\n",
       "      <th>dof</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scale</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>plain</th>\n",
       "      <td>-4.766501</td>\n",
       "      <td>17.533001</td>\n",
       "      <td>25.774773</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>9.533001</td>\n",
       "      <td>0.225774</td>\n",
       "      <td>1000</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log</th>\n",
       "      <td>-4.602970</td>\n",
       "      <td>17.205940</td>\n",
       "      <td>25.447712</td>\n",
       "      <td>0.948276</td>\n",
       "      <td>9.205940</td>\n",
       "      <td>0.186813</td>\n",
       "      <td>1000</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LLF        AIC        BIC         A  deviance  \\\n",
       "scale                                                       \n",
       "plain -4.766501  17.533001  25.774773  0.965517  9.533001   \n",
       "log   -4.602970  17.205940  25.447712  0.948276  9.205940   \n",
       "\n",
       "       p-value(deviance_bootstrap)  deviance_bootstrap_samples   n  k  dof  \n",
       "scale                                                                       \n",
       "plain                     0.225774                        1000  58  4   54  \n",
       "log                       0.186813                        1000  58  4   54  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# goodness of fit measures\n",
    "pd.DataFrame(\n",
    "    [logit.goodness_of_fit(x, y, theta, bootstrap_seed=random_seed)\n",
    "     for x, theta in zip(xs, thetas)], \n",
    "    index = pd.Index(scales, name = 'scale')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad4c94ba24a",
   "metadata": {},
   "source": [
    "### 4.3 Fitted risk curve and asymptotic confidence bands\n",
    "\n",
    "**Coverage note.** The displayed limits are pointwise confidence intervals evaluated separately at each predictor value; they are not simultaneous confidence bands for the complete curve.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "91ed23f07d87",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABHIAAAHaCAYAAABsPG7IAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAA9X9JREFUeJzs3XecXHW9//HXmbK992x2k930ShISAkmAgAKhSFGUYqGoCCoicv0JqHQFRfFiAZErggUB9SoiKEi9KCA9KIQWSAKkl92dnV7O9/fH2V2yyZaZ3Slb3k8fa9iZUz7b5nznc77fz8cyxhhERERERERERGTEc+U6ABERERERERERSY4SOSIiIiIiIiIio4QSOSIiIiIiIiIio4QSOSIiIiIiIiIio4QSOSIiIiIiIiIio4QSOSIiIiIiIiIio4QSOSIiIiIiIiIio4QSOSIiIiIiIiIio4QSOSIiIiIiIiIio4QSOTIuHHLIIRxyyCG5DmNQZ5xxBiUlJWk/7v3338/ChQspKCjAsiza29vTfo7x7IwzzqClpSUn57Ysi8svvzzl/R577DEsy+Kxxx5Le0wiIiORxgIaC2RSuscCl19+OZZlpe14IjK2KJEjMsbt3LmTk046icLCQm644QZ+/etfU1xcnJVz33jjjViWxf7779/vNpZl9ftxzjnnZCVOERGRsUxjARGRscWT6wBEJLOeffZZOjs7ueqqqzjssMOyeu7bb7+dlpYWnnnmGdauXcu0adP63O7www/ntNNO2+vxGTNmZDrEcevggw8mFAqRl5eX61BERCTDNBYQERlblMgRyaBwOExeXh4uV+4mv23btg2AioqKtB0zEAgMeidv3bp1PPnkk/zxj3/k7LPP5vbbb+eyyy7rc9sZM2bwyU9+Mm3xyeBcLhcFBQW5DkNEZMzTWEBjARGRdNPSKsmYjRs38pnPfIbGxkby8/NpbW3l85//PNFotGebt99+m4997GNUVVVRVFTEAQccwH333dfrON21PH73u9/x7W9/m6amJgoKCvjgBz/I2rVr9zrvzTffzNSpUyksLGTp0qX84x//GNbXccghhzBv3jyef/55li9fTmFhIa2trdx00019xnnnnXfyzW9+k4kTJ1JUVITP5wPg97//PYsXL6awsJCamho++clPsnHjxj7P+fbbb7Nq1SqKi4tpbGzkyiuvxBgzpNhPP/10APbbbz8sy+KMM87oeT6ZmLrX6r/11lscffTRlJaW8olPfGLQc99+++1UVlZyzDHH8NGPfpTbb7895fh3FwwGee2119ixY8eg27755puceOKJNDQ0UFBQQFNTE6eccgodHR29tvvNb37D0qVLKSoqorKykoMPPpi///3vPc//+c9/5phjjun5HZ46dSpXXXUViURi0Bhs2+b6669n7ty5FBQUUF9fz9lnn01bW9ug+3Z/z4fye7Bhwwa+8IUvMHPmTAoLC6muruZjH/sY69ev77VdXzVyun/X16xZw6GHHkpRURETJ07k2muvHTRmEZG+aCygsYDGAkMbC/QlHo9z1VVXMXXqVPLz82lpaeHrX/86kUhkr/NefvnlNDY2UlRUxKGHHsqaNWtoaWnp9bMXkdFLM3IkIzZt2sTSpUtpb2/nc5/7HLNmzWLjxo384Q9/IBgMkpeXx9atW1m+fDnBYJDzzjuP6upqfvnLX3Lcccfxhz/8gQ9/+MO9jvmd73wHl8vFV7/6VTo6Orj22mv5xCc+wdNPP92zzS233MLZZ5/N8uXLOf/883n77bc57rjjqKqqorm5echfT1tbG0cffTQnnXQSp556Kr/73e/4/Oc/T15eHp/+9Kd7bXvVVVeRl5fHV7/6VSKRCHl5edx2222ceeaZ7LffflxzzTVs3bqVH/7whzzxxBO8+OKLve6QJRIJjjzySA444ACuvfZa7r//fi677DLi8ThXXnllSnF/4xvfYObMmdx8881ceeWVtLa2MnXqVICUYorH46xatYoDDzyQ73//+xQVFQ167ttvv52PfOQj5OXlceqpp/LTn/6UZ599lv3222+vbcPhcJ+DsrKysp6lP8888wyHHnool1122YDFfaPRKKtWrSISifClL32JhoYGNm7cyL333kt7ezvl5eUAXHHFFVx++eUsX76cK6+8kry8PJ5++mkeeeQRjjjiiJ7vUUlJCRdccAElJSU88sgjXHrppfh8Pr73ve8N+PWfffbZPd/j8847j3Xr1vGTn/yEF198kSeeeAKv1zvg/kP9PXj22Wd58sknOeWUU2hqamL9+vX89Kc/5ZBDDmHNmjWD/uza2to48sgj+chHPsJJJ53EH/7wBy688ELmz5/PUUcdNeC+IiK701hAYwGNBYY3FtjTZz/7WX75y1/y0Y9+lP/6r//i6aef5pprruHVV1/lT3/6U892F198Mddeey3HHnssq1at4qWXXmLVqlWEw+GUziciI5gRyYDTTjvNuFwu8+yzz+71nG3bxhhjzj//fAOYf/zjHz3PdXZ2mtbWVtPS0mISiYQxxphHH33UAGb27NkmEon0bPvDH/7QAOY///mPMcaYaDRq6urqzMKFC3ttd/PNNxvArFy5ckhfy8qVKw1grrvuup7HIpGIWbhwoamrqzPRaLRXnFOmTDHBYLBn2+645s2bZ0KhUM/j9957rwHMpZde2vPY6aefbgDzpS99qdf365hjjjF5eXlm+/btKcd/6623GqDXz2IoMV100UVJn/O5554zgHnwwQd7voampibz5S9/ea9tgX4/7rjjjp7tur+/l1122YDnfvHFFw1gfv/73/e7zZtvvmlcLpf58Ic/3PN71q3799MY0+vn2O3ss882RUVFJhwO9zx2+umnm8mTJ/d8/o9//MMA5vbbb++17/3339/n43tK5fdgz+9JXzE/9dRTBjC/+tWveh7r/n4++uijPY91/67vvl0kEjENDQ3mxBNPHDBmEZE9aSygsYDGAkMfC1x22WVm97dqq1evNoD57Gc/22u7r371qwYwjzzyiDHGmC1bthiPx2NOOOGEXttdfvnlBjCnn376gOcVkdFBS6sk7Wzb5u677+bYY49lyZIlez3f3Urxr3/9K0uXLuXAAw/sea6kpITPfe5zrF+/njVr1vTa78wzz+xVmPWggw4CnKnHAM899xzbtm3jnHPO6bXdGWec0XPnZag8Hg9nn312z+d5eXmcffbZbNu2jeeff77XtqeffjqFhYU9n3fH9YUvfKFXTZJjjjmGWbNm7TV9HODcc8/t+W/Lsjj33HOJRqM89NBDw/o6hhPT5z//+aSPf/vtt1NfX8+hhx4KOF/DySefzJ133tnnVOTjjz+eBx98cK+P7v3BmRpujBm01Xb3z/qBBx4gGAz2uc3dd9+Nbdtceumle9Us2L3V5+4/x87OTnbs2MFBBx3UM7W7P7///e8pLy/n8MMPZ8eOHT0fixcvpqSkhEcffXTAr6HbUH4Pdo85Fouxc+dOpk2bRkVFBS+88MKg5ywpKelVoyAvL4+lS5f2/J2JiCRDYwGNBTQWSM9YoNtf//pXAC644IJej//Xf/0XQM/P6+GHHyYej/OFL3yh13Zf+tKXUjqfiIxsSuRI2m3fvh2fz8e8efMG3G7Dhg3MnDlzr8dnz57d8/zuJk2a1OvzyspKgJ51xt3bT58+vdd2Xq+XKVOmpPAV7K2xsXGvgn7dXRT2rD3S2tra6/PuuPr6WmfNmrXX1+lyufaKt79zDVWqMXk8HpqampI6diKR4M477+TQQw9l3bp1rF27lrVr17L//vuzdetWHn744b32aWpq4rDDDtvro76+PuWvrbW1lQsuuICf//zn1NTUsGrVKm644YZea+LfeustXC4Xc+bMGfBYr7zyCh/+8IcpLy+nrKyM2traniTHnmvsd/fmm2/S0dFBXV0dtbW1vT78fn9P0cmBDPX3IBQKcemll9Lc3Ex+fj41NTXU1tbS3t4+YMzdmpqaeg1gwflbG+p6fhEZnzQW0FhAY4HhjwV2t2HDBlwu115dvxoaGqioqOj5eXX/u+d2VVVVPX8vIjL6qUaOjBput7vPx80QCv9l0u53bsaK/Pz8pLttPPLII2zevJk777yTO++8c6/nb7/99p5155ly3XXXccYZZ/DnP/+Zv//975x33nlcc801/Otf/0p6ENre3s7KlSspKyvjyiuvZOrUqRQUFPDCCy9w4YUXYtt2v/vatk1dXV2/RR1ra2uH9HUl40tf+hK33nor559/PsuWLaO8vBzLsjjllFMGjLnbaPk7E5HxabS8RmksoLFApsYCe95sEZHxSYkcSbva2lrKysp4+eWXB9xu8uTJvP7663s93j1NdfLkySmdt3v7N998kw984AM9j8diMdatW8eCBQtSOt7uNm3atFebzTfeeAOAlpaWpOJ6/fXXe8XV/dieX6dt27z99ts9d95SOVeyUo0pFbfffjt1dXXccMMNez33xz/+kT/96U/cdNNNGR/kzp8/n/nz5/PNb36TJ598khUrVnDTTTfxrW99i6lTp2LbNmvWrGHhwoV97v/YY4+xc+dO/vjHP3LwwQf3PL5u3bpBzz116lQeeughVqxYMeSvc6i/B3/4wx84/fTTue6663oeC4fDtLe3DykOEZGh0Fig77g0FtBYYKgmT56Mbdu8+eabPTPWALZu3Up7e3vPz6v737Vr1/aaGbZz507NrhUZQ7S0StLO5XJxwgkn8Je//IXnnntur+e775odffTRPPPMMzz11FM9zwUCAW6++WZaWloGneq6pyVLllBbW8tNN93Uq63pbbfd1ueb2Ndee4133nknqWPH43F+9rOf9XwejUb52c9+Rm1tLYsXLx40rrq6Om666aZe7SH/9re/8eqrr3LMMcfstc9PfvKTnv82xvCTn/wEr9fLBz/4wZ7H33rrLd56662k4k9HTMkIhUL88Y9/5EMf+hAf/ehH9/o499xz6ezs5J577kn52Mm2HPX5fMTj8V6PzZ8/H5fL1fO1nnDCCbhcLq688sq97qZ1/3523/Xd/S5vNBrlxhtvHDTWk046iUQiwVVXXbXXc/F4POmkSjK/B3tyu9173Zn+8Y9/nFSbVBGRdNFYYO+4NBbQWKBbKmOBbkcffTQA119/fa/Hf/CDHwD0/Lw++MEP4vF4+OlPf9pru91/n0Rk9NOMHMmIq6++mr///e+sXLmSz33uc8yePZvNmzfz+9//nn/+859UVFRw0UUXcccdd3DUUUdx3nnnUVVVxS9/+UvWrVvH//7v/yY9fbeb1+vlW9/6FmeffTYf+MAHOPnkk1m3bh233nprn+viZ8+ezcqVK3nssccGPXZjYyPf/e53Wb9+PTNmzOCuu+5i9erV3HzzzYO2jvR6vXz3u9/lzDPPZOXKlZx66qk97T1bWlr4yle+0mv7goIC7r//fk4//XT2339//va3v3Hffffx9a9/vdc03O6B3FDWyqcaU7LuueceOjs7Oe644/p8/oADDqC2tpbbb7+dk08+uefxN954g9/85jd7bV9fX8/hhx8OJN9y9JFHHuHcc8/lYx/7GDNmzCAej/PrX/8at9vNiSeeCDjrxr/xjW9w1VVXcdBBB/GRj3yE/Px8nn32WRobG7nmmmtYvnw5lZWVnH766Zx33nlYlsWvf/3rpKbvr1y5krPPPptrrrmG1atXc8QRR+D1ennzzTf5/e9/zw9/+EM++tGPDniMZH8P9vShD32IX//615SXlzNnzhyeeuopHnroIaqrqweNW0QknTQW6B2XxgIOjQWSHwvsbsGCBZx++uncfPPNPUu+nnnmGX75y19ywgkn9BSFrq+v58tf/jLXXXcdxx13HEceeSQvvfQSf/vb36ipqdHSLJGxIut9smTc2LBhgznttNNMbW2tyc/PN1OmTDFf/OIXe7UDfeutt8xHP/pRU1FRYQoKCszSpUvNvffe2+s43a0m92whuW7dOgOYW2+9tdfjN954o2ltbTX5+flmyZIl5vHHHzcrV67cq+UoSbYhXblypZk7d6557rnnzLJly0xBQYGZPHmy+clPfpJUnN3uuusus2jRIpOfn2+qqqrMJz7xCfPee+/12ub00083xcXF5q233jJHHHGEKSoqMvX19eayyy7bqzXm5MmTe7W57E9fLUeHElMyjj32WFNQUGACgUC/25xxxhnG6/WaHTt2GGMGbjm6+88n2Zajb7/9tvn0pz9tpk6dagoKCkxVVZU59NBDzUMPPbTXtr/4xS96vv7KykqzcuXKnjapxhjzxBNPmAMOOMAUFhaaxsZG87Wvfc088MADe7Xt3rPlaLebb77ZLF682BQWFprS0lIzf/5887Wvfc1s2rRpwK8hld+DPb8nbW1t5swzzzQ1NTWmpKTErFq1yrz22mtm8uTJvVqO9td+fO7cuX3Gk8zvmojInjQW6E1jAYfGAoOPBfZsP26MMbFYzFxxxRWmtbXVeL1e09zcbC6++OJebdCNMSYej5tLLrnENDQ0mMLCQvOBD3zAvPrqq6a6utqcc845A55XREYHy5gRVh1OZIQ55JBD2LFjx6Dr/EXS5YwzzuAPf/gDfr8/16GIiAgaC8jo197eTmVlJd/61rf4xje+ketwRGSYVCNHRERERERkjAiFQns91l1b55BDDsluMCKSEaqRIyIiIiIiMkbcdddd3HbbbRx99NGUlJTwz3/+kzvuuIMjjjiCFStW5Do8EUkDJXJERERERETGiH322QePx8O1116Lz+frKYD8rW99K9ehiUiaqEaOiIiIiIiIiMgooRo5IiIiIiIiIiKjhBI5IiIiIiIiIiKjxLiskWPbNps2baK0tBTLsnIdjoiICMYYOjs7aWxsxOXSfZbh0rVeRERGGl3rJV3GZSJn06ZNNDc35zoMERGRvbz77rs0NTXlOoxRT9d6EREZqXStl+Eal4mc0tJSwPkDKisry3E0IiIi4PP5aG5u7rlGyfDoWi8iIiONrvWSLuMykdM9xbqsrEyDOxERGVG0DCg9dK0XEZGRStd6GS4tzBMRERERERERGSWUyBERERERERERGSWUyBERERERERERGSWUyBERERERERERGSWUyBERERERERERGSWUyBERERERERERGSWUyBERERERERERGSWUyBERERERERERGSWUyBERERERERERGSU8uQ5AZESxE7DhSfBvhZJ6mLwcXO5cR/U+OwHr/wnr/gEWMPlAaD1o4BhH+tckMhT6vRYRSd1oee1MdbwzWr4uSZ1+tiJ9ynki5/HHH+d73/sezz//PJs3b+ZPf/oTJ5xwwoD7PPbYY1xwwQW88sorNDc3881vfpMzzjgjK/HKGLbmHrj/QvBtev+xskY48rsw57jcxdVtzT3wly9DaNduD34PCivh2B/1HeNI/5pEhkK/16OOrvUiI8Boee1MdbwzWr4uSZ1+tiL9yvnSqkAgwIIFC7jhhhuS2n7dunUcc8wxHHrooaxevZrzzz+fz372szzwwAMZjlTGtDX3wO9O632hAPBtdh5fc09u4uq25h743af2GNR0CbU5z+0Z40j/mkSGQr/Xo5Ku9SI5NlpeO1Md74yWr0tSp5+tyIAsY4zJdRDdLMsa9C7dhRdeyH333cfLL7/c89gpp5xCe3s7999/f1Ln8fl8lJeX09HRQVlZ2XDDltHOTsD18/a+UPSwnOz/+f/JzVTOQePrUtoIX3nZiXGkf00iQzHGf6/Hy7VJ13qRLBstr52pjndgdHxdkrrR8js7BLo2SbrkfGlVqp566ikOO+ywXo+tWrWK888/v999IpEIkUik53Ofz5ep8GQ02vDkIIMGA76NznatB2UtrB6Dxtelc9P7MY70r2mUMcawMxBl/Y4A77WFaAtGaQ/GaA9G6QzHicRtIvEEkbhNNG6TsA0JY7C7/jUG56PrWDJE0QC0nTfwNtuB7/8d8oqzEhLA14+ezcEzarN2vvFA13qRNBotY4JUxzswOr6uUSQcS7ClI8ymjhBbOsJs9UXoDMfwR+L4I3GCkQSxhE00YRNL2MQTXeMdA7ZtsLvHPF3HG/KYZwRe71fNbeArh8/IyrlEkjHqEjlbtmyhvr6+12P19fX4fD5CoRCFhYV77XPNNddwxRVXZCtE8Plg2zbYsQO2b4e2NggGIRRyPiIRSCTe/+h5l9f1sbu+HpP02vEmrA0Pvt3a70DN/6Z06A0uHzfl/YeQFR9icECoHdr9yW37w9OgsCL5fbq3l14SxsNOWtjKNHYxmU5qiLP3a4vkwqTBN9llA50Zj6Tb4w/dwcEzBhlwSkpGxbVexr5oFAKB9z/CYeexSMT5NxZzPuJx599E4v3/3v3zRAJs2/nYfezX/Vj3WG/3/+7rA5xtunU/Ntg4cccb8OYA45zu3VMc5ySw+WH+at5xpen1NtXxDmisMwzGgI96djGJNppoZyI+6hgBlTe6jKzrfVXkbVAiR0aQUZfIGYqLL76YCy64oOdzn89Hc3Nz+k/U1gZ/+xs8/jh0djoXfMtynvN4wO12Plwu5/HuD3j/3z3/WzIvZkHUTm67QCClQ7+Wv5nXvNupSeQPMTiABFhJxNe9bTyYwj7d24vBYjtzeM+1nDamYVvePTawKaCdQnaSZwJ4CeAliJsQUTtI0PjxmBilxkWB5Sbfsshzech3eXC5XFiWC8tl4bVcuLBwY2FZFi6XGwsLy+UCCyycf7s+oev/HOP9tSHcAVv/M/h29fOhoDzz8QBul5uTDjsrK+eSgWXtWi9jT2cnvPsubN0Ku3bBpk2wcaNzY647YRONOkkZ2957zLZ7omX38V1/472+Pu/+792P29d/9/V5X3bfJgJE0j/Oed3TxvP5m8hPuHCbdFyfUhzvgMY6QxCmjC3WYjZbS/BbE/Z63m0i5NNOAR3kmw48hPAQxkMEmzBBV4gYUQqBEtuiwLjIwyLPQB4WFs7IxWXAY8AVi+OKJXDFYliWheX2gNuF5fZgudzgdjl/K66u90guy5mRs3OtE1Bfv1rdj9XPhoIy54E+/472+o89jpPc722eN59jDv1YUtuKZMuoS+Q0NDSwdevWXo9t3bqVsrKyPu/QAeTn55OfP5w30oOIxeCf/4R77oH166GuDpqaoKBAb7xGg8YJ0P4viPh5/7bU7izIL4VZ+4KV2l2KN3iPPIqZyHCWXRjw74B4ZODN3PlQ0YpzsereJ0q/X5Mnb7ftxy/bWKyzZ/Gf+P60m/d/TkV00uheT6NrA5XWdkqtdjw9M6sMJhxmS3gH21whKlxFNBfUsrhxCdMn70tdw1QqapvxVNdCcbFeB9KlZ838Zvr9vS5rhPNvGXVr5qW3EXmtl7EjHofXXnv/4913oaPDGc9ZlnPzragI8vOd13Cv9/2P3RMvo0WGxjn/YhthvEynfvCNk5LqeAeNdVLQYVfyXHwl79lTMV2zblzEaXC9R421mRrXZmpcWygksNeveJg479BOHMM8SplLLdOpooUK6imhEI+TwjHGSYC2tYHf7/ytFJdBSQm0NkNLC1RVQXm581FW5vyNFRY6f1/dkr7e/1LXexm3Rl0iZ9myZfz1r3/t9diDDz7IsmXLchNQOAzXXw/PP++8CM2b58y6kdHDcsG0w+GVP9GTBHn/SeefaYelnMQJE+d1dlJBwXADhKopsO3VgTernsL7A5XufV6j36+pavftx6ctdhNPxI6k01QC4CXCLPeLTHGvocLaufdY3Rjo9OGL+NhQFKeyuo6Pz/4QyxceS2PrAiz97WeWy+20HP3dafT7e33kdzSoGwNG3LVeRj9jnJk2q1c7N9/Wr3eWSBUVOW8oW1udxM1YlIFxzk6CPM171JHO+iRDGO9orDOomPHy7/gBvJLYDxvn+lhnvcc09ytMdr9OvtV/4ixKgg20EyXBLGo4iuksoZE8drvOGuMkbbZscd4XlZZCQwMsWADTpsHEiVBb2ztRMxhd70UGlfNEjt/vZ+3atT2fr1u3jtWrV1NVVcWkSZO4+OKL2bhxI7/61a8AOOecc/jJT37C1772NT796U/zyCOP8Lvf/Y777rsvN1/Arl3w6qvQ2AiVlbmJQYavdibM/TCsfRAiu621zS91Bje1M1M+5Lt00EaIiaShIn1RDdTNhh1rwY71fs7lhZppzjZ77TMLdr3d++6WJ88Z2Oy5/ThiDPw7cQCr4yswuMgnyBzP88xyv9j/gCYcxnS0sbbcYDdPYOXMD3L8sjOZXNmS1djHvTnHwUm/gvsv7F3ksqzRGdTNOS53sUm/Rv21XkY3vx/+8hd46CFnpkBJCTQ3O0mc8SLN45zn2cx2gsyjLr1xpjre0VhnQO8kpvJ07DACXWPRJtdbLPE8RoWrj/buewgS4012MZsajmIa+9PUO4Fj27B5M+zc6cyqmTEDVqyAWbOc90WuYdba0fVeZEA5T+Q899xzHHrooT2fd69vP/3007ntttvYvHkz77zzTs/zra2t3HfffXzlK1/hhz/8IU1NTfz85z9n1apVWY+9l1SyzDIy1c6EmunQ/q6zNjevGCqaU56J020DHYSIU5iuP7OiGphU7dQJCXU4NyTyy6GwnH7vNhXVQFHXPokYuL1dtUPG792psCnk8dgxbLKdadlTXS9zgPchvFas7x0SCWhvw1jw5uQyKqbO5fRln2e/ifvhGuLvhgzTnONg1jFOJxL/Viiph8nLdWduBBsz13oZXYyB//wH7rrLuenW0ADz54++pVHpkqZxThybx9lAMV5cmRhPpDre0VinTy/Hl/Bc3HndLbHaWep5hGbXW0n9+vuJ8hZtrKCZz7GYMnabrWaM09RlyxaYMAFOOgkWL4apU4efvNmTrvci/bLMOOyF6/P5KC8vp6Ojg7KyYc6W2LQJLr7YmTJYUpKeAGVMuJFneYz1zBlWfRxJpw67kgeiJxOkFDcxDvA8yHTPK/3vEI1C2y5MbQ1vTyqnoK6RLy79IgsbFmYtZhk/0nptEn0/x7twGP73f+GBB5zX8qlTddMtTf7NVq7hHzRTThH6no40xsBz8UN4JbEfALPcL7DE83+71fkbmI8I62nnUFr4NIsoJu/9J9vb4Z13nDo3hx4KRxzhvAeSpOnaJOmS8xk5ImNRlASvsYNyxuh6+1EoYEr4e/RjBCml3NrJId57qHTt6H+HcBh8HdDSyjtTqvF4vXxm388oiSMiMtIlEvDb38J99zmzcPRGM62e4l1i2ErijEAJ4+KJ2FG8bc8BYInnMea6n016EloHYd7FxyqmcjoLKeh+q2gMbNjg1JU6/HA45hiYlER7cBHJGCVyRDLgPXzsIsQENEtrJIiYAh6MfowA5ZRZuzgq7w4KrFD/OwQDTgvWadPZ1FpD3MT47MIzOKDpgOwFLSIiqTMG7r0X7r/fqYNTUZHriMaUbQR4jk1pLnIs6WAbi8dix/OuPQ2LBAd672eqe03S+yewWU8HRzGNM1mIt7seTjQKb7zhJETPPBMOOij9S6hEJGVK5IhkwHraCRHT3aoRIGa8PBQ9kXZTQxGdHJH3+4GTOH6/Mxtn9hwiM6ayq/0tzlh4BgdPPjh7QYuIyNA88QT84Q/O0g8lcdLueTaxk1D6ixzLsK2Or+BdexpuYhzqvZsm9/qU9t9ABy2UcyKz30/idHQ4Hd722QdOPx2mTEl73CIyNErkiGTAWnbhxoU1zgvt5Zpzd+o4tptG8ghxeN7vKbF8/e8QiUAoCPP3gZkzWbfzdebWzuXwKYdjjdfimCIio8Urr8Avf+nMFmhoyHU0Y9JzbKIoU0WOZcjWJ2bw78QyAFZ4H0g5ieMnSoQEH2E21XR1c2tvh3ffhaOPhlNOcdqKi8iIoXlxImkWx+ZVdvSu8C858UpiPzbaU/AQ5bC8P1Lp2tn/xrbTnYqWVpgxA1+0E5fl4riZx5Hv0c9SRGREa2uDW26Bzk5oacl1NGNShDib8VO6e/Fbybk2u5p/xo4CYK77Waa4X01pf4NhHW0cwESW0+w82NHxfhLnjDOUxBEZgZTIEUmzjfjYSZAKCnIdyrjWZtfwYnwFAPt7HqbOtWmArQ3s3Am1dTB/Psay2NCxgaUTl7JowqLsBCwiIkP3+OPOEpDp08dve/EM206QAFEtGx9BIiafR2MnECePCa4NLPb8X8rH2EgntRTzUebgxuUkcd55B446Cj71KXV7ExmhlMgRSbP1tBMgSrEGOjmTMC4ejx2DjYdm11qmuV8eeIf2DigsgoULoaCA7cHtlOeXc/zM43FZepkUERnR2trgwQehshLc7lxHM2ZtJ0BQ9f9GlCdjq/CZKorpYKX3L7gsk9L+YeK0E+ZDTKeZcmdG2zvvwKpVcNppkKfZVyIjld6hiKTZW7R1VcfRHcFceSm+nDZTRz5Blnn/PvDN2XAIEnGYNw+qqkjYCTb7N3NY62G0VrZmLWYRERmixx+HjRuhsTHXkYxp2wliwJm1ITn3bmIqG+yZWCQ4NO/PAzdy6Md7+JhFDYcxFWIxePtt+OAHncLGSuKIjGh6JRZJowQ2r7Bd9XFyaLs9gf8k9gdgmfdBiqzAAFsbZwpxSytMngzAe773aC5rZtW0VVmIVkREhqV7Nk5VlWbjZNhW/LkOQbrEjJd/xT4IwFz3c9S4tqZ8jAQ2IWIcQgsFxg2vv+7c1PrEJyBf41iRkU6JHJE02kqAnQQpV32cnEgYF/+IHYXBxRTXGlrcbwy8Q6cfiktgxgywLIwxdEQ6OHzK4VQXVWcnaBERGTrNxsmadbRrWdUI8VJ8GQHKKaaDBZ6nhnSMbQSoo5jFTHCWU9XUOIWNy8vTG6yIZIQSOSJptJOg1o/n0GuJRfhMNQUE2N/70MAb2zYEgzB1ak83hrZwGxUFFew7Yd8sRCsiIsOi2ThZEyHOFvwa34wAbXYNrySWAHCA92G8VizlYxgM2wmyjGYq28IQDjstxqdOTXe4IpIhSuSIpFE7YWwMHv1pZV3E5PPv+DIA9vX8k3wrMvAOvg6oKIfW9+vgbPVvZW7tXBpLdWdXRGTE02ycrNnRdaNKjRxyyxh4KnY4BjeTXG/Q7H5rSMfp7GrKcUC0zmkzfvjhcPDBaY5WRDJJ7zZF0qiNcK5DGLf+HT+ACIVUWDuY5v7PwBsn4hCNwfQZUOAsg4slYtjG5oCmA7DUulZEZGQLh+Hhh9WpKkvUenxkeDMxn22mCQ9R9vc+MuTjbKKT2dQy7a022GcfOPlkcOltochoor9YkTTaTkC9qnKg0y7j1YSzHGqJ57HB22+2t0NtDUya1PPQtsA26kvqWdCwIIORiohIWrz9NmzfDnV1uY5kXNhOAINRx6ocihsPq+MrAFjoeZJiq3NIx4mRII7NwZ1VuPLy4SMfgeLidIYqIlmgV2ORNNqCnwI8uQ5j3HkhfhA2Hia4NjDRtW7gjWNRMMCMmeB5/2e1K7SL/SfuT0leSWaDFRGR4Vu7FiKRnlmVkllOxyrdqsql1xMLCFJKMT5mu18Y8nG24GeiKWHR+jAcdBDMn5/GKEUkW5TIEUkTG8NWAhRq2nFW7bAbWGfPAQxLPI8x6KqoDh/U18OECT0PBaIBCjwFLGlcktFYRUQkDYyB1auhqCjXkYwb6+mgUDeqciZmvPwnvj8ACzxP4rYSQzqOwbCLEAftLKa4vhmOP57BB04iMhIpkSOSJj4ihIhpRk6WPRdfCcBU1ytUu7YNvHEiAcaGlpZea8G3+LfQWtnKjOoZGYxURETSYvt22LDB6VYlGRclwWY6KSYv16GMW68m9iVMMaVWG9Pcrwz5OB1EKE94WbqzEI491rmxJSKjkhI5ImnSRogQcSVysmiL3cQWexIu4uzr/efgO3R2Qnk5NDT0PGQbm0AswIGTDsTtUsFMEZERb+1a6OiAiopcRzIu7CBIgJgKHedI1OTxcnw/wKmN47LsIR9rB0Fa2wzN0xfDoYemK0QRyQElckTSpJ0wESVysurf8QMAmOZ+efCif8Y49RRaWsH7/mB0V2gXlQWVLGxYmMFIRUQkbV57zVkOoi47WbGdgDpW5dAr8SVEu7pytrpeHdaxQrEgi6LVWB85UfWlREY5XQFF0qSNMAZwqRhgVuywG9hkt2JhM9/9zOA7BANOPYWJE3sfJ7iDuXVzaShp6GdHEREZMaJReOklZ3alZMV2ghjAo7cNWRc2BaxJOPX7FnqeGLwr5wBCxMgPRpkxYxksUIdOkdFOr8giadJOONchjCsvdc3GmeJaQ6mrY/AdAgFoaurVYtMYQywRY36dOjaIiIwK69fDjh2qj5NFWwngtHuUbFsTX0KMfKqsrUx2vTGsY+2MdVBrCplyxMkqcCwyBiiRI5ImW/Hj1p9UVrTZNbxrTwcM8z1PD75DJOIsp2pu7vVwIBagyFvEtKppmQlURETSa+1aCIWgsDDXkYwb62lXR84ciBkvryUWAbDA89Swcy/toXYW1i0gb55m44iMBXrXKZImm/GrNWeW/LurBWeL63UqXLsG36HTB7V1UF3d6+FdoV3UldTRXN7cz44iIjKivPSSU9tDMwqyItbVsUr1cbLvjcQ+RCmgzNrJJNebwzpWIhoGy2L2AR9SbSmRMUJ/ySJpECPBToIqdJwFPruC9fYsgORm4yQSzozwyZP3Gvj7Ij4W1C/A49LPTURkxNu1C95+W8uqsmgHQfxE1Xo8yxLGxStxpzbOPPezw85btgV2UlnewIylR6UhOhEZCZTIEUmDdsJqPZ4l/0nsj8FFk2st1a5tg+8Q8ENZaa+W4wAJOwHAzOqZmQhTRETSbe1aaG+HyspcRzJubCdIUK3Hs26dPZsgZRTiZ6p7zfAOFoux0x1h+swDqCqpTU+AIpJzSuSIpEE7YcJK5GRc0BTzVmIuAPskMxsHIBSGiU29Wo4DtIfbqSioYGrV1HSHKSIimfDaa2AMuN25jmTc2E4AG1sdq7LIGHg5vh8AczzP47YSwzuer51IeTELFmo2jshYoldlkTRoI0yUBPlocJlJr8UXYeOmznqPOtemwXeIRcHr2Ws2DkBbuI2WihaqC6v72FFEREYUY2DNGigtzXUk44rTsUr1iLJpo91Ku6nFS4QZ7peGd7BEHD8xiidMZnrdrPQEKCIjghI5ImnQ3Xrc0mAnY5zuDQsBmOt5Lrmd/AEoL++znkIwFmRB/QIsFcwUERn5AgFoa4Pi4lxHMq5solM3qbLsP/GlAMxwv0S+FRnewTr97CrPY8KkuUwun5yG6ERkpFAiRyQNdhHKdQhj3trEPKIUUmq10exam8QeBqJRaGreq0NDOB4m352vZVUiIqPFjh0QDEJRUa4jGTcMhq34tWw8i7bbDWw1k3CRYI7n+WEezUAkgq+mjEVNS3C7lJATGUuUyBFJgy348erPKWNsY/FKwuneMNf9HC7LDL5TJAL5eVBfv9dTbaE2qouqaa1oTXeoIiKSCTt2QCjktB6XrAgSw0dEiZws6u5U1ep6lWLLP7yDhULECvLwVFUzq1rLqkTGGr3zFEmDLfgpVEeHjHnHno7fVJBPkGnul5PbyR+AyipnadUe2sJtzKubR6G3MM2RiohIRuzY4fzr0tA1W9TIIbv8ppQNttNJM+kl5AMeMEBHbSnlFQ20VurGlchYo6uhyDCFiNFBWAOdDNm9e8Ms92o8Vjy5nRJxaGqCPWrgGGNI2Alm1ejulIjIqLF1616v55JZbV2JHN2oyo7X4vticNHg2kCVa/vwDpZwxkodtaVMLGuiPH/vm1oiMropkSMyTO2ECemOVcZsMxPZYRpxEWeW58Xkdgp3Tb/vY1lVZ7ST0vxSplaqPo6IyKjxzjtQqFmU2dROmARGrcezIGa8vJ7YB4C57uHWxgH8figvI1RSwOza2WrsIDIG6ZVZZJjev2OlRE4mvNI1G2eq+xUKrWByOwUCUFvXZ3eTtlAb9cX1TCybmM4wRUQkU2Ix2LxZhY6zrE2NHLJmbWIeMQoos3bR5HprmEczEI5gNzWB282k8klpiVFERhYlckSGqZ0wcWzdscoAn13BO/Y0IIU7VMYG20BjY5/T8P1RP7NrZ+Oy9PMSERkVdu5Ux6oc2EEQzePIPGNgTWIxALPdLwx/BWEoDAX5dNaWU5JXQnNZ8/CDFJERR+9kRIap+46VpeFO2jmdqiyaXG9R4dqZ3E7BkDP9vq5ur6eMMdjGZnL55PQGKiIimbN9uxI5ObBZrcez4j17Kp2mkjzCyTd0GIjfD7V1+Lw21UXVNJQ0DP+YIjLiKJEjMky7NPU4I8KmkLWJeQDMdT+b/I6hINTU9FlLIRwPU+ApoKmsKV1hiohIpu3YAYkEeJRUyBYbwzYCSuRkwStds3FmuP+N14oN72B2AjAwaRKdMT+zqmfhdrmHH6SIjDhK5IgM0ybdscqI1xMLSeCl2tpCg+vd5HYyBmwbGvq+++SL+CjLL1N9HBGR0WT7dnWsyrJOIgSIanyTYTvtOrbYk7Gwme15YfgHDAahuBhTW0vCTjClcsrwjykiI5ISOSLDYDBsVSIn7eLGzavxRQDM9Tyb/Pg9Eob8fGdGTh98ER8tFS0UeTU9X0Rk1HjvPcjLy3UU40p3IweNbzJrTdyZjTPZ9TrFVufwDxgMQcMEwi6bfE8+zeWqjyMyVimRIzIMfqK6Y5UBbyfmEKaYYjpocb2R/I7BEFRUQElJn0+H42Fm1sxMT5AiIpJ5tu0kclQfJ6valcjJuKApZp09G4C5nueGf8BEwpm51tCAL+KjPL9cS8lFxjAlckSGoZMoERLko/XH6WIMvJJwWo7P8TyPy7KT3dNpUTuh725VCTuBy3JpUCMiMpp0dIDPp0ROlrURwsbg1luFjHk1vi82buqs96h1bRn+AbuWVVFbS0ekg9bKVs1AFhnD9OosMgydRIgQJ193rNLmPXsKHaYaL2FmuP+d/I7RGHi9/S6r6ox2UppfqkSOiMhosmOHOlblQDthdePMoJjx8npiAeAsIU+LUAgmTACvl0g8wqyaWek5roiMSErkiAxDJ1Fi2Hj1p5QWxsB/4gcAMDPV7g2hIJSWOkur+uCL+KgurKaueO+25CIiMkLt2AGRiFP/TLJmGwFcSuRkzNrEPKIUUmq10ex6a/gHTMTB5SyrittxXC4XzWWqjyMylundp8gwdBLBAt21SpMtdjPbzERcxJmT6nrxSMS5E+Xq+2XNH/Uzs2YmLksveyIio8aOHc5yWXWtyqrN+CnUbOOMsI3Fmq6W43Pcz+OyzPAPGghCcQnU1DgdOvPKVOhYZIzTOxqRYegkmusQxpR/J5zZODPc/6HICiS/YyLuJHBqa/t82hhDwk7QWtGajjBFRCRbNm/uN0EvmRHHZgdBFTrOkHftqXSaSvIIMc39cnoOGg5DYyN4vfgiPupL6qkurE7PsUVkRNKVUWQYOoiQhvsoAmy3G9hst2CRYJ7nmdR2DoacAn9VVX0+HUlEyPfkqz6OiMho8847qo+TZR2ECRFTIidDXok7DR1mul9KbQl5f7qXVdXXA84M5Dk1c7A0i01kTFMiR2QYdhAkTx2r0uLfXbVxprrXUGL5Uts5FIL6BqfYcR98ER9l+WVK5IiIjCbBoLO0SomcrGrraj1eSN/XVBm6bfYEtpkmXCSY7XkhPQcNBKDEWVZljHN7cXLF5PQcW0RGLCVyRIZhJ0G1Hk+DXXYt79rTAcN899Op7Wy62pPX9V/E2BfxMal8EsV5xUMPUkREsmvnTnWsyoF2wkRIaHyTAe/ftHoltSXkAwmHYWITeDyE4iEKPAVMLJuYnmOLyIilRI7IECWwaSesGTlp8O/4/gC0uF6n3NWW2s6hMBQWQHX/a8HD8TAzq2cOJ0QREcm2HTucGZdK5GRVGyFAjRzSbaddx3v2NCzs1G9a9ae7RmDXzazOSCeleaVMKJmQnuOLyIilRI7IEPmJEiFOvtaQD0uHXcl6exYA+3j+lfoBQiGorILCwj6ftrtm7Kh7g4jIKLN9OxijYsdZ1k4YowqAafef3W5albna03PQPWoE+qN+JpZNpNDb95hIRMYOXRlFhqiTKFESmpEzTC/GVwAWza61VLm2p7i3gXi8p8BfXzojnZTklag+jojIaLNtW64jGJc249fYJs3a7SrW287M4CHdtOrPHjUCg7Eg06qmpe/4IjJiKZEjMkSdRLSGfJh22A2st2cDhkWef6Z+gFgcvJ5+u1UBdEY7qSiooL64/2SPiIiMQNu2QV5erqMYd7bgV8eqNHNm41g0u96k0rUjPQe1bbDoWVZljAELGksb03N8ERnRlMgRGaJOosSx8ejPaEiMgefiKwGY6nplCLNxcO5EFRdDRUW/m/ijfqZUTsHtUsJNRGRU2bUL8vNzHcW4EiFOGyElctKo0y7nbXsOAAvSPRunoLCnRmAkESHfnU9DSUP6ziEiI5begYoMUScRLFQMcKg22q1ssSfhIs4i7xBm44DTqaGuHtz9J2liiRiTy9WGU0RkVEkkoKNDM3KyrL2r9bgSOenzcmIpBheNrvXUuLak78ChINTW9tQIVKFjkfFFiRyRIeokqlKAQ2Qbq2c2zhz3C5RYnakfpLvteE3NAOexsSyLCaUa1IiIjCrBIEQiSuRkWVtXIqcQb65DGRP8pow3E/MA2MfzVPoObIyztGq3GoH+qJ/6knpK80vTdx4RGbGUyBEZog7CmoszRG8l5tJuaskjxPyhTjMOh6Fg4LbjgWiAYm+x7k6JiIw2fj9Eo0rkZFkbIaIk8OotQlq8EDsQGw8Nrg3UW++l78CRsLPscLebWcFYkOlV09N3DhEZ0fQqLTJE2wmqq8MQxI2nq1MV7ON5mnwrMrQDhUJObZx+2o6Dc3eqNL+U+hIVOhYRGVWUyMmJdsJYXf+T4dlp1/G2PReAJZ7/w0rntzQYgopKKCnpechgVOhYZBxRIkdkiHYSJF9ryFP2UnwZQcoopoNZ7heGfqBYHBoaGGhk5I/6mVQ+iTy33giIiIwq3Ykcr5b4ZFMHEdDC8WFzGjocAkCraw01rq3pPDrEYjBhQs8YKJqI4nF5tJRcZBxRIkdkCOLY+IhoRk6Kdtp1vJxYCsBS7yN4rMTQDhSPgWfgtuPgdHBorWgd2jlERCR3/H7nX5eGqtm0nSAejW2GbZPdwmZ7Mi4S7OsZYkOH/kRjkOfttaxKhY5Fxp8RcXW84YYbaGlpoaCggP33359nnnlmwO2vv/56Zs6cSWFhIc3NzXzlK18hHA5nKVoRp2NVhAT5GuwkzTYu/hk7CoOLFtdrTHavHfrBQiEoLhqw7bgxzh1FTTMWGRl0rZeUdCdyJKt2ENDYZph2b+gwy/0ipa6O9J4gGISS0l5jIH/UT3VRNRUFFf3uJiJjS84TOXfddRcXXHABl112GS+88AILFixg1apVbNu2rc/tf/vb33LRRRdx2WWX8eqrr3LLLbdw11138fWvfz3Lkct41kmUKAnNyEnBfxJLaTN15BNkf+/DwztYd9txT/9L20LxEAWeAhpKGoZ3LhEZNl3rJWVK5GSdjWEXIS0bH6a37Tm0mTq8hNPbqapbNOIsq9pttpo/6mda1TSstBbiEZGRLOeJnB/84AecddZZnHnmmcyZM4ebbrqJoqIifvGLX/S5/ZNPPsmKFSv4+Mc/TktLC0cccQSnnnrqoHf2RNLJmZET12AnSe12NS/FlwHOkqpCKzj0gxnjLN8foO04OIOakrwSrRcXGQF0rZeUtbWBWzdLsslPVLONhylq8nghdhDgNHQosNI8izCRcBI4e4yBDIbmsub0nktERrScJnKi0SjPP/88hx12WM9jLpeLww47jKee6juDvXz5cp5//vmewdzbb7/NX//6V44++uh+zxOJRPD5fL0+RIajkygJDG51dRiUbSyeiB2JjYcm11tMcb06vAN2t9wcpD6OP+qnsbSRIm/R8M4nIsOia70Myc6d6liVZT7dpBq25+KHEKSUUquN2cNp6NCfUAgKi3qNgWKJGC7LpRtXIuNMTl+pd+zYQSKRoL6+d2vg+vp6XnvttT73+fjHP86OHTs48MADMcYQj8c555xzBpxufc0113DFFVekNXYZ3zqJYIHacybh2fghbDeNeImwzPv34bffDIWhohyKiwfcLBgLMrVq6jBPJiLDpWu9DMmuXUrkZJnq/w3PpsQk3kgsAGC55wE8Vjz9JwmFYPLkXn8bPTOQVehYZFzJ+dKqVD322GNcffXV3Hjjjbzwwgv88Y9/5L777uOqq67qd5+LL76Yjo6Ono933303ixHLWNRJVM05k/BGfB9eTSwBYIX3foqtNNQ8iEWhfuC248YYjDFMLJ04/POJSNbpWj/ORaMQCCiRk2U+IsSx8Yy+twc5FzNenoyvApwCxxPcGXj9MQaMDXV1vR72R/1UFlZSXVSd/nOKyIiV0xk5NTU1uN1utm7d2uvxrVu30tDQd4HSSy65hE996lN89rOfBWD+/PkEAgE+97nP8Y1vfANXH20q8/Pzyc/PT/8XIONWGyFcmo0zoM2JZp6KO0spFnn+SYv7jeEf1O5aGz7IsqpoIkqeO0+FjkVGAF3rJWV+v5PMGWTmpaSXjwig2cZD8UL8QPymgmI6WOz5v8ycJBLpc2m5P+pnceNiXJYScCLjSU7/4vPy8li8eDEPP/x+Bxvbtnn44YdZtmxZn/sEg8G9BnDurmJ43e2GRTJtByF1rBqAz67gsdjxGNy0ul5lH3eaujaEwlBQAJWVA27WGe2kNL9U04xFRgBd6yVl3YkczcjJqk6ioPnGKdtqT+TVxGIAlnv/jteKZeZEoRCUlUFpaa+HEybB5PLJmTmniIxYOa9mdsEFF3D66aezZMkSli5dyvXXX08gEODMM88E4LTTTmPixIlcc801ABx77LH84Ac/YNGiRey///6sXbuWSy65hGOPPbZnkCeSaTsJag15PwKmhIdiHyFCITXWZlZ47x9+XZxu4RBMaHTuSA2gu9BxWX5Zmk4sIsOha72kRImcnNhFCJeWVaUkbAr5R+xowGK6+99MdK/P3MliUWcMtNugyjY2gGYgi4xDOU/knHzyyWzfvp1LL72ULVu2sHDhQu6///6eoojvvPNOr7ty3/zmN7Esi29+85ts3LiR2tpajj32WL797W/n6kuQcSZKAj9Rzcjpwy67loeiJxKklCJ8fCDvT2ks9mectpt7rA3vSyAaYFrVNKy0ZZBEZDh0rZeU+P0Qj4Mn58PUcWW7blKlJGHcPBI9Ab+poNRqY4nnscydLB4Hl3uvZVWBaIBibzH1JfX97CgiY5VlxuEcZZ/PR3l5OR0dHZSVDfOO/aZNcPHFUFsLJSXpCVBGtJ0E+RoPUko+FRTkOpwRY1NiMo/GjidGPuXWDg7P+19KrDS2/43FoNMHKw+B6oEL+r289WXOXnI2h089PH3nF8mwtF6bRN/P0eyRR+CnP4W5c3MdybhhMHyVB2knRDPluQ5nxDMGnogfydrEfLyEOSbvdipcuzJ3ws5OcLvh8MPB6+15eIt/Cy7LxfVHXk+eWzPYRgNdmyRdNH9SJEWdRImQ0IycLsbA6/EFPBg7kRj51FvvcHTeb9ObxAFnbXhREVRUDLhZLBHD7XJrmrGIyGjlT0OHQ0lJmDgBouTnfrL+qPBKYglrE/OxsDnE+5fMJnHAWVpeX98riQPOUvKmsiYlcUTGIb1ai6SokwhRJXIA2GHX83Tsg2w3TpvvVtcaDvTej9tKpP9kkTA0NTl3pAbgj/opyStRIkdEZLTy+Zy7BJI1zk2quGYaJ2FDYjrPxQ8BYKnnkczWxYGutuPGmf2/h3A8zJTKKZk9v4iMSErkiKSokyg2Bs84ntAWNMW8GF/Bm4l9AAsPURZ6nmSu+9n0FTbeXfcgZpAlVQCBWIDygnKqiwbfVkRERqBdu/aaeSCZ5eu6SaUZOQN7Iz6fp+JHABYz3KuZ5X4x8ycNd3Xs3KM+DjhL4uqLVR9HZDzSq7VIijqJ5DqEnAibAt5JzGCdPYstdjOmK5E1xbWGxd7/o9jK4FT4SMTpVNXHIGZP/qiffer3wWWN30SbiMiotmuXOlZlmY+Ilo0PwBhYHV/BS4nlAExz/4cDPA9n5ubVnkIh50ZWcXGvh6OJKF6XV4WORcYpJXJEUtRJNNchDJkxkMBNAg82bgzOCMT51yJmvMTxEjN5RCikw1TRYVfTbqppMzWY3QZ4ddZ7LPY+Tr1rY+YDD4egtCypguKxRIzJ5ZMzH5OIiKSfMdDerkROlnXfpHKhbo97so2LJ+NHsDYxH4AF7idZ6HkiO0kccJo9NDSw5wm7l5JrRo7I+KREjkiKdhHCPcIHOj67go12K+2mGr8pJ2DK8Jsy4gxvYFxlbaXV/RotrtcpdXWkKdokRKN9DmL2ZIwBC92dEhEZrUIh50OJnKzyjdPZxoPZYjfxr9hhtJtaLGyWef7ODM9/shdALAZeT59LywPRAJWFlVQWVmYvHhEZMZTIEUnRDoIjcurxNruRdYmZbLSn4DODL0ECsLC7/stgYfAQw0sMrxXFS4Qyq40K104qrJ1UWNspdaW5E1Uy7ARYrqSWVQVjQYo8Rbo7JSIyWvn9TvK+qCjXkYwr7Urk9BI0xTwXO4S37TkA5BPiIO99NLnXZTeQUAiKiqFy72RNIBZg3wn7aim5yDilRI5ICgyGXYRGVCJnp13HC/GD2Gi/37XARYJ613vUWpsosToosXyUWB3kWRHcxHGRwIWdvWnBwxEKQ2HfRf72FIgFKM4r1owcEZHRqjuRoxk5WbWDwIga2+SCMbDNTOTtxBzeTswmRj5gmOl+iUWef1BghbMfVDgMzc19duyM2TGay5uzH5OIjAhK5IikIIZNgOiIGOx02uW8ED+QdV13iywSTHG9yiT3Wia4NpBnjd5aPr2EQjBxolPseBD+qJ8plVMo8upOrojIqKRETk7sIEj+CBjbZJMx4Dfl7DK17LAnsN6eRaep6Hm+xtrEAd6HqHFtzVGANlj02XbcGAMGzUAWGceUyBFJgZ8oMWyKyW1b1E2JSTwaO54YBQC0utawyPMEZa72nMaVfgYSCairS2rrUCzE1MqpGY5JREQyxu933mH3MQNBMiOOTTvhUdV63DYuOk05IYoJmWLCpogoBdjGhcHCxoXB1bVwHMAijpeoySdKAVGTT4ep2qt2oIcok11vMNW9hgmuDbmduRzqajveR32cYCxIkbdIM5BFxrHR84otMgIEiBIlQWVXAiUX3ozP48n4ERjc1FqbOMD7INWubTmLJ6OiMcjzJrWsCpylbxNKJ2Q4KBERyRi/P9cRjDudXa3Hi3J8k2ogCeNiiz2JbfZEtpmJbLcnDLuBA4CLOBXWTiqt7Ux0r2eS6008VjwNEadBKAgNE6CwcK+nupeS1xUnd6NLRMYeJXJEUhAgRowE3hxMPzYGXowfyL8TywBnFs4K7/14rETWY8maUAiKS6C8fNBNY4kYbsutacYiIqOZEjlZ5yNChHhOb1L1J2wKeT2xgNfiiwhR0us5D1GKLD8FBCm0AuRbYVwkeubjOHNxnH8tDG4rTh4R8qwweUQosToot9pwWXbfJ88pA/EE1Pc9ptFSchFRIkckBYGupVVestshwBh4Kn4EbyQWALCP+0kWeZ4YHcWKhyMSgdZWcA3+/VahYxGRMaC9nbF/cRtZOokSITGillYFTTGr48t5KzGXRNdMoQICNLrWU+/aSJ1rIxXWjrH7qxKNOjOS+1hWBc7SKi0lFxnfRs4rtsgoECAGgEV2Rw5vJPbhjcQCLGyWex5guuflrJ4/J+yuIn/9DGL25I/6Kcsvo6aoJrNxiYhI5uzapULHWeYjgo3Bk+WbVP15L9HKP2NHEaYYgCprK3M9z9Hieg33iJw9kwGhEJSUDjgjuaGkIYsBichIo0SOSAoCZL8T1A67gafjHwRgkecf4yOJA07LzYLk2o4DBKIB5tfNx2WNjIGoiIgMgRI5WecjkusQAEgYN8/HD2ZNYgkAldY29vc+TL313tidedOfSASmTO1zRnLcjuOyXJqBLDLOKZEjkoIAMUxX/4NsCJtCHo0eh42HZtebzHc/k7Vz51wo5KwN76PIX19idozJFZMzHJSIiGRMIgEdHZCfn+tIxpVOIlkc2fQtYEp4JPphdhpnlsls9/Ms9vzf2K4D2B87AZYLavqeYRyIBijJK1FNQJFxTokckRR0EMadpanHtrF4PHYMAcoptdo4yPvXcXRHykA83m+Rv722Ns4QVIMaEZFRLBBwaoMkmcCX9NhBMKfLqiImnwejH6Xd1JJPkAO999Psfitn8eRcKOT8DfQzI1lLyUUEGCGLYUVGiTbCWSt0/HJiKZvsVtzEONT7Z/Ks7C/ryplYHLyepJdVheIhCjwFmmYsIjKadXZ2FXnV0qps2k6Q/Bx04wSIGw8PRz9Cu6mliE4+lPeb8Z3EASeRU1vb78y0QCzApPJJuF25+ZmJyMigRI5ICtoJk5eFwU7QFPPv+AEAHOB5iCrX9oyfc0QJhaC4GCoqktrcH/VrmrGIyGjn9yuRk2UGw84cJXJsY/FY7Fi2mSbyCHN43h8odXVkPY6RxTjNHurq+t0imojSUtGSvZBEZERSIkckSTaGDsJ4szDYeSm+nDh51FibmOYeJ8WNdxcOQ109uJP7XgeiAeqL6ynOK85wYCIikjF+P8Ri4PXmOpJxI0CMMPGstx43Bp6Mr+I9expuYnww749UunZkNYYRKRKBvPx+ZyQbYzDG6MaViCiRI5KsEDGiJDI+I6fDruSNxD4ALPH+3ziqi9PF7motWlub9C7BWJAplVMyFJCIiGSF3w+Wxfi78OWOjwgRElmfkfNmYj5rE/OxsFnp/Qv1ro1ZPf+IFQpBaSmUlfX5dDge1lJyEQGUyBFJWqArkZPpGjnPxw/G4KLZ9SYNrvcyeq4RKdxV5K+6OuldDIbG0sYMBiUiIhnn9+c6gnGnkwiRLM/ICZpinosfAsBiz+NMGu81cXYXjUJDQ7/JzEAsQHFesWbkiIgSOSLJChAlhp3RGTlb7Ym8Y8/Awmax5x8ZO8+IFgw5SZwku5bE7Tguy6W7UyIio10g4Ky5kazxESGGnbVGDgDPxD5AlAKqrS3McT+XtfOOeIkEuFwD3sjyR/3UFtVSkleSxcBEZCRSIkckSX6iGV1aZQw8H1sJwHT3f6hw7czIeUY24wxkGhqS3kOFjkVExojOTueNrGRNJ05HTIvsLGd7NzGF9fYsLGyWex/AZSlx12OQtuPg1AScUjkFS8sPRcY9XS1FkhQgho3BlaHBzjv2NLaZibiJsdDzREbOMeJFIk67zRSWVfmjfsryy6gpqslgYCIiknEdHSp0nGV+ollK4UDMeHkqdjgAc93PUu3alqUzjxKhENTWDdi1zWCYWDYxi0GJyEilRI5IkgJdg51M3bVaE18MwBz38xRZgYycY8QLhqC8rN8if30JRAO0VrTidmW/daqIiKSRzwee7HZPGu98RLDJzqyYF+IHEaSMUqudhZ4ns3LO0cOAsQds9JCwE1hYmoEsIoASOSJJCxDL2LE77Eq2mklY2MzyrM7YeUa8aBQmNKbUsSSaiDK5YnIGgxIRkazw+TQjJ8t2Ecp4N06AdruaVxP7ArDM83c8Vjzj5xxVutuODzAjubvQcV1xXRYDE5GRSokckST5iWbsntWbXe3GJ7rWUWx1ZugsI1w85tyJTaVblTFYlkVDSfI1dUREZASKxSAcViIny7KVyHk5sR9gMcn1Bo3uDRk/36gTDEFZqdN6vB+BaICSvBIlckQEUCJHJGlthPBk4E8mYVysTcwFYIb732k//qgRDEFx8YBF/vbaJRak0FOoacYiIqNdKOQkc7S0KmsMhnbCGU/kBEwJbyfmADDf80xGzzVqxaLQMGHAGcmBWICmsia8biU7RUSJHJGktWVosPOuPY0wxRTip8n1VtqPP2qEw063Knfy3+PujlWakSMiMsoFgxCPa0ZOFoWJEyae8UTOmvgSbNzUW+9Q69qc0XONSon4oG3HAcLxMK0VrVkKSkRGOiVyRJLUThhvBv5k3kgsAJyW4+O2Dadtg8WARf76EogFaChpoNBbmJm4REQkO7pn5CiRkzV+okRJZDSREzH5vN41ztFsnH6EQlBUNOCMZGMMxhjduBKRHkrkiCQhSoIgsbQPdjrtcjbZLYCTyBm3QiEoLEypPg5AMBpkauXUDAUlIiJZo6VVWZeNRM7riQXEyaPC2s5E17qMnWdUC4WdtuMDJDGjiSh57jzqS7SUXEQcSuSIJCFIjCgJvGke7LyZmA9Ao2s9pa6OtB57VAkGoaYWCgpS28+CCaUTMhOTiIhkT/fSKiVysibTiZy4cfNqfDHgzMZJoSHl+GGM81E3cAHj7o5VqgkoIt2UyBFJQoAosTQPdmxj9SRyZrhfSttxRx1jO4OYCaklZKKJKB6XR9OMRUTGglDIKfSqd/tZ4yeKjcGdobcDbyfmEqKEYny0ul7LyDlGvUgY8gduOw5OTcCqwioqCiqyE5eIjHhK5IgkIdAzIyd9fzIb7SmEKKGAAM2utWk77qgTCkNhQer1cbracCqRIyIyBoRCuY5g3PETzdixjeluOQ5zPM/hsuyMnWtUC4agosLp2jmAQDRAa0UrlhKdItJFiRyRJDgzcuy0Lq3aYE8HoNX9Ku7xPMAJBpxlVUVFKe3mj/qpKKigsrAyQ4GJiEjWBIO5jmDcyWQiZ5uZiM9U4SXCDPe/M3aeUS8Wczp2DpKgSZgEzeXNWQpKREYDJXJEkhAgBoCL9NwJMQbeS0wBoHk8txw3xulYleKyKgB/zM+Uyim4LL2MiYiMesGgc02QrOkgkqZRzd7WJWYBMMn1Jl4rlqGzjHKxGHg9gy6rso1zs0/1cURkd3oHJJIEP9G0DnZ2mgbCFOMlQr3rvTQeeZQJh6CgMOVlVQAJO0Fzme5OiYiMCR0daj2eZbsIZaTQsW0s1idmAtDqVm2cfoWCzpKqyoFnFgdjQYq8RepYJSK9KJEjkoQAUdJ5n/DdhNMyu9G1fnwvqwoEoaZm0LXhe7KNjYWl+jgiImOFz6eOVVm2i1Dau3ECbLEnEaaYfII0ujak/fhjRjgCDRPAPfDPIBANUOwtpq544M5WIjK+KJEjkgQfEaw0zsl5z3aWVTWN+2VViSEtqwrGghR6C3V3SkRkrOjs1IycLLIxdBDOyIycdbazrGqy+w0VOe6PbYOFczNrEP6on8ayRgo8BZmPS0RGDd36EEnCLsLkpSnvGTTF7DQNgKHJvS4txxyVwiEoSL1bFTiDmpK8Eq0XFxEZC4xRIifLAkSJkEh7Iidh3GxIzABQy/GBhLqWlg9SHwcgFA8xpWJKFoKSkSqRSBCLqdbUeOD1enEPMkuvmxI5IkloT+P04+4ixzXWZgqtcdylIxB0ZuOkuKwKnETO7NrZ5HvyMxCYiIhkVSTifGhpVdYEiBElQQl5aT3uRruFKAUU0Tm+awAOJhSCiROdG1oDMMZgMDSWNmYpMBlJjDFs2bKF9vb2XIciWVRRUUFDQwPWIN3sdMUUGYTB0EEkbXet3rOd+jjN7rfTcrxRafdlVYO8SPUlFAsxtXJqBgITEZGsC4UgHh9SYl+Gxk+UaAZm5HR3q2pxv47LUheyvnWNgeoHn1UcTUTJc+UxoTT1Zegy+nUncerq6igqKhr0jb2MbsYYgsEg27ZtA2DCIOUnlMgRGUSIOBHieNOwtCph3GyyJwPjvD5OOAz5Q1tWZYzBslToWERkzAgGu1oxa2lVtmQikRMzXt61pwHqVjWgSBTy8pJaVtW9lFxjnvEnkUj0JHGqk/hdkbGhsLAQgG3btlFXVzfgMisVOxYZRIAoMey0DHa22M3EyaOITqqsbWmIbpQK+J0CfyUlKe8aSUTIc+cxoUR3p0RExoRQyEnkaGlV1viJAuBKcyOHOHmUWO3UWJvTdtwxJxiEsnIoKxt0085oJ7XFtZTnl2chMBlJumviFBUV5TgSybbun/lgdZGUyBEZRPc68nTUyOnpVuV+eygrisYGO+EsrWpuHtKyqs5IJ6V5pVovLiIyVgSDztIqzcjJmu5ETjqtS8wGnCLH43aMMygD0ahTHyeJb1IgGmBa1TQtqRnH9LMff5L9mSuRIzIIZ0bO8KcfGwPvdtXHaXKN4/o4/gCUlELD0KYJ+6N+6kvqKc0vTXNgIiKSE6EQJBLg0rA0W9KdyIkbNxvtFkDLqgbUvYQwibbj4CwnbyprynBQIjIa6YopMogAMeLYuIc5/bjDVOE3FbiIM8G1IU3RjTbGGbA3Nzvrw4cgEAswo2pGmuMSEZGcCYWc2Qm685w1bYTSuqxqmz2RBF4K8VNpbU/bccecQBBKS6GyctBNY4kYbpdbS8lFpE9K5IgMIkAUq+t/w7HFngRAves9vNbAax7HrEgE8vOcKcVD0N2Gc2LZ0PYXEZERKBhUEifLdhFOa6HjzV2NHBpdG/SjHEgk4oyBkph91l3oWB2rZLQ544wzsCyL73znO70ev/vuu3stGzLGcPPNN7P//vtTUlJCRUUFS5Ys4frrrycYDGY77FFHiRyRQQSIpeWe1TbbqelS79qYhqONUp1+qKmFiooh7R5JRMh356s+jojIWBIKOeuPJWvaCGUkkTPBPV5nHCchHgOPO+mOnf6on8rCSqoKqzIcmIx1Cdvw1Fs7+fPqjTz11k4SduZfbwsKCvjud79LW1tbv9t86lOf4vzzz+f444/n0UcfZfXq1VxyySX8+c9/5u9//3vGYxzt1B5AZBB+oqTj5W67cWaR1FnjNJFjJ8C2YdKkId951d0pEZExKBDIdQTjShybTiJpS+RETD47jFP3bvwuHU9CMOh066xKLjHTGe1kSeMSXJbuu8vQ3f/yZq74yxo2d4R7HptQXsBlx87hyHmZG08fdthhrF27lmuuuYZrr712r+d/97vfcfvtt3P33Xdz/PHH9zze0tLCcccdh8/ny1hsY4VeGUQG0UYIzzD/VIKmmE5TARhqXOO0Jac/AKUlQy5yDE7HqvriekrzVOhYRGTMaG9Xx6os8hMlmoYmDt2cpeMW5dZOii1/Wo45JoXD0DgR3Ml9321jM7licoaDkrHs/pc38/nfvNAriQOwpSPM53/zAve/nLn3JG63m6uvvpof//jHvPfee3s9f/vttzNz5sxeSZxulmVRXl6esdjGCiVyRAbRRhjvMP9Utnctq6q0tpNnpb/l58jXXeR40pCLHENXoePqGWrFKCIylvh8SuRkUboTOT3LqjQbp3+JuFMXJ8llVQk7gYVFQ8nQb37J+JawDVf8ZU2fqwq6H7viL2syuszqwx/+MAsXLuSyyy7b67k333yTmTNnZuzc44ESOSKD6CCMd5iDne76OLWuTekIafSJRJwETuPQa9sYYzBGhY5FRMaczk7waLV/tgTSnMjZtFuhY+lHMAjFxVBdndTmPUvJ1bFKhuiZdbv2momzOwNs7gjzzLpdGY3ju9/9Lr/85S959dVXe59fddGGTYkckQEksPETTcOMnK76OOM1kePzQX19Uu02+xNNRMlz56nQsYjIWGLb4PdrRk4WOTNy7GGPbQD8phSfqcLCpsH1ThqiG6NCYWiYkPTvuT/qp6ygjLriugwHJmPVts7+kzhD2W6oDj74YFatWsXFF1/c6/EZM2bw2muvZfTcY50SOSIDCBIjhj2su1YJ42aHqQfGaaHjSMRZDz5t2rDay3ZGOynNL9XdKRGRsSQUgnhciZws8hPFAqw09OTcnHBm49RYW8bp0vEk2Aln/FOXfFLGH/XTWtGK25W+zmIyvtSVFqR1u+H4zne+w1/+8heeeuqpnsc+/vGP88Ybb/DnP/95r+2NMXR0dGQ8rtFOiRyRATiJnMSwllbtNPXYeCggQKnVnr7gRgtfh3MXKsl14f3pLnRcll+WpsBERCTngkGIxbS0Kov8pC/h8n59nPVpO+aYEwhCcVFK46CoHaW1ojWDQclYt7S1ignlBf2may2c7lVLWzPf3n7+/Pl84hOf4Ec/+lHPYyeddBInn3wyp556KldffTXPPfccGzZs4N577+Wwww7j0UcfzXhco50SOSID6J6RM5zpx9t3q48z7mr0pmk2DkAwFmRa1TQVOhYRGUtCISeRoxk5WdNJFPosgZoaY96vjzNBy6r6Fwo63aqSbPZgGxsLiwmlmoEsQ+d2WVx27ByAvZI53Z9fduwc3K7sjKuvvPJKbNt+PwbL4re//S0/+MEPuPvuu1m5ciX77LMPl19+OccffzyrVq3KSlyjmW5/iAwgkIYZOd2FjsdlfRxfB0xsGvZsHACDobm8OQ1BiYjIiKGlVVnXRghPGgodt5sawhTjJjY+xzjJiMfA5YYJySdlgrEgRd4idaySYTty3gR++sl9ueIva3oVPm4oL+CyY+dw5LzMJAtvu+22vR5raWkhEon0eszlcnHOOedwzjnnZCSOsW5EzMi54YYbaGlpoaCggP33359nnnlmwO3b29v54he/yIQJE8jPz2fGjBn89a9/zVK0Mp4EiZHA4B7iOnJjYFtPoeNxVh8njbNxIvEIXpdX9XFERjFd66VPWlqVdTsJpaVjVfdsnHrXe7itxLCPNyYFAlBWBjU1Se/SGemkLL9MiRxJiyPnTeCfF36AO846gB+espA7zjqAf174gYwlcSR7hnXVjEQi5OfnDyuAu+66iwsuuICbbrqJ/fffn+uvv55Vq1bx+uuvU9dHUbBoNMrhhx9OXV0df/jDH5g4cSIbNmygoqJiWHGI9CVIDBh6QUC/KSNECS4SVFtb0xnayJfG2TjdbTjVsUokN4Z7vde1XvoVCjn/ukbEvcUxz2BoI5yWRM5mtR0fhIFwBGbMdG5sJckf9bOgYQF57uSWYokMxu2yWDa1OtdhSJqldNX829/+xumnn86UKVPwer0UFRVRVlbGypUr+fa3v82mTalPq/zBD37AWWedxZlnnsmcOXO46aabKCoq4he/+EWf2//iF79g165d3H333axYsYKWlhZWrlzJggULUj63yGC6EzlDtd04s3GqrK14rHg6QhodwmFwe9IyGwecjlV1xXUqdCySJem+3utaL/3qTuRIVkRJECQ67ESOMe/XAGxwvZuO0MaeSNSpi1Nfn9Ju4XiY6VXTMxSUiIwVSSVy/vSnPzFjxgw+/elP4/F4uPDCC/njH//IAw88wM9//nNWrlzJQw89xJQpUzjnnHPYvn17UiePRqM8//zzHHbYYe8H5HJx2GGH9WpPtrt77rmHZcuW8cUvfpH6+nrmzZvH1VdfTSLR/5TOSCSCz+fr9SGSjMAwOzuMy/o4xoaOdpg0KS2zcQAC0QDTq6er0LFIhmXieq9rvQwoGExLwl+S4ydKlMSwEzmdpoIIhbiIU2ltS1N0Y0zAD1WVUFmZ9C4JO4FlWTSVNWUwMBEZC5JaWnXttdfy3//93xx11FG4+pj6etJJJwGwceNGfvzjH/Ob3/yGr3zlK4Med8eOHSQSCer3yFTX19fz2muv9bnP22+/zSOPPMInPvEJ/vrXv7J27Vq+8IUvEIvFuOyyy/rc55prruGKK64YNB6RPfmI4BrisioYp/Vx2tuhogLmzEnL4NwYg21smstU6Fgk0zJxvde1XgYUCsFunUwks9KVyNlhnPotVdY23JZ+fnsxxini3dSc0ljIH/VTmleqRI6IDCqpRE5/d8z2NHHiRL7zne8MK6DB2LZNXV0dN998M263m8WLF7Nx40a+973v9Tu4u/jii7ngggt6Pvf5fDQ3602hDK6dyJBbj8eMlzbjzEgZN4mcSAQSNsyeA0VFaTlkOB4m35OvjlUiWTBSrve61o8jPl9K9UNkeLoTOUMd23TbYTuFUmtcW9IR1tgTDkFBQcrLqnwRH1VFVdSXpLafiIw/KRc7DofDFBQU9Pnc5s2bmZBCe72amhrcbjdbt/YuArt161YaGvqu1D5hwgS8Xi/u3S76s2fPZsuWLUSjUfLy9i4Mlp+fP+yizDI+dRAecuvxXaYOg4tifBRZgTRHNgIZ48zGaW2FNL558kV8lOeX6+6USJal63qva70MqKNDrcezKECMODaeYSdynL9dJXL6EQg4s3FKSlLazRfxsX/T/rgsFf8WkYGl/Cqx7777snr16r0e/9///V/22WeflI6Vl5fH4sWLefjhh3ses22bhx9+mGXLlvW5z4oVK1i7di32btNw33jjDSZMmNDnwE5kqAwG3zBm5OyynU4sla5xsna8owNKS9O2pKqbL+KjtbKVIm96ZviISHLSdb3XtV4G1Nmp1uNZFCCK1fW/obKNxU7jzBipsTanK7Sxw7bBNtCYeqdN29i0VrRmICgRGWtSfod6yCGHcMABB/Dd734XgEAgwBlnnMGnPvUpvv71r6ccwAUXXMD//M//8Mtf/pJXX32Vz3/+8wQCAc4880wATjvtNC6++OKe7T//+c+za9cuvvzlL/PGG29w3333cfXVV/PFL34x5XOLDCRCgjDxIc/IaTM1AFRZyRX/HtVCIYjHYPbslO8+DSacCDOzemZajykig0vn9V7XeumXz6cZOVkUGGY3ToB2U0MCL14ilFu70hDVGOPvdG5s9TPjsD+ReIQ8d55mIItIUlK+BXLjjTdyzDHH8NnPfpZ7772XzZs3U1JSwjPPPMO8efNSDuDkk09m+/btXHrppWzZsoWFCxdy//339xRFfOedd3oVXGxubuaBBx7gK1/5Cvvssw8TJ07ky1/+MhdeeGHK5xYZSJAYMRKUMLS7v222Ux+n0jXGEznhkDNomTkLJk9O66ETdgKX5dKgRiQH0nm917Ve+hSLQTisRE4WOd04zbCO0b2sqtq1RQ3H9mKcm1vTZzitx1Pgi/goyy/TmEdEkjKkuaxHHXUUH/nIR/jpT3+Kx+PhL3/5y5CSON3OPfdczj333D6fe+yxx/Z6bNmyZfzrX/8a8vlEkhEgSgx7SJ0djKGn0HHlWJ6REw6DrxNmzoR586CPLjfD4Yv4KM0rVaFjkRxJ5/Ve13rZSyjkJHP6qcUk6ddGGPewO1Z1FTq2VB9nL8EgFBZBU+rJGF/Ex8yamZTml2YgMJHse+qppzjwwAM58sgjue+++3oeX79+Pa2tfS8hfOqppzjggAOyFeKolvK7rrfeeotly5Zx77338sADD/C1r32N4447jq997WvEYsOfrikyUnTPyBnK0iq/KSdOHi7ilFltGYhuBIiEnSnx06dnJIkDzqCmrriO2qLatB9bRAam671kXDDotGjWjJysaSNMngodZ44/4NTGKU09GROKh7SUXDLDTsC6f8B//uD8ayeyctpbbrmFL33pSzz++ONs2rRpr+cfeughNm/e3Otj8eLFWYltLEh5Rs7ChQs55phjeOCBB6ioqODwww/n6KOP5rTTTuPBBx/kxRdfzEScIlnnJHKG1tlhV9dsnAprJy5reFOYRx4Dfr9zJ3XaNNhnn4y1jvVH/RxScwiW5m6LZJ2u95Jx3TNylMjJmnZCQ679BxA3np4Zx7UuFTruJRoFjxsmTUp5V9s4hd01A1nSbs09cP+F4NstkVLWCEd+F+Ycl7HT+v1+7rrrLp577jm2bNnCbbfdtld9verq6n67V8rgUn6HeuONN3LnnXdSUVHR89jy5ct58cUX2XfffdMZm0hOBbsKArqG0NlhzC6rSiRg+w6nI8P8fWDBgowlcYwx2MZmckV66+6ISHJ0vZeM607kqGtVVsSx8RMd0pLxbrtMHQYXBQQoojON0Y0BPh/U1EJt6rOIA9EAxd5i1ceR9FpzD/zutN5JHADfZufxNfdk7NS/+93vmDVrFjNnzuSTn/wkv/jFLzBmrN3czq2UEzmf+tSn+ny8tLSUW265ZdgBiYwUAWJDbs7ZXei4aswUOjYQCMCOHVBTA8tXwKxZGUvigDPFuNBbSHOZ7k6J5IKu95Jx3UurlMjJigBRoiSGlch5f1nVZhU63l0iAcaGlhaG8o3xRXxUFlbSUKLZCZImdsKZidNncfOux+6/KGPLrG655RY++clPAnDkkUfS0dHB//3f//XaZvny5ZSUlPT6kOTpyinSjyCxIfd1GDMzcmz7/WVUhQVOe/FZs1LuxDAUHeEOyvPLdXdKRGSsCoWcN73KCGRFoGvJeNkwauTssJ1Cx7Wqj9NbZyeUl8OECUPa3RfxsWjCIjwuvTWTNNnw5N4zcXox4NvobNd6UFpP/frrr/PMM8/wpz/9CQCPx8PJJ5/MLbfcwiGHHNKz3V133cXs2bPTeu7xRK8WIv3wEx3SfnHjwWcqgVHaety2nW5UoSAkbCgtganzoLkZysqyFoYv4mO/ifuR78nP2jlFRCSLQqFcRzCupGVGjulqPW6pPk4PY5wGELNnD7neU8JOMKVySpoDk3HNvzW926XglltuIR6P09jY2POYMYb8/Hx+8pOf9DzW3NzMtGnT0n7+8UKJHJF+tBPGO4S7Vu2mBrAoIEChFUx/YGlhnIFHIuFMa+/+iMbAwmkF2zDB6bzQ2Aj52U+mxBIxZlTPyPp5RUQkS8LhXEcwrgSIER1iN06AiMnHZ6oAdazqpbMTSkqdG15DEE1EcbvcmoEs6VVSn97tkhSPx/nVr37FddddxxFHHNHruRNOOIE77riDI488Mq3nHK+UyBHpRwfhIQ12dnXVx8n5bBxjIBpxkjOxmJOoASdRYwxYLnC7nNoEHi9UlEJFhVMDp7oaCgtzFnrcjmNZlgY1IiJjWTjsXI8kKwJdM42H0sQBYGdXfZxSq50CS0k4wKmLEwo5HTyLioZ0iM5IJ2X5ZRrzSHpNXu50p/Jtpu86OZbz/OTlaT3tvffeS1tbG5/5zGcoLy/v9dyJJ57ILbfc0pPI2blzJ1u29E4KV1RUUFBQkNaYxiolckT60UFkSDNy2kwNAFU5qY9jnPaXfj/E4lCQ79Szqahw1m4XFjrTfr1eJ4Hj9TqzbwoKwDX0NfPp5ov4KC8oV6FjEZGxzO8fUdeesS7Q1Y1zqLSsqg+dnVBa6hQ5HqL2cDtTKqdQnl8++MYiyXK5nRbjvzuNrru4uz3Zlcw98jvOdml0yy23cNhhh+2VxAEnkXPttdfi8/kAOOyww/ba5o477uCUU05Ja0xj1ZCunscccwybN2/e679Fxoo4NgGiQ5qR01PoONszckIh2LrN6S5VVQVLlsAHPgirjoSDDnLuFk2f7gw2Jk6E+npnu6KiETeQ7gh3UF9cT1VhVa5DERnXdL2XjPL71bEqiwJDrP3X7f2OVVpWBXTNxgnD1KnDmsUcjAWZXz8fS0W/Jd3mHAcn/QrK9ijCXdboPD7nuLSf8i9/+Qv33Xdfn88tXboUYwz77LMPxpg+P5TESd6Qrp6PP/44oa4Cdbv/t8hYEezq7JCfYiLHmPdbj2etY5WdgLY2Z6lUd6KmsnJUdwHxR/3MqZ2jQY1Ijul6LxmlRE5WdRIZ1v7tPTOOt6UjnNHP1+k0gZg8eciHiCViuF1uplZOTWNgIruZcxzMOsbpTuXf6tTEmbw87TNxJPt09RTpQ7CrIGAJqbXZDlFMhCIsbMqtnRmKbjfBIHT6nJo2c+dBQ8OoTuCA07nBsiymVmlQIyIypimRk1W7CA+5Y9XuHTkrXFkY34x03R0+Z892lqcPUUekg4qCClorW9MYnMgeXO60txiX3NPVU6QPzoycRMo1croLHZdZu/BYiUyE9r5On1PIePYcmDkzJ52lMsEX8VGWX0ZrhQY1IiJjljHOUmAlcrJmqN04ATpMFWCRT5BCAukNbDTq9EH58GbjALSF2phbN5eKgor0xCUi44auniJ9CBAlhp3ynat2k6VlVZ0+p5jxggXO2uxRPgtnd23hNiaXT6auuC7XoYiISKbEYk5xfiVyssJgaB/GjJzuZVUV1o6xNOQYmkQcIhGYM3fYN9HC8TDz6ualKTARGU9GVoVTkREiSAwbk3KLzu4ZOVWZLHTcncTZZ58xl8QBCEQDLGhYoPo4IiJjWTgM8bjTPVEyLkycMPEhJ3LabCeRU6llVU5dwtq6YXWqAogmonhcHs1AFpEhUSJHpA/BrhadVoqJnJ6OVdaOtMcEdCVxYmM2iaOifyIi40R3IkczcrIi0LNkfPgzcsa1UBDcHpgzZ9hJyPZwO5WFlaqPIyJDokSOSB+6EzmpsI2LDlMNZKj1eCTifMwfm0kccAY1KvonIjIOKJGTVQGiREkMe0ZOhWscJ3KMDT6fMxOnbvjLv9tCbUyrmkZJXsnwYxORcWdIiZzJkyfj7cpC7/7fImNFYAiJnE5Tjo0bD1GK8aU3IGOgvR2aJ8G0aWMyiQNOfZzWylYV/RMZIXS9l4xRIiernBk59pCKHceMlwDlwDifkdPeDhUVMGtWWsZhkUSEObVzhn0cERmfhnT1fPnll/v8b5GxooNwyvVxfKYKgDKrLf15lo4OKC2FuXPHbBIHnKJ/8+vm5zoMEemi671kjBI5WdXdxMEzhERO97KqQvwUWOF0hzY6RKOQSMCs2VBYOOzDheNh8t35TKmckobgRGQ80tIqkT50EEn5rlWHqQSc1uNpFYs6dXFmz4aSsTv9VoMaEZFxJBx2Zpu6NBTNhgAxLFKv/QdaVoWxnQLHjROhuTkth2wLtVFVWMXk8uG1LxcZiQ455BDOP//8tB7z8ssvZ+HChcM6hmVZ3H333WmJZyTQ1VOkD0Np0dk9I6fcaktjJAZ2tcHEiTB5bF/s28PtVBVW0VLRkutQREQk08LjdGZHjgSIDnnf7hk5GWvkMKIZ2LkTKiudRhNpSjy2h9uZVTOLQu/wZ/eIjAdf/epXefjhh5Patr+kz+bNmznqqKPSHFnuKJEjsgeDoZNIyp0dfN0zclxpTOT4fFBc7CypGuN3LdtCbcysmUmRtyjXoYiISKYpkZNVQ6n9121cd6zq6ID8fFi0KG2zoo0xxO04s2pmpeV4IuNBSUkJ1dXVwzpGQ0MD+fn5aYoIYrGhv66mw9h+ZygyBGHiREikvLTKZ3cvrUpTIse2nYHuzJlQXp6eY45QxhhidozZNbNzHYqIiGSDEjlZ1U4Y9xCWVcE4XloVDEA84XQLra1N22FD8RCF3kJ16JRxo62tjdNOO43KykqKioo46qijePPNN3tt8z//8z80NzdTVFTEhz/8YX7wgx9QUVHR8/yes2wee+wxli5dSnFxMRUVFaxYsYINGzZw2223ccUVV/DSSy9hWRaWZXHbbbcBey+teu+99zj11FOpqqqiuLiYJUuW8PTTT/f5Naxfvx7LsrjrrrtYuXIlBQUF3H777QD8/Oc/Z/bs2RQUFDBr1ixuvPHGXvs++eSTLFy4kIKCApYsWcLdd9+NZVmsXr16yN9TGGKxY5GxLEiMGAkKSD5jGzNegpQCaUzkdPqcBM4YX1IFEIwFKfIWqT6OiMh4oUROVrURSnmmMUDEFBDCmYlSYe1Md1gjVzQK/gDMmZP2cdiu0C5qi2qZVD4prceVsc8YQyiWyMm5C71urCE2XDnjjDN48803ueeeeygrK+PCCy/k6KOPZs2aNXi9Xp544gnOOeccvvvd73Lcccfx0EMPcckll/R7vHg8zgknnMBZZ53FHXfcQTQa5ZlnnsGyLE4++WRefvll7r//fh566CEAyvu4Ie73+1m5ciUTJ07knnvuoaGhgRdeeAHbtgf8Wi666CKuu+46Fi1a1JPMufTSS/nJT37CokWLePHFFznrrLMoLi7m9NNPx+fzceyxx3L00Ufz29/+lg0bNqStflDSiZyHHnqIww47rN/nbdvm6quv5pvf/GZaAhPJlaG06OxeVpVPkPx0dHQwNoQjMGcu5OUN/3gjXFu4jZqiGg1qREYAXe8lKzo7x/yS4ZFkKLX/ANqNs5ShmA7yrKHX2RlVwmFnSVVLi9NoIs3dQtvD7Rw8+WDy3GN/fCfpFYolmHPpAzk595orV1GUl/ockO4EzhNPPMHy5csBuP3222lububuu+/mYx/7GD/+8Y856qij+OpXvwrAjBkzePLJJ7n33nv7PKbP56Ojo4MPfehDTJ06FYDZs9+f1V9SUoLH46GhoaHfuH7729+yfft2nn32WaqqnDqn06ZNG/TrOf/88/nIRz7S8/lll13Gdddd1/NYa2sra9as4Wc/+xmnn346v/3tb7Esi//5n/+hoKCAOXPmsHHjRs4666xBzzWYpK+gRx99NOeeey7BYHCv515++WX2228/fvrTnw47IJFcCxIjSiKlO1fdiZy0FTru7HTajaepO8JI1x5uZ37dfLxub65DERn3dL2XrOjsVOvxLElg4yMypERO97KqyvGyrCoQcGZET58OixeDO/Xv2UBiiRiWZTG3dm5ajysyUr366qt4PB7233//nseqq6uZOXMmr776KgCvv/46S5cu7bXfnp/vrqqqijPOOINVq1Zx7LHH8sMf/pDNmzenFNfq1atZtGhRTxInWUuWLOn570AgwFtvvcVnPvMZSkpKej6+9a1v8dZbbwHO17bPPvtQUFCQ1NeWiqSvoP/4xz8444wzWLBgAbfddhsrVqzouSt31VVXceKJJ/ZMXxIZzYLEiA9xRk5pOhI5xkAoBAtmwm5/9GNV3I5jYTG/fn6uQxERdL2XLPH7lcjJku6ZxvlDmpEzXgodG+jwQTwOc+fBrFkZmTG2M7ST2qJaZtbMTPuxZewr9LpZc+WqnJ17JLn11ls577zzuP/++7nrrrv45je/yYMPPsgBBxyQ1P6FhUPrGFdcXNzz336/H3Dq++yeqAJwpzkJ3Jekr6D7778/L774IhdddBGHHnoon/vc5/jXv/7Fu+++yx133NFripHIaBbs6uxgpVAU0Gd3tR537Rp+AP5OKCmFSeNjmdHO4E6qi6rVvUFkhND1XrIiEFAiJ0sCRImSoJTUl/K8X+h4DNfHiUaho93pTrXvvs6SqjQvp+q2K7iLD0z5ACV56emAJeOLZVlDWt6US7NnzyYej/P000/3LK3auXMnr7/+OnPmzAFg5syZPPvss7322/PzvixatIhFixZx8cUXs2zZMn77299ywAEHkJeXRyIxcC2hffbZh5///Ofs2rUr5Vk53err62lsbOTtt9/mE5/4RJ/bzJw5k9/85jdEIpGejlnJfG3JSOk3oaCggP/+7/9m27Zt3HjjjRQXF/Pcc88xc6ayyjJ2BIim3Nehp/X4cGfkGAPBEMybB0Xjow33ztBOVk5eSVl+Wa5DEZEuut5LRhmjRE4WBbqaOKRa7NiY92fkVFrbMxFabsViTgLHsqBxIsyYkdbuVHtK2AlsbObXaQayjB/Tp0/n+OOP56yzzuJnP/sZpaWlXHTRRUycOJHjjz8egC996UscfPDB/OAHP+DYY4/lkUce4W9/+1u/xZXXrVvHzTffzHHHHUdjYyOvv/46b775JqeddhoALS0trFu3jtWrV9PU1ERpaelebcdPPfVUrr76ak444QSuueYaJkyYwIsvvkhjYyPLli1L+uu74oorOO+88ygvL+fII48kEonw3HPP0dbWxgUXXMDHP/5xvvGNb/C5z32Oiy66iHfeeYfvf//7AEMuHt0tpTmDb731FgcffDCPPPIIN910E/PmzeOQQw7hz3/+87CCEBlJgsQwKe6TtkROIOAkcFpahnecUSJhJ7CNzYKGBbkORUR2o+u9ZFQ06ryJViInK7pn5KSyZBwgTBERigBDuZWGGccjgoFwCLZvdwoa19XD8hWwfHlGkzjgNHaoLKjUDGQZd2699VYWL17Mhz70IZYtW4Yxhr/+9a94vU5tzBUrVnDTTTfxgx/8gAULFnD//ffzla98pVddmd0VFRXx2muvceKJJzJjxgw+97nP8cUvfpGzzz4bgBNPPJEjjzySQw89lNraWu644469jpGXl8ff//536urqOProo5k/fz7f+c53Ul4S9dnPfpaf//zn3HrrrcyfP5+VK1dy22230draCkBZWRl/+ctfWL16NQsXLuQb3/gGl156KUC/X1+ykr6C/uQnP+Giiy5i1apV/PGPf6S2tpbPfvazfO973+OUU07hox/9KD/+8Y979XsXGY38XUurkhU2BURw1lkOO5ETDMCs2bDb+suxrHtQM7tm9uAbi0hW6HovGRcOO7VIhlijQFIT6LpB5U4xkdO9rKrUasdjxTMQWZYY4yQOwyEIhZ0lVE1NTlvxhoasdU/bEdzBfo37UV1UnZXzieTKY4891uvzyspKfvWrXw24z1lnndWrk9NZZ53Vq4vU5ZdfzuWXXw44S5r+9Kc/9Xus/Px8/vCHP+z1uDG9b9VPnjy5z+360tLSstf+3T7+8Y/z8Y9/vN99ly9fzksvvdTz+e23347X62XSMMtoJJ3IufTSS/nZz37Wa/2Xy+Xiwgsv5EMf+hCnn346c+fOZePGjcMKSCTX2gilWOjYWVdZhG94A51Y1Lk72dg49GOMMhrUiIw8ut5LxnUncjQjJysCDK1t+PvLqkZDoWMD8YTze5WIO//G45BIABZ4vU4DiSlTnSROeXnG6uD0GZ0xxBIxFjYszNo5RUaT73//+xx++OEUFxfzt7/9jV/+8pfceOONuQ4rLX71q18xZcoUJk6cyEsvvcSFF17ISSedNOSCy92SvoK+8sorTJgwoc/n5s6dy9NPP83VV189rGBERoIOwkNrPe4a5mwcfwDKK2CIBbdGG9vYxBIxFk1YlOtQRGQ3ut5LximRk1WBFGcad2s3zk2WkdexykA44nT4jMfoWQ/vcYPb4/xelZQ6s5srKqCk5P2PHP3OdUQ6KMsvU7cqkX4888wzXHvttXR2djJlyhR+9KMf8dnPfjbXYaXFli1buPTSS9myZQsTJkzgYx/7GN/+9reHfdykX836G9R1c7vdXHLJJcMOSCTXOoiQl0oix05HfRzj1Axobs7aFN9c6wh3UF5QrmVVIiOMrveScUrkZFXnEGfk9NT/G+6NqrToagYRDIBtQ14+VJRDVbVTW7CoyFmqV1DgLJ3KQuvfVOwI7mB61XQmlk7MdSgiI9Lvfve7XIeQMV/72tf42te+lvbj6goqspsYCYLEUlxa1Z3IGUYhwHAYCvKhvn7oxxhltge3M7d2Lg0lDbkORUREskmJnKxKdcl4t/TcqBomY4Pf7yRxCgugqdkZK1VXQ2lpVpdHDZUxhmAsyOLGxcPuUiMi0i2pV3WXy4Xb7U7548orr8x0/CJpNZQWnR1dNXKGNdAJBKC6BsrGRwtuYwzheFiDGpERRtd7yYpw2JlVMU5moOZaG+GUZhoDxI2HIM6YZFg3qobMgM8H27Y7s2vmzYNDDoUDDoDWVme8NErGD4FYgGJvsbpViUhaJXUrZN26dUM6uDpayGgTJEaUBGXkJ7W9MdBpKoBhJHKMDQnbKb43SgYlw9UZ7aQ0r1TLqkRGGF3vJSvC4XFzvcs1g0m59h+8P9s4jxAFVjgTofXPtmHnTmeJ1KJFMGmSs2RqlNri38KUyim0VrTmOhQRGUOSSuRMnjw503GIjAgBosSwkx7wBCkhTh4WNqVWx9BOGgw567rH0bKqrf6tTK+eTnN5c65DEZHd6HovWRHOcmJgHIuSIEQs5Rk57y8bz/KyqlgUdu1ylk4tXAQ1Ndk9f5rZxiYYC3LgpANxu0ZW3R4RGd2SntO6bdu2AZ+Px+M888wzww5IJJeCXUurkh3w+GxnWVWp1Y7Lsod40gBMmOAkc8YB29iEE2FWNK/AZWlavchIo+u9ZJwSOVnjJ0o0hXFNN99wZxsPRSgIbe0waTIsXzHqkzgAO4M7qS6sVttxEUm7pN9FTZgwodfgbv78+bz77rs9n+/cuZNly5alNzqRLAsQwwAukpvyPew7VokEYDmJnHFie2A7NYU17Dth31yHIiJ90PVeMk6JnKxxav/ZKRc79nXX/8tWx6poFDr9MGsWLF3qdKEaA7YFt7GwYSF1xXW5DkVExpik2wUYY3p9vn79emKx2IDbiIw2QWJJpnAcw07kBAJQWgJ14+cCvz24naOmHUV1UXWuQxGRPuh6Lxnn96vQcZYEhjojJ5sdqxIJaNsFLa0wd+6Iax0+VJF4BAuLpROX5joUGWs6OiAYzN75ioqgvDx75xvFLr/8cu6++25Wr16d8XOlte+jus/IaBcgSipvTzqGm8gJh50ifl7v0PYfZYKxIF6XV4MakVFO13sZls5OtR7PkgAx4th4Up6Rk6VEjjGwcwfUN8DChWMmiQOwNbCViaUTmVc3L9ehyFjS0QFXXQU7dmTvnDU1cMklKSdztmzZwre//W3uu+8+Nm7cSF1dHQsXLuT888/ngx/8IC0tLZx//vmcf/75mYl7jNNVVGQ3AWKDb7SbnqnHQ2nNabpq6oyBNeDJ2ty5mSmVU5hdq25VIiLjlt+vRE6WBIgCYKUw3zhq8ghTDGQ6kWNg104oK4d993W6VI0Rxhjaw+0cPf1oCr3jowaiZEkw6CRxCguzswSx+3zBYEqJnPXr17NixQoqKir43ve+x/z584nFYjzwwAN88Ytf5LXXXstg0AOLxWJ4x8BN9KTT85Zl0dnZic/no6OjA8uy8Pv9+Hy+ng+R0a6dcNJ3rWxj0WmcF7QyV3vqJwuHnXaaVVWp7zsK2cYmEAtw0OSD8Lg0gBcZqXS9l4wLBJTIyZJUb1DB+7NxCgiQZ0XTHdL7Ov3g8TotxsvKMneeHOiMdlKSV8KihkW5DkXGqqIiKC3N/McQk0Vf+MIXsCyLZ555hhNPPJEZM2Ywd+5cLrjgAv71r38ldYzbbruNiooKHnjgAWbPnk1JSQlHHnkkmzdv7tnGtm2uvPJKmpqayM/PZ+HChdx///09z69fvx7LsrjrrrtYuXIlBQUF3H777ZxxxhmccMIJXH311dTX11NRUcGVV15JPB7n//2//0dVVRVNTU3ceuutvWK68MILmTFjBkVFRUyZMoVLLrlkr+Xn2ZJSjZwZM2b0+nzRokW9PtdUaxnt2gknXRAwSCkGNy4SFNGZ+slCIaipHTfdqnYEd1BdWM3iCYtzHYqIDEDXe8koY5TIySI/qSdisrKsyk44d/gXLID6+sydJ0c2d25mbt1cWitbcx2KSNbt2rWL+++/n29/+9sUFxfv9XxFRUWf+51xxhmsX7+exx57rOexYDDI97//fX7961/jcrn45Cc/yVe/+lVuv/12AH74wx9y3XXX8bOf/YxFixbxi1/8guOOO45XXnmF6dOn9xznoosu4rrrrmPRokUUFBTw2GOP8cgjj9DU1MTjjz/OE088wWc+8xmefPJJDj74YJ5++mnuuusuzj77bA4//HCampoAKC0t5bbbbqOxsZH//Oc/nHXWWZSWlvK1r30tfd/AJCV9FX300UczGYfIiNBBGG+SBQE7bWc2TrHlw2UNofBnPO4MXsbJG6JtgW0cPuVwaotrcx2KiAxA13vJqGgUYjElcrKknTDulNo47JbIyWTHqrZ2qKmGKVMyd44cSdgJonaU5c3LcVkq6i3jz9q1azHGMGvWrJT2mzBhArZt93osFotx0003MXXqVADOPfdcrrzyyp7nv//973PhhRdyyimnAPDd736XRx99lOuvv54bbrihZ7vzzz+fj3zkI72OXVVVxY9+9CNcLhczZ87k2muvJRgM8vWvfx2Aiy++mO985zv885//7Dn+N7/5zZ79W1pa+OpXv8qdd945shM5K1euHHSbXbuGUCdEZIRIYOMnmnRnB3/XsqpSqyP1k8XjTkG/cbKsKhQL4XF52L9p/1yHIiKD0PVeMiocdq6B42Q2aq61EUr6BlW3jHesikQAA7NmQ15eZs6RQ5v9m5lQMoF9J+yb61BEcmKonS2vueaavR4rKirqSeKAk+zZtm0bAD6fj02bNrFixYpe+6xYsYKXXnqp12NLlizZ69hz587FtVsHxfr6eubNe784udvtprq6uud8AHfddRc/+tGPeOutt/D7/cTjccpytDQ0LWniv//975x00klMnDgxHYcTyYkgMWLYSS+t6k7klAwlkRMKOWtOKytT33cUetf3LlMrpzK3dm6uQxGRYdD1XoatO5GjGTlZ0U449dbjGV1aZaCjHSY2QWNjBo6fW7ax2RnayaEth1JRUJHrcERyYvr06ViWlZaCxnsWJbYsa0iJor6WePV17L4e654l9NRTT/GJT3yCo48+mnvvvZcXX3yRb3zjG0SjGawlNoAhJ3I2bNjAZZddRktLCx/72MdwuVz86le/SmdsIlkVIEaMRPJLq3oSOe2pnywchtq6cTGQDcfDxO04R00/Cq979FeIFxlvdL2XtFIiJ2sS2PiIJH2DqltGEzl+vzMba/bsMbm0fFtgG7VFtRw46cBchyKSM1VVVaxatYobbriBQCCw1/Pt7e1pOU9ZWRmNjY088cQTvR5/4oknmDNnTlrOsbsnn3ySyZMn841vfIMlS5Ywffp0NmzYkPbzJCulq2g0GuWPf/wjP//5z3niiSc47LDDeO+993jxxReZP39+pmIUyYogMaIkUp6Rk/rSKuO0Hh8nbcffaX+HWTWzWDpxaa5DEZEk6XovGaNETtYEumYa56cwIydsCojiLHtLeyLHTkAgCPPnp9TGeLQwxrAtsI0Pz/ow9SVjr4CzSCpuuOEGVqxYwdKlS7nyyivZZ599iMfjPPjgg/z0pz/l1Vdf3Wufiy++mI0bN6Z0s+j//b//x2WXXcbUqVNZuHAht956K6tXr+4phpxO06dP55133uHOO+9kv/3247777uNPf/pT2s+TrKSvol/60pe44447mD59Op/85Ce56667qK6uxuv14nanNmVTZCQKEO1aWpXsjJwKYAhLqyIRyMsfF/VxQrEQcRPnqGlHkecee+vgRcYiXe8lo5TIyZoAUaIkKCX566/POGOTIjrxWPH0BtTZCRUVsFu9i7FkZ2gnFQUVrGwZvM6YyLAFgyP6PFOmTOGFF17g29/+Nv/1X//F5s2bqa2tZfHixfz0pz/tc5/NmzfzzjvvpHSe8847j46ODv7rv/6Lbdu2MWfOHO65555eHavS5bjjjuMrX/kK5557LpFIhGOOOYZLLrmEyy+/PO3nSoZlklxk5vF4uPDCC7nooosoLS3tedzr9fLSSy9lZPpSpvh8PsrLy+no6Bh+caJNm+Dii6G2FkpK0hOg5MTTvMe1PMk8arEG6fAQNx5+E/kKAKfk/4QCK5T8idrboLQMPvCBMTmteHev7XiNWTWz+MZB39CyKpFBpPXaNAxj5Xo/Ur6fsoenn4Zrr3VmZUhGrWUXl/IokyinIMl7t2sTc/ln7GgaXBs4Mu936QvGGNi2DRYuhJkz03fcEcIYw8vbX+aIKUdw9pKzcx2OjGDJXpvC4TDr1q2jtbWVgoKC95/o6ICrroIdO7IQbZeaGrjkkjE5k24k6vdnv4ekb4f8+te/5he/+AUTJkzgmGOO4VOf+hRHHXVUWoIVGQkCxLBg0CQOgN84L7weouSTQhIHnNarDQ1jPokTjAUxxnDUNNXGERlNdL2XjAqHx/z1b6RwZhonUip27LMrACgbSv2/gQSDUFQITU3pPe4I0RHpoNhbzKGth+Y6FBnrysudpEq2ZuSA06BFSZwRJ+lEzqmnnsqpp57KunXruO222/jiF79IMBjEtm3WrFkzau7QifQn2JXIScbu9XFSGo/aCbBc42JZ1Tsd7zCvbh6LGxfnOhQRSYGu95JR4XCuIxg3AsQwgCvp0c37S6vSXh8n4Idp06GPzjFjwcbOjSxvWs70qvQv5xDZS3m5EiuSeteq1tZWrrjiCtavX89vfvMbTjzxRD75yU/S1NTEeeedl4kYRbIiQBSb5NrZ+YfasSoUhsKCMZ/I8Uf9ABw57Ug8LtVBEBmNdL2XjFAiJ2v8RFNI4Tje71i1K32BRCLg8cKkSek75gjSHm4n353PYVMOw9JsMxHJkiG/w7Isi1WrVrFq1Sp27drFr371K2699dZ0xiaSVR1E8CSZ23y/9XiKhY7DYaivh/z8VMMbNWxj83bb2xw8+WD2nbBvrsMRkWHS9V7SKhx26qVIxgWIJnl7ymFMhlqPd3Y6Y5/q6vQdc4QwxvBOxzscMfUI5tXNy3U4IjKOpDwjpy9VVVWcf/75vPTSS+k4nEhOtBFOumPVkFuPJ+Jjvu34+vb1NJU1ccq8U3C71OFGZCzR9V6Gze8HdT/Lik4iKW0fopg4eVjYqY9v+pNIgLGhpWVM1kba1LmJuuI6jpt5nGbjiEhWJZXI+c53vkMolFxB16effpr77rtvWEGJ5IKPMN6kZ+RUACkmcowNWDCGu6d0hDuIJWKcNPckGkoach2OiKRI13vJuM5OtR7Pkl2EUyt03DUbp9jy4bYS6Qmis9Op5dEw9sYEsUSMnaGdHD3taBpLG3MdjoiMM0m9a12zZg2TJk3iC1/4An/729/Yvn17z3PxeJx///vf3HjjjSxfvpyTTz65V7tSkdHAxuAjkvKMnJSWVkUizpKqMVqcLG7HWd+xnpUtK1nevDzX4YjIEOh6Lxnn9yuRkyXtKdygAvDZaV5WZYwz9mlpBe/Y6165rm0dM6tn8sEpH8x1KCIyDiX16v6rX/2Khx56iFgsxsc//nEaGhrIy8ujtLSU/Px8Fi1axC9+8QtOO+00XnvtNQ4++OCUgrjhhhtoaWmhoKCA/fffn2eeeSap/e68804sy+KEE05I6XwiewoRI5pki86IySdKATCERE5xkdPCbwx6e9fbTKucxklzT8JlpWXVpvz/9u47Tqrq7h/4506f2d4LZdkFBOlNELuCYDQ+ah591J8FjWmWGGOJJSqiUWwxGmPUqNEkjwZjHjVFxYJiRVAQBQVCW5rsLtumz9yZuef3xzADw065d3Znd2bn835e+0Rmzr337N1yzn7vOd8vUT/L5HjPsZ4AAG43Azn9QECgW+OKnMhq4z4rPe71AlYrMGRI35wvizj9TggInDn2TBSYBmclLiLKbqpH0smTJ+Opp57Ck08+iS+//BI7d+6E1+tFZWUlpkyZgso08368+OKLuPbaa/HEE09g1qxZePjhhzF//nxs2rQJ1dXVCY9rbm7G9ddfj2OPPTat6xIdzI0AAlBgQ+onRpHVOBa4YZQC6i8iy0DDiEG5R3yXfReMeiPOm3Aeyq2DuyIX0WCXifGeYz0BCK/QYCCnX/gQhA/BtAI5RX0VyPG4gSFDB13JcUUoaO5uxjHDj8ERQ44Y6O4QUZ7SPJLqdDpMnToVU6dO7ZMOPPTQQ/jhD3+ISy+9FADwxBNP4LXXXsMf//hH3HTTTXGPCYVCuOCCC7Bo0SJ8+OGH6O7u7pO+UP7yIIAAQqq2VqW1rQoiPIEtLU2vg1lsl30X5JCMBVMWsEoV0SDSl+M9x3oCEH6gEQgwkNMP3PtXGhdDfZXMAxU5u3vfAaEAigLUD77cMdu6tqG+qB7njD+HK5BpQNh9dngCnn67ns1oQ4klO1JDPPfcc7jmmms0zQkkScIrr7wy6Fb2qh5JQ6EQHnzwQfzzn/+ELMuYM2cOFi5cCKvVmvbFZVnG6tWrcfPNN0df0+l0mDt3LlasWJHwuDvvvBPV1dW47LLL8OGHH6a8jt/vh99/IHO/w+FIu880OLkhQ0ZI1V7ytEqPB4KAwTjoEh1HgjiXTLkEJzWexIoNRINAX4/3HOspyucDgsHwdhvKKDdkBFRuGY9wpVPIIRGvL/x1rqrq/bmySJu7DRIkXDjpQgwtHjrQ3aE8ZPfZcdcHd6Hd095v16y0VeK2427THMxpaWnB3Xffjddeew179uxBdXU1pkyZgmuuuQZz5szBiBEjcM011+Caa67JTMfjaG5uRmNjI7744gtMmTKl366bCaoDOffccw/uuOMOzJ07F1arFY888gja2trwxz/+Me2Lt7e3IxQKoaamJub1mpoabNy4Me4xH330EZ555hmsXbtW9XUWL16MRYsWpd1PGvw8CCAEAYOKQE5apcf9fsBiHjSBHCEEdjl2IagEGcQhGmT6erznWE9RkUAOV+RkXGRFjtpkx35hhh/hAFufbK3yuIGhwwZVXkBPwINWVyv+Z8L/YOaQmQPdHcpTnoAH7Z52WA1W2IyZ//mKXM8T8GgK5DQ3N+Poo49GaWkpHnjgAUycOBGBQABvvvkmrrzyyoTjP6mnej3gn//8Z/z+97/Hm2++iVdffRX/+te/8Pzzz0NRlEz2L4bT6cRFF12Ep556StMe/Ztvvhl2uz36sWvXrgz2knKRG+FcNxJSByPSKj3u9wGlZYNi8uqW3fh639fQSToGcYgGoYEe7znWD2IM5PQbF2TVD6iAXuT/i0cogCKAurrenSeLhJQQtnRuwexhs3HmmDM576EBZzPaUGQuyvhHusGiK664ApIkYdWqVfjv//5vHHbYYRg/fjyuvfZafPrpp6rP89xzz2H48OGw2Ww466yz0NHR0aPNP/7xD0ybNg0WiwVNTU1YtGgRgsFg3PM1NjYCAKZOnQpJknDCCScAAD777DOcfPLJqKysRElJCY4//nisWbNG+yfej1SPpDt37sSpp54a/ffcuXMhSRK+/fZbDB2a3tLCyspK6PV6tLa2xrze2tqK2traHu23bt2K5uZmnH766dHXIhNLg8GATZs2YeTIkT2OM5vNMJvV7xGm/OOB+klLWjlyQiGgokJrt7JKSAlhh30HfEEfjqg/AmePOxsjy3v+vBFRbuvr8Z5jPUX5fMyR00/ckCFB3QMq4MBDKm35/xLwegGrZdBsq1KEgk0dm9BU1oSLJ18Ms4G/Z4iS6ezsxNKlS3H33XejIE6y89IEOUMvueQSNDc3Y/ny5QCAlStX4rLLLsPixYtx5plnYunSpVi4cGHMMR9++CEuvvhi/Pa3v8Wxxx6LrVu34kc/+hEA9GgLAKtWrcLMmTPxzjvvYPz48TCZTADCD5EWLFiARx99FEII/PrXv8app56KzZs3o6ioqBd3I3NUj6TBYBAWiyXmNaPRiEAg/ai9yWTC9OnTsWzZsmjyIUVRsGzZMlx11VU92o8dOxbr1q2Lee3WW2+F0+nEI488gmHDhqXdF8pvagM5QgAuEd4epXqyoyiApMvJbVWKUNDl7UK7px3+kB/DS4bjzLFn4pjhx8Cg40ScaDDq6/GeYz1FcUVOv3EjAKGh/YFt4929v7jHM2i2VYWUEDa2b0R9UT1+MO0HqC5IXGWPiMK2bNkCIQTGjh2r6bi6urqY1b+PPPIITjnlFPziF78AABx22GH45JNPsHTp0mibRYsW4aabbsKCBQsAAE1NTbjrrrvwi1/8Im4gp2p/gLmioiLmYdJJJ50U0+4Pf/gDSktL8f777+O73/2ups+jv6geSYUQuOSSS2Kedvl8PvzkJz+JibS9/PLLmjpw7bXXYsGCBZgxYwZmzpyJhx9+GG63O1rZ4uKLL8aQIUOwePFiWCwWTJgwIeb4SETv0NeJtLDDp+qplRcFCMEICQoKJZWJNP1+wGwCSgYu27sQAnJIhj/khyIUKEKBEAKKCP+yFBAQQiAkQvAH/fCH/AgoAUiQUGopxdS6qZhaOxXT66ezvDjRIJeJ8Z5jPQEIr9QAAB0r/WSaCzKgIZST1rbxeAbRtqqgEsSGfRvQVNaEy4+4HE1lTQPdJaKcIISWMPIBixcvjvn3hg0bcNZZZ8W8Nnv27JhAzpdffomPP/4Yd999d/S1UCgEn88Hj8cDm8qAcmtrK2699VYsX74cbW1tCIVC8Hg82LlzZ1qfS39QHciJRLkOduGFF/a6A+eeey727duH22+/HS0tLZgyZQqWLl0aTYq4c+dO6DjgU4bZ4YdJQ6JjG5zQSSrzRfj9QFFRv1bpEEKg09uJfZ59CCrhPaJGnREWgwU6SQedpIMkSdGymZEglsVgwZDiIai2VaPCVoG6wjqMrRyLSlsl94MT5YlMjPcc6wlAOJDDsaRfdMELvYaKVc6+WpHj9Q6KalVySMbG9o04vPJwXH7E5axQRaTB6NGjIUlSvyQ0drlcWLRoEb73ve/1eO/Q1cXJLFiwAB0dHXjkkUfQ0NAAs9mM2bNnQ5blvuxun1IdyHn22Wcz1omrrroq7vJqANE9cok899xzfd8hyjvd8MGoYsITnejoNDyxkmWgsrJfJq9ySMZe517Y/XaUWkoxa8gsNJU1odJWiUpbJUotpTDqjdBLeuh1euglPSRJgoRwUEev00eDO0SUnzI13nOsJ/h84T3KlHFd8Kl6QBURXZGj6+7dhT0eYNjwnN1WJYTAXtdetHvaMaV2Cn4y4yfcTkWkUXl5OebPn4/HHnsMV199dY88Od3d3Qnz5Bzs8MMPx8qVK2NeOzRR8rRp07Bp0yaMGjVKVd8iOXFCoVDM6x9//DF+//vfR3ME7tq1C+3t/VfiPR3cpEx5T0DADr+qEp0uzckA909YVfyy6g0hBL51fotObycaShtwxtgzcET9EagvqudKGiIiyg6RrVWUcd3wwaRyRY4ipGj+v15trRJKOC9gnCTmucDhd6C5uxkV1gpcNOkizBs5DwWmnolaiSi1xx57DEcffTRmzpyJO++8E5MmTUIwGMTbb7+Nxx9/HBs2bOhxzM0334w9e/bgz3/+MwDg6quvxtFHH40HH3wQZ5xxBt58882YbVUAcPvtt+O73/0uhg8fjrPPPhs6nQ5ffvkl1q9fj1/96lc9rlFdXQ2r1YqlS5di6NChsFgsKCkpwejRo/GXv/wFM2bMgMPhwA033ABrP+6mSAcfvVPe8yEIP4LaVuSonegEAoDRkNFExyElhE0dmxASIXx/6vdx90l348yxZ2JI8RAGcYiIKHu43QPdg7wQhAIXZFXzGgBwiyII6KFDEFa40r+wzw9YrOFVyDkiEApgr3Mv1retR4urBSeMOAG3H387zjr8LAZxKKt5Ah44/c6Mf3gCnrT619TUhDVr1uDEE0/EddddhwkTJuDkk0/GsmXL8Pjjj8c9Zu/evTE5aY488kg89dRTeOSRRzB58mS89dZbuPXWW2OOmT9/Pv7973/jrbfewhFHHIEjjzwSv/nNb9DQ0BD3GgaDAb/97W/x5JNPor6+HmeccQYA4JlnnkFXVxemTZuGiy66CFdffTWqq7N7NR5X5FDe8yCAAEIoQupykppLj/v9gNkSzpGTAd6AF5s7N6OxrBELJi/ApJpJGbkOERFRrzkcrFjVD9yQEUAIVhhVtT+w2tgBndSLrW9eL1BRMWDbqoQQEPtXQkeSrSpCiSno4Av6oh+egAc6SYdKWyXmjZyHGfUzMKV2CreYU1azGW2otFWi3dMOb7B/VjlW2iphM2r/ua6rq8Pvfvc7/O53v4v7fnNzc8y/422j/v73v4/vf//7Ma9dd911Mf+eP38+5s+fn7AfhyZf/sEPfoAf/OAHMa9NnToVn332WcxrZ599dsJzZgOOppT33AggAEXl1iqNpcdlGaipzcjE1RPwYHPHZsweNhsLJi9ATWFNn1+DiIiozzgcgFFdcIHS54IMGSEUq3hABfRhouNAILytKsOrgUNKCN2+bnR6O+EP+QGEizYoUKDbP5eLrEiO5ACMFHiwGCywGqwYVjIMw4uHY3z1eIytHItCU2FG+0zUV0osJbjtuNvSXimTDpvRhhLLwFXfpfgYyKG8F1mRk2oJsiIkuLUGcoJBoKyst13seVoliM2dm3H08KNx1cyrYDGoz8pOREQ0INxursjpB5EHVGpz5PRJ6fFgEDDogfLy9M+RhBAC+zz7sM+9DwICpZZSjKsah6ayJhSZi1BkKkKhqRAGnSEmiBMp4qCX9DDoDCi1lKLYXAy9Tn1FL6JsU2IpYWCFGMghCi9BTr0ix4NCCOigQwg2VXvIBQAJKOzbpzxCCGzu2IzR5aOxYPICBnGIiCj7CQE4nQzk9AP3/hU5anPkOKPbxrvTv6jXC1htGSnu4JJd2N61HaWWUpw88mSMrxqPwyoOQ6WtkrkAiShvcTSlvOdBAED4yU0ykfw4BZJD3arhQDA8Ye3j/Di7HLtQbC7GpVMuRVVBVZ+em4iIKCNkOfzBrVUZ594/r9GlmNdEuPpiRY7PBzQ29unXN6gEsb1rO0IihKOHH42zxp6FxrLGPjs/EVEuYyCH8l5kwpOyXXRblUPdiWUZMJn6dEVOp7cTnoAHP5z2QxxedXifnZeIiCijvN5wDpUBSoSbT9yQNbXvfY4cES493ofVqvxBPza2b8ToitE4a+xZmDlkJrdDUV46NFEvDX5qv+YM5FDe86gM5GiuWCX7gfKKPns6FVSC2OXYhTPGnIETG0/sk3MSERH1C683nEeFK3IyzgUZgLo/BGRhgh/h4FragRy/HzCZ+yw/jhySsbF9I6bVTcMVR1yBCltFn5yXKJcY9/+u9Hg8sFqtA9wb6k8eTziRtTHFeMlADuU9O3yqFh+7tK7ICQT7NOnfLvsujCgdge8d/j2WxiQiotwSCeQwR07GOeCHpKISJ3Ag0bEFbhgldQ+2evB6gaLiPtlKHgniTK2byiAO5TW9Xo/S0lK0tbUBAGw2G3NCDXJCCHg8HrS1taG0tBR6ffJViBxNKe91wKuqssPBOXJSE+GPPsqPI4dkuGQXLpp8EYrNxX1yTiIion7j84W3VjGQk3Fd8MGkMpCjebVxPLLcJ2XH5ZCMDe0bMLV2Kq484koGcSjv1dbWAkA0mEP5obS0NPq1T4ajKeW9LtWBHA2lx4MhQG/os/w4zd3NGFM5BkcPO7pPzkdERNSvuCKn33TBq6FiVSmAXmyrUkKATtfrFchCCPyn4z+YXDOZK3GI9pMkCXV1daiurkYgkOaKOcopRqMx5UqcCI6mlNdCUGCHP2UgRwiNyY4jiY77YEWOJ+BBUAnitNGnwWrkHlkiIspBXm94xQa3BmSUgFA1r4k4kOg4zRU5Xh9gsfQ6kLPXtRelllJcNOkiVNr6Lmky0WCg1+tV/3FP+YOJNiiveRCAjFDKCY8HhVCghwQFNjhTn1iWgQJbOJjTSzu6d2Bi9UTMHDKz1+ciIiIaEF7vQPcgL3gQgB9BGDXmyEl7RY7XGy7sYDandzwAX9CHDm8HTj/sdIwsH5n2eYiI8gkDOZTXXJBVBXIie8htcEInqagEEZDDE5tePnl0+p3Q6/T47mHfhVHPSh9ERJSjIityKKPcKh9QRUQCOenlyBFAKATU1KRx7P4zCIEtnVswpWYK5o+an/Z5iIjyDQM5lNfUBnLcWvLjAOFcx32wrWqnfSem103H5NrJvT4XERHRgPH5wvuUKaOc8KsO5ChCiub/K9J1a79YMAgYDUBpqfZj92txtaDEXILzJpwHi8GS9nmIiPINAzmU1yKBnFRJATWVHldCgE7qdSDHG/BCr9NjbtNclhsnIqLc5nSGk+JSRjn3z2vMKtJgelAEAT10CMEGl/aL+fzh/DjF6VXT9Af9aPe047uHfRejK0andQ4ionzFEZXymgsyAECH5Mu9D5TnVBHI8cuAydzrilV7XXsxonQExlWN69V5iIiIBpzDwYpV/cAFGQKp5zUA4FQOlB5XtW38UH4fUFYOGNPb+r2jewfGV4/HKaNOSet4IqJ8xkAO5bVIICdlOy1bq2QZsFoAa/oVpoQQcMkuHDv8WObGISKi3OdwpP0HP6nnhF99217lxwEQDAEV6ZUJ9wf9CCgBzBs5jxU5iYjSwEAO5TXtgRyVpcfLynuV1LHD24EySxmm1U1L+xxERERZw+nkipx+EFmRo6pttPR4t/YLhUKAXgeUlGg/FsBux240lTVhRv2MtI4nIsp3DORQXuuGL+XyYyE0JjsWStoTm4hWVysm105GXVFdr85DREQ04EKhcNUqrsjJuE54YVCxrQoAnNFAThorcvw+wGxJa74TCAXgCXgwb+Q8JjgmIkoTAzmU1zrhTVnZwYsChGAEIGCTnMlPKJTwSpxe5MfxB/2QJAmzh85O+xxERERZw+cLVzjiipyMa4dHc+nxtFbk+PzhJMcW7YGYb53fYnjJcMwaOkv7dYmICAADOZTnuuBTXXrcBif0kpL8hLIMmEy9CuS0ulsxtHgoJtZMTPscREREWcPrBQIBBnIyTECgCz5VFauAgws5pLEiJxgAqqo0HxZSQrD77ZjbNBeFpt4VhSAiymcM5FDeCkKBE/6UgRxt+XEC4YpVBQVp9UkIgW5fN44ZfgyXGxMR0eDg9YZX5HBrVUb5EYIbsqoVOQFhhA/huYrmFTmR1celpZr7uNe1F3VFdTh6+NGajyUiogMYyKG85YIMGSEVgRwNpcdlObzUWJfej5bdb0exuRjT66andTwREVHW4YqcfuGCDL+KeQ1wYFuVGV6YJHWFH6L8/vBDK435cRShoMPbgRNHnIhSS6m2axIRUQwGcihvqQ/kaEh0HAoBZWVp96nF1YJxVeMwvGR42ucgIiLKKpEVOQzkZJQTflXzGuDgh1Td2i/k84dXHttsmg5r97Sj0lqJY4cfq/2aREQUg4EcyltqAzlu1VurRPijF9uqAkoAR9QfAakXpcuJiIiyis8XLgGpV5eEl9Lj3D+vMatakdOLilWyDFRXh7dXabDPvQ9T66aiprBG+zWJiCgGAzmUt9yQIUOBMcWPgepkgMEQoDekHcix++0oNhXjsIrD0jqeiIgoK3m9A92DvOCCDAUCehXT+/QrVu1/aKUxP04gFAAkYEb9DI3XIyKieBjIobzlggwJgITET5SEOLC1qiDVipyADJiMaQdyOjwdGF4yHPVF9WkdT0RElJUYyOkXTvhVt3WluyJHlgGjSXN+nDZ3G2oKajC+ary26xERUVwM5FDeigRykvHDiiBMAFQEcuQAYLEClvSqTbkDbkyvn85tVURENLgwkNMvXFCftDiyIkdz6XGfH7BZgaIiTYd1ejsxc8hMFJjSe9hFRESxGMihvOWCDAGRvM3+1ThWuGCQQslPGJDDS43TCMR4Ah5YDBZuqyIiosHH7R7oHuSFLvigS/mIKna1seatVX4/UFGpqTqnL+iDUW/ElNop2q5FREQJMZBDeasTXuj7svS4IsKlx9PQ4elATWENmsqa0jqeiIgoa7lcrFjVDzrgUVWxyosChGCEBAUFklPbRYQAyss1HdLqasWQ4iEYWzlW27WIiCghBnIob3XCC1PKRMeR/Dgplh6L/St7CgvT6ovdb8fU2qkw6U1pHU9ERJS1HA4GcvpBB7wqK1aVAghvGddJivoLhIKAXqdpW5UQAna/HbOHzuYch4ioDzGQQ3mrC76+Kz0eDADG9CpWBZUgJEnC4ZWHaz6WiIgo6zmdgNE40L0Y1GSE4IKsakXOgYpVGvPj+GXAZNa0+tgdcKPAWIDJNZO1XYuIiJJiIIfykowQ3ComPKq3VskyYDKlFcjp9Hai3FLO/DhERDT4CBEO5HBFTka5IENGSF0gR4nMbbq1XcTvD6/GMZtVH9LqakVDaQO3jhMR9TEGcigvqZ3wOIXKyY4cAAoK03ri2OHpwLiqcSixaCvlSURElPVkeX/Jaq7IySQn/PAjqCqQk3bp8UAAqKhQ3VwIAXfAjdlDZ0OvS90vIiJSj4EcyktqAjnhqg4qJzuBAFBWprkfQggElSAm1kzUfCwREVHW83qBYJArcjIsMq8xI/V9PrC1qlvDFUT4Q8O2KoffgWJzMec4REQZwEAO5SUXZASgJA3k+GBDECYAAgVqqlalkejY4XegyFzEbVVERDQ4+Xzhhx1ckZNRTsgIQUCvovx4WityAkHAYNQUyOn0dmJI0RAMLR6q/jpERKQKAzmUl1yQEYQCQ5IfgchExwYXDFIo8ckUBZCktPLjdHg7MKx4GIYUDdF8LBERUdbjipx+4YQfACClCOQEhQEehKtOacqR4/cDZpOmilUu2YUptVOgk/jnBhFRX+NvVspLLsgAkk94DuTHSbWtSgZMxrRW5LhlNybXToYkpX6CRkRElHO83vCKHAZyMioyr0nZbn81TiP8MMOn/gKyHygpVf11lEMy9Do9RleMVn8NIiJSjYEcyktqJjwHlh53J28oB8LlOG02TX0IKSHoJB0aSho0HUdERJQzuCKnX3TDp2JT1cHVOO3Q9AwpGNSU6LjL24VyazlGlo3UcBEiIlKLgRzKS2oCOapX5MhyeM+4TtuPUyQJYEMpAzlERDRIeb3h7cdceZpRHfCqKz2eTqJjIQBImrZVdfm6cHjl4Sgyqz+GiIjUYyCH8lIHvDCm+PZ3RSY7uhSBnFAIKC3V3Ae7347awlpU2ao0H0tERJQTfD4GcfpBBzwaK1ZpSXS8fwu5ykTHQggEQgGMrx6v/hpERKQJAzmUlzpVPLlyqtpatb8cZxr5cVyyC+OrxzM/DhERDV5e7/4VHZQpQShwwK9yRY7KbeMH8/sBi0X1XMclu1BgKsCo8lHqr0FERJowkEN5R0CgO0UgRxES3PsTAibdWhUKAXq95opVilAgINBY2qjpOCIiopzCQE7GuSBDRkhVIOdAjpxu9Rfwy0BZueot5J3eTtQU1GBY8TD11yAiIk0YyKG840MQXgRhTDLhcYtiCOigQxA2uBKfTJYBk0lzIMfpd6LIVIThJcM1HUdERJRTnM7wAw/KGCf88KsI5AiR5tYqRQHKytT3x+/E1Nqp0Ov4dSciyhQGcijvqHlydeCJlSP51n45AFis4SXHGtj9dlTZqlBXVKfpOCIiopzicLBiVYZF5jXmFIEcP6wIwgRAoEByqDu5ooRzHKnMjxNUgpB0EsuOExFlGAM5lHfUBHJUV6wKyOFExxrz3Dj8DoyvHg+dxB9BIiIaxFwuwGgc6F4Mak7ICEKBIcW03rF/NY4NLhikkLqTy37AbFJdsarL24UySxnz4xARZRj/iqS840ZA9YqclMkAFaH6KVWEEAJCCDSVNWk6joiIKOc4nVyRk2FO+AEAEpI/VHKK8PaoYl2X+pP7ZcBqA2w2Vc07vZ0YVT4KZVb1W7GIiEg7BnIo77ggIwQF+iQTngNVHZKsyIkkb9RYscodcKPAVICGkgZNxxEREeWUUAhwuxnIyTAXZFXtHMr+QI6kIZAj+4GKClUrj4UQkEMyJlZPVH9+IiJKCwM5lHec8EPa/3+JuPYvP066tSoQAIwGzYmOu33dKLeWY0jxEE3HERER5RSfDwgGubUqw+zwqWrnEGkEcgSAkhJVTX1BHywGC0aWj1R/fiIiSgsDOZR37PuXICfjVLO1KpBexSq7z47DKw+HQccnlERENIh5veGHHlyRk1Ed8KkqPa45kBMKhUuOq8yPY/fbUWIpYdlxIqJ+wEAO5Z19cEOf5Fs/IIzwIRycSboiRw6Et1VpeNIohIAiFCYBJCKiwc/r5YqcftABT8qKVUKkEcjRmOjY7rOjqawJVqNV3fmJiChtDORQ3mmFG5akiY7DyYuN8MEsJVm9EwgApdqS+XmDXlgMFjSUMj8OERENcpFADlfkZEwICuwqVuT4YEMAZgAChakKOUT4ZcBWAFgsqpoHQgGMqRij7txERNQrDORQXglBQQc8sCDxpDKSHydpomMAgFD9lCrC7rOjzFrGZcdERDT4uVyAvH8bMmWEA354EUw6rwEOrMYpgEN96fGArDrRcSAUgE6nw7ASzm+IiPoDAzmUVyITHnOSCY+qilVKCJB0mvPjOPwONJU1wWwwazqOiIgo5zid4SCAikAApacbPvjUBHKUUgBaSo+L8H4slYmOHX4HSswlGF4yXOX5iYioNxjIobxihx/+FBMe1/5ATtKlx3IgrUTHsiJjZBmrORARUR5wOge6B4NeJJCT7AEVADhEOQCNiY71ek2JjmsLa1Fm0bblnIiI0sNADuUVe3TCk3gvuaoVOQEZMJsBm031tRWhQIKE+qJ61ccQERHlLIcjvKqDMqZ7f+lxHZKvetKc6NgvAyaz6kCOJ+DBuOpxkLj6ioioXzCQQ3nFDj8EkLRqVSRHTsqKVSUl4bKcKrlkFwpNhQzkEBFRfujoYMWqDIsEclJJq2JVYaGq/EaKUAAADSUs5EBE1F+yIpDz2GOPYcSIEbBYLJg1axZWrVqVsO1TTz2FY489FmVlZSgrK8PcuXOTtic6mD3FhEeIg1fkdCduGAoCpaWaru30O1FiKUFNYY2m44iIBgOO9Xmoo4OJjjNsHzzQp1iNE57blALQEsgJqE507JJdKDAWMD8OEVE/GvBAzosvvohrr70WCxcuxJo1azB58mTMnz8fbW1tcdsvX74c559/Pt577z2sWLECw4YNw7x587Bnz55+7jnloi54k053/LAiiPCks0ByJGglAEia8+M4ZSdGlo2EQccyrESUXzjW5yEhgK6u8DZkypi9cKVMdOxFAYIwQYKioiInEJ7nCKC4WFUfHH4HKmwVqC2sVdWeiIh6b8ADOQ899BB++MMf4tJLL8W4cePwxBNPwGaz4Y9//GPc9s8//zyuuOIKTJkyBWPHjsXTTz8NRVGwbNmyfu455aIWuFVVrLLBmbg8ZzAIGPSaAzlBJYjG0kZNxxARDQYc6/OQ2w34fFyRk0EhKGiHW3Xp8ULJDp2kpD5xMAjoDeoTHfvsGFMxhg+qiIj60YAGcmRZxurVqzF37tzoazqdDnPnzsWKFStUncPj8SAQCKC8vDxhG7/fD4fDEfNB+UdAoA3upImOD1SsSpEfx2gK7x1XKaSEg0LMj0NE+YZjfZ5yOgG/n4GcDHLAD6+q0uNpJDo2m1QFcoQQEBAYWc6KnERE/WlAAznt7e0IhUKoqYnNGVJTU4OWlhZV57jxxhtRX18fM0E81OLFi1FSUhL9GDZsWK/6TbnJiyBckJNOeFRXrLJaNU1OXbILRaYiBnKIKO9wrM9TLhcgy9xalUGR0uOpV+RoLD0u+4GiYlXzHF/QB5PehGHF/HkjIupPA761qjfuvfdeLFmyBK+88gosFkvCdjfffDPsdnv0Y9euXf3YS8oWB0qPJwvklAJQsSKnrExVAsDoeWUnSq2lqC6oVn0MERFxrM9ZTmc4kMMVORnTrWJeA6RRsSoQBJKsfos5t9+BEnMJhpUwkENE1J8GdDNrZWUl9Ho9WltbY15vbW1FbW3yhGkPPvgg7r33XrzzzjuYNGlS0rZmsxlmPhHKe3b44U/x5MqhhCcuJbrOxCcS6hMARjhlJ6bWToVel3hbFxHRYMSxPk85neEHHhoeepA2kdLjuhRVqxyaKlZpS3Rs99sxpXYKbEabqvZERNQ3BnRFjslkwvTp02OSF0aSGc6ePTvhcffffz/uuusuLF26FDNmzOiPrtIgYIcPASgwJvm2t+9fflwidcRvIBRAguZExyElhBGlIzQdQ0Q0GHCsz1NO50D3YNCLBHKS0Vx6PBAEDEbViY79QT/GVo5V1ZaIiPrOgKeXv/baa7FgwQLMmDEDM2fOxMMPPwy3241LL70UAHDxxRdjyJAhWLx4MQDgvvvuw+23344XXngBI0aMiO6vLywsRKGG5LOUf+zwAwCkBE+u/MICH8IBmoSTnUAAMBo1JToOKkFIksT8OESUtzjW5yGHIxxFoIzZBw/0KVbjuFGEEIzQIYQCSUUCcNmvOtFxSAlBJ+kwtHio2i4TEVEfGfBAzrnnnot9+/bh9ttvR0tLC6ZMmYKlS5dGkyLu3LkTOt2BFRSPP/44ZFnG2WefHXOehQsX4o477ujPrlOOsad4chVZjWODA0YpEL+RLAMms6YVOUx0TET5jmN9HuroCD/4oIzZC5fqilWFUjd0korAml8GKitVfe1csguFpkLOb4iIBsCAB3IA4KqrrsJVV10V973ly5fH/Lu5uTnzHaJBqRVuGJJtq1KTH0eWgZpaQK8+143T70SZtQxVBVWqjyEiGmw41ueZjg4mOs6gEBS0w62iYpXGRMfBAFBRoaqpU3aixFKCKhvnN0RE/S2nq1YRadGWYsJzID9OkkBOMBSuWKWBU3ZidPlo6CT+uBERUR4QAujqYunxDHLAD6+K0uPOaCCnO/VJhQAgASUlqvrgkl0YWTaShRyIiAYA/7KkvBCEgg54YEbiyUbqQM7+Sg4a8zMoQkFDSYOmY4iIiHKW2w34fFyRk0GR0uOpAjl2LStyAjJgMqquWBVUgizkQEQ0QBjIobzggB9+hHq3IicYBAwGzYmOdZKO+8eJiCh/OJ2A389ATgZFAjnmvtxa5fcDFquqeU5ICUGChLqiOlX9JSKivsVADuUFe4oJjyJ00fKcJboEpcdlOTwp1RDIcfqdTHRMRET5xeUKj5ncWpUxkdLjuiRVqxQhwRUpPZ4s/1+E3w+UlwO61H8euANuFJgKUFfIQA4R0UBgIIfygh3+pEuQnaIEAnoYIMMGV/yTyHI4iKPhCaNLdqHMWoYKm7rEgURERDnP6Tzw8IMyojtFJU4AcIkSKNBDjwAK4Ex9UkWozgPo9DtRbC5GTWGNqvZERNS3GMihvGBP8eTKLsKBlhKpE1Kih1uBAFBWrum6roALjaWNTHRMRET5w7k/aJBwQKXe2gcP9ElW4wBAlwhXkyqV2lN/KZRQ+OulMj+OS3ahsawRBl1WFMAlIso7/OuS8kKqJ1eqKlYJAEVFmq4bDAUxrGSYpmOIiIhymtPJIE6G7YUrZaLjTiUcyCnTtac+od8f3gqnMpAjKzKaSptUtSUior7HQA7lhU54kz63igRyEu4hD4UAnaQpP44iFEiShJoCLjsmIqI84nDsL2VNmRCCgna4UwZyIityyqR9qU/ql4HCAsBiSdlUEQoTHRMRDTAGcigvtMKdtLKDXUmxIiegPdGxN+CF1WBFbWGtpr4SERHltI4OwGgc6F4MWg744VVRsSoSyClXE8iRZaCiUtVKKrfsRoGRiY6JiAYSAzk06AkItCV5ciWEiq1VshwuyWm1qr6uS3ah0FSI6oJqzX0mIiLKWR0dTHScQZHS48lW5ASEMVqNs0yXKpAjwh8lJaqu75JdKDIXMdExEdEAYiCHBj0PAnBDTjjh8cEGGVYAAsVSV/yTyHK4koOGPf8u2YW6ojpYjeqDP0RERDlNCKCzk6XHM0hNIKdbVAKQYIULFsmb/ISBIGAwqs6P45SdaChtgEnPYB0R0UBhIIcGvUjpcTP0cd93iHCpzULJDoMUjH8SRf2TqghvwIumMiYCJCKiPOJ2hxPnckVOxnSnqMQJAF1KJQA1q3GwP9GxSXUgxx/yY1TZKFVtiYgoMxjIoUHPDh/8CCV8cmVXDpQej0sogARN+XEAABK4f5yIiPKL08lAToZ1w4dUqaQPJDpWUbFK9gOlZYAhdSlxJjomIsoODOTQoNcBL4JQYEjw7Z4yP04gEE7aqCGQEwgFoJf0THRMRET5xeUKb0fm1qqM2QcP9ElrcR5celzFipxgECgvV3VtT8ADm9HGB1VERAOMgRwa9PbBDQEBKcGkJ1p6PFmiY5MZKChQfc1IomMmAiQiorzidO4fN7kiJ1O+hTNpfhwhNJQeFwoASfW2qsj8hg+qiIgGFgM5NOjthB2mBPlxAJUVq0pKAH3icxzKHXCjxFKCcqu6J1xERESDgtMZ/l8NxQFIPR+CaIULBUhc3t2DQsiwQoKCUqkj+QllWVN+HKffieElw2E2cMUVEdFAYiCHBjUFAjtgRwHiPxkMCj1cIpzEuESXIJATDIUrVmngkl1oLG2ETuKPGBER5RGnk0GcDGqFC24EUJhgXgMAXfu3VZVIndBLoeQn9PsBi1X1qmN/yI9R5Ux0TEQ00PhXJg1qXfDCDl/CJ1dOUQYBHYzwwQp3zwZifzrBoiJN1w0oAQwvGa61u0RERLnN4TgwdlKfa4UbHgRgTbIiR/W2KiAcyKmqAnSp/yQQ+7+u3FZFRDTwGMihQa0NbrgRSLgi5+BtVXEfIMoyYDKqXnIMhCc6EiTmxyEiovzT0REuEEAZ0QoXgFSlx9UmOhbhoJvKRMf+kB9mvRk1BZzfEBENNAZyaFBrgxsBhGBM8K3eLVKUHvf7AatN04ocT8ADq8HKJ1ZERJR/OjqY6DiDdsGRsmKV6hU5cgAwmoDSUlXXdskuFJgKUF1Qrao9ERFlDgM5NKi1wg1p///F06GEgy0Vutb4J/D7gIoKVUuOI9wBNyc6RESUf4QAOjtZejxDBAS2oyvhKmMACAkduvevNk65IsfnA2xW1auO3bIb5dZyFJvVr1ImIqLMYCCHBrUdsMOcoGKVEMA+pQ4AUKlridcCEFC95DjCJbtQW1gLm9GmsbdEREQ5zO0Or2TlipyM6IIPXfAlTXTsEOUQ0MMIHwrgTH5Cvw+oqlb9sModcKOxtBESk1kTEQ04BnJo0ApBwS7YYUuQENCNIvhQAAkhlEttPRsEg+GS4xry4wCAN+DFyLKR6XSZiIgod9nt4VUeXJGTEeGKVXLS0uOd0W1V7SmKh2nLjwMAilAwtHio6vZERJQ5DOTQoNUBL1yQEy5Bbt+/Gqdc2geDFOzZwO8HLGagpETTdYUQqCuq09xfIiKinLZvH+DxADauSM2EVrghIwRTgpXGwIFEx+WptlVpzI+jCAUAuG2ciChLMJBDg1a4YlXiJ1eRQE5F3G1VAHx+oLRMU/WNQCgAvU7Pig5ERJR/WlvDqzz0iQMNlL5IxapEef8ADYmONebH8QQ8sBltrMhJRJQlGMihQasNboQgYEzw5KpdhBMdV0l7458gFAIqKzVdM5LomBMdIiLKO3v3IsV+HuqF7eiGNcm2KkBD6XGt+XFkNwqMLORARJQtGMihQatl/5OreBQhoX1/xaq4iY4VJTwZ1Zgfxy27UWwuRoW1QtNxREREOW/7dm6ryhAZIeyGI2l+HLcoggdFkKCkWJGjPT+OO+BGXVEdLAaLhl4TEVGmMJBDg1YzumGBIe57dlGOIEwwQEaJ1NGzgd8fTtaoMT+OO+DG8JLh0Ou4rJyIiPKI1xveWlVQMNA9GZT2wQ0n/EkrVrUowwAAFVIrjFIg8ck05scBwlurGksbVbcnIqLMYiCHBqUAQvgWztT5caQW6CTRs4HfBxQVAlarpuv6g340lDRo7i8REVFOa2sLlx9nICcjWuGGG4GElTiBA4GcGt2u5CfTmB8ngoUciIiyBwM5NCjtgyd5xSoRnoxUJUp0LAeAyipNe/2FCAeEuH+ciIjyTltbeFWOxgcgpE440bGAPsnUPRLIqU0VyNGYHyeoBKGTdJzfEBFlEQZyaFDal7JiVSQ/TrxEx/tX6GjcViWHZJj0Jk50iIgo/7S1hf9XZXCAtNkNB3RJpu0eUQCnKIMEBTW63UnOlEZ+HNmNQlMhK3ISEWURjrY0KLXBDZHgyVVQ6NG5vzxn3ETHsgyYjGnlxykwsaIDERHloT17GMTJEAGBbehKmug4shqnXGqDSZITnyyN/Dgu2YVCUyEqbdoqeRIRUeZwxKVBKVyxKv62qE5RDQE9LHCjAI6eDXz+8NLwoiJN13TLbpRaSlFqKdXeYSIiolwlBLBtG/PjZIgTMjrgTbhdHDg4P06y1TgIb38rKNCUH8cdcKOhpIGFHIiIsggDOTQobUd3woSAkUTHlbq98VPg+P1ARaXmJ4vugBsjSkdA0pBXh4iIKOc5nUBnJwM5GdIKF1yQVVWsUpUfp75e0xxHDsloKGUhByKibMJADg06fgSxV0XFqriJjoUAhAJUVGi+biAUwLDiYZqPIyIiymmsWJVRrXDDjxDMiL8ixiMK4BAVAETyFTmhIKDTA1VVqq8dKeTA/DhERNmFgRwadNpSlOhsF+FExxVSnETHfh9gtgCV2vaBs2IVERHlrba28GpWi2WgezIotcIFCYCUYMt4qzIUAFAm7YNZ8iU+kccDFBZoeljlD/lZyIGIKAsxkEODznZ0w4NA3L3kfmGGQ4QrNcRNdOzxAGVlQGGhpmv6gj5YDBZOdIiIKP+0tYVXtHJrcZ8TENiEjoSrcQCgVe22Kq8PqK0DDAbV13fLbhQYC1BTyBU5RETZhIEcGnQ2oh0SAF2cJ1eRbVVFUhcsPZ5aCSAQBOrqNE9GWbGKiIjy1s6dgDFxRSVKXwe82IYulMOasI2q/DhKKDy3qdY2T3EH3CizlqHErK2SJxERZRYDOTSo+BDEOrSiLMGEZ7fSCAColvb0fFOWAZNJ87YqIPzEqsJagUKTtpU8REREOU1RgB07NK9kJXU2owPd8KEU8bet+YQV3SI8b0maH8fjAWw2zXMct+xGU1kTCzkQEWUZBnJoUNmOLnTAi7I4Ex4hgJ2hwwAADfr/9DzY4wmX4ywt1Xxdd8CNxrJGTnSIiCi/dHcDdjsTHWfIhv2rjPUJpuyR1Til0j5YJG/iE3m9QE1N+IGVBiERwpCiIZqOISKizGMghwaVLeiED0FY4yQ6bhe1cKMYBsio1zX3PFiWwyU50wjGKIrCiQ4REeWf1lZWrMoQP4L4Ei0JV+MABxIdJ99WpQAC4UCOBizkQESUvRjIoUFDQOBLtMKK+En8duxfjTNUtw0GKRT7ZiAA6A2aSnJGKEIBJE50iIgoD7W1hR+EMEdOn9uObuyDJ2F+HCGAb5UGACkCOV4vYLVq3lblDXphMVhQVaB9bkRERJnFQA4NGh3wohndcSc8QgA7lCTbqrweoKgwXLFKI2/AC6vBykAOERHln9bW8EpWbi3uc5vRAX+CVcYA0CFqYBeV0CGIOt2OxCfyeMIPqqyJEybHPSzgQYGxAFU2BnKIiLINAzk0aGxBZ8KEgF2iCk5RBj0CGKrb1vNgnx+oqwf0ict7JhKpWMUnVkRElHd27ADM5oHuxaAjIPAFWhIGcQBgc2giAKBB9x+YJX+CE4nwR22t5j64ZTfKreUs5EBElIUYyKFBYxPaAcRPCBjZVlWva4ZRCsS+GQoBOimtbVVAeKJTU1ADm9GW1vFEREQ5KRAAdu1ifpwMaIMbzehGRYJtVUFhwLbQ4QCA0fr1iU/k9QBWS1pzHHfAjRGlI1jIgYgoCzGQQ4NCEAq+QitKEP+pYGRb1YhE26psNqCiIq1rRypWERER5ZVNm4B9+9Kq9kjJbUYnHPCjJEGi4x3KaARgQQHsSbZVCcDlAoYMTSvYpggFQ4pZyIGIKBsxkEODwg50ow1ulMV5ctWtlKNbVEKHEIbqth7yrgjvHa+tSz9RowDqCuvSO5aIiChXrVkTTnRs44rUvvYN9kEHQIf4q2G27N9WNdqwPnF6Ip8fMJqAhgbN149UrGJ+HCKi7MRADg0KW9AJNwIoiLOXfKcyGgBQp9vRcw95LyY5ABBSQpAkiYmOiYgov7hcwKpVaa9mpcR8COIrtKI0wbYqp1KCvUoDAIFRybZVOZ3h3Djl5Zr7EKlYxfkNEVF2YiCHBoX12AcT9JDiPLmK5Mdp0MXZVuVwAHV1aU1yAMApO1FkLkJdEVfkEBFRHlm3LlyxKs38cpTYVnSiPUnZ8S2hCQCAet0OFEqO+CcJyOFKYiNGpFVRzC27wxWrWMiBiCgrMZBDOa8TXvwH7XEnPA6lFB2iFhIUDNdviX1T9gMGPdDUlHbZ1G5fN6oLqvnEioiI8suqVeGxM91tyZTQ19gHGSFYYOjxniKkaCBnlH5d4pM4HEBVJVBTk1YfPAEPKmwVKDAykTURUTZiIIdy3gfYgX3wxK3ssCZ4LABgiG47LJI39k27A6ip7dXTRJfswsSaidBJ/FEiIqI80doaXpFTzYcYfW0f3HgP2xNWq9qrNMCNYpjgxXDd5vgnCYUARQEamwBdevMTVqwiIspu/OuTclonvHgb21AGS4+y421KPZqVsQAEphk+jD0wsuS4F6txFKEAAJpKm9I6noiIKCd9+SXQ2Zn2tmRKbBm2oQUu1KGox3tCABtC0wAATfoNMEih+CdxOIDSMqC+Pu1+KEJBfVH6xxMRUWYxkEM5bTmasRdO1B8y4RECWBU4EQAwWv8VynX7Yg+0O8IrcdJccgwADr8DRaYiNJSmlyiZiIgo5ygK8MkngNWa9moPim8vnFiG7ahBYdxqVc3KGOxWRkJCCGP1a+OfRFHCD6saGwFDz61ZakQqVnHbOBFR9uIITDmrHR68g22ogLXHapztyli0i3oYIGOq4ePYA4MBAAIYObJXk1C7z47qgmrUFtamfQ4iIqKcsm0bsHVruBoS9am3sQ3t8KAGPfPSeIUNnwbmAgAmGz5Fqa4jzhkE0NkRriQ2bFja/fAGvbAarCw9TkSUxRjIoZz1LrZjb5zlx0FhwOrAcQCAiYaVsEnug94VQFcXUFkZrlbVC07ZiYnVzI9DRER5ZO1awO0GCgsHuieDyk7Y8T6aUYeiHhU4hQA+DZwMP2wok9owUf9p/JM4XYDRBEyaDJjNaffFLbthM9q4IoeIKIvxL1DKSW1w411sRxVsPZYffxOaDjdKUAAHxus/jz2wuxuw2cKTHL0+7esrQoEQAo1ljWmfg4iIKKfs3g28/z5QWpp2fjnqSUDgTWxBF3yogq3H+83KGOxQDoOEEI4xvgG9pPQ8SUAGvF5g7Nhel4R3B9yotFXCZuzZFyIiyg4M5FBOWoZtaIMbtYh9Itim1OOr4JEAgGnGD2CQggfe9LjDe8cnTup1gkan34kicxFGlI7o1XmIiIhygssFPPss8O23wNChA92bQWULOvEJdmFInNU4B2+pmqT/FBW6tp4nEEo4+fSwYcCoUb3ujyfgYcUqIqIsl14WNKIB9CF24E1sRTUKYlbj7A414r3AGQjBiFrdDjTpNhw4KCADLjcwblyv9o1HdPu6UWWrQl1R77ZnERERZb1QCFiyBPjiC2DMGCY57kO7YMfTWAMXZAxHScx7DqUU7wXOjG6pmmSIt6VKAJ1d4VVSkyb1arVxBCtWERFlPwZycpgiBPZ0eeGWgygwGTCkzApdnKcnydpF3nP4Atja5kIgJGDUS2iqKoAcFLAa9fAGQrCa9Cg097xG5HiXPwiPHIIvEIKAgD+gwOUPIhhSYDMZAAi4/SF4AyGYDDpUFJhhNuiwz+mHXgcIAP6gAgnha08dXgZJQvTcXjkEo0HCP7yb8UbxRkgAhijF6IYDgZCCTtNkNBeeBSHpURb4D8YEXsJeXQhuOYiAHEC514HAkOFwlw1BQZc34b1Syyk7cVzD8Vi5rQttTh+qiyyY2VgOvS7/nl6FFIFV2zvz/j4QEWVCpn/Hqjr/O+8Ab78NDB/eI/dKvDlGSBH4cPM+dHsCKLUZccyoKrQ6fHDLQdiMBkACPAe1B6BqPhPPwde3GQ0QCP8bQPjcAtjT7YUAYDHo4A2E4PIHUWQxYuj+93d1edDq8CGoACVWAw6vK0Z9qRXrdtvR7ZHh8gdRYDagxGqEBKDbG4B7/2tlNhMmDyuNuWcxfTIZAAG4/EHstXvR7vJDDiqoKDAhWC5jScGX2KHrRq2nGP+RnDAadNBLElqlUfiP9WwEJQtMwoXD5f9Dp+yByx+EHFSg00koNOpgc9mht1hgmzAJuoKeSZK1UkR421ZVQXh7Vr6O8fn6eRNR7siKQM5jjz2GBx54AC0tLZg8eTIeffRRzJw5M2H7l156Cbfddhuam5sxevRo3HfffTj11FP7sccDb0ubE8s37YPLf2DrUKHZgBPGVGFUdZGqdgB6vBexrd3d47VDrxHv3GrttfsSvvet3YuPtrTDqNchEApPKAQENlW04PPa7dAHdSjzFcCOIEKSCV0Fx6O9aB4AoNjzBaq7X0IrFAAC1oAfBbIPzQUl+EJXDf+GfT0+D62EEGixe/Hg691wOw88HasrsWDh6eNwyoT8WaWzdP1eLPrXNzFfz3y8D0SUGsd67TL9O1bV+deuBV58ESgqCq/6OEi8eYBeJyGkiOi/d3YCX+22J+yDxRheQeILhKKvqR2jU85DmpMejs/ivL/XDmxscSY/8BAfbt6HaQ1lOHZ0leq50SalAx+VbkZ3wINqVzHcUOCGAgE92otOQkfRHACAVd6B+s7n0ak40HnQ8cZQAJLPhd3WYmwoGQZ5mw8nGJ1pzWsO5g2EK1ZVF1Tn7Rifr583EeUWSQghUjfLnBdffBEXX3wxnnjiCcyaNQsPP/wwXnrpJWzatAnV1T2z5X/yySc47rjjsHjxYnz3u9/FCy+8gPvuuw9r1qzBhAkTVF3T4XCgpKQEdrsdxcXFvfsEvv0WuPnmcGK5fqrgsKXNiX9/tTfh+9+dVBcNtCRr1xvTG8qwekdXRs59KK9BxuayVqyt2QlTyIBSvw2yvhJdBbNht02HorMAAMpcH6Ha8RokCOiVEEp8LgT0BjSX1aG5bAgC+p5xy8i90uKrPd/irY1bURq4GAZURl+PPKd5/MJpeTHQL12/F5f/7xoc+gsk3+4DUV/p07Epy+T8WD8AMv07NtX5/zivHifuWAt8+GE4ie5hh8UkOM7kHCMi2RjdH9fXqqmqANv2xX8QFiHrgthV3Ikvq3fBYfai2l0MCRL8xiGwW6fBYZ2MkD48nyx1fYIax2uQcCDIJQkBW8ALczCAb4ursLF6BHyGA6uk0pnXHGyfex9CIoRTht6Any/ZmHdjPOc2lGm5PjZR9hjwQM6sWbNwxBFH4He/+x0AQFEUDBs2DD/96U9x00039Wh/7rnnwu1249///nf0tSOPPBJTpkzBE088oeqauRzIUYTAHz/anvRJT6HZgEuPbsSzHydv1xsS0GOQ60sCAm02L7aVurG9zAuXyQaDGA7ohsNvqIXfNCTa1hTch3Ln+yj1rII1KMMSlCEJgQ5bCf5T1YAua+KvcaHZgO8f06hpCffjH66EX7ahLHgJJMTuRZcA1JZY8NGNJw3qJbghReCY+95NuLIqX+4DUV8azJO7nB/r+1mmf8cmOr9eCaHG1YHpuzdg7t71OH2oGbr6eqCyMiaIo2Yu0hcSjdH9df2+5DD6sK3Mic3lTtjNAHQ1sISGQzbUwGccAtl4IKCpDzlQ7XgdJd61+18RMIUCsMk+6IUCr8GM5rJ6NJfXQ5Fi8xVpndccTAiBzZ2bMaKkEe99dlLejfGc21B/yOWxibLLgG6tkmUZq1evxs033xx9TafTYe7cuVixYkXcY1asWIFrr7025rX58+fj1VdfTXgdv98Pv98f/bfD4ehdx/fzyEF87/kNQOU8QG8A/JlP/ieHFDjGjU7Z7i9uA9wq2vUXsT/0E/3fSI4eCRCSBEUCQpIOIZ0ERdIhJBkgdKYkJ1RQ7P0GtY73Ueb+BgYRAiDBazChpbAC+wrLsLeoEiFd8qR/Ln8Qe7q8GFaursTmni4v3AEnbGJsjyBO+PMMbxtbtb0Ts0dWqDpnLlq1vTPp9rh8uQ9ElFquj/UAcM2SLzRvt+kNtxxS9Tv2hAeXo8CUZJw7+Fld5L+FgMcfRHuXGyYhoBPhVazGUBCmUAACEtYMGYtVw8bjkQILTHodIMeeVu1cpC+84DWG+zBA148QMdWkBAAp/EBLOvBgKzqnAaDoJIQkCSGdDoqkhyKZgEOCLq6D/lsSARR716PM/RlKvJtgUAIwKiHolfBqnIDOgA5bKfYWV2JfQRn8hvhzJK3zGiBcparF1QKX7EKZpQwlhsPzcozn3IaIcsmABnLa29sRCoVQU1MT83pNTQ02btwY95iWlpa47VtaWhJeZ/HixVi0aFHvO3wIRQAbO3yAsTT8Qn+sbdIBUJHLrh0AjBnuS38QCiThgV5xwBDaA0NwNwyhXTCEmiEJO/ZZ9NhdaILfYIPXaIbbZEVAZwDgA7Bb1SW+ae9Gd8Cqqu23di8kGGEQyZfVtjkTTwQGA7Wf32C/D0SUWq6P9QCwo9PTr4EctXZ1etI/WK9ykhBvbqNyLtIX2uP1oR+v3+dEEDrhgT60D4bQXhiC38KgfAtTYBMk4YVXJ8FTIEHR6eA1muEz2CDrDfAaLfAYzQjPb5JvKdMyrwEAk96EEaUjcGzDsZhaOxWrtioA1qY8brCN8ZzbEFEuyYpkx5l28803xzzZczgcGNYHJagtBh3+fMl0LP/8JTj9/TPB63DJ+HRbe8p24+pK8M3exMkFMyP8hCqWdND/j/yXBElIkCBBJ/SQhA56oYde0e9/T0CCAkkEASgQ+59whSQ9gvoaBA1D4DMfDY/R0iPvjU0BoGjr9Q8mT8TEoSWpGwJYt9uOW7d/DaMYnrRddZFFWydyjNrPb7DfByLKHpka6wHgjtPH4+PPl2L7xk9jV7lkiMMbwIa9yVcUCUgYV1+MYmucgIwU/X/hLVESAJ0+/N86Hbp8IXy+x4GgTt9ja87BjmyqREVhz5UfaucifSFeH/rz+rGk2PmMiLwSntfooINOGKATeugUPXQQ4XU7QkAngpBECAfWKAOKTgdFqoYi1SFgmoGA3gjZYERAZ4DfYIz52hgBlKjcSaZlXgMABcYCHFZxGIz7g3vVRR2qjhtsYzznNkSUSwY0kFNZWQm9Xo/W1taY11tbW1FbWxv3mNraWk3tAcBsNsN8SLnMvmDQ63Dc2FocN/anfX7uRCL7d1vsvrgPySL7d1+4/kQc/8B7Cdv1lk4Kr0gaSDoA5l70IXKvLjlC/V7n6XUCT7+rS3n/ZzaWp9+xHDCzsRx1JZa8vw9ElFquj/UAMHlYKSYPOw/AeRk5/6HUjvW/72WOnFTnf+qn8c+f6vi+kKwP/XH9gWYENK/0TmdeE0++jvH5+nkTUW7KfFKXJEwmE6ZPn45ly5ZFX1MUBcuWLcPs2bPjHjN79uyY9gDw9ttvJ2w/2Oh1EhaePg5AorUvwMLTx8Fk0KVsly4JwA+Pbez1ebLBwtPHaZrsqL3/gz0JHu8DEanFsV67TP+O7e35kx3fF1L1IdPXT9fJ46r3r88ZOH0x9ubrGJ+vnzcR5aYBDeQAwLXXXounnnoKf/rTn7BhwwZcfvnlcLvduPTSSwEAF198cUyCxJ/97GdYunQpfv3rX2Pjxo2444478Pnnn+Oqq64aqE+h350yoQ6PXzgNtSWxSztrSywxZRGTtXviwml44sJpqCvRtjy0bv81bj51HB5P43gtbMkSOPZS3SH3Sgu193+w430gIrU41muX6d+xvT1/ouPNBm1TyzKbEaW22O1havqQ6PoDQScBPz6uEU9dfMSA9ak385p48nWMz9fPm4hyz4CXHweA3/3ud3jggQfQ0tKCKVOm4Le//S1mzZoFADjhhBMwYsQIPPfcc9H2L730Em699VY0Nzdj9OjRuP/++3Hqqaeqvt5gKfsWUgRWbe9Em9OH6qLwUs9ES6ATtYu8t7vLg7e+boFHDsFm0mPu4dVw+UMos5nQ5ZFRXmBCbYm1xzUix7fYvWh3yej2ylCEgN0TQKvTD58cREWBGYDAPqcfHR4ZhWYjxtQUochiwDffOmA2hCs82L1B6CQJJ4+rwaVHN0Kvk8LndvjQ6fKj0GLA21+3YOs+Nww6YGiZDZIkoc3ph0kHWMxGVBYYodPpUFdiQYnFiA17ndhj96C+xIIJQ0pRVWxBbXHie5WJ+z/Y8T4Q9Y3BMjYlwrE+PZn+Hdvb88c7Xg4quOf1b9Dc4cGIChtuPOVwrNtjR5vTh8oCMyAB7S5/tD2AtPtw8PUrC8xQhMDK7Z0ABGY1VgACWNncAQGgxGxEp9ePFrsfQ8qsOHL/+59sbcdXe7rhCwgMK7fiv6cNxYwR5Xhh5Q5sb3ej1eFDVbEZDeUFkADs6HCjzelHdbEFTZUFuGj2CJgOCmDF9KnQDAigxeHD6h2d2NjihMsfwJjqIowfUgKHN4jd3V50uHywGvWoLragyGxAm9OP2lILym1mlBeY0O2RUWI1Yu2ubrQ6vLCZDBhfX9Kn8xq1X998GOPz9fOmzBssYxMNvKwI5PQ3/gAREVG24djUt3g/iYgo23Bsor4y4FuriIiIiIiIiIhIHQZyiIiIiIiIiIhyBAM5REREREREREQ5goEcIiIiIiIiIqIcwUAOEREREREREVGOYCCHiIiIiIiIiChHMJBDRERERERERJQjGMghIiIiIiIiIsoRDOQQEREREREREeUIBnKIiIiIiIiIiHKEYaA7MBCEEAAAh8MxwD0hIiIKi4xJkTGKeodjPRERZRuO9dRX8jKQ43Q6AQDDhg0b4J4QERHFcjqdKCkpGehu5DyO9URElK041lNvSSIPw4GKouDbb79FUVERJEnKyDUcDgeGDRuGXbt2obi4OCPXyKRc7j/7PnByuf/s+8DJ5f73Zd+FEHA6naivr4dOx53PvRVvrM/l7zUgt/ufy30H2P+BlMt9B3K7/7ncdyA7+8+xnvpKXq7I0el0GDp0aL9cq7i4OGt+caQjl/vPvg+cXO4/+z5wcrn/fdV3Pp3rO8nG+lz+XgNyu/+53HeA/R9Iudx3ILf7n8t9B7Kv/xzrqS8wDEhERERERERElCMYyCEiIiIiIiIiyhEM5GSI2WzGwoULYTabB7oracnl/rPvAyeX+8++D5xc7n8u9z0f5frXK5f7n8t9B9j/gZTLfQdyu/+53Hcg9/tPlExeJjsmIiIiIiIiIspFXJFDRERERERERJQjGMghIiIiIiIiIsoRDOQQEREREREREeUIBnKIiIiIiIiIiHIEAzm98Nhjj2HEiBGwWCyYNWsWVq1albDtc889B0mSYj4sFks/9vaADz74AKeffjrq6+shSRJeffXVlMcsX74c06ZNg9lsxqhRo/Dcc89lvJ/xaO378uXLe9x3SZLQ0tLSPx0+yOLFi3HEEUegqKgI1dXVOPPMM7Fp06aUx7300ksYO3YsLBYLJk6ciNdff70fettTOv3Plu/7xx9/HJMmTUJxcTGKi4sxe/ZsvPHGG0mPyZb7Dmjvf7bc93juvfdeSJKEa665Jmm7bLr/EWr6ns33Ph90dnbiggsuQHFxMUpLS3HZZZfB5XIlPeaEE07o8TX7yU9+EtNm586dOO2002Cz2VBdXY0bbrgBwWBwwPvf2dmJn/70pxgzZgysViuGDx+Oq6++Gna7PaZdvHFwyZIlve6vlnkQkPrnWgiB22+/HXV1dbBarZg7dy42b97c6372Rf+feuopHHvssSgrK0NZWRnmzp3bo/0ll1zS4z6fcsopA953Nb+Xsvnex/sZlSQJp512WrRNf937TM2htf4spUtr/19++WWcfPLJqKqqis4/3nzzzZg2d9xxR497P3bs2AHvu9q/Afrr3hP1NQZy0vTiiy/i2muvxcKFC7FmzRpMnjwZ8+fPR1tbW8JjiouLsXfv3ujHjh07+rHHB7jdbkyePBmPPfaYqvbbt2/HaaedhhNPPBFr167FNddcgx/84Ac9fpH3B619j9i0aVPMva+urs5QDxN7//33ceWVV+LTTz/F22+/jUAggHnz5sHtdic85pNPPsH555+Pyy67DF988QXOPPNMnHnmmVi/fn0/9jwsnf4D2fF9P3ToUNx7771YvXo1Pv/8c5x00kk444wz8PXXX8dtn033HdDefyA77vuhPvvsMzz55JOYNGlS0nbZdv8B9X0HsvPe54sLLrgAX3/9Nd5++238+9//xgcffIAf/ehHKY/74Q9/GPM1u//++6PvhUIhnHbaaZBlGZ988gn+9Kc/4bnnnsPtt98+4P3/9ttv8e233+LBBx/E+vXr8dxzz2Hp0qW47LLLerR99tlnYz7HM888s1d91ToPUvNzff/99+O3v/0tnnjiCaxcuRIFBQWYP38+fD5fr/raF/1fvnw5zj//fLz33ntYsWIFhg0bhnnz5mHPnj0x7U455ZSY+/zXv/51wPsOpP69lM33/uWXX47p+/r166HX63HOOefEtOuPe5+JOXQ6X8/+6v8HH3yAk08+Ga+//jpWr16NE088Eaeffjq++OKLmHbjx4+PufcfffTRgPc9ItnfAP1574n6nKC0zJw5U1x55ZXRf4dCIVFfXy8WL14ct/2zzz4rSkpK+ql36gEQr7zyStI2v/jFL8T48eNjXjv33HPF/PnzM9iz1NT0/b333hMARFdXV7/0SYu2tjYBQLz//vsJ2/zP//yPOO2002JemzVrlvjxj3+c6e6lpKb/2fp9L4QQZWVl4umnn477Xjbf94hk/c/G++50OsXo0aPF22+/LY4//njxs5/9LGHbbLv/Wvqejfc+X3zzzTcCgPjss8+ir73xxhtCkiSxZ8+ehMel+pq+/vrrQqfTiZaWluhrjz/+uCguLhZ+v79P+i5E+v0/1N/+9jdhMplEIBCIvqZmvNRK6zwo1c+1oiiitrZWPPDAA9H3u7u7hdlsFn/961/7tO/p9P9QwWBQFBUViT/96U/R1xYsWCDOOOOMvu5qD309B821e/+b3/xGFBUVCZfLFX2tv+79wfpqDt3b+5GudH8vjBs3TixatCj674ULF4rJkyf3XcdU6Ku/AQbq3hP1Ba7ISYMsy1i9ejXmzp0bfU2n02Hu3LlYsWJFwuNcLhcaGhowbNiwlE/Ts8mKFStiPlcAmD9/ftLPNdtMmTIFdXV1OPnkk/Hxxx8PdHcAILr0vby8PGGbbL73avoPZN/3fSgUwpIlS+B2uzF79uy4bbL5vqvpP5B99/3KK6/Eaaed1uO+xpNt919L34Hsu/f5YsWKFSgtLcWMGTOir82dOxc6nQ4rV65Meuzzzz+PyspKTJgwATfffDM8Hk/MeSdOnIiamproa/Pnz4fD4ejTr21v+n8wu92O4uJiGAyGmNevvPJKVFZWYubMmfjjH/8IIUTafU1nHpTq53r79u1oaWmJaVNSUoJZs2b1+c9+uvO4g3k8HgQCgR5j4PLly1FdXY0xY8bg8ssvR0dHR1b0PdnvpVy798888wzOO+88FBQUxLye6XufjlTf931xP/qToihwOp09vu83b96M+vp6NDU14YILLsDOnTsHqIc9JfobINfuPdGhDKmb0KHa29sRCoViJnUAUFNTg40bN8Y9ZsyYMfjjH/+ISZMmwW6348EHH8RRRx2Fr7/+GkOHDu2PbqetpaUl7ufqcDjg9XphtVoHqGep1dXV4YknnsCMGTPg9/vx9NNP44QTTsDKlSsxbdq0AeuXoii45pprcPTRR2PChAkJ2yW69wOR4+dgavufTd/369atw+zZs+Hz+VBYWIhXXnkF48aNi9s2G++7lv5n030HgCVLlmDNmjX47LPPVLXPpvuvte/Zdu/zSUtLS49tswaDAeXl5Um/d/7f//t/aGhoQH19Pb766ivceOON2LRpE15++eXoeeN9P0beG+j+H6y9vR133XVXj+1Yd955J0466STYbDa89dZbuOKKK+ByuXD11Ven1dd05kGpfq4j/9sfP/vp9P9QN954I+rr62P+CDzllFPwve99D42Njdi6dStuueUWfOc738GKFSug1+sHrO+pfi/l0r1ftWoV1q9fj2eeeSbm9f649+lINYfu6urq9fdif3rwwQfhcrnwP//zP9HXZs2aheeeew5jxozB3r17sWjRIhx77LFYv349ioqKBqyvqf4G6IvfA0QDiYGcfjJ79uyYp+dHHXUUDj/8cDz55JO46667BrBng9uYMWMwZsyY6L+POuoobN26Fb/5zW/wl7/8ZcD6deWVV2L9+vUZ2UPcH9T2P5u+78eMGYO1a9fCbrfj73//OxYsWID3338/YTAk22jpfzbd9127duFnP/sZ3n777ZxL+ptO37Pp3g8WN910E+67776kbTZs2JD2+Q8OekycOBF1dXWYM2cOtm7dipEjR6Z93ohM9z/C4XDgtNNOw7hx43DHHXfEvHfbbbdF/3vq1Klwu9144IEH0g7k5Lt7770XS5YswfLly2N+N5x33nnR/544cSImTZqEkSNHYvny5ZgzZ85AdBXA4Pq99Mwzz2DixImYOXNmzOvZeu8HkxdeeAGLFi3CP/7xj5ig83e+853of0+aNAmzZs1CQ0MD/va3v8XN19VfsvVvAKK+wkBOGiorK6HX69Ha2hrzemtrK2pra1Wdw2g0YurUqdiyZUsmutinamtr436uxcXFWb0aJ5GZM2cOaADlqquuiiaxTPWEPtG9V/t9lgla+n+ogfy+N5lMGDVqFABg+vTp+Oyzz/DII4/gySef7NE2G++7lv4faiDv++rVq9HW1hazAi4UCuGDDz7A7373O/j9/h5PS7Pl/qfT90Pl0u/6bHXdddfhkksuSdqmqakJtbW1PRJUBoNBdHZ2avremTVrFgBgy5YtGDlyJGpra3tUMYl8f6o5b3/03+l04pRTTkFRURFeeeUVGI3GpO1nzZqFu+66C36/H2azOeXncKh05kGpfq4j/9va2oq6urqYNlOmTNHcx2R6M4978MEHce+99+Kdd95Jmfy8qakJlZWV2LJlS58FEzIxB82Ve+92u7FkyRLceeedKa+TiXufjlRzaL1e3+uvZ39YsmQJfvCDH+Cll15Kuc24tLQUhx12WFaOewf/DdAXP0tEA4k5ctJgMpkwffp0LFu2LPqaoihYtmxZ0pwVBwuFQli3bl3MgJmtZs+eHfO5AsDbb7+t+nPNNmvXrh2Q+y6EwFVXXYVXXnkF7777LhobG1Mek033Pp3+Hyqbvu8VRYHf74/7Xjbd90SS9f9QA3nf58yZg3Xr1mHt2rXRjxkzZuCCCy7A2rVr4wZCsuX+p9P3Q2XT93yuqqqqwtixY5N+mEwmzJ49G93d3Vi9enX02HfffReKokSDM2qsXbsWAKJfs9mzZ2PdunUxQZa3334bxcXFqlb0Zbr/DocD8+bNg8lkwj//+U9Vq8fWrl2LsrKytII4QHrzoFQ/142NjaitrY1p43A4sHLlyj7/2U93Hnf//ffjrrvuwtKlS2NyGSWye/dudHR09OnPfybmoLlw74Fw+Xq/348LL7ww5XUyce/Tker7vi++npn217/+FZdeein++te/xpR8T8TlcmHr1q0Dfu/jOfhvgFy490RJDXCy5Zy1ZMkSYTabxXPPPSe++eYb8aMf/UiUlpZGq1pcdNFF4qabboq2X7RokXjzzTfF1q1bxerVq8V5550nLBaL+Prrr/u9706nU3zxxRfiiy++EADEQw89JL744guxY8cOIYQQN910k7joooui7bdt2yZsNpu44YYbxIYNG8Rjjz0m9Hq9WLp0adb3/Te/+Y149dVXxebNm8W6devEz372M6HT6cQ777zT732//PLLRUlJiVi+fLnYu3dv9MPj8UTbHPp98/HHHwuDwSAefPBBsWHDBrFw4UJhNBrFunXrcqL/2fJ9f9NNN4n3339fbN++XXz11VfipptuEpIkibfeeituv7PpvqfT/2y574kcWiUo2+//wVL1Pdvv/WB3yimniKlTp4qVK1eKjz76SIwePVqcf/750fd3794txowZI1auXCmEEGLLli3izjvvFJ9//rnYvn27+Mc//iGamprEcccdFz0mGAyKCRMmiHnz5om1a9eKpUuXiqqqKnHzzTcPeP/tdruYNWuWmDhxotiyZUvM7+ZgMCiEEOKf//yneOqpp8S6devE5s2bxe9//3ths9nE7bff3qu+ap0Hqfm5vvfee0Vpaan4xz/+Ib766itxxhlniMbGRuH1envV177o/7333itMJpP4+9//HnOfnU6nECI8P7n++uvFihUrxPbt28U777wjpk2bJkaPHi18Pt+A9l3N76VsvvcRxxxzjDj33HN7vN6f9z4Tc+hU92Mg+//8888Lg8EgHnvssZjv++7u7mib6667Tixfvlxs375dfPzxx2Lu3LmisrJStLW1DWjf1fwN0J/3nqivMZDTC48++qgYPny4MJlMYubMmeLTTz+Nvnf88ceLBQsWRP99zTXXRNvW1NSIU089VaxZs2YAen2gHN+hH5H+LliwQBx//PE9jpkyZYowmUyiqalJPPvss/3e70g/tPT9vvvuEyNHjhQWi0WUl5eLE044Qbz77rsD0vd4/QYQcy8P/b4RIlxK9rDDDhMmk0mMHz9evPbaa/3b8f3S6X+2fN9///vfFw0NDcJkMomqqioxZ86caBAkXr+FyJ77LoT2/mfLfU/k0GBItt//g6Xqe7bf+8Guo6NDnH/++aKwsFAUFxeLSy+9NPqHthBCbN++XQAQ7733nhBCiJ07d4rjjjtOlJeXC7PZLEaNGiVuuOEGYbfbY87b3NwsvvOd7wir1SoqKyvFddddF1Pee6D6n2hMBCC2b98uhAiXMJ8yZYooLCwUBQUFYvLkyeKJJ54QoVCo1/3VMg8SIvXPtaIo4rbbbhM1NTXCbDaLOXPmiE2bNvW6n33R/4aGhrj3eeHChUIIITwej5g3b56oqqoSRqNRNDQ0iB/+8IcZ+4Owr+eg2XzvhRBi48aNAkDM2BfRn/c+U3PoZPdjIPt//PHHJ20vRLicel1dnTCZTGLIkCHi3HPPFVu2bBnwvqv9G6C/7j1RX5OE6EX9SSIiIiIiIiIi6jfMkUNERERERERElCMYyCEiIiIiIiIiyhEM5BARERERERER5QgGcoiIiIiIiIiIcgQDOUREREREREREOYKBHCIiIiIiIiKiHMFADhERERERERFRjmAgh4iIiIiIiIgoRzCQQ0RERERERESUIxjIISIiIiIiIiLKEQzkEFHO6OjoQHV1NZqbmzUd9+Mf/xgXXHBBn7dNx3nnnYdf//rXGTs/ERH13vXXX48zzzyzT8+Z7hhGiaVzT7NpTgDEnxdwrkBEqTCQQ0Rp2717N37yk59g1KhRsFgsqKmpwbx587Bu3ToAgBACpaWlePTRR3sce8UVV2DmzJkAgPHjx2PhwoVxr7F48WJUVFSgo6MDd999N8444wyMGDFCUz8XL16MP/zhD2m1/fnPf47vfe97mq6XzK233oq7774bdru9z85JRER9a+3atZgyZUqfnjPdMSxX9PecAEjvnmbTnACIPy/gXIGIUmEgh4jS0tzcjKlTp6KjowN/+ctfsHHjRvz973/HuHHjYDabAQBbt26F3W7HjBkzehy/evVqTJ8+HQAwceJErF+/vkebvXv34p577sGdd94Jq9WKZ555BpdddpnmvpaXl6OgoCCttqtWrYrb/3RNmDABI0eOxP/+7//22TmJiKhvffnll30ayPF4PGmPYbmgv+cEFRUVad/TbJoTAPHnBZwrEFEqDOQQ7ffXv/4VVqsVe/fujb526aWXYtKkSX32RKS5uRmSJOH//u//cNxxx8FqteKII47Azp078eGHH+LII4+EzWbDnDlz0N3dHXPswoULMXHiRBQUFKCmpgaXX345AoFAv/X9UI8++igKCgrw4osvYvbs2RgxYgSOPfZYPPzwwzjssMMAhCdmBoOhx2Q4EAjgq6++ik7aJk2aFHfSdsstt6CxsRE/+clP8Prrr8NsNuPII4+MaVNdXY2nn3465rXPPvsMFosF27dvj97zyLJrRVFwzz33YPTo0dEnhpdccgkAxLSVZRlGoxGffPIJfvnLX0KSpB7XPlRLSwskScIjjzyCqVOnwmKxYPz48fjoo49i2p1++ulYsmRJ0nMREVFP/THe7d69G+3t7Zg8eXL0tfXr1+PUU09FcXExamtrcd1110GW5ej7K1euxDHHHAOr1YopU6bggw8+gCRJ0bEt0RiW7rwg2Zygv+7Twfp7TgDEv6fZNCcAejcv4FyBiJISRCSEEEJRFDFp0iRx1VVXCSGEuP3228XQoUPF7t27e7S9++67RUFBQdKPHTt29Dju1VdfFQDEnDlzxIcffijWrFkjhg0bJo499lhx6qmnis8++0x8+umnoqKiQjz00EMxfbvtttvExx9/LJqbm8Xrr78uqqqqxO9//3vNfe8rl156qaipqRHbt29P2OaGG24QkyZN6vH6F198IQCINWvWCCGE+Oc//yn0er3wer3RNp9//rnQ6XTivffeE0IIcfXVV4tTTjmlx7lOOukk8fOf/zzmtRNPPFFcffXVQojwPS8tLY2+96tf/UpMnDhRvPvuu6K5uVl8/PHH4plnnunRNhQKiZUrVwoAYu3atWLv3r2iq6sr6T154403BAAxadIksXz5crFhwwZxyimniOHDh4tQKBTTzmQyCZ/Pl/R8REQUqz/G6n/961+ipKQk+u81a9aIoqIi8ctf/lJs3rxZvPfee6Kurk7ceeedQggh1q1bJwoKCsQvf/lLsWHDBvH3v/9dVFdXC7PZLAKBgBAi8RiWzrwg1ZxA633qC/09JxAi/j3NpjmBEL2bF3CuQETJMJBDdJB//etfwmw2i1/96leirKxMrF+/Pm67jo4OsXnz5qQfkcnbwe644w5RXl4u2tvbo69deOGFYsSIEcLtdkdfO+WUU8QvfvGLpH09//zzxc9+9jPNfT/YsmXLxIMPPqjq/a1bt4p//OMf0fdWr14thg8fLiRJEjNmzBA33nij+Prrr2OOP+mkk8T3v//9Hud9+umnhdlsFrIsCyGEaG5uFgDEF198EW1zzDHHiHPOOSf67zPOOCPuua6++moxf/786L+XLl0qioqKxL59+4QQ4Xt+3HHHRd8/9thjxS233BL38z207SuvvCIqKirito3n3nvvFUajMWYi+/nnnwsAYufOndHXvvzySwFANDc3qz43ERGFZXqsvuuuu2LGgunTp4srrrgips0tt9wiZs6cKYQIBwrOP//8mPdPP/10MXXq1Oi/E41hfTUvOHROIETfzwuyaU4gRPx7mk1zAiF6Ny/gXIGIkjEMwCIgoqz13e9+F+PGjcOdd96Jt956C+PHj4/brry8HOXl5ZrP/+WXX+Kss85CRUVF9LWdO3fi3HPPhc1mi3ntjDPOiP57x44duP/++/H+++9jz549CAQC8Pl8uPfeezX3/WAnnXQSTjrpJFXvv/HGG3A6nfiv//ovAMC0adOwbds2fPTRR3jrrbfw0ksv4de//jVefvllnH766QCANWvW4Oyzz+5x3tWrV2PixIkwGo0AgIaGBpSUlGD9+vWYMmUKXnzxRaxevRobN26MHuP1emGxWHqca+LEiXjllVcAhBMp3nzzzbjhhhtQWVkJoGeeg//6r//CjTfeiM8//xznnHMO/vu//xtlZWVx237xxRcxS+tTWbt2Lb73ve/FJF0sLi7u0c5qtQII50wgIiJtMj1Wr127Nvq7f+PGjVi9enWPXCUmkwl+vx87duzAe++912MrkNlsjhk/Eo1h6cwL1MwJgL6fF2TTnACIf0+zaU4A9G5ewLkCESXDHDlEB1m6dCk2btyIUCiEmpqahO3uueceFBYWJv3YuXNnj+PWrl2LWbNmxbz25Zdfxuyz9vl82LRpU3SysG/fPhxxxBHo6OjAQw89hI8++giffPIJdDpdzIQiUd9XrlwZnURF2l100UUAwhOYSDWJp556CtOmTcOECRNw7rnnxrz//vvv47bbbsMzzzyDqVOnwu12AwD0ej2OP/543H333fj6669RXV2NF154AQCwa9cudHd3x504vvPOOzjqqKNiXpswYQLWr18Pn8+HG2+8ETfeeCOGDx8efb+yshJdXV09zjVhwgTs3r0bLpcLS5Yswd69e3HttdfG3POD79P111+PDRs2YM6cOfjNb36DUaNGYfv27XHbHvrvVOJVOVmxYgUqKysxZMiQ6GudnZ0AgKqqKtXnJiKisP4YqyO/y7/++msYjcZonpeIb775BhMnTsTatWthMpl6jHUbNmyIGT8SjWFa5wVq5wTJ7pOaeUG2zwkS3dNsmhNEjkl3XsC5AhElNdBLgoiyxerVq0VRUZF44YUXxLx588TZZ5+dsG06y7XtdruQJEl89tln0de2bdvWY9nsqlWrhE6nE06nUwghxDPPPCPKy8uFoijRNo8++qgAINra2lL23W63i9GjR0f/fdRRR4n//Oc/QgghRo8eLWRZFp2dnWLy5MkiGAwKIUR033fkfSGEOP7445Pufff5fKKiokL89Kc/FUKEl10DEK+99lpMu7feeksAEB9//HHM65dffrk47bTTxF133SUaGhqEx+OJef+BBx4QkydP7nFdp9MpJEkSH3/8sRg5cmRMjoDIPV+9enXcPnu9XmE0GsW///3vuG0bGxvFn/70p4Sf88E8Ho/Q6/XiV7/6VfS1UCgkpk6dKq677rqYtk8//bQYOnSoqvMSEdEBmR6rHQ5HzFjw5ptvCp1OF5OnZNu2bcJoNIo33nhD/Otf/xI6nS4mn8s777wjAIh33303+lq8MSydeYGaOUGq+5RqXtDW1pb1cwIh4t/TbJkTCNH7eQHnCkSUDAM5REKI7du3i9raWrF48WIhhBCffvpp0sE+HR988IEwGAwxk72XX35ZlJeXx7T7wx/+EDPBevXVV4XBYBCvvvqq+M9//iN+/etfi8rKSjFkyBDVfW9oaBCyLIvXXntNLFiwQAgRnqxOnDgx+t/Dhw8X1157bXQP/cHvCxGewERceOGF4p577hGffvqp2L59u1i2bJmYM2eOqKioEFu3bhVChBMtjh07VkyaNEm88847Yu3ateLJJ58UlZWV4pJLLulxf37/+9+LqqoqUVBQIF566aUe73/11VfCYDCIzs7OHu+NGDFCHHnkkWL06NExk/LIPY9MwO+77z7xpz/9SXzzzTdi48aN4uc//7mora0VnZ2dPdpG7tstt9wi9uzZI7q7u3tc92ArV64UBoNBjB07VnzyySfim2++EWeffbZobGzskRBxwYIFcfMEEBFRYv0xVn/44YcxY0F3d7coLy8X11xzjdi6datYtmyZOPzww8VFF10khBDi22+/FWazWVx11VVi69at4p///KdoamoSAERHR0f0vPHGsHTmBanmBGrvU7J5QS7MCRLdUyGyY04gRO/nBZwrEFEyDORQ3uvo6BBjxowRP/7xj2NeP/XUU2MS5vXWo48+KsaPHx/z2sKFC8WcOXNiXrvyyitjnpyFQiHx4x//WBQVFYnq6mpx7bXXiiuuuEKcdtppqvs+d+5c8c0334jZs2eLLVu2CCGEWLFihTjvvPOibRwOh/jzn/8sxo0bJ1555ZWY93ft2iWOPvroaNuHHnpIHHXUUaKyslJYLBYxevRoceWVV4pdu3bF9GPr1q3irLPOEhUVFaK4uFhMmzZN/OEPf4g+5TvYRx99JACIE088MeE9nDlzpnjiiSd6vH766acLAOJvf/tbzOuPPvqomDBhQvTfixYtEocddpiwWCyisrJSnHHGGeKbb76J21YIIf7yl7+I+vp6AUBcf/31Qgghnn32WRFvMeOTTz4pJkyYIP785z+Luro6YbPZxFlnnRWTzFCI8BO/kpISsWLFioSfJxERxerPsfrQseCDDz4Q06ZNExaLRTQ1NYnFixfHjGPPP/+8GDZsmCgoKBBnnXWWWLRokRg1alSPcx86hqUzL0g2JxBC/X1KNS/IhTlBvHsqRP/OCYTIzLyAcwUiSkUSQoj+3s5FRP3r6quvhsfjgRACzzzzDIBwTpx9+/bhlltuwebNmzF69GgAwBVXXIHjjz8eDocj+v7HH3+Mhx9+GC+99NJAfhp47bXXcMMNN2D9+vXQ6QYmxdfChQvx/vvvY/ny5TGvX3nllejq6ormA0jk8ccfxyuvvIK33norg70kIqKBoCgKTjjhBBxzzDG45557Yt7LhjEsItm84JxzzsmJOQGQHfc0E/MCzhWIKBUmOybKA4cffjj+/Oc/49Zbb42+tm7dOkyYMAEA8Ktf/QpjxozB1KlTIUkSzjnnnJj3J0yYgG3btmHixIn45ptvBuRzAIDTTjsNP/rRj7Bnz54B68Mbb7yB+++/v8fra9euxaRJk1IebzQa8eijj2aia0RE1M8++OAD/N///R+2bduGVatW4dxzz8WOHTtw/fXX92ibDWNYRLJ5Qa7MCYDsuKeZmBdwrkBEqXBFDhFRLwkhUFJSgiVLluDUU08d6O4QEVE/eemll3DTTTdhz549qKmpwdy5c3HPPfckraZFgx/nBUSUaQzkEBERERERERHlCG6tIiIiIiIiIiLKEQzkEBERERERERHlCAZyiIiIiIiIiIhyBAM5REREREREREQ5goEcIiIiIiIiIqIcwUAOEREREREREVGOYCCHiIiIiIiIiChHMJBDRERERERERJQjGMghIiIiIiIiIsoRDOQQEREREREREeWI/w99qU8pcsoEzAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting results\n",
    "fig, axs = plt.subplots(ncols = len(scales), figsize = (6*len(scales), 5))\n",
    "\n",
    "quantile_methods = {\n",
