diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 7e190d2..0000000 --- a/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -data -**/data -.ipynb_checkpoints -**/.ipynb_checkpoints -__pycache__ diff --git a/notebooks/.ipynb_checkpoints/Replication of PAPER TITLE by AUTHORS-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Replication of PAPER TITLE by AUTHORS-checkpoint.ipynb new file mode 100644 index 0000000..c47170b --- /dev/null +++ b/notebooks/.ipynb_checkpoints/Replication of PAPER TITLE by AUTHORS-checkpoint.ipynb @@ -0,0 +1,42 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Replication Notebook for \"PAPER TITLE\" by \"AUTHORS" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "nbTranslate": { + "displayLangs": [ + "*" + ], + "hotkey": "alt-t", + "langInMainMenu": true, + "sourceLang": "en", + "targetLang": "fr", + "useGoogleTranslate": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/C.justinCook-The_natural_selection_2015/.ipynb_checkpoints/Replication-checkpoint.ipynb b/notebooks/C.justinCook-The_natural_selection_2015/.ipynb_checkpoints/Replication-checkpoint.ipynb new file mode 100644 index 0000000..5e06e05 --- /dev/null +++ b/notebooks/C.justinCook-The_natural_selection_2015/.ipynb_checkpoints/Replication-checkpoint.ipynb @@ -0,0 +1,3012 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# _THE NATURAL SELECTION OF INFECTIOUS DISEASE RESISTANCE AND ITS EFFECT ON CONTEMPORARY HEALTH- A replication study_\n", + "## C. Justin Cook\n", + "### _Review of Economics and Statistics Volume 97 | Issue 4 | October 2015 p.742-757_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This study pretends to do a replication of _\"THE NATURAL SELECTION OF INFECTIOUS DISEASE RESISTANCE AND\n", + "ITS EFFECT ON CONTEMPORARY HEALTH\"_ whose author is C. Justin Cook, an assistant Professor, Economics University of California, Merced. The replication data for paper is available [here](https://doi.org/10.7910/DVN/27669)\n", + "\n", + "The replication is done by Sara Ariza Murillo, a colombian economist from the Universidad del Rosario. This includes two jupyter notebooks:\n", + "\n", + "1. **Notebook one:** presents the stata do file of the author that replicates tables 2-7 within the paper. It also replicates in stata the figures of the paper that are not included in the author's do file.\n", + "2. **Notebook two:** presents the code elaborated by Sara in R that replicates the tables within the paper. \n", + "\n", + "Both notebooks include a summary of the document.\n", + "\n", + "If there are any questions, please contact Sara Ariza at sara.ariza@urosario.edu.co" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook one" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Abstract\n", + " _\"This paper empirically tests the association between genetically determined resistance to infectious disease and cross-country health differences. A country-level measure of genetic diversity for the system of genes associated with the recognition and disposal of foreign pathogens is constructed. Genetic diversity within this system has been shown to reduce the virulence and prevalence of infectious diseases and is hypothesized to have been naturally selected from historical exposure to infectious pathogens. Base estimation shows a statistically strong, robust, and positive relationship between this constructed measure and country-level health outcomes in times prior to, but not after, the international epidemiological transition\"_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### I. Introduction\n", + "

Prior to the major medical discoveries associated with the international epidemiological transition, infectious diseases were a major determinant of mortality and subsequent differences in life expectancy across countries. (The discovery and widespread use of effective medicines (e.g., penicillin, streptomycin, a range of vaccines) in the late 1940s to early 1950s is labeled by Acemoglu and Johnson (2007) as the international epidemiological transition.) \n", + " \n", + "

This paper answers the question what were the causes of the initial cross-country disparities in the virulence of infectious diseases? the above, by empirically investigating the role of genetically determined differences in resistance to infectious diseases \n", + " \n", + "

The main hyphotesis is that innate resistance did influence country-level response to infectious disease prior to the international epidemiological transition, but the effects of innate resistance are dissipated by more efficacious health technologies.\n", + "\n", + "

The measure of genetic resistance is found within the human leukocyte antigen (HLA) system. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### The HLA system \n", + "

TThe HLA system is responsible for locating foreign proteins in order to direct cells of the immune system to initiate an immune response .This high level of genetic diversity within the HLA system is hypothesized to provide increased resistance by increasing the number of potential immune responses to a foreign pathogen.\n", + "\n", + "

Populations that are genetically similar in regard to the HLA system are more susceptible to infectious pathogens, as a pathogen is better able to spread within a homogeneous population, increasing the rate of infection and virulence, and lowering life expectancy. Therefore, variation within the HLA system is hypothesized to provide population-level resistance.\n", + "\n", + "##### What did the author do?\n", + "\n", + "

Using country-level aggregations of ethnic-level genetic data, the author constructs a cross-country measure for diversity within the HLA system: HLA heterozygosity. He tests the hypothesis by estimating the association between country-level health measures (e.g., life expectancy at birth) and HLA heterozygosity in periods both prior to and after the international epidemiological transition " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### II. Background\n", + "_**A. Historic Differences in Infectious Disease Environments**_\n", + " \n", + "

As reviewed by Wolfe, Dunavan, and Diamond (2007), the number of infectious diseases humans face increased substantially with the introduction of agriculture, commonly referred to as the Neolithic revolution.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1.\tFirst, agriculture allowed the development of large, dense, and sedentary populations, which fostered an ease in the transmission of infectious diseases, as well as providing a large number of potential hosts. \n", + "2.\tSecond, the domestication of animals in the Neolithic provided closer contact between animals and humans.\n", + "\n", + "

The Neolithic revolution provided the conditions for the initiation and sustainability of novel infectious pathogens. Therefore, societies that domesticated animals earlier and developed large, dense populations also encountered a resulting increase in the infectious disease load at an earlier date. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**B.\tThe Natural Selection of HLA Heterozygosity**_\n", + "\n", + "

The set of genes comprising the HLA system represents one of the most genetically diverse regions of the genome (Jeffery & Bangham, 2000), and this high level of diversity is hypothesized to have been naturally selected as a mechanism of resistance to infectious pathogens (Hughes &Yeager, 1997; Penman et al., 2013; Spurgin & Richardson, 2010).\n", + " \n", + "This natural selection for diversity within the HLA system is from balancing selection. Balancing selection results from two distinct reasons: overdominance and frequency dependence (Slade & McCallum, 1992)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. _Overdominance_ implies that heterozygotes, or individuals with differing alleles at a single locus, have an advantage compared to homozygotes, or individuals with identical alleles at a single locus. \n", + "2. _Frequency-dependent selection_ results from a comparative advantage of rare alleles. Infectious pathogens are not a static selection pressure. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**C. Out-of-Africa Migration and Genetic Diversity**_\n", + "\n", + "

The overall level of genetic diversity within a population has recently been shown to be a function of the population’s migratory distance from East Africa (Ashraf & Galor, 2013; Ramachandran et al., 2005). Modern human populations originated within East Africa (roughly Ethiopia) and subsequently migrated to all other continents, excluding Antarctica. \n", + "\n", + "Given that the entire set of genetic diversity was contained within the initial East African population and that migrating populations contain only a subset of this diversity, a strong, negative, and linear association exists between the distance along migration routes a country is from East Africa and the genomic diversity of populations within a country.\n", + "\n", + "This implies that populations that are farther along migration routes from the initial point of Ethiopia have less overall genetic diversity, resulting in lessened diversity within the HLA system." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### III. Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**A.\tHLA Heterozygosity**_\n", + "\n", + "

The measure of interest is referred to as HLA heterozygosity. HLA heterozygosity is constructed with data on SNPs from the Allele Frequency Database at Yale University, referred to as ALFRED (Kidd et al., 2003).\n", + "\n", + "ALFRED provides allele frequencies for anthropologically defined ethnicities, providing genetic data for 156 SNPs within 19 HLA genes for 51 distinct ethnic groups. Expected heterozygosity is calculated for each ethnicity. This gives HLA heterozygosity at the ethnic level; However, outcomes of interest are at the country level.\n", + "\n", + "The author aggregates ethnic groups, and their respective measure of HLA heterozygosity, to the country level with the use of ethnic compositions and constructs genetic diversity scores for 175 countries, of which 131 are used in the baseline regression model. \n", + "\n", + "For the base estimation, continent fixed effects are used to account for any unobserved continent differences that may be associated with selection into the base sample." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Import essential packages to run stata code and manage databases in python.**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using matplotlib backend: Qt5Agg\n", + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "from __future__ import division\n", + "%pylab --no-import-all\n", + "%matplotlib inline\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib as mpl\n", + "import ipystata \n", + "from IPython.display import Image \n", + "import seaborn as sns \n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#The directory \n", + "import os\n", + "\n", + "pathout = './raw/' #in the directory I create the folder \"raw\" where are the files downloaded from the internet.\n", + "\n", + "if not os.path.exists(pathout):\n", + " os.mkdir(pathout) \n", + " \n", + "pathgraphs = './created/' #this is the folder where the graphs will be saved\n", + "if not os.path.exists(pathgraphs):\n", + " os.mkdir(pathgraphs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Load the databases used by the author**" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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countrycodeln_hla_hethla_hetln_mort40ln_le40ln_le50ln_le60ln_le70ln_le80...frac_migrantpnativ_60ln_ypc60ln_yr_sch1960ln_pd60ln_urb60ln_youngln_mix_hetoecdfstdistwtd_usa
0AfghanistanAFG-1.1116260.329023NaNNaN3.4531573.438053.5569463.668782...0.1300.8706.99026-1.0358492.7127412.0794423.769448-1.0921960.05.684600
1AlbaniaALB-1.1299040.323064NaNNaN4.0109634.131234.2045354.243828...0.0370.9637.709311.7032264.0815563.4242633.715973-1.1243900.04.577419
2AlgeriaDZA-1.1019320.332228-1.1631513.6627923.7635233.849843.9687444.087718...0.0001.0008.24249-0.1387121.5217843.4177273.778703-1.0882670.05.960433
3AngolaAGO-1.1092020.329822NaN3.5553483.4011973.496053.6112263.692916...0.0200.9807.51752NaN1.3475622.3418063.777649-1.1092020.020.419653
4Antigua and BarbudaATG-1.1426330.318978NaNNaNNaN4.132824.202506NaN...NaNNaN8.09377NaN4.8191813.681351NaN-1.1055600.013.315693
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5 rows × 49 columns

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" + ], + "text/plain": [ + " country code ln_hla_het hla_het ln_mort40 ln_le40 \\\n", + "0 Afghanistan AFG -1.111626 0.329023 NaN NaN \n", + "1 Albania ALB -1.129904 0.323064 NaN NaN \n", + "2 Algeria DZA -1.101932 0.332228 -1.163151 3.662792 \n", + "3 Angola AGO -1.109202 0.329822 NaN 3.555348 \n", + "4 Antigua and Barbuda ATG -1.142633 0.318978 NaN NaN \n", + "\n", + " ln_le50 ln_le60 ln_le70 ln_le80 ... frac_migrant pnativ_60 \\\n", + "0 3.453157 3.43805 3.556946 3.668782 ... 0.130 0.870 \n", + "1 4.010963 4.13123 4.204535 4.243828 ... 0.037 0.963 \n", + "2 3.763523 3.84984 3.968744 4.087718 ... 0.000 1.000 \n", + "3 3.401197 3.49605 3.611226 3.692916 ... 0.020 0.980 \n", + "4 NaN 4.13282 4.202506 NaN ... NaN NaN \n", + "\n", + " ln_ypc60 ln_yr_sch1960 ln_pd60 ln_urb60 ln_young ln_mix_het oecd \\\n", + "0 6.99026 -1.035849 2.712741 2.079442 3.769448 -1.092196 0.0 \n", + "1 7.70931 1.703226 4.081556 3.424263 3.715973 -1.124390 0.0 \n", + "2 8.24249 -0.138712 1.521784 3.417727 3.778703 -1.088267 0.0 \n", + "3 7.51752 NaN 1.347562 2.341806 3.777649 -1.109202 0.0 \n", + "4 8.09377 NaN 4.819181 3.681351 NaN -1.105560 0.0 \n", + "\n", + " fstdistwtd_usa \n", + "0 5.684600 \n", + "1 4.577419 \n", + "2 5.960433 \n", + "3 20.419653 \n", + "4 13.315693 \n", + "\n", + "[5 rows x 49 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "hla_country_web = pd.read_stata(pathout + 'hla_country_web.dta') # Acording to the author that gives the necessary country-level data\n", + "hla_hla_ethnic_web = pd.read_stata(pathout + 'hla_ethnic_web.dta') #Acording to the author that gives the necessary dataset for the ethniclevel analysis conducted within the supplementary appendix\n", + "\n", + "hla_country_web.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Table 1.—Summary Statistics: HLA Heterozygosity verses Aggregate Heterozygosity**_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

In addition to summary statistics for HLA heterozygosity, table 1 also provides summary statistics for the overall level of genetic diversity from Ashraf and Galor (2013). In comparing the two measures of genetic diversity, African countries are found to have the highest levels of overall diversity, while countries from Europe contain the greatest amount." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#In order to identify the regions, we create a new variable with the name of the continent for each observation\n", + "\n", + "hla_country_web['region']=(hla_country_web.europe)*1+(hla_country_web.asia)*2+(hla_country_web.africa)*3+(hla_country_web.americas)*4+(hla_country_web.oceania)*5" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#Instead of number, we constructed the new variable with the name of the continent\n", + "\n", + "hla_country_web.region.value_counts() #cuenta las operaciones\n", + "hla_country_web.region.astype(str).replace(['1.0','2.0','3.0','4.0','5.0'],['europe','asia','africa','americas','oceania'])\n", + "hla_country_web['region']=hla_country_web.region.astype(str).replace(['1.0','2.0','3.0','4.0','5.0'],['europe','asia','africa','americas','oceania'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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hla_hetln_hla_hetln_le60ln_fracln_abslatln_arableln_suitavgaa_ln_atdeuropeasiaafricaamericasoceaniaregionaa_mdist
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10.323064-1.1299044.131230.1992003.7135723.048799-0.2613658.9249111.00.00.00.00.0europe4.466400
20.332228-1.1019323.849840.2922223.3322051.169381-3.2188768.2940490.00.01.00.00.0africa4.806958
30.329822-1.1092023.496050.5803822.5257290.879627-1.3862947.1641000.00.01.00.00.0africa3.511567
40.318978-1.1426334.132820.1520822.8361502.900322NaNNaN0.00.00.01.00.0americasNaN
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" + ], + "text/plain": [ + " hla_het ln_hla_het ln_le60 ln_frac ln_abslat ln_arable ln_suitavg \\\n", + "0 0.329023 -1.111626 3.43805 0.570609 3.496508 2.495682 -1.832582 \n", + "1 0.323064 -1.129904 4.13123 0.199200 3.713572 3.048799 -0.261365 \n", + "2 0.332228 -1.101932 3.84984 0.292222 3.332205 1.169381 -3.218876 \n", + "3 0.329822 -1.109202 3.49605 0.580382 2.525729 0.879627 -1.386294 \n", + "4 0.318978 -1.142633 4.13282 0.152082 2.836150 2.900322 NaN \n", + "\n", + " aa_ln_atd europe asia africa americas oceania region aa_mdist \n", + "0 9.072699 0.0 1.0 0.0 0.0 0.0 asia 6.002551 \n", + "1 8.924911 1.0 0.0 0.0 0.0 0.0 europe 4.466400 \n", + "2 8.294049 0.0 0.0 1.0 0.0 0.0 africa 4.806958 \n", + "3 7.164100 0.0 0.0 1.0 0.0 0.0 africa 3.511567 \n", + "4 NaN 0.0 0.0 0.0 1.0 0.0 americas NaN " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Choose the variables we need for table 1\n", + "\n", + "hla_country_web_1=hla_country_web[['hla_het','ln_hla_het','ln_le60','ln_frac', 'ln_abslat','ln_arable','ln_suitavg','aa_ln_atd','europe','asia','africa','americas','oceania','region','aa_mdist']]\n", + "hla_country_web_1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**According to the author this are the variables we need for the summary statistics**\n", + " \n", + " \n", + "1. _hla_het_ is HLA heterozygosity\n", + " \n", + "2. _ln_hla_het_ is the natural logarithm (ln) of HLA heterozygosity\n", + " \n", + "3. _ln_le60_ is is the ln of Life expectancy in 1960\n", + " \n", + "4. _ln_frac_ is the ln of Ethnic fractionalization\n", + " \n", + "5. _ln_abslat_ is the ln of Absolute latitude\n", + " \n", + "6. _ln_arable_ is the ln of Percent of arable land\n", + " \n", + "7. _ln_suitavg_ is the ln Suitability of agriculture\n", + " \n", + "8. _aa_ln_atd_ is the Ancestry adjusted ln years of agr., 1500-1960\n", + " \n", + "9. _europe_ is a Europe dummy\n", + " \n", + "10. _asia_ is a Asia dummy\n", + " \n", + "11. _africa_ is Africa dummy\n", + " \n", + "12. _americas_ is Americas dummy\n", + " \n", + "13. _oceania_ is Oceania dummy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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hla_hetln_hla_hetln_le60ln_fracln_abslatln_arableln_suitavgaa_ln_atdeuropeasiaafricaamericasoceaniaregionaa_mdist
00.329023-1.1116263.438050.5706093.4965082.495682-1.8325829.0726990.01.00.00.00.0asia6.002551
10.323064-1.1299044.131230.1992003.7135723.048799-0.2613658.9249111.00.00.00.00.0europe4.466400
20.332228-1.1019323.849840.2922223.3322051.169381-3.2188768.2940490.00.01.00.00.0africa4.806958
30.329822-1.1092023.496050.5803822.5257290.879627-1.3862947.1641000.00.01.00.00.0africa3.511567
50.337511-1.0861584.177700.2271363.5263612.318458-0.9162918.8596020.00.00.01.00.0americas6.660737
................................................
1700.304496-1.1890984.091500.4031962.0794421.078410-1.0498228.5270970.00.00.01.00.0americas11.140847
1710.298078-1.2104003.787730.2137462.7725892.947067-0.9416098.7096510.01.00.00.00.0asia11.293782
1720.329822-1.1092023.718510.6284620.0000001.085189-2.0402218.7797910.00.01.00.00.0africa2.952760
1730.329822-1.1092023.808480.5770632.7080501.957274-1.0216517.6765440.00.01.00.00.0africa3.066750
1740.329572-1.1099603.941090.3274312.9957322.118662-1.3470747.2827700.00.01.00.00.0africa3.300056
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131 rows × 15 columns

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" + ], + "text/plain": [ + " hla_het ln_hla_het ln_le60 ln_frac ln_abslat ln_arable \\\n", + "0 0.329023 -1.111626 3.43805 0.570609 3.496508 2.495682 \n", + "1 0.323064 -1.129904 4.13123 0.199200 3.713572 3.048799 \n", + "2 0.332228 -1.101932 3.84984 0.292222 3.332205 1.169381 \n", + "3 0.329822 -1.109202 3.49605 0.580382 2.525729 0.879627 \n", + "5 0.337511 -1.086158 4.17770 0.227136 3.526361 2.318458 \n", + ".. ... ... ... ... ... ... \n", + "170 0.304496 -1.189098 4.09150 0.403196 2.079442 1.078410 \n", + "171 0.298078 -1.210400 3.78773 0.213746 2.772589 2.947067 \n", + "172 0.329822 -1.109202 3.71851 0.628462 0.000000 1.085189 \n", + "173 0.329822 -1.109202 3.80848 0.577063 2.708050 1.957274 \n", + "174 0.329572 -1.109960 3.94109 0.327431 2.995732 2.118662 \n", + "\n", + " ln_suitavg aa_ln_atd europe asia africa americas oceania region \\\n", + "0 -1.832582 9.072699 0.0 1.0 0.0 0.0 0.0 asia \n", + "1 -0.261365 8.924911 1.0 0.0 0.0 0.0 0.0 europe \n", + "2 -3.218876 8.294049 0.0 0.0 1.0 0.0 0.0 africa \n", + "3 -1.386294 7.164100 0.0 0.0 1.0 0.0 0.0 africa \n", + "5 -0.916291 8.859602 0.0 0.0 0.0 1.0 0.0 americas \n", + ".. ... ... ... ... ... ... ... ... \n", + "170 -1.049822 8.527097 0.0 0.0 0.0 1.0 0.0 americas \n", + "171 -0.941609 8.709651 0.0 1.0 0.0 0.0 0.0 asia \n", + "172 -2.040221 8.779791 0.0 0.0 1.0 0.0 0.0 africa \n", + "173 -1.021651 7.676544 0.0 0.0 1.0 0.0 0.0 africa \n", + "174 -1.347074 7.282770 0.0 0.0 1.0 0.0 0.0 africa \n", + "\n", + " aa_mdist \n", + "0 6.002551 \n", + "1 4.466400 \n", + "2 4.806958 \n", + "3 3.511567 \n", + "5 6.660737 \n", + ".. ... \n", + "170 11.140847 \n", + "171 11.293782 \n", + "172 2.952760 \n", + "173 3.066750 \n", + "174 3.300056 \n", + "\n", + "[131 rows x 15 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hla_country_web_1=hla_country_web_1.dropna() #similar to the author, we need to drop the Nan observations\n", + "hla_country_web_1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Table 1.—Replication**_" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hla_het
countmeanstdmin25%50%75%max
region
africa37.00.3158500.0176440.2843830.3038780.3264630.3298220.335216
americas24.00.3078380.0253130.2587750.2915260.3087680.3263300.350252
asia35.00.3148540.0146130.2710950.3100950.3190510.3250650.329846
europe32.00.3342710.0099920.3184470.3263180.3315930.3432460.352915
oceania3.00.3021960.0589820.2347120.2813470.3279820.3359380.343894
\n", + "
" + ], + "text/plain": [ + " hla_het \\\n", + " count mean std min 25% 50% 75% \n", + "region \n", + "africa 37.0 0.315850 0.017644 0.284383 0.303878 0.326463 0.329822 \n", + "americas 24.0 0.307838 0.025313 0.258775 0.291526 0.308768 0.326330 \n", + "asia 35.0 0.314854 0.014613 0.271095 0.310095 0.319051 0.325065 \n", + "europe 32.0 0.334271 0.009992 0.318447 0.326318 0.331593 0.343246 \n", + "oceania 3.0 0.302196 0.058982 0.234712 0.281347 0.327982 0.335938 \n", + "\n", + " \n", + " max \n", + "region \n", + "africa 0.335216 \n", + "americas 0.350252 \n", + "asia 0.329846 \n", + "europe 0.352915 \n", + "oceania 0.343894 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Table_1=hla_country_web_1[['hla_het', 'region']].groupby('region').describe()\n", + "Table_1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_1.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Figure 1.—Cross-Country HLA Heterozygosity**_\n", + "\n", + "Figure 1. shows a global cross-country plot. As expected, countries with populations derived from Eurasia and Africa have higher levels of HLA heterozygosity, while those from the Americas and Oceania have lower levels" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6IufpHDly5MiRo/oi5+kcOXLkyJGj+iLn6Rw5cuTIkaP6IufpHDly5MiRo/oi5+kcOXLkyJGj+iLn6Rw5cuTIkaP6IufpHDly5MiRo/oi5+kcOXLkyJGj+iLn6Rw5cuTIkaP6IufpHDly5MiRo/oi5+kcOXLkyJGj+iLn6Rw5cuTIkaP6IufpHDly5MiRo/oi5+kcOXLkyJGj+iLn6Rw5cuTIkaP6IufpHDly5MiRo/oi5+kcOXLkyJGj+iLn6Rw5cuTIkaP6IufpHDly5MiRo/oi5+kcOXLkyJGj+iLn6Rw5cuTIkaP6IufpHDly5MiRo/oi5+kcOXLkyJGj+iLn6Rw5cuTIkaP6IufpHHNg9kJGOsz8Ie2rypg5c+aUBJMnT45CMVRmKK554403fvzxx+iheNCQIUeOHDl+HeQ8nWMOpFy00JAOM39I+6oycp7OkSPHooucpxdnBKkEu1SOaA/Kn3/++QcV4/333y9byBA1UOoQPvvss//973/ZKNmgWTkrFCMqA2lVUqmr9957b2KCSQmychSKce21166++uo1a9bcaKONNt10U4WqYNttt3388cfTLhJMnTo1Y24ISbJCIlppRANIj3MsOIRWi3WbaLqA9DhHjsUCOU8v5kj9VhnguS+++OLjMvjoo486d+7cqFGjxgkUoGGCqIzDMxJEfZQDDrNm0TIKcM0110ybNi1GmTFjxk8//RSSKLzzzjuvvvoqFvQZhUCUv/nmm+nTp8dhhpdffvmoo45aswh//OMfs0IxomadddZZZZVV/vrXv5588skrrrjiqquuuvzyy6+99trq11hjjSWXXLJGjRrLLLMMel5iiSWUYemll3aquE/M/eSTT5LkzTff/OGHH0L+KiJdjxzzgVSVVUN6TY4ciz5ynl6skLqoEielgI+//PJLuWwpSHA7dux4ZgmaNGmCXKN84okn/uMf/zi2BMqHJzimBMoIDw477LAjjjgiygGn0suOPfbvf/+7BllXJ5xwglH0b6ALL7zwpZdeeivBc889t+uuu6633npbbrnlhhtuuNVWW/msXbv2BhtssO66666//vq33nqrfpRBMw222247mfEKK6wQhBpYaaWVglMzYNb4/L//+78pU6YIFDbZZJPNNtuMMG3btj3ggAMGDhz4yCOPtG7dOrqqVatW8+bNl1pqqeiwLDD3WmutRaQ99thj1KhR2FqE8e2333744Ycxl3Lx9ddfx3JUQ2TWEp+BqIT0eMGhoj5juFIot/7TTz8Nrb799tuzZs2KSmGftYh6th2VEJ3PLdKLiwSI+koQzcoiPV2E9MS8ypbj94mcpxcTxOaXm86cORMxZ5Af46Rzzjnn3Dlx9tlnn3TSScGggF//9re/HX300WgVKR555JEKQbHKwcGHluAvf/nLnxMoqPcZNegQi0eH4PKM1/WZYb/99pOnImN0u8MOO0hSUenKK6+MvDfddNMePXrUrVvXJ0L9wx/+IMFFnxg6uRVdU/p74403Pv/884ccckjUBFZbbbWLL744vT1dBu+++654hTcXHDiU1n/xxRc33XRTnTp1tt9+e/EBeXSi8y222ELBuJFV42yZdzJCYYhIuzW+9tpr9957bxIStX///qa/9dZb68qMhAKmlkE80atXr2rll8NUqoL0ggWKtOsy+O9///vxxx9PT/Dvf/87Kq3aJ598EpVg4Ro0aBBaperevXuref/99y+44AJGAk41atTIcqsXHukhHXVu4Cr7SOxlRNtHOT1RMRJhC+GCcUPOr776io1FOasEWzIaB9Lrc+SoFDlPL3rIdngUAj/++CPH9Morr1x22WXnl+C8885DyfLjlDkrBjaV/mJWPJqxMoZ2SqVTAUycMXQUMqD5ArEn1O5Tno2eYf/995fURiqMCBHe2muvjfw233zzQYMGYVwYMWLETjvthCPlypMnTzYLfk3Oessttzgr4Y5mMH78eIEIn/7GG28U1ytzqakuqobPP/983Lhx6fVJDxluu+02oQO2JuRDDz00duxYafddd91FfvEEwhZbyMIJ3KJFiz333HOVVVZp1qzZww8/3K9fP1NwoRqfUbj55psNF2u3sBFTSw8qAO1hlHgAAUgRHdJqepwgY8pAemWRyWWH2WdVoKXRpbyGsF4QwylMnTr1oIMOEvpQ4ODBg//zn/9oJj8WkIneAhG6CZWWWWaZpk2bMtGoZE4MmLmKrihcD+K/7t27z5VgxWB+f/rTn/S87777Ei9tVDFcgs5FwzsmqF27NsOw+/SwzTbbRCUI5rQRAWDxGAjSLnLkqBg5Ty96iO3NL8yaNevbb7/97rvv+Fn0dumll2Ll4447LpiS50KrSQJ8aCTKUODkSpEScgI9xFUZsDhKTvLnn6HGENqfcMIJ0h1Az5GAcqCIDb1hviFDhnTq1Akf33PPPaNHj8aRhEcPIgxO+YUXXsCFzz777Pfffx+574QJE8Kd+YyHweqlIxonCpj9zTffZHSiKxdGmVroJMoqNVOgN2XNot6FwhodKhPAEFHWuTI8/fTTo0aNevzxx13FsZom74+YBw4ciLCFGlK3FVZYYYMNNvApz15//fU12GWXXZwdM2bMk0VAQnqOtVvYCBXRnijEZzGymiA/0oKs1HKgoiZNmkQNoJk+ffpE40yr0b8C3cYpyO48x9lfhJa0cfDBB9epU4clGCgdcqutZMMMZqWVVmIwdevWHT58+IEHHrjlllsyJJWlHkbgY5Efzl5uueWcBRm2FYkyg7R2kteqC8YGpL9UJ4xw1dtvv41idYVlP/jgg7RRxXAJzYsvLf2wYcNMcI011ogbMKeeeuqDDz4oJPVpj6g02YsuuigUyCDTLnLkqBg5T/+WKPYjWbm4EhlzlKUQpMWVtGrV6uqrr5Y3cJ0SzdNOO03q/M9//hPdolgJbvAo4FGHwFOklFsBZ2enoiBHiWvjM+5yo3/ED0YRFhx//PEY+qSTTurYseOkSZPko7fffnuxY11++eUlE2STpjilwEOZgkNlmbEyakQVfKKyREqHEydOVDbZxo0b8338IFYwIuZTj1AvueQS+asy3HDDDXfccYeCZi1btpSIRz2+ad68uUrK7N+/f7t27ZTV85vnn38+ZSqLGE455ZSgcwEExxo0L8MzNSSN1GXVkiEcTJ7XXntNGocheGGEseyyy0YBYbdp08ZEXFsWsaALD4YwtXfffRcHb18EmSXao3/lKOAeKxJLQ+y4h4w/oiaA83beeWdhR8+ePTP5o9CrVy/1AebHRONsVaAlZVrBxx57zBoJd0gSWH311Tt37vxoAsFQ/fr1iSQ57tChw9ChQ0kezSg5K+BRi6iruCqDmDVWueqCjR07drfddmPbrFEAhz4JqatnnnnGiqeNKkaMFaATQRvxll56aepVjoUA5RB+vfXWo141rVu31n8SGabIQslAOkCO3zdynv7NIIS3RUuh2OspvPTSS8j4yiuvvKIIuAfBCP9lnOj55Zdfxiu4TUr68MMP/+tf/wrGDSo9qgTxmDnoFltHm2JgZemys65SPuKII5Rlxq6NfpL+CgWXo2dUes0118g7JcHA0yGwu+++u169ergBjSGwgKQTiXJ8piyr4/siD+aSnnrqqXiOiHf1EATprHqptrL8Rj2yVHY5Hv3000+j3nzRkjLgVGlilBF8cD+IZjRToE+Np0yZEvVCBPQc2hY0EEnnyp988oly1HOaZM5EUg5RhQuDBw++8cYb11lnHe5YHCBDUrj44otdrgHECv6aMKjRrRcaCK5FEsssswwyIyHeFTmdd955SBp/xIP2UlCfLtiyy1q+M844Q2RDmeYbiFV78803mVnA2WDEVIi5AWWOGDEi7ejhh4ViTLqguyQ4e+KJJ1Q+8sgjDMZaMzA6h2gchSFDhkTSDGmn8wpkL1AQFggI7ALdpifmBiEJnYSEWD/V5rLLRth60EEHiSxpFUSlKgWFPXr0iKDHxtl9992FjKZM2xFEpl3n+H0j5+mFiLLbTA0OQAkgRcNzaNhnBvxU2OsJuCee984774xb2QFMKQeVMpa6MHDppZdKATVDtD5dyHGjW5AEo1487VNWmnQ2B1wSNOysq5D6n//8Z5/xlNqnU9FSt1Ln559/vn379nvttdceRdhiiy14HM7ovgT3JpAPBdvNFbKUqBjlVlYFofz5gaHBoiAqrtw0cfMf/vAHEzTxpk2bZkEDxHL/mjAoJdO8XA3jwqqrrip0EEzgZmyNtmvXrr3GGms0bNhw3XXXRRt4QmXC0TUw9z//+c++ffuKAgGRCPj2SbD33ntHgYHF7MoiFWJukF65gJB2Oq8Q5g4aNCiMFv1/88036YkqI5WjCAJEthG7wFYSM4mWQpNgLSjfQmy88cb0r7ztttsKZ2XYFL7nnnuec845Qpns/lk6TI7fJXKeXiiwr/h0fjPuZdlvUZAj9urVy1bk8rjLoL0MJ598Mh+R7PECEOFVV10lB+JAcbPQWwNsKp1Fxg5LIcukAw0aNOAj8IerUC8OVqmfuDFeFrhZG/0HSRdulydw6KwRdRgD3XTTTRJc3id8TTj6QJ06dWQSzhbeEfroI6kqYhOURFZkUpyOtCnKQhb1zipT15dffhmJrLPq4zm0eu0jt1DmTyOrA8rMbhJyrFmZnqUjUVYZeblFsRyREIMO1etQmUiGy8qGi3KInYlkLmqQNCcrQ4r54r9bb7115513pgrJn5ZhAL8VyD9q1CiS3HbbbZtuuimpZMYhKiBjVocz4utql19+OQpPz9WoscEGGyR53S5ir0cfffTFF19kjcWIJxG/+RyrIeiE9TL4iEfT2hKoeeWVV4Q+PXv2FEiFMps3b16zZk0ZPMQtkMjma9WqZZnwt40vw7Ycdlz2jkWO3ydynl5gsJGAi0cSqAKjdOvWDU0mb16nsPew6T333IOnkd9JJ50UzAcY9Pbbb3///fddG1Q0cuTI008/3SlE27lz58mTJ999991Bxjb5Sy+99PKckBDopEC5SXIs673gggt4bXH92WefrVJXknhxerQpBfQvTUTVSF05QyTf/P57JQjm+/TTTzl6vkZaGY4e8BZ62HzzzXdIoDfTmTp1qk7invC4ceMISTnK06ZN0/Nrr72mrM/TTjtt+vTpys6S3wSVEY96xBOKPeKII8xIPRa/8MIL77rrLmXo0qUL7UX5iiuuiHd9oXfv3pq51gLRXps2bYKDH3jgAToJr/rcc88RKSj8iSeesChR/+qrr5p+PDI3WSKp0UBKms42Yb611lqLBiSv+tcyjOG3AgECTGjYsGEWgveX9GevC6y99tr4+M4772QVjO3ggw9OmGIONG7cGK+EEspFOthvhFSICsRQT/IIE4uBRyu6pHIkQxVQqlwMwdwZZ5zB4OOFCTXFzRRY12WXXbbVVltdffXVYj7hIw1j68Kt/MGDzzrrrFT1q6yy4oorin0FguwqgmBRoB2UTqMItkkYcyDGWkhIxyhCeqI8pC1K9GCr2vsEtoki6o1mOaqOnKcXGGw/DoJFdurU6aKLLsKRPHt2r7gYZ555JspUuPnmm/GrTfj6669fe+21jRo1ciEMHDgQJ7FsZ+1nmx9hq5fIYiYXYm70E40DmNsnFi9Ol1G1QEFLPEQYOTFyyri8LLCpZhpoqZDWJsm0ICDZeinM1+fHH3/89NNPizmwFIbOssyNNtoIET744IOPP/64NvhYzCHJ5lnefvttRKssPcUTo0ePjlxBqjplypQIUGxsqVvU80QClHjvzCaX5IXDVY/gda4MoocgVJDyBt+DgehWgcD0afSo5zKQbqT1Igl6Dg8imVaOes6UGCGScEF50qRJO+6449JLLx3TDHCm3Ot1110XQia28NuDJCQfP378k08+OXTo0Lj1DSuvvLKYJnRrmnT7VBmccsopKMcKagNpj9UGRCI/bVcE26p27dpbJkCNPk1feBrLmvZSZbiEbTAMPWe3f9JzJWCN9inVhUGqKRaS2V9yySUi2r/85S+CQjU33HBDiAdks+NsFsukBxF5vPcO8mzGtuaaa7K6Ogl22mmn+DTBfffd96233sruToUkCwM659niHhWjiklVguy1+bjWxtlnn32ILSh85513Fqqoiytynl5g4O67du3aokULnConw5GysWC7im41N2nSBL9K0eyB66+/Pq099tiGDRva2ODsHXfcIeKOfmSWkRkHVAaUpbb2g7wzu/sdd8uzoaPZL0J7eaGoPw4xtJiAbDyUDVa8x+IQcJ4poGTxh8wSuKS6devGTdRy4Sxsv/32e+21V3wlOmjyF1GcQBSXfxGZwMWSVx0xFgpfffXVU34uwXLLLWfRBQHRMtFNtUDIA6jl+eeffyyBZeJJ0xMlSC9I4JD5XXrppUKcOIz63wqJgAXEI4zAueeey++HLeGw7eZEfGUOCq9WJ9h///3tjljEtN+qQXuUI4Lcb7/9WCxqFKsl4hQQDcD+RU4Ei8/AlVdeGRJCvOm9wQYb6MThpptuGkEtGj700ENbtmypUlZtF9hogtpYKR5g9913t/1j7TII1s2R4WFr5GelQpiFBJ13795duC8m6Nu3ryhBGGciEv1U49ttt+222/pUr5JIguNUCzNnCpoffvjhAQMG8E7imKDwhSrw4oecpxcYZIGskysH3GOPoUk44YQTKuJpQJ+ol6M/44wz0qoEGa3i5rhTjfj1JgKIPtWIA+IFMY1bt2790ksvXXbZZZol1x0rK4pbas4edthhmsUd7F+EFFyooeBCkQcHV4kjiC0XePfdd40Yj99+EZKetdZaS9Jgbwu35eWcHV/MmYL4Pfaz8vfffx9JDKjPGF1oH8+wQb5bXIYouzASYqIqR32Uox50qFsDRTmehQMBsvoQiY8WggQ9Z1hyySWlRGYUgiVaqUZIplIO0tNl4JQkO25OpFW/NSifbTRu3BgHFJLKOnXWXXfd888//957773nnntwJ84rBk596KGHwswCL774YjLpAtJOqwbt5aw6/OMf/4irhg8fjnhUMhIiBbBXgwYN6tWr96c//QndRgEuuugiYsSOGDhw4KBBg0IYqbNDZxHtQQcdNGHChPfee0999n0HYHvs0yqMGzeO0Rou7FC9PSJB7927tzzbZDfffPN4gTGVeCFA51ddddU666yz6667brHFFkskLyTKH6644oritx9A9BDP3a2UxrjcYilnEDDZRGaRdp2jash5er5Q2FIJGQCeyGBfvfLKK88lP5g1ZsyYIEvMJz0NlGJu5Ip645RCueV27dq9UIL77rsPW+vz6AR6wKx2iMZZz4jflrjggguUs8xePz6z718V2iU44ogjtFGI98Mxeox73nnnie5jmoGYdUwforJcFJ+NxpAeJ0/s+KYHHnjg1ltvlV7IrXfbbTe5BacjpTj11FOFO5pRpuRJDqHMQ5k1Ro96boIeCn3Nno31s2fV11577W233RZlflwzPs7Q999/f+fOncPf8Z7NmjULSuYoGzVqxOEqP/XUU02aNAl3Kerif/lQl8hB4+l+WZ4G7lJWN2LECFfFNKsPiFQWOMDcy3WXaYsEadVvgVSCBJbGAq222mpLLbWUqIjCGSfr3XPPPUV4YlNWVAzcFiueIe1oTlR0KuoDDhkqE91qq610yzAiehMlyHQDe+yxB8pUw8CIJJIOMc4880x5pB4Iw/BuuummpPvZjLZ9+/ayTCG4DJu5Rj0jF5EzbGXGzz6jzOA5kE8++cS4Mvu//OUvdgrOc+0tt9wybNiwjN3LRTqTCpRQCmnTMiCPTLp///4mtfzyy6+xxhomLsKO9x4w9zIJVl11VRsZW/fo0UNYI7hRWfjuQQLlFVdcsX79+q+++molY+Uoi5yn5wusjcuzjW22tkVo06ZNWmrbtlWrVnEvGhnffffdzzzzjI0nPnUY4HREx0g9kH0dWcto7FNNRM32vLwNbfTr1y9IN4CkBQRidtQSNbrlwmJoHCP5wNmx0/i4+P5VtAQkHYcKTtlm2ejvvPOOEUEsEiQH6fznFdFJgJehlo033tiel2FL33nGoUOHxu1ZbpH/QpbK3BZ+jVRPPU6Nd9DgpZdeEhhFmeqC4w00depUzaKMd1Fy0qTwverRo0eblDIPiGLDJ+o8yqC+W7duFogGvvrqKyTtklI8TeYVVljhwgsvFBAs7LRmQUFGeMopp/z1r39FPGlV9UNhkZIVZwxB0hgaBcrSqH3LLbdce+211SOPzAZKIe2oBGrYTHKrJb31ktWz6qyyuB6ivYz59ttvpzFgaQ4POOCAMAD0I5YVI06bNk0/9kuQEFBvfKdf/fjx4+O9SCCw8D0KLrTlo56RP/7444RUZvyjRo0SSxmdXXEjBx54oLRevi4ojKEZnuD7448/DiHJH4UM0VVMB5Rt4fRcGZRqXAz1smejt2zZ0iZi6sSGSy+9NPJpQQwf2LNnz14JHnzwQZv6m2++iR/vKwXRTDxES3vPUQXkPD1fYG2CfRvVhol7y8WIm9IZEKHcrmMCxopWA08++WQ8tgk4xAd2smYdEiioMZYNj4E6deqk5sorryxOyhs2bIi5cRVKNlAmTMiAs12C5Lp06ZIReVlEzg2XXHKJ9hkyMUi1QKg6Lo9+gD8aMGAAZ4Sn9957b2EEfk3PlUEmQFaoHNEsGy4Z/+ehy8WkSZMOP/zwP//5z3vttdcqq6xCpEMOOSQOw0viDFgp+QkRMdb777+fXrkoeB+RJfPjTwUlaVV1QqhRCIUOzzjjjIMOOmj11VeXscG22267/vrr0zx7xgpvvvlmNC4XaXdJh2wART3xxBOI1jo2adIE/dtr6g0ksLPcWN+Ica1T6gVqRx55pPZSWFmg5bb68siDDz44exkbT6vRQ1xYCaporhDS8gMhLdSrV8/QYXXFWHnllffff//CtyeL/iMOuMSh4MAsMvBU9nV0mDXL8MADD2Q7pRRUvvjii3YlYPRExgIE0ASgBOk1nUQ/p59+erzpWTnSrnNUDTlPzyPC2hBMnz595Ky4sJTdgxr1tnogEtYjjjiCuct0b7jhhq5du/qMQgbJmcQXAWiMOIM777zzTsMh6RYtWhSINIHcl3+JBj6vvvpqBCNVQtJGAYNKjn3iZvm0zhW0dJU2xIh+MrikwO3/93+uim5LgVTC/8zjhCrmE9EVyFlR9aqrrsol0V6Wl4Q/zcpRgOJ6hcrLBlJwSQya1QdKdcsBxddj+KCyIOHll19Ombfcckv2wjlEz9UfqbgJ0qrqBFIFQ2MaqkaETHqzzTazHJiJMV9//fWTJ08O+SG9rGJoM3ToUBdiuwLbL7GE7JyRX3vttSNHjrQr99hjD6PUrl37ww8/tPrCl9NOO81esH/V16xZ06YT4IKhA8LWWrVqySbPPvtslpB9j2CubLKistnZa3/605+WSn6IBjbccMM2bdrE0JkYF198sahFjhuHnTt3jkK7du022GADuhJWhhMImLLwggE7JXwXXoQ9B7bbbrv77rsvBEgVV4JEusItq+HDh8cOcqjl9OnTsTLxXB5ywhZbbBFqTGYzB1RGV9FtjqpjHnk61F0u0hYLAmmPVUN6za8FI3733Xd9+/aN/LUQnRbxtLJdYZPEQ18FvBjgfewZlT4dlv3iVhCk+mjvWqQe+aWNgdSjmUENYaBoo6Zx48Z4TqoRLCt6uOeee3gokEm89957fAqGjqug1NBORb0GIXa5QNU333zz008/beMliv8ZmWayclUQjeOq1157bZ111rHt+UeKmjJlyn/+859WrVoZzlkKv+iii+KdIAkHlzRs2LDkutn9+vV78MEHo4w+5YtRHjJkiGYKhuBl5L5RTyFcbbxKZpQrr7zy2+RnUiZMmHDiiSeefPLJBj3nnHNWSL4azvsUPFkJ1l133XLT/WQ2lSFtV4T0xK+LdOwEZWuqgrhqgSM6F/taZYkaFmTSrVu3thysgklfd911LyS/AluM9OI5kZ4rARvYaqut9LDppptaWf3885//ZOdsqX379naK0BBPP/PMMw0bNjz00ENFabHWlp4ktthxxx0ndIivFTEbVzVr1qxTp05vvfWW+Diep9gROHvw4MHJmIUfQmdmUc/wbMaoZ7TZ+xMMtUePHlE2usky7GnTprE9AjskqumLMOIxkFNixIgOqYJaCOzUp59+eumll8bjoa+//rpu3bquEohwHTie5MrXXHMNEo1J7ZT8e7qYY4btt99eNF8QpQyyzZ4V0LagXyigN1FUiEqYNddck2ejLhqOz4DymWeeGW+kVhGxjrGgCxDReSmk56or5pGnBXeQ8EgBt99+e6mnO/MPXTFZ3QYkWwwdUaXHRchMJ73y18LMmTM5FLwIXADrjFtPCA+J2ic2qn2C2zCisxlojI/QGKP7VC5G0CfqDcoX+Z511lmjRo36qeRh6ugEdyVfpNZbsHLxs+oAqfgR7UM5drJ++KAgYyjOp/XglOHUK8SpkDbOGiUGCjRt2lT8HkmGAneDRGM5rFqMmKqpatCeqMb6Q9H/ft5zzz1N387nd+Kxcf/+/cMZ0cajjz4az6FBlp+900tX48ePj/LEiRPjHTRDxHeFox7RcpchKt8xaNCguKH37rvvnnvuuauvvrp1iReJV1llFR7QHLnmtddem2zB0yH2XEH/piAgoKsYOj3xW4Mk9Ek2GU8UykVY4MITW8+UQ9t//OMf6VmshgbsJunstttuy8k4CyT5RTGc1UzjPn362Er4w3Lj1IceesgsWIIc2jrabs4yM2NZ6N12282Ksz1GiA7Fo/Zv5PTkids8ll7PYr54HcE6Mp4oA2NjclF+8skns3tCDC97Ds1ox4wZE2WGGr/bA/HD6aFkeyGSV2UGT+yIKT///PMBAwZwhlHu2bPn3XffPWvWLNz8wAMPqDF34olZMbo2NEC8YEeiCltNKoP5CobQrf0lyTZx+5pxusqOphl+4M477+THogwcAvfl0NKIYrfZZpubbrpJtyGqAIJIVLfaaqtRV82aNQUHBqJGNbR6wAEH8Es0YIgYKArlYiHtEX2Slg2QRJb1WYL0XHXFPPJ0ws6HJa/4FbDxxhuLWIMv0xZzA1dlsDbsDNiiSLNly5bCVbjiiivYk2QuDjMIF9h3XOLaYkRldrOlEqRyzClJMUwtetNtVoZXXnkFPYwbNw5nNGrUKDiMnPaVePPLL7984403+J0CE5Yg0taEglMkpJnCIYZATsGaceiSJk2a2MMxaEztiy++sBulAkLvZ599FoExO/1Hnu1ynTj7+uuva0xmPG0TxmtiAS1DJDJnUhXL4DMKxWUbVfu4MANzv+qqqywWCKdwHiFDdalmfwla2rHhR7jpQnhfBE7khhtuyHhiHhBDVA798w78YPG3TbgbycdBBx3EpXbv3l12JYmZ5y/jtmvXjoPbZ599fpMfLytMMgHhTbawSRLgAF7e2uEP8ZBCbK5AsqoFDBw4kC2VuwrpAPOKtJeEXMmAktdbb73wLcsvv/x9992HtJgrXoE77riDgVkpmXclJiG0QsYbbLDB3nvvXfxeBUhzV1ppJUYlFpRG20EOY7iAQ3b+zTffiMXtO7YX7w9afeyb9rKQUSxwWRQrrRjl1pfblUomzdoxuhhCVCoc4ckpDWTbPmn+7LPPVsmfaMlIxPpWROYtAHIo/sg6z4bmugcPHuys7WxTq6dGZdtHpLviiivWqVPHLvhF9OvXL2PrKkJjIxY/RC8X9q/+xRmmJgwCQlZnzCNPM1boW4S4H5Wenku40GLbcrikW7dubZP3pQViCC/lgYpx3HHHCdZcUhFsez1XhGDxVI4iSUpBJC6d0tstt9zyzjvvdE1AWu2ZhXhC5jpy5Eh8+cwzzzB6DB3m65NNnHnmmSEthouYNKXKBA6zXLbAzEXIuFMAq5OYUYYbb7zRcAICftbQJyY/NIaJEaqrfDoU35D8gw8+YPE2lW1GY5LvgAYYGvsaAsReIU/URHIfUFYT3Trr0CepynL2GWecYfnuuusuUqVqrQLoKkC9G264YXBkMbbaaqvHH388Yo5oaU9amijHMkW5VL1LYghr7VRxOdooR7fc1oEHHph92wRkUcqArXfeeed69epx4vwUVcQt0OititAeYdgsSPo3+WpKTJZOMJAkyTKFISk0btzY2p133nnnnntu2TUNxF1oiSk/GMh0mA4wr9CDJYjNCAhYpEtRt912G8LYcccd69evL3pDJEAG0QNe2X///YUOqShFCKl4JLZqEUkeNbHQCvInMasF5WHWXHNN5n3rrbeyWCP6FAf4xDS6ivZy6PXXX19XGU/rUG9lJVdfbIdZvcqsXqVTYcZl66PsbFZWH/YZ9VE2dJRVZmJEfZRj6Ky9MhSLVFzW5umnny748b597Wu6VeAuyGZTKE+YMEHZJcxeIC4t4U+i2xBPIRMp6rNyVi/pl9xnGtZtcRlKlU8++WSSiA8MYehY3EpAQjtrv/32wwhMKK0tA10xACGIUcjDgQMhqzPmkacLxvXTT7EepZC2+CVo6XILEPjoo49EW3bgSSedFE6hFNKnHHMiPVeCsjVw/vnnJ28rlw9pKANKhUggA74+eU+kGFdeeSV605sMtUWLFrjW3pYbCcR69uypktiET1t36NClS5e33347OuQTL7roohAGKQbDgUJWDmbNEIyIvBGnMrgwegiYKZcqjxcLixiMyDRDQk6WbBEKhMNVLxkSYVCyS+INc5sQzAWvaxztswxbpTlmh4ZT1oaoIWEgODvuifmMxoHTTjvNlss28C8isZ0C3n333Y022ihufYNC3JFTMAVBQNw/tNmwi20WV1mFeLTGqCRSgv2oF9RrFs7iwQcfFNmE0SKbq666Ku4lCnQoc9asWR9++KHsARMjZoMCJ+5yiJ8hU+PUGmusYYh4qTWEryIKApUgDqN+ISEGgrBDoLT777+ftVx77bWnJr9cWwoMxkKX4mmHUSMFQVdY85BDDjk4gS1Ah7rlDXxGIR01QSpKmbkX17hEyCs+ENHqIfMqysjACmJrkGSDwqRJk9iq5bAW22yzDWHibfwMIo+Qyt63ZGRu1aoVUxw1atTFF1/MU+ucj5ayywvFf82bN7fuX3zxhUtE5ALieIF/xowZ4lqfCEb+IFBYZZVV7B0cwHiEyGahfOGFF4qStSe5obN3I1zCzKKe4RE+psZo7dko33nnnWwpysQjSWyZV155hc8hj/LYsWM5EDIoo0nZf3w1kYcJsYlhaxNVjTKlmXhsE2fZNo0pE1Wf8S1/IxIv+3o33yXiUQYqOuWUU6Iso8WXUbYQzCZEMnfNeDZl0fOll14a9ZMnTyZebA27iUg+ifTmm2+qJ6R6+iSSlspE5bgi5I0vzggLyMNWuax4tz9+mYCnSle3PIRBBoMIv5o1axYutFyEhAQOGBoIWZ0xjzx9XQIhZ+x/CFOr+oS1ZEPC2CQ17coVxm3bQIGEFwTS7soArwRNCtlwasgQiG82p+1KwE+hLqwZzBrcibfE9Ww6axM0DEjLlogOpdraJL6uwKBOBcrSnj7j2gJtHnkkITMS1bPPGAhM7fTTTyc5vUmLQzZtDJG2SORRCc46FEnYZkIQbGefWy9AUVyDszoPyaMxtzJy5EhbpdDRscdyc65VIFIiTqExaTOxI9RQH+0Dl19+eRBhuuS/hMR8Cr+KzIPEi6mSp4YNG3JknKnDXXfdFcfEs0DOnRPkgMLwOMrsu9Ekj++nggkOHz5cQf8ai1Ginh8cMmRIhGhiHcE1L6ZPbjp7/Z6uTNyUuYCVVlpphRVW4I+c7d27d/woFYTk1RPEoxy5Ak7KZtSoUaN0eeYEeg4wAJ9q2JUtCXvssceGCQQrnKZ+pCzCJhBRsToIkwauFqOETyjWT6KtnyOGgJqPP/5YSGdfsBZWzWgRjFOff/659F3P9M93R/uAJeDB2YbGO+ywAzEYocoMWC2EwbikZasoWVfiaSQakYTEy7VGXGeddXbZZRcBGTPWRo6+ySabTJkyJVQnjvepvUlhrB49enzyySfM5pFHHsEu5sXRS/iCaYCxFb8nEXdNdMXwgstBOJI9k2ao2e8IyRBQeIhHLewzygxePftUpha2yvwMjfDEppELfvXVV0RVo0w804xt4qxy8DpReex4BZJIZHj55Zdj+wwdOjS4HOg/E4/ms6+cmRe+JJJLBAREit392muviYCtizImJhJRQyRln8qRvyprg4+JFI/MifrQQw+ZlLL4SX08VjcEDVtKrgDv2vjcVCxuuRBBMgNBvA27wQYb8LfWPT1XBjwzewujFa1SWuitOmMeeVqqB3Xr1i2QYQLOi3IDFF0K6WVFQNI333xz3KrNkPa14FBunyoxSnAMdrGNg8/AyvFTPjPC47OsqFMZPUNcq6AyLvcJcSo+nUq6nAPBghmC9goepQSlGmRQz7aizA3h2ix79klI12qQiQ0qI00niSk4BZq5EPPxLKJpfCwk15jwWpIwfLRghauSpCozfe7GbhTSFvpNQJ6ybO3y9HQCnjecS0VITaEIUW8nY5S1115bwrTvvvsSmzO1D4UaNny0mSsUd56BoXJJ+kzNIgGx01ICCpRFUbioVFRUdvTouXpCxBMMXRxKRqEsnLL02ScjoRnEBltvvbUgCZmJ1XhnenviiSdk0hGsl0Lc16E6ZoMYEn+QOgROXJQQigUJNH3KbMSa1ldKJAvceOON9957b2cZquTVorOBffbZh4Rxlc7jO1rque94dCq9y0YBjBLi4Wntd9ppJ+1r166NyYJOAE8LMvSgHwUGLyyI1/tXXHFFV0XyVzkoOS3NK4rFnisUD11Rea4wz5KUiwUoHj+z3HLL9erVKz0uD4899pjlliiLI4VfAwcOjIUud+j33nuPA9SeBW655ZYCGnC2OmMeeZrrB1uLfQtPdtttN5u5QYLu3btnNxMypJcVQXwqepLOhpsIxFb8FWDt8UogaCYQN3J9JqxaQDH7FiPal4vsbNmWpaj0F4EOyUDa9DgBZuUxzSI9LgNDRGwRPA3EKMQRyUvmggOpkh6aNGmipc6zluYegUIQuXBbpBy/8zVu3DjjapmFC67VONMPZHrjZDWQCsevFAUEznFPL0NqCnMiTn366accK6dZjIMOOigyg8AvepasN8jKySCFskRBh9KytPcEskZMw6QzdOzYUUwZ1waih+oPLHjWWWfFYpUCMi6LsEx2JYdG0vZm8PTBBx+87rrr8mj4PntwUIziGk4Q7zI/KaxrXXLFFVcIvJySNl166aXCr/C8aJgFsi4EqbzsssuyHyERnYdXAVmUoZdcckniRU0ga6CQ3TspFiPK3Lc2BxxwAO90zz33ZCQNIhgMvfnmm7PV3Xff3RJPnz6do49vZCFsHP9dye9xUmYUoCrlsiBPsXjzj/kXaYFjbkUqK165KkITLVu2zL7ZUQlGjRrFHh5I/s9bdFXucNko4jm8vjjzdDj9Tp063XXXXcOGDXvkkUdssLj5INq18TgIhh7grMtdAPG+lM4ylE2sIeHThYWMp4NgAqVqig+LC2URpzKktUl9WippQ2lcWCVAoun8E5LW3lUyhvR0pTCpcLUuLGbobPQo49HMKbskrlVZTNXxvc9i8Gtxs8gn33ffffdZOFmLrqIHF0b/OskIG/hBbYyFtuM9kSFDhlRyP1y9GO6SSy4p9Z+pmJZusydbP/30E+7P5BRPPProo1Em6uCS77A+/vjjt5f8X+rRo0cTO8rPPvss+pGNNW3a9OIiMEVRZohROULaDGltGe8TZ39lxNBledpCVA5tkHTQcyDYWo4Sq8Agsd2AAQPs6379+tnXTz75pA0eofmrr74qwkNvPCCTWH/99RnYWmutJfSxpmIv8qy55ppB/Hpo27ataGmLLbYwhFOWgyfRj8bSHcTJTsRJG2ywQbZwvXv3ji/aifmMG1+sB9aYPWG10A6jzDsZiEkQlVTXX389j2/RySDycEqsEIGvYEIUSIygahQuVCVG3K0VZIRIyoa+5ZZbJk2aRNU//PADNxgsYgg2yeQKA8+eLX2X6Ec9+Zlo1KOT7LvU9gLZojxhwoQbbrghNGloQ0RZ58SO2+8ktOlo0tDKRKIr5RkzZpiCmhCJ0mKbEF75jTfeUFbPvOM+NpGIF+/EWdDbbrst20p8cibe8OHDs600cuRIyo9b8RMnTuzatWvEMeKkbt26Rf1bb71FPI6dGMyPeD6V33//feW4vz1z5swOHTpoqUwkeV3cA//++++JGlkBkXr06BFPqWj7pptukidE/d133x1GArb8nXfeGeGX1S/e3bqN+tdff52dKBODAdBw3JYXUJ566qlsb3HmaU4Z6tevv+2227Zo0SK0Q2sC0n333Vf8G7s6QBcXXnghs4u1zBA8Tbnl8nQg5dUFjYynIaGw8gl4gSPGKovsFK1mCSuGq+SSskDn2Je35SKT0cpBtORtgz6NFQm0CzPOtuFjF0Esd/g4DiXw0ksvTZ061cLZk5QZ0uonLi9GRtURFgQQ9qBBg2zy6LwUDMq5SLyKeRpJMzbbr1WrVuGAuDA+MXuQJlkv5mnuL8qcJl8siATGmZWDpJEKJkgHnpNrixGV/DhecS0ZuMWozMCbkCc6h2bNmoXHgbSXXxEGtbnuvffeuONN+aH/hIsLT6AhysVQqVklPI3YOHTGgKd33nlnlfa1bNUGFz/Zxdw3lQajoIENN9yQUUmgOcTzzz//tNNOW2mllfSz2mqrEeyCCy6gKBdKmhEMgV0bT08ZACOMR5UUqz6enhoaVcRCF/O0ellUxtP8eyRVMHTo0D59+rAcY9kgKJ9Zxk0UYrMHFMi2u5T8T+6PPvqI7WFrOdzHH38cPK1/CbdyiIRdqsjT8UxavbVgolEvjBDiRLksT5uXcjFPM3hCxj0JlBY8rYz8lIlHDAZJPDXKTJpIsU2QIvKLH1gtxdNkCCJUtqzZVuKTM/EyntamLE/HQpfi6fbt20foUMzT4hv1sWtCpPAwRL3xxhuDpzkE9dkT/Yynia3NL/I0l0LJUU/tLqkKT8d/G1tseVpmA+FAM54G+mWFqFoo7XPTTTf905/+pBlwi1TPCr/88kvWKbqUWvEC4UEqQtDqAkcxT1eOILb0oMqo6JJKuopTPnkTxAY4O2rKotx6K+IqbigOsz6xcvBlIKvPAoLAcccdZy/16tWLj7COsaDWOgrFeO2117hRbj3kLO5HZaluEYA2xat8+umncwTx/ktZ6N/otne8OxZgZtQSrqfqYIpSfzONTvDKPvvsE7d8GjRowPtzAZqlAycoewg8i8Y4CcHoZ5VVVuHH9XxFAgXg3O35GAiWWWYZZBZqTPv6VRACoxwJpTmGwi1BgaiTGA4KLF3E08WH2jCDuOkdDB2fgiTLceCBB+KemBRS5OZ23XVX+jRf+x31Xn755aENQMwHHXTQUkst9cc//lGE5OzRRx+9QvI/oWkPyclig62r8iR4PoGEjAVkBiaBqjfaaCOJXdjAvCFT+G+LYjEWRZF+HZkzn5YBbS/m9705TbBFARM3TXDuuefakCLK5557jru/5557ateuvffee0uvNbMrhF14+o033rBhkGU4kVIIhxLl4NSFgarzNCSsVyC2ucK8XQVxYYa0tgqIxsWXRDm72Y4v+WKfWTn0HJBISZQFnqXYRVlqmCTSKXhh7Tl3kYQhxAfoP4ZQY7hYwSZNmnTv3l23SfcpVIrS4v2ydIA5UdhAyXuqgnGpWNAeMCGJUcTIAXJmey8r60FBrs8aWSbbC/OD7bbb7pIEogThf7SPSypCNHj77belhnpIRUmw4oorIjC9sXy2zaKUpdGyQxS13nrr0cP8/KjAPMBYJiXBsvvwK20H9UKBhMtDMHSsl9gLu4s8gqrNDoKn4eCDD15nnXVMKiwkIJ1VT8P007BhQ0y87LLL0gY90D8XQQ+0IU+SY4m9Wrdufc011yiEqBALGvI7jHKcndtyHAaU1UNWH4UQXiQqu5CKRcIaZ31ml0S5uH5uy6AMWX2Us3qVWX2UoWw5u/xXKAeUs8MoawNRjnqIcvHl81MOVF4fUI5DzbJyqfo4LC6DvJ8pdunSReIeNbbnYs7TgxJIv6BdCQ455BA7doMNNrDzbfu99tpL0EoRcVYkHvdGZCePPfYYX5nF+4gzCqUQnLowkPF0VYhQm6o0qzoWSIfRQ0X9JCPMgaiP3DoWKICwkzT7MM66TZs2N91008033xx3nwLjx4+XLMqSXVsqV65IjVribHlz27Ztjz/++DPOOOOOO+7g6/nuMWPGiNVSM6oYxpXo3HbbbQggJcYaNXbbbTf8EeTHw9pyQ4cOTWQs/J/p7Pkf+bdIfrsYTxebaDROB6gaokMco5M11lgjo+qVV15Z7vjJJ5/IXCkHt4k+zcsoEydOlCOuvvrqUkkxzdyOOA8IIWHWrFn0gBTFRhk3RyHLmEtBve0gnBoxYsS05D8zktnKFlN1oE6dOiZuZe1iDAcxaNeuXeM2srx5j+QfWoiu9KDMG+y0007ihrfeekv4Eo8nZ8yYgapVKr///vuCmw+SdwPViP/i/jYXoX32CLP4ZbEbbrghFpq2ZcNxD5zY8epD0qRwt7b4W7/Zu29jx469+uqrXaj87LPPdu7cOXhaXmUIq6w8ffp0IsWd2C+++IIY8Rah2EJZxKb87+T3vccl/xVUmdj2iHqjsMmHky8lw+233x7fuVIve7k7+QU6YIcMO8r33nsvcw3xBD00E+K9/vrrhgjxJkyYQOy4LW+N2FvcgXjvvfeI+uWXXxKDKRIv+EYQLAqJbeKscjyCIarYN+5vG5F48V1qsQtVZDeTOepbb701ygMHDszugQ8ePNhah0jm3rJly7gVb0cLv6Ke3yBe3Ir/+OOPieRTWWCknpDa0Ke9Ew84iMrnxFN/9ElUqYIykSxufPucAXAj2aP0Hj16ZM+hH3rooeyWO7ul5Kg3R5dEvbmfmPyr/vi/JqyU13IICGuzzTZbnHk61AHFZXZm8Vg8iKxj99arV4/WwhCLYVfYThQNLBtkS2eddRYTOe200xIWqBY8nXDcL7T5rVC5YJgSBwekvEm6m9bgTs4La1Iyzxv9xCtszNendbSNw2nGXW5nXc6tt2rVyraMlg71lvWQIQ5xv7OanXrqqbfccovAjoXEfg4rqgSJjRSy6l122aVAjAmWWWaZ8847L16KsQklf5n7fvzxx+NRJWCpaC9QiJpipANUDek1Jd/qZp/x1R2fNr/tzbn07t17ypQpPKAaQeqBBx5Ys2ZNHgHVqXdt2tdCQMjGF9s+fDeiOqXkK1gQSwkVkXSAMXC1duhTTz1l0flTCoRSPG1BxUwbb7yxfYodbd4YHYvYTeYbOg+IkzgB7GI787NojwEEEXLiOJ7elJGfBvHY0lrzs8HNCIA7DrJkMDQcbh0GDBgQC60eowQbEZ482QsKWCRcPJgaw4uy5cDfYYHK3H24chZliBDp008/NXSEFBad2EGQQgf1QZCoFHvZF5ZAuVevXsrqiSEsiGfSMGTIEBFA1OPmeG6t/Nxzz2UvZ/GNHGCUJ0+eTDNE0kYEE3d91ONsYod4qI7GInSIcjw/YgPEU6NM4USKbUJ4WsXoykQVxokjlQ2BmOOhr7JwgVTKILbIxMOO2ftl5mV2IZL5EjXuiumQ4UW9MCITiZBE8qmMrYUmdKgNkZhZ9qQfv8bjdmGE+jAAfGxxQzxia5O9AUC20ck/+YVnnnlGJKGxstXPonZk75Kot0m5LKYY4Py5ESYq7BZPi6sYZAQW1RnzyNOVg3a4+Msuu6xZs2aNGzfmWy2q7R2gaMuTtbSElsRKYw6Nr7/++szXBKcuDGQ8PQ8IEqqcI6sD0CRm5YhpMkJI3tZOQG88lJ1/ZvJrpk4lDJ62zKB9eBBLyb6jEkMLmUEnIOalTBe6PO6BH1rm1fSg6rOTH07SW6x7FWGLyp+QX3j/5ZdfnjDpuTmhZ7CNeajsC10CvrkdsRLoikMRwbDqRo0arbXWWkcddVSTBAICHGbuTgWwHS0lQi1gno4+M9AqR1xq4wCdF0i4PBTTtmZWUJBhh1500UViC9FGdq87Q9TsuuuutLrtttsKCwydCYOqzTedeQK0F14yZM6RY/4RxlYK6bmqQXsBnxh02WWXXW211S688MJwSnPbz6+PhcXTGYR1Qhvo1KkTYuAOxHEZTwuob775ZooLz5J9BhJKXSiYH56GX5+kjRhIj38JWWNOOZTJz1K+uNui4L8RI0ZYFGHT+eef37ZtW6f4+mjZvn37u+++29lgGpFs8HSQtJpXXnlFAD5+/Pg333xThI6t+XcJd3L7fI5ve2OC+eFp7cXpGCjuflfO00gaPWy55ZZaysKNGO9/pi3mG7oKKJuLfNEQIMn2KRiNm5/FLSG5dEEiupXYySxNUMoi8S3FypnyA2ygmJvBjuvWrVsYwDnnnONyKwiRQ5fL08CikPTaa6998cUXx9RCmEoQMufIMf8Q+fE2yBWyu/TpuaqBK7vkkkt4qnXWWUdesdVWW0X6Prf9/PpYKDxdFhQxffr0YcOGcbtZzaxZs7iJ45P/BjH/9Fz2wkq6kgllTBaFDAnNLcLIZvF/yaNok+3SpYsss3///qVu71gCyzFq1KjXXnvtmWeeifRa+6eT/y394osvJi8hFBBvd6PzBx980GGrVq2slxRq4MCB48aNe+edd9A8jkTnFIuYC6s455pisnng6YD92blz54022miZZZYhpIx2ypQpJMdS8SUTePTRR5988sn4DgKSjjsB6fXVDASjXmps0aJF3KpNT1QBGsvpze7GG2/EtTQcSOm3CCpLcTOoUe9Cl8fDQhGzxbXope5yVwRGdcABBwhNshvLDKBfv35xM1ncdvvtt8ftUMGcctR/8MEHN910E/NT9skav02+0hNhenbf+7bbbsvue6vP7nvrJ7vvff/99z+V/HNS9bx2xEYwdOjQ7Fb8888/n90CHT16tIAmyszG0HGHdvLkyXZEiCeXMER2Y/mWW24Jkb7++mtixH1vAa4phEjCXIFsxKzKxM7EY5PxnWkYPHhwdlt+yJAh8bVvYLRxQwJGjhyZvVdBpEw83tIQUbY91ReLF7flP/744xCPYRBPOR4GW1Mivf7669o4e+utt76b/IZo3EDObsUTLx4bKVvBbCuRMxNpzJgx2dfMnn32WcqP2yTmTqRY6JdeeumOO+6I+vfee48Y2X1vGvOpLKwkdiQJtCq4zO57cx1hiv/+97+JGl8hI5LFTb5OcQXB7rrrLmKrESba4LDbbruhapbwwAMPxNASD0pWAI11G9ozdx5DP7Yb77TeeuudeOKJp59+OmOuXbt2vHAAhKzO+PV4Gmg/04iCxTvjjDNSRz5/2XPaRYLsME6Vi2KezrgtEDVzhfTK6sHxIQZ/yi/zyF27drV7E/UXEMoPpFXJuvBxNhLYQrFV7DE+vXnz5mouv/zyw5OfLsfBxRCZSq1iw9sV/KDGNmp0hQCkaLEW88PTrrIVO3XqhGZWXnllbG1eEmt5tizWKLYrRyMvjF+4vOaaawqzqq57L6ZDUZRD7LmSU2Nu0fRDqwFqgWDigMOMvEudUt+xY8d4IgjB0yQpm0OXghhIALT33ntz0PwmRA/cuqXBAcrxiCReL2ISFkL/ythXVJe9R9a6det4Vo2/L7vssuw9squuuioeQ86cOVP78KF60GfGx926dYsgTL1xsxe1+OXsRS0uWwIQZaEkgwzPw+MLMUNUZfFrlKmUKw+RECRrz94jI0awC7GVhaTKGIUOQyTlTDyj2G62Q5QpKh7uKoti0YwyCHGEBVHGfCwhxEPw2QtZWLZDhw4h3oQJE2gywgX0RmOxo5EiUYnHMCyoejXK6JMmg4+dvfLKK7P3yNq3bx8Pd43Ys2fPCBNZY/ErmUKNTDzRZPbSFhak/BDPfIkaCy0+FsFHvXiFSJEPEJLGInSgN/VhdbSKNeOJPlGZX7xHZtHr1KnD0tS0adMme3pF5vr16x955JHbbLNN1Gy33XYsVnDAErip4GmrT8kKYI7XXXedIdq1a3faaafxGBtssIGdIie0WMIIcqpv2rRpfPUACFmdsRB5OuYfSKsSpFWJDcmTIp+GYNCsUEUUXxgoW84+M2T3vasbv1aEkBPS4zIoPsulcuXsmwuIvZGqvmJoY+sGksUpvJl1ww03PPfcc9wlr4SnS43u8NBDD6VJbH3BBRdgepfws2kv//uf7WcX6QRsqtjV6Xhzg5BHh3o49dRTV1999aWXXprZcNM777zzkksuefDBBz/99NNi5CWWWGL77bfnmudtoF8TpsO/+EyPS0ByTi3LzyA9kcAh/1uKp7FvwtSl+TgKxfXK2qMl40bnwdMq9Rl8DMHZ0g6QZ1tr6cu+++5rrbk/18blIQ9kvUHl5bik3DL8YpsoQ+X1cZgVyi1DlMvtamGUoaKyQiDKv9jVQioHlLPDrKxZcTlOLagyIOzbki937LHHHpIoYGzxYgrPs9VWW9npUmGH9jj6jx7iWgUQrMer7FyQsANOOeWUNddcU3YRdsvUi9tnZWJUfyxEnhY38Z4RN6VVCRxyuM8++6xArH///nxB4m3mQEqnlSKaRftilFsZSK4rALVYP8A0pejnt0LlYjgbSI/LoPiseTVs2PD999+PSLOU/stFNCsGI5Yi+7RSF110EVtH/zEKxEBRDrZGAxQrnxAZWFmpldFBmsIM8H2kVul4c4OQJyBUP//880MAn+sn/xUYVR900EFSPXtYFlL1Wf+GCAkDaVUChwMGDJD2xcYpPhuH7777rgwpu1EBQcDFfAx4ulQNqFEv87BAYF2kO1yYTnQYxJwhhhADUTLFcqASU6lqeDfIRIJiCStBqWZZubg+K0Cp+orKWSErQ3EZSh1mSC4q56ri+iqWowDF5WIkzcu5FkqVs8NS9WmpTJsoRwFK1ZcqF6PcylIodW3xYXEhUG45aTJHGaL83Xff9evXT6bOPg877LDnn39eah6QVbM9afTaa6/NIIWMQdUcjgzk1jnBAwglFRjtOuuswz/wVzJmXKOrzCeURUhVzbEQefqtt96S8TzyyCM2dlqVgGo++eSTdu3a0TtfIEMSH2VZdYYg1IqQNqoYVqhtCWQMGdSnLRIg7L8l/3gDFAJB3hkZFBhpTpRbWQpJBwusWYZfbG8iciOaj9uM82OIcbnlGzRoEP9uF6VjFCHkAUpDA9dff/2rr75qce+55x7b45tvvmEAAtvGjRvHvfHoMx1g7uFa3aJ8sMPx2X4JNtlkE2xdu3bteBiZtl6kkCimgAceeIBlZt/6TU+XQI3IidvSJpLmYj7Oylk+HSiut+mAkbRs2VI0cN9998U2iRcUSqFu3bp/+MMfIo9ZaqmlJDdc3gsJEmELD3fHjBkTK/vRRx8h8gjIpk+frhz1THHo0KHxTNrn448//u/kB95FXep9KqvRPu7oyq7Ux4MSPTz66KOCTmUQW8RdXPXGjWc0MH78+Eyk119/fULJPzmdPHkydUV52rRpjz32WIj03nvviVTCfX/44YeGCJFmzpw5bNiwuAfOwIgRT2G5+2KRRo8eHT/SqUzsTDxZXTy3hnHjxmWvKSm/lHwzGOg8HgyDcDZ7MEwk4oVIM2bMMESUP/jgA/Wh1RAve2SuTDxihHhxDzxEiqcJzg4fPjweANs4+oynCUyLSPEMW5kqsriQnJl4tBff44KpU6dSPpG0N99MpHfeeWfkyJHqrZ1d3ydB8mDkrihkn1EI4GbtiaSw1lprrbrqqk888cR//vOfYps3EYbKYrfeeusddtjB1mbGEZfb75tvvrmEe/85UatWLSSNUKwm8fSf9bZIYyHyNI3Te+yKtCqBQ6ds6Y4dO/IUvXr1YgE8u3KLFi3CAVWEUiQttTr99NMtpEo5nx7gmgSjRo0yunUqhhp7O9pE44svvlg/RyX/YUI/yj6DvBFP8FAQUoaoKVtfjMrPFkPLBduY2KeddppdxFXZY3xNqve5h5UCKyWRskPoJB1jToRU4oMjjjgintW9/PLLVB0k3aBBAyp1+QUXXGDbl7WHuUKIlMEoPJE5yuN33313O3k++/8NgcxefPFFXMJpsswHH3yw2GdlUPPcc88V83QxoqZsffC0enChsFhSjuS4Vzrkr7FF69atndWz0DmDZZXTrLvuutQLl1xyiUviWUaIJ4a77LLLwmUTDNkHi9iAZ511VpAf6tJVPILBGeck/6hf+d1332Wr8ZaTmiZNmgRDUIXYLm7+60Gftq2yERkYLx/1hIln1eoZQPfu3ZWBGTiMMvdiXlHGBIYOUVELbxNlRE7U+HYvGiNSvOX02WefESMeYdoFphBkxuSaN29OJMvB9ohtydTTDK1mj8n5t+xZdefOnW2iqBcYdejQIcq333675CHK9uy5555rOZTtIENEGTueffbZEc1YMh4vHugqE4muiCHUUBYzKYuEiBSvs+H7Ro0axbNqonJ38aY0UYkXT4iUqSK+HAFSVd44ynfeeacMNcoIlfJ5UWVzp8mIZsQKzMnlcgMR894JhHcrrrjiCiusIBtefvnll1566ZVWWmnXXXd16k9/+tMqq6wi+Lviiiss5ZZbbikEXHnllWOJAwVD/3//jzFIkbfYYguftgMl77HHHngajH7TTTcJ003WcmSw3EHSaUcJordFGr/Se2RlwXaDOH0qK4CIMr4dJNhv06YNSyr+SigUkzTIOVgzp9atWze7OjoJML6yK6RGfdoiAVvncXSS9pgg3FklzFQufpFBfzUceeSREYfa0vHmSzr/uUTBxpP0jtrFMXqOOZadqZr/S34uFCiQW+EHBdcnJ7/fjr9FDwqomk+MrQ7pMPOH6MpSckNB0pCeq5YICXl8TFYM/hFHcothohUFHCrL5Wk1PD4C+Ne//qU+iDlDHLrkqquuuv7663EGKrLv9GasLl262HFsRgM207RpU84O5QCHy5Pecsst1AtBbIWdk1AIWM2oBJJnLpL8pepjOJ/qo1yqXlmNORaXndJeb1k5s5+oj3IpkcotF4tUkXhZOROjuFwsUrllY2XilSpDlCsRzxBR1mGxqFUUL8qaKYdIUa+c9FRaY1m5uL6UeJCVi0VS1rmwRnjEf7IuQYBgC1myExGDvX/SSSc9/vjj4T3OOOMMAZlTEn1hRLyFyiwVnNWm+J0MPaP/evXqxZf1RX54mmVuvPHGiF/73r17kyfkNM2ActnKxQO/GU8XVqMMhLR9+/blFO644w6RIAu7//77uRWuhwcpF6xBlpZen6C48xgrQ9nKqBG98pKixSwOCL5hRvIJJBRISakC/GKDqiOGg/S4vJrKcVjyfyrpLW5zpbNNEFOG9LgCpI2S102tyHnnnZc9zi8rhhqnjkr++5YogersWztQOQgeHKqXG2W3PdOR5g8hZCmk56oliMdvxoO0QPBolC+//HIZz5SKf8hMvdhLHnbdddfF06IGDRpIH2+77TbJJacm7kS6GTcHlJm09jgeDDFixAhbTG/8mkwoRo/fJrILGjZsKLdD2BzlRRddxG9qOVeYh0t+ETH9BYKFId5vhWK1zLOK5kEhQj0eZv31189eUy+F6NNnICohPZ4T6nUig2/btq0kDSsz17ghIWnmYWRNO+20kxw6GgcKWyJBelyCtHZxwW/G06UQmvUZawbKlo1PGThw4GnJL4laNuuHtou/zWXZ4i4QRFdzi7jWiHKacHwZODiExz4SbiogKGdhIx1sPoZzbTyo7t+/P17M8Nhjj40ePTpu8aXzLw+hkwyU8/TTT3PctFGuYFGDqhFzIB5mR2NQJg8ip1XR9BNPPIGq08F+Zwh90sD5558fZhaPXaQXwOQuuOACa6RZesGcSBak0AOzjy+/CX1kG4MGDeLa7BRbxmeplDp4WsbsbMuWLSXfMm89iBheeOEFnvHss8/G9x07drTFXH7OOeeo8Sl92X///Z966qnx48fHF5QN/WSCkESMO2TIkMjGOG6BdQRhb731lnLUf/LJJ/fcc8+s5IV/udGAAQPkVcpffPFFv3794h64Gu3jhjO/rH3cJ9eD+gg3QZoVz57VGzeL0ceMGUPIKE+aNCm7ifr888/Ht4/gzTffzH75UsInWImyPE+9RFD5888/z0QiMDEIo/z1118rxz1wfgl/xLNn5fvuuy8Tb+jQodljcksc8RaNWVA7KOonTpwYXycDy2FLRvn111/PHtx8/PHHQq4oC8vUh3hCK+LF/iVqiMcqiHf33Xc7qyzwIlI8TXBWOZ5VqxfJxdP9sECKijJNZo/Pn3322Uy8cePGZU/3X3zxRcoPkcy3W7duV155JYvNSDoxz3lB0n0hp5eebbLJJrvttpsdITqkKLLpnAHIxzbYYAM8HffqIb3494HfmKdD42XBpPgFFnzWWWdFjoty2rVr9+GHHzrFUsX7Mg95QPHdkrTTuUR6cfIzCCwvS6kzcHPFqSQUqOnXRTpwlYeOlrw/i4fgAFA+6aSTeCVbvXj6Uc6ghtfg7ALxIPPmm2/O2DcZZA4k0v2MtLakPgpx95tKURQvE0MXIx1+sUbMlD579+4dBoZE4+6f+MYC8VbcU0XaiMsDOokHlow27FZ+IzKzUyKlDoaOQsbT9957b/fu3ePVJ85RYxGY3ST8jU5sOofydTExp7nEEkuocRVqD8FuTZCIUPharVg57ohiUO41+BhrSs2j/rXXXkP5QXLY95JLLomnrVjZ0BFnM8gLL7wwSA5/ixJQqbIeLr744njaanTSIp6ov+aaa/Bi1AtZsm/9YrgePXpEGZMJPkJssUXz5s1DJFYtIuFMlLGRIUIk9EakUA4TJXYYajxWp21lgYhYJwgMfZpyvAtmFEqzuZSBkrPI5qabburVq1fUC6qQXIh01113UXKUcTnNhEivvvrq1SUPgy0ZTcZTf5xNPG6QJSBLZbpS/uijj4inRlnEQ5PxpN9ZYV/wMVGvuuqq7DvTxItsVZkqxHnKwPyylw/YkgQ36sUKlI+nBSimL3uJp+YujwYF65wnuJZsVort8bQCGkowRPwaDG1fdtlllIykmXH2Xhuk1/8OUL142mqJ2hi9zI9r4FY4DrG/NWPN8eMDYBUZEFplNGFPaXfzAZ3oSrDJGTGLeFZXDP4uHlcH6ywSSCiygPQ4AUowOwqkZ+AFuM5UCwkSHc/m+DSDpk2bCpjee+89brGifLqKkFKjIuFCo0aNOM1IvIqRSrBYI2bK2DjE1LYS68LQCtiUr4/8LL2gYmijJZ52IRfm036hW5x6yimn6Coh6AJDq2/RogU/i/w41siKAGkZ7tRTT23cuHGTJk18utynYM66c+v169dfeeWVDzzwQNIKLPr06YPVXJ71oLegEzCpuS3byz6z+ihD1Mccoz4rZ0MXlxOJfhap3PoYIsrFbWIKMbTP4qGVkyYFlBLJVVkb5aRJWh9l/ReXi4dTjuGinNVn4jlbLGq54mVtMjGiPsoxdLTxqQzKUQ/Gzcql6jORitvwt2EArAL3i6uMot6I8wmdcEF16tSRr8cs1GQiKSDsHXbYIb7QwXGpLIW0o8UXvwFPp6otgTjxpZdeEokDeuYmjk/A6SCJSy+9tEuXLoLZbP0CDCg7TPudP0RXYdZSyWuvvbbst8UQDLKZZ5aqHMF/kB6XoGxN5YhOMpStAZVYgfsGc7T9Qv8goYkvnzz00EOI+cwzz2zfvv348eM7JT8HJtrNephbZBfGDXCsMHLkyBiUAUQSBul6LF7IpsbAMGvMmmJTwyqCFfGZpYZxebmIBhKp1q1bR/YcPYCNEwWV1lfcyZ6lQcWbyA6S5orSnJU922WdO3cu/DxEUrA6LsfuEvT99ttviy222HLLLbfaaqt1113XKZcXb8aKUJU2lSOb5jxDYnp/8o9n4hNYXcZDpTC3As+/eAsExWIsJJGo8eSTT1522WWXWWYZlPnII48ESZcLMswVXIKnd9xxx+wJRTG+/fbbAw44gPnttdded5f8lip8/PHH8ZQkQ9rd4ojfnqeFaRICSTM0aNAgXjTlOzgI7uOdd96J+1TpxQsZIZI4zqBya3lG+LsAosIxwToLHEFjkB6XoGzNL+IXL0nGSdscnvynLB45YBXihp545d133+3YsaOJq5eF/2K3VYR+gqpRCEbBMQzAFo1nlpAuxmKEmBfPIiJp1aoVJZt4IdVNyPVvf/vbvvvuSy0OqeXII4+U+E6aNOm95N8Rpl2UQfTJVhF/w4YNg+CLEUGY7Ec/33//vUxanwFiyFEkzRJlw0mPWiYQOlx//fVI3YrYjGeccYY92KFDB/WgoLG1c63dEaymczFHeM8ZM2Y888wz4cFNliFFSiT3evLJJ8mg7HPs2LGxqQVnTz31VIRornJtvNrmrPqvkp+f1IN+4hvAQPJ4MKzeuNOnTw8xyDN16lRzJFuGwYMHs67sS2WyseWWW27rrbfG1s66PMT77LPPDIEq5HPqH3vssRDp3//+NzHirg+xleObSCYrciVSiEfsTLwXX3wx7oXA5MmTs1dneLnsCZ1t9XLy86gQixLlDz/8kGZCpC+//HLcuHFRJpj60KqshhjZI3NapSti2DvqnVUmHpFcpQ3hlSPyVq/P7KvexAsDA6rgaaMsesvEe/PNNx0CQ8r+dam8NjjVEtNDtDTc0yX/tRaIMVdwOfpv06ZNPByxpnqOR+yWZqeddjrrrLOIEf1r79N877rrrokTJ1Kgy6OfxRULhaczVcZhBjUB1iNnFaNZCZZ69dVXo2fgnuQHtoTVuvHGG+MeXVwYPSxshHgBo8f7awLJpk2b4io8fWiCBcVYlcMo8zBQXAXpcXlIWyRIq0pw9NFHZy+83HHHHfYnIk+bzqUw5V4SNXQoNafPww47DDnRLVU/8cQT/EIWp6dLslggZnTvvffGb/vEnYl4CsDAuCFpysYbb0z58ZSaQrTMHhNmyLoK8M7xGkEYaing6VNOOYU/xWF82SWXXILOz0yAhg1qxE022USUgI+BnZ933nmycw0IYD926tRJeo25M+hw9dVXX2GFFawa4iGePPWiiy4K3uVShbZBZo8//rg+7XRtsBRD4tbV+2zUqFG8qIXShCzBELjEtfJ7ZWytPbHNlzcI21CvKxF87+RnnPV8/vnnk9YEgepMWWyx/vrr75KgXoI6deoQT6CA9QcMGLDZZpvhm+UTiI3kha5FD5qJn9ZYY41VVllF7BIExvsTI5gDiSpzWcrUbmgiEU+ZePHeH/EoOXugK9yJh77qeTarGfX33HOPEaN88803C480UB4+fDjNhP1bL2sRZetr4UxBmVNiGJFHWnrai3fHsK+IRI0yFrdM8aIf4ekk2JSo+sy+cd62bVsGqczHXnzxxdnXvrt3706rUb799tv5YTU777wzHl1vvfWWWGIJVE2H22+/Pcn33ntvoYnexDcsatSoURmVzhWoepttttluu+2YkMuJFLGjmIn8eraU2RN07X0aiGa0EYkqRH2GpGHaMvtcdLGweDqQHpdAjV0nHBYKNW7cmH0zAtbDsASwPpXDZKNlhJPpxb8iQgBbIvLpZs2asR5bInxfsHWQEAT9VCvMs2Cu4u+4M3zJr9kYXG3G02mjKqPcq9REMh35X+gTFGj4nHPOEdqHe0oXY7GA6fCnrN2UMRySpgqfyjR80EEHrbbaaltttZVD+he7oGrla665Jt7UzRBd2Sw2kTVCD/YRfkWrFFgKodV//etf2qBeA3FqHTt2xL4+sUjhR8jatpUlR82FF16IA7S3MVk+95eScxE0Q2M8dfzvMjsUX8YrTmDhmE1s4eKyTE45trPP4nr041NZjfpYem2iPuYb9Yki/5+xeIZoj9oZjHR5pZVW8snLb7vtthiU06ftDJEcg04efPBBbahabr3BBhu4EGrWrOkQD/ncbbfdhKf2e/gfYmRiZyIZWiwSIhWLDeopJMrGLS5zbgra69mhgkOVmXhinQhxAAllZZ0rR/tMk4YO7alXzkRV1ixEAgn0kCFDhBRoG5kNHTpU/KQMSD0Tr1jscMhZORCavPXWW4VBQdUrr7wyhW+00UbCHfTMkJZbbrm6detG7k6MuYIlk6MPHDiQzGYksBNC7b///laHcRr6888/J2HausRF01JwR6ngIM46ZfuE3iA9t2hivng6m3/xJ1AQvYeCoibAQNnH2WefLeq0naTRgutMjxUhvfhXRIwrpm7evLnsh/9iiKLyeLWNq+UBUfXCuwc+DygmxeJy1RFX4Ql+n5u2n22Mnj17HnHEEWbqVNquaqiofWGM5MVvA1Gjz9CkUSR2eOXcc88NckoXY7GA6XCX+JLxiHtCDz7NWsrSr18/heBmEQyeBoeMDZtKN4u3iU0k8UK6CBUyhnZtBorNCjqRg7Zv3x7FFifHpRJlMFaTJk1sTC2Rd9kGUUNsxLbppptKyqW/IobKt/AvbvB5BlqSKe6zzz6YA2F/lABFVTIik6bMaNmjR49atWrJsAVMgvKoHDt27J577rnlllvGq93VFmFUgYrKZir4WHXVVbGpaW6++ebZ59prr80PY/G4sVFFIOzbbruN0qy+bYvp77nnHsy9yiqrrLHGGuuss44YKF6PLxh9laE9viDqTTfdJGJA1VZEnzokavz8XCC9oGiOxUjPJWdxkFxcYDqt5Pd80nNz6mpRwQLgacY9adKkl4tw//33t27d+oUXXqCjtGmCd999l7/o3r27VbGdhMalNFhNEPMSrr799tvXXXcdtpbZiOlsZknG8cnLZeEZqw9VB/+lB/OE6AFPmB0ggw8//JBrNtNIqdN2Cw6hvRg3DqkUA72f/HLy4gTmhG4ZP+IE+uTmcDPddu7cWZqCbiOTdgqCqjVQL2bCoJItXlKe8dBDD8l6sbhmBX4u89NjxXDKUmpvPwbXVoLzzjsPqXPfvFuGdu3apacT4O8DDjjAYvXq1cvQcZcYW9ss5miPyET5WWUkkT1tleo9VfSgV/u4T84DPP3005FQqhHEi2bkfAxPey5CvR70Wfb5tBHVS7lk9qiC8MUPgF9JviUM6Db7Jk+xSLyWyw2kEt8oh0hEvfnmm03wgQceIF4mNuUrR45r6AkTJmTPp4vFq+T59GslP6BNPMmimQYqej4tBIkyb5k9nyYAjUW+G2ViEyMTT5l48Xxah3hu+eWXb9SokYXLYE3tuK222oreTJMT5qvjYTCQkw+PMu1RTpQZMOUbmsbIPGzYMENTDhex5JJL1qlTh0nstNNO88bT5sv+yWP1TUo/QgEhrLFMbR46JLZdIxW88847Q3XpuRL3niGtrd6YX562xhSBukBI26YErVq1atu27YABA9KmCTT+seirFBnS09UMIRuBGWifPn3ihoxUz+xOSP6xID+VZdUZ2fxWKCtA2ZrKEe15fxuGixc4289x/wBhpI3KIK6C4sMoV45oCXQokwZBNJUGT1dbq5g3mM6jjz5qg9Ak9cpZRX5gmzRt2jSUTOeRVfsMnrYWETZdeeWVX3zxBRcW37aiK2e1Vw4y9lmMqPEZBXvTQCnTVgzx6MnJf7SU9wcQPJnFpvydTyB5/OSyjFNeteaaay611FK77LJLvESNgM8s+n1vlB8k92ry+95BZj5NOdK49957T1LuE3nfd999XDPSxSsuRDD8rMq7776bBsaMGWP3YUTJrulgF3R48cUXE6BGjRo0Rtobb7xRn8DtZA+Di3/fm5CmEyIpF/++txExtLJuiYTnGjRooJPI55ATXjeFIC3SXnLJJaIKK6tsysrqSWilpJjK0KlTp3J/31t5rbXW2iaBvFaocW8C9nDuueeGSDg++y413iJ2RC2Ik3ixQZRPO+00vEgMlEw8iYR60jZp0sRVtLTeeutJoC+//PJb5gRddevWrV69euIb+i/+fW9Ro4BSgdj8XvZF+X5Fv+9tvkQVFvCHxsLTjMFyUJdBaTix+qpCh8ZiGOj5wgsvlJTXrl174MCBxUyRNq0atBfiEBWyTtJzyVmhoZhD8BQaLj5bPbEA8mnu44oEDz74oBViZxmoKW26yMIE2ZCJxGQFd3a4LRE8jVq4XZ/IJuWfBCkF/XbMPW9DZ1dhCxMMcPRYoaIOC5NMUHwY5V+ElvRmCJsTPdCn8uLK0/zF8OHDbRNsJz2KDWK/BHkkmk6f1mOdgFUInVx00UU2V6iIvUE0LibjiuCspJzTlxx37Ngx5eQEXbt2TUsJ8AcGPeuss6677jpl4HkbNmwoG4vPKKjkx0FmJmDdZJNNsE7dunWfeOIJM8Jb9kvMNytzmjZOlH1GPXD0mOyhhx7q3bs3OuGdUb6u/vSnP6GQ3Xbbbdddd8XEl1566aBBgzTbY489UMLSSy+94oorNmvWrH///hrg6Q033BDxZD7XbiVJlFUGMUOxSNoU1ytHPVG18SkL32uvvcQN6667rsTO6IIGqyAVjmelroqV1V456alwX11uWnjX/OGHhQvIRgH3kycTj97EOuJgEAkJeqS2CPXAAw+MEEf/4pLsuXUpTUbZ0GW1qibKrhUniQNolfZuuukmUUJK0SVQQ8nLLbcc5RM7c3HkLNZeJnYpjYUY2N0CoWfh2qqrrmoiWFYSnFh9VaFDo1CsfrbeemurT9uGS08vaBhu+vTpDLh79+4R/UB6rrpiAfA0hVozKKXZOJseLMqIiQSYoPA88ukM4V5lOcE98RlI+OjXRoxbFQHKbaASMQs+fKIEn+mJEiQdl99z8SkcXDlQ0QUXXCDVmzRpErcbyizm6fhcDJDYzs87JVw8fPrppzInTjPmLv5DqBxrIONvROszeDeSaZCHHXTQQRLuinjaJZJCKbhuJV4oQee0HZDtBT1nz6Hlr1JDKbWkMGqCrbUshpo460KH7dq1M4q8sE6dOmgsefm6AHQVFFIRcJJ0bZ111pFAY4tVVlllyJAh/GZAdgvSRBPE3Mkb3LvIt7YvgUo0o2a77bYjRkYhCwrWCL/ijB133NHQxgJzNFPhzmOPPRbvgWcwWdRu4uxfmy0TxC0HwU3oBFzosH79+ncm6NmzZ48EJ5544s477zxs2DBt9I9ihSYjRoxw6PP55wv/ldkomS0BMhbkJb0WoJlQSTJN8qeeeorYK620kggPSQcrJ+z8M9QccsghIh5t6DntdC4hVGKHcm7LZ6Xuv/9+S/PRRx/F2TDyyqGZsECyR1dI2iysfjbZhQE9C2gYjHEzE03PVVfMF0//3hALPG3aNI7s+Dl/AgV4xoyqM6769WHoqj81L1fOUpWFyZSpyT7LBQ6mCq7n7LPPxjHCGpSjIKAB9ehZWoYtKJO/njJlyvklP3aNVJ555pk33njjrbfeQmmp6hdT8Noy7Msuu4w2QN4m0ufuA3379o0QUEATWhU2OeQQ5ZHoDQcg7HJ5OvQsd8+eQzVu3DgIWwSmN2whez43+Wcb0KJFC4vibPPmzTPmriLwimhgo4022nTTTaVEIDvEcGjjpZdeQhuWWCGyNJ8vv/wyR/ziiy+uv/76yy67rKvkYXhFYhc3n2VX2uMhbvTLL7+UGrIHWSY8++yzLldAk08n/4t6xowZ0kqcmj1hxRMOo0yf2RNWLV0bSafL43tlyoYIkZR1ZWiGp8yVm4LQ3HD6RIoCStyGiiTZwponizB69Oj99tvPxOmBqWNHkLGJpTbeeGN5rbwZxCWrrbaaBQ2ezmCnuDCaUciGG24Y7SF+11oQk45UAuoSEGgczeJaCS6qo8w//vGPFoIBlGXoDEIulwiSRMnRp/343nvvvfnmm6EZ2nOoAB988MFrr70W9RQivqSxfv36HXzwweItQQw9d+vWTYcEiCw8tfKKoY2Ww4cPR9K1a9cWbVTxwt8bcp6eO7ChoOqrr76adyuVWPOP+AlNVh2cb6lCJahKGwLw45kYCW+WRka0lXBtdjYrFKPcSjAi5gie4PcfeughAQ1KkChIEM9Kfq39wgsvlDF//fXXHLFtaXtHMo1vQIN//etf/H6jot8WTbW/2AFp0QNt8HqoAnPcd9995i5DpQTWFQxdDCuLrffee+9VV10Vzx2e/ExNKVAj/buczgtPlRNgYtESraINzl3uwjPWrFkT36+wwgqYQDKNe7J0uSrIGB1Vty6CoddYY40//OEPe+yxhwmaHakQnqWMB71ffPHF+PHjtUEnY8aMcQoHHHfccUI0bRCq9qI3ZZQpxceRykiCqYhmlBmGIIO6lOG6667rUPJPne+55x6HUcZSwqAo4wO6jXCBQQoNo0wA2o5H5gICphiU/8knnxDj0+QHQ2ScVEoksjnLqmkv5iiVRKto0nLoB1fJL1P6vfNOubIawNk+zfGAAw7AkenpEphUNAtE4yiYZvbCtrVbffXVN9lkE2WLKO22ZJrdcMMNQrGTTjrJsqoniYW2KDfeeGNQcrlwVji11lprkV//CmecccYll1zCuZkyED570n/HHXdQfvDo448/TpOYm57x9F133bX77rs//PDD4gZRxZZbbikApdvUyiuGrgRMQp8ddtjBJdE5pKdzlCDn6bkGM0LVX331lWCzZcuWAuFitraZS8FWT0vlwdkMaVUZVHIKis9GmRhR4K8riRs4/XK5dt6gw2DoDHwZD0sAXIKbBeYTJkxQsLf5bmqUafHO8kjcHHIKRMjs2lAmrxHOOlX9Yor4npVcFuIhNI2FSkO3ZZeJazvwwAPjzbtY6NB5MSjQEiCGgGbc8YorrsjXc76PPPIIkuPoa9SoEf+of88994zV4d/bt2+PAIqBBYOSqwKNCRA/XGWn8L+YOBJZn1yzT8aA53j2eDdbDUaMsEwZR7qKcnCz+riz4pRMLh7iqleON59BIm6gKIv/Ii8Hlhbf6AW5sqEjI1R2eZQNaogQT45o6MitQwxWquwzRIp6QYbMlT6RtFBDXNWmTRsBihw6Jd4ywHM+MZ/QIWqqCBSevUkg8Npuu+2yBxMYWlatjW6FWdYOxGQauAQTV5JMg7PWOq6C+vXry/XZSawI/TyVYOLEicIUas80ZjlocvTo0TRw4okn4mlxnrkfdNBB999//7bbbisWEX0m1l0ZQplCN5QfMVN6IsecyHl6rsGYAkxWDI6tJdbBK8DK01L1QLjssuDZ4/4nzA9bF9Lq5F/D6jPmLttDt2oMkfGHaIa7DH8tgQ63KDVp3rw5LnFhxtMuJ5hLdCUAkutUJTBfpCGTwNDxKgBVRA4dis0Q2ob0OKkJno5VzlQdagenHOoN64PG66233jLLLCOPlL7IezhZfhlzr7TSSjvvvDMBKFxOJnI666yzxFjKEMk9I68iVbdr104Ke9RRR8nUs3ejymLSpEkSLzw9ePBgJhG3misBRaWl3xqCA4pdc801MbS0FT1LeRs2bEhRAveePXsGvy4M6BwBBzcXA+kSoG3btni3cePGwj5SWQvcGZRcFXTu3Hn//fevWbOm5X711Vc7deq0xRZbSM3NVCadTr4ElNCvXz+rjGXvueeeFVZYoW7duvJs9e+88860adPEOmHelSDtqwjpiRxzIufp+QLD4l+uuOIK+UqBFRPIDqsDUmkqRvh07nt+cuvgjIwwQIHH12fwriFUCvbl0yKbN998Ew1I1+xkCQqHztMBL4OrMtaJgllcdNFFU6dOTdW9mIJre/jhhymBez3llFMima4IsUw+IxIKXSk4jJpQnU+ndIX7QcGiaMDtrrvuunzxvvvue2QCRK7e2bj/4TPYOsq6IliTJk2kTdgIDXDflk8hWDnAxasEmberVl11VdwvGnjttdcEZCIt2VUkqRJTnj0qhw4dSpjNkze9aeCtOcHdy3TZjGSLiijK5SK8yJXVs5+4MQNSPYiyxDori6RdEmV5trwwMkJll0dZbm0IIikLJZUjbyYqsSOfJjyLDcFefvnlvfbaa9lll7VeMlpZKe6kAfphw61atVqoVF0u5NNBz3Dqqadec801Ii0FUiHgyrPqYlhWoZXgqVatWjj76quv3m233eLtelqlydCY5ZA3M7b4zjrCXmqppShk9913nzJlCk2GYedYUMh5er7ARhmu2NP24NECQZPVHyFtsDVPXUwDVUFGGJEEBz1kQADRIMiD7xbsS6bHjRtnzw8bNoxDf/vtt/lNbjEQb5OdnoC/C8IYNWpU3PNcjBGOL1QxcOBAjBhUTYGhw2JEJZ3HwmmJaA8++OB1EoiNQv/Oqs8Y2mHUR6XPqKThIGMFxCyRijQ6KzsLOABbK6Aiy+fUhRdeiJVl2PHZokUL2SRi0KccncsWCvD4KNDsEJvh4vm0aYpFgkdfeeUVnh09SO5xg/x+66233i7Btttuq/z8889bffI89thj2oO47bbbblNQf/bZZ2ff+mVdJIny3XffzbqiLKG89NJLbVJgdYSMW+tChPPOOy/utY4ZM8ZM4/b4G2+8YQrZ82liB80jaYyFvZL3uLeUYpqgnoMC4zPYmmZQdcqfCx9xO12GbaYCpngJv2nTprGOygX6rTJMhBoL37K94goq1WF8Yd2qGYLyxSvW9L777rPf4yVBysHTSy+9dI0aNay7COazzz5LLTux7YCyxmHkEQZFgxxVQc7TCwD2My8WfhBSGlxEEDLzTcVsXRUEYYAyesAH0VXgqKOOinqIbBsTyKopCg/Jnn1KpjnB0KF9axvTpGTLZpZLtWnThl+QkP2utjTCfuCBB4QpGVUXdD0nVMaNCqsmHvKJTtQEeVO+ZY18GkrFT05xu1rGL2Dss88+rrUWQczFUBPQ/rjjjvPpckyMh9CAdYxmasRVetAPApaBLb/88pw7braI2NHy4VQxWeTTPt97773w77JhabTQBEOTR1Z91113uXDQoEHdu3ffbLPNRo8erQesmd08f/bZZ3H2Cwm0jJe/QBDAeKKsMmICwBnMKcqG01WIFO+XRFnn6kMkLE7U4G+iKqt3OHz48E022UQ4Hi/lgZlKplN+SxCJtejhyiuvDBL9FRAMfeONN2LoJgkIgGKDrcUNpIJUxLmBHlia+MlMKUpohWJ9ipOsuBgr8maKwtMrrbTSxsm/dalfv34pnmbSEydOtF6DBw+uU6fOAQccEP/OJG2RowrIeXoBIOPphPgWPSQOvABuHVUXuLckVw4Ul4N6SwETByWkPSa/ghmVgXSAkuf3nD4n0rJly2KeLga/MGvWLI41vGe0+T3AZLECqkbAFEvhofwCP5cgDq0UZQZCt5BqPyHjs846K8gVitvoWTrIsXK4DlG1rhRQclBvIEgaBFjJdcei4Z49e1oU6Nu3rxqnsMIFF1xgRP2smEDPLo+FqwTcN4aOm94777zzyJEjGYNc7cUXX6xXr57MdY011oj/QwXsQcT23HPPGXHTBBrI/CTcGT0vWBBv/PjxRhw7dqzl2Gijjf7whz9IoFMSS1CWAi+55BJZbPPmzVE4iDZuv/32lFEXHHBz/KAYUIKoTgAh9oWOHTuqxNwE0ybEKyVkJdDyuuuuMwsQzLETmzSCJKEPbSNvJC2YjhAnLJZ+dt99d2fVz5gxI54gAEu2mmwmHrVsvfXWVlakFRFbGHyOqiDn6QWARZ2nA+TnzZFrkEEgY4goYI4jjjgi5d45UXDkiQaiAMENPp3NeELixbNfc801kio5TUWPsmKfZ0hrfwcw2WBBSpNSl8vTAW60mH0h1hGU5VUTJkxo0aIFqo6aOCUzFkLxm1hHLjtt2jSnVl99dYmOAlYOkg4U8zSSRg/YK1Zk6NCh6KFx48bSayn1OeecQ2D0udZaa+24446PP/74Sy+99Oabb4bLxr6vv/563O30+dZbb/HUcizpl3QNQ8e9UNTuKrn4qquu2qZNm7333pvr14N+pHHCi/XXX98UMAfIWTVA861atUIhesjIA1VkObS9KW+O8syZMw0dIinHCxPKFB4iKcuniepTvih6+OMf/2hS5FTYYIMNrrjiipTQKgBqvOiii2gGZfIJwNRVIk5RUUqz8werIGun8CBmDG0VOnTocEOCjJKrzs2lwANYRMHTFltsYbfGb5FSo+CD/pE03dJb2Gp8ol5mhuDffvvt9xO8++671nf48OEWiHVtuOGGGFrQY43ym97zgJyn5wsMDhYPngZT4G1L3f1OEuYCgqFLcUNZyCfOTBDP7F1y2GGH6RxzqOR/+c0vv/wyC7pTVZaHaADp8SKOmAuG4K1QSwY8oR6ZUQsPOGTIEOxI25RfzNNZqAQKePrvyTt6XCRPDUHJsY4OX375ZVR92WWX0XxwrW5xKsbVFaoePHiwVUDV+kGNPKlmli9D3O6OgtRwVskvWQJpBw0adG4Cp2Ii3LTUaquttuLiEZsMO/4vBZctONDAheZOgC+++IJ48b73sGGF/+jgFM2wFgGENG6XXXZ57LHH8GU8n9bmwAMPZJnZ/wXp0qVL69atEecBBxwQbyOaJnJ99dVXUSNSScT8+fm0sk4EHxU9n45b6GiJhaMZp9APLTFjHRqrbdu2GDdls4ohnSUeKgWFZs2a6YHar7rqqniWPJ8wxFlnnUWk7MvrQc/zTMyB7PKDDz6YPVg1QL36/+yzz0RjFoUxiIdCmZlJW6BHH31U8E0MKrUQogcGs/HGG7MEwU2k2nZ9REIZooccVUHO0/MF1sZqxfvBSeElF2mYhYA6S+BQLNcZKGbotHURVPKndin3ETwkfLZ7Y99ecskltCSgkRjFPv8dblRT/uqrr/h6jAJBrtCrV69PP/10xIgRQXh9+vSRO6LhWALIliO42VpEA58MD0tNnTqVK+RMg6qtBUdJ83I7dChxlIA6lB41aNDAZ+TT2X+JQEu88zbbbGOJI5MOWNCsw/h1kZhFQG5dIOePP44XsAOROrMBORmqXmeddbjpsWPHSpoJ+c4772jAZUud8SWy1Iw3J6RrVU6ZMgVDS5GRNF2pJJ5IAoniaeIFM2UItha44HsQH0SIQOzIwoGEiWiF59DxTBq1OEtOrKwwadKk0aNHv/baa8po/oknnnAKqWgsfNloo42uvfbaYiarCKUaxCHaJqSFuPzyy+f/HjimR/8Wnf0oxEAwnyQd6NSpkzA6XlmgChg3bhy7IrxlYjahPZrM7Fk0Q11M64ILLujbt6+ZRkRoRWTS1teiZ8/+swtzzC1ynp4vsDx7nhPkFstlr0UIIb9P3IwSkrStkEOXS8/FZXBoG2OgeNMk9qQ9HK93Al+ZJdAZUiX+bmDK/fv3l7pR6VFHHXV4Cbg2GdJpyU9wx9dpkDFQvpjJp4WQSqqh59NPP52b9qkZ149pKNmnbE++eOaZZ2ZLo6DPyZMnS15FTtgapVkmQ8fXpnv37o0C4a233rLiq6yyCgfdeU7Em8NG5IVj1aoCzI38kEf8Ytdmm22GjGXPO+64o+SVJAYlEtlq1aqFX1944YW4EDcfcsghuFbQMHz4cM1wA0b/z3/+k/F0x+TfYwdIqCYSaIgHq+YiPth0002lsNFtFhrarfp86KGH6MeFBtpuu+2233772rVra7/ttts6NDQ5n376aRRF+PXXX59u0ZjpzC0dRnufck2JtfW6tQKUS+ECuPR0Cch8zjnnSHMx4k1z893oXwRN7rfffquvvvoKK6xg1ejEesmGqUikwtjeT351H8KSQSgjAhOFCxrMkcA8gLiTtWivMnv6EIhdkGMekPP0fIHx2fkSmnCLiXtcJBHC+wTpWvAEKMepyqHN1VdfjY9jQ1aCVHG/S5h+vLqVkbSQKBCHAcTsU5tYCJ/oU8GFfLSED8+BNAXViX58IgBsKp/mNE855ZRYEZ/Cx7PPPhuByVPjDWcJE6rmf5dddtn4Qel4J4tr3n///RFAPEzluCX3cSdTQX4ZX1uKWSDOwrtkJdAzZI+uow2oHDhwIMnl/SQUgqBPYyFCvLj33ns/99xznHudOnVGjRoVjy0lXvJaedh6662HIFEF7hw0aNCsWbMOOOAAekDS8TvkQQbZL3MVQzgiBdxpp53MnVTE0LN9SlH33Xcf4jF98yWMKT/11FMouV27dvXq1RMZIJ6ePXsKLCgHNDv//PNpIMhs3hBsfemll1oaqWfcRCkFZ9kGtgaFKJMK27kkg1kDko6e5xkhkn7al2DffffF0PHdKsYjk27RooXQzaDKLCdZ0gISWy4s8dtvv808NGZ75557Lm1fccUVli/sE6/Hje5on2N+kPP0fIEVcpQTJ06U34RnXHRB/oqQtpgTWX20oQH+tLCPSzx18WeGgtZ+rzB9tHT88cej3gz4OOXqMkDYAbSNov71r3/x5hgrugowP8k0eqb/+H8VskC+NVsXwNbSYmwd73ZNnToVIeG/P//5z6houeWW22233eTrhOFnDSEN7datG6pDCZFMW1mEB0YHh7LDSF4DOkfwX375pbMYkYOOxjNmzJDlIxvpGp6++OKLI+vVLQpEw7vssguSMDoyyO64yMMwJdrYa6+9DjrooJ133hm/IpI999wTN7uWnF27dtWhrjp16oS8I7EuhuzwzDPPRHUohBg0LzjA/UssscTGG2/slEDBzjUoqhZMjB49+pNPPiHGwQcfLJpZd911l19+ee3FLsFq8wkCm2l6H6AMBBwpYxdB4FV8SYFO27c336pk0pnMCuIMcFXxRMgjTBGsBFZbbbVlllkGSbPPt956yyrQBs1MmzYtFiWz4awgZGSTDAyji+RGjhzZqlUr6540L90+x/wg5+n5BUNcPN4jS1x6irSqYqTtEg44KflZjGuvvVa+kuzQfGeWD5rBQC1btsTN2Bf1UmDk1ikzl4Fm//hH4avnGA6pyGDwStpd0uEHH3yAtPhWwK+WIF6A4uWtCxSvF7a+7LLLJk+ejFARlUwRPxndIoIG1tElnC/Wl3YjYyTn8njDALC4zBjvohBcyNcHDO2SOCsfHT9+PN+trAbf6xDZ69mna0ENAt59991r1qy54447NmjQQA+CDBwvpSaemr/+9a/omcyIE53jde1NkxjSzV69epFfhhoxioFkhxAkDQQ49NBDpc5rrrnmdtttt+GGG+KhpRKsuOKKCPiQQw6J37J95513hDhPPPGE/rfeeutVV11Vik+B+hdVzGcyXS7wZYaoERgJCEQVxUDJWYNA8SW/CC0FT1aHDmlJh1F5ww03UBTlmGn8FxY6sSJom7FZhWQTz4HU4IqgMuPpoGeQRkewlTbKsYCQ8/R8ISxysXnfu4oouPyEobnya665Rpb28ssvR9yd6SRHWdDMv//9b8kf9sXBGBpVV07S9IztaDg4DAdnL81GhzgGo8SKYEGOUqVR+NBJkyYh7HPOKfxH0WgAeE6HU6ZMQdhI6LTTThMEBEmXhbOIMIkBUkhhZa7A3ctQMyBOrj9O4YPWrVsrXH311eghkl1nRXI6dIrNGFRMgFyFC2jbKZUCDp8yM1T9wgsvCBEuuOCCEMC4IoPoFtkY8Y477sCgBWbu0kWz6C2l6BKYqY25xx57LL300n/84x+l4xJ0nI2WllxyybXWWuvss88eOHAget5111233Xbbtdde23A0hqGRWUJ2CwwFgk2QHlcB6QVzc0kGV5kCHVIvbdMhNkXb1CLu2WyzzYQvTg0YMKB///7CoE022WS99dZjEolxFRAGFoWyUM/GhFOMx7LefvvtmWVC2ijHAkLO0wsAvyueDg+uwLdKm/BEdrsSUo3kKA/0M2TIEGx6ZIJfvOktuUFdU6dOlfPJpIPM7rrrruyxn8/vvvtu5MiRp556qkXJeDqDCydMmBA5sQZcqsvxGZKWg3KyPLg0V4EfjwYFNi55x1sSH3dcM0j7gpgrAc7WDI8qSOayLyMhD3KKKuK2vEGTpLGdbK9NmzYokyRoUlBC8pkzZ2JfkiB7TK8rPZT9XpOaPn36oJ/zzjsPkQdtB0lnMFnx0A477ICqcbMMfokllsDWLqfebbbZRs5tXIKRpJie540gf1uQ+aYSXHTRReKkBx54wLwo3Iqbo2BltdVWc9i3b1+mJQASplCLzPj+++8XwFF+mGvl0AxPU3vcTWnZsqUgsorX5phb5Dy9APDZZ5+x13BtoLBYImYH+IPLzt7/zJCqYxGHicjnvk7+PXb2nnAgbTGv0AO+iRw6u+md0nIZxLtXL7/88osvvoieuUJZC8/LOWbfjUmEKvwzf+7YAuHj4vd9AuaiE5461g5pPfHEE2qyX0GBIGxnsZQU3CkE5hB7XXfddcGIgWKmRJBpqQyDZofF9b17927fvr3ENxGkEBOYEc42tIFg6NCheCJ+/eqrr74i6oknnsjSXJh2UQZZ//iJumTnl1xyiUs6deoUJG2OEZHgpxVWWGG55ZZbNgFmkmdvtdVW8kh6w/RBcvGZIeG+RQYENpHzzz/fjEQtjz766CuvvCJ0a9asGfMQJMmeVVLFhhtu6PO+++7785//vMoqq1gInB0GE7ZaEaINxD0PPffr108ghafjTcC0XY4FipynFwDkNxwfqo77hwmpLW4I3xrgPe++++6Mw1ItLC4wI66N35GZYQuEjTkWyFM3PeA2RiJXjpveKSeXBzyNYNAMbibMW2+9RaoPP/wwu3sRHcIHH3zANTO/iRMnhq/UJnsNW+Gjjz7SiYXDhSNGjPg++Q+S2kybNg0Xqs+W2IgYfdiwYW3bti2sdPIafxDhAgEiIYOxpM5wxhln4FSfylgkfss9QDxTNikhiOghews60KtXr7THIqjv3r173Gxv3ry5nuHiiy+mcIOec8452NqMDjnkkL/97W/KKmMtpPKSwkWOlcvCFLp16yZpHjJkyOTJk7/99lurL9KiYfFQ/NPJf//730xrySWXFJ/xWk8++eTgwYNZI5VSexhqJdCGu9Ot8PGCCy4QDVAdszFKdpsnxwJHztMLAGG7xVQN4fsWG8SkQPZz6623zpgxYzHek5js7bffRmMff/xx3G3+JPkR6fT0vEIP8l2ZMUfJM/7jH/9QSGl5TqhnSO3atXvjjTfQFSYuldkXI96EkjnNnDmTI9aS2OiKDwUMzZ8iwiBpDVwSwpimqVlTksTigvWVlQalgcS63HS2VAJdETQrfNPo9tt79uzpE4/+K/nXHQo9evTArAI+n5gYWYoziCdRS6ZV+FkSyTEJMRDeLbx+lgANCyOi27KyMU7McWnyJSgQdoB8Md6JowfGLKd3qE/K0SHGwuLxvtiiztZmgac5IhNE1QI7oVjQ9meffUa3eJppLbPMMmuuuWbt2rXj6+N77bWX3DqIFsJcS0G9BrNmzZowYcKVV15JY+yEtjt16sRIGHZ2myfHAkfO0wsAiW0XjJijOeuss8LBBb0tNohJAUdmw8eU0/kvXoipBX744Yfx48fzdPGLDWmLeYUeBg4ciKXQME2ih7jvfXiCYGhwlsLlguiZY/3uu+8Qqs9KoCXqQk4ybx7z/fffj8e92cJh3+HDh3OyybR+5mlRiGZZcBlBmKGjTEK02ir5h8rBiD4lXtkhYGKHFeH6669HhJI5wB8o1ljSL1f179//3hL06dOH08eaCEC8G/MVC8qGUaxTQhaGd1PyzSLBhx5U6lm9UYrZWlluh6ETsy0k7lg5JnLiiSf6dKEanK2soNJkZZyLQUpNfujYsSMNULX4TKAj5ELbCi1atMDW2HTSpEl//etfl1122a233trExSvMQGAn4a48Jx43bpy9QFGWDE8b8Z577mnfvj3bq5zjc8wncp5eYGCjX3zxRTwKsvPDTSw2KHjxBOLu39WGRNUyWlnI/E9ZD0gOMaBAuSB/R7GRW2OvDEiFq5XNG/qll16SrAT42WKktQm4WpTDNaMlPvfzzz+fOnXq008/jZOOOeYYHQoOJNzFU1D+73//++677xpCzmpciyvH/fTTTzFfPNrAguo5ehR71VVXEZ5TFomCSxwCUsd5wgJtAHcaNAMaCOMhGNo4//zz9Qwo+a677uLumRP069cPwSB1PeBmY4E5olu6atOmTd++fTVLOP1e4skapeCoAmEbEU9gaNxPHtM3WeyOnCAE0I9AATGHDBRChkTZKRyaRfFLZDcmIFV6vEiB2NaFKsRtlo8OTZASnn/+eeuOqmUUluCII46gny5dutCe6Meyjhw5UoNyIWQcMWKEflwidDOKSEvk5JBVOJsaVo6FgJynFxiYsqBS+sV9cEwFclu8UGDpY4/lUosfkS72iJkG0qp5hR4kuy+88MKLL76IDmW9XOQ//vGP1q1bP/fcc5IVYD/wzjvvcKmcLO8ZLFIJIvFFV5hGGWXGD26j+VGjRu26664YFxnH+1mpKEXz4rXffPNNnte1gwYNirvoRv/kk0+wOBesc0B++hcKiCEQKqcfxIzLx44dyyq0CSJEAMVwFZJWkCsDGm7atKkL0aqrgqqDgPl9BIMsi9GtW7eM0aNZQGNRhVjHdsPoekPnmIacZKM3GSQY13AOjRv5JfKmUs3ivfeAU4Qkqg5xj09lmhQQLHJUHQL7pDqzEMo8/PDDrI4NxP8aCTBFi051t956a/C03U3JcdYeZwnAp0Xh2WeflabHouif/i0fA9bDG2+8wYpSw8qxEJDz9ALGV199xRew+CC2YLjFAAWKTqYjHRld8l+B0znnmHvQHteGQWkVu8gIA0OGDHnqqacUEEwwNP7AfxVBGq0Bio30sZinQVK+ww478LDK6cDlIaga3fbp0yfECEyYMAFViyGKoea9994TFsTvWG211VaXX375k08+KYUNMr66BJEWN2vWTErHmytH5RVXXMGK0KedYkSkix6KEUwM6fGcDA1ZDbZo3rw50sXW6BkwU/AxGi4XtERv5JFuZjCFCEcEEJZDJ3pwSMh4br1oAUmLNlAvkmZREaIV24AyJjY1cVjv3r1FOcU87ZSQMTSTmkLbtrQq1sHNdA6CJDWNGjWSuEfgnnadYyEg5+kFDOFn/LhEsNpiA9NBG/IMHlm+Vdj0+c6cD6DGJ554AmdQbDGQBI9J4cUMjYyjUBZOWRf0jFFAAcdkPP3tt99KkeN9gnTg8hCNeVsuO5Y7MHz48DhVCjKwzTbbbIkEdevWHTZs2DPPPIOMzzzzzFI8je0wnxmpz3ha/orUcSoXf8899wTvFiM4GNLjSnlaHBA5NGAOTFwJSQOday/5lh2KGIiHbMhjvvRP5yhcotmuXTvK1Gekj4sQgqQpwaRk0mVJGhwyP8Zm+oX47rzzzNT0KdYpJI2ShWIWiFGp/8c//iH+a9myJQ1bTXAojqG0nKd/BeQ8vYDBXm0M4Xls++LPKCxyyCTnsOJHx/I9WRahFkiPy0M0wNAQpMj9hZIh9BzgAVMerhRIBUlHFsjV8svIUv4UvzhRjFSCCpA2mj07fqX58RLEV+TTRiVQI1Y78MADkXS9evUeeeQRc5GLCyz4eswXZCzNbdiwIUYkksog6UCkqlK9O++8M2HheUGQtIQvEmgJuk89Q0rIc0K9BgYlT8bTKikwdE6ftu0111xz4403duvWDT+ZEWkdpgS46ABVm6DJXn/99dm/9YR0CZNFfO2110QkDE/UZa2tUadOnYTgzz33nNCHQmjpouQ33kM/rAtz0+Sjjz6qPTsJU5k0aRJ7Lu48xwJHztMLGOw1nuUMTcBpcqDZ917CIy9CCLGBQ8xIGtLZ5ihBqpdKNeMsSuPm4gmipIRiU0UXQWXVeVrj4pvewO1+9913IQz8og+NZhnKrSlGVKJqJhEkjd2xAsLLeBpJx/PyINEsmQ5gxwXF04iEHnBqRsZRKAunCENdlIaHCJ/xNLGl+Bg63icH9EY8NKYmmG8RQliXT6rGtV27ds2oOl3CZBGffvppNsPeFBy+/PLLffv2daG1o6VyQY0TJkzIsudCj0WInnMsDOQ8vRDBdnnJiRMnjhgx4uyzz2bokf2EL84+qy1C4Hbt2o0aNerdd9+dh92ovV2NNr7//vvFNeg2KZHZgw8+KAl7++23K5mjU/H73pXHbeoZSSX3ujNog5iDaXDVmWee2ahRo2OOOSZ+7yzgLBdsCTL3GrAchPnyyy95c81cNWDAAM0q/2ZOKWipfZ8+fTbddNM6deogQsxHBvJgCJwdDB1IOLoA9bYD4TVG1ckN7AJSBi4PaYs574T369cPT8vzIpMOPq4IEm4ak0wrB0lnPE1gaxf0FgzXpUsX8lPpovgeWYDYhKf/xo0bM4BSu8/hHXfcceKJJ9L/p59+Kt568cUXRXgs06xFkOyQaVFU+wQ8AMRv4aVd5PgVkfP0QoSNEfj222/DI9j/d911l50QbjoQ3jlQ6vA3QUhFWmxhq8dXhzOkc6satH/llVcOOeSQo446ijuY28sXCZgUCpw6dWrLli1feOGFgo7KTDMqNXv44YfjRmKq6zII5QMvGWRcObAdb+tC5XPOOYdTRplrr702N33//ff36NFjvfXW23HHHffdd9/4Tk4mzFdffYWK9t57b6lw4b2g/v0Jttdee1lxQZXII170DXYPKKsMZPUPPPDAlltuudRSSxlIqioNJUbDhg0ZD7Ymj6y0VD6tjKovvvhifOCzd+/eRi/m4GIk7Pwz0toinhad2FyBoORipCcuuURkEKFDRtLQqlUrOSWxsbiJB8NJQDVm/+rxd1QucmAA1CsEYR5mHa9kx5KFDbz//vvMdebMmZbyhhtu0NjCMSSNr7nmmtatWzurzQcJ4sJAYtE5flXkPL3QwbLx9GWXXcZzIWnpFH/BEcezSe6g4J5LED46yqVO/WoggKEF188991xs0XQmcw/Xfv7557zqoEGD4mZsemKxA2fHFcb3XtKqIqiE0aNHn3VW4WdwUkXPiWTlC5rnWAFJVCWlhsiqjzjiiK0SoJytt9467n9MmzZtm222adasWa1atQYPHvxDCbhs67LbbrvJRN98802u2QJZbvaJourXr89l47O2bds+++yzWPmnn37yOX78+Hj1F0aOHKkGkXfu3BlJiwz+/ve/YwVGLmclUhRAvoutDZEk1aXZOqhablfMwcVI2LmA9LgEavC0a41F2iDjlJwTRI1TkdxrJnooJumAlnTuVEbJgunQqsBr0f0K9U033WRSYVrWQqhkocMOU6MsgRqnhHHQoUMHuhKxmXXZljl+K+Q8vdDB3Lm5cePGcaCC9yDp2D9SjV69evE1cRhwlu9Qz3mJajmRxIHPe55dfG2pfsrtViWSzv6ZNKQzmXuk1xchPbHYIZ1egrSqCFF/zz33FL84Vgw6j7XA0MG+gCrS0i9BSz3vt99+klrRANKN3weN970//PBDCav+/5Lg6KOPnjJlyqeffrrHHnsgPMb5+OOPZ/+GAVur33///ZskuPzyy0WWgXi9yHDYlyWradq06XbbbbfEEktI4qW2KpkraIAduft4tQ0isS6m6ijrXwrO3pKU/udfKAsyhvQ4qUlLJWXt0Qkx7CB8nLBzCgwdCXSk+M0TGCsl5xKgbY0JaaNlj6JxM8HIrAdhhFzTKBBnFxUQWDhFz4KhsJAJEybEEqdGWYLiyr59+1pi2hB0lm2Z47dCztO/EuRbwlXpiz3Ap8SjI47gzjvvHDBgQOF/CxRh4MCBzzzzTLyMllF1OPT5RKl+HAbS45Ia2VKpB1o55geJJyyfp0PhoFxM0nMFXlg6K41ea6214v9CQvHQcl/E1jWBnIlFHXrooeuuu+79998fDSAa9+7de5NNNkHnKArYKoothpqolD2T2aB42iUa40v1ZoEakXSGYGuFIP5inr7sssucwvdoHrky/oSIK+TpqISoRNXtkv+MGY+oiyGbBCIhqrJpdMAp1xaTdMD2vOaaa4iK5m3SeBuO6hYtwhZwmD754/ZM8HRYRUV466236LNnz56/2DLHr4mcp38lsPv//Oc/Nr+dw2Ehaek179ypUyd8XAlQtcb8e7jy7LP4sBTibLng0Pv06ZO8ij502LBhpd7tzC7nQOM/0QbSOeQoD6Gf7DOQlYsrS/F06ByUT0h+2TFl3bnHMcccs+WWW6622mqsK/v19Yrw3XffoUPUJRONtCkQoj7xxBP16tXLeLpcnHnmmVJVpKt84IEHLrnkkvJpZMawpdFledohsD2nEHMwNBbUg/oIQ0EBHQZVZ0xcjGKGBiSNawUBeka3BIiBAphJTfBxuUDSZCNDqW9eZUwsk7YumWyiE/Pt0qVLnC2LuDCh8hTFh0mTnzvPCgsP5sV10AP5M56OhS6LOAVvvvlmqZ+YzfGbI+fpXwnsXkr95JNPnjfnT1twJTxOZM8V4aGHHuJ/NeYp+A4+Lrx89lmMQqfHHitHueuuux588MFBgwY98MADUp+o59Tsw0ykcePGaQPacL7BItESixd2bYJon6MipGoqwk8//USB7733Xnqc3E+W5EW8FeD68VYA06SUO/dwrVXbaaed1ltvPQv64YcfylY///xzg37xxRfK8Ra6MFGqNH78eOVvvvkGUU2ePDnqpY/oOSbCGrfaaqtinpbppqUSsMMgXTZzwAEH1KxZc8899yQJMdSzPTQZDF0MlU4h8t13333nnXcm8HbbbXfkkUe68M4772SEOFjyh6rlc8V8nLBzAelxEeInwXV+VgKxrxzaEArQIvnRsWDlYqi0iTQQNLRs2bKiL1/ZawQWy2oGzZo1i4CgLFUH6RbYuKSQVBcKQnMzShV34YUOixssbKBq4QixIZYewmhLIU4VIz2Roxog5+lfCWH63Hfbkv/smwEHY+Kg5IoI+/777+/cuTPqVR48eHBQdYa0o+TdNIkOr6fN119/HZvNoMOHDw+/xhvy3SFMMbR59NFHs7ecfLZu3TrCaogp5CgXP/74I9X1TUDDCDJIGpnhZtqzpk4h6dAtYGiJmhQn4dkFg6OOOmrjjTfG05988gkiiTfaZs6cicnii+/4GD28/PLLyrNmzcLNr7/+ujL5GQy7MheHDz/88Pbbb195Po25UaPpCPuk8kg6CzWSVLZ8ko5Pc992222XWmqp5ZdffscddyS2C3v16sUsf/jhhxEjRiBC7FKWlcvlaTBBDIqbXYiDZZCXFn3zqlwgy7iBX/nXo/G0GVm4eBXcJ85D24azZxW6du0aLYN3Ub5TgqGotIM0oyIpu04iVnaoMv6Pxa8AmhRhUI5y9sWNsNtSGDt2LCsN86ioTY7fCjlP/6oo5mnOjqcQ8scbLhg0YeTS4OWLyTvKQ4YMcSGXF10Fs0puevfurZ94hyhg0LRUhBCmGCp5SfQfHQLK11VF7XNkwH/Y5Y477ogs88033/z3v/9tQelQ3MMpH3jggdskwE/xiQWD1RYgjj76aDyd5UxVREyhVFkSWTlPg8TXFNCzxuyQAAlBFxDEXBE0OP744zH0LrvsoofoxCfTFeJ89913SBTbIbn46e9iZFRdSK5L4BCh4t3K73LD1VdffV4COwV1Oaw8r0Vy9qYVxLJgTQ2E2m09mzey9jgVECLbNTqPQ5NVNhdD683G1xU5GYZTprlQs2qdk1/IEuFgRsAQC10KGtv+kyZNqqhBjt8QOU//qvjvf/8rteXj7FVb6IILLhBZy5KlAhlVF7NyMbL6KDzyyCPFVM0jyKFxRmzFDAZNS0UIYYqhsping/hl+dE+Q9o6RxFCM7LSxx57TGL62WefKVvKP//5z4cddhgXickyICRsuuuuuxaoNbllDVGeH+gET2+00Uby6ZBnHpBNh5y/yNPQsGFDRoL2UE58Cysj6V9k62JoTH50Qnsff/zx0KFDcaG8045o06ZNfGWrc+fOdgrceuutCVOXhkw3LglKLgusHPef41PajcmqwpS4GdeaJgnRsyHsu06dOhlRP6hafbJpCjfGCAyxwSXQ7du3j07k9y6EuGUFCpVn8/OMbFIkFzqccMIJ8v5yv2AZNYW1nz17zJgxeTJdbZHz9K8Ke+B///sfopUHiLhjx3Jzd999d9xMUx9kXBXcf//9cpHohHeI/5E8z9tMDDFy5EhJSfYMFU8PGjRIDAH81BNPPDHPnS/2CM3HS/Iinnbt2m244YYiMDUUqzBs2DCF77///tBDD11vvfW22267gw8+OCg2uHaeoQeLJU1fd9115dOorkOHDtl9b+V4TC6G47jjbfBZs2bx3fFdLFFFz54948cjQYRBNjyNk6BAyAmKy4FzzjnH6Pp5++23feIDh3PF0BmCqhmzrA6j0BK27tu3r+zTpsCIkaqaJsZNmbkI5tWkSRNniVTRHW+ZsUii6vScIdp37949bn3Zp6aJZRViz4b+wY6mbbCJHGLlGEh4YY52EKsA5E17ejDZGGJhwNAEELv06tUre7UwNdYSqGGfGJr/yb+IVZ2R8/SvimSzpJg8eXLkr3KR0aNHT5w4kT/iaPh0RB5Jc+WQwHXs2DFeSW3bti2ejiFirLlCiGTTSsh45ILXSXgaN4eb22effXxGM0gvy1GCVC8Jvvnmm7p16/Lmr732GpVef/31m2yyCfL75z//iWzw9//93/9R5t/+9rfNN99877335ughGNfn3MJVRxxxxNprr80vf/LJJ59//nnv3r3j7QSSKMdX4X/66ad77rmHSMrfffcd1xz/90y9aCz7NteoUaN+8X3vDAgJdQlBDIHs4w72PFO1jXDccceJOONxtcBC9KDzwYMHH3TQQZtuuun+++9/5plnGlEmmlJ0cg/cxtEDeRQSay28KVaqIJ92LfEuvfTSKiaywbIZ4tC1AmsDoW24LPleWewX60shKuOsvRlXybkxOuU//vjj9qzPLl26LIx8OhNYgWBIWij/1VdfMUKI9YXUZBOjFTuKGwQ68RJDeiJHNUPO078Z7ApU3bJlS9u7devWr776ao8ePex2W73qVG3Pi8o5Dv4oeHqeUdjBCVU/99xzsgSMEve9OfFTTjmFW5HcKAwYMEBlek2OOUEz3377rfCLAoMR6ROJHnDAATK5AMIQDKmXMjo8/PDDg6pT1p17uJYJ7bHHHriq+H+lVAUhM5Anm0IV73sHWEWnTp1wqhDh9ttvnx+eNpd44CJ7nj59eggGDNvsllxySWOxSQx09tln+8zQqFEjl6gsCzluRtVxB8tYCzCRxYgWNKNqEqZ6ufBC40YDPI2zg6T79OljtzZt2nQh3fc2HOg5dPLee++J2Bikw7I/aqts3Z9//vnMbNITOaoZcp7+zRAbI75TK4149NFHbST5gWA8qBoNV4WqoV+/ftzQAuHpAKomSdwX5XGWWGIJxCPdiUK0gfTKHEXakwjutNNOQ4YMiUN+EBO3bds2DjOolwgikl122SW+1xT3jecZRx111JprrsmW2E8k06VgREgPKsVc8TRblaEiIXn5yy+/LJKbN54GF6K6unXr0klEOd9//327du1QGkXJp3X+5ptvxjahPcjyafGucoY4i59cWJxSN2nShK4uv/zyeeZILJiWEujH6DSGoePxUHzGw3KNsbJ54ekskzZHNepLdbVAQB6p/EXJvxG77bbbhDvEYBjLL788tcT6piY755YPpCdyVDPkPP0b46WXXmrWrJk0gh+5//77UaOaoOq+ffv+VjxdDL64Ro0ahJQKcHOc5uDBg+NUemWORHs//vgjR3nBBRecc845kydP5oXjX/PKp/fZZx9uWhtkRo2WVT1m2nLLLY8++ui///3vvHzz5s1R9Ynz+msnTEh8sPXWW9epUwc/zZw503BfffXV9ddfH7mplJdI2fey5JTBhdn3spTB4u6www7B02WfSZetAVaBnEaOHPnBBx/QADbFW/PA1i4RC6611lpIxYyMZRc4XGqppf7v//4PT9erVw/pFv82SwBzF7/PnMG+IEwQZyAeYxtoQaXUwdOklawnwcB18nsD4WkFZKwgnxZ2B0937tzZSpnsguXp6MqkDHryySeL+wUEb7311pVXXrn66qsvs8wywriJEyeGWlKTzbHooPrydNhTGBZE5eIHU3v66ad59sJds2OPHThwoJqMquMGOB9aEWGr5x/btGkzz/e9E+2m+O9//6vPhx566OGHH87+keWAAQMQABZRxi6R3MSpQNrR7xv0IPnbb7/9OET8hxS57Oeff16whTvliPEQASl2796dv6bJhg0b8qexjmDpLSJymjeq5p0DKA23oQpsLZeyWIK/SLMaNWoUP0ol4Zbil/s7J/379xc9VD2fBtaLmBHPd99998knn8gdzWseqFr7U089FRmvuuqqhx9+OIoNmNS666672WabRUAj8hBMFP/+fIZYC4hD/H377bfjKjuLPEKoADKjENy2QJhSV6QSAcjX9WwKhvvHP/6hbAtbCJFZ3PT2md331sy2LRYgK3ft2tXCtWrVquzZSgRG0pZYqEf5ooH333//ww8/3GqrrQ444IANNtggi60h1VGORQfVmqeLkdYudjA17Dh8+PCg6uBpEPzadb+YVTuFADSzD3FA2uncwFg4g0cbNmzYI488wosV4oVjj7XV1QjJNSAV9x0t8fSee+6JhIYOHUpylWlHv2/QA54++OCDn3zySWWQOt97773SKcq0OlQX9VEoy9OWEu677z5OfB6oGpnFJ2Lef//9ZfB/+tOfatasqbfWrVurl8YJttq1axc/T5bJA1HOJqKfP//5zyKGKgKVMolDDjmkd+/elPDOO++0bNkSYcwDT/tEYLvttluz5GvHnTp1mjZtmgB0r732OvLIIxGh4EN6ihR79uw5YsSI7F8uhvAZohJMTaqNqi2EDSX8XeCJLKBVSjsz+TnV2D5gp1jibt26ZSQdUL7zzjvxt4mkvdxyi+0W4vmkUoahK2VrF9LGZwaHeragAoJYWQm9hdDPrbfeGmrB0yJsm3fXXXcVeaeqybEIovryNNYZP368XNPOjy2XIW2xWCBmxKdzOjfccEPx71RMmjTJRuUCnnrqKR48dedlgKftUnkS1kw7nRNpdxWAI5syZYoeeBYbPlxMwGGHDh1kWlL2zLlLUA477DA5IkdsjVSmw/y+QRUc4vbbb599wemHH36oX78+6rIu9AZRH3BYiqcz3HXXXQKveHe6wMC/BM0yoDcRFafMKpCBuGGXXXZZc801raB9ZJXlqcigR48e2a9TZYiJKOhz2223PbDKOOigg3SLDKS86BOtstiTTjop2LfqwNMBEyGA3mrVqoW0unTpoiB0kKHiaQjeZYHmVSx8hqgMUDVqDJMmFSVIWFH+tddeW+DYeeXsuBakxSFPDFEMQZJVCG7OSDo+LYEsPL5gjVxxdlwi7w/CjogZ95s+aRG2xD2GljrLti+99FI6WW655VZbbTWXmJRTX375ZWZplpgahQsoXyCuJtVOjkUN1ZenuRUOC3Ux6+eee67Ub2wtZjAp3jwceswRlM3dBn7ggQdSF14G+FvWe/HFF0s+KrrvHb05KwjgQAPoxOenn34qRKDk8BGIWVgQ3wHlILAIqPTJlWgvTickTrI0/Ckv6TAd5vcN9omuOE3e87bbbhPNcJqiGTxN+ZLs+OITdVHaq6++anGpHRlrWRyEFXLq5HdGg6rxShBwUHK5cDZIHUn369cPSUvfIdj6jjvu2GSTTUj1/vvvq3njjTdY1KGHHopdnJKtMgAiTZ061SxI+Pzzz0ted9hhB/nxeuuthyAVQMFhlHUISEJ5m222+ctf/vLNN9+Y4+67716nTp0vvviCdRGJhaQMPDdImLoAzIe0hD4bJxAQkDzjaQ3YajyWjiWoCBoIeWmAMqkI27Vr1+74448XJwVVJ9w319BP27ZtCVOcQ8tubUZrp2zXaJMxdDFU3n333WeffTYSbdmypXlFiCwhtvVokg0I++TcphnpNVWYtcaAlc3F0gtcLKW5WNyC+0jub4VCfFoIVx155JEvvvhiOJZQSI5FDtX6vjcPwtS6d+/eqVMnWR0Pgml4hIIlLl42FzMKFB+ataTBzky9eBmEW7fng6ddEh0WQ6XcbsyYMZzChRdeeMkll/BWPALXwCNQ8oMPPshrA7fFX6sBPp0bgszpgEDeKgibhg4dqvLvf/+7nhNJfxY7Bv29AU8jkqWWWkr+Ko0O7Lfffo8++igXSbFIkXL+85//8LbxdEO4Q7dHH320FUyXM0GsKaq2TBlbByUXI6HvAkMry9ssa5B0MbC1yn333Xf11Vd3qNno0aPJ8NlnnyEAAgdb16tXT9CQrN5s1LL++uvfeuutVlYaJ85QAAV2yMbg+gQK6gWR2EIZSTAkXX3++ed4mt2yMaOk9Dv3cC1ga0RojswvHrqDyUookXQW16bLUB6c1Uxjk73mmmsiE9UbreonS1KrjqB2fol4ONL0Y6fo36fdMWLEiHOSf/0pmS6VSWdwSIc2UTC0aVodO9Ry3HfffRTIBnr16qV/p8gsKKFSjS26dTEWMThDS2BBQwOJJlJVRNkpqIqWclRnVF+eDrAt7gxb8yO2ve3K9QRVpy0Wa+BpG9W2TF14BeD3u3TpgmL5yvTKItDVjBkzeAF+f8iQIdJiboLLE/vz2s5i5djPYEvHJVmlZCuoGng3zgL23HNPCRYPpYHGpRDj/q4QPL3CCitQ13fffff999/7BGqkEFqKZwTAsSq7RNmiHHXUUTxyupBFUGml7r33XlkXtQcfF4O/li3hA2SjGY7MuDkKEGWUf8ABB0jUpk+fHvJYZcyKUJs0aWIp99hjDzQwYMAAhoQkhBc9k/9AHKIWhE62ocMoF0+nuN5wu+66q24nT54cX+SbH6pOaPp0PQSwtT6RNJaVClNRpI+h/8qhmcakat26NaqGeP0t3vqeh5TaJSJjUtHbG2+8UdgnJTAQJYvP0LDgxroEKwdPxydYF2LYg+bYvHlzHYp9TU1uja1FV1paMnqwFrNmzXrllVcicXdW9mygbAnSSeZYfLEI8HR8fvXVV+yVpXJP99xzD1+QmOjibKNmx+kHT48dO7aQZFXwlFr0zcN269ZNLG8Dh2aKgaf5OK4hvL8wn4OW2L366qtpiyLE0Bk4HZ1zKMHTwJV06tTp9ttv54wwfTHK7RBiRosx8DQuXHHFFak3nXOlcInPing6WepCpU/Kt1KWT/4X97dBAUkwjPvvvz9j6HKBO/UQDzuL334IYGuSf/311+3btyeJ2GubbbY58sgjBYhpi7lB8LSoGle9/vrr8m/WK5hARUG9FSFh5ALS46TGhSCbdIjJKAFJizU5gSuuuOLNN9+MQUP/lSNaik4SJi3AlhF9zlsyDfJmglkI3J9lq5CNRQO33XabiJbYDzzwQELTBWBo8ZAekDTGFSuYEYa2ccTZtnnXrl0pTSAiY9bAtWK+mTNnKhguePrjjz+OUWJ2lSCaZUhrcyxqqO48HQgjE1Sy6X/+85/XXntt9q2htMXiCLOzdVu1asWb29gSnXDiFUH4YtuPGDGC8w3lgKCbS3rooYck3NwEv88F81D2fHxFJx2sPEQPMHHixMQ7FcC18SZ8E+cSt+wAbZx55pk8l9wu3lgphbTHxRdySo51k002CZ6mdilRfEEZHnnkEZpX4L7R6osvvugSh5Xk0wGnAviY06dqnzjssssuk88NGjQoI+MMUVOMu+++GwEPGTKEhcT/spToq4xNRNQQSUBmBffdd9/nn38+uMcl1loBhIA6j3omlBH5Sy+9RIxI0zXYfPPNe/XqFc2k7+hfeFFMwJWDAMId+T3Tcti4cWMpZnKru4CLLroIcwsZWXIyeAGh/7mFeeHCeJsaUhL+JYT925KdO3dWFqpmXw9L+y2BmjFjxtiPJiKxRs8JTT927733Wjv17dq169+/f79+w4455kYOjamAxULSsafM3RaLpHnKlCm0Ea+MBE+nw/wSEtF+RlqbY1HDosHTAfTD0XTv3r34xxHTc4sjYoJ2r0RZOnXhhRdy1qn/Lg/ImFOTKNvtEvHAsGHDbGzuT0EbLMKNcgFcQ/y8czpYeQgBOGgxfiB+8PLtt9/Ws0M+KPLsf/3rX8TTrbJYyiU6v/HGG2+44Ya+fftihegq7XdxhNmZ5kEHHRQ8LWO7PHlRP5n3bJ46vtjG7XLQaDsuqQpPR8El0lO6lUZbu8pz6FKwa/C0ApF0aNyvvvqKqUREJcJA+ShBDIFQt9xyS52rB2wkOowy23No9aFngqhnV+QxX2Vkj2W33nprY+lNSwQjIkSues7YOgrZoRnVr19/zxIceOCB66yzzjHHHIPPUn5OYAvoRyXDi29+B0L/cwVXMdHgvLj1/YvAylri9auvvtpqCndighXJoNJa05gdIUQOnvaprKZjx45BzGef/XmNGv/v5JML34EkkrPUIti1sy644AIxUAxhvo0aNRKmnHrqqYSxfOkwlcKiCEfCD4wePfr3cA9yccWixNMszN6IyP13Ym2maaOaNU/doUMHhN07gai8rHNXw5vzaNxxoEUCHtnmlwe4sEePHspoVYocLiAdqQJo4Cq+e8kll1x66aU5KTUG4jLOPvts2ZLOI/VR5n0ICXfeeefAgQOXX375pZZayrWRc6Q9LqYwQXFkxtPAUDM/bgUhyuqV45Jf5OkM2jz99NMYlOYjjS43dYZCTp0gPS7haUxQLFKpMpEcsg3xViZqQeiismZZOat3VVavPGLEiJVXXnm77baTi2sjpW7Tps0JJ5yAYlld/IuwYGifKByvy+Dp7c8JDjvsMJm6dFNBUMLe2HPc9tcDAkNdujVQqH3eQFREy3TZcNV5WtBJbEGJSYXq0u7Kg7PkpHyhgD0oykHSPu3K4GkCQN++Ew4++Ak7iNqRtHhazCSSJpswwlaKZ/8yE6O7SgYfvzSXDlMxtEHnrAXf02TDhg1/8f5ZjmqLRYmngZ1lSKsWa6RTLfGGdho3wVtxZLgw3gtNHXkC3hxPgPoojBkzRkYr03WVzc8zcgp8ZfxDeEhHKg9xlrtB1ZzpfvvtZ1CVavQvJhCw64dnufvuu7///nsSBjQgSbjgE0888YsvvnDVp59+KqeUt2WvHcUo1R8hbSmk50qgphRPB8Khl0VcgiS441JfzSoXGkDwdOXJdMrSJVAT971pPoauCFYNd2LE9HjuISDDqbJhKbWlzzimVatWyEmuvOqqq+622274OF40kTprdsQRR0ydOlXjADGYCrWoZ+cIDNMg6ebNm2fBJYTa5w0u14+A4Nxzz7UEldz3jlNiXNGGWXTq1On9998PASDtrgJoYIOIre04FwZJK1MF5kbbZj1gwID4bRNQ6N69u9xXyHvHHXeYuPZTpkyhkNj+gaoMDdrgabve6ouZ9Gb3xU5MW+RYdLCI8fTvFsneLHjSoMZrrrnGDhSq9+nTR7Ykec0cPWKWbYNCHLZu3VoeYJeikHjExSO8/fbbade/BOP++OOPhXeXv/sunpaFJOEyQqSoTy9IoD4uwV7hW1944YX999+f873rrrtkXU6lTas9ChNO7g8PT/Bt8lX+9FwJ1Jip0KRt27acL1iU7NeyBFicsgK1PP/885nyLeWRRx4pdywVb1WEhx56CI8W83TKxhWDGFJS1Dh27FgGED9vYi4M45NPPgmRnnvuOSJZJjx9+OGHZ69ojR8//rXXXovZSf6y19BeThDld955x+VhDEK3ddddVyfobZ999jGKa3UrAcW18uxll11Wqo3IcZWYBhDSZ599pk2IhKH1I+Zj2BtttJH8Gz23bNmSwdNVGBIkyptfjB49unKejnoNCCCwQNUWNGRIu/gl0K3Lg5iRrq2nfMopp8hu9fnKK6+wCp8Z4m0yNiYD7tev3/HHH+8q6qV8Gk47rRrI+eKLL55//vn6EaKdd955utJ/ejrHIoWcpxcNhGtIfFTBsaIEWy7uCsqSYzcGPXNwcppGjRrJfTk+BICh8XSTJk2GDRuGpyXT3D2vN7kIwvagH4gRixGVcbYSRONAWlUEYs+aNYsrX2ONNbbaaqt33303bVqNkYqeAIVskSB+mi1tUbQoeHSHHXaQue64444Kq6++uqWRYr700ksIyUppJiOUGmYPfbt27brBBhsceuihxTydhVyQZNEFKOMVmbfEqBRPF39mSE4+jKQZCZJ2ucCIVUjjjPvll18yCZXKCBIryPaYhJbrr7++DCyRbjZD6tWrlwKnryC8C5bqkiDKDzzwgNBBJ/QjItx5552FAj179qQBZkZyMZyWooS11lpr7733lsUecsghsmo2wCBPPPHEeJ1NqofgcZIypW255Zbbb789w37jjTd0DjTsFKR6n2/Qp00hea2Ip6XR1113ncCLHq699tqMpKsuA3KVIuNmDC2qxpRnn302zu7QoQN7QMzCoOT+d4rkmfVrjz76qACCwl3iQiqyiPbO3A5tCMYmjhcZN23alG5Dh2mLHIsOcp5eJGGz2XJJvvod2rCNkztnBXTq1ElILva3S/v27Yu5neXv+IjHHnsMT//zn/+0/+U3xWjcuDHvwEdwB+kYCwecBf+LzBYhnhYVoTQp8oYbbli/fv33338/PZ0g1kIOKi3GPRLEJ598cuTIkdioZs2aVkSSvdRSS/G2Ko844gieWsJUeLdn5EiJztFHH/23v/2tVD5tpfBf5OUZkLSszjoGT6eEXPQcOj0u4WyXSEOlpHQeNzy++eabKOCbrBz1L7zwwl//+tf11lsPIZlv1AvdsrJCFsnJdyHKGFS9DgcNGiRRpoSvvvqKhJtuuukKK6wge0aHBtI/5cRb5aNGjVp77bUPPPBAmjR0kIceZs6c+fzzz1OLruIGgJ6dTRW9oEEkWWYlj6hvuOEGCkfVH3/8sfkGSacXVw0mKyZrnkCEareKnERFkmncj6dTfp4TNGbDaqCxrXryySe3atXK3tRb1QXQmKkIyhkkklaI59NzO4Uc1QE5Ty96iM2WgSNDrhn4AluUg+Nc2rVr17BhQ2w9fvx4LhIr4GM5hPia1wjItmMncwc8SNyeTUdaCOCyDbr11lv7DA5IT1RLJAqeLaQgcO3atffZZx/uMnglbZG04cSlZdKm3r17U7UEmrax0V/+8hdUvcwyy6y22mqbb765TjbbbDMZuUJ0COr50IynsRSSk2/JdwVVeOKcc86RXfnEKCIqgVfGylEIJASdImpuu+02owRNJvOoELj2+OOPR9JmwX7S2rkEQtp2223jnQlzl2QvueSSApRtttlG4CJWMFkkpOXLL798+OGHS5RDk/Cf//yHxkQnaH7HHXfca6+9xo0bF7QEqaIXNPQvubcdOnbsWG5KjacpXHI/t2l0Bpcw8tiY5ujQcljWE044oVQ+rTBp0iR6AzbQp0+fSy65ROjco0ePiy66yGHr1q3j34qnXf8SzO76669nP2bRqFEjYaLtT4zCNEqQNl3QSHtfaP3/DpHz9OKAdFvMCc7lk08+sb1RtcDcLuXokXG3bt34eoeyFiQt0kcJ4BSe4NPTThcOCPbUU0+tvvrqKKTUDeRqiESRhe8By0o5UPosm95pgOfkZA0aNOjZsyeyGTFiBA8rNXTJ3nvvvfLKK1NvSqFzIvjVJc8884zGyl26dLFeWBlVS6ClU7169UJ+9yeQZFtKYVa8lxCXl4V6ZN+5c+eNN97YEgf1sgd88OWXXypbZWWJb9QLziT9l112GT8ux42HxCBAEe1FWSFuUIM2EOz12WefxbNt4pnpKqusstZaa+2www6bbLLJEkssUaNGDVRtrbt37y7DFpxJoPETTSISHIac5HmPP/44Df/pT38aMmTIhx9+aKyILVIVLxzoX3AgBqJk4qXkXAQMhyNpLL1g7lFQ1pxgKpbmn//8JxoWuyQcXcDEiROtKTsRMVt9oZvIyWF8HcuGtZHfeuutqt9dMJZ1vOaaa05JfiEY4suZ0xJYBQ3SpgsawlYWZabpcY75Rs7TizyS7V/OllPJe37++eeI2Sa/+uqrTzrpJDmfT1F2p06dzjvvPKE9p6AspcbinEV6cRUQ4xYjPVEpNMPTa6+99kEHHYT/qnjVbwgSco4zZszImMNnFAJxyJu3adOGbnlY4BxvvfVWmeV111133HHH8fhioHjMXFzIgKrRKqZH0nI7DTjxAQMGWBRu+qGHHgr2RdJdu3aV5Fk7tIpLJM2AyzX2GYdgRK4Zca677roiBhLymzhJPqc8c+ZM6x71vOrf//735ZZbrm3btg4FHFhBAZiNmCDKCg5ZFGA1iHqsc8UVV3D9V155pfBLNmzuH3zwAdquV69erVq1IqteaaWVll56aVRt9dEPaUWKVLT55pvvtttucecfPRMyui2LVN0LDvq0pmPGjKHkBc7TOpeIR1QEWSUMHz6cSaDhli1bKuNOc6eQ008/XY1V7tu37/nnn2+xKJxgzZo1k2FbFKRr4aKrX4SBZPAMyR6PHzkAlomtWc5CugeuQ8Ef86tbt67oLa3NMd/IeXpxRmxFzsiOlfdw8fFgzHblJlACT9SvXz8pF25o3LjxPPD0F1988dJLL2X3MNNzFUMbnnqnnXaSQ/D4VbnkN0dhngnS4zkRpxBMu3btMocINEy9UmG+nsPljvGxfFGqHdycgbO+9tpruWktLcQLL7xApaAwaNCgq666Cmf0SCCi4sHvvvtuqyb8Ovnkk0UGTZo0ie/d6oFT5t8DDrfddttVV11Vah6/cUHIuJlM88qxZHj6L3/5CzYlg0PWok3MK3uTH1TGnVtQWaoetYi9yMPMgmt1Lo/HQPvss892222HpOXWq622GiMU0Ggse95zzz0PPfTQ1157jQwhDMTQUu0RI0bE/YN4Lz1R9oJEMtrscePGsXwxStlb3/PJ02JfQUCMktYmMK948VsgxUjg8ssvp5aLL76YJYjYQBtgKrfffnvz5s3V33TTTVLwqgtj0G+//RbN2+xhkAGD6pNpLQyt6vCVV1455JBDDj/8cLFaWptjvpHz9O8FXDN/yofa6nImORD3feedd4rT8bQU7cQTT8TTlW9dZ4uBpF3OEXBztn12LzSQXlPmqkmTJp1wwgmyjfT0YgG6xdOpL0zAIUrUMKgCv4yqcXaLFi06duzIEWcpNRdMe5w1Ph47dqxE58UEeNonfyqZbtq0aTh0a3TppZfGu/2SUTkx545g4n0on1zwPQm0Qee4fIsttlhjjTUijS4XGFE/2gRPB8woLZWH4lUGHtm1p5566owZM0qdwuKson379iussAKeXnbZZSXcu+6665FHHvnee+998803s2bNyhiaDqlFQCOq0JU2ggyJuODG2dDzAsfUqVPxH6pGaTSZsbVDS3bhhRfOLU/HXGwHQdWoUaPiMD2XnLXiwdPQqFEjJiF0tgGFVnZi8DSY9V133UV1lthSNmjQYK6EsRBvvfWWmJhhxFgZGJLwTuJeSrb5h96spjWNVwvT2hzzjZynfy+IPQn2D9cct6fiDRpsLUvDnVXkaT6UdwNuxYY/5ZRTunTpghj0FvXxIDO9JrlKfJCxON8db/mmpxcLyC/lT1wqhYQ35IvDz2JZoGHJGSrF3DQfJA2SNokvH3399ddHJh0kLRMdOXKkBvypNdKtZhChFVA+942SoxDlOBWwIjqX4kten3jiCQrXczx7xojGil+9UI8d99hjjzPOOAN3vvnmm9OnTzcjp1599VU1CqDgUMEiagNR/+GHH0r4pFB6xq+61bl6RmI4ncspa9asGc+q//jHPz733HMzZ878+uuv9cObIxLTDDz44IMyb9wslXSWwfztb3/baaedIsgIPS9wkHDKlCktW7YUtspZg6QBywpALR9R06ZVQ6KVwlt1lqxcnhaNnXXWWWEk4qrvk3+tRi1hJ0HVghX2YLnVxO+f2J5zxdMMUpwhBBHYNWvWLLJqISNEgRFW8afNqo5krj8jrc0x38h5+vcIW4gf5CA6d+4sReOkBN2V83Sy7wrgi3v16iUPQDkNGza05+ObQg55n/jkuN944w3hPFfLDyJpaSKvET/jgHuQEIcuXNBhOsAiDhOhT1wlMQo/SEWDBg16/vnn+WUwa58YUfLUo0ePRx991Flok/zTYme19CnZ4qMBxcq0tJSnnnnmmbrt2rUrhatJeTgBPiiLOCVu0F7ytN5662FB2hZRGZeoGJrjtgohtsx1mWWWWX755Vu1ahV3WdQDL8+bR1nBIbMBbRCYgnoxxIorrrj//vuLvXSo23g9zUCGM6gQQU6MpGGttdZCivIttEQVYotNN910m222kWQHttxyy9VWW21o8tNp+kdL+D6IP1X0goaeBY5CCoxI50HSJmWOtGdBiZE2rRp0CI888gh2zH4ZJj2XnMWgA5N/QGdlH3rooahXidQtloiNWshw8skni+3EXt26dRNCMQAiVSVo+P/tnXlcFVeav+3lN1tMd9vbjOlJJ93GaWdiZ+mM3THj9GinTTptJzEatRVD1CTiEgOuICoqYIyCC7gvqKAoKrghKiouuOCCiiIaXFBUUHFF3CJIfk/Veygvl3sRBPQC5/mjPnVPnTp16izv932r6lZxCOqMszV8+HCmG63NWTBJbW/KABNf3Ai1m8aF0TpdGzEshwlxMDMW64CusMSRx7VXmYoj+bHvRABEhBh09ABjLTsSRxIHmI8kL8WsiIoDCoRgY60Qb/wADPe77777E5M333yzWjxHVkakfdC8r776CouPXGHrkSJiQbnRyDpigL8SHBxMeIRvhCEGmmXEiBGSBy2k6fqYf8RCm2lnYjJSKGrBggXYbopFLOW6tyDCbAfp5JkyZQpKQMv/4he/oGRMPD6W+EY4T+if3KJGOP/nf/6HHvnBD36AHqSnpxPmyukQb2HlZZ0VEWCgKGAFAaDrn376acYAe1EghxD95kAcgnX8D5QYX+F73/vej3/8Y6oXHR39+9///re//e2LL77YpEkTvJNsE0Lzr7/++vXXXyeuZRxKORaqoSsbKfz48eOErXJ5CeRFoYSzpNNEKmvZkAJpCppLPAxQ24q2rl69GtXELWDAWOkHDhxAmOkvRoVclenYsSO+woABA6jYhQsX8ITK4jSQB/+YASYXSCiZrsE7NNX5Acxc5jtb1W4aF0brdO2FKYroYhSYtO3atcNwIAwEXtgONmGjz507h+kUsMsEN5h78hAZx5ggw8x2diccx/GXPwURI1r6ERYWhpVBtzB57NilS5eWLVv+8Ic/bNiwIcqELathZgITiXXGpMo/1+fMmUNMjOVFa4mGESRsMYJNA6KgNIjAOh4M+TGsyLA0HW1I/ISKsxXzjU7jVFEOZRr6bKPQ8tNCEufOnUub09qEsPXr13/ppZc4Oq1tCwqUlpZGHkSUY5H/Zz/7GUEYmzgXyVM6KA3lM3KsmxoloZczMzM3btxIrEzIzvLtt98+dOgQEgLsaPtIWkpKyiuvvEJtEWxSpFUfA9SBrsGzlJCazqLZGdve3t4nTpygYpVVGfNEjWibRhs3bhyjxUoXL0ecWplWzM2JEycynJh9ZVFoCqFPmbZ4fnhODBvWSUSn8UIMcS6OXFxRO2tcGK3TtRemKPExhqlnz549evTAecd2YKwxqZiGGTNmkGhBfDZz5ky2ikijx8iGdS+WFX9//1UlWLFiBXE2+8pS/mRMSN26detBgwYRrGABVW1qBDQpYFIJJZFqdJcWwxliHT3G5u7atWvfvn20IdAmAiE4quDr6zt27Fh0XbXdqlWoBcoxfvx43CCajhJYQbBt9RjkpwUplElESL88++yzRMkcV94LiygSk9HmCCRCSE3efPPNf/u3f+PQbEI7//KXv1BhuQnKiZw/f17uYQMr1n+pc0xYGT169P/+7//u2bMHeeAnQfCZM2dYpwU4kKyTTmxNX2/atIkwGteNAXbkyJGMjAzyI+HirlErKtCsWTOif+r/mH04jnXs2DGaF3+FpiN+7WW+uQ+1s6RaZa0YHAg/mG6SWUNnWelCXl7eunXrOpvQcdb1CZCcpUBr04+INPvi9iH54mHQzpwXiRyUUSSuOSxbtoyuUTtrXBit07UXmfwY5ezsbAz3wIED0WlmL5a6X79+KAc/P/74Y5nV6CvriHF0dLSoiKXT2DLyI8Mkot+y1cI2hVAbIyiRilhAkWqqoepUI5CGRcmwmIi03IsNCAigufB+tmzZQsyKJBsXH0zweyIjIzHZkyZNIp7mpzSX6DTGGv1gE97SrFmz0DBLj8FaEWQTMTeZaecBAwb88pe/fO6553CYrl27Rlci8wSOb7zxRqNGjV588UW2oknkJB21RtcJqUeNGoVaIA90EMGlnA41x7UiEXAshgwZcvLkSWpbr149JE0up2/btg0xsO5PczhJR4Dbtm2LbCcmJsrftIA4ngr/3//9H4nfmP8bbN68+dNPP43jiEuhmvJxQSXxGzw9PT08PHChGM9Sfzc3Nwbq4MGDqZ7KWjE40NatW5lilMx4oHnlzoK1lRnB4agGvp3cOLC2PhR8I/qFLsBBpC8sXwf9xi2jszgXRhpDkTxUgGwHDx6UfTWujNbp2gtz2IIpjXnC12YmI72oCDYaOzJ//nyURvQbMNAiIagLgi3XveXJl5UrV0q6ZBBMGSqWYkk14TvCP2zYsEoMVlwEaVJOivaZN28eYQ1SzTqWEQONCUYAbHVaNc2qVYhx165dCX3kJzrNLnhIvXv3Rr2QQyuYBlFlaymwjpBj/Xv27IkjRd/5+fmJVBM8JSQkEED/x3/8Bz+Ja+XBchy1U6dOIQmEtqQwBoiwcQgQS1IuFr2bjBWcOVnPyspC1Cnnxz/+8Z/+9Cfrr/DohOV4odasSzpFkQcxZpgR2XMUwHX43e9+99RTT73++utHjx4lWEe8f/rTn3Li7KKa8nHBEalbcnIyjhHtjH9DrzFKR44cySglBV9WZa0YHCje/P80bjEeEt1Nl9FK1lY8G2JifBq5YiHI1odCm+PSiUiLhyT7Ek+PHz+e6cxsldsrOAFDhw6lGnv27JF9Na6M1mmNAZYCu4BdZj6ju0RjeNyINJZ3zZo1cq2M6c1WkRBRl8mTJ2NoyI8aiYSQKIJtIVIE6veqVcSL48aNozS0Sv7JvW/fvsd8nfMxQBAzbdo0YiOaEf8G+8hZy+M8mH6iZJpCNY0JzYL1xD1aunSpNFRJnaYoGlkQVbZAnpFehJlOoUfIT6eEh4eTwu4///nPiZ6bNm36m9/8hmxpaWlop608WI2PXuJMvPLKKzgNiLdsKsmFCxeQanQa4bd9U3fZuX37NudIfVavXs0ww5X585//LEOOrVKZx8y9e/fQ43PnztEIuI+0A11A3/Xv3x/3Qu6jq6yPCiUkJiYSuCOozB0GA+6UdXNBZSo/7EsX4BjR9Tt37rRmk6TjYBFM4yCyleGEI8gYI/JGp3fv3i0laFwZrdMaA8NImGC+r127hnEnsCD8IgXHHBlAbwiaiYZFQgBp4afc3kYJsDsjRozAzkaYyGVwySYrFogHIo32cwiUjPV+/fphpjEuVmVsqwSSXr2g2jQmIQ5tgsX39/dfu3YtRhmPh4aiSW1bhnV0Ed8I8bNSEAlalUCcFkbCsbNTp04l0p07dy672+q03PsnA41J+QSCM2fORKflQbZnn33217/+NfHr1q1biVzpUCTn66+/lgvUqPXJkyflui4/ieTQXbTqgw8+GDRokHVPmpWMjAxZJ+CDvLw8IsJnnnnmrbfeQsbQg9zcXMqXeJpRZMXZtMORI0fk4WeWpJOHREL8//qv/2JfDve///u/eHgiMCBtWEXIIWwpmU6VGPNIGudIF6DWcmlBcpYRKcpaEWgBWn779u14w+g0nY7ThpRaly5k37LDLrQnLT9gwABcQNtgmnQ6lzFAOv4BjhfOMSMtICAAkaYChw4dUqVoXBit05piyPTGBOPjy4PfgAElsENjREJskae7BcwNgg3oCiZDRL2kTqMfhIYICXLOOiJNyewVHx9/0ATbIaSmpj7B6KpSoPI0Jsoq1w8ImjGa6Cim37rTL8hDdkQ58pOcaAPGlMx9+vShlVgS2LHs27ev9dQ3INLot1zPRFdofJoXOccfQul/+ctfPvXUU2RDGzDW69ato0p0LvIg/5/GphNmUQjrgENAbRHdd99991/+5V/4KekzZswg1kd3gYoBK/QOnV6/fn1CYWJ0lIBiRf7pTTwGEQy6sm3btiL5BKydOnXasWMH5/iHP/zhtddekx0bN268adMmMqiGq0o4CpWkVgw2nA9OVm2wAYVDO2lSGs3Pz49s4n+ozWWD/CB/nQBpGQG1pi/oXJFqGR5yrVvtXDbIL17XwIEDcQdxfxkA9GxWVhZOD0fEKWTy4vBxvuQMCwvDt8YFIef69euphipI48JondbYY5qRYjCf5XKZSEhJRIwJjzD3QJTs6elpG3zbgjyjNNOnT0ekyYwqjB49ukGDBg0bNjQfMGpMjPWLX/ziN7/5zSuvvEIGedBGUFWsPki10TNUEznEHcEpwe537doVqTZbS0HYPWrUKLSQ9YULF2K1xebS7GgbFlzYuXMniQi2XLogMwKPlQdUGf3GAUJTaVuOyLF+9rOfodPyPjKkQhoT65yUlJSSksJP5BYFxaBfuHCBmIzyd+/eTfj75ptv1qlTB/suTzNdvXrVCvhIAVlnR6rNIf76179SAofgQKRTMuvsyDpqTfyN23f8+HGiZ3oceUbaqcOpU6dOnDjBTwaA+A2q4aoSjrJmzZqXXnrphRdeeO+993IdvTxETg33yNfXl5BUTgrU5rJBfnaMjIz0MKFHaCKQf5/TIPQgIopaMx7obvpLrq6DVYKFpFhIImJMfxEZ4xXR74wKph5OG77arl27qDk67eXlxTqdTn76kUGCt8dsFUdcFadxYbROax4CMxmjjA+OYKDHJRH1tVYAkbB0mlicYG7JkiWsSPCNCFEaZgvjgmVBsP/zP/8TYSYPoRVs2bLlP/7jPzBh+/fvRy0w6wjJ47kiWkVQbUwzZ0GYi6COGzeOgIYoCmOKy4IYACtYT0IiSWQd3UWh5ealVQ7tQKiK5FMI2QhnKZCYD13H/qIEtlDCc889V7duXbmFIRAXIpYtW7ak2THrRIpYcxQUqSD4Ri+FH//4x//8z//MctCgQeQRLKkWUDIScbO+//3vo7WEp4RxlqQBSkAKS/R469atv/3tbzkoh0ahOSKaBMjMG2+88Thlg6PQHYw0dLF169YOdRo4ETQV3+gRImmBvSiERkZEiZvp3AHmu2PxrtgE0dHRNC8zAi0fP348Ho/dvWpW0FdqiMcjKRZsYjDQs0yWn/zkJx988IH8HYAlkwvVJ8L29/fHG+MsZPrIXsT3jCscKdDxdLVA67TmITCxExMT0dTg4GCRYRCFFlSSDbY6vWDBAtbRFawVcTYWxIz9PkOl5A4Z8o8qIDPWCyWIwzDomPVbt261aNHimWeeadu27Z49e9gEqlrVCqk5XL58GXOMBiPGWG2k9EwRctM3JiZm9uzZkkJmtVuRhRX27dtHmAuETSyxyLQk63g/+DSi0IJ1fxo9IKjF3+IQiHTz5s0Jf9u3b/+73/0OKw8NGzb86U9/itZSFHTq1EmWjRo1+td//VfJQzfRa1ZVgV57/vnn/+3f/u1HP/oR2X7zm9/gVxGLq81nz6K+b731Fif14osvspX4lRTiZontEGnO5f3334+Li6Ov5eykxaoUORAQXOKjMOrUhuKoTEWo1PIgOyK9aOdHH32Ec4Ykf/XVV1OnTkUsOWX6hUTah5mydOlS/AYcL2kcqwT8icmTJ980/6tmlqfAA9i5cycN+w//8A+4U++++y6ziZnFuGIwbN68mZ5iKklpYBUIlMa5M09xrSRd48pondY8BGY1IQWRme11byXRRajUoqjaVqdZYpUI+DAimA838+2hiIePjw9GH40RnSaSQzyw7BwO49K4cWN0GktEbP3qq682a9asSj/G8Hig/kRFhKQSmwK20u6kiJwseVZJxSEdI0t4qorIyCA2xTp36dKlZ8+eNLWS6CImTZr0q1/9iih2xYoVKCJiieL+4Ac/aNOmDRpPF6DW8r87lnQHPoQtdJxkgP/+7/9GD1BuoEDAhfp//+//NW3aFE9u165dOAr//u//Tt8hHvSpqHu9evVwFFhhMKDK1JyjcOKIdHJyMuGsJdLqDGscnBrxKzOIE8dLQ4nx0gijcWHRaRIDAgJonJkzZ9KPhNS2sS8gpfhDotPApLhx4wbjBL+2b9++f/rTn2he/GDkWf5/QZ8SZDvze0ihBAYDFWDA0CMOs2lcClfXaRlDFipV87igzbGn69evR2WtP2VBSW22BZ3GcFj/L8IeEUYjzMjzsGHDhg8fPnr06BEjRmBZsFnE2aSj0wQTmDNUigACQ7969eqsrCwiaRSFWIQ6PPLlRxfBHMIOUJtNVJKJSiqO2mYDbg2tGh8fj/VHlcNMCMpZ0hGE1P7+/hjlP/zhDz/84Q/feOMNAi8iYBpWlBi1lhU7jIDdRNZZduvWjX3feecd9oUPTfCiWrZsiSdHTVDfbdu2EcnRoU899RSb1qxZQ/hIb1I9kR/C+kOHDt2+fXvv3r0ffPABvfw4I+knAqfGLMBJHTduXEREBOOfJevMqblz58oDeuLO0m5ff/213NSXHQGFposRV0m8dOnSl19+6evrO3DgQEqYMmXKyJEj6Xo0nqnERGOyoL5WfjvMIo2n9xH1xYsX5+Xl0S8corpPrppNNdBpRi2DCf+RkadSNY8FGh+RXrduXe/evYcMGcKsNmLnIkSDQdZXrly5bNkyzA1LFAJjwS62ITV2Cmsif3EBdBrLInfUpk+f/utf/7phw4avvPLK7373u5deegkrT9DGCtEY8QHWB+Hfv39/ybt0tRPD1pp3mrGwaB5RNfYdJabN0VTMN6CsNLup2mFYcwJcRJrojRAZlaU7RH3LDvnpso4dO+JgqaQ+fVq0aPGTn/yEFOTWqhgywLDZvXv39u3b//a3v+FjiUiDhIME32g8Y6NmR9ICgxYFZUbQXF7mh02Dg4OZL4TOdBOTwt3dHa1dtGgRripzgT61G+c0ESm4OHD69GkKCQ0NxRULDw+PjIz0NN+fTziOV4QHwGQZMGBAenq6w4Yl8ZtvvmHMoP34VRRIIYMHD05KSqrxHVF9qQY6ffbs2ffff/+tt946cuSIStVUPbQ8MrB27VrsSP/+/TEihiab2Om0QFiAGUJNsRrs8ve//x11F+UGzEeXLl2wJtgmS6dJIf+0adP69ev35ptv/uAHP3j66aeJnjt06PDzn/+cHifsY4n2YHookIicuE3Vr3ZD7wBhE6J70fz2Ympq6nPPPfeLX/zi+eefl6Z2c3Oj3eiLnub724mDSUdrUQVR8bLrNDklM8XSO/JXMUmhAnhXb7/9Ns40Gozdl7oxeBADxs/LL79Mr7FOJQGZISiHFStWkCiZ1Vk9RuS4oH5XJRwlKysrJSUFRzM5ORnflAYcPXo088LPz08eBaAZaUycp65duyLVNKPa2YR2O3nyJLsQixNJ4y0xoayZRW8i0jhGGzdujIuLo8fp+j179rAXkoyHBKwAKawvWbKE2bdt2zb6i93xG+hT+cOeOp7GxagGOo3HnZiYmJCQ4OyxTE2lI80eHR2NLcZwMKuZ2wLSK8Jsp9YhISFiwfHNhw4dKv/0RbyRbQSbCAC5lSveotME1uREObAyLMnPCgQEBKDl7EJmjo4hE1EnM6qD06aqWLuhgzDlxGR//OMfCYxOnTr15z//GS/nn/7pn376059iecVY49wA4iptS6vSpJZOlx2RZOSBMlEUCpd0ysHBwpciGkOY0QZyyl/et2zZQgYGBiq+Y8cO6pCdnX3w4EFkCZ+PFAQD2VDn83ihergIV65ceZwV4KDbt2/39/eniTp37swcWb16Nc4KoTYjnBSmj8BQl4sTAjsi0uRnmhCUi6gzoZgyIPMLwUakLZ2m0/F9d+3aNW7cOOadwFTauXPnvHnz6AtKIPimywIDA+WgeFQcSB1S42Lo+9M1GWk07ObVq1evX78ut6xAbXaC5FmwYAF2edKkSRiX2bNnY5GZ3oDQ2qq1xcSJE7EO2BFCZPIgw5gG4gMMEEo/bNgwEsHUaAU/R44ciQaztK6HC3Y5sSYYI4zL0aNHr127RmQglbRF1b52wPkihC1btsQW37t3D6mmneURblZEjwmjpf3pOxFaYBN9ZKfTbLUyOEQy0PtoPCVICktE+kc/+tGHH34okTEj7bjN+71PnDiBGKDTeFdpaWlo+d/+9rcNGzag0EaHmajzebxwXASSdsB1QKofTzU4CpEGc4dmDA0NZQbhj6KOIq4IKtNHJHPQoEHW/99kx9TUVPR4zpw5YWFhKDRB8NIicKZjYmLWr19v6TQdxIzDV2MAyOxjXUDCcXapAHvRWbY6jdLj+dGP2Aq5GeEMOR3N48TVdVpTEZhUTLng4GCca+QwIyNDJqfa7AT2YroSHyO9+/btS09P379/P3YEG41SEjGj30xv28CaJYqO9UePiRhEXJFeYmLg0Ha6W3bYEXOGfSEOg759+3p7e+/evRuTJ1iarWpfO+B8Mc3Nmze/cOEC7heKSOPTPphmU1gNaDQJv0gUWWVJTGaXrSyISLMvZVpFsWzWrFmLFi3QYMsFBKmeLNEhdPr8+fP4E61atYqPj2domWfwJKFiixcvfuaZZ2g08TDUhqqEowDuy7Fjx6KiolBTmnTs2LEi1TNmzEBT0Uu5/U+6bcUOHz6Myg4cOHDlypWoMs0oqmwHPhD+B44y6zhM5EekExMT6SCUnllMSM3up06dYuJQOL1m6TQdum3bNiYyU4zqyeQS0G9cK1YeW1tp7NA6XZNhUt28eRP3HFedGTt48GBkj9hLbXYCexFME4rNmzfv6yL27NmTkJCwbt26gIAADD1qzYptVI2HzpyXYNqSWFl5ZFBoyhSRxpQQUnAKnAg2BbXmvACTxDnWNrXmTK9du3bw4EFOHBXE/iLJdo+GsU7w5ObmJtEzPyWYxuLbZgN+2qVYSDpLRBoJ4RCUxk+KYvk///M/7777rrxgi8CUyB5HUCJUKsY6zsTvf//71atXE/ojSHL3Wp3Dk4M65OTkbN26Ff0TD0NtqHo4FlOJaTJ06FCCY/oiKCgIfSUOHj9+PFLdv39/xBjv9vLly9JccOTIEWl5VJbMYOryAyQRkcYjp9PJn5eXh9OM5O/du3fChAlo9oABA/jJWUuZYOk0x124cCEjav78+fwkG/PL18THx2fNmjVbtmxh6pEHwcb1tyoG6sRKYG21VjSPjNbpmgzTAw0bMmQI8dOOHTtwq1HchwY02FkiaeY2MRATXnSaqJrlnDlzCKYRYMJrpJpJjmlArYEVAndMScXlWSAcR6FRZRFpIKDnoKgFeHl5URNWEAysCWZOLreqc6gFmNbP4OzZszSCRMmW3LICNCAmWKJn8nz88ce0GHZc8pQd9v3jH//485//nBj0tddeoyOEJk2a/OxnPyNipi+uXLmyc+dOhAfNplZ4dZh7nMKQkJAXX3yxUaNGZ86cIV3V/oliNptCJT0uOOJV882dDOyZM2dOnjx5xIgRorV4M0g1DUgwjUOMyqKauD7swiymPZFq5DM2NrakTgO7s5WOYJ2ofdasWcxi5iPCT7/jqeOxMRgoB5UV0HJ/f39mVmRkJLXiWHPnzmUgAXrPOqDTeO24YtSWrkxKSsLDoHAOYd2/UOdWHMwIGTiKZFOpmkdC63RNhumBeh07dgyFLn1S2cIEQ6SZovj7ixYtEoVGsFNTU7EjKKi8AhqpZg5PnTqVMBdJQLOxL5Wo0wTT2Cx0RUTaunsnIk00j4HDBUF1SESQEhMTieFsEeUuyylXR+TUAFO43fz4ErGySDVLRJSf2GWaSxIlIMbRkVBYKfDDkJwtWrR4+umn33jjDXZ/9dVXCc4EShazTlSNQhw9ehQZoOWpFVE+PbJ582ZU/Fe/+lV0dLTrXDU1m02hkh4XctC1a9eimtIvhM7x8fESEItUI+G0LQOeWSD/b0ZBkWoEmJGPQ0xOaxeL5cuX01OEy3J5idlB1D5lypTp06czNhgJUVFRfn5+RNXMHZk+gPYzKuSPc6tWrWJsIMbsQh2Y10CPh4eHE09zaDKzpLvxwKg22fDJ2FFqKKcmcKYIP2YEP2DGjBmcBSkYFipGZpZy0cVsEs3D0Tpdw5FpY6FSS4UpFBoaypzH8jIVMb6I9MKFC/Gj+UlcLuG1/KMDW8M8x6YgA0hmJeo05RBS4ApgMiif+kgw3bdvX6yMyvTVV9gLzAdSjQXBVwAxQ0BQgvmo8XE2Z4dUb9u2jb5Am0Wwxa1hSYuRgh5gqcsr0iCZiZu/+93vygtEf//732PfaXZbCL/ee+89Ym6GgbyhusB8l5yI9NKlS6mhMf60aTYhPN27dy8RKn3EJELPrAfBmFb0lHQfExCdYyTjNOPlnDx5knZGj1euXEkigo1aA/sSZA8ePJiJkJ2dTSMjhCNHjmQGkYjkUz6azRFx2VkKzB0qgM/t6+tLzE2BjB9ynj59mkPI4JElY4BaTZs2jbGEcs+ePXvOnDlBQUEcjurhT7MvGagh000EW86R+J4zIkggkRSUG7f+yy+/DA4OzszM1FJddrRO12RkGrAs13xg/qDE2GJmKb4wkRBuOBMsJiaGuA2FFpKSkkgRJk+ejCFgwjPVH0GnS9mFTfgHqDUWQWQbJVbbinZESKgqITV2QS798ROnAf0YM2YMUYLdU041DDkvwhTiV2QY60msRsTDikAiYP1pk3KJNEj+Nm3aNG3aFBn+05/+RNuKNluIbCMhbdu2bdWqFSvYZUT6d7/7XYMGDRYvXkzdpJKgKl1m1G5FqNRqjpwLfiQqiM7hBBNGS4iM4s6aNQsloxPRRYGRf+PGjYyMDPqRgFguZdPOMuCZnpSA3uOHWTpNp0in47Qhq6j1nj178FlFHVlKonzXXPSYA+EEk0L5rJMIzDgOwewmM1sXLFggLgX1RLBRekSafRlv7Dh27NiwsDCqyozDMzt06JCtf8b6vn37OEdKYxafOnXqMT8cUH3ROq0pBtOGORwXF4eBRhq3bt2KH41XzgraLJG0rMi6rKCFqOOgQYMwKKKglYIz/bZLx05xaAwT62yiDvwU/SCURL+JPywLpc6zxsGpWVLdqVMnzpoeFJVlKRe9scim+JYb3C+EAeVA9bHapjobzSvIT4EDPfPMM/QIBv2FF15YtGiRBFgVaXn2rZHdl5WVhTdDjIu4RkZGos1WVI3uIpCbNm0iA+zfv59mRP927NixefNmNBKpY1SzadmyZWgkCookI9W28bQILf1OENzV/G9kSEjIkSNHyEBjEqOjr0AelrjazHGmDz8RUUqePn066XQ94BzgqYtIm1fZFVSVbFSGjo6KiiIPdgC1RsKvF31sW51tkXdC3TgQuzByQkNDz5w5ozZrnKN1WlMMS6SZrjjsKB+TfNy4cSLJpkarFVkyV/GjEXLMAdpg6mZVIfLsTLxtsXJiCzAxyMakSZMSEhLE1luoc64RyBldu3YNC4h5dTf/RIdqsgR6B0tNBynhLTOm1htijwZQLFZYCXIRdjrNmPnrX//6zjvvvP7663/4wx9s/4mrKlpO2BHLTsm4II9ciGtitooBwxJdpIWZaCLV6DTTynJx7Lh9+zbBsbQtOodIx5h/oUbCJZE4dffu3fQaw4C4GdfNUOwOHfC00EizjELmL7sIhPLylB9OA+tHjx5lHTdCLqozlfB3hw4dyiTip6XQskSn2YT8Yw0Il6k5nj3jhHRGo3koA+t8b968GR4ezslSH2L91NRU21kpLaOxQ+u0phhMFUwA8Sj++MCBA9u3b49fv3btWkOfi8M8Z6LiemOaoX///kOGDDFV0uXgXJAoLy+vmTNnzpgxA3MmpkGdcw2CkHqF+QYPscsWItKW6JYX9sKwovo4PaLHghFNF6GSTMj80ksvUQ257Kkq90iwO4U0a9aM+K9GdhknhchJ+NutWzekGnV0qNNqBxuBZ50ge/v27YifSiqCfZmPdERKSgputAyDTp06WTr9UJjjO3fuZAXxXr16NUE8XUA96d+JEyeuM19TigWw1Wnx3Tkis4zpdvz48TVr1liXvnNzc1euXCl32akMTgNht7xqLSMjQ/LICWrs0DqtKQZTBQ1jHs6bN49JiL3YtGmTNQMFWcfpXrBgAda/9FeJPVmsyvj7++N2gIeHx+TJk2vqw2WcFBEV1hBNFdNMEGyF1yDPlNmptd3PkpABi9+9e3eRYbATZjuI8DgoK4TCFWxndqezcD5Qi5raZefOnUPYcIhRvujoaNoWR7lnz55Mw5CQEFLy8vJEs5mbrIBtDGqLFAjk8fPz+/TTT5mnO3bsQBpxmxDa8+fPSwZB6gDqdxGEuXQfgS9OAHXYtm0biUTYWIOwsDBGAm4EUo2JIHq2dFrAPiDwDBssA2cxfvx4ZlxoaOjYsWOpT0BAgPh8ixYtunz5MufesGHDv/71rwcPHrx9+3bNfpTkkdE6rSkG0wMTwKTCNGMgmHKCmoJFMPmXL1/u6+uLLXApYX4oGKxJkybJk03qnGsQpn0zroXijmATWWJSZQmdO3dmKarM0g5TkR3DVtFputsUYoOSUm2E1WaiBEysoNOqZo+KnJGFSq1ByHmhWESfTD2CzuTkZPwSwk18nY7m68nwMmfNmsWm7OxsZA/9vnDhAkos+6qCikNR+/btQ/tPnDiBo8OE5edV83vngsrnCMSSA+3du5eBhC9OHebPn692M///vWTJErqYIBs/Hk+dwN1Op5F5Imn8RTwDnDYsCWfBGRHQM5DYtH//fvmfPdpM4b/4xS9eeuml5s2bJyYmklJT3ehHRuu0phjmTDSudGEasM5MMJl4dlKNSDM5q5FIW/Xs378/loX6YwvUOdcgpPvOnj1L3EwMLde6La0lohKd7m2+ApoMFqRLNoeITiPzrJuKbOBQp2VF63TZsU5KThBQx1GjRiFpNDvRKqE24/Yz82W9tDCdCEzPmTNnEmczjB2qGikIOXpJHrNUI4OsCAg5O9rG5awLSUlJcXFxkohOBwUFnTp1Sn4CGZBw6kMdYmJiiI8lnsZE4A3YghiPHDnS29tbVJ+wHvbs2WN3lZ7h+sILL3zP5JVXXmnZsuV68//cOra20DqtKYaaFuZjJsxAzISp0QZKok1mz56NBlSvSFrA0vU0/yeKFVPnXOPADq5duxbp5UxFaEF0Gq1Fwrt0MR7/RqqxtgQ6JLJU+Yogv1oz1ynq448/Jr8VUtvqNOuC/KxEna6FXL9+nbAyPDycTiEg9vX1JSRFtmlSOo6hO3XqVJYeHh5jx44lvCbUvnbtmkgsIG8lsUtHoTdt2kQhBPFqwpuvkNtpMnDgQCa+JGZkZJw8eVLWgXIwBagv9cG9owJy3TstLY06416wCbsBqLu8Q43SbON4QZ2qya1btxYtWjRu3Dii6jp16nznO99BrVu1akU4LtW2y18L0TqtKYY1JVhhQoaFheEymzJtr9NY7QEDBlQvqaa2Q4YM6datGxaqBksIfYdUr1mzhrgZRHFZYjTd3NxQa0QaMPSSTleW1Glb2JE8si/r/fv3Fz22Q+t0pUD3QU5OzoYNG4ynsYtYuHAhftLgwYOHDRvGEu+KRm7fvj16GRwcPLmISZMmqTUT25/WOivyDAExsdz4Bya18eVa8+vUhw8fJkUqY1UJsANoMIOHQwOunqenJ7VCofEnSMcLXLVq1dKlSxldFD7PJDIykrDecCKKh+9qzQQt/+1vf4tII9WWWq9YsSIkJATXQfKYzVMb0TqtcYxMjPnz53/xxRdyo1pJtAnOOJPZkupqpNZICAYlOTkZM6FOtcZBxxGT4WNhRu1CalT2ExOEuZf5N63SdVp2QRKAvZAErDP5Mce0pKiyhRFQm1KNimCmWdE6/cjIBLSFPkWtV9sQEREhPVIKCLlaM/9LjewRo6vfZsr27dulfLRZZjcrJWeH5Dl79mxcXJw6/OrVBNMUYh2CwbZx48a7d++iysuXL2cMhIaGrlu3DmcRz2DGjBnYE3ld3Z07d9BghmhWVpaUTwqOAuPnueeeQ6Rbt24dGBi4a9cuCsdBkaNLzlqI1mmNU5gYqampOMV46zKBbdU6PDxcHiQeOHAgFtnb21spYREjR45E47H1eO642yr1iSL/VCGecHhLr8bAqWVnZyPD6DQmUhRXQHfVWtH6Q3WaQjDBXbp04SeG2FR54141e/GTzjUkugTE3NhcdNqwryaqcpqyUbLRJAWsnyh3bGys8UbA4hAT03GinfQUAimJuFbMRLnawQqJy5YtQ3qlWDvkKLaoDSbyk2E20fzwPAUy3uLj46XH2Xrz5k0KJ3HcuHFUgGzYCkCML1++fOPGDQYJMn/x4kWzbFU4E3PWrFnMUzxpVojUX3jhhbfffhvBlmJlWdvQOq1xijlxCpnedjotSxznqVOnMtmw1zjOmGwlhl99FRAQ4OnpSYpMYCYnKWrbk8PPzw+BwZ/YuXOnXHNT51nj4NQwoLS/6DQmG0R3S8ImepA+kr9sqVQb6FxsPUWxbmWQREDg7aTaDKpHoNMcWl6xXoOb+gliTE6TkusFBQUbNmygQwlGcbAOHTq0efPmU6dOMQsM6TZF+oj5YjLBLO9RYN/Tp09HRUUtWrSII4r7KwWyvHXrFhF/9+7dEemgoCDGGIf+6KOPCKMZn5s2bWIpmS3MvQ2Yoezr5eUlj5i98847SUlJKlPtQ+u0xikyYVJSUnCB169fb3fpW2D+h4aGYp2x40wqAWuOIzx+/PjAwEB53uSJ6zTBPfZCRFrOC9R51jg4tdzcXMxct27dUGtRVmfQWWJAWUqsjH23NrFEkrGtmHtLpGWFnG5ubuyFVNv+X0tAp/HSOLoVY9mh6lqESq25nVLpqPYqwi4FqY6Li5s3b154ePiCBQu++OKLCRMmyJUtBoatSNtiW7JdgRaSR5AU5Hb58uVbtmwpeZnqypUrCxculLvUuPWMIsYS4TVhtOxrIfmtdVkB9nrmmWe+//3vT5kyhZ+SrbahdVrjFJknsHLlyiFDhmzdutVOqq3wOjExEVXGZUYIMeuszJ079+jRo8Tc2HF4sjqNSPcy30dW4yNpQXotJycH61y6TqO4gNDiThEco7uWYLMjXYlmI/ak43iJPFuITrMX4Tg/OZYt7MhWfKMlS5bIv9Ux2XLNU6qn6moiKYJK0lQM1ZrmH5SXLVs204RAVv6lrbbZtDbrhN2xjmAiS2ZB7VAEKXv27GH89O7dG6/d7vKJuYcC35GwGy/h0qVL/CQn83HdunU3btzgp9rBBhI5+u7du0ePHs0Yo3CjlFo5QrROa0pDJgbe97Bhw3DMbeVZsKRawHceMWIEE1t+MoHHjRvHHEMjceSf1BNneA8ox65duywLpU6vhiLnSMiCfCK3dvpqh6nUxvUPlJVwB90VYUatWRL9GFe3zUREV+1jQgr50fiOHTuSzfTHHsAm8PX1lTdOA6Poueee69q1Kz2SZ36Q2KoqedasWRMdHW3drdRUEGlYC1ttFlS+IkhBxY1r4iVg7qOvwvbt2+3+KMU6Uvr3v/+9bdu2TDT5u7baZlMN23Xhm2++2bRpEz6EfLFD8ttCIgPv7bffTklJcehb1B60TmsegsyNyMjIgQMHxsXFob6i0A4RnRaR5ifL/fv3T5s2DZONYOBx9+3b1+49o1XKkCFDOChawkFrzx8xOU1sH8Eroli6TotI0z4iqwgz6ks6kmxcATdBg8Ve429ZRZGBzOyCwGNMKYEVO2Sr9V1L4umJEycGBgYyHm7evGlVFcsul+gpn5BO0jWPGTri8OHD/v7+dPSHH374gQ2tW7dmSWL79u2Dg4OtJ8UE1tFp9nr//fcZLWg5Ebza5hz2skNtsIHE1atX49nj96uk2koV6jROEDYCYyHdAGqDprpB36Wmpg4ePJjeFD12Bjo9fPhw5pX6bYJUz5s3b+bMmWPGjMEWi1oTXistrTKY4f379x80aFBMTMyOHTtwydX51HSuXbs2Z84cmlqeIxNldYjotETAosoeJb59KfenLZ0GEpFVUh6q03bPAJfkxo0bCxYsIDNBG5kZZmqDpmowLbFjdu3a5ePjgx6juO+VAKlGp8eNG0dvqrKKSkOn2cRebdq0YcY5i481j0wV6jTe2TvvvNOwYUOMu1y1UBs01QpzJhrXzQiDHk2nLTAE06dPnzJlyvjx44nOq1qqRadDQ0Oti2bqlGo68n7vh4o0iO4iwCilSioBGeQutVz35idLS6fxxVlCSakmkR0JiUp5m/qFCxeoJBXAkxswYIDW6aqGjmA6oKxMVYuMjIzSRZqQWuLpkjp9+vTpCRMmtGvXjmzsyzBYtmyZ3OxQmTQVpgp1mn7auXMn9gL7rnW6ukP3EZLOmjVrxowZ8plLubJtwU95+YnotN1WOxITE7Hv/v7+SlGrDNHp2jb20GkEkvDU1NmH8FCdtoVeQ6GBXeTRM9ZxCPgpUm2r1mwiw6BBg6yr3LbQKQTTS5YsQf5HjhwZExNDT0VGRsoHnVQmTeVBqwoFBQW4y4hu27ZtiZKR2ODgYG9vbxwvAmIRZlveffdd0nGsAwICRKcpxCpz27ZtHTt2RMXJiU5TyCeffLJ8+XLruQTJWblIyVu3bh08eDCOoPxU22oiVavTdqgNmmoI3YezhVRjdidNmqT01gaEOSIigsnv5+dXFp3GOj+Gq99apx9K2XVaQuePTFhBnpFh+d+d3K62FWkoXadzc3Ojo6PZEd9u5cqV8fHxkydPJrbev3+/thVVAa2KQicnJ69atWrIkCEoK1Ey4soK4iqUotPssmjRoo0bN9o+58GKnU6TE5DqFStW2D1TVolQLAQGBn73u9/9wx/+gD2pogO5CPo5Mk05YDIg1TNnzpw/f/66detEdJFkUWV0GnkIDw+3UpyxZ8+e0aNHY+gJpJSiVg39+vWrVTrNmSKKmEg0Fc2Ta9SlIzqNxDrMLIl0KxnQY1SZzAiwSLIoMSDbdjrNukTYCxcuvHv3rlW9O3fuEAatX78+KiqKbMOHD0c2NmzYQFREhfEAtE5XBTRpfn7+3r175aWhlkgDITWBtWAl2kE6Ybevr+/atWvpIOt9JoBOUyCFqKymWuOvMwKzs7PV4SsbOTTxPTr9ne98589//rN8IRtUDudINrh06VJkZCT+IqhtrorWaU05YHATVe80v6jDhMe/Rq3lbjTCHBcXN2HCBGZy6SItMK+w/oTUxFve3t64xkpaK5VaqNNZWVkeHh4E02URaSAbAoymYlgd7kI3kQEZxq8im4Vc8SaWwkzLG2QtnSacQs7RXeyg3JyWLkCkExISOBA9jtH39/dHpAnRRKe/+OKLUaNGZWRkSGZNZUF7oqxE0sw1I2ouLqtlBPVFyBH4AQMGUBQiRxR78OBBJjKqrDIVQc7HoNNLly5t3LgxOg1eXl6SqHI4hzxULCwsbMiQIY0aNTplora5KlqnNeVGpHqsyZQpU6Kjo+Pj41FrEgm1Z8+e/dDr3oBrj45irAmh0IBhw4aJslYutfC6NzqNXkL3EqCg1tKKtlmiuATK7CLCbAvWlq2oMtpPYwokEkPTayLV7GsF0yxJoU9ZX7Bggd2T3hcvXvT09KSvcemML0CtX49IA97erFmzOBx237C1WqcrFUSaIBjfWkTaNph+BNiXMTB//nyGwbRp0+bMmSMXvW1B1Bkb9C+emapEpSKDpKCgAOPTsmXLf//3f2dcPXTYkOHs2bPUfPDgwXXr1n3qqac4lwsmKoeronVaU25khtwzwbCitYTFy5YtGzduHHESsVFZ4mnyHDlyBLUmkib2YuZX+r1qeQ1ZLdRpZJggGDMq0AiCrLMJlSXkNaTbBE2VW87qtw1YW/bq16+fKdAPYBcsPuWIMEswDbISEBBgibRt42MQ2TcmJoZBIgotINt+fn6UmZqaareLpuLcvHlz+PDhRL2ApiKiSk7Lz7vvvssSeaOoL774Yu7cuX//+9/bOLqrzYEYaVUkgTJIABfkwIEDLVq0oDL8VJudQAbM1NNPP/29733vX/7lX6hkZmYmUQeoHK6K1mlNhWCIo7jyQQ4x6KijXAkvHdFylkyziRMnIhtYaiWwlQTeA7OXWP+hE7jGwJmeO3eOEHnChAl0ikMmTZo0aNAg4g+gfVBWFBeRRqqxufhMyDMCzE8EmLBbhFkYMGCALC2dZgVhZkkPUhTLpUuXotByC1NVqwjiaXZfvny50uciiKdnzJhBcHbmzBmVVVMZ0AX0xfbt2+loxPWDDz6oiEjbQjkocbt27ZxdQicDAwOdpg4lR0KlICUvWbKEajBQt27dWvqB2Lpnz54333zz+9///gsvvHDixInS87sOWqc1FcKYKCabN28mMg4PDy/lf1m2iSkpKYRcc0yw0R4eHlWh09OnT5fqqerWdDjTa9euzZ49G29p8uTJKB8tYIdt4pQpUzBwiCvKTXMRAKGj9CAWFq/LUGZHiE537NgRdbdEGs2mE6Ojo60b0qCqVQQ6zb7ENHbxdGxsLB4ew6PkLnZIsYJK0hSh2qUIScnNzfXx8Wnbtm1lKXTZwW9buHAh8lnKH+jLhXVSsrQFR4RxKOtmXgfI1jVr1jz11FPoNE5hKZldCq3Tmsphy5Yto0aNQqeJ0jDWDkNqDPG+ffuYuvPnz0ee0XXkZNiwYcRwKETl/p0a1ScuRIqqy1SsFExDVHj58uWZM2cGBAQQOoseO4PwGp1GodFmC5oOq4foAitKnG0Qne7UqVM3E7qvT58+HCsmJub27dtSB1Wh4hBdeXl5hYSEEECLQiPYa9euxVGjwg/95zRb4+LiCInMI9Sibi0j0ixQUFCwd+/eeJOVK1cyuR6/SEObNm06dOiAw0dH37lzR1XuUTtO7VwcK52B+vrrr8vFM5BdHHLw4MGPPvqIcXjlyhWV5PJUmk5L69iiNmhqB5ZO//3vf0eqjxw5YkXP1kpycjLaQOzl6+s7evRoZu/27dvXr1+Pla/058jMy7qeyECtGo1yspCVlcXpjx8/XvTYGQ51WsDwESIjwyWlGp3GB5Lr3og0hpgQ+Ztvvrnv/GMJkiifBhkxYgTabMq0odMrVqygTLkICWoHGySRJeV/+OGH7777blpamsOctRaj4cwvUCUlJW3evJmG7devHzOxXbt2tFhFnhqrIEi13KhOSEiwpFpVuvwwALAzU6ZM4TRtBxvg9//tb3/DBSRd5XYCGfLz8+Vf4KBSXZtK1mms87x58xglNITaoKkdMH/kujfmG8suOi2ISAPWmTiMbExaFHqHCZa6cnUaD0CUhpKr12ysLDjfq1evYs7wnFhOM1HKXBxbnUY+BWk9oB/lyW2RZwvS5R9Z7u7u6DReFzEKTa0O7wjphZLXvVkh4KPMkydPqqzFYS9KJv6eO3fusWPHvvzyyz//+c+xsbGkqxy1FbNFFUggTjDtSWchz/K8WNu2bZVaPlHkTraHh8emTZvkcos6gXLCjsgK4+173/veO++8s3v3bvPUFQwS3BR5KgLUPjWFStbpJUuWtGjRwsfHp4oex9e4JnS96DQm+PPPPxedPnDgALEyKyLSCPaaNWsw01gT5Hnnzp2y5KedTiO0oH48EhS4YMECS6RBVbR2wPkSNJw/f97Ly8vPz2/y5MlKlm0Q5Uane/XqZem0LG2lGvOKJJPHNqpGDPDGkPBPzYe9O3XqNGbMmNLvQbIJm0CPM0jkxSaGSptPkBEJTZw40dl/bWXHP/7xjy+++CLyTNTOT4cPqdU2aIFbt24xy/bs2YPjS8xKp4g8P5Gr3KVAfagVloFeptqCOo0ywy7MaCzDM888g1S//fbbCxcuLBlYC2qfmkIl359m/jB0mEg1srE0DpG+lufI9u3bFxER0bNnT0wq9tfT03P58uWrTeLi4mbPnt2vXz/cauTZQuJprP8Qk6FDh5brw5d2is5PZKZ79+7MYamYqmXt49q1a0FBQZ988glL0WZb0OmxY8cScNNWJXUaZB15lkfBWRJ5g/V/a4JpBBsIcWj20mc96fgNgwYNWrp0KT1u6TTDY+DAgRkZGc4uV7Ij2owIrVixgoDJWfm1EJri1KlTtIybmxsxNArtavJsC3VjKDL3U1NTH+0rHewCuIPYhyZNmvzyl7/8h3/4h+bNm6ekpMgmla8mUsk6Le1loVI1NRfp6Bs3bixatCggIADvnpAa497ZfPMz5oMVAWsCSPiSJUu2bdtmXPI2YeoS8xH5gagC8qBUt2yMHDlSBN6Cn2V5oqRmg/LJ/5WDg4OVONtAkE1f0NqI7oABAxzqNAwePFjuRnfs2FF6UBSaSFoQnabNjx8/Lhcw1OGLQzo6jZdm/Xla1BqdpoalPHlrdKEZOGqRtoPWwL+h+1wwgHZIu3btsAO9evXCvTB7tdy9KXvhEebm5k6YMOGVV1559dVXGd6SrjLVRCpZpzW1EGYI8ty1a1fi6f379ycnJ69ZswYN6GCCVIPtOjGZJdXE0yy3bNlCOA6rVq3y8PDw8fEp4ztP5HFxeXcHigLsm5CQcPPmTYKwGj97S4dzv3LlCirrTKdpNISTBsRPslVoEJH29fVFhhFm5NnSZlbADKTVP6fRadRizpw5pVz6Jt1Op0EuehPNZ9yo/tAAACvTSURBVGZm1uaeejRoMXSaeVctRBqoJ0E/FWbuV/CaKzvevn2b4X358uUq/TCXi6B1WlMhZIagzZ988snEiRPR6aSkJHR62rRp1pufS4KyLl68eOXKlbGxsYmJiSLYgOHG9ON0o7ilXP0ePXq0v78/6oLSoCJEe4RlxPSCpdCgalkr4fQxZKXE07SeRNKCkugikR4yZAgaLA+L0bmmRitVtkshKA8ICLh27VopbU66rU4LrDMSiMXltqXKqikDNBfjnInz0UcfKRmsDohUM/C2b99e+l/4Horsa4vaUBPROq2pKMwQdBr7vmvXLkR61qxZuMyYD6XJTsDcY9+JniMjI+Pi4lBrdHr9+vWYcnSXrciw3b1nC8Jo8hCye3l5eXp6rlixooJzvkZCa1y/fh13h2acMmWK0uciRKdpPeJpEWZbbEXa0mOBdTpXQmrWEWzWOQTBtDqwI6hMTk5OYGDg/Pnz4+PjEWl8snnz5hFMHzt2TD8XVl5orrNnzzIFHuGLGs54PHG5SDUTnynPKRBYq1PSOEfrtKaiYDLQaV9f34SEhAULFmDErTDaNp42LnwX/ykrYvFR9y1btuBlY8Hnzp2LDA8cOLDk1W/0AP0mP3K+bds2YjjQzy06hAa5f/8+oaqPj09QUJDdX7PQaRq5U6dOBNwI8+DBgwcNGkSbsxSRZhPekqnRxUSaJV3cuXNnvLHevXuTGcLDwwnv1IEdQWUKCgoyMjIoX76LSn1wFHAjLl26ZPae7r6yQlvdu3ePycLccfHHx5xBtRld+BlHjx5VZ6VxjtZpTSWATuMgz5gxQ/4/LYgMl47K2qEDk3b58uU7duwgpMZ8k4JOlPxTtTyfjK0/cOCA9bcrVQmNI3Jzc2musWPHKn0uAp2my4iY8XhwfUSz5TEx2r+b+Y0sO5EGomfkmWz0HdnoMpwkFLqMD3ldvXp1woQJI0eO9Pf3Zzlx4kRE2tmT3hpn0NRXrlz54osvmCbt2rV7gq8xqQhINQ7f4cOH1VlpnKN1WlMhTKE04umAgACWlk6LDNtSSjpq4enpuXr16sTExMjISERCEh3qNPJAJKFFuozY6rRtSC3xNO1PTEwYjR6LMBOiIdWIsSi0nU7jTkmvtW/fftasWbaPAoA6pHPIc/fuXaRdYF3+/Ko2a8oGLXb9+nXmAnOEjniE95nIZ6+eLG3atOncubPW6bKgdVpTUbAaKHRgYCAxrug0tsOhJDuEzLjVU6dOjY6Onj9/PmJAYik63bNnT3kVkTq8plRu3rw5fvz4L7/8EmFWEm0i8TRNbfzXys3NVpgtTGkuBjpNvwAdFBYWpkPhJwKDn5bPzMz08fGhI6rjdW+g2niEWqfLgtZpTYXAZKAEa9euJZ7euXPnpEmTRGUNBS4bkhnPmkgOwZZEVlAOvxJf0NI6XV4w6Dk5Ob6+vmPGjEGwg4OD5U2iotPWxW1BqXFxbDeRH5HGvNJHWqefFAx+KCgoOHLkCNOkml73BtFpOR11bhpHaJ3WVAgm2N69e3v06BEUFEQwjdyK0JYX1NoCMSCSJkC3fd6bdRhuvslS63TZoaFu3bqFPNOedBMOELH1hAkTxo4dK7orAuwMFPqTTz7p1q2bSLX1kxB8zpw5uheeIDT+2bNnBw4cyJRRuveoPJGInIPi823dulW/weahaJ3WVAgmGGG0j4/Pxo0bCabbt2+vhLf82Iq0rUJbjBw5kuihd+/eBw4c4Lh6bpcFWklCaqTa3d2ddqZ5u3fvjmDLfWjRY2fI366wp+RnnRSkmnW6afHixTqefoLQs/n5+WlpabhNElK7wl3ncoG5GDp06MWLF/VcLh2t05qKkpSUNGTIkPnz52PQH1mn7UTaTqeJBYkCe/XqhUjjFtzRf8QqJwgq1pBWRWvlHjNtTmsjuiCSXBJLkuXxMdYlc8+ePenuGzdu6C544mRnZ/fv35+5Ux3vUjMOmc763/MPReu0pkIwwUSn58yZU67Hx+xgRzc3N4evIUNdUOiBAwdij1JTUy2RBlUJzcOQ5kKqhw8fjugSgYGp0Q8Raekd0XXR7ClTpmRlZeXm5ur2dwUQOSYFnYVOV7t4Wt+fLiNapzUVBZ328fGZPn16RS56i8YjHgiJ7Z1pVviJbHTu3Hnt2rX37t1TR9WUH2z6qFGj0FpR4odC8C0PHKDQdA0/QX+IzNUgpPbw8KiO8bR+3ruMaJ3WVAjsNTqN6e/atWtFLnq3a9fuww8/ZEWCNl9f35EjRxJb+/v7y+s4kI39+/dreagI6DTRsMTTosSlQ5SGMItCs969e/dp06aV/h5vzWOGjrh48eLgwYPbtGmj1O+RQOYdojZXAa1bt+7Zs+exY8fUmWico3VaUyEwE6LTWPOK6DQiLVINlIMwE8nJ+zeYzH379t28ebN+LrSC0Hq5ubnDhg0ro04LItU9evSYN2/ezZs3dRe4FHSHvJCVDiqXrIoMI5Ys6V/PEtD1VffFTIp1c3Pbvn07vqM6E41ztE5rHhEMBNy+fXvt2rXe3t5Tp06Va9dKe8sMu9jqtK1adzLfZLlly5bLly/LNxNBHV5Tfmg9bPrIkSOxyzhAIErsEMmASH9k/pc9IiIiLy9PCpHSNK6AOSeMb5z07t27jJpKNnd3d/JbrF+/nilmx4YNG9iEC06kblvyBx988AhvQLOF0pjmffr0kZvT6kw0ztE6rXlExEDs2rWre/fu6PScOXNEcZX8lhl2QZKZt3ZIdE7hhw4dkmOpA2seFdpQdBrdLUWkJYAGVmh/AjX9dLcrQ0ianp7uMJ4mxQ4klp5duXLlxYsXL5iwYvtspgUuOFsXLFjw8ccfWxfVEWkmJm60/Hw0KOHzzz/fvXt3KR8s19iidVrziMhkTkxMxDf38/ObO3fuo+m0Q5EGikIktm/fridzZUEzotMjRozo2LGjSDVKXBICaMng4eGxdOnSrKys69evm72te8HloFPk/jRdhgwrJTThJ/OIfqRP2YrcdurUCZGOjY2V+xfSodaKw/Xc3FxccLkAzlSlECYmOm13rLLDjliMpKSkqrtCJsXeu3dP/pUgqG3VE63TmkeEof/NN9+sW7eOYDo+Pn727NmmTD9iPC2IQmMFSESkt23bJp6+OqSmYtCS6PSYMWMw3CLSDqNqSSfkyszMtCyd7C7laFwHOuX8+fP9+vVjKsnNZgFlRVMJW3ft2nXs2LEvv/xy7dq1QUFBq1evtkQaVClOkDyLFy+Wuenv7z9//nzEnp+PfOmbcnr16nX8+HEpXB2pUpGS09LSJk6cmJOTUwMeqtA6rXlEGPo4xX369CE+W7ZsGbGXKdOPeH8a8LK7FEFpRNK3b9+WA8kRNRXEsF6FhcTHJ06cwIoFBgbiYJ0sAVtZyoVuW1QpGleCfsFdPnTokJeXF8JsgbOVkJBw6tQp+ShZdnZ2Xl7ehQsXbLtVFeEcycbsJiiX6ys9e/ZkqopsK+EtDzgQzO4dO3ZU6auKpOR9+/ZRZzyYGTNm3Lp1q4qO9XjQOq15RBj3Gzdu9PHxWb9+/dKlS/GymcBKe8sJO3700UeE5kePHv3ahAgAka7WU8sFMaxXEQTWZ8+exXar345Qu2lcGOmpe/fu4VrJ3BHS09NFnByidi4DZL506ZKUGRkZ2dF84w3Lzp07P0JILcE03oPUoVw1KTvGGRYWpqSk4BNgW1Dr8PDwai3VWqc15UamQX5+PspKML179+6oqCg3Nze87EeWamTe39//6tWrMpfkECBH1FQK0p7SsBYlUyzMnTQujeoqR1hbS2Yzdy0TVn6WmzdvRvlwqYcOHbp8+XL0r1xSTTDdrVu3xMREccEFOUrlIiXfuHEjLi7uY/OG+qeffmpJtS1qB5dH67Sm3DC+EekdO3b069dPLnp7enqitY8s0sC+PXr0yMnJqUaTR6OpPTAxL1++TJC6f//+48ePX79+HRXEvVYiXAbQ6c8++ywzM9NQyCqe5nIIkWp3d3eR6oiICLkL8M0338jLGEDt4NpondaUGwb3rl27+vfv7+PjEx8fv2TJErm+pCS3/Mi+Xl5ely5dqi4zR6OpVZiiVoz09HTEz3ho7WHPfkuedu3aMcfPnTsnu6tyqww5ys2bN6OjozuaBAQEXLhw4dChQ/7+/qGhodXor4ZapzXlhsGdm5sbGRnJuN+3bx+B9dSpUytyf7pr1669evXau3cvYXp1mTkaTa3FEMDCQmLTDRs2EK0iwHLHGjFu06aNxK/y360PPvgAJ54J3q1bt379+qGRxLKyuyqrKuEomJSEhASOTlxBBfr27du7d29sDj+tu2yuj9ZpTbkxZ1lhXFxcYGBgcnLysmXLevTowcwsi05Lno8//pjZK6DQKL08mKoOoNFoqgNEq6tWrUIFhw8fHh8fzwrTefXq1SyZ48x05PDw4cPm3whOZmZmPs5X/3IgRJqjf/HFF8T9Pj4+gwYNIqCnbm5ubr6+vvKmepXbtdE6rSk3DG7m24oVK4in9+zZI3+pLKNOt2/fnmlDFJ5exIkTJ/T3pDWaaofM2dzcXGbxmTNn0Gwi108++cTf3//AgQO7d+9GGvfv35+Xl3f79u379+9LflD7VzEcCOcAPSYYIJL29vZGpIn1AcFOS0t7nE5DBdE6rSk3DO6tW7f26dNn1KhRS5culetIpWCr36wPGDCgGl1x0mg0ZYEZfezYMSJXT0/P7OzsGzdu4IKfPn0ab37ChAnXr19X+aoSsSosiaTx/i9fvhwbG4swE9x37979888/J6JAsBFpMsgu1QKt05pywzQgmMZZjouLCw8P79ix40MjaaYHS3K6ubmxYzW64qTRaB4K0xkIqdevX9+jR4+hQ4eOGzfu/PnzhNpdu3YlkL1y5crjmfIcpaCg4ODBgyNHjhw+fDihM5YHA4XxQbAxPqmpqYj046lMZaF1WlNuGOLo9GeffcagJ6p+qEgzb2NiYrp06UIkvX379sOHD8uzJKo4jUZTI2BSi1SjzR999JGfn9+QIUMQyL59+6LTKlNVQgXu37+fkpJiybNYJ+rA0tfXF5FGxVXu6oPWaU25EZ2WOWBhCLITPD09mR7M2H379uHJWneqVHEajaZGIPP69OnTOPHt27f/4IMPWrduzVLiaZWpKuHoiLS3tzehMyGEh4cHCj148OAZM2YQ4h86dKjaRdJCpem02UEGsi6JmhqGdDFCu3z5cgRYSfTD4mkiabkGJe/r1mg0NZjMzEx0ul27dua/pt//8MMPH4NOi2kiViaaxyIR0Pv4+HBc1Hr//v1ZWVmHDx+ujpG0UCU6rampSBfv3bsXFxUBFpEG0WNnSB520Tqt0dR48vLyVqxYgXeOQgNzv6p1WuxScnLylClTiJs5opub2yeffEIdfH19bb+urXaoblSCTsv547PMmjUrMTGRYEtt0NQ4pK+//vrrVatWBQUFMR/knd6gNLkEbPr000+XLFnC2Lh3754qSKPR1FAwEUj1smXLunXr1r1796VLl27evLkqPqtjWiODpKQk1AdJ/vjjjzmoWCQgmObQ1fRaty2VptP0SmBgYFpamtx9VNs0NQvp6127dk2aNKksIbVs6tevnzztCaogjUZTQ5GZjjDv2LEDW2G9gAxUjgojpRUUFFA4h+jbty/y3Lt37x49eri7u2N5vL29N2zYQIRNnko87pOi0nQ6Kyvr9OnTNaBFNKVA/+KcRkVFWRpsYeqyQiV16NCpUydc2t27dzOdVBEajUZTMUzNMQKGUaNGeXp6fvrppwMGDPDx8enVq1f79u2HDRuWnp5OBpW7+lNpOm2hUjU1C+lcnNNNmzZ5eXkpHTbl2VqxkBSc3C1bthw4cKB6fZpGo9G4OGJPYmNjsTZdu3aVbwKJTmN5xo8fXzPCaItKe45MU7Nh0N+/f19E2ronzfTAn+1Y4j0n/CQb3i4KLROmJs0ZjUbzZBGTsnr1aoxP586du3Xr9omJu7u7n5/f4cOHJYPKXf3ROq0pEwx6dDosLMwUaANEetmyZfPnz3eo0927d580adLkyZMXLVokj5DUpGmj0WieIGJPMjMzQ0NDxeBAQEDA+vXrDx06JFtB5a7+aJ3WlAkGPTqdmpo6cuRImRW9evUaPXq0t7e3zBNToB8geaBfv37Xr1+vSXNGo9G4AliV06dPxxaRlpZmiLOJylFT0DqtKRMy+nfv3j148GClwEV/ypKl0ucizO0GWqc1TxYZumC7DrJVU61RfVkCtbmmoHVaUyYY+nLd27o5bYsSZxtI7NGjR0RERFxc3N27d2vezNFUFxh727dv79u3r5fJhAkT9IDUVC+0TmvKhOj0nDlzSuq0Uuaiq9+S2LNnz9jYWNsnvWWp0TxmGHho83e+8506Jq+++mpubq4ejdWa2mZStE5rygRTwu45MgsRaUFS0PLhw4frqEXjCjAIN27c2LlzZzcTPz8/eZGk2qyphtB9tqjUmovWaU2ZYDKg0/v27RsyZIiIsR2WSHt5eS1cuBDL6PAvjOa0MqA0tWauy0+VSaOpPGzHmKBHWnWHHkxLS4uKitq1axcdqlJrLlqnNWXCtHUGCxYscHaLWlbGjh1r/Wca1P5FSOLp06d79epFiGPxySefnDhxQmXSaDQa52BDjh8/vnr16pSUFK3TGo1C9JUpgU4jxg6lWujTp8+KFSskagG1fxGknDx5EmH+p3/6J7lfCP/4j//YrVu3M2fOqEwaTdUj41NQSZpqguq2IlRqzUXrtKYcMCVwYIcPH6402QkDBw7Mz89X+xSHEk6dOjV48GB5+FbgJ+JdG+abxnXIyMjYvn17DfiYkqbGo3VaUw6waLBkyRIlyE4QnZbMYLtv6UjOSkSVWwK1WVOL2bJlS//+/Vnqz61qXByt05pyICKXnJw8c+bMYcOGKVkugYeHx6pVq6xgxRZV0ONCDnro0KGAgAB/f//4+Hj9GJFGOHbs2KxZs+Li4rROa1wcrdOaciCyJ6SkpEyZMsXPz896gsyOPn36YATXF7Fr1y5nF8OrDqnq5s2bX3/99e9+97tvvPGGSLXarKnFyNgQVJJG45JondaUGzFtqF1BQUFaWlpISEgpak16ly5dxo4du2jRosf/FWqrqlu2bHn33XdbtWpFbam22qzRaDQuj9ZpTbkR8ROQQKLkY8eOBQcHjzHx8fHp2rXrV199JT9h5syZ169fd/h36seD1BMv4d69e/q5IY1GU73QOq2pEIZWmyqIBAqHDh1CmO/evat+F0kjqH00Go1GU2a0TmsqhAiwLcTNaDMrKodNHvVbo9FoNGVG67SmQigFtkHrtEaj0VQiWqc1Go1Go3FdtE5rNBqNRuO6aJ3WaDQajcZ10Tqt0Wg0Go3ronVao9FoNBrXReu0RqPRaDSui9ZpjUaj0WhcF63TGo1Go9G4LlqnNRqNRqNxXbROazQajUbjumid1mg0Go3GddE6rdFoNBqN66J1WqPRaDQa10XrtEaj0Wg0rovWaY1Go9FoXBet0xqNRqPRuC5apzUajUajcV20Tms0Go1G47pondZoNBqNxnWpRJ0uyMvcNq1L4zrNB4XFLI2Y6ufRsU9Q7NG8QrXZEQV56XGhXh08Ys6ohFpPYU7qvrN31Q877h+LaP3WsMTL6udjo/B6emxQlxbjk/NVgg10+uaQDg3rNPFefDgnvzAvc2toh3r1mgxauHPv2gfpKvMDCq+s69s5KvMhY2N1UJdOQcl5KsERpTXXY+LuyQj3xsO25hrr+Tn7D569byaXjcK8U1tD3OoxZxYfvJRv05ipJ06ollxy+FLJEyy4sm5o56iMUtqvMO9obFC3FkGOOs2i8OL+fefKU9/K5/7JiNaNRyTmGrUoV2/a7lgBCvOzkyICujykoQxKzVl6S5Y2gx7Gk5r1GleicuPpvOSg5nXcY7LNH4W5u4JavtElIu2O+dMJZ2LcX3PXOi3kZ8T0aOdcmQryzmZeyi9N3KqI/OSgBg2cWbJinS5ZzQ4tnl6Me1kxPevVaTMt7aZKcIhRVPPSdPohzfWYKMzLOmVIaWF+ZkyPJg+393YYZ1nHIyZbdrNptActWYLCzJiujeq0nJ52r5TBYBTltNMM7mTGeJa/vpVNYd7ZU5eNOpS3N60dK8r1pMBmpTaUhbOcD2/JUmdQ6TyxWa9xHapQp02vf2D9+oMTrl85HDHcO3RGoHu30OQrhfkXU2K+dHP/tFuTRq0j1i0xdPpUXursrp2+jE46nXfvbELoxLDIwA5vjdqaczMnJWaUm/tn3ZrVax1x0oG/WpB3ONLbOyQ8sFub0D2592xLPng2YWZQWFhgh3bDt54vtMtZmJ+zNcQ7aN7soW49ljsyhcUqmX43e1No0OzIwM5vDd+c803O4dggN/ce/dxbvVbvHQr/JudAzKjP3D9za1Kvc8TXx23qn/9t4fmto4cFLZwx9L1+y7Pz8x2U02dwjw7Nm/SISL9+LXW2e/3G7kFLEjNKChg13rvAu6Nx7SHfbkdM293shMl+IeFhg9q+F7Tj4sVS6nO9WF+UUj0sQ/7ZhODhQeHhEz2a1a1EnSZE8PAJ7NesceDOWyrJEUZRjdp6fN6lXYsmXRak3yk06vPgXO7YNNflbJu+LtYdJ69nb5zgHRQRFdKvi1/syTv3smOGek5bHLMwsF29598P238uwb9pnTZBSRfy88/E9vUITblWmHcwrO/QqbMChy/PMM+YQGrDl97jF0VN9OwSGHvyZrE+zTyXvGBwK4/l2YVXUqd3rV/fPSh6w45VI+zLNE/IIY+g0/dPRnoMHdGvUYvApGsqyQFGUfXbevTr0rZokHAiD/r34tX9090bG/VNPHmjWL/ftZl04V+fLX7u2cu9PKdEx4QHtmtY9/2w1HMb/Zo2aBW0/VL+3Quxg9uGJucVMtHC+3qHzvIbszzTdNHzz278cljQosgQzx5+scfvMHwfTL1jOcmR3q36xmTffdCb6ekJfi3rtJqQdOlu/oXVfdtOSckrMM/IlrtFO+YX5mwe7T1+4Wzf93osdzDYwBjGgSGLZgxq1Sko6VKh3RQwG8oc3bRPQrBf6KIw71bvTdiZmbrA4+UGQXvvXNofZqyQwcpZ3Jg4bUk5dOkzqCDvxEKP+vijly+lhpsrebZnlFnarC/R1JoaSpXq9LeFZ6I61PlLyIYlnvWwRHeyYzzqD9p4+dLBBZ6vPeudcD3v4vm8U8TTnWcvjwxectiYjQVXEka4RRwv/DYr1uPVt6ZuObzA89lnfROuXz9/3uEV9AvrPJsZtiw7xr2+z9oT+x+UnBnv7bYgo7AgJ7ZP3bfC0u/b5hyckHsm1uPTsPRb3+builpfcnYX5herZHaCt1dExt1vc2I96rabnZyymE3DEm9KXNgsOCF1oeezTbwTzuedz87caFP/sKP32eX9sPT8gtxty9dfzHJQDocoxE//46shKQWGaXYWQRbknUkIavUyp1CYl263Y35WTDc34zLy/czoT9tO2lpKfa6ss+mLhFzn1QtLz8tZ5/OXkAPffFt4N2nU85Wm04XfpEzziEi/mRzctP7AdVdKmuAijKI4i5zCm4nDnv1LSEpusbHBuRQ1V+GVjTZ9HZp42Dr9izfOxXR9h97nsOdie7zRMep47tGjZ+8zzIY2ahKQeLXAvK7YpGtMZuH9o2EDl2YWfpMZ1a0h50rM2uXV5h4hCZnpMV27hqVjB/MvxnrW7xiVefFBo8WfPLEpqI2cpnnqZivZl6lOyCHGXuXT6byU0KERJy8mB/2lvtc6dMYJRlHmILmSOOwNY3QVXizev5fIYNTXLv3wmaPWpMtOjbY799wTaWfvGg3eqKVfIkp092RE5/pdY7IK76SHjY7KZLBkRHVsZXRKdkyXei08gtfsWNLnHTqLHrgY26N+t6jMjAdTL/7rM5uCWsnpF/UmVTeuadfvGZN17356xECHl/cL84p2vJsT2+/9sKP5317eFrUlR222hUna180o5G5mTN+2YUeJHopNAbOhzHbIjOnWxziF+6diPvUIS89R6VaGByt2xiTXcUuWcQZZJ65Wrtuc0caTzmd9gX1TbzxTwZsAGlelanXaGMfo9IGMlPX7sm9lJ45qZW61hjuciXFvUKfOyz1jz5mzEZvyVrvA8JiYyCD3xnU9YrMNY+VkfBvkX0pJTM7OzUkc08KYtNeKSsa2D2/QLnBhTMzSIPf6dfvE5twpnjP3ZFTPhg3dgtYdd3IH3aaS6ESDDoELo82yGnnEfm1tMh0RnN2kIhNjX/+ce8eiur7WsEPQupO5ht44LqfoWDamyhHWPQKrbrKy6+K6/o2kqsKDckrUpzCnWF+UVr1kZYyUZNgewBajDnVecx8WZDLGs1W90nX6WtKXwzHB395LndayccdSbrLa269M+3NR6Tfs+zp7V9HpY5P711N1uHcmqquxF6u39gQ1fdnQUSPduOrzLHYxdcFQI+VmSshfTCeM+rcyTkSZdePkje6m/HNHHjTat/mYezlNm1ayK7M0jL3qNHUfNsZsvkDPVg1Uo5nFSfsX49bOL/sszyosvJc2vWVdZO8blW6P3SBJznc2/OzTsx6cSMlzz8Gvuo6LUM/QZuMwhVfWeT3bKexo8tyhK4yUgpSQV98YlnjFrD/7nrNG0beFp6I6NPGIPVZs6hkmwjyEkV96jZzn13k1ez8sOXVusDFUHFK0472TUV0bvmr0hoOwG9DUNsXmlN0UsNqHky02i0o04IOVB2anSZ3AB2bn0WaQdeJFK8XPyNmsT863b2qHpWtqAlWq04ZYPttoRGLutZPrpg4PXhQ9ViIPa8ABo/Dlv3zWtWVj73UXSeBnS2PkFVHa+DYoyDsZHzo8JDp6tOlcWzptWE/T2lrY5TSuAKcvH/Feg9e6Rh1zZAlsKolFeHZ44k3L3hbfhEN9ZXeRibGvPxTmHV3u16ZBw55RW+c5KadoxZqxjnE2Y5POqMigiAfllKhPYW7xviileuz7aoeoU/wutReMOjzodDOrVUmbwcCBLp7PK8Cs92vtaYrS2ACPFnVLuclqnYVaSbM/F5V+zb6vrR1Fp42rKUYq2ep5rrvy7Z3MKI/6BMTWcQ3Zbtauo3+sMQIL7xye8V7ToesyD0f1HBSTedfUKmIj87oi3V2v/7orBQ8a7WSuI522K7M0jL1kQBrYNJpZnFI4g4K88xfzCjmjoa09A01RH+7RvGHLaalOdMxukCRz/o6Hn326zYk4Ovf8zMVd6ncyAmIFst2qSbsuw5SrnXd4Wqem3msyTyzu+Vl0pnEdq4lx8cPYxIhq5rnuQrGpR7EldfrbwlvJwU2btO44LO6ik9Fh6bTRMunL/d5r3LBr1EkHY4mDNh+UcEn9AvspYNMORnBsxaQlGvDBijImI0dG1qnzbWffO05asmwzyDrxBy1ge0aZTnXavqlVeZqaR1U+R5aXMr1Dc6bi3ZMRrY0riYahLDYxjFwM5dfcow/sDWrduO/ai4W5yUFvNfLCpTdvRs7bmLHH+fgG4wLjO8bIVpP2QTx9Nzno5UYDY7Pumvdul8SfSi2eM2N5UGx2YcH1BN/nJcayx6aSd/cGvfyGV2ymeQdr87z41D0PjhLc3HvjlQcTzL7+Z7JWBxFRFeYkeL/lsWSpk3KKjlXMVJXEbCtHM/Zu2rRm9TpPT71OuVsnRybn0mqO63PqRPG+yC6lel9/HdauXpfFmfnmVbvnRyXddWgyjTpYnS4GyZFO3zoRERSZkZcVM2x40cOrxoXQen8JSr4uP+2xt19Z9m17T2Ww7+uM7VYzFmbFdDWutRJ05qWEdPFal21zzfZmSnh0mhGDmRdve8SaksC571ocERUTuyVNHrQ2Htpqasb9hd+khP7Fa22ObaPFZjrWadsy8y8e3Jaa4+RRIGOvsuj0vfQI38UZ9zNjPh9b9JCzcS26XtPg5FtO+6XY6HI2jO3TM412lRMpee7Gpd0/NvHbcrWQyHlJeJqh1uZlas8ij+RudlKM0X6b0synn4x7Q8YFc1a/ORDyF591OcWnniW3VncLxtRu2kNpvyPUjneyl09n98LrCd7PS7hvx620ae/X6zArNa8g/+LWyXP35TkzRwWp05o17jA9Ja/w7sWtc+cmnyP9+cAkPLXEYU2LtWSR2bn29aqX68SGJt5w0sJlm0HGiTcLTLpuXLd7lha4VvyMMpzrtF1Ta2oslajTBXlnksI8Xjb/lxUdNXuUZ9d+8r+swqvbRrV4tXnnfmP8ujZq8L53+OKpXRrX7zJ9Z+b1vIzVw5o3aBG47sTeme/XfdU9ZGN6evyo9xrVqVO3wXtjEjIz90/vVr9+t2k7Tzu+Ol14aeeo9xo2d/MaM+yzRv/1x/Z9Pd2l5LxCTPmotg3q1KnToO2ohLP5xXI2bjV88lj3NoNmL14Y5Dc+6VKJsgvzc/ZMV5XkyFj/Me81qFunTqP3Rm3MzjfmSd3mXlMXzQ3yCU3Izs3ZP6tL/cZdpm3LxBZkb3xQ/+y7WJMuLQbNjo4I8p6adPWmbTlZV48s8Hi5vsfC49nGEyusnDBuhDdr0KJ36M4LJapUkJcZH9iCtorPPK/y2+x45XDYZw05apM+EYcvXnRen+J90Xp42Gh3J9XDUBhPVHVp+lort0/bNW/03ojl6dcd1crJ/7KSVgW1a1h0PXxsoOd7DZqFJh1bPqjJ+4HxGWZvsm/csKb16nWYsien5L9x5Pmalz0WpGYfl5UjV7OKt62hGWZzbd2z8UFfn8iyOX1Tucd29AhatCjULzghO//G4Wkd6jbyCImOiYma4OEpTx4V3kwc20dJgqGvdRs0b+fu7t7Fa1TM4bxCbO7GUR17By1aEOI32a5Pd2Yeiw9sVafFqHjGieEBNGrRY9pO459oD8o0ZMx4oq3kOeLLOvtf1vH0jcHt6tV7zd3XDJ39PVu93GzqtmPLfZrIscydM1f7Na3TsENoUkkngIi/xCC5Vbx/842b9A3e6hG6Yf9Gm/TbF2wmnd253zEuNtRt4hGyKCYmMsRjsHIvbiYO7yNejuhrgwbN2xrt5zk2Jg3fkZn4qUdQxKKQwGBmIu5ml6KptzMjM35UizqtzCFR1Jtq8F9JHD7c6dUIzl3tmJ4e/XmLQTOiFwZ7j9+J91AScxi/WqdO/SY9FhzOK7A3R/OXzTbaJzz10q28w/O6NKxbp26zHhEHOfc76Qu6NGrSyt3D/Z0Gz3ouPZaxF/tm5Mw5V9yYrE3d6KglyzSDIC89wqPRa63cP3d/p95rnovTbM4oMbOUWZ971L6pVYGamkblxtO1BMuf1VR3CvMvnTyWk5kwIkQFqflnN/qPWX7uVt6Z1OTkpNjRk20uhJaREmXWYPIvHTt2/mLC+C/VNRJ0fb3/8FXn7uWeOZicvHfF6KDND27HlJW7l46dzLm4ccSXieXft/ZQKU2tqR5onS4vWGHjfxr1PcL2nHEc5GuqD7kpIa3r1n3HbyNxnsm9E6v6vtXQiFG6fOY5am5SVvm9sRJl1lgKb6WEtqz7bAu/9cSOknIvY0Xf5q82b9fZ3f0zz8CIpGwHVxEewq19IS2frdsiYOMj7FuLqIym1lQTtE5rNBqNRuO6aJ3WaDQajcZ10Tqt0Wg0Go3rUrU67QKfSagK7mYnRQa4O/5ERNEp37X5PINGo9FoNI9IVeq0a3wmoUq4mxT4vM1/PS1sTrno8wwajUaj0Tw6ZdfpQtuX1CddLTD+wWL7en3718TbflWi2GcSCgvzMrfN8+r82SCvrq2avD9sYWSIsdJ61M6ce5dSY4M83D0+N75w0cL8bgTBa7HvKNyx+UjAnAMHnXyEY/bG9Y5f5V+YX/IQjk/E+szGuRslXpT/4J0Mxd7pn3P1wbcETqjPMxjtVvxLBiXfp1/8YxhmPTUajUajMSizTtu/pD43K6bY6/UXHT7k7KsSxT+TEJZ+90JSUJs6HaLOFBqvXK7zfkSGuVJ/0Ppzp6I9n21qvBvSeBHSy82mpeZnFf+OQnhS2oMvcxxf++Bt+MU/wpFX4ORV/gV59ofYf8bhiTz4zMa0tLtFqmzJs7WiXoBc9E5/65TzTqnPMxhHKf4lgxsl3qdv+zEMBy9U0mg0Gk3tpcw6bf+S+qIvxoB6vb6zr0qU+ExCzt2iN/ZZr+4rWrH0z/xqQh33xaklvqNg82UOZx/hMHH2Kn/7QwRPc3oiJT6zYe1rrdi90/9B4UVnpITcqFTRlwyu2zeU7ccwjHpoNBqNRqMos04bMavtS+pLvl7/uL38KNEq8ZmEkvLsQKeNFCLsjBLfUbhIFlFRgmPHH+EQnLzK3/4QcyOdnkjRWVuf2bD2tVbs3ulfvPAinbb7kkEJnaauD77rUNoXmTUajUZT2yizTtu/pP5SidfrWzJpp9MlPpNw5pbSMEvMrJUHOpebHNTZO+Hi/RLfUTCyiLg5/QhHEQ5f5W9/iLPnnJ6I7Wc2bF+U/0Cnjavrtu/0f1B4UYqDLxlY8ly0Uuy7Dufyc1K3HXzYV5Y0Go1GUzso+3NkBcVfUo8SFXu9/j0H7/0v+qpEsc8kkDPD/HTBmMSsU8bl4haj4k+kb5WVkxvHNni2uWfoovAgH+OrCRym+HcUin0kwPlHOFSdHb3K35DS4oew/06AIZ82n9nAvbB7Uf7BjEPhxpchwvbnXCz+WYuNyRvllNduW6s+z3DjXvGvODj4QEL6YttvdZyM6FyvdcRJ68qFRqPRaGoxZdfpx8KDeLSCOH+V/8MP8eBytEaj0Wg0TxaX0um75h+fiFOTzqi/UT0qTl/l/9BD6M9saDQajcaFcLF4WqPRaDQajQ1apzUajUajcV20Tms0Go1G47pondZoNBqNxnXROq3RaDQajeuidVqj0Wg0GtdF67RGo9FoNK6L1mmNRqPRaFwXrdMajUaj0bguWqc1Go1Go3FdtE5rNBqNRuO6aJ3WaDQajcZ10Tqt0Wg0Go3ronVao9FoNBrXReu0RqPRaDSuyrff/n/qUFS3atBKZAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Figure_1.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**B.\tHealth Outcomes prior to the International Epidemiological Transition**_\n", + "\n", + "

