Vertical Microbiome Deprivation? A Systematic Review of Cesarean Section as a Driver of Childhood Obesity in Sub-Saharan Africa

Vertical Microbiome Deprivation? 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A Systematic Review of Cesarean Section as a Driver of Childhood Obesity in Sub-Saharan Africa Oluwafayoke Owolo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8712827/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sub-Saharan Africa is currently facing a "double burden" of malnutrition, characterized by the persistence of stunting alongside a rapid rise in childhood overweight and obesity. Simultaneously, rates of Cesarean section (CS) delivery are surging in urban centers, often without medical indication. While global studies link CS to increased adiposity via disruption of early-life microbial colonization, evidence from the African context remains is limited, particularly within Sub-Saharan Africa, where specific regional factors influence outcomes. This systematic review aims to synthesize observational evidence linking delivery mode to childhood obesity in Sub-Saharan Africa and explore the biological plausibility of the "dysbiosis" mechanism in this population. Methods We systematically searched PubMed and African Index Medicus, and supplemented with Google Scholar for additional studies published up to January 06, 2026, for observational studies conducted in Sub-Saharan Africa. Eligible cohort, cross-sectional, and case–control studies reported quantitative associations between CS delivery and childhood anthropometric outcomes, including overweight, obesity, or body mass index (BMI). Methodological quality was assessed using the Newcastle–Ottawa Scale (NOS). A narrative synthesis was undertaken for all included studies, with an exploratory quantitative synthesis conducted for high-quality studies reporting comparable effect estimates. Results Four studies met the inclusion criteria, encompassing 10,215 participants from Ghana, South Africa, and Ethiopia. Across all studies, CS delivery was consistently associated with a statistically significant 1.64- to 3.23-fold increased risk of childhood adiposity (adjusted odds ratio [AOR] 3.23, 95% CI: 1.14–9.13 in Ghana; incidence rate ratio [IRR] 1.64, 95% CI: 1.01–2.68 in South Africa). In the South African cohort, the association persisted into early adulthood (ages 21–24 years). None of the included studies directly assessed gut microbiome composition, and residual confounding could not be fully excluded. Conclusion Despite a limited evidence base, available data consistently demonstrate a positive association between Cesarean delivery and increased risk of childhood adiposity in Sub-Saharan Africa, with effect sizes exceeding those commonly reported in global meta-analyses. These findings suggest that Cesarean delivery may be a critical, underexplored contributor to the growing obesity epidemic in Sub-Saharan Africa and support the biological plausibility of microbiome-mediated metabolic programming. Prospective African birth cohorts incorporating high-resolution microbiome and metabolic profiling are urgently needed to confirm causality and inform context-specific preventive strategies. Epidemiology General Microbiology Cesarean section Childhood obesity Gut microbiome Sub-Saharan Africa Hygiene hypothesis Dysbiosis Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Sub-Saharan Africa is currently navigating a complex epidemiological landscape defined by the “double burden of malnutrition” (DBM), a phenomenon characterized by the coexistence of persistent undernutrition—manifesting as stunting, wasting, and micronutrient deficiencies—alongside a rapidly rising prevalence of overweight and obesity within the same populations, households, and even individuals (Mbogori et al., 2020 ; Wariri et al., 2020; Zemene et al., 2023 ). This dual burden is fundamentally driven by rapid urbanization and economic development, which have precipitated a "nutrition transition" away from traditional, fiber-rich diets toward the consumption of energy-dense, ultra-processed foods and sugar-sweetened beverages, coupled with increasingly sedentary lifestyles inherent to modern urban environments (Sofroniou et al., 2025 ; Wanjohi et al., 2024). Consequently, while the region continues to battle high rates of stunting, recent meta-analytic evidence indicates that the pooled prevalence of overweight and obesity among preschool children in Sub-Saharan Africa has reached approximately 14.77%, with distinct regional disparities showing prevalence rates as high as 21.01% in Southern Africa (Shibeshi et al., 2026 ). This trajectory underscores a critical public health challenge where the pace of rising obesity is beginning to rival that of undernutrition, necessitating comprehensive surveillance and policy intervention (Danquah et al., 2020 ). Across the continent, the landscape of maternal healthcare is simultaneously undergoing a rapid transformation characterized by escalating rates of Cesarean section (CS) deliveries, which in many specific demographics now frequently exceed the World Health Organization’s recommended population-level threshold of 10–15% (Lokko et al., 2025 ; Ntiyakunze et al., 2025). This aggregate rise, however, masks a profound structural disparity: while rural and economically disadvantaged populations continue to face critical barriers to accessing life-saving emergency obstetric care, urban centers are witnessing a surge in CS utilization that is increasingly disconnected from medical necessity (Atuheire et al., 2019 ; Shibre et al., 2020). In these urbanized and wealthier settings, the prevalence of the procedure is often driven by non-medically indicated factors, including rising maternal requests, provider convenience, and the financial incentives inherent within private healthcare systems (Tilahun et al., 2025 ; Visser et al., 2023). Consequently, this polarization - defined by the simultaneous underuse of surgical delivery among the vulnerable and overuse among the privileged - represents a massive public health shift that is fundamentally altering the early-life environment of the African child (Islam et al., 2022 ; Yaya et al., 2018 ). The biological plausibility connecting delivery mode to long-term metabolic outcomes is frequently framed within the "hygiene hypothesis" or the concept of "microbial seeding," which posits that early-life exposure to maternal microbiota is critical for establishing a tolerogenic immune milieu and metabolic programming (Kahhaleh & Barrientos, 2024 ; Li et al., 2013 ). Physiologically, vaginal delivery facilitates the vertical transmission of maternal vaginal and intestinal flora, specifically seeding the neonate with beneficial Lactobacillus , Prevotella , and Bacteroides species (Gholamipour-Shirazi & Gholamipour-Shirazi, 2025 ; Martin et al., 2016). In contrast, Cesarean section bypasses this essential colonization event, resulting in a state of "dysbiosis" characterized by a depletion of these commensal anaerobes and a predominance of skin-associated taxa, such as Staphylococcus and Corynebacterium (Stinson et al., 2018 ). Extensive global research has established a robust correlation between this early-life dysbiosis and increased adiposity in children, supporting the theory that an altered initial microbial inoculum may predispose offspring to metabolic dysregulation (Butel et al., 2018 ; Tun et al., 2018 ). While substantial research has synthesized the risk factors for childhood obesity, systematic reviews and meta-analyses have predominantly focused on Western and Asian populations (Jiang et al., 2025 ; Talukder et al., 2025). Evidence regarding the long-term health consequences of CS within Sub-Saharan Africa remains fragmented, often limited to single-country analyses that do not isolate the obesogenic risks of surgical delivery (Afakorzi et al., 2025 ; Pérez-Muñoz et al., 2022). Due to unique environmental, cultural, and dietary factors, findings from high-income settings may fail to account for specific African microbiome variations or dietary patterns, a region characterized by a unique epidemiological and environmental context. Specifically, variations in diet, pathogen exposure, and the gut microbiome—such as the higher prevalence of Prevotella and distinct short-chain fatty acid profiles observed in rural African children—suggest that the biological pathways linking delivery mode to childhood obesity may operate differently in this setting (Deering et al., 2020 ; Kozyrskyj et al., 2016 ; Tette et al., 2022 ). Therefore, this systematic review aims to synthesize existing observational evidence to quantify the association between Cesarean section delivery and childhood obesity specifically within the Sub-Saharan African context. METHODOLOGY 2.1 Protocol and Registration This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al., 2021). The protocol was registered with PROSPERO (Registration ID: CRD420261292360) prior to data synthesis. 2.2 Data Sources and Search Strategy We searched PubMed and African Index Medicus, and supplemented with Google Scholar for additional studies published up to January 06, 2026. The search strategy utilized a combination of Medical Subject Headings (MeSH) and free-text terms. The core search string for PubMed was ("Cesarean Section"[MeSH] OR "delivery mode" OR "c-section") AND ("Obesity"[MeSH] OR "Overweight" OR "Adiposity" OR "Body Mass Index") AND ("Africa" OR "Sub-Saharan Africa" OR "Ghana" OR "Ethiopia" OR "South Africa" OR "Nigeria") AND ("Child"[MeSH] OR "Infant" OR "Pediatric"). References from relevant reviews and included articles were hand-searched to identify additional eligible studies. We analyzed data from four distinct observational studies representing West, East, and Southern Africa. West Africa : Sulley & Saaka ( 2022 ) conducted a retrospective cohort study in the Tamale Metropolis, Ghana, utilizing primary hospital data. Southern Africa : Sogunle et al. ( 2019 ) performed a retrospective analysis of the Birth to Twenty Plus (Bt20+) birth cohort in Soweto, Johannesburg. East Africa : Gebremichael et al. (2022) and Walle et al. (2024) utilized nationally representative data from the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS). 2.3 Inclusion Criteria and Confounder Adjustment Eligible studies were required to report quantitative estimates (Odds Ratios, Incident Rate Ratios, or Beta Coefficients) linking Cesarean section to childhood anthropometric outcomes. All included studies performed multivariate adjustments for key socioeconomic and maternal confounders, and Sulley & Saaka ( 2022 ) notably adjusted for birth weight to control for macrosomia. 2.4 Quality Assessment and Risk of Bias Methodological quality was assessed using the Newcastle-Ottawa Scale (NOS) for observational studies (Wells et al., 2014 ). This tool evaluates the selection of study groups, the comparability of cohorts, and the ascertainment of the outcome of interest. A critical review revealed that none of the four included studies explicitly screened for or excluded children with pre-existing chronic medical conditions, representing a potential source of residual confounding. 2.5 Data Synthesis and Statistical Analysis Due to the significant heterogeneity in study designs (e.g., spatial regression vs. logistic regression) and reporting errors identified in one primary study (Gebremichael et al., 2022), a narrative synthesis was prioritized. A limited meta-analysis was performed on the subset of studies that provided compatible Adjusted Odds Ratios (AOR) and Incident Rate Ratios (IRR) with valid confidence intervals. These results were visualized using a Forest Plot to illustrate the direction and magnitude of the effect. Statistical heterogeneity was assessed visually based on the overlap of confidence intervals. All visualizations were generated using Python (Matplotlib library). RESULTS 3.1 Study Selection and Characteristics Our initial search yielded 156 records. After removing duplicates and screening titles/abstracts, 17 full-text articles were assessed for eligibility. Of these, 13 were excluded. The primary reasons for exclusion were reverse causality (studies assessing maternal obesity as a predictor of C-section, n = 6) and incompatible outcomes (studies focusing solely on macrosomia/birth weight without childhood follow-up, n = 5). Two additional studies were excluded due to missing outcome data. Ultimately, four studies met the inclusion criteria Our search strategy yielded four original research studies meeting the inclusion criteria, comprising a total sample of 10,215 children. The studies represented distinct geographical regions: West Africa (Ghana), Southern Africa (South Africa), and East Africa (Ethiopia). Table 1 Characteristics of the Four Observational Studies Included in the Systematic Review . Citation Country (Region) Study Design Data Source Key Confounders Adjusted Medical Exclusions? Sulley & Saaka ( 2022 ) Ghana (Tamale) Retrospective Cohort Primary Hospital Data Maternal age, education, occupation, birth weight No Sogunle et al. ( 2019 ) South Africa (Soweto) Birth Cohort Bt20 + Cohort Sex, birth weight, parity, household SES No Gebremichael et al. (2022) Ethiopia (National) Cross-Sectional DHS 2019 Child age, sex, maternal education, region No Walle et al. (2024) Ethiopia (National) Cross-Sectional (Spatial) DHS 2019 Wealth index, residence, child sex No *CS = Cesarean Section; BMI = Body Mass Index; DHS = Demographic and Health Surveys; AOR = Adjusted Odds Ratio; IRR = Incident Rate Ratio. 3.2 Synthesis of Results All four eligible studies reported a positive and statistically significant increase in childhood adiposity, with effect sizes ranging from 1.64 to 3.23. However, effect estimates varied by study design and population: Magnitude of Effect : Sulley & Saaka ( 2022 ) reported a 3.23-fold increase in obesity risk for children born by C-section, highlighting a particularly strong association in West Africa (AOR 3.23; 95% CI: 1.14–9.13, p = 0.027). Sogunle et al. ( 2019 ) demonstrated that this risk persists into early adulthood. In their South African birth cohort, C-section was associated with a 64% increase in the incidence rate of obesity at age 21–24 years (IRR 1.64; 95% CI: 1.01–2.68, p = 0.045). Gebremichael et al. (2022) reported an Adjusted Odds Ratio (aOR) of 1.75 in the Ethiopian DHS dataset (p < 0.05), though a discrepancy in the reported confidence intervals precluded this study from the pooled meta-analysis. Spatial Analysis : Walle et al. (2024) confirmed a significant positive spatial coefficient (0.01, p < 0.05), indicating that regions with higher C-section rates in Ethiopia spatially cluster with regions of higher childhood obesity prevalence. 3.3 Quality Assessment and Risk of Bias The methodological quality of the included studies was evaluated using the Newcastle-Ottawa Scale (NOS). Two studies (Sulley & Saaka, 2022 ; Sogunle et al., 2019 ) were rated as "High Quality" due to their use of longitudinal data or primary hospital records and robust adjustment for confounders like birth weight and socioeconomic status. The remaining two studies (Gebremichael et al., 2022; Walle et al., 2024) were rated as "Moderate Quality." While these studies utilized large, nationally representative samples (DHS data), they were limited by their cross-sectional design and reliance on maternal recall for birth details, which introduces potential recall bias. Additionally, the exclusion criteria in the moderate-quality studies were less strictly defined regarding pre-existing medical conditions. 3.4. Meta-Analysis (Forest Plot) An inverse-variance weighted analysis was performed on the studies providing valid, calculable confidence intervals (Sulley 2022 and Sogunle 2019). The pooled analysis confirms a consistent direction of effect, with risk estimates significantly exceeding the null value (1.0). Sensitivity Analysis and Data Exclusion While four studies met the inclusion criteria, only two (Sulley & Saaka, 2022 ; Sogunle et al., 2019 ) provided compatible effect measures (Odds Ratios/Rate Ratios) with valid confidence intervals for quantitative pooling. Gebremichael et al. (2022) was excluded from the forest plot due to a reported discrepancy between the point estimate (aOR 1.75) and the lower confidence limit (1.84). Walle et al. (2024) was excluded as it employed geographically weighted regression (OLS coefficients), which cannot be mathematically pooled with logistic regression estimates. Discussion 4.1 The African Consensus: Clinical Evidence Our findings are consistent with recent epidemiological data across the continent, confirming that the obesogenic footprint of Cesarean section (CS) is detectable in diverse African populations ranging from West to East and Southern Africa. Specifically, the significant association we observed in Ghana and Ethiopia aligns with longitudinal data from the 'Birth to Twenty Plus' cohort in South Africa, which reported a 1.64-fold increase in obesity incidence for young adults born via CS (Sogunle et al., 2019 ). The persistence of this association into early adulthood in the South African cohort suggests that CS-associated adiposity is not merely a transient childhood phenomenon but a long-term metabolic trajectory. Furthermore, the magnitude of risk we identified in our West African dataset (AOR 3.23) exceeds the global pooled risk estimates of 1.23 to 1.33 reported in recent broad-scale meta-analyses (Chiavarini et al., 2023 ; Zhang et al., 2022 ). This suggests that in the context of the rapid nutrition transition occurring in the Global South, the lack of exposure to vaginal microbiota may act as a potent synergist with environmental obesogens. From a microbiological perspective, these consistent associations support the "hygiene" or "microflora" hypothesis, which posits that CS delivery disrupts the vertical transmission of the maternal microbiome. As noted by Faúndes et al. ( 2021 ) and confirmed in the discussion by Sogunle et al. ( 2019 ), infants born via CS are deprived of this essential microbial inoculation, resulting in a gut microbiome characterized by limited diversity. Instead of protective commensals, these infants are often colonized by maternal skin flora (e.g., Staphylococcus ) and environmental opportunistic pathogens (Magne et al., 2017 ). This initial dysbiosis is critical; Murata et al. ( 2020 ) demonstrated that CS-born infants exhibit a strikingly high Firmicutes-to-Bacteroidetes (F:B) ratio, a microbial signature strongly correlated with increased capacity for energy harvest from the diet and subsequent adiposity. 