EDACheckpoint_Group141_WI24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - EDA Checkpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Shivangi Gupta\n",
    "- Joseph Hwang\n",
    "- Zijun Yang\n",
    "- Johnny Gonzales\n",
    "- Tanishq Rathore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizing clinical MRI Data and personal details of an individual, can we predict via machine learning model whether an individual will have an onset of Alzheimer's disease? Features the model will be trained on include variables such as Mini Mental State Examination (MMSE), visit number, Clinical Dementia Rating (CDR), gender, age, years of education, socioeconomic status, Estimated total intracranial volume (eTIV), Normalize Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Advancements in healthcare, improvements in living conditions, and\n",
    "breakthroughs in medicine have collectively contributed to longer life\n",
    "expectancies worldwide; simultaneously, developed countries are also\n",
    "experiencing declining fertility\n",
    "rates.[<sup>1</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/)\n",
    "The combination of these two circumstances has resulted in the\n",
    "proportion of older people within populations to steadily increase. The\n",
    "World Health Organization (WHO) reported that “in 2020, the number of\n",
    "people aged 60 and older outnumbered children younger than 5\n",
    "years”.[<sup>2</sup>](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health)\n",
    "In addition, they also state that “between 2015 and 2050, the proportion\n",
    "of the world’s population over 60 years will nearly double from 12% to\n",
    "22%”. As a result, it is reasonable that we examine common health\n",
    "conditions associated with older age, one being Alzheimer’s disease.\n",
    "\n",
    "So what is Alzheimer’s disease? Alzheimer’s disease is a progressive\n",
    "neurodegenerative brain disorder that impairs memory and cognitive\n",
    "functions. It is the most common cause of dementia and affects about 6.5\n",
    "million people in the United States who are aged 65 and\n",
    "older.[<sup>3</sup>](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
    "At the moment, there are no cures for the disease but medicines may\n",
    "improve or slow the progression of\n",
    "symptoms.[<sup>3</sup>](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
    "As such, it is our project to create a model that is able to predict\n",
    "Alzheimer’s disease based on clinical data that include factors that\n",
    "show risk and progression of the disease.\n",
    "\n",
    "There are several other projects that have asked similar questions and\n",
    "approached similar problems for other diseases. For instance, one study\n",
    "tried to use machine learning methods to predict risk of cardiovascular\n",
    "disease based on major contributing\n",
    "factors.[<sup>4</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/)\n",
    "Similarly, another paper used machine learning and ranker-based feature\n",
    "selection methods to predict eye diseases based on\n",
    "symptoms.[<sup>5</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/)\n",
    "Lastly, there was a paper that predicted thyroid disease using selective\n",
    "features and machine learning\n",
    "techniques.[<sup>6</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/)\n",
    "All three papers seem to have been relatively successful in predicting\n",
    "the disease based on distinct features. Evidently, training machine\n",
    "learning models based on datasets which contain factors and indicators\n",
    "for a given disease is not a novel format of question and method; we\n",
    "hope to achieve similarly for Alzheimer’s disease.\n",
    "\n",
    "<u>In-Depth Study Analysis</u>\n",
    "\n",
    "Our group analyzed two studies published in the National Institute of\n",
    "Health’s (NIH) journal database. The first study[<sup>7</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/) employed\n",
    "machine learning models to predict early-stage Alzheimer's Disease using\n",
    "Open Access Series of Imaging Studies (OASIS) data, focusing on metrics\n",
    "like precision, recall, accuracy, and F1-score. The authors, with\n",
    "backgrounds in technology and health research, aimed to enhance early\n",
    "diagnosis, potentially lowering Alzheimer's mortality rates. The study\n",
    "demonstrated that machine learning techniques such as decision trees,\n",
    "random forests, SVM, gradient boosting, and voting classifiers can\n",
    "effectively predict early-stage Alzheimer's Disease with an accuracy of\n",
    "up to 83%. This achievement highlights the critical role of data science\n",
    "in identifying Alzheimer's at an early phase, leveraging feature\n",
    "selection and advanced algorithms to enhance diagnostic accuracy. Early\n",
    "detection is crucial for timely intervention, potentially mitigating the\n",
    "disease's progression and impact on patients and their families (Kavitha\n",
    "et al.).\n",
    "\n",
    "The second study[<sup>8</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/) titled\n",
    "“Application of machine learning methods for diagnosis of dementia based\n",
    "on the 10/66 battery of cognitive function tests in south India”\n",
    "investigated the use of machine learning for diagnosing dementia in\n",
    "South India, employing the culturally and educationally fair 10/66\n",
    "battery of cognitive function tests designed for use in low and\n",
    "middle-income countries. Through the analysis of neuropsychological\n",
    "data, demographic information, and normative data, the research applied\n",
    "Jrip classification algorithm among others, achieving high diagnostic\n",
    "accuracy. This approach demonstrates the potential to streamline the\n",
    "diagnostic process, making it quicker and more accessible for clinicians\n",
    "and patients in India, thereby addressing the significant healthcare\n",
    "challenge of efficiently identifying dementia in community settings\n",
    "(Bhagyashree et al).\n",
    "\n",
    "<u>In-Depth Analysis of Similar Projects</u>\n",
    "\n",
    "Our group also delved into actual Kaggle projects that are directly\n",
    "associated with the dataset we’ve chosen to use, delving into EDA and\n",
    "prediction models using Scikit-Learn and Tensorflow. The first\n",
    "project[<sup>9</sup>](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri)\n",
    "I will discuss starts with an introduction to Alzheimer's disease and\n",
    "the problem statement of estimating the Clinical Dementia Rating (CDR)\n",
    "using MRI dataset features. It progresses through data loading and\n",
    "preprocessing, including null value handling and normalization, and\n",
    "employs machine learning techniques, specifically mentioning model\n",
    "training with hyperparameter tuning for XGBClassifier and\n",
    "GradientBoostingClassifier. The notebook concludes with predictions and\n",
    "performance evaluation, indicated by confusion matrix and classification\n",
    "report visualizations, and the model was able to reach a final accuracy\n",
    "of \\~80%.\n",
    "\n",
    "The second project[<sup>10</sup>](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf) tried to predict the Clinical Rating of Alzheimer's disease (CRA) by\n",
    "integrating data loading, visualization, and extensive machine learning,\n",
    "including the use of TensorFlow for neural network models. It explored\n",
    "various machine learning models, with a special emphasis on model\n",
    "training and evaluation, culminating in the finding that the\n",
    "DecisionTreeClassifier performed the best among the models tested. The\n",
    "conclusion stressed the need for more data to enhance the precision of\n",
    "Alzheimer's disease predictions, highlighting the challenge of data\n",
    "scarcity in achieving accurate diagnostic models.\n",
    "\n",
    "**References**\n",
    "\n",
    "1.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/) Nargund G. (2009) Declining birth rate in Developed Countries: A\n",
    "     radical policy re-think is required. *Facts, views & vision in\n",
    "     ObGyn, 1(3), 191–193.*\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/)\n",
    "\n",
    "2.  [^](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health) World Health Organization. (1 Oct 2022) Ageing and health. *World\n",
    "     Health Organization*.\n",
    "     [<u>https://www.who.int/news-room/fact-sheets/detail/ageing-and-health</u>](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health)\n",
    "\n",
    "3.  [^](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447) Mayo Foundation for Medical Education and Research. (30\n",
    "     August 2023) Alzheimer’s disease. *Mayo Clinic*.\n",
    "     [<u>https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447</u>](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
    "\n",
    "4.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/) Peng, M., Hou, F., Cheng, Z., Shen, T., Liu, K., Zhao, C., &\n",
    "     Zheng, W. (23 Mar 2023) Prediction of cardiovascular disease risk\n",
    "     based on major contributing features. *Scientific reports, 13(1),\n",
    "     4778*.\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/)\n",
    "\n",
    "5.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/) Marouf, A. A., Mottalib, M. M., Alhajj, R., Rokne, J., &\n",
    "     Jafarullah, O. (24 Dec 2022) An Efficient Approach to Predict Eye\n",
    "     Diseases from Symptoms Using Machine Learning and Ranker-Based\n",
    "     Feature Selection Methods. *Bioengineering (Basel, Switzerland),\n",
    "     10(1), 25*.\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/)\n",
    "\n",
    "6.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/) Chaganti, R., Rustam, F., De La Torre Díez, I., Mazón, J. L. V.,\n",
    "     Rodríguez, C. L., & Ashraf, I. (13 Aug 2022). Thyroid Disease\n",
    "     Prediction Using Selective Features and Machine Learning\n",
    "     Techniques. *Cancers, 14(16), 3914*.\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/)\n",
    "\n",
    "7.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/) Kavitha C, Mani V, Srividhya SR, Khalaf OI, Tavera Romero CA.\n",
    "     Early-Stage Alzheimer's Disease Prediction Using Machine Learning\n",
    "     Models. Front Public Health. 2022 Mar 3;10:853294. doi:\n",
    "     10.3389/fpubh.2022.853294. PMID: 35309200; PMCID: PMC8927715.\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/)\n",
    "\n",
    "8.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/) Bhagyashree SIR, Nagaraj K, Prince M, Fall CHD, Krishna M.\n",
    "     Diagnosis of Dementia by Machine learning methods in\n",
    "     Epidemiological studies: a pilot exploratory study from south\n",
    "     India. Soc Psychiatry Psychiatr Epidemiol. 2018 Jan;53(1):77-86.\n",
    "     doi: 10.1007/s00127-017-1410-0. Epub 2017 Jul 11. PMID: 28698926;\n",
    "     PMCID: PMC6138240.\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/)\n",
    "\n",
    "9.  [^](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri) Reddy, Shreyas. 2021. Alzheimer's analysis using MRI, February\n",
    "     8, 2024.\n",
    "     [<u>https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri</u>](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri)\n",
    "\n",
    "10. [^](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf) Andrew. 2017. Predict alzheimer disease sl and tf, February 8, 2024.\n",
    "     [<u>https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf</u>](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our project's hypothesis is the following: \"It is possible to predict the onset of Alzheimers based on the combination of (1) clinical data* and (2) personal features such as gender, age, years of education, and socioeconomic status.\" We believe that we will be able to successfully train a model that is able to predict the onset of Alzheimer's disease because, as mentioned in the background portion of the proposal, there has been numerous successful machine learning models trained on clinical data to predict the onset of a disease. The clinical data provide variables that capture both the cognitive and structural changes associated with the disease's progression. Incorporating personal features such as gender and age in the prediction model is justified by extensive research indicating that these factors can influence the risk and progression rate of Alzheimer's disease. Together, we believe that this will be enough data to train a model to predict the onset of Alzheimer's disease.\n",
    "\n",
    "*Clinical data include Mini Mental State Examination (MMSE), visit number, Clinical Dementia Rating (CDR), Estimated total intracranial volume (eTIV), Normalize Whole Brain Volume (nWBV), and the Atlas Scaling Factor (ASF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data overview\n",
    "\n",
    "- Dataset #1\n",
    "  - Dataset Name: OASIS-2: Longitudinal MRI Data in Nondemented and Demented Older Adults\n",
    "  - Link to the dataset:https://www.oasis-brains.org/#data\n",
    "  - Number of observations: 373\n",
    "  - Number of variables: 15\n",
    "\n",
    "The dataset provided by The Open Access Series of Imaging Studies (OASIS) contains a collection of 150 subjects aged 60 to 96. Each subject was scanned on 2 or more visits, separated by one year for a total of 373 imaging sessions. All subjects were right-handed and included both men and women. 72 subjects were charaterized as nondemented throughout the study. 64 subjects subjects were characterized as demented at their initial vists and remained so for subsequent scans. Lastly, 14 subjects were characterized as nondemented at their initial vist but were subsequently characterized as demented in later visits. Important variables are years of education (EDUC), socioeconomic status (SES; 1 to 5), mini mental state examination (MMSE score), clinical dementia rating (CDR rating scale), estimated total intracranial volume (eTIV), normalize whole brain volume(nWBV), and atlas scaling factor (ASF). \n",