According to the primary hypothesis, if a country’s population has relatively low levels of HLA heterozygosity, then in periods prior to the international epidemiological transition, a greater fraction of the population will die from infectious diseases, thereby lowering life expectancy. \n", + " \n", + "In order to measure this relationship, the author considers a number of country-level health outcomes prior to the discovery and diffusion of medical technologies associated with the international epidemiological transition. These include both predicted mortality from infectious disease and life expectancy in 1940, as well as life expectancy in 1960. \n", + " \n", + "However, the use of 1940s data, while truly before the epidemiological transition, is problematic due to a lack of data in relatively poor countries, leading to possible selection bias. Therefore, the primary dependent variable is country level life expectancy at birth in 1960. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### IV. Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**_A. Explaining HLA Heterozygosity_**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Figure 2.—HLA Heterozygosity and Migratory Distance from East Africa**_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Replication of Figure 2 in python.**" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['hla_het', 'ln_hla_het', 'ln_le60', 'ln_frac', 'ln_abslat', 'ln_arable',\n", + " 'ln_suitavg', 'aa_ln_atd', 'europe', 'asia', 'africa', 'americas',\n", + " 'oceania', 'region', 'aa_mdist'],\n", + " dtype='object')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hla_country_web_1.columns #this print the name of the columns of the database" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "africa 37\n", + "asia 35\n", + "europe 32\n", + "americas 24\n", + "oceania 3\n", + "Name: region, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hla_country_web_1.region.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Replication figure 2\n", + "\n", + "#Use function seaborn\n", + "sns.set(rc={'figure.figsize':(12 , 6)})\n", + "sns.set_context(\"notebook\")\n", + "\n", + "Figure_2 = sns.lmplot( 'aa_mdist','ln_hla_het',hue='region',data=hla_country_web_1,fit_reg=False, height=5, aspect=2)\n", + "sns.regplot('aa_mdist','ln_hla_het', data=hla_country_web_1, scatter=False, ax=Figure_2.axes[0, 0])\n", + "\n", + "Figure_2.savefig(pathgraphs + 'Figure_2_python.pdf', dpi=300, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "log file already open\n", + "r(604);\n", + "\n", + "Unknown #command\n", + "\n", + "C:\\Users\\USUARIO\\Desktop\\Summer school\\Trabajos\\Replication_paper\\raw\n", + "\n" + ] + } + ], + "source": [ + "%%stata \n", + "\n", + "set more off\n", + "log using \"replication_hla_base_tables.log\", replace\n", + "\n", + "#for this code to work it is necessary to declare the directory of the computer where the database is located\n", + "cd \"C:\\Users\\USUARIO\\Desktop\\Summer school\\Trabajos\\Replication_paper\\raw\" \n", + "\n", + "use \"hla_country_web.dta\", clear\n", + "\n", + "local cont \"europe africa asia americas\"\n", + "local base \"ln_le60!=. & ln_hla_het!=. & ln_frac!=. & ln_abslat!=. & ln_arable!=. & ln_suitavg!=. & aa_ln_atd!=.\"\n", + "local bc \"ln_frac aa_ln_atd ln_arable ln_suitavg ln_abslat\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Replication of Figure 2 in Stata.**" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 400, + "width": 500 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "%%stata \n", + "graph twoway (scatter ln_hla_het aa_mdist if europe==1) (scatter ln_hla_het aa_mdist if africa==1, msymbol(oh)) (scatter ln_hla_het aa_mdist if asia==1, msymbol(S)) (scatter ln_hla_het aa_mdist if americas==1, msymbol(T)) (scatter ln_hla_het aa_mdist if oceania==1, msymbol(Th)) || (lfit ln_hla_het aa_mdist) , legend(label(1 European) label(2 African)label(3 Asia) label(4 American) label(5 \"Oceanianic/ No Majority\")) graphregion(color(white)) bgcolor(white) xtitle(\"Migratory Distance from East Africa (Ancestry Adjusted)\") ytitle(\"In HLA Heterozygosity\") ysc(r(-1.5 -1)) ylabel(-1.5(0.1)-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Original Figure 2.**" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Figure_2.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Figure 2 plots the measure of diversity, HLA heterozygosity, as a linear function of migratory distance from East Africa. From figure 2, countries with a majority population from Europe and the Middle East tend to break from the linear association between HLA heterozygosity and migratory distance from East Africa, containing higher-than-predicted levels of HLA heterozygosity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 2: Explaining HLA Heterozygosity (author´s code) \n", + "\n", + "The role of both the migratory distance from East Africa and the Neolithic revolution in explaining HLA heterozygosity is tested in table 2.\n", + "\n", + "\n", + "Table 2 regresses the log of HLA heterozygosity on the number of years since the Neolithic revolution, the number of potential domesticate animals, population density in 1 CE, and migratory distance from East Africa. Given that the relationship between overall levels of heterozygosity and migratory distance from East Africa has been established (Ashraf & Galor, 2013)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "log file already open\n", + "r(604);\n", + "\n", + "Unknown #command\n", + "\n", + "C:\\Users\\USUARIO\\Desktop\\Summer school\\Trabajos\\Replication_paper\\raw\n", + "\n" + ] + } + ], + "source": [ + "%%stata \n", + "set more off\n", + "log using \"replication_hla_base_tables.log\", replace\n", + "\n", + "#for this code to work it is necessary to declare the directory of the computer where the database is located\n", + "cd \"C:\\Users\\USUARIO\\Desktop\\Summer school\\Trabajos\\Replication_paper\\raw\" \n", + "\n", + "use \"hla_country_web.dta\", clear\n", + "\n", + "local cont \"europe africa asia americas\"\n", + "local base \"ln_le60!=. & ln_hla_het!=. & ln_frac!=. & ln_abslat!=. & ln_arable!=. & ln_suitavg!=. & aa_ln_atd!=.\"\n", + "local bc \"ln_frac aa_ln_atd ln_arable ln_suitavg ln_abslat\"" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Linear regression Number of obs = 131\n", + " F(1, 129) = 4.65\n", + " Prob > F = 0.0329\n", + " R-squared = 0.0217\n", + " Root MSE = .06738\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_ln_atd | .0211444 .0098049 2.16 0.033 .0017451 .0405438\n", + " _cons | -1.326062 .084678 -15.66 0.000 -1.4936 -1.158525\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(2, 128) = 37.36\n", + " Prob > F = 0.0000\n", + " R-squared = 0.4265\n", + " Root MSE = .05179\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_ln_atd | .0299843 .0086383 3.47 0.001 .0128921 .0470766\n", + " aa_mdist | -.0120634 .0014224 -8.48 0.000 -.0148779 -.009249\n", + " _cons | -1.321902 .0729942 -18.11 0.000 -1.466333 -1.17747\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 89\n", + " F(1, 87) = 17.05\n", + " Prob > F = 0.0001\n", + " R-squared = 0.1176\n", + " Root MSE = .07228\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_lanim | .0266128 .0064443 4.13 0.000 .0138042 .0394215\n", + " _cons | -1.189394 .0126485 -94.03 0.000 -1.214534 -1.164254\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 89\n", + " F(2, 86) = 52.15\n", + " Prob > F = 0.0000\n", + " R-squared = 0.5968\n", + " Root MSE = .04914\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_lanim | .0360532 .0050961 7.07 0.000 .0259225 .046184\n", + " aa_mdist | -.013321 .0014938 -8.92 0.000 -.0162905 -.0103515\n", + " _cons | -1.109316 .0107794 -102.91 0.000 -1.130745 -1.087888\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 113\n", + " F(1, 111) = 11.23\n", + " Prob > F = 0.0011\n", + " R-squared = 0.0626\n", + " Root MSE = .06849\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_lpd1 | .014363 .0042865 3.35 0.001 .0058691 .0228569\n", + " _cons | -1.154714 .0074313 -155.39 0.000 -1.16944 -1.139989\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 113\n", + " F(2, 110) = 37.73\n", + " Prob > F = 0.0000\n", + " R-squared = 0.4578\n", + " Root MSE = .05233\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_lpd1 | .0158342 .0032576 4.86 0.000 .0093785 .0222899\n", + " aa_mdist | -.0123827 .0015009 -8.25 0.000 -.0153571 -.0094084\n", + " _cons | -1.072103 .0100197 -107.00 0.000 -1.09196 -1.052247\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "***************************************************************\n", + "*** Table 2: Historic Determinants of HLA Heterozygosity ***\n", + "***************************************************************\n", + "\n", + "*Col. 1\n", + "reg ln_hla_het aa_ln_atd if aa_mdist!=. & `base' & ln_le60!=., rob\n", + "\n", + "*Col. 2\n", + "reg ln_hla_het aa_ln_atd aa_mdist if aa_mdist!=. & ln_le60!=. & `base', rob\n", + "\n", + "*Col. 3\n", + "reg ln_hla_het aa_lanim if aa_mdist!=. & `base' & ln_le60!=., rob\n", + "\n", + "*Col. 4\n", + "reg ln_hla_het aa_lanim aa_mdist if aa_mdist!=. & `base' & ln_le60!=., rob\n", + "\n", + "*Col. 5\n", + "reg ln_hla_het aa_lpd1 if aa_mdist!=. & `base' & ln_le60!=., rob\n", + "\n", + "*Col. 6\n", + "reg ln_hla_het aa_lpd1 aa_mdist if aa_mdist!=. & `base' & ln_le60!=., rob" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_2.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-

Columns 1 and 2 regress the natural log of HLA heterozygosity on the natural log of the years a country has practiced agriculture. Agriculture has a positive and significant effect on the main measure of heterozygosity in both the bivariate estimation and when controlling for migratory distance.\n", + "\n", + "-

Columns 3 to 6 consider the more proximate determinants of HLA heterozygosity. Agriculture provided close contact with domesticate animals as well as large, dense populations, the two necessary factors that led to an increase in the infectious disease load within a country. A positive and statistically significant relationship between the number of potential domesticate animals and HLA heterozygosity is shown in columns 3 and 4, while the same relationship is exhibited with population density in 1 CE.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**B. HLA Heterozygosity, Country-Level Health Outcomes, and the International Epidemiological Transition**_\n", + "\n", + "

The primary hypothesis of this paper is that genetic resistance to infectious disease, measured by HLA heterozygosity, is positively associated with country-level health outcomes prior to the international epidemiological transition. To test this hypothesis, the author uses the following estimating equation:\n", + "\n", + "$$ln y_{i}^{tThe baseline controls are adopted from similar estimation in Ashraf and Galor (2013) and are intended to control for the persistent effects of historical development. Additionally, controlling for ethnic fractionalization is intended to capture any unseen effects from using the ethnic compositions found in Alesina et al. (2003).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 3: The Effect of HLA Heterozygosity prior to the International Epidemiological Transition (author´s code)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Table 3 gives estimates from the baseline estimating equation. The estimates consider three alternatives for measuring country-level health outcomes prior to the epidemiological\n", + "transition: the mortality rate from fifteen infectious diseases in 1940, life expectancy at birth in 1940, and life expectancy at birth in 1960. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Linear regression Number of obs = 73\n", + " F(1, 71) = 49.51\n", + " Prob > F = 0.0000\n", + " R-squared = 0.3488\n", + " Root MSE = .49154\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_mort40 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | -5.007995 .711708 -7.04 0.000 -6.427101 -3.58889\n", + " _cons | -6.672417 .8309391 -8.03 0.000 -8.329263 -5.015572\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 73\n", + " F(10, 62) = 15.93\n", + " Prob > F = 0.0000\n", + " R-squared = 0.5133\n", + " Root MSE = .45476\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_mort40 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | -3.861032 1.128212 -3.42 0.001 -6.116297 -1.605768\n", + " ln_frac | -.3970101 .3960854 -1.00 0.320 -1.188773 .3947533\n", + " aa_ln_atd | .5975243 .2358444 2.53 0.014 .1260782 1.068971\n", + " ln_arable | .0448605 .1220245 0.37 0.714 -.1990631 .2887841\n", + " ln_suitavg | .0361975 .0844383 0.43 0.670 -.1325923 .2049874\n", + " ln_abslat | -.3260423 .1296545 -2.51 0.015 -.585218 -.0668665\n", + " europe | .1074832 .1696108 0.63 0.529 -.2315639 .4465303\n", + " africa | .4589723 .2152117 2.13 0.037 .0287701 .8891744\n", + " asia | .1722149 .2033102 0.85 0.400 -.2341963 .5786261\n", + " americas | .1102584 .1877282 0.59 0.559 -.265005 .4855218\n", + " _cons | -9.575309 2.730034 -3.51 0.001 -15.03257 -4.11805\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 71\n", + " F(1, 69) = 41.94\n", + " Prob > F = 0.0000\n", + " R-squared = 0.3329\n", + " Root MSE = .21596\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le40 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 2.143586 .3309808 6.48 0.000 1.483298 2.803875\n", + " _cons | 6.25536 .3783145 16.53 0.000 5.500643 7.010076\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 71\n", + " F(10, 60) = 44.93\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7192\n", + " Root MSE = .15026\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le40 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.077113 .3770962 2.86 0.006 .322808 1.831418\n", + " ln_frac | .072362 .133847 0.54 0.591 -.1953719 .3400959\n", + " aa_ln_atd | -.0030757 .0549058 -0.06 0.956 -.1129035 .1067522\n", + " ln_arable | -.1059797 .0365892 -2.90 0.005 -.179169 -.0327905\n", + " ln_suitavg | .0529932 .0238341 2.22 0.030 .0053179 .1006686\n", + " ln_abslat | .098031 .0409504 2.39 0.020 .0161181 .1799439\n", + " europe | -.052723 .0434553 -1.21 0.230 -.1396465 .0342005\n", + " africa | -.5020729 .0592995 -8.47 0.000 -.6206896 -.3834562\n", + " asia | -.3398571 .0565811 -6.01 0.000 -.4530363 -.226678\n", + " americas | -.3291928 .0621135 -5.30 0.000 -.4534382 -.2049474\n", + " _cons | 5.316719 .5542282 9.59 0.000 4.208097 6.42534\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(1, 129) = 43.55\n", + " Prob > F = 0.0000\n", + " R-squared = 0.2271\n", + " Root MSE = .20359\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.620043 .2454809 6.60 0.000 1.134353 2.105733\n", + " _cons | 5.814552 .2829132 20.55 0.000 5.254801 6.374303\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 100.89\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7446\n", + " Root MSE = .12133\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.01512 .1899264 5.34 0.000 .6390794 1.391161\n", + " ln_frac | -.1210012 .0927409 -1.30 0.194 -.3046217 .0626193\n", + " aa_ln_atd | .0369984 .0397486 0.93 0.354 -.0417011 .1156979\n", + " ln_arable | -.0086997 .0159756 -0.54 0.587 -.0403303 .0229309\n", + " ln_suitavg | .008039 .0128307 0.63 0.532 -.017365 .0334429\n", + " ln_abslat | .0199086 .0156461 1.27 0.206 -.0110696 .0508867\n", + " europe | .0387395 .0716524 0.54 0.590 -.1031272 .1806062\n", + " africa | -.3122101 .0754901 -4.14 0.000 -.4616753 -.162745\n", + " asia | -.2056222 .0755278 -2.72 0.007 -.355162 -.0560825\n", + " americas | -.0400593 .0736294 -0.54 0.587 -.1858403 .1057217\n", + " _cons | 4.962063 .4156831 11.94 0.000 4.139039 5.785086\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "***************************************\n", + "*** Table 3: Premedicinal Health ***\n", + "***************************************\n", + "\n", + "** Mortality from infectious disease in 1940\n", + "*Col. 1\n", + "reg ln_mort40 ln_hla_het if `base', rob\n", + "\n", + "*Col. 2\n", + "reg ln_mort40 ln_hla_het `bc' `cont' if `base', rob\n", + "\n", + "** Life Expectancy in 1940\n", + "*Col. 3\n", + "reg ln_le40 ln_hla_het if `base', rob\n", + "\n", + "*Col. 4\n", + "reg ln_le40 ln_hla_het `bc' `cont' if `base', rob\n", + "\n", + "** Life Expectancy in 1960\n", + "*Col. 5\n", + "reg ln_le60 ln_hla_het if `base', rob\n", + "\n", + "*Col. 6\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `base', rob\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_3.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Column 1 shows the bivariate relationship between HLA heterozygosity and predicted mortality from infectious diseases in 1940. The coefficient is negative and statistically significant at the 1% level indicating that greater variation within the HLA system is associated with lower mortality from infectious disease in 1940. \n", + "\n", + "

The use of 1940s health data is ideal; however, data are relatively sparse for this period. Therefore, life expectancy at birth for 1960 is used as the main dependent variable. The complicating factor with the use of data from the 1960s is that the presence of medicine should dissipate any effect of inherent genetic resistance to infectious disease. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 4: The Effect of HLA Heterozygosity after the International Epidemiological Transition (author´s code)\n", + "\n", + "

To show that show that 1960 is an early enough period to capture the effects of innate resistance, table 4 explores the effects of HLA heterozygosity on life expectancy in more contemporary periods. Table 4 explores the effect of HLA heterozygosity on life expectancy from 1960 to 2010 and tests the effect of HLA heterozygosity, or innate resistance, in post epidemiological transition environments" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear regression Number of obs = 131\n", + " F(10, 120) = 100.89\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7446\n", + " Root MSE = .12133\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.01512 .1899264 5.34 0.000 .6390794 1.391161\n", + " ln_frac | -.1210012 .0927409 -1.30 0.194 -.3046217 .0626193\n", + " aa_ln_atd | .0369984 .0397486 0.93 0.354 -.0417011 .1156979\n", + " ln_arable | -.0086997 .0159756 -0.54 0.587 -.0403303 .0229309\n", + " ln_suitavg | .008039 .0128307 0.63 0.532 -.017365 .0334429\n", + " ln_abslat | .0199086 .0156461 1.27 0.206 -.0110696 .0508867\n", + " europe | .0387395 .0716524 0.54 0.590 -.1031272 .1806062\n", + " africa | -.3122101 .0754901 -4.14 0.000 -.4616753 -.162745\n", + " asia | -.2056222 .0755278 -2.72 0.007 -.355162 -.0560825\n", + " americas | -.0400593 .0736294 -0.54 0.587 -.1858403 .1057217\n", + " _cons | 4.962063 .4156831 11.94 0.000 4.139039 5.785086\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 76.19\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7340\n", + " Root MSE = .11485\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le70 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.04047 .1695943 6.14 0.000 .7046852 1.376255\n", + " ln_frac | -.1163928 .0881347 -1.32 0.189 -.2908934 .0581078\n", + " aa_ln_atd | .0246249 .0372331 0.66 0.510 -.0490941 .098344\n", + " ln_arable | -.0141638 .0153671 -0.92 0.359 -.0445896 .0162619\n", + " ln_suitavg | .0007926 .0135757 0.06 0.954 -.0260864 .0276715\n", + " ln_abslat | .0045722 .0158262 0.29 0.773 -.0267626 .0359069\n", + " europe | .0344497 .0460362 0.75 0.456 -.0566989 .1255982\n", + " africa | -.3192781 .0522129 -6.11 0.000 -.4226561 -.2159002\n", + " asia | -.1420921 .0484679 -2.93 0.004 -.2380552 -.046129\n", + " americas | -.0238244 .0420173 -0.57 0.572 -.1070157 .0593668\n", + " _cons | 5.198611 .3846817 13.51 0.000 4.436968 5.960254\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 55.68\n", + " Prob > F = 0.0000\n", + " R-squared = 0.6995\n", + " Root MSE = .10588\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le80 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .8420929 .169953 4.95 0.000 .5055977 1.178588\n", + " ln_frac | -.1462667 .0786894 -1.86 0.066 -.3020663 .0095329\n", + " aa_ln_atd | .0348747 .0381473 0.91 0.362 -.0406544 .1104037\n", + " ln_arable | -.0199753 .0150991 -1.32 0.188 -.0498705 .0099199\n", + " ln_suitavg | .0003745 .0142482 0.03 0.979 -.0278359 .0285849\n", + " ln_abslat | .0070102 .0152294 0.46 0.646 -.0231431 .0371634\n", + " europe | .0176881 .0373365 0.47 0.637 -.0562357 .0916118\n", + " africa | -.2552229 .0450962 -5.66 0.000 -.3445103 -.1659355\n", + " asia | -.1194574 .0422942 -2.82 0.006 -.203197 -.0357178\n", + " americas | -.0064243 .0328959 -0.20 0.845 -.0715559 .0587073\n", + " _cons | 4.945296 .3733601 13.25 0.000 4.206069 5.684523\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 32.32\n", + " Prob > F = 0.0000\n", + " R-squared = 0.6886\n", + " Root MSE = .10684\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le90 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .5490235 .1798274 3.05 0.003 .1929779 .9050692\n", + " ln_frac | -.183742 .0828562 -2.22 0.028 -.3477915 -.0196925\n", + " aa_ln_atd | .0383219 .035843 1.07 0.287 -.0326448 .1092886\n", + " ln_arable | -.0220742 .0174598 -1.26 0.209 -.0566434 .0124949\n", + " ln_suitavg | -.0046492 .0148575 -0.31 0.755 -.034066 .0247677\n", + " ln_abslat | .0247666 .0225831 1.10 0.275 -.0199464 .0694797\n", + " europe | .0189598 .0440979 0.43 0.668 -.0683511 .1062706\n", + " africa | -.2203019 .0524047 -4.20 0.000 -.3240596 -.1165442\n", + " asia | -.0726899 .0444554 -1.64 0.105 -.1607085 .0153287\n", + " americas | .0346061 .0405436 0.85 0.395 -.0456675 .1148796\n", + " _cons | 4.554555 .4118077 11.06 0.000 3.739204 5.369906\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 43.79\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7683\n", + " Root MSE = .09193\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le00 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .2512166 .1540813 1.63 0.106 -.0538535 .5562868\n", + " ln_frac | -.1775283 .0728385 -2.44 0.016 -.3217434 -.0333131\n", + " aa_ln_atd | .0843881 .0303567 2.78 0.006 .024284 .1444923\n", + " ln_arable | -.0062233 .0115616 -0.54 0.591 -.0291145 .0166679\n", + " ln_suitavg | -.0168243 .0100998 -1.67 0.098 -.0368211 .0031725\n", + " ln_abslat | .0137408 .0156327 0.88 0.381 -.0172109 .0446925\n", + " europe | -.0010578 .0524527 -0.02 0.984 -.1049104 .1027948\n", + " africa | -.2497432 .0608725 -4.10 0.000 -.3702665 -.1292199\n", + " asia | -.0924549 .052818 -1.75 0.083 -.197031 .0121211\n", + " americas | .0264391 .0515306 0.51 0.609 -.0755879 .128466\n", + " _cons | 3.85075 .309451 12.44 0.000 3.238059 4.463442\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 43.16\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7480\n", + " Root MSE = .08408\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le10 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .1787627 .1334796 1.34 0.183 -.0855176 .443043\n", + " ln_frac | -.1508934 .0700744 -2.15 0.033 -.2896359 -.0121509\n", + " aa_ln_atd | .0733 .0301988 2.43 0.017 .0135085 .1330916\n", + " ln_arable | -.0045715 .0094332 -0.48 0.629 -.0232486 .0141057\n", + " ln_suitavg | -.0138008 .0083383 -1.66 0.101 -.0303101 .0027084\n", + " ln_abslat | .0066167 .0136851 0.48 0.630 -.0204788 .0337121\n", + " europe | .0065547 .0508292 0.13 0.898 -.0940836 .107193\n", + " africa | -.2235135 .0590873 -3.78 0.000 -.3405023 -.1065247\n", + " asia | -.0839933 .0521013 -1.61 0.110 -.1871502 .0191637\n", + " americas | .0163142 .0512064 0.32 0.751 -.0850709 .1176994\n", + " _cons | 3.912044 .282363 13.85 0.000 3.352985 4.471104\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "*************************************************************\n", + "*** Table 4: Additional Years: The effect of medicine ***\n", + "*************************************************************\n", + "local le \"ln_le60!=. & ln_le70!=. & ln_le80!=. & ln_le90!=. & ln_le00!=. & ln_le10!=.\"\n", + "\n", + "*Col. 1, 1960\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `le', rob\n", + "\n", + "*Col. 2, 1970\n", + "reg ln_le70 ln_hla_het `bc' `cont' if `le', rob\n", + "\n", + "*Col. 3, 1980\n", + "reg ln_le80 ln_hla_het `bc' `cont' if `le', rob\n", + "\n", + "*Col. 4, 1990\n", + "reg ln_le90 ln_hla_het `bc' `cont' if `le', rob\n", + "\n", + "*Col. 5, 2000\n", + "reg ln_le00 ln_hla_het `bc' `cont' if `le', rob\n", + "\n", + "*Col. 6, 2010\n", + "reg ln_le10 ln_hla_het `bc' `cont' if `le', rob" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_4.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Column 1 reproduces the baseline regression. Columns 2 to 6 regress life expectancy at birth in 1970 to 2010 (by decade) on HLA heterozygosity, respectively. As the set of technologies that comprise the international epidemiological transition becomes more widely distributed over time, the coefficient of HLA heterozygosity is hypothesized to move to 0.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Figure 3: The Effect of HLA Heterozygosity following the Diffusion of Health Technologies from the International Epidemiological " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Figure_3.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**_C. Robustness_**\n", + "\n", + "**_Controlling for regional ethnic differences_**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 5: Robustness to the Influence of Regional Populations (author´s code)\n", + "\n", + "

Controlling for regional ethnic differences. One potential source of bias associated with the measure of HLA heterozygosity lies within the aggregation from ethnic groups to the country level. Given the limited number of ethnic groups from which HLA heterozygosity is constructed, the aggregated measure may simply be accounting for the fraction of a country’s population derived from a region. \n", + "Specifically, populations from Europe have been shown to have favorable institutions and human capital (Easterly & Levine, 2012). In other words, the relationship between HLA heterozygosity and life expectancy in 1960 may reflect some underlying role of European populations in promoting greater health outcomes." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "note: europe omitted because of collinearity\n", + "note: americas omitted because of collinearity\n", + "\n", + "Linear regression Number of obs = 55\n", + " F(7, 46) = .\n", + " Prob > F = .\n", + " R-squared = 0.4183\n", + " Root MSE = .12654\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .7240941 .3623023 2.00 0.052 -.0051826 1.453371\n", + " ln_frac | -.1662651 .1477015 -1.13 0.266 -.4635727 .1310425\n", + " aa_ln_atd | .0109134 .063677 0.17 0.865 -.1172619 .1390886\n", + " ln_arable | -.0062449 .0196735 -0.32 0.752 -.0458456 .0333558\n", + " ln_suitavg | .0015924 .0193779 0.08 0.935 -.0374132 .0405981\n", + " ln_abslat | -.0104055 .0199072 -0.52 0.604 -.0504766 .0296656\n", + " europe | 0 (omitted)\n", + " africa | -.0788309 .129284 -0.61 0.545 -.3390661 .1814043\n", + " asia | .0581873 .1263473 0.46 0.647 -.1961366 .3125111\n", + " americas | 0 (omitted)\n", + " _cons | 4.663561 .7141303 6.53 0.000 3.226092 6.101031\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 52\n", + " F(10, 41) = 19.53\n", + " Prob > F = 0.0000\n", + " R-squared = 0.5891\n", + " Root MSE = .13973\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .8048964 .2923111 2.75 0.009 .2145622 1.395231\n", + " ln_frac | -.1580961 .1784565 -0.89 0.381 -.5184962 .202304\n", + " aa_ln_atd | .1411788 .0914403 1.54 0.130 -.0434887 .3258463\n", + " ln_arable | -.0640976 .0343788 -1.86 0.069 -.1335269 .0053318\n", + " ln_suitavg | .0491992 .0384154 1.28 0.207 -.0283823 .1267806\n", + " ln_abslat | .053768 .0361827 1.49 0.145 -.0193045 .1268405\n", + " europe | -.0364848 .0463389 -0.79 0.436 -.1300682 .0570986\n", + " africa | -.2468748 .0834365 -2.96 0.005 -.4153782 -.0783713\n", + " asia | -.2803521 .0860084 -3.26 0.002 -.4540497 -.1066546\n", + " americas | -.1194736 .0524145 -2.28 0.028 -.225327 -.0136203\n", + " _cons | 3.976339 .8431324 4.72 0.000 2.273599 5.67908\n", + "------------------------------------------------------------------------------\n", + "\n", + "note: europe omitted because of collinearity\n", + "note: africa omitted because of collinearity\n", + "note: asia omitted because of collinearity\n", + "note: americas omitted because of collinearity\n", + "\n", + "Linear regression Number of obs = 24\n", + " F(6, 17) = 5.33\n", + " Prob > F = 0.0030\n", + " R-squared = 0.5162\n", + " Root MSE = .04194\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .728073 .3110997 2.34 0.032 .07171 1.384436\n", + " ln_frac | -.0734813 .0922997 -0.80 0.437 -.2682168 .1212541\n", + " aa_ln_atd | .0357073 .037792 0.94 0.358 -.0440268 .1154413\n", + " ln_arable | -.0090738 .0212053 -0.43 0.674 -.0538131 .0356655\n", + " ln_suitavg | .0184685 .0208964 0.88 0.389 -.025619 .062556\n", + " ln_abslat | .3230336 .1173578 2.75 0.014 .0754303 .570637\n", + " europe | 0 (omitted)\n", + " africa | 0 (omitted)\n", + " asia | 0 (omitted)\n", + " americas | 0 (omitted)\n", + " _cons | 3.517317 .6836917 5.14 0.000 2.074853 4.95978\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(15, 115) = 165.25\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7891\n", + " Root MSE = .11264\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .8869916 .2871503 3.09 0.003 .3182021 1.455781\n", + " frac_eur | .3177208 .1395855 2.28 0.025 .0412289 .5942128\n", + " frac_me | .020621 .1596132 0.13 0.897 -.295542 .336784\n", + " frac_easia | .1401225 .1415624 0.99 0.324 -.1402853 .4205302\n", + " frac_africa | .0173643 .137623 0.13 0.900 -.2552404 .289969\n", + " frac_am | .1451349 .0934837 1.55 0.123 -.0400383 .330308\n", + " ln_frac | -.1295757 .0868859 -1.49 0.139 -.3016799 .0425285\n", + " aa_ln_atd | .0426266 .0416563 1.02 0.308 -.0398866 .1251398\n", + " ln_arable | -.0099352 .0143495 -0.69 0.490 -.0383587 .0184884\n", + " ln_suitavg | -.000756 .0145277 -0.05 0.959 -.0295325 .0280205\n", + " ln_abslat | .0068563 .018 0.38 0.704 -.0287982 .0425108\n", + " europe | -.0518747 .055794 -0.93 0.354 -.1623918 .0586425\n", + " africa | -.1408858 .0945556 -1.49 0.139 -.3281822 .0464106\n", + " asia | -.0978798 .0933299 -1.05 0.296 -.2827483 .0869887\n", + " americas | -.057943 .0612781 -0.95 0.346 -.1793231 .0634371\n", + " _cons | 4.598251 .4873273 9.44 0.000 3.632949 5.563552\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "*******************************************\n", + "*** Table 5: Pop. Composition Trunc. ***\n", + "*******************************************\n", + "local pop \"frac_eur frac_me frac_easia frac_africa frac_am\"\n", + "\n", + "*Col. 1, countries with no fraction of population derived from Europe\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur==0, rob\n", + "\n", + "*Col. 2, countries with part of the population derived from Europe\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur>0 & frac_eur<1, rob\n", + "\n", + "*Col. 3, countries entirely composed of European populations\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur==1, rob\n", + "\n", + "*Col. 4, ethnic fixed effects\n", + "reg ln_le60 ln_hla_het `pop' `bc' `cont' if `base', rob" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_5.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Table 5 shows the explore the effect of HLA heterozygosity in countries with differing concentrations of European descent as well as controlling for the fraction of the population from each region.\n", + "\n", + "In order to account for the fraction of a country’s population being from each region (the Americas, Europe, East Asia, the Middle East, Oceania, and sub-Saharan Africa). While the point estimates of the coefficient are slightly smaller in magnitude when compared to the base estimate of column 6 in table 3, the effect of HLA heterozygosity remains both positive and significant at conventional levels, indicating that the portion of the population from Europe is not substantially altering the protective effect of genetic variation within the HLA system." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**_Omitted variables_**\n", + "\n", + "