4.2 Mechanism: Vertical Transmission and "Bacterial Seeding" Vertical transmission, often described as "bacterial seeding," constitutes the critical developmental event where the maternal microbiome serves as the initial inoculum for the neonate. During a vaginal delivery, the neonate passes through the birth canal, acquiring bacterial communities that resemble the maternal vaginal microbiota, predominantly Lactobacillus , Prevotella , and Sneathia (Dominguez-Bello et al., 2010 ). Additionally, this mode of delivery facilitates the transmission of maternal intestinal anaerobes, specifically Bacteroides and Bifidobacterium . In contrast, infants delivered by Cesarean section bypass this natural colonization route and are instead colonized by bacteria characteristic of the maternal skin and hospital environment, such as Staphylococcus , Corynebacterium , and Propionibacterium (Fig. 3 ). These infants often exhibit a "stunted" microbiome characterized by a specific depletion of Bacteroides and an enrichment of opportunistic pathogens (Stinson et al., 2018 ). The elevated Odds Ratios for childhood adiposity observed in this review - specifically the 3.23-fold increase reported by Sulley and Saaka ( 2022 ) - may reflect the downstream metabolic consequences of disrupted early-life microbial colonization, although this mechanism remains to be empirically confirmed in African populations where the absence of protective, immunomodulatory pioneer strains alters the trajectory of metabolic development. 4.3 The Consequence: The "Energy Harvest" Hypothesis The "Energy Harvest" hypothesis posits that an "obese-type" microbiome—often characterized by an increased Firmicutes-to-Bacteroidetes ratio—possesses a superior genomic capacity to hydrolyze non-digestible dietary polysaccharides (fibers). Through fermentation, these bacteria convert complex carbohydrates into elevated concentrations of short-chain fatty acids (SCFAs), primarily acetate, propionate, and butyrate (Ma et al., 2020 ). Mechanistically, this process drives adiposity through two primary pathways: first, excess SCFAs serve as direct substrates for de novo lipogenesis in the liver (Amabebe et al., 2020 ). Second, the gut microbiota suppresses the intestinal expression of Fasting-Induced Adipocyte Factor (FIAF), a circulating inhibitor of lipoprotein lipase (LPL). The suppression of FIAF disinhibits LPL activity, thereby facilitating rapid cellular uptake of fatty acids and hypertrophic triglyceride storage in adipose tissue. Applying this mechanism to the results of the current systematic review, we propose that the altered microbial energy harvest between Cesarean section and obesity in African cohorts (Sulley & Saaka, 2022 ; Sogunle et al., 2019 ) may represent one plausible biological pathway linking Cesarean delivery to later adiposity, alongside established behavioral and environmental contributors. Even if these children consume a standard caloric diet, their dysbiotic microbiomes—deprived of vaginal Bacteroides —may biologically amplify the caloric yield of their food. 4.4 Global Comparison The "C-section effect" observed in this African review appears to be consistent with, and notably stronger than, the global standard. While global meta-analyses by Li et al. ( 2013 ) and Kuhle et al. ( 2015 ) established a pooled risk increase for childhood obesity ranging from 33% to 35% (OR 1.33–1.35), the African studies identified in this review reported Adjusted Odds Ratios ranging from 1.64 to 3.23. This elevated magnitude raises the possibility that while the association is underpinned by a universal biological driver, regional environmental modifiers or the rapid nutritional transition occurring in Africa may be amplifying the obesogenic consequences of cesarean delivery in these populations. 4.5 Limitations of the Evidence Base This systematic review must be interpreted in the context of a limited but highly consistent evidence base. Only four eligible studies were identified across Sub-Saharan Africa, reflecting the historical underrepresentation of African birth cohorts in life-course epidemiology rather than deficiencies in the present review methodology. The scarcity of longitudinal datasets linking delivery mode to later-life adiposity remains a structural constraint in the region. The small number of included studies renders the synthesis inherently sensitive to study exclusion; the removal or reclassification of a single dataset could materially alter the strength of the pooled inference. Nevertheless, confidence in the observed association is strengthened by the complete directional concordance across geographically and methodologically diverse African populations, with all included studies reporting a positive association between Cesarean section and childhood adiposity. Additional limitations include residual confounding, as none of the included studies explicitly screened for genetic syndromes or chronic pediatric conditions that may independently influence growth trajectories. Furthermore, the biological mechanism linking Cesarean delivery to obesity risk was inferred rather than directly measured, as no included study employed metagenomic or metabolomic profiling to characterize gut microbial composition or function. As such, the proposed microbiome-mediated pathways should be regarded as biologically plausible but unconfirmed within African populations. Finally, while an exploratory quantitative synthesis was conducted for studies reporting compatible effect measures, heterogeneity in study design and outcome reporting precluded a comprehensive meta-analysis. The findings should therefore be interpreted as hypothesis-generating rather than definitive causal estimates CONCLUSION AND FUTURE DIRECTIONS 5.1 Conclusion This systematic review synthesizes the currently available observational evidence examining the relationship between Cesarean section delivery and childhood adiposity in Sub-Saharan Africa. Despite the limited number of eligible studies, the available data consistently demonstrate a positive association between Cesarean birth and increased risk of overweight and obesity across West, East, and Southern African populations, with effect sizes that exceed those reported in many global meta-analyses. These findings support the hypothesis that disruption of vertical microbial transmission at birth may represent an important contributor to early-life metabolic programming within the African context, particularly when acting synergistically with rapid urbanization and nutritional transition. However, given the observational nature of the evidence and the absence of direct microbiome measurements, causal inference cannot be established. Rather than providing conclusive evidence, this review highlights a critical and underexplored public health signal. The concordance of findings across diverse African settings underscores the urgent need for prospective birth cohorts incorporating high-resolution microbiome and metabolic phenotyping. Such studies are essential to determine whether the observed associations reflect microbiome-mediated mechanisms and to inform evidence-based obstetric and early-life interventions tailored to Sub-Saharan Africa. 5.2 Future Directions To move beyond inference, future research must prioritize prospective longitudinal studies employing high-resolution genomic tools. Unlike cross-sectional snapshots, longitudinal sampling is essential to capture the temporal trajectory of gut colonization (Dogra et al., 2015 ). We propose the initiation of African birth cohorts that utilize shotgun metagenomics rather than standard 16S sequencing. The choice of sequencing technology is pivotal. 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AIMS Public Health 7(3):425–439 Ntiyakunze, G., Kyaruzi, A., Moshi, L., et al. (2025). Mandatory second opinion to reduce cesarean section rate… BMC Pregnancy and Childbirth, 25, 698. Pérez-Muñoz, C., Carretero-Bravo, J., Ortega-Martín, E., et al. (2022). Interventions in the first 1000 days to prevent childhood obesity… BMC Public Health, 22, 2367. Shibeshi AH, Asfaw ZG, Hasen AA et al (2026) Prevalence of overweight and obesity and its associated factors among preschool children in sub-Saharan Africa. Advances in Nutrition. (Advance online) Shibre, G., Idriss-Wheeler, D., Bishwajit, G., & Yaya, S. (2020). Observed trends in the magnitude of socioeconomic… inequalities in use of cesarean section in Ethiopia.BMC Public Health, 20, 1200. Sofroniou A, Basilico S, Conti MV, Martin HD (2025) Obesity in Tanzanian youth (15–35 years): From nutrition transition to policy action. Nutrients , 17 Stinson, L. F., Payne, M. S., & Keelan, J. A. (2018). A critical review of the bacterial baptism hypothesis… Frontiers in Medicine, 5, 135. Talukder, A., Kelly, M., Gray, D., & Sarma, H. (2025). Cesarean deliveries and double burden of malnutrition… Journal of Public Health, 47(4), e546–e556. Tette EMA, Intiful FD, Asare AA, Enos JY (2022) Pregnancy as a fundamental determinant of child health: A review. Curr Nutr Rep 11:457–485 Tilahun WM, Simegn MB, Abate A et al (2025) Cesarean section delivery and its associated factors in Ghana. PLoS ONE, 20(2), e0318223 Tun MH, Tun HM, Mahoney JJ et al (2018) Postnatal exposure to household disinfectants, infant gut microbiota and subsequent risk of overweight. CMAJ 190(37):E1097–E1107 Visser, G. H. A., Ubom, A. E., Neji, K., et al. (2023). FIGO opinion paper: Drivers and solutions to the cesarean delivery epidemic… International Journal of Gynecology & Obstetrics, 163, 5–9. Wanjohi, M. N., Kimani-Murage, E. W., Asiki, G., et al. (2024). Adolescents’ dietary patterns… in Kenya’s urban slums. Journal of Health, Population and Nutrition, 43, 181. Wariri, O., Akhimienho, K. I., Alhassan, J. A. K., et al. (2020). Population and individual-level double burden of malnutrition… in Nigeria. Annals of Global Health, 86(1). Yaya S, Uthman OA, Amouzou A, Bishwajit G (2018) Disparities in cesarean section prevalence and determinants across sub-Saharan Africa countries. Global Health Res Policy 3:19 Zemene MA, Anley DT, Gebeyehu NA et al (2023) Concurrent stunting and overweight or obesity among under-five children in sub-Saharan Africa. Archives Public Health 81:119 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted 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. 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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-8712827","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":581296451,"identity":"e17b1e41-26f5-4d66-a2fd-4f93f9030bb9","order_by":0,"name":"Oluwafayoke Owolo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBAC9gYGBsYGMJO5geEDQoIZpxaeA3AtjA2MMxgYJEjTwsxDlBb2M4YPZ1QwyBkcb2x8bLvncJ28e+/jDwwV1okNuLTw5BgbbjjDYGxw5mCzcc6zwxKGZ46bSTCcScepxZ4hx0zyYRtD4rYbiW3SOQeAWmaksTEwth3GbQv/G6CWf1AtFiAt858xf2D8h0eLBNCWjQ1QLQxALfISbAwSjA34tDwrNpxxTMLYHugXw54D6ZIbeNLYJBKOpRvjdljyxoc9NTZyku3NBx/8OGDNL99+jPnDhxprWVxaGBg4DBgQsQEEBgeARAJO5SDA/gCVL4/b9FEwCkbBKBihAADKmVsde7ePEwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-8470-4708","institution":"Rutgers University","correspondingAuthor":true,"prefix":"","firstName":"Oluwafayoke","middleName":"","lastName":"Owolo","suffix":""}],"badges":[],"createdAt":"2026-01-27 16:41:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8712827/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8712827/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101366135,"identity":"16bd30c7-dc46-40e0-a5e5-628c4c481c30","added_by":"auto","created_at":"2026-01-29 01:00:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67669,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram of study selection. The flowchart illustrates the sequential screening process, from identification of 156 records to the final inclusion of 4 studies. \u003cstrong\u003eParts of this figure were drawn using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8712827/v1/0903532b202e90d315639670.png"},{"id":101366137,"identity":"406e8310-3368-48c3-8700-44378b5eaa34","added_by":"auto","created_at":"2026-01-29 01:00:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest Plot of the Association Between Cesarean Section and Childhood Obesity in Sub-Saharan Africa.\u003c/strong\u003e \u003cem\u003eSquares represent the point estimates (Odds Ratio for Sulley \u0026amp; Saaka, 2022; Incident Rate Ratio for Sogunle et al., 2019) for the association between C-section delivery and childhood adiposity. Horizontal lines indicate 95% Confidence Intervals (CI). The size of each square is proportional to the study's statistical weight (inverse variance). The maroon diamond at the bottom represents the pooled effect estimate derived from a random-effects meta-analysis, illustrating a consistent and significant positive association (pooled estimate \u0026gt; 1.0) across the sampled West and Southern African cohorts\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8712827/v1/886fc4e0a923de087419e7f8.png"},{"id":101366134,"identity":"6624e13b-973b-42cf-b7e9-c90e6c6a263b","added_by":"auto","created_at":"2026-01-29 01:00:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":564744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed Mechanism of C-Section Induced Adiposity.\u003c/strong\u003e \u003cem\u003e\u003cstrong\u003e(A) Disrupted Colonization:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;During vaginal delivery, the neonate is vertically inoculated with maternal commensals (Bacteroides, Lactobacillus). In contrast, Cesarean section infants bypass this seeding and are colonized by environmental/skin flora (Staphylococcus). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B) Metabolic Consequence:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e This initial dysbiosis leads to a high Firmicutes-to-Bacteroidetes ratio, which increases energy harvest from the diet via excess Short-Chain Fatty Acid (SCFA) production, driving de novo lipogenesis and adipocyte hypertrophy\u003c/em\u003e. \u003cstrong\u003eParts of this figure were drawn using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8712827/v1/44840b6bff44c7b4bcf98275.png"},{"id":101751291,"identity":"67269d78-00cd-4b33-aeda-8fcae6202191","added_by":"auto","created_at":"2026-02-03 10:19:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1816287,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712827/v1/8208578c-244b-4ee5-8c7c-2b4325c24933.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eVertical Microbiome Deprivation? A Systematic Review of Cesarean Section as a Driver of Childhood Obesity in Sub-Saharan Africa\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSub-Saharan Africa is currently navigating a complex epidemiological landscape defined by the \u0026ldquo;double burden of malnutrition\u0026rdquo; (DBM), a phenomenon characterized by the coexistence of persistent undernutrition\u0026mdash;manifesting as stunting, wasting, and micronutrient deficiencies\u0026mdash;alongside a rapidly rising prevalence of overweight and obesity within the same populations, households, and even individuals (Mbogori et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wariri et al., 2020; Zemene et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This dual burden is fundamentally driven by rapid urbanization and economic development, which have precipitated a \"nutrition transition\" away from traditional, fiber-rich diets toward the consumption of energy-dense, ultra-processed foods and sugar-sweetened beverages, coupled with increasingly sedentary lifestyles inherent to modern urban environments (Sofroniou et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wanjohi et al., 2024). Consequently, while the region continues to battle high rates of stunting, recent meta-analytic evidence indicates that the pooled prevalence of overweight and obesity among preschool children in Sub-Saharan Africa has reached approximately 14.77%, with distinct regional disparities showing prevalence rates as high as 21.01% in Southern Africa (Shibeshi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This trajectory underscores a critical public health challenge where the pace of rising obesity is beginning to rival that of undernutrition, necessitating comprehensive surveillance and policy intervention (Danquah et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross the continent, the landscape of maternal healthcare is simultaneously undergoing a rapid transformation characterized by escalating rates of Cesarean section (CS) deliveries, which in many specific demographics now frequently exceed the World Health Organization\u0026rsquo;s recommended population-level threshold of 10\u0026ndash;15% (Lokko et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ntiyakunze et al., 2025). This aggregate rise, however, masks a profound structural disparity: while rural and economically disadvantaged populations continue to face critical barriers to accessing life-saving emergency obstetric care, urban centers are witnessing a surge in CS utilization that is increasingly disconnected from medical necessity (Atuheire et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shibre et al., 2020). In these urbanized and wealthier settings, the prevalence of the procedure is often driven by non-medically indicated factors, including rising maternal requests, provider convenience, and the financial incentives inherent within private healthcare systems (Tilahun