    "\n",
    "The variables may be proxies for Alzheimers/cognitive state (conjecture):\n",
    "- __EDUC__: Represents years of education which may proxy for cognitive reserve. Higher education levels may result in better cognitive function and reduced risk of dementia. \n",
    "- __SES__: Ranging from 1 to 5, is a proxy for income, education level, and occupation, which represents environmental influences on cognitive health. \n",
    "- __MMSE__: A widely used screening tool for cognitive impairment. Scores range from 1 to 30 with higher scores indicating better cognitive function. The examination assesses domains such as orientation, memory, attention, and language. \n",
    "- __CDR__: CDR scale is also commonly used to assess severity of dementia symptoms. The scale ranges from 0 to 3 where 0 indicates no dementia, 0.5 is questionable dementia, 1 is mild dementia, 2 is moderate dementia, and 3 is severe dementia.\n",
    "- __eTIV__: Indicates the total volume inside the skull (including brain tissue, cerebrospinal fluid, etc.). It is measured in cubic centimeters (cc) and can help establish baseline brain size. Decrease/varying sizes may proxy for cognitive function and Alzheimer's risk.\n",
    "- __nWBV__: Represents the volume of the brain normalized to the subject's eTIV. It is expressed as a percentage, reflecting the proportion of the brain occupying the eTIV (nWBV = brain volume / eTIV). Changes in nWBV may be indicative of brain atrophy which is a common feature of Alzheimer's and dementia.\n",
    "- __ASF__: A scaling factor used in brain imaging to adjust for individual differences in brain size and shape. It accounts for variability in brain morphology allowing for accurate comparisons of brain structures.\n",
    "\n",
    "The dataset provided by OASIS is relatively clean and organized. However, some cleaning may be required, such as changing M/F and group to numeric values, dropping columns such as MRI ID, Visit, and Hand which will not be used in our predictions. All of this will be done by manipulating the dataframe. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format ='retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>Subject ID</th>\n",
       "      <th>MRI ID</th>\n",
       "      <th>Group</th>\n",
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       "      <th>M/F</th>\n",
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       "      <td>Demented</td>\n",
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       "      <td>76</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
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       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>1895</td>\n",
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       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>OAS2_0185_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>2297</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1688</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>763</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1327</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR3</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>3</td>\n",
       "      <td>1608</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>373 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Subject ID         MRI ID        Group  Visit  MR Delay M/F Hand  Age  \\\n",
       "0    OAS2_0001  OAS2_0001_MR1  Nondemented      1         0   M    R   87   \n",
       "1    OAS2_0001  OAS2_0001_MR2  Nondemented      2       457   M    R   88   \n",
       "2    OAS2_0002  OAS2_0002_MR1     Demented      1         0   M    R   75   \n",
       "3    OAS2_0002  OAS2_0002_MR2     Demented      2       560   M    R   76   \n",
       "4    OAS2_0002  OAS2_0002_MR3     Demented      3      1895   M    R   80   \n",
       "..         ...            ...          ...    ...       ...  ..  ...  ...   \n",
       "368  OAS2_0185  OAS2_0185_MR2     Demented      2       842   M    R   82   \n",
       "369  OAS2_0185  OAS2_0185_MR3     Demented      3      2297   M    R   86   \n",
       "370  OAS2_0186  OAS2_0186_MR1  Nondemented      1         0   F    R   61   \n",
       "371  OAS2_0186  OAS2_0186_MR2  Nondemented      2       763   F    R   63   \n",
       "372  OAS2_0186  OAS2_0186_MR3  Nondemented      3      1608   F    R   65   \n",
       "\n",
       "     EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
       "0      14  2.0  27.0  0.0  1987  0.696  0.883  \n",
       "1      14  2.0  30.0  0.0  2004  0.681  0.876  \n",
       "2      12  NaN  23.0  0.5  1678  0.736  1.046  \n",
       "3      12  NaN  28.0  0.5  1738  0.713  1.010  \n",
       "4      12  NaN  22.0  0.5  1698  0.701  1.034  \n",
       "..    ...  ...   ...  ...   ...    ...    ...  \n",
       "368    16  1.0  28.0  0.5  1693  0.694  1.037  \n",
       "369    16  1.0  26.0  0.5  1688  0.675  1.040  \n",
       "370    13  2.0  30.0  0.0  1319  0.801  1.331  \n",
       "371    13  2.0  30.0  0.0  1327  0.796  1.323  \n",
       "372    13  2.0  30.0  0.0  1333  0.801  1.317  \n",
       "\n",
       "[373 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('oasis_longitudinal.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 373 entries, 0 to 372\n",
      "Data columns (total 15 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Subject ID  373 non-null    object \n",
      " 1   MRI ID      373 non-null    object \n",
      " 2   Group       373 non-null    object \n",
      " 3   Visit       373 non-null    int64  \n",
      " 4   MR Delay    373 non-null    int64  \n",
      " 5   M/F         373 non-null    object \n",
      " 6   Hand        373 non-null    object \n",
      " 7   Age         373 non-null    int64  \n",
      " 8   EDUC        373 non-null    int64  \n",
      " 9   SES         354 non-null    float64\n",
      " 10  MMSE        371 non-null    float64\n",
      " 11  CDR         373 non-null    float64\n",
      " 12  eTIV        373 non-null    int64  \n",
      " 13  nWBV        373 non-null    float64\n",
      " 14  ASF         373 non-null    float64\n",
      "dtypes: float64(5), int64(5), object(5)\n",
      "memory usage: 43.8+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# slightly skewed distribution of SES\n",
    "sns.histplot(df['SES'].dropna(), kde=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['SES'].fillna(df['SES'].median(), inplace=True) # impute missing socioeconomic status columns using median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# left-skewed distribution of MMSE\n",
    "sns.histplot(df['MMSE'].dropna(), kde=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MMSE'].fillna(df['MMSE'].median(), inplace=True) # impute missing MMSE columns using the median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensure no more Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SES'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MMSE'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subject ID    0\n",
       "MRI ID        0\n",
       "Group         0\n",
       "Visit         0\n",
       "MR Delay      0\n",
       "M/F           0\n",
       "Hand          0\n",
       "Age           0\n",
       "EDUC          0\n",
       "SES           0\n",
       "MMSE          0\n",
       "CDR           0\n",
       "eTIV          0\n",
       "nWBV          0\n",
       "ASF           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data cleaning approach was quite straightforward. Once we imputed the missing values in the 'SES' and 'MMSE' columns using the medians because of their non-normal distributions, we simply had to ensure our relevant columns were of the correct data type, encoded numerically, and renamed for easier understanding. We chose to use a subset of the data, only subjects' first visits as that is the most appropriate for our prediction task for determing the early onset of Alzheimers/Dementia based on an initial visit. Then, we renamed the 'M/F' column and encoded it to be binary, updated the target variable, 'Group,' to have binary values of (1,0) representing the final categorization of Demented or Nondemented, respectively. Lastly, we dropped extraneous columns such as 'Hand' and 'MRI ID' that will not be used in our prediction models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Nondemented', 'Demented', 'Converted'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Group'].unique() #'Converted' are individuals who did not initially have dementia but later developed dementia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.mode.copy_on_write = True\n",
    "\n",
    "#Only use date from subjects' first visits\n",
    "df = df.loc[df['Visit']==1]\n",
    "\n",
    "#Convert M/F to numeric values and rename\n",
    "df['Gender'] = df['M/F'].map({'F': 0, 'M': 1})\n",
    "\n",
    "#Change 'Converted' to 'Demented'\n",
    "df['Group'] = df['Group'].replace('Converted', 'Demented')\n",
    "\n",
    "#Convert Group to numeric values (these are our target variables); 1 = dementia, 2 = no dementia\n",
    "df['Group'] = df['Group'].replace(['Demented', 'Nondemented'], [1,0])\n",
    "\n",
    "#Drop variables that will not be used in predictions\n",
    "df = df.drop(['MRI ID', 'Visit', 'Hand', 'M/F'], axis=1)\n",
    "\n",
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Subject ID</th>\n",
       "      <th>Group</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OAS2_0004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>18</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1215</td>\n",
       "      <td>0.710</td>\n",
       "      <td>1.444</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OAS2_0005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1689</td>\n",
       "      <td>0.712</td>\n",
       "      <td>1.039</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OAS2_0007</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1357</td>\n",
       "      <td>0.748</td>\n",
       "      <td>1.293</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>OAS2_0182</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1661</td>\n",
       "      <td>0.698</td>\n",
       "      <td>1.056</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>OAS2_0183</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.746</td>\n",
       "      <td>1.174</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>OAS2_0184</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1354</td>\n",
       "      <td>0.733</td>\n",
       "      <td>1.296</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1704</td>\n",
       "      <td>0.711</td>\n",
       "      <td>1.030</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Subject ID  Group  MR Delay  Age  EDUC  SES  MMSE  CDR  eTIV   nWBV  \\\n",
       "0    OAS2_0001      0         0   87    14  2.0  27.0  0.0  1987  0.696   \n",
       "1    OAS2_0002      1         0   75    12  2.0  23.0  0.5  1678  0.736   \n",
       "2    OAS2_0004      0         0   88    18  3.0  28.0  0.0  1215  0.710   \n",
       "3    OAS2_0005      0         0   80    12  4.0  28.0  0.0  1689  0.712   \n",
       "4    OAS2_0007      1         0   71    16  2.0  28.0  0.5  1357  0.748   \n",
       "..         ...    ...       ...  ...   ...  ...   ...  ...   ...    ...   \n",
       "145  OAS2_0182      1         0   73    12  2.0  23.0  0.5  1661  0.698   \n",
       "146  OAS2_0183      0         0   66    13  2.0  30.0  0.0  1495  0.746   \n",
       "147  OAS2_0184      1         0   72    16  3.0  24.0  0.5  1354  0.733   \n",
       "148  OAS2_0185      1         0   80    16  1.0  28.0  0.5  1704  0.711   \n",
       "149  OAS2_0186      0         0   61    13  2.0  30.0  0.0  1319  0.801   \n",
       "\n",
       "       ASF  Gender  \n",
       "0    0.883       1  \n",
       "1    1.046       1  \n",
       "2    1.444       0  \n",
       "3    1.039       1  \n",
       "4    1.293       1  \n",
       "..     ...     ...  \n",
       "145  1.056       1  \n",
       "146  1.174       0  \n",
       "147  1.296       0  \n",
       "148  1.030       1  \n",
       "149  1.331       0  \n",
       "\n",
       "[150 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check to ensure all Correct Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subject ID     object\n",
       "Group           int64\n",
       "MR Delay        int64\n",
       "Age             int64\n",
       "EDUC            int64\n",
       "SES           float64\n",
       "MMSE          float64\n",
       "CDR           float64\n",
       "eTIV            int64\n",
       "nWBV          float64\n",
       "ASF           float64\n",
       "Gender          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "\n",
    "As mentioned in our hypothesis, we are exploring and taking into account two types of variables in order to predict Alzheimer's outcome in individuals: (1) personal features and (2) Clinical features. We first perform EDA on the personal features.\n",
    "\n",
    "## Personal Features EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - Social Economic Status and Clinical Dementia Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdr_by_ses = df.groupby('SES')['CDR'].mean()\n",
    "\n",
    "cdr_by_ses.plot(kind='bar', color='lightgreen')\n",
    "plt.title('Average CDR by SES')\n",
    "plt.xlabel('SES')\n",
    "plt.ylabel('Average CDR')\n",
