(Re)Emerging Disease and Conflict Risk in Africa, 2000 – 2018

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Leveraging a new geolocated monthly outbreak dataset on 23 zoonotic pathogens in Africa, this study explores the impact of (re)emergent disease on armed conflict. Zoonotic pathogens are considered key drivers of reemergent and new epidemic risk, making them a useful test case while also ensuring their probability of being reported by media and health policy outlets is high. Results suggest that over the January 2000 – Dec. 2018 period, zoonotic disease outbreaks intensified social conflict but had a dampening effect on state-initiated conflict. Social conflict intensification was due to civil defense mobilization rather than security outsourcing by the government. Rebel-initiated conflicts are not noticeably sensitive to outbreaks. Results are robust to system GMM models that account for endogeneity and a battery of additional robustness models. Social science/Politics and international relations Social science/Geography Civil war Georeferenced data Infectious disease Social conflict Zoonotic pathogens Figures Figure 1 Figure 2 Main In recent years, the media has been inundated with stories about the outbreaks of deadly diseases such as Ebola, swine flu, and COVID-19. One important, yet poorly understood implication of such outbreaks relates to their destabilizing impact on political stability [ 1 – 2 ]. Although several studies explored the impact of some protracted epidemics like malaria, HIV/AIDS, and COVID-19 on civil war [ 3 – 8 ], no study (to the author’s knowledge) has examined the effects of new and remerging disease outbreaks, broadly, on armed conflict. Furthermore, we know very little about the local-level impact of disease outbreaks on conflict, even though environmental drivers of violence operate primarily at this level [ 9 ]. This study fills this gap by exploring the impact of infectious zoonotic disease outbreaks on conflict in Africa, a continent that has produced many emerging pathogens in recent decades. By “zoonotic,” I refer to diseases whose reservoir is in animal hosts, and who can infect humans directly or via an intermediary [ 10 ]. Zoonotic pathogens are crucial drivers of emergent and reemergent disease, with outbreaks posing a grave threat of becoming deadly regional and even global epidemics [ 11 ]. Considering this risk, outbreaks involving these pathogens are often immediately and significantly impactful, requiring swift and decisive action and, given the potential danger they pose to regional and global health, are highly likely to be reported, allowing for comprehensive data collection and analysis. Leveraging an original geolocated monthly outbreak dataset on 23 zoonotic pathogens, including diseases such as Ebola, bubonic plague, anthrax, and Lassa virus, and using a 0.5° gridded analysis (10,674 grids over 228 months), I estimate the influence of outbreak events involving 23 zoonotic pathogens, identified by the World Health Organization (WHO) as outbreaks of concern, on conflict in Africa. I use the Armed Conflict Location and Event Dataset (ACLED) [ 12 ] to capture multiple nuances of conflict, including not only civil war involving state and rebel forces, that focus of many studies, but also social conflicts , defined here as initiated by civil defense forces, mercenaries, vigilantes, and repressive other nonstate actors often referred to as “militias” [ 13 ]. My estimation procedures accommodate local features and heterogeneities, and account for potential simultaneous relationships between conflict and disease risk as well as serial correlation in conflicts over time. Results from multiple analyses suggest that disease outbreak events are associated with a statistically meaningful reduction in state-initiated conflicts and a statistically significant increase in the number of social conflicts by progovernment and nonaligned civil defense groups, but have no meaningful impacts on rebel-initiated attacks. The Discussion section draws on extant research to explain these results. Results Measuring conflict and disease My unit of analysis is grid-cell (0.5 degree) month from January 2000 – December 2018, 1 measured across the entire African continent [ 14 ]. This framework includes climate and socioeconomic indicators at the same resolution across the continent, reducing the risk that any identified relationships are the result of local-level confounders. The dependent variables are actor-oriented operationalization using data from ACLED [ 12 ]. The first two dependent variables capture the government vs. rebel conceptualizations of civil war based on the number of conflict incidents initiated by (i) state and (ii) rebel forces. The next two dependent variables operationalize social conflict based on a wide spectrum of nongovernmental actors (mentioned in Main) often referred to as “militias” [ 13 ] and classified as to whether these groups were (iii) politically-, or (iv) self-defense (identity) oriented (see Methods). The explanatory variable, zoonotic disease outbreak events (ZDOs), was constructed via a multi-year effort to create the first (to the author’s knowledge) geolocated dataset on zoonotic disease outbreak events involving 23 infectious, potentially lethal pathogens that were identified by the WHO [ 15 ] as posing potential or real epidemic risk in the African context. A detailed discussion of this indicator’s construction is provided in Methods and the SI file. Briefly, for each outbreak event, information was recorded on location (village/town or grid cell, my cross-sectional unit) and date (day, week or, if these weren’t available, month and year, my temporal unit). Outbreak information was collected from reports in reputable English language sources, such as NYT, Associated Press/AP, Reuters, CNN, and CBS as well as situation reports from leading health agencies (CDC, WHO), to avoid the risk of misreporting, based on a set of guidelines discussed in the SI file. For illustration, Fig. 1 reports total frequencies for each of the four dependent variables (plots a – d) as well as the ZDO explanatory variable (e). Key clusters of disease outbreaks emerge in Nigeria, eastern DRC, Sierra Leone and Liberia, and Egypt, with additional smaller clusters scattered across the continent and neighboring islands. There also appears to be some spatial correlation with state-rebel and social conflicts, especially the latter. However, a more systematic evaluation of these associations over time is needed to evaluate whether different conflict types directly track disease outbreaks. Statistical models of conflict and disease Table 1 reports estimates from eight baseline and country-control grid-month OLS conflict models. ZDO events it have a negative and statistically significant coefficient in the state-initiated conflict models. Examining rebel-imitated conflict, ZDOs appear have no statistically meaningful coefficient, and the sign is ambiguous. Moving on to social conflict, there is evidence that political militias may be more conflict active during outbreaks (a positive coefficient), but this relationship is not statistically significant according to any meaningful threshold. Examining identity militias, ZDOs’ coefficient is positive and statistically significant to p < .1 (two-tailed tests) in the baseline model. The results become more pronounced once I adjust for country-level fragilities ( p < .05). To provide a more substantive illustration, Fig. 2 plots the change (in percent) from each conflict type’s mean for a one outbreak increase in ZDO. Statistically significant coefficients from Table 1 are in black. A zoonotic disease outbreak leads to an approximately two-to-threefold reduction in state attacks (from a sample mean of 0.008 state-initiated conflicts). It has a much smaller impact on rebel attacks, with zero being included in the confidence intervals in both models. For political militias, the predicted change is bigger than zero, and is on average about twofold increase in the expected number of conflicts (from a sample mean of 0.005 political-militia-initiated conflicts). Finally, one zoonotic disease outbreak change corresponds to an increase of two-to-fourfold increase in the expected number of identity militia conflict (from a sample mean of 0.003 to 0.011–0.018 expected rate of identity-militia-initiated conflicts). Overall, then, it appears that zoonotic disease outbreaks harm the ability of the state to initiate conflict, but may increase the risk of social conflict. Importantly, these results are robust to key local and nation-level confounders, including population, development, conflict history, environmental stressors, and all time constant local features (e.g., elevation, distance from capital). Tab. 1: Determinants of Armed Conflict in African Locations Standard errors clustered on grid cell in parentheses; fixed effects by month and grid cell were included in each regression, but not reported here. * p < 0.1, ** p < 0.05, *** p < 0.01; 1 Natural log. Tab. 2: Determinants of Armed Conflict in African Locations, Accounting for Endogeneity Coefficient estimates are reported with standard errors in parentheses and two-way effects; internal instruments for system GMM estimators are t − 2 to t − 4 lags. * p < 0.1, ** p < 0.05, *** p < 0.01; 1 Natural log. Accounting for endogeneity and serial correlation Often, the determinants of a zoonotic outbreak are not influenced by conflict, and include, for instance, outbreaks resulting from animal migrations into new areas or deforestation [ 10 ]. It is possible, however, that conflict may impact zoonotic disease outbreaks, e.g., by inducing population movements. The direction (positive or negative) of this endogenous risk can go both ways – for example, conflict may push people toward more contact with contagious wildlife, but it can also induce people to move away from such areas. To adjust the estimates for these potential issues, I use a two-way systems GMM approach, a well-established method designed to account for endogeneity and serial correlation (see Methods). These panel data methods are computationally intensive and are not recommended for very temporally long data series [ 16 ]. Accordingly, the models from Table 1 . are estimated on a series of 72 months (Jan. 2013 – Dec. 2018) in Table 2 . The results are robust and become, if anything, more statistically robust in the case of social conflict by identity militias (in both models ZDO events it ’s coefficient is significant to the p < .01 level), despite the loss of approximately two thirds of the sample due to temporal limitations. Sargan test estimates suggest the models are robust, although weakened by the many instruments (due to the large number of panels). Table 2 . hence suggests the findings are robust to endogeneity and serial correlation risks. Additional sensitivity analyses In addition to endogeneity, several robustness models are estimated and reported in the SI file. The first model accounts for the inclusion of controls, which may pose the risk of inferential biases due to multicollinearity [ 17 ] by including only the ZDO events it variable and grid cell fixed effects (Tab. S2). The next set of analyses then account for environmental variabilities due to flooding and droughts, which can increase vector transmission risk, e.g., via proliferation of pests or mosquitos (Tab. S3). The ensuing two sets of analyses then account for conflict dependencies between different actors. Here, the models are first estimated where each conflict type excluding the one operationalized in the dependent variable is included as a control in each respective model (Tab. S4). The next set of models then add to each analysis from Table S4 t-1 conflict lags (Tab. S5). The final set of analyses then add orthogonal binary spatial conflict lags to account for the possibility the results are driven purely by conflict spillover from nearby cells (Tab. S6). The results hold across all these models, thereby confirming the impact of zoonotic disease outbreak events on state and identity militia conflicts. [1] While information on zoonotic disease outbreaks was collected until and including Dec. 2019, the lack of availability of data on key controls forced me to omit the last year from analysis. Discussion This study shows that in Africa between Jan. 2000 and Dec. 2018, zoonotic disease outbreaks reduced civil war activity by the state by about half but intensifies social conflicts rates by identity militias by about 2.5 times. Political militias may also be more inclined to engage in higher rates of social conflicts due to disease outbreaks, although the results do not reach any meaningful threshold of significance. There is little evidence for the existence of a clear directional effect on conflict initiation by rebels engaged in civil war. Past research on the impact of COVID-19, malaria, and HIV suggests several potential explanations for these observed trends. First, outbreaks can adversely affect the state and its administrative and security capacities. For instance, infectious disease outbreaks can constitute a governance shock. Outbreaks force the government to “shift its focus from other administrative functions to combating the disease, while simultaneously being forced to reduce its bureaucratic and even security operations to avoid infection and the spread of the pandemic to its employees and troops” [ 6 ], which can impact its ability and willingness to engage in armed conflict. Zoonotic disease outbreaks similarly constitute sudden shocks to governance – often, vaccines are not readily available, and containment of movements and activity is necessary. This compels state militaries to reduce conflict activity as the rate of zoonotic disease outbreak events increases. With respect to rebels, zoonotic disease outbreaks may cause similar impacts in the short term, but not impact their general activity levels over the long term [ 5 – 8 ]. This can be explained by the fact that rebel groups need this time to adjust for the new situation, but unlike governments, do not have a high dependence on long logistical supply chains and other administrative services that can be affected by the outbreak. It is also possible that rebel groups might respond differently to endemic disease than to (re)emerging pathogen outbreaks [ 3 – 4 ]. Examining social conflict, if the government is temporarily weakened or incapacitated, a governance vacuum is created, which can increase the risk of violence involving nongovernmental militia actors. During outbreaks, militias may provide services the government cannot, such as aid, food, and healthcare. For instance, the onset of the COVID-19 pandemic has been “increasing stress on its precarious health-care system and exacerbating youth unemployment, which surpassed 25 percent in 2018...This further undermines the fledgling government’s legitimacy, as militias have stepped in to supply medical and humanitarian services” [ 18 ]. Militias can also act as security providers, stepping in to provide security, both to react to rebel attacks, and to preempt any potential intensification of rebel violence [ 6 ]. There are two explanations for the observed rise of conflict activity by militias in the lack of effective state protection and reduced government security activity. First, governments may “contract” such groups to engage in conflict or provide security when and where the government is unable or willing [ 13 ], such as in the wake of the outbreak of a deadly disease. Second, civil defense militias might organize independently or semi independently by local communities in affected regions to provide security [ 19 ], or get recruited to protect, e.g., areas where natural resources are extracted or pastoralist zones [ 20 ]. Such militias are less affected by the constraints faced by government forces, and – unlike rebel groups – do not face similar pressures to advance state or regional takeover goals, thereby using the opportunity provided by the outbreak to expand operations and security relevance [ 6 ]. The finding that identity militias – groups that include civil and community defense forces – rather than organizations tied to political parties and leaders (political militias) intensify their security activity during zoonotic disease outbreaks is in line with the second (community mobilization) explanation. Considering the risk of emerging zoonotic pathogen outbreaks is poised to increase in the coming decades due to population growth, deforestation trends, and climate change [ 11 ], this study’s findings provide a template for potential impacts and suggest mitigation strategies. The results underscore the importance of studying the entire spectrum of actors involved in conflict rather than focusing only on civil wars and the often-used government vs. rebel conflict dichotomy. If disease outbreaks empower militias, this can still lead to loss of state power and legitimacy over the longer term, even if over the short term, identity militias provide security and protection. Therefore, considering (re)emerging disease outbreaks can pose threats to the state sovereignty, interventions designed to bolster the state and its capacities (assuming such bolstering does not cause more harm to its citizens, as in the case of repressive states) can assist in preventing protracted conflict and improving political stability over the short term. Future research should address some of the weaknesses of this study. Most importantly, follow-up data collection efforts on zoonotic disease outbreak and conflict data should be conducted in other world regions to determine whether the results are viable at a global scale. Furthermore, research should more specifically study the specific conditions under which conflict dynamics might shift as an emerging disease and epidemics become endemic. Finally, it would be useful to study the impact of disease on other forms of violence, such as repression and authoritarianism, considering the viability of this risk [ 21 – 22 ]. Methods Sample The sample is constructed using the 0.5-degree grid cell – approximately 55km x 55km at the equator, which decreases in size toward the poles – measured for each month between Jan. 2000 and Dec. 2018. This empirical construction uses AfroGrid, a recently released data framework specifically designed to study environmental conflict in Africa [ 14 ]. Its subnational/localized geospatial resolution combined with subannual (monthly) temporal disaggregation make AfroGrid an especially useful tool for assessing local level zoonotic outbreak events' impact on conflict as unanticipated shocks [ 9 ]. Dependent variables The key advantages of ACLED over other datasets for the specific purpose of this study is that it disaggregates all local conflict and violence incidents by the initiating actors, while coding a diverse actor typology. Using ACLED data [ 12 ], two dependent variables capturing the standard (government vs. rebel) conceptualizations of civil war were created based on the number of conflict incidents initiated (based on interaction code from ACLED) by (i) state and (ii) rebel forces; and social conflicts initiated by civil defense forces, militias, vigilantes, and mercenaries that are (iii) politically, or (iv) civil defense/identity oriented. Using an actor-oriented operationalization helps to capture the direct impact of a zoonotic disease outbreak on their active engagement initiatives, which past research suggests is key in the case of disease-conflict analysis [ 6 – 8 ]. Each dependent variable lags were created by using conflict values from the previous month. Zoonotic disease outbreak events The Geolocated Zoonotic Disease Outbreaks Dataset (G-ZOD) records monthly information on outbreak events involving 23 infectious, potentially lethal pathogens that were identified by the WHO [ 15 ] as posing potential or real epidemic risk in the African context: Ebola-Zaire, Ebola-Sudan, Ebola-Täi forest, Ebola-Bundibugyo, Marburg, yellow fever, Rift Valley fever, West Nile fever, H1N1 flu, H5N1 flu, SARS, MERS, chikungunya, Lassa, dengue, monkeypox, septicemic plague, bubonic plague, Crimean-Congo hemorrhagic fever, shigellosis, rabies, zika, and anthrax. Attempts to code other deadly flu strains (e.g., H9N1, H7N9, H5N6) were also conducted, but no such outbreaks were reported in African states over the Jan. 2000 – Dec. 2018 period. The efforts focused on Africa to maximize the availability of time and resources, and because the continent experiences a high share of both global conflict events involving state, rebel, and militia forces, and zoonotic disease outbreaks (including those involving emerging pathogens). The ZDO events it indicator was created by aggregating the total number of outbreaks occurring within each 0.5 grid to the monthly level, creating a framework that directly corresponds to AfroGrid’s unit of analysis. A detailed discussion and illustrations of the data collection procedures are provided in the SI file. Control variables The controls used in the main and sensitivity analyses were aggregated from other databases into AfroGrid. Due to concerns related to inferential biases from including too many controls [ 17 ], only key confounders were included, while adding fixed effects for each grid cell to account for all constant (time invariant) features. NTL it (accounting for local state capacity and development) was created by AfroGrid using VIIRS-adjusted DMSP data and a high sensitivity re-calibration method [ 23 ]. Population it was obtained from the Global spatio-temporally harmonised dataset [ 24 ]. Both variables were measured annually and aggregated into monthly using the last-value-carried-forward approach. Controls for drought and precipitation and temperature anomalies were created as normalized deviations ( Z values) from long term trends using CRU-TS data [ 26 ]. Country level controls for life expectancy in birth (a key indicator of development), government efficiency (a political capacity indicator) and GDP per capita (state capacity) were obtained from the World Bank [ 26 ]. All robustness models correspond to the country specifications. The spatial lags for each conflict indicator (Table S6) were created based on whether at least one conflict event from each respective type was recorded in an orthogonal grid cell during the same month t (= 1) or not (= 0). None of the variables were lagged due to misspecification concerns [ 27 ]. Summary statistics for all variables are in Tab. S1, SI file. Analysis Table 1 and S2-S6 were estimated using ordinary least squares (OLS) with cross-sectional/grid cell ( i ) and temporal/monthly ( t ) fixed effects per econometric recommendations [ 28 ]. Identification was conducted using the following formulas: Where y it is a vector of each of the four conflict types and y it−1 its one-month lag; z it is a grid-month vector of zoonotic disease outbreak events; n it is a control for nighttime light emissions; p it is a control for population densities; E it is a matrix of climate controls (precipitation and temperature anomalies and drought); C it is a matrix of country-level controls (life expectancy, government efficiency, and GDP per capita); β is each respective independent variable’s coefficient; ω i and ϕ t are fixed effects by grid cell and month, respectively; τ t is the time trend for each month during each year in the sample; and ε i are standard errors clustered by grid cell. Some specifications in the sensitivity analyses include additional controls for conflict, conflict lags, and spatial conflict lags added to Eq. (2). Figure 2 was estimated by normalizing each coefficient by each respective dependent variable’s mean and then converted to percent as: {[β 1 – mean(conflict)]/ mean(conflict)]} x 100. The upper and lower bound were calculated by first normalizing the difference between the model’s standard and the standard deviation, multiplying it by 1.97 ( p = 0.05 threshold, two-tailed), then adding (for upper bounds) or subtracting it (for lower bounds) from the mean, and finally converting the value to percent as follows: {[β 1 – mean(conflict)]/ mean(conflict)] +/- 1.97[SE 1 – SD(conflict)]/ SD(conflict)]} x 100. Table 2 was estimated using two-way system general methods of moments (GMM) estimators. The system GMM estimator is the more robust estimator, and uses past variations in each dependent variables to, in effect, ‘exogenize’ its variations at time t [ 16 ]. The two-way effects approach is akin to a unit fixed effects estimation, where the units are indexed according to the time series for each grid cell, removing – in effect – the need for grid cell fixed effects. Based on research recommendations, where only shallow dependent variable lags are preferred as instruments [ 29 ], two-to-four month ( t -2 to t -4) lags as my internal instruments. Considering the sheer size of the sample as well as the fact that GMM estimators are not recommended for very long time series [ 29 ], I was forced to limit the period of analysis to the Jan. 2013 – Dec. 2018 period, leaving a total of 72 months for each grid cell for which information on all controls was available. Declarations Data availability Upon publication, all data required for replicating all tables and figures in this study and its SI file will be made openly available on the Harvard Dataverse. 