Tables 6 and 7 include omitted variables that may be associated with either the measure of HLA heterozygosity or life expectancy in 1960. The additional variables are broken into two classes: endogenous and exogenous. \n", + " \n", + "- The additional exogenous variables, found in table 6, include genetic, geographic, and historic population controls.\n", + "- the endogenous controls of table 7 consist of income, human capital, and demographics in 1960." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 6: Robustness to Exogenous Omitted Variables (author´s code)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear regression Number of obs = 131\n", + " F(11, 119) = 90.88\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7449\n", + " Root MSE = .12179\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.077102 .340135 3.17 0.002 .4036012 1.750604\n", + " aa_pdiv | -.2783647 1.199801 -0.23 0.817 -2.654091 2.097362\n", + " ln_frac | -.113391 .1067678 -1.06 0.290 -.3248019 .0980199\n", + " aa_ln_atd | .0406652 .0470243 0.86 0.389 -.0524476 .1337779\n", + " ln_arable | -.0083386 .0157507 -0.53 0.598 -.0395265 .0228493\n", + " ln_suitavg | .007875 .0127592 0.62 0.538 -.0173895 .0331395\n", + " ln_abslat | .0206019 .0161684 1.27 0.205 -.0114131 .0526169\n", + " europe | .0390449 .0703013 0.56 0.580 -.1001588 .1782486\n", + " africa | -.3031556 .0870473 -3.48 0.001 -.4755179 -.1307932\n", + " asia | -.2058545 .0739893 -2.78 0.006 -.3523608 -.0593482\n", + " americas | -.045389 .0776443 -0.58 0.560 -.1991325 .1083545\n", + " _cons | 5.19642 1.000954 5.19 0.000 3.214432 7.178408\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(14, 116) = 78.86\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7741\n", + " Root MSE = .11606\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .7713477 .2040145 3.78 0.000 .3672712 1.175424\n", + " malfal | -.1613359 .0481473 -3.35 0.001 -.2566976 -.0659741\n", + " tropical | -.0003105 .0005798 -0.54 0.593 -.0014589 .000838\n", + " desert | -.0015805 .0015453 -1.02 0.309 -.0046411 .0014802\n", + " distcr1000 | .011631 .0384694 0.30 0.763 -.0645626 .0878246\n", + " ln_frac | -.0863313 .0921872 -0.94 0.351 -.2689197 .096257\n", + " aa_ln_atd | .0120531 .0330839 0.36 0.716 -.0534738 .07758\n", + " ln_arable | -.0019372 .0127372 -0.15 0.879 -.0271649 .0232905\n", + " ln_suitavg | .0037507 .012647 0.30 0.767 -.0212982 .0287996\n", + " ln_abslat | -.0149382 .0184394 -0.81 0.420 -.0514598 .0215834\n", + " europe | .0444248 .0730941 0.61 0.545 -.1003473 .1891969\n", + " africa | -.2552539 .0778437 -3.28 0.001 -.4094332 -.1010745\n", + " asia | -.1849297 .0777094 -2.38 0.019 -.3388429 -.0310165\n", + " americas | -.0619446 .0777163 -0.80 0.427 -.2158716 .0919824\n", + " _cons | 5.007553 .388945 12.87 0.000 4.237198 5.777907\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(11, 119) = 85.46\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7504\n", + " Root MSE = .12047\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .8699479 .223888 3.89 0.000 .4266273 1.313268\n", + "frac_migrant | .0982091 .0632069 1.55 0.123 -.0269468 .2233651\n", + " ln_frac | -.1440908 .0987111 -1.46 0.147 -.3395486 .051367\n", + " aa_ln_atd | .041594 .0395006 1.05 0.294 -.0366212 .1198093\n", + " ln_arable | -.0085389 .0155162 -0.55 0.583 -.0392626 .0221847\n", + " ln_suitavg | .0113689 .0119537 0.95 0.343 -.0123006 .0350383\n", + " ln_abslat | .0157883 .016349 0.97 0.336 -.0165843 .048161\n", + " europe | .1133226 .0846705 1.34 0.183 -.0543335 .2809787\n", + " africa | -.25279 .0810031 -3.12 0.002 -.4131842 -.0923958\n", + " asia | -.147021 .0801837 -1.83 0.069 -.3057927 .0117507\n", + " americas | -.0426537 .0676053 -0.63 0.529 -.176519 .0912116\n", + " _cons | 4.708444 .4388107 10.73 0.000 3.839555 5.577333\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(16, 114) = 64.92\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7824\n", + " Root MSE = .11491\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .5372995 .3093499 1.74 0.085 -.0755202 1.150119\n", + " aa_pdiv | .138512 1.028011 0.13 0.893 -1.89797 2.174994\n", + " malfal | -.1730445 .0487015 -3.55 0.001 -.2695218 -.0765672\n", + " tropical | -.0002674 .0005692 -0.47 0.639 -.0013949 .0008601\n", + " desert | -.0012792 .0014481 -0.88 0.379 -.0041478 .0015894\n", + " distcr1000 | .0299095 .0381407 0.78 0.435 -.0456469 .105466\n", + "frac_migrant | .1203078 .0733916 1.64 0.104 -.0250803 .2656959\n", + " ln_frac | -.1187416 .1035394 -1.15 0.254 -.3238522 .0863691\n", + " aa_ln_atd | .0120886 .0375722 0.32 0.748 -.0623416 .0865188\n", + " ln_arable | -.0020202 .0121886 -0.17 0.869 -.0261657 .0221252\n", + " ln_suitavg | .0097049 .0121508 0.80 0.426 -.0143658 .0337755\n", + " ln_abslat | -.021768 .019187 -1.13 0.259 -.0597774 .0162413\n", + " europe | .1426791 .0957523 1.49 0.139 -.0470055 .3323637\n", + " africa | -.1868511 .1054909 -1.77 0.079 -.3958278 .0221256\n", + " asia | -.1124693 .0903735 -1.24 0.216 -.2914986 .0665599\n", + " americas | -.0646517 .0742432 -0.87 0.386 -.2117268 .0824234\n", + " _cons | 4.584323 .881409 5.20 0.000 2.838259 6.330388\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "***********************************************\n", + "*** Table 6: Exogenous Omitted Variables ***\n", + "***********************************************\n", + "local ov1 \"aa_pdiv!=. & malfal!=. & tropical!=. & pnativ_60!=. & distcr100!=. \"\n", + "\n", + "*Col. 1, genetic capital\n", + "reg ln_le60 ln_hla_het aa_pdiv `bc' `cont' if `ov1', rob\n", + "\n", + "*Col. 2, environment\n", + "reg ln_le60 ln_hla_het malfal tropical desert distcr100 `bc' `cont' if `ov1', rob\n", + "\n", + "*Col. 3, population\n", + "reg ln_le60 ln_hla_het frac_migrant `bc' `cont' if `ov1', rob\n", + "\n", + "*Col. 4, all\n", + "reg ln_le60 ln_hla_het aa_pdiv malfal tropical desert distcr100 frac_migrant `bc' `cont' if `ov1', rob" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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t2KvLRWPflPzd0qBy21HuHIUoPsHt1hM43qaau/9lSL9LPXgjC7Y6TiFDk4zs2h/ku5DBQQGrOzEV/E4psFsevDHu9pECp23hs1zwgLPqawzyuMHXjvLQgINKM3Z1IBMrWFEHuow4cxNCjqq1R7QgjgfEp+3ovnJcNcggH1TuPTngTrgukaKt26IyeOMpFrwGAyM+SN7+Fxy8keP7owbvS0kK9nengMov5K4q4sR/43iGHvnSIJLsown3UwAT3Y3IDhu8kX91nMQC+1TA+0+cvvxQSozE297y4I3ZYeMYSDOcBAdvZP8pjRWQeRau0Pbk594ZfMG70/DAvvzU8oCX0kCHMcakYMuWk2DpKLaqzoVgF5ewFijBxMLyjCtZ5rt4ztbn9SQWGOm4c2B4IfK9eYx3n0FOaBvFoSQgpO4QqmNIHHPg4HFVUkJ8hI6KbdCVODDGPqZeRzmJtgUZ1d8pLQwit8EbKgu7L77geUcIIeR1osQCIYSo8P7k7Z9XYAe/66fbhZoL/4J7ch7+oXZUZRSg4fthJUBZhH+0CwIfST6vCd+576HXdpQF20rzX+FjmPK2I6/6bxj0P4d/+AcIhXpVE99mNBwrQvr2/y967ke+J6D5lAEZ2y1wP49Vaeb9jdtgzN4NU8MZNFQdQXUwkbA0AkdpOvSlV+CN1ehhVVhgNnwDdqEYeo0G2uwq/OWaW74rLs1gUAitJhk7TK3oGn7G4o8/0G02oqD4BJr57y/cj+rL/XKA6/8P+mJuHx5Ys+DUdx1C0Q72/sfs/dOoqWrC5UEluGUBZON7eTCYatiy61CWXwKh+w8luGPbwnsd5iK2DTOqYXd5w74zq8iC7tB0fTwIVkaTF2cw3HkWhTpVk3T2vacM72G7+Q5m+s7gvTwDTDWnIdQbkb/3HLp594+46xIp+rrJ5uHrtqCkoBSnmtnxYNqHwtD2CMePy2t/qUI2Py5T89k+b5bXt7kGR/LT2X5IR9V3AxjsMMOwpRStfFBP/lsn+3G5ajs02p2ott/BBA/K4uwnGVuvvm9Qlf8+DHy6SaEeVWUfoMLyE8aePkSnsBc6TbArBF+HEzBk7MKnPf3xt3fwue8nmEsKlaksTSgsPLW8nzm+fsIBZOQdQQM/F2qOoy60ftPoa9yNPIMJNWyb1ZftwV7huhIAR8O/rxfWUweQV3SULa8ex4oKUHTqAvqCXXmkmRcuoCLLAHP3CPuecXSdOAXH2HO2/e6x7ccCXd1+9t49DN6+xMoBHZKKW/BTaDrMEbY997NtsgNV9lsYVe2/qO/FO+4+vY77Ye+x86BsN/KNnyhTcibCu4A0o1AaN0J5xe9Be1Uushu+V6Z8lNfrR2E3Oy52of7yd7j6qbKOl+9JXSDkwSL1yG9woNfVAzvfBkmlaPlpEMO/vqGykI9DserzjhBCyOtGiQVCCCGbzCKe/TlDAclrQ9ubEEIIeddRYoEQQgghhBBCCCFrRokFQgghhBBCCCGErBklFgghhBBCCCGEELJmlFgghBBCCCGEEELImlFigRBCNqK5YfTYm2HQa6HRZqOsvhnNNUdQlJ2N/CNNuPDjQ9Uo/m8an31gAN9bq5GXrprrfj3MDaHLYkS6hs/aUYSGSzcwPKdsCHEC7mufw5SRBL3hHDr5TBWrtDRkwy7tdtT0PFFeiU6aaeKKgOKMQ1GnFuTv33S2oiqvYHmqvbXisxXcvIrWqgKkR0xvmFgAk+5/wlyWBQ3bVrr8GlzoHsac8q5a9N/Op620oLrmKAr1KchuvIGnqzjepFlVzMr+UY3sL80sYciOOSXj5jSPIdsBaNM+Qs/MWqdvYefexF04hFJkrzhGnmHI2QSj6SMI9YdQVGKRZzWJFBiHu53ta+OHEGw/whOaOeIebOUHYKw/g3rjAZTbXtH0tYQQQt5JlFgghJANS54zf3n+eEacwVCHgCIe7NX+EGeavdeJTzd3CzZTevi6rhfRC0dJGjTaUti9z5UXFUv3Yd1ZAttQlAAqnjkPnBYbeqIFXiE8gXIf3/Np+qIGyPx9D3qtRiSp5/Bfq8AkPL1WlCVpkPrCiQVZwCWwYD4VBseo8koUUX67lGzQ830ZwESPBZVnuzGx2kMt4IKQqoHG4FCmmFSIj9BRcYwSCyEi2/TfwmK9scbzWMTCyHVc/OZjFOoijxERs24LcrLOwjXLlz0Lj3UfdAVWeBaWv4snD6zFWciucWJIPc2pOApnOXvdfEdORs3dgTk7C+XOUbZkQgghZCVKLBBCyIYVJbEgmceY8xjSNCkosA5iQXn1zfLDJex4NYkF8U+4LQLaYwagS5jp+Yj9/iTktPSH/X4eEO/eZ4d33aKfAHyOshiJBZkczL+CxAKnBOnrmlhYYQGj9pK178tYiQW23/z3+nDPv9a78y8p4XH1lop6jEyiqyodyTU9CLbbEYdtKNCko6prUn5hbhC24m3Q7W3DQ1WygX0Sz90WZGkKYfUE27jMwWMthCbLAvdzSi0QQghZiRILhBCyYcVKLDDP78KcpYNmSxP6pDuSb9qrSizwJvj1yNYmaDIf9fc/w2BLMYzOP9bxrupmSCy85L6MkVhYGr2Ff4++YEuSV2aVx9XbKNoxstiPlu268Nfm+9CQokFKQx/bGnILBq1mJztOp5UPBClJhKQT6Ap10VjEVMeH0IUlGwghhJBllFgghJANK05iQbkjqQlW9ANj6G09hYqaBpwy7ECmsQ0D/ufwD9+A3VyJvK2lqBc+QIaWBXypBai+3K/0l2YBV+9fUF1RgzOnDMjMPALbwDRE/xB67J+iIm8njrW0oGJHMvuuNOQ3/RRqEi/6+2GvLJX6YDdUlCDvvRTVusZYrjiD4Z7LMFfkY9uxz2CryGHBjRap+QJ6JgIQfT/hrCGTfVcmDDXNON/lRfT728/htZey/92Gio5HciKBJxveq4RznK2AOI3By3UoqzqPSxdqUbTrDLp882ydPfje2oCy7Fwc+/wTFOu10OZ8hL9esaCqMAP6YCAW4//ZhlYSC3moEo4hL1Urb5fGzlBz9hWJhaj7hjc7X4L/9ifI1uVDuP1U/mwkddC4in0SKWFiQelfv/zb/4TbLsCUx447XR5MZwQIdjf8sfanspgwURMLfH+dULbJIjsuf4BQqGffUQrrfS/6Ww8gzWBB1/CM6tjLxsH6j1GckST9zrzqv2FQ2m7x1oUte/DvqDVVo0E4CcOOPFS03cNMtOMq5jGyumP/cu1RVDWcRo0hF9kVFzHw/zzFcEcT8tg5llR2Af2T7HgR/Ri5XAF98QXcV3c1YGvLx0Zot1ShMD1X2i6r+d6ooiUWlNfkJIJCvV+CibnkDyBYm2AqLoWp4QJ+5Ns/WLZElDvysaRq8UAIIYSoUGKBEEI2rHiJBeWushTAjuOh7TA+cHjl4GqmC1VJOmQKtzCLafQ1bGcB3AG09rPgK/AIvcIeaLV70DLgx8LDNuz/wIEx6R+VgCKT98tewnxfI1I0ehS1ssBS5HcsjyFJsx+2YR4w8T7YOSi2P1QCcd5/fpuyrmKc5bIXlDunuqKv0M+CLXGqAxVJySiw/cbDrYQtAoLEcSeMOg20e3nXBxGzfU1Ir+rCDHtvcbAF20PBvbwd5WbhovK7UpBv+Rljv36Hr9sH4J+T1ykYnMX+/+D67UDV5XuYXJjBUPtxZLDl7bI9kJIg4YmF2Tj7hm2nYSfqKsxK0iKKsKAxuO4x9kkUq2qxoOyP5cA0ssVCgv0ZKRjApu5AocEAA38cyEO6VpVsYcucG/gC+dosVH93A5dKP4RjLNipJfJ3zmOi9xz7bDJyW+7hWZxjdmHIjpLcZvTNyAkIaXBE3YfomJpfcVwlPkZibOeFB7CX7EVj3xT7pNz9Zpd2K0wdj9mq/wGncQu0obEM+N1/Exr6/uRfGSH4PcF1ePH9K4mWWMBjdJi2QpPTisFgNwdVYmFM6hahRZrxG9ydmEdg8h4uV7F9nlaNjvGhqOXOqo4lQgghmxYlFgghZMOKl1iYQk8NvwNbCOv/voWW7WnIMzUoI/CfhCFdB00uC26WVjZrlwPyJORaXfhfLQXQ5ZlwRvq/M6iR7urKrSDCA+RgYCE/l4OyEthHg8Gg+ntmMRhnuSsCobDnq08sAH+ir4EnV3ajZdDLtsf+5QAu4IPr2254/Avwe79HQ3ZS6A565O+SrFinWP8fZf3EYdj3sv1UYMMwi+HCli81SY+1b+R/jytiveLtk2jk9xMEg5G/fUViIcH+jKQsL6zFAtuePY1l4espTqGvMRfa1G0oihgrZMXvUgJ2Te4nsP//Yq3LJNzmvIi79JN48GCS/a4o++0FjhH1c2n8gZRG9M0rATv7tskHQ5iUWqyImHWdRaYmC9Vd4xD5YKIHPo2egGHifU+051Gt2H/cIp721CNDkwFj2wD8C+Nw208gW6vFNrMb89Jyw48L0WvHXi1P8P0bndRigRBCyAuixAIhhGxYcRILPGDJZYEQH0xt5AoMMe9qrkwsyIEIDzCu4LJhq9JSYKXYQc6fUYJ/9ffw9Y693BWBUNjzF0ksBAeZ02LLoSMoTVePt7AIv+cahOqP0XrNgU/3pLxYYiHm/0dbv/BtHLZ8nyPOvlmFiPWKvU+iB57y+y+bWEiwPyMpywtLLDDRxlgIBrM5wk3MqBa+8ncp67TNgP/P/zvWusjnS1hiISTaflv9MbL8XDn2wxILEZRZS3QlVzBy9zwKzXcRMXdJSOzvWd3+lazYf0Hz8PVdRIPpA5RVf4oLn5uQoYypIA/kGHFchJbTg8EVYywsYabrBJJiJZMIIYRsepRYIISQDStWYmERM31nkKnRY6/Ng4WpDph0aSgJNreX8KnsbsI1+efKxMKzHtQk86bbd6Xm0rqSYLNyxdxvuOEajxPkzChBRo70t0wdjMrNsGMtV4wMhMKev0higQm2FtDokBUK4JSp9pKPoWOKN4lXtuOqEwvx/j/a+sndTXSmDkyxZ2HLj7tvVvH7IrZV7H0SPfCU34+WWOC/8Wuc/XFyxXesTCwk2J/K0xBleZGJBYn4GH3/uIVJ/k/iFG6fPYaa+lLotQUQbstdC7iVv0tuoaMzfYELMdelX77THtZFQ0TA1497j/5PxH57sWNk+Xnw2I8Y9DAwhl/uPVbWny27rwlbNOnIyyuLG4jH/p7V7V/Jiv0XhTiGjsos6MuvwsdXUhpjIQlbhduq1h18OXoUs2N1XkrYqZMINCsEIYSQ+CixQAghG1a0xMI8Jlw2VOxIQ3btD/KAgdJ4Byyg0pfA3DEg9fv33riA43XfsiBCCRJDd1hZ0OM6i6wcFiDMPofPWQm9ZhsM5u8wODkLv/cGLhw/g2u+hbhBjjjVieo0HfRlF+Hhg9IFRuAwblW+J/5yVwRCYc+Do8/zu/zs/0a8eBI3jglg3Glin+fdIYIT6z2D27wTmiQ5aBT9P0OQmrlfgXd6GtO3owRrYesQ7/+f4MGViMTC7C0IWfukMSu4sO0Wd9+86BgLEcuO8jyS/H5kYoEPHNgHS9FBuSVF5P5YkVhYiL8/IynLW5lYmIevowbvS8HsIp72nkN56z3MBYbZsZMObc453JbGRgiu93Y09CnBu7SN98DsfhJnXeaVaUiTkV31DTr7buN290XUnbiCoYXI42oQ359b/TEStp1nulGTpoU2uxoXOnvhun0dl+oacHloVvqsZGEQ1oIUZfwP5bUoEu3PyOdRrdh/EcRpDFjLkKEMkCrjY3+UQJd5RhmPgh0THivy9ZVw8n0qHbdZy9O5LvSjJScL5c5RJXlCCCGEhKPEAiGEbERzw+ixN8Og10KjzUZZfTOaa46gaEcmsssaceHHh5BndeD4Xdl/o6VsO7QaFtBpM1DU8A854A8Gidp05B+pQXPzSZSVNMI5pAThfMaClnJ5tghNEtKLGtHumWaB1gi6zftZILYFxS034PU9VJ7rUWTpw0RgEf6BNhilEfs10Gbsx8E925Bh+BjW7z3wL8RYruiHt9uCIp0GScWt6PWOhZ7rij7DrYl5iBPXUZedzNb5fdR2/h49WFKb7UVDoXrMAhawD11BOV83bRaKBTuuNO9i2yYN/6PSggtn9kq/o1BwwjXqhxiYwOB3Agr5OhQK+G5wHLMx/j+/6Tr+eOhEbeF2ZBpOoFlowKmKOnzRO8bWk+2HyQF8J8jLLzL/AM/kfJx9k2BWCPV6FZnReftndMbdJ+qQL4BJ97doMb3Htr8WqTv2yIMohgZSZOuyvQWD85G/fRS+wW+VGRuK0PjPn+GVEkcx9qfybUHipBvX/lKFbP651HyYGprlsSWaa3AkP51tg0zU9Pgw4b6Iisy9sLj/ZMuYx2Pnh0jmCYFqO9x8MEEpoGbfk38INc2ncarMiDrnb5Dun8dbF2mmh5PKbB1JyCj7Ar1K0ib8uBqNuY//R2UL/nouwbE/+DdU56Wx7+DHfjlapP2vNo9hW1lESxW18GOlUPgWA8O/JjjnVmxttoxfceOfp0P77183PcpYD+xdvxeuzr+iuaQQJZ9+jyHpmFPhCQebCdn5R9Ag1KPKWIM21Uwfov8ebOUHpFlf6o0HUG67pypzCCGEkHCUWCCEkHda5N1nQja+Vd2p38j4OAsHa5VuFm/CIvwPbuGG60Eo0UAIIYSsJ0osEELIO40SC+Tt83YmFgKY6GvD2RYb/m4+jN3mX6DqHEEIIYS80yixQAgh7yzeHN6BBqVZe8NfO+BezYCBhLxBvDuFo6FI7iLQYMO37okY3Qk2mnl4HYeh12ihL/4GA5FdDwghhJB3GCUWCCGEEEIIIYQQsmaUWCCEEEIIIYQQQsiaUWKBEEIIIYQQQggha0aJBUIIIYQQQgghhKzZK0ks8Hmc6UEPetCDHvSgBz3oQQ960IMe9KDHiz3eBdRigRBCCCGEEEIIIWtGiQVCCCGEEEIIIYSsGSUWCCGEEEIIIYQQsmY0xgI96EEPetCDHvSgBz3oQQ960IMeb+jxLqAWC4QQQgghhBBCCFkzSiwQQgghhBBCCCFkzSixQAghhBBCCCGEkDWjxAIhhBBCCCGEEELWjBILhBBCCCGEEEIIWTNKLBBCCCGEEEIIIWTNKLFACCGEvCniNDzt53G+5xFE+QUEJu7CIZQiW3AhIL3GiI/QYzmHy4PTyucICVqE3/NPnD3fgwl2cIj++2iv3Y1UaQqzNOTXtsPjX2SfC2Ci50s0Xe6Hnw4iEkE6bs7+BT0TrNSRyqWPkJ+qZceQFvqic+j2zcufm+iBpekKBqVjihCVFdezoDl4HSboDQ74+FO6nr2zKLFACCGEvAmsEjZgrcZxp1dJIIhYGLmOi998jEKdBqnqxAIXGEVHXQ1sHr/yAiGL8A9cwKHj1zAW4FmFCfTUHUT1peu47erFd+YS6DXJyDH/glnp8/PwdZxBpW2QVfUJkYn+e7Ae+hjOMZ48WMTTnnOotN6DXxQRGO9EbWYS0mq6MSN/GgFfB+oqL8IzR2EhUay4ngWx48XrgFGvhSaYWODoevZOosQCIYQQ8totYub2OewsdWAssm4ecEFIjZJYYMQxB0p3WuCepQo9YcfDzE0IO01wjC3Iz8e/xYmzt5QkAiN64ShJg2Z7CwaDN5j5a6UHYHbLYSLZ5MQp3Bb2o9ThVe4eP8ODm/14Gipi/HAJO6AzdWBKeQVYwJjDhJ2hhBXZ3OJczxYewH70JGqOZIQnFhi6nr17KLFACCGEvG5LD2DblQmj84+VTUHjJBakoLA4CyWhIIBsXvMYsh1AktGJceVgEP2/Y+SJ+qiRg0JNrhWeJeUldlT5HEYklUQJAsimszRkw64kE5zjK0obmTgM+769EG5PhZU5os+B4qTyUFKLbGIxr2ezeGg7gY96hnCbl0MRiQW6nr17KLFACCGEvGZLHityNYWweqI0SI+XWMAzuM07oSmwYZhqYpvb0n1Yc1ORa72PUM5ghcfoMKUjy3wXz5VXuEW3Gds0+2EblvvNk81qDh5rYUTiaZnof4gucwnS8ppDYyyELLph3paCAttvFBRuctGvZyLmfrWh8vQNPBWVBGdkYoGuZ+8cSiwQQgghr9USZrpOIElTBocvyl3CuIkFfre5DJrkOvQ8o5rYpjbThaqkVBgco8oLK/GmxiVbq9EReTfa54BBk4manuXG7WQzmkRXVXqUgI+ZHYTj7GnUlOVKA4FqcyKbrI/CYUhFck0PCw/J5hXjejZ3D62HzuP2DO+DFSuxQNezdw0lFgghhJDXSqlMvUxiIdb/ks1DSg7ESSyIU+hrKkddd+QI7Uyi/yWbhJwciJpYCJnHmPMY0lYkolbzv+TdF+2a5MdA6wmcDXWfSZBYoOvZO4MSC4QQQshr9QpaLOg+RMcUTfe2qcVtsTAPX+c5HG+LMfuDlFjYClPHY+UFsjnFabGg9qwHNclpKHOOKS9wcmIhfFBHsvlEuZ4p1zCNNOVt5GMHBFdwJgi6nr1rKLFACCGEvGZyn9QCtAxGGVM9bmIhfp9osolIYyykYHtLP8Kr5HwKyjacsPyMmVBThXk88U2Fjqe4Y3yQTUQpT9SzhkQz34eGlIjjZVVjfJDNIO71TBKrxQJdz941lFgghBBCXjc+0vreDBRHGw07XmJB/ANOYyZ22R5QZX7Tew6vvRS6YlZZDx1E8pzx5QV1uNJ7Gy6Xiz1uo+/qJ6hrDx5rAYw7TUjaZcMQHUSbnui1Y6/OqGo9NQ/fTSccPUPwSwfMImb6zmJf3XVMqAorcdwJY9IB2IZoANBNL971TBIjsUDXs3cOJRYIIYSQ1y7avN8sKJz8FTf+eRqFOg10hQL+ddODycByVU0ajC+rHj1PqdkoYcfDzE0IO02hKf/Eieuoy05e2fw46dhyU2M+xVvJLtT2TMQIAsimIk7htrAfpaGgcAbuln1I1SQhw3ACzYIZrQ4XJlTlELCAMcdhZNX+hKd0EJGo1zO16IkFup69eyixQAghhLwJ4jQGrDVoWm2AFxhFR90pWAemKSAkCt7t4QIqm7pXGeDNw9dxBpXWe8rdaEJYUeS/B2vluVUGeCICvg7UVV7AgJ8CQqKg6xlhKLFACCGEvCm8Mnb5PL7sexy/ciU+Qo/lHC4PUiWMROLJhSs4+2VvWFP1lQKY6PkSTZf7KalAVhD9/bh89gL6JlaO7KImTvTA0nQFg5RUIJHoerbpUWKBEEIIIYQQQggha0aJBUIIIYQQQgghhKwZJRYIIYQQQgghhBCyZpRYIIQQQgghhBBCyJpRYoEQQgghhBBCCCFrRokFQgghhBBCCCGErBklFgghhBBCCCGEELJmlFgghBBCCCGEEELImr2SxIJGo6EHPehBD3rQgx70oAc96EEPetCDHi/4eBdQiwVCCCGEEEIIIYSsGSUWCCGEEEIIIYQQsmaUWCCEEEIIIYQQQsiaUWKBEEIIIYQQQggha0aJBUIIIYQQQgghhKwZJRYIIYQQQgghhBCyZpRYIIQQQgghhBCyBiICj/6D3+dE5TnZrDZJYmERfu/PcLZWIS9dgCugvLxac8PovlCDfB2fZzQLZeZ/wj3JFiJOwN3+KcrSdez1VOTXXMBPfT1oNxuRzuck1e1CzYVuDK/mRHt+F+a8M+ibTfTZZxjuvoCa/FT2nTqkl32KdvcEP6Ux6f4nzGVZ0lyouvwaXOgexpzyX5ve0gPYdqUiraYbM8pLhBDycljJO3EXDqEU2YKLlcJxiNMYsB1FkbEOQv0hFJW3YcC/KL811YGKpPD5rKVHrhWepUVMdRxDUuR7miTkWu9jSVoCExiH2yGgOPuTKNc4P1yCAYLLrzxXBFwQVlwTY3yWrI84x0W4BMeB+AgdFdsi3uOPQlg9wZpAnOOVrYen/SPkp2rZ/2ihLzqHbt+88qYs4BKQvuI4j368RP8sWR+sjjvQhvKiQ6gX6mAsOgrbwDTb29GwY8D3E8zlh1Dd3IwG0z4UNXbCF4j89Dwm3P+ApfowKoQ2dHmU5c39BmfdEZjqz6DeuB8l5p9U/ytibsiJOmOlsh7lMHf/EX4MRC1zYhwvMT5L1ofovwdb+QEYpX17AOW2e/DHDElEzLrOIlNV1uhKHBiTPs+OR087avPT5Pf0+yGEjoPE1zPRfx/ttbuRKr2+FUXC9Yjjk65RG9kmSSzMY9LTA2vZVmhS11pIjcJhYMH8iv/nB/gOdvCXweFT3uCFYSo7IQwO+ORXEmAnaF8Ttmi2oaLjkVx4xxWAz1HGvnNH1It5qiYVBseo8gqR+eFxfgVrT8RFjhBC1kTEwsh1XPzmYxTqNEiNG0QtwOeshD7bAreUaJ6B27wL+vKr8IkLGHPU4qjtR9x2ueCSHtfZ9Sod21v6sSh64Th6Crbun5X32OPfVpQlFaBlcFZZ/Ah+vPgV6gv1EdeoJcz0fYJdxc240FyH5gvNKN71CfpmJtAnFKNY+BLNJwVcEA5il3ADv0f9bCh1QV65eMeF/ImQBMeBONaOo0cvoPu28p7rNv5tNSJpewsGpTxFvON1EU97zqHSygMJFniOd6I2M2k5ET/TC2HXQQgXBJxs/hJCcTGEvonox9bvN6J8dkr6FrI+RN9VlOt3wezme4sF924LsvWVcPoW5A+oSTdZti/XNaWEVDZKHN7luidPdlkPIi27Ds6hmeXXpeNzD7KEW5BKnoVBWAu2osA6yI5kZvYXmHMKWL10mj1hx5vHigLdPlg9/NNTUcqcXsxEPbY80T/Lv4OsD3EUzvIsZJvvyDck5+7AnJ2Fcueoav+riOPoOnEMLT/dCpVH90b90mfFp91oqrwgJ0gDY+iqzYEm7SP08GtJouuZOIGephpYpcTYPMa7PkamZjtqep6wJce6ntE1aiPZRF0hlATAhkwsTKKrKp0tQwPtXju8Uc9itVeUWBD/hNsioD243puM6HfBUssqcMpzQgh5YUp5HzexwFukZaWEtTBYYpXuXE0eCwbGMeK6j6fqcn+xHy3bC9h7rHxf8MJ170lY5W5xsAXbs1gw+lz9auxr3NLwJexL1SF13yUMh+pg8xi2H2TXiy3YZ3+4vF5RP0vWRdzjIuJuXILjYGHEjXtP1S0dZjHYshtZ5rt4rrwiiXq8PsODm/2qY1A+lnSmDhYOKpYewr5vCzu+DsI+vNySIerxEuOzZD34WbCfp7RuUl5aug9rbsrKfc/5HDBoMlmgFtyz0+hreA/bzG7IR48fHlsZ9LoS2B4qicugmS5UJan/l5Uhtv3QJJ1A10yAvX0CScl16HmmHEjib7AVJCOpqktJCkQvc6IfLzE+S9aBiOduC7LCWjfNwWMthGbFdUYmeu3Ym3kUtr7/RLRqYMt64MIdVVkkxSW6D9ExxV5LdD17PoSbd1TvS+XVVpg6HisvxChzyIZBiYVVW7/EgjjuxOHjNvxdSi7sRsvgM+WdWF5FYmEevs56ZGtV672ZBH5HZ+370K46+UMIIVGsIrEgB4vpqOqaVF5hpjpg0kV0Z1BIn9/SFKNrHK/wFWFLQ6981zAk2jVOucNT9ikuCZEtFkpQZvkaQmSLhRWfpZrbennR4yJcrONAIQWXuWjo+1N5QbGaRJg4zAK9vRBuT8kVfOmu8iFYLn2yssVC5PEitViI/Cy1WFg30n5OUgXv3GN0mLaGJxuCnt9DS24ydEVfod+/CJHt28asPaHWDlIrA60OmcFWCSpSABhW7xQx39eIFM12dpz5MNhSEFH+8KTFdmhSGtE3/yRKmRNssRB5vPAWC1E+qyyVvGpKEkFKEAUPGN5l4UPowpINQUoyK9iNIayrQ6Tn8NqNyBVuYiba5SxBOcYTGPtyz+H2DE9UxLqe0TVqI9mkiYVF+IdvwG6uRN62SrTYjmGHlp0cqXvQ1BOrK8J6JRb4SXcCjezCK3eHiF6gh3uRxMI8fL1/QXVFDc6cMiAz84jU927J9xPOGjLZMjJhqGnG+S4vO2Wjf1b0e/C9tQFl2bk49vknKNZrod1lw9DiNAYv16K47CSam0/AkL0H1Zf75ewl3wYZhahqFiAITagqZBc5TRpKHMP4f4a/Q0NeCivEjLD2T7Bfw/bHyBWY9B/Aep83f+J9ADvRVFKOU2eOoVCfjPS8fTAYqmHnBZwY53vFJ7htqUb15xdh/7wahdlmtr+UvoJVRUjX8/23AF+PGQY+Nka6ATXCV+gcHEBHwy5oNVtRZr2DyQBbC/a7L5tyUWxVlk0IIZESBmqsMsTv5EWW18r/hQcEnFzJS67pQdQUsxRIvKe6axgUL3nO36MxFjaWFz0uIsQ8DmRS0kJ99zgowfEq+h+iy1yCtLxmGmPhbSC1Iojcn0pZEBYoBvHxGL6R63E7SmAyHof19mPlf4MB4zaUCZ+hwVSKYlMTLvz4UKoDyfVLnkTgXR1ky3XOQfk7pSRC8JhT1iOyfkxjLGwwSqvpiGuHvG8jEp9Boh+j7h557Do+Lou2YDkRGSTOYLjLAkPabjTGSjzELMd4nNYJs2E78hppjIW3ySZusaBkUnUH0NrPgtngwEcFNgxHXIdlSmJBm468AwYW5AYf+5CXnrSy4FxtYoGfVIbPlCZAvFkkC3aDfZFiCiYWkpSAe3l9DuSls+A4mFgQsfCwDfs/CA6oohQemWfhmmXBtbSM4HrH++ySkpVOQb7lZ4z9+h2+bnfjP52nsKXACs8C/wf5//fq0uU+WYtufPW5C7xaIs7chJCTDN3eNjyUPhvAuNMEnTbY906uBBUGM5ZSH8CtKLKP8KVizFHOPlsKu5c36lvEVFfs7w3wjHrowvYn+gRr+P4O7X9lf6r2EW85YtSlLPcX5PumsDnGXUNCCGESBGrL5XX0AHLFdYLfLd67I3pljpGaoCZHCxgir3FkY3vB4yJC7OOA4zcsSpEcLTkR73idHYTj7GnUlOWygEIDbY4Fbrr+bWxS14YYiQV1vTTMLDzWfayuyI4z7fuo7fxd/l+l64Im7RCsd8cRCExg4HI1MjSZqOwYkwaZ5a1pclr65ToSs5xYGFHucKtb3SZaD7IxRL9xurxv47WA5jcCf0BtNqvjG50YDxUXMxh0WNBccxDZUuIhOAZIuOjlmMiKonacbT6Fsmw9O36SkWP+JcENV7JRbOLEQqLnkdajxQLv1/QZDoQK6WdSn0iNZguMzj/Yu7HEqJAw4QUB72NZAF2eCWcE3nLgDGqkVgq8aZM/IrEQ77NzynJV38cHYCnWh1/MlNfCkzN+DLQUQRvZX2/2FgQ+OFR1J6ZEfofuQ7ZsJQsuXSiXCzNx2IaC4Hcn+N6HfzhQokvGDtNX+HF4BgvT05Bv2ETu35WJBZ58cAk72UX1FLqmFrHk+RoHErYeIYRsagkTCy92Z1pk5V9xktIfdQVe9huRpO77HpLoGkY2lpdpsRDvOGCka+J7Yf2SQxIer9w8xpzHkMYCylgtIsgG8cItFmYxfPUkio7/C17vdQhFW1nQl4fG3kmI0equUqKT1ZV4vW5pAj212ayOdBhtv05jYeIO7NU7odXsZEHjM4hPf0JtBqvXGS/iV/8sJtx2VLOAU7PNDHe04oxsEGtosRBGuQEY9dojIjDmREWaNkorvATlGBfwwlnBYpFora/IhkSJhZjPI61HYuFP9DUWhreAOJCHdG2iQRxXm1jg67wVBbbfoiQpgssIrne8zwaXq/q+qJWTyG0oYm7gC+RrVa0AQpSCSFcOx8hNmAvPLw8QI7VYSEFSBSts2EtSYkFfhY4JttCE38u7c3wGg55nSLfDGJouJ3LdoiUW2HeN8cQE77LRj7tm08oBtAghRG0VgVrUvvRSQBDZl15uzZVUzMqlaAWx+Aecxu0oVo/gHpLoGkY2mtUfFxHiHgfsbd76LskY/S7xKo5XybMe1CSnocw5prxANiSpKXnkGAtKoBhljAW5ZeZyv3nR70ZrkV6ucy4pLRbC6kUR5UpgDH22JpiKjag2W/G56T2lZSs/Evnd6z7YGipQXHYC5gsWmDKSV9G9l7xZcve78ERUMPEZbYyFlaQYIWYCaQo9NZnQljkxobwiSVCOyURWFNUhWXsEzgnKTr0NKLEQ83mkV5RYmP0F58/+JF0A+BRBhyuvqZoOMfxOQwmf+zXeII6rTSzIA/gszy2rmPsNN1xjEYmFeJ8dV5ar+r6orRPk7gahkaSlqYhSoM3/AgPSVFqL8D8aXx6vYLYXDVt4d4587AurRAUznNtQWPUxqo2Vy3MhJ/jeJ0+8GOEDEvl/Q4ewH3q2zvLgVZH7N3piQUr2NLDPpe9E3r6vVw58RAghaqsJ1FY9+j8PCHZgr304ekWLB53JwW5hkRJdw8iG8yKzQqjFPQ7kgCA51s2J1SYW5vvQkLK6oIK8SS82K4Rcl1PVV7GAUXuJUm4oy9rK/o4YJ0EbJdkpjn+HyrSMGFMSBjDeUY20WNNekg3kxWeFCMcHejyGrcHpaVfgdfTslcnSuOVYkDJAaLSBSMmGRImFmM8jvYLEQuAxbllKsFtqFcCb3e9bnks4JJgljDeI42oTC8oc2ZptMJi/w+DkLPzeG7hw/Ayu+eaU/nD7YRtmr4/8BvfVWJ9dUJar/j55rIM0bRFaBpTXFvrRkrtHGcBlFg9tJdCp3+dJgaZ2jIZ+sNLPLzR+QhCf7qgRFtfTiG3Dxf/egMuC8mD2UxyBvShLaQ4auX+VUZOl5n0zGBkJDjojKqMip8au3BNCSFCMQE18+jNaK+vQJs3HrZTFOa0YlMaG4d3e9kBffjW8si7drY4VzCnXhpgVrETXMLLxJDgu+GDErSdwvC3Y8o5LdBzw5FRW7BYPUY/XefhuOuHoGVK+ZxEzfWexr+46JtTHJ9mQ+E2qcv0e5WYUK20GW5ETCugX8fT2X1B5/K8Y8C+yMqYbNWnpqronTybkI00KCoNjVuVKA4pL7wfuw5q/fUXyQPTfg7U4G/lNP0U5RpQBIjMK4wyITjYUcRTO8qzl8TN4vTonK7Tfw65n/gG0W75Cu3tcLkMCD2Ev/xD2IR6x8FYrP6PdcQPD/Hjjr8z0omnfaXTzVschMcqxwBhutv8TPcP8aGTEKfQ1laOum46jt8UmSSzMY3LwWwiFemh0+2HuHMDww+swF7HnSaVo6R2Cz6s8Z+9bbgVHyFXMDaPH3qw0r8+B6fOrcE+yT4gTcF/7ElW8DxkPyAU7uvtu4FqLCRl8UJzUHSgMDawYHOSxAC39I3C3VSNbq0dhg0NelkKcdOOqYEAS/3/dLtTY72Ay7Gx6huEeOwTDNrYsPpbAeXZyT/BTGZPub/GXKt7fTQu9oRn2nmHMsZO0t6UcGXzWCz7YY1Ej2j28ostX/zrq+LoHB++J9Vn/Q3QKe6HTsPUVnHCN+pUTns/OcBJ5GUWoaj6NGlMlhC55EKAlPkWMji1HmnWBj9kgoJnPNGEMbwrFuznsjmwlsTQCRyn/fXw9+EOL1B0fhJYd73sDrrNIzziAmr9cwpULTag83QlfQL3/90L4bgCTgQVMdDeyfaCBNrsenerRr/kARrtNcIxRlp0QEgsrdSd/xY1/nkYhK+t0hQL+ddMjzSjDLXmvoFSfjlLHiBzgsXJrwHYURcY6CPWHUFTeJlf0Q5QmnzEDRt6c9L3oAWNgEp4bV9AYLOP+dROeyfAR/ckGFe+4kK6F6dCXXoE3tNPjHAec1IUhWnIq3vE6A3fLPqSy636G4QSaBTNaHS5MhI3ETjYuHsi3obzoEOqFOhiLjsozeknvzcPrOAy9/jAcXl4msM8O/g3Vhftgamhm9bJyFB//GwZDZZG8LGP2bvb+GTRUHUF1KLHF3hu9g85LZ1BSYMSnHb+pEl4MnynA9d+41FyGgpJP0TGkBIfkrcCTRbbyAzDWn0G98QDKQ12Jw69ni097IeSnsdghC4ZTTWhs/FK1r0XMultRkKqFNsOAU81nYW5th2si8noUoxybvYuWguCyT0MwfwWHKyImIxvaJmqxQDYePs5CNY5GtNrghdvXdd/grm8E91wuuPij71uYS2pjDGj2avFxFg4elcd3IIQQQgjZ9MRpPLjZC5dnMmqgx29C3bzhoqQmIZsYJRbIaydO9OHrsy2w/f1TGHefj5jOSp6dIqnsr3iozqAPd0Bo+lf0Ac1eBfEx+r62oMV2EWbjB1GnxSGEEEIIIYQQshIlFshrJzep0kKjPwhrqLleEO+f9RPMxVnyHMu8G0R2GRpsffCtZ7PMUPeLbSi2qvuzEkIIIYQQQgiJhxILhBBCCCGEEEIIWTNKLBBCCCGEEEIIIWTNKLFACCGEEEIIIYSQNaPEAiGEELJa4gxGHsjzd8ebniuS6L+P9trdSJXGjtmKIuF6jHFj5uB1mKA3OOBTXok/nZwiMA63Q0Bx9idwhYZsn8ejQQ9NG7jhsP05MoRHfL8knIZUhX3W0/4R8lO18vhD+R+Fpo+WrHpZL3KMiQg88mBwxXRx5E0T/V48eKRMIZmofAhhn/W0o5ZPF8jLotTdqG2/v1xuJTjG4pdjIuaGnKgzVirrUQ5z9x/yDBKBxxgclMtNsoGs0/Us/rKeYcjZBKPpI7mcKrGgWzXle+xl0/XsbUCJBUIIIWQVpArP2S/RxStB4iic5VnINt9hYRozdwfm7CyUO0dXVujFCfQ01SiD1c5jvOtjZGq2o6bnifKBIBbEeR0w8sFtVUGf6LuKcv0uZbYaVnl3W5Ctr4TTtyB/YGEEP178CvWFehYoCKrEAh8M90dYmq6o5qknb5QUuJ2Hpet3VplfgM9ZCX22Be45ftTMwG3eBX351SgzIC3iac9p7K2+iO7bt9H3nRkGdpxoc9j/SjMrrXZZazjGAr+jy3IOlwdjBavk9eLJgX/irOVHKeBKWD6oiE9/Qt3eU7jU/TNcfddgNmyDRhv83wTHWKJybPYXmHMKILim2RMRCx4rCnT7YPXMsufz8HW1oulyPw2OvUGs2/Us7rJEzLLjMyfrLFxSuTULj3UfdAVWeBYSHWN0PXsbUGKBEEIISSTghbPyiFJJFvGcVY6yNIXsuVR1YuZYBakQmixWCX8eURV7PoSbd56w/1IEXBBSt8LU8Vh5QbHwAPajJ1FzJEMV9PlZgJgHTS6reC1JLwBL92HNTUGW+S6eKy/xz7mEHRGJBY5X8L/G/konxuhOzxs2jzHnSey3DkIK+Z7fhTkrBbnW+wjtWhaM5WryWKDnV15RiH/AeeJzpTLOLWDMUQ6dpgAtg+yYXO2y1nqMLQzCuv8knGPUcuHNYsHVmBOV+7+WA7FVlw9cAOPOMzgrBf4yccyBEp0O21v6sZjwGItXji1hpusEkpLr0PNM+YT4G2wFyUiq6oI8gTcPIo+g0umllgtv2rpdzxItawJdVelIrunBM+VdcdiGAk06qromV3GtpOvZRkeJBUIIISQufjf4KLKqOzEl1WWUilLSCXTNBGvzi5jq+JBVwtUVquhErx37cs/h9oz6rsssHtpO4KOeIdzmCYJg0CcFCUmqyjn3GB2mreHBRMzEAiM+QkfF+9HvPpHXRrqznFWDril5v8uBv1KhDprqgEmXFJYgkPAmyyNTYfsv4BKQqhxvq1vWyxxj/PiuwtaorSnIayPdDd6F6q5x+VhYdfnA8S44XjwJP4hY4KYcIwmOsUjh5dgsBlsKIsqfafQ1bIcmpRF98/JSRXZMVmyN3pqCvC7reT1LsKz/fQst23VIFVzLyaX5PjSkaJDS0IfItGXUayVdzzY0SiwQQggh8Uh3gzNVQdukdNclMoiXK+ERwV0YVrEf7oTZsB15jRF9k3+1ofL0DTwVlQRBMOib6UJVkia8IhZMIoRV3uIkFoIVu5xWDEp3OcnrJ99ZXg4AlTu8mh0QXKoWBVKgp4kIFKNR9ql0F3BxFct6BceYlKjYjZbB4L1G8nopd4PV+2TV5UMM0j6N0kJGoj7G1OVGtHJM+U5VEiH0mqYMDl9w7XjSIwM5Lf1yqx3y+q3r9SzBsjqcUpkUlkRQyqnlFlRcrGslR9ezjYwSC4QQQkgci4Mt2K7ZD9twsCo0CochNUblKRUGx6jyipqI2cF2nG0+hbJsPatsJyPH/At4Q1TM3UProfPKXZmIoM/ngEETI3AIq7Arr0VNLAR/Q+K7T2SdLPajZXsqCmy/KXfZAmzXlrF9GD0ZEF7JjkL0wlGyE5UdY2x5q1jWqzjGpN8Q3t2CvE5Kq4ACG4aD8dSqy4doeFeHQ9ha+R3Go8VnYcdYUKxyLHhXWp14irYeym9Y0ZqCvC7rez1LtKybcmsadVJgRZkX51qpoOvZxkWJBUIIISSmRUw4j0AbVjle6x0eBe/fWpEJjdQfeQYDrSdw9nawCXJE0PdKWiwwUgCSiZqeKeUF8lpNOFGmVVfSX6bFwiJm+s5iX911TEgHTaJlOXDjlRxjctCg7h9NXqcxOMvSVAEY8xItFsSZXjTtO43uiWgFRuQxFkVYOSZKA0PWZiQhzXgRv/pnMeG2ozo7GZptZrhDLdmVJJh6LAbyGq339UweQyH2sh7haU89MjQZMLYNwL8wDrf9BLK1Wmwzu9naRYg4xkLoerZhUWKBEEIIiSl4N1hdEYvWjzQY3K3mLoqIZz11SNYegdN3U75bI02tFflggeLPt6L0oVYqgqsdY4GTKmKx7j6RdRdl+0cdF0EKFKOMsRDCR0b/bzQdvwSPNPuDLO6yWr7E6VdyjCl3I9WBLXmNomz/qGMsRNt3EQK/o7OpHm2eaF0goh9jK6nKsQkeEvL/64OtoQLFZSdgvmCBKSMZmcIt1d3maOUpeX3W+Xo28WwVy5qHr+8iGkwfoKz6U1z43IQMzU7Is4lEijzGFHQ927AosUAIIYTEJGK+rxEpYRWxFxxFewVlmVEr/hF3k9nzl5sVQiFVxFZx94msD2mAsoiK8IvMCqHg88N/feJL1WBmLJh78hhPnt15gWWt9RiTA9v4rSnI+lEGQwxL7LzIrBAKcRoDXzfCEmrBwgSm4HsiN42PeYytGIU/XjkWwHhHNdJWTHupBLYJWlOQ9bLe17MXXJY4ho7KrBhT7HIxjjG6nm1YlFgghBBC4hBZJaZYG9HMXJmrOzQI2UI/WnKW5/0Wn/6M1so6tPH5uANjuNn+T/QM8yH0+JtT6GsqR133I/l5mMigj38/n6d+j9J3WcTCYCtyVlTY4ycWpD6p2lLYvVFDDbLeeH/1Yn1Ek3U+Onsl9KH+xs8w2LJnuZItPsHt1hM43nZPnvs/MAxH+QHUXLkBl8slP/ra0VR3FWNigmWFWeMxJo2xkIa99uEoxy1ZfzwoN0IbcY7H33eLeHr7L6g8/lcMSHP/z8Hr+BAFNX9Db/AYcv0bV5ua0T7GPh/vGHu+2nJsEf6Bb1CcUYimnsj35DEWtHvt8NJB9Eas+/UswbJCeILLWoaMfAE9we44q7xW0vVs46LEAiGEEBIPn9/dmKEaeE/G7+zZWCXcWH8G9cYDKLcpASCz5L2CUn06Sh0jWJq9i5aCNGi0WTCcOg3B/BUcrseqAFNtZdAnV9TbUF50CPVCHYxFR2HjFTzlXQQm4blxBY2Femh0eyH86yY8k+qJu+SAJKnEwQJQ5SXymgUw7jRBpx54j+OVa9tRFBnrINQfQlF5mxIAMksjcJSmQ196Bd7FR+iuex/aFV0ZtqGiI1ihj7OsMGs4xhgpIEkqh4MHoOSNEMedMOrUA+9x8fbdPLyOw9DrD8PhfYaJ7kZkayOPIQ2SKjowtZTgGEtUjol+jLr+G5eay1BQ8ik6hpTgUE1KsGWhxOFd+R55PV7D9SzeskS/F67Ov6K5pBAln36PIXUZtaprJV3PNjJKLBBCCCFxiZgb+AIFu6zwvI3TW4nDsO8rRstA9Ob15DWZu8cqzSWwetTjm78tnsNrL0dByz0k6nFN1pMfAy0G7LIOyneDNxDR/xA3b7gikprhRK8d+wq+wEDcsRvI+nqT17NF+B/cwg3XA0yu6FqzSnQ929AosUAIIYQkNIsh+0kcd3oj7p5sdPMYc36Mw/YHGy4Q2XxELAxdxuHj1zC21kr1GyEiMHYNxw9fxtDbmFh71yw8gP3wx3COxQ7gNyQ+wv/xk7APvY2JtXcNXc/I+qDEAiGEELIa4gyGrrXgk46Rt6RSMwdvpxWt3z4INUMlb9oi/EP/guWT7+F9K4J0EQveTpxv7QhvskzeKNH/ANcsLejwviXtRxb+g87zf8G30bpHkDeDrmdkHVBigRBCCCGEEEIIIWtGiQVCCCGEEEIIIYSsGSUWCCGEEEIIIYRsMCICj/6D32nAz7fC5k0sPL8Lc94Z9M1utgN1Ef7h67BWFSA9bD5tssLSA9h2pSKtphszykuEEBIUb0qtSKL/PtprdyNVmr4tDfm17fAofdbFqQ5UJEVO8cYeuVZ4lvgn5uHrPo9y43E0Cx/DVFiMxq7fo5TfrALmdcCoN8Lho9KdEELI6rzI9UwSGIfbIaA4+xO4wi43LM7wtKM2P02+jun3Q+j+Q3W9SnQ9EzHrOotM1bVQR1NLvjU2aWKBHbR9Tdiinv95s+DzDN9pgylZg1RKLCTgh8f5Faw96gKREEIYcRTO8ixkm+/I0+/N3YE5OwvlztGV1xRxAj11B1F96Tpuu3rxnbkEek0ycsy/YBYLGHPU4qjtR/aeCy7pcR3WsnRsb+lnVTRgaciGXcnH0DGlSkSklMMxFjHkVmAYDmM6q4iVUWKBEELI6rzI9YxbGMGPF79CfaEemlQhLLEgPu1GU+UFDPDEeWAMXbU50KR9hJ4ZKUue+HomjqPrxDG0/HRLuR66cG/Uv7litbfYJk0sTKKrile+NNDutcP7lh6tot8FS+1V+JTnqxZwQUhdY2JB/BNui4D2TVppXfM2J4S8Q0Q8d1uQpSmE1RMclX0OHmshNFkWuJ+HX1TE8W9x4uwthCZZE71wlKRBs70Fg4uzGHHdx1P1vyz2o2V7AcxuPk93AD5HGTTJdeh5pnxovg8NKTvZ+8/k5xI+fdhHqKs5iBRKLBBCCFmVF7ueLfPDJeyISCywZT1w4c5TOWnABVwCUnUfKomExNcz0WvH3syjsPX9h2Z/eAttysSCOO7E4eM2/F1KLuxGy6C6cvaWCPyOztr3oTU4XmNiYR6+znpkazdppfVltjkh5B2iVLqSTqBLuQvDm39OdXwIXVjlTCb6f8fIE3WZqVTIQl0dwi15rMjd0qR01WMVtYFW5Gr1KGp1s4rWImb6ziArh1X4Ql35RCw8vIhDH/2EidusEkeJBUIIIavyYtezZdESC5Gew2s3Ile4iRnpcpXoeuaH25wX6gKxshsF2eg2YWKBH+Qn0Ng3pXSH0CFTUN1JkogI+DrRVFKOU2eOoVCfjPS8fTAYqmGXTjAWYHcJKCk7iTNVhdBr05F3wADD4Tbc/t8/wNpQhuxtlfi89SD0mhTssnkw7/s3Wqs/RM2ZEzBkZsMY6rsU/7tEfz8uHz+Mqs/bcKHmAHY1dsIXeA5fjxmGdB006QbUCF+ha3SeBb5j6G09hYqaBpwy7ECmsU1uihRJSSwklzXBUrYdWo0WqfkCeiaUUzfGckTfTzhryGQneyYMNc043+XFUqzP+j343tqAsuxcHPv8ExTrtdDusmFocRqDl2tRzLZdczPbFtl7UH25X94WfL0yClHVLEAQmlBVuJV9VxpKrtzDw44m5Gk1SCq7gP5J9ltFP0YuV0BffAH3pd8Ya5/Y4WFvy9vxIMpOnUbzqWJk55/E5cFptvUZ8QluW6pR/flF2D+vRmG2mRWS85hw/wOWqiKk63mhuRCxzVvwf3X+X2jIS2GFsRHW/glW8C3CP3IFJv0HsN5Xlk0IeQcprd4iKlTSnRlNOqq6JpVXYnmMDlM6ssx32RUpklzJS67pQSjlLU5jwMqvJ8nYcfAwjJVf4/YEKweD5gZgrTSj9+misg6UWCCEELIaa72eJUgsiDMY7rLAkLYbjerkQKLrGe+y7e6Bs7UKealaaLQFEG5PUZ36LbH5EgtL92E1fCY37eEDOGaxQFHV90ciDdq3FUX2EXYg8/6v5dBpS2H3ylVAqX+QtgT20QV2AshNWpe7VEyjr2E7OxEKYbk7gl+vXUT73T5c2H9U6T+0hJmuE0jS7ITgmk7wXbMYbClYPml9DhhYUF/TM8WejMJhSIUmdPd8Fg9th/GBwyuffDNdqEqKljRhlMSCrugz3GInM2/BYdQlo8D2G/vfeMtRmjCFKq3xPitivq8RKZoU5Ft+xtiv3+Hrdjf+03kKWwqs8Czw/+B32dqwV5cu9+NadOOrz13gxYs4cxNCTjJ0e9vwkH9W/ANO4xZoQ//LK98mNPT9yb85/j7h/bWqc1BgHWRbmOPrXQKdvhJO3wIWB1uwPaURffP8w3+iT7AqhaSyL0OFZuQ2D2DcaWL7ax+sHnkr8zuNhQ297BsIIe8upSyIWhFLhcExqrwSnTjmQMnWanSMq/45SByGfe+OlZW5hUFYC1Kkuzja7Hp0+oIVMT8GWk/grFLxosQCIYSQ1Vvr9SxeYmEGgw4LmmsOIltKDuyC2a0aBj3m9UyN33j9AbXZLBYwOjHOL3Bkw9tkiQXej+gzHGjpVwLMZyxw380O7C0wOv+QKmUSKYBfPpnEYRsKNDsguFT9XUMVt3kM2/arTqyVJ5oUuOryYDrD78SzR40B6Zok5FrvYynud/GTyoVvf/TAL/rh/b4R2aHPRgS5Up/cNOSZGuTvEE7Kd9ejNbWN7Aqhfh53ORG/PcF3yoVS8LcwPOAv1od3wVBe0xTYMBzaAayi3FIEra4EtofBED04SmwWqrvGIfIE0YFP4ZKaTsXfJyLbxsVa1Xow8mtyMmWJV/J1ydhh+go/Ds9gYXoactevyH0ZmVhgZm9ByExCWnUnpkSe7PhQThgRQt5hL9FiQZxCX1M56rqjDxwslU1Jwf6oioWHuFpZguMdD+DtPoci3gIsuxm9TwOYG7Di0NlgM9PgOlBigRBCyGqsU4sFCYtjxpyoSNMut8KLeT2L0sI6eMM17neQjWSTJRb+RF9jodxE3qA8DuQhXRsxiKPUiiAFSRUdLFhkpwUP9vVV6FC6Csh3x4MzSvAg9gD0Fd9hImowqgS9YYGzSoLv4k2GPM5PUV37Ba45P8GeWIkFKUGxH7bhaFm/CPESC3GXExHAJ/jOFYmFyO+VRG4vkVWUv0C+NkXVwkChtETQlVzByN3zKFQ1I463T1asBxe2LvPw9X4GAyvcNNrtqm4qq0gsBAs9XTkcIzdhLjwfZ6AbQsjGNQZnmTI9VpyHtsyJiah9UoOt0eL1SeXj1JzD8bZBtoRo5FZQScWsjAkVI0rLqFCSeBH+/q9QpEvFXrsbt3gZFWU9+SO8rCWEEEIirfV6tprEAjeFnppM5doZ73o2HDXZLtXht5nhjpZ3IBvOpkosiL6rOFx5Lbw5jRKshg/iGMywbUNh1ceoNlbCHDEH65jzGNL0hag6cxzG8vPoVjVLDT/RlAFQeOAZNjWYH54bbkyK8b5rBm7zLiQrSQc5kI+RWJjqgEmXhpJgtwQJC9I9N+GajDjj4yUW4i5nLjyxkOA7VwT0UVsnyN0NdCb2G/lTpXmUNv8LDMzxD7FC59F4aDwKeVyMdOTllUUUdrH3ibp1QuhrpVFot8LU8RjiEy9GpHEhfkOHsB96ts5yF4vVJBaY2V40bElCel4+9vFWKMrLhJB31VpG0WZl2UAbTlh+DrUu4OXWE9+U6trC7xztiKhgKeWQutwRR2AvSomaOJDLXWqxQAghZDXWcj3jVptY4PX8bLmV9gtez+QY6hi21nSziIi8DTZRYmGaHcz7lDvaasGsnHo8Ahb02xphcT2N+KxibhC241/CFW1gxCgnGk9olOt10BvM6BicwIJ/BDcufIy6a3x+2DjfteiGeZtWac3AKqWuc0pXiIeY/nMI35m2ykH60gxGhu/gank6NPoSmDsGMLkwA++NCzhe963qzpciXmJBmss21nKCo8TyVgqz8A//hNay2N+5sqUA+/+uU0jTFqFlQHltoR8tuXuUgVmUsQ/U7/NkRFM7RoO/IZh4iJwmNN4+kcZYyFIlK0S2mFbk5pzD7Rk+2JkF5cHkiFTAZUkJh5X7kg+4ptrmI8HBZGZZAbwPWtU4HISQd5wy73dOsGsdL8tyluf9Fp/+jNbKOrQN8IFcRQS8DpQX1OFK721lbu7b6Lv6CeraVYlZaZyayDtE7BrV8xHSkpbn/ZbHB9qBmp4n8nMVSiwQQgh5IS90PQuKlljgXbh/RrvjBoaV+rg404umfafRLbXEjn89E/0DaLd8hXb3uBKfPIS9/EPYh2jksrfF5kgsiBNwt1UjW6tHYYMDbtUdfHHSjauCAUm86ahuF2rsdzC5OAJH6TZVk1ItUnd8AKHrd+lAX/JeQSlvNh96X48dxefQ5ePB9g8QCvVsWXsh/OsXjAZnLOj9AmUZSdLnten70dB+X74LvxTvu2YxdKUSGbyrRkYphPZLaM5NZifxHjT1jGK8u5H9puDAJ3PsZP43WqRZHthytBkoavgHPJGBNh+ltfMsCnV88EYLur1j8HZbUMSfF36K3gTLESeuoy6brYP2fdR2juL/xPis6H+ITmEvdGzbFApOuEb9cmEk8lkhTiIvowhVzadRY6pUbVc79rH1kGddkMejaD5lQKaRN58K4t0cyiJaScTbJ0qrBT4rRPUuZBQeQ3NzDUzln4beC7jOIj3jAGr+cglXLjSh8jSfeWMek4PfLu/L7wYwGVjARNg2X+4Cwruw7C5xYEy9UoSQd5rovwdb+QEY68+g3ngA5aFuVMEyKR2ljhEsBsvNUPmkPNSVK1aiPeupQ3K0cXGkcvMUCgtNaBBO41TZQRwPzqYTgRILhBBCXtRqr2fS5SkwCc+NK2gMxTs34eEztrHr2Ky7FQWpWha3GHCq+SzMre1whc36EPt6Jj7thZCfxuKJLBhONaGx8Ut0DM2E1ffJxrbJxlhYHX5yfV33De76RnBPurPEHn3fwlxSyyqBz+EfuIi6L27B5+1fvvP0gwUlR5Xm/C8g/ndFJAUI22BeOA5GbhvexPjV7ZMXx8dZqMbRFa1hCCGEEEIIIeTdR4mFFeQpHpPK/oqHobv9LHAd7oDQ9C/4AnwmhHSUtSktDjjeCqDDgiapa8OLSPBdFKUqApjoa8PZFhv+bj6M3eZf2JZTkWaneFX7ZPXEiT58fbYFtr9/CuPu83BLM1QQQgghhBBCyOZCiYUVeP+gn2AuzpKb9/OuCdllaLD1wRfggeM8fN0WFCvdGniT++yyRtj6xuT+QC8k0XcR2Ty8jsPQs+2jL/4GA5HdO17pPlm9UPcL/UFYw/qdEUIIIYQQQsjmQYkFQgghhBBCCCGErBklFgghhBBCCCGEELJmlFgghHdJefQf/C5NRUkIIYQQQggh5EVsvsSCOA1P+3mc74kygj+fSzXvDPo28yB8gQkMfv8XVOUVQHD5pZeWhmzYpd0edc709ceC/skBfG+tRl66eq7cROYx4W6HULwv9DuWiZh1nUWmNB6D/NCppoqMN+UOP34GbEdRZKyDUH8IReVtUcZ8iCfOes39BmfdEZik792PEvNPUcbaYNtj4i7aLSdgrBBg6/Io68ZnxmhDedEh1At1MBYdhU0Z90Gc6IGl6QoGX2g9CSFRiTMYeaDMsa2cjw6hFNmCK/GYLoFxuB0CirM/iSjLRMwNOVFnrFTO33KYu/9QLe8ZhpxNMJo+ksudEgu6VdPdIvAHus2VMFY3QWgwobCoWZlOdx6PBj2YoDF7CCGERFrr9SxuXZjVRz3tqOXTRvI6dupu1Aan2JfwcdHOo9x4HM3CxzAVFqNRmXZeEnPZdD17G2yuxAI/WFmAetzpjXLCsGCzrwlbNNtQsYmnDRT9o7hjMyFZs2M58J3zwGmxoUddkX1tWAE1egs2UzornFabWJjFyI+X8U19EXTq3xEkjqPrxDG0/HRLmZrShXujfnmfi6Nwlmch23wHc/z53B2Ys7NQ7uSzSyzA56yEPtsCt9S6YQZu8y7oy6+ucgaPeOvFl7UHWcItecaLhUFYC7aiwDrIvjWIJw++QXFaHmqcD1SFNFtt31WU63fB7J7hzzDntiBbXwmnj/83u1j4OlBXeREeapVByJqJ/vtoP/ulErSzEmHkOi5+8zEKdRqkJqqILYzgx4tfoZ7P+x1Zls3+AnMOT+ZOsydsuR4rCnT7YPXw0oDPC25BTtZZuKSk9yw81n3QFVjhWeDP5zFkO4Dkig5MSaf3IqY6jiFFSpbyc/9HSiwSQggJs/brWfy6sPj0J9TtPYVL3T/D1XcNZsM2aLTB+qlyszL5WGjaeHGqAxUp5XCM8fpqvGXT9extsIkSC4uYuX0OO0uX70yHm0RXFQteNRpo99rhjfqZ10f0u2CpZSeS8vx1CrgEpEYLyF/S2n+THy5hxwskFmSxfofotWNv5lHY+v4TFpyzd/CcVeCzNIWsQi+lFZg5VokvhCaLFXL+OzBnpSDXeh9LyrtLLADI1eSxAnP12yrqes10oSopEzU9U8oL8xi27Ycm6QS6Zvi3iZjztKFYn4a9No8q2cD5WcGbB00uCzRCK3Yf1twUZJnv4rn0wgLGHCbsjJyqkxCyOgEvnJVHlGBfJeCCkLqKxIIkWlm2xE7/E0hKrkPPM6VAEn+DrSAZSVVdrFolX5uSa3rwTH4X4rANBZp0VHVNsmejcBhSVe+LmO9rRMo2M9xS3YsnKr7G/konxuhODyGEkJe5nvHW3THrwn9i3HkGZ6UkuUwcc6BEp8P2ln4WiQXgc5RBo77ezfehIWUn+192BYu7bF5npuvZRrd5EgtLD2DblQmj8w92WK4kjjtx+LgNf5eSC7vRMhiswr0Bgd/RWfs+tAbHu5NYeKnf9CoTC0oQHuwGod8PIdTkWEkihIJ5jt/9+xA6TSG+cJhZ4RaszCumOmDSJYUVgolEW6/FwRZsD3tNCQ4029HQxwpoqQVDCjSZwbuWKlISIUkJQoIeo8O0NSzZIPocKE4KZoUJIavH76IcRVZ1p9IqQOWlEwuzGGwpiHhtGn0N26FJaUTfbD9atuvCly9VxDRIaejDPJ5hoGUPtLoDaO2fhihOoa8xHznqJKL4CB0V7ystrwghhGxeL3c9kwP9WHXhAUyPePFEvVxpmcF6sojnA63I1epR1OqGX1zETN8ZZOVY4GZ12/jLVurZdD3b0DZNYkE+WNV3otWew2s/gca+KaU7hA6ZwSbpnDiD4Z7LMFfkY9uxz2CryIFWo0VqvoCeCeXUE6cxeLkBpqp6CDXF2JH9IdqkPu686c6/0Vr9IWrOnIAhMxvGUJ/9RTy9/QUqqs/Dbj+PqsJ9MLuewtdjhiFdB026ATVCCxzfXYW1oQzZ2yrxeetB6DUpKDD/E9827GLrsRVl1juYDLBv8ntw2ZSLYmt/xJ14Rlq/OpRVncelC7Uo2nVGaf6kvMd+v4H3Z2o4ig/yskJN9UX/EHouCTBmZ8Dg8CIw8QvaKt5jQXkZHL45TLq/RYsp+Jxvi9X8pq/QNcq+OzCG3tZTqKhpwCnDDmQal/toif5+2CtLpXEOGipKkPceC6pfUYsFtnCMunvgbK1CXqoWGm0BhNtTbE8prVYivkdezv8L+/5rF5Iil6cUwuFBfXzR1kt+TUkiKOTXUqXtLrek0CC5rJkdCxUoLq5Aw4XrGObbS2rtEHkhUAIYdZJk0Q3zNnbs2H6jwpiQFyHdRckMr+wEvXRiQXmNJxHmg2em8hovV0f7pOXLSQSF8p0aJVHLx4WxFvPmptk4aDqESuvPEf1QlQRpTisGpe4ThBBCNqWXup4pLexepC4sJQZULXulbuk8lknGjoOHYaz8Grcn+NVttcum69lGtkkSC8GDNRj8RuB3fA2fwf2cHaDSCccC4LSP0BO6a80od4h0RV+hnwVzUp+gpGQlSJvFkP0wcht7McOPcal1RAp0pg5MLXhg239UuUscXI+d7KRhAeQivxO1Qwkm+RgPX+C8dDLJTVuDlcbQ3SttISx3R/DrtYtoH5yWWlkYdSxQDPbD57+jsDnq4JPhd8TVTWfl/kxbii/DK1VE5f656hNbHLWjSApwR9kzpRlTaFtGPF/1b5rFQ9thfMCCZmltpeBYSehI4xzkoNj+UC7YpOwkqzS/qsRCCE/6/IDa7GTojE6Ms++V1jFqYkFp4RCjwFv+XYlFXS8lI5vT0i/vS2Y5sfBA7hahyYDR6mIBwzwmB/6GqowkpFV+h/ExBwxs3aImFsKO+cgm04SQ1ZDLz/2wDYdC+2UJK2Jq0RILwVZR6pZy6vP3D7n1kboStaLcUcZl0PIyKhnZtT+sGPhV/g2xkuuEEEI2g5e7ngXr/KutC/NuuIewlddV1ZekYCtcVnfVZtejU7rRufpl0/Vs49okiYXIYFiN96v/DAdCAd0zDLbsZp/dEt5tIvJkUz+XkhHBYJpjAevkEB5MzssHvy4PpjMCBIE9agxI1yhNekQvHCVp0O6oxF9+fAj/wgz+fMbv2EcG4dEqo9w0e30nNGmn0DW1iCXP1zigbmmhFvDB9W03PP4F+L3foyE7SV6+lAjQo8g+EvqtKwJfHw9cV5lYWO1vkr43DXmmBnm7CCflFg25Vvzv/8ULjBLYR4MhdqzfH1/ixALHC71y6KRlv7kWCxAn0FObzfblYbT9Oo2FiTuwV++EVsP7nT1WBRnBFeOtbErZ++zicOfb1bVYWHFcEUISW8SE8wg716JdP5jIa0Nc0csyPthVLU8UGi/iV/8sJtx2VGcnQyONk7CIpz31yOCJxbYBVqaOw20/gWytFtvMbrZ2IhaG/4HKoo/Q4X2IbmG/dCcou/EGnqorclI5rh7HhRBCyObystezF2uxIM70omnfaXQHW3dzCw9xtbIExzsewNt9DkV6LbTZzeh9+nz1y6br2YZFLRbwJ/oaC5F3wACDQXkcyEO6NmIQx8iTTf1cOsDDm7HLlKC7wIZhdQUvhN8xvwELHzFVk4SMUFeA1SYW2BKkQVHSUOLox12zabmp0Qp8+pdrEKo/Rus1Bz7dkyIvPyxpIHupxMJqf5O0zGgZ08jlceuZWFA+J1Xgo42xEDx2YoyxILW0SELux5/gdLHqGIp4FAs9eKr8S8z1Coyhz9YEU7ER1WYrPuddTKQxFeaUFgvhx29oOT/fijLGgpIkUQ/oqOwDqSWN8gohJJFoZZJKwoqYWqyyjJebfbDxbk5lJ2C+YIEpI1nVJW8evr6LaDB9gLLqT3HhcxMygi3fxD/gNKar+p9Oo7/1AHTaUti98tCtkihlPSGEkM3k5a9nUcdBCNaF1eON8bHVmurR5lHXdQMYd5qgC9VNWWzS/xWKdKnYax/G4mqXTdezDWuTjbFQgJbB8Pv5fJq+w5XXwpvoKHfdwwZxjJdYUDfjlz7MsYrgL/cw8N2H0OkiB8zzw3PDjcmlKYyMzEAUZzDUIaBIr8OWhl62jNUnFqTESAN7L30n8vZ9rQoi1ZTpykLTu6iWL627FluF26H+uy+VWBBX+Zukpv88IaJ0hZDwmQ968d//8ygL5HPY9wfD5PVMLMhdP7bWdLOg/A3OChFBHP8OlWkZyuA0wfVSbxNlOVoj2+5PVzErBCO9lhpeOBNCEggOpLqeiQU1VvHqqEZaaLrYCOIYOiqzlqe5lb4/vIIld1+LKGOkcjyiwkYIIWQTeQXXs4QzNzB8HIWvG2GRxi9TBKbge/JEvgaG4htGHIG9KEX+ztUsm6Pr2Ya1aRILEIdh35uB4rBAlncl2IeKjkeq17jgXWpVsiBeYgFP0FPDx0DYiaoLP6DP9TO6LzXhxOUHeO67inIWXOsNZnQMTmDBP4IbFz5G3TUWMLJlnC0PTn+5gFF7iXI3WRnRn7d0WJphgfp/cCtmZTTYt1bO9oX/jqBnLPDcCU2SnFgQ/T9DkLpCXIH3z1/RXpUFjf4Q2jw8tJ7HH44j7LerWmCEJRYi+gMH/kBXLVu2Zq88bc1qf9PwHVwtT2ffWwJzxwAmF2bgvXEBx+u+xdiTTlSnsW1WdhEe3tohMAKHkf2vMrgZH6isrdqEWsevLOyPLVoAL/oH0G75Cu3ucWU/PoS9/EPYh5SUkDS+Q9byWAcL/WjJyVICfGV+3VBfZ95tZs9yBX+VEiUW5IHYspHf9BMmgsvlY3Xs3YLM4Dge7Kjkc9mH5g2WjrM9SiKMrelgK3IiAhNpTI6kA7ANRelXRwiJSZpRRRvjnI1REROf/ozWyjplEN+gRImFRfgHvkFxRiGaeiKvS4w06FUZMtQDByvXn6QKVs5K/6AkIiPGCZK65UW2YiCEELKpvPz1LFFdeA5ex4coqPkbel0uuKTHv3G1qRntY/OY6fkIaUo8IlG6k9f0PGFPVlfPpuvZxrV5EguswjZz+xx2lipBrzgBd1s1srV6FDY44J5cPoXESTeuCgYWXGug0e1CzaXruP2TBUU6DZKKW9HrHYO3W36uK/oMtybmWTDYj8vVBSxgZP+j3Y6yln8rg2fNw9f7BcoyWCDPBylJ34+G9vvyrA38BE7fjgM1X8B+xYqGynPKTA0BTHQ3snXjg5rUwtH9TwiFerYueyH86xeMSl0LVPic57tNcaYRZMXA0BWU83XQZqFYsONKM59RIk0KXsdn7sFm3M6ey+teerAAyRnFqLf+IAf2YYkFZu4+Lkmf5zNjnEHHlY+xo/AkWq+5MTF3exW/iQ/UMgc+W0ZLWfB7M1DU8A/5+6TKdRuMwW2WsR8H92xDhuFjWL/3YPpXK3byQcp0Hy4XTGHmMen5N/7ZWASdhu1f4SpueialQlJ82gshP03aDoZTTWhs/BIdQzyhsowH9rbyA9KMFPXGAygPzeLB32QVe9tRFPEZNOoPoah8eSaLxGKvl/SbR++g89IZlBQY8WnHbytm9pDWy5iHfNPHEBqqYKz+q+q75W1WXnQI9UIdjEVHYQsLaPhYEoeRVftTeL9rQkhiUneDjIgZVfhYOr/ixj9Po5BfCwoF/OumR5qhh1vyXkGpPh2ljhH5zktgEp4bV9AYKstvwjOpJPn4TDWu/8al5jIUlHwapUzywtX5VzSXFKLk0+8xFFHmyNef/SjkZUPzSZQV1+IyH+BXeZ+Xvz6HEUklwaQvIYSQTelVXM9i1oWX6/rygOfLj1Dym/3v4OVTKCw0oUE4jVNlB3H8smo2u4T1bLqebWSbKLHASHd7atDUMxFWaXvb8XEWDh4N3q1aB1JiYStMHY+VFzYA8Te0HfkLBvhMHu8Cdmw+uNkLVyjR8Crx/tsdqKu88AJJEELIMhFzA1+gYJcVnnWY3kr0P8TNG67lREOYRfgf3MIN14NQJe+F8RZ7+4rRMhC9lRQhhJDNYn2vZ+uOrmcb2uZKLHA8uXD5PL7se/x2JxfEx+j72oIW20WYjR/A7F7ue/9qzGPC3YMbwzNSsynDRmpyJI7j9pcWXAy7I09iESd6YGm6gkFKKhDyEvi0widx3Oldh+TfeprHmPNjHLY/kLt3EUII2eToekbWx+ZLLLwrlkbgKOUzL2xDsVXVVP+VmUB3TTa06QacrCjH0SvxxzN4rcRZ/DlN/aoIIa8ZH5T2Wgs+6Rh5Syo1c/B2WtH67YN1uEYQQgh5a9H1jKwDSiwQQgghhBBCCCFkzSixQAghhBBCCCGEkDWjxAIhhBBCCCGEEELWbHMkFvjUku2foixdB40mE4aaMxAEgT2a0VBlwI5U/fJUiutiHkO2A9BGzCv++vBZATohVJ9EVeFWaLOb0ft0AwzkJ/rhvXkVrVUFSI+YM3dVlh7AtisVaTXdeNVDV756qzwGAuNwX2tFVV5B9DmG1yLwOzqFU6ipKoRem4fG3sl3btDLpSEbdmm3K/Mgb1Ave7xzb9UxTwhJKOHUapH4wMrtEIr3xb5G8OtIuwXVxg8h2H5UpnHmWF1g4i4cQimyI8sgth6e9o+Qn6pl9SQt9EXn0C1NFU0I2QziTrUeDS9nHAKKsz+BS1WYiFMdqEhaOd2kJtcKD6/+JiprXrhMJBvJJmqx4IdL2MEO4jI4fOrL6SKmOk6hdF0TCyLmPN/CYr0B31qnC3sZUjDyvlQJESe6cbbSgp6JNYU1rxaf173XijJWAKWuKdDyw+P8CtaeP96CUW1XcwzweYQ96LUakaTZ8YoSC3JCQ8+3r/gIPWdP4mzPo3cuscA2LpwWG3o2ckX4pY937m065gkh8S3A56yEPtsC9xwvlWfgNu+CvvwqfFEL6VmM/HgZ39QXQRfjGsGDA2txFrJrnBgKq4yLWBi5jovffCzNUx9eBi3iac85VEoDQbPr0HgnajOTViQwAy4hSlKU160MK9Yl+mcJIRuSOApnOSs3zHfkgdrn7sCcnYVy52j0+uLCCH68+BXqC/XQpAqqxMICxhy1OGr7EbddLrikx3VYy9KxvaWflTSJyprVlInRyxxW6EBIV6+LjMqi14sSC5x/ED33/lSevAqL8Lu/Qm37eiYrVk8ctaPolQWqrxgvCFJXGWiJf8JtEdAeuf/eANHvgqWWFXTK81eJF4Kpr2p/iSOwF6W8RCD7EtZzf22gY+GFvMjxTgh5tz2/C3NWCnKt9xFsx7bksSJXkwezO3b5H/MaMTcIW/E26Pa24WGs+emjlkHP8OBmP56GVdx3QGfqwBR/OtMLYddBCBcEnGz+EkJxMYS+Ccz0fYJdxc240FyH5gvNKN71Cfp+vxHls9JSCCEbkojnbguyNIWweoLzv83BYy2EJosF+M9jlCXBuCossTCLEdd9VVnCLPajZXuBUqYlKGvilokz0cucmQn0CcUoFr5E80kBF4SD2CX0YiZquUVl0XqjxMIrTyrwbgc/oDZ76zp3r1i9VxqovmqrDrTm4eusR7Y2SmLodeNdC2rfh9bg2PiJhTcWyK7n/tpAx8KLosQCIUQhV5jTUdU1qbzCTHXApEsKq1hHin6NmGWBwD5oNTvZ69PKa1GspgwSh2HftxfC7anlu5VLD9lrW1gQcRD24eWWYUvDl7AvVYfUfZcwHIoEon+WELIRKUmEpBPoCnXV5a25P4QuLNkQKVpiYSWpnNvShL7ZKAmKiLJmNWVi1DKH1QuH7QdZubgF++wPl8tOKoteu02eWBAx23cWhxwj8Ht+gLWhDNnbKvF560HoNSnYZXuARX8/Lh8/jKrP23Ch5gB2NXaGmrKL/L3ao6hqOI0aQy6yKy5iYGYUPWdLkK7RId1wEsL5Tgw9uot2SxUK03OXKwLiNAYv16K47CSam0/AkL0H1Zf74RcX4R++Abu5EnlsXVpsx7BDq2EnxR40xWrCHnNZS/C7/45mUx4rHFKRZ2qAIPwdbn/olGNU37e1FPXCB8iQvq9AWYbymcG/4XhxOU41n8Ypw/vIr/4bBv2LbBsMocf+KSrysnGw/mMUZySxbZyGPP7+Ux/cbUeRwb6bJ1nESTeutZhCzyWRlRzpt9ShrOo8Ll2oRdGuM+jyzUP0/YSzhky2bD5GRjPOdz3AI/c/YKkqQrp+uVCT9snxgyg7dRrNp4qRnX8SlwenIYozGO65DHNFPrYd+wy2ihxWAdMiNV+I0S1E+c1lJ/D5pb+gpugAGrt+Z+u4AF+PGQY+Xke6ATXCV+gaVRdW7P9WHEvJyPn4Aq5EHAPi05uwVJzC5/aL+Jz9jmyzvA3UlUbRfx9tZZnYYfoSHTc9mFzRjSL2vmE7H/ZmE/J0GujyTDgjmGF3R0miSdu8Aaaqegg1xdiR/SHaBtg242+tcXuu2F+d93D/eysaynLZ5y1oLd4GjfYAbA8fR93f8dZrKXLZ393EL+0WVBVmKF0+VrOv5+HrElDCzpkz0tgT6cg7YIDhsB2esK58vD/zt7DWs9/ecAXdLeXK+VEIofsueoPPtdtRfuU3eS7oGMewZLXHu9+D760NKMvOxbHPP0GxXgvtrlb8379EHPPxvosQsoEtYabrxMpub0oZkVTVFXMclaiJBelOH7suJX8AwdoEU3EpTA0X8OPwTHi9IUFiQfQ/RJe5BGl5zcv9nqU7f4dgufTJyhYLZZ/ikhDZYiHys3SXkJCNaxJdVekrEgRyORMR5IdZTWJBTlok1/TgmfJK0MqyJlGZ+N/4I1qZI7VYKEGZ5WsIK1osUFn0um3CxIIeOwoPwGBgQYRhj2rgxmn0NWxnAUIhLHdH8Ou1i2gf9GGgpWD5pPE5YGDBTE0POzAXHsBesheN7CDlF2158LitMHU8Vj6nCp7ZJ+b7GpESOlkWMdV1ClsKrPBIzRVFLDxsw15dutKfSVkX3QG09vMg7hE6KlggVmDDcGRcmXBZMSohYSK+L/AIvcIeaLV70DLwDOJUJ6q37IfVMyt/fMED2940pc9T8LfpUdTqhl9kgVjvOeRrk5Hbcg/PI7dF5POISs7iYAu2h9Z1FA5DqlIgBdi/lrH9p04MKesd3D/iOLqqc1BgHZQDPMzioa0EOn0lnD72ynwfGlJYkF30Ffp5UkQaYCYZBbbfpO0URmq6pVPWS/nu5Dr0POOflNdLE7PFQrRj6U/MhR0Dsxhkx1ZKQx8LXflTVgCel7dBaH/dHsOQU8Dxr2MPoBN/37DnCSqRfD2G7IeR28gKYf55aTyOFLlZ2kttzyj7S/m8Nv8z3B0bwLWvr+FeryXG/o6zXnGWHfqdCfa1fL6WwD7KfofohaMkDdq9dnijbWelO4lmR70ctAfPR10prL/ySvszti93Q7OVHYfzYpxjmFn18R48r1KQb/kZY79+h6/bB9hxEH7Mx/0uQsgGFizHguevQikjYl9fVNcI1f+JwzYUaLRIM36DuxPzCEzew+UqVudJq0bHuFJOcvGuCbODcJw9jZqyXLZ8VlbnWOBW3WXk30tjLBDyrlHqtFETC+pYJtIqEgu8RcLeHSuTE1HLmoVVlonRyxzpczTGwhu3uVssiDN42FapDNwY7QTh3Rpc+PZHD6vQ++H9vhHZ0knmlfsjpTSijwUSsnlMPhiS7yivSCwET1DlZOGBTLE+/MKuvCYnDyLXJc7Jm3BZEd8d1crli+NOGKWmR/8LvzuM0IZ9N68Qsdc0+2EbZhWYyOWLf8Bp3CKPAPvHiyUW2AaH69tuePwL8Hu/R0N2klKYRAkmI9ZbZMsu1kZUtqTXlIByxXfFqWDxO9quH/CjZxqifxjfN+SpvlsphGNW/KLvr/DtxAe4KYdOmwPTX65j2D+H6T9npaBX/lwa9hj2YV8oqI9G2Q9x9k3838hId7l2oKEv2HSWHfOTQ3gwyVuJvMz2jLK/oq1LrP0dZ73k35hg2S+0bvMYtu2Pfn5JIvd35P6NWF7MY5h/NHK9Yn82+nkb8d3xvosQsoG92hYL8mvh9Q7Ra8feYHmtvLaiDIpqHmPOY0jTKDdSCCHvsPVrsSDVGZM+RMdUrFkd1GXN5JrLRLJx0BgLoTEWYpwg4jQ8zk9RXfsFrjk/wR7pwj0sBxJhiQUVdiLFTSxEvbCrvz9yXeKcvAmXFfHdUUVZvrRcLbaZu3Ezynerl7ly+crytpnhHn3BxILUleAahOqP0XrNgU/3pCiBUpRgcjW/U738yO+Kuu2W8W4ITuEUalvb4fx0r+q7X0Vigb8whl5LCfQ8W5txBDal+4H8uWSkp6clmBp0Fd+T4DfK+2O7KoBf9nLbc5WJhVj7O856sQUlXnaC53KLhW2o6ODdi3hi4QD0Fd9hIsrp/MKJhZjHMP9o5DaI/dmo23/Fd8f5LkLIa7aICecRaIPTq8V6aI/AObEYvT/xTBeqkl58jAW5xUJ4vWNlecNEey2aZz2oSU5DmXNMeYEQ8vYYg7MsLXr5o3poy5yYiDrGQjDx+TJjLAQw7jQhqZjVSaLWrRSqsmatZSLZOCixEBLtBJGnOUmu4M3C2dNQUDyinHARgySxQPGXe4+lDF3cxEJEiwKZ3MRZbuoduS5xTt6Ey4r47qiiLF860XnXjt/Zz1bdAZcozbR1chZy5fKn0FOTKX9/5LaIfB5WyREx67YgJ/mYkt1UB3SJEwthd9OVT8hN4pUuKpEVqngVrNlfYM7JUgLPyO9+FYmFAJ6M/A5pTI2hDghFW6FRBrcJfq75v3+AkJOCtAonxqJOUcnXK/6+ifsbOanA1iFTuAWlMwUzD98vA3j0x8tszyj7a8W6xNnf8dZLXMWyEz0PZsn1hag6cxzG8vNx5mx/kcTCQpxjmH9UvR7xjnf+0Wjnrfq74/8/IWSDe5WzQkjLSsJW4TYr3RRSeaNHscO7XIavKAtjkMr6eEEFIeTd8CpnhVDjLSF2YK99eLn8iUZd1qyxTCQbByUWQqKcIItumLdpkSQlFlgA6DqndIV4iGnvtziVpoU2uxoXOnvhun0dl+oacHmIhUHSCKZyQLbk/x0jT1iYFVYRkMdFSNMWoWVAOVEW+tGSu0cZGTVyXeKdvImWFStAUVOWH2qBwQIW11lkKf0rpX78aanIb7nHihqO9ynfixzhptT/XV6+6u7y7C0IWXukqWGCo7nmtPRjgQeGXfXYoUlGPis0pJ8SVsl5Brd5JzRJcqAk+n+GIDXtvgLv9BS83/ERankQPQv/iBdPIruMSGMCZEGb/wUGpPlvRSwMtiI35xxuz0QJsuNUsBbdZmzTKHe0xafse/LlY8c7hT+fjaHDtFVO5izNYGRENXK2JPr+Ct8P7DNn6+AYkzs6SFOCrkjUzGBu4Avka/Uosg4o2z5con0T7zfKnqCnho8HsRNVF35An+tndF9qwonLD7DwUtszOKKwan8tRK5LvP39ENdOxViv1Sw70b7m07Id/xIuPshlQi+SWJiO85um8SxsPeL9/mlM34523qq/O/7/S8OBEEI2MGXO9pxWDEpjJPHye8/yGDniE9xuPYHjbeHj7ES/pitj4GSeQR8vn1l5HWAV8vzgmDhBkWWhhF2bbzrh6BlSvmcRM31nsa/ueoxWXISQd4o4Cmd5llJXZ3gckZMVGqtNfPozWivrQgN7y6LXdUOkG0SRyclEZU2CMpFseJsjsSBOwH3tS1RlJ7PK/zYYhIv41j2hOjkW4R/+AUKhHhrdXgj/+gWjUsAxi6ErldKo79qMUgjtl9Ccy5YhzdDwB2YG/4bqPLmpkTajHC29Y/KFWnyE7rr3oWUBdHbtD/D6+vGdsJcFQnoUCt9ikPcTl0ZzP4m8jCJUNZ9GjakSgjTrAFsX73WYi9i6JJWyZQ7BF3yu2w/LrceqyoAi5rJ4v/SB5e9udOCm16/63UFK4aBNR/6RGjQ3n0RZSSOcQ8Eh4Ng6Sb91BwqrmtBcU4FyoSs0O4ZcyUlCev4h1PCZCcqMqHP+pgS6fngumZSR9PegseMyGnbsxanWb+EeG8XgdwIK+awFRWZ0eiYwO3QF5XxmCW0WigU7rjTvYtsxDflNP2F8/Drq+D7Uvo/azofwDX67vM++G5DGt5BmMajehYzCY+x31MBU/qky4J4f3m4LC941SCpuRa93LPRcV/QZbk1E3K1e+A1Xyrez705CRrGA9itNyNUGZxb4P5jobkQ2Py6y69EZdqc7+rEUth+kY+AJ2+Z5yDhQi7/Y/4YLDcdxumsU/2fCjauNRexzW1Dc8iM8Pg+cFZnsN29HmdkJd+R6xts3gYnl7Vt4Gv+8+Z+wymmQvM0K2D5k+4h/T8u/w2c+WeP2FCfU+2sAns6zyroI+JfLy9ZFxEK8/T0TZ73Uy/7OBXfodwr4buAhHiZYtyXvFZTqtdK5Kz/02FF8LsqMCvOYHPgbqjJ00OyoRfvgIzwNnY/8uOuHb+wOW89ctoydqGn/X/D9+vfov6n2Ijq/Xd3x/j8qLbhwJni8OOEa5ectW5ewY559d6zvYtuPAgJC3gLs+j1gO4oiYx2E+kMoKm/DQDDhuTQCR2k69KVX4JVu37EywPNv/FO6RvCy4SpueiaX6wTSskzIzj+CBqEeVcYaVSDA6wO/4sY/Ty+Xw6GZhmbgbtknXcczDCfQLJjR6nBhQilvCSHvPtF/D7byAzDWn0G98QDKbcsJTbnOlI5Sx4jckiAwCc+NK2gM1XVvwiONgRUk4llPHZL5WGth/RdWUdbEKxPJhreJWiyQ2BJkHROIfveEkI1qEf6Bi6j74hZ83n64XC72uI2+HywoOSp3HyKEEEIIIYSsHiUWCEOJBbKJSFOJpqOs7f5yCw5xBsMdFjRdk5v9EUIIIYQQQlaPEgubXgCTbgcapOZMRWj4awfck6vPLoiTbjgalGaZDbaILiaEbETz8HVbUMy7ECjdILLLGmHrU7oyEUIIIYQQQl4IJRYIIYQQQgghhBCyZpRYIIQQQgghhBBCyJpRYoEQQgghhBBCCCFrRokFQgghhBBCCCGErBklFgghhBBCXptF+EeG8IjP3f5Cc7bzqXLbUF50CPVCHYxFR2EbmI4yYLKIgNcBo94Ihy84JC17zfcTzOWHUN3cjAbTPhQ1dsKnnj8e85hwt0Mo3qea5Yn93yMPBifUc9QTQt4J4gxGHoxLA1eL/nuwlR+Asf4M6o0HUG67tzxzVqRVl1tz8DpM0Bsc8CmvyANon0e58TiahY9hKixGY9fv8uDZ4iN0VGxTBtZWPwph9Uzj0aAHE2FlFtloKLFACCGEEPI6sAq5p/08LFJFegE+ZyX02Ra453hleQZu8y7oy6/CF6XuLPquoly/C2b3DH+GObcF2fpKOH0L8geCAsNwGNNZZbxsObGw9AC2XdtR0fFITkRIFfhslDi88nPMYuTHy/imns/yFDF9dOB3dFnO4fJgtCQGIeRtJPrvo/3sl+jyzbMno3CWZyHbfAdz/M25OzBnZ6HcGW0K7tWWW8EEpxYaVWJhaciGXcnH0DElJyLEqQ5UpJTDMbYAcawdR49eQPdtF1wu/riNf1uNSNregsFFnhz9EZamKxiMmXwlbxolFgghhBBC1t08xpwnsd86yKrmzPO7MGelINd6H0vS+6zS7bEiV5MHs1sV2Ev8rPKeB02uFZ7Qh+/DmpuCLPNdPFde4gmCIftHqKs5iBR1YsHngEGTiZqeKfk5ptHX8B62md1QV9EDLgGpkYkFbmEQ1v0n4RyjlguEvPUCXjgrj8DqmWVPRDx3W5AltQqQ0grMHDzWQmiyLHA/j0gtrLbcWngA+9GTqDmSoUosBFhRVAZNch16ninLne9DQ8pO9r/PsDDixr2n6hJpFoMtu1VlnIgFz9fYX+nEGLVc2JAosUAIIYQQss6kFgdZNehS7tTJlfF0VHVNSs8lUx0w6ZLCKu0SKYmQhKSqLvD2CrLH6DBtVSUbWKX74UUc+ugnTNzmCQJVYuH5PbTkJkNX9BX6/YsQZ3rRmLVHaf2wLGZiAYts1aqwNUZrCkLI24K3ODiKrOpOTEnnspJESDqBrplgqcPP9w+hC0s2yFZXbs3ioe0EPuoZwm1hhyqxIOL5QCtytXoUtbrhFxcx03cGWTkWuGejFCxSuZeLhr4/lRcYqbXV+zFaU5A3jRILhBBCCCHrSm5xsJwYWMJM1wkkreh24IKQqolIIDAzXahK0iBVcMl9kSV+uHilPRgQzA3AWmlG79NFJUGgSixI4zN8g2K9FtodJTAZj8N6+7FqWbLYiQVGCh52o2XwmfICIeStI7U4yFQlBibRVZUOTaoAl6pAkMuCiATCqsotEXO/2lB5+gaeikoZpR5jgY/PYD0IvSYZOw4ehrHya9yOMYaLlMRQt26QKEmPnFYMLlBqYaOhxAIhhBBCyHpa7EfL9lQU2H5T7rIpTYJjVNDDKuKc1JUhRmJBSiA8xUDrCZy9PSUtf2VigZuFx7oPWj4YmvZ91HYqA6apxE0sSL8hvAk0IeTtsjjYgu2a/bANB4P5UTgMqTESC6kwOEaVV7hVlFtz99B66Dxuz/CWWVESCxzvWlWQwpajgTa7Hp18nIcVnsNrL0VyZJKVkX/DytYU5M2jxAIhhBBCyHqacKJMq66kv8oWC9W4euMLHDp7EzPKDbyViYVZDF89iaLj/4LXex1C0VZotHlo7J0Ma04cN7GgBCDJNT2gNguEvI0WWVF0BNqwsuFVtlhw4IYqwRk1sbDwEFcrS3C84wG83edQxFtRZTdLLa3CiF44it+DqeOx8oLKijFjyEZBiQVCCCGEkPUkVYTD7/5F7assJRBWO8aCEhDkCrCe5i0XglOzhT+kZMS4E0bd8h0+0e9Ga5Ee2r12eFWZhdUkFlbcfSSEvCWCLQ7UiYVoYywEEwirHGMhWG61fInTvOVClHJIbuXwJ8adJuhC48Iswt//FYp0qdhrHw5Lcoq8zEpST5mrEqU8JRsDJRYIIYQQQtaTNPJ5REV4XWaFkEW2WFjZgmEBo/aSGHcp4ycWVrSmIIS8JURWFDWGzxjDXluXWSEkkS0WorRgEEdgL0qJaI0lJzaSIxKfIVJiIbI1BdkIKLFACCGEELKepGa9+ojKszIffGgQsmcYbNmzPB+8+AS3W0/geNs9+NlzaVYJ/R5l8EQRC4OtyNFXwumTJq8MsyKRMNONmrR0VHQ8CjVRdpvzkVbTHZYkiJtYkMZYSFtxZ5EQ8vYQWVBerI04x8VROMuzkNPSL0+Fu8DO9Zys0MwL4tOf0VpZh7aBaV7yxC+3wkQmEpYw0/MR0pKOoUOZHUdOVOxATc8T+bmEt8bKijmeizTGgrYUdm9kSpW8aZRYIIQQQghZVwG5CXCBDcPqyjcfId12FEXGOgj1h1BU3oYBv1LhXhqBozQd+tIr8AabDQ+0obzoEOqFOhiLjsImVfRXWtlCgf3v4N9QXbgPpoZmNJ8qR/Hxv2Ew+F2Yx6Tn3/hnYxF0Gj0Khau46ZlUJUGUgCSpHI6xlYkMQshbQvwDTmOGaiBZmei/B1v5ARjrz6DeeADlNjmhyS15r6BUn45Sx4gc6Mcrt8JEa6EwjcHLp1BYaEKDcBqnyg7i+OX+0HdJnvWgJjnW4Iy8O4cRSSUOjEUr/MgbRYkFQgghhJD1NncPLQUlrLI8q7zwNuEjtJejoOUeaBx2Qt5mIuYGvkDBLis8b+N0jeIw7PuK0TIQrbsWedMosUAIIYQQsu5ELAxdxuHj1zAWeJsq9CICY9dw/PBlDNG88YS8A2YxZD+J405vWKukjW8eY86Pcdj+QO6yQTYcSiwQQgghhLwWi/AP/QuWT76H960I0kUseDtxvrUDQ1GbOhNC3kriDIauteCTjpG3JEifg7fTitZvH4R3myAbCiUWCCGEEEIIIYQQsmaUWCCEEEIIIYQQQsiaUWKBEEIIIYQQQggha7bJEgvyVE3GnL04UlYO4fZT5XU+GMgJ7Db/glc1VvPSkA27tNsj5mUNYusxfB3WqgKkh81pvSz+/28ASw9g25W6Yg5sQgghhKzSqqdt4xJMN5lgWfGmk+P/62n/CPmpWmg0WuiLzqHbN6+8SQh518UtHyK9TFmToBwT/ffRXrsbqRoNK4u2oki4Dt9bNdjt5raJEgt8NGY7SvQ70dg3xZ5N4YZQiVPNp1FzZBfSt5xC19QrHJhozgOnxYaeaBdm0Y/RO20wJWuQGiOxEPf/NwQ/PM6vYO354y0bUZYQQgjZCBbgc1ZCn22Be45XnGfgNu+CvvwqfFHq0aLvKsr1u2B283S+iDm3Bdn6Sjh9fOi1BMsSR+Esz0K2+Y48XeTcHZizs1DuHGVLWsTTnnOotPLKv4jAeCdqM5PoxgEhm0Xc8iHSy5Q1CcoxcQI9TTWwSomGeYx3fYxMzQa+yUpW2ESJhSfoqdkO7S4bhpb4cz9cZitcARGzLgs+sA6u76io4p9wWwS0+5QwPOCCkBonsfCSRL8Lllp2kivPCSGEELKBPL8Lc1YKcq33IVVLmCWPFbmaPFbpjpyj3c8q73nQ5FrhCX34Pqy5Kcgy38XzBMt6zirvWZpCWD1SVZ+Zg8daCE0WCw6eP8ODm/14GoogWP1I2AGdqQNTyiuEkHeVmKB8iEgtvFRZk6gcG8LNO0+WkxlSrLQVpo7Hygtko9s8iYVnPahJTlo+EcTfcPHsf2NqYRDWDyxwzUacOK/UPHyd9cjWlsHxOhILgd/RWfs+tAYHJRYIIYSQDUiujKejqmtSeYWZ6oBJp6qrBEmV7yQkVXWpWhE8Rodpq1RJv38/3rLu4D6v2CedQNdMcKmL7O0PoQsLABTiMOz79kK4zVt3EkLebUrgv8ryIX65laCsuX8nbjkWSjYoRK8d+3LP4fYMTXX7ttg8iQWfAwbNdjT0TbMnIhY8f8XpayMY7/gIhx3exBfPwCg6qndAo8lDY48Ps74eCIV6dvKUovXOOAI8edBVjx2ZZ9A35YO73YKqwgzoBRcWfD/hrCGT/W8mDDXNON/lxZKSWEgua4KlbDu0Gi1S8wX0TATYd42H/X9AnMFwz2WYK/Kx7dhnsFXkhH8+zAJ8PWYY0nXQpBtQI3yFzoF7+N7agLLsXBz7/BMU67Vyy43FaQxerkVx2Uk0N5+AIXsPqi/3wy/yMSBuwG6uRN7WUtQLHyBDq4EmtUB5fx4T7n/AUlWEdL0Al7IKor8fl2uPoqrhNGoMuciuuCj3uRKf4LalGtWfX4T982oUZptD/0MIIYRsPkuY6TqBJM0OCC5V6wSlbhBe8WZmulCVFHkzQm5ZoEk6hr///VicZf1P/L0qnV3Dl6/XXMAlIDUiQBD9D9FlLkFaXjONsUDIpjCJrlWWD4nLrQRlzd//Z5xyLDwZ4R/uhNmwHXmNNMbC22TzJBZ4y4SCHajuGmeB/g1YhGvwzvwMofQrDMyJ7GI6hB57K862XMHtqBdTkS2iFTnsZGro+5M9V04ubSns3ufS+/MuM4ptD+S7DPN9aEgJnjwB+Bxl0GhWtljQFX2GWxPzEMedMOqSUWD7TU5yhP3/8nNd0VfoZ8G6ONWBiiTV58OMwmFIhSbUYoGtW18jUjQpyLf8jLFfv8PX7W78p/MUthRY4VngS2C/72Eb9urSlX5Q0+hr2A6N7gBa+6chBh6hV9gDrXYPWgaesc8r7wcLj4UHsJfsVcavYFtHGnxSbr60ONiC7SmN6Jvn7/yJPoF3QWF/EkIIIZtSsF4QvYK+fP1WSDdHYlTINf+F5ub/irOsejTzOkHUyn4qDI5R+YXZQTjOnkZNWa40cJo2xwL3urbmJIS8eUrMkKh8kCQqtxKUNc31ccqxYIwksqKoHWebT6EsW89eT0bOKxxcn6yvTTV4Y8B3C5dbzGhtv4uJwDN4rCYc73qMBd8PqM1+D8a2AczMuPHFycvKOAwRePPAvXpsaehlBzgfm+EsMtmJstc+zJ6xgLmpDo4xZaQG5SRLlFgInVwv+zxMZGKBf5yf1KqCQPTCUawP/3/lNU2BDcOicqKrCgc5+RFsoql+X+mfFUoecPOYfDCESfaeOOZAiS4ZO0xf4cfhGSxMT+MZ1VUIIYRsWhuzxYKMz5R1DGmaTNT00CgLhLzbNmKLBUXAC2dFJjTJdeihwOGtsIkSC+HEqU6c+OAiHj7jLRlSkVbdiSnpmJ1G36cX4I7anSeAcacJOnYR73gyhq7jBhgOZEGT04qBP67i8NEOZRn8o+rAfwMmFqL+vzpZsDKxIP+PFtvMbt5ISfW+8vvCEgtq8/D1fgaDXguNdjuM8aawIYQQQt5Ki5hwHoFWmiYtzkN7BM6Jxeh9laUEwmrHWFACglhjLISWFa3fczBAiDLGAieNS5WGMueY8gIh5O0xBmdZWvTyR/XQljkxEXWMhdjlQ/xyK0FZE3WMheVyLHKMBRatsaKoDslKmUk2vk2aWJhmQXE5C7Kn5Wb6LOCWuzcwC/348uz18DsFatLJk4YDp45h3/FOPHnIm/y/h4NlRjQGl8GFBe4bt8WC3DpBfinYvUEeCTpKYkGqaARHZ1W/Hyw0dkrbNCQwhl/uPcbSEy9GePcN/2/oEPZDr97ehBBCyGa0YWaFiJLpl7pfxkg6EELeIRtoVgjlpWVKV+6oSQeyEW3CxII8lsD+DxwYY+eKnFgIBvxz8LZbYB2IvKCr/cmCbxZQa/exk2ZWDtBL0laefGGBf3BE1P2wDc/CP+LFk4WXTCTETSwoI6zypMHSDEZGprAQmVjg69R1CmnaIrQEf+9CP1py9ygjQSuJg1ArBLnrR1aoz2VE4mGmGzVpWmizq3Ghsxeu29dxqa4Bl4dm2apaUB4cIFMcgb0oi6aOIYQQsskp88HntGJQGuvoGQZb9qjmg3+C260ncLxNbuUnz/++By2DfJwjZdyn4PzvCZclzy2f09IvT63Nr/c5wbnl5+G76YSjZ0hpTbiImb6z2Fd3HRPSc0LIOy1u+cDefvozWivr0DYwzUuelyhr2NvxyrHAGG62/xM9wzPSZyFOoa+pHHXdj+TnZMPbfIkFcRxdx01yUoCTpmbcg3xTA5rr62H58fcogbqaiOcDX2DP8WDXCR6gf4wDwUEbucAEBr8TUKjTQFco4LvBCTyfuI667GRotO+jtnMAns6z8vtFFnR7x+DttqBI+vyn6PWOhv//wEM8VN5PKm5l76s+rwz+GC6Aie5GZGs1LNCvR6dnAJ3CXug0ehQKTrhG/coJy2eFOIm8jCJUNZ9GjakSQlfw9yuJA2068o/UoLn5JMpKGuEc4gXBPCYHv5VnxdDthfDdACYDi/AP/g3VeXLTK21GOVp6x6RlBVxnkZ5xADV/uYQrF5pQebqTRnglhBBC2HV4wHYURcY6CPWHUFTeJs+mxC2NwFGaDn3pFXilCga7zg60obzoEOqFOhiLjsImVfQV8ZbFiP57sJUfgLH+DOqNB1Ae6pY4A3fLPqRqkpBhOIFmwYxWhwsTdJ0mZNOIXT6wosh7BaX6dJQ6RuRYZ81lDRenHJu9i5YCFkdos2A4dRqC+Ss4XI8TxGVkI9m0YyyQRKJ0hSCEEEIIIYQQQiJQYoHEQIkFQgghhBBCCCGJUWKBRBHApNuBBqmrQxEa/toB9yRlFwghhBBCCCGErESJBUIIIYQQQgghhKwZJRYIIYQQQgghhBCyZpRYIIQQQgghhBBCyJpRYoEQQggh5LVZhH9kCI/4dI4Jpm0Lt17TTarNY8L9D1iqD6NCaEOX508sPPJgcMW01oSQt544g5EH49J0jqsrHxTrNd2kWmAc7nYLqo0fQrD9CI9/Fo8GPTQN7gZHiQVCCCGEkNeBVcg97edh6fqdVeYX4HNWQp9tgXuOV5Zn4Dbvgr78KnxR6s6i7yrK9btgds/wZ5hzW5Ctr4TTt8CeJ1iWOApneRayzXcwx55i7g7M2Vkod46GJyasB5GWXQfn0Mzy64Hf0WU5h8uDUSr/hJC3kui/j/azX6LLN7+68iHk5cqa+OWYjCcmrMVsGTVODIUSFiICvh9habqCwZjJV/KmUWKBEEIIIWTdzWPMeRL7rYOsas48vwtzVgpyrfexJL0PLHmsyNXksUq3X3klyM8q73nQ5FrhCX34Pqy5Kcgy38XzBMt6zirvWZpCWD1SVZ+Zg8daCE0WCw6e8+q+Hx5bGfS6EtgezsofUVsYhHX/STjHqOUCIW+9gBfOyiOsPODnuriK8kHlpcqaBOUYfz43CFvxNuj2tuHhQsR3s3Vd8HyN/ZVOjFHLhQ2JEguEEEIIIetMulOXVYOuKflum1wZT0dV16T0XDLVAZMuKazSLpEq30lIquoCv88ne4wO01apkn7/frxl3cF9XrFPOoGumeBSF9nbH0KnBAALbF0KtDpkCrcQJa3A8M9XYWuM1hSEkLcFb3FwFFnVnZiSzmUl8I9TPqjFL7cSlDX378QtxzxLs2xd9kGr2QnBNa28H0F8hI6K92O0piBvGiUWCCGEEELWlXynbrlCvYSZrhNI0uxgFWhV64SAC0KqJqLizcx0oSpJg1TBJfWHlvnhEnawSvwx/P3vx+Is63/i71Xp0KQKcC3/M3tbQKoUIAzLdxE121AmfIYGUymKTU248OPD8D7WUvCwGy2Dz5QXCCFvHanFQaYqMTCJrrjlgyqBkLDcSlDW/P1/xinHTqBrwsXWTQdN8gcQrE0wFZfC1HABPw6rumYFExU5rRhc0aKBvGmUWCCEEEIIWU+L/WjZnooC229KBTkAn6OMBfPRK+gagwM+5SWJzwGDJkaFXPNfaG7+rzjLqkezITVGZT8Vhis/wFaQDE3aIVjvjiMQmMDA5WpkaDJR2TG2XKGXfkN4E2hCyNtlcbAF2zX7YRsOdmsahSNe+eAYVV7hEpVbCcqa5vo45VgZrnRbUaDRIs34De5OzCMweQ+Xq9h7adXoGF/+D/k3rGxNQd48SiwQQgghhKynCSfKtOpK+gZqsdDhXJnMEIdh38sChAIbhkOZBTkASa7pAbVZIORttMiKoiPQsiDe4QsWBhunxULHD6dXJDNErx17tcmqpCwjJVozUdMzpbxANgpKLBBCCCGErCepIhxeYY7aV1lKIKx2jAUlIIg1xkJoWdH6PQcDhEJYf/1fcouFsFYSSmU/LEBQ7mxGtqYghLwlgi0O1ImFaGMsqMqH1YyxsNqyJuoYC8vl2K+/2VAQ2UpCSVqEJSOilKdkY6DEAiGEEELIeprvQ0NKREV4w8wKoSx7qwDXfPCeoJxY0BY7VIM1yomFFa0pCCFvCZEVRY1ICUssbKBZIaRlJ2GrcBuh+WekxIIexQ5vRIuFyNYUZCOgxAIhhBBCyHoSvXAU6yOaACvzwYcGIXuGwZY9qvngn+B26wkcb7snDaIoz/++Rxk8UcTCYCtyQvO/J1qWPLd8Tku/PNXlQj9acoJzy7NlPWzDXl0uGvum5Mp7gFX287eHj7wujbGQhr324eXXCCFvFZEF5cXaiK4MccsH9vbTn9FaWYe2gWleWrxEWcO/P145NouHthLoMs+gb4bPniMi4LEiP/S+TBpjQVsKu1eaoJJsIJRYIIQQQghZVwGMO03QhY1ZwIjTGLAdRZGxDkL9IRSVt2HAL09HiaUROErToS+9Aq90d28R/oE2lBcdQr1QB2PRUdikir4i3rIY0X8PtvIDMNafQb3xAMptcsJCJi/bmL0bpoYzaKg6gmoloREkBSRJ5XCMLVfwCSFvGfEPOI0Z4WMWMPHKhyXvFZTq01HqGJFbKbyCsiZ+OWZCdv4RNAj1qDLWKAmNIN6dw4ikEgfG1D+AbAiUWCCEEEIIWW9z99BSUAKrZ1Z54W3yHF57OQpa7oHGYSfkbSZibuALFOyywvM2TtfIB5bdV4yWgcjuYmQjoMQCIYQQQsi6E7EwdBmHj1/DWOBtqtCLCIxdw/HDlzFE88YT8g6YxZD9JI47vaquWW+DeYw5P8Zh+wO5mwXZcCixQAghhBDyWizCP/QvWD75Ht63IkgXseDtxPnWDgypmjoTQt5y4gyGrrXgk46RtyRIn4O304rWbx+EddEiGwslFgghhBBCCCGEELJmlFgghBBCCCGEEELImlFigRBCCCGEEEIIIWu2yRILynRKOXtxpKwcwu2nyut8MJAT2G3+Ba9qrOalIRt2abejpueJ8ooaW4/h67BWFSA9bE7rZfH//0WICEwO4HtrNfLSBbjerlFaCCGEkHdXgmnbwq1mmrZ4y2L1gYm7cAilyI5R9+D1oQn3P2CpPowKoQ1dHvU0b4SQd1X8KSIjvNR0kxwvZ9ohFO+D4Aqf3UH030d77W6kajTQaLaiSLgO31s12O3mtokSC3w0ZjtK9DvR2DfFnk3hhlCJU82nUXNkF9K3nELX1CscmGjOA6fFhh7fvPKCiujH6J02mJI1SI11cY/3/y+EVURGb8FmSocmlRILhBBCyMawAJ+zEvpsC9xzvOI8A7d5F/TlV///7P3vTxRn+/+P7x+wd/YmN0hMyCbkGxNjCDckpIEbkja8I6aGL/FH9g3GBog1gP0KagT6qaCRMW2X2EKvuu/Wfalde7lvrrr2Ku0LasV+wOrWri/hZdcKXEtlFRCF18oFsjjP73nOzC4zs7PLglpBjofB7M7Mzpw/jvM4j+OY8weCBna0GLyAcutm2H2T/Bumfc3ItVbBE+RLry10L2YDDV7CmS8/QKElju3BnQXHO0jPrYenf5ICCgSxWhCH4CnPQq79hryd7PQN2HOzUO4ZMtADC+iaBe81hcEfz+HLw0WwmHK0gQVxFF1HauGQAqYzGOn8ABtML+IlK/FXsYoCCw/RVZsN82Yn+p/x7yF47Q7maIuY8jZjl6Pv5a6KKj6Gr1lAW1DpysNeCGkJAgsvFJZXIeevDSzo80sQBEEQxDxPf4M9aw3yHLchmSWMZ34H8kz5sPv0e7SHmPGeD1OeA/7oxbfhyFuDLPtveJrsveLaHiH4naWwWkrgvPuixm4SBLH8EfHU14wsUyEcfikUwJiG31EIU1YzfE91oYUFdE2y9wp7BaTpAwtP+3H1xsP5YIakr9ahov2BcoBY7qyewMKTLtSmpsw3BPEPnDn+nxif7YNjVzO8U7qG80KZQbDjMHLNpXCvisCCQX4JgiAIgogiG+MZqO4cU44wxttRYVHZKhGkIEIKUqo7wccryDxAe8U6Kdhw+3aS9zK0PUTMsrQUmC3YIFx7YVNCCYJYCSiOf8oBdE5GNMUcUx/vwaIJEMgk1ls3cDvJexkGFnSIARe25X2E65O01e1KYfUEFoJu2EzZaOiZYF94J3oWRy8OYqT9fbzrDsxHx+IRHkJ7DXPOTflo7ApiKtgFodDKGs9OtN4YYR00c6Y7DyNnwzH0jAfha2tGdWEmrKzzng3+hOO2Dey3G2CrbcKJzgCeKZ17aukRNJdmw2wyI22TgK5R1tWHRzS/D4uTGOg6B3vlJqzf9wmclRu11xsghm7BVbVTmt/UUFmC/DfWqAILLK3dJ1FTWYtjh2zYsGFPdJ6m+OgqmisP4VPXGXxaXYRcu2J8hIfR3XoIlbUNOGTLwYYyeT6VGPLje0cDSnPzsO/TD1FsNbN8rsX/2qrLL78HQRAEQRCMZ5jsPIAUvWGt2AbaAAJjshPVKfqAgPLSIGUf/v73fcndyzCwoIyGMK1HqfAJGip2orjiCE79eJf2iyeI154xdFbHTpeWHX9dAGFBvfUf+HuS90ocWOBr0XXAbstGfiOtsbCSWD2BBT4yoSAHNZ0jzNG/gmbhIgKTv0DY+Tl6p0XmIPejy9WK4y3ncd1wXQOR3aIVG1kjaOh5zL4rjcu8E67AU+n8jNeOYucd2Yme6UHDmkjnHUbQXco67dgRC5aiT3BtdAbiiAdlllQUOP+Qgxya389/txR9jlvcoR9vR2WK6no10vymjSh23ZV/K95He+V6paGzfNw9je273BiWfqgolA3H4Z16gr6WAqxp6IFUAlPdEE7w50/hrvNd7IoEYCQDJ/Jmg+W7pxFrTGuwqfkXDP/+Hb5o88F/XpdfgiAIgiAUInaBsYFusrkRVA5JSC9H4gQWTP8bTU3/O7l7GQUWxD/gLEiFKX03HL+NIBweRe+5GmSaNqCqfTjWxiAI4jViCG5bWpxgQBps7iHlCGchvXUYTUneK35gQcRUXxuONx1Caa6VPSsVG1/g4vrEy2VVLd4YDl7DuRY7Wtt+w2j4CfyOCuzvfIDZ4A+oy30DZad7MTnpw2cHzynrMOgQB+DaasXahm7JoZ7yHscG1lC2ugbYt8foOVIP97CyUoOm844fWIh27s/7XcVcXwuyTSVwDUVWjVBPhZiSggeW/AocEwQIwjHUSqMp+BCl/8GwuxwW80ZUnLyEgdA0Jh6znM7dQkt2OvIrGtj1/DcHYcuwROd6xioHg/wSBEEQBKGwjEYsKMc0AQjJ3mEOQoETAxRZIIjXmOU4YkEhHICnkvkoqfXoekKKaCWwigILWsTxDhzYdQZ3n/CRDGlIr+nAuCSzE+j5+BR8htN5whjxVMDCOvH2h8Po3G+DbUcWTBtb0XvvAt7d267cg1+q7rz/ysCCkVOvDizwyOQ645EOHD7lobkEVpMJ5kxlioT0pmQ7nAPGO1RQYIEgCIIg5jDq2QOztE1agj/zHnhG54znKksBhGTXWFAcgnhrLBjdy8h2iIxY0IySUNsNyiGCIFYIw/CUphvrH9WfudSDUcM1FiIBhCTXWIjqGqM1FozvlVRggXkqT7rqkaroTGL5s0oDCxOswyxnwjyhvN2PTG9gzN7C345f0r4pUCM1nnTsOLQP2/Z34OFdJzab38A7pWVojNyDo+m8/8rAQqQBb2T5i+RCbSDIiz1ZSiJTIRSm/8AV7z2MDf6JkDiHUH87hKJ1MK09gp6h71BhSUeJZi0KEdP+q/COsdxRYIEgCIIgFsey2RVCufc6ZiPMRHp52W4wF7sNt74kCOJ1YRntChGDMt1arfeIZc0qDCxo1xiQAwsRB3gagbZmOHoTCflj9DQwJ928jTWaKXa7ANwl6bGNT9N5R1ZE5W/9pxAaDODh7CIDBwt9V8FHY9SkW2AtPQN/aI5dOwh32TqY1jSiZ+apvP+saT1s9u/QN8bSE7iCU/uP4WLwEbzH56dziEMuFFneQ/vDQXjKM2CylsDe3oux2UkErpzC/vpvJYMjVjkY5FenlwiCIAhidaPsB7+xFX2zvJPk6xxtUe0H/xDXWw9g/+mb0iKKYvACyq1b0NL3hJ+U132yVsET5H32AveKYGg7yHbRVkseGnvG2TdG+DYcm7Lj7GNPEMRrhbQ2WxY2ttySt96fvYWWjVnR9i8++gWtVfU4LS30vpDeSnyvCIaBhfAwrrZ9g66BSflacRw9R8pRf/k+6aEVwuoLLIgj6NxfIQcFOOE/0VG3BZsqGtB0+DCaf/wzxlHXIuJp72fYsj8ydYI50Z0fYEdk0UZOeBR93wkotJhgKRTwXd8ono5eQn1uKkzmt1DX0Qt/x3H5fFEzLgeGEbjczJx4fv3H6A4MaX/fexd3lfMpxa3svOp6ZfFHLXMI9Z5GWWaKPNQpczve2bIembYP4Pjej9DsMLpbypFp5kOhUpBR1Ig2P1cW/A1FPjJ31OGk62ucatiPo528PPj6FD+jRdq9gv3GnImihn9IQQsxdBcdwlZYTFYUCh54h0JS4xc1+V2oTAmCIAhiFSJOoNe5F0Vl9RAO70ZRubzjksSzQbh3ZsC68zwCkoEh9+3lRbtxWKhHWdHe6I5OEonuxfvxsd9x5ZujUdvin1f9GIuutq7YDblvo6LhGBqq96BGCWgQBPH6I4Zuwlm+Q9pN7nDZDpQ759v/s8B57LRmYKd7UPZ1EuqaxPfiO9ON+X/GN41Fiu9wAVf9Y7KfMPUbWgrSme+QBduhoxDsn8PtfUA+xApi1a6xQBAEQRAEQRAEQRDE80OBBYIgCIIgCIIgCIIglgwFFgiCIAiCIAiCIAiCWDIUWCAIgiAIgiAIgiAIYslQYIEgCIIgCIIgCIIgiCVDgQWCIAiCIAhCS3gEA3/yrS0JgiAIYmEosEAQBEEQBPGXMYfQYD/u860eF9i2TcvzbDfJEREe/Q1uYSdyBW/sFm5T1yBssEjbVEt/lnK4h58ifN+PvphtrQmCWPGIkxi8MyLpgsRbROp4ru0mOTMY9bVBKN4GwRtSjulh+irgRpm1DO4gT+EM7vf5MRrdIpdYjlBggSAIgiAI4q+AGeT+thNo7vyTGfOzCHqqYM1thm+aG8uT8Nk3w1p+AUED21kMXkC5dTPsvkn+DdO+ZuRaq+AJzrLvC91LxOzgJZz58gMUWkxIiwkszGG8sxHlLf8Jr9cr/90ckp2B8J/obP4I5/pUQQyCIFY0Yug22o7/DZ3BGfZlCJ7yLOTab2Can5y+AXtuFso9QwZtfgFds+C9pjD44zl8ebgIFlNO/MBCeADusgyYTKVKYEFEOPgjmo+cR1/c4CvxqqHAAkEQBEEQxEtnBsOeg9ju6GOmOePpb7BnrUGe4zaeSeeBZ34H8kz5sPv0xnaIGe/5MOU54I9efBuOvDXIsv+Gp8neK+yFkGYQWBAH4Nr6Fiqd3QgYGe2zfXBsPwjPMI1cIIgVTzgAT9UeOPxT7IuIp75mZJkK2XcpFMCYht9RCFNWM3xPdaGFBXRNsvcKewWkxQ0sTKHf9T7qa9/BmmhggSNi1v8Ftld5MEwjF5YlFFggCIIgCIJ4yUgjDrJq0TkuO+6yMZ6B6s4x6bvEeDsqLCkao11CCiKkIKW6E3y8gswDtFesk4INt28neS/DwELEsVCmQJjWoUi4hKDGcJ9jt6vGujijKQiCWCnwEQd7kVXTgXGpLSuOf8oBdE5GNAVv7+/BogkQyCTWWzdwO8l7xQ8siJi9ewa73/8Jo9f5NerAAkO8j/bKt+KMpiBeNRRYIAiCIAiCeKnIIw7mAwPPMNl5ACl6w1px/LUBBMZkJ6pT9AGBELxCDjPi9+Hvf9+X3L3ijVjg6zcM3USXpxXV+ekwmVKxUbiKSbXlLjkPb6OljxZ0JIgVizTiYIMqMDCGzuoMmNIEeFVKQXb8dQGEBfXWf+DvSd4rbmBhuheOKju6H80p1+gCC5FAxcZW9M1SaGG5QYEFgiAIgiCIl8ncLbRkp6HA+Yfyli2MoLuUOfDGBrrJ5kZQOSQRdMNmihNYMP1vNDX97+TuFTewoCL8Jzrq3oLZUgHPiOoqKQ/aIdAEQaws5vpakG3aDudAZFrTENy2tDjBgDTY3EPKEc5CeuswmpK8l3FgIYTe1gM4fn1c0pPGgYVIHmJHUxCvHgosEARBEARBvExGPSg1qw3r5TZiQYs47EaJRW/0yw5Iam0XaMwCQaxE5pgq2gOzxllfLiMWREz3OrD7+PxIqXiBBTnQugG1XePKAWK5QIEFgiAIgiCIl4lkCGvf2BnOVZYCCMmusaA4BPHWWDC6V5KBBfm6N2H3qUMIyptN/WgKgiBWCJERB2pn3WiNhUgAIck1FqK6xmiNBeN7xQYWIiOwImu9aP80OstAnxLLAwosEARBEARBvExmetCwRmcIL6ddIfTw9RTWvY+uqHPAkQMLMaMpCIJYIYhMFTXG7LSw/HaFkEk8YkE/moJYDlBggSAIgiAI4mUiBuAutuocemU/+OgiZE/Q17JFtR/8Q1xvPYD9p28ixL5Lu0pYtyiLJ4qY7WvFRmsVPEG+eeUC94pgFFgQJ9DX9hla237DqLQTxDQCrn3Y5bojb4sZQVpjIR1bXQPS/GeCIFYeInPKi806h14cgqc8Cxtbbsltfpa19Y1Z0Z0XxEe/oLWqHqd7J7jmWUBvJb5XhOcJLEhrLJh3whV4qhwhlgsUWCAIgiAIgniphDHiqYClwIkBtXXNnPpe514UldVDOLwbReWn0RuSt6PEs0G4d2bAuvM8AtKrwTmEek+jvGg3Dgv1KCvaC6dk6Cskuhe7Kjz2O658cxSFFhMshQL+edWPMR5IEMfQIxQyAz4FmbYDaGo8hpb2P6RghhrJIUkph3tYE24gCGIlId6DpyxTtZCsjBi6CWf5DpQdPobDZTtQ7pQDmpxngfPYac3ATvegPEohoa5JfC9gBmP+n/FNYxEsJisKhQu46h8zHEFlHFjg0znKkFLixrBORxGvHgosEARBEARBvGymb6KloAQO/5RyYCXxFAFXOQpaboLWYSeIlQxfJPEzFGx2wL8St2sUB+DaVoyW3vgjHYhXBwUWCIIgCIIgXjoiZvvP4d39FzEsTTlYKYgID1/E/nfPoZ/2jSeI14Ap9LsOYr8nYDhSYPkyg2HPB3hXP02LWDZQYIEgCIIgCOIvYQ6h/n+i+cPvEVgRTrqI2UAHTrS2o1811JkgiBWOOIn+iy34sH1whTjp0wh0OND67Z2YaVrE8oECCwRBEARBEARBEARBLBkKLBAEQRAEQRAEQRAEsWQosEAQBEEQBEEQBEEQxJJZHYEFcRS+iw7UbkqDyZQyv7eqDnHYjRKLiV2ThVL7N/CNvYIlTcKj6Pv+JKrzC5S9XWfQ79wBc/r76JqUNnkhXhee3YFzcxrSay9jUjlEEARBrCIW2LZNywLbTXLCI/C5BRTnfghvjAkjMhPjN7iFncgVvHEWbZvBqO8faK55F5XCaXT6dfcnCOK1JPEWkToS6i2mp/xtqNuUzvwp5lOlvY26ttuqez1Bv+cIyirel39b0ozLwRnlHGP6D3jq96BCSsd2lNh/QnBFLXa7ullFIxb4vqelspCvPYKeKb2QTsHv2Aaz1AgEgw75r0EMDeGGswKpphwlsCBi2v8tmh1XqGG9doTg93wOR9e9FbYqL0EQBPH8zCLoqYI1txm+ad6/T8Jn3wxr+QUEDbp7MXgB5dbNsPt4KJrZBr5m5Fqr4Akqr0pmB/Hjmc9xuNBqYMeI7PQlnPnyAxRaTEgzCixwZ8HxDtJz6+Hpn6SAAkGsFsQheMqzkGu/IW8nO30D9twslHuGDPRAYr0lPvoJ9VsP4avLv8DbcxF223qYzPN6a4rprY1Zx+GV/DDZ97IURLa+5PfagizhGjvDmO2Do2AdChx9tAvECmGVBRbKsCZtDcymtSjz3NM2lqlrEN58C/nZKa80sMAJewWkRQMLySOGvGiuYw1b+f6qWW7pScRKSitBEATxGvD0N9iz1iDPcRuR8YjP/A7kmfKZEa7v/0PM4M6HKY8Z4NGLb8ORtwZZ9t/wVDnEr/MKOfHtmLAXQppRYCEEv7MUVksJnHclk54giFWBiKfM2c8yFcLhl8IKjGnm8BfClNUM31ONt7SA3nqMEc8xHPdOKGfY3aXR4BZkt9zCHMbQWZ2B1NouPImcH3CiwJSB6s4xYLIT1SkbUNs1rpydwYBzO0wpB9BJo7ZXBKsssFCBHR8JKLOYYN7qQiDaVp5hsusw3hTO4pQtbWUGFsJ/oqPuLZht7uXhHC+39CRiJaWVIAiCeC2QjXHFoI4w3o4KS4rGaJeQgggpSKnuVE2de4D2inXaYMOSAgsiZllaCswWbIi8KSQIYpWgBBE0zvscU0XvwaIJNsgk1lu9mBgM4KE6FiHpHEWnzd1CS7ZFq39metCwxoQ1DT2Y6mtBtsb/EdnpRqwxZaOhZz5YQSxfVl1gweb+FV212TCp3wiI9+ApL2WN5zbc+sBCeBjdrYdQWduAQ7YcbCiLzCOaQ6jva+wvPYBPvzqJ2qIdaOz8E2FxEgNd52Cv3IT1+z6Bs3IjzCYz0jYJ6BqNE60QJ9DnOgAbn6vUsBe78rNYY5YbFp8P2dZcjcKMPKWhzeHR9c9QWXMCLtcJVBdug937CMEuO2wZFpgybKgVPkfn0AzE0C2c2/8uqj89jVO1O7C5sQPBMEv3wBW47FXIX1+FFuc+5Jj59I8tONJ1XxnFwfP2d9RV1KBBOAhbTj4qT0fmWs0g2H0SNZW1OHbIhg0b9sTO8eTDpAzSI+XzXB2KSw+iqYnlN3cLas7dijOHK1EalLIvLsehpqOsXt7Cppqv0afM7zLON/9h8mWnR77nOyg9dBRNh4qRu+kgzvWxfLM8+b93oKE0j9V3M1qL+ZCvHXD2q+5heM3/wgdnz6K5uggZVi5vLE/+r1GZydKx4QN0jvDfz2C06xg2bvoI3aMzCAd/RmvNe6g9xspuQy7KpPlvSTyfIAiCWGY8w2TnAaToXyIojr82gMCQ3uTpAwJKEEHjECwlsKCMhjCtR6nwCRoqdqK44ghO/Xg3Tv9MEMTrgzyKQK8z5JecugDCYvUWRwo6KD6Xch0PIkStVOWYyebGkPRMbRBBTkca89+GlCPEcmYVBhYCypAfc3TRPDHgwvadbgyLQ7rAwhTuOt/FLvYbqW+VOnYloq+JuvF7l8KUWo+uJ+xKJfpmKfoct5izK7JGVZmSigLnHzoHnCPPVVpbfA4Bxfkdb9+narSRaJ3yXXpujtLoREz1fIYT0nVK2qNv3afQ11Iwn5egGzZTZHjRBHoasmGy7EDrLe4c30d7JXNIC5wYEEXM9rtQkteEnknuqCuLR1reQ/v4HGbvnsb2Xbys+DMUZbQhMldKjT49LF+dh7A2Oo+KPYfda6slw2AOV+I0iOMdqFm7HQ6/8l5l1g/n1nRlfleCfCdddjrEEXTWbFTN8eJyUQJLZG6rUt/mTZ/gt+FeXPziYjTIEcXwmnG5HqLyFslnBTwj/ABP40fYw+WP53H7XriHeQoiiv1NJhMsL8k8nyAIglhGKHZDHAM9pj+S+rI4gQVTKdzByNElBBbEP+AsSIUpfTccv40gHB5F77kaZLK+s6p9WNc/EwTxeqH3fWSMHfpF6i1mNQ+7d2Nd1XcYkRSJMspqYyv6JF+Aof6tMvJBvcg+BRZWFqswsMAEUxyAaytrRJZy5qg9Zo7oTlS287f1usYlOaLpyK9ogCAI7O+g/GZbGnY4g6D3B/zIV0wODeD7Bh7tVzp3fcdt1JFHkJ5hRZFrMNp566dCaL6LAbhL0mHOqcJJ/jZhdhKPn3AnUu8ciwgHvfj2Rz9CYgiB7xuRG22YesND/V1+c6GNJo7hzp0xlnbZabfkV+CYVB7HUGvbwPIdO1QqJj083cVWnTEjH5MDGsoxiURp4PVYBrNGASrHTNvhHOBv9uPkO+my0yIyg67YrFWi8jElWJSofiMYXhNrAMpBqDRsdQ3IAZ/3PpCCKXN8eJglHxXHeLmzv1obMkzK0LJknk8QBEEsI5bRiAXlmKYPjNhJMf0zQRCvFy9vxII42Y0j247icnTE9hwedR1GpikTZad7mR0+Ap/rAHLNZqy3+zAnjqKrLhem9Hdx+vcJzI7egKvmTWbfvwm7L7IqA7GcWZ2BBfZ5xFMBi2kNNn9yEkff/kh5464LLEhvCGRn1QgxdBse4RDqWtvg+Xjr0gIL0jO0kbiEgQUpYHAFzXyVVeZYZkanZhg4x3yYvOdj1NR9houeD7ElqcCCfB+NUx+Fn1sXZ+SFHl16DMsgngGUKA3Gv9EGX+LlexFlp0JfHxLq/CSq3whJ53+CHXtTiubeGnTj3Xo+qkaJEMcz8JJ5PkEQBLGsMJyrLAUQkl1jQXEInneNhciIBU0fuMB9CIJ4TTBaYyESQEhyjQUjvcXXLztyGKf9KttZYgbBnjNoqNiF0pqPcerTCmRGRuBywsPocR5BRXEZauwOfFrxRpyR0cRyZJUGFhhT3WhYa5YczPkhN4qDGelIpSE56SiJTIWQ4Ns/XoU3cBX2jVnKSAfF8VtKYEFqjGasE65HneiEgQVxHIODkxDFSfS3CyiyWrC2oRtTMc6xvP1LamU7xnniNQEMvcGg/m40vYE75Ldw8/6QNITJUhKZCqEw/QeueEdUZcTRpcdwdII8JcNSwdKoHJFJlIYplpX50QmRc9J0EWmqxKP4+U667LRoRicox+TpB+tQ0f4gcf1GMLzGyHCLLKK1HlsK31EUrbKIjjTCJjI4jBOC/4oPY8k8nyAIglheLJtdIZR7r2O/mYn0cvJ9zMWsX4x2fARBvH68yF0hFL0lTqD3i0Y0Xx+ft5vD4wg+1L0uFIfRXpUVf4vdke9QlZ4ZZ9tLYjmyigILsxhylaqcwyfoa3kbJvNOuAKRLlkXWJD2dWUOrrUE9vZejM1OInDlFPbXf4uh3+xYb1ovBxbER6wD3iQHFgLjeDxxVdtxJ3L8pPn7WewZu3Haz/eNnsE99x6kqBYv0QQW2L2Ol0cce56nEsUxV+Ytccf92SQG//gBH603I0VysOcQ8n6kTAm4i4nHI7geN7DwDJNd7yPdlIrc6i/R0XMd1y+fQf2B8+iffSrvXcvybbN/h76xKYQCV3Bq/zFcjOyjHUWXnsERPOw8hHRzEVp6FcUzewsteVsgqBWPRKI0iPIaC+lp2NRyU95vV6rLrdgoXMVk2Ad7vHyPXYKQTNkN6tKj1JF502folfbsZc5/XyvyNn6E63wNiGQc+zjGnKEBqEzZUM9Bk/cvt8Bqs6O9bxSzoUFcOfUB6i8yZZvM8wmCIIhlhrIffFTX875sy7yRLT7E9dYD2K8sXCz3A1vQ0seHBMv90MbIWj9RlhJYiKx5lIfGHqX/C9+GY1M2GfQEsRqQ/J2s+Ret3D7fmBVt/+KjX9BaVY/T0mLtC+gtZpkH3O+hoPZrdHu98Ep/P+PCkSa0qV+O8eCDoxSZcRa3F0M34SjOxaYjP2GUlNCKYXUEFsRR+C7+DdW5qTDnVuPkRR/GmJDyRRt3VClvtqcHcPlULTZZTDCZ3kBFy7fwjc2Cr8TfUpoNs4kdN2eiqOEf8PPh87N/4Hw5P56CzGIBbeePIM9sRtr/2oeTZwUUsfukFLeiOzCMwOVm6bul6BNcG40d3M8bj7Ms8oxs7HynAKmZxTjs+AG9AzfxnbAVFpMVhcK36Lt/BUJGNnbUfgbXeQcaqj5CZ5DfM4zRy43INZtYHg+jI/gI/eerkMm/Z+6E0PYVmvJSmbHxv1B18hQ+KrLClLITLd39CAYuwc6/W7aj+doDhFlj7zt3EPlp8oiOzNLP0C09gz9mGN0t5dJ9+bmMoka08XUm5LMq9Olhv4/cN7MI1U1HUVtRBYHvpKH8QkOiNEDeFaImPweF1UfQVFuJcqFT2flhKk6+t+BIZxuakiq7yHPmkXaFqNmMzMJ9aGqqRUX5x/Jv+S4gHcdRyOu3UMA/vYHYVbQNr5nBWN+3EAp5uW+F8F0vxqT0c/hCl414VzNShu/G8RlKM1NYebB0ZmxHQ9ttdp8knk8QBEEsT7hx7dyLIr4r1OHdKCqPTNFjPBuEe2cGrDvPIyC9GmR9X+9plBftxmGhHmVFe7W7MoXH4L9yHo2RfuWfV+Efi/RnIjv9O658c3S+v7jq1/Q7/N5luW+jouEYGqr3oCa6ExNBEK87ki9SvgNlh4/hcNkOlEs7j8nnngXOY6c1Azvdg/Iohbh6a96e5raq+k9+4cefE4C34yyaSgpR8vH36I/oOwmmh4ZuoOOrYygpKMPH7X+QDlphrKIRCwRBEARBEARBEMRfzxxCd67hiveOKqipQpzAnavd8Pr5Yu3ESoQCCwRBEARBEARBEARBLBkKLBAEQRAEQRAEQRAEsWQosEAQBEEQBEEQBEEQxJKhwAJBEARBEARBEARBEEuGAgsEQRAEQRCElvAIBv7kW1sSBEEQxMJQYIEgCIIgCOIvYw6hwX7c56uiJ9puMobE202Kodtoq3sbadL2butQJFxStmGWziIc/An28t2oaWpCQ8U2FDV2qM4zpq5B2GCZ3yLOUg738FOE7/vRZ7BVNkEQKxxxEoN3RqQdGBJtNxlDQr3F9JS/DXWb0mU9Yt0O4fI91S4PMwhePoHysv1oEj5ARWExGvVbz4dH4HMLKM79EN7oiRnc7/Nj1Gg3CWLZQIEFgiAIgiCIvwJmkPvbTqBZMqRnEfRUwZrbDN80N5Yn4bNvhrX8AoIGtrMYvIBy62bYfZP8G6Z9zci1VsETnGVfR9F1pBYOKdAwg5HOD7DBlI3arofyj5/dgXNzNirb78uBCPE+2itzUeIOKIGJOYx3NqK85T/h9Xrlv5tDsmMR/hOdzR/hXN98EIMgiJWNFIg8/jd0BmfYlyF4yrOQa7+BaX5y+gbsuVko9wwZtPnEekt8dBlHqk7JgYbwMDrrNsKU/j66Jp9Jv37W78Tm1H1oH5cDEeJ4OyrX8CAm02Oc2UH8eOZzHC60wpQmqAILPDj6I5qPnEdf3OAr8aqhwAJBEARBEMRLZwbDnoPY7uhjpjnj6W+wZ61BnuM2ZJObGd1+B/JM+bD7QsqRCCFmvOfDlOeAP3rxbTjy1iDL/huePu3H1RsP552AsBdC2jpUtD+QvwfdsJk2oLZrXP6OCfQ0vIH1dh8kE10cgGvrW6h0diNgZLTP9sGx/SA8wzRygSBWPOEAPFV74PBPsS8invqakWUqZN+lsAJjGn5HIUxZzfA91YUWFtBbT+94cePRvA4JewWkWd5TAglhpopKYUqtR9cT5b4zPWhY8yb7rXraVQheIUcXWOCImPV/ge1VHgzTyIVlCQUWCIIgCIIgXjLSiIOsWnQqb+pkYzwD1Z1j0neJ8XZUWFI0RruEFERIQUp1J/h4BZkHaK9Ypw02KIgBF7blfYTrk4qB//QmWvJSYSn6HLdCcxAnu9GYtSU6+kF2LJQpEDHTKDhzLGnVWBdnNAVBECsFPuJgL7JqOjAutWUliJByAJ3KqAK5vb8HiybYILMovYWnCLjKkCdcxaT0LKZreluRZ7aiqNWHkDiHyZ5jyNrYDN+UWrHECywwpNFWb8UZTUG8aiiwQBAEQRAE8VKRRxzMBwaeYbLzAFJMORC8qtEJ0kgDky6AwJjsRHWKCWmCVzUXWTG+dQ5BaKADdls28hvVwQG+PsOXKLaaYc4pQUXZfjiuP1Ddi50fuokuTyuq8/nc6FRsjDoDCpLz8DZa+mhBR4JYsUgjDjaoAgNj6KzOiHHipZEG+gDCYvSWOImBzmbY0t9Go3qNBb4+g+MdWJmOyXnnXZRVfYHrMWu4JAgsRIIeG1vRN0uhheUGBRYIgiAIgiBeJnO30JKdhgLnH8pbNmVIcBwD3WRzI6gckpCmMsQJLJhK4Q7yoyKm+tpwvOkQSnOtcnDA/iv4YGeZKfgd22DmoxLMb6GuQ7dgWoTwn+ioewtmSwU8I6orpDxoh0ATBLGymOtrQbZpO5wDEWd+CG5bWpzAQhps7iHlCCdZvTWJPnczmmrfQW6amembyNowCnxqVcEadh8TzLmH0cHXedCQKLAQyUPsaAri1UOBBYIgCIIgiJfJqAelZrWR/rJGLCjwOdSVG1RzmacwcOEgivb/E4HAJQhF65ixn4/G7jHD4cTisBslFl3aFAcktbYLNGaBIFYic0wV7YE5GozkvKQRCxIiwsMeVKab5/XG7F1cqCrB/vY7CFz+CEV8FFVuE7pV6zIsFFiIXTOGWC5QYIEgCIIgCOJlIhnC2rd/hnOVpQBCsmssKA6BwRoL3KB/0lWPVPMeeEbnII54UGaZf8MnhnxoLbLCvNWFgFFkQXIU9AuqKW829aMpCIJYIURGHKgDC0ZrLEQCCEmusRBPb0mMo6t2A8ylHoyy5494KmCJ6qw5hG59jiJLGra6BlRBzmQCC/rRFMRygAILBEEQBEEQLxNp5XOdIfwid4VQDs0jskc2Yo1yvfz2Ue1MzGLIVRLfcOfrKayb3yJORg4sxL6VJAhiZaDoBY0ueLG7QsTCd6DJVa5XAgbq4KQ4CFfRGuPRWAkDC/rRFMRygAILBEEQBEEQLxMxAHexVWc8K/vBRxche4K+li3R/eAhPsT11gPYf/omQuy7tKuEdYuyeKKI2b5WbLRWwROcBd8v/mrbN+gamJTf+onj6DlSjvrL9+Xvk5dRm56BynbluxSo2IT02suYFCfQ1/YZWtt+w6i02OM0Aq592OW6I2+LGUFaYyFd92aRIIiVhMic8mKzbiqDOARPeRY2ttyS2/wsa+sbs6I7L4iPfkFrVT1O906w74n0lohw8Be0ua9gQNm2lu9Ac2TbUVwe5ZrvGSa73kd6yj5l+0mGFKjIQW3XQ/m7ROLAgrTGgnknXIHYkCrxaqHAAkEQBEEQxEtFGQJc4MSA2ivnK6Q796KorB7C4d0oKj+NXsUgx7NBuHdmwLrzPALSq0G+s8NplBftxmGhHmVFe+GUDH3G1G9oKUiHyZwF26GjEOyfw+3V7frQ9zVqCrehoqEJTYfKUbz/a/TxZ4lj6BEKkWZKQabtAJoaj6Gl/Q8pmKFGckhSyuEe1oQbCIJYSYj34CnLVC0kKyOGbsJZvgNlh4/hcNkOlDvlgCbnWeA8dlozsNM9KI9SiKu3REz5WlGQZoY504ZDTcdhb22DV73rAw9knjuEwsIKNAhHcaj0Hew/d2te34TH4L9yHo2FVpgsWyH88yr8Y+rFHfl0jjKklLgxrNNRxKuHAgsEQRAEQRAvm+mbzPkvgcM/v0/DyoHvR1+OgpaboHXYCWIlI2K69zMUbHbAvxK3axQH4NpWjJZeo2kXxKuGAgsEQRAEQRAvHRGz/efw7v6LGJamHKwU+MruF7H/3XPop33jCeI1YAr9roPY7wmoRjWtBGYw7PkA7+qnaRHLBgosEARBEARB/CXMIdT/TzR/+D0CK8JJFzEb6MCJ1nb0R6ZoEASx8hEn0X+xBR+2D64QJ30agQ4HWr+9EzNNi1g+UGCBIAiCIAiCIAiCIIglQ4EFgiAIgiAIgiAIgiCWDAUWCIIgCIIgCIIgCIJYMqsjsCCOwtf2MUozLDCZNsBWewyCILC/JjRU25CTZoXNPaRc/DKYQb9zB8zp76NrUtqo5S+G7yvbAaHmIKoL18Gc24TuR3/RXMlnd+DcnCbvla0cSp4ZjPX9AEd1ATKUvb+f9Tux2Zyt2++WWBRiCIGrF9CqKtdF8Vx1ShAEQRDEiyPBNqQGJNpWEHiCfs8RlFW8L28jWNKMy0HVVn/he7hsr0JZzREIDRUoLGpCZ/Q8S4e/DXWb0pmtbYIp7W3Utd2m+fArhMRyYUB4BD63gOLcD+HVGJILyUFieRVDt9FW9zbS+G9N61AkXEJwRS12u7pZRSMWQvAKOUxIS+EOqlvAHMbbD2HnSw0siJj2f4tmx5VX0zgkR/AtCN4QxNHLOF7VjK7Rv2od2BD8ns/h6Lq3eAeWOcBDN06jItWEtIgDPO2Hp9mJLnVHRywOvkdwtwOlKapyXRTPUacEQRAEQbwwxOAFlFs3w+7joX5mb/qakWutgidosCSfOARPeRZy7TfkbUOnb8Cem4VyzxD7pYgp9tuNWcfhneK26hT8jm2wFES2JZRfkqVWtmNcMmW5/bwPa0rcGGbfxUc/oX7rIXx1+Rd4ey7CblsPkzmSLmJZk1AuDJgdxI9nPsfhQitMaYImsLCQHCSUV3EUXUdq4ZACDTMY6fwAG0z0MnElQYEFTqgPXTcfK19eBHMI+T5HXdvLDFYkjzjkQpEpRwosrDjCXghpS3WAibgsplzFx/A1C2jTtxuCIAiCIF4hIfjs+TDlMec/MiD22W048tYgy/4bniqHZEQ8ZU5clqkQDr/kPjKm4XcUwpTVDN/TUXRWZyC1tgtPlLPigBMFpgxUd46xb0Nw29JU55nr19OINevt8M2FMeI5huPeCekMRxx2o8RiQXbLLWYVE8uXheTCMLTAUPwqTWBhITlYQF6f9uPqjYcsRQqSrboOFe0PlAPEcocCCy88qMCnHfyAutx1L3l6RfKEvQLSKLBAqEm6XGcQ7DiMXLNBQI4gCIIgiFeH5JSlIKW6UzU18QHaK9ZpnTcJxVlMOYDO6LRcPurgPVi4U/nf19CSbdHaBTM9aFhjwpqGHmYNPEFvyxaYLTvQemsCojiOnsZN2Gj/FVPcZRwM4KHaB5XsjBTkOW7jVUwCJpJlAbmIBhv0GAUWFpCDRckr86gCLmzL+wjXJyk0tVJY5YEFEVM9x7HbPYiQ/wc4GkqRu74Kn7a+A6tpDTY772AudAvn9r+L6k9P41TtDmxu7IhOZxD5ubq9qG44ilpbHnIrz6B3cghdx0uQYbIgw3YQwokO9N//DW3N1SjMyJt37sUJ9J2rQ3HpQTQ1HYAtdwtqzt1CSGSNcuAKXPYq5LO0tDj3IcfM5yhtwZGu+/NRPDVx7/UMId/f0VSRz5RDGvIrGiAIf4cvpG65yTyPXdP3NfYXl+NQ01Ecsr2FTTVfoy80x8qgH12uj1GZ/yb2tbSgMieVlXE6Nh35CaPiDEZ9/0BzdREyrFzxJPssF6ps7+Kw8AEqdxXgDYviAPP5XG3NqC7MhDXa8cnrRxwpYWk7tg+F1lRk5G+DzVYDF1eG4WF0tx5CZW0DS3cONpSdRm/o6XPlOTz6K05XvqHI0jTGfN+ipSLynadqDo+uf4bKmhNwuU6w9G6DXR/UEScx0HUO9spNWPfO+xCKs2A2mZGWfxDn+pS5ZsnIyLqdrJx2IVNKf4F8fpaV0+m9yGR1zoNb4pgPF1sqot8l9IEF6Vn1KK0+ga9O1aFo8zFp3qQY/AnHbRtY3vjaJE040XkH9zV1yn+8VPngvyUIgiAIYklMdqI6ZlqjYu9qHEXOmDQiQT90XX75lIHqdo9kF8hBBAXFVjDZ3Aiyr3wevqOYD23PxTsVu1Hl+AWj8ab4jrejwpIPu28FvtRaVSwgF9JoFSOMAgsGqOUgaXnldm4H7LZs5DfSGgsriVUYWLAip3AHczxt7G+LauHGCfQ0ZDNlWYjm3wbx+8UzaOsLorelYL7RBN2wMQertmscmL0DV8lWNPaMS06gvKigMlxHuk7lxLErpOFi0VEDcxjvPIS10XlrImbvnsZWS4Yyn0lJSzQqfB/tlUyRFzgxENO2FrpXRDkkGrGQ+HnieAdq1m6Hwz8lXz7rh3NrOqzlFxAUI3mzoqjVJzm9fM5dimk7nAO8a1LuHVU8CzyLz71aWwZXQI6QikwhVaqVkBI9j36X1o9YhyLXIM85ht3lsJh3st/zAYBTuOt8F7vcAakcZIVmwQbhGjvzPHkOsyouVQUSdN/nbqElOwcNPXwoGA9efYYTRmWv5MVS9DluSQGLKxA2rYE5rxW9T8OLk5HwfXQLW2A2b0FL75NYGdR/1wUW5vpakB2VEfVwR31eOdo6fT75IAiCIAhiSUh9exxHTdNvc+S+3diB5PbBVfnN8cZW9El2h3RSE1iQbBG/AwX8ZYYpFbl1P8Rx+rg9thvrqr7DiNFpYhmxkFxEfBk9yQQWdHKQlLwyu7mvDcebDqE01yrJmTwqhlgJrO4RC+Ik7p6uUhZuNGog/G24F9/+6GcOUQiB7xuRKzWygDwfaU0jemYiGnMGY3f6McYVbExgIdJAFcdNDMBdbNU2LOWY7Njq05Kg8S54L92zDUn0PO5YlsGsebZyTHEO9ffXfl8oL+rvU+jjgZwiF4Yixap/s67/ritreT6g8mzJwU9XRmrwXUAOwsZ3BpGGWz1Pnp+wzwkCC7z8S9JhzqnCyR/vIjQ7icdPDIZx6fPC/h/xVChDz/yLlBF2esSDMosy3Ewvg/rvMeUahPfby/CHZhEKfI+G3BTFkNDlTWIxZbWQfBAEQRAEsSRe5IiFzvt41HUYmaZMlJ3ulUc/ug4g12zGersPczyoMPAPVBW9j/bAXVwWtsPKgwuNV/AoYrMpiJPdOLLtKC7/ZQuFE0vn5Y1YiJGDRckrIxyAp3IDTKn16HqiEzJiWUJrLETXWIjTQMQJ+D0fo6buM1z0fIgtknM2IDtbmsCCioUCCzEOJUf9fH1a4qSNs+C9dM82JNHzjJ+tvqf+/trvie6t/65ETaORcUaMA6z7Lo1YWIMUZZViKbBgrUY7V2JSPcjObSyJ0qE/JzOfr8dy/ccLLEgBqSto5ivhmlKQKU2/SCawEHnGm7B7Ly1SRhjS/RQDYLGBBT7szH8RQs0HaL3oxsdb1iQZWFiorBaSD4IgCIIgloThnHXFUUxqjYVnzNc7gJToXPoZBHvOoKFiF0prPsapT/k0yjdZfz3BTJt78JRlzK+ZwOzjW607VKNEFcJ/ouPIYZz2Ux+/MkhGLowwtv+iGMnBouSVI+JJVz1SzXvgGaV1FlYCFFiIYtRAJuGzb57fWifqnA0qDU5RthHCw/j15gOICwUWdCMKZOTh5ZYK9qyYtCRovAveKxlHLtHztG+fZZTh7Zb30D4+t4DjuFBe1N8V5bKOfY4EbBYKLHAnftiDyvT1KKz+ADVlVbBfVrZBlOZ1paMkMhVCgm/9eRXescfPkWfW8SYKLIjjGBychChOor9dQJHVgrUN3bHDuAzyIilQ6RkDi5QRxpMu1KbGmY6TMLCgbDGVuk+qz+iwuMWMWFiyfBAEQRAEsTRe5K4Q85aShDiM9qosZVoj+y7ZDVrbNmbXMXECvV80ovm6PE1YIjyO4EOa+rh8eZG7QijElYOHi5BXjmJPGgYdiOUIBRaiGDSQOR/s683K2/A5hLwfKVMh7mIi8C0OpZthzq3BqY5ueK9fwlf1DTjXz9xHyaFNRYHzDzwL/YnBh2GdMyWvi5BuLkJLr6KMZ2+hJW8LBKkR6tOSoPEueK9kHLnEz5Pm0KenYVPLTaZqOE/Q17IVG4WrmGQPSOw4LpQX9XclL6YMlJ7uQ0gUEb7XhrIU1WJCMc54CH4nU17eR/PKK4K0L28GTNYS2Nt7MTY7icCVU9hf/y3rJJ8nz3ydAL5a7tto6XvC0nQPnXVvMtnaypQyq3+WxuPl8r7OfH7ZkKskGuTRoORlfqGkCZaGgugKy0nJSHTUjIgp73FkbWSdAN9/WpLBFGxsucVSMINg52HkmFKxyXFbLjdNOT5hip6lP0UOLIihXyBIUyHOIzAxjsB3PK88cDClrPa7mLLij0okHwRBEARBLBVpbSrrFtkeYbbAbF8rNlqr4AnOsu98MemTqNp/Vh45KdlFWYptwOB2xcas6JpcUbhj6ChF5iYBXdHpDA/RVZsdHSHKnyU5pOnvo0t60z2NgPs9FNR+jW6vF17p72dcONKEtmHpacRyZQG5EB/9gtaqepzunVDJid6OjpBYDhLKa3gYV9u+QdfApPwcvvPIkXLUX46zeD2x7FgdgQVxFL6Lf0N1Ll+Rfj1swhl86xtVCSlfffQHCIVWmCxbIfzzVwxJQ9en0H++Slpx35y5E0LbV2jKY/eQdg+4h8m+r1GTn87uyc+Xo6V7WHbaxPu4XP8WzMrCNoHgLXwnbGXOmRWFwrfoG2NuJFPafecOIj+zCNVNR1FbUQWh80/2e5aWwCXYi1haUnaye/YjGPlu2Y7maw8Uh1pF3Hsxx3ysd/7ZjW5cDYR0jTOZ57FrpLzmoLD6CJpqK1EudEoL9oihQVy2b2f3X4vilissr3eV71YUNV9Cr+/b+XL97iYG7i7wLN6ZOffIuxzwaQQ7d2JLahZshx34vvcOer8TUGgxwVIo4Lu+UYSfDcK9k0854NfzPzPScnbN5z/4M1pKs1ldsHPmTBQ1/AN+vivEc+RZYvo2virj92XP23QM7ec/QE7hQbRe9GF0+jqEjGzsqP0MrvMONFR9JO2wEIPi3JszNmNP7VE0HSpHSb0H/dPKM+LWK0dR6OYMbNpTi6amgygtaYSnX95dWgq4fFWh7BaxBY3t59CQsxWHWr+Fb3gIfZFyLLKjwz+Kqf7zKM9MYffLQrHgwvmmzSxv8u4NIyOXUM/bjvkt1HXcRbBPXae9GOO7fSxJPnriryZNEARBEEQSsD649zTKi3bjsFCPsqK9cEYdwBnm5L0Lq/VduAOyHcJ3dnCW70DZ4WM4XLYD5c6bCEXMjlAA3o6zaCopRMnH36NfN41T2g2tZjsKKz6AwO2O4jplJ6swRi83Iley3bR/84EIYjmTSC6eBc5jpzUDO92D8jSY8Bj8V86jMeo3XYWf+zZJyUECeZ36DS0FzK9itqjt0FEI9s/h9hr4PcSyZRWNWCBeR7gi/KL+S/wWHMTNSGS051vYS+qUYf3LmJjRF4shXqSYIAiCIAhisTCH7841XPHekRciJwiCWCQUWCBWMPIuEimlZ3E3GlVnHeNAO4Qj/5TnBC5nKLBAEARBEARBEMRrAAUWiBUMn+rwE+zFWfJUBz4tIbcUDc6eOPsqLyP49Bx3ozKtowFnv/VhLOkkhzHmc6NBGoJWhIaz7fCNUXSBIAiCIAiCIIhXAwUWCIIgCIIgCIIgCIJYMhRYIAiCIAiCIAiCIAhiyVBggSAIgiAIgiAIgiCIJUOBBYIgCIIgCIIgCIIglgwFFgiCIAiCIAhiBSGGArhzf4Z9mkOo9zTKi3bjsFCPsqK9cPZOIN560Hybbmf5DpQdPobDZTtQ7ryJUMzFMxj1/QPNNe+iUjiNTr98PzF0G211byNNWjB7HYqES6rFslk6/G2o25TOzrHzaW+jru22fO/wA/T1jSxhByzipSJOYvCOXC/JyYWCOIFe514UldVDOLwbReWn0avsziaOt6MyhcuH7i/PAf8z+bf+tvexKc3MjpthLfoIl4NcjvWICAfcKLOWwR3kKZzB/T4/Rmkr1GUNBRYIgiAIgiAIYkXAHfhvcLz5R8mpF4MXUG7dDLtvkp0TMe1rRq61Cp7grHy5GnEInvIs5NpvYJp/n74Be24Wyj1D84EI7jQ63kF6bj08/ZOq46PoOlILhxS0mMFI5wfYYMpGbddD+fSjn1C/9RC+uvwLvD0XYbeth8kcSdcMgp2tOHLuVnxnlfhLkYJEx/+GTu7UJyMXUWYR9FTBmtsM3zQ/OwmffTOs5RcQFGcx7K7DXuePuO71wiv9XYKjNAPZLbeY5M7hUddHqHLwoIWI8EgH6jakIL32MruLjvAA3GUZMJlKlcAC3wnuRzQfOY++6BbzxHKDAgsEQRAEQRAEsexhztWwB1Xbv4B/ljt1IebU5c+/DeY8uw1H3hpk2X/DU+WQjIinvmZkmQrh8EvuI2MafkchTFnMSXwq38/vLIXVUgLn3Sn5kghP+3H1xsN5RzPshZC2DhXtD/gXjHiO4bh3Qj7HEIfdKLFYFIeSM8WetQdVngCNXHjVhAPwVO1hcsDrOBm5UPH0N9iz1iDPcRtRkfM7kGfKh903gkHvbTxS/2TuFlqyC9i5EPvyBHeu3lKdD8Er5MBS0Y5x5YjMFPpd76O+9h2siQYWOCJm/V9ge5UHwzRyYVlCgQWCIAiCIAiCWO5Ib5Y3o6ZzRHbwpSBCClKqO1VvfB+gvWKdNtggoTiLKQfQORk5MYfx9vdgUZzKWeYgFpgt2CBcY65dYsSAC9vyPsL1SR42mENoMICHal9PCjykaBxQaZj8ujijKYi/CD7iYC+yajowLtXXwnKhRg4iZKC6c0w5wmD1WmHR1nUE6fq1R9AzZRAIEAfg2rYVwvXx+YAVDx7cPYPd7/+E0esC0jSBBYZ4H+2Vb8UZTUG8aiiwQBAEQRAEQRDLGuXNstoBnOxEdYoJaYJXNQpAfgusdRQ5Y+iszoApTYBX5aeFvdx5447igDz6wbQepcInaKjYieKKIzj1413d9IU5hAY6YLdlI79RvcaCDsnZ5G+x+ZvqCDzokYmNLbeYe0u8EqQRBxtUgYGF5EIVQMAzJnIHkGLKgeBV1asURDLpAlwcOWiRWtuFJ8qRCGLoLjrtJUjPb9KusTDdC0eVHd2P5pQ06AILkaDHxlb0SaN2iOUEBRYIgiAIgiAIYlkzhb6WApgKnBiI+FNBN2ymOIGFGIdsCG5bWhwHMg228z/AWZAKU/puOH4bQTg8it5zNcg0bUBV+7DydljEVF8bjjcdQmmulT0jFRvtvxqMbuBz7XdjXdV3GNH4fkoeYkZTEH8Vc30tyDZth3Mg4swvIBfuIeUIJ8xErpTVu3FgwWRzI6gckuAjErbm6IITjKk+uI8fRW1pHnuGCeaNzfBJIxpC6G09gOPKCAbjwEIkD7GjKYhXDwUWCIIgCIIgCGJZMwxPabrWeXuRIxbaPbHOoeQYMqdTHcyIwOfpV26AKbUeXU+0J8XJbhzZdhSXR7UOYdQxNfgN8Vcwh1HPHpg1zvrLG7EgBt0oTnkP7ePxFlucwbBnH9JNG6RFQKd7Hdh9/ComFdGIF1iQA2r8N9qVGYhXDwUWCIIgCIIgCGJZo7xZVjv+hmssKI5iUmssRBzFQjh+/y95xILmrbMSpNA5nTIinnTVI9W8B55RleMY/hMdRw7jtF89BSJC5I23gbNI/AUYlf8CcpHMGgtSgEu/xgJf0LMCKcVMnhLFkJ50oTY1HaWe35WRNqotKlV/muCZFFjQj6YglgMUWCAIgiAIgiCIZc0EehqyYxz/F7crhHKvdQK8MxFPUA4smA2dQxEzPY1Yo34236ryi0Y0qxfjC48j+DAy7F5xbGNGUxB/DUqdaQILL3JXCHUwiQe4crDVNTAvC0bM9KBhjfG0hsQjFvSjKYjlAAUWCIIgCIIgCGJZw53yMph1owfE4AWUW7egpY8vjyditq8VG62RnRfm8Oj6SVTtP4tevve/tKtE1vziibO30LIxS1lhn6/GfxpbLXlo7FECA+HbcGzKls+Hh3G17Rt0DUzK58Rx9BwpR/3l+/J35owG3O+hoPZrdHu98Ep/P+PCkSa0DUeWapTXWDBvdSGQ0NskXhbS9ASzbipDQrlgpx/9gtaqepzuneBSgqCnCtbo4olPWJ1ugbX8gjb4JI1i0AcMZhC86oG7q19ZEHQOkz3Hsa3+EkYN5CHhGgvmnXAFtKEz4tVDgQWCIAiCIAiCWOaIIx6UWdQL73HmEOo9jfKi3Tgs1KOsaC+ckgPImWHO/ruwWt+FOyD/RgzdhLN8B8oOH8Phsh0od95U7fog36ss921UNBxDQ/Ue1JxWzk/9hpaCdJjMWbAdOgrB/jnc3gfK8PQwRi83ItccO4Q9pbJd2daQIQbgLs5CiTugpI/4yxHvwVOWiQLnH5o6SCQXzwLnsdOagZ3uQXmUAh+Z4tyLorJ6CId3o6j8tBy4iqJMk4mZjjMJX8s2pJlSkGk7gCbBjla3F6NxdhYxDizIAbaUEjeGSYiWHRRYIAiCIAiCIIhlTwi9LTZsdvStyO0axYAL2wo+Q+80eYSvDhHTvZ+hYDNz+lfido18QdFtxWjpNVrDg3jVUGCBIAiCIAiCIFYCs3fgevcDeIbVoxZWAHwXif0H4eqP3ZyS+KuZQr/rIPZ7AsqIk5UC30XiA7zrurMiA2urAQosEARBEARBEMQKQQzdwcXmFrQHYhe8W5bM/gsdJ07i235lfQbi1SNOov9iCz5sH1whTvo0Ah0OtH57RzV1h1huUGCBIAiCIAiCIAiCIIglQ4EFgiAIgiAIgiAIgiCWDAUWCIIgCIIgCIIgCIJYMqsksPAEA11fw16aBZPJggzbQQhNtdizKRPW/Bo4eoZfzuIlYgiBqxfQWl2ADMErP+PZHTg3pyG99jImpYtef8TQv3DV04rq/ALtvrkEQRCriel+dLZUIkfakm0N8mud6Brge8/zcwPoclQjx5SKnIpWdEaOE4QGZs90tqIiJ1Xays+cXwtn1wDkmfbc1vkC1TkpMOdUoqWzXzlOEGpEpm460VKxEWa+JaS5ALXOLgxIOzXwc5fhqM5jxzeioqVTOU4QOqg/IwxYVSMW5P1Q02BzD8kHxMfwNRfCbN4Mu+8luPnhMfi7HShNMSEtElhACH7P53B03VthK7EuFZEVgx/djjKkmHIosEAQxConhN8dNlhMFmwQrmF+fXQRU97jyN56GndX4hZgxF8Ic/5+/wJFFmbQbzgO75RKXqauQcjeBeddWnmfWIDpXjiKrMwpfJPZZhPKQc4EvMIWbHX6aeV9YgGoPyO0rO7AAkMccKKANYjslluYU469UMJeCGnqwMLKQwx50Vx3AUHl+1KQy54CCwRBEJjtg6NgDUyWknkHkB/bXAKHnxxCIhmm4Hdsg9lkVTmA/FgJNjv6yCEkkkDErN+BArMJlqgDKB/bvNkBPzmERDJQf0aooMDCkAtFJjPW230UWDAi/Cc66t6C2eamwAJBEMQLQcR072fYZDbDWn4BQXEWQU81Ntt/Vb3xIYgFmL6Jlk3MoLdWwROchRi8gN2bT8CnHsFAEAkJobelCGZTBso9QxDFIXh273w5o3iJ1xTqz4h5VvlUiAncat0Bi3oqBDvWd64epdUn8NWpOhRtPobO4Ixy7iGuN9eg5tMzcH1ag8JcO7xStGAGwe6TqKmsxbFDNmzYsAfO3gnW1BiawMIMRn3/QHN1ETKsAvvtJAa6zsFeuQnr930CZyWf72ZG2iYBXaNKGCI8jO7WQ6isbcAhWw42lJ1Gb8g4BCKGbuFc3V5UNxxFrS0PuZVnotdK5/a/g9JDR9F0qBi5mw7iXB9LY6gfXa6PUZn/Jva1tKBSmreZjk1HfsIoVw5ddtgyLDBl2FArfI6O3pv43tGA0tw87Pv0QxRbzTBvdqJ/jpdbHYpLD6Kp6QBsuVtQc+5WdK9ZfWBBfHQVzZWH8KnrDD5l5ZFrX7kjOgiCIBaNOI7rQgHT+VmoPvsFqvMa0PUoottFhIM/o7XmPdQeY/p0Qy7KnDdlfRq3HyJWH3OYvP4RNjKDPr36C5yt3o66rlHZ9uDEtR/m8Oj6Z6isOQGX6wSqC7fBTkH/VYs4eRXCRmb7pVfj7Nn9yKv7CY+iQhTfviU7johC/RmhsAoDC8xxz9kCm82GHfkZrBGsxVbn7eiwwbm+FmRHHeAhuG1pSK3tAl92RDq3phE9M7w1PEaP4GANQMTs3dPYvsuNYUkRj6GzOmN+3mPMiIUJ9DRkw5TGAwvs60wPGtaYYCn6HLdYhy+Ot6MyJRUFzj9YU5zCXee72OUOyIbCZCeqU/TzmBRm78BVshWNPePStc/6ndhsXoeK9ges4Y6gs2YjCqLDI/l9S2CR3nI8ZUloxBqTFUWtPtbQ5zDevg8ppu1wDvCAilwGpuiIBVG5fg02Nf+C4d+/wxdtPvyr4xDWFkSGzsllstWiRMDZEW1gYQp9LQVY09DDuiz+tRvCCeqQCIJYXYiPfkJdpgUm0wZUtQ/Lep4z64dz+164h7nGfsZU/wGmk+V50Mb9kPQrYjUijqKrLpfJkBnpVd9hJCpECeyHuVtoyc5BQw+fV88sjZ7PcIICC6uYOTzqOoxMvpBjeg3aRyIKJZF9+4TsOEID9WcEZ/WOWBBDGPL+AGft20gzZ6Psq9vy6snhILzfXoY/NItQ4Hs05KZEnWpx2I0SC1/h9HP8ODCJ2YkJPBFlJ9mSX4FjggBBOIZa2wbWsArh8LM7xgQWQvAKOfOBBf159Xep809HfkUDuy+/90F59EAec+Cf8YsjiHjqa0ZWtHFyZjB2px9jYRFi0I1is3YagnxMDmDM6kYTaIMA+sCC/jxDDMBdbFXlkaEcMxU4McCSpP3NLIbd5bDwFYdPXsJAaBoTj6fmlRBBEMSqQOkPTKVwB+etKcnYsuSj4hjX++yv1oYMUwryHLcxZ9gPKT8kViUa2yZCIvthjvXPJekw51Th5I93EZqdxOMnxiMhiVWCYnuqbb3ISyBj+/Z/yI4jdFB/RtAaC8zHZZ3vxhSYzDvhCjxlB+YQ8l+EUPMBWi+68fGWNSpFy4eEfQKb1cyuz1aG8nDHe50ywsCA5wksMOffFh05kIgwgu5SmDSBhXliAgEc1XOmdee11ycRWIjJI0ebz9jfDKO7uQRWvl1WpmrqCEEQxKrByBBT9LkSlI3FqB9SThGrEkPbJqH9wIcmX0GzbT2TvRRkJphiSawSFDtOG1hYyL4lO45QQ/0ZQYEFhuI4mwrQ0vcEU75mbEzdh/Zx3slqnWrxYQCDfLpC6A+0C9uZMuVDCW+jvWIdLCWRoWIK03/gincEYozTvYjAwng7KizpKIkMZZQQMe2/Cu9YpNFytEOLojCl/+vNB3imGp0QvY80BUOeKqF3+rXfkwgs6EYnyMhTPiwV7Rhn37S/CePh4J/StItQfzuEonUwrT2CHlpwiiCIVYWRIcano70Hi6VcGToaIQT/FR9Gx4z6ocfKNcRqxNC2SWQ/jI5gcHASojiJ/nYBRVYL1jZ0x06xJFYPhoGFBwns23sYIzuO0ED9GbHqAwsiwoFzKLaYYN70GXqnQ/DZ34QpRQ4siKFfIEhTIc4jMDGBievNKI900uIgXEVZzDEfQtBTxRrDetjs36FvbAqhwBWc2n8MF4OsET1PYIGvzlueAZO1BPb2XozNTiJw5RT213+LoF53T15GbboZ5twanOrohvf6JXxV34Bz/cxUkNZYyFLyyH8oYravFXkbP8L1yTmlXOIFFuSORQoaPJtkxsh4zNQJSXF0HkK6uQgtvcoxPhIkbwuE6/KaD9p7sjI4Xh9VMtLOHJb3lGAOQRDEasHIEGM6MXgB5czZs9rsaO8bxWxoEFdOfYD6i0NM/xr1Qw+k3xGrE8PAQiL7YdaL4+URZ3EWQ66S6EsAYpViGFjgq/vHs28fkR1H6KD+jFg1gYUnGOhqw8nqN6VdF6yF+9AkHEXtns3IMFuRW/E5uqWdH5jD3X8e5Zl8akQWigUXzjdtZr+Rd0kIXjuOjMwdqD35Fc6fOoKqox0Ihllz4MPBWsqRaWZK2ZSCjKJGtPknIIZH0fedgEILX5zRjg7/PQT7voVQaIXJshXCP7txvcPOlLEJKcWt6A4MI3C5WfpuKfoE10ZnpJVUW0qzWRrYvc2ZKGr4B/yGQxbnEOr7GjX56SwNfFhaOVq6h5VgBssZ3xWiZjMyed6balFR/rG024XIGvhl+3ZYTGtR3HIFgeBd5bsVRc09GA3PYvRyI3JZ3sy5h1keetEhbJXOFwoeeIdCikLgu0IcRH5mEaqbWNlWVEHo/JM9X0R4rBffKb8psv8A/9hDpnzykbmjDiddX+NUw34cla4lCIJYJUwPoOtcMyqknXiYPm1w4ltfZEV/Pjz0M5Tyvojr84ztaGi7LQ0RDXvj9EPEKoTbNn/HpxV8RylmNxQ24Oy3PozJnXJ8+4E7kRnZ2FH7GVznHWio+mh+9ytilSEyVXQF5z6tRA63YS1FaDjbDl9kVGw8+1ZyIsmOIxSoPyMUVtWIBYIgCIIgCIIgCIIgXiwUWCAIgiAIgiAIgiAIYslQYIEgCIIgCIIgCIIgiCVDgQWCIAiCIAiCIAiCIJYMBRYIgiAIgiAIgiAIglgyFFggCIIgCIIgCIIgCGLJrLLAAt9+qRuOQ1uRYc1Boc2GHflZyCzciw8qClGi3gP6BfGs34nN5mzUdj1UjrxK+JaUf0ddRQ0amvah0LoWm4RuPHqRO7vwLTa/P4nq/AII3pBycDHMINjRjJravSx9a5DbeAWPZv9Eh3AItdWFsJrz0dg9pmxhQxAEsYKY7kdni7Ktm2kN8mud6Bp4opwbQJejGjmmVORUtKIzcpwgNDzBQGersq2bCeb8Wji7BjAdOdf1BapzUmDOqURLZ79ynCDU8C0mO9GibFNqMheg1tmFgWluWfFzl+GozmPHN6KipVM5ThA6qD8jDFhFgQXmVPd+iWLurNb9oNonNXLcCttLCCxg2g9PM2tsy2GP6FA3GrLehXt4ln3hDnwDCnadR+CZfPpFIIaGcMNZgVRTzpICC1IgxirAGw5jtKsZVcfb8f+e2gGr4EVYvI+u4wdxvOs+BRYIglihhPC7wwaLyYINwjVMKUe5QT/lPY7sradxd5Y0HJEI5vz9/gWKLMyg33Ac3imVvExdg5C9C86785JFEIZM98JRZGVO4ZvMXptQDnIm4BW2YKvTD24tEkR8qD8jtKyewALvbDdYYC5wwB8j5LMYdle9kBELYsiL5roLCCrf/zrmEPJ9jrq2eHl4hsnOA0hJ4067cuglEfYKSFtSYGEWQ64SmNRpFAfhKlqDNB5YUA4tlVdXNwRBECpm++AoWAOTpWTeAeTHNpfA4SeHkEiGKfgd22A2WVUOID9Wgs2OPnIIiSQQMet3oMBsgiXqAMrHNm82spUJwgDqzwgVqySwEMaIpwIWUyoKnH8wtRmL+MiHH28+Vr4tkfCf6Kh7C2ab+y92XvkUjx9Ql7suwaiLaWZwFGqd9pfE0gMLIXiFHG0aw14IaabnDyy8srohCILQI2K69zNsMpthLb+AoDiLoKcam+2/qt74EMQCTN9EyyZm0Fur4AnOQgxewO7NJ+BTj2AgiISE0NtSBLMpA+WeIYjiEDy7d8Lum1TOE8RCUH9GzLNKAgvj6KrdAFNSzi5fh+Br7C8ux6Gmozhkewubar5GX2gOYqgfXa6PUZn/Jva1tKBSmuOYjk1HfsIob0hddtgyLDBl2FArfI7O/gB8bc2oLsxUhvJPYqDrHOyVm7B+3ydwVvL5bWakbRLQNaq4zeFhdLceQmVtA3t2DjaUnUZv6ClCA1fgslchf30VWpz75DlNaVtwhE8LEIfRdbwEGSYLMmwHIZzoxJBmesMMhjo/QUV+GkyWfFQcEyAIx1Br42VSCneQPZunreM4Ci0mWGyncDv4K1oL34DN3oEBlndp6kT3SdRU1uLYIRs2bNgDZ++EHKQRJ9DnOgBbWT2Ehr3YlZ8FS9yy5kGQn9Fa8x5qj7HfbMhFmfMmQuJj+FyCNo2ftuDTpgrk8zTlV+CYYIfL9zhOGfE08tuztJxrQEX1YQi1xcjJfQ+ne0dj62ZoBuKjq2iuPIRPXWfwaXURcu3PPyqCIAgiKcRxXBcKWB+QheqzX6A6rwFdjxQ9FldP8lMPcb25BjWfnoHr0xoU5tpferCYWK7MYfL6R9jIDPr06i9wtno76rpG5X6ZE7evnMOj65+hsuYEXK4TzEbZBvsSpi4Srwfi5FUIG5k9m16Ns2f3I6/uJ9XaW/FtP7KhiCjUnxEKqySwMAS3jTmsSQQWxPEO1KzdPj98Z9YP59Z0JQonYqanEWtMVhS1+lijmMN4+z6kmLbDOcDXUFCeo34rPtODhjWqN+7Kd0vR57jFgxXj7ahMiYykmMJd57vY5Q7IhsFkJ6pTIvOWJtDTkM2c7h1ovcWUungf7ZXrYSpwYoBfHHTDZkpLMGJBPxogzH5SOh9YkFAi1+kH8d2vZ7BzlxvDUkJEzN49je3R72PorM5Q5nY+RdBThbXF5xCQ1q2IlEmcsubluX2vss6DMj0jOr8vmRELicpoCv2ud5HX2I1JfvLZHTg3r4Gloh3jMXUzhb6WAqxp6GHdJv/aDeEEdYoEQfx1iI9+Ql2mhenhDahqH5Z1GieBnpzra0H2mkb0zPCrH6NHcJAhtpoRR9FVl8tkyIz0qu8wEhWiBH3l3C20ZOegoYf3u8zy6PkMJyiwsIqZw6Ouw8jkCzmm16B9JKJQEtl+T8iGIjRQf0ZwVklgQVGGpmylI40Hd7bLYNZMF1COKcED/TB/7XeDwILeMU70Xers05Ff0QBB4KMKDspv2fMc8D/TO9267y8ksMAUw2Q3GjekIs1arJobJTvh8qgB9WiHQjj++xpLsxVFrsGoEkk0FUJSItFRE+yv1oYMUwryHLeZukkisJCojKZ/gz0rYixxRITH+nFnzCjow9fVKIeFr3p88hIGQtOYeDw1rwgJgiBeOorO0+nhRHpybtiNEgtfaftz/DgwidmJCTwhxbWqkftcXf+fqK+cC8Bdkg5zThVO/ngXodlJPH4SebtIrEoUW0tjvyay/fz/QzYUoYP6M2LVBBaeIuDaCXPUgY2HgWPLUDvKLzWwIAUHIqMf9OjTpvv+ggIL0bIyF0C4Pq50Ejxf64zXpzB4bvzAgvLMyCiLGAzKX19eicpIOhcveGRUN8Pobi6BlW/Zlama2kEQBPGXoOg8jR5eSE/yocmfwGY1w2TOnh9SSqxaDAMLCe0JPjT5Cppt65nspSBTPZ2QWJ0YBhYS2H4csqEIDdSfEasmsMC60eAFlHPB1W/NFEGcxODgA9xTjU5QTsjTHyzvoX18LsZpfqGBhfF2VFjSURIZuighYtp/Fd6xxzqn++UEFvhcu+O7D+LwOxkwb/wI1ye5sfEA7RXrYCmJDIdTmP4DV/7zFPalmLFOuC4Ph2PEDyzwaRLvwWIpV4ZERQjBf8WHMTGJwEKiMrrbrpoWEYEprV97cV/U100YDwf/lKazhPrbIRStg2ntEfTQolcEQfxlGBliifXk6FgAg3waXegPtAvbmVHPR2k958LDxIrGMLCQqK8cHWH2ziREZvf0twsoslqwtqFb1W8Sqw7DwEIC2897D2NkQxEaqD8jVlFgQXIwOw4j15yK3FoP+qPReRHh0Rs413gAQuefmOVrLKSnYVPLTUxL5/k8sq3YKFyV5u0nDizISliKzD3jgYpxiIsJLPDVeMszYLKWwN7ei7HZSQSunML++m8RjHG6dd8lI0Jeq+FZ6E8MPow06gj63xsEFsQxdB+pQWvvJMIBN8qsa5R88xVeq1iDXw+b/Tv0jU0hFLiCU/uP4eLwn+isyWJp3o3TfmaosHK+596DlDgjB+QAjwVWmx3tfaOYDQ3iyqkPUH9xiP1Wn0aGvrwSltFDdNVmw2R+E9WnfkCP9xdc/uoIDpy7g9mYuvkXrh2vjyo6cciFIiV4RBAE8ddgZIgl1pOz3maUR5xFaTveLFS0P5B+R6xODAMLifrKWS+Ol0ecRXmbZ3ktImLVYhhYSGD7BR/BSzYUoYH6M2JVBRY4cwj5L0Ioy2OdsBU5hTuwY9MmFB1ySHN75DirvCtETX4OCquPoKm2EuVCJ4JhESJrDJft22ExrUVxyxUEgneV71YUNfdgNDyL0cuNyDWbYM49jI7AEPq+E+SdFgoFfNd7F3cvNzPla0JKcSu6A8MIKN8tRZ/g2uiMtHJqS2k2zHwRHXMmihr+AT/fFSJwCfYiK0wpO9HS3Y9g5LtlO5qvPUBYvI/L9W+x36Uit+4HKb3zsDxFr98KoeMuHo3exLlqrgDeQOXpXzH67wfwnd6LDZs+gY8HXcLMKKlkRglz0mtcN9j5e+huKUcm343ClIKMoka0+ZWVgUM34SyLpDkbO98pQGpmMQ47fmBp13cyfNjTZyjNTGH3YeWUsR0NbbcREmcw1vcthEKexiI0fvMLAo/uq8rvKL65+i92nby6bGwZyc8RQ7dwrqaA1a+cltKWn5WyCGvrJviQKcB8ZO6ow0nX1zjVsB9HO/+UgxcEQRAvm+kBdJ1rRoW0u5AVhQ1OfOuLrOgfT09y+/84MjJ3oPbkVzh/6giqjnbo9D2xeniCga6/49MKvsMU7ycbcPZbPvqPn0vQV3InMiMbO2o/g+u8Aw1VH6EzaDRlgnj9EZkquoJzn1bKu40x+6vhbDt8Y4o1xKc7GNp+3IkkG4pQoP6MUFhlgQWCIAiCIAiCIAiCIF4kFFggCIIgCIIgCIIgCGLJUGCBIAiCIAiCIAiCIIglQ4EFgiAIgiAIgiAIgiCWDAUWCIIgCIIgCIIgCIJYMhRYIAiCIAiCIAiCIAhiyayqwIIY8qNdKIbVlII8x208U45DnMRAx3EUWqwobPzWYItEgiAIgnhBhEfgcwsozv0QXs3+bHxL5DbUbUqXtuUyWbdDuHxPt4XbDEZ9bRCKt0HwhpRjxOojvhyIodtoq3tb3nbZtA5FwiXdFm4iwqO/wS3sRK7gpS0CVy0J5ECcgL/tfWxKMzMZMsNa9BEu67ckjavHiFUF9WeEilU3YkEccqGIC3hWM3xPVR3tky7Uptej6wntn0oQBEG8JGYH8eOZz3G40ApTmqAxxMRHl3Gk6hR6eXA7PIzOuo0wpb+PrslIGHwKgz+ew5eHi2Ax5ZAhtmpJIAfiKLqO1MLRO8HcxhmMdH6ADaZs1HY9jFzARPASznz5AQotJqRRYGGVkkgO5vCo6yNUOW4iJIrMb+xA3YYUpNdexqRyRSI9RqwiqD8jdKyywMIzTHbWIicnCymmN5kQTyjHmRrta0FORTvGle8EQRAE8XIIwSvk6AwxEU/veHHj0fyIubBXQJrlPbSPa0fRScfJEFv1GMrB035cvfGQSZNC2AshbR0q2h8oBxSk4xRYWPUYysET3Ll6C4+iQiTrK0uMjWykx4jVB/VnxDyrLLAwyYR/F4RL36JhrUUVfZ3BgPMdFLsD850xQRAEQbwUkjHInyLgKkOecBWTuo6JDDGCk4wciAEXtuV9hOuTWmPe2KEkVh3JyIE4ANe2rRCuj+tsZAosEBzqz4h5Vldg4dltOPJr0Tk5CZ89H6aUfUrk7AHaK3bB4Z+WryMIgiCIl8YChhhf96ezGbb0t9EYMyeVDDFCJrEczCE00AG7LRv5jfo1FhgUWCA4C8iBGLqLTnsJ0vObYtdYoMACIUH9GTHP6lq8ccCJzcVuBFn/+qyffTavRZnnHsSZ6xA26dZcIAiCIIiXQiJDbBJ97mY01b6DXL5wmnkz7L7ozGYJMsQITnw5EDHV14bjTYdQmmuFyZSKjfZfMaWclaDAAsFJJAdTfXAfP4ra0jwmZyaYNzI7eUptJ1NggeBQf0bMs4oCC3MYb6/BW5HdIMQA3CXpMG91YfB3BzbVduGJdB1BEARBvEySMchFhIc9qEw3I1XXP5EhRnCSkoNwAJ7KDTCl6hanpsACwUlKDmYw7NmHdNMG1HapV1mgwALBof6MmGcVBRbG0VW7nQluJFIWxoinAhbTW9hX/Q4q9QsbEQRBEMRLIVmDnPdbG2Au9WBUOcIhQ4zgJCcHIp501SPVvAeeUdU6CxRYIDjJygHfOS01HaWeYeUAhwILBIf6M2Ke1RNYmLuFlpxq7Wqkk5dRm86H5uyEK/BUOUgQBEEQL5NkDbEJ9DTkIi8y0k6BDDGCk2xgYaanEWvyHPBrhYgCC0TycjDTg4Y1hbq1yCiwQHCoPyPmWTWBhWd+B/IKnBjQLKMwwRrDmzDpO1yCIAiCeGkYGWIiwsFf0Oa+ggG+7zc/MtmNI9uO4vKo1lojQ4zgGMpBeBhX275B18AkkyiGOI6eI+Wov3xf/h6BAgsEx1AOZhC86oG7qx8hSWjmMNlzHNvqL2FUI0QUWCA41J8R86yCwAIT7tFfcbryDZgy9+K0b0SlPEXM9n2GQuE6U6MEQRAE8ZIJj8F/5TwaC60wWbZC+OdV+Md4DyRiyteKgjQzzJk2HGo6DntrG7yj6t5pBmP+n/FNYxEsJivruy7gqn+MHMNVRwI5mPoNLQXpMJmzYDt0FIL9c7i9DzR2T3jsd1z55igKLSZYCgX886ofY/pdI4jXnERyMAlfyzbm7KUg03YATYIdrW4vRtUyElePEasK6s8IHatojQWCIAiCIAiCIAiCIF40FFggCIIgCIIgCIIgCGLJUGCBIAiCIAiCIAiCIIglQ4EFgiAIgiAIgiAIgiCWDAUWNITg/6oCmWYTTGlbcKRLt4oyQRAEQRAEQRAEQRAaKLCgQgy04ei5uwhjBsOefUinbSgJgiAIgiAIgiAIIiEUWIgiYvbPuxicVsYoBN2wZbegT95+lSAIgiAIgiBWF+ERDPz5RPlCEAQRn9UVWGDK0XfuAHJMJpishahu+gAV+VnILfsEl4PqvVVFPPWdwBbhGqaUIwRBEAShRgzdhLN8B8oOH8Phsh0od95EyGj+nHgf7ZXrYeJ9j+avEA7/tHJRhGkE3BWw2twIKkc0hAfgLsuBzT2kHGBM/wFP/R5USOnYjhL7Twiq95wnli/iBHqde1FUVg/h8G4UlZ9Gb8jojcYcxtv3ISVGhlKQ57iNZy9ExmYw6muDULwNgjekHCOWP3MI9Z5GedFuHBbqUVa0F87eCcOpvOJ4OypT9DLC/tQjdKeuQdhgmT9nKYd7eFY+l7S8LqDHiGXHi+vPRISDP8Fevhs1TU1oqNiGosaO+T6JyZC/7X1sSjOz35iRtul9tPmN5JXdJ+BGmbUM7mBYOUYsd1bfiAU+EoE1gJTqTkzy71PdaFhrhnmzE/0RpSqOoPP9Y2gfIUEmCIIgDBCH4CnPQq79BjOhGdM3YM/NQrlnKMZAEofbsHfvKVy+7oXXy/+u42dHGVJiRsVFDClmcBka5IqxbkpTBRYm4bNvQVYkED7bB0fBOhQ4+qC4AsSyZRZBTxWsuc3wSaMleV1uhrX8AoIxQhSAe+8hOC//osgQ+/vZgdKUArT0Tb0AGZvC4I/n8OXhIlhMORRYWEGIwQsot26G3cetWhHTvmbkWqvgCeo1wCyG3XXY6/wR1yMy5L0ER2kGsltuQRaTOYx3NqK85T+V8+zv5pDiYCYrrwvpMWLZ8SL7s2d34Nycjcp2ZZ06KRCRixJ3gH2fw6Ouo9hac4b9/jp6vrPDxuTEvJHJ1JTuSVIQPQMmUykFFlYQqyywIGLGK2CdaQ2KXINKYxmC25amEtwZBL//FM3dYzGNiSAIgiB4X/KUGe9ZmrfB0/A7CmHKYgbSU3XvIWJ20Iebj9Te3RT6Wt5Glv03PFWOSMzegWvvQdTuyTQwyNl9+r/G3vpD2LNGFViY7ER1ygbUdo3L31kfNuDcDlPKAXRO0iJBy5qnv8GetUYecaAceuZ3IM+Uz5xEnWM/G2AO3kONXTLX14JsRd5ejIwxW57ZSGkUWFhBhJhzn68dcfDsNhx5a2LrngePvLfxSCNEt9CSXTAvb+IAXFvfQqWzGwH9SIRk5XUBGSOWGy+4P5Ne4Kr7pAn0NLyB9XYf5sR78Bz4FN5oEIEHu8phMckB0nmm0O96H/W172ANBRZWFKsssKA0FLUASwo4Baa1R9AzFUao9+9ovjgILsLi5E38+GukYRAEQRAER+lLNM47H6r+HjOQjIae65D6nTw09DxWDnCmcNd5AO939eO6kBNrkM/64dwtoGv0KoS0+cCC5FxqHEERMz2NzBjLZvefUI4RyxHZKctAdeeYcoQx3o4KizK9QTlkDJfBIqxt6GaSY8BSZIxBgYUVhmLDRkfhSjxAe8U6bbAhDpIMSvYvd/QiDmZkaPs6FAmXokPYk5PXhWWMWG684P7s6U205KXCUvQ5boXmmC/VjcasLfKIGnESg4PjmgCprHPUzxExe/cMdr//E0av83MUWFhJrK7AAh9KWGxVNZ45TPYcwwbTehQ7+xAKfoeaTPW8sgp4aDoEQRAEoWEMndUZMKUJ8Kq6CJP2YcIAAL34SURBVNlA0hneBkgGemo9up5EzCsR0787UXX0Ch6JIXhjDPIQfnfU4igfSRf2agIL8jO1QQT5mHq6BLH8eIbJzgNI0TvxUv2qpmvGQzLm31C9FdSyeBmTocDCCkMasWRCmuCVXojJKPW74Kgl2aFMre3C/NKMcwgN3USXpxXV+enMFk7FRuEqJsVk5DU5GSOWGy+6P+NrfnyJYj7FIacEFWX74bj+QCWfapQAhnpkxHQvHFV2dD+aU9JAgYWVxOoKLCgKWF64sQkN+7YiZ2M5BM9t4wVKCIIgCCIGZQqdoSG2kEP/FAHXTqSqHcfpm2jdfQLXJ/nwUr1Bzoz1Xgd2H+fGPfuqCyxE3hhubLkVXVOBAgsrgTCC7lLmuBk7ags5ZGLAha2p8RzHxcrYPBRYWGEo64YZBhYWcsikaQ858R3H8J/oqHsLZukl2/TC8pqkjBHLjRfcn0lMwe/YBjN/SWt+C3UdfxoHFvgL35I3UdU+zHo6Tgi9rQdw/Lo8qoECCyuPVRRYiKyvkIoC52088FRICxRphwkSBEEQxEI8xxseaeTcG6hof6Ac0BpSMQa5xlhn6AML4ii66nJhSn8Xp3+fwOzoDbhq3mQG3Zuw+2iLuOXL84xY4EGJMqRUtMNwvMJiZUwFBRZWGM8xYkEMulGc8h7ax9Xz5bWIw26UWLg8TC4gr25cSVLGiOXGi+zPOFMYuHAQRfv/iUDgEoSidTCZ89EYs3YdHzV+HNvqL2FUOqELojMosLDyWEWBBWUOkTKPR95yx4z02ssJOm+CIAiC0GM0JzXiKCaekyqOeFCWoto+K/LGLzIFT/OXgwahmhlWRufYX8QQDA+jx3kEFcVlqLE78GnFGzBtOK5aIItYjhjOWZccxQXWWOALoJVlo1haZT2WxcqY2lGkwMIKw3CNBcVRTLjGQhgjngqkFDPHP5GakGRHDlImlNeWv+FokjJGLDdeYH/GkI5Z5n8nhnxoLbLCvNWFQFTW+JaU/4kj+7+CX9phhKMEowxlSB88I5YrqyewIA35SoNpHTPEZrgQP0RXbTZrSPsSRmsJgiAIQstiVtFWIxtrqRoDS88Cb/okQz/+8FRx5DtUpWcabhNGLDMWsyuEGu7Mpe6EK6Bd81/m+WSMAgsrjcXsCqGGBx9ysNU1kFhP8KlW695HF3c4FyWvNGJh5fBi+7PYUQazGHKVaEZEiKGb+OLA3+ZH4vFAw8MHeKgsFBqBRiysPFZPYEGah6oeXihiynscG0xrUea5RwYYQRAEkTzKvt/RtQ1mb6Fl4/y+3+KjX9BaVY/TvROq/oUb81kLrPi/9MACN9YcxbnYdOQnZWgpsbyZRdBTBevGVvTN8gp7gr6WLbCWX5DfIosPcb31APafvqlaB0p5kxj3bfTzyRgFFlYeYvACyq1b0NLHpz6JmO1rxUZrFTxBrpnm8Oj6SVTtP4te9faR0kgD3dtocQJ9bZ+hte03jEoO3jQCrn3Y5bqjrN+ygLxqoMDCiuJF9meTl1GbnoHK9vvKtTz4tWl+hHh4AO7yHag9fwVer1f+62nDkfoLGNbJEQUWVh6rIrAghvz4rrEIFpMJKbZm/DTA165lSNFXC8y5B+DyjdAQG4IgCCJpuCPvZAZS2eFjOFy2A+XOeQfwWeA8dlozsNM9OG90PelCbWrioaWLDyzwVdxvoOOrYygpKMPH7X/QYsQrCebM9Tr3oqisHsLh3SgqPz3vAD4bhHtnBqw7zyMQFaJxdNW+ET9wsGQZm8GY/2d8I9lKVhQKF3DVP0Z20YqAr8J/GuVFu3FYqEdZ0V44ow7gDALud2G1vgt3YEY6woSOiUk9UvXBKXEMPUIhc+RSkGk7gKbGY2jR65NE8qqBAgsrjRfXnzF57PsaNYXbUNHQhKZD5Sje/zX6uJyI93G5/i15UUfN33pVIGIeCiysPFbRGgsEQRAE8ZrBDP07V7vhJSeQIAiCIIhXCAUWCIIgCIIgCIIgCIJYMhRYIAiCIAiCIAiCIAhiyVBggSAIgiAIgiAIgiCIJUOBBYIgCIIgCIIgCIIglgwFFgiCIAiCIAiCIAiCWDIUWCAIgiAIgiAIgiAIYslQYIEgCIIgCIIgCIIgiCVDgQWCIAiCIAiCIAiCIJbMCwksmEwm+qM/+qM/+qM/+qM/+qM/+qM/+qM/+lvk3+sAjVggCIIgCIIgCIIgCGLJUGCBIAiCIAiCIAiCIIglQ4EFgiAIgiAIgiAIgiCWDAUWCIIgCIIgCIIgCIJYMq88sCCOt6NqhwsBUTnwMhEn4PcIKM79EN6wcuxFEv4THe9Xo/HLY7BlfYCuyWfKicUhjt7Cr3/OKN+SJ/q7Z3fg3JyH2q6Hyhkd4WH0OA/DJpXDDII9Z3DYtg2CN6Rc8JJ5QeVkhBi6DY+wE7mCF+GFyoFImmf9Tmxe+75SV2GM3riJP5VqS1ZeNXWjHHsetGnSM4eQ/yKEYr1cvwJ5V5E4zfFYZJoXo+fiXLu4dEbkIV6ZL0wiGTKWGxHhYA+ch4tfmDy9OrTt6VWQdN9hhEaGZtDvtGFt7WVMKqdfCPp0iSO48es9vMIi++t5zn4zUscvWg9r0dd/ItmOry/m9c+UVvfp5CDZvic+XI/8gPcrj+JLoQRZzyW3S9FJS20vSrnOLdWmfVH5VtXvy7S3XqLNuCRemv6J034S1PNy6quT5wX2E0pdzC1ZrzH7qkNAZWMLBNvmpcuvSiaWZue9PrziwMIsht3lsJjeRkvfE+XYyyXsFZCWJixSCSfHXF8Lsp/33uEBuMsKF93Ytb9jSuPPAMbC8aI1ImZ6GrEmktaZHjSsyVn8M5fICymnuITgFXKQJimXhcqBSBoxhD8HH7IyZQZJwI2yTKX+FiWv6rp5AUTTFIewF0KagVz/xfKuYaE0x2ORaV6MnjO8Nul06uUhTpknYkEZiic3E+hpyH5x8vRK0JXfq2BRfYcxahkSQ8MYHHtew1OPOl3TCLgrkLmi633xPFe/qanjF6yHdczXfxKyHU9fqPWPRvep5GBRfU88ptDXUvACy2LxOmnx7UVbrkuzaV9EvvX1+/LsrZdrMy6Wl6t/4rWfuPW8rPrq5Hkx/YS6Lpao1+ZuoSV743PmSScTS7XzXhNebWCBRzhL96NhbzbWNnQzVacjPIpej4Dttj04UFaEnMwyOP0TCN09i9IU3iAeYuzWKeVziNXlIK6cqsGOkipU7tqEzI21OOv6WPnchO5Hc3LjtLyBwl3bkZ+RiaLmq3gksgYX/BHNwkmcadiBvPpLGBVnMOpzo3G7De/szIZlsxP9keATj54eqYXw1RnYK8pQ5/kDU2M+XBBsSEmxQfj79/CNKSIVzUMZ9trykWF5C/WX77Mmzp/5nzhS8yG+YmmsKG6Ap38Cj2+dhC1lLWzC1+gaeIhg598gOBxoKCqUfxenTOZ/dw4d3Z04XbMVpe4hlgCm7HvPoKbGjlMNO/F28zVM8j5ZraSiCuYhRjo/wAbT2xB6HiAcHoKnqhTNvscstQos753HG2D/6jNU529j142xNMWWxzRmMNbrgbC9GHsOlKMoJwfFzj6Dcgoh2PERagQnXPa9KK7zoP/fE7h7uhwpPH3To7jlKJM/h43vOS2VZSeO7xdw6uyHKM1MYcrlOqZHr8+Xg2G5KYZK7ylU1TTj87pjaAtMy/mMMqOrg39hpPsjbDJnoPKCD90sL8WtVzEqGTpG5cB1zE04qg7h088bUN82oCgb/X3v4+nYLXiEUtj2vIeyolxkFp+GfzrOfcXJOGUUxuhlOyvPL3Hy0HaUtXEZiMAjs7qynmbqOHAFpyqLUVJdiV352dhY+yU7v0/+3HiFtQ/WFrxMhvKr4A6O4FZriVx/5y/B+8vfEsurYd2oFH84iK7GfJhyP4I3xNrng4uo2vIJfgvegLPmEJpPNcD29gl4J6d07bEV/+8vkTSFpTLWXj/H7s3leg1yCouxIz8T6UWf4DrTAZoOlctz88dwnGlAUV4jLo+ylIn3cfkoK++zn+HQpr1oY/ePEPscfbpO4fc/9bpE+TEr//lyDCOcoL5j21gkzfH1n/y7eginTuHj0myYJXkw0m0sKYbXyqnUpvNJnDanII6r5OEKBkLXWDrTsaX0XRQX6vKkL2eJOa3O67+HXmc9apo/Q4NtJ5q940yCZIPBklOIXTuYDk3fgebrD6PHZXmKbU8iO9bv3KHV3QxDWREn0Os4iJpPP0Fd/TcIKMaxYdsNj8DnPsJ0yS7szEzHZudN/BnzbGO9Evts1nbV5Tc9bZCPeQzTY5D3RLpEWw/TCfoOpe3WNeMrRw3yN32EHt5+IhjJ0PQDeE8fQH5pG4Jx2rbv0b90aZhl6vk3uBvfge2d7ci07MCp//efOMr6xrMnD2JTWRsCKl0uPr6BVttapNgEnO/6b/xroT5LwbDeFZKxHYzKWeRyo5fXGP0xbqyrp5ORIxkxpt9kZRZjP4RiytHZzw13XRsbuC+1m5QtpdhbvEXVFxq1oQi6fuX/3oqTJ1X963RD/6gvSR3NdKpKT6r1dTgqB4PaPPn96KzbCFM+k1HmrERljcmdjJG99Rhjvm8gKLL09299GFMynJQ8GNW9RicZ2F6zujo/eQm/RMrL6Hq9EHN05eq/fAxpKQUo3bsThWr9HFfnhnX5voreG+o+7P/gt5sG6ZDau6pf6v4d/6VOR+CaSncY2RrTifsSDntG0rY1I1GbluX5BOrsp+Co3oJNQrdkyyRtb7LPRjajRv90/IxuTX124Ie4cshkMHAOxRZmO7J83ZP0Vj4au4KYDf0C4a06dD4cjtt+jOv5VfXVvK+4rNM1Ewnq3MbSXcx8LityuQ3yVKUn+K31ssVsnmejlzR9gHSdDm1f4MNlQ70WxwbiiKPwXRCUPJ3Ft9dv4oa6Pk9dxc2YNh7bL3b3X9P0SQGN/jKw3+P6I68HrzCwIOKprwWlTj+eeI9jQ8o+tI+rlQK7IuTHuYoMpNZ0YoIp8Z6GN7De7sOcxjFQdTpjP0PIX4si1yDr3AfhKlqLAucfyuc3UN05xi5nDnVqPbqePFMaeSEcfmbo1FTCOcA64VEPSs3sWO8Qbp+uQGrqQXROTOD+faZQpFTxURZ78JbjtjTkRRzxoCxlJ1yBp/K9uUKa13vzeajtwpPICI3sFvSFA3CXlLBn82YfxoinAilbXQjMzudHHO9AzfbTGBDnWLL2wJzngH9yoTJhjsdQJyuHdbBJCv4B2iuy5c9BN2tAB9A5yfJuGFhg5SkNZ8tEiTsA8dltOPb9XTVNhae/CttdA6wsZhBgn7c4bmLIsDweYvBcpVJ+c/IIifV2+FgVq58tDrtR8hbLl/Tje/CUZWEru/+s6pr560PG92QGbDtzjO0+3jAjbwyu4rGqHOLKkjgA19Z8ubxZ+RRbclF6vANDiiNiWAdzI0xecrDxxFl8vseBvlleQPHkIoSAayes3NAQWZ0Xr0dOqR0dNy7q7tsKb/95VKRmoqZzFKL0luZNlqfHScnb/OdheEp3MbmaAiavwvXDvDo2Luv/xmjPR8g3lcA1NAtxyIUi03apLUifubww+R/6UWDXlMoOseq57EtieZ0zqhtVYIEhBlzYmlIBz0gY4oALB1138LD9PVhsbtaZDMFty0H1d3269hhEIJqmGYzrr2dtXU7bBtR2jbPPd+FiZZ/Hy1HVVsY738d2riPAy005z3VAAUt7eA6TV9rww2hEL83FPicmXXfRodclUhtniCFVOc4gNBivvvVt7HZcnTf/+TFG2/fjTftveMp/GR2RxGVVn55QnGvlZGrT+ShuO44QKw9Knp50oTaV52nSuJzln2vzM96OCgt/LnOw3aVIqe7EpGKsyzp0mrWnUli4bD1TGfFGsveMGQCjt3Dl5ohK3gzqkMmKJINWnocwe24ZLDllON55B4MxbfdjfOf9BacjuiQ0gvuB7w3k3kivfI8b38Y+W11+hm0oWlBPn1OXxKmHaPnr+g7+jJ17JF2DZ4Nw7yqdl2VWokYydP3xIH4U3oZJymOcth2TBh9Gbp9V0nsfIda2+y/sRQFLWxgPccXVgX5Nn6Z23hgJ+6wIxvUuw+QkCdvBsG7GDOTVQH/E6mrmnN6/ubAcRete18543cTYD/8H1/vU5TiCUKQc1G0s0p6kvnAcXbVZUl9o3Ibkn0OqK22/Epun2PqfvyZO+Uvp0unok79gUNXfzKddJ5+aPCnDj1PK4R6exTO/E/skHapgWF58Cq5OliSSk4dEukq+n9722o/v7t7Q1jnrL+bLy9hWMyKm7CX9HMaTrnqkSvqZlXcinatOZ3hM14f9ge9i0jFj2C/NqOpXXTeGtsZXPgwk7EsWZ1snbtPs9ywNO7fL06yfBc5j1xYHbg8lb29efxzHZoyW3XVMj+nacGhuATnkz8xFRfsDps5/gz2Lj9ZmbWrmOj784Ed2j3jtJ049M5v41fTVDzGm0dkj+J97ieqc+1zMJedyYSnApz19qnwa+RW3cN+j7gN+wqicAh3q9iZ/1uu1OdHIBor0YwyVHgnr6zPwbWwbN+wXR+fTobGf/m0o01/1/lcc2Xo9eIWBhcfoOVIvNT7M3kLLxrVSA9faA7FCIytCVYei6Vy4csmQFWKcz5qGJf4BZwFThP/Hgdq0IjScPQ/333n0Kh2lnmHttVFUyp8jdTSZCa5X54FdLjluTOBueRSBlS+Wjpv3wBO8quTnfyTlkVbYgLNuN/7OI7b8/OjEwmWiyTuPTnfBG5zEaNcx5CqddayCifyWK+t9SGWG0e1bp3GIG2vsqAzP+9vKdRGSKw/jz+qOgTOLIVcJzKUeBBf8rerzSKSDZwc15a0uhziyNOeDfX2WYtjwcpivE5aZOHUQxhQPhpnSZWNWujZeOfjhs7+pOEX8ufnsmoDxfaN1r5brPxZZvlPod5XDat0Oof2PecNS0wlz5st6lBsPESMuic/q586nM4683r4Yp25URDv4Ptw9fRznmMIWmYL/wTuMqdGf0Ji7Vsq/5rkcVZqMrp9PG5fXGQw4t8sdWfR4gHU+eShsOAW3+4z0Bkcqj9k7cJVkwFokoL1/UqOTFkwX76ANdEkUdZmq06epb30bYxheG19OommaMEqP1/hadaXEqW+jazXHDNMWp5zln2t/w98g/PArglPK225JXrVyIw44USCl7bFy/BomDNupcTdtVIdzPjvWK8aPlB/puU8M2i4vM3V64uiIoNdQrySWn3j6JpIPo/QsRpfEqQd1+at1Jnec0nVyEUWr7+bzwAMzpVHDOLZt308iDSJm+10osa5HkdCOfultXzxdzknUZ81jqCOiqO9v9DlOOY8EY+XVQH+o28j85yTkSCXD6ntIdZPQfuDlqEJTvurnRj4v1IZi+xXjPGnrX33NonR0PD2prhvNcYZ4H+2V2cwZ8eLWF8dl2zJCvPLS2FNqFpIH9nlBXWVke0V0VuR56vIyttWMMC579WfuWCXQubo2pL4HT1NsOu4Z9kva30XKZjCurRHPrpNZrG0dR6YkZHsnXVOvi7U3jdpJos8KieSQXcWDWmu4gyr5PWmS4z7tc+CoFBSJ337ifX5lfbWm/SVXtnKdrmf1NKDKp5FfYdQHGJFEvSxkk2nyoatPozZu2C/qfhetE95ujGT69zjpfj14ZYEFvmjj3s0VOCYIEIQmHC7NhXljq/L2N0IcQYkr3At3BtrGxo/noNphh00xKtUYNUxZ+WWqonj8HtlSBNL4egOB4xHgAR4JU0XO+HHLe2gfiRgGj6WGJxuRapIoE03e5xDq/x7N9XacP380+hYgroLhTF2DsCEbhVs/kN40zaOUlyoqnGx5GH9WlFH0zYisVC0V7RhZ8Leqz0O8Ectv3LXloy6HOOXGPve2bMOGmn8icPccyt85j4CcEYacntg6YMxch7AuHUWOXmWoWbxyuI/p3s+wacMhtAd64SqvhjvwxPi+hnLNHcbFla+0mFrb+9iUloES1x2m4jnxy3pcbcQl8VnzrGg648ir9DujulHD08Ycgy27UXngHxhmzVAMsXw3N+L4+a/QpLwF0TyXo0qT0fXzaeNyLedXijhHj/exstwoO386+AJnbXVvI81aDlf//CStBdPF02SgS6Koy9Swvnma9G2MYXit/nesE+Zv2KTLlTTxthGTHi5DBteqKyVOfRtdqzkWN23G5Syh/o04if72T1F//Cucb4q80dDJDU+b9BZtUjneg6F47dQAQ1mZvomWTW+hpt2Pu659eIcZx89Y6cS2XT60XJ2eeDrCWK8klp8E+kbCKD2L0SVx6kF9rVpnRss5qhBVxJMhOQ8Rwzi2bfPfLZQGDuu3/G2o27QW1hIX+mcD8+ky0iNx+6x5DOs9iirfhp9lQzimnA3llT9Lqz+M21AycjSPpp1JjnIi+yFSjgqa8lU/N/I5iTak61f+bZgnbf2r07woHR1PT6rrJkZmRDngn7kZW2u/xYha5cUrr/HnCSwspKuMbK9EgQVjW80IY3lSf+bpTKBzdW1IfQ9jm3GA3S+2X9L+LlI2SmBhEXadzGJt6zgyJaGSJeVIIhvIOF1G7STR5wgJ5JDBfZ/KtBp880Mz9pRux9o8Ac6WL9AzJWlSlTxoyyjeZ/aDV9NXa9pfcmUr1ymXo/uqfEaO6WweLoeaPkBXkBJJ1Asvn0Q2mSYfuvo0auP8fjH9ou530TrhgQUjmf4jTrpfD15RYIEPfalFvWr1TWmIiOVNVrkTyhFOHEGRBCEbDT3sWmkIT0QoIootfmegaVh8KBKf1xTsYU5iFio9AfYcPn/mEr78PqAZHjUPT3u5MoyOfeX3eHM/2kd1DlcUdR5EzDCFk1PTgfFnfGjeBmWUBp8W0ow3K7/TvLnhw8zWpe+DZ5gbs3y+2Nf4fmg8QZkYlIM0TPQt+XgcBaRtWBx5bnJKmUenFOUFfyxFn+NWaA7hkcs48UUP7iRRHvE+S0OjlOFuvKx89mJUtivDLNc0omfmmfQmJzXRfZ7ehiNvPYpdd1mZyMPt1zT0sFyoZSCOLPFy7XHD6XLD82OfbuEhXl9GdfAQvZ8dgeOHUyhL34qWXl5uCeQieBXnnC64PT+hT1mUx/C+Az/P10O0Th4llreYMhpCm+BBUJzDROdBrFFFm+OVNR+qHTXikvisroP5dMaR10FfnLrRMdmJ6pS1KPPc46UjyZ805Dte2+VE0/TE8Hp12uT87kRN54hqvYJhJgd5SK/0YFhZI6Pzyw4MDV+EwN96iqPorMlTRbeTSJcUcIrVJVGpUpepOn3RzyMGbexXTMVcq9d/Y/A7CmEpPietDyANg+SyMckcrpj0/I7fja6dUcl+nPqOqQNGPHmY/xynnCOPU/1GGkYanZIQMTy0OpTryre4Do0OY76OJywNse10xmAqhHEdSjvlnDsDl/sifuwbVa7n5aVvuxxteox1xKSBXllIfuLdi7dXjlF64vzGUJfEqQfV9DtN3yEtarUWRa0+hMQZjFz+Al9E++dpY3mb0RrGEpq2zYNBC6WB3aOtVZI9caITNWv4m2Xu2Cjp0pR/BF2fFWbt4eaQasRWnHqPoj5m9DlgWM7/7PgsVl6DsfpDquMYXZ2MHEXqnlejqp1JQ/sT2w8aNMfVz12oDUWeH9uvSG8iY/Kkrf/5NMdrd9p0RXV0PD0Z97cKkr2zQZE1FfHKy3AqBGcheRhaWFcZ2l4JAgvxbLWxP3BzKKTJj1oWjD/HaWfRm2jzrZEtw3RMGvZLk+rfqcomoV1nkG6ZxdrWidu0tOCjZQdab02wPj+IyyfO4Pqd84uwN43aSaLPKuLJocQYOqszYNpwHN6JX2HPSlGtMRev/cT/rJGVeNcoaI6p20/0c/J9teYzI5k6l+yjnFp0jjP9Es2nkV9xFXdj+oApjPlvY0gzeiGJelnIJtPkQ1ufhm3csF+8p/ndfJ3wqRBGMq3WA3FkaAXzCgILPBrahurMAjR8P6B0/OxY4FvUbrAw4foE10Z5x8YENXQbp0vXIaX0LP4I3op+vhuagN9ZivSMfNjeteEtSwYqzv23NEexITcNuY2XMDx8CY38c0M77vZ3Kp+/x2DveezOKkTtKReczc0418uUjtSJHsOmNDNMpnRsauxA8N8PcKN1J1JYQ2np/pfKQGHwxta4C6WCE1/ZG3C880/MhgK4xhd9ibleFhpzTiU+/eoLCPub0Rnk+eMC3oHGrbshfHUa9roT8nFpPlA20nJ3o/nyNXQ0bkGayQRT2hY0Ss+JVyZB5XfvoP7U3/CBkt9A6AG6GzfBmrMdlcdq8U76WuTXfIFvT/K0lsFxawhBaQG4dSh13FAMYD4sswl7DJSitHhY8XpWTinILDuNXt7IDcojHFlcMKUcp/+4hz+Uz/9x47905cTLvglbSwV89VUz6o53IsjTMN0HZ/EGZOTb8K4tD5bUSpwbnL+P+p6n7z7C//R+ieL0TOTvKEZhTiY21Z3H9VvfyvLAy+H+fFlpym3yNusA0pCWswU2mw3FFU1w93GZUJDypqqDjl787nkfm/dzx2YUXXW5MOcfQfvApLSIZUw5KJ2fmSmuQnZ/W3ElGt29CM3q7tv5LzySFuVj9XD6FoJ/RD7fVq7V35dhWEa9OF/8NqpPnsNZoQ4f8oX/pIxwDMp69n8Q+L4RudIiQoMYloY+5rO2eRv9l5XP7T70tsvXSG2Wz8VNT0duWQu6R+4llFd5ASt93bTBr+kcOHxqVIPytnEOj7qbkGvNxY7KD1D3zgakvbEdByuKVHITiqa74fs7COivz29E5707uLD7TRTUOnDeeQLHz92S5G1MJe/BAGuDm9KZPJuRtumY3AZZp1CcW42T550Qaj5VLViXTLoMdInUphi6NN+P1rGuvv9H38ZYh6ppoyED/edXfsflYTt2FeYgfdP7aPOPYNggPXI71l+ryL06ne03cOM/jNrc/BQRae65JA/N+O7KlwZ5+m88GjYo5whqnffNWRzOXY+cHXtxrK4E6WmbUd/Zj39deA9ZBbU4df4Umo+fl/ROeOwGHFI7PoVbwbu69jTf9rSLNxrUIZOVHy5/js1mK3IKdzA9sBMVjW3oY+Ue23Zv4dHINbQWr0VKcSu6A8zg1+sI/mzJsNTrlYcYN3h2x61/zren4QGDfER4Xl0Spx6i5a/vO57KbdfKZMecjTLnTVW/xsUkVob+7/Vf0d7AF2xsxPe8bKQr1W1b6fc0aZjE6I3PUZyyFsUtV9hzudH5LnKrP8P5s8dR82EXApG+XUnXeOchJht5KGv+WVmIS91nKU66KRvzW4cZ13tnkI+k4vbHwrZDgC86qaub2UdX2HmdvDqOwqbXHzG6ugL/cfU/0bKQHMmJZ2Wtty+M7Ad9Oap0rLqNfdcBp1FfaJC/edljjpu+XzHqf/7Lp61/lW745vwHSepotZ78bwz8V0T3XcfA3fZ5OfifiM3D8tT9QJG1cXTV1xuMWjEqrymEhnpkHRKpA+naJOUhwIffq+u+ADX/8X9xMqKTRu/pbK//D974/1ahwqaq89AAvo+W10CsrVZ/Du5jG2FK125dFy3X0v8HnzUUG+rn2Zh2FtG5LH/qfN/9Q2frjhmk4z8RnIy1/TS6/5pH1UanDGwNY5tQ3ZcszrZO1KYZ4gR6He/AyuTZnLkHzqitn6S9+V9X8R9G7SSqfzbif1fUYZ+6PuUnM+LJIecZJpmueleassV1+j409jyWzmjlQdV+4tUz+/xq+uodqDhaDb7o4byuMS5bKbBg3oiKT0/jlFCv1KkunzF+hb4P6MYjKUBgQbpmi8q5aF9QWv8xGt4xqi8ui/FtsqFrDpZ3no8u1r57tH27kX7vDGAypl+cT0dZ87e49l1EfzF72cB+j+/H6e3ilckrXGNhtbCSolFMCYz1485oAJ3v21fBHqzc2PgBH9RfwD3W6Q3d9MJ7/R84KlxSKa7nhHWUHR8cQ9s9bsTcgtfbA89RZtS89mVLECsFZsR0fIj6tkHW4Q/hpteL6x47hO9+XWLbfUl6hXSJAfH6LGboff8ZvuSLDRKrgBmM3enH6EgH3j/S9eL67+WAeB/ff+iC33AoOLG8eI3lcIloRiw8Jzww8uGXfcr0XmK5QoGFlwo3eiJv1Ry4phvOtvyYhM++GWbzW6jrUL+teF0RMTvwD1TlrFfeVL6DigYnetQR2udl9i4uVOXBKr25LMY7FY34omd4FZQtQawUpjBwYT9yrMpIgHeq0PBFN4JTS227L0mvkC4xIE6fNfU7frxyj/TsamHqV9g3psKcexgdL7L/fuWImOq7giuvVZ5eY15bOVwiYWX7Tj46+lpANdpkKUyi78df5kcbEMsWCiwQBEEQBEEQBEEQBLFkKLBAEARBEARBEARBEMSSocACQRAEQRAEQRAEQRBLhgILBEEQBEEQBEEQBEEsmZccWJBXx36/8ii+FEqQpdkm5NUhjt7Cr38ucWEVcQQ3fr2HZ+IE/B4BxbkfJrfaaeR3ylcZvtWMDWsXUy6Lfe4iWLhcwhi9cRN/PptDyH8RQvG26B62rwt8i1OPsBO5fBcPacu4PNWWZXoi5aF8fZXo0hqpS01+pDMKC+Zt+fBC2qvyddnxUtK3BL3yMkmgsxaU0wV5UX2Mqi2/1LaRKL1KGuZeno5fMuFh9DgPw5YwTfNlKO3/vVa7Rd5SWKp8xOqMv7jPemEylGwfEz9/SdXFIvXQ87fbl8iy0Pl8pxkBlY0tEGybdXIwg1Ffm66u+AKyTahtPoOvhH2o0mztKl9/vO4YTrp/xgDfajfYgSPl9Th5/m+oLT+u3RJQA98mNA+2Q0ch1NqQYUpDfkUDhIY9yF+/Fyf/71L0zBL7l6R0SCwvSpcYk0AfR+ToJdrcL4xFyTzPcw+ch4sX1W4X39Yjuus5dO+ytFNZ2+45g8O2F9OXLEsd+py85MDCFPpaCpbXVovhAbjLCpcoENMIuCuQqeQn+W1UtL9TI4aGMTi2OKfpRW7fEmXBcmHKKOBGWaby3LAXQlrOC2lYywv19qBMIf4ZwJjhKrS68njlqNKqqct4250mytsy4gW21+XHy0vfUvTKy8RQZyUlpwvxIvoYfVt+mW0jXnq1aXgpOv65EDHT04g1cdOkK0MxhD8HHz6fXC9VPuLpjL+0z3oRMrTIPiZe/hasi0XqoRfSbl8Wy0Tnz91CS/ZGY1mbvoe+2//E4TXzdSUGXNj6Rgv6+Db2z27DkfcWGnrG2RceoGhAQcmX6I3scc+cyM6afJR57nEJwYinEutqOjBuKGpDaGtuxwg/F3TDZoo8MwRfixM9PUvTM0vrXxbSIXF4EbokLvH0sVaOlp8+VrMUmZ9AT0P2ItvtYtq6TnctWfcuUzt1pgcNqvb7fCw3Hfr8vKDAAhOi4H/iSM2H+Mr1MSqKG+Dpf4wx3zcQbGuRYhPw9299GFPJhhi6CWfNITSfaoDt7RPwTipKM0L4T3Qeb4D9q89Qnb8NQs8YRL6P95FaCF+dgb2iDHWeP/Dv0G2cLl3HKuU6ppWtHeXPt+ARSmHb8x7KinKRWXwa/ukwHt86CVvKWtiEc+jovgx34zuwvbMdmZbtOPmzB3Ub0pAv/MwEeQYPPAewpdkbjRyLj2+gVcnP+a4BhLiySSlA6d6dKMzJQbGzjzVx1hB6z6Cmxo5TDTvxdvM1TD7S/m5avh3L4wN4Tx9AfmkbgkykRi/bUSN8iZOHtqOsbUi5SEEqj3oIp07h49JsmCUlx8v9RzQLJ3GmYQfy6i9hVDS6D+ucOv8GweFAQ1Eh6i/fx9PR31R5Z0bC13alXL5G18AT+ZlqxHHcai1BSooNwvkrGAhdY4oiHVtK30VxYaR8WUHxdDZ/DMeZBhTlNeLyqLqpGJTNbBBdjfkw5X4EL4/EP7iIqi2fwBeaWiDNO3Dqt19iZUicQK/jIGo+/QR19d8gEFFICdPFy7ETx/cLOHX2Q5RmpsgyNHodp2u2otTNy1BXrv/3hrY8/v0Ivc561DR/hgbbTjR7xzFrKINKGell26CO5psL6zhcpbCkvgfPnwPorNvIyusYukanmAx+hLeYUfFwJJLWAZWM87q8LymtlC2l2Fu8BTmZZXD6Qwir8xYeRa9HwHbbHhwoK4peo+Z1aK/TBumJtkUOlx1dHc7XAYfX0QnU2U/BUb0Fm4RuPBK54fcRkwsnXPa9KK7zoJ/VsVF5iaOXcJTpyLMnD2JTGWvz+vQpT5HlUa9PeZ1p5d/Zrxh24RH43EdY/e3Czsx0bD55Cb9E9Eo4Tvt69C9de3iCUZ8bjdtteGdnNiybneg3eP1hlK9wQjnX6yzlRkwXJCOnRrprvk7Cuj7mKnpvaPNwd8JAbvWy2v07/kvVlv2Ba6p2b1S/0xjr9UDYXow9B8pRFNX9ahbZJ+r0q//yMYO+xUjfK7/nRNtxGfba8pFheUspr0R5sLFnFCM/w4pcfr9nk7h7uhwpvK6mla3ClHqbN64N9Lgm/f+J7sv/gZr8KriDvMKXUoZJykdMmx3FI83vVH2ZZNzG9lmGuk28j8tHma44+xkObdqLNikfMmJoEFdO1WBHSRUqd21C5sZanGV1LH9uQvejsFa/Sv3eKVTVNOPzumNoC/BcsrpcsD9j5a/uY1iZxbYF3j6asV/4Amc/LkOmWW/szmDUy+pKqosnhmX+70XpyRfRbjn8fBK69N8TxvLIPhv1WRqdf+kafonqRSs2/j8CDiboLzhJ6W3lWhmDNE8xfXxBUMroLL71jeryztA4WnMYb38PFptbubfs9KXWdiE08h2q1m6Hwz8lnZEYb0cF6/+cA7L+FwecKLC8h/ZxXZ+sRxNYkJHadIye4ScS2Esau3UmTjtWbKq6ZnzlqEH+po/Q84j1FVEdEk/P6O3XOyr5Xaou4Rj97mlcfZycrW+kj5OzvzXyoO+PIvZgkvnUtN+On9GttgWcfszE9ENcJ6qd2QV8jyjybyw5hdi1g/Uv6TvQfP2hcVr//TA5fyFCnL7rqUaPGpW3kZyxHMVcx3Rw4ApOVRajpLoSu/KzsbH2S5beffLnxitM/xjd31gnaNvvPLHXTiVuHxqfgwILWsQA3CUlTPnxpswjqBVI2epCQIwXiVEr0iG4bTmo7hxTznFmMeyuwnbXgCS0AfZ5i+Mmhtx78JbjtjTcRxzxoCxlJ1yBcdUzIs+7iseD51GRmomaTqbUpejSm7D7WIOKCsRDjN0+q1xzH6H7I6yD4UO8diClxI1hcRp+x1F2/6dykiS0+ZGUZOpBdE6E8aSrHqnr7fDNPUB7RTZsvCFwRZ5yAJ2Tk4blwA2UH4W3YZLKYRie0l1yBzJ5Fa4f1N0Xa/jt+/Gm/Tc85SUSjfryyHWl3MGMelBqLmS/vxtzH3G8AzXbT2OANa5Rzx6Y85rR1avLexIRRU3UVrpeKd8nXahN5eU7ifHO97Hd+QdLJc/PeuQp9SVjVDbP5Gh9SgU8I2HWSbpwkNX7swXTHETgu1gZku5llTuooLuMKcEyHO8cwFiidIlBtDOFY/fxvEciuUyGhjoh5K+T02tQP5rykDr7Utb5MePPXYqU6h9wz1AGHxvI9m3MxeTXAb/KsZPkPVU2Hp76mpGVzd9usDt4W/FB5wOE1GnV1KUss6k1nZhgxn9XbRbW272YUF0vhvw4V5GhXMPz/wa7xsdaaYTXob3yNBqlR3V9TB12aoZ6isNu7NzO9RrwLHAeu7Y4cHvIjZK3lLoS78FTloWtrjt4GFNe91m97kUBe34YD3HF9RNGdfokiqE+/T+43qfPv3Qxc+xv4nSk/kIjuH//rkqv8Nvp2xdLn749fPoTek9XIFXSZxPsHsxI57fXYCQHTPYM624ijs5SbsVZUE59CC/QLjR1HB7DbU0eJgzSyzStQfubibZlZoCo2war85j6/cqHgXOVynPm5LxJul9OkcSi+0ReHPP6xLBvMdT38+ZztB0zh+SJJO/lsDA9cctIRqN5qEfXE+Zc83xaCtDSNxWbjpjPxnp8/nwIQz8KyDfxtsRkbqllmIR8zBm1Wc3vVEjHFTlV91lGuo2XbwFLM5OHyStt+GE0kjDe3n5m8rEWRa5BiOIgXEVrUcDbkvT5DblNqPWxOADX1nwpPSIrr2JLLkqPX4Q3if5MXf6x/ThL3/3vUPlmM3xPWWs1eovG5G2+Lh5h0LDM1TKZhJ58Ae02eV06gFkDGbz+OF6fNZ+Xab1eDE0t0F8Y6TcjvT2PoWxz3RJPBiNozivPjZaRnAeT7StcYzaM2VqI6qajOGTLxca6HzB0/WOsV9qWhBQwUPrLREjXadMkladez8yx9CSwlzR2K5MvQ5kKM/23c49cvs8G4d5VKukqtTwbf9bZWd///kJ0Sdx6Uvcf8qUK2uOG5bRk+ztSzxwj24nJ2qLyGUkrf0Gjk/n/GUiiH4q1bY2RfyP3L8rLLpaX35Not6wADXTvvLwa913N6BlU61GD8v7991g5+33QoF4mMNbzEZOjEriGZiEOuVBkkoNz0mfeh00EDX73xLh/MGzfBvqjYyBO+zDyOWJtgpXKiwksRDt3uVikijLvgWd0Ik6jZdewBvCDdxhToz+hMXetLDhRuOHytq7SVMYMR+rEM1Hq+V31DFVjUVd8Mp8VRJaXytRtcNz24otD/2AdkHJCwkDZxChG/hatC97gJEa7jiFXUoiP45QDd35LFQeAdXquclit2yG0/6E4DRG0eY8+a4I10LQiNJw9D/ffeYQ8nZXHHd19REkRphU24Kzbjb8LNqTwugle1ebdsKFoUedXc330c4AZFnkobDgFt/uMFAk2l3pUHbFR2bCbRZVRH+6ePo5zgZmk0mwkQ3M+O9YrxrKUXqlsucGTIF0a+VXXMVcOGUq5x9aPpjzEUfh++BXBKeUNMX+uYRn9YSDbceooaswypDLKY0ptFLN9rdho4kpvDL4TJySjXppLGUmrpi7V+YmXt3jXzLPy22u89AzL3zlGdaicinQa6ZpyUXcknFkMuUok2RqJKa8AZvtdKLGuR5HQjn5pWKtxWcfVp/o2G0V/H7VeYcS0r/uG7SGoluc4GMqBYR1xOTfQWep7a+pTnYfI52uYWKhd6PKuf05seq8ayKr+d5G2MRi3ftVlZZi3JfSJ6vsYfjbU9yr51ZWFbDyV4ETLuwvmQW4P66X6WjAdhn1cWHOt7MTw4zNLL8MF5YN9XkjvqjGUU+bsG8n07B24SjJgLRLQ3j/JNLQate5M4vOcD/b1WajtGleeq8iFLp1G6ZgvF6M+ohSfn9wzX7bx8h2tC20dzX9Wl20SelLzHKN6WajdLk6XGstJHHmI+1kmcX9hVAdGejtC/DSPxquLCPq6D15AeXoBGi/zZ19Da/FarGloxxWW/jUNPczlZEx1o2FtLuqE6hcbWIgp2wXsJVaa6v7F8B4jTP+ly8fUGD9P/dnADn5uXZKgngxkREZ73DCtz2N/R9uCke30otoCI6l+KJHvoUZ7b2mkTLL9i1reDduG7t5S38XTPTCvR3lAQl/en38SK2dG13H9pdKDhp/vdBr+bkGbR0ViHa6qM94+DH2O14MXGFjgjo7y9oRXlDQ0K0FgIcQMz+ZGHD//FZoiEakovFNWIkNReAPMVEVO+TXZqGANYf4ZqgqKJ8gLCjhPcz4yC4tRK81hU6MVAEOB4W+8+r9Hc70d588fVSKtyQQWGOIE/G3vY1NaBkpcd1gTjcDzyjo2/oaEfYs+a4iVs+JEa9Dcpw8B9gw5EqhCn3fDstCizq/m+ujnPpbOjbIBZYhR2fCbcUW6D6lbdqPyAO/s5XJZKM2GMjR9Ey2b3kJNux93XfvwDnMOnknllyBdkmKRI5naOlYZhxxd/fxbXR7iJPrbP0X98a9wvkmJ5sctI71sx8mvBrmM0qq/xg/Hq1C66w3knfgCLSeusm6Bo0qrppzU+YmXt3jXzLPy22u89DyQvkkY1aFyKlJH2lEMigEQfQMhX2OpaMe4YXkx+fe3oW7TWlhLXOifNS7ruPp0ZImBhZj2xfMe2x407TsOhnJgWEdczg10lvremvpU5yHyuQdDC7YLbd71z4lN72UDWdX/LtI2lMCCQf2OqK43zNsS+kT1fQw/x9P3UXRywJ+Z8v/D2TMVC+ZB3YYXTIdhH7dAYGEpZbigfLDPC+ldNYZyGoqr2/iiWm11byPNWg5Xv6xlZSLywa9L5nMIvS3bsKHmnwjcPYfyd84jwMtCl06jdMyXi1xu2ragtPMiF4bkRmac72hdJBtYWEBPap5jVC8LtVs53cnqUmM5iSMPcT9HSNRfxNFvMXo78qsE+j9eXUSIOc+f8QMc9o/Qcuo4StPfYPr5HvyOQlX6FZk6YWe65QVOhYgpW/6cRHacIndK/2J4D66rlJFMaoyfp/0cYwc/ty5JUE+GMsLRHjdM6/PY31F4Wev7oxfVFhjJ9kNxfQ81unvzeyXbv6jl3bBtGN2by8/gvB7lx/TlHb1OJWdG13H48YjPYfT5usvwdwvaPCoS63BVnXHZMfQ5Xg9e4FSIDdLwF5H940O136z8DqNxh33KQ5it0nF1BxxBXlDFUvQ5bvF5wSOXceKLHtxxlyvDeNglT3+D/c39aB+VnXY5qssjrRvk58UT5AUFHPIqtMrQYS3a/BgKzFO+iulb8j2jwptMYGEIbYIHQXEOE50HsUYTIebD9gphKT4nrRkgDadZ04ieyWsQ1mWh0hNgd+Jzdi7hy+9/gltznwsYYmlbl74PnmHeEfH5Xl/j+4GftXnXlMUMxvy3MaSJzmvzq7k++nmY5TMP6ZUeDPO1DfjcsS87ZKOHI63wqi8bpUQmO1GdslZZkIjlccE0x5EhvvLwuTNwuS/ix75Rpbz5VJRE6eKLJa1Hsesuu14eliTLk1o2Y+tHHZGVZCY6BUOpU8MyGjGQbS8eGeV3SD1MkyGVkQUbhF8wwadDMCOhoeexclJdBuq6VMus+rM6b/GuifA6tFd5eF1seuZ/Y1iHyjnOXF8Lsi070HprAmI4iMsnzuD6nfMoiQ4VDsFnL0Zl+wDuxpTXAIJtrZK8ixOdqFmT4O11PH0aJ/96vcQKKjb9mvZl3B4GrqnaN7sqPPYHbg6pp0TEa3NGdTRmrLNm5u+mrU8jGbyOJwu2C23eNTrKML23DWT1V0xqfjefN2kIbUz9aqdBaZ+psOg+UXsfw89Prhvo+4CqftT35jr0OHL4+iv3Fs4DZti9c2rx/2/vfX+iuvb2Yf4A3vCSFyRNCAl5YmKM4YWNaeCFpIYEjYabWM3cg9FnMKcGtCloI+C3HWx0fDzC7RE9la9CEcaWOZzj1CNYRgV7g1VqscChKD86KCCDKHSkIOBcz1pr75lZe8/azIBotV3XyTlnOey9fnx+XJ/PWnvvtVxkcsJ+Z7p6wZ62xen7IYxxRgsL6uvLS5FhWPtow/NwvMtDaKePxTY9/A1sDiJbnweu/FTdmyHcdRGVid22OlBpd8B5tSu4EZmmn2LfCspFHBP/ffl/kBpjIbIlEwf2KcRaEhMmWPUBGOgoWOZlG54ntf0W6SW830bOpaqdhNij2B6My0EYxwuRDkS8HcyLjGx7cZ9C8KBPj/+Cd1VuZp89bqhEP9WF7z7xu+2o7O1HfW4aaYd+q08noXlYybiFcIKnAzfujmrGGwCzA22bYnsIky+R2vn4Iqxjhm5euQKZp9pBP1kcbTqLs20TodeG6FWQBy8DlxjqycBGtHZtMEYhH0eYfwd8QZQ7/YBnixrnAjYfURwSyHyOxO+7g7q3F/h7lLrY/l6RxBfe3oW2r63bH7vG2UMQlUdpjNLL+1Id/qa3s+8axHGSsyOeEwPlgRbBfT2CXI70RTiGcBzO6ey50Zzjj4Fl3LyxEcVb/gJbTRVKCk/ANTwF72Ar25wt1nwKLW4+QZ3Hk5YjSElIwbbcT1G4k0wu0orJPcF1MroJRrl5FaKiYpGUXaXsiEsJrngHLLZK1JRYcdT1gBnAdE8VzIlJSDNZYFofj7gcO366UwlL7EpYqjowfL9aLXcT46Pf6axFfMo25HyeB7rBjrnsBty0fj+eNaNot1PZSVcDQuKuA0iMT0V2qRM3KkkAjt2FqvsPcZ9uRMPKvWgpTkdC8lbkHi7AzsQVSCu6jI5G/33fsQ1BKHzefjRY6cZqxWhwd6LWvAl5Zy6g2laIY2wDlyAUeazB6rSt2JGRjMT0g6jrGcWQ6zDS46OJnBKRXtxIkipCJPp6mNw2Iz4qClHxm1Hs6sXQnS9g5scekMtfUHq1DodXxiBRd5wQ+1YsMREp2aW4fOOcQL7/wZMhYgfpiaQ/0YhPP6w9Bsk3JpDNt4TQ6B+fovWQNRjsw/b5udCGrjR9gY3RCUjO2AaTaTtyiuvQRQmb2qdRv4hevZ3nFBvaZkZGchLSC2txu+MSrCnxSLE2kPYGQuUakEcZWrqvojhlFZK37cXhwiyi6/XYdawQO0U2+KuRbfPjpbatx2M0FxXAMUT8hC7SmGxonaLCI/13N3B9pd+Kqbq83IhK6oOWatwf7mAbJ8ZaKnGnkxvbiP93/ppq9AZ84o/gr8Tvnov6E4TvyQ2dDjeiyMU91fJNoLN8JxKIjqKTdqOykyTFLFE4gi0WG2pqSlF41EV8cE4gr0KUH8lCSt5p1FYfRf4xulmZrn/BhgR8OgmP3mf916qvzgZ4VsMrft7l/Uutn/cHtxt3Tm0nHLYdZS2/ELnT5HIdohL5Y75EdnAQ/7h2Vqw7Zud6zqIyU8FzjtBOiQ3STSYN/YLYPR9jeu/rxiC228ae2yG2quG2W07Ol+gmsjr9zqobj+m5v5d/ZX6xMZHc4e+D5f/gtNUsqN8j4Hu+BiU5i07Oxcmas7DtK1V5TmSjpH80yYleh5yTVaiwFQX9YboLlUxvJnxoSkVMXC4u3OvFT3SDtdhslP/UiSYRjz/29/8zVJQXICUqDdaGfpbwLUmGEdjHfTd95VTns41taPTf1/JIrY/YSq/fNnk77YBbxG23q2FOycOZ2krY8k+yjecUcFxbfA1DQ9dI+9RW6tHb51LLl9DJ8+tkN5kQxiM+eTOJSSaYc47A0TWK0QjiWWPHv4MxZqg/1BdUTkpcnYZtOzKQnLgJhXWUN9XukgmEu6FY0UX9Hdz5UiTzJ4viyZf3W4KIuZQMRGSPP93El6J2iRz9Y/l/D30BG8+LatMLxYvIeFu9nEFk279i8FY5sS9BrKKgOmk5RXRP7K+8VXmAQ34bbKtHReFu5JZ9F/RrKqfK/dhVXI6a0gPIZ8dQKtwSetykMrERbbzr87rRZs9HUlQCMkuuoIee6EA43sgHZxfIl7TxhbtPZ1O/0pwqgXBV9Fpk037Pqps0Ug7p8GBOpNcBXR7c0h+035fhEqFt6eKHbo4SNtcX8nEk+bfWF4S5k9CujMbp9991+O+cQnxMN3IMjEcUh6Yxp26cHWupQMfYPV2fPcoiZtRa6I9JdV/8CO9uKEBFbQVKj9Ya95XYb/j5AsdTwthF4qUmr6WxWC9vokO9nTEb0V1HfNJvR8XNAxhin/BRm+pGX5O/3IWeRn39olxuP768dFoZQ/kd7sQKAX+s341jhf8t1BnzD82cow49eq54S7FMCwt/EMyN4d69EYy6juHQG3Vu6usHJYVj57oMXol6U0EIpfEYiuoGMOsdxN22Ntx2lsCme+1Z4g8C6a+vFr4RNByzoyfw6q/Emw3+qU94zPFPlCReEWhifwWfFl3EQzIxGLzbhrbb/8TntmuaRXuJ14A3OV7QJ8Q/3OMmKRISvyfmMd5wGufoho6vBYuLXRJvNuTCQgA+TLWXYl10HFIKr7AVtz8vJtF19fu3UAZT6L+4D8kJycgwmWDauQfWsy1/cl3+USH99dWCyLfrBm5o3uyReHNBJrCBp1DluKX5hEWAOe7p4S039+RIYnnhw2z/P7EneZX6Ft1O5Fgr0Sr96jVDxgsJiYgx9TOu3nj4mib5i4xdEm885MKChISEhISEhISEhISEhITEkiEXFiQkJCQkJCQkJCQkJCQkJJYMubAgISEhISEhIfHGYwaPBj1v2d5HEhISEhJ/FizTwsILTDYfxArBbrRB0B1rTVihO2kgBL5R3PnhIV7QYwMrP4Mp5diSN5ei51A7bduR8rtuCDIHz527eGAoFz9mMNx6Hp+ZPggcYeLzdOCHB0v7FlO5dwrenm9gMwfrfF1gRzqtCO4oHxgLO3IyVbfb7HIivLyDcg1vky/Vb40Nh+p3sdDLNHLox0l30q1BYeFJ1FR8hi2ph9Gy6DrFiNxmlU3NDuZ+jnO2LLxbUI/uRhtyi8tgM21chJwj9S8Vcw/QeDAPxecOw/Tup6os6U7v11CSmYCopHxc6PRgju7c3VSKzJgYJOV9hY7+n9FUshUxUcnIu3DXeJMt3wjq9+xRj14KxaI5yc+H6j/fdBj6/UuB2korKj8zB+Tmr/dVcnwkdS/eJ1V7nZ9Aj9MG86Lj2yuIteo/FwfO75aJ0xfDyy8HusnvUrjGj1B7fKWgnJJLd42PIv9djV3OwQW/QVbk+PvF/hD5NvWis6oYhWVVqLCakGr9zlCvS49x4jgQcX3LkG8uJwy555XnT0uAvk+L4JVXnqP7IufZtyG/Wjz0eZaeU7V56aJ4bZHxY1li9two2h06fdJTNw7tRcGZapwp2ItD/lM3AieqnMTJUno63BQ8beewZ9836kl0PJaR098wLvk9sHxvLMw9xsCDhTfd8HmHMECPuTHENNyOHCQxxfqUs89fatfq33unUWKsbgeykyIcAzuH2n/Waz8c2RlLSwr4e8Odp/yqQCZmDwYeK3LXjIWQ7gP3K9r9OAJ56+S6oE2+dL91NszrdyngZbpIaMbp64d9S9ry28SibFY5vzngm/P03Ot1i+zTIv2LgJ2fLuQUhSv8Z3MzMN+JV84tZv+0Id5/9rEB2HnZMbFYV9Zh8FRxMZzE8+FbAkO/f1koZz0zuWnqfZUcH0Hdi/JJrb0u+VSGZY+1i4Xe75aB0xfDyy+LJXGNHpw9qr+8Gvgw21OB3eXdkSXwvBx/r9ivk6/PbceWhAjtfEkxboE4EHF9y5FvLieMuOdV5k9LBd+nxfLKq+RvBRHz7JueXy0JujxLBE1eGimvLVLPyxKzfZge7EF3/UHOT5VjQVdmK8fH+kadyF55AK7xGeV38wW4iV3OtZ2AxVaOkuwclBuedLFcnP6mccnrx/IsLPgm0Vd/PLiKRJ8KHiqAreY8SnKyUei8j+m5R2ir2o80Sx1J3Gcw1umEbasZu/fvQmZyMsyVXfjt6R2comewmmyobe6HN0AIMxh1fYo1UZtga31EEstBOPdYUNr+lKhQD2JEwy4c3WdDRfUxWJJiVUOhq+h/Rb6tEvaSvTAXOtE3Te6mq12lx1F+3orM1GI0ecgAfCNo+pz0v/o0DqTvRR0/kZjzoNNpw1bTbuzPzkRyUjYqe6izCOr/7TE6TmUhNtYEW+0N9NP2VNCzayvzD6C0wgrTphNom5ynDKgmAhN42nGGndlvsn2F5v7HGHb9HbbyclgzM1DUNAIfW7k7RPqxA9uTErGx8p6aeMxr7+25SupMxGbLhzBnpCDJXIUeo3EH4NePCZa9ZqStTkBK0TV42PnJ3+JQ/jHU2I8jx2yFs+8ZuX4OnqYSMvZzOHNgK7Lr7sHTdh75aXvIJGyG688FNLa4UJW/BRaHm4iSnkEejxTb9+zs2UfO/dhc2oYnQ1dRajuD89ZtSGXtKr0Kha7df9zRyvu3J+isLEJ+6WlYTdtR2ubBE41sejmb1I+h36DfdJKp2lhhKWrK85GW/le0PnkObycZc34JKqzbsan0FiapmPmgFtDv4wjseSGZ0spIMO+swJ78UnxReBh17mnym9ivtL73DP3XjqvjOg/H1zZS9gcVYsOuEygsqUB53mak2+iZ3XTVl5fhOGbHOuC0WWDa/RGyM/02NaezWWoXftB6efsdhKf9X7Cpvv618zLq/0n7Qe+txqX2UTwf1tuAQObjHkP/EnHQ1Fg7LtpMyvVfN6B9jLd5Jdi93MICfXKxDwesf0HiikNonRIZrtJO7GYL9po3a/lD5+MvNHzYh1/1Mnmu54AezIj8c7gOuTl/Q62jAtaMBERvKEfHw0YUrolHmu07khAGfc/Lzpf/BPkn/4bCon+xoMwg4vTF+P3//sOgPaV6HkJuDCQjNzGmsbMRY3mG8PGEcg449cdp9VQE1TfZWeJ7DuDkF1YUEd9XNGykKz9mND45J/QLboC+cY299jQdRnzsBsKx25Hh91XGLQvw32JjrUG88uliLWUPBaL2F+bZHvetIDeS5Nx94xxyt+1EXm4W0pI2oKCa9I2VN6K4ZQy+EE5ZiJcJhGM0isM8BDYwRXzmIs81Ho5zRbxO9CqMeVxy7O1GlWUlKd/GtLq7ufL7AG5U5GNb1h7k7khH0roCVJM6lPIRtNC6w9kMSXbbbO+ztxWik3ahrGVItU0RIoz9Aq7hW2S5wVErSmpOIy/tAxKjiM5EshTlEaNDWvm2tOBqgHMd+JqWA/FQ385DjGpiXGg/n4vkZZhnaf1zwbyOIBirI/CBOmrrofWF3PfcyE5Fbaj2F5K7+kH+7rkdzJ/C2g7JEdw3UJFrRlZeLnakrcW6gnNEfx8r5eIbJL6TWkV8K4oDBj4X7NOghle+dtbhlMgv/H4mytEXssuIcxHaV2pbRbBVVOC4ZS2i/TYXwNuWX1FJiO5fSGZzGOPzrEvt8EwuNO+gfeR4bQF/CYkfIo5Wrw3hpYVi9kL6V6FdKHqE+pw12FB5X7nWdx+VG9Ygp/5H8nsKsp0Pye/P4f66Eq6Bb/H5sRaWl4vBjT2kL79gtPkwUqLSiZyekPmXP2d/gllBvI54MesPiuVZWJgbQ3dVDuKYIGcx5NiN9eoKO1tBit2Oms4eEmA2KYk7ST4GLuQiLu4TuCbmldWdVSVon+cVq1MOe90qCVmEUH0vulH+8ddwiwzEN4x6QqQl7dRQgytQs/RJ4vpy9LBOPYQz+11ssd/DY9dBbGVGOQSnZRVSab89TlhI8t0zN4/JG3W44qGJrQKftwcXclYjLt+FCUIKrdb3sKqkHXPC+vsxKzSweYzXf4QYNokZhMOUjDzXmNbBubJvvBH5W6vQ75uHx7kb0aln0Dl6F1X+fnhHMeIN9jG0niTku0gC9awZBXHvE9lMYlw0bvV2mhwq+ilC8zOf+hR2A8o6/gNHVhbKeyhlzGHUmYPYLXaiB1rHDmUlcPIm7A0/Y/CqDWn+SVigPxPwDrpgS1upTtaIw9u3I5atNs6gv8oGu3sQrvxcVPbPgAwWlugMtT0RdO1eGSZNcfIer0dODO3DNIYdFsTmuTDJy5UkfgGbFNRl2G+fG47tu5XX3V8MwLHDQu4bIES2Vvn7sIOQ6H64Jl9o+8PrJaw9h5EptyruI+2ZY1JgsdXiWrXAryb4cRLw/eDlQfS8fSvVJ+meuxY7NhN79uhleAUPB2qRE6faFFvtpjZFkm2+Xg6h9kv9ROvrmnt9o6E28PPPApmTqa3Qv8QcRO81JnylPy+1sPD8R5RazqPv2S3Y1iQht14UGJV2FP4YR3PBuwp/hJNRiEw242TzTS0H/Nov9s/JXnTRVxBpG4nrUNhMJ1N0EWQbYrMcGPJNo6f8cyaf4NPFOaLvbMQkZ+OoqxeDQnn2R+j31B7E7YXCgBv5oK+p10CeEfBx0BYUHkpgcia+bV6FZEsJXIOPhXUHmJbwZHC8M/Aa+QUH3v5YmfnqHJ41FyGO+uqcwPZ5/ltsrDWIVyRtDspTqVmByPfIRN+YZ0lNPDeSJLuVtp1px6BvFoP2LERtqCR2rZQZBy+Kl43G+JPBuIJgcUtgAz6N/XAQ8Xp3t0HM4+UnKtPJ1HdELiuQaR+AzzcAe+YKJQFm5feIXT+KyGbYhKbnBmqLP0BC9AeKHozAj42V9bH/mQEfq/czee/BVion0q6blDcT2Yv9ySB/0sk3xOYDtqtr58xNDHD8EdrPU2jrE8uLbyMAjX+SP1B7NsjrKIJ1ROYDIfWNDoXcd6atXWynQj/rE+auwSHxvjYQge0Qu2n9Kxl/FuyDs/AN2pEZtZW1ycosR5kV8q0oDtj+cRnVIWNpw4QmpwvnF6RskKOLYyAbiIIQ3jDKRSbgqd+H90t+JBmm0dPjty+/Et3/syeMzDQ6iGDewV+/oL/w9RrnWwEI2tDH1bD6V6Hx9fl2lKxKCORoYONKIPU14GrBFlhbR+G5fR7H6m6h5agVjiEvxrqu45uGu/D4H5oEwI1J1Jd5ahPvKosVvvuo+qQW7hfieC3koz8Rlu1TiKAg6QqSOsGiYEE0CRanmzmO3/l4wQfLvLHqDIg5xceII4be3VGFA3RCRn/WI0A+9CZ/fTcxGnAoCiXJibZ8gZqCVGRYK+BwnGcre9EWJzyz92DPWo2ETBvq+yZ17fB9DFe/E8MGBuYbu4srbUOYYk/tV6hJmYiQfmVJZ3yGFdUOdcU/ejecHvokIygrDYT18GU3cWbBuNXbKTSyZzpcBdOJEk625GcanFhffkWffRcSErbCVn9feQpJJ9fCCQZ1/NUB+wiQ0EAHqg4QR/WSBCg+E9bqWnW1N5HYzhC7NhRTIe1q++1B+5UfMDw1jObiNMX2NH2hQdNvk6F1Gfab2liiXqd0dbgZbcOT8LCVTWXsmv5o6gtnz4L+8DJlhPouCprH1XpD2wuW+XES8P0IlCdY0EnU21M4GRqVA/BFZr/8vTQJ1tvAF38TyJzexsk3ACMOGjK4nkLx5ajVJhTYbLDR/x7OQVpMpAsLPkyRJG430+MzdJVtQjSbgKh/DkDEH7cwEU5GIpk4f9bKUMN9pEcB/6QJgfLkM4ZN7NmfSeJej9w4MlHpbsPZA/9kv8+3l2CVuqDIxstsxkie9xbh9+L2RBByIy83Tb0ieUbGx0FbeIb2kvcRV9BMNEfrSAttU1PmYDReoS/wbRqUJ8LzX/C+SGKt0RgMxiO0s1A9833XcjrPNQblRfGy0Rh52xeNhU+iKYI24DHQjZDXDX2K5y8jufJyMShHYDMBsMnoe1hb1kFGZwCj+gJlo3zCXyOV9yZdH4xkaZA/6cYhtnlROwQBf5oV93P4pnB8WnvkwPvngnkd1zehDwq4Tl9fWI7mbMMoxoXkrvQ+Hou0HX78BmUR34rjQCR2bnQNV9b4lP93oxjIWXrEuch9DWeIbeNty69E9xvlVkYLAOSOcPMO/vqI50FGHM3FLaM2FqN/FRp9Gi4stGPO24cbzku42jWKqZ5KfFR+G932D7Eu/99wD1zE4fP+t7z9CNeXmcDC8kCvHQcu9Bv4/EJ55p8Dr2hhIYl7Ak4VvRY59Q81zid2UN7gdAZEMUWfBK5FxpZP4Rw10BgjB2WFNmgoaqIZWAFTiCAmpwQnTOsU8tCBbjBSV7gJ8Qm7YO/jnxCInMKo/nqMGhiYz0vIr7QYR2trcCTwtEdEKE9ZXUrSy0MrKw2E9fDlLqIT8bj90Mqe6jAZedXlJCBwT9CorGM+Qv04cX7fBHrqDiI9fjWy7Pcwy5M03wdNECJgG1O9h805e7CfTsrofWpAiwi6dn/j+81eGz6JoqM1qD2irihr+qILCPoxGPWb9lF9IyGIeXj7GlBaVILa2s8DK98aOWrqIwhnzwvJlOi/s+wDrKEk2XsBu3bS1VOt3oLlSAKfYmfs6SG9xo9wMjQqB6C0HdZ++XtFNiCUOb2Nt1M/jDjokcH1FEp/AjKiYH0Ks7Dg82JkxAsfteO925BzWF2U+MyC5OhNKOvSP30U8UcrBsPJSOgXOhmyhE3sn3PuCzDH6p920slRGpIyzChgrwwSTN9FWfp65Nf3oNf+MXY6BogMjeUZud9TCNoTQMiN/Fg19YrkGRkfB23Bh+nO00hfcwD17k7Yd+XB4Z4xqFuVtR9G4w0ZuwLe/oTlQZGetQjeF0msNRrDAuMRtb8Qz2o4necag/KieNlojPfDjEWdDAtsYNxAN+SCUI4x9KnfYWGBjSFbeeKt/hICo/oCZaN8wg/aN/9bQn4YydIgf9KNQ2zzonYIAv6kPMEN6afB+Pg2NOD9k8A4r+P6ZuSDeq6jP/H1NZ0Lw9FcWeRnrK/63JXex2ORtsOP36As5FthHIjEzo2u4crCcRrFQA4R5yI0v12lvClEbjO0jbcqvxLdr7S7oMw0OqD2GmbeEXJ9JPOgBfIDPwzbWIT+VWj1SdsWfQrBte0bhHP/cTR11yE7LkfJtZ99hxMV/v76Eb4vysORjcjJ/QyOIWK/1GYEXGFoc38SvIKFBfpazC71dUHyh+c/ouT9faj3KIHC73xiB9Uadahy1Ndp1Y066GuhPXcHldVGP+hr5amrYLb3kjqU16zesbZimr7KF3g9x4v2EjNy67tx25aKxFwnhtg3ZA/gOteIwaFvYKOTXJ8HrvxU7cqb0CnUTy1C6udfGVXuVqCMg716G0GgmCF1rEz8GM4hmuzSb3++QsPguEZWGhjUEywPkXsF4+bkqOn3zG3YkgvgetwHR9Ya5XVS8p/n7aV4P/cyPMRx62xOkjDOY8L1Cd6hTy14kub7oAlCFC8w6dqP2BjV4WlbK99FrtNNxkW/xbuGcw1EFyJdk7r07fJPJNkuv4HX+VTb0/SFDwiCMRj1m21OtQKZp9pJf2Yw2nQWZ299j8qN65VrubFr5Kipj0JnzxqEkSm1g1YHKu0OOK92BTZz4tsLliMJfF5lY8OYbTjVMUHkPYymE+dx66eKhWVoVA7AF5n98veKbOBSHf6ml3nbBLmNk28ARhyk04cGii8HZETB+rTQwoIPs701+OT8fbwYqsOHRdyOy+x14VVYY7sFbfoq4o/beBZORiKZNHTgFi9D+iq30D+VJ51JhdfZN7Xz7V+hokvpFfORWNX3KOiOxhfOw+74Ble7PEq9hvLsX4TfK9C2R1/x7sYg/xmX6hMh3MjLTVOvSJ5h+PidYrTOvGBPe5RPCqg8b+JCpR0O53V0BTYNFNetSkqB0XgFY6fg7U9YfmbAf+r9FMH7Iom1RmMwGI/Qzq7DsQDPajmd5xpx+eGieNlojE/DjoV9CiGwAcNPIUS8frvdwKdCZUnzjBnQV3vXqL/zcjEoR2AzQZAxlO4F/ZSRcs/c2H3cHfRqbMOwPq4s5mP/a8vKZm8xmV+gg/jl3GgTTpz9Ac8Wkz/NaschtnlxO1OcPwn72f+dcEx8Gxrw/jm8UF7H9U3og6E+4NHX99XXYTiasxmRn/37G3whyF39bKRgkbajGb+o/MwgFxXFgVCbD7XzCPxCmKO3YFKk74BdRpDPBcokVyzPQIy6cR/7ZIFxPu8pb19+Jbr/u+tHFpSZVh8RzDv46xf0F75e43wrAKM2AmWjHIgfiwKtr8/BU5+Hlbn1GCdts4n/yjyu7Xk8abZhLxkH+wySfaI3j/GrZ3E+5JOySPoyBlfeasT4c3aRH5N4rfkEfm4Ud290CD69+OPiFSws0H+QAFO8AxZbJWpKrDjqeoBZbz8arGmISilGg/sh7tNNtGJ3oeo+V+59wnbyTIxPRbbtAq6cySa/Z6O8w09s9NWfI9jNnnapE5aotdAevUM3XTkHc2IS0raZkZGchPTCOvR4p4hxHMEWiw01NaUoPOoiDkwNoRHF6YmIiopGfPphuIaJIRGCMafk4UxtJWz5J9H6JJj40hU8ulFTrKUa94c7AuVeg/qV75oTkZJdhhbe2FuOICUhBdtyP0XhTkK4afvx5aXTsMSuhKX8DsZmHxFnXov4lL+gtOkWGos3k0lNFKLiN6PYNYjfPLdwyrwCseZTaHHrkgs2kaD37kTRaSt20jqrOjB8v1qpv+o/eDIkGDcHps/odcg5WYUKWxHToeI45L4tf4GtpgolhSfU+whJmTch78wFVNsKcaylH+6GYqREpcHa0E+SNK4/FX/Hp3TDRmsD3P4JxVQLDn30jTq5pk58GOnx0aRviUgvbmRyFOta1y7dZIqXd/dVFKesQvK2vThcmEXsaiOKGtvQ6Jfr5Ru4HLDJTtTq6zLs93PFxhJIH6PXIrvyLhnjGFqK05GQvBW5hwuwM3EF0vLP4lLAhgcx3FER1C8jGd6e9Qgj03m6R0M84pM3w2QywZxzBI5Ot9CvvrxzB/WBcU7A2+u3A94muskYJtBZvhMJbKOw3ajsJAHsyQ2dDNdj17FCgU3R+/3yIrJteRQcE+MD3n5/wZPBVraZk2K/w3DfKif1rIC57AaRL/UlvQ2ofq2ROQ0kIv8iEHKQG7fohn0kQS5r+YXdr4DUHfa4yS402rYgJmoNTAWHlbcSrLuRRmzqZOsd1OWlamza5+3FNwXrEBWzFaW3Hqn8tQB/PPlFJyPqbyQA+vmw9FvcadTKxD2k5wCRf04rT+Ojk2ApqWGvLJdY9qvJE8GzZhTtDi5sseQtOgHJGduIXW1HTjE9pomMSSBPlpxE5PecPfDtzdCgHINEzRFYIm604nL7TcVeLBXo8LiD9V5uRKVIngZ8jOkuVJrXYHWaCR+aUhETl4sLA2PoJQlXNEl8Mogvmcy5KHZ04ldDrld5i9pIYLz3MBLwBb1fKJdTBOzV8n9w2moO8dWqXg+GBPzHYzGxtr7zJr4UjuE5Z1vfsQ2nFIj4l+47YcSzpbh8ywmrnxtH7qltH0bz0IDyei/lnd4eNKnl+jvfkOsj5WVi18IxhtENAx2LzgZmf8Wghmv460UcI/apOXUzOmaPY8THeqqUnMNkgWl9POJy7Pip85Iil+JrGBq6RniUyqgevX0utXwJnXcqF7aZJ43IS1iB9IK/w15RhvKr6lFqxGPabIRfEnXH44WN/aTuWT0f++tUwDYyNdPjLWORlF2FTiYjgSyJXYbkT0NjOvk+UTZMDbHzSbzQt/PrhJY/QvpJ4oZR7BLFAY1/kvoWyOvoxoRsM1c1Vrsj8YGQ+p6F+M7Qk/8Y2OkCMS4kd/X3k8apBtXXIrAdbvzFzQMYYp9oUll0o6/JX74HdwjfFuNK0xchcaCT639wLJW447dzNTcK8grRf7feL2ox4M+f9OMUxsAgFpWL/Epti/L8VuzISEZi+kHU9ZB8Rq3r7cyvBPeH5Fa8zIi9aPKspxgPM+8YHuZ4rZ3ozshfNLkJiR/PQzma112QlxaI2WH0T+Hz/oKWsu2Kn95yK7ZOZSA6bpJi+i5O7SlHJ938cuoHlHzwEcpq/o7SCx1BP2EgXMZz+nCvQV/oJ6/H8VEgZxfEy99GOS7xYIbmVDHbUNmnnWP9kbG8Cwtskyb1h2UFVXof7tGE8mAJF0iJcTecxrmFNjOSWBI0yesbgT+Sro3sOVLQhO4KPi26iIezkxi824a22//E57Zr2tfsJCSMMDeGe/dGMOo6hkOBxToSJBuPoahugEzcBnG3rQ23nSWw6V9XXgqE7RFLJhOCY+e6QF+K/d1AEpPGTw+j7uEUScQ60NbWCufnZAK5aL98PXi1sVbirYFvBA3H7OiZlYYgsZx4hXHgrYDMryQkXgYvvbBAn3BtWvcxThwpwOFm8fEgL49JtJdsRHT0ehQ2cqtRUz/j6o2HIataEi8JfvXevyr4e+MPpWsDe44YPsz2/xN7klepTxR2IsdaiVbdWycSEmL4MNVeinXRcUgpvMI9EZ9C/8V9SE5Qn9zv3APr2Rbu70uFUXuT6Lr6/TLU/5KY7cXFPalIYE+nzNiZU4yzrUNvHNe8nlgr8XaA+FTXDdyQnC+x7HhVceBtgcyvJCReBsv2xoKEhISEhISEhISEhISEhMSfD3JhQUJCQkJCQkJCQkJCQkJCYsmQCwsSEhISEhISEhISEhISEhJLxu+wsBBuB/ZqtNy+rP27eqce9GiRPdvUI05EoOfUOm0wpxyLbBNC3yju/PBQd7bpG4wXdOfa1MBJCT5PB354EOl3YPToGRNWaHZk/6ODbkpkQ25xGWymjboTJn5fBHSn0+krQyTtBPzhddmKvp05eO7cxYMXhDN6voHN/IF6XJERFnu9HuG4iZ4O8TOaSrYiJioZeRfuBo6hCoFvBPV79qjHs70C/G5cZSxbdqLECt0u9QFEppOF63hzEez3E4OYQ7in9Tw+M0Vqk8oGYgdzP8c5WxbeXZLvqf4wv8g4+KbAML69aj5aSPbLoReK3yf+KjKcWiI/vib4HqHlqAWZOZ/iSEEOTGkZb2Y/I0Yk3BfhNZ01KCw8iZqKz7Al9TBaFuBJPZe+9hyDB90g92Aeis8dhundTxfmd3rUZeVnMFG+mubKy8ZdC/sePbnJaduOFHYcIge+X28aj/4eOlUhtrMFOGYJfX0reEsEzRz09+H83wO/0xsLynmhgXNfKdg5p/4z4wV/D4FydmpM1CaUdT1TfwtF5KcbTMPtyEGSnkzeaJBA88CtTG7m+uHIXlwA9nmHMBA4s/1PAHZO+bo3j5Q0uuN0+koRrh2tP7wuWwm2QxJ4twPZSf5j9fhzkEVY7PVGCMdN9J+EUwLnXYvBztCPicW6so5XcOLB78xVRrL1efFg4LFxnyLRSbg63lRw/TaMOTOtsL4TqU0q5/wrZ30vBVp/ePNO+YkExvHt1fLRQrJ/Wb0E8drjLy/DJfPjq8eLnnKkvlOM1hkamyjX5SFb5d63FpHIO9w1vn7Yt6RFrjOeS3+XHCOI+a4yrI2Yf3yEKovxDrueL6t/XgYs7HtKDhDq46+mL8uD16/TAIzszNCeF9nXt4S3jMDH3tfO+b8T3t6FBbrqZdkH6961WGFtIeFeDKbU2A2w7N2OjORkmCu7SKiiTx2uotR2Buet25BadA2jT+7glGkFYk021Db3Y5qejVp6HOXnrchMLUaT5xk87Q4UbzVh5/a1iNlYib6ZB2g8VABbzXmU5GSj0Hmf1D0IR24eymprUW3NREz0ByjvuAdX4TpEpf0VrcSo5h59gz2b/4Z2dp5vBfbkl+KLwsOoc0+rnRbVS4zScw2f5x9D9ZlPkJ5dB7fnNqryt8Di6MfTjjMwxa6AyfYVmnt6DNrjz21+hLaq/Uiz1BEZz8HTVIJ82zmcObAV2XW6IE7Pz60sQn7paVhN21HaNk4kyIEQi/vGOeRu24m83CykJW1AQTXpOytvRHHLGLk+VOYeHyGYzvPIzy9BhXU7NpXewqRP35cB9VxgSiaPMdZREShr9VGBnx/o61f7R+HzoP2iTZVRNS61e9gZwCFynhtFu+MQtpp2YHtSIjZW3tM8FWZnfO85gJNfWFFU168GHnqW7d9hKy+HNTMDRU0jeD7WAafNAtPuj5CdmYIkcxV66Fm6Idc+xJOA7i6gscWl6pTqgMrMhaOFpagpz0daOtGn5izhGXjY2dTpRB5PyHgG4dxjQemPA+jQ6WvW8yMcxTth2rkVSTFbceZ/m4PtiPT7lPOHxhtoCtgKgVBuHnQ6bURuu7E/OxPJSdmo7NGR/1wv7OZExOU68eDhtyhc8w5Siq/DMztO/D0L+Zfv4ra/Hd84Ok5lITbWBFvtDfR7bxF+SMRmy4cwZ/DyVBHp9SF+rc8QlmNhga5M78MB61+QuOIQWqc03hKA3p+H54bEfjv5EE2fE3lXn8aB9L34R9etMFw1A6/7BipyzcjKy8WOtLVYV3AO9pKPlXLxDTwRd4mAJPJ2C2LiPoLzQb/Sn5TDaPZMwdv2V6zPd2GCyUMv22l42ogvp+1R5EL7dNSKkprTyEv7ADZ67rvwPr4jxJ75OnwjmnHXaeRNro3A/0M40++XpA+mtNWISVFtYFE2reeoe5p+M/uIeQ8ZO7YibXUSMktvKvLmk6IF7XAOY+3/gk3V8deX2jH2XBQT9DIgMclPVjp/6Gk6HFEc1HCmqH4S80L6LdS1QQwT8aeBnOcM41svF7voGL7FIaLjGvtx5JitcPY9M6xTC/oG21+JHiuJb+yFudCJvunnobIPyCRCvYTEkB7M6OX8nI+/i5NLEGTsRvYcwukeLs5QGV418EWRTKgjkfrKP0H+yb+hsOhfcLNJgcB+nhv0eUFf1oItLNA3wup6lBOppvvQ1jVBCoK+/TaB3qpdiKWJ+7R6mhUri3Sgj6XEV0Psfwm5kKGeaKwvxT7bWVQfz0ZStGhCZHBNCD88Rf+146r+zsPxNc1llGsDNqDJNXg+nuH8R5Rj6P3HqE6SxeljljIIFWJf9I2146LNpHDR1w1oH+N1L8oByfC5yRhfDoK05bmO4pR4pNi+J3Yyg0fO/dhc+j2GO3T1zeps4cw1fB/wPVH7Sg4Qk5yBHdvSsDpxG0pvPyYt8n0R2P6SbEdvp3fxQJdL0nZ5hHIFlYWfK90GcmnDkyF9f2cw1umEbasZu/fvQmYgLnAIl7O5BjEqtDMjjnmh7athnuzHfAR10uupHy0gtwX9Nht7TUTPMeu1ubsgR9D65DNj+bGYWARbRQWOW9YimnESx/lzI2ixbUY0ybMu3muCbUM2Tt1+pPD+HwBv6cKCD8/by2AhweJZ21Gsif0Y9eP8hCsIRgRxn8A1MYdnzUWIW1WCduLMrvxcVPbPAB4nLNEZKO8ZY20qq5TzGHcdxNbK+6SlITgtq5B68jo6q3IQx+qawMjIEzx07Mb68m428fSNOpEdux129yi6u4bxwkfbeA9JhddZUsleF4rdBcfQLAmclfjY3g8ftwLtG3bAHJMCy9F/43+/EtU7Rbq6FxvI73N4jBv2RvQNumBLW6nIjE9aCYTtsb8o8HkHcNW2SZUxHeMOIoMpYPIm7Fd0Uh+vR04MnUhNY9hhQWyeS/sqz9wjtNK6Mu0Y9M1i0J6FKHbOulJm1zN56GU+gPqctUr/yfhNsfvhmiSTY31fNAm5Wm7pR7dGH71oDKlfQ5E6GdE3XvRyzkLF7VuoylmNODqB8o5ihF+MwXMy6dqOBGojPjcc5lVItpSg8c43yN9aRcY7T5rejejUU2jrq0VOXBIhXg987Enl+yhpJ0F2vFF3bTl6nvv7NQEvr1Paxvbdyuv0Lwbg2GEJHROzoXeR7XxI7Ok+qj6phfuxXl+X0NtdrfZnBN6RYYy4uXaE+lV8kPmDxlZEctuOms6fcMEvN0Lirdb3sKqknXgSjzmMOonOcuoxTupvL9mAtWUd5JpJtB07jsaHfVw7VF1cQsF0p8rzWTMK4hR58gh//WSoX6vjCGIZFhae/4hSy3n0PbsF25ok5NaLjgak+uf9mQRr8qvQb6k9byB2MkckdaMOVzwTC3NVeTtGW/+KtKgs2Adn4Ru0IzNqK/MNVmZ+ph01D6ZTEvAopz5vL8W7a8vQNe/DTNspfErPMhfKdgSDV22kTSqX34iN7MFWxjkzcJPyZjLO+XA6JAldsA4i25Bx8wujYxH4/zOdjAlnDlC/XIOC5nHFv7JWEhu8g8FF2bSOLxt+1vRbiTlFaH5Gkij3BcLrKhex8St+HqkdKjoW+5y996FOBiSOKDcz8P6g9CmSOMjxS4iMRf2+S2Sn1zX9TRTDvGL+7LorkHMbJgzimyZ2MR1mqf1W+CV2ix3uX3vC8hF7q2g9sS/WyYdwZr+LLWwcvOz1CKcXUQwZDpHzmbZujusM4opQLvwYyKRIaM+EU0WczslQkWeoLxrJ5IXbji0J1JbmSH3ZiEnOxtHGm7gSMq52cZ/p3418WQ9yX2f5TiRExSE5txJtHlI//dmgb7N6O48/gpaRu1od/NofGkt/HhDYf6/WtyPIhSa8Ylub81xG7vulaH9OvNLgbSWf8BoDfgjRn3KtYgOqLgO5Bs/HTDDB6/U5Roj/VKGzX1TnpI5PlZgVgJEvkqHxXKTFI0EOSHgzRKeiexW/ic12YpRMkvurbES/bl19+3C5947WFkisCPqeqH2SjxAfjytoxjMyTWQL7TRPI3YX7IuIOxdrO414PKazU3dDaH5I7T0AEVccxT+aLgV1KpTLoKC/HgxcyFX5fV55G4PGBY1rLpyz0bggtjMjjiGZZcD+BgxsV/cGetg6DfJqXm4L+S3Ts/oG/NpStPaKOLUdHr1PnvkefUL5zcFTvw/vl/xINBF8y+X2Uz6PJlXTPiduxonak9h96lW82fr74fddWFhtQoHNBhv97+EcpMVEurDwFK2HiljyjdkOlK1boSYEoRAS1AQxyPhMWKtr1ZXfRFicP3MJwziaC1KRYa2Aw3GePaGItjgxrCE4jpAofAOwZyaReoboPzBFFzxilAmC8vcR1OeuJaTcho6zR5Xf59tRsupdxYCZw1DDf2hQ70PM9tmRlbAKmbZ69LEJ7yAcptXKtbzzsfsE7WlAEwSLKuMp9Nl3ISFhK2z198GeEvCgT/uv/IDhqWE0F6cJ9MLXZVCmJBAi818w1t6MtmESsNiTdzr+ydC+hBCLUtYEHGH9VBccNDIy0h9vB3o8I8T6vkpE1EbTyP1ulqjHZ1hR7XDga7oyH70bzuGbgj7/Gv5aXqeUDBP99maEYAAf6LXjwAXiByJ96e2Db0eoXz6B5nUaidz4e7Vgyfc7NHh70VVGSJYGgOkfceLzayRU8e1QsXH6NbABHuGvdwv9WpMcheUmtR1/IA0B8X0yqd9N7MJHrISOMVpNrrTwCfyZ/izw29l7sGetRkKmDfV9k+ROXr5irvLQJMnfR6OyEVjSnoo8Erxnu05hXRRNQsbQfuKEsiBhpItA3ZTDNoXox/A+Hnz/QsatRXj/F3Cmpl11EdR0FGWLsmkBX3L91vTLdx+VG/ST48jtUGnPONZo2tKB/5uwLIyDWs7U1i+ytf/BmRBdG/W3R8Cf9Bp+rHzZKL5xPBFIGBUBsIUzyqmaxTe+Tj/mCb1+hBiVa/y2oOhBdL0f/N8ijCFC23QHxyCMKwvJhYPQnkmd4WKAsEwmtAYyGW4vwSq2WOZTbIJeIxyXgc+E8eVQzGC4tRw5yXGITtqD2j6vcd+Edq6TlyiWCvt/bwm5kEhPNzHK91ejJz90Nhi4xoAfwupPV+a5lP9dn2OI/EeYv3jFMcsPQ1+cJ1XwPMKDvgWkzwG1HGp8L2nDv2g50IGqA7Ukzorqe6q1BfK/Qd8Lf72vvxIb9P0ScufL2g7JGET5oWYRzogrOJ0ShMjFK+b6SOS8cM5GYGRnRmUjXtdcwyFsnQZ5NS+3sLInl7AHMGQcg61cP/ycegKVAp8Uc482LgR/1+a3AI1R7yOKnyf+QfBWvbHgU58g000b927MwWGW+B/BZ5YURK87ha7Z0HAldJxB4ghqkAyCNzJq+OuUCT8HreNR40ninjTRe9Yip/4RuZC+PpREJgddxCz9UBcbkjZiS8EljLKmvegs+wBr8v8Nd+8F7NpJCODFAvXSJxQ9dShMX4GELDv6Zt1iB2UQtcdDZ+S+CfTUHUR6/Gpk2e9x/Sbwkcl+/UkUHa1B7ZHgilsQWqIWlin5hMicjKevAaVFJait/Ty46qnviwGxaPQhrF8HjYyM5HxfQzZa+DDdeRrpaw6g3t0J+648ONzP2BgVoucg7PPT8NfypEvHFObpMgX1h9y4jcjJ/UwhKJG+NG1QcO0I9cv7A6/TSOSmJWwN2MR5PfL+9S8c3Z2NHSsycKLyFE60PiV/1NqkRr9CefrHoiD89V2kr6F+rUU4blLb8duqHz6v8sSYjm/vNuQcVhclPrMgOdpoHxi9P1PbFfst3VCqrnAT4hN2wd43ysmXyl8wJj7YG5UNQRPejxGf9xWuHN0Dy473kHriLMpO3FQ+OTPSRaDuftKnZOTRtxt4GN3HQ9c/7bhZ6wFE5v86Gf92m2tXsbfYvLM4v1ib1nMU129Nv1hdqiwCY47cDpX2jGOCti0t+L8Jy8I4qEXoWPT95sYXgFF/RwT8SZ9G82Ply/Q+UXzjeIJNZrg3LageYujbNhEuLASebCl1xqhP5kKv9yMSvehiiNA2ea4TxZWF5MJBIBflDcEwMUBYVhcWRDKZvouy9PXIr+9Br/1j7HQM4IVwXMZ9XsiXgyATve57gc9PfN42lKa/Q8Z0Bf0GfRsV2rlOXrSv+lgq7D/BonMh0ZhbMUj1y97iJPdp9OSHagMh1xjwQ1j96cp0fH4u5X/n/crIf0bFCwvimKXC0BcXWlgQ54BCvgq5l4DlE+9hc84e7GeL+aL6FlpYiOB6znYCfTHizpeyHaVfIfmhBkZcwemUXaaTi4GtRyTnBXM2AiM7Myob8brmGg5h6zTIq3mElT2BX8/j33P9UHQSm1eG0wKfFMuPjm8VMu0DRFv877zdUdC3QG1YGWNC+c+6Mb/leIsWFqbQW2nD+X4vhhwFKOJ2FGUrajHvE0Og3+FpIVT8M5JcrnwXuU43MRv6rdQ1nGvowK2AkdFXoVKRmOvEEP2WkH4vc64R/beCddGVLPrqjP9VL/r6c8n7+1DvmcG46wASkz5DM/0efv4uKio6CX0RsN1Q1yivrdN/s1V5ByrtDjivdqmbmRjVO43hulPMeX0TLuS/Q1fkaAIvSrxUhLTHgzfyQdTZnBj2zWPC9QneYU9tgmCvZwdeg+Qdww/+d4PyjEDm/76Mio3rlT4HyKk/tC9sbGthbSX6pU8Z4pRx8rqFqP4GGmg4aGRkJGd9EOJB672JC5V2OJzX0cU2YVHJIfFjOIfov+m3Xl+hof+7YFtcu2Gv5UmXbTa5Apmn2kG/lRttOouzAhsHxuDKW40Y9uqbgb5C7CPYjli/oQFP+T0SuekIW4MXmHTtR2wU9deHaC9JQ1RgHwK+HSo2Tr98/0PGoiD89UNCv2ZJXQDhuEltxx9IGXyY7a3BJ+fv48VQHT4s4nb9ZU//V2GN7ZZuHxgy1hB/VlfY9X47/A1sNDnweeDKT9U9FRRz1eAQF+z5wK9JAsbQc3cw9MkKxaQLebExpN/fY4J+DhGVTPxPTSSMdBGoe5JtcBeT+QU6vCSpHG3CibM/YMroPh6avurHvcDTdKH/92JIL2PN20FUdtuQ73qAh4uyaQFfcv3W9IvWtb6QJCtEt4ExR26HSntGPqdfxNCC/5uwLIyDWs7U1i+ytX/i4t/0um7FPaP+hvAnBT9WrZzF8Y3jCfaq6hr1jUX6iWQp3s+9DI/6nXRonUGwV+vZJxr05Bb6mq8ZufV0mi++XgH/twhjiJFtBrhOFFcowo9BKxe/PY9iPlwMMCgbyoTuiH/hPOyOb3C1Sz2pSzguPo/i+izy5blR3L3RQeK7xuJIfwtR2OwhNVIonxYkFjRhwqBvzEbZZo8v2JPLOGavOnmJYul3DYL+X4dj0bmQWE/P6V4RMRbSXzLRZq95qzkMB7afRMg1g6QOAT/Mhtefpqzheu533q+M/EdYJ5nAGcUsCkNfpFXwPMKBxTp9DqjlNcN7GdR8IiYHzlFygbA+fU7H6S7s9co41uc3Ypwfh5A7X9Z2DHLJQWrvfhhxBadTBp1chL7qFnxGJJLzQjkbgZGdGZWNeF1zDYcI6gwnt8hkfxTJVM+afvg59R5uC3ySnxMG5TeNnvIMxJgvsL1o2KcQjJ/4tgmm7+L0/nJcqd6NxPTT6NTsLfF243dYWJgPf9xk60XYMsjfA68jH4E1Jx3x9PuXe3XIS9oAa0O/mhDT+i6hYE0MSW7+hlvq93gMvkllY5/YXai6/xD3/eVeD4Zch5EeH42oqESkFzeSoDunLAjEpyK79AaGHjaiOD2R/D0a8emH4XK7cefUdnL/dpS1/KK0TY2reAcstkrUlFhxlCSoc2xlPx6JluOoddTCXpKLnICzj6O5qEhxdApGavGIT94Mk8kEc84ROLom2MZXIfWS/ww7PkRK3mnUVh9F/rFmuN0NsNJNWqwNcLPvONciPuUvKG15RNyEQtceB5+3Hw1WQhApxWhwd6LWvAl5Zy6g2laIY3TzLfU6Ct+TGyhOWYXkbXtxuDCLyGgjilzBxYpgXYfRPDSgvGpE6+3tQZO/7B4LlfmsBy3F6UhI3orcwwXYmbgCaUUncMSk74sXPZUWJK5Og+lDE9bHrEbOl9dwvYzXByUTvU65UdDvt2+VwxK7AuayG0ReNNHXy3kQv3lu4ZR5BWLNp9Di1n6zTAmrr3IbognpZBB9mcy5KHZ0wjtL69lMJptRiIrfjGLXL3jCNpxcCUtVB4bv+8vdgmuJbtn+E1R3O1FU8Xd8GtDpc3g7z8GcQMYUvRbZlXfFk0DSy6nW4/hInYiG6us9/NcnuaAb4ChjJ/XytuOmm/3o9evGY9UfLEWnUf6pX49EJgL7nPV2o8qyErGWatwf7giUe/WvS1JMNqHowzoMkc6+6KuCqVjZfFVrk6Qd9g1aIlKyS3H5xjmxPHkVh73+P3gypPPrYY4vwnITPW6yC422LYiJWgNTwWHlrQTrbqQRmZ1svYO6vFRVd8q4fd5efFOwDlExW1F6i9+cR+/PLdyGijq/JYHbnJKHM7WVsOWfROsTdfHSiKuGxuBuKEZKVBqKmwcwxF7xTCOc2Y2+Jn+5D49vk2ActRbiY58ekz4UKG/AUJ4y2dREgshIZNtf3kRnvdIm4+Zf76LcvIr0KRZJ2VXopDZn5BP+cVNZq/1mdYSMm7MlMinR8rHI/wUyZklDHJJzSlFTYcO+oy6FJxZl0wNw8HzZ0q/p96T7n/jLuxkoqLCjsrSU2BHhdMIdY2zjWTLm8jsYdoexw8FWlNP2/DwkigkhMlBvVxHwB8v/wWmrWYl9YeMgV0lI/TQhDe0320xMo2sRt9IYJubPX4VyrsSdzkvi+Hb5Bi4HeOJXzNI+bfkLbDVVKCk8ofYpEj6iNnMEWyw21NSUopDZgkD26tWR6UUUQ0Jtc+hJH8d1Y+iNWC66MRjYszBmN7ah0R9nTluxU+iLIpn4lMQ8OgHJGdtIrrIdOcV16PJOCcb1H3Gfey6E+DKrM2YbKvt426eJ+GG8l7YTBWXVqK2wwrzliOof4r5huguV5jVYnWbCh6ZUxMTl4Mub36JMowOiu5BYKuIMMjleZC5U+I/LqBTqaZztFUHzlm07MpCcuAmFddqYRZ9wh14jilNTBvzZgRGubMjHhjnGvOLTGv8xausn9NQaxSwKlR9CfNGNW3RTTRFP+cZCc8D8s7h0hl6fjfI7HbjD7iXlDoOj56dacOijb5S3+0Lq+3/w3n/tQY6JswVNntEvyEEvouWfH+HdDQWoqK1A6dFaldPUzUFZXwbhfmnb2YD8igs4ydsp4xNdfqjer0DEoXdJPOHnAio/8HIRcNDwrNH8SPCpkkHORoSpjdm8nQk5hthroK+X0HmnUmBneh8JVye5XpRXq7dTiOcw3WwRNDo5FydrzsK2r1ThGQNODYl9Qx5j+bGYSDlpK3ZkJCMx/SD+cfsH1Kt2V9/ZhosFWdjnGiV9u47CpASkWS+jXxOf3l78Tm8s/NlAksp7ffCMNuLgoWYoTzOpoV7Bp4TEHhIHH7zbhrbb/8TnNvW7pZeCqD2JlwYh/cZPD6PuIQm8gx1oa2uF83P1u3MJiZfG6/bbeYw3nMY5utnUnwWapxESrxWSP8V4Gbm8Fnsmk5LGYyiqG8CsdxB329pw21kCm/5TJwkJiVcLyaHLCN1bTX7IHOGlIRcWXgemfkDJujhEp3yGRrbyTuHDbP8/sSd5lfoUYCdyrJVoDfz9JSBsT+KlMduLi3tSkcDeMDFjZ04xzrYOiVfRJSQWi9ftt1M/4+qNh38i++XfGmjF4B/k6cBbA8mfYixZLq/LnqfQf3EfkhPUp6Q798B6tkV5W0BCQuL1QXLoMsGHubE7yttnlnLcGqRvNVHIHGE5IBcWJCQkJCQkJCQkJCQkJCQklgy5sCAhISEhISEhISEhISEhIbFkyIUFCQkJCQkJCQkJCQkJCQmJJUMuLEhISEhISEhISEhISEhISCwZr3hhQTn54GDu5zhny8K7Bdz57ksFO6Ix1eCINNKitxtO23akaHb6pP1oReVnZt3vSwA9trCtBnlJMYg12fB17TkcycpE9qmbGHupvYxmMNx6Hp+ZPlB2Iw0zztcG3yju/PAQke05uxg502NzTFixWJug52lXfgZTyjHBebuhENvD4uDzdOCHB8oRZoupix2nteIgmiPasZfuvG1DbnEZbKaNL6H3OXju3MUD0uTi2g8HrX0utW6/LJes/7cWQb28Cmht8zGaC1KxsfJehH6rIlJf902gx2mDOUIffLOwDDFJldP8MnDLHxqLih0c3mj7Wi5+nYe35xvYzGq8XxYsU0xdqt4ihBIDpl5i/G96rNRjefNgwzxkMbnRIvMoEZacW+lz27B8+vbkCsH85mXgt8tXwRFa6O1+of4b6pvTpzb/1+ltWXjl9c8pxRDMdV4xb75NeMULC1PoKtsQepzHS4E42wM3xgx3JDY4QgQTaLWuXaa+DMJhig/U5Rt1IjsmGdbWp8qfl4qZVljf8R9zEm6crwPTcDtykLQomUUuZ593CANjiyVhes51Md6Jt0UYEI3sIULM9cORnaHqZJF1+bx4MEDIVv3ngpjvQNnadS8ZQAjZuR3ITlJls5j2IwFvn0upWyNLWsVS9P82QqeXVwLeNkl7Y248WNSOxovz9bk2G+Ij9sE3CS8bk3g5vSS3/KGxlNgRxJtpX8vMr6/gWLOXj6kvp7ew4GPAUsf/NsRKDZY7DzbincXkRovNo0RYKv/xuW1kfPpW5Aq6/GZp0Nnlqz76kLf7sP030g+vT23+H9TbcvHK7zGnNAI/1lfMm28ZlmlhgTjD8Lc4lH8MNfbjyDFb4ex7irH2f8FmWqE82b/Uzj3Rn4Gn+TBSotKJET+Bb24Qzj0WlLY/xq+d55GfX4IK63ZsKr2FydlRtDsOYatpB7YnJWDdp+WoyN8Ci2OQ1EMMQn89cRRq/DHJGdixLQ2rE7eh9PZj0kPeKWh/r6LUdgbnrduQWnQNHt8cPE0lyLedw5kDW5FdR+s3gnZhQXH+RFicQ0QUE+isLEJ+6WlYTdtR2jZOWwute+4BXKXHUX7eiszUYjR5SE0cicx5bqOKjdNNJgkdcNosMO3+CNmZKUgyV6FnmgpzBsOuv8NWXg5rZgaKmkZIWxxoG0etKKk5jby0D2BrHSN/p6v9fyV9qYS9ZC/MhU70TU9jrNMJ21Yzdu/fhczkZJgru/Db0zs4peqvtvE7tAT0kIiNlT2YCdH5M9IoL+cFZDr3CG1V+5FmqcMw6ZOo/WmmJxeOFpaipjwfael/ReuTeSImf9I5id6qXYil5WkPOsqzlTIhCHbfPhsqqo/BkhSr9ieMvEL+/hBPOs7AFLsCJttXaO4fYWOL3WzBXvNmJCdlo7KH6Eqon2l42ohtpu2BY5haCa37BApLKlCetxnpthY88Tfu86D9ok1tpxqX2gcx2u5A8VYTdm5fi5iN/xc/3tXbOblPr9+Wn/HTqSzExppgq/0WLU1fatuPUO/TQr+i7fnt87F2bMN1yM35G2odFbBmJCB6Qzl6nj/V+YFHK8ueXk7/VF96WzKSq1ZjPHzeu6jMP4DSCitMm06gbXLKYHw8BGOd9aDTaSO2vhv7szMDehbrUGBTvhE0fV4AW/VpHEjfi7rhUXTwemmp5XR7Ct9d+RRrojYR/3yEuQAXPtXappBXDOzcex/1x3cgxXYTT3urYYlVdKYco6QmKbo+/qPrVtDXm/sxLeQnam9FsFVU4LhlLaJ1SanPO4AbFfnYlrUHuTvSkbSuANVEn0r5CFqI7wo5KxLO/EeH2Nen+fjg5yU9tyv9IwMQxCSxX3g0/leJPvUxhI/nxOZ2NAn4gFwliC/K/exvo40oXBOPNNt3JJmZwSPnfmwubYN3ViBzgWxmPT/CUbwTpp1bkRSzDRX/+298Tnyn+swnSM+m/sRBKFseBjatl8lvEwbyF/uKRk762HGmEVcK1yEqjfA5STrnHn2DPZv/hnZuIYxxfOwGWPZuR0YYXvJ5rhmM3x9XTKQeM9JWJyCF6YLkH2H5VfUtPvaMk3EvxK+RcAO7rhT7bGdRfTwbSdGhk4ZQHiNyoXos/wT5J/+GwqJ/wU2T4Dmd7Z+5hu81nKrv/xODeKnKm5Rvj3F6a7qDnyLyubt4sGBc9WMeTzUx4CrLmzZbPoQ5g+N3Eff4saRYuQRdRhwreYjue75AHizS9VQEtmmU4wb1qORARhykIHityK/C5GQhuZUfZIJltyAm7iM4H/TDRf085TCaPVPwtv0V6/Mb8XjUn9sORsanfK44ZxSbOYTjvEjz4kVx3oTOtnu185b/Y8MnIs7X6QS+cc4ub6Dfe2vxPkKg5cRa9AjzAD6Pm9H2v++hQIYiuxsj8SioT03+z+tNo+f/4BdXuJyH2tli5pQiX4qEN/VzylCuaHF3oMqykozpNqbVIyoVm+fGyo9v2Xnz7cPyLCz43HBkZaG8h9LsHEadOYjdYodbJUAt8ajw9cO+5V1kOx+SxPI+qj6pJdc/Qn3OWpiogocdxMj34XLvHVTlrEZcvgsT3mHc67oCW9pK5Rror98P1+QkazOuoBnP/CSXSiY6L7i++Ebhys9FZf8M4HHCEp1B+t4Lp2UH+f8pYPIm7Fc06ZkO3MKCbxJ9dfuQlLAHzuFZYLweOTEW4qjTpEsWxOa5MIkhXd2DGHcdxNbK+8SQ6N9WIbW8Gy8CE7cJeAdd6jgH4B2oRU5cEvJdHvjYU+P3UdL+jHBQI/K3VqHfN0+GsRvRbJxqFzGLIccebLX3kzZm4CblzaSNuSEHstar1/kewpn9LrbUtKP/Qi7i4j6Ba2JeWcleVYL2eb/MqEPd5fQwipFf+yPQuX7cQZnSSchV2yZEmRwYJvcMiNqfI3a1fTfs7ufAiwE4dlhYe8GASFoWlodRn2smMqIBh1tVXFBepE+ivz/nV4yVsTEZkADQXPAuVpW0YUKonxEMXrUhLYrawhx8RO7bt1L5kKG4a7FjM6mbf6DMr0zPjaG7KkeVxwRGRu7jcoidzwj1OxOQgTek/cj1LvIrcqO/j7eHNXXD24su+qkIlV/iOhQ2EzmI/IAbo1b/Iv6oQme/2O7FmCeu9xFiaH3MP5OR19gvtite7gIOaXzYjQt+WyeBqdX6HtFzO/MdvQ5/9ghsZoRwCl1cIQnb5I06XPFwi2HTet0SWbBX8ZKQ5XCTcjfKP/6ataGBSJ4+kZ0HfZXZvMauuDLlPU0fJzi/JbIM4acOjNTvw/slP+I5tbiQp10kII99RzhrBTLtA4TTB2DPXIENtA5Wfg95LpLAiXxsLBLOHOb8mw6Flo+gZUTHSyRGhHI7n/5rY5LQLypa0aXXEbuXgr9fxAftmBfGF74P9BXRbYjNcmDIN42e8s8Jx02JY0KI3i+ht7ta9YsReEeG0XdxLzZQbsdj3LBfh0dthUEYj4IQ8VL3oEAmhGdmQ+RPJqJPe4S+QqYqqpwEscM7r7yCG7sLjqFZvOipxMeMx4Jg9TP5z+FZcxHiDHnpORGxwfgDcaUIzc98iq5jNqCs3R2eXydIfFsw9oTya2TccBm575ei/Tm5UPOGoh8CHqN+47ZjSwJtd450MRsxyRYUf+lABS/XkV4tpy4pdmr9I/Qagc+5GxaMqxqE8JHK78+aURBH+X1S7Afq7QyaOiKIlUvQZeSxUukShfA+ZtdamQYh0PXlrghiv1GO69cRHVM4DuKvFfgVaVuckxnkVkqVDOwN3riPUD8+j+ftpXh3bRm65knMaDuFT12PuNyWtBcBn87xuYLXiG84LMh5i8iLF8t5nF3OhXDelIDziT0KENQL+8cSfIT6oY4TQ/yOlPV5HH+NUIaqfjR2dwptAwb65PWm94FwOc+i55RL5U3dnFLImx6uTb59o7JWh0r5JXnzLcPyLCwEjFBRtW/Qjszo3XBqklU9gsYy0GvHgQvU0ekTpWa0DU+qbzTQOp/q6qBGs1o14vDX+/orsUH/O3XQ+ExYq2vh+JquftO3De6hz74LCQlbYau/H7qSqAHtQzxi0nJw+MgBWFJWYLXl72gdJiROV9Sv/IDhqWE0F6epTkVIRVM3Jc1UZFgr4HCcZytw0Ran1vn5cYpIoe1XlnDFZ1hR7XDga5sJsUzmfoqlwWKTWpcfvPNRzGLQnsXaHhYQqDbJ0Dl0RDrXj5tdqoI6ukWVD98mVx4lbSQqv/EQXqu/L9A3f79vYWJBefnE8hy+yenEQB5C/ZAyDdLM9maY3BP9shNBo3vtuMi/BHb+UKBf3X269iPXu6g9doFgbP4RUb2/jxgWOMk/RX6gGSOnfyNb4mWvk48IPhLMr7QNYcpzHcXEJ6nvCO3D32WGcBzi1/NNjIbo0MBmHv4H9qzVSMi0ob5vklylbTu0H9QvP0Ycmeh3d1ThAA226l8CEMlTI7dF2ubsPV0f+ftF/PQ/OONPPgnEsuS5WVR2i+U1OhwBZxrJkO83gZDbh+hfVPDXR8aHWhjImS+H7QNRJ9FdbtwHKO9uw9kD/yQ+YxATwvqRD7N9dmQlrEKmrR59+s9fhPHID2X8WpterhhhVFbhGyETlLUk+W1Dx9mjbIGBh1jXYl5aaPx8PWALXKtCeIFcFVpvhzNs7FmY30XcYMEXZ3YHZWvAaSIem28vwSp1gYT1gdWhl6uOU5cSO8MuLOivMeDAQFzVwYiPAmW32A/U2xl0cuP7SP61LLqM3P7ZRQTGfuMR2b+KcDFLOB7DHHcueO9EeA4KtiP2K74fgbIwt9KNi03MU8nEzoPZrlNYF0UXNcbQfuKE8oBCExv4OozKfK5odA2HBTnvFebFGrsM7Vso56t/0IGXO/nHEnxEEBOE9ZAyn8fxvwtlqB1T0O76DfRppDcKKvMFch6jnHCBOeXSeZOzRyFv8tdHUqaiDGc3i+TNtwzLuLDArYhSY42hK5YLLSwQQ2COthE5uZ+picU8vH0NKC0qQW3t5+pK2kILCxFcT/vCrfKy3+lvqrFp4JtAT91BpMevRpb9HqEXI9A+cJ9CUCLdtRLRdEXtxST66k+i6GgNao/4V+voNXzd9ag2rUNB8zj9SxC8Y/PjFJLCU+a0yuqhCPR+ZdUuCJVAAytjiuPH5NRjNKwjaB0nYp0bypQnHb5Nrjzo1512GU/stLr7orJgH6St+fvdisEF5aX0J+TvGp0YyEOoH1IOkLb4aaEGmna4sTAhiuyckrlev7r7Au2ryVLEehe1xy4QjI02REjbfQHmWBI06RNmCvomj94PNGPk9G9kS6OLXFignwCUFuNobQ2OqCvQQvugXQ4gHIdo7UerQwObIaAbHdUVbkJ8wi7Y+6bC92PqFmxr1iJjy6dkok3+MN8D+4dmmEwmmMx/RfP4k1B5Mh3o7TxC2yTQ9nGUu59yh56f6G8kUaFvI5B/iWXJcZaw3C+Wl8hW2O9a7vhNKEN+3ARUJiJuD4C/PjI+1MJAznw5bB8oKE+mISnDjAL61p5Q5gRh/YiC2HBPHQrTVyAhy46+Wa5dI9kyKOPV2vRyxQijsh8+TLUdxZqkjdhScAmjOlGJ/cWAlxYYv9ZOqYwVztT+Lqj3tj1s7GG2xvog4ncRNyi/RWXaMUi7aMBpIh7D9F2Upa9Hfn0Peu0fY6djAC9C5KrW7+eGpcROnT+Fv0Y0zgVgxEeBcpfYD3jo5Mb38eV1udhYSa+hMPabcaH9KwgXs4TjMcxxXwTvpTlQGA4KtiP2K74fmnqFMYeHMmmMz/sKV47ugWXHe0g9cRZlJ25CyQ742MDXYVTm7NrwGg4Lcl6QA4JYJs7T2KWob3rOF4OXO/nH0nyE2g3Pib/dFtRDygG7ZwMM/i6UoW5MAbsbMNCnkd5U6HMeHkuYUy6dNzl75HwpCP76SMpaHYrtRpFNxLz5lmEZP4VYo7725WOvP72fexmehT6FYBiDK281YrKdSmLBXo9ZrzP4BRYWwl6v9IV+1zXO92WGONnKd5HrdLNr5oav4VzDdThsTgz75jHh+gTvsBXAGXju3sRdzwxrOQjaB25hgT3hW4MoQkzd9yqxMfDqjd+pBlGnqbsCF22pSMx1Yoh98/MArnONGJzliYkbp5BcvOy195WJH8M5RPtHv938Cg2D/terlE1OYjK/QId3HnOjTThx9gc8o698xW6H8hqWF+0lZuTWi14z1juCTpcR6Vw/bn5VlZcP3yZXnqGbNK1A5ql2eH0zGG06i7NtE6HXvlOM1pkXbAUwjv7+vBvlqatgtveSVpTX9d6xtmCSXGssL/qqnuDv/d9xOjGQh4F+gjZJpjBdZVgbsw2nOibgmxtG04nzaJviQgt/H/tncIxiO58U6peOMXAf1z57TTNSvT8XtccuEI6N7obryn8PSYXX2b4R8+1f4f/Wngj1A80Yud+NbMlIrkQ3Yz3dGNQ8nVReL09Q7c7vO8LxUdn4EZZDgnqeEejwu+tHQm2mpRY2ugLv8xC5pLInRWH7ofY/1s+FOrBXx/XypK8Qhth5K6lJb5trYW2dUJ7mx/n1942ujz9zY6aLsHp+uojLX2QgxnyBfafIXo1lfsd3luMsYdkt9LF/N56OgDNJmcpN7+u6iRBmRNzOPw3RXh+JX2jB329QDtsHBcrnADlqUiWSeSMGegV61/tR3SnFxydcyH9H+9RDaDfq3yhEvHT7Xq2xTBaUfyRlDsz31iifQ6o/+SH0FyEvkUn9AuPX6JHqJbkArnHCl/zvIg548GPY2BPsg5jfRdzw78v/g9QYC5EtSZjZpxCqbwYg5jG2i/+F87A7vsHVLo8qR71cOR2zDQ4N+h+iQ15O2jrF1/PXGMTNwRnMjd3H3UH+MyICI04PlIdI3YLciK9EY//0n8ury0XFSnqvCqP7jD+FCB+zwscnPsfl+vUsPAcFrjWI98LxCnMrGnN0mHQhLzYGa2zfY4L0790ofnNzPjbwsjEq89xldE0QC3PeEvLiSDlPY5cL9C3A+aI8RmdbS/IRQUxgb3+K8oCgvvm2xDLkx8TbnZE+je71Y4GcZ9FzypfhTe56IW8+ZNcrdq7M9ZR7+Xq0dYa3GyPepPb39mMZN29sRPGWv8BWU4WSwhNwDU/BO9jKNrqINZ9Ci1sXYBh8mGo9jo/8iYVvDC3F6UhI3orcwwXYmfj/4L3/2oMc0wq1jgl43Q2wpsQjxdoAt/eR7voVSCu6iJZ/foR3NxSgorYCpUdr0UkJRN10I5ZM6jvGJokSDyM9PhpRUYlIL24kyRoxZPMm5J25gGpbIY7RDV0Yqa/UHttGDHuwrRYFybHKBiKOr1F5/EOkpX+CC10kqXhyA8Upq5C8bS8OF2YhMX4jilzfoVZTtwezVF7piaT9aMSnHybymlQ3VVkJS/lt9PfWq+O8hM47lcrvVR0Yvk83YaHlbmWzr+LNiI+KQlT8ZhS7HqiOooBuZlJuXkXaiEVSdhWTg2LAR7DFYkNNTSkKj7owPKtughi7C1X3H+K+v9z7BOOuA2QM6/DfOYX4OKAHqkuRzqd1cr4XKlOla6Rv/WiwpiEqpRgNbq5NXfu/dp6DOYHoKXotsivvkjGrm6HEZqO8gxDFdBcqzWuwOs2ED02piInLxYWBCeW+xCSkbTMjIzkJ6YV16Hnyy4LyYgSt/zubNK9FfMpfUHq5EZVsbNW4P6xs6BJrqcQdkX6+vInO+mKkRKXB2tBPSIpuIrMTCaTu6KTdqOwktqI2y2zqVjm5bwXMZTfgfjKMO6e2kzFuR1nLL+RevV9QO/8Ww5Oh+lX2OUhEiuUzVJQXcO0vQu/3e4V+1X7Xb5/XcNtpVevuhafjNNKjk2ApqWGv5pVY9qO241qoHzS2oTEgyxu4HND/r4o/6PmDbTgksHs2cYtBouaooXk8aTmClIQUbMv9FIU7Cfmv341jhf8dOr5e5fMEBoFs1++ykvv1eq5Gr3c8VIcimyHB2pyShzO1lbDln2Qbjgb08t978fnHWzjdqv0gPXrWfAS7DZ5kiHnFjUmRnXv5VX0veiotSFydBtOHJqyPWY2cCz3whvSRhEzm66nILr2BoYd6fqLHrVJ7o762FTsykpGYfhB1PX47ng9yc/E1DA1R/VP+qkdvn0stE84W+OBsRJxJuCPE13Pw5c1vUWbmeYnauZ7b/RIlfQyJSQK/+O2Rzv/U2xnmA3KyFB2HVWgnUwv0gQNJ8Ip2+5MqlU/1Mg+RzXv4r09yQTfZYlzhJf13fIiUvNOorT6K/GPcprAEYrvhbEzISwKZ0P6LuPanm/gyhBOpDJ4vEDv8IElaUVHo0yrfYnjp37hdazx+luBFr0POySpU2IpwlPrn3GgE/HoZPR262EPqNeZXgRyF8US5jvrjth0ZSE7chMI6wmmBPgt4LK0YV5q+wMboBCRnbIPJtB05xf9Ae28rTvG2r4mpagzU9V+ow3u9+CkQU4cxGuCB7+CZisDnRONkC2XrEJWoO8IxEE93oui0FTtD+P0/eDIU6gcBLClWLlaXi4iVfCwR+o2Ic/wQ6Pq9rfgkJzPMeMQ5Lub43GgQ7oU4iL/2p0406dsovIBrlaLxqjlZSMzRTo5BjzwuKlDeRKZ5NMmVW9mDFC5OsBzezxPGfHp/5F7Arus7jfgm2H44zos4L14059H9faht74Kt9h84qYlLKnjOF+YxnF1ml+LyjXOCHCiMjxDvC40Jojzgti5H5XLdf1XjsxAZ9uGXi3q7e87pk8T6/tuB/P+n/v9wfPSUyy8IrzCBLJTzLHZOuVTe1M8pyXhCeNOH6Z4qxeZNFpjWxyMu52vc48baMUb3SFoO3vxjYJkWFiQkJCReH2jwPXaua4HPld4WkAA61od7HjdcB0uW6Rx1ujL+vrJxovqLxBuEuTHcuzdCJnDHcGjJZ/C/zZjB2L0+eEYbcfBQsyapXm6whQX/07+3FmTS03gMRXUDmPUO4m5bG247S2DTfQb3xsE3goZjdvTwn+ZISPwZYcD5f5w8ZjF4FTmPCG8pb/4BIBcWJCQk3jJMouvq9+KnwG8dJtFeshHR0etR2PiyK9b0dcDtWJd3FEfyS9GsO4JK4k2AD1PtpVgXHYeUwit/EBteJKZ+QMm6OESnfIZGzZO2ZQb/VPaWmz2lfjsxhf6L+5CckIwMuufKzj2wnm15w22H2HnXDdx4lfqVkHgrYMT5f6Q8ZjFYzpxnIbyNvPnHgFxYkJCQkJCQkJCQkJCQkJCQWDLkwoKEhISEhISEhISEhISEhMSSIRcWJCQkJCQkJCQkJCQkJIwxN4bBEfUYSAkJAV7bwgI7vmRFcIdgn6cDPzyYgrfnG9jMH6hHtAShv/6NhW8Ud354GDw14rWBnlMrlp0e9Lx6p207UtSjUBYNemRL5WcwpRxbeBOsSGURSX1cXVpbmIPnzl08iETg9Bx8pw3mcP0WgfaxfB8y0i0oOHIE1lwzMjanwcKO1FkkAmOh38CbsEK3C/DLQPGj0O9Y6c7HVYWfoaymHNYtmbC2LHTm8UKI3M4U0A1zbMgtLoPNtAEfV1TgYO7nOGfLwruLGLfRuF49wutocX3j5bERBYLN+l7aPyPGy9of3an5ypL0qYdvvB57ttnh1n/uOPcAjQfzUHzuMEzvfqrlf3ZKT6pQhktBuBgUOeg56AewjR2Ntfx4XbHQUCc6vM7YLPYNurP8NZRkJiBqtQkFNhts5L9H8jKQoDla7vcC9ZPrKMneifyyatRWHEa2uVA5NcrnhbupFJkxMUjK+wqdYzoe8T1Cy1ELMnM+xZGCHJjSMl7CLo2g46SmXnRWFaOwrAoVVhNSrd8Z+vXSdb+IuP0WINIYYMTtr82HXiYHCgGxm9bz+Mz0Mly5FBDbaSmFOTMH1iMFyDGlIZnJ8yXimS6WLF++pMNCObHvPio3F8LF28DcKNodOn3R0wMO7UXBmWqcKdiLQ/7TA+gJM5X7sav4JE6W1qHLOwVP2zns2fcNhiMKRDPor/wQefrNDAX9orEhNzYKUfQEg4Q9cA7T7SYJD3eex55dxfjiZBm+6nqKWc8tlO/5TP17ePj6K7E5z6XTX+T9Espryf3i4kpyHsod1SjL+wDpuefVk0PEWI787ffLd18NXt8bCySgPhh4rAh+rh+ObDVg6s4kDoC//o3FNNyOHCS98gmBAYxkFwL9ua2LhU85t37B3bUXI4tw9enqCtgCSdjcDmQnRb7L95J2BVePeErMdrAz+xXQPuUhe9ELC9qx+LxDGNAnk0sF70caPIfbvl0903cZELGdEbBzgNep1ypnRi/a7gzH9XqwoI4W2zeNPIzwsv4ZOV7O/paozxDMYsixCzFRm1DW9Uz9TcF8VxnWGvorCfwP3Bhbjs2XIolBkYKduZ2IqHWn0PUqdsB/LbHQWCcheK2x2cg3lN81Z9T7+nHhaD086j9/LyjH3a1HYbNHXWjyYba3CltW7IK9b0q1t3j13HctXvSUI5Wdf07vXGrMCQMdJ/ncdmxhZ9azfy6MJel+8XH7jcaiYoCB/b5GH1rWk1FmWmF95yW4cil40Y3y1BRYWyfIP1Rbyq5jfr/0eMbHkmXOlwIwyonVTZYLq1BRchq1Z7KRartFoqsP04M96K4/yOXGyvHGK7OVIyp9o05krzwA17hyPPRK8wWWo861nYDFVo6S7ByU9xCOCQO6sLVp3UHUVZzAidqTMKeSifnUtEG/ptBTfpDUq31LgR2LuTIbdjf5nXDaMcsRnC/JQVZ5BKdc0IWdTfSo3QqUnDiPM2xhf2KR/RLJ6yX7pY8rzPYSsbHy3gIPTF8yf/ud891XgeVZWJjrhd2ciLhcJx48/BaFa95BSvF1eGbHicCzkO8axGjbeeSn7YFjeAZPO86wc7hNtq/Q3HOVBNlEbLZ8CHNGCpLMVeiZnoYncP0c5sY64LRZYNr9EbIz/dfwCZyyQpWfX4IK63ZsKr2FSf7PlIyGXThaWIqa8nykpf8VrU9IvcPf4lD+MdTYjyPHbIWzb1I9O58S52OMddBz+9VypxO2rWbs3r8LmcnJMFd24bend3CKns9tsqG2+T9wtztQvNWEndvXImbdJzj+yTpEpZG2CPHNPfoGezb/De2ilS+28liE/NLTsJq2o7RtHLOGY57BsKsU+2xnUX08G0nRepIXyUIx/JjkDOzYlobVidtQevsxfL4RNH1eAFv1aRxI34s6Imv2xPAQ+a3mPEpyslHovE/okfqnEpxuP+1m5/bG225jeuwOO8eVOZRGFn34dfgqSm1ncN66DalF19Rza4MIBjtBf5/o5BqwhVF0nMpCbKwJttrraP/pvEBXRBZ0hfdoEWwVFThuWYto1g61gYX75Ad7mkDkam19qv6iYqod1XU9mBfJaM6DTqcNW027sT87E8lJ2ajs8cLHy6XxBpqq9iPNQoPijNCmppmt6vr5XFT3hNaP+v2TAUK2/Y2wqW1+7TjPykxHcyNosW1GdNxHuHivCbYN2Th1+xEhQ2pTf4etvBzWzAwUNY2QWsLZmaCfLzxov2hT+3QOp8v+B4f9/bjUjjF2PrionRMoLKlAed5mpNua0PcTP66n8DSVIJ/Ud+bAVmTX8Um2yK+Jfy1CP/RINI0PuB+iLaAj8mfPNXxOOKL6zCdIz/4aXUKZU5BxNP6V9LMS9pK9MBc60Tc1ysmjGpfa/RMNCrXv+2yoqD4GS1Is0dFNPBXyDz1r+QYqcs3IysvFjrS1WFdwjrTzsVIuvoEnPuJH4a6ZfaQZG/OT0uMoP29FZmoxmjxzuvGq1zHMYaz9X0G7ovp8LpIzfYJwiMh5B7YnGQRkmlRY9sG6dy1WWFtIgqDAN9aOizaT4t9f1+N25y04infCtHMrkmK24sz/NqMqf4v61pDebuhZ3eHigB/zEcQgcqNAPiK86CM8dOAg9ibS5NfPGRHog/YtpI1n8PBxZOMp/O/3fv5T+Nl11IqSmtPISyPJWOsY4fGJkPgh5PaFYKATqnet/93TxGZR7DKgVTIJuIvK/AMorbDCtOkE2ianFuBAvW/okzb9wgLhvYGfMTA9ZxjDtXKtwM8P9PHfG8w1iC2Y0lYjJsWvdxF3iTBNkvAMRK0kMYctDqigi0/mBMTSp3PhFhaikpFH4gw7xWK6D21ddELFQ2T7Av4R2fDokJaTWlpwNeBzDnxNy/4kPcTWHnJ5nFgmz0V5y2+Pubh9A/1Tw8a2KbKnEE65iweGupjR6bkSfTOhPqbYnQmWvWakrU5ACssHqN0JbMLzI8dDZALwVQkXAx4L7CKc/ZI+Liq/pfXpcxcR16nt6uIhy7ViN5CxbkdGwMd4LMae/IuwJCYtOVYZ5T4GYJO7d5CUV4c+lj97ca+N+MecNp5RfinfcwAnv7CiqK5fkbdB3J/z3FZjids4X/LrUSfPSHlMOz/o141vHk9uHUd6zLvY5ejVTHi1C0GPUJ+zJnh0NH1qv2ENcup/JL+nINv5kPz+HO6vK+Ea+BafH2sxiHmh8D1phS09Eat31aI/sCAu6NfULdjWxCAqKhZJltNoYSe90Lf0PkKcf8HD7cRpVwdcn59C66Tx030NfI9xi+SjMauJH/QHI07E/VKhldfL9ksXV3wDsGcmYG1ZB6mZgHBi6PxIML+61Yf7VbsULp1WTydS+xh5Tvn2YpneWJjDqDMHcTn1GCdCbi/ZoCpiEm3HjsM1MYHBqzakRVnU5Ih7QsTKSch3kaT7WTMK4t5HSfsId/0MvAO1yIlTr2ErpvQaXgHU+dYqgXrYQZS0X/u6DA3q23fD7n5OSGoAjh0WlHd3w5GVpa7CKf2P3WKHe1bfN1K+PYyBC7mIi/uEjGVeedq+qgTt89xK1dwYuquIDNg1ExgZ8WKeTlBjd8ExNEsShkp8bPSq7Hg9cmLoWKdJ9y0k+biChwZj9nkuI/f9UrQ/JzUJV49FsiB6IP2MK2jGM+IGbrsFManl6BlxwrKB/D+Z3E/eqMMVzzSGHLuxvrybTQbY6mjsdia3oPNyY1YdKqTsG4UrPxeV/YSAPKSN6IyQ1c5gfcb9VeryamxHQyIhdkTLZCJavw/vl/xI6JZ/MyJ8nxTMkz/vRrTfVkNAn+yFyqim8ydcyFmNuHwXJkhy1Gp9D6tK2kltnFy8AySJ26SQFhmX0KYE/TzT1i6umx+/BkY6Iv2lq7mJm9lq8O5THYyg2W9bq9BPkhU2dmYbYezMSMeaPgna1rXz86AD27cqr1+/cNdix2bS9gxfxxCclh2kbhJ4Jm/CfiU41RX6dc/k4vRD+875QENfb1BHzBb2YgOpaw6PccN+HR4DmfuGHMhaT+phjT6EM/tdbKH+bqQj3zDqSfJV0k5/p/1Zq8iJvz5QJolb61+JD2TBPjgL36AdmVFbmexZmfnMb2GvaXzYpxnbuOsgtrKEhcp4FVLLOzCiH6/SWxW8PkV+kIWK27dQ5ZezdxQjIQupJA1qL4OlsgfP2o5iTezHqB8PXhP0b5J8dlerHDgC78gwRtwu2NJWMq6g8g6xm/kwcYBHiJz1MWhSIB9lrFqQeFd6AJV9o0Q27yM2l8RARvKk/2F19jy0jZPX0amJI8NwB/jvNyLzPdjK4sgM3KS8mfRpPiR+kMmrzq6veBZKqhbSic7/Gn7WxnJR2+qdWigJXwyzvUE4TGTy3NhvwIEGvqFUpEKxxWACOI7W4oNKn0Q+1NKvjc/DHagNif9V6OyncXcNCprHSZ30TZSVSi4j4kihaVF5JQb7FYDaXzo+xm/ihQU2sS7fiYSoOCTnVqLNQ5N4LUS23014NJR/7uGxyIZ5+RDwMTVYpv6ts7UzNzHA6T6Uz0+hrU+ct2ji9kK2GWJPjXg8dlfLKe4GY12E5GETAj9uQz+zuyI0P/Mp3B2zAWUd/xHkhP8Xt7t4HholE9qg/ISxcz6M/WpymgjyW2GsHQjluol+QTycVmTP5DGHZ81FiGO5q1o3QeT2pItnwnIEsYroRZxPK/0JBV1EOQdzQjSikz9CZRt9GELFyOVT/FsHbBFvFZItJWjsuiuI+22YGAzGEm1c48rC/OJZ5DzGzw/YOPygfc3mnsBbNJ8Vanxlvh0lqxI4rqBtJpAxNOBqwRZYW0fhuX0ex+puoeWolcw1vBjruo5vGu6SXIUFIiHoW0of8G8GsM8O6dzAoF/Er3qav0Jx5kpEU9+dJb7bfBBJ1hZ4Pd/j7LF/oqPlBD5y/ILnY124+k0j2gXcFYCvH/YPNnNvLCifiS66XwQaeZEI/VL94uOK9xe01uxHSqL6pplB7m930wfoofOrru+D/Qr2MfKc8m3Gsn0KwYT8Dk2WvOgqI85OCXb6R5z4/JpiAJT8/MlIWHIi5UiuD4A+TWtG2/AkPM2HkeK/zw8arBL9hqciEMCUHxnpRe+Gc/imsC3eeINlLXFoDZzAN0ICzFpiRG3oOHuULTAI4fOg/coPGJ4aRnNxmmLUwjGTIBkgNYKIZfFU00/6XdMG+ru7C/as1UjItKG+b5IEXi4xp2CrdUmwOIcMxmxQpsl5fCas1bVwfE2fjiSyOngE6wvfX94WNDIWyui+ZgyB6yfC90mBmiDobSgAIxn9HF4u5D80WfInnfxYFu6nQd1C/VMYtU8xQf79PqJilAUvMgCWbMRnWFHtUJ9YRVvwxZndC9uZkY411/JtG7eTGOibCk0dU+iz70JCwlbY6u9rz6IX+fVi9TN7T+cDvI58mO2zIythFTJt9crTEqHM+UkTxSwG7VmItjiNg4aGfwx0ypd5PlxyWUnWlbGNo7kgFRnWCjjUpzTRlot4qB+vBrw+I5GzCE/ReqhIsb3ZDpStW6EkrOpfDf2bgSZVq0mbA0zeIXZD/rVgHOBhJOdA2S2QD9GnensAUy04tLsOQz5iK12nsC5aWYhlCKuPUWEbw7wMNPU8JDLfxMlDhSh+hNj1QlhIJwL/48ciapvdFwofmSBeaRvClOc6ilNWMNsRcuCogW8o1ahQfg/usVAES/KHyj0GutXYViTxX/XjKFMt7oVwF7lWuFjzkgsLDDMYbi1HTnIcopP2oJYltX4oXKO1fSP++QI1Ihvm5UMg1AFb8BfYWkD3swI+jyx/wkK2KbQn3gZEcUSrC01bQp7T+RjjrlUwnSiJwCYIAuP6VdyX7m/C2y/vQwb2GoAw1v4SynUdTkE81MpDKxuKxdiTznaMyvzYDMoL90kE+vZAC8pz1iE6ei121d4nveJj9TO0l7yvTu6ozNNU/+Llz5f9sWSBa4T5BelJpDymmx+EgL6JkVYQsvitkYfhwkI75rx9uOG8hKtdo5jqqcRH5bfRbf8Q6/L/DffARRw+v9Dr+xT07SpL6F4GBv2iUB5MbURZF+Ek3yT6b1yC82oXxqa6UP5RBTq6a5C1rgD17m7UHa5F38IdYG9opYUsRi+uXyH281L9UnQWlZwLa857iEqkn534uSWynMc/v7I7rAKbiDSnfLuxfHsssEn0euT96184ujsbO1Zk4ETlKZzwvxoaCZEakZPRNQHMw9vXgNKiEtTWfh5YUQ+A1qV/esUSC+6pNb0m5iPUjy7jwgIhwyn6BChpI7YUXGKv5ghBHKGv/iSKjtag9oi6Aisc81OFSDPtGKR1RSwLwURdlQfdeKSucBPiE+iqXD9xnCTuyRwlsbXIqX9kMGaDMq1ffRpghGB9EfZX1alGxkIZdZE+E6e1D7CEJXD9YPg++aG8jqo+tQoBJReRjO6Hlwv5T9iFBWE/DeoT6p/CqH0KH2ZIWytjTCj/md6n9EkJyH6o/VzIzox0rLk2dOyidkKecurb802gp+4g0uNXI8t+jyQUKjg7DmKx+iHVa3xgUhl7IKki9tlTh8L0FUjIsqPvt9sCmauJWODJmTKuGPoGl5GOmE0rT3U0cuKv58ucDyy9zC8sULmsE9i4bryaPQN4uUUi51DQTaD2bszBYTYZPILPLCmI5vYmMPRvBtoGTQb72ThCn46HiQM8jOQcKFMeEcmHB9X7AWzMsbLNA5XJbQLWlSlvAoXXR7+wDY0MKDTXJws2tRLED/qzxq75yakW4XQS4n/8WAzaFsHnvY/60mIcra3BEfVpoSEHinxDqUaF8nuwvRfw3m3FXS+xRgPdauQaSfxX/Tg2rx7d5P+13GWEpX4KQWx35CH6/3MPY36xe9tQmv6Ozs5FnGnEPyU4IbJhXj7snwIdzFFfE9haQPcKl4TIJBLZExjaptCeeBtQxraQLrRtiXku9Boy1uryCGyCIDAuJR8L6QuTURj75X3IQGYB0GtDYq2A627bBfFQO1a9HsgvbAyR2ZMunhmV+bEZlBfukxa+sXvoHlP/6HuK9tIMRLNxPmf9UmzEh+nO00hfc4BMHjth35UHh5s+meblz5f9sWSBhQXaX4E8I+axcAsLBtDKg8ZZ0acQj+i/FPgG4dx/HE3ddciOy4FzlNz47DucqPDH5uUEldsm7Rs1JNoNOz/HoaYfcDE7Wf08YxzNJ74Kvkn0CmFsP0vpl6IzZlOTxO/XrUB6aZv6QCvCnEe1m/ornwtsgl4QSU75dmMZN298gUnXfsRGvU8E9BDtJWmIWnEIrVMqGfIEs1hyMrrGD7bD6/rQ+/xgmxWtQOapdmIgMxhtOouzt9vhyFqjPpmhr4KW4v3cy+qK7Fploxj2WqzSViTEITRw1rc1qlH7MDd2H3cHvaQUBPumn22epBA4M2qDMbNJb4wFbGMS9tqc2lc/hLLgJ+rKWNfnN2J8+BvYHG74fB648lNhcQ6AbuDFPgmhHXz+I0re34d6Dx8IlDG/Y23FDHsasEatl5PFDHGUle8i1+lm7c0NX8O5BtKO0kOGQH3Pw/WXgNOpRsZCXY2xxC5G3dSGvZZGN8OavCXu09wo7t7oIHrneucbhmtfMmIyv0BH4KmtsmJ+vr4LA0IZ8X3m7YIvc/olt/JjCZSfiWTXgVui+kS+wGDUPsH0XZzeX44r1buRmH4andMvlIWGxI/hHKKBmH5v+RX+ffl/FrYzIx1r+sS3rS5o6Nq5dPH/w9qYbTjVMUHuHUbTifNoo7oK1DGIOpsTw755TLg+wTv8U2ORXxP9i23YQD8hPuDmdET0VXeK2Z1vwoX8dwRPr1Sw12nVz4bouNtLzMitJ+mOkY7Yd6OrYLb3klaU12WZTwltmtzL89qSy/zCAv3cKBWJuU4MUdun31Ofu4wWh268mqeyvD6Vzf4WlrMe9J4CFHGnOrA33WJozFBsK9S/edkFk0G2yaPebrw9wjgg4lxN3cLykEA+jcpCmx90svjhEW5nd+X16Zg1R9FG415YfdBXKEPb6L/FyYAicP0k2zzTz0tzo004cfYHMsEQxI8Qu34olkNYnQj8jxuLMHYR3x7r6cag5o0XumHZNnWDtKAehRz43MA31JoUKLbo51IFRP71p3C+q1noQxrbYp85GMV/v81RH9mGfNcjIXc1DP4Kz92buKt7tdbn+Rb7ktajuNW/3wThR/cFmOPUxVzWhm5hYZbY7idVaKot5DZ9VF7dTtTtei+y/dv3agX8043bIhvmP/ckEOpgTtmoVW9rU5zuhTLp/y5YNydLjexDbDP45qDYnsLHkYZB9S0hAk1bQp5rxMBt7hoay5IL4HrcF4FNEHD/FvZloD28/fJ8wNevb4tCFGv/fRkVeq578KMgHk5o5KGVjYLI7UkXz1h5abFK3CcyNk8HbtwdJePkMFyH3MLryp40VP80p0ukJ2poFxbmSGy+UGmHw3kdXYENHXnb4csRLCwI84vRyHlsWRYW5uCpz8NK9RM7djrDyjyWkyuYx5NmG/YSf5ql97GHQfMYv3oW59kmjiIufgmQOF+67W9o98/rCHxPruPg3joMMV5RP4EZv44T57tJdCF6Ecad5YNWXkGE7ZfI1lT9+21q+uezyIxNQ3HLGPlXJDlPcH41SjmGbcT7grhHEeJYHyPPKd9mLOPCAsFkE4o+pK+F0gBRBVOxugmUzwt3QzFSotJgbegnTkq/GVuL+JSdKDptxc7YlbBUdWD4Pt0MhpS/vInOev/19zDi/52/pqqb1MNaJfUru/gnJG9F7uEC7ExcgbSib7kjV+YD32hFRa9FduVdci8lokYUb/kLbDVVKCk8ARfblMSLnkoLElenwfShCetjViPnwi3c+ZIYVOwuVN1/qGzKQcu9T0B3Zk2MT0W2zY5LJ7eT37ejrOWXYN/o5LuoSFlFZEFuHaIYKQaXzJRdpFchedteHC7MIvWtx65jhaFyYWNWvsGk/du2IwPJiXRn1XCyuIiWf36EdzcUoKK2AqVHa5XjUwjRm1PycKa2Erb8k4GN71zFO2CxVaKmxIqj9GibOXXzkdhslHeM4teeKpgTk5BmssC0Ph5xObUY8NLvhVVZlH6LO42HkR5P5B2ViPTiRgzToO4HX99PnWgK6e9ldDT667qEW5c522GvYiUiJbsMLZ4nAl31wPvrXZSb12B12lbsyEgmE+iDqOsZxZArtE8skSFBtbJPE/aVVenjO7AmKRMfWw/DeuAj5JdeV8YhkNGsV9nUMtZSjfvDHYFyLycXS9FplH+ahqiUYjS4OTvS2JQnpJ9DT/5jUPew6kd/QWnLowBx06dBouvvj3TDWZCFfa5RRrqFSQlIs15G/5NfyHg2I54eJRS/GcVU5+HsjCVROnnO/orBW+XEVlfAXHYNnV032OaeseZTaHGTwMLkJm4ngfwWnbQblZ30SDY/P9Bx3USteRPyzlxAta0Qx+hmdWoPxH5Nfl6MfnpIws/5QIv7Hhqsfh09IS7yIVLyTqO2+ijyj9FNrfi+BWWuyOMItlhsqKkpReFRl04eN+DWBHe179SPtpmRkZyE9MI69HgnBDZ9O8CHxc0DGGKvvlJ/6EZfk1qubw97zWcXLuPCZ/6x/YpZyn/piUR/0YhPP0z475lgvGp3aX8HWwX65OU8iN88t3DKvCJ4jXo3u7+vDnlJG9QYoP7mvoSCNTFkAvM33Oq7h1uMFyiH9qDvzhcwB2T3nFzbAGtKPFKsDeTf4wK7EXHfBTgOh3Ku8t3yAjGo6j94MqSXD8cR9Mlq3T4kMVmq46Rx7ptCrIlKQGbJZdxyWhfUh7WhL7QNtxt3TvFxRBc7GbetItfHIim7ivF4aPzYiKLyz2HScPsTQeyJQCeee3Dw/tfSr+3PuKDtS1/h8MoY3YSYJMAtR5CSkIJtuZ+icOcaxK/fjWOF/y3gwCf4Vegbfv8hfey/AluG7rjJAyYkvUOf7P4a6kNfXsP1Ml6uBvGfTZbikJxTipoKG/ZRPw5wvo672CL+SsEGpbTu6yjZ9SEKz9SgtvIYci1F6nGTk+hvPIqMmBisNn2ivunyKXLSlL0cploP4720nShgx1RaYd5yRGt3FCLOFPEPnajpfXxoTMdJT9ArjEOTxJx1tvbrhFb3s3qZ/IInbEM/Qd7Cx+0fa0LzDhWhtrwB+RUXcJLnFJEu1Pvp8XNa/xHIgMiTTUai1yHnZBUqbEVKnsP0preJSXg0PET6yseApltoDOkLsc+F7Ffj0xHkt8JY6xFw3WX0dOjj4aSxftXaI7cnMkllmzSS/pXfwRiZOC8tVt0xyKc96CWTdrbhJu9QM60ofi8dOwtOwl57FlbzdibnWW8/F6vH2L3RZIKWYTLBZM5FsaMTvwrjfiXudF4KxpIRLkfS5W6h+cXieCyYE39nuFk4D5/3F7QwniK58S23YgPU3kXHTVJM38WpPeXopBtrTv2Akg8+QlnN31F6oUO5ly1K6bl4MSDjdX2ChPhNKCg/j4rSClzV8JEXnaeKcKqTToon0V6yC7llFThV6lCPaBTPeZYLQnkxhOuXstCttTUa+9TjJpPycaHTQ+Ts/xQwCyWuXpXzdPMjUpf7omB+Nd2FSjYPMeFDUypi4nJxYWAxOeXbi+VdWJDgQEj4Xh88o404eKg56NS+ETQcs6NH84qxhISEhMQrgeRcBa9RDvRb3GPnIjne6w2D6ImxxB8ORk85Jd5SkMl346eHUfdwCt7BDrS1tcL5+YmQzxj+jPjduVjG3z8d5MLCqwJdPVwXh+iUz9AYWOHzYarrBm7on0BISEhISLwCSM5V8DrlMImuq98rT/rfKvBPZFuX7/VhiTcL/BuTmqecEm8tZntxcU8qEpI3w2QyY2dOMc62DgWf7P9p8XtzsYy/f0bIhQUJCQkJCQkJCQkJCQkJCYklQy4sSEhISEhISEhISEhISEhILBlyYUFCQkJCQkJCQkJCQkJCQmLJeKULCz5PB354sNRva+bh7fkGNvMHi99IyTeBHqcN5pRjf5jNedjpBStezc6qS8ccPHfu4sGLl9CVhISEhISEhISEhISExFuNV7ewMNcPR3bGy000X2KH5j/crr8+Lx4MPH6DNqPxYc7tQHaSKmO5m7aEhISEhISEhISEhMSfEsu0sDAHT1MJ8m3ncObAVmTX9eNpxxmYYlfAZPsKzX0P0VlZhPzS07CatqO0bRyzYx1w2iww7f4I2ZkpSDJXoYeexcrOzy3FPttZVB/PRlK0MlmlZypX5h9AaYUVpk0n0DY5BU+7A8VbTdi5fa1yHunMA7iOFsFWUYHjlrWIDllYoHX/HbbyclgzM1DUNALf3CjaHYew1bQD25MSsO7TCnxVvBOmnVuRFLMNlT/fR+OhAthqzqMkJxuFzvuYJvVo2z6D/73Gj39QbU9BxH0vPY7y81ZkphajycN3nLTXdh75aXvgGH6GsU4nbFvN2L1/FzKTk2Gu7CJ94jDnQafTRsaUjb2mNKyOWa+MlZ5XrNeD50c4+PH2TYTI6LlIV789RsepLMTGmmCrvYF+7y3Y4hOx2fIhzBmcPukZvLpx+TzX8Hn+MVSf+QTp2XUYposUw1dRajuD89ZtSC26Bo9Pb1NamUpISEhISEhISEhISEi8GVimhYUhOC07UN4zBUzehP3KsPYJ9ng9cmIsZFI8jWGHBbF5V/BwoBY5cUnId3ngm2mF9Z33UdL+jEw6LyP3/VK0PyeTUvY7rWOCVPERYkwOMgkdhMOUjLzLXeiuykFc3CdwTUxgZOQpRuv34f2SH/GcTFRnWovxjm5hgZ7nmr+1Cv2+eXicuxGdegado3dRlbMacfkuTHgHcff7SrVfI/COPMTPtbuxvrwb9AME36gT2bHbYe99qGv7Z1zUjz+A+Qj6TsbnOoitlfdJz6ksVyFVbZPB58XgVRvSoqgMn2DgQq5677wyzlUlaOdOxvJ5e3CBjqmgGc8wiyHHLsSsLUOXR6+HS+jtrubGOwpviIxOoa1PrCvNWyFM3+o1z5pREEevmRSMqwMjzr3YQMY3h8e4Yb8Oj28UrvxcVPbPgDQKS3QGkWVvqE1JSEhISEhISEhISEhIvHFYpoWFKfTZdyEhYSts9feVc4H5hQWfB+1XfsDw1DCai9MQRSfZ/N8DZX4BQVuHb+wurrQNYcpzHcUpK2ByDGontniE+py17HflVv2nED48ay5CfIYV1Q4HvraZEBu9G07PBNpsyYi3tSmfGfD90tUJ3wDsmUmwOId09QvGzyF838fRXJCKDGsFHI7zsJlWINrihIfdrWLYARNbWJjT3Bs6TgqvZky+QTsy6b1DwwvrwUhGwzcFuvJq2+brCZTdgnFdxMM+O7ISViHTVo8+elY4XYiIz4S1uhaOr20wxSYSGd9bUKYSEhISEhISEhISEhISbwaWb48FumFi3UGkx69Glv0eZvmJpm8SffUnUXS0BrVHNi2wsPCUPUmPyrRjULc44fPeR31pMY7W1uBI2krB5Jy+DUAmq/YBMj2mt+on3HOsbuUpPg/tJJxvU1lYSOLeHqBtrEVO/aPQ+vXjV3+miKzv61DQPM6uF+IlFhbYvbH74Zp4urAejGQk1FUkCwtdBuOimz3WoTB9BRKy7Ohz18IUV4TmZ/oVGWOZSkhISEhISEhISEhISLwZWKaFhUHU2ZwY9s1jwvUJ3qFP27mJJjvRIIFOQJWJq/HCArm2pxypMRbY3dPqpxBrYW0dRV/lNiSwiTKdhK8WTM6n0VOegRjzBbjn1E8h3ilG64x/skp+I9evTPwYziF6UgXdb+ErNAyOL7CwoHxGELvFDjet5vmPKHl/H+o92sm9cPz0Z4aZCPo+SfqQisRcJ4ZI39m+BOcalcUVP5a8sEDHfRTJ+Y0Y6w2jByMZ9X8n1JWmbaE+hwTjuowWxyk2Dt+EC/nv7IZzsBW2le8i1+lm/Z0bvoZzDdfhCLGpUdy90UH+nxeMhISEhISEhISEhISExO+JZVtYcJg3Ie/MBVTbCnGsdQw+9t38WsSn/AWl/6rGZymrkLxtLw4XZiExfj12HSvEztiVsFR1YPh+NSys3A0v3WCwfCcSV6dh244MJCduQmFdB9wtR5CSkIJtuZ+icOcaxL+3FZ/kZCI2djvKWn5hr8rTTRLLzWuwOm0rdmQkIzH9IOp6JtgbDAx0Ylu8GfFRUYiK34xi1yB+89zCKfMKxJpPocX9GKN3voA5dgXMZTfgpq/ps3t2wGKrRE2JFUddDzBHJrh3Tm3n2haMX22SPp1/ErbvdDLdiOL0RERFRSM+/TBcw9wxnT4v3A3FSIlKg7X+Du58uYvcuwtV9x/ifpVa7p3k2lQWFqKTc3Gy5ixs+0pZfb4nN1Cs0cN7+K9Pctkmm9rx8jL6BU96/frR6Yrux5CYiJTsUly+cU5wzX/wZEg/rmcYdnyIlLzTqK0+ivxjLXjiowsYh5EeH02uS0R6cSOG59whMp2nC1Rsg8mlHmEqISEhISEhISEhISEhsdxYvk8hJN4g6D6FkJCQkJCQkJCQkJCQkJB4RZALC384+DA3dgfllpWItZTj1qCXe5NBQkJCQkJCQkJCQkJCQmJ5IRcWJCQkJCQkJCQkJCQkJCQklgy5sCAhISEhISEhISEhISEhIbFkyIUFCQkJCQkJCQkJCQkJCQmJJUMuLEhISEhISEhISEhISEhISCwZcmFBQkJCQkJCQkJCQkJCQkJiyZALCxISEhISEhISEhISEhISEkuGXFiQkJCQkJCQkJCQkJCQkJBYMuTCgoSEhISEhISEhISEhISExBIB/P+YBEnjgYSgQgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_6.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