    "    \"normal\": lambda x, p, theta, cov: logit.get_model_quantiles_normal(\n",
    "        x, p, theta, cov, seed=random_seed\n",
    "    ),\n",
    "    \"delta\": logit.get_model_quantiles_delta,\n",
    "}\n",
    "\n",
    "for ax, scale, x, theta, cov_theta in zip(axs, scales, xs, thetas, cov_thetas):\n",
    "\n",
    "    ax.set_title(f\"cond. prob. for AE: scale {scale}\")\n",
    "\n",
    "    if scale == \"plain\":\n",
    "        xlab = r\"$x = max_{visit} SUV(visit, p)$\"\n",
    "    else:\n",
    "        xlab = r\"$x = log(max_{visit} SUV(visit, p))$\"\n",
    "    \n",
    "    ax.set_xlabel(xlab)\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # plotting data points\n",
    "    for i, group_label in enumerate([\"NC\", \"AE\"]):\n",
    "        ax.scatter(x[y == i], y[y == i], label=group_label)\n",
    "\n",
    "    # plotting fitted model \n",
    "    dx = max(x) - min(x)\n",
    "    xp = np.linspace(min(x) - 0.1 * dx, max(x) + 0.1 * dx, 100)\n",
    "    yp = logit.model(xp, theta)\n",
    "\n",
    "    ax.plot(xp, yp, label = \"logistic reg\")\n",
    "\n",
    "    for ci_label, color in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        # get result of model quantiles at probs\n",
    "        # theta and cov_theta are obtained via MLE method\n",
    "        res = quantile_methods[ci_label](xp, probs, theta, cov_theta)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *res, color=color, alpha=0.5, label=f\"CI:{ci_label}\")\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(1.02, 0.5), loc='center right')\n",
    "\n",
    "fig.savefig(results_path / \"logit_mono_asymptotic_fit.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32da73e5cac3",
   "metadata": {},
   "source": [
    "### 4.4 Model confidence-interval comparison\n",
    "\n",
    "**Coverage note.** All analytic and bootstrap envelopes compared below are pointwise in the predictor. Their nominal coverage applies at a fixed predictor value, not simultaneously over the complete curve.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cfe79b472ce4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model CI:scale:plain,method:normal\n",
      "model CI:scale:plain,method:delta\n",
      "model CI:scale:plain,boots-method:normal\n",
      "model CI:scale:plain,boots-method:nonparam_boots\n",
      "model CI:scale:plain,boots-method:nonparam_stratified_boots\n",
      "model CI:scale:plain,boots-method:parametric_boots\n",
      "model CI:scale:log,method:normal\n",
      "model CI:scale:log,method:delta\n",
      "model CI:scale:log,boots-method:normal\n",
      "model CI:scale:log,boots-method:nonparam_boots\n",
      "model CI:scale:log,boots-method:nonparam_stratified_boots\n",
      "model CI:scale:log,boots-method:parametric_boots\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting results\n",
    "fig, axs = plt.subplots(ncols = len(scales), figsize = (6*len(scales), 5))\n",
    "\n",
    "boots_theta_results = {}\n",
    "quantile_methods = {\n",
    "    \"normal\": lambda x, p, theta, cov: logit.get_model_quantiles_normal(\n",
    "        x, p, theta, cov, seed=random_seed\n",
    "    ),\n",
    "    \"delta\": logit.get_model_quantiles_delta,\n",
    "}\n",
    "parameter_generators = {\n",
    "    \"normal\": logit.get_normal_theta,\n",
    "    \"nonparam_boots\": logit.get_nonparam_boots_theta,\n",
    "    \"nonparam_stratified_boots\": logit.get_nonparam_stratified_boots_theta,\n",
    "    \"parametric_boots\": logit.get_parametric_boots_theta,\n",
    "}\n",
    "n_parameter_samples = 10_000\n",
    "\n",
    "for ax, scale, x, theta, cov_theta in zip(axs, scales, xs, thetas, cov_thetas):\n",
    "\n",
    "    ax.set_title(f\"cond. prob. for AE: scale {scale}\")\n",
    "    \n",
    "    if scale == \"plain\":\n",
    "        xlab = r\"$x = max_{visit} SUV(visit, p)$\"\n",
    "    else:\n",
    "        xlab = r\"$x = log(max_{visit} SUV(visit, p))$\"\n",
    "    \n",
    "    ax.set_xlabel(xlab)\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # plotting data points\n",
    "    for i, group_label in enumerate([\"NC\", \"AE\"]):\n",
    "        ax.scatter(x[y == i], y[y == i], label=group_label)\n",
    "\n",
    "    # plotting fitted model\n",
    "    dx = max(x) - min(x)\n",
    "    xp = np.linspace(min(x) - 0.1 * dx, max(x) + 0.1 * dx, 100)\n",
    "    yp = logit.model(xp, theta)\n",
    "\n",
    "    ax.plot(xp, yp, label = \"logistic reg\")\n",
    "\n",
    "    for method, color in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        \n",
    "        print(f\"model CI:scale:{scale},method:{method}\")\n",
    "\n",
    "        # get result of model quantiles at probs\n",
    "        # theta and cov_theta are obtained via MLE method\n",
    "        res = quantile_methods[method](xp, probs, theta, cov_theta)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *res, color=color, alpha=0.5, label=f\"CI:{method}\")\n",
    "\n",
    "    # plot quantiles of models at sampled or bootstrapped parameters\n",
    "    for method, color in zip(\n",
    "        [\"normal\", \"nonparam_boots\", \"nonparam_stratified_boots\", \"parametric_boots\"],\n",
    "        [\"orange\", \"pink\", \"purple\", \"cyan\"]):\n",
    "    \n",
    "        print(f\"model CI:scale:{scale},boots-method:{method}\")\n",
    "\n",
    "        # generate sampled or bootstrapped parameters\n",
    "        btheta = parameter_generators[method](\n",
    "            x, y, n_parameter_samples, seed=random_seed\n",
    "        )\n",
    "    \n",
    "        boots_theta_results[(scale, method)] = btheta\n",
    "\n",
    "        # quantiles\n",
    "        quant = np.quantile([logit.model(xp, p) for p in btheta], probs, axis = 0)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *quant, color=color, alpha=0.5, label=f\"CI:boots, {method}\")\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(1.17, 0.5), loc='center right')\n",
    "\n",
    "fig.savefig(results_path / \"logit_mono_ci_comparison.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121501281f2e",
   "metadata": {},
   "source": [
    "### 4.5 Parameter confidence-interval comparison\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2ed7732fc9ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta CI:scale:['plain', 'log'],method:Wald/delta\n",
      "model CI:scale:plain,boots-method:normal\n",
      "model CI:scale:plain,boots-method:nonparam_boots\n",
      "model CI:scale:plain,boots-method:nonparam_stratified_boots\n",
      "model CI:scale:plain,boots-method:parametric_boots\n",
      "model CI:scale:log,boots-method:normal\n",
      "model CI:scale:log,boots-method:nonparam_boots\n",
      "model CI:scale:log,boots-method:nonparam_stratified_boots\n",
      "model CI:scale:log,boots-method:parametric_boots\n"
     ]
    },
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "coef",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "LCL",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "UCL",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "method",
         "rawType": "object",
         "type": "string"
        }
       ],
       "ref": "c0d1b721-15a2-4b7c-b395-d0b7e23abb75",
       "rows": [
        [
         "0",
         "plain",
         "p0",
         "-123.99205696060534",
         "-234.62243769320924",
         "-13.361676228001471",
         "Wald/delta"
        ],
        [
         "1",
         "plain",
         "p1",
         "-1.900645071045952e-09",
         "-4.178295911726185e-09",
         "3.770057696342804e-10",
         "Wald/delta"
        ],
        [
         "2",
         "plain",
         "p2",
         "5.582661715376665",
         "2.8839605756750313",
         "8.2813628550783",
         "Wald/delta"
        ],
        [
         "3",
         "plain",
         "p3",
         "-12.798181418791243",
         "-18.64851560843598",
         "-6.947847229146507",
         "Wald/delta"
        ],
        [
         "4",
         "log",
         "p0",
         "-40.70878964503654",
         "-80.63179986011647",
         "-0.7857794299566123",
         "Wald/delta"
        ],
        [
         "5",
         "log",
         "p1",
         "3.3991848080423114e-08",
         "-1.3703677763387472e-08",
         "8.168737392423369e-08",
         "Wald/delta"
        ],
        [
         "6",
         "log",
         "p2",
         "15.072426370420516",
         "6.6715809018928045",
         "23.473271838948225",
         "Wald/delta"
        ],
        [
         "7",
         "log",
         "p3",
         "-12.365102187959996",
         "-18.643587328887083",
         "-6.08661704703291",
         "Wald/delta"
        ],
        [
         "8",
         "plain",
         "p0",
         "-123.99205696060534",
         "-233.94943123214563",
         "-9.562456807989498",
         "normal"
        ],
        [
         "9",
         "plain",
         "p1",
         "-1.900645071045952e-09",
         "-9.595711131465509e-05",
         "7.713982008034643e-06",
         "normal"
        ],
        [
         "10",
         "plain",
         "p2",
         "5.582661715376665",
         "2.7944366226901765",
         "8.270209121704738",
         "normal"
        ],
        [
         "11",
         "plain",
         "p3",
         "-12.798181418791243",
         "-18.63648977078935",
         "-6.719472346319873",
         "normal"
        ],
        [
         "12",
         "plain",
         "p0",
         "-123.99205696060534",
         "-662.077284728864",
         "-20.9316993950732",
         "nonparam_boots"
        ],
        [
         "13",
         "plain",
         "p1",
         "-1.900645071045952e-09",
         "-2.42017933708336",
         "0.0003871658766217858",
         "nonparam_boots"
        ],
        [
         "14",
         "plain",
         "p2",
         "5.582661715376665",
         "-11.027819990575658",
         "17.817659569531223",
         "nonparam_boots"
        ],
        [
         "15",
         "plain",
         "p3",
         "-12.798181418791243",
         "-32.8388006577751",
         "5.78917152016678",
         "nonparam_boots"
        ],
        [
         "16",
         "plain",
         "p0",
         "-123.99205696060534",
         "-647.740989994033",
         "-21.412504234536605",
         "nonparam_stratified_boots"
        ],
        [
         "17",
         "plain",
         "p1",
         "-1.900645071045952e-09",
         "-2.530518347267342",
         "0.21072214516873888",
         "nonparam_stratified_boots"
        ],
        [
         "18",
         "plain",
         "p2",
         "5.582661715376665",
         "-11.034868065816198",
         "17.537220601664792",
         "nonparam_stratified_boots"
        ],
        [
         "19",
         "plain",
         "p3",
         "-12.798181418791243",
         "-32.43663897803941",
         "5.801689872262745",
         "nonparam_stratified_boots"
        ],
        [
         "20",
         "plain",
         "p0",
         "-123.99205696060534",
         "-519.9185959807692",
         "-14.267927545473208",
         "parametric_boots"
        ],
        [
         "21",
         "plain",
         "p1",
         "-1.900645071045952e-09",
         "-2.558558838295493",
         "0.017587628573496706",
         "parametric_boots"
        ],
        [
         "22",
         "plain",
         "p2",
         "5.582661715376665",
         "-17.294438383114326",
         "14.72733658434196",
         "parametric_boots"
        ],
        [
         "23",
         "plain",
         "p3",
         "-12.798181418791243",
         "-28.083488712335097",
         "26.4111328579993",
         "parametric_boots"
        ],
        [
         "24",
         "log",
         "p0",
         "-40.70878964503654",
         "-80.51482391483258",
         "0.5768096898392844",
         "normal"
        ],
        [
         "25",
         "log",
         "p1",
         "3.3991848080423114e-08",
         "-4.475512960882313e-09",
         "2.942177378758347e-08",
         "normal"
        ],
        [
         "26",
         "log",
         "p2",
         "15.072426370420516",
         "6.428671163878268",
         "23.468982296502062",
         "normal"
        ],
        [
         "27",
         "log",
         "p3",
         "-12.365102187959996",
         "-18.600564651751515",
         "-5.869816698776138",
         "normal"
        ],
        [
         "28",
         "log",
         "p0",
         "-40.70878964503654",
         "-362.3418986772154",
         "-14.457646430958098",
         "nonparam_boots"
        ],
        [
         "29",
         "log",
         "p1",
         "3.3991848080423114e-08",
         "-1.2949692152748325e-05",
         "1.3701590772882096e-05",
         "nonparam_boots"
        ],
        [
         "30",
         "log",
         "p2",
         "15.072426370420516",
         "-35.74117319597362",
         "78.60083374376462",
         "nonparam_boots"
        ],
        [
         "31",
         "log",
         "p3",
         "-12.365102187959996",
         "-44.06299924037829",
         "2.2799625576498834",
         "nonparam_boots"
        ],
        [
         "32",
         "log",
         "p0",
         "-40.70878964503654",
         "-369.35112799570214",
         "-14.205808684448709",
         "nonparam_stratified_boots"
        ],
        [
         "33",
         "log",
         "p1",
         "3.3991848080423114e-08",
         "-1.1407057756070862e-05",
         "1.2099347078543717e-05",
         "nonparam_stratified_boots"
        ],
        [
         "34",
         "log",
         "p2",
         "15.072426370420516",
         "-35.75099430893984",
         "79.27185653189909",
         "nonparam_stratified_boots"
        ],
        [
         "35",
         "log",
         "p3",
         "-12.365102187959996",
         "-44.49996309035288",
         "2.2802825280843164",
         "nonparam_stratified_boots"
        ],
        [
         "36",
         "log",
         "p0",
         "-40.70878964503654",
         "-310.97179686469656",
         "-8.945695488818114",
         "parametric_boots"
        ],
        [
         "37",
         "log",
         "p1",
         "3.3991848080423114e-08",
         "-0.00022031918968224803",