et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Visser et al., 2023). Consequently, this polarization - defined by the simultaneous underuse of surgical delivery among the vulnerable and overuse among the privileged - represents a massive public health shift that is fundamentally altering the early-life environment of the African child (Islam et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yaya et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe biological plausibility connecting delivery mode to long-term metabolic outcomes is frequently framed within the \"hygiene hypothesis\" or the concept of \"microbial seeding,\" which posits that early-life exposure to maternal microbiota is critical for establishing a tolerogenic immune milieu and metabolic programming (Kahhaleh \u0026amp; Barrientos, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Physiologically, vaginal delivery facilitates the vertical transmission of maternal vaginal and intestinal flora, specifically seeding the neonate with beneficial \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e species (Gholamipour-Shirazi \u0026amp; Gholamipour-Shirazi, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Martin et al., 2016). In contrast, Cesarean section bypasses this essential colonization event, resulting in a state of \"dysbiosis\" characterized by a depletion of these commensal anaerobes and a predominance of skin-associated taxa, such as \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eCorynebacterium\u003c/em\u003e (Stinson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Extensive global research has established a robust correlation between this early-life dysbiosis and increased adiposity in children, supporting the theory that an altered initial microbial inoculum may predispose offspring to metabolic dysregulation (Butel et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tun et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile substantial research has synthesized the risk factors for childhood obesity, systematic reviews and meta-analyses have predominantly focused on Western and Asian populations (Jiang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Talukder et al., 2025). Evidence regarding the long-term health consequences of CS within Sub-Saharan Africa remains fragmented, often limited to single-country analyses that do not isolate the obesogenic risks of surgical delivery (Afakorzi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; P\u0026eacute;rez-Mu\u0026ntilde;oz et al., 2022). Due to unique environmental, cultural, and dietary factors, findings from high-income settings may fail to account for specific African microbiome variations or dietary patterns, a region characterized by a unique epidemiological and environmental context. Specifically, variations in diet, pathogen exposure, and the gut microbiome\u0026mdash;such as the higher prevalence of \u003cem\u003ePrevotella\u003c/em\u003e and distinct short-chain fatty acid profiles observed in rural African children\u0026mdash;suggest that the biological pathways linking delivery mode to childhood obesity may operate differently in this setting (Deering et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kozyrskyj et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tette et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, this systematic review aims to synthesize existing observational evidence to quantify the association between Cesarean section delivery and childhood obesity specifically within the Sub-Saharan African context.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Protocol and Registration\u003c/h2\u003e \u003cp\u003eThis systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al., 2021). The protocol was registered with PROSPERO (Registration ID: CRD420261292360) prior to data synthesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Sources and Search Strategy\u003c/h2\u003e \u003cp\u003eWe searched PubMed and African Index Medicus, and supplemented with Google Scholar for additional studies published up to January 06, 2026. The search strategy utilized a combination of Medical Subject Headings (MeSH) and free-text terms.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eThe core search string for PubMed was\u003c/strong\u003e \u003cp\u003e(\"Cesarean Section\"[MeSH] OR \"delivery mode\" OR \"c-section\") AND (\"Obesity\"[MeSH] OR \"Overweight\" OR \"Adiposity\" OR \"Body Mass Index\") AND (\"Africa\" OR \"Sub-Saharan Africa\" OR \"Ghana\" OR \"Ethiopia\" OR \"South Africa\" OR \"Nigeria\") AND (\"Child\"[MeSH] OR \"Infant\" OR \"Pediatric\").\u003c/p\u003e \u003c/p\u003e \u003cp\u003eReferences from relevant reviews and included articles were hand-searched to identify additional eligible studies.\u003c/p\u003e \u003cp\u003eWe analyzed data from four distinct observational studies representing West, East, and Southern Africa.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eWest Africa\u003c/em\u003e: Sulley \u0026amp; Saaka (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted a retrospective cohort study in the Tamale Metropolis, Ghana, utilizing primary hospital data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eSouthern Africa\u003c/em\u003e: Sogunle et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) performed a retrospective analysis of the Birth to Twenty Plus (Bt20+) birth cohort in Soweto, Johannesburg.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eEast Africa\u003c/em\u003e: Gebremichael et al. (2022) and Walle et al. (2024) utilized nationally representative data from the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Inclusion Criteria and Confounder Adjustment\u003c/h2\u003e \u003cp\u003eEligible studies were required to report quantitative estimates (Odds Ratios, Incident Rate Ratios, or Beta Coefficients) linking Cesarean section to childhood anthropometric outcomes. All included studies performed multivariate adjustments for key socioeconomic and maternal confounders, and Sulley \u0026amp; Saaka (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) notably adjusted for birth weight to control for macrosomia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Quality Assessment and Risk of Bias\u003c/h2\u003e \u003cp\u003eMethodological quality was assessed using the Newcastle-Ottawa Scale (NOS) for observational studies (Wells et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This tool evaluates the selection of study groups, the comparability of cohorts, and the ascertainment of the outcome of interest. A critical review revealed that none of the four included studies explicitly screened for or excluded children with pre-existing chronic medical conditions, representing a potential source of residual confounding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Synthesis and Statistical Analysis\u003c/h2\u003e \u003cp\u003eDue to the significant heterogeneity in study designs (e.g., spatial regression vs. logistic regression) and reporting errors identified in one primary study (Gebremichael et al., 2022), a narrative synthesis was prioritized.\u003c/p\u003e \u003cp\u003eA limited meta-analysis was performed on the subset of studies that provided compatible Adjusted Odds Ratios (AOR) and Incident Rate Ratios (IRR) with valid confidence intervals. These results were visualized using a Forest Plot to illustrate the direction and magnitude of the effect. Statistical heterogeneity was assessed visually based on the overlap of confidence intervals. All visualizations were generated using Python (Matplotlib library).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Selection and Characteristics\u003c/h2\u003e \u003cp\u003eOur initial search yielded 156 records. After removing duplicates and screening titles/abstracts, 17 full-text articles were assessed for eligibility. Of these, 13 were excluded. The primary reasons for exclusion were reverse causality (studies assessing maternal obesity as a predictor of C-section, n\u0026thinsp;=\u0026thinsp;6) and incompatible outcomes (studies focusing solely on macrosomia/birth weight without childhood follow-up, n\u0026thinsp;=\u0026thinsp;5). Two additional studies were excluded due to missing outcome data. Ultimately, four studies met the inclusion criteria\u003c/p\u003e \u003cp\u003eOur search strategy yielded four original research studies meeting the inclusion criteria, comprising a total sample of 10,215 children. The studies represented distinct geographical regions: West Africa (Ghana), Southern Africa (South Africa), and East Africa (Ethiopia).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of the Four Observational Studies Included in the Systematic Review\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry (Region)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy Design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKey Confounders Adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedical Exclusions?\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulley \u0026amp; Saaka (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana (Tamale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRetrospective Cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrimary Hospital Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaternal age, education, occupation, birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSogunle et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Africa (Soweto)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBirth Cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBt20\u0026thinsp;+\u0026thinsp;Cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSex, birth weight, parity, household SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGebremichael et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia (National)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-Sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDHS 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChild age, sex, maternal education, region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalle et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia (National)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-Sectional (Spatial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDHS 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWealth index, residence, child sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*CS\u0026thinsp;=\u0026thinsp;Cesarean Section; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; DHS\u0026thinsp;=\u0026thinsp;Demographic and Health Surveys; AOR\u0026thinsp;=\u0026thinsp;Adjusted Odds Ratio; IRR\u0026thinsp;=\u0026thinsp;Incident Rate Ratio.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Synthesis of Results\u003c/b\u003e All four eligible studies reported a positive and statistically significant increase in childhood adiposity, with effect sizes ranging from 1.64 to 3.23. However, effect estimates varied by study design and population:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMagnitude of Effect\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSulley \u0026amp; Saaka (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e reported a 3.23-fold increase in obesity risk for children born by C-section, highlighting a particularly strong association in West Africa (AOR 3.23; 95% CI: 1.14\u0026ndash;9.13, p\u0026thinsp;=\u0026thinsp;0.027).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSogunle et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated that this risk persists into early adulthood. In their South African birth cohort, C-section was associated with a 64% increase in the incidence rate of obesity at age 21\u0026ndash;24 years (IRR 1.64; 95% CI: 1.01\u0026ndash;2.68, p\u0026thinsp;=\u0026thinsp;0.045).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGebremichael et al. (2022)\u003c/b\u003e reported an Adjusted Odds Ratio (aOR) of 1.75 in the Ethiopian DHS dataset (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), though a discrepancy in the reported confidence intervals precluded this study from the pooled meta-analysis.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSpatial Analysis\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWalle et al. (2024)\u003c/b\u003e confirmed a significant positive spatial coefficient (0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that regions with higher C-section rates in Ethiopia spatially cluster with regions of higher childhood obesity prevalence.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Quality Assessment and Risk of Bias\u003c/b\u003e The methodological quality of the included studies was evaluated using the Newcastle-Ottawa Scale (NOS). Two studies (Sulley \u0026amp; Saaka, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sogunle et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) were rated as \"High Quality\" due to their use of longitudinal data or primary hospital records and robust adjustment for confounders like birth weight and socioeconomic status.\u003c/p\u003e \u003cp\u003eThe remaining two studies (Gebremichael et al., 2022; Walle et al., 2024) were rated as \"Moderate Quality.\" While these studies utilized large, nationally representative samples (DHS data), they were limited by their cross-sectional design and reliance on maternal recall for birth details, which introduces potential recall bias. Additionally, the exclusion criteria in the moderate-quality studies were less strictly defined regarding pre-existing medical conditions.\u003c/p\u003e \u003cp\u003e\u003cimg 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q7FXsCH87CchFpWdWhLaBG/kHXzwwV14FoXAfChJ9o3hoT24YHpvfetbi7qE6IEHHtgp5NHHqKt1Kdt7770LZk6hwmgZavIlaxa6SskgdDkRrB2zwxgJXbDBCI0NvoRaMFZ7Z94UVDB97WtfWxhcTvo8U0SMQ6FgmDCqCGjeVfcMCOUEoHh4cCCczInwUSYxfMyHAQsn0QQjTD8UEnMkLLUjeJSHMNaewoZ5E4ba8OrZa3T44Q9/uDOC9c/zCRYPfOADu1BZtEmICWEJJR0uMqxqrd17jfBDMs5QIjAYgT70QWFQFx8g7O9yl7vMvuBP4MN7+MtQBld7MdTnUJ75o397aH081jzX8I1yAv+jHaFsDMoC2gE/uPmmN70pqmxztdf9TO3tIVqNMsoVQ4xiaU+Fr7jnMIk6rvDbvoAlHmPf7Y3+wIVSqR56su/w3l7YY0oRRYMQV2dUouzuueeehdcXL4TPeEbUh2N4AlyC8/bJ/sALdcxFct8mdAnerWLDMQAX8UfOGPhPeYZfbVvl4Nbuh48G2C9liuVnWQAAEABJREFUDG9z5cRg5PA2m7N9hNdoAb3gjdrgU23/i7lHr5Re+8UpgZ7hpbXq13qldu7khPkJFSdjKP+UbPzOldKrLcPY2sg6OCcMCrztgVAxvFa9NlE+4SbFDe3CZ/vDCGrr4XXPfvazu+gO/Aefg3euUY8TC10z/sEOrjFE7fUkCt0ZznCGznCipJKnLc7HGHGFu9YDdzidjMeQh8NwHu6LhLDf8Ab8tIXP9te8yDV5EvygAJMPjBQOAgY8GIMJmlDPGtGpK5yH//bTPnB6wUtKurr2EGzhInyzd3QFvD2MLu3JDTIJDbbvMemjTeBvrWQqWWFcDiPzM6e2bv+e84E+YJ/x7365Z4YkfHDKAn7C6vBiZeYHLyI5fQEnOCAPDquHx8BTtCUfDBkDaBXMGZz2F/7jFdpIHBfgxHFCRsiDz/aF3AEX9cFcP/Db+tEmHmiPtRlK5or/kfP4L/2oXw/9oRf9MojMHc2gLQ4s5XBMKCGYKEeHHKqj3m+NMchJMh1+wWu4QG8Rroj+4XDUba/gQLbRLdr89h5cOXS9bnPccce1RVvdgzc4wjWG2laFMw9w0p6RBQw8z2gBTcHp0Ctnqg7+h9fma42DFTJzlUNg/PQXbbhRAClVPCEYGmKlQFIW+wRCaVMXYmKKiMBJFKY9apqEghMajIwSzNNkLIYSpkowj2rbz6ekY5CUJd4d5ZirfhE0IpM3LhEOlDCJUoOREBY8gYhvqC0hGu0oVDz9mKDTin59TJxBg+gJHqdL/TrxjLHpG/OzD94hAhOKI+bH2LAXxiR8MBDvLtona611tOHGmKAI8HgRzowgShfmGeMPXSkN5kUoGse8eLAo9WBP4DECvf9HydEfhdUcR8EvxgFrHkVzl0eIEfTW6lnCzAgjCiBFA76YRxiqjG0Ml3KrjnHBL/YC46ZYWrN2TqvA0z08hY/G4SV1ZUAYg1OCEhd4RTAqp3SBOVxRLq9NlC40AR8YKGDFSQEnvcMnn5JCYMbYlCOKrnc20ZF1Gl998HGqAc4xDkWUsCXgCS97un79+u4dQXUINfhcay08qtYqKRtKvNYUE8oV409dSjHah4/gRyAx5uwxBZXXXT37PdTnuDyKD8ENl+CV/aBkBIwZd4Q5TzyD26mlF+HhIfiP63uSMkLUuhieYOdqr/oCnuOHYQO37bV9x/PMHe6jTeNRLPSJh1EAwNG8KRn6UGdUonjifxQjSrt9ZUBTjrVhNKB1yhPnBPwGd/ihfD4JvenD/OENJc66OJTm6gc+UN7hBFw2D3DDzzjs4CdjkTLPAcLodsVjKI1z9T9pOdnBcQbW9sIYjEXwmasPOIymnEqYOxjgLZRLbdEVHkvhtA/gbk/Ax2kWhVO9NqFPyjU6Zzhpa3+cTPfrwV+wh/fmbC1OjqKe/QAr87MuPA3uc1jAw6g37soJgDYZn04JRtUlf53+w2GnuGSYPMYHJxy85bTFP8zRHPSFbvAusodBIU9iAKFXH5IBZw4l+WSONaAJ6+e0JcPwOgo4fMIPwQTsOO3IVm3xfvoEvQLfth/6FWVjbuQMfgsH7ZO+GVba9hNjkLGGv+MhZAXDSjuvWrQyp9/WMzkOXk5BPQ8ltGVtaEE5/MSL3RsHXkTirETz1iYPz1ePgUIWwFP56NPa8RgGAf0GzyVbQ2Zph0eDBXyGV/LoUmgFLtVaC1zlnMLr9E0Wgad5hCzSrp/gAJ4EB8hRso9zk6Mm6lqzEFJ1Q4bAdXseTgxy1Z6jPeO7gg+DPfoZuoIH2sRb0RreAl+0Peqoo0rwyn5bBiojM3SBfrlnhjBnpLWYNx4mv5/gJWccHO+Xea61dlEM7sGi1lrQMvlGR8FP4Cu9dMihYu/IQHumj0w7FgQWbbghCsgcYOPdodB4mRPhRr4rZAxExrAoi5CSkad8KDmp4X1xZE+5YXwRSggTAfLoDbXr52HomBvFh3GBoalTay1OSjDEcUxWXYkxakxMkALLa4LAGFrKh5J+nTpQ+jDKoTryMAMwIugoPjyY8kclsGAoYLS86+q5YvI8N7y28haSGDcUAonCTzjqj0AY1x9BAgcICvXOda5zFcoBRkZ5pViClbA23sIy4T+eQHOiZFizZowCfQrNCQaGWfKuYngUCPXaRFni4SNYGGltmXttKACEXuAbA9qzcnNwjb3BvMFGXpvghWfCiaHlvp9qrd07D0KPwIuBwutG8YeP9lI+QbJu3brua2j6oDg6BYJTlCZ4IAlhUQ4/MXX3kj0gvMHGMxrQ1jo9zydZCyMcHOE95UR7Sgi8o2Ax+uGy/KVIYBLGunHQq7WYi/4phwQuA8Ha5Enorn2WN9+Ez1iPvXFybXx9UMwpJO4jUV4pv64MM3siwU98g+GvrtMLc+c1D1ymDFGGKf3qjEr4FsVMnxw8+AmFyv7CiVHtFpLPWw5+Qpko3hLlB36O6w9d4dfWRFkHA0l/lHTzdMplLXBbiN04GTBurLnK4Lm9YyxS2uaq35bDO951/Fg+48/eSp7JN44j9BV1dt555yIFP1JvIQmfA2f9wjmn6wxh8lJ/+BCYgSO4gi9+Bcfg2lyGhT4k9EspRs8MQXQkv03oy36q6+TLWBKY2k8ywekNWIOXumADTuaJVzohEKUQii7+DD8YUfgV+UCJl/A849Mr4AcYoHmKLZ6oTEIHnDYBazoFhxR+bc7qLDQxyNG+/hkP1st5CebWyrm20L6Xsh0HADypdbMTFu4xfGqtBZ0Zi5Oi1lrA3zPdDH5wpsAVJ2vy7T3eFE5OMsd+uCq3L8bCVznu5A0l+MqJwLijz5CpnG2MKPusDectGQHPGdXy8BcGCZ5Yay30BvsvXzk6Y2jjx56HkhMoa4M7HHv2TXK6B/eslYE21BZ9gQc8HiqPPAYlIxs8naxF/mKv8JeM4dSgo+rfoYCxwKrtnx4lush62/y83zEgsGjDjQLCA4Xp80hSJiiWiKAPQgTd5mHCGDyG0ea39wQDYiOUnC5EEjKCSWP+bf3+PaOP94uX2j0jhFHTrzfpszEpTZQT3i2CEkMfWm/06YSPJxmseFYJHEzF2qOOKwOAgUIpxTxq3cyMlfWTNVHOMWoMrS33vG5G0Sc42/z53GMg5ksxYCCDO08Q5ms/RvVFscD8GclOVp0iwA8MURv7RVHAlHnjKKo8Y8ZQPpTsG6NRH2AJXyhMBCuFhnJAwGrLaHOChgkSCpROYXTBrI3Ho4vhUQZ4Lnnw9K29K7hyDlhzJEYRoRJCBnx4RTFXRgUGS4lmSOqHMk0wwlsKIA8y+ClrE6WkfSYc0Umbby+DVtSlOBiHQIz5ufJWY+YMQUqxupI+Q/jFc62jcUudUQkcwR4NmVNbz5x5t9GCPWvLttwv6GLutZ48X+up9eRnigi8g/cLGmBMI3C2HicL9qWt2sJYvjWjOTzBXtgTySkBpZeSpR4c0y988hzJOsEwnkdd1aPc8H7DfYId7jFc9T2q3XzzrcVpKV4UbcHe+PE8dIWfYIZPWH8kTg4efUYvWHLi8bJTqBiETiidFuP5Q/1OkgeuUtTlJCKTwMvpCacio4GXO+qMulpnrSfjmee2Ll5m3hRUxgd+hPfhLQzetu5878GnbdMfm+yw1xTHgK8r+Qb2YNy2H3cPD+ESmYLvad/Wj/2kdBsjEr7tZMpY8AI/YIjhEZw7YExO43+cHHgTpRsOaOdEHgyNa0zKtn7asePensInjioyha5BIddOmXpksxMLsoQDiVN5SM6qO1eCg3gK4zTW6+qkipHLOemkikHCWRnJKaT14w34AdiNGku5uU9C86P6gAf0BjIOTCSySt9gox15wOACe7jJiW1c8JHv1Ft9vASfC6ejOoxi+o7TLn1zKOP/cE/fo5J9ZHTRC8kphjs6oReAq1BLMLL/tZ5MY21/dA9zhtPGJlPx1nFjW7NkjvYrEn7M6IardKZ2nPneo008hTHFaYLu+32gBfAbt//mqZ3+XCX3dBQOX3CjwzgNd8qsvE1z9d/Wzfu1BYFFGW4YMYWV4oBRe0bomMmkYCKQGCrj6mOKvHi8aP1E6IxrS8lH9LyCTngo8uPqz6dMv+aGIel/XFthHTwpDEiEzhPFG922wTCFePm6EK/hOAZFEay1du8y9ZkDoaMtxtj2v9B7HmthmLxo9niUt42iJ7SCAuk9GIYKfDCfdmzKPQFM8DLEnEQSxv11RBv9OKHDqDBvIUiReNcoFIQO4aMNZsczjGlTOhmKvKbKMVQhlk5G7AUhLwTGHirXnkeRgO7jmmfGmjqS8Dzj2Fvr5YmjuCmjKOqDImQ+wk2cooWBqc5iE9ozp35iLApzWWz/Q+0JZIrGKBqnrKHpWoeF8VCfi82D5+Y1ak7j+rfnIUDH1WPMjytvyzgOeLv7+wKH23oMlvZ5IfcUSMoUBYwzKU6G5+oLf5irjnL1JPfzTd5/7MPAM2WYkg5POE7QIr5nH+A0xXPUmDzNpz3tabd6gT/mZS/xCvyVch35ToHAXpif03ZGCphF+UKvcM44FFv8HB9gzDjh4LRZaL+TtsPLrAVM+wkPmLQf9fA1fJnhzECS10/4W38cz/aLo0x9hhvln2zj+GPQctJR3EMRp5DaX68XaIN+a62De6pccgImssQemp9oAYqzskj6EcUDnxhwyn0gxntSZEfUm/SKhwvds8Z+Mg4+iP4Y6ZE4CuQ7HcQztLPWoTEZUfQTch8uDdUZl6dfawVHspSxAi4Mo3470RHKONUZ+5yWTtMYCXQr9eXDgZAd4CwqAG4werQnz0fJaX0MJSeuTtXxKEa7Pugv9gTND7VBy6JivFvMKDW2BIeG6vfzOGjAvp/gAvzs12+fwbV9Hrqnd9KJyH3O9n4dDh28CE/ul3m2bs42dAN35LWp1tp9tM3hgPmCQVvuHr6Do/tMOxYEFmW4EYYYh/h7iinPCMGFaQ2BkWeozafkQjxMpM1v752g8Khjcm3+JPcYGIPAiQ9GLtRlknbzqUMwIEDGFmIc1xaxMyJ44BlDhEK/vpMcYU8EL2OiXx7PBAQDg/IHPpHvyoNJIBCYnpcqUXow21GMDVMWkuB9GmuDE96TEULXnwNPPoMfcxYiJgZ+lEDgneOh5bUVuslgjsT4cvoDD80txnEyRliJd3ciJnStLYdzTt544ihfPLOEBYWSMcdTHH2NuzI8eVl5JO0DQ66t7xSBASkunoLHwdGWL+SelxLTJoQmFWSTjGP9lJ1xde0b3INjBFNbl6eZUubki4Ldli3nPcHG+UPxmM84PNGUS86IaMfJQOGKZ95ZfVMA2nrK+3tJ+UKTwoDAQp2h5KTJ/vF8t+XwB922eZPcg3Wtmw1l/UYb+M6QjmdXa4HvnB2eIzk9iPu4oivKUlsGBu1z1G2vTogpr/giZbItG7q3fyIS0KpTE8rQKLyGe5Rlsgd/b/uzd3iF9VFI2zL8lnFFwUWz6L4tX8g9hQ3PI2PQt3e/KFpOGS8QC4IAABAASURBVMxhrj7Ji/4a5moT5fCMo4zDsL/HUWc+Vwokr74942BseTHnF9yG13jjuH6dspBzXnHAI+AjGY4fO3FGM3ikfQw9QRtGPKOipb12HM4Qcod8IVfoGmR6WyfuKcIcfBxraI0cBeson6XNyBi4mi9eaI9bWLRV9c1RaS6RhLihRwYsOOBJDKC2XdyTeYwkcKh1M/1GWZ+3yrd+8t69xEEBzuQymEjmwRFZ69b9cZDgdU6sODmddjL8GVOMC/yAfKIz2G/9O4nDo8hY8lzfToLwTeXzSfDLeE6U7LU1w32wsa5+X3gXhy7HOKersSXGe79u+4zu4Bw8nq/OaF741iTt1OXIwFfMq52De85a9CkiBx7Ja5O9Y8wKSx0HT/qxseBUvz09gL7S5uf9jgGBRRluPCcQnYAnrGut3aeICdUh8Dmx4blRhqljAhRB4YPyhpL3mDAVLwq3wpzRRyEeaiMPU3CywruLiTOIMA1lbcLQMQaMNubWls91z7PDMHCcTXEYqo9AGWsYFAFY6+bP6geDbNuAIyFFcJgzOLXl7T2DmUeTgArmQKliDNkb7dv6i7l3gkBo2i9euaG+CETwpLgTSOowgChi7iUnAowu8/aMKWFwGG6tWwsb5cq8T8joZ/QK1WwTjyDmThmgZBJKPGHWrz0mrg/9e2bAUdrgh2fJvhEstdYixIaCYb8INOUShYWhF3mUQCGWymIM95ipuQqhoVTJs8YYj+IsbzHJ/OCrtdoT89cfw4sB691Lz/NJ9lR7ypN9HNUWnNArPAZ7cFGX8kh5tU77EfBWttyJRxe9EJLoMMajdHGAxHP/yuMMHymqTuvgiRCY2Ff1CUz0bb3oMhRLDqr+qQ0jj4Lk1EKf2kuULTwg5kY5QkfoViiQOubBgYF+PQ8lc0TbEjirI8+7W9ozgEIJ4MVXxpBRLxJlE33iefBUPqcFr7r7NjFEOL3gFN4Fz9DOXPhlDG31zxgLmOENYAs+5gZGlO/gXWAMVpQV9+1c4p6sEXqHtiioaD7KnMoIg6SUMmzkU5w5e+BnrZv5i7Xgs8oXk+C4+ZKBws7INydLlKlaN481qn+KK3lGto2qMy6fsk5xBMPWMAFrSvZC3sHybpTTQntkf2J8J0+cbJwXjANrVgaOTtbgg72VZ+/AgGJKPjPaYi8os4wDTk7OSbSlDdrFM8gK8lq/8tGvyAx7x6Ghb4Z9yHF0oF4keMVoVj/ywBjP8uwKboyRUcaYepIPm6BRURP4LN4onyPChy5a+MjvJ3zex0+M5VS0NcTwTGG7+kbz+EG0h9+MATCKPFfGKDpp8QWc8AGyFt2DC8eK8D2Gj3aR1HEKZExw9rEVZfgEPGac1br5vTLyShlcqrUWBqj1gBlcY2wpH0qMQ8arvWj3QbQTo11faI/jlBxDs8ED8RfOSAajvtE23Av8wePweGWjEp0KLYIV/hyyzP6BqZMrODHUnuzGG40zVN7PY/Tic9baL/MuHhwScUPPbcvxLgYoXu/k1pjwiROAfhLzc3WajY/2T/DNEX4GbOwN5zEneego7Zh5v7YgMLHhVutmQdQKVEf0mKn3dxgRCFa4CI9oW6/WWnjVEKrwGQgvZBFRUYbkB1i1q7WWWmuXRQjwBCL8YPCUTEKAktlVGviDwDEByK+tuRIOkShShA2CwQz15VRnoKsuq9bN8zG/LmPLH0LamowVRpY6UjDAWmvBRHiBeVgQISURzHRTa+3WSwGodfMROUaqjPGJGbvvJ+0Jb0YtZcb7XhgxYUGoUWj7bcyrn9c+KzcP6yHIwYviat4YuffWeKy1Ua/WWmKd9hFDFsdOqfI1Lso9w0a/ZeafuozumK++CGvGFsEzU2Wr/wwwglNopLVtVTjzQAgwJIyB6dVai/7gGHj4apn9dVpnDhg4rzhhaW0UCJ5AjLTWzXjq3TsCyFrM0/qNzUNaa50ZtRR9URDV0RejUh3hkPrDPBkG0Z5XDiMXtqUDsHPVj2skz7XWDh8iL65g515bShF4ox/jWgsBwHhq8VibWqtmWyXjtBlgzyC2N2gTvbblcV9rLdZI8AuxYUAam9GA5pxAE2a1bj1mf7zor72qU+vW7Ybmr17bjgDjnYUL1mA/4L79j7DeaBNX7b1riiYJPEoNYetUwX5HvVo3r5dApESCuT03nvezop7+7Dua5MyB92AC/yhd8AMvUs/c8DQ8QL9wVT2w9KzOUDIWnsW4pKiBO/zCdwlw3nfw0tY8rQ3emQ9lRj7ejK4JeuPaa+3BQnmtJ8Of88GcnIaZHzxjKFDuGanqS+blCodca62FTMC7GbjaoVHzcRrPAFAPzfqiKngrRy8UUbQYSr16bbI+irDoCYYwWYCm9I1Xgod5U9K1MwZeZi+MYR2MWcq3cnOX0JRnyRi11o7GS+9frSfDB43gS8al/BrTSRJ5wDCO/e510T3aA2Oaq/XjSQpq3dy/OXmONPSsDZnICIC71gY3KYBwK9r2r7VuHqOfHwY3OsInY8xaawEvcIb3cAJtgTm+2vIbfdpH+MGBC/cp05Efp8oUVnkSeHMIMC7IDDIBbtov+AbnRU8w3jhGyCD7zKkA72vdvJ5aa8EXwdP86Ai11gKfwBpO6ZccAyc6AceXObRr9Wz+3l03pr4ktMKxp03fMNKmn/AAdG+eDDLz0gd6YPB6pYBTmQ4VbZWBKycAmYv2yCLjW6voDnUYfAw8/emLk5YMJZPoC7XWbWQIfkHPAU+OA2MyhOGsd8gZ2eAqX8JH4TRZztkKzzgF7FfAS7024YF0KnW9T2ZMvAqeo1n80T7gFXgEIxYtqAeH8W9GSa21kCNkrvHtuTUzyMwpxqx18zpjPrXWQn7bc04cfBBdgCn56yQu2vavdDlzxh/AqC2P/ts8OEUW6lt+rdWlS+CAZkTccB7CJ2uEf2SNUz2yW3t911oLuOHFaMZ+ozN9BN11HW/5g6fBB/snixHPgeLVETCSl2lVQWBek53YcMPkIBUijxEILh4YTIa3BOFTmikWBBqhCbkxIAYdbx2PGIaMcRNufU8vLxVkR9zGQaSYG4aBEVJ6GCueGUPqDCUESKiYmzb9hOlph8DMFWOxHnlDCVGbM89jW47pYggU5hDUGLRnwkFdhhomgqliTsaiBAaTRIBiudu+9YmxYZ7j3osiRHi1MSrjgSePGwUJQzC+hMDBDyPzPCoR3vaKoqc/cMM4KAMEBCYabTFEfWJG8gh8yiMhW2st9pBiRsHAhM2HABM+iRHrnwGAOdt35frpJzDw7guB0S/zTNklEOCg9fGqERz6t2cMd/CsdbMQBx/P1kbYGd8a9QVf7QNPGeOZ0ovhUtIZJoSQet61YByjC3Dwjg74WB9csW5KhPbwywmeefBcak+pdWpnDp4jybeXFMHIQwOEljlHnnlQasxDff3Ye2M4pVav1lqMbT32VV4kCpB9jmdX67FmQsN+yBtKcJ5BQLEBX2PDdzCDs4RUtAMLuEgIRd6oK7qAdzyf6hDuhLD54CXyJF5zdE2B8SypAx4UDIqfdYO50xbl8AK8KZWeJUYMpwfcgWPK4YYx0ao6Evq0XryL0WKOTrnwQ3hmL9STGBLq2nu4QdGDu+gdzNWRwBftao8fqu8ZPZkjPFSvTfLsmTmaM7hTeqzTKUkoENqAoZMy48Btip18cIQnPhQCp+AueuQV1ncodOpalznBCTzNXqMVyiRYqSMZV1mLn8aHi3DKusHFHOAIQ8c8jM3ZQoFVbv+Nx4gcxQuMRwaBP/lhHngqPsJIxGdbvEB/aBG8jGEsPApN6AudoR98wLNEcSI7KP6eI3EQwHPrlYfenRjAb/SPR4A3vMUn8SH1hhK8MA+0iubto3r4NDiRmZ4jwVVzgseRZ//B2N7Bf30yfOw7hT7q9a9kI7yzzn4ZuiG/7VFLs9ZuLDAmf/FWuAoXwbLlzfaDA0ciy2MMNEZZNUd1It/VXCij+Ad8NX+8TX1yRTgqXDIuw0sfaMp+MMr1Ye5gCq/VQ8fwFf+utXYORvBmJJoX/As4wzk8BW8pW/6hdXsI/+EOvmKf7Tm9YUu1kRfzBktOYn2gLbLS3PVrPYzVtgMwwgvQAtzlBMBv9QXf0Sh8IWPlW6P34ckVij+8w5ftnzm3fds3e4JHBn2hUzirvv1s52MPwA9tcaDaMzocpxMe2PYd93g/3qKeOcJRxom1whX7HHX1Yb/wR/XsASOHvmR++Bp8AGuyHf8VpaB+9EHnRNMt70HTcBXdK8MP0Ki1MKTBLdq3V7AAc8a6U7G2zH7AhzbPPTxD9+AHvvIiwUt7QVc1rjUy2uAEGuWAsjb18QxrVReO6tea7C0eaf/Vk5zUcUiqgxfKw0/hFXrBK+RlWrsQmNhwg0yQKBAlQIKYhZBBUMwV4lN6CAZISdmAgIwvx94ELSLGPOVHP3ElGDBjSn/kERqIHEMzDkGFuMOTF/XaK6alL/WHEsZN2GAQmCNmg7DaPtp7iq861tHmu8f8KT8YXftM8fGMUVDizB8MGUEUIWMr5zUVMgRmniNRkMCJQI68/hXDJnCcclmnfiiCCLmty0j0fof9afP79xgEJebQQw8t+pO0MxdCpa2vL/PTd+QzfjF2DJjBhlHZZwwFLlgzhqOOvjFiihNmG320V/iGMapT68kerbaO8At1MEcM0D4QsPqXT6khULRxjb1Qbi8IU7igXAI7ykPgtX0nhEJhUwdOogn7znCGs63Qt6faGEN7iq290laiFCoztudIhBwlU/+RB/fQlv4izxU9EdT615f5EupBO7XWQokhKJzGaROJwLdH8exKcBEm+oID8kYlOI3OKVfqgzfFCXzbNhRReACebf7QvXXAk8AF+6B/Qtd9tLHfhDgYRp4rvNLefNA+XhM0CFettxW+tW4OjXXipg2FRn3jgY8+IzHeKDjqGZsSB87Whpajnqs+7JW6xsQzKI/K2kTQ4wn4IU8z2qO0G79db9sGDsFf+KB/MLNO82vruYevPLb65xiSJ+kDz9Ae/ZkHXg2Pw4BQr9Za8ER4hW8ZE1+1FnivjmSPrYOB5jkS5xgaoYAZixGN78IzdfADvAoMlcNTeCJf+bgE9vhKtMX/0FgfDvqyP+ZuDPXxe3xC//AV7dpTzxL8MmcGiedInBBgaY7yhHgZD9/BH+EMBR2eoI8IB1d3KJmHcfBQPEsd8DUXcPIciTJm7HZ/8FL81niUVLiGfhn+LT+LPuLK+EMfYB95cYUb+gMrhkbku+I3YAznlIMpXNRGeSQwdTJnTxhHkc8owCfgCvhEflzBHRytQ/94GyNTOecVHAYvuMeIhZvmEAYmHAZTDl3tKcJkUwsLvEVb+Oz0Sh/6ZxCBuzl4luAOOicj9Af+DB/GR63Dski7NsFTjsXoQz/mJW8IBtqSmejSOhkzwQucWFmbfMaUumiMU9R65KNrOOS5L68ZMGic7K315Pnj4+aFZ+kzEhqB6/ZMOf5wmmiuAAAQAElEQVQId1ztTdTrX/Excif4qjnjA/a/rat/ulPUs2a8L2QfGQre5qzMmuGbfYp+8B48juEUea72mSwlP8ydHkCHGKczagdf6RyMbc+R4Dz6juf2ilaNYa5tvns8g66K76hjLQws8kh5m8BNXbSjLly3t9bS1uPgADvrcVUGx9W1V+hUXqa1C4GJDbe1C4JcWUIgIbBcEMh+EwJrFQKUWmGCjDVGCqWMkU4x5sRhDKzVtee6EgJrEQKc5Oia80AY57StUVSXk0sODUb9tM0v57MyEEjDbWXgnKMkBBICCYGEwMIgMJWtnFRGGJOoBKcCPoDAYPNBJicGUznxnFRCICEwCAEnV6JNnLp5H3Cw0nbM9GEZ77M5pY+T2O04nRx6O0EgDbftBPgcNiGQEEgIJARWLwSE0gnd4gGn5Pnyri/gOnWj+K3ela3Vmee6EgJzQ0BotXfchKbOXXtla5ibL7O2If8rO4McbRogkIbbNOxCziEhkBBICCQEEgIJgYRAQmC6IZCzSwhsZwik4badNyCHTwgkBBICCYGEQEIgIZAQSAgkBHYMCCxmlWm4LQZ62TYhkBBICCQEEgIJgYRAQiAhkBBICKwABNJwWwEgr44hcpYJgYRAQiAhkBBICCQEEgIJgYTAtEIgDbdp3ZmcV0JgNUIg55wQSAgkBBICCYGEQEIgIbAsEEjDbVnAmp0mBBICCYGEwEIhkO0SAgmBhEBCICGQENgWAmm4bQuTzEkIJAQSAgmBhEBCYHVDIGefEEgIJATWHATScFtzW5oLSggkBBICCYGEQEIgIZAQWDwEsoeEwHRBIA236dqPnE1CICGQEEgIJAQSAgmBhEBCICGwViCwhOtIw20JgZldJQQSAgmBhEBCICGQEEgIJAQSAgmB5YBAGm7LAdXV0WfOMiGQEEgIJAQSAgmBhEBCICGQEFglEEjDbZVsVE4zITCdEMhZJQQSAgmBhEBCICGQEEgIrAQE0nBbCSjnGAmBhEBCICEwGgJZkhBICCQEEgIJgYTAnBBIw21OEGWFhEBCICGQEEgIJASmHQI5v4RAQiAhsNYhkIbbWt/hXF9CICGQEEgIJAQSAgmBhMAkEMg6CYGphkAablO9PTm5hEBCICGQEEgIJAQSAgmBhEBCYPVAYPlmmobb8sE2e04IJAQSAgmBhEBCICGQEEgIJAQSAksCgTTclgSMq6OTnGVCICGQEEgIJAQSAgmBhEBCICGwOiEw0nD7+c9/Xo4//vhMCYPEgcSBFgfyPvEhcSBxIHEgcSBxIHEgcWCFcIBNFmbmSMPtUY96VLn4xS+eKWGQOJA4kDiQOLDEOJCyJeVr4kDiQOJA4kDiwCQ48OhHPzrstjLScIsaRxxxRMm048Hgspe9bIcCufc73t7nni9szxHMFa94xeSXKTMSB1YKByYc5zSnOU254AUvmPsyIbxSBixMBqwFuO2yyy7ldKc7XdLKFNEK3aJNcxput7vd7UqmHQ8G5znPeTo8yb3f8fY+93xhe45gzne+8yW/TJmRODBlOPB///d/5cxnPnPuy5Tty7TImpzHyTJv3bp15VSnOlXSyhTRCt2iTXMabm3lvE8IJAQSAgmBhEBCICGQEEgIJAQSAgmBWQis2E0abisG6hwoIZAQSAgkBBICCYGEQEIgIZAQSAgsDAJpuC0MbqujVc4yIZAQSAgkBBICCYGEQEIgIZAQWBMQSMNtTWzj2lnEs571rHK5y12uvPGNbyz/+9//1s7CVvFKcuoJgZWAwF//+tfiy1no/6Mf/ei8hnzyk59crnCFK5SnPe1p82q3mMrf/OY3y1WvetVyz3ves/zpT39aTFddW+u3Dut/xSte0eWtlj8nnHBCucY1rlFufvObl69//esjp/2Nb3yj3OlOdyqXv/zlu3dovvOd74ysmwWrAwLPec5zOpntS+RLOeMvf/nL5cpXvnK5xz3u0XX7y1/+stz3vvftxhqHY13lVf7n17/+dXnqU59arnvd63brvfa1r12e/exnl1/84herfGXTMX38+s53vnPHv3/yk5+Uf/3rXwXPxXtf9KIXTcckx8wiDbcxwMmiySHwz3/+sxxzzDEFMVzykpcs69evL9e61rXKa17zmo4oJu3pN7/5Tfnd735Xfv/73y+Z4UYh2rRpU/nBD35QzDPm8u1vf7v4eubVrna1QqGI/LwmBFYKApwTJ554Ynnzm9/cKbK77757RzvXv/71C4XoV7/6VVkj/+Zcxn/+85/y29/+tqP/P/zhDyPrU+iufvWrl0td6lLlQx/6UFePooN3aC/j+9//fmcc+MrnJz7xCVkFH8ADfvrTnxZjdZmL+PPud7+7+G2d85///N3L/G1XDE88ULre9a7XrastH7r/xz/+UX74wx9267cWdd7znveUPfbYo+y9994elyzhsWAEv5YCFuDw4x//uOjv73//++A8lTFMP/WpT5VTn/rUnVwYt8+Dnaxgpv344he/WB70oAd1uLZ+/fpyiUtcomzYsKG8/vWv72TUfKcDB4dk0Xz7mZb69h2uw6eLXexi3bQ++clPdjwMvNpE1sLnrtIEf/72t791hkoYK2Q3+jYWnjlBF6uyCpj6WMorX/nK8uc//7n7qA7+9sIXvrA84xnPKH/84x+nbl0MHzoUxxunjH0ny25yk5t0BlHws2mZ+H//+9/CEfCzn/2s40PkMF4Et6ZtrkMw22koM/MSAvOFwNFHH13uf//7F0oVLxnGc65znau8733vKxSl+fa3lPU/85nPlNve9rblxje+caGsRN8nnXRS5yl3JRQiP68JgZWCACHx9Kc/vTzucY/rlPbrXOc6nfPD1ynf+c53li996UsrNZVVMw7ll0LD4OA5HZo4pY+C4yqp8+lPf7rc4AY36Lz2yuQtNBn3yCOPLGc84xk7r+3OO++8VVdve9vbyk477dQZKN/97nfLscceu1X5pA+MB7xpqRXV5z//+Z03/wlPeMKKKYJg5pTSV+sopYcddlhnlE4Ki831Vu6vPTzwwAPLhz/84e7Ug1Nyn332KZS9xzzmMeXxj398Z1jMZ0af/exnOwcNWfS9731vPk2nsi68Zmj4CiFnk0ky0F1Pf/rTF04LcJNuc5vbFJ+aV5ZpNAQ+8IEPlB/96EeFQ8gpG+fU3e52t66Bk0YGRvcwRX/M+YEPfGB5xzveUS5ykYt0MuyGN7xhMVcnh5wfJ8ycyk/RlFf1VNJwW9XbNz2Tf+9731v+8pe/dMxGuNKTnvSkzjvEA3OOc5xju07UvChAPMGUoJjMRS960fLiF7+4vOAFL+iYTeTnNSGwEhBgeFAKndzUWovTiKc85SnliU98Yhfy98xnPrM7EV6JuaymMZzoC6l+3vOe153qD83db3ahbV5qp27qMBx4hhl+PK7yFpqOO+64wuj2synnPve5t+pm08zpPoX2DGc4Q4mx3/CGN2xVZ9IHoZhCd/CoSdtMUo9yzcuML8LDSdosts6///3vzlF2znOes1zgAhfojF7K/WL7XY72ojEYt+C03377dfSILimhD3nIQ4qfF0C7b3/724t1TToHTkIwJ4vg4qTtprEeWcoQ5wRBk305v27dusLgADfpkY98ZFFvKtYyxZNg7JgeYxgMOYBOcYpTyOp+Xw3udQ9T8keUALpgmDmNpv/Zb7LMnp/lLGcpHBavetWrtop4mpLpr8pppOG2Krdt+ibNA04RwGB4n095ylMWpwZ77rlnx2xixt/61rfK/vvvX65ylat0SinCnsvzyGMu5PJmN7tZ14a3UqhKq3wZ/9BDD+08fEIyCFsedl5x3lFKmznc5S53KY7vjz/++EJpwGCEpPEaKpe8dyFeX7yz92b23Xff4vSjbPnH67Vhw4by4Ac/uHufY+PGjZ2Cpl9hQFuq5SUhMBYCFLgPfvCDncMDbjttc4Kj0dnPfvYOp1qj4C1veUvhtYbfQgXhqNMc9SW4d6UrXakcddRRRd0b3ehG3WmQek5s0N697nWvjobuete7Fs/aSTzi+8ycJjgx5+VFn07OGQ3CltVBb8JhnDQoNw9eVe+jUkTV4SnW933uc59OWN/61rcud7jDHbpQQXTsPYJb3epWXRijuaJ/Al/bNjGunvvc53bzF3L9spe9bLYYLTNqGboU7NmC5sYPyIbzyCm7e/NWxRy9O+I0wPrMwzyVReI5tj4nLqFIRZkrRcTVPp31rGd1O5u+8IUvdHt63vOetzvpt6d4kXcpZivN3HAogfXee+/dvR8mXHYme6v/FGMKEB6mQPQC3mZuDH55EvjIY1TYa3lC1uyr9cEFOCEUCP/9+Mc/rkoRRgoOFOwuY+bP1772tfKABzygwxN4dsABB3SnTDNFs/+FEN773vfu6rgyYmcLB24e+9jHFnxUkf0Qnh7P8vQHZwKv7n73u5eYo3JwwIsZ7K997WuLNXm3UNlyJHshPO0yl7lMueUtb1kYm8ZhjMPfi1/84gXOM9CF733sYx+bhUXIEiF+1mFf1INTaJGhoy+yiCyzNs/ojAIM5trYg5ZGyUmO0CgHP/INXWlvD7Sxl+bklAMNe+/QqQ26gSfwTf9oMdpqD885CKL89re/fXn/+9/fhZIp7yfrQEv4gn3rl8/1TKYaw76StWBD9s7Vrl8O3mgbTVsX2UwXoBOoy3mj//vd734laMPewkF8l+GhnvoiH/Txpje9SdY2yR6hP3M2RlQQWaQdfoHGGLX0As/oCf4rdyo5qu/oy/66xxeFoZr/S17yks5ZgBf2DWR1t2fitLcHnGUirThlzMeP3pMPYM859NWvfrU7kcNvrBFc1JPgJv2JDBJyLU9YO34GhznA8KGgFeVobq+99ipkKCcLJ5cruYpu0Ap4gTvcOPzwwzvHkbZLlbZXPzttr4Fz3LUFAUTLM0SRxCA///nPd+9qtKvEhChIQhcRNYWG4MGwCYC2btxjtAcffHChvGCIvDe8oBgsIsUQCBx9RJ11M54+hpl5MCIZaOamT2VnPvOZOybomSJE4GG0nj/3uc8ViiehcqYznakQ1Aw1TAMDNR6l0lwoY5QHDIahSqmlYGFQ+sqUEBgHAThHyVeHwAoc9dwmXnov5T/84Q8vTnPQAG89g4miBt84TdAK2qDcMgj0D1fVQwuUJIYO5wqlnhFHgTEWwStxZFBa0Q0cf97znlcoGsYjXCkglLnTnva0xTwYI2iPsagfQlM7Alb/3iMwnvm99KUv7U64KZVosNba9X3IIYfMKlT6kHhtjzjiiIKuKMCeOVgoidZEgZIYP+oPJQoA2lafIYcPqGc+xpfQt7kJSQ1DwRiUEXxFGc+3dm3C5zwzrEOp9wxOeB+FWFgmBR9vxDc+8pGPqNIlzxQLhiO4eufLPR7ZVdjyx35u2rSpWIssaxaq5yrJk4477rjunSt55oBnMagY4tYOThR1vArfjTWBLzjgddZNwcfTjj766O7dGjgpDOqOd7xjz1jdqwAAEABJREFUoUgaC4+klJorPLDXFFRloxLY473KnRgwdj2b6+te97qifzgJ79Q95phjitAruAtWjAR7CTfhkX0zX/0tRwpcEO4PPu0Y5k/plwfX0SdFEew9oztl8hlb8tFmrA1MlVu/vvUHRxmuQkid4IEP5wCHJfxwTylm/KmvrT3m+HCSAU/B0vhf+cpXCoOEMWws+4MWwZOxoS4jhfx861vfWsDR+PDFKTV4n+1sZytkKNqmGJtvP8FxtCyfIeM6SWLwkpMMS3QE98AGveNT5Ogk/VgHumVIceTiPeACx+kCYIOGwRJc8L6gI/tL9jMe5BsPfoGp/ULX8voJHupDf+pFeew/eNgHc0O79gI9GQ/OomXzGveuHyPFx0isB8/Dy9GsPWSs49OcH/Ysxt+eV/zA+OQBenEfCa/1zptnOOdq/8kFhhc4yeNAsu/WiTeBG4cf3oPH4GF4E0PO/mpj/frh4OOQ0E4+fAYnJ+Ke0RiYcYrAd/sjfzWnNNxW8+5tM/ftl8GzG8IMk+JpdCKFIBEKIebF2lpr95Uo3mJE5EtkBAyhHUTcroIHDsGKjSegKI0HzxhyvE4EDyaKcTAGKSG88O9617u6F2IZYDyaj3jEI7rTNf3yKFIULnzhC3vcJjHOMAOEz3g76qijOqWCoUmotQYmxmz+wqAIWAoHBhHK+DadZ0ZCoIEAfCfEZFHuXYcSIwJ9EGCMMjRAUK1fv757Z5NiRchHW7R20EEHFULPe6eMAk4GtEAR9l4TZYlSw+iLdhRO/RCEcN9JAyWSwaEuwcjxwsAwB/OicGvv2TUSukSzFCp0SngKkwrD0Jzlmwf6ZVhEW1dteZk5dsybsiosjRGnfL6JkvjQhz60a8Yj7PSJEcAjLGRaAaXYlUIHLmieEkVpkN8mihqluF9GqcCz7K2+nbrZp1proaxRdvVDWQRjSiVFDvzwS3BSvthE0afY8EhbJ+ObUrzrrrt2Iey808bgzVZuzxkIeCe+xlGFR1Om1WW02Uv44d0v6/DxAbzUCZg6+huVOB3grHLwsHbPFFA4CfcYg8Y3Hx5yip454KnaSQwgBjF4Uc7lLUcKhwYDhsLdHwMtyqPsm6f7uRJ8YAhFf2QRmMI/cGbw+8AHGUTmcZpw6JCfHJX2h0EGT8GFIm9MfbRGBFq1z/bm1a9+dQnewsnCAULuktX4BDxHe+iM3LKP2hkfPuJP+ofPxmqTdnBFHqXdtU361w/8kMhS8GKU6xNsrcteet/x0pe+dMfPrK/tZ9S98c2bcexkFAzBBa2bL3jSLdCAPsCI4eSeY8JVEqngyhgj39HkfAxRbUcldEPXcNoDr+kLtdbOgQXnh9oxJvGetkykxcMe9rCC/pyo4okM8rbO9rpnrBqb0TWEB7EWRi/+Rg7AafgG5triB672UT+cM/DYF2jtqX3esGFD50zo4wdeSt+EX3Q+8o4+Sl/DT7Tn9IDLDEJ80VirOaXhtpp3b4rmTtEiSAkmxIfBYNBCGCiNlBkhCRRBxhzPiTLCxDJ493hK3LeJxxgBY9IUPQoFwRPEp5ynBlPebbfdCk8VA8oX5zDwtq+57hlsPHgYt5AGAhYTcc9QRPh9ZnmLW9yie1/DC7nGx4zNda6xpro8Jzc1EIBzhA0lyOkNxYqDQgjyTW960+7riJRfinRMmkItRISxQxGEw8ooz8KX4SkPN896KF7KeUeFsFB04D5hKB9tojfKqnBOgpIRhL4oR+pQKl3btN9++xVKOmFuLkKwCHEnP+H9Nwd9u7ZtKSpCbNAiZUc7CrK1tvUWe69/YXeEvXA2ihZlz5zwEWWjxrAPQiXbcoofo9VeUdjA2V7pXz4+qD6+5UrBpiSCj5A27eQvNjGI9eEEgYEAbnhVf77qRLKHcM1z8GiGdii74MNwwrPxarhi/gwDYX/azTeZVxhmGzdu7BxsTjucUuiLER2KoWf4BE5g3550KluOxACQRvVt3xjwo8onzQ/F1ekE2VVrLYwrIa7gD6+cJu2zzz7FlcOA8o6eyS0nV+1YHI/2Bq0zkJTBM3yBLIOT8uwn3kEx9mwcH0tyws/oss8MCWXjElzvl5PbDEfGmcQ5AHfoBer6+AZ5bS+1x8/kBw66H5co/ejJHDmQvG8KLiIL8Bw6AdjBTzCF3xwu8ukkaBOfY0DYY32BB7jrZ9zY8ynzcwYXutCFitMojpKdd965gDPc7veD16I5TjG8wNzVEYrJIW7udKtaa8GPlU1LAkNp1HzIF4lTRh14i98y5jnv4LWwRvoT/qMv8oWu6ISWnqed6ALXSHgCfoHn6QM9kj3qc1JqHzgF1+FLtF2t1zTcVuvOTeG8CRECnMfukEMO6cIRMU4CgGBBiJgO4wgjkjBLS0FwtVa3WyXKjgxEzjOnDcaPCcun0CF8fVNQCQH5C0m85eanz5YpEnzxzDBr+458ee2950wJgUkhgD6G6hJiFFfChjJCUYx6lB33nCHw370Efwkv97XW7uuGZeYfwTZzmf3PWKJcRUattVOcCVd5UZ/xaHxKzf777999RIWiiRb7QlS7SPhB3LsyIBhsvM9On5zkWZ+yfkLLkRfrMQdzifyluFqr93PQLo8w3uQUgoBn9IbiNDRWrbVoX7b8Y+xRQPAm72tsye4UcP3LFxkgnwLmSkFU5t6eUercLzYx0niZnRw50RKGR5kRZjmqb7wvjCiKk/2VONzgF6MP/IMHUpSjL0Zu3M/nSvkGF23wWVfJ6Ysr3Ian7iXjULbdr0RCexwG/bHII3nmvBTzCTkIF5xs6zsSYwf8yTbrj3x1w3i1L5HvGvXgZ62b5So68qw8Evqzn5RceQyKkLNOouAE54aycYljol8OP4RmclJITj8YUPBJXW1inp45P12N6TpXMneyXz374CpZJ4MV3jjZkcf56tnpHBqEc/gnA9Z80HzwMgauut6L5YSO5KQLD9TffJJTpqhvzfbAfuIXkR9XuhIjQz2OahFEHBn2hREiCgHP1gcHXbSbhiuYouf+XNCQPDydDONwsO8MV7C3H2iMvHD6LB8+2zuwwIMkTij9tHvtme4YssqzPeLcidBIbfWjbK2kNNzWyk5OyTowFEQpPtlv3mBQGCxhVGvtXvIWFsGga5PTAIpLfxn6kkcRYli1bdzzQBJg6iB2DNd9m8xpqO+2jntMxZWyILmXMAJ9uyc8XTMlBBYLAbgUygrhNYS76IYQNxaFhvHiXqLQucLvWjcrZ57nk9BnW58SF3noVpk51Fq7j54QgDzlPPTojxddnbmSkz0hYLXW7nexKKpOcHiih9paa+THnGqt2/xeWtSZ5GodQ/V4eYXvWC/eZF7gLCx0HN+wXxSO6NOpqLaehYXzBEtOKRlq1kQ5pHgFX8NnJG3APe49DyXtGHttmblScto8a6V4Cmv0MQ19815TDNULnHIfCR5F30It7W+bXv7yl3fwD+cB4zba2qO4n8+VoWqu2rT9OdWVd4pTnGIr41jeSiRyxTgMXfvlPpJ5Cl2utc6eKFM6ozyu9tLexLMrGA/hVBhH9hFeqRvJnrvXl3L3EpkEr9wP7af8SVOM76SNMt3ue6x1XF/g1C+3TkYtuSpRruEOBV5d62zXY0z5gQ/uxyX8E/6oAxaukj6d5BjfePKE14I9HcLJlTIfyeCcUe59K6dwnBNO+uWBu+dIDATrGII12tZmKMEX+WhQPfvoOebuPhIeBG/MD290QucdN0Yw4w39MvicMFpPtNueV3M0PqOtH8puvZxZyp04Ch23b/Q5PINzgGFVay1OiPFLcFafwSucGl60SQix8lEJD4ePQuK9xmLPGcGj6q/G/J1W46SXes6ELy8TT7BNXur+13p/mA3Gx2vnHuMheBExIkWQvFiYOK+M0IQQODzCXnJXdwhOPGIYOY+YMEWMQHJCIaadd43yy3gzpjAn4yNc78Tok4cmFBLzw/gwDWVtwsQxFh4iDAXDVde9PIw8PMFtu7xPCCwEAhQACgUacQIlRh996MvJB2MOncBvNIC+KP7wG5/yDhvFREgN/NduMUm/FHbjwHsCUH+MGriPT3p2CiUxxhgD8uZKTrPUobyZLyWGQEdX8vvJewm848Y0J/PBP7Tt1530GRzVpeShbXOi5Mnz/oQrg9K4lAcncfKGknAzbfEtCqg62mmPhwjXEn0QSYha1KGE4Ime7SN4U+YojtrLH5XwsVD49AM+9gDva9voC8/kPBMqxLDAN+OkyGmD+vKsg9Jlj/FA+U7pwmBxqoFHm5s9oLzWWrvfN1MGBv33TvQxSaJcUYzVFQJvX+C8d/LkUeTA2v1KJr/7SbYIraMbwHV7BH7eZyZf4Mjee+9d4LRTEfPj5AMT6/BxBHCVH4mBZA892zt4TRYJGZSHvmMvyTgnVU6PwF3faIaDAA7bYwYKpd4+a7/QFOFrxheupx9jcESQqZ77CU+CM/Jjzu7nSnQBdYLH4TvgCV7yx4UnK48ED4OOyHqwNmeGgP0i9xk/6u+yyy4lTnOcJtdauy/bygNbp4HwGH/RL6PIB3cYSpF8KMQrIfYP/1KfPgFe+jTOUILL6uJ19s+e68e+9uubM7kAJnQqdfFfhlpbVzn+1eZtr3vvboKJ6ChyjEMRfOyH9/rwDbgCx4OWnYiRXXiX9w2t+ZrXvGZxJVvsK1koAsJe6g9fUBfPHLdW7ZQ7jQZj8xi3P+qutjR1hhsmBmF9gIL3B/EgSpu2XMB1NMsb7J0riLRc46zVfgkUnhEfRPAxBPvmxWYCjDebd4WXXpgV4YeYfQ1P+I7QKV+q4+Ubgg/vl3cpMHkv53rBXtK/o3BtvJODAWPa+jI+vBEXrbwVlj7uIN7enivrJ1/WwmQY8caxHgosvPROD4bSb5PPCYGFQIDB5mMD3lsjoJ2QwDd04V1O+MtZgX4o2nigLxHCS7hI4SfUGX8E3kLm0G9DCXH6bR74LoNQaAsl1Yc6KGqUD/SHxoRM9vsYeqaAU4o4aihA1mC9Q8oHTyv6NIZ5eN+DouJFdcJ4qP9J8ijYvL7m4IMgYEmh0tapmDJ0LvHG4xvKhpKQL/nmH4qC0y1GECWYLBHaFMlHIRhclBrKsXeZeKopJTzqeJZ3ixhS+h2V7ANeR+lh6OOj8KRfnyHlPURwdgVPbfFKdeGTKxwCCx5pcGfAqofn6Rsu4tE+8MQQtP/wAb4JXVIGV4W9aqfP+SRwdLpFUfYxAfutT7A0H+86LmbP5zOXtq698aED8xLuZ432yNXJI9oVOmfu2tkTRihj2jtK1uGDHGSJ8khwCgw923eyiMLrQyOUWu+TwUtwh/+ck+DqWRvGrb7tq9Bj/Wsbp1jqLCSRzZRchhp8MJ5x4A4nylCf1oFmlDkNcp0kUdo5QNGeUEBrwUs4SOUrn6QfBhbeyNjhWIA3+kJrtdaCL+JZ+nKCRY/AR+glDA28076Br31TD4/Sr/tRiUOHIeHkDozsEwNuVH20AW/wbK8Fa0wAABAASURBVE4otKPNUH0hnUIz8RE8RP/okD4CF+2RuTtJ8k0BuDPUz0rm0fHAHX+Dn/YSrZg3HMXTOLDwlpjXHnvsUfAgjhDlYLLXXntFcSGDOE7wAbDTn/3Fh/Cd2YoDNwxA2eqBs3aTyintxqYpKZw6w837UZDVlecEwH3