The first column of table 6 includes the measure for overall genetic diversity into the baseline estimating equation. Ashraf and Galor (2013) show that genetic diversity has a nonlinear association with economic development: societies with too little genetic diversity lack innovation, whereas societies with too much heterozygosity have increased conflict. Therefore, the measure of HLA heterozygosity may\n", + "be accounting for the indirect effect of overall heterozygosity on income. Additionally, historically rich societies may have attracted an increased number of migrants, thereby increasing total genetic heterozygosity within an ethnic population. Conversely, historically rich societies had the means to prevent migration, thereby lowering ethnic heterozygosity.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 7: Robustness to Endogenous Omitted Variables (author´s code)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear regression Number of obs = 109\n", + " F(10, 98) = 81.14\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7378\n", + " Root MSE = .11998\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .9999595 .2118165 4.72 0.000 .5796165 1.420302\n", + " ln_frac | -.0687966 .1026132 -0.67 0.504 -.2724292 .134836\n", + " aa_ln_atd | .0220791 .0427207 0.52 0.606 -.0626988 .106857\n", + " ln_arable | -.0203005 .0163016 -1.25 0.216 -.0526505 .0120494\n", + " ln_suitavg | .0070124 .0139013 0.50 0.615 -.0205742 .034599\n", + " ln_abslat | .0169459 .0166373 1.02 0.311 -.0160703 .049962\n", + " europe | .0821774 .0783703 1.05 0.297 -.0733459 .2377008\n", + " africa | -.3038598 .085273 -3.56 0.001 -.4730812 -.1346384\n", + " asia | -.1987743 .0862051 -2.31 0.023 -.3698456 -.027703\n", + " americas | -.0337169 .083025 -0.41 0.686 -.1984773 .1310434\n", + " _cons | 5.075976 .4274974 11.87 0.000 4.227622 5.924331\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 109\n", + " F(11, 97) = 78.18\n", + " Prob > F = 0.0000\n", + " R-squared = 0.8062\n", + " Root MSE = .10368\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .6574856 .2123752 3.10 0.003 .2359796 1.078992\n", + " ln_ypc60 | .0977108 .0220749 4.43 0.000 .0538981 .1415234\n", + " ln_frac | -.1265308 .0889372 -1.42 0.158 -.3030465 .0499848\n", + " aa_ln_atd | -.0006487 .0401033 -0.02 0.987 -.0802427 .0789453\n", + " ln_arable | -.0105789 .0161119 -0.66 0.513 -.0425566 .0213989\n", + " ln_suitavg | .0290353 .01418 2.05 0.043 .000892 .0571787\n", + " ln_abslat | .0089705 .0178069 0.50 0.616 -.0263713 .0443123\n", + " europe | .0967318 .0609128 1.59 0.116 -.0241633 .2176268\n", + " africa | -.1342939 .0807126 -1.66 0.099 -.2944862 .0258983\n", + " asia | -.0661328 .0708522 -0.93 0.353 -.2067548 .0744893\n", + " americas | .0155833 .0634731 0.25 0.807 -.1103931 .1415598\n", + " _cons | 4.0475 .4733963 8.55 0.000 3.107939 4.987061\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 109\n", + " F(11, 97) = 108.99\n", + " Prob > F = 0.0000\n", + " R-squared = 0.8727\n", + " Root MSE = .08403\n", + "\n", + "-------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "--------------+----------------------------------------------------------------\n", + " ln_hla_het | .6127261 .1633457 3.75 0.000 .28853 .9369221\n", + "ln_yr_sch1960 | .1320134 .019477 6.78 0.000 .0933569 .1706699\n", + " ln_frac | -.0241461 .0610461 -0.40 0.693 -.1453058 .0970136\n", + " aa_ln_atd | .071103 .0282958 2.51 0.014 .0149436 .1272624\n", + " ln_arable | .0043996 .0115413 0.38 0.704 -.0185067 .027306\n", + " ln_suitavg | -.0081406 .0108674 -0.75 0.456 -.0297093 .0134282\n", + " ln_abslat | .0059583 .0124948 0.48 0.635 -.0188404 .030757\n", + " europe | .0285669 .0269261 1.06 0.291 -.0248739 .0820078\n", + " africa | -.1239058 .0453816 -2.73 0.008 -.2139757 -.0338358\n", + " asia | -.1525689 .0349806 -4.36 0.000 -.2219957 -.083142\n", + " americas | -.0317955 .0268294 -1.19 0.239 -.0850445 .0214535\n", + " _cons | 3.99027 .3344223 11.93 0.000 3.326535 4.654006\n", + "-------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 109\n", + " F(13, 95) = 58.98\n", + " Prob > F = 0.0000\n", + " R-squared = 0.8256\n", + " Root MSE = .09939\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .7528768 .1943835 3.87 0.000 .3669768 1.138777\n", + " ln_pd60 | .0001866 .0151894 0.01 0.990 -.0299681 .0303414\n", + " ln_urb60 | .1137048 .0222823 5.10 0.000 .0694688 .1579408\n", + " ln_young | -.0614236 .0886641 -0.69 0.490 -.2374441 .1145969\n", + " ln_frac | -.1367887 .0831761 -1.64 0.103 -.3019141 .0283367\n", + " aa_ln_atd | -.075599 .0396077 -1.91 0.059 -.1542302 .0030322\n", + " ln_arable | -.0064507 .0170226 -0.38 0.706 -.0402447 .0273434\n", + " ln_suitavg | .0133635 .0119674 1.12 0.267 -.0103947 .0371218\n", + " ln_abslat | -.009797 .0191409 -0.51 0.610 -.0477966 .0282025\n", + " europe | .0512724 .052395 0.98 0.330 -.0527449 .1552897\n", + " africa | -.2301635 .0573917 -4.01 0.000 -.3441005 -.1162265\n", + " asia | -.1139926 .0547566 -2.08 0.040 -.2226982 -.0052871\n", + " americas | -.0694854 .0447219 -1.55 0.124 -.1582697 .0192989\n", + " _cons | 5.523092 .5104961 10.82 0.000 4.509629 6.536555\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 109\n", + " F(15, 93) = 88.15\n", + " Prob > F = 0.0000\n", + " R-squared = 0.8897\n", + " Root MSE = .07987\n", + "\n", + "-------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "--------------+----------------------------------------------------------------\n", + " ln_hla_het | .529581 .1672842 3.17 0.002 .1973878 .8617742\n", + " ln_ypc60 | .0280348 .016719 1.68 0.097 -.0051658 .0612354\n", + "ln_yr_sch1960 | .1036748 .0189568 5.47 0.000 .0660303 .1413194\n", + " ln_pd60 | .0062813 .0126177 0.50 0.620 -.0187749 .0313375\n", + " ln_urb60 | .0342884 .0194949 1.76 0.082 -.0044246 .0730014\n", + " ln_young | -.0148322 .0685018 -0.22 0.829 -.1508632 .1211988\n", + " ln_frac | -.0582462 .0703963 -0.83 0.410 -.1980392 .0815469\n", + " aa_ln_atd | .024198 .0292272 0.83 0.410 -.0338415 .0822375\n", + " ln_arable | .0014524 .0146563 0.10 0.921 -.0276522 .030557\n", + " ln_suitavg | .0023245 .0111488 0.21 0.835 -.0198147 .0244637\n", + " ln_abslat | -.0001829 .0152382 -0.01 0.990 -.030443 .0300772\n", + " europe | .0255092 .0397006 0.64 0.522 -.0533284 .1043467\n", + " africa | -.0977018 .0504283 -1.94 0.056 -.1978425 .0024388\n", + " asia | -.1094775 .0438173 -2.50 0.014 -.1964899 -.022465\n", + " americas | -.0341979 .0351158 -0.97 0.333 -.103931 .0355352\n", + " _cons | 4.048212 .4632424 8.74 0.000 3.128304 4.968119\n", + "-------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "************************************************\n", + "*** Table 7: Endogenous Omitted Variables ***\n", + "************************************************\n", + "local ov2 \"ln_ypc60!=. & ln_yr_sch1960!=. & ln_pd60!=. & ln_urb60!=. & ln_young!=.\"\n", + "\n", + "*Col. 1, sample adjustment\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `ov2', rob\n", + "\n", + "*Col. 2, income\n", + "reg ln_le60 ln_hla_het ln_ypc60 `bc' `cont' if `ov2', rob\n", + "\n", + "*Col. 3, education\n", + "reg ln_le60 ln_hla_het ln_yr_sch1960 `bc' `cont' if `ov2', rob\n", + "\n", + "*Col. 4, demographics\n", + "reg ln_le60 ln_hla_het ln_pd60 ln_urb60 ln_young `bc' `cont' if `ov2', rob\n", + "\n", + "*Col. 5, all\n", + "reg ln_le60 ln_hla_het ln_ypc60 ln_yr_sch1960 ln_pd60 ln_urb60 ln_young `bc' `cont' if `ov2', rob" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_7.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Additional controls are considered in table 7. These include income, human capital, and demographics in 1960. The additional variables of Table 7 constitute more endogenous controls, as the effect of HLA heterozygosity may be associated with some general level of economic development, which is correlated with health, human capital, and income. Controlling for these alternative channels of economic development allows for a greater focus on the relationship between HLA heterozygosity, which represents innate resistance, and aggregate health.\n", + " \n", + "Once these additional correlates of development are controlled for, the positive effect of HLA heterozygosity on life expectancy prior to the international epidemiological transition remains, providing further support for the role of HLA heterozygosity in measuring genetically determined resistance to infectious disease, mortality rates thereof, and resulting life expectancy differences across countries prior to the international epidemiological transition." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### V. Conclusions\n", + "-