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Last updated December 5, 2020. https://cran.r-project.org/web/packages/wbstats/vignettes/wbstats.html. Bellemare, Marc F., Takaaki Masaki, and Thomas B. Pepinsky. "Lagged explanatory variables and the estimation of causal effect." The Journal of Politics 79, no. 3 (2017): 949-963. Angrist, J. D. and J. S. Pischke. 2009. Mostly Harmless Econometrics. Princeton, NJ: Princeton University Press. Roodman, David. 2009. “A note on the theme of too many instruments.” Oxford Bulletin of Economics and statistics 71(1):135–158. https://www.rand.org/blog/2022/01/the-future-and-past-of-war-and-disease.html. Additional Declarations There is NO Competing Interest. Supplementary Files Appendix62523.pdf (Re)Emerging Disease and Conflict Risk in Africa, 2000 – 2018 Cite Share Download PDF Status: Published Journal Publication published 15 Jul, 2024 Read the published version in Nature Human Behaviour → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3120431","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":215962421,"identity":"09c7a473-a5c2-4bd4-b054-1488c33ceda9","order_by":0,"name":"Ore Koren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAmklEQVRIiWNgGAWjYDACHjBpA6YkSNGSRrqWwwzEa+HnOXzsw8cd52UMDjAfvM1DjBbJ3rbkmTPP3OYxOMCWbE2UFoPzPMbMvG0gLTxm0sRr+dt2DqiF/xuRWs72GDMzth0A2cJGnBbJnmPJjED/8EgeZjO2nEOMFn6e5MMMP9vs7PmONz+88YYYLQjATJryUTAKRsEoGAX4AAACYSpCuL0NGgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9011-1510","institution":"Indiana University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ore","middleName":"","lastName":"Koren","suffix":""}],"badges":[],"createdAt":"2023-06-28 12:47:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3120431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3120431/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41562-024-01929-1","type":"published","date":"2024-07-15T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":39652784,"identity":"6edc8687-c4b1-41a5-a948-951893a69e8b","added_by":"auto","created_at":"2023-07-06 17:45:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":530399,"visible":true,"origin":"","legend":"\u003cp\u003e0.5 grid map of total conflict and ZDO frequencies across Africa, Jan. 2000 – Dec.2018\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3120431/v1/22e73bc64d47232bfd230245.jpg"},{"id":39653429,"identity":"9b55fccd-bd75-4582-abe4-6208736cb98a","added_by":"auto","created_at":"2023-07-06 17:53:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56210,"visible":true,"origin":"","legend":"\u003cp\u003ePercent change in expected conflict rates for one outbreak change across four actor types.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3120431/v1/b2bad2d1b1cd61bdc543ce7c.jpg"},{"id":60381940,"identity":"b2aa75ab-4b7b-4895-b151-e144c7f210b8","added_by":"auto","created_at":"2024-07-16 07:22:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1130731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3120431/v1/18dbd699-3065-40d0-8dc5-2b8ccd1adace.pdf"},{"id":39652786,"identity":"84e3c896-d428-4904-a137-d4999a4d77c9","added_by":"auto","created_at":"2023-07-06 17:45:31","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":433191,"visible":true,"origin":"","legend":"\u003cp\u003e(Re)Emerging Disease and Conflict Risk in Africa, 2000 – 2018\u003c/p\u003e","description":"","filename":"Appendix62523.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3120431/v1/558bc528aba614d1e2d9d8e8.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"(Re)Emerging Disease and Conflict Risk in Africa, 2000 – 2018","fulltext":[{"header":"Main ","content":"\u003cp\u003eIn recent years, the media has been inundated with stories about the outbreaks of deadly diseases such as Ebola, swine flu, and COVID-19. One important, yet poorly understood implication of such outbreaks relates to their destabilizing impact on political stability [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although several studies explored the impact of some protracted epidemics like malaria, HIV/AIDS, and COVID-19 on civil war [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e], no study (to the author\u0026rsquo;s knowledge) has examined the effects of new and remerging disease outbreaks, broadly, on armed conflict. Furthermore, we know very little about the local-level impact of disease outbreaks on conflict, even though environmental drivers of violence operate primarily at this level [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThis study fills this gap by exploring the impact of infectious zoonotic disease outbreaks on conflict in Africa, a continent that has produced many emerging pathogens in recent decades. By \u0026ldquo;zoonotic,\u0026rdquo; I refer to diseases whose reservoir is in animal hosts, and who can infect humans directly or via an intermediary [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Zoonotic pathogens are crucial drivers of emergent and reemergent disease, with outbreaks posing a grave threat of becoming deadly regional and even global epidemics [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Considering this risk, outbreaks involving these pathogens are often immediately and significantly impactful, requiring swift and decisive action and, given the potential danger they pose to regional and global health, are highly likely to be reported, allowing for comprehensive data collection and analysis.\u003c/p\u003e\n\u003cp\u003eLeveraging an original geolocated monthly outbreak dataset on 23 zoonotic pathogens, including diseases such as Ebola, bubonic plague, anthrax, and Lassa virus, and using a 0.5\u0026deg; gridded analysis (10,674 grids over 228 months), I estimate the influence of outbreak events involving 23 zoonotic pathogens, identified by the World Health Organization (WHO) as outbreaks of concern, on conflict in Africa. I use the Armed Conflict Location and Event Dataset (ACLED) [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] to capture multiple nuances of conflict, including not only \u003cem\u003ecivil war\u003c/em\u003e involving state and rebel forces, that focus of many studies, but also \u003cem\u003esocial conflicts\u003c/em\u003e, defined here as initiated by civil defense forces, mercenaries, vigilantes, and repressive other nonstate actors often referred to as \u0026ldquo;militias\u0026rdquo; [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. My estimation procedures accommodate local features and heterogeneities, and account for potential simultaneous relationships between conflict and disease risk as well as serial correlation in conflicts over time. Results from multiple analyses suggest that disease outbreak events are associated with a statistically meaningful reduction in state-initiated conflicts and a statistically significant increase in the number of social conflicts by progovernment and nonaligned civil defense groups, but have no meaningful impacts on rebel-initiated attacks. The \u003cspan class=\"InternalRef\"\u003eDiscussion\u003c/span\u003e section draws on extant research to explain these results.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2 class=\"Heading\"\u003eMeasuring conflict and disease\u003c/h2\u003e\n\u003cp\u003eMy unit of analysis is grid-cell (0.5 degree) month from January 2000 \u0026ndash; December 2018,\u003csup\u003e1\u003c/sup\u003e measured across the entire African continent [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. This framework includes climate and socioeconomic indicators at the same resolution across the continent, reducing the risk that any identified relationships are the result of local-level confounders. The dependent variables are actor-oriented operationalization using data from ACLED [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. The first two dependent variables capture the government vs. rebel conceptualizations of civil war based on the number of conflict incidents initiated by (i) state and (ii) rebel forces. The next two dependent variables operationalize social conflict based on a wide spectrum of nongovernmental actors (mentioned in Main) often referred to as \u0026ldquo;militias\u0026rdquo; [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] and classified as to whether these groups were (iii) politically-, or (iv) self-defense (identity) oriented (see Methods).\u003c/p\u003e\n\u003cp\u003eThe explanatory variable, zoonotic disease outbreak events (ZDOs), was constructed via a multi-year effort to create the first (to the author\u0026rsquo;s knowledge) geolocated dataset on zoonotic disease outbreak events involving 23 infectious, potentially lethal pathogens that were identified by the WHO [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] as posing potential or real epidemic risk in the African context. A detailed discussion of this indicator\u0026rsquo;s construction is provided in Methods and the SI file. Briefly, for each outbreak event, information was recorded on location (village/town or grid cell, my cross-sectional unit) and date (day, week or, if these weren\u0026rsquo;t available, month and year, my temporal unit). Outbreak information was collected from reports in reputable English language sources, such as NYT, Associated Press/AP, Reuters, CNN, and CBS as well as situation reports from leading health agencies (CDC, WHO), to avoid the risk of misreporting, based on a set of guidelines discussed in the SI file. For illustration, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e reports total frequencies for each of the four dependent variables (plots a \u0026ndash; d) as well as the ZDO explanatory variable (e). Key clusters of disease outbreaks emerge in Nigeria, eastern DRC, Sierra Leone and Liberia, and Egypt, with additional smaller clusters scattered across the continent and neighboring islands. There also appears to be some spatial correlation with state-rebel and social conflicts, especially the latter. However, a more systematic evaluation of these associations over time is needed to evaluate whether different conflict types directly track disease outbreaks.\u003c/p\u003e\n\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical models of conflict and disease\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e reports estimates from eight baseline and country-control grid-month OLS conflict models. \u003cem\u003eZDO events\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e have a negative and statistically significant coefficient in the state-initiated conflict models. Examining rebel-imitated conflict, ZDOs appear have no statistically meaningful coefficient, and the sign is ambiguous. Moving on to social conflict, there is evidence that political militias may be more conflict active during outbreaks (a positive coefficient), but this relationship is not statistically significant according to any meaningful threshold. Examining identity militias, ZDOs\u0026rsquo; coefficient is positive and statistically significant to \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.1 (two-tailed tests) in the baseline model. The results become more pronounced once I adjust for country-level fragilities (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). To provide a more substantive illustration, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e plots the change (in percent) from each conflict type\u0026rsquo;s mean for a one outbreak increase in ZDO. Statistically significant coefficients from Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e are in black. A zoonotic disease outbreak leads to an approximately two-to-threefold reduction in state attacks (from a sample mean of 0.008 state-initiated conflicts). It has a much smaller impact on rebel attacks, with zero being included in the confidence intervals in both models. For political militias, the predicted change is bigger than zero, and is on average about twofold increase in the expected number of conflicts (from a sample mean of 0.005 political-militia-initiated conflicts). Finally, one zoonotic disease outbreak change corresponds to an increase of two-to-fourfold increase in the expected number of identity militia conflict (from a sample mean of 0.003 to 0.011\u0026ndash;0.018 expected rate of identity-militia-initiated conflicts). Overall, then, it appears that zoonotic disease outbreaks harm the ability of the state to initiate conflict, but may increase the risk of social conflict. Importantly, these results are robust to key local and nation-level confounders, including population, development, conflict history, environmental stressors, and all time constant local features (e.g., elevation, distance from capital).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTab. 1:\u003c/strong\u003e Determinants of Armed Conflict in African Locations\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"974\" height=\"623\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eStandard errors clustered on grid cell in parentheses; fixed effects by month and grid cell were included in each regression, but not reported here.\u003c/p\u003e\n \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e1\u003c/sup\u003e Natural log.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTab. 2:\u003c/strong\u003e Determinants of Armed Conflict in African Locations, Accounting for Endogeneity\u003c/p\u003e\n \u003cp\u003e\u003cimg 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7ef13gdkG714rAIWe64TYKv3hbhjPA/+pdtoxHSyUv2tlCi8650WN6/1Kflwhy6Jh6FHpsfBvNZvWTvPotiE+/HPYwpCrgZXiq7MildOk1TNKiTJUYEYmyRsOOuaJPTDO58ydkvSK33ZTqlluLDAKzUNETR7lzoKm0cJMPigJC0TqH3dOS4R1Xqv9MldBK+4qfqub+5XZPwoPa982Xsf53nM52/dKiRuioPZh6SWB3r/y/vJRes90u0prf+Wc3/9QpX0nEd690+Kaa1NW+q92381q3U55zn9nf2TlPDv7jDiY6+0/Pxn26Tkxa3S9HMbvZoYz0FLy1R6nj9EnhKWogKRPHAVtZvZiZMmO6k0NCOSiaD1naT81BmVpG8PUaT0IP6U2DnOMTGtWxLdl1JlZ6MjqSkHSkwxxRRTTDHFFFNMMcUUU0wxxRRTp5fRfrb1cuSY6Ywy8vz37nQ+pS75RfeWx/SLkPnUBaE7uWbbieSsorON5zGrYjz/PNLZSWd4kIkZ7HYDizhMIzieaSVou+1Mn1GG3T5IyIh0velQeObnZR/ki+E8Q2KD8+xDug4+ngEkPAXHs3oWndGNfc7oXZqlz7bfqEjvfbaZgo5kkKQSg1uy+d6eCaXUVWjAQHPIOhZlSAikIO2z3itVDOEL1p0xJe+u6AUZ+/1OAc0Jov6Q7HNym/TKrRu9T55BY578wStXCumfe3/j59gnBFF/kPY58Yj0xs0ve5afQWM+4P1XfiOkf+T1NwF02lnlTEiSHYySdOaSBWmbG+nH1DX7+Umtx5Z89qNg/W2PNEdAqAf53kZfmkMtNH9Yd8gPz9n1QVz3ofnbNI8zrU26KbhfIQypv285H58Sf/DD85g9QUwMDdbf/tBL60FLI87YLSrhixaaWpL4rZ/5n0OPdNKYg9OLeNZLEk3u8k2I0ug1KfRIk76iILdvBiiFUYqUVo/p9TrKt0dVqDSUZ8/sbRwUOComFJ51Xqk/vtTJWfw0elU0z2ij93l+Beb3obW29Hg5pOm1eGopglavF1W0IcvTeGPgKVeh86xpk/6Av4JGnvilSuujE/DxJ6kLw9dLHr4ILCy+QgCWRlVQrsA0vV5ISNP3UaXhhvQfGm8OnBY/FJ7VR6SheP6+qWzqtL4pCkr/Fx3El7e9ypfYmXalAN1T9XgXTtX375qqT1FgFb1UaT0FGPGdNFjOKXh2iHi2EXK6RoOZnkucplo7pDRZS1y+XSKS6MUKfDI0mgrr+rBiyd3VLkmCdr/krJaa+kKR1GQPfPWHwnOud7UAhoOjrN5lAhSUDK+0C4aDE8toWcqIxQ5JC9vLyTp4a3Ftb9jgXd2jwNug0B2QFlZ5m3qzVd6mOe2LYXas0Hme4XleCZoP7jIdwuclJcM3z1Ekff7XoorjIYThi30vPdJeTtp/7s7f+zvhcc8n8RPapC4JH3vXVXlbrscNTfOQ9gAKhefpra/GQ+pn4yytL5wP04vv3LQ6fsBXkxaURyU+pNHrC9wAGPHZ6CW7fis82vrGBY+0fsISP/Q+ts3zbWLXR1qlh5D2s0aS5CqRStA8O6UUqJZUKYifysYaakEnRSNSKkWixCVcqoNuQeMtE8xeesb2dCXWVSA50DuOrr+92tW8DGkq9faF/VIvlWeZoMrtuq9BofcO8e0TiXR0w/C9PLRFkeZ5RZHnXY3PDQrYK00VNnvvFfibgAqV59VOz/NXCTDFc5vwpvdaXduTCjZFsaMxLrH1zihckUmt0jwfz+Jn78aJR9+K47GUR6QusMLzpHJW26NBBksHz/Ory35c9zsBHmsbrfjEm9r12+VxbF7c9oPnpB6JhhOe0CLN9x2J+tt3z1G3bD9n9JE3zkk9/AmDVzyL4p9smx6nbumsMb6nF9lnHTGnsjJDg6RRtUi1OqSwwe48blgjEHq+7Z1dVgktvrflRJ7Hgl1yRZ3nOUKGVxouaHK6Gg5KfcRDkrsPHkfTHJf3mGGNQKrjPOejG6DxSD1P4PlGYZV3zhniea4i8UfpXoWY3W3wXummHj9I794gJLY2zXV6/h6Nq//oMZ5Htq5Rwkfei0/geZ5yTOsaGfCACsU+px6VRisSpl+Y9pWU3u0LafvvBfXhpnlFbdHoJeveIi3yHe60o2+cA82SZsxxnico/3b0ofizkWe0y9UAljpbtaQBU6PUYjRJku/ziNUsOXyvEF3kWer1X+AZ7XKtANlVU+ulPpB5UGoaiHVE4ZrnQqfadyRFMs9X/Nd4hk3efysha+ecGu9NQsY+SfrLoDYpWguif+Zd54Noehvn+Tf/BZ7x4ZMuMHLHXz/3pitG/MfbMlR/5As5V2fkajjmb6OfTTynZf1P8OyW3FCIL9ySRieKDsmNFjtaI7h2qZH/YCrRzHlu7MA+i9YAiYpC4rkeec73ThWQZ41Kld+8ur+3KdLh4nblees4gSqGHCPPLSf62yfwLOb25ft3qDB4Xu99V/kQmuNN3pv6qNjsQ29d3SpdG9zvElDT276g/m1IUGVw+9wcf3qexSnX+K8xJJ63I8+TW6cpP/Sm99GIs35c9+vDkjpKZzTtKJKLShAnc/vcojrB3z6BZzbpmuD+B794iRKlFGlpMUCD5NQ1SFozMlftglrJxfRRyZDPaniGQINbw/YTx1bQepsEk1fqgTxbkecSAX3hXiaHVO3/vxgkz8wjLRW0XmkUsjzVeFDqqy8WoNwu1Eiru0anh5vt5A734MqeqgOU/fNBxLdWkeNtIp7vQJ4XClqP1DvD6n3L/1USEs+sVfq7QvxRulvYI625Yo90m36DEl4uU27y/uPC5OhMGeU5iCD55Su7flQbl3D078qEoy3xya3SZcLTnkeUE9peiEs8Kl02wdr2ln8jHSzPX0jr4hMok9Brnpk3fiUN1a+Ph2eXxr3qeeH81Oj0cL/qoQb0gJd+JX7SwBIPPxbfD1kefeRbEXmeHDe39bH47s2Sdozl6BdnR9YxnVuSGt3VJToAW4vkcEkl5uoyh1sLmlqpMVqTTGx1de4Kp4i11TvdDvzhiqQ6S6PbYmiWGvrXS039oVyq6GrwOqLBs6ZckprwjAYCZDe3PFjmXTqoqsxZ1gs0NVIjT5cfBeXurnWX2dHtSN63rJqc7w3ehjn17qmmQ1LtFfukhitww/Pd0jzLosezZqNXatpSXXajABMOSQ87PK8YtlUsrLpBUO3wNkWj+UyavrNhS9myCwES/7Wsctm5AIs9//7L51vmXPO19MnNH3hbbkaw37go7Ycn/fMaHM/q5zx0Rv+4SQGjv5YecbY9e9PW5xeW/05IeNvb9Lfg7ggBNW1H3Zbnl14kQL8Pnyx/GFsmT7b9+5HPtzx865fe9377b+8XyYon27YnJH37FB/Xium/qGD97c6j0PztzqNg7XNM/8uK8dxZFOM5psCK8dxZFOM5psCK8dxZFOM5psAazxPdn01asJBIHldytvF8T3GM55gC6YG1ZxvPBdw+P/DM2cbz1GdiPMcUSDF/u7Mo5m///BI1AFarhQFEKarwdOLhi0YeSqWy2bQAKYHDqo4pFJ5ZCj60r7yTgRWxM3RWyXRGWXzmmS7P3se3IUiFwXMa/gaqLP6FzLxBArD2hamirCSa0zfMFxrJno6DpMBhkscUCs8803/CYH5Gw/Ls9hsUAZLlh6sBPbCiof3lwhPGD1EO8C2CdVZKZQOosIOhloEmKtPCTiczAmaskCiQUFvfi1XpgTmCBzoEnsUFXUHMa15NX9AXeCm1gcF0Ji78LIMAgyu9lNggs6bE23QVsLzgL/yQeWYP9QQ25cAWJb58wuE8cK8CkjLOxHmNxFtF+gaPL7HB4rYuALMCx0m2KwSexVnngjj58zU04Yx96pW+uACSprRHbUZTI0xKSH26Tb5xTNpwGd4KJ51NcZInqZCBoQVNi9uKyJ2hFeUMPDBD7yWeS+1YT7UAquAhDYHncm4wK+UvaDw8VUn+QHqMrtJyqYbBHuI5XwDNodUKUBUHjWfIPC/hGVEeqsQLfnBfAWaU44uMlOhflUlLCCnxqMxz2ioP8syeCxrSEHh+6lKq4rXVxPPoQfIPMXlI0D9I0Oo3n2pIPMx5nraBzwZjL0Qr7uMXJyvaZEcjvrDT4pEVIfiMIaiGWy7Os8aLdInevgBFQd88guc5w8n3/CnPqppgvx602GZ+4aURzyLFr2xw4Yb8IcFWFCrPSfl8z9nEM+khG71/OfrmbOuv+Skc4TwnPHPlUbr+Z40N9jiD5zld3lPmeXv13EuoQrb9AtoYVW3pTo/qFuJ56He+5eqmjYr+T/eLEGvC1rNTQt/XXodvnWckPaChlv+KnGcDN9LNQ9BI18m/bWAFz3PVcL7nT3mGmlHBfj9YZZfyK5zzzLWTUE6rDPa6D5XnTXfwM2jnOXEDf14f9fNK8yUJk3lecaWG85z2sbw1sILn+bW/8FPgPIuLdnqbeBqDV+WtUdSI17kplnleV/P30qXkF/R778wYrp9dOop2NlAikcIyfGUu4VujLHsFv+44z2bOcz1WqPEGG34aPM+HZNf6JJ6LAsR7hK6iQl7iMZ5VnG/NvmD5DJXnryl32DGep1R5qSENKx3Bfj9YTdgs88h5nmBSqDnP6qMX862BFTzPn97Cj122z1jFa/vJNC93Bvv9YPVIEa9A5rnh0XhYt5sM9sGzKvT5uEw8e4FVqi5poGQihlp6G22VyvnIOM9GzvP+4cQzJe4LRkHzrOGFn8pzVLKHnaidcp6SYzwXyF1hbT35U2CFyLOq7Ur+fMzfnuAhv3hlWbSt2eLK4zwnPqFEknl788gfg/wBg+ZZPPxTniHh8ACsY+bGYD2BYPWCXAHnOaFlmBKGHiGym/3l8e3EknlG6VooqcCZ4blEtvqcZy01nYFQ1vBXwShonlW+Ik/muSTa/7xtcp6Sdp4z5aTH7MzxfDWv7xjP8J+7cEP0eZ5/As8rPRKJMhFFn2d2Ms/wxnVYR/R5XncCz9A8UgndW8bgGZ2tPGslX0OCureRHHK6oy6rm/94nGeoQVdbtx/dnTPhb++XAT6ZZ1u0/3n5cgU+njnO+Kc5ECSfIfvbn8u5t4/z/DEBvtIZ7PeD1chtx3kmnTl/+73b+bEf45l9+it8XL4w2O8Hq5nF3MOQed7ytzjkWSdA9+az1N+GRj7dgrnkVNmFwSxXEbJ0cs+XnltPAzq/BVQNDVoFp+B5LpZ7iCplg6zxyFk9a4J17INWRg2/GtM8V+FjVuUcu93VC7mWe8mCUKg8PzOV77mkCnlOzuoKaTwtyQ65lyyKSpR7vsSjv5dLlnlO2xGs1Qye5xX38lvTa//AolOzzoURufT+1duC/UWCVepH3GD1OzwMSx76UYIwYj2e0i3/PFsHrAx2pNngkCdRsdoz0+3H544YC92FtJSV2Wq1UzVFQa9rFTzPqjzcU8yrqLBjg1ZX4HbROo6q4mhf9gAbaJw7rchdOEjQVrpRNFC2Mej7Rqg8q6hDW5xd7p7TE5K31S3kA2OqHe3WOnqaRes9pT1RuewOfmgJrxOfs4YHe5zB85wwi/acubFi7oWQvrV67q1UBTuWNDt6Wk49DakrK5fdhDWMWFWy7GJ8v+7mYM+o04nSALXLeobmk7DTmH1d8J1UwfMMWadJLZgXrXSAJ4jfOE6SriDoiyRUnmFC/1PrWzIo6J8laLFZp94jEl4K2gkOnmcYeZozmil3E0RVCdNPPaR+T3Mn/KwU48aSy2jxvYi6dKcArXEG7wqEwDPkyT3cJyjrjNykdKcsO6fND75NFjLPMHvgyfWNMQf/7eCVyCeInSjx8Qt9rwIrBJ5h3im5QmlmZvTVb8bJx6Sex7OZnqVix1YiDZA2NxJpT6Y3lLpC4Zn6pX4iPn3rDEgTyRmFzjNQ9MJP5GeltUikPvmuFEo9ofB8asFn6IwSaGW6E9XvTNwIYwpaIfHcKRQGz51CIfEc0/+oYjx3FsV4jimwxBBipTuH5CbO2XdeLPhOkU4q0R5TTDF1Vt14kgul4ZPmYoopps6oh04aEY/xHFNMnVcn8xzzt2OKqfPqZH87pphiiimmmGKKKaaYYooppphiiimmmGKKKaaYYooppphiiimmmGKKKaaYYooppphiiimmmGKKKaaYYooppphiiimmmGKKKaaYYooppphiiimmmGKKKaYzJ/HYCh5nizQ8U+/Zd17sDC1xEdPZpFg+/c6iWD79X640Jv50xtaoO6YFnFbjyQtPHVeM586iCHlOSuE/yGS+LN28S87cf30NX19yBC2OC2xSRMfMfIkDU0QrPppSfJvBYLefodVaTyed3dK+5CMzW+2cJZWTL91mstv1+MamMVh1YFWZU2y0NVyZLbZ2ULU2C90cmN1dRmu3qWxWG7qdVm2hrlBIMet1rh58t1MVHM86LL59v2yrndZiVmVbaCFo0FS63fIS79FQlsXWvtCzJleuM8Nm0eOzakHlxKAWmQyFZ1WOxdrN9zrDYqVFYtWrKpf1xLeDF9iiuMjkmFxeOEk9Q65zzGY3rbAuzp9qD2qRSX88D5g90eJbP5KNt+TR6pJJz7n/ipse4ytod0nKuirloWuFyReOH5SlHGZ46MrHwloNmo225F7pO49+M8aasQI2b/Pz8gr1CePNlwiTrnzqulnxQw2Gvs9ewGaeN27UkAjWtdRXGPV6Q1OdCFrJCfoSN29Ead0mUVcbETmhiDVowFoov3bYQNWIgBkqJDoUSyGwWryzaOwleK9hlhLZTIcpqwPY7r78JdvdC+sUoKCwsLGpF0CpCXQ1XYEZSwhrvbOwwwUag+KZ1feFLCyelFMo8Fpr+kMGVgH5eLmMCqKMoJTjENiuq/hLtrc31QWDyxWwbZCgKh5urp8aTD2h8PzmNULGxnP5y8EblGzHjYJqx5xi7ytKmFCkhJcXRgvoKaVxsPUmfvTso+uEMRvjYLFr4V7v7Qr457VC8seXBXFifnhO+PRyYZZNBmfRRGXC+5cJA16fu80zP0587+92e/HuLpA4v/BaAcTJa+/C3YZtvS/4dWpP1LypcQnv/ZYfbMK73eGROXHwwtLCbxoTcNPQ9XQW3UdsfeA8gFseX/hrARKmr70tApwBbR9AWQtevHqJjHNFNQNIkcg2m1uCWNvU7HuOSNYy/Ke18DuJhp7KXPhKz3luwgO01ILKbnU4NGAqtBdG4DaomtGSuUr4a2eZABpvXx2egKp5FKQcxF+2xgh6e6HTBBqHw2bvyLQFxXMOFs8O8dWfVc2DAPJcQk49XusHhguqgiC+H6xUh64BKCrhFOW7FSB6rmI7xwqQU6PIRF8gT/4kgELgOXMvXm1f30FnwHbco4DZNcoZvZE+z0XwMW7N/PEieb9IxT67WeBMo6ZsxqevruuxWAkJ3z+pHPYBGsr3/x7EVe+H50WuOFB/9yt6mXQkAeC1h+PmIVTPHjgntQ+e3Dx7XMJ0dFgvEcbn2503KpIeL/2rvWNj37HU3yYKsG4pN+2PuuKh37eadIMC+h0eqYRbPiKoYWju2mW3KdXzHFNt54nTbAsc1wbzP/Mjq2TER0cjvbYg1WID8oUeI6eaC8FmiLy8YJlIq3zhS6LdVosPTIxw4a8KcrarucdtacIHKz1wnjV0DHpJZKLGKIqAZty3eFpYMkn4+1lbOE677fhw0MZLQ44dyB/kFwoqKBK0IIp6XYc3s6B4LrfiTlXcOmZ60Vc0HFSUk49dVSjkeuseDH6t9QDK9OA/P7uZXwG7FmIF+x7s60HEdfQAsHZIEMcaCs/ry5CjN+kBktrQfA1uu5Z+KPGH88TW2wTc9odgKgysxKPnYw0/cA6eXojVPfdgT7r8Xl+qnLUTtz5XE4T364fn7fdhmZ/+mc54+h5svi7a1YWKH/0xb8rCh78XmJik78VALUxWqgWRLbr05HWbg9KkrxGNufQA0DgtDs3WdN4Qf+sWReK3k/nhqYV1XRKF7mzoFRoB30w6j1MevvSSg55qySiCUTJDocTbmCaOOcrgsLq1ZUiuE3fUFJqbRE1FrVjSYgB9dYUNdGWmxgggQzWQlS9z0ktnAz6YJXJ5iWfWgk4/f+UbTenQCw5Gjib8pcwSWU3mpdtHPUeTHewBpfTK7sYH7rH4hnBOq6B4rkcbCaVO2nPBQbxqdN6e9Utpk1vQ5dVIDejhR0V5VHiG5wp67aHbx07XcHqn8ZANzSpvb+r6VQg8f7Aad1pfSzxntmG7MKntTjrHjI1K1jpXAerWu4MoJAgN+wZ5VR/9DRX+9v1Y5ko0qKivbleMab0YYEV9ZDx/+ycsc/tc+vypGmR4Zus5tHlRIf9G0vuEdYJ8DchL1w8I7xb86F5EedJRuhcktIzHn63xUTrw7o0ibn1ss/uvVN8t9HO2L10f6fLumiY3f5J4y9SGLNfxDeAkqlCWCjCVgUpCP9wCukaNpolBod0C1Xjl05+jBOwMNPx2EJ7wHoJ+MC+gROYZ4ZX97ZJatMlkrqOhkgb8Wc0Sur+g9VKd9QQwmCxoNznP9HlABcWzl3gu4nsWU6kab5/mObSpgf5dWc2rgygjGBWQE5/muRGLU3nuwcfN7olezjO+ycj3vhXM1RECzz8Qz6vq6QKc4UGeNW1/oW+tvEKA9buvFSa0YRswGpqwl/OMTjfAZ1TFykq68tOQUPH7eeeqtj7fIarH1THP6iO3YJmvrabP33BjVTNbqY3LtsvN8plh+dan0wv1yPPoo1osdsBh4rmOd2ZPGq6EdRVdQN0yPVo1+cSqGzi2lhb+VNIEosQtJWuspidIaVSZKvAzxMvKWJ1VV40ucIOdMcRabMGjlf1yFdo72YQGJ0adiFyqE+2z46f2GTRljWVO+f4Spoy+atxGsMn2mWyjzz5Tj5WGqJPtcwU+BFS49nk3vSuVq0AzTU+RK1e2z9iEBTjE7fOyIcftM35yaxAVhWyfq0+wz+hlwxgjPojPHHxlaX2ULk+ffeZ4yfa5EOtkz2EbF0a+Wf/w3ge4TfMvP/b58Agsczt1Z8NjO8k+/0j2eZFvfaj35O7nKMhnn4mtE+xz4nx8eIvIfusV7hdET64W6gZTQRmHJkWyoKUmvLCdKY/sOGvtfBSrQa9JAWOLzcQR1oEBv2Ck5jOUNeAmVIrc0xS63DRwVM2Hls3UirfSg8wzqTZag85GL/6bfO3nern9jNeIk47eSY1bbD/TRwEUFM+VE3GnKnqALC9eJIb9Ar9lVMpVUHdZVJRxQvt5JzkAex/s47mK2s9yn/cBjnUAhdJ+LsFrctMy2jOp7WoB0touxVc57d98M1r920mtx9vPz8ntZ3yYjG4AV9L33BMPID88f/gXLE5uP0/2tZ8BUg3y0au5ux0VTfoPXl0zP+DQNlJ7uRkfui+i83qyHGt5pDy6PFs5vCorb6iCqoGa0o10qWsafcNV1bQJD6raiFg5CGARbGi6nQ4NFDpVYAAt0q2pM2rc7jD7nq3oarMm3gHFqCnOTfUxnu2+kazIxfZT/7ZcHO/fbu4DzIH3Tq1K5+vfDkJB8cz7tw/yVjKvNc8pZNTg9w6Qs49uN3WXRUNsH/Vvy0eUU6oA8dBV4Ovflj+mgeFACoHnDN6/LY8jUf/2lEolqHPxi2m0aUKlPJIVBb2Ddfj6t4fJ/dsCx1lMxorY23cGcaz+eJ6cT/3bl9NBJ1A392toqpP0WGg6/k2PmrsNCZ9Q/zZvp8MjRbx/W2DUkz5AMfQzvPLWLeS3rGgppYXQSXFrjJIVm8e13DJbGjSgq20f6LU3aDQmrNldxg2zgRm13KBWO0zQaNFCNYhkTptEcMtdSaGLoc9twwNxVND8EbyrkHti5ONnWH+4Vv80MjhArOkFqvq+oMJnG964SmvRFUcnuMgMKQRcYAXFM6vqC1noYOdVC5DpEsR6RLt4IGSWCqzUImiKo9bBPXiBIO7tDeK+IYJq1xWQi3UOrlTAzoFCVlF/lmsI5pRC4JntuEHILFVC1jYlDN6oZDuuEdR7tuBP+CB+eUJ5eGO0p1P6hjj2zrWC+NltCnj9WmHMhjiYvBcrqr1UgIT19wZxqH55Zv+8THjMGQdPueJgvEWpfvsCSHofi6+mG8UbUXO3Acbnxqk/7A6Jn6qFhHcThEenxrEXarCif3SBF+bG9/tQHb2qUGY+OcxuVfHHdh4NJQ7zseEaZpL9bStBBjq7EbG24acmNGYW/IbTbERDqqtDC0uOa1gSbVYLfllP9tFotfKGs9Vux2e9OdybxGlltFHhzIqVaWxWvHvp+PmTX2GxWTscovqJguIZxBy7BaHVU8sywybPSsvim7Irp5qi+E9Ms9pohkxuHzylXBufH5ZmteoF0BZVTA2uGz0EntvPKzELd07LtQ0UYEwe/YRXCMmZdK5RU1qOjeaHTUGbzKbYLN1AnEX1TFSIg7OCcTpQfngGcbJtLLoaw4x4HsNybJeg9afiaYoJm44PUdPoXCqcTUObrJ5uG6WE1NlUEbbU2bS1S/Hu9IsUusx6Pmp99is4njuTQuG5M8kfzzH5lcuks8p2/KxXjOfOohjPYctgotHp/wnFeO4sivEcU2DFeO4sivEcU2DZtpxtPBfRJBHIdZ9tPM+ojOpgUExnpSauPdt4zuf2eeIzZxvP9/D5nDHF5E8xf7uzKOZv/xyi0eAUp72Qj2+j7HYnTWtJKbQX0kC0xeJwMtAFNXXLv1K0IDrlwkkGp8NFddrtDpoKp7PbHEY8mPbD6FCBeWbZAhjw8H0ZTli+rdCM39Hl21wU25Vjszt6gD6oqR7+laEXWL7dQYWT0rACqjPXYbdRfXk2xxABcgPOXQmWZzalByQusRXxaC7UDLt9LH4nebP3FZqIOWNX89+7QVJm5LeFZJ3A5jt44aS0J+YUXIjVz9/XSDOuE9d73rpWgFkBJ68E5pmN7AYDVj5Y4IvaZvPsDhqVTlz8YDHWADMdDmccpNIYdYRK1SvYTLlw0oDFcwouxgomLXjQhsc4YMWcDdcrYNKFvn9kZxZzisAaVGCQwz/A4uQZSVijFrQNDMw0b8yK9EUMtNlCk1SB1cizUlX7e4GpWgBLiQBVFmD1vUDl7QusKNDlH5BnsbgHL91QJe+4wCGwmiFYQx9Iqe8KOU4BCrDSzIhnlWRYBFhgFdheOcpZtbcnZFYpIKNYARsmCmzfFSDu7QZifqB6guV5CYK840pI3iPP6szAL7w8SkjaeGvevlolTLEP2eCdq4DBpkgv//QcBUwpUMJLt/KS2EeXCulvxsHTU4dv9o5TJKy/y7Kn8WJgiwMBHZBntuIiYB/+RhiwVZ6wTbNA37hdAduvFxK/vFygt8sR6PSsSCeYpOfEwaT8eHhrqHxG/1YLt2w9B255rgusuTcuoVElDPhCI7C55/O9O7WsKYgZBYO0cM5YIz7Za8FCm2qN4LQTQfj6mP0OUyq3AGIz2kebHAvhQCzZwf5s/3A8hgZB5+0FcLAvgNEe4PIPyHM+nsEC2ukAT+zFs6Lklgk5NJ103yihtFCAHAr/qAxu9laHUm1WgOrQVVhhBb9K8ssUwDw3sH3DBciqV2gozupAT7R27fa7IwXJcxZ6FFNq8NL+nE+3ZJ/fIcCEvXEzuiE5bZfCDNz48Rb8+CEe7RW+2EtxwD77PwHG0MRtgCkb8emjm/vMUAL7fk3cmN4CDGv9kwKSngiAa0CeJw9XwOSN8QAf3kxnlPDdLQoY/WYXHqHx3gPK97CS1CP9BHgqwihQ9sIFkNCUroCZ23j4xSOb8engMOVbk+Jg6GfiI3sYwP5F8dBvSoAD/uVLRREetRTzUc0DJQwUhmGQtIW0yVUI5qYUVkGkmyIMy3CieU7Zj/8XfTP/55TTcHmlLcWLiOu9fdn+kq5GHsW4m3xiPwrEs6oePy+niZeV5PVCNsVX6ZsV5fS9yhIh5+AgVjkEP8gq9l9QIBWMRSNP8VVZlAEFYBePr1rYn0KrtJ4bhH2rFZnldJ1uCzA/Mjie2Q5kVY6vqiB75Yuv4glJVN/IF+IzLvxk8Fr6OHwttikhqRUp40lKAJ6bg+XxJCUAW+fyp4Sj12O1byPz/hSI54RPLwB4dg7u9NRC2nPY0e5Y6RH1cyVY+VMV8RRUqaaHYS9H1lH+yIPI7RE0SOlHNHTIbz2M5W158tKWwVhBS8ZLFCT91vN4E9nCI0Q6syi2Clooostdwd9LNJFbMlTTJgrQKGlx88nPuhZ6DF9key2UWkBLlhjvh9SOLi60eNG/1nqHQMrBWgf/MYt9gWUdKRDP3Pbup/jnYh4cmU83Eax0P6UDKkYPv7i5guck1HqCyh7SoejrC/YjhIN5cCTzUHjmtpJRcvzzXULygdplvPGwIUD2weB4Jjce9tDQ1jO7CaosymeQ2HY3lZ1ZJCO8g8Iz1a0UpRy+KHXYyM/xilcf5YER75M7sLKEs0lhVqhhewiwxXI2kQ4ViOfxm7GU9yjz0AoiWE5Soj6S+v4afLd8V5ftf49DnkcrQTwcWdhEY5IA047i/yL1cBr9yAfn44G9UnH7ER36iM2TV7Uh5a9UYOVr7vd/xL98UQ4jkAghN4/Atkv4oJdMdbTJjpsM1VI1udoaKaJcQxqi2FaH/xYNWWSA5lH4UOSyUk4xjXcUsNK6FrKa6Lb6/98F4plHLzYTQnIcY5EvP8lBnp8Ej8BQJVUQZ6Kc4ytcaQ7hpZEv5ye5AYsW5fwkFWMpuwHlJ2GrdrfwdAareF6FjhUczzPo868p9FnOTzLFcynW48tPInsASavoA7FVhi5MiUTxGAJWfZQb4I94fpJyutKH5cn3jae5VzBrG1HYsQLxvKgYv//hOOK5nEpaREkN1EfSP3oSv7d87zkzvxnXe3IrLbLRIrd7w1T3FuR15g9oqFIPo0XGq2MW8ez+0xHKW9I869rDz16W/tE8PIQnC/2f0S9fLrLDddw+88BIk5ybRFdBm5wloHeDoYVsuEaKKNJKT9yaCC0dWWR0q8k+lzqM3h60qT+U61l5C1luO0HvR4F45ikLdvP8JNzg8xQiOm+3Gp6fpEzQV3Y1NFMuMfBElLU3jVDOkfOTcJwO8PwkSwfK9vlWYeMgcWMzsg2r5PZ1hwqO5ycK8fMdPD8J97cz5PwkRNYMOVM2e1nuoYosiVhy628ESP8aL2z10Ytpw1aen8SJdSasly3/yFyO9SxKSuRHgXh+kcan37ifeHbSnpNl+9zvpdVY7vKqLjB587KpX+M24KlFwpa6Ba+4EUe4febZyN6dRzz/47rDyQro3jxSOeDpsru/G4lVPFlJtXVmOSkAu4Ie5HzfKWSGjS2skJrLJTYowWaumdIaRWiftWSVUw7ib2qQeS2n6qpMGi/6vgZvD81BdH32j8VtCyK0z8X0eTk1nas4r3J+z91CEfnhVU6Bchnw3L0sMvusOoBlpJHTnU1Qo2nm+T2n9pLze/ZO2asAtpd88LXRs8+reH7PpbSnRs7veRH6+76e+tly+hDWGlGSz4SjaN7VPL8nQY0oU9P5JcSOzZfvF+oJ8pFGap8fK8Xvr6Cm87MENaQeTQAY8N05fPuzy/iX5ZyBkdln1qISYMBhfBj6HV7JAC9QAt+3/nZe0zAlJLYg1ACT/oVtd3iyJGqZUX4mWcgqU94iPGm+gRIpOJygo0RiDTogO62iNrWWHiIQ9TJDDQVs080D6+T93b0okT7YS+T+7SrqIyuSm9EdKhDPuXQ/MFZS7zlvqLPdg9Avdgo6nn+7P1D/toGavZoI288H6Os7BwrtR5xTqUDIe9NYFeUqSaPPN5LN3szzHnWs4HimNAaQsQ8fvqaxWYBNExUwo1gJg7Pxi5kCzOiL5zwYWWyNLAn3V7djea/foYAp+UQZpO2IB/bVZQLM7y1AQrKgptjrkXhy8yNsP0+m+0HqO4jQp3I/1PY/KWCmMy7xU7SSn1CPG8wktIFF2H5+tx9+/ZU/KeERJxUHQ985B1iTRlj31zgYvZVMciLlMcH2M8/32ZmlqcMHVm0AO4JL7WV0sFW1iHahBczIuqHOoHJQG9scYXi1ixIA6t2CZn8vbM0iYuV6KMCCtRWCuBvBK3Vp9LvJE6/inVUdKxDPWur/gvIhkIeYUeNZX84rhQKjkIubUupHqYqo+zujyH9BgVQwHL+fViRodnXDe8NYge28Bgqw4LSabrRkBtvo6KXfRZ74PnK6/Sg4nlWfE10v3yHMcChgNjah07YpxQ8ugaRvKK/HVMVsSh9Sjz54xgaOYdh63I7fT9txnvjxpYL6KDaeX7pVMcuhhBW7sYL954vvUHbHMrzwX5fvKx0qEM/qLy/Ax2fvUU6ngea9XSDxpfiEDxHt6Rbl6FVIHlvk4rem1PUcw7A1k09SebFLwrtqgTWNV7Ittytm2uMhcXt3tuV6/NmHvk75RtGYRTddyc8hB/VeMwsNQ4OKXGqd1cKHmk0WnvJI61tJqyS47CEdSsUza2qtZipcj7beVyeoLBZetMXO54bpIh5/zuNTX7IsdNga3xnxqWIZfBNo82hJLuSRm+/wxfhCG5psXngaHruYbUnhm3ItvOhsu5U+ygg0oSQ4nmEKzz+C54VPKh0+aHKoniQ9qQdLoyeqfz0tBhWBaPwZYZthoQXa0ugMxljQCxF5BTpBzZ/7oKmeH+C+EYhnPv4MMNo6BJ/UdEbqCVae7n5YDn2SNJjWykAtijCNCHuE7gfqyVaaADagB7ox4603UNXqyWOxvoSRA+Wfvt/iyO4bvwTR/LAgZCBXOCKZ2jOi+RVzBHLrA/LMijm9gZQRcXqewUFNGWVrA7n1QfLM1gYYx5aVTOkBI1I6OfABxRYHSj4YkGdYEVTLIPL5YalBlcCW0xzQzi7RHgTQhvZZ1xHIJCcg9iuR0n36V0Ceg5hhicqIQhKxzGCAzg/oBgTJM7CHggA6mVq3EWqkL3GuX83nltSfAvMs0sBRIEWOM5ZBuUMD6ZEoLUYQUygKzHNnU7A8dzYF5jmmmGI8dxbFeO5AEa4feXbpbxFOu/7l6ZlZRPJf155tPGevi/F8GjFDhOtHnl2K2efOoph97kANMZ6PK8ZzZ1GM5w4U4/kERZlnMaJprFFR5+KZFrACSOTLq7Nh/o45xnMH+m/wrLMcG04STbSoDoDWImfo11t4/VpaXiciMaPp2KyUkwpXtddplg/DYO7onIPiub10FDPIiwdpzHw2CaRYfATT2Yh6nVEb4a8rZpiODXjrzHx2G8sw8VEqbTafYILb+WMHCprnxOyB7efeXoMmW47FSM7uS89ipj6IX6djqY/XAGnZfD54whi5BlBT7PCwbrOUyUKiLuuKtK4wrOfiS+Tl1k+roHhWT/BN5sBzSs+iKSyQMNLIh+yTsq6it2yYiW9OnMDfhitf4ST1GLnOAVn8DLuPMPKh9MQJ18iHEsEsMUMLhR2nNLoGNLnsjXXO2gYwtrScNNr7X+DZ7gAHhVShVLUaDa04bXExG0VWuU2sDFEwNTRK7og65liNXiUHUyOVZV05mJUmsRQL17l76aqQCnOhYKcJYpUGvvl0CobnrLKuWb7dWM0gsbI//tCVPQzlXQGKLF2LKGFIFl9MnhXYHc6gpp90KNXOK7S7fCm98mzCqrECsJ3XqDZjnVnlPTPL8dLUlXr9DaEGy3NWUbds305sxzWqTdcIkFF6YeZGvBhXPqhYOVYBSRtc+9+KYLR28OaLBvOYS9Trt/V4/TYsctNFaW9ehNf4Sg+t7j5s1dT84Qr1SufUBefCfEfpMgoV7UDB8Dxs/UUjX/al+dl+e88XrxMg6fXL0rZeDLSq+2Nz4oBtv77Xa7h59KZ/HHo0fIP/ys29XqSpnaj0Zy4YvaoLwJP3nLvuL0pIeOu3mhd/JcC4VedPs8cD9HuWFo0OV9U8bsnN0B4xyitgx6eTI/rPPM+qRob1yqxRiiFbGbBGNGsNRjC66X4DIm61SEHN8epIlDvB1EhztIHt7wOw28CjMlL2d4VSvIMVuQQVvmQHh4CxgmIm+J6nKAie5dIpFAIgB3fPrBKghqaMW3lUhuoQTagu4NlQMu2OgbRb+Fpro3xk/ErR7cLCPVdBTqEA2TUKtg8v9s10GGmeKPDMPr8EYI+8oPQU3DlzpxJ2YA2bpiqS9sSB6oeLYIoC1K2/C/j7dChaJfYlefHICRvjIO0/8fDcXQp4eikeftJR4lk92z62G4gT8ieisUzPK8ZPO1QwPG//PwW8xqOreBYSis54424lLF8Yl/Bpd2CHBwiTi+JhxDtdxDFKmPZDAv9SGBr3chdI/ZRzyv6dKMC7k+L6vccg8VuN8MiceJi2oQtrQLNcR+vV/vmj8HkWHU1O9JrKAA2iXkKgdGCkp5/ozPNcSMkM3GSNQUWBznpJY6ZNhdVQQXeXJoMWz5E1UVBF2KrHu4Ho5b6HmYImnSVCOfkE9UN41iFzc1d7LW6uLBF2o40WvRQ9eaqC4DlrN+6ST3mHEOtRWJSnr6kZ7WRWvZx1aC+lNsgjnjUmrcEY0cx0DYVHZjXzmVuryvBq2LlQOIQ2Wue50UQRXXlyKFcUeJ5Baz+vcvOSfrhHAZq2m/S0aca+ODk2moMofh1+dpK0r9FSTtnGKfzofgWwr25PoWjoKT/idjXneUzPKb0zFUkZ5kFZPWFyz/k30LTrDhQEz6kfo52c+TmPUnwNbSX79hb9EYR2xH/OeawSt25fHf/tOCWoDxNnMF5emD0c1U1DS9/Im/ujv8JL+cmqc15YjRU0zudZh1IPa8dswsIfdeG21A/D59mU4kTTSEun4vVNCzkjQ0386bhY47HG4JkSR9nFU3zyhdw1ksFJm+wt0Eg2uUF2GXwWPDyJXrKEXgrWAidFZ9jrhHo68Uqb2Ytv9d6+pbS5qK4rR9lL8cSnKgie86mYPJ7Vi3moUs8oB7Gl93arJ5TxjuHjGe+fEebtzaR0JHKGEti5DB83V/T13CggwvfaK7HOLDldSRR45ijPpoBJQhlrar3XWonvxrRd+iYt/7xpGX00W042Gpam7EIAx/xIQQkJR/+MRX70V0sbXuBj2n5PIdHEMxpufpjD+tObhBn+zisInqdTyqHRrTxc8guKcfrwPtsPyFXqEfU/KavBq9W/4Si3TMKK2LNhT8hMaEHzDnUz6YAe3YUVzPyha8N8rKBu3c2HtXhyLRmLi/BM//YmVR4Bz07QohNbxg1wLbeP0OCLTGTt8KToKR7pjKqB80wxz2CWeTZX0Caz9Fuel8TNswGmkMkOW9wIQz0/yXJeeLPAwa0stFP2Er130G5itailP99cf3pwg+CZG+EsXp1OrvTBsga8KvTeKzycZ0LZx3OkyqN0g2meu6iwQ1T4qt3DiWdom+OiFWyyCPWo8LyJ8+yhSzJN5nluIfGc1vaHb9Bcw8oKJajWe99CrzxMPV6Jhae1XoZlp7USva8vc3rwKudv2nkOXkHwvIKWxBndmoqHn3CY8zyvaC/WqD6S+hXPIrb3liOc5/lxkLrVs50CLMNR6hFKNPTKMiwSXnETz639WojnV9x/oaxD6pYJL5O5Hk2udmQ8Q0U1cMOoklunWsnXG6aPMKNmQOko8I0kyrcSF+X6RHef82wskXkWZZ65Va0I3e9nvir0KXKKEqjn6QzkwhuFZs6zg2cR03v7V3GeG/vKPJ8+rUdHPGt9FelVcoqSLC/5wL5KJ5bIPPc8wHmmlAeR8KzyVaXXQo7MM08Uto/zXD1E5nlqocxztOzzes7zAWIkSeb5fofM89Wfc54p9yckf77aX11+NV/mmbqnkmSeFzrIPqe1Xh9NnuWIS1Qf4TGZZ8o3wGSeH8iXee73Kee55nqZ52l0UiO+Hx3KubFUXz0pin48cdgrlNcT1lFSoZmtKpnn5++UeR75DLnao9+JjGcdNkiNEiWt5+zQk4148knkGJ0pod2XJUIZ55k7BjqZZ52DNtkboJFuLw30YOugx9mfRF8Vej0wymIAXh546SCY7G6BZxGrNBtlf7sH76MqcsuYe6fSnqeoI56P3Z00sIDbX54FiHmoUs8gG/e3DypqOM90p4iEZ42vKr0OBlO6sDR6ANjowko2u3rJ/vYdNu5v02FEheclBO9sSsANYivn+U6L7G/HvTkHv7uJHnAP2haeJlBCMJ4KEERKZAAf/cXUilf5mLZj7edQ1BHPie089xV8/jYCBvAJ5/lPOfQu9cg5L3J/+x8Xyf72CH5ur4aUP6R7O896RXeegayOcg/CXEq3PXMPq6UMgHWPXCv728nzub+9AR8i4NlGPnUj5bBHnGRv1s29Xp1R50pRVTsj6qsJXlaehpv3drFGI95jGpmRjqOkBMrwnsJatNjUx6NkQcRWdqgaRJnnC6Py8doockAx3slYc1+e895aKxipD6vGLlQhcBovZSg6VUH420ZKUVIk5yGrGSuAtrmHjqx1XpnAs4ceoMwiUfK32T6El+cjA1hAAO+dKOybKkDaod6GQ9iCpyz70eE5g0zzeiffa8dUBST9eInux/Pw61XKJWSaP76Nn9AEX+7eMJT0w8UAs4o5M+/MVYL4/e9U318nwCzqIjsj/nb6990BFsnrUFISMfV3lycd+a0A49/sMnkHovXv+5TbH1BC0nfykPCjNwX4jTrUW/PioHsTHyzv9y1exOtWd1lT1gVY07BuDSOUlFV/KLnaPGFvBDxzD9dG+fZAbOR9TqoWyuglVjPArWX/JZwRYg3o6hhoETknHoAL7WZtCqiaVKBrYGBCW2122+32kkh4NlYLYEWAKU1IjQHEml6gq+8KJkSM0gbTiFJNX9Ds7gpG/LPy7NmnKgieoWYIiPV9QJvXFTLKBci3C1BqEVhNf74Qjr6SSliARxMN5TgE2DxIgNyBgmpfb0iuUfBMYvk2AXaOEthOys2tjQbPsONGQbXnElDlKmBKlZJ/bT3WsONaQfX5JZC8WQmDuwI8I2cFDEvPTVSwrVcLCbN7C8k7zoMpeGtYbFfC25RY6IzwDNvvVrAPfy8kzDoXkrZ2Aco89NiDSth+s4J98lsh6e0uMPnNLjQUDcMUwJZz8MPRgLe7w6RV8TDCpIR1tyu6v6sW+n2hEoau7wKPFHeBJ+9Rwlt/VCT8m67vW+RxrdDFzG5qnKpoSonW5qbUP8zuLkNuKhygcwPjuP9XpKu1V6ANNuChsAqXi/x8XbWDH5652lEh4uGQTh4ZD032CmcJ/lQ0SKWpcfBlN8zVzopeWGdRYRl5B7rd9jJyWexup+v0w89B8UylD8GzocRkeWWFVJRY5aygvHz6SkcFNalTityDwr4N/0RFhSVoj9lOdAQya+aUUeGrygrJpddsdpZRjlFdTuUo2tyBguVZs21hOToDSdvO5TUUovXXbF5aQjUM3jmn8kqB/adprtWXoycsiVuXlmNTPAlvETBm89JSWoXuuaWl1DpXT9n81xDnZwXDs/r1peV3K2DAO78XYPLGZSUXYY3PLiulEemkrXM3/wZrXF62rPA8SPj2k0nmQBlR/Gj0y8tKLxZg+dJ4UG9/eCMtsDPuzYc3/gr/cy+sdi3EG0W/fz68mVz+frM2//nYVLLoyWYDPU+JHdNPFAzPnUvB8tzZFAzP/ztyp2ic1p8/WOAXpxjPnUUxnk9UrQXc9uh4hGeVYjx3FsV4jimwYjx3FsV4jimwxv/jbON5wUIieZwc03EW6R555CummPzogbVnG88F3D4/8MzZxvPUZ2I8xxRIMX+7syjmb//XpVUBmCx8gRiSyianBTFaTNQTx6xWHhOis9twv4iCQrQAGl/hJItcp8om9+CnyMvgMAutrcNX3elYAXjW4FFmHT8jrVXOf5JpNdN4NsuRzwhUFFZFo+thK00Aba712LBytpWvtyHmWvgEuMEWs5wmIQO3inKdHSkonjV4GplWU/ugvDjDwhOSDM7m9bAJFp5FJDHP3hsguYOh+6CUKEDC7LG8cNJIi4mOC+vjEVXJ2WZ5bHZYHwFEOfdQRwrAcwL+y32l87fTrXwmTHq2mSbes8nWQfwjkYIcKWAjbA1QYOEUry1rRPZwmt3TfZqVpyMZMIXWvEGl9hSgO19sstPKYAVwWPj0L5KqljFKIOJwgL4CuajVQ6EZwOYukxpVIAZciqZj2UXQ1XVlVb4Lu8RCmUhAtbsXlCPBhhJBW9UDVJUlDdJUeWJpx/LPsx5JLrYIWb7JZbqarmJNf6C16fSUnaSyP1B2EtF4cDXuIOaFfeGzgh6g3dWNbfNNSi0aK1DuELazN2wcIkBOsUK7DS9LjZyZxP9CGsHwnJyjoP0GUzISlOqDS+DlWwWawZW880KAN28VZtsUMGHnPzxNvxaCWkijA83vLSR8fCnbept8NIvtypGrlMDe/53w0l0KSNsQp/7XrwVQP+OhGRnpx2g8nfzzPCBbCYvscSN9OyV8eDn753UCjF8VP4Cyk7x6s2KePQ5Y6mdbuuB/arq8VG0YYjMvEhLeu7zrFporgpo7MW7Eqi7A3uonrLlHCUNfP0f9Lt3DlrfR6u4jhoQ9W/bnl6Yar3iKtm6S7VmJDXGpBVUL3jndVp6ZRGwRVXbcS0LydWFHh9jR5JZiCWbKV4AFHcSn+v586Vj9foHVD+SLr9uw2lJae7vYz2Qq/zyrCoRjpZNKxwqQVSmomtGIVU4UssvxY77GOg/BAh1fUi4cFWP5G6Zi4VX8KtHtw6dDtwoL8D6Ssb8b23cFwCqaIuab6ZnvL6dREDyrNipBRZO15aQFsMSpgIx9cezzKwVYv1SRsROR+/pScQnlJ3lYAar2pEEha9YNAp/iOexzngQo4St8evtOxfyiOEj6z/nwNmL+eAkSmHiUeIb5/ta+88szWxEHCbTAJC0Si0K0Ydg7XRK+/K0Ay+fEDXuzCyQcprirpyivASQ8G+5Uz0euVcCaOXF8kVhUvy/QKjWOVE5a2wUGfKsS3kLMHynFKro3E8/wtwjXvvs5RWnBnBSrWUEzTkGU8L1WSrHQNFNHI/DZnXUWykwCdTQbs8K/39ihdIX4/6PwCq13CL23U1BEcSHzDsftzUOMXmTY1iiQn21sxtcpfIJ1B/LLM90J8uTS+V4edH413l4L6D6SVy3U0ErNhx7EhyK5lHKKvgpDulIFonoNFu6h+dmwlqIutpUoDkxEr8Nzl3E3vs1uRqh8kRhaf7AGwfMTowQ5tOqJKl5e651YT+ttxl34bsqhuGcoSuPNB/vQdH8ea7WSIjbDkJq6sL76PzRXPJ8BTNmI7xcXx311H7776m5DK1rOCT/ig1rmWb3VD2Z+eV50owImb8RvLyrmRXzxRwWIh/tN+RypG/1Blzf+jt/95BF8WM55hkfvoPpCV7/XukD3FvTZu7ekUQmPbMMKXqjo0kAJS5onD/oSjciII5TqUOY54SleXWcUX7OdoyxnMkiRUtASSGYnbbJJIo+UdPNYTmgilK0c+9BVhgXxBdtVXrotQAlFdhe4dTyAst5qb8Ef0uzlJszKo54O+sHMH886is/iKMvZwbRUKXgGldK7rIPdOMp7qQAfz2i76SlkkWnO4EGSHp7QaycVt7H2Cp7XwDOHR0pm0uftkVUHeEDl6RWYZ9U36GavPR7/nOiLf7ZRbGRm2693UH6Sl128qo/pozGb5WpD1cqpaOGP0qzpo/OohMepzOk7f83pfn+ulaKhR/qiofnhvn13x0ftj2f1l4jVYy7cYfpObn8Po12Gb0es2oXvUo+c88U8/Oi95xF1H8/D/sXNa8hCW4/lEcpNFDJJKAM8uTehZQK+O/jYxI/RWqXy6GiZZ6iOqK3+c4r8aajjkc8SvTfxyGvJ7stMouGJFeSkYik8NFovJ0QKWfv1VDj9Wl7KFwaV9GhvMMk8210N+JGZJx6gVd5RlRz708sfz9mEZyUlCM3j2cP0lGwAPGN3c569V3jQ/Ya99MbHs54HSYeuXWgvc72cZyoSDlFzfFX9jTyTgWc1z2SQJodBy2DtlfP4nVaBeU7bhqV0nJ/kph95PgMe+KzeRI9p33SMkj+9Q21kShkGR9dQOS9RcoQxe26SsxusLjwxWwk/3BULabfTyx/Pw/6FJb24GncYTSnEYABFScKH4za58V3qkT8cmY8fvUpvfDyrD3enp5C15S9KGH2UUK2jWwS8uwaLe7It7TDxXLdmDgVDp/I3Pp5fuS+8n+7nF+XbhAqK+Wi3z+jxMsnsok0WSWw6Zp8Z5e/Fq1/OuBCqRC9Cqienmsn2uZQa4g53is8+OykYWrbPJnk1d5+3fFr54zmfUMWWOMgpDdApoBxix+xzTw9FWO2lwn08ayh7SBgiVLn99dnnXcTz2tpreJ4Sz4M8cxjPdHCMZ/IMOlBgnrMI1dPkJ7HT9sFtv95DrepnuH1ezzuJ1a2X0vdC1veIapLMM7fPK6nMCTt/d/R2rGDrXBvZ5/RWOZsYP9wVZR1f/f545okMVpB9Hk9xzqDmPH/yp7VV+A7t87cE3xsn2Gf14QEd/4J+1IhWOf0IBT43yPb5H8TzXjW3zw2PTpXtM9nvdp7/zv3/TigLceskI13iaz8jdxopxU6b7I00gxzdD3qwyw1nvURN6ZDFiGcN2V+N3H7mqQaLCkX+bv8QC2UnsRDUGhnnsHnmFOfTDkW8BNGDlTJPLx7rnF0l8IRAlG0gUp4PIc86ajozuf28gSZ3bS7p5rlHwE13mKmXLOsQXjHR4TmTuH2Ims78AVjrbVhP621msttjDsXx1CSbyOmewUeVwucZuRWPIq9Mbj/z5Ab48D3PVnK3sfUCrO9HJOIYz9Q51oH88rwNqZlFTeeZcvuZ930d7jdjDxae/q8u2x/G7/77hPaz+rCfzP1+RBQntFCOk2a5/bwZ/fY15V0aJ2PZzZOHf4aX9C2He2HZ7TzzpESdUTy9n5F8aDkzCTQgu8ZGpm/CcyxxQSFix5Ny05itiL54uP72bhqAomy9xka+qAPv5t5thBoKhG7upaKcJc4SdE7JIaBbRyXdRDqQP54p0bacrbeGUpBgpfikrxFSyK0ucgoFWIl4iLq+2/1tyqYbhnYOwUt+Hxaeto88AMitwXL2jRU2L0TOD/TWUmaSBSdmJonM304ibpPJh97E54byTq+kby7RUpf3klLl7HL8/OtrBZiCODO88MP2t8lxfx8f0n6grICQTsmHXrpf8dxSpOL7y3i2kvmlWPYxnh+Uz+908utvk5edTg8vytm330C/eMBnFyQdQbd6pjNuUWk8MJ5qqJ3nb8NrP7+A5cJb6EMP+JQ7l6MJ4LceiHthaTwkfpvUqwUpnkRZPdt5rp0U3k/380vkeFbrQVcNoC1EG4mAlxlxkxlUtSJoGjVAqc3slGGBlsawhhmPXULtZQvaznITMJceWH0K6JFpfW1XKMBbidPOk5VoKrGeQkL5IJ+TcXr541lLveOsfhCk1AiQUiQADVBVInalowRNfVfQ7O4FudwHKKWUvQhRmIvP5lvxe5nlCtiIhRcMF1T7bhDS8O3g3d2gAFvUG+wCDUUTz3woWOQp/DtQYJ7ZHhp+XT9KSNx5LiQ+oYCsXefCE2MVsP5BBaOUJZ9fLWSWKmF26Ry7vfwSRAnfhKPFTvzesB3nwcqJClg8XMG23iQkfXw+JH18MczCz6bnK9nbNKKj5h44wNsIeEfyx3PCd+hfs3/erUj86AIYsPI8GPbOBfAUov3UPcqEDy8XEt77jTD6Cfr+cvLA8aDWh2c25zrwe+mvd4Hl98fBo3co2b/TFf3+eQ6kfqoSZtri4JHCeLaFfIPuzTyVODvoJ6P4L1wOsptiSYkL71zEM9hKKNEQiK6SQho80pWUFDJ0gkmUe7R9sZpQpSJHAN37EmSVeAaVq8RFYyuGMo46OuAuLNrMK8L7RsrpM3vK8sczLKCBdFVxiQv9AOIZsktKaHUbVuBy0T1CV+TiZevtNt7xVupvpNuPVFQ2IuiiBjnyDNriEr5uTlaxi1BnBSUltBKU1mQ30S+Zmd8xrEHwDFNMVOgTrkJskhPPMKHYNRWf2CpXIaX+TlxbUtiTFq9AIeaw0t+4sB+pNhFCY4pKqHDkGcRnXK6rsazktSULaSrL9OKSP+BbdWYen0KmXunHmPnjGaZb8c4hPs1LJ55h9FoX2Xq2qNj1B6xcvcK1kFYGGDDDfiO5QI9SzqMwxPj409BnSiiVIPIM6mdXF16MZQ19xjWHbhGPFLsoK1niyLxRNIUs9Wl+++iUYr5M30HKEHYC4ePzL4NSkb/ZF355ZqUh/df1C8K7SBDQ0NLwb/bXTA+CZ3iCu/XBKins+SQjjf6O4hQ9Jk+VPL388gzP8gkrwSpptr+y/Ck9M6Tf4knezuik0oSSMCH86WFo+EOw7Mz/zn55Bk0ohKbYwv/fZfsmegalXL87B8OzODsEoNWh7HySJsgdasFpul83wD/PCYvJ/AappBnh4oyGf0gIt6hJN4Z0P/ulKZT8uxHlGg3hy6L/e4x/ngN9+yeK6IxC+OUCHFMwPIdU3y/kvPzzDCyE/1RC+DfekL7MxEgqiikMBeC5Eyo4njufAvAcU0yoB14423guWEokP/D82cZzNk3Pjils6f2M2p49mnjW5SfJ5/Z54lmXn+Qems4ZU7hS8WlXZ71i/nZnUczfjkz26PPs67OQO1hC6CkJWXLZvh4Sv/05EfLsO6MTz+vMSD4j32/mt+snKjz7ejblxzN5XvKp+LrCfKd3ekXK84lV+K0oQnXnF5Sv10x+c+Z0LEVPYJ0Bnk18CVsmF8ynfpwhyVGZ8nI6Wt8s7tMrQp4N8tr4ubyQMGeHBaWNHM9sPsFVleWvoqjwPNjMv76eD7bO8DfcHaHm8xom8LCPhMf9ERshzwP0/Iz4GuzwWNhLuQfWK3wm6Qh+RmxaZPcgXYnbZbV1CK255aTIQRvNNupAofJsKjm2vpy2zF5Ik8VMFXaaLKaqkOrMeC+psFgKRXCaHHYzWFxmeV3bkKVxy4WTrCWOQrzviiV2J81EM9RJtHyVw2a2WkBfZjEUdmWlRrtDhu608sdzDp5GV/mlptTuonCPTJfdRdNTVPkUoaErHmUo6AMFZrPNJGQWOQzhjUBriu2FQ3zfzHHxOjXlDifNRIM0iq2CBRanzSxkFE80FCnEtUMsDp7e6/QKzHNm8RyXL4WQZuMcF4VoZpbPceEVKL7s/eReBT6NtRRfITw+cYHdosgonjpkbVhLPalfXuhsH62dUrywmNavWrnQSfO20j/wvoVMTXeMtVgUSeunDnniQvb0HfnOWzs+an88T94wp+gi+aX6uTlF92ANqevnFGMN4ouef9+nBPXTd5kWXCnMG2vKHaVMXem8Y0lYoHV/dm4BFU4avXZO8cUCdF+HZ4SFjf6Xd/tvBfjb1PwHrXEDnrtvyHMXiE/dlpV3q2/38ORoQYpaOrp+NScsBk1ifBG3DhQiz4Y6xIuvJA9QawCxQQe6RhGMuMmhYbZGxE5VzYE3N9DkSF1deDhDjQlYvTxHxFItgK0EoMoCbLcBjHa9pYlin8wNFDqpraHJmKyo2s/0bX88G2u6Qo48MZsmcasO9gdDfVfIqsKrJIWvFQ9ieQkBv6CWZnik1PuJevIjtneIIO67hr/OrlJAXolCXksSGddkefgVnrWf1onT7lxKNTxR7SedQWCek/aeC5kUkoHacZfAPr9VXlFypxIeukqYcgjBEF+uoCneE/bdhaUkvrksvGty670KtlWeWDlh53kwpTQOnkaY375Xkbbkxuw9dfEAw/ZQBgP11tU0G2RFjZ/p2/54TvroAhi5Qe7+3n67gn15vZD02QUw7OMLYNHVwswfLxeAPVtxMX467QMK/Ej6jC/oHrq2/FnJGobyXzb9/XN45MW6v8bBW5Pibnnoxilfv4Wm+ZZPKZAy4dXVNGHtkZ0DOvw3BKUKN1HbUfy+mQcmy9IEwjVEnqsRTw2lJEG0KTLDVUZBVQBNKdQWEyURdBVGo0sFJSl2iwUsDqM2rBliBkoJRqnCUPvxRDXeXgYKdnZWCOTNO6UeUGQwG6xgLDFpS7uySr2V0nx2JD88V6GjLvKUJJBNEVWVhUIp7i16huNXeESVvmiQLr8vVA4yWkxCjtPet8jP9dihMpq7AWyQYzp3zqHIyWt0FFGVR1FWGs5z/pD8UVYhM39Ur3xB3NDXZPUzPzQgz2tdaIEpxRBe2Dyiqkb5EK34vOeOrpQo54crBdXLt5qKrhFWDskba1WMXDC2zxPh2Gd5/WcKn2pf//k67dHrBZi1K56SbE5u+40wf6zJkKdMfuaeQasuYVuvm20b1eFR++P56YVxwL7FohEzWgL62bL4p8ricdNopVqAhMMDhMSn/6BfcrXw1F1Gs0WZ/sSca54OZ/Ar/YhKgOXlfEr2lr/Hg7olrS+t3z7pPyKFUT3a2hXm3rXkjtnxqY/f2XfR+eIL12fk8uyf4Yq1YKvUIGlBZTSrQNTbVUbyvlPwuk9Bh9RFgDGTyaAHXUO1UUsJOWlgCilkegNYyGc9Jnto4U8t9GWJN4ptPOJZglra5OabzPKjXIF8owiz/WylwuVMgCo5g4HZKmckoU1gpNcgcyU3nNujoE8vPzzzNEX1PGEIzzdU1CDsp4jnejKS7RGSPA4D8vibsGhG607Lt+fxNdwZz0/imWohLzuLgqlkngE286csHvChiaz9/DmFN+/ho9QTKBI6y3PRyzzimW/KKKDHtCx6BJ6dBGaH136esBeZGfMNgcOO8ojn+0e1IgtjEGTclvw1fTKL1zCGJ9P1G47hj+f3KIrxwweoqHl7sYaZ35zz3lzc+8M1VMXkAvpi6ont5+XhtZ+nUfqRmUgtiucbap5/B63bnnqYJ+Ydx3MEyu3n0bz9DH8L57ZxXAZJD6ZGGxhKmKoJLWKL0eSqBpW7DC03mrQG5ElTIrIGA6S0WLTaatyuKtNBmQn0ktli5XmCfNJaQpklDVqeTqiBo8rtslnScMQpw5Bo5cnDQJTb13J/aZi9poWc5yb6tQy+DEOFJ2QYKuBG/8QamN+KOubZd7cggqGKeM7z9uS41VOGQB/Pvo5ZuYoQOhtP1AZKJ5THcxnp5AxDSx3Ec5qcIZBfhr6yfafi66Y4vQLxLLbdg3XseZ6u/odknq/9nAKVV5WhDZ1y4K/0Rd9/Cq0byu8P2LHmVyI7aZTzD5LkDENLHZSRhGcYQpIL6AjkGhLkn1F+04E65pkdpuS5ry2lz591I0EzWy8/TBmGXnN3gYTJn/PMQL6RCLnf2W9FHWvNXixETjeUeJh4rlvzoJyRZCSdy5PUjvaV7evYjmhmKV7sTXa7SQV6SpFbZ8KWtA4Rx/asGV1hI/6JINah3SbnlxLrImWszkiBjaBpsYgmysQZZtZNnUTersxzmcxzIt/EM4ZpXVh9dMSTn8g8G70D8bHeXlKH72SeVZQWOxR1zLNWLp3zXFOBj3nevt6TeY6Gth3nebCPZ2cFvvsJz8ErEM+JJ/C8hDIXIM/ftPMM6ic8cg7fyLX4OM/pPp6dh5A2meeE10MIoCB1zLN45DjPr8k8/+bITDwZ4hnU89vCbC2fonW70fbKPPc7PAYLrVuz8APcJPOsfpY74tFUg+wjN6AtZi16oNRdllrkVwMuZNiMvGKzFgz4bCbTjNvtxLDLCebGFHrCyxivoTCQZtw+k7tPTVl8sEncHfD521ARWjBlx+Lpi2R/W+vzt23H/e2iUG2kH39bLp3720VknwsaBJ7Rs34hPkSR5wJKxyv72yo5o+dEK9nnzBP97eAV0N/+kSyw7G9P8fnbckZPvglWVkTp6p+yCwEbyf1tOV/v+/db5IyeFEQ4X3ZIg5cff/tLKv097m8vooye6G9/wjN6Psm/8pgrMq/3mOaRvz3tB7L0rIXsc9P8sbJ9pqwFa6IeG6njUOGTgRqSDNCvhjIrGGtBX0YsO/Ew0E5SUj7KNIDboRZfqxr13KY24Gc6HejpOWQ1ILcit8hgpXuEsw4qCOUGvol3Q0dFJoJXbtuzZjwJ1jzQQHlE7NxIh+zy+uF5N6KMpdPLbMrvWVohVDlx0yHqao4iz7yzrYgsMsCBqZTE8wYj0Z1D2YvOAM87kFv2A7fCGXvxopy9T7meUPYl158QZjKSUzSMUJ6yjSP12QNY9le36yk54BTaPqZPqL+fH563E8pf8H7n0V+jvVxR1eW11VjJF7JhHhHe4NSpGv09+r2P7OQNZMrv2b1lzMBvcdPo7/ECnHll1K6Idtkk3tTR0pM7BbQtDFKqGTjt6ErbDcirTifp0LgZtawRjbDToWXVVkSvEFhTCpprUSO6XGDDr4YuBxplfS0DrQZ9d8QK3X1KOJRCA1WoEj99zCFJ04x+9W4DiHpsXhQi37UCowy+5cg2x5nf0oKWH54X4EeZNQLoNKBq7oV2eSDkVAqQsptc+ijyrDp0BcCuQQJLEaCA6qxUiIduEGAjZRo8AzzPQKOcvPdcSNSA+OMfBNg0UZG5Swmaz+VFYCjxUFTEKJP+63cpWFpXWOxSQtqO8+D9uxU8K3ca4TwypIr88Dx9YzykvnM+JGoFRitivHe7YuS/uoD60wv4x4/5GdUOSawxVQGv/knJ8Gnuxi6Q+nZ31jhaCU/OiYdJtHwVvo6iNHWUbg/lsmusyI+1DvQ0n6MMDWWDS4cusZY1ue26phKdrsUqog+cgn63xuxgaJMRvSYLsm0GW0fDXX6lqjZp3eisu8nHL9PayXcvsWrdBmANhVpDaJT5ExWOzoTR2x/ECr2uGk85pUxnKRHQ5ZZQoWQG8Mszqxyuq8LSarC9bCzqn0eZf4otunKy2OLuOn+j2qEpo7h/nkPAxnNPUG0epatCvDPK+2aX4gUiZnun+kuvchoF5JltnNp3A7bT12N7OWPjVblkj1fZ+28cJbAPll2Vkhm1G9WwjdfOKI3DxvPtCvbSXX3fvEmApPVXZ22IgzGt9J+Sl6YJVn54Zi/ee9XWmxXwz+ouMGzt72bk456PTb3quZsU8MayKwfnRI2y9Gd+Nz0/HhvPSULCG3++6rXfC5C+9foxmy7AJjud0bCo8nxcGm5hy5x6bq/pnQ65TsE/hq9TcCuNs9BykGjncCsXo5tBrQhl8hhMyNLxkjRyofKNRcsPQOd7FyXJhdOJtNcJTB+OS0Hyw3N76b6fSZ5K56swulLJhfNfKVnPe/RYGlrrsBSQZ4BEPZ/jRufF0uSEdYl6sphJ+nBrPa1E+ST4upGJafJ9Kezz8sNze+lqGj9nqb4zSqO0uYlpKSHdNQKoe2p/Ko7PEumX1pMqYqlRrcGP6kLHSKxlrCGUpBydXX557pQKgudOKb88/y9Ij83nUGVED5YvZPG/ohjPnUX/6zyL+jCcUDEFfVff6/8JxXjuLPqft88xBaEYz51FMZ5jCqyc5882npfzNWxyaHmcs0qT5ciOmGLyo5h97iyK2eefRbwP7oQ0CRpfp1z7JnoblSEfKqi9cJKvApFGYUj4HERO5mB4PuFs2kv3bWK++Wj8OCI6K15A+6GjTjqb9kOg5wAnFRzPVEji8VNvr6d9k+9ZRbsFqM+/5PM6XtFJFRwLjEgQA9UTiOdTzshXg29Cf3uF9DbiM2InBHT4XrZvaq+vH/7+oeQE/4VKawWwFBrKfENlYpmJzyyxlOlpTSnQFNLcbo2cCSgimTSgdZvs7SXZC41lCJdYbraU0S9q5pPHA4eI+edZzOkKxtJBRXxdWvz/lI+1UAKDlPIhBZQ9JNdhpCWrdLslaZkAeloVKkxl6wW2YaK52HcxZBQPKqaMJQVWU2lPgOTK4QtsArBVXqnxGmB5fmeYBMOzOLsnJG+4lda641o1Va5n4x0FNvzehKIbN1wjgLi+pvnvCkjK9n9r8Ke0FIE9N3XURl/ykGEbb11Fk7Uedwzf/GsBEtbfkUdL1uEvu/UmBZvld0q3f57FMd1g2PrbeOko9uK95rXnAyS9fucSyocyvuDW9dcJoP6nR3oqHlKzwp/3MXSggq27f9Trv5KPNf25m1fehnXOdNyx/jcCJL7257z8eIC5rdIXqQr2CC2215lF69DpGhlofdM8C5G3CitQ8KbYiLdgDQ9fjgLQJUhqqQX/5ME1XX1X/BN4uGQxTUlXecfS9iKejsCP/PLMirqCqr4XiAflYmgW1wKnwOoHAqvvDym7BcpYwvL7qPK8owTIDBvoXPzmgkJeOL1V7esJqkO9+ZKTlLGEMpVsGy5kG4W0XdUKYPn+6gmCZ7axN7A9VwvsG1/2kHIln7a941YBdtwhJO45FxI/Pw9md4O0Hy/Fu4vFT1l+NdKsgCnFcTBdbvuyz34nsI9+J4zcFgfDdsTD0/co4CW+wOuYVsob8py/kCv/PD91HrBPfy+wD3/Pz2jy2niYXBTPtt+ugDfuVCS9fwEM+Kw7PPZrcVbrJCUMCHvBm2ljlTBtbReYtIEHU3X/RC10b0xW3LL1HBjxzjnw/H1x8PwDcUOzlKnv1zFgj0Y95Oq/K0ooxEOpGnn2bp6wxNrI7LTJTZvMnGdwnODDhiMj3jc0lJbETPmFeKgJQP0QDcVEmZvIysk86+R8QR3LL88L8OhzKbSqkuZ6AhzC0nUHu+ZSAEV+iVCJBDLPcJo/xvZRkoPKQHePDqRCblWeOwTQHeJ5vQoQWti1UKBl27Wea/QH8G1ejWIwVpFDdWfw1GIdKAiep1gVMGMfcrSe8pIA/HC/AhLbrtbTMs+za5RPuHDrx3cI+L9kP5Bl3XQJ7RW62Hos8KM70cS38kxCY3ai6Vq5UPnRXCWw769LaUX4pn+O29L0nOdh+X7spl+eJ49SwOgdCM+KhXyvD/+kAPV3CWN+6I6fbYx/sQQr+XbSFeSDv0dJDp4NMyFgwvZzgFEqA/W3NBcNZlK45Ja/x9c+Eg8JLcMG0lLuk/4jjsDzGHoEL8PUKeHeOH4R0hOtdRRWxcOewUgJy/SSjq/jyrOj+Hg2ktsdgaqNAAaKtNLLaUmqyExX2g0UBK330hRumWfYzeOjOpY/nlX78TOKkIAiCnkGFQ+x9vQvJsSz6hUHKDZ6L0/pBbvIdc2TkwaFrHw7zd6mpGA8/hm2UZ2b3T09FKl8YrYSoGANunvw16dXYJ7Fz5HPJyhUcnY9AaRquwnrab3HQpvGtF1Ey7u3ZyvheT5n8NQDoWuWXQniUcpGcpTiq2B+EZYzq/IiX7YSHj05pvU3gjhFeYQv6P5ZmOs/ixR5Ma8Id5i5GWkFdvi3WNDhW5ZQtpLUr+ToyX/zbCXwKjkEo33ZxkLVXCfN3qaF4Zt5CoM1lcjzkzu7N0/Ad83zR52QrWTA2xSH1cCx76yyEbI86ZCbc2ujjCd6yVhLm+TIZZlnrRRZnwSRxdMMaeS0JAeJ5yKXVeKbhuMbH89F6CL7kz+ejZX4WSWlK8rj8dZ6nnPAM7yG8+ztzbOV7OWoa9Arxx0owjEM7cSCcrycZ96iPbAaH1fV39Ce3YASHvDsBriVgjVhLzrGHSkwz8m78OLb5MaH2R5iJE3mea6TeE5rk7MbrCxDI7TKs4Yyh6V9Hp6V2YoFJVOIJBz9O139z5HhH7Pn/9qzG7RnK5l1Lsg8Pz2Vdju9/PGc/jGW9KwLdxhNryDpCPH84biNlNAg9chvjlC2klfpDbBPLsePEr/kQY8h69W/KGHEEUpoUMdT8b61Bst88pvkw8Rz3Zo5FCDty1byt4m0wyt/6viMfvkqJG55UgM3N8A+ng0yz7TJx7NGCthT5U86crV9PHMfV+a58FSeF1BmET/yx/MC7mqfyjPPPpTV7OOZe/TF3A3QyjY0ZB1Cbn08Uz4DH8+7fTwvPIHnNLnpvJdCKTtQYJ4ziduXOc+H6JpLlnn+6yk8A5vhmYuvE1t9/Vkh6ivkNu0Unj/4w095/mNWX8HH84pCOp7Tyx/Pk8kqyzx/cALPf375JzxXkE1edAdVpD58Qg91CGpAV7tjnpf7eM7AN4mPcQ/glb91fNS/fHG/msPr5qu1+/xtVRltctGmYzxHNKvUIGHbhKf+0zfz/0sN97dtPn+bQhl9PPM0B37kj+d8uhfwQOciDq3sC3j68uyAWbsF2d+mlCW+9dg13GcOWYy49fnbN1BBsr9d4fO37+LZSrI8aClVPkr38ixIp1dgnrMpy9AS7m/vxgcQWznPd1tlf/u8Hdzf5tNS5N3UrWE1N9lR5FYt+9v3UWkUBQ2zys+T/e37fP72r7fOtdvb1pBpXlHe8dXvj+d5xPMi4nkmX7VOlP3t1If2EM+f+fxtylYyzExnjDyndvz7+BFFOyfK/jblKPX521U+f3vWWNnfRh+7+yPyAvOvLKP7S2eVnZDl6QDl/jAtmWFLIzgon8iJ/WEnZwIPUbwlq20+3h9WQmkA64eIzdh0NlO6knaeCwJ0iPnjOZv87ZxjRhrtJpaua+7Gs5Xkl8jZSjxDAFLo5iTic3NYFwklMwCRjLSvPyyfDPLOhcI+NMNaz1WGffg2p14BbAGeGQ1z7+Vu+ekVhL+9GS++CZSaxNcfRukAE9uuNuzDC3723rhneH8YGWlftpLkto5R8qd/UXLtr26n/jCyyDBhM5azcqFyKzZh2fc39yXUJ++5eJb9GM8L+fGcVv54HklWefybiLKvP+yTobw/LP0rxG3y5i5PUcahLyYpISkDjwh/QfXh7vyLoWoLZRJtGIFNkRYy0jBpB1awZXWXt+bFQ/eWYf2/VQkw7jMGbO5FeG3QFx4I76f7ZchELjVlOtLWMdBRaiMrsmYGHY1X4Wbij++oa+FPYYsyknCjXGoEZhPBWNcVdDUCFGN9RTwfg4/n4gAjY/54TiFutQd7gIitY02OAEWIdZ5TYPv7AtvdHwxYoQGZ19n0er0F/f7BVR0X5k/FhCe19LFwyNJxrGnMKq9MAdlFCrYPUd84XGBrh1NFuO8BbsZPryD6w/bgRck+v1qgPzFHAUsQ6wlYz+dopzfdIaTReNUH5/I7x0py+QdTJrAw9AwN/ZJRnl4cB2P6CwlfXQzso0sF4noY/r2O94yXuP/r87dfovz9Hcgfz4lfXQCQ8N1vBfbh5YJ6ihJtdTxMzo9jH14vwKt3KlI/ugAGvHMBJC6+Ua/P6i9A0vvhtZ8fnYoHQZ1ik1bFw+hBikQEmDWoBcqsP+LlLvDWOCWsQebX3DlIr6e+7Qay5Z1WrJaYtZUYaXjYjuhqKsx2Mp2mMlMZZSVLKZPMNBXJSuNLEchC39dUWBx83ApNpLXEVJaCl6rLYiNCVVavmycQqfI798I/z1BOHeWG8lGl2CDPa+4GYpHVVoIF6oqHF9MIVrbTVN4HiafUFP/A9/lGP4X5ka4Av8cKrNbSHsAOLUSmy4cXD8QbfJHdXEz1lY914q1kI8+/chW+3+nnIgnMMyyh5XKSNgwvGoXt2x/PA3HtnLHF6BkkbbxrAX17TOmojTcI7OPqewbx7AOzreFdlEmb8IpmK+eM3XAhsM8eVkLahjs2kN2fXjB249XokzxzT75dtsicZ7ZVdlJPK388w1M0pyP15TvX3quE0T/8CtiKOdYNlwqQ+PRdTzjweyNW3fHM74SED+kXfAFt9WTZ7Q5ZamoVs0cenPjyZQLUrYlHiO9edRN6To8U3vUMblK/+Jc8Rzw8yrOVpKGPsJ2A6LwyBpmrqILu/ZGoNLjfyUjtan/yy7N2QUh8qsJekG5BaAPX+ch6hwqCZ9VaPx+eKrYjrOWrUIuvCekXmc7T6ncgvzwnrPDz4aliT1ErOxxNCy3r3yN3hHff+MUouCT8IaXqP600QRl4XSCc/fMMmaFkMRT9z8P0J5EaxkErze88tCB4hoyQZrLNviqka/gEiYtDuROkZfo7Zr88t/dzBSc285Jwz4jNw4Zx0BrQuaeTkPRBGE5KZhaptEHcEuQcY37ln+egzqZdkfTYiyFk1eJ53jpWMDxDcgi3nsQI/lsizx0WnFiS33398wzpIQwVJoVy9zxJlNszWLEIV6OLKWQF4LkTKiieO6EC8BxTTCjzU2cbz3NlntecdTyvjvEcUyDNpH7ps0qraUYX/K3kbOPZQsEdMf2SpOVRVUxeTidaC2acTnJTVm5nqvy2ayP0t3lu7vaaopeJ/lTJZafxhp/oNzgtKv52opzDOoM/Do6guRlIcvs6mVJlAxvs75gj9LfVcn4DufUbSmM7VFFsFcCAHvyMOnMDOuVY55GehBcd0/uyz6fQBS+mGI06BingwgtSK1rD7UDS+FLbk3S+Hi+xnV2qzwB2wBq0VuIsRWNX+ZmL5pdn/YndYVrf4aYYsQ6VnsrUaPU6jYhnlNNVAymqIiFw/9tpdeIZyegm+35B3xklCxu6aUCnyuqhF1iKmNXDl07kdAqC52SeT98nVRr9Asl6PmTG0vT4jiXps/qKMFhYeW6ykKSa0o3vErrUaX1P+OIEOiLRl79f5Anwk7tOVyYJidqsK3RdIbnH4gtPyGRysvzynHrCGYntIU1JGb0FSOAJ9cUknb6PiDRPPjdRUKsWXcpnd4WuhBPPqL1nrHs6rS2dqqebUmq3dRckKdSaYVckK1iqYtJF4c0T9ymlTnLYnXYGolNy4P9cV1cYQgdtZKowatwyVSk0mC7ZQFWh19YiBaYyjcGtAdHhKsRDMxWWuJC2QmdheBbaXKGy0HRwlK5Cp+WzSExNtIUVm1UUEaIvdBVqQeUsdJgArIVlTj9Xvx+eWc0oFaUfkcV28znTKaU0PyujtKQZHXUx1+m0dYWMQoezF2iKShzhzScZXNQjV55QSsqop2Bo+gUXCmzt2F40eWxwfpmrL2jyXU6zANmOQruf7unAPK+a2GvD8QCt9TRbe5W996ohAqh2XJuy8UJgs4tcjp6QkV9aeJWgfsLlCm9Jq8Gbr8ykCElZw1rjEbGXr8zYeB7A0/f0XuxUQtLKhc5rBHF+6cKJCpiQX77QzziSP55fu7f3c3JqBhCHfT2O15n03L34POCly0a+fDGw6fmOqedC+hKn80ohYUX5X0eFMLx1XOnPXDZ5lW/out/TbbLfMmnT7Qpg6+7s/cJflJC6uHz1/ykSHi1eOjUORi8omhNZhpLaOnxw1CHQfMFlVYRhxiHIjFXp5awkTjIsbi2PzjDjJlrm0mZDrvGCR2NkKbGIwMyFVpn+EMX2I8Bu+VZQihWYymiibD3xbKzFK9LbA1TWMivVUEKLeOnthWY/9zQ/POdUCKCvaf+4oJZ41tVzwHMFyKSojwy7cxBNOyk0dwUxu3RseGe07wqAXcN9FalKGxSQScNXqwYJGbuR7X34l1X6INrPLKe9Pxq1PKK6QwXkOWOzEpJ/bE8FkrUTeU7aowQVblqF351RrESSHYiwKrv8wd4IUoG9v5/6OhT7100CvCRPA0fL9roXQXj9LgWsdCrTPo4H8ehlQsKUgqloQ8cUTR2igOSHSu/14wf74XnYpngY8JEc+MDgMOdZ/RHFgQAlKFk+Jw5Jtg9XgHpWwUS8XY3fdJ+/mSsdir37RwW86puRzcYf5TzPLSDAJ63qAgO+1QjdR7/8V7wpjVh5/40K6De94J5wp+JwiRJNrhQpArmOJi47wrrAwhJlMfCtF0tSNQDQQhtavK/UOQFcBtBYDQaLChyiS2sCU4olxJUgfTphvVhwuQWwEckyz/rmPjwHkFW0qsygN1rwFXNo7Sl+3GA/PJ+wXixCYHWhCdXUt2MHhuaeoDPptWYV5PXI1hqFTH1R1+wOC/MjnnLEt14swIIhfCVo1C4FpB3qDclVCqxhba9MIc1gxhpUuV2zUsgr7kABeT5hvVhsKGcX4f5POPHdjnt7UaCV+seLVDMGZRn7CLN7LumdqUjTZyumdOM7h6YT14tFi3wD2mceDD3lm/OTW68X1P+Jh+m9p/QcqUjOMg/K6gnzL5x/lZ8WtB+eX6QUBfJ6sSjOs/ghpzqRAidHf9AlKeuqNGN/YeaVE3oPU6TrH7pocjj2eQRfL3aXb+r3aM7zuP/wJuWWuXiyzdPjJl351GXj424xGPqMOZfNPG/cVWkdn1FgGeUwxNoydH/xklcR3f8d+dZzP1YhTbCm24pGsoJJspr5dC6D3ASVQySOoR+a5KwIchSkttmlK+Q/J+eZVTX0p4nj7TXI9wtE24/88Cyv5z6Rf64qFXgwxv5R5ehak4o46Dq54ZvFd0KrHY7k9dx9qRBy++h9PKchkWxX7TW0xCVizssezP1slRyd2YEC8iyv576Mf86eUJBNfpOv575sSBvFRbb+QYAkuYrZfKcxfrx7P5LXc/+RrBfArEF9jsbDBHk9998LL9X/31oeUzKG15DOHYCEKf4g88Pzp3w997/5vs15nvzDuI3LrhbUR0YoIPUIEpgkt9vlFV2nhTc/TF7P/Si/4tp5rnty4cb74uRA6LpH8WQJbIBb0O1AyZ1jYcvZxF3LJuTZiZ63k9fsq/4EMT8NyjAlLyXfwLMMkdyIbjWip6XtFqkhovDIE+XiPLfI/w+Dt4lf7TLPIO6WJvK3watjnnkuMqiXI41Le/Hgqsra/tryRtyiLfZWh3eVn6rNFB2Zx3MlQLJJSPPxnE+ugGafRPHVoSgQz5o2iqre8wq/0mb35j721+QVr6qY6uE83x1ijR3o8UoEMK31Miot+SGlGnle7MGrnjKSsNe980K81DvmWTxyCx7+a8t8n3Oen635P+1L+y+A956Mg2EUNRkNrduLeI0+6utL4zyrW+ZfOezLRfGPfoOYNc6Xb15RUy33QzVkGK0S0/EYZHDwa/0E6ar9mqywxLmFRp7FAKVqwgdto8tZImlArDA3NkUL6ELOMzGFKrFUt/BcujLPlsJCnuwoBHXMs0rmmeOUZ5SDJQ+htdbz7WA4GCpoHWkD55mHTrM8Adp53kU+bo7L5fWTu+B0CsSzKPP8D/p8MJ4X8UwB0PBMyUSZ59uic2Lzief0VgqZYs+dB8TzfLLP6ehsjyxwtD0aGtAd88wOE8//fPhEnj+cFwdJR4Yqxrc+WljOEw1FQWuI53FHfZ3wnOcRR/C2/uSPXRPe3fJwUZvfAbfQJXKowE5ZNQ2S7pixBFF2dNtljz7P0EJlonMtiycSA6bX2LAdXWYFsfb4wUQmG+eZkgzgPcsFzMVdb86ztl4Ae0toZtOPv93MeabcRaqDbre7scEt7EaG2SEZsHCT/52ifAI4r4Yuhdwat7u2ZQul3k6m5dzT0BXPw4Z6KArob39DE072UK827MDz2t/gVsoZAOcMlP3tsJKRnKoJe9FajdlDJmv6ri3uau+Wv45qRbLGtJ2v/ioexsiWO2j587c5wZRqgMR5fmMu7v3Fo3GgTuvx7NIOvxmappG/PfNz7gP7eE49jHSPPqIRILV/xnenusIRyShRgRqOllaSc9ozvRUMdRbfMcg6Ezy7sYXM0/NyVftax6oGvJM04Z8RuY6KDE14Fbjkdno53j7kBGKcZ56r5CC3nkHLD8+UpFfTTFHUjEaDy12DoJgY3i83qXPkHL6RK6MZLfEGfhwarMe6/0ZqDuVRH3YezfLae6wPLigF5PlllwJE2QjTMPdG1yDhCdwE/7m16w93on8QZvK/U5T8A5rm+cVUmjpNr89svbmv5uj1AszaFkfJt3ki3xDkrz9sThyI3/Xz/Uyc53mlWMP2BwjxYe/8Sv4gYqUeQXiXl/isPec5oSVZAelfEl7s3T/77ijRkqsWHww+liSfRdRXox/8E5zPCM9GrNuC7raVatf4lp5W1ZLL70bYrJSBOxpi9QYQ61WgLVGBk0aUGqlXop4A1zX3OJb6Plj54dlYJUCuU4AF8ugQ+dua/T1Atb8XpODplYdWkR/tHCSIO3uDqojOpN3f3tUbHzL2K4AduIreB62APGfsPRcyihWQJafJJ387ac+5MHibEh4qVsITU0PCzI+23qFg/7pOSHiG9z6Rvw0rsfCt1wqJ3/9GoOT68n7ByQ/P6e90gfGUQd9CQB2ehI8JX/4K2LeUyjP9HT5wFRW9+icle/dyIeEpakSM4x1j6xbGw0xaEqP7Cw+TKxJFGd1uu91u9Rn9al+nl93O02IzC36GIqN9JngGq9PuwMucm04bH/dOcbi4n69y2M2WqLkimkK7y0hj3X2B2e1mmxabE0Z3IXkGRofNEuIkFT88Q7bD4UTvvVJetCKXsNYX2QsHAqtpHGWMWg8faPKxUAF0e7ljrUPU0L3axqnKcljNfnuzT1VAniEr3+7Eqp4o5LvMoPNKK7C7aLWZh+wORzhjU6eV+Li9cBTeLP7Ds2onvI48svk2B26C9AW2UYaOj/B08sMzjFxgL7gI4CkX2ukxm5beiCUPWGJ33oCmc7Yz7FjnU9V9+Zz8mxSQ+KlagAGLN/+5pwDdH33Q7jgP1LOK7oyydT5J7X3Y7hSgVZ64z4girs4Iz6cq+r3o0Zc/njunAvPcOeWP544VNTMSQN3/W5cRa2SsVnfm/e1OqhjPnUXh8XzWSVdrFFvkiKZ2pbjlWRcxxXjuPIrxLAvbl37mO/6vK8ZzZ1GM55gCa/xZl89gAV/YYtxZl8/gHj7yFVNM/vTA2rON5wJunx945mzjeeozMZ5jCqSYv91ZFPO3fxZRV7rFemx+qc7K18PAZ76JGc0abM9HYSSLUTlWc/t4BLNa+Qg0H2PHF1qL8ZRprqdTEDxrsfQcy/E+CBV/yXIoFhk/zaMT9OVhiUiUhzfZYj4+X1WO2krOM/F5JoPz7PY+LHCmkKB5pjy8WZZjo9uJOWY+Q4bqoYHwDEtfAZKiMeaYKEDijLFywBGe5hQLLc8BCbPt9ol4kCNzhisCpBriCoJnNf5Q463HQpuTJlioUjY6T85ewGgd1wE8tVFkYkkKSJxiOTawPc5KI96gnmXjyblH5E2MA5Z+Zoej/0syIUJlBprEwqVzg6ZOA6xWDyYnvi1jYoUBWGHEl4kKKzBUC5r2KKcqPRSYge2vdburqwUoN4OhvisY5MBJPwrMcxaeUdUQef4lSsw6SPO3WanD1TxcgJRSe3VjN2AFxxKZhCvNgq4wuFih2eabrq0r5yFXWcX23WX4gm1zu7FNPDhgJvxgeaac+kuGCzN8O2o2K8U3rxSAvYn1KCFx04VsJZ7fQzTLJCKJM7pB+o7zxK2+aeFbb1PQOu8wq9Ltvk0BK+5QTNhwHiTPDnT5B+Z5mF4Br/1FOd0u7zjg9fiEf/5GYG+4673LcVPq1vpz8HecGdoKF6dR95nnwYDXzknYTotYotbdrphpj4db3t7i+QLvXc9jfc92gVtyzgJ/wmBGRGrxV5PkwOBaE4CzDPQ0e7sJX+LHJhd+HGl0BqtEjmuQNCcPrAajWwCxuZeexuHMNtDRKrI0k/u4o9CBAvKcbRX4wu7s2ERSHkKZgcatuESgrIYqzyj8OOy1bnxim3sC23eDAEXydDQ0lB4OpgDJh7phhb4hxhy+LJcfBclzco4C0vYhRD/I4VSb0FbOKFXCFDkXCZWSvFEJbEmg+0IgvYRm7J07FTBFTj1EU7cZTfecz9+m0yTvzx5QwmSaNuZPAXkelhcHw97pAvAF54y99ycFzCyOn36NAI+1JeCW8XsobpK96GeRrKD0wmUCe3eEEh4p5jO5R79zDrBmnTAtDhJbnuoyeqACph3VCTDpmkh/up9djCaQVxNitTxiIoWwNkoqWu5ZW414VTNw0JRuS5jZDNplRfOsO4julUGOnCwlQ1wlx4eW90G0R4FqP37Mariv2rEC8czQymPpuFOVHIbh45lURKnE0LJx0vPkN2Erxy5AhgfBzW5fdDaN80wvKOHBNvccfkNhGwNcJUHy/Oa5AGtpzfY3+ZqxiW3XCjC47WrhTfccmjk+Zu+FMIFKGiPP8g5bE/LR2B9FhJJ+4NObn6YlYV96UDlsx+pbseTpO3HrS9XI+PsX8/07VCCe2Ye/AniKlot99q90RulHkeEB3yXQqhsDjhDPoz/mcdDTbJEZznFPxEM6RWYM/Y5HTm75O57CW49cRHO2nl/dhbIEdm+mGOnnoxR2/fPJTFEXjeRru3n8hYUCvvQIs6vJUoYNXlWjm0hEDCMMt6pHu8szD+m83OHeTTPeSrmtVtGqzXZpqot7CJUBpsIF4jmLSqPMQ1i6b892ng2+DUU8Alsf5trP7Tp0BUAuZR7KaF8Uvp1ncSM21MW8Em/jNfR2G299dqzgeE6jNaB3rMad1tfS1Z/Rhm5oUtudvWYXexsRbXFP7T0LqIikHyO7+t9H05z+NV7x8sLusPV+LHWlK27kqnrvmjgY8yOivqIeP39uDh1HxwrE88gdaC3foMxDK2hhd5jcijipjwygSkfQZ+08J30X3trP7aq7RQHj5FjJZPqFGuZjda88z2113VB+Ev0+JLjX3e3/jH75ouVbmUS20k1hyuCQ8EFPUdllUgmdol5q4UZUwyM7w5ZI67nbKPJZQ68A0B4jWJxNC28zF0kVHPSiAFHKgXjOp8+b0aH2lY7y8ZxSIy3FF2Lu/hYK0dR4QguDOkkqgreAQqvSPLTkMsrHsyr/YBMvOrmqkZrWq8hZ8KPgeJ5Cn/9ASYdW1dNVN8NzKZ5D219wQ/IHjReiwf5RIvJA3RpaGNRJYkf/T5AjodVH+Zrun1EVKynVAUz48T5FwtHbFfCSG9/OKvXPayCe55VjJTwSekUlvoLHfuQ88/WXn7qJHn08QwtnPFwxWsl97g94Nacepl481jyLeHZT2f2w3Uyay5eVfHIhHUdnlouML8+dK9tnq4SnrZdSwGY3t1Tgxe/WlfCcC8cjpcOSXsL/iJnss1a2z/VUaDG3z5XUwDTbDQdrydW2U0y0HwXiuZI+ryf7XHyyfYZ8L7ekYlUFbfCEFqZ8kpL34qWRS0nEBp9sn0F3aBkvWsdJXxVgpkhwPC+hiOcPyD4/U03XXibZ58Q23pZOa/uDIK6/scBDicag9c5IziuJMgAO+5zzzO3z25SIEO0zffg0Ps0/sKWsfi4ewqxd/q/+QDw/S2Vuv4945qVPP26fqWWNOsaznEQsTKlb8LqadIJ9bjxmn9m6C/guqYv5qTxZLtfXeVVIrebqYw9gkNDJNkhiCrarU1q04ETUHeRqR8gz97L11HTmD4gdWX3+oNqNG8RmAXQ8v0ikPJfS55XUdOYPpGM8s0Ny7gTuk4OHsoqELe0+vDQyieCsU9rPsIHgQ+2jrNnR4fkhKvJlajpv4pkBk7HpjCCjkUZ9/QdhtlMBD+0jDCLjOfHo79GPp9TbiXL7mbvVTz/IiRpJiDKV5ugfsYpIeX6qBL//7Bzc6Sle+sij6FYnHsEHNQGHig7P3VvQtUw/0v6A7WeKe+YPc+UufPU8+Uw6P89W6rd2kKvdwuOQRXqyVYOF4qGrzeBG4jSSih4i8rcZJb8WKXeIrYz/hI4S6oGmAWGevzflIG6tIsc7Un97ARYsPxyQMw+eyPMBucs7k+rQeHrzN+HqEFKlOoSOdT6NTpGO87xKvpWoDpG/vTlA3rLgeJ5AUdazd+OF/fkdVB77+i70i3fyC131w0Xw8oMKYJR5KEJ/G75HL5t9j073mA2cqlnl+PQZj4iGWVZe3+tL6SlSf3v6ZqRo5kZ8+PB6Kj3hO2znjseGs7gIv5iIm6Ljb0MDlsWa0I+fuY2XN3czPh0cpoRJNBKWKHSnLJ9U35NLZa47r/TUv61r0YCpBFHBP2chsGodaJpE/Ak0SB+AsRr3MTby/cMW3hvQG8DCd+tAVZMCmoN9wMzRriJ3W6xHy7mfAIy0P8xQj59T6ZmlAuRyx7qe8NUZBcjGlmwGOiA8U0l7mt1wtXkglppvF9jeK4Dtoj6vDLpDiDk9QNzYDbIsXdkC7tHvPb60xWkVHM+Je5Ah1Y/XCmkblZCBf0vKlLDpBmGMpRt7COuZUqWEtA8QhEjTD71OrfDFLiW8fq3A3rlJSPjqd8LIDXEJs3sL6vWU1F98poLX8JwvI3hHCsRzKmXxTPiyn5C6Ph7GI9wzi+LZ9psV4j8fttsLHPjlyV/zjrCkzyIzm2soccG0gni25XoF2/InZb9vE4URz3TB+ubY7RsvF7dgffmFiPJbcudYZ1YZTRTROqx2NL8mcrmtVgdZaq3VYqT+ZqPdZCLL7PxpBGfI0nF2LbxwVQWWjHU6qGBRHgfWWC0mqo9xV9iPAvEMpXRX0OZZHOjh51ATWpVrxZcZlY6BVMEC91h5blheiGlETpaWZ9jOsjiRa7aReM604z1D3FAxipLnp5WX2PnIs2ZzgPtGcDzDqmuw1EQ8L7T5g2nnCVYHthgGby6Zw5doysgbZaT71GxrgIICKJEvCzkhj9KSsOduxEb67Il5vQW2uHIOzQ5TT1kwRK7g9fYVOzpQIJ5hxR+woIT5U/MvEWA0rUoz2pJ/hwIbzzRlEFsTMD6PT+OaHN5aN8eUsIjM7oic/FvxjNbdrIDE+VPzLhLUs6ieUcqhs+n5WgV0fyuy+8YvQSp5ekcgqSLOJhbkyh+UjcivAvKsKgqwgyyNnPA+AuXIznsg5be7/R0pSJ5VqwLsIIutj9TG0HyOIDSZ/BN/Csiz+FiAHWQlBLebH40ObqLIXN6p3sllaM/Y60+ijcauIhKT18UIIHNA6gPyDIYA40NcmjDXxjhRebynPoCy/ax0IytIniEpN4jLjU3wzT6NQIuDmTJKczX9KyDPkOR3fQ2f1NMjWlGKK6gpo0MNnd7bJolBoIptzsgVRCFiYOYD8xzU0UbljIKY1B7EyibB8hzMrwMRrXbariAKYb4c9X4UmGdICOLa+6+dUUI0KoopRAXBcydT0Dx3MgXBc0z/85r4wtnGc/5SInni82cbz9llMZ5jCqSJZ11+knxunyeedflJsmP5SWIKqJi/HVjJ8lKr8tKuP5ui7G+ro7x23BkRrX/EY4B/ZjGrxdreN5Fis/KRYIuVr9yhtfIVcehVcGNbfqW3WdtPV7RZeKhHitXG56aZLBbeeSTa/fUhBcuzmGM5FhBpsNpoKJhlW2kkmgakeZKBzCJrxEkNAAZbbe0jN2KOmdepzbXywe1sm0UOmcxpP5LTKUie2RSrhSa4k5Jz5DonWK3Una3KtvATUs22260K9YyBKTk94aHeWQMjGWJPzrEeOy+smhbhSJxi42PPI3N4HpGE+UV3+DnooHgencuLIiXOsPLSR+TYrsJNbLzZIvdss8fiQJx1yUgTH2cPXwOm2G7wHS+bZLHS8HniZMsQ6tUenWul5CUJMyNdNMMh6VJKqoMZyjmjqjCAkS95g3jV4ltkrswC+loGrEEDDh4BxWh56AhlrBYYZTUg1Rg4nLrdXaES7xg2u6DdjZe/tlDyR1mwPNcMgSzfrhlVAqNF1ostgqGmK4hYS75NgAVWU1VtAIgCa3CVQp4dhto2UMhGLDW7usGGUQLkOwQtrWqlzff6u0iC5Hn9RAVNDSMl7zwXNt0owENWRdK2C4F9cLXAC5lht9uRwcRVLgRCnFF5VwRnl7TjPHj9NrmA5+5RpG+MA/XHF8PTWOaEAmXCO78WEhaPyvnxTx1XEQzPixxxie/Ly1YmfHq5sPwuJa1rlfDe7wV48SbF6Jf5/MvH9tJEshVl/xfB6aAGvN0dttwul/HC7cqhL3WBhHcThDV/UcLfCuIT3vqtkPjUnbnfR+YG0Ip0mmOrtv5cMvN0JHwKGKvDgzHXgo5WmndbwenCA+Sr1dn4PO/IVIPgWmv5/89I0ZP1A6HKTnMvBW0zUsxDlvVyRGUHCpJnI0V5YOmkGmQrq1ww0LKQeycKFEepa+7NUvDe5Y1sDjf+XHvHCpBXya+B7BoFsEM3CpvnCJCxr5vuEBa+gc5I44mc56Rv0FjtkKduf3C/AjJ3KpN+vIgWjVU8VKkEsfXXgiqbFyE+8aC1oCfMtlkKfIGc4eidexQw8nOeGWQYLSH7/m0Kmrad/J/zVV9dJ1BIVBp6C8+VdMxsMONV310ugG912EWFcZD02QWqL29R0MxPPnf7ixF4RsOeQJ7Zool5BRGti8venRYHk/7Fp4CN/hgv6MaRyjVL4yH12x4JTVoBniztMhp/4r9HFjPZiD6sTqJJkIwS5oFG9jVPyrqjYxo8APzIF+TEXVaR8edAE6qCUQktK1/G54BpKUOJTtLZaJOjDviCk42IYYrVHDHPKi9eAzovx6yAZpcWO1XeIfhbewdaKe7KTmvLRoXnAgfuJkdMqrzIsdbbs4Ait0orhAMULOGZSh/llNNjJEqmKGo+cRtgI4VAbSvs4blVQITvsFTh25xmRDkaPM+geAyas41oU2iV2Hpt7l58t2S38gNK4P3xXxVLDjxPOYBETZq+hwhJwhQFBRmEJ0ZhVuJRbn75ErKLCy+kyGf46k8mWhN68o88cNjfErJB8DzyTSrqB87Ye1T6FyNMFGWV/v05z1L01atl8ZAwwYxos0TNmHMDj3n7UeLhJKE9YnINLSG7rqzLwfFK6N48xvQZ0jXuKE88uOWPHZ9RYKVIRhAr3Exlb1HVukHrMDSi740NygayiD7pygy2JtCU1IkOSi7AHCZjLVMVSqKj1qFxtkQBaL54O1+8FowUMamRjC7aZJWAL71TawfmhMh51nvxJ9N4echFJcVVOcsMXpoPedBcSMHRVh5WGQ2eK+24Wz6fNK6nNH2ip38lvStq6OWh4OgDlNpAuzvQXMyAyvZiA9aX0WAvlbnBPZDeiZ6pTjfWm0XB0dHgeS3dLGbXUEGZbVfiY+udxfRutieulSIodqxWpuVVeZ+nHRI1dDgwmD+Gp6RWyiZ09AE6qOconGpW5R9aKb/BRw8sOICkjaEwaZhV7GcOdxA8z6MQq9GtPH3YYbTL8OEDC75BupOOJL6/Bj96tqYLzDtPDrPieYgi0NAjaIT7HaaUofDWGuT5yZ39Wuhd80P2HVjB0COIOptWwO9T4cre4rCXUU+U0W1impQGlYhurhM9XuvxPiFtnchXYy50mqHOCIxS9jVYwFZrgLJCi0iByU7eoRS+GjjPPKWQWebZ7KZNZun/JHIV8I1dFQWezZTWQOMlkuUV3e1uM6e33l5BPPM3UeGZx0jmuWnfLJ520zNoN32zyDvQSzzvxTcpeftbIgU6jwpP47cIOER1rqofxelum+OSeb6BjDVdRB0pOJ43cZ59qUk4z/dUuvHdbE9/nqRkE72BCW33+S8mWKVRBDQcnUeFvk4R0LP23i3zPLeIkg2Nab1ZwaYUeNZ1zGwQPPPEJKNbiWT1Ec7zvKK9CJT6SPr3T+FHy/eeM/4qoT1sMjKNPoo8q1sm0BnVcZ73pvN8Jc1PuSqxAp68ZNLathWRAF3dHufgrKauJ7PWbYCUn6bqYuTxuhDxBiejKORCMqNNZiir0EBDIZgpkNGkBRaymfatL223a6Ca80xxkXJGA41kkO1zC6ADgfbZZsA7Rpg8a33V2C3t9pn3l8v2uUQv2+dRPvuM/nj4POf6KrL39dlnCoNut899y7l9rush2+eF9JFY0x4aHZJ0PAIIZRKyZPuM0ALs4va5wmef7/XZZ/w8bJ6n+OqxDxTWc56rqKAM2T7fJtvnA3E/cPu8jNfx5mp/VQUQ9Y9zTVT47DO/O8j2ufxann/oo/t89pn3Yo1slRN8nE4d8ZzeXsutikUyz7SKu88+3+ezz+r3uH2u+v0Y5bEw6DCUyGOoUGOV6bJ9HkZntGU18Vzls8+zfPaZj/JNk93u8KQ61hPWYEMb3WIyoqm2U8v1uIxNeN016rBNmwImZLkFv0IRy012fFKBq5DvJVKXdNhy0W2ljNerkbAgnaS10yZsP6MnQO1nl8QVKJtuAGnIn/a1n4to9KvYIba3n6npvID6yKJin3kS3WJqRGOliJTW2y3f137eh01n5pEzDuQFE7vhT7oT2s8bKBEJtZ/vEEDrudVag29zKCNRNOwzbzovOZ7aU2y9Oo/SkSypUu6g9vN/eK4heMjpv5hgJcrtZ94dvFhuP597hFrTX92eRQz72s/wZcdJPoOwz5Mp7Z+v/fwhlf7F0Kz29jM2neGNZXNa+XXnS1gSidQt6E+nH+Fe+5OlWO+6ki7Nk+Oo/WyW2888vKZ7s9xYCUtmgoekRTLRg+avC386zOtEsCzItB2to9PJVNR5VmblLBP5DXoRStxgabGf0OIOVcbj/dvQIPdv63n/th0K0U5r6OjCts8nqp76t+v4MKqZurnr+0Ml1qff35XnCSwlqxoVnrN4/7bsTddT/3apoKdknHvHCpQxUHeQEwgFEeUcIu0dfqx/O8fXv72N+rfru2mJ8lXR6t8efOBY/zbsuEfg/dtt1L89VbGE+rd/oIBhtM/yHpHrlP7tOxVkp6l/+/vref82fZTwDn7UgYLgOfEI9W/LRS13xsGAzy5QUZaSmaW+/m2y2ZHY5xP11jglTHqTF9Xev/0C9W9/qlZRxkBfcm71JxFwVNHie2Gjwd8UScdMOjDUqZhFhFpfog57nd5krjWJ1Wgoa+0ptN2MxNvxG9VmtPCWFFrcQh8ZaxVGnqHEVEecMebG20yFFfR1DDQNWt6gR0WBZ1N1V1aTAqqDZqBxaAu6wCnVXaESbyU2h6DlybdN3APvSEHyzGqGC8gw5NbjpV/VVaSer2KzYMBNqpo+UGAWxPxBkBVpUn20zeUKtvMKYAceFMS9N/Lx58G7ewIlOHjCKWh20jyMwX5TGwXHM7w8VZG2TQkTDlwIaZvPZW/eKsBDNkXyZiWoP7+Wxp/VG24UMuUc+FHQsB3nsa23KdhHf1Wy1+9RjNwWB4n/ulSgpCQTiuIS3rkI0nN7ahZTSu4OFATP8JgzLvH9X6E97ifQOPSz9yph/Ib4hA8R8xdvU/AkB1HjOfX1C9iWfgK8Oy+OsutThpIBH6oFyl4yt6hLwltqYbTlQvUiSrIQpgzYApNfmfhAlN7Ol3cy2i14j7D4GsSi1SKK2MalWVNmvO5VFiN5vfSAm5gd0UP87HzKR9hiViP51Vp60Fvkqi1GPj9MtJt8B6kLkAooGKXIhVPaAl+dlJqEu/FGk43q01rsVj/RjEHyDCzHSNOpUmjCVoqFz3iDLJOVbhiqXBP1H+bY7RE2H7h02VaafJY7EP3TXBNfZEeXY06h50wTn4+my7X7m4gWJM8wwWjFm0MyRUEnZlv43K1MYy6fHzZ7FM0Pm2G3B0j1HYqScngd0+lhyig+PyxhipnP4ErLyu1NeUvsfHZaRwqGZxhpyrkQC56Fzkd76alZuTxcefIoi3xzCipMOgglTrDQxLNxN1A/9vBsOrrE6WMHUp1Ds3Ivwdb1LPvEAElX/htiaFVrI2k+dyYFy3PnUbA8dzYFxXNMp0pfZlS1mCJw+zuTYjx3FsV4DlMqp0bjitnnzqoYzzH97yrGc2dRjOeYAsv2zNnGc9FsIjn36bON53v9xGrEFJOs8U+dbTwv4PZ53Jqzzj6vjvEcUyDF/O3Oopi//V+XSQSV02RvH+412yw0xKxymHj2e32h0TcdNcWN7yLocVPhdy02a/v4tbbQxNfGMNr5WrHMbqJRdBIt7c4HpTtUAJ71DDT5JlobgyvHajPi/qoFRgdNUklZYLJ3xQoXuN1DBMjwjfCHI22KANlWKx9uRmnyhy+gOgfnmWkGqWqB2U71Da50F/YAMdvvIQfFc0YPUD00fIFvJUvIyBtLy7arHhqV1x8LT1oyKq8bntdDle57FJDGB7/Dk5gkwJi8sWbfwagfvyOPsnGnLxmbq8QKpphzaYg78WX3sguBTfB7yAF4HtAV1IvvyGvPjD0+z2rGGsRFdy24ls4IP6Kh4Jmb3bcrYIA2/DNKSFfA6LyJFt/BJCy6i9eZ+tBdubip+7yxObQIx4DX3MsuFtj4Tn0LKkSKqlNA1yAHcxkqAMoQsDITqBq1oGpSgZnPDme1NDPM5meWh3/paHJ6hQClvkkvNVhnTVdIwT+nA8BhBbabrw3D46s0NOmzQ/nneQF+v2YgL52UUSpA+Sj+p9rfC1S7u0IOHscCq7mKIqtyeaXhaDAeY2a5AjbI08DZzv6QXNMNdFUKyC8UYMODAtt1FSQXDC84+A8F6Pwm7w+G5xkDBXj5DkHzwSX8bfJOJSyxKWD9VIXqg6sFtudKgZa0WjnVvNl7mwCz+WSTcKSeoYRhO+JgpbyeJLxzpyLxXxdB0sfnw6wCJUzPV7K3bxKSVt6x4MCWOFA/5G+ih3+ep+NtYjuW/v6v+Nth73SB5VOV8NT9SvG93wvih5cLI16Oh3lTR23yIJDT+MI34ajfrDhIf/scWP6gfDRb/qjo96FaUP9bpCWt4BFnPHvrFkW/p+9c8s0LXUA9vRMDbUaWeHgWpSRBUeiFsZGl0MztskIoLMELlUdX223Es8qXkCh0ubDA3SYsvIljZqKoixoj7LajnWvuoaVlJwsoFEpr4lO3rfJE8tPLL89GE2JGM7dr5LWdd+OTYbeQSZlJih1CEVai8vTX4G1MdWipACzcdAZsWzcQ940VYDCFW+CNYTc+7ZsobF4oQPKh3lpaVzK/TJGHxefTrPEcMqIdKQieB+crIPMQXmqbKPQC4IM5Ckj+8SItJSh5okw5oxy5+vpaTY4C2A9/x4eX5cRboesZ/OLWe9HE/8DDp9JpHeiX7ueZSRK+v0xDy07Or4qnRIPT23CPye12/HTyy/MAvHGk/6cLwIv383vCG/cpafZ26pEEgHmFcY+VxwM7PLTnMDyZL5bHAXuj4zgu/3oBv1g3LQ4GfEoeIYym0It3p8W9sCweElt0mib0R/6245xJeKh8iehJYd84fnaxRjS4LiK1hEdKaih6SiMZrLTJ3iJnJqlGk6o3yjO3S8Kc72lFbkVueeVISUphBEUlPSiyCvabLBREaWlGvOwC51mzX7aup5U/nlk9fpHCLbB02kv0YHHM05dHVmXvFvZRdOS+hbz0KkpQUmCl3UIXrXPHI6uYHCnJI6s2V3Tz3MNDt8yUmSTLcy65PYMpykq1089VEgTPO64RKJAK4CFaMBbEVjTCrPVu8zZ8N6btvPWIN6Lek6YUbZqKr5fQQxgalq+UM5MwOVJyFkVWzSo/7yhFWH9033CKrBrTehmRkfwDvhY/knN/nVZ+ed7+GwFLx7sFXzAW4HA/LOhw6kN7EPH0z87Z/jB+999P4u0KiZ+Lr2fa/RTmR0NXIreH0cJDM4+UfJJCnddUndM4GctrnjWc8L7lsI5iJBP/ja/ZW3xJy84oPVFcTUGSLr4YrIEyDTHJQnYZLJKqifB1F4Lo8EVimDn2oavagC1XimxmXu5wl1KmIUd1ipdWV6+3OSnTkNnbi3I4cJ6hfjg9nl7+eDZU4WelFCS5gK8Kr6NMQ+AZUk55DbKae/Igyb0y6gfI184g2MLQNjTNmZR5BDx3UWm7VuPj2rprKNMQeBY6KN5qMPFOvde0wz4/a7wF5jnpc+R2Pc9eQEYaktpuwuJa/+qoxE2D267+gFZufYbHUsLXFGeVsTO8q/8lbH0n8cwjRx+h0p524eOEXdfxuMmtC21tRFsrLeYO02lZaHjdz5qx/nge8BGW9BQFVo3/gMKaEo9QjpJP/ryhCt+lHun+7Tz86I0KjvoXlAEoNcw1Y9fdGwdDeZBkA9lg2PIPrODJfXJeg4blUynQKvVwBp3suCeoui2dds1YM/nPdTyTAQ/zMlEmA5BsFbTJLGkkzrMLHMzHs76JHkPXQbT7JspMAnJmEp7JwF5nknm2u3yZSfSIvcxzpa+dfTr54zmHuOWZDPLI6ZYzGYBn1G7Os/cKnslgL18TOpdb5pTmji9HfzowRIBcL+dZzkxCPK+qv5FnJvEsK6RMBnKSA9UGuqXAXmzEd6TAPGdsxmuMJyCZ7aGLMk3meW4h8ZzW9ocfEUNYWUEX4hhsVaPx/LFjlPzps9vQ1ZZ5fpJKe4myI4zZcxPPTPL6skIPwpDG37Ctl9IZrXDSbqeXP57Hv4nsvEgDWqM/Jp4HcJ4/HLfJzXm+7ggFPL9Kb2D0AipGfRgd8TD07jilnJkE6mZSOe/yzCQ/ph0mnuvWzNnDeR6Db9g67tK/8kDHR/3Llo245UkA5cxhOgrGVknGQtpkk1gj59lhsun1jlpaM1kvyf1mIUrlRZ5TKJOBysv7rksowLugTCfzbOFJAM1eVbFer/eOokAzysDZkfzxXEDc8iSABTxzmJYyGYCnfzHneb+CJwHkWb50+fQ5aLiNDV3EbYZsn/li7TvpoDZWX0GZDMAz1Ub2OZMyk8AqeVHZvX7yoATmOYu4XcV53kf8aGSe77XR9sy2iyjfJ7xMy+YkrqXPQd0aXipMykCSeJTzzDOHPU72efq2S3lewPfvt5J9Hskzk8yXHY4V3EqfXv54nkWJwx4j+zx9G0GbcJh4/nboEzuJ56/O+YLs83v/wJ3UT3MjrT6c2vHv40dN47FdzpMANvHMYevKiOedCdw+H1w0UbbP9Pmj8k/2yjI6ns4oI3FrJx9azuzJKFOCVtLZaJOjAdxkJWvNNrfbXddEEdH69mjtEEX2WeVF/1bLCQYHOfTFTtaMbjU7OMhErri1oX8lViRVkzdczvOKnF4B7fMCKkHO7IlNZ6q5Z24Nvst1C7spyRCl9xR9Hc5aD7eeIWvfIEoKdg0WLt8QisrwgtjmUhyisj23mqh7LJs6wtp70PfydKKnV2CeB5N9nkHZhSh1AeqHOylByU2mXfhuwoG4l6mX7E1sM4tL5PNJbOUtz5D12Z0KYNTp5cvsOb0UsZpfpPyIWtOf3amnJEQTvkHAhvk6+FYUhsfzeEpMMp2azvOK+F7folPNDv92zH/QrR6xo8urlMP3E4RanCcHMKoPh7fOJCUxYE3UrUbpSQDmbsMKEGrqImPNEwZ9iXZq6BG0NZN8a0S/8reOj/qXLS1xqyNGG6kjDG0xAoyetVbCc6ywAPWLiZRulG9FGeroMXRVU391FaU9wVathoGeHOyD/aEU7bS+tivbPxABlF1s2d+u8gVcn05+28/Erf4gPlBmEi363ugNZ5UKKurfrhwl0FiVdn8PEGm9Ch06G3oCLwxtpO7znQhvVrmCziiDurkPXANrCwUYXNNNPISUP4F+fw4fhMZ993EzfnoF5lm1A7lV/YhX9g5sruN/6hmnAgbvPFf84UoBVtoVE3biRfv1pSCuOhevfJ0AaZ+Hd1G+RM3h5+YoYdgH59FirolfI3Fv36ZYjJY06T/ns8+wHf04fswX1KFOpqfDbT9/hjwnftcdYDsWiag+NScOhr3ZhSfkXj5VOfnNLpDw5eUCo8Wf1eguJ34aXvt5DbafYd1fsRH9zjmg7gqp36HxaEhXUDaSAR+KDF/C3IJ4GD0IT+QW/JnrRvM7ZmcUX5bD5gSrA9u3yLW+TqtyIwW2EkZD0azazHxd2jLPjp8mQgpaVhrE1tVqVTVavCdi47nQzsedVTUDVZXoE5jKehh4o9bHM6tvnw5yGvnjme0n77bAKeSiec5DiHVVfbQ1yFROYdesSgFY6RCxeAiwckpJRUtj5Pod6u5YmfQ9bdUVmp1Y+KFlAmywCwtseLPYe41qI27KLu2ZUtUNsjxUEWXj3ufHDwjMM7xJXsCUgm6U/SQNuU7cc61qI3q8U0ovTN58LrD1UxWPT1Wwl71YXSNiPyU/vIty5Fq8+hP/dV2vrX8Q4DNsQi92KqcXKUF8/bYeL92BdW+4KPnN8yD9BzovsuDh9m+zt2mxi0WF501G80wJARPf/73mn9cJMHrVecPewYbss/ecu+IuJXuRfsF3kf3R+eHdoYYuIZ/9td/3egMdgLo18bBuafykBfGQsH1oz3U3K2DE65cn/bM7jD5CZ4TN6O6NEaQP+5klz/7SWWhulohcgcpo4uZYa6F3CLic1gM/oF1YbVjNZ1QFNlJANJrp+0Z6kOsEZjTzOSoas1wf1kh7Gv2Ni/njGc+IPtRZqRGuojJFk9F3RuiCoPQWrFBFi4bpqeZyPnQVhgroi2KGie4fg6mkZG6KcZOZN5g1WQbcI41XhIeU429cLAiek/Lpw8RsmifCyN6315OYJS9RhdULoOL1Ud/Py/K0k9D1Eq3iJA7OojWl0vrgQxKvE41xFr2DhDFD8LyS+HnhIaXn+Llv+OMZBiyhD5MmUHYQkUaSxGEmvpCVegJZStwhm9bs4WdENS8PrwGBzWJKgcbkwtOppAETuGfN0rP51DT1mOHoAgzgZ4Q33RE54d03fhFycZqCVXtupNCV4ps2Gpw0flrPAXiGAj+W/VTlypCHIa3fGV8nSyzy10wPgmdYElK/3Rh/0zz8KtHvjK+TJT7ub96KX55hXvtEz6A0nmciCkehzfjq/gjPgNhZZeHGKzgZuUkNT3rfOj3BSOX/tuGfZ/8rOZ6kwT7vIxzpQli6Ucz0u28wPEOW3E8elJIjmL+dFMJybOJIv/v65xnG+8s9dpJSI1hZsl8IZ8RoBauY/pvyz3NnVFA8d0IF4DmmmFAxnjuLYjzHFFjT15xtPK+YSSRPX3228fxALJ/BL1Th9mufCcXscyD9Uv5bEdpnkYY68OmExzOj7vyCSpAvK9/TGVcEXTcRi9HE0F+KYjwHUOITv5AJERHyPIAGxAD4rGt4LKKl3P3rFT5TZQSlbQA2LTKfIqXE7XS0J+foWOYGHshwJqRzWZzt9TvsFgp90pVY5AVojXxed6HJ4fQXkRy8VGVWe/vYsrnEWoh3XVZks/FpKroyf6POxxWYZ32xpZAPBvtKx/21pdZCCs7MLLQW0jzyUm+LnwmYwSvPaSn0DWFriyY6aTGszAJeBVtltVOmkrTiifxgWLa8xtVpFAzP4qqJ9vZcKJkFE/Opb/iJqTYKvkjbONUpT6ZOpCiMx4fnF/oJ5gpGiwsnFvj6eZPX31tApU0pmlh0IZ7FyokLbHS7SNt0k7/D5fLH8+Siewt8I8rqFffmj8Uyhz0xsYDGsMRZpTQFZMVdpiVXCtPGmnKHK1Ifd97xUFigJSy/dwEVThqXf8+qiwVIePb+PBsVlvo6zRmfZMl/cGJc6nP3DV/fhT16W9aCCNa7QVU3AWjcgSZesZYQBnxCEqvTgKpRdtHMaIdp3latHlgDMq61SfL2hrDHaX8qpw2gSp46oqrvCoZqAex46gU0V8zoHcs/CKSAPLP9fYCSkZAoTrnAIUDNIGD1/YHWx8qoEqB4uKHUG/FKdAAZyGhOsfz/33kNqPZdA9qd3bAKBZIuwLaJAtt3Baj2dkMA8us7uk6C4XnVWAF2yHNGE3ecCxnblDDFqYD1ExXsP1cLqj00f4St99Jl+tA+ytcTgYZtiIP5FPiMeudaIeHjS4W0HfEwATcvnqqEf96rAHVW680BL3s/PCf96wIYth6xRb12s4J9er2Q8OEFkP7xBcCSX+dhVeypClq6ctoHFC2Z9Nm08JyOdXcq2b+H8kPt9/Y5MO7Nc2DN/XGw5b44UA+jBWQBbvl0PJad8OpqGnl+ZCfFYYcv1kKxCVopwFQNrS9SUQxzxmXHstDKkrVysRRQpW9RGSgamq8gq+c8l6Q4LP5zegUpsbkPVkgxE+gKEJb7B4nNg7Aanr8gWjxnUxB0JY/HwAp46Tw0oqhQKMXvip7hWrx9sQMUZhWhaOVZjbzWHK9ic6GwwaXAKm4QD90oQPb+btmU2WAz+QJZkfCcRCs+L6GADABaCFpsvbMHxWRk7TtvAuU0WE/5C2YMb4sDtunWPHtktypaWTKp9ToqI51Wlnx6jvLtuUoQv79Oe/R6AWbtIvN5NCKen16IR/otFoZ3j++7AzxbFv9UWTxuosnTyytpbvfTf9AvuVpYfpfJbFGmPzHnmpUy/aEp/YhKgOXldH+ALX+PB3VLWt/DSPGk/1CDvJl4nnvXE3fOjk99/M6+i84XX7g+M3d4eHcOWXoeZAwNJaA1mcGqBdFES9PpaUU2+lPZTDSHy1IGRhsDa0NFdBzf4yqhmdlyvKRWzk9i5MmF7ISyzDN+Lj9FKgPFYfAl3dFM03lX2g18wjZfUDJaPBfQDkVUFah46Z7+PI4SgTpAUYs8XhLtKQ9cjkgqHu68lxdUQCHPq9qrmDOE4q7SPNcU0Crsqyj6KiKep1DYc5aHe6hfz8Ud9ywd2IbOaVrbTQUU0ji7VAlJOX1aCSD1qkguSLzrnhAvOZ+Wq5xVeZGcn+T+Ua1IxhiKj46Q5/f+guV++AAd6KK9WObMb855j7KQfEiLuHOe0R0+sf28PLz28zQKb57ZyptEFDkJzfNHfXQsQpLz3N5+Hs3bz/C3cG4bx2WnWCbk2Q2sotDiNuoqRGgwgAE36/BP26iBFqSpzGo2o8utb6Lpx1FVNbHq4ubfIOczsJbQJjO9aec5Sj2mFoKMr/OMlpN4LnLxXEPgpXwk0eK5nOcz4JkMUuR8BsOrOM/e3nI+Ax4arTrAzWpEGizzzEObtxHPeVjFPbRp9UQvXjwazx3l9GEeZUGJiOcn6rG4LA850qo2qmHP8xM9yLOm7S/llGtodo2SPaFUc55ZhAEFyXI+g78TbDw/yZg9/yfnM1jqoPhnns8gMp7ZYVrX+TWKiYQX3UjQzNbLD1MGg9fI1fbxzOT+bbnDObxgSVizFwuR0xkkHiae69Y8KEc8UzC0j2e5bLmXO9L+bTcPXBIldHirC0VsyWKz1V0IejSYTvzIVSF7wcgxRTHqKFNf+Gn5Tqfa4zwbfTyXncJzlGSlPIA+ng+Owocil1VOWxJNnquO82zw8VxD0VtZ3r7e4zwvCGG+ZkfK4BlIZJ53VmBVec1X+Xi2+niuig7Pa4/znOjjearnUqyh7e5tPp5n9AaZ50iVzvMVyDw/twwfj+UnWeo8xHn+Y6Q8i0eO8/yazPNvjszEql6jDEM+nqOhdbvR9o4+Sne4fjwJSd2ahR/gpp/wHEWxFh72a5aMoCLPu4RYbbCCvRCbzHYEzQHGBhWkSFowI+I2nncAQQspvsGveI3t/jZ6+irJ6KRNNoqLjjLPRrLPKc38R6wh+1xuM3ip4yqq/ja2k9H9pVsH3juodE//Im6fdwuHHsTnvZTUQN/eWRyyNHl2WWZBK9tnnkpoLdnnghqFXMWDBmpVJ3uuKSrEzU9E7G/PoLRhPn/7h7/ijnuWDiF/O7nt2lVUw+zSGzbPsTs9c6Ow/rP6BH97Mc9PUn7R0T/ju/fvt8j5SS6OuP38JZX3nuxv78Iy0d+m5AXw3t/xIYo8zyN/e9rXZOlZC9nnppljZftMAdzR51lPKX6ANZbgddqCXm0T4q1r0UC1GWxN+JFYpnfgZjsS5kIT7kaOtXoNs1WTJY2KbISymywyQAs2znWSxkKbnJS7IMo865qxIWPkpEEpDcDVmFSEsoa2R43nXLK/pXI89SFeerfsenyXVyFUOQVgh5A/TXQ67A+hvWeHeIbPXMJ1g0uxk9IAH7qrFyUvyTjULbeUNpPHEBHPg9sQ5dlkpAHeXKoA9sM9vVpvEiDjx/NmYNMZVtr7420mOjzD97I/TSVN2Yx8LS5UfkRJdb+6OYVc8cnURxYhz/+k8r4gIw2jv0Z7uaIy/sXVWOwX43B7FHke8T02Zx/ZzMt7F28Y3VvGDPwWN43+nq646PNsJ0dXLCnDW4aLOrrJt66w4L1EYzU2qUSwyqC5cXODQcdaUrQIv1FVdmxFi4ilQheeNWp5/HMZMmZzgQpvKFBHw0pR5pnn4HbhPcko8hzclJe3FE9NHoCOFs9012D7+/P451KbALlOQTyIdnr3IKCcBpTEn+OcFfkUHUoNnFmpoEAt1SE0yLuugjx8S5n1d6KdfsIu6A51A7aPmuoR8Qx70BN4Ge8emT1gSqUSknedC28+qKCvJlHaEjnNfnT8bVhZqISRCHJaH0H99fkAH10nTNmIb3fEw9t3K2DFWDzQCHmevDEeUt/pAgN0Avv0twK8