         "0.00024100793866809866",
         "parametric_boots"
        ],
        [
         "38",
         "log",
         "p2",
         "15.072426370420516",
         "-74.06005091397371",
         "48.61999601158131",
         "parametric_boots"
        ],
        [
         "39",
         "log",
         "p3",
         "-12.365102187959996",
         "-34.97653145139742",
         "17.689024661963625",
         "parametric_boots"
        ]
       ],
       "shape": {
        "columns": 6,
        "rows": 40
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scale</th>\n",
       "      <th>coef</th>\n",
       "      <th>value</th>\n",
       "      <th>LCL</th>\n",
       "      <th>UCL</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239921e+02</td>\n",
       "      <td>-2.346224e+02</td>\n",
       "      <td>-1.336168e+01</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>-1.900645e-09</td>\n",
       "      <td>-4.178296e-09</td>\n",
       "      <td>3.770058e-10</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>5.582662e+00</td>\n",
       "      <td>2.883961e+00</td>\n",
       "      <td>8.281363e+00</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279818e+01</td>\n",
       "      <td>-1.864852e+01</td>\n",
       "      <td>-6.947847e+00</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-4.070879e+01</td>\n",
       "      <td>-8.063180e+01</td>\n",
       "      <td>-7.857794e-01</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>3.399185e-08</td>\n",
       "      <td>-1.370368e-08</td>\n",
       "      <td>8.168737e-08</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507243e+01</td>\n",
       "      <td>6.671581e+00</td>\n",
       "      <td>2.347327e+01</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236510e+01</td>\n",
       "      <td>-1.864359e+01</td>\n",
       "      <td>-6.086617e+00</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239921e+02</td>\n",
       "      <td>-2.339494e+02</td>\n",
       "      <td>-9.562457e+00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>-1.900645e-09</td>\n",
       "      <td>-9.595711e-05</td>\n",
       "      <td>7.713982e-06</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>5.582662e+00</td>\n",
       "      <td>2.794437e+00</td>\n",
       "      <td>8.270209e+00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279818e+01</td>\n",
       "      <td>-1.863649e+01</td>\n",
       "      <td>-6.719472e+00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239921e+02</td>\n",
       "      <td>-6.620773e+02</td>\n",
       "      <td>-2.093170e+01</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>-1.900645e-09</td>\n",
       "      <td>-2.420179e+00</td>\n",
       "      <td>3.871659e-04</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>5.582662e+00</td>\n",
       "      <td>-1.102782e+01</td>\n",
       "      <td>1.781766e+01</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279818e+01</td>\n",
       "      <td>-3.283880e+01</td>\n",
       "      <td>5.789172e+00</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239921e+02</td>\n",
       "      <td>-6.477410e+02</td>\n",
       "      <td>-2.141250e+01</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>-1.900645e-09</td>\n",
       "      <td>-2.530518e+00</td>\n",
       "      <td>2.107221e-01</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>5.582662e+00</td>\n",
       "      <td>-1.103487e+01</td>\n",
       "      <td>1.753722e+01</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279818e+01</td>\n",
       "      <td>-3.243664e+01</td>\n",
       "      <td>5.801690e+00</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239921e+02</td>\n",
       "      <td>-5.199186e+02</td>\n",
       "      <td>-1.426793e+01</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>-1.900645e-09</td>\n",
       "      <td>-2.558559e+00</td>\n",
       "      <td>1.758763e-02</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>5.582662e+00</td>\n",
       "      <td>-1.729444e+01</td>\n",
       "      <td>1.472734e+01</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279818e+01</td>\n",
       "      <td>-2.808349e+01</td>\n",
       "      <td>2.641113e+01</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-4.070879e+01</td>\n",
       "      <td>-8.051482e+01</td>\n",
       "      <td>5.768097e-01</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>3.399185e-08</td>\n",
       "      <td>-4.475513e-09</td>\n",
       "      <td>2.942177e-08</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507243e+01</td>\n",
       "      <td>6.428671e+00</td>\n",
       "      <td>2.346898e+01</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236510e+01</td>\n",
       "      <td>-1.860056e+01</td>\n",
       "      <td>-5.869817e+00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-4.070879e+01</td>\n",
       "      <td>-3.623419e+02</td>\n",
       "      <td>-1.445765e+01</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>3.399185e-08</td>\n",
       "      <td>-1.294969e-05</td>\n",
       "      <td>1.370159e-05</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507243e+01</td>\n",
       "      <td>-3.574117e+01</td>\n",
       "      <td>7.860083e+01</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236510e+01</td>\n",
       "      <td>-4.406300e+01</td>\n",
       "      <td>2.279963e+00</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-4.070879e+01</td>\n",
       "      <td>-3.693511e+02</td>\n",
       "      <td>-1.420581e+01</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>3.399185e-08</td>\n",
       "      <td>-1.140706e-05</td>\n",
       "      <td>1.209935e-05</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507243e+01</td>\n",
       "      <td>-3.575099e+01</td>\n",
       "      <td>7.927186e+01</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236510e+01</td>\n",
       "      <td>-4.449996e+01</td>\n",
       "      <td>2.280283e+00</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-4.070879e+01</td>\n",
       "      <td>-3.109718e+02</td>\n",
       "      <td>-8.945695e+00</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>3.399185e-08</td>\n",
       "      <td>-2.203192e-04</td>\n",
       "      <td>2.410079e-04</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507243e+01</td>\n",
       "      <td>-7.406005e+01</td>\n",
       "      <td>4.862000e+01</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236510e+01</td>\n",
       "      <td>-3.497653e+01</td>\n",
       "      <td>1.768902e+01</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    scale coef         value           LCL           UCL  \\\n",
       "0   plain   p0 -1.239921e+02 -2.346224e+02 -1.336168e+01   \n",
       "1   plain   p1 -1.900645e-09 -4.178296e-09  3.770058e-10   \n",
       "2   plain   p2  5.582662e+00  2.883961e+00  8.281363e+00   \n",
       "3   plain   p3 -1.279818e+01 -1.864852e+01 -6.947847e+00   \n",
       "4     log   p0 -4.070879e+01 -8.063180e+01 -7.857794e-01   \n",
       "5     log   p1  3.399185e-08 -1.370368e-08  8.168737e-08   \n",
       "6     log   p2  1.507243e+01  6.671581e+00  2.347327e+01   \n",
       "7     log   p3 -1.236510e+01 -1.864359e+01 -6.086617e+00   \n",
       "8   plain   p0 -1.239921e+02 -2.339494e+02 -9.562457e+00   \n",
       "9   plain   p1 -1.900645e-09 -9.595711e-05  7.713982e-06   \n",
       "10  plain   p2  5.582662e+00  2.794437e+00  8.270209e+00   \n",
       "11  plain   p3 -1.279818e+01 -1.863649e+01 -6.719472e+00   \n",
       "12  plain   p0 -1.239921e+02 -6.620773e+02 -2.093170e+01   \n",
       "13  plain   p1 -1.900645e-09 -2.420179e+00  3.871659e-04   \n",
       "14  plain   p2  5.582662e+00 -1.102782e+01  1.781766e+01   \n",
       "15  plain   p3 -1.279818e+01 -3.283880e+01  5.789172e+00   \n",
       "16  plain   p0 -1.239921e+02 -6.477410e+02 -2.141250e+01   \n",
       "17  plain   p1 -1.900645e-09 -2.530518e+00  2.107221e-01   \n",
       "18  plain   p2  5.582662e+00 -1.103487e+01  1.753722e+01   \n",
       "19  plain   p3 -1.279818e+01 -3.243664e+01  5.801690e+00   \n",
       "20  plain   p0 -1.239921e+02 -5.199186e+02 -1.426793e+01   \n",
       "21  plain   p1 -1.900645e-09 -2.558559e+00  1.758763e-02   \n",
       "22  plain   p2  5.582662e+00 -1.729444e+01  1.472734e+01   \n",
       "23  plain   p3 -1.279818e+01 -2.808349e+01  2.641113e+01   \n",
       "24    log   p0 -4.070879e+01 -8.051482e+01  5.768097e-01   \n",
       "25    log   p1  3.399185e-08 -4.475513e-09  2.942177e-08   \n",
       "26    log   p2  1.507243e+01  6.428671e+00  2.346898e+01   \n",
       "27    log   p3 -1.236510e+01 -1.860056e+01 -5.869817e+00   \n",
       "28    log   p0 -4.070879e+01 -3.623419e+02 -1.445765e+01   \n",
       "29    log   p1  3.399185e-08 -1.294969e-05  1.370159e-05   \n",
       "30    log   p2  1.507243e+01 -3.574117e+01  7.860083e+01   \n",
       "31    log   p3 -1.236510e+01 -4.406300e+01  2.279963e+00   \n",
       "32    log   p0 -4.070879e+01 -3.693511e+02 -1.420581e+01   \n",
       "33    log   p1  3.399185e-08 -1.140706e-05  1.209935e-05   \n",
       "34    log   p2  1.507243e+01 -3.575099e+01  7.927186e+01   \n",
       "35    log   p3 -1.236510e+01 -4.449996e+01  2.280283e+00   \n",
       "36    log   p0 -4.070879e+01 -3.109718e+02 -8.945695e+00   \n",
       "37    log   p1  3.399185e-08 -2.203192e-04  2.410079e-04   \n",
       "38    log   p2  1.507243e+01 -7.406005e+01  4.862000e+01   \n",
       "39    log   p3 -1.236510e+01 -3.497653e+01  1.768902e+01   \n",
       "\n",
       "                       method  \n",
       "0                  Wald/delta  \n",
       "1                  Wald/delta  \n",
       "2                  Wald/delta  \n",
       "3                  Wald/delta  \n",
       "4                  Wald/delta  \n",
       "5                  Wald/delta  \n",
       "6                  Wald/delta  \n",
       "7                  Wald/delta  \n",
       "8                      normal  \n",
       "9                      normal  \n",
       "10                     normal  \n",
       "11                     normal  \n",
       "12             nonparam_boots  \n",
       "13             nonparam_boots  \n",
       "14             nonparam_boots  \n",
       "15             nonparam_boots  \n",
       "16  nonparam_stratified_boots  \n",
       "17  nonparam_stratified_boots  \n",
       "18  nonparam_stratified_boots  \n",
       "19  nonparam_stratified_boots  \n",
       "20           parametric_boots  \n",
       "21           parametric_boots  \n",
       "22           parametric_boots  \n",
       "23           parametric_boots  \n",
       "24                     normal  \n",
       "25                     normal  \n",
       "26                     normal  \n",
       "27                     normal  \n",
       "28             nonparam_boots  \n",
       "29             nonparam_boots  \n",
       "30             nonparam_boots  \n",
       "31             nonparam_boots  \n",
       "32  nonparam_stratified_boots  \n",
       "33  nonparam_stratified_boots  \n",
       "34  nonparam_stratified_boots  \n",
       "35  nonparam_stratified_boots  \n",
       "36           parametric_boots  \n",
       "37           parametric_boots  \n",
       "38           parametric_boots  \n",
       "39           parametric_boots  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nr of parameters\n",
    "d = thetas.shape[-1]\n",
    "\n",
    "# CI of params:\n",
    "#   - using Wald approximation \n",
    "#   - assuming asymptotic MLE distr of parameters \n",
    "#   - cov = hessian(nllf)^{-1}\n",
    "#   - this could be considered as delta method\n",
    "\n",
    "print(f\"theta CI:scale:{scales},method:Wald/delta\")\n",
    "lst_theta_CI = [df_theta_CI_wald.assign(method= \"Wald/delta\")]\n",
    "\n",
    "for scale, theta in zip(scales, thetas):\n",
    "\n",
    "    # parameter quantiles from sampled or bootstrapped parameter sets\n",
    "    for method in [\"normal\", \"nonparam_boots\", \"nonparam_stratified_boots\", \"parametric_boots\"]:\n",
    "    \n",
    "        print(f\"model CI:scale:{scale},boots-method:{method}\")\n",
    "\n",
    "        # get sampled or bootstrapped parameters\n",
    "        btheta = boots_theta_results[(scale, method)]\n",
    "    \n",
    "        # quantiles\n",
    "        quant = np.quantile(btheta, probs, axis = 0)\n",
    "\n",
    "        df_tmp = pd.DataFrame({\n",
    "            \"method\": f\"{method}\",\n",
    "            \"scale\" : scale,\n",
    "            \"coef\" : [f\"p{i}\" for i in range(d)],\n",
    "            \"value\" : theta, \n",
    "            \"LCL\" : quant[0], \n",
    "            \"UCL\": quant[1]\n",
    "        })\n",
    "        \n",
    "        lst_theta_CI.append(df_tmp)\n",
    "\n",
    "df_theta_CI = pd.concat(lst_theta_CI, ignore_index=True)\n",
    "df_theta_CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a93d455317b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cubic_monotonic_results = {\n",
    "    \"model\": logit,\n",
    "    \"fit_results\": ress,\n",
    "    \"parameters\": thetas,\n",
    "    \"covariances\": cov_thetas,\n",
    "    \"parameter_ci_wald\": df_theta_CI_wald,\n",
    "    \"parameter_ci_comparison\": df_theta_CI,\n",
    "    \"bootstrap_parameters\": boots_theta_results,\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e64342b71314",
   "metadata": {},
   "source": [
    "## 5. Collected model results\n",
    "\n",
    "The principal fitted objects and uncertainty results from the three sections are collected below. This makes later cross-model comparisons possible without rerunning or reconstructing section-specific variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4fd30ddf0b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['linear', 'cubic_unconstrained', 'cubic_monotonic'])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_results = {\n",
    "    \"linear\": linear_results,\n",
    "    \"cubic_unconstrained\": cubic_unconstrained_results,\n",
    "    \"cubic_monotonic\": cubic_monotonic_results,\n",
    "}\n",
    "\n",
    "model_results.keys()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base (3.12.3)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}