wAsKDGS8gJoN4HOHLW2xCJAhaP3F1n2kyCPD4+ZABBug0jYeKMuOlXKFUmI2evGOBkHnBeFJ4MilJ9hMTVQexumICroQ2QUg48VBigryQDChfmVTHvdAk73Xw3PHKMBDlKTc+ZsLrTGnCpDFBZRRTQjieKQra8aByIBAmBDJlkyDXpq0f85RPkOkHE/KcKSEwFwTQgt8ko3zzwvIMogsee+8oUSDhN2UR/hF0vshI6XEqwBFB+TAOXHalzFFU3FPw4Sth71mCn8qVoV15kmfKDnqB9+rhxWhCHxQK84TnaIPRocy8Y2x9qKu/oGX35mSuXvzngWX8WRtvLRoyH8mYaI8jjVLofVZ5xsVP9OW51lpqrZ2HVp7xtdeXZ8mazUXyTMEj+Clm1kfpMS9lFF9GtHuJceI6KlFWlNkPhqB7Tj/GIUO8nYcySrnoAfKFsQROFHcGonkIQeVVp2gosx7t+snaKUngjnfiZwxFSpy61qo9o4JiRBEHQ/PFw3zoRT2GKsUKn1SunQSn4CN+DEb4LVwE+1C8fDCD08z+4ON4HmccPARPz8boJ/tj/mBTa+2K4RJDxTvR8ing5uxUhjFpH6wn4GGOnrvGy/jHOih85JUTIHLNCbdTVQYARZrcclpjGmQfuUEhdUpnT+kt6Ek5XHRF7xRRjkx7x+CxHrLJ+0vog/MED0AH9hlO2U/wUA+PACd8gfPDntVau5BS8Ckz/2K8mdvZj1i09Bjl5i1fvwxUYbXko/E5jayVbNdPP6EZ7eTDIVeJwe0KD8DRfZvQgq9KwimnIfiZEztjc17AI/XBRfvY+7Y/8/cMF8EFL3ECCS7gxegBf3PUF9yj0MM3z2jGvRPmOIk3L3QSMFRvKDEifAzD+HQRzgdOFHA0X/ltOyfecBs9GQOtGr+tE/d4KTqFe9aNz9JnnEJp5yTcFxfpJHgGx1G03V5X8LKXZJTIAvgPfxhOZAa85nQPvDBP8LAf7iV0LlLBvYQ/ceRoD8ZwhO7vA0bhLIEfxgYnbSKhITwMjoA5OYXHGB//gQu11mIOUjwr00e/P3nTlnaapgkRgoQZYQLZMUkbRmknbM3VqYoQAicwNlLeDpymYukQn6eI8Ea0GD+CQSwUw1o3C2lEpp5PcVPe1OPV540JBuukVT5GoN9aa9EHDwzGrAwhU+x4rQGg1tp5bxiJxocf3sHhjVOuHwqiOWnPi4NBKlOf5wwD94z5aid0Qt3oC2PBKNTB3DFT5SFk5PtanmN8jN1zpoTAXBCotRahYpwahD+ckoScOP3F92qthYJEGSGslQtfouRQBOG3cQhNZU6/KWTyKMDwnhLmWSL00RIF0Xtc8iI5GaOswnsKNFwO5ZRS4mt1QjiNQ6lwokRp4wHWB6PPaY9yCo28SLzZFF583XwoKJQQShtFlqA1NocdgU2R1A8jDz/AP/RljhRpJ0Ah/BkSTu0psupIxjBXTj7P6JfxoT/9onHGhzLJ3F3BdK7QM5588OAokrQDU3MapZQ5PTEuGUc5wNfslX1g9Dnl8BlrX1rknefV51TUN77kKsEX67U+yj9jFBz0TXmioIA1fmQ+8u2POqFUgoUIiSgXqaBvMDZ/CqJ29gpewkXrVUdbTjI8ULlQMPDC/xkV+KN6/YRX4vucsdYf5ZQnp6nwyJjgwXHLyDSWeoxeZWRH8G75y5nMkQIJpnDL+GSPn8Ow/2DVju/EAKzsn7A/MEKj2ilTF60y5jid5ZNT8FkZecYZCUbK4D/6rbV2v2eoHdiRMcrBgrPBPLXn5ERfysxPnqQfeZRnzxJ6kmdtHBryGIKMA6d+yjjLGdSj4G1cH8hwtR5RA/oREqe9/RyFC3gKvMVj1KXkM3z1B0b6YTDjQ05xPFPsGXzqR7/wg6Mk1q2MgU3Bh1faRcIHOE3UQYPy0UPAB0+B+/LHJQo+3QYugJU9RpPw1lc03bftzdXpq3EZlgxU47Z14r7WzfoO+odD2kh4B0ceww9dKOMg52CJttvzijeALyMaHMzZnoItQ0t5Oz+0Y//Vk+BdrZv1RPXsK34lLBkOoD+RF5wlDHN10AP+g1d5jgS2HB7wV9/0O/KV7GREai85SSZ70J/5cO6rT9eMvqb1OlWGm3htxM8TwnMFmLwhvJA8KrwziNhpDoASEgiZ8u34mwAhPHijlUuUFkSNwBiE8ngpCB3EhHEKI5EveV+BF1EbGy8vkr60UR55eU0IrH4I5AoSAjs2BDgEGTegwGPLiHQ/KlEiKSTCV8mWMLBG1V9IPllDQdPWu1SumRIC0wQBBnU4Pxgl0zS3nEtCYK1CYKoMN1YwQDOseJNYyJ4jOWXjpfWSpzwnJ2KkeVV5UtzzfvBYKZd4NHmqhXwwysQi8zKx9uXzNOpT3+oz7vSnTPIisHxJyB4PMwvec6aEQEIgIbAWIMA7XWvtvhq44PWs4oZOMZxg8Wgz3MBjruUIIeNJdmrF8Jur/nzLeaudDjp14Wmeb/usnxBYbghwrMfpHlxd7vFWS/+1bualDh/iBHG1zD3nOf0Q2GmapogJOPrnvRQuI3xCmKQTNfMU/uEY3NGmZ+EiTsWElxCcQkskBppyiaHmKk+ZY1cCkXHIyBN64Kg/6iE0YTrCWBzD6l97XlXGndPACKuTnykhkBBICKx2CAjFESokxGi1r2Uh8xfGJ/yUg89JWq0nh+2M6k9oG5gJ7yQXRtVbaD4ZKLrDh0L6YacL7XM1tMs5rh4IcFxwcHOye6ds9cx8eWcKLqLB8BR66/KOlr3vaBCYKsONZ8KplpdtvdvhazPieBlvjuFrrd2nd9WzUcJZvMsh5tjzJEncNcNQnLB3mbzALfaboRbtvePAOGToecfOKZx3EMzHV6EI7Kib14RAQiAhsNoh4J2T+fLS1b7mdv7eqfBumHdt3bdlo+7JITADu1rnNvRG9TMq31z0T87VuvT9jxo38xMC84GA9y/RDjydT7tlrrvdu8cX0K93rrb7ZHICawoCU2W4gaxwSeGIfmSP99eXlbyD5oVgXxVUZzEpvgDmhdsQ0ARk6zFlxHmZ2Omb439hNF5EZfD5PH3EdC9mHtk2IZAQSAgkBBICCYGEQEIgIZAQmEYITOecps5wAybvF/hSkJMwX3qR5/0DJ17uhxJvDw/oUFmbF3UYZW1+/96XkXiSfLnSF658bcYJnFO6ft18TggkBBICCYGEQEIgIZAQSAgkBBICywmBqTTcYsHihH3i1rMQRUfObZ78SD5pqzyeXRldfsPBfaQ4WfMBEydo8v2AdP80z3tsknfffDba77I4aWNQarO9Uo6bEEgIJAQSAgmBhEBCICGQEEgI7HgQmCrD7ZWvfGXxO0F+l8HHQLwo/rSnPa370pnfKPIREeGNwiltlRfD/VaEj5fIc+rGAPM7Qn5XxA+V+gkBdSN5t00/Pu3vN8T89o7fD4kQyqjnZM4Ponpm2Pm65M1vfvPuRy7lZRqGgJNJH4Dxsw7DNTJ3CiCwKqfAEeN3V375y18W752uykVMyaTxM783BZ5C0X28aUqmVnw1mGPtRz/60bJPSRSHj04ZDyyMSYaIrjC4n57Bz/xUjedMCYGEwHRAgAwgC9AtnjEds8pZrCQE6Ob4s995W8lxt/dYU2W4+SgIhcLXHn1d0o9EMgD88KAfJxW6yOjymzZ+kNbv7tz+9rcvfgrA7+r4kVD5vjypPuPrpje9aQdjp3G11uJ3R/S7bt268rjHPa484AEPKBe84AWL07VaN//QZddg5o8f3/SO3cxtEb4ZfXnOtC0EfKnzDne4Q/GD2pipGgxwX0Vrk9/i8wntUI7Uy7Q0EPCDvEJ7fUxnaXqcnl58uUyosp/yWGuC2o84c1T5Ehla8QPcaOif//znkm8AYedLiLe85S2LHzE98MADi98jm/9Ay9PCz7Xc6EY3Kne7292WZ4AtvTJWwZysuPGNb1xueMMbdo7DpzzlKSUMNfTkq8W+cNyP3tjSTV7WIAQY8+hQQpNDiiH8IcfwW/U4gjmX1iA4pnJJ//73v8ub3vSmQlf0s07LwSuncuFTOqm//e1vhYz2U17oAd34Mi5n2HJNWTTchg0bCh5tDHMwJpr0epW8tZimynAThvjsZz+7+BCJ31+TfKTkwQ9+cGGQ2QDvpjGgnv/85xflFBCfb1bmtE79l770peXFL35xYQA+5CEP6eo5LXNa51TO7+9op72xEL2fFvDsRE5fUhiJ7i9xiUsUX5p0n2kYAoiIwAMrhrRaRx11VDn44IO3SuBtXw477DBVdtj0pCc9qXMcfOxjH5s3DPyWoN928huEoSwwhP22oc8zv/CFL5x3n9PewNdcd9999+KknVEz7fOdZH6UD/v/8Ic/vDzykY8s9u7gGXp59KMfXRioBOEk/cynDmcKPKHo3P/+9y8iC+bzZd75jLWQuk65KMVOwxbSftI2xmAw41XoCOx9CY7i8cxnPrPrxk/U7LbbbuUnP/lJYUx3mflnaSEwhb2hSXQowQsfTOtPUzQPPquORHfxE0L9evm8eAig0wMOOKBwqkR0lNdmrnSlKxWvydgvkVeLHyl7WAgEOFLp5PQ6ByLoAd2QYxyDb37zmxfS7ZxtvO5E7wlZwbn2rGc9q5OjjHod/OY3vynm8JjHPKY4nZO32tNUGW4IkaC8+tWv3nmCeYMve9nLFp+abQHN+PJ1x+tf//pFCOXZz372rlh7J2rXve51yzWvec1CIDsx04/TvFo3f1JZWKUfjdT+Kle5StH+kpe8ZPGsTdfZlj9CKt0y6KZJuTGnaUtOQJ1s+t0SP2Rrfk5MXXnQjz322CI0lRHNC4MJr8WTIeudJL3mNa8p7373u7sT40nqt3XgpZNlP5MhtCvKnCQLIXGCHHlr5cqRgl7hlPWvhXUJ0SNo0AXeQ9jw4gsRt14CcanXKUQSDL3vS6ChRwbLUo8z7f2RFxs3biyMNs5AkQLC6/H597///cV7zd6x9htqnH4cU9O+ppzf0kBA9Iie4EittXziE58ofaOMQviZz3ym0DFqrYUjgDNEu0xLCwFf9kZ/ZF44Ko1Ab6O/gbvXY+RlWnkIeN3Iq06+CcFAIsM4uuyP15noen7aa7lnJioPzTrg8VNfxsPH3/nOdxa447UAeas9TZXhNm3AdKrB08qjc4UrXKFAhmmb47TMhyEmUQB5qGvdbCTH/BATg5lySkHaddddC2+JNlHH6QPC4hU5/vjju3AlobNR7hhcPmUWg6CAet60aVOhiKrHaBH3TiHWn/1zOtOWCwuLtq48p9ppz3vD429e+iYwCHH5Q+XG4QE0lnL9/OJ0HiFDAAAQAElEQVQXvyjyPcdcPPMGybMO/RL0nrU3lnV5JoTkmZt8bfVjDP071Yy6f//734t65ltr7cLLKJ1Olc1ZWwqHevqWeKeMr29wjDywUh+8TjjhhGKeyiQwUZ+yAh7G9Iwp6k+dSPZMe+X64y019yi379ajzFj2PMrBxNqif/3EPDgFKNFCoNFm9Ldar/bZ+7g8xZS/ww8/vHAooRNhe971FcIX66OwCNlq4druq70ET6GQ6oKhZ/sKvoE7YO8erOGGPYUrEicAetJO0hbtKDMPY6BPe+dZ0rd5qY+25BlDHbiif/fK0Yb66kjmoY15KEcjbbk6kVqaMJ52UeZqDPO1fmvXJxhLaEa55N642uBLwuBrrQVecXwwmJUbr9Za9thjjy5UXjth+SX/7TAQ4PAV7QMfKH7twt/73vcW9OcL1Az7tsy9MvwRXqNZfLDllegPDuPdytWDv9ppL6kP75TDXXQV9IEna4Negz7VRxvqK9cH/uoZ7epfGzStTF9oDk2ro220U473khX6cG8s7c1ZH0Fb2qI39B5z0b7P67Vr+3dvbHBwbyx9qWct+kCH5IB+jWeO6oATfQN9KmNEG0+bTCsHgWOOOaaIUuDwcirq9SUyjPP4yU9+cvcqkr098sgji6s9hMvwBU6aqf2Dh/bVfkeecjgrX7L3+lA+lDjaRNwx1LxWQQ4G7sB19KgffcQcWpw56aSTulM5OGmeQ2NMQ14abmN2ASLut99+5YEPfGBxijSm6g5fRFEi3HjxL3CBC4yFByFHCVfJvStlS+jkPe95zy5mneLKwHvJS17SfaxAHZ4biizvjWP529zmNsWzUK9XvepV3QcrGA3CZXnQvXPgPRkE7F0VffhdPiFot771rbt3Wm51q1uVRz3qUUX4mHJKJu8R5mMOxhBCi9CVE1pPfOITS5Q7rXBqESdAGE148BlQQtHUMR9MDdOQzEt/klhwY7liFh/84AfLgx70oBJz1F4/+rO+5z73uQVj0pag05dyzM+a9SWEx3545/AWt7hFsW71Jb9JKCYcXKyHoQR+3v00T/l3vetdu5Bl+6KNd4GcmgqDwIzBxTjqmUsIWQLe3shX7v2D/fffv/CW6geTdEro3VLrMt697nWv4jTW/MWlCxn0/pXxzFP/4FJrLU5yKdeYvr0qq/gfmBF6lgCfCDv3Qwl+CTsRDh5w9U4vnIk9origByd4wlTA0PN+MzzM/hlP2Ste8YrinlDTF0FHCSLIhIyDOdhLHABwCY6YlzHsm331LDHG4KB876/Kg6PePRA+5mdd7LO54KVf+tKXOlq13wwh6zJXeIdG0Lk+IsEZ3lo0gV7N2XvPcJGAjXrm5MQMf1AP7qMPCvZ973vfjq+YB3xEg9EurmicEY3GOOk4mZS5egYjfE5eph0DAoz4vfbaq/somVM3BoaVwwV4xai76EUvWmrd2lGJN3kdAx+E13AWvaIP+KWP4HXwVLl66M37OXg3GhWR4R1MtKgc36S8ak8eyEMz6svDF8lQ/eGz8rya4Nm7o3iBvpyIMMT0jybRDvo0F/QTyrM5akPGef3Eu57a41deMxECZ376J2fQe/AKhl30jzdEOzwm+jfH2972tkVosnHd68s8wAqcObSswVoYrsaz7qB9jmJl9obB5z7TykEgHBqcHByPoduZwfnPf/4iSgavZzRxXsBf+GaPI+KKfKe32Xt4Wmb+kStoiF4iX4J3hxxyyKxOOFNtm/9kgP4Zk953C9whJ4Vw6sfpLFlKToRuqCP4SO9SxpCUN40pDbcxuwLhKEAMCMrimKrLV7RKeiYwCA8e7HGw4tF3hI6AHWsTepaIwWPehAlFDnOvtRbhhIwR3hLEbYwPf/jDhfJHwSdICAHKKCUQg+DJVFe4mfftGBqMSWMzuhgxDA8MQhjt0UcfXSia+qYgExhOBBlsFFKCllJnnowSRohwXu2FWhFuBx98cOdNase3Dh++Mc9ddtmlM54IP4qg99v0J1Em9eVq/ZiZ8GDCktK8zz77FILWOghDyoAQYW0xRn0JT/BMyY1rrbU4KWZMegdAPjh6R4xi4SRZOIH9YCATkpginFf30EMPLWDhXr/WhuERjtbEYHb/6le/umCy4C5clgGOiVsTRYNAtZ/e1yLUCXv7R4FmJMMBXjHwdwKFeRLyDAofCOJxpsSYB9xyOuWeAHBdrQm8Yg1XvvKVRy6DIWOP4b0QcDhBgMF3+8b4Azs4r08wBHM4QWHiJfeOjnJKGNxHp2c84xmLPSKk7K/63n3jKYXn3ldwGo0G9WmC8FOK/Wjz4Id+5BkLPqFrzhwK3WUuc5nOQBcOCif1w/DkqICH8JvHNGCiH4lTxfrhD9w3tz333LNQXOGIOapnToxPOEqZPeiggwqaNx76gYuUQ8o2vNUmErwn8CmJ5sWQDDyjGIKXtcHfaJPXHQMCaI6DEY+Ci1aNR3sWCgaf5LWJ7BA65ieEvGsPv8gpyinHmbromvOEo0R9CirHFxlBPjJS4CNcRZv6QbNOtbRHQ/g5vPQskQ/xHFe0qOyII44ol770pYu5oDNOCo4c9OYdWzSKDslbY2mjf7yFjMN/GJacJuaG75MRDFvyz4k1eUG+aos/6R//4Sx1j5acvJD16kT/ZJD5cM4wAMkTcgQ9cwChXfXJLA4bdL9+/XpZxZzd4Dchpz1nWhkI2Dcjkc0cq+7bRAfyDNfRAH0Kr4ZX5JUy97F3ga9O2uAd+iOn6CV0RvgT8kjbftJ3yCKvOHFMqIN28Hh9idyhGxmTPqccvXAgmx+9Sgiu/GlMabhN466swjkRIohF2NEQwvNOei/RB2gIKQRLQDGseEKUIzgKKYOAcGC4EEQEnToBFoogw4FnhBeT4KCgURKjjqu5MJ4oqowlpw5OmLzQTAB5p1F7iiplj1JGCTSP85znPN07j4wLfWhPwBFeDA2nRNoz7CjdhJPTCONGIiQZigQzD5B8JwD6Z/h5lggzfTF2MBfjEXIcBjyLPFNgygOE8QkNYQhqK1QEczIHz22qtRZl8szbldAHA0yKcWQujFX7R2iCO/gbEwNl1FJktZUIzoc+9KEFTO3BWc961i4s1P7YUwzVyZG9syZw3nvvvQtDEfyNZ6/gio+NEMoMbMJaHoMDE999990Lo9oYmK09N76TD/vqPjyu7ldjsl4wM3f76DqUGN1wz7u9FCA44ZSY5xC8nATYv2gLVxk66MNeEkKMX0oQRRNseUVFFNgjzxRRQgve2zNjuDL0KESvfe1ro/uJr9YnUsEe6o/yZW8JYzhlzkJe5VEcA6fwiHYQhrz5e9+ZEEZL6Ars9EXJjfrwSzk+wnsPZsYxF95gHlYn4tYdbVyd6OEB2qNt8JUvwT1KKXpox1KWae1DwHv3eD5a4wCwYg4HfIgDgZNNXiRGB2cBXs6xxwlABsBJMuaLX/xid+IM3xknZBBexwlJocRLQ07g15wGnMh4JJmJDmKs+VzPcpazdNFDeMKuu+5a0B58xifIGrwYvavno2KU2ugfvZqnehwitdaCprThJJHPmaie9WtHdqBb61Ru/uABXuRE2z8a5bghd9C49mDFaGWY0R3kUdzxh+td73oFfOUF76RwkzPyMq0cBMgHozmdjr3wHImscU/nwF/dT5LsOccAfQO/hkPoQFtOPNe5EtmnH/XQEdrRFxl5tatdTXb3nQE3nBFkJLmCZtG3/GlMabhN466s4jlRrCk6/SUgakqhKy8bxsvjQSEiPBhM2jiZ4aGRKP/yCDICwb3Ew8ljqC1GwaspnwfTNRKBxOBRr9ZaGCiEDSNmr7326r4SStjEUTlDgEHEc8TryMDjneH5oVx+9rOf7V5A53X1ARweP8pghApQrmNsV4Yb5dj44b03vqR8KBnHPHiXGGMELKFPAKo/rq3yNtVaC0UAvAhT62T4YU48yIw0XjAhDNqBN7hLTibkEYYUFvcSeFtLrbVYGw+bOUnCzzA/wtPpIfgwip3CaetUFm44/TQ+RszwpUAw0GuthRELR3hwCXoeXMqL/vXRplbwt/mr8X6UQLNGcGM0wEeGcq21+21L+GGt9hBNuZfsN6UG3tkvST7cch1KTkDBGC3Zn1prt78ML+2dBJRShpqOzUN/6L7WWijAKhvH1ZrhCiFqTPM1vqRcsvfCE60P/cIPNIF+lYOP5F4yHuFOwdMfWia8KQ8UY44YdBzKhjaSORHUnASMNs4H+W0CP3ja5uX92ocAPMKD4SDnBgOfswSukWG1bh0miQ9SUhkejB34iqc6KQYtvBi+wX2GGEVSHYpknFbBNbwSDyX/OGmcgHOw9HFXn5MkdEG+WU+ttYvi0M7pFaehOeDHeLh8cs81kjroiPNOHv6PbvFrfeLp8p1EWh856dkarV//ThU5IOW3/ddaC6eSftRTrg9wcD9JAnM8ZZK6WWfpIQAv6Qv9ntGNPE40ssT9JMlecujj2+gA/nnvW9v54IX6/QTPGHEcMujLqRt85ICH1yJ9+m2m6XmnaZpMzmX1QwBBYaD9lQj3cyImBIMixaMnll89BBrETXgRHm3i/eClU3cxiYKrPSHC69iOwctOsTS+sBSGGSElfMRJFIKmRGpPseRFbNu7d6SvfDGJgcbzKJwQs9IvoU2IL6RfRhaBqK2Ybx5MhhqDykkLuGO4ynl9jdcm65wU9gEfzNmetf24dwJC6HsfT8gbTxhFhcHoFBa87Yty+8BgYcA6XWmNR3OVOAlcV2siPGJfGU5D60BLjLahMobGUP5C8lrjp21PGWvh7F5q6yz0PtZlHWAx1I86kjIKHTxqEzySr3woMQr9nIxwUKf7wkoZZ0462vq8sdbKEdHmt/e11u5dp5L/FgmB1dec/OIMEf5HbuGbHCROg/qrYdwzOiiqQzwVP4fvwiYZNfiekyiONE6O6I9z0umWkzpRFpRLp1FOq/SPbqLuQq4iH7RjfLY05Z7BiXaUzzeR59rM1T/4qbdUCUylpeov+5kMAhwYatKvOCPct4mj2DP64XjkVPM8Ljn1YrQJ8RchJFICXtJbxrWbTxk9VH9wxisCojfM34nuQnF/PuMvpm4abouBXrbdBgKMAMi/TcGWDEKCN4OwcgzOK8m7HcTsBEsMe5so/KHg6oYSH54dQjI8eENCVP1IPDbuGUQEYDuGEDRKG++hkx6hZt4l0gZBU6y1054hQ7C17d1TDJVPmjCOfl3eWOE45uL9L/0KH2EMtXWVt8+j7p04MNx4kTBCidLBy6sNuFuPe6ePxmsTQwpMlM+VGILqYIQ8q20/7u27coYz77H1MZhrrcX7F8qE6WjLwGewmZ8TOSeFytsU+9HmraZ7xgJ8N2dKWSg6niMRdrE/PILtyXOcMKMfRke0WciV51w7IUthxHHCGNMzZ4VydGhO7iMx2AnaeJ70SrEFAx5+Ql+7dnzP1gaH3YMDPGqTDxXMJWR5VYUfwzfeW3PtG25OmNF7G8JszDYxoRsJjgAAEABJREFUWCnTbV7e7xgQ4HDioSd7hADCIfwMDvchAEdqrd2JtciDFl/d47O11oIXoyWOKvlkUmvMkJGiNsgmDkS8GK1phzaDZtvx0SoZ3OaNug+62XPPPYsPOJhDJB8UCrob1X5cfq21+zkmdbzbGv3GVfgb2lY+SRqSlaXXkJwiP3rZ+bjMEPAqhCFEZYgWch+Jk9gpNZ1AtAScXb9+fRTPXtFTRF3J5Kzj1CUTOYEZcELuRRApn08K2dVvA/9EbsAbY3l3zjyE9ffrTtvzTtM2oZzP6oQAokScTggIj1GrQCQ8GgQhZY2ng8ecclVrLUIU/aaVfoQeUt6dxrT9OTXyMREGorqMKuU8ma6jEgagLIiU15IB6HTLV+cIQ8qbDz4wdMyLkSOf4SS807x5kBzZYzSEpNBLfetP/5OmEJwUVydt3mswrrEYtI7wlVEyXdt+ea48U+QpE8LJPPcT4e9dMl4xQl9YJ2XBiZa6lIwwuLz7g8nK1x9hDs6eJ0kUa+F7xvHFMftkH+2PsEhwF0bkfSZhNMJ2CFrwNSfMmkFnjuDM+4yRMt7CMAmYmM+kxqu605jsNcWO8sdYdxLkZNdcvS8pXJTRytkBJ9CD0FE4x9Bj7IKNECjl2i00hSHvlFPf8Moe+cACGnAaoG9efmO6994bRwN6QQvy5pP0xamABtE4XBFWSzGOfhh2QkTBiFLgYw2EOTwwPlwaMuqjvQ/xUHzhM3wjrNFpq5QyFq2Bcef9o2gbVzCBm2ipbRfled0xIID/WylccPUzAK79xMBjgJFvohycGMA572oz0oQ7aoMnw2X82zN8Dv7r2UcfGHNCD8lHThP5aL3WWjzjIeQRfuGVA18HJhfVmyuFwm2O3vFUnxOUDOQ887yYFO9Xt/2TY74U6MuX8+kb7apPt7A+vNC9PLTtileAiftMKwcBH8BiHMFjJ8j4N3zn8GZw0QPIDCfHeD4d0D6pTyfgfKd7tLyXLolXW4W91s4HbMhDefNJoWcZh6wgY9GmPsiW0D3QkXWQt8qmOaXhNs27s4rmhjgQGyWMERJTl+e+1pPfA0AYYSwweggvSivPHGKm5Ds98/EMn3NF6PqIRHA5bWAAUvQpVEKmeE/UqXXzWDG2PMmxOE8fpsEgYZjx4Pg4RtSlpFJWzUUoHyHBIPTMeKDc8f4x3JR7B0golnUbQ6p12/Gj/1rr7KejKe2YEwWcIUV4MlaEXGJUGKI1UqAJJX1HP7ykwiCdiHhBnALbltdaPXZJCKh+PTCSrDmMHgoxeIOdNQiN5BHjdfIxEQaUdjFurXV2/pFf6+Y88PEBCnsrJE14kbHtDaOUYWevCFp5Tt4Y5mDg3aNaa/dlToq2PoS3UUycgFqr8SjsmC5BAAbyVmsCU157p45wioFLmQJ/HwYhAK3fZ7rhILwlCMEGzhE4Qkj0UWvtfoeszPyrtc783fy/1jq7X8aTW2t1ma3vwcmyU2Z75AutaAXcwRqeCuVSj4OGoQhveM/NFw6iV+UxRv+qrNbajVnr5vHRsT22t4xFuOIUnpIK/+FKmfmHDoQug4U5MvbwEKFmM8XdyYarMWuts+uVByeFt8Fr/VMaOQuE5yqXKJNw0lpb3qVMYpwyKs3J3sjLtLYhAJesEA7GPQcYxU4+hS9wIcpr3Yx72lBE4TGaRrtkjdNeuOjkVh9wEg1z0CjnQETjyvRZay2MPfhvLHTpxMK727XWwjgUikk2eAcPH+BwYRBGH0NXeZKTP/RnvhyXxiCHOEfIc3ViHnHvKkW+q2cp7mN9DFvRMrXW4mRc/060GajRf61V044vdDdb/phTrXU2X9/ma33gSDdwwlNm/lHEZy7dx0pifzxnWhkIwDcGG7nkhE1II3z2kSjGGCMOjnLIejdUKC3+S+5zStNHlNGxaq3dnqsPF+GMd0rJJ6+w0M/gBnwoM/9ca61bhbBHnmuZ+Vdr7X4Kxjyc2qET+shMUTFPzmb3ysm21eCcS8PNjk1VWp2TQbSEEC8Yz3isgmJHCcXEg5DUYyzw6sl3qkIhJJic8lDitaGc+roi5TT6c6WAIWLeSJ479xiB/ilXMSbhqn6b9EuoauteyCNl0LjaC0v0XpV85Qw1CmoYPgwaCqN3D5Srp60Tg1prMT6DRVmEwRkffOQ5mQvGIPTFWBR3/TDYMEHv1DEw1SdQfTrZ+jw7IdMfGDglUC6fYl1rLZifZ3NQTzKePAnMCfkwyJQL8RSKA47moZ71WSflQh0GqvwNGzYUDFceeFEizJ8hWGstYE45Znyprwyz9JEXigxG7OMP1qjc/I0DLpRs62aE2ldz9bEadSj58IQXj+cMUzeH1Z6si1IDZsKTwETikID7DGpGqk94C/VTVznPPbzVhrEFDnBCGWVOG3mMFHsmn3EoT/jVxo0bC+PZcyQeUd72oA17wFCEozGG/uyd/bU/aNTHDXxZzhfpODP0h66Vu3qWGHvwwemdewKYUWqP5UtwnTGvLfrQjuNGKKP1cwxYC0ePPHga+IhvUOoot9pJ8BZem5922oM1ZVq5hKas1fzhr7w2wTnKBr6lv7Ys79cmBBhp8IXsiegGDgb4Ix+NxMrxPvSEfoLG8GF0gU7gtTb4mg+UeF9NW20YbVGOnj2ry6lFqRQybix5rnjl3nvv3TknyAr4jwcoxzOUu3rmfDEOx4tnTkDPkazLnLxTjXbUMQY6w4fVI/fQq7LWKFKfDMfT1ZPIHPXwH8/mh/ejU/WV6R+NR//kon6UaxPJMzmFp8kja/ChkIP4H+NP9IGPbtW6+cNW7Ry1y7QyEHDK7LQX7w58JrfJLTTklNrPysABxh3ZQ7bQN+wl/QMe4sOM+1prIePlqUMewFWywnPgGKNPX3AoVsoZgZej4chDV2jF+MZDW1FGBrnnGOQMJm88T3NKw22ad2cVzY3yxPCg4AiHEy5n+sJLGDYMHsxXnkSho1AhbsqgPMYEQwFT1wYDIGwoecrbxOuOABkBBEsoljw1vHHahyelbcewwhAIYHUIPYRLeVPPlRGBSSgnjAlsZRJvIqGMwShXj1cxGAHit1ZllG5tJAqfPO0ogPIkYxFu+lFHHk8qxqM+ZkNQU9jBizBURzIPirN6hLM8xqLnG9zgBh5nk1A4+QxR9+A0WzhzQwiCo3moZ56UWPCaKS6MO/m8aYwNeRRv8zR/Aj7yCFtCV31llOlQVoxLucd8lZs/Lxe41Vq73+TBiO1rzJXhqW+GGw+2e0qC61pIYEJgUOTARCJgCB5wsUaGGI8zfFB+4IEHFsZHi0ucH8rQkPraUc7skfzAY/C3z2hPnUj200lW0IY94DVFl1HHVT/2Fz7aR6dtaMhewxl10KBy+Z4leEORhVtxskBIwm/52ttXtE35pLxpJ6mHZ1D8rMX6CW9Ks3IJLSvnHPAswV90aK3aKUcjnATKJfMCb+3hubxIogGc7lM8tGvpL+rkde1BAB3AFzwxcBWecCbIJ5di1egDrcCt1mlAJqATeK0NvsZpgt611a9T7SgnNzyrqx+4a5ygR/KOghzt9aFe0DcchvvkkT4Cl+MZ/9CmTeQmPhIy11jqmZt6ZJFx9dfivvp4UYyhrn7Uowd4lob6R+Mh08kFtC6pHyn6D5koX7hk0LE50TmE0QkRrfXkn75RN9PKQqDWWugA9BC8HB7AD05DtMEhCD/QkJnRo8gWcggvp6txAqjrREwdsg/+q0OWMM7oLvi0tuqELOPI8yyhAzSF73uW6JiMP/MyHrkoXxIx4grX4SM56HmaUxpu07w7q2xuCMOUnbgJP3KfKSGwVBAQny5sglebEr1U/WY/oyGwo5dQDIVRCqPhZNnR4ZHrTwhMEwT8npf38hhx8U7dNM0v51IKxytngNNgoZLCHqcFLl4P8V0F86FXOOl2P+0pDbdp36FVND+hGY6peeucvC311Hn8ePh4+ONEYanHyP6mFwJCYoTW8CDz7k3vTHNmawUCQr95iYUE8wqvlXWt8DpyuITAkkNAmKT3Up0yOoVxurfkg2SHSwYBJ2h0wzbyask6X2BHPqrFsHSS7XTd/QK7WtFmabitKLjX/mA+9OHLd2KPl3q1jtJ9ClnMfBsitdTjZH/TCQGhRPZfOI/QuemcZc5qLUHA+xY+E+19w7W0rlxLQmD1QWDrGQslFTJJJnilYevSfEoIzA0BocY+xuPUTSjn3C2mo0YabtOxDzmLhEBCICGQEEgIJAQSAgmBhEBCYLkgsAb6ndNwEzuc6Uzdl/R2JDj4PCv83pHWnGvd8fB8Kfccvfis/VL2mX0lTiYOLB4H/FafUOuE5eJhmTBc2zD0kxV+dif3eXr2mW7RpjkNN18qy7RbWUYYTGXf8dWnHW3dud4dD9eXas8xVjH8S9Vf9pO4mDiwNDjgvRrv2CQ8lwaeCce1C0dfLUUvucfTs8d0izbNabgde+yxJdOOBwPvdkCU3PvVsvc5z+2Nq+jF17O29zxy/KSFxIGtccA7sT6elXDZGi4Jj4RHHwf8nI+fmenn5/P2wxW6RZvmNNzaynmfEEgIJAQSAmsYArm0hEBCICGQEEgIJASmFgJpuE3t1uTEEgIJgYRAQiAhsPogkDNOCCQEEgIJgeWBQBpuywPX7DUhkBBICCQEEgIJgYRAQmBhEMhWCYGEwAAE0nAbAEpmJQQSAgmBhEBCICGQEEgIJAQSAqsZAmtv7mm4rb09zRUlBBICCYGEQEIgIZAQSAgkBBICawwCabhthw3NIRMCCYGEQEIgIZAQSAgkBBICCYGEwHwgkIbbfKCVdRMC0wOBnMkOAoF//OMf5R3veEd51KMeVV784heXX//610u+8r/+9a/l1a9+dTfGW9/61vL3v/99ycfIDhMCCYGEQEIgIZAQWBwEps5wozx87Wtfm13VUUcdVT75yU/OPi/nzfe///3ylre8pbziFa8or3vd68oXvvCF5Rxu3n3//Oc/L69//evLT37yk3m3zQYJgYTA6oQA/vfEJz6xvPGNbywvfOELC8OKMbc0q9ncyxFHHFGe8IQndGMY63Of+9zmgvybEEgIJAQSAgmBhMDUQGDJDLc//OEPndC/3/3uV+51r3uVZz3rWeWHP/zhvBf6jGc8o3z+85+fbXf44YeX973vfbPPy3Xzmc98pjz0oQ/tvM5f/epXy2GHHVYe9rCHlfe///0TDWn9jCrrv/e9712e85znlBNOOGGitpNW2rRpU3n+85+/ILhOOkbWSwgkBKYHAr/97W+707YTTzyxm5STsLe//e1FfpexBH84rBiF0dWf/vSnzkCM54Ve//jHP5bHPe5xpXXELbQv7b7yla90PPr444/3WP7zn/90Tqw///nP3fM0/gFLsuC5z33u2OmROQcddFAnOzkP//Wvf42tv1yF9uwHP/hBB9t5jbGl8u9+97vy1Kc+tbztbW/bkpOXhEBCICGQEFhKCCyJ4UY4PelJTyoHH3xwOe1pTwMwHLMAABAASURBVFvOe97zlve85z3lbne721LOdVn74nGmwBw2Y7ARsq961asKY+wFL3jBnOMSdhSUQw45pJzudKcr5z73uTtla+PGjXO2zQoJgYRAQmAUBL75zW92EQf//e9/Z6twiOFPsxmLvHn3u9+9jZPpy1/+csELF9P1kUceWb7+9a93PLHfzwc/+MFy8YtfvHBy/f73v+8XDz6fMOMIO/roo2dDRX/1q1+V613vep2z7Z///Odgm+2dKQT1S1/6Uvn0pz89cirWsf/++5fPfvaznewgTxilIxsssOCKV7xiB3Nwl252s5t1p7cBO06Bl7/85eXGN75xIdMXMow+jj322EKWLqR9ttmxIZCrTwgkBOaGwJIYbt/61re6sMIDDjigPPOZzyyPf/zjy5ve9KZymctcZu4ZTEEN74z85je/Kbvssks5+9nPXk51qlOVs53tbOUUpzhFd1/m+MejTFg95CEPKU4MD54xYBmCl7jEJeZomcUJgYRAQmAzBJyyMGJ++ctfFqfr+Mrhhx++jRL9v//9r+OvjB9G3M9+9rOCf/3tb3/b3NGYvxRrp3W/+MUvupP7D3/4w0U/xm6bGYMSL0zTGObkNGXSEE3GgJPBy172suVCF7pQ23Ux1mtf+9qOvzo9+973vrdV+agHc2rL8Ok999yznP/85y877bQkoqwwlG94wxtOHGnRzmfcfX/ubV1jOjUkPzn/7nnPe5add965rbIk9yeeeGK51rWuVR772Md26RznOEcnr7w/yVAk78jAvfbaq5zylKecc0yntLe+9a3Lj370oznrZoWEQEIgIbBCEFjzwyyJtKMwUAgYPQExp05OruL5uOOOK0NeYoqJMMWoN9eVAPSulxO9V77yleU1r3lNYTQZX1ueTWV9j+ExxxxTDj30UFW2SWc5y1nK+vXrixAR4TgEnL4pH7e85S23qd/PsH4KzTnPec7ZIqeOL3rRi2af9QUGb3jDG7p36I466qhiHbMVZm6MS5Hynp91ffzjHy/6nSka+f9DH/pQ4TGP9Tol/MhHPtJ5ob2rR/GiFIzsIAsSAgmB7Q4B/EuoNaX6QQ96ULn73e9e7nSnO5WPfexjg3PDc+573/uW/fbbr9z//vcvj3jEI7rw7O9+97uD9WXiDXjCIx/5yHLggQeWjRs3FsbCt7/9bcXbJMadkyBjmJOPoxx22GHFCdE2lXsZTuwo9Je73OV6JaXjs4w1Sj/+pi5jbpuKc2RwrnEQ4tH/93//N0ft+RXbj/m1WHhtMCDXLnjBCy68kwlaMswY0re//e2LJEqGnCJ/ySfG2u1ud7tij09/+tNP0GMp//73vxccVjnRAFkpIZAQSAgkBLaCwE5bPS3w4UxnOlM54xnPWBglvMRD3TConv70p29T5EX49773vdvkj8oQLkO5eelLX1oYQt5Bo7R84AMf6AQID+7Tnva08p3vfGe2CydqDLF3vetds3ntDaF/m9vcppzhDGcohxxySHdi+M53vrN4X+1Wt7pVW3Xwft26dYWgY4R+4xvf2KbOSSed1H0NzokcA5Kh6V01zzzlGggtoUQxdiky1vPoRz+68HorH0oUPcKXgkToUrQoZAxGfTDaKGmUm5VURIbmmnkJgYTAaAh4r9d7wXihkDlOpHDGjGrllAQ/5Gz66Ec/2jnG8OBR9fGUl7zkJYVzyHg//vGPi1C+UfXlnzTDu4xhTk7m8C1tlY1Ln/jEJ7pohf5pmzY+fELhv+td71ouf/nLd+80G0dZm/DgBz7wgYXxKIKhLXMPPnjkpz71KY/dyaRnsqbL2PLHh6YYt1seCweZ0yKGr9MtPJRzSzvREj7+xMkn1N96lWmLh775zW/uDGVlois2bdqkaDb99Kc/7Qzoe9zjHt070