d7Ni2L+6gPp9SskXRZ7ZJ6kKePVmBRuhhLkbu0DqP89hDaOV8CRPrh91nlMaG00mh4uPyDbQkJW5RFtoIae3RG8ktu0NDTbkDQFTSYWiXiI/u5FBbdRwJj60DqzJSOPgtfqUMnQHTGVaGhYGlVMyk6sSNemqdaaKrqCj9nKBXVeG/oCmXGeoJq9Y73XJY8YBFJBnyHH1pbzaed6eoKkcaKjCYg1F/WlAGPItuvK+INZQMornI/9nsg3DdVV9sfGMDvXggr75WCvbOLF/KW7S/X975wLfVHn//+9JWnzwMg6o6wHdGp37N7iNBnU0uo0Gp23V0QY3IOigwSENDmhQLmECPSA0DLHxRuMFWm+080I7RVod0OgUqpu08wKdF+jPC3RToU6l5dbm//0+z0lBaa4tmmI+L8jl5PQ85yTnfb7f5znP83m2XZRTdWrXzmBC3hnMcigSntPXXUgrpB+5WceemjV07UUSpG25KGfzadTV46I7nPxve4lneeN1Q5/CZP5jrEJPKr3oDlUP7LFpQzf9AsP2Yz/JX0eFTDq84kyxdnCF4Jk9etOFG3+pg1cbkbHHfjqXzLUXOC98kjqpyK+3jej5T6Np2KM/nVOSjJXnDB17/vcX/u1nEjL980lP4RWDTT+08KxeKyiIZG7QARYFGKbbqgnTU62ujB8w6lRiqgVZuxvdO1K4Uwfj0cos8lC5J3NVhZQYCM0fjHiQ/E10uW94nvGI6GDoCzzuiAKF9pZM5HsKdENbKzWwCAKzSAUWB1ckPAc2l0F5YppFuDZkkvUJlpDZ23Em3cKLoVJSMsXQ47RMrczIx1aH4DmwvVRqqWIjqTJLi8jVvpc1YORw+l5H0MOwzEFUABtpDv1VnzBRXw+n6OvRJZeT2SiIflcVAc99TJHx3PcUkufvpFxOxcAnYjxGtariIfPc76oSPPcVJXg+XsfnakYDGMJlcCezEjz3FSV4Tii8Ejz3FSV4Tii8CnuhXTq+9ACfA7aQOp2cVJpBk2sklFBIJeJzX1EiPn/zkgHYMfeLsHLOJe6aCRt9LnzxtXa56ETbC2wcJWtl0szqJHNg4wYWrpxwPGNJ3R6R2GzgiEy0uEdHRH9sPNqQIYt7R11tG5li43yx5pwTTJHxjNtjmXQbScig3aPSbiPJGeJtGi3u0d0P2le+FaFAmTSLDilDlMcycW/DHFc4numXGnnUmDxQ6EhRQgqfbgpgBL3t0SgJGmlx7BFpE9ulpmsFiLtkfFe0MSB9VbKLQU61JeAMwiodNpoDx1Kb46V+5Wp5Tq3AgYZQmrS1YlGOAViNw+YTPxXYai2V1ImmwmWrPw/PzWqrW0w8I+85D5gakrPQPMs2CcZXjaogMxIUq5vsqMYT0lx3VRmVMM9trRuOh7LD779PAguuHKvyh1LXEftWzUQ6r2pUFY06LnXa6s7Hs37rGJr3ma3p9LdcBKxYO+7uFQnPckF/yNhwdRkfm4laM8tWcyaWs/naUvrrmWWX0/Q3aS93+v96GqQXhtpUaGWZJbZ+1oQtmnlI7oar19FktPe6x7x8gQQp668tovnjZnzpb/2lBHPEGMMgCs2zPPU0yH32mqdu4J1YgT1zk/3lswHSN/72DupdcuOaa569RILUvzd23p0E6flirVg0epSOPX7zhJe+L/Y168krnqTRmkvc167/MX5lf/t9UVk/fHvY/xleo2bzWXD7qBQfRsMWBeR2EbVcXgB3NbAmE7BmK5ibGZj5FHTGRhoGZuX92GKRDQMm9Thz86FNoOwaDNQF01ErcXuDGltgCjrVj3izsqNh6HiF5JmVSl1bJxW5JSj2SmzXpcB2jQLzDgly9pzKis9TijonIPkxAz0vh28YisXeKLsHgbL/fBhfo4OCBh1sQ7a3TJMKrFLm9nodMDE7bRBFwDPbcBqw934isU9/wbc0tUbP/5NlyWs36dLeOw3SPjqdPXWhvKjjjzrIc8QK9Di7DmZWJMGcLZxF9sFPJfbBJdI4fJv7WjI8imyvn68fZz81482WZIAnxeSP3Ss0zw+eDuz9n0lkTUJvZ6xNhhnrkhlNT/fi9fr0N74HIz4eADMQ+kPDJBgxN9S2Qmn2GD3MXtsPpq/jvUgH/F+qNKA1Q3flS6fA2DcZ/PWPSfDXPydfma8f+Qae73BXr/U1/eblQYZUGnzBB0EDo/EYFr/BRt1YyhuhlrzEWnEZ84r56Pj8dTFIoa7p3IiED6TCaI8/YZ0KbQ68THcOt9DU0O5GXGYz8cGU1lAT3oTkudiMqImt87X2Y6GmtlPn7cF3JdUSuZawjsk0syTbTbYGdRF1MT1eCnKr0NTPJuq2rbkZbLtTomlhjR2XW3bj26IGXT4WUdiGwSDPwXene0XA80ynDubuxA09Vc0D1Rc36SDtyMUWmkZyUYN+FQ2DeveG4ZQTv0tDHF+Ocf4q9hhu8J1ruvxJJhHWqz36d5bogX2ujcf46AxK7sfR69xVITALyfOMCTqYSJO6P+jha71N00z+74eTvhiAn23uJ0ZnzNbjGZdyIA2Le+SnIb7BEEp59RRgZGWQ2s5T7gWvnQLwwkP9Gm9LhgHt4y79TOFuJeSLMvogxpHYLxzfugw0mrqaSC3nUdgk/Elsblqk+qGJEmwfjdEw9Gx+SS8NCyNSDZ08Z/fS5aOsVswGvcvOx0DbOyUwOLX5JfdpsyZ3p1A8G4hb4U/Cx1dp/iSXivncA+YhNJ4SYDulrkWhLg4hVIqXi2wxnzsNmYQttJ3NvkF89HPHfCd5/+ULIyIarwFsb4jJXcLzLH+KufUqmvpZjLFSxHzuNzhp0aQj575MY6A3EcjCLQhDLH8dtZaq+oA/ya20BT5essuf5E98vCT3J8GK+ycUyTB20991q1A8y//9HsDtNJ/7AprZHWQ+n/uBK+8gt5KRn4vRk/8mtxK4jfv5TVxHr6PXEg/13qapbdto/DM8Xoc83799QBvN597lVkKtATDiH/gRNPfZ+SWtBGkIPwNuheQrB7MtMP8zHyYdvXbgdcIqjEi4TSmGZkoMrIJn1UvDou2IXrEUmP85hF1ZKJ7H1+FnPDQXUbjHfIB+p44xDZznr/iTGHYRYRaatzkGkVlRYSfnmddo9z6Ej2t2Xcbp7rjTQ+E6k4ZM4lKaKhp2BhuMgQrPcybN277Jhw+LyHiIbIZwc4eXeIjnzCMXf0oMr+ahW36Xar6xzv/8Em7oWH+SJ7/qT+Lp8icBuGcxrfDoLHrsXqF4znoXt/QE8TyRXkH6QeL57SkbyKBk5MEfHyS3kufxTdq9R54nX8C0/xJs0esFrCuP5cZ/NIM7wCsPYwH3f5pxgHhuengxDZAW7gZwK79wPPe74EcU33KSO4LwGyIHwIDfkEPjWWnnPHsZpt2CZwt5HEQv1nmUZ27fUMN5rs8RPLs0ngeTX5rguUa4+XWrUDwXE7fCb6ie1tL8hq4SfkNtGs+V9FHFpfQHxo4oJkU7RtztT/B8HW1tN+d5h8bzco3nC/GTTFF1pkQ8mMLznE/cPst5JmMDyBA8/0nj+SeC50o6Ee/lo7HTDmvtWVHqY0RV43kFbU3w/NYlfDT0xuWa39Cv8E3asxzWB738qVuF4nkGRWXB81vH8Pz7dYLnFO4+9Dy5lcDET1bg69QDsVkONWOqPVrwPJ12B1Nt5Hl3ejvn+QHNfSgP36TdwzOA524NvtfxLT642kukltfTe0Xk2zki327X8m2X6lXV6mYacW3xx3QnRO7EWq2Wb/NWL5FvVyqCZ5vIt9uG1qmq6vfQcLKyEMyG4rmEsmyRb3NotXx7aAnneYck8m1yEBkv7IYMPGeOWoy4zRD5NudnA5W5uVbLt69ziHz7NNwDjVJhStS9wvNcUIPn2x08396BDyCTlwEcvt4h8u3TRb5N3VK0mWJTD8fkn8eI2xSRb/+RNiT8AGtOF/n2H23CDxDjJbtXDJd8sCb42R+K59uI59uoqXzBBsKIiXx75LL3iOcPTnmd59v38w0sKMM1Ug9ot5miVAvWjNNEvk2zTMPdPN/eeso+7g64UMu3MccOzBT73H10femLyiFfIQdVnetFxbgFabO0K1ZqD/PWQyUtbc2xImYazzGO09yD1wlGD5ZOXjHmlr11LmhAduXOoSZyBXLXn0cmPoJn/CyoQvFcQPk2f6gjL3vh12tq0+Xx9rBKqQqrzqzjKm0UpYzPbTGdJLB9FG5o91XUHsYDfDHZ8m5fLpGLgbHjQtNefDuvQQesuD8vRzMB7V7hec7YjCdf3m58eIoHYXjrZmoP+4npUzzhFzXo7+DtYZgpZM7DjWB5GUeCoxRKb16Pf//Bb7vaw6hNG1Yv17/E28N+Ofjzn0kwh/x6Z+BhIwnw4HK+P90qFM/jKCrnvoYPDy7na736O2oPS8n6HHGbsSH59gos5LPpfONji3GN1AMD6HXUonwbyMIgtZ1XjKe8iQW8cFfyC39OhpT23KHtgyWY8gEDtuQs/EnpD26J7av79qUQt4zuVzUroGCV1U33qzC7Ji/9JjOY6H6VqDKLfNvGH6NXLVWH+f0qLMBpQrYHgrJrIFjpfhXWZqvofhXPf7V8e4d2+7g7heLZTNyyPYNBxq0bCiWYR/erMHlvGMr99E10v6oGKXRZLBYnXkXIXD8WVRCehdrGC4aDgljTPatCul+FFw26X7XhKomtHWOx5NjxRNnLw3j3iqA97D06sV+7UGIf/QjkQh3MpPtVpTp+v+rlq6XUj07nNvvpay+3WAqHS5C9PbaT8jGqFPP7VRiYJ10qsQ9+ILF3zpVyX06C3A1JsHKaDtZfo4OleFyWebju+uuC73UontP+h3jy+1Vv/1BKnasX96tKkuDVX+rgxd/qUl7/Hoz45/f4tfAJurSMeCO2+vPds3Anppcm8/8TR+kGtBqkAf9OlcbS/apn+8Hjf0yCh6/Xw8O/HWWxFOC6zRTL+6aqqb5srrZR3w47VqHlWpejEq9R5lqbhxB0VlqrxZ1pwXNljObfZqqoy5UuZzVeB9uw8pxTbSNvEvC4HbWUhlfaeP8VFOfZEIqyUDxDDd0Py6mZUDUGq9Btp4Jc5nRVD8QYXTGmglxCCjy2mvPAuI/8SajKW8Ybq6KXiWrFrNTprBoIbP9yCfJrxlRcityWqfYKKq9usger8Rs6qSCsRmdS/A6m8DzDHeQLlr5uTNkEjMFfng7y2sWTKzBEpm+4rpj+elLVhA2XSWmfUnGvIGZ3kL9+DEqnWjFbvXjyujOBffBnPWSuu5b3J5lTOnnDTySQH7uhRNXDnCNU0M/x/UsiSe1WoXiGB6/GrY589rdrb9LDxC++D+zBxc5150qQ9uh1ayhij11z7WM/ldjrjUvK8PqBSXdg1poolfogBnq2Yv60Z38gQdPDyQjx9Wt+jZnTCu91j+Gi1Gf+UOROhrsP0xFl6mDYqxSk+6YMdIM5cpl4LTsWeaKrd5dqnTK7VUieDcVR8Wmsig1nvFhEd+N6HcbMoIqAZ2VViA+Pl4KxOjYtjW7qq6Uh0o7QPKfcG+LD45XyBGIZk6ZfGNURrbgmtutGXMjGbwdHKFYec79g2R3NRc8Wsh9aSJ5hfDR92OTSkP0wQ4kVherD9nWND+kOHAHPkB1NTzZ2R8h+mKEUaOeKTNSbLLhC8gxZ0XThlO+Jrb0exW6LxiTsyvF9GGfMhKl+EqHMPUhEZO4YFpnkUNE5HM+BkSQRiRrFYpUcuY9WuHUj4TkwDiIiRbPu1yVnh9yNrygwCCSIQvMMWVHcKjxm2EbUCoy/iESM+4wl9A0qDM99UBHx3AcVhueEEkLZHznZeF4ieH74pOP5oQTPCYXTgr+cbDw/RBNGwq2VJxvPjqcSPCcUTr2Zb3PncVCs3+4Vovfz7TQxpiDzhB/XOL7P6d3X1/tevj26TzeGfUVGcW6TmDC9B4PmBG8R3h5GbXHPFNg4ySTM7ZlFNGJ1Oc+bQrXShef5GM983DR/DhydOVASvZWNhSYsMVspGdx17DFItmjWJCjtiCCTG9J3fYMZR9foRpHynHbMT6RZ6WMB/DldOOzjYkuOJBstlvNkyJZmnkojDaNWYOOktMzz+GvtgFIyNZsSyMLlmf0fPD1dSlUmnc/HJ31dEfCcqrn1o5g233OqVkS6hfuGpGSKxT3QCAu30ScFZgcYJqz7A2XiB+k6SBu4Ijm2TqVdsjarbn+lQ8wa+23K5ZG92s0rpd5Es1CDq1J208AJn0OhXiW25ha/rwdN3EKmRpOpXrucl3rkcixTbrAYt54HkFM52EmsGj1kahBUYXme5x5cpt0fUrYON9VhafMqB3K7kIZpg8meJL+msm25BHJxtdspQb671hvjMGiSYdtwc8ClpFgdTA4ibMsow+bzqZwhBUipXFa5q3WIWKM7Rcjz3IpBy/iaeFyvXWTa8iMJ8soGzaUe1o/NGvwUDeCa2eH3/0HHZqqq81TIK3Z7YrlvJW+82EReJKSpJWeuVPWQ8uZvz3sWy8t6+YLxAtK0z6/QQeayMu9lUsqiiuU0pdVxCs/znIqzlpIZCSrljStML/1YgtxnfzB1TTLAPfPPfPQaHWS929T5Kk1qFbue/sP5T1wi9i/rsR/MWNMP4O75Zz1wjR4GvPiroc8I25Ln/qCHYSur7uzh3WeXDBa/FUwh+it/IzK10CxZ4kZRJULsqgSlFUNNs5WPqjQ3g+LBCpGfD6foiaqcAKpwKTHt6Q+sbTCUeiWw+7iHCNRR722L6PQZROF4FpvlvglQ4ZKgqFpSyK1k5xipEAu27JBYPpLQNgjkwmongm92VLl6cINnw3xJDAHBMLyTXA7O58Yl+Q06tvsigM2zpHmDge3u0fgqkvLpOQDv0QzQ1C1TBzOr9PJHyNhbN+jGb9dD6henA1skNjGpyGnXQcY8tyvEVSSo7qUOYHWcMpqOjn18hbTSo4dxNB3dryV48iYq5MHdyHPaXNV9vsQmlU4b092+h+U55b8/lODt3/G/vd2TBBM3JMvv/0oHT9ykz6LZ6f6Xym5LgqxDNNYqZk3Z1A+Gvc8DEfs3ht/nbknKeodBSqtBuntxMszmtiXT35yCeM9Y96fLQ/8I4YRhUG2XgUVxG/iEiOaNp3nkUQYaZWVuV5yUM3jqoZ4Qa80xI92slV73RDKhqo3K8BAEVapMk9PJnUMdNC90CU0m2zOeK2iMVQP3LDB24iUir3NwEY3/KKuXGqjX9V4a1AXmHTow2RxG+0AoOq94eOyTu5tolPN4vDqg1pHl7rbl/WkMpanjMjvNKFkkRlhvIXOxIIqMZ5pWEvhkz5BGwyUNR84t3Isn+qqt+tfuxKWf3KSbWTOLsuP0/FEW63nS3FOnDh4fepvdST6EgGUc/hnt78oGLGB9ZRINo5QP/cbyCQbOSVvwYc5F7yLPM85ffeY4XZZl0fkzuwtrYXnmgyZv38LHM9HksfKB/zeTZo+d+HK/F+/Dv/3PrT9ScDf465jVTCO1mkbTFzHlc8R6xfZTnqMxk423ntU+DoMyzQQ9YvxGGiY9fOUZN/akKC6Ra+fYK3NkR7Uxp9EBaqMCtnqb2viV7ph2p9NtlV3VBhuugueRy+GWwVppNtV7wMFnd+2BxCho3p+Tj4I2+HM8tMjVDq0Uk5tEAkFTUvdIJhoFrY2a5DNAl1aaCTpom+ChUdCuTqym9YznBvq8hg9/zqMNmTuvqqBFRW06jvKOafRRKS9iPK/eyoWhtxhS48mFJFOMmtx2Hz5uqB1OoymVjpvcR11KMueFKCIynleV4+eLthI4eUd+hI+HrymlwZOL9id9QjO8v3mf/t66tnYa2Ajpogo6KfQWu1fG4V/S9M804Tqsr8XTe/WOs/gIyv/d6qCRVZlf9IOMQv07fL7YOZzjGUO6PbqwPHN/kjlf0EAL4U/y2ehVNLv7yM9P+Q9R+PpfsDyAV2mIVKwawKdxf/5PtCsr+OzuhwY2U8B/7i+XkC1JSvskPVuSTPk2wK28vB6JtdNp7a4Gjwdymk3mctUu+81gbHeRlZdYh+R10FTrYG82m2iUkrWWQaUTmN9kdTbajPXIm7EHzVXNnGcab0XGJMSzvZYW2f3DKFxrodvc42q+jYY6G4SrwQ46cNVHxiTkalAbcCnpIc9tfJwFbQzm0YYMnZPrOM+d6Xy8NdmXmGr89dH01wyhIrJMyBSuBvtpkPOaHRO4q8GRxeXkapCPqLOCjvYe+ZOQNnGeud3Q3A7O8w11wrFk+BGCeFMtvkl9tpNGRvdEk44gvKnCbuidh/Bx9f5fcoeDd+5fTFl45uEfsHv1IHgOqbA8P0M3qCcexqQbRmj+JJuEn8ElXf4kmJW//X2xekwacYDzfB+R+vQu5HniQWM759n3e25z0LYwaclZkuC5F2T1Y8yztyhWnwxqbQ40eg2GdgZWrNAa6CNN9kb834RYV5qh3I6440fV1WBptGKubGTtyLI9utEVGCwtmpjmOsRdxLjrkMFvq6RFDr/sp9BfT9tm1EoWg2StGIsJckR85nZCW/mFrNbKs+89jkpC0EHWgDHw3HUouCX6vILbDRXQhoydE2oEz4M6iOetZGcABW38KUYpWmkWIxR2nko8c1x3L8fHtfVXEc/syHyv4JlcSgzbWoKf/iF4ztDKsQyGZznP3M5gvIjP11fV47tFHWcdpirty3/hZ+SHdwUvKLjkTK0co5RLqXaqsDP451/wxF+9+6c8Pn/wZxe5A2Ye/t7C86Ve4fkRis83HqaBzWmc5//7/bqtnGfhT/Ii9yd5IkYnQKE0wfOfaUsP70SepxxSWjjPtb85iGkMa5thH4W16t7i2UMeAa1eGw0fbKoE2W8DB4ZBDwbEHOQ6oBb8nPwFKJC2GKCS6rvNLvAg5i1uMHO/IDytCZGI1QWBDBzeShrWCEbi2eg3uWmR2gw0rTw004Mnxmz7GJ6NxLPwAYQK2tkyj3Ap6RzlIp7VFnyIgWezVgJemji8ddyexEwbsnQO581VxTuk3VTSTjFEsqq7zUSqY3jmLiXCGhA2EHKbywcTz4aOa12UbxeQvScRH3ycQ2Q883x7WRVtLZ14ZocvLqZovWyH/i2yJ3mX90qBe8naIGp18WyS0sijJO0QJt0Aj1JAfrQ26XPi+eAU23tIRdZ7P/jgBZ+v7V/cYCyUwvIs/EnIkSTgTzJiLr3Leref8Ce5Cx9uvKqb7yUKkbencDWA2WQutOBD9sIS3O4Ld53bnq2D1Pbs9/Fw9v77hdjGV39djZUIlp8HPiPm2RaMkdV4otdjVPQdbfU2ItqmdhtxRo3N0Izx0tqiUL3WhB+Rc5Cl0mhp+frkspHKSbYFzfxqwOjSYW2VuUtJdSVU41VE9mNF04U4s647u7GJu5TY23lzspOauRsc0IDXClPbQBOFZm4T1LN8u7ABP9/FK8lsD7Kbv2egdRe+w6S7ClNtuUN4h5bScOheENuN8BaKRq95BPDeadK2WRJk7j/ftB9DdwkFabwC7A1+9keWb+eRXe8mIhfYR9diAZ+emfHl6QBPlXO7XvbFNfyAHpsfejNhxcggNPdT8uCDmTsR4H8s0a/HRamHfqZ8jksXVpxO8H94S9iRlWF5zuWmgJUUOnklOf1/Kenkc7CgIvn2qmRgn03UQ24OHk9Mt9E1PX4nbqk1nbYw7IAiwf2V/VZUYbktN+obp+hh9P+UkXg4/1jx6x5+b0JGP+aeZHwt27BujPmnBwvPsbF2GRxIuk0LuAitwd5qMVGjs1pukatdwOqtmBYb8C1ArR1r3K0MMEmPTQxjvqmJgRGvE24sl+4LN5r5PStyKbHjIqdPVdXyWAsIyIl5RxUestcMbMdgMDf0Bysy7MFyq+zAdtGN4BzR/h1E4Xhme84DEzJtcUtQgutWTZDYnku5y75pR38o9EqAlRO5rpfqz9ylZPNVEqy5VFJ2D4HMBh0UItclLgnWYdHbLgQDXgSLQ4yYjIxneO0ySXnvNMhYeyrMxHVXzdLBU1dL8lvnQPpHZ0LeOj2bOQhS3wrlcB+RctcmwerJ5EGiIzv91DfPgNzNSTCjRA9PztKzfwiH3t7It8mMhO5Zpa0+HXKfTYZ75uvhiWt07PWfSynvf58vynp2iaqW9mTSimFvD4CxT/WD6a4kePwPevbvVCntP6nSyI2nwPS1/eD+m/lO9lK+bSr3qRj6bI38pq8Tz3MLsEYXxkdzDjUCqzwFRnlrXQxjthsXOn3Insfuwr+zIstunokjc9Vgir29yuh1enCDZCnG3C4XZQa0iJrYctwur4zbJh3NGGIUcztV3IhM02GYPU4vNTDbXU4aHy27nW5qezO6fY5jGgK/rnA8g7HE4cUAT969rNjlojhsLHV5qBU93+0qHwhKTdPintxx/prmOV3kKEQWvtmlDg9dkeapLjIZVUpdbiS9cNd9bvIXCaYIeU5bNctzqQTjt2HmPtfpcmPwl5c53bgIsoudpYOAPbtjsSuKQYjBNNXpcuHZvR7pylg1q4T8ACYVOUtOw/JWzndrXUceFXfCQyk8z2n3ziq5TAdZ/8S4P8flcuO1SF46q3gClj5i2aySsyDlGTrreON0zMq6Yz5dEO6u6AcDbp9VTHeYcVEJLmK3zXci5aQTbGPgJSu+6FTuAJcWzU9qheW5zylCnvucwvP8XRFrif5+cpNJrrVSuDvJleC5ryjBc0A2nz3qimq9/dgGtJNXCZ77ihI8JxReCZ77ihI8JxReN550fgbFNLEFTDnp/AxuqEjwnFA43bL2ZOOZz8gOtzx2svE8iyafTSihkErk231FiXz7G5dRBrA5qK8pl+Ky8zu/VoeN2uOY08m7eJpUehuNE+5xoj7h2sZJDlGm4nLyFnmz004d22QX3VRXQncrDcOzgnuZf/SIjE5xL3u8004dSFihOKIclW5A093umJUpgXGes6snW4GTuxbJ8xy8M0y2w053uJUi9SqpV/x6DXgY4522QC8Yea6DO21kF/By2FQHv8OdWaQOgR759VJfrJRFk7tsQ8Y5bLRfWB6fEiCjwE6DqdgcdYyuh369KfiTa1vnb+c4uftCVoGd7qGzGc5R9NHIRa5zpB759cIIHW58Wpet/tiCMXSvecBs50VUwIiZk7EAGLBAvVYPAzJC/gjxrhw87z0OsFFXbZShkTEfnu9uN1hqGbAmC5BpCQJPgzplb8xAMwcDU1N/tlVDtdIBdq8Eyo7BUJODl49Kybh1ILBSq4sGQ/fETz/HDFDhkPL5+Ge8EjX0lxuGAxS5JUtNf2B1w4G8SwrrKjubB4FMs8XFJrloIBi3n8q2COMEKJsscSuSbUNo3ioorNAZtwwCZUvlLv8sKTCbZRBFwnN2oY7Wy94gJr5Q3joHnr1agpll+gzqX/Ly1dIilw5mbvtLR+sFElsWc68StnAQpLx7LnuJTzIDsFLVj1ujB/nNn0o0VVXmuqTUNy+Q5I1/2dG5Qg+5XTR2p9A8j8jTwe1q0jhtpZS3f8j+fokEM8qSR7x0NsDzV+huU5Pgxpf/sr/1h5J8W+x++gvOklJf/2H/F64U+7piWtLYNf2AvTJMevgGPUzcdErqv9KklFf+srNzWTJM5Kz3UZkqMTTSnM7UZRtV60JcGkFpxyunzwnUuVRulxl+aKCZYk3RDuDqEs13Q84kdj7sCUz78GnXcKjwYIzcI8l7LkUI3VIOXi/40MaKmPtvG4ukrq2TqiZLkF8nmdowYtVNk+bVYlBtG2JApE0di/GhNMS2QqpiMMAWJDVfTEyVTR24918tkddK9u5Tlf3nA6zxSDSJxjr6qCRUxIyAZ2WDHtKpt/Zr3B0EHvPosJwk5csfSfDUnbq8bXpgn5wrr9JD6uE/60BBBGPTUgxjTy5GUj/iE1Olf4FP//it7t7yJEj/8Az2DmJ+b2XSHIzR93R8P8z0OCF5TlmJW/x8gJhYEvXE4iTI/We/1P/9PwkeWJx048v9IOXAsIFzkvDpbnyIeb6bB34qwdNLkmD0P/mAixGf4W/SPFa/pKIfvlYYTT+3oqrfbIzRKz7Fj27tSd/Sb1m1GMw8ZGJQTcO1gA+NNPrNDlrkbgEaVA3cPEGbuqo2dCYcVCaM/4w6ZRs7+cSR1EMcKryMgjHbd5WNTAycNLIKYAcN5DLTjK/BFJLnKrwSFNOYjgoeoBkNvlA6BxfTdWTeVmkHDWjcO5+7hW2jwco1fKRX9Mqu0oGRnEkUPhwS1pIzyZZK3f5pNOLjOusOfFvQpqd8NI9cSoyhYI2A57UY85c1IKR3cEMDdvi3WM7ha6zb8d3U/UmPUQv5y/PPo9EyfC7o1TQCOwalr0UEP/8luZFwQ4OZG/D90oqkz2n45AfX59DUz1O/PJvGR2QcQp5TXwqBWUieH/yFjuaFBbiNZoYF+M+vdMAODJv7EVI39s1+L9LQqv9b8SNKoJ65E1/ffW2IryeEhv0NLwztubjxdm5auGILFvB4bb/m2Uk0VvKq/2LsGn3QQEc07D9YWsojNNyrT8pM451rqYe4cDIw03hr2W/30CKnX+YjJX3U81QW00y6OPbRq9aGTNPwKbmTGxbxiaU9PpOYz93pbsUv006f8ywctS8EZqF4Nu/Azyo8+MBncMfrByWeHaOq+Hzu+07tIBevnWID3KwEYze9jlobcEN5Yj73a2kL22ib6xov5O86FvORkuJzKKRxjrCXv+5e4Xk2fIrcruXuBWQxJAyH4PDNLvIgyjtywWs3498+JUZKvksfTaIJo2PQ6lk6SBPzud9GW+CjL2du++khCqL/WOI8gid77uGf4+cwiaaGhjdowuggCsVz6n8Rq3to/PMMmgQagzCNl/zP2LUN+G7kQeFP8upfOer/psJzad7mGPTA/CTIOkjV4hYaOAlPV2MB9+9MaZ+K71runkZDKEce4JP8jH6K9qSphxaf3574dM5NVHcu9/P3NPIZ/KrmTJLG3f98+Eb2tjfTpd+iDbOOVjRIkjuTQCfvYc6dDNRmG3ca2qVyJwPuTGJr9LtpNW4LGESheOZTuW+loRBFNEQScsg8BDom7yBPsfzOoZ1kILaTo57Hp5a07Ivtx9s5QYJ5nUPwVQdtEvaT09CaXdzJADoe8tbiZjP5GyCjT/wDjn33Cs9z9hY8+V4mA5JFHcRI9pFf4OYOL/HSoswjv/6SpnRdTXBD+jp6zKSJ3mMQzeeedfgHuPFDD9N21t+Hj5Peu+bwFbho40Pe/XjGZ/I3sJEPMOzRfO7PkDHYRBo0CSMOpuJG357ysvAn+cXBBfiR8CdJ30iPsc7n/soUPUw5REYkTXSJgH89jFu7/0jmgUm4400PL3+L80xww8PcM+25P8b21X37chG3tZT7BuKzEVM5v72cFjn8cmtXfAZjCxkOWcQ47WgldyLPFkqqmYjPVVQRd/vMWnz2UKot4jOUtFOGJbLl7hWKZx6Vq+iSUMyhNZF5yNH4PKiDhkXvpI3LVVQOGLi7V/QiVMcfE5+3k8nR2saLuE9Jx3yV4rMwOSgSI6t2hrBDCc9zPqH61NH4nK7FZ5WWZx+54D2qVT9GsZQ9y30TUg+fS09Ri7wM0rm7kIjPq2mbUzE+/wYLeGmJi+JzFvcKnOSkz+HB6uBnfyieF5IV4IPcn4TH51ThT/K7tcKf5JTPCL4XKT6zJ3iNNvXACHqKWmRnkHWQhj83i/j8F+J5ZyqPz813zxLxmVLxKaIp7Lm7eFLQB0UDrDHtxQdud4KpNnJn8JtVWqS2iKpzPa8/g4fWsPhjauFmxLOB4q9B1J+51WCZV+bv9lzlINMSh6g/k4Fv7DxzimnQM26d1pI7sFDWMbiY7AELtkq7qc7MrXPniQaqWHneizybqOrMRP15HXXu2lx5ascNEi661k6tZPn78fzI1sY994znPEqfl1HVmT9g/fkaLOfwNXaK25P2J3Gbg03kZKDNFBsrzx9fo8OqM/LKRP15IXXxwoePyaHsneuth7+H5X2JRKRqjp4PVsbG8wxyCV1IVecFov58YBju+IFhM7k/yZv9Xv0z/u2/V+DDDO2IDnBLgqhFFA9oz8LdbxP1582Ytz9c069lBm67bcaYD/CUvvLAYAlGaPPXPsetifqiuL2fiXLoJpHfErvWFmZqxWOsLCcT7i5TbpVCaqz59g66AdWA2bu1hTdFWah5aoeVO3wa2wYrbUPJ6JP/XIxqwFAnLiLdKhTP47fiZxbaRINwFarDp5wGydSGPyXWq0uwEGU/OeojzgxrFxZhLBK1uP3uNky6s/fwO6P51AC2e7K0DnODjL1DlL1Yby+u1kGGA9ejW+w9y7fTiNv0L/EM3MT7hsKzCHDGp+con2I0vqNKP7MGP//kYgkWDUcUDWReEhylUHqJEvf1GO0zv6CkG9LfxZN7/c06MuBO+fwH7INfSrC0KglS83E9akN6cL6goDuF4jmLsuz0D/DhmZv5Fp64SQ8jPvieQv4kt3mSqKmMHUAMJyLODFFO/Sy2+vPTZFTw+E1JMOJ9nlxmkRX3v2Yn3e9NhrTP0vu34LZnv3wKDJuJO0tH1Dg9tq/u25fM/f+qLbz1muCmCnW1Ff/bQWmUQWkxgN0DzGkAYzV9GQR4LPJSfdmKsbPGBozshRrMnGlTQ38oxUuJWwW5YTCYbCB7qM4O+0IYlITi2bCPsuiGUWBuoFtJ/SGvRuJMV0yQDLv6Y+GDYR5Wr4s8qqr6cN0CcRWJWsXkkpBdo4MNuPHN0yS2+zIpE99m7jgVSrFGvUaV2LYhYNq2WFXL8b28P0QeEJ5neI+y6LUTpLRtp0H6ltMgb/tpsGqyDtbM17H3zgH23k+k8VV6WFSF5W09B1HiPmPRaylyC1mvnQ6rp+k41xt/LaW/ewakvHs2LMTPppbo2T/OleSNWE4ZrgJvCLeSbhWKZ5ms9OFv1+vS3vkeZP3zezAS/z+CaN8+WZ/y9g8l9vqPpYmrkmBGGZa0Da8tuU/FFjZn0/wb1Mj9wM1J8NyfkuBfV+qG/R35fV+RFriSYElpMnthmJT66m14RIg92xeY0afvyUlxkzlVL8JK95Qgx+3RFpFZCSiq6mbAKn0uqknjpSvG+1UKT+dzVA9uhlUjsbJL9dAFwuz28CGeNpcXN53jK3fxzlw51EIdTKF4hiK6kS7PU2kCGwtVkXNUr43Wz1e9dI1Q5qkYQQ1FiLNKfTzqMB+PRayMNpoxz0PpdBnGaaVIdVMKn1Hk5hl2gdt7qQSFVI6Ky/M9IWCNgOd8Ogg2U/XgZSFt3Wl4LSkSRiFTVQ91fFIWqZ5BkLqIiqPlT4U19+pebCMhmLHIQxtZfZ2Oyiyl7lNpi9zkWQLjikovQ7j4FzgE69qrQwSzUDzDjVT/ZjO4l9DIp84AGIGFUgk3FpX+AguXZ6jq6SAvpILIr+SREBeOUGIP0HVgxKKSG3AjxPSABar7LNzWiIUq9yYZW1SC5U3kR4T7MvoxzBn6qFjAzSgy2bkJdyw62v8yErEaUbntXiF5ZrzVOmLlu2I7STCzj2pGDbYuRHiOhGdYFVWHx8yY+5OMs4bai6+LPUqsB1NInuEJ3mElUuUuC7WtUMoaH9V38RzlDX1VBjWKBi5ziEptOJE3aKRiKt0bC6qQPIOhOIqfw6I1VsWiAq2jZyRiRSHTgEh4lhdFAXR6YTRQflVTeTftyMTIiDu4QvOcsjIK58KRBbFeoAAmRmP4O/snsZ8ScaBoJs6KYtXjFRLRrypMOaF5BjmKK1SPjiiaby70PkXCc1TlRfFlH6/eO67QPAOL4pdK6QllUfxxNPuUUK8oDM99UJHx3PcUhueEEkLd8vTJxnMpTQ8Jt/z1ZOO5gLpnJxS5DOU9GsHbNzXtpPMnKeHxedpJ509yA003l1DksrR8B3lO5Nt9RYl8O1o1n3w8c78S7jISRAme40E0eiLQ0sTEm+OU4Dmq9mbUSchzBT83yGYkiBI8x4MW8vtH4jHt3u65/S7ybK32eXmfKBfd3bU7o7MM6GM8y25VFT3NAGxeZzlevGSPStN2AZiq6R6tw6U63JK52marDNanJN54ZsWqGuiWklHioqmzoNDtpEFehjLVQx29i/b4/xLCASjeeE5dvdgTuGc7s9hVShNXLXWpE3DRuPc6Hz9HgqWuMYUOXeaaaaNWJbEnry12X9bdbxJ3PC9RXSVaE92wexa7L8Cdnlg8q/QMTDTuXs57oU15t/MJ3nE9ZtW3iruF1PFRqQULhztS9TGey/GaxcdmYFq9oz/Y6sXEkjT6QtEmiTU1UAdQua42aBexeOO5yCPBBj49LSjbh0DGzlO5e0kJLt48RmI7L5cK7NL4PQ8FpzXeeH7yWh17R0yinvVyEqws19N8k+yNa3S5hadmv/cCUpr7Hk0vmfrPu8jO7MGt3c+4Hm88T1nbDx5fzoFm/0qVhr2dIo38+ykwmxb/MYn9a7R+9LzTR7/x1550+GTt1G9akzvEMP5u1bd4NhGyHhq6CHw6dbnzKpmGVjlbaBQF59lhVa3lksltMXn6SHxW9l/eNf9zsTb/M43IzNw/JIfmf15TqyvCHS4i16EgijOeuWfYahqYAfDBLXyoVTpZlqzcmjQVd3HOkbNh4Sir3anPvGPC0KdOhwcvnltydXf7Hmc8D2gdp4eJH/Ma7ZJtpwA0zU565bZkGHbAZPxMkWDFtlPuRtinf85Hxscoi58noIrFKZvUlnIEWslxBSrRJkcOGBw2UBzM4jJZaGbZr6pZBLs+IidZlDj8HNR9NORqjyeHhj5buK+BiM9Qxmm19Jn6M3cVyiYrMYAtZC20uXowuR0YOm7grkMFHTzTzqbXQRRnPM/cjBhOfY/iWAr5G6Qe+gP3GZqKIKOyPqFP5hxTf05dyZ+OU5zxPOKAUQpYCz39EB7Qw3XfJ1cD1jbHQa4GEw+SiwmMeLUnfcRUzfLD0QwGl99qAks1o7QUxTx2uRVkW7uiOkD2O2zu9q+VZFHtPera9w3Ly32F/HycUyddxnZVOmmRsXMCvtF4Fl9HiK80znieR5ZGmdyXBPaSXcLaeu46xI7M9/rwo3xheLBKZOTdKs54vrcOMZz0JVGbRdZC7NCtpTvx3STuSwL3eole8QNpP1Yg/nxNccbzWOI1jfsMaa5D2wXdbbd7yOTgSu5SArdqM0LHJh83zhTeP85mDMmtitwk4nB5NR/RJPtp1LGl3QhG8utDGY5J0fuSNJ5pIIPGczXn2dA5Gd9oPIdVfPLM7Qo4z2sax3DjsCOLywXPFLqNpSFgjTOeV3Oej3TxDAdv03gmH8CU9REPo4hHnoXPkMbzTo3nezjP4k3qPT2rPnODPOTaBlCNnDaW292iSczitzh5fbqRWohU/CwnYPeDvAeaieNeOTTBPikHnO14PthFvt1GB7XLY23DRcJHrC/xnFmnHdQsKY8cizK1fJtMjjZUDyWelY4buItYfsdp+DOvDTU6Kh54Xhg4out0CznPR/PtlEN/mEfRehKZDcGDP4r4648Hnkf8TTuuW5J4vj3imHz7/roftlO0bpvjfBPJ4ryzu8/uyell8fOKYg4jC/tmJyj+romfPfVa14omGnaMOAD36UOaLQq4mvrgnWcLWf65hE/YVrIZbHMYaZG1k1yI+mh8NhDK4zs4r6Xkwr1tubT/OnIXu8xGbWDzduiAFQdr3OOKs/g8jlBeWMNZ/OB6HaQf+iX33Z6znaA+L/JvP+7awzCfHn3QQAewZAPy/Nx9yeTCndKel/Pf/lpD2K10Eyt2qa30aLFbG2WDjOm0kSw2Bark0SdjqHZaG8EsI+/mFgWM+LHcYjH4hJFHH1MDHlkVzYmBVPrwlG8bDFWYl3iq6TvsozzDuskSlJBVmAIZO3UgI990ryq/Qcd2Y9V53SwJaPizMj7obscZz+wDPKdfukEnZ0ows0QPk7Ylwz9+LcGTs/A14ixTK3ckijOeYcWsJFiwrh8MGywN+w8D1pqhW1DWD0Z/IMMrv9LB/Xcmw4qfSjBgYsy7bWltV1W11m9wNlkhh9z2fI2q1rxtail3WaGynMntHoO11WCtVDAlb8RPWhg09qV2sC6Za432agbGzuHAahzGasw5TFuH5/gwejF7pzuyY4o3no01w3PqBgHrWE62YUPLEG/DtjGmmvPJ43u4vUoHazr9KN4s1q3ijGfIXXPR3IokyD18icSevHb4s5hhZzx1cf66JEy56Ui4nX4EijeeU564wrL++xI0350Md3suXol4pzz/+wv/9nMdXLn+Z5Me+x7ceoiOjzeL9UwM4y5VkfEE7zqrGcVpM8KN/8urMclG0SyDpmqQuV923xOz8BY9/mC0iOuWRaQjESveeAbIsNAemehwDOJnwkX8JqYsjjeM4o1nkDO5Jx6fPTJdHAnLDD1HZneKN56xNm2h7zhtIB5KaqaoOQzL5FUlNtLcu1+/CWhmx2BqPqb9S3Uya6Vg4buo+OO5p4o7nntJ8cfzNye5yRLCnsvCZ6LRVKvK1bHODHcSKMFzX9F3mefQXkyWY5NSDM0hhhOe9Erw3Ff0neY5oQiV4LmvKMFzQuFV+NeTjecH+Bw2hXTz+qTSjKoEzwmFUyI+9xUl4vPJLfmYG8yi7xufE46kvUXJSpim+3jhmZowujmiwKLAEdHbMK0d8cWzOK6j3zHN0XbMc5e3UIoihTmueOGZdvNYTyTtZWBRamB85ABZSvhvRyarAVi1w1apfV22Wks5DfUsc9mqBwIYqq1u6s/N9vj9Lf2ZGpLoOOF53qUSWzfNXqGdDHkVoyrIiqTUaasaBJBRN6aYTEtWle9qGgKsqO/098zF43py1oQNZ2lvN1y9hgY33+ses/kCCVKeurZIjIxmL92g69H8GN+YJo7SscdvnrDx+2Jfs568YvU1eEQL3Nc+9WMJ0v72+yLNtOS5K3VsBZ9sOqHQspmBzzjrFj3QlV0DQd43GByVEjiqJaixAmzF/06nhfxZWGngitmd4oPneTkSFLvxv5g6T9l9Kij7h8D4Kh0UbNXBtlESbBkj0aQ4u5dLCHSofY4nnrNtOphZqoc5ou7LPviBRF4l4zYkQe5ryfDoDTpYzyd4nXp4Cu7vo6EGW8UHzzRN++zSZJi+jg+cGvB/KdKAlgzdlRtPgbH/PAX+er0e/noL7eiUj7N0wFYIthMKIYV6vrWNArB08olgXTTtep0T2iZjatc5lI/TcNdLrMEuslSbGuL0jwuelRodKDSMyrSf2xWU0tDI7culnbMkMHZcZNmNb4sadFSboA7ckBdqpqw44pk9hmf2Oxi+0g/z+RwnbcH3q5fr30GG2eeXmA9jRJvzEZ7ymRbOc24xD9bdKy54Tnm+HzByJkn9jI/EWPDaKQAv3JXceFsypLTnXkrOJNM/xIxwRN47NOv7yIJ4uAjFtzxOrE/SeAsDH/YMXuK7rNrIx0nusnPfEnunZG7w+8spM5Vjnf/5G1MJXnGyyZkE+Fhn2EI7tdk3qOMGfO6Y5RQjJQl1Qxn1J2Q0wXswxRHPC1U9yGQuBIduod0h7zBYWHfWoT/gu3dudpBPySSEWp6rP0g8w39DdOaOC56XeMhRiEhtQ6gBHq9Dnu/fNqCNxkG3LZ3wDlYBRx7I0LEFyU20FjRz7BMKIRpXZSVnIejkw73r6FFttAqe1XLuc0C8KzV+TuvWEH3l4oLnbZMlKOzkPOMrgL0P4eOaXZdzujvu9FC4pkHRrGBPMx8avZMbmHSvOOIZK8WQSUEYDt1FZ/+TxPOk937NfQ023ukhnjPxzdLTQPD86HxarXvFBc/P/0EPEw+Rk1DTbNqdV7iTwacZB4jnpocXv8d5Hqe/9XQQPD/3++BHlBCJdSLP3CkMOqnZC2oEzzmCZ5fwLeGDoKHUT3XnmhATuscFz2QCWCB4vo52ZzfnecdlguflborPwuQgc3sznSWUiAdTHPH8MXKbcQzPj3Ke37qEfA2QZ7fg+Vf5QyWN5wcrgzMbFzw336iH0dwZTPD8Ancy+DSd+5Q0PTCfnMNGHsgbf6kuwPOfEzXo0JI7zV35to0WiHy70iB4tol8m5xKyKuEsC4jo48gigeeGXGbKfJtbj29jufbtVq+fZ2jy5kEIIOvtpOsiIIofnhmwo+E8/xH2p2lZXjWL6w5XeTbf7Qf5vn2BS8tUdUjT8/Czx6sjnOeWybqIU3k27l0RPfzfHvrKSLfXqjl28NevE1VP3nkBlz23F0JnsNoD8ZnRg8m0R5GbdqUdO/B2rTcOdSktYfRR4Z99FQXwrg4LuLz9lESyBSktfawQgJ423JpO28Pu9BM7WGFwqKXHaE1dk4IvtdxFJ/fvB534oNrdJDGIzKMI6/P1cv1L/H2sCuGEuoz3jt7odrF83J8CKK44PkFzLeheaweUtspSMOUf1J72EP9XliSDAPac4dSe9iUD5QFeESC5xd4Y3dCIVRLrWBuN4ALQXaagO3qD8q+wWCrlLipfpUVEbaCQcY1eEK+iwJ3EMUFz2RKAvNUCeZV6iAfj2inuF9VWIVv8T/dr9owTZLxiMYTp2x/tzNICMURz0/Owp2g+1XUsD1puMTeORvYB5dIudr9qnsn62D9TXxHRb698brgex0XPN89C3dieom4X0W3optTJNaSoRur3a96/Ho93H8z31GRb+/hjwmFkKUaH+Rqh61cBtaGlWdbpaUaGYZSl63agEG5kvcnKW318Fl/wFAXAtm44NlUgjvByibbqwYiq/dJkF01qgwZZmtn8f4kppoxLrcERXuWu13UIJBJ8TuY4ojn9E14ZrPHZo1ZdyZyfL8eMtddvo74Xeoes+EnmJM8ee28EhGROc/yS2fwN90qLnhOuwfTZ3b3TROe/YEETQ8nQ9Z63p+EPbD82tUXSJD6zPWFpSLD5jwP65H99ndEXt4JktzQgHeAJPsGLrEICeZfYqCrZ4m2tFvFBc9QxFvvROdOsdfdH5HW+3Pt8BA7HUc8w8qLaEdFV0hxXFo/z+N6R3LN4asHUVzwDLO5IekA3oNVzIcZOKJAD1ayK+nSiovj4WeIc8nuaC56tpCzfsQHz3JxqD5sX1e2LdQ+xxPP8kreiBehsqyh9jk+eGazIzYMR105Pnh7QEJdko/xYwgnQ/C5bkjxwTOwzMh3Qw7tvRVPPEd3XNxhLKjig2dgIyL/YgdkJXD+hhUnPPei4ornXlSc8JxQXCvBc19RgueEwmvOX042nh9cQiTPqTzZeL4lRN+xhBIS6ivxuZDvZh4f+Ry6Ap2Iz9+62Fheb57IH1MzevZLmC2GYww6gki22fB/n5rv+Wtix5iUmmicM8piEbd1NN95Pvw5jOKCZ4Pl6A+WKRznA8cgbshlSutONYJJyR9okWSTnD8wxIHFI8/aQZFSM4fybzzDwq9NLJNPH9C1GGBckB3/1nkecIxJ/rDMwbS7bCS31A/svWajP1J3/9npumEDVySP1A3IGJo35Bh/lijF3D5njqeZ5rEJJVaptI6olJv77s1uZavZVK+d1S6v4rHjQdXkGLcOBzBWD7YhpfJWX2vrVWKN4IoHnjPLBlLnMK51owxb8Biy687nPcGUeW10huSVVZcPB0NZudshQaGr3M1Z6F7xxzPbeN3gjReLA8zacM5U6sV97/whT12tg5SNPzG/fBZNinMmDa5EZXzej694nL5tnlP/9uPsp2hOTNSS+Wc9cA3u/asjTH//vgRjH/vBHDUZBjyyfMseZHfssoryS3TDHqy677d6uNXtWRzNLa6vSKnns9w4abrFUHK6QHE6tc4YfVIeF3IsOmkre/oD23ceqIimrVHi4ylrnKAagdWJ6SdDKA54ZrvPB9g+hu9HIe7O+F06tvNCgA2zJGCGDuJSGV8zfzDA+FIVWc8o9PQRPwNNMyuSIPND3vWL3Elg47W6jNeSIfXzM/isdAsrklI+OBvgnd/iLrMHO+KU5+d/p4cH7uR9v0a+wyCl1SA9fBOfko41p0rQeKVuwVkSH4CVNumxm86U2Oj1Sy7WwciZa5DqGFXbwhHlc1WFUqMZWEv4ZDSO1YYntqkTH0Q3bqhyc38S1naplcZjqE0SfQc2eh1SccBz4T48j8tqOX97kWFDx9VWGnxRuAcfBM8F/dcOHi9lGvP7F0hKfv98s0hSu1X88fzGDTpgn/MRVrna7LFP3onn+D9uGn7o5xLkfnH2XJo89kGaVHbpRYfik+cRBwwSTPyY93J7+iHc3aYFZ9GYjGEH0sfRoIwVFXy/X/iVDqYk3f/TbP3oIbefcWNSyrjzs3OGx/hb2PzHjNx3OJw2sDTmgFx9TFptt1Uzq7fdvcDbqlKf5z4qEw2EVDr54VbSjNaltSYxXtLh4vO8d/IKmx3DdWjFAc+lO/DnLiJ4QeHjnzvmc0eS8TS3u+AZP+e7mc0vT0oImuOQZz5sEt65n6LUHBphNemj5H/8Cd+tv+/awz+QIOPQrx6jSd8X7k6GXMt5ccrzjYeQopEH0+mrb74Nd+W5v1xyAE+ylPZJd9TgLs9+iygbvYgH8Ol8V2/lr9Nin2Cy2t+VQbNqrFG2mqDcjVFb68iMZ0K9GZoVcPrwRMb/XH0yTNtokLPmT7KV+xk02TR/ksoufxJMu/kA6VCKA563kP1IUSf96tnCweA+r3AkuYyCdbRnQ9zxLAZLvvMcneOPEriTjvT7H42AfrBmPjkapB76/cs+XLzwSL+URfrUOOX5tsOcZxpkwdqX4q485/v9QQoabQs3kKnBxM+x1jf9UHtuzMn18WqmOZ/B3OR3MLWWynWAoxLPdf4hqbIaPPiuEuvXXm0p40MK+5q4Pwk7xp/EXW/R/Ek8R/1JLCGMSTTFAc/rOM+83ct0rCNJdgdWovssz5kWTVKK4PkvhOO9nOcvkz8gnh+tvIEcDdIO/WYd5/nLfvechnTHJ8/TRXzmwbaF81x7BcVn1jZjbTnu8pQPKT6nvbqLnnpJrfX8qR4ZbsfIZPIbKAxbu26GmPwOF90GacJMu/FoHVumAYh9S5o/yRh6LfxJyhXBs9VBqNvJyEB2h2c1DngupspyUQOdKIz7h3U5kuBDX+WZHeUZPuc8Lzmab29LfonebVx8GTkaZB7+8TKeb2+7/q0XfPWdL2J1uxt9yzxnHeQ881tPLywhnu86tx15Tm3PXrYZ688L6AHXIMZ7S5U8PrN2O5KLmbcHT3RLk/Fozulq4k8yci77xXXEqFpNjb5w7WdxJ7YnB49NJNVW8uvd6oAGJyF+nomWenAZI1uSrqpGEMUBz3ltp2oWQwDbFksYpM837cdFRVv7MM/H6snFZPP5G9qh9C/O4DafK8uRiA9+0/8DRB2r07mfYIR7svwcvAiMP3ylmBr96/qWeU75LEMH08kfDGBFFe5uy436f12pg5H/U3I/wKX3L+e15WH/14vx2dBOCagdWZbbZTDV46YtLQ6Acq1vgo2WmiEHsbc04ls64ZvNUN3ncAZwYg5CgdljAbZrMBj3DAZrvcTbuqvswBpGAXNjcLCHsPbkigOeyX5E3nkhKGUSjMfAPA93acMEiW2jGTKMHXzC/ygUfzxnvpYE47YkwUy7ju5VsXcukVLePQOy3joDZpTo4dFpOvjHJZL8wU/pp4jXfBtWzEri9p53/1RK+48ijXhjAMwuSYYV7mR4ZbR+wL+HSakYvFeQyVCvSams9arlFJAdHruLrhQWPOflFi1EM2+zG9l1efE/Vjq9WMc2tABr6cVLyjcl5napbgbyLgTW7FHLaVSk0+32YjyW3S4PHrDLR+JGASEUDzwbSlTvKMw7dw