    "plt.xticks(ticks=range(len(cdr_by_ses)), labels=cdr_by_ses.index, rotation=0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the analysis conducted using the provided dataset, our method involved calculating the average Clinical Dementia Rating (CDR) value in respect to each Socioeconomic Status (SES) level. The graphical representation derived from this analysis indicates a trend where higher SES stages are associated with higher average CDR values. This observation may suggest a correlation within this specific dataset where individuals with higher socioeconomic status appear to have higher average levels of clinical dementia severity. It's important to note that this analysis reveals a pattern of association between SES and CDR within the confines of the dataset analyzed and does not imply causation. Further research would be necessary to explore the underlying factors contributing to this correlation and to assess the potential impact of other variables not considered in this initial analysis. There are potential confounding variables, some examples including how individuals with higher SES may have better access to healthcare, greater awareness of dementia/disease, genetic predisposition, and occupational factors resulting in the graph shown above. It is possible that the lower SES groups are being under-represented. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 - Age and Dementia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 382
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum age: 60\n",
      "Maximum age: 96\n"
     ]
    }
   ],
   "source": [
    "plt.hist(df['Age'], bins=range(60, 101, 5), edgecolor='black')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of People')\n",
    "plt.title('Age Distribution')\n",
    "plt.show()\n",
    "\n",
    "min_value = df['Age'].min()\n",
    "max_value = df['Age'].max()\n",
    "\n",
    "print(\"Minimum age:\", min_value)\n",
    "print(\"Maximum age:\", max_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above graph shows that the age of subjects range from 60 to 96, which is in-line with our earlier description of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 390,
       "width": 720
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create age groups from 60 to 96, in 5-year intervals\n",
    "bins = np.arange(60, 101, 5)  # 101 is the upper limit to ensure the maximum age of 96 is included\n",
    "labels = [f'{i}-{i+4}' for i in range(60, 96, 5)]  # Create age labels\n",
    "\n",
    "# Categorize ages into groups\n",
    "df['Age Group'] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Calculate the probability of dementia for each age group\n",
    "grouped = df.groupby('Age Group')['Group'].mean().reset_index()\n",
    "\n",
    "# Bar chart to compare the probability of dementia across age groups\n",
    "plt.figure(figsize=(12, 6))  # Set figure size\n",
    "plt.bar(grouped['Age Group'], grouped['Group'], color='skyblue')\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.ylabel('Probability of Dementia')\n",
    "plt.title('Probability of Dementia by Age Group')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The observed graph depicting the probability of dementia across different age groups (chosen arbitrarily) appears to be within expectation, demonstrating an overall trend where the likelihood of dementia increases with age. However, a notable deviation is observed in the age groups 85-89 and 90-94, where the dementia rates do not continue to increase. This anomaly could potentially be attributed to a significant reduction in the population size within these specific age groups. As shown in the our earlier graph, the chosen age groups are not uniformly distributed so each group will have a differing number of sample size. Smaller group sizes may lead to statistical variations that do not accurately reflect the general trend. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 - Gender and Dementia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender\n",
      "0    58.666667\n",
      "1    41.333333\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Calculate gender count, normalize count and convert to percent to find percentage distribution of gender.\n",
    "gender_counts = df['Gender'].value_counts(normalize=True) * 100\n",
    "print(gender_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f7541f24af0>,\n",
       "  <matplotlib.axis.XTick at 0x7f7541f24ac0>],\n",
       " [Text(0, 0, 'Female'), Text(1, 0, 'Male')])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dementia_by_gender = df.groupby('Gender')['Group'].mean()\n",
    "\n",
    "dementia_by_gender.plot(kind='bar', color=['blue', 'pink'])\n",
    "plt.title('Dementia Prevalence by Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Probability of Dementia')\n",
    "plt.xticks(ticks=[0, 1], labels=['Female', 'Male'], rotation=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 447,
       "width": 382
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Demented = df[df['Group']==1]['Gender'].value_counts()\n",
    "Nondemented = df[df['Group']==0]['Gender'].value_counts()\n",
    "gender_dementia = pd.DataFrame([Demented, Nondemented])\n",
    "gender_dementia.index = ['Demented', 'Nondemented']\n",
    "gender_dementia.plot(kind='bar', stacked=True, figsize=(6, 6), color=['pink', 'blue'])\n",
    "plt.ylabel('Number of patients')\n",
    "plt.xlabel('Group')\n",
    "plt.title('Gender and Dementia Count')\n",
    "plt.legend('MF');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two above graphs indicate that within the study, males had a greater probability of getting dementia than females."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 - Years of Education and Dementia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 386,
       "width": 605
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    150.000000\n",
      "mean      14.533333\n",
      "std        2.874714\n",
      "min        6.000000\n",
      "25%       12.000000\n",
      "50%       14.000000\n",
      "75%       16.000000\n",
      "max       23.000000\n",
      "Name: EDUC, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df['EDUC'] = df['EDUC'].astype(int)\n",
    "\n",
    "education_counts = df['EDUC'].value_counts().sort_index()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(education_counts.index.to_numpy(), education_counts.values, marker='o', linestyle='-', color='blue')\n",
    "plt.title('Distribution of Education Years')\n",
    "plt.xlabel('Years of Education')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks(np.arange(min(education_counts.index), max(education_counts.index)+1, 1.0)) \n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "print(df['EDUC'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above graph shows the distribution of years of education, with the average being approximately 14-15 years. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 458,
       "width": 720
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(df['EDUC'].min(), df['EDUC'].max() + 3, 3)  \n",
    "labels = [f'{int(i)}-{int(i+2)}' for i in bins[:-1]]\n",
    "\n",
    "df['EDUC Group'] = pd.cut(df['EDUC'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "\n",
    "dementia_rate_by_educ = df.groupby('EDUC Group')['CDR'].mean()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 7))\n",
    "dementia_rate_by_educ.plot(kind='bar', color='teal')\n",
    "plt.title('Dementia Rate by Education Years')\n",
    "plt.xlabel('Education Years')\n",
    "plt.ylabel('Average Dementia Rate')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the graph derived from our analysis, there appears to be a observable trend indicating that the rate of dementia decreases progressively with an increase in years of education. The graph indicates that individuals who have received 6-8 years of education exhibit a higher rate of dementia compared to other educational groups. It is interesting to note that this particular range of educational years typically corresponds to the completion of elementary and middle school education. This may suggest that education may play a crucial role in cognitive health, potentially offering protective benefits that reduce the risk of developing dementia. However, it is equally important to note that there may be other confounding factors as mentioned previously that may impact how the graph is interpreted. Many variables are highly intertwined; for instance, individuals with higher socioeconomic status may be able to afford better health care in addition to more years of education."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Clinical Variables EDA\n",
    "\n",
    "- Group 0: No dementia\n",
    "- Group 1: With dementia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 726
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "sns.kdeplot(data=df, x='MMSE', fill=True, hue='Group')\n",
    "plt.xlabel('MMSE Score')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Density Plot of MMSE Scores')\n",
    "plt.xlabel('MMSE Score')\n",
    "plt.ylabel('Density');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above graph shows that individuals who had dementia had much lower *MMSE scores than individuals without dementia. \n",
    "\n",
    "**__MMSE__: A widely used screening tool for cognitive impairment. Scores range from 1 to 30 with higher scores indicating better cognitive function. The examination assesses domains such as orientation, memory, attention, and language.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L8NzfKMXvvoiIiEipKcAWERER8ZGZHQP8MX5zpnNuwh6nvBi/Pt3M9hV2YWb19vV4MWqK7OeU/DYeRW1/sCN+XdQVqvltRM7BC/ehdJs3JtpHeF9TGPj9ng/G2yvcG7853Tm3bo9T8r8fdUs4/9t4fxRoAFxRyPw1CtT19j42eiyWeO/jEfGbY51z2/ZxWQFMi597UYEx2ppZ+/3MUwM4JX7z20TUvp/5uptZ0/2c04RdgfKvNTnndrJrE9UHzKxWEacdG78eaWbN9nzQzI5m10r3MUUcs6AX49d9zazQzTQLzFWvwHGif/dFRERESk0BtoiIiEg5M7M6ZnaMmb0OfIC3+eIqvB6ze/oX8CXe67bxZjbKzOoXGKuxmZ1rZlOBUQkq8Xgzm2lml5vZry0OzKyGmV2Otyoa4MMijrcUyMVrs7DfFaHOuTl4IWFdoBveitdXi15+2XLOpQN/iN+8wczuNrOaAPEV2a8Dh+GFzPcUMsQP8esLS9JL2Dn3M/CP+M3HzOwKM0uKz98Rb2PKA/F6Nj9c3PH3YTjQKn5clM008885v8DX2Q1YbGZvm9lZBcNbM0s1sxPx2ui0jd/9dALq3p9hwE9m9oqZnbjH71dtMzsfr31IHbyf6XN7PP8uvI1SOwKfmdlwMwvEn1/XzE4wsz3/MPUc3kaiKcAkM+sbPz8Y/x15I37eZOfclOJ+Qc65Sez6/v/bzB7Y43tdz8xONrNxeBs15kv0776IiIhIqYX2f4qIiIiIlMKhZlZwBW4toGDbB4e3wvLaeO/f3Tjncs3sZLwwahDwF+ApM9uGtwK4ZoHTP01g3YfEL5hZJpCFFyjnt734gF0h6j4559LjYf2FwFgz2w5siz98q3NubCFPe4FdQeH7hX1vfPYnvP7CF+KFxA+Y2Q52fY9iwPXOuc8Kee4LwKHAjcBVZrYB79/BWOfcrUWc/xagPXAU8HfgOTNLZ9eq7mzgvHjv5kTJX0n9tXNuZRHOfwd4FmgGHA1MxPtDRhA4NX7J//eVw+4r9KPAfc65ogTlpZULJONtWnhBvKadeD+Tgiuqs4Cr9mxl45xbVuB3tBcwBcg2syz28qkD59xWMzsFmAT0AGabWRre73Ry/LR57AqMS+JCvD98nQLcB9wX/90zoHaB817c43kJ+90XERERSQQF2CIiIiJlKww0iR9H8dpHrMPrgfsV8Lpz7qd9DeCc22BmQ4Gz8QKtPkB9vNBvEV4/4reAyQmqeQpeq4gjgd7AAXhB3Ga8ldGvAK8652J7G6AQVwG/4PXybs2uzetq7uX8t9kVYFeUzRt/FW/LcZGZvYfXxqMPXii4Fq91xp/jK8kLe+5/4iuSL8cLwVvihYMNizF/hpkdB4zEC5Z74P1h5GfgY+AJ59zSkn11vxVfYZ6/er5IobJzbo2ZzcQL6y8CJjrnPjSzTngbcx4GdAea4/072AYsBz4DXnDO/VDowAnmnPu7mc3C62F9GN7PpCle0L4Z7xMEU4B/xlujFDbGp/Gv62bgBLwV5CFgCTCHXX3dCz5nlpl1BW6LP6cVXj/tr/F6TT/nnMsqxdeVDpxqZifg9ZMfADTC++PKMmAW3s/ygwJPK4vffREREZFSMefc/s8SERERESlH8bYNr+KF3q0T1cdZREREREQqF/XAFhEREZGK6Kr49b8VXouIiIiIVF8KsEVERESkQjGzS/FaOWQDz/tcjoiIiIiI+Eg9sEVERETEd2bWApiBt2le/fjdTzjn1vhXlYiIiIiI+E0BtoiIiIhUBCG8jR1jwE/AP4HHfa1IRERERER8p00cRURERERERERERKRCUg9sEREREREREREREamQFGCLiIiIiIiIiIiISIWkAFtEREREREREREREKiQF2CIiIiIiIiIiIiJSISnAFhEREREREREREZEKKeR3AVWVmf0E1AZW+FyKiIiIiIiIiIiISGm0AXY459qW98QKsMtO7ZSUlPpdunSp73chIiIiIiIiIiIiIiW1cOFCMjMzfZlbAXbZWdGlS5f6c+bM8bsOERERERERERERkRLr06cPc+fOXeHH3OqBLSIiIiIiIiIiIiIVkgJsEREREREREREREamQFGCLiIiIiIiIiIiISIWkAFtEREREREREREREKiQF2CIiIiIiIiIiIiJSISnAFhEREREREREREZEKSQG2iIiIiIiIiIiIiFRIIb8LEBERERERERERESmqWCzGli1bSEtLIzs7G+ec3yVVKmZGUlIStWrVon79+gQCFXuNswJsERERERERERERqRRisRirVq0iIyPD71IqLeccWVlZZGVlkZ6eTsuWLSt0iK0AW0RERERERERERCqFLVu2kJGRQSgUomnTpqSmplbo8LUiisVipKens27dOjIyMtiyZQsNGzb0u6y90k9XREREREREREREKoW0tDQAmjZtSq1atRRel0AgEKBWrVo0bdoU2PU9raj0ExYREREREREREZFKITs7G4DU1FSfK6n88r+H+d/TikoBtoiIiIiIiIiIiFQK+Rs2auV16ZkZQIXfBLPS/aTN7Awze9bMppvZDjNzZvZqKcYbbGZvmdlaM8uOX39kZscnsm4RERERERERERGRiiI/wK7oKuMmjvcAPYGdwGqgc0kHMrN7gIeATcB4YC3QEDgYGAZ8UMpaRURERERERERERKSEKmOAfRNecL0MGAp8WpJBzOxMvPB6MnCacy5tj8fDpaxTREREREREREREREqh0gXYzrlfA+uSLnM3swDwOJABnLdneB2fJ7ekNYqIiIiIiIiIiIhI6VW6ADtBDgXaAmOBrWZ2AtAdyAJmOedm+lmciIiIiFQj2fG1FIEwBCOgDYlEREREytSSJUt4/vnnmTp1KitWrCAtLY1atWrRoUMHBg8ezLnnnkufPn38LlPiqmuA3S9+vR6YCxxU8EEz+ww4wzm3cX8DmdmcvTxU4t7cIiIiIlIFRfNg0xJY/wOs/x7WzYf182Hn+t3Ps6AXZNdoAC36Qsv+0HIANO0BoYg/tYuIiIhUAc45HnzwQR588EFisRi9e/fm7LPPpn79+qSlpTFv3jyeffZZ/vznP/Pcc89x7bXX+l2yUH0D7Mbx66uAn4Ajga+A1sCfgWOA/+Ft5CgiIiIiUnLr5sM3r8C8NyFz628fDyaBBSCWB7FccFHIy4Qdq2HBaljw7q7zDjgYuvwOep0PNeqX65chIiIiUtk9+OCDjB49mpYtW/L6668zaNCg35yzYcMG/vKXv7B9+3YfKpTCVNfPJwbj14a30voT59xO59wPwKl4m0QONbOB+xvIOdensAuwqOzKFxEREZEKLWsHfP0f+MdweH4QfPW8F17XbAItD4Ge58Kwu+C0F+D8/3mXEe/Ahe/DiHFw/lg4+W9w6A3Q4Wio0xKi2bDqS/joHniyK4y7DtZ+5/dXKiIiIlIpLF++nIcffphIJMLEiRMLDa8BGjduzB/+8Aduu+22X+8bOXIkZsby5ct59tln6dGjBykpKQwbNuzXc5YuXcqFF15I8+bNiUQiHHDAAVx44YUsXbr0N3Pkj7dixYrfPDZ16lTMjNGjR+92/7BhwzAzsrOzueeee2jbti1JSUm0b9+eBx54gJycnBJ9XyqD6roCO3/py3Ln3G6v+p1zmWb2IXAp0B9QP2wRERERKZqsHfDZH2H2C5Cb4d0XToV2w6DDUdDgwH0/38xrIRIIQt1W3qXD0d5j2Wmw7ntYMhHWfOOt6v7mFa+9SP8roNtp6p8tIiIishf/+c9/yMvL47zzzqNbt277PT8U+m1sOmrUKKZPn84JJ5zA8ccfTzDorZGdPXs2Rx55JGlpaZx00kl07dqVRYsW8d///pdx48bxySef0Ldv34R8HWeddRazZ8/mjDPOIBwOM27cOEaPHs3XX3/Ne++9h5klZJ6KpLoG2Ivj19v28nh+wJ1S9qWIiIiISKUXi8H3Y+Dj+3b1tG7SHTocA60PhVBS6edIquWN1fpQ2P4LLJ4Ayz6BVV95l5l/hd896bUZEREREZHdfP755wAcfvjhJR5j7ty5fPPNN7Rt2/bX+5xzXHjhhezYsYNXX32V888//9fH3nzzTc455xwuuOACFixYQCABiw0WLlzIDz/8QL169QB45JFHGD58OOPHj+fVV19lxIgRpZ6joqmuAfZnQB7Qwcwizrk919h3j1+vKNeqRERERKTyWfMNfHAbrJ7l3W7UCfpfCQ07lt2cdZp7q64PHgHLP4Xv3oA1c72WJX0vgSPuhZR6ZTe/iIiISCWzbt06AJo3b/6bx1asWMGLL764231169blxhtv3O2+2267bbfwGuCLL75g0aJFDBw4cLfwGuDss8/mueeeY8aMGcyYMYMhQ4aU+uu49957fw2vAZKTk3n00UcZPnw4//73vxVgVzZmFgbaA7nOuR/z73fObTKzN4HzgfuAewo85yi8TRy3A5PKt2IRERERqTSyd8JHd8OclwAHyXWhz8XQfri3KWN5CKdAp+O9FiXfvg4Lx8HX/4IF4+CoB71e22orIiIiIoJzDqDQFhsrVqzggQce2O2+1q1b/ybA7t+//2+eO3fuXGDvK7sPP/xwZsyYwTfffJOQAHvo0KG/uW/w4MGEQiG++eabUo9fEVW6ANvMTgFOid9sGr8eaGYvxo83OedujR83BxYCPwNt9hjqZmAAcLeZDQFmAa3xNnGMApc757Yl/AsQERERkcpv01J48wLYuMjrWd3lJC8sjtTwp55wDeh3KRx4hLdh5Pr5MO4a+O51OP1fUKuJP3WJiIiIVBDNmjVj0aJF/PLLL795bNiwYb8G3Hl5eYTD4ULHaNq06W/u2759+6/j721egG3btpWk7N9o0uS3r+uCwSANGjRgw4YNCZmjoqmMyzF6ARfFL8fE72tX4L4zijKIc24DXoD9FNASuAE4HJgADHbO/S+hVYuIiIhI1bDgPa9Vx8ZFUKclnPiMFx77FV4XVK8NHPMoHHaLtyJ8xXT4+xD4WfuSi4iISPU2aNAgAD755JMSj1HY6u06deoAu1qU7Gnt2rW7nQf82gs7Ly/vN+fvL+hev379b+6LRqNs3ryZ2rVr7/O5lVWlC7Cdc6Odc7aPS5sC567Y8749xtrinLvZOdfWORdxzjVwzp3snPuyvL4eEREREakkonneJo1jRkBOGrQZDCc8CfVa+13Z7sy8NiYnPettJLlzHbx4grfJY3xlkYiIiEh1M3LkSEKhEGPHjmXhwoUJG/fgg70NtKdOnVro4/n39+7d+9f78ntYr1q16jfnf/311/ucb9q0ab+5b/r06eTl5f1aS1VT6QJsEREREZFyt3MjvHIKfP6019+672Uw5DavB3VFlVIPjn4Eup0GLgof3gVjL4bsNL8rExERESl37du355577iEnJ4fjjjuOL774otDzitvqY9CgQXTq1IkZM2YwduzY3R4bO3Ysn332GR07duSwww779f78Xtr//Oc/dzv/+++/5+mnn97nfA899BBbt2799XZWVhZ33nknABdffHGxaq8sKl0PbBERERGRcrX1Z3jpRNj2s9eWY+gd0LS731UVTSAIfS+BRp3g87/AD+/A+h/g3DegQXu/qxMREREpV/fddx/OOR566CEGDRpEnz596N+/P/Xr12fbtm2sWLGCyZMnAxR5w0Uz46WXXuKoo47i7LPP5uSTT6Zz584sXryYd999l1q1avHyyy//2jYE4OSTT6ZDhw68/vrrrF69mgEDBrBy5UrGjRvHySefzJgxY/Y6X5cuXejWrRtnnHEG4XCYcePG8eOPP3LCCScwYsSI0n2DKiitwBYRERER2ZtNy+A/x3nhdYMOcOLTlSe8Lqj1IDjhKajTCjYtgX8dDWu/87sqERERkXJlZowePZoFCxZw4403kpeXx2uvvcbjjz/Oa6+9xvr167n66quZM2cOL7/8cpHHHTBgALNnz+a8885j5syZ/PGPf+SLL77g3HPPZfbs2QwYMGC385OTk/nkk08466yzmD9/Ps899xzLly/ntdde4+qrr97nXGPGjOGSSy7h/fff57nnniMWizF69GjeeuutQnt0VwXm1AevTJjZnN69e/eeM2eO36WIiIiISEmsXwAvnwzpG6BxVzhidMXYqLE0cjPh0z/A2m8gqRacNwZaH+p3VSIiIiJFlt+/ukuXLj5XUr6GDRvGtGnTSHSWW9TvZ58+fZg7d+5c51yfhBZQBFqBLSIiIiKypzXfehsfpm+AZj3hyAcrf3gNXs/uI+7zVmRnp8Erp8KSD/2uSkRERERkrxRgi4iIiIgUtGqW1/M6cwu06AdH3A/hZL+rSpxg2NuAssPRkJcFb5wH34/d//NERERERHygAFtEREREJN/Kr+DlUyB7h9daY9hdEIz4XVXiBYIw8HrofjrE8uCty2D2C35XJSIiIiLyGwqwRUREREQANi2F18+G3HRoNwyG3O6tVq6qzKDPxdB7JOBgwi0w50WfixIRERGRwkydOjXh/a8rCwXYIiIiIiJp6+DV0yBzq9c2ZNBN3irl6uCgM6D/ld7x+zeqnYiIiIiIVCgKsEVERESkestOg/+eCdtWQsOO3srr6hJe5+tyIhx8IeDgnSu1saOIiIiIVBgKsEVERESk+ormwpgLYd08qNWs6m3YWBwHnbmrJ/aYC2HFDL8rEhERERFRgC0iIiIi1ZRz8N718OMUSK4DRz7oXVdXZl4/7I7HQl4WvHY2/DLH76pEREREpJpTgC0iIiIi1dOUh+G71yGU5K28rt3M74r8ZwYDroa2QyFnJ7x6OmxY6HdVIiIiIlKNKcAWERERkepn/lsw/U9gARh6h9f7WjyBIBx2k7eZZeZWePUMSFvvd1UiIiIiUk0pwBYRERGR6mXDQhh3vXfc7zIvqJXdBUJesN+oM+xYDW+cC7mZflclIiIiItWQAmwRERERqT6ytsObF0Buutcmo/OJfldUcYWSYPg9ULOJ1wv7nasgFvO7KhERERGpZhRgi4iIiEj1EIvBu9fA5mVQrw0MvN7r+Sx7l1IXDr8PwjVgwbvw6SN+VyQiIiIi1UzI7wJERERERMrF50/BovEQToVhd0E42e+KKod6rWHo7fDJA17f8Abtodd5flclIiIiUqg2d0zwu4QiWfHYCX6XUGloBbaIiIiIVH0/ToEpD3vHg2+B2gf4W09l07wP9L/KO37vBljxub/1iIiIiAgAq1ev5pJLLuGAAw4gKSmJNm3acOONN7J161a/S0sYrcAWERERkapt20oYeym4GPQ8F1r297uiyqnz8d6GjgvfgzfPh8s/hfpt/a5KREREpFCvX36I3yUU6tx/fpmwsX788UcOPfRQNmzYwMknn0znzp2ZNWsWTz/9NJMmTeLzzz+nQYMGCZvPL1qBLSIiIiJVVzTPC68zt3iriHue63dFlVvfS6FFP8jcCm+OgJwMvysSERERqbauueYaNmzYwDPPPMO7777LY489xpQpU7jppptYvHgxd999t98lJoQCbBERERGpuj57AlbPghoNYPCtYHr5WyqBoPd9rH0ArP8ext8EzvldlYiIiEi1s3z5cj766CPatGnDtddeu9tjDzzwAKmpqbzyyiukp6f7VGHi6BW8iIiIiFRNK7+Ez/4IGBx2CyTV8ruiqiES3wQzlATz3oBZ//S7IhEREZFqZ8qUKQAcffTRBAK7R7y1atVi0KBBZGRk8OWXiWtZ4hcF2CIiIiJS9WRth7cu9/pedz8dmvXwu6KqpV4bOHSUd/zhnd4fC0RERESk3CxevBiAjh07Fvp4hw4dAFiyZEm51VRWFGCLiIiISNUz4VbYvhIaHAi9zve7mqqp7RDoejLE8mDMRZC2zu+KRERERKqN7du3A1CnTp1CH8+/f9u2beVVUplRgC0iIiIiVcu8MfD9GK/FxeBbIRj2u6Kqq8/F0KQ77FwH/xsJ0Vy/KxIRERERwMX3KTEznyspPQXYIiIiIlJ1bF0BE272jvtdAXVa+FpOlRcIwdDbvU0yV86Ej+7xuyIRERGRaiF/hXX+Suw97dixY7fzKjMF2CIiIiJSNUTz4O0rITsNWg2EDkf7XVH1kFIPht3phdlfPQ8L3vO7IhEREZEqr1OnTsDee1wvXboU2HuP7MpEAbaIiIiIVA0zn4NVX0JKfRh4PVSBj0tWGo06e+1EAMZd662EFxEREZEyM3z4cAA++ugjYrHYbo+lpaXx+eefk5KSwiGHHOJHeQmlAFtEREREKr+NS+DTP3jHg0ZBcm1/66mOupwELQdA9g4Yewnk5fhdkYiIiEiV1b59e44++mhWrFjBX//6190eu//++0lPT+fCCy8kNTXVpwoTRwG2iIiIiFRusSiMuwai2XDgkdC8j98VVU9mMOhGSG0Ev8yBTx7wuyIRERGRKu1vf/sbjRs35oYbbuCUU07hzjvv5PDDD+epp56iY8eOPPLII36XmBAhvwsQERERESmVL/8PVs/2Wof0u8zvaqq3pFow5DaYdLvX0qXNYOh0rN9ViYiISDV07j+/9LuEMte+fXu+/vpr7rvvPiZNmsQHH3xAs2bNuOGGG7j//vupX7++3yUmhAJsEREREam8Ni2DKQ95x4deD5Ga/tYj0LgL9L4Q5rwI714FV82AOi38rkpERESkSmrZsiX/+c9//C6jTCnAFhEREZHKKRb1NgzMy4J2w6FFP78rknzdToN133utRMZeCiPHQzDsd1UiIiJSDax47AS/S5AEUw9sEREREamcZv0DVn0JKfWg/xV+VyMFWQAOu9lr67LqS5j6qN8ViYiIiEglpQBbRERERCqfzT/C5PgmgYdc6/VelooluY7XD9sCMP1JWDHD74pEREREpBJSgC0iIiIilYtz8P4oyMuEtkOh1SF+VyR707Q7dD8TcPD2FZC51e+KRERERKSSqXQBtpmdYWbPmtl0M9thZs7MXk3AuCPiYzkz0/b1IiIiIhXVt6/BiumQVFutQyqDXudCw06w4xfvDw/O+V2RiIiIiFQilS7ABu4BrgN6Ab8kYkAzawk8C+xMxHgiIiIiUkbSN8NH93jH/S732lRIxRYIwZDfQzgFFoyDb0q99kREREREqpHKGGDfBHQEagNXl3YwMzPgP8Bm4PnSjiciIiIiZeijeyBzCzTrCe2G+V2NFFWtpjAg/tJ94m2waam/9YiIiIhIpVHpAmzn3KfOuaXOJeyzhzcAhwMXA+kJGlNEREREEm35NPjuNQiG4ZBrwMzviqQ42g33epbnZsBbl0Fejt8ViYiIiEglUOkC7EQysy7AY8DTzrnP/K5HRERERPYiNwvG3+Qd9zgHajf3tx4pPjPvDw81m8Dab2HKQ35XJCIiIiKVQLUNsM0sBLwCrATuKsU4cwq7AJ0TVauIiIhItTfjSdjyI9RpCd1O87saKalIKgy+FSwAXzwLP2kNiYiIiIjsW7UNsIH7gIOBkc65TL+LEREREZG92LgEpj/pHQ+8zmshIpVX4y7Q42zAwTtXQeZWvysSERERkQqsWgbYZtYfb9X1n51zM0szlnOuT2EXYFFCihURERGpzmIxGH8jxHKhwzHQpJvfFUki9DgbGnaCHb/AhFv9rkZEREREKrBqF2AXaB2yBLjX53JEREREZF++ex1+/hyS60KfkX5XI4kSCMHgmyGUBPPHwrz/+V2RiIiIiFRQIb8L8EFNoGP8OMsK373+n2b2T7zNHW8sr8JEREREpIDMrfDxfd5x30shqZa/9Uhi1W4O/S6Hmc/BhJuh1SFQt6XfVYmIiEhlN7qO3xUUzejtfldQaVTHADsb+NdeHuuN1xd7BrAYKFV7EREREREphSkPQ8YmaNId2g3zuxopCx2OgdWzYdVXXj/si96DQNDvqkREREQqhbFjxzJt2jS+/fZbvvvuO9LS0jj//PN59dVX/S4toap0gG1mYaA9kOuc+xEgvmHjZXs5fzRegP2Sc+6F8qpTRERERPaw5huY/S+wAAy4Cgr/1JxUdmYw8HrYuBh+nuGtxh40yu+qREREpCq4aLzfFRTupd8lbKiHH36Y7777jpo1a9KiRQsWLaqaW/JVuh7YZnaKmb1oZi8Cd8TvHph/n5n9qcDpzYGFwCflXaeIiIiIlFAsFt/Yz0GXk6BeG78rkrKUUndXaP3JQ7B2nq/liIiIiFQWTz31FEuWLGHHjh383//9n9/llJnKuAK7F3DRHve1i18Afga0lbmIiIhIZfXtq/DL15BSH3qe53c1Uh5a9IOOx8GSifD2FXDFVAgn+12ViIiISIU2fPhwv0soF5VuBbZzbrRzzvZxaVPg3BV73lfEsdU+RERERMQPGVvg4/u9476XQqSGv/VI+el7KdQ+ADYuhCkP+V2NiIiIiFQQlS7AFhEREZEqbMpDkLkFmh4EbYf4XY2Up3AyHHaL1/d85nOwfJrfFYmIiIhIBaAAW0REREQqhl/mwtf/AQtCf23cWC016gQ9zvaO370GMrf5Wo6IiIiI+E8BtoiIiIj4LxaDCbcADrqeDPVa+12R+KXH2dCgA+xYDRNv87saEREREfGZAmwRERER8d+3/4U1c+MbN57jdzXip0AIBt8CwSSY9yb88I7fFYmIiIiIjxRgi4iIiIi/MrfB5NHecd+LIayNG6u9Oi28TR0Bxt8EO9b6W4+IiIiI+EYBtoiIiIj4a9oTkLEJGneFtsP8rkYqik7HQfM+kLkVxl0LzvldkYiIiIj4QAG2iIiIiPhnwyKY9XfAoP+V2rhRdjGDQ0dBUi348ROY/YLfFYmIiIiID0J+FyAiIiIi1ZRzMOl2iOVBx2OhQXu/K5KKpkZ9GHgdTH0UProX2g6FRh39rkpERESkQnj33Xd59913AVi3bh0AM2fOZOTIkQA0bNiQP/3pTz5VlzgKsEVERETEH4smwPKpEKkJB4/wuxqpqFoPgvaHw49T4J0r4NKPIRj2uyoRERGp6F76nd8VlLlvv/2Wl156abf7li9fzvLlywFo3bp1lQiw1UJERERERMpfbiZ8eKd3fPAFkFzH33qkYut/JaQ2hjXfwGd/9LsaERERkQph9OjROOf2elmxYoXfJSaEVmCLiIiISPn74jnYthLqtYGOx/ldjVR0kVQ47Cb48C747E9w4FHQsp/fVYmIiEhFNHq73xVIgmkFtoiIiIiUr+2rYfqfveP+V0Ag6G89Ujk0PQi6nQou6rUSyUn3uyIRERERKQcKsEVERESkfH10L+RlQpvB0LSH39VIZXLwCG/V/pbl8OHdflcjIiIiIuVAAbaIiIiIlJ+fv4Af3oZgEvS52O9qpLIJhmHwrRAIw5z/wJIP/a5IRERERMqYAmwRERERKR+xKEy83TvufjrUbOxvPVI51WsDvUd4x+OuhZ0bfS1HRERERMqWAmwRERERKR/fvALr5kFqI+h+mt/VSGXW9RSv/Uz6Rnj/BnDO74pEREREpIwowBYRERGRspe5DT55yDvuewmEkn0tRyo5C8BhN0E4FRZ/AHNf8rsiERERkUrHVZJFAAqwRURERKTsffZHyNgEjbtB68P8rkaqgtRGMPAa73jSnbBpmb/1iIiISLkwMwBisZjPlVR++QF2/ve0olKALSIiIiJla+MS+Op5wKD/FVDBXyBLJdJ2KLQbBrkZ8PblEM31uyIREREpY0lJSQCkp6f7XEnll/89zP+eVlQKsEVERESkbH14F8TyoMPR0KC939VIVTPgKm819pq5MO0Jv6sRERGRMlarVi0A1q1bR1paGrFYrNK0wqgInHPEYjHS0tJYt24dsOt7WlGF/C5ARERERKqwJR/Bso+9XsUHj/C7GqmKIjXhsJu9P5RM/xMceCS0GuB3VSIiIlJG6tevT3p6OhkZGaxevdrvciq9GjVqUL9+fb/L2CetwBYRERGRspGXAx/e6R33PBdS6vpajlRhTQ+C7qeDi3mtRLJ2+F2RiIiIlJFAIEDLli1p1KgRycnJFb5/c0VkZiQnJ9OoUSNatmxJIFCxI2KtwBYRERGRsjHrH7B5GdRuDp1P8Lsaqep6ne+1EdmyHCbeBqc+73dFIiIiUkYCgQANGzakYcOGfpci5aBix+siIiIiUjnt3AjTHveO+10OwbC/9UjVFwzD4N9DMAm+ex2+H+t3RSIiIiKSAFqBLSIiIiKJN+UhyN4BzftCi75+V1NlxJxjc6ZjfYZjR7Yj5iDmIOoczkHUQdCgTpJRP8WonxygVgQC1eWjtXVbQr/L4Mu/wviboEU/qNfa76pEREREpBQUYIuIiIhIYq39Dua+DBb0wkQpltyoY9m2GAs2R/lhU4yVO2JsyIixPt2xKdMRdcUbL2hQL9lokGK0rRPgwLoBOtQL0r5egPZ1AqSEq1i43fFY+GUOrPoS3r4CRk6AoN72iIiIiFRWeiUnIiIiIonjHEy8A3DQ5USo08Lviiq8NTtjTFuVx7cboizYFGXx1hg50b2fXzPsBdI1I0bAvJ6AZnjHBtEY7MyFtBxHWo4jMw82ZXrh9+Itsd3GMqBlbaN34xD9mwXp3yxI+7qByr0Zkhkcej28t8QLsaf/GYbd7ndVIiIiIlJCCrBFREREJHF+eAdWfgHJdaDnOX5XUyFlRx2z10aZtiqPqavyWLo19ptzmtQw2tQJ0Lp2gOa1jPrJRt0ko26yEQoUL1zOizl25sDWLMeanTFW74yxJs3xy84Y69IdK3c4Vu7I5d1luQA0SLZfw+xhrUK0rRNMyNddrpLrwOCb4aN7Ydpj0G4YtBrgd1UiIiIiUgIKsEVEREQkMXIy4OP7vOODR0Ckpr/1VCA5UceUn/N4a2kuM1bnkZm367HkIHRvFKRrg8CvoXWNBLb1CAWMuslQN9loW3f3PdzzYo5fdjoWb4mycHOMRZujbM5yTPwpj4k/5fHAF9l0qh/gmDYhjmsXpnP9SrQ6u1kv6HYq/PA2vH0ZXDXDC7ZFREREpFJRgC0iIiIiifHFs7B9FdRvBwce5Xc1vnPO8cOmGGOX5DJuWS5bs3Y1r25V2+jVOEjPRkE61g8Ue1V1ooQCRuvaRuvaAY5u49W8Lt2xaEuM+ZuifLM+yuItMRZvyeGZuTm0rm0c2zbMSQeG6dawEqzMPniE15N9y48w4RY47Z9eixERERERqTQUYIuIiIhI6W1fDTOe8o77XQGBShBulpG0HMebi3IYuziXRQV6TresZQxrGeKQA4LUTwnsYwT/mBnNahrNagYY3ipEXswxf1OM2Wvz+HpdlJ93OP7+XQ5//y6HHo0CnNM5wkkHhqkZqaChcDAMQ34P40fB9/+D9odDr/P8rkpEREREikEBtoiIiIiU3sf3QV4mtBkMTbv7XY0vNmTE+M/3Oby6IIe0HO++mmE4rEWIIS1DtKltlaf9Rlwo4K0U79U4yKU9vE0gv1wT5fNf8pi3Mca8jVk8PDOLkw4Mc26XCD0aVcAWI3VaQP+r4IunYcKt0KIfNOzgd1UiIiIiUkQKsEVERESkdH7+Aua/BcEI9LnY72rK3c/bY/xjXjb/W5xLTtS7r3P9AMe2DdG7SZBwsIIFuiUUMKNLgyBdGgQ5v2uYr9ZEmbIyj0VbYryxKJc3FuXSo1GAq3olcUybEEGf2qIU6sAjYe238NM0GHsxXPYJhJL8rkpEREREikABtoiIiIiUXCwKE2/zjrufDjUb+1tPOVq+LcpTX2czYXkesXh7675Ng5x0YIgO9ap2C5VI0BjcMsTgliF+SYsxZWUen63yVmVf83Em7eoEuLJXhFM6hEmqCAG+GRxyLWxcDOu+9z4xcNzjflclIiIiIkWgAFtERERESm7uy14gmNrIC7Crgc2ZMZ6Zk81/F+SS5yBoMLRlkBPbh2leq2L2ti5LzWsFGNEtwtmdw0xdlcf4ZXks3x7j9mlZPDk7m8t6RDi3S8T/PtmRGjD0Nvjg9/DV89B2KHQ+3t+aRERERGS/FGCLiIiISMlkboVPHvSO+14KoWR/6yljWXmO/8zP4W9zs0nLhQBweKsgp3UM06CCbspYniJB4+g2YY5oFeLLNVHGLctlVZrjkS+z+ds3OVzXO8IF3SL+rshu2BH6XARf/xvGXQPNPoc6zf2rR0RERET2q9K90jazM8zsWTObbmY7zMyZ2avFHKOBmV1mZu+Y2TIzyzSz7WY2w8wuNbNK930RERERKXdTH4PMLdCkO7Qe5Hc1ZSbmHOOW5nLEmzt5/CsvvO7ZKMBjQ5O5vGeSwus9BAPGoBYhHh+azG39k+hUL8DWbMdDM7M5/I2dvLMkh5hz/hXY9RRo3sf7A8zbl3ttcERERESkwqqMK7DvAXoCO4HVQOcSjHEm8H/AWuBTYCXQBDgNeAE4zszOdM7PV9YiIiIiFdiGhTDrn2AB6H+l12O4Clq+Lcqdn2Xx1Vov5GxV2zi/S4Qejat2j+tEMDMObhKkV+MA32yI8cbCHFalOW76NIt/zMvh9v7JDG0ZxMr7344FYNBN8P718PPnMO1xGH5X+dYgIiIiIkVWGZeL3AR0BGoDV5dwjCXASUAL59z5zrk7nXOX4IXhq4DT8cJsEREREdmTczDxdnBR6Hgs1G/rd0UJlxt1/PWbbI4dm85Xa6PUicAVPSM8OiRZ4XUxmRm9mwR5bGgyV/WK0CDZWLg5xsiJGZw/PoOlW31YAZ1SFwbfChhMewJ+/LT8axARERGRIql0AbZz7lPn3NLSrI52zk1xzr3vnIvtcf864Pn4zWGlKFNERESk6lo0Hn6aBpGa0OsCv6tJuHkbo5z4djp/nJVNTtTboPFPw1MY3ipEoIquNC8PATOGtgzx5OHJnN81TGoYvlgT5bj/pfPYV1lk5Jbzhx+b9YSe5wDOayWStq585xcRERGRIql0AXY5yI1f5/lahYiIiEhFlJsFH97tHR98ASTX9reeBMrMdTwyM4tT3kln0ZYYjWsYdx2SxFW9kqgZUXCdKJGg8bv2Yf5yeApHtg4RdfD8tzkcNWYnH/6US7l28etxjhdkp2+EsZdCVG8BRERERCoaBdgFmFkIuDB+c1IRnzOnsAsl680tIiIiUrF98Sxs+xnqtYGOx/ldTcLM3xTlhLfT+ee8HHDwu/beJoQHNVK7kLJSM2Jc2iPCg4cl0aa28ctOx5UfZXLppExW7ojtf4BECAS9ViIp9eDnGTDtsfKZV0RERESKTAH27h4DugMfOOc+9LsYERERkQpl20qY/mfvuP8VXvhXycWc44V52Zz6TjrLt8VoXtN4cHAS53eNkBzSquvycGC9II8MSWZk9zApIZiyMo+jxuzkX/OyiZXHauyUejD4997mjp/9CZZ9UvZzioiIiEiRKcCOM7MbgFuARcCIoj7POdensEt8HBEREZGq48O7IS8T2gyBpj38rqbUNmbEuHhiBg/PzCY3Bke1CfGHIcm0r1v5g/nKJmDGMW3DPDk8hUHNg2RH4aGZ2Zzzfkb5rMZu1gN6nsuv/bB3rCn7OUVERESkSBRgA2Z2LfA0sAAY7pzb4nNJIiIiIhXLj1Ng4XsQSoa+l/hdTal9ujKXY8emM21VlJphuKVfhEsOihAJatW1n+omG9f1TuKWfhHqJMGstVGO/d9OXl2QU/a9sQ86C5odDBmb1Q9bREREpAKp9gG2md0IPAfMxwuvtf24iIiISEF5OTDxdu+4xzmQ2tDfekohL+b4w8wsLp6YyeZMR7eGAR4fmkzfpiG/S5MC+jYN8cTQFAYeECQjD+6ZnsWFH2SwZmcZrsYOBGHwLZBSH1Z+AZ88UHZziYiIiEiRVesA28xuB54CvsULrzf4W5GIiIhIBfTV87BpCdRuDl1P9ruaEtucGWPEhAz+MS+HoME5ncPcdUgS9VOq9UviCqt2knFDnyRu6BOhVhimr45y9JidvL8st+wmTakLQ2/z+mF/8QwseK/s5hIRERGRIqnSr9bNLGxmnc2sfSGP3Yu3aeMc4Ajn3KZyL1BERESkotuxFqY97h33vwKCYX/rKaF5G6Oc+HY6M9dEqZME9wxM4uQOYQKmliEV3cADQjwxPIW+TYLszIXrP8nkrs8yycoro5YiTbpDn4u943evgU3LymYeERERESmSSvdZSTM7BTglfrNp/Hqgmb0YP97knLs1ftwcWAj8DLQpMMZFwINAFJgO3GC/ffOywjn34p53ioiIiFQrH98HOTuh5SHQvI/f1ZTI/xbncPf0LHKi0KFegBv7RLTqupKpm2Tc3C/C5J/zeOWHXF5bmMvc9VGeOzKFA+uVwaabXU+BjYvg589hzAi4bDJEUhM/j4iIiIjsV6ULsIFewEV73NcufgEvrL6VfWsbvw4CN+7lnGnAi8WuTkRERKSqWPE5fD8GghHod5nf1RRbTtTx0BdZvLLAazlxROsQF3ULE9ZGjZWSmXFUmzAd6gV5ek42i7bEOOntdB4enMxpHSOJngwOHQVbf4YNC2D8TXDq3737RURERKRcVbqlJ8650c4528elTYFzV+x5XxHHMOfcsHL+0kREREQqjmgeTLzNO+5+OtRquu/zK5itWTEuGJ/BKwtyCQXg8h4RLusRUXhdBbSpE+APQ5IZ1Nzb4PHmT7O49dNMMnIT3FIkUgOG3QmhJJj3Jnz978SOLyIiIiJFUukCbBEREREpB7P+AevnQ80m0P0Mv6spluXbopz6Tgaz1kWpl2zcf2gSh7eujB88lL1JCRnXHhzhip4RIkEYuySX08elsyotltiJ6rWGgTd4x5PugNVzEju+iIiIiOyXAmwRERER2d2ONfDpI95x/yu9FaiVxFdr8jjt3XRW7IjRurbx8GFJZdMjWXxnZgxvFeLhw5Jpmmos3Bzj5LfTmbkmL7ETtRsKnU+EaA6MuRB2bkzs+CIiIiKyTwqwRURERGR3H969a+PGlv39rqbI3lmSwwUTMtiWDQc3DjB6ULI2a6wGWtYO8PDgZHo2CrAly3HB+AxenJ+DcwlsKdL3EmjUBXashv+NhGhu4sYWERERkX3SK3oRERER2eXHKfDD2xBMgv5X+F1NkTjneOrrLG76NIvcGBzbNsSt/ZNIDqnfdXWRGjZuG5DESQeGiDoY/XkWt0/LIjuaoBA7GPb6YafUh59nwMf3JWZcEREREdkvBdgiIiIi4snLhgm3esc9z4Gajf2tpwhyoo5bPs3i6Tk5BICLuoW5qHuEgCm8rm4CZpzbJcJ1vb2+2GMW53L2exmsT09QX+wa9b0QOxCCL/8G88YkZlwRERER2ScF2CIiIiLi+fwZ2PIj1GkBXU/xu5r9ysh1XP5hBm8vzSUpCDf3i3Bsu7DfZYnPBjUPMXpQMg1TjG83RDn13XQWb4kmZvDGXaBf/JMJ790Aa+clZlwRERER2SsF2CIiIiICW1fA9D95xwOu8VomVGBbs2KcNz6Daaui1IrAvYcm0adpyO+ypIJoW8fri92hXoA1Ox2nv5vOF78kaHPHTsfBgUdBXia8eT5kbEnMuCIiIiJSKAXYIiIiItWdc/DBbZCXBe2GQbMefle0T2t2xjhzXAbfbojSMMUYPSiZ9nWDfpclFUydJOOegUn0bxZkZy5c9EEGby/JKf3AZnDI1dCgA2xbCWMvhmiCwnERERER+Q0F2CIiIiLV3aIJsPRDCNeAvpf6Xc0+Ldsa5Yxx6SzbFqNFLeOBQUkcUFMvaaVwkaAxqk+E49uFyI3BzZ9m8cycbJwr5eaOwQgMvwuS68DyqfDJ6ESUKyIiIiKF0Kt9ERERkeosawd88Hvv+OARkFLP33r24dsNUc4cl8GanY6O9QLcf2gy9VP0clb2LWDGiG4RLuoWxoAnv87m9mlZ5EZLGWKnNoKhd4AF4Ytn4bs3E1KviIiIiOxOr/hFREREqrMpD0PaGq8dQqfj/a5mr774JY/z3k9na7bj4MYB7jokiZoR87ssqUSObRfmpr4RIkEYsziXyz/MJDO3lCF204Og/5Xe8XvXwy9zSl+oiIiIiOxGAbaIiIhIdbV6Dsz6B1gADr0eAhWzj/TUlXlcPDGDjDw4rHmQm/slkRRSeC3F169ZiHsHJlErAlNX5XHhBxnsyC5liN3pOOh4LESz4Y3zIW1dYooVEREREUABtoiIiEj1FM2F90cBDrqeAvXb+V1RoSb9lMvlH2aQHYUjWoW4+uAIoYDCaym5A+sFvfYzycbsdVHOHZ/OpsxYyQc081ZhN+4KaWvhzRGQl524gkVERESqOQXYIiIiItXRzL/C+u+hZhPoeZ7f1RRq3LJcrv04k9wYHNc2xKU9wgRM4bWUXvNaAUYPSqJpqvHDphhnvZfBmp2lCLGDYRh2l9cXe/UsGH8zlHajSBEREREBFGCLiIiIVD9bV8DUx7zjQ66BcLKv5RRmzKIcbvwkk6iDUw4MMaJbGFN4LQnUqIa3EWir2sbybTHOHJfO8m3Rkg+YUheG3w3BJPj2Vfjq7wmrVURERKQ6U4AtIiIiUp04560OzcuEtkOheR+/K/qNl+fncNu0LBxwVqcwZ3eJKLyWMlE32bh3YDId6gX4ZafjzPcyWLC5FCF2gwNh0Cjv+MO74McpiSlUREREpBpTgC0iIiJSnXw/Fn78BCKp0O8yv6v5jRfmZXPf51kAjOgW5tSOYZ8rkqquZsS465AkujcMsDnTce776czfWIoQu+0QOOgscFEYcxFsWpq4YkVERESqIQXYIiIiItVFxhb48E7vuO+lkFLP33r28MK8bB6e6W1+d8lBYY5vp/BaykdyyLitfxJ9mgTZng3nTyhliH3wBdDyEMjeAa+dDZlbE1esiIiISDWjAFtERESkuph0B6RvhCbd4cCj/K5mN//4bld4fVmPCEe1UXgt5SscNG7sG6FvPMQ+b3w635c0xLYADL4F6rWFLT/C/0ZCNDeh9YqIiIhUFwqwRURERKqDJR/CvDe9DeYOvQEqUE/pv3+bzR++9MLry3tEOKJ1yOeKpLoKBYxRfSP0bRpkRw6cPz6deSUNscMpcPi9kFwXlk+FSXcmslQRERGRakMBtoiIiEhVl7Ud3r/ROz74fKh9gK/lFPR/32bz6FfZGHBFzwiHK7wWn4UCxqg+EfrFQ+wLShNi12wMw++GQBhm/xNmv5DYYkVERESqAQXYIiIiIlXdR/dC2hpo2BG6nOx3Nb/66zfZPB4Pry/vGWF4K4XXUjGEAsYNBULsUq3EbtwFDr3OO/7gNm81toiIiIgUmQJsERERkaps+VSY+5K3AvTQURAI+l0R4K28/uMsL7y+spfCa6l48kPs/k2DpMVXYv+wqYQhdvsjoPvp4KIw5kLYtCyxxYqIiIhUYQqwRURERKqq7J3w3g3ecc9zoF5rf+uJ++d3u1ZeX9krwtCWCq+lYgoFjOsLrMS+cEIGP24rYYh98IXQ8hCvpc9rZ0LGlsQWKyIiIlJFKcAWERERqaqmPATbfob67bzVnxXAv7/P5pH8DRt7KryWii8UMK7vHaFHowCbsxwXjM9gVVqs+AMFgjD4Fu/3cctybyV2Xk7iCxYRERGpYhRgi4iIiFRFK7+Er/4OFoi3DvE/KH55fg4PfuGF15f1UNsQqTzCQePmvkl0rh9gbbrj/PHprE8vQYgdToHD74OUerBiOky4GZxLfMEiIiIiVYgCbBEREZGqJicd3r0GcND9TGjQ3u+KeG1BDvd9ngXAxd3DHNFa4bVULkkh4/f9k2hXJ8DKHY4LJmSwJbMEIXZqQzj8XghG4JtXYOZziS9WREREpApRgC0iIiJS1Xx8P2z5Eeq29npf+2zMohzumu6F1xd2C3N027DPFYmUTI2wccchSbSoZSzdGuPCDzLYkV2CFdQNO8JhN3vHH90Liz5IbKEiIiIiVYgCbBEREZGq5McpMPufXsuQwbdA0N+w+O0lOdw+zQuvz+8a5rh2Cq+lcqsVMe46JJmmqcb8TTEumZRBZm4JQuw2h8HBIwAHb10Ga79LeK0iIiIiVYECbBEREZGqInMrvHutd9zzPG+zOB+9vyyXW6dm4YBzOof5XXuF11I11Es27jokiQbJxtfrolz/SSZ5sRKE2AedBe2GQ246vHYO7Fib+GJFREREKjkF2CIiIiJVxcTbIW0NNOoE3U/3tZRJP+Vy45RMYg5O7xjm5A4Kr6VqaVQjwJ2HJJEahsk/53HP9CxccTdkNINDb4DGXb3f3dfP8XrYi4iIiMivFGCLiIiIVAULxsG8NyGY5PXWDQR9K2XKz7lcPzmTqIOTDwxxekdt2ChVU/NaAW7rn0QkCG8syuWpr7OLP0gwDMPvhppNYe238M6VECvB5pAiIiIiVZQCbBEREZHKLm09vH+jd9z3Yqjd3LdSpq/O46qPM8mNwfHtQpzdOYyZ+VaPSFnrWD/IDb0jBIBn5ubwyg85xR8kuQ4ccT+EU2Hh+/DJAwmvU0RERKSyUoAtIiIiUpk5B++Pgswt0Oxg6HS8b6V8uSaPyydlkBOFo9qEuKCrwmupHvo0DXFpjwgA983IYtLy3OIPUrclDLsTLACf/wW+eTWxRYqIiIhUUgqwRURERCqzuS/Bkoneys1Bo7zwywdz1uVxycQMsqIwvFWQkd0VXkv1cnjrEGd2CuOAG6Zk8uWavOIPckAvGHC1d/z+KPhpeiJLFBEREamUFGCLiIiIVFYbF8PEO7zjQ66C1Ia+lDFvY5SLPsggIw8GtwhyWY8IAYXXUg2d2iHEUW1C5ETh8g8zWLIlWvxBOh0HXU+GWB68eQFsWpb4QkVEREQqEQXYIiIiIpVRXjaMvRTyMqHdcO/igwWbo4yYkM7OXBjQLMiVPRVeS/VlZozsHqZ/0yBpOXDxxAw2ZJRgQ8Y+l0CL/pC1DV47CzK2JLxWERERkcqi0gXYZnaGmT1rZtPNbIeZOTMrUYM4M2thZv82szVmlm1mK8zsL2ZWL9F1i4iIiCTU5Adg/fdQs+mulgPlbOnWKBeMz2B7NvRtEuS63hGCAYXXUr0FzLi2d4QD6wb4Zafj8kkZZOa6Yg4ShCG/h3ptYcuP8OYIyCvB5pAiIiIiVUClC7CBe4DrgF7ALyUdxMzaA3OAi4FZwFPAcmAUMNPMGpS6UhEREZGysHQyfPlXsHjIFalR7iUs3xblvPcz2JLl6NkowA19IoQUXosAEAkat/RPolGK8d3GGDdOySQaK2aIHU6BI+6DlPrw8wyYcJO3aauIiIhINVMZA+ybgI5AbaA0y43+BjQGbnDOneKcu8M5dzhekN0JeKTUlYqIiIgk2s4N8O5V3vHB50OjTuVewqodMc4fn8HGTEe3hgFu7pdEOKjwWqSguknG7QOSqBGCD1fk8dhX2cUfJLURHH4vBJPgm1fh86cTX6iIiIhIBVfpAmzn3KfOuaXOlXz5gZm1A44GVgB/3ePh+4F0YISZpZa4UBEREZFEi8Xg3ashfSM07QHdTi/3EtbsjHHu+HTWpjs61Q9wa78kIgqvRQrVvFaAm/olETT457wcXvmhBG1AGnaAwbd4x5NHw8L3E1qjiIiISEVX6QLsBDk8fv2Rc263XVWcc2nA50AN4JDyLkxERERkr756HpZNhqRacNjNXp/ccrQ+PcZ572ewOs3Rvm6A2/onkRxSeC2yL90bBrm8ZwSA+z/P4tOVucUfpPWh0PsiwMFbl8OabxJbpIiIiEgFVl0D7PzP2i7Zy+NL49cd9zeQmc0p7AJ0TkShIiIiIgCsngMf3+cdH3oDpDYs1+k3ZsQ4b3wGK3bEaF3buGNAEjXCCq9FimJoyxCndggRc3Dd5EwWbo4Wf5DuZ8CBR0JeJrx+LuxYk/hCRURERCqg6hpg14lfb9/L4/n31y37UkRERET2I2ML/G8kxHKh8++g1cBynX5LZowLJmTw47YYLWsZdx2STM2IwmuR4jizU5hDmwdJz4XLJmWwKTO2/ycVZAaHXAtNukPaWi/Ezskom2JFREREKpDqGmDvT/47sv322XbO9SnsAiwq2xJFRESkWsjve719JTToAH0vLdfpt2U5LpiQweItMZrXNO4emEztJIXXIsVlZlzZM0L7ugF+2em4+qNMcqLF3NYnGIZhd0LNprD2W3jnSu//CBEREZEqrLoG2PkrrOvs5fHae5wnIiIi4o+Zz8KSSRCpCcPu8AKscrIj23HhB+ks2ByjaaoXXtdReC1SYpGgcUu/JOonG7PXRblnehbF3ps+uQ4ccT+EU2Hhe/Dpw2VTrIiIiEgFUV0D7MXx6731uO4Qv95bj2wRERGRsvfzFzD5Ae/4sJuhZpNym3pnjuOiDzKYtzFG4xrGPQOTqJes8FqktOoleyF2JAhjFufy7+9zij9I3ZYw9HawAEz/M3z3RuILFREREakgqmuA/Wn8+mgz2+17YGa1gEFAJvBleRcmIiIiAsDOjTD2EnBR6H46tOxfblOn5zoumZjBNxuiNEzxwusGKdX1ZaNI4rWrG+CqXhEAHvkym6kr84o/SPPe0P8K7/i962Gl3rqIiIhI1VSl34mYWdjMOptZ+4L3O+d+BD4C2gDX7vG0B4BU4GXnXHq5FCoiIiJSUCwKb1/mbdTWuCscPKLcps6Ih9ez1kWpn+yF141qVOmXjCK+GHhAiNM6hog5uG5yBsu2Ros/SOffeZdoDrxxPmxbmfhCRURERHwW8ruA4jKzU4BT4jebxq8HmtmL8eNNzrlb48fNgYXAz3hhdUHXAF8Az5jZEfHzBgDD8VqH3J346kVERESKYOpjsHyq1+t2yG0QKJ+XbJm5jksnZfDV2ij14uF1k1SF1yJl5fSOYVanOWatjXLZpEzePTWVusVt1dPvctjxC6z5Bl4/Fy75EJJqlk3BIiIiIj5I6DsSMzvVzIKJHLMQvYCL4pdj4ve1K3DfGUUZJL4Kuy/wIl5wfQvQHngGGOic25zIokVERESKZOH78NkTXm/bwb+H1IblMm1WnuOyDzOYuSZK3SS4Z2ASzWoqvBYpSwEzru4VoXVtY8WOGNd/kkE0VsxNHQNBrx927eawfj68cyXEYmVTsIiIiIgPEv2u5C3gZzN70MxaJXhsAJxzo51zto9LmwLnrtjzvj3GWuWcu9g518w5F3HOtXbOjXLObSmL2kVERET2aeNieOcq77j3RXBAr3KZNivPcfmHGXz+S5Q6SXDPwGQOUHgtUi6SQ8at/ZKoHYHpq6P8cXZ28QeJ1ITD74NIKiwaD58+kvhCRURERHyS6HcmfwNqAPcAP5rZ+2b2OzPTlvUiIiIi+5K1Hd44D3J2Qpsh0O208pk2z3HFhxlMXx2lTgTuHZhM81oKr0XKU8MaAUb1SSJg8Py3OYz/Mbf4g9RpDkPv8D69Mf1PMO9/iS9URERExAcJfXfinLsOOAC4BPgaOAEYh7cq+z4za57I+URERESqhFgM3r4CNi+Dem3g0BugHP7+n5XnuOqjTD5bHaV2BO4+VOG1iF+6NgwyomsYgN9PzWTh5hJs6njAwV5PbIBx18LqOQmsUERERMQfCX+H4pzLcs696JwbCPQA/g+oCYwGfjKzd8zs2ETPKyIiIlJpTX0UlkyCpFow/B4IJ5f5lPltQ6auyqNWxGsb0lLhtYivjmkbYnCLIJl5cMWHGWzLKmY/bIDOv4OOx0I0G944F7b/kvhCRURERMpRmb5Lcc7NL7Aq+2JgPXASMMHMfjKzW80stSxrEBEREanQCm7aOOQ2qNW0zKfMzHVcNslrG1I73jakZW2F1yJ+MzMu6xGhbZ0Aq9JcyTZ1NIMBV0HTg2DnenjzfMjNLJuCRURERMpBmb9TiQfUFwI3AM0BA74DGgBPAIvMrFdZ1yEiIiJS4az/YY9NGw8u8ykzch2XTMpgRnzDxnsPVXgtUpFEgsbNfSO/bur4xKwSbOoYCMHQO6FmE1jzDbw/ClwJVnOLiIiIVABl9m7FzA42s+eBNcDzQEfgBaC3c6433qrsO4CGwDNlVYeIiIhIhbRzA7x2trdpY9uh5bJpY3quY+TEDGauiVIvybh3YDIt1DZEpMIpuKnj378r4aaOybW9lkShJJj3Jsz8a+ILFRERESkHCX3HYmY1zOxSM5uFt4njFcBqYBTQ3Dl3hXPuWwDn3E7n3BN4oXbfRNYhIiIiUqHlZsIb58H2VdCoEwwaVeabNu7McYz8IINZa6PUSzbuPTRJGzaKVGAFN3W8bWomS7eWYFPH+m3hsJu944/vhWWfJLBCERERkfKR6Hcta4B/AL2At4DDnXPdnHPPOud27OU5vwBlv1ORiIiISEXgHIy7FlbPhtRG3grJYKRMp9yR7bjwgwxmr4tSP9m479AkmtVUeC1S0R3TNsSg5kEy8uDKjzJJyylBG5DWg6DHOeBiMPZi2Pxj4gsVERERKUOJfueSBowGWjnnznLOTS3Cc/4GtE1wHSIiIiIV07THYf5bEEqBI+6HlHplOt2WzBjnjU9n7vooDeLhddNUhdcilUH+po4taxnLt8W4bWomriS9rHudBy0HQNZ2eP1cyNrb2iIRERGRiifR715aO+cecs6tK+oTnHM7nHM/J7gOERERkYrn+7Ew9VGwAAy9Deq1KdPpNmTEOOf9DOZvitGkhnH/oCSaKLwWqVSSQ8ZNfZNICcHEn/J4YV5O8QexABx2C9RpCZsWwztXQiyW+GJFREREykCi38FMNrML93WCmV1gZlMSPK+IiIhIxbZqNrx7jXfc91Jo0a9Mp1uzM8bZ72WwZGuM5jWN+wYl0aiGwmuRyqhZzQBX9/JaDT32VTZfrckr/iCRGnD4PRBJhcUfwGd/THCVIiIiImUj0e9ihgFt9nNOa2BogucVERERqbg2/wivnw3RbOh4HHQ5qUyn+3l7jDPHpfPT9hitaxv3HZpM/WSF1yKVWb9mIU46METUwbWTM1mfXoIV1LWbw5DbAPM+DbLkw4TXKSIiIpJofryTSQFKsGRAREREpBLauRFePR0yNkPzPjDgSjArs+mWbY1y1nvp/LLTcWDdAPcMTKZ2UtnNJyLl56xOYbo2CLAp03Ht5ExyoyXoh928Dxx8AeDgrcu0qaOIiIhUeGURYBf6Kso8rYHjgVVlMK+IiIhIxZKTDq+dBVt/ggYHwtA7IBAqs+nmb4py9nsZrM9wdK4f4K6BSdSMKLwWqSqCAeOGPknUTza+XhflD19ml2ygg86ElodA9g548wLI3pnYQkVEREQSqNQBtpnFzCxqZtH4XaPzbxe84K26Xg70At4o7bwiIiIiFVo0D/43EtbMhZpN4Ij7IZxSZtPNXJPHOe+lsznL0aNRgDsGJJESUngtUtXUSTJu7BshaPCf+Tl8sDy3+INYAA67Geq0gA0L4L3rwJVgNbeIiIhIOUjEEqDP2LXqegiwElhRyHlRYDPwCfBCAuYVERERqZicgwk3wdKPIKk2HPkgpNQrs+k+WpHLdZMzyYnCwAOCXHNwhFBA4bVIVdWhXpDzu4Z5+YdcbpuaSZcGAdrWCRZvkEgNGH43TLgZfngHDugNg24om4JFRERESqHUAbZzblj+sZnFgP845x4s7bgiIiIilda0x2HuyxCMwOH3Qp3mZTbVmEU53PFZFjEHR7YOcfFBYQJl2GNbRCqGY9uGWLQlxqy1Ua75OJN3TkklubifuqjT0luJ/ekjMPl+aNYD2g0rk3pFRERESirRPbDbAk8neEwRERGRyuPrf8PUR72P6A+5DRp3KbOp/vFdNrdN88Lr0zqGuEThtUi1YWZc2TNC01Rj4eYYoz/PKtlArQbCQWeDi8HYS2H7L4ktVERERKSUEhpgO+d+ds5tT+SYIiIiIpXG/Ldg/M3e8YCroNUhZTKNc47Hvsr6dQO3i7qFObNTBFN4LVKt1AgbN/ZJIhyANxbl8taSnJIN1Os8OOBgyNjk9e7PK+E4IiIiImWgVC1EzOw+vP7Xf3XObYnfLgrnnHuoNHOLiIiIVChLJ8PbVwIODr4QOh1fJtPkRB13fJbF20tyCRpc1SvCYS0Ssa2JiFRGresEuPigCP/4Loe7P8uie8MgneoXsx92IAiDb4X3R8HqWfDxfXDcY2VTsIiIiEgxlfbdzmi8APtNYEv8dlE4QAG2iIiIVA0rv4I3L4BYLnQ9FQ46s0ymSctxXPNxBtNXR0kKwqg+SRzcpJhBlYhUOcNaBlm0Ochnq6Nc/XEm752aSs1IMT+RkVwHht0Bk+6Ar/4PWvaD7qeXTcEiIiIixVDaAHt4/HrlHrdFREREqod18+G1MyEvEw48EvpeAmXQymN9eoyREzNYuDlGnQj8fkAS7esqvBYRrx/2JQdF+Gl7Fsu3xbjzs0yeOSKl+G2FGnWGvpfCrL/DezdAk+7QqFPZFC0iIiJSRKUKsJ1z0/Z1W0RERKRK27IcXjkVsrZ7G6ENvL5MwuslW6KMnJjBmp2OZqnG7QOSaJKa6L24RaQySwoZN/ZN4q7Psnj/xzwGHpDLeV0jxR+o8+9gw0JY8Rm8OQIunwJJNRNfsIiIiEgR6Z2PiIiISElsXw0vnwzpG6BZTxjye6+PbIJ9uSaPM8als2ano2O9AA8clqzwWkQKdUDNAJf18ELr0V9ksWBztPiDmMGh10OdlrBpsdcX27kEVyoiIiJSdAl992NmbczseDNLLXBfyMweMLPvzOwLMzs1kXOKiIiIlLu0dfDSibBtJTTsBMPvhmAJVjrux7iluYyYkMGOHOjXNMjdA5OoVdy+tiJSrRzWIsThrYLkROHajzPZmVOC8DmcAsPuglAyzB8Ls19IfKEiIiIiRZTo5Tv3A68A2QXuuwe4FzgIOAQYY2aHJHheERERkfKxcyO8dJLXPqR+ezjqAQjXSOgUMed4cnYWo6ZkkhuDY9qGuLFvhEhQ4bWI7N9F3SO0rGX8tD3GXdMzcSVZQV23pbcSG+DDu2Dtd4ktUkRERKSIEh1gDwQ+cc7lAZhZALgGWAS0AvoD6cBNCZ5XREREpOxlbIFXTvE+Vl+3NRz1EEQS2xs2M9dx/eRMnpmbQwAY2T3MyO4RAmXQW1tEqqZI0OuHnRSE95bl8frC3JIN1HYodDwOojkw5iLI2pHYQkVERESKINEBdhPg5wK3ewENgb8651Y7574GxgH9EjyviIiISNnK2u5t2Lh+PtRuAUc/DMm1EzrFhvQYZ7+fzoTleaSE4PcDkjimbTihc4hI9ZCQftgA/S+Hem1h60/w/g3qhy0iIiLlLtEBdhgo+IpmUPz2lAL3rQaaJXheERERkbKTnQavngFrv4WaTb3wOqVeQqeYvynKye+kM29jjMY1jAcPS6ZX48RvCiki1UfBftjXlbQfdjACw+6AUAr88A58/a/EFyoiIiKyD4kOsFcDPQrcPh7Y5JxbWOC+xoA+eyYiIiKVQ/ZO+O+ZsHoWpDaCY/4AqQ0TOsWk5bmcOS6dtemOTvUCPHRYMi1qJfplmohUR/n9sJeXph927eZw6HXe8aQ71Q9bREREylWi3xmNB44ysz+Z2cPAUcB7e5zTmd3bjIiIiIhUTNk74b9nwMqZUKMhHP0HqNk4YcNHY44/zcriqo8zycyDwS2C3D0widpJ6nctIomxZz/sMYvVD1tEREQql0QH2E8APwE3A3cBa4H78x80s9bAocBnCZ5XREREJLH2DK+P+QPUTlwXtO3Zjss+zOS5b7zNGi/oGubqXhHCQYXXIpJYB9QMcGm8H/b9M7JYvKUU/bDrt1M/bBERESlXCQ2wnXMbgIOAk+KXrs65NQVOqYkXbr+QyHlFREREEiq/bchu4fUBCRt+yZYoJ7+dzqcr86gVhjsPSeKE9mHMFF6LSNkY3CLE0JZBsqJw3eRMMnJL2A976O0QjvfDnvty4gsVERER2UPCmys65zKdc+Pjl7Q9HvvBOfe0c25RoucVERERSYhfw+svoEaDhIfXE5fncso76azYEaN1beORIcl0b6TNGkWk7I3sHqF5TWPp1hijP88q2SC1m8Mh13jHE2+HDXprJyIiImVLuwOJiIiI5MtO2yO8fjRh4XVezPHYV1lc/XEmGXlwaPMgDwxKplENvRwTkfKRHDJG9UkiHIAxi3N5Z0lOyQZqNxzaHwF5mTD2EsjNTGyhIiIiIgWEEj2gmdUHLgH6A/WAwpYUOefcEYmeW0RERKTEsrbDq2fA6lkJD6/Xp8e4/pNMZq2NEgDO7RrmhHYhtQwRkXLXsnaAi7pHeGFeDndPz6Jn4yDt6pbgUyADroKNi2DDD/DRPXDCnxNfrIiIiAgJDrDNrDMwFWgE7OsdmXb7EBERkYojcyu8ejr8MgdSG8HRiduw8fPVedwwJZPNmY56ScZ1vSN0baiWISLin8NbBflhU5CZa6JcOzmTd05JJTlUzD+ohVNgyG3wwa0w+wVoNwy6nFgm9YqIiEj1lujPrP4JaAw8DrQDws65QCGXUr1rM7MWZvZvM1tjZtlmtsLM/mJm9Yo5zglm9pGZrTazTDNbbmb/M7OBpalPREREKpGMLfDyyV54XbMJHPtYQsLrmHM8MyebCyZksDnT0a1hgD8MTVZ4LSK+MzMu6xGhSQ1j4eYYD88sYT/sBu2hz0jveNx1sG1VwmoUERERyZfoAHswMME5d5dzboVzLprg8TGz9sAc4GJgFvAUsBwYBcw0swZFHOdxYDzQG5gEPA3MBU4GPjezCxJdu4iIiFQw6ZvgpRNh7XdQq5kXXtdsUuphN2fGuOiDDJ78OhuA0zqGuOuQJOomqWWIiFQMNcJeP+xQAF5dkMvE5bklG6jLSdCiH2Rtg7cug2heQusUERERSXSAbcCCBI+5p7/hrfK+wTl3inPuDufc4XhBdifgkf0NYGZNgVuB9UBX59xl8XHOAI7B+zoeLLOvQERERPyXth5ePAHWz4faLbye16mNSj3sjNV5HDc2nemro9SKwB0DkjizU4SA+l2LSAXTtm6A87uEAbhtWiar0mLFH8QMBt0IKfVh1Zfw2R8TW6SIiIhUe4kOsOfghchlwszaAUcDK4C/7vHw/UA6MMLMUvczVGu8r/0r59yGgg845z4F0vD6eIuIiEhVtP0XePF4bwOyOq3g2EchtWGphsyJOh79MosLJmSwIcPRqX6AR4ck06OxWoaISMV1TNsQfZoEScuBGyZnkhstwXZFyXVg8C2AwWdPwMqvEl6niIiIVF+JDrAfBI43s2EJHjff4fHrj5xzuy0PcM6lAZ8DNYBD9jPOUiAH6G9mu71bNbMhQC1gckIqFhERkYpl68/wn+Ng8zKo1waO+QOkFGsbjd9Yvi3Kae+m8/fvcggYnNkpzH2HJtEgJdEvtUREEsvMuLJXhPrJxjcbovxpdnbJBmrWE7qdCi4Gb18GWTsSW6iIiIhUW6EEj9cSGAd8ZGav463I3lbYic65l0swfv7q7iV7eXwp3grtjsAnexvEObfFzG4HngQWmNm7wGagPXAS8DFwZVEKMrM5e3moc1GeLyIiIuVo849ez+sdv0CDDnDUg5BUq8TDOecYsziX0Z9nkZkHjVKM63pH6Fhfq65FpPKoFTGu7x3hoZnZ/P27HAYeEGJYqxK8VTx4hLenwJYf4YPfw2l/T3yxIiIiUu0kOsB+EXB4PaRHxC97fgbN4veVJMCuE7/evpfH8++vu7+BnHN/MbMVwL+Byws8tAx4cc/WIiIiIlLJbVgEL58EO9dD465wxP0Q2V/Xsb3bnBnjnulZTPzJ27BsUPMglxwUoUZYva5FpPLp3CDIGR3DjFmcyy2fZvLBGak0SS3mp0iCYRhyK7x/I8x7AzocBQedUSb1ioiISPWR6AD74gSPV1z57xj327jNzG4D/gA8AzwHrMNbNf0o8F8z6+Wcu21/4zjn+uxl/DlA7yLWLSIiImVp7Tx45RTI2AxNe8Dh90I4pcTDfbQilzs/y2JzpiM5CJf0iDC4RaJfVomIlK+TO4RYsDnK/E0xbpySyasn1CAYKOYf5eq0hH6XwZd/hfE3Qot+UK91mdQrIiIi1UNC32k5515K5HiFyF9hXWcvj9fe47xCxXt0Pw6845y7ucBDc83sVLwWJbeY2fPOueUlL1dERER8t3oOvHoqZG2H5n1g2F0QSirRUNuzHQ98kcXbS3IB6NogwFW9IjSqoV7XIlL5Bcy49uAk7piWycw1Uf72bQ7X9y7B/5cdj4Vf5sCqL+GdK2HkBAiotZKIiIiUTGV7t7U4ft1xL493iF/vrUd2vt/Frz/d8wHnXAYwC+97c3BxCxQREZEKZMXnXtuQrO3QaiAMv6fE4fX01Xkc+7+dvL0kl3AALuwW5u6BSQqvRaRKqZtsXHOw9//kU19nM2ttXvEHMYNDr/c2yF05E2Y8meAqRUREpDopk3dcZtbIzK4ys6fN7IU97u9vZiX9zG5+4Hy0me1Wu5nVAgYBmcCX+xkn/51ro708nn9/TkmKFBERkQpg2Sfw6umQsxPaDoWhd3j9WYspLcdxz/RMRkzIYG2648C6AR4bmsxx7cIETP2uRaTq6dE4yEkHhog5uOGTTLZkxoo/SHIdOCz+YddPH/VWZIuIiIiUQMIDbDO7FFgB/BW4nt37YjcBZgLnlWRs59yPwEdAG+DaPR5+AEgFXnbOpcdrCZtZZzNrv8e50+PXV5hZ8z3qPw4vCM8CvihJnSIiIuKzRR/A6+dAXiZ0ONoLUUrw8fVPV+Zy9JidvLogl6DB2Z3DjB6UxAE1tepaRKq2MzuF6VAvwLp0x++nZuHcfrcZ+q0DDoYuJ4OLwttXQE5G4gsVERGRKi+h777M7CjgH3gtPE4F/q/g4865+cAPwCmlmOYaYAPwjJm9a2aPmtkU4Kb4vHcXOLc5sBD4ZI8xxgKT8QL1hWb2kpk9bmbvARPwNoO8wzm3uRR1ioiIiB/mvwVvXgDRHOh8Igy8rtjh9ZbMGDd+ksnFEzNZm+5oVyfAI0OSOaVDuPgbmomIVEKhgHF97wipYfhkZR7/+r6EH07tcxHUaQWbl8HH9yW2SBEREakWEr186HZgLTDUOfceXtC8p3lA15JOEF+F3Rd4ERgA3AK0B54BBhYldHbOxYDj8ULvBXhh+y3AIcAHwDHOuadLWqOIiIj45Jv/wluXeav9up8B/a8AK/rLHecc45blcuSYdN5dlkskCOd3DfPgYUm0rq1V1yJSvTSqEeDKnhEAHv8qm3kbo8UfJBiBIbdCIASz/wlLJye4ShEREanqQgkery/whnNuxz7OWQ00Lc0kzrlV7N6aZG/nrcBbTV3YY7nAX+IXERERqexm/RM+uNU77nUB9Djb20isiFalxbh/RhZTVnoblnVtEOCKnhGapCq4FpHqq1+zEMe0jfHhT3lcNzmD8afVpHZSMT+JUr+d9//y3Bdh3DVwzZdQo36Z1CsiIiJVT6LfkUWA9P2cUxcowZ/uRURERPbi82d2hdd9L4We5xQ5vM6JOv72TTZHvbmTKSvzqBGCK3pGuGdgksJrERHg/C5h2tQ2Vu5w3DU9s2T9sLudCo27ws718P4oKMkYIiIiUi0l+l3ZCqDPfs4ZACxO8LwiIiJSHTkHUx+Hj+/1bg+4xgtJiuirNXmc8FY6T8zKJisKAw8I8qfhKQxvFcKKsXpbRKQqCweNG/okkRyE8T/m8cai3OIPEgh6G+qGU2DhezDvzcQXKiIiIlVSogPsccBgMzuzsAfN7GKgB/BWgucVERGR6sY5mHw/TP2D1+d60E3Q+fgiPXVzZoxbP83k7PczWLo1RtNU485DkrihTxL1khVci4jsqVnNAJf28Pphj/48i8VbSvCh2lpNof+V3vEHt8K2lQmsUERERKqqRAfYTwArgdfN7E1gIICZXRe//Q9gKfBsgucVERGR6iQWg4m3wedPgwVhyO/hwCP2+7RozPHKDzkc/uZOxi7JJRSA0zuGeHxoMj0aBcuhcBGRyuuwFiGGtgySHYVrP84kI7cEbUDaHwGtBkJ2Grxztff/uYiIiMg+JDTAds5tBYYCM4AzgaPxNlF8Jn77C+AI59z++mSLiIiIFC4WhfGjYNY/IBCG4XdBm8H7fdqstXn87u107p2RxfZsOKhhgCeGJnNGpwiRoFZdi4gUxcjuEZrXNJZti3HfjKziD2AGA6+D5Lrw8wz46vmE1ygiIiJVSyjRAzrnVgLDzKwH3grsBsB24Evn3JxEzyciIiLVSDQPxl0L896AYBIMvxua997nU9alx/jDl1m8tywPgIYpxohuYfo1DarPtYhIMSWHjFF9k7hnehZjl+QysHmQ0ztGijlIHRh4PXz6EHzyABx4JDTqWDYFi4iISKWX8AA7n3NuHjCvrMYXERGRaiaaC29fDj+8A6FkOOI+aNpjr6dnRx3/mpfDc3OzyciDcABOOjDEie3DJIUUXIuIlFTLWgFGdo/wj+9yuHd6Fj0bBTmwXjHbMLUaAO2PhB8nwztXwqUfQ7DM3p6KiIhIJZboHtgAmFlrM+trZn3MrFVZzCEiIiLVSF42jLnIC6/DNeCoh/YaXjvn+GB5Lke+uZMnZnnhdf+mQf483GsXovBaRKT0hrUMMqh5kIw8uG5yJll5JeiH3f9ySG0Ea+bCjCcTX6SIiIhUCQkLsM2soZk9aWZrgeXAV8As4CczW2NmfzSz+omaT0RERKqJ3Ex443xYPAEiNeHoR6Bxl0JPnbcxylnvZXDNx5msSnO0qGXceUgSN/VLolGNMvm7vYhItWRmXNojQtNUY9GWGA9+UYJ+2JFUGHSjdzztcVjzbSJLFBERkSoiIe/kzKwD8DUwCmgCRIENwMb4cVPgZuBrM2uXiDlFRESkGsjJgNfPgWUfQ1JtOOYP0LDDb05buzPGzVMyOentdGavi1I7ApceFOaxIcn0aFTMj7WLiEiRpISMUX2SCAfgtYW5vL8st/iDNOsJXU6EWB68cxXkliAIFxERkSqt1AG2mQWA/wKtgGnAkUBN51wz51xToBZwNPAZ0AZ4tbRzioiISDWQvRNeOwuWT4XkunDMo1B/97+D78xxPDk7i+Fv7OTtpbmEAnBi+xBPHZ7CkW3CBANqFyIiUpba1AlwQbcwAHd+lsmK7bHiD9L7IqjdHDYuhE8fTnCFIiIiUtklYgX20UBfYAxwhHNuinMuJ/9B51y2c24ycDgwFhhgZkclYF4RERGpqrLT4L9nworpkFIfjn0M6rX+9eHcqOOVH3IY+vpOnpmbQ1YUBjQL8qdhyZzXNUKNsIJrEZHyclTrEAOaBdmZC9dOzih+P+xQMhx2M1gAvngOfp5ZNoWKiIhIpZSIAPt0IBu43jm311cq8ceuA3KBMxIwr4iIiFRFWTvg1dNh5RdQoyEc+yjUaQF4GzROWp7L0f9L594ZWWzOcnSsF+CBQUnc2DeJJqnqcy0iUt7MjCt6Rmhcw/hhU4yHZ5agDUijTtD9TMDBu1dDTnrC6xQREZHKKRHv8noDnzvnNu7vROfcBmBG/DkiIiIiu8vcBq+cCqu+gtRG3srr2s0B+HpdHmeMy+CqjzP5aXuMZqnGzX0jjB6URMf66nMtIuKnGmGvH3YoAK8uKGE/7J7nQL02sPUnmDw60SWKiIhIJZWIALsl8EMxzv8BaL3fs0RERKR6ydwKr5wCv3wNNZt44XWtpizaHOXSiRmcMS6DOeuj1InAJQeFeWJYMv2ahTBTuxARkYqgXd0AI7ru6of90/Zo8QYIhuOtRIIw6x+wfFoZVCkiIiKVTSIC7NrAtmKcvw1vY0cRERERT+ZWePlkWPMN1GwKxzzKKteImz/N5Lix6XyyMo+kIJzWMcRTR6RwVJswIW3QKCJS4RzVpkA/7I8zi98Pu347byU2wLjrvLZSIiIiUq0lIsCOAMX503os/hwRERGRXeH12u+gVjM2D3uMB76rzRFv7OTtJbkEDI5pG+IvR6RwZqcIKSEF1yIiFVV+P+wmNYwFm2M8VJJ+2AedCQ0OhO0r4aN7El+kiIiIVCqJ2umomH9WFxEREWG38HpHaluebPoEQ8ZF+M/8HHJjMKh5kD8PT2Zk9wh1kxRci4hUBgX7Yf93QS7jitsPOxCCQTd513NfgqWTy6ZQERERqRQSFWCPNrNoUS7AfQmaU0RERCqzeHiduWYhzwfPY3DawzzzfZD0XOjVOMCjQ5K5rncSTVIT9XJFRETKS9u6AS7s5vXDvuuzTJZvK2Y/7HqtodcF3vF713ub/IqIiEi1lKh3hFbMi4iIiFRnmVvJeelUXlnViKG5T/NY+u/YnmN0rh/g/kOTuH1AMq3rKLgWEanMjmwdYuAB3h8mr/k4k8zcYn5wt9up0KgTpK2BibeXTZEiIiJS4ZX6naFzLlCCSzARxYuIiEjlE03fwlt/vZsjfr6Ie/MuYUOsDm1qG7cPSOK+Q5Po3EAvE0REqgIz47IeEZqmGou2xLh3RjH7YQeCMOhmCEZg3huweGLZFCoiIiIVmpY2iYiISLlwzjFx7o8c+9j73LLpBFa5xhxQw3FjnwiPDEmmV+MgZvqglohIVVIjbNzUN4lIEMYuyWXMopziDVCnORx8oXf8/ijI2JL4IkVERKRCU4AtIiIiZco5x9TFGzjxmc+4eswiluY2pJHt4KqueTwxvAYDDggRUHAtIlJltaod4JKDIgDcOyOLBZuL2Q+7y4nQuCvsXK9WIiIiItWQAmwREREpM18t38xZf5/JyP/MZv7andQljYuTp/Lk8CSGtq9NMKDgWkSkOhjaMsTwVkGyo3DNR5nsyC5GP+xAEAbdCMEk+H4MLBxfZnWKiIhIxaMAW0RERBLuu1XbGPGvrzj7H18ye8VWagWyOT/wMX+p+TJHHzqAUGo9v0sUEZFyNrJ7hNa1jRU7Ytw2LRPnihFi1z4A+lzkHY+/EdI3l0mNIiIiUvEowBYREZGEWbRuB1e8/DUn//Vzpi/dREo4wBl1FvGXwF/4XY0FJPW/GFIb+F2miIj4IBI0buybREoIJv2Ux7++L2Y/7M6/gybdIX0jTPx92RQpIiIiFY4CbBERESm1FZvSGfXGNxz39HQ+WrCeSDDAiQc14emmH3F6xv+okRSBfpdCakO/SxURER81TQ1wVS+vH/ZjX2YzZ11e0Z9sAa+VSCgJ5r8FC8aVTZEiIiJSoSjAFhERkRJbtz2LO9/+niOenMa4b9cQMOPork34y1ndOS/jv9RaNxMiqV54XbOx3+WKiEgF0L9ZiOPbhchzcM3HmWzMiBX9ybWaQp+LvePxN0P6prIpUkRERCqMkN8FiIiISOWzJT2H/5u6jJdm/kxOXoyAwdCOjTi9dwsa1QzBZ//f3n1Gx1WdbRi+9xT13mxZki1Zcu8dY5opBgyhQwgllJBAIBAIBBISSEjvIZCQLw1IL4QemikGg21scO+9W8XqXRrN7O/HGdmysWzZlmZUnmutWWd02rxiMZ45j/Z5989h50LwRsOkm53AQUREJOgzI7xsqQiwoSLAnW838LcLYvB0dGLfYbNhx3woWgWv3AtX/blrixUREZGw0ghsERER6bCaRh+/fHMjp/74Hf7w/jaaWwJMy0vhJ1eM47bT80mP88KCX8O298Ad6YySSxwQ7rJFRKSb8bgMX54cSVIkfLjXz08XN3X8YOOCk78MnihY+wKsfq7L6hQREZHw0whsEREROapGn5+/L9rJb+ZuprzOmXRrfE4SV03OIS8tNriXhcW/h01zwO2FSTdAUk74ihYRkW4tOcrw5UmRfHdhE79b0cz4DDfnD/Z27OD4/jD5ZvjwCXj1Psg9FeLSu7ZgERERCQsF2CIiItIuf8Dy3NLdPPrWJvZUNgAwrF88V0/JYXhmQps9LSx5Cta9DC4PTLgeUvLCU7SIiPQYw1PdXDvCy1/X+rjv3QaGJLsoSHZ37OCh5zutRApXwCtfgav+AqaDbUhERESkx1CALSIiIp9greXNtcX89I0NbCqpBSAnOZqrpwxkwsAkzKEBwYp/wapnndu6x18DaUPCULWIiPRE5w/2sKkywId7/dw2p4EXL4sl1tuBINoYp5XIi3fAupdg9bMw5oquL1hERERCSgG2iIiIHOTj7eX88LX1LNlRAUB6XCRXTs5mRn4arsNNsLX2RVj2Nye8HvdpyBgR4opFRKQnM8Zw67gIdlU3srkywP3vNvDrs6M/+cfSw4nLgCmfg4W/PtBKJL5f1xctIiIiIaNJHEVERASAzSW1fOEvH3PF/y1kyY4K4qM83DB9ED+/ahynDkk/fHi9aY7T9xpg1KXQf2xoixYRkV4hymO4Z3IkUW54ZWsLf1rV3PGDh5wLAyZAQ4XTSsTaritUREREQk4BtoiISB9XUt3I159bxbmPzmPO2mIiPS4um5DFo58ez3mjM/G62/m6sP19WPC483z4hZA9OXRFi4hIr5MV7+K28REA/PDDJhbsaenYgcbAyXeBNwbW/w9W/bcLqxQREZFQU4AtIiLSR9U3t/DLNzdy+k/f5Z+Ld2Kt5azhGfzy0+O5cnIOMRFH6DS252OY9zOwASg4G3JnhK5wERHptaYN8HBRgQe/hS+91cDumkDHDoxNhym3OM9fvQ9qirquSBEREQmpHhlgG2OyjTFPGmP2GmOajDHbjTGPGmOSj+NcpxpjnjXGFAbPVWiMmWOMmd0VtYuIiISbP2D5z0e7OOOn7/KrtzfR4PMzJTeZn1wxjltOHUxyTMSRT1C0Ct75PgRanF6j+WeGpnAREekTPj3cy7h0F+WNllvn1NPg62BLkIJzIGsSNFbCy19WKxEREZFeoscF2MaYfGAJcBOwGPglsBX4MrDQGJN6DOf6JjAPOA14Hfg58DKQDJzRqYWLiIh0Ax9sKuWCx97n/mdXUlLTRH56LN/61Ei+cs4wspKij36Csk3w9nfA3wzZU2DY+c6t2yIiIp3EZQxfmhhJvxjDmtIAX5/XiO1IGG0MTL8TvLGw8XVY8c+uL1ZERES63BHuDe62ngAygLustY+3rjTG/AK4B/g+cNvRTmKMuRL4LvAWcJm1tuaQ7d7OLFpERCScNpfU8v1X1jJ3wz4A0uIiuHrKQKbnp+LqaABdtQvefBh89c5kjSMvUXgtIiJdIi7CcO+USB76oJEXNvsYne7ilrGRRz8wNg2mfQE++CW89gDknQ6JWV1fsIiIiHSZHjUC2xgzGJgFbAd+c8jmbwF1wPXGmNijnMcF/BioB645NLwGsNb6OqNmERGRcKqq9/HIy2s479F5zN2wj2ivm6un5PDzK8czoyCt4+F1bQnM+SY0VkP6UBhzJbh61NcIERHpYXISXHzxeCZ1HHwm5EyDpmp46U61EhEREenhetoI7NYmm3OstQfN5mGtrTHGzMcJuE8C3j7CeU4G8oD/AhXGmAuA0UAjsNhau7CjBRljlrSzaXhHzyEiItLZ/AHLPxfv5OdzNlBR78Nl4KzhGVw5OYfE6GO8yaihEt78JtSVQnIujL8W3D3tK4SIiPRE0wZ4uKQqwAubW7jjzQZeujyWnPij/AHVGJj+JShZC1vehqV/hkk3hqReERER6Xw97epzWHC5sZ3tm3AC7KEcOcCeElwWA0uBMW03GmPmAVdYa/cdf6kiIiLhsWBLKd95eS3ri5wbjEZkxnPD9FwGpR7xBqXDa6512oZU7YH4TJj4WXAfZZJHERGRTnTlcC/bqwMsLwnwhTfqefbiWGK8R7mDKDoZpt0G834Kb3wDBs+E5EGhKVhEREQ6VU+79zcxuKxqZ3vr+qSjnCcjuLwNiAbOBuJxRmG/gTOp4zMdKchaO+lwD2B9R44XERHpLHsrG7j970u45g+LWF9UQ3pcJHefPYSHLhh5fOG1vwne+S6Ub4GYVJh8E3g7MNGjiIhIJ2qd1LF/rGFdWYB75zYQ6EhbkNzTYNDJzh9jX/oSBAJHP0ZERES6nZ4WYB9N65/hj/Ztxt1m/yustW9ba2uttWuAS4HdwOnGmOldVKeIiEinaWrx85u5mznr5+/x6qoiIj0urpqcw8+uHMe0vFTM8Uy0GGiBd38MRashMgGmfA4i4zu/eBERkQ6I9RrumxJJjAde29bCr5Y0Hf0gY+CkOyAqEbbNg4//1PWFioiISKfraQF26wjrxHa2JxyyX3sqgsut1toVbTdYaxtwRmEDTD3mCkVEREJo3sZ9nP/o+/z0jQ00+PxMy0vh51eO49IJWUR4jvNj3gZgwWOwaxFExMDkm51bsUVERMIoK97FnZMicQG/WtLMK1t8Rz8oKhFOut15/ubDULalS2sUERGRztfTAuwNweXQdrYPCS7b65F96Hkq29neGnDrPmkREemW9lQ2cNtfl/DZJxeztbSOAYlRPDh7BHefPZTUuMgTOLOFj5+EzW87va4n3gjx/TqrbBERkRMyPsPNNSOdyYjvndvA6n3+ox80aAYMPgN89fDCFyHQgWNERESk2+hpAfbc4HKWMeag2o0x8cAMoAH48CjnmQe0AEOMMYebiWp0cLn9+EsVERHpfC3+AH+Yt5VzfvEer69x2oV8ZupAfnz5WMZktXeD0jFY9QyseR5cbhh/HSTlnPg5RUREOtHswR5Oz3HT6IfPv1FPSX0HeltPvQ2iU5y7ixY81vVFioiISKfpUQG2tXYLMAfIBe44ZPMjQCzwF2ttHYAxxmuMGW6MyT/kPKXAv3FakTzcdpsx5hzgXJw2JK93wa8hIiJyXJbvquSiX8/n+6+uo775QLuQi8YNwOPuhI/0ja/Dkj8DBsZcBelDjnqIiIhIqBlj+NyYCIYmuyiss9w2p4Em/1GmQYqMgxlfdp7P/QEUr+n6QkVERKRT9KgAO+h2oAR4zBjzgjHmh8aYd4B7cFqHfKPNvlnAOuDtw5znK8Bm4BvGmHnGmJ8ZY54BXgP8wOettZVd+HuIiIh0SHWjj4dfXM2lT8xnbWE1aXER3H/usE5oF9LGjvmw8DfO85EXQ+bYzjmviIhIF/C6DfdMiSQ1yrC02M/X5zVi7VFC7KxJMPQ88DfD87dCS3NoihUREZET0uMC7OAo7MnA08A04F4gH3gMmG6tLevgeUqCx/8SyAHuAs4EXgFOtdY+0+nFi4iIHANrLa+uKuTsn7/HXxbuwACfGpvJT68Yx4SBnTipYuFymPdTZ/LGgrNh4LTOO7eIiEgXSYo03Ds1kkg3PLfRxxPLOhBIT/4cxPWDolUw7yddX6SIiIicME+4Czge1tpdwE0d2G87YI6wvRxnJPZXOq04ERGRTlBS3cg3X1jNnLXFAAzJiONzp+QxKDW2c1+obBO88z3w+2DgdMg/s3PPLyIi0oXyEl3cMSGCX37czE8/amJQoosL873tH+CNhlPugde/Du//AoaeD9mTQlewiIiIHLMeNwJbRESkN7PW8p+PdnHWL95jztpior1ubp6Ry7cvGtX54XX1HnjzW+BrgMxxMOJCMO3+3VdERKRbmpLp4ZqRTmj9lbkNLC1uOfIB/UbDyEvA+p1WIs31XV+kiIiIHDcF2CIiIt3EzrJ6rvvTIu5/diU1jS1MyEnip1eM5ZyR/XF1drBcXwZzHoLGKkgbCqOvAKOvBSIi0jNdMNjDWQM9NPvh8280sKsmcOQDJl4PiQOdO5He+nZIahQREZHjoytVERGRMAsELH/6YBvnPjqP+ZvLiI/08KWZBXz13GGdN0ljW8018ObDUFsMiTkw/lpw98iuYiIiIgAYY7hxjJfRaS7KGiw3v1ZPddMRJnV0R8CpXwHjhsW/g81vh65YEREROSYKsEVERMJoR1kdV//+Q777v7U0+PycnJ/Kz64cx4yCNExXtPNoaYS3vgMV2yEuAybdCJ6Izn8dERGREPO4DHdPjiQrzrCpIsAdb9Xj8x8hxE4tgAnXOs9fvAPqy0NTqIiIiBwTBdgiIiJhEAhY/rpwO+c9+j6Lt5eTGO3l3llDufPMISREH2HyqRN60RZ478dQshaiEmHSTRAR0zWvJSIiEgaxXsP90yJJiID3d/v51vxGrD1CiD3qckgfATWF8L974Ej7ioiISFgowBYREQmxPZUNXP/kIh56cQ0NPj/T81P56RVjmTwopete1AZgwWOwa7ETWk++GaKTuu71REREwiQjxsW9UyLxuuAf63z8dnlz+zu73HDqveCJhrUvwKpnQlaniIiIdIwCbBERkRCx1vKfj3Zx7i+Dva6jPNx91hDuOnMI8VFdNOraeWX4+Emnv6c7Aibe6LQPERER6aWGpri5Y0IEBvjJ4iae23iEEDu+P0z9vPP8lXuhcldIahQREZGOUYAtIiISAuV1zdz61yXc/+xKaptamJKbzE8uH8u0wald/+KrnoU1zzujzMZfB0k5Xf+aIiIiYTZtgIfrRjl/IL7/3UY+2N3S/s4F50DOSdBUDS98EQKBEFUpIiIiR6MAW0REpIu9u6GEcx+dx5y1xUR73dx+Rj73nD2UpJgQTJ64aQ4seQowMOZKSB/S9a8pIiLSTcwe7GX2YA8tFm6bU8/aMv/hdzQGpn8JopJg+/vw4W9CWqeIiIi0TwG2iIhIF2n0+fnWi6u58amP2FfTxPD+8fz48rGcOiQdY0zXF7BzISx43Hk+4kLIHNf1rykiItLNXDvSy/QBbmp9cNOr9eypaWd0dXQSnHyX8/zt70DhypDVKCIiIu1TgC0iItIFVu+p4sLHP+DPC3fgdhk+MyWHhy4YSXp8ZGgKKFoF7/3Ymbyx4CwYdHJoXldERKSbcRnDF8dHMCLVRXG95cbX6qlqsoffOWcqDJsN/mZ49nPQXB/aYkVEROQTFGCLiIh0Imstf3x/K5c+MZ/NJbUMSIriuxeP5qLxWbhcIRh1DVC+2Rk55vfBwJMg/6zQvK6IiEg35XUbvjI5kux4w6aKAJ9/o57GlnZC7Mk3Q2IOlG6EOd8IbaEiIiLyCQqwRUREOklZbRM3P/0R33tlHT6/5ZyR/fjBpWPIS4sNXRFVe2DOw+Crh/5jYfinnL6eIiIifVxchOFr0yJJjjIsLvRz19sNtAQOE2J7ouC0r4LLCx8/CetfCX2xIiIisp8CbBERkU6wYEsp5//qfeZu2EdcpId7zxnKzTPyiPS4Q1dEfSm8+RA0VkHaEGfSRpc+6kVERFqlRrv4+rRIYr0wZ3sLX5/XiLWHCbFTBsOkG53nL34JqgtDWqeIiIgcoKtaERGRE9DiD/DzORu49o+LKAlO1Pijy8YwOTcltIU0VcOch6C2GJJyYPx14PaEtgYREZEeICfBxf1TI4l0wzMbfPxoUdPhdxzxKRgwERrK4flbIdDO5I8iIiLSpRRgi4iIHKe9lQ1c/fsPefydzRjgsolZfPOCkaTGhWiixla+Bnjr21C5E+L6wcQbwRMR2hpERER6kKEpbu6eHInbwO9WNPN/yw8TYhsXnHIPRCXCtvdg4a9DX6iIiIgowBYRETke724o4YLH3ufjHRUkx3j5xuwRXDkpB3eoJmps5W+Gd38A+zZAdDJMugkiYkJbg4iISA80PsPNFydEYIAfLWriX+uaP7lTdDLMuNt5/vZ3YO+yUJYoIiIiKMAWERE5Jv6A5edzNnDT0x9RUe9jXHYiP7p8LCMHJIa+mIAf5v0M9iyFiFiYfDNEh6EOERGRHmpGlocbx3gBePD9Rl7f6vvkTtlTnEmRAz545iZorA5xlSIiIn2bAmwREZEOKqlp5Lo/LtrfMuSqyTncf95wEqK8YajGwsLHYcd88ETB5M9BbFoY6hAREenZZuV6uWKYl4CFO99u4P3dLZ/cafJNzsSOFdvg5S/D4SZ+FBERkS6hAFtERKQDFm4pY/av3mfh1jISo708OHsEl07IwmVC3DIEAAuL/wib3gS3FybdCAmZYahDRESkd7hsiIfz8jz4AvD51+tZtPeQENsdAac9AJ5oWPMcLP1zeAoVERHpgxRgi4iIHIG1lt++u4Vr//ghpbXNjMxM4EeXjWFUOFqGtFrxL1j7ArjcMP46SB4UvlpERER6AWMM14/yMnOgm0Y/3Px6PcuKDwmxE7Ng+h3O89cegOI1oS9URESkD1KALSIi0o6aRh+3/W0JP359PQELl4wfwIOzR5AUExG+ota9DMv+BsYFYz8N6UPDV4uIiEgv4jKGW8ZGMCPLTZ0Pbni1ntWl/oN3GnwGFJwDLY3wzI3QXBeOUkVERPoUBdgiIiKHsbmkhot/M5831hQTE+HmvlnD+PSUgbhd4WgZ0lrU27Do/5znoy6F/mPCV4uIiEgv5DKGL46PYGqmm+pmuP5/9WwsPyTEnnorJA6E0o3w6lfDU6iIiEgfogBbRETkEK+tKuTiX89n6746cpKj+d4lo5k0KDm8RW1/H+Y/6jwfNhuyJ4e1HBERkd7K7TLcOTGCCRkuKpos1/yvnq2VbUJsbxSc/gC4I2H532H5P8NXrIiISB+gAFtERCSoxR/gh6+u44t/X0pds5/p+al85+LRZCZGh7ewXYth3k/BBqDgbMg7Nbz1iIiI9HIel+HuyZGMTnNR2mC59n/17KwOHNgheRBMu9V5/spXoGRdeAoVERHpAxRgi4iIAJX1zdz41Ef8bt5WXAauP2kQd84sIMrrDm9he5fBuz+AgB/yToP8M8Nbj4iISB8R4TbcOyWS4SkuCussV79cd3CIXXAODJ4Jvnr493XQWB2+YkVERHoxBdgiItLnrS+q5qJfz+eDzaUkRHn4xgUjmT0mE2PC2O8aoGQtvPM98Ptg4Ekw9DwId00iIiJ9SJTHcP+0SIYlu9hba/n0S3VsrwqG2MbASXdA0iAo2wwvfQmsDW/BIiIivZACbBER6dNeX13IZU8sYGd5PXlpsfzg0jGMzEwId1nOxFBvfQtaGiFrEoz4lMJrERGRMIj2GB44KZJhbUZib6sK9sT2RsEZD4I3Bta+CAt/E95iRUREeiEF2CIi0icFApZfzNnAbX9bSn2znxkFaXz7U6NIjYsMd2lQvhXefBia66H/WBh1GRh9ZIuIiIRLtMfwtWlOO5GiOsvVL7WZ2DExC2bc7Tx/82HYsSBsdYqIiPRGuhoWEZE+p6bRxxf+uoTH3tmMy8C10wZyxxn5RHi6wcdixXaY8w1oqoH0ETDmSnB1g7pERET6uCiP4YFpkYxIdVFcb7n65Xq2tIbYg052/uBs/fDMjVBTHNZaRUREehNdEYuISJ+ys6yey3+7gLfWFRMb4eb+c4dz4dgB4e93DVC5E9540JkEKn0YjL8G3J5wVyUiIiJBUR7D/VMjGZnqoqTeGYm9uSIYYk+8AfqNhtpi+O9NzhwWIiIicsIUYIuISJ/x4dYyLv7NB2wsriUrKZrvXTKGcTlJ4S7LUbUrGF5XQdoQGH+twmsREZFuqDXEHpXmYl+D5aqX6lld6geXG05/AKJTYMd8p52IiIiInDAF2CIi0if8a/FOrvvjIirqfYzPSeI7F4+if2JUuMtyVO+B1x+EhgpIyYcJ14PbG+6qREREpB2RHsNXp0QyPsNFeaPlMy/X8XFRC0QnOyG2ccOHT8Dyf4S7VBERkR5PAbaIiPRqLf4Aj7y8hq89t4qWgGX2mEy+OmsYMRHdZHRzTaEz8rqhHFIGw6TPKrwWERHpASI9hnunRDIt001NM1z/Sj3v726BfqNg2m3OTi9/GXZ/HN5CRUREejgF2CIi0mtVN/q4+c8f89T87bhdhi+cNpjrTxqEy9UN+l1DcOT116CuFJJzYeJnwR0R7qpERESkgzwuw50TIzg9x01DC9z8Wj2vb/PBsPNh2GzwN8O/roXqwnCXKiIi0mMpwBYRkV5pZ1k9lz2xgHkb9xEf5eGbs0cwc1hGuMs6oGo3vP71A+H1pBvAExnuqkREROQYuV2GL4yL4Lw8D74A3PFmA89vbIapXwhO6lgE/74WfI3hLlVERKRH6pEBtjEm2xjzpDFmrzGmyRiz3RjzqDEm+QTOeb0xxgYft3RmvSIiElofby/nkifms7mkluzkaL538WiGZyaEu6wDKnc6I6/ryyAlDybdCJ5u0o9bREREjpnLGD47ysulQzz4Ldwzt5G/rA3AGV+H2AzYs8RpJ2JtuEsVERHpcXpcgG2MyQeWADcBi4FfAluBLwMLjTGpx3HOHOBxoLYTSxURkTB4YdkePvOHDymva2ZcdiKPXDSKjIRuFA5XbHfC69YJGyfeqJHXIiIivYAxhquGR/CZEc5cFg/Pb+RnKyOxM7/pfNav/Bcs/E2YqxQREel5elyADTwBZAB3WWsvsdZ+zVp7Jk6QPQz4/rGczBhjgKeAMuD/OrtYEREJDWstv3hzI3f/ezk+v2XWyH589dzh3WeyRoDyrU7bkMYqSBsSbBuintciIiK9yUUFXr4wLgKXgV8va+aBVZm0TL/H2fjmQ7Dh9fAWKCIi0sP0qADbGDMYmAVsBw790/W3gDrgemNM7DGc9i7gTJwR3XWdUKaIiIRYo8/PXf9azmNvb8Jl4Ibpudw0Iw93d5msEaB0I7zxIDRVQ/owmHA9uL3hrkpERES6wMyBHu6dEkmEG/6zwccXNkykfvR1YAPw35tg7/JwlygiItJj9KgAGydoBphjrQ203WCtrQHmAzHASR05mTFmBPAj4FfW2nmdWaiIiIRGaW0T1/zhQ15esZdor5v7Zg3jvNH9w13WwYpWwRvfgKYaSB8B469TeC0iItLLTezn5pvTI4n3wjs7W7hm54WUDzwPfPXwj09D5a5wlygiItIj9LQAe1hwubGd7ZuCy6FHO5ExxgP8FdgJPHi8BRljlhzuAQw/3nOKiEjHbC6p5dIn5rN0ZyVpcRF8+6JRTBh43PP5do3dH8GbDzsXq/3HwYRrwd2N2pqIiIhIlxmS7Obbp0SRFm1YXhLgiuIb2JU6A2qL4B9XOW3FRERE5Ih6WoCdGFy29ynfuj6pA+d6GJgA3GitbTjBukREJMQWbinjsifms6u8gcFpsXz34tEMTIkJd1kH2zYP3vku+JshewqMvQpc7nBXJSIiIiE0IM7FI6dEMijBsLXKcmnZHSyPmQ4la+E/N4DfF+4SRUREurWeFmAfTWuzU3vEnYyZijPq+ufW2oUn8oLW2kmHewDrT+S8IiLSvmeX7OazTy6iurGFyYOSeejCkSTFdLPJEDe+Ae/9BAJ+yDsNRl0Krt72sSsiIiIdkRLl4uGToxid5qK0ET5d9SVedZ0BW+fC/+4Be8RLWBERkT6tp11Jt46wTmxne8Ih+31Cm9YhG4GHOq80ERHpatZafvHmRu59ZgU+v2X2mEzuOXsoUd5uNqp5zfOw4DHAQsE5MPQ8MN1oQkkREREJuRiv4YFpkcwc6KbJb7i9/gv81n8Jdulf4f2fhbs8ERGRbqunNeHcEFy21+N6SHDZXo9sgLg2xzeawwcKfzDG/AFncse7j7VIERHpfE0tfr727CqeX7YHl4EbTs5l1shuNlmjDcCSp2D1c87Pwy+E3BnhrUlERES6DY/L8PmxEWTGtvDPdT5+7LuKbe4Mvvf2D4mISYPJN4W7RBERkW6npwXYc4PLWcYYl7U20LrBGBMPzAAagA+PcI4m4E/tbJuI0xf7A5yw/ITai4iISOeoqvfxhb9+zKJt5UR6XNx11hAmdrfJGv0+mP8r51Zg44LRV0DWhHBXJSIiIt2MMYZPFXjpF2v4zbJm/uM/g102nf97+RskRic5bcdERERkvx4VYFtrtxhj5gCzgDuAx9tsfgSIBX5nra0DMMZ4gXzAZ63dEjxHA3DL4c5vjPk2ToD9Z2vtH7vq9xARkY7bVV7PjU8tZsu+OpJjvHz13OHkpcWGu6yD+eph7g9g7zJwR8D4ayG9vZuFRERERGBqpofUaMPPFjexsGkUlzY/wu+feZiCqETIPzPc5YmIiHQbPa0HNsDtQAnwmDHmBWPMD40x7wD34LQO+UabfbOAdcDboS9TRERO1IpdlVzyxHy27KsjJzma7148uvuF1w0V8PrXnPA6Ig6mfl7htYiIiHRIfpKb754SxcB4w1Y7gEsaH+btv/0Edn8c7tJERES6jR4XYAdHUk8GngamAffijLJ+DJhurS0LX3UiItJZ5qwp4tO/X0hZbTOjsxL59kWjSI2LDHdZB6veA6/eB2VbICYVpt0GidnhrkpERER6kLQYF4+cEsW0TBe1xHBLwx08/sc/YIvXhrs0ERGRbsFYa8NdQ69kjFkyceLEiUuWLAl3KSIiPc7T87fxyP/WYi2cPjSdW07Nw+PqZn9zLVoFc78PTTVOaD3xBoiMC3dVIiIi0kNZa3lxUzP/2dCCxXB+xAp+dvunie2fH+7SREREmDRpEkuXLl1qrZ0U6tfuZmmAiIj0ZYGA5Xv/W8u3X3bC6ysnZXPraYO7X3i9+S2Y800nvE4fAVNuUXgtIiIiJ8QYwyVDI7lvsodo08xrzeO4/Ndz2bltY7hLExERCatulgiIiEhf1ejzc8c/lvLHD7bhdhm+eHo+l03MxhgT7tIOsAFY+mf44JcQaIHcGTDhOvB0s9YmIiIi0mNNzIzkezMiGeCuYn1LJp/6/Qre+XhVuMsSEREJGwXYIiISduV1zVzzhw95bXURMRFuvnbecE4bmh7usg7mb4L3fgQr/wPGBSMvgeEXQncbHS4iIiI93oDkaL57RhITI3ZRZWO4+b87+emLi2nxB8JdmoiISMjpqltERMJqe2kdlz0xn6U7K0mNjeDbnxrF6KzEcJd1sPpSeO1rsH0+eKJg4o0wcFq4qxIREZFeLCYmmntnDuQzsR/jIsBvFu7j+t/No6SmMdyliYiIhJQCbBERCZslOyq47LcL2F5WT25qDN+5eDQ5KTHhLutgJWvh5buhdCNEJ8O02yB9SLirEhERkT7AFRHNRadO5RsJc0ikloU767jg0fdYtLUs3KWJiIiEjAJsEREJi9dXF3HNHz6kvK6ZcdmJPHzhKFJiI8Jd1sE2vAqvfx0aKiAlH6bfAfH9wl2ViIiI9CXeKEaePJsfprzCcLODfXUtXPOHD/ntu1sIBGy4qxMREelyCrBFRCTk/vTBNr749yU0tQQ4c3gGXz13ONER7nCXdYC/GRY8Bgt/40zWOGgGTL4ZImLDXZmIiIj0Rd4okqd+hm+mvstFrvn4Lfz49fXc8NRitRQREZFeTwG2iIiEjD9g+fZLa/ju/9ZiLXx6Sg63nJKH22XCXdoB9WXw+oOw8Q1we2HMVTBCkzWKiIhImHmjcE+5mc9k7OKr7n8RTz3vbyrl/EffZ+76knBXJyIi0mV0NS4iIiHR0Ozni39bwtMLtuNxGb40s4BLxmdhTDcKrwuXw8t3wb51EJ0EU2+FrAnhrkpERETE4YmEiZ9l4oAofuT5PaNc2ymra+ampz/iOy+vpanFH+4KRUREOp0CbBER6XKltU185g8fMmdtMbERbr4+ewQzCtLCXdYB1g/L/w5vfBMaKp1+1yfdAYlZ4a5MRERE5GBuD4y9mpSBI3jQ9Teudr+D21ienL+NS3+zgM0lteGuUEREpFN5wl2AiIj0blv21XLTUx+xs7ye9LhIHjhvOFnJ0eEu64CGCpj3UyhcARgoOAsGn6mWISIiItJ9uVww8hJc3hgu3vouo9jG454bWFtYzace/4AHZw/nupMGda873URERI6Trs5FRKTLLNpaxmVPLGBneT15abF85+JR3Su8LloJL93phNcRcc5EjQVnK7wWERGR7s8YGHouDJtNgWsvP/T/nFMSS2nw+XnoxTV89snFFFY1hLtKERGRE6YrdBER6RIvLt/DdX9aRFWDj4kDk3n4wpEkxUSEuyxHoAWW/gXe+IYzAjslD06+E9IKwl2ZiIiIyLHJOxXGX0OMx3JH/W+5O+0j4iPdvL+plFm/nMdzS3djrQ13lSIiIsdNLURERKRTWWv5zdzN/GzORgDOHdWfz540CJerm9zCWr0H5v0MSjcCBgbP1KhrERER6dn6j4GoJFj6F6ZVvc6w+C38IfVGlu5t5Cv/WcEba4r4waVjSI2LDHelIiIix0xX6yIi0ml8/gAPPLuSn83ZiAGuP2kQN56c203Cawsb34CX7nLC6+gkmPp5GDpL4bWIiIj0fEk