HLA heterozygosity has a positive, statistically significant, and robust relationship with life expectancy at birth in 1960, a period argued to be before the diffusion of health technologies associated with the international epidemiological transition.\n", + "-

The strong statistical relationship between HLA heterozygosity and life expectancy is substantially lessened by the introduction of medicines and vaccines, which dissipate any benefits from genetically determined resistance.\n", + "-

An important source of the variation in HLA heterozygosity is the differential timing date of the Neolithic revolution, which provided the means of development for a much more severe infectious disease environment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Figure 3: The Effect of HLA Heterozygosity following the Diffusion of Health Technologies from the International Epidemiological Transition (replication code in stata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we are going to write all the regressions from table 4 and save the coefficients in stata in order to collect the data we need for the table." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear regression Number of obs = 131\n", + " F(10, 120) = 100.89\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7446\n", + " Root MSE = .12133\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.01512 .1899264 5.34 0.000 .6390794 1.391161\n", + " ln_frac | -.1210012 .0927409 -1.30 0.194 -.3046217 .0626193\n", + " aa_ln_atd | .0369984 .0397486 0.93 0.354 -.0417011 .1156979\n", + " ln_arable | -.0086997 .0159756 -0.54 0.587 -.0403303 .0229309\n", + " ln_suitavg | .008039 .0128307 0.63 0.532 -.017365 .0334429\n", + " ln_abslat | .0199086 .0156461 1.27 0.206 -.0110696 .0508867\n", + " europe | .0387395 .0716524 0.54 0.590 -.1031272 .1806062\n", + " africa | -.3122101 .0754901 -4.14 0.000 -.4616753 -.162745\n", + " asia | -.2056222 .0755278 -2.72 0.007 -.355162 -.0560825\n", + " americas | -.0400593 .0736294 -0.54 0.587 -.1858403 .1057217\n", + " _cons | 4.962063 .4156831 11.94 0.000 4.139039 5.785086\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 76.19\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7340\n", + " Root MSE = .11485\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le70 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.04047 .1695943 6.14 0.000 .7046852 1.376255\n", + " ln_frac | -.1163928 .0881347 -1.32 0.189 -.2908934 .0581078\n", + " aa_ln_atd | .0246249 .0372331 0.66 0.510 -.0490941 .098344\n", + " ln_arable | -.0141638 .0153671 -0.92 0.359 -.0445896 .0162619\n", + " ln_suitavg | .0007926 .0135757 0.06 0.954 -.0260864 .0276715\n", + " ln_abslat | .0045722 .0158262 0.29 0.773 -.0267626 .0359069\n", + " europe | .0344497 .0460362 0.75 0.456 -.0566989 .1255982\n", + " africa | -.3192781 .0522129 -6.11 0.000 -.4226561 -.2159002\n", + " asia | -.1420921 .0484679 -2.93 0.004 -.2380552 -.046129\n", + " americas | -.0238244 .0420173 -0.57 0.572 -.1070157 .0593668\n", + " _cons | 5.198611 .3846817 13.51 0.000 4.436968 5.960254\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 55.68\n", + " Prob > F = 0.0000\n", + " R-squared = 0.6995\n", + " Root MSE = .10588\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le80 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .8420929 .169953 4.95 0.000 .5055977 1.178588\n", + " ln_frac | -.1462667 .0786894 -1.86 0.066 -.3020663 .0095329\n", + " aa_ln_atd | .0348747 .0381473 0.91 0.362 -.0406544 .1104037\n", + " ln_arable | -.0199753 .0150991 -1.32 0.188 -.0498705 .0099199\n", + " ln_suitavg | .0003745 .0142482 0.03 0.979 -.0278359 .0285849\n", + " ln_abslat | .0070102 .0152294 0.46 0.646 -.0231431 .0371634\n", + " europe | .0176881 .0373365 0.47 0.637 -.0562357 .0916118\n", + " africa | -.2552229 .0450962 -5.66 0.000 -.3445103 -.1659355\n", + " asia | -.1194574 .0422942 -2.82 0.006 -.203197 -.0357178\n", + " americas | -.0064243 .0328959 -0.20 0.845 -.0715559 .0587073\n", + " _cons | 4.945296 .3733601 13.25 0.000 4.206069 5.684523\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 32.32\n", + " Prob > F = 0.0000\n", + " R-squared = 0.6886\n", + " Root MSE = .10684\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le90 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .5490235 .1798274 3.05 0.003 .1929779 .9050692\n", + " ln_frac | -.183742 .0828562 -2.22 0.028 -.3477915 -.0196925\n", + " aa_ln_atd | .0383219 .035843 1.07 0.287 -.0326448 .1092886\n", + " ln_arable | -.0220742 .0174598 -1.26 0.209 -.0566434 .0124949\n", + " ln_suitavg | -.0046492 .0148575 -0.31 0.755 -.034066 .0247677\n", + " ln_abslat | .0247666 .0225831 1.10 0.275 -.0199464 .0694797\n", + " europe | .0189598 .0440979 0.43 0.668 -.0683511 .1062706\n", + " africa | -.2203019 .0524047 -4.20 0.000 -.3240596 -.1165442\n", + " asia | -.0726899 .0444554 -1.64 0.105 -.1607085 .0153287\n", + " americas | .0346061 .0405436 0.85 0.395 -.0456675 .1148796\n", + " _cons | 4.554555 .4118077 11.06 0.000 3.739204 5.369906\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 43.79\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7683\n", + " Root MSE = .09193\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le00 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .2512166 .1540813 1.63 0.106 -.0538535 .5562868\n", + " ln_frac | -.1775283 .0728385 -2.44 0.016 -.3217434 -.0333131\n", + " aa_ln_atd | .0843881 .0303567 2.78 0.006 .024284 .1444923\n", + " ln_arable | -.0062233 .0115616 -0.54 0.591 -.0291145 .0166679\n", + " ln_suitavg | -.0168243 .0100998 -1.67 0.098 -.0368211 .0031725\n", + " ln_abslat | .0137408 .0156327 0.88 0.381 -.0172109 .0446925\n", + " europe | -.0010578 .0524527 -0.02 0.984 -.1049104 .1027948\n", + " africa | -.2497432 .0608725 -4.10 0.000 -.3702665 -.1292199\n", + " asia | -.0924549 .052818 -1.75 0.083 -.197031 .0121211\n", + " americas | .0264391 .0515306 0.51 0.609 -.0755879 .128466\n", + " _cons | 3.85075 .309451 12.44 0.000 3.238059 4.463442\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 43.16\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7480\n", + " Root MSE = .08408\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le10 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .1787627 .1334796 1.34 0.183 -.0855176 .443043\n", + " ln_frac | -.1508934 .0700744 -2.15 0.033 -.2896359 -.0121509\n", + " aa_ln_atd | .0733 .0301988 2.43 0.017 .0135085 .1330916\n", + " ln_arable | -.0045715 .0094332 -0.48 0.629 -.0232486 .0141057\n", + " ln_suitavg | -.0138008 .0083383 -1.66 0.101 -.0303101 .0027084\n", + " ln_abslat | .0066167 .0136851 0.48 0.630 -.0204788 .0337121\n", + " europe | .0065547 .0508292 0.13 0.898 -.0940836 .107193\n", + " africa | -.2235135 .0590873 -3.78 0.000 -.3405023 -.1065247\n", + " asia | -.0839933 .0521013 -1.61 0.110 -.1871502 .0191637\n", + " americas | .0163142 .0512064 0.32 0.751 -.0850709 .1176994\n", + " _cons | 3.912044 .282363 13.85 0.000 3.352985 4.471104\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + " %%stata\n", + "*******************************************************************************************************************************************************\n", + "*** Figure 3: \"The Effect of HLA Heterozygosity following the Diffusion of Health Technologies from the International Epidemiological Transition\" *** \n", + "*******************************************************************************************************************************************************\n", + "local le \"ln_le60!=. & ln_le70!=. & ln_le80!=. & ln_le90!=. & ln_le00!=. & ln_le10!=.\"\n", + "\n", + "*Col. 1, 1960\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `le', rob \n", + "estimates store y1960\n", + "local coef1960=_b[ln_hla_het] //Save coefficient\n", + "local lb1960 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub1960 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge)\n", + "\n", + "*Col. 2, 1970\n", + "reg ln_le70 ln_hla_het `bc' `cont' if `le', rob\n", + "estimates store y1970\n", + "local coef1970=_b[ln_hla_het] //Save coefficient\n", + "local lb1970 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub1970 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge)\n", + "\n", + "*Col. 3, 1980\n", + "reg ln_le80 ln_hla_het `bc' `cont' if `le', rob\n", + "estimates store y1980\n", + "local coef1980=_b[ln_hla_het] //Save coefficient\n", + "local lb1980 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub1980 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge)\n", + "\n", + "*Col. 4, 1990\n", + "reg ln_le90 ln_hla_het `bc' `cont' if `le', rob\n", + "estimates store y1990\n", + "local coef1990=_b[ln_hla_het] //Save coefficient\n", + "local lb1990 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub1990 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge)\n", + "\n", + "*Col. 5, 2000\n", + "reg ln_le00 ln_hla_het `bc' `cont' if `le', rob\n", + "estimates store y2000\n", + "local coef2000=_b[ln_hla_het] //Save coefficient\n", + "local lb2000 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub2000 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge)\n", + "\n", + "\n", + "*Col. 6, 2010\n", + "reg ln_le10 ln_hla_het `bc' `cont' if `le', rob\n", + "estimates store y2010\n", + "local coef2010=_b[ln_hla_het] //Save coefficient\n", + "local lb2010 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub2010 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge) " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "number of observations (_N) was 0, now 6\n", + "\n", + "(5 missing values generated)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "/ is not a valid command name\n", + "r(199);\n", + "\n", + "(5 missing values generated)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "/ is not a valid command name\n", + "r(199);\n", + "\n", + "(5 missing values generated)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "/ is not a valid command name\n", + "r(199);\n", + "\n", + "(5 missing values generated)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n", + "(1 real change made)\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "********************************************************************************\n", + "****** Make another table where it stores the information we need ***********\n", + "********************************************************************************\n", + "clear\n", + "set obs 6\n", + "generate year = 1960 in 1\n", + "replace year=1970 in 2\n", + "replace year=1980 in 3\n", + "replace year=1990 in 4\n", + "replace year=2000 in 5\n", + "replace year=2010 in 6\n", + "\n", + "// Coefficients\n", + "generate coef = `coef1960' in 1\n", + "replace coef=`coef1970' in 2\n", + "replace coef=`coef1980' in 3\n", + "replace coef=`coef1990' in 4\n", + "replace coef=`coef2000' in 5\n", + "replace coef=`coef2010' in 6\n", + "\n", + "//lower_bound\n", + "gen lower_bound= `lb1960' in 1\n", + "replace lower_bound= `lb1970' in 2\n", + "replace lower_bound= `lb1980' in 3\n", + "replace lower_bound= `lb1990' in 4\n", + "replace lower_bound= `lb2000' in 5\n", + "replace lower_bound= `lb2010' in 6\n", + "\n", + "//upper_bound\n", + "gen upper_bound= `ub1960' in 1\n", + "replace upper_bound= `ub1970' in 2\n", + "replace upper_bound= `ub1980' in 3\n", + "replace upper_bound= `ub1990' in 4\n", + "replace upper_bound= `ub2000' in 5\n", + "replace upper_bound= `ub2010' in 6 \n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 400, + "width": 500 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "%%stata\n", + "******************************************\n", + "****** FIGURE 3 **************************\n", + "******************************************\n", + "twoway (connected coef year) (line lower_bound year, lp(dash dot)) (line upper_bound year, lp(dash dot)) , graphregion(color(white)) bgcolor(white) legend(rows(3) label(1 Point Estimate in Baseline Estimating Eq.) label(2 \"95% Confidence Interval (lower bound)\" ) labe(3 \"95% Confidence Interval (upper bound)\")) xtitle(\"Year\") ytitle(\"Coefficient of In HLA Heterozygosity\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/C.justinCook-The_natural_selection_2015/.ipynb_checkpoints/Replication.R-checkpoint.ipynb b/notebooks/C.justinCook-The_natural_selection_2015/.ipynb_checkpoints/Replication.R-checkpoint.ipynb new file mode 100644 index 0000000..54bf6a1 --- /dev/null +++ b/notebooks/C.justinCook-The_natural_selection_2015/.ipynb_checkpoints/Replication.R-checkpoint.ipynb @@ -0,0 +1,2319 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# _THE NATURAL SELECTION OF INFECTIOUS DISEASE RESISTANCE AND ITS EFFECT ON CONTEMPORARY HEALTH- A replication study_\n", + "## C. Justin Cook\n", + "### _Review of Economics and Statistics Volume 97 | Issue 4 | October 2015 p.742-757_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This study pretends to do a replication of _\"THE NATURAL SELECTION OF INFECTIOUS DISEASE RESISTANCE AND\n", + "ITS EFFECT ON CONTEMPORARY HEALTH\"_ whose author is C. Justin Cook, an assistant Professor, Economics University of California, Merced. The replication data for paper is available [here](https://doi.org/10.7910/DVN/27669)\n", + "\n", + "The replication is done by Sara Ariza Murillo, a colombian economist from the Universidad del Rosario. This includes two jupyter notebooks:\n", + "\n", + "1. **Notebook one:** presents the stata do file of the author that replicates tables 2-7 within the paper. It also replicates in stata the figures of the paper that are not included in the author's do file.\n", + "2. **Notebook two:** presents the code elaborated by Sara in R that replicates the tables within the paper. \n", + "\n", + "Both notebooks include a summary of the document.\n", + "\n", + "If there are any questions, please contact Sara Ariza at sara.ariza@urosario.edu.co" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook two" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Abstract\n", + " _\"This paper empirically tests the association between genetically determined resistance to infectious disease and cross-country health differences. A country-level measure of genetic diversity for the system of genes associated with the recognition and disposal of foreign pathogens is constructed. Genetic diversity within this system has been shown to reduce the virulence and prevalence of infectious diseases and is hypothesized to have been naturally selected from historical exposure to infectious pathogens. Base estimation shows a statistically strong, robust, and positive relationship between this constructed measure and country-level health outcomes in times prior to, but not after, the international epidemiological transition\"_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### I. Introduction\n", + "

Prior to the major medical discoveries associated with the international epidemiological transition, infectious diseases were a major determinant of mortality and subsequent differences in life expectancy across countries. (The discovery and widespread use of effective medicines (e.g., penicillin, streptomycin, a range of vaccines) in the late 1940s to early 1950s is labeled by Acemoglu and Johnson (2007) as the international epidemiological transition.) \n", + " \n", + "

This paper answers the question what were the causes of the initial cross-country disparities in the virulence of infectious diseases? the above, by empirically investigating the role of genetically determined differences in resistance to infectious diseases \n", + " \n", + "

The main hyphotesis is that innate resistance did influence country-level response to infectious disease prior to the international epidemiological transition, but the effects of innate resistance are dissipated by more efficacious health technologies.\n", + "\n", + "

The measure of genetic resistance is found within the human leukocyte antigen (HLA) system. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### The HLA system \n", + "

TThe HLA system is responsible for locating foreign proteins in order to direct cells of the immune system to initiate an immune response .This high level of genetic diversity within the HLA system is hypothesized to provide increased resistance by increasing the number of potential immune responses to a foreign pathogen.\n", + "\n", + "

Populations that are genetically similar in regard to the HLA system are more susceptible to infectious pathogens, as a pathogen is better able to spread within a homogeneous population, increasing the rate of infection and virulence, and lowering life expectancy. Therefore, variation within the HLA system is hypothesized to provide population-level resistance.\n", + "\n", + "##### What did the author do?\n", + "\n", + "

Using country-level aggregations of ethnic-level genetic data, the author constructs a cross-country measure for diversity within the HLA system: HLA heterozygosity. He tests the hypothesis by estimating the association between country-level health measures (e.g., life expectancy at birth) and HLA heterozygosity in periods both prior to and after the international epidemiological transition \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### II. Background\n", + "_**A. Historic Differences in Infectious Disease Environments**_\n", + " \n", + "

As reviewed by Wolfe, Dunavan, and Diamond (2007), the number of infectious diseases humans face increased substantially with the introduction of agriculture, commonly referred to as the Neolithic revolution.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1.\tFirst, agriculture allowed the development of large, dense, and sedentary populations, which fostered an ease in the transmission of infectious diseases, as well as providing a large number of potential hosts. \n", + "2.\tSecond, the domestication of animals in the Neolithic provided closer contact between animals and humans.\n", + "\n", + "

The Neolithic revolution provided the conditions for the initiation and sustainability of novel infectious pathogens. Therefore, societies that domesticated animals earlier and developed large, dense populations also encountered a resulting increase in the infectious disease load at an earlier date. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**B.\tThe Natural Selection of HLA Heterozygosity**_\n", + "\n", + "

The set of genes comprising the HLA system represents one of the most genetically diverse regions of the genome (Jeffery & Bangham, 2000), and this high level of diversity is hypothesized to have been naturally selected as a mechanism of resistance to infectious pathogens (Hughes &Yeager, 1997; Penman et al., 2013; Spurgin & Richardson, 2010).\n", + " \n", + "This natural selection for diversity within the HLA system is from balancing selection. Balancing selection results from two distinct reasons: overdominance and frequency dependence (Slade & McCallum, 1992). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. _Overdominance_ implies that heterozygotes, or individuals with differing alleles at a single locus, have an advantage compared to homozygotes, or individuals with identical alleles at a single locus. \n", + "2. _Frequency-dependent selection_ results from a comparative advantage of rare alleles. Infectious pathogens are not a static selection pressure. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**C. Out-of-Africa Migration and Genetic Diversity**_\n", + "\n", + "

The overall level of genetic diversity within a population has recently been shown to be a function of the population’s migratory distance from East Africa (Ashraf & Galor, 2013; Ramachandran et al., 2005). Modern human populations originated within East Africa (roughly Ethiopia) and subsequently migrated to all other continents, excluding Antarctica. \n", + "\n", + "Given that the entire set of genetic diversity was contained within the initial East African population and that migrating populations contain only a subset of this diversity, a strong, negative, and linear association exists between the distance along migration routes a country is from East Africa and the genomic diversity of populations within a country.\n", + "\n", + "This implies that populations that are farther along migration routes from the initial point of Ethiopia have less overall genetic diversity, resulting in lessened diversity within the HLA system." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### III. Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**A.\tHLA Heterozygosity**_\n", + "\n", + "

The measure of interest is referred to as HLA heterozygosity. HLA heterozygosity is constructed with data on SNPs from the Allele Frequency Database at Yale University, referred to as ALFRED (Kidd et al., 2003).\n", + "\n", + "ALFRED provides allele frequencies for anthropologically defined ethnicities, providing genetic data for 156 SNPs within 19 HLA genes for 51 distinct ethnic groups. Expected heterozygosity is calculated for each ethnicity. This gives HLA heterozygosity at the ethnic level; However, outcomes of interest are at the country level.\n", + "\n", + "The author aggregates ethnic groups, and their respective measure of HLA heterozygosity, to the country level with the use of ethnic compositions and constructs genetic diversity scores for 175 countries, of which 131 are used in the baseline regression model. \n", + "\n", + "For the base estimation, continent fixed effects are used to account for any unobserved continent differences that may be associated with selection into the base sample.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\f" + ] + } + ], + "source": [ + "rm(list=ls())\n", + "cat(\"\\014\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Import essential packages to run code, manage databases and make regressions in R.**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "package 'lmtest' successfully unpacked and MD5 sums checked\n", + "\n", + "The downloaded binary packages are in\n", + "\tC:\\Users\\USUARIO\\AppData\\Local\\Temp\\RtmpAv8m5N\\downloaded_packages\n", + "package 'sandwich' successfully unpacked and MD5 sums checked\n", + "\n", + "The downloaded binary packages are in\n", + "\tC:\\Users\\USUARIO\\AppData\\Local\\Temp\\RtmpAv8m5N\\downloaded_packages\n", + "package 'stargazer' successfully unpacked and MD5 sums checked\n", + "\n", + "The downloaded binary packages are in\n", + "\tC:\\Users\\USUARIO\\AppData\\Local\\Temp\\RtmpAv8m5N\\downloaded_packages\n", + "package 'data.table' successfully unpacked and MD5 sums checked\n", + "\n", + "The downloaded binary packages are in\n", + "\tC:\\Users\\USUARIO\\AppData\\Local\\Temp\\RtmpAv8m5N\\downloaded_packages\n" + ] + } + ], + "source": [ + "install.packages(\"lmtest\")\n", + "install.packages(\"sandwich\")\n", + "install.packages(\"stargazer\")\n", + "install.packages(\"data.table\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading required package: zoo\n", + "\n", + "\n", + "Attaching package: 'zoo'\n", + "\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " as.Date, as.Date.numeric\n", + "\n", + "\n", + "\n", + "Please cite as: \n", + "\n", + "\n", + " Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.\n", + "\n", + " R package version 5.2.2. https://CRAN.R-project.org/package=stargazer \n", + "\n", + "\n" + ] + } + ], + "source": [ + "library(lmtest)\n", + "library(sandwich)\n", + "library(stargazer)\n", + "library(data.table)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dir1 <-(\"./raw/\") #the directory where the base is\n", + "setwd(dir1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Load the database used by the author**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "hla_country_web <- read.delim(\"hla_country_web.tab\", header=TRUE) #The database \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Table 1.—Summary Statistics: HLA Heterozygosity verses Aggregate Heterozygosity**_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

In addition to summary statistics for HLA heterozygosity, table 1 also provides summary statistics for the overall level of genetic diversity from Ashraf and Galor (2013). In comparing the two measures of genetic diversity, African countries are found to have the highest levels of overall diversity, while countries from Europe contain the greatest amount." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Figure 1.—Cross-Country HLA Heterozygosity**_\n", + "\n", + "Figure 1. shows a global cross-country plot. As expected, countries with populations derived from Eurasia and Africa have higher levels of HLA heterozygosity, while those from the Americas and Oceania have lower levels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**B.\tHealth Outcomes prior to the International Epidemiological Transition**_\n", + "\n", + "

According to the primary hypothesis, if a country’s population has relatively low levels of HLA heterozygosity, then in periods prior to the international epidemiological transition, a greater fraction of the population will die from infectious diseases, thereby lowering life expectancy. \n", + " \n", + "

In order to measure this relationship, the author considers a number of country-level health outcomes prior to the discovery and diffusion of medical technologies associated with the international epidemiological transition. These include both predicted mortality from infectious disease and life expectancy in 1940, as well as life expectancy in 1960. \n", + " \n", + "

However, the use of 1940s data, while truly before the epidemiological transition, is problematic due to a lack of data in relatively poor countries, leading to possible selection bias. Therefore, the primary dependent variable is country level life expectancy at birth in 1960. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### IV. Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Variables in the data base used by the author:\n", + "\n", + "1. ln_hla_het: ln HLA heterozygosity\n", + "2. hla_het: HLA heterozygosity.\n", + "3. ln_mort40: ln Mortality from 15 infectious diseases, 1940.\n", + "4. ln_le40: ln Life expectancy, 1940.\n", + "5. ln_le50: ln Life expectancy, 1950.\n", + "6. ln_le60: ln Life expectancy, 1960.\n", + "7. ln_le70: ln Life expectancy, 1970.\n", + "8. ln_le80: ln Life expectancy, 1980.\n", + "9. ln_le90: ln Life expectancy, 1990.\n", + "10. ln_le00: ln Life expectancy, 2000.\n", + "11. ln_le10: ln Life expectancy, 2010.\n", + "12. europe: Europe dummy\n", + "13. asia: Asia dummy\n", + "14. africa: Africa dummy\n", + "15. americas: Americas dummy\n", + "16. oceania: Oceania dummy\n", + "17. frac_eur: Frac. of population from Europe\n", + "18. frac_me: Frac. of population from Middle East\n", + "19. frac_easia: Frac. of population from East Asia\n", + "20. frac_africa: Frac. of population from Africa\n", + "21. frac_am: Frac. of population from Americas.\n", + "22. frac_oc: Frac. of population from Oceania.\n", + "23. aa_ln_atd: Ancestry adjusted ln years of agr., 1500-1960.\n", + "24. aa_atd: Ancestry adj. years of agriculture, 1500-1960.\n", + "25. aa_mdist: Ancestry adj. migratory dist. to East Africa.\n", + "26. aa_mdist_sqr: Ancestry adj. mig. dist. square\n", + "27. aa_lanim: Ancestry adj. no. of potential domesticate animals.\n", + "28. aa_lpd1: Ancestry adj. ln pop. dencity of 1 CE.\n", + "29. ln_frac: ln Ethnic fractionalization.\n", + "30. ln_arable: ln Percent of arable land\n", + "31. ln_suitavg: ln Suitability of agriculture.\n", + "32. ln_abslat: ln Absolute latitude.\n", + "33. aa_pdiv: Ancestry adjusted predicted genetic diversity, 1500-1960.\n", + "34. malfal: Frac. of pop. at risk of contracting malaria.\n", + "35. tropical: % within tropics.\n", + "36. desert: % within desert.\n", + "37. distcr1000: Mean dist. to coast or river.\n", + "38. frac_migrant: Frac. of non-indigenous pop., 1960.\n", + "39. pnativ_60: Frac. of indigenous pop., 196.\n", + "40. ln_ypc60: ln GDP per capita, 1960.\n", + "41. ln_yr_sch1960: ln Years of schooling, 1960.\n", + "42. ln_pd60: ln Pop. density, 1960.\n", + "43. ln_urb60: ln Urbanization rate, 1960\n", + "44. ln_young: ln Frac. of pop. under 15, 1960.\n", + "45. ln_mix_het: ln HLA heterozygosity, mixed country-level genome\n", + "46. oecd: OECD dummy\n", + "47. fstdistwtd_usa: Genetic distance to the U.S. (weighted)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**_A. Explaining HLA Heterozygosity_**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Figure 2.—HLA Heterozygosity and Migratory Distance from East Africa**_\n", + "\n", + "

Figure 2 plots the measure of diversity, HLA heterozygosity, as a linear function of migratory distance from East Africa. From figure 2, countries with a majority population from Europe and the Middle East tend to break from the linear association between HLA heterozygosity and migratory distance from East Africa, containing higher-than-predicted levels of HLA heterozygosity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Table 2.—Explaining HLA Heterozygosity**_\n", + "\n", + "The role of both the migratory distance from East Africa and the Neolithic revolution in explaining HLA heterozygosity is tested in table 2.\n", + "\n", + "\n", + "Table 2 regresses the log of HLA heterozygosity on the number of years since the Neolithic revolution, the number of potential domesticate animals, population density in 1 CE, and migratory distance from East Africa. Given that the relationship between overall levels of heterozygosity and migratory distance from East Africa has been established (Ashraf & Galor, 2013)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_ln_atd, data = hla_country_web, \n", + " subset = aa_mdist != \"NA\" & ln_le60 != \"NA\" & ln_hla_het != \n", + " \"NA\" & ln_frac != \"NA \" & ln_abslat != \"NA\" & ln_arable != \n", + " \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.29871 -0.02954 0.01560 0.04933 0.09588 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.32606 0.10613 -12.49 <2e-16 ***\n", + "aa_ln_atd 0.02114 0.01251 1.69 0.0934 . \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.06738 on 129 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.02166,\tAdjusted R-squared: 0.01408 \n", + "F-statistic: 2.856 on 1 and 129 DF, p-value: 0.09344\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.32606 Min. :0.009805 Min. :-15.660 Min. :0.000000 \n", + " 1st Qu.:-0.98926 1st Qu.:0.028523 1st Qu.:-11.206 1st Qu.:0.008225 \n", + " Median :-0.65246 Median :0.047241 Median : -6.752 Median :0.016449 \n", + " Mean :-0.65246 Mean :0.047241 Mean : -6.752 Mean :0.016449 \n", + " 3rd Qu.:-0.31566 3rd Qu.:0.065960 3rd Qu.: -2.298 3rd Qu.:0.024674 \n", + " Max. : 0.02114 Max. :0.084678 Max. : 2.157 Max. :0.032899 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_ln_atd + aa_mdist, data = hla_country_web, \n", + " subset = aa_mdist != \"NA\" & ln_le60 != \"NA\" & ln_hla_het != \n", + " \"NA\" & ln_frac != \"NA \" & ln_abslat != \"NA\" & ln_arable != \n", + " \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.189302 -0.028152 0.003329 0.033802 0.138810 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.321902 0.081579 -16.204 < 2e-16 ***\n", + "aa_ln_atd 0.029984 0.009661 3.104 0.00235 ** \n", + "aa_mdist -0.012063 0.001269 -9.505 < 2e-16 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.05179 on 128 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.4265,\tAdjusted R-squared: 0.4175 \n", + "F-statistic: 47.59 on 2 and 128 DF, p-value: 3.522e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.32190 Min. :0.001422 Min. :-18.110 Min. :0.0000000 \n", + " 1st Qu.:-0.66698 1st Qu.:0.005030 1st Qu.:-13.295 1st Qu.:0.0000000 \n", + " Median :-0.01206 Median :0.008638 Median : -8.481 Median :0.0000000 \n", + " Mean :-0.43466 Mean :0.027685 Mean : -7.707 Mean :0.0002357 \n", + " 3rd Qu.: 0.00896 3rd Qu.:0.040816 3rd Qu.: -2.505 3rd Qu.:0.0003536 \n", + " Max. : 0.02998 Max. :0.072994 Max. : 3.471 Max. :0.0007072 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_lanim, data = hla_country_web, subset = aa_mdist != \n", + " \"NA\" & ln_le60 != \"NA\" & ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.28924 -0.03132 0.01314 0.05735 0.08659 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.189394 0.012819 -92.780 <2e-16 ***\n", + "aa_lanim 0.026613 0.007815 3.406 0.001 ** \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.07228 on 87 degrees of freedom\n", + " (86 observations deleted due to missingness)\n", + "Multiple R-squared: 0.1176,\tAdjusted R-squared: 0.1075 \n", + "F-statistic: 11.6 on 1 and 87 DF, p-value: 0.001001\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.18939 Min. :0.006444 Min. :-94.03 Min. :0.000e+00 \n", + " 1st Qu.:-0.88539 1st Qu.:0.007995 1st Qu.:-69.49 1st Qu.:2.078e-05 \n", + " Median :-0.58139 Median :0.009546 Median :-44.95 Median :4.157e-05 \n", + " Mean :-0.58139 Mean :0.009546 Mean :-44.95 Mean :4.157e-05 \n", + " 3rd Qu.:-0.27739 3rd Qu.:0.011097 3rd Qu.:-20.41 3rd Qu.:6.235e-05 \n", + " Max. : 0.02661 Max. :0.012649 Max. : 4.13 Max. :8.314e-05 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_lanim + aa_mdist, data = hla_country_web, \n", + " subset = aa_mdist != \"NA\" & ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.173322 -0.027746 0.009552 0.035496 0.127879 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.109316 0.011777 -94.190 < 2e-16 ***\n", + "aa_lanim 0.036053 0.005395 6.683 2.22e-09 ***\n", + "aa_mdist -0.013321 0.001318 -10.110 2.73e-16 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.04914 on 86 degrees of freedom\n", + " (86 observations deleted due to missingness)\n", + "Multiple R-squared: 0.5968,\tAdjusted R-squared: 0.5874 \n", + "F-statistic: 63.65 on 2 and 86 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.10932 Min. :0.001494 Min. :-102.9112 Min. :0.000e+00 \n", + " 1st Qu.:-0.56132 1st Qu.:0.003295 1st Qu.: -55.9145 1st Qu.:3.620e-14 \n", + " Median :-0.01332 Median :0.005096 Median : -8.9177 Median :7.240e-14 \n", + " Mean :-0.36219 Mean :0.005790 Mean : -34.9181 Mean :1.258e-10 \n", + " 3rd Qu.: 0.01137 3rd Qu.:0.007938 3rd Qu.: -0.9215 3rd Qu.:1.887e-10 \n", + " Max. : 0.03605 Max. :0.010779 Max. : 7.0747 Max. :3.773e-10 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_lpd1, data = hla_country_web, subset = aa_mdist != \n", + " \"NA\" & ln_hla_het != \"NA\" & ln_frac != \"NA \" & ln_abslat != \n", + " \"NA\" & ln_arable != \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \n", + " \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.28985 -0.02536 0.01560 0.04899 0.09343 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.154714 0.006867 -168.154 <2e-16 ***\n", + "aa_lpd1 0.014363 0.005273 2.724 0.0075 ** \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.06849 on 111 degrees of freedom\n", + " (62 observations deleted due to missingness)\n", + "Multiple R-squared: 0.06264,\tAdjusted R-squared: 0.0542 \n", + "F-statistic: 7.418 on 1 and 111 DF, p-value: 0.007501\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.15471 Min. :0.004286 Min. :-155.386 Min. :0.0000000 \n", + " 1st Qu.:-0.86245 1st Qu.:0.005073 1st Qu.:-115.702 1st Qu.:0.0002755 \n", + " Median :-0.57018 Median :0.005859 Median : -76.017 Median :0.0005510 \n", + " Mean :-0.57018 Mean :0.005859 Mean : -76.017 Mean :0.0005510 \n", + " 3rd Qu.:-0.27791 3rd Qu.:0.006645 3rd Qu.: -36.333 3rd Qu.:0.0008265 \n", + " Max. : 0.01436 Max. :0.007431 Max. : 3.351 Max. :0.0011020 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_lpd1 + aa_mdist, data = hla_country_web, \n", + " subset = aa_mdist != \"NA\" & ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.180134 -0.026307 0.008982 0.032460 0.114983 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.072103 0.010613 -101.019 < 2e-16 ***\n", + "aa_lpd1 0.015834 0.004032 3.927 0.00015 ***\n", + "aa_mdist -0.012383 0.001383 -8.955 9.65e-15 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.05233 on 110 degrees of freedom\n", + " (62 observations deleted due to missingness)\n", + "Multiple R-squared: 0.4578,\tAdjusted R-squared: 0.448 \n", + "F-statistic: 46.45 on 2 and 110 DF, p-value: 2.381e-15\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.072104 Min. :0.001501 Min. :-107.000 Min. :0.000e+00 \n", + " 1st Qu.:-0.542243 1st Qu.:0.002379 1st Qu.: -57.625 1st Qu.:0.000e+00 \n", + " Median :-0.012383 Median :0.003258 Median : -8.250 Median :0.000e+00 \n", + " Mean :-0.356217 Mean :0.004926 Mean : -36.796 Mean :1.304e-06 \n", + " 3rd Qu.: 0.001726 3rd Qu.:0.006639 3rd Qu.: -1.695 3rd Qu.:1.956e-06 \n", + " Max. : 0.015834 Max. :0.010020 Max. : 4.861 Max. :3.912e-06 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "##############################################################################\n", + "########## TABLE_2: Historic Determinants of HLA Heterozygosity #############\n", + "##############################################################################\n", + "\n", + "#to do regressions in R we use the function lm, the dependent variable here is ln HLA Heterozygosity \n", + "# this are OLS regressions\n", + "\n", + "regt1_1<-lm(ln_hla_het~aa_ln_atd,subset=aa_mdist!=\"NA\" & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\",data=hla_country_web)\n", + "regt1_1robust<-coeftest(regt1_1, vcov = vcovHC(regt1_1, type=\"HC1\")) #is the model with Robust Standard Errors\n", + "summary(regt1_1)\n", + "summary(regt1_1robust)\n", + "\n", + "regt1_2<-lm(ln_hla_het~aa_ln_atd+aa_mdist,subset=aa_mdist!=\"NA\" & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt1_2robust<-coeftest(regt1_2, vcov = vcovHC(regt1_2, type=\"HC1\"))\n", + "summary(regt1_2)\n", + "summary(regt1_2robust)\n", + "\n", + "regt1_3<-lm(ln_hla_het~aa_lanim,subset=aa_mdist!=\"NA\" & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt1_3robust<-coeftest(regt1_3, vcov = vcovHC(regt1_3, type=\"HC1\"))\n", + "summary(regt1_3)\n", + "summary(regt1_3robust)\n", + "\n", + "regt1_4<-lm(ln_hla_het~aa_lanim+aa_mdist,subset=aa_mdist!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt1_4robust<-coeftest(regt1_4, vcov = vcovHC(regt1_4, type=\"HC1\"))\n", + "summary(regt1_4)\n", + "summary(regt1_4robust)\n", + "\n", + "regt1_5<-lm(ln_hla_het~aa_lpd1,subset=aa_mdist!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt1_5robust<-coeftest(regt1_5, vcov = vcovHC(regt1_5, type=\"HC1\"))\n", + "summary(regt1_5)\n", + "summary(regt1_5robust)\n", + "\n", + "regt1_6<-lm(ln_hla_het~aa_lpd1+aa_mdist,subset=aa_mdist!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt1_6robust<-coeftest(regt1_6, vcov = vcovHC(regt1_6, type=\"HC1\"))\n", + "summary(regt1_6)\n", + "summary(regt1_6robust)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 2: Explaining HLA Heterozygosity\n", + "=========================================================================================================\n", + " Dependent variable: \n", + " -----------------------------------------------------------------\n", + " ln HLA Heterozygosity \n", + " (1) (2) (3) (4) (5) (6) \n", + "---------------------------------------------------------------------------------------------------------\n", + "ln Years since Neolithic Revolution 0.0211** 0.0300*** \n", + " (0.0098) (0.0086) \n", + "ln No. of Potential Domesticate Animals 0.0266*** 0.0361*** \n", + " (0.0064) (0.0051) \n", + "ln Population Density in 1 CE 0.0144*** 0.0158*** \n", + " (0.0043) (0.0033) \n", + "Migratory Distance from East Africa -0.0121*** -0.0133*** -0.0124***\n", + " (0.0014) (0.0015) (0.0015) \n", + "Constant -1.3261*** -1.3219*** -1.1894*** -1.1093*** -1.1547*** -1.0721***\n", + " (0.0847) (0.0730) (0.0126) (0.0108) (0.0074) (0.0100) \n", + "=========================================================================================================\n", + "=========================================================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table2<-stargazer(regt1_1robust,regt1_2robust,regt1_3robust,regt1_4robust,\n", + " regt1_5robust,regt1_6robust,type=\"text\", keep.stat=c(\"n\", \"rsq\"), align = TRUE, \n", + " title = \"Table 2: Explaining HLA Heterozygosity\",dep.var.labels = \"ln HLA Heterozygosity\",\n", + " column.sep.width = \"5pt\", digits=4, no.space=TRUE, order=c(\"aa_ln_atd\",\"aa_lanim\",\"aa_lpd1\",\"aa_mdist\"),\n", + " covariate.labels=c(\"ln Years since Neolithic Revolution\",\"ln No. of Potential Domesticate Animals\", \n", + " \"ln Population Density in 1 CE\", \"Migratory Distance from East Africa\") )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-

Columns 1 and 2 regress the natural log of HLA heterozygosity on the natural log of the years a country has practiced agriculture. Agriculture has a positive and significant effect on the main measure of heterozygosity in both the bivariate estimation and when controlling for migratory distance.\n", + "\n", + "-

Columns 3 to 6 consider the more proximate determinants of HLA heterozygosity. Agriculture provided close contact with domesticate animals as well as large, dense populations, the two necessary factors that led to an increase in the infectious disease load within a country. A positive and statistically significant relationship between the number of potential domesticate animals and HLA heterozygosity is shown in columns 3 and 4, while the same relationship is exhibited with population density in 1 CE.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**B. HLA Heterozygosity, Country-Level Health Outcomes, and the International Epidemiological Transition**_\n", + "\n", + "

The primary hypothesis of this paper is that genetic resistance to infectious disease, measured by HLA heterozygosity, is positively associated with country-level health outcomes prior to the international epidemiological transition. To test this hypothesis, the author uses the following estimating equation:\n", + "\n", + "$$ln y_{i}^{tThe baseline controls are adopted from similar estimation in Ashraf and Galor (2013) and are intended to control for the persistent effects of historical development. Additionally, controlling for ethnic fractionalization is intended to capture any unseen effects from using the ethnic compositions found in Alesina et al. (2003).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Table 3.—The Effect of HLA Heterozygosity prior to the International Epidemiological Transition**_\n", + "\n", + "Table 3 gives estimates from the baseline estimating equation. The estimates consider three alternatives for measuring country-level health outcomes prior to the epidemiological transition: the mortality rate from fifteen infectious diseases in 1940, life expectancy at birth in 1940, and life expectancy at birth in 1960. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_mort40 ~ ln_hla_het, data = hla_country_web, \n", + " subset = ln_hla_het != \"NA\" & ln_frac != \"NA \" & ln_abslat != \n", + " \"NA\" & ln_arable != \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \n", + " \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-1.18727 -0.34339 0.01533 0.26435 1.09459 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -6.6724 0.9301 -7.174 5.63e-10 ***\n", + "ln_hla_het -5.0080 0.8120 -6.167 3.79e-08 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.4915 on 71 degrees of freedom\n", + " (102 observations deleted due to missingness)\n", + "Multiple R-squared: 0.3488,\tAdjusted R-squared: 0.3397 \n", + "F-statistic: 38.03 on 1 and 71 DF, p-value: 3.795e-08\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_mort40 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-1.01614 -0.30628 0.05079 0.28246 0.91429 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -9.57531 2.25891 -4.239 7.6e-05 ***\n", + "ln_hla_het -3.86103 1.04886 -3.681 0.000489 ***\n", + "ln_frac -0.39701 0.38210 -1.039 0.302832 \n", + "aa_ln_atd 0.59752 0.21443 2.787 0.007060 ** \n", + "ln_arable 0.04486 0.10042 0.447 0.656616 \n", + "ln_suitavg 0.03620 0.08595 0.421 0.675104 \n", + "ln_abslat -0.32604 0.10993 -2.966 0.004283 ** \n", + "europe 0.10748 0.35411 0.304 0.762501 \n", + "africa 0.45897 0.40337 1.138 0.259562 \n", + "asia 0.17221 0.37414 0.460 0.646914 \n", + "americas 0.11026 0.35364 0.312 0.756252 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.4548 on 62 degrees of freedom\n", + " (102 observations deleted due to missingness)\n", + "Multiple R-squared: 0.5133,\tAdjusted R-squared: 0.4348 \n", + "F-statistic: 6.538 on 10 and 62 DF, p-value: 8.296e-07\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le40 ~ ln_hla_het, data = hla_country_web, subset = ln_hla_het != \n", + " \"NA\" & ln_frac != \"NA \" & ln_abslat != \"NA\" & ln_arable != \n", + " \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.44116 -0.18547 0.07159 0.18426 0.34939 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 6.2554 0.4174 14.985 < 2e-16 ***\n", + "ln_hla_het 2.1436 0.3653 5.869 1.39e-07 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.216 on 69 degrees of freedom\n", + " (104 observations deleted due to missingness)\n", + "Multiple R-squared: 0.3329,\tAdjusted R-squared: 0.3233 \n", + "F-statistic: 34.44 on 1 and 69 DF, p-value: 1.386e-07\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le40 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.32401 -0.10245 -0.00067 0.09843 0.36017 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.316719 0.686427 7.746 1.33e-10 ***\n", + "ln_hla_het 1.077112 0.348335 3.092 0.003013 ** \n", + "ln_frac 0.072362 0.128129 0.565 0.574344 \n", + "aa_ln_atd -0.003076 0.068392 -0.045 0.964279 \n", + "ln_arable -0.105980 0.033565 -3.157 0.002491 ** \n", + "ln_suitavg 0.052993 0.027554 1.923 0.059195 . \n", + "ln_abslat 0.098031 0.037499 2.614 0.011290 * \n", + "europe -0.052723 0.116216 -0.454 0.651708 \n", + "africa -0.502073 0.128697 -3.901 0.000245 ***\n", + "asia -0.339857 0.122322 -2.778 0.007282 ** \n", + "americas -0.329193 0.116623 -2.823 0.006451 ** \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1503 on 60 degrees of freedom\n", + " (104 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7192,\tAdjusted R-squared: 0.6724 \n", + "F-statistic: 15.37 on 10 and 60 DF, p-value: 3.688e-13\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het, data = hla_country_web, subset = ln_hla_het != \n", + " \"NA\" & ln_frac != \"NA \" & ln_abslat != \"NA\" & ln_arable != \n", + " \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.5756 -0.1791 0.0458 0.1764 0.3490 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.8146 0.3023 19.233 < 2e-16 ***\n", + "ln_hla_het 1.6200 0.2631 6.157 8.72e-09 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.2036 on 129 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.2271,\tAdjusted R-squared: 0.2211 \n", + "F-statistic: 37.91 on 1 and 129 DF, p-value: 8.721e-09\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.48975 -0.06072 0.00716 0.07354 0.27776 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.962062 0.408122 12.158 < 2e-16 ***\n", + "ln_hla_het 1.015120 0.192335 5.278 5.89e-07 ***\n", + "ln_frac -0.121001 0.075280 -1.607 0.110605 \n", + "aa_ln_atd 0.036998 0.037781 0.979 0.329412 \n", + "ln_arable -0.008700 0.015168 -0.574 0.567349 \n", + "ln_suitavg 0.008039 0.014414 0.558 0.578063 \n", + "ln_abslat 0.019909 0.016277 1.223 0.223684 \n", + "europe 0.038739 0.081110 0.478 0.633792 \n", + "africa -0.312210 0.084269 -3.705 0.000321 ***\n", + "asia -0.205622 0.077827 -2.642 0.009339 ** \n", + "americas -0.040059 0.076638 -0.523 0.602141 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1213 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7446,\tAdjusted R-squared: 0.7233 \n", + "F-statistic: 34.99 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#########################################################\n", + "########## TABLE_3: Premedicinal Health ############\n", + "#########################################################\n", + "#to do regressions in R we use the function lm, the dependent variable here is ln HLA Heterozygosity \n", + "\n", + "regt3_1<-lm(ln_mort40~ln_hla_het,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_1robust<-coeftest(regt3_1, vcov = vcovHC(regt3_1, type=\"HC1\"))\n", + "summary(regt3_1)\n", + "\n", + "regt3_2<-lm(ln_mort40~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_2robust<-coeftest(regt3_2, vcov = vcovHC(regt3_2, type=\"HC1\"))\n", + "summary(regt3_2)\n", + "\n", + "regt3_3<-lm(ln_le40~ln_hla_het,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_3robust<-coeftest(regt3_3, vcov = vcovHC(regt3_3, type=\"HC1\"))\n", + "summary(regt3_3)\n", + "\n", + "regt3_4<-lm(ln_le40~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_4robust<-coeftest(regt3_4, vcov = vcovHC(regt3_4, type=\"HC1\"))\n", + "summary(regt3_4)\n", + "\n", + "regt3_5<-lm(ln_le60~ln_hla_het,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_5robust<-coeftest(regt3_5, vcov = vcovHC(regt3_5, type=\"HC1\"))\n", + "summary(regt3_5)\n", + "\n", + "\n", + "regt3_6<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_6robust<-coeftest(regt3_6, vcov = vcovHC(regt3_6, type=\"HC1\"))\n", + "summary(regt3_6)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 3: The Effect of HLA Heterozygosity prior to the International Epidemiological Transition\n", + "==================================================================================================\n", + " Dependent variable: \n", + " --------------------------------------------------------------\n", + " \n", + " ln PM1940 ln PM1940 ln LE1940 ln LE1940 ln LE1960 ln LE1960\n", + " (1) (2) (3) (4) (5) (6) \n", + "--------------------------------------------------------------------------------------------------\n", + "ln HLA Heterozygosity -5.0080*** -3.8610*** 2.1436*** 1.0771*** 1.6200*** 1.0151***\n", + " (0.7117) (1.1282) (0.3310) (0.3771) (0.2455) (0.1899) \n", + "ln Ethnic Fractionalization -0.3970 0.0724 -0.1210 \n", + " (0.3961) (0.1338) (0.0927) \n", + "ln Years since Neolithic Revolution 0.5975** -0.0031 0.0370 \n", + " (0.2358) (0.0549) (0.0397) \n", + "ln Fraction of Arable Land 0.0449 -0.1060*** -0.0087 \n", + " (0.1220) (0.0366) (0.0160) \n", + "ln Suitability of Agriculture 0.0362 0.0530** 0.0080 \n", + " (0.0844) (0.0238) (0.0128) \n", + "ln Abs. Latitude -0.3260** 0.0980** 0.0199 \n", + " (0.1297) (0.0410) (0.0156) \n", + "==================================================================================================\n", + "==================================================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table3<-stargazer(regt3_1robust,regt3_2robust,regt3_3robust,regt3_4robust,\n", + " regt3_5robust,regt3_6robust,type=\"text\", align = TRUE,\n", + " keep=c(\"ln_hla_het\",\"ln_frac\",\"aa_ln_atd\",\"ln_arable\", \"ln_suitavg\",\"ln_abslat\"),\n", + " title = \"Table 3: The Effect of HLA Heterozygosity prior to the International Epidemiological Transition\",\n", + " column.labels=c(\"ln PM1940\", \"ln PM1940\", \"ln LE1940\", \"ln LE1940\", \"ln LE1960\", \"ln LE1960\"),\n", + " column.sep.width = \"10pt\", digits=4, no.space=TRUE,\n", + " covariate.labels=c(\"ln HLA Heterozygosity\", \"ln Ethnic Fractionalization\", \"ln Years since Neolithic Revolution\", \n", + " \"ln Fraction of Arable Land\", \"ln Suitability of Agriculture\", \"ln Abs. Latitude\") )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Column 1 shows the bivariate relationship between HLA heterozygosity and predicted mortality from infectious diseases in 1940. The coefficient is negative and statistically significant at the 1% level indicating that greater variation within the HLA system is associated with lower mortality from infectious disease in 1940. \n", + "\n", + "

The use of 1940s health data is ideal; however, data are relatively sparse for this period. Therefore, life expectancy at birth for 1960 is used as the main dependent variable. The complicating factor with the use of data from the 1960s is that the presence of medicine should dissipate any effect of inherent genetic resistance to infectious disease. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Table 4.—The Effect of HLA Heterozygosity after the International Epidemiological Transition**_\n", + "\n", + "

To show that show that 1960 is an early enough period to capture the effects of innate resistance, table 4 explores the effects of HLA heterozygosity on life expectancy in more contemporary periods. Table 4 explores the effect of HLA heterozygosity on life expectancy from 1960 to 2010 and tests the effect of HLA heterozygosity, or innate resistance, in post epidemiological transition environments." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.48975 -0.06072 0.00716 0.07354 0.27776 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.962062 0.408122 12.158 < 2e-16 ***\n", + "ln_hla_het 1.015120 0.192335 5.278 5.89e-07 ***\n", + "ln_frac -0.121001 0.075280 -1.607 0.110605 \n", + "aa_ln_atd 0.036998 0.037781 0.979 0.329412 \n", + "ln_arable -0.008700 0.015168 -0.574 0.567349 \n", + "ln_suitavg 0.008039 0.014414 0.558 0.578063 \n", + "ln_abslat 0.019909 0.016277 1.223 0.223684 \n", + "europe 0.038739 0.081110 0.478 0.633792 \n", + "africa -0.312210 0.084269 -3.705 0.000321 ***\n", + "asia -0.205622 0.077827 -2.642 0.009339 ** \n", + "americas -0.040059 0.076638 -0.523 0.602141 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1213 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7446,\tAdjusted R-squared: 0.7233 \n", + "F-statistic: 34.99 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le70 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.47914 -0.04940 0.00058 0.07175 0.24402 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.1986107 0.3863098 13.457 < 2e-16 ***\n", + "ln_hla_het 1.0404702 0.1820558 5.715 8.15e-08 ***\n", + "ln_frac -0.1163928 0.0712564 -1.633 0.104998 \n", + "aa_ln_atd 0.0246249 0.0357620 0.689 0.492418 \n", + "ln_arable -0.0141638 0.0143576 -0.987 0.325870 \n", + "ln_suitavg 0.0007926 0.0136433 0.058 0.953771 \n", + "ln_abslat 0.0045721 0.0154071 0.297 0.767166 \n", + "europe 0.0344496 0.0767749 0.449 0.654450 \n", + "africa -0.3192781 0.0797647 -4.003 0.000109 ***\n", + "asia -0.1420921 0.0736678 -1.929 0.056115 . \n", + "americas -0.0238244 0.0725423 -0.328 0.743166 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1148 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.734,\tAdjusted R-squared: 0.7118 \n", + "F-statistic: 33.11 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le80 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.42788 -0.04509 0.00724 0.06262 0.25050 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.9452963 0.3561394 13.886 < 2e-16 ***\n", + "ln_hla_het 0.8420928 0.1678374 5.017 1.84e-06 ***\n", + "ln_frac -0.1462667 0.0656913 -2.227 0.027842 * \n", + "aa_ln_atd 0.0348747 0.0329691 1.058 0.292271 \n", + "ln_arable -0.0199753 0.0132363 -1.509 0.133893 \n", + "ln_suitavg 0.0003745 0.0125777 0.030 0.976298 \n", + "ln_abslat 0.0070101 0.0142038 0.494 0.622533 \n", + "europe 0.0176880 0.0707789 0.250 0.803088 \n", + "africa -0.2552229 0.0735352 -3.471 0.000722 ***\n", + "asia -0.1194574 0.0679145 -1.759 0.081137 . \n", + "americas -0.0064243 0.0668769 -0.096 0.923631 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1059 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.6995,\tAdjusted R-squared: 0.6745 \n", + "F-statistic: 27.94 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le90 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.42359 -0.04798 0.00311 0.06009 0.26370 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.554555 0.359376 12.674 < 2e-16 ***\n", + "ln_hla_het 0.549024 0.169363 3.242 0.00154 ** \n", + "ln_frac -0.183742 0.066288 -2.772 0.00646 ** \n", + "aa_ln_atd 0.038322 0.033269 1.152 0.25166 \n", + "ln_arable -0.022074 0.013357 -1.653 0.10101 \n", + "ln_suitavg -0.004649 0.012692 -0.366 0.71478 \n", + "ln_abslat 0.024767 0.014333 1.728 0.08657 . \n", + "europe 0.018960 0.071422 0.265 0.79111 \n", + "africa -0.220302 0.074203 -2.969 0.00361 ** \n", + "asia -0.072690 0.068532 -1.061 0.29097 \n", + "americas 0.034606 0.067485 0.513 0.60904 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1068 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.6886,\tAdjusted R-squared: 0.6626 \n", + "F-statistic: 26.53 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le00 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.39356 -0.05137 0.00062 0.05057 0.21952 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 3.850750 0.309218 12.453 < 2e-16 ***\n", + "ln_hla_het 0.251217 0.145725 1.724 0.087299 . \n", + "ln_frac -0.177528 0.057037 -3.113 0.002319 ** \n", + "aa_ln_atd 0.084388 0.028625 2.948 0.003845 ** \n", + "ln_arable -0.006223 0.011492 -0.542 0.589157 \n", + "ln_suitavg -0.016824 0.010921 -1.541 0.126047 \n", + "ln_abslat 0.013741 0.012332 1.114 0.267420 \n", + "europe -0.001058 0.061454 -0.017 0.986296 \n", + "africa -0.249743 0.063847 -3.912 0.000152 ***\n", + "asia -0.092455 0.058967 -1.568 0.119534 \n", + "americas 0.026439 0.058066 0.455 0.649695 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.09193 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7683,\tAdjusted R-squared: 0.749 \n", + "F-statistic: 39.79 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le10 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.36822 -0.04284 0.00248 0.04303 0.22940 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 3.912044 0.282804 13.833 < 2e-16 ***\n", + "ln_hla_het 0.178763 0.133277 1.341 0.182360 \n", + "ln_frac -0.150893 0.052164 -2.893 0.004537 ** \n", + "aa_ln_atd 0.073300 0.026180 2.800 0.005960 ** \n", + "ln_arable -0.004571 0.010511 -0.435 0.664390 \n", + "ln_suitavg -0.013801 0.009988 -1.382 0.169608 \n", + "ln_abslat 0.006617 0.011279 0.587 0.558550 \n", + "europe 0.006555 0.056204 0.117 0.907355 \n", + "africa -0.223514 0.058393 -3.828 0.000207 ***\n", + "asia -0.083993 0.053930 -1.557 0.121995 \n", + "americas 0.016314 0.053106 0.307 0.759223 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.08408 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.748,\tAdjusted R-squared: 0.727 \n", + "F-statistic: 35.62 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "##################################################################################\n", + "########## TABLE_ 4: Additional Years: The effect of medicine #############\n", + "##################################################################################\n", + "\n", + "#to do regressions in R we use the function lm, the dependent variable is different in the 5 regressions\n", + "\n", + "regt4_1<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_1robust<-coeftest(regt4_1, vcov = vcovHC(regt4_1, type=\"HC1\")) \n", + "summary(regt4_1)\n", + "\n", + "regt4_2<-lm(ln_le70~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_2robust<-coeftest(regt4_2, vcov = vcovHC(regt4_2, type=\"HC1\"))\n", + "summary(regt4_2)\n", + "\n", + "regt4_3<-lm(ln_le80~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_3robust<-coeftest(regt4_3, vcov = vcovHC(regt4_3, type=\"HC1\"))\n", + "summary(regt4_3)\n", + "\n", + "\n", + "regt4_4<-lm(ln_le90~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_4robust<-coeftest(regt4_4, vcov = vcovHC(regt4_4, type=\"HC1\"))\n", + "summary(regt4_4)\n", + "\n", + "\n", + "regt4_5<-lm(ln_le00~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_5robust<-coeftest(regt4_5, vcov = vcovHC(regt4_5, type=\"HC1\"))\n", + "summary(regt4_5)\n", + "\n", + "regt4_6<-lm(ln_le10~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_6robust<-coeftest(regt4_6, vcov = vcovHC(regt4_6, type=\"HC1\"))\n", + "summary(regt4_6)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 4: The Effect of HLA Heterozygosity after the International Epidemiological Transition\n", + "===============================================================================\n", + " Dependent variable: \n", + " ---------------------------------------------------------\n", + " ln Life Expectancy \n", + " 1960 1970 1980 1990 2000 2010 \n", + " (1) (2) (3) (4) (5) (6) \n", + "-------------------------------------------------------------------------------\n", + "ln HLA Heterozygosity 1.0151*** 1.0405*** 0.8421*** 0.5490*** 0.2512 0.1788 \n", + " (0.1899) (0.1696) (0.1700) (0.1798) (0.1541) (0.1335)\n", + "===============================================================================\n", + "===============================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table4<-stargazer(regt4_1robust,regt4_2robust,regt4_3robust,regt4_4robust,\n", + " regt4_5robust,regt4_6robust,type=\"text\", align = TRUE,\n", + " title = \"Table 4: The Effect of HLA Heterozygosity after the International Epidemiological Transition\",\n", + " dep.var.labels = \"ln Life Expectancy\", keep=c(\"ln_hla_het\"), \n", + " column.labels=c(\"1960\", \"1970\", \"1980\", \"1990\", \"2000\", \"2010\"),\n", + " column.sep.width = \"10pt\", digits=4, no.space=TRUE,\n", + " covariate.labels=c(\"ln HLA Heterozygosity\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Column 1 reproduces the baseline regression. Columns 2 to 6 regress life expectancy at birth in 1970 to 2010 (by decade) on HLA heterozygosity, respectively. As the set of technologies that comprise the international epidemiological transition becomes more widely distributed over time, the coefficient of HLA heterozygosity is hypothesized to move to 0.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**_C. Robustness_**\n", + "\n", + "**_Controlling for regional ethnic differences_**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_**Table 5.—Robustness to the Influence of Regional Populations**_\n", + "\n", + "

Controlling for regional ethnic differences. One potential source of bias associated with the measure of HLA heterozygosity lies within the aggregation from ethnic groups to the country level. Given the limited number of ethnic groups from which HLA heterozygosity is constructed, the aggregated measure may simply be accounting for the fraction of a country’s population derived from a region. \n", + "Specifically, populations from Europe have been shown to have favorable institutions and human capital (Easterly & Levine, 2012). In other words, the relationship between HLA heterozygosity and life expectancy in 1960 may reflect some underlying role of European populations in promoting greater health outcomes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = frac_eur == 0 & ln_le60 != \n", + " \"NA\" & ln_hla_het != \"NA\" & ln_frac != \"NA\" & ln_abslat != \n", + " \"NA\" & ln_arable != \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.23551 -0.07323 0.01446 0.06769 0.29570 \n", + "\n", + "Coefficients: (2 not defined because of singularities)\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.663561 0.719793 6.479 5.52e-08 ***\n", + "ln_hla_het 0.724094 0.362643 1.997 0.0518 . \n", + "ln_frac -0.166265 0.129578 -1.283 0.2059 \n", + "aa_ln_atd 0.010913 0.066160 0.165 0.8697 \n", + "ln_arable -0.006245 0.020622 -0.303 0.7634 \n", + "ln_suitavg 0.001592 0.021353 0.075 0.9409 \n", + "ln_abslat -0.010405 0.022727 -0.458 0.6492 \n", + "europe NA NA NA NA \n", + "africa -0.078831 0.192005 -0.411 0.6833 \n", + "asia 0.058187 0.183382 0.317 0.7525 \n", + "americas NA NA NA NA \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1265 on 46 degrees of freedom\n", + " (11 observations deleted due to missingness)\n", + "Multiple R-squared: 0.4183,\tAdjusted R-squared: 0.3172 \n", + "F-statistic: 4.135 on 8 and 46 DF, p-value: 0.0008798\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = frac_eur > 0 & frac_eur < \n", + " 1 & ln_le60 != \"NA\" & ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA\" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.49173 -0.05557 -0.00057 0.07756 0.20268 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 3.97634 0.84821 4.688 3.04e-05 ***\n", + "ln_hla_het 0.80490 0.36724 2.192 0.0341 * \n", + "ln_frac -0.15810 0.14726 -1.074 0.2893 \n", + "aa_ln_atd 0.14118 0.08746 1.614 0.1142 \n", + "ln_arable -0.06410 0.03803 -1.686 0.0995 . \n", + "ln_suitavg 0.04920 0.04322 1.138 0.2616 \n", + "ln_abslat 0.05377 0.04204 1.279 0.2081 \n", + "europe -0.03648 0.12133 -0.301 0.7652 \n", + "africa -0.24688 0.13633 -1.811 0.0775 . \n", + "asia -0.28035 0.12115 -2.314 0.0257 * \n", + "americas -0.11947 0.11174 -1.069 0.2912 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1397 on 41 degrees of freedom\n", + " (26 observations deleted due to missingness)\n", + "Multiple R-squared: 0.5891,\tAdjusted R-squared: 0.4888 \n", + "F-statistic: 5.877 on 10 and 41 DF, p-value: 1.93e-05\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = frac_eur == 1 & ln_le60 != \n", + " \"NA\" & ln_hla_het != \"NA\" & ln_frac != \"NA\" & ln_abslat != \n", + " \"NA\" & ln_arable != \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.079159 -0.024165 0.002131 0.022136 0.057548 \n", + "\n", + "Coefficients: (4 not defined because of singularities)\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 3.517311 0.758054 4.640 0.000234 ***\n", + "ln_hla_het 0.728074 0.365006 1.995 0.062365 . \n", + "ln_frac -0.073481 0.070289 -1.045 0.310474 \n", + "aa_ln_atd 0.035708 0.044750 0.798 0.435913 \n", + "ln_arable -0.009074 0.022862 -0.397 0.696384 \n", + "ln_suitavg 0.018469 0.024473 0.755 0.460780 \n", + "ln_abslat 0.323034 0.129588 2.493 0.023290 * \n", + "europe NA NA NA NA \n", + "africa NA NA NA NA \n", + "asia NA NA NA NA \n", + "americas NA NA NA NA \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.04194 on 17 degrees of freedom\n", + " (7 observations deleted due to missingness)\n", + "Multiple R-squared: 0.5162,\tAdjusted R-squared: 0.3454 \n", + "F-statistic: 3.023 on 6 and 17 DF, p-value: 0.03371\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + frac_eur + frac_me + frac_easia + \n", + " frac_africa + frac_am + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_hla_het != \n", + " \"NA\" & ln_frac != \"NA\" & ln_abslat != \"NA\" & ln_arable != \n", + " \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.42848 -0.06734 0.00682 0.06718 0.24529 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.598250 0.497114 9.250 1.45e-15 ***\n", + "ln_hla_het 0.886992 0.286872 3.092 0.0025 ** \n", + "frac_eur 0.317721 0.194336 1.635 0.1048 \n", + "frac_me 0.020621 0.204333 0.101 0.9198 \n", + "frac_easia 0.140123 0.190495 0.736 0.4635 \n", + "frac_africa 0.017364 0.194295 0.089 0.9289 \n", + "frac_am 0.145135 0.180890 0.802 0.4240 \n", + "ln_frac -0.129576 0.073851 -1.755 0.0820 . \n", + "aa_ln_atd 0.042627 0.039200 1.087 0.2791 \n", + "ln_arable -0.009935 0.014528 -0.684 0.4954 \n", + "ln_suitavg -0.000756 0.014302 -0.053 0.9579 \n", + "ln_abslat 0.006856 0.016666 0.411 0.6816 \n", + "europe -0.051875 0.088836 -0.584 0.5604 \n", + "africa -0.140886 0.115650 -1.218 0.2256 \n", + "asia -0.097880 0.112253 -0.872 0.3850 \n", + "americas -0.057943 0.096103 -0.603 0.5477 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1126 on 115 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7891,\tAdjusted R-squared: 0.7616 \n", + "F-statistic: 28.68 on 15 and 115 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "########################################################\n", + "##########TABLE_5: Pop. Composition Trunc. #############\n", + "########################################################\n", + "#to do regressions in R we use the function lm, the dependent variable here is ln Life Expectancy in 1960 \n", + "\n", + "regt5_1<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas, subset = frac_eur==0 & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA\" & ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\", data=hla_country_web)\n", + "regt5_1robust<-coeftest(regt5_1, vcov = vcovHC(regt5_1, type=\"HC1\"))\n", + "summary(regt5_1)\n", + "\n", + "regt5_2<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas, subset = frac_eur>0 & frac_eur<1 & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA\" & ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\", data=hla_country_web)\n", + "regt5_2robust<-coeftest(regt5_2, vcov = vcovHC(regt5_2, type=\"HC1\"))\n", + "summary(regt5_2)\n", + "\n", + "\n", + "regt5_3<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas, subset = frac_eur==1 & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA\" & ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\", data=hla_country_web)\n", + "regt5_3robust<-coeftest(regt5_3, vcov = vcovHC(regt5_3, type=\"HC1\"))\n", + "summary(regt5_3)\n", + "\n", + "\n", + "regt5_4<-lm(ln_le60~ln_hla_het+frac_eur+frac_me+frac_easia+frac_africa+frac_am+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset= ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA\" & ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\",data=hla_country_web)\n", + "regt5_4robust<-coeftest(regt5_4, vcov = vcovHC(regt5_4, type=\"HC1\"))\n", + "summary(regt5_4)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 5: Robustness to the Influence of Regional Populations\n", + "=======================================================================\n", + " Dependent variable: \n", + " -------------------------------------------------\n", + " ln Life Expectancy in 1960 \n", + " %European=0 %European=(0;1) %European=1 Full \n", + " (1) (2) (3) (4) \n", + "-----------------------------------------------------------------------\n", + "ln HLA Heterozygosity 0.7241* 0.8049*** 0.7281** 0.8870***\n", + " (0.3623) (0.2923) (0.3111) (0.2872) \n", + "=======================================================================\n", + "=======================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table5<-stargazer(regt5_1robust,regt5_2robust,regt5_3robust,regt5_4robust,\n", + " type=\"text\", align = TRUE, title = \"Table 5: Robustness to the Influence of Regional Populations\",\n", + " dep.var.labels = \"ln Life Expectancy in 1960\", keep=c(\"ln_hla_het\"), \n", + " column.labels=c(\"%European=0\", \"%European=(0;1)\", \"%European=1\", \"Full\"),\n", + " column.sep.width = \"10pt\", digits=4, no.space=TRUE,\n", + " covariate.labels=c(\"ln HLA Heterozygosity\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Table 5 shows the explore the effect of HLA heterozygosity in countries with differing concentrations of European descent as well as controlling for the fraction of the population from each region.\n", + "\n", + "In order to account for the fraction of a country’s population being from each region (the Americas, Europe, East Asia, the Middle East, Oceania, and sub-Saharan Africa). While the point estimates of the coefficient are slightly smaller in magnitude when compared to the base estimate of column 6 in table 3, the effect of HLA heterozygosity remains both positive and significant at conventional levels, indicating that the portion of the population from Europe is not substantially altering the protective effect of genetic variation within the HLA system.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**_Omitted variables_**\n", + "\n", + "

Tables 6 and 7 include omitted variables that may be associated with either the measure of HLA heterozygosity or life expectancy in 1960. The additional variables are broken into two classes: endogenous and exogenous. \n", + " \n", + "- The additional exogenous variables, found in table 6, include genetic, geographic, and historic population controls.\n", + "- the endogenous controls of table 7 consist of income, human capital, and demographics in 1960.\n", + "\n", + "\n", + "**_Table 6.—Robustness to Exogenous Omitted Variables_**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + aa_pdiv + ln_frac + aa_ln_atd + \n", + " ln_arable + ln_suitavg + ln_abslat + europe + africa + asia + \n", + " americas, data = hla_country_web, subset = distcr100 != \"NA\" & \n", + " aa_pdiv != \"NA\" & malfal != \"NA\" & tropical != \"NA\" & pnativ_60 != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.49329 -0.06037 0.00781 0.07372 0.27135 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.196418 0.811731 6.402 3.18e-09 ***\n", + "ln_hla_het 1.077102 0.267620 4.025 0.000101 ***\n", + "aa_pdiv -0.278363 0.832380 -0.334 0.738652 \n", + "ln_frac -0.113391 0.078912 -1.437 0.153364 \n", + "aa_ln_atd 0.040665 0.039475 1.030 0.305031 \n", + "ln_arable -0.008339 0.015263 -0.546 0.585860 \n", + "ln_suitavg 0.007875 0.014476 0.544 0.587445 \n", + "ln_abslat 0.020602 0.016469 1.251 0.213395 \n", + "europe 0.039045 0.081417 0.480 0.632416 \n", + "africa -0.303156 0.088810 -3.414 0.000877 ***\n", + "asia -0.205855 0.078120 -2.635 0.009531 ** \n", + "americas -0.045389 0.078557 -0.578 0.564501 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1218 on 119 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7449,\tAdjusted R-squared: 0.7213 \n", + "F-statistic: 31.58 on 11 and 119 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + malfal + tropical + desert + \n", + " ln_frac + aa_ln_atd + ln_arable + ln_suitavg + ln_abslat + \n", + " europe + africa + asia + americas + distcr100, data = hla_country_web, \n", + " subset = distcr100 != \"NA\" & aa_pdiv != \"NA\" & malfal != \n", + " \"NA\" & tropical != \"NA\" & pnativ_60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.51779 -0.06648 0.00606 0.06247 0.24479 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.0075525 0.4057770 12.341 < 2e-16 ***\n", + "ln_hla_het 0.7713476 0.1998741 3.859 0.000188 ***\n", + "malfal -0.1613359 0.0486509 -3.316 0.001219 ** \n", + "tropical -0.0003105 0.0005464 -0.568 0.571036 \n", + "desert -0.0015805 0.0016191 -0.976 0.331046 \n", + "ln_frac -0.0863312 0.0760023 -1.136 0.258338 \n", + "aa_ln_atd 0.0120531 0.0374891 0.322 0.748402 \n", + "ln_arable -0.0019373 0.0147608 -0.131 0.895810 \n", + "ln_suitavg 0.0037507 0.0152505 0.246 0.806161 \n", + "ln_abslat -0.0149382 0.0226380 -0.660 0.510645 \n", + "europe 0.0444247 0.0784266 0.566 0.572182 \n", + "africa -0.2552539 0.0830358 -3.074 0.002633 ** \n", + "asia -0.1849298 0.0749077 -2.469 0.015015 * \n", + "americas -0.0619447 0.0746850 -0.829 0.408575 \n", + "distcr100 0.0116310 0.0358164 0.325 0.745964 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1161 on 116 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7741,\tAdjusted R-squared: 0.7469 \n", + "F-statistic: 28.4 on 14 and 116 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + frac_migrant + ln_frac + \n", + " aa_ln_atd + ln_arable + ln_suitavg + ln_abslat + europe + \n", + " africa + asia + americas, data = hla_country_web, subset = distcr100 != \n", + " \"NA\" & aa_pdiv != \"NA\" & malfal != \"NA\" & tropical != \"NA\" & \n", + " pnativ_60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.47729 -0.06295 0.00722 0.06580 0.28144 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.708443 0.433304 10.866 < 2e-16 ***\n", + "ln_hla_het 0.869948 0.210203 4.139 6.55e-05 ***\n", + "frac_migrant 0.098209 0.059433 1.652 0.1011 \n", + "ln_frac -0.144091 0.076038 -1.895 0.0605 . \n", + "aa_ln_atd 0.041594 0.037615 1.106 0.2710 \n", + "ln_arable -0.008539 0.015060 -0.567 0.5718 \n", + "ln_suitavg 0.011369 0.014452 0.787 0.4330 \n", + "ln_abslat 0.015788 0.016352 0.966 0.3362 \n", + "europe 0.113322 0.092317 1.228 0.2220 \n", + "africa -0.252790 0.091068 -2.776 0.0064 ** \n", + "asia -0.147021 0.085022 -1.729 0.0864 . \n", + "americas -0.042654 0.076108 -0.560 0.5762 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1205 on 119 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7504,\tAdjusted R-squared: 0.7273 \n", + "F-statistic: 32.52 on 11 and 119 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + aa_pdiv + frac_migrant + \n", + " ln_hla_het + malfal + tropical + desert + ln_frac + aa_ln_atd + \n", + " ln_arable + ln_suitavg + ln_abslat + europe + africa + asia + \n", + " americas + distcr100, data = hla_country_web, subset = distcr100 != \n", + " \"NA\" & aa_pdiv != \"NA\" & malfal != \"NA\" & tropical != \"NA\" & \n", + " pnativ_60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.50848 -0.05118 0.00504 0.06153 0.25524 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.5843220 0.8102615 5.658 1.16e-07 ***\n", + "ln_hla_het 0.5372992 0.2842763 1.890 0.061289 . \n", + "aa_pdiv 0.1385137 0.8873441 0.156 0.876231 \n", + "frac_migrant 0.1203077 0.0637473 1.887 0.061668 . \n", + "malfal -0.1730446 0.0508256 -3.405 0.000915 ***\n", + "tropical -0.0002674 0.0005420 -0.493 0.622759 \n", + "desert -0.0012792 0.0016128 -0.793 0.429317 \n", + "ln_frac -0.1187415 0.0780980 -1.520 0.131175 \n", + "aa_ln_atd 0.0120886 0.0398070 0.304 0.761925 \n", + "ln_arable -0.0020203 0.0146392 -0.138 0.890481 \n", + "ln_suitavg 0.0097049 0.0153976 0.630 0.529769 \n", + "ln_abslat -0.0217680 0.0228029 -0.955 0.341791 \n", + "europe 0.1426789 0.0933093 1.529 0.129011 \n", + "africa -0.1868513 0.0966412 -1.933 0.055660 . \n", + "asia -0.1124695 0.0829847 -1.355 0.178000 \n", + "americas -0.0646518 0.0753915 -0.858 0.392941 \n", + "distcr100 0.0299095 0.0365692 0.818 0.415128 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1149 on 114 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7824,\tAdjusted R-squared: 0.7519 \n", + "F-statistic: 25.62 on 16 and 114 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#############################\n", + "##########TABLE_6#############\n", + "#############################\n", + "#to do regressions in R we use the function lm, the dependent variable here is ln Life Expectancy in 1960 \n", + "\n", + "hla_country_web$distcr100<-hla_country_web$distcr1000\n", + "\n", + "regt6_1<-lm(ln_le60~ln_hla_het+aa_pdiv+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset= distcr100!=\"NA\" & aa_pdiv!=\"NA\" & malfal!=\"NA\" & tropical!=\"NA\" & pnativ_60!=\"NA\" , data=hla_country_web)\n", + "regt6_1robust<-coeftest(regt6_1, vcov = vcovHC(regt6_1, type=\"HC1\"))\n", + "summary(regt6_1)\n", + "\n", + "regt6_2<-lm(ln_le60~ln_hla_het+malfal+tropical+desert+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas+distcr100,subset= distcr100!=\"NA\" & aa_pdiv!=\"NA\" & malfal!=\"NA\" & tropical!=\"NA\" & pnativ_60!=\"NA\" , data=hla_country_web)\n", + "regt6_2robust<-coeftest(regt6_2, vcov = vcovHC(regt6_2, type=\"HC1\"))\n", + "summary(regt6_2)\n", + "\n", + "\n", + "regt6_3<-lm(ln_le60~ln_hla_het+frac_migrant+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas, subset= distcr100!=\"NA\" & aa_pdiv!=\"NA\" & malfal!=\"NA\" & tropical!=\"NA\" & pnativ_60!=\"NA\" , data=hla_country_web)\n", + "regt6_3robust<-coeftest(regt6_3, vcov = vcovHC(regt6_3, type=\"HC1\"))\n", + "summary(regt6_3)\n", + "\n", + "\n", + "regt6_4<-lm(ln_le60~ln_hla_het+aa_pdiv+frac_migrant+ln_hla_het+malfal+tropical+desert+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas+distcr100, subset= distcr100!=\"NA\" & aa_pdiv!=\"NA\" & malfal!=\"NA\" & tropical!=\"NA\" & pnativ_60!=\"NA\" , data=hla_country_web)\n", + "regt6_4robust<-coeftest(regt6_4, vcov = vcovHC(regt6_4, type=\"HC1\"))\n", + "summary(regt6_4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 6: Robustness to Exogenous Omitted Variables\n", + "===============================================================================================\n", + " Dependent variable: \n", + " -----------------------------------------\n", + " ln Life Expectancy in 1960 \n", + " (1) (2) (3) (4) \n", + "-----------------------------------------------------------------------------------------------\n", + "ln HLA Heterozygosity 1.0771*** 0.7713*** 0.8699*** 0.5373* \n", + " (0.3401) (0.2040) (0.2239) (0.3093) \n", + "Aggregate heterozygosity -0.2784 0.1385 \n", + " (1.1998) (1.0280) \n", + "Fraction of population at risk of contracting malaria -0.1613*** -0.1730***\n", + " (0.0481) (0.0487) \n", + "% within tropics -0.0003 -0.0003 \n", + " (0.0006) (0.0006) \n", + "% within desert -0.0016 -0.0013 \n", + " (0.0015) (0.0014) \n", + "Mean distance to coast or river 0.0116 0.0299 \n", + " (0.0385) (0.0381) \n", + "Fraction of nonindigenous population 0.0982 0.1203 \n", + " (0.0632) (0.0734) \n", + "===============================================================================================\n", + "===============================================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table6<-stargazer(regt6_1robust,regt6_2robust,regt6_3robust,regt6_4robust,\n", + " type=\"text\", align = TRUE, title = \"Table 6: Robustness to Exogenous Omitted Variables\",\n", + " dep.var.labels = \"ln Life Expectancy in 1960\", keep=c(\"ln_hla_het\", \"aa_pdiv\", \"malfal\",\n", + " \"tropical\", \"desert\", \"frac_migrant\", \"distcr100\"),\n", + " order=c(\"ln_hla_het\", \"aa_pdiv\", \"malfal\", \"tropical\", \"desert\", \"distcr100\", \"frac_migrant\"),\n", + " column.sep.width = \"10pt\", digits=4, no.space=TRUE,\n", + " covariate.labels=c(\"ln HLA Heterozygosity\", \"Aggregate heterozygosity\", \"Fraction of population at risk of contracting malaria\",\n", + " \"% within tropics\", \"% within desert\", \"Mean distance to coast or river\", \"Fraction of nonindigenous population\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

The first column of table 6 includes the measure for overall genetic diversity into the baseline estimating equation. Ashraf and Galor (2013) show that genetic diversity has a nonlinear association with economic development: societies with too little genetic diversity lack innovation, whereas societies with too much heterozygosity have increased conflict. Therefore, the measure of HLA heterozygosity may\n", + "be accounting for the indirect effect of overall heterozygosity on income. Additionally, historically rich societies may have attracted an increased number of migrants, thereby increasing total genetic heterozygosity within an ethnic population. Conversely, historically rich societies had the means to prevent migration, thereby lowering ethnic heterozygosity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**_Table 7.—Robustness to Endogenous Omitted Variables_**" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_ypc60 != \"NA\" & ln_yr_sch1960 != \n", + " \"NA\" & ln_pd60 != \"NA\" & ln_urb60 != \"NA\" & ln_young != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.48437 -0.04816 0.00643 0.06730 0.30371 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.075976 0.435901 11.645 < 2e-16 ***\n", + "ln_hla_het 0.999959 0.202655 4.934 3.29e-06 ***\n", + "ln_frac -0.068797 0.080539 -0.854 0.39508 \n", + "aa_ln_atd 0.022079 0.041794 0.528 0.59850 \n", + "ln_arable -0.020301 0.016533 -1.228 0.22243 \n", + "ln_suitavg 0.007012 0.015235 0.460 0.64633 \n", + "ln_abslat 0.016946 0.017460 0.971 0.33417 \n", + "europe 0.082177 0.081488 1.008 0.31572 \n", + "africa -0.303860 0.086142 -3.527 0.00064 ***\n", + "asia -0.198774 0.078794 -2.523 0.01326 * \n", + "americas -0.033717 0.076160 -0.443 0.65895 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.12 on 98 degrees of freedom\n", + " (66 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7378,\tAdjusted R-squared: 0.711 \n", + "F-statistic: 27.58 on 10 and 98 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_ypc60 + ln_frac + aa_ln_atd + \n", + " ln_arable + ln_suitavg + ln_abslat + europe + africa + asia + \n", + " americas, data = hla_country_web, subset = ln_ypc60 != \"NA\" & \n", + " ln_yr_sch1960 != \"NA\" & ln_pd60 != \"NA\" & ln_urb60 != \"NA\" & \n", + " ln_young != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.36913 -0.05115 -0.00077 0.05649 0.25210 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.0474992 0.4156969 9.737 4.97e-16 ***\n", + "ln_hla_het 0.6574851 0.1846542 3.561 0.000575 ***\n", + "ln_ypc60 0.0977108 0.0167017 5.850 6.64e-08 ***\n", + "ln_frac -0.1265309 0.0702960 -1.800 0.074974 . \n", + "aa_ln_atd -0.0006487 0.0363256 -0.018 0.985789 \n", + "ln_arable -0.0105789 0.0143837 -0.735 0.463824 \n", + "ln_suitavg 0.0290353 0.0136934 2.120 0.036524 * \n", + "ln_abslat 0.0089705 0.0151501 0.592 0.555156 \n", + "europe 0.0967317 0.0704638 1.373 0.172985 \n", + "africa -0.1342940 0.0798851 -1.681 0.095963 . \n", + "asia -0.0661328 0.0717676 -0.921 0.359083 \n", + "americas 0.0155832 0.0663525 0.235 0.814816 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1037 on 97 degrees of freedom\n", + " (66 observations deleted due to missingness)\n", + "Multiple R-squared: 0.8062,\tAdjusted R-squared: 0.7842 \n", + "F-statistic: 36.68 on 11 and 97 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_yr_sch1960 + ln_frac + \n", + " aa_ln_atd + ln_arable + ln_suitavg + ln_abslat + europe + \n", + " africa + asia + americas, data = hla_country_web, subset = ln_ypc60 != \n", + " \"NA\" & ln_yr_sch1960 != \"NA\" & ln_pd60 != \"NA\" & ln_urb60 != \n", + " \"NA\" & ln_young != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.259833 -0.044515 0.001145 0.046709 0.179913 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 3.990270 0.323522 12.334 < 2e-16 ***\n", + "ln_hla_het 0.612726 0.146980 4.169 6.66e-05 ***\n", + "ln_yr_sch1960 0.132013 0.013021 10.139 < 2e-16 ***\n", + "ln_frac -0.024146 0.056578 -0.427 0.6705 \n", + "aa_ln_atd 0.071103 0.029667 2.397 0.0185 * \n", + "ln_arable 0.004400 0.011832 0.372 0.7108 \n", + "ln_suitavg -0.008141 0.010774 -0.756 0.4517 \n", + "ln_abslat 0.005958 0.012276 0.485 0.6285 \n", + "europe 0.028567 0.057315 0.498 0.6193 \n", + "africa -0.123906 0.062887 -1.970 0.0517 . \n", + "asia -0.152569 0.055372 -2.755 0.0070 ** \n", + "americas -0.031796 0.053339 -0.596 0.5525 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.08403 on 97 degrees of freedom\n", + " (66 observations deleted due to missingness)\n", + "Multiple R-squared: 0.8727,\tAdjusted R-squared: 0.8583 \n", + "F-statistic: 60.45 on 11 and 97 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_pd60 + ln_urb60 + ln_young + \n", + " ln_frac + aa_ln_atd + ln_arable + ln_suitavg + ln_abslat + \n", + " europe + africa + asia + americas, data = hla_country_web, \n", + " subset = ln_ypc60 != \"NA\" & ln_yr_sch1960 != \"NA\" & ln_pd60 != \n", + " \"NA\" & ln_urb60 != \"NA\" & ln_young != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.300764 -0.043457 -0.009262 0.061716 0.245806 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.5230912 0.4641918 11.898 < 2e-16 ***\n", + "ln_hla_het 0.7528765 0.1766469 4.262 4.77e-05 ***\n", + "ln_pd60 0.0001866 0.0141599 0.013 0.98951 \n", + "ln_urb60 0.1137048 0.0177831 6.394 6.02e-09 ***\n", + "ln_young -0.0614235 0.0915465 -0.671 0.50388 \n", + "ln_frac -0.1367887 0.0734167 -1.863 0.06553 . \n", + "aa_ln_atd -0.0755990 0.0374562 -2.018 0.04638 * \n", + "ln_arable -0.0064507 0.0173545 -0.372 0.71094 \n", + "ln_suitavg 0.0133635 0.0131086 1.019 0.31058 \n", + "ln_abslat -0.0097970 0.0155962 -0.628 0.53140 \n", + "europe 0.0512724 0.0733536 0.699 0.48627 \n", + "africa -0.2301636 0.0746173 -3.085 0.00267 ** \n", + "asia -0.1139927 0.0715206 -1.594 0.11429 \n", + "americas -0.0694855 0.0673081 -1.032 0.30453 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.09939 on 95 degrees of freedom\n", + " (66 observations deleted due to missingness)\n", + "Multiple R-squared: 0.8256,\tAdjusted R-squared: 0.8017 \n", + "F-statistic: 34.59 on 13 and 95 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_ypc60 + ln_yr_sch1960 + \n", + " ln_pd60 + ln_urb60 + ln_young + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_ypc60 != \"NA\" & ln_yr_sch1960 != \n", + " \"NA\" & ln_pd60 != \"NA\" & ln_urb60 != \"NA\" & ln_young != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.21803 -0.03882 0.00079 0.04623 0.22772 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.0482107 0.4849748 8.347 6.35e-13 ***\n", + "ln_hla_het 0.5295807 0.1467296 3.609 0.000497 ***\n", + "ln_ypc60 0.0280348 0.0183296 1.529 0.129539 \n", + "ln_yr_sch1960 0.1036749 0.0149946 6.914 5.84e-10 ***\n", + "ln_pd60 0.0062813 0.0116550 0.539 0.591220 \n", + "ln_urb60 0.0342884 0.0196702 1.743 0.084609 . \n", + "ln_young -0.0148321 0.0771438 -0.192 0.847953 \n", + "ln_frac -0.0582461 0.0607576 -0.959 0.340214 \n", + "aa_ln_atd 0.0241980 0.0330670 0.732 0.466138 \n", + "ln_arable 0.0014524 0.0140344 0.103 0.917800 \n", + "ln_suitavg 0.0023245 0.0117470 0.198 0.843568 \n", + "ln_abslat -0.0001829 0.0126272 -0.014 0.988474 \n", + "europe 0.0255091 0.0607972 0.420 0.675762 \n", + "africa -0.0977019 0.0649752 -1.504 0.136052 \n", + "asia -0.1094776 0.0599032 -1.828 0.070820 . \n", + "americas -0.0341981 0.0551987 -0.620 0.537072 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.07987 on 93 degrees of freedom\n", + " (66 observations deleted due to missingness)\n", + "Multiple R-squared: 0.8897,\tAdjusted R-squared: 0.872 \n", + "F-statistic: 50.03 on 15 and 93 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "####################################################################\n", + "########## TABLE_7: Endogenous Omitted Variables #############\n", + "####################################################################\n", + "\n", + "#to do regressions in R we use the function lm, the dependent variable here is ln Life Expectancy in 1960 \n", + "\n", + "regt7_1<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset = ln_ypc60!=\"NA\" & ln_yr_sch1960!=\"NA\" & ln_pd60!=\"NA\" & ln_urb60!=\"NA\" & ln_young!=\"NA\", data=hla_country_web)\n", + "regt7_1robust<-coeftest(regt7_1, vcov = vcovHC(regt7_1, type=\"HC1\"))\n", + "summary(regt7_1)\n", + "\n", + "regt7_2<-lm(ln_le60~ln_hla_het+ln_ypc60+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset = ln_ypc60!=\"NA\" & ln_yr_sch1960!=\"NA\" & ln_pd60!=\"NA\" & ln_urb60!=\"NA\" & ln_young!=\"NA\", data=hla_country_web)\n", + "regt7_2robust<-coeftest(regt7_2, vcov = vcovHC(regt7_2, type=\"HC1\"))\n", + "summary(regt7_2)\n", + "\n", + "regt7_3<-lm(ln_le60~ln_hla_het+ln_yr_sch1960+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset = ln_ypc60!=\"NA\" & ln_yr_sch1960!=\"NA\" & ln_pd60!=\"NA\" & ln_urb60!=\"NA\" & ln_young!=\"NA\", data=hla_country_web)\n", + "regt7_3robust<-coeftest(regt7_3, vcov = vcovHC(regt7_3, type=\"HC1\"))\n", + "summary(regt7_3)\n", + "\n", + "regt7_4<-lm(ln_le60~ln_hla_het+ln_pd60+ln_urb60+ln_young+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset = ln_ypc60!=\"NA\" & ln_yr_sch1960!=\"NA\" & ln_pd60!=\"NA\" & ln_urb60!=\"NA\" & ln_young!=\"NA\", data=hla_country_web)\n", + "regt7_4robust<-coeftest(regt7_4, vcov = vcovHC(regt7_4, type=\"HC1\"))\n", + "summary(regt7_4)\n", + "\n", + "regt7_5<-lm(ln_le60~ln_hla_het+ln_ypc60+ln_yr_sch1960+ln_pd60+ln_urb60+ln_young+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset = ln_ypc60!=\"NA\" & ln_yr_sch1960!=\"NA\" & ln_pd60!=\"NA\" & ln_urb60!=\"NA\" & ln_young!=\"NA\", data=hla_country_web)\n", + "regt7_5robust<-coeftest(regt7_5, vcov = vcovHC(regt7_5, type=\"HC1\"))\n", + "summary(regt7_5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 7: Robustness to Endogenous Omitted Variables\n", + "==================================================================================================\n", + " Dependent variable: \n", + " -------------------------------------------------\n", + " ln Life Expectancy in 1960 \n", + " (1) (2) (3) (4) (5) \n", + "--------------------------------------------------------------------------------------------------\n", + "ln HLA Heterozygosity 1.0000*** 0.6575*** 0.6127*** 0.7529*** 0.5296***\n", + " (0.2118) (0.2124) (0.1633) (0.1944) (0.1673) \n", + "ln GDP per Capita in 1960 0.0977*** 0.0280* \n", + " (0.0221) (0.0167) \n", + "ln Avg. Years of School in 1960 0.1320*** 0.1037***\n", + " (0.0195) (0.0190) \n", + "ln Population Density in 1960 0.0002 0.0063 \n", + " (0.0152) (0.0126) \n", + "ln Urbanization Rate in 1960 0.1137*** 0.0343* \n", + " (0.0223) (0.0195) \n", + "ln Fraction of Population under 15 Years in 1960 -0.0614 -0.0148 \n", + " (0.0887) (0.0685) \n", + "==================================================================================================\n", + "==================================================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table7<-stargazer(regt7_1robust,regt7_2robust,regt7_3robust,regt7_4robust,regt7_5robust,\n", + " type=\"text\", align = TRUE, title = \"Table 7: Robustness to Endogenous Omitted Variables\",\n", + " dep.var.labels = \"ln Life Expectancy in 1960\", keep=c(\"ln_hla_het\", \"ln_ypc60\", \"ln_yr_sch1960\",\n", + " \"ln_pd60\", \"ln_urb60\", \"ln_young\"),\n", + " column.sep.width = \"10pt\", digits=4, no.space=TRUE,\n", + " covariate.labels=c(\"ln HLA Heterozygosity\", \"ln GDP per Capita in 1960\", \"ln Avg. Years of School in 1960\",\n", + " \"ln Population Density in 1960\", \"ln Urbanization Rate in 1960\", \"ln Fraction of Population under 15 Years in 1960\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Additional controls are considered in table 7. These include income, human capital, and demographics in 1960. The additional variables of Table 7 constitute more endogenous controls, as the effect of HLA heterozygosity may be associated with some general level of economic development, which is correlated with health, human capital, and income. Controlling for these alternative channels of economic development allows for a greater focus on the relationship between HLA heterozygosity, which represents innate resistance, and aggregate health.\n", + " \n", + "

Once these additional correlates of development are controlled for, the positive effect of HLA heterozygosity on life expectancy prior to the international epidemiological transition remains, providing further support for the role of HLA heterozygosity in measuring genetically determined resistance to infectious disease, mortality rates thereof, and resulting life expectancy differences across countries prior to the international epidemiological transition.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### V. Conclusions\n", + "-

HLA heterozygosity has a positive, statistically significant, and robust relationship with life expectancy at birth in 1960, a period argued to be before the diffusion of health technologies associated with the international epidemiological transition.\n", + "-

The strong statistical relationship between HLA heterozygosity and life expectancy is substantially lessened by the introduction of medicines and vaccines, which dissipate any benefits from genetically determined resistance.\n", + "-

An important source of the variation in HLA heterozygosity is the differential timing date of the Neolithic revolution, which provided the means of development for a much more severe infectious disease environment." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/C.justinCook-The_natural_selection_2015/C.justinCook-The_natural_selection_2015.Replication.R.ipynb b/notebooks/C.justinCook-The_natural_selection_2015/C.justinCook-The_natural_selection_2015.Replication.R.ipynb new file mode 100644 index 0000000..45d4fe7 --- /dev/null +++ b/notebooks/C.justinCook-The_natural_selection_2015/C.justinCook-The_natural_selection_2015.Replication.R.ipynb @@ -0,0 +1,2089 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# _THE NATURAL SELECTION OF INFECTIOUS DISEASE RESISTANCE AND ITS EFFECT ON CONTEMPORARY HEALTH- A replication study_\n", + "## C. Justin Cook\n", + "### _Review of Economics and Statistics Volume 97 | Issue 4 | October 2015 p.742-757_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This study pretends to do a replication of _\"THE NATURAL SELECTION OF INFECTIOUS DISEASE RESISTANCE AND\n", + "ITS EFFECT ON CONTEMPORARY HEALTH\"_ whose author is C. Justin Cook, an assistant Professor, Economics University of California, Merced. The replication data for paper is available [here](https://doi.org/10.7910/DVN/27669)\n", + "\n", + "The replication is done by Sara Ariza Murillo, a colombian economist from the Universidad del Rosario. This includes two jupyter notebooks:\n", + "\n", + "1. **Notebook one:** presents the stata do file of the author that replicates tables 2-7 within the paper. It also replicates in stata the figures of the paper that are not included in the author's do file.\n", + "2. **Notebook two:** presents the code elaborated by Sara in R that replicates the tables within the paper. \n", + "\n", + "Both notebooks include a summary of the document.\n", + "\n", + "If there are any questions, please contact Sara Ariza at sara.ariza@urosario.edu.co" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Notebook two" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Abstract\n", + " _\"This paper empirically tests the association between genetically determined resistance to infectious disease and cross-country health differences. A country-level measure of genetic diversity for the system of genes associated with the recognition and disposal of foreign pathogens is constructed. Genetic diversity within this system has been shown to reduce the virulence and prevalence of infectious diseases and is hypothesized to have been naturally selected from historical exposure to infectious pathogens. Base estimation shows a statistically strong, robust, and positive relationship between this constructed measure and country-level health outcomes in times prior to, but not after, the international epidemiological transition\"_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### I. Introduction\n", + "

Prior to the major medical discoveries associated with the international epidemiological transition, infectious diseases were a major determinant of mortality and subsequent differences in life expectancy across countries. (The discovery and widespread use of effective medicines (e.g., penicillin, streptomycin, a range of vaccines) in the late 1940s to early 1950s is labeled by Acemoglu and Johnson (2007) as the international epidemiological transition.) \n", + " \n", + "

This paper answers the question what were the causes of the initial cross-country disparities in the virulence of infectious diseases? the above, by empirically investigating the role of genetically determined differences in resistance to infectious diseases \n", + " \n", + "

The main hyphotesis is that innate resistance did influence country-level response to infectious disease prior to the international epidemiological transition, but the effects of innate resistance are dissipated by more efficacious health technologies.\n", + "\n", + "

The measure of genetic resistance is found within the human leukocyte antigen (HLA) system. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### The HLA system \n", + "

The HLA is responsible for locating foreign proteins in order to direct cells of the immune system to initiate an immune response and is broken into two major classes, class I and class II, with both classes being associated with the recognition of certain pathogens (Piertney&Oliver, 2006).\n", + "\n", + "Using country-level aggregations of ethnic-level genetic data, the author constructs a cross-country measure for diversity within the HLA system: HLA heterozygosity. He tests the hypothesis by estimating the association between country-level health measures (e.g., life expectancy at birth) and HLA heterozygosity in periods both prior to and after the international epidemiological transition \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\f" + ] + } + ], + "source": [ + "rm(list=ls())\n", + "cat(\"\\014\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "package 'lmtest' successfully unpacked and MD5 sums checked\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"cannot remove prior installation of package 'lmtest'\"Warning message in file.copy(savedcopy, lib, recursive = TRUE):\n", + "\"problema al copiar C:\\Users\\USUARIO\\anaconda3\\envs\\GeoPython38env\\Lib\\R\\library\\00LOCK\\lmtest\\libs\\x64\\lmtest.dll a C:\\Users\\USUARIO\\anaconda3\\envs\\GeoPython38env\\Lib\\R\\library\\lmtest\\libs\\x64\\lmtest.dll: Permission denied\"Warning message:\n", + "\"restored 'lmtest'\"" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "The downloaded binary packages are in\n", + "\tC:\\Users\\USUARIO\\AppData\\Local\\Temp\\RtmpsvIWcj\\downloaded_packages\n", + "package 'sandwich' successfully unpacked and MD5 sums checked\n", + "\n", + "The downloaded binary packages are in\n", + "\tC:\\Users\\USUARIO\\AppData\\Local\\Temp\\RtmpsvIWcj\\downloaded_packages\n", + "package 'stargazer' successfully unpacked and MD5 sums checked\n", + "\n", + "The downloaded binary packages are in\n", + "\tC:\\Users\\USUARIO\\AppData\\Local\\Temp\\RtmpsvIWcj\\downloaded_packages\n", + "package 'data.table' successfully unpacked and MD5 sums checked\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"cannot remove prior installation of package 'data.table'\"Warning message in file.copy(savedcopy, lib, recursive = TRUE):\n", + "\"problema al copiar C:\\Users\\USUARIO\\anaconda3\\envs\\GeoPython38env\\Lib\\R\\library\\00LOCK\\data.table\\libs\\x64\\datatable.dll a C:\\Users\\USUARIO\\anaconda3\\envs\\GeoPython38env\\Lib\\R\\library\\data.table\\libs\\x64\\datatable.dll: Permission denied\"Warning message:\n", + "\"restored 'data.table'\"" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "The downloaded binary packages are in\n", + "\tC:\\Users\\USUARIO\\AppData\\Local\\Temp\\RtmpsvIWcj\\downloaded_packages\n" + ] + } + ], + "source": [ + "install.packages(\"lmtest\")\n", + "install.packages(\"sandwich\")\n", + "install.packages(\"stargazer\")\n", + "install.packages(\"data.table\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"package 'lmtest' was built under R version 3.6.3\"Loading required package: zoo\n", + "Warning message:\n", + "\"package 'zoo' was built under R version 3.6.3\"\n", + "Attaching package: 'zoo'\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " as.Date, as.Date.numeric\n", + "\n", + "Warning message:\n", + "\"package 'sandwich' was built under R version 3.6.3\"\n", + "Please cite as: \n", + "\n", + " Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.\n", + " R package version 5.2.2. https://CRAN.R-project.org/package=stargazer \n", + "\n", + "Warning message:\n", + "\"package 'data.table' was built under R version 3.6.3\"" + ] + } + ], + "source": [ + "library(lmtest)\n", + "library(sandwich)\n", + "library(stargazer)\n", + "library(data.table)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dir1 <-(\"./raw/\") #the directory where the base is\n", + "setwd(dir1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "hla_country_web <- read.delim(\"hla_country_web.tab\", header=TRUE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===============================================================================\n", + "Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max \n", + "-------------------------------------------------------------------------------\n", + "ln_hla_het 175 -1.158 0.093 -1.560 -1.174 -1.109 -1.042 \n", + "hla_het 175 0.315 0.027 0.210 0.309 0.330 0.353 \n", + "ln_mort40 89 -1.095 0.802 -3.507 -1.619 -0.477 0.181 \n", + "ln_le40 87 3.820 0.253 3.292 3.620 4.039 4.215 \n", + "ln_le50 155 3.893 0.250 3.401 3.665 4.116 4.286 \n", + "ln_le60 158 3.975 0.223 3.438 3.790 4.175 4.298 \n", + "ln_le70 160 4.047 0.205 3.525 3.906 4.224 4.313 \n", + "ln_le80 159 4.112 0.178 3.654 4.010 4.252 4.332 \n", + "ln_le90 159 4.157 0.175 3.491 4.083 4.275 4.367 \n", + "ln_le00 160 4.185 0.174 3.682 4.117 4.306 4.395 \n", + "ln_le10 159 4.231 0.153 3.858 4.174 4.332 4.418 \n", + "europe 171 0.240 0.428 0.000 0.000 0.000 1.000 \n", + "asia 171 0.246 0.432 0.000 0.000 0.000 1.000 \n", + "africa 171 0.246 0.432 0.000 0.000 0.000 1.000 \n", + "americas 171 0.211 0.409 0.000 0.000 0.000 1.000 \n", + "oceania 171 0.058 0.235 0.000 0.000 0.000 1.000 \n", + "frac_eur 175 0.344 0.422 0.000 0.000 0.928 1.000 \n", + "frac_me 175 0.155 0.327 0.000 0.000 0.031 1.000 \n", + "frac_easia 172 0.139 0.316 0.000 0.000 0.012 1.000 \n", + "frac_africa 175 0.268 0.416 0 0 0.6 1 \n", + "frac_am 175 0.046 0.148 0 0 0 1 \n", + "frac_oc 175 0.044 0.186 0 0 0 1 \n", + "aa_ln_atd 147 8.496 0.463 7.164 8.205 8.857 9.247 \n", + "aa_atd 147 5,503.902 2,129.866 1,356.990 3,865.574 7,105.059 10,403.160\n", + "aa_mdist 150 6.520 3.496 1.193 4.276 7.502 19.390 \n", + "aa_mdist_sqr 150 62.027 79.294 1.423 18.456 64.049 442.036 \n", + "aa_lanim 100 1.326 0.973 0.000 0.061 2.301 2.303 \n", + "aa_lpd1 124 0.452 1.229 -2.535 -0.441 1.540 3.114 \n", + "ln_frac 170 0.335 0.180 0.000 0.171 0.491 0.658 \n", + "ln_arable 167 2.249 1.227 -2.659 1.568 3.094 4.129 \n", + "ln_suitavg 141 -1.329 1.232 -5.809 -1.661 -0.580 -0.041 \n", + "ln_abslat 170 2.971 0.936 0.000 2.565 3.711 4.174 \n", + "aa_pdiv 150 0.725 0.026 0.628 0.718 0.742 0.765 \n", + "malfal 148 0.280 0.409 0.000 0.000 0.597 1.000 \n", + "tropical 173 41.897 46.000 0.000 0.000 100.000 100.000 \n", + "desert 173 2.233 7.479 0.000 0.000 0.000 55.087 \n", + "distcr1000 143 0.308 0.418 0.008 0.053 0.347 2.292 \n", + "frac_migrant 150 0.260 0.339 0.000 0.018 0.410 1.000 \n", + "pnativ_60 150 0.740 0.339 0.000 0.590 0.982 1.000 \n", + "ln_ypc60 164 7.996 0.940 6.052 7.308 8.653 10.792 \n", + "ln_yr_sch1960 130 0.981 0.907 -2.008 0.285 1.639 2.305 \n", + "ln_pd60 153 3.333 1.542 -0.487 2.393 4.419 7.990 \n", + "ln_urb60 162 3.270 0.857 0.693 2.824 3.846 4.605 \n", + "ln_young 159 3.635 0.215 3.058 3.537 3.779 3.904 \n", + "ln_mix_het 175 -1.130 0.088 -1.558 -1.138 -1.085 -1.039 \n", + "oecd 171 0.175 0.381 0.000 0.000 0.000 1.000 \n", + "fstdistwtd_usa 164 9.722 5.240 3.145 4.855 12.999 20.880 \n", + "-------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "#############################\n", + "##########TABLA1#############\n", + "#############################\n", + "\n", + "data<-data.table(hla_country_web)\n", + "stargazer(data,type=\"text\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_ln_atd, data = hla_country_web, \n", + " subset = aa_mdist != \"NA\" & ln_le60 != \"NA\" & ln_hla_het != \n", + " \"NA\" & ln_frac != \"NA \" & ln_abslat != \"NA\" & ln_arable != \n", + " \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.29871 -0.02954 0.01560 0.04933 0.09588 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.32606 0.10613 -12.49 <2e-16 ***\n", + "aa_ln_atd 0.02114 0.01251 1.69 0.0934 . \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.06738 on 129 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.02166,\tAdjusted R-squared: 0.01408 \n", + "F-statistic: 2.856 on 1 and 129 DF, p-value: 0.09344\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.32606 Min. :0.009805 Min. :-15.660 Min. :0.000000 \n", + " 1st Qu.:-0.98926 1st Qu.:0.028523 1st Qu.:-11.206 1st Qu.:0.008225 \n", + " Median :-0.65246 Median :0.047241 Median : -6.752 Median :0.016449 \n", + " Mean :-0.65246 Mean :0.047241 Mean : -6.752 Mean :0.016449 \n", + " 3rd Qu.:-0.31566 3rd Qu.:0.065960 3rd Qu.: -2.298 3rd Qu.:0.024674 \n", + " Max. : 0.02114 Max. :0.084678 Max. : 2.157 Max. :0.032899 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_ln_atd + aa_mdist, data = hla_country_web, \n", + " subset = aa_mdist != \"NA\" & ln_le60 != \"NA\" & ln_hla_het != \n", + " \"NA\" & ln_frac != \"NA \" & ln_abslat != \"NA\" & ln_arable != \n", + " \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.189302 -0.028152 0.003329 0.033802 0.138810 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.321902 0.081579 -16.204 < 2e-16 ***\n", + "aa_ln_atd 0.029984 0.009661 3.104 0.00235 ** \n", + "aa_mdist -0.012063 0.001269 -9.505 < 2e-16 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.05179 on 128 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.4265,\tAdjusted R-squared: 0.4175 \n", + "F-statistic: 47.59 on 2 and 128 DF, p-value: 3.522e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.32190 Min. :0.001422 Min. :-18.110 Min. :0.0000000 \n", + " 1st Qu.:-0.66698 1st Qu.:0.005030 1st Qu.:-13.295 1st Qu.:0.0000000 \n", + " Median :-0.01206 Median :0.008638 Median : -8.481 Median :0.0000000 \n", + " Mean :-0.43466 Mean :0.027685 Mean : -7.707 Mean :0.0002357 \n", + " 3rd Qu.: 0.00896 3rd Qu.:0.040816 3rd Qu.: -2.505 3rd Qu.:0.0003536 \n", + " Max. : 0.02998 Max. :0.072994 Max. : 3.471 Max. :0.0007072 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_lanim, data = hla_country_web, subset = aa_mdist != \n", + " \"NA\" & ln_le60 != \"NA\" & ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.28924 -0.03132 0.01314 0.05735 0.08659 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.189394 0.012819 -92.780 <2e-16 ***\n", + "aa_lanim 0.026613 0.007815 3.406 0.001 ** \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.07228 on 87 degrees of freedom\n", + " (86 observations deleted due to missingness)\n", + "Multiple R-squared: 0.1176,\tAdjusted R-squared: 0.1075 \n", + "F-statistic: 11.6 on 1 and 87 DF, p-value: 0.001001\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.18939 Min. :0.006444 Min. :-94.03 Min. :0.000e+00 \n", + " 1st Qu.:-0.88539 1st Qu.:0.007995 1st Qu.:-69.49 1st Qu.:2.078e-05 \n", + " Median :-0.58139 Median :0.009546 Median :-44.95 Median :4.157e-05 \n", + " Mean :-0.58139 Mean :0.009546 Mean :-44.95 Mean :4.157e-05 \n", + " 3rd Qu.:-0.27739 3rd Qu.:0.011097 3rd Qu.:-20.41 3rd Qu.:6.235e-05 \n", + " Max. : 0.02661 Max. :0.012649 Max. : 4.13 Max. :8.314e-05 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_lanim + aa_mdist, data = hla_country_web, \n", + " subset = aa_mdist != \"NA\" & ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.173322 -0.027746 0.009552 0.035496 0.127879 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.109316 0.011777 -94.190 < 2e-16 ***\n", + "aa_lanim 0.036053 0.005395 6.683 2.22e-09 ***\n", + "aa_mdist -0.013321 0.001318 -10.110 2.73e-16 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.04914 on 86 degrees of freedom\n", + " (86 observations deleted due to missingness)\n", + "Multiple R-squared: 0.5968,\tAdjusted R-squared: 0.5874 \n", + "F-statistic: 63.65 on 2 and 86 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.10932 Min. :0.001494 Min. :-102.9112 Min. :0.000e+00 \n", + " 1st Qu.:-0.56132 1st Qu.:0.003295 1st Qu.: -55.9145 1st Qu.:3.620e-14 \n", + " Median :-0.01332 Median :0.005096 Median : -8.9177 Median :7.240e-14 \n", + " Mean :-0.36219 Mean :0.005790 Mean : -34.9181 Mean :1.258e-10 \n", + " 3rd Qu.: 0.01137 3rd Qu.:0.007938 3rd Qu.: -0.9215 3rd Qu.:1.887e-10 \n", + " Max. : 0.03605 Max. :0.010779 Max. : 7.0747 Max. :3.773e-10 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_lpd1, data = hla_country_web, subset = aa_mdist != \n", + " \"NA\" & ln_hla_het != \"NA\" & ln_frac != \"NA \" & ln_abslat != \n", + " \"NA\" & ln_arable != \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \n", + " \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.28985 -0.02536 0.01560 0.04899 0.09343 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.154714 0.006867 -168.154 <2e-16 ***\n", + "aa_lpd1 0.014363 0.005273 2.724 0.0075 ** \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.06849 on 111 degrees of freedom\n", + " (62 observations deleted due to missingness)\n", + "Multiple R-squared: 0.06264,\tAdjusted R-squared: 0.0542 \n", + "F-statistic: 7.418 on 1 and 111 DF, p-value: 0.007501\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.15471 Min. :0.004286 Min. :-155.386 Min. :0.0000000 \n", + " 1st Qu.:-0.86245 1st Qu.:0.005073 1st Qu.:-115.702 1st Qu.:0.0002755 \n", + " Median :-0.57018 Median :0.005859 Median : -76.017 Median :0.0005510 \n", + " Mean :-0.57018 Mean :0.005859 Mean : -76.017 Mean :0.0005510 \n", + " 3rd Qu.:-0.27791 3rd Qu.:0.006645 3rd Qu.: -36.333 3rd Qu.:0.0008265 \n", + " Max. : 0.01436 Max. :0.007431 Max. : 3.351 Max. :0.0011020 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_hla_het ~ aa_lpd1 + aa_mdist, data = hla_country_web, \n", + " subset = aa_mdist != \"NA\" & ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.180134 -0.026307 0.008982 0.032460 0.114983 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -1.072103 0.010613 -101.019 < 2e-16 ***\n", + "aa_lpd1 0.015834 0.004032 3.927 0.00015 ***\n", + "aa_mdist -0.012383 0.001383 -8.955 9.65e-15 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.05233 on 110 degrees of freedom\n", + " (62 observations deleted due to missingness)\n", + "Multiple R-squared: 0.4578,\tAdjusted R-squared: 0.448 \n", + "F-statistic: 46.45 on 2 and 110 DF, p-value: 2.381e-15\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + " Min. :-1.072104 Min. :0.001501 Min. :-107.000 Min. :0.000e+00 \n", + " 1st Qu.:-0.542243 1st Qu.:0.002379 1st Qu.: -57.625 1st Qu.:0.000e+00 \n", + " Median :-0.012383 Median :0.003258 Median : -8.250 Median :0.000e+00 \n", + " Mean :-0.356217 Mean :0.004926 Mean : -36.796 Mean :1.304e-06 \n", + " 3rd Qu.: 0.001726 3rd Qu.:0.006639 3rd Qu.: -1.695 3rd Qu.:1.956e-06 \n", + " Max. : 0.015834 Max. :0.010020 Max. : 4.861 Max. :3.912e-06 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "##############################################################################\n", + "########## TABLE_2: Historic Determinants of HLA Heterozygosity #############\n", + "##############################################################################\n", + "\n", + "regt1_1<-lm(ln_hla_het~aa_ln_atd,subset=aa_mdist!=\"NA\" & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\",data=hla_country_web)\n", + "regt1_1robust<-coeftest(regt1_1, vcov = vcovHC(regt1_1, type=\"HC1\"))\n", + "summary(regt1_1)\n", + "summary(regt1_1robust)\n", + "\n", + "regt1_2<-lm(ln_hla_het~aa_ln_atd+aa_mdist,subset=aa_mdist!=\"NA\" & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt1_2robust<-coeftest(regt1_2, vcov = vcovHC(regt1_2, type=\"HC1\"))\n", + "summary(regt1_2)\n", + "summary(regt1_2robust)\n", + "\n", + "regt1_3<-lm(ln_hla_het~aa_lanim,subset=aa_mdist!=\"NA\" & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt1_3robust<-coeftest(regt1_3, vcov = vcovHC(regt1_3, type=\"HC1\"))\n", + "summary(regt1_3)\n", + "summary(regt1_3robust)\n", + "\n", + "regt1_4<-lm(ln_hla_het~aa_lanim+aa_mdist,subset=aa_mdist!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt1_4robust<-coeftest(regt1_4, vcov = vcovHC(regt1_4, type=\"HC1\"))\n", + "summary(regt1_4)\n", + "summary(regt1_4robust)\n", + "\n", + "regt1_5<-lm(ln_hla_het~aa_lpd1,subset=aa_mdist!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt1_5robust<-coeftest(regt1_5, vcov = vcovHC(regt1_5, type=\"HC1\"))\n", + "summary(regt1_5)\n", + "summary(regt1_5robust)\n", + "\n", + "regt1_6<-lm(ln_hla_het~aa_lpd1+aa_mdist,subset=aa_mdist!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt1_6robust<-coeftest(regt1_6, vcov = vcovHC(regt1_6, type=\"HC1\"))\n", + "summary(regt1_6)\n", + "summary(regt1_6robust)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 2: Explaining HLA Heterozygosity\n", + "=========================================================================================================\n", + " Dependent variable: \n", + " -----------------------------------------------------------------\n", + " ln HLA Heterozygosity \n", + " (1) (2) (3) (4) (5) (6) \n", + "---------------------------------------------------------------------------------------------------------\n", + "ln Years since Neolithic Revolution 0.0211** 0.0300*** \n", + " (0.0098) (0.0086) \n", + "ln No. of Potential Domesticate Animals 0.0266*** 0.0361*** \n", + " (0.0064) (0.0051) \n", + "ln Population Density in 1 CE 0.0144*** 0.0158*** \n", + " (0.0043) (0.0033) \n", + "Migratory Distance from East Africa -0.0121*** -0.0133*** -0.0124***\n", + " (0.0014) (0.0015) (0.0015) \n", + "Constant -1.3261*** -1.3219*** -1.1894*** -1.1093*** -1.1547*** -1.0721***\n", + " (0.0847) (0.0730) (0.0126) (0.0108) (0.0074) (0.0100) \n", + "=========================================================================================================\n", + "=========================================================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table2<-stargazer(regt1_1robust,regt1_2robust,regt1_3robust,regt1_4robust,\n", + " regt1_5robust,regt1_6robust,type=\"text\", keep.stat=c(\"n\", \"rsq\"), align = TRUE, \n", + " title = \"Table 2: Explaining HLA Heterozygosity\",dep.var.labels = \"ln HLA Heterozygosity\",\n", + " column.sep.width = \"5pt\", digits=4, no.space=TRUE, order=c(\"aa_ln_atd\",\"aa_lanim\",\"aa_lpd1\",\"aa_mdist\"),\n", + " covariate.labels=c(\"ln Years since Neolithic Revolution\",\"ln No. of Potential Domesticate Animals\", \n", + " \"ln Population Density in 1 CE\", \"Migratory Distance from East Africa\") )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_mort40 ~ ln_hla_het, data = hla_country_web, \n", + " subset = ln_hla_het != \"NA\" & ln_frac != \"NA \" & ln_abslat != \n", + " \"NA\" & ln_arable != \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \n", + " \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-1.18727 -0.34339 0.01533 0.26435 1.09459 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -6.6724 0.9301 -7.174 5.63e-10 ***\n", + "ln_hla_het -5.0080 0.8120 -6.167 3.79e-08 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.4915 on 71 degrees of freedom\n", + " (102 observations deleted due to missingness)\n", + "Multiple R-squared: 0.3488,\tAdjusted R-squared: 0.3397 \n", + "F-statistic: 38.03 on 1 and 71 DF, p-value: 3.795e-08\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_mort40 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-1.01614 -0.30628 0.05079 0.28246 0.91429 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -9.57531 2.25891 -4.239 7.6e-05 ***\n", + "ln_hla_het -3.86103 1.04886 -3.681 0.000489 ***\n", + "ln_frac -0.39701 0.38210 -1.039 0.302832 \n", + "aa_ln_atd 0.59752 0.21443 2.787 0.007060 ** \n", + "ln_arable 0.04486 0.10042 0.447 0.656616 \n", + "ln_suitavg 0.03620 0.08595 0.421 0.675104 \n", + "ln_abslat -0.32604 0.10993 -2.966 0.004283 ** \n", + "europe 0.10748 0.35411 0.304 0.762501 \n", + "africa 0.45897 0.40337 1.138 0.259562 \n", + "asia 0.17221 0.37414 0.460 0.646914 \n", + "americas 0.11026 0.35364 0.312 0.756252 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.4548 on 62 degrees of freedom\n", + " (102 observations deleted due to missingness)\n", + "Multiple R-squared: 0.5133,\tAdjusted R-squared: 0.4348 \n", + "F-statistic: 6.538 on 10 and 62 DF, p-value: 8.296e-07\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le40 ~ ln_hla_het, data = hla_country_web, subset = ln_hla_het != \n", + " \"NA\" & ln_frac != \"NA \" & ln_abslat != \"NA\" & ln_arable != \n", + " \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.44116 -0.18547 0.07159 0.18426 0.34939 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 6.2554 0.4174 14.985 < 2e-16 ***\n", + "ln_hla_het 2.1436 0.3653 5.869 1.39e-07 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.216 on 69 degrees of freedom\n", + " (104 observations deleted due to missingness)\n", + "Multiple R-squared: 0.3329,\tAdjusted R-squared: 0.3233 \n", + "F-statistic: 34.44 on 1 and 69 DF, p-value: 1.386e-07\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le40 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.32401 -0.10245 -0.00067 0.09843 0.36017 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.316719 0.686427 7.746 1.33e-10 ***\n", + "ln_hla_het 1.077112 0.348335 3.092 0.003013 ** \n", + "ln_frac 0.072362 0.128129 0.565 0.574344 \n", + "aa_ln_atd -0.003076 0.068392 -0.045 0.964279 \n", + "ln_arable -0.105980 0.033565 -3.157 0.002491 ** \n", + "ln_suitavg 0.052993 0.027554 1.923 0.059195 . \n", + "ln_abslat 0.098031 0.037499 2.614 0.011290 * \n", + "europe -0.052723 0.116216 -0.454 0.651708 \n", + "africa -0.502073 0.128697 -3.901 0.000245 ***\n", + "asia -0.339857 0.122322 -2.778 0.007282 ** \n", + "americas -0.329193 0.116623 -2.823 0.006451 ** \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1503 on 60 degrees of freedom\n", + " (104 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7192,\tAdjusted R-squared: 0.6724 \n", + "F-statistic: 15.37 on 10 and 60 DF, p-value: 3.688e-13\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het, data = hla_country_web, subset = ln_hla_het != \n", + " \"NA\" & ln_frac != \"NA \" & ln_abslat != \"NA\" & ln_arable != \n", + " \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.5756 -0.1791 0.0458 0.1764 0.3490 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.8146 0.3023 19.233 < 2e-16 ***\n", + "ln_hla_het 1.6200 0.2631 6.157 8.72e-09 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.2036 on 129 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.2271,\tAdjusted R-squared: 0.2211 \n", + "F-statistic: 37.91 on 1 and 129 DF, p-value: 8.721e-09\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA \" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\" & ln_le60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.48975 -0.06072 0.00716 0.07354 0.27776 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.962062 0.408122 12.158 < 2e-16 ***\n", + "ln_hla_het 1.015120 0.192335 5.278 5.89e-07 ***\n", + "ln_frac -0.121001 0.075280 -1.607 0.110605 \n", + "aa_ln_atd 0.036998 0.037781 0.979 0.329412 \n", + "ln_arable -0.008700 0.015168 -0.574 0.567349 \n", + "ln_suitavg 0.008039 0.014414 0.558 0.578063 \n", + "ln_abslat 0.019909 0.016277 1.223 0.223684 \n", + "europe 0.038739 0.081110 0.478 0.633792 \n", + "africa -0.312210 0.084269 -3.705 0.000321 ***\n", + "asia -0.205622 0.077827 -2.642 0.009339 ** \n", + "americas -0.040059 0.076638 -0.523 0.602141 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1213 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7446,\tAdjusted R-squared: 0.7233 \n", + "F-statistic: 34.99 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#########################################################\n", + "########## TABLE_3: Premedicinal Health ############\n", + "#########################################################\n", + "\n", + "regt3_1<-lm(ln_mort40~ln_hla_het,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_1robust<-coeftest(regt3_1, vcov = vcovHC(regt3_1, type=\"HC1\"))\n", + "summary(regt3_1)\n", + "\n", + "regt3_2<-lm(ln_mort40~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_2robust<-coeftest(regt3_2, vcov = vcovHC(regt3_2, type=\"HC1\"))\n", + "summary(regt3_2)\n", + "\n", + "regt3_3<-lm(ln_le40~ln_hla_het,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_3robust<-coeftest(regt3_3, vcov = vcovHC(regt3_3, type=\"HC1\"))\n", + "summary(regt3_3)\n", + "\n", + "regt3_4<-lm(ln_le40~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_4robust<-coeftest(regt3_4, vcov = vcovHC(regt3_4, type=\"HC1\"))\n", + "summary(regt3_4)\n", + "\n", + "regt3_5<-lm(ln_le60~ln_hla_het,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_5robust<-coeftest(regt3_5, vcov = vcovHC(regt3_5, type=\"HC1\"))\n", + "summary(regt3_5)\n", + "\n", + "\n", + "regt3_6<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_hla_het!=\"NA\" & ln_frac!=\"NA \"& ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\" & ln_le60!=\"NA\" ,data=hla_country_web)\n", + "regt3_6robust<-coeftest(regt3_6, vcov = vcovHC(regt3_6, type=\"HC1\"))\n", + "summary(regt3_6)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 3: The Effect of HLA Heterozygosity prior to the International Epidemiological Transition\n", + "==================================================================================================\n", + " Dependent variable: \n", + " --------------------------------------------------------------\n", + " \n", + " ln PM1940 ln PM1940 ln LE1940 ln LE1940 ln LE1960 ln LE1960\n", + " (1) (2) (3) (4) (5) (6) \n", + "--------------------------------------------------------------------------------------------------\n", + "ln HLA Heterozygosity -5.0080*** -3.8610*** 2.1436*** 1.0771*** 1.6200*** 1.0151***\n", + " (0.7117) (1.1282) (0.3310) (0.3771) (0.2455) (0.1899) \n", + "ln Ethnic Fractionalization -0.3970 0.0724 -0.1210 \n", + " (0.3961) (0.1338) (0.0927) \n", + "ln Years since Neolithic Revolution 0.5975** -0.0031 0.0370 \n", + " (0.2358) (0.0549) (0.0397) \n", + "ln Fraction of Arable Land 0.0449 -0.1060*** -0.0087 \n", + " (0.1220) (0.0366) (0.0160) \n", + "ln Suitability of Agriculture 0.0362 0.0530** 0.0080 \n", + " (0.0844) (0.0238) (0.0128) \n", + "ln Abs. Latitude -0.3260** 0.0980** 0.0199 \n", + " (0.1297) (0.0410) (0.0156) \n", + "==================================================================================================\n", + "==================================================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table3<-stargazer(regt3_1robust,regt3_2robust,regt3_3robust,regt3_4robust,\n", + " regt3_5robust,regt3_6robust,type=\"text\", align = TRUE,\n", + " keep=c(\"ln_hla_het\",\"ln_frac\",\"aa_ln_atd\",\"ln_arable\", \"ln_suitavg\",\"ln_abslat\"),\n", + " title = \"Table 3: The Effect of HLA Heterozygosity prior to the International Epidemiological Transition\",\n", + " column.labels=c(\"ln PM1940\", \"ln PM1940\", \"ln LE1940\", \"ln LE1940\", \"ln LE1960\", \"ln LE1960\"),\n", + " column.sep.width = \"10pt\", digits=4, no.space=TRUE,\n", + " covariate.labels=c(\"ln HLA Heterozygosity\", \"ln Ethnic Fractionalization\", \"ln Years since Neolithic Revolution\", \n", + " \"ln Fraction of Arable Land\", \"ln Suitability of Agriculture\", \"ln Abs. Latitude\") )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.48975 -0.06072 0.00716 0.07354 0.27776 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.962062 0.408122 12.158 < 2e-16 ***\n", + "ln_hla_het 1.015120 0.192335 5.278 5.89e-07 ***\n", + "ln_frac -0.121001 0.075280 -1.607 0.110605 \n", + "aa_ln_atd 0.036998 0.037781 0.979 0.329412 \n", + "ln_arable -0.008700 0.015168 -0.574 0.567349 \n", + "ln_suitavg 0.008039 0.014414 0.558 0.578063 \n", + "ln_abslat 0.019909 0.016277 1.223 0.223684 \n", + "europe 0.038739 0.081110 0.478 0.633792 \n", + "africa -0.312210 0.084269 -3.705 0.000321 ***\n", + "asia -0.205622 0.077827 -2.642 0.009339 ** \n", + "americas -0.040059 0.076638 -0.523 0.602141 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1213 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7446,\tAdjusted R-squared: 0.7233 \n", + "F-statistic: 34.99 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le70 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.47914 -0.04940 0.00058 0.07175 0.24402 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.1986107 0.3863098 13.457 < 2e-16 ***\n", + "ln_hla_het 1.0404702 0.1820558 5.715 8.15e-08 ***\n", + "ln_frac -0.1163928 0.0712564 -1.633 0.104998 \n", + "aa_ln_atd 0.0246249 0.0357620 0.689 0.492418 \n", + "ln_arable -0.0141638 0.0143576 -0.987 0.325870 \n", + "ln_suitavg 0.0007926 0.0136433 0.058 0.953771 \n", + "ln_abslat 0.0045721 0.0154071 0.297 0.767166 \n", + "europe 0.0344496 0.0767749 0.449 0.654450 \n", + "africa -0.3192781 0.0797647 -4.003 0.000109 ***\n", + "asia -0.1420921 0.0736678 -1.929 0.056115 . \n", + "americas -0.0238244 0.0725423 -0.328 0.743166 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1148 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.734,\tAdjusted R-squared: 0.7118 \n", + "F-statistic: 33.11 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le80 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.42788 -0.04509 0.00724 0.06262 0.25050 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.9452963 0.3561394 13.886 < 2e-16 ***\n", + "ln_hla_het 0.8420928 0.1678374 5.017 1.84e-06 ***\n", + "ln_frac -0.1462667 0.0656913 -2.227 0.027842 * \n", + "aa_ln_atd 0.0348747 0.0329691 1.058 0.292271 \n", + "ln_arable -0.0199753 0.0132363 -1.509 0.133893 \n", + "ln_suitavg 0.0003745 0.0125777 0.030 0.976298 \n", + "ln_abslat 0.0070101 0.0142038 0.494 0.622533 \n", + "europe 0.0176880 0.0707789 0.250 0.803088 \n", + "africa -0.2552229 0.0735352 -3.471 0.000722 ***\n", + "asia -0.1194574 0.0679145 -1.759 0.081137 . \n", + "americas -0.0064243 0.0668769 -0.096 0.923631 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1059 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.6995,\tAdjusted R-squared: 0.6745 \n", + "F-statistic: 27.94 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le90 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.42359 -0.04798 0.00311 0.06009 0.26370 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.554555 0.359376 12.674 < 2e-16 ***\n", + "ln_hla_het 0.549024 0.169363 3.242 0.00154 ** \n", + "ln_frac -0.183742 0.066288 -2.772 0.00646 ** \n", + "aa_ln_atd 0.038322 0.033269 1.152 0.25166 \n", + "ln_arable -0.022074 0.013357 -1.653 0.10101 \n", + "ln_suitavg -0.004649 0.012692 -0.366 0.71478 \n", + "ln_abslat 0.024767 0.014333 1.728 0.08657 . \n", + "europe 0.018960 0.071422 0.265 0.79111 \n", + "africa -0.220302 0.074203 -2.969 0.00361 ** \n", + "asia -0.072690 0.068532 -1.061 0.29097 \n", + "americas 0.034606 0.067485 0.513 0.60904 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1068 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.6886,\tAdjusted R-squared: 0.6626 \n", + "F-statistic: 26.53 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le00 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.39356 -0.05137 0.00062 0.05057 0.21952 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 3.850750 0.309218 12.453 < 2e-16 ***\n", + "ln_hla_het 0.251217 0.145725 1.724 0.087299 . \n", + "ln_frac -0.177528 0.057037 -3.113 0.002319 ** \n", + "aa_ln_atd 0.084388 0.028625 2.948 0.003845 ** \n", + "ln_arable -0.006223 0.011492 -0.542 0.589157 \n", + "ln_suitavg -0.016824 0.010921 -1.541 0.126047 \n", + "ln_abslat 0.013741 0.012332 1.114 0.267420 \n", + "europe -0.001058 0.061454 -0.017 0.986296 \n", + "africa -0.249743 0.063847 -3.912 0.000152 ***\n", + "asia -0.092455 0.058967 -1.568 0.119534 \n", + "americas 0.026439 0.058066 0.455 0.649695 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.09193 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7683,\tAdjusted R-squared: 0.749 \n", + "F-statistic: 39.79 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le10 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_le70 != \n", + " \"NA\" & ln_le80 != \"NA\" & ln_le90 != \"NA\" & ln_le00 != \n", + " \"NA\" & ln_le10 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.36822 -0.04284 0.00248 0.04303 0.22940 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 3.912044 0.282804 13.833 < 2e-16 ***\n", + "ln_hla_het 0.178763 0.133277 1.341 0.182360 \n", + "ln_frac -0.150893 0.052164 -2.893 0.004537 ** \n", + "aa_ln_atd 0.073300 0.026180 2.800 0.005960 ** \n", + "ln_arable -0.004571 0.010511 -0.435 0.664390 \n", + "ln_suitavg -0.013801 0.009988 -1.382 0.169608 \n", + "ln_abslat 0.006617 0.011279 0.587 0.558550 \n", + "europe 0.006555 0.056204 0.117 0.907355 \n", + "africa -0.223514 0.058393 -3.828 0.000207 ***\n", + "asia -0.083993 0.053930 -1.557 0.121995 \n", + "americas 0.016314 0.053106 0.307 0.759223 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.08408 on 120 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.748,\tAdjusted R-squared: 0.727 \n", + "F-statistic: 35.62 on 10 and 120 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "##################################################################################\n", + "########## TABLE_ 4: Additional Years: The effect of medicine #############\n", + "##################################################################################\n", + "\n", + "regt4_1<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_1robust<-coeftest(regt4_1, vcov = vcovHC(regt4_1, type=\"HC1\"))\n", + "summary(regt4_1)\n", + "\n", + "regt4_2<-lm(ln_le70~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_2robust<-coeftest(regt4_2, vcov = vcovHC(regt4_2, type=\"HC1\"))\n", + "summary(regt4_2)\n", + "\n", + "regt4_3<-lm(ln_le80~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_3robust<-coeftest(regt4_3, vcov = vcovHC(regt4_3, type=\"HC1\"))\n", + "summary(regt4_3)\n", + "\n", + "\n", + "regt4_4<-lm(ln_le90~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_4robust<-coeftest(regt4_4, vcov = vcovHC(regt4_4, type=\"HC1\"))\n", + "summary(regt4_4)\n", + "\n", + "\n", + "regt4_5<-lm(ln_le00~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_5robust<-coeftest(regt4_5, vcov = vcovHC(regt4_5, type=\"HC1\"))\n", + "summary(regt4_5)\n", + "\n", + "regt4_6<-lm(ln_le10~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=ln_le60!=\"NA\" & ln_le70!=\"NA\" & ln_le80!=\"NA\" & ln_le90!=\"NA\" & ln_le00!=\"NA\" & ln_le10!=\"NA\",data=hla_country_web)\n", + "regt4_6robust<-coeftest(regt4_6, vcov = vcovHC(regt4_6, type=\"HC1\"))\n", + "summary(regt4_6)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 4: The Effect of HLA Heterozygosity after the International Epidemiological Transition\n", + "===============================================================================\n", + " Dependent variable: \n", + " ---------------------------------------------------------\n", + " ln Life Expectancy \n", + " 1960 1970 1980 1990 2000 2010 \n", + " (1) (2) (3) (4) (5) (6) \n", + "-------------------------------------------------------------------------------\n", + "ln HLA Heterozygosity 1.0151*** 1.0405*** 0.8421*** 0.5490*** 0.2512 0.1788 \n", + " (0.1899) (0.1696) (0.1700) (0.1798) (0.1541) (0.1335)\n", + "===============================================================================\n", + "===============================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table4<-stargazer(regt4_1robust,regt4_2robust,regt4_3robust,regt4_4robust,\n", + " regt4_5robust,regt4_6robust,type=\"text\", align = TRUE,\n", + " title = \"Table 4: The Effect of HLA Heterozygosity after the International Epidemiological Transition\",\n", + " dep.var.labels = \"ln Life Expectancy\", keep=c(\"ln_hla_het\"), \n", + " column.labels=c(\"1960\", \"1970\", \"1980\", \"1990\", \"2000\", \"2010\"),\n", + " column.sep.width = \"10pt\", digits=4, no.space=TRUE,\n", + " covariate.labels=c(\"ln HLA Heterozygosity\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = frac_eur == 0 & ln_le60 != \n", + " \"NA\" & ln_hla_het != \"NA\" & ln_frac != \"NA\" & ln_abslat != \n", + " \"NA\" & ln_arable != \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.23551 -0.07323 0.01446 0.06769 0.29570 \n", + "\n", + "Coefficients: (2 not defined because of singularities)\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.663561 0.719793 6.479 5.52e-08 ***\n", + "ln_hla_het 0.724094 0.362643 1.997 0.0518 . \n", + "ln_frac -0.166265 0.129578 -1.283 0.2059 \n", + "aa_ln_atd 0.010913 0.066160 0.165 0.8697 \n", + "ln_arable -0.006245 0.020622 -0.303 0.7634 \n", + "ln_suitavg 0.001592 0.021353 0.075 0.9409 \n", + "ln_abslat -0.010405 0.022727 -0.458 0.6492 \n", + "europe NA NA NA NA \n", + "africa -0.078831 0.192005 -0.411 0.6833 \n", + "asia 0.058187 0.183382 0.317 0.7525 \n", + "americas NA NA NA NA \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1265 on 46 degrees of freedom\n", + " (11 observations deleted due to missingness)\n", + "Multiple R-squared: 0.4183,\tAdjusted R-squared: 0.3172 \n", + "F-statistic: 4.135 on 8 and 46 DF, p-value: 0.0008798\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = frac_eur > 0 & frac_eur < \n", + " 1 & ln_le60 != \"NA\" & ln_hla_het != \"NA\" & ln_frac != \n", + " \"NA\" & ln_abslat != \"NA\" & ln_arable != \"NA\" & ln_suitavg != \n", + " \"NA\" & aa_ln_atd != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.49173 -0.05557 -0.00057 0.07756 0.20268 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 3.97634 0.84821 4.688 3.04e-05 ***\n", + "ln_hla_het 0.80490 0.36724 2.192 0.0341 * \n", + "ln_frac -0.15810 0.14726 -1.074 0.2893 \n", + "aa_ln_atd 0.14118 0.08746 1.614 0.1142 \n", + "ln_arable -0.06410 0.03803 -1.686 0.0995 . \n", + "ln_suitavg 0.04920 0.04322 1.138 0.2616 \n", + "ln_abslat 0.05377 0.04204 1.279 0.2081 \n", + "europe -0.03648 0.12133 -0.301 0.7652 \n", + "africa -0.24688 0.13633 -1.811 0.0775 . \n", + "asia -0.28035 0.12115 -2.314 0.0257 * \n", + "americas -0.11947 0.11174 -1.069 0.2912 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1397 on 41 degrees of freedom\n", + " (26 observations deleted due to missingness)\n", + "Multiple R-squared: 0.5891,\tAdjusted R-squared: 0.4888 \n", + "F-statistic: 5.877 on 10 and 41 DF, p-value: 1.93e-05\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = frac_eur == 1 & ln_le60 != \n", + " \"NA\" & ln_hla_het != \"NA\" & ln_frac != \"NA\" & ln_abslat != \n", + " \"NA\" & ln_arable != \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.079159 -0.024165 0.002131 0.022136 0.057548 \n", + "\n", + "Coefficients: (4 not defined because of singularities)\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 3.517311 0.758054 4.640 0.000234 ***\n", + "ln_hla_het 0.728074 0.365006 1.995 0.062365 . \n", + "ln_frac -0.073481 0.070289 -1.045 0.310474 \n", + "aa_ln_atd 0.035708 0.044750 0.798 0.435913 \n", + "ln_arable -0.009074 0.022862 -0.397 0.696384 \n", + "ln_suitavg 0.018469 0.024473 0.755 0.460780 \n", + "ln_abslat 0.323034 0.129588 2.493 0.023290 * \n", + "europe NA NA NA NA \n", + "africa NA NA NA NA \n", + "asia NA NA NA NA \n", + "americas NA NA NA NA \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.04194 on 17 degrees of freedom\n", + " (7 observations deleted due to missingness)\n", + "Multiple R-squared: 0.5162,\tAdjusted R-squared: 0.3454 \n", + "F-statistic: 3.023 on 6 and 17 DF, p-value: 0.03371\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + frac_eur + frac_me + frac_easia + \n", + " frac_africa + frac_am + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_le60 != \"NA\" & ln_hla_het != \n", + " \"NA\" & ln_frac != \"NA\" & ln_abslat != \"NA\" & ln_arable != \n", + " \"NA\" & ln_suitavg != \"NA\" & aa_ln_atd != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.42848 -0.06734 0.00682 0.06718 0.24529 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.598250 0.497114 9.250 1.45e-15 ***\n", + "ln_hla_het 0.886992 0.286872 3.092 0.0025 ** \n", + "frac_eur 0.317721 0.194336 1.635 0.1048 \n", + "frac_me 0.020621 0.204333 0.101 0.9198 \n", + "frac_easia 0.140123 0.190495 0.736 0.4635 \n", + "frac_africa 0.017364 0.194295 0.089 0.9289 \n", + "frac_am 0.145135 0.180890 0.802 0.4240 \n", + "ln_frac -0.129576 0.073851 -1.755 0.0820 . \n", + "aa_ln_atd 0.042627 0.039200 1.087 0.2791 \n", + "ln_arable -0.009935 0.014528 -0.684 0.4954 \n", + "ln_suitavg -0.000756 0.014302 -0.053 0.9579 \n", + "ln_abslat 0.006856 0.016666 0.411 0.6816 \n", + "europe -0.051875 0.088836 -0.584 0.5604 \n", + "africa -0.140886 0.115650 -1.218 0.2256 \n", + "asia -0.097880 0.112253 -0.872 0.3850 \n", + "americas -0.057943 0.096103 -0.603 0.5477 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1126 on 115 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7891,\tAdjusted R-squared: 0.7616 \n", + "F-statistic: 28.68 on 15 and 115 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "########################################################\n", + "##########TABLE_5: Pop. Composition Trunc. #############\n", + "########################################################\n", + "\n", + "regt5_1<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas, subset = frac_eur==0 & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA\" & ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\", data=hla_country_web)\n", + "regt5_1robust<-coeftest(regt5_1, vcov = vcovHC(regt5_1, type=\"HC1\"))\n", + "summary(regt5_1)\n", + "\n", + "regt5_2<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas, subset = frac_eur>0 & frac_eur<1 & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA\" & ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\", data=hla_country_web)\n", + "regt5_2robust<-coeftest(regt5_2, vcov = vcovHC(regt5_2, type=\"HC1\"))\n", + "summary(regt5_2)\n", + "\n", + "\n", + "regt5_3<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas, subset = frac_eur==1 & ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA\" & ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\", data=hla_country_web)\n", + "regt5_3robust<-coeftest(regt5_3, vcov = vcovHC(regt5_3, type=\"HC1\"))\n", + "summary(regt5_3)\n", + "\n", + "\n", + "regt5_4<-lm(ln_le60~ln_hla_het+frac_eur+frac_me+frac_easia+frac_africa+frac_am+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset= ln_le60!=\"NA\" & ln_hla_het!=\"NA\" & ln_frac!=\"NA\" & ln_abslat!=\"NA\" & ln_arable!=\"NA\" & ln_suitavg!=\"NA\" & aa_ln_atd!=\"NA\",data=hla_country_web)\n", + "regt5_4robust<-coeftest(regt5_4, vcov = vcovHC(regt5_4, type=\"HC1\"))\n", + "summary(regt5_4)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 5: Robustness to the Influence of Regional Populations\n", + "=======================================================================\n", + " Dependent variable: \n", + " -------------------------------------------------\n", + " ln Life Expectancy in 1960 \n", + " %European=0 %European=(0;1) %European=1 Full \n", + " (1) (2) (3) (4) \n", + "-----------------------------------------------------------------------\n", + "ln HLA Heterozygosity 0.7241* 0.8049*** 0.7281** 0.8870***\n", + " (0.3623) (0.2923) (0.3111) (0.2872) \n", + "=======================================================================\n", + "=======================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table5<-stargazer(regt5_1robust,regt5_2robust,regt5_3robust,regt5_4robust,\n", + " type=\"text\", align = TRUE, title = \"Table 5: Robustness to the Influence of Regional Populations\",\n", + " dep.var.labels = \"ln Life Expectancy in 1960\", keep=c(\"ln_hla_het\"), \n", + " column.labels=c(\"%European=0\", \"%European=(0;1)\", \"%European=1\", \"Full\"),\n", + " column.sep.width = \"10pt\", digits=4, no.space=TRUE,\n", + " covariate.labels=c(\"ln HLA Heterozygosity\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + aa_pdiv + ln_frac + aa_ln_atd + \n", + " ln_arable + ln_suitavg + ln_abslat + europe + africa + asia + \n", + " americas, data = hla_country_web, subset = distcr1000 != \n", + " \"NA\" & aa_pdiv != \"NA\" & malfal != \"NA\" & tropical != \"NA\" & \n", + " pnativ_60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.49329 -0.06037 0.00781 0.07372 0.27135 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.196418 0.811731 6.402 3.18e-09 ***\n", + "ln_hla_het 1.077102 0.267620 4.025 0.000101 ***\n", + "aa_pdiv -0.278363 0.832380 -0.334 0.738652 \n", + "ln_frac -0.113391 0.078912 -1.437 0.153364 \n", + "aa_ln_atd 0.040665 0.039475 1.030 0.305031 \n", + "ln_arable -0.008339 0.015263 -0.546 0.585860 \n", + "ln_suitavg 0.007875 0.014476 0.544 0.587445 \n", + "ln_abslat 0.020602 0.016469 1.251 0.213395 \n", + "europe 0.039045 0.081417 0.480 0.632416 \n", + "africa -0.303156 0.088810 -3.414 0.000877 ***\n", + "asia -0.205855 0.078120 -2.635 0.009531 ** \n", + "americas -0.045389 0.078557 -0.578 0.564501 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1218 on 119 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7449,\tAdjusted R-squared: 0.7213 \n", + "F-statistic: 31.58 on 11 and 119 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + aa_pdiv + ln_hla_het + malfal + \n", + " tropical + desert + ln_frac + aa_ln_atd + ln_arable + ln_suitavg + \n", + " ln_abslat + europe + africa + asia + americas, data = hla_country_web, \n", + " subset = distcr1000 != \"NA\" & aa_pdiv != \"NA\" & malfal != \n", + " \"NA\" & tropical != \"NA\" & pnativ_60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.50609 -0.06769 0.00790 0.06232 0.25659 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.4368764 0.8027329 5.527 2.03e-07 ***\n", + "ln_hla_het 0.6126416 0.2805173 2.184 0.030977 * \n", + "aa_pdiv 0.7093535 0.8347645 0.850 0.397206 \n", + "malfal -0.1729166 0.0507154 -3.410 0.000896 ***\n", + "tropical -0.0003448 0.0005208 -0.662 0.509231 \n", + "desert -0.0014115 0.0015310 -0.922 0.358459 \n", + "ln_frac -0.0967928 0.0763874 -1.267 0.207647 \n", + "aa_ln_atd 0.0008407 0.0396231 0.021 0.983108 \n", + "ln_arable -0.0026086 0.0147429 -0.177 0.859863 \n", + "ln_suitavg 0.0045518 0.0151566 0.300 0.764471 \n", + "ln_abslat -0.0196719 0.0225771 -0.871 0.385378 \n", + "europe 0.0424909 0.0779337 0.545 0.586650 \n", + "africa -0.2728139 0.0854900 -3.191 0.001823 ** \n", + "asia -0.1811694 0.0746724 -2.426 0.016798 * \n", + "americas -0.0496188 0.0755212 -0.657 0.512471 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1157 on 116 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7753,\tAdjusted R-squared: 0.7482 \n", + "F-statistic: 28.59 on 14 and 116 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + frac_migrant + ln_frac + \n", + " aa_ln_atd + ln_arable + ln_suitavg + ln_abslat + europe + \n", + " africa + asia + americas, data = hla_country_web, subset = distcr1000 != \n", + " \"NA\" & aa_pdiv != \"NA\" & malfal != \"NA\" & tropical != \"NA\" & \n", + " pnativ_60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.47729 -0.06295 0.00722 0.06580 0.28144 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.708443 0.433304 10.866 < 2e-16 ***\n", + "ln_hla_het 0.869948 0.210203 4.139 6.55e-05 ***\n", + "frac_migrant 0.098209 0.059433 1.652 0.1011 \n", + "ln_frac -0.144091 0.076038 -1.895 0.0605 . \n", + "aa_ln_atd 0.041594 0.037615 1.106 0.2710 \n", + "ln_arable -0.008539 0.015060 -0.567 0.5718 \n", + "ln_suitavg 0.011369 0.014452 0.787 0.4330 \n", + "ln_abslat 0.015788 0.016352 0.966 0.3362 \n", + "europe 0.113322 0.092317 1.228 0.2220 \n", + "africa -0.252790 0.091068 -2.776 0.0064 ** \n", + "asia -0.147021 0.085022 -1.729 0.0864 . \n", + "americas -0.042654 0.076108 -0.560 0.5762 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1205 on 119 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7504,\tAdjusted R-squared: 0.7273 \n", + "F-statistic: 32.52 on 11 and 119 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + aa_pdiv + frac_migrant + \n", + " ln_hla_het + malfal + tropical + desert + ln_frac + aa_ln_atd + \n", + " ln_arable + ln_suitavg + ln_abslat + europe + africa + asia + \n", + " americas, data = hla_country_web, subset = distcr1000 != \n", + " \"NA\" & aa_pdiv != \"NA\" & malfal != \"NA\" & tropical != \"NA\" & \n", + " pnativ_60 != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.49909 -0.05211 0.00440 0.06217 0.24834 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.6502914 0.8050756 5.776 6.63e-08 ***\n", + "ln_hla_het 0.5817241 0.2786370 2.088 0.03903 * \n", + "aa_pdiv 0.1578063 0.8857528 0.178 0.85891 \n", + "frac_migrant 0.1079735 0.0618486 1.746 0.08352 . \n", + "malfal -0.1678646 0.0503568 -3.334 0.00115 ** \n", + "tropical -0.0003982 0.0005171 -0.770 0.44285 \n", + "desert -0.0009068 0.0015449 -0.587 0.55837 \n", + "ln_frac -0.1038242 0.0758290 -1.369 0.17361 \n", + "aa_ln_atd 0.0113972 0.0397407 0.287 0.77479 \n", + "ln_arable -0.0020442 0.0146181 -0.140 0.88903 \n", + "ln_suitavg 0.0101676 0.0153650 0.662 0.50946 \n", + "ln_abslat -0.0243936 0.0225432 -1.082 0.28148 \n", + "europe 0.1273729 0.0912817 1.395 0.16559 \n", + "africa -0.1934715 0.0961629 -2.012 0.04657 * \n", + "asia -0.1168021 0.0826962 -1.412 0.16053 \n", + "americas -0.0608349 0.0751385 -0.810 0.41982 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1147 on 115 degrees of freedom\n", + " (44 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7811,\tAdjusted R-squared: 0.7526 \n", + "F-statistic: 27.36 on 15 and 115 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "###################################################################\n", + "########## TABLE_6: Exogenous Omitted Variables #############\n", + "###################################################################\n", + "\n", + "regt6_1<-lm(ln_le60~ln_hla_het+aa_pdiv+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset= distcr1000!=\"NA\" & aa_pdiv!=\"NA\" & malfal!=\"NA\" & tropical!=\"NA\" & pnativ_60!=\"NA\" , data=hla_country_web)\n", + "regt6_1robust<-coeftest(regt6_1, vcov = vcovHC(regt6_1, type=\"HC1\"))\n", + "summary(regt6_1)\n", + " \n", + "\n", + "regt6_2<-lm(ln_le60~ln_hla_het+aa_pdiv+ln_hla_het+malfal+tropical+desert+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=distcr1000!=\"NA\" & aa_pdiv!=\"NA\" & malfal!=\"NA\" & tropical!=\"NA\" & pnativ_60!=\"NA\" , data=hla_country_web)\n", + "regt6_2robust<-coeftest(regt6_2, vcov = vcovHC(regt6_2, type=\"HC1\"))\n", + "summary(regt6_2)\n", + "\n", + "\n", + "regt6_3<-lm(ln_le60~ln_hla_het+frac_migrant+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=distcr1000!=\"NA\" & aa_pdiv!=\"NA\" & malfal!=\"NA\" & tropical!=\"NA\" & pnativ_60!=\"NA\" , data=hla_country_web)\n", + "regt6_3robust<-coeftest(regt6_3, vcov = vcovHC(regt6_3, type=\"HC1\"))\n", + "summary(regt6_3)\n", + "\n", + "\n", + "regt6_4<-lm(ln_le60~ln_hla_het+aa_pdiv+frac_migrant+ln_hla_het+malfal+tropical+desert+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset=distcr1000!=\"NA\" & aa_pdiv!=\"NA\" & malfal!=\"NA\" & tropical!=\"NA\" & pnativ_60!=\"NA\" , data=hla_country_web)\n", + "regt6_4robust<-coeftest(regt6_4, vcov = vcovHC(regt6_4, type=\"HC1\"))\n", + "summary(regt6_4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================\n", + " Dependent variable: \n", + " ---------------------------------------\n", + " \n", + " (1) (2) (3) (4) \n", + "----------------------------------------------------\n", + "ln_hla_het 1.077*** 0.613* 0.870*** 0.582* \n", + " (0.340) (0.319) (0.224) (0.313) \n", + " \n", + "aa_pdiv -0.278 0.709 0.158 \n", + " (1.200) (0.974) (1.015) \n", + " \n", + "malfal -0.173*** -0.168***\n", + " (0.049) (0.048) \n", + " \n", + "tropical -0.0003 -0.0004 \n", + " (0.001) (0.001) \n", + " \n", + "desert -0.001 -0.001 \n", + " (0.001) (0.002) \n", + " \n", + "frac_migrant 0.098 0.108 \n", + " (0.063) (0.071) \n", + " \n", + "ln_frac -0.113 -0.097 -0.144 -0.104 \n", + " (0.107) (0.104) (0.099) (0.106) \n", + " \n", + "aa_ln_atd 0.041 0.001 0.042 0.011 \n", + " (0.047) (0.037) (0.040) (0.037) \n", + " \n", + "ln_arable -0.008 -0.003 -0.009 -0.002 \n", + " (0.016) (0.013) (0.016) (0.012) \n", + " \n", + "ln_suitavg 0.008 0.005 0.011 0.010 \n", + " (0.013) (0.013) (0.012) (0.012) \n", + " \n", + "ln_abslat 0.021 -0.020 0.016 -0.024 \n", + " (0.016) (0.018) (0.016) (0.018) \n", + " \n", + "europe 0.039 0.042 0.113 0.127 \n", + " (0.070) (0.077) (0.085) (0.094) \n", + " \n", + "africa -0.303*** -0.273*** -0.253*** -0.193* \n", + " (0.087) (0.088) (0.081) (0.102) \n", + " \n", + "asia -0.206*** -0.181** -0.147* -0.117 \n", + " (0.074) (0.081) (0.080) (0.088) \n", + " \n", + "americas -0.045 -0.050 -0.043 -0.061 \n", + " (0.078) (0.082) (0.068) (0.071) \n", + " \n", + "Constant 5.196*** 4.437*** 4.708*** 4.650*** \n", + " (1.001) (0.888) (0.439) (0.876) \n", + " \n", + "====================================================\n", + "====================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table6<-stargazer(regt6_1robust,regt6_2robust,regt6_3robust,regt6_4robust,\n", + " type=\"text\", align = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_ypc60 != \"NA\" & ln_yr_sch1960 != \n", + " \"NA\" & ln_pd60 != \"NA\" & ln_urb60 != \"NA\" & ln_young != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.48437 -0.04816 0.00643 0.06730 0.30371 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.075976 0.435901 11.645 < 2e-16 ***\n", + "ln_hla_het 0.999959 0.202655 4.934 3.29e-06 ***\n", + "ln_frac -0.068797 0.080539 -0.854 0.39508 \n", + "aa_ln_atd 0.022079 0.041794 0.528 0.59850 \n", + "ln_arable -0.020301 0.016533 -1.228 0.22243 \n", + "ln_suitavg 0.007012 0.015235 0.460 0.64633 \n", + "ln_abslat 0.016946 0.017460 0.971 0.33417 \n", + "europe 0.082177 0.081488 1.008 0.31572 \n", + "africa -0.303860 0.086142 -3.527 0.00064 ***\n", + "asia -0.198774 0.078794 -2.523 0.01326 * \n", + "americas -0.033717 0.076160 -0.443 0.65895 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.12 on 98 degrees of freedom\n", + " (66 observations deleted due to missingness)\n", + "Multiple R-squared: 0.7378,\tAdjusted R-squared: 0.711 \n", + "F-statistic: 27.58 on 10 and 98 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_ypc60 + ln_frac + aa_ln_atd + \n", + " ln_arable + ln_suitavg + ln_abslat + europe + africa + asia + \n", + " americas, data = hla_country_web, subset = ln_ypc60 != \"NA\" & \n", + " ln_yr_sch1960 != \"NA\" & ln_pd60 != \"NA\" & ln_urb60 != \"NA\" & \n", + " ln_young != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.36913 -0.05115 -0.00077 0.05649 0.25210 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.0474992 0.4156969 9.737 4.97e-16 ***\n", + "ln_hla_het 0.6574851 0.1846542 3.561 0.000575 ***\n", + "ln_ypc60 0.0977108 0.0167017 5.850 6.64e-08 ***\n", + "ln_frac -0.1265309 0.0702960 -1.800 0.074974 . \n", + "aa_ln_atd -0.0006487 0.0363256 -0.018 0.985789 \n", + "ln_arable -0.0105789 0.0143837 -0.735 0.463824 \n", + "ln_suitavg 0.0290353 0.0136934 2.120 0.036524 * \n", + "ln_abslat 0.0089705 0.0151501 0.592 0.555156 \n", + "europe 0.0967317 0.0704638 1.373 0.172985 \n", + "africa -0.1342940 0.0798851 -1.681 0.095963 . \n", + "asia -0.0661328 0.0717676 -0.921 0.359083 \n", + "americas 0.0155832 0.0663525 0.235 0.814816 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.1037 on 97 degrees of freedom\n", + " (66 observations deleted due to missingness)\n", + "Multiple R-squared: 0.8062,\tAdjusted R-squared: 0.7842 \n", + "F-statistic: 36.68 on 11 and 97 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_yr_sch1960 + ln_frac + \n", + " aa_ln_atd + ln_arable + ln_suitavg + ln_abslat + europe + \n", + " africa + asia + americas, data = hla_country_web, subset = ln_ypc60 != \n", + " \"NA\" & ln_yr_sch1960 != \"NA\" & ln_pd60 != \"NA\" & ln_urb60 != \n", + " \"NA\" & ln_young != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.259833 -0.044515 0.001145 0.046709 0.179913 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 3.990270 0.323522 12.334 < 2e-16 ***\n", + "ln_hla_het 0.612726 0.146980 4.169 6.66e-05 ***\n", + "ln_yr_sch1960 0.132013 0.013021 10.139 < 2e-16 ***\n", + "ln_frac -0.024146 0.056578 -0.427 0.6705 \n", + "aa_ln_atd 0.071103 0.029667 2.397 0.0185 * \n", + "ln_arable 0.004400 0.011832 0.372 0.7108 \n", + "ln_suitavg -0.008141 0.010774 -0.756 0.4517 \n", + "ln_abslat 0.005958 0.012276 0.485 0.6285 \n", + "europe 0.028567 0.057315 0.498 0.6193 \n", + "africa -0.123906 0.062887 -1.970 0.0517 . \n", + "asia -0.152569 0.055372 -2.755 0.0070 ** \n", + "americas -0.031796 0.053339 -0.596 0.5525 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.08403 on 97 degrees of freedom\n", + " (66 observations deleted due to missingness)\n", + "Multiple R-squared: 0.8727,\tAdjusted R-squared: 0.8583 \n", + "F-statistic: 60.45 on 11 and 97 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_pd60 + ln_urb60 + ln_young + \n", + " ln_frac + aa_ln_atd + ln_arable + ln_suitavg + ln_abslat + \n", + " europe + africa + asia + americas, data = hla_country_web, \n", + " subset = ln_ypc60 != \"NA\" & ln_yr_sch1960 != \"NA\" & ln_pd60 != \n", + " \"NA\" & ln_urb60 != \"NA\" & ln_young != \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.300764 -0.043457 -0.009262 0.061716 0.245806 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 5.5230912 0.4641918 11.898 < 2e-16 ***\n", + "ln_hla_het 0.7528765 0.1766469 4.262 4.77e-05 ***\n", + "ln_pd60 0.0001866 0.0141599 0.013 0.98951 \n", + "ln_urb60 0.1137048 0.0177831 6.394 6.02e-09 ***\n", + "ln_young -0.0614235 0.0915465 -0.671 0.50388 \n", + "ln_frac -0.1367887 0.0734167 -1.863 0.06553 . \n", + "aa_ln_atd -0.0755990 0.0374562 -2.018 0.04638 * \n", + "ln_arable -0.0064507 0.0173545 -0.372 0.71094 \n", + "ln_suitavg 0.0133635 0.0131086 1.019 0.31058 \n", + "ln_abslat -0.0097970 0.0155962 -0.628 0.53140 \n", + "europe 0.0512724 0.0733536 0.699 0.48627 \n", + "africa -0.2301636 0.0746173 -3.085 0.00267 ** \n", + "asia -0.1139927 0.0715206 -1.594 0.11429 \n", + "americas -0.0694855 0.0673081 -1.032 0.30453 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.09939 on 95 degrees of freedom\n", + " (66 observations deleted due to missingness)\n", + "Multiple R-squared: 0.8256,\tAdjusted R-squared: 0.8017 \n", + "F-statistic: 34.59 on 13 and 95 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ln_le60 ~ ln_hla_het + ln_ypc60 + ln_yr_sch1960 + \n", + " ln_pd60 + ln_urb60 + ln_young + ln_frac + aa_ln_atd + ln_arable + \n", + " ln_suitavg + ln_abslat + europe + africa + asia + americas, \n", + " data = hla_country_web, subset = ln_ypc60 != \"NA\" & ln_yr_sch1960 != \n", + " \"NA\" & ln_pd60 != \"NA\" & ln_urb60 != \"NA\" & ln_young != \n", + " \"NA\")\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.21803 -0.03882 0.00079 0.04623 0.22772 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 4.0482107 0.4849748 8.347 6.35e-13 ***\n", + "ln_hla_het 0.5295807 0.1467296 3.609 0.000497 ***\n", + "ln_ypc60 0.0280348 0.0183296 1.529 0.129539 \n", + "ln_yr_sch1960 0.1036749 0.0149946 6.914 5.84e-10 ***\n", + "ln_pd60 0.0062813 0.0116550 0.539 0.591220 \n", + "ln_urb60 0.0342884 0.0196702 1.743 0.084609 . \n", + "ln_young -0.0148321 0.0771438 -0.192 0.847953 \n", + "ln_frac -0.0582461 0.0607576 -0.959 0.340214 \n", + "aa_ln_atd 0.0241980 0.0330670 0.732 0.466138 \n", + "ln_arable 0.0014524 0.0140344 0.103 0.917800 \n", + "ln_suitavg 0.0023245 0.0117470 0.198 0.843568 \n", + "ln_abslat -0.0001829 0.0126272 -0.014 0.988474 \n", + "europe 0.0255091 0.0607972 0.420 0.675762 \n", + "africa -0.0977019 0.0649752 -1.504 0.136052 \n", + "asia -0.1094776 0.0599032 -1.828 0.070820 . \n", + "americas -0.0341981 0.0551987 -0.620 0.537072 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.07987 on 93 degrees of freedom\n", + " (66 observations deleted due to missingness)\n", + "Multiple R-squared: 0.8897,\tAdjusted R-squared: 0.872 \n", + "F-statistic: 50.03 on 15 and 93 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "####################################################################\n", + "########## TABLE_7: Endogenous Omitted Variables #############\n", + "####################################################################\n", + "\n", + "regt7_1<-lm(ln_le60~ln_hla_het+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset = ln_ypc60!=\"NA\" & ln_yr_sch1960!=\"NA\" & ln_pd60!=\"NA\" & ln_urb60!=\"NA\" & ln_young!=\"NA\", data=hla_country_web)\n", + "regt7_1robust<-coeftest(regt7_1, vcov = vcovHC(regt7_1, type=\"HC1\"))\n", + "summary(regt7_1)\n", + "\n", + "regt7_2<-lm(ln_le60~ln_hla_het+ln_ypc60+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset = ln_ypc60!=\"NA\" & ln_yr_sch1960!=\"NA\" & ln_pd60!=\"NA\" & ln_urb60!=\"NA\" & ln_young!=\"NA\", data=hla_country_web)\n", + "regt7_2robust<-coeftest(regt7_2, vcov = vcovHC(regt7_2, type=\"HC1\"))\n", + "summary(regt7_2)\n", + "\n", + "regt7_3<-lm(ln_le60~ln_hla_het+ln_yr_sch1960+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset = ln_ypc60!=\"NA\" & ln_yr_sch1960!=\"NA\" & ln_pd60!=\"NA\" & ln_urb60!=\"NA\" & ln_young!=\"NA\", data=hla_country_web)\n", + "regt7_3robust<-coeftest(regt7_3, vcov = vcovHC(regt7_3, type=\"HC1\"))\n", + "summary(regt7_3)\n", + "\n", + "regt7_4<-lm(ln_le60~ln_hla_het+ln_pd60+ln_urb60+ln_young+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset = ln_ypc60!=\"NA\" & ln_yr_sch1960!=\"NA\" & ln_pd60!=\"NA\" & ln_urb60!=\"NA\" & ln_young!=\"NA\", data=hla_country_web)\n", + "regt7_4robust<-coeftest(regt7_4, vcov = vcovHC(regt7_4, type=\"HC1\"))\n", + "summary(regt7_4)\n", + "\n", + "regt7_5<-lm(ln_le60~ln_hla_het+ln_ypc60+ln_yr_sch1960+ln_pd60+ln_urb60+ln_young+ln_frac+aa_ln_atd+ln_arable+ln_suitavg+ln_abslat+europe+africa+asia+americas,subset = ln_ypc60!=\"NA\" & ln_yr_sch1960!=\"NA\" & ln_pd60!=\"NA\" & ln_urb60!=\"NA\" & ln_young!=\"NA\", data=hla_country_web)\n", + "regt7_5robust<-coeftest(regt7_5, vcov = vcovHC(regt7_5, type=\"HC1\"))\n", + "summary(regt7_5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Table 7: Robustness to Endogenous Omitted Variables\n", + "==================================================================================================\n", + " Dependent variable: \n", + " -------------------------------------------------\n", + " ln Life Expectancy in 1960 \n", + " (1) (2) (3) (4) (5) \n", + "--------------------------------------------------------------------------------------------------\n", + "ln HLA Heterozygosity 1.0000*** 0.6575*** 0.6127*** 0.7529*** 0.5296***\n", + " (0.2118) (0.2124) (0.1633) (0.1944) (0.1673) \n", + "ln GDP per Capita in 1960 0.0977*** 0.0280* \n", + " (0.0221) (0.0167) \n", + "ln Avg. Years of School in 1960 0.1320*** 0.1037***\n", + " (0.0195) (0.0190) \n", + "ln Population Density in 1960 0.0002 0.0063 \n", + " (0.0152) (0.0126) \n", + "ln Urbanization Rate in 1960 0.1137*** 0.0343* \n", + " (0.0223) (0.0195) \n", + "ln Fraction of Population under 15 Years in 1960 -0.0614 -0.0148 \n", + " (0.0887) (0.0685) \n", + "==================================================================================================\n", + "==================================================================================================\n", + "Note: *p<0.1; **p<0.05; ***p<0.01\n" + ] + } + ], + "source": [ + "Table7<-stargazer(regt7_1robust,regt7_2robust,regt7_3robust,regt7_4robust,regt7_5robust,\n", + " type=\"text\", align = TRUE, title = \"Table 7: Robustness to Endogenous Omitted Variables\",\n", + " dep.var.labels = \"ln Life Expectancy in 1960\", keep=c(\"ln_hla_het\", \"ln_ypc60\", \"ln_yr_sch1960\",\n", + " \"ln_pd60\", \"ln_urb60\", \"ln_young\"),\n", + " column.sep.width = \"10pt\", digits=4, no.space=TRUE,\n", + " covariate.labels=c(\"ln HLA Heterozygosity\", \"ln GDP per Capita in 1960\", \"ln Avg. Years of School in 1960\",\n", + " \"ln Population Density in 1960\", \"ln Urbanization Rate in 1960\", \"ln Fraction of Population under 15 Years in 1960\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/C.justinCook-The_natural_selection_2015/C.justinCook-The_natural_selection_2015.Replication.ipynb b/notebooks/C.justinCook-The_natural_selection_2015/C.justinCook-The_natural_selection_2015.Replication.ipynb new file mode 100644 index 0000000..a1ebec1 --- /dev/null +++ b/notebooks/C.justinCook-The_natural_selection_2015/C.justinCook-The_natural_selection_2015.Replication.ipynb @@ -0,0 +1,1915 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# _THE NATURAL SELECTION OF INFECTIOUS DISEASE RESISTANCE AND ITS EFFECT ON CONTEMPORARY HEALTH- A replication study_\n", + "## C. Justin Cook\n", + "### _Review of Economics and Statistics Volume 97 | Issue 4 | October 2015 p.742-757_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This study pretends to do a replication of _\"THE NATURAL SELECTION OF INFECTIOUS DISEASE RESISTANCE AND\n", + "ITS EFFECT ON CONTEMPORARY HEALTH\"_ whose author is C. Justin Cook, an assistant Professor, Economics University of California, Merced. The replication data for paper is available [here](https://doi.org/10.7910/DVN/27669)\n", + "\n", + "The replication is done by Sara Ariza Murillo, a colombian economist from the Universidad del Rosario. This includes two jupyter notebooks:\n", + "\n", + "1. **Notebook one:** presents the stata do file of the author that replicates tables 2-7 within the paper. It also replicates in stata the figures of the paper that are not included in the author's do file.\n", + "2. **Notebook two:** presents the code elaborated by Sara in R that replicates the tables within the paper. \n", + "\n", + "Both notebooks include a summary of the document.\n", + "\n", + "If there are any questions, please contact Sara Ariza at sara.ariza@urosario.edu.co" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Notebook one" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Abstract\n", + " _\"This paper empirically tests the association between genetically determined resistance to infectious disease and cross-country health differences. A country-level measure of genetic diversity for the system of genes associated with the recognition and disposal of foreign pathogens is constructed. Genetic diversity within this system has been shown to reduce the virulence and prevalence of infectious diseases and is hypothesized to have been naturally selected from historical exposure to infectious pathogens. Base estimation shows a statistically strong, robust, and positive relationship between this constructed measure and country-level health outcomes in times prior to, but not after, the international epidemiological transition\"_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### I. Introduction\n", + "