0i1yHVgxmbUqrym8zGwrppIdzDu1P88QyTilXvmdSerYeUe+eXTsBdy1q1uPRCzKGW4s+IBKSuVN2X8z1O2cjj3PH6tnlmC1xuRHrAvyfqYeIdi0svwL2/bb7qPh1rzffML71GDws+XKGKg/hmRem1TwvZrnLh3/NdVTzw3LuKQ557Rd82z3Eqk1WpN1sNAYZrbYrb2Qfjc28pwXNfUYLnbmVxu2SLxxloJ1Jt4OSJ+XdUCZ77ihI8JxReCZ77ihI8JxReN6492XguXkokT3nspOO5IsFzQuHkqjrZeC77M5E8b93JxvNNIebCSSghoUS+3VeUyLdPXhkBFIe9a/BJjspNeWW7k3dtNbis1MIn21Va76j1R3eKE56ZCfe0wMH7u5Gyiy6l/WL5YpGxyErdCJUCdWjYI4ovntMkkKc6zg98yZlFV9F+sUnOC2lR6twxYkBr5nm4nuZIH0TxwvNIHQy48egRjVjEbzmziYU/oUVpcyacyT8aNlDq8thPKKSYUwZT02C5QbuHXq6CU0V+dwyV63KQ7krJXN0fDFs99f5pSGwf8NNXSgeCYfsQeRsf0A1Q4pDIsYRtOZ9tvkqCgqpTzVWngmnb8gb/DRLM46wHUzzxzBYOgpQ3L5Bf0txJlrr15E7C/nlJ//VX6yDj2dPTyZ0kddmRK3B3x+WE2uf44HnA3WdLKa/+v4Ev/lzs6wI1acqafsCe/7nu8d/o4cqNA9L/niJBygw+SfTYq3qzt9jJKg8G5ionsihGN5to7MWu4VDmAbDskNieS/FjF5TgWhWt+FFpqBGh8cFzxWCAzbMkyK/hZ0nmbh2fc67EK0HenlO5gckat1SMa62lwRvFoVwa4onnpRiFV6p6GPcR7/SV8vEZAG9co6Pe2xkfnsHwJTzope5hh4hnvnpQxQfPD/9UgseXJPHJ51DDPsO8qWWcfkFFPxj5X5k1Z+ngbu5s0Mbnn7z13Hg4veJbpnK88NNUscZObg/mrsSHCg+j0VZs31U2sjJwtkgUvLlXiSn+56Or0oGR5mtXaIgkQluNZ/eWSt3+aZiFdlxrpbFXBXt0NNdE3l58bQwFaxzxnPYYIvj5JXgQYjTGzCp8v7QiiXuVfPDbnMNnA0z9Eh80nlNfCtLXkxQXPA/7Wz9Iac/Fy207t+1csQWxfry6X/PsJBo5edV/8dwb/T8ahCV4HvBwkN7oCXWJnBr4JM9yJ5/YuYrc0jw+zZ/E6aaYbBdGQw4ewfeFcAWMC57XYWjOE/O5X0u7s412al3jhfxdx2IXjZ4Un2PqTafRXo5994ojnlfPoqmf+Xzut1HieW85Ps7c9tNDv8Od+8cSJ7kZ5B7+uRTgGf5BE0YHUVzw/MDiJMg6SCi33EhH9HQ1HsL9O1PaaT73lrtn0firkQey8XPBMzRidTuhkKKp3O3H+JPU0aPabOvEPBt2qeXH+JPU8ZFjdSEsG+KC5+0TsFLcOQRf8fHPQFPGwppdlwt3g4fKyVssk7+BDXx0Rh+Zz/2f1+kgkyyF4NDDdPZvvA8fJ73368M0IHrjQ979CEMmvQnwHPfzub8yRQ8TedW4iTyH4F8Uf+8/knlgEu5408OLyXRo5AGCW+P5uT/GwV7HteROhNRCSTXT4rMbH9w+cyA+k6eniM92/jlUhGA2LngmVMd/PT6vxfh8NT53zFcpPmfzzwutfHd3Lg6+13HEMxmTpB8+Gp9XU3yeuu0Cnn2/tMRF8Tnr8M+O4TnEPea44JmisjARIyNPjM9/4fE5lcfn5kB8JhOTAM93hahCJIRixLNB+JPwRJpsG6DMK/N3e66ykz+Jg6A2aHG5IoTtUFzwvBd5NlHVmWn1Z5qsZnOlruMGCRddaydjg/z9eMZkaEa9O+cH3+s44vnja3RYdUZe2aHf0+4spC5e+PAxuQ29c7318PcQ0y+RiC6eQ8xpExc8E8UD2slErI07D60gV5KHa/q1zMCda5sxhpwNrjxAXvsBnv+c4DmMdtANqAYbQE4bH92cQ5XkHQ6owrzb2DZYacOUtLRaAgPWs/lMIXUhTA3iguct5FywDXPo7D3cvSCPHD93z5LWuSXI2D9E2TsIoBihNtGsN3REO3kY715xxPNL5By08SYdZFAQBkh/F0/u9TfrVnr0kHLoZ/0/+KUES7fisi6e51PQ615xwfPTf8AdfPymJBjxGXceGvY+AvyvFckP35cMae0Z/fdh6J79GjV9B+rPU+Jgr+Nb5VRftvkk8GL8JQfPHWaw7OoPph39oRTx9LhA3jUU5DpVVT2E8p4QjgBxwXOxE3cir0YHGzCPXodV6G2XShkNp0Ie/i9VcdF8iW0bJRm2LcYjwhAt76e6dhDFEc9LkVvIfe10WOnVw6MI9z9/LaW9e65E9vkUq2eU6Nk/fo3Hni7av+GNS4L/GnHB8wIVd2LE3/rBA8uT4YGbk+DpK3TD3k6VRryvSLNLk2GFN5m9ciUeS0q7iQ6F7eNPCYWQwl0PbS4nzXFTjTwrqsNLfcUsLjH00+H0mAGsiLOq4ntzYC6Z7hQXPMtltBPZLtWBz2UYp5Vip0oNANnFTkKdFTo8l0qQz48Il48PZXQYRzzLGwnBrKJpTtyZ1ddh8j3XWUL3mDMWTSs6DT8a5yi5jA4lq2gypiCQvioEsnHBM3uA0uesQpcTd4Z4HrBgWjHdY75y2SxaBNOdxVfjRYyNLeL9xEbfkUi3w8oZ1Tx6rJS6SgZTXPAM+VpPtwi1NpSnWBzxDJOsUe3FPcR4MMUFzzDaGrxG0I0e/348nF7xLlcUcwAwd8iV44NnKNA6ekakeV3dvLtTPPEMU4dH8fXO6eoU3Z3ig2eYclEUX+ytF8fFrxD36kgNrcoAADyXSURBVBqJEV4KZeDBFSc8RzNLiRz6iOKKZ0iN/OsNc1xxwjOkRH5EA+T4OLm+Q4oXnntP8cVz7yleeE4onjXt6ZON55I7ieRpfz3ZeC5I+BlEKjv1yPhuatpJ5zdUwuPztJPOb6iABnckFIEUV2Cuye+eEvl2X1Ei345YXXPHfvfUWzwz0UQXB5PvJngOIa3NTbRpRdFW17d0gng+phU68FJ7lqNooD6xipDn4IcCwO8dZ9tpgAiUSeEmwDvhioVnw9EvIdDKHDe/UECR8HwMoYGXgUZzemaQZRlITwuSZHx85IJQPRPiQyZveXWlWg1grvZ5vB4rLVPKfd7KSj50sHudEJ4tXlulFq7kSrubOlNbKq1eXKT4/P7q0DcnvjFFxHOO11ah3fNlZXY+sZSlzFYuFo3fpQNDxQS7ZyiUTVB5f65vVdHznL5qTJl261deNbnIhX+ZvnZC2UW4iM2tiqr/xAlUeJ5nFE9ed4Z4mfrgtWuupm/gxg+5j4h8e10/gBH3Xm2+9wLpgWtshRP0Y+/w/mJRvKfwriaMFqyWZrOpbEVyGmuJG9VvBos/eK+qE8Gz0sjA0iyorbQBNOaA3CSDoRmgPMfg8YcYk/xNKhKelQYJ8vA/qcQmsR1XgbyjP5j4ImVrM543rMRH/TPzd4wJv7kTrKh5Zm+dBunvia5Zdzh1sGmajr17DijvnQ4su6Suz/Ccvj4ZZqwV/Sv/donE3v+5BOkrjxDPbORLPuQZ5EcrCfgFm2l4yMg3bomXYwsmF4KLUvhEsNRorfhp8EJ1Cx4Sf9W9TgTP5XhNYe08PzC2YxKqNgpjoMYcA+4da4mTJvVIeK7wku0PH5tp7BwMUOyTiivxz3bSohIbxmel5Cq7eygUmIucYoDyt6ioeV5Wg6f1JzfR+vIX5wLM3Jk0dQsuQq6Ros19hudHbtZDyv9+SN//xE+Q4ifuxD+YeIjHZ3iA4nPqwsvNK89iCy7KKxylGzG35BfL4js+m9ppaL+QwU8ZLrQ04kOrF/Nvv9Vgcck2DJX4oS3n2Hqeg49w6F010eWDX1PAgTEZ7H5DLS0qJ3cggHphNfCtKxKe95F7wK47acWCTjzH8zsH1dAYjzqPBONzLOTQh/Vnvp3S8Fs70Yqa59cewlU3VRO3eTvxBM88cvFaMh5YTYv6EM//oXFNb0+h/b2nDsP0gvcQ4a/wDJBl5l/LbL6pR+LdrM/rP2rN7PDzymtLE2Lut2Hqi2hV+mwm4szhVpz4tqsKayWj6t6VSAfqa+m1h8J/jt/cRFeYcgrSwFqjG4FwwhQBz0onmQDtoogMJbvwMadz6B5aVFEpKcWS4JmJL/Nbbw2LgecvluCqm+qJg7mC52u2LcZFy7b2KZ5TDnKeb6X9fcZHPH+JKH+NZ+33iZsfK7Qa2+kxp9bnYVCNIOMu+5FbZ6uqOohuH7KEcOVgzDbg+8qoxilFJ5nz7OOJPOfZ4rc0c575TLWaE9C3r8h5ru7i2dI5fB8tKquWivuD4Dl+FCPPjcTBIsHzbznPixr6FM+pgmdu6yV4Pnw8z31L7ZRcI0X4xFp5XmunrLtam+yZtWPt2ueBJg0mvEoZQrR6Ry/mC4hBC/HcxHfCSZcWm1/xURsYz7eNWAGIDwXjmdVpR+LrD23k7qPl2214zozvHNRAi2o8VtVice4ZFVf3diLj+Snt6Hznw1v34aqbKonbfC3fXkem2Pf2gXw7baN2GL4z4ADnmRsbiXx7+/H5dt9SPY/JrBW5sfipKUppQbKgldekcRnWY5V2kywazZjFDlYeMk+IaqnqLErOacUHZzt4CWUfLpJFHToeFEn9eSvWk9l+ishg7hwEULhL4pZkDZPtSH1ju483lcWLoo7PjxG37/2J1s/48nQEZ//py+gu1VMUpPtQ/fn1P+iBffYrOo4bqer8QA1C3ad5tnFSLVSLVtsRZMXXjHUEs6hJI1KImNcNcjsGZUy1HT4MqOKTEyA7XlsMzQwULLwJCyx3ggmvJwwXyW7KDPhF5VtXJDwX1Elg3tEfFIzDu0ZhXJ4mmalRW5h99fl8O3unHpSPTgPZIMFr10mwStUpX5wO7MNz8MM+xPOMsiQY91o/SFUk9v4PJXj75/jTBnh+pC/yDM5mh8Xmw2TW0NKuquW1doK6NtBKVutW3JRql9dayBFAdYHlxAVKVu9UqvGq4akHsNYarHTlKPfI5XZgtX5Ui1jt21YkPLOt05QK5LgEq9Dj686z0nwbZergCtGk3ed5hqfmD1ozQYJlWF/O3PIj8+bTcCPeIXfw7TzWdkH4L+gbUVie2d+uOe/vV+jg1b8mw7g1Zy300PoPHBlNhyG/38xvZPU1KRaLhWpzZnzWorIJX/IXrN1iQb5RRh4bfSZw8/vDJ0hiDwx0MTFou2Cy4DsZd8hi6Tvt2yjxpVIbIh6KSCzMdCgkOT4SjS5FzzNkWM7Db0EhR0ol08K/kHQLN7LLwF+Km6h++wrLM8BICznlplPv1dRMvv9pmbj/WFsaiccRxRfSJ2Thtesuyc2MNRkE4N9dRcZzX1IMPPcJRcDzd0vVX+01Ymm0m6jB+7utBM99RQmevyrMebUMUZMR2FcXfBeV4LmvKMFzQuGV4LmvKMFzQuF1a8XJxvNjC4nkP6092XgueDzBc0LhlIjPfUWJ+Hxyi3rTm482ABi0e3GmwI0w6sgSvsd9PPFM98QytbuMKIO4cwQZ4iAN/PZf+COKP55pnzPE9GyklExhC5Ju1halGKQIXK3jiWfa2xGmrm84JVNMWDJMLBrA53CPwqc7IQDVAEafyUa9Svlb1VRNVg5V5px6chYAtlWVwBG2e3r88MyKh4NSNjS/TNuhAufQquG4eN0oSxWeLmv2+P03STAv7EQaccfznIuklGd/nbPudPF2avGFj12tA/bktZZNZ/ElGxfrYcZV4fY3jnhecbEu5YlLxj/7PfF2StHFj16jB3j4CvMz35dg+ss79mXpEnNjRCUXwltlA6gUY0tMOyQw7ulPc0uCiyPqaKEhyiXhWvDjh+dSJHXdVRJsmMX3yLRdB6bdp0KRS4Iij5RXLBmq2pDronDdO+KN55nDJVg5TQdLVd5zNOXNZEj5/AxYWKqHqesI0alvLcFPVpK9USjFD89LkNQHbtDDA3cm09u010+B1FYZphcnw+y1/YbNSYL7K/vhavE+vDqOZPRgAtqGgdhez7+0cjJy2HWVoROhMLVhPmfO2UU8K1VhvtO44TmjQgeZHTTagyZ/BthMIz72Thi8/3IJsvcPopmfTR1D8IhKwuxwnPGcslqP/P5Ygqz3+Nm/kqwS/nHDkM9/o4PUz8/GtHv8ZozPkPIS/zi44obnYc/0g2EHsP4w5UNexXv8IYS3efppreP0MOwzA/VOm7IKD2bA3WGOKKEuVVsBrM34zVk6+endQAO56lRrJ3n0IdRMlTjP0BBm7FPc8LwNQ3Oh5n5C7/eSKcrOOy/tOB+vXB0INV7EdhKj2y6kj4Mrznh+8gaazh3P7NRDCC/AeoJ3teeyw4g4HLxCYkv1lG+Led9DKW54fvzmJBh7EGPGSIIa4F805Pq5hy45kI1XqHaEGtgDZ9MHL/w8Tn6DuBcjbp3Es4ETDPuI57Jyp5++x84x4JZB8FxGYS6E4oVnRqG5hIZ2ZHZchO/ljhtwxzZXT+vkPNMbKOTTPq8V+XhQxRnPH18hwdSdnGc+WfsHf8AdW1190xHi+Z0/6OYMkQTPSytC8xo3PLdi5XjJYc4zEgysbSnx7PvdQaK7DbPtK/++/3YKzffPj5M9jnuZOjHV7oZnB4/WnWOsOZh9c56LffQYXPHCs3E/nhrFR3lmGs8TRHymN0oFb0VdUx6a1PjiOfUQcjuJUm2NZ0SYeL6Ox+d3/jBqvI63hwEs3BY6PY0XnlPakefZh7p4hn2C59908QzD/t6Rh4d0f1VfHKP5bchCcVjk2210kmj5titH5NsjGlRVbfUR0Goj/zyo4oVnE+XSmtsgx3Y3z7eXDxc8XyYBWyNawtZUhyY1vnhOJ24zRb7NvbE5vKuXX0Scw8FfvbZEVXe+sPhM5JmCeAjFC8+pxPNokW/zeQhe4fn2fT9t5/k2gQxs343Ec50Ye51QOCnErfGY9rBK0R4mt/H2MAVx1ngu4TZ+wRU3+TbFZyMl3UWiPWwtheG9EwZSe1jeflrOExGAdWFqEPHFMzv0Swlkag/LFe1hS8vwTP/HDYM+xzw89fOzF+IvpfFcFZrXuMq30w6kSzBdtIc9TI3ZzdOTmsfqYeRn4j77K+R9dL839BUqoS7tG4UPVQ78bwVml8G0S/jb0/0qtzBpEPl2RRjf/rhpD9s+BB/WTZBgyxgJCgZCHubeGQ06WIOV5lI8lFVOi8VCk29s4cZHwRVn9ec3fos78ugsHdyL/6cOgrQPz+D/V7r1MKOEgpmWb68U97OCKm54fuV3uKOP/ykJnv6DHqacI438rwxprw+AFYjvEg9HOPXFAfhI6yQUkZwUj431NrUcnzrHYF7ttfrMAHKFzVEr8lLB8w7Mi0IpbnjOd+COGCrGqBh+5Q5kdo33qrqhuGjzZHvVqVDUSaYuWI2Wd4u+SEEVZzxPonicuukGx4bTQT6EleeZZVevu1YH8pM32DeLO7SC57+HuV0bNzyPpllyUp+4vnBNMgxoxfrykrJrnvylDlKe/+OEp76HIfoet0rHwpoSXcQiVg3nVOHdH0UfSK3niFgUkD2cAUvc8AzivrIwmBCGoYEjEu80FdrC7HCc8Qzrz6EdTlHoUfw2aeIIZL4ooFx7mB2OG57hbt4QkCLT7g/gxzBM7PsAfkRMO77pzkR4jljGMHm0kAEz8tCKH54N+ZHsiak43FrxxnPqstB5tFDq3HBrxQ/PKQsi2ZNhKxO15yhkDNeTkxTepCx+eAYlkiPKC7u78cYzpGRG8BVnht3d+OEZUjIi+HLHRnIZS6iXFUc895LijudeUhzxnFDcKsFzX1GC54TCy3zSOSLm8fvUGYHBxSeNTr4jSiihhBJKKKGEEkoooYQSSiihhBJKKKGEEkoooYQSSiihhBJKKKGEEkoooYQSSiihhBJKKKGEEkoooYQSSiihhBJKKKGEEkoooYQSSiihhBJKKKGEEopUzJJQQgn1VZ33NQMlA02ZkFBCCfVJLfuaQXeC54QS6rv6Os+KL6GEEuqrmpIwIE4ooYQSCiprmNmU+54KaVJZyCs52fz0c+9IhLOEwikxP0Zf0Xdyfgw5R3XwSdiM4SZKPUYWi/aip7KoqpojXjloklO5ayeYQ+WFyA4Xn3XNqDq+Mq9rlHKpqpi0XcESZCxVFZsnKVbaBbu2I2AXT8EUhmczblkchMVGOyx3FcMKVD61hlzgGkrPhiL7QHqOTaxIVV18T4z5Em6LjmhUYM+U8TQ1RL5qFQvCTO8WEc8ZWMIEvp2MfJpPg40PbJTlF11Fr+UCdSg9py5yiPm0Y9NMVXUPohdskg7Ywq8cl5xnxZ3MLRojdjWXrxdUYXhOX4RHxDc0YvwQLIFlBb4AdmPR5fSa3ei6kB/RHAetEKsW4BHxhqyUSXr+TlWvDUw9l5I7CksaWzRBLBjd0ynpcpochpxaLwNjU7m2KAJ5vdqLnqrR72834LPFRzAxV7OPL8YzstEE1UggazIy+iynlpnrYwfa6vf7K/mrKjxMZ6vP52udwN8DeKoJqxorlFTij2ar94f+6cLw3IBHNByfzTWj8FEu3BNYnW0dBVWqBKzhPLb1KoDsqv6mrbEDnd/p999Jm2ZbfDoo2odH1DZNK6qoHLfLtlwllXjxZMmuaQ6cqN0rIp6f7fS3X4zbz9h8HZUydXuzduqx166W7sBy2Fu/7v/s1RJkbz4t/bUzxWcxKP2w3/8853BlXRKM+/IFn2/PH7U9W1hFM0PfO18/o+p0gKxnO36sHXD3CsPzEx1+/5W45fT11+Ajm/PeC1rrMXvxN7pH7kwC9urP+j+Dn6VvPCP1jf8XsqhQGn3Q73+Eb/o53ykw+r8v4hEF5o+dvfZs3O5fr9ffuq4fwJXrj/BJymOXqZ1iCWvEc708Qp5txF8vyazNtmxq4htl4A7w7K7HpX4DOJFpSxOwFgxt5RHN4Nytuo7NVo0vOdm7tK/O46NfylYvgdw2BvfB3hn6lwvNs1VLMEy7+KRQMpQFVp+HVwtj53Aowfp3/g6JNVyKF5fY6+IbTtVeFNYgzzTHs7x/iFhSzLc6b5cODB0X4RFl7wxJakQ8p80Tnxre+xF/Zot2aTzP3ZkEqYcvlmaW6SHjoyT22rUS3OsJXWII3Xua9iJrgy8JVtK7d84V39LCbURFxof4+PEtejyuwz3hOX2mOIDUd35GW2Fw42aN5xs39YOUAyOk2aVJkPvhAHj1dzp4wKt9GL0e4JO5o6a/jDzPxisRvKBdHWYTxTDlAwYD2sbhEY080EOea5v4k8NvIp6NfF5/o2BLZLugHBMUaZGplT7mCw1iZjYZxDTEFsafolG1z0lFKs028R7UAM/1xJzfCU3IB2s32dpx4/Zm8Vn0MjV7RTJtcKoa2hYKxig7IoYqJaq3VuNDz3iuqnVSsmncF4iVXTzXuPHF7sXSfsxaTZ2jrJ0I5Lw9sZ73pt33Xco3nGEvQ55J4+vEc/4ujvqGeny7m0J4Zi/wfEfdfJ5lv3WTtlYXz8/W44sPl+heuwE/+eTX1iNnAeR9FDIyhlD6hw9fzUtImVqE8ZmUsUk8jzv8SzriOVuQrI3IOmT2iOcntt7Ms+zXb9UOZOImDdlHyvHFv+9K+s90PcgHRpsPpkow44sB4sOoNez9hyn+44uZa+pO4YtSXxTPY/+n0AE8vBNP7qaHEe0e89xey58sfheUV9bWI0BQaXHiKe9SXa0m5m61Nnvtfi8wzLBdTkuTEVx+j91Qj9gZKt2uZgvYWh3lTY0MFLeluquqGKGYp9Hfiijb/S6fm18UunhubMSHZg/z0zWj2eYhlC3+6K8YQjm1rf5q+mM3BHj2apeQhtryWheD0hb8Zusa8aFHPLNiPCJMpos6F/s8vJbcxfNWumLsLB/aiXm40jHN1YY/cn6nFlKjVmZNm/855JaVSgGe12qXkG3V5dWz+sOWVly8sxofeoPnZQ2d7Yhy3pGb6+7kdcounl/eh9S8+5D+k1/j4jdvcu3HtxlHzhUfRq3MdXs6n6cQtvC0hRrPS6eJHdvYeF/dnwbBnC/xxH9wFyLXM55v39LRioE399DSzXdeRiV08fxEA5bwfPXZBybiIX62wHkYz5yxh9JDlhVcI57c3fni9/CXeiD5AY3n2S6xY0/77qu55XR4/EusHlEu3mOejX5xdhsR2XIkyOM3WTDRtUIOprm+elxulBVej61k0I4xXAUDJsEcO58dK6ItCjRjJG21gOoEJYaZVHOa2hUor5XBzevRXTx7KSC3qkYqDZpdtYJnnjnEJNnrd+P+GgI8sz3im5M7VZB3lEuWTgw/HLmexWc8oh2tg6GufjAUtFHk7+K5uA3x231nDvEMHYsrqVKb34lJd4yS57XNl6BkMGg8My3dZvunSfL2Wl1x5/lY3kP4WW/wjFvZ1vYjuGPXOTB1P9Wju3he1nGBBB/eOeQw8bxpccUuPFdTD/8i9JcUQvKy/ffrYepQKcCzlm6zz2/Ty2/+JSn10M8leLKhxzwjPf9s+3/Sgm0/kG48PBKPvYvnOYdTAF596BcHqYHs3/d7j+CpOJK/iU1swRf3JMOSn0gBnv+lpdsttyez5+pPmX0oQwev/LUX4rOsNRKZ/CqvP5v8TsVfiylwebWqIqEGP31qbWEKIi1jmDvKs0LPzG+FZhWBs4O1HaNcDFLac6AJNyHjLhzDs1LvU1W/RfFT2G52eAXPvD4Qo8obJTNegjSec7R0m+OltkisplFVm1Vc2FOeQekcA22LJQSLkt0unpWteERtYzDVxu+tY76HUu38zgvFhzGptFY33i4FeM7T0u2MDuS4sPN8trlxsdp2A5beOzwD++I66WW8PrAv7sI1u3hWXvYtdu//bX/O88vz3bsRorTDPw3zJYXS0t3Jafk60HgOpNsZh6/AtPfLM+DRhiXqzruw9J7yDPL/puufeQhX+s/D+NDFM3vGt0T9dMr5B0bjcf7fCpXic9ZBQj5WzX75lJFzk0DjOU1Lt9PaTRJMPJjR/+mG29yfzMFd6HG+3UJpLTX/WkV7WKsDjLWtdvCJpifBM7RYXSY89cuVY+KzGavc+Ik9wDPktDSKenSUqraCD0MnNFKbeRfPWLbsaMWkgHL4FouTeM5p5R/EKGuzVOXz+ZpbfHRVKNfSbSPhxYOzohh5tOwxz7B1stRATVJ1tGYXz3RE1rZBrAMLNHSMcXTi+VFADzEro063HY+opdVHicAaLd02EM+mjsskLC97P77uLZ5h1WTpqUrkaFM5rtnFM0ZjOfvLs+BD4vnda21H8LzMpIeYlfFJ8uo6n29X6wuUCKzU0u2UQ5gdp1Nwlg3Kod/gwh7zDM/crH+wGil+ohYfunjGsuSM//0/6bMpeIjtN9oOIc8TD2HMjllpr5/yHP5Se1pf/Dnu9wIt3R7QjnE5tT1bBylKxgFku+c8ezDbpadmxnnGcGhi4PYbqolzg6zxrNZiGDdQYkw8I7WIHWvHujaFT41nI3UO52tHqXoDuKvxuRG3dyzPoLTkIO14pTC0y/gPP6TVYpZDpCIiPgfSbYA9CLalk7+roxarXuB5z1Aoppa1Oro/fAzPwHaNkaDGKYGlbYiJsCupDbetUMov439dxAMz2xuoiu9GlE0ddGuWbeeM9xbPmy6UljXgKf7sfFzzGJ6BvXWtBE8t1oH8xbnKF5h+z9x69MPoNWkzP9+1+PzBD7Tv6I2b9cgzf/fonbT9nvP89hW6G1/DPPeJO3HNY3gGePGPenhicRKk/2+Y8tkICRZsw9ViVtZj/K8nbuKBOZBuQ/NYPfJsoHcv3EL72mOeFX7X2dyCUbAcaXG6wW4F1sqsfq/FirFatEApfgdGR7/J2FLOWLtdAU8TrtvI+N0kzrMDPAxM0d6VNmCxRsRMwboz41cWD9beQammirjsI8DNtQAuLMXjANbI+73EIjvmFDWi8i14zuFwO3DDTqwzu4g+VkKPxHPo7zQkzzkOCXIwNiv7MOLvpZbuMkrs5QqK/HINAW6qw1ovvqhC1rbxtvVYlD8QYAO/JabxnMkfCz39YR6Gz4IqfCevFbfD8naHJjUCnuU8DPfr9KB8iRXjj87BJYsor5bXXk5Ht5YAT3/tNJhZooM7XDr4J/Idm3KHSrD+J/yvBc+ZPN0eV3oa5L52OkxdR++W1tAj5B7md5qCKiTP8lQ9jFydBOy/GPHfx/8wYwtCx574NR4Le9CDf5r+Uj+YXZIE9yDXr/4q9HcYXKMHSfDAJfyvBc8i3R57xxkwfUM/GPsUlardDht5IPYOCULMXV7uqSSUjF6nC2NVTrWNukvZfdUOJnt8Hh6/yzE6Mq/PafVZMITac6prcRWbV0XMbb5Kk9VXLru8FhtfNwpZmpoc/A602auWI8Msp9pnYWDwVYLB5hH02lWXl0rH+rx2szoGeVvdqrZzdl6TcPLbV64mxMzlVrEA2aJ1DzNX+vCCFUIheR6/q2majT43l6keYjivqnaUDEpdtWTI8Qh681WnipcMuUQN9N+KXqxiz3KXwBnyObUFvNiiXQhYsUv1DAQlr0T0qzKV+SaE7LAVAc/p25pudtJtsPS1i710oyx7ne9qLGNTtV7JKx7Oy8krcZVSVrDM5XbGevLDyv13qbz9HHEsoSA81Ubbmrods/mpJW7vmSBnzxPdw9LurbspZLetkDynv9F8i4MKSL93cSndIst6rO6KgcD+VpuckrmMdw+DicWzis/CL/ue+a7JMSccD3x693xxgYKsO4jasXba1pR3J+phgVt1nyUNyL2DLiIAV66u+8P5oY6oj0qOveNIdLLH0IQXPt/uRjKn7RtQSQwX+Ejz7W4kF8bwR7FoZuiund0qfL7djeQZMZMbnRZovWS+A3LG1FIevayxJPAx8TzvG/rtsi0xFNQDnmfyrisnXhmxHFdMPM8I9E87wRqW/Q1dNxIKrZh4jmv1gOe4Vkw8J/QdU4LnvqIEzwmF141rTzaei5cSyVMeO+l4rkjwnFA4uapONp7L/kwkz1t3svF8U3WC57iVuFll4y1YJ7L92yTayERx3TXM9Vq+nc+3k8dvjjHx5oQoM4e2zUSTXGE35fRyvj2ObydblDr3xF0k0rL5tkVx47pp/+61fFs0eWeJ23JjT1w71gjRRraE34seG5OFgtHjsX1TzcldsgUsQGxOp9bETF2r8eenLiSaTOJ+sLhX7FAcih1MFodFlZEyd47WXTO0mEd7ITsd/CAVfosdN+fk4zRJilO1ieM3Oc1YqNFiN2EJDqMVLGZ3/6LjvtRueM5RVVXr62pyuPioFKvDxe/5zlNVj3bzl5xLHBIohU58hIKBBcZCyWK2Dy7sDwVSQQ4//0Mrs8St3XMGudDBXb+UAie/FZZXNjlwp8pYXElGIuOxONUpGaw5Oa7zYF7/NZfapTxj0cDi4wgLwXOeXZzEWNAd9PFM2qidFuV33dLOK50mXk+idcedVzB4ri41z5ZTOAhmnrr68vxuNvt1zcVvSWtXnuogL5C0RVTQ+dI47ruiEcRm8lM8s5AMS1Lzz8/OGS5NOnXqhVN1GeaCUxce1zDdLc+5Nm6iguJuIXAjlUB3l9mMLoByV02j1/KciptxozcOmXr+nKR0c87QuedIN55246XjwxOdQr4qonQ23ekgGwaYraolZ2C9xun6UaAgGDDjKixp9OrlP5EgZeKofPv50vTTV1w8NenKS/PPuT0wcDpyGZsN4OAjmyLSceDHdCUwtNPQC5RaC6yRD7Jk1MsMLD7RvxRlVFu1lSx+RF1xecmPyF5JvT7Mdl9k3kPuZvHFsR14lD5EacdgbopQaQfjLj6aEQw7BoKrHldjFW46GBt3CzI4yu0yMHut4/iv5nie2VafTxveYdrVH2povKRbMjTgWZ63w+cTnbQAanw+33Aw7DkPirwSGAq9RPx4j3opXUq8HJLQyveOqWoRN4jY9vOhoEaHz0N4Z7MSl72BelWjMuqW7/LPl+TtD6lqea0Oxpd5EG9jQdWsgSDnl6riuI9VUJ7TNkwO7FX+Tj+enoa37lPVmuU6yNjQGTgpF3kmbG7EMzxjTQf14Janls4agliXea5CLPOrZkVw4zh3i89HXcxQT14mpb6UBKtrFqvq3h+wNx9arJZtFWBkbeqk/p3j1ibB+uV6GFdYOkGHUJZOGwRs0rqbtF4ox6gbnlO5FwlJFn4kaW/cv1itui8Jt94R6IZ5u+falxv7Qcrf76rpeCIZUmeWzseDzV3mvlwHaQtLp0VwR2v2yz7fHwT2T/9Sl/afVIT2JZ/vrmS4f3FS6uv4lmvis9TDbfba+z6lrtyjF5Vcp4fUSc/eco404MaSxRcf94OElYt6P9ojHbds+XqHTmMsiS9TObz4lZI5ioN6eIKrki/S+otz+bSVnMSz2at6LCC7q90YwW3OWm/ILlyaLKrGs7MaS22zggeP1tjW30g9tKtE707q7mlqo/c0tgoTBNXtYmDxqG4DKKWVIpx/RcfzXHj0NnYVspWzQ1LaMI6WuiUoO9p31Cw6g1XUSiDvHw6mYtV7KcjzvC6M1uPdHndXvAuqbAnYXm7VJVjeO0Yqxp0x7R8i40eZWq/OEixye5Mun7gtsktykbfc1R/yXF7vUFCK3W6xgWMVjGflrcuOrpvfgXCMp518jPp4ssMazywTsRXDnfkIq7RVbu9VkryorHzxqZBXWFp+PGfHaXVXwJM/xpj00k8GUhKduikplyZ4WejSPk49RDyvX6yHOTVJbKqrZP4gyFymei6UUpZW3Dn5uLP/eJ7lN355dK2F5Ecyka6PD1L3rNSDGs8sVwcjDw6TFuARLjg8AFJXur2/0MlzStyz9JB7h9uj9fYKpQd4ykwa9hmGnuaROm3RsANGCe72iI9Hv5OCmxqwIhlS2m9PZrOLPfPPkkYuWnffT6WU2aWLNU+xaORop26WdD6aeS9HE7MZQLZbefgz2SmJNMlG7aRWmmstDGSrlQKZBauyhubySC8Fx8ihaDybyazATF3Ec8zlwXh2M+LZhJm4CWSD04L7apI9SgQphey0aDxX03VohxfaqFa8x2Zrx8UqPWDJnZeCA4OrFsplg8likLEoJzOCQXEbjqIa0HE8sz1NwroAtW84vu8c7tyD6+TtkEyd9WqA06qWcuoAuoMi+Y75kgkK+xtBUfIHGxmYoXBwREmSsl2Eu1LayuZK3e5ZyHgHH3Uhun3id4j/i7eK03bbIDwiqxVLMElrBhnAIBdJxw+CC8bzy3ces4TzTGJv8dMswDMpc4NYxHmWZ+rSJdkw3jq0P6RLq4fw4QYhlXv4uZsC/VI+vkUnvyvO9jkOUfwbF2hbEDw/uOsH0sabdLLBkDdYkdKlGaemSSnKzItofNJXdTzPf6fBkQFxnknsdRot0cUzaeLL/YCCaNr/BkBa/xmnpWMRucONdEQzzgx/RKM/f/6PWhQf0Hp7ctr7DEYffP7m0wGmH8K/nvgBz2tHfDaOvrcBMm7vhduSWVpm/vkDpWG6+89Ok1IHTjwz/es/SHgprX6eZiqNiqlZVqqbXfWq0pTj9jfaodGkNJtA9aueRuFiYmpEnk1ei7eegddoaARjSww8W8SQaZSZhnnQ+EvFSSO3UMfzjFcN4hlXpgcw87gsB6rWIaWyLp7Jd6Su1tg5Bt80qBayGFL9ArSKdlXFb6ChXvVU0xKDiPyiuBz+vX9Vx/EsO8rb24W5oEIDL2HfGC8VaOnUGYrq/a1EMSrfuw/TYNjRRPuCCbdWhEkUId6Ek1yhJcBFu/C33uwb1EHv99I4a2MdZrkBreXJBhgxiuOjIBhDOCpD/P1XFIRnw5H71Ap3ALQunrPFesfwrGwSrznPgSLSMA6hRKmhlbZoW+e/zxKvpx6538trmwD/FB0i014OMCh4Tn23+X4ei+U0/rkoLmOw+KNjdRzP8sHHF5ctD6TLXTxnraL1juU59R80QAM1di2tM4IfcbooQrwJrRFL3+r8N8Ve1MRDj9yJRzJsIV80/RBG/4kHedq24vBSdYOWlTeTXUKq+M5Ei9uw0MOCgshY76dhy+Zm3KQFcmiQE3nytTpBbmHkQWLwW3GxOOl8mK/W2ixW5LAFl4qxVVFKxj/R/oy1VAJYcdtuCMazEYkXPEctvBIEeHb6EbMdHgu3Bqnzsn2YtLtbxWdsj7+6P8idCGRNUyRfYTftYWCoa+etUXwgNeyaXENGJ5ZOuj7k7KLaOZdSSnaAnVjdo7Aag4xVzf6r+V+aOy7ErPqhSznPOx+SILuurbWrpsq2CAox3ebPYRSE57z9PwLDpzdpm+jieY1I2I/ynLFhXysNVA7wHIvGffqwOK/hyY7/u4RvJk3zMehKtzWeIf2Q/5bAopA6jufcL34oye/fpS3t4pmn28fynPXsnnbx5pGYfRmy/rtCK+fuw/8WGxv98SPJwz67PRkWvMd5eq62H4ZtHqOHPdqT4Zhfkb21lS7gxpwWCwKAr9RWpJYqxooFo7TBj4vJwwBFPDdTgyNWRVuoKToGnikYBv7MVF/tqvaRC1AQnhkZHcTGswH3L8AzuJs8zlarofMqfL1VBVtTudMn6s+symxtq5TMnZgx2yNy/+mOZxCbxifiec8YLzGM8ZmWjT/qWcD2zpJYadNyl/AgjEHyZs0srHjHnc62yYM5z9z0DzL2dzVerRFWgZRuR6IgPC+iQczrfBo6XTx/JOLbsfl26kc8M+8Bz7B0e4Dei95t4YF5oeZj0JVuazynbzpr6ZGAg29IHcfznLeQmgd3a+gEeBbp9lfz7dT3+SVjIh+BFZtu2yzKWaD+5O1mYSa44EPMu994eP7mh/hHLVSl3vMEvmbPfT/2r+4YybyC7PcAq8UsWOPZ0OjhVtdup4jPX+GZBTz57K24Tgw8i4nyjnLb5ITmrkVf59kiPqG9ilaq+NOua4G1dSDso3e7eGsA469xca0Ets6BChHGvUrCqlueYQfnGeiygA/OXbhOzg6+Itt39Nyj5jIUWenGqPwdXX+at28I7KWqs6g/wzqRZOMHWlg28XQ7vILwnL8dT+aZO7/Gc+ZaseBYnmFZFS3sCc/Zb/HNK5+fAfK73EP0DZFup3el2xrPG2/Qwb1kHxZWx/E87l1k58ZPBGddPOfydPurPMOcElyYvuhrG4hGWRt5OQNaZUj9P5FCZ4l6c0orj8nwAmH9eDU+3BpDU3Z3YnxYf0s5OLGGHOAZbBay3rXi6+N5Bh6Xc7C+Z2ixxcIz6Zg/C3iOBG0P4zsRo7riM54oeHTg8WARbbzeXE41WJQLM2OGMDdgIea2SAYbdsuzvE/8aZVDAsue/gptSdhgg+WY/mQ7OfXGPcffM4pU87T9xiPaOUqCUnxr2i9MyDZrLdfZuA+8VlkcWbodjGfl03OQ1EptaYDnVVoW8BWeV7lorZ7wvLCMn+KZHyFjKz34OmAbtrQr3dZ4/vg3Osj6JCaeU/77QwnmBNLsAM+PiMHHX+X5tqt1kLoS/z5mG8AFbl76iP9haHz8Zv569lqO+ON3ioKnv3YKwCu3JcPsy3TAYmj9Ol6NqgyWdjOmzzmOVtVgJVufnGorzWfjbLfam8pNRqo/c68w5KHJIrvb3XavDIhhtQLlPgtvCY9SxLO9lnIDV70W7gXPRu745+JXmfrAzbGe8az4KB4b6mkjyo6B4KY+JsxD7r1KXQ4Y9w0G467+kFPfH1TuNxROx/Fc4OwPRVjIPKyHm7b2hwqnRCgpuwZDIX6PZRj553kkw7yBkFdGf2puELE8apVcJSllA3G3KR4b6ghf085BUOSSjKVDwVSMaNcNh4zti1XVSz1KtLbwsArCMyzz6GDTRVJeDd1ymttBLroAb4mpL5TDdNcmv0Kv3HGplLqWcnB2+HpcFL3k1ZdL8rNnAlt/k05+9xod/JPuEQdcet+hdJutJ3PvDG7AvbIiCaZG1DP7OJ7h0cV6+Ns1uokbKPYv3M7pYm/zJ0jjjn8TS5LYPb/Wpc5JgtR/LFFVtzeSkr4mtuBH0oCnvy+xp/+oZ/+aooemEboFl+vY83S3mT1QiQWmPP8bPWvK0g34d6o0fRMWtC5gq9QjOW0ebyXCKrtUxeZKddEkUXK9z9fczJhTNVlUxa46ZYcqkDLQM75B6FSLGf+ML4heTjzNzdVOyHFos0lgNkAN5UaHakPKHUgys6pOzaDffvw9owhFdWiGGYBi5ZNjYQGqw8Fw22KTrIrvh537quS4IzyW43i21FQ76dqUU4GcWlSX6D7lUJHjvDovt9cfr0pyhc9FPbmMNmckWUB3Kqxz07ZZUbVOydHmvsooduIy3PhywlvZMBQKqYGDG6AUfG1HgykYz1CgqlgTT1/r1kHGPHUMlSgX8LXSCtUJQyW8QOlgVd1yPq+Vkl80Wau3R6mVdfdNpga8uVVJoDw2y8G7pE0VvbXkmby8J6/FcDm1aALdj55adq0t0OoeUsfzzBY6Sy7XwYjV0/QwYq56NbVYp4oOo7T18yXIWpYE99T9GYuDibxvWgQ3m4/Xgs133UCXuNlYh0555GbnRTqYsum+m87EXHBsAb7BNPzpX+og5fZZJedKA/iMVtNiuG5EJoy7eJpHdgOlB/KIHP6ES47pkhNc3defSQUx3WGIXoZe7uwdlGdNMwN9Pk+wNHZ7TcfzHNCMb8jVZ8CME0ZpxDL4nUaj9YT36dbGPZx4RXSvOgoF5VnME/QNqLfHboThOS0Wb5BYFMm96mgUlOeUWOe6iFY0wca3LmulzyeGFyXUjYLH576qcPG5ryp4fE4ooYASPPcVJXhOKLymPX2y8VzCe4NM++vJxnNBws8gobCadtL5DZXw+DztpPMbKngswXP8SdyGFl79x/od9LY0894KTmugq1U36nm+XcI3UMjvIBhOoJNvtplvW5RwvDFDl3or317EN6CV2stt1ccqYx5vZrqH07qwy4XgOPU8317ANzDxUjqWlNtP3NVhRB4/oum8hFsj6SsTQuW8KdtS6fP5PIFmbQc+m6oNAHLXHeITJDuNwOay8LmrZNVGjgIk1YL/je4cF5jtOUaPLHtk+9E71lHJ6qvW2rnlOjxOnwty6MmHDDucLtwmFoMlQInisTvAkeMyBqU2LM/jAxeDQieNbQa5zFdLHcMNxTZ1IIJlVs0OyeJwmKv6s7Kh89QIjEm6lawNeAalxO7iHcJNNMsNlPIj0xnmXWoqOQ9WDc6326XxVsd5xcHKiYRnedWY4oCxR/Zm32K6ATzV4aIZODJWTSgZBDD30nmXrtJnYKl3nC/dOyTfHtvkH2y9z1evDbYCeaaT7glv8t1HvVgW1vlu0IG88vKC0h9JSy+8Y8w8/Thr0YXPBuMsNM8pD15XHLi5PHGT78+nY9lzJhfRzBjp915XjAXOuKzgoqVJI+ZOyHnsLLj73KWzYuvNPXrZZMeZoqCU266bRwUMW3rtItE9J/WZUyD1tovzll0g3XruxDEFSWPt8859RPRwiU0GmsQdZfK7QPE18zhI94ZzqnkvT9OJnavC2Brg2VbPO3vSBHRNvIu1hc8taauntnZDrQt3THZVx3aXy6EaHe1ifKSzUlXVRjtUuvG5dSDYqiWowfIMVVQCs/sIPXO1M2i/t3A8G/YsFisUenDLbonVuVy7sHDWMBwsW/GjwmrqH2bauhh3SC6oi2QMSLcq1QZ6sO0Xgbz3fOSshpYoWxarak29DgylXiyBFdTw+WlqJge9Ox4Jz69dJBk+5aOoIK9iQkXnn3WQV6WHly+X0r88HcbvPI2WU5eu1FXeIVjqzJpjvBCi0cwSVZ2g7Un6azSNdNaG69bsfzwJ7nVP3tL5Ox2krq6kEZWT1vI+Y5uWiA5r3Sgkz+z93+lSP/gh38mJa2/Y0LEiCWaUJcOLV+hS3v4ejH0ZmZq47nokOG39EixPvvG1K0J9QUE1+p+nwMQ1Iua+8js9a0mX2L/TpNHrObR8GveUuyuoJ+v0dVTCiL9PCYzojEnuRjHcwUBjF3I43A4CKdBr28PfnCAxN/UJF7JznluRrUq6hsgO6nJtdZd7rWD0qnZVZm6Xqs1qFaUotd0lIju/eO0aqNAlw1grsT1jsJBGSSl3u70KuFzlXguY3VhSsNMxHM9F5dqf7h8jQX7bwBybBObOyVJhAy7eO0bK93hUm2Ss8Lrc/VmRw1se41ir8XaNZ1MHArT7InyV2YFLMulKtNYlKaVO1T0Q5rlcnkuljIrKaUGPKAKeM/ciGs9W8/hEafWmfUnsreskmNmgX9WgB/bFb6XsIrd7mi71jlmqexDMdbo8sQG9/iiD6R/ykZP34un96N5keaUe5M8fTmKr55d6z5HmuNzeq3Xpd1TMKg4WNUPyPPEwgvQIzSsJQJn06439Ut6/UgczNiQvqEoG9p/p+tw15Yun6dMeXKx6TocZjrXeX4T4hoLq4cp+kPZ3moIO0g6kS/DKaN2CzacAa83ErU2Zt+kUGLBiVrHnXGm2y1l8tX7Ek3fOKu5Bxs1qafJnFOeZJnYHRUwKrfFsbDqBXUtU5es8+xplQxPv2s2IZ+opbsBHK/kMyEhjLP3FSYaGo/HJTMOTUaodSTsPt9t5HpOt1I1VhjJJ4Y/82LtTGJ4LB5cJcPgQSkvnpTzh2TsKaujv6spx81X4n7FCE5VnKOrPV4hayrzziF4U2/uQlM0NSTjPYtH5WEKmCb8sBoX8iO4YGLScCHieSTyv2sfPfvoF5jbo049gtpp55BfcPOzdO3UyrNanSgwyTVipYFh/jum4cg+3Ph7Igj8WY6LJF2DqtmSZTsONixHqzEJM72V4MClFkmFlcLuQkDzffmQAZvB8Flim4BZur0rOPYSL0g+mPlmJPL1+f5IMDySnSIxNHIVHJMu3n07rRa0pn2fp7r5OXHOan/5e+usD4IU/YwGv3J+M9eZ8mnKSpecNYjBAmo5HlCLdesHAWMrRZLNCI+eXeJarW/BXcPPxEF2jqpp7u4fVUVnsfMyWkODZ0NzMJ1y3GWnAF0p0PFX4RYUFpSycFG2Gei4yISM1DAR7J/VKpoGSijj/RPwPkQWE5tlskzSelc75EvLM+zYb9vWHnTRKuYquJGLjBn5lkmM9opL+hgC9mXvrvfxi1cWzwFsR1z66OOJj8KtyJPH5CObaiwJDoAGemq8bf+RHEqQf+e2GNqwJvvsQnrCi15VWasDyLkopeaUd7cJEIP3w03fW1YvRz6uXCyA+phEZKYJgUVxa8E62IXm+7cgICRa+11VT/dv1ujkUslMPjlhES5+nKd5FEan8iJiwQ4leD3xJfbW5hv3nlb+cLUHLUtyx5/7ajy1IFlPIDhBXCvGdhTiiCFTJwMmHTxn81ZXt3Lq2Wtjcdo2SPGGNygqy/HWewdFMiYIR/wuee0Wyq5YyD027yHWAp9vg8tN32DmZL4hIIXmWiyTQeIaa+v5QwA0OYB7m3PupWl3WHAKaaJRvgi6eoXRPK3n0HeW5VIyzjlAR8Mw+/ZMOnqJIzKVsOg3mdiDPhiN/sB+5RlI+vbmXjguV9uYLnMOZR34gwUv19FreKFqPsu4I7EEkCslz+ue3JbFnqgOZbeqTyXDPEc5zlvngSJ389m0h/jgqpb7d+cz3tNd372ybpIc2zrPvlAWnB6Z47zWZ6lXV7adByBifje385lCTOPO7eA4MXOxNWVVVlWttFgt5kolFgmeXGzx+M6u0WCwtAXPungjLERXnyi7fHzO5e6HceM2wivgcRaNUMJ7zsaSBpTkWS025sEWQy8rdHjI2AHMpPjbQcOgqvIT0UHKRqtrPKxtlsXVcLn6gUtfADW1d9WfS7vPFc2QKzXMmljcIMjbf6azr8gd87EIJ8kR8vkaaWrN4wpeiqaxHWqiqwqNz6qecspUd+LTwyNn48l5hEio/KbCOUMF4zsUj+pE0YuOfp70VMC5iT5wrwQwRn4dJMzYvmfAJ1qV7KBo4NVmf+uoPM/77uMgDlqinP3AkW9fMef7L9fMutyx77cpBPf/qjsqDSFlqiVzKt+28Oaz2a/H5RLRwG7FYRvdVmlt4eo3iPLNWLNTnVeij9saAH34PhOWIhDOHjPi43Fqk3oEoG8hRROZQR6hgPJvoiCpwt1uateo5qoGCslxKGVQZWXJuDZhxxy6G5Zhy6ny++s4XuLeA3DEI2Lbl+DLAc6Zm7xmhQvNswPJ4Bmj4klqbSfk04CqNUvBs4dQ7c1s0cTOIMvG4eAEZH3KeJx3Bp6zDZ+Crq8SurQzalN2tgvGchiXxH3zkxwELgwUX4YuRh1Iwbh/k3kAT3wqE1NjFRlosw3ULypJh5EFeRWCtmLM/91C/p8nQ4JUV8zf7fDv2vfjzqH6s0GLcuVPxk3EHuW2WU9uY56v15/oTV39GqV/NtxU/0ucUO9CL+TbK0VXQLtGibOaxsgoPmO5ZRaww7WEI7tGG5CLaMCOczbKJTIb2xtiY3Z0C+TZv3+ZmKAGei6NKtyPJt1HsZc1hALIJuzzptRt0MJMbDaW9d6xZSY+10MuvDuyDKySYuSUJsmxYcK4O5pwvQUp3BqVBFDLfRslv/0Y7ohlkEJKre/V3WMg6upikvn2sWUmPdHcFxuYWXhNPacen6ev6TfznKUD3rXBRL+fbHhGqWtsNkENZN2tqz9Fch5Av3hTFmoO3pPSCiGejn8h1c3OSSry0+ESG3Hs8V7sVM/kS1xHTZs2FiNJtLLtSYlujoSwini1tGPZZSTWSzGoaMWgj2KWjIL/LLagXxHneXKljm+dLbAsdQkGnyLN3RpVuR8azYcMs/Dzty2uk7O14PPWXS5kb9Ow1Ajl7Wy9k25rudQ9Kewqz6o21STBuQxJb/2sp48MXsLzrdAu34/MOSr8jVBie018ir9yRh0bqJlIJjb/UjXwqmb1OTr0jXzrGd7+HGvF+mpT10inwcN0p3Gnohd/o2CsjdFPETane5RlrscSME6t+FvxP7VCqaoJyqrdSDZc+tAu4TpQsuHmZprugnaCEwK46RILMfT97R9ZKN++i4qBHs3ZA2pw5Jkd01fSwPJPViqFkIJi5awmftkqlaXqgwE7dxXpN1PYGBTYJWKFqo84yeVgOYS13N+lcCEXSHpZZwDNUtupCaS4dDxmGZRY6h9JEUmS70lvKXrOcpwGTyEw/d54TM3s+Z5Z6msKntArkCJEoJM8s187tmFLuOZfRZFMq+ZSNLHRiTT0l19YL1YcujVh5wxjc3kRHErDpxdeRlQKb7tAmvxjGJ9E6wVJEF1AuQy/UYU8qheW5zymyfLvvKVy+/d2R4gkAbeTTZyR0VAme+4oSPHepC+IEzV9Xgue+ogTPCYVXgue+ogTPCYWX/ejtqJNEi4TDMKf6ZNK4om+grSmhPi520lVBRDv/yXdcJ39lkXe7SiihhPqkpnytAmEQk7QllFBCfVDLeB+Uo0rwnFBCfVdf55n68ieUUEJ9UzR9V0IJJZRQQgkllFBCCSWUUEIJJZRQQgkllFBCCSWUUEIJJZRQQgkllFBCCSWUUEInqQD+P3ko85FGnXi0AAAAAElFTkSuQmCC\" width=\"975\" height=\"564\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eCoefficient estimates are reported with standard errors in parentheses and two-way effects; internal instruments for system GMM estimators are \u003cem\u003et\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/em\u003e to \u003cem\u003et\u0026thinsp;\u0026minus;\u0026thinsp;4\u003c/em\u003e lags.