5MP0OiO9PUs0m7qv6EV8YF0G0180ba4qZ9ct5vLKyUKOxRUSkx9EVu4iIdIrqRh83PfUR//l4NxEeF/ecPZTZYzLDXZajqRre+T4seAxaGiFzHJx8l9M6RERERKS3iE6CabdC+nBMcw0z1z/Cj8eVMDIzgbK6Zu74x1K+8NclFFU1hrtSERGRDlOALSIiJ2xXeT1X/HYBH2wuJTHay0MXjGRKXkq4y3LsXAgv3O4sPVEw9ioYdzV4u9FkkiIiIiKdxRMFE66H3FMg0EL6iif4RtzL3HxSFtFeN2+uLebsX7zHPxbtJBDQaGwREen+1ANbREROyLKdFdzyl48pq20mKyma+88dRkZCVLjLgsYqWPQ72Pae83NKHoy5EqKTw1uXiIiISFdzuWD4BU5bkVXP4do+j3MqtjHx3Ad4cmUzS3dW8ODzq3hx+R5+dPlY8tJiw12xiIhIuzQCW0REjturqwq5+vcfUlbbzOgBCTxy0ajuEV7vmA8v3u6E124vjPgUTLlF4bWIiIj0Lf3HwvTbIa4fVO0i9e37uG9IMXedWUBClIdF28o579F5/PqdTTS1+MNdrYiIyGEpwBYRkWNmreWJdzdz+9+X0tQSYOawDB44fzixkWG+saehEt77Mcz9gfM8ZTDMuBsGnQxGH3kiIiLSB8VlwElfdOYAaWnEzPsx00v+zc8uHc6pQ9JoagnwszkbOf9X7zN/c2m4qxUREfkEtRAREZFj0twS4KEXVvPvj3dhgM9MHciFYzMxxoSvKBuATW/Cx09Ccy24I2DY+ZAzVcG1iIiIiCcSxn4akgfB+ldgw6vEF63k9lPv5bQhI3hq/ja27qvj2j8u4qJxA/jmBSO6x111IiIiKMAWEZFjUFnfzBf/tpSFW8uI8Li444wCpoZ7ssbKHbDg11Cy1vk5bSiMvBhiuskkkiIiIiLdgTEwcDokDoJV/4Gq3fDqfYwedw0/uvRyXllTwvPL9vDSir28s76Ee2cN5fqTBuFxazCAiIiEl7FWsw53BWPMkokTJ05csmRJuEsREekUW/bV8rmnP2J7WT1J0V7uO3cY+elx4SuopRFW/AvWPAcBP0TGw/ALof8Y5wJNRERERA7P74ONbzjzhgCkj4DTvkKJTeHPC7ezdGclAMP6xfPwp0YyoyAtfLWKiEi3MGnSJJYuXbrUWjsp1K+tEdgiInJUH2wq5fa/L6G6sYVBqTF8ddYwUuMiw1SNhZ0fwuI/QG0xYGDgSTBkFnijw1STiIiISA/i9sKICyF9OKx+BvatgxfvJGPiDXx11gV8vLOKvyzYwYbiGq794yLOGdmPb8weQW5abLgrFxGRPkgjsLuIRmCLSG/xtw938K2X1uAPWKbkJnP7GQVEed3hKaZqJyz6Pexd5vwc3x9GXQpJA8NTj4iIiEhP11wPa1+EopXOz2lD4eQ7aU7I5dVVhbywfA9NLQEi3C5umpHLl84sID7KG96aRUQk5MI5AlsBdhdRgC0iPV2LP8D3XlnH0wu2A3Dx+AFcNTkHVzjaczTXwvJ/wvqXnXYh3mgYcg5kTwOX+jKKiIiInLDitbDuRWisdibBHn0pjLuG8iYX//5oJ/M2lQKQFhfBV84ZxlWTs9UfW0SkD1GA3QspwBaRnqyq3seX/rmU9zeV4nEZvnDaYE4dkh76QgJ+2PwmLPsrNFQCBnKmOO1CInQLq4iIiEinammETW/CjoWAhbh+MP12yJrMln21/GXhdjYW1wKQnx7L/ecNZ9bIfhjNPyIi0uspwO6FFGCLSE+1uaSGz/9lCdtK60iI8vCVc4YxrH98iKuwsGcJfPQkVO5wViXnwvBPQeKAENciIiIi0sdU7nImyq4pcn7OmgxTP4dNyOHDrWX866NdlNQ0ATB5UDJfnz2cSYNSwliwiIh0NQXYvZACbBHpid5ZX8xd/1xObZMzWeO95wwjPT7EkzWWb4WP/wR7lzs/RyfD0HOh/1jQ6B4RERGR0Aj4YccC2PKOMzLbuGD4BTD+Glo8cby1roTnlu2mprEFgFkj+3H/ecMoyAj1wAcREQmFcAbYnlC/oIiIdD/WWn773hZ++sYGrIVpeSncdnp+aCdrrC2B5X+HzW8DFrxRMPhMGDgd3Pq4EhEREQkplxvyToUBE5yWbrs/hnUvw5a5eMZfw3kjZnPa0DT+t7KQV1cVMmdtMW+tK+aS8VncddYQctPU7k1ERDqHRmB3EY3AFpGeoqHZzwPPruSlFXsBuGpyDpeMHxC6XoaNVbDyP7DhFfD7nIulnJMg/0yIiAlNDSIiIiJyZNWFzve1si3Oz3H9YPw1MHgmFY1+nlu6m7kb9uEPWNwuwxUTs7nzrAKyk/V9TkSkN1ALkV5IAbaI9AS7K+q57W9LWL2nmiivizvOKGByboj6FzbXwdoXYM3z4Gtw1mWOg4JzIDY1NDWIiIiISMdZCyXrYdNrULvPWZeYBeOvhdxTKalp5rlle3h/0z4CFrxuw9VTBnLHzAL6J0aFt3YRETkhCrB7IQXYItLdfbCplDv/uZSKeh8Z8ZHcN2sYOSkhGCHT0ggbXoWVz0BTtbMufRgMORcSMrv+9UVERETkxAQCULgCtrwN9WXOuuRcZ0T2wOkUVjfx7NI9LNhcigUi3C6umpLNraflh+b7poiIdDoF2L2QAmwR6a6stfxu3lZ+8vp6AhbG5yRxx8wC4iK7uM+0vwk2vA6rnoGGCmddci4MmQUpeV372iIiIiLS+QJ+2LvMCbIbKp11idkw+nIYfAa7q1v475LdLN5WjgU8LsMlE7K4/Yx8BqfHhbNyERE5RgqweyEF2CLSHdU2tXD/f1fw6qoiAC6bkMXlE7Nxubqw37W/GTa+4QTXrSN0ErOg4GxIGwah6rUtIiIiIl3D3wJ7PoJt7x8YqBCTCqMugaHnsafW8OLyPczfUkrAOl//LhiTyR0zCxiRmRDW0kVEpGMUYPdCCrBFpLvZuq+WW/+6hE0ltUR73dx+Rn7X9rv2N8OmOU5wXVfqrEsY4ATX6cMVXIuIiIj0NgE/FK6Ebe9BbbGzLiIWhp0Pwy6gOJDAyyv28u5GZ7JHgNOHpnPraYOZnp8auknERUTkmCnA7oUUYItId/LaqkK++t+V1Da1kJUUzVfOGcqApOiuebGWRtj4Oqx6FhrKnXXx/Z3gOmOkgmsRERGR3s5aKN0AW9+Diu3OOuOCnGkw8iLK4obyv5VFzN1QQlNLAIDRWQl84bR8Zo/uj8ftCl/tIiJyWAqwj5ExJhv4DnAekAoUAi8Aj1hrKzpwfCpwKXABMAbIApqBVcBTwFPW2sAJ1qgAW0TCrrklwI9eW8+T87cBMC0vhVtPyyc6wt35L+arh/WvwprnoLHKWZcwAAafCf1GOBctIiIiItK3VO6EHQugeLUzQhuceVCGX0jNgBm8uamaN9YUUd3YAkB2cjSfOyWPKyfndP0cLSIi0mEKsI+BMSYfWABkAC8C64GpwExgAzDDWlt2lHPcBvwWJ/ieC+wE+gGXAYnAs8CV9gT+4yjAFpFw21vZwJf+sZSlOytxuwzXThvIeaP6d/6tmU3VsO5/sPZFaK511iVmQ/5ZkK4e1yIiIiICNNbA7kWwc9GB74zeaMg7jeb8WcwrT+KVlUUUVTcCEB/p4crJOdx4ci4DU2PCWLiIiIAC7GNijHkDmAXcZa19vM36XwD3AL+z1t52lHOcCcQCr7QdaW2M6Q8sBnKAK6y1z55AnQqwRSRs3t1Qwj3/Xk5FvY+U2Ai+fNYQhvaL79wXaSiHNS/AhlfB1+CsSxoE+WdC2hAF1yIiIiLySf4WKF4FuxYfaC8CkDKYwJBzWeKZyKvrK1hfVAM4XynPGt6Pm2fkqk+2iEgYKcDuIGPMYGALsB3IPyR8jscZUW2ADGtt3XG+xoPA94FfW2vvPIFaFWCLSMi1+AM89vYmHp+7GWthbHYid8wsICHK23kvUlvk9Lfe/Cb4fc66tCEw+AxIzlNwLSIiIiIdU1sCuxfD3uXQHLyEd3th4HS2pZ/Ja8VJLNxaTktwwsfh/eO57qRBXDIhS+1FRERCLJwBdk/7F//M4HLOoT2qrbU1xpj5OKOzTwLePs7XCKYxtBzn8SIiYbG3soG7/7WcxdvLMcCVk7K5ZEIWrs4KlMu3wepnnVnlW/8J7jfKCa4TszvnNURERESk74jLgOEXwpDzoGQN7P4IyrbCtnnkbZvH7dHJXDPybN4OTOatbU2sL6rhmy+s5kevreeSCQO47qRBDO+fEO7fQkREulhPC7CHBZcb29m+CSfAHspxBNjGGA/w2eCPr3fwmPaGWA8/1tcXETleb6wp4v7/rqSqwUdyjJfbzyhgdFZi55y8eDWs+q9zQQHOZIwDJkDe6RDfr3NeQ0RERET6LrcHMsc5j4ZK2LsM9i6FulKSNjzD5TzDRckFLM6axVtVWazf18jfPtzJ3z7cyZTcZK47aRDnjupPlLcLJioXEZGw62kBdmsaU9XO9tb1Scd5/h8Bo4FXrbVvHOc5RERCptHn5/uvrOOvH+4AYHxOEl88PZ+E6BNsGWIDTl/C1f+FknXOOrcXsqdA7qkQnXRi5xcREREROZzoJMif6dzlV7UL9iyFohV4KzYzo2IzM4Bd/abzpusUPiiN4aPtFXy0vYKkGC+XTsji6ikDGda/k+d+ERGRsOppAfbRtN4nf8yNvY0xdwH3AuuB6zt6XHt9X4Ijsyceax0iIh21uaSGL/1jGeuLanC7DNdMHch5o/ufWMsQvw+2vev0uK7a5azzRsOgk2HgdIiI7ZTaRURERESOyBhIGug8hl8IpRuhcAXsW0dO+UJuZiHXmCg+SJ3N200j2F7r46n523lq/nbG5yRx9ZQcLhw3QL2yRUR6gZ72L3nrCOv27otPOGS/DjHG3AH8ClgLnGWtLT++8kREup61lr8t2sn3X1lLoy9A/4Qo7jyzgMHpccd/Ul89bHwd1rwI9aXOuugkGHQKZE8GT2Sn1C4iIiIicszcHug30nn4GqFkLRQuJ6psC2dXP8fZwLaIbN6JOof59Vks31XJ8l2VfOd/a5k9JpMrJmUzNTcFl0uTjYuI9EQ9LcDeEFwObWf7kOCyvR7Zn2CMuRv4JbAaJ7wuOe7qRES6WEl1I/c/u5J3N+wD4NSCNG6akUd0xHH2+2soh7UvwYZXD8z8HtfP6W+dORZc6iMoIiIiIt2INwqyJjqPplooXgNFK8gr387nGp7iOjws8o5jrudk1jck8d8lu/nvkt1kJ0dz+cRsLp+YzcDUmHD/FiIicgyMtcfcbSNsjDH5wGZgO5BvrQ202RYPFAIuIN1aW9eB8z2A0/d6OXCOtba0E2tdMnHixIlLlrQ3x6OIyLF5fXUhX39uFRX1PmIj3XxuxmCm56ce38mqdsHq52DrXKdtCEBKHuSdBmnDnFs2RURERER6ioYqZ/LxwhX7W+EV2hTmMZH3mUBZS9T+XafmpXD5xCzOG51J4onOHSMi0kdMmjSJpUuXLm2vnXJX6lEjsK21W4wxc4BZwB3A4202PwLEAr9rDa+NMV4gH/BZa7e0PZcx5iHgO8ASYJbahohId1Xd6OORl9by7NLdAIzNSuTW0/NJiY049pOVrIXVz8LOD4MrDPQfDbmnQVJO5xUtIiIiIhJK0YmQO8N5NFRA0Soyi1bx6aq3uNK+xVp3Lu8xicWBYSzeVs7ibeU89OIazhyWwSUTspg5PJ1Ij+4+FBHpjnrUCGzYPwp7AZABvAisA6YBM3Fah5xsrS0L7psLbAN2WGtz25zjBuBpwI8Tgh+uZ/Z2a+3TJ1CnRmCLyAlbsLmUr/53JXsqG/C6DddOG8Sskf0wxzJC2vph1yJY9RzsW+esc3mc2y5zT4XYtK4pXkREREQk3OrLoWglFK2C6r3U2wgW2RHMt+NZG8jB4nyvTojyMHtMJhePz2JqXgpu9csWETmIRmAfg+Ao7Mk4o6fPA2bjtA55DHikgyOp84JLN3B3O/u8hxNyi4iEXE2jjx++tp5/LNoJQF5aLHfMLCArKbrjJ/E3w+a3Yc1zUL3XWeeNhpyTYNB0iIzvgspFRERERLqRmBQYfIbzqC0lpngVM4tWMrPmz5S74lkQGMUHjGNHYwb/+mgX//poFxnxkcwek8mnxg1g4sCkYxs8IiIina7HjcDuKTQCW0SO13sb9/H1Z1eyt6oRt8tw2YQsLho/AI/L1bETNFXD+ldh/cvQUOmsi06G3FMgaxJ4IrusdhERERGRHqG2xBmVXbQSakvYbdOYHxjDAjuGEpu4f7espGguHJfJhWMGMDorQWG2iPRZ4RyBrQC7iyjAFpFjVdXg4/uvrOU/Hzu9rgenxXLb6fnkpHRwlvTaIljzAmx6E1oanXUJA5z+1v3HQEcDcBERERGRvsJaqC2GwpVQtBJbV8ZWm8kCO4oP7RjKbdz+XQemxHD+mP5cMCaTMVmJCrNFpE9RgN0LKcAWkWMxZ00RD724muLqJrxuwxUTs7lg7ICO9d4r2+xMzLj9A7ABZ13aUMg7FVLyQV+sRURERESOzlqn9V7RSihcQaChio02hwV2FIvtaKrsgXZ+2cnRzB6TyewxmYzLVpgtIr2fAuxeSAG2iHTEnsoGvvXiGt5aVwzAkIw4bj0tn6zko/W6trBnCax61vmCDWBckDnOmZgxIbNrCxcRERER6c1sACp27J8AMtBUz3qbwyI7gsWMoTJw4Pt6ZmIU547qz7mj+jMlNxmPW3c+ikjvowC7F1KALSJH0uIP8NT87fzyrY3UN/uJ9rq5anIOs0b2w3WkUdd+H2x7D9Y8DxXbnXWeSMieCoNOhuikUJQvIiIiItJ3BALOXY+Fy6F4LYGWJjbaHD60o/jIjKbcfyDMTomN4JwR/Th3dD9Ozk8jyusOX90iIp0onAG2J9QvKCLS1y3bWcGDz69mXWE1ANPyUvjs9FxSYiPaP6i5Fja8DutegvoyZ11kAuTOcMJrb1QIKhcRERER6YNcLkgf6jz8zbhK1jF873KGl87hs4HX2eoewEdmNIvNWIrq4N8f7+LfH+8iNsLN6cPSmTWyPzOHZZAY4w33byIi0iMpwBYRCZGy2iZ+Nmcj//poJ9ZCWlwEN8/IY8LA5PYPqi2GtS/BpjfA1+Csi+sHeadB/7Hg1j/jIiIiIiIh445w2vZljoPmelxFqyjYu5SCyjlcbeew25POYvdEPnaNYXtDNK+uKuLVVUV4XIZpg1M4Z0Q/zh7Zj+zkDk7ULiIiaiHSVdRCRERa+fwB/vbhDn755kaqG1twuwyzR/fnsonZ7d9SWLbZaROybd6BiRlT8yH3NEgbookZRURERES6k7oyp8XI3mX775jcZxNZEn0yH7vGsK46kkCb+GV4/3jOGdmPs0f0Y0xW4pHbCIqIdAPqgd0LKcAWEYAPNpXyyMtr2FRSC8DYrEQ+Oz338JM02gDs/tgJrg+amHEsDDoVEgeEsHIRERERETlm1kLlTifILloBvkYAak0sy5JmscSMYkW5m0ZfYP8hGfGRnDUig7OG92NGQRrREeqbLSLdjwLsXkgBtkjftrOsnu+/upY31hQDzpfS66cPYtLAZMyho6f9zbDlHVjzAlTtctZ5IiF7CgyaoYkZRURERER6Ir8P9q2HPUuhdOP+Oyt9kcmsy5jNEkaypDhAWV3z/kMiPS5Ozk/lzBH9OHN4BllJhxn4IiISBgqweyEF2CJ9U3ldM4+/s4m/fbgDn98S6XFxyYQsZo/OJMLjOnjnhkrY8Cqs/x80VjnrohKd0Dp7iiZmFBERERHpLZpqYO8K2LsEaor2r7bJg9mReR5LGcbSvQ1s2Vd30GHD+8dz5vAMzhyewficJDxu16FnFhEJCQXYvZACbJG+paHZz5Pzt/F/726hpqkFA5wyJI2rpwwkJTbi4J0rd8LaF5xR136fsy5hAOSeCv3HgEu3DIqIiIiI9ErWQvVe2LPEaTHSXO+sd3kgZxqVA89iuW8Qy3ZVs3JP5UGtRhKjvZw+NJ2Zw9M5fWjGJ68zRES6kALsXkgBtkjf4A9Y/rtkF794cyPF1U0AjMtO5DNTBzIoNbbNntbpg7fmBefLaqv0EZB3CiTnaWJGEREREZG+xN8C+9bBno+hdPOBydujU6DgTHx557CuPo5luypZvrOSourG/YcaA+NzkjhjaAYzh6czeoAmghSRrqUAuxdSgC3Su/kDlldWFfKrtzbuv80vLy2Wa6YOZHRW4oEdWxph67uw9kVn5DWA2wtZk2DgDIhLC33xIiIiIiLSvTRUQeEyZ7BLXemB9f1GwZBzYNApFNbDsp2VLN9VybrCaloCB/KctLgIThuazhnDMjhtSBpJMRqdLSKdSwF2L6QAW6R3CgQsr60u4tG3NrKppBaA9LhIPj0lh+n5qbhaR1HXl8K6V2Dja06/O4DIBBg4HXKmQkRMmH4DERERERHptqyFih3OqOyiVc6E7wDeaKfl4JBzIGMEjb4Aq/dWsWKXE2iX1h6YCNJlYFxOEqcHA+0xWYm4NTpbRE6QAuxeSAG2SO8SCFjmrC3i0bc2sb7ICaTT4iK4ZEIWpw9JD06mYqFkHax7GXbMh4DfOTgxGwadAv1Hq7+1iIiIiIh0TEsjFK12RmVXbD+wPjEbCs6BgjMhOgVrLXsqG1geDLPXF9XgbzM6OznGy6lD0jltaDqnDUkjI0GTxYvIsVOA3QspwBbpHVr8AV5ZVchv392yP7hOiY3gkvFZnDEsHa/b5YyK2DYP1r0EZVucA40L+o2GQSdD0kD1txYRERERkeNXu88JsvcuPXCHp3FB9hRnVHb2FGciSKDR52fN3mpW7K5kxa5KSmqaDjrV8P7xnD7UCbQn5yYT6dEgGxE5OgXYvZACbJGerdHn55mPd/H797eyq7wBcEYuXDw+i5nDMojwuKC2GDa8Dpteh8Zq58CIWMie6rQJiU4K3y8gIiIiIiK9TyAApRudFiMl6w5M/BiVCINnwpCznQnig6y1FFU1OmH27irWFVbT1BLYvz3a6+akwSnBEdpp5KfHYTT4RkQOQwF2L6QAW6Rnqmrw8bcPd/DU/G37+8j1T4jiwnGZnFqQToQb2LsM1r8Cuz868IUxYYAz2rr/WGeSRhERERERka7UVOtcm+xZ4gyuaZVa4IzKzjvNmYenDZ8/wIaimv2B9q7y+oO2ZyZGceqQNE4dks6MgjRSYjUZpIg4FGD3QgqwRXqWLftq+fOC7fx3yW7qm53e1XlpsVw8bgBTclNw+Wpg05uw4TWoKXQOcrmdNiEDp6tNiIiIiIiIhIe1ULXbCbILVzi9s8EZWJMzDQrOggGTDjsfT3ldM6v3VLFyTxWrdldS3diyf5sxMGpAAqcUpHNKQRqTc5OJ8qrdiEhfpQC7F1KALdL9BQKWeZv28dT87by3cd/+9aMHJHDR+CxGD4jHFK922oTsXAB+n7NDdLLTIiRrMkTGhal6ERERERGRQ/h9ULLWCbNLNwPBzCc6GQaf4YTZbVqMtBWwlp3l9azc7YTZG4pr8PkPZEaRHhdTclM4ZUgaM/LTGDkgAbdLg3hE+goF2L2QAmyR7quqwccLy/bw54Xb2bqvDgCv23DqkHTOHdWfgTE+2PwWbHoDqvYEjzKQPgRypkPaUHC5wvcLiIiIiIiIHE1DJRQuhz1Loe7AgB1S8qHgTKfFSHRKu4c3tfjZUFTDqj1VrNpTxY6yg9uNJEZ7OTk/lZML0piRn0peWqz6Z4v0YgqweyEF2CLdi7WWJTsq+Mfinby6qpBGn9O7OjU2glkj+zFzaBrx5SudNiG7FkEgeOtcVIIzo3fWZE3KKCIiIiIiPU/bFiNFK8AXbDFiXDBgPOSfCQNPAk/0EU9T3eBj9d4qVgcD7dY5g1oNSIzipPxUTs5P4+T8VAYkHfl8ItKzKMDuhRRgi3QPFXXNPLt0N//6aBebS2r3rx89IIGzR/RjcnID7q1vwea3oaHc2WhczijrnKmQNkyjrUVEREREpHfw+2DfBmfyx9INEHDm/8ETBYOmQ97pMGACuDxHPI21lpKaJlbvqQqG2tXUNrUctE9uagzT81OZnp/GSYNTyIiP6qrfSkRCQAF2L6QAWyR8mlr8zF1fwvPL9jB3/T6a/c5o68RoL2cMS2dmXgz9yhbB5rmwb92BA2PTIGsSZE6A6MQwVS8iIiIiIhICzfVQtAr2LoXKnQfWRyZA7gwnzO43yhngcxSt/bPX7q1mzd5q1hVW0+DzH7TP4PRYThqc6jzyUshIUKAt0pMowO6FFGCLhFYgYFmys4Lnlu7hlZV798+ebQyMy07izCEpTHBtwrPtHdj98YEWIe4I6D/WCa6TBzkHiIiIiIiI9CV1ZVC0EgpXQG3xgfUxaZB7ihNopw/vUJgN4A9YtpXWsXZvFWv2VrOhuIamlsBB+wxOi2Xa4BSm5qUwNS+VLLUcEenWFGD3QgqwRbqetZaVu6t4dXUhr6wsZHdFw/5tg1JjOCU/hZPjikgp/AB2LoBmZ8JGjAtSCyBzPPQbCZ7I8PwCIiIiIiIi3Ym1UFPkBNlFK6Gh4sC22DQYdOxhNkBLIMC2fXWsLaxmbWE1G4o+GWhnJUUzLc8JtKfkpTBYk0KKdCsKsHshBdgiXSMQsCzbVclrqwp5bXUReyoPhNYpsRGckp/CKUnl5JTPh+0LoKn6wMEJA5z2IJnjICo+DNWLiIiIiIj0ENY6rUWKV0HxGmioPLAtJs2Z+HHgSdB/zFF7Zh+qNdBeV1TD+kJnhHZ988EtR1JiI5g0KJkpuclMzk1h9IBEIjyan0gkXBRg90IKsEU6T6PPz8KtZbyzroS31hVTWNW4f1tyjJdpuUlMTShnWPVCXLsXHfzFKi7DaRHSf4zzXERERERERI6NDUDVbqdndvHqg6+5IuIgZ6oTZg+YCN5jbwUSCFh2VdSzrrCG9UXOCO3KBt9B+0R6XIzLTmLCoCQmDUxm4qBk0uJ0N61IqCjA7oUUYIucmOLqRt5ZX8Lb60qYv7n0oAlAUmMjmDYonmkxRRRULcC152Pw1R84OCbVCa0zx0JcP/W1FhERERER6SzWQvUeKF4LJWugtuTANrfXGTyUPRVypkBc/+N8CUtJTRMbimrYUFzDhqKag+6+bTUwJYaJA5OYOCiZcdlJjMhM0ChtkS6iALsXUoAtcmwamv0s2lbG/M2lvL+plPVFNQdtz0uLYWKGmwnubQyuXIApWQ2BNreYxWc6M2RnjIT4/gqtRUREREREQqF2H5QEw+zK3UCbnClpIGRPgayJzrWaO+K4X6am0cemklo2FdewsbiWLftqP9FHO8LjYtSABGek9sAkxmUnMSg1Rr20RTqBAuxeSAG2yJH5/AFW7ali4ZYyPthUypIdFTT7D3z5iHC7GD0gjomJNUwIrCal+EOo3nvgBMblfBnKGOkE1zEpYfgtREREREREZL+mWijdCPvWQ+kmaDnQ/hFPFPQb7YTZWRMgMQc4/mDZH7DsLK9nU3ENm0ucQHtvm3aTrRKiPIzJTmRMVhJjsxMZk5VIdnK0Qm2RY6QAuxdSgC1ysEafn2U7K1m8rZzF28tYuqPyoLYgBshNi2FMsp8x7u0MrVlMROmag0dZR8RA6hBnxuu0oc7PIiIiIiIi0v34W6ByB+zbAGWboKbo4O37Wz+OcdqOxGdyIoE2QG1TC1v31e4PtLfsq6PqkF7aAEkxXkYNSGDUgMTgMoG8tDjcLoXaIu0JZ4B9bNPEioh0gLWW3RUNLNtVybKdFSzfVcnqPVX4/Af/wWxAYiTDkwKMjihkVOMyEspWQlWbXtbG5fxVPq0A0oY5z13qZyYiIiIiItLtuT2Qmu88ABprnCC7bBOUbob6Mtg613kAxKY78xhljIR+IyEh27kmPAZxkR7GZicxNjsJcK5NK+p9bN1Xy9bSuv3Lynof8zeXMX9z2f5jo71uhvWPZ0RmPMP7JzC8v7NMjPF2xn8NETkBCrBF5ISVVDeyem8Va/ZUs2J3Jct3VVJa23zQPgYYlBzFiIQmhrv3MqxpJUnlK2HvIbd4xaRCaoEz0jplMEQc+wzWIiIiIiIi0s1ExQfbh0x0JoKsLYbyrc6jYhvU7YPNbzsPgMh4yBjhPNJHONeJ3mO7PjTGkBIbQUpsCpNznbaT1lrK65rZVlbH9tJ6dpTVsb2sjtLaZpbvcq5n28pMjGJY/3iG9otnSEYcQ/vFU5ARR2ykIjWRUNG7TUQ6zOcPsKOsjg1FtawtrGL1nmrW7K2mtLbpE/vGR3koSDQURFVRENhOft0yYmu2Qu0hO8alQ3IeJA+G5FyITgzJ7yIiIiIiIiJhYgzE93ceg04GG4CaYKBduQMqdkBTNexa7DzgwDxIaUMhbQikD4XEQeA+thHSxhhS4yJJjYtk8qADcynVNrawo7yOXeX17Aw+dpU3UFjVSGFVI+9u2HfQebKSohnSL4789DgGp8fuX6bHRaq/tkgnU4AtIp/Q3BJgZ3k9W/fVsqmklg1FNWwsrmHrvrqDJlpsFeN1kZtgyI2qZTC7KWhcS0bNGky5/+Ad3V5IyHK+dCQOhORBEBkXot9KREREREREuiXjgoRM58EMZ4R2YyVUbHfC7KpdTg/tiu3OY9Mc5ziXx7m+TBnc5pEHEcd+nRkX5Qn2xD4wqCoQsBRXN7Kzop7dFQ3sqWhgd0U9e6sa2VPZwJ7Khk8E2/GRHgZnxJGXGsOg1Fhy04LL1FiSY7wKt0WOgwJskT7K5w9QWNnIzvJ6dpTXsW1f3f6eYLsqGvAHDj/Ba0aMi+wYHwM9FeQGdpHXsJb0hq2YaqC6zY7GBXH9IGEAJGY7Xyri+jt90ERERERERETaYwxEJzuPAROcdX4fVO+Fqt1OoF29B+pKD7QhaSsmDZKDA6eSBgafZ0NE/DGV4XIZMpOiyUyKZlregfX+gKWoupE9FQ3srWpgb6UzUntvZQM1TS2s2FXJikNakYBzp/LAlBhykmPISYkmJyWG7ORocpJjyEqOJiZC18sih9Mj3xnGmGzgO8B5QCpQCLwAPGKtrQj1eUS6o+aWAEXBvwrvDT72VDawq8K5FWpvZWO7IbUBMqIhM7KJLHclOXYv2U1byG7aTJTPB1WHHODxOhNuxGc6gXVClvPcE9Hlv6eIiIiIiIj0AW6vcxdv8qAD61oandYjNYVOuF1T6PTWri91HnuWHnyOqATnejUhq821az9n8FVkPM7VcAdKcRmykqLJSjq4J7e1lqoGH4VVjRRVN1Jc3UhRlbMsrm6iprGFNXudVpyHkxzjJTMxmgFJ0QxIimJAUjSZiVH0S4iif4KzjI5wH8t/NZFeoccF2MaYfGABkAG8CKwHpgJfBs4zxsyw1pYd4RSdeh6RUGtuCVBW10RZbTP7apooqWmkqKqJ4ppGSoIfikXVjZTWNmEPn08DzsdyaqSfDG8TGa5q+rOPAb5dZPp20o9yIlr80HLIQW4PxPRz+lbHBT/k4/o7Ey+6jm12aBEREREREZET4on6ZKgdCEBDBdSWQF2xE2jXljiTRDZWO4+SdZ88V0Rsm+vcfhCb5lzrxqU7I7qjU8B15PDYGENSTARJMRGMyEw4aJu1lurGFvbVNFJS00RJTRP72jxKa5uoqPdRUe9jbeHhA26AhCgP/YOhdnpcJGnxkaTHRZIeH0laXCRp8RHOxJUxEXjcuk6X3qHHBdjAEzih813W2sdbVxpjfgHcA3wfuC2E5xE5bk0tfqobWqhq8AUfzVTU+aiob6aivpnyOh+V9c2U1TVTWuuE1lUNvg6d24Ul1esj1V1PqqkmLVBOqr+EDFtKP1NBGlV4A344dP5Ft9u5TSsmxfmAjk6FuDSIzYCoJAXVIiIiIiIi0n25XBCb6jwYcWC9tU54XV/mjM6uC47SbqiA+gporjt8O5JWxgVRicHr5GSICbY4iU5xRnZHJUJkIkQnQmSC05+77eHGkBjtJTHaS0HGJ1uZBKylusFHWV0zZbWtGUATZXXNVNb7KK9zcoLqxhaqG2vZWFx71P8UidFeUmODgXZsBEkxXpJjIkiM8ZIUHUFyjJfEGC8JUU5dCVFe4qI8uF3q0y3di7FHGqLZzRhjBgNbgO1AvrU20GZbPE4LEANkWGvruvo8R6l1ycSJEycuWbLkeA6XbsrnD1Df7KfR56eh2U99s58Gn5/65hbqmvw0+Jxl68+1TS3UNrZQ0+SjprFl/8/VjU5g3ej75ISIR+MiQIKrmQRTTyK1pNhKkqkmmRqSTU