Prior to the major medical discoveries associated with the international epidemiological transition, infectious diseases were a major determinant of mortality and subsequent differences in life expectancy across countries. (The discovery and widespread use of effective medicines (e.g., penicillin, streptomycin, a range of vaccines) in the late 1940s to early 1950s is labeled by Acemoglu and Johnson (2007) as the international epidemiological transition.) \n", + " \n", + "

This paper answers the question what were the causes of the initial cross-country disparities in the virulence of infectious diseases? the above, by empirically investigating the role of genetically determined differences in resistance to infectious diseases \n", + " \n", + "

The main hyphotesis is that innate resistance did influence country-level response to infectious disease prior to the international epidemiological transition, but the effects of innate resistance are dissipated by more efficacious health technologies.\n", + "\n", + "

The measure of genetic resistance is found within the human leukocyte antigen (HLA) system. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### The HLA system \n", + "

The HLA is responsible for locating foreign proteins in order to direct cells of the immune system to initiate an immune response and is broken into two major classes, class I and class II, with both classes being associated with the recognition of certain pathogens (Piertney&Oliver, 2006).\n", + "\n", + "Using country-level aggregations of ethnic-level genetic data, the author constructs a cross-country measure for diversity within the HLA system: HLA heterozygosity. He tests the hypothesis by estimating the association between country-level health measures (e.g., life expectancy at birth) and HLA heterozygosity in periods both prior to and after the international epidemiological transition \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using matplotlib backend: TkAgg\n", + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "# Let's import some packages we will use \n", + "from __future__ import division\n", + "%pylab --no-import-all\n", + "%matplotlib inline\n", + "import pandas as pd\n", + "import ipystata \n", + "from IPython.display import Image \n", + "import seaborn as sns \n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "pathout = './raw/' #in the directory I create the folder \"raw\" where are the files downloaded from the internet.\n", + "\n", + "if not os.path.exists(pathout):\n", + " os.mkdir(pathout) \n", + " \n", + "pathgraphs = './created/' #this is the folder where the graphs will be saved\n", + "if not os.path.exists(pathgraphs):\n", + " os.mkdir(pathgraphs)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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countrycodeln_hla_hethla_hetln_mort40ln_le40ln_le50ln_le60ln_le70ln_le80...frac_migrantpnativ_60ln_ypc60ln_yr_sch1960ln_pd60ln_urb60ln_youngln_mix_hetoecdfstdistwtd_usa
0AfghanistanAFG-1.1116260.329023NaNNaN3.4531573.438053.5569463.668782...0.1300.8706.99026-1.0358492.7127412.0794423.769448-1.0921960.05.684600
1AlbaniaALB-1.1299040.323064NaNNaN4.0109634.131234.2045354.243828...0.0370.9637.709311.7032264.0815563.4242633.715973-1.1243900.04.577419
2AlgeriaDZA-1.1019320.332228-1.1631513.6627923.7635233.849843.9687444.087718...0.0001.0008.24249-0.1387121.5217843.4177273.778703-1.0882670.05.960433
3AngolaAGO-1.1092020.329822NaN3.5553483.4011973.496053.6112263.692916...0.0200.9807.51752NaN1.3475622.3418063.777649-1.1092020.020.419653
4Antigua and BarbudaATG-1.1426330.318978NaNNaNNaN4.132824.202506NaN...NaNNaN8.09377NaN4.8191813.681351NaN-1.1055600.013.315693
\n", + "

