notebooks/Fase_1_Equipo6_v1.1_ftorres.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ https://experiencia21.tec.mx/courses/530360/pages/evaluacion\n",
    "+ https://archive.ics.uci.edu/dataset/565/bone+marrow+transplant+children\n",
    "+ https://arxiv.org/abs/1806.01579, https://arxiv.org/pdf/1806.01579\n",
    "\n",
    "# Dataset Information\n",
    "\n",
    "The data set describes pediatric patients with several hematologic diseases: malignant disorders (i.a. acute lymphoblastic leukemia, acute myelogenous leukemia, chronic myelogenous leukemia, myelodysplastic syndrome) and nonmalignant cases (i.a. severe aplastic anemia, Fanconi anemia, with X-linked adrenoleukodystrophy). All patients were subject to the unmanipulated allogeneic unrelated donor hematopoietic stem cell transplantation. \n",
    " \n",
    "The motivation of the study was to identify the most important factors influencing the success or failure of the transplantation procedure. In particular, the aim was to verify the hypothesis that increased dosage of CD34+ cells / kg extends overall survival time without simultaneous occurrence of undesirable events affecting patients' quality of life (KawÅ‚ak et al., 2010).\n",
    "\n",
    "The data set has been used in our work concerning survival rules (WrÃ³bel et al., 2017) and user-guided rule induction (Sikora et al., 2019). The authors of the research on stem cell transplantation (KawÅ‚ak et al., 2010) who inspired our study also contributed to the set.\n",
    "\n",
    "`Has Missing Values? Yes`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cargar Dataset Raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from icecream import ic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ic| current_dir: '/home/fernando/Git/MLOps_team6/notebooks'\n",
      "ic| root_dir: '/home/fernando/Git/MLOps_team6'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/fernando/Git/MLOps_team6'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_dir = os.getcwd()\n",
    "root_dir = os.path.dirname(current_dir)\n",
    "ic(current_dir)\n",
    "ic(root_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ic| full_path: '/home/fernando/Git/MLOps_team6/data/raw/bone-marrow.arff'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/fernando/Git/MLOps_team6/data/raw/bone-marrow.arff'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relative_path = 'data/raw/bone-marrow.arff'\n",
    "full_path = os.path.join(os.path.dirname(current_dir), relative_path)\n",
    "ic(full_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outs:\n",
      "- md5: b1828b3d79e4b9416c60a8f7f7d270b9\n",
      "  size: 27934\n",
      "  hash: md5\n",
      "  path: bone-marrow.arff\n"
     ]
    }
   ],
   "source": [
    "# !head '/home/fernando/Git/MLOps_team6/data/raw/bone-marrow.arff'\n",
    "!cat '/home/fernando/Git/MLOps_team6/data/raw/bone-marrow.arff.dvc'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descripción del Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bmt Dataset\n",
    "\n",
    "El conjunto de datos describe pacientes pediátricos con varias enfermedades hematológicas:\n",
    "\n",
    "- **Trastornos malignos**: pacientes con leucemia linfoblástica aguda, leucemia mieloide aguda, leucemia mieloide crónica, síndrome mielodisplásico.\n",
    "- **Casos no malignos**: pacientes con anemia aplásica severa, anemia de Fanconi, adrenoleucodistrofia ligada al cromosoma X.\n",
    "\n",
    "Todos los pacientes fueron sometidos a un trasplante de células madre hematopoyéticas alogénico no manipulado de un donante no relacionado. El objetivo del estudio fue identificar los factores más importantes que influyen en el éxito o fracaso del procedimiento de trasplante. En particular, se verificó la hipótesis de que un aumento en la dosis de células CD34+/kg extiende el tiempo de supervivencia general sin la aparición de eventos indeseables que afecten la calidad de vida de los pacientes.\n",
    "\n",
    "<!-- ### Fuente\n",
    "\n",
    "Proporcionar nombres, direcciones de correo electrónico, instituciones y otra información de contacto de los donantes y creadores del conjunto de datos:\n",
    "\n",
    "- **Marek Sikora** (marek.sikora '@' polsl.pl)\n",
    "- **Lukasz Wrobel** (lukasz.wrobel '@' polsl.pl)\n",
    "- **Instituciones**:\n",
    "  - Instituto de Ciencias de la Computación, Universidad Tecnológica de Silesia, Gliwice, Polonia.\n",
    "  - Instituto de Tecnologías Innovadoras EMAG, Katowice, Polonia.\n",
    " -->\n",
    "\n",
    "### Información del dataset\n",
    "\n",
    "El conjunto contiene 187 ejemplos caracterizados por 37 atributos:\n",
    "\n",
    "- **Recipientgender**: Género del receptor (Hombre - 1, Mujer - 0).\n",
    "- **Stemcellsource**: Fuente de células madre hematopoyéticas (Sangre periférica - 1, Médula ósea - 0).\n",
    "- **Donorage**: Edad del donante en el momento de la aféresis de células madre hematopoyéticas.\n",
    "- **Donorage35**: Edad del donante <35 - 0, Edad del donante >=35 - 1.\n",
    "- **IIIV**: Desarrollo de enfermedad injerto contra huésped aguda en estadio II, III o IV (Sí - 1, No - 0).\n",
    "- **Gendermatch**: Compatibilidad de género entre el donante y receptor (Femenino a Masculino - 1, Otro - 0).\n",
    "- **DonorABO**: Grupo sanguíneo ABO del donante (0 - 0, A - 1, B - (-1), AB - 2).\n",
    "- **RecipientABO**: Grupo sanguíneo ABO del receptor (0 - 0, A - 1, B - (-1), AB - 2).\n",
    "- **RecipientRh**: Presencia del factor Rh en los glóbulos rojos del receptor ('+' - 1, '-' - 0).\n",
    "- **ABOMatch**: Compatibilidad ABO entre donante y receptor (Compatible - 1, No compatible - 1).\n",
    "- **CMVstatus**: Compatibilidad serológica de citomegalovirus entre donante y receptor (mayor valor, menor compatibilidad).\n",
    "- **RecipientCMV**: Presencia de infección por citomegalovirus en el receptor antes del trasplante (Presente - 1, Ausente - 0).\n",
    "- **Disease**: Tipo de enfermedad (ALL, AML, crónico, no maligno, linfoma).\n",
    "- **Riskgroup**: Grupo de riesgo (Alto - 1, Bajo - 0).\n",
    "- **Txpostrelapse**: Segundo trasplante de médula ósea después de la recaída (No - 0; Sí - 1).\n",
    "- **Diseasegroup**: Tipo de enfermedad (Maligno - 1, No maligno - 0).\n",
    "- **HLAmatch**: Compatibilidad del complejo mayor de histocompatibilidad (HLA) según criterios internacionales BFM SCT 2008 (10/10 - 0, 9/10 - 1, 8/10 - 2, 7/10 - 3).\n",
    "- **HLAmismatch**: Compatibilidad HLA (Compatible - 0, No compatible - 1).\n",
    "- **Antigen**: Diferencias en antígenos entre donante y receptor (sin diferencias - (-1), una diferencia - 0, dos o tres diferencias - 1 o 2).\n",
    "- **Allel**: Diferencias en alelos entre donante y receptor (sin diferencias - (-1), una diferencia - 0, dos, tres o cuatro diferencias - 1, 2, 3).\n",
    "- **HLAgrI**: Tipo de diferencia entre donante y receptor en HLA (compatible - 0, diferencia en un antígeno - 1, diferencia en un alelo - 2, diferencia en célula DRB1 - 3, dos diferencias en alelos o antígenos - 4 o 5).\n",
    "- **Recipientage**: Edad del receptor en el momento del trasplante.\n",
    "- **Recipientage10**: Edad del receptor <10 - 0, Edad del receptor >=10 - 1.\n",
    "- **Recipientageint**: Edad del receptor en intervalos (0,5] - 0, (5,10] - 1, (10,20] - 2.\n",
    "- **Relapse**: Reaparición de la enfermedad (No - 0, Sí - 1).\n",
    "- **aGvHDIIIIV**: Desarrollo de enfermedad injerto contra huésped aguda en estadio III o IV (Sí - 0, No - 1).\n",
    "- **extcGvHD**: Desarrollo de enfermedad injerto contra huésped crónica extensa (Sí - 0, No - 1).\n",
    "- **CD34kgx10d6**: Dosis de células CD34+ por kg de peso corporal del receptor (10^6/kg).\n",
    "- **CD3dCD34**: Relación entre células CD3+ y CD34+.\n",
    "- **CD3dkgx10d8**: Dosis de células CD3+ por kg de peso corporal del receptor (10^8/kg).\n",
    "- **Rbodymass**: Masa corporal del receptor en el momento del trasplante.\n",
    "- **ANCrecovery**: Tiempo hasta la recuperación de neutrófilos (cuenta de neutrófilos >0.5 x 10^9/L).\n",
    "- **PLTrecovery**: Tiempo hasta la recuperación de plaquetas (cuenta de plaquetas >50,000/mm3).\n",
    "- **time_to_aGvHD_III_IV**: Tiempo hasta el desarrollo de enfermedad injerto contra huésped aguda en estadio III o IV.\n",
    "- **survival_time**: Tiempo de supervivencia (numérico).\n",
    "- **survival_status**: Estado de supervivencia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import arff\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "arff_file = arff.loadarff(full_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(arff_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# arff_file[0] # raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset: 'bone-marrow'\n",
       "\tRecipientgender's type is nominal, range is ('1', '0')\n",
       "\tStemcellsource's type is nominal, range is ('1', '0')\n",
       "\tDonorage's type is numeric\n",
       "\tDonorage35's type is nominal, range is ('0', '1')\n",
       "\tIIIV's type is nominal, range is ('1', '0')\n",
       "\tGendermatch's type is nominal, range is ('0', '1')\n",
       "\tDonorABO's type is nominal, range is ('1', '-1', '2', '0')\n",
       "\tRecipientABO's type is nominal, range is ('1', '-1', '2', '0')\n",
       "\tRecipientRh's type is nominal, range is ('1', '0')\n",
       "\tABOmatch's type is nominal, range is ('0', '1')\n",
       "\tCMVstatus's type is nominal, range is ('3', '2', '1', '0')\n",
       "\tDonorCMV's type is nominal, range is ('1', '0')\n",
       "\tRecipientCMV's type is nominal, range is ('1', '0')\n",
       "\tDisease's type is nominal, range is ('ALL', 'AML', 'chronic', 'nonmalignant', 'lymphoma')\n",
       "\tRiskgroup's type is nominal, range is ('1', '0')\n",
       "\tTxpostrelapse's type is nominal, range is ('0', '1')\n",
       "\tDiseasegroup's type is nominal, range is ('1', '0')\n",
       "\tHLAmatch's type is nominal, range is ('0', '1', '3', '2')\n",
       "\tHLAmismatch's type is nominal, range is ('0', '1')\n",
       "\tAntigen's type is nominal, range is ('-1', '1', '0', '2')\n",
       "\tAlel's type is nominal, range is ('-1', '0', '2', '1', '3')\n",
       "\tHLAgrI's type is nominal, range is ('0', '1', '7', '3', '2', '4', '5')\n",
       "\tRecipientage's type is numeric\n",
       "\tRecipientage10's type is nominal, range is ('0', '1')\n",
       "\tRecipientageint's type is nominal, range is ('0', '1', '2')\n",
       "\tRelapse's type is nominal, range is ('0', '1')\n",
       "\taGvHDIIIIV's type is nominal, range is ('0', '1')\n",
       "\textcGvHD's type is nominal, range is ('1', '0')\n",
       "\tCD34kgx10d6's type is numeric\n",
       "\tCD3dCD34's type is numeric\n",
       "\tCD3dkgx10d8's type is numeric\n",
       "\tRbodymass's type is numeric\n",
       "\tANCrecovery's type is numeric\n",
       "\tPLTrecovery's type is numeric\n",
       "\ttime_to_aGvHD_III_IV's type is numeric\n",
       "\tsurvival_time's type is numeric\n",
       "\tsurvival_status's type is numeric"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arff_file[1] # metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recipientgender</th>\n",
       "      <th>Stemcellsource</th>\n",
       "      <th>Donorage</th>\n",
       "      <th>Donorage35</th>\n",
       "      <th>IIIV</th>\n",
       "      <th>Gendermatch</th>\n",
       "      <th>DonorABO</th>\n",
       "      <th>RecipientABO</th>\n",
       "      <th>RecipientRh</th>\n",
       "      <th>ABOmatch</th>\n",
       "      <th>...</th>\n",
       "      <th>extcGvHD</th>\n",
       "      <th>CD34kgx10d6</th>\n",
       "      <th>CD3dCD34</th>\n",
       "      <th>CD3dkgx10d8</th>\n",
       "      <th>Rbodymass</th>\n",
       "      <th>ANCrecovery</th>\n",
       "      <th>PLTrecovery</th>\n",
       "      <th>time_to_aGvHD_III_IV</th>\n",
       "      <th>survival_time</th>\n",
       "      <th>survival_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>22.830137</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>7.20</td>\n",
       "      <td>1.338760</td>\n",
       "      <td>5.38</td>\n",
       "      <td>35.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>23.342466</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'-1'</td>\n",
       "      <td>b'-1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>4.50</td>\n",
       "      <td>11.078295</td>\n",
       "      <td>0.41</td>\n",
       "      <td>20.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>26.394521</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'-1'</td>\n",
       "      <td>b'-1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>7.94</td>\n",
       "      <td>19.013230</td>\n",
       "      <td>0.42</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Recipientgender Stemcellsource   Donorage Donorage35  IIIV Gendermatch  \\\n",
       "0            b'1'           b'1'  22.830137       b'0'  b'1'        b'0'   \n",
       "1            b'1'           b'0'  23.342466       b'0'  b'1'        b'0'   \n",
       "2            b'1'           b'0'  26.394521       b'0'  b'1'        b'0'   \n",
       "\n",
       "  DonorABO RecipientABO RecipientRh ABOmatch  ... extcGvHD CD34kgx10d6  \\\n",
       "0     b'1'         b'1'        b'1'     b'0'  ...     b'1'        7.20   \n",
       "1    b'-1'        b'-1'        b'1'     b'0'  ...     b'1'        4.50   \n",
       "2    b'-1'        b'-1'        b'1'     b'0'  ...     b'1'        7.94   \n",
       "\n",
       "    CD3dCD34 CD3dkgx10d8 Rbodymass ANCrecovery PLTrecovery  \\\n",
       "0   1.338760        5.38      35.0        19.0        51.0   \n",
       "1  11.078295        0.41      20.6        16.0        37.0   \n",
       "2  19.013230        0.42      23.4        23.0        20.0   \n",
       "\n",
       "  time_to_aGvHD_III_IV survival_time survival_status  \n",
       "0                 32.0         999.0             0.0  \n",
       "1            1000000.0         163.0             1.0  \n",
       "2            1000000.0         435.0             1.0  \n",
       "\n",
       "[3 rows x 37 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(arff_file[0])\n",
    "df = pd.DataFrame(arff_file[0],en)\n",
    "\n",
    "# df = pd.DataFrame(arff_file[0]).str.decode('utf-8')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Donorage',\n",
       " 'Recipientage',\n",
       " 'CD34kgx10d6',\n",
       " 'CD3dCD34',\n",
       " 'CD3dkgx10d8',\n",
       " 'Rbodymass',\n",
       " 'ANCrecovery',\n",
       " 'PLTrecovery',\n",
       " 'time_to_aGvHD_III_IV',\n",
       " 'survival_time',\n",
       " 'survival_status']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numeric = df.describe()\n",
    "numeric_cols = list(df_numeric.columns)\n",
    "numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Recipientgender',\n",
       " 'Stemcellsource',\n",
       " 'Donorage35',\n",
       " 'IIIV',\n",
       " 'Gendermatch',\n",
       " 'DonorABO',\n",
       " 'RecipientABO',\n",
       " 'RecipientRh',\n",
       " 'ABOmatch',\n",
       " 'CMVstatus',\n",
       " 'DonorCMV',\n",
       " 'RecipientCMV',\n",
       " 'Disease',\n",
       " 'Riskgroup',\n",
       " 'Txpostrelapse',\n",
       " 'Diseasegroup',\n",
       " 'HLAmatch',\n",
       " 'HLAmismatch',\n",
       " 'Antigen',\n",
       " 'Alel',\n",
       " 'HLAgrI',\n",
       " 'Recipientage10',\n",
       " 'Recipientageint',\n",
       " 'Relapse',\n",
       " 'aGvHDIIIIV',\n",
       " 'extcGvHD']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_categoric = df.describe(include=['category','object'])\n",
    "categoric_cols = list(df_categoric.columns)\n",
    "categoric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['Recipientgender_decoded'] = df['Recipientgender'].str.decode('utf-8').astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recipientgender</th>\n",
       "      <th>Stemcellsource</th>\n",
       "      <th>Donorage35</th>\n",
       "      <th>IIIV</th>\n",
       "      <th>Gendermatch</th>\n",
       "      <th>DonorABO</th>\n",
       "      <th>RecipientABO</th>\n",
       "      <th>RecipientRh</th>\n",
       "      <th>ABOmatch</th>\n",
       "      <th>CMVstatus</th>\n",
       "      <th>...</th>\n",
       "      <th>HLAmatch</th>\n",
       "      <th>HLAmismatch</th>\n",
       "      <th>Antigen</th>\n",
       "      <th>Alel</th>\n",
       "      <th>HLAgrI</th>\n",
       "      <th>Recipientage10</th>\n",
       "      <th>Recipientageint</th>\n",
       "      <th>Relapse</th>\n",
       "      <th>aGvHDIIIIV</th>\n",
       "      <th>extcGvHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>...</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'2'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'-1'</td>\n",
       "      <td>b'-1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'2'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>112</td>\n",
       "      <td>145</td>\n",
       "      <td>104</td>\n",
       "      <td>112</td>\n",
       "      <td>155</td>\n",
       "      <td>73</td>\n",
       "      <td>75</td>\n",
       "      <td>158</td>\n",
       "      <td>134</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>94</td>\n",
       "      <td>159</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>94</td>\n",
       "      <td>99</td>\n",
       "      <td>89</td>\n",
       "      <td>159</td>\n",
       "      <td>147</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recipientgender Stemcellsource Donorage35  IIIV Gendermatch DonorABO  \\\n",
       "count              187            187        187   187         187      187   \n",
       "unique               2              2          2     2           2        4   \n",
       "top               b'1'           b'1'       b'0'  b'1'        b'0'     b'0'   \n",
       "freq               112            145        104   112         155       73   \n",
       "\n",
       "       RecipientABO RecipientRh ABOmatch CMVstatus  ... HLAmatch HLAmismatch  \\\n",
       "count           187         187      187       187  ...      187         187   \n",
       "unique            5           3        3         5  ...        4           2   \n",
       "top            b'1'        b'1'     b'1'      b'2'  ...     b'0'        b'0'   \n",
       "freq             75         158      134        57  ...       94         159   \n",
       "\n",
       "       Antigen   Alel HLAgrI Recipientage10 Recipientageint Relapse  \\\n",
       "count      187    187    187            187             187     187   \n",
       "unique       5      6      7              2               3       2   \n",
       "top      b'-1'  b'-1'   b'0'           b'0'            b'2'    b'0'   \n",
       "freq        93     93     94             99              89     159   \n",
       "\n",
       "       aGvHDIIIIV extcGvHD  \n",
       "count         187      187  \n",
       "unique          2        3  \n",