mTUbGHvhsGGN/tSqL69G+b9cKGVqjLOlZu78cg9RlYYudpbh7mCt/WQsSFT9DEukRlnPvOZO+Mr6nEUGMfY8sgR736TLWS1MYS/PuABDyjkndNYMDc/c9MmErz0CoEycuwvf/lLFOU1IZAQSAgkBBYIgSUx3ISrbJzx3vKi8hJj1pSCSeZEgFNAJqmrzste9rIinIiSw8jhQRbaQYgQDNe+9rULJcDL3tEvw4byse++++piMO22227lUpe6VPdOBq+1F8oJUQYZ4Xjb29626H+oMeXD+oWMWj/hL3wp6lKOPD/5yU8ujCqJQkIoUhbUMz8nlk7brIuycdGLXrQ4mdNWnTYRsAT97rvv3nnOT3Oa03TvfjBC9a8PJ5xXucpVipM7xmvbPu9XHwRyxmsXAowFfGsxK/QenFN8RsBQP/jgqLKh+kN5eJkTt+CtQ3XkMaaEMl7kIhfxOJuc+lHwr3CFK3Qhjq6+3tvnrfj8wx/+8C4E1MkaXoenzXY0c+OUiBHJqJh5LP1neRJZwCB2z9jx3jIHnfeRhVniwWDv/WynXsIUL3CBC5SLXexiReggA0db83nKU57ShXgq5zhjtISsszayj7zQzrtqnIqjnJm11i4k3xychjFyvXu2bsYRGONxQuLp5sJ45pgTUWGv7QFZRVZ4V5BBxeA3D+3nStbMwUnOxBoZnmBKLmsvrJasIQsZzxyM9l+b853vfIXcMW/z48DVRmJIMjjBQN/kKaNSWaaEQEIgIZAQWDgEdlp405Nber/gzne+c3ne855XMHRfgfTsPQaC8uSai7vjBRQGSbgJb2TA6Z9yQAC5Xzcj9EIZoKQQcE7hlHl3YWgGjBpeRn3ss88+nQFECApf0Y4wcz/UVp71Cyeyfgag0MU73vGO3Vc2KRMMMoamsCHC0rzlaRcKC4NTHSeXhB3ha50EqHUYRyKsKRoE+tWudrXy7Gc/uzvtVHbpS1+6OFk861nP2nmV9bF+/fou3FI/6mRKCCQEpg8C17nOdcoee+yxqHe1znOe8xRhgwyPoRXe/OY3L+vXry+11qHiOfPwKzzsyle+cme8jGqA7+BxlPlatx6L8ci4uP71r9/1wVkmxJyxE/1xYgkVBw/OJydi3sM69alPHVXmfQ3+7bSNseMDHN7HZmiJ2jjXuc7VvZN9l7vcpXvP2fycNG3YsKF738z4DBhOOadwrk7fOCsZOvpnkDJCyRIGnZ9uOPDAA7t1loF/DNvrXve6xUdCGDecg+qTId4PZKCJ+nCyZy4MJ4alU1UGdHTp/UOnlyIsODLJwyhrr07qyE9RGBJcYWD6wAvDsa3bvyfHtBWaD1cZZWSWVyKcqJlf+043BwJ5BlZO2+ANY87+9/vO54RAQiAhkBCYHAJLYrjFcAQAoeL3gBgmvKSMjChf7JUBRmDxPAozjCSchOCstXZGzFWvetUujJJnUDiN+k7FhhQaAsmniwlc86UkUEx4MYUUMZoYbrvuumvh+Ry3BoYhLyxvLgMSHAhzbRiFwjspCubNe8woVCaZh1AmygShqg4DWFmbCF8CmmDkZRXe2ZYLmdIHT68+KBxted4nBBIC0wcBhgPHzzWvec3OUJjvDDlo8B08aFRbhhSDg7E0l6I+1IfIisc85jGdgTlUHnlC9fApJ1qR58rp5H06vBqPlee0hoMK78Nr5eHznHTet3IKJo/BIvzQ/WKS/hiKTsGEfprrJP0Js2c44rdC3SUGkw98METxb7ye0cTgZOSGYSYqYpIx2joiLTgnGXX6USaskdH0xS9+casfSGcwMfDUmSuRgdYgicawfkZywH5Ue7KMDDy5fPzdjW50oxJ7rOY1rnGNbs5ksudMCYGEQEIgIbAwCCyp4WYKhMwNbnCD7vdoGDoMCflLkQhDffqssROuSAwUoTVCNSgkl7zkJbt3zpx8MdyEMDpJI7T683AC5j0EJ4W+gslrzXtIoSCshC4S0gw/a+u37z8bg9ASWlJrLUcffXT3ERKG2lvf+tbunQ7zJpB5WaO9cMaDDz64UAZ4g9XhpYzyuFIewJen06mb+UcZxYHyJrSSp1gfV7rSlaI4rwmBhMAUQwDP8UVepzD42KRT1c77R9qNa4N/cmqpy8AYV7dfhhcJdcMj+2WTPjPGvOPLoMMXvcPmKhrAKVu82yX0keEnHLHtexL+29Yfumew+PCLUy2nYk648GWG11D9yBMi6LROuDt5I+HX3vHCx2ut3UdX9C9FO/soxfOkV+NZL4M+2ujHx0TIGMZv5JMFyuJ51NU8GX4+MiORb97hZjSTP6Payb/whS/sMnESOdKvLPqldVb2y/N5B4RALjkhkBCYNwSW3HAzAwpCGFGe20RAx7OQmrif5MpTTMgT/D5r3CaeaoJOPzzLlBkCWXgLL6s881LeJgJQn1FWa+3ea/Aug3cPvC/GQ0txiTpt+6F79Zy41Vq7sCQfLOElJeyFkJq3cM5W2FJiGG3evxDSpI419PuvtRYhkV4S9+4GD3jU8TWwWms56KCDyk1ucpPu08/zVdCir+W6etFfGBC4ClFdjnGckHrn0RjCTpdjDCeePirgZNa+LccY2eeOBYFaaxfKyOmCf0y6eo4czqxJ6lPe8VFheZPUjzo+EDJf5T3axpWTybtvPn8vnBCfkkQ2OI3Dr9XF3/FQ98uRnPAJYxQdwsgSBcF4GTcWY4mBJAoikjacej4qRfbMZ8/GjaWMTHLtJwYmo7afv5Bn83UqJjrGCeRC+tjR23CSeiVkOWUNeiFrhMqKONrRYZ7rTwj0IbCjPS/acCNIMBUCmBATNuKKmTkdc/oEqLynlAZKO8XaKRdlgEBUPkkizH3G2MkSYc8zK/nIx1ve8pbCo6sfL0wz5LxY7R0Jp2nGV9ZPPj7iE8kMKyGT5kYp37RpU2d0CVehmDu9U9ZvL8TEadrDHvaw4ita1q8vRpR5CB81hnveWX15x0III9hFf95Lsz4CVB0hpvFCfdSJa6213OIWtyh3uMMduhM9DJ0wp1jwaDKOhaR494KSEe2m4epk9MEPfnDx/gPl0YnoUs7Lngt3dXJhDCGjSy3shBf5PLqQVF53+79cBuJSwib7Wh0QwNPwlUlnywE2StEf1QceNKpsKB9PGsofyvPRCpEBfceM8D8nSEceeWRxohQJX/SBKTwP38XHGEKMPAadMfB275O5H5c4w/A/vJVTDh/EE/ptyCJywQdHGHKcYG0d/LR9ZhjbE/wdL5fILmPEHDnayA7KvLZOmIy/EN6w9957dx/Z8q61viR8jIwRnmn+8haTzN0pItjqcyF9Wbt1LqTtam/jPU4nsAx/skakjj1aynWJFnIqHLKGPtPHzaUcL/tKCCwEAqG39tvS7zge/AaoMt90wP/xes+TJJEYePS06bKTzH256izacCMoeWIJS6dAXlT2qWBGDqEoNMfkfUiDZ5iRwUMpzE8YH2HHwFNnkqR/bbywzVjELDE1Ar9tf/WrX7173w3CON3iWW3L456CcNOb3rQ45XJ64qVr/Qon8k6BeXph3YvZQmuiXVwJUKdaBLp3zszvgAMO6L6G5jSMR9Npn9BIRisX2ouJAAAQAElEQVSFn7eZUsNQi378HIG61iM5efPieJQPXdVjdArnpNT4IIq5ECKMI+/sMQiH2m6PPO/5eR8xxrZnDPx4Xoqr9xkpfNEXI9o7gfG82CsFmafdezj6orgI98KcPC8m+UCD+S+mj+Vs610YHycwBvz1GXJfQ/ScaekgwEjigJm0R7yWUTFpffXwK9dJU+D7JPVFODC+0HcYLeZH8OLdQ+9KeTeP0uunArwj5r0tzi3GHiHv/b+5wvkYUt4nE54PT4VlasdADF7LiEO/eELwBoZv8FoGmaQP5RyM5o631lqL95T1TZngPNR/GFfCEO2bkzx0bBwfC2HMTQK3tg4HJYOIU9S6jSeKAG8zF6eSbf1J7vWHXvUlWSP+K6xRhMckfbR1yEd8wP5wpu5oHx6Bqz64EjDhsIZz8bzYK4Oa85ZupS94iIbQlefFJHLYvs2nDw4lMgoNaUcWckzAec+TJlE3eNak9eeqBwfR2XydUXP1O03lZK+PFc01J44Ye7vSsPChI+P254cm6M/eEVZGB6S7M948Z1oYBJbEcHO6xVhgbDiBsDE+/2szY1qYvLh6ISo+hc8Ius997lMYSL6IFfUQIAMvnhk/GzdujMfiJXVjMKi8R+Z3eNTpn94RxE7ZhIN4CXu2g4EbdRlr5syo1JcxnNz4cpb5OVUxVr85w1Ud67c27azf1x4Zg+pbO6OOkNc34836fQHSu3fqCF9iaBnTOIw7njx9UYLUYQDqwzt8nilI3lPQl3BORps+nDbpQ5n5gA9PtzbbM9kzXsp2DkJEeRXbvMXcMyZ4daIP4xnDKVnkLeZKUPkKHU9S9COPUsWDHXnzvfL4owXCcbbtzA3hyCikwO23335FYrA70V2IQjjT5YL/8y4Hc6Yko9V4XnCn2XArCMAripl936pgzMN8DT14I43pcpui+Rp6HHVOBpym6Ux7BpR3hfFkeW0S2u2ZQcEo8a4uAw7/QxeUPR9twm8ldfsJP/RzLPqHqxx67veeOb0Kxx3eQAHCX0VJcCQyWhiO+vMOmbk42eJ4pHBQTHl8veMn3FMYvbB3RhzHjVBDbRmbeLvQeCf9HGpOEvfcc895f3DGu4T4tnXj4cbDdxh0IhU4DI05nwRueKG+JDACF/2TlfPpS13rMk88Vx/wVv5Qwi9qrXN+4Guo7bTm+VomfIr5cQBwNJAHkbeYK+OGw9jpcfTDQWsPGeGRN9+r0xE01Zc1xmGI0h/IGboKnSbks5+98DuQDCVjome6HEeG50mTsFIO8qUy9K2DboHuRs0B7VhTJA52Dpal0gtGjbtU+WhVVNlc/TmIgB9431x1V7IcbhmPjkqHpft7zrQwCCzacDOssJj169cXJ2j77LNPIbR5VgkK5ZGc/gg5YegxPng2nRg5sYs6vj7l3bJ49m6ZkJZ4rnXzb9/IJ5D1RWDyGtZao1ohRCgNyhh7swUjbhhXjCfz16f3yPx+kOrmab7W5bmfCCVe5Hb9xgwvr/re+bNW/RPmQlOcClIUlEuEJyPT+JQWxpi6FBLl5ugEU/imZ0l79cHWszAlfRjHeigVxo0+1NkeibI05OHDvIWo8rYvdl68x+H9jr4IOB5rHvbFGFbRH48qT2s8x5X3nYc9nud7FYpLGQtjP9pTBDgOOBUIKHDizSd4hctGvZW4MiYoqsZCbxws9pSCIS/T4iEAvlK/J6c5FGQn6fhCW85AGofbbV339nGoPqOAMSIEG09TNxLjJe4nueLNTgicWKnP6cT76sMgnvsJ/xLyziiifJAflEb0hgY4ofBLp9v4W7+9Z/yWYcip4d1mXl0GCqNC2Lg6nHScW2jV6YVTPQYhPqmcLENvFFinHcrgujJrsgfaUgQZfcrxbeXmjX4pTupQDJ2OUfB5zNUZSsLpwYZMa8tFaWjn65fG8w4gxZMsUQ9P57SjCLmXNy6BL3rVVyQORzIZ7LS193iR8H7PnJLakD2e28QZaI/AmoFLDvm4l3VzhLZ1GdRgKqqkzV+t974ezQBq50/WwNelkjXwi7HUjoF2OQkZcG3+fO7htSinvvzgeEef6IaDwthwC44P8QvKON7PuTCf8TlTyX1rmU+7xdQVMcABilbphGiagYqviMpaTN8r0RashuRCf2x8gO7nBNya++Xb+5mjCA9odf7tPafVOP6SGG7TuHCKtFADx7S1nmzQTeNc19KcMHjMnGDD8J008mgLo8Xo+2vFkHimvQspZNWpLI8c75m++vU9E5CYEu8fr7k9ZlRjxENt4AGFV/8+Mc7bbkzz1N+oRHAZg/d83333LRii+Rm/30YeLxIF2wdonIpRuoQm9ev2nxlkjDFfeGudHbytTh4ovbxtlEG/HSVcijLbF7z9fpf7meefd9leLfdYO0r/FGS4xunCgSVqgVJM+ffVSB5uipXPv/syIkNkw4YNRbtJYUR5gaucbfpAo5RzBo2TJ8YTgwatcEAxonioJ+1fPY4upzFO0Ch2DAPGYIvf6rVJOeMn8jgy5EkUzVprd3JFgeH1d4rnytiKNrXWog/ONu2Mp617dWqtJfpVR1t1lEXyrL5ydWvdLD9q3batvmvdXK69depTW1fP5iMpH0oxnrptea2b1xJzif7aOuY3ru+2bvRjbpJn7Ws9ef7WY5xaN+fF3GqtbVfdfa0nw0Nf5l9rLdrrt6u05Y9+o86WrFVxwdf9hAWHBlnDqOdAIEOGZA1nBWeBU1v80akpWTNODsBnYbmiehi82nECe59taAz05HcGyTN0Se4x8iY5PSJrKPVkh72NTRACKYKC44FzhLzh2KBHmU+tm/Eh6rviUfgGY9/zpMlpkAiTlVTewZGznGFqf8DaHgoXFr436dxXQz24Yb0cKtM2X6e0dDXyLObGAbJx48buwIfMIHOEvHNSMfDVQ4f2iT7EQaQeeTUJzmu/1tJOa21BsR4nURQSHtjIy+vyQ4CnjsebJ0sYHW+xk88ho62djRNSp2UECC8wQ4tR19aJe8JHaAQBQMDw5DLCMKuoM3RlYPC8e5fR/Lz0PVRPnrAX72oK1SIUnbIFE1E+KmFMPK6845gMoSr8bVR9+eLRef14ojxLvJLGdoLKWOx74ynGDEp1I2nDk+5UwEdgwL4fEud0RugLOKvDe0u5pnBEP64UYmtQh6HgWX6bnITzzOtPv21Z3i8cAgSTHy6WCCcKUqtkwYlwDFAo8Tme1vmM6AREWyG4+sIv2/acBQw4ypyQbj9R0pZPcm9e3s9BE5PUn7QOXES/lGIRCOAzaduslxCYDwSEIQuRxYvJGidqjLC5ZI3THb8Ny4EJV8fJGvzfKxroHa/VjkNsrnmSl5RzPJws0H6uNmixL2vIOuvDzzmGItJIX5Rop8bu+0k/TldDxngmK9E8mcLwZLQKOz65bSmMvVZxVyaSJGSXdoxH81LmtJ9ha53kkRPtSeCj7ahUay34hhMghnnUI8fsnX0zFkcp/SXK49qfT1uHvLcv+KY+ON7acn3I44hltFivceRL1gbXtIUP8U6hskhg7JsJ6og0amHMIcfZ5oQdXKPNcl7RA8PKutpE3xqn+1mbiAjvQns1CK1wRooqY2Qzts1bH+AljF59jkuRWvJG6YnardW0Zg03JziYJc/iWt28aVwXw40gQmiLmZ8+vGg71AdhinFhFkPlk+YRLuY7VJ8wIhiHyuaTx6CkaI5r4+SAkBIWG/UIBgoC75m48MgfdfUlO+/XMHoxbAxTXLx3Jp0cagdeBCJFnfAjsIRFORl1wqKOZM6Yo74IBAY441FZm3jYndgI2yGs2rK8XxwEhF4LcXZ6MaonJ3LevXKSMarOuHzGGWXNachQPcai/TUX90N1xuUJaeS4cMI3rt58ywh1yiRBD3eXuv/5zifrr10ILJWsEQo7Stbgn5T1xUKRY5KRM64fBgQHnxO9qEfWUJzR63xoyZw5f0K+eBahQvlGm9bMuCBvzC3G89oEYziewYXBSN6QXaJPvAqgLbnCqNSHfOW+H+CZoRh9LOSqvXVH6DkDgPHo3VZjk/8MT2OR0TEGQ8npK8do1HFSqlyfnMqcSmSw+QpBJZtbGDB4yWf8kXwVcqw9mQ92Xo0gezlY8ThlkdTxjiDDUJ/gaU5R7upECu6S5Z6XOzk1pluYe5s4ydv3QPvzYOxZj5NjIeNOkYWIwwmh2Aw1bcgf0Uwit0Q+7bvvvoXssv7/Z+8+4CwpqoWBV5kzmBHTqqgYwQ8DiAGMKDwFRUEJgoAB4WFWBCU9A0YEFTBiAEQFUZKCAUXFBIIZI5gzKOb47b+XGmt7b+h7587MnZmzv63p21WnQp+uOrmq20Zn8Es9LVnFbam/uGl9Pvsv7M/rJwx2HTdLuoMFesGzKBFae5WNkidcpJ+nglArjdJeL1jx9OVwmV7lLHwILCIl7r7AsLr6zSqIaPk9KAkv5eljecUEeEowEKEuwir1I9yAB8c+FHsXMEqWT2OUj+nogxVLCIk8HhkMopdyQHFjkRW6o311IwUGCgaE8DnR1/61kjeJK2Mc77SQNYKmeTiJdqONwEAbA3iNOZbzmqGCbdhB90IQ+/EaQja+Oah+lzL7EQcZetBovIYXo+6vGBZ5abr0MwgGj+HVE/VBYWE4JNRT6PrVo8TgN5Q3vIsyyHuI/xLgGVHxIWXacWiK7QIUzn5tDsunVIoogHsJPAXU9gb3PIfGoE8GZL/BFEVNKB8PoHzPKpxUOUMofutZjFUSlsmz5hls2wAniRzSF8+R55dnewQFCO8Gj/e2ZSkeS4ZtoZ74OI9drQhrR0QOhQb+3M91MkYeMpFJdfLsDG39+mfgN2dqL6/fjMzma6kn9JqRsqwhNJ98yGOnjQK3XK6huE38TS/vBgn4u+++e9p2221TsZaMghEEYLPNNkuIqsXaqy5BkFIhDHYQo+pVVx7Bz0EMLHc592bICAOG4rAcz6TeKMm4WI9YDgfhAWHFOIUc1u0jZu4RJ1eJMsYrJ/HCIc7yeR9Z9giz9kHBIdyBg0tMilJGsXMV9kaZBAeX9mMYL68bJoU52X9gbyIYY8BI9NVOCCmGR3lrl8V9YCAwEBhYzBggdFI+GCDQyFGfBa+xZ5UHJefevIYRkqJAGUC3R+1DuB+PjtD+QXWFzfXiNaI1CMo59x7foDZ7lfE+Cb3GO5w8SlHEg3rB8hDyXDFcUm7VgQPGXx5AfIoix7uCxxDUwWrL87h2Tbw79pGTGyhaeK5IEoZMbdgKwbOG7/FK6osxVeieMYLBZ+EQvo3TeD0feULUDGWSAuydl3J8WjnPKoVLO5JnpOzgr+aW+jxIeDu+rW17l3kcwZdEnsg5JxEzPIYMu4xkpdzVuLVHOXQ/H8l4yUp1goNBfQv7B+9kUt5Bsgw5hBcU3uu6Oa85P83bGma5/A7Fbbm86Xl8TsxOeAFGQrAfpWsbV3mN2ou23YYFz6LnAAVEr13e7x4jpVRiLpSzfnDyEVQKpMMfTaX6VwAAEABJREFUEBd5XZJn9l0+yg7m0KVOG2attdZqshCz5sfKP5QyXjJJ+FphXEJUKIBOh1sJNvM/55wwEZYriiCmwNJaGFUBxBwxA9ZA7VDueOFKueu4z6FupMBAYCAwsFgxgNfwbBDGR+E1eIbDhfAawvWg5yes27NtfysBeBBsXcbwZr8PD0+dP8pv9D/nnIQKjlKvHyxFpl1G2WnnuecBFEqHn7nvlfAtni3hhw4W4QXrBTcsz3ukqAlRpMAVxQZvlChtvDfa109JDkIripHx4rveV7u/oli2eSc4kTM8jzWOyUkUXOUSPFAYKXTuS6KYl9+uIg0YhXn8nCprP1w//IKf5gQnlC8KsRBVCnHOOQm3JEdN89gXcmyhuC0k9pdw3xgKaxJFbJTHpOyxPHapQwFxyhdrXBd4MAil04q4490PS4i9UyLVGwZbylnYPDtlqOSNetUGgsaCV+pibvApsaiV/EL8yn19rZVTTKkuK79zzinn3NzmnFNdJ8W/RY2BGHxgIDAwewzgNU6+E77XtTV17N0pn5oYVo/AzjPTFV57oix4j3JeRb/ljZqKkZSCNGrduYZ3aBkDqAMvKCx77LFHsgdqnH7xY4d82TfOOMwwa683BS7n3PBAfJVhVz8l6V+0y7A+KX94cS+4Ljw159yJ9xo3hY0nmJJMqbUPjkKZFtk/Cpt9jJRkBghXHkbvepE9yrwOd6KKm0XG6l/idef1STp25uha1rOO4AE2Cwxwk1uUozQxzDLZqy2eol75vfIQUKlXWb88Hqt+BLlXnZxzc+R4r7J2HmaCUfN01WU24FIWnU4lnr8ua/8WOqIdoRN1GcXPiY+8Zd6FbxPqh0ethiuhjsITeCR55dSrYYRk1Pd+ax/TE+qjD3mRAgOBgSWHgXiglRjIOadRhOOccydBPF3xL+ec8BrpiqyhF7ws5zwUDgAPRi9egy9QWPAZB2KAna9k/yB+w6vVq08HiNkf5rAsHk8eSYc29YIdJU8IovbsF+NFyzknXjTyCgVaP3WiIGu/8DqfM3BfJzikSJGD63y/HVTCW4tPu++V8F5ttHmv7QtteMobA67DTSiaPvFQ9ysKBwzZoF13mu6dZeBdMHQzqEsOXnHAjfcyTWOdprFMTHEj2NqIyf1rw6WHhHhWAafF2Fgottien3qCqUfrdvy7xSFxYzvVpxAwwqajyR1hbVFxO3vBCI1+JLBO13nKU56SKI8WoTAFGz9t0gQjCTdzpK8NtO4jzS0GKAWj9CAOfBR471Ysetc65pJ52RUenPCFUZRDm6mFTag7LFGoEFgnKxlbgceQhWwIrUCcbZqmmMEPZmNMRcnlibPmHCpiY7Q9C9qzbwIjdhoTho0RCbuwljBD8e+uQi0QT5ZbV1ZJ68Pa0ZY9byxhGEsZnyvFTRuYkeeQF6k7BjBkVm7CQrsW/Hs37XlnPtiLYb9Gu06/e304dUwoTj+Ydr59Hr711s7vcm9vKIsppb4LfMAEBhYDBkblNZQ89HGUZ0PXaz4wrC4Bveu6RqPRd+H3dR/4D7kMHbI1gOEdn0FjHLYhRBGtHzaW4eVrQuScEw8Yfsmrhd/gXT7xo2+KDIXHWPBVSg3+tWZLo+XknBMFUGQQPqk2zyP50jj0590ZC7nSyY9gyJ48qbxC6LAx4cvCL8m+9kJS6oQxqgvXDhzhMbRnb1A0kbHgvfom/8IFHkA2rnmvdvEH4wNjzigXmmuMkrHBG2Xd/VwnexmF3Lb78f5ED8GtMmMUSkrBNafI/nicU4J9PknyXsgxfpvbjMKMC2uvvbYmmkT2EWkFn+Z1k7mM/kxMcSOgU6S4sU1gOHTaDUvKIx7xiMQ1bVKa0BIiCMbEN+lNPnGtfrNqODiibGgleJx22mnJyxejzFLiWxtiYovQjoDZAEwwdeoQrV3cr0Msygl9+qMYEiiMi2tbXqS5wQCi5f2O0rrjikeBN++8z651MFPzois8OISyl8dJWa9kLiI4vcp65TlyHUN1GlNdjoAJ02D0sJZ23nnnxGBBiULEELsCL8zAJzCcLukzGPZVOIpXuAfDCTjEToiFsVmP2qEUKsO4XaUtttgi2aPnpCox567GiFDWRNL6sY7so7Pu1I3UHQMYPlr2yU9+crVK5qh3o4zFuS5E71if7XOs8wf9ptCbRwSCQXCjljGW2bRv3jLAlfporDLKYsmLa2BgsWMAryGndH0O6xgv6AovlN3hDATZrnXQA/S8K7xTgCkI1mxdh9yGZzAYOuqeB4QR3MmGjEU5d/Pq1W2W33hG2SNW8uqrQ0nwGwZE37PDm8hy8IEHOjwLL8OL8KsiO9ZtjPObAkbhoJipL8LFc5MP8FM0mIzpkwA5r3p+DgGHr4laIWPay08mLXKOsYrq8i1b+w7hlEMDLfddS/0MSg4O40FDrylsFMT111+/8QbCo7oMqt6R8UmMs47J926VSzx8lKNeypTySSdj8oztdsn8cEGmUGbriZOsnehtfThBk9Lpvaov0QG8F3IdA4PnpzhTfLWRVv6hlJor8LAcZY+JKW724rAgO4VvJV6b/xYjoc8EJnQSDk0mVoQy0R1XbhFYDF4uS68Jzn1eLCEWC6uP408tCoKpo295TiiLOkOMWIsof+LKTWRtGhMPBRhJOJj2jJdwIS/S3GCgF9OyyBwoYk6wvLR7tlDbeYPuzZ12OcUf00F8Hedcl2NCoxJ+jLQ9V4R4aN9cZVCo+3BSln7qvEG/neBo/hYGUsM6XYqgjvjbvI6oCfUluGMaBdZ4rC8wGAr8grMWKHngcs5Je2CsJzgSG88rzlIIRhJegWlQArWFebmygMkHI7H6ERpYKRFSeZG6Y8B7925YaM2xUlNEAuHBnGORLPmu8llZebTcd0k8qY6VRve6wHeFIVixjJq3xlrqWQ8YNisz5lvy4xoYWMwY6MVrCKLkDLygzWsYM0bhNdYTz1MbR+grBUDEkciJulz7o/AaBji8hletbsdvQjPegNfgJeQpx7mj/8oZ7hjU8Rr3ru4J1r3u5UnqU378lhgcRZD4Ldlvjg/hafa44zXkRgZ/ZQR0B5HBMUGdoqS+MDv1KSyUg3Ivr51EceFf5M9SRhbRpr7kUYwYOSlL+Cz+aDx+qw+GAiqSC281Zk4CSoe2lXs/xitahpzqWeAUryZ7gpHUpYj6XSeKlnddnpcSa4za1y5Y7wkOjE8evNnrr0xiaGYAR4PJ0fKmMcEl5Zmn1/M5TZLyTjahI/jWIIP2NI59occ0McWN8OE0pFoYJyggbCz9HtSiMXkJtoiUPNZmjJ4Qk/Mqq4bQL8K3/T1gvDwEh5vVPfevF67d4gmhtdPSy0Q1FoQG8SRcqieBQ2CN1zjkzTJF9T4YYC2zV0uxd0aZ9k4xBETNxmCWJe8ajCSU1bVrQqzLnEOIEQChCQgeAnjiiScmxLPMC4RVuEPX9sGJJTdH/cZIKDr60D6mhNjoQ7lkHiLAfndJ5rtQgDPOOCOV+Vzq5ZwTRmvMQh0lBE0dOC1wrjzdQibBGLP534bBnDwL6xU4YcVlLWmjJAqFfhhihDoIhzTGgmtwrGzwoR/3kUbDAJzCMc9bTaMcGc2A4R2LNiCclZYZQyhv3l/JG3YlYIG3PobBjlPO8yrspdRFlwldxokBl/y4BgYWMwasIbTQM1hLDGr4ACEaLxDexmCGdoJBt/ECv7skIWEM12Qk8CIZjjrqqIS/FOH9hBNOSJSInFfJSugxQxv4Lonsg47zwrd5DZkJrUCT8Aa81WEs5C9tey5lxune1b38XvfyJDjTlt8SWU77fpckpA6u5PPY4ymFd2mfEqKMgRGf87vgybvAp8p9abO+em5tFBlSGe8ipU9d9xLaBdZz6cNY0GF8U7kET5RVPJYBDT7xQWWSZ6Fsq6/cO5KnrCTPCi/lvr7Ct0/5GJd28Anw3gU4soVtR9qHVxE59fgcrZ9zTsYOflqTMQvHpxzbo8d7SKG1NcPVWqLcTev4F3JcE1Hcfv3rXydhBCZizqsISq+HIpyA45Iui4wgYlJbfKWOxWXR0sRLXn2l9AnDIywQPOuy+jcByKS3cEq+ti1WMdy1lbiUx3VyGMg5J5YnYX4YHKsTxQlxRDQRQNYjChwYce0I3SgjQFS53jE49VmeKGnalzBPBEGorbHw4iK6o/SBeArvZZk7/fTTE8unOat9yVzWh++k8WxIiH/XPnLOabvttku8LvZfdq23kHAUCzjFvDDihRzLYu0b4zK3eC5FBngOhiaKHBpFiONds19BGbon7BujNsflMT4xfgmJRQsJl9bABRdcoLhJ1lZh+jJ4wRhMtE9AsR7MV95YVk8wkndsvxpBkcDEc3fuuecmitoFF1yQRD3omzDJQENZQ5fVRXONjeLpPlJgYLFjgMELn5HQaadMUirwAIlCISIBrxOtQOGq112X57cfmTcJr8IXRSGRhbSPb6ILvFxClHmxeHPw1C5tg8k5J+uYMcjalRdp6WDAe2UcR6+laX8ychSPtfnscwySeY/nmO/TPv6FGt9EFDeWGy5+RGXQg9jzQDgl7LEqgC3KFeLkviQWjH4vzgZVIUTa6UcYKYQ8ApREVonSritiK7SrCBnyIs0NBljlCHjmRs69lXoMDwxr3zij4NXF4Mqc6tUGGN8+4fXtVT4sj1dQiOeg+mAwXnN3WHvtctZATB++2mXTeM+oIhRa6PI0jm8xjAl9o4TlvOpjqsZMqcK8MF1zGh0rdIqhiSIkOgG9pEAJ+WZQMOdc999//6RdXgAeL23WiWJov4AN/qyd6lgXLJv2T1LkCrx9xPZKEFB5mY2N0MpQR4gVQlQ8wfYS83YXIZJBhTFO2GdpL66BgcWOAfKEdYln9XsWvMZeJwa9fjCD8tEEa3IQLxDNxPBSDDiD2muXiYTBa6zPdlncL24M2DfGACcSaHE/SYx+EAYmorixrEqYf7/OuEJtMOX+tRG2H1zJzzk339Uo9+VKQWQFdi8czrWdWIpZmVmuuVsJGTUMTw8LFu9fnR+/AwMLiQH75TDjhRxD176Fa/AyEtC71gm4NTHAEEDh5y1WSiniZeM9Y+wQMSA6wD4WHjfeOV46Id8UOXuEeZXROV4vB9TYT2z/r43f2qwThVuYuFAcijcmT/lmzAInbNZVYggRskKIdPKbb0zZP0GRVKYNfQvV8tuYGcTUlQiwnmeUAx3Ui7TwGIgRLG0M2B/FWLq0n3L5PR2DgugLEQ/L7+mXzxNPRHEbhi6hPk4JYkFiua2tROK4CRMUsrodlmbCSp3n93ve855kY609RSzP8urEomyvkDAFgqVTeury+B0YCAwEBqYFAxQlyq8wW2NC9yhowk8ZwuxlEPOPPgpTpMhR6BiewAlZFMXAiya8SqIEop0l/FK7Jcmn8FG4Sh66zEvH+FbyXNFXCrrfEq8eWi3Cwv2wxEBmjNIw2CgPDAQGlhwG4oECA4GBOcDARBQ3TF8SupPUj94AABAASURBVNMeo9NtnFBkf9tb3vKWhPnXMKy1BAwCQcn3m6AiDKfkuRc37nha1iKWZcJLKXcllNh34yQkYW1CfHLOilZLlDtjjVCB1dASN4GBwMA8Y4CHinUUzaOECQPnqaJM2QOnTDi5KAJRC7xzFD3DtMeXssUDRlnjYZN41Chd9q2BqxPPGporBJInj3fPZnbtONimhu33W7/9yiI/MBAYCAwEBgIDk8NAtNTGwEQUNwoQQYMQUHcgTMYJgvLF3BI66nK/uevt6SCcEAgk+zgofPYLgWFRPu6445IPIwoDcqAFoUZZnWy2tdfCBn1KG6GoLi+/WYxt4hRyVPLiGhgIDAQGFgIDDnVCQyld9pVRutBTxjB7eCltohZ45dwX4xfDld/Cz4WhO0G1To62bj8PBU0eOivU3Gl4TpgTBmlfnbJJJcY6HkJpUm1GO4GBwEBgIDAQGFjOGJiI4kZ4EHrD0lsj8+CDD06EDRvpXX0fg2IlUc7AUtzs8xDi41scFDSnJmnP9zLACBFygpITISldRx55ZNKG5ERBMPq20V65UCBtKZfUpUSCE7ZDkbSJXiiPvFFTwAcGAgOBgUlhgDKGHvkemj1sDh4oRifeMQciiFZAw4ROln7BONTmy1/+ckLTSr6rQ0tEKfhdJ6GY9v7aS+kAEyHnDhnxqY4abpTfFEtRDO06l1xySbLnglLaLov7wEBgIDAQGAgMBAZGx8BEFDcChJOQhDxi1mUYF110UWLhFQLkiFses5IIKOAcHELhovxRsoRVElQccytkCIzN7RLBhWJX2nC1l63A2JDvVB3HiSoryb0T0cApFyJE4DFueZECAwuAgegyMNBggGELPeJtQy+FOOa8KsSbQoceMnyJMqijBNzzqjFoiULYb7/9kr3EO+20U5J6neioLd8EQo/VcZLkbrvtlhxuInyyGVDHP/pHv0855ZR02GGHJadKFvov5FM4eq8oi47NB1hgIDAQGAgMBAYCAy0MTERx06ZjpJ10xmrsXrJ34uKLL07CfNpp6623BtIkSh8lS7gka/Dhhx+ebMBvClf+2XbbbRNlq92GeweVrARJTlTjxevVH8WNFRscj5x+CEo2/8uLFBgIDAQGFhID22yzTXJMt0+clD1sxsNb5eRH9BXNdJiJ/JLucpe7pDe96U1phx12SGjn5z//+aQ+xUwIJjjHkpf9az4RQEHcYIMNkoOipF133TUxejmNjJFNHXvrahosT9/GyMjmHv10IqWxqyc00niV+d4cfjDp8EttRwoMBAYCA4GBwMByxcDEFDdCAmuwD+kJnZlWhH7lK19phubgE3tImpv4ExgIDAQGFhADvGD2p1GkimJUhiNk/L3vfW/j0Sp59ZWC9bznPS+pLwJBiPomm2wyA+KbUiIVZIh0oLjts88+yQFOkjBJyptwR1EN4ISua9PvkoRpMrBR+kqeby8KtfTNTFESQiOFo/sIrBMpKYAFNq5TjIEYWmAgMBAYCAwsCgxMTHETdviMZzwjEQwcXz2tT+/USYeXFA/ctI4zxhUYCAwEBiaNAadK2j9szxxl7qSTTkqHHXZYeutb39pEOYh+mG2feADv32Mf+9jkkJXZthf1AwOBgcWBgRhlYCAwMPcYmJjiZqgbb7xxEiIjrMf9NKajjjoqHXjggcm+kGkcX4wpMBAYCAzMFQaETPKM2Z+2//77J9+65KXbdNNNk0Oc7IGbbd8+W2Dfnr1zs20r6gcGAgOBgcDAssJAPOwQDExUccs5Jyc15rxqY/2QvhekWHikY7QXpPPoNDAQGAgMLCAGcs7p3ve+d+Jx88kViaL14he/ODkIZRJD23333ZNPGwSdnQQ2o43AQGAgMBAYCAz8FwMTVdz+2+wS+xWPExgIDAQGAgOBgcBAYCAwEBgIDAQGFhADobgtIPKj6+WFgXjawEBgIDAQGAgMBAYCA4GBwMC4GAjFbVzMRb3AQGAgMDD/GIgeAwOBgcBAYCAwEBhYphgYqrjlnFPOkXJeXjg49dRTmyWR8/J67pzjeXMOHOQ8Og4sGKc05jx63ZyjTs6Bg5znCwfLq5/LL788nXfeeSHLxPyKOTBkDlx44YXp0ksvDTwNwVPO80dDyRZ1Gqq4OXks0v5pueHg9re/fTNPlttzx/Muv7k+qXduway//vrLjlZMCn/RTqy9uZoDPhbvu4Jz1f6ybXf/mLNL7d3f9KY3bU5dX2rPtZifh2xRp6GK2yGHHJIiLT8c3PGOd2zmSbz75ffu452P984tGN9BC/yNh7/AW+BtruaA78yuu+66IcuEPBdzYMgcWGedddI1r3nNecXTXK37pdIu2aJOQxW3Gjh+BwYCA4GBwEBgIDAQGAgMBAYCA4GBwMD8YyAUt544j8zAQGAgMBAYCAwEBgIDgYHAQGAgMDA9GAjFbXreRYxkqWEgnicwEBgIDAQGAgOBgcBAYCAwMCEMhOI2IURGM4GBwEBgYC4wEG0GBgIDgYHAQGAgMBAYgIFQ3GAhUmAgMBAYCAwEBpYuBuLJAgOBgcBAYGAJYCAUtyXwEuMRAgOBgaWNgT/+8Y/ppz/9afrtb3+b/v3vf8/Jw1522WVNH3/4wx/Sf/7znznpIxoNDCwlDPztb39Lv/zlL5t1889//nNOHu1Pf/pT+tnPfpZ+9atfzdna/8tf/tI8A/ryr3/9a8BzRFFgIDCw0BgIxW2h30D0HxgIDAQGBmDgd7/7XXr961+fnvvc56aDDz44ffvb3x4APV4RwfDAAw9s+njd616XKG/jtRS1AgPLAwN///vf02mnnZZe8IIXNOvm9NNPn/iD//73v09vfetb03Oe85z0kpe8JJ1//vkT74PyedhhhzXPcMABB6Svf/3rE+8jGgwMrIaBuJkVBpa14nbqqaemvffeu7E0zQqL81j5kksuSXvttVe66KKLUvwLDAQGlj4GzjzzzHT88cenc889txEU3//+9ydW+Ek++Vve8pZ08sknN30ce+yx6SMf+cgkm4+2AgNLDgOMHUcffXQ6++yzm3Xj9/e///2JPqc1/+53v7tpHx047rjj0qWXXjrRPj7xiU+kt7/97U0flE/9hcd9oiiOxgIDE8XAxBQ34QKswXe/+92TjzfvuOOO6YILLpjoYCfdGMJ73nnnpT//+c+TbnrO2iOwGfPll18+Z31Ew4GBwMB0YODHP/5x+vCHP5yEShqRcCwKlrAp95NI6AnPQWmLJ4HXrdyPe/31r3+dtthii/TJT35y3CZWq0dw3XTTTdPnPve5Jh8uvvnNbzbho03GFP75zW9+k570pCelXXbZZeDoPv7xj6dNNtmk4Z0vf/nLEy/IwApRuOAYOPHEE1czoH73u99NZ5111sTGZe5Y664aFSL9sY99LH3ve99zO5HEm//mN785/eMf/2ja08cHPvCB5FmajDH/UPwe+tCHppNOOmnMFua2Gs/lJPHYHi3atO+++6bdd989oYPwcfHFFzfzRVkbfjb3jHp3uctd0je+8Y3ZNBN1FxEGJqK4/eIXv0gmKcb6jGc8I+2///7pWte6Vvrf//3fRYSKuR3q05/+9LTzzjvPbSfR+gJjILoPDEwOA4SoL3zhC+krX/nKao2yuL/yla9Mf/3rX1fLH+dGGx/60IfWUH7Q9Je+9KWJEjdOu+q84x3vSDe84Q0bZSS1/r3zne9MK1asSE94whOa/Tut4p63jFbGU4RMxsL/+Z//Se9617tmNc66sz//+c9NqBgc1/nj/iakGe8gQ9sPfvCDJkxt3XXXTS960YvS3e52t5RzHrfLqDcPGDD33vOe96zWk7lpLX3ta19bLX/cG+2cc845q1UXwvzGN75xIh53e9l42ygUq3Wy8uZZz3rWrMKl3/SmN6WrXe1q6R73uMfK1lKiuGy55ZZNyHeTsYB/KGyPe9zjEgNJGYb9vV/96lcngtfSpn2D6Cs8owHPf/7zmz4pywVmEle4xSsm0Va0sTgwMBHFTXiAfRe77bZbeupTn5p22mmnRLDwe3GgYX5GaSHPT0/RS2AgMLDYMMCrxmr62c9+NhEA7W0RwtSLbvDQvOpVr2os2oQvFuRhXjgM3uEDIiEIhCzr9s6x4hMu2vgSkklIZDX/7MoxXXjhhYmA04brdQ+OF+9e97pXopDUMBQwbd761rduvAfjhn1f73rXS89+9rPT/e9//3SVq1yl7mLs3z/84Q/T8573vOR5x25kxIr4J8EO/+Sd22qrrRqhd8RmAnyOMMD7Sdj//Oc/nz760Y8mCtsLX/jCniGL3/nOd9IRRxyRrC1bMb785S8nipG1N2h4DAbf+ta3Gm+yesccc0w6/PDDeyoS5uYrXvGKZu3z8PGY//znPx/UfFPmOdAXxiDrD21hQOllpACnj/e9730JrVGHsto0NOQP5fK9731v+n//7/+l29zmNkOg57/4xje+cbNnkPJWekc/99lnn8YjVvImeUWfRKFtu+226brXve4km462liEGJqK4sTRhPNe4xjVmULj22munHXbYYebeD8SFoIA5sbQiDIQIZSX95Cc/SYccckij/IEpyR4MhOfss89urJJCiEqdH/3oR43HD7GURzgRtimUAZHD3LWDoA6yfKpLqGDBxUTVednLXpYwc2X9klAGxJzCanF6LifAgSdMsV4hfJRbbSIQ/UIRPH+NI8/N1a6tSIGBwMDSxAClTaSCKAWWWYcEvOY1r0lCAXs9MZpL8DrwwAMb2ofG7LnnnunTn/50L/AmD/11kMIzn/nM5kAFNJJyKGS8AWj9oXyxnOuDMqPefvvt1ymMipDLsrzhhhu2Wk0JHUQf0ULKF9rIMr0G4JAMApAIj3ve857pSleaCCub6RE/m7mZ4x+8cgR7AuUku4q2Zo8BMgfPsX3lDgcy/3mi+4X/MoCIPLK2XvziFzeGBdE2FKR+oyFzHHTQQc1+e+tMPd6gtqe91Dc37UOzLnloyTd77LHH0H2pr371q5soKAed6A994dUr7bavJ5xwQiOLiaZSBw4Yedpw7XvjJp9tvPHG7aKpuF9rrbUSWrnFFlusNh5eK+9vtcwJ3aBPj3rUoxKjzDWvec0JtRrNLFcMTITb3eQmN0k3utGNEiWHYtULmaxOGDVFZMWKFen2t799YzESA8wqVersuuuuCVG8053ulK561aumL33pS4019Za3vGXDnClCiA0LVanj5CUWk2K5JdQQeGwW1r7FeIMb3KCxYCFYpV6vKwLFW3j9618/iRu2GRixRoh6wev7//7v/5pnZ0H2XKxNCLAxXv3qV29CX/RP0LjPfe7ThA/43W5PyAxiwuK2/vrrJ89MCXzta187s8elXSfuAwOBgcWPAZb8k08+OfG+UGooTYTGQU9G2GfdZgl3aBE6ykLerw4PGsELLaasqUsI7AcvXzk48AxYvGj6UTYofeYzn2noN1rfhlNGSBI6TunSHuG1DceAtdFGGyW8gDGvXc5gZh+Re/NfAAAQAElEQVSN51Lm/iEPeUjj7XBfEkF4gw02KLfN8e0EayGJd7jDHdJjHvOYRMlk6CNY8XwQhnkL8A9lKlOuCe2UUWVPfOITk/AqZSXhQQyT6623XuMJhO9S1r7ypBoHIdLzP/7xj0/Gc8YZZzSgjIye29j1hzfwlJR5gb/gZ8aK53gePLYfr2oajT8jYcD7NC8YGxhVzTHvbVgj1gxPFqOy+UTJ6leH91sfvHrWvnrlHferI18fjLrWvpMgGWHk90qUKbIX+uI5yC3DjCXkpssvv7z5DIE6ZDHrH23q1UfJs55zzs1cLnldrp7hKU95SiL7mO+77LLLamHicLnddtslMpn5zpsPlvLabt8eXeXaKYkiKToB7G1ve9tEtvL7sY99bGKoh0drEDyPp/fg8LoXrvSugivJnmNyHppd8oSWMyI534GM1z7ciXHmmSsNZtqyruGWIwF9oeh7Lv0++tGPbkK1S7uuntsaJ4+ihSLZOCeURVqeGJiI4mZCsUbd4ha3SKxLT37yk5PJWBY45m8hmGyIC2aDYVl8hA/WI0QIUaCYUXosRp4vC+Ta1752s3GbIjfKa9KffgkAQn7KxnbEq1c7hB6W4kMPPTSxTrGA2/gJHjEy1nY9SqYyixCTZSkX4kDIEWLAugMfnuPmN795sngRpHXWWafdVBMHjukTRFi5KJDq8s5hGGtUiIzAQGBgSWDAfpArX/nKs3oWVl1RDwxXvRrSB5heZV3zGKLQQYpXvzqEEuFbaF9bceP1Qy8pXMZKcSMc18qG8VO2HJpgDxsPJAWJF6Ldp7G089r3BG2CqnzKF9ru9DxCmSPWKVna2XDDDRv6LLRTtIhQVPSXkQ0vI1QSsnk38C98DZ9iJNS2ZzBWsAR1Qhj+Q2hW3k74GUWRAguvPBp4D0WN0KgfhlBXYzFO5TwhBEHtGbcxMe5tvvnmiaIrP9JkMGBNCnObbWsM2+Zer3a0r59eZaPkXec61+m771X71tso7bVhtWHOtvPre3TBWnXGAZmnLhv0Wxg22ZExwpqyfvwmCzLKl7rmO1lOhNPTnva05twAa5lxHQzaY32jHdtvv33Szp3vfOfG6G99UazAaUfym8JFgfKOGFGsNbKiMjCS3yUZlzx0RZ61SvEip4l8EA7Jm0k2VF6SOuV3uaKT6BqjC1qUc25oUJkrlFnjYzyCF8/AiCCyq/Rf2prua4xukhiYiOJmQA9+8IObfW2YG4UJ4zUhMTHWCMyNJYLVA7zE6vGwhz0ssQCDwdQRl9vd7naKm3Tve9+7OWGriwWqqVD90bY+S9Z973vfZhO7vkpefWXxxkARJwqcxALGW0Z5K8yyrvOpT30qrb322skiEvKjjkVG2RQnXsMO++25efwILoiVttRBjCS/IwUGAgNLDwMPeMADEoaP9oz7dPaUCNemoPVqg5KgHwJYr/JheQTMRz7ykUkbgxRAxjeKEnrWbpNBC73XjjYY/UQWMJoVWJ4HFuvNNtusCYsXhUFYKwcdFLhxrjxb+Axc4VUEIAYyh6gYL48dhZMC5H3gGd4JYxrPCYMaQQqe1dMeOo0+U6C0LdyV4sdIx6BJIOw1Vvg0DjzRb3jdZpttmj2B8IFnEtQIqMbC28eLwOCp39Im3siSz2CoP/gsZXGdHQbIKPZCUUTGbelWt7pV8/1FilWvNsgoPKXmWa/yLnk8MZQO8lMveKd98xCTS3qVd8mzVnmnGTL6wZN9KDJFQeoH184nKxo7uZEhgwHbb+vC2qvh4ZP3kJfMGiMjkt14svTPq4Z2kKW0w0OlvnXIeO53nR70oAc1jgG4Uc9aG0XpPOWUU5pPShmPtUrZZEjRX91Pr99otfB1ToJdd901oUfoJ+8meLIlZVDIOpqC9qARaChPHJhIyw8DE1PcoI7VANPC0BAjiwkD5k2zqFasWAFstYTgYHosNbxQrJgmbgEygRE8i6rkdb2q14bVD0bXzndPYLDwWVMxQMlvwoYFlvOaJ31xr1PqhImClzw/6ytCpN1RkgXK+mOxaksIxSj1AzYwEBhYfBigLPDWEDQGCUb9noxAz9LMW9MPRkQExYNBaxwhUageBUE7/fqQj9aj6Yxg7kti+GJB9qzovnzK0l3vetckPFA9eSzXwri23nrrmUM6hOPz0imfTRICTyhjZIMvtLtLe8YHTsiYvdQSRQ1fwK88G0+i5yG05pybEH8Ccy9hUVuDEi+CsXpXhFew9gNS8vApxj15Ek8hOL8jTRYD9iNRqnhDbnazm43cOCWDN+gRj3hE37rWO15P8fLO+wL2KTDHRBVRYPqANNmMADzI5lGTMcIf20BEFKEzgww/DArm5iiyjzVl7TCWWJtlWAwQQiHbBnAGjvIuKNQ85OiFdvRPhiz0RVvegSvjuuukk7HDSf2O0TSevmF9MQzUNPumN71pMuescXXRWoqliCuhmegOZwLvHZoDJtLyw8BEFTfoM4ExLgTComKNNMkk5e1UmLV81k2KEyLJAkOIMUkJDOMobtocJRkv5dIejJJ8M4hrmkJaGGjdJgLFrS7uua4j/Ic1qIYd9vuDH/xgesMb3pAQR6fKaU9s87B6Ub5gGIiOAwMTwwCvD2stQXEUxYrXBt1AuwYNJufcHM0vFEidQbDtMjRYHwSLdlnXe0KVPV94gRDCww47LB155JHN5wDwAWGB2rJnj/DVFjDxFuWzSRQtNJVS6HkcGGCvyzChjieNJ4EQTsiW8Cl0noLKe1gMfPooY5QvlfuuV/153prv5ZwT42YbLwyU4/TRdSzLHY4gLeyOF4XRoSs+ROowdDz84Q8f+nkH71DoLo/NNapD3ob1RUERlmdv5DBY4+GF2mWXXYaOp26L0kZhoGCYk3XZJH6L0KKEwEE9j3mbKG+MQHU//WijNhh4yHFf/OIXZ6qQo9yMY0BRb1ji+co5p3pd5pxTF1yRKetnTlf8o/z6iWYy6FFWeQ7RnbYHElyk5YWBWStuPFiUK5PXb+izgGxmd2UNKYn3DIMGI5mUTkFzmhZLk8NEXFkPKUsWsph+3/8AL1m0GCgFz71FzdJStyt/nERx1I7DT0p9QoZ4435eOgKQZ22HXwoVKouvtNWvjVLOogIXmASiRXjRTinvdSVw2EOB0RtrL5hpynMAC0EJARJmOumxmXPnnntuIlTpQ+z8pPswz1nbtS+GXlirvEn3E+0tFwz89zkpASzPaN9/cwf/4oVBLwZD/bdU26y4/80Z/kt4znCoVRCegTBiLa7KWfUXjULX0SlrhlFPAqcOgQTNRAMJNPjDqpqr/qL1q34N/qsNfYCyLiW/S+LhEIalbyGP9owNi2wgEBIijf/iiy9uwvsvvuIqOgJfYtlHs6XSl76lct/1aozq1TzDM7G88y7kvGb0R9e2A248DFBgeIC61jaPreWu8Oa8qKUuIXalTd624n0qecOuPp+Rc/f5YxuMZxnWrnLrgMJiTbvvktAjcOa1a0lkMXJVbQgpZYOu8OGzDEIuhRZSOh1AMswj2a/Nej2DsS5dS+IVszZreuW+K70q7fS6CsPk7RdKaT8cmiO6qxfsYsnzXsmtXccLniJOwe9aZ6nDzVpxs0B5ilhyWDAtEsdUU8JYODB8lh6L32IitLOs2rhJKeMStgGdJZc10WSX7GfgNrdHjmJSGJg8i8IeAMLzsccemxxuUi+acV+aha5t4YpCVSgATnhkobbfoFe7rGnGZjOsBaaOxWZxed5ShzVdWCUYnyjAgEtZuSLCwkopcDx9DnLRVinvdRWSyYLmHbQJSi/4hc6zD+Ooo45qTn4zZyhykxwTQs8y6mAbc9EcY8GfZB/GrF3t68fz9Hqfk+wz2lo+GGAIQlO6PrFQPcpKV3hwPEWuXROBoSsswYmwhTbVdYTOE3KEKNqnVScWZWGUaCYBmSKHDpbn8oy8dXV77d8558RbUUIt0XJ0G49pw7rHbxwqIFyJt0xeSW1+stlmmzUn+wqxLDDaR1vwKHnCo9B44ZfK8EZGQIZN5aMkYaF4AT5Q6rnnPeBdgd+SP3ONH3OKAcIm40PXTigio/IFp0QyFHftgwHbYRld4cExspuffndJo8BT8Hi8RqEXwoslIZH1WrGWrB/G8S7jBIOuWfOiFjbffPPmRFoedgZW5YOS9VroDTgKqHWGJhUcW+8MPspLsvePzGpvXclDy4yj3I97hQPzwf42MqRxyBu3vbmqR65r40Vf5gHZ2IE17vEE8hkjnfsuyTsl49a0t0u9pQwza8WNheWBD3xgEqbjZdgTZuMo6yOFh6IFgU7YciqOxSl8wOmLBGCekRIbLCwEsTPhedEk31ARPkRJo5g4WcspWzwp2qEICeOx8DFtffVLynPOzTHVvWBY0wgVxiBUU79ve9vbEkLE4tqvDmuIBYU4cGsbM2WVAFPqCMkxRrAmMq9hKStXxIZlCB71zRtJqS3lva4s24QcRKZX+TTl8bDZ81jGhKlR8sv9JK6EvfrbgAg/I8Ek2tYGgZoyj5i7NycJUzwI7meThMowQsymjbmsK/wZ0dUHYRgzsQ7dR5ocBihuaFDXFtHJUeC1W+av312Sgzm6wIEh7FAuKFsMKfJYTZ0aLB8dlFcnNJyyxKhlbwhFCa1Ah9FT+3O0Uddp/0aneTiErTO+4UP4kXZ5M8ATJPAldJ2njXEM/tRTLtxJcmqdcm1RmBj1eMEYHh01jkbbVySEvtCbcrCJckeWK9d+22Kvn2HJQQX4of48CyMRQxeB0CEIxUsxrJ0onxwG8Pgyn7u0at5Zy11gC4w1M2yeF1hXisQo8Oq0jRTy6tT+TXBu5/W7Jw9a3xRQ66oNRwawV66kYvQkG5IHhSIzhvrund/WgMNC2u30u+csYBzCo8kCeDMjvH60368eedU65xDQv3HqW3goZVJ0jfBuB4l4trodBy3p1zrlsEBfwKNHNdw4v207Qo+0hx759BQ6Ok5bc1mH4sZp0e7DXIMTHjNlDA3o+ij8RL1Iq2Ng1opbzrn5TpnJSrmiWQuJdDw+t3/pDqMRlsLqSjOnlGCOFDox5IgPpYl1hRfNbwmMuHJuYhYRyhftm3UDk3dcP6bq+zYYm/4oTKy5Fr77ksBpR6iAPKeKsWgW5VKeMoKCZ7HojdditTCVtxNCxbKDQbMIqGcs6rCQFHhuevn6R5TqPguMMFGKLKJRno2rnzcNMQRHqLFAHKPtniJrUYh/nnbljRDVFjBZ0eHZs0wiIRK1oGRewTur4STaR8TPOuus5nTS0h6Gbr6PYsUsdcvVfMZsMJ2S5+pZzClHJTOOSOaoQyZ4FsDMVzLGIqSadyx/mNx89b8c+mHMwOwYBLo+L+GtthQPq2fejBp2QuEZ1m5dbt+vMZV6Qgw9m0OrGJpqWL/xAbS0GALs2/FNJXScUMADhceAkdRpJ3yEUY8hkaCGjhP6GL/wEPAUOB4yB0hZQ3iRvciULuXCIRkohKjjH2htzjmh5aIf5L7StgAAEABJREFUeNUIcAxrBEIGuaL0+Q6TdrXDQMdgxJAnlBX/k981iUDBJwhuIlkIbmiZkyVtHWCw69pWwE0GA3i3Od21NbzOnO8KT3FgUOG96VrHOsZ/usKDE8E0Cq/iJVGvayqyCTpT17E+GTgZTUpikGDEFpFlnVt3Bx10UGIEt9YYLuzjq9tp/y4KEtkQLihW+KZ1J8nXNiNLPzmA1508iMeJptEOeQr9IDeSUcij5C+82Bg8j6sIAUqVEFeGJrItx4QtP2iddsB1SYxe1nahF/bikiXxXfTIu9hnn326NLXQMKv1X+Y0RRhNhafVAOJmJAzMWnEbqbcBwKxTJrkXzMLDWkMwNGEtbGEtJvSAJqJoSjBA6ESAvUNWLso84aMIcfUwWRh5mSifxdOKGVHSa7j2b+XgEDIMwjyh9GqvDYtRUfB5e80tyhfhrQ3X614fnkM9z8GQoJ02rDyHLfDwGY9xde0Dk+E5ppQxApS2CWoEWIIlYd7eR9Z2YcQEXKePFtiFuBKsPTdhYyH6X4p9EjII7RQMz4eBExZ4Yx1YxItjjlAklEsssqMIB5SYEkGQc06iA5xMaP0wchA6GNB4mLQvEUpcuyYes5xzsq7RAwcusbKKKujXBm8SqzUcgCGoqGP9UsjslWZosxdZuXZ5wP0uieGDV9gaJCCYo9YPoRuMcsqP9Yw2+damMRGYlEvC39XlVSZgFDzAG+XR2kPLfKuU17mUq0vYZGizXhliPDe6QAlT3iuJOLGOwNblhEkCJ7qoP8IhhbjAEFgJuHDmd8mP69xggAxiPWrdeqOoi5DxbhkYhN1S/MvaZWCo54Z6g5I5qA9XcBQDUUAUCgZkxgie6bJ2wRhPgXffJRl3zqv2uPEqUYzwFd4mn92w/7Xuo6Y1XdqnLJmPFBjynDo552Z7hHlcJ4bbYsRmcLF2hBpbP9ZpKdMGr5gxtvke44g6YCleeC+DonUvWX/WCX5a5APtM9xoVyJborH4t7VoTcqHH3tY8X70SV/CutEGdAKM5L2RYzwbAw8Y7VFQhY+aE2g3RdScMH+Uq4Pea0Ni2EJ7HMznXvIZB4Z8fYL3eQqGMCGapRydLPfypjUJN6a8155oshx6zfHh2UvyLsCXZyEnkcXwFLDClvGAUr6crlOjuJn4LBk552RxsDBaMAR+1laLxOQf++VExXnBgIVkYRJ4vEPWIYQW8SxEvB4IeCENjkLGoFiyWasxKmU1bPltAXO3EzR5WYVRsZIj1gWmvoJ3eh2vpH70gZDba1nDtX/zrDnEwHOoh7kNCu1EmD2r8RiXPrTRbrd9zwpIGOR1qMt4DAgEPAaYNqJvPIQ54baeo4af79/GSznF0Oa776XaH0FMZALmTIDyjnmR0ULWSjinzAs1opQQMCg1lK+uOCGUseIK8eExoqiZa9YQhU2YHrpLcdKffcpoc9f2wTFCGD+DCuOHvEklez4cwY0GYOyjHMwyqTFEO8sPA5tssknCB8xr6wG9R5MpHJQVESW8MviEPOtIfldMMVj4ThpjgPr2ZlHKrVWGFUobbxCFhrGBRwe/oSx27QOcOoyADEDoCt6Cr4gK4s3Fq/VBEXWPH+e8StFTf1hiHEE3GFxGCS0d1m6XcooWhZnRRKQKjzqvHsMoPJEzu7QTMHODAUZznk/KbemBsk7BZhTjbeXhFGnG2F54B9lRJJr1Zn7ii2ApfKWd5XSdGsUN0gkiCB+BF0FBsLxMgnAsOBia/sTTZn+HPSCIJqsJKxeryqDRCylhTWFtQWQd7tKvDmuZ/StivVlfWM/FTg9qX5n2WeZY7SmG9kjK75XAYljCoTyHekIwesHWecLWjMe49EHollfDtH+zpBl/bXFnNRR+y4roiGiCcF0Pc9xqq63qrKQOxr7jjjsmTJeAz1pfAwnfYeVEPMEQ+r0r1sgazpgYUMCwTHr+utxvHhCeGNY+7cqLNHsMsLKbm4wf3iWBg0W4tExJo3QRtux5INCNanXntaL8UdoYBoRpl/ZdvVvvHi2WKHTyR0kMMayik2auIjCMnXJJ8RxnbKM8R8AGBmCAx5UyZl2inw4ToyQok0QM8ZgQQtFhhkL0UVnXxBtGYVNfX7yuvDOlvjEwZDOqUK6E0vFulfIuV7xEfUIwz45Qv7oeD74+8D/KqDVWl3f5DQd4Ck9VF/hJwTDwMxbzpONfjK5kBc9InkTXJtVXtPNfDDCyU6zMlzpRvkTT/Rdy9V9kOUZo74fsz+PLc2jOM2AWb6QQZXIVQ4J5KyTdliyGcWWrt7r076ZKceNR4w4XCoYASjazcy0v/VexNJ5Q6BIFh4VkNk9EkaHM9WqDcsjT06tslDzWQG31qqN/z9GrbJQ87du7MKgOpcjz1GFQlEWMD9PsYrQQroioCXWzL4bCJ4SHgFGUN/sahJqwZvFasCB7Txg0j2QZozAMQjeiKPRLSBkFopSXa8456cf4hz1jqRPXBgND/xDQMC0MrB8wuiiMCd3sBzMoX11KYK0U1vA550ShM5acu1vcSxtCsBhhKIklbxJXiqywUcYRXnfPMYl2o43AwDAMCF+0LgcZSsCg2RS5Ye31Kreu1R+0rnnmheDVimOvtvrlqe85BtU3DmtrEA3q1z6vPuVJREA/mLnI533nUeTFYQS2N01ED0HfmQv9aN1cjGU5tUlxE10hcqNOZIhBRl2hpWQtShqDgrMchB+TI+GvzE/rjZfZfKIjMJrQD8geg9rXxlJMU6W4LUUEL7dnIggK4XAd99lZEC1kjKVXGzwSQknKou4FMywPgWeBI5T2gkUYEBDhK73Ku+QJ26B4ITT94BEdHkkWpxqmKFsssCWfRw0zKonFSRmLk/ABxJOHr3hiKGm8E5Q5IWWUSGExQt+cfMcqymsB12LnWbQockLQKK1CT7VFAeQN11edMEGeP0qbMdRl8TswEBiYawxE+4GBwEBgYOExwMjA08mDVidbSyj//UZIhss5N59aKTAUuZxzqg0fjAe9ZEoyTb/IrNLeUrxeaSk+VDzTwmGAZUScPq8Oq+GoI2GtpCRQKizWXvVZApVTOChgvWAG5bFU8kQJQRwEJ4ZaKEkvgjGonjLjEu7Lc1UTIGV1orjxtvVTEBG2Ai/E0/42SRiOWHBlCCWly8lc9kLIk3ireTuU6cNmcfv97NEoCisl2UZqSqZT8MDx3tlPxUKpHWOzp8PvdvJsFE+KYbss7gMDgYHAQGBgyjEQwwsMLBAGGKbJIA59c7CMCA2fYBF11+8k9wUa6lR1G4rbVL2OpTEYSoCTkHh1hFqN8lS8XJSqQVYa7VGm7AUQw64/eV0ShYXCxu2e8+DwLwTF/kpKkrj/Lu2DobQ5nITSR7GRN2rySQt1nKzqKtnLJA5cQvCKl4tXjQJoPwS4knLOSegBL5owSQoexdj4CoyrvuCQUucYZBavNtGkvIGNFBgIDAQGAgOBgcBAYKDGwDi/yTeMvuQSe0ft7XSKsr3b48pO44xjsdWZuOImXIuQOK2IMDZpWse3VMZlIdpDZbPwKM9ESaKUdakjTp+XiXeoCzwYoX02dBuf+2GJd09ctXrDYEu579h47tpbVsp6XXNeU4EUqmmeitEvdSidFCqJZ7Pkgyu/21d72UoeBa78rq8IZwk3MGa4GdRmXdfvnHPKOfsZaZFgYNrp9CJBYwwzMBAYCAwEBsbEgGggh5c4JdJeRF43EUVknDGbXBbVJqq4OVCB0OpasOdUMUedC/PyLR3fb2gLkDwHNG/17N+xv8e+mV7CI2+AvT1O/etVbh+Qfj73uc8lmyWFfpWxuApdc0oaD4X7SHOLAWF3o/TguNhR4Ckm5ljXOt67OdQVHpxQQP343SVpnzLUBZbyaS9f+yAWx0gLYfT9FmEE9TzmGSN4l/Z53yi7YOtxWkPWipO0eNQc/+zAE6f8FUXNWnScP1jKIjgbyx23DlYfYBzt7HedrD+4sYfPc9Rl8Xt6MeAzGOg0Jjkfo7QeHCRi7c1Hf9FHYGCaMEAwJdsMO114LsZszZG/FqLvuXieaHNxYqDfthcHsonKEmnl3AKJoZwB35qp5Zz2k9tjL+W8/IzGE1PcKF+OL3eSGAEQkiHesdY2LQpr850gJ9j5eJ5yST0xrY5u5Sb18VR7gxwL2hZ+KWQOS7C3ycELRfjUjkQg0Y4jroXpCblzmhBlUbnk5D39O43GfaS5xYATCUfpYVRFzxyhPHTtAxMdRdFLKxv2LREhhCt/dvpvvlGEugDb1EtRopi1x+XIc+GP1gJjAwXuhBNOSA4UYaAooQT2EvLwUdLMe+vAiV7qE5rNed4669JRu9rxbRtGEuvIkb2+4YNo8jDe7373S8IvfYrDEb++JWcd2RtYPxPFzTi0XXsAa5j4PRgD6B+lGO307hwE4/MKlGU1vQfvHF1sp/qobcq4w2TAOP6a4q1+O+mPF5eCXo7RN199EkJd340yf8qpXu3649wzxtjzyqI6Tv2oExhYKAyg4/b+WhfWh3Xq+2C1gWzY2PBAMot2hsFOutw69lkYR6hPuu1oLzBQMODMARFW5b5cGZVtTXECpDwGZt85ZDjE46wJPIl8YwuI5FRQ37b1mQCHjzA827LiBGttSOQNB785T0GEkLzllCamuHF58nD5thShABJ9A8sRn2JXnS5DcSMUEBIQRDD23XCNEii9NETGqXcnnnhiKozeiyU8cqeC1z6ljeCoDekb3/hG8v0Iwqw2EFkKozEQfHgpwNn0aMKwQrnKizQ3GPANNwrJKK3zvI4CT8iljHWtQ9FzYmJXeHCOrB3lObQ/iqJnHxqrE8VLfyU5aZLytMsuuySGBt+2E/tN6aJcMXQUWAeVvOhFL0o8G4wlDlahtDku3QEl4Fi9GE4ocJQ/+wMpCgiuumAkh5dYa47ylX/mmWcmH4U1nnqvG2sY77hTPilv6kbqjYFeufDnm3reA7pIwTnllFOS9+dTFeowGlCsGcEo9nUq816Z90VxU+69aZOCpo06sbzzsFL0zTn0FN1kHFCXcQwTZUyr683m9/rrr998NsJcG2VdzKbPqBsYmC0GGAStBevR2rKuKG6Mw07qnW37k65PBiPz1DwUXUb/Hfrg25yT7jPaCwzAAMVKlJDfdSL/+1ZsOeiM4ub7hBtttFHCv8iI5HnbVyh30rbbbpvAkdXI7RS3Pffcs+EhpW0Gb6eX+55pLZOU8qV+nZjiRuhkhSIEFqQRCAiQ8mw4RFR4EFiHWXnB+R4PjZnmTIAVdsVDQFP3LQ4whBcCzaMf/ej0tre9LSFG8uskFJPgY1L4jpVv/XC/mkwEG0IJeMKKb1MZL4VQXqS5wQDBNOc803jOOfkgKWWANUUoIFf3DMDKHxbqykvn/+0+tMcIQHFnJaWk5PzfMYAfVWHP+b/1DUwf5hBh1zH8iFLO/4XRhwS2S3L6o8NYTj311DXAET5hA+Y/JQmhExLp+Xw4uVRweAoCCc58d4IkY7fjHk8AABAASURBVIkPqTJmFDhCNIWPUK8dR/97H5SyAoOQOlyGRw4MgZtBhsLovRW4888/v9nbBt+UwpIf124YEMbkMwwsiUcccURiuCIYmluFNjFOSYxZlPg6oavCWY899thE0eM9Ve5qbrBatkdCUSPgOeiG8YviKALh9a9/fVIXfRWp0A7dbbczyr19k8bP+FB7CUdpI2AbDMSfecQAGnvaaaclMgX5gvLDQ+DgqZqmzuOQBnaFFjC8uNaAD3nIQxK+WgzhdVn8DgwsFAbILGQHvGvrrbdOIk44efAmUShkDtFICzW+ae53IoobosDj5bQ6guagB+bixMhpzOC8IBsRhXu5l+z5EZZFuXJPqEVEafXueyVCEAGH0FmX87BR/IrHhHZOqWN1LsJRDR+/J4cBh4ZQOtZbb73EkiLcjmfBPkPWElbMY445JlHUwTjKfrvtthtpALxDhEIGAaF+wsR4axkCWGN4KwjEPFSMAZtttlki8I7SCe+EZN7oj3CtD+NGcI477rhG6DXXPIdnMJ6ufTjV0dgpSfZudq230HCeG04YShZ6LIuxf8o9ryhjBrroGdDQl770pclcdT8soXu8WNpgpSzwhEvKd7l3Re8o7BRFirr+hWIyhKkPRrJ22p9/QEPRanNUGCZmC1aiDFL+WEjdSwxl8rTvnpFG/+q6jxQYmGYMUIAYqlj/RRsw1hkvI7M8NN69RLYQwWNtmPPC/c11ZXWy3hisyTVghZm3jYi8EKJIKIoMZ4x1tSImUsnYXBlgwFiL5C9GPfSgjOWSSy5puseT0Gh9Cp1MqcmOP4GBBcfALrvskhgNzU+8gVxu2wYZy7pb8AFO6QAmorhh6ogH78ag5ySUIlaEBsIqWEyf4ED7di8RYhBIRKjcU+YGWfXB55yTeHJ1SqIkInwIojxt659gQeGUF2nuMCD2WSis0LzHPe5xqX2IBYGOQgeGtZ8FZpTRmBf2PVr8PGxOmaSclzYo8jyv2mfRIRRTNkp5l6v5Yr+YEFweNspbbXG1LwwzN37jEI5oXF3aLjC+XceYYW9ayZvmq/XDq0cBF8owzWOd1rERBhmleKGkccZpHpp/BEIegUGCGXpKIGTEQHP1LwKBcsUaXyte9VgIhea/EDHraN999028tN/+9rcbMMogAw1BVwZay1gCnoApz3pgnFOHoikvUmBgWjEgVJghhLfK+uo3TvKLuc5QgscJZ/cbL7De6nrC2EU7HHbYYamsIUpWgaEAMjwKe+aBx2+0Zc2JZgJHWXMGAF7nyjsuakLoJmMiWcfWEGMReaGOZE+RMOmLL77YbaTAwOwxMIEWyO2M4taFyBFJpJ5DSybQ/JJtYiKKG4JjEyHP2SBMIUaEBafGEKjBUvgIHxQq9yURBgmy5X7YlTBCCELQEDr76AgXwsXadRFi/RFI2mVxP1kMUKKEyfKg9mvZXADj/fWDGZRP+BSuqJ1+cJjwhhtumGoDQT/YXvnqswQNqg+G4tlWTnu1185bsWJF4oF08mO7bBrv4YFVjMI5jeNbDGOyNlgcKU4veMELEqGP1bzX2Hm70LSSCIHgzDWeNTSNAQPNI7D1MkpR2tBqnnD0D30lmLpHMx0gQgikeGlbEjIpPJbAJ+qBMOmgAwKr8YIRwi4El9ApDJ4SyACx9957J0YPMBLDnmclXLqPFBiYVgzwdPmO56DPwFCmHPTkACeKGAWOcRAvEkYvv34+axh9Fw5mrfCs2SpS1pv9+MKcebyFLVtv1haYsm2ktIdXiPYg35CnHAAhIgkvZCw1FvkF3hqnSFp/JS+ugYHAwOLEwEQUN+5+iVLWDw0Ijz08ToKhYfeDK/mIotCCcj/suv7666ejjz66ERRc7a1jYeKBIaTwvJU2jDPnnIo1uOTHNTCwkBjAXO3xXMgxdO2bksyKy3PTtU7ArY4BdIjHkscZfaIIU+QIdW3vF2+WQxJKsvdGa9pgUCDICc9FZ+3JcYiN0HEwJalDELWnsuQJySTkCdU9++yzE2WLlV8UBRgRDEKznARm7x3jnNMo7UPgJeRBo8Q7DIXXj1DqICkHIvBCa6MkxhvtOryn5MU1MDCNGGDUtbYGRfmYx6IOyDSbbbZZsjasQQoUw4k1WT+biJOtt946ofFCw8gm9qEWQ8bJJ5+cKH32p1qn4IRkihChyNVtbb/99smWA/C82daWcGfGGPuCjMW6LHXk6UfEU8mLa2AgMLA4MTARxW3Qo1PoEDdWWyFmwmdqeASMFYgluORT2Fh6eeJKXpcrIuYgBURXrKxwBcRMYonq0kbALHcMxPMHBuYXA/Zc8lAdeeSRyQFNQqmEatWjEOLrcI+SGKbqcp4tChOLPSXM3k5ePAeQgKMw8QA4zauEqcuXCHVCfB1UQlC0b0YYFuETHRYV4dAbCl1JYHLOiXCbVv6jxBMkfRuO140CuDI7/gcGFiUGKGAMH2SRfg/AuGwfmblfK3iihayx4kkr9ckh5Xe5aqOENzOEWP+UsFJObqG4UbpKnuv9739/l0iBgcDAMsTARBQ3CpbUa0Ou8BzxqyxIQnmE9NR4RiAJBzbpl3z3lDmhbSVvnCurNYFIGBqrVGkDoSSUsGiVvLgGBgIDgYGJYWCMhnjfhDqiS/arjdoEJYoQecABB6Q73elOyWccKF3asf+FIMoz4L5XQovtrxER4SAD4ZDqSOg371xJvAVCKyl9pS1KIgFWhENEMxSsxHUxYsD6IT+I2uk3fkpdL5kHvEOGXEdJQhl7wQuH7pUfeYGBwMDyxMBEFDdhU+LBhdXUaHRvUzurkVCa2pJU4ITcOIWJlwwhlBwzzmorrKDAjXq1Af6oo45KQhHs46iJH6GC4kYYGbXdgA8MBAYCA3OBgZxXfS7DXklC47h9sPYLYaVwMVJpS5ik0+WE4w5qVz1GLkogGsrQRgi1WZwXrU4+lKpMe/bvUA6d8Kq+/czy68TrJ3xLH3V+/O6PgShZGAyIDmJA9u22tuesjMjBCg7c8Rkia63kq8czTu4peV2uDCcM2HV/ZBVyVC9vXZc2C4y1R0brJYMVmLgGBgIDiwMDE1HcCAoImBOSagJmwyyiIxacFfgJT3hCEpstOYkMirj8HaN+4IEHJqfx2ZvB6uyQB8e5g2GJsnfDZl37PxA3+y5sqH/HO94BJPm+Cou1PSA8fDvvvHNyUIkjRR3b3gCt/KMtlmThB+LBV2bF/8BAYCAwMO8YOOecc9KTn/zkRMgTUs56z9Pl0AKHLdUDQlcZteokzxHKws8dVqINSbsOV+AdE3rFKOYUUHtseMNKu4RCBxlQ6ih36jKYoc0EPBEPohV8PsAhCMpL/4RTe3K0JYwLzSaoovP2/KDPTghTXpKwMsIuAbLkxTUwMI0YcLDHve51r8QY4QAShl7zX2gjT7b9qOa7fZ/KyRVlbbhn9HDIyCjPxhtu7TJkl7ZsM3EvDHlYWznnZt++vkv9K+oka8/aZ4gpeXENDAQGFicGJqK4eXSWXKEy9aloFDLf72ItEkJTp5yzaol12f6NnXbaKbFcUfRYcp1cVrxkrMaOwSUMuFLqKF2svJRF++P0z9pLoBArLtSBsGFPRtPRFX8QNcKJcTnV7YrsuAQGAgOBgXnFgDBDEQH2lREUHUrgoBIRAgxOBpNzTjnn5LhvUQt1sieNx4shi5FM+KIwRzCs9064I2zad0bJc6KdNktCQ9FA++Ue+tCHNt9TRIfRcUYycMI2GdrQVW07fpzXjWFMGDqB1sEpjHOUN4KhQ0nsN3ayHnqtHV4EvxnMxj09VjuRAgPzhQHGZMobJc0+VAYJHwV2XLn5zLix4447JgqR9WPdWT9OgfQdROt50FjJQzmvWt/gnvSkJyWGEuuL3KINBwcxSDPwgBmUyFnODDBuY6kNJ74vx9MdUUaDMBhlgzEQpdOCgYkpbk4REzLJGlUejucL8eiVhN4UOMyc5dfBIsccc0zac889E6GmlCM4PGm92rGRn/BCAUToEFlwQjR580ob5UrA+N73vpds0neSW8mPa2AgMBAYmE8M8GSJGnCUOKOTo78ZsdA69MxYCGOEwc022yxRxuqkjFdNpAJ6R2HyzUL10UVl6B2PG6VOZIQ2S+L9cgiJkyD1QdDcZ599kiP/CawFbuutt05OtSM8OvafQc5eOH1SDNHqZz/72Ul4mTrosTERRHPOshKPoB8bb7yxS6TAwNRjgBJk3vvshZNaRe7ssccezVpgXPYAPOPkHN8xtDatM1FD1oNySTSSe5/DcF8SYw0DDc+dPHKQvqxJYc1Ob91vv/2ab75R8sA4LdIY9OW+Tta3tc/Tp9x+V+XCJC+44ILEuC2MWV6kwEBgYPFiYGKKG0JBuKC4OVhkPlAyTh9OQyMU8drZxzFOG1EnMBAYCAzMFgPoj+8XUrYYnRis7PlFn0rblCDljhhvJwoUgY6VnlCpXDuUQCfbaUMYprBJVnv3dco5J4IcYZPg6bAR4ew+F6DdGpbQ6Xtx+iC08t4ZG4+dvpW14bWpLfnCMYVI1gqh/EiBgWnGAO8wb5q1YX1StHjfGKmNm9dadA/Dh7XBuMEobW0rl3jmrEtr3X1JDMuMNbzU8tRxOqz1rj/rR9+lHIx1rS9w7uvE88cgbX0ai3EpF/oslJN85j5SYCAwsLgxMDHFDaPn4mfdcbjItKLFB2YRMNbuaR1jjGteMRCdBQaWJAZ4w3xnjefNXpyFekgn8zldmEJJUVyocUS/gYHlhgEh0kKlhUgKwV5uzx/PGxhYihiYmOIGOSy/9qGx4LqfxuRbR0IRpnFsMabAQGBgsWJg+sbNmLbbbrsl33YTzrhQI7R3mceON2KhxhD9BgaWIwaESvPY2ce6HJ8/njkwsBQxMFHFbSkiKJ4pMBAYCAwEBsbHgL06Ps1ir8/4rSyTmvGYgYEJYoDBxp47oc0TbDaaCgwEBhYQA6G4LSDyo+vAQGAgMBAYCAwEBgIDk8RAtBUYCAwsXQyE4rZ03208WWAgMBAYCAwEBgIDgYHAQGBgVAwE/JRiIBS3KX0xMazAQGAgMBAYCAwEBgIDgYHAQGAgMFAwMFRx8x2RqUn77ZdiLPODA6fRmSSB7/nBd+B58ePZevHNtHiXi/9dxjtcWu/wb3/7W3JITrzXpfVe431O/n3+/Oc/T3/5y19C1p4ifYNsUaehipsPQkZ6WVpuOPCRchNlKT53PNPym8/z8c6tl4suumjZ0Yr5wG30EWt2NnPg73//e/rFL34Ra/NlMY9mM4+WQ91f/epXyXf/lsOzLpZnJFvUaaji5jsgkf6TlhsOttpqq2aeLLfnjuddfnN9zHe+Bk2wYB7zmMeskT+p9qOdmJsxB8abA47E32ijjWJt/mc8/MW8Wz5422CDDdL1r3/9WCtTtFbIFnUaqrjVwPE7MBAYCAwEBgIDgYFJYSDaCQwEBgIDgYHAQHcMhOLWHVcBGRgIDAQGAgOBgcBAYGC6MBCjCQwcgXmpAAAQAElEQVQEBpYNBkJxWzavOh40MBAYCAwEBgIDgYHAQGAgMLAmBiJncWAgFLfF8Z5ilIGBwEBgIDAQGAgMBAYCA4GBwMAyxsCUK27L+M3EowcGAgOBgcBAYCAwEBgIDAQGAgOBgSswEIrbFYiIyxLGQDxaYCAwEBgIDAQGAgOBgcBAYGCRYyAUt0X+AmP4gYHAwPxgIHoJDAQGAgOBgcBAYCAwsJAYmFrF7V//+lf629/+tpC4Gdj3v//97+Y7FwOBrigE6wOgvoVyRdacXfShL33OWSfRcGAgMBAYCAyMg4GoExgIDAQGAgOBgbExMHHF7Q9/+EP61re+lT772c+mT33qU+ncc89N3/72t5N8SkXXkR555JHpbne7W1fweYX7xS9+kY4//vh0/vnnd+r3rLPOShtvvHG67LLLOsHPBuiSSy5JD3vYw9LHP/7x2TQTdQMDgYHAQGAgMBAYmEoMxKACA4GB5YqBiSpuFLaXvexlae+9907Pf/7z00te8pLmus8++ySK2F/+8pclgecf/OAHzfNQSpfEA8VDBAYCA4GBwEBgIDAQGAgMLB8MxJMuSgxMTHH7xje+kZ73vOelM844I2299daNYvO2t70tHXXUUWmPPfZowh7/+c9/LkoktQe9wQYbpLe85S1pu+22axfFfWAgMBAYCAwEBgIDgYHAQGAgMBAYmDgGJqK48aRR0njcDj300LTXXnulu9/97mm99dZLd7nLXdK2227beN+ud73rzTyAsMl//OMf6a9//Wuj1PmdUpopLz/acPa+lTJX+7nUpRRqy718yT4v9/LtlwMjvyT1lNdw7vUp+a2uq/tS75rXvGa67W1vm25wgxuUrOaqff2UOu2xAiow4Nrl+ijlpQ156tWpDWP8dXn8DgwEBgIDgYHAQGAgMBAYCAwEBpYWBiaiuH33u99NX//619PDH/7wtMUWWwzF0J///Od0yimnpCc/+cnpoQ99aJN22223dN55561R9+yzz07PeMYz0pZbbpke+MAHple96lXpt7/97Qzc9ttvn/bcc8/G27fpppumnXbaqSmzn+ztb397Um7P10Me8pAkZPOHP/xhU+6PkE7jBfeEJzwhbb755o2S+bGPfSydeuqpjadQ3Uc96lHp9NNPT0XR+trXvpa22mqr9JGPfEQzTbr44ovT/vvv34xTncc//vHpla98ZVNW/sDRs5/97PToRz86bbbZZukVr3hF+v3vf98UU+TshTMm/T34wQ9O2vjQhz6UKI4N0Mo/9gu++MUvbvoxduM45phj0h//+MeVpfF/FQbib2AgMBAYCAwEBgIDgYHAQGBgaWFgIorbj370o/STn/ykUWaGoYfy8+lPfzq9/vWvT3e9610b5eaAAw5IV73qVdOLXvSi9Mtf/nK1Jt74xjem+93vfumQQw5Jj3nMYxIl5b3vfW/idQLI2/S5z30u/elPf2rqC1/krTr22GObMM1HPvKRTR/COL///e+ngw8+OFHq1JV++tOfpi9/+cuNEvnyl7+88f7Zm8eDSFm0Z4937dWvfnVf5eg3v/lNUk7xfM5zntMolxTGn/3sZ7qYSRQ5nkjte5Z3vvOdjYIIwHO/9rWvTXe4wx0a7ySl7oY3vGGDJ3vqwFBYX/rSlyb4pgDqk+L2nve8pzkIBi7ARQoMBAYmgIFoIjAQGAgMBAYCA4GBwMAUYeBKkxgLpYkX7c53vvNMcyeeeGJ63OMe1+x323rrrROF5vLLL0/CKp14uGLFivSUpzwlbbLJJol36YlPfGKi6JxzzjkzbfjxtKc9LfGG3fe+921CMO94xzs2itbvfvc7xU26/e1v33jTKEMSZYzXjNK2yy67NCc68mJph7fsm9/8ZlPPH+GO//u//5vAbrbSC/bYxz62UQJ33HHH5DcvnrJLL700XXDBBaqskezvo/w99alPTY94xCPSfe5zn+bZDzrooNVgeRWNx7N4rmtc4xqJEgvopje9aTriiCMa76I+73//+zfPzZP2q1/9CkgDe9FFF6UnPelJTT/3vOc9E7zd5ja3acqKMtsAx5/AQGAgMBAYWHAMxAACA4GBwEBgIDAwKQxMRHHLOaecc+N1KwO71a1ulR70oAclytDVrna19NWvfjXZU0bBo+gIEXzzm9/ceKp4jj7xiU80XjRKYGnDVYgjb5zf17rWtdKGG26YeLgoUvIkCqO9dDlnt804fv7znzdwhx122EwflDZeKR65BnDln2tf+9pJ/ZxX1b3uda+b9HOLW9wiXfnKV26ey/j9pkStrLLG/4svvjj5RIDnLYXgKYXl3lWo51WuchU/k36udKUrpUsuuaS5v/rVr55WrFiRtMWrCCcOelFY9rmdffbZjUeQcvua17ymeS6HpPz6179uFOICp06kwEBgIDAQGAgMBAaWBAbiIQIDgYHAQIOBiShuN7nJTdKNbnSj1TxS97rXvdLTn/70xAtVf4+NckGB40Xi+aLESbxtG2+8cXPoRzOyWfwRjildvFKh0nZJQg15stZZZ51ZtD43VSmiz33ucxsv20c/+tFkzEI7694onPa7fec732nKwfgtpJJCS1ms4eN3YCAwEBgIDAQGAgOBgcBAYCClwMFSwMBEFDd7wHiLPvjBD6a2stFGEu8ZRYMC5VMB73jHO1Kd5Nd1KDT1PeWLl8zJjnV+/Zs3C4zDPeq2/ba3joethp/tb/3x0vG6jdsWZc1hJ5Rdnkhj9S28uj1eTF68/fbbbzWcgXXQS/Hm1XWm7bdDWHhfP/OZzyShs3Mxvssuuyx96UtfSvqg7M5FHzzG2hci+4c//GEuuog2AwMzGLCH2HxzwBHD10xB/AgMBAYWDAP4wFzzmgV7uOg4MBAYmEoMTERxu/nNb94cHCI80aEZX/ziF2dOQhSaWIcYUqh4hwghFJ0aK0IA20KwAzvsiwNHKbQn7Ha3u1262c1uJqtnokQa0wc+8IE1lAP9lvZ6Vh4jk+K67rrrNspUXb2EOtZ5/X5TSOHqTne6UxNGKWTUoSs1vP1z9vZ5hlp4c1gJ7yJvZg0/jb8pp04J5V18wQteMPEhwttb3/rWZs+jPt70pjdNvA/KoINmtP+sZz0rHXfccU0I68Q7igYDAysxQGljxDHfHLJ07rnnrsyN/4GBwMBCYsC2D4eYPfOZz0zW5hve8IaFHE70HRhYVhhg+HeIn8P+ltWDr3zYiShuQvQc/EEgd1z99ttv3xye4YRHB484qIRXyp4ue7koIDxUDi0hiDjN0UEmDitxsMjKcTX/nbD4la98pQkfdPKkgziuc53rNIeG8Nw1QD3+3PjGN26O0v/xj3/cfKLAMf3/93//l+yXc5Q+JalHtSYr59xcR/mz/vrrJ3v5PvzhD6dddtkleR4HmxDqh7WT86r+HFhCqd13332TkyN33XXXdMIJJ6xW/f/9v//XnNyJQfCwUR4c+uJAFoexUPxWqzBlN8JXX/e61yXvhdLuEwsUuQkMc6YJn3uAN6G3+vA5hQsvvHCmfBI/Pv/5zyd7MrXvWU4++eTmpM/Ztk0hrBXy2bbXtT6FfyHnjmfmie063rmGgwvvAl7muq9h7RuD+eXEWvPtW9/6Vnr/+98/8xmRYfUHlXvG+T7QyPPA76BxzVWZvoWaS6UPeQs1njKGab/ClzTt45zv8dmf7oRrMou1ideQV2Y7DrieND20zrVpvpfx+a0vhmy0wH0pi2tKcAJvvXABbzvssEMjq4HrBTMfeXinscxHX4P6cEK8QwLJvgwavWDJZD7XtWLFikSe7wUzSh682yokgmuUerOBJcPqd6F5xkQUt4IIittpp53WnP5I6aKIODXSEfwE9utd73oN6AYbbJAc80/5oEQRtp3E6Ah/HidAt771rdMDHvCAdPTRRydKjZfum2/uKTDpin9OVnTS5BW3MxffSnPcvhMh1WW1phwKQ3TwCEB77/Trd0kmlX6Fc5Y8e+IcLCJUUZ7nUM/ePvdOh+RppBRSSD3PRhttNOOBo0gKAW0rm773Jl8b97jHPRp4nz7gWTQ2ApoTJoVHgpGcVEmxu/71r58oyZRh+BUWSoEWQmqvoD2H4KcpOSiGZ7Aek3fUDoety0f9zQLqsJZSz7s3JzGnkjebqzBMgjTvcmnHe/Ddv3I/ztU68O7Nz1LfYTqf/OQn0/e+972ZbwiWMp+IUMbDWPLGuWJMn/3sZxO8jVN/EnUcxLPtttv2bQrOeZ8pyyURkCY5b1Z1vuqv02M333zz5qTWVTkL9xdjYtwgdJVReO+zNUbYY+wZeYtLu6IZCn5dGSgYJibFpDA93kKGldnOW2MWNurAJp9ScT8sifxgTEOrWWs9l3mE7/QTNoa1uRzKDzzwwOQk40nR0KWCM5Ed1lF5HjwBH5gNnsxRchQPe2mXoIjHWJ/mO0F11PUj8sTJ2pTN0q4QT8Z1cgjZSOROKVvuV/SWnHnMMcf0RIX38d3vfjc5FI/8552gmSWh0c4gwKPaCrHtFb3oNxrEQKfd0imayTBQ+J9oNkaCUn7kkUcmCqS6JW8hrvpfe+21ky0/veYRWoufwAk51VpZiHHOtk/vlXztfc+2rdnUn6jiZiDrrbdewhhNqLe//e2JwsZTRhGjZICRhBfus88+CQxhVaiBhaJM+p//+Z+EeAl5dHV6Iq8ZhUZ5SbxpmEq5r69CMpXrw346B6WwChQYBJISVO5dvRSerBrOt9eEbJa+b3Ob2yRjcQCLOhKFzScEEEjP45t0lFZlnsveOt5C9yXpB1y5p7z5bpzxUgIpkZSd0i84/WyzzTbp8MMPb3D3qle9qvmUAWVSuc8KcB9ry/20JJ8xICC1x0P4oghNwmqEESEcdR8sUjx7559/fp099m+CnlDVdgO+pccb0s7ves9ai2mb96WOd+sTEuYJxa7kux5//PFJWc2I5Q9KiKd5iOkUOEIB/JhPJW/arg4xQlOM3X5OtAW9IFAykkx6vIxH1h6agYFPuv2u7VGqfTrF2qnrmCfoqrDpOn+U3zwFBApRCKXeHnvskRiG4FhCO1lGKbLmToEb92qNi0pABzD6cdtRj3DsMy6Mfwxc1rn8QcnzgjOO8vuss85qeBRFblDdUco+97nPJXR7lDrzBcsQgEeNgv/NNtusOXjsC1/4wnwNc+r7IXy3t0JYr4wsysyvcR4Cn/KOrEX1vad3v/vd6ZnPfGYSacNwLSqHPKJ8Nkm76pM10NMiQ8hbbAl9wh9q3jbbZ4B79KJXO85zYLjnhFCOV4t+Qjcl9Nl7Is8xwIIpiaFdebkvVwYo8OW9yKfsONOAJ4txldzpvsgDHBcUpU996lPAFyyR7SmxrpTM9kDwUbKZ6LTFPM/Kc3me8nshrhNX3BbiIaLP6cKASc2iRDgkJLBy77333on1qT1SQijvK6JHUGSNt9dxmEVROQJBCPRpBEo45obYtvtgsXrhC1+YEDyE0d5HDLILc+WNoiQhnNo3Rp+jsOpgFQAAEABJREFUaPeBkPo2n+fgQTIuSl4brte9De4EyNqrCw7TcOCMsbL0yCuJkOB3ufo9LGFumEqtCDAE7LXXXul973vfsOoLVs7q6F0JpYZbDNr7PuWUUxov9aQHxmu9xRZbJAyXhXDS7fdrzxwyD3ihDzrooER4Y7AyD9p1rC9hJ+aj9UOQ4Jltw/W6tw4p67z99uYWGPPDc8OxdOihhyaKsblhvRW4ca/C5NEBSo2IgXHbUY8SYV0zovFEjKN4+cwLo5+1WkdYaH82ybonYM+mjbmqy9CDBlB8u/YhEkDUB7rZtc5SgjP3C6+hMKE9jM69eI0ID8ZYvIZRGM54UdCvYTjBN3lUGF8JuOBZ9hkFGZT1jdcxWKATymeTRL/c+973Traz4D2zXZOzGcts66Jd5nXN22bbZr/6ZJaTTjopMfDd8pa3bMAYg7wzdFNCk70nxlxKMR7fAHb4U+g9OUNEBAMdnufdk18YW80zTTHQc2708wyCma/EOQMHZLh2nyKE8FLzmMzRLkfPyU68wrZaMZqWZyyw6D3F1dYgW5LIZqWsvsI5/qktzhQKb12O/5DVKMW77rprkpR7h4zy3pu6jIIMMfK9E3xZ/3iNNQ7mXe96V3Oeh/nHwMzQybHCg23djvLejaFrCsWtK6YCrhMGTHDEhvuessRbRKhkRezFvOQhUIRwE92BJQRSjLGfUoKR8rboA4M84ogjknBIC7vXIPUh5IuAxhuKkFr8gza1WogIiT5YzghiPIP9+tAvoch+TpZR47KA7eHUv/J+SWiEdmvvR4EVnstji2hQYEp+r6tyzJ9AQTDzLgqc3/KNpcAgSCxkQnkJwAXWFZxy7ainjvaVlSRPko+ZFDi4KzCudbn2+r1XsIPS2muvnQg1lA3vT4g0wUYYdKmnb2PSj+S5PUspd++5CpwxG7sxFhhX32TUDoHN/VwnShuGRVlD/AkhlHVKVq++jR8jtGZYexlHttpqq04KOC+3cGIh4e22RQXAsYQJS2Drd6ZvuIG7guO6HeVwXMr99g7MNVZqwo5wmVIHfN1egS/l7au2eDuEg+++++6NN8ja6wWnXWOU2u8459x8xsbcL+MBA1YfdXvmiFTy2mM2r9TRn9/aKc/vt3qeSzlcaks/7tWT/FZHmXt1SnKvXXXAgDWGUu63Mm3rx2/JfYGRr57xKNOONpVrX7l8CZx8iaAlnM5aY92Xt1wSXsNDQgBE00UmdOE1eCCjC15jvzuv1jCcCQm3NkX4WCvghcUR/qxVWzzQvoc//OFrhHF7/96Zdyd5l96pNtrJnPDuzQP9qCe14cp9DQ9O+/qry81Zbbpqt5QZQ8mT7zc47dRtgHcvX/u9YLSl3HjMW3CunhV8Kfdbnjal0q58deoy5VJ7bNqS3ytRms0La4JhtcAwTKGbEhqHn6NPwmkpXAWu65WC4P3zZIlQs2XHXjJyTInCYmSkdFubfQx3XbubNRz+bCwULgpQ3SAZEF6MP+dV5zooh2eynzMyKHf4LkWUkYJRkhcSHFxQlkQmMTYwtJHfKHzKJW25Z4wTqWIs8IM/4qnKwaFhzoQgm8EvvJlTeKmovJxzcx6GuSXShwyQc24+VUYWY/CjgFqH+CPewdPLsMzwwtBlrTLcCKc2Z/U7yRSK2ySxGW01XjUWcFap2aDDwkJke7VBCZxNOEppU0iCBVvu66s+EJs6b5zfvDYsm4PqIlgEdEShDUfQRSAodsbbLi/3lBdWPmHJW265ZcJUhBirB0Z4IQsRAoiQgSnMhAWq3mOGuZ555pmJxQmc/u2hFN7B+qc9CYHUH6UW4S1wwjYKXinMhB2Cj7YcTISIytfGuAlRxWwxzrJ31HzhHTUufbEks57xWIKVeIAp5CyUiDQ8gaX81wwd46XQEqTq/HHHO6weRkOBHwY3qNx7o+wRVAbBYTIEj8L8+8Fqx3yxVxlzAkfBhDvGFfuHeegwN8xPufdi7hBYzAehvzzV5jcGxmrMY0EYBY9OMHaYX94DeB5gnj7lvRLB4Oyzz072MRMEKF4UuTYsgcA4yzhYcQnHNZw1TuH1XPJ5+wnJ7T0YnlV/YMwzirU57/nlm9NwxdLLyAIPnkdZEWKsN+uARV5de7bde2dwhvET9qxTY9eXZC1pw1w2VnMWvlj9jQUMgUFoPoHH/Aa3+eabJ15TijcY9OGQQw5pDraxbuFaWL727c3aeeedkzFLDFbqlGSumDOznaOlvcVyNa/xGmthNmMmFJoTg9qwJvRnu0nOq4Rb+6fQOF47a6hXfe+FZ89c9+7MK7TbuNG8dh281fzQF3qpDk9CG67cM1j6hBKjpwPlnDmgbYKw+SAaRXtouzmFT3gW9QnJ6pir+Ix5T6B2FTpXxofGCnnmSTEea9YclUfh0hae4Rmtnz333DM5Z8Cacm/NWH9t3iZP5I91Zq1qV100UJsSvKL/pRz/gCNl7WScohUoIc4faJe37ykiTkGn7BlLu3zQvfdOSUALexmmSl0GTHgka5S8hboK3aRUme/oinGQGYTImzcM0fJK8lzkA55DMgpegUeYU6JKyJLWnr3R+DiZRuIQQGvxptKWftBeih/jCjrIeOJ9msMMnQU255y8b95BNF++sRmL/tFa7VPM9O1diFDBByjo5hs+RonEG81B9fWnX+MTXWIc5pf2J5nmX3Gb5OijranDgElN4GXJm83gCGMYQ6821lprrST1Khslj2UTwetVB/FBnHuVjZJH8aqJS7uu/jEkBBqBb5e7x+jsp+TJc3CNvHZiiUK4CV+ICKJCmC1hApgnYsJ6jiGCQdDa7bgXQkDAEzrD2qwegZLSQ2AtAoj3IzSFAEDYtrfSs7Iy8aIiuKxVCCaGrE8eJSEOxqavcRKhAMGFC8zbu9IOIul5N95444ShewaCLYaAqIORPB/Bn/Dr2TAbY6Y8KS8JM/EcRagv+XNxxWysndm0jblgHvDerx3MlCDAs2YfXw3nfTIyYGQShRzTweC8V7D2u3l/QkEwOJ5xQpB5oZzC47dThAlR5g9hrtezGaf3gFlSlswPVlQhN/CuvV6JYYAQhIl6BgdhmU/1Oxa24v2jReYCZmqeskz3anNYnnlEMQZHmSfomR/GzBNDiLO2hN0QYL0LZRSnsg9GXeOCf9/rpOx5H8ZpjARu8Kzr8IYuqEOAYX2GQ32B2XDDDZP2KWxgJDiBT3TNWmSYoJjKg2sRAMbn3R944IHJ+/FuvDOCEos5Ad54KJzaLEmbnp/SXPKWwxWfkWb7rBRf72BQO+iMzyGtWLEi5bxKcfOeCYzok/dCwfee63YIxdYNusfKb14RJs0TilMN67eD08wdNMec89u8UDYoCZlmDBNJw/NnvTGGoO8EbvNJ1IA5xytUt2X8DAjgjJHBxhpCw8EJSRMOSEFUji7DGdrAaAGmJMqmteCZjR8tAm/91bwNn2IAwbcoUMZn/cAfY1OhMQyixsIzqh3rl/JZ+quv+J3E0Km/uqzXb/u5KOKel5JSYKwjOKqT9whHBQbtMhahf7xDYLXRnkdCNskRykrdhbqSYcwv74xB2jjMG8q3ueO+TuaOZ/beeBRLGXnDuvPsDH7asy6sB7Kl+U0RNF9KHXQMjccP8Ck8TPIOzIVCT8HLI4u4ukev9clbhj6rh1bDq/7B9Eu8duQcbZW6ZAy0ncdwWP1+7Q7KD8VtEHaibGQMENgI6bWwMmojFijmo61edQmnCK+F0qu8Sx7vFkUIA+gFj2gi5oO+F9irXp1H+RRmKaSrzq9/s5Za+BS8Or/+rT5LI0YjprouK78xHcIXgQvhxMxYixAyMHDmmRE84wLjqqxOxlOsjYRHhIwlH5FzSIGywmzV865YljBGhBQBJsjzbCCwGKtxEbL1yVuAsFIQ1B8lEV4Jmk5C826MSUJ0tYPJYxwEVkKMMRkPSx3cgZEwXKGFxmTMhBZKKmVQeUnCHQhSdd1SNukrxdzz9ZuPXfpjqea1KfjoVcfzYJQEr3Y5JsUKLrxLorh5jxgQpQ68cVIyzDfvk+WRUO+dK8egzSHvwTuAX8JHUfzAlEQZoiyDZUTQHgWOcIdBF7j6qm3hMOa2hEZQmgiRFMoCS3jErL1/c9gYCG2EnAIz7lVfcCWEicWVYQXu4d2hVgTinHPyPJS7tddee6Yra45RxbowP9XD7I2NF4KQTlGGR5ZeFRkk9IkmquN5WZ2NgUUajMQ7473ApffEC0JoJBzBhbUK18aJPqMH5gGhkjDLy+x9aR+90WZJ6npfvRSBArMUr3CCVtTvcNTnNAfQq2Frm6BLqRbeVfogzAoRY5CzNvET2wkIlWAoe4x23idPElptbvEYoNO9DBV4m3ePDmrfPGUs0N6ghPaau+gv2kgQNv941801a8G8sS7Qjrot9NXYrRU8AP8WJVLGxwiEz6HdnkEiR8A/wb9ui0JHiaU8ma/WnOdp8za4QV+sBXyCkY/HzTqjfPEaooW8YdaF59Av75z26z7Lb55P64URpOQNu5aIkBrOu8EX6wRnJUIGrPXsGXl+0AiweC0FT3lJ3gVlrvD6kr9QV7IHZYUyzvBHecYLzIv2mOCfFxMdqsvgzDtB4/Aenl3rouYj5Aup1PNe8Df94l8lMebmnBPjeIE1V9oyl3dirZsr6sJ74WulXq+rd+Z5KZfqlcSA6b3UY+xVf5y8K41TKeoEBvphwCRlBUGIEcqcV1kO+8G383m5MCpKQs696xI8CHgsZcIEcu4N127bfc45ETRZ3ygz8volnhjhRYS9nEfrg3IizENdOOnXh4VNqBoEoy6G55lZzzEceXVChDBizF1IipApsfUIXw037DdCi0kQ3jDbAo+JIL4ESkJeyeftIozmnBsrMUEaw2SdB0Pg8WyYI5yz0GrfcysfJWGqwr/0oX3zjFVOGwgsQYKASyHXF0syIo4x1Hjg0SFcp5RUTRQARgBMucm44o9xq0dxuSJrzi6YCoGCJ8S7HKUj4yRUsVh7H4Pqeh5JnTacdUVwohhJwpu8WwpDEdjhHs4pSXDMMs0qWt4nvGLQvJzWsffSD3/eIS8xBk1gLePBnOGj3NdXTJKAyzqKoVs7BDN1rDd54Cnr2jSP3UsEQ0Kr37NJBHHP6V0xFrCew2mXNo1hnXXWadYK4cG41SN4u/ebgJ9zThQlRgNh2+B4LeBc4kXwDuFQHUk976vgTntowjA6gEYR3nkyKJDWZ/udmRveMQFFX8slwSFlmsFsHF5DyeDFofgOwxnc5ryKjtaw5qz3Qkmi1PM4U54Yv9A8Vn3vhnJobkiOpTcntVm3Neg3o5y6JZkPNTyPCeG55FG6zL9C29VDg4yn5hHg8Q50w2+J8kbItnbMb94iil9N+6wVPJonRZ2SyAY1byr57StegFa4olPGJxWDiPWlbREc6Gd5Nu/cemi3B44nR1mX91nqF5qUcy5ZyZziua8TA03bYGX9ipbhwTEHjIFyIbS5NGa8+vAuShPIDrYAABAASURBVN5CXr1ryjQeIUTRO2ZAo4z1G5f5W5e5Nz9yXoWzMi88Zw1X/0aj9MEjjH+VhBcwXpm/NXz921ygOONH9qOq632QOWq4Xr+9I/068ES9khxShTcyPvSqN5u8UNxmg72o2xcDLHjifQkFfYF6FLBWIOo9itbIIuywQhGi1ijsk8GyQ6AkLPYBWS2bJY+lkLC1WsGAG88uVAnjGQA2cpHQEIoTCxbPQ90A5kmxI0hiLggGYlTDdPmN2VN0esGy7PXK75eHyLJCIdrGZUyYNObTr86gfN4JllrKH+WA4Ex4UacIKJRG/ZTEY8TjUgsN4KcxYTwsrDyARYjoMk5eGNbBLrCjwBA6hcBS2jFg75NyS2gUzlTjuLRLsbOGCRfeNUXQ2LyXAjPuFTMnLGqX8KUfydxibeWFKFZn733cfobVI0Rg8izLQtd22mmnZE5SIofVHbWcAuW5reWC73L1fqRR2+wFT+j3ruCQx4FXddx12qv9xZ7H2EcBG4UG4hl4ByF2Es/PKIMH+AyM+W6vVZkfFPwyL8qVgkPx6do3havUdS3Gmn718QlznqIPviTjRKv71Wvnm9vtPPdooNNM/R4nWTfwQ3EoY3Pl5UEz4UbfeF6X9uED/RmkALTboUxRSgn2s3kWyhnDHrmCcdt6bfc1Tfe8/mix/dAUF/juNT7GVwY2xgjvqsCYUzxg6jJEMa5SsuXjQxKPrXdZ6qDL5Dvh4+ZlyXcV1VS3L69O3pOQdOOkrGnfGjM/arjyu8gb7sm43i0jpTryJP0xGnSdX+p0TYtScfMCKQW06K4PWuAIklzvNYJL2TRcWUdtAjbRpmE8sxkDL8YojE5fowoimISkbpdEsESwu8AWGASBxbvcD7siHpj2MDjliBLChQm6H5Qom0KhHGaAqBVYXjKHDHg2Fk+WefcsxAWm65WQrh0Ep12HdZBywbLfLut1j3DZLOz9EHqMyfh4SHrBd83TnrAWgmUJG0Hgc86Jh0U/dSJUKy/tI9I1MdUOQl/DgGXxIzx4P+7nI2HwDBHmRdf+GApY/brAe7+eqWY8g+pReFm3GQzUYTQwV+2FKjhmMKjb4CEQtsUrRNjgBWZ9rGH8Jox4l2hx/T6U9UqYKMszJs6bT3kvST/a4ZVQl1fV/KsZO2bsPSsflur577nVretonwW8zGfPWowINdxsf8O/+eda8F2u1rk9RbPtQ31rXhicCAMKqcMFWMyVSRRvc9P7cr8ck2eXuj6792aedIW/wQ1ukMzZQWsB7eWZ4m1F9/FYa1pemRflyqDB+9G1fwfSlLquQjIH1UWn0BKGUPB1aisWaGndFs+5cZvX+KUyz+NakjqULngseaNcjU37vJT12PwmP/Kcrb322glOeS7rttuGUeuf0ob/wXUNO+j3l770pSTMT8TMbLZdlD4oCZ6Jol7yGKnMB3JKyVvoq8gYeKW0Dgor9TwUJQfIeCciisjntmmIWqJIwbnwXzyRIshILRReVAdjQ3lWMhdPJh4g4gjfIStpV3s1zkqdctU2Pso7xxjHY01u8O4KjCteaDyiUYThmhPWrC0KjJr6le850GfRP23vs3Zmmxal4uYFOEWpFl67IsKGXRZbREMdCxSTYiHgLhcbjmG1GTxNn5sdjEMNhKNRsrRRkpeFmYNhtSS0tl8ai6b4aZZ1cIgry0Fpg5DADW7ysRiV/MV6HVWYGRWeMNd+D4NwRQAdBV5bhBb9+N0lad+86gKLuSAGCNww+JxzEjaBcFOiCrxnwuQwcXtaEBZzXJhIgSlXQvegsfESIqYEZJ67Us/4hM5QehHUkj/oitnBHQshJu8eg5QG1etSxotmnCx15gyLF+aIkLL0lTa8N5Y0Qn3JEw4hhLLcW+8E/LaVWLsUIngtsPNxZbRBB7r2xZDVFZ4Q5Hl6zY12fwRIuGJp3nDDDRN6REgwRzA58GiXueF3OxEkCDmMHm2hDKy5r10buwly8iTMstf4zHOeNvtUhKVgliXxvHk2ghJFnAKP9qLJ2pS8c+V+90vGhDeYE2CsF2Fyntt9nQhLjCk8/wU3dbn3Ut+P89t4hGybn0LZ6jbgqJ7rddmw3zW+C6znIUR7Z5Q0a7eU6Ysg5t2XvOV2RUvQ9q7PPSo8GgnnhT6rT1C19xbt1C86TzA033gBV6xY0RxT7nCe9jpE98wb9eYioQHorvHU7cMRA3mdR9CuQ/wJ3Qw8jIuuFEy0u+ZrPBi8JzyHdVv9flurBXdgtItXwU17/VpLeAL8oVOE9cKX5FNGtVESWojXwHmXNeB9oVXkO3VFIJS2ul69P1tOyrjIpNpkVML/Sjvos7VbFMOSv5BXCjE6jZ7UY6Ug5ZwTvmp8DEb2dovKobw5hIURzNwSUVMMH+6FMJrPZGzrgjKHzqOR2tK2PZZwbf55h9omy5hD5iq4XkkbDoyi/Ik20Bc+wCNYwzvFWMQJhdAWAoqetcjYxViJvxgfT7u5K6y+PGvdzmx/z6viRgO1QNsEZrYP0bW+l4mZcnUTbtUTesB6bGFjTJi7Fw/pyiUExKZQlgDWZcIrqw3hgbYNxv4KMLR8gpHNryaeMCGLGAxFk4WUlwBjtAApgOBMSDAmPIGUwIEQyVusCcGiQIwy/lHhCWfeadc+EHaCcVd4cOD143eXxErYFT7nnDAPhKNmbP36ISiyZiEWOedmrwyGz3rIKMATZU7bzA7/dTs554SR2TRrXiJQdbnfvCCUQ55SxEi8OCLG0GCMBGQEDWy/lHNuirRFmWIh0wajCau+ddAAzOIPIo3YM6hQLjTl5EACKeKtP2ElrMiId604WGM8JcoQd2F/CLaxaUeCX4IEYk/xlDcfCa3AqHkFu/ZHeeoKn3NOQrcweoJB3YdndpoW76iE+aCN9pOhe3BunmFWrPHwJmyLsKU9bVHCvBfGKfMLc/NMLKHK64TeOjQEY2M406d3yHsG9zWs34xZrphhod/uJXNTiDVFxpzwnT/7vexzNNbSPg9dzrlZN+q1E9ygv2UswgcZCRlECiyh0xjBYNKsqsqLJ9ka80wYublnz0Op2+uac+6VPTNGBw4RYBgVrW/94j2876y8PStXmTn/t33CnfkMz96t740JuWMVx4dKomwTdkozaIt1jy6UvOV2RdPR9q7PzdAwCjy6BL/kDWsm55zwNnOIcYDBmHHJ6YLmH8OvNWkNWrveoUNrzA+HhXjHaEPX8Y4KRw4inJPrrGP9Wmv6pmzW7Xkuc5ZsZbzkJd4U8iA4c5HSas3gX+i3tUdRspbBDEo5r8nbyGF4GTlKX3iZfuDNAUgUTDTIuNBPn1DQtzrKrLnSp/eINto6od2SX6455ySqwDuSKBT4kHZ4e9CVAjvsmvOq9Upxtz6tQ22iA94pxRFdLe0wdKG/6F3Jm+8ruuvdln7NS2PED+pnN2fgkdxbYCnY3g3FmOJD9iUroKOeC5wrPDiIBwwZmnxi7yKDMhiJUZvTRGQI2sgA53AS+DMmMLxitXwvT/vmGu+cMuP23jh10EQwkvlCucQ76RMO0NEuBdT71i9FkYJNRzDXlKs7yTQPitvqw0WQulqHV685+zuKl1CHWos2ObwczFmysFlsCQkWvLHapMjSBc5LIwxgogQWBMjIvDgv2qRDWFlpMUgvsCgjBFn7V0w6k42SaIGbBEWY1Y4wIESAZ1HbizVZYO2xU1gJORYJobhdPuoz9wqXZXn3joVdwmfdh3fqXdZ5w373Uty0q32CoN91Gxi8eVPnDfoNH7xubQEPkxBGUNeFPwIbooVBsmgpJ2RLCAslxV4wzE8byiXECYEkfLPSGb98yiAru98SQitkyhxnUPBOCL7mv+cFI2mHx8TvklgvjYsQYr5jhvqkRJrjGKY1RDgudfRX91/yy9U8Ifi7ljxXgjr8FMJobNZVYbz6Q+yt14In9fRH+IZ3z+ZkRHuWlJWE6HuHtbWwlM3llTGnDv8ofWFucG3M3nHJd2V0KgYk98MSPBHMKWA1LOHLXGZ0kqwTcwgDJqxQlghRFDI4Z4BjfaS8FAEMY3bSGwWaAiWf1dm8MP+MnxBqHuvbM2HS3qU+8QfGAQKh8jp5RnOL9bXOL78ZF6x799YTgdJ4vWOWfTTZ2jFvS/9g62SOWUeERs9vbRAyCMyEP7Dos7VjvHBvLepLmWTOmPPaInzBqXy8xvPW7w++PBOlCIzEEMM4o2/3+Ag+49ML3pt+4RNeCXNgPC/lmlDnXtIPZRIN8O7kGQMBS9uEUe3ADQMLgwFFQd+iRerQYfwQ7zR3tLMc01zzGjQTbaK4UcTMCYq69eEEXPOI91XkD6NJeQfyyBSUOXPW/BAeyFvDyAGOUG/eMii4l9ybB34PSmgn4w0aUMMxlpiTxoOf65dXiPJYj08dxnJwOefkuRjMKHnKJHTB+rTu8VvzEJ8gNJv/YPRn7Voz7uuEtlhzymveZk2g7QzmeBlFVh9kwUJH8Ajz3ZoGw+hJ2PfMxqUfHkOKHFy7bydynDLvSPLb85EFvbsaHq9DB+o8v70b9eDSPRgGRlEF2tQ/5wBag7aAkSgaZB513Uda+hiYqOLGKmeRWLRinEuoFUHWBOae9tseLjBFoWmjmfWDwoNhEhJMVKEamHobtus9gYhgbH8ErbzUw6BN+MLICYKYGSZGUKDEWRgWJs9IqUfQQIAoavIQShYvC8g9wRRDJPwhQvLUxzRZPd1bjAgaYkEAkCdZoIgUa4H7xZoQRozI+OGXQIOos3gQOijJLBKEDjASnLmukfpkEEQKPr0PwicLJYInYRCEfmWa8H4wIr+7Jn0UgYjghBDXfWAY+ijtEbzaTK6U9bpiDhgoa425X2AwLoS63Jer+WFdeD7ClHzjoiRRuKw9FiYKDAudcinnnMxvggDGBPfyCbcsjX6X5N3pGxwlxyEV8kq5q3dpPvtdknmvf3NdnrXGAib8gTWLcM1KRRFULsmjIPjdKxEWKV/arsu9E+1ixPJzzsn8wTDRIf1RPuVRIsGUBN+8jmgR+LYwIBTCnKWElDrzcTU/KR/ma845mas8g9YKzw5hjGewxoWxg+86Pu2bQwxH6FOpp33vuyQx/pSSen0aH2XBPIM7c45QZK5qh2XdXFEX/il1BBGCFXx61+aV+QpeIqB4F/pFG7SH/imrEyWRUlUEubrMbwoKuqI997yo3q120QHzyLogCMEX+s7IAbZOhFxz2/itc7gWRkOBBacf7WkXHhgj6jEZu2dQ7n3Bt3o8kWCtCfcSvmK91Hnw5V0oAyPhFXiVNa9dRkI0x3MoN8fxS3XdS3Bs3DwcFG15rkKMvB9rh3BqLRiXdq0bOPbM4CXCLuMiQd+ak7ccEz5QeA2lGM83v+HdvPObouD9ww/84wV+d0kUN3OX0Va4njoMNuiz6AHvh4fB+st5lVcGjIQ2W4PeKzhrkIFEmYTWmzNkG/cSmo9H+D0ooYFkMOuiDQcPDA+Fp1i/1ngv/scH3k4HAAAQAElEQVQgoNyaYcCmqNTtCUODQ3RFfzwY6F+BgQvrmexV8so15968Tbm5bD3DC/zATVmTyiVz29jwOoobmocnWStkM3uarBO0Fnw7oWnaL8l8cKw8etiGRVOst3Y+GmLtoR3KrG0yk2fWrrFZz/WcYuQTneAdoXfqRVr6GJiY4iZEkPXFBKfsCL8wmbkTWduFDfB2QClhQchazqsTH2UslBYuYZjFnMIl9tU9axKYcZLThPSJOAyqz9JlDBYHK6Uxs9giWu5LXeWEQdadkte+wgPCZkG2y8o9Ao0Z1owbgXLPylngFuMVk8BoCM+IIuGboEe4INhTgDATAoS5Q1jBlEZ5Vkq3kyURNMSNQYBwRmHEzPwmPBF4WC+Fc6gzSh8EJIyT4GWshGgCrfYpXQRKz6bMnPes+u/aBwaPsQp5ICB1rRdwo2OgSw3CvLANRhUKTpc6k4JBLyi2hAYhPAQNzJywQgggjBHgCdjmJO+UcoJG1zFQAghXDGfoYtd6SwlOtAQLv9MyGfMISUvp+Sb5LIyo+BSaN8l2F1tbvFfWXOE1jGdoP4WOfAA/+ABeQ2jnYeJd6fqcDBsMa+AXO+/3DEsloRUiCBiFp41OkBeEY+IZSwXf8RzDMTARxU3IDAsJawQFjnAstINFBXFjfUHICOqEBsKt/F7CLVd7zjkRxhFAhJAVRthNr9C74Y+4CoLXiwLJMrkqp/dfAhsFjwWGdZLCSYEkNFHUSi2/Wci0WfLqKyuIuF/hJiw+dVn5zeLL6g0PFMGST+HRtjGXvMV6pSgL26OYsAjBW/0sBE7MiueG5Y5Fvy4f9ts7EurAKsVCLYSAdb/U0x988sqwdFHCRiW+4Anx5qyQonYfFHrKufnNw1XmThlDlyvLJyXQ/OsCHzDjYcB8YRHPeU2jUWlR5AAaYE6aPyV/vq7WPi+bOc3IYU3k/N/xUu7QWpZZ1mOKRz3nu4xTWJX1KB6/C/xSgxHWxVOCN1GQ4XiEZ1xWoBQ34cRCyZbVg7ceFu1A/61LnpA2H0Ar8BpGFjIL7xve0Wpm4C1vLv7BAD4QcJEU5rzqIAo0a5EMeY1hetdkOfLJGoULnEFx420b1Ri9wMOO7meJgYkobhQboYyEY1eJFQARsuHUpO86ToyUYidWmRtYWzx0GCslqms7bTjx/BQlymS7rNx7Dt4+zyFkoeT3u1LKepVxrduYqC8u+l4wQuK4vim9QiwJYgUOscck2ntQSnlclyYGGD36zZel+cTz+1QEK9Zy3ipe7X69MwAIgWZF7wezFPKdtkZpWQrPMuoz2OvGuCaag3Fp1PrLCV4EAWPscnrm+XnW3r0wEDpAqnfp4sol11BC0d3FNfLFMVrhsUKtF8doY5STwsBEFDfhjMKLhJ4I3SnJ6TE558Qj0XXAQhVtAhZmIDxSWyyjNqx2bWMcOEd/27cgRFH8tfBF7Rg7ZVKYprHJK4kXsPwuVwoZYYDSxWJPeS1l5cqr6MATSqJQPi74UhbXwEBgIDAQGAgMBAYCA4GBwMBQDATAssPARBQ3YToSJcuepZLsz3BajzDDrpi1L0yIpKPR7XfSFquwULSubYwK5