5 rows × 49 columns

\n", + "
" + ], + "text/plain": [ + " country code ln_hla_het hla_het ln_mort40 ln_le40 \\\n", + "0 Afghanistan AFG -1.111626 0.329023 NaN NaN \n", + "1 Albania ALB -1.129904 0.323064 NaN NaN \n", + "2 Algeria DZA -1.101932 0.332228 -1.163151 3.662792 \n", + "3 Angola AGO -1.109202 0.329822 NaN 3.555348 \n", + "4 Antigua and Barbuda ATG -1.142633 0.318978 NaN NaN \n", + "\n", + " ln_le50 ln_le60 ln_le70 ln_le80 ... frac_migrant pnativ_60 \\\n", + "0 3.453157 3.43805 3.556946 3.668782 ... 0.130 0.870 \n", + "1 4.010963 4.13123 4.204535 4.243828 ... 0.037 0.963 \n", + "2 3.763523 3.84984 3.968744 4.087718 ... 0.000 1.000 \n", + "3 3.401197 3.49605 3.611226 3.692916 ... 0.020 0.980 \n", + "4 NaN 4.13282 4.202506 NaN ... NaN NaN \n", + "\n", + " ln_ypc60 ln_yr_sch1960 ln_pd60 ln_urb60 ln_young ln_mix_het oecd \\\n", + "0 6.99026 -1.035849 2.712741 2.079442 3.769448 -1.092196 0.0 \n", + "1 7.70931 1.703226 4.081556 3.424263 3.715973 -1.124390 0.0 \n", + "2 8.24249 -0.138712 1.521784 3.417727 3.778703 -1.088267 0.0 \n", + "3 7.51752 NaN 1.347562 2.341806 3.777649 -1.109202 0.0 \n", + "4 8.09377 NaN 4.819181 3.681351 NaN -1.105560 0.0 \n", + "\n", + " fstdistwtd_usa \n", + "0 5.684600 \n", + "1 4.577419 \n", + "2 5.960433 \n", + "3 20.419653 \n", + "4 13.315693 \n", + "\n", + "[5 rows x 49 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#We load the databases used by the author\n", + "hla_country_web = pd.read_stata(pathout + 'hla_country_web.dta') # Acording to the author that gives the necessary country-level data\n", + "hla_hla_ethnic_web = pd.read_stata(pathout + 'hla_ethnic_web.dta') #Acording to the author that gives the necessary dataset for the ethniclevel analysis conducted within the supplementary appendix\n", + "\n", + "hla_country_web.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['country', 'code', 'ln_hla_het', 'hla_het', 'ln_mort40', 'ln_le40',\n", + " 'ln_le50', 'ln_le60', 'ln_le70', 'ln_le80', 'ln_le90', 'ln_le00',\n", + " 'ln_le10', 'europe', 'asia', 'africa', 'americas', 'oceania',\n", + " 'frac_eur', 'frac_me', 'frac_easia', 'frac_africa', 'frac_am',\n", + " 'frac_oc', 'aa_ln_atd', 'aa_atd', 'aa_mdist', 'aa_mdist_sqr',\n", + " 'aa_lanim', 'aa_lpd1', 'ln_frac', 'ln_arable', 'ln_suitavg',\n", + " 'ln_abslat', 'aa_pdiv', 'malfal', 'tropical', 'desert', 'distcr1000',\n", + " 'frac_migrant', 'pnativ_60', 'ln_ypc60', 'ln_yr_sch1960', 'ln_pd60',\n", + " 'ln_urb60', 'ln_young', 'ln_mix_het', 'oecd', 'fstdistwtd_usa'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hla_country_web.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "hla_country_web['region']=(hla_country_web.europe)*1+(hla_country_web.asia)*2+(hla_country_web.africa)*3+(hla_country_web.americas)*4+(hla_country_web.oceania)*5" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "hla_country_web.region.value_counts() #cuenta las operaciones\n", + "hla_country_web.region.astype(str).replace(['1.0','2.0','3.0','4.0','5.0'],['europe','asia','africa','americas','oceania'])\n", + "hla_country_web['region']=hla_country_web.region.astype(str).replace(['1.0','2.0','3.0','4.0','5.0'],['europe','asia','africa','americas','oceania'])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "africa 42\n", + "asia 42\n", + "europe 41\n", + "americas 36\n", + "oceania 10\n", + "nan 4\n", + "Name: region, dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hla_country_web.region.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14, 6))\n", + "Figure_2 = sns.lmplot( 'aa_mdist','ln_hla_het',hue='region',data=hla_country_web,fit_reg=False)\n", + "sns.regplot('aa_mdist','ln_hla_het', data=hla_country_web,scatter=False, ax=Figure_2.axes[0, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "log file already open\n", + "r(604);\n", + "\n", + "Unknown #command\n", + "\n", + "C:\\Users\\USUARIO\\Desktop\\Summer school\\Trabajos\\Replication\\raw\n", + "\n" + ] + } + ], + "source": [ + "%%stata \n", + "set more off\n", + "log using \"replication_hla_base_tables.log\", replace\n", + "\n", + "#for this code to work it is necessary to declare the directory of the computer where the database is located\n", + "cd \"C:\\Users\\USUARIO\\Desktop\\Summer school\\Trabajos\\Replication\\raw\" \n", + "\n", + "use \"hla_country_web.dta\", clear\n", + "\n", + "local cont \"europe africa asia americas\"\n", + "local base \"ln_le60!=. & ln_hla_het!=. & ln_frac!=. & ln_abslat!=. & ln_arable!=. & ln_suitavg!=. & aa_ln_atd!=.\"\n", + "local bc \"ln_frac aa_ln_atd ln_arable ln_suitavg ln_abslat\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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o0AmrDiiaePvRi//////////Gg2eb2zwe1zFgRQeb1B/XMaChGVmaUu756oHl/2Fg6qo9Cj7FUu75yEg/ovbUlbv/R8EoGAWjYBSMguEIRhvuRIHRhvsoGOFg3b4zUu75xCN5byxj89IeBZWTV5dNXCntUeAdEhIf5YmMEqK94qM846M8c9pr4qM8MZGUe76Ue35W+8L///975fZKuefjQrldi/+PglEwCkbBKBgFww6MNtyJAqMN91EwmMHuE5fLJq50zepyzugs6V++49il/9QGOZ2Lpdzz6YC0Qovz+lp0w0uk3POxIpuk1voZ66Tc8/GjU1fv/R8Fo2AUjIJRMAqGFxhtuBMFRhvuo2Bwgs9fvzuktkm556Mh66SWl+8+/qcesEvBYgstUGBZbV5fS0hFrZR7Plak5FuCfKANLmQQWft/FIyCUTAKRsEoGF5gtOFOFBhtuI+CQQj2n74u51Uo5Z6PFcl6Fm4+eO4/lYBZbKOUez6tkV5ESV5fCwQZRGG510nKPV/Gg3CrHYKu33/2fxSMglEwCkbBKBhGYLThThQYbbiPgkEIdEKrpNzz8SCNoIr/VAIJ9bOl3PNpjUIr6/L6WtLam/L6WsKr66Tc8zGRPMaGVFyod/H2/6NgFIyCUTAKRsEwAqMNd6LAaMN9FAw2QGRLOqJy2n9qgLkbDkq559MUGUWX5vW15PW12CSX5/W15PW1mMSWSbnnoyE5bNtesaLQsin/R8HwAn8+Pv4/CkbBKBgFIxiMNtyJAqMN91Ew2IA2oeF2CFJDOj+RIHj57uPsdQfi6mbF1MyYvmbv01fv/yMBggP8FKLImvq8vpbAslop93z/kpq8vpbounop93xkJONZaJ3UIuWeTwyqnb72/ygYXuDVXLu36xP/j4JRMApGwUgFow13osBow30UDDYgTfRS7//EgZCyKVLu+WjIr6D/PwwcOHNdyj2fRsgsviyvryWvr0UrtFjKPV8juCivryWvr8UisVzKPR+OGmdtyOtaIuWeTww6fP7W/1EwjMCv1zce1zE8rmf6PwpGwSgYBSMVjDbciQKjDfdRMKjAqSt3pdzziUR7Tl79jxfcfvRCLRClfYyMlP1KT8GuNOpetA3/5akynoVS7vlkoNiGhry+Ft+iGin3fAjyLqzO62uJb2qQcs+HoPTWBf///1+//6yUez5BpOBT/H8UDC/wcpbF4zqGx3UMb9dE/R8Fo2AUjIIRCRj+jwIiwGjDfRQMNkDFEXezuEYp93w8SD+i5j8MfP3+0yG1XRbjNBtFn+KYmhkbDpyTcs8nFVklQRe1qwUWyXoV5nQtUvYrNQjKzutryetrsU2p0A2rPnT2xn8YiKicJuWejx9NW7X3/ygYRuDH/f2P6xigqJ7p/9/f/0fBKBgFo2DkgdGGO1FgtOE+CgYb0AkjasU5wYNlivuXS7nnE0SQ0W5kcOfRi9nrDxy9eHvBpsPvP3/9DwMx1TOk3PNJQonNjXl9LZ751VLu+e3zNv////////+vl3gvaXfP62tpnINlj6lGUIWUez4u5JXX+38UDC/wYorO4zoGOHq1wOn/KBgFo2AUjDzA8H8UEAFGG+6jYLCBjNYFUu75BFFC/ez/eIF+BGJ1Cn4k7VGgE1YdUDTx3PUH//EC/8IJUu75WJFmcIW0e74UErJLrcjra8ntbVHyKwwsnvj/////////+fj4cT3T7Xqu4t66vL6WvWeO/8cAWMfdZTwLGmau/z8Khhf4dmvr4zoGVMT4+wOBdDgKRsEoGAXDDzD8HwVEgNGG+ygYhMAgslbKPR8P0g6t+k8IyBBxCykmSmuZX9S3zCm9QzWgzCSmIbZm5oy1+/4jAdesLrSbkpR8S1rnbv7////F24+Mouuk3POl3POl3fNT25ry+lq88msWbDr8HwZezbV7XMfwuI5hZZtjXl9L1fTeX79//8cAGw+cy+9e6pDWrhlc4ZXXWzd93TPUk3BGwfAAz/oVH9cxoKFXc23+j4JRMApGwQgDow13osBow30UDEJw+up9Rd9iKfd8rEjeu2jf6Wv/cYOivmXED7cTg9yyuv7//79+/1n1oHIp93w0JO1RUDphxX8YePTi7dRVe1Na+vP6WoomdZy7efXK3VsQdPH8/gPN6hC0r1mztLcmr69l+/FD/0fBiARfz817XMeAFf14jGUqZhSMglEwCoYxGG24EwVGG+6jYNACj5weKfd8NOSS2fn3739c4ObDF6axDVLu+VRHchibVtFQTPWM/zDw/eeP8qk9eX0txKDiie1fviFW0o+CkQOe9cg+rmPAip5P0vw/CkbBKBgFIwmMNtyJAqMN91EwmMG5Gw/b5m4OKJroVzihec7GM9fu/8cLlHxLpNzzBwptOHDu////////33Jkf8OcyQ1zJtfPmdwAQ/Uzumt6SjBRbX/lhoN7/o+CEQY+Hel8XMeAB32/t/f/KBgFo2AUjBgw2nAnCow23EfBkANbDl+on7H+6MXb/1GBV16vlHv+ACL1oPL/uMGL6UaP6xiwoh8PRlfLjDjwtE3gcR0DHvR8gsr/UTAKRsEoGDFgtOFOFBhtuI+CoQJOXb2HtmlV2qPANavr////////P0nKzU20Q/9xAGyHhyDQ84nq/0fBSAIfdpU9rmMgiL7d2PR/FIyCUTAKRgYYbbgTBUYb7qNgSICkxjlS7vlYkYJP8ZSVe5pmb5Byzx9wtPP45f/YANbDQ5DR99vb/4+CEQOetPI+rmMgiJ51S/8fBaNgFIyCkQFGG+5EgdGG+ygYbOD9568LNh3ecezSfxgIKZ0s5Z6PB8l6Fnrm9Eq55w84mrfx0H8MgOfwEDh61iP7fxSMDPB+W97jOgYi0Zezc/6PglEwCkbBCABDruG+r4CBbFCw7z+ZYLThPgoGDyjoXqrgUyzlng9H6oHlvgX9Uu75BJEiqsaBQm8/fvmPAZ7hPjwEGX09v/D/KBgB4EkTx+M6BiLR03ah/6NgFIyCUTACwJBruF+ZacHLQCYYbbiPgqENrt59ahJTL+WeP6SRgk/xfwxA8PAQOHraIfJ/FAx38PXSsg97qkhC329u+T8KRsEoGAXDHQy5hvv//79eHmowZGdgYGBgEAyesHUn8eD8k/9kgmHTcH+9ft+tzNYXi7d+v0d2YIyCAQPG0fVS7vlDHeV0LvqPAQgeHoKMvpye/n8UjIJRMApGwSgYeWAINtz///////vV6Y7sDAwMDNbTb/76T3swbBrul7xzD7KaQNBJNf8bCXXPZq/7evXu/1Ew6EF+91Ip93zKkZIf4UPcrRKaiVFGBjKKqvuPAT7ub3jcwIKEmB/XMWBDjI8bWB43sDztFP0/CkbBKBgFo2AUjDwwRBvu////f7srS5SBgYE9YAUdho6HTcP95bLtNxLrTqr5H2Q1QUbHZT2uRpQ/mbT889lr/0fBoAQKVFqento8T8azQMo9HxeS8Sx48urdvE2HZTzwKcOKvPJ6g0omSbnnY0WGkbUXbz36Twi8nG35uI4BK/p+a9v/UTAKRsEoGAWjYKSCodtwhzfdZRtO0nzQfdg03OHg+72nL5ZsvZXZelo/7CCrCTI6KuJw2b/gUeeCD0fO/x8FgwO8+/hVyj2fKmjjgXPLdhxX8S+Tcs/HRCr+pct2HP////////8fPHtjEd8k5Z6Phrzz+myTW6Xc85GRgk/x9FV7/////////4Vbjqj4l0q558ORvHdRVjtRm0p/PT//uI4BF3rWI/N/FIyCUTAKRsEoGKlgKDfc//9/siKAnYGBPXnbx/+0BcOv4Y4Mfj5/83rtnjtFPWfNYw+ymiCjwzyWF90yHzTOfLf7xN9vP/6PggEC8zYeknLPpxxJexT8h4GAoomKvoj1MIq+JQFFE/9jgL2nrlZMXuWW3e1fNKF17uZz1x/8////////X77/WLz1aGrzvI75Wy7dxj6Ovnbv6emr9z559e4/0eDlDOPHdQx40Nfz8/+PglEwCkbBKBgFIxIw/B/S4NejMzt37tx76cV/2oLh3XBHBr8/fH679fC96skXHFIOspqgofN2SfcqJr3ZfPD324//RwEdwbYjF6Xc8ylHZRNW/EcFX779WLz16NE587eUV8/zC9lSXn16weL/AwS+397+uI4BP3raIfx/FIyCUTAKRsEoGJFgiDfc6QVGTsMdGfz98ev9vlMPmmdf8sw+zGd9kNUEGZ01jb5d0P1q1a4fT1/9HwW0B1Lu+RQi3bDq/xjg9e07K5PTJ5haI6NVKRlv7t77T3fwfJLG4zoGgujjvrr/o2AUjIJRMApGwcgDow13osDIbLijgY/HLj7qWXQlsOiouNNBVhNkdFon5GZ684uFm7/defx/FNAGqAWWS7nnk410wqr+YwNrMnInmFpPNLc5PnPO3YOHj8+cM9HMZoKp9drMvP/0Bd+urnlcx0AMetLC838UjIJRMApGwSgYeWC04U4UGG24o4HP5288nbryWlTlCXnPg6wmyOikiu/1uJpnM9d8uXzn/yigHli566SUez55yDqx+T82cGXDpgmm1hNMrV/fRkTW69t3JphaTzC1vrJx8386gme9co/rGIhEH3aV/R8Fo2AUjIJRMApGGBhtuBMFRhvueMDXG/efz91wI7nhlEbAQVYTZHRM2u1qWOmTCUs/nb76fxRQDMIrpkq55+NBEZXTlJD2m0q556v6l81ad+A/DrC3vXuCqfW+jp7/qGBvB3Zx/ODTka7/5IIvp6Y9bmCBIebHdQz4UAPLk1be/6NgFIyCUTAKRsEIAwz/hz/4/u45BLz7/p9MMNpwJxJ8f/Ds5bLtt7LbzxiGH2Q1QUZHhOwv++Y9bJ/34dC5f3///h8FZIHcrsVS7vmYSDWgbOXOk//////////3n7+XbT+28cC5/4TA6rTsCabWF1at+Y8Kzq9YPcHUek1Gzn+iwbvNmU8a2f5TAzyfrPW4jgEPers25v8oGAWjYBSMglEw8sBIaLjvK2CAgIJ9/8kEow13MsCvl29fr9t3t6TvnGXcQVYTZHSIy+KiS/r9+unvdh778+Xb/1FACjh19V5A0USdsGppjwJ57yKz2Ib4+ln/yQKH+idPMLXe2dDyHxXsrG+eYGp9aMKU/7jBvSev/sPA3+/vnzSyPa5jeLch6T9l4NuNjY/rGAigeqb////9HwWjYBSMglEwCkYYGG24EwVGG+4Ugj+fvrzdfvR+7dQLTmmH2M0Ospogo/M2CXfLJrzZuP/X6/f/RwER4MPjJ19ev/lPMbixY9cEU+sJptZ39h/8DwO39x2YYGo9wdT65s7d/zFAQsNsvfAaGc8CKfd8Oa9C4+j6qimr3yzzf1wHb1JTBJ71yT+uYyCIXi10+T8KRsEoGAWjYBSMMDASGu5XVpdDwOor/8kEow13KoJ/v36/P3DmYeucS965RwRsD7KaIKMzxpG38zpfrtjx4/GL/6MACXz9/nPxtmO14em9Vo4TTK0nmFrP9vI/t3TFf8rAzobmCabWE0ytNxSUHJ8xe0NByQRT6wmm1rsaW/6jgn2nr+mEVkm556MhHe/Uh3WMj+sYIOjtmuj/5ILPx/of1zEQheoZ/3x8/H8UjIJRMApGwSgYSWAkNNypAEYb7rQDH09cfty3+EpwyTFJl4OsJsjolFbQzdSm5/M3frv18P/IBgn1s+XccmssXCaYWqOhDQUl/ykAv3/83NPaOcHUGhntbev68/PnfyTw4s0HGc9CKfd8TLShQOZxHQMC1TP9/0fmHoZnnWKP6xiIRK/m2f0fBaNgFIyCUTAKRhIYbbgjwKlTp07iAJ6ento6Op9//BhFNEWvTl+5N2nZpciK44peB1lNkNFxRa9LURX3Ji97debK5x8/hgp69eHT3I0He5fuuPbw2ecfP8hArz991g6tknLPT7QJmGBq3WFm5+oQI++Wq+KWHWQXNsHUeoKp9Yklyz7/+EEJunHk6JE58zeWVh6ZM//GkaOff/xAQ5YJzVLu+ZjI1C/hUR3DY1T0fJ7j5x8/SEXvzi1+2i1NAupT/Pj08ucfP0bRKBpFo2gUjaJBi/5TFYw23BGAkZGRATdQ19R89enzKKIbenL+2q3pqy4m1B7HOGXyiITzOf+C6+1zH+4/9erT58GJuhZvU/QplnLPhyNpjwLN4Eq1wHLVgDKHtPacrsWvPn2GozX7z7hld2sEVUi758t5Fan4l0JUQlaTK7rlTDC1nmBq7eAYJ+WeD0chkLa7q69f4QSdsGoZzwLd8Gr/oolTVu959ekztdCcTQel3POxoi2Fko/rGDAQ48t7J199+jyKRtEoGkWjaBSNcPTvPzXBaMMdAQ4dOnQQB3Bzc9PW0fn0/ccoGhD05vajh4u3XM1qO2UYcZDVBBkdFrA975l9q3Hms13HP37++un7D/qjC7dPR1eZI1ClWWiJUXCxIVYk550l5Z4PQfLeRROW7/r0/Udc/Swp93w8yNIpaYKpdbuZvZR7PjLScM2cYGo9wdRa0S1Hyj0fGfkVTvj0/QdVUGrLfCn3fExkGxDzuI4BK3o23eTT9x+jaBSNolE0ikbRCEf/qQpGG+5EgdE17oMH/Hr9/s2G/XfLJpyzTjjIaoKMDnGYXXBKu1879e32o38+ff1PL7D31PrQUn0ikbJfqpR7PjKS9SyQcs/Hj6ydEiaYWreYO0i556u4Zls5JSq5ZUu556u6Zk0wtZ5gaq3imi3lno+GdMOq/1MD2CS1SrnnY6KdReKP6xhwoZ+PT/wfweDy3Vv/R8EoGAWjYBSMAqqC0YY7UWAYNNwP9k1akZA60cJuSWTcrqa2b+/e/ScEvr17//3Dh/+o4MvrNz8+f/4/EODVzdvbqusXBIVPtXddk5FT3RJa0xPZmx0wz8t9jYrlfnaTg6woaK2UyQwT02Yfs6ZIp2mObmuz8s6vWP3///////5tr2mcHxg20dx2aXT87ub27x8//v///93DhzvqGheGRk2xcVqVmnVizvz/RIC/v/9MbcoILdUnEmE23EPsQkss3XvMbGstnNOs/XRc0qXc89GQslv2BFPrCabWzebQI2UmmFpXWbjG2gZNMLVuMneUcs/HipKb5v6nGFjEN0m55yMjOe+sshL1x3UMeND5eu70Ftf0Fte7j6/+H2Hg87evE1Ys+D8KRsEoGAWjYBRQFTD8HwVEgCHdcH9z5+7i8JgJptbIaIaz5519B/7jACfnLpjrGzTB1HqCqfU8/5Azi5f9////6NQZc7z8J5haTzC1XhAUfnH12v8Ug0enz5xeuGRrRc2RKdNv793/Hzc4s2jpBFNrZBRepB9aikBhJfr5UQad9oaLVI13c5scZEVBO/hMV0uZz1e3XOAV1Gtl02duPcEUgWa5+ezr6JloZjPB1BoZrUrJ/Pb+w3/c4P3DRwsCwyr9zEJL9YlEeQHW/vYRUu75Uu75ym7ZpZbuE0ytkVG/qbW3Q6SUez4aqrRwnWBqDUGt5g4TTK3hKNQuVMo9Hxe68/jVf8pAStNcKfd8ZKTsl3qmluNxHQN+VFmuHFqqP3114/8RBtYf3J3X13LmxpX/o2AUjIJRMApGAfUAw/8hBo62qJMNWo7+JxMM6Yb7mozcCabWM5w9Lq/f9OrGzevbdiwKi55gaj3HJ/DXt2//McDqtOwJptZoaJqD2wRTazS0qaTiPwXgyJTpE0ytkdGG/OL3jx7/xwAvb9ycYGo9wdR6XXb+7X37n128dHrhErSGOxrKiTVodTacp2m0g9/kICsK2iRqvFjFZLGSxQw9qwmm1shoVUrGrd17n1++en7F6gmm1hNMrbfXNPzHDdbnFU8wtW6Icgot1SceTTC1LrDylHLPT7f2nWBq3W9q7WMfaeyc4uQYC2nH95naaLlkSLnnw5GuS/oEU2s46jCz74V1M/pMbdRcs6Tc83Ghict2/qcMLNx8RMo9HxnFxbo+rmMgiM7XsoeW6rfPy/k/ksCbD+/z+lry+lo6F8/+PwpGwSgYBaNgFFAPMPwfYuBoJS8DuWAk3px6a8++CabWE0yt39y5+x8G/vz8Oc8veIKp9YlZc/+jgmPTZ00wtZ5gan1++covb958fvXq9MIlE0ytJ5haT7K0v7x+47d37z89f3F85pwJptYTTK3JHnc/PmP2BFPrCabWG/KLj02fvb2mYYKp9QRT65VJaf8xwJayqgmm1utyCv8jgegK09BSfWJQRqJBo7vhbF2jLcLGB1lNkNEeLtMNomZLFS1m6ljN9Q36jwQenjg1wdR6gqn18ytX/2MDj8+em2BqPcHUesPG6aGl+sSjCabWE0ytIcc7TjC1tnJKlHLPh6MyS7cJptZJNv5S7vlwFGYXOsHUuszSHdLWh6M2M/sJpta+sCF8CJLzzlbyTYOj8Ir2F28eYUWfv378jwO8unHz8MSpazJzV6Rm1UZmGrmhLOA5U8HzuI6BGFRfoTjSGu4r9mzL62uBoGOXzv0fBaNgFIyCUTAKqASGXMP9//+35ya6Qxvv/FYJ5SSAkXhzKmRUezvGsPGp+YsmmFpvLCz9jwomWzlMMLW+tHbDfxj49e3bBFPrCabWU+1c/iMBSNt9QVD4f9IBfAT90pr1/2HgxdXrEy3sJphaH5855z8qmOsbPMHU+t7hI/+RQEy1RWipPqkoJdWgzttwuqHRTlm7g6wmyGg/t/mVoKLHPYs+Hr/4////////r0rNmoC7c3J2yfIJptbrcgpI2pwaWqrv5+fX4GZR72bR4GaR4+moFRKHjDz9AzrsLKssXKXc8+Eoz8prgql1kH24lHu+vFuuuXOSkXOKtHt+lG3wBFPrVBs/Kfd8OPIrIHbpTm6Hz39s4PTCxRNMrdFQgH24lHu+lHt+cYLd4zoGItGVWtYR1XB/+vplXl9LXl/LjPUr8/pamuZN/T8KRsEoGAWjYBRQCTD8H4rg16PNybIMDAwM7LGb3/6nAxi6DfcddY0TTK0PT0JvPVzbun2CqfXS6IT/SOD17TsTTK0nmFr/RwJPzp6fYGoNQR+fPvsPA5+ev5hgaj3B1JqMvaqX12+cYGq9IjHtPyo4s2jJBFPrDXnF/1HBRHPbCabWr27c+o8EyGu4w1HPouIfT16+WrVro5L9Dj7Tg6wmyOgwn80lz+w9jlFzNS2PTJj2Hxs4Om3mBFPrbVV1pDbcCaJ6D3O0kx9zIQ13uzAp93xkRIuG+50DhyaYWk8wtW7xCvW1j/Czjyi08pxgaj3B1NrWKUHKPf9qFeHV7cho1STn/yMGLNq2Ia+vZcKKhZ+/fS2e1JHX17L/7Mn/o2AUjIJRMApGATUAw/8hCn7dnO3IzsDAIFqw7+N/moOh23A/t2zlBFPrtVl5/1HBgZ7+CabWu1s6/iOB51euTjC1nmLj9B8J3D92fIKpNQS9uXvvPwx8e/d+gqn1BFPrL6/f/CcR7G3rmmBqva+r9z8qeHTqzART65mu3v9RwYqE1Amm1hdWrf2PBChvuP////////+QnbtTjKw2uIbfrZh43jbpIKsJCmIzueCQcq96ytuth39/+PwfBu7sPzjB1Hp+YNjeE+tCS/WpiOo9zPOtPKXc8+EIslSmxNJdyj0fGdVYuEzAWCpDYcN9rm/QBFPrhfnlUu75cJRi4z/B1LrB3EnKPV/KPV/ZLzW0VJ9INHJG3O8/e5zX15LX1wI5C3LbsYN5fS3VM/r//P3zfxSMglEwCkbBKKAYDNmGO6Lpbj37zn9ag6HbcH9x9doEU+sJptan5i38DwNXN22ZYGo9wdT6xo7d/5HAvz9/JphaT0BdlPLt/fsJptYTTK0nWzn8RwKQUfM5Xv7/SQcXVq6eYGq9Oj37Pyo4v2LVBFPrNRm5/1HB8VlzJ5haTzS3fXDsxP/////////v79+oMuPQUn2yEaTh/u3Dh0kWdhNMrSHozv6D//////vt+7vdJw56JW8RNDvAZnKQFQWds4i9U9T7eu2ez3cfzvLwnWBqPbkmJbRUn4qo3sPcH7UtrgPbnJpm7afuminlnq/hmplt7TPB1LrTzE4VdXMqJQ33r2/eTjC1nmBqbRxYIuWeD0dqrlkTTK0nmForu2VLjTbccYDZG1fl9bVMX7fs//////////X7V+X0vry+lh0nDv8fBaNgFIyCUTAKKAYM/4cw+HWxW5eBgUG0+tiv/7QFQ7fh/v///5PzFkwwtZ5gar0gKHxbdf2SyLgJptYTTK13NrT8xwC7GlsnmFrPDwh9cBzaRL578PBka8cJptYzXb2fnLvw////7x8/Hpk6Y4q1wwRT66NTZ/4nHTy7dHmCqfUEU2vkIyC/vXs/29Nvgqn14YlT/mOAlUnQluuKhNRNJRVzvAPwnypDEOVl2azPLZpi4zjB1HqCqfUMF88JptYTTK2XxSVvKauCbN6dYGp9ZcOWD0fOP+qcf9m/4KiIw0FWE2R0VMV7rYT5ZGPT5HSD0FJ9aqF0b0cp93w05GsfOcHUGoJ6rJ0nmFpDkKtDjJR7PjKipOH+8OTpCabWs9x9pNzz0VCHmf0EU2sLpySpkd1w/3Iae5q/8eBeXl9LXl/LrUcP/sPA3tPH8/paSid3ff3+/f8oGAWjYBSMglFAGWD4P6TBx5PzysvL23fc/09bMKQb7v///z+3bMUEU2tkhLnqHQJ+fv06G3ZY+ywP35mu3hNMrSeYWsPPOJ9kaT/B1BqCJls53D1I5lDi3vbuCabWE0ytD/ROuL51x/FZcyFN5/mBYX///PmPAb5//LijrmmCqTUcRRRT1FbOSDCeYGoNQTPdvH//+LGntXOCqTUcTbN3vbp56////xPr7UJL9SEoO86gxcVwnpbRdgGTg6woaLOI8Uw9owYPw/RkihwWWqqvFxIt5Z6PiXwCsucHRUwwtYagpTGJNdX90h4FUu75yIiShvuPT58mmFpPMLXWcM2Ucs+HIy3XjAmm1hNMrRXccqVGdsP9aSvfzyen/mOAKWuW5PW1zN285j8qaJgzOa+vZdPhvf9HwSgYBaNgFIwCysAQb7jTCwz1hvv///8/PX9xe9+Bo1NnXN++892Dh/9xg+8fP0K2tMLR7ub29w8fLo9LmWBqjYnOLFr6n3Tw59evbVV1E0ytkdGi0KiHJ7A0ieDg1c1bl9dvOjln/v2jx2OqzENL9clG8Ib7bE+/Vzdu/v///////69v37m6acvxWXPvHT7y5c2b/////0dtuCOj9CSDBg/DWXpGmzFOmdwmYDJXy6jF1TArnpxGvHZIjJR7PjKS9ijI6Vz8////////f//w8dGpM/A9wdfvPzOJaZByz4cjShru////XxaXNMHUOtvaW9o9X8o9X8o9X8Y9D7I7tszSTco9X2oEN9zfbc54XMfwfLLWf1Rw8faNvL6WvL6Wh8+f/kcFRy6ezetryetref/p4/9RMApGwSgYBaOAAjDacCcKDIOGO6ngx8dPD0+cenTqzM/PX/7////t/fsZLl4TTK1XJqff2rPv17dvX9++293cPsHUeoKp9etbt/+TBa5u2rK7uX1JVPyWsqpT8xf9JwVQuDk1I8F4cXjM3o7uv79+/8cLcDXckVFymkGtj+E0I6P14uiN+F08JgvVjNsdDfOiiW3Ea4fEmMU2nLp6r3n2xqbZG05dufufCLDhwLmCnqUbD57LaHELLdUnBmFtuD+7eGmCqfUEU+t6C+c428A428AGc6cJptYTTK2NnVOkRnDD/e/3908a2R7XMTyuY/hx/8B/JNC3fH5eX8uSHZv+YwPtC2fm9bWs3rvj/ygYBaNgFIyCUUABYPg/CogAI7DhjgYurlk/wdR6UWjUf1SwNitvgqn1kcnT/9MdUNhw715Q+J84QEzDHRklZOlXBhhOMjVaI43eiN/HYbJU2bjb1qgo3CCyUD+0FDvSDonxLej/Ty6gsOH+////mzt3t5k7TDC1hqNWcwcnx1gp93wIIrvhfvb6ocxWd2JQVpvn/0EG3izze1zHAEHPJ6r9h4FT1y7l9bXk9bU8f/v6PzYAV/ACh4JRMApGwSgYBaOAGEDPhvvbp0+//6cHeHJ+586dO88/+U81MNpw393cPsHU+kDPhP+o4NzSFRNMrddm5f+nO6Cw4Q45VQYPWLlzWmqTU2qTU1iZYWipPnkoJk+/NMSg39JohTx6I/4gq8kKeeN+S6OyYMOYXP3QUgTSDolpmr3hP7mA8ob7////k6omezpEJdoEJNoEeDpEKbrlSLnnwxHZDfclWyeEluoTif4PJvDn8/PH9cyP6xjg6Putrf////8PG1CvnNbbsWgWLlQyqTMP95D8KBgFo2AUjIJRQAxg+E8/sK+AgV3UMLB13YVHn379pyHYV8DAwMBQsO8/1cBow31PS8cEU+sDPf3/UQH09tDsgv90B1ltnvG1NsgotFSfeESw4d45Py+0VJ+KKKJIvyjcoNvWcImy8V5Ok4OsKGiNtPFkU6NKf8OELH3tkJiTxC2PwQqo0nD///+/dmiVlHs+ViTnnRVaqo+JqsuVasuVQkv1kdG0VfX/YWDoNtxfzXd4XMeAjJ71yPxHWsJOJHr44tn/UTAKRsEoGAWjgCzA8J9+ANKehgBe7cDK2ftv0aYBD7FotOFOTXBp7foJptYLgiP+/fv3HwmsyciZYGp9ZMr0/4MAhJbqE4/o33BHQ3nRBu2OhgvUjXbxmhxkRUEbJexvpre8WLTl+93H/0kH1Gq47z11VTO4Qso9HysKKnarm5aIhi43CVxv5K6bloiMzt848h8GhmjD/ffbO4/rGB/XMaChr+fnn752mVT0fxSMglEwCkbBKCAL0LPh/v3phR2zc92keRkQgFfaLXf2/uvvvv+nIhhtuFMffP/4aZa7zwRT641FZR+ePP3////nl6921jdPMLWeYGr95g75w8NUBJsOLgwnelkLLRruBd2BD5/dxIra0/xCS/Vxoax4g2ZXw7naRtsE0FfUnFTxvR5f+2zm2i9X7vxHBfefvZ60fFdMzcwZa/Y9e/3+PwxQ3nD/+fj4fxiIqpoujXripLx30cTlu/5jgG/XNzyuY3hcx/Dt2rr/OMAQbbi/nGP1uI4BEz1t5fuPDXz6+j6szIBIdPLy3v+jYBSMglEwCkYBEYCeDXcY+PXp1omlrYHayA14dlH7+AnrLlBnEfxow50m4OauPRNMrSFohrPnBFNrCDq7dPn/QQMmLa9qnZNJDNp7av1/vICMhnvZhIj/OMCixuLQUn1iUFqyQZqpz1Sd0JPqAQdZTZDRcRm3q2FlTyYu3TF3o2ZwpZR7PjLSj6g5cfnuf2o03J9P0viPCo5futMwc/20VXufvHz3Hwd41if/uI7hcR3Ds27J/zgASQ33Q+e2Hjq39dz1w/8HFPx6cfFxHQMu9HFf3X8M8OzVg9BSfSIRwaQ4CkbBKBgFo2AUQMBANNzhADIEby/KzoAAvNqUL4IfbbjTCjy/fGV5AuI09wVB4fePHPs/TAF1G+77188ILdUnEmmHxEi555vGNnx/8PTl0m23strOGIQfZDVBRjs4zSdKuGZqBDuZJ8IPXJdyz8/pXHT70eU9J9YSg24/uvwfA7zfmvO4juHL6Rn/SQGfj/U9rmOAI1zaSWq4Q1BYmeH/AQUvZ5o8rmPAhZ40cfzHAKMN91EwCkbBKBgFtAAD2nCHgV+fbp1YOiEeZRENLwWL4Ecb7rQFv75+e3z23PcPH/8Pa0DdhvvTxT6L20vr3c3r3c2bvG36k4Lr3c0rAs3yI0z9Yp2tE3yQkVlMoEFEuEFEeHpL1YHTmw6c3nTy8t6fL968Xrd3c1DxfGGHg6wmyGgvm+k0Med81UAP03hFl9ydJy7ffPC8d8n245fu/Pn7d+Xukyt2nnj++sPs9Qe2Hbn4Hxv49+/f6t2nlu04/uPX7ydN7I/rGJ608P4nGizY1HO3geNxHQMc3Wtg65yfh4la5mSGluqThAa24f79zs7HdQz40YddZf9RwWjDfRSMglEwCkYBLcCgaLj//////69Pjy7smF3pLsuACngtcpecfkla83204T4KqAA6qddw//X6+uM6hg97qi6sXDPRzGaCqTUEpUcTe/lrWJnB////////r+xXKuWer+yc420SV6QSMFPU6QCryUFUNEfEsVTZ3884Vt0pW8o9HxOpBZav3Xv6////////L+xdJudVJOWeD0Gzs9Uf1zFA0Ifdlf+JA3Nr5R7XMaChBVVioaX6aCi92TW0VJ8kNLAN9+f9yo/rGPCjJ43s/1HBaMN9FIyCUTAKRgEtAMP/AQbfn15YNyEebbmMtFvuhAmViFXw7IYNx97+Jxp8f/f8+fPn1NzwOtpwH4GAig33l7MsHtcxPGli//////q9Z0xcU10dYvRc0myTPENL9YlBkIZ7WPkUKfd8NCTnludqlpCjHjRZ3GUXh9lBVhNktEjQvlrRN8QgWschU8o9HxnphKGf9qjpnX6/lulxHQMEQRxMDLhdx/y4jgEN3a9jDC3VR0NDq+H+7dq6x3UMxKD93drt83LgqGFGcmipPpFotOE+CkbBKBgFo4BIwPB/YMD3d9f3z0ZqmjMwMDBgLo/59enWukxldgYGBgbRgn0f/w8YGG24j0BArYb7j/v7H9cxQND7HUXqgeVS7vkQRFLD/fLtx1Lu+QSRvUVSmmZIr6TbFi6Lg6wmyGgFv22jnHekXqSRbZqUez4mWpGn8LiOARm925T2nxB4v6PocR0DVrSqSji0VB8Zkd1wP311f2l/GDGoYlLUfyqBZ71yj+sYiEGP6hhCS/XJQ0O64f7m/YubDy8Sg968f/F/FIyCUTAKRgFlgM4N91+fbp1Y2hpoiDLAzsCrHdi69ARSgx0Z/DrXrs7AwMDAXnrw/4CB0Yb7CATEN9zDyw0PdxkdbFRbPbX50ekz/1HBiyk6j+sYIOhBHYuUez4ckdRwN46ul3LPJwlZWqckaId1yHis57E6yGqCjNbyWrfJeMbrhFtYp0i550u55xv5Jj3COKr8UR3Tf0LgSQvP4zoGrOghxqA72Q33hZt6Qkv1iUT/qQE+He15XMdAPFpfLRhaqk8GGloN953HVjVMT4aj8HLD0FJ9YlDdtMT/o2AUjIJRMAooA/RsuB+tR22vQ69Rffr9P17wfXsqAwPDaMN9FNAZkNBwLzF4WMP0qJZxgqn1BFPrNZm58IPtv93c8riOARlNz9SScs+HIBIa7qUGUu75lCAT27Ro3YgmOa+V/LYHWU2Q0WYuyx4p952OandSuB/XMaCht+vi/+MG77dkPa5jwIPWVQmGlurD0RBquH8+OeXjvlpi0JMt+XOrxKdXSoaW6pOBhlbDvW0u9ktzCaLRhvsoGAWjYBRQDujZcIfsGWVgIPHM9pvz/NTV1d3qDg7gPOtow30EAuIb7huqBB/XMTyuY7hQoTXB1HqCqfVsL/+fX7/+////WZ/i4zoGZPSwjlHKPR+CiG+4h1LccEdGevYZoQZRNQo+iwXtD7KaIKPD3Ean1VUvO0veTuB9XMP4uI7hcT3OQfe/398/aWR/XMeAB6GtIRlCDXfiwbM3j0JL9clGow33UTAKRsEoGAVEAno23A/W2+fO3kF0g30wgdGG+wgERDbc48p0HsBXmNQzvb59e75/6ART612NrV/PzXtcx4CJ5marS7nnS5HWcNeXcs+nBdJwzPI3itlrpn5CXvMgqwkKYjc+qax+0V76QYX7n89f/2OAN8sDHtcxEESbqgVCS/UhqKA7IKfdO6fdO67GKrRUnxhEt4b7lTunpq9uIBL9/vv7PwyMNtyJQaMN91EwCkbBKKAc0LPhTiz49enV/QvXnvwfRGC04T4CwdbDSwp6AvGgvDaf+GzDPRX8j+sY4OjVApd7h49OMLWe5e77rEvicR0DJnpUxyjnkSs1OBruUu75vqFBj+sYHtcxPKxgvhnDf9Fe+qSy+kF244OsJsjonFX83dL+1xv2/Xr17v///3++vHrcgOUwGUz0qI4hslQvtFQ/tFQ/vsqysiagpikkvcE5tFSfGES3hvv6fXNDS/WJRD9/IkYgRhvuxKDRhvsoGAWjYBRQDujZcIcslSF4vDqRyugKRhvuowATXN+2Y6atySP4cDsE1TN+e/1sgqn19nCFx3UMuND6Ahkp0hru1Fwqg4YOlgo/rmNARzWMtxN4L7tInlZXPcxteJDVBBmdMYq45Gp1PVjofgHb4zoGgmh/DU9oqT55aLThPqgArRvuX79/vnjrOCY6cGbThv3z0NDBM5su3jqOhl68efR/FIyCUTAKhikYfA33j5tjGRgIK6MvGG24jwJM8PTCxfO5fI/rGNDQkymmE0yt71WxPK5jwIUe1THYB0YR33APK6NVwz0s3O9xHQNBdCeF50Fj59WQkmNSrgdZTZDRMXHds8YKV/1F7uZwPKpjwIUKy9VCS/XJQKMN90EFaN1wf/P+RWipPiUop8P7/ygYBaNgFAxTQNOG+6Pd3eVIIFiXgYGBQTe4HB+It+BlYGBgEG04+X8QgdGG+yjABN+fXn5cx4AVHU0WfVzHgB/tKhYnqeGe3bFIyj2f6uhUOd/jOgZi0IspOv////////+XS7efzlh9Pbb6hJLPQVYTZLRV0HiOtlGzm2FmgkFoqT5V0DBruNdNjZ++qh4Z3X189f/QAaMN91EwCkbBKBhAQNOG+/+3m2NRDoAkGsg2nMR6qPtAgdGG+1AHH589u3vo8JfXr/9TD0DuQ8WKHtUyPK4jjJIzLUNL9YlBYWUG////N4yqk3LPx0R6YVG2yZ7EIOOoECn3fDjKiHF5XMdAPPrx8Mh/VPDt9qPnCzbNNjbbKIa+Jn4nr8kCDaM2J8PcGIoa8cOs4b735Lr/QxmQ3XAPLzOMrjKPrjLffWLNf9xgtOE+CkbBKBgFeADDf9qCJysCiG+680qrq6urmwW27no0qJrtow33oQxu7dk329Nvgqk1BC0Oj3l06vR/igHyfahko5NV3KGl+sQgSMP9////oWVTpNzz0ZBntk1oqT4xKLDIWMo9H462FEodLxPAgsoFrjVJPp+ggoZezXf8jw1AjohJzDSo8jOcYmK0Vgq9Eb+X02SxinGXnWFhhEF4sX5oKQpKaXR4+OwmLvTu46v/ow33QQPIbrjD0ZKt/f9xg9GG+ygYBaNgFOABDP/pB4hb4z4owWjDfYiCU/MXTjC1hqB5fsETTK0h6NqWbf8pAy+mGz2uY6AcVZcrhZbqE0Twhvv///+XbT8WUjpZP7xGxrNAO7TKv3BCYoNPaKk+MSio2Ng8vjGpcc7E5bvkvYuk3PPRkIxHgUd295sPn/+TAlKbnCMrTJBRYr5JeZDhBAujlXLojfgDbCbLFYz7rIxKQgyi8/VDS/XTW1z/EwKjDfdBAkYb7qNgFIyCUTCAgOE//cDRFnV1dfWWo/+HHhhtuA9F8PLajQmm1hNMrfd19Pz88uX///9f377bVlU3wdR6mqPbl9dv/pMLvt3a+riOgSB6hBMxwtG5GqHHL+4SRJBRZ1ygclJ0aKk+MSi+1uY/DPz9+zeva4l9apuyX6lmcIVnbk/NtLX/qQdyO31DS/VDS/UjC/SLwwx6bIyWKhnvZzc5yIqCVssYz7S1fr1u788Xb//jBqMN90ECRhvuo2AUjIJRMICAng33IQxGG+5DERyeNG2CqfX6vKL/qGBJZNwEU+uLq9f+xwE2HVw4d0MHHnSrVehxHQNBdKWWNbRUHxMZR4VKuedDkFFU3X+KAXkNd1oDeMMdGYWV6OdHGXQ4GC5SM97NbXKQFQWdMQi/ldX2cum27w+e/UcFg7nh/vnrh6hKUyLRlTun/g9lMNpwHwWjYBSMggEEow13osBow30ogjWZuRNMrc8vX/UfFRzsmzjB1HpXU9t/HCCq0iy0VB8X6q6UfVzHQCTqrpQNLdVHQx7ZtvYp7hAUUVH8n2IwhBruaCg7zqDVxXCJntVxGbeDrCbI6KR6wI3E+mdz1n+9fv////+Hzm1NrLcjBiU3OvwnC5DdcKcniKo0DSszIAZNWl71nwZgtOE+CkbBKBgFAwgY/tMQQBa1M8DWtcO5xACIlsECRhvuQxGsyymcYGp9dvGy/6hgf1ffBFPrPW2d/3EA/A33y7Wsj+sYiEQ3allCS/XxoLTm0P8Ug6HbcIcgyBr3L1fuPJk070pA/EkV34OsJsjouJzHtciKJ1NWfD53/T/NwJBouEdUGIeW6hODuheizzVRBcxe2xJTbQFHUVVmUZWmUZWmoaX6RKLRhvsoGAWjYBSQDUYb7kSB0Yb7UARHp82cYGq9KiXzPxL48+vXfP/QCabWl9dt/I8D4Gm4N1co7K3hgaNTbTKvFrq+Wuj6dI7d3hoerKi2Qim0VB8XapqV/p9iMDwa7v///381z+Fph/D///+/3X38YtGWmxktp3VDDrKaIKOjYo5XAgofdS/8eOzCf6qC0YY7JSC0VJ9INNpwHwWjYBSMArIBw38agiuryyFg9ZX//5G4xACIlsECRhvuQxG8e/Bwgqn1BFPrbVV1Hx4/+f///9t79yHD8HO8An5+/fofB8DTcEdDrXOgvQKyWxvxtdZVk2NwoVOX9/4nAgyPhvvvt7cf1zE+rmP4dKTrPxL4+fTVq9W77xR0nzWLPshqgowO81pddM960DTr/d6Tf3/8/E8ZGG24UwJCS/WJRKMN91EwCkbBKCAb0LThPnzAaMN9iILzK1ZPMLWGoCm2ThNMrSHo9t79/3EDejbc8aP5Gzv+EwGGR8P95Wyrx3UMj+sYnrTw/McBfr/7+GbzoXuVk87bJR9kNUFD5+2T71VNfrPl0O/3n/6TDnYeWxVWZkAk+v339/+BAKMNd4KIpg337z++Pnx2k0j0/vOb/6NgFIyCUUBVMNpwJwqMNtyHLnh06vTi8JgJptYQtCo16+WNm//xgtGGO1VA2YTwiHIjYlB+l9+v5xce1zHA0Ydd5f8Jgb/ff7zbc/JB48yL7lmHeawOspogo7NmMXcKe16t2f3z2ev/wwiMNtwJIpo23N99fBVaqk8k2n50+f9RMApGwSigKhgcDffv907sXDqhZfb6g1de/vo/CMFow32og1/fvz+/fOXvnz//iQCjDXf6g5czTB7XMcDRk0b2/ySCj0cvPOpacDmg8Kio40FWE2R0Wi/0Vkbri8Vbvt978n+Ig9GGO0E02nAfBaNgFAxjQPeG+9tzS3LdpDM3f/wPBb9uznPnZUAA2bAVNwdd43204T6iwGjDnc7g++0dj+sY0NC7jSn/yQWfz15/Mnn5tYiK43IeB1lNkNFJVb/r8bXPZq39evXu/yEIhl/D/cfdPf+RwNfvny/cPIaJ9p/euG7vXDS07/SGCzePoaFnbx79pxkYbbiPglEwCgYW0Lfh/utkgywDAwMDg9n0m/8h4MkSTwZ0IFt6EN6uHxxgtOE+osBow53O4Hm/8uM6BnRUz/yfGuDrtXvPZq+7kVh/Us3/IKsJMjou4341vOzJpGWfzlz7P0TA8Gu4P+0U+/d30A3W4AKjDfdRMApGwcACujbcYY10XovKfU/+////////qxN1GRgYGBhkC3a9/PX/+711ybIMDAwMuhOv/h9MYLThPqLAkGu4109PCiszIAYlNzj8H2Tg27V1j+sYsKK3a2P/UxV8v//0xZKttzLbTuuHHWQ1QUZHRRwu++U/6pz/4cj5///+D1owaBvurXMym2alEYPOXjv0HwY+7Kl8XMfwaoHT/yECIA33WZUSoaX6BNFow30UjIJRQHXA8J9+4MWKAAYGBgbr6Tfhoyt3ZlszMDAwMHgugTbk//86Vi3KwMDgOO/+/0EERhvuIwqQ0XD/+v3zhKUVcBRebhRaqk85IrLhPqTBs165x3UM2FE903+agZ/P37xeu+dOUe85i9iDrCbI6BC35UXXjAcNM97tPvH324//gwkM2oY7eeBpK//jOobHdYy/3z/4PxQApOF+u455fZVgaKk+fjTacB8Fo2AUUB0w/KcfONkgysDAELDixX8YuD/PkYGBgYHBevad/zDwa1cWA8PoBUyjYAABGQ13NBBdRawJ+NGwb7h/OtrzuI4BD3qz1Pc/7cHvD5/fbj18r3rKBYeUg2ymB1lNkNF528S7FRPfbDr4++2H/wMNhlPD/f2Owsd1DBD0aq7N/6EA3n18tbRK5HEdw6M6htBSffxotOE+CkbBKKA6YPhPPwC5ORWpRf5xcywDAwMDg3r3xf8IgKFsEIDRhvuIAhQ23G/t2RdeYhBaqk85GvYN96ftgo/rGPChesZ/P7/8pyP4+/PX+32nHjTPvuSZfZjP5iCrCTI6axJ1O7/r1apdP568/D8QYDg13J+08DyuY4CjH4+P/R/04N3HV3frmB7XMTyuY9hbwxNaqo8HjTbcR8EoGAVUB/RvuGftgi2U+bWvgJ2BgYFBtOHkfwSALKhhLz34fxCB0Yb7iAIx1RahpQahpQahpfq4kUFoqUHXgoL/qODskuUTTK3Di/VDS6mAhnfD/cPuisd1DATRq3n2/wcOfDx+8VHPoitBRUfFnQ+ymiCj0zrBN9OaXyzc/O3Oo//0AsOm4f5uc+bjOgZk9HyS5v9BD16siXtcxwBBj+oYokt1Q0v1caHRhvsoGAWjgOqA4T/9AGRhjGj1MUjLHdZuZy/YBxH4////f9gGVuQFNYMAjDbcRyZYnZo1wdR6aUzCk3MX/v////Xtu0MTpkwwtZ5gan334OH/GODNnbsTTK0nmFpHl5mElRlCUalBaIl+aIl+WJlBWJlhWJlhaKk+kWh4N9yftvI9rmMgBv18evr/IACfz994OnXltajKEwpeB1lNkNEJZZ/rcTXPZqz5cvn2f1qC1btnzF7bQgw6fmn3/8EK/n5//6SR/XEdAxr6jno05GADf7+/f9zI+riOAY4O1HCHlurjQqMN91EwCkYB1QHDfzoC6BEysslLL1y/sBRyfgxSu/3Xp1vrMpXZGRiQN6sODjDacB+B4OmFixNMrSeYWr+9/+A/Etjd3DbB1HpTcfl/DHByzvwJptZrMnL/o4LVadkTTK1PzVv4////////h5YahJbqE4OGccP9/faCx3UMRKLnk7X/DzLw9cb953M33EhuOKUReJDVBBkdk3K9Glr6eMLST6eu/B8F2MCbZf6P6xgw0fMJKv8HMXiz1PdxHQMaKipXDS3Vx4pGG+6jYBSMAqoDhv/0BB8PlkJa63DA7jjvzv//////h25dZWBgYGCQbTgJbcsPFjDacB+B4PzylRNMrddmorfCHxw7McHUeraX/38MsKm4fIKp9cm58/+jghOz500wtd5cVvX/////ow33////////pIX7cR0D8ejHgwP/Byv4/vDZy2Xbb2W3nzGMOMhqgoyOCNld9sl72D7vw6Gz//7+/T8K/v//8+Xl43rmx3UMWNG3G5v+D0rw59PTx/XQ1e3I6HQtR2ipPlY02nAfBaNgFFAdMPynL/j18lCrvSg7AwMDAwO7ctTsc2//QwFkbTsDr0XroZe//g8yMNpwH4Hg7NLlE0yt12UX/EcFD0+cmmBqPcvd9z8G2FZVO8HU+ui0mf9RwZEp0yeYWm+vafj///////8T62zjqq3ia60Joj0n1v4fBUMK/Hr59vX6fXdL+s5ZxR9kNUFGhzjNL7qk36+b9m7nsT9fvv0fqeDVfMfHdQy40LNu6f+DEryaZ/+4jgErqihXCS3Vx0SjDfdRMApGAdUBw/8BAd/fPX/3/T8quLJv6YlbnwZdm/3///+jDfcRCZ6cPT/B1HqylcPHp8/+I4F9HT0TTK03FpT+xwBnFi2dYGq9ODzmPypYGBo1wdT67NLl17ZsWxAYPsHUeoKp9TRHtz2tHb9//Pg/CoYp+PPpy9vtR+/XTr3glHaIw+wgqwkyOmedcLes//WG/b9ev/8/YsDvd/ce1zE+rmPAg76cnfN/kIHfb289rmPAhc7VsoeW6mOi0Yb7KBgFo4DqYIAa7kMNjDbcRyZYHp8ywdR6VUrmyxs3//////vHj+Mz50wwtZ5gan1rD5YDS7++fTfD2WOCqfX6vKLXt27/////1c1b63MLJ5haz3T12lHbOMHUGg3N9Q3+/PLl/1Ew3MG/X7/fHzjzsHXOJe/cIwK2B1lNkNEZo8jbuR0vV+z48fjF/2ENXs6xflzHgB89aeX7P8jAy9mWj+sY8KDT25sOnduKhu4/vf5/FIyCUTAKqAoGrOH+/d31Ezt3Lp1QXl5ePmHpzp0HL9zHGIMfPGC04T4ywbNLlyeYWkPQDBevCabWELS7pf0/DnB9644JptYQNMnCboKpNQQd6O6fYGo9wdT60IQpn56/+P3jx50DB+f6Bk1AWvv+////b+8/vEPdCzsKhiX4dPLy477FV4JLjkm6HGQ1QUantIJupjQ+n7/x262H/4cX+PXy8uM6BmLQh73V/wcN+PHg4OM6BvzoWY/M/1EwCkbBKKA9YPhPd/Dr0a5K2DJ3VMAual+569Gv/4MPjDbcRyx49+AhZMgcgqY5uF1cTWDd+Ytr11cmZ0w0s5lgaj3R3GZVaubL6zfWpOdMMLXe19nzHwk8PX9hgqn1BFPrt/cfXNmweZ5/yART6wmm1lPtXXY3t//69v3/KBgB4MvFW0+nr7oeU31CyfsgqwkyOqHofS2m6um0VV8u3vo/9MHLmaaP6xiIQU+aOP4PGvByhvHjOgaC6Ov5Bf9HwSgYBaOAxoDhP33Br5uzHbG12RGA3XH2zV//BxkYbbiPcPDz69fHZ86+f/T4PykAssAGAqbauUwwtX526fJ/VLAkKn6CqfWq1KwJptZoaI5P4OcXL/+PgpEEvt16+Hz+xpupTae0gg6ymiCjYxIuV4KLH/ct/ngCPRUNCfD9zq7HdQzEow87i/8PAvD99vbHdQzEoKcdIv9HwSgYBaOAxoC+DXf4cZDsylETdyAWx/z69Or+hR0ToyCHuDPIlh78+H9QgdGG+yigEECWzby8fuM/KliekDLB1BqCjkyZ/vnV6z+/ft09cGieX/AEU+vNpZX/R8FIBT8evXi5Ysft3M4zxpEHWU2Q0REB20teOQ9b57w/cObf79//hwJ4vzXn1QJn4tGbFcH/BwF43q/0uI6BSPTpaPf/UTAKRsEooCWga8MdeiuqbNgKHGPqv26uCJNlYCD2AqZfn149f/78+fNXn7AbBwff3z1//vw55kE2RIPRhvsooBCsTEqbYGp9fOac/0jg7f0HE0ytIehAz4T/SAC+iubdg4f/R8GIB79ev3+zcf/dsgnnrBMOspqgIHazC06p92umvt1+9M+nL/9HAfXA1/MLH9cxEI+etPD+HwWjYBSMAloChv/0A/fnOTIwMLDHbn77Hzd4uzmWnYGBIWDFi/94APo6eXZR+9wl53Cau6+AgYGBgaFg338ywWjDfRRQCC6uXjfB1HqCqfX5Fav+////////l9dvrkrJnGBqPdHcdoKp9fMrV/+jgiWRcRNMra9v3/l/FIwCJPDny7d3O4/dr59+0SX9EJfFQVYTZHTOMu5OSd/rdft+vcRZIg4qsOXQ4qhKU2JQdJXZf7qD91uyH9cxwBDjz2dn/4+CUTAKRsHAAXo23CHN59jNH//jAx83xzIwMLCXHvyPC+BcJ8/rPhF74x1i82jDfRQMKIDcqzrB1HqSpf1sL/8JptYQBGm4v7px6z8qWBabNMHU+uqmrf9HwSjAAf79+fvh0NmH7fMu++YdEbI/yGqCjM4YhN/Kbn+5bPv3h8/+D1awYFN3aKk+keg/ceDfT+rMPPz9/v5JI/vjOgY4ejFF+/8oGAWjYBQMHKB/w51g65mQsqsTdRkYGBgY2A0zZu+4cP/59RPrJyZo8zIwMDAwMLArl+7DbLtDjMRjJiEw2nAfBVQBJ+fMn2BmM8HUGoLW5xa9e/BwRULqBIxVNG/u3J1gaj3B1Pr1nbv/R8EoIA58On3l8YSlV0NLj0m7HWQ1QUanNAJuJDU8n7vh6437/wcToEXD/fUyv//UAG/XRD2uY0BDPx4e+j8KRsEoGAUDBOjZcL/Yrc7AwGA9+85/fODObGsGBgb17ov/sYJfx6pFGRgwz555e242ZHk8A7tj97m3/1HAaMN9FAwm8PLajQfHjn97//7///////+/uHrtBFPrCabW55ev+vzy1fVtO7ZV1c3xDpxgar0mM/f/KBgFZIEvl28/m7nmelzNSWXfg6wmyOi4vOe1qMqnU1Z8Pn/j/0ADqjfcf7+5+biO4cOusv+Ugb9fXz9uYH5cx4CGnk9S/z8KRsEoGAUDBOjZcP+1r4CdgYFBtmDf2/+4wNt9BbIMDAzsyds+/scKbk43Y2BgEK0+htJs////////v17uKpBlYGBgYEBv1o823EfB4AabSiommFpjouXxKa9v4+/pjoJRQBh8u/PoxcLNN9ObT+uEHGQ1QUZHxZyuBBY96ln48fjF/wMBqN5wh9xy+qSR/T9l4NUCp8d1DFjR99s7/o+CUTAKRsFAAHo23P//Otkgy8DAwMDu2H36JXLDGgJ+vTzdDVm8jrVdDgGQNnjWLuzyvx5tTpZlYGBA6x5ANI023EfBIAbbquommFrD0Sx33wmm1hNMref5h/z5+fP/KBgFVAI/nrx8tWrX7fyus6bRB1lNkNFhPutLHtkPmme933fq70/shSzVAXUb7j+fnXtcxwBB7zam/CcX/H7/4HEd4+M6BqzoWY/s/1EwCkbBKBgIQNeGO3xAnYGBgYFX2i2+vGX2+p07d66f3VIe7ybNywABKG1udABpg+NpgsO3riINu0M04dNFAIw23EcBTcHf379ne/hNMLXeWln78OTpH58+/f///82du3O8AyaYWu9t6/o/CkYBDcDvtx/ebD54r2Liedukg6wmaOiCQ8q9qslvtx7+/eHzf5oB6jbcUW45rWf+Ty54NdfmcR0DHvT1wsL/o2AUjIJRQHdA54b7//+/Hm3OhN6zhA2wK2dufvTrP24AWSqDcwn8////kboHsC7AaMN9FAxu8OzS5Qmm1lPtXP7++fMfCdzed2CCqfX8wLD/o2AU0Bj8/fb93e4TDxpnXHTLPMxteZDVBBmdNY+9U9Tzeu2en8/f/KcqoGLD/futbY/rGJDR27Ux/0kHv15dfVzHgB8965b8P0zBm1WjBc4oGAWDF9C94f7/////v15eWTch3k0Z5SB2Zbf4CeuuvMTXaP///z98c2rsOnxKEcPuyqX73o423EfBIAcXVq6ZYGq9Oi37Pyr49OLFBFPrCabWkDH4UTAK6AT+/ftw5PyjzvmX/QuOijgcZDVBRqf1Qm9ltr5YsvX7/af/KQZUbLhjueW0nuk/6eDlLLPHdQwE0ZfTM/4PO/Dn09PH9UzvNqb+HwWjYBQMSjAgDXeKwK+L3boMDAwMDLJurduv47409e2+UujIvmxYmDUDA8Now30UDAj4+eXLnrbOZTGJE81tVySk7u/u+/fv339U8ODYiQmm1rM9/f+jgkenzkwwtZ7p6v1/FIyCgQOfzlx7MmnZ1fDy47LuB1lNkNFJNb8bCXXPZq/7eu3ef7IAtRruX88veFzHgIleLXT5Twr4fm/P4zoGYtCwvCf11Tz7x3UMlKwyGgWjYBTQFAy9hvt/xHA6AwMDA76rmhBtdwgYbbiPAnqDB8dOzPb0m2BqjYwWBEe8uHrtPxL49v7DBFPrCabWF1at+Y8E1uUUTDC13pBX/H8UjAIc4Pfv37tOrCES3X546T9l4OvVu89mr72RUHdS1e8gqwkyOi7rfi2i/Mmk5Z/PXv9PNKBWw/1Zr+zjOgYsqJ7xz8cn/4kGLyZrPa5jIBJ92F3xfxiB329vPa5jgKC362L/j4JRMAoGH6Bnw/37u+dEAdyj6HDw/d72SgteBgYGQufCf7+6JAreeB9tuI8C+oJ//xYGR0wwtZ4fEHpt87ZXN29d2bBplpvPBFPrlUlp/1HBiVlzJ5haTzC1PtDTf3vv/svrNq5MSp9gaj3B1PrVjZv/R8EowAG+//waWqpPJFq/b+5/6oHv9568WLzlVmbrab3Qg6wmyOioiMNl/4JHXQs+Hr3wHy+gSsP905Gux3UMuNCreXb/iQPfbmx6XMdAPHrSxP5/GAHISZpQRNYqo1EwCkYBrQHDf/oB6EpzogFk3fv2e9//Ywe/Pj26cHDnkVsf/+MHv16eXpLrpizKPtpwHwV0BWcXL5tgaj3Tzfv7B0Qiff/o8SQLuwmm1te2bPuPCnY1tkwwtUZGE81tL6xa+38UjALcYAAb7sjg57PXr9bsvlPYc9Y85iCrCTI6zGN50S3zQePMd3tO/v3+4z8qoErD/Wmb4OM6Bjzo55NT/0cBXvDr+fnHdQzI6M0y3/+jYBSMgkEGBnPDHQZ43efd/PV/QMFow30UkAEgR7MfnTbzPyrY09oxwdR6f3fffwxwY8eu7dX1C4IjViSm7e/qe//o8f9RMArwgkHScEcGv99/ervl0L2qyeftkw+ymqCh83ZJ9yomvdl88Pe7j/+p0XD/sKv8cR0DfvR8stb/UYAXoJykCUH1jP9+ff0/CkbBKBhMgJ4N9+/vnj+/sTySn4GBgYFd2ad16c4T159DwPUTO5dOiIcufjEsWbFz586d62e3xNtDD56RLT348f8AgtGG+yggA6xMzphgan1pzfr/qODE7HkTTK03l1b+HwWjgGIwCBvuyODvj5/v95580DzrkkfWYV7rg6wmyOisafTRuPzZWZG9fWnt83Lwo+6Fhf+xgSetfI/rGAiiH/cP/B8FOMD329sf1zFgolfz7P+PglEwCgYTYPhPT3BnniM7AwO7YcOhl7/+YwHfr053ZGdgYHecB1u3/uvlLsiZ7PiXstMajDbcRwEZ4EDPhAmm1rub2/+jgk1F5RNMrU/OXfB/FIwCisEgb7ijgY/HLj7qXnglsPComNNBVhNkdFon5GZ6y4tFm7/dJWGi6f22/Md1DMSg5xPV/o+C///frU/8jwGeT9J4XMeAFf18eub/KBgFo2DQAIb/9AO/9hWwMzAweC7Bt8H/7eZYdgYG0epj8Jb9x23J7AwMDI7z7v8nDxxtUYeAlqP/yQSjDfdRQAa4s//gBFPrCabWl9Zt+A8DZ5csn2BqPcHU+snZ8/9HwSigGAythjsy+HzuxpMpK65FVhyX9zzIaoKMTqr4Xo+rfTZzzZcrd/7jBU+aOB7XMRCJvt/a+n9kgz9fXj6uZ0Y7DOfbtbWP6xhwoedTtP+PglEwCgYNYPhPP3CyQZSBgSF288f/+MDHzbEMDAyiDSf/wwBEBM+xjwQAfG396ObUUUBvsLe9e4Kp9QRT68URsTtqGxeGRk0wtZ6AbeH7KBgF5IGh23BHBl+v338+Z/2NpPpT6gEHWU2Q0TFpt6thZU8mLv10+up/VPBuU9rjBhbi0fOJ6v9HNng13/FxHcOTRvb/SOBZr9zjOgY86McDcqvfUTAKRgG1AT0b7pAGNMHWM6YyTBGSAEQ7AwUmjDbchwl4ePLU7pb2pdHxKxJS93X2vrx+8z/twdFpMyeYWiOj0wsW/x8Fo4BKYHg03JHB9wfPXi7ddiu77YxB+EFWE2R0RMj+sm/+w455Hw6f+//33/9RQAr4/e7u4zrGx3UMj+sY3m1K/f///////z8d7X5cx4AfjXZ4RsEoGDyAng33i93qDAwM6u3n4KtgsAGIKuQR9xcrAhgoGXGnAhhtuA8DcGjilAmm1miIPm3odw8e3tix6/Ckabd27/3wBN9SsVEwCkgFw6/hjgx+vnj7et3eO8V95yzjDrKaIKNDXBYXXdLv109/t+v4n6/f/48CQuDlHOvHdQxQBLsbleBJmhD0/fb2/6NgFIyCQQDo2XCHrnFnd5x9E1fTHXYpKnvBPriSJ0s8GRgYGAJWvPg/YADScP/7798oGqLo0vpNE0ytJ5hab6uuv7pl+6X1G9flFE4wtZ5gan3v2PG///6NolE0RNHXH19CS/WJROv2zfn77x+t0c0Hl5Ztn0wk+vT1w99//4hBvz58frPt8L3qKecdUw+ymx5kNUFG56wT7pRNeLVx/4/X7//++zeK0NCPFxcf1zEgozdr494TcZImBD3rkfn7798oGkWjiAz0n6qAng33///vzLZmYGBgYGA3zFhy/BHKBam/Pj26sK7UkJ2BgYGBQbf74q////9/f3d9/0TIxafsAStoPk7pjhtISUlpamu/+vR5FA1RNMXWeYKp9a6+ia8+fYajDZW1E0yt54dGvfr0eRSNoiGKnrx5FVqqTyRavH3aq0+faY3WHVgaWqpPJLr+6NarT59JRS/fvn+w9dC1uqln3DIP89scZDVBRicNwy9ltNxZsPHpjXuvPn0eRa8+fX46zehxHQMKqmd63ML7uI6BSPTi+MxXnz6PolE0ikhF1G2507fh/v/XzRVhsgxwwC6qrK6urq4uzcuAALJhKyAj8r92ZTFAAL5BeuoBV9xAUlJytOE+dNHty1cnmFpPMLV+8e7Dq0+f4ejutRsTTK0nmFo/f/P21afPo2gUDUU0MhvuaOjhvpPX2+ac888/Iu58kNUEGR3XCLyYUHtrxqonF66/+vSZclQ1JSG8zBAThZUZoKHoKvMpq5ow0e5TW199+kxP9OLyxsd1DNgQ4+N6JiLRk07xV58+j6JRNIpIRUO64f7///9fL0/PTtBGbqkjAK92wuzTL2FN9JvTzaBi597+H1gwulRmSKPb+w9OMLWeHxD6998/NDTB1HqCqfXLW7f//vs3ikbRUESDcKnMruOrQ0v1iUQv3j7+++8fFdGz46d3FpfscYvaL+VwkNUEGe2XdtzjHr2zpHTbgv7NhxZvPrT49uOrf//9IwmVT4wMLdWnBBX3hvz994+e6Plkzcd1DFhQPeOfn1///vs3ikbRKKId+k9VQP+GOwT8+nTrxPrZLeUw0DJ7/Ynr777/Rwbf3z1/jiY0UADScP8/CoYm+Pzy5QRT6wmm1p9fvfqPBJ5fuTrB1HqSpf3/UTAKhiwYhJtTd51YE1qqTyR6+ZbKyyCfvLoXWqoPQakpBvVehjMMjDaKGh9kNUFGO/hM5msYHSiq+HTy8n9SAFUa7v/pCL5dX/+4jgEXejXP4f8oGAWjYOgAhv/0A79+/fo/RMFow32og6XR8RNMrbeUV//9/fv/////////+ubtisTUCabWGwtL/4+CUTBkwe/fv1++fUIk+vLt43/ag8HTcEdGSRkG1X6GU4yN1kmiN+KP8Fuftw142Db3w8Ez/37/+Y8XDLmG+7Ne+cd1DLgR489nZ/+PglEwCoYIoGfDfV8Br3Zg5ez9tz5R0ID//u452YD84fvRhvtQB0/Onp9gaj3B1Hp+QOiuxtbtNQ3THd0nmFpPsrQfPZ9xFIwC6oLB2XBHRnE5+uVBhhPNjQ4a+B9kNUFGhzjMLjil3a+d9nbH0T+fv/7HAEOr4f75WO/jOgb86MXo3aijYBQMHUDfhjsDFLCL2ufO3nHhKRkNaYQhpIPRC5hGNLi1Z99sT/8JptZwtDgi9tHp0aGmUTAKqAwGf8Mdjvaf3vDlyrabMfwXHaTPGrse4jQ/yGqCjM5Zxd8t7X+9ft+vV+/+////f6g13J+2Cz2uYyCIfjw49H8UjIJRMBQAPRvuT47PrgxE2ZbKLmofP2EdSQ34J/tgZ0aSDkYb7iMd/Pzy5cb2nQf7Jx2ZMv3W3v3/R8EoGAU0AEOr4f5istbjOobHdQxPmtj//fnz4eDZh+1zL/vkHRG0O8hqgozOGEbczumYmOCTlGEQWqpPNqJbw/3DnsrHdQzEoNG7UUfBKBgqgJ4Ndyj4/vTCugnx9qLIzW9e7cDWpSeIXEPz69HmZFkGBgYGBv7I5TeeEw9Gl8qMglEwCkYBzcEQarif3FL9uI4Bjt5tSv+PBD6duvK4f8nV0NJjUq4HWU2Q0SZR4xn6RvVehmkpJDfi6dZwf9rK/7iOgUj0/faO/6NgFIyCQQ8GoOEOA9/fXd+PPgTPK+2WS9Qi+Lf7CmQZGBgYZEsP0mOv1WjDfRSMglEwCogEQ6jhfq9D7HEdAwLVM//HAb5cuv10xurrsTU7xCwOspogo62CxnO0jZrdDDMTiGrE06fh/n5H0eM6BuLRsx7Z/6NgFIyCQQ8GsOEOA78+PbqwbkK8mzQvAxywE14E/+tity4DAwOD7sSr/2kORhvuo2BYglu79x6eNPVQ/6QbO3b9/v7j/ygYBdQAQ6XhPqlS6nEdAxp6uz7hP15QPjEyNdWgzttwuoHRBjH0A2p28posUDdqczLMjcHZiKdPw30UjIJRMCwBw/9BA359un1oRowyAzJgF7WvXHflJfYBeGjTnT15G80H3Ucb7qNgmIEHx08uDImcYGoNR3N9g+4cGN2gNgqoAIZKw/1aLcvjOgZ0VM/0Hy8oR92cmphpUOVvONnEaK0UeiN+L6fJEhXjLjvDwgiD8CL90FIoGm24j4JRMArIBgPfcP/16daJpa2Bhmhr3t3cECLsypmbH2FrvH/cVyDKwMAQsOLFf9qC0Yb7KBii4M2du/8xwNt79yeYWk8wtZ7p5r2jrmlXY8tc36AJptYTTK2fX77yfxSMAsrAkGi4T6+UfFzHgBW9Web/HzdAa7gjo9hc/bIgwwkWRivl0BvxB1lNlisY91kZlYYYlLcG/R8Fo2AUjAKywMA13L8/vbBjdi7KAhkGdlHDwFbEKTPfn15YBztDRrf7IramO+RYd/J3nRIJRhvuo2BogTd37q7JzJ1s7TjB1HqylcPqtKxXN2/9h4F12fkTTK1Xp2V///Dh//////////39x8bC0gmm1ssTUv6PglFAGdhzcm1oqT6R6PX7Z/+pCohsuN+qY35cx4Ad1TP++/PjPw6Ap+GOjCIL9YvDDHpsjJYqGe/nMDnIioLOWcTeKe59vXbvzxdv/o+CUTAKRgHRgOE/nQG2LakM7HiOhYSOqjOINpz8P2BgtOE+CoYQuLV77wRTa0x0Y/vO//////v7d4Kp9QRT61c3b/9HAh+ePJ1gaj3B1Prb+w//R8EoGLKAmIb7oirRx3UMeNCr+Y7/cQAiG+7IKKxEPz/KoMPBcKGa8W5uk4OsKOi0ftitrLaXS7d9v//0/ygYBaNgFOAFDP/pB64sQlsQw8Auap87e/91/APmHzfHMjAwUHIKO+VgtOE+CoYK+PH582wPvwmm1msycl9cvfbv37+X166vyy6YYGo9w8Xr27v3zy9fmWBqPcXG6T8GgFxQ9eD4if+jYBQMWUBMw/1OHdPjOga8iPHn8/P/sYFD57ZOXlGNiaKrzEJLDYhBbSWBTyYuuxpedlzG/SCrCTI6qeZ/I7H+2Zz1X6/f+z8KRsEoGAUYgJ4Nd8Slp0Sf+vj/////d2ZbMzAw8Potv/9/wMBow30UDBVwfvmqCabW8wPD/v758x8JLAqLnmBqfWbhkh+fPk0wtZ5gav3pxcv/SODn168TTK0nmFp/fErlpQujYBTQExBsuC+vEnlcx0AQvZii85/24MuVO89mrb0eX3tS1e8gqwkyOi7ncS2y4snkFZ/PXf8/lMHXS0v/j4JRMAqoBOjbcOfVDqwkusEOA9+/f/9PGjjaoq6urt5y9D/VwGjDfRQMFbCjrnGCqfXhSdP+o4Jj02dNMLXeWln7////BYFhE0ytdze3/0cCB/smTTC1nuHi+X8UjIKhDL7//P7x81tc6MObe48b2R/XMRCDfjw88p+O4Pvdxy8Wb7mV0XJaN/QgqwkyOirqeCWg8FH3wo9HL/wfUuDDzpInrbz/R8EoGAVUAgz/6Qd+/SKpwU4+gAztU3NpzWjDfRQMFbClrGqCqfXxWXP/o4JT8xdNMLXeWFj6////uwcPTzC1nmBqvbGg9MrGzVc3b91aUTPB1HqCqfX1bTv+j4JRMHzBmxWBj+sYiETPJ2n8HyDw89mrV6t33ynsPmsWc5DVBBkd5rG66J71oGnW+70n/37/+X9wgyctvI/rGD7sqfw/CkbBKKAGoGfD/cn5nTt3nrj3/T9+QPl4+WjDfRSMXABpoK9Jz/mPCtblFExAatCfnDN/gqk1Gjo6dcb/UTAKhjH4++dJE8fjBhbi0fc7u/4PNPj97tObLYfuVU46b598kNUEDZ23T75XOenNlkO/33/6P8jA+605j+sYHtcxPGlk+z8KRsEooAagZ8Md0p7GeSg7DECUUdLsptwEdDDacB8FQwW8uXtvgqn1BFPrfZ29//78/f///79//w70TJhgaj3B1PrVjZv/YeD5lav7OrqXx6csi03c09r5+My5/6NgFIyCwQ3+fv/5fs/JB00zL7pnHea1OshqgozOmsXcKex+tXr3z2ev/w8C8KQJsSrp3ab0/6NgFIwCigH9G+4MDAwM7IYNh3BchwpTRkmzm3IT0MFow30UDCFwdumKCabWELQ0OmGCqTUEnV645P8oGAWjYBiBj0cvPOpecCWg8Kio40FWE2R0Wjf0VkbLi8Vbvt998n8gwNs10Y/rGBConvn/KBgFo4BiMDANdwYGBgZe99lXv//HAiDKKGl2U24COhhtuI+CoQVu7d47y913gqk1BM109bqxY9f/UTAKRsHwBZ/PXX8yefm1yIrjch4HWU2Q0UlVv+vxtc9mrf169e5/uoC/394+aWR9XMeAjN6uj/8/CkbBKKAM0L/hXrDu3ER3XgYGBgYcq2agyihodlNuAjoYbbiPgqEI3t67f23r9jd37/0fBaNgFIwk8PXavWdz1t9IrD+p7n+Q1QQZHZdxvxpe9mTisk9nrv2nGXi9xPtxHQM6qmf6PwpGwSigDAxAw33f//+/Hm3OVIZcxYS5agahjFxA+fZWdDDacB8Fo2AUjIJRMBTB9/tPXy7ddiur7Yx+2EFWE2R0VNj+sl/+o875Hw6f///v338qgT8fHj2ux37F1ZvVEf8HGny7sfH77e3/R8EoGJpgYBru///////96pIwWQYGBgYGBl73iefe/ocBVGWDA4w23EfBKBgFo2AUDHXw88Wb12v33inuPWcRe5DVBBkd4rK46JrxoGHGu90n/n79/p8C8Gqu7eM6BuyonvHf7x//BxQ865N/1iPzfxSMgqEJBq7h/v///18vDzUYQgbeGWTDllyFlBQYygYBGG24j4JRMApGwSgYTuD3h89vtx25Vz3lgmPKQTbTg6wmyOi8beLd8olvNh389fbDf1LAr9fXH9cx4EGv5jv+Hzjw+Vj/4zqGx3UMX88v+D8KRsEQBAz/6QewtsiRV80ol+56+Qu7sgEGow33UTAKRsEoGAXDFfz7+ev9/tMPWmZf8sw5zG9zkNUEGZ01ibqd3/Vq5a4fT17+JwRezjJ/XMeAFzH+fH7+/wCBp+1Cj+sYHtcxPO0Q+T8KRsEQBAPecP//////t/Dtqgy87rNXNakzYFU2gGC04T4KRsEoGAWjYISAj8cvPe5ddCWo+Ji480FWE2R0Sjv4Zlrzi4Wbv91+9B8DfL+373EdA0H0YorO/4EAnw61Pa5jgKMvp6b/HwWjYKiBQdFw//8fZdUMAwMDLmUDBUYb7qNgFIwCZNAwIzmszIAYlNzg8H8UjIIhCz5fuPl06spr0VUnFLwOspogoxPKPtdja57OWP3l8u3/////////xRTtx3UMxKAfD4/8pzt42sr/uI4Bjp608P4fBaNgqAGG//QD+Bru///////r5grYdlV8ygYCjDbcR8EoGAXIoHJSdGipPjEovtbm/ygYBcMCfL3x4Pm8DTeSG09pBh5kNUFGx6RcL3nGXvEQv5PC/biOgSB6PknjP33B+x1Fj+sY0NCH3eX/R8EoGFKAng33K6vLy8vLV1/5jwfAV82MNtxHwSgYBYMXjDbcR8EIBz8ePn+5fPvtnI4zhhEHWU2Q0WFuw9PqqpddJG8n8D6uZXxcx4AVfb+z6z8dwZMWnsd1DGjoSSP7/1EwCoYUoGfDfQiD0Yb7KBgFowAZjDbcR8EogINfr969WLzsZkruGSP0+54OcZidswq5nVP0Ym7/l/Nrv9/cDEc/Hhz8Ty/wfkvW4zoGrOjdxtT/o2AUDB0wgA337++eP79+YufOnTtPXH/+/NWnX/8HLxhtuI+CUTAKkMFow30UjAKs4M+nr293HL1fO+2Cc9ohDvODrCbI6Jx1wt3S/tcb9v96/e4/vcDf7++fNLI/rmPAjuqZ/4+CUTB0wEA03L/f294aqM3LgA54tQNbt9/7/n8QgtGG+ygYBaMAGYw23EfBKCAI/v3+8+HgmYdtcy955x4RtDvIaoKMzhhF3s7teLl8x49Hz//TErxZHvC4jgEPersu7v8oGAVDBNC94f52X6ky8ukx6IBduXTf2/+DDYw23EfBKBgFyGC04T4KRgGp4NPJy4/7l1wNKTkm6XqQ1QQZndIMvJnS+Hz+xm83H/ynKvjz9dXjBubHdQz4UD3T/1EwCoYIYPhPT/Dr5mxHSKudXdmncvb6E9efP3/+/Pnz+xcOrp9d6QNt0bM7zr45yNbNjDbcR8EoGAXIYLThPgpGASXgy6VbT6evvh5bfULJ5yCrCTI6oeh1Lbrq6bRVXy7e/E8xeDXf6XEdA0H0Zpnf/1EwCoYCoGvD/eO2ZHYGBgZ2yA2pmODXy12Q4Xj25G0f/w8mMNpwHwWjYBQgg9GG+ygYBdQC3249fL5g083UplPawQdZTZDRMQnnK8HFj3sXfzxx6T/p4Pf7+4/rcB5rg4LqGf/9+vZ/FIyCQQ/o2XB/sSKAgYGBwXr2nf+4wZ3Z1gwMDAyxmwdVy3204T4KaAR+fv36+PTZR6fP/Pz85f8oGDpgtOE+CkYBLcCPxy9erdx5O6/zrHHUQVYTZHSE3+aSV87D1jnv95/+9+v3fyLAqzk2j+sYiESv5jn8HwWjYNADhv/0AwdL2RkYGAJWvPiPD0Ca96INJ/8PIjDacB8FVAe/vn3b3dw+wdQajnY2tIw234cKGG24j4JRQGvw6837NxsP3C2fcN4m8SCrCQpiN73gmHq/Zurb7Uf+fMRebP758OhJI/vjBhYi0ZNG9p/PzvwfBaNgcAN6NtwJ3ZwKBUQqoysYbbiPAuqCv3/+zA8Mm2BqPcHUeoaL10xX7wmm1hNMref6Bv3+/uP/KBj0YLThPgpGAT3Bny/f3u06fr9++kXXjENcFgdZTZDROcu4O8V9r9ft/fly8J1uMQpGAVUBPRvuJxtEGUZH3EfBKPj///++zp4JptazPPzuHjj0////////3ztydK5v0ART6z2tHf9HwaAHow33UTAKBgz8/fvh8LmHHfMu++YfEbY/yGqCjM4YhN/Kbn+5dNv3B8/+j4JRMOwAPRvuHzfHMjAwMFhPv4ltZyoE/Lo53ZqBgXDzns5gtOE+CqgLJls5TDC1vrUHZWLpzoFDE0ytJ5ha/x8Fgx6MNtxHwSgYJODT6atPJi69GlZ2XNrtIKsJMjqlHnAjqeH53PVfr9//PwpGwbAA9Gy4w06VYZAtwHmqTIEsA8PoqTKjYHiD17fvTMDRQJ9kaT/B1Prltev/R8HgBkcv7Ji+qp4YtOXQov+jYBQMbvDt2tr/wwJ8uXLn2cw11+NrT6r4HmQ1QUbH5T2vRVY+mbLi8/kb/0fBKBiygK4N9///78yDneMuah8/YenOgxfuQ45x37l0Qry9KFRu9Bz3UTCswftHjyeYWk8wtf7948d/JPDv798JptYTTK3f3L33fyDAhydP7x0++vXN2//////////Lq9d3Dx7++Oz5f0Lg9a3bj8+e+/Xt23+YIe8fP3585tzr23f+j4JRMAoGN3i/vfBZt+T/YQe+3Xn8YtHmm+ktp3VDDrKaIKOjYk5XAosedS/8eOzi/1EwCoYUoHPD/f//t8caDCHtc+yA3bDhGLTZMIjAaMN9FFAXTHN0m2BqfX7F6v9I4NKa9RNMrafYOP2nO7ixY9dMV68JptYQND8wbJ5f8ARTawia7eWPtqoHDk7OmQ9Z9gNByGwImmztcGrewv+jYBSMgsEKnrTwPK5j+HJ6+v/hC34+ffVq9a47Bd1nTaMPspogo8O81pc8sh40z3q/99TfHz//j4JRMLgB3Rvu//////Xy9JJcN2leBlTAK+2Wu+T0y0E21g4Bow33UUBdcHLO/Amm1hNMrc8uWf7lzZuvb9+eX75ygqn1BFPrYzNm/6cvOD5zzgRTawiCbJCFo3l+IRNMrSHo9ILF/1HBhoKSCabWEDTFxmmCqTUutKm4/P8oGAWjYPCBd5szH9cxPK5jeNLC+39kgN/vPr7ZfPBe5aTzdkkHWU3Q0Hn75HtVk99uPfz7/af/o2AUDD4wEA13GPj16dVzGHj1aVA22GFgtOE+CqgO1mTkTjC1RkOrU7P+0xc8vXBpgqn1BFPrAz0Tfn39+vnV66l2LhNMrSeYWs909f7+8dPPz1/2tndPMLWeYGr98sbN/zBwfsWqCabWE0ytz69Y9ezS5Qmm1hNMrSFr9Kc5uN05cGhLefUEU2u4aRdXr/s/CkbBKBhM4O/3908a2R7XMUDQh90V/0cY+Pvtx7s9Jx80zrzonnmYx+ogqwkyOmsec6eo59WaPT+fv/4/CkbB4AD0bLj/OlYtq+zTuvnWx/9DDYw23EcBLcDpBYvhI9xzfQJPzl3wn+5gf3ffBFPrTSXQCvvCyjUTTK2XRMUvCI6YYGp9ddOW////////f11O4QRT6yOTp/+HgaXRCRNMrU/Omf////8DvRMmmFqvyciZYGoNQe8fPf7////CkMgJptabisommFovj0/5PwpGwSgYTODNMv/HdQxw9KSJ/f/IBh+OXnjUteCyf8FRUceDrCbI6LRe6K3M1heLt36/9+T/KBgFAwcY/tMPXOxWZ2Bg0J149f+QA6MN91FAO/Dt3buvb9/9HyCwMiltgqn15XUb/v///////12NLRNMrQ/1T97b3jXB1HpfZ+////////9/bumKCabWa7Pz/////////79//kwwtZ5gav323v3///+vTM6YYGq9q7F1gqn1ZGvHCabWN3fu/v///76Ongmm1lsrayaYWk8ws/k/CkbBKBg04M+XF4/rmR/XMSCjd5vS/o+C////////+ez1J5OXX4soPy7rcZDVBBmdVPW7kVD3bPa6r1fv/h8Fo4C+gOE//cBgvBKVSDDacB8FwxWsTs2aYGp9YdXa////////f3dLxwRT6wM9/bub2yeYWh/onfD///////+fWbR0gqn1+ryi/zAwwdR6gqn161t3/v//vzo9e4KpNaTRD9mfenvv/v///+9p7Zhgar2tqnaCqfUkS/v/o2AUjIJBA17Nd3xcx4CO6pn/jwIM8PXavWez191IrDup5n+Q1QQZHZd1vxpR/mTS8s9nr/0fBaOA9oDhP/3AndnWDAwMut0Xf/0famC04T4Khis42Ddpgqn12qz8/////////9La9RNMref5h8729Jtgan196/b///////9/ZXL6BFPrY9Nn/YeB5XHJE0ytD0+e9v///0MTJk8wtV6ZmDbB1BqCPj599uvrtzneARNMrdfnFU0wtV6ZlP5/FIyCUTA4wO93dx/XMT6uY8BEb9fF/R8FuMH3+09fLNl6K7P1tH7YQVYTZHRUxOGyf8GjzgUfjpz/PwpGAW0APRvu/z+ebDVkZ2CQTV53a3DvRcUAow33UTBcwetbtyeYWk8wtd5R1/Th6dOfn7/MdPWeYGo9wdR6rk/g39+/3z9+sq2ydoKp9UQzm/cPH/2HgSsbt0wwtZ5gan181tzHZ89PMLWeYGo9ycJugqn1VDuXC6vWrM3InWBqPd3JfYKp9QRT62uwPsAoGAWjYMDByznWj+sYsKN6pv+jgDjw8/mb12v33CnqPWcee5DVBBkd5ra86Jb5oHHmu90n/n778X8UjAIqAXo23J+c37lzQ7efIAMDAwMDA6+0urq6W3w5DrD6yv9BBEYb7qNgGAPIMhgIgqxQh6Mpts4TTK0h6MKqNf9Rwbaqugmm1sSgHbWN/0fBKBgFgwP8enHpcR0DHvRmVfj/UUAi+P3h89uth+9VT7ngkHKQzeQgKwo6b5t0r2Lim80Hf7/9+H8UjAIKAD0b7pA17kSCwbUUfrThPgqGN3hw7ATkBBgIWh6fvCwmcYKpNQQtjoh9dPrMf2zg4qq1c7wCJphaTzC1nmztMN0ROr4OR3O8Ay+tXf9/FIyCUTBowMuZpo/rGPChesZ/v779HwXkgr8/fr3fd+pB8+xLntmH+awPspogo7Om0bcLul6t2vXj6av/o2AUkAjo2XA/2qJOPGg5+n8QgdGG+ygYCeDX168vrlz79/fv//////////f37+eXr/z+/v0/IfDlzRvI8TL/////9e3b8ytX//39+/buva9v3v4fBaNgFAwm8P3Orsd1DATRq3kO/0cBlcDH4xcf9Sy6Elh0VNzpIKsJMjqtE3IzvfnFws3f7jz+PwpGARGA4f8oIAKMNtxHwSgYBaNgFAwD8Hyy5uM6BiIQ489nZ/+PAmqDz+dvPJ268lpU5QkFr4OsJsjopLLv9biaZzPXfLl85/8oGAU4wGjDnSgw2nAfBaNgFIyCUTDUwbfr6x/XMRCJXkzR+T8KaAm+3rj/fO6GG8kNpzQCD7KaIKNj0m5Xw0ofT1j66fTV/6NgFCCBgWu4f3/3/PqJnTt37tx5/sl/CPj48ukgPW1mtOE+CkbBKBgFo2Cog2e98o/rGIhHPx4c+j8K6AK+P3z2ctn2W9ntZwwjDrKaIKMjQvaXffMets/7cOjsPylVkoYAAH0ASURBVNhSxlEwYsEANNx/vTy9JNdelJ0BBuD7UO/MtmZXjlpxc/A13kcb7qNgFIyCEQI27J+X0+FNDCqfGPl/FAwd8Pv19W/X15OEvt/d838U0B38evn29fp9d0v6zlnGH2Q1QUaHOC0uuqTfr5/+buexP19GNxCPREDvhvuvm/PceRlQAbzhfrJBlIGBgYE9YAVsDH6wgNGG+ygYBaNghICOebmhpfrEoMhKk/+jYBSMAlqCP5++vN1+9H7t1AtOaYc4zA6ymiCj8zYJd8smvNm4/9fr9/9HwcgA9G24fzxYKsvAwMDAwGuRO3vHhfvrUxkYkBrub89NhLTqRQv2Da6DTkcb7qNgFIyCEQJGG+6jYBQMTvDv1+/3B848bJ1zyTv3iIDtQVYTZHTGOPJ2bufLFTt+PH7xfxQMX0DXhvuTJZ4MDAwMsgX73v7///8/7GR3eMP9////v042yDIwMLAX7BtU62VGG+6jYBSMghECRhvuo2AUDAnw8eTlx32LrwSXHJN0OchqgoxOaQXdTG16Pn/jt1sP/4+C4QXo2XC/P8+RAXU0HbPh/v//zelmDAwM6t0X/w8iMNpwHwWjYBSMEDDacB8Fo2DIgS8Xbz2dvup6TPUJJe+DrCbI6ISS9/WY6qfTV325eOv/KBj6gJ4Nd0gzPWvXr/8wABFBbbhjF8QBvr97h3o5zK+Xp2cnGMJ3vvJKu+UuOf0SYSOZYLThPgpGwSgYIWC04T4KRgEe8PPRsf+DG3y79fD5/I03U5tOaQUdZDVBRsckXa6ElDzuW/zp5OX/o2BoAvo33JFb5Jgi/3EIooFfj3ZVuknzMjAwMDAw8Frkrr36/f9/rDtfGRgYGHjdZ1/9/p8SMNpwHwWjYBSMEDDacB8FowAPeD5J4//QAT8ev3i5Ysft3M4zxpEHWU2Q0REB20veuQ9b57w/cObf79//R8EQAfRsuF/sVmdAXQSDrY2OqQod/Lo525GdARWwO86++WBFAESYVzswq7y8vLw8K1Ab2pBnj90MXVdPFhhtuI+CUTAKRggYbbiPglGAC3zYWfq4juHL6en/hyD49fr9m43775ZNOG+dcJDVBBkdYje74JR2v3bq2+1H/3z68n8UDGJAz4b7r30F7AwMDKJZu2BtaIyGO6xNjm9z6tWJugwMDAwMvBa5s9fv3Lm01UeZnYGBgV1ZWZaBgYHdsfsczPj//////35vXbIsAwMDytp6ksFow30UjIJRMELAaMN9FIwCXOBJC+/jOoYnLbz/hzj48+Xbu53H7tdPv+iSfojL4iCrCTI6ZxV/t6Tv9fp9v16+/T8KBhmgZ8P9/6+L3boMDAwM7Ial2+99/4/WcP/18vTsMFkGBgYGBtmGk7ja7b+OVYsyMCC3/v///3VzujUDBHguefIfHcCsjd1Mdst9tOE+CkbBKBghYLThPgpGAVbwfmvu4zoGCPqwu+L/cAH//vz9cOjsw/Z5l33zjgjZH2Q1QUZnDCNuZbe/XLb9+8Nn/0fBIAB0bbj//w8bUWdgYGAXVVaX5mVgYGDQDS7PCjSDsBkYGBhkSw/ibmFDVtJYz77zHxl83JbMzsDAwBCw4sV/LODObGsGBgbRhpP/yQSjDfdRMApGwQgBow33UTAKsIInTRyP6xgg6EkT+/9hCj6dvvJ4wtKroaXHpN0Ospogo1MagTeSG57P3fD1xv3/o2CAAJ0b7v///397Djaujg3wWrQeQwylYwEog/QI8HFzLAMDFnEowKGLaDDacB8Fo2AUjBAw2nAfBaMAE7xdG/24jgEZvduU9n+4gy+Xbz+bueZ6XM1JFd+DrCbI6ISC17WoyqdTV34+f+P/KKAjoH/D/f////9/vbyybkJWoJky7NxGXml1t/iW2fvvff9PABwsZWfANrIOaZo7zrv/Hwt4sSKAgYGBvfTgfzLBaMN9FIyCUTBCwGjDfRSMAjTw99vbJ42sj+sYUFA9839ywbcbm/4PNfDtzqMXCzffTG8+rRNykNUEGR0Vd74SVPSoZ9HH4xf/jwIag4FpuFMAIG1wBuvpN7Gsgsc41h0CICtlsLT2iQajDfdRMApGwQgBow33UTAK0MDrpT6P6xgw0dt1cf9JB5+OdD9p5f0/lMGPp69erdp1O7/rrGn0QVYTZHSYz/qSZ/aD5tnv9536+xNLS20UUAjo2XB/cn7nzp07zz/5jx88OT67JSuw/+h/7AC2nJ3XfeLxp9ia6agAfqoMe8AKQjbjBqMN91EwCkbBCAFr9szKbHUnBhX3hvwfBaNguIM/Hx89rmd6XMeABdUz/ScdPG0TfFzH8GFP1f9hAX6//fhm88F7FRPP2yYdZDVBQWwmFxxS7lVPebv18O8Pn/+PAmoAejbcIatZCC40J6js7b4CWQYo4JW2nXzhP1bwZN+EeHtRdgYGBgYGBnbH2Td//ScbjDbcR8EoGAWjYBSMghEIXs21e1zHgAu9Web3nxTwYVf54zqGx8N0e+vfbz/e7T7xoHHGRbfMwzyWB1lNkNE5i9g7Rb2v1+75+fzN/1FALmD4Tz9AsEX+/////9/PtesyMBBQ9uvRrkoLXgYGBgacy9r//7853YwBAtgNG/DveCUIRhvuo2AUjIJRMApGwUgDv15ff1zHgA/VM/77/e0/0eBJK9/jOgYIerc5/f8wBv/+fzhy/lHn/Mv+BUdFHA6ymiCj0/phtzLbXizZ+v3+0/+jgBRA04b7282x0AFvkgH6eY9YwPenF9ZNyIqfefY/dvBxcyy7qGFg67orLykYa4eA0Yb7KBgFo2AUjIJRMNLAy1kWj+sY8KNX8x3+Ewfeb8t/XMeAQBRsbx1y4NPZa08mLb8aXn5c1v0gqwkyOqnmfyOx/tnsdV+v3fs/CggBhv80BXfmOZLTdJct2Pf2/2ACow33UTAKRsEoGAWjYESBH/f3P65jIAIx/nx29j8RAPkkeAh6uz7h/8gDX6/efTZ73Y2EupNqfgdZTZDRcTmPaxEVTyYv/3z2+v9RgA0w/Kct+HVzQ0s5FATrMjAwMOgGl+MCE5bu3Llz58ELROw5pTMYbbiPglEwCkbBKBgFIwq8mG70uI6BGPRiis5/QuDt2tjHdQzoiKztrcMJfL/35MXirbcyW0/rhR5kNUFGR0UdLwcUPOpa8PHohf+jAAYY/tMPELXGfXCC0Yb7KBgFo2AUjIJRMHLAt5tbHtcxEI9+PDz8Hzf4+/39k0a2x3UMmOjN6sj/o+D///////98/vrVmj13CnvOmsccZDVBRod5rC66Zz5onPluz8m/33/8H8GAng13Io+DpDqA2LuTEqtHG+6jYBSMglEwCkbByAEvZ5k/65YkHr2aY/0fN3iz1PdxHQN2VM/478/P/6MAFfx+/+nt1sP3qiZfcEg5yGqChs7bJd2rnPRm86Hf7z7+H2GAng33gQKQkX4GSgb7Rxvuo2AUjIJRMApGwSggA/z59PQxrpPg6xge1zG8mu/4fxTgBn9//Hy/99SD5lmXPLIP81kfZDVBRmfNou8UdL9avfvn01f/RwAYkIb7r0+39s/OdVOX5mVgYEBqT99ZnJAw+zTlZ8CggdGG+ygYBaNgFIyCUTAKBga8mmf/uI4BL2L8+fzC/1FAHPh47OKj7oVXAouOijkdZDVBRqd1Q26mt7xYtPnb3cf/hylg+E9n8OvR5kxl1JNm4O1pyMnrsmErblK17T66VGYUjIJRMAqGMHj26sG564eJRJ+/fvw/CkbBoAG/39x6XMdAEL2Yovt/FJAOPp+/8WTKimuRlcflPQ+ymiCjkyq+1+Nrn81c8+XKnf/DCNC54Y649JRd1DAwC3LODLzhfrYL0qRnD1hBdhObNmC04T4KRsEoGAUDBfacWBtaqk8kevHm0f9RMAoGDXg52/JxHQMx6Mejo/9HAQXg6/X7z+euv5HUcEoj4CCrCTJ6Pn/j/+EC6Npw/3WsWpSBgYFBNnndve///8MWscAb7v//f786yZqBgYFBvfvif7qDPXv27MYBXFxctLV1Pn3/PopG0SgaRaOIzmjLkeWhpfpEortP73z6/n0UjaLBgN4/OPm4joFI9Gyi+qfv30cRVdCbm/cfLNh4NaP5pH7YQVaTZ3tPfPr+faDQf6oCejbcP25LZmdgYNDtvghbCYPRcIc37h3n3f9Pb8DIyMiAG6hrar769HkUjaJRNIpGEZ3R+gNLQ0v1iUTXHt549enzKBpFgwE9nWr4uI6BePTiyuZXnz6PIuqi54+evfr0eQDRv//UBPRsuJ9sEGVgYAhY8eI/DGBpuP//uDmWgQFdkC7gwoUL53EAHx8fbR2dzz9+jKJRNIpG0SiiM9p6ZEVoqT6R6N6zu59//BhFo2jA0YdHp9+eXfD2+GTi0bvLaz7/+DGKhhn6T1VAz4Y7ZjMdU+Q/DsEBBqNr3EfBKBgFo2CgwOga91EwCkbBKIAAejbcD5ayMxAecX+xIoCBgYG99OD/QQRGG+6jYBSMglEwUGC04T4KRsEoGAUQQM+GO6RJzuC5BH5kDGbD/dfN6dYMDKjN+0EARhvuo2AUjIJRMFBgtOE+CkbBKBgFEEDPhvv/JysC2BkYGNgdp1+F7LFFa7j/erkLelokUuMeDRxtUScbtJB90tJow30UjIJRMAoGCow23EfBKBgFowAC6Npw///r5mxHdgYGBgYGXovc2et3dnozMDAwpK5/fv/CwaWtPsoQOeRzZzDA0UpeBnIBvIdAMhhtuI+CUTAKRsFAgdGG+ygYBaNgFEAAfRvu////f7uvFNY+xw5kkzc/wtls//////+35ya6Qxvv/FYJ5SSA1Vf+kwlGG+6jYBSMglEwUGC04T4KRsEoGAUQQPeG+////389OjwxQRva9kYC7KL2ldshNzMRAL8ebU6WZWBgYGCP3fz2Px3AaMN9FIyCUTAKBgqMNtxHwSgYBaMAAgai4Q4Fvz69un/h4M6dO3fu3Hni+vN3xLTYEQC26ka0YN/H/zQHow33UTAKRsEoGCgw2nAfBaNgFIwCCBjAhjulANZ0t5595z+twWjDfRSMglEwCgYKjDbcR8EoGAWjAAKGcMP9//9fF7t1GRgYRKuP4V0UTwUw2nAfBaNgFIyCgQKjDfdRMApGwSiAAJo23J+c30k2OP/kPxHg48l55eXl7Tvu/6ctGG24j4JRMApGwUCBF2+fXLlzikj0+Ssdlk+OglEwCkbBwACaNtwhx7STB8g/upEWYLThPgpGwSgYBaNgFIyCUTAKBhaMNtyJAqMN91EwCkbBKBgFo2AUDEXwYVfZ/1EwXABNG+7f3z3HCtanMjAwMDCkrn+OG5B4yAyNwWjDfRSMglEwCkbBKBgFQw6831H0uJ7p/7+//0fBsAA0bbjjAtCReJoNqkPW1hO3Sp44MNpwHwWjYBSMglEwCkbBkANPWnge1zG8WuD8fxQMCzAsG+4Q86lp/GjDfRSMglEwCkbBKBgFQwu835L1uI7hcR3D4zrGPx9GD1waDmC04U4UGG24j4JRMApGwSgYBaNgCIG/398/aWR/XMcAQa/m2v4fBUMfjDbciQKjDfdRMApGwSgYBaNgFAwh8HZ15OM6BmT088mJ/6NgiIPRhjtRYLThPgpGwSgYBaNgFIyCoQL+fH31uIH5cR0DMno+Wev/KBjiYLThThQYbbiPglEwCkbBKBgFo2CogFfznR7XMWCi7/eo2DgaBQMARhvuRIHRhvsoGAWjYBSMglEwCoYE+P3+/uM6xsd1DJjo+UTV/6NgKIPRhjtRYLThPgpGwSgYBaNgFIyCIQFezbF5XMeAC327ueX/KBiyYFg23CEXP1HzCqfRhvsoGAWjYBSMglEwCgY/+PXyyuM6BjzoWY/0/1EwZAFNG+6QBjQmGL05dRSMglEwCkbBKBgFo4D64OVMs8d1DPjRl3Nz/4+CoQlo2nCHjqyTBWg2HE8WGG24j4JRMApGwSgYuuDrj0E1GjYKaAW+393zuI6BIHrSyvd/FAxNMNpwJwqMNtxHwSgYBaNgFAxdsOnwvu8/f/wfBcMdvJis9biOgRj0cW/t/1EwBAFNG+5Pzu8kG5x/8n8QgdGG+ygYBaNgFIyCIQref/qY19ey5cj+/6NgWINvNzY+rmMgEj1p4vg/CoYgoGnDffiA0Yb7KBgFo2AUjIIhClbv25HX11Iwoe3Dl8//R8HwBc8nqD6uYyQefdhZ8n8UDDUw2nAnCow23EfBKBgFo2AUDEXw4u3rvL4WCFq7f+f/UTAKRsFQBqMNd6LAaMN9FIyCUTAKRsFQBEt2bMrra2mcOzWvryWvr+XVu7f/R8EoGAVDFow23IkCow33UTAKRsEoGAVDDjx88SyvryWvr+Xi7RvdS+fm9bUs27Xl/ygYBaNgyILRhjtRYLThPgpGwSgYBaNgyIF5m9fk9bVMWbPk////529dy+tryetrefzy+f9RMApGwdAEow13osBow30UjIJRMApGwdACtx49yOtryetruf7g7v///////z9p1eK8vpb5W9f9HwWjYCSBDzuK/w8XMNpwJwqMNtxHwSgYBaNgFAwtMH3d8ry+ltkbV/2HgWv37+T1teT1tdx58uj/KBgFIwO831H0uJ7x35+f/4cFGG24EwVGG+6jYBSMglEwCoYQuHL3Vl5fS15fy72nj/8jgZnrV+T1tcxcv+L/KBgFIwM8aeF5XMfwar7j/2EBBl3D/denW/tn59qLlu77P4jAaMN9FIyCUTAKRsEQAhNWLszra1m4bcN/VHDnyaO8vpa8vpZr9+/8HwWjYLiD91uyHtcxPK5jeFzH+PP5+f9DHzD8Hyzg+9ML61oDtXkZIKBgtOE+CkbBKBgFo2AUkAHO3ria19eS19fy9PXL/xhg/pZ1eX0tk1Yt+j8KRsGwBn+/v3/SyP64jgGCXkzR+T/0wSBouKO12KFgtOE+CkbBKBgFo2AUkAM6F8/O62tZsXvrf2zg8cvneX0teX0t529d+z8KRsHwBW+WBzyuY0BGPx4d/T/EwQA23GGLYtgZkAG7qH3u7P23Pv36P5jAaMN9FIyCUTAKRsGQAMcun8/ra8nra1mxZ+vGw3uxoo5Fs/L6WrqXzv0/CkbBMAV/vrx6XM/8uI4BGT2fpPF/iIOBaLj/+nRrO+YQO692YOu6C0+//x+MYLThPgpGwSgYBaNgSIDmedPy+lqIRCeuXPg/CkbBcASv5js9rmPARN/v7Po/lAHDfzoC7ItiGBgYGMwmXf31fxCD0Yb7KBgFo2AUjILBD/afPblo2waS0P9RMAqGHfj9/v7jOsbHdQyY6PkElf9DGTD8pwP4hW1RDK+0W+7s/bc2ZzEwMDBYz77zfzCD0Yb7KBgFo2AUjIJRMApGwZAAr+bYPK5jwIW+Xd/wf8gChv+0BL8+3do/MQF1iJ1XO7ByNmIR+74CBobRhvsoGAWjYBSMglEwCkbBKKAc/Hp55XEdAx70rFvq/5AFNG24QxrlUMCrHdi69AS8wQ4DEDWjDfdRMApGwSgYBaNgFIyCUUApeDnT7HEdA3705cys/0MT0KPhrhyz8PjjT/+xA4ia0Yb7KBgFo2AUjIJRMApGwSigCHy/u/txHQNB9KSV9//QBPRouEMAr7Rb7uwdGMfGQNSMNtxHwSgYBaNgFIyCUTAKRgFF4Plkrcd1DMSgD3uq/g9BQNOG+//v765j7EplFzVEPvdxtOE+CkbBKBgFo2AUjIJRMAooBd9ubHxcx0AketLE/n8IAto23GEA6zmQkEXvc1MZGEYb7qNgFIyCUTAKRsEoGAWjgBLwfILq43pG4tH7HUX/hxqgT8MdBrA24BkYGBjUy/fChuAHJRhtuI+CUTAKRsEoGAWjYBSMgoEF9G24Q8GvT7f2z65Eb8Czi9rnIp0TOajAaMN9FIyCUTAKRsEoGAWjYBQMLBiQhjsM/Pp0C2MJPKwBf29QDcCPNtxHwSgYBaNgFIyCUTAKRsHAggFtuMPA96cXdqA34Av2/R9EYLThPgpGwSgYBaNgFIyCUTAKBhYMioY7DHxHWgI/2nAfBaNgFIyCUTAKRsEoGAWjAAEGVcMdCn59enRh3b4r/wcRGG24j4JRMApGwSgYBaNgFIyCgQWDr+H+5PzOnTt37jz/5P8gAqMN91EwCkbBKBgFo2AUjIJRMLBg8DXcITcyjS6VGQWjYBSMglEwCkbBKBgFowAJjDbciQKjDfdRMApGwSgYBaNgFIyCUTCwYLThThQYbbiPglEwCkbBKBgFo2AUjIKBBaMNd6LAaMN9FIyCUTAKRsEoGAWjYBQMLBhtuBMFRhvuo2AUjIJRMApGwSgYBaNgYMFow50oMNpwHwWjYBSMglEwCkbBKBgFAwuGa8Mdcqgk1c6UHG24j4JRMApGwSgYBaNgFIyCgQXDteEOMYVCQxBgtOE+CkbBKBgFo2AUjIJRMAoGFow23IkCow33UTAKRsEoGAWjYBSMglEwsICmDfcrq8tJB8G6DAwMuNvcR1vUiQHSvAwMDAy80urIoOXofzLBaMN9FIyCUTAKRsEoGAWjYBQMLKBpwx0y7E0ewNVwp4WZhMFow30UjIJRMApGwSgYBaNgFAwsGHIN9yfbMpXZGSCAXVRZHQcYHXEfBaNgFIyCUTAKRsEoGAXDCtC04f793XOywbvv/3GBXy8PtVrwMjAwMLAblm6/h1UhpM+Aq/VPMhhtuI+CUTAKRsEoGAWjYBSMgoEFNG240xD8enl6ojuk8a4cNfvc2/9oYLThPgpGwSgYBaNgFIyCUTAKhhUYqg33/////////eqSKMi6GV6L1kMvf/1HgNGG+ygYBaNgFIyCUTAKRsEoGFZgSDfc///////7ve2lhuwMDAyo62ZGG+6jYBSMglEwCkbBKBgFo2BYAZo23Mk6DhIKVl/5TzRAWvQOWzcz2nAfBaNgFIyCUTAKRsEoGAXDCtC04Q5pPZMHSG1z/3p5enaYLAMDA2TdzKosBgbSDcEJRhvuo2AUjIJRMApGwSgYBaNgYMGwabj/////P9KidwggyxBsYLThPgpGwSgYBaNgFIyCUTAKBhbQtOGOC0Ab9FRrVqOAX492QRe9U9GG0Yb7KBgFo2AUjIJRMApGwSgYWDD8Gu7//////+vllYM7d+7cef7Jf+qA0Yb7KBgFo2AUjIJRMApGwSgYWDA8G+5UB6MN91EwCkbBKBgFo2AUjIJRMLBgtOFOFBhtuI+CUTAKRsEoGAWjYBSMgoEFI6HhfrRFHQJajv4nE4w23EfBKBgFo2AUjIJRMApGwcCCkdBwh1pHiYWjDfdRMApGwSgYBaNgFIyCUTCwYLThThQYbbiPglEwCkbBKBgFo2AUjIKBBSOh4U4FMNpwHwWjYBSMglEwCkbBKBgFAwtGG+5EgdjYWD19/T9//46iUTSKRtEoGkWjaBSNolFEJPpPVTDacEcAS9xAVFRUU1v71afPo2gUjaJRNIpG0SgaRaNoFBGJ/v2nJqBpw/3J+Z1YQac3AwMDA4N3507cgGp3JxEPgnEDOTm50Yb7KBpFo2gUjaJRNIpG0SgiCQ2hhjt0ZJ0sMLiG42NjY/X19f/9+z+KRtEoGkWjaBSNolE0ikYRkYi6YLThThSANNz/j4JRMApGwSgYBaNgFIyCUTBAgKYN9+/vnpMN3n3/P4jAaMN9FIyCUTAKRsEoGAWjYBQMLKBpw50WYGA6A6MN91EwCkbBKBgFo2AUjIJRMLBgyDXcB2b5zWjDfRSMglEwCkbBKBgFo2AUDCwYcg33J/tKDdkZyAOjDfdRMApGwSgYBaNgFIyCUTBUwZBruP/////Xo83JsgwMDAwM/JHLbzwnHowulRkFo2AUjIJRMApGwSgYBUMVDMWG+/////+/3Vcgy8DAwCBbevDjf9qD0Yb7KBgFo2AUjIJRMApGwSgYWDBUG+7///+62K3LwMDAoDvx6n+ag9GG+ygYBaNgFIyCUTAKRsEoGFgwdBvu8KY7e/I2mg+6jzbcR8EoGAWjYBSMglEwCkbBwIKh3HD////jvgJRBgaGgBUv/tMWjDbcR8EoGAWjYBSMglEwCkbBwIKh3XCHHetO/q5TIsFow30UjIJRMApGwSgYBaNgFAwsGOoNdzqB0Yb7KBgFo2AUjIJRMApGwSgYWDDacCcKjDbcR8EoGAWjYBSMglEwCkbBwAL6N9y/39s/uzLQTJ0AaDn6fxCB0Yb7KBgFo2AUjIJRMApGwSgYWEDfhvuvmyvCZBmIAuTfcvr//74CBgbKTEAHow33UTAKRsEoGAWjYBSMglEwsICuDfcnKwLYGRgYGBgYeKXNArPK8YHVV/6TDUYb7qNgFIyCUTAKRsEoGAWjYLgBejbcX6wIYGBgYGB3nH6VtqfAjDbcR8EoGAWjYBSMglEwCkbBcAP0bLhD2tN0uOkUYtFow30UjIJRMApGwSgYBaNgFAwfQP+GOzXb0zgA9S0abbiPglEwCkbBKBgFo2AUjIKBBfRsuN+cbsbAwBC7+eN/GoPRhvsoGAWjYBSMglEwCkbBKBhugJ4N9/9XJ+oyMDB4Lnnyn7ZgtOE+CkbBKBgFo2AUjIJRMAqGG6Brw/3/230FsgwM7I7d597+pyG4srq8nLJzadDBaMN9FIyCUTAKRsEoGAWjYBQMLKBnw/37u+fPH5+d5cPPwMDALmqI/zxIaja7KQejDfdRMApGwSgYBaNgFIyCUTCwgJ4Nd8gKFiIBNRe6UA5GG+6jYBSMglEwCkbBKBgFo2BgwWjDnSgw2nAfBaNgFIyCUTAKRsEoGAUDC+jZcB/CYLThPgpGwSgYBaNgFIyCUTAKBhaMNtyJAqMN91EwCkbBKBgFo2AUjIJRMLBgtOFOFBhtuI+CUTAKRsEoGAWjYBSMgoEFow13osBow30UjIJRMApGwSgYBaNgFAwsoGnD/WiLOtmg5ej/QQRGG+6jYBSMglEwCkbBKBgFo2BgAU0b7iQdI4MGRk+VGQWjYBSMglEwCkYBTcChC2f2nz35fxSMgqEGaNpwHx1xHwWjYBSMglEwCkbBoAP1sydVz+j/8+fP/1EwCoYUoGnDffiA0Yb7KBgFo2AUjIJRMDzAntPH8vpa8vpadpw4/H8UjIIhBUYb7kSB0Yb7KBgFo2AUjIJRMAzAj18/K6b15vW15PW1lE7u+vr92/9RMAqGDhhtuBMFRhvuo2AUjIJRMApGwTAAW48eyOtrqZ05oWHO5Ly+lo2H9/4fBaNg6IDRhjtRYLThPgpGwSgYBaNgFAx18Onrl8KJ7Xl9LQfOnTp88WxeX0teX8u7Tx//j4JRMETAaMOdKDDacB8Fo2AUjIJRMAqGOlh3YHdeX0vz/Gn///////9/+8KZeX0tq/fu+D8KRsEQAaMNd6LAaMN9FIyCUTAKRsEoGNLg9ft3eX0teX0txy+f/////////09du5TX15LX1/Li7ev/o2AUDAUw2nAnCow23EfBKBgFo2AUjIIhDVbs3prX19K1ZM5/JNC7bH5eX8uSHZv+j4JRMBTAaMOdKDDacB8Fo2AUjIJRMAqGLnjy6kVeX0teX8vZG1f/I4GLt2/k9bXk9bU8fPH0/ygYBYMejDbciQKjDfdRMApGwSgYBaNg6IKF29bn9bVMXLnwPwaYsmZJXl/L3M1r/o+CUTDowWjDnSgw2nAfBaNgFIyCUTAKhii49/RxXl9LXl/LlXu3/2OAGw/u5fW15PW13Hr04P8oGAWDGzD8HwVEgNGG+ygYBaNgFIyCUTBEwawNK/P6WqavW/4fB5i9cRV+BaNgFAwSMNpwJwqMNtxHwSgYBaNgFIyCoQiuP7ib19eS19fSOHdK+6JZWFHT3Kl5fS15fS1X7t76PwpGwSAGow13osBow30UjIJRMApGwSgYimDK6iV5fS1EognYFsGPglEweMBow50oMNpwHwWjYBSMglEwCoYcuHD7+unrl0lCZ29c+T8KRsFgBaMNd6LAaMN9FIyCUTAKRsEoGAWjYBQMLBhtuBMFRhvuo2AUjIJRMApGwSgYBaNgYMFow50oMNpwHwWjYBSMglEwCkbBKBgFAwtGG+5EgdGG+ygYBaNgFIyCUTAKRsEoGFgw2nAnCow23EfBKBgFo2AUjIJRMApGwcCC0YY7UWC04T4KRsEoGAWjYBSMglEwCgYWjDbciQKjDfdRMApGwSgYBaNgFIyCUTCwYLThThQYbbiPglEwCkbBKBgFo2AUjIKBBaMNd6LAaMN9FIyCUTAKRsEoGAWjYBQMLBhtuBMFRhvuo2AUjIJRMApGwSgYBaNgYMFow50oMNpwHwWjYBSMglEwCkbBKBgFAwtGG+5EgdGG+ygYBaNgFIyCUTAKRsEoGFgw2nAnCow23EfBKBgFo2AUjIJRMApGwcCC0YY7UWC04T4KRsEoGAWjYBSMglEwCgYWDL+G+/d3z6Hg3ff/1AKjDfdRMApGwSgYBaNgFIyCUTCwYMg13J+c37lz586d55/8Rwff722vdJPmZUAC7KL2uUtOv/z1n1Iw2nAfBaNgFIyCUTAKRsEoGAUDC4Zcw31fAQMDAwNDwb7/yODXo83Jsgw4ALty5uZHlDXeRxvuo2AUjIJRMApGwSgYBaNgYMGwaLj/OtkAbbWzi9rHT1i6EwKWToi3F2VnYGBgYGCQLdj39j/5YLThPgpGwSgYBaNgFIyCUTAKBhYMh4b7ndnWDAwMDAyyBbswVsX8enm625GdgYGBQbT6GLok8WC04T4KRsEoGAWjYBSMglEwCgYWDIOG+/15jgwMDAy63RdxNMw/HiyVZWBgYE/e9vE/mWC04T4KRsEoGAWjYBSMglEwCgYWDIOGO0TEcd79/zgBZExetOHkfzLBaMN9FIyCUTAKRsEoGAWjYBQMLBg2DXckASyAGDX4wGjDfRSMglEwCkbBKBgFo2AUDCwYBg13yFKZ2M141sG8WBHAwMDAXnrwP5lgtOE+CkbBKBgFo2AUjIJRMAoGFgzVhnv4svvw+5WuTtRlYGCP3Yzr2JhfF7t1GRgYGAJWvPhPJhhtuI+CUTAKRsEoGAWjYBSMgoEFQ7XhzsDAwMDALqpsFpg1YenW/kB+BgbRrO0Yh8r8//Xy9ER3XgYGBor2po423EfBKBgFo2AUjIJRMApGwQCDIddwP9tvpgw7nB0D8FYe/Q8DLw5OjkdcpMruOPsmRqueeDDacB8Fo2AUjIJRMApGwSgYBQMLhlzDHQJ+fXp1/8LB9bNbsgKR2/HWs+/8hwHISTIMDAwMDLzuE8/hWkdDHBhtuI+CUTAKRsEoGAWjYBSMgoEFQ7ThjgZg7fgNyEe57ytg4JV2i5+w/R5sMTz5YLThPgpGwSgYBaNgFIyCUTAKBhYMj4Y7zcFow30UjIJRMApGwSgYBaNgFAwsGG24EwVGG+6jYBSMglEwCkbBKBgFo2BgwWjDnSgw2nAfBaNgFIyCUTAKRsEoGAUDC0ZCw/3J+Z0QcP7JfzLBaMN9FIyCUTAKRsEoGAWjYBQMLBgJDfd9BQwQgHTdKolgtOE+CkbBKBgFo2AUjIJRMAoGFow23IkCow33UTAKRsEoGAWjYBSMglEwsGAkNNxHl8qMglEwCkbBKBgFo2AUjIIhD0ZCw50KYLThPgpGwSgYBaNgFIyCUTAKBhaMNtwRYO3atWtwAHt7ey1t7Y/fvo+iUTSKRtEoGkWjaBSNolFEJPpPVTDacEcARkZGBtxAXVPz1afPo2gUjaJRNIpG0SgaRaNoFBGJ/v2nJhhtuCPA/fv37+EAgYGB2jo6X3/+HEWjaBSNolE0ikbRKBpFo4hI9J+qYLThThQYXeM+CkbBKBgFo2AUjIJRMAoGFow23IkCow33UTAKRsEoGAWjYBSMglEwsGC04U4UGG24j4JRMApGwSgYBaNgFIyCgQVDruF+tEWdbNBy9D+ZYLThPgpGwSgYBaNgFIyCUTAKBhYMvYZ7JS8DuWD05tRRMApGwSgYBaNgFIyCUTBUwZBruP////bcRHdo453fKqGcBLD6yn8ywWjDfRSMglEwCkbBKBgFo2AUDCwYgg33/////3q0OVmWgYGBgT1289v/dACjDfdRMApGwSgYBaNgFIyCUTCwYGg23P/////r5mxHdgYGBtGCfR//0xyMNtxHwSgYBaNgFIyCUTAKRsHAgiHbcEc03a1n3/lPazDacB8Fo2AUjIJRMApGwSgYBQMLhnDD/f//Xxe7dRkYGESrj/36T1sw2nAfBaNgFIyCUTAKRsEoGAUDC4Z0w/3//48n55WXl7fvuP+ftmC04T4KRsEoGAWjYBSMglEwCgYWDPGGO73AaMN9FIyCUTAKRsEoGAWjYBQMLBhtuBMFRhvuo2AUjIJRMApGwSgYBaNgYMFow50oMNpwHwWjYBSMglEwCkbBKBgFAwuGZcP9yfmdO3fuPP/kP9XAaMN9FIyCUTAKRsEoGAWjYBQMLBiWDfd9BQwMDAwF+/5TDYw23EfBKBgFo2AUjIJRMApGwcCC0YY7UWC04T4KRsEoGAWjYBSMglEwCgYWjDbciQKjDfdRMApGwSgYBaNgFIyCUTCwYLThThQYbbiPglEwCkbBKBgFo2AUjIKBBaMNd6LAaMN9FIyCUTAKRsEoGAWjYBQMLBhtuBMFRhvuo2AUjIJRMApGwSgYBaNgYMFow50oMNpwHwWjYBSMglEwCkbBKBgFAwuGZcP9+7vnz58/f/f9P9XAaMN9FIyCUTAKRsEoGAWjYBQMLBiWDXfqg9GG+ygYBaNgFIyCUTAKRsEoGFgw2nAnCow23EfBKBgFo2AUjIJRMApGwcCC0YY7UWC04T4KRsEoGAWjYBSMglEwCgYWjDbciQKjDfdRMApGwSgYBaNgFIyCUTCwYLThThQYbbiPglEwCkbBKBgFo2AUjIKBBaMNd6LAaMN9FIyCUTAKRsEoGAWjYBQMLBhtuBMFRhvuo2AUjIJRMApGwSgYBaNgYMFow50oMNpwHwWjYBSMglEwCkbBKBgFAwtGG+5EgdGG+ygYBaNgFIyCUTAKRsEoGFgw2nAnCow23EfBKBgFo2AUjIJRMApGwcCC0YY7UWC04T4KRsEoGAWjYBSMglEwCgYWjDbciQKjDfdRMApGwSgYBaNgFIyCUTCwYLThThQYbbiPglEwCkbBKBgFo2AUjIKBBaMNd6LAaMN9FIyCUTAKRsEoGAWjYBQMLBhtuBMFRhvuo2AUjIJRMApGwSgYBaNgYMFow50oMNpwHwWjYBSMglEwCkbBKBgFAwtGG+5EgdGG+ygYBaNgFIyCUTAKRsEoGFgw2nAnCow23EfBKBgFo2AUjIJRMApGwcCC0YY7UWC04T4KRsEoGAWjYBSMglEwCgYWjDbciQKjDfdRMApGwSgYBaNgFIyCUTCwYLThThQYbbiPglEwCkbBKBgFo2AUjIKBBaMNd6LAaMN9FIyCUTAKRsEoGAWAjYJRMLBgtOFOFBhtuI+CUTAKRsEoGAWjYBSMgoEFow13osBow30UjIJRMApGwSgYBaNgFAwsGG24EwVGG+6jYBSMglEwCkbBKBgFo2BgwWjDnSgw2nAfBaNgFIyCUTAKRsEoGAUDC0Yb7kSB0Yb7KBgFo2AUjIJRMApGwSgYWDDacCcKjDbcR8EoGAWjYBSMglEwCkbBwILRhjtRYLThPgpGwSgYBaNgFIyCUTAKBhaMNtyJAqMN91EwCkbBKBgFo2AUjIJRMLBgtOFOFBhtuI+CUTAKRsEoGAWjYBSMgoEFow13osBow30UjIJRMApGwSgYBaNgFAwsGG24EwVGG+6jYBSMglEwCkbBKBgFo2BgwWjDnSgw2nAfBaNgFIyCUTAKRsEoGAUDC0Yb7kSB0Yb7KBgFo2AUjIJRMApGwSgYWDDacCcKjDbcR8EoGAWjYBSMglEwCkbBwILRhjtRYLThPgpGwSgYBaNgFIyCUTAKBhaMNtyJAqMN91EwCkbBKBgFo2AUjIJRMLBgtOFOFBhtuI+CUTAKRsEoGAWjYBSMgoEFow13osBow30UjIJRMApGwSgYBaNgFAwsGG24EwVGG+6jYBSMglEwCkbBKBgFo2BgwWjDnSgw2nAfBaNgFIyCUTAKRsEoGAUDC0Yb7kSB0Yb7KBgFo2AUjIJRMApGwSgYWDDacCcKxMbGqqurnxwFo2AUjIJRMApGwSgYBaOAaPDnz5//1AOjDXeiwKpVq3h5eRlGwSgYBaNgFIyCUTAKRsEoIBr8+PHjP/XAaMOdWHD27NmDRANnZ2c1NbWDo2AU0Bfw8fFFR0cfHAWjgI5g4cKFDAwMXV1dB0fBKKAjKCoqYmBg2Llz58FRMAroCGxtbXV0dA6SAkZH3IcAiIyMNDY2/j8KRgF9gbCwcGVl5f9RMAroCK5cucLAwLBt27b/o2AU0BFMmzaNgYHhy5cv/0fBKKAjCAwMtLa2/j9wYLThThMw2nAfBQMCRhvuo4D+YLThPgoGBIw23EfBgIDRhvvwBKMN91EwIGC04T4K6A9GG+6jYEDAaMN9FAwIGG24D08w2nAfBQMCRhvuo4D+YLThPgoGBIw23EfBgIDRhvvwBKMN91EwIGC04T4K6A9GG+6jYEDAaMN9FAwIGG24D08w2nAfBQMCRhvuo4D+YLThPgoGBIw23EfBgIDRhvvwBKMN91EwIGC04T4K6A9GG+6jYEDAaMN9FAwIGG24D08w2nAfBQMCRhvuo4D+YLThPgoGBIw23EfBgIDRhvvwBKMN91EwIGC04T4K6A9GG+6jYEDAaMN9FAwIGG24D08wceLEurq6/6NgFNAX5OXlrV69+v8oGAV0BK9evUpNTb19+/b/UTAK6AhOnjyZmpr669ev/6NgFNAR9Pb2NjU1/R84MNpwHwWjYBSMglEwCkbBKBgFo2AIgNGG+ygYBaNgFIyCUTAKRsEoGAVDAIw23EfBKBgFo2AUjIJRMApGwSgYAmC04T4KRsEoGAWjYBSMglEwCkbBEACjDfdRMApGwSgYBaNgFIyCUTAKhgAYbbiPglEwCkbBKBgFo2AUjIJRMATAaMN9FIyCUTAKRsEoGAWjYBSMgiEARhvuo2AUjIJRMApGwSgYBaNgFAwBMNpwHwWjYBSMglEwCkbBKBgFo2AIgNGG+ygYBaNgFIyCUTAKRsEoGAVDAIw23EfBKBgFo2AUjIJRMApGwSgYAmC04U5N8P3e/tm5bsqi7AwMDAwMvNJu8RO23/v+fxSMAtqBj5tjGfCAgn3/R8EooBr4dbJBlkCy+vXyyrrWQDNpXgYGBgYGdlHDwMolp1/++j8KRgH54MmKAHYGBuvZd/5jBaPF4CigFvj+9PiSSngBxsAuqmwW2LruCs4ibADafaMNd6qBt/tKlSExhwpkkzc/whXjo2AUUApONogy4AGjNdYooB54u69AloGBAU+y+nVznjsvAyZgd+w+9/b/KBgF5IBfN2c7sjMw4Gm4jxaDo4Aa4NfNFWGyDFgBu2HDMcwibGDafaMNd+oAeMHC6z7x+KNPv/7///Xp1v42aBWm233x1/9RMApoAF6sCGBgYGBgD+zZiRWcf/J/FIwCKoBfL3chqigcDSFYw55dOXPd9Xff//////3phaWZEG3ssZsx671RMAoIgO9XZ0MrUtwN99FicBRQAXw8WCrLwMDAwMBuWLodUoD9//Xp0fHZ0MY8u+Psm8hNuQFr94023KkCPm5LZmdgwIwpeLx6LhktOEYBLcDBUnYGBgaGgBUv/o+CUUAj8Ovl6YnQ2ggCsDfcr07UZWBgwGyhw9rzotXHUMrHUTAK8IPv99ZBu30QgKvhPloMjgLKwZMlngwMDJjtuP///7/dHMvOwMDAwJ687eN/GBi4dt9ow50aABbfsZsRcQoFv45VizIwMDDoTrz6fxSMAmqDm9PNGBgYGNS7L/4fBaOABuDXy9Ozo5TZGRgYGBjYld0d1RkYGLA33GFlHbbECC0iUaq9UTAK8IDv97ZXWvAyMDAwMDDwurtbMzAw4Gy4jxaDo4BycH+eIwMDA9Z23P///y92qzMwMDCwlx78DwXQQg2belhZSKt232jDnQoAOk2Hvbv/a1cWAwMDA4PjvPv/R8EooC6AbslCKkxGwSigJrgzG9JiYleOmn365S8YF1vDHbrMWLTh5H9MAG1ajabUUUAc2FfAwMDAwMDAa1G5/d53GBd7w320GBwFVABHW9TVlUXZzabf/I8FYBR9A9nuG224UwFAKywc3X2M+B4Fo4BaAJr0AuafRtrXzi6q7JY7e/+97/9HwSigENyZ52ePlJjwlGawegzL+NP////xt7xGwShAA/sqtZGP8sCbfEaLwVFAawAbQ0f0DqGpbkDafaMNd8oBtLuPo0yBd71wSI+CUUA2gM3tYQeyYStuQqu9UTAKqALwVEfQqWRsUv//w4fccUmPglGAD+BruI8Wg6OA1gBzjfuAtvtGG+6UAzx12f////EXOaNgFFAAoEmLgYHXonLdhUeffv3////7u+v7J0I3wTPIFuxD3SY4CkYBJQBPYQdNjLjKOTw6R8EoIATwpS6o3GgxOApoBGC76xlkG07++g8BhAo0aKrEmmIpBqMNd8rBgEbgKBjB4P7ySHV1aV70M6r+/8de0oyCUUApwFPYESjn8OgcBaOAEMCTukaLwVFAS/D2XDfkjBjUVESoQMOTYikHow13ysGARuAoGAXYwdt14QwMDAwM7AX7EIXNKBgFFAE8hR2Bcg6PzlEwCggBAqkLJxgtBkcBJeDtsQZDdgYGBgaMG5UIFWjkpliiwGjDnXIwoBE4CkYBDgDbLIhjk/woGAWkAzyFHYFyDo/OUTAKCAECqQs3GC0GRwGZ4NejzcmQCRuMVvt/wgUa2SmWGDDacKccENiFAJOm0blAo2AU4ADQkgNnyTIKRgGpAE9tRWD3KVQacSjDKBgFxANoYYajksUHoDpxpctRMAqwAcQCGXbl0l0v0Vrt/xENOxxJEiZNm3bfaMOdCmAgjwUaBSMa/Pr06vnzdzgOPIPWWDjS5SgYBaQDPKUZbGhz9DjIUUB9gDf5jBaDo4Ca4NfNFbB9zbzus6/iSFgD2e4bbbhTAcDOBcJaY9G24zUKRjCAXvONPd3Bhzix3w8xCkYBOQBfdQQ9DxJ7RQZNjaMD7qOALABtfmNpuI8Wg6OAmuDXzdmwoXbDhkNYhtphYCDbfaMNd2oA6NW3iCM+EQB2bD+trr4dBSMXwAoObOnu//+rE3UZGBgYGDyXPPk/CkYBdQC+hju+sg5PETkKRgFhgLvhPloMjgKqAUSrXTZsCa6hdhjAU6jhKwupAUYb7lQBT1YEsDMwMDDotp9DietfN6dD6rnRYmMUUB983JbMzsDAwMBgPf0m6sAAvPwRLdiHUaiMglFALsDXcP//62SDLAMDAwN77LqXKOnx7a4sUQYGBgbR6mMo4qNgFBAJcDfcR4vBUUAl8Otity4DAwMDA7ajRbGAgWv3jTbcqQPgJQS7Yen2W59+/f//69Ot/W3uvAwMDAwMut0Xf/0fBaOA6uDOPEiyY+C1qISku//f313fXmoIER29eGQUUBfgbbj/Rzo3O2z28aff//////3phaWZyuwMDAwM7LGbR1PjKCAP4Gm4/x8tBkcBNQCscGMwLFmxEyc4cgvRBxywdt9ow51q4O2+Ukj9hAawHCQ0CkYBtcDbcxOhxQQaYFeOGr3qexRQGcDqNhwN9///f92chzU9sjt2nxttPY0CcgHehvtoMTgKKAfQzaaEAFoSHJh232jDnZrg+739s3PdlEUh8cgr7RY/Yfu97/9HwSigKfh+b/uEeKR0ZxZYuQQy3jkKRgFVAcGG+////3+9vLKuNdBMGtKQYhc1DKxccvol7SqxUTACAIGG+///o8XgKKAIwIo2QgAzCQ5Au2+04T4KRsEoGAWjYBSMglEwCkbBEACjDfdRMApGwSgYBaNgFIyCUTAKhgAYbbiPglEwCkbBKBgFo2AUjIJRMATAaMN9FIyCUTAKRsEoGAWjYBSMgiEARhvuo2AUjIJRMApGwSgYBaNgFAwBMNpwHwWjYBSMglEwCkbBKBgFo2AIgNGG+ygYBaNgFIyCUTAKRsEoGAVDAIw23EcBpQDl/NPYzYhrxbAC+P3UDKiHQRNxTi9dwfd72ystKpEcOMgASqijAXZRZXW3+AlLT+A4TJbisB7sgUMV8P3e9kr44bwMvNItR/8PGQCNYWIA+amARPD9HjmJBlFe6E68+h8/+PXy9OwE2PnxDOyiwcvv/8cDoIFEtwAgACC3zlpPv4njxHtSQmLIgV8vT8+Oipo3MDFxdaIuAwMDAwMDe/I2AtUXLgAtjpHTEp2TF21DEIv/7sy2Zhi9lHaAwGjDfRRQCu7MtmZAAAItd0TtwzCIG+4vNkTxMjCgOnCQAdRQxwV4LVoPvcRoCFAW1kMgcKgA4LeowwCZoTUwABrDxAD6+IvcRPNiRQADDIhWH8NIykjg48FSWQYUQMAyaCDRJwAIAUizXbbhJC4vkhASQw6c7VJmZxiwmEC+MdNzyZP/5ABocYzsA7omL1qHIBb//f91skGWgUE0a9do053uYLThPgooBdA8DQN4W+4fN8cyIAByxUrXYo4QgHkJ2YGDDMCcqJ61aCcaWDqhPCtQm5cBCjAHRSgLa5jNgzhwKAf35zkyMDAwMOjW7Ifcvfjr11BqKkFjmMG7cychcOQWnvxKNUBmonmyxJOBgYHBMyCAnYHQgOjBUnYGBgYG9sCFtz79+v+fcJRBA4nMbEBVAOl0sMduRsupCEBKSAw5MJAx8etYtSgDA4NoQIA1AwMDubMZ0PSN7AO6eorWlmHx3////99ujmVnYBAt2DecEuOQAKMN91FAKYDmaQZ1XV12Bga8LXdou11dXZ2BgYHkWpx+AOalQetAhBPRilIE+H5vXbIsAwMDAwMDuyM1Z1BhNg/iwKEcQD2p3n3x/1AE0Hp88MQRNDxJdBB0DYN693FIwYF3QBTqZzzFz2AFvy526zLgbzOSFBJDDkCjDmdZRkMAmwKO3Xy8W52BgYHM2Qxo+h4IH/z//5/2IYjLf5B0iWeiaBTQBIw23EcBpQCapxmsp88uYGdgYGDAWXVC2+2x3d0QHaTV4nQEMC8NWgcinIhRlCIDyIgIAwNVB0VgNg/iwKEcQD2JN3QHMYDW44MnjqDhSZqDLkKbUg0n4e0rPI1bqJ9JsmFQgCcrAtgZ8DfFSQuJIQegUTcAuQ1aJTEErHgBbYSSN5sBTd8D4AMIoHUI4vQfZCaIPWAF7sQ7CqgPRhvuo4BSAM3TDNazb+4rYGdggJSCWABklSZ78raLUB3IdSzOkufXy9NLcmGbBNlFDQNbt9/7DlOPpBzqjIJ9/98emwBZKcIuahjYug9eovz6dGv/7MpAM6hZDAwMvNJmgZWz99/7/h8OIOaiggIkd/7//+vllXWtCFN4pdXdclHNgABcDppYqMvAwICzooZVzDi7PxAANR05BLABSLnKgGoc1I9oWn99urV9QrwbzF8MvNLqbvETtkPXHfz//x+uEQUUIAfO96cX1k2Id1OX5mWAAHZRZbf4CeuuvEQY8v//f5hJ1rPv/P///d721kDYlkJeaTNoBGMBaC7klTYLbMU0+v//////f7+HqhJHHGEBEJehA4gvIXLWs+/8urkh116UnYGBgVfaLXfFRUTYfr+3f3YuPADYRZVxuBFu1P9fL6+sg++CZRc1RPgfJenzSpslzD6NaRBWADEd5mwSAdGR+P8/epQw8EqTnmhwAOgaBugQKCxbQHgoAJYVUIH17Dv/YZZbY40xiByD9ew7/9HA96fHl1TC0yS7qDKu1ENKUOECkPYiO57mItEh8f//f5i3rGff+f//+wBnLdRwZGAXVTYLrFxy/ClCMba4s5595/9/uFQBevm5ZTF+/0PCk4GoyTJY8Qgpi6E6oTwc4NenW4hAZRc1DGzd9egXzLFQp////x8WD4SE4AAqV4CaMygKQRj49fI0kiHsosq4I/c/8f6DAmhyHE79yCEARhvuo4BSAM3TDNaz7/zC13KHt9s/wnQgF1LQcssatWR4u69UmZ0BA8iGrZiexcCAohxqaGxpqSwDAsBL97fHGgyxmMTAwMDAwK5cCl8GDnUHCkC489ejzZnYHMTAwCAbtuQqojj9j8dBNyES2OsH6BAQezLuevz///9w05FDACuAmsfAkLULEhD/4X5E1vrr5jx3XgasgN1x9k2oVqhGFAAPnF83V4TJMuAAvO7zYIb8//8fZpL19F3Y9WCuy8cdgeyO3edQFL89NxGHX2TDVqC4AhuAuAwdQHwJkbMuKHVEdgk8HnFbzG7YgLZJGGrU9F3Ywp3dcfbN71hjhDdsDdQyvABiOszZJACSIpEKiQY3gDYIGEQbTv7///8/vPWKmTNgWQEVQFI3xHLsMQaRgyqEA9whIJu8+RHUT///41PIgBlUOAHUW5i+QgDiQ+L///8wb1kPeNbCUXYzMDAguQFb3EEjBCqFWX5+hdYyorCyHQVAJyeIakjCWuqw+grWjsepGbv/2Q0blrRbMzDAnf7//39YPBASggOoHHLOoDQE////j6fGYjdsOAY1Aw5I8R8MQCt97LExCmgDRhvuo4BSACs5rGffgWVieEmIBCCFInvyto9wHciFFLTcsp6NKBl+3ZztyM7AwMDArpy59MLT7//////+7vr2UkN2BhhAUg4zlIGB3bB0+73v////+nTrxPkn/////w+bjWZgNyzdfv3d9/////////3d/eOzYXWbbOvZ/wgAMwvZgf///4ec/MDAAHHQo0+//v////3d9e2VFrwMDAwMDKhryWGGMDCwG6I6CBIUsPYDCoD0bhjYC/b9+o8XwExHCgHs4OZ0MwYGBpQRKMywhtZg7IaI4Pn16dFxWCmOUpHBbEYNHMgRAwwMDLJhs49Dgub/r0+vENHFHrYGKU1AnMDOzs7AwG5Yug4Su8hWsidv+/gfDqCrgBkY2JWjYMZ/fwqPPqSTDZASDVzlr0+Pjs+OglRfiBoPL4B6EimI/v+HuZqBgYFBNgwy+v396YUzkN2dcIsZeN3b9kNCEdliBtnSgx//wwHEKHZ2doh6yBD196f7q3UZGBgYGBhkZWUZGGRhYfnr0y3YjgWiKkiI6ehxRBCQFomUJxo8ANZaRZgCa69iyzb//8P9jGoDVJCBgUEWI8agcihxDF9cxmtRCfXVr0+34P5HHNZIWlDhBDBPIU+IoQFSQwLiLXZ2dgYG9oHLWjBXIwXP/+/vrq+DtiJR3QBxMmpMwJMMAwO7IWr5CfM/tqwAPSIG1SgcAJaAEW6BlczYTEZKHHBP/fr0CBJS7OzsDAyo1mLxFBYhOIDKIZIv5SH4H1FjIZLz/++IGgupe/3//38S/QcH0LEhwnXWKKAaGG24jwJKAayAtZ595z+8TMVouUOKREjmhulAFFLYSh5YYxulPvj//z+iAkFRDjMUqYKDA1gJjeXUBqgUskkIs5AdiKiqrSdd/f4fBcBdhGwDzBAsDoI2zzErXkgo4ag3UADMdBR3YwO/dmUxMDAwIHsGI6yhhiG17aHg47ZkXlFldfWsLa//wwBUMZJ5/////witZXS7L6K7HCaFogHqBCwaYMrZSw/+hwFosGAmBfipjbAwxplo/sMrMSIC9z/Mk4gg+v//P8LVqJXm////ERaLZm1/iWY8zGIUv8KMQhH8/x+egRgw1o3COsWYsYQJ4KYTAigehAU9mpP+/0dIIUUiNIgwnfOR2ESDB8BiHBav////hzfKUAXhAOpnVBuggthiDCaHFASwwEdr0Pz/Dz32hQFuzkdoeBAXVLgB1Etm02/+xwFIDgmotxgw3QZzGT2yFtQVmB2SqxN1eaXV1W0nX/gPB1DFSDHx/z88ycAdgASg/keyDwJgEYhRsGIBMLWwKP3//z+iZEYRhAC4evTE8evlOmiDF8UHWDyFRQgOoHKIRAMVoCAEYTUWhov/I2osREiR6j8EgI4NwWaERgHtAcP/UTAKKAOwAhaSqWHZH63lDqkfoAUtTAeikMJS8kB0MDCEr0OvJhCWICmHG4rZkPj/YkOaurqyKG/Bvl//MQC0DkCuzOBmITsQ1m6C+gEdXIV2ABAFIcwQLA6CV6FI7v//H+5nHFagAJjpaCZgAdCQRfIMVAShFWoY0ugaHgBVjGTe////z/abqatL8yKMRAKwytBx3v3/MAB1Anoy+f//P2wAB0k5zEZE2CLAR0gr0ax87+v/8FjAUu3+/w83hx1rOkAFUKVo/oG5GrMyharH6sT//z9CoxspYmFGYQQAzPuYroTZgRLw2AHMdIIAxYOkRSLUORQkGtwAphzNKdDoxdaUg/sZ1QaoIJYYg8sh7MCbxa9Cmkvec6///09qUOEEsFNHUR2NDEgPCai3MFMWIm0hXAYzHlu6/UhJ1oK6AjFFgQ/AFKP4EWok1vIT7hq4ff////8Prxew+QYdwCIbzYiP0KyK6U9oRYErcTAwMKD6AIunsAjBAVQOkRKgAuSHIMwrWNLIf4T/4VpI9R8SgNqOSFajgMZgtOE+CigFsAIWmqlhhSdKvQFpkcLKBJgORCEFz/tQQ/7D6xgUYxAAWsrAlSMMRVrLTRSA6UN2DDYx6NpJtHIeAaDDDgj7YYbABZABrFBF8gAslIgbuYCZjmIAVgANWSTPQEUQWmFxxsAuap87e8eF+5BJVewAZjPCPEIAwz64ENZKGSoHVw5LCoTrBViTCUeigcUhEQEM9STcDRAAdRnm+CjMiWjq4QA27YHQCTUKSwBAZTCNgrqJmICHmkH4HHfiT3GHGolwFg0TDaydgNEAg0nAShJkAHUgqg1QQUS4IwGoHMJH0OSB0ocnB2AYjBNAz57HrRLmYRJCAmo7lpQFl4PbB0u31M9a0LKMgYFXO7B16Ynrrz6hORMZoDvr////iCSDtfyE+R+lOIalSFxuRAawAhgj/GAS6EEOK99R0xcMQL2P7AMsnsIiBAdQOYTxFIcgtI6ERwkagMU9NLRI9h8SgEUVdr2jgPpgtOE+CigFsFwLy9RYik+IEngZCeGi5nP0kgebGiQAlYYrh4vg0oAEfn169fz59RM7189uyQo0FGVnYGBgQNWHzSx0B6IDWNsMrgCbIQgACyWk4RCoBiQRPACqGGEdLgArn5HcgcUrsMluOOCVNgusnL0DskIWBcBsRpiHCb6/e/78/oWDO5dOKI93gxxQgGofFifAAbocMRZCALSqIggImwS1E+4GCEB3GRxAleM2GKNaxGkUbhmCliAA1AxilOIGBCORyokGBmBZA7kEgQFYawZzQBTmZ1QboILIroYDDDkMAaIBwaDCDmB5E1e7mayQwOcLdDnio4XkrAVfjAED7KLKbvETlp64hdkARXfW////CboN6n8k70PHkJFEcAOobmytWnigoxbEWN0IB9CYRJbFogGLEBxA5ZB8S2EIwrpahABUDxYTkAAW/yEB2MQRlmQ6CmgCRhvuo4BSACtg4ZkaVvLBszFEBaKMhPBRCimMcgPKR1GDBKBGwJXDRXBp+PXyyjqkU8wwAbI+bGZBHYRkIxpAV4DNECQACyV49QAdQ4Lz8QOY6XDrcAHoWAnyoCO6SyHg+73trZBT11AAu6h9K8qJKDCbMfz1/d7+2YhTxzABsn3YnQAB6HI4LcQAUJ0EAWGToHbC3QABUPPRRP/DleM2GEMBTqNwy2CYgRtAzSBGKRogJRL///9PcaLBBLCMQQCgDYjC/YxqA1QQzdUQgCGHIYAfkBhUWACBUCEvJPD5Al2OgAOQAFQnQYBs0q+Xp5dAz+BEAbzaCagncEENhzvr////hN0GbXvD2+nQoXI4Hx+A6iUA4CNN///jcCMCQKSRZSEihITgACqH6ltKQhAWfoQAVA8WE5ABRBq/LE7pUUBtMNpwHwWUAlgBgZRrocMz0JY7pEWKaLfDdSAXUuhZH5saJACVhiuHi2DVgD52wSutru4WXz5h6cELjy5Nx9SHzSx0B6IDEkfc/8NDCdpSh4QSQjsBADOdkHpYFYU8p4zXK9/fXT+xfnYLYuCQgYEB5Wo8mM2o/kIbfWUXVVY3C8xqmb3+xPV32wsYGBhQ7cPnBHQ57BZiA9CVBxSvdYDbCXcDBKC7DA6gynE7cWiMuJMYiQhAZqLBBqDtL8IAmm/gAOpnVBugglhdjSGHIYAHkB1UyAB/qJAZEvh8gS6H3wHIgIKs9evTowsHl05Amt1kYEDdeo3urP///xPhNuiQMrSlDg0t5HIOJ4CWtQQB1Oj////jcCMcQEt/ZFksGrAIwQDUAOy+JSsEYcPguKZz0AAWE5AA1Hk4ZAloHgVUB6MN91FAKYAVsMi5FtomDVjxAlZKIi+5hOlALqTQsz50bg5XsQO1AK4cu6EQANtcz8DrDjunDwlATULRh80s6Mg1zooBKo9YkonNEFQACRlI0EC1o42e4QYw05FCABuAWoFSBWGENS7w/emFpdBDCJEqbJjNyP6CnTiBdE4mAkArWBT78DkBXQ6mH3tSeLEigF1UWV295SiirgpY8eI/ZQDqSbgbIADdZXAAS62YMhAAk0dMe+A0CrcM1E0oAY8DQM0gRikCkByJWAHxiQYrgNnDHrvq/nOs4P4q6BkXKAOicD+j2gAVxOpqDDloUYDkbGRwskGUV1pdPXL5fWoFFaw5hD1lw4whNSQwvIUE0OVgdmB1wP8XK6idtX59urW/ETqGgmQnurP+//9PTJKBttUZYjd/hOUwlJDABU5C4plBt/34c+zgeLsuAwMDA3J5DOt6w8t3FACVRfYBFk9hEYIBwr6FABJCENrXQhoxwwegPiDef0iAOoljFBAPRhvuo4BSACtyUDI1tGwMWPEC0nRU7774Hw5gOpALKYySB6YodvPH/xgA2sxFUg5Xj2woBEDdgvV8GviINIo+bGZBV1Diqhog3kR2ETZD0ABUk+7Eq1BWANElH8x0hH1YAOJgXqTBT8ywvr+j0E1dWVQXOY5gAGZP2JrX/yEAJoLkrxfQ+l+9/RySNVAAn/CH2/cf0wnIAEMOZiO2pACrvCGqYekCUd+SCaBWQkyFAwyXwQFUPQ6LYY5E6vXhNgqnDMwOpIDHBaBmEKMUDkiMRIoTDVYAy444ctn///+R3ILSG4X5GdUGqCDc1cgAQw6WxdWx+QmaQ9kL9v0iNahwA2jbCtGfQwCyQwLDW0gAQw4WL1TOWp/PzAg0U5fmxWYszBFIV2dARSAWwQDMaagRigKgLmRP3vYEwiKmkQoPNHw+gYU+gy58NgNajWCND5hyZB9g8RQWISiAJT24b6kQgtC+DHYXYwJS/YcEoM16HD3eUUB9MNpwHwWUAlgBi5qpocVAbGOjLgNy8ff/P0IHrJD6/x9byQMb1ZJFPzcYaeELknJshkIAtHbEVjkhTXgj68NqFnzgHt857kjTl1gNQQPQAtG6tNSRgQGrC3EBmOlIIYAKvt+DXdXBgHy6/P//mGENrf+wnewHq+WQakSYzUj+go24YGnxwIMGyb7/mE5ABphy0GDClhSgC51glTC0ecWA5Th1iGJ2UWX1wEW3/xMAUE8iueH/f2wugwNYasViMTyRIXefcBuFUwbqJuSAxwWgZhCjFA5IjESKEw02AI8+bK0HBIApg0f7//9wP6PaABWEuxoZYMrBHI5xlPX/t7uyRBkYYA4jMahwA1gPAHOYE+ZFiIU4AUwZUkhgegsBMOVolLVgirEcZgizEWmQAtNZ/4lLMtAIYy8oLWBnwBofGACWbuH+wg7gyhARgNNP8AyO4gMsnoI2cDGsRko0cN/itI34EIT7AbPG+g9J0bzS6vp1B7/+////Px4bsfsPAfBOHI0CWoDRhvsooBTACli0TA0boWFgQG+3w3XAC6n/WEseeHHGjnQB4K39bdALABkYUJRjMxQC4OUX/EK4//+/v7t/YR30/jgIQNYHq0/RCjx4AYY0Q/4dcQ8dWn2P20FIAFYGMzAwMLAjDZsRBDDT1bMW7UQF62e3lMcjbWjCqJIxwxpxe6Jh6Tqox/5/R7pZFqEUe+DAynx2Q/gtmr8+vbq+fyL0UkUGBgYGFEMwnIAEsMihOvDp9/////+CXerHwIB81QxcJfJlgb8+PbqwDnalJZaWJiaABi+yG/5jdRkcwFMr0oqsX58ewe+VRIsH3EbhlIG6CX+CggCoGYSPg9y5c+fO89BWBImRCA9pdsNSshINJoD29tHLC0wAzzZIKqF+Rg0dqCCyU+AAmxxijipsIuw623fw61DhPWASgwo3gJaSGC1OCkICm7dgAIscajQ+/f7///9fnx7BI5HcrIUSkHC1iJtlRQv2ffwPA1DfonYJiEru0JY7BMCDAA+AJUSChS3cZIRK+NANOzzavz9FZHAG1IDFEtYo6QaRuioteBlgd5MifEt5CCKZgXTZ7f/v767vnwiNXpRWOmn+QwBciXgU0AyMNtxHAaUAVsCiZ2podmbALFBhOhCFFPZi7v//t/tKEaUGHLArlxaEMzAwoCjHZigU/Lo5D6mxjwzYlTPXrSoTZWBAMQpRwEIAYiTh16PNmVgcxMDAwMDrPvEcrNb6//8/XgchAdh8JgNSBUEMgJmOH2A46v///1jDGncQMbArZ25+BC/c/2MPHOwRxcDAwMDrPnH7JE8GBgYG9oJ9cGOwOAEOsMu9PdZgiM0GduXSXS/h5v7/j1slAwMDg2zYipsoinEAaPCiuQG7y+Dg7bmJOAIRIwzxGIVTBuomAgnq////cDOIA3DzSI1EShMNGoA3ldDLCywA3hlHDIhC/Qz3zf///+GCmIH5H5fcr5srYI0aVMDrPvvq9/9QQGpQ4QTQFhfS4On//5SFBHZvQQB2OVwZhrKshTuIGHjRiiW4HxgYGBhgKy6ITO64qxlsANbPIaawhSdZxAA5jvKfHVYhIQcs1rD+eLIVWwDyus/bNd2agQHVt5SG4P///3EnZwYGBnbDhmPIhvwnyX9wAO0LwdLfKKADGG24jwJKAayAxcjUsCIVQwKmA7mQwlrM/f/////3e/tn58KOceSVNgts3X7vOxbl2AxFgF8vTy9BOrqNV9ossHL2/nvf/yPKPETx/P///18vD7W6wVQjlYP/////9fLKulY0o5Ycf/r9PxrA7yA4gFlPYrkHMx0LYBdVVjcLrJy948pL7CZiBt7///9hIa0O8xhun2EPnO/3tiOdt8kuquwWP2EdxAnQKhDJizic8P//fzxyqCHPLqrslguNQ3Tw69MtZMcwsIsq4/ALDgANXjQ34HQZAkACEWYxu6iyW/wE6OAaCsBtFE4ZqJsIJaj//+FmEAeQzSMpEv////8f4l+yEw0SgGUE1IyIC8AXBcObYFA/I/sGLogZmP/xyUE8BQsFXmmzwFZoICABkoMKO4C23JFXyVEWEri9hUeONlkLaiqyWjfs2eH71SUJ8CNTIN06YpM7NLCJCiyYWqJiBu4C1C7Br5enl8ATB6+0GXKFhBywEBEUof///+PSD7MMzbcUhSAMfH96HLnyY+CVVsdhyP////9jdx/ENxie+f8fNvREXJCOAuqA0Yb7KBiSAFKOoFR3QxRAa+nRcm8UjIKRCKCNyWFQlA0MgIYfMe12ugJoFYWtrTucALTdjtK3GQW0BqMN91EwSMH9ee6iyupu8auv/McE0MF8pN1vQxVAy73R9YGjYBSMTABdiDzoWp5DA0CrgsHX7xkZDXfI2iP45o9RQB8w2nAfBYMVQOeQsRy59nZXligDA8MwGKX+dROysnF0vGIUjIKRCqC7PpH2gY4CIgG0KoCuEhpMALryG33tyzADkOmO0ZRLbzDacB8FgxXA97gjHQaDfIYBu+O8oTqWcWXr7PU7d66fnWvBy8DAMBirnVEwCkYB3cDHfQWiDKPjlsSCFwdXLN25c+fSVh9ldgYGhsE17vH9+/f//3+9XBfLzsCA/Yj+YQMgc0Uox9uMArqA0Yb7KBi8AM+pFbwWrceGbiUHnd6FAOSz30fBKBgFIxFAho6RT/ofBTgBdH0hFAyuQIOd8c/AwMAwlAeXCAPIRBHSEaCjgG5gtOE+CgY3gBzwAD+1Av+G+KEC7i+PlOZlYGDglXar3PVoSHtlFIyCUUANALklYpgviaYSONqiLMrOwMAuapgw+9ygajd+3Vsiys7AMCjdRl1wZ7Y1A9rlFKOAXmC04T4KRsEoGAWjYBSMglEwCkbBEACjDfdRMApGwSgYBaNgFIyCUTAKhgAYbbiPglEwCkbBKBgFo2AUjIJRMATAaMN9FIyCUTAKRsEoGAWjYBSMgiEARhvuo2AUjIJRMApGwSgYBaNgFAwBMNpwHwWjYBSMglEwCkbBKBgFo2AIgNGG+ygYBaNgFIyCUTAKRsEoGAVDAIw23EfBKBgFo2AUjIJRMApGwSgYAmC04T4KRsEoGAWjYBSMglEwCkbBEACjDfdRMApGwSgYBaNgFIyCUTAKhgAYbbiPglEwCkbBKBgFo2AUjIJRMATAaMN9FIyCUTAKRsEoGAWjYBSMgiEARhvuo2AUjIJRMApGwSgYBaNgFAwBMNpwHwWjYBSMglEwCkbBKBgFo2AIgNGG+ygYMmDt2rWTRsEoGAWjYBSMglFAHJg1a9b/UTC8wGjDfRQMGWBmZsYwCkbBKBgFo2AUjALigICAwP9RMLwAw/9RMAqGCDAzMwsODv45CkbBKBgFo2AUjAJCoLKycrThPvwAw/9RMAqGCDAzMwsLC/s/CkbBKBgFo2AUjAJCoKamZrThPvwAw/9RMAqGCBhtuI+CUTAKRsEoGAVEgtGG+7AEDP9HwSgYImC04T4KRsEoGAWjYBQQCUYb7sMSMPwfBaNgiIDRhvsoGAWjYBSMglFAJBhtuA9LwPB/FIyCIQJGG+6jYBSMglEwCkYBkWC04T4sAcP/UTAKhggYbbiPglEwCkbBKBgFRILRhvuwBAz/R8EoGCJgtOE+CkbBKBgFo2AUEAlGG+7DEjD8HwWjYIiA0Yb7KBgFo2AUjIJRQCQYbbgPS8DwfxSMgiECRhvuo2AUjIJRMApGAZFgtOE+LAHD/1EwCoYIGG24j4JRMApGwSgYBUSC0Yb7sAQM/0fBKBgiYLThPgpGwSgYBaNgFBAJRhvuwxIw/B8Fo2CIgNGG+ygYBaNgFIyCUUAkGG24D0vA8H8UjIIhAkYb7qNgFIyCUTAKRgGRYLThPiwBw/9RMAqGCBhtuI+CUTAKRsEoGAVEgtGG+7AEDP9HwSgYImBQNdz3FTAQC6xn3/k/CkbBKBgFwwF83JbMzsDAwMDAoDvx6n/84NfL07MTzKR5GRgYGBgY2EWDl9//jwdAS9XREpNqYLThPiwBw/9RMAqGCBhtuI+CUTAKRsGAghcrAhhgQLT62K//uMHHg6WyDCigYN9/fABaqo6WmFQDow33YQkY/o+CUTBEAJEN9x8/f/cu3h5aNkU/osYzt6dm2tpD5278pzaAVjEM3p07CYEjtz7+HwWjYBSMgqEPnizxZGBgYPAMCGBnYGBgT96Gp3A7WMrOwMDAwB648NanX///////69ev//gAtFQdbbhTDYw23IclYPg/CkbBEAHENNz7lmyX9y6Scs9HQ0mNc/5TFUCrGEJDSKNgFIyCUTBswNWJugwMDAzq3cc3xzIwMDAweC558h8XgBaSsZvxNO5HAU3BaMN9WAKG/6NgFAwRQLDhHlQySco9HxfSDav+Tz0ArZNGG+6jYBSMgv//z69YtS67YLqTx8KQyB21ja9u3f4/DMHFbnUGBgYG0YaT8KXueBa6QwvJ0TJy4MBow31YAob/o2AUDBGAv+G+cPMRKfd8/CiubtZ/KgFonURqpQTVhnUuGCqHZOKd2dYMDAwMBfv+vz02IVCbl4GBgV3UMLB1H2KQ69fLK+taA82URdkZGBgYGHil1d1yZ++/9/0/GoAaZT37zv/v97a3BhpCNLCLKrvlLjn98td/rODXy9NLKgNhm8vYRZXNAlvXXcGh+tenW/tnVyLcwsDAK20WWInpGqhbCvb9///r5ekluW5QHez4HTMKRsGgBP/+bSgomWBqjYbOLFr6f3iBX8eqRRkYGKBL22EtdwgPBUBzOBqwnn3n/39oMWc9+86vmxty7UXZGRgYeKXdcldc/AiTY7Cefec/Gvj+9DhqSYS9mPv///vTC+smxLupQ1VC1MZPwCi1oE4c7oXQaMN9WAKG/6NgFAwRgL/hruxXKuWeTxBtPXzhPzUAtIphKNj3nxQA1YalZoLLIZkIrVxiS1H2eMEryl+PNmcqszNgA7JhS66i1GpQo6zbZ5di0cJu2HDs7X9UgNt0bKrfHmswxKqYgYGBXbl0H7J6qFsK1mHXw+44+ybEg6NgFAx+cKCnf4Kp9QRT65Nz5j85d/7W7r1rs/InmFpPMLV+ce36/+ED4C31hpP/////D2/HYy50h+ZwNAAp9SDFnHVBqSM7AwJAVtxA5KAK4eDXzRVhyAUgAsgmb36EVFTgVsjAwMDrPg+5WIE6cbgXQqMN92EJGP6PglEwRACehvvJK3el3POJQQU91BkGg1YxyM1sYgBUG1rNBAFQOSQToZULAwMDu2Hp9nvf////9enWifNP/v//////230FkEqKXTlz6YVHn379////+7vr2ysteBkYGBgY2B3nIVmCMIqBgd2wdPv1d9/////16dHx2dCqDlU5wnRei0qIYhTT0eq1JysC2BkYGBAm//////u7+3DDGWRbz/6HA6hb2NnZGRhkwybuvwVz+zpYTwHP9PsoGAWDCHx4+nSCqfUEU+sbO3b9RwLr84ommFpvKi7/P2wArN2OyJywlju02Y0BMEu0/3BBBgYG2bDZp1/++v//+9MLZyAb+KFyKMXj282x7AwMDMgl0a9Pt7aXQpvb1tNv/vr///////9/nWyAlDayYbOPQ8rD/78+vboOV8oetubFfxgYIYXQaMN9WAKG/6NgFAwRgKfh3jhrg5R7PjHIMa39PzUAtIohDFAqIZg2VEEogMohVXPQygVbHQKvpawnoQ6t////6+ZsR3YGBgYG9tjNb/9DAdwoBt32cygafj1YAm11IwbOYKajtc////+PMB2psobuWEO2DwagUig+hrsFUwNMueO8+/9HwSgY9ODW7r0TTK0XhkT+RwUPjp2YYGo928v//3AB0ONkUMuikw2QljuKIBxglmj/4YLIxQ0cQOWQCgtY3wCzJIKdNQkzB96v6L6IqhAhheySEVIIjTbchyVg+D8KRsEQAXga7sGlk6Xc84lB6oHl/6kBoFUMYYBUCf2Ha0MVhAKoHJbKRb374n9U8GtfATsDAwNi2QwqgFU9iMY1zCisGqDKYVUgvKLDXh3D7IZ74sWGNHV1ZVHegn2YRsOqdvbSg/9hAO4W6JQ7MoDufkNWPgpGwaAFZ5cun2BqvS674D8qeHP33gRT6wmm1v/+/v0/HAAs08JzPQRAiw7sRQVmifYfLoj1rBmoHMIOWFmDq9jilVZX9557/f/////P9pupq0vzIvQiAdjZ80hNcZh/hnkhNNpwH5aA4f8oGAVDBOBpuHcv3Cblnk8Mcsvu/k8NAK1iCJ/jjnqKO1Qb1toFKodUzcEql6xd6LUWrGrB2lb+////zelmDAwMSDqhRuGoi6DGwSoxaGMbxkUHH6EnwQWsQMw84wRQi7F5C1vVDZNDUj4KRsGgBU/OnZ9gaj3D2ePPL2hOg4DL6zdNMLVeGpPwf3iAq9AGOmIoAApgEtia1pgl2n+4oNn0m/8xAFQOUTxCCyYcxRbxAMNgeEEzzAuh0Yb7sAQM/0fBKBgiAE/D/erdJ1Lu+cSgismr/lMDQGsCUst2qDakCgQBoHJIJuKuQKBqsZrz/////792ZTEwMCApgBoF56OC+/McGRgYYE1x2PgUIYDDsF+fXj1/fv3EzvWzW7Jgp9eg+AHqFmQhOMAnNwpGwWADv75/n+MVMMHUem9bF3xw/dHps1NsnCaYWh+dOuP/cACwBSvQAgIFwJbQwOfrEABaSqHmZagg1sIDQw5DgGjw/d3z5/cvHNy5dEJ5vBvsiBkkc/AVNPjkhhgYbbgPS8DwfxSMgiEC8DTc////rxNWLeWejx9JexRcv/fsPzUAtEYhtWyHakOqQBAAKodkIu4KBKoWqzn////HVAA1Cj4CjwZQlEPVEgQQ1RDw6+WVdRPiYWeqYQFIfoCZjyQEB/jkRsEoGITg9r4DE0ytJ5haz/UN2lxauSolY4Kp9QRT62VxSf+HB4C32/EDjNF4aKmCmpehgsiFBxxgyGEI4Aff7+2fjTg0EhMgmYOvoMEnN8TAaMN9WAKG/6NgFAwRgL/hfuDsDSn3fPyobMKK/1QC0BqF1LIdqg2pAoED2CA5kom4KxA85vz//x9hGFwB1Cg4HxXARtwh88YwHtKKUPwAvmEVCnil1dXd4ssnLD144dGl6dYMDAwofoC6BVkIDvDJjYJRMDjBzV17Zrn7TDC1hqNtlbVf37z9PywAbMsLQYC+0B1aSqHmZagg1pIIQw5DAA+AH4QFAeyiyupmgVkts9efuP5uewEDAwOKOfgKGnxyQwyMNtyHJWD4PwpGwRAB+Bvu////T2uZL+WejwtR6zwZCIDWKKSW7VBtSBUIHGCpLbAIQQFs7SeuNe5QecQIO9QozG2u////x1jUfrCUnYEBwSUAYEfQMPC6t+2HnNeGBKBGI/sB6hZkITjAJzcKRsGgBT8+fbp3+OiJWXMvrln/4uq1/8MHwFbOsceuuv8cK7i/CnpiI9pCd2hph5qXoYLYikBMOWjpgWON+8kGUV5pdfXI5ff/I06kRRyOiwAwLyAMxlvQ4JMbYmC04T4sAcP/UTAKhggg2HD/////7hNXtEIqpdzzkZGsV2H7vM3/qQqgVQypZTtUG1IFAgOw8xOQTcRdgcBUo9WUMADbMYawB2YU+qDY////4crh3QDY7lMcpqOBk5DKlSF8HZYRRtgKWGQ/wNyCJAQH+ORGwSgYBfQGsByMrzCAr6VBXegOLe1Q8zJUEFE0IQEMOVg5h3XAAbnYgjXN1dvPYboS7jyEwXgLGnxyQwyMNtyHJWD4PwpGwRABxDTcIeDw+Vu109d55vbmdS1Zv+/sfxoAaBVDatkOO+0FfTko0mITJBPxVCDwYW5857gHrIDbAjOKAePYYrhy2YaTsDoPPjmOafr//293ZYky8Eqr69cd/Pr/P3x8HrrOBgUgzV4j+QHmFiQhOMAnNwpGwSigM4C2jvG22/8jlKGUbNBCEjUvQwWR2tAIgCkHa3NjnuP+dleWKAMDzGGw5X1YWvjwAg7FYHwFDT65IQZGG+7DEjD8HwWjYIgA4hvudADQKobwcZA7d+7ceR5Rl8EqOHbD0u3wy/og95Gys7MzMKDUFngrEHijGGlyGG4WAwNaVQczioGBgQF+s+CvT7f2t7lDlMsW7ENu0MPvK2RXjoKq/v//+7vr+yfCLkOFX1kIb+XDzf3///u7+xfWwVzCwMCA4geYW5CE4ACf3CgYBaOAvgA2nYZ1pg4ZwAbmkVVCC0nUvAwVRGpDIwA2OXhJhHK/Kew6VPg4BHLBCl2v9+vTq+v7J0YpszPAAJLB+AoafHJDDIw23IclYPg/CkbBEAGDsuFOFECuAD6ebDVE1CRwwOs+bxdkGyeSYgIVyK9HmzOVsZjFwMDA6z7xHKRCgwKYUd7JybIM6IBdOWoFtBWOAL9urgjDVAoB7IYNxxDG/7o5D9r6RwfsypnrVpWJMjCMxDpzFIyCIQ5g493IrXFcAN5/hwyB//8PLyRR8zJUEKk8QADscjhLIl732YgJwbf7SpUhDkAHvO4Tt0/yZGBggKyqgQB8BQ0+uSEGRhvuwxIw/B8Fo2CIgOHRcP////+vl6eX5MJOTuSVNgts3X7vO7y6QFIMFUEWQge/Xl5Z14o4AI1X2iywcsnxp9//owEko77f214JtZ1dVNktF5tyKPj+9PgS5OPVeKXV3eInQKcKkMGvl6eRFfJKmwVWzt5/7/t/RIWOmENHcgsGwCc3CkbBKKAjgGVdpLyLB8DWoyMWukMLSdS8DBVEa5xDAG657/f2z4aXmQy80maBreuuvEQrhr7f2450JC27qLJb/ASoKuiAPKJTga+gwSc3xMBow31YAob/o2AUDBEwqBruQw0Mo7poFIyCUTAKRgERYLThPiwBw/9RMAqGCBhtuFMARhvuo2AUjIJRMLLAaMN9WAKG/6NgFAwRMNpwpwCMNtxHwSgYBaNgZIHRhvuwBAz/R8EoGCJgtOFOARhtuI+CUTAKRsHIAqMN92EJGP6PglEwRMBow50CMNpwHwWjYBSMgpEFRhvuwxIw/B8Fo2CIgNGGOwVgtOE+CkbBKBgFIwuMNtyHJWD4PwpGwRABow33UTAKRsEoGAWjgEgw2nAfloDh/ygYBUMEjDbcR8EoGAWjYBSMAiLBaMN9WAKG/6NgFAwRMNpwHwWjYBSMglEwCogEow33YQkY/o+CUTBEwGjDfRSMglEwCkbBKCASjDbchyVg+D8KRsEQAaMN91EwCkbBKBgFo4BIMNpwH5aA4f8oGAVDBIw23EfBKBgFo2AUjAIiwWjDfVgChv+jYBQMETDacB8Fo2AUjIJRMAqIBKMN92EJGP6PglEwRMBow30UjIJRMApGwSggEow23IclYPg/CkbBEAGjDfdRMApGwSgYBaOASDDacB+WgOH/KBgFQwSMNtxHwSgYBaNgFIwCIsFow31YAob/o2AUDBEw2nAfBaNgFIyCUTAKiASjDfdhCRj+j4JRMETAaMN9FIyCUTCYwdfLK/6PglEwaMBow31YAob/o2AUDBEw2nAfBaNgFAxm8LSN//8oGAWDBow23IclYPg/CkbBEAGDtOH+cVsyOwMDAwMDg+7Eq/+pAfYVMDAwMDBYz77zfxSMglEwRMD7LdmP6xjerI74P6wB9Qs8+oA7s60ZGBgYGAr2/R85YLThPiwBw/9RMAqGCBicDfcXKwIYYEC0+tiv/5SD0Yb7KBgFQwz8/f7+SSP74zqGx/WM//78/D9sAQ0KPPqA0Yb7KBgugOH/KBgFQwTgabi3z80OLzciBiU12P+nJniyxJOBgYHBMyCAnYGBgT1528f/FIPRhvsoGAVDDLxdE/W4jgGCXs13/D9cAS0KPPqA0Yb7KBgugOH/KBgFQwTgabjXTk0ILdUnBsXWWP6nIrg6UZeBgYFBvfv45lgGBgYGBs8lT/6PglEwCkYS+Pv19eMG5sd1DDDE+PP5+f/DEYwWeEMLjDbchyVg+D8KRsEQAYOw4X6xW52BgYFBtOEkfOXn0Fr3OQpGwSigGLxe4vW4jgEZvZii838YgtECb4iB0Yb7sAQM/0fBKBgiYNA13H8dqxZlYGCArvSEVWQQHlbw69Ot7RPi3ZRF2RkYGBgYGHil1d3iJ2y/9QlVA86lMt+fXlg3Id5NXZqXAQLYRZXd4iesu/IS1YBRMApGAb3A7w8PHtcxPq5jQEM/Hh39P7wAaQUepBiznn3n/6+XV9ZVwoo9dlHDwNbt977///////9fL08vyYXJ8EqbJcw+jb0s+/Xy9JLKQDNoyccuqmwW2Iql3IMuhynY9//tsQmB2rwMDAzsooaBrfuewOUYCjCWynx/ehzVcLfc2fuhLkQBRBfACL///35veyvcbF5pM4Tn6QFGG+7DEjD8HwWjYIiAwdZwh1dcDSf/////H16t4Vj3+evmPHdI6Y0B2B1n30Qq+SGlPqTYh4NfN1eEyTLgALzu85ANGAWjYBTQC7yaa/O4jgETPZ+k8X9YARILPEgxZj19F7Zyj91x9s3vWEtE3rA1T/6jgF+PNmcqszNgAeyGDcfe/kcC0MZ5bGmpLAMCQLoWUDm0hjvuclU2efOjX//hALdCBgaMAhjud+x6ZAv2oTibdmC04T4sAcP/UTAKhggYZA13WDWGmCqGVWRY131CF4eyG5Zuv/4OMuLy69Oj4xOhVRfCFFipj9Jw/3WyQZaBgYGBQTZs9vFHn379/////69Pr65vLzVkZ2BgYGBgD1vz4v8oGAWjgK7g16urj+sYcKHvd3b9HzaAxAIPWoyxs7MzMPC6t+2HzCt+f7q/WpeBgYGBgUFWVpYBUZ79+nRrXbIsAwMDrJ0NA2/3FUCEeS0qYUXn93fXt1da8DIwMDCwOyIPesAa5wwM7Ial2+99////16dbJ84/+f8fIYfccH+7OZadgYEB2fBfn27BS1Xr6TCzSSyAkfzObli67sJTiMmI8h5HZ4fqYLThPiwBw/9RMAqGCBhcDXfo6QpI1dj///9PNkAqMhTB////w2sN9e6L/1HBx23JvKLK6upZW17/hwJIqY/ccP8IqzO7L/76jwpgUij10SgYBaOAHuDlLPPHdQy40PMJKv+HCyCxwIMXYwzopRa8uc/AHrDiyX8k8GtfATsDA0opCWswo7bP////////r5uzHdkZGFC6DdBilgGbg2ByiIIS5hJMwz8ehAzZw9rXsFIWzSv//yOkkMzF7XeEcvbSg//pAEYb7sMSMPwfBaNgiIBB1XCH1QJIrev//+Hj6pj1BlS9aNYuwnOk0FIfyeiz/Wbq6tK8SCIIADtX2XHe/f+jYBSMArqB7/f2Pq5jwI++Xd/wfzgAaAHGgFYG4Szw4MUYQ8AKpKHo//////8IPZCGvWAfaqMWbgm8DQxr5mIx/v9/eEMf4SaYAUhNfziAycENh2lHHeGHgqsTdXml1dW9517//5/0Ahin3//DfY+snHZgtOE+LAHD/1EwCoYIGEwN96vQ+gppsAcCYBLolQFscIeBXdQ+d/aOC/ffff+PC0BLfay1BBZAovJRMApGAVXAi8naj+sY8KNn3VL/hwGAlWtEF3jwcglLGxoqg1liYbStoSP6sFX16ADWCIa3j2EGZO1Cc8x/hBzccOgRORSPfWPxDlQIi9/hcsjKaQdGG+7DEjD8HwWjYIiAwdNwh7fC4RUGAsBmlGGTrHAAX6sJA7zSZoGVs3dA1j8iA4Il+/d3z5/fv3Bw59IJ5fFu0PMK8CgfBaNgFFAbfLux6XEdAzHoy5lZ/4c2IKvAw12M4ZRBb1vDBrMJAbhJ6AYgAww5nM4gCAgWwPiMxidHdTDacB+WgOH/KBgFQwQMmoY7vBrDDzAGp/5/v7e9FXJGGQpgF7VvPfTy138YwF6yf7+3fzbizDJMgKZ8FIyCUUBD8KxP4XEdAzHoSSvv/yENyCvwsBdj///jkUFvW8P4hADcJJgGmAHIAEMOpzOwA1IKYHxG45OjOhhtuA9LwPB/FIyCIQIGS8Mdtu6SIMC+MPP//+/vrp9YP7sFMVbDwMDAINtwEtZ0x1Kyow3Xs4sqq5sFZrXMXn/i+rvtBQwMDKjKR8EoGAU0BF/Oznlcx0A8+rCn6v+QBWQWeFiKMSjAKYPetr4/z5GBgYGE9eDoBiADDDmczsACSCyA8RmNT47qYLThPiwBw/9RMAqGCBgkDXfY9C177Kr7z7GC+6ugZ4xhrvtEB9+fXlgKPQQNsdQSo2R/siKAnYGBgYFdOXPpBehZZHAAcw9C+SgYBaOApuD91tw3K4KIR+82pvwfqgBWwJBa4GEUY3CAUwajbX2wlJ2BgQHnGncMgGEAEsCQgy6gRxS8KOBkgyivtLp65PL7ZBTAOH34H78c1cFow31YAob/o2AUDBEwOBrusCWdKHUUGoBPLcPXfd7fUeimriyqi22zEqxCCVsDPRASvWSH1Qzq7ecwrYTbBVc+CkbBKBgFVALkFXj/MYsxBMApAysK4W1r2O5TfJYjAwwDkACGHOxUGaxbSKGbbtkL9v0iowDG6cP/+OWoDkYb7sMSMPwfBaNgiAA8DfdDZ7dMWlZJDFq9a/p/SgC0PCdUk8CUwdZ9wmabsRwHCSv4EaNK6CU7bMIYSwUDP8sYSfkoGAWjYBRQB8BKMhILvP+YxRgC4JTBaFvDC04G60lXv/9HA293ZYky8Eqr69cd/Pr/////2AxAAEw5WNGLeY77211ZogwMMF+TXgDj9OF//HJUB6MN92EJGP6PglEwRACehjvdwEnI7Craak4sADZOBVP562I3pGZjNyxdB5tt/f7u/vHZ0DuxkcpxjJIdViuyG8KvXf316dX1/ROjlKGVBgOy8lEwCkbBKKAGILvAw1KMwQFOGcy29X/E5absylHwS0u/v7u+fyK05ES63xSrATCATQ5uuGzYRNjdru/g16Gyx26GDLOQXADj9OF//HJUB6MN92EJGP6PglEwRMDAN9xhIzSIygk3gA8VQQZt/v//dXMe9LprDMCunLn50a//MIBZsr/dV6oMMQ0d8LpP3D7Jk4GBATKpOwpGwSgYBVQCFBV4mMUYDOCUwda2/v//180VsDY6BmA3bDgGaVz///8flwEQgF0Op+G87rMRY/ykFsA4ffgfvxzVwWjDfVgChv+jYBQMETDgDXd41YSYDsYDYCsokdd9fr+3f3aumzr8NBleabPAyiXHn8IriP///+Mo2b/f2z4h3k1ZFGqmqLJb/IR1V17++g8fD4JVmKNgFIyCUUA5oKzAw1qM/f//H48M9rb1/////39/enwJ8lmMvNLqbvETtkMGyREAtwH45CDFMqxs5ZU2C2yFFq1IgKQCGKcP/+OXozoYbbgPS8DwfxSMgiECBrzhPgpGwSgYBaNgFAwVMNpwH5aA4f8oGAVDBIw23EfBKBgFo2AUjAIiwWjDfVgChv+jYBQMETDacB8Fo2AUjIJRMAqIBKMN92EJGP6PglEwRMBow30UjIJRMApGwSggEow23IclYPg/CkbBEAGjDfdRMApGwSgYBaOASDDacB+WgOH/KBgFQwSMNtxHwSgYBaNgFIwCIsFow31YAob/o2AUDBEw2nAfBaNgFIyCUTAKiASjDfdhCRj+j4JRMETAaMN9FIyCUTAKRsEoIBKMNtyHJWD4PwpGwRABow33UTAKRsEoGAWjgEgw2nAfloDh/ygYBUMEjDbcR8EoGAWjYBSMAiLBaMN9WAKG/6NgFAwRMNpwHwWjYBSMglEwCogEow33YQkY/o+CUTBEwGjDfRSMglEwCkbBKCASjDbchyVg+D8KRsEQAaMN91EwCkbBKBgFo4BIMNpwH5aA4f8oGAVDBIw23EfBKBgFo2AUjAIiwWjDfVgChv+jYBQMETDacB8Fo2AUjIJRMAqIBKMN92EJGP6PglEwRMBow30UjIJRMApGwSggEow23IclYPg/CkbBEAGjDfdRMApGwSgYBaOASDDacB+WgOH/KBgFQwSQ2nA/f/Pa/1EwCkbBKBgFo2BEgtGG+7AEDP9HwSgYIoDUhnv30rmv3r/9PwpGwSgYBaNgFIw8MNpwH5aA4f8oGAVDBJDUcD9x5UJeX8uyXVv+UxN8v3di6YR4N3VpXgYIYBdVNgusnL3jystf/4cT2FfAwMDAwGA9+85/ugAq2fdrXwE7A4No9TF8sXFntjUDAwMDA4P19Jv41EHdxFCw7z+VAdwFDAwMDLGbP/7HCz5uS2ZngAIK3AL1DqVBjB3gMPz7ve2VFpXku3kU0B2gpE18AJ4UYTrgAhCAI+5/vTw9OypqHmo6oRqAugU9HdITQJ2AHhwDBEYb7sMSMPwfBaNgiACSGu6tC6bn9bXk9bU8fvn8PzXA23Ozo5ThDShMwGtRuesRvnbgkAI4GmI0A1Sy72SDKAMDe8E+vPEAq1kZGPA33aFuokENjOQCBoItd+R2OyVugXqH0iDGDrAZ/mJDFC8DA0VuHgV0B6hpEw+ARytMB1zgP+64P9ulzM6Alk6oCaBuoZn5RACoEzC8PjBgtOE+LAHD/1EwCoYIIL7hfvD86by+lvy+1ry+lgVb1/2nFPx6tDlZloGBgYGBgV3Zp3Xdhfvvvv///////1+fXl0/sbTSgpeBgYGBgYHdsOHY8Fibg60hRktAHfsudqszEGwIw2tWBgYGBnxNd6ibaFADo7iAgIM/bo5lQAAK3AL1DoVBjANgMxzmSwrcPAroDmCxpp61aCdecP7JfwiA6UCOZ2xi//9jTyfUBFB7aWY+EQDqBAyvDwwYbbgPS8DwfxSMgiECiGy4//v3r27WREiTPa+vJa+v5c6TR/8pAG/3FcgyMDAwMLAbNhx6ibWZ9/3eOmjLnt1x9k2sSkYBzQGk3R6w4sV/vABWs0IBzqY7tJFBgxoY5gJ1XV12Bga8LXdou11dXZ2BgYEWbqEdgPlyKLl5FMBijbK2L8wU9LiH5inKDMcDoPbSzHwiANQJGF4fGDDacB+WgOH/KBgFQwQQ2XDffepoXl9L+dSe7z9/zFy/Iq+vZeaGlf/JBh8PlsoyMDAwMMgW7MM3mv7r5mxHdgYGhoGtNUYygNSYhEMfoo6BwdoRFmHYm+7QRgYNamC4C6bPLmBnYMDTcoe222O7uyE6qO8W2gGYL4eSm0cBLNYI5yN8AGYKetxD8xRlhuMBUHtpZj4RAOoEDK8PDBhtuA9LwPB/FIyCIQKIabh///mzfGpPXl/LrlNH////f+fJw7y+lry+lmv3ySzJnyzxZGBgYGAQLdiHo22FALAiG9vmyO9Pjy+pDDSD7mplF1V2y529/x5kwQ0q+PXyNKpKs8DWdbg2v/76dGv/7MpAM2VRdgYI4JU2C6zE1Bm3qwAAGHNJREFUNBrqtoJ9////enl6Sa4bVAe7qLJb7pLT6MbjqmDpbd//X59ubZ8QD9PNwCuNMzRerAhgwGYCBoA6jaFgF6Kvha3pDnUTQ8G+/+jg+739s3Phe5TZ8ccRJoC5wHr2zX2QljuOaQKIn9iTt12E6sB0y/enF9ZNQNsv7RY/AdM1UO9gBtCvl1fWtSKilFdaHXvahBhgPfvOr5sbcu1F2RkYGHil3XJXXPwIk2OwhhoO5aKAgn3/r07UZWBgYGDwXAJbZoECYKv5cXZjRgHtASJtQqKSMIDpKICkTRxxD1OFDNDs+PWStJJveytMMbuoYWDrrke/YLagGYwOYIU6gXSIIv2LxJKPoQASHP//w4MEq6ugckiqYeD7PdSSD0e2/P///3/U2oWBXVTZLLByyfGn30cb7sMTMPwfBaNgiABiGu5bjuzP62upmzXx379///////////yt6/L6WiatWvyfHAArhUUbTv4nDCDtLAb07ZG/bq4Ik2XABmSTNz9CUfloc6YypKBGA+xYls+/PdZgiFUxAwMDu3IpygQB1CMF67DrQV/hA61OUKsaetuH20J2x+5zyLb9/w8dm1bvvvifEIA6jaFg3/9f+JruUDcxFOz7jwzenpvozsuADbDjXEqFBmAusJ595xe+ljskPbEnb/sI04HqFtwJi4GBgdd93k1kx0C9Y40SxLgTHINs2JKrKK0EiAHWBaWOyBogrRuIHNxwKBcFFOxDeByiCQ1AYpCBPXnbx/+jYKAALIpgUUkYwHQU7Pv//z/OuIepQgZIduBOiOzYSj6seZDdsGFJuzUDA4rBWAEkY+FKh5jtdpwFEQMDrpKPoQASHP//w4MEq6ugckiq////j8ODDAwMDLJhK1Cy9f//b/eVKuNwnGxBTPFow30YAob/o2AUDBFAsOH+8cvngglteX0tB8+f/g8Dj18+z+tryetrOX/r2n9SAbQtwcCQtQu1sMQB7s9zZGBgQG3nv90cCylWeS0qt19/9/3///+/Pt3aXgqtCZBajPDF9AiV/7+/u74duvkVrbH7ZEUAOwMDAwO7YSlM8f/v7+4fnw1ry8m2nv0PB9DqhJ2dnYFBNmzi/luffv3////7u+vrYPWl7sSr/+EAWp0gVzX0tu/XxW5dBgYGBgZ25ajZxx9B9D+FWyeatevtfwSAxBRyuOMEUKdB6ko8TXeomyDqoAChnNe9bT8kGH59enQcfuKQbOlBws1OmAusZ9/5j6flDhkYZE/e9hGuA8UtJxtkGRgYGBhkw2Dh8//Xp1fX4UmLPWwNkplQ71jPRgQxPMGxK2cuvQAx4jsiwTGwOyIf2wc1gIGBQTZs9umXv/7///70wplbH///h8shG47VzRAvoTSKYADamELr9BIH/v0bRVgQOQAWayhRiRfAdCDHMzax////Y00n/5ESIlElH7xElYUl/F+fHkEKBnZ2dgYGTPMxAJbGOQxAShLkDElWyYfsdRy+/v//P1wOSTWikEEq+X59egQvZFAWbcJ8Ag+L////f0cUs8wGkfyjDfdhBxj+j4JRMEQAwYb72v278vpaWhdM/48Klu3aktfX0rN07n9Swc3pZgwMDDiKXGwAWgwjSv1fx6pFGRgY0Oue///hi+fZk7d9/P//P7wdhqkSUZIj1TPQZQfssZvf/kcDUCkUV8OqEywaYMod593/DwNQfyCZAFWFqR0uhayacvtgTTyUWur//////9+ZBwkM5JY/pPlLXKMP5jRoXQkPXAbd7ovI2qFugqn7//8/vAoXzdr+Elnp//+ItgeaKdgAzAXWs+/8h6cQeJKBAUgIQPwE04HkFliNjcU+mBSycph3rGff+Q8BsATHYD0JdWj9PyJMkGMPagA8wSIDqBzC8P9Y3QxvniOnZAiA+BbrMjP84LJ/wUFWk1GEiS66Z/0nGcBiDSUq8QKYDuR4xib2/z/2dAJPiMSVfLD8gqH618t10AY9mvnYACyLYKiEttuREjm0vELOCjAAlUIxBYvXsfoaCqBySKphhQxmyQcvZJByCVQ/5vKyqxN1eaXVheXceUcb7sMOMPwfBaNgiAD8DfdX79/m9bXk9bWcuHLhPyp49Q4udfE/SQBaKmIvcrEBWKkN0wBpUKKUtAgAKVrVvede//8fXpMgN0cRAGYOzNz/Lzakqasri/IW7MM0F3KcOQMDe+nB/zAAcxi2MWnIaSwoyqEeh1tHd/tg+hEVNgJ83JbMK6qsbla+9/V/KDjZIMqAXNniAzCj4XUlvHWA2giGugmhDq4Tm6P+//8IjUGscY0CYOZAvAtriaC13CEtWahhMB0It/w/22+mri7NCzEDDcCax/j6RrAkBbUBHVyFtkkQfoUagKWNAJeDG/7/P1Y3/0eEEopSmG9xOQYfGG2440IUNdzxAdS4g+lAjmdsYv//Y08nsBRBZMl3skGUgQFHSoElWrhi3ACW69CUQnMOUlFCbsmH5HVsvoYBqBxCNdQPSC5ABlDT2eGugerHmC+EgtE17sMSMPwfBaNgiAD8DXfIsHo3jmH1tft35vW1tC6Y8Z8kAC0VsRe52AC0XIVrwNJGxQGgVQC2hu7///9hA0HorTvsAOYKRG0AF8LW6oLJISmHehzmDQIAiwEwITLtg3kXueWJB0CCGZtdWAAW67E23aFuQqiDOQruSjTwa1cWAwMDA4PZ9Jv/8QKYC6AGwdoQKHELacnCmicwHXC3EABQt0Mt+P//P6YQJMyQmgBoADbbBF8lBjUAq++gcnDD///H6WZYOw1FLcS3OFM/PvDr5dsvl2+PIkz06+Xb/yQDWKzhAyhRB9eBHM/YxP7/x55OSCv5YMkS3WQIgCZqFPNxAFiuQ+kvwNIhNNsRBFj8iUUIm69hACoHVw3tOcC8iwGgPoSHFtTFDAy82oGtS09cf/UJxeWjDfdhCRj+j4JRMEQAnoY7YiH7zWv/sYEPsOXvh5CWvxMG0FISe5GLDUCLYXixC+UT1g8rrwkBHCb9+vTq+fPrJ3aun92SFWgIPYgAURtgrU7gAIscIXfT2j4sSvAAyCAVLMwJAaxmY2m6Q92EUIdVIzLA36hAAJhBMO/C2hBIPoAogTcgIFx8Jn9/9/z5/QsHdy6dUB7vBjteAmbB//9w78CF0PnoANYNgSvApwGbHC43w3yL1GKCKkUSGQUDBKBRgfcCpiO3kHvIMB3I8YxN7P9/rOmExJIPiwlIANrKxyGLBqA9BqRUB20Fw7MdFkBOyYfPzVA5uGqoowgCuAZEyQUF7KLKbvETlp649enXaMN9mAKG/6NgFAwRgKfhDrlrqXBCW9X0PlyoZFJnXl9L3exJ/4kH0HqAmBHz////Y9mcCi2WsRbZKABW3BMCyCb9enllHdKBYZgAXrgjzEcSggMscljdTUf7sCjBDSCK4XoJAYhyDLMRFSC06Q51E0IdDo0IQFABFMDUwZ0Ma8vCW+4QFfBkBOVjmPz93v7ZSMfAYQC4Bf/h3oELofMxALoCdD4ywCaHw80I38JbTJCOF4I/CgYOwGINJSrxApgO5HjGJvb/P9Z0AlNKCED1YDEBGUCkccmiAfR0B2u3w7MdDFBY8kEchd1VUDm4aiifIIBr+P///6+Xp5dAD2hFAbzaCQEZFaMN9+EHGP6PglEwRACuhvudJ4/y+lqIR7tPHf1PLIAW5UQdM4jUzocvP4AWw1iLbBQAa/ITuTbkP1I7EwJ4pdXV3eLLJyw9eOHRpekYdQeW6gQOsMhhupu+9mFRghNAhuyIi6H///GYjfCibvfFXzA3IdTh1AgD5I64/4cPtEFb7pAWBVIDAqYD2WTYTjUoYBdVVjcLzGqZvf7E9XfbCxgYGFAsgHkHLoTORwc0G3H/D/cttMUE8S2q5lEwQAAWa8THBkwHcjxjE/v/H2s6IbHkw2ICEoAmWhyyGACa8qBlB9TR0FQJB4hiAQJIL/nwuBnqYITqg6XsDAwMRA8VIYNfnx5dOLh0AtJEAAMDA7M6N5/A/1EwvADD/1EwCoYIwNVwv/nwPqnox8+f/4kDsC3+ZF7ABG2h4CiGTzaI8kqrq0cuvw8vsJEaa/gA7CAGpFMJkcBJiK2I2gDhNiQhOMAih17V0Ns+SGMcV23+YkUAu6iyunrL0f+w3hK07iUGYLEeDmAn1jDodl/cVcDAwMCApA5iE5Ir0QBMHusqcGQAcwGyQdAwDFjxAtaeQPYSTAfcLfCEiXSSIwK8gAYfsgXoQQxdBoboZKIBqDzGGne4AcgAmxymmxEA4kOIF6EWIXbBjoIBBLBYQ4lKvACmAzmesYn9/489nZBW8sE6x/BkiQKgsijm4wPQgRn17otwN6O126lR8mHzNRRgqIb1YwJWvPhPPvj16db+RmhRxsLF938UDC8w2nAfBUMG4Gq40xbAC25ZzNO5kAFiXAa5fIadiaDeffE/BoC2XiBNJ1irD6nVjwfAKozwdVjcBK2NkGoDLBUEEsAih17V0Ns+uBpsG05RtjdCOETW+v///0cYjWQ9EkA03XV1GRgYGJDVwXRib2NCXAKLT3wAZo717Dv/4QAaxAErXkDSBUqSgemAuwXWNFdvP4dpGWwtCooF6EEMS5o4EhzEEUjqMQxABtjkMNyMDKDG6068CmUFUNRQGQXUArBYQ4lKvACmAzmesYn9/489nZBW8sHyCVbVsJIIxXy8AKpDvfsi1MnoeRtqHUUlHzZfQwAsEyKphvZjMRyCFXw+MyPQTF2aF1sxCbOWiYP3/ygYXmC04T4KhgwYmIb7f/iB6wzsOG/G/H51CfQyDvTThWFtKHTx///f7soSZWBA1ECwdh+WY7Whinml1fXrDn79/x8+SoWtXYu0ggJRG2CrTuAAixy0zIdXNfS2D1EnYvSXft2ETk97LnnyH1rzEW4rIwEs1qMAeNMdCpDUwYa58Z3jLttwEk0KA8BcgOTd//AmQmxjoy4DA4MuysAfTAfcLbCBOXXk5j0EILqQKBZgBDG8S4qZ4OBGsAesePIfCjAMQALY5DDcjAKg8WtdWurIwMCALWGNgoEAsFhDiUq8AKYDOZ6xif3/jz2dkFbywft8mCcgwrMgmvl4AbTbYF1QYM3AAC1UkAE1Sj7oPACG4fBshqIa5j8shcz/XzenWzOwiyqrBy66/f//f4RijMCA5zAGVm7+/6NgeIHRhvsoGDJgoBru////erkLdqs0u7JP67oL96FTpr8+vbp+Yin0fj8GBnYst3Mj3/OHdIEo7HpL5Gs94CqRbsz7//3d9f0Tob0CRPEMr+uQLsz7/u7+hXVwtzAwoNQGWKoTOMAih17B0tu+//+Rbk41LF134en3//////r0CH5doW73xV//Ya1dLLUqboDFejSA2nRHVoeoahEz578+PYJfaojZz8AGYC5A9u5/xFgbAwMDWrsdrgPhFliNzY64zvHXp1fX90+EuYSBAdUCLEEMb3kgrbf5/g5+YSVabxOLAXCATQ42KYCtOfYf0a5gYGDAdWj1KKA/gKU0lKjEC2A6EGkTd9xDsitGm5SEku//f0SPE5H2vz9FZEEGBhIcj1S0MTBgm/eBS1NS8iFnVkQVUGnBywC76xVZNbzkQ75I9tenRxfWQSsNBqRro+FBJxs2cT9cLeL+ZC4zHn6B/6NgeAGG/6NgFAwRMHAN9///////fg9S0OICvBaV2+99/48N/Lq5Alb/oAJe99mo9RpOlQwMDKi9gl8357nzMmAD7MqZ61aViTIwoNRf2KoTGMAih9EQo7d9////////7bEGSO2DBtiVS3e9/PX/P6yti1nd4gNYrMcAyE13NHVvz03EHRKbH/36TxjAXIDmXahvGBgw2u1wHUhuebsP1plEB7zuE7dP8mRgYECZicAaxL8ebc7Ebco5RHr7j8MAKMAqB2uuQADGdgXoWCcDw2i7fRABWEpDjUp8AKYDKW3ijHt4M5iBgYEBeesP8SXf////cSVbduXSgnAGBlIc/x/ZTVjTIVVKvo8nWw2hliADXvd5u6ZbMzCgqf6Ps+RjYGBgkA1bcROeq///x1MQMPC6u6cUj54qM/wAw/9RMAqGCBjYhvv////////+9MK6CfFu6vBzwXil1c0CK2dDR9LxgO/39s/OhZ8nxittFti67spLbJq+Pz2+BPmIP15pdbf4Cdsxbfj18jSyQl5ps8DK2fvvff//H14XIeZmsVYnUIBFDltDjN72/f//////Xy+vrGuFW8ouquyWC7Xz/3+YSaS126GaUKzHAhBNdyzqUGOTXVQZewzhAjAXYHgX1nLHkIDpQHXL93vbJ8SjOQOaqqANJ9g6rP9EBzEkXpccfwoLYzjAacB/XHK/Xh5qdYOZi9RKgwJYqkFy5CgYaABLaWhRiQfAdKCmTVxx//3qkgT4qSfwBv3/////E1/y/f//////Xy9PL4HnQV5ps8DW7fe+QxMi8Y7///8/Yjkjtnb7//////96SYWS79dLbO6FKUdX/f//r0+3kHM3A7uoshn2jPn/PzQLI6uFhtzoOe7DEjD8HwWjYIiAQdBwHwWjYKgCSJOGxDYNTQG00TPabh8Fo4BGYLThPiwBw/9RMAqGCBhtuI+CUUA2GHQNd+hSGSzba0fBKBgFVAGjDfdhCRj+j4JRMETAaMN9FIwCcgFsuyDmnPzAgF83IYt70Vfzj4JRMAqoBkYb7sMSMPwfBaNgiIDRhvsoGAUkg+/fv////+vlOsjhEwQvh6IxuLJ19vqdO9fPzoVs9Ma5rHgUjIJRQDkYbbgPS8DwfxSMgiECRhvuo2AUkApgB74zMDAwMLA7zhvghTKw7bcMDAwMKKfEj4JRMAqoDkYb7sMSMPwfBaNgiIDRhvsoGAUkgq97SyBHTbCLGibMRj3fcSDA/eWR0rwMDAy80m6Vux6NbkodBaOAlmC04T4sAcP/UTAKhggYbbiPglEwCkbBKBgFRILRhvuwBAz/R8EoGCJgtOE+CkbBKBgFo2AUEAlGG+7DEjD8HwWjYIiA0Yb7KBgFo2AUjIJRQCQYbbgPS8DwfxSMgiECRhvuo2AUjIJRMApGAZFgtOE+LAHD/1EwCoYIGG24j4JRMApGwSgYBUSC0Yb7sAQM/0fBKBgiYLThPgpGwSgYBaNgFBAJRhvuwxIw/B8Fo2CIgNGG+ygYBaNgFIyCUUAkGG24D0vA8H8UjIIhAszMzLS1tWtHwSgYBaNgFIyCUUAI2NnZjTbchx9g+D8KRsEQAba2tvyjYBSMglEwCkbBKCAOiIuL/x8FwwuMNtxHwSgYBaNgFIyCUTAKRsEoGAJgtOE+CkbBKBgFo2AUjIJRMApGwRAAow33UTAKRsEoGAWjYBSMglEwCoYAGG24j4JRMApGwSgYBaNgFIyCUTAEwGjDfRSMglEwCkbBKBgFo2AUjIIhAEYb7qNgFIyCUTAKRsEoGAWjYBQMATDacB8Fo2AUjIJRMApGwSgYBaNgCIDRhvsoGAWjYBSMglEwCkbBKBgFQwCMNtxHwSgYBaNgFIyCUTAKRsEoGAJgtOE+CkbBKBgFo2AUjIJRMApGwRAAow33UTAKRsEoGAWjYBSMglEwCoYAGG24j4JRMApGwSgYBaNgFIyCUTAEwGjDfRSMglEwCkbBKBgFo2AUjIIhAEYb7qNgFIyCUTAKRsEoGAWjYBQMATDacB8Fo2AUjIJRMApGwSgYBaNgCIDRhvsoGAWjYBSMglEwCkbBKBgFQwCMNtxHwSgYBaNgFIyCUTAKRsEoGAJgtOE+CkbBKBgFo2AUjIJRMApGwRAAow33UTAKRsEoGAWjYBSMglEwCoYAGG24j4JRMApGwSgYBaNgFIyCUTAEwGjDfRSMglEwCkbBKBgFo2AUjIIhAEYb7qNgFIyCUTAKRsEoGAWjYBQMATDacB8Fo2AUjIJRMApGwSgYBaNgCIDRhvsoGAWjYBSMglEwCkbBKBgFQwCMNtxHwSgYBaNgFIyCUTAKRsEoGAJgtOE+CkbBKBgFo2AUjIJRMApGwRAAow33UTAKRsEoGAWjYBSMglEwCoYAGG24j4JRMApGwSgYBaNgFIyCUTAEwGjDfRSMglEwCkbBKBgFo2AUjIIhAEYb7qNgFIyCUTAKRsEoGAWjYBQMATDacB8Fo2AUjIJRMApGwSgYBaNgCIDRhvsoGAWjYBSMglEwCkbBKBgFQwCMNtxHwSgYBaNgFIyCUTAKRsEoGAJgtOE+CkbBKBgFo2AUjIJRMApGwRAAow33UTAKRsEoGAWjYBSMglEwCoYAGG24j4JRMApGwSgYBaNgFIyCUTAEwGjDfRSMglEwCkbBKBgFo2AUjIIhAEYb7qNgFIyCUTAKRsEoGAWjYBQMAQAAiabZdecoL9UAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 400, + "width": 500 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "%%stata \n", + "graph twoway (scatter ln_hla_het aa_mdist if europe==1) (scatter ln_hla_het aa_mdist if africa==1, msymbol(oh)) (scatter ln_hla_het aa_mdist if asia==1, msymbol(S)) (scatter ln_hla_het aa_mdist if americas==1, msymbol(T)) (scatter ln_hla_het aa_mdist if oceania==1, msymbol(Th)) || (lfit ln_hla_het aa_mdist) , legend(label(1 European) label(2 African)label(3 Asia) label(4 American) label(5 \"Oceanianic/ No Majority\")) graphregion(color(white)) bgcolor(white) xtitle(\"Migratory Distance from East Africa (Ancestry Adjusted)\") ytitle(\"In HLA Heterozygosity\") ysc(r(-1.5 -1)) ylabel(-1.5(0.1)-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Figure_2.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 2: Explaining HLA Heterozygosity (author´s code)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Linear regression Number of obs = 131\n", + " F(1, 129) = 4.65\n", + " Prob > F = 0.0329\n", + " R-squared = 0.0217\n", + " Root MSE = .06738\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_ln_atd | .0211444 .0098049 2.16 0.033 .0017451 .0405438\n", + " _cons | -1.326062 .084678 -15.66 0.000 -1.4936 -1.158525\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(2, 128) = 37.36\n", + " Prob > F = 0.0000\n", + " R-squared = 0.4265\n", + " Root MSE = .05179\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_ln_atd | .0299843 .0086383 3.47 0.001 .0128921 .0470766\n", + " aa_mdist | -.0120634 .0014224 -8.48 0.000 -.0148779 -.009249\n", + " _cons | -1.321902 .0729942 -18.11 0.000 -1.466333 -1.17747\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 89\n", + " F(1, 87) = 17.05\n", + " Prob > F = 0.0001\n", + " R-squared = 0.1176\n", + " Root MSE = .07228\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_lanim | .0266128 .0064443 4.13 0.000 .0138042 .0394215\n", + " _cons | -1.189394 .0126485 -94.03 0.000 -1.214534 -1.164254\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 89\n", + " F(2, 86) = 52.15\n", + " Prob > F = 0.0000\n", + " R-squared = 0.5968\n", + " Root MSE = .04914\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_lanim | .0360532 .0050961 7.07 0.000 .0259225 .046184\n", + " aa_mdist | -.013321 .0014938 -8.92 0.000 -.0162905 -.0103515\n", + " _cons | -1.109316 .0107794 -102.91 0.000 -1.130745 -1.087888\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 113\n", + " F(1, 111) = 11.23\n", + " Prob > F = 0.0011\n", + " R-squared = 0.0626\n", + " Root MSE = .06849\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_lpd1 | .014363 .0042865 3.35 0.001 .0058691 .0228569\n", + " _cons | -1.154714 .0074313 -155.39 0.000 -1.16944 -1.139989\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 113\n", + " F(2, 110) = 37.73\n", + " Prob > F = 0.0000\n", + " R-squared = 0.4578\n", + " Root MSE = .05233\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " aa_lpd1 | .0158342 .0032576 4.86 0.000 .0093785 .0222899\n", + " aa_mdist | -.0123827 .0015009 -8.25 0.000 -.0153571 -.0094084\n", + " _cons | -1.072103 .0100197 -107.00 0.000 -1.09196 -1.052247\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "***************************************************************\n", + "*** Table 2: Historic Determinants of HLA Heterozygosity ***\n", + "***************************************************************\n", + "\n", + "*Col. 1\n", + "reg ln_hla_het aa_ln_atd if aa_mdist!=. & `base' & ln_le60!=., rob\n", + "\n", + "*Col. 2\n", + "reg ln_hla_het aa_ln_atd aa_mdist if aa_mdist!=. & ln_le60!=. & `base', rob\n", + "\n", + "*Col. 3\n", + "reg ln_hla_het aa_lanim if aa_mdist!=. & `base' & ln_le60!=., rob\n", + "\n", + "*Col. 4\n", + "reg ln_hla_het aa_lanim aa_mdist if aa_mdist!=. & `base' & ln_le60!=., rob\n", + "\n", + "*Col. 5\n", + "reg ln_hla_het aa_lpd1 if aa_mdist!=. & `base' & ln_le60!=., rob\n", + "\n", + "*Col. 6\n", + "reg ln_hla_het aa_lpd1 aa_mdist if aa_mdist!=. & `base' & ln_le60!=., rob" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_2.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 3: The Effect of HLA Heterozygosity prior to the International Epidemiological Transition (author´s code)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Linear regression Number of obs = 73\n", + " F(1, 71) = 49.51\n", + " Prob > F = 0.0000\n", + " R-squared = 0.3488\n", + " Root MSE = .49154\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_mort40 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | -5.007995 .711708 -7.04 0.000 -6.427101 -3.58889\n", + " _cons | -6.672417 .8309391 -8.03 0.000 -8.329263 -5.015572\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 73\n", + " F(10, 62) = 15.93\n", + " Prob > F = 0.0000\n", + " R-squared = 0.5133\n", + " Root MSE = .45476\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_mort40 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | -3.861032 1.128212 -3.42 0.001 -6.116297 -1.605768\n", + " ln_frac | -.3970101 .3960854 -1.00 0.320 -1.188773 .3947533\n", + " aa_ln_atd | .5975243 .2358444 2.53 0.014 .1260782 1.068971\n", + " ln_arable | .0448605 .1220245 0.37 0.714 -.1990631 .2887841\n", + " ln_suitavg | .0361975 .0844383 0.43 0.670 -.1325923 .2049874\n", + " ln_abslat | -.3260423 .1296545 -2.51 0.015 -.585218 -.0668665\n", + " europe | .1074832 .1696108 0.63 0.529 -.2315639 .4465303\n", + " africa | .4589723 .2152117 2.13 0.037 .0287701 .8891744\n", + " asia | .1722149 .2033102 0.85 0.400 -.2341963 .5786261\n", + " americas | .1102584 .1877282 0.59 0.559 -.265005 .4855218\n", + " _cons | -9.575309 2.730034 -3.51 0.001 -15.03257 -4.11805\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 71\n", + " F(1, 69) = 41.94\n", + " Prob > F = 0.0000\n", + " R-squared = 0.3329\n", + " Root MSE = .21596\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le40 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 2.143586 .3309808 6.48 0.000 1.483298 2.803875\n", + " _cons | 6.25536 .3783145 16.53 0.000 5.500643 7.010076\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 71\n", + " F(10, 60) = 44.93\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7192\n", + " Root MSE = .15026\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le40 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.077113 .3770962 2.86 0.006 .322808 1.831418\n", + " ln_frac | .072362 .133847 0.54 0.591 -.1953719 .3400959\n", + " aa_ln_atd | -.0030757 .0549058 -0.06 0.956 -.1129035 .1067522\n", + " ln_arable | -.1059797 .0365892 -2.90 0.005 -.179169 -.0327905\n", + " ln_suitavg | .0529932 .0238341 2.22 0.030 .0053179 .1006686\n", + " ln_abslat | .098031 .0409504 2.39 0.020 .0161181 .1799439\n", + " europe | -.052723 .0434553 -1.21 0.230 -.1396465 .0342005\n", + " africa | -.5020729 .0592995 -8.47 0.000 -.6206896 -.3834562\n", + " asia | -.3398571 .0565811 -6.01 0.000 -.4530363 -.226678\n", + " americas | -.3291928 .0621135 -5.30 0.000 -.4534382 -.2049474\n", + " _cons | 5.316719 .5542282 9.59 0.000 4.208097 6.42534\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(1, 129) = 43.55\n", + " Prob > F = 0.0000\n", + " R-squared = 0.2271\n", + " Root MSE = .20359\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.620043 .2454809 6.60 0.000 1.134353 2.105733\n", + " _cons | 5.814552 .2829132 20.55 0.000 5.254801 6.374303\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 100.89\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7446\n", + " Root MSE = .12133\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.01512 .1899264 5.34 0.000 .6390794 1.391161\n", + " ln_frac | -.1210012 .0927409 -1.30 0.194 -.3046217 .0626193\n", + " aa_ln_atd | .0369984 .0397486 0.93 0.354 -.0417011 .1156979\n", + " ln_arable | -.0086997 .0159756 -0.54 0.587 -.0403303 .0229309\n", + " ln_suitavg | .008039 .0128307 0.63 0.532 -.017365 .0334429\n", + " ln_abslat | .0199086 .0156461 1.27 0.206 -.0110696 .0508867\n", + " europe | .0387395 .0716524 0.54 0.590 -.1031272 .1806062\n", + " africa | -.3122101 .0754901 -4.14 0.000 -.4616753 -.162745\n", + " asia | -.2056222 .0755278 -2.72 0.007 -.355162 -.0560825\n", + " americas | -.0400593 .0736294 -0.54 0.587 -.1858403 .1057217\n", + " _cons | 4.962063 .4156831 11.94 0.000 4.139039 5.785086\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "***************************************\n", + "*** Table 3: Premedicinal Health ***\n", + "***************************************\n", + "\n", + "** Mortality from infectious disease in 1940\n", + "*Col. 1\n", + "reg ln_mort40 ln_hla_het if `base', rob\n", + "\n", + "*Col. 2\n", + "reg ln_mort40 ln_hla_het `bc' `cont' if `base', rob\n", + "\n", + "** Life Expectancy in 1940\n", + "*Col. 3\n", + "reg ln_le40 ln_hla_het if `base', rob\n", + "\n", + "*Col. 4\n", + "reg ln_le40 ln_hla_het `bc' `cont' if `base', rob\n", + "\n", + "** Life Expectancy in 1960\n", + "*Col. 5\n", + "reg ln_le60 ln_hla_het if `base', rob\n", + "\n", + "*Col. 6\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `base', rob\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_3.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 4: The Effect of HLA Heterozygosity after the International Epidemiological Transition (author´s code)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear regression Number of obs = 131\n", + " F(10, 120) = 100.89\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7446\n", + " Root MSE = .12133\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.01512 .1899264 5.34 0.000 .6390794 1.391161\n", + " ln_frac | -.1210012 .0927409 -1.30 0.194 -.3046217 .0626193\n", + " aa_ln_atd | .0369984 .0397486 0.93 0.354 -.0417011 .1156979\n", + " ln_arable | -.0086997 .0159756 -0.54 0.587 -.0403303 .0229309\n", + " ln_suitavg | .008039 .0128307 0.63 0.532 -.017365 .0334429\n", + " ln_abslat | .0199086 .0156461 1.27 0.206 -.0110696 .0508867\n", + " europe | .0387395 .0716524 0.54 0.590 -.1031272 .1806062\n", + " africa | -.3122101 .0754901 -4.14 0.000 -.4616753 -.162745\n", + " asia | -.2056222 .0755278 -2.72 0.007 -.355162 -.0560825\n", + " americas | -.0400593 .0736294 -0.54 0.587 -.1858403 .1057217\n", + " _cons | 4.962063 .4156831 11.94 0.000 4.139039 5.785086\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 76.19\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7340\n", + " Root MSE = .11485\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le70 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.04047 .1695943 6.14 0.000 .7046852 1.376255\n", + " ln_frac | -.1163928 .0881347 -1.32 0.189 -.2908934 .0581078\n", + " aa_ln_atd | .0246249 .0372331 0.66 0.510 -.0490941 .098344\n", + " ln_arable | -.0141638 .0153671 -0.92 0.359 -.0445896 .0162619\n", + " ln_suitavg | .0007926 .0135757 0.06 0.954 -.0260864 .0276715\n", + " ln_abslat | .0045722 .0158262 0.29 0.773 -.0267626 .0359069\n", + " europe | .0344497 .0460362 0.75 0.456 -.0566989 .1255982\n", + " africa | -.3192781 .0522129 -6.11 0.000 -.4226561 -.2159002\n", + " asia | -.1420921 .0484679 -2.93 0.004 -.2380552 -.046129\n", + " americas | -.0238244 .0420173 -0.57 0.572 -.1070157 .0593668\n", + " _cons | 5.198611 .3846817 13.51 0.000 4.436968 5.960254\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 55.68\n", + " Prob > F = 0.0000\n", + " R-squared = 0.6995\n", + " Root MSE = .10588\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le80 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .8420929 .169953 4.95 0.000 .5055977 1.178588\n", + " ln_frac | -.1462667 .0786894 -1.86 0.066 -.3020663 .0095329\n", + " aa_ln_atd | .0348747 .0381473 0.91 0.362 -.0406544 .1104037\n", + " ln_arable | -.0199753 .0150991 -1.32 0.188 -.0498705 .0099199\n", + " ln_suitavg | .0003745 .0142482 0.03 0.979 -.0278359 .0285849\n", + " ln_abslat | .0070102 .0152294 0.46 0.646 -.0231431 .0371634\n", + " europe | .0176881 .0373365 0.47 0.637 -.0562357 .0916118\n", + " africa | -.2552229 .0450962 -5.66 0.000 -.3445103 -.1659355\n", + " asia | -.1194574 .0422942 -2.82 0.006 -.203197 -.0357178\n", + " americas | -.0064243 .0328959 -0.20 0.845 -.0715559 .0587073\n", + " _cons | 4.945296 .3733601 13.25 0.000 4.206069 5.684523\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 32.32\n", + " Prob > F = 0.0000\n", + " R-squared = 0.6886\n", + " Root MSE = .10684\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le90 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .5490235 .1798274 3.05 0.003 .1929779 .9050692\n", + " ln_frac | -.183742 .0828562 -2.22 0.028 -.3477915 -.0196925\n", + " aa_ln_atd | .0383219 .035843 1.07 0.287 -.0326448 .1092886\n", + " ln_arable | -.0220742 .0174598 -1.26 0.209 -.0566434 .0124949\n", + " ln_suitavg | -.0046492 .0148575 -0.31 0.755 -.034066 .0247677\n", + " ln_abslat | .0247666 .0225831 1.10 0.275 -.0199464 .0694797\n", + " europe | .0189598 .0440979 0.43 0.668 -.0683511 .1062706\n", + " africa | -.2203019 .0524047 -4.20 0.000 -.3240596 -.1165442\n", + " asia | -.0726899 .0444554 -1.64 0.105 -.1607085 .0153287\n", + " americas | .0346061 .0405436 0.85 0.395 -.0456675 .1148796\n", + " _cons | 4.554555 .4118077 11.06 0.000 3.739204 5.369906\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 43.79\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7683\n", + " Root MSE = .09193\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le00 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .2512166 .1540813 1.63 0.106 -.0538535 .5562868\n", + " ln_frac | -.1775283 .0728385 -2.44 0.016 -.3217434 -.0333131\n", + " aa_ln_atd | .0843881 .0303567 2.78 0.006 .024284 .1444923\n", + " ln_arable | -.0062233 .0115616 -0.54 0.591 -.0291145 .0166679\n", + " ln_suitavg | -.0168243 .0100998 -1.67 0.098 -.0368211 .0031725\n", + " ln_abslat | .0137408 .0156327 0.88 0.381 -.0172109 .0446925\n", + " europe | -.0010578 .0524527 -0.02 0.984 -.1049104 .1027948\n", + " africa | -.2497432 .0608725 -4.10 0.000 -.3702665 -.1292199\n", + " asia | -.0924549 .052818 -1.75 0.083 -.197031 .0121211\n", + " americas | .0264391 .0515306 0.51 0.609 -.0755879 .128466\n", + " _cons | 3.85075 .309451 12.44 0.000 3.238059 4.463442\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 43.16\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7480\n", + " Root MSE = .08408\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le10 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .1787627 .1334796 1.34 0.183 -.0855176 .443043\n", + " ln_frac | -.1508934 .0700744 -2.15 0.033 -.2896359 -.0121509\n", + " aa_ln_atd | .0733 .0301988 2.43 0.017 .0135085 .1330916\n", + " ln_arable | -.0045715 .0094332 -0.48 0.629 -.0232486 .0141057\n", + " ln_suitavg | -.0138008 .0083383 -1.66 0.101 -.0303101 .0027084\n", + " ln_abslat | .0066167 .0136851 0.48 0.630 -.0204788 .0337121\n", + " europe | .0065547 .0508292 0.13 0.898 -.0940836 .107193\n", + " africa | -.2235135 .0590873 -3.78 0.000 -.3405023 -.1065247\n", + " asia | -.0839933 .0521013 -1.61 0.110 -.1871502 .0191637\n", + " americas | .0163142 .0512064 0.32 0.751 -.0850709 .1176994\n", + " _cons | 3.912044 .282363 13.85 0.000 3.352985 4.471104\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "*************************************************************\n", + "*** Table 4: Additional Years: The effect of medicine ***\n", + "*************************************************************\n", + "local le \"ln_le60!=. & ln_le70!=. & ln_le80!=. & ln_le90!=. & ln_le00!=. & ln_le10!=.\"\n", + "\n", + "*Col. 1, 1960\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `le', rob\n", + "\n", + "*Col. 2, 1970\n", + "reg ln_le70 ln_hla_het `bc' `cont' if `le', rob\n", + "\n", + "*Col. 3, 1980\n", + "reg ln_le80 ln_hla_het `bc' `cont' if `le', rob\n", + "\n", + "*Col. 4, 1990\n", + "reg ln_le90 ln_hla_het `bc' `cont' if `le', rob\n", + "\n", + "*Col. 5, 2000\n", + "reg ln_le00 ln_hla_het `bc' `cont' if `le', rob\n", + "\n", + "*Col. 6, 2010\n", + "reg ln_le10 ln_hla_het `bc' `cont' if `le', rob" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_4.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Figure 3: The Effect of HLA Heterozygosity following the Diffusion of Health Technologies from the International Epidemiological Transition (replication code)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we are going to write all the regressions from table 4 and save the coefficients in stata in order to collect the data we need for the table." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/ is not a valid command name\n", + "r(199);\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 100.89\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7446\n", + " Root MSE = .12133\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.01512 .1899264 5.34 0.000 .6390794 1.391161\n", + " ln_frac | -.1210012 .0927409 -1.30 0.194 -.3046217 .0626193\n", + " aa_ln_atd | .0369984 .0397486 0.93 0.354 -.0417011 .1156979\n", + " ln_arable | -.0086997 .0159756 -0.54 0.587 -.0403303 .0229309\n", + " ln_suitavg | .008039 .0128307 0.63 0.532 -.017365 .0334429\n", + " ln_abslat | .0199086 .0156461 1.27 0.206 -.0110696 .0508867\n", + " europe | .0387395 .0716524 0.54 0.590 -.1031272 .1806062\n", + " africa | -.3122101 .0754901 -4.14 0.000 -.4616753 -.162745\n", + " asia | -.2056222 .0755278 -2.72 0.007 -.355162 -.0560825\n", + " americas | -.0400593 .0736294 -0.54 0.587 -.1858403 .1057217\n", + " _cons | 4.962063 .4156831 11.94 0.000 4.139039 5.785086\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 76.19\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7340\n", + " Root MSE = .11485\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le70 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.04047 .1695943 6.14 0.000 .7046852 1.376255\n", + " ln_frac | -.1163928 .0881347 -1.32 0.189 -.2908934 .0581078\n", + " aa_ln_atd | .0246249 .0372331 0.66 0.510 -.0490941 .098344\n", + " ln_arable | -.0141638 .0153671 -0.92 0.359 -.0445896 .0162619\n", + " ln_suitavg | .0007926 .0135757 0.06 0.954 -.0260864 .0276715\n", + " ln_abslat | .0045722 .0158262 0.29 0.773 -.0267626 .0359069\n", + " europe | .0344497 .0460362 0.75 0.456 -.0566989 .1255982\n", + " africa | -.3192781 .0522129 -6.11 0.000 -.4226561 -.2159002\n", + " asia | -.1420921 .0484679 -2.93 0.004 -.2380552 -.046129\n", + " americas | -.0238244 .0420173 -0.57 0.572 -.1070157 .0593668\n", + " _cons | 5.198611 .3846817 13.51 0.000 4.436968 5.960254\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 55.68\n", + " Prob > F = 0.0000\n", + " R-squared = 0.6995\n", + " Root MSE = .10588\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le80 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .8420929 .169953 4.95 0.000 .5055977 1.178588\n", + " ln_frac | -.1462667 .0786894 -1.86 0.066 -.3020663 .0095329\n", + " aa_ln_atd | .0348747 .0381473 0.91 0.362 -.0406544 .1104037\n", + " ln_arable | -.0199753 .0150991 -1.32 0.188 -.0498705 .0099199\n", + " ln_suitavg | .0003745 .0142482 0.03 0.979 -.0278359 .0285849\n", + " ln_abslat | .0070102 .0152294 0.46 0.646 -.0231431 .0371634\n", + " europe | .0176881 .0373365 0.47 0.637 -.0562357 .0916118\n", + " africa | -.2552229 .0450962 -5.66 0.000 -.3445103 -.1659355\n", + " asia | -.1194574 .0422942 -2.82 0.006 -.203197 -.0357178\n", + " americas | -.0064243 .0328959 -0.20 0.845 -.0715559 .0587073\n", + " _cons | 4.945296 .3733601 13.25 0.000 4.206069 5.684523\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 32.32\n", + " Prob > F = 0.0000\n", + " R-squared = 0.6886\n", + " Root MSE = .10684\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le90 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .5490235 .1798274 3.05 0.003 .1929779 .9050692\n", + " ln_frac | -.183742 .0828562 -2.22 0.028 -.3477915 -.0196925\n", + " aa_ln_atd | .0383219 .035843 1.07 0.287 -.0326448 .1092886\n", + " ln_arable | -.0220742 .0174598 -1.26 0.209 -.0566434 .0124949\n", + " ln_suitavg | -.0046492 .0148575 -0.31 0.755 -.034066 .0247677\n", + " ln_abslat | .0247666 .0225831 1.10 0.275 -.0199464 .0694797\n", + " europe | .0189598 .0440979 0.43 0.668 -.0683511 .1062706\n", + " africa | -.2203019 .0524047 -4.20 0.000 -.3240596 -.1165442\n", + " asia | -.0726899 .0444554 -1.64 0.105 -.1607085 .0153287\n", + " americas | .0346061 .0405436 0.85 0.395 -.0456675 .1148796\n", + " _cons | 4.554555 .4118077 11.06 0.000 3.739204 5.369906\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 43.79\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7683\n", + " Root MSE = .09193\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le00 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .2512166 .1540813 1.63 0.106 -.0538535 .5562868\n", + " ln_frac | -.1775283 .0728385 -2.44 0.016 -.3217434 -.0333131\n", + " aa_ln_atd | .0843881 .0303567 2.78 0.006 .024284 .1444923\n", + " ln_arable | -.0062233 .0115616 -0.54 0.591 -.0291145 .0166679\n", + " ln_suitavg | -.0168243 .0100998 -1.67 0.098 -.0368211 .0031725\n", + " ln_abslat | .0137408 .0156327 0.88 0.381 -.0172109 .0446925\n", + " europe | -.0010578 .0524527 -0.02 0.984 -.1049104 .1027948\n", + " africa | -.2497432 .0608725 -4.10 0.000 -.3702665 -.1292199\n", + " asia | -.0924549 .052818 -1.75 0.083 -.197031 .0121211\n", + " americas | .0264391 .0515306 0.51 0.609 -.0755879 .128466\n", + " _cons | 3.85075 .309451 12.44 0.000 3.238059 4.463442\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(10, 120) = 43.16\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7480\n", + " Root MSE = .08408\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le10 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .1787627 .1334796 1.34 0.183 -.0855176 .443043\n", + " ln_frac | -.1508934 .0700744 -2.15 0.033 -.2896359 -.0121509\n", + " aa_ln_atd | .0733 .0301988 2.43 0.017 .0135085 .1330916\n", + " ln_arable | -.0045715 .0094332 -0.48 0.629 -.0232486 .0141057\n", + " ln_suitavg | -.0138008 .0083383 -1.66 0.101 -.0303101 .0027084\n", + " ln_abslat | .0066167 .0136851 0.48 0.630 -.0204788 .0337121\n", + " europe | .0065547 .0508292 0.13 0.898 -.0940836 .107193\n", + " africa | -.2235135 .0590873 -3.78 0.000 -.3405023 -.1065247\n", + " asia | -.0839933 .0521013 -1.61 0.110 -.1871502 .0191637\n", + " americas | .0163142 .0512064 0.32 0.751 -.0850709 .1176994\n", + " _cons | 3.912044 .282363 13.85 0.000 3.352985 4.471104\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + " %%stata\n", + "*******************************************************************************************************************************************************\n", + "*** Figure 3: \"The Effect of HLA Heterozygosity following the Diffusion of Health Technologies from the International Epidemiological Transition\" *** \n", + "*******************************************************************************************************************************************************\n", + " /* local le \"ln_le60!=. & ln_le70!=. & ln_le80!=. & ln_le90!=. & ln_le00!=. & ln_le10!=.\"\n", + "\n", + "*Col. 1, 1960\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `le', rob \n", + "estimates store y1960\n", + "local coef1960=_b[ln_hla_het] //Save coefficient\n", + "local lb1960 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub1960 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge)\n", + "\n", + "*Col. 2, 1970\n", + "reg ln_le70 ln_hla_het `bc' `cont' if `le', rob\n", + "estimates store y1970\n", + "local coef1970=_b[ln_hla_het] //Save coefficient\n", + "local lb1970 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub1970 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge)\n", + "\n", + "*Col. 3, 1980\n", + "reg ln_le80 ln_hla_het `bc' `cont' if `le', rob\n", + "estimates store y1980\n", + "local coef1980=_b[ln_hla_het] //Save coefficient\n", + "local lb1980 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub1980 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge)\n", + "\n", + "*Col. 4, 1990\n", + "reg ln_le90 ln_hla_het `bc' `cont' if `le', rob\n", + "estimates store y1990\n", + "local coef1990=_b[ln_hla_het] //Save coefficient\n", + "local lb1990 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub1990 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge)\n", + "\n", + "*Col. 5, 2000\n", + "reg ln_le00 ln_hla_het `bc' `cont' if `le', rob\n", + "estimates store y2000\n", + "local coef2000=_b[ln_hla_het] //Save coefficient\n", + "local lb2000 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub2000 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge)\n", + "\n", + "\n", + "*Col. 6, 2010\n", + "reg ln_le10 ln_hla_het `bc' `cont' if `le', rob\n", + "estimates store y2010\n", + "local coef2010=_b[ln_hla_het] //Save coefficient\n", + "local lb2010 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (bottom edge)\n", + "local ub2010 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] //Save confidence interval (top edge) */\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "%%stata\n", + "********************************************************************************\n", + "****** Make another table where it stores the information we need ***********\n", + "********************************************************************************\n", + "/*clear\n", + "set obs 6\n", + "generate year = 1960 in 1\n", + "replace year=1970 in 2\n", + "replace year=1980 in 3\n", + "replace year=1990 in 4\n", + "replace year=2000 in 5\n", + "replace year=2010 in 6\n", + "\n", + "// Coefficients\n", + "generate coef = `coef1960' in 1\n", + "replace coef=`coef1970' in 2\n", + "replace coef=`coef1980' in 3\n", + "replace coef=`coef1990' in 4\n", + "replace coef=`coef2000' in 5\n", + "replace coef=`coef2010' in 6\n", + "\n", + "//lower_bound\n", + "gen lower_bound= `lb1960' in 1\n", + "replace lower_bound= `lb1970' in 2\n", + "replace lower_bound= `lb1980' in 3\n", + "replace lower_bound= `lb1990' in 4\n", + "replace lower_bound= `lb2000' in 5\n", + "replace lower_bound= `lb2010' in 6\n", + "\n", + "//upper_bound\n", + "gen upper_bound= `ub1960' in 1\n", + "replace upper_bound= `ub1970' in 2\n", + "replace upper_bound= `ub1980' in 3\n", + "replace upper_bound= `ub1990' in 4\n", + "replace upper_bound= `ub2000' in 5\n", + "replace upper_bound= `ub2010' in 6 \n", + "*/" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "%%stata\n", + "******************************************\n", + "****** FIGURE 3 **************************\n", + "******************************************\n", + "/*twoway (connected coef year) (line lower_bound year, lp(dash dot)) (line upper_bound year, lp(dash dot)) , graphregion(color(white)) bgcolor(white) legend(rows(3) label(1 Point Estimate in Baseline Estimating Eq.) label(2 \"95% Confidence Interval (lower bound)\" ) labe(3 \"95% Confidence Interval (upper bound)\")) xtitle(\"Year\") ytitle(\"Coefficient of In HLA Heterozygosity\")*/" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Figure_3.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 5: Robustness to the Influence of Regional Populations (author´s code)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "note: europe omitted because of collinearity\n", + "note: americas omitted because of collinearity\n", + "\n", + "Linear regression Number of obs = 55\n", + " F(7, 46) = .\n", + " Prob > F = .\n", + " R-squared = 0.4183\n", + " Root MSE = .12654\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .7240941 .3623023 2.00 0.052 -.0051826 1.453371\n", + " ln_frac | -.1662651 .1477015 -1.13 0.266 -.4635727 .1310425\n", + " aa_ln_atd | .0109134 .063677 0.17 0.865 -.1172619 .1390886\n", + " ln_arable | -.0062449 .0196735 -0.32 0.752 -.0458456 .0333558\n", + " ln_suitavg | .0015924 .0193779 0.08 0.935 -.0374132 .0405981\n", + " ln_abslat | -.0104055 .0199072 -0.52 0.604 -.0504766 .0296656\n", + " europe | 0 (omitted)\n", + " africa | -.0788309 .129284 -0.61 0.545 -.3390661 .1814043\n", + " asia | .0581873 .1263473 0.46 0.647 -.1961366 .3125111\n", + " americas | 0 (omitted)\n", + " _cons | 4.663561 .7141303 6.53 0.000 3.226092 6.101031\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 52\n", + " F(10, 41) = 19.53\n", + " Prob > F = 0.0000\n", + " R-squared = 0.5891\n", + " Root MSE = .13973\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .8048964 .2923111 2.75 0.009 .2145622 1.395231\n", + " ln_frac | -.1580961 .1784565 -0.89 0.381 -.5184962 .202304\n", + " aa_ln_atd | .1411788 .0914403 1.54 0.130 -.0434887 .3258463\n", + " ln_arable | -.0640976 .0343788 -1.86 0.069 -.1335269 .0053318\n", + " ln_suitavg | .0491992 .0384154 1.28 0.207 -.0283823 .1267806\n", + " ln_abslat | .053768 .0361827 1.49 0.145 -.0193045 .1268405\n", + " europe | -.0364848 .0463389 -0.79 0.436 -.1300682 .0570986\n", + " africa | -.2468748 .0834365 -2.96 0.005 -.4153782 -.0783713\n", + " asia | -.2803521 .0860084 -3.26 0.002 -.4540497 -.1066546\n", + " americas | -.1194736 .0524145 -2.28 0.028 -.225327 -.0136203\n", + " _cons | 3.976339 .8431324 4.72 0.000 2.273599 5.67908\n", + "------------------------------------------------------------------------------\n", + "\n", + "note: europe omitted because of collinearity\n", + "note: africa omitted because of collinearity\n", + "note: asia omitted because of collinearity\n", + "note: americas omitted because of collinearity\n", + "\n", + "Linear regression Number of obs = 24\n", + " F(6, 17) = 5.33\n", + " Prob > F = 0.0030\n", + " R-squared = 0.5162\n", + " Root MSE = .04194\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .728073 .3110997 2.34 0.032 .07171 1.384436\n", + " ln_frac | -.0734813 .0922997 -0.80 0.437 -.2682168 .1212541\n", + " aa_ln_atd | .0357073 .037792 0.94 0.358 -.0440268 .1154413\n", + " ln_arable | -.0090738 .0212053 -0.43 0.674 -.0538131 .0356655\n", + " ln_suitavg | .0184685 .0208964 0.88 0.389 -.025619 .062556\n", + " ln_abslat | .3230336 .1173578 2.75 0.014 .0754303 .570637\n", + " europe | 0 (omitted)\n", + " africa | 0 (omitted)\n", + " asia | 0 (omitted)\n", + " americas | 0 (omitted)\n", + " _cons | 3.517317 .6836917 5.14 0.000 2.074853 4.95978\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(15, 115) = 165.25\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7891\n", + " Root MSE = .11264\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .8869916 .2871503 3.09 0.003 .3182021 1.455781\n", + " frac_eur | .3177208 .1395855 2.28 0.025 .0412289 .5942128\n", + " frac_me | .020621 .1596132 0.13 0.897 -.295542 .336784\n", + " frac_easia | .1401225 .1415624 0.99 0.324 -.1402853 .4205302\n", + " frac_africa | .0173643 .137623 0.13 0.900 -.2552404 .289969\n", + " frac_am | .1451349 .0934837 1.55 0.123 -.0400383 .330308\n", + " ln_frac | -.1295757 .0868859 -1.49 0.139 -.3016799 .0425285\n", + " aa_ln_atd | .0426266 .0416563 1.02 0.308 -.0398866 .1251398\n", + " ln_arable | -.0099352 .0143495 -0.69 0.490 -.0383587 .0184884\n", + " ln_suitavg | -.000756 .0145277 -0.05 0.959 -.0295325 .0280205\n", + " ln_abslat | .0068563 .018 0.38 0.704 -.0287982 .0425108\n", + " europe | -.0518747 .055794 -0.93 0.354 -.1623918 .0586425\n", + " africa | -.1408858 .0945556 -1.49 0.139 -.3281822 .0464106\n", + " asia | -.0978798 .0933299 -1.05 0.296 -.2827483 .0869887\n", + " americas | -.057943 .0612781 -0.95 0.346 -.1793231 .0634371\n", + " _cons | 4.598251 .4873273 9.44 0.000 3.632949 5.563552\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "*******************************************\n", + "*** Table 5: Pop. Composition Trunc. ***\n", + "*******************************************\n", + "local pop \"frac_eur frac_me frac_easia frac_africa frac_am\"\n", + "\n", + "*Col. 1, countries with no fraction of population derived from Europe\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur==0, rob\n", + "\n", + "*Col. 2, countries with part of the population derived from Europe\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur>0 & frac_eur<1, rob\n", + "\n", + "*Col. 3, countries entirely composed of European populations\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur==1, rob\n", + "\n", + "*Col. 4, ethnic fixed effects\n", + "reg ln_le60 ln_hla_het `pop' `bc' `cont' if `base', rob" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_5.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 6: Robustness to Exogenous Omitted Variables (author´s code)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Linear regression Number of obs = 131\n", + " F(11, 119) = 90.88\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7449\n", + " Root MSE = .12179\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | 1.077102 .340135 3.17 0.002 .4036012 1.750604\n", + " aa_pdiv | -.2783647 1.199801 -0.23 0.817 -2.654091 2.097362\n", + " ln_frac | -.113391 .1067678 -1.06 0.290 -.3248019 .0980199\n", + " aa_ln_atd | .0406652 .0470243 0.86 0.389 -.0524476 .1337779\n", + " ln_arable | -.0083386 .0157507 -0.53 0.598 -.0395265 .0228493\n", + " ln_suitavg | .007875 .0127592 0.62 0.538 -.0173895 .0331395\n", + " ln_abslat | .0206019 .0161684 1.27 0.205 -.0114131 .0526169\n", + " europe | .0390449 .0703013 0.56 0.580 -.1001588 .1782486\n", + " africa | -.3031556 .0870473 -3.48 0.001 -.4755179 -.1307932\n", + " asia | -.2058545 .0739893 -2.78 0.006 -.3523608 -.0593482\n", + " americas | -.045389 .0776443 -0.58 0.560 -.1991325 .1083545\n", + " _cons | 5.19642 1.000954 5.19 0.000 3.214432 7.178408\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(14, 116) = 78.86\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7741\n", + " Root MSE = .11606\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .7713477 .2040145 3.78 0.000 .3672712 1.175424\n", + " malfal | -.1613359 .0481473 -3.35 0.001 -.2566976 -.0659741\n", + " tropical | -.0003105 .0005798 -0.54 0.593 -.0014589 .000838\n", + " desert | -.0015805 .0015453 -1.02 0.309 -.0046411 .0014802\n", + " distcr1000 | .011631 .0384694 0.30 0.763 -.0645626 .0878246\n", + " ln_frac | -.0863313 .0921872 -0.94 0.351 -.2689197 .096257\n", + " aa_ln_atd | .0120531 .0330839 0.36 0.716 -.0534738 .07758\n", + " ln_arable | -.0019372 .0127372 -0.15 0.879 -.0271649 .0232905\n", + " ln_suitavg | .0037507 .012647 0.30 0.767 -.0212982 .0287996\n", + " ln_abslat | -.0149382 .0184394 -0.81 0.420 -.0514598 .0215834\n", + " europe | .0444248 .0730941 0.61 0.545 -.1003473 .1891969\n", + " africa | -.2552539 .0778437 -3.28 0.001 -.4094332 -.1010745\n", + " asia | -.1849297 .0777094 -2.38 0.019 -.3388429 -.0310165\n", + " americas | -.0619446 .0777163 -0.80 0.427 -.2158716 .0919824\n", + " _cons | 5.007553 .388945 12.87 0.000 4.237198 5.777907\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(11, 119) = 85.46\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7504\n", + " Root MSE = .12047\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .8699479 .223888 3.89 0.000 .4266273 1.313268\n", + "frac_migrant | .0982091 .0632069 1.55 0.123 -.0269468 .2233651\n", + " ln_frac | -.1440908 .0987111 -1.46 0.147 -.3395486 .051367\n", + " aa_ln_atd | .041594 .0395006 1.05 0.294 -.0366212 .1198093\n", + " ln_arable | -.0085389 .0155162 -0.55 0.583 -.0392626 .0221847\n", + " ln_suitavg | .0113689 .0119537 0.95 0.343 -.0123006 .0350383\n", + " ln_abslat | .0157883 .016349 0.97 0.336 -.0165843 .048161\n", + " europe | .1133226 .0846705 1.34 0.183 -.0543335 .2809787\n", + " africa | -.25279 .0810031 -3.12 0.002 -.4131842 -.0923958\n", + " asia | -.147021 .0801837 -1.83 0.069 -.3057927 .0117507\n", + " americas | -.0426537 .0676053 -0.63 0.529 -.176519 .0912116\n", + " _cons | 4.708444 .4388107 10.73 0.000 3.839555 5.577333\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 131\n", + " F(16, 114) = 64.92\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7824\n", + " Root MSE = .11491\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .5372995 .3093499 1.74 0.085 -.0755202 1.150119\n", + " aa_pdiv | .138512 1.028011 0.13 0.893 -1.89797 2.174994\n", + " malfal | -.1730445 .0487015 -3.55 0.001 -.2695218 -.0765672\n", + " tropical | -.0002674 .0005692 -0.47 0.639 -.0013949 .0008601\n", + " desert | -.0012792 .0014481 -0.88 0.379 -.0041478 .0015894\n", + " distcr1000 | .0299095 .0381407 0.78 0.435 -.0456469 .105466\n", + "frac_migrant | .1203078 .0733916 1.64 0.104 -.0250803 .2656959\n", + " ln_frac | -.1187416 .1035394 -1.15 0.254 -.3238522 .0863691\n", + " aa_ln_atd | .0120886 .0375722 0.32 0.748 -.0623416 .0865188\n", + " ln_arable | -.0020202 .0121886 -0.17 0.869 -.0261657 .0221252\n", + " ln_suitavg | .0097049 .0121508 0.80 0.426 -.0143658 .0337755\n", + " ln_abslat | -.021768 .019187 -1.13 0.259 -.0597774 .0162413\n", + " europe | .1426791 .0957523 1.49 0.139 -.0470055 .3323637\n", + " africa | -.1868511 .1054909 -1.77 0.079 -.3958278 .0221256\n", + " asia | -.1124693 .0903735 -1.24 0.216 -.2914986 .0665599\n", + " americas | -.0646517 .0742432 -0.87 0.386 -.2117268 .0824234\n", + " _cons | 4.584323 .881409 5.20 0.000 2.838259 6.330388\n", + "------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "***********************************************\n", + "*** Table 6: Exogenous Omitted Variables ***\n", + "***********************************************\n", + "local ov1 \"aa_pdiv!=. & malfal!=. & tropical!=. & pnativ_60!=. & distcr100!=. \"\n", + "\n", + "*Col. 1, genetic capital\n", + "reg ln_le60 ln_hla_het aa_pdiv `bc' `cont' if `ov1', rob\n", + "\n", + "*Col. 2, environment\n", + "reg ln_le60 ln_hla_het malfal tropical desert distcr100 `bc' `cont' if `ov1', rob\n", + "\n", + "*Col. 3, population\n", + "reg ln_le60 ln_hla_het frac_migrant `bc' `cont' if `ov1', rob\n", + "\n", + "*Col. 4, all\n", + "reg ln_le60 ln_hla_het aa_pdiv malfal tropical desert distcr100 frac_migrant `bc' `cont' if `ov1', rob" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(pathout +'Table_6.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### TABLE 7: Robustness to Endogenous Omitted Variables (author´s code)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear regression Number of obs = 109\n", + " F(10, 98) = 81.14\n", + " Prob > F = 0.0000\n", + " R-squared = 0.7378\n", + " Root MSE = .11998\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .9999595 .2118165 4.72 0.000 .5796165 1.420302\n", + " ln_frac | -.0687966 .1026132 -0.67 0.504 -.2724292 .134836\n", + " aa_ln_atd | .0220791 .0427207 0.52 0.606 -.0626988 .106857\n", + " ln_arable | -.0203005 .0163016 -1.25 0.216 -.0526505 .0120494\n", + " ln_suitavg | .0070124 .0139013 0.50 0.615 -.0205742 .034599\n", + " ln_abslat | .0169459 .0166373 1.02 0.311 -.0160703 .049962\n", + " europe | .0821774 .0783703 1.05 0.297 -.0733459 .2377008\n", + " africa | -.3038598 .085273 -3.56 0.001 -.4730812 -.1346384\n", + " asia | -.1987743 .0862051 -2.31 0.023 -.3698456 -.027703\n", + " americas | -.0337169 .083025 -0.41 0.686 -.1984773 .1310434\n", + " _cons | 5.075976 .4274974 11.87 0.000 4.227622 5.924331\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 109\n", + " F(11, 97) = 78.18\n", + " Prob > F = 0.0000\n", + " R-squared = 0.8062\n", + " Root MSE = .10368\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .6574856 .2123752 3.10 0.003 .2359796 1.078992\n", + " ln_ypc60 | .0977108 .0220749 4.43 0.000 .0538981 .1415234\n", + " ln_frac | -.1265308 .0889372 -1.42 0.158 -.3030465 .0499848\n", + " aa_ln_atd | -.0006487 .0401033 -0.02 0.987 -.0802427 .0789453\n", + " ln_arable | -.0105789 .0161119 -0.66 0.513 -.0425566 .0213989\n", + " ln_suitavg | .0290353 .01418 2.05 0.043 .000892 .0571787\n", + " ln_abslat | .0089705 .0178069 0.50 0.616 -.0263713 .0443123\n", + " europe | .0967318 .0609128 1.59 0.116 -.0241633 .2176268\n", + " africa | -.1342939 .0807126 -1.66 0.099 -.2944862 .0258983\n", + " asia | -.0661328 .0708522 -0.93 0.353 -.2067548 .0744893\n", + " americas | .0155833 .0634731 0.25 0.807 -.1103931 .1415598\n", + " _cons | 4.0475 .4733963 8.55 0.000 3.107939 4.987061\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 109\n", + " F(11, 97) = 108.99\n", + " Prob > F = 0.0000\n", + " R-squared = 0.8727\n", + " Root MSE = .08403\n", + "\n", + "-------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "--------------+----------------------------------------------------------------\n", + " ln_hla_het | .6127261 .1633457 3.75 0.000 .28853 .9369221\n", + "ln_yr_sch1960 | .1320134 .019477 6.78 0.000 .0933569 .1706699\n", + " ln_frac | -.0241461 .0610461 -0.40 0.693 -.1453058 .0970136\n", + " aa_ln_atd | .071103 .0282958 2.51 0.014 .0149436 .1272624\n", + " ln_arable | .0043996 .0115413 0.38 0.704 -.0185067 .027306\n", + " ln_suitavg | -.0081406 .0108674 -0.75 0.456 -.0297093 .0134282\n", + " ln_abslat | .0059583 .0124948 0.48 0.635 -.0188404 .030757\n", + " europe | .0285669 .0269261 1.06 0.291 -.0248739 .0820078\n", + " africa | -.1239058 .0453816 -2.73 0.008 -.2139757 -.0338358\n", + " asia | -.1525689 .0349806 -4.36 0.000 -.2219957 -.083142\n", + " americas | -.0317955 .0268294 -1.19 0.239 -.0850445 .0214535\n", + " _cons | 3.99027 .3344223 11.93 0.000 3.326535 4.654006\n", + "-------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 109\n", + " F(13, 95) = 58.98\n", + " Prob > F = 0.0000\n", + " R-squared = 0.8256\n", + " Root MSE = .09939\n", + "\n", + "------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "-------------+----------------------------------------------------------------\n", + " ln_hla_het | .7528768 .1943835 3.87 0.000 .3669768 1.138777\n", + " ln_pd60 | .0001866 .0151894 0.01 0.990 -.0299681 .0303414\n", + " ln_urb60 | .1137048 .0222823 5.10 0.000 .0694688 .1579408\n", + " ln_young | -.0614236 .0886641 -0.69 0.490 -.2374441 .1145969\n", + " ln_frac | -.1367887 .0831761 -1.64 0.103 -.3019141 .0283367\n", + " aa_ln_atd | -.075599 .0396077 -1.91 0.059 -.1542302 .0030322\n", + " ln_arable | -.0064507 .0170226 -0.38 0.706 -.0402447 .0273434\n", + " ln_suitavg | .0133635 .0119674 1.12 0.267 -.0103947 .0371218\n", + " ln_abslat | -.009797 .0191409 -0.51 0.610 -.0477966 .0282025\n", + " europe | .0512724 .052395 0.98 0.330 -.0527449 .1552897\n", + " africa | -.2301635 .0573917 -4.01 0.000 -.3441005 -.1162265\n", + " asia | -.1139926 .0547566 -2.08 0.040 -.2226982 -.0052871\n", + " americas | -.0694854 .0447219 -1.55 0.124 -.1582697 .0192989\n", + " _cons | 5.523092 .5104961 10.82 0.000 4.509629 6.536555\n", + "------------------------------------------------------------------------------\n", + "\n", + "Linear regression Number of obs = 109\n", + " F(15, 93) = 88.15\n", + " Prob > F = 0.0000\n", + " R-squared = 0.8897\n", + " Root MSE = .07987\n", + "\n", + "-------------------------------------------------------------------------------\n", + " | Robust\n", + " ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", + "--------------+----------------------------------------------------------------\n", + " ln_hla_het | .529581 .1672842 3.17 0.002 .1973878 .8617742\n", + " ln_ypc60 | .0280348 .016719 1.68 0.097 -.0051658 .0612354\n", + "ln_yr_sch1960 | .1036748 .0189568 5.47 0.000 .0660303 .1413194\n", + " ln_pd60 | .0062813 .0126177 0.50 0.620 -.0187749 .0313375\n", + " ln_urb60 | .0342884 .0194949 1.76 0.082 -.0044246 .0730014\n", + " ln_young | -.0148322 .0685018 -0.22 0.829 -.1508632 .1211988\n", + " ln_frac | -.0582462 .0703963 -0.83 0.410 -.1980392 .0815469\n", + " aa_ln_atd | .024198 .0292272 0.83 0.410 -.0338415 .0822375\n", + " ln_arable | .0014524 .0146563 0.10 0.921 -.0276522 .030557\n", + " ln_suitavg | .0023245 .0111488 0.21 0.835 -.0198147 .0244637\n", + " ln_abslat | -.0001829 .0152382 -0.01 0.990 -.030443 .0300772\n", + " europe | .0255092 .0397006 0.64 0.522 -.0533284 .1043467\n", + " africa | -.0977018 .0504283 -1.94 0.056 -.1978425 .0024388\n", + " asia | -.1094775 .0438173 -2.50 0.014 -.1964899 -.022465\n", + " americas | -.0341979 .0351158 -0.97 0.333 -.103931 .0355352\n", + " _cons | 4.048212 .4632424 8.74 0.000 3.128304 4.968119\n", + "-------------------------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "%%stata\n", + "************************************************\n", + "*** Table 7: Endogenous Omitted Variables ***\n", + "************************************************\n", + "local ov2 \"ln_ypc60!