       "top          b'1'     b'1'  \n",
       "freq          147      128  \n",
       "\n",
       "[4 rows x 26 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[categoric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Donorage</th>\n",
       "      <th>Recipientage</th>\n",
       "      <th>CD34kgx10d6</th>\n",
       "      <th>CD3dCD34</th>\n",
       "      <th>CD3dkgx10d8</th>\n",
       "      <th>Rbodymass</th>\n",
       "      <th>ANCrecovery</th>\n",
       "      <th>PLTrecovery</th>\n",
       "      <th>time_to_aGvHD_III_IV</th>\n",
       "      <th>survival_time</th>\n",
       "      <th>survival_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.472068</td>\n",
       "      <td>9.931551</td>\n",
       "      <td>11.891781</td>\n",
       "      <td>5.385096</td>\n",
       "      <td>4.745714</td>\n",
       "      <td>35.801081</td>\n",
       "      <td>26752.866310</td>\n",
       "      <td>90937.919786</td>\n",
       "      <td>775408.042781</td>\n",
       "      <td>938.743316</td>\n",
       "      <td>0.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.271826</td>\n",
       "      <td>5.305639</td>\n",
       "      <td>9.914386</td>\n",
       "      <td>9.598716</td>\n",
       "      <td>3.859128</td>\n",
       "      <td>19.650922</td>\n",
       "      <td>161747.200525</td>\n",
       "      <td>288242.407688</td>\n",
       "      <td>418425.252689</td>\n",
       "      <td>849.589495</td>\n",
       "      <td>0.499266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.646575</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.204132</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.039726</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>5.350000</td>\n",
       "      <td>1.786683</td>\n",
       "      <td>1.687500</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>168.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.550685</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>9.720000</td>\n",
       "      <td>2.734462</td>\n",
       "      <td>4.325000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>676.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.117809</td>\n",
       "      <td>14.050000</td>\n",
       "      <td>15.415000</td>\n",
       "      <td>5.823565</td>\n",
       "      <td>6.785000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1604.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>55.553425</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>57.780000</td>\n",
       "      <td>99.560970</td>\n",
       "      <td>20.020000</td>\n",
       "      <td>103.400000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>3364.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Donorage  Recipientage  CD34kgx10d6    CD3dCD34  CD3dkgx10d8  \\\n",
       "count  187.000000    187.000000   187.000000  182.000000   182.000000   \n",
       "mean    33.472068      9.931551    11.891781    5.385096     4.745714   \n",
       "std      8.271826      5.305639     9.914386    9.598716     3.859128   \n",
       "min     18.646575      0.600000     0.790000    0.204132     0.040000   \n",
       "25%     27.039726      5.050000     5.350000    1.786683     1.687500   \n",
       "50%     33.550685      9.600000     9.720000    2.734462     4.325000   \n",
       "75%     40.117809     14.050000    15.415000    5.823565     6.785000   \n",
       "max     55.553425     20.200000    57.780000   99.560970    20.020000   \n",
       "\n",
       "        Rbodymass     ANCrecovery     PLTrecovery  time_to_aGvHD_III_IV  \\\n",
       "count  185.000000      187.000000      187.000000            187.000000   \n",
       "mean    35.801081    26752.866310    90937.919786         775408.042781   \n",
       "std     19.650922   161747.200525   288242.407688         418425.252689   \n",
       "min      6.000000        9.000000        9.000000             10.000000   \n",
       "25%     19.000000       13.000000       16.000000        1000000.000000   \n",
       "50%     33.000000       15.000000       21.000000        1000000.000000   \n",
       "75%     50.600000       17.000000       37.000000        1000000.000000   \n",
       "max    103.400000  1000000.000000  1000000.000000        1000000.000000   \n",
       "\n",
       "       survival_time  survival_status  \n",
       "count     187.000000       187.000000  \n",
       "mean      938.743316         0.454545  \n",
       "std       849.589495         0.499266  \n",
       "min         6.000000         0.000000  \n",
       "25%       168.500000         0.000000  \n",
       "50%       676.000000         0.000000  \n",
       "75%      1604.000000         1.000000  \n",
       "max      3364.000000         1.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGsCAYAAAAPJKchAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOxdZ5gUxdZ+e2ZzYMm75AyCklRAEBAFLxgwXxUDBsSIqKgYABUTXgkqqHjVz4CiqCjmgAkjKiCYBckqGSQsm3f6+zHb3RVOV3fP9OyM136fR5ntrq4cTr3n1ClN13UdAQIECBAgQIAASUIo2RkIECBAgAABAvyzEQgjAQIECBAgQICkIhBGAgQIECBAgABJRSCMBAgQIECAAAGSikAYCRAgQIAAAQIkFYEwEiBAgAABAgRIKgJhJECAAAECBAiQVATCSIAAAQIECBAgqQiEkQABAgQIECBAUhEIIwECBAgQIECApOJvJYx8+umnGD58OJo2bQpN0/Dqq696jkPXdUybNg0dO3ZEZmYmmjVrhrvuusv/zAYIECBAgAABXCEt2Rnwgv3796N79+648MILccopp8QUx1VXXYWFCxdi2rRp6Nq1K3bt2oVdu3b5nNMAAQIECBAggFtof9eL8jRNw4IFC3DSSSeZz8rLyzFhwgQ8//zz2L17Nw466CD85z//waBBgwAAv/zyC7p164Yff/wRnTp1Sk7GAwQIECBAgAAc/lZqGieMGTMGixcvxrx58/D999/j3//+N4YNG4bffvsNAPDGG2+gbdu2ePPNN9GmTRu0bt0aF110UcCMBAgQIECAAEnE/4wwsnHjRjz55JN46aWXMGDAALRr1w7XXXcd+vfvjyeffBIAsHbtWmzYsAEvvfQS5syZg6eeegrLli3DaaedluTcBwgQIECAAP9c/K1sRlT44YcfUF1djY4dO3LPy8vL0aBBAwBAJBJBeXk55syZY4b7v//7PxxyyCFYuXJloLoJECBAgAABkoD/GWGkuLgY4XAYy5YtQzgc5t7l5eUBAJo0aYK0tDROYOncuTOAKLMSCCMBAgQIECBA7eN/Rhjp2bMnqqursW3bNgwYMIAMc/jhh6Oqqgpr1qxBu3btAACrVq0CALRq1arW8hogQIAAAQIEsPC3Ok1TXFyM1atXA4gKHzNmzMCRRx6J+vXro2XLljjnnHPwxRdfYPr06ejZsye2b9+ODz/8EN26dcNxxx2HSCSCXr16IS8vD/fffz8ikQiuuOIK1KlTBwsXLkxy6QIECBAgQIB/Jv5WwsiiRYtw5JFHSs/PO+88PPXUU6isrMSdd96JOXPm4M8//0TDhg1x2GGHYfLkyejatSsAYNOmTbjyyiuxcOFC5Obm4phjjsH06dNRv3792i5OgAABAgQIEAB/M2EkQIAAAQIECPC/h/+Zo70BAgQIECBAgL8nAmEkQIAAAQIECJBU/C1O00QiEWzatAn5+fnQNC3Z2QkQIECAAAECuICu69i3bx+aNm2KUMie//hbCCObNm1CixYtkp2NAAECBAgQIEAM+P3339G8eXPb938LYSQ/Px9AtDB16tRJcm4CBAgQIECAAG6wd+9etGjRwlzH7fC3EEYM1UydOnUCYSRAgAABAgT4m8HJxCIwYA0QIECAAAECJBWBMBIgQIAAAQIESCoCYSRAgAABAgQIkFQEwkiAAAECBAgQIKkIhJEAAQIECBAgQFIRCCMBAgQIECBAgKQiEEYCBAgQIECAAElFIIwECBAgQIAAAZKKQBgJECBAgAABAiQVnoWRTz/9FMOHD0fTpk2haRpeffVVx28WLVqEgw8+GJmZmWjfvj2eeuqpGLIaIECAAAECBPhfhGdhZP/+/ejevTseeughV+HXrVuH4447DkceeSRWrFiBq6++GhdddBHee+89z5kNECBAgAABAvzvwfPdNMcccwyOOeYY1+EfeeQRtGnTBtOnTwcAdO7cGZ9//jnuu+8+DB061GvyAQIECBAgQID/MST8orzFixdjyJAh3LOhQ4fi6quvtv2mvLwc5eXl5t979+5NVPYAAJFIOTZufBIVFdsBAHl5B6Bp03+b77dufRN79iwHAKSn10WLFhcgLS2Pi6O6ugy///4EKip2JjSvXlFQ9xAUNj7W/PvPTS9gf/EqAEBGZmM0azoSS5euQJs2bVBUVAQAqKzcg99/fwpVVXy9p6UXoEXz85GeHr2scN++X/DXX4vRvPlIhELRrrRr15fYseNDz/mMRCLYsnUL6tWrh+ys7Jp8VGL79u1o2KghMtIzzLDpGfXRssUFCIdzAAB79n6HvXtWoHnzkeZlTDt2fIyqqn0oKjrB/G7z5pexb9/PnvJVUPcQZGUejiVLlqCyshJ68cdo0Lgc9Zo09FxGFTQtjCZN/4283A4AgMrKv/D770+jqmpfXPGGwzlo3nwkMjMbAQBKStbhz00vQI9USmGrKquwec0faNCkEXIKcqX327dvR3pGBuoWFJBpZee0RvNm55htsHzFUygrW4EG9RuQ4cX+l5m5Edt3LAR0nQwv9j8D4vilUK9+PzRqONj2vd34FevPgK5H8PsfT6Os9A/bOL0iO6c1GjY4Dd9++y26dOmCevXqSWHY8UshN7c9mjUbYf69bdt7gAY0bhTd+FVXV+PztyYju+5e1C1qgMysIrRofoGr8Vu6Zz92/FmCPkdMRF69xgCANWs/xprV76K6eiDC4XQcfPDBaNgwOjYqKnbg9z/moLpqP51ZLYSiwuGoU6cbAKCqaj9+//1JVFb+RQbfvn07CgoG4KCDonOzrlfj9z/moF7d3sjPP7Amjn3Y+PtTqKrcbVtHjRodjXr1DgMARCKV+P33J1FevtW2/nbv/gbl5eXYuXMnGjdujLQ0a9n0Un982TUUNj4WBQUHA/B//dj5xzbo5S3R75jrzWfs/JeR0RAtWlyAcDgLALB23SxUVe5GixYXIDu7uS958IqECyNbtmxBYWEh96ywsBB79+5FaWkpsrOzpW+mTJmCyZMnJzprJnbu/BRr1k7lntWvfziyspqiqqoYP/08Drpebb7LzCxE06anc+F37PgQa9ZOr5X8esEffz6Lhg0GIRzOQWnpH/j115u593v35uK991ajXbt2OPfccwEAW7YswLr1M8n40tProUXzaLjVq6dg119fIC+vE+rXPxwA8PMv41Fevjnm/O7YIT/bulV+lp3VHEVFJwIAVq68Ffv2/YCCgp7mxPbjT1ejuroEDRocgfT0ApSXb8PPv4z3nJ8//nwWlRVTsXjxt8jMLEbvPgtQXA0U+7cGmSgp3YDu3f4LANi06SWsWz/Ln4i1ENq1vQYAsG7dLGzZ+pp92Dxg577ofxTKyoF9CvmoXt3eyMvrhOrqSuzYfjdC4WqUlNiHZ/tfu/ZPobR0vbIobP8zQI1fEX9uegGDjvgemkZrppXjl6k/K9/f47ff7lSmGQs25ebi/fe/x86dO3HCCSdw76jxS6FevcOQk9MG1dXl+PGnq6BpGhoO/A6hUAZ+/2UxqvLmYl8VsK+mD+fndXE/fusAyz9viAHDbwEA/PrrZGRl/Y4ff9yHvXsbY8+ePfj3v6PCwp9/Po/169Xq/D17vkWvQ18GAGzf/h7WrrtPGX7fnx+bwsju3Uvx2293om7dPjjk4OcARDeO69bdr4xj+44PcHi/TwAAf/21GKvX/Id7L9afrluC++Ytcnye6o/BX399hT693wSQmPVDz9CwZ9sIFDRuTs5/2TmtUdg4quXYvPlllJX9jsLC4/93hZFYcNNNN2HcuHHm33v37kWLFi0Sll5VdVRyz8pqjoqKHYhEylBdHZ1BI5EyUxDJze2A/ft/M8OzqK55lp3dimMikgUdwIYNs6HrVYhEKhAO55h5DIWykZFRH2Vlf6KiIrqyVFRUmN8a5cvL64yGDQYBAHbs/BjFxb+a9cKGq2bqw/jdrOkIpKfXdZ3fLVu2YNVvv6F+/fo46MDoLmftunX4448/0KxZM7Rr2xYAsG37uygpWce1gZGm/ExHJFIGoMAMo2kZaNVylGN+2PorL49+27Bebk3caWjXdrTrsjmheP9v2LHjA64ejbLk5x+EBvUHxBTvX399hT17l5Nt1qDBEcjP68KF3/jzOqzf+Qfqpeej62E9uHelpaVYsnQpwuEwDu/XT0rrjz/noqpqr1mG6upKhMLRcdOkyXnIzMjhwm/Z+rrU/4xvmzY5HRkZPJtC9T+xTFlZzVFUOJx7Vx0px++/P4FIpBTRVqVBjV+q/sTw6en10azpGbbxuoVRfxWV8ngU0wyFstGyxfnS+9//mIPq6v1mfnW9ArpeCV2HOYeVleyOxlWVjpzcBigv3+J6/P7260tIz94hzH9lAID69XOwdy89j9Sp0wP16/Xl4ior24QtW18j+3xOTjs0bvQvLnxx8Xbs2DkfoZAlGFQT848RR25uBzRqyDPyFZV/YdOmeeR4yMxsYvZfsf4AoLx8ELZu3YE2rdugRYvoYr15ywJP9WeWvXwLtmxZQH7n1/qxbv1saJqO8tJ9XPyaloGsrCKUlm7k0jfHho2wXhtIuDBSVFSErcLWduvWrahTpw7JigBAZmYmMjMzE501C3oEAGqk4ZIaAST6TDcpYw35eV2wf/9vJI1shM/N7YB27a6rlWyroOs6NmyYXfM7wv2blpaPnOzWKCv7kygnzPqoU6e7WZaKip0oLv7VfMeGY7814mvZ8iLk5LR2nd/du5dhw/o3kJ7WHu3anQMAWL36PWxYvxhFhX3Qrl1Ugt9fsg4lJeu4NjDS5POjC2WP/h0OZ7tqH67+EI2jYb36AKITuZ9tvHXrm9ix4wOrHIBZloKCQ2JOa82aadizdznZZo0bDZPYvT+/idZ3OKcZ2rXjha0dO3Zgw/oHkZ6ejnbnyvnZuu0dVFXtlfoaADRvdinq1GnMhd+793up/xm/W7Q4H3l5nbjwZP8TypST00aqq6jK8QkzT5oWlr8HPX7J+hPCZ2YW+dIXjPqjxpSYZlpaPpnm5i2vorp6PzmmjWeRSFQoqarKRFZWc5SXb3E9fn9Z/inSs3dAZ4Q6reZ3/fr1sH59JTmP1KvbW8rvX399jS1bXyPD5+UdIIXftOl77Ng5H6xAaY17OY78/IOkOPbvX4NNm+aR4yw7u6W5QFP1V1Y6DBvW/4T27QajXbvo5uCv3Us81Z+BPXuWY8uWBeR3fqwfkUgEa9f9F5oWQSQiz385Oe1QWrqRrDcNWlxpx4OEi0F9+/bFhx/yOrT3338fffv2tfmi9mEsNpoWYmhco6Gsd6jRhRvh7eJIBRh6+ygi3L/RstTkk5q4asrOlYUou/Gbrw/iWxcw0ucHqPzMiJdP08iHEY6dyIl2dAGu/oyBaj7yecBKfc6mDWKMl2ozaugb1awTDALVFlxSZlrR98aix8ZL5Y0SIMndWYxjj39GCDKqOMi+xsfFj7PYYdafQhhx6sN2cxf72xyzumYzluz7nZklRmVthg9pUr51ZXsaz+S+SacdjV/j+qY8/5jjhujfmipNaMr6M8eG41zkYtx6LLtncDJGFZcWu8aR80IS1y/PKRcXF2PFihVYsWIFgOjR3RUrVmDjxo0AoiqWkSNHmuEvvfRSrF27FuPHj8evv/6Khx9+GC+++CKuueYaKvrkwOxgIRhVIu/wQlYHJ3dnbBypAqMsPEtQM/S4Z9QOhS0LWXZzhLKD1n6xU8GtMGIKAlyafD50Ij9s2d2D7wshI2ndX2FEE9KJ/hHh3sUTL7kDIhZRlcDhJIxA6B86t2DJaVH9T1eUOfaxpzHB7NU0VBxk/ZnBY+vn9nAWRpz7sN3cxcRnCoka6LGkElYNgZDJmxb9bY0Nd7ttsf1r/qh5p2hHTZ4fKOaPXlTt04yGV9WfkCabJ4/zn6rsfvQnPaJbbRWh5j853+q6rx14Tnnp0qXo2bMnevbsCQAYN24cevbsiVtuuQUAsHnzZlMwAYA2bdrgrbfewvvvv4/u3btj+vTpePzxx1PqWC/NjETMt9F3GrNTIiaKFGNGAGonwEjtip2YTu36iLLTzEhs9eAPM8IzQPxv70yDuFsN+UyIMAnV/KB2KnEkSu6A7Cc9sy/ExIzwzIVBD0e/IcpA9j9F34lx7CWOGfGBuWJg1p+SGVGnqUn9iBgjZvzqsUSnQbFTBjMCKd+q3baKpaD6vBWtev5hmQ77NIm5DiFl/blnRlzMf6p8+NCfogJItCwWQ2n1HZHFZNP/W9mMDBo0SLnDoLyrDho0CMuXL/eaVO2BkBq9MyOx7LwTDWeWx6JtY2FGan4TAoTXenAtjBC7VVF3TOliY9vJ8vVnTbiJYkaoXWU8zAi1A1JR4ca/MQgjCmaE1NIQ/U+184997Fnl1KlvFXGQ9SfF5ZMwohqPrtNUbDAIhpAeS/bj11LTyGpQmhlR7ba99Xm9Jj+cnKJidclF1Z6BjEZsV38hD3OR8/ynYkL9WD/UzIjFAFGMzv+0zcjfAZaeUWMaQ9QbMnSvSm+dQsKINXAFuwlo5kuaGbHqw4rMeCfvQqidgdcdvVc1DbU7o5kRouwuoQllTlzbUsxIbEIdFS9tMyLHG4mDGbFsOnQurpqH9uEpmxGqzET/s6In+qv4oW1G+Hj5OOztVKz+5BPMzqZiRtT9UBPagGJGzHbRozFFf7odvwpmhBBGVH2NZgMV7W9oUzQqfjkOdV+gmRS7+tM0zcNc5Gb+U+cjXrCsZMTcFDDlVLRjMjfTgTACWJItq74A3/l4o0+F7jmF1DSSzYg1omF1OmKQcXpUPi6y7MROyeuO3tkmoQYan28ufWvrJsfLld0tomUwvrAmXJ+ZEbJfUW3gQ7y6oh7k6mM+o3Z/LIS0uDAqWw623VVqCBf9j1QHuLQZMT9g2EDFeFcaZ8YEgimwTVNtMwKxz4NpUvOdxnZoLhXAhp0gVCWa2WayAauqD1usk9O8IwczF1tivLvrC2x44yXLGIj1RzMjXuvP+kzR1n6oaXSLGYG4wdAc2v3vZMD6vwhObyhRWAytqdBb+0Gr+w1JL8sIVuJES9qMsJQ1pWeU6oh5liibEYpiFHZIPB0f4cJ7aR/ZZiRRuweCwfCFNqV0w6rFRl7ExHfibwOivp1T01BzLtH/VGoI1dhjx68MdzYjdP9QjffYhG47iDY35EKlqxcMebzLagC9xoaAPU3jevwa1D8br8YHp1RDZB9W2Vs4MFxW+rrwt0NfIIUAZu63qT87ZsRz/UllYYUi/9YPnbXXihibA3mNo+abZIoEqbNyJhOs3tBmUtA4ydleTROXwaHvsLcZUVrvEwOK1tnz33K72wTZjFATilwG4l1MO1nBZsTc/dUGMxKbUMfHa28zQu8+jfqW43LLjIh9zX6jT/U/vp5ZuLIZob5jyqmyGaHGL1l/Unh/mRE3fkbsp22V6pVSqQrMqcP4JXgW87fyNA3JjChsvxRHewFL9aDyM0L2BTgJD3b1Z8OMeKw/Mx/CJgfwd/3QIzojOFbVxM/Mf4r5xq+j6rEgEEbAS9PywmBNAOLuhY8kFZkR2jqcOmtO24wwZaH8jEjMCLVrcAf3zIisbzV3SCQzIu8M3EJsb0Nf7YLs9whqp+LDzlvhT8B/ZsSIL/ouwhwhVYXnDVgVAqMbPyMOR3s9MyOq0zQ+G/wl3M+IK2ZEPX4tgUAeX7TNiLWYy5ml+qY988PLG7wdhFs/I+IJLvZblZ8RTbMxYPVYfxZkochvZsQw+LVUWk7MiKKtagmps3ImE9wkSO/wHJkR1USaNIiSu5FvxmYkHmZEiJcXAhIjjJATis4POGrnE9vOgy9zyLIe4YzE4oUlAMusU1zMSAL8jNi9t2dGaGGE7n/2E7IrPyO2dgKUPYMYhRyHKz8jvtuMuGFG1DYjKj8jvJGoyAQ4jF/Kz4hCGFH1NdrXhor5kdU0ZloumT/2cIL1rfPcb8+MeKw/4ytNLruf60eEY0ZqhE92/iMZP7WgWxtIpZUzaSCZEUnadbAZScnTNHRZOGZEQenyp2ko+wNxZ2J17sT7GZEpRpoZ4ancePyMmMyIroHsAjEjQTYjnv2MxMOMqGxGFMwI0/+UC3zMNiPUOKCg6vPUdz4wVwxc2Yw4GBm68zNiMCMyO+U0fs0c6Wy4mjyZcps8P6g9sBLzjtLPCMu6UcyI/bjhy6QL4e39jDjZjHif/+Sy+7l+RJmRmlgj8vxnMSOEiiw4TZNsGFIp+4SfmNkdltLvQErZjPA7GXJXpJho+bIQRlfSwhD7Cq3ajVO7ETIflM0IWXa34HfU7ERhGob5AbLPUG0QK4i6UjIjRAyOzIjQ11wyI+QumwitGnvU+HVOS4hBNX5rZbwrTreJadoWVFVOnfuHY0fFMHbQpR/mb5oZsR9zsgsF9rdaGFE551OPG00Ox4W3qz/10V45TjWoUz2xnfajEZ2bBNaGrdvgNE0Kg9CnWQ69LKqOHkDgnqWSzYg0gZPltGck2LJQtKqoHqGMu7wiptM0ghBCqpxishbnd0rWHKIJ7Et8oJ0gxd+fqHhVLteZxGNOy8g374GVYEYkg001xa3ZLhTg+jUNhYrHikSKg9pByuF9shlRjEdVHtVxEOpKc3Gi2kA9fnWyDVQCpCq/VJ9XtaP1TFTPuB83bBzivCA7gXMyGPZaf+I792X3DmtKpNYxmSnUFYJjbSGVVs6kgdPXiX5GWF2agrb13+9A/BBVGpTekNZRE/QqSXUL8daCmoY2ZBQnJVktoaKA7WCrpgF8ZkaIycGP0xoEFa5yysQuVPI7B2ZEMki1DCXp8Pb6dpU7eErV4qRvp31gCHFQ45esP+OJD+3DwoUBq6Pdk9gG1Fhl2kVmRx3UNCoDVqWahlKZyHG5nUMtOwhTGUGkaWc/ZIBnDKj5nZ373c1F7uY/2hW+f+sH6w5etJnTiLmfzUvAjCQdbpgR2n2yiVQ8TSOVxdo1qNxPU/YKlMtymRlR725VcH+ahmJG+MmANIqL5TSNUEccM+KnAavCmC+enTfNjNjvHE2XBNTi62QzIrBwrEpBxYzQdj4UMyL3PybnXBgZLmxGiP5BMlZmeH+ZUPGUGFlOhzQl9/UKZoRT07g2YJV+mGOCPE3jgukgDV5JNQ1rwFptm2+1rZU9u6KxzDcx97uZi9zPf4Rw7OP6EWHcwZv2NQQrTtuMBMJIUsHpf0VDMoXkzMXh4zlx3yCVhdk1aOJAIlQbjswIX0f8JOWtHjyfpiF2+6K6JvpLyKMXyV/QrVpKOi1qse4TKD8j/jAj1E5WEa/yJIdLZkRUB9gyI6JQ6dB3lMyIeuxRfh2kOKjxS9afGD4ZzIhNmtLOnhgH5kILyE7d1G1gqWkIZoSyGVGMOfVFeRQrY/02VIBWPhxYXSlNKxzPFtP1Z8+MeKs/OR9U2f0xYJWZEZn9p0/TBGqapII9my6xH4TNCL1T+jswI9auQdTBO/kZcWczUhvMiGzIaL2nmBF5B+QW4s7YvLXXZ2aE8jPix85bbTOiMhKMhRnhVRrWiQe78LTBaxQUM2I/9pS+JWreROEnMxI/c8Xn0J6pdJum/dxl/bYWWu/MCNU/NIUw4o4ZcdfneQNWFTOi6gv2NiOquV+zOdobOzOSWJuR6EV5Nb91FTNC9bVAGEkuWCbAhk1gL5ejd0qG5JmKzIi9zYiSGQGxS+QGnMpmJFHMiEebEWES9jTWpPZOjM0I5WfEl50SdReH+VthM0JqCJyYETsbELXNCOUbhmZGFKdpKCaP+1Sl4qmJghq/ZP2J4f1iRngWLjabERsmFDZCn6mW8zp+2fFoCCNUvu3zS9uMuOvz8gkRgvkj+jdfJiG/irkfdhflGU3mtf5q8zSNyCJpcBh7gZomyWAnaFE6tjqJJQlTUfirQ/YHYlms5+JOlqJX2QGl8thJT6DxMyMU1Nd225fFCu8lXyGbvzS6D8QJTvVk9qf4bUaoulL5kYjLZgRiWzjkjWwze5sRGvYCFv9c0WjE+FXbiDml6Q30TapimsYPBwZInLuIZ6wBq9vxq5NOz7iUhU2Nqg9rbBDH8FE/NDV2EIINHHmcmFxUWbsTvp8Ccn2wbaxmRrzNfxqVDx/XD+q0Ez//8ePBj1OQfiCVVs6kgbU0lpw3MXrDv5vNiFwWo5xyWRydFZFlt9+hJMVmhMiHqFf25vRMUA0wY9hXNY3CAZQfNiPkFeekgaLF/MivnJgRO4NUtc0IZcCqshlRuoN3shlRLPQqmxF1mv7ajLhhRpzcwatsKax31uLrdvzqxC+lmkbBHnm3GWFUDxHBqygxbpydnsnMt139ubcZcTf/OdquxAk9QghuTFlkx5EBM5I64CyJBUqXsBlRUcWpyIyoztSTg4zcJVK2Gny8Oid9e0OsNiOUeol+pliEbcFPmNbRXp8NWD3qz73GS7UZNelEpN0imx0nZkRcTNVHe+XTMWp9u5ux58wYqFgHefy6upzPb5sRhQGrU5qe/IxwzIjL8Usd7TXVNEY68tjzx2ZEN/OrtFVTMH9KPyMa5XvJ6lfubEbczn9q25V4wRmwRoRLBSGfGA1sRlIIOik1OrMJVBx/Bz8jGqMfpTqke2ZEF57Fvlv0zozI+k76Gb/YxeVnxIzUZwPWBPsZoVgk2s8IiPDGO2/MSIRZON3YjLj1M6Iae86Mgb0AqfIzonRB7zczojBgdUxTMTZMAZNbuOnwdvFbWZLVAJTNiHc/I07MiCGMuPAzQgm0Hv2MOLmDl5kzd32CZkb8Wz/0CJi6kstil285b7WL1Fk5kwlOKhV2UQSbQOuQ7enB5EHY2TE7j1j9jIAIFx/7YMTllhmx3/3RO6V4WBtRfWKpMZxsW7wg0X5GqJNH5O7T7AtyXK5tRqQduF14lUpBYTOiHHt2batgOKQ4qD5PMSP+6fjZdJW2Uw5p2vt6YdrDbGOmX7kcv9TRXpWfkVg9qtrZjBgQb6Il07TddLhnvr0zI27nP8pmxL/1w2n+U86hgc1IcsFK8DIzopKcqThSp0qly7fYPAp2Co7+AVSnWITFOpaz6v4wI7KagWS4XEK+XdOYOBLFjLjTt7uPV2EzQvoZEdJmX7lmRmrazDza6+BnBPKEqDpNE4u9ljebEac+7y5Nz/DAjMTiZ8S6MNLaXNn5GbEbvxQzoinUNO5sRqy8eWdGFOyoA0sm29FptvXn1c+I0/zntexeQdmMqBl+tn8HaprkgrGatpgRUXJmZFbFTimZjSnDjuWRd1EciLLQfkb4Oorn5kfXwogrmxF2ZyC3o3vwVLJmDmy/bUYSxYxQbWYfr2EzEpMwotyVOzMpxqJm1z5qHz8ux56KzXLZ563wsfd1GqoTDmKaDnFIfd4qg9m2uplUDOM3+iF7/xCoo73KPsyyAyJza8eMuLcZsS8DHYfT3O9uLnJbf/Zl96M/ce1CCqH2/TpwepZkkE7PRGkXrERJ7Vr8pW39gMwCsCwPb0lP6Xq5shC7d3PilHYG/tSByoCVZkHkZ3I7xmIzQi3OrqNxAQUzEk9/IttBMenFUSjJPokxlFTZjIj9z8keAjGMPXlMyyDjUPZjn9U0Uv1ReVXXkSbVEdVvLYEaNmPJ3mZEFJjYzQ2VW/u+xqchziNOdaoz/xd/OfQFwceHlcOQbf3Z17e3+rNgX3Zf+pMOQLIZseY/8Rh+oKZJJTAW1RCoedbVtFJvnYJqGtFindYb2jMMLAVNl91uh5JAmxFNzDehH2VpW6kdY7EZ4Xfvut930ygvrvLXZgSKRd+cUknhK1ZmRH2aRna85CBQqBgDJ/WFklmQ41DZjJhl8m28Kxg/MU2HU0PU5YOUqtG+zdQ2I0YcEd3ysqu0GXHr80Nh78H5GRFsRgBdtgHxajOiWfaCKsd9pE8Oj/MfWz7pNJkfahpdN9vKdHrGrXHiWGJVpIEwklRQZ81Fr5s8M0JNTvHT6n5Dtlin9IYUM2K/SyQXevCdOqE2I5Kem9UXy/Q02Y4uId/2au2jfL21l2JG4hDsTBA2D6rTBlZ7ynA0YLXxG+J0N43MjNj0HTd+RmxVPOKYlkGOX4XNCBzS9Aq5/uw3PHH5GWHbWHJi5jB+jeFlxMW4/KfUNH7bjFjCgmgzYmWOZHXJdCNCeO9+Ruz8dTjPf0TZ/TRgjUSYtpXnP/UllYGaJrngdjm85MxKlK78jKQ0M2JJ+m78dWjcLpEKrxPPrHS9wD0z4sJmBFQcTjsmCrRgGj0h4iczQuzcndQWbuL16GfE3CyS9L6DMKL0cUGFF8eSEzPiwsbJlhlR2H6YcVB9XsWM+MuEqr15CmnaLhiqNjDGi3FXiWYxHS7Hr2XWYK+mie00jcF02G8YosyIGJ5gfhwZK2Gsce1oV3/++hlh+5jkZ8Q3ZoSPn5v/RHaZ2WQFNiNJBitNy8wII7G68HWQSlUq6kA9e2AlmRGRJWDDxz6gfPUzohPPYjpNY1fmRDEjVL7jmBxIGyerDCIsfzQynE/TCCe3HJyeyScXHNrHjc8Ph92wKz8jyj4vh/fPzwjPYsbiZ0Te2cvMgTUXaJB3z+r4JTVNhFXTUMyI/QaA9vnhlhmRNx0y0+FkM8LHwc79ifYzwo89sSzx96cI4/RMFPY1ZlMtzpfJ3kinzsqZTCj1hpak7crXQQq5gxfLwmxtAGFiib62192qfBjU5mkaV35GQOUtlsEunkYy6idRNiNqdspzvJ6ZEVnQlN/ZCCOK/qEMby4G4nMxtDMr6bQbduVnhLSTUqTp2xRqb1BupWkvSPLhqDYgFnDdLjwdv5UlI4+smsYIQzEjTqdp+HmHTlu3hCfhJloyjphsRvh8O9uMxHaaJtrHhDHv4/qhR6KbADZPnL2gYDPiy8bHBwTCCFjpmB048m5YnDDEWKxwqQG7stCnhpjFnyyLPTNiIfY6oAc7Bbv2YX5xcYhl93CaRghr3MOhi0nEDUIw9LE/uWVGwJRPeuNWTSO2i24nXNiEd1BBqE+y2UGuXxnOjBEV3jebEaVxvJimnSpLHtPit5bRuQZ5+le3gTjSOJKAEKKsX2o1jdXv7MvHqx6oOUiMw65dxNM07NxP15/mpKYR8uBuzKrWmTjBMcPU/CesY3Go1/1EIIwAvHQsGtZxqg3RIJSJIhVP09hQ4bzekGJGdO776Ce0MSwff+zUtXs1jaAO4HZ68jPR86Sn9pFUdkakfjMjVDpyG/gTr4oZMVKOgRkRjkGzR0hV4U07AU3MsxBcMfacmBH6tJIQBXWaxq79IYwlP+DCgNUxTZu5i4pXZxY9t+NXVNM4MiOKvkYZsKr6PKumiZAGrM5xsOlKqizF3A8bA1aVkzQn2F074tdpGvFoL+nwUjhhlWxWP4VWzuSB9DMiGUmxl8tRE6J615IMSLstxqCMurLcmqgovStfdn5xEnSPiTRgtTNAY8vCtY9Ydi/MCD1x2S6wMUPuV3ocdSnHSwkSBAMQh5rGqleBsra9KI+29refkuzHnh9+RujxK9efFd6P9rGgNCh3mab93AVpDojSe6IaVx2/Sk1DMiNKtYUmhVP1efZor6x2dhcHl65Qz6q5X7NR00hO8Tz1CTEt/9YPPcJsJ4iyiGPPl5N7PiB1Vs5kgmNG6IZiL9EjJ2u/d0p+wEZyh01ZVHpXUc9I7bbjqQOvBqzmhEjZh6iOH8fCjFC7VD89sDKOmES/A3H5GfHKjMShphF9ebBHSFVHga1dmXpXKfc/NnMOu0oXfkaovqtmRuJnrjiI/ZpkRhzStLv+gXjGGrC6Hb8mM6JFw/EeWEHkW8WMUGpv+/S5/heRywcXcbB5kdMk5n6XzEgs859oJ+bn+hEhmRGm79jM5YEBawqAO2suGa2xEqtid+bzTskPSEZ/XB4p40aV7tZOmpaFmOQxI7r0THTM5snPiLDb18yyJ+o0DSAvRvH0J1k9odoFqex2HJkRyW+ImhmR+589Y8M+T7yfEaLPKwxYa9XPiGdmRKHC1DWGaXA5fj0zI+o+bOfA0JkZqebCs7+d5mG7y+3IuZ9hK9RzUSzznyDI+Lh+6JEIpEsNOVZc7GsqBqv2kDorZzLB2kjYTapaSK239nun5AdEb7Km9M28UzIjTFkE/x78/lmUvhPJjIjqAMWEy8YRy85DcB3NLph+qmlIvwM+7FZoJi9BzIhEf6uZEan/mTYjdvYQqtM0LhkD1WkaYvy6Y0L9Gu+iPYYqTbsobHbb3G+rXSy4G7+68IuvTyrfTmPORlVny4yI7UhtOryxRyQrTswZruYiD/Of7FvIv/VDbTOi2c7lgc1ISsCQgGVnYNSNhmpmJHXUNJKunLAZUVHQ1HXqkpDG/o5D9+ieGbGfkGibEfFZPDYjuvmPr7f2cnnyc7dCMSP2QlQ8Bqzybkt9a6/MOjnsohWnaUD0V/FrJ9DjV8WMOKXpDeJYjYsZocaBZA/EqElcjl/RZoT1M0JtDpznRK/MCB8vlZYTSyYJzdzcb1d/3mxG3M1/tI2VPx5YGcGRtH0UWW4/WNj4EQgjEHY54gVeptTI6NpsYjHjSBVI1uHMc9VpGmpHI+0S2cmSf+aXhK3W04pp03kj29EtpDoy4tJI04VYQV0aptoluo+Y2tmr+mkchZIuIDPazt1pGisatd0HlUe318bTbt2tWKQ4lGn6PN6ty11q/qHSFPNFxyH2ee4Zoz6DcPGd4/g1whMeWOk1VF1HcjpOfd6OqaSeqW1G5O8c5n4yMnpOdDP/2Zfdh/6kRyAxbcp1LH4W1g+k0MqZPLjRG1KXy/GRpCIzYm8zQu00xcWf3aGIthqU6/J4rLJVjAh5tt9VPiC1Y2w2I8LCoGvCNd3xgj1ZYG7/at7EoaZR2IzY7T4Bvk9I+RJ+W2nZtYv6ojxrknRYRFzZa7nbgZMg4qDqTxU+Hoi78kTZjFiLEyzhwuX41YVflJqGzTs1j/Cg7b9s+6bN6R8+TaeF1SZNLXabkdjmP7uy+2HAajV9hCiL/VweqGlSAIxU6sLPiNIdfEoyIwq9IXG0l7zBtJb8jLC/3fgZ4adI6hnfjt5sRoQdtZMdRIzwemmY93gJtVWiTtMIKkHoduHF/qTuO/HYa2mKcWvFIMfhzs+IX8yIqAZVMUB2djX03MX/tpgR0cjRafxGBCaF8zNCCCNON9HKl9bZt6OuE/etUP3aNRtDjLOk+hnxb/3QSWaEnfvFfh37vO0nUmjlTB7Y3WLsfkbUu5ZkQO1nRKagpd2FwucCZTjqh80IlYZbZoRmbZx3XXaQvWIa9agBvjIjVp783SnZtxkVr2gTwL9zYkbsDFjVzIg1ETJ0OQm+LHzenNqWVgnxkTj3eSFRhzS9QWThEuVnxFpoNauZ3dqMCL94dlDBUjjYb3hnRgx38Gyf5PudezZGZgy8+hmJbf5TnXSMD3qEqit27heMW+OYt/1E6qycSQUrHSt2C/9DfkZkL3zUjoahrCVpmmJGLMrTK9wzI/a7PzesTWweWI0Jy5rI/WVG2PqSd2Wxx2vfZsrTNETzuWdGhAnO0WaEF+rs+o6KpXC7A1czI276PBXeX2ZE5fTMDz8jFjMCiBflOY1fXbIZcceMOPqOccFeRpkRmxMiXPrqeVg6gq6c+70yI+7nP/t8+GDAytaVGT8x98NdO9UWAmEEvARvv7uQdW18JKnHjJhQWFRTkwit67WTpmHtUOLQPcbKjNB6Y3vWJrb2EQQaIGF+RsSTAvH1J0WbUfEy9SbaxLhnRvhdsZ1KS+x/TjYj0i6ay5u7Hbg7mxH7Pk+H98tmxD0z4lRHSj8jJkPCGGF7Hr98G4tpyePQXZuq2pE6OcOPfbfjhmZW2Ttb5PpzYkZimf/sWBg/1DS6Na2r7AXJeSF5SMGVM5mwbCksYt7o3BrTxxS7liQbAbGQpW9j4mf1htQiQ5TF1ucGv0iz6XqBsx8LITzRBuq8eRcebC3vdS3q5dA3yMwI2Qaeo1WdPiAmfNs/XLSPaDPilH8bltH+5IKNMTEfyOYpv0ukQI5fRZq+j3ep/mRw45eKwozD+gLSb6sPi7tnMR4pfTMY0Z80qn+omRxbPy52NiOCkzb6Akh1u2i2Y0KzrT/NkRnxPv9JwqeP/YllRtRjn3+X7I10IIwAltSohWAnsYKytubisDpu6oC2GQHnZ4SYRAiLdNlynNp1xa57dM2MSHQ2uzsj6FvR0t1T+9CGXok82uur0zNFm1ELGtcGAvPj2WbEVNc4+RkxFiwjjJM9hD1j4HgEU2kzIo/fuNL0CPluHxUz4s5mhPYvo5v/N6vcxfiNRCLQhT7Ds2eKecQvmxEzvL3NiHO7iGlaApObE2EqmxFP85/oiM/H9YMdu0rnneZYVQuNtYVUWjmTBmvxshpK0gPC0rXR15jHrqJIFOwstln7F6W3Um6XKOrd7fXRtWkz4uT0LJ6TPtL9KeauEj4bsLL1JUxsPvgZodqMdAfPqaG8qWlsbUYcw/N16yRQqNzBOztMs28zd32eDa8WnjxDtJ9Q2Iw4sUeUzYh0eoR1B+9m/Opm1mDcTcPZjFDj1/VpGjG8jc2IMfxImxHxmZPtEZ+mxvkZEeZ+lzYjXuY/ycmdj+sHyyLBvOGY7TvCHJoiJgZpSU09VaCznUhkRuwlZzqOVJLv7C22qfs6JJsRbpdotwuAWUep7WcklrwJNLbDCZFYYV0axlyU54PNiL3VPN1PebLLKzNCs3D2zIjQ/xz9jCjsPnSHRUDlSl6KQ+7z9jYjWYhEslBWVmYfr0tEqnMRCjVFelo95OXlAQBKS0u5MlVXZSEUaopIdT6ZZqQ6D6FQU1RXZaOsrAxVlekIhZoCACorwygrK0M4vS5CoabIyGwIHdE09UgdlJWVoaICNeEbSfFXV1YjK7seQqGmCKc1QFlZGSorI2b8kUiemW/jWw2NEQqFUFkBMr+a1gShUAbKy6uQllYG6NG8VVWmSeE1TUNaWhFCoUxE9HSUlZWZ5Y2mWYVQqAzQ6yMUamqWV0ZjhEJ7UV6uo6ysDHokP5pmdaZt/el6PeTk5AAAMjMzzXiN9nBTf1LZQ0UIhYDy8uqafBRE81GVHnd/0kNARnpjhELlQFq0r1RXZUb7TqQOqqoyavJdgLKyMpRXRNtR0xrHlHZ6ejrC4XBceQYCYQQAv8uxs3KmJGcyjhQSRmRmhNl5EPpw6TQNuxC6YkZipxpry8+IF9ZGOk1hLJi6JjEH8SK6+6qGtMOLpz/ZWM1HQcVr1ZtXA1Z7ZsTuNI1gwGo+joUZcVDxuDpNQ4xfG5sGXddRWdkZdQsGYf/+hli3bp1tvG5RVdUDdQvaoU5+Bho1SgcArFu3juuvlZWHom7BAaioyCPTrKzshboFnc33VVWtUbfgVgDA3r11UVKyDg2b/xta6ATk5aYjLS0TdXEAdD0L69atQ3V1HdQtuBWaliHFr+s6unc7CRkZw6Dnh2rizzLj1/VsHH54tL7+/PNPhEIh5ORciRxEsGVLBKGQnN/c3KsAvRpbtlQjFFqHtLRzULegArt21ceePXz4OnXqIC/vaoRCEYRCBVi3bh10fRjqFhwBANi8uRSh0DpkZJyP9PRK7NyZj9275TQzM85HRnoldu7MrXk/HHULjsa+vXWh683p+mPKFg6HzbqpqmqLugW3uqo/ETnZVyA7qwrbtoWwY8c6pKWdgboFJ2H37vrYty++/pTWOBut9YsRDlcB+dlYt24dKqu6o25BW1RV5mLPngzULbgVoVAm1q1bh0gkXJPvtJj7ct26dVFUVBQTK27mO+Yv/5fA7IpsT2twNiMKHXJKab7srb3Vp2lUu0RCQCB0rF7hDzNi/yweZsSsI5N+jno59BchANWE/jz2wS1bzTP1SFHhnJrGGzMisnCWOsCOSbGzGXEwQo3JfsONzYg8fu1Oz23ZsgWa1hmFhVnIz2+EzMwG9vG6REXFDlRU7EJ1dSbKy7MBAI0aNeLaqbx8GyordyM9vS4yMxtLcZSXb0dl5V/m+4qKv1BRkQkAyMhohIyMeti3Ow1aWikqKzORmZGDiP4XwuFcZGc3Q2VlMcrLwwiFMpGT04qLOxKJYNeuTGRl7UckEkadOm1QUbEfFRU1i3RaHvbtjS4nDRo0QDgcxv79Eeh6NbKzWyEczpTyu3+/Dl2vQnZ2C4TD2SgpCSESKUdmZlOkp+dxYYuLi1FdnYFwuBqhUEPk5NRHaWkmqquLAcBMY/9+DbpegaysZkhLy5XSFNMoLc1AdfV+ZGQ0hq5XkvXHli0cDqNBgwY1bfYXKioyXNWfXHZA1yuRldUcaWk5KCkJIxIpQ2ZmE6Sn5yu/dULJ3v0or96G9PRKoDoPeQVNub4RCmWhvDwdoVA2cnJaoKqqBGVlIWhaOnJz23hKS9d1lJSUYNu2bQCAJk2axJzvQBgBOwlrzDPB1oA5+qXWW6eQzQhEZoSl/mU1jQVq0hbDs8wIv/OOZQF1zYxIJwDsbVfYZ9Tlf06Q9boMM+L7cTjRut6P/sQbRVKXPrLgDVi9MSPy5VtOKi2+P2lc36Rg31+dTiLYuvXn4qDqW6w/oLq6Grt370ajRvWRm1uOzMxMZGZm2cbrFpqWAUBDdXUY1dXRaTkrK0sQGtOhaRoyMjKQlUWlabxPi34bSjfLkJWVgYyMLJRnhKGlaQDCyMxIR7WuIS0tGj4cLoeuawiH06T4I9URpKeHkZGhIRIJ1eSt0ow/LT0NpWlWvsPhMCoro3YpWVmZCIfl/FZVhRCJWO+jZdeQlZWB9HQ+fEVFBaqrQwiHIwiH05GVlYVIJIyqKqN80TjYONPS5DTFNIw4srIyEImArD+2bOFw2KybUCgz+t5F/anKnpamLrtXVJVWQg+HkZ5eBVQbebH6RjicUZPPaFmqqqoQiWgIhcKO+aaQnR0Vnrdt24bGjRvHrLJJpW188sAaC4rULGszonB6ZqkoUkcYsT3ayZaFJThEewWWGZEMv9g6EJ75pKpSnqah8mFkny8U/9KT0zOeWWKdninWtZhgGk4LC3dcp2kUR3tpp2fMb482MVZ/EsaNnc0I0f9qXqjjpyreydhXoxkOIZKaKKw45PoDKisrAQBZWTX7uAQOd7neHNpEOffIfcD66bat+fhVx5C9QBd+xTaH+pAXu76XkLSTtU5Ym2o/JzHDpsYYH7EgEEbAMwayKoalb+0N2qyJI3Wq1E6lwTn4oQxYSZsR+hge/8xpd2sPz8wI4djJUg2wz3gVVTzMCDdt+q6n4Xf+/rhoNhZhXv1mH2/szIhooW8xUnbhhf6nOfQdsf9xuVZ/K7I2FOjxGxLecZGy//iI2PuVtcQIAqEvqcibD0F6dZEzm+dEdO7z4hViXixWzb7+Eik4SNK4z7ETQqgt4lAJ+7AJT52VM5lgfVDYGeJpmgMzkoKnaTRxMrXKSZXFk58Rrg7iN7r07GeEOEJK+xmJI29GWE0ocwLUNJq0e4+/P9leRAYbmxGmSSNeT9OIvgtMYdGBGTEFCTVz5cZey95NugubEaK+1S7orZz5Aw/xxHJ0XvhXvTap+4b3VL2GsznaaytIUM+c6kj4NqZmdBCyPMHPzY3XuIyx52MWYkAKrZzJA3tiRjoOSdhZqFxSJ71FGdg5UmJ9plDCiCufCyQzUhs2IzYCll3eBLbE20V5Ws2/xreWHYS/HlijsUcRvzGwBaG/OrAtfvoZcTwGLfY/k2lwULUo7LWcLpBTMyNUfSvUO763vzOsucgOPNPAf6zaIbvbPctv6fAxH3t3/Z2qDB6TNOuUYYs9R+WFfTCQSP2eKn57NU2yV65AGAEYJoC1GRFP04QcdkqpzIwIBozMqSHK6RlpM2J3WoIJH5uXUyFt5rd3ZoRgbXxkRszBq2u+q2ksZoRPyx+bEb4ObN19K9RQXv2MkP2KCC86PbNnRhLsZ4RkRmRV5t8D9OJ4/vnno16DDigo6IamTTujTfuuOPHEi/H00y/yR7mpatR1hw20/Xiwp/A9Ln8KdY770SikSX7oTriw729esiGO9/hFAh1szp3LwsyucacdD1Jo5UweOD8jop6d8jPyt7EZ4U+eWDYj7P8pZoQqi6B3p9gHbpfhDTHbjHDDjsqbWHYvNiNsTVlp6VKe/EACbEZEAceRGWF+C13c7dFe17f2iv3P7UV5JDNiwEHFo5qQqfErCYj2+YofLowKfehygwcPxKpVH2HZskVYMP85DBjQC+PH34Hjjz8eVVVVNTlxVybZ2DpWA9eosz8jfRfBxR/EM49qGiWYGYDoC9LdNC7qz43CKS54IWuSwPJRSJ2VM6kwJkSN0ccKramxuzOq8Rws+pMBSRVj7T6pkwKybQl7skDVVQQhxidmhPxbyLfyVA/zjGtjt7CrP9sFNnZY1xD4t1OyO9JqFyXfBl7VNEJapmBrZzPCh9fM5zYChdLuw+k0jVi3FIg+Txh5JwxE1u1P08TONGRmZKCwsCGaNClCj+7dcN11ozHvhUfwzjvv4Ok5cwEAv//+J0488UTk5eWhTp06OP3007Fl61Yzjin3PIQePXrguefmoWvXYWjRoh/OO+9KFBcXm2HKy8sx/vopaNfuCOTk1EH//v2xZMkS8/2iRYuQn98J77//GXr3HoDMzEwsXrwMa9f+jlNOOROFhYXIy8tDr1698MEHH3Bl2LJlC4477jg0aNAZXbsOw0svvYX27Tvj/vvvN8Ps3rMbF110ERo1aoQ6dergqKOOwnfffaeoIlZ9ERtiOV2UGDFAEasGYo1LDQTCCNhdM3OBHKlnl1UbTCQ1caSOMCI7cNOZ5wqbEZJF4FU+/N0jibUZYSEyVyrbFe53TDYjhppGyBcTn3+wMQD10WbEC9uiEgjV7SKoBG2rye6kkl157cceO34pKK9xsCKpCUv1eXVb67qOkoqquP4rrahGSUUEJRURlFZWo7SymghnhIm+s88XtS2mdvRRDDqiH7p3745XF7yJSCSC00+/BLt27cInn3yC999/H2vXrsVZZ5/FfblmzRq88cbbeOGFWXjhhVn47LOv8eCDD5ohxo8fj9df/wCPPHInli79Gu3bt8fQoUOxa9cuLg+33fYA7r57Mn755RcceGBH7N9fgmHD/oUPP/wQy5cvx7BhwzB8+HBs3LjR/Obii8di06ZNePfd5/HMMzPw1FMvY9u27VyZzjzjHGzbtg3vvPMOli1bhoMPPhiDBw/GX7v22NSCU/0p4DF4FMLGVheexwOlLKLyupPctStwegaA2xUpLpdT2Yz44r7bb9hdnOV0moZgOOSys3XAq7QSaTMi2vToqnyALrtraKIqy2JG/DZgFa8hANPvYo9T3Nl7sRmJzYBVZKfsmRHhNI2mLq8bey1nZkRhwEr0D7c2I6WV1ehyy3vKMP7iDwDAz7cPRU6G1ymc7rcHHHAAvvtuORYt+ho//bQS69atQ4sWLQAAc+bMwYEHHogVK75Hv36tAUQ9sj766IPIyoou7iNGnIxPP/0cN9xwA4qLizF79mzMnn0Hjj56APLyDsBjjz2G999/H//3f/+H66+/3kz35puvwJAhRyItLR/FxVWoWzcHffocY3pPveOOO7BgwQK88847uOiiE7Bq1Tp8/PGnWLJkCTp3qY/qqv2YOfM2HHzw8Wacixd/iyVLlmHbtm3IzIx6fp02bRpeffVVvPrqOxg5cnhMLIYMO2VLsk/TyPGqy5saDEkKrZzJA3vKxO7CL7d+RlLJHbyda3tHPyPkTlM4ZVSLfkbY39LlZZRqgV38pHb0YjMiGrAyJ0QS5WckBhbDMU5E688pTj8MWCV2ysFmRFzo7ftO7GPPDTNCxxGS3jNfcPlKDajyoiuCRG02NE3DqlVr0bx5E1MQAYAuXbqgbt26WLVqtfmsdevWyM+3XLYXFTXCjh07AABr1qxBZWUl+vTpYb5PT09H79698csvv3Ap9+zZhfu7uLgE48ffjM6dO6Nu3brIy8vDL7/8gj/++AOAht9+W4+0tDQcfPDBZpHatWuJevXq1RRFx48/rkJxcTEaNGiAvLw8879169Zh7doNXHXwdRer+iuWuaCW+w2XRZW5QfIQEzPy0EMPYerUqdiyZQu6d++OWbNmoXfv3rbh77//fsyePRsbN25Ew4YNcdppp2HKlCkxuZ5NDJhdkeJyOTd+RlKLGeF32yQzolGLDHWygN75suETxYwYvzWuDShmRH4WHzMSDUsxI34PZPkyt/hPZ/HfRmCxEDbnIVgZzuvdNDYnd3Sb8FL/M/91YkZiGHuuDFEpZkSsP8LNtQZkp4Xx8+1DFXE7o6JiF8rKNiNSnYHSsugiL7rWLi39E5WVu5GZWYjMzIbIThfyI5zQ8LL7/+WXX9C6dSvX4dPT0/mkNU26XNENcnKyWREYEydOxyefLMW0adPRvn17ZGdn47TTTvPk2bO4uARNmhRh0aJPpHcZGXvNtPh/4ar+3NmKeRE0xPj8OU0j/+VGeP6bqWleeOEFjBs3Do888gj69OmD+++/H0OHDsXKlSvRuLF8edNzzz2HG2+8EU888QT69euHVatW4fzzz4emaZgxY4YvhYgXrI2EbDPC7pjs/Q7Ecloj0bAvC33WXBYC2LKIjITMqKiPTqohLnb2NgvebEbiYW3M+pOO4bmdlLxAtBnxY+fN7uwjLtiWeJgRu/7hxIwY7eJUnypmRD32rDZXMSP2dlLGe02ThRENGjRNi0FdwiMNYYQiIVRXh4DqaDrZ6WGkpVnxatUhVGohZGaEkUmkJ41oZZVq0fcasGjRYvzwww+48srRaNQoE3/8sRm///67yY78/PPP2L17Nzp27MDFYDcE2rVrh4yMDHz99XK0bNkUgIbKykosWbIEV199tfwBY4v19dfLMXLkOTj55JMBRC/IW79+Pfr27QsA6NChNaqqqrB8+XIccEBdAMCaNRvx119/mYXu3r0ztmzZirS0NLRu3ZpLqqRkPaqq9tnViJkP/ocd/DwTk6D5JClpxwbP264ZM2Zg9OjRuOCCC9ClSxc88sgjyMnJwRNPPEGG//LLL3H44YfjrLPOQuvWrfGvf/0LI0aMwDfffBN35v0DsyuzZUb+jn5G7G1G7K5HZ5+pvFHSjETsdg5uhRFlPig/I77ajDBqmoTbjPjBjLATUsSsAzc2IxHPp2lE5sxBTWPn9CwmmxE1q+LlNA3tZ0RO1697WSy4WTyMcsZgIF7zbXl5BbZu3YFNm7dgxXffY9q0x3DmmZfi+OOPxznnnIEjjzwMBx7YCWeffTa+/fZbfPPNNxg5ciQGDhiIHj260fkRkJubi0svvRSTJt2HDz74HD///DNGjx6NkpISjBo1SpnPtm1b4dVXX8OKFSvw3Xff4ayzzkIkEjH7UMeObXDkkQNw8cUXY+nS7/Ddd7/gqqsmIzs722yvI488DIcd1gcnnXQSFi5ciPXr1+PLL7/EhAkT8O23P9BliOsUZIqpaRyPov8PnKapqKjAsmXLMGTIECuCUAhDhgzB4sWLyW/69euHZcuWmcLH2rVr8fbbb+PYY4+1Tae8vBx79+7l/kskWHsEi00wX9b8UJ81t9o1lZgR0f6FKCfJjFAnOTQuDG2XwbMX8UB5vJdLk3tI5I0P76V1bFkUPZHMiBda1WWcRrQObAtfld7KJzk9Y3a7KiZFZkacbEbsqXN7VlJgCCmQ41eoPzJ87cO+RwhzF4uaZx9++Bk6djwKhx5yBE4+bQQ++2wJpk6diNdeew3hcBiapuHFFx9DvXr1MHDgQAwZMgRt27bF3GefdcgVn+g999yDE04YgosvnoBDD+2N1atX47333rNsO8godNx993WoW7ce+vXrh+HDh2Po0KFR+xAG//3vAygsLMTQoSNwzjnX4PzzT0V+fp6p9tc0DW+++SoGDhyICy64AB07dsSZZ56JDRs2oHHjhoqaU9RfwuC/miZmJHnp8sQt7tixA9XV1SgsLOSeFxYW4tdffyW/Oeuss7Bjxw70798fhmObSy+9FDfffLNtOlOmTMHkyZO9ZC1OMPS1DTOiwTppQ/XW1DxNY28zonIHz9WHEZWdzw0QdZVAZsSWueJ++2szYt1Nk0BmxM7pWQJsRtwII56dnkknVgzB0C0zombVXNmM2BmwSqyTDPo0jVh/ZOy2cfoN9z1OHqMA8NRTT+H++25GKFyOsrJc5GTkIhLahnA4G6GQVdYWLZvjtdde476tLKvAzt1bAAA33XQ57rnnYZSU7DDfX3HFBRg5cozJYmRlZeLee2/EvffeiPz8LpKKa9CgQSguXo3q6hIun61aNcMHH7yDcDibifsK/PXXX4jo2wAARUWFePvtt7F//2pUV5fizz+3YNu27Wjfvr35TX5+HcycORMzZ87k0i0p2YCqqr1mmnSdutwQOHn89QSeIfQfzr0n2SYGCV85Fy1ahLvvvhsPP/wwvv32W7zyyit46623cMcdd9h+c9NNN2HPnj3mf7///ntC82jqizXKz4i1CMq+FNhI/k42I8ypIYXNCE9T82Wn3LCDZFTcwbWaxua0k33e4rcZoQxYfWdGGO++0biddvtuQNuM2AuLOheee+PyNI14csv2aK/Q/zTmDQ0X9lq25XI+TUOPX77+hA/s44oJLqhzn9lXXUpTXSZdr625zSadmux98snneP3117F+/e/4+usVuPDCG9C6dUsMHDgQntsljjpleDMxMi8fu3nsCbriLyoV/1ne2OCJGWnYsCHC4TC2Mt74AGDr1q0oKioiv5k0aRLOPfdcXHTRRQCArl27Yv/+/bj44osxYcIETiI3kJmZaZ4Prx1YJ2bsmZG/o58Re/sXV8yISz8jqcKMWLsde9YmVmYkKpjVls2ILj2PLU7R5sHqyxTiOdpLsXBa9IfPzIi9vZbdjlSL28+IXbo+gsi6/XFiD5G4+NYND6DUTNf85d1bsFcD0Gj4yspKTJp0O9auXYO8vBz06dMDzzwzB+np6Sgrc4pClaZz/ml38Py/bmpBuqrDQx7cwLYmNY1JIoVURPDIjGRkZOCQQw7Bhx9+aD6LRCL48MMPTWtnESUlJZLAYRxXSxWJzI2fkegkZW/RH8vOO9Gw95nizmaE9jPCh2HjjacO3DMj4qkNYjevYG1i8TOiIbowmYu1DskpWPywdu98/4qnP7E7e8bPiN3RXua3eEzTOzNiTLJ2zEgibEbiYUaoOPj6U+UrXvjLqKp26rp9MCVLQEkjqvnbyyLvvA6w9T9kyBH48ccfsX3791i9ehHmzr0frVq15GJyFozc5N2l8Oeq/pyS8nEt1HUwUxWXkM05Sv/SjgOez6ONGzcO5513Hg499FD07t0b999/P/bv348LLrgAADBy5Eg0a9YMU6ZMAQAMHz4cM2bMQM+ePdGnTx+sXr0akyZNwvDhw7kz9MmFzBhQNiMqvXVKMiOm9M2fdgE0c0HSSD8jPAtRE7Dmh2ATALmuEqmmsT21weTNNz8jRjkEZgTQfJ07AJYZsRgM9nk8cUZh2YwkhBkx47DqO8qM2NmMyP0vmjcnPbz30zRW3drDu81IMidwPwQXNo4Y1AwJhbvy0eoIdRnslWF+nKbxUn+JPtHCxy+OslSEZ2HkjDPOwPbt23HLLbdgy5Yt6NGjB959913TqHXjxo0cEzJx4kRomoaJEyfizz//RKNGjTB8+HDcdddd/pUiXpDW+Dr3LjoZ2jMjqehnxO6iN02ztxnh7RWYqCAKAYpdVyxHDx0WO/nEhNA+3G/qmQEPedN4ZoTdbSSM1WPqP37w/Vl3aB9dtz5RnWZSOjETTlbZ1ZUkEGnqvMVnr+V8moYev5r0PrlwuVOP6VsPSZPJiPOIizileGpDGIpFTaOhdvKmykMC4o9nbkwAYvLUM2bMGIwZM4Z8t2jRIj6BtDTceuutuPXWW2NJqlbA7ujFSc/0nWFzuRwbixFHqkBUaXBOr4hdpC4thCwzIiwGyovy/FfTiHmSLmQDIyDpBDMSQ94sp2dRZkTXjHJqNsJY7GCFPb7s8TBtwmLqwemZOFF5tRmxJDc7pkM0YHVQtbhhJW3GnqWfVwkU1Pil2INEwc+dsmrD4CUv6vh5dtQujH18sW7czJidDVkUkTiraWR7DhEOwq8nJLh/uVapJQ+ppFNIHszTI8QFcozNiPriLCuOVIG1W+UXbk2ztxlhd4CcAygmvK7rtHpEUqW4h3unZ3wbcOEEAbImABfeC2vD9oVousbklIjTNKyQ5ZeahmHzYLWZrTt49rdXZkS8W8fB2Ffqf8afdqoWof8JmYuGcXAHr/YQLMch1h8dPhmbD7Whrmvo4g+V+s16Z7sEx1gXYns6xuNqYY3hdIzL/NsbFseipvEfzkJo6myYWaTOyplEWAurxX7owqTK+hmh1QhOu85kQDjhQDIj4iJDG0/yE73OTQgiSxHLjidmd/CUnxGfmBEjLc1gRlivoj5flMf6GfHPgFUQSIU6FMF5YPV8a6/APrhkUtwyI1L/4/5Sjz1NGAcU7MavLNBTOUgMvJ+mMUIx+kTxWVw5V6g4YhLO+fjU7eMUv9v07crAsIhE/SUOiVaVqOJPLTVNKq2cSQPnZ0RkP5jdvvqOC/WuMxkw8yKedmHKIhqwsoOe8jMSDRcBx4yIJ2xq0x08cXKGzxtvcOuFaWDLzx/tdTM5egTjZ4SdJOLvT5ZhrLM/DgtenZ7JN+MygpsivCZdlGdXXr7/8XlzKJcrZsRu/PIG7UTkiji9wAtj58QcSD/UwdzkxbNGxEsf9jqWaMHK25gU2RjqlZNQkqJqGlJD5oJNSvLSFQgjAHjp2I4ZYU7aKK4xT60qFU/TsLp1Wk1jx4zwPTUCkn0wd7e1wYzYn5zhduWS0OL9NI3BjFjGmYn0wKrDX2aEFUjVdcCdpvF6N42gznD2Vsv3P1NL42qCtzvZErvNiN34lQR66wPbuBKFuOyUJM2W19M0ztLI2LFjceGFF3oaG127DsPMmY84Jw/A22oZy4mcWONKLTVNrGknexsd31WT/zNgGAPpVIBla0DZWVhwv+usNajKYmsQyO5oaAdQom2J6HWzNm1GuEWBEFBEmxFPTINgM8IJPn8Dm5EoLLWWxSC4sBnxemuvyD6Yfc4hvLkrsxhICmL/o3LuyIwoFwu78Wuvmq11uF6sqYA6zj//fDz99NMAogcLmjZtgpNOGoxJk65GXh4RjWPSXI/BnXfeicrKSps80fn9+OPnUL9+OyE+5/4pvWPa57jjLsTBB/fFAw/MJNMUPmSeuzMi1vXofGA7lbiZYmwYC/+YdTeZ05n/Jx+BMAJ2Fyh3SH63z0+I9ASZbPnSgt1x3OhzZwNWviwqnT2/M4j3NI36b0OdIefF0vWqnnmgwxmnZ3xeEtHGdgaa8abFtrNTHbCCndcpSmRGjH/dMSPOfUfR/xyuIbBOldmXyX78+nnKxQV87VpyngcPHoDZsydj375M/PrTKlw25kqEQmHcd9/jTBZoaYQ/RS/HXadOHVRVVdmmTaFhw/rIysp2DugqytjVNPHEJ4aKiRn2/IXXuBQpmK8Cm5EUALMrsrvwiztpY31DxpEqcFEWVhAXj/byNhN82Wk37N5PrFDwZDOiygcXTr3zJmGUg3R6prI/8A7eZXlEeh57vIxhrAODwMpYkVj9jIjXBdjZjAj9T7NekHmLa+wpXcmr41C7oQf8msCtdnZSh8WXZmZmOgoLG6JZs6Y4/thjccQRffDxx18CAKoj1Zg+/XEccMBhyM7ORvfu3TF//nzu+19+WY3TTx+DgoICNGnSHsOGnYe1a6N3hxlqGgPHHXchrrvuLowZEw3fsGFDTJo0iSsXp6YBsHv3XowefQkaNWqEOnXq4KijjsJ3331nvp8y5WH07z8MzzzzDA488Ci0aNEPF1wwHvv27QUAXHbZRHz++VLMnDmrhr3QsH79elRXV2PUqFHo3LkvCgt7oWvXw/HAAw9wZauqqsL48fegWbOeaNCgASZOvBuXXjoBZ5xxvhkmEongnnvuQZs2bZCX1wCHH34aXn31PQDspswZRGu7/tYZun28GjXEUoMbSaGVM3lgd0WSnxHuFIbKiC51mRF1WVTMSIj8zfusYOoigX5G7GxG6HzoxDPvebOYEUNNUyPs6Bp8PkwDy2W5LpU1PjCGsQq7Gen0jNfTNKI6wzQmVoe3BGT3zIjXsSddIUDAiRlRCp+6DlTsj/O/EqCyFFplKbTKEmiVJbZhUFHzTqpX1dwjt8HPv/yCb775Dunp6QCAqffOxLx5b2DWrHvw008/4ZprrsE555yDTz75BACwefNmHHPMBcjISMcHH7yPTz99F+ecczKqq6ukuA08//zrSEtLwzfffIMHHngAM2bMwOOPGyyMzCyfd9512LZtO9555x0sW7YMBx98MAYPHoxdu3aZYdet24hXX30VL740Gy+8MAtffLEUU6dGBYt77rkBvXt3x0UXXYTNmzdj8+bNaNGiBSKRCJo3b465c/+Lr79egJtvHoebb74Zr7zyjpmXqVPvx4svvoXZs/+DL774Anv3FuOttz7myjNr1iw888wzeOSRR/D998tw+eXnYvRF46N15GlOMGyRLHWmr7CJj2X4/faVFC8CNQ0AnhkRF2nLDkJ9cVYqMiN0WYwdA//OmBBoewW+7DZ+RmJhH7i0rd+u/YxwA8oFWxKLzYhWk67GxOs7M8Ke2vCvL7G2GSo/IyobEfFvf5gR0YBV3XfE/sfDz9M0dsyIYuKuLAHubqqI2xlpAOrU/K5rEyZXfHDzJiBDegpR9cXivfc+QdOmfVBVVY3y8gqEQiFMmzYR5eXluPfemXjttUfRv/+RyMpqgrZt2+Lzzz/Hf//7X/R5tBeeeupp1KmThyefvBf16nZHSek2tGtXYJNW9O9mzYpw3333QdM0dOrUCT/88APuu+8+jB49WrKbWLz4W3z77Y/YsmULsrOj5Zo2bRpeffVVvPHGGxg5cjiAqOD81FNPAdqf0COVOOOM4/Hxx58C0FFQkI+MjHTk5ORwl7eGw2FMnjwZZWWbUFGxEx079cTSpT9jwYJ3cfLJRwMAHn74UYwbNwonnHA0cnPb4777bsd7730II6Pl5eWYNWsW3n//fRx++OGori7D2WefiK++WhGtoz4zalKLZ0Pqw2ZWl6NxJ3gkdyMdCCNgdz0hBZtgnbSJPrfTW6eOMCKXhd1x2x3tdcuMqNiHxJ+mMZ8pGBr2WWweWO2ZEf/tGa1TG/72JaYPKBgXiQmRDo84MSM8g+BsMyIe7TX+iYUZUddXPH5GZI+/cuypQnOLoHI1YEBvzJgxAX/9peHxx+YgLb0CJ554NFavXo2SklKcdNLFALN7rqioQM+ePaHrwM8//4x+/Q6uYVJ02CkDouM3+n2vXt04QbJv376YPn06qqurhVlCx48/rkJxcQkaNSrk3pSWlmL9+vXm3y1bNkd+fj727YumWlTUCNu27XCsj4ceegj/93//xcaNf6CsrBwVFZXo2rUTAGDPnj3YunUbDjnkIDN8KBxG9+5dzL/Xr1+P0tJSDB06lIk1goqKSvTseTDi6wf+9SEyJuqhrnpZ+wiEEQDsrkj2surVZiR11DSQysKeprFjRuxsRnhWyG+bEe9+Roh8UH5GiHZ0DclmhBDAfILJjDB+RvzoSyzjoisYBJEZiXg92iv1J/XJI3tmhCqF3P94qG1hvJ2m4TPAM1ZUmgDSc6IsRRyoqtqPktL1iETCKC2Jsg3169dHZmamGWb//rWojpQiO6sl0tPzo+nymeXyRuU3Jycb7dq1RGlpPh6aeT/6DxqAOXNexqGH/gsA8OKLD6FVq07IzGxsfmnkISsry4qNnyp8QXFxCYqKGmLRok+hafwlqtXV1ebvtHR+2YqeulePx3nz5uG6667DPffcgoMPbof69Vtg5synsHjxJ0YsTGi6YPv37wcAvP7662jZsiWqq8tRUrIemhZCgwYHkd/Yo7YMoylVXi0bZbtEIIxAVL3ylvc8M8LqOO0m29QRRuzuplHbjNjpzt3YjDjp/WlQgofTaRoyH6T3T4rhcgf2KDfv9CwRNiPsaRr16ZCYwNUVpaaRqBDhTwc1jWTLY+jC1cyIJSQ62cm4sRmhYWkD6AXL1fhVLXaaZqMu8YCQDlRlA5EwdEPIyMgFMixhBJXZQDWAjBwgXU5PWmK4U2Vy6JAWxrXXXoSbb56G3367GpmZmfjjj8046qhhyMpqwoUu3VeCzp07Y/78F2qO7wox2hy3Xrr0B+7pV199hQ4dOtTc2M7XdffunbF1606kpaWhTZt23LsdO3YA2C0WUMhC9O/09HROeAGAL774Av369cMll5yPioodyMhoiDVr1pjvCwoKUFjYGN9++xMGDOwPAKiuqsL33/+Cbt26AQA6duyIzMxM/P777zjqqKNQXV2G/fsj0LQw8vNboKxsM2JB7awfqa+mSR2dQlJh2YXY3XQLiDYjOvk7NW1GDBgTv+VnRD5NY6c7F21G5PLHajPidKyXzwd/xFP4knzKvovFZkQDODUNdI2YfOMDr0pw2Ol7iZdwo04JZGJxvNqM2I0b3c6olOh/cn7Z5842I7b9zpbdkOOztxmxg18TONUmNn3MR/b1pJP+hXA4jP/+97+4+upLcdNNU/Hssy9hzZo1+PbbbzFr1qyobxJdx/nnX4B9+/bjggvGY+nSZVi9ei3mzXsDv/22zjb+P/7YjHHjxmHlypV4/vnnMWvWLFx11VVk2COPPAy9e3fDySefioULF2L9+vX48ssvMWHCBCxfvtx1mVq2bIpvvvkG69evx44dOxCJRNChQwcsXboU77+/CKtXr8fkyfdiyZIl3HdXXHEJZsz4P7z55gdYuXIlrr9+Mnbv3mv2vby8PFxyySW49tpr8fTTT2PNmrVYseJnPPLIXNN/SxTJ3pASRiOO4T1+kgAEzAh4xkBya805iqJtRvz0mOkn5LKwzAWtplHr30OI3p2ik+xDrDYjsTMjzjYjUjvGYDMiqmkSc1Fe4m1GzDy7UNN4dnomnTrRzX/ceWB1w6ox/Y/Lm7ptrXFAt5l6/NrZjCSK4lbFa9SRHTQ2mKto09LScPHFI3Dvvffil1++RL16mZg6dSYuv/w61K1bFwcffDBuvvlmAED9+vXwxhuPY9KkGThq8GCEwyF07doRffr0tM3RmWeegNLSUvTu3RvhcBhXXXUVLr74YjKDmqbhpZcexj33zMUFF1yA7du3o6ioCAMHDkSjRo1s0xAxdux5uOKKu9GlSxeUlpZi3bp1uOSSS7B8+XKce+6lAHScccYpuPzyy/HWWwuiNadpuP76cfjjj9W45OLxCIfTccEFZ+Coo/ohLc1ip8aPH4/WrVtjypQpWLt2LQoK8tC9exfccstQ+0omkSxVicjwp46qJhBGAPDMiHHsSTytobIZoU+gJB1SWdiTQbSaxtqVy1OepoVgeAhV24wkTk3jyd8JiHaMwWbEYEasEyJ27EvsoE/T+GczwtYVtWirjvKKf7s5TeNkwGrvgVWx1DL9j0e8zIj9+HX2M1J7cN/jaGnkqaeewu7dPwOo5kKMG3cRbr31PpSVbcJll52Dq66+BlmZRdzXJXuj9hIHHdQRCxY8gpzsTigt2w5d32XG/+CDD6KioqKmvaPtmJ6ejocfno3Zs2cTedLwww/vIpNJKz8/FzNnPoBZs2ZxIbdv3w5gD2666XLceON47t3ll5+La8aNN8vdvn1rfPnll9L4efLJJzF79hRUVGxHRkZDZGU1wU03n20KqWlpaZg69SZMn34b8vI6orh4HXr2PBKnnnqKlWNNw9ixY3HNNdcgEilHcfEqaFoI+fkHoqxsk1EsZ3BhWGau9tU01jYvOE2TVLATpcb4GbF03qzagtZbp67NiA3Lw5wMEtU0zswIkmwz4t52hX8WPzOiM8PW/x2F1e8S7mfEDTPiVU1jd5uyg82I6PTMmRmxH3t2k6mTnxH1+FV/m0g1jQQzm27TpBgs65WugzwL5GpRcrRg9TI+dFdaT136pVMvAbhZ1HXuHwDYsGEj3nprPgYM6Ie0tHLcf/8UbNjwJ04//RT+S4Gljan+EghGFoRzO/AsdzKRQtv4ZEHYFQm7KPZyOXuL/hRlRszBIjIGmrXr18RFxv7EhcZ0cNrPSGyGl96YEfFUj879zecj+hf3zNPOw2BGRDUNfB/AtepnxIUBq2enZ3an0BzCS7f2qpQQ5ATrYuw5nqZxw4yI3/qtphP+JeGUZqyLoNGvFfHrYvx+L2Dxbuhc5sc2ag2hUBjPPfc6Bg06DYcffjh++mklXnvtURxwQEdXaXvzwMrPzbUGTVzHUgcBM8JNlCFrohZPYZiTkqy3TlmbEcHZE6vq0KSJhT9F5MyMEItNjBflxc6M6DbMiD+sjVl/hs2I4fRM1yR36fGDshnxY9Ig2oxon0i8Ts8kBsE6eaQKDyuUbd4sqJkRJz8j/tqM+AuqpWNn3wjmgHzm/EoOZBdUh2hk/NZbT0ALpSvidG83oeuyKTSVBz5eKkX7WFq0aI6FC+cgFMpAXl4nlJSsQ1VVsWPe3NWf6lv/mXW7ulHHHpymSTLYScYy7pElVmMnRw2g1FTTyGWxFnTS1wTDjJCxcbtEufz8pYLuQU26bgxYoylSw84+b97ahw/L2kH4DX6nFEte1fGybUafphGZEK8JCQyC2eWcbEbMjNrmTf6GZiXt64u3nZJhP37VN3Wr0vQKjfm/u7BxJBN/IN3d2quMyXMxnMURt/CHkVD3t+RBB3Rx7nIInwL4xwsjnM6ZOfJqTaribp8yhuPjSBVI17pz9L985JNlJGjKmzmZQS0IEosUG7wYsJIsCKdLpsruDtbxU8Hpma0dRBzQrN2704V23uK1FmI9gTYjMvtgMCN2O3y+/3ljRui82I495qQSDUUc0mWT8je1Bz/VNJTKxT5+cqetGAPea8fLHErFzjxzWQ1yv4xPzeWt1MlaJ/jTNKkiiACBMAJ+Z2Ut0pZba97wUSMmtlRV01j2L4awYdlN8PfOWMKIVR+UzQhjF0C5XGeYFy+Ix+kZaTPCOUITyu5lEpDu5jGYkXgodJukWCE3BmNb23g1VkhQ2YyIf3sTRmRbK7XgJvU/x4vyhP5nwnnsObmDV41fvv5SHIJNDcUQcvBSJF35J5dWVH0qZUqCxQbGCNsPVWPcmL+J5zb1l9imF+c6n4UUwxbLVSECNU1SwU5EGnfkVdztGw0l785S1YDVnZ8RC+xpGjUzIg4gnbPf8MoOeTdgZVQapFDILC4CW+KJGWHuT4mepnE6IRIHWD8jcbjVl8GwWSo/I7b3vRh/OzEjos2IpdJS+xkR/vLMjDiPPZkhFKGKQ20z4h8T6t5+wm7RkJ46RMWNYJcLohnKz9M0MS/GNnNEDN+S39kIVLY+j+xjqlV4kjF1r18kDqmzciYNwq5I4ydVcbdP6a35iSp11DTu/IyIzIg80KxwbNll2ro2/Ixw+ZDURSILwpQ9FkdijH1Q1IDVaYGNHQlnRthTOkS88Rqw2t527