\u003c/p\u003e\n \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e1\u003c/sup\u003e Natural log.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eAccounting for endogeneity and serial correlation\u003c/h2\u003e\n \u003cp\u003eOften, the determinants of a zoonotic outbreak are not influenced by conflict, and include, for instance, outbreaks resulting from animal migrations into new areas or deforestation [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is possible, however, that conflict may impact zoonotic disease outbreaks, e.g., by inducing population movements. The direction (positive or negative) of this endogenous risk can go both ways \u0026ndash; for example, conflict may push people toward more contact with contagious wildlife, but it can also induce people to move away from such areas. To adjust the estimates for these potential issues, I use a two-way systems GMM approach, a well-established method designed to account for endogeneity and serial correlation (see Methods). These panel data methods are computationally intensive and are not recommended for very temporally long data series [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Accordingly, the models from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. are estimated on a series of 72 months (Jan. 2013 \u0026ndash; Dec. 2018) in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The results are robust and become, if anything, more statistically robust in the case of social conflict by identity militias (in both models \u003cem\u003eZDO events\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026rsquo;s coefficient is significant to the \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01 level), despite the loss of approximately two thirds of the sample due to temporal limitations. Sargan test estimates suggest the models are robust, although weakened by the many instruments (due to the large number of panels). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. hence suggests the findings are robust to endogeneity and serial correlation risks.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eAdditional sensitivity analyses\u003c/h2\u003e\n \u003cp\u003eIn addition to endogeneity, several robustness models are estimated and reported in the SI file. The first model accounts for the inclusion of controls, which may pose the risk of inferential biases due to multicollinearity [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] by including only the \u003cem\u003eZDO events\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e variable and grid cell fixed effects (Tab. S2). The next set of analyses then account for environmental variabilities due to flooding and droughts, which can increase vector transmission risk, e.g., via proliferation of pests or mosquitos (Tab. S3). The ensuing two sets of analyses then account for conflict dependencies between different actors. Here, the models are first estimated where each conflict type excluding the one operationalized in the dependent variable is included as a control in each respective model (Tab. S4). The next set of models then add to each analysis from Table S4 \u003cem\u003et-1\u003c/em\u003e conflict lags (Tab. S5). The final set of analyses then add orthogonal binary spatial conflict lags to account for the possibility the results are driven purely by conflict spillover from nearby cells (Tab. S6). The results hold across all these models, thereby confirming the impact of zoonotic disease outbreak events on state and identity militia conflicts.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e[1]\u003c/sup\u003e While information on zoonotic disease outbreaks was collected until and including Dec. 2019, the lack of availability of data on key controls forced me to omit the last year from analysis.\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study shows that in Africa between Jan. 2000 and Dec. 2018, zoonotic disease outbreaks reduced civil war activity by the state by about half but intensifies social conflicts rates by identity militias by about 2.5 times. Political militias may also be more inclined to engage in higher rates of social conflicts due to disease outbreaks, although the results do not reach any meaningful threshold of significance. There is little evidence for the existence of a clear directional effect on conflict initiation by rebels engaged in civil war. Past research on the impact of COVID-19, malaria, and HIV suggests several potential explanations for these observed trends.\u003c/p\u003e \u003cp\u003eFirst, outbreaks can adversely affect the state and its administrative and security capacities. For instance, infectious disease outbreaks can constitute a governance shock. Outbreaks force the government to \u0026ldquo;shift its focus from other administrative functions to combating the disease, while simultaneously being forced to reduce its bureaucratic and even security operations to avoid infection and the spread of the pandemic to its employees and troops\u0026rdquo; [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which can impact its ability and willingness to engage in armed conflict. Zoonotic disease outbreaks similarly constitute sudden shocks to governance \u0026ndash; often, vaccines are not readily available, and containment of movements and activity is necessary. This compels state militaries to reduce conflict activity as the rate of zoonotic disease outbreak events increases.\u003c/p\u003e \u003cp\u003eWith respect to rebels, zoonotic disease outbreaks may cause similar impacts in the short term, but not impact their general activity levels over the long term [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This can be explained by the fact that rebel groups need this time to adjust for the new situation, but unlike governments, do not have a high dependence on long logistical supply chains and other administrative services that can be affected by the outbreak. It is also possible that rebel groups might respond differently to endemic disease than to (re)emerging pathogen outbreaks [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExamining social conflict, if the government is temporarily weakened or incapacitated, a governance vacuum is created, which can increase the risk of violence involving nongovernmental militia actors. During outbreaks, militias may provide services the government cannot, such as aid, food, and healthcare. For instance, the onset of the COVID-19 pandemic has been \u0026ldquo;increasing stress on its precarious health-care system and exacerbating youth unemployment, which surpassed 25 percent in 2018...This further undermines the fledgling government\u0026rsquo;s legitimacy, as militias have stepped in to supply medical and humanitarian services\u0026rdquo; [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Militias can also act as security providers, stepping in to provide security, both to react to rebel attacks, and to preempt any potential intensification of rebel violence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are two explanations for the observed rise of conflict activity by militias in the lack of effective state protection and reduced government security activity. First, governments may \u0026ldquo;contract\u0026rdquo; such groups to engage in conflict or provide security when and where the government is unable or willing [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], such as in the wake of the outbreak of a deadly disease. Second, civil defense militias might organize independently or semi independently by local communities in affected regions to provide security [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], or get recruited to protect, e.g., areas where natural resources are extracted or pastoralist zones [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Such militias are less affected by the constraints faced by government forces, and \u0026ndash; unlike rebel groups \u0026ndash; do not face similar pressures to advance state or regional takeover goals, thereby using the opportunity provided by the outbreak to expand operations and security relevance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The finding that identity militias \u0026ndash; groups that include civil and community defense forces \u0026ndash; rather than organizations tied to political parties and leaders (political militias) intensify their security activity during zoonotic disease outbreaks is in line with the second (community mobilization) explanation.\u003c/p\u003e \u003cp\u003eConsidering the risk of emerging zoonotic pathogen outbreaks is poised to increase in the coming decades due to population growth, deforestation trends, and climate change [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], this study\u0026rsquo;s findings provide a template for potential impacts and suggest mitigation strategies. The results underscore the importance of studying the entire spectrum of actors involved in conflict rather than focusing only on civil wars and the often-used government vs. rebel conflict dichotomy. If disease outbreaks empower militias, this can still lead to loss of state power and legitimacy over the longer term, even if over the short term, identity militias provide security and protection. Therefore, considering (re)emerging disease outbreaks can pose threats to the state sovereignty, interventions designed to bolster the state and its capacities (assuming such bolstering does not cause more harm to its citizens, as in the case of repressive states) can assist in preventing protracted conflict and improving political stability over the short term.\u003c/p\u003e \u003cp\u003eFuture research should address some of the weaknesses of this study. Most importantly, follow-up data collection efforts on zoonotic disease outbreak and conflict data should be conducted in other world regions to determine whether the results are viable at a global scale. Furthermore, research should more specifically study the specific conditions under which conflict dynamics might shift as an emerging disease and epidemics become endemic. Finally, it would be useful to study the impact of disease on other forms of violence, such as repression and authoritarianism, considering the viability of this risk [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eSample\u003c/h2\u003e\n \u003cp\u003eThe sample is constructed using the 0.5-degree grid cell \u0026ndash; approximately 55km x 55km at the equator, which decreases in size toward the poles \u0026ndash; measured for each month between Jan. 2000 and Dec. 2018. This empirical construction uses AfroGrid, a recently released data framework specifically designed to study environmental conflict in Africa [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. Its subnational/localized geospatial resolution combined with subannual (monthly) temporal disaggregation make AfroGrid an especially useful tool for assessing local level zoonotic outbreak events\u0026apos; impact on conflict as unanticipated shocks [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDependent variables\u003c/h2\u003e\n \u003cp\u003eThe key advantages of ACLED over other datasets for the specific purpose of this study is that it disaggregates all local conflict and violence incidents by the initiating actors, while coding a diverse actor typology. Using ACLED data [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], two dependent variables capturing the standard (government vs. rebel) conceptualizations of civil war were created based on the number of conflict incidents initiated (based on interaction code from ACLED) by (i) state and (ii) rebel forces; and social conflicts initiated by civil defense forces, militias, vigilantes, and mercenaries that are (iii) politically, or (iv) civil defense/identity oriented. Using an actor-oriented operationalization helps to capture the direct impact of a zoonotic disease outbreak on their active engagement initiatives, which past research suggests is key in the case of disease-conflict analysis [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Each dependent variable lags were created by using conflict values from the previous month.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eZoonotic disease outbreak events\u003c/h2\u003e\n \u003cp\u003eThe Geolocated Zoonotic Disease Outbreaks Dataset (G-ZOD) records monthly information on outbreak events involving 23 infectious, potentially lethal pathogens that were identified by the WHO [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] as posing potential or real epidemic risk in the African context: Ebola-Zaire, Ebola-Sudan, Ebola-T\u0026auml;i forest, Ebola-Bundibugyo, Marburg, yellow fever, Rift Valley fever, West Nile fever, H1N1 flu, H5N1 flu, SARS, MERS, chikungunya, Lassa, dengue, monkeypox, septicemic plague, bubonic plague, Crimean-Congo hemorrhagic fever, shigellosis, rabies, zika, and anthrax. Attempts to code other deadly flu strains (e.g., H9N1, H7N9, H5N6) were also conducted, but no such outbreaks were reported in African states over the Jan. 2000 \u0026ndash; Dec. 2018 period. The efforts focused on Africa to maximize the availability of time and resources, and because the continent experiences a high share of both global conflict events involving state, rebel, and militia forces, and zoonotic disease outbreaks (including those involving emerging pathogens). The \u003cem\u003eZDO events\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e indicator was created by aggregating the total number of outbreaks occurring within each 0.5 grid to the monthly level, creating a framework that directly corresponds to AfroGrid\u0026rsquo;s unit of analysis. A detailed discussion and illustrations of the data collection procedures are provided in the SI file.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eControl variables\u003c/h2\u003e\n \u003cp\u003eThe controls used in the main and sensitivity analyses were aggregated from other databases into AfroGrid. Due to concerns related to inferential biases from including too many controls [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], only key confounders were included, while adding fixed effects for each grid cell to account for all constant (time invariant) features. \u003cem\u003eNTL\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e (accounting for local state capacity and development) was created by AfroGrid using VIIRS-adjusted DMSP data and a high sensitivity re-calibration method [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. \u003cem\u003ePopulation\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e was obtained from the Global spatio-temporally harmonised dataset [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Both variables were measured annually and aggregated into monthly using the last-value-carried-forward approach. Controls for drought and precipitation and temperature anomalies were created as normalized deviations (\u003cem\u003eZ\u003c/em\u003e values) from long term trends using CRU-TS data [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Country level controls for life expectancy in birth (a key indicator of development), government efficiency (a political capacity indicator) and GDP per capita (state capacity) were obtained from the World Bank [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. All robustness models correspond to the country specifications. The spatial lags for each conflict indicator (Table S6) were created based on whether at least one conflict event from each respective type was recorded in an orthogonal grid cell during the same month \u003cem\u003et\u003c/em\u003e (=\u0026thinsp;1) or not (=\u0026thinsp;0). None of the variables were lagged due to misspecification concerns [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Summary statistics for all variables are in Tab. S1, SI file.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and S2-S6 were estimated using ordinary least squares (OLS) with cross-sectional/grid cell (\u003cem\u003ei\u003c/em\u003e) and temporal/monthly (\u003cem\u003et\u003c/em\u003e) fixed effects per econometric recommendations [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. Identification was conducted using the following formulas:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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fJKVlJSErLZwu91B0+V03rp1CzU1NaitrYVWq0VTUxNyc3MxMTEREMAsZ+E+A4DS0lLk5OSErO4qKCgIaufl8XjQ29sbFAhnZWWFrBoK1bhWTofT6YTb7YbBYEB5eTk8Hg927dqFoqKiJQNt/+8Wa4fW0dEBj8eD6urqoO+ampqCeqb6fD74fL6gHp6LlTCFCrSA+aDC7XbzBxN5faFuWIWFhRgYGIDb7YbL5UJnZ2dAezx5P0mShLi4OLjdblRUVKCnpyfkNi9GFEUMDAygoqJiyflsNhva2tpCbmuoY7mvrw9xcXFB8wLB+XL//fcDmN9f27dvx9TUFN9WtVodtJyl2O12PPzww/yY9fl8qK+vX/SY0ev1yMjIQHp6eljt+0RRxFtvvYU333wz6MbudDpDNjYXRRFPPPFE0LJC5dG5c+fAGIPZbObL93q9Qdep5bhcLlRWVgalxe12h0zL6dOngzoB9Pb2Yvfu3RAEAUNDQ9i9e3fA9/4Ps5EqLi7GzZs3A6Z5vV4IghC0r/R6fVC7Of8OJUNDQ3jyySeD1uFyuUJ2POnu7kZbW1tA3sil0P/4j/8YMK9SqcT58+eD9lNeXh7eeecdPPXUU0hPT8fo6Ch+/etfIz4+HsB83jz77LPLZQP5oorWOBE6nY4BYGazmVkslkXnk8dJMhgM0Vr1kvr7+xkAdvPmzYDp8jhaXV1dfJpGo2Ht7e1By5icnGSCIDAAzGg0Lrm+/Px8Fmm25ufnhz3v7OxsULoZY+zmzZsMACsqKmI3btwImF+pVAZMk9Povw80Gg0DsGhaqqqqGADmcDjCTmtVVVVE8+t0OlZUVBQ0HQBTq9UB0yYnJ5lWqw2ZRv/8n5iY4NNaWlqClt3f388EQYho/DGHwxHR/OXl5UFpZYwxvV7PAPBxtLq6utiNGzdCHj+zs7NMrVbzfbTUOTYxMcE0Gs2qzrGdO3euaPyoSI7llpYWJghC0Drkc/ODDz5gjY2NrKqqKuj8DdfCMcra29tZbW1twLSCggJ+jFy6dIkxNp/ftbW1PG2NjY1By56YmGA7d+5k+fn5EeVTRkZG2POH2g8qlYpduHCBf7ZarUyhUPC0R6KgoCDstGzatIkZDAY2NzfHmpqaWE9PD5ubm2PJyclMq9Wy119/nVVVVbH29nZ28+ZNVldXx89/m83GWlpa2J/+9Kew09bR0RHR9UOWkJAQNAab3W5nubm5YW/rxMQEy8jIYJ9++mlY8yYkJDCdTsf++Mc/LjrfUuPldXV1sdbW1rDSVllZyURRDGtesjFErfK4pKQEwPwTZGpqKp8uiiJvQC73tBJFkZcgLFbsXlBQEFTcv9hfqJIImdvthiAIQU/NNpsNgiAs2xgZAK/eEQSBN6C32Wz4zW9+wxtgy40u5Wq66urqmPTkW6wUQC5dyMrKCigNlCQJSqUy4hJCq9UKhUKB6upqeL1eXqrT2dkZ8ZAN4XK73QHVSAB4flosFj5NrsYNp7TG//jS6XTo6OjgHQGA+e2RJAlDQ0NB1cbREqqqVz4X9Ho91Go1PB4PJEladD8JgoDCwkIA8yVw/iUa/ucYYwwDAwPweDyLltJtFHJDeH+SJKGzs5MPe+Hz+TA4OIhdu3bBZDJF3HtsYSnuO++8w6uTlyIIAsrLy6FQKGCz2TA1NQVRFBEfH8/z2W63w+PxQBRFNDc3x6RaSBCEgG2QS+j9h8NwOp3Ytm0bzp07h02bNqGqqipmaZFLW2/duoXU1FSMjo5CFEWUlZXBZDLh5MmTqKmpQU9PD7q7u2EymVBVVYWbN2+iuLh4TRrtezwevo6amhpcvXoVNTU1mJ2dhd1uj/r6u7u7MTExgUOHDgVV+0uShObmZmzatAkfffRRyHUXFxfDbrfjG9/4Bp82Pj6OtrY27N27Fw0NDQHzP/vss8jIyFjX9rQkQtGK2OSSE0EQgiJ3g8HAALCJiQnG5o+0NSvRKioqYoIgBJTotLS0MABBT7ahSrQcDgd/2j1y5AhjLLA0RS4dkp+85JKhSERSCtDV1RVUOnPz5k0+ovVCLS0tQaPfWywWBoDpdDo+TU63XFIib7dccrNwO8MRSYmWXJLjX1IzOTnJdDod02g0/JianZ1ljY2NzGw2B5XU+ZdoXb16NWCavK1yCZecV+3t7RGPqB9piZZWq2VarZbNzMzwbcjPz2eCIPD88V/eYsfP5OQkA8CUSiWbnZ0N+E4+x+bm5tjExARTKBQbvkTLZDIxtVodUPogl2adP38+YF6Hw8F27drFioqKVjwS+czMTMjSpOzsbH7c9PX1BXzX1dXF4uLieHr880XO51iWaMmlVfKo+bm5uUyv17MPPviAMTZf6qNQKNiFCxdYe3s7UygU7MSJE2EvP5ISLTktH330ES8dslqtzGKx8PzwL/Wqrq5mCoWCnT17ls3NzbGEhAQGIKxSInnbVlKitWnTJqbX65nFYmEXLlxgc3Nz7Ktf/Sr7xS9+EfYyIinRqqurYwqFguXl5bE///nPfHpTUxP76KOP2MzMDIuPj2f/8z//E/Tb1tZWlpOTEzTdarWyxMREBoDV19cHfS+KIktMTKSSrc+JqLTRAuafdvLz8+H1eiNqCxRrHo8Her0eVquVP5F5PB7U1tbyBu1L8W+rIggCMjIyIIriuo19NDIyAo1Gg+HhYbS1tfHegkajMajNm9vtxq1bt1BUVMSn2Ww2PkikKIro6OiAJEm89M3lcsWsxGopckmdWq3Grl27oFar4XK5oNPp0N/fz48pQRBw5MgRHDlyBCaTCQMDAyFLgbq7u1FaWsqXEWlbpWiR23oUFRUhLS0NarWaHz89PT0wGAyoq6uDVqsNyHen0wmdThfQPkXueKJWqyM+/lwuF9/voZjN5jUf7FYURRgMBhw4cABKpRJutxuSJKG9vR25ubkB8xoMBrz++usB56M8EPFiFpZWDwwMYN++fbxtj8fjQXNzM/r7+/k8xcXFMJvNcLvd8Hg8IcesW0tlZWUYGBjAI488wj+bTCY88cQT2Lx5M9xuN958802YzWacO3cupml5/fXXIUkSnnjiCSiVShQXFyMpKQklJSXrPs6YP/lNEklJSTCbzbh79y48Hk/A+HzRVFJSguHhYZw7dw7d3d28Hdjhw4eh1Wpx7tw5ZGZmBrXrmp6eRkVFBa5fvx60zNOnT+PKlSt47LHHQq5zz549eOWVV3D06NElz2uyMUQt0ALmu56v15AHixFFESUlJSgvL4coipAkKaKRuK9evRrD1EVOroZqb2/nr29ZeEMG5m8iNpstaKwwi8USUA0n8w/GAKx5sDUyMgJBENDY2IiqqireSWGpoD0rK2vRG21jYyOvjovl6NXLkW/UeXl5aGlpgdPp5K8VksmN4v27xzudTmg0mqD9mpqaGlBtFC69Xr+hRqCXexw+/fTT6OrqCuvc1Ov1ATerSLfn3Xffxauvvso/azQaNDY2orGxMfINWCMajQbj4+NBDbk/+uijRRt3x4ogCDh79ix8Ph8qKytDdgDYCN5//314vV5+jg0NDSE9PT1maRUEARcuXIDP54MoikHX48HBwZCN88+cOYN9+/YhISFhReu1WCzIzc3FlStX8Oijj644/ST2Vh1oFRQUQKlUorGxEd3d3WGXHMhtDfx7XEWbx+OB1+vlT6Nr8VTq39VZkqSwtk1u3xYOl8vFexyq1eqQgYjP50NNTQ3y8vJ4wORyucIqwVuOz+cL2eYoFIvFEnbppv/I6UqlMmTbOfndZ/JFbHh4eMlBZ8PJe3l/3b17N+ztChXYLkbOfzktobYrPz8/4HNNTQ1vWyVJEnJzc3lQMDIysux+9B9l3efzQZKkiEuZ5QDW5/PxdxCGI9xjeWG+hHNuDgwM4LnnnlvRDTMW15qF+fzJJ5+Elc8rGWk+VEAVjSDr8OHDEadFFMWgHof+5GPnk08+Cfgs8/l8vDfeUtLT01dUc+D/FhBgPtB5/PHH4Xa7w36dkkajwalTpyIaA2ux69bg4CC+853v8Fduyd566y0cOnQo7OWHkpmZifPnz1OgtcGtemR4u92O1tZWFBQUhOxyLDd8Bz67uBqNRrjdbphMpphWM8pFqstdkOR3B2o0GoyOjoY98J78BAPMN6oGPhu3KS0tLex0hhsAyoGjf2eDUGpqauDxeFBTU8P/Ig0y5fGaOjs74fP5+HZZrdawx8uRB1UNhyiKy+4nl8uFtLQ0VFdXo6mpCSUlJQHHj3xhl5cXinwMysel0WiEUqlEU1NT2I1LI+lcMDQ0BI1GE9Fx7n+x9vl8GBgYQGtrK3JzcwOGTZGJosiP2ebmZqjVaj48gdVqjWjdcgP6yclJAEBDQ0NEnToiyZfNmzcvOb/H40F8fDysViuampogSVJQyWu47HZ7QLXhSoiiyDtX2O12Pjiz3W7HwYMHwx59PFalUPJwI++9917Yg/Ou5OFTHoZlYV663W4cP34cWVlZ8Hg8uP/++3HixAnExcVhamoKzz33HKqqqnDr1q2w1hPpebOYPXv28DdORCJa+ykzMxNTU1N8SCFgvrH72NgYrxJeqYceegjvvffeapNIYkzB2Oq6YMhtP+QRgsO1Fm25KioqIEnSmlcNxGrb7HY7mpub0dXVtS7t4Hw+HwRBiHr7NHnE6cbGxhWXOvr3+JRZLJawL66x2mfZ2dlIT09f1Stl5FeJRNqOaqO1l/Qnt5ncyNV24drI+RwLzc3NePrpp7Fz5871Tsrnltz+6vLly4uWRsnz1NfXo6ysLOQ8DQ0NOHr0KA2EusGtOtAihBBCSPgo0Lq30EuYCCGEEEJihAItQgghZA3dd999AICPP/541ctKTExc9TJIbFGgRQghhKyhhIQEJCYm4le/+tWi88hB2N27dxed59KlS9i3b1/U00eiiwItQgghZI0dOnQIly5dCvmd0WjEU089BWDp4UCuXbuGr3/96zFLI4kOagxPCCGErLHp6Wk8+OCDuH79+ooGLbXZbHjttdfw4YcfxiB1JJqoRIsQQghZY1u3bsXp06eRk5OD6enpiH47PT2Nl19+GW+//XaMUkeiiQItQgghZB1YLBbs3bsXRqMx7GBrZGQERqMRr7zySsze30iii6oOCSGEkHXU19eH3//+9ygsLFx23uLiYhw4cIBeu/M5QoEWIYQQQkiMUNUhIYQQQkiMUKBFCCGEEBIjFGgRQgghhMQIBVqEEEIIITFCgRYhhBBCSIxQoEUIIYQQEiMUaBFCCCGExAgFWoQQQgghMUKBFiGEEEJIjFCgRQghhBASIxRoEUIIIYTECAVahBBCCCExQoEWIYQQQkiMUKBFCCGEEBIjFGgRQgghhMQIBVqEEEIIITFCgRYhhBBCSIxQoEUIIYQQEiMUaBFCCCGExAgFWoQQQgghMUKBFiGEEEJIjFCgRQghhBASIxRofcGNj4/j2LFjiI+PX++kEEIIIfecVQVa4+PjKC4uRnx8PBQKBR544AE0NDRgenoaxcXF0UojWYXKykqcOnUKMzMz650UQggh5J6z4kDLZrMhMTERd+7cgcPhAGMMw8PDuP/++/Hggw+ipaUlmukM27Fjx3DlypV1WXe0TE9Pw2g0RmVZNpsNmZmZUVkWIYQQQiKzokDrypUryM3NRU5ODmw2G/bs2QMA2Lp1KywWCxwOR1QTGYnu7u51W3e0/PSnP13vJBBCCCEkClYUaL3yyisAgFOnToX8fs+ePbBarStP1QoVFxdjbGxszdcbTSMjI1TtSgghhHxBRBxojY+PY3BwECkpKUhISFh0vtOnT/P/T09P8wbZCoUCe/fuRV9fH4D50jGFQgGFQoGGhgYAQENDA58mVwPabDYYjUY0NDSgra0NDzzwQMBvjh07xqsrH3vsMSgUCkxPT/N5+/r6YDQaeRrkP/8qOqPRyKcvpq+vD3v37uVt0mw2W8D3V65c4d/Hx8ejuLgY09PTPO/kfBgfH4fFYuHLGR8fx/j4ODIyMjAzM4PBwUG+fVeuXALVCt8AACAASURBVEFxcTGMRiNsNhvi4+Nx7Ngxvkx5OQqFAhaLBSMjI0vuw5GREb6tNpuNL0vmv3xCCCGErAKL0OXLlxkAlpmZGfZvMjMzmdVqZbdv32a3b99mVquVAWAXL15kjDF28eJFBoDV19fz33R1dTEA7PLly+zy5ctMpVIxACwlJYVdvnyZMcZYZWUlA8DGxsYYY4zV19fz38if5d9VVlYyURSZSqViFy9eZCkpKUylUgWkc2xsjCUmJrLbt2+H3I6LFy8ylUrFl5+Tk8MAMFEUed74fy9/TklJCZhf3tbbt2+zsbExplKpmNVq5etZmL/yNiQmJjJRFJnVamU5OTns9u3bTKVSsdbWVp5+ebvkPJHz339X5+Tk8N/I+9OfSqVilZWVi+1OQgghhIQp5oGWHET53/gZYywxMZElJiYGLNM/0JKn+Qcty82zMNDynyYHdQt/29XVxadZrdaAzwslJiYGfC8HbnKglZKSEhAwMfZZwCj/bmHQI0/zz89Q+SsHmf4qKyt5HsrGxsYYgIB0LFxnZmZmQD4uTDMhhBBCoiPiqsO//du/jWj+X/3qVwAQVM2Yk5Ozpu2p/u7v/i7g86OPPorMzEy8/PLLAOarN9977z08+eSTiy5jbGwsYDl79uzBnTt3eGeA69evQ6PRBPxGXt7vfve7VW/D1q1bAz7//Oc/xwMPPBAwLSEhASkpKRgfH190OQ899BCOHj3Kh+P47ne/u+q0EUIIISRYxIHWnj17kJiYiMHBQd72KBwL592yZUukq466l19+GWNjY7DZbPjpT3+Kffv2BQUzC/3kJz9Z8vu7d+8GfF5ueasVah8st85XX30VXV1dUKlUOHr0KB588MFl23URQgghJHIr6nX4xhtvAACOHz++6DzHjh3D+Pg47r//fgDAj370o4DvPR4PUlJSAAC7du1aSTJWzb9U67XXXkNhYeGS86tUKrS0