=. & ln_yr_sch1960!=. & ln_pd60!=. & ln_urb60!=. & ln_young!=.\"\n", + "\n", + "*Col. 1, sample adjustment\n", + "reg ln_le60 ln_hla_het `bc' `cont' if `ov2', rob\n", + "\n", + "*Col. 2, income\n", + "reg ln_le60 ln_hla_het ln_ypc60 `bc' `cont' if `ov2', rob\n", + "\n", + "*Col. 3, education\n", + "reg ln_le60 ln_hla_het ln_yr_sch1960 `bc' `cont' if `ov2', rob\n", + "\n", + "*Col. 4, demographics\n", + "reg ln_le60 ln_hla_het ln_pd60 ln_urb60 ln_young `bc' `cont' if `ov2', rob\n", + "\n", + "*Col. 5, all\n", + "reg ln_le60 ln_hla_het ln_ypc60 ln_yr_sch1960 ln_pd60 ln_urb60 ln_young `bc' `cont' if `ov2', rob" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/dev/null +++ b/notebooks/C.justinCook-The_natural_selection_2015/raw/app_tables.do @@ -0,0 +1,216 @@ +set more off +set matsize 1000 + +log using "replication_hla_app_tables.log", replace + +************************************************************** +*** Table A2: Explaining HLA Heterozygosity: Ethnic Sample *** +************************************************************** + +use "\hla_ethnic_web.dta", clear + +*Col. 1 +reg ln_het ln_atd, rob + +*Col. 2 +reg ln_het ln_atd md, rob + +*Col. 3 +reg ln_het ln_anim, rob + +*Col. 4 +reg ln_het ln_anim md, rob + + + + /*********************/ + /*** Tables A3-A12 ***/ + /*********************/ + +use "\hla_country_web.dta", clear + +local cont "europe africa asia americas" +local base "ln_le60!=. & ln_hla_het!=. & ln_frac!=. & ln_abslat!=. & ln_arable!=. & ln_suitavg!=. & aa_ln_atd!=." +local bc "ln_frac aa_ln_atd ln_arable ln_suitavg ln_abslat" + +************************************ +*** Table A3: Correlation Matrix *** +************************************ + +pwcorr ln_hla_het aa_ln_atd aa_lanim aa_lpd1 aa_mdist if `base', obs + + +**************************************************************************** +*** Table A4: Interaction between Years since NR and Domesticate Animals *** +**************************************************************************** +local es "aa_ln_atd!=. & aa_lanim!=." +gen ag_anim = aa_ln_atd*aa_lanim + +*Col. 1, years of agriculture +reg ln_hla_het aa_ln_atd if `es', rob + +*Col. 2, domesticate animals +reg ln_hla_het aa_lanim if `es', rob + +*Col. 3, interaction +reg ln_hla_het aa_ln_atd aa_lanim ag_anim if `es', rob + +*Col. 4, interaction controlling for migratory distance +reg ln_hla_het aa_ln_atd aa_lanim ag_anim aa_mdist if `es', rob + + +************************************************************************************** +*** Table A5: Interaction between Early Population Density and Domesticate Animals *** +************************************************************************************** +local es "aa_lpd1!=. & aa_lanim!=." +gen pd_anim = aa_lpd1*aa_lanim + +*Col. 1, years of agriculture +reg ln_hla_het aa_lpd1 if `es', rob + +*Col. 2, domesticate animals +reg ln_hla_het aa_lanim if `es', rob + +*Col. 3, interaction +reg ln_hla_het aa_lpd1 aa_lanim pd_anim if `es', rob + +*Col. 4, interaction controlling for migratory distance +reg ln_hla_het aa_lpd1 aa_lanim pd_anim aa_mdist if `es', rob + + +************************************ +*** Table A6: Summary Statistics *** +************************************ +local lvl "frac arable suitavg abslat ypc60 yr_sch1960 pd60 urb60 young" + +foreach i of local lvl { +gen `i' = exp(ln_`i') +} + +local yr "40 50 60 70 80 90 00 10" +foreach i of local yr { +gen le`i' = exp(ln_le`i') +} + +gen mort40 = exp(ln_mort40) + + + +*Dependent variables +sum mort40 le40 le50 le60 le70 le80 le90 le00 le10 if `base' + +*Baseline controls +sum frac aa_atd arable suitavg abslat if `base' + +*Exogenous omitted variables +sum aa_pdiv malfal tropic desert frac_migrant distcr1000 if `base' + +*Endogenous omitted variables +local ov2 "ln_ypc60!=. & ln_yr_sch1960!=. & ln_pd60!=. & ln_urb60!=. & ln_young!=." +sum ypc60 yr_sch1960 pd60 urb60 young if `base' & `ov2' + + +****************************************************************************** +*** Table A7: The effect of HLA heterozygosity after the I.E.T., bivariate *** +****************************************************************************** +local yr2 "60 70 80 90 00 10" + +foreach i of local yr2 { +reg ln_le`i' ln_hla_het if `base', rob +} + + +****************************************************************************** +*** Table A8: The effect of HLA heterozygosity after the I.E.T., 1940-2010 *** +****************************************************************************** +local yr3 "40 50 60 70 80 90 00 10" +local le "ln_le40!=. & ln_le50!=. & ln_le60!=. & ln_le70!=. & ln_le80!=. & ln_le90!=. & ln_le00!=. & ln_le10!=." + +foreach i of local yr { +reg ln_le`i' ln_hla_het `bc' `cont' if `base' & `le', rob +} + + +************************************************************************************************************************** +*** Table A9: Replacing within-ethnicity HLA heterozygosity, with country-level, mixed-ethnicity HLA heterozygosity *** +************************************************************************************************************************** + +** Mortality from infectious disease in 1940 +*Col. 1 +reg ln_mort40 ln_mix_het if `base', rob + +*Col. 2 +reg ln_mort40 ln_mix_het `bc' `cont' if `base', rob + +** Life Expectancy in 1940 +*Col. 3 +reg ln_le40 ln_mix_het if `base', rob + +*Col. 4 +reg ln_le40 ln_mix_het `bc' `cont' if `base', rob + +** Life Expectancy in 1960 +*Col. 5 +reg ln_le60 ln_mix_het if `base', rob + +*Col. 6 +reg ln_le60 ln_mix_het `bc' `cont' if `base', rob + + + +***************************************************************************************************************** +*** Table A10: Replacing within-ethnicity HLA heterozygosity, with its ratio to genome-wide heterozygosity *** +***************************************************************************************************************** +gen ratio = hla_het/aa_pdiv + +** Mortality from infectious disease in 1940 +*Col. 1 +reg ln_mort40 ratio if `base', rob + +*Col. 2 +reg ln_mort40 ratio `bc' `cont' if `base', rob + +** Life Expectancy in 1940 +*Col. 3 +reg ln_le40 ratio if `base', rob + +*Col. 4 +reg ln_le40 ratio `bc' `cont' if `base', rob + +** Life Expectancy in 1960 +*Col. 5 +reg ln_le60 ratio if `base', rob + +*Col. 6 +reg ln_le60 ratio `bc' `cont' if `base', rob + + + +************************************************************ +*** Table A11: Controlling for the diffusion of medicine *** +************************************************************ + +*Col. 1, full sample, including genetic dist. to USA +reg ln_le60 ln_hla_het fstdistwtd_usa `bc' `cont' if `base', rob + +*Col. 2, excluding OECD countries +reg ln_le60 ln_hla_het `bc' `cont' if `base' & oecd!=1, rob + +*Col. 3, excluding OECD countries and including genetic dist. +reg ln_le60 ln_hla_het fstdistwtd_usa `bc' `cont' if `base' & oecd!=1, rob + + + +****************************************************************************************************** +*** Table A12: 2SLS, using migratory distance and its square as instruments for HLA heterozygosity *** +****************************************************************************************************** + + +*Col. 1, base +ivreg2 ln_le60 (ln_hla_het = aa_mdist aa_mdist_sqr) `bc' `cont' if `base', small first rob + +*Col. 2, controlling for income +ivreg2 ln_le60 (ln_hla_het = aa_mdist aa_mdist_sqr) ln_ypc60 `bc' `cont' if `base', small first rob + + +log close diff --git a/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_country_web.dta b/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_country_web.dta new file mode 100644 index 0000000..3fc682f Binary files /dev/null and b/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_country_web.dta differ diff --git a/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_country_web.tab b/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_country_web.tab new file mode 100644 index 0000000..900f954 --- /dev/null +++ b/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_country_web.tab @@ -0,0 +1,176 @@ +country code ln_hla_het hla_het ln_mort40 ln_le40 ln_le50 ln_le60 ln_le70 ln_le80 ln_le90 ln_le00 ln_le10 europe asia africa americas oceania frac_eur frac_me frac_easia frac_africa frac_am frac_oc aa_ln_atd aa_atd aa_mdist aa_mdist_sqr aa_lanim aa_lpd1 ln_frac ln_arable ln_suitavg ln_abslat aa_pdiv malfal tropical desert distcr1000 frac_migrant pnativ_60 ln_ypc60 ln_yr_sch1960 ln_pd60 ln_urb60 ln_young ln_mix_het oecd fstdistwtd_usa +"Afghanistan" "AFG" -1.1116264 0.32902342 3.4531572 3.43805 3.556946 3.6687818 3.745481 3.8131492 3.8770628 0 1 0 0 0 0.04362281 0.91275436 0.04362281 0.0 0.0 0.0 9.072699 8750.0 6.0025506 36.081318 0.8684827 0.5706092 2.4956818 -1.8325815 3.4965076 0.7289717 0.0592746 0 4.355736143291409 0.942351 0.13 0.87 6.99026 -1.0358487 2.712741 2.0794415 3.7694476 -1.0921965 0 5.6846004 +"Albania" "ALB" -1.1299044 0.32306415 4.010963 4.13123 4.204535 4.243828 4.2717295 4.307286 4.3425183 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.9249115 7518.0 4.4663997 19.9496 1.9972702 0.19919997 3.048799 -0.2613648 3.713572 0.74057233 0.0 0 0 0.077544294 0.037 0.963 7.70931 1.7032259 4.0815563 3.4242628 3.7159731 -1.1243899 0 4.5774193 +"Algeria" "DZA" -1.1019324 0.33222845 -1.1631508 3.6627922 3.7635229 3.84984 3.968744 4.0877175 4.2057633 4.2488403 4.2884374 0 0 1 0 0 0.0 1.0 0.0 0.0 0.0 0.0 8.294049 4000.0 4.806958 23.10685 -0.1746841 0.29222175 1.1693814 -3.218876 3.3322046 0.7380005 0.0 0 23.277136053771727 0.76593 0.0 1.0 8.24249 -0.13871193 1.5217843 3.4177268 3.7787035 -1.0882667 0 5.9604325 +"Angola" "AGO" -1.1092016 0.3298222 3.5553482 3.4011974 3.49605 3.6112263 3.6929164 3.7170153 3.811206 3.9250114 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 7.1641 1356.99 3.5115674 12.534498 -1.5863429 0.5803815 0.8796268 -1.3862944 2.5257287 0.7477829 1.0 44.34624572755408 0.425228661821526 0.481495 0.01999998 0.98 7.51752 1.3475616 2.3418057 3.7776492 -1.1092016 0 20.419653 +"Antigua and Barbuda" "ATG" -1.1426333 0.31897795 4.13282 4.202506 0 0 0 1 0 0.0255292 0.0 0.0 0.9712796 0.00319115 0.0 0.15208225 2.9003222 2.8361502 100 0 8.09377 4.8191814 3.6813512 -1.1055597 0 13.315693 +"Argentina" "ARG" -1.0861584 0.3375106 -1.5837702 4.016383 4.1351666 4.1777 4.1979313 4.2408895 4.2696733 4.3002467 4.3258815 0 0 0 1 0 0.925 0.0 0.0 0.0 0.075 0.0 8.859602 7244.1455 6.6607366 68.790215 2.1754115 2.0202506 0.22713557 2.3184583 -0.9162907 3.5263605 0.72400135 0.0 0 0 0.271642 0.9439142 0.056085855 8.98695 1.7343782 2.0193183 4.298645 3.425399 -1.0525403 0 4.5911674 +"Armenia" "ARM" -1.1488335 0.31700635 4.1713057 4.18744 4.247415 4.258164 4.2159314 4.2625313 4.301136 0 1 0 0 0 0.32429615 0.32429615 0.3514077 0.0 0.0 0.0 8.988633 8015.481 4.1052976 16.860508 -0.42495933 0.11969587 2.865054 -0.597837 3.6888795 0.7432992 0.0 0 0 0.420939 0.020100474 0.9798995 8.67266 2.0508783 4.1838765 3.9376907 3.6489425 -1.1049696 0 4.863554 +"Australia" "AUS" -1.0674232 0.34389353 -1.4620098 4.201703 4.2427645 4.2601 4.262941 4.3085637 4.3437357 4.3724074 4.402994 0 0 0 0 1 0.9794239 0.0 0.0 0.0 0.0 0.020576132 8.582729 5681.3315 6.559279 47.70375 0.920452 0.08883654 1.8794651 -1.6607312 3.295837 0.7247675 0.0 14.680003945458534 10.888616350076296 0.35488698 0.96753925 0.032460764 9.46118 2.2295575 0.2991587 4.400603 3.4075983 -1.050965 1 3.533981 +"Austria" "AUT" -1.0648363 0.3447843 -1.2073117 4.0976725 4.1850986 4.22808 4.246933 4.282533 4.32453 4.357053 4.3868017 1 0 0 0 0 0.99155796 0.004221021 0.004221021 0.0 0.0 0.0 8.7810545 6511.2217 4.981763 24.819952 2.3025851 1.7988685 0.10145145 2.8314471 -0.8915981 3.8572147 0.7366804 0.0 0 0 0.0794891 0.01449275 0.98550725 9.21682 1.3994938 4.448228 4.169761 3.092362 -1.0517367 1 3.4436648 +"Azerbaijan" "AZE" -1.1344188 0.321609 4.11578 4.10818 4.170296 4.169472 4.1704774 4.201062 4.2557054 0 1 0 0 0 0.06101237 0.006963933 0.9320237 0.0 0.0 0.0 8.9712305 7905.0 4.525599 20.523287 -0.48721895 0.18620732 3.0591764 -0.4942963 3.701302 0.74012524 0.0 0 0 0.696768 0.06999999 0.93 8.35114 3.8426223 3.8649313 3.6471694 -1.1278471 0 9.896091 +"Bahamas" "BHS" -1.1329259 0.3220895 -2.8134108 4.091006 4.147 4.192422 4.2150474 4.240421 4.27149 4.3204455 0 0 0 1 0 0.1284 0.0 0.0 0.8716 0.0 0.0 0.35265848 -0.35667497 3.1884167 100.00000000000001 0 9.73625 2.4170015 4.089332 3.7467296 -1.087695 0 12.442552 +"Bahrain" "BHR" -1.1169273 0.3272839 3.9318256 4.01551 4.1568246 4.246701 4.283103 4.3013053 4.317806 0 1 0 0 0 0.0 1.0 0.0 0.0 0.0 0.0 8.933499 7592.5967 4.3647532 19.125345 0.4068841 1.0367368 3.2580965 0.74133986 0 0 0.06404406 0.93595594 8.7653 0.22708532 5.425065 4.4103713 3.7110178 -1.1060562 0 7.8275714 +"Bangladesh" "BGD" -1.1300751 0.32300898 -0.40289426 3.3978584 3.6000483 3.69539 3.7349505 4.0116124 4.085491 4.17023 4.2288 0 1 0 0 0 0.0 1.0 0.0 0.0 0.0 0.0 8.62121 5560.0 8.280665 68.60893 2.0794415 2.172011 0.04443211 4.128746 -0.2613648 3.1780539 0.71176815 0.1786233 75.03817542532649 0 0.041123502 0.01999998 0.98 6.68085 0.018395416 6.038814 1.6292405 3.7746344 -1.1300751 0 5.391045 +"Barbados" "BRB" -1.1410776 0.31947455 -1.6194882 3.8918202 4.046554 4.16628 4.226651 4.27631 4.309688 4.322917 4.3382425 0 0 0 1 0 0.03343433 0.0 0.0 0.96656567 0.0 0.0 0.13308054 3.6165776 2.5776885 100 0 8.64717 1.6412594 6.2921677 3.6054978 3.6396046 -1.1046499 0 13.313827 +"Belarus" "BLR" -1.1342833 0.32165256 4.1881385 4.22452 4.255937 4.2532644 4.2603755 4.232833 4.2542624 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.423949 4557.5 5.1710763 26.750273 -1.0054749 0.27928492 3.3864222 -0.99425226 3.9702919 0.73525083 0.0 0 0 0.046762697 0.11500001 0.885 8.51719 3.695552 3.4781585 3.3625166 -1.1342833 0 3.9154067 +"Belgium" "BEL" -1.0585636 0.34695384 -1.8604666 4.1239033 4.2121277 4.25374 4.263895 4.2929854 4.330316 4.3589263 4.3812337 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.61697 5534.7876 5.8688965 34.477123 2.3025851 2.120625 0.44173273 3.2809112 -0.7765288 3.9285524 0.72998106 0.0 0 0 0.0383926 0.018499494 0.9815005 9.23611 1.9384083 4.527209 3.1562545 -1.0470694 1 3.1448045 +"Belize" "BLZ" -1.166397 0.31148723 -0.44832042 3.8066626 4.0552573 4.11766 4.1903205 4.250593 4.2834044 4.2977085 4.328625 0 0 0 1 0 0.45979616 0.0 0.0 0.16874292 0.3714609 0.0 8.407985 4900.8765 10.475933 157.4827 1.1229197 0.5455194 0.5315337 1.0331844 -0.56211895 2.8478122 0.6951902 0.1 100 0 0.025455201 0.8678933 0.13210672 7.94272 2.0235448 1.3930993 3.988984 3.819916 -1.070555 0 10.174174 +"Benin" "BEN" -1.1649737 0.31193087 3.523415 3.70534 3.683618 3.812139 3.8846216 3.962057 4.017924 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.038867 3099.9 4.0854588 16.816587 0.0 0.09532832 0.5806591 3.0689828 -0.6539265 2.2512918 0.743449 1.0 97.48924860569808 0 0.352174 0.10100001 0.899 7.45298 -0.34856805 2.9219692 2.2300143 3.6477892 -1.1512645 0 14.091902 +"Bermuda" "BMU" -1.1114053 0.32909617 -3.506558 0 0 0 1 0 0.3828829 0.0 0.0 0.6171171 0.0 0.0 2.9957323 3.4760985 0 0 -1.0554413 0 +"Bhutan" "BTN" -1.2412044 0.2890359 3.5610461 3.61325 3.703432 3.83642 3.962519 4.1169896 4.2033315 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 8.626513 5580.5 8.032378 64.54252 0.47312376 1.0919234 -2.3025851 3.314186 0.71364313 0.4601328 0 0 0.136476 0.16100001 0.839 6.66696 1.5066838 1.2809339 3.7416854 -1.2412044 0 10.187919 +"Bolivia" "BOL" -1.3517965 0.25877494 -0.051293306 3.5409594 3.69883 3.75345 3.8232143 3.9505427 4.073769 4.142558 4.1937156 0 0 0 1 0 0.23695697 0.0 0.0 0.0 0.76304305 0.0 8.449744 4866.9033 19.390005 442.03564 1.0857565 -0.35065788 0.55366963 0.9932518 -1.0216511 2.8332133 0.627874 0.0113571 64.87835699245787 0 0.45398 0.27632314 0.72367686 7.76726 1.132724 1.1537342 3.6054978 3.7485664 -1.2702974 0 11.575732 +"Bosnia and Herzegovina" "BIH" -1.1078757 0.3302598 3.9852734 4.0978 4.1894517 4.252396 4.2116365 4.3082204 4.322813 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.853665 7000.0 4.625043 21.39103 1.773173 0.48860332 2.9719517 -0.5108256 3.7841897 0.7393743 0.0 0 0 0.0672865 0.0 1.0 7.83201 4.151503 2.944439 3.6532874 -1.0900704 0 +"Botswana" "BWA" -1.1219177 0.3256547 3.749504 3.92187 3.9971812 4.1019692 4.1591516 3.9272168 3.972356 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 7.2942986 1563.6 4.2961955 18.524406 0.02314156 0.3437656 -0.40047753 -2.9957323 3.0910425 0.7418576 0.3615499 0 1.4546963718673023 0.79926395 0.68799996 0.312 6.40853 0.27429748 -0.13190684 1.1314021 3.819742 -1.1031724 0 20.076466 +"Brazil" "BRA" -1.1252364 0.32457572 -0.6439761 3.6027768 3.9318256 3.99814 4.070069 4.135112 4.194804 4.2504687 4.291822 0 0 0 1 0 0.6810735 0.0 0.0 0.18353604 0.13539049 0.0 8.563128 5777.3516 7.818273 94.50489 1.9749652 1.0075002 0.43232512 1.9183921 -1.609438 2.3025851 0.71525997 0.0332967 88.56728695544824 0 0.315764 0.90919244 0.09080754 8.14787 0.7195397 2.1371377 3.8044379 3.7637374 -1.0500991 0 4.7821884 +"Brunei" "BRN" -1.1938754 0.30304456 4.1009893 4.12981 4.2062607 4.258689 4.2993736 4.3337855 4.355837 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 0.43282083 0.5364934 -1.4696759 1.5040774 0.0 100 0 0.0335407 9.09043 0.9925921 2.7572558 3.7704594 3.740489 -1.1819456 0 12.535214 +"Bulgaria" "BGR" -1.1442994 0.31844693 -1.0834527 4.079231 4.1604443 4.23769 4.266284 4.2648964 4.271674 4.2719803 4.2974515 1 0 0 0 0 0.5073628 0.032643393 0.4599938 0.0 0.0 0.0 8.92503 7521.96 4.210897 17.742395 2.3025851 1.5141054 0.33798116 3.4616652 -0.35667497 3.7612002 0.7425018 0.0 0 0 0.0730677 0.0431 0.9569 8.33615 1.8492125 4.264288 3.613617 3.260332 -1.1118743 0 5.150665 +"Burkina Faso" "BFA" -1.2503567 0.2864026 3.50255 3.61784 3.7111576 3.8358245 3.8805952 3.916041 4.005954 0 0 1 0 0 0.0 0.033950616 0.0 0.9660494 0.0 0.0 7.972466 2900.0 4.405797 19.41105 0.0 0.1667514 0.5525843 2.692598 -1.0498221 2.5649493 0.7410299 1.0 49.82000666128512 0 0.73184496 0.0 1.0 6.70441 2.8783193 1.5475625 3.7207837 -1.242334 0 12.694204 +"Burundi" "BDI" -1.1101888 0.32949674 3.6635616 3.71932 3.7774837 3.8471498 3.8335671 3.828751 3.9095645 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.155135 3484.0 1.7224591 2.9673407 0.0 0.25862187 3.6211357 -0.7765288 1.2527629 0.7612937 1.0 75.35375738491533 0 1.00966 0.015999973 0.984 6.05209 -0.44048804 4.6971507 0.6931472 3.7843983 -1.1089377 0 20.069086 +"Cambodia" "KHM" -1.2068169 0.299148 -0.9187939 3.673766 3.74725 3.775807 3.6538758 4.0145307 4.0511613 4.135746 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 8.424557 4572.872 10.293791 105.9916 1.0241967 0.19100375 3.042616 -0.7550226 2.5649493 0.6965657 1.0 100 0 0.147114 0.036646068 0.96335393 6.83841 1.5903113 3.4854703 2.332144 3.7493503 -1.2045943 0 12.039059 +"Cameroon" "CMR" -1.1462754 0.3178183 3.583519 3.72581 3.8303006 3.9354765 3.975056 3.9134474 3.9330554 0 0 1 0 0 0.002155172 0.0 0.0 0.9978448 0.0 0.0 8.005749 2998.7866 3.1823225 10.474392 0.0 -1.6137158 0.62245107 2.5502262 -1.4696759 1.7917595 0.75026923 1.0 94.11981439901624 0 0.405651 0.09182644 0.90817356 7.16935 0.080687694 2.4736028 2.6318889 3.7026744 -1.1131879 0 15.975799 +"Canada" "CAN" -1.0491015 0.3502523 -2.1150687 4.162003 4.2355547 4.26455 4.286348 4.318528 4.3486905 4.372438 4.3919497 0 0 0 1 0 0.977261 0.0 0.022739017 0.0 0.0 0.0 8.674815 6163.034 6.5350823 50.46665 1.029808 0.5379076 1.6174061 -2.8134108 4.0943446 0.72495025 0.0 0 0 1.46667 0.9534299 0.046570096 9.46622 2.123662 0.69752425 4.2355547 3.5127919 -1.0398507 1 3.8812585 +"Cape Verde" "CPV" -1.1128999 0.32860464 3.775057 3.881564 3.95058 3.9626975 4.09173 4.1756206 4.240582 4.301007 0 0 1 0 0 0.365 0.0 0.0 0.635 0.0 0.0 8.1911545 4244.8525 5.6391807 34.120655 1.1727729 0.3488242 2.390596 2.7725887 0.7317158 0 0 1.0 0.0 6.6107 3.888259 2.8154087 3.7389233 -1.0571066 0 10.199737 +"Chile" "CHL" -1.2619838 0.28309187 -0.21989879 3.7376697 4.001864 4.04352 4.129337 4.2368813 4.298693 4.3414874 4.3680005 0 0 0 1 0 0.44945395 0.0 0.0 0.0 0.55054605 0.0 8.655232 6015.409 13.550474 274.71185 1.6740706 0.8365244 0.1707094 0.97077894 -1.9661129 3.4011974 0.6719722 0.0 0 15.489202436018472 0.112301 0.62693894 0.3730611 8.72794 1.6530211 2.3225648 4.2165623 3.6731575 -1.1595422 0 8.958856 +"China" "CHN" -1.1387327 0.3202246 -1.2361517 3.7819145 3.708682 3.84165 4.141582 4.2046003 4.240747 4.2660785 4.2941933 0 1 0 0 0 0.0 0.004420587 0.9955794 0.0 0.0 0.0 9.1049795 9000.0 10.00298 100.0596 2.0794415 1.737333 0.14305247 2.6878474 -1.0788096 3.5553482 0.6987618 0.0157621 0.3007317472089089 5.7695296359254815 0.76506096 0.0 1.0 6.80462 0.82469743 4.245027 2.7725887 3.6790419 -1.1360557 0 11.988264 +"Colombia" "COL" -1.1821961 0.30660465 -0.6254886 3.634951 3.9239516 4.03807 4.108111 4.1814566 4.223991 4.2626934 4.296328 0 0 0 1 0 0.56 0.0 0.0 0.125 0.315 0.0 8.414064 4997.9824 11.785647 202.38837 1.4373991 0.40731418 0.47087824 0.99694866 -1.1086626 1.3862944 0.68529963 0.2616948 89.7632432491355 0 0.200267 0.6282475 0.37175247 8.16905 1.1212083 2.6657186 3.8066626 3.833694 -1.0777125 0 7.8001385 +"Comoros" "COM" -1.1092016 0.3298222 3.6054978 3.6888795 3.77143 3.865085 3.9503813 4.0181727 4.0583105 4.104728 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 3.152943 10.572561 0.0 0.0 3.5799012 2.4987 0.7504911 100 0 0.5 0.5 6.94986 4.5883574 2.533697 3.7495883 -1.1092016 0 20.56829 +"Congo" "COG" -1.1092016 0.3298222 3.6532521 3.87561 3.9784956 4.029572 4.029745 3.9916813 4.0423527 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.018698 3000.0 2.987377 8.924419 0.0 -1.654471 0.6284625 0.35767442 -2.65926 0.0 0.7517414 1.0 100 0 0.507805 0.0 1.0 7.70886 0.17393723 1.0766151 3.4531572 3.7221184 -1.1092016 0 20.880098 +"Costa Rica" "CRI" -1.0737902 0.34171093 -0.40496525 3.897924 4.0483007 4.12077 4.202436 4.2842875 4.327399 4.3541937 4.371883 0 0 0 1 0 0.93867975 0.0 0.0 0.015406591 0.04591368 0.0 8.006368 5382.6675 10.839661 165.36447 1.4434999 1.1086504 0.2125274 1.4838747 -0.43078294 2.3025851 0.69244343 0.0 94.01733788998786 0 0.0357302 0.68594575 0.31405425 8.27843 1.3694592 3.1963122 3.5351453 3.799073 -1.0491661 0 3.9223287 +"Cote d'Ivoire" "CIV" -1.1845194 0.30589315 3.583519 3.70687 3.7821088 3.9397206 3.963555 3.9152353 4.002623 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.470341 3549.679 4.543559 20.70295 0.0 0.18296112 0.5990694 2.2772672 -1.2039728 2.0794415 0.7399896 1.0 100 0 0.272162 0.41948444 0.58051556 7.45819 -0.08211851 2.4199712 2.8735647 3.7794282 -1.1517875 0 13.832062 +"Croatia" "HRV" -1.0863314 0.3374522 4.114147 4.19012 4.232625 4.2509975 4.2790313 4.287823 4.3369718 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.168315 7043.6377 4.858727 23.625177 1.7695369 0.31409955 3.260785 -0.1863296 3.8103595 0.7376095 0.0 0 0 0.0338831 0.069446445 0.93055356 8.45766 1.6638083 4.279303 3.407842 3.304904 -1.0701506 0 4.5900817 +"Cuba" "CUB" -1.1038427 0.3315944 4.082609 4.15739 4.2438507 4.2981334 4.3102593 4.335535 4.3689938 0 0 0 1 0 0.63131315 0.0 0.0 0.28282827 0.08585858 0.0 8.859371 5629.0767 5.982806 42.64647 1.2142997 0.46423703 3.3796332 -0.22314353 3.068053 0.72912085 0.0 100 0 0.0210312 0.9746697 0.025330303 8.00936 1.4367771 4.180985 4.067316 3.5569844 -1.0403683 0 7.077656 +"Cyprus" "CYP" -1.1146576 0.32802758 4.0430512 4.204693 4.22909 4.283954 4.314129 4.3373523 4.3561563 4.3742514 1 0 0 0 0 0.66944736 0.16527633 0.16527633 0.0 0.0 0.0 8.504016 8732.541 4.3141093 18.764896 0.08970724 2.3617969 3.5553482 0.7417223 0.0 0 0 0.0110395 0.20499998 0.795 8.44398 1.6237346 4.1273437 3.5723455 3.6027298 -1.0836405 0 4.540665 +"Czech Republic" "CZE" -1.1338451 0.32179356 -1.245953 4.0656023 4.2121277 4.25347 4.2404666 4.2524595 4.2680726 4.3170652 4.349302 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 9.071817 6502.487 4.387732 19.262966 2.3025851 2.0608103 0.27926978 3.6861258 -0.7339692 3.9070106 0.7411664 0.0 0 0 0.0769749 0.0153846145 0.9846154 9.04759 2.1203055 4.8284245 4.085976 3.2507339 -1.1331612 1 3.3421216 +"Denmark" "DNK" -1.0777297 0.34036738 -2.1086645 4.18205 4.26268 4.27912 4.2951527 4.3054385 4.3148894 4.3385015 4.3707128 1 0 0 0 0 0.99527675 0.002361639 0.002361639 0.0 0.0 0.0 8.614495 5512.538 5.714107 32.65847 2.3025851 1.5527161 0.07868178 3.9845297 -0.63487834 4.0253515 0.73115 0.0 0 0 0.022714399 0.010131717 0.9898683 9.4618 2.0570815 4.683232 4.3000026 3.227059 -1.076539 1 3.4749274 +"Dominica" "DMA" -1.1504012 0.31650975 0.18065347 0 0 0 1 0 0.04037957 0.0 0.0 0.9232788 0.03634161 0.0 0.18253987 1.8976198 2.7354493 100 0 -1.0969236 0 13.465435 +"Dominican Republic" "DOM" -1.1318495 0.32243633 -1.6194882 3.3978584 3.8264651 3.94735 4.067942 4.1431537 4.215148 4.261182 4.2931957 0 0 0 1 0 0.525 0.0 0.0 0.3533 0.1217 0.0 8.432793 5272.0303 6.0253344 47.40447 1.6156462 1.0015699 0.35725477 3.1201599 -0.08338159 2.944439 0.7287997 0.0 87.81660113130476 0 0.029725 0.9619676 0.03803243 7.55224 1.015029 4.202853 3.407842 3.8689075 -1.0460073 0 8.211717 +"Ecuador" "ECU" -1.2871528 0.27605566 -0.07235562 3.6712246 3.8795 3.97264 4.0565724 4.141863 4.23203 4.2958155 4.323633 0 0 0 1 0 0.35 0.0 0.0 0.05 0.6 0.0 8.44669 4927.002 15.929182 317.41995 1.2034088 0.45053443 0.503801 1.7647308 -0.6539265 0.6931472 0.654009 0.195974 73.16508036609258 0 0.18535401 0.38897616 0.61102384 8.18758 1.1688334 2.7695408 3.523415 3.767727 -1.1770949 0 10.406094 +"Egypt" "EGY" -1.1107237 0.32932052 -0.9853819 3.4339871 3.7471485 3.8272 3.92103 4.0280304 4.1273413 4.235304 4.2901206 0 0 1 0 0 0.0 1.0 0.0 0.0 0.0 0.0 8.861781 7079.0 2.381034 5.781835 2.175943 1.304883 0.16855389 1.0331844 -5.48224 3.295837 0.7563203 0.0 0 16.497606799128874 0.22420199 0.055000007 0.945 7.28551 -0.2128107 3.2946997 3.634951 3.7856526 -1.1107237 0 4.83833 +"El Salvador" "SLV" -1.2043937 0.29987374 -0.030046945 3.48124 3.813307 3.93776 4.035224 4.0400224 4.189516 4.2442236 4.272942 0 0 0 1 0 0.455 0.0 0.0 0.0 0.545 0.0 8.444102 5100.0 13.395143 227.77272 1.1512926 0.8335745 0.18048654 3.4304326 -0.12783338 2.6270812 0.6731452 0.0 100 0 0.0442959 0.5 0.5 7.92805 0.67754847 4.818803 3.6454499 3.8080184 -1.1187958 0 8.450272 +"Equatorial Guinea " "GNQ" -1.1109744 0.32923797 3.4781585 3.5409594 3.60361 3.6824553 3.756869 3.8451445 3.8854792 3.9286993 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 3.3004065 11.550396 0.137208 -1.4309951 0.29765716 1.5325569 -2.8134108 0.6931472 0.7493775 1.0 100 0 0.0851343 0.9069453 0.09305473 6.99301 2.1626344 3.2386785 3.6117692 -1.1069928 0 20.528912 +"Estonia" "EST" -1.1148995 0.32794824 -1.292804 4.22747 4.2499022 4.2345853 4.240976 4.254436 4.3231955 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.24305 3817.9888 5.7362638 32.91544 -0.9730633 0.40957 2.990217 -0.967584 4.0775375 0.7309827 0.0 0 0 0.084040396 0.014285743 0.98571426 8.98005 1.941786 3.3520002 4.051785 3.1218696 -1.0999763 0 8.865375 +"Fiji" "FJI" -1.3432668 0.26099166 4.010963 4.02175 4.092689 4.14328 4.183131 4.213716 4.237374 0 0 0 0 1 0.0 0.46249074 0.0 0.0 0.0 0.53750926 13.325858 213.98515 0.97617155 0.810603 0.4369263 2.3933394 2.8903718 0.6736684 100 0 0.46785718 0.5321428 8.37747 1.4854184 3.0694344 3.3911471 3.877596 -1.1876262 0 11.476062 +"Finland" "FIN" -1.102931 0.33189684 -1.4986571 4.0483007 4.19419 4.23149 4.251056 4.2964687 4.314994 4.3498373 4.3804097 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.160519 3500.0 5.845452 34.16931 2.3025851 0.12354181 1.9699056 -3.506558 4.158883 0.7301581 0.0 0 0 0.18167 0.0 1.0 9.11019 1.7348881 2.677098 3.6402142 3.4146502 -1.0965271 1 10.922708 +"France" "FRA" -1.0415289 0.3529147 -1.2758268 4.0943446 4.1972017 4.25191 4.271912 4.304757 4.338597 4.3689227 4.398986 1 0 0 0 0 0.976881 0.023119 0.0 0.0 0.0 0.0 8.922159 7499.542 5.9949703 36.298573 2.3025851 2.1690361 0.09823031 3.5121422 -0.38566247 3.8286414 0.729029 0.0 0 0 0.053724498 0.03107661 0.9689234 9.31677 1.434236 4.4433393 4.12552 3.2721014 -1.0394766 1 3.2156706 +"Gabon" "GAB" -1.1092016 0.3298222 3.637586 3.67803 3.8423703 4.00449 4.1156025 4.0891805 4.1317477 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.016713 3050.1663 3.327243 11.166387 -1.6126094 0.5703918 0.23111172 -1.7147983 0.0 0.74917483 1.0 100 0 0.236024 0.25771284 0.74228716 8.72274 0.0102146715 0.54864603 2.85647 3.4120116 -1.1092016 0 19.394001 +"Gambia, The " "GMB" -1.2574322 0.28438333 3.4011974 3.58929 3.643944 3.861596 3.9726486 4.010179 4.063198 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.009549 3014.4026 6.052353 36.634247 0.009475186 0.17305362 0.58020353 3.349904 -0.7765288 2.6002176 0.7285957 1.0 89.35480344032106 0 0.0262397 0.004999995 0.995 6.90575 -0.7678344 3.5609126 2.4932055 3.6649318 -1.2574322 0 13.81199 +"Georgia" "GEO" -1.1540947 0.3153429 4.119037 4.1499 4.2069244 4.2393503 4.25203 4.2705793 4.2949333 0 1 0 0 0 0.8796095 0.029284164 0.09110629 0.0 0.0 0.0 8.745197 6365.0 4.205682 17.761122 2.3025851 -0.24751504 0.40030113 2.4344902 -0.7339692 3.7376697 0.74254113 0.0 0 0 0.24033299 0.125 0.875 8.6403 4.088854 3.744787 3.3561625 -1.1361127 0 4.9958506 +"Germany" "DEU" -1.0784254 0.3401307 -1.7004446 4.15104 4.2121277 4.24195 4.2576094 4.287972 4.3216944 4.35577 4.381874 1 0 0 0 0 0.99472016 0.005279831 0.0 0.0 0.0 0.0 8.987403 8002.761 5.4196167 29.404339 2.1616652 0.15546581 3.521348 -0.7133499 3.9318256 0.73337394 0.0 0 0 0.041169602 0.005268693 0.9947313 9.38017 1.6330646 4.2682977 3.0575209 -1.075307 1 3.51613 +"Ghana" "GHA" -1.1721678 0.30969486 3.7376697 3.82554 3.8899605 3.9723735 4.0402937 4.067012 4.1563373 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.156115 3486.6 4.291841 18.432783 0.0 0.16329266 0.5148168 2.8541687 -0.79850775 2.0794415 0.74189043 1.0 100 0 0.27757102 0.01999998 0.98 7.28276 0.07332057 3.4203615 3.1484532 3.7865138 -1.1500481 0 13.81199 +"Greece" "GRC" -1.1078757 0.3302598 -0.8935241 3.996364 4.1881385 4.23193 4.272407 4.3096614 4.3430133 4.3552694 4.3868623 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 9.047821 8500.0 4.270529 18.23742 2.3025851 2.741866 0.1463749 3.0568273 -0.2357223 3.6635616 0.7420514 0.0 0 0 0.0411515 0.0 1.0 8.52258 2.0040526 4.168271 3.7588718 3.2768476 -1.0900704 1 4.5524073 +"Grenada" "GRD" -1.1437064 0.31863582 -1.6194882 3.8501475 4.14485 4.1607223 4.1745305 4.233053 4.296029 4.3262553 0 0 0 1 0 0.06352459 0.0 0.0 0.9177254 0.01875 0.0 0.23596816 1.0784096 2.494582 100.00000000000001 0 7.54486 5.580262 3.4111476 3.8937593 -1.0957901 0 12.894178 +"Guatemala" "GTM" -1.2947992 0.27395287 -0.21616788 3.4144425 3.7376697 3.81841 3.9521644 4.04851 4.1317477 4.2157383 4.260218 0 0 0 1 0 0.28061223 0.0 0.0 0.0 0.71938777 0.0 8.307875 4290.252 16.470167 310.3165 0.5219234 0.06890005 0.41356555 2.554899 -0.105360545 2.74084 0.6499236 0.0153678 78.37599235323688 0 0.104894996 0.26275557 0.7372444 8.08425 0.37421212 3.644351 3.437208 3.822222 -1.1981584 0 10.147902 +"Guinea" "GNB" -1.2574322 0.28438333 3.4275146 3.48124 3.54117 3.6165454 3.6902966 3.757102 3.804101 3.8649452 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.03587 3913.1594 5.9516726 35.494366 0.06952595 0.22420031 0.59233963 2.3674362 -0.4780358 2.4849067 0.729356 1.0 100 0 0.0422559 0.03833002 0.96167 6.14847 3.0878725 2.6100698 3.7204854 -1.2574322 0 13.81199 +"Guinea-Bissau" "GIN" -1.2574322 0.28438333 3.4339871 3.5952 3.5251224 3.659315 3.7766771 3.8734932 3.9822686 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.054873 3151.5 5.629013 31.797571 0.0 -0.28807354 0.5532769 1.3787661 -0.56211895 2.3978953 0.73179257 1.0 100 0 0.280026 0.394 0.606 6.44572 2.510834 2.3513753 3.6888685 -1.2574322 0 13.81199 +"Guyana" "GUY" -1.1588354 0.31385148 -0.68319684 3.8918202 3.9569964 4.02708 4.027679 4.086373 4.112536 4.165863 4.2420335 0 0 0 1 0 0.0424 0.494 0.004 0.3798 0.0798 0.0 8.502184 5839.3506 6.525871 59.38956 0.48211622 0.89199805 -0.7765288 1.609438 0.7250198 0.5083628 100 0 0.22974001 0.9497708 0.050229196 7.50988 1.5748637 1.065804 3.3672957 3.8346014 -1.063647 0 10.530728 +"Haiti" "HTI" -1.1418484 0.3192284 -2.141317 3.5409594 3.627004 3.74153 3.8518903 3.9294646 4.008976 4.066838 4.1233044 0 0 0 1 0 0.025030036 0.0 0.0 0.97497 0.0 0.0 7.9046826 2903.2632 3.9368956 16.33278 0.111617826 -0.53083354 0.09075436 3.342862 -0.09431065 2.944439 0.7445709 1.0 100 0 0.0248002 1.0 0.0 7.34537 -0.19783068 4.8988166 2.7472708 3.6966798 -1.1063133 0 13.54531 +"Honduras" "HND" -1.1949586 0.3027165 -0.49511635 3.48124 3.7328963 3.83543 3.959668 4.0846086 4.1947837 4.2539377 4.288072 0 0 0 1 0 0.46244624 0.0 0.0 0.0210021 0.5165517 0.0 8.404693 4924.39 13.656617 236.16644 1.0704035 0.71961075 0.17117634 2.2565413 -0.3147107 2.7080503 0.67117065 0.1 94.6362638034219 0 0.0798539 0.4798783 0.5201217 7.63434 0.6727118 2.8589091 3.122365 3.821888 -1.1090127 0 8.39684 +"Hong Kong" "HKG" -1.138013 0.32045513 -3.1700857 4.1108737 4.19029 4.269032 4.313121 4.348735 4.3929424 4.4173703 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 9.070395 8772.263 10.114602 102.5271 2.0272014 0.060108725 0.6979189 0.0 0 0 1.0 0.0 8.43468 1.5910022 7.990016 4.4450016 3.7106352 -1.138013 0 12.117326 +"Hungary" "HUN" -1.104762 0.33128974 -1.3182299 4.0253515 4.1526136 4.21955 4.23649 4.2350006 4.2386703 4.2661433 4.306863 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.908495 7399.4185 4.76933 22.766657 2.3025851 1.1491886 0.14167316 3.9112227 -0.1863296 3.8501475 0.73828465 0.0 0 0 0.0487502 0.07132709 0.9286729 8.67368 2.0068016 4.708436 4.0235643 3.2315588 -1.099912 1 11.004282 +"Iceland" "ISL" -1.0770804 0.34058845 -1.7573581 4.1108737 4.276666 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 0.07677213 -2.65926 -5.809143 4.1743875 0.0 0 9.085976039395224 -1.0770804 1 3.148021 +"India" "IND" -1.1374893 0.32062298 0.11871381 3.4011974 3.6558397 3.74777 3.8948288 4.0136237 4.0665083 4.1208873 4.176406 0 1 0 0 0 0.0 1.0 0.0 0.0 0.0 0.0 9.044097 8475.5 6.8982544 47.60848 2.081896 2.172011 0.34938845 3.988984 -0.6931472 2.9957323 0.72220767 0.3326023 39.44693720919765 4.494773291059892 0.34261498 0.021000028 0.979 6.95368 0.10493894 5.0093217 2.8848007 3.7058144 -1.1278645 0 6.0438747 +"Indonesia" "IDN" -1.2091099 0.2984628 -0.13067833 3.5351453 3.624341 3.7173 3.9479687 4.0533686 4.128808 4.1842823 4.2325063 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 8.301788 4047.7134 12.22726 149.55354 1.107972 0.11458785 0.55108464 2.4265711 -1.1086626 1.609438 0.68196476 0.4516813 99.83455984240312 0 0.076184504 0.009542704 0.9904573 7.25205 0.44969612 4.0124354 2.6810215 3.6871843 -1.2091099 0 11.818276 +"Iran" "IRN" -1.1574267 0.3142939 -0.9902065 3.6627922 3.830813 3.87523 3.9366314 3.9578419 4.125689 4.244769 4.2870545 0 1 0 0 0 0.0 0.7525773 0.24742268 0.0 0.0 0.0 9.159308 9502.552 4.4400883 19.716759 0.89357626 0.5118651 2.1690538 -2.3025851 3.465736 0.740771 0.1246762 0 0.5426872857539721 0.493337 0.0051020384 0.99489796 8.0861 -0.08345506 2.5825744 3.5174978 3.7256653 -1.1296242 0 6.3418403 +"Iraq" "IRQ" -1.1217827 0.32569867 -0.3744026 3.6627922 3.7841897 3.91579 4.0551724 4.0480337 4.212634 4.2590604 4.22663 0 1 0 0 0 0.0 0.98 0.02 0.0 0.0 0.0 9.1846695 9773.04 3.804231 14.530263 0.7130956 0.3140148 2.5241272 -2.5257287 3.4965076 0.74557275 0.0281283 0 0.00978036350881942 0.500556 0.11140001 0.8886 8.29854 -0.64552283 2.7471328 3.7588718 3.740989 -1.1090702 0 4.8610964 +"Ireland" "IRL" -1.0585636 0.34695384 -1.18526 4.091006 4.203199 4.2441 4.2626266 4.2842674 4.314033 4.337823 4.385669 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.520781 5021.6196 6.6366262 44.076572 2.3025851 0.38860992 0.11389549 2.7491922 -1.1086626 3.9702919 0.72418344 0.0 0 0 0.0436382 0.018999994 0.981 8.75337 2.0789201 3.7162273 3.824284 3.4302657 -1.0470694 1 3.1448045 +"Isle of Man" "IMN" -1.0770804 0.34058845 1.0 0.0 0.0 0.0 0.0 0.0 -1.0770804 +"Italy" "ITA" -1.0843426 0.338124 -0.20285276 4.0724397 4.189655 4.2359 4.2705193 4.303297 4.341973 4.374836 4.4035015 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.979707 7948.5 5.0742135 25.752434 2.3025851 3.113893 0.10842292 3.3614166 -0.2876821 3.7573166 0.7359823 0.0 0 0 0.0516288 0.041999996 0.958 9.08783 1.58112 5.1397686 4.0842943 3.2192676 -1.0815504 1 3.9915917 +"Jamaica" "JAM" -1.1376541 0.32057017 -2.141317 3.9721768 4.069027 4.1625 4.2223134 4.2554073 4.257676 4.2552195 4.288363 0 0 0 1 0 0.07206395 0.014750591 0.0 0.9131855 0.0 0.0 7.9799047 3214.1292 4.189245 19.015778 0.44215873 -0.36133608 0.3456401 2.7769542 2.904165 0.7426653 0.0 100 0 0.012252499 1.0 0.0 8.36962 1.3499773 5.0151067 3.5204608 3.730053 -1.0948466 0 13.20756 +"Japan" "JPN" -1.1590977 0.31376916 -0.88071465 3.8774316 4.1573195 4.21458 4.2759748 4.331939 4.36738 4.395388 4.4180293 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 8.411833 4500.0 12.02971 144.714 2.0794415 -0.1947441 0.011857422 2.5071573 -0.2876821 3.583519 0.6834565 0.0 0.006131225749628325 0 0.039792098 0.0 1.0 8.6694 2.0803242 5.54748 3.7635229 3.4062405 -1.1588765 1 12.993673 +"Jordan" "JOR" -1.1117826 0.32897204 3.7658405 3.84726 4.109691 4.2052417 4.2546835 4.2777076 4.2944193 0 1 0 0 0 0.022222223 0.9777778 0.0 0.0 0.0 0.0 9.247303 10403.163 2.9727018 8.938369 2.3025851 1.9027282 0.4653679 1.0079579 -3.218876 3.4339871 0.7518522 0.0 0 15.331683542107124 0.235173 0.66663444 0.33336556 8.00803 0.87869 2.2634275 3.929863 3.7647665 -1.1107595 0 4.7108746 +"Kazakhstan" "KAZ" -1.1435266 0.31869316 4.0342407 4.130316 4.199071 4.2244453 4.1823106 4.2238417 0 1 0 0 0 0.38241526 0.56567794 0.05190678 0.0 0.0 0.0 8.72008 6182.661 6.274377 40.06004 -1.1023928 0.48064804 2.0769384 -0.7339692 3.871201 0.726919 0.0 0 9.747162957702038 2.2916799 0.3312096 0.6687904 8.9118 1.3116695 1.3076438 3.788725 3.5901275 -1.0974965 0 8.026226 +"Korea, North" "PRK" -1.1450148 0.3182192 3.8607297 4.01135 4.1202617 4.215066 4.255134 4.176086 4.227303 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 10.771979 116.03965 -0.078592315 0.038451187 3.0722303 -0.4155154 3.6888795 0.69295454 0.0 0 0 0.0697366 0.007000029 0.993 7.39203 3.693867 3.6068935 -1.1450148 0 10.521579 +"Korea, South" "KOR" -1.1450148 0.3182192 -1.6847004 3.885679 3.8607297 3.99178 4.114266 4.1866493 4.2668242 4.3288302 4.3915057 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 8.411833 4500.0 10.94793 119.8572 2.0794415 -0.0913934 0.0019960066 2.85647 -0.2613648 3.6109178 0.6916258 0.0 0 0 0.0529981 0.0 1.0 7.37776 1.4672905 5.525569 3.3214324 3.7106338 -1.1450148 1 10.521579 +"Kuwait" "KWT" -1.1133709 0.32844993 4.021774 4.08395 4.2021875 4.2549987 4.286092 4.3007145 4.312204 0 1 0 0 0 0.0 1.0 0.0 0.0 0.0 0.0 8.991628 8074.4717 4.0978527 16.792982 0.50705856 -0.5798185 -5.2983174 3.3843904 0.74335545 0.0 0 0 0.0340444 0.7514868 0.24851322 10.6553 0.8811058 2.7972164 4.316154 3.5851662 -1.1101335 0 5.258344 +"Kyrgyzstan" "KGZ" -1.1320877 0.32235956 4.01458 4.0980268 4.1410728 4.223874 4.227688 4.2394323 0 1 0 0 0 0.25963718 0.0 0.7403628 0.0 0.0 0.0 8.7756 6502.7383 6.5176263 42.908226 -0.93607104 0.51592183 1.9401795 -2.1202636 3.713572 0.7250821 0.0 0 0 1.8949699 0.09267205 0.90732795 8.18535 1.4885514 2.426619 3.5322256 3.5936034 -1.1151893 0 8.658093 +"Lao People's Dem Rep" "LAO" -1.3052865 0.27109486 -0.9187939 3.6323092 3.78373 3.8293483 3.8869557 3.9946756 4.117322 4.2056475 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 8.736007 6270.0 10.934579 119.65185 2.0794415 -0.59432197 0.414664 1.3350011 -1.2378744 2.8903718 0.6917266 1.0 54.79420381215124 0 0.26799798 0.100000024 0.9 6.89061 0.41500798 2.3031743 2.0668628 3.739901 -1.3052865 0 12.823391 +"Latvia" "LVA" -1.1342833 0.32165256 4.189655 4.24544 4.2461405 4.231328 4.2380576 4.2529798 4.297053 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.29912 4063.84 5.479636 30.03419 2.3025851 -1.1533133 0.46163622 3.3921566 -1.2729657 4.0430512 0.73292065 0.0 0 0 0.127878 0.0 1.0 8.92532 1.5396646 3.526701 3.9684033 3.1014993 -1.1342833 0 8.20096 +"Lebanon" "LBN" -1.1135515 0.32839063 -0.6618423 3.6627922 4.0253515 4.11086 4.1708817 4.19832 4.229342 4.2575545 4.282327 0 1 0 0 0 0.02 0.96 0.02 0.0 0.0 0.0 9.240045 10325.0 3.1103497 9.78866 2.207566 0.12345581 2.9215474 -0.34249035 3.5214465 0.75081277 0.0 0 0 0.044134602 0.27999997 0.72 8.14729 5.162538 3.744787 3.7166271 -1.1079882 0 4.713632 +"Lesotho" "LSO" -1.1092016 0.3298222 3.6480575 3.83902 3.889746 3.9826443 4.083062 3.8625462 3.8578851 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 7.3132205 1500.0 4.445932 19.76631 0.0 -2.535299 0.22713557 2.3860066 -0.40047753 3.3843904 0.7407268 0.0 0 0 0.238319 0.0 1.0 6.41673 1.0833108 3.3421202 1.2237755 3.7625716 -1.1092016 0 20.56829 +"Liberia" "LBR" -1.2172633 0.29603922 3.624341 3.69426 3.7211387 3.780733 3.7442386 3.8278654 4.0279837 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.076126 3227.4875 5.402836 29.316347 0.1312585 0.6462589 1.3737156 -1.5606477 1.8718022 0.73350066 1.0 100 0 0.087426595 0.039900243 0.96009976 7.80466 -0.32067168 2.3934684 2.9231615 3.7236755 -1.1790112 0 13.81199 +"Liechtenstein" "LIE" -1.0622944 0.34566182 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 0.45273665 3.218876 3.8558054 0 0 -1.0502657 0 +"Lithuania" "LTU" -1.1342833 0.32165256 -1.1805818 4.24631 4.259921 4.25536 4.264941 4.276937 4.294128 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.272214 3960.9746 5.2375627 27.438559 -0.8381111 0.27934995 3.8456695 -0.8675006 4.0253515 0.7347487 0.0 0 0 0.0604537 0.13522398 0.864776 8.89151 1.6170553 3.7867029 3.6763008 3.300918 -1.1342833 0 10.080332 +"Luxembourg" "LUX" -1.0761585 0.3409026 4.1271343 4.1881385 4.23308 4.246716 4.276114 4.3155303 4.3550816 4.3831234 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.609088 5485.404 5.7197833 32.717976 2.3025851 2.1188254 0.42542326 -0.597837 3.9070106 0.7311071 0.0 0 0 0.0223941 0.009132445 0.99086756 9.72442 1.9106864 4.2427645 3.0604634 -1.0547082 1 3.2939878 +"Macedonia (Former Yug. Rep)" "MKD" -1.1095381 0.32971123 4.0073333 4.10629 4.1946864 4.2435775 4.2666125 4.289229 4.312393 1 0 0 0 0 0.9726871 0.013656433 0.013656433 0.0 0.0 0.0 8.931271 7577.77 4.326451 18.746653 1.7734281 0.4069693 3.0828269 -0.82098055 3.7336934 0.7416291 0.0 0 0 0.142786 0.10869998 0.8913 7.93916 3.9838588 3.5263605 3.6171107 -1.0898882 0 4.807308 +"Madagascar" "MDG" -1.2017819 0.300658 3.6296601 3.6859 3.7819054 3.8755276 3.9267616 4.088906 4.196707 0 0 1 0 0 0.0 0.0 0.93 0.07 0.0 0.0 7.608833 2033.0 3.265208 10.7053795 0.013815511 0.6307929 1.6074358 -1.3470737 2.9957323 0.7496433 1.0 65.22437388940375 0 0.097816594 0.0059999824 0.994 7.49164 2.2434945 2.3608541 3.7511275 -1.182943 0 11.688062 +"Malawi" "MWI" -1.1092016 0.3298222 3.589059 3.63161 3.7025166 3.7918015 3.8521514 3.8292875 3.978983 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 7.4980674 1808.4 2.750419 7.777364 0.0 0.5154549 3.105483 -0.63487834 2.6026897 0.7535308 1.0 39.22364985094371 0 0.519043 0.08499998 0.915 6.286 0.08154882 3.599988 1.4816046 3.825682 -1.1092016 0 20.56829 +"Malaysia" "MYS" -1.1827935 0.30642155 -1.1491691 3.7518542 3.881564 3.9875 4.157956 4.210687 4.2494287 4.278643 4.304397 0 1 0 0 0 0.0 0.07973422 0.9202658 0.0 0.0 0.0 8.629282 5900.7524 10.646887 114.74739 2.0703874 -0.21928807 0.46245143 1.7119945 -1.3093333 0.91629076 0.6938992 0.2543278 100 0 0.0717327 0.3266778 0.6733222 7.71824 1.0431656 3.244684 3.2809112 3.8175488 -1.1565223 0 12.10972 +"Mali" "MLI" -1.2201633 0.29518196 3.5351453 3.58917 3.540842 3.6790774 3.7878697 3.8553102 3.9309397 0 0 1 0 0 0.0 0.11235955 0.0 0.8876405 0.0 0.0 8.010838 3014.5 5.1295977 26.328043 0.020723267 -1.5412965 0.5250835 1.3350011 -2.207275 2.8332133 0.735564 0.9899238 11.687950070578438 26.132130661449526 1.0364499 0.028999984 0.971 6.6053 -1.6559631 1.3039013 2.4069452 3.7005239 -1.1809266 0 13.757853 +"Malta" "MLT" -1.0827438 0.33866504 4.0253515 4.1881385 4.22762 4.2548084 4.2895336 4.319829 4.3592696 4.3938165 1 0 0 0 0 0.97883916 0.021160847 0.0 0.0 0.0 0.0 8.927566 7548.8 5.10896 26.152863 2.3025851 0.04059689 3.218876 3.5788786 0.73571986 0 0 0.04699999 0.953 7.68064 1.4238127 6.934023 4.5009203 3.6753578 -1.0817914 0 3.95868 +"Marshall Islands" "MHL" -1.5602771 0.21007785 4.1780577 0 0 0 0 1 0.0 0.0 0.00513347 0.0 0.0 0.99486655 0.058587726 2.4078455 2.1972246 100 0 7.37901 3.5723455 -1.5576743 0 +"Mauritius" "MUS" -1.1412287 0.3194263 -0.113168694 3.465736 3.9318256 4.08425 4.145022 4.204513 4.2399573 4.2719803 4.2900114 0 0 1 0 0 0.02 0.68 0.165 0.135 0.0 0.0 8.809308 7440.0605 6.299044 41.27764 1.9250883 0.3807625 3.8971124 3.0097995 0.72673273 0.0 100 0 1.0 0.0 8.27792 1.2619992 5.7890844 3.50255 3.8419306 -1.0887758 0 8.043985 +"Mexico" "MEX" -1.2303797 0.2921816 -0.47666574 3.775057 3.9239516 4.0442 4.1162148 4.198282 4.259733 4.307764 4.33969 0 0 0 1 0 0.3939394 0.0 0.0 0.0 0.6060606 0.0 8.463685 4991.4336 14.286494 246.50139 0.72777796 0.4845673 0.43295056 2.56418 -0.82098055 3.1354942 0.6664141 0.0411304 27.92822795690076 1.704476130709012 0.207121 0.37393826 0.62606174 8.46295 1.0177667 2.9879732 3.9278963 3.8206306 -1.1384102 1 9.044684 +"Micronesia" "FSM" -1.5586437 0.21042126 4.05329 4.1212068 4.1772075 4.192644 4.20877 4.2306924 0 0 0 0 1 0.0 0.0 0.008998875 0.0 0.0 0.9910011 0.5309435 1.742219 1.933934 100 0 7.7626 4.0890207 3.1045866 3.81505 -1.5541105 0 +"Moldova" "MDA" -1.1165642 0.32740277 4.067316 4.12395 4.1716943 4.1708226 4.2112927 4.2030234 4.23271 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.853913 7018.425 4.4009676 19.43491 -0.59471446 0.4404963 4.0142183 -0.32850403 3.8501475 0.7410664 0.0 0 0 0.0280568 0.17955512 0.8204449 8.54675 4.5134673 3.152736 3.4590473 -1.1005678 0 4.536134 +"Mongolia" "MNG" -1.1534504 0.31554615 3.744787 3.84418 4.0207386 4.048875 4.103276 4.145059 4.222371 0 1 0 0 0 0.0 1.0 0.0 0.0 0.0 0.0 8.528109 5072.0 8.973991 80.74109 2.0794415 -1.6004549 0.31351355 -0.2876821 -2.4079456 3.8286414 0.70653236 0.0 0 1.4896948137870085 1.6758101 0.032999992 0.967 6.81564 1.0518641 -0.48695743 3.5751507 3.609023 -1.1534504 0 10.03703 +"Morocco" "MAR" -1.0929813 0.3352156 -1.5230309 3.6627922 3.7588718 3.84329 3.9416718 4.0540648 4.161162 4.2293873 4.274784 0 0 1 0 0 0.0033 0.9967 0.0 0.0 0.0 0.0 8.219117 3799.5 5.4852896 30.17649 2.279397 0.88759696 0.39477968 2.9775684 -1.8325815 3.465736 0.7328779 0.0 0 0.6138048618807664 0.178334 0.097 0.903 7.5999 -0.7511851 3.326256 3.3809946 3.8026788 -1.0726395 0 5.950972 +"Mozambique" "MOZ" -1.1091015 0.32985517 3.8066626 3.5115454 3.55524 3.6690805 3.756676 3.765291 3.8551955 3.9059432 0 0 1 0 0 0.002026342 0.0 0.0 0.9979737 0.0 0.0 7.2442274 1400.0 3.947621 15.58371 0.0 0.5266297 1.6034198 -1.0788096 2.904165 0.7444899 1.0 87.3647570459732 0 0.203434 0.0 1.0 7.23201 -0.17989673 2.2515087 1.3083328 3.7438114 -1.1088024 0 20.534752 +"Myanmar (Burma)" "MMR" -1.195198 0.302644 -0.47612143 3.6000483 3.6082115 3.77697 3.9116306 4.00798 4.0478415 4.1248045 4.169175 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 8.546351 5195.0 9.13593 83.6 0.5014614 0.40958062 2.712706 -0.7765288 3.0910425 0.70530945 0.7168632 50.24476883718649 0 0.261538 0.055000007 0.945 6.54391 0.073532246 2.9549103 3.7058964 -1.1723076 0 10.529254 +"Namibia" "NAM" -1.1366284 0.32089913 3.57683 3.6558397 3.84703 3.9613333 4.057039 4.1072183 4.0545244 4.1282644 0 0 1 0 0 0.075 0.0 0.0 0.925 0.0 0.0 7.5062356 2223.0996 3.952092 16.42796 0.19974269 0.49036002 -0.010050327 -3.218876 3.0910425 0.7444562 0.6194981 0 13.860907342860697 0.35894698 0.54236346 0.45763654 8.182 1.1148058 -0.33194774 2.8848007 3.7288868 -1.0868943 0 18.539692 +"Nauru" "NRU" -1.4200472 0.24170262 0.15384616 0.0 0.0 0.0 0.74358976 100 0 8.95764 -1.3103738 +"Nepal" "NPL" -1.1424056 0.31905058 3.5918176 3.65031 3.7534173 3.8744168 3.9881825 4.119389 4.2252975 0 1 0 0 0 0.0 0.8834842 0.11651584 0.0 0.0 0.0 8.699514 6000.0 7.683035 59.02903 2.0794415 1.944911 0.5087567 2.7880929 -0.79850775 3.3322046 0.7162813 0.1628608 0 0 0.288941 0.0 1.0 6.68211 -2.008401 4.25096 1.2527629 3.6801844 -1.1160828 0 8.835381 +"Netherlands" "NLD" -1.0585636 0.34695384 -1.7145207 4.210645 4.278054 4.29583 4.2984495 4.327348 4.3422203 4.3565526 4.390769 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.696217 5998.9575 5.914501 35.11211 2.291234 1.760106 0.10018219 3.2906382 -1.0788096 3.9608133 0.72963667 0.0 0 0 0.028670799 0.024113 0.975887 9.42771 1.8327005 5.8296075 4.091006 3.4007065 -1.0470694 1 3.3418128 +"New Zealand" "NZL" -1.1147954 0.32798237 -1.5413121 4.215086 4.2427645 4.26601 4.26652 4.288118 4.322516 4.364837 4.390769 0 0 0 0 1 0.79581153 0.0 0.10209424 0.0 0.0 0.10209424 8.346976 4835.063 8.12216 86.085976 0.3342197 1.7227666 -1.171183 3.713572 0.71296513 0.0 0 0 0.039581 0.8669153 0.13308471 9.53626 2.3045945 2.1980512 4.3307333 3.4929755 -1.0486218 1 5.117172 +"Nicaragua" "NIC" -1.168182 0.3109317 -0.7431781 3.5409594 3.744787 3.85012 3.9815128 4.0683503 4.1613493 4.2434216 4.3003993 0 0 0 1 0 0.515 0.0 0.0 0.09 0.395 0.0 8.433162 5086.7227 11.903568 193.50693 0.8280876 0.39501065 2.7593768 -0.63487834 2.5649493 0.68440914 0.1 100 0 0.0839011 0.59908664 0.40091336 7.93451 0.79248536 2.5180209 3.6788292 3.8531773 -1.0777868 0 7.9500976 +"Niger" "NER" -1.175643 0.30862048 3.4965076 3.62505 3.6451116 3.6770494 3.7240741 3.8767414 3.993891 0 0 1 0 0 0.0 0.37348178 0.0 0.6265182 0.0 0.0 8.18097 3606.3623 4.505045 20.667576 0.01391163 -1.4811914 0.5018426 2.4362414 -4.6051702 2.7725887 0.7402804 0.9842288 0.03212717192259343 39.20989211883996 1.1802601 0.41489428 0.5851057 7.29777 -0.96947074 1.1278024 1.7578579 3.8521388 -1.1211549 0 10.159941 +"Nigeria" "NGA" -1.1651031 0.3118905 -0.6052519 3.5973122 3.63419 3.746428 3.8174546 3.8207262 3.834544 3.9398332 0 0 1 0 0 0.0 0.12213303 0.0 0.877867 0.0 0.0 7.9131236 2734.5 3.6876 14.026362 0.033589724 0.6154342 3.432696 -0.8675006 2.3025851 0.7464536 1.0 73.43996903855937 0 0.47306 0.11500001 0.885 7.11639 3.8203022 2.7850113 3.730437 -1.1169881 0 12.008705 +"Norway" "NOR" -1.0773575 0.34049407 -1.5399119 4.2091603 4.286341 4.29796 4.3052545 4.3264046 4.3377786 4.364806 4.394419 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.517193 5000.0 6.146944 37.78492 2.3025851 -1.119232 0.05694728 1.0577903 -3.912023 4.1271343 0.7278814 0.0 0 0 0.33258402 0.0 1.0 9.31452 2.0270207 2.465516 3.910021 3.253727 -1.0762614 1 3.3729966 +"Oman" "OMN" -1.1158568 0.32763445 3.5945687 3.72628 3.9214487 4.10707 4.257497 4.3055544 4.292165 0 1 0 0 0 0.0 1.0 0.0 0.0 0.0 0.0 8.925118 7597.4 5.205855 27.73305 -0.912548 0.36273575 -2.1202636 -5.320563 3.0445225 0.7349882 0.8682069 0 19.027367888056602 0.110127 0.13548338 0.8645166 7.31122 0.66079086 2.7972813 3.7984638 -1.1061811 0 4.877651 +"Pakistan" "PAK" -1.1256831 0.3244308 -0.20653225 3.4011974 3.6609943 3.90007 3.9740858 4.0589733 4.10705 4.145702 4.177442 0 1 0 0 0 0.0 1.0 0.0 0.0 0.0 0.0 9.100408 8960.0 6.4482236 41.597866 2.2845533 2.161498 0.5363764 3.318902 -2.8134108 3.4011974 0.7256062 0.4946105 0 11.183842195733146 0.637725 0.089999974 0.91 6.85541 0.14161459 4.1799536 3.0955777 3.700002 -1.1181641 0 5.318209 +"Palau" "PLW" -1.2997522 0.27259934 0 0 0 0 1 0.02 0.0 0.63 0.0 0.0 0.35 0.35851327 2.014903 100 0 -1.2050636 0 +"Panama" "PAN" -1.2160314 0.29640412 -0.5188578 3.7471485 4.010963 4.10766 4.178986 4.247934 4.2814903 4.307076 4.3303943 0 0 0 1 0 0.5104167 0.0 0.0 0.072916664 0.41666666 0.0 8.360127 4972.9883 11.531368 188.75269 1.2279683 0.8185696 0.44005975 1.9823799 -0.7550226 2.1972246 0.68721986 0.0309956 100 0 0.0204568 0.6343384 0.36566162 8.16678 1.524388 2.7367876 3.7184384 3.7585015 -1.1066844 0 8.891032 +"Papua New Guinea" "PNG" -1.4493964 0.23471193 3.558201 3.65005 3.8295307 3.9680953 4.020148 4.07407 4.134216 0 0 0 0 1 0.0 0.0 0.0 0.0 0.0 1.0 8.294049 4000.0 15.49179 239.9954 1.0986123 -0.3364722 0.24043322 -0.79850775 -0.56211895 1.7917595 0.657312 0.5892759 100 0 0.0796111 0.0 1.0 7.31455 -0.28712744 1.3500665 1.3083328 3.7454712 -1.3738748 0 18.887417 +"Paraguay" "PRY" -1.2393917 0.28956032 -1.0100521 3.8416004 4.1367655 4.15565 4.1804323 4.2005906 4.219105 4.2491407 4.280506 0 0 0 1 0 0.48924732 0.021505376 0.0 0.0 0.48924732 0.0 8.607158 5715.9395 15.443572 333.2394 1.5015578 0.15606314 1.9699056 -0.6539265 3.1354942 0.65767616 1.715E-4 57.033901637704666 0 0.171161 0.53960454 0.46039543 7.73674 1.2315929 1.569926 3.5723455 3.8671813 -1.1363213 0 8.336868 +"Peru" "PER" -1.3284181 0.26489595 -0.18374251 3.703768 3.7819145 3.86475 3.977215 4.0946145 4.182806 4.255276 4.3008842 0 0 0 1 0 0.29044613 0.0 0.0 0.0 0.7095539 0.0 8.485702 5066.2827 17.34885 370.0454 1.1675372 0.24387273 0.5047534 1.0612565 -1.3470737 2.3025851 0.64328814 0.029737 55.78815256359179 0.5402389003518683 0.254983 0.35717 0.64283 8.43229 1.2491481 2.0488012 3.8458831 3.760479 -1.2378155 0 9.727567 +"Philippines" "PHL" -1.2084538 0.2986587 -0.023793373 3.8565102 3.8607297 3.97391 4.1066365 4.1458254 4.1768365 4.2016306 4.226605 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 8.517193 5000.0 12.04161 145.0003 1.0986123 0.21393386 2.941804 -0.3147107 2.5649493 0.6833667 0.75 98.97271722866223 0 0.0273757 0.0 1.0 7.60887 1.4405462 4.560707 3.4111476 3.8496377 -1.2078023 0 12.617162 +"Poland" "POL" -1.1342833 0.32165256 -1.1790363 4.0091496 4.11578 4.2148 4.246612 4.249888 4.2611327 4.3006644 4.3339696 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.702406 6020.1006 5.0904794 25.914202 2.3025851 0.51303047 0.111845024 3.8282065 -0.597837 3.9512436 0.7358594 0.0 0 0 0.060476 0.010050237 0.98994976 8.48281 1.7979983 4.575923 3.8691156 3.51665 -1.1342833 1 3.1883547 +"Portugal" "PRT" -1.0614929 0.34593895 -0.47258294 3.918005 4.082609 4.162 4.2057843 4.2656984 4.3036036 4.3348646 4.369787 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.783726 6532.796 6.9593 48.781315 2.2995856 1.6972399 0.045697168 2.9790947 -0.82098055 3.6763008 0.7217467 0.0 0 0 0.0929028 0.018009841 0.98199016 8.38 1.1618172 4.5927105 3.5553482 3.376034 -1.0505667 1 3.929611 +"Qatar" "QAT" -1.1268014 0.3240682 3.871201 3.96914 4.191901 4.266193 4.305804 4.334715 4.3579593 0 1 0 0 0 0.0 1.0 0.0 0.0 0.0 0.0 8.873721 7895.1855 3.7401826 16.37054 0.5570983 0.49469623 -5.809143 3.2386785 0.74605644 0.0 0 1.781965039862531 0.014175801 1.0 0.0 10.7921 0.98929524 1.3695018 4.4450016 3.718901 -1.1090568 0 5.0256 +"Romania" "ROM" -1.1073227 0.3304425 -0.86259794 3.9019728 4.1125116 4.1814 4.2221894 4.235424 4.2447915 4.264979 4.296721 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.924109 7513.0386 4.153515 17.26376 2.3025851 1.261836 0.26763517 3.709662 -0.44628713 3.8286414 0.74293506 0.0 0 0 0.110138 0.022000015 0.978 8.06432 1.4594127 3.5322256 3.3387873 -1.0858418 0 5.191068 +"Russian Federation" "RUS" -1.1353285 0.32131654 -0.90446436 4.028917 4.2091603 1 0 0 0 0 0.94899046 0.051009566 0.0 0.0 0.0 0.0 8.528025 5065.081 5.4286966 29.490686 0.21927206 2.0268316 -2.4079456 4.0943446 0.73330534 0.0 0 0.056381835557723206 0.03323263 0.9667674 -1.129809 0 4.3221254 +"Rwanda" "RWA" -1.1101631 0.3295052 3.6888795 3.74388 3.7906075 3.8753622 3.4912508 3.839672 4.0083714 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 7.837505 2540.0 1.5583248 2.4295373 0.0 0.2805064 3.596764 -0.7550226 0.6931472 0.76253307 1.0 68.87955709384018 0 1.0579599 0.04000002 0.96 6.71052 -0.2629392 4.8113236 0.8754688 3.8729672 -1.1089443 0 19.87945 +"Saint Lucia" "LCA" -1.1464639 0.3177584 4.03881 4.15271 4.23388 4.2613926 4.2641664 4.3099923 0 0 0 1 0 0.0264 0.032 0.0 0.9233 0.0183 0.0 0.16285157 1.8809906 2.6306891 100.00000000000001 0 7.48437 4.96807 3.068053 3.8113356 -1.0968186 0 13.245343 +"Saint Vincent and Grenadines" "VCT" -1.1529967 0.31568936 3.98952 4.101626 4.184749 4.237267 4.2538657 4.278228 0 0 0 1 0 0.048378687 0.0 0.0 0.9033469 0.048274424 0.0 0.26742682 2.8875902 2.5839975 100 0 7.65681 5.336317 2.6318889 3.9037864 -1.0937343 0 13.81199 +"San Marino" "SMR" -1.0821474 0.33886707 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 0.25676012 2.8136108 3.7788725 0 0 -1.0821474 0 3.9154067 +"Saudi Arabia" "SAU" -1.1091305 0.32984564 3.6863763 3.79145 3.9595346 4.1319246 4.230775 4.269587 4.3020415 0 1 0 0 0 0.0 1.0 0.0 0.0 0.0 0.0 8.926317 7570.3433 4.0777297 16.807383 -0.74119174 0.16551444 0.5128236 -5.35383 3.218876 0.7435074 0.0931783 0 37.25825144794916 0.34132698 0.039153457 0.96084654 8.82026 1.0955429 0.7861249 3.443618 3.778453 -1.104234 0 4.9762583 +"Senegal" "SEN" -1.2574322 0.28438333 3.5973122 3.69626 3.7074444 3.8572133 3.9749665 4.0206842 4.076759 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.02453 3095.8826 6.1091785 37.36917 0.030174932 0.20468688 0.5270241 2.503892 -1.2729657 2.6390574 0.7281666 1.0 34.99866826068084 0 0.107650004 0.018000007 0.982 7.46394 0.6189703 2.832235 3.1354942 3.7362807 -1.2574322 0 13.81199 +"Serbia" "YUG" -1.1077342 0.33030653 -3.4227169 3.908015 4.0621657 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 8.919 7481.4 4.525183 20.504202 1.7281299 0.45335835 3.5082564 3.7841897 0.7401284 0.0 0.07976695 0.14420003 0.8558 -1.088147 0 5.301443 +"Seychelles" "SYC" -1.110366 0.32943836 0 0 1 0 0 0.3120312 0.3440344 0.0470047 0.2969297 0.0 0.0 0.1843761 0.7747272 1.5224266 100 0 -1.0485874 0 11.27059 +"Sierra Leone" "SLE" -1.2574322 0.28438333 3.4011974 3.50621 3.5596871 3.762215 3.656394 3.6821465 3.8586686 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.066058 3206.9321 5.506894 30.65905 0.0 0.09206493 0.5983364 1.9227878 -1.0498221 2.1400661 0.7327148 1.0 100 0 0.12732701 0.24529225 0.75470775 6.40026 -0.6175163 3.5103068 2.85647 3.678352 -1.2574322 0 13.81199 +"Singapore" "SGP" -1.1477444 0.3173518 -0.12715174 4.1009893 4.15389 4.2236376 4.2722363 4.3252206 4.357365 4.402337 0 1 0 0 0 0.0 0.080214985 0.0 0.0 0.0 8.989209 8271.589 10.181059 104.56108 1.9699668 1.5274527 0.32618666 0.3987761 0.31237468 0.697417 0.0 100 0 1.0 0.0 8.133 1.2998796 7.8068237 4.6051702 3.7667558 -1.128526 0 11.695582 +"Slovakia" "SVK" -1.1306038 0.32283828 4.16356 4.2485 4.2505536 4.2543144 4.261731 4.2911606 4.318983 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.795315 6611.35 4.8971543 23.996716 2.3025851 1.9822562 0.2262555 -0.5447272 3.8849943 0.73731935 0.0 0 0 0.0774823 0.12129998 0.8787 8.87291 2.1272128 4.419334 3.5115454 3.4463344 -1.1236247 1 4.237059 +"Slovenia" "SVN" -1.1078619 0.33026436 4.1835756 4.22655 4.2284346 4.264156 4.293262 4.322969 4.374775 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.852653 6993.164 4.9927588 24.927647 1.7735728 0.20020077 2.1505988 -0.6539265 3.8311744 0.73659736 0.0 0 0 0.0376239 0.013669848 0.98633015 8.87235 1.8623666 4.348296 3.339322 3.3090527 -1.0898731 0 4.5773177 +"Solomon Islands" "SLB" -1.551881 0.21184911 3.7841897 3.89958 3.996138 4.0756836 4.037858 4.1405 4.2116117 0 0 0 0 1 0.004069176 0.0 0.01881994 0.0 0.0 0.97711086 0.10528031 -0.44628713 2.0794415 100 0 6.91771 1.5073113 1.7578579 3.7583747 -1.539303 0 16.442823 +"South Africa" "ZAF" -1.1088874 0.3299258 -1.6642462 3.7256935 3.8066626 3.89257 3.9674706 4.0425253 4.1198435 4.0032578 3.9528096 0 0 1 0 0 0.17531742 0.0 0.0 0.8246826 0.0 0.0 7.7311583 2790.55 4.7965565 23.805965 0.40375987 -1.6225455 0.56061125 2.4973292 -1.4271164 3.3672957 0.7380791 0.022699 0.17921839668223408 4.385847427835768 0.306891 0.21350002 0.7865 8.47303 1.4793321 2.6631217 3.8416004 3.7123063 -1.0758983 0 17.923338 +"Spain" "ESP" -1.0623239 0.34565163 -0.94910365 3.9160151 4.1573195 4.23569 4.2770457 4.322134 4.3416934 4.3690157 4.402158 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.88142 7203.58 6.3957744 41.005154 2.3025851 2.1835034 0.34816292 3.2902658 -0.5798185 3.6888795 0.7260022 0.0 0 0 0.12218 0.023000002 0.977 8.44891 1.1929004 4.115933 4.036009 3.3120077 -1.0503497 1 3.1625924 +"Sri Lanka" "LKA" -1.13646 0.32095322 -0.4835753 3.744787 4.036009 4.05803 4.1385016 4.222766 4.2438946 4.262459 4.3137827 0 1 0 0 0 0.0 0.9892473 0.010752688 0.0 0.0 0.0 8.517193 5000.0 8.138092 66.22854 2.0794415 1.535104 0.34712952 2.6282852 -0.38566247 1.9459101 0.7128448 0.1753118 100 0 0.0389524 0.0 1.0 7.54062 1.5518866 5.063131 2.7972813 3.7256157 -1.1267253 0 6.6583233 +"St Kitts & Nevis" "KNA" -1.1408497 0.31954738 -1.6194882 0 0 0 1 0 0.036078755 0.0 0.0 0.96392125 0.0 0.0 0.16906744 2.9673328 2.8526316 100 0 -1.1041396 0 13.014447 +"Suriname" "SUR" -1.1388123 0.3201991 -0.68319684 3.8918202 4.0253515 4.08925 4.147447 4.1886706 4.2121115 4.218556 4.253274 0 0 0 1 0 0.16835018 0.37751248 0.17345169 0.26027957 0.02040608 0.0 0.54996943 -0.99425226 -1.0788096 1.3862944 0.1893433 100 0 0.196855 8.04173 0.600167 3.8565102 3.8625429 -1.0608183 0 8.518781 +"Swaziland" "SWZ" -1.1077226 0.33031034 3.5723455 3.78898 3.8704014 3.993164 4.0833516 3.8850672 3.8783174 0 0 1 0 0 0.03 0.0 0.0 0.97 0.0 0.0 7.308084 1616.1097 4.0391026 16.49592 0.06923499 0.14524809 0.056569353 2.3369865 -0.84397006 3.2771447 0.7437991 0.1037715 2.876148802408021 0 0.110120006 1.0 0.0 7.27725 0.5423325 3.0182729 1.3609766 3.8111362 -1.1034231 0 20.045586 +"Sweden" "SWE" -1.0775307 0.34043512 -2.0768847 4.200205 4.2738843 4.29054 4.312801 4.327319 4.350753 4.3775654 4.4000044 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.611989 5499.1533 5.8988266 34.994396 2.3009665 -0.6803879 0.05826292 1.8855534 -2.5257287 4.1271343 0.729755 0.0 0 0 0.16026899 0.01267159 0.9873284 9.46351 1.9837825 2.9030554 4.2835865 3.0912855 -1.0767082 1 3.3489523 +"Switzerland" "CHE" -1.0708028 0.34273326 -1.9394476 4.1604443 4.237001 4.26708 4.2907367 4.323593 4.346949 4.3780246 4.409719 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0 8.623216 5569.273 5.5514073 30.873465 2.301316 2.0070877 0.42618236 2.3340838 -0.94160855 3.8501475 0.73237866 0.0 0 0 0.123443 0.028506279 0.9714937 9.86313 2.024187 4.898213 3.9318256 3.184144 -1.0578808 1 3.3779871 +"Syria" "SYR" -1.1149786 0.3279223 3.8286414 3.89779 4.094547 4.19271 4.263592 4.3043203 4.326812 0 1 0 0 0 0.045263156 0.9547368 0.0 0.0 0.0 0.0 9.245051 10370.539 3.137555 9.898311 2.3025851 2.312754 0.43173176 3.207208 -1.7719568 3.5553482 0.7506073 0.188119 0 3.445484907249226 0.301359 0.090216756 0.90978324 8.21528 0.3182889 3.2053027 3.6054978 3.8067355 -1.1058795 0 4.663206 +"Taiwan" "TWN" -1.1421665 0.31912687 -2.8134108 3.713572 0.0 0.0 0.99 0.0 0.0 0.01 9.09513 8930.0 8.846459056725854 0 7.66856 1.6037242 -1.1387804 +"Tajikistan" "TJK" -1.1126546 0.32868528 4.01998 4.02837 4.0950794 4.1310663 4.14086 4.155053 4.2085643 0 1 0 0 0 0.0 0.732334 0.26766595 0.0 0.0 0.0 8.853292 6997.484 6.0054507 36.073387 -0.96188843 0.41258502 1.893112 -1.7147983 3.6635616 0.7289499 0.0 0 0.024920510934136986 1.5481801 0.014358997 0.985641 8.27563 1.6183233 2.6990356 3.50255 3.678729 -1.0891343 0 6.288034 +"Tanzania" "TZA" -1.1194372 0.32646346 3.6109178 3.7756 3.8434014 3.9210508 3.9238951 3.919461 4.049826 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 7.824046 2500.0 1.761773 3.103843 0.0 -2.178823 0.551178 1.510722 -0.7133499 1.7917595 0.7609968 1.0 88.2944968767263 0 0.477395 0.0 1.0 6.54247 0.55694866 2.4494877 1.6486586 3.824459 -1.1041473 0 20.56829 +"Thailand" "THA" -1.2766272 0.27897665 -0.68042845 3.7518542 3.8501475 3.99795 4.0916185 4.1820164 4.2831855 4.2837963 4.303087 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 8.675969 5951.0464 9.766345 95.38976 2.0794415 0.20512114 0.4909233 3.4355989 -1.0788096 2.7080503 0.70054877 0.4175675 99.54566955065245 0 0.214576 0.12864834 0.87135166 7.31721 1.4328135 3.9862523 2.9806187 3.7533731 -1.2592685 0 9.199355 +"Togo" "TGO" -1.1911303 0.3038776 3.583519 3.80087 3.8131428 3.904448 3.9701405 4.0038633 4.0358095 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.054321 3153.254 4.2030163 17.710175 0.0 0.15203094 0.5364513 3.831897 -0.46203548 2.0794415 0.7425613 1.0 100 0 0.261366 0.34100002 0.659 7.04141 -0.71248525 3.2871993 2.3125355 3.7515335 -1.1546617 0 13.81199 +"Tonga" "TON" -1.3599863 0.2566643 4.11683 4.172618 4.212868 4.2425103 4.259307 4.2787566 0 0 0 0 1 0.028484233 0.0 0.4857579 0.0 0.0 0.4857579 0.08333145 3.0363944 2.9957323 99.99999999999999 0 7.65917 1.8377043 4.482971 2.867899 3.8156602 -1.2544513 0 11.188929 +"Trinidad and Tobago" "TTO" -1.135008 0.32141957 -2.141317 3.9702919 4.079231 4.15146 4.1789985 4.2089863 4.233306 4.2253084 4.244989 0 0 0 1 0 0.06720672 0.46434644 0.0120012 0.45644563 0.0 0.0 8.498657 5756.5176 5.560788 33.79748 0.90029883 0.4992772 2.6823905 2.3978953 0.7323078 0.0 100 0 0.00795231 1.0 0.0 9.01213 1.7358551 5.099664 2.8507063 3.760649 -1.061811 0 11.360499 +"Tunisia" "TUN" -1.1102095 0.32948995 -0.39958236 3.6627922 3.7977338 3.88337 3.9834206 4.129119 4.252876 4.284965 4.3121405 0 0 1 0 0 0.01010101 0.989899 0.0 0.0 0.0 0.0 8.411833 4500.0 4.216083 17.77536 2.3025851 1.638867 0.038643625 2.9139798 -1.7147983 3.5263605 0.7424626 0.0 0 20.66402115622988 0.108772 0.0 1.0 7.57096 -0.096158594 3.2849162 3.624341 3.7695942 -1.1096964 0 5.931992 +"Turkey" "TUR" -1.1529517 0.31570354 -0.48936316 3.775057 3.91717 3.9119043 4.035472 4.144094 4.240562 4.2999573 0 1 0 0 0 0.2667 0.4666 0.2667 0.0 0.0 0.0 9.170745 9681.542 3.683668 13.697084 2.3025851 1.770957 0.27763173 3.432696 -0.46203548 3.6635616 0.74648315 0.0 0 0 0.160468 0.09897959 0.9010204 8.1044 0.56993705 3.6017735 3.4499876 3.7425938 -1.1055062 1 9.271221 +"Turkmenistan" "TKM" -1.1482712 0.31718466 3.9702919 3.99717 4.0662503 4.108185 4.1377664 4.157249 4.172285 0 1 0 0 0 0.26820603 0.3674642 0.36432976 0.0 0.0 0.0 8.985064 7985.8896 5.09033 25.92092 -0.95767176 0.33062232 1.3711808 -2.8134108 3.6888795 0.7358606 0.0 0 55.086906236993556 1.34813 0.020726204 0.9792738 8.43728 1.2156423 3.8372996 3.6759005 -1.1028867 0 9.92043 +"Tuvalu" "TUV" -1.353625 0.2583022 0.046277665 0.0 0.47686118 0.0 0.0 0.47686118 100 0 6.75809 2.766319 -1.245136 +"Uganda" "UGA" -1.1092016 0.3298222 3.6888795 3.78326 3.9092872 3.913454 3.857709 3.8306324 3.981822 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.160519 3500.0 1.193059 1.42339 0.0 -0.6785411 0.65761065 3.245323 -0.99425226 0.0 0.7652915 1.0 97.78262396425042 0.019939726224867293 0.969328 0.0 1.0 6.90775 0.14928171 3.5930243 1.4816046 3.826503 -1.1092016 0 19.756868 +"Ukraine" "UKR" -1.1342038 0.32167813 4.203199 4.23876 4.2511625 4.2316036 4.2504444 4.217497 4.2524247 1 0 0 0 0 0.9959514 0.004048583 0.0 0.0 0.0 0.0 8.741683 6283.8667 4.851765 23.601883 -1.0420306 0.38775724 4.0290947 -0.43078294 3.8918202 0.7376621 0.0 0 0 0.074536696 0.15083128 0.8491687 8.45681 1.7164772 4.298735 3.8458831 3.2527177 -1.1335893 0 3.9473352 +"United Kingdom" "GBR" -1.0621135 0.34572434 -1.3096832 4.1743875 4.237001 4.26446 4.2762933 4.2996716 4.3291597 4.353389 4.3870444 1 0 0 0 0 0.95310754 0.032550097 0.0 0.014342387 0.0 0.0 8.599542 5450.4927 6.168838 38.1972 2.2883976 1.0443099 0.11427377 3.1900647 -0.7133499 3.988984 0.727716 0.0 0 0 0.0294346 0.023453712 0.9765463 9.44928 1.8404087 5.377479 4.361824 3.1502268 -1.0462401 1 3.369691 +"United States" "USA" -1.0929179 0.33523688 -2.0236244 4.155753 4.2341065 4.24521 4.2599626 4.29944 4.320346 4.3390746 4.3598 0 0 0 1 0 0.80435246 0.0 0.0 0.12865372 0.06699381 0.0 8.63146 5949.747 6.6866603 57.130753 0.9191499 0.3988507 2.9559515 -1.1394343 3.637586 0.7238056 0.0 0.3578068896175714 0.277523272798737 0.446154 0.9620742 0.037925784 9.71968 2.2168975 2.981945 4.248495 3.4285877 -1.0429595 1 +"Uruguay" "URY" -1.0767835 0.34068957 -1.066823 4.0342407 4.191169 4.21886 4.2281284 4.252035 4.2833753 4.315978 4.3338447 0 0 0 1 0 0.93 0.0 0.0 0.03 0.04 0.0 8.841314 7131.1177 6.6351333 62.56666 2.18175 0.2234635 2.059239 -0.040822018 3.4965076 0.7241947 0.0 0 0 0.120051004 0.95757854 0.042421456 8.87543 1.5769023 2.6714575 4.3845234 3.3265083 -1.048942 0 3.8868735 +"Uzbekistan" "UZB" -1.1341125 0.32170752 4.0324693 4.07567 4.139586 4.178479 4.199937 4.203959 4.2195225 0 1 0 0 0 0.006507592 0.16160521 0.8318872 0.0 0.0 0.0 8.773556 6466.765 6.105359 37.56202 -0.9602513 0.3453711 2.3532782 -2.040221 3.713572 0.7281953 0.0 0 32.15238476660473 1.71954 0.035962045 0.96403795 8.49577 2.9984355 3.5263605 3.6537514 -1.1133345 0 9.608268 +"Vanuatu" "VUT" -1.5558891 0.2110017 3.7376697 3.83961 3.9604285 4.0697565 4.1462545 4.212942 4.260134 0 0 0 0 1 0.010111224 0.0 0.0 0.0 0.0 0.9898888 0.040506426 0.49469623 2.7725887 100 0 7.77654 1.6903561 2.3418057 3.832697 -1.5475057 0 11.107593 +"Venezuela" "VEN" -1.1890981 0.30449575 -0.702087 3.523415 4.0091496 4.0915 4.1562963 4.222774 4.263558 4.2941446 4.305784 0 0 0 1 0 0.545 0.02 0.0 0.1 0.335 0.0 8.527097 5491.6753 11.140847 182.23225 0.6007972 0.40319586 1.0784096 -1.0498221 2.0794415 0.690169 0.0022341 96.75154181243138 0 0.12757 0.6733005 0.32669947 9.39049 1.1381743 2.147912 4.1206617 3.8201833 -1.0838544 0 7.996057 +"Vietnam" "VNM" -1.2104002 0.29807797 -0.035570074 3.2924979 3.69883 3.78773 3.8647525 4.0194607 4.18172 4.275909 4.3151956 0 1 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0 8.709651 6075.0 11.293782 127.59223 0.24441281 0.21374594 2.947067 -0.94160855 2.7725887 0.689014 0.7154578 56.53464972904928 0 0.0799946 0.024999976 0.975 7.00579 1.1048462 4.5773606 2.6878474 3.6902754 -1.2050174 0 12.04933 +"Zaire" "ZAR" -1.1092016 0.3298222 3.6661224 3.71851 3.7809322 3.824823 3.846739 3.8210452 3.8726497 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 8.779791 3054.5 2.9527595 8.844928 -1.5956272 0.6284625 1.0851892 -2.040221 0.0 0.7520028 1.0 94.86599394668549 0 1.1153901 0.05519998 0.9448 7.06817 3.1045866 3.7797651 -1.1092016 0 20.880098 +"Zambia" "ZMB" -1.1092016 0.3298222 3.6323092 3.80848 3.8921208 3.9515903 3.860322 3.7359986 3.8806455 0 0 1 0 0 0.0 0.0 0.0 1.0 0.0 0.0 7.6765437 2237.6096 3.06675 9.60026 0.03468741 0.5770627 1.9572738 -1.0216511 2.7080503 0.75114197 1.0 17.1324540317762 0 0.99219596 0.397 0.603 7.42833 0.8137716 1.4764196 2.895912 3.8035684 -1.1092016 0 20.56829 +"Zimbabwe" "ZWE" -1.1099604 0.329572 3.7256935 3.94109 4.005562 4.080483 4.1031213 3.798137 3.9092367 0 0 1 0 0 0.005 0.0 0.0 0.995 0.0 0.0 7.28277 1479.4609 3.3000555 11.162635 0.02308227 0.3274315 2.118662 -1.3470737 2.9957323 0.7493802 0.6186963 8.400756715623329 0 0.489782 0.13999999 0.86 7.16395 0.93548644 2.3387415 2.533697 3.8124862 -1.1076409 0 20.56829 diff --git a/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_ethnic_web.dta b/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_ethnic_web.dta new file mode 100644 index 0000000..c8b8806 Binary files /dev/null and b/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_ethnic_web.dta differ diff --git a/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_ethnic_web.tab b/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_ethnic_web.tab new file mode 100644 index 0000000..29ee391 --- /dev/null +++ b/notebooks/C.justinCook-The_natural_selection_2015/raw/hla_ethnic_web.tab @@ -0,0 +1,52 @@ +ethnicity region het ln_het ln_atd md ln_anim +"Adygei" "Caucus/Russion/Georgia" 0.31363973 -1.1595103 8.699514 4.15503 2.3025851 +"Amerindians " "Columbia" 0.23165898 -1.4624889 8.131531 22.662779 0.6931472 +"Balochi" "Pakistan" 0.31103814 -1.1678398 9.1049795 5.84206 2.3025851 +"Bantu speakers" "Congo" 0.3298222 -1.1092016 8.006368 4.126315 0.0 +"Basque" "Spain" 0.31869102 -1.1435332 8.881836 6.01226 2.3025851 +"Bedouin" "Jordan" 0.3345718 -1.0949037 9.2591305 2.84495 2.3025851 +"Biaka" "Central African Republic" 0.29812178 -1.2102532 8.006368 2.38486 0.0 +"Brahui" "Pakistan" 0.31375128 -1.1591547 9.1049795 5.84206 2.3025851 +"Burusho" "Pakistan" 0.33376667 -1.0973132 9.1049795 6.4756002 2.3025851 +"Cambodians, Khmer" "Cambodia" 0.2984628 -1.2091099 8.411833 10.26055 2.0794415 +"Dai" "China" 0.27109486 -1.3052865 9.1049795 9.34396 2.0794415 +"Daur" "China" 0.31571922 -1.152902 9.1049795 10.21313 2.0794415 +"Druze" "Israel" 0.3097564 -1.171969 9.2591305 2.88725 2.3025851 +"Estonian" "Estonia" 0.3313382 -1.1046157 8.216088 5.9564 2.3025851 +"French" "France" 0.35331923 -1.0403833 8.922658 5.85748 2.3025851 +"Han" "China" 0.32045513 -1.138013 9.1049795 9.98897 2.0794415 +"Hazara" "Pakistan" 0.3320295 -1.1025314 9.1049795 6.13257 2.3025851 +"Hezhe" "China" 0.31524745 -1.1543974 9.1049795 10.89621 2.0794415 +"Italians" "Italy" 0.33886707 -1.0821474 8.987197 5.24904 2.3025851 +"Japanese" "Japan" 0.3137423 -1.1591833 8.411833 11.762111 2.0794415 +"Kalash" "Pakistan" 0.30818847 -1.1770438 9.1049795 6.25362 2.3025851 +"Karitiana" "Brazil" 0.22980385 -1.4705292 8.160519 24.17734 0.6931472 +"Koreans" "Korea" 0.3182192 -1.1450148 8.411833 11.762111 2.0794415 +"Lahu" "China" 0.29901153 -1.2072731 9.1049795 9.29963 2.0794415 +"Mandenka" "Senegal" 0.28438333 -1.2574322 8.006368 5.46991 0.0 +"Maya, Yucatan" "Mexico" 0.26794487 -1.316974 8.318742 19.82571 0.0 +"Mbuti" "DR Congo" 0.30119488 -1.1999978 8.006368 1.3355 0.0 +"Melanesian, Nasioi" "New Guinea" 0.2096218 -1.5624503 8.294049 16.16851 1.0986123 +"Miao" "China" 0.32147437 -1.1348374 9.1049795 9.87532 2.0794415 +"Mongolian" "Mongolia" 0.31554615 -1.1534504 8.517193 9.869849 2.0794415 +"Mozabite" "Algeria" 0.34386027 -1.0675199 8.294049 4.41817 2.3025851 +"Naxi" "China" 0.2890359 -1.2412044 9.1049795 9.131371 2.0794415 +"Orcadian" "UK/Norway" 0.34058845 -1.0770804 8.517193 6.63669 2.3025851 +"Oroqen" "China" 0.30907822 -1.174161 9.1049795 10.29053 2.0794415 +"Palestinian" "Palestine/Israel" 0.32932052 -1.1107237 9.2591305 2.88725 2.3025851 +"Papuan New Guinean" "New Guinea" 0.2391923 -1.4304874 8.294049 14.843121 1.0986123 +"Pashtun" "India" 0.33031923 -1.1076957 9.047821 6.1787596 2.0794415 +"Pima, Mexico" "Mexico" 0.24633333 -1.4010696 8.318742 18.015789 0.0 +"Russians" "Russia" 0.32165256 -1.1342833 8.517193 5.9564 2.3025851 +"San" "Namibia" 0.26264745 -1.3369427 7.130899 3.8724198 0.0 +"Sardinian" "Italy" 0.3206436 -1.1374251 8.987197 5.30581 2.3025851 +"She" "China" 0.2751423 -1.2904668 9.1049795 10.817809 2.0794415 +"Sindhi" "Pakistan" 0.32300898 -1.1300751 9.1049795 6.2017 2.3025851 +"Surui" "Brazil" 0.2309923 -1.4653709 8.160519 24.17734 0.6931472 +"Tu" "China" 0.3 -1.2039728 9.1049795 8.868139 2.0794415 +"Tujia" "China" 0.30365384 -1.1918669 9.1049795 9.8325 2.0794415 +"Uyghur" "China" 0.32191154 -1.1334785 9.1049795 7.07197 2.0794415 +"Xibe" "China" 0.32032564 -1.1384171 9.1049795 7.11029 2.0794415 +"Yakut" "Russia/Siberia" 0.3150577 -1.1549995 8.517193 9.91911 2.0794415 +"Yi" "China" 0.30629873 -1.1831944 9.1049795 9.32879 2.0794415 +"Yoruba" "Nigeria" 0.31490898 -1.1554717 7.901007 3.6296499 0.0 diff --git a/notebooks/C.justinCook-The_natural_selection_2015/raw/replication_hla_base_tables.log b/notebooks/C.justinCook-The_natural_selection_2015/raw/replication_hla_base_tables.log new file mode 100644 index 0000000..e8ddb32 --- /dev/null +++ b/notebooks/C.justinCook-The_natural_selection_2015/raw/replication_hla_base_tables.log @@ -0,0 +1,1620 @@ +-------------------------------------------------------------------------------- + name: + log: C:\Users\USUARIO\Desktop\Summer school\Trabajos\Replication\raw\rep +> lication_hla_base_tables.log + log type: text + opened on: 24 Jun 2020, 01:17:27 + +. +. #for this code to work it is necessary to declare the directory of the compute +> r where the database is located +Unknown #command + +. cd "C:\Users\USUARIO\Desktop\Summer school\Trabajos\Replication\raw" +C:\Users\USUARIO\Desktop\Summer school\Trabajos\Replication\raw + +. +. use "hla_country_web.dta", clear + +. +. local cont "europe africa asia americas" + +. local base "ln_le60!=. & ln_hla_het!=. & ln_frac!=. & ln_abslat!=. & ln_arable +> !=. & ln_suitavg!=. & aa_ln_atd!=." + +. local bc "ln_frac aa_ln_atd ln_arable ln_suitavg ln_abslat" + +. qui log off +graph twoway (scatter ln_hla_het aa_mdist if europe==1) (scatter ln_hla_het aa_m +> dist if africa==1, msymbol(oh)) (scatter ln_hla_het aa_mdist if asia==1, msymb +> ol(S)) (scatter ln_hla_het aa_mdist if americas==1, msymbol(T)) (scatter ln_hl +> a_het aa_mdist if oceania==1, msymbol(Th)) || (lfit ln_hla_het aa_mdist) , leg +> end(label(1 European) label(2 African)label(3 Asia) label(4 American) label(5 +> "Oceanianic/ No Majority")) graphregion(color(white)) bgcolor(white) xtitle(" +> Migratory Distance from East Africa (Ancestry Adjusted)") ytitle("In HLA Heter +> ozygosity") ysc(r(-1.5 -1)) ylabel(-1.5(0.1)-1) + +. qui log off +reg ln_hla_het aa_ln_atd if aa_mdist!=. & `base' & ln_le60!=., rob + +Linear regression Number of obs = 131 + F(1, 129) = 4.65 + Prob > F = 0.0329 + R-squared = 0.0217 + Root MSE = .06738 + +------------------------------------------------------------------------------ + | Robust + ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + aa_ln_atd | .0211444 .0098049 2.16 0.033 .0017451 .0405438 + _cons | -1.326062 .084678 -15.66 0.000 -1.4936 -1.158525 +------------------------------------------------------------------------------ + +. +. reg ln_hla_het aa_ln_atd aa_mdist if aa_mdist!=. & ln_le60!=. & `base', rob + +Linear regression Number of obs = 131 + F(2, 128) = 37.36 + Prob > F = 0.0000 + R-squared = 0.4265 + Root MSE = .05179 + +------------------------------------------------------------------------------ + | Robust + ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + aa_ln_atd | .0299843 .0086383 3.47 0.001 .0128921 .0470766 + aa_mdist | -.0120634 .0014224 -8.48 0.000 -.0148779 -.009249 + _cons | -1.321902 .0729942 -18.11 0.000 -1.466333 -1.17747 +------------------------------------------------------------------------------ + +. +. reg ln_hla_het aa_lanim if aa_mdist!=. & `base' & ln_le60!=., rob + +Linear regression Number of obs = 89 + F(1, 87) = 17.05 + Prob > F = 0.0001 + R-squared = 0.1176 + Root MSE = .07228 + +------------------------------------------------------------------------------ + | Robust + ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + aa_lanim | .0266128 .0064443 4.13 0.000 .0138042 .0394215 + _cons | -1.189394 .0126485 -94.03 0.000 -1.214534 -1.164254 +------------------------------------------------------------------------------ + +. +. reg ln_hla_het aa_lanim aa_mdist if aa_mdist!=. & `base' & ln_le60!=., rob + +Linear regression Number of obs = 89 + F(2, 86) = 52.15 + Prob > F = 0.0000 + R-squared = 0.5968 + Root MSE = .04914 + +------------------------------------------------------------------------------ + | Robust + ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + aa_lanim | .0360532 .0050961 7.07 0.000 .0259225 .046184 + aa_mdist | -.013321 .0014938 -8.92 0.000 -.0162905 -.0103515 + _cons | -1.109316 .0107794 -102.91 0.000 -1.130745 -1.087888 +------------------------------------------------------------------------------ + +. +. reg ln_hla_het aa_lpd1 if aa_mdist!=. & `base' & ln_le60!=., rob + +Linear regression Number of obs = 113 + F(1, 111) = 11.23 + Prob > F = 0.0011 + R-squared = 0.0626 + Root MSE = .06849 + +------------------------------------------------------------------------------ + | Robust + ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + aa_lpd1 | .014363 .0042865 3.35 0.001 .0058691 .0228569 + _cons | -1.154714 .0074313 -155.39 0.000 -1.16944 -1.139989 +------------------------------------------------------------------------------ + +. +. reg ln_hla_het aa_lpd1 aa_mdist if aa_mdist!=. & `base' & ln_le60!=., rob + +Linear regression Number of obs = 113 + F(2, 110) = 37.73 + Prob > F = 0.0000 + R-squared = 0.4578 + Root MSE = .05233 + +------------------------------------------------------------------------------ + | Robust + ln_hla_het | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + aa_lpd1 | .0158342 .0032576 4.86 0.000 .0093785 .0222899 + aa_mdist | -.0123827 .0015009 -8.25 0.000 -.0153571 -.0094084 + _cons | -1.072103 .0100197 -107.00 0.000 -1.09196 -1.052247 +------------------------------------------------------------------------------ + +. qui log off +reg ln_mort40 ln_hla_het if `base', rob + +Linear regression Number of obs = 73 + F(1, 71) = 49.51 + Prob > F = 0.0000 + R-squared = 0.3488 + Root MSE = .49154 + +------------------------------------------------------------------------------ + | Robust + ln_mort40 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | -5.007995 .711708 -7.04 0.000 -6.427101 -3.58889 + _cons | -6.672417 .8309391 -8.03 0.000 -8.329263 -5.015572 +------------------------------------------------------------------------------ + +. +. reg ln_mort40 ln_hla_het `bc' `cont' if `base', rob + +Linear regression Number of obs = 73 + F(10, 62) = 15.93 + Prob > F = 0.0000 + R-squared = 0.5133 + Root MSE = .45476 + +------------------------------------------------------------------------------ + | Robust + ln_mort40 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | -3.861032 1.128212 -3.42 0.001 -6.116297 -1.605768 + ln_frac | -.3970101 .3960854 -1.00 0.320 -1.188773 .3947533 + aa_ln_atd | .5975243 .2358444 2.53 0.014 .1260782 1.068971 + ln_arable | .0448605 .1220245 0.37 0.714 -.1990631 .2887841 + ln_suitavg | .0361975 .0844383 0.43 0.670 -.1325923 .2049874 + ln_abslat | -.3260423 .1296545 -2.51 0.015 -.585218 -.0668665 + europe | .1074832 .1696108 0.63 0.529 -.2315639 .4465303 + africa | .4589723 .2152117 2.13 0.037 .0287701 .8891744 + asia | .1722149 .2033102 0.85 0.400 -.2341963 .5786261 + americas | .1102584 .1877282 0.59 0.559 -.265005 .4855218 + _cons | -9.575309 2.730034 -3.51 0.001 -15.03257 -4.11805 +------------------------------------------------------------------------------ + +. +. reg ln_le40 ln_hla_het if `base', rob + +Linear regression Number of obs = 71 + F(1, 69) = 41.94 + Prob > F = 0.0000 + R-squared = 0.3329 + Root MSE = .21596 + +------------------------------------------------------------------------------ + | Robust + ln_le40 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 2.143586 .3309808 6.48 0.000 1.483298 2.803875 + _cons | 6.25536 .3783145 16.53 0.000 5.500643 7.010076 +------------------------------------------------------------------------------ + +. +. reg ln_le40 ln_hla_het `bc' `cont' if `base', rob + +Linear regression Number of obs = 71 + F(10, 60) = 44.93 + Prob > F = 0.0000 + R-squared = 0.7192 + Root MSE = .15026 + +------------------------------------------------------------------------------ + | Robust + ln_le40 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 1.077113 .3770962 2.86 0.006 .322808 1.831418 + ln_frac | .072362 .133847 0.54 0.591 -.1953719 .3400959 + aa_ln_atd | -.0030757 .0549058 -0.06 0.956 -.1129035 .1067522 + ln_arable | -.1059797 .0365892 -2.90 0.005 -.179169 -.0327905 + ln_suitavg | .0529932 .0238341 2.22 0.030 .0053179 .1006686 + ln_abslat | .098031 .0409504 2.39 0.020 .0161181 .1799439 + europe | -.052723 .0434553 -1.21 0.230 -.1396465 .0342005 + africa | -.5020729 .0592995 -8.47 0.000 -.6206896 -.3834562 + asia | -.3398571 .0565811 -6.01 0.000 -.4530363 -.226678 + americas | -.3291928 .0621135 -5.30 0.000 -.4534382 -.2049474 + _cons | 5.316719 .5542282 9.59 0.000 4.208097 6.42534 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het if `base', rob + +Linear regression Number of obs = 131 + F(1, 129) = 43.55 + Prob > F = 0.0000 + R-squared = 0.2271 + Root MSE = .20359 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 1.620043 .2454809 6.60 0.000 1.134353 2.105733 + _cons | 5.814552 .2829132 20.55 0.000 5.254801 6.374303 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `base', rob + +Linear regression Number of obs = 131 + F(10, 120) = 100.89 + Prob > F = 0.0000 + R-squared = 0.7446 + Root MSE = .12133 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 1.01512 .1899264 5.34 0.000 .6390794 1.391161 + ln_frac | -.1210012 .0927409 -1.30 0.194 -.3046217 .0626193 + aa_ln_atd | .0369984 .0397486 0.93 0.354 -.0417011 .1156979 + ln_arable | -.0086997 .0159756 -0.54 0.587 -.0403303 .0229309 + ln_suitavg | .008039 .0128307 0.63 0.532 -.017365 .0334429 + ln_abslat | .0199086 .0156461 1.27 0.206 -.0110696 .0508867 + europe | .0387395 .0716524 0.54 0.590 -.1031272 .1806062 + africa | -.3122101 .0754901 -4.14 0.000 -.4616753 -.162745 + asia | -.2056222 .0755278 -2.72 0.007 -.355162 -.0560825 + americas | -.0400593 .0736294 -0.54 0.587 -.1858403 .1057217 + _cons | 4.962063 .4156831 11.94 0.000 4.139039 5.785086 +------------------------------------------------------------------------------ + +. qui log off +local le "ln_le60!=. & ln_le70!=. & ln_le80!=. & ln_le90!=. & ln_le00!=. & ln_le +> 10!=." + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 100.89 + Prob > F = 0.0000 + R-squared = 0.7446 + Root MSE = .12133 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 1.01512 .1899264 5.34 0.000 .6390794 1.391161 + ln_frac | -.1210012 .0927409 -1.30 0.194 -.3046217 .0626193 + aa_ln_atd | .0369984 .0397486 0.93 0.354 -.0417011 .1156979 + ln_arable | -.0086997 .0159756 -0.54 0.587 -.0403303 .0229309 + ln_suitavg | .008039 .0128307 0.63 0.532 -.017365 .0334429 + ln_abslat | .0199086 .0156461 1.27 0.206 -.0110696 .0508867 + europe | .0387395 .0716524 0.54 0.590 -.1031272 .1806062 + africa | -.3122101 .0754901 -4.14 0.000 -.4616753 -.162745 + asia | -.2056222 .0755278 -2.72 0.007 -.355162 -.0560825 + americas | -.0400593 .0736294 -0.54 0.587 -.1858403 .1057217 + _cons | 4.962063 .4156831 11.94 0.000 4.139039 5.785086 +------------------------------------------------------------------------------ + +. +. reg ln_le70 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 76.19 + Prob > F = 0.0000 + R-squared = 0.7340 + Root MSE = .11485 + +------------------------------------------------------------------------------ + | Robust + ln_le70 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 1.04047 .1695943 6.14 0.000 .7046852 1.376255 + ln_frac | -.1163928 .0881347 -1.32 0.189 -.2908934 .0581078 + aa_ln_atd | .0246249 .0372331 0.66 0.510 -.0490941 .098344 + ln_arable | -.0141638 .0153671 -0.92 0.359 -.0445896 .0162619 + ln_suitavg | .0007926 .0135757 0.06 0.954 -.0260864 .0276715 + ln_abslat | .0045722 .0158262 0.29 0.773 -.0267626 .0359069 + europe | .0344497 .0460362 0.75 0.456 -.0566989 .1255982 + africa | -.3192781 .0522129 -6.11 0.000 -.4226561 -.2159002 + asia | -.1420921 .0484679 -2.93 0.004 -.2380552 -.046129 + americas | -.0238244 .0420173 -0.57 0.572 -.1070157 .0593668 + _cons | 5.198611 .3846817 13.51 0.000 4.436968 5.960254 +------------------------------------------------------------------------------ + +. +. reg ln_le80 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 55.68 + Prob > F = 0.0000 + R-squared = 0.6995 + Root MSE = .10588 + +------------------------------------------------------------------------------ + | Robust + ln_le80 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .8420929 .169953 4.95 0.000 .5055977 1.178588 + ln_frac | -.1462667 .0786894 -1.86 0.066 -.3020663 .0095329 + aa_ln_atd | .0348747 .0381473 0.91 0.362 -.0406544 .1104037 + ln_arable | -.0199753 .0150991 -1.32 0.188 -.0498705 .0099199 + ln_suitavg | .0003745 .0142482 0.03 0.979 -.0278359 .0285849 + ln_abslat | .0070102 .0152294 0.46 0.646 -.0231431 .0371634 + europe | .0176881 .0373365 0.47 0.637 -.0562357 .0916118 + africa | -.2552229 .0450962 -5.66 0.000 -.3445103 -.1659355 + asia | -.1194574 .0422942 -2.82 0.006 -.203197 -.0357178 + americas | -.0064243 .0328959 -0.20 0.845 -.0715559 .0587073 + _cons | 4.945296 .3733601 13.25 0.000 4.206069 5.684523 +------------------------------------------------------------------------------ + +. +. reg ln_le90 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 32.32 + Prob > F = 0.0000 + R-squared = 0.6886 + Root MSE = .10684 + +------------------------------------------------------------------------------ + | Robust + ln_le90 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .5490235 .1798274 3.05 0.003 .1929779 .9050692 + ln_frac | -.183742 .0828562 -2.22 0.028 -.3477915 -.0196925 + aa_ln_atd | .0383219 .035843 1.07 0.287 -.0326448 .1092886 + ln_arable | -.0220742 .0174598 -1.26 0.209 -.0566434 .0124949 + ln_suitavg | -.0046492 .0148575 -0.31 0.755 -.034066 .0247677 + ln_abslat | .0247666 .0225831 1.10 0.275 -.0199464 .0694797 + europe | .0189598 .0440979 0.43 0.668 -.0683511 .1062706 + africa | -.2203019 .0524047 -4.20 0.000 -.3240596 -.1165442 + asia | -.0726899 .0444554 -1.64 0.105 -.1607085 .0153287 + americas | .0346061 .0405436 0.85 0.395 -.0456675 .1148796 + _cons | 4.554555 .4118077 11.06 0.000 3.739204 5.369906 +------------------------------------------------------------------------------ + +. +. reg ln_le00 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 43.79 + Prob > F = 0.0000 + R-squared = 0.7683 + Root MSE = .09193 + +------------------------------------------------------------------------------ + | Robust + ln_le00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .2512166 .1540813 1.63 0.106 -.0538535 .5562868 + ln_frac | -.1775283 .0728385 -2.44 0.016 -.3217434 -.0333131 + aa_ln_atd | .0843881 .0303567 2.78 0.006 .024284 .1444923 + ln_arable | -.0062233 .0115616 -0.54 0.591 -.0291145 .0166679 + ln_suitavg | -.0168243 .0100998 -1.67 0.098 -.0368211 .0031725 + ln_abslat | .0137408 .0156327 0.88 0.381 -.0172109 .0446925 + europe | -.0010578 .0524527 -0.02 0.984 -.1049104 .1027948 + africa | -.2497432 .0608725 -4.10 0.000 -.3702665 -.1292199 + asia | -.0924549 .052818 -1.75 0.083 -.197031 .0121211 + americas | .0264391 .0515306 0.51 0.609 -.0755879 .128466 + _cons | 3.85075 .309451 12.44 0.000 3.238059 4.463442 +------------------------------------------------------------------------------ + +. +. reg ln_le10 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 43.16 + Prob > F = 0.0000 + R-squared = 0.7480 + Root MSE = .08408 + +------------------------------------------------------------------------------ + | Robust + ln_le10 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .1787627 .1334796 1.34 0.183 -.0855176 .443043 + ln_frac | -.1508934 .0700744 -2.15 0.033 -.2896359 -.0121509 + aa_ln_atd | .0733 .0301988 2.43 0.017 .0135085 .1330916 + ln_arable | -.0045715 .0094332 -0.48 0.629 -.0232486 .0141057 + ln_suitavg | -.0138008 .0083383 -1.66 0.101 -.0303101 .0027084 + ln_abslat | .0066167 .0136851 0.48 0.630 -.0204788 .0337121 + europe | .0065547 .0508292 0.13 0.898 -.0940836 .107193 + africa | -.2235135 .0590873 -3.78 0.000 -.3405023 -.1065247 + asia | -.0839933 .0521013 -1.61 0.110 -.1871502 .0191637 + americas | .0163142 .0512064 0.32 0.751 -.0850709 .1176994 + _cons | 3.912044 .282363 13.85 0.000 3.352985 4.471104 +------------------------------------------------------------------------------ + +. qui log off +local le "ln_le60!=. & ln_le70!=. & ln_le80!=. & ln_le90!=. & ln_le00!=. & ln_le +> 10!=." + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 100.89 + Prob > F = 0.0000 + R-squared = 0.7446 + Root MSE = .12133 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 1.01512 .1899264 5.34 0.000 .6390794 1.391161 + ln_frac | -.1210012 .0927409 -1.30 0.194 -.3046217 .0626193 + aa_ln_atd | .0369984 .0397486 0.93 0.354 -.0417011 .1156979 + ln_arable | -.0086997 .0159756 -0.54 0.587 -.0403303 .0229309 + ln_suitavg | .008039 .0128307 0.63 0.532 -.017365 .0334429 + ln_abslat | .0199086 .0156461 1.27 0.206 -.0110696 .0508867 + europe | .0387395 .0716524 0.54 0.590 -.1031272 .1806062 + africa | -.3122101 .0754901 -4.14 0.000 -.4616753 -.162745 + asia | -.2056222 .0755278 -2.72 0.007 -.355162 -.0560825 + americas | -.0400593 .0736294 -0.54 0.587 -.1858403 .1057217 + _cons | 4.962063 .4156831 11.94 0.000 4.139039 5.785086 +------------------------------------------------------------------------------ + +. estimates store y1960 + +. local coef1960=_b[ln_hla_het] + +. local lb1960 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub1960 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. +. reg ln_le70 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 76.19 + Prob > F = 0.0000 + R-squared = 0.7340 + Root MSE = .11485 + +------------------------------------------------------------------------------ + | Robust + ln_le70 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 1.04047 .1695943 6.14 0.000 .7046852 1.376255 + ln_frac | -.1163928 .0881347 -1.32 0.189 -.2908934 .0581078 + aa_ln_atd | .0246249 .0372331 0.66 0.510 -.0490941 .098344 + ln_arable | -.0141638 .0153671 -0.92 0.359 -.0445896 .0162619 + ln_suitavg | .0007926 .0135757 0.06 0.954 -.0260864 .0276715 + ln_abslat | .0045722 .0158262 0.29 0.773 -.0267626 .0359069 + europe | .0344497 .0460362 0.75 0.456 -.0566989 .1255982 + africa | -.3192781 .0522129 -6.11 0.000 -.4226561 -.2159002 + asia | -.1420921 .0484679 -2.93 0.004 -.2380552 -.046129 + americas | -.0238244 .0420173 -0.57 0.572 -.1070157 .0593668 + _cons | 5.198611 .3846817 13.51 0.000 4.436968 5.960254 +------------------------------------------------------------------------------ + +. estimates store y1970 + +. local coef1970=_b[ln_hla_het] + +. local lb1970 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub1970 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. +. reg ln_le80 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 55.68 + Prob > F = 0.0000 + R-squared = 0.6995 + Root MSE = .10588 + +------------------------------------------------------------------------------ + | Robust + ln_le80 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .8420929 .169953 4.95 0.000 .5055977 1.178588 + ln_frac | -.1462667 .0786894 -1.86 0.066 -.3020663 .0095329 + aa_ln_atd | .0348747 .0381473 0.91 0.362 -.0406544 .1104037 + ln_arable | -.0199753 .0150991 -1.32 0.188 -.0498705 .0099199 + ln_suitavg | .0003745 .0142482 0.03 0.979 -.0278359 .0285849 + ln_abslat | .0070102 .0152294 0.46 0.646 -.0231431 .0371634 + europe | .0176881 .0373365 0.47 0.637 -.0562357 .0916118 + africa | -.2552229 .0450962 -5.66 0.000 -.3445103 -.1659355 + asia | -.1194574 .0422942 -2.82 0.006 -.203197 -.0357178 + americas | -.0064243 .0328959 -0.20 0.845 -.0715559 .0587073 + _cons | 4.945296 .3733601 13.25 0.000 4.206069 5.684523 +------------------------------------------------------------------------------ + +. estimates store y1980 + +. local coef1980=_b[ln_hla_het] + +. local lb1980 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub1980 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. +. reg ln_le90 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 32.32 + Prob > F = 0.0000 + R-squared = 0.6886 + Root MSE = .10684 + +------------------------------------------------------------------------------ + | Robust + ln_le90 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .5490235 .1798274 3.05 0.003 .1929779 .9050692 + ln_frac | -.183742 .0828562 -2.22 0.028 -.3477915 -.0196925 + aa_ln_atd | .0383219 .035843 1.07 0.287 -.0326448 .1092886 + ln_arable | -.0220742 .0174598 -1.26 0.209 -.0566434 .0124949 + ln_suitavg | -.0046492 .0148575 -0.31 0.755 -.034066 .0247677 + ln_abslat | .0247666 .0225831 1.10 0.275 -.0199464 .0694797 + europe | .0189598 .0440979 0.43 0.668 -.0683511 .1062706 + africa | -.2203019 .0524047 -4.20 0.000 -.3240596 -.1165442 + asia | -.0726899 .0444554 -1.64 0.105 -.1607085 .0153287 + americas | .0346061 .0405436 0.85 0.395 -.0456675 .1148796 + _cons | 4.554555 .4118077 11.06 0.000 3.739204 5.369906 +------------------------------------------------------------------------------ + +. estimates store y1990 + +. local coef1990=_b[ln_hla_het] + +. local lb1990 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub1990 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. +. reg ln_le00 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 43.79 + Prob > F = 0.0000 + R-squared = 0.7683 + Root MSE = .09193 + +------------------------------------------------------------------------------ + | Robust + ln_le00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .2512166 .1540813 1.63 0.106 -.0538535 .5562868 + ln_frac | -.1775283 .0728385 -2.44 0.016 -.3217434 -.0333131 + aa_ln_atd | .0843881 .0303567 2.78 0.006 .024284 .1444923 + ln_arable | -.0062233 .0115616 -0.54 0.591 -.0291145 .0166679 + ln_suitavg | -.0168243 .0100998 -1.67 0.098 -.0368211 .0031725 + ln_abslat | .0137408 .0156327 0.88 0.381 -.0172109 .0446925 + europe | -.0010578 .0524527 -0.02 0.984 -.1049104 .1027948 + africa | -.2497432 .0608725 -4.10 0.000 -.3702665 -.1292199 + asia | -.0924549 .052818 -1.75 0.083 -.197031 .0121211 + americas | .0264391 .0515306 0.51 0.609 -.0755879 .128466 + _cons | 3.85075 .309451 12.44 0.000 3.238059 4.463442 +------------------------------------------------------------------------------ + +. estimates store y2000 + +. local coef2000=_b[ln_hla_het] + +. local lb2000 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub2000 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. +. +. reg ln_le10 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 43.16 + Prob > F = 0.0000 + R-squared = 0.7480 + Root MSE = .08408 + +------------------------------------------------------------------------------ + | Robust + ln_le10 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .1787627 .1334796 1.34 0.183 -.0855176 .443043 + ln_frac | -.1508934 .0700744 -2.15 0.033 -.2896359 -.0121509 + aa_ln_atd | .0733 .0301988 2.43 0.017 .0135085 .1330916 + ln_arable | -.0045715 .0094332 -0.48 0.629 -.0232486 .0141057 + ln_suitavg | -.0138008 .0083383 -1.66 0.101 -.0303101 .0027084 + ln_abslat | .0066167 .0136851 0.48 0.630 -.0204788 .0337121 + europe | .0065547 .0508292 0.13 0.898 -.0940836 .107193 + africa | -.2235135 .0590873 -3.78 0.000 -.3405023 -.1065247 + asia | -.0839933 .0521013 -1.61 0.110 -.1871502 .0191637 + americas | .0163142 .0512064 0.32 0.751 -.0850709 .1176994 + _cons | 3.912044 .282363 13.85 0.000 3.352985 4.471104 +------------------------------------------------------------------------------ + +. estimates store y2010 + +. local coef2010=_b[ln_hla_het] + +. local lb2010 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub2010 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. qui log off +clear + +. set obs 6 +number of observations (_N) was 0, now 6 + +. generate year = 1960 in 1 +(5 missing values generated) + +. replace year=1970 in 2 +(1 real change made) + +. replace year=1980 in 3 +(1 real change made) + +. replace year=1990 in 4 +(1 real change made) + +. replace year=2000 in 5 +(1 real change made) + +. replace year=2010 in 6 +(1 real change made) + +. +. // Coefficients +/ is not a valid command name +r(199); + +. generate coef = `coef1960' in 1 +(5 missing values generated) + +. replace coef=`coef1970' in 2 +(1 real change made) + +. replace coef=`coef1980' in 3 +(1 real change made) + +. replace coef=`coef1990' in 4 +(1 real change made) + +. replace coef=`coef2000' in 5 +(1 real change made) + +. replace coef=`coef2010' in 6 +(1 real change made) + +. +. //lower_bound +/ is not a valid command name +r(199); + +. gen lower_bound= `lb1960' in 1 +(5 missing values generated) + +. replace lower_bound= `lb1970' in 2 +(1 real change made) + +. replace lower_bound= `lb1980' in 3 +(1 real change made) + +. replace lower_bound= `lb1990' in 4 +(1 real change made) + +. replace lower_bound= `lb2000' in 5 +(1 real change made) + +. replace lower_bound= `lb2010' in 6 +(1 real change made) + +. +. //upper_bound +/ is not a valid command name +r(199); + +. gen upper_bound= `ub1960' in 1 +(5 missing values generated) + +. replace upper_bound= `ub1970' in 2 +(1 real change made) + +. replace upper_bound= `ub1980' in 3 +(1 real change made) + +. replace upper_bound= `ub1990' in 4 +(1 real change made) + +. replace upper_bound= `ub2000' in 5 +(1 real change made) + +. replace upper_bound= `ub2010' in 6 +(1 real change made) + +. qui log off +twoway (connected coef year) (line lower_bound year, lp(dash dot)) (line upper_b +> ound year, lp(dash dot)) , graphregion(color(white)) bgcolor(white) legend(ro +> ws(3) label(1 Point Estimate in Baseline Estimating Eq.) label(2 "95% Confiden +> ce Interval (lower bound)" ) labe(3 "95% Confidence Interval (upper bound)")) +> xtitle("Year") ytitle("Coefficient of In HLA Heterozygosity") + +. qui log off +use "hla_country_web.dta", clear + +. +. local cont "europe africa asia americas" + +. local base "ln_le60!=. & ln_hla_het!=. & ln_frac!=. & ln_abslat!=. & ln_arable +> !=. & ln_suitavg!=. & aa_ln_atd!=." + +. local bc "ln_frac aa_ln_atd ln_arable ln_suitavg ln_abslat" + +. local pop "frac_eur frac_me frac_easia frac_africa frac_am" + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur==0, rob +note: europe omitted because of collinearity +note: americas omitted because of collinearity + +Linear regression Number of obs = 55 + F(7, 46) = . + Prob > F = . + R-squared = 0.4183 + Root MSE = .12654 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .7240941 .3623023 2.00 0.052 -.0051826 1.453371 + ln_frac | -.1662651 .1477015 -1.13 0.266 -.4635727 .1310425 + aa_ln_atd | .0109134 .063677 0.17 0.865 -.1172619 .1390886 + ln_arable | -.0062449 .0196735 -0.32 0.752 -.0458456 .0333558 + ln_suitavg | .0015924 .0193779 0.08 0.935 -.0374132 .0405981 + ln_abslat | -.0104055 .0199072 -0.52 0.604 -.0504766 .0296656 + europe | 0 (omitted) + africa | -.0788309 .129284 -0.61 0.545 -.3390661 .1814043 + asia | .0581873 .1263473 0.46 0.647 -.1961366 .3125111 + americas | 0 (omitted) + _cons | 4.663561 .7141303 6.53 0.000 3.226092 6.101031 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur>0 & frac_eur<1, rob + +Linear regression Number of obs = 52 + F(10, 41) = 19.53 + Prob > F = 0.0000 + R-squared = 0.5891 + Root MSE = .13973 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .8048964 .2923111 2.75 0.009 .2145622 1.395231 + ln_frac | -.1580961 .1784565 -0.89 0.381 -.5184962 .202304 + aa_ln_atd | .1411788 .0914403 1.54 0.130 -.0434887 .3258463 + ln_arable | -.0640976 .0343788 -1.86 0.069 -.1335269 .0053318 + ln_suitavg | .0491992 .0384154 1.28 0.207 -.0283823 .1267806 + ln_abslat | .053768 .0361827 1.49 0.145 -.0193045 .1268405 + europe | -.0364848 .0463389 -0.79 0.436 -.1300682 .0570986 + africa | -.2468748 .0834365 -2.96 0.005 -.4153782 -.0783713 + asia | -.2803521 .0860084 -3.26 0.002 -.4540497 -.1066546 + americas | -.1194736 .0524145 -2.28 0.028 -.225327 -.0136203 + _cons | 3.976339 .8431324 4.72 0.000 2.273599 5.67908 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur==1, rob +note: europe omitted because of collinearity +note: africa omitted because of collinearity +note: asia omitted because of collinearity +note: americas omitted because of collinearity + +Linear regression Number of obs = 24 + F(6, 17) = 5.33 + Prob > F = 0.0030 + R-squared = 0.5162 + Root MSE = .04194 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .728073 .3110997 2.34 0.032 .07171 1.384436 + ln_frac | -.0734813 .0922997 -0.80 0.437 -.2682168 .1212541 + aa_ln_atd | .0357073 .037792 0.94 0.358 -.0440268 .1154413 + ln_arable | -.0090738 .0212053 -0.43 0.674 -.0538131 .0356655 + ln_suitavg | .0184685 .0208964 0.88 0.389 -.025619 .062556 + ln_abslat | .3230336 .1173578 2.75 0.014 .0754303 .570637 + europe | 0 (omitted) + africa | 0 (omitted) + asia | 0 (omitted) + americas | 0 (omitted) + _cons | 3.517317 .6836917 5.14 0.000 2.074853 4.95978 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het `pop' `bc' `cont' if `base', rob + +Linear regression Number of obs = 131 + F(15, 115) = 165.25 + Prob > F = 0.0000 + R-squared = 0.7891 + Root MSE = .11264 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .8869916 .2871503 3.09 0.003 .3182021 1.455781 + frac_eur | .3177208 .1395855 2.28 0.025 .0412289 .5942128 + frac_me | .020621 .1596132 0.13 0.897 -.295542 .336784 + frac_easia | .1401225 .1415624 0.99 0.324 -.1402853 .4205302 + frac_africa | .0173643 .137623 0.13 0.900 -.2552404 .289969 + frac_am | .1451349 .0934837 1.55 0.123 -.0400383 .330308 + ln_frac | -.1295757 .0868859 -1.49 0.139 -.3016799 .0425285 + aa_ln_atd | .0426266 .0416563 1.02 0.308 -.0398866 .1251398 + ln_arable | -.0099352 .0143495 -0.69 0.490 -.0383587 .0184884 + ln_suitavg | -.000756 .0145277 -0.05 0.959 -.0295325 .0280205 + ln_abslat | .0068563 .018 0.38 0.704 -.0287982 .0425108 + europe | -.0518747 .055794 -0.93 0.354 -.1623918 .0586425 + africa | -.1408858 .0945556 -1.49 0.139 -.3281822 .0464106 + asia | -.0978798 .0933299 -1.05 0.296 -.2827483 .0869887 + americas | -.057943 .0612781 -0.95 0.346 -.1793231 .0634371 + _cons | 4.598251 .4873273 9.44 0.000 3.632949 5.563552 +------------------------------------------------------------------------------ + +. qui log off +local ov1 "aa_pdiv!=. & malfal!=. & tropical!=. & pnativ_60!=. & distcr100!=. " + +. +. reg ln_le60 ln_hla_het aa_pdiv `bc' `cont' if `ov1', rob + +Linear regression Number of obs = 131 + F(11, 119) = 90.88 + Prob > F = 0.0000 + R-squared = 0.7449 + Root MSE = .12179 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 1.077102 .340135 3.17 0.002 .4036012 1.750604 + aa_pdiv | -.2783647 1.199801 -0.23 0.817 -2.654091 2.097362 + ln_frac | -.113391 .1067678 -1.06 0.290 -.3248019 .0980199 + aa_ln_atd | .0406652 .0470243 0.86 0.389 -.0524476 .1337779 + ln_arable | -.0083386 .0157507 -0.53 0.598 -.0395265 .0228493 + ln_suitavg | .007875 .0127592 0.62 0.538 -.0173895 .0331395 + ln_abslat | .0206019 .0161684 1.27 0.205 -.0114131 .0526169 + europe | .0390449 .0703013 0.56 0.580 -.1001588 .1782486 + africa | -.3031556 .0870473 -3.48 0.001 -.4755179 -.1307932 + asia | -.2058545 .0739893 -2.78 0.006 -.3523608 -.0593482 + americas | -.045389 .0776443 -0.58 0.560 -.1991325 .1083545 + _cons | 5.19642 1.000954 5.19 0.000 3.214432 7.178408 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het malfal tropical desert distcr100 `bc' `cont' if `ov1', +> rob + +Linear regression Number of obs = 131 + F(14, 116) = 78.86 + Prob > F = 0.0000 + R-squared = 0.7741 + Root MSE = .11606 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .7713477 .2040145 3.78 0.000 .3672712 1.175424 + malfal | -.1613359 .0481473 -3.35 0.001 -.2566976 -.0659741 + tropical | -.0003105 .0005798 -0.54 0.593 -.0014589 .000838 + desert | -.0015805 .0015453 -1.02 0.309 -.0046411 .0014802 + distcr1000 | .011631 .0384694 0.30 0.763 -.0645626 .0878246 + ln_frac | -.0863313 .0921872 -0.94 0.351 -.2689197 .096257 + aa_ln_atd | .0120531 .0330839 0.36 0.716 -.0534738 .07758 + ln_arable | -.0019372 .0127372 -0.15 0.879 -.0271649 .0232905 + ln_suitavg | .0037507 .012647 0.30 0.767 -.0212982 .0287996 + ln_abslat | -.0149382 .0184394 -0.81 0.420 -.0514598 .0215834 + europe | .0444248 .0730941 0.61 0.545 -.1003473 .1891969 + africa | -.2552539 .0778437 -3.28 0.001 -.4094332 -.1010745 + asia | -.1849297 .0777094 -2.38 0.019 -.3388429 -.0310165 + americas | -.0619446 .0777163 -0.80 0.427 -.2158716 .0919824 + _cons | 5.007553 .388945 12.87 0.000 4.237198 5.777907 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het frac_migrant `bc' `cont' if `ov1', rob + +Linear regression Number of obs = 131 + F(11, 119) = 85.46 + Prob > F = 0.0000 + R-squared = 0.7504 + Root MSE = .12047 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .8699479 .223888 3.89 0.000 .4266273 1.313268 +frac_migrant | .0982091 .0632069 1.55 0.123 -.0269468 .2233651 + ln_frac | -.1440908 .0987111 -1.46 0.147 -.3395486 .051367 + aa_ln_atd | .041594 .0395006 1.05 0.294 -.0366212 .1198093 + ln_arable | -.0085389 .0155162 -0.55 0.583 -.0392626 .0221847 + ln_suitavg | .0113689 .0119537 0.95 0.343 -.0123006 .0350383 + ln_abslat | .0157883 .016349 0.97 0.336 -.0165843 .048161 + europe | .1133226 .0846705 1.34 0.183 -.0543335 .2809787 + africa | -.25279 .0810031 -3.12 0.002 -.4131842 -.0923958 + asia | -.147021 .0801837 -1.83 0.069 -.3057927 .0117507 + americas | -.0426537 .0676053 -0.63 0.529 -.176519 .0912116 + _cons | 4.708444 .4388107 10.73 0.000 3.839555 5.577333 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het aa_pdiv malfal tropical desert distcr100 frac_migrant ` +> bc' `cont' if `ov1', rob + +Linear regression Number of obs = 131 + F(16, 114) = 64.92 + Prob > F = 0.0000 + R-squared = 0.7824 + Root MSE = .11491 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .5372995 .3093499 1.74 0.085 -.0755202 1.150119 + aa_pdiv | .138512 1.028011 0.13 0.893 -1.89797 2.174994 + malfal | -.1730445 .0487015 -3.55 0.001 -.2695218 -.0765672 + tropical | -.0002674 .0005692 -0.47 0.639 -.0013949 .0008601 + desert | -.0012792 .0014481 -0.88 0.379 -.0041478 .0015894 + distcr1000 | .0299095 .0381407 0.78 0.435 -.0456469 .105466 +frac_migrant | .1203078 .0733916 1.64 0.104 -.0250803 .2656959 + ln_frac | -.1187416 .1035394 -1.15 0.254 -.3238522 .0863691 + aa_ln_atd | .0120886 .0375722 0.32 0.748 -.0623416 .0865188 + ln_arable | -.0020202 .0121886 -0.17 0.869 -.0261657 .0221252 + ln_suitavg | .0097049 .0121508 0.80 0.426 -.0143658 .0337755 + ln_abslat | -.021768 .019187 -1.13 0.259 -.0597774 .0162413 + europe | .1426791 .0957523 1.49 0.139 -.0470055 .3323637 + africa | -.1868511 .1054909 -1.77 0.079 -.3958278 .0221256 + asia | -.1124693 .0903735 -1.24 0.216 -.2914986 .0665599 + americas | -.0646517 .0742432 -0.87 0.386 -.2117268 .0824234 + _cons | 4.584323 .881409 5.20 0.000 2.838259 6.330388 +------------------------------------------------------------------------------ + +. qui log off +local ov2 "ln_ypc60!=. & ln_yr_sch1960!=. & ln_pd60!=. & ln_urb60!=. & ln_young! +> =." + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `ov2', rob + +Linear regression Number of obs = 109 + F(10, 98) = 81.14 + Prob > F = 0.0000 + R-squared = 0.7378 + Root MSE = .11998 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .9999595 .2118165 4.72 0.000 .5796165 1.420302 + ln_frac | -.0687966 .1026132 -0.67 0.504 -.2724292 .134836 + aa_ln_atd | .0220791 .0427207 0.52 0.606 -.0626988 .106857 + ln_arable | -.0203005 .0163016 -1.25 0.216 -.0526505 .0120494 + ln_suitavg | .0070124 .0139013 0.50 0.615 -.0205742 .034599 + ln_abslat | .0169459 .0166373 1.02 0.311 -.0160703 .049962 + europe | .0821774 .0783703 1.05 0.297 -.0733459 .2377008 + africa | -.3038598 .085273 -3.56 0.001 -.4730812 -.1346384 + asia | -.1987743 .0862051 -2.31 0.023 -.3698456 -.027703 + americas | -.0337169 .083025 -0.41 0.686 -.1984773 .1310434 + _cons | 5.075976 .4274974 11.87 0.000 4.227622 5.924331 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het ln_ypc60 `bc' `cont' if `ov2', rob + +Linear regression Number of obs = 109 + F(11, 97) = 78.18 + Prob > F = 0.0000 + R-squared = 0.8062 + Root MSE = .10368 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .6574856 .2123752 3.10 0.003 .2359796 1.078992 + ln_ypc60 | .0977108 .0220749 4.43 0.000 .0538981 .1415234 + ln_frac | -.1265308 .0889372 -1.42 0.158 -.3030465 .0499848 + aa_ln_atd | -.0006487 .0401033 -0.02 0.987 -.0802427 .0789453 + ln_arable | -.0105789 .0161119 -0.66 0.513 -.0425566 .0213989 + ln_suitavg | .0290353 .01418 2.05 0.043 .000892 .0571787 + ln_abslat | .0089705 .0178069 0.50 0.616 -.0263713 .0443123 + europe | .0967318 .0609128 1.59 0.116 -.0241633 .2176268 + africa | -.1342939 .0807126 -1.66 0.099 -.2944862 .0258983 + asia | -.0661328 .0708522 -0.93 0.353 -.2067548 .0744893 + americas | .0155833 .0634731 0.25 0.807 -.1103931 .1415598 + _cons | 4.0475 .4733963 8.55 0.000 3.107939 4.987061 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het ln_yr_sch1960 `bc' `cont' if `ov2', rob + +Linear regression Number of obs = 109 + F(11, 97) = 108.99 + Prob > F = 0.0000 + R-squared = 0.8727 + Root MSE = .08403 + +------------------------------------------------------------------------------- + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +--------------+---------------------------------------------------------------- + ln_hla_het | .6127261 .1633457 3.75 0.000 .28853 .9369221 +ln_yr_sch1960 | .1320134 .019477 6.78 0.000 .0933569 .1706699 + ln_frac | -.0241461 .0610461 -0.40 0.693 -.1453058 .0970136 + aa_ln_atd | .071103 .0282958 2.51 0.014 .0149436 .1272624 + ln_arable | .0043996 .0115413 0.38 0.704 -.0185067 .027306 + ln_suitavg | -.0081406 .0108674 -0.75 0.456 -.0297093 .0134282 + ln_abslat | .0059583 .0124948 0.48 0.635 -.0188404 .030757 + europe | .0285669 .0269261 1.06 0.291 -.0248739 .0820078 + africa | -.1239058 .0453816 -2.73 0.008 -.2139757 -.0338358 + asia | -.1525689 .0349806 -4.36 0.000 -.2219957 -.083142 + americas | -.0317955 .0268294 -1.19 0.239 -.0850445 .0214535 + _cons | 3.99027 .3344223 11.93 0.000 3.326535 4.654006 +------------------------------------------------------------------------------- + +. +. reg ln_le60 ln_hla_het ln_pd60 ln_urb60 ln_young `bc' `cont' if `ov2', rob + +Linear regression Number of obs = 109 + F(13, 95) = 58.98 + Prob > F = 0.0000 + R-squared = 0.8256 + Root MSE = .09939 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .7528768 .1943835 3.87 0.000 .3669768 1.138777 + ln_pd60 | .0001866 .0151894 0.01 0.990 -.0299681 .0303414 + ln_urb60 | .1137048 .0222823 5.10 0.000 .0694688 .1579408 + ln_young | -.0614236 .0886641 -0.69 0.490 -.2374441 .1145969 + ln_frac | -.1367887 .0831761 -1.64 0.103 -.3019141 .0283367 + aa_ln_atd | -.075599 .0396077 -1.91 0.059 -.1542302 .0030322 + ln_arable | -.0064507 .0170226 -0.38 0.706 -.0402447 .0273434 + ln_suitavg | .0133635 .0119674 1.12 0.267 -.0103947 .0371218 + ln_abslat | -.009797 .0191409 -0.51 0.610 -.0477966 .0282025 + europe | .0512724 .052395 0.98 0.330 -.0527449 .1552897 + africa | -.2301635 .0573917 -4.01 0.000 -.3441005 -.1162265 + asia | -.1139926 .0547566 -2.08 0.040 -.2226982 -.0052871 + americas | -.0694854 .0447219 -1.55 0.124 -.1582697 .0192989 + _cons | 5.523092 .5104961 10.82 0.000 4.509629 6.536555 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het ln_ypc60 ln_yr_sch1960 ln_pd60 ln_urb60 ln_young `bc' ` +> cont' if `ov2', rob + +Linear regression Number of obs = 109 + F(15, 93) = 88.15 + Prob > F = 0.0000 + R-squared = 0.8897 + Root MSE = .07987 + +------------------------------------------------------------------------------- + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +--------------+---------------------------------------------------------------- + ln_hla_het | .529581 .1672842 3.17 0.002 .1973878 .8617742 + ln_ypc60 | .0280348 .016719 1.68 0.097 -.0051658 .0612354 +ln_yr_sch1960 | .1036748 .0189568 5.47 0.000 .0660303 .1413194 + ln_pd60 | .0062813 .0126177 0.50 0.620 -.0187749 .0313375 + ln_urb60 | .0342884 .0194949 1.76 0.082 -.0044246 .0730014 + ln_young | -.0148322 .0685018 -0.22 0.829 -.1508632 .1211988 + ln_frac | -.0582462 .0703963 -0.83 0.410 -.1980392 .0815469 + aa_ln_atd | .024198 .0292272 0.83 0.410 -.0338415 .0822375 + ln_arable | .0014524 .0146563 0.10 0.921 -.0276522 .030557 + ln_suitavg | .0023245 .0111488 0.21 0.835 -.0198147 .0244637 + ln_abslat | -.0001829 .0152382 -0.01 0.990 -.030443 .0300772 + europe | .0255092 .0397006 0.64 0.522 -.0533284 .1043467 + africa | -.0977018 .0504283 -1.94 0.056 -.1978425 .0024388 + asia | -.1094775 .0438173 -2.50 0.014 -.1964899 -.022465 + americas | -.0341979 .0351158 -0.97 0.333 -.103931 .0355352 + _cons | 4.048212 .4632424 8.74 0.000 3.128304 4.968119 +------------------------------------------------------------------------------- + +. qui log off +local ov2 "ln_ypc60!=. & ln_yr_sch1960!=. & ln_pd60!=. & ln_urb60!=. & ln_young! +> =." + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `ov2', rob + +Linear regression Number of obs = 109 + F(10, 98) = 81.14 + Prob > F = 0.0000 + R-squared = 0.7378 + Root MSE = .11998 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .9999595 .2118165 4.72 0.000 .5796165 1.420302 + ln_frac | -.0687966 .1026132 -0.67 0.504 -.2724292 .134836 + aa_ln_atd | .0220791 .0427207 0.52 0.606 -.0626988 .106857 + ln_arable | -.0203005 .0163016 -1.25 0.216 -.0526505 .0120494 + ln_suitavg | .0070124 .0139013 0.50 0.615 -.0205742 .034599 + ln_abslat | .0169459 .0166373 1.02 0.311 -.0160703 .049962 + europe | .0821774 .0783703 1.05 0.297 -.0733459 .2377008 + africa | -.3038598 .085273 -3.56 0.001 -.4730812 -.1346384 + asia | -.1987743 .0862051 -2.31 0.023 -.3698456 -.027703 + americas | -.0337169 .083025 -0.41 0.686 -.1984773 .1310434 + _cons | 5.075976 .4274974 11.87 0.000 4.227622 5.924331 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het ln_ypc60 `bc' `cont' if `ov2', rob + +Linear regression Number of obs = 109 + F(11, 97) = 78.18 + Prob > F = 0.0000 + R-squared = 0.8062 + Root MSE = .10368 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .6574856 .2123752 3.10 0.003 .2359796 1.078992 + ln_ypc60 | .0977108 .0220749 4.43 0.000 .0538981 .1415234 + ln_frac | -.1265308 .0889372 -1.42 0.158 -.3030465 .0499848 + aa_ln_atd | -.0006487 .0401033 -0.02 0.987 -.0802427 .0789453 + ln_arable | -.0105789 .0161119 -0.66 0.513 -.0425566 .0213989 + ln_suitavg | .0290353 .01418 2.05 0.043 .000892 .0571787 + ln_abslat | .0089705 .0178069 0.50 0.616 -.0263713 .0443123 + europe | .0967318 .0609128 1.59 0.116 -.0241633 .2176268 + africa | -.1342939 .0807126 -1.66 0.099 -.2944862 .0258983 + asia | -.0661328 .0708522 -0.93 0.353 -.2067548 .0744893 + americas | .0155833 .0634731 0.25 0.807 -.1103931 .1415598 + _cons | 4.0475 .4733963 8.55 0.000 3.107939 4.987061 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het ln_yr_sch1960 `bc' `cont' if `ov2', rob + +Linear regression Number of obs = 109 + F(11, 97) = 108.99 + Prob > F = 0.0000 + R-squared = 0.8727 + Root MSE = .08403 + +------------------------------------------------------------------------------- + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +--------------+---------------------------------------------------------------- + ln_hla_het | .6127261 .1633457 3.75 0.000 .28853 .9369221 +ln_yr_sch1960 | .1320134 .019477 6.78 0.000 .0933569 .1706699 + ln_frac | -.0241461 .0610461 -0.40 0.693 -.1453058 .0970136 + aa_ln_atd | .071103 .0282958 2.51 0.014 .0149436 .1272624 + ln_arable | .0043996 .0115413 0.38 0.704 -.0185067 .027306 + ln_suitavg | -.0081406 .0108674 -0.75 0.456 -.0297093 .0134282 + ln_abslat | .0059583 .0124948 0.48 0.635 -.0188404 .030757 + europe | .0285669 .0269261 1.06 0.291 -.0248739 .0820078 + africa | -.1239058 .0453816 -2.73 0.008 -.2139757 -.0338358 + asia | -.1525689 .0349806 -4.36 0.000 -.2219957 -.083142 + americas | -.0317955 .0268294 -1.19 0.239 -.0850445 .0214535 + _cons | 3.99027 .3344223 11.93 0.000 3.326535 4.654006 +------------------------------------------------------------------------------- + +. +. reg ln_le60 ln_hla_het ln_pd60 ln_urb60 ln_young `bc' `cont' if `ov2', rob + +Linear regression Number of obs = 109 + F(13, 95) = 58.98 + Prob > F = 0.0000 + R-squared = 0.8256 + Root MSE = .09939 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .7528768 .1943835 3.87 0.000 .3669768 1.138777 + ln_pd60 | .0001866 .0151894 0.01 0.990 -.0299681 .0303414 + ln_urb60 | .1137048 .0222823 5.10 0.000 .0694688 .1579408 + ln_young | -.0614236 .0886641 -0.69 0.490 -.2374441 .1145969 + ln_frac | -.1367887 .0831761 -1.64 0.103 -.3019141 .0283367 + aa_ln_atd | -.075599 .0396077 -1.91 0.059 -.1542302 .0030322 + ln_arable | -.0064507 .0170226 -0.38 0.706 -.0402447 .0273434 + ln_suitavg | .0133635 .0119674 1.12 0.267 -.0103947 .0371218 + ln_abslat | -.009797 .0191409 -0.51 0.610 -.0477966 .0282025 + europe | .0512724 .052395 0.98 0.330 -.0527449 .1552897 + africa | -.2301635 .0573917 -4.01 0.000 -.3441005 -.1162265 + asia | -.1139926 .0547566 -2.08 0.040 -.2226982 -.0052871 + americas | -.0694854 .0447219 -1.55 0.124 -.1582697 .0192989 + _cons | 5.523092 .5104961 10.82 0.000 4.509629 6.536555 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het ln_ypc60 ln_yr_sch1960 ln_pd60 ln_urb60 ln_young `bc' ` +> cont' if `ov2', rob + +Linear regression Number of obs = 109 + F(15, 93) = 88.15 + Prob > F = 0.0000 + R-squared = 0.8897 + Root MSE = .07987 + +------------------------------------------------------------------------------- + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +--------------+---------------------------------------------------------------- + ln_hla_het | .529581 .1672842 3.17 0.002 .1973878 .8617742 + ln_ypc60 | .0280348 .016719 1.68 0.097 -.0051658 .0612354 +ln_yr_sch1960 | .1036748 .0189568 5.47 0.000 .0660303 .1413194 + ln_pd60 | .0062813 .0126177 0.50 0.620 -.0187749 .0313375 + ln_urb60 | .0342884 .0194949 1.76 0.082 -.0044246 .0730014 + ln_young | -.0148322 .0685018 -0.22 0.829 -.1508632 .1211988 + ln_frac | -.0582462 .0703963 -0.83 0.410 -.1980392 .0815469 + aa_ln_atd | .024198 .0292272 0.83 0.410 -.0338415 .0822375 + ln_arable | .0014524 .0146563 0.10 0.921 -.0276522 .030557 + ln_suitavg | .0023245 .0111488 0.21 0.835 -.0198147 .0244637 + ln_abslat | -.0001829 .0152382 -0.01 0.990 -.030443 .0300772 + europe | .0255092 .0397006 0.64 0.522 -.0533284 .1043467 + africa | -.0977018 .0504283 -1.94 0.056 -.1978425 .0024388 + asia | -.1094775 .0438173 -2.50 0.014 -.1964899 -.022465 + americas | -.0341979 .0351158 -0.97 0.333 -.103931 .0355352 + _cons | 4.048212 .4632424 8.74 0.000 3.128304 4.968119 +------------------------------------------------------------------------------- + +. qui log off +clear + +. use "hla_country_web.dta", clear + +. +. local cont "europe africa asia americas" + +. local base "ln_le60!=. & ln_hla_het!=. & ln_frac!=. & ln_abslat!=. & ln_arable +> !=. & ln_suitavg!=. & aa_ln_atd!=." + +. local bc "ln_frac aa_ln_atd ln_arable ln_suitavg ln_abslat" + +. local pop "frac_eur frac_me frac_easia frac_africa frac_am" + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur==0, rob +note: europe omitted because of collinearity +note: americas omitted because of collinearity + +Linear regression Number of obs = 55 + F(7, 46) = . + Prob > F = . + R-squared = 0.4183 + Root MSE = .12654 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .7240941 .3623023 2.00 0.052 -.0051826 1.453371 + ln_frac | -.1662651 .1477015 -1.13 0.266 -.4635727 .1310425 + aa_ln_atd | .0109134 .063677 0.17 0.865 -.1172619 .1390886 + ln_arable | -.0062449 .0196735 -0.32 0.752 -.0458456 .0333558 + ln_suitavg | .0015924 .0193779 0.08 0.935 -.0374132 .0405981 + ln_abslat | -.0104055 .0199072 -0.52 0.604 -.0504766 .0296656 + europe | 0 (omitted) + africa | -.0788309 .129284 -0.61 0.545 -.3390661 .1814043 + asia | .0581873 .1263473 0.46 0.647 -.1961366 .3125111 + americas | 0 (omitted) + _cons | 4.663561 .7141303 6.53 0.000 3.226092 6.101031 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur>0 & frac_eur<1, rob + +Linear regression Number of obs = 52 + F(10, 41) = 19.53 + Prob > F = 0.0000 + R-squared = 0.5891 + Root MSE = .13973 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .8048964 .2923111 2.75 0.009 .2145622 1.395231 + ln_frac | -.1580961 .1784565 -0.89 0.381 -.5184962 .202304 + aa_ln_atd | .1411788 .0914403 1.54 0.130 -.0434887 .3258463 + ln_arable | -.0640976 .0343788 -1.86 0.069 -.1335269 .0053318 + ln_suitavg | .0491992 .0384154 1.28 0.207 -.0283823 .1267806 + ln_abslat | .053768 .0361827 1.49 0.145 -.0193045 .1268405 + europe | -.0364848 .0463389 -0.79 0.436 -.1300682 .0570986 + africa | -.2468748 .0834365 -2.96 0.005 -.4153782 -.0783713 + asia | -.2803521 .0860084 -3.26 0.002 -.4540497 -.1066546 + americas | -.1194736 .0524145 -2.28 0.028 -.225327 -.0136203 + _cons | 3.976339 .8431324 4.72 0.000 2.273599 5.67908 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur==1, rob +note: europe omitted because of collinearity +note: africa omitted because of collinearity +note: asia omitted because of collinearity +note: americas omitted because of collinearity + +Linear regression Number of obs = 24 + F(6, 17) = 5.33 + Prob > F = 0.0030 + R-squared = 0.5162 + Root MSE = .04194 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .728073 .3110997 2.34 0.032 .07171 1.384436 + ln_frac | -.0734813 .0922997 -0.80 0.437 -.2682168 .1212541 + aa_ln_atd | .0357073 .037792 0.94 0.358 -.0440268 .1154413 + ln_arable | -.0090738 .0212053 -0.43 0.674 -.0538131 .0356655 + ln_suitavg | .0184685 .0208964 0.88 0.389 -.025619 .062556 + ln_abslat | .3230336 .1173578 2.75 0.014 .0754303 .570637 + europe | 0 (omitted) + africa | 0 (omitted) + asia | 0 (omitted) + americas | 0 (omitted) + _cons | 3.517317 .6836917 5.14 0.000 2.074853 4.95978 +------------------------------------------------------------------------------ + +. +. reg ln_le60 ln_hla_het `pop' `bc' `cont' if `base', rob + +Linear regression Number of obs = 131 + F(15, 115) = 165.25 + Prob > F = 0.0000 + R-squared = 0.7891 + Root MSE = .11264 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .8869916 .2871503 3.09 0.003 .3182021 1.455781 + frac_eur | .3177208 .1395855 2.28 0.025 .0412289 .5942128 + frac_me | .020621 .1596132 0.13 0.897 -.295542 .336784 + frac_easia | .1401225 .1415624 0.99 0.324 -.1402853 .4205302 + frac_africa | .0173643 .137623 0.13 0.900 -.2552404 .289969 + frac_am | .1451349 .0934837 1.55 0.123 -.0400383 .330308 + ln_frac | -.1295757 .0868859 -1.49 0.139 -.3016799 .0425285 + aa_ln_atd | .0426266 .0416563 1.02 0.308 -.0398866 .1251398 + ln_arable | -.0099352 .0143495 -0.69 0.490 -.0383587 .0184884 + ln_suitavg | -.000756 .0145277 -0.05 0.959 -.0295325 .0280205 + ln_abslat | .0068563 .018 0.38 0.704 -.0287982 .0425108 + europe | -.0518747 .055794 -0.93 0.354 -.1623918 .0586425 + africa | -.1408858 .0945556 -1.49 0.139 -.3281822 .0464106 + asia | -.0978798 .0933299 -1.05 0.296 -.2827483 .0869887 + americas | -.057943 .0612781 -0.95 0.346 -.1793231 .0634371 + _cons | 4.598251 .4873273 9.44 0.000 3.632949 5.563552 +------------------------------------------------------------------------------ + +. qui log off +/* local le "ln_le60!=. & ln_le70!=. & ln_le80!=. & ln_le90!=. & ln_le00!=. & ln +> _le10!=." +/ is not a valid command name +r(199); + +. +. reg ln_le60 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 100.89 + Prob > F = 0.0000 + R-squared = 0.7446 + Root MSE = .12133 + +------------------------------------------------------------------------------ + | Robust + ln_le60 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 1.01512 .1899264 5.34 0.000 .6390794 1.391161 + ln_frac | -.1210012 .0927409 -1.30 0.194 -.3046217 .0626193 + aa_ln_atd | .0369984 .0397486 0.93 0.354 -.0417011 .1156979 + ln_arable | -.0086997 .0159756 -0.54 0.587 -.0403303 .0229309 + ln_suitavg | .008039 .0128307 0.63 0.532 -.017365 .0334429 + ln_abslat | .0199086 .0156461 1.27 0.206 -.0110696 .0508867 + europe | .0387395 .0716524 0.54 0.590 -.1031272 .1806062 + africa | -.3122101 .0754901 -4.14 0.000 -.4616753 -.162745 + asia | -.2056222 .0755278 -2.72 0.007 -.355162 -.0560825 + americas | -.0400593 .0736294 -0.54 0.587 -.1858403 .1057217 + _cons | 4.962063 .4156831 11.94 0.000 4.139039 5.785086 +------------------------------------------------------------------------------ + +. estimates store y1960 + +. local coef1960=_b[ln_hla_het] + +. local lb1960 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub1960 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. +. reg ln_le70 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 76.19 + Prob > F = 0.0000 + R-squared = 0.7340 + Root MSE = .11485 + +------------------------------------------------------------------------------ + | Robust + ln_le70 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | 1.04047 .1695943 6.14 0.000 .7046852 1.376255 + ln_frac | -.1163928 .0881347 -1.32 0.189 -.2908934 .0581078 + aa_ln_atd | .0246249 .0372331 0.66 0.510 -.0490941 .098344 + ln_arable | -.0141638 .0153671 -0.92 0.359 -.0445896 .0162619 + ln_suitavg | .0007926 .0135757 0.06 0.954 -.0260864 .0276715 + ln_abslat | .0045722 .0158262 0.29 0.773 -.0267626 .0359069 + europe | .0344497 .0460362 0.75 0.456 -.0566989 .1255982 + africa | -.3192781 .0522129 -6.11 0.000 -.4226561 -.2159002 + asia | -.1420921 .0484679 -2.93 0.004 -.2380552 -.046129 + americas | -.0238244 .0420173 -0.57 0.572 -.1070157 .0593668 + _cons | 5.198611 .3846817 13.51 0.000 4.436968 5.960254 +------------------------------------------------------------------------------ + +. estimates store y1970 + +. local coef1970=_b[ln_hla_het] + +. local lb1970 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub1970 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. +. reg ln_le80 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 55.68 + Prob > F = 0.0000 + R-squared = 0.6995 + Root MSE = .10588 + +------------------------------------------------------------------------------ + | Robust + ln_le80 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .8420929 .169953 4.95 0.000 .5055977 1.178588 + ln_frac | -.1462667 .0786894 -1.86 0.066 -.3020663 .0095329 + aa_ln_atd | .0348747 .0381473 0.91 0.362 -.0406544 .1104037 + ln_arable | -.0199753 .0150991 -1.32 0.188 -.0498705 .0099199 + ln_suitavg | .0003745 .0142482 0.03 0.979 -.0278359 .0285849 + ln_abslat | .0070102 .0152294 0.46 0.646 -.0231431 .0371634 + europe | .0176881 .0373365 0.47 0.637 -.0562357 .0916118 + africa | -.2552229 .0450962 -5.66 0.000 -.3445103 -.1659355 + asia | -.1194574 .0422942 -2.82 0.006 -.203197 -.0357178 + americas | -.0064243 .0328959 -0.20 0.845 -.0715559 .0587073 + _cons | 4.945296 .3733601 13.25 0.000 4.206069 5.684523 +------------------------------------------------------------------------------ + +. estimates store y1980 + +. local coef1980=_b[ln_hla_het] + +. local lb1980 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub1980 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. +. reg ln_le90 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 32.32 + Prob > F = 0.0000 + R-squared = 0.6886 + Root MSE = .10684 + +------------------------------------------------------------------------------ + | Robust + ln_le90 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .5490235 .1798274 3.05 0.003 .1929779 .9050692 + ln_frac | -.183742 .0828562 -2.22 0.028 -.3477915 -.0196925 + aa_ln_atd | .0383219 .035843 1.07 0.287 -.0326448 .1092886 + ln_arable | -.0220742 .0174598 -1.26 0.209 -.0566434 .0124949 + ln_suitavg | -.0046492 .0148575 -0.31 0.755 -.034066 .0247677 + ln_abslat | .0247666 .0225831 1.10 0.275 -.0199464 .0694797 + europe | .0189598 .0440979 0.43 0.668 -.0683511 .1062706 + africa | -.2203019 .0524047 -4.20 0.000 -.3240596 -.1165442 + asia | -.0726899 .0444554 -1.64 0.105 -.1607085 .0153287 + americas | .0346061 .0405436 0.85 0.395 -.0456675 .1148796 + _cons | 4.554555 .4118077 11.06 0.000 3.739204 5.369906 +------------------------------------------------------------------------------ + +. estimates store y1990 + +. local coef1990=_b[ln_hla_het] + +. local lb1990 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub1990 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. +. reg ln_le00 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 43.79 + Prob > F = 0.0000 + R-squared = 0.7683 + Root MSE = .09193 + +------------------------------------------------------------------------------ + | Robust + ln_le00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .2512166 .1540813 1.63 0.106 -.0538535 .5562868 + ln_frac | -.1775283 .0728385 -2.44 0.016 -.3217434 -.0333131 + aa_ln_atd | .0843881 .0303567 2.78 0.006 .024284 .1444923 + ln_arable | -.0062233 .0115616 -0.54 0.591 -.0291145 .0166679 + ln_suitavg | -.0168243 .0100998 -1.67 0.098 -.0368211 .0031725 + ln_abslat | .0137408 .0156327 0.88 0.381 -.0172109 .0446925 + europe | -.0010578 .0524527 -0.02 0.984 -.1049104 .1027948 + africa | -.2497432 .0608725 -4.10 0.000 -.3702665 -.1292199 + asia | -.0924549 .052818 -1.75 0.083 -.197031 .0121211 + americas | .0264391 .0515306 0.51 0.609 -.0755879 .128466 + _cons | 3.85075 .309451 12.44 0.000 3.238059 4.463442 +------------------------------------------------------------------------------ + +. estimates store y2000 + +. local coef2000=_b[ln_hla_het] + +. local lb2000 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub2000 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. +. +. reg ln_le10 ln_hla_het `bc' `cont' if `le', rob + +Linear regression Number of obs = 131 + F(10, 120) = 43.16 + Prob > F = 0.0000 + R-squared = 0.7480 + Root MSE = .08408 + +------------------------------------------------------------------------------ + | Robust + ln_le10 | Coef. Std. Err. t P>|t| [95% Conf. Interval] +-------------+---------------------------------------------------------------- + ln_hla_het | .1787627 .1334796 1.34 0.183 -.0855176 .443043 + ln_frac | -.1508934 .0700744 -2.15 0.033 -.2896359 -.0121509 + aa_ln_atd | .0733 .0301988 2.43 0.017 .0135085 .1330916 + ln_arable | -.0045715 .0094332 -0.48 0.629 -.0232486 .0141057 + ln_suitavg | -.0138008 .0083383 -1.66 0.101 -.0303101 .0027084 + ln_abslat | .0066167 .0136851 0.48 0.630 -.0204788 .0337121 + europe | .0065547 .0508292 0.13 0.898 -.0940836 .107193 + africa | -.2235135 .0590873 -3.78 0.000 -.3405023 -.1065247 + asia | -.0839933 .0521013 -1.61 0.110 -.1871502 .0191637 + americas | .0163142 .0512064 0.32 0.751 -.0850709 .1176994 + _cons | 3.912044 .282363 13.85 0.000 3.352985 4.471104 +------------------------------------------------------------------------------ + +. estimates store y2010 + +. local coef2010=_b[ln_hla_het] + +. local lb2010 = _b[ln_hla_het] - invttail(e(df_r),0.025)*_se[ln_hla_het] + +. local ub2010 = _b[ln_hla_het] + invttail(e(df_r),0.025)*_se[ln_hla_het] + +. qui log off diff --git a/notebooks/C.justinCook-The_natural_selection_2015/raw/tables+2-7.do b/notebooks/C.justinCook-The_natural_selection_2015/raw/tables+2-7.do new file mode 100644 index 0000000..7f56a71 --- /dev/null +++ b/notebooks/C.justinCook-The_natural_selection_2015/raw/tables+2-7.do @@ -0,0 +1,144 @@ +set more off +set matsize 1000 + +log using "replication_hla_base_tables.log", replace + +use "\hla_country_web.dta", clear + +local cont "europe africa asia americas" +local base "ln_le60!=. & ln_hla_het!=. & ln_frac!=. & ln_abslat!=. & ln_arable!=. & ln_suitavg!=. & aa_ln_atd!=." +local bc "ln_frac aa_ln_atd ln_arable ln_suitavg ln_abslat" + + +*************************************************************** +*** Table 2: Historic Determinants of HLA Heterozygosity *** +*************************************************************** + +*Col. 1 +reg ln_hla_het aa_ln_atd if aa_mdist!=. & `base' & ln_le60!=., rob + +*Col. 2 +reg ln_hla_het aa_ln_atd aa_mdist if aa_mdist!=. & ln_le60!=. & `base', rob + +*Col. 3 +reg ln_hla_het aa_lanim if aa_mdist!=. & `base' & ln_le60!=., rob + +*Col. 4 +reg ln_hla_het aa_lanim aa_mdist if aa_mdist!=. & `base' & ln_le60!=., rob + +*Col. 5 +reg ln_hla_het aa_lpd1 if aa_mdist!=. & `base' & ln_le60!=., rob + +*Col. 6 +reg ln_hla_het aa_lpd1 aa_mdist if aa_mdist!=. & `base' & ln_le60!=., rob + + +*************************************** +*** Table 3: Premedicinal Health *** +*************************************** + +** Mortality from infectious disease in 1940 +*Col. 1 +reg ln_mort40 ln_hla_het if `base', rob + +*Col. 2 +reg ln_mort40 ln_hla_het `bc' `cont' if `base', rob + +** Life Expectancy in 1940 +*Col. 3 +reg ln_le40 ln_hla_het if `base', rob + +*Col. 4 +reg ln_le40 ln_hla_het `bc' `cont' if `base', rob + +** Life Expectancy in 1960 +*Col. 5 +reg ln_le60 ln_hla_het if `base', rob + +*Col. 6 +reg ln_le60 ln_hla_het `bc' `cont' if `base', rob + + +************************************************************* +*** Table 4: Additional Years: The effect of medicine *** +************************************************************* +local le "ln_le60!=. & ln_le70!=. & ln_le80!=. & ln_le90!=. & ln_le00!=. & ln_le10!=." + +*Col. 1, 1960 +reg ln_le60 ln_hla_het `bc' `cont' if `le', rob + +*Col. 2, 1970 +reg ln_le70 ln_hla_het `bc' `cont' if `le', rob + +*Col. 3, 1980 +reg ln_le80 ln_hla_het `bc' `cont' if `le', rob + +*Col. 4, 1990 +reg ln_le90 ln_hla_het `bc' `cont' if `le', rob + +*Col. 5, 2000 +reg ln_le00 ln_hla_het `bc' `cont' if `le', rob + +*Col. 6, 2010 +reg ln_le10 ln_hla_het `bc' `cont' if `le', rob + + +******************************************* +*** Table 5: Pop. Composition Trunc. *** +******************************************* +local pop "frac_eur frac_me frac_easia frac_africa frac_am" + +*Col. 1, countries with no fraction of population derived from Europe +reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur==0, rob + +*Col. 2, countries with part of the population derived from Europe +reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur>0 & frac_eur<1, rob + +*Col. 3, countries entirely composed of European populations +reg ln_le60 ln_hla_het `bc' `cont' if `base' & frac_eur==1, rob + +*Col. 4, ethnic fixed effects +reg ln_le60 ln_hla_het `pop' `bc' `cont' if `base', rob + + +*********************************************** +*** Table 6: Exogenous Omitted Variables *** +*********************************************** +local ov1 "aa_pdiv!=. & malfal!=. & tropical!=. & pnativ_60!=. & distcr100!=. " + +*Col. 1, genetic capital +reg ln_le60 ln_hla_het aa_pdiv `bc' `cont' if `ov1', rob + +*Col. 2, environment +reg ln_le60 ln_hla_het malfal tropical desert distcr100 `bc' `cont' if `ov1', rob + +*Col. 3, population +reg ln_le60 ln_hla_het frac_migrant `bc' `cont' if `ov1', rob + +*Col. 4, all +reg ln_le60 ln_hla_het aa_pdiv malfal tropical desert distcr100 frac_migrant `bc' `cont' if `ov1', rob + + + +************************************************ +*** Table 7: Endogenous Omitted Variables *** +************************************************ +local ov2 "ln_ypc60!=. & ln_yr_sch1960!=. & ln_pd60!=. & ln_urb60!=. & ln_young!=." + +*Col. 1, sample adjustment +reg ln_le60 ln_hla_het `bc' `cont' if `ov2', rob + +*Col. 2, income +reg ln_le60 ln_hla_het ln_ypc60 `bc' `cont' if `ov2', rob + +*Col. 3, education +reg ln_le60 ln_hla_het ln_yr_sch1960 `bc' `cont' if `ov2', rob + +*Col. 4, demographics +reg ln_le60 ln_hla_het ln_pd60 ln_urb60 ln_young `bc' `cont' if `ov2', rob + +*Col. 5, all +reg ln_le60 ln_hla_het ln_ypc60 ln_yr_sch1960 ln_pd60 ln_urb60 ln_young `bc' `cont' if `ov2', rob + + +log close