ZkZicdmxK6+KNWqBdX4tfMzUhunIOR6M4RJuy/smQZ3+XUjXNgITXq8gplT2ai8OD3zEg+7yYkVgZomHgTCCANNc/IzApAW/cxATSmbEeaEBvuveM+OtYg72StYghq5kyaNTJ3hVRgBsXCbeah5wzys+RGDzQhjVMjbjCRgCBM2I16FOpuIa6J1shmxZ0LEv70xIzbCi9T/jDI7U+xeWQra6JxNXTV+1WkmA46jy6xvdhxIP2KD7ed2L1yoTNwkq5wP4k+TPwhE1F/CwDNMyXB6xiSeoLTd4R8vjMj6Yhs2QerElCogdQSRKASbEa4sNsyIsizWpC7vtNQePlXwLoywKg1ZXaZmRryAP9rL1p/vt/ay/c7H/qRxbWaf53jdwVv9SRAIHS/KM9RgrArRDrJQ4c5ey0GgUNS37Wka/6VRxxCJXRrVagaxCXkW1Sk+exx33IW47rpJyrTdQGf+7+WL+KCha9dhePjhZ+BUf7UKT0VjBCHfM+IN/3hhhNcXE6dphB0bvbNWsQnJg53DpqhtjI3NiNLpGSvcUBSym92tDG+7HnH3oBO/7Z/FcpoG4H0o6HoChi23ezfy6kM6ghFzNF6iDhzq3JEZkdgHL23IvlAZsFKGqPz4VcZpW0ZF3yDT5ALYPE8k7NJ0zsvWrTtw/fVT0KdPfxS1aI4uXY7GGWeMwYcffghAR9euw5CV1QiapiE7OxutW7fG6aefjk8+/YSLZ+fOnTjllJHo1GkwGjU6BF26HI3x48dj3759ZP/44osvkJaWhh49etjkzBBGVVC9te9vmzdvxllnnYWOHTsiO7sQN974H/Kb+fNfwaGHnoBGjXqga9eueO+9Rerc2BiZfPX1Ehx11FHIzc1FnTp1MHDgQJSWlrooil99ydvYq3XHazZIrdUzCbBlRkRHYWZVUY6XYrBHqBWIZWHz6Z0Z4QxiCQNHpwvL7BCzmkZQF0nO1yC3o7c2MgRQ8E7PiDzHC7Zu/exPdJs5H+2NePXAKhmXGn3KJTNiLkaq0xeyIaobZoSzxyGgqm8qzaTBdZ+jbSrWr1+PQYPOwKeffoNJkybgs48/wcsvz8aAAb0xZsyV5le33nojNm/ejJUrV2LOnDmoW7cuhp98Ah544AEztlBIw7HHHo3nn5+JZcvewMMP34FPPvkEN954o5Cuht27d2PkyJEYPHiw9M5z0czyuWeqysvL0ahRI0ycOBHduh1Ihvnyy8U4++zzcO65J+Pzz+fjpJNOwplnXoaff/5NTt3OeR6Ab775DicMPwP/+te/8M0332DJkiUYM2YMQiHhlBZrhJ8QYcBONUmMr9SQRVJu9ax18DtGihnh9ey8225wYVPJXgRgd3r8KRP+1JAFR5sR1mcFeVlZbLYOMRuwSoa0otExILZjPMyI7rjAxgHNWswT5mdEUQcRB6dnzsyIjZrGxtjXzumZsu8QPj/E8av6zsnPCPm9rZ8Rf2Gm7WoKcTvP8PV++eWXQ9OAjz6ai+OOOxbt27VD587tMWbMSCxe/IUZLi8vD0VFRWjZsiUGDhyIRx99FDdcNx7Tpk3Db7+tAwDUq1sPF110Lg4++EC0bNkUgwYdhlGjLsTXX3/Np60Bl156Kc466yz07dvXMZ/vvvspCgoKMHfuXABAVVUVxo4di44dO6JTp0Nwyy334dJLx+Pkk08GAOzYsQsdOhyJe+99wIzn669XICMjo4btAVq3bo0HHngAI0eORJ2COmTdzJw5E0OH/gtXXXUBOnVqhzvuuAM9enTBo4/OM8Ps2LED5513HgoKCtCmTRvMnfucVIabbroXV1xxMW688UYceOCB6NSpE04//XRkZmbCET4tH/aiEsvmUOxyYDOSXEi7Ip75sHb75mwpfJfKzAi/W+V9bVgdj2ZGHHaJhAFr7TEjrFEmYT9AMiOx2GHwR3u5Bdbn7YRVZ2oGI+Z4HVgv2UYkVmZEMGC1Fdz4/mcxIwo1DcVSuKgr935GVH3eXk2j6zpKKkvi/K8UpVVlKK0qRVl1Gcqqy6QwpVVWmJLKEqJe7fv2rl278e677+Kii0YgNzdHel+3bl0lPXHZJZdC13W8/fbHRq2BXfY2b96GN998SxI4nn3mZaxduxa33nqrbdxGfC+99BZGjboec+fOxdlnnw0A+M9//oO5c+fivvvuwxtvvIh9+4rx9tsfmF81bFgfDz10O+68czqWLl2Gffv2Y/ToGzFmzBiCieH5Arb+Fi/+CoMHH8WFHTx4AJYs+c78+5prrsGmTZuwcOFCzJ8/H7NnP4Lt23eZ77dt3Y6lS39Ao0YN0a9fPxQWFuKII47A559/rio2kTv/Yd+yKUKLILibRtot2u7wJGZE1lunms2IJQaLZRFPDVjCiFUfql0iZcCqW+kk0M9INHqWveIZKrYMUfBl99RGGrtgimoa99F4SYv1buuvzYhl7EuyYvGepvF4UZ7U/zSrb9qC6X/Wly7Gnks/I2qbEZkJNVBaVYo+z/WxTz9B+Pqsr5GTLgsWVv6sfK5dux66rqNDxzZSKDeoV68+GjZsiI0bN5nf6gBGXTgeb7+9CKWlZRg2bBimTp1qvl+zZgNuuWUaPv/8S6SlEUsN09QPP/wIJk26Gy+88BCOOeZ48/msWbNw00034dhjj0UoVIJp027GwoVfcNH8618DcOGFZ2HkyAvRvXsH5OZmY8qUKR5KB2zZsgWFhY1r/orWTOPGDbB1646asqzBRx99hLfeegt9+vRBRkYGHn/8MXTpYql91q/fCAC48857MW3adPTo0QNz5szB4MGD8eOPP6JDhw5E4S2hzk9RxHQpJL1hGDipAwTMSHIh2RLQdhamAyyl3jrF1DQOZWFPi5hQ2la4ZUYSK4zY2YzUhCTteWJhr8yjvVri1TTc7t1PPyMsk6eI18mI2JkZ0YR3hspMVVfMaSUXNiMgmRFVfzVy5pYZUZym0e0E0dQa8yw4ZaXyuK9dmwrx6Ywht64DOjBlynh8+ukLeP75B7Bu3TpMnjwZuq6juqoKo0bdiAkToioWCkbdLljwFq69djwWLPgv+vfvZb7fs2cPtm7dit69e5vPwuEwevSQ7T6mTLkFVVVVePXVhXj8/6Y6q0W8jF9dw+rVq5GWloZu3bqZjw844AAUFOTXRKebvnouuug8XHDBBejZsyfuu+8+dOrUCU888YQvWYkZShYtNdiRfzwzYu2Ya/6UdmaCPo3UIaemzYjsiIrfGWtaCLoesWR0zl6BmJiZExM0MyKm6w7URKhmRuRdBZkP7m8XO28RdierEqCm4U/TiM/8iVd1WWDcd9PYnNyCbr/Qif0v+lB1mkY19mw/Y4rrv82IpgHZadn4+qyvyfduEYlUYf/+lQCA/fsbAADq1KmDnByL+Sgu/gW6HkFubgeEQhnITsumskqiXbvW0DQNv61a5yI3ckQ7d+3Ezp070apVM+55YWFDFBY2RMeObVBQ0B7HHnscJk6ciLp1M7F8+U/4/vvbce21t9eUMapWTUtLw8KFC3H44VGhonuPg/Ddip/w7LMLcMgh3V3kT8batRuwadNmRCI6Nm74E4e5IqqsfllUVIRt27Zxb7dt24HCwobyVzb+lIqKosxK586duOedO3fGxo0b3WTIJzjPTRYxkhqCCBAwI9KOWRN037J7dGZnYMbhvDtLBqyyGDt6cQfIL4BONiP8dfQyM+LXjt6LzYgo8Yt5E8vuhbWx2DBDTWPEhQSM4cScpuHshkynZ4QwIlmsxpqOYDOiZEaY/ufKz4hsv+GmrsRxIEI9fo007XOlaRpy0nPi/C8b2WlZyE7LQlY4+l92WjYXxnhvPLd10GYWzHpTv34Bhg4discfn4f9+0ukMuzevVtZyNmPzEYoFMKxx7F2GHx44wRWeXk56tSpg8WLX8aXX76BFStWYMWKFbj00kvRqVMnrFixAn36WNJC2zat8MEH7+LttxfhuuvuMp8XFBSgsLAQS5YsMZ9VV1fju+9+5oZ9RUUlLrxwDP7971MxYcIVGDNmkiRYOKFv37748MOPuVJ99NGX6NUrKhy1a9cOVVVV+P77781vVq5ciT179pl/t2rVDE2aNMaqVau5uFetWoVWrVoJKVKnXRKxmVUxIrXEyrhEwIw42ozwEzi9A0xVmxHa/kX0Jmsxr+79jKicntWmzQh1qkdlM+KJtTHC1oYBK7cD989mRKNsRlwc7fV8N41kM2IYEzuraTSNmSJj9DPiymbElhlx0+dr/2ivPaPk+KXwb/TXQw89hH79+uCoo87GtdeOR7cu3aGlb8HHHy/Gk0++imXL3gUAFBcXY8uWLaisrMS6devw7LPP4vHHH8dNN92Etm1bAojgnffew6bN63HIIe2Rm5uDX39dg0mTZqJXr15o1aoVQqEIunTpgHA4B7m57QAAjRs3RlZWFg466CAAQHn5fjN/HTt2wJtvPo7jjhuFnJyrcf/99wMArrzySkyZMgUNGjRAx44t8NRTj2P3nj3c2Lj99pnYs2cf7r9/KkKhbXj//S9w4YUX4s033zTDrFixoqZs+7Fjx1/47rufkJ+/Gy1bRtv3qquuwhFHHIFZszpj6LCBePONl7B8+Y944IGJAID27dvjyCOPxA033ICHH34YOTk5uPrqq5GdnWXWsKZpGDv2PEyZ8ggOPvgw9OjRA08//TR+/fVXzJ8/30V7+QPOAkXjnyqRZGb/Hy+MyDsr0c5CnMDFHSDFOKQG7GxGrEVAq5GMKWaEKovKZiR2W4d4bEZU+TDDMP96yRvrfVPTNOiaEVcCjvYmjBmRbUboi/LE+rZ/7+Q3hPXOqWJGuP7n4m4aymbEGzNCCxTq8UsdC07EblbU4RPslds0qerWgbZt2+LTT1/AtGmPYfLkO7Bt2zY0bFgXPXp0wUMPPWh+OHnyFEyePAUZGRkoKirCYYcdhjdeeQ09ex8C6H8BGpCdlY05c+ZhwoTVKC+vQLNmRTj++BNxySWXei65kcEOHdrgzTefwnHHXYhwOIzp06fjhhtuwJYtWzB27FiEwyGcf/6pGHxUf2RmRoWAzz5bgtmz5+Ldd19CnTp1UFKyA4899h/063cKZs+ejcsuuwwA0LNnTzOlb7/9Di+99DZatWqF779/A4CGfv364dln52DixJtw++0z0aFDRzz//EPo0sUyOp0xYwauu+46HH300SgsLMSdd96JiRPXcmW4/PJzUV2djWuuuQa7du1C9+7d8f7776Ndu3Yx1ksMoLuPgMQa0MaCf7wwYrEFdswHP4HT15iLjEOKwG63Kqhp2KO9qhMX7O6dZiTsbRJUiMfPiDof0dwaz/lvXcDsExCYEXF3Hj8o77Z+9CeeGbFXVUlOz2JmRgDO54xN+Jqvar61DFhVfUdtM+J8CsfZZiS1mBEZTn1ONe6i3xYVNcK0aTdj8uR6SNcykZGzBQCQl3cAysr+wA8/vIusrObIyKjHfb135x4UM0zGEQP7Y+HC+dA061kk0gwlJWW2Objttttw2223cc/eeusJpKdbaR1wQDts3brV/DstLQ2zZs3CzTffjHC4DBkZe9Gr10k444wRAIABA3ph585vkZ5R3/ymVavm2LNnD1/6mj5YUbELZWV/Ii0tH1lZTVFcvNKstn//+zQcc8xBADTUqXMQiotXIRIpN+No3Lgx5syZgwYNGpgGsiee2EMq5/XXX40JEybb1gMApk8mWk0jJSw9SRVNzT9eGJH1xTY2I2YjqpiR1BJGZJsRceLmBa8oq6DSn7O7bHtbDa+LaDx+RpxtRkRWyLvNiMGM1MatvYnzwMrajMjxih5XJTtUT8xIxBIudJVKi+l/mr1AYIE62aLqr8ZXaruP+PyM1CJcZ0FW0wB27WYI2ayw72aM2GeGZcbU440xrraJbsOGDVi4cCE6d+6Mqqp9ePrpJ7Bhw58YMeJMIjve0rQHXX/28XmvP8Faz0U68UKRhvkqUNMkGU42I7ytwd/Lz4id/QvP8rA2Iyp7Bd5mhDhSa3upoBr+2oz46WfEqCf+aC90Dbrm8wSSIJsR135GKKHO5r2zR1WLGYmuEfZqmui/bI9JnM2IPbuhiMPxW38mcG9tHWuabBvWRMMQfkq2jzxBwoePr7vSgkQoFMJTTz2FH374Abquo0uXdljw6hPo3Lkziot/tc2LLbjo3agGiZyy/dlt/blBLdhsyB5YeUEqmfjHCyP2O2ajh4kdljhNkyKNKUKT8mqUhWeBWGZEzSCwu0RCWNDtFzsVnHxcyDAWbh3iQqcLeZP9K3hhRqywrJqG24T5BqutYlV3KcGxXkS8EhMi/u3EjGjMe+ZP5aWCFDPixmaEzYuzAEz5CmHhavymAjPiQU0jtxHBPjj9rcqJ27BxduEWLVrgiy++wObNmxEKlyE7qxgRPSPm+DSxnwpvrXfs/O6yEJ66iNEnvXwTC8QEkm0ZYo8U28onA8IEzdwREn0rGn0Slvk+OqnyFcKuTpdoevGYl7WQU2Vh7WVkY0DGViPRd9OwKg3KZoT71hC0YlB9CMyI6RxOqXqIDbzTM/s28BwvYV9DOz2zZ0LEv70xI26O9rJQMSP29lpuqHlbdkMxfsnxXkubj/hVgc7SB80KxKem4WNyY8vjIjVdZwRbp928c7zuxq/O/UOnFGv9sWkY490vyBl2V97kCioptnrWPkQ/ItwdIcR7zl7BiCPGRTjRYPXdvKChce85A1aFXUGi/Ix4thlhVRqkeoGy5/Gu+uBtRmAJI9DEZOOHRghYvvQnRrhW+RmJ0+mZaDNiTeSK0zTc3T9uWDV7ey3Vd5qwwRChHr/qb/1zdKiOh7KZ8gylFka9wHvt7m4EKYaHIJ96h9GO7oPGn2ZNdA715zorPkAHoCsZSTBzQOqoaVJr9UwKhAVUEyc8fudF+hn5WzAjDLNg+s8QzKhYmxFigPK2GmIHToKfEd3GzwgnKPFHtGPxM2Ko1q28JIIZYQUs+zbwHC/HjNgzLqIBq3dmRDxNU1PvNuFrPjJihBuGQ+njx42zNN+YkUQjtr4l3vcjtKDwRIuzB9svYl4FJ0b5GWd+3KSosnlR1Z/fYNLSiWd+Q0gjFZU1KbZ61j50YbfI+pZgmQLRURjPjMS2CCcarJ8RfoHm7V80xhhTvUtkT7Go/IwkVhhx9DPC2YwIzEgsfkY0XWpa3/2MsAKWkp3yHHFNtE5+RsS/vTIjvL6dO03j5mivm4vyKFZSwfaYX0kbDB7q8SunKb5LWUiNSgVyaw/hQa1hnxiddgxDiepT1jOf1Ex2TJofLBWXj9pgJuzKwv4VqGmSDH63yNO9FlPAOgqLgrUZcUMxJwPszpOdTBU2Iwp7BUvIcPLvkWCbEWK3z+WDcFAV2wJvCKisH4xEMSMyi+WnzYiTnxHEyYzwbR7hN34ebEZUZWb7HxO743e0Twc2Cvvxq/Qz4uvc7RRZfAugM/vgjp0yQtDsmOdsCbGq06bD+wE/GtJd/aUOAjVNysFiAoTTMjVvzclOeP+38MDK0drsYm7kk7AZUe0SCcdcBuJx1qXe5SjCOOTDeML+66WFLHUWpPrw32aEYeSkPhlXxMxv+3i9nGhyNmC12kDtrdaoX0bYc3NElzrJpmJGzB243e5QNX6pb2tn8ra3U7H7wl4FYf8s0XDbh412jDWdWMomfuNUf4lGAtYPTxOe/8l7wT9eGJEvhuMN8UTqj96BxcYIJB4WPU2pOcQdMmfkqNq1csdEjY8jvgllnk7TiPS58Myu7O7AGrDyzIjfkxXnut9PmxHOhb89i6Bi9N2ppFg1DWujpDJgZfTXhkbMjfMywl5L3a5OvkLsxy+ZJvM2EfBP28vWk51jMY+J6W4XbPrdbbfdhh49etimTeWG7j/iJsQpFjfv1GnEHx/1XaKZCXEudSG0JwmptnrWOqyL4TTu3yhYB1SCgSt7YsNPJ1U+glcpRZjnfFlAnKZxcgAlTuw6owpKuAdWjqGh1AuiAStRdhew+gSvpkHcxn9UYgk6TUO58Kc8sBJCnfnTBWsSrSsjLZfMCNf/RAbSPrzXsacxdUtBGQfpgr7mlX1OPcN57vDDTkHH1q07cP31U9C3bz80b90CnTsfhTPOGIMPP/wIgI6uXYchI6MAmqYhOzsbrVu3xumnn45PPvuEi2nnrl049dRR6NRpMBo1OgRduhyN6667Afv27aOTVmDv3r2YOOl2HHroCWjQ4CAUFRVhyJAheOWVV8y+c9ppp6GoqA0KCrqhsHF3tGzZFmecMQavv/4BVz4AOP30i9GyZUtkZWWhSZMmOPfcc7Fp0yapLgBgzZqNaNr0UNStW9e+1lwNdpd92EBt2Koqk2E2D4GaJkVg9jSKGWGdgAlsAjuxpeppGuZYIs8g8DYjGixhxFJnKGxGbBmJ2BiiuPyMUP5OBPsGuuxuYIVl55fE2oxYrJMvNiNEXZE7IKm+2d/uVDjsos/b2NjVFWOTozn3HXrsuelz9gIFGx/d52VBxncVnW/gjYh56Fi/bj2OOOJMfPrpN5g4cQIWffgxXnnlvxgwoDfGjr3GbLHbbpuAzZs3Y+XKlZgzZw7q1q2Lk047BQ888IAZW0jTcOyxg/H88zOxbNkbePjhO7Bo0Se48cYbPRl47t69F0cddRrmPvs8xo0bhc8/fxWffvopzjjjDIwfP567Y+acc0Zg1aqPsHz5e3jxxefRqVM7XHjheIwdO5lrlIEDD8OLL76IlStX4uWXX8aaNWtw2mmnSWlXVlZg1Kgb0LffIS7qzxlevrBOz/khZDrlRJWGnhyNFIHAA6voZ0Ry3uSGGXGh704CeGaEtRkxf9X8IzMjZFmMXaLOxwcY9VE7R3u9+BmRmRH3eeOZEXWe40bCmBGCRaJO00h+RrwxIzUR16QVgbt7fNj+58JOhut/fD7d+BmxtRlRjV/SBb350j6vKQE+z1eOvQqaBnz00VzoegukIYzsOvVxwAFtcMkl4wAUAwDy8vJQVFQEAGjZsiUGDhyIenXqYtq0aTj11CNwwAEtUK9uXYwadRY0rawmXFNcdNGFuP/+WVza06Y/jIcefBIlJSU4/fTT0ahRIy5Pt98+Exs3/oGffvoW9epVIBzORm5ue3Ts2BEjRoxAVlaWGTY7OwuFhQ2h62no1Kk1unatj44d2+CKK27BaaedjKFDjwcAjBkzCrm5bQEArVq1wo033oiTTjoJlZWVzPjXMWnSZHTs2AaDBvXDN19/71h/5lNxLopnKtCZdGrBIFo9/QVqmiRDxYwobEa4rWOqMyMigyAyI6gJxzIjqpMOItsA5523ArEzI85+RnyzGZF2FwmyGfHbzwjHjChsRiRhhP3tVhhhWYSaMLobZsSaJD3bjLiy11IJFFCOX4oZkQRxXUekpCT+/0rLESktg15aAr20FJHSUvNd9f79iJSWRf+reeZFIN61azfeW7gQF110JnJzc6T3desWMI0u97tLR18CXdfxzjsf1dQA77dk8+ZteOONN9G3b1/z2SuvvIe777ofd999N5YuXYomTZrg4YcfNt9HIjpefvldnHHGiWjatImUdl5eHtLS0kiWx8BZZ52AunXr4PXX3wU1Jnft2oW5c+eiX79+SE9PN58vWrQYL7+8ANOm3Sx9Ext0Zf25Qe2KAsk21pURMCNKmxHCDkKht045PyOcwybKboJgRlS7cs5fA2UzEhtDFLvNiLOfEbYNAa82I8xpI+Yzyn43bnCO3Hxk2ogL+Gg/I/ZHe92rabSaumHbwE9mhO1/NXnxYjNio6ZRnyBzUPEA0EtLsfLgQ2zfx4q9ADYTz41nnb5dBi3HEizkucvC2rUboes6OnZswz03xGqn7lyvXj00bNgQGzf+yT2/8MLxePvtRSgtLcMxxwzF1KlTzdRnz34W5513OkaNGgUAuPPOO/HBBx+grCzKpuzYuRO7d+9Fx45tXeSAHnOhUAjt27fChg1/cIW64YYb8OCDD6KkpASHHXYY3nzzTfPlrl27cemlN2LOnCdRp06e9Zmi/uzgtv7oL6Pp6NIzv0GoaTTqXXKRalv52od0AoTXG9o60uG3jnSYpMOgtcUFxLB/CTGhasIoysI6hJOPX1h15Yc7eDUM2wHKE6ycN9rhmxtoxK/oX4nywOrHDouM16Ft5Vr0Lozwp4+MsCoTOqv/acIzOjx7AsH46f4El53TM1d9PnUNRWzBieROAqFD8fSaOEShb8qU8fj00xfw/PMPYN269Zg8ebL5wcqVa9GrV08uPMucxDeE+P4p5uv666/H8uXLsXDhQoTDYYwcOdKsgyuvnIx///t4DBjQ32UKKrirP3VCie1b7qNP7voVMCOmBbTaZgTCe36npBPfJh/2zIgoeMnMiPNpGrGHq3feKnh3ekbbwlh5E3f51cS3zmD9tLBHe5HA0zScczKfbUZUTs90yekZ+9stM8L2Nw/MCHSTnXPlZ4Sw11LWFelGnoUiDhc2I1p2Njp9u8w+fZfYu+9nADpK9hdA18PIyc1FQZ06AIBIpBLF+1cBAOrkH2imS+fJyKuV53btWkHTNKxatc4mdWL3zGDXrp3YuXMnWrZszoUvLGyIwsKG6NixDerXb4ehQ4/HjTfeiLZtGzqWt1GjhigoyMfKVWuUaatQXV2NNWs24tBDe1p9ARoaNmyIhg0bomPHjujcuTNatGiBr776Cr16dcVnn32Dd95ZhJkzn4iWRNcRiUSQlpaGBx64FeeeexJXRsd7XpiwsZTBb0QFR/EJD2HmT3SWXOEfL4yo/Ywwi5jCZiSmG2FrBazNCKFbZww0o+GcmBGm7MRFebVlM2J/SgigTtjoepX0rTsYAijLahK7cx9A+RnxhxlhbB50+0VXqm/uskG3zIhs4Cyzcmxw1kDYWkicy0IxI862Jrbu4BVx0DYjQhhN49QlsSJUlQ0gAi2SDehhhLKzETLijVQiFIkac4ZcpaVzXbRe/QIcffQQPP74PFxy6VkAGoANsHv3bmRl2ffpRx77L0KhEI49brBtGON+o4qKCgBAp05tsWTJClx0kRXmq6++ssobCuHUU4fhhRdex6233IJ69fj4iouLkZWVhVCI6q/Rf5977nXs3r0XJ544zHZIGvkqLy8HALz//jPQ9TAyMhqjrOwPvPPOZ7jvvv/Dl19+ifx84/SObhufff/3MidQ/TwxQgyd39Rj/P7xwojKZoTfUdvbjMRyI2xtgPIzwi9E/I4xyoyACGc8lHfvBmrTzwjPTilsV8xviXZ0Ae40jSmDUEfyfAB3CaGfzIhl82C1mbMwwmlCPDMj7F0+LjywxsWMxG8zohy/KpuRpIx3hzRtbat1PHD/DAw68kgMPupsjBt3Aw7qfCDSs/bgo48+wZNPLsCSJa8DAIqL92HLli2orKzEunXr8Oyzz+Lxxx/HTTfdhLZtWgGoxHsLP8CWbRtxyCEHIDc3B7/+ugaTJs1Er1690LJlSwBVuPTSs3H55ZNw2GGDcPjhh2Pu3Ln46aef0LZtWzOzkyaNxeeff4v+/Ydg4sTLccihh6KgTjU+++wzTJkyBUuWLEF+fj4AoLS0DFu37kBlZQS7dm3F/PnP4OGHn8GoUafjiCP6AtCxdOn3WLFiNY466kTUq1cPa9aswaRJk9CuXbsaFVEVOnVqi1AoE1lZTVFSko0VK1YhFArhoIMOwt59P3pcoOPtA7FZnHhPw4DzabVk4R8vjIBiDBBC9FSGvKOmfB3E6l8j8aAEK2rCZX67KQtxQR3vZyTRzIjVBvIi6iczQhm0WXY4foLbvcdoe6OKl20zatEVT9NEYjBgJZkR5htbgYFhnlyxarH6GbE72quIw/40TeInbj6/sXQ4nfvZtk0bfPrpC5g27THcfvvt2LZtGxo2rIcePTrjwQfvN4PedtuduO22O5GRkYGioiIcdthhWPDSK+jVtzeiZrVAVlYWnp7zEiZMWIPy8go0a1aEE044AaNHX2YmeOqpw7Bx4w6MHz8eZWVlOPXUU3HZZZfhvffeM9OqX78AH388H/ff/zSmTn0Mv/9+O+rVq4euXbti6tSpKCgoMJmNuXOfx9y5zyMjIx0NGjRA9+4d8eSTUzF8uMXWZGdn4bXX3sNdd83E/v370aRJEwwbNgwTJ05EZmYmqqqqzPx5qj9PYVNhU8rkQcWMRANIT5KBmISRhx56CFOnTsWWLVvQvXt3zJo1C71797YNv3v3bkyYMAGvvPIKdu3ahVatWuH+++/HscceG3PG/YNMXWtaCOJxWHceWFNLGGHzo5MLkaF6cmczonH2BxStX1s2IypmhHjGtWMsNiOAptXEkXBmhPUz4sP0QNiM0B5YRaHOuzDCtgt7a6/xjVz3TP9z4/SMtN9wthnh72iS4c5Oyl7v7jc0zc51u3OimmlcLUeg6zqKihph6tQJuO22+ggjhNw6exGJlCMnpw3Kyv7ADz+8i5zcdkgL86qgXVt3oqy63Px74IDDsXDhPIS0CibfTbBvXwXXP8aPvxK33vofLq7//If/u6AgH3fedQsmTrwQaWl5yMnhT/wAwPz58xEKVSEnZw90PYTc3NYoKVlrla2mvAce2BHvvPMccnJaqyuq5isAOOfcU3HppTdGy6CoP7fxueoY5OWNfvUocW5m504xjVTxvxqDMPLCCy9g3LhxeOSRR9CnTx/cf//9GDp0KFauXInGjRtL4SsqKnD00UejcePGmD9/Ppo1a4YNGzYo3e/WJmh9sTGBVkvhSV1bil6UR9u/EGoazmbEXjcOVmdPLV7mjj4+ZoR6RjMjNvmwVdN4bR9GGBFYn4QNYeFYbPxg60p4xiYr+RmJnRmBbgkXupKVsBd4acgshbv7kFjGhoCri/JU48Iv+Dd/yNXtvr+6ywUhnGnUe+d2sYnOG7jvXaapik6nf7uBK1nEW5TeIBJ3rgvwN1PTzJgxA6NHj8YFF1wAAHjkkUfw1ltv4YknnsCNN94ohX/iiSewa9cufPnll6bTmdatW8eXa19hGF1asPwlsMKIeNqG2J2lmDDCMyPV0jNrZ2qEYU9cqJgR2WYEDjtvL3Dt9IxNk82H9C1VdmfwzAi/0/cbvDt4/5zoaYTNiPqyR+Mv76sDOTZ0lTDC2uQYu0pn2w8+b26Mpp1sRuzHr1V/TlR37SCuOYZqA9ZGjkmF+NjlMzF+5yCxsQPx5kddWtdwXX80eGVmkpAiRqyeZruKigosW7YMQ4YMsSIIhTBkyBAsXryY/Ob1119H3759ccUVV6CwsBAHHXQQ7r77blRXU6xDFOXl5di7dy/3X6JgTpKEYSdrayA6CqNcUqeaO3h2UFhlkYURc0iwTANZFmaXSJymidXWIWZ38EyaXD4kNQ1VdjdgJxWeXfHbHTx7rwvdJ2OFbDNCLfhyfave2ZXdsnsxT2gpv2H7n5u+o7DXUqppnGxGVOPX+TRNbcB9f4v1tBfBMtYqfKVGbMETBhR741x/dFt4rT+jL9tkLg6IuYi1nLUNT7Pdjh07UF1djcLCQu55YWEhtmzZQn6zdu1azJ8/H9XV1Xj77bcxadIkTJ8+HXfeeadtOlOmTEFBQYH5X4sWLbxk0xOo3aK5A1MwI6SfkRQzYKWZEcJmhPDA6syMiJ049jtVYrUZAcnQUAasVNmdQTEjmm7Ugd+Q7WB8cQdP+hkhTtOIahqmhHH5GfHMjLixGSHstVRjz/E0jf34VdmpJBrxCbyE6lNiv2JZ/9wtYtbyF//pDZU7eJuIHd8516x34SY2sHxKopg2Y2w5hUsus5/w1TMSiaBx48Z49NFHccghh+CMM87AhAkT8Mgjj9h+c9NNN2HPnj3mf7///nviMkjuiihmROPe8QZ+Phoc+gqKGaGkYyOMDtXu2ZLmmXDmt8nxM0IyNAlgRjTyCLF/MPsXazjtAzNC+Rmh3cH7YTMSRSRSLXUfNTPCPnGh76fstVRCjFs/I0SfV/sZScZ4j0ONIe2QLXGEtMmiPnULFx9QCg5V+XjnYwo1jZsqcsyfNybKqf7UWUmwcEuq57gAiU3fJTzZjDRs2BDhcBhbt27lnm/dutW85VFEkyZNkJ6ejnA4bD7r3LkztmzZgoqKCmRkZEjfZGZmIjMz00vW4oCsL7ZoXbfMSGqepnE2YJWZEeUukdm9U7Ya7H7IC/xkRnQfbUY4A1bpNI3fkE8qJcrPCMmM+CCMGPFGGCFe98qMKPqOBmrsyeNX/lB9mkY5fl3cTeMfVOPG6+IYSxwxqHaopx4XV3/sJvQYZh979UVs8kEMahrHZ/FDp9Y4kyFKDUEE8LhVzMjIwCGHHIIPP/zQfBaJRPDhhx/ydw4wOPzww7F69WrzrDgArFq1Ck2aNCEFkdoGvQs1Jj3KWZZCb51yahp272FvwGpOB6wQoJiY7WxG3FznTiFWPyPyjbw1eZMEFEoQcwan1pGOnvrNjBC2HQnyM+LGZoQtn2tmxBDUI6wQ7yyMsIKs2q07NfbcMCNWOuqTW/YCeO3ajPjRtxK7yHi3YVG8iyGrZPrmo/hP0/D93+UnMYHdxPkJJxbJ+VVtw/NsN27cODz22GN4+umn8csvv+Cyyy7D/v37zdM1I0eOxE033WSGv+yyy7Br1y5cddVVWLVqFd566y3cfffduOKKK/wrRVygdlaykaq5iwO1U/JPx+8vWGaEOr5osD0sM2JfFv4Uiwh2UMUvjKigEYsYmw85vlgXErYciWZG2J2Ki92+x3ituOl4/WVGqpmwKmHEOFpOPFPET/oZcb0AUflQjd/UOk0TT5pGGfiSuN2hu+AvYqb+Y1dx2MfjZ1jhS0eVTIqtA66Lmtx8ez7ae8YZZ2D79u245ZZbsGXLFvTo0QPvvvuuadS6ceNG7i6BFi1a4L333sM111yDbt26oVmzZrjqqqtwww03+FeKOEDtrCwDVsJmhNK3m7rKVGNGWGGkSnpG2Ywo7V/MerGEFvNbzuV8gm1GOKNC9zYjcR3trUktYfQmazOSEKdnjFGpq7tpFO9smRFD8Kii38sf1PzfUtO4uvCOtNdyNnw1wotpKMcvoeKxwttn1Q/E5IFV02qCUsyBKg7vO3Sx+PFVh32dSv2NTCie8cgyoIr6U8IPhsOnDiXaaynUUTwr6U/ysSImD6xjxozBmDFjyHeLFi2SnvXt25e7ICm1QO2KRJsRdlEiaNsYDTcTD9aAtUZNw+48DYMw7jSNLoczYzPik0+sgBMgEnyahmkD6eQModun7WXcgDdgjc5RvGrLLyTMzwin0kqwzYippnFpM8L1P2eNP9v/LHjxMyJ+a+RDxQYSLGnCZJH4Y9Sg4jB06h9v0KUf1EshRxZuu+02vPrqq1ixYgUADZddNhF79uzHK6/MI0I7ZkKZMg01S6auPxnDhp2Frl074IEHHnKdAz4f/qpp6HGmjj9VNDWptZVPAigbCZEZ4XZShEFb6jIjGizBqsp4yIZg/g/+dIrKNTZpl8EyIwm2GWHbQAon78pjZ0Y0xoJf8DPiKSY3iTEClp/9iTvW6sHPSCw2I4aaJsK2gQsDViZLbpgR3sePc13xl19SC5kbZqQWT9MoNU6xLtfRZ1u37sD48Xejb9++aN2mNTp16o8zzhiDjz76BADQteswpKVlQdM0ZGdno3Xr1jj99NPx6eefcZnbtesvnHrqxejUaTAaNToEXbocjXHjbsS+ffs4Q3i3YLcaTrAfgbRAW1ZWhvPPPx9du3ZFZmYuzjrrKv77muCLFi1C//6noFGjQ3DAAd0xd+5r0Vjd66ZQXFyCsWOvRvPmzZGdnY0uXbqQJ0drg4XQiV9MDlw+qz2k1uqZFBB+RsyjgF6ZkVSsTpHlYW0IjEeUDt6eGaFOrPAsRWLVNBwzIqlkZGd6sbuDtyB6YPXd6Rl7UsRHps3qy5YKzh0zonpnV/ZofiOkQKjYzXpkRmibEdXYY99RO0eFbx32GHktga4Bt+kb6j45/IYNG3DEEWfis8++xsSJE/HhBx9iwYInMWBAb4wde51Zr5Mn34rNmzdj5cqVmDNnDurWrYtTTz8NDzzwgJVKSMOxxx6J55+fiWXL3sDDD9+Bjz/+lPTC7Qrx1q/i++rqamRnZ2Ps2LEYPPgo6b0GDevWrcNxxx2HAQP64PPPX8KVV16OK6+8DR988IXbDECHjptvnor33luIZ599Fr/88guuvvpqjBkzBq+//rrNV3xO4oZK2LazBarFvq1CKq6etQpyVyTovvlTFfLk5KuO32coWR5GZx8No6vLwp74UNhqJJ4ZkVUaVkDCs2/MR3sBq454AcHv4auRNiM+MyMe/Izwdhlu1TQ1dRNxa8Aaq82I7PTMrc0IxXC46vOKo726rqOyvDr+/yoiqKzQUVURqfnPeldRXh19Vx4xn6kFJPnd1deMh6YBH3wwD8cddxzatWuHLgd2xJgxI/H55++b4fLz81BUVISWLVti4MCBePTRR3HtNeMwbdo0/PbbWgBAvboFGDXqTBx88IFo2bIpBg06DKNHX4Cvv/6aS3PqtFkoLCxEfn4+Ro0ahbKyMkWegaVLv0OjRo24y/TuvvtudOvWDR06dMaYMbfi1lvvx6GH9gcAlJWVo0+fk3HllZPM8GvXrkd+fj6eeOIJAEBubi5mz56N0aNHcy4o2Pp75JFH0KZNG0yZciM6dWqLyy8fjRNPPBoPP/yMGaakpARjx45FkyZN0KRJE0yfPl1a37/5ZgVGjjwXgwYNQuvWrXHxxReje/fu+Oabb4SSskKjf3o/rtUNVtf1ZPU3M2D9nwO5CxX1xKztAKG3TtmL8gDWRTf7dxQ1i4E5LnRlWTg/I1x8UeNI2u2wM4zvomoRnRNG2GdWPqyBbEn90XzwNiTis1jaR2jvRN3ay9mMuNntu4OlgmP9jBBqmhoPrJoeLSKlpqHagk9LZBSjgoCuOwgjmvxMGT8x9tzajJAnq5R9nvqO7+dVFRE8etUnivS9YofD+6i364sfOALpmWGHsFHs2rUH73/wESZNuhK5uTkoKQEA3ay3unULUFq6z/b7i0ddjOn3zcA773yILl1GSu83b96G119/y3TxoEPHK6+8h7vunI6HHnoI/fv3xzPPPIOZM2eibdu2NV/x9f3JJ1/j3HOvxb33TsXFF18MAJg7dy7uuece3H333ejV6xC8884LePDBOeb9ZllZmXjssSkYPPhsHHfcsRg8uDsuvPAqHH300bjwwgttyyOO38WLF3PXnADA4MH9cNNN95p/33HHHfjqq6/w3HPPoU2bNrj55pvx3Yqf0PWg9kak6N27B954402MGjUaTZs2xaJFi7Bq1Srcd999tnnxFeTQdKumSS7+8cKIxYwwAoe5AyN21MTFWb46qfIZxqV/ULqDj/7FGbCq/IxwFtjhmoWOfRYbMxIKhcw7i5QLIGe3I+SDOwHFP4uHGeGFngSAqVuqT8YeL3saxF5YZI/9so7X+Cxq6t24FqPNiGbkz4kZkY2H3dQVfZss+0TV5xPjVyZxoA2s163bCF3X0bFjG/Irvr3luqxXry4aNmyI33//k3t+4YXj8fbbi1BaWoZjjx2KqVOnmu9mz34W558/AqNGjQIA3Hnnnfjggw9MdoRtl9deewujRl2Fhx+einPPvdh8PmvWLJx//vk444wzoGkR3HDDpfjoo8UoKak089qt2wGYNOlqXHbZOJxyylBs3Pgn3n57oaKOrFIbcWzZsqXmRKhVf40bN8DevcUoLS1FSUkJ5s2bh5kzZ2LQoEGoU6cOnn76aTRv3oyrv6lTb8K1196P5s2bIy0tDaFQCI899hgGDhzoIheJAjFGNPZdavTtf7wwQjuY8moz4t9O1n/Yl8VcDELMguFCf85e5mbsfHmbkdjqgb3QjBVQIhHeuRl1msbagVO78nhsRuzUNAmyGWGck/lpMwLWZoRadE2mOHqegLIZMdrCeCYe4bbKYAkjoVAYkYidSsHqf5rkVE5VFoqVdGczQjMjKjsp+TixiLSMEC5+4AhF+u6wd98qAJUoK8tHdVU6MjMzUL9+AwBAVdV+lJSuR0hLR15eRzNdZxjtSdnKiL/VfZpq8ylTxuPGGy/D6tXrMXnyw5g8eXJUfaEDK1euxcUXj+LC9+3bFx9//DH3bOnS7/Duu4swZ850nHzyCdy7lStXmiyJ0V8OOeQgfPbZcrZ4GDv2Arz11iI8+ujzePW1Z9CgQQNlWbwO3/Xr16OiogIHH3yw+ax+/fro0KEtE5+O//73OXz99dd4/fXX0apVK3z66ae44oor0LRpU4F5YYRGXXzmL1JD1HDGP14Y0Qk7B2tRVNiMcLuzv6nNSI3KIcQxIy78jECHZR+SBqAcfvgZMfzTiMIIG8YxHxwzwj+LiRnRNUCzhBFLneIzzDpLjM2Is5+RaJohaKgGraZh/QdRC5NRBpYZida5jTAi9D8rvFNZ5LGn+s4PmxHaHbzB7Giu1SUqpJeHAGiojoSghUJIywib8WrhMNKrNYRCYVdpWaeMAOga2rZtBU3TsGrVOhx3XE0YpgxOPXrnrl3YuXMnWrZsURM6uqMuLGyIwsLG6NixDRo0aIN//esEXH/99WjXri6sDNgh+q5Nm5Zo0KABnn12AY4//gRkZzsWj4khyuRt374Tq1evQTgcxprV691HUIOioiLumhNd17Ft207UqZOHrKwsFzHoKC0tw+23z8TLL8/H8OHDAQDdunXDihUrMG3aNEkNxH7rGxRHe2VOXEw5OE2TXCg8k/7vn6ahmBHVLpExsjR32WHzmZimW3gVRmhmJCyUk3rmvX2MOoIm1l+imBG/bUaINiNP09SEt3R2zDtaGKFSi76rMqNg2S4pTZIZcVcWKxI39kDubEac+nxy4bW/8eHr1y/AkMGD8Pjj87B/f4kUevfuPcrYHn38MYRCIRx7LLGg1lSRwZqVl5cDADp1aoslS77lglL+pho0qIt3352PtWt/xznnXIbKykrzXadOnbB06dJoMjV96dtvf5QSv/zyCTjwwM545JE7MXHiXfjll1+U5eHVExr69u3LXXMC6Pj448Xo1as7AKB169ZIT0/Ht99a5fnrr7+wevU68+/KyipUVlYhFOKFxXA4zF2HkkjQvYRSBBEGtElGwIwQ+mLjd+Rv7mcEsAYwVRZjouWZEUVZuLKzjAR/nDMemxEzH0pmhLIZofLBP/PK2NTEUvNvTXkNA1a/NxHU5X++MyMqPyMWMxIN710YEccNoCmFEb7/uRhDirGnZkbY8trbjCh96xCX8yVqIxkXwSrY1WjQoGtRVcCMaXdjyNDhGDLkTIwbdwM6H9AZObll+OCDD/DEE6/gm29eBgDs21eMLVu2oLKyEuvWrcOzzz6Lxx9/HDfddBPatm0FoAwL3/8I27b/iUMO6YK8vHz88ssqTLplJnr16oWWLVsCKMell56Nyy+/BX36DMDhhx+OuXPn4qeffmIMWC00btwIb7zxOIYPvxgjRozAvHnzkJaWhiuvvBKjR49Gx44d0bv3oXjnnRfw00+/oU0by/blscfm4ZtvVuCbbz5CYWEG3n//a5x99tn46quvzPvPfv75Z1RUVGDXrl3Yu7cY33//KzIzd6JTp6g659JLL8WDDz6IiZOm4pyzh+PLL9/FggUL8dJLDwOInsgZMWIE7rzzTjRp0gRt27bFhAkTmDGho06dPPTvfyhuuOFG5ObmoVWrVvjkk08wZ84czJgxQ2worp34Z36hRoflUthINrH/jxdG6F2oyH4wxq1/O2bEvizGztQYT7zNCHWygLU/sAxH+fiZNF3CT2aEzYf8LJ6jvfLuIhKJcAt0POA8fSbMZsSecYnofHg7mxHxGY+abyOGEbJaGGH7n+n0zJXNCJc5x++svBEXK0YzbBuHymYk0R6X/fNtEl2U2rRphU8/fQFTp/4fbr/9dmzbtg0NG9ZHjx4HYNYsy/D01ltvw6233oaMjAwUFRXhsMMOw8svzkeffocB+n4AQFZWFubMmY8JE9aivLwCzZoV4cQTh+Oiiy43co9TTx2G33/fjfHjx6OsrAynnnoqLrvsMrz33ntMviwUFjbE2++8gGOGjcDZZ5+N5557DmeffTZWrVqFO+64A+Xl5Tj55H/hrLNOwPLlqwAAq1atw6RJM/DgQ3eiRYumqKjYgQcemILevf+FSZMmmUeEjz32WGzYsMFMa8CA0wEAe/Z8DwBo06YN3nrrLVx11eWY/fDTaNasGWbNug1DhhyOioroN7fccguKi4sxYsQI5Ofn49prr8Vff/E32D/xxL24666ncPbZZ2PXrl1o1aoV7rrrLlx66aWK9kmsmsapG7EzazLxjxdGrB0PI3CYEyhh+Eiepkk2hWsPy2bE/tZeVk1D1YcVnNXZ88KIaDjqBSrBQ82MWP93o6bxhRlhKf+I7qOi09opxXIZun20bH9VxOsLMyKPG5UwYvY/zdjBwYEZkVkKZX8V8hbNgsJmhP7QOYxv0IR/WZh6NFcxiEtMtPV1FBU1wtR7J2Ly5AJAB+rWq0RV1V5kZTVBWdlm/PDDu8jP72IJ8jXYvnkrKvVqM4WB/fti4cK50LQINC0Nul6FtLRC7N7NO7y74Yarccstd3HPWB8is2ffGT31VvN30yZFWLlyJRf+5ptvxiWXXIJwWEN29g6ceOLFaNs2yox07NQOW7Z8Ay2UboavW7cAGzdu5OJYv349gKg9U3FxVIVjlNko06BBg/Dll6+hurqMeWchNzcXs2bNQm5uLgoKCgAAV1xxOqqq9pr1XVjYEE8++YRUf8mF/dizf1/7+McLI/S16vykyqs27HdnqaimEcvCrZ66xoQwmBH7snD6c4WthtdFlD3Ga/wtPuPhxmaEmZbjsBmx7k8hFj0fx7AlNDLslA/9iW4zhc2I2V/Yd3JbKJkR3WBGHARAo/9pOsOM2Idn/ctYcbg1HjcMae1P06j7fC1O2HHJoGL7MYKeAvEVj89wTIyOzTclJSV48MEH0atXL6Snp+HNN+di0aKv8NZb15op64rvXSXJZV+uP/VcpMy+LTRuQ8WnHQ906YdT/KkhiACBMGLpnFkjVfEECruIxai3Thbk0zSy0GVdhmoxIyTtzexMrXCGEFB7NiMa1wZG+/H50LQQxKOmsbSPaWQJWdWjRyIA/NoBsTYjuvAsDhBtRk1KZn0Tt5Z6tRnRXdqMGPkIccPLKzPiTk1jMSP2NiMqPyOq0zR+QxWr+xSp+haeaWCEOPWiRNWaq4VMKSQ6l0bTNLz77ruYMmUKysvL0aFDKzzzzAwcddRAVFRstYlDIdAS9kN0aJeLtMv6swdTj350JzIbbvU0gZomuSAvhhNO07AO0WL2dZAs2J8MMnX2jAGr6qI8jhUydgshihlJsAErxYxI+QhZC4kPfkYoIc3wWuoHrIWcZTD8tRkx20zhDp7yo+L1NI1pwOrSZkQLEW3rVBYzErfGvkz9ShmxH79u/IzUBtx7N1YZRhJxkFtpSo3nJk35A3c92D7t7OxsvPPOO9i5cyfC4TCys7cpMuS1jag6dRYu6L7vUH9OWfAN9sI2D7acqcGOpOLqWaugXGRLfkY4ZoS1mxDiSLY5MgGLdq/i/ubCsJob6Lbh2IXZKnMaFz8bzi28MyNsG9D50DTNF2bETBPV0jNfj+vVgp8RnWB3DHDMCPj6ds2MCCpBHXBgRmpyw2XHeSdNnWxxMia12p6arBXjV3WaJlFgNwcxQyV4qMKrQdaz+YidE73CefGnPenGw0yovpHrj+oflI2OO8hf+m4Qbd5NYzAvTPypt1QFwgitLxaZEdZmhNHBm3GkMDMiGhUSTs+4BUNlr8Bec6/075FgYYT0BEsxIwp7Gdd5s4QELm34zIzUgp8Rtc1ITX0TTt1cMyPGwh1xqaYxnZ7JKji6MDIrqSecGZHHO2+P4T8St074KUSx41HFpDirTNznilj6SQHSH0WXO8Qbn852qLjhNSq/bVbiQQqunrULlZ8R04CVZEb+JjYjEMvCDmhZTaOyV7AofNa/RxLUNKS/E/HkTIg4SRS7moa6kVj3lRlhbUYSwYyoT0pZfdheTeNkwCpflOdWTcM+ValpYrfX0jS+Hfl8uLEZSTyVrSsXA7dqmprQXH2rFn2qfVyOE036wcTlpb5iSNsU2NlYvKiG2DLb21Cxv9UGrDGqaWoFcr0kxTDbAam3etY2SA+OwqTH9S2KFvQ2UdQqROGJONrL0cKK0wnWBYLWZGMajjKTfLwGrOwzejGT9Z1yPjRI7RiHzYh4a6+cp/jATw4JiJeNk5pUBTUNlQVHYU5gonTHujKYOVkFR0evsBlxaUuh1vdTNHw8aoBkQCiDg8O31IFz3uj+53f7qOov0fAjLUvwoLYLqYx/vDBCGSaKdhaUO3idmBAT7QQpFkh2E8TRXupuGroszC7bFFrk0zSxdnq7i/KMZ2KZoKvyoVn5pcruElayCWZGWHscX53oWeoJV+7gzXzQO0OlR1XCYNiNmoZ3Ba8qs8Jey5XTM4BiRtTjl1LvJGjzQaj2446MBJWA02JOldlNPbh4F5Mc4dUmhsqPs82Ic1PEKgwlSEikhpkr2xgg2cLKP14YIZkAlaMwTZ7UfKXV/YbCZsQczubazugvqdM05q7ZXj3CLkBuEfNFeXq1WQpaTaOwl3ENkd634ogk4DQNx4z44WeEU0/Y91PLgJVY8F0KI0bduHUHbyoEXdqM8P0P/G8nZ2AaJVQY+VCcIFOodxI9d6vYJFuYr5nF1NXxU3Ub2CvZ7ELFqKZxPXd4Y7hcx6HZv1P3fZd2T27zES+4LKj6EZtv/7PhBSm4etYuSD8j0lXoFGvCTohyHKkDkeVhYO5Ma/508jPC7d5FIaCKC+MF3g1YjXxQ3latNpNZodjVNBrBGPnLjDCnXhLOjNjbjJinadh3bpkRs6/JxsxKZoQ72uu8k47Fz4hyB6scv/ZCTO3O3e4WLLlteSsxFTZs+BMFBdFbZr3AzeI7aNAgXH311bbvVfYepCAr2HvEZ9OjMb/k+ksMVCqn2MHOkvLD1GPuWaTi6lnLIHahCsNHawf792BGXLmDZ21GVFwxt3uvcR9uHqml4neH+JgR2OdDDBdL+5jJMgbANc8S5mdEdaV9jPHCrZ8RTa5v18yI4PTM6W4aq/+5tDeibEZcskgawfhYMaiYESpN860yzZgRq9aDAVXOSy+7DgUF3VCv/gFo1aoVDjvsMEyYcDfKyso9LOaysOpLPcQ4lNR2JOrw1gWJVHa8ZcjrFQ4aJyv4qaahBHv7+Pm0kyusBE7PiN2iuMOjLtHjaezUtRkRjylzR1NB2Iy4uDSM271TR2o9Itajvap7aKKtoTgV5TZv0m48BA1a1G7G1+tKrInDX2aEYbO8+BmJxWZEYg1dXpTHDRvVaRr5JJtbey3SWaERhTIOmY1JHDTm/3Yh3Eoj9CIzZMjheHDWf7B3XwZ++OEHXHPN1dD1ckyZcrvHvNovYvxpGreSlfOCrGka9JrRJ4XX3cXBf2OwMZQ0Qvd/IldSeM9I+IkWhSAdnKZJHejULlQTJlXiHT+p+beT9RuaVBZq94eaME7MCCtpG+EMISB2x28qwUN5tJe7oTeNexZlRhTt6Bri7oLZWfkpjVB6fT/6E9lmhJpGsBmh3rllRuD6ojwjOy717cTYI8ev4ltaqFDEQY53viy6rqOyrCz+/8rLUVlegcryclSVl0f/rnlXUVpqvjOeubMp4f/OzMxAYWEjNGvWDMOGDcORR/bHxx8v5sL8+uuv6NevH7KysnDQQQfhk08+YWLT8PnnSzF4yKlo1OgQdOx4FG655T+oqrLUwCUlJbjoomvRtGkftGrVGdOnT+fiv/3223HQQQdJOe/f/9+47bboJXrnn38+TjrpJNx9991o3bo1OnfujGnTpqGqqgoTJ05H8+Zd0bnzEMyZM5+L45Zb7sNBBx2GnJwctG3bFpMmTUJlZaX5/rvvvsPxx49Cs2aHobCwMwYOPAPLln0HANiwYQNOO+0itGx5OBo27Ig+fU7Ge+99KuWTn4uU1e0NCV8+Um99YhEwI4S+2I3NCDch6fx3qQSl3YSuRfunV2aEdDYm15VbUMd4VbsR+bQTJBUBW7D47FlqvtEsdkVDzdLu69FeRk3jIzPCt5nKZqQmvB9+RgxbHkZNQ8Lsf5GaON2xGxy17NpeixIqjDj4+B3TFFBVXo6Z553mkL7/GPv0fKRnZdm8VRtZAlGh46uvlqFFi0Lu3fXXX4/7778fXbp0wYwZMzB8+HB8/eVXKKhfF5s2b8G//305Rpx5Kv776B1YtWodxl51OzIyNEy65VYAUWHj88+/xnPPPYDmzbvg1lvvwbfffosePXoAAC688EJMnjwZS5YsRefOuQCAFSt+wI8/rsILL8wx8/HRRx+hefPmWLhwIT788ENce+21WLr0c/TrdzA+XvQa5r80H2PHTsARR/RAs2ZFAID8/Fw89thMtG59IH744QeMHj0a+fn5GD9+PADg7LPPxkEHtcGMGRORmVkX3367BOnp0WXwiiuuQHl5Bd5550kUFBTixx9XIDc3R9kG8nrgdo6RWSFfmHVS86SapwJmJGVA6otVzrJU15inoM2Iym7CdDrFMSOKsnAute2djXmFHzYj7pye+XGaRrOEIR8NWK28Rdzv9l1FzLaZwr5CVNNw671bZoRXb+ou1TQGM+Io2xEu3d2OvVhtRqg0mZfKNJMDtQ3Fu+9+iuYteqJt27YYPHgwtm/fgbFjz+dCjxkzBqeeeio6d+6M2bNno6CgAM/Nex4A8MQTz6BZsyJMvfcWdOzYBscffxQmTrgGDz44B5FINfbv34/nn38ed919EwYNOgxdux6Ip59+mmNOmjdvjqFDh+Kpp542nz3zzIvo3/9QtG3bxnxWv359zJw5Ex06dMCZZ56J9u3bo6SkFNddNxrt27XGuHGjkJGRjsWLl9d8oeP66y9G33590Lp1awwfPhzXXXcdXnzxRTPOjRs3YtCgw9CxYxu0b98GJ5/8L3TrdqD5rm/fQ3HggR3Rpk0rDBt2BPr372XVoovxmOweQffvGrCZIzMa2IwkFdRu0Y2fEX62Tl2bEUc/Ixq7GOjK3TO5y07KaRqRBbHLh6LsrvNm/GKEHOOdjwasFouj1wIz4mzAyi34rm1GqHZRCC9C/3PqOxx7ZObN7diztxlRjV8qTRFpmZkY+/R82/dusWfvGmhaGSKROigtSUM4HEbjxo0BABUVf6GsfBPS0vKQk93KTNcOdkzOgAG9MH3aHdi5S8djjz2GnJwwTjzxaC583759rbKlpeHQQw/Fb7+tAgCs+m0NevfuDo0x9DnssENQXFyCP//chI0bdqOiogK9Du1hvq9fvz46derE5WP06NE1DMkohEIhvPjia7j77uu4MAceeCDnCLFRo0Y48MCWZgnD4TDq16+HHTt21TwBXn75XTz22EtYu3YjiouLUVVVhTp16phxjBs3DldeeRvmzXsDRw0ehBOGD8QBBzQEAIwdOxaXXXYZPvhgEY466ggcf/wAdOvW1baOWcRjhJo47748a0OucQEzkkpww4yw72Tdc2ozI9F/qBuILWqe+o7aJbIsgSGMiKdYEi+M0MyIfJomMX5GWGbERzUNx4woGAzP8cptRvoZgVDfMQgjIjMChByYEeMzlwwT6fPDXV1Z9eCNGVH6GWHCpGdlxf9fZibSMzOQnpmJtJr/rPfWO+OZWgVG2wfl5majbdtWOLDLgZgxYwaWLFmOOXNecUFLqeDWSNXC8OHDkZmZiTff/BDvvLMIlZVVOPHEo7kw6enpfEyahrQ0+ZlxYeXXXy3F6NE3YdiwIXjzzTexfPlyTJgwARUVFWb42267DV9//SqGDh2ITxZ9iT59TsJrr70DALjooovw00+f4owzjsdPP/2KQYPOxCOPPONgwFoDz/XHqsu9qngU0Knfiryxm7wk2zym4OpZyyD0xfJpDerd38XPiFgWdhDUPGHVNEqbEcpwNBmnacRFzy4f4mmaWAQl4xdbf/4LI5w/i4T4GVGzXvJpGvmde2bErQfWmlAmM+fEjMj9z/3YUzAcrvyMULN8bU7e8aRJC2KhUAjXX38l7rzzQZSWlpnPv/rqK/N3VVUVli1bhg4dOgAAOnZsj2+++Y6rx6++Wob8/Fw0a9YUrVu3Rnp6OpYsWW6+/+uvv7Bq1Sou7bS0NIwcORLPPvsa5s59Daeddjyys+3sXyjI/emrr75FixZNcOON1+LQQw9Fhw4dsGHDBilchw6tccUV5+KNN57B8OGD8cwz88x3zZs3xahRp+P55/+LMWPOw1NPvSSnbHNSif47gFuk4upZq4jVZiQWvXUyoLKboGxGlPYKLhkJr0gMM6JJZY+PGbEWLKNqIj6epkmUzQgbr4pF8MUDq9mRPF6U51JNkwybEb7+xJfKJD1DHI8xQRP7K+R8MlVwysnHIxwO4b//tew3HnroISxYsAC//vorrrjiCvz1118488wzAQCjLhyJP//cgvE3RI1X33rrY9x11wxcccW5CIU05ObmYsSIEZg48T/45JOv8eOPP+P888/n1C0GLrpoFD799Bt88MEXGDny30L+1aDasX37Vvjjjy148cVXsGbNGsycORMLFiww35eWlmLMmDH47LMl2LhxExYvXoJvv/0JnTp1BABcffXVeP+DT7F+/R9YvvwHfPbZN+jUqS1Ttaq8eduY8HH5J9zS/k6oZ/ZMYbLwj7cZUV60RbAJ5G2Hui6ESiWIxy0FmxHw49+NzQg4IUB8Fr+ahn1GLWZWGzjlQ14cveeNUtOYLz3H55wemP7kR4+y+quuiNcoCbVocLGp3LWLfkCcLsoT6lbXNei6bpuGyl7LuW2NelAxI6rvamHS1o3kFAKfp8iicWmKBSctLQ2jR4/Affc9isGDo8aa99xzD+655x6sWLEC7du3x+uvv44G9RsgAh1NmxThpZcexqRJ9+Hww19EvXoFOO+8M3H99ReZ9Tdp0iRUVOzBmWdeifz8Orj22uuwZ88eKe0OHTqgT5/u+OuvPejVqweqqord93iiSMcdNwSXX34Orr76RpSXV+C4447DpEmTcNtttwEAwuEwdu7ciUsuuQnbtu1Egwb1MXz4UZg0KWqrUl1djXHXTMKff25Gfn4+hgzpi//855bYM+Qa/vct9zNHMlg+Gv94YYS6GE5iE9gF/G92mka0m+CYEVNFxe6C7R1jyTYBsnokthMrYl5jYEZElYwWktQ58eRNB9sXDDVNzNHJSJDNCG/j5Oz0zOlorxcPrI7MiKmmYQ1S7YURlb2Wo9MzYtyKcajspPg04SrN2KFYnByYA9Xb2bP/A2hliFTxocaNG4Xx469CVVUx9u79Gfn5nTFixAguzOZNm83f/fsfig8/eAmhcNQWIz29Piord5nvc3Nz8fjj01BdfQeys1shPb0Orr/+eik/ug5s3rwdF110hvTuqaeeYsJF6+P1119HWtoWsCzfryu/QmWFlfYdd4zD1KnTkZFR33xmuKHPyMjA888/j337foauVyMtLQ9VVcXIyIiqh2bNmoV7770elZV7zHfhcDaKi+1qFEg5NQ3XdTywH8mXRQJhxNwVcRNR9Ld54RfzTqN2WH8Dm5EIcdqFpIVZp2ECNCkuSxiJ+OBnxKvNCFcmKh+afdnd501hwOqnmibBfkbA2oyQC5qopmHexGgz4vpoLyybEaVzNOpEDDl+6a+lb8U4FH5GeDbGIamEwG2iVH3Li5Jm/uWVhfGH3t++fTuef/55bNu2A2effRKTvrcxyt2845FJ0i1pmIuRj4vdpNrnLTYWK9oKfvorck6P/117aTvjHy+MUC6yZWZEthmhrjFPSWZEZAfYshBjUYeK9qaYEcFWwwc1jVdmhDo5E2VGFKeiPMM6jWROx34asCbYz4jO2oyo3MGH4mBGzA/cXpRnBOKZEXsQeVOwPdyXcfoZqR138PbwvlSzX1Asll147xAXaTcLXOPGjdGwYUM88MAtqFevjqv0aTsLODwjY+LC03Uq9381Yqg/s2kooShWeK0XP1XC8eEfL4yAmWgtCIsux4xQu7PU9TOiviyuJr8atfDYG/MpT7HUwtFe+rQTcWqIOGrqPXNGeVgPrDXMjJ9Oz7gdnp/MiMUI6ArWy6heq77Zdx7VNLDq29NFeU7MCHFJJT1+KRAMhxmHyk6KYlQSq2dXEFcxpumFrndSAxlzBhU/C3W7GO28d+8PznkScsDGr9o0JQrU1RS2f3uAL6II1b1NW6TURupt5WsZ1K5INIbkbUbs9dapzIxQZVGpaeiy8HFFv02zjd8tYmVGzDSZkzPg2BL5mee8maNYNmBNjJ8RWPXri80Iu7NX2IyAr29qZ+iopuFuHgbgUk1jpKVDLYxYzIj3safyF/J3YEZi3fXbhxFXJxVLQMWn3oF7HxneBDxe4eQxVcnnjLwRpeKir6bg0/ay5pNXiyQEBPOi8LuTLKTe6lnbIPXFxqQn7/ZVNiMpKX6Ku1VeJ1PziKK9iYEn7XwZlgKxL6CeL8oz88rswMV8sGwJk1/veTPSYvtJIvSt7LQWu8pLjlWerKlJVWqDmNQ0Rtt79TPijRmJzc+Iws5HMX61hLS1O/h1mkZ85qSWcB2dCeKhaybHeVGk68HoPE7PVHBWX7iPzO+2ig8a8//aTjtW/OOFEWrxla+eZxuKshlxOyHWPuSysKdpqEVJoSJwcydMrVyUR6hkzLisNpPtZeJQ02jMAmsl5j0+W7DtEvtRZLt42csNqXjNNw4GrOIzFtIN0YwwQkE0DtZt4rUSoBgdd0d7rbanhFrV+LX/LjkTuFfmwF1AmmmIPX33LIWn0FL0nHoT8jNFkq5EEWcYG1cxdvdIjJirFkJTjxcJhBGL4SAoLNJmxKT+2Wb00eDQbyjKoj5N40YIkIURP2xGAMseg7QZoRy5SbYxGvEsFjWNAbd2ELGBX+j9MLg14rUMYy0X/kS8RBtYrxLDjFAqMO+naVyOPYpVMWEfh9Lpmc/QlQuZRzWNdW7feqbaBTv0ZflLhd1ELHDBLjsasHodj2Sacv0lxh08kZYfcKHmTFjaceIfL4xYfgpkI1X6gjVCb+2r+25/IV76R56mIVgeumvwcQFgTtP4d1Ee9YxfpPj2iZZKbjO57LGwNnx7J8qAlWdGYj8mLYFhK1Q+ZPy4m0Z20+9OTWM5PXMS8OxtRpza1ur3hM2Ikg2k1DsJmsB9ZcyN3TAdmT035gRZAUAKKK4R6x5dbbMSf5r29adUG8WEBKtKNOdyBmqaVIApAbNGqjUTckTFjMiTkx87Wd8hlYXdgRthmPDK0zQaHxcTznjmhzt4wIkZ4fPBMSNsmynK7h1E/fkoi3DO6OKoSyle7k4WLzYj8ju3p2lcMyOmMOxS1WK6m6dcszvUleJor2r8kn5GrAyp04wRccVKfWzpFV184FeZ3C1y0lvH5Kn8+lgGxac0M5Ko+osNqcNzeEcKrp61C0pfrGQTyLs73Pk6SAZULI/odCr6TLUwiIwEqx7xz+kZoBZGqHxQbaZmuNzmreZfULYoiWZGfJjYJDf5fFpmmklgRiy1RI2TNAcDVtJeyyUrqRIq1F6H7U/TJHrZieVyPuqEhjtDaLc2HoYNlV7zFSUMxII4mAilsOWcJpv/2E+41CYr5DYbKvVc6lmNpN7qWeugbEaECcjJz4ipi08+1SVBURarjxKTu8rPCGd7IKgxatNmhM0HVU7xGHZMrI1A77MO1nw92suS3QrbDq/xEqoNtZ8RY7Gx2sA7M+LuaK/V/1zajJCspEebEdL2Q2UnpWBjkgGXw0tlUPnNkuVo1qI5zj33XC78hg1/Ij+/Exo3box9+/Zx3xz9r6Mxffp0LrY1azbi8ssnol27HmjU6BB07twPl19+Ob777jvPBXIzkjSNumUnFiWRkCZlQkPEr4IfSho/hTrP/SQF1q5/vDBCXQxH7fDAvAX4SY09PZBqUJ0osRZa9j4QLx5YCS+nPjEjapsRYgdO5EN9Kspt5owfVj8xa8pXmxFWfebnaRqxruh4TWYkHGYf1nzrlhkRhQW1sS/b/6J/2+2ChbKQ9lpuZ197mxFln08ZD6xuyymPFyOWZ559ERdecCG+/vprbN68RQq/b98+TJs2zSbiaFzLl/+IQYPOxJrVG/Dgg1Px9devYt68R9G+fXvcfvvtoG+PBSorK13k1xk6t4QbAqNH+wfzWLg6P8bpvupqykVAjGnbpOUXXOciMGBNJShsRohTDdairdoZphCoUyY14KYr0VDPhQdWTbPii+fEipgHwFroycVMcv2uEfmweeYRurQYWWyQn+AvMPTP6RntNZeKV7YnUR3fpRMz2AfqyDWlpjHeWeFdMSO8RQuXttO3StWaos/z4BceXdcRqaj24b8I9AodekUEemX0P+OdXlFtvjOfKbza2u3Vi4tLsGDBWzhv5HkYPHgwnn32RSn8lVdeiRkzZmDbtm1kHLqu4/LLJ6Jt25Z4552nccwx/0Lbti3QvfuBGDduHJ544gkAUaYlPT0XL7zwAo444ghkZWVh7ty5AIDHH38cnTt3RqNGPXHooSfgscee4/L/xx9/YMSIEahfvz6KiopwzDHHYOnSpWYeHn98Hrp3Pxb5+c1w8MHHY968N8wynHvuKJxxBn/5XmVlJRo2bIg5c+YAiM4v06c/iq5dh6GgoAW6d++O+fPnm+l/9tnXKCjohvfeW4Rhw4ahTZs2mDdvHpo3b44VK1Zw1f3ww8/gwAMHR+csL1ODuJb4Pa3o0g/bQKmwjf7Hu4OndM6irQHfVMROPaVP0yhsRnTLZsQyDrTX3dK2Gn7YZXhU07iwGeHZkvhtRgw/I1HGpeZdgk/T+HO9gFhX0ZhFWGoa1pA2AiDs2WbEvQGrwMa5tRnhdq0ebUbII6GqOJyFGL0ygk23fKlM3xv2mL82YR3xLvqs6e39oGWEoYYGa7HTsWDBe+jQoR06tG+PU045BbdPvg1XjxvByekjRozA+++/j9tvvx0PPvigtBH4/vtf8csvq/F//3cPeRS8oKAA0Peaf994442YPn06evbsaQokt9xyCx588EF06JCP7777EWPHTkZOTiZGjboSxcXFOOKII9CsWTO8/vrryMvLw1dffVUzJ2h4440PceMNUzBlyngMHTocb7zxCi6//BY0a9YEAwYcihEjTseIEeehuLgYeXl5AID33nsPJSUlOPnkkwFswfTpj+PFF9/GffdNQpcuvfDVVz/gnHPOwRtvPIc+fTqYeb/llv9gwoRb0bJlS7Ro0QIDBgzA888/j6OPPtqs3blzX8NZZ52EUCgU14j1Y7zzKh8xPspmxO7v2kfqrZ61Dmc/IzwzIurFmV1zCujdJKjYAXOSsYQR5fX1VL1IzIv3LHo2YHXhZ0RjbUbiYhqMerHK93f0M+LEjFhqGlYYMdQnbm1GRJWgy4v1DANWp3CkS/f4/Yyoxq/az0gKjncTlJoGeOaZBTj99JMAAEceeST27N2Lzz9fyoXXNA333HMPHn30UaxZs4aLVUPUVgQAOnRo4yr9q6++GqeccgratGmDJk2a4NZbb8X06dNxyimnoHXr5jjhhCG44oqRePLJ+QCA5557Dtu3b8err76K/v37o127djjhhBPQu3dvAMCsWU/jrLNOwujRZ6JDx/YYM+Z8DB8+GDNnPgkA+NfQIcjNzcWCBQvMPDz33HM44YQTkJ+fj/KKCsyY8Tgeeuh2DBlyONq2bY3zzz8f55xzDh5//Bku/5MmjcPAgQPRunVrNGjQACNGjMCCBQtQXl4OAFi+/Dv89NNvOOeckxR1QSMWaxdHkFERzIsmvEsB/OOZEYsJsGdGSD8jOqW3TkXZjmcMqLJAk21G6OvURfZBZkZq4zQNxVzRfkZ8ZEYYNY1ZD76bEYQAROKqSxlim9HxWm1g7bR1jwasVv169TPiUk1DjD1q/Kq+pSZf9fgl0hRDpIfQ9PZ+yvTd4K9d65CWVoJQKB9796UDAIqKiqBpGsrLtqCiYicyMhogM6vITNcO1BHm335bh2XLfsSzcx4DAKSlpeHUU07EM88swICBvbmwQ4cORf/+/TFp0iTMffZZ5g3VRobATuPQQw81f+/fvx9r1qzBqFGjMHr0aBjjqqqqGnXq5AHQsGLFCvTs2RP169ePxiukt3LlWpx//mlMysBhh/XA7EeiKqD0tDScfvrpmDt3Ls4991zs378fr732GubNmwcAWLtmA0pKynDSSaNrvo7WY0VFBbp3P4gry8EHd+PSHjZsGCZOnIgFCxbgzDPPxJw5L2DAgF5o2appTV/0pKepSctPgcB5/LC//U07PvzjhRGSCRB3UezdNOROyZ3eOhmQdOXcDlw2YFXZjIj1wttqxH4CJGY/I2yaUjmpZ7HQNrzNiKZZVGzEZ2lE00JgnZP5y4ywp2koA9YoWGYk4pkZEcaG5taANWL+7c4dPKOmcTv2lKdp7OOg7VTEMJoLdYkztIwQtDQNWihkChqhjHA0/khUCNYyQggp0zLUrWbmTIFtzpxXUFVVhQO6HGaG1nUdmZkZmDr1Zimme+65B3379sW1117LRd++fSsAUeGmW7cDHXfZubm55u/i4mIAwGOPPYY+ffqgeP8a6JEqABrC4WhE2dnZdMnI0zQMmJdnn302jjjiCGzbtg3vv/8+srOzMWzYMADA/v0lAIAXX3wITZo0RmZmITIy6tbUxx4urpzcHKt7aBoyMjLw73//G08++SROOeUUvPDCy7jnnuvjIxjMbxPFtCkyl/C03SP1Vs/aBmkzItLNbDXFrrdOBtR30xi/IpJDKfo6dZHyD0l15Rcz4s4Dq30+eD8j8Zz0MX7JJ0Tgo5rGiDsabSLupnHpZ4RT03hlRmQbEDdOzziVpwt38Dwz4p+fEfc2I3GcnKg18GqaqqoqzJv3Bu666zp89snb+GDhB1i4cCEWf/khiooaYf78t6UYevfujVNOOQU33XQTE5OGbt0OwAEHtMOsWXMQiUSkWtizZ4+QvoXCwkI0bdoUa9euRfv27dGuXSu0a9cS7dq1QOvWzQEA3bp1w4oVK/D/7V13nBRF2n66J+xsXuLCIjmjJFERFERBERMoKgcoqJiz6HeIIoieWdE7T+XUU+9OFMx6ZkQxIkZOEQXlFE4lw8KmiV3fHzOdq6urZ3oCbD+/H7rToeqt/NbzvvX2zp07KSkI6N27Gz799GvlNyDg009XoU+f7sq14cOHo2PHjliyZAkWLVqE0047DYFAkm3q06cHioqC+PXXTejevRN69OiGHj16oEePHujYsQOl/vSYOnUq3nnnHTz44IOIx+M48cTRyKxPuDeP6L4CwCWLx4wUDKg7JdYxV+ppmix5Q7sBY1ko36bRQt1pUgpDiSNhvJZJnBHaSQ5qeiaTkkApp/a99JkRdeOgVdLc9xkBtJtY93yQzEe2AWpHpSiEtKO96uMsZkSdmJkfyjOYTtJjRjjZKWPMGYMkKXEpr5nHe/Y2kzwJ2vnGmH8SAXjzzQ9QW7sHZ555MirKOiDcVIQEJFSWizjppDH4179exJgxh5mSu+WWW7D//vvD7/crCQoQ8OCDN2H8+PMxbtw0zJ79R3TpUoVw5He88Py7+OCDD/D223+3FHH+/Pm4/PLLUVlZiREjeiIcbsTXX69Bbe0e/N8f52Ly5Mm49dZbMWHCBNx2220oKyvDZ599hi5duuDgQzrh8svPwllnXYMBA/pg7NiT8Morz+Pf/16Gl19+WJfPlClTsHDhQqxbtw7vvfeecr28vAyXXTYds2ffBUmScMQR49DU9As+/vhjFBcTnH76kcwq7tmzJw499FDMmjUL06dPQXFxiN0mBYnCW6wKbyufY9AocdMOSTCzJrSPdRUyM6L+1i4o8jVCYUasfUaU34JIeS59ZoS2cLF8RjSC0OUQjGVPRzbj7K49TZMdZkTJykWfEd0VqgNrSgLdaRpnZhpqu3AxI2oKTn1G+E1alHGrJOLQZyRLew+qW43GyZwHZkYz+ftf/3oBo0YdisrKcs3VZLLjx4/B119/h7q6BlN6vXr1wtlnn41wOKxLb8iQ/li+/Gl07doJF188E4ccMh6nn3Yu1q1bh/nz5zNlPPfcc/Hoo4/i8ccfx6GHjsdxx52Dp556GZ07d4AAIBgM4u2330bbtm1x3HHHYfjw4XjggQfgS8XAOeGEo3D77bNw//3/wODBh+Oxx57Bgw/ehBEjDtblM3XqVKxZswYdOnTAYYdpFS0Bc+Zcij/+8XwsWPB3DBw4DMceeyxee+01dOnSiVp/gH5+mjFjBqLRKM46ayqzrGzQ28od0LntQkazZ0ao9mLTIiaY7uk/1lW4PiPMDk/UPwTjzpFxmkaXluFaOsfTjIuddkGin6YxK0WmNhNE8wKVFmtjSALsBTYTmOV1z2fEcNV0RfksQgZmGlpf43NgVX87P03DN/bUqL0UpShDn5FChwBgyZK/an6r7B6QVCx27/4GAFBf/xNKS7vr3l/44EO48cYbTen26NEFCxfeiuLidgiHf4ffX4Ha2kDq7i507twB8XgYPl+R6d0pU6ZgypQpqK9fC0mKmu537tw5FfcjafZpaGhAaWkpCJJB2s49dxLOPXcSQqEOiES3gkjaYGrJsvXt29eyPwmCgIsuOgMXXXQGQqH9EAy2AABEIlsQiWzFiBEHY/fub+APVKJ2l/n93377Df3798dBBx2IcPg3ah75QZqacgGcBC3E1TOnoMcZMe+G1XuUxbGAmRHTBKsLriVfIyZWguUzovymMSMZOLBqFzsZXMyIxmdEK22mTIPV8VV1h55tZsQ9nxFdqjQH1lRZfBk4sJrTtXNgNfy2KS997BHdPWtY+4ykH2ckDxN4WouGk3doiqo2+2zu5tnpZetzG07SbWhowPfff4+//vWvuOyyy5BddiMN6Lo3S5b8Kx9GFODqmWtQfCQou2zzvb0jzojJtGITDp7nNI0uLRaLxAmnygi1fajMiLVSySuXyUwDUWlmd4OeUSbFLDAjVuYMRS/N4GgvrS/wnaaRL/D6jDgfe8bvGemydRxnJMssCc2lhztSZj6UBbYCk+38nZeQf5HW/hIEAddffz3Gjh2LUaNG4ZxzznGcsz5x43jP9vrBUPSynDMPPGWEEYFVBe2kjfNYB3kBDzMCZ3FG1AsC5VoumBGKOYDaZpkpSoQSN0DQndzZG31GrNKU2wDKdxPl8qXtM2KrjJglcB5nhJeVZPmMsMZvttraIVzNXrD42/wzmbdm7DGHkNmp3551yHQZ1Juc3EnTGvfddx82bNiAJUuWwOfzUTYQ6afthtTpn+nJvzqS1mz3wAMPoEuXLgiFQhg6dCg+++wzrvcWL14MQRAwYcKEdLLNCugfdGKYLCh2a0I7dVMgMO2MNTLSj/YyujPVIdSaeeGFY2bEmCevz0g6ygih5JW1BSoLkyonra4c7RU15cuQGbGPM2JKIP04I7YmnpQcLGaE2udpfipqqtmANpqG2YHVJTONRTL2CrsdAyXPI/ZSpZN+7qHKw2POcbLhKbSS5huOV44lS5Zg5syZmDdvHr766isMHDgQY8eOtfyokoxffvkF11xzDUaMGJG2sFkBzRuf4fioxivQJWJOo2BgTdPrfUL1kzWtLPTTEpk7iepfNy7+5sWMX8lwwUxjFhAmvc0lmE0qmU9XZj8fC2VEY60UDNd4ZbI6yZFMi4MZsTHTUGOF8J6mESjvqolYpsH8po3LcCcHpwo3xzMu5+kqWPoj9zsMpiiryAajY71p061jBehS4Hj1XLBgAc477zycffbZ6NevHxYuXIiSkhLlS400JBIJTJ06FfPnz0e3bt0yEtht0HZFLD8L6mka5ifI8wuaU6EMms8INbaKkhjFFMLpk8CCc2bEzNCYjzCbnVrTcmA1MiO6cPDZZkZcUG5pbUYB0dDqcvnSj8Ca+mnrwErxNeBgRkgarKTi/EpjRljjl+drv24hK/oO3cfDkfeAlpxhLmI8J6yMt+3nTJaJNh0fNXccw9XU2L9zDKWqzHLYjZB8w9FsF41G8eWXX2LMmDFqAqKIMWPGYMWKFZbv3XTTTWjbti1mzJjBlU8kEsGePXt0/7IGYt4VsY5YUuOMFDQzYq0dq9CaaeTH7H1GBGp8D+edmkcZ0T/Hw9CYFaW0/FmMF3QLsvvh4Fm/00qTZtJiwfTVXgfKCKWvOToGbcuM0Oqdc+xRgpfxpKH9po3VN1lyC4d5ClbvOElHqwyY06Hvst3XrKx387lRCAqRTUgfhVcWR7Pd9u3bkUgkUF1drbteXV2NzZs3U9/56KOP8Pe//x2PPPIIdz633XYbKisrlX8dO3Z0IqYjqES8tioYjo9MZqTwlBGeoGdaZkQtl7UznwrRpNxky2dE+xw9Tx62xAWfkSw6sLoRQM6UJqdJi8aMZBz0DOznnTqwagxImjRo45f2pmymYTEjFGVEV39G/41cgjdPCnPg6rpjnVg6H11zun93ByzlJZdMh/t56VugwFgbG2R19ayrq8OZZ56JRx55BK1bt+Z+b/bs2di9e7fy73//+18WpaREYGUcsaR/48K9D5u5DuZpGmszDW0XQNu55+o0je45SmwTc5sJGZuQkj4jlPJlKeiZSclyZSfGVwfabwGpPiNOHVjNk5+bR3tpH/3jHnvKu7T0WWlo+59ekcneTjmDfsUhEltumunM+mmj61wybSfyG/u8g1cp71tfyyA5A/gU5vzCnXrMLRxFYG3dujV8Ph+2bNmiu75lyxa0a9fO9Pz69evxyy+/4MQTT1SuyV9j9fv9WLt2Lbp37256r6ioCEVF5qh92QDVXswKI85iRgqQxmPHGVH+oiwa7IlZ/Z0Z+6DNk58ZoeVp7zPiVPe2ZEYUS12WmZGsRGC18hmRs9QoEBkzI86DnrEnejl95/5apo/46eTgiTNCfzfbcGoacuohkvQDcSAQdY5zptxkBppfj3t5pOOBUlDQnUpgPVhgcsPh7BwMBjFkyBAsW7ZMuSZJEpYtW4Zhw4aZnu/Tpw++/fZbrFq1Svl30kkn4cgjj8SqVauyan7hB436t/az0NqQjWm46xjlEhinXbTKiAoWM0LxGXHDL0Oz2NnlCVDqWRDME5LVNYdyUX1GstXOJvOT+/lY74rNbUCLM2K8p0/brCQyP5RnTMKWGbEee7Zty/Tz4R2/2TXPsIk2uZx2qVg/cNFFc1BZOQAVLfZDh8774bDDDsPtd96DeDyODz/8HJWVA1Bbu1t5ftSoURAEAcFQEB06dECH/TqguLQSlZUDUFk5AMcffw5v0RyAxsxk2VGcNfdnFbleLxj5FcDS5fjbNDNnzsT06dNx0EEH4ZBDDsF9992HhoYGnH322QCAadOmoUOHDrjtttsQCoVwwAEH6N6vqqoCANP1fIEeDp612zdParx263yAdspEBj3omRNmRICJZs2FzwiF8aCfpnHfZwQa05TkcgRWOvPkdppWPiOpuxn4jJjychhnxN5nxGxqoY1fGtTTNDQ5+Pp8LgOfGb/RZNXzOVKCVokaM+YwPPTX+1G3R8Kb77yN66+/Hr5ABIccMtCUwwsvvIBoNIpYOIpv16zG8ccfj9dffQU9epUBAAKBgO75WCwKoISSv518nCWxYGUyXUcFALFYLFUe2l1W/tbPpyeJWzDPzeyn86+NOJ7tJk2ahLvvvhtz587FoEGDsGrVKrz55puKU+vGjRuxadMm1wXNHijhz1k+I9QPZxWyz4j1LoCqjDDiNpiZEbc+RpfZaRpB48ehkyND1oZuishmnBH3fUb4w8Gn6lbUcH+Og54Zf7r81V7T8XPN39xxRmgKJKvP08w0etWAEIJoNJrxv1gshlgsrvk7prkv31Ov8StHasMUFQVRXV2Njvt1xPTp0zFq1Ei88cZy2qNo2bIl2rVrh3bt2qFVq1YAgFYtW6K6ujWqq1uja9cR+Pvfn8bJJ/8B7dsfgjvvfBAA8NZbb2HEiNPRtu1B6NGjJ+bPn494PK6kW1tbiwsuuADV1dVo1aofDj30ZLz55vvK/eeffx77778/ioqK0KVLF/z1r+pH/m666R4cddQUU/kOO+xU3HHHQuX3o48+ir59+yIUCqFPnz548MEHlac3bPgfKisH4Pnn38Rxx52NsrI2ePjhh1FRUYEXXnhFl/Irr7yJHj16oL6+3qJqTZ2eH5kRt/sc0vpq76WXXopLL72Uem/58uXMd5944ol0sswa1J2V1hTD8BmhfDiroOOMcPmMqEd7WYf4eHxG0qkD58wI5dRGluKMGB1YBd1uf29kRjh8RqBXIHLmM2K7ttLGHp+JhRW8jD1+jQ6soimFWCyGW2+9lZl/NnDdddchGAym+XayrMWhEHbujDGf1JXXMD7vuP0B3HrrTbjllstRVFSGTz/9FFdccSXuvPOPGDZsCLZsEXDBBRcBAObNmwdJkjBu3DjU1dXhySefRPv2fqxZ853ygcYvv/wap59+Om688UZMmjQJn3zyCS6++GKUlZXhvPPOw2mnnYQFCxbiv//9H7p1S5r516z5EatXr8M//7kAAPDUU09j7ty5+Otf/4rBgwfj66+/xnnnnYfS0lJMnz5dKfuNN96HW265BkOHHo2yslb4z3/+g3/84ykcd9xBSvn+9a8lOP7441FeXq7WB2HPkrzIBhuhbStWEMJCXKvSUkb2LVB226w4I9TdGX3HXhBI99s0trtEeXHK7MRKUg43mBFKOTNkbahmGk0IfNeP9mYjzgiFzaKB6HxG0jTTUOO/uMmMmB1Y+U/TyMo2w2fElg3Mx5Fep6AtQGYnWAKCDz74AMveXY7zz/+D5dsAmFriqacej+nTz0BT0wb4fMU4//zrcckll2DKlPEAgAED+uHmm2/GH//4R8ybNw/vvPMOPvvsM3z//ffo1asXGhr+i06d1JOW9933F4wePRo33HADAKBXr1746quvsHDhQpx33nno27cX+vfvjeeeex1//OMFgCDgmWdewUEH9Uf37p0AAPPn34x77rkHp5xyCgCga9euWLNmDf72t78llZFUIS+++AycdNIYlJR0gd9fhnPPPRfDhw/H5s3b0K5dG2zbtgNvvfUuFi9ebFfp7PrL0dtJyMo5O3nzVJh/5aTZKyO03Tbb18Dabr13+IxQzDSE12eEtjCz8+NBpj4j1hFYWQwXn1xmZsRhIC9HyJ/PiDKJaY/2Zniaxj4Cq/GC3Wkac1rZjjNCPdqrZJ+UJxAI4LrrrmPmz4Nt2zcgVFQPUSxBXV0xCCFo3bo1AoEAGhs3IpGoQ1FRewSDLZV8+aC2+ZtvfoD2+/VALBaHJEk47dRTMHv2Rfjqq+9Mz1JTMozPQYP7635/9913+Pzzz3D//X9OXRGRSCQQDofR2NiIVatWYb/99kOvXr2o6f/ww1qMHz9Bd+2QQw7BwoULkUgkAACnnXYcnnzypaQyQgieffZVXHLJGQCAhoZGrF//X8yYMQPnnXeekkY8HkdlZaWuhIMH72/Kp1+/PnjqqVcwc+YMLFnyGjp12g+HHnooq0ZsfucYDOei/KsbbDR7ZYS6s6LES1BvsezWBdjcHMwI0UZg5fqcuuZ3HnxGaD49PHFG0mNGjElojva6zoxkw2fEqLjR03TDTEPz23HzaC+LGeE+TUNlN1h9XjA/R3kmfXOJioA/gEDAD5/Pj2AwCEmSEAwGEQgEEI/7EY8nrzvKy0CMjBhxMO5bcA+QKEaLtq3QojwE4tti+bodSkuKdRuZxsZGXH311Zg4cTgAoKysp9JuoVAIxcXFZvl4iyIIIAQ49dRxmDfvPqxatQaEbMavv27CKaeMBQA0NDQBAB555BEMHTpU977P59P9LimRZVGFOPvsM/DQQ3/DzJkzsGjRSzjzzNOV8rkfVyaf60XhrVXNXhmh2f3ZIbRpzAjv0cDcg+0zYlSytLZQHp8Ruq+GU+g/gme9IFsGPUtdNctmvJI5M6I9TeM+a5+NXRafEy8hyeySp2nka7Tju9YysU9u0ZQRQ1okk9M0nD4jVGaENX5pZShkcw3bTFNaWozu3boh0uRDRGL7iihwUNz+/ftj/fr16N49afopL++hmz8HDBiAX3/9FevWrUOvXr1Mdd6nT298/PHHumufffYZunXrBp/Ph3hcQIcO7XD44Qfh2WdfRywWxFFHHYY2bZIOtm3btkJNTQ3++9//YurUqRZSWveVKVMm4brrbsTChYvwww//xdSpp/MXPmO4uX6o5la+9PO/djV7ZUQZaSyfEa0Jh7o7o6RRKOCKMyKZlRGe0zSgsQ/p1wGNGaEqIxwMjaUfiQPQfUb2sm/TmNrMKk31NI1iDsnYZ8TdoGcsfy370zT2PiO0NJjMSEEyoeaflO8RWuK779Ziyxb15IsgCOjTszfXuwTJ0A9nnnkmunZtifHjj0Z5uQ/ffPMNVq9ejT/96U844ogjMHLkSEycOBELFixAhw5F+P771RAEAWPGHI6ZM6/E0KGH4eabb8akSZOwYsUKPProo7jlllt0eZ122nG47baHEIslcPvtehPZvHlzceWVV6GyshLHHnssIpEIvvjiC+zatQszZ85klqFFyyqceOJo3HDDAhx11DDst18Ndu2yNx1a/84t0j8Cnn8U4OqZW3DFGaEExUkn1kE+wPrKpTaKgXnHy8E+0MLBpzEMWGYa+gJoljUbcUZo0IbAdz/sRDZ8RgzpWCygqgOraoaSHB7tpfU1pjJCk4PDZ0T3HJMt074pMyPWp2ms0zAzMnpp3AW9BviWGVoft43AqsHRR5+KwYMHK/+GDBliSIyWlsoUHnXUUfjHPx7Hu++uwJFHTsGwYcNw7733onPnzsrTzz//PA4++GBMnjwZQ4Ycg7lz70UikWyDAw88EM888wwWL16MAw44AHPnzsW1116LSZMm6XIcP/5o7NxZi8bGJpxwwtG6e+eeew4effRRPP744+jfvz+OOOIIPPHEE+jatatFJWgYYgg488yTEY3GcMYZJ+vi7+iqzZXBn40exCtX4akrHjNCZUbMi656KwOP/rzAeoEznqYRBDtmhOagmPmCz6OMEEJsmBEenxE3zDSqjJLrzIj7PiPJdER1wbU8TZN6Np8+I5zfpklCAuBTmQ7eb9MwfEas6katv+yGg9eOR/Vapoueyng+9NCfUomaKfwRIw7G7t3fIBBogeLi/XT3Ig1hdOzYEZt++x2tWlWhKfITAGD37m8gEe1nO5L5HHnkKJxwwkAAQHn5AaZ+3LJlSzz22GMAgMbGXxCP1+nuT5w4ERMnTlR+79ixA5FIRFMeoKqqAlu3foGSkm6IRLcgEW/QpTFlyhRMmWKMR5JE586dsHv3N+oFQ7fdtGkrWraswvHHH0l9P3twYbzruouDjWIBsHzNXhnZ5+OMMI52KmITibLwcfiMCKL5OZeP9tIXQD7fFTfijJi/TeNTi7w3MiOWcUa0p2kUaiR5L1txRkxrO++3aZJjThB8DnxG5N076zSNVRrmMc9+3jl4lQ63nShprWgjgKP03Xf6pOSRITsrP93Y2IiNG3/Gvfc+hrPPPhXBYEBXQVYRYDPJO2soEDGcoBC38jkGh8/IvhRnREd1p/6fNjNiZh/cjDNi9GLPR5wR42QtCALEvchnJJmOtk7ZzIgoaiPMZugzYmemyZgZ0UjOzYw4izOSvG7UPrPswEooZoGM6Hea+Yz/dTVvK5XPzZXPOq3sKTbJdO+88070738I2rZthZkzz7WVh3rbiYimjZeDd7nS579ZCLpLAa6euQWdGWE1DcVuXcBuQ2b/F7qZRv//5Ju01AyJUerKfWXEHjQ5zFS0W9+mUe87So4D2dpl8aeTrHK9Twz3h/IofY3VhrT6c+ozwsuMKO9S07cbvwwFLFdIO2tKmTLqVqyXTap7hum58Tx/GjfeeCMaG7fj3/9+FGVlxm/s6KGewHIT7ip1zhij/K9dzV4ZUXdY2qqwpvf1uyf9hFiQ1ekgAquWGaEGNKP4jJiZkcx8RmhpmXbXNDlou78MzR724eDdXZzccAampqtl9rjMNEk4dWA17fQch4N3wozIz/GNPfVDeU6DnmnzzZ6ZxiAR457LeRqyoip18rCjZG8OgyAYXsg2zJsOx2wGTy6cVJKTDU82qqdwt8X2KMDVM7dQHfsYVDYlzoj2XdUBbm/oAmYzDWD2GeGyjwrmEyvpDAPHZhqT74o5LD0oTq1OzR60oGd2TpkZQdArxO71J4YJEimlI5WVoGG7aMyIk9M0Th1YrZ7T5qA+J+n+b1tXcrmZH8qzSsPKZ8Q9WJXbndgmbiq5PH4T7kJXN+YuZpbGsTj5mrezqY5QckuLdc4dmr0yosJ6wrZmRvQ7x0KMM8JyxpXk6NaEjxkxXqP6jGTBTMPHjNgf7XU6+IkcCcyUbraYEat+lmG62jqltY92rs/AgZXaBoznJePabhsQg+YzQs/bCKXNqB/KY49fup9YoYJX8XAyFrS+ZKyk3DDTsN9xx0hTqOYKt/N2kl7+lZPCWz1zDvOxPtrOW/ND+UudYAs3zgjLjMJmRmhmGhozYu1TwQvnzAhFDmrArcwcQpPHiWl55YIZcbMvaRVt86QjBzcDMnNgpbeBAzMNeIOeUcaeTdsKPMyIwzgj2Vs8aHF/MkzSAjynafTFNt/PSN1xUMxkndCUl+yYN+n5m67mJG9uKHaa/CsXTlGAq2duQbUXU2zf6i0aM8IZ6yAPYPlNsH1GaJ2Zz1fDKTI+TQOKSQaCSd60IrAakNVv08BcdlfStfEZIRqKQhDVNiWOmRFzGzgy09h+KI8x9mynMtlnhCYHe/yafUZy6RMhIz1vgKSfh9kEK+ifcJqi4bfMOjkHv2qTTThnSgo16BlbKvqmulBQeKtnzkHxGWEaJs12a7kLFF7zgjIRacw0JmZES8dSpgkO9iGTBZ/bTEPzTeA6yeHcTEMLegbmgpwBssaMaMpgw4wIojaoW+p+lpgRyXCJWDxHS9+pv5Zyn8qM2I1fuQwS5WphgdbnqXKqJIzD9Bk302KOjPOTM3nSawVGS1sIUIg+FiwImv/uLWj2ygh9gWP5Qeg9+vWTZ+FVJ8tvQvvtEUEQAIUZsSoHL1vCD1OIbaoSZFzQKL4rHPStK0HPBJ/G/8Bd6JcNNycS674NGBZZQddDzCkxJ2XWMXIKI2HSRuyYEW3eehOSfX3JbaZXKPjGr37lztYBX+qh46wcJ06W55NPPkFVm7aord1jumf9FqfSlzXwmEpyCTfNNIVUjtyj8FbPnIPiM8IIlqUfbARap7ZCDHrG8pswmWks3lHf1S/wVPYhgzrgN9OY86Sfpsns2DEt6Jn2hAh9l50BWH0wk2SpQftUSDpmROOg6/BDefTPBfCbafh8RmSlwpnPiHqaxpi+/fiVr1NC4LHzdAD9yS2qWpKShU/pol07/vhzcO21dyhXDzroIKxd/S0qK8vthFPTYdIsqV8CvwL1r389i06dDrNMK5m9TXqmV/jaZcuW7Zg163b07t0PoVAI1dXVGDnyaDz66BI0NjaZs7Go+4sumoMpU64wXV++fDkEQUBtba3utyAIEEUR7dr1w+GHn44bbliAzZu3cckMJOOhDBo0yOI3wT333IPDR43gYpluu+1BHH74aYAAXHbZZejbty/1uY0bN8Ln8+GVV17hltMpCm/1zDHoNmfWjlpPFeup20KsTpbmLv9tNNOwymHcZRsXIGdIz0zD57uSaZyRZKZmM5cqj/PkWMgWM2LN7CWh9RkRM/lqryljZz4mfOnqFUHrTwQY35Lv69PnGb/qR/bk42c2ImYBbnsnAQKCwSCqq6sdKums+SQPFYN0RoqAn3/+FSNGnI53312BP/3pZnz99ddYsWIFrrnmSrz11gdYvvzTLEiaxNq1a/H777/jww9fw5VXno3lyz/FoYeegm+/Xe1eJoLyH+NFyt/JuWfGjBn44Ycf8Mknn5iSe+KJJ9C2bVscd9xx7sloQCGunjmGecfB8oMwO7Bqd1b5p7pMYDAX5qO98issm6q+LvLhM0JjrrIVZ8Qc9Ey7wO4dzAhYfRtGnxGtg67Db9M4ZEZoR3vtlBGVpXAY40eWw3Q8l2P8mt7Vm4YIIUgkGjP+J0lNSCTCSCTCICSc+i3fD6f+qdf4TDhJGS+6aA4++ugLPPTQIpRWtELL6lZYsmSJzkyzaNHLaNeuN1599VX07t0bJSUlOPXUU9HQ2IhnnnkGBw09CK1atcIf/3g7EomEkkMkEsH119+N3r2PROfOnXHccePx4YefK3lbYfny5bjggquxe3cdKisHoLJyAG68cT4AYNeuXZg2bRpatGiBzp0744wzzsD69espaZrNsTt37sTkyZPRoUMHlJSUoH///nj66ad1z1x99Z/g9/uwfPnTOP30U9G3b19069YNJ510Ap599gGMGzcKAHDWWRfhwgsv1L0bi8VQXV2Nf/7zn2b2gWMJaNu2Ldq1a4eevbrh1FPH4e23/4nWrVvgkksusX85ixg0aBAOPPBA5SOGMggheOKJJzB9+nT4/dn7nF2z/lCetb2Y5QehsagTaa/zGRF08qtUNz8zYqwLF/wy5HczYkYoC6EbcUZMzHw2mZHsOLBa9V8Z+tM0atAzyeHRXvPpJbvTNAaWwsZMo8vD4dhTIrAS5z4jJmbEAElqwvL3+zPzzwZGHfEtfD522PIkBNx++yz89NMG9O3bA/NuuBnRJmDVd9+YnmxsbMJf/vIXLF68GHV1dTjllFPwh6mTUVJagkX/WoTaut049dSJGDp0ECZOPBYAcPnlV2H16m/w+ON3o2XLPnj11ZcwceJFWLHiJQweTKf9AWD48OG4664bcfPNd+OLL5L0f7t2QwAAZ511Fn788Ue88soriMfjuOmmmzBhwgSsXPk2AgF2acPhMIYMGYJZs2ahoqICr732Gs4880x0794dhxxyCHbs2Il3312BuXMvR2kpvf7kfvuHP0zE1KnnoqFB/Srw8uXL0djYiJNPPhk0hoEX8rPFxSGcc85pmD37LmzduhVt27blTsMIu2PYdvdmzJiBa6+9Fn/+859RWloKIFnen3/+Geecc07acvGg8FbPnIJuL7b3GdHarQvbZ4Q/HLxq62WVw8gg5YcZ4fEZEUyyuMGM5CrOiLvMCDtdk8+IXL4c+4yA2NepPj0HY0+579xnRP3IXn7MEM5AX2QqK8sRDAZQUhJCu+p2qG5bDZ/PZ3ouFovhoYcewuDBgzFy5EiceuqpWPHpCtxzzz3o3bs3TjzxRIwYcXCK+QD+97/f8cQT/8Q//nE3hg8/EN26dcNFF12AQw8djEWLXmJKGgwGUVFRDkEQUF3dGtXVrVFWVq4oIY8++ihGjBiBAw44APfffz9+//13vPraW/rSUvxFOnTogGuuuQaDBg1Ct27dcNlll+HYY4/FM888AwBY/98NIISgZ88uujpr3bo1qqraoaZmKObOvRcAMGbMkSgpKcHrr7+u5PDSSy/hxBNPRHm56mvz5psfoKZmKFq16oaysjKUlZVh3LhxzPJr0atXVwDAL7/8wv2OU+jnVtNdAMCUKVMQi8Xw7LPPKncef/xxHH744ejVq1fWZAOaPTOi3ekwTBNUapCkdmd8aeQLPBFYkyAw0s902DEjabAP8rsWyoj5WXtuVIDTD0VZyGb0GdHENNk7FidAX24bZkTjyEwLegbDPet8ZGWV30zjiBmBs7GnRs01MiM8aej9VEwSicUYdcS3zPztEI/HsWPHJhQX74EgBhAJt0QsFkNVVRWKi4tRX/8TJCmCkpLO8PvLlHztoWU8DZcpKCkpRvfu3ZXf1dXV6NypE0pLS5VX2rZthW3bdgIA1qxZh0QigSFDTkzdFUEIQTQaRatWLTjl0+P777+H3+/H0KFDAST7TsuWLdGrVy+sW7sewOHM9xOJBG699VY888wz+O233xCNRhGJRFBSUsIqOj777DNEo/U444ypiEajAAC/348TTzwRzz//PC655BI0NjbirbfewlNPPaV7d8SIg7FgwRwUFbVCMNgaALBy5UqcccYZHHXA2Gg5RmZzUlVVFU455RQ89thjOOuss7Bnzx48//zzeOCBBzKUyx7NWhmx3BXZ7PYFQUxNYtoF3I2OlAUwfEZ0n3xIx2cE7jMj+rwEi+uUUzIcPiNuBT0TFZ8KR8nZgsnOZZSuRsGzCQef1C8F3Q034ozQ4DzomTaPdJkRk6OKJg26rIKhPjR3lPt85hJrEBKHKBbD54tCEAMQxWKIoh8+Xwl8vmL4fKFUPqUZ5wXBQkEBEDDYQARBgN9vviYrcQ0NDfD5fHj//cXw+QKIxVojHm9CKFSHiooKDlmMoqUzhwq6P++66y78+c9/xn333Yf+/fujtLQUV155paJgdO/eBYIg4Mcff9Gl0q1bN8TjDSguDinXCICTTz4Zp556KrZu3Yq33noLoVAIY8eO1eVdWlqM7t07oaioGkVFSTPLr7/+yl2CtWt/BgB06dLFUclZSCcAJZA01YwePRo//fQT3nvvPfh8Ppx22mmuyWWFArQr5A76ic96wrY6lbF3+IzQmQtzfA/A8WkaQTA9mw1mxM5nBBT/EJrPSDqymcw0ouosS/sCbGbIjs+IbTh4uV4JIGZwtJfGklk9b/9hOCuoDIfV+KW/ZcWM8Ixfo79JlhkxQgsHz5snTaFPXgsEAkgkpBSfmeGin8KAAfsjkUhg27ad6Na9E7p164auXTuje/dOqK5uY5tiMBDQOcMCQN++fRGPx7Fy5Url2s6dO7Fu3Tr06dPTNs2PP/4Y48ePxxlnnIGBAweiW7duWLdunXK/VauWOPLIYXjkkafR0NBoKJehjAQ4+OCDUVNTgyVLluCFF17ACSecYFLa0kMyr6amMJ544jmMHDkSbdrY1xl32hamGPPfehx55JHo2rUrHn/8cTz++OP4wx/+oPiPZBOFt3rmFNqFkMWM0Hfsjndn+YBFWYzfXdEWkdtnBLSdb+59Rix9V9xgbahHe+UHHCVni+wxI+x0ZTONvEDJ5XPswEphEFnKiPG7PybFj1oWbeVbjF/qi1b3048zUohEKLUKU9c6darBF198iw0bNmLHjh3JrzWnAW0t9OjRDVOmTMYFF1yPV15Zig0bNuDrr1fhnnsexZtvvm+bVufOHVFf34jlyz/Fjh270NjYiJ49e2L8+PE477zz8NFHH+G7777D5ZdfjpqaGhx//FhDCmYFuGfPnli6dCk++eQTfP/997jggguwZcsW3VP33HM94vEERo2ajCVLnsH333+PtWvXYtFTT2Pdup9N/jQTJ07EwoUL8cEHH+CUU07JyF9s69at2Lx5M3766b947rk3cMwx07BjRy0efPDBtNOkw0kH1c+755xzDh566CGsWLECM2bMcFkuOgpw9cwdrGIMmNkEFjNS2HFGrFie5GDSKyNqsCJOnxFBND2bTmAx7bt8zIheDt44I+mc9DFNORrFR8pgQqKDb+fiHGzGRXZglXNMO+iZA2dfY/9LXuT3GXE69qxOxPClYXWaJlvaCKsO7Bgg2rXk1csvnw6fT8Tggw5F97498dtvv6UvogaPPfYIJk8+EddfdzcOPfRQnH32+fjqq+/QsWN723eHDTsY55xzGs4++4/o1u0I3HnnnQCSTpNDhgzBCSecgOOPPx6EELz88ssWjIS+1HPmzMGBBx6IsWPHYtSoUWjXrh0mTJige6Zbt4748MNnMGrUobj++jkYOHAgDjroIDzw14W47LLpuP76S3Rpn3rqqVizZg3atWuHgw8+2DJvnj7Ru3dv1NTUYPjwY3HffY9h1KhD8emnL6Bfv36279rBrRnprLPOwu7du7H//vsrvjvZhuczkgJvnBH9/b03zkhyZ6p9TN1pspkRvdKWn2/TQOO3A0DgizPiBjMiCqI637isjOTEZ4SLGdE7xWSPGTHIYZWuIc3ksw7HXgZxRgTLd7MPtT5S/7edYmiLY/LdHj264J13nkTQ3xmRxgQaok04a+oZ8IU2AwCmTh2Pc865QPf2jTfeiP+78mrsbqxTrj304C3QRlkNBIK47rpLcN11lyIer0E0WoeSkj0QxSBXGe+99wbce+8NgCCgovwAAECLFi3wz3/+EwCwZcsWJBIJtGrVCtHodtsit2zZEi+99BIjx+QL7dq1wV13zUZ5eT8IQpIJSSSa0NDwk+mNXr16gRCCTZs2mfroQw/9iZrLqFGjdM8af4fDmxGNqpFXedePG2+8ETfeeKPl76uvvhrz58wDEDG8ad7szJ59MWbPvthUh/vtt5/JfJZtFN5WPodIL85I8kryfa3dujCr0spvgs2M8PqMiKZn040zQnNWdcaM5MhnRBCTColJnsyRvzgjBvODwa+InxnhP9qbPjMiK0rpxRkxMyP2fd4uzogboJmtdPcVWTKH4DAlwtSDtFflfkK7x5aGH2ZlK3vfdKLkXogbTi2UIUE7TchC/stVmCtozmBxrM+mw9Hs1gXbSS2YC7PPiNYXxLos2WJGrE7OaK/pmRHdE6DvCI2KRBomJMPamDzam7qfhU/lqX+52J9s2ta02CjECO3EDF/fMP7m9RnhP00DWI5f+pupPExHeFLpMt63vJePMZ9OntamBLMZ0t0yjRs3Tom7Yfx36623GqTiy3vixItQUzMUNTVDUVFRhdateyq/7777YVflt4M55lHm9WdVX2VlZfjwww8dCAelXmpqhqKqqlpJp7y8HJ988qX20byjWZtptPZi4yKrhZna1izoyoRYmHod/2kaojIjzAHF9hnJFOkwI7QjpALFdJOezwjLD8JRcvbQexG7mDDDORvqOFDMNGkyI0awnqe+T5yepuF3HlfuG5kRrvGb49M0oJym4e5sfP1GecqkbDuEwH7r0UcfRVOT+aNzQNKcYjYlMLJK1cn999+Ipqbke6Wl3RGL7UA0WptKswV3epqULf7OMgR6vqtWrbJ8pUOHDjaJ6hv0ww/V4GXFxZ3g96uxacrLd/FImTM0a2XEmhmhKx/Kba3PiDyRFyozYhpc2t2q3kyjvsGirAXdc24s+Mn8nZlpTMojtc3MioRjGKlz7QLr+ufLcuAzQv1Qnt4IYKzv9MPB+5jPp8WMaIKe6SfeNJkRjvGr3su9z0jmEGDWOJzOVdZGIhsO2XbxDIc3O5JEEATU1FQrv8vKeiIarUA0uiN1n2NJc2GutuqnaRtGND969OiRjkj6tFNKYvfunZRrJaXd4dfEqNmzRxuoL//rV2Fu53MEzdLGnLDNC4N2d1bgPiMW1DnNZwQKM8Ioi2n3ntmC74bPCASKfwjluG8mMVCUXMXchIPPaZwR2YE1dSttnxFKfbvuMyJoxp5mkbTbDFgyI1zjV3uCx4kClB7s1DHnSHuJTILpsGJkJDUvpEezOHvDhplxJxeWsmptAssHnJ3Dyr8CokVhrqC5grIrsvN7sDLbEKhfDS3UqrRSpMwxEwTZfs7jzKf8nS9mxKA8Utssc3+WXH6bRsc6ZSvOCJUZ0efvHjNi58BqkMMyXU2S0Iw9q/FLfVE+hWNM3z4N/XjPjolGX25Cue6emSYZsTiThchsEk1PEj64rvSbwG+mcZcB185hbisGlI2iaS5zOcsMUagraE5gZS/m9xnR2q0LsyrtmBHFeVHHjDB6qSE4XKYOXO4xI+Y2c8WEZAp65tPImE1mxM2ZgtdnJPVI6g+ZMeFnRmhtwGZG9GswDzOiGXsO/LXs44zwMiM5MNW42K3sFk+erNI/ycPxhuPF3W6B5UmPP0+eQHyFCmdVm/9yFuYKmitY2ovZv9WFTx/roDBhLIvxWKq84BOdnm6dmtHRN19He/W7Cq44I2mYaUyTtejMidMJBEOZ3EuXnocMogQ9E3T/l4uXrTgjNOkc+YwoigH/AsSKM8J+U342mx/GNPd91jP8yZrHgXLFfJzGaeLO5bF8O11WIosLqe0QLywzjQIu81WByJpCs1ZG6BMihQmxCqnuJBx1nkD9qBy0FLym7AKhvmNIQPc36zgnD6xCvFtdo+ZD812B+Zx9WrJRvtqrOrC6DNYnCVxLl6KMGBUAQ/+mtZHTOCNWeWr7H3HiMwLC7Dvm1xS6x+I+q8/TTTzZ81mn1QFfb6OOowylsc9bq0RlnBl3Xs7vWrxD2QDZwZ2NCL95iB/yxsIuv0JTRZq5MgJLmtfGbKOhfAvdTGOluVOP9jr8UB4tDHs67EMy/0yYESvfFbbvD59s/KaHTGH2x8lNutbMiFOfEadmGjP4g56p/lp8Y4+uUPCMX3W8E9fbPPtwpuRTkXaRBXTp0gX33XdfugmYsGHDRlRWDsA33/xgmacdjj56PK699g5H+RbuaUkWnMic//I186O9dCaAZvvWX1AnNlLoDqxMnxF1Z6onPHgZCXmxEaB60OdGGaHLoS+DG6yNae3J4tFeI+vkXrp2PiP63ZRCIjg+TeNcGdExI1zOl6r/htX4Zb1nXFm5xq9FCPpcTOA0R3PnoLyUVkK0d9jpfP755zZffLXv87o6MD0iWL63fPlyHHnkkdi1axeqqqqU60uW/AOJxDb1/QxQaEqK9oSofdEE6p/5QrNWRoilzdmOKdE4wzmyW+ceZh8BK5+RJDuSvGLvzKdPW1VGcnaaxsZ3hRpnJC3ZjAusD4Ioy+MoOVtkjxkxskh6mL9Nk+ojDj+UR4vH48hnhPAwI6rPiPX4pUGWw2Cm4UhDqb8sOrBSFV/tfTcDwjMXUIoZz3RBQDQWQzAof7TO8I7muzVt2rRJR0KGdJmboVq2bIFwuDG9/G2Vj3TZCJfWD003MdcVq4/nHwW6nc8VrJgRmx21sjNWYx0UKjPCPk2jvQ6o7AbrmKNWmxbNeeQqzohBDlo57RgvHtlM3wvxaWJaZDPOSF58RgTNf9NhRtJRRjTXwBMOXqtUpMOMGCOw8vhJadkYMwghaEgkMvrXmJDQJBE0JpL/miT5t5S6L9+TlHd4TGXa3y+99DaGDTsFFZUt0LlHF0yaNAn1jQ04/vhzVLNF6vEJEybgrLPOUt4dOnQo7rn3HkybNg0dOx2CK66Yj6OPPhNz5+lDum/fvh37deiFjz/+AjCYaaZMmYJJkybpno/F4ujadSSefvoVCADefPNNHH744aiqqkKrVq1wwgkn4JdffkmKxuVXlvz9yy+/4MgjjwSQ/OieIAhKeY4++iRdebt06YI//elPmDZtGioqWuKAA8bi9dffw/btOzFp0pno2bMnRowYgS+++EKX00cffYQRI0aguvpg9Ot3NP74x9vR0NhgktEShaABKMi/MB4zAsCOCTHvjlUbsnIip2D1OnpZzGYaohTTWZwRfR75YUas/EPs2pVPNl2q2qiirkdgFah/Z54uu30kY9Azl+KMODXT8Bzt1cYZceavRVfCecYvPc/kHQBolCR0/+Bb03uZ4dfU/7cZrqu+EutH9kepz8eRloDNm7dhxoxrcdNNV+G0iTOwY+tOLPvgPfOmhNbvUs/89aEHMG/ePMy8agogSFi27GP8+c//wIJ7HlAefeGFF9GuXVsMHz7ElMzUqVNx2mmnob6+HmVlZQCApUvfQ1NTGCecMBqAgIaGBsycORMDBgxAfX095s6dixkzZuDtt99mls+Ijh074vnnn8fEiROxdu1aVFRUoLi42PLde++9F7feeiuuv/5a3HXXfFxwwfU45JCBmDbtbFx77XW4/fbbMW3aNLz33nsAgPXr1+PYY4/Fn/70J/zlL9di+/aduOaaW3HlFbPwj38sYsjKkto9CIANA5ZtCZyjUFfQnEC1F5v9DQwXDG9qF6P0fCVyBXtmRNFAoDiwMllcbXpmBcJNnxE1STtmRKA0kdmW7AYzkgywlh1lJHs+I+x0lVgzpva09wth3ZM/y57Mw95Mw8OM0P21OOpKecbIbnCMX4Ey3vcybN68DfF4HCeeOBpdunbB/vvvj7POOgtlpWXcaYw4fASuvvpqdOnaCd26dcTJJx+DTZu24OOPP1aeef755zFhwonJNjFU6dixY1FaWooXX3xRubZ48QsYN24UysuTfiUTJ07EKaecgh49emDQoEF47LHH8P3332PdunWpN/jGhc/nS337Bmjbti3atWuHyspK03NyascddxwuuOAC9OzZE7NmXYg9e+px4IEH4OSTJ6B79+64/PLL8f3332PbtqRyeNddd2Hq1Km48sor0b1HZwwdOgh33nktnnxyMcLhMJeMekGyv34U6BKloFkzI9b2YsoCpAPtNE2Bt7QC+k41qUk7O02jKiY0P5I0peM+TcP2GRFgDoTmhs8IRBGiIo+j5Gyh72fZ8RmhpisZFmiDFSr90zQOP5THuG4Wzpm/lnpCyCroGcueruZJM9WUiCLWj+xvKwML0UgUO3fuQGnpTgBAIlGDcDiS/MpqeRnq9nwHACgr6w1B9Cv50qQ1/ybo3783jjhiKIYPn4hjjh6LI0aMwuhjRqNtC36fjkEDB+p+t27dEkcdNRKLFi3CwIGX4JdffsVnn32OO+6YT33f7/fj9NNPx6JFi3DmmWeioaEBr776Jh577HblmR9//BFz587FypUrsX37doW1++233zjKmj4GDBigpNm2bSsAQL9+PRXVs7o6+T2cbdu2oWXLlvjmm2+wevVqLFq0CLKCSwiBJEn4+eef0bdvX45cs7hmCG541+QWzVoZsbIX28UZUWl6Z3brvMCmLNrTNHJXZYfGNpsS8uEzYvRdoQc9y+w0DWDeBwuCoDqwZjECq6v9SZeutc+IerRX7yORDZ8RNW+HZhrd2KPnS3+RfpqGa/zqTtOY+6AgCJzmEmv4RAHFooASXzLNBEQIooASn4hSnw+J1PUSnwhRdJBXUheBz+fDyy8/jJUrV+HDD9fhb4/8DfP/NB9vvfYGRFE0MVWxWMyUVGmJ+VTM6aefjFmzbsQtt1yAZ599Hf369UO/fn0A1FM3JlOnTsURRxyBrVu3YunSpSguDmHMmMOU+yeeeCI6d+6MRx55BDU1NZAkCQcccIAqjzZJwfRH2tv/QCCg/C23ayDg1xBn+n7c0NCACy64AJdffjnq639UxkooVIPu3bs7zt8tNUE/J9mkKgia7px/RaVAV9AcgVgxAcZdtgVTshfEGTEHojIuDvKEqqWgOU/TKBO4VkHJkQOr7TdyBIosaZiQTF/t1RwjzmIEVjcnBzvGRYkzotR38rr8Md+0v00DZ6dpiGW6lDyIM58RVcEyyuEszkiuzDTaNdUqvg5XOtr/CgIOPXQwbpw3Dx+//xECgQBef+MNtG7dAps3b1feSSQSWL16tZo/I/3jjx+LcDiMd975GM899wZOP/1U5hvDhw9Hx44dsWTJEixatAinnHKSogjs2FGLtWvXYs6cORg9ejT69u2LXbt2GcrDX/5gMKiUxxrpjbNBgwZhzZo16NGjB7p376T869Gjm5KvM2RBGci/fuEIzZoZsfpIltlnhM4uEN2H8gq05Tl9RnRHe5l+AeaFLVs+I5nEGbE6YeNUNmPsC91uf69kRigOrIY4I6KibGX72zTaXMHJjGhZCucfyjP7jHCMX8s4I4UH2ukSQSD44otvsHz5Shx11HB07lSKjz74CDt37kTPnj1R0SKB6667E2+99QH69DkE99//CGprazVpmPhB5a/S0hJMmDABt9zyV6xd+19MnDiR+pwWU6ZMwcKFC7Fu3Tq8/bbqP9KiRSVatWqFhx9+GO3bt8fGjRtx7bXXWpbNzhDRuXNnCIKAV199FccddxyKi4sVx1kr8M7jV199NY444ghceumlmDz5SJSUFOGHH/6LDz9cjQcffJgrjWxqC8li2LkfFJYHVGFu53ME3jgjtDDXyQT2BmaEXjbTaQaBmJ6hg0b55/40Dd1PxSiHCz4jhtEqiCIEUa5DR8nZolDijCgOmxkyI+kEPeNnRpz5a9n7jLCYEbbPSPaReUcrLy/DJ598idNOuxh9+/XDzbfcjLlz52L0UaNx5pmnYPLkk3DBBddjzJjj0a1bN+VYrH32AqZOnYpvv12L4cMPRKdOHWxlmTp1KtasWYMOHTpg+PBDleuiKGLx4sX48ssvccABB+Cqq67CXXfdZcqPJgPt7w4dOmD+/Pm49tprUV1djUsvvdRWNisYlZQDDjgA77//PtatW4djj52GESNOx623PoD27dulnYe7MH8Og/YM/e/8oJkzI1Y+umcWVwAAVv5JREFUI2yNUpmbdHFG8t+YVPAyI9AwIyxnPoOvhjZNWn52cMtnRJZDWSyofiTpsDaG3YToU5JxnRnJ1uRgU26Tz4igX7jT9xnx2SojzpkRrc+IA38ti9M0XOOXxoTlabinN88I6N27G154YSEAoKy0H5rqmrC7cQ+ApL/EggVzsGDBHBQXd0YgUGFKYeXKlSj2F1FTHzduHPbsWU1V1OQYIVr07dtXaedodIdGTAFjxozBmjVrdM9rnVc7d+6E3bu/MRbPEjfccANuuOEG3bV33nkNTU0blZdpMhrz6NSpEwgh2LZtm+K/cvDBB+Ptt99GXd0aEJI0BRUXd7YWJtco0CXJCoW5nc8RuJkR0wSgsSFb+p0UBqz8JsyLgfaYMh8zor7rns+ITlLmxKvNkyaHAGO7Oj3pQw16Jqi+KG4zI3bBydKHjbJoOuWtMiNWCiAXMyLafyhP20bOfEb4TsIosljGGXHgJ0Wy922a9E8XGWGzkbK4lB1knhG7/FqPGCfgm1eyj1wzE5ybzDyhMFfQnIEe8Mi807K6r/UZKdSqpE9O1AishsWInppZ8cik7K74jFj6rmTuM2KCICqnady2uOrq1sX+pE2LLxx86rph8c2Oz4j2ogNzi9ZnhGcaszxNYz9+qX4qWVk8tO2fheS1+QiG3wxoeoA5JZqgjmQXKH/xPX/llfNRVlaGli07o6ZmKGpqhqK6eiDKyspw4YUXOhHCEsZuSl+087+Qy9AfjikcuXjQrM00Cu1qY5axUk7IXhBnhLZAAGabve6rvRzHHFMvyX+Y0ueFKz4jFr4rloyWA9lMQc80yojrEVgpCpZLKWv+pDmwGp7S9BEnyoiaimo+cRSBlZmuQX4iMcYvTSpZw3IeZwSad/PvM5KemUb3SxeDwrgpoaWvd3Cmy+BUqaCB/Way/6m/r7/+UsyefQui0V2IRLYCAHy+EEpKOqOiwmxqskg0XWEtZczGs47lSGbA81TBoFkrIyDqpKmFPTOitT87sFvnBVaKFMVmz/GhPCozkkGwLld8RmgMjQtxRqx8RmC7IKeL7Jym0TMj5glIXmDlOpWJHwLnykjSbyeRSsfh0V6ucPAaZsRi/NJf1LIbOkls09AzI4V0/oAG2sbKWmau0hgf4q6C7LJHbdq0Rnl5D0SjOxAOhwAAPl8JSktt4nwILMWJLrNRcXB97LummKjjSuBSNJW7LuWfPgp1Bc0JeL9NY7XDJg5jHeQDVs65tDgj6s6Y0TFpzqo6BSIfzIhovgZ34oyYDjUKAkRlcXIX+n7m3uSgqwdK+yhxRpS+kAkzom0Xhw6s4A8Hn26cESPv7jTOCF3u7ML9dU9jkyUwjHen5bJmUviSsjERZewzk84TTrPMzrjNCHuhmaYwV9CcIb04I/RYB4Xa8HzMSHJ+4tglUo6f6hd95wt+Mk+nzIg5T9MJGzeYEaMfg+hT18O9Js6Imc3SgtoXkjfSYEb0iqn7zAht7GXCjPDEGdGwMTmOM0JMgdYyN9MYL3GnyHzQ0HecpOvoDbaJyDkyn7fdMEtlZfWwtdIU1prVrJURa/uvgZq3cAJNEdnUdwoFvD4jSZ82jrKYnESR0SLKUkbUNGkLGkUO3eDO/NsMlkHPtLtKV5GtHRatrjRQ2kD/PNV/13YCo/cF93xG5Lp35r9hGWeEa/yml6cT0E+3OYd5vKcvk4z0uzkHS5GJgGk7z1o/ZBfWIXubTvfTzXT+yzXSUkYeeOABdOnSBaFQCEOHDsVnn31m+ewjjzyCESNGoEWLFmjRogXGjBnDfD63sPKRsDHbaJ3hiFUahQL64DLHGSFw7jMiv5s+M2JKn5cZofiu6NtJhF072oEW9AyCAMhBz1w/TcM+9eJOuub2USOwGuo7DZ8RnULolBnhMNOo8quMAV9dWSzyHONXvZcpQ8EP99Y8ekLG7RZfSmalP9cLnF29uH/KiWeMF94iTwttYH7G+lc+4HjGW7JkCWbOnIl58+bhq6++wsCBAzF27Fhs3bqV+vzy5csxefJkvPfee1ixYgU6duyIY445xuIrjLmFNkCWFrxmG0IklfYtUAdW3oBuerMn72kas89IroKe0eQQDAyNfdl5oH9HFEV1c+66mcaGwUg7XU6fEZno0tR3Og6salb2H8rT1S/hP02TZK04Tn+Z5DIoRTzjV8cm5sOB1cpx2wrWu3l76WnpM95S9BM3+qvN4inoF1h3jBxss48pzlBa6eUOuqO9e9lpGscr6IIFC3Deeefh7LPPRr9+/bBw4UKUlJTgscceoz6/aNEiXHzxxRg0aBD69OmDRx99FJIkYdmyZRkLnzmsdkXGRjIyI5qJTT6JUGANq4J+0sW0M9Uc7WXHGaHt3s1sCS/S9hmxkSP5d+bMiPGrskBSIQGy4MCar3DwlhFYM2RGHH4oD1zMiCy/pBl7PHWlbiD0gtiPX/Ve4X+bxh6Kxpn6nVkvVtlROrp06YL77rsvozy02LBhAyorB+Cbb35g5GoH5+9l5zRNdhmmfdpME41G8eWXX2LMmDFqAqKIMWPGYMWKFVxpNDY2IhaLoWXLlpbPRCIR7NmzR/cvG1B3RbSdhJ5uNjyQfH+vYEbo5aCFg1d3ObzMiLx4ZcdnhJcZESzksPKXcSqbeiHFwIh2C3KayCCsfibpKuWg1HdGzIihDbTvpOvASmcl7SdZ/XF8Fc6YEU2eeZnYOfMULH+YrVQ86abtFifg888/x/nnn898hvankrWpP1CW2DROs2zY8FtKqfmeKRINHTp0wL///W/HeeYWgiO2qhAOYDia8bZv345EIoHq6mrd9erqamzevJkrjVmzZqGmpkan0Bhx2223obKyUvnXsWNHJ2Lyg2EvZgWgUu/tDT4j6TAjvD4jVr4a/EifGdEOHtqpHprPiHPWRsuMmI6+7i0+I3ZxRkxHezNhRrTKiM+RMsLHjKTydzz2jKfIFEFs09C1t+uxZWQx6Ct+eqYhuik2vXd1RiLlr2g0Rn9P0L/Rpk0blJSUcOac/wUxCQZLVvCmD63JtdBl1SOnK+jtt9+OxYsX48UXX0QoFLJ8bvbs2di9e7fy73//+19W5GHvisyLovFe0m7NvzvLBwQKgwCwHViZZaH6ajBYJBukz4xQ8jS8m6nPiCnomayYZGvUZMtnBOx01aBn8iPygg/Hyoi2zkXRKTPC7zMC3dhz4jNixYyw6jt1Txv1VZsGIWiMxjP8l0BTLIHGqJT6l/zdFE2gMZq6HpN071i2gUUpXnrpbQwffjKKi4uxX+eOmDRpEhoaGzFu3Jm49to7dNUwYcIEnHXWWcq7Q4cOxV333IVp06ahU6chuOKK+Tj66DNxww036/LYvm0bamr64uOPvwCgN9NMmTIFkyZN0j0fi8XQtetIPP30KwCAN998E4cffjiqqqrQqlUrnHTSScqH7IyMtd1iumvXLkydOhVt2rRBcXExevbsiccffxyAgAEDxgEADjtsAgRBwKhRowAAn3/+OSaMPxddu45Ex47DcdxxJ+Lbb79V0hw0aBAAYPLkyRAEAV26dAEAXHTRHEyZcoWuG1155ZVKugDw3HPPoX///iguLkarVq1w7LHj0dDQyFWWtGBIshDYDxYcRWBt3bo1fD4ftmzZoru+ZcsWtGvH/nTy3Xffjdtvvx3vvPMOBgwYwHy2qKgIRUX0L0S6CmYoaOsTInqfkcI+2svNjADgOdpLO5nhZpwRXV6MwUPP0zhRmdkTp7LpHdjk8sqLtdvMiJmFcSddNuOiWGkMzIg2zgi/zVyrEPos2zBznxFiM37pcpl9Rpz0efp4b4ol0G/uWxwyOMGvFtfVjdmam8aiJGg3hQsACDZv3oYZM67F/PlXYcqUi7F9yza8vWypI6bn/gfux9x583DVldMgiFEsW/Yx/vznf+Kee/6iPPPCCy+hXbu2GD58CIx1OnXqVJx22mmor69HWVkZAODtt99FU1MYJ5wwGgDQ0NCAmTNnYsCAAaivr8cNN9yAc889F2+//TanlGqeN9xwA9asWYM33ngDrVu3xk8//YSmpiYAwLvvPoWjjpqCf//7CRx00FgEg0EAQF1dHSZPHo8775wNQgjuv/85nHnmmfj6669RUVGBd955B71798bChQsxfvx4+Hw+ALW2Um3atAmTJ0/GnXfeiZNPPhl1dXVYvnyp+0SbZv/kbAbJ//rlSBkJBoMYMmQIli1bhgkTJgCA4ox66aWXWr5355134pZbbsFbb72Fgw46KCOB3QRhBDwSBEGdpE0+IzJtK2nSKEwzDY1pAOhmGvU5lv2csjOhfq+GD+6cptGbT+S/M2FsTPklUwGgcWB13YM1Wz4jWmaEkq7JgTV1GWZlJN2gZ8Z36Ed7OaCwNtpPMdj3ObvTNDx9Ph9Bz9zC5s3bEI/HceKJY9ClSxe0a12Nmo4d4OMyZSbrbOSIkbj66qtRu2sdRF8EFRXH4Npr78RHH32EwYOTpvtnnnkBJ598QrJNDEmNHTsWpaWlePHFF3HmmWcCABYvfhbjxo1CeXkpAAETJ07UvfPoo4+iXbt2WLduHfbbbz+L0tHbf+PGjRg8eLCy5sgsRiLRiNatWwAAWrWq0m2kjzrqKBx8cLWitN53373o3LkrPv74Y5x++ulo3bo1AKCiokJ5r76h1laWTZs2IR6P45RTTkHnzp0BAH37dkFj4y+s19KHyUxDyyD/CogWjr9NM3PmTEyfPh0HHXQQDjnkENx3331oaGjA2WefDQCYNm0aOnTogNtuuw0AcMcdd2Du3Ll46qmn0KVLF8W3pKysTNGO8wZlXqJNRAyfEQ1tW/g+I3R2IN04I/qdL+00Te59RvjijKSrjGjykT8qmLWv9mbrNI2Nz4jJgVX1r3CqjBiDnjnyGeFxYNWaTJRneerKQm4ncUZ0eaooDviw5qaxHDJYo6GhEXv27EZJaS0ESBCE9qivj6CkuARl5cVoaPgRgiCivLyvLl8riY2/+/fvjSOOGIrDDjsFY8eOw5EjR2HU6CPRqsr6IIERgwYO0v1u3bolRo8ehUWLFmHw4Jn45Zdf8dlnn+Ouu+ZR3/f7/Tj99NOxaNEinHnmmWhoaMC///06HnvsduWZH3/8EXPnzsXKlSuxfft2SKkvSquhIPgX0IsuuggTJ07EV199hWOOOQYTJkzA8OHDDU/p09uyZQtmzZqHDz/8HNu370QiQdDY2Ihff7ViqvgwcOBAjB49Gv3798fYsWNT8hwL2QDgFhOq8+8pLF3DFo5nvEmTJuHuu+/G3LlzMWjQIKxatQpvvvmm4tS6ceNGbNq0SXn+oYceQjQaxamnnor27dsr/+6++273SpEmWLsiK1+L5AXVoa3wT9PY+IwoX+3V9F2WecTGZyS/cUaMu3JGG3LKpl979PnsM3FGlDaQs1br2zkzovUZ4XBg1ZjBuIKeUcYeD+tl7zPCcZpGF3FZm7aAkqA/w38+FAd8KAn6UBIUld+hgKi5Jure4WOEks/4fD68/PLDePbZh9CvXz889LeFGDlyJDZs3AhRFE31HosZHVSB0tJS07VJk07Fc889h1gshmeffR37798P/fr1TuZNWQ2nTp2KZcuWYevWrXjppZdQXBzCmDGHKfdPPPFE7Ny5E4888ghWrlyJjz/+2FIe6hjRXBs3bhw2bNiAq666Cr///jtGjx6Na665BqxVevr06fjmmx9wxx2z8Pbb/8QHHyxHixYtEI1GLd8RANs69Pl8WLp0Kd544w3069cP999/P/bf/0D88ktmSg5LJlsFR7D8kRektYJeeuml2LBhAyKRCFauXImhQ4cq95YvX44nnnhC+f3LL78ok5r234033pip7JmDaXO29jfQ7pT2mTgj3BFYabt39s6bBXfijMjvGP1I3GVGYGBGXLfS5CnOiLz7dMNMY2TO3GZGaGOPbyKV2Z4M4ozo8nQX7HJn6pemtt2hhx6I+fPnY+UnnyIQCOD1N15H69YtsXnzduXZRCKB1atXa2SjpwcAJ5wwDuFwGEuXfoTnnnsDp58+0fiwDsOHD0fHjh2xZMkSLFq0CBMnnoxAIAAA2LGjFmvXrsWcOXMwevRo9O3bF7t27dLnTFXArOulTZs2mD59Op588kncd999ePjhhwEAwWAyz0RCX7iPP/4YF154Bo45ZgT69u2BYLAIO3fu1OUfCASUMSPn36pVi1QdqrKsWrXKJPthhx2G+fPn4+uvv0YwGMSrr75rKXtGEGC7oSm0NcuxmWZfAj8zYvQZUW3Ihc+M0P0mTGYazdFe9i7RvHunsSW8yG4EVnrZnchGO9orZikcfP7ijKRuyfWt8YlxzIxkcLTXeHqHnoF57PExI3I+mTAj2jgjhQz9IiMIwBdffIPly1fiqKMOQ7du5fjo/Q+xc+dO9OzZEy1b+TB79q14660P0K+fhD//+QHU1tZy5VRaWooJEybgT3+6D2vX/hennXYK7NT0KVOmYOHChVi3bh2WLn1Nud6iRSVatWqFhx9+GO3bt8fGjRsxa9YszlKar82dOxdDhgzB/vvvj0gkgldffRV9+/YFIKBNm5YoLg5h6dIP0LPnoQiFQqisrETPnj2xePG/MXhwX9TV1WPOnAdMJz/3228/LF++HKNHj0ZRURGCQWDkyEPwl788gX/9axEOO2wknnzySaxevRqDBw8GAKxcuRLLli3DMcccg7Zt22LlypXYtm07evfuyihNZhAoc3UhozBX0FyBaS9mOf+YfUYKgeaig37ShWpvF+zLYsdIuOkzoqRpuwAKyl/aq/T/O5VNu3MXdUm5z4yw+pxb6ZqhHO01XSemOrdlRjTtJytt2vSMf+s+ROiYGXEy9uhKEd9pGkF9NktxRsxwK5+k7OXlZfjkky9x2mkXo1evXrjx5vmYO3cuRh91FKZNOw2TJ5+ECy64HkcddQy6deuGI488kluWqVOn4ttvf8Dw4QeiY0etkym9TqdOnYo1a9agQ4cOOOywYcp1URSxePFifPnllzjggANw1VVX4Y477qCWxzp99VowGMTs2bMxYMAAjBw5Ej6fD4sXLwaQ9F+5445ZeOyxp1FTU4Px48cDAP7+97+jtnYPRo6chPPPvx7nn3++4rQqY+7cuXj33XfRsWPHlLIhYMyYw/DHP56Pa6+9HgcffDDq6uowbdo05Z2Kigp88MEHOO6449CrVy/MmTMHd955K44+eoRVtWYIp/NH/tcvjxkBQGsI2ifqjfeI9mNdBcqMWA1e8yJDNMs2q2OazVfpREC0loPTTEMJSkf3ZxFBSMIlnxGjjO6rI+Y83EiWT1k0R7IllHt2cjkz0xj7Gq8Da3Lk8YeD1/cXCYDs/MkxfmlB7nK009TXR/p59u7dDS+8sBCEiKis3B+RxjB21CbND4FAAAsWzMGCBXNQWtoLPp85rMLKlStRXpTyGdE1kYBx48ahvn4dEomwSUQ5RogWffv2VcoVj9fr0hozZgzWrFmjXInH4/jtt9+UftSlS2fs3v2NPkGLtpgzZw7mzJljup5IhAEA06dPxDnnnInS0u7KvcGDB+P9958BIfHUsx1wzDHHoKKiQnnmmGOOwcSJE1FeXg4AaGhYDwC47rpLcMstf6bWX9++ffHmm2/qrsXjDWhs/C9V9owhMH+ar+ZfF2nuzAgr4JFmaTbcV3dnRGODLoDWpMA+VLu2Q/KYaWjpZb6IOjbTwEYOE2uTblenKF/Z+jYNI+pvRukaFAQjiKTvw5owIxmdpgGslRGtdOp9/nDwOmaEq8/RTJTgGr9anxGTz4nr0LdBKmOHSVgwuboraYxTQZ0TzVml5M5kVFBEYm1WGK9llinTgd/Ned5dJpQQoiTjtJYKYfVq3soIY1ckCJTdtnoz9XbhxxnRL9C0CVk2PWiYEUZZaKYEcxh2fqTtwMr5jRzzbt+5bEqacnkVB9bs+Yy42p9slByjz4jWJyaTOCN8EVj1DKSjcPBpnaYBtH4jfHFGRM2zdJNWplCZosxh3DrpM9KvVpn3YF4W1f4Zp2W//PK5KCsrQ1VVe9TUDEVNzVC0adMHZWVluPDCC9OWK2cLcyFoAAryL0zzNtMw4xRY76jpduvCVEbUEMpEp/Ezw8GzykJbMDNYRNN2YKUqWbRF18yaOJNNSE7gAlEmclGJw+E4SSZyEmeEsrNjKRyZxRlx6MDqkBlJJ86IUQ6e8atlQjM/2ZIOnDBA6YDNbPKV2H01SneHwsrMmXMlZs++CfF4A5qakkdkA4EKhELtdWaVNLMEwDvG02OGXW9N3fSomjNZnF8hoVkrI6ydlXFnrb+p2TkWPDMC1W+C90N5vHFGlA/UmU0ZvHCHGTG3lZHZyowZSSpzgmKmkfNxWxsR6H9nnK4dM2I82qsyP5kc7RVFn/KO0RmWHg7e3mdE76+VD2YkVw6sFDHSfjKbiw5tfsxidhq0bdsKpaXdEY/Xo7Ex2UaBQAsUF1tFak1CN8yowuZqkXZ7vFuZQfPXZ52gcFfQHIBtL5av2TEjhe0zkoSZwTBF3QT4mBGar0YGwcWyG2dEey1TZUT9f7bMNDmJM8I82gvdH2kFPaP0D9o7xv6XvMYTgVXLjDiPM5LMR+P3kYbPSFJJyz5LISMTBs44Hol6g5WlQQDjc5wcCddc4Kwe7Z3l3c/TnK+h/xfI1E8hRrQNbvN2ZoWwH7f2aN7KCPQ7Qi3ox0XNKSh+J4XSIylQFhKGz4h2YWUGgKI6kJlZCl4YFzu7vLS5Gv9mHY1Np31Myohib1cpUHdhN9FmJ13jyRZttVu1D8/RXkHDjFjl6dhnRMvaMMYv5U3N31onVJ7xq+bp8yVApBgikQRHnpnDjdM0bKdPwhw3Vm+padOec+D/Ilj+SKbEtcjld+5140i+KyUwnHLKUa4AgMbG5NeH5QB26aB5m2mU3RnNSZXlwCr7DGg+KV7AZhql01EWGXUh5wx6pqMWZWZEswA57ODpBz2j7PapDqxmGZ3KZmZGstPWmQSP40+XoYwYnH1ZzIh837zQacw0PMyI4wlRZSnUSzxmGno+PONXqT8iQRAlNIXfw7btlSgq2oGSkhLHbCAN0WgU8Xgc0WjSFCQIMcTjcQiCgHBYQDRKIAgS/P6wbVqRiOpPEw6HU+knf0uShHA4jFgkmV/y+YQSiTQcDkMU9YpWLBZFHAlEolH4w2HEogkIUvJ5QmIAwohEJEgSgSjGEI0mUvnHQAhb3kQiopEtDkHQPy/XS7IewojHo8rzopiAzxdGPK5Jg5jTMEKS1DQSCQmiqH9eW3/xeDL/SCQCn8+HWCyWaqeoUrd29Wdd9qim7Albue0gJSSlTcPhMESfiGhUgiAAgiAp8irljMaRiCfz9/ki8Fl96ogBQpLf7tm6dSuqqqpSXzFOD81bGVE0eIYphrob0LAJir29kJURs6mC5jOi6iwsytpsSsiLzwjFWZUuh0h53plsqsKWTEPM1mkarTnF1d0e2/xjMtlpypeJMiIIembEDQdWAemOPbaZhp2GNk+CSORFFIc6YOvWlhz58iEcDiMcDqMoFIaABAQhgqamOAKBAIqL/YhGt0MQ/MqH1ViIRLak4mQICIUCqfS3JssgiSgu8SERT6Cuvg4CgOKSOCQpAgAoKhKVdpNRv7sOcZJAcTCEop0hNDVugyAmv9Xi88URCBQjGt0OSYpAFMOIxhrh98Xh9zfB769jyppUDLal0qpHINCkux+Px1FfXw9RFFFfXw9JiiMaTZZFFIsQDMYNaTQgEGhk5klIApGInEYIwaD+uzPa+kskoojFYigpKUEwGERTUxMikQhCoZASmVUuu1X9WZc9ppQlWfbMlBEiSdi9Zw8AoL6+HoIgoKlpGwSBAPAhFNL38Wh0JyQpWd/BoABRTF8dqKrSf/04HTRrZUQNBU2Z+JlHQjV2a1YaBQKaE2fazAht954HnxH7Uz2C5T2nspmYEUUZdRfZY0YobJYGcjlFU32z24emOGgXdfmIMD8z4vA0jYOxZ+fAykpDKbOSJwGEz9Cr12X0D7ilgU8//RRffPEFBh/4GURxE0pLLsDHH29Gp06dcMQRXbDm+/kIhTqiT5/HbNNa9Z9b0NS0AaIYRN++yXDrKz69GEAckcYqjDrqWez8dRvefOtN+IiIUUf/htrdnwEAhhz4DILBFrr0nvvbU9gc24mR/Q5Fn6P64v23/opg5TcAgJr289G5cz98t+Yh7NnzNSrKZ+Dnn99BmzYb0LHjWdivw1SmrA0NP+Kbb+cDANq2PQ5du16lu79582a89dZbKC8vx/Tp01FXvxWrV18MAKioGILeXW9HXd0arP4umUa76vHo2vVSZp7R6A58+dVFAIAWLYaha9ebLOtv29aL8Msvv+DII49E79698eGHH+I///kPBg8ejMMOS37g7/sfHmXWnxUaGzfgP98k5W7degy6drUOfc+Dpj0NeP2NNwAAF55/AfzBAD788DL4AxH4xdbo2/dp3fPrflyM2l3Jb+MMGvi4reOvFQKBQEaMiIxmrYywdkXq5EW7J+8cJYe7s3xBnmjNyogqNwHfh/LMu/e8xBmh+ozQ5KDdcyabkXlR1kPX1ZHsOLDaRWBVmZHU/zhO0+je0+Ult5W5HekRWLV9kuM0DdIde1q5HTIj2tM0mpNHPp/PlUkYABKJRGrnvx3A7xCECOrr6xGJRBAMSpCk3wGUmL6TQhUXO1PPFyvPS9ImADEk4jGEQiEEA0HU19fDR0SIwp7U80AoVIRgUJ9HuLEJ9ZF6iAkgFApBitUqz/sDAkKhEERhNyTpdwhCE2KxrZCk3+H3RW3ljcX8Slo+scH0vM/nQ319PQKBAEKhEGKxIuV5QeiOUCiESNSnpuFrss1TENU0RGGP6Xlt/UUiEYWZCYVCkCQJ9fX1SCQSynt29WeFRCLALLtTJBqjqK9PRrQtLi6BL+BDIrEJoi8MIhBz3Yr1Sv5FRYGM888UhbyCZh1sezHDZ0Tn0V/4PiO0Y8qso72OI7DmI84Ipxyu+oykvk2jHO0VYPh6Z2bIGjPCG2fEcHSZx2fEDNnfRJN/1pgR/rGnxtvRK5E845cacdnl8W6uD6JcV45e8/Zhqr+bXHb5EXpfY562Uo600/y1NOZeSrqWotr0eXP/U5U/qpmYJ0+75zX1x3WaLM35z46xdApJ0mzWlDFsbjONAJo/879+5V+CfIK5K2L5TWgnNSce/fmC2W/CfLRS6zNiDX1dyaxDPnxGzMyIUVL6/53LZkxDN3DdP1Kjy9H91BjKCIMZUd62VUas6zt7PiO8taWaW1TwpKFhY7J0es54okmrjLB822ig+7sZ+7Cci/E0DUUhMJ3tpbGjGnOv4KSOtGkxWDtZOqrzuFke3jxp/VRbf1b933oucjL/OZWbDaLdGAmGPyhH0bP1Yc500ayVEdVebL7HDCOuXCOa3Vn+G9MKgrrKKNeUwUTkSQSczAhFm3fBZ4T1Pu9uRKDI4V7QM3XwiiKlHt1AlpgRapvpoF88WG1g37603TMvM8JRnxrl2am/lurro/UZ4Ri/ujyzw4SanIgF9brjOcagVAKAIC9GRN+HiSFdevsSwz0aMyIab/HJyxnvhDof08a2Q/8h+vyuz1Ofvx3DwD//OZXbFpqhY1baaHKnz2hnA/mXIJ9g+Ywww8Frdlh7qc+IestMrzr1GbHbaXBJmNFpGsFwzXwvk65uLKeW4iZummlyEQ6e49s0POHg5fu03LRpGtMzP67vO9w+I5pPMfDXlWpuUcAxfhU2huh9RtyEOe6KWUb+PswaB8Y+TCye08qWuiMax5lG2VfYV3VTw/U1ZRt/M5aZhja2nZ6sos9Xav3xBf2zS89eDjfGuyKrVjRiPf/R5tB8opBX0KxDabN044xg74gzwvYZ0UxKDr/aS6uj3PmMWO/OWB/MS0c20zFhLTMisRdPR8gaM8Legak+CfLj6q7Z+Wkap8yI1nTowGckjbGnyq6Rw5GflJaNya7PiGzqSDIjznxG6GygrIgZxpRgP37lOhIFWtumlAPB4TxCyY/ur6Lvf/InBrRlcuprJdDmDopMvD4j6c5/bvuIGb++DQCEMh41AtBlyRPyL0E+wcWM0Gxtmh3WXsGMmHdKpqibmqO9TuOM6OsoN8oIO84I7Z57ZprsMSM01smNdNk7MFmfMi5iaZ2mMSx62nfop2mcmWlUliKdsadhOJQMnTIjTtkYPjDrg6K4sUEbl9bsnt6fgKUQmOcRlRlRGVaeecQsF71OrQLy6d91Om7sGAm1/viYkXTnP3bZnUJ2YNXVAMOBNZNTkNlA/iXII9KOM6JjRvb2OCNuMyPO6iEbp2loabgTZ8TMjEguMiPZOk1j6zNiYEbk4qVlplGcX3PBjKTnM+I0zoh6T8o+M6L0ZRoz4owBYi3cWr8nUNpKJ5t8TzHTqM8oTIWilBBNVs6YETprZ82MqD5OTpkRdp7a+nPOjPDPf277jMgbI7pjKm2NS3/ezgaatTKSqc8I2Yt9RminadRuy9olmnchhRdnxG1mRL/TFLPEjGQrzgj9o4IqVJ1LVrY0Zok0fUZoCxxVGcnIZ8Tp2NO8qwhSaD4jKlOpXHfTZ0Q204iUexZ5yGYa9Ug7jRlR+4wTnxHe6MB0nxEKU+NCnk59RtKf/9wd74TBjNC/v+YxIwUDppe6YN2I2gWcmK4VHvh8RgDV84m1S6RMYhnYHo2DXZcV125V8zdDoUln7TAtEISSlosuI9QyuZ0uTRmBYRKT1y049xlhxRnR5UllRuzNNPQ4I7zMiDpuYfyLs69l6/ScqT4E7XXrMUKDyXSiTRC08WAzfk2Kkjkv0MZZFuKM0I72Cjb92/y87pelTDRmxCiXMUFH85/L410ViZIW7SvTuuzzv341a2WEveMwDDIN9j6fEfNuVbVRK1fUpxkdk/45emeTgRZu+IyYfVdoGn/6zIj5NI2m1tw00zi2ffOmSzFpacDa/bGURbbiwMeMCBbvWKeqeSZNXwqd3A6YEWh8RrI3dZqZEatj0HZp6OYuYt2H7cavUmLlFq0/yTcl6I9zOIF1H9MrwcbntauqM2aE/jxlnuT2GeEft/rx7gIzovRlzTWWmcZjRgoHhLHj4Ikzstf4jFBMFTQbtcDlBW9nAsmNMsLyXaF5y7sRZ0T1GcmSmSYXPiO0IW9sA1Hu3/TFgHlUl+I8yTbTOPQZgXnsOWUM0vUZ0eeZHZ8RrSOofF3Jk3O6pptPjcqI5h5hL6aqmYbWtvLHENM002hNPpzKiGk82qRhytLmeW39OTfTOFBGXPcZodQVkfuuNQNkkiVPyL8E+QQPM8LwGdlr4oxQnDjpcQ3sJxG2c6jgmO5zw2fEfIqGNjlkoIwY7K5Zc2DNhc8IZdKRjFS41gzpWBkx78R4HVit09SkLpjHHn/bynKk5zOiz9PdzYdRORO0152GoDc4dEOboiHome6exfhl+W0oH0OERm45AiuPmcaGpaAzI8Y+5nRRtVH6NfXHxYykPf+57TNiZkZo45GeZ/430wW8gmYfKjNCs0MydtQ6ZoSHTcgvaDZkswlC4mNGKLELqF7tnHDzNA09EquZLXEqW84cWLPGjLB3YKovqV7Zoh3t1f7NUkb4P5SnX2R4fUa0Y4//lIlW8Zb/4u/zBM5PtvDCzMLRmBFOBojJWFKYEZvxq9QWgxmBRoFV7zplRviUEc3LqXvOzA22cUY09cfHjKQ3/7kfZ0SVSJNw6ial/jLw9csG8i9BPsH1bRqGRqnzGcm/ZmkN807JvBOjsw5GUHfZlPgDvHAzzgi9zVyQjUJ7y3qbvVmBH/mKM2I0d6j1nY6ZxiEzYti988YZSc9fK6VUUJkRjj6fkzgj8phSlRFQ64oBg0N36ofu/zqfEUaUztQD+mQpc4BOboGlQBhE1dZ72swIha21BWNe0NSfM2bEaZ9wd7wTWl/mjsCaf1Ug/xLkESx7sfEDUMa78vvqdy4KVxkxH7+j78RUhYRRFhoz4gL74JgZoWj/1OifzHbkk42u5KRkcvVoL23xcBuUdA3MiI4JoCgHTpURJRuqmUawfIaKDPy1mD4jHPWdzfFuPOosUJkRZwyQ/oOOBmWEYsKxGr9KBFbN16o1mUF/UTuP8LAUWkWZ3WeM8ir/d3iaJvmK9bxAO01jfI8WZ8T5/OfueKce7WWmm4v5hh/NWhlh2n+pdtfUJcVurfpZFLKZRgH1WKZ2Z+KsLOrgM9O3vLAa7LRr1mYa/SJKdQxzxYFVq+TIzzhO1hLZcijjD7mdeoa6WeTdffK1H10Z4XFgVRJI239Dx4xwjF+lznTjPUvKCMVMA+I0T2vlWY0zQts9WygjRjMeUetKNMyT+qBnPPJq5x8GO0VTOBSFIh1zhyy39WZTN94pmyXW8zxw3UxDGVNaLtksAGUOzSP2ghU0e1DjK7Dthuabmt1ZlmhbN6GWxZoB0tp6uZz5Ur/0aaRfB5kFPdO3Fc0skRkNap705PTcdGDN2lE7miOjBqoyknxOVE7TZODAauMzoj6t7zu8Zhrtl3f5T5lQfEZ4xq+GKcqWw7pZDm09pOczQnfkprAbCqx8Rgx9QLeI+Qyy8TnC0/Lk9RkxHe218TuhQaApMqY0nJ6mcdonnMvNAivoGc1nhDaH5hP5lyCvYOyKqHbX1CWt3TpL4aFdBYXloR3tVZkRxqRH22WzjkHbIG2fEYYcOvkZDBe3bAzmx9Vv03CzDw7TtTltYJrbNfWdtgMrZVfJ8hkROJURaFgKx6HZtUqFAvs0tD4jdF+XzGE00+iO9jr2GaGYDQzshyiKmoanMIo0iOZ0BUM4eH2IAPs+LNDGqgZcR3s52RU9GPOCpv74fEbSm//syu4UNKdwkxKqEyA7TGy6yL8EeQTV4ScFmp+FelPLjBS+Aysrzoh6TQLPjob+gbrMzTTOfUas5aAxGK58KI9i33b1q71ZY0bYOyC5D8uUe2ZxRqwpbvppGr0i6YQZcTr2dI6oihwcaciyQYJTloIXVswI0Wx43IkzYu7DtmYaUzwms5mGdlLJOTPCYu0YJqe0mBF7tth5nBGnfYJddqegHu2VTWpUZoTNmOYazVoZYe+KrDVnapyRAtAsLUGhV007MRANscCirCm7d9YxaBukz4xYy0E9IpuBcy01kJfyjJvMSHZ2KvYRWOV78v/lxTcdZoRSV0xmxJmZRnmeqIqB01MmWhMPz/jVMyO5+VCeoLnuOE8aE0pTEg1/WR7tJbIDK0XZV2KOqMoIX/BE+X3KWKXkzRtnxLnPiPVm02mckUyO9rox3nmOQesvqWPV8xnJM1j2YpbmTI0zUsBVSfOboO/E7MtCZ0YokwIn3IgzYop3QvMZychMQ9lVyjJljRlxcXKw8xkxxOtQ44ykwYxQdtk8zAhbwdFKr5panPpr6R1RoaSTBEsB1+TpMLYJL8x9TWOmcSPOCK1dZHbP5mivwg9SPpQnGkw3Qg58RtQyZMKMWDOm6cYZyeRorxvrh0T9ai+LGUnPvJQtFIYUeQPraKBgf0/r0V8AmqUlKL4Upp0p0QY9YyVlXpCpNmpOsLR53lMbgrF8unmLTUHzyKZMegwa2Q3obcju9Sc7m7raF4xyZP/bNI7bh8qM8NaVLIeZGWGlQWNCXVUWYe5r7sQZoWVkPZ9ZBz0zLMi6ttUzYUTrLcRRRQJ1rGryZvU/2wSZOVs/r6k/q/zpPiPO+oTb451F0rLrqjDWrmatjDB9RhjarnZ3pu4CCqNB6RAN/2ezQuxuYda6afE9eJG2mYbpu2JmAtzwGaHuKl1lRrTpuzk07dI1tEEmzIiycFOURW2ORmaE0jfpJdGMParDHgtmvwa+8asutM52/WlAe0QWcl0Y+6FtIqn/s1hMTWqcXVgdj2YzjdqORFGksuYzwgoH77AvsOJIOT9Nk84awJLDIZhBz2hrHG2+zB8KQ4q8gbHjYNkBlYGhDbxUuFVJm0SoPiNyfVB3T0pimj/1dtfchoOnMSNmOcxRYtOQjVBs5fIzrp6mScf27Sxdts+Ivp/k1GeE1jfphUn94Xzsqb4wtA/lMUyTyr3sfRiTy0zDXU7KeFSq1KxQKwq7HTMimhc20XiaRmvuzZbPCEtuh3VEjyOl1l82fUbs5HAKY7ygVMKpmzQGKH1GOxso3BU0F2D5jDC0XfUYYvbCQ7sLc1moPiPKp79ZlDWNdbBmkeyQOTNCsxdTfEZcMNPQJnI3zTRuxx2gpUtrW1MbKI9n/2gvy5+JhkzGHu00DV8acp6qWTZbcUb0i7o+T96dNzvOCOWaQ58R6refNEqUauXIjc9IeuPGmjF1Hmck/fnPzOqmDzXOCKuutHcykdt9FIYUeQJzV0RhE9Rb6oSRrZ2Sm6DtlMyTn0QjcCmJWbMO+fhQHi3aKvX8fgaysSZyN800WYszYseMmBxYZWbEuZmGemqDyYyY24fNjMgKhTtxRnjGr54Zcei/wQnzUWctM+LQaZYyDswnUNQiy23G7zOiPsdmRuz7cEZxRmTGMg3fCyYjoam/7DMjsuaW+XiXTPOV5m8mM1IYakBhSJEvMH1GWBqrujsjWdopuQvzJOIOM2Kso/TZBzeZEbfjjBhPfCSvpZ7ZG+KM6NqFNeGnnhbV+s5VnBGqokxNXVYoJOb4Zb1LjzPCqm9BfTZLcYV4mBHnkWZpzAjFTGMTZ0Txk5G/2svwGYHmaC//mLNmL537jDirI9bcn1ufETeYEVqcETNTKSMzud1HIa+gWQebGbHWnNWBoQYKK2yfEbksFCqc4jNC67iaxDR/KquXPh8HyNRnxJYFoZTdqWz0Sc99M03evk2Tanf1mGb6zIhTZURtF15mRF5A0/DXoviM8IxfOhOaLWZEHY/ydcfsK9VnxKyUK6kpTeHcTCOaFBQzY2ArLmOuzZbPCM1XyZjG3uszQtl8UJkRNiOWaxSGFPkCwxufx7semmN3hX2axrxAmBdaAsM8aJEShQJk2CXtwFJGlFyYdlrKTo/TZsoPeSKnMCOu+ozQ5HY7XQoMRVCbgF42pgmJ4iip3GIoL/w+I7JCkc7Y04xbNTOONDT9L0vj3ayc6W7q5LAHb5/nG79GB2e1PswpQVA3NU7bJf06pc0BmeRpXR/sgGLpyJ9p2TWQ24k7ZzfmRvfQrJWRdH1G9EGQ9oLTNAwqkB70jMWM0Mw05l1XunDbZ4R6soATrKBnyoTsqs+IuSzuJMzeORpPS7hytJeTGTGGg7dO1yi/6kvheAdO9RlhpJEPn5GUXDqfEW4TBG2Bo7F7cuZ249fQP5SyU1hJTnOvTl4KO6bkzNjtq74uzpV4tQy0OVGtPycOrOnMf0Z2OROoJ/s4N2Oez0gBgcmMsDqrxva8F3ybhkZJGm3UumBFjLLQQovTzEC8yPZpGmQwUSj5EXP55D8lF4/26nd4LpppbNI1n6bRKNs5cmB17DOS1tjTvKtkZp+GapLLnc+ILs6IG+HgmRFY5QsWzIjRwVmpR1q/4ptH9JD7gDWbxj5No03DoZ8Krcya+uNSRjKY/9yMMyLvi/T+Nea2kuH5jBQQeHxGWHFGSBZ3Sm6CFtrevOuXlB0Nr8+IcbdVKD4jzC/6piMbRaHJts9ItuKMMJmRVB2JOWVGzLvibMcZgS7OiBOfkezFFVLrWc/e6OKMONz104/hU5gRhWHg9BkRrefL9JgR640f+zSNmQF13BeoPoFq/XH5jGQw/7nrM0LrJ+a6Um95zEjhgBFjgD/OyN5wmoa2UzJPfjxBz7T1YlYgcqeM0JkRmhzWJipe2ejRKxVqxHG61qCVJfvpqj4BqXKKap9wrIw4/TZN2swIYY5fOjTvKpnxjN9M8uQD9XSbkqdTBYiigDOYEfs4I7KZhsWMaBU9jk0NRV56QD6KMkI9/eN0nLOeV+uPjxlJf/6jKYnpQo0zYk6fnnMmcruPwpAiT8jUZwT7VJwRzh0N1VcjH8wIS470d0xU2SiDVjnU4SozYmZ53EmXwhRpYPIZketbyIQZEZnPm80SvMxI+iyFekqIFoGVMWnnkBmhHe11GmdEoMxdNPOZcddsx4yInD4j6gE9XmaEJlsqCQYzQgusxzsPsyOwquXM3WmazNcPZl15zEiBg2n/tR4gubAhuwtzWYwLLSFq0DPW8ko/xZJ+N6IPIFCv2Z2mYbUZU8GykU31GTHT3tn7Nk22+hMlXaUNzI8oXwJl2PN112g58phptBtfntM0uo/W8UJefJ35jKhy5eJDecoV9brjPCkNSYz3tAq1rXS659V61OaobWOnY8JakaAf7bVOw/HJKuY9MzNilMs+LT453Bjv7M0B7U5hrVnNWhlRIzHSFkIGjaeq/po0CrcqaT4PVBu1YFggLFJLgm2P5oUTZoQuh1kp0tvFzSYWp7IZc9T+LbnIjGQt6BnlBJQWqv9iqr41PgEK9esCM6LL09T/RIt2tko//VMm2uWMa/xSTvBkayJXd8oUZsRxQC/2GDWWwSp91WdEv5PWm2HkPNWgZ47NNG74jPDWEWN+19afk6BnaZ2mYRyUcAozk5v8lbxpbY4qFBeDwpAib2DRn9b0mf40jTMKNS+gUIEmWphI6k6JpU1TTCHKizk001CP7NKoV1fMNOYJw3QSwQVkLegZt2KQvCeK6jM0ZsTdoGdqO7LThfJc6iGwxy/tZdnXgfJtGpYDKzR5ykxK1o/20nxGOBd3Wp+nMCPqPesxIkmShriQx2MqBDz1y8xaf5zMTSa8ZhrnTqSMOcvhaZpM5j+mO4BD0DYOShtQhpSbx4rdQGFIkSeokxLDbkhlRuSdgcZnpMAoLy1omrtx8tN+Hp09idAW5vQ17Ix9RigKAu0oayb+LMquUjDn5a4DK0XBcyVZO2bE4KCoYUakRCIlDqcy4tSBVdOfeJQRNcaExBy/NBgXejkdo7zmFzV5ZokJZZ+mSY8B0p2eYynUym9K+rohZxxL2v6q1pHg8DSNMr64mRFz/iy/E2qOTGZErb/cMSPumWlo8x99g+kxIwUEa3s47QSKDGqckQLRLqmgaMCmyU/DjLAGBpuRSN8UkvZpGjtnVTdO+hDKRC7on3EDyXKmb/KyTBeUOtJAPU0jm2nSZ0acxhnRMm3OmBEJ7PFLfTmVPsVnhMmMaN4jDvPkhJUDa/JewnDPBgxmRKAt4LICSWPNND5RouFoL6EsetBsanjNNFS2VU6N00yTbpwR6pzlmBlJf/5z04lUlVWXQeomheF30XnWDRTwCpp9sJkRhtYod8i9Jc4IxW/COPkRSOD6Ng2nrwYvMo4zYnOMl/aRO6ey0QMTpWRylRmxUKgyTpTNjKjmDv1iAwAJp2Ya2jdQOE/T8DEjmrHnlBnRnIpR5OAZv9TTc7kx0yTvpfdBQD0Tat6QKIYVAtPzynua/q32C1kh1Gaqyq1IyWumYfhN8J+msU6DmieD+c6Lz4ibZhqK+Yrp9Fsga1dhSJEvMOzFtJ236Z7WhlzIVUlZ4GjMiDo7OWVG0rc9Zs6MmBc9XZs5nKSoslHsy3Jerh6mAeBmREYZtj4jhqO9Wp+R9B1YnTEjcOgzoov54XA37DTOiD6ukDOTCS/MZhrzl4WdMyPmBZwaK4fhM6JlRpT+QTHT0IIncoPhN8HPjDhdWBmMBKX+su4z4ooDK4O1YzEjBbJ2FYYUeQKPvZh17JfoTtMUBtVFA9WGbPCHINB+tZedmv7/2nTdNdMo6VMXKVqe1hNuZos7rcwy3NZG0q9L+zTp6So9gbIo0JgfFsukVRdN92yUFyfMiI6l4GYM1HcVmbjGr/a97Ix3lSmSF1rd3VSOvOU0/0W7a0yf3maa9leaSm4nq36Vns+I4+cFyjXuFKzzZNUfdX7KaMy6N97pw4bWn9zP2w00a2WE6aXODIqj3b1kx4bsKhiau47l4WBGeIONpQvnp2koLIjbrA3RywGoE5C7cUboCoFbaVqla1wIM/EZSdeBlddnBLqxZ1jA7aAxSaqwH7/qPW3Qs+woI24yI/ogh6x2sd4ISDpmxGiOoIw9TdAzpz4jVJ8Vp0d7M2KPDPcodcUOeua8T7D8ZRyDUldMB1Y383YBac14DzzwALp06YJQKIShQ4fis88+Yz7/7LPPok+fPgiFQujfvz9ef/31tIR1GywvdbYdUO6QksmvoBDB8hnRRqUUOHb57KBnmZtptH8zJwAlf4p/CPVa+qwN7QQRsqSM0PNyK02rdJNlkOn3TE7TEPUh5vOsOCO8Qc+IQxOpapbQLvQ841c17zjNkxfZ9hlR7snHcrXpMcLBa5kx1YFVPtprzjMpKzGkawdrebl9Rhz6bbC+tEs9ys9jMk6rT7g33tlxRmiMTvq+LtmAYymWLFmCmTNnYt68efjqq68wcOBAjB07Flu3bqU+/8knn2Dy5MmYMWMGvv76a0yYMAETJkzA6tWrMxY+czCc1xias/743d5wmobXZyRlpknTZ8SNcPDav53HGaGwIBk4hBoHtz4OiPxMdhxY3WVGbHxGDMNAy4yk68DKGw5eW95044w43Q0TndLNcZqGysZk22fErIxksuunf7VXvic/buczok9Xt9vWsEdOj/ay4l1w+4w4ZUBZ84LiVM3LjKQ//7nJhLKOQdOGVKHFGfE7fWHBggU477zzcPbZZwMAFi5ciNdeew2PPfYYrr32WtPzf/7zn3Hsscfi//7v/wAAN998M5YuXYq//vWvWLhwYYbiZwaFgmacNQcR8WNDGD82hrEpEkNYIhgaUHcB6mJkPfB2xuL4tLYeIgQMrihBdVFAd58Qgp8aI/ioth6r6xoxpLIUE6tboEhMenPvjCVQFfDBx0Gn7YrFsbquCT1LQ2iXyqdeKsJa9EFJvBjdCIEgCIgTgpjo003QavKsfPSafDghYX2kCF9hCHolWuHAVPo0xCWCHbE42gT9EBUH0OTSoB3ku4vLEIXZl0G/SMltIGJbNIat0Ti+b6zAWgxHz1g1eiYklPhECBAhQcDaaBXe2rAFAVHAkIpS9CsrhgggQQhq4wlsj8XRmJDgFwT4hOTA2FFagZJomLrA7ioqxv/K2qCOMsojkoT/NkbQubgIJT5RubYpEkPHUNCmHZP3EgTY2BQBARAQBLQI+FHss5404hLB+qYI2gb9aBEwDmt9m8UlAr/WSdVwmkbb/FZmmoQg4PuohC9+2472wQCGtyhDhd/n+Giv1sE43TgjjQkf3t9Zh+oiP3qXhCz7n+5IviIHj9+JiAiC8Ek+EzMSTkjYFouDEIKOoaCS9554AuGEhDZBPwRBQFNCwg8NYZT5RPQoKVKe2xaNISSKJmVEIhJ2lpRDEgRI0k4lz62RGIpEAZWpNg4nJKyub0JQFFBTFESrgI++67c45RT1+fE9KvETjkSnWBt0j8VRpek/+tM0yTREWtCzVLoJScJ6oTvqUA6hkWD/SEwZ77tjcby3sw7f1jfBByAgChhQXoIQCaTS4DXTiJAgYhcJ4Pv6JnQIBU0MaFwi+GpPA/4XjiJGCHyCgIMrS9GluAgA0ECKsRr9sXJ7FX7fuQF9y4oxuX1LtAz4lTQSRERjoAh+KWHLjEQQxM+Jtti0tRZRQtClOIhuxUW6uqRBa/IihGBHLIH/NoaxPRZHVEqOzM6hIHqWhuATBGyOxLAzFodPEFAkCggIAoKigCq/T7OeURQ3zbWdsTi2R+MoI3pmhF7XuYNAHARKiEajKCkpwXPPPYcJEyYo16dPn47a2lq8/PLLpnc6deqEmTNn4sorr1SuzZs3Dy+99BL+85//UPOJRCKIRCLK7z179qBjx47YvXs3KioqeMW1xcGPPoNd0XIEYxICsWRzJVINEww0QgyEsZO0QUQImt6twi6UkzoAABEENEarECNBEMU7mgAESIgiGgN65SOYSCCQSCCQkBDziQgHAkgYOkAgkUBpNIb6oiDioggBBEXxBESJKHNAIJFAMJGAQABJFBD2+9EQVGUtiUYhgqA+GITcGX2EQCAE8dQupyQRRYVvOyTiR4NQgjj88MUF+BIqGaqAAAHEEArtRoSEUJeoQsSvH2zBRBylkWS+QJJsEQCE/T40BIsgCQJEQlAci4IISZmJIKA0Gkd5NIHdRQE0BnyptCS0bIqiye9DXZEPAgFKYjEEEwnE/QSxoIAwQpAsCL5AIgFRTCAhCIgjQH2GByVSBJXiDvgiAUQbBdSXlKGxKJQsHwjKI1EE43EIACI+H+qLkuUUQFAejkAAUJe65pcklEciCMYTqXZMGsfkNvWX7EFEKMIeUoWEYcdSFI+jKJFAVBQR8/vhkySURGMQQFBXVKS0aSgeR3EsBp8kQSQEgiAhUFqLMIqxJ9ESUZ8PIiHwSRL8kgSBSPCRBIKxGAhpSkoklgAARCJAEggCkg9BSUTUJ2JPMNlnJZ1tmqA4FodfjCDgC0NIiAg0liDuE1AXFBHz+RCMx1Eci8NHCKJisswhMQ5/sA6JRAj1iTLERRGS4EPcJyqKjV+SUBKLIRSLQRTjCJTsSd4jApqEIuxEa92zZZEofCQBERLigg9x0Zese18Yfl8UgiRAkASIRELAHwYEgnCkAjFSBIEQiJp/BAIaivxoDCTHVTHCCKEJkUQJokJQqfNkf02gLBJB2B9AYzDZ33yEIJCII+L3KzIGEwkUx2JoDAQQ8/lSz0nwJ+IoI2EEA/XYhZYII9nHgoiiErWokyoRFotSbRxDICGhIRjUtYOPEJRIjSj31YLEAohHShHziYiEgJjgR3EihmB9BAnRh6aSEkR85sWyKBFHUTw5R8VEERF/UsbipiYEY1H4g0CgrB6S5INY1ypV+DDixQ3YjraIoEiXngCizHU034UAYmiJHYhHSpGQApDEpLIrgMCfSECU4ghKIoKSiLDfh7oSCWEhpEurguxBhbAb0aYKxEgQdaEiXdvICMVjAICw3zwfiISgLBJBIkAQ84mIQp03S6NxVETjICBo8iUACBCJiIRPQCTgQ0Skzy9+SVLaSpZXIAQiCESJIFhSCyIS1MVaoclHl5kXoXgMoWgERQkCxCIgAiCWEZBAAtFEMeLxECJ+P6KpPieCoAK1IEREOFGKqN+HmYFtuOq809OWgYY9e/agsrLSdv12xIxs374diUQC1dXVuuvV1dX44YcfqO9s3ryZ+vzmzZst87ntttswf/58J6KlhV3bShHfLSGuuyrvBEKpfzH4ETO9W48g6tHKcDVueg4A/Iiacoik/gEJCIiZGoIAqAcANCn3jKnHADTq3tDLKufqp8jl1zyzHZWaOwkkqKWQIWI3WqT+jsCPiO6uBKCO+l4CIhoVtUF+S0zJG079AxKKbBKA7cq7STSl/iVBIKLJ0tZIAKUsxjZwgiiAbdo6qkvAjwblZyO07aAvp3xdey1ZPxa7jx0VqbtNpj6R0KQnIAoJch8BjP1EbQMBgA/YIffVsPJcsseoSKZQTpdLeSOBZBljpnqPAohCAFCskViVImbIDwAa4QeU/pRI/YvBZ3oOaIQAIAAYxp1PU/uAtk7ElMzJ/h+BTyObDO1vY8+X2yiujKFkGYpSz+rbSAKwBwAQ1fW3OACf5rcEpHpPQvd+AsBu+ABUpu41KM/vQqlOjnjqn4i4qR2SY6TKIFlSruS91IZll3n8ynI06n4l66VeW3875XqTNytFqX9x6nwjAaY2lUEA7FD6HW328WmejAM7zG3eCB8a0VKTm3n8AOocajUfqKlKunJEAGxTfqkmKVlm1vyizm0U7KyylZkXcQD1EFCvHYN1xifiujzq5b6QGh3rWzUhX8ik7FnD7NmzMXPmTOW3zIy4jaOFb7C1dRkaIi3RhGIISGrHAJDwAUIojNI6oLIhplwHgKjPhx2tBcR9AohAIEgifI1BBOLq8VggaVOVd8fBlCNgkikJIuoXERN98EsJlETjKI7HIKTyIIKA7aUliPl8KA9HURqNIObzIez3p5iF5K4y6vcpOxuREAQSCVQ1hRFMxBHz+bCrpBgEAlpE6lAe2o5wfUs0+VK7rUQCPgiI+kPYUxWDX4yiONEExASEI1WIi6LKwhIBEFJLkSiCFEfgiwkINQoppiKGYPlORMIV2BWoRNgfSH5+HgAggAgCgok4KsJRFMdiaPL70VAUTDEkcQQSBNFACPXBAMqiMVSEmwAiobGoBLWhIIrjcQRjEcTE5E4w6vMhmEigNLgTgSYfAmE/hBQDECzfiVhjJcIkhKjPD/gSCBbvQbA2BEjqtJ1QTgYAIiNwmYgAmkIB7K6Io7FJhEQEhKJhtGkIIxAoQtgfQG1xMeKiAAgCfAkJleEwSmIxNAUC2FWcrO+qcBjFsRjqg0HsCYWUHZBAkvslAQAIgc8fRUCIoGSPH8XRmDLtxXzJfhPxJ8teFE8g5hPREAgiIQqoDEeSuzpRRF2oCGG/HwlBUMrpC4YRiEsobhBQHIshIYiIiyLivuT/EwLQIDYgLrNS8VIg1bcEAgQlMakKJKQkQyFFEEqElXk5IgTRFAxCEgGhuAFCuBwSCcJHCIqjMSQQRTjgR1OKURF8SQUzmBBBgmH4wwGIMREEcQQSiSQ1TtTx1lAURDjFwvmCTRB8yYXClwAqdgooicVBhCQD1RgIIBYQEPeJCCQSCEWSDBHxSRBLGiClzEwSREiCCCL5ITQVwZeixSVBSP4Tk/xeaTSKluI2+IONaPAVI0yKITSUIBiTEEwk4JckSIKAPaEi1BUVIRSPoaopAr+UQJM/gKjfj+JYFEXxROq5EJoCfpRGoyiLRkEARHx+xP0BENGPSFkYlVIdquK7kRASqPVXYI9YhlBdEOVNUUgQUBcqQtTnQ0U4gpJYTBmmTf4AGov8iJbFkYgm+7xfklAiNaEkUItd9SVoDIbgSyRQHk2gDCL8JI5g2U7Em8oRlYrRGAwg7Pcj5vMhICVQFEuAJOKoFyVEA0EQAQiWEojxIHwxmQWRgFADWkh70CJei1i0CLGGquQ85fMh4vcjkEigOKZXVAiAunIRjSUCSFNJipGS4JMIJEFA1OdDzOdX+l8wIaE00YBg0TZgdwUEknxmT4WIcJEAEi6GSAjKIxGFlVTHvIDa4hBEAlQk6lAc3I1ofUuFlqwrKkJjMICgFENp0U6I9cXwxUSICCAcCKGuKJBUqoUEJEgQSJLRKo7HUFG0FUJjMaSYygpJooBGfwBNQT3zTeT+JQgg/gR8/iiC9f7kOhDTrzUy5PnCbzFXxUURe0JFqA8GkUjEUvIR+AWCouIYEA5BlAQUxRMojUYhShKaAgGEq2Ig0SACERHBeByHtTRbAXIFR8pI69at4fP5sGXLFt31LVu2oF27dtR32rVr5+h5ACgqKkJRUZHlfbfw0LWzs56HBw8ePHjw4IENRwaqYDCIIUOGYNmyZco1SZKwbNkyDBs2jPrOsGHDdM8DwNKlSy2f9+DBgwcPHjw0Lzg208ycORPTp0/HQQcdhEMOOQT33XcfGhoalNM106ZNQ4cOHXDbbbcBAK644gocccQRuOeee3D88cdj8eLF+OKLL/Dwww+7WxIPHjx48ODBw14Jx8rIpEmTsG3bNsydOxebN2/GoEGD8OabbypOqhs3boSo8QgePnw4nnrqKcyZMwfXXXcdevbsiZdeegkHHHCAe6Xw4MGDBw8ePOy1cHS0N1/gPRrkwYMHDx48eCgc8K7fhRF6zYMHDx48ePDQbOEpIx48ePDgwYOHvMJTRjx48ODBgwcPeYWnjHjw4MGDBw8e8gpPGfHgwYMHDx485BWeMuLBgwcPHjx4yCs8ZcSDBw8ePHjwkFd4yogHDx48ePDgIa/wlBEPHjx48ODBQ17hOBx8PiAHid2zZ0+eJfHgwYMHDx488EJet+2Cve8VykhdXR0AoGPHjnmWxIMHDx48ePDgFHV1daisrLS8v1d8m0aSJPz+++8oLy+HIAiupbtnzx507NgR//vf/5rNN2+aW5mbW3kBr8zNoczNrbyAV+a9tcyEENTV1aGmpkb3EV0j9gpmRBRF7LfffllLv6KiYq9t6HTR3Mrc3MoLeGVuDmhu5QW8Mu+NYDEiMjwHVg8ePHjw4MFDXuEpIx48ePDgwYOHvKJZKyNFRUWYN28eioqK8i1KztDcytzcygt4ZW4OaG7lBbwy7+vYKxxYPXjw4MGDBw/7Lpo1M+LBgwcPHjx4yD88ZcSDBw8ePHjwkFd4yogHDx48ePDgIa/wlBEPHjx48ODBQ17RrJWRBx54AF26dEEoFMLQoUPx2Wef5VskV3Dbbbfh4IMPRnl5Odq2bYsJEyZg7dq1umdGjRoFQRB0/y688MI8SZw5brzxRlN5+vTpo9wPh8O45JJL0KpVK5SVlWHixInYsmVLHiXODF26dDGVVxAEXHLJJQD2jfb94IMPcOKJJ6KmpgaCIOCll17S3SeEYO7cuWjfvj2Ki4sxZswY/Pjjj7pndu7cialTp6KiogJVVVWYMWMG6uvrc1gKZ2CVORaLYdasWejfvz9KS0tRU1ODadOm4ffff9elQesbt99+e45Lwge7Nj7rrLNMZTn22GN1z+xLbQyAOq4FQcBdd92lPLM3tTEvmq0ysmTJEsycORPz5s3DV199hYEDB2Ls2LHYunVrvkXLGO+//z4uueQSfPrpp1i6dClisRiOOeYYNDQ06J4777zzsGnTJuXfnXfemSeJ3cH++++vK89HH32k3Lvqqqvw73//G88++yzef/99/P777zjllFPyKG1m+Pzzz3VlXbp0KQDgtNNOU57Z29u3oaEBAwcOxAMPPEC9f+edd+Ivf/kLFi5ciJUrV6K0tBRjx45FOBxWnpk6dSq+++47LF26FK+++io++OADnH/++bkqgmOwytzY2IivvvoKN9xwA7766iu88MILWLt2LU466STTszfddJOu7S+77LJciO8Ydm0MAMcee6yuLE8//bTu/r7UxgB0Zd20aRMee+wxCIKAiRMn6p7bW9qYG6SZ4pBDDiGXXHKJ8juRSJCamhpy22235VGq7GDr1q0EAHn//feVa0cccQS54oor8ieUy5g3bx4ZOHAg9V5tbS0JBALk2WefVa59//33BABZsWJFjiTMLq644grSvXt3IkkSIWTfa18A5MUXX1R+S5JE2rVrR+666y7lWm1tLSkqKiJPP/00IYSQNWvWEADk888/V5554403iCAI5LfffsuZ7OnCWGYaPvvsMwKAbNiwQbnWuXNncu+992ZXuCyAVt7p06eT8ePHW77THNp4/Pjx5KijjtJd21vbmIVmyYxEo1F8+eWXGDNmjHJNFEWMGTMGK1asyKNk2cHu3bsBAC1bttRdX7RoEVq3bo0DDjgAs2fPRmNjYz7Ecw0//vgjampq0K1bN0ydOhUbN24EAHz55ZeIxWK69u7Tpw86deq0T7R3NBrFk08+iXPOOUf3Icl9rX21+Pnnn7F582Zdm1ZWVmLo0KFKm65YsQJVVVU46KCDlGfGjBkDURSxcuXKnMucDezevRuCIKCqqkp3/fbbb0erVq0wePBg3HXXXYjH4/kR0AUsX74cbdu2Re/evXHRRRdhx44dyr19vY23bNmC1157DTNmzDDd25faGNhLPpTnNrZv345EIoHq6mrd9erqavzwww95kio7kCQJV155JQ477DAccMAByvUpU6agc+fOqKmpwTfffINZs2Zh7dq1eOGFF/IobfoYOnQonnjiCfTu3RubNm3C/PnzMWLECKxevRqbN29GMBg0TdjV1dXYvHlzfgR2ES+99BJqa2tx1llnKdf2tfY1Qm432hiW723evBlt27bV3ff7/WjZsuU+0e7hcBizZs3C5MmTdR9Ru/zyy3HggQeiZcuW+OSTTzB79mxs2rQJCxYsyKO06eHYY4/FKaecgq5du2L9+vW47rrrMG7cOKxYsQI+n2+fb+N//OMfKC8vN5mU96U2ltEslZHmhEsuuQSrV6/W+U8A0NlU+/fvj/bt22P06NFYv349unfvnmsxM8a4ceOUvwcMGIChQ4eic+fOeOaZZ1BcXJxHybKPv//97xg3bhxqamqUa/ta+3rQIxaL4fTTTwchBA899JDu3syZM5W/BwwYgGAwiAsuuAC33XbbXhdW/A9/+IPyd//+/TFgwAB0794dy5cvx+jRo/MoWW7w2GOPYerUqQiFQrrr+1Iby2iWZprWrVvD5/OZTlNs2bIF7dq1y5NU7uPSSy/Fq6++ivfeew/77bcf89mhQ4cCAH766adciJZ1VFVVoVevXvjpp5/Qrl07RKNR1NbW6p7ZF9p7w4YNeOedd3Duuecyn9vX2lduN9YYbteunckhPR6PY+fOnXt1u8uKyIYNG7B06VLbT8sPHToU8Xgcv/zyS24EzCK6deuG1q1bK/14X21jAPjwww+xdu1a27EN7Btt3CyVkWAwiCFDhmDZsmXKNUmSsGzZMgwbNiyPkrkDQgguvfRSvPjii3j33XfRtWtX23dWrVoFAGjfvn2WpcsN6uvrsX79erRv3x5DhgxBIBDQtffatWuxcePGvb69H3/8cbRt2xbHH38887l9rX27du2Kdu3a6dp0z549WLlypdKmw4YNQ21tLb788kvlmXfffReSJCnK2d4GWRH58ccf8c4776BVq1a276xatQqiKJrMGXsjfv31V+zYsUPpx/tiG8v4+9//jiFDhmDgwIG2z+4TbZxvD9p8YfHixaSoqIg88cQTZM2aNeT8888nVVVVZPPmzfkWLWNcdNFFpLKykixfvpxs2rRJ+dfY2EgIIeSnn34iN910E/niiy/Izz//TF5++WXSrVs3MnLkyDxLnj6uvvpqsnz5cvLzzz+Tjz/+mIwZM4a0bt2abN26lRBCyIUXXkg6depE3n33XfLFF1+QYcOGkWHDhuVZ6syQSCRIp06dyKxZs3TX95X2raurI19//TX5+uuvCQCyYMEC8vXXXysnR26//XZSVVVFXn75ZfLNN9+Q8ePHk65du5KmpiYljWOPPZYMHjyYrFy5knz00UekZ8+eZPLkyfkqki1YZY5Go+Skk04i++23H1m1apVubEciEUIIIZ988gm59957yapVq8j69evJk08+Sdq0aUOmTZuW55LRwSpvXV0dueaaa8iKFSvIzz//TN555x1y4IEHkp49e5JwOKyksS+1sYzdu3eTkpIS8tBDD5ne39vamBfNVhkhhJD777+fdOrUiQSDQXLIIYeQTz/9NN8iuQIA1H+PP/44IYSQjRs3kpEjR5KWLVuSoqIi0qNHD/J///d/ZPfu3fkVPANMmjSJtG/fngSDQdKhQwcyadIk8tNPPyn3m5qayMUXX0xatGhBSkpKyMknn0w2bdqUR4kzx1tvvUUAkLVr1+qu7yvt+95771H78fTp0wkhyeO9N9xwA6muriZFRUVk9OjRprrYsWMHmTx5MikrKyMVFRXk7LPPJnV1dXkoDR9YZf75558tx/Z7771HCCHkyy+/JEOHDiWVlZUkFAqRvn37kltvvVW3eBcSWOVtbGwkxxxzDGnTpg0JBAKkc+fO5LzzzjNtGPelNpbxt7/9jRQXF5Pa2lrT+3tbG/NCIISQrFIvHjx48ODBgwcPDDRLnxEPHjx48ODBQ+HAU0Y8ePDgwYMHD3mFp4x48ODBgwcPHvIKTxnx4MGDBw8ePOQVnjLiwYMHDx48eMgrPGXEgwcPHjx48JBXeMqIBw8ePHjw4CGv8JQRDx48ePDgwUNe4SkjHjx48ODBg4e8wlNGPHjw4MGDBw95haeMePDgwYMHDx7yCk8Z8eDBgwcPHjzkFf8PTu21/VAxsTEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[numeric_cols].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Donorage</th>\n",
       "      <th>Recipientage</th>\n",
       "      <th>CD34kgx10d6</th>\n",
       "      <th>CD3dCD34</th>\n",
       "      <th>CD3dkgx10d8</th>\n",
       "      <th>Rbodymass</th>\n",
       "      <th>ANCrecovery</th>\n",
       "      <th>PLTrecovery</th>\n",
       "      <th>time_to_aGvHD_III_IV</th>\n",
       "      <th>survival_time</th>\n",
       "      <th>survival_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.472068</td>\n",
       "      <td>9.931551</td>\n",
       "      <td>11.891781</td>\n",
       "      <td>5.385096</td>\n",
       "      <td>4.745714</td>\n",
       "      <td>35.801081</td>\n",
       "      <td>26752.866310</td>\n",
       "      <td>90937.919786</td>\n",
       "      <td>775408.042781</td>\n",
       "      <td>938.743316</td>\n",
       "      <td>0.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.271826</td>\n",
       "      <td>5.305639</td>\n",
       "      <td>9.914386</td>\n",
       "      <td>9.598716</td>\n",
       "      <td>3.859128</td>\n",
       "      <td>19.650922</td>\n",
       "      <td>161747.200525</td>\n",
       "      <td>288242.407688</td>\n",
       "      <td>418425.252689</td>\n",
       "      <td>849.589495</td>\n",
       "      <td>0.499266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.646575</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.204132</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.039726</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>5.350000</td>\n",
       "      <td>1.786683</td>\n",
       "      <td>1.687500</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>168.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.550685</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>9.720000</td>\n",
       "      <td>2.734462</td>\n",
       "      <td>4.325000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>676.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.117809</td>\n",
       "      <td>14.050000</td>\n",
       "      <td>15.415000</td>\n",
       "      <td>5.823565</td>\n",
       "      <td>6.785000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1604.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>55.553425</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>57.780000</td>\n",
       "      <td>99.560970</td>\n",
       "      <td>20.020000</td>\n",
       "      <td>103.400000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>3364.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Donorage  Recipientage  CD34kgx10d6    CD3dCD34  CD3dkgx10d8  \\\n",
       "count  187.000000    187.000000   187.000000  182.000000   182.000000   \n",
       "mean    33.472068      9.931551    11.891781    5.385096     4.745714   \n",
       "std      8.271826      5.305639     9.914386    9.598716     3.859128   \n",
       "min     18.646575      0.600000     0.790000    0.204132     0.040000   \n",
       "25%     27.039726      5.050000     5.350000    1.786683     1.687500   \n",
       "50%     33.550685      9.600000     9.720000    2.734462     4.325000   \n",
       "75%     40.117809     14.050000    15.415000    5.823565     6.785000   \n",
       "max     55.553425     20.200000    57.780000   99.560970    20.020000   \n",
       "\n",
       "        Rbodymass     ANCrecovery     PLTrecovery  time_to_aGvHD_III_IV  \\\n",
       "count  185.000000      187.000000      187.000000            187.000000   \n",
       "mean    35.801081    26752.866310    90937.919786         775408.042781   \n",
       "std     19.650922   161747.200525   288242.407688         418425.252689   \n",
       "min      6.000000        9.000000        9.000000             10.000000   \n",
       "25%     19.000000       13.000000       16.000000        1000000.000000   \n",
       "50%     33.000000       15.000000       21.000000        1000000.000000   \n",
       "75%     50.600000       17.000000       37.000000        1000000.000000   \n",
       "max    103.400000  1000000.000000  1000000.000000        1000000.000000   \n",
       "\n",
       "       survival_time  survival_status  \n",
       "count     187.000000       187.000000  \n",
       "mean      938.743316         0.454545  \n",
       "std       849.589495         0.499266  \n",
       "min         6.000000         0.000000  \n",
       "25%       168.500000         0.000000  \n",
       "50%       676.000000         0.000000  \n",
       "75%      1604.000000         1.000000  \n",
       "max      3364.000000         1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recipientgender</th>\n",
       "      <th>Stemcellsource</th>\n",
       "      <th>Donorage35</th>\n",
       "      <th>IIIV</th>\n",
       "      <th>Gendermatch</th>\n",
       "      <th>DonorABO</th>\n",
       "      <th>RecipientABO</th>\n",
       "      <th>RecipientRh</th>\n",
       "      <th>ABOmatch</th>\n",
       "      <th>CMVstatus</th>\n",
       "      <th>...</th>\n",
       "      <th>HLAmatch</th>\n",
       "      <th>HLAmismatch</th>\n",
       "      <th>Antigen</th>\n",
       "      <th>Alel</th>\n",
       "      <th>HLAgrI</th>\n",
       "      <th>Recipientage10</th>\n",
       "      <th>Recipientageint</th>\n",
       "      <th>Relapse</th>\n",
       "      <th>aGvHDIIIIV</th>\n",
       "      <th>extcGvHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>...</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'2'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'-1'</td>\n",
       "      <td>b'-1'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'2'</td>\n",
       "      <td>b'0'</td>\n",
       "      <td>b'1'</td>\n",
       "      <td>b'1'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>112</td>\n",
       "      <td>145</td>\n",
       "      <td>104</td>\n",
       "      <td>112</td>\n",
       "      <td>155</td>\n",
       "      <td>73</td>\n",
       "      <td>75</td>\n",
       "      <td>158</td>\n",
       "      <td>134</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>94</td>\n",
       "      <td>159</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>94</td>\n",
       "      <td>99</td>\n",
       "      <td>89</td>\n",
       "      <td>159</td>\n",
       "      <td>147</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recipientgender Stemcellsource Donorage35  IIIV Gendermatch DonorABO  \\\n",
       "count              187            187        187   187         187      187   \n",
       "unique               2              2          2     2           2        4   \n",
       "top               b'1'           b'1'       b'0'  b'1'        b'0'     b'0'   \n",
       "freq               112            145        104   112         155       73   \n",
       "\n",
       "       RecipientABO RecipientRh ABOmatch CMVstatus  ... HLAmatch HLAmismatch  \\\n",
       "count           187         187      187       187  ...      187         187   \n",
       "unique            5           3        3         5  ...        4           2   \n",
       "top            b'1'        b'1'     b'1'      b'2'  ...     b'0'        b'0'   \n",
       "freq             75         158      134        57  ...       94         159   \n",
       "\n",
       "       Antigen   Alel HLAgrI Recipientage10 Recipientageint Relapse  \\\n",
       "count      187    187    187            187             187     187   \n",
       "unique       5      6      7              2               3       2   \n",
       "top      b'-1'  b'-1'   b'0'           b'0'            b'2'    b'0'   \n",
       "freq        93     93     94             99              89     159   \n",
       "\n",
       "       aGvHDIIIIV extcGvHD  \n",
       "count         187      187  \n",
       "unique          2        3  \n",
       "top          b'1'     b'1'  \n",
       "freq          147      128  \n",
       "\n",
       "[4 rows x 26 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['category','object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Recipientgender          object\n",
       "Stemcellsource           object\n",
       "Donorage                float64\n",
       "Donorage35               object\n",
       "IIIV                     object\n",
       "Gendermatch              object\n",
       "DonorABO                 object\n",
       "RecipientABO             object\n",
       "RecipientRh              object\n",
       "ABOmatch                 object\n",
       "CMVstatus                object\n",
       "DonorCMV                 object\n",
       "RecipientCMV             object\n",
       "Disease                  object\n",
       "Riskgroup                object\n",
       "Txpostrelapse            object\n",
       "Diseasegroup             object\n",
       "HLAmatch                 object\n",
       "HLAmismatch              object\n",
       "Antigen                  object\n",
       "Alel                     object\n",
       "HLAgrI                   object\n",
       "Recipientage            float64\n",
       "Recipientage10           object\n",
       "Recipientageint          object\n",
       "Relapse                  object\n",
       "aGvHDIIIIV               object\n",
       "extcGvHD                 object\n",
       "CD34kgx10d6             float64\n",
       "CD3dCD34                float64\n",
       "CD3dkgx10d8             float64\n",
       "Rbodymass               float64\n",
       "ANCrecovery             float64\n",
       "PLTrecovery             float64\n",
       "time_to_aGvHD_III_IV    float64\n",
       "survival_time           float64\n",
       "survival_status         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DTypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Recipientgender          object\n",
       "Stemcellsource           object\n",
       "Donorage                float64\n",
       "Donorage35               object\n",
       "IIIV                     object\n",
       "Gendermatch              object\n",
       "DonorABO                 object\n",
       "RecipientABO             object\n",
       "RecipientRh              object\n",
       "ABOmatch                 object\n",
       "CMVstatus                object\n",
       "DonorCMV                 object\n",
       "RecipientCMV             object\n",
       "Disease                  object\n",
       "Riskgroup                object\n",
       "Txpostrelapse            object\n",
       "Diseasegroup             object\n",
       "HLAmatch                 object\n",
       "HLAmismatch              object\n",
       "Antigen                  object\n",
       "Alel                     object\n",
       "HLAgrI                   object\n",
       "Recipientage            float64\n",
       "Recipientage10           object\n",
       "Recipientageint          object\n",
       "Relapse                  object\n",
       "aGvHDIIIIV               object\n",
       "extcGvHD                 object\n",
       "CD34kgx10d6             float64\n",
       "CD3dCD34                float64\n",
       "CD3dkgx10d8             float64\n",
       "Rbodymass               float64\n",
       "ANCrecovery             float64\n",
       "PLTrecovery             float64\n",
       "time_to_aGvHD_III_IV    float64\n",
       "survival_time           float64\n",
       "survival_status         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}