1wWUsSNbjNYd7fnkjnwzQ6yQmko5IOPI9JCX7QKqQWERERERGRPsJa8NU7YXZDOTRUOZNINlYHl1XQVAscQ4YWEetMJhkZ57QniYx3fo6IAW+ss90bG/w5BrxR4GldRoE32gnNDyrTUtPUQkVdMxX1ziC4qnoflQ3Oo6reyRqqG33UNrYcS7X7GQNxkR4SorzER3mIi/QQF+UhNtJDfKTzc2ykh9hINzERzjLa2/qzmyivm2ivsy3a6yYqwkWE26XJK3uBSZMmsXTp0qXW2kmhfu2eNgL7zOByTtvQGcBaW2OMmQ/MAk4C3g7BeeQQPn+AFr8lYJ2HBWwALJaAdSY6sNZ53rpPIOA891tLIHBgv4C1+AMH1rc+9wcsLQFnXUvgwM8twdf2BYJLf2D/tuZmH77GWnz+AD5/gOaWA8vmlgDNwedNfktzS4DGFkuz39LYYmnyQ2OLpdEPftu5/+C68RNLo/MwDcTSSDz1xJkG4qknngbiTT1xNJBo6kikljga+MQfQ93e4AdhLEQkQETmgZ8jEyAq3llGxjsBtoiIiIiIiIg4jAleT8c6kz0eTsDvhNjNNdDU5tFcG3zUQXO989zXEPy5zmlhcrzcEc41vCcKPJEYdyQJnkgS3F4GeSKdLMAd4TxiIyDeAy4vuL0EXB5qbSQ1LV6qW7xU+z3Utrip87uoaXFT12Ko8RlqfVDfYqj3Wep8loYWqGlsoabx0J6ix88YiPK4ifS6iPK4ifK6iAz+HOF27V9GeFxEeNzB5wav27X/EeE2eNwuPG6D1+UsPW4XXldw6Ta4XQaPy+B2uXC7wO1y4XEZXMbZ1vrwuAzGOL3M3cZgWrcbZ73LZXAZcBkTfDgj6FuXZv82iPa6Fc6HQE8LsIcFlxvb2b4JJ3geypGD5846D8aY9oZYj1u3bh2TJoX8jxJhU9fUwrbS4xqw3icZLK7j+nuoiIiIiIiIiEjns4DFYDs4oWVfFxfpITctNtxlhMS6desAcsPx2j0twE4MLqva2d66PilE5zkSf0NDQ9XSpUu3n8A5ehRXZFyCJ6lfQbjr6CksBn8P+EDwle0CwJuaE+ZKRORQen+KdF96f4p0X3p/inRfen9KT1NTV1+3dOeGDeGuI0TGAXHheOGeFmAfTWsaeKLDWjt8nnD0fREJpda7DJqKNuv/dZFuRu9Pke5L70+R7kvvT5HuS+9Pke7rCF0oulxPm6mtdWR0YjvbEw7Zr6vPIyIiIiIiIiIiIiJdpKcF2K1D8oe2s31IcNleb+vOPo+IiIiIiIiIiIiIdJGeFmDPDS5nGWMOqt0YEw/MABqAD0N0HhERERERERERERHpIj0qwLbWbgHm4Mx4ecchmx8BYoG/WGvrAIwxXmPMcGNM/omcR0RERERERERERERCrydO4ng7sAB4zBhzFrAOmAbMxGn58Y02+2YFt+/ACauP9zwiIiIiIiIiIiIiEmLGWhvuGo6ZMSYH+A5wHpAKFAIvAI9Ya8vb7JcLbAN2WGtzj/c8IiIiIiIiIiIiIhJ6PTLAFhEREREREREREZHer0f1wBYRERERERERERGRvkMBtoiIiIiIiIiIiIh0SwqwRURERERERERERKRbUoAtIiIiIiIiIiIiIt2SAmwRERERERERERER6ZYUYIuIiIiIiIiIiIhIt6QAW6QPMsZkG2OeNMbsNcY0GWO2G2MeNcYkH+N5LjDGzDHG7DbGNBhjthpjnjHGTO+q2kV6K2PMFcaYx40x7xtjqo0x1hjzt+M8V6e8x0XE0RnvT2NMqjHmFmPM88aYzcHPzSpjzAfGmM8ZY/S9XOQ4dObn5yHnvT54LmuMuaUzahXpazr7/WmMOdUY86wxpjD4HbcweD06uzPrFukLOvn6s8uzIU9nnUhEegZjTD6wAMgAXgTWA1OBLwPnGWNmWGvLOnCeHwP3A2XAC0ApUABcDFxujPmstfaELx5E+pBvAuOAWmA3MPx4TtJZ73EROUhnvD+vBH4LFAJzgZ1AP+Ay4I/A+caYK621tlMqFuk7OuXzsy1jTA7wePCccSd6PpE+rNPen8aYbwLfxbnu/B/O52kaMAE4A3j1BGsV6Ws66/ozJNmQAmyRvucJnGDrLmvt460rjTG/AO4Bvg/cdqQTGGP6A/cBxcBYa21Jm20zgXeA7wAKsEU67h6cLw6bgdNxAq7jccLvcRH5hM54f24ELgJesdYGWlcaYx4EFgOX44TZz55wtSJ9S2d9fgJgjDHAUzgX4s/hfOcVkePTKe9PY8yVOOH1W8Bl1tqaQ7Z7T7BOkb7ohN+focyGdKuiSB9ijBkMzAK2A785ZPO3gDrgemNM7FFONQjn349Fbf+BArDWzgVqgPTOqFmkr7DWzrXWbjqR0Zed+B4XkTY64/1prX3HWvty2/A6uL4I+L/gj2ecQJkifVJnvD8PcRdwJnATzuemiBynTvp+6wJ+DNQD1xwaXgdfx3cCZYr0SZ30+RmybEgBtkjfcmZwOecwF9A1wHwgBjjpKOfZBDQDU40xaW03GGNOA+Jx/jouIqHVWe9xEQmt1gvvlrBWIdLHGWNGAD8CfmWtnRfuekQEgJOBPJwWIRXBXrsPGGO+rLmXRMIuZNmQWoiI9C3DgsuN7WzfhDN6cyjwdnsnsdaWG2MeAH4BrDXGvIBzm2U+zu3RbwK3dlLNItJxnfIeF5HQMcZ4gM8Gf3w9nLWI9GXB9+JfcfrTPxjmckTkgCnBZTGwFBjTdqMxZh5whbV2X6gLE+nrQpkNKcAW6VsSg8uqdra3rk862omstY8aY7YDTwKfb7NpM/D0obePiEhIdNp7XERC5kfAaOBVa+0b4S5GpA97GGcyuFOstQ3hLkZE9ssILm8DtgFnA4twWhf8HDgXeAa14RIJi1BlQ2ohIiJtmeDyqD2QjDH3A/8Fnsb561osMAnYCvzdGPOTLqpRRI5fh9/jItL1jDF3AfcC64Hrw1yOSJ9ljJmKM+r659baheGuR0QO4g4uDc5I67ettbXW2jXApTiT0J2udiIi4RGqbEgBtkjf0jr6MrGd7QmH7HdYxpgzcCbSeMla+xVr7VZrbb21dinOl4g9wL3BCeVEJHQ65T0uIl3PGHMH8CtgLTDTWlse5pJE+qQ2rUM2Ag+FuRwR+aSK4HKrtXZF2w3BuyVa716aGtKqRCSk2ZACbJG+ZUNwObSd7UOCy/b657a6MLice+gGa209sBjn35cJx1qgiJyQznqPi0gXMsbcDfwaWI0TXheFtyKRPi0O53NzBNBojLGtD+BbwX3+EFz3aLiKFOnDWr/fVrazvTXgju76UkTkECHLhtQDW6Rvaf1HZZYxxmWtDbRuMMbEAzOABuDDo5wnMrhMb2d76/rm4y1URI5LZ73HRaSLBCe6+RGwHDjHWlsa3opE+rwm4E/tbJuIc9H9AU6IpvYiIqE3D2gBhhhjIqy1h15jjg4ut4e0KhGBEGZDGoEt0odYa7cAc4Bc4I5DNj+C06voL9baOgBjjNcYM9wYk3/Ivu8Hl18wxmS13WCMOR8nJGsEFnTubyAi0P5781jf4yLS+Y7w2Ykx5iGc8HoJcJbCa5HQOtz701rbYK295XAP4KXgbn8Orvt3eCoX6f2O8P22FPg3Tou8hw855hycSRyrgNdDVatIX9MdsiFjreZxEulLgv/gLMCZzflFYB0wDZiJ01bgZGttWXDfXJyZnndYa3PbnMOF02vsbKAGeB4owrn18kKcCTbuttb+KiS/lEgvYIy5BLgk+GN/nC/jWznwpaDUWntfcN9cDvPeDG7r8HtcRDqmM96fxpgbcCa38QOPc/he9NuttU93cvkivVpnfX62c+5v47QR+by19o+dWLZIn9CJ328zgPlAQfDYxcAgnB67FrjGWvtM1/0mIr1PJ32/DVk2pBYiIn2MtXaLMWYy8B3gPGA2UAg8BjzSkUmkrLUBY8xsnBGeV+N8cYgByoFXgcestXO66FcQ6a3GAzccsm5w8AGwA7jvaCfpjPe4iHzCeE78/ZkXXLqBu9vZ5z2ckFtEOm48nfD5KSJdYjyd8/22xBgzDfgmzrXnSThh2SvAD621ao8ncuzGc4Lvz1BmQxqBLSIiIiIiIiIiIiLdknpgi4iIiIiIiIiIiEi3pABbRERERERERERERLolBdgiIiIiIiIiIiIi0i0pwBYRERERERERERGRbkkBtoiIiIiIiIiIiIh0SwqwRURERERERERERKRbUoAtIiIiIiIiIiIiIt2SAmwRERERERERERER6ZYUYIuIiIiIiIiIiIhIt6QAW0RERERERERERES6JQXYIiIiIiIiIiIiItItKcAWERERERERERERkW5JAbaIiIiISJAx5hvGGBt8DDvKvgOMMb80xqw1xtQbYxqMMTuNMe8ZY75vjMk/ZP+n25z7cI93j6HOc4wxzxtj9hpjmo0xFcaYjcaYZ4wxdxljzHH+JxARERER6VY84S5ARERERKQ7CIa+nwMsYIDPA/e1s+9o4D0gBVgF/BmoAgYCo4EHgW3AlsMc/iKw/DDrt3ewzgeB7wMtwOvABsAL5AGnA1cATwS3i4iIiIj0aAqwRUREREQcs3BC4KeB84EbjDEPWmubD7Pvozjh9bettY8cutEYMxiIaOd1XrDWPn08BRpjBgHfAaqBU6y1qw7Z7gLOAfzHc34RERERke5GLURERERERByfDy7/APwdSAMubWffk4PLXx1uo7V2q7V2feeWB8A0wA3MPTS8Dr5uwFr7hrXWHrrNGDPVGPNvY8weY0yTMabQGDPHGHPVYfa9yhgzzxhTFWyNssoY83VjTORh9t0efCQYY34RfO4zxny7zT7Dgy1UdgVfu9gY84+jtWkREREREVGALSIiIiJ9njGmH3ARsNFauwB4KrjpC+0cUhZcDu3q2tp53cHGGHdHDzLGfB5YAFwSXP4ceAXIAG4/ZN8fAP8GRgD/AH6N01LlB8AbxhjvYV4iAngneP45OMH+tuD5zgOWAtcCHwW3vQ1cBiw2xkzs6O8hIiIiIn2PWoiIiIiIiMBNOH2knwaw1q42xiwFZhpjCqy1mw/Z/9/AvcBLxpjfAnOB5dba6g681iXGmNzDrH/UWlt5lGM/BHYAY4C5xpingUXAemvtYduGGGNG4vTErgZOtdauOWR7dpvn04GvA7uAqdbaouD6rwPPAxcCX8UJs9vKBNYCp1tr69qcLxn4J1APnGatXdtm26hg7X8EFGKLiIiIyGGZw9xdKCIiIiLSZwQnb9yE0/96oLV2T3D9ncBjwI+ttV875JhI4HGc4Lt1UIgFNuJMrPiYtXbrIcc8DdxwhFLyrLXbO1DvWJxJI8e3Wd2AM7r538CfrLVNbfZ/HPgS8BVr7S+Pcu4/ALcAt1prf3/ItqHAOmCHtXZwm/XbgUHAeGvtikOO+TJOv/AvWWt/c5jX+yVwNzCqbbgtIiIiItJKAbaIiIiI9GnGmLOAt4A3rLXntVmfCuwFKoAca63vMMf2A87D6U09EZiM06O6AbjKWvu/Nvs+jRNg33S8kzge8tqTgZnAJOAknBAZYAUw01pbEdzvo2BdI47Wl9sYsyT4eww5zKhzjDE7gIFAcuto8WCA3Q+IObT3tjHmX8CncfqK7z3MS04BZgNXWGufPfpvLSIiIiJ9jVqIiIiIiEhf19rn+um2K621ZcaYl4HLgYuB/x56oLW2GGc09J8BjDEpwI9xRjE/aYzJttY2d0XR1tqPgY9bfzbGTA3WMQ74Fs7IZoCk4HJPB06bGFwWtrO9ECfATgQq26wvOdzEkUBqcPn5w2xrK64DtYmIiIhIH6RJHEVERESkzzLGpONMPAjwT2OMbfvACa+h/ckcD2KtLQduBXYC6cDoTi75SK+9GKdVCMCZbTZVBpdZHThNVXDZv53tmYfst//lj3K+cdZac4THnztQm4iIiIj0QRqBLSIiIiJ92Q1ABLAEWN7OPhcBZxtj8qy12452QmttwBjTOpGh6ZQqO67mMK/7IU4LkfOBI7YQAZbhtBA5A9jSdoMxpgDIBrZ1YLLJtq99OXAqsLKDx4iIiIiI7KcR2CIiIiLSl90SXN5urb3lcA/gdziBcOu+GGO+ZYzJPdwJjTFXAMNxemev7sxijTFTjTE3GmOiD7PNCzwQ/HFem02/BVqAh4wxIw9zXHabH58MLr8ZHJ3euo8b+BnO9cOfjqHkp3BGgH8r2OLk0Nd2GWPOOIbziYiIiEgfo0kcRURERKRPCganc4FV1tqxR9gvF9gKFAEDrbUtxphKnD7Qy3D6UO8L/jwRmI4TGF9rrf1Pm/M8zQlO4miMuQR4HqgDPgDWAo04rT3Ow2n9sRk4Jdifu/W4zwP/F6zrRWATTn/qyUCNtXZmm31/DNwPlOD0/a7DGb09OviaZ7Xt6x2cxBFrbW47NZ8VrDkOeBtYAwRwemlPB1KttVHH899DRERERHo/tRARERERkb6qdWLBPx5pJ2vtdmPMW8A5wKdwwtgLcULd03GC43444fDu4Pkes9au6oKa3wauAWYBk3AC6CSgGqc9yK+A31hra9oeZK39gzFmNXAfTnuQS4BSnLYefzxk3weMMctw+ml/FvDitBP5JvDzY52U0lr7tjFmbPC1z8VpJ9IM7AXeAZ49lvOJiIiISN+iEdgiIiIiIiIiIiIi0i2pB7aIiIiIiIiIiIiIdEsKsEVERERERERERESkW1KALSIiIiIiIiIiIiLdkgJsEREREREREREREemWFGCLiIiIiIiIiIiISLekAFtEREREREREREREuiUF2CIiIiIiIiIiIiLSLSnAFhEREREREREREZFuSQG2iIiIiIiIiIiIiHRLCrBFREREREREREREpFtSgC0iIiIiIiIiIiIi3ZICbBERERERERERERHplhRgi4iIiIiIiIiIiEi3pABbRERERERERERERLolBdgiIiIiIiIiIiIi0i0pwBYRERERERERERGRbkkBtoiIiIiIiIiIiIh0S/8PfXNUjJUFoRAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 728
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 739
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "sns.kdeplot(data=df, x='ASF', fill=True, hue='Group')\n",
    "plt.xlabel('ASF Score')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Density Plot of ASF Scores')\n",
    "plt.xlabel('ASF Score')\n",
    "plt.ylabel('Density');\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "sns.kdeplot(data=df, x='eTIV', fill=True, hue='Group')\n",
    "plt.xlabel('eTIV Score')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Density Plot of eTIV Scores')\n",
    "plt.xlabel('eTIV Score')\n",
    "plt.ylabel('Density');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above two graphs, we can observe that the distribution of *ASF and *eTIV scores between the groups are relatively similar. Note that these scores vary widely between individuals regardless of disease status. Unless new studies indicate that initial brain and skull size impact Alzheimer's risk, not much weight should be given to these variables. What should be examined, rather, is the nWBV since it represents the volume of the brain normalized to the subject's total volume inside the skull.\n",
    "\n",
    "**__ASF__: A scaling factor used in brain imaging to adjust for individual differences in brain size and shape. It accounts for variability in brain morphology allowing for accurate comparisons of brain structures.*\n",
    "\n",
    "**__eTIV__: Indicates the total volume inside the skull (including brain tissue, cerebrospinal fluid, etc.). It is measured in cubic centimeters (cc) and can help establish baseline brain size.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 710
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "sns.kdeplot(data=df, x='nWBV', fill=True, hue='Group')\n",
    "plt.xlabel('nWBV Score')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Density Plot of nWBV Scores')\n",
    "plt.xlabel('nWBV Score')\n",
    "plt.ylabel('Density');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph indicates that based on the *nWBV scores, individuals with dementia had lower brain volume than individuals who did not have dementia. This is expected, since it is known that Alzheimer's disease causes brain tissue to shrink.\n",
    "\n",
    "**__nWBV__: Represents the volume of the brain normalized to the subject's eTIV. It is expressed as a percentage, reflecting the proportion of the brain occupying the eTIV (nWBV = brain volume / eTIV). Changes in nWBV may be indicative of brain atrophy which is a common feature of Alzheimer's and dementia.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ethics & Privacy Considerations:\n",
    "\n",
    "Biases/Privacy/Terms of Use Issues with Proposed Data:\n",
    "\n",
    "1.  The potential datasets considered, such as OASIS MRI, UK Biobank,\n",
    "     and NACC, may have biases and privacy considerations. For\n",
    "     instance, the OASIS MRI project's terms of use and participant\n",
    "     selection could introduce biases. UK Biobank, despite its size,\n",
    "     might not include a diverse representation of certain populations,\n",
    "     leading to potential biases in the dataset.\n",
    "\n",
    "Potential Biases in Dataset Composition and Collection:\n",
    "\n",
    "2.  Biases may arise in dataset composition and collection, affecting\n",
    "     the equitable analysis of Alzheimer's prediction. For example, if\n",
    "     the data predominantly includes participants from specific\n",
    "     demographic groups, it could introduce biases in the model.\n",
    "     Additionally, variations in data collection methods across\n",
    "     different research centers, as in the case of NACC, may impact\n",
    "     standardization, potentially leading to biases.\n",
    "\n",
    "Detection and Mitigation of Biases:\n",
    "\n",
    "3.  To detect biases, the group will conduct a thorough review of the\n",
    "     dataset sources, including participant demographics and\n",
    "     recruitment methods. During data preprocessing, the team will\n",
    "     analyze variables for potential biases, ensuring a balanced\n",
    "     representation. The group plans to collaborate with experts in the\n",
    "     field and seek external input to validate the fairness and\n",
    "     inclusivity of the dataset.\n",
    "\n",
    "Other Issues Related to Privacy and Equitable Impact:\n",
    "\n",
    "4.  Privacy concerns arise from the sensitive nature of medical data,\n",
    "     especially in Alzheimer's research. Ensuring participant anonymity\n",
    "     and adhering to privacy regulations are paramount. Equitable\n",
    "     impact considerations involve understanding if the model's\n",
    "     predictions could disproportionately affect certain groups. It is\n",
    "     essential to communicate findings responsibly, avoiding\n",
    "     reinforcing existing biases or stigmatizing specific populations.\n",
    "\n",
    "Handling Identified Issues:\n",
    "\n",
    "5.  The group commits to transparently communicating any identified\n",
    "     biases throughout the research process. Mitigation strategies will\n",
    "     be implemented during data preprocessing and model development.\n",
    "     The team will consider alternative datasets or additional sampling\n",
    "     methods if biases persist. Ethical review boards will be\n",
    "     consulted, and the group aims to publish findings with a clear\n",
    "     acknowledgment of potential limitations and biases, promoting\n",
    "     responsible and equitable use of the predictive model.\n",
    "\n",
    "In summary, the group is dedicated to addressing ethical concerns\n",
    "comprehensively, from data collection to analysis and post-analysis.\n",
    "Transparency, collaboration with experts, and continuous evaluation of\n",
    "potential biases will guide the research, ensuring responsible and\n",
    "ethical development of the Alzheimer's prediction model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Expectations "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Clear communication and relatively reasonable responsiveness to messages.\n",
    "2. Shared Responsibility and Accountability: finish all tasked work by the designated due date.\n",
    "3. Maintain quality work and attention to detail.\n",
    "4. Attendance and Participation: during designated days in which we meet, everyone should be present unless notified previously. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Timeline Proposal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Meeting Date  | Meeting Time| Completed Before Meeting  | Discuss at Meeting |\n",
    "|---|---|---|---|\n",
    "| 3/9  | 12 PM  | Finalize wrangling/EDA; Begin Analysis (Shivangi; Zijun) | Discuss/edit Analysis; Complete project check-in |\n",
    "| 3/13  | 12 PM  | Complete analysis; Draft results/conclusion/discussion (Johnny; Tanishq)| Discuss/edit full project |\n",
    "| 3/20  | Before 11:59 PM  | NA | Turn in Final Project & Group Project Surveys |"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}