tKCtrY1Pkxu4A0BiYiK6u7sDgh+5ZOkrX/lK1NOfkJCA69evBwVJv/3tb/HQQw8t+rvi4mJYLBZ8+OGHuHz5MgBgcHAw6ukjhBBC7nUrCrT2798Pq9WKlpaWoF5u8vAEX/va15CQkACLxYKUlBRUVFTwHoRXrlyBzWZDY2MjgM9KYH7xi1/w78+fPw8AyM/Ph81mw8cffwwgsMRInib/+8gjj/DPbW1tGB8fDyphWkgu1dq6dSuvAlxMbW0tAKCoqIj38tu3bx9MJhOA+QB0bGwMx48fx/T0NKanp/H9738fmZmZ2L9/P4DPSqD8gzF5XllmZiafJo+0H8p3vvMdqFQqfOtb3+IBXVtbG+7cuYMXX3wxYPn+/8oj+k9PT2PXrl2Ij48PKGGkXoeEEEJIlKymgdfly5cDetLJvefkxuEyuadhqJ6Dsq6uLqZSqZhKpWL19fXs8uXLvPH5T37yE74O/P8G8XIjdyzoxSf3uuvq6gqYJzExMWidspSUlCUbwftrbW1liYmJfJkLG9nLPRrl/KisrOS9GOVG6fIfYyzgszxNbkCfmZnJbt++HZC/OTk5AesTRTFgH+Tk5ITscei//Pr6enbx4kW+HQt7GFKvQ0IIISQ6FIwxtiYR3QY1PT2NBx98EHfu3FnvpBBCCCHkC2ZVL5X+Ijh+/Pi6jGJPCCGEkC++v1zvBKyHtrY2FBUVAZhv4P5f//Vf65wiQgghhHwR3ZMlWv/wD/8AAEhJSYHD4Yj5EAyEEEIIuTfd8220CCGEEEJi5Z4s0SKEEEIIWQsUaBFCCCGExAgFWoQQQgghMUKBFiGEEEJIjFCgRQghhBASIxRoEUIIIYTECAVahBBCCCExQoEWIYQQQkiMUKBFCCGEEBIjFGgRQgghhMQIBVqEEEIIITFCgRYhhBBCSIxQoEUIIYQQEiMUaBFCCCGExAgFWoQQQgghMUKBFiGEEEJIjFCgRQghhBASIxRoEUIIIYTECAVahBBCCCExQoEWIYQQQkiMUKBFCCGEEBIj90yg5fF4kJubi4yMDCQnJ8NqtcLn8613slbM6/WitLQUaWlpSEtLQ3Z2Ntxu93onKyo6OjpgMpmQlpaG5ORkDAwMrHeSosLpdPLtSktLQ11d3XonaUMQRZGfm2lpaaioqIAkSeudrA3F4/Hg4MGDPI+sVitmZmbWO1mEkDD85XonYC243W6kpaWhq6sLRqMRHo8HO3fuhCRJaG9vX+/kRUySJJhMJuh0OjgcDgiCgOTkZGRnZ+PGjRsQBGG9k7hi1dXV6O3thcPhgFKpREFBAUwmE27cuAGdTrfeyVsxm82GgoIC3LhxA1qtFna7HdnZ2VCr1cjPz1/v5K0bURTxyCOP4Mc//jFMJhM8Hg8SEhIgSRIaGxtjuu6Ojg50dnYuO19ZWRlMJlNM07IUt9uNRx55BK+99hrOnDmDu3fvIj4+Hl6vFxcuXIBCoVi3tMk8Hg9OnjyJmZkZqNVqbNu2Dbdu3UJqaiokSYJSqUReXt6GSOtG8i//8i/4wQ9+gE2b7pkyj3sTuweYzWZmNpsDpqnVaqZUKtcpRavT3t7OBEFgk5OTfFp+fj4DwCYmJtYxZaszOTnJALCenh4+rauriwFg7e3t65iy1dNqtezIkSP888TEBAMQdFzea7Kzs5nZbGZzc3N8WlxcHNPpdAHTYqWlpYUpFAoGgBmNRr7O2dlZ1t7ezuLi4tjZs2djno6lZGdnM71eH5Afu3btYgDWJI+WY7PZmEqlYhqNht25c4dPn5ycZMnJyQwAO3v27IZI60YyOTnJrFYr5cs94J4Io51OJ1JTU4OmGwyGdUhNIKfTCZvNFtFvBgcHodPpoFar+TSfzwe1Wh0wbT35fD5YrdaIfmO32wEARqMx6LuNVJqVkZER0fw+nw9utzvgGJSrxtLT06OatvVUWloa8W/kc1Mu6ZAkCYIgQKPRRDt5IWm1Wv5//3NHEATk5+fj6NGjUS2FefHFFzExMRHRb5xOJ55++umg6WazOVrJWjG52ndmZganT5+GSqXi36nValy4cAHbt2+P6joPHDgAxlhUl7lSBw8eXHFaRkdH8aUvfSnKKfrM0NAQampq1i2vvva1r2Fubm5d1r3RfOGrDr1eL3w+X8AF1el0AgBqa2vXK1kBIm2P4na7odfr+Wev1wun04mWlpYNU23o8/ki3q7R0VFotdqAbWhra8ORI0c2VKAVKbntnP8xaLPZoNfrUVRUtF7JirpI2zzK52ZSUhKfZrPZsGXLFpw6dWpdq5lsNhssFgtycnLg8XiittyV5pFWq+X54XQ64fP51j2PAKC4uBiMMajV6pAPDRqNJurBqtfrjfg3Pp8Pzc3NEEURZWVlSEtLg9frRXFxMbZt24YTJ05EPSBcSnd3N9ra2rB582Zs2bIF+fn5674vgfl8amtrw+zsbMD0wsLCiPNnamoqoiBPkiS0tbWht7cXjDGkpqYiJycHOp1uQ+TNqqymOMxisTAADABTKpWsq6uL3bx5k2k0GqZUKpnD4Vh9mdsq9ff3MwDsxo0b7MiRI8xgMDCdThdQ7bYYuXpHEARmMBiYVqvl21tbWxuV9DkcjoirxQRBYI2Njaynp4cZDAam0WjYjRs3lv2dvG/kbbhx4wYzm81MqVSy/Pz8qFY7TkxMsPz8/Ih+YzQamdlsZhMTE3w/hZvPBoOBb1dLSwszm80MANPr9VE/Dg0GQ0Tzt7S0MABsdnaWWSwWZjAYmNlsZrOzs8v+1v8cEwSB9ff3s6tXr/Jz7ObNmyvdjKiLdH/39/czhULBPvroI35uGgyGgOqnWHM4HLzqMD8/n83NzbHZ2VlmMpliUqVTUFDAxsfHI07fL37xC1ZbW8vPiz/96U9RT1ukJicn2aZNmxgAZjAYFs2v2dnZsK634crIyIho38zMzLCEhASm1WqD8k3+zmAwsE8//TTitBQUFEScFpPJxE6fPs2OHDnCqqqqWFxcHCssLIz68eZ0Oll1dXXYy52cnGRf/vKX+fXG/++Pf/xjxOtPTExk//d//xfWvLOzs2z//v1Mq9WyX//612x2dpYlJiYyQRDCXsZGtuo2WnLwYTQa+bSWlhbW2Ni4quU6HI6w/pa70TQ2NvKb3I0bN5jD4WA6nS6sG518w5fn0+v1/IYXrQtHpIGW3I6pv7+fzc7OMofDwfLz85lWqw0rUOrp6eEnj9FoZD09PXy7Ir1RLmUlgZZWq2Xl5eV8u7q6upggCKyrqyus9cnbJQdXcoAjCEJUA5JIA60jR44wrVbLGGPs6tWrzOFwMLVaHVYQOTs7y9RqNQPAdDodn97S0sJaWloiS3iMRbq/GxsbmSAIbG5uLujcXKt2KwsDrRs3brDc3Fym0Wg2RKDV2trKFAoF+/Of/8xu3rzJHA4HM5lMTK/Xsz//+c9RT18k5EAZAMvKylqzfRZpoJWbm8vi4uLYz3/+c8YYYwMDA2zXrl08sKqurmYKhWJF7cgiDbSeeeYZduTIETY3N8dyc3PZp59+yp555hmmUCjY2NhYROteTqSB1sGDB9mFCxfY3NwcS0hIYNPT06tafySBVkdHB9u0aRP78Y9/zNPb0dHBysvLVxQAbzSrbqNVWFgIYL44Wy7SHR4ehsViATDfG8VqtUY09IDT6Qz7z2azLVlFNTo6CrVaDUEQoNPpYDAYUFVVBbvdjqampoB1VldXB/xWEAQcPXoUgiDAbrfD5XIBAI4ePQq1Wg1JkmC1Wtd0+AFRFAHMF8kLggCDwYDa2lqez0DgUBYL238plUr+/8bGRpjNZmRmZgL4rI0UALS2tqKjoyPWm8NJkgS3240dO3bw7bJYLNDr9SgoKIDP54PP50NraysyMjLQ2tq66LLy8vJgMBhQVFQEpVIJSZJ47zKn04nS0tI1HT7A7XbzakO9Xs+3raKigh9TMpvNFnA8yccgML/v5WqswcFB3lvR6/VGfI4tp6Ojg1exx4pcVQyAn5slJSXo7e3l+1eSJFRXV/M/+fgPl3xe+/8ttl2iKKK0tBRdXV0hv/d4PCgtLcXk5GREaViNW7du8euXVquFwWBAWVkZhoeHefsbURTR1NS05LaFQ76esgXVPfX19Tzv/KuUpqam+P/9rytLGRgYQEVFxZq1G/J6vejt7cWzzz6L5ORkAEBzc3PAuSK3B/ztb3+LF198EVevXo1JWrq7u9HT04N/+7d/C6gK27JlC4D542ut8mUhURTx2GOPwWw249atW1AqlWHv02iQYwf/debl5aG2thabNm3iVZrh5o/NZkNbW1tY8x47dgwjIyORJzoCq26jJd8wJEmCzWZDUVERb5gNzJ+8ra2t2LFjB8rLywEATU1NMJvNizZ4NRgMUWuo7na7g9r3yJ8HBwdRXl4Ou92O5ubmoN+q1WoYjUZIkoSKigo+Td4Ol8uF1tZWeDwe3oDb5XLB4/HwQNOfKIpBbTREUcTU1FTQBVIQhIB2WP7bI190/dOpVqsxMDAAn8+HmpoaZGVlYXBwELm5uVCr1SHzc2F7Lv+01dfXAwC/mft8PtTX14ds1+bxeILasXi9Xt52bKFQaZEvfAv3lUajgdPpxMDAAG+bVlJSAqvVym88S22XTqfjQTkAdHZ2oqOjAzk5OTx/rVYrWlpagpYjSVJQICTnRajt0ul0IS9Obrc76HjYs2cPgPkbj5wOeWy0hXnsf451dHTgyJEj/OYrL2PhOdbR0QGDwRBRo3J5+fKNaKmhT0Jt/2L7W6PRhEyHf6DlPy8AvPvuuygqKkJHRweGhoYAzF+E5Qe7cDU0NAQEB3IwH+q40el0OHv2LIaHh3Hw4MGg751OJ5qbm7F7924UFBQAmD9PcnJyQm6fy+UKCui9Xi+Gh4dx69atoO0OtQy3282PFf95AaC3txdVVVWorKzExYsXoVAo8MQTTyApKSni9jS9vb1obm7G448/HjC9s7MTjDFUVVXh3XffxTe/+U388Ic/hEKhwLZt2/h8/kHXUhoaGuBwOPD8888jISEBAFBTU4MTJ04EtcPx+XwhA2v5/Fs4f6jzr7u7G7Ozs/xhEpg/7vzTLvvDH/6AH/zgB5iZmYFerw9Y/mLXAvmYX5gWrVYb1DGpt7eXpzPaD3qhrsHyg9nC9AmCENABRU6T/33x8ccfD7tdlM/nw8jISFAQNDs7i6GhoaChK1JTUxEXFxcwraSkBNeuXcO//uu/4tlnn8XU1BTKysoC2mddu3YNzz///LLpKi4uxp07d/Dmm28CAKanp/HCCy+gu7sbAJCTk4OKigp+Xj377LPIyMjA6dOnQ963o2HVJVpqtZr3fmlra4PNZkNOTg7/3mw2w+Fw8Ea/LpeLBy1rwe12L3uzMZvNS/b+am1t5YFAVVUVv8EZDAY4HA5+k/b5fMjNzY1paUmom5M/u92O2tpaWCwWtLe3w2Aw8BM8Ej09Pejv7+efCwoKVtQINZRQ+ROqwbi///3f/0V+fj6MRiPMZjMsFktEpRtyEFlVVQWHw8GDm6ampkVLJCPdj6HmlyQJHo8n6GYZSmtra8gAwP8c6+7uht1uR1ZWFv9+4TkmiiKsVuuKno4tFkvIXp/+onV8y0HEUhdOr9cLh8MBh8OBnp6eiAIIr9eLw4cP8987HA7s3Llz2d56er0+IA/kjh1msxmXLl1Cbm4ugPlr2VK9uiLJp8XmXe765XQ6A36rVqsjLvUDgKysrKBroM/nQ0VFBTIzM6FQKPjDm3wd8L8JhtvI/+zZs7h69SoPsiorK6NWchoqDwcHBwEgKLAwGAwBPV2B+WvPpUuXUFdXF3RMRuOYHx4e5qVXoigGHfv+HR4iFc17zrVr1/Dwww8HTX/hhReCGsmvRKi0dnd38961WVlZKCwsDMgPpVKJM2fOLDveWFtbG+7cuQObzYatW7cCAI4fP45vf/vbYIzh8uXLGBsbQ0ZGBqanpwHMP/Q6HA68/PLLMSvZikqvw7y8PNhsNrjdbtTX1+PGjRsA5jO0tbWVZ6xarYbJZIIkSWhubkZeXl7I3mT+JRDhKC8vD9nbzufzwev1BvRqAj6rfgtVYrSQ1+vlpTs6nY7fzDweDwYGBuD1evlQCxkZGfB4POjt7YVSqQy6oC/Wc87j8YRdgieKYlAwIkkST8fCwS91Oh127NgR1rJlcv4rlUpotVpYrVbY7XbodDo0NTXhyJEjAfOHehr3eDwYHBwMe7tGRkagVquDnkjlp7Qnn3ySPyHK1YxVVVVhb5NGo4HH4+HVoRqNBi6XC6WlpVAqlaiursaRI0cC1q9UKkOmf7HpocgB5ML8GR0dBfDZMSH3cpOPtYX8z7G2tjZeveF/jimVSgiCgIyMDEiShDfeeGPRcywUQRAgCAK/GSw1X6jt7+zsDDtf5HNz4bEp51dqaip6e3tx8uRJdHd3o7CwMOi4W45cIi3zeDwQBCGsKpHnnnuOX+RPnjyJsrIydHR0BFzL9u/fj9nZWTQ3NyM/Pz8on0Plxblz55CamoqdO3eGtQ0ejwfPP/980DRgPo/0ej1EUURrayvS0tLg8/miVhvg8XiCrp9JSUkYGBhAfn4+1Go19uzZgxs3bsDlcmFychL33Xffostzu93o7u4GYwxJSUk4d+4c6urqsGPHDn7++Q8Psdz5F05QIkkSGGMB559SqcThw4f574eHhwEAMzMzvEdnVlZWwPIXS8u5c+fCTgvwWWn7yMgIz9eRkRF+vL/wwgvYsWMHnnvuOXzve9/D5s2b8bOf/Qzf/va34XK5UFZWhtra2pAlaAvvCwqFAnfv3o0ofcD8Peall16CQqGAx+OBJEn45je/ifHxcaSkpAT1kFQqlSELKuLi4pCeno6/+Iu/WHJ9Xq8XL7zwAv7pn/4Jp0+fDgqm3G43ent7sXv3bhiNxkW3ZXp6GhUVFbh+/TqfduXKFRQWFvIH3UcffRSNjY147LHH8KMf/YiXkO/ZswevvPIKjh49GpOmQFEZR8toNPIDWa/X84NJvqjV1NTA6XRCq9Xy4KOkpGTRG4BcJRTu32JDGix2k5Prbv1L3hZTX1/Pn+DkG/vAwADsdju0Wi1qamrQ29sLQRB4KUNWVlbMxrjxeDxBgaN8ow21PS6XK+LiUIPBgLa2Nl6dKi9Xp9NFfLMLl8fjCbpQiKIIp9MJo9EYUBUtB7SLlbD5PzHJQXVmZiY0Gg0fW0au3tVoNDzQikWbhFAldXIVnVKphNFohNfrhSRJS5ZU+ueB/wOCIAhQq9WoqanhQbh8Xh0+fHjDDouxWAnmW2+9BcYYsrKyoNfrcenSJRiNRpSWliI7O3tVbVgGBgbw7LPPhnXTkfNNPte3bdsGpVKJkydPwuFwICkpKaxr2Wq43W5IkgSNRhOQZv88UqlUOH/+PF566SWYTCb88Ic/DKqWWSn5YcD/+ioIQkC159mzZ3na5GrGhWpqalBdXY2kpCQMDg6ipqYGMzMzKCkpgUKhgEajQXV1dUCQFS1KpRIKhYJfE0RR5E04GGOQJAmDg4PYs2cPMjIy8N3vfjegrWo0abVaTE1NwefzYXR0FElJSRgZGYHb7cabb74Jq9UKhUIBn8+HkpIStLe34+OPP8Ybb7yB999/HyaTCW+88UbMX73k8/nQ3d2NyclJnDx5El/60peQmpqKo0ePxmQYit7eXszOzoYsQBgaGkJSUhL+/d//HZs3b15yOWfOnMG+fft4aSkwH1gtrE149NFHAQB3794NmG6xWDA4OIgrV66sZnNCitqApXl5eQH/ylYyrpPcpijcv8XIN9mamhreXkKuKiovL1/24ujxeHijXLPZDLPZDJfLBavVuqYNBWVyKUBzczPsdjskSYLT6URNTQ30en1QEGSz2VBYWBjQVsC/Hl8u7v/kk0+Cpq31eFxut5s3XPZ6vfB4PCgoKIBSqQxoP2UwGNDV1QWlUrlo6U9zczMGBgZQXV0Nn88HjUazbuNVyUXRpaWlcLvd8Hq9qKiogNfrRXt7OwRBQEdHR1iv4ZHnCecBYaMTRRGMMVRWVkIURUiShLq6Oty4cQO1tbVITk7m14HGxkbcvHkTvb29K6oWk7377rsBVa7AZ6UZAAIaSEuShMrKSmRnZ/MHm7U+J+Rz9eTJk7wEs62tDXa7HYWFhTzQm5qaQmFhIRQKBQ4cOBBQvbNUR6Jo0Ol06OrqgkqlQmVlJZqamnggIIoinnnmGXi9XlRVVUGhUKx5Hsr7u7u7G263G5WVlfj+97+Pd955Bx6PBy+++CJmZmZw5syZqAWoiykrK0NcXBxOnjyJiYkJqNVqvPDCC3j77bd5Q30AvCG6fN2WS323bdsGSZLW5B29J0+exH333YdvfOMbUCgUGBoaiqjdViTS09MhCAI6OzsxMDAAxhhGRkZQX1+P3bt34+7du/D5fEhPT19y/W+99Ra++tWvLrs+ucrQv92eLDMzE+fPn1/5xiwiagOWFhUVoa2tbUOMti6Te+y0tLQEPPF3dXWFVcrjdDp56YHP5+Mjgms0Guh0ujV/KbV8I+jp6UFbWxtKS0uhVqtRVFQU0HYMmC+OHRkZCWhY7Xa7A6p3KioqUFVVBZfLFTAtVMPwWHO73aitrcXdu3d59bJer0d/f39Qo1KNRoP29nZkZ2eHXFZhYSGv87dYLKitrV23gVw9Hg9vAyj3DNTr9bh69Sr0ej1cLhc6Ozt5WxK32w2XywWv18sbtsvy8vJgt9vDqvL2J0kSD/RCSU9PD+pxG2u3bt2CVqvFiRMnUFFRwc/N/v7+kG3E5FJu+Sl0uWDBbDYHPEhJkgRJkgKOpbq6OgwODgZUezzxxBMBy9Hr9UHVSGtldHQUSqUSp0+fxne/+13+nswzZ87wdmLyOf2zn/0MJSUleOKJJ9DR0YGioiIMDw8vG1Atdb1erMnBwuk5OTlIT0/HG2+8gXfffReVlZW8qu348eMBQcRay8vLg8/nw/e+9z0kJSXh1VdfRXJyMurr6/HUU08hKSkJ77//PpKTk3mni1hJT09HX18fOjs7ea/R//iP/wi7GnmtHDp0CJ2dnXj++edhMpnwySef8KYpsaDVatHX14fm5mZ861vfwo4dO5Ceno6SkhKoVCp0dnYuG+SNj49jbGwMjzzyyLLr++lPf4rMzMyQ7WYfeugh3mg+mlYdaHm9Xj7UwUZ7Oa7c41C+YUcqPz9/yW2Kdff3hfzbli11gZRHQfYPsuTRpR0OR9D8oaatViSBjVxFotPpYDQawxqxf6nXDSmVyg3zsnC32w2DwbDosaTVagMC2/r6euh0uoCqZ/kc8/l8QSXG4RAEYU2C50j2udypI5Jz0799VaQ9k+12O/bt2xdwsS4vLw8KZmMtkjy6desWkpKSoNfr0dfXF3KekZERHvhoNBqUlJTgl7/8JYD560SkQbk/ue3p6Ogodu3axdf35ptvBt301Go1Tp06teJ1RSLSh6aSkhKUlJQETDt69CgfNmUtpaenIzU1FQcOHMDrr7++IV8mvTBvhoeH+cvB//qv/zrs5WzevDnsB5T09PRFO6Rdu3YN//zP/4xPPvlk0VLHP/3pT2GtZ3p6Gq+99tqiwdSWLVswNjYW1rIisaq9XFBQgO3bt8PlcqGtrS3oYA5FPkkW6woeTS6Xa8l2L/7u3r274p4b8u/km4DcUD4c8phK4RgeHg56RU2otMhdz+Wxb+Sn35WS1+fxeHg1z3LUajUaGxvDWv5iQzv4k6t95XW3trYueqGMZD8KgsCrZEN13w6lp6cnrPnk7ulLdUaQn/zlP7VajaSkJH7cZmdnY/v27XA6nejt7V0y8PdvqA3M36hX0rBT7qof6atnInmlldPpDGp7tJBcsienZcuWLWH13gzlvffei3rXbUEQoFAoIrqWnTp1KuwSDKfTuWwpQmZmJgYHB3np+tTUFHJyclZUAjc1NRXQbkUew21wcBCMMbhcLuTk5IR9TQ2HUqnkQxOEOw7c+fPnvwsFHwAABBtJREFUN8wrWV5//fWI0yKKItRq9aI9G+V9KR/78j6Rz8twa1LkUqHV5lVvby+2bNnCj4Nwvf/++1EJJOUmF263e9XjjB0/fhxvv/12QDuutbCqXJADC3mgzIUlDJIkYWRkJKDIPycnB2q1GgUFBTF9cWyoHjOLEUURSUlJyMzMDPtmK//OYDDwLvzy2GCdnZ0RPXWFO6/coWApLpcLe/bsCWg4mJmZGVGbMvkmqNFoIIoi7+7ucrngdDrDTm+46xwaGlr2hdiCIGB0dBQFBQVoamqC0WgMqGLyv0j733j8eTweHtjIQURhYSEfeDbcp/9wt0u++UZS8pKVlRVwc5XPEfmFzUudYyMjI5AkCXl5eVCr1bBarREV90uSBLvdDq/XC4PBgNHR0YgGrQ03X+TGyKFe9O5vYGAAJpOJD57b3t6+4puGPH7WSkmShGvXriE9PR2/+93vAMyPv6NWq3Hw4MGwlx1uHslB+u7du5dd3tmzZ/HNb34TTU1N2L1794peVC6KIu6//37s2LEjoN2afKOuqanBwMAAXnvttVXtA7nH6vDwMBhjOHz4MG7duoWampqwrtVA+HkYKZfLhfT0dN4mKFZpGRkZCblfu7u7cfz4cZSUlGBoaAhutxvHjx+HUqmE0+nEww8/jBMnTuCXv/xl2AFHNPIqMzMTmZmZMJvNEe37aO2nw4cPIy0tbdXvPGxoaMDjjz++4oe1VVnt0PJXr16N6jvyokV+D2A037G1nmZmZpjBYGD9/f3rnZSos1gsq3p3pPzKHv+/cN79GGuNjY3MYrGsejlXr17dUO80XC353FzLdxp+3vT397OMjIwN8U5DEl12u31DXJ++SMbGxhgAdvHixZDfd3V1Bb3KzWq1Br32qL6+niUmJkY9fQrG1mnMf0IIIYSQKHjggQdw6NAhlJWVBUxvaGgI2cwkMTERv/3tbwOmGY1GJCQk4PTp01FN28ZriUcIIYQQEoFDhw7h0qVLAdOOHTu2aFveUMPkXLt2DV//+tejnjYq0SKEEELI59r09DQefPBBXL9+fUWN3W02G1577TV8+OGHUU8blWgRQggh5HNt69atOH36NHJycvigpOGanp7Gyy+/jLfffjsmaaNAixBCCCGfexaLBXv37oXRaAw72BoZGYHRaMQrr7wSsx6JVHVICCGEkC+Mvr4+/P73v+cvjV5KcXExDhw4wN+BGAsUaBFCCCGExAhVHRJCCCGExAgFWoQQQgghMUKBFiGEEEJIjFCgRQghhBASIxRoEUIIIYTECAVahBBCCCExQoEWIYQQQkiMUKBFCCGEEBIjFGgRQgghhMQIBVqEEEIIITFCgRYhhBBCSIxQoEUIIYQQEiMUaBFCCCGExAgFWoQQQgghMUKBFiGEEEJIjPw/3CcruP2Ku28AAAAASUVORK5CYII=\" width=\"602\" height=\"178\"\u003e\u003c/p\u003e\n \u003cp\u003eWhere \u003cstrong\u003ey\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eit\u003c/strong\u003e\u003c/sub\u003e is a vector of each of the four conflict types and \u003cstrong\u003ey\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eit\u0026minus;1\u003c/strong\u003e\u003c/sub\u003e its one-month lag; \u003cstrong\u003ez\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eit\u003c/strong\u003e\u003c/sub\u003e is a grid-month vector of zoonotic disease outbreak events; \u003cstrong\u003en\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eit\u003c/strong\u003e\u003c/sub\u003e is a control for nighttime light emissions; \u003cstrong\u003ep\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eit\u003c/strong\u003e\u003c/sub\u003e is a control for population densities; \u003cstrong\u003eE\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eit\u003c/strong\u003e\u003c/sub\u003e is a matrix of climate controls (precipitation and temperature anomalies and drought); \u003cstrong\u003eC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eit\u003c/strong\u003e\u003c/sub\u003e is a matrix of country-level controls (life expectancy, government efficiency, and GDP per capita); \u0026beta; is each respective independent variable\u0026rsquo;s coefficient; \u003cstrong\u003e\u0026omega;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003c/sub\u003e and \u003cstrong\u003eϕ\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/sub\u003e are fixed effects by grid cell and month, respectively; \u003cstrong\u003e\u0026tau;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/sub\u003e is the time trend for each month during each year in the sample; and \u0026epsilon;\u003csub\u003ei\u003c/sub\u003e are standard errors clustered by grid cell. Some specifications in the sensitivity analyses include additional controls for conflict, conflict lags, and spatial conflict lags added to Eq. (2).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e was estimated by normalizing each coefficient by each respective dependent variable\u0026rsquo;s mean and then converted to percent as: {[\u0026beta;\u003csub\u003e1\u003c/sub\u003e \u0026ndash; mean(conflict)]/ mean(conflict)]} x 100. The upper and lower bound were calculated by first normalizing the difference between the model\u0026rsquo;s standard and the standard deviation, multiplying it by 1.97 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05 threshold, two-tailed), then adding (for upper bounds) or subtracting it (for lower bounds) from the mean, and finally converting the value to percent as follows: {[\u0026beta;\u003csub\u003e1\u003c/sub\u003e \u0026ndash; mean(conflict)]/ mean(conflict)] +/- 1.97[SE\u003csub\u003e1\u003c/sub\u003e \u0026ndash; SD(conflict)]/ SD(conflict)]} x 100.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e was estimated using two-way system general methods of moments (GMM) estimators. The system GMM estimator is the more robust estimator, and uses past variations in each dependent variables to, in effect, \u0026lsquo;exogenize\u0026rsquo; its variations at time \u003cem\u003et\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. The two-way effects approach is akin to a unit fixed effects estimation, where the units are indexed according to the time series for each grid cell, removing \u0026ndash; in effect \u0026ndash; the need for grid cell fixed effects. Based on research recommendations, where only shallow dependent variable lags are preferred as instruments [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e], two-to-four month (\u003cem\u003et\u003c/em\u003e-2 to \u003cem\u003et\u003c/em\u003e-4) lags as my internal instruments. Considering the sheer size of the sample as well as the fact that GMM estimators are not recommended for very long time series [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e], I was forced to limit the period of analysis to the Jan. 2013 \u0026ndash; Dec. 2018 period, leaving a total of 72 months for each grid cell for which information on all controls was available.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon publication, all data required for replicating all tables and figures in this study and its SI file will be made openly available on the Harvard Dataverse.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon publication, all code scripts required for replicating all tables and figures in this study and its SI file will be made openly available on the Harvard Dataverse.\u003c/p\u003e\n\u003cp\u003eKoren\u0026rsquo;s work was supported by the Harry Frank Guggenheim Foundation, XCEPT Research Fund and NSF Grant No. 2149053. Koren\u0026rsquo;s opinions\u0026rsquo; do not reflect these of the Harry Frank Guggenheim Foundation, XCEPT, or the NSF.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDavenport, Christian, et al. 2022. \u0026ldquo;Civil Liberties and Covid-19.\u0026rdquo; Political Violence At A Glance. Mar. 16, 2020. Last Accessed, May 20, 20223. https://politicalviolenceataglance.org/2020/03/16/civil-liberties-and-covid-19/Mar. 16, 2020. \u003c/li\u003e\n\u003cli\u003eSpirtas, Michael and Stephen Webber. 2022. \u0026ldquo;The Future and Past of War and Disease.\u0026rdquo; The Rand Blog, Jan. 27, 2022. Last Accessed Jun. 27, 2023.\u003c/li\u003e\n\u003cli\u003eBagozzi, Benjamin E. 2016. \u0026ldquo;On malaria and the duration of civil war.\u0026rdquo; Journal of Conflict Resolution 60(5):813\u0026ndash;839. \u003c/li\u003e\n\u003cli\u003eKustra, Tyler. 2017. \u0026ldquo;HIV/AIDS, life expectancy, and the opportunity cost model of civil war.\u0026rdquo; Journal of Conflict Resolution 61(10):2130\u0026ndash;2157. \u003c/li\u003e\n\u003cli\u003eIde, Tobias. 2021. \u0026ldquo;COVID-19 and armed conflict.\u0026rdquo; World development 140:105355.\u003c/li\u003e\n\u003cli\u003eKoehnlein, Britt and Ore Koren. 2022. \u0026ldquo;COVID-19, state capacity, and political violence by non-state actors.\u0026rdquo; Journal of Peace Research 59(1):90\u0026ndash;104. \u003c/li\u003e\n\u003cli\u003eBrancati, Dawn, Jóhanna Birnir and Qutaiba Idlbi. 2023. \u0026ldquo;Locking down Violence: The covid-19 pandemic\u0026rsquo;s impact on non-state actor violence.\u0026rdquo; American political science review pp. 1\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003ePape, Robert A., and Christopher Price. \u0026quot;A Slow-Rolling Disaster: Assessing the Impact of the Covid-19 Pandemic on Militant Violence.\u0026quot; Journal of Conflict Resolution (2023): 00220027231180101.\u003c/li\u003e\n\u003cli\u003eTheisen, Ole Magnus, Nils Petter Gleditsch and Halvard Buhaug. 2013. \u0026ldquo;Is climate change a driver of armed conflict?\u0026rdquo; Climatic change 117(3):613\u0026ndash;625. \u003c/li\u003e\n\u003cli\u003eRahman, Tanvir, et al. 2020. \u0026ldquo;Zoonotic diseases: etiology, impact, and control.\u0026rdquo; Microorganisms 8(9):1405. \u003c/li\u003e\n\u003cli\u003eMills, James N, Kenneth L Gage and Ali S Khan. 2010. \u0026ldquo;Potential influence of climate change on vector-borne and zoonotic diseases: a review and proposed research plan.\u0026rdquo; Environmental health perspectives 118(11):1507\u0026ndash;1514. \u003c/li\u003e\n\u003cli\u003eRaleigh, Clionadh, Andrew Linke, Haavard Hegre and Joakim Karlsen. 2010. \u0026ldquo;Introducing ACLED: An Armed Conflict Location and Event Dataset.\u0026rdquo; Journal of Peace Research 47(5):651\u0026ndash;660. \u003c/li\u003e\n\u003cli\u003eCarey, Sabine C and Neil J Mitchell. 2017. \u0026ldquo;Progovernment militias.\u0026rdquo; Annual Review of Political Science 20:127\u0026ndash;147. \u003c/li\u003e\n\u003cli\u003eSchon, Justin and Ore Koren. 2022. \u0026ldquo;Introducing AfroGrid, a unified framework for environ- mental conflict research in Africa.\u0026rdquo; Scientific data 9(1):1\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). 2023. \u0026ldquo;Disease Outbreak News (DONs).\u0026rdquo; Last accessed, May 30, 2023. https://www.who.int/emergencies/disease-outbreak-news.\u003c/li\u003e\n\u003cli\u003eBlundell, Richard and Stephen Bond. 1998. \u0026ldquo;Initial conditions and moment restrictions in dynamic panel data models.\u0026rdquo; Journal of econometrics 87(1):115\u0026ndash;143.\u003c/li\u003e\n\u003cli\u003eSchrodt, Philip A. \u0026quot;Seven deadly sins of contemporary quantitative political analysis.\u0026quot; \u003cem\u003eJournal of peace research\u003c/em\u003e 51, no. 2 (2014): 287-300.\u003c/li\u003e\n\u003cli\u003eBussemaker, Nathalie. 2020. \u0026ldquo;Iraq\u0026rsquo;s new government: What to know. 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Obtained using the \u0026ldquo;wbstats\u0026rdquo; package in R. Last updated December 5, 2020. https://cran.r-project.org/web/packages/wbstats/vignettes/wbstats.html. \u003c/li\u003e\n\u003cli\u003eBellemare, Marc F., Takaaki Masaki, and Thomas B. Pepinsky. \u0026quot;Lagged explanatory variables and the estimation of causal effect.\u0026quot; \u003cem\u003eThe Journal of Politics\u003c/em\u003e 79, no. 3 (2017): 949-963.\u003c/li\u003e\n\u003cli\u003eAngrist, J. D. and J. S. Pischke. 2009. Mostly Harmless Econometrics. Princeton, NJ: Princeton University Press. \u003c/li\u003e\n\u003cli\u003eRoodman, David. 2009. \u0026ldquo;A note on the theme of too many instruments.\u0026rdquo; Oxford Bulletin of Economics and statistics 71(1):135\u0026ndash;158. https://www.rand.org/blog/2022/01/the-future-and-past-of-war-and-disease.html. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Civil war, Georeferenced data, Infectious disease, Social conflict, Zoonotic pathogens","lastPublishedDoi":"10.21203/rs.3.rs-3120431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3120431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe frequency of new disease outbreaks may rise in coming decades due to deforestation and climate change, yet their impact on conflict is poorly understood. Leveraging a new geolocated monthly outbreak dataset on 23 zoonotic pathogens in Africa, this study explores the impact of (re)emergent disease on armed conflict. Zoonotic pathogens are considered key drivers of reemergent and new epidemic risk, making them a useful test case while also ensuring their probability of being reported by media and health policy outlets is high. Results suggest that over the January 2000 \u0026ndash; Dec. 2018 period, zoonotic disease outbreaks intensified social conflict but had a dampening effect on state-initiated conflict. Social conflict intensification was due to civil defense mobilization rather than security outsourcing by the government. Rebel-initiated conflicts are not noticeably sensitive to outbreaks. Results are robust to system GMM models that account for endogeneity and a battery of additional robustness models.\u003c/p\u003e","manuscriptTitle":"(Re)Emerging Disease and Conflict Risk in Africa, 2000 – 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-06 17:45:26","doi":"10.21203/rs.3.rs-3120431/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-human-behaviour","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nathumbehav","sideBox":"Learn more about [Nature Human Behaviour](http://www.nature.com/nathumbehav/)","snPcode":"","submissionUrl":"","title":"Nature Human Behaviour","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"252617d8-a658-4637-a134-e27acf20c962","owner":[],"postedDate":"July 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":22999349,"name":"Social science/Politics and international relations"},{"id":22999350,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2024-07-16T07:06:09+00:00","versionOfRecord":{"articleIdentity":"rs-3120431","link":"https://doi.org/10.1038/s41562-024-01929-1","journal":{"identity":"nature-human-behaviour","isVorOnly":false,"title":"Nature Human Behaviour"},"publishedOn":"2024-07-15 04:00:00","publishedOnDateReadable":"July 15th, 2024"},"versionCreatedAt":"2023-07-06 17:45:26","video":"","vorDoi":"10.1038/s41562-024-01929-1","vorDoiUrl":"https://doi.org/10.1038/s41562-024-01929-1","workflowStages":[]},"version":"v1","identity":"rs-3120431","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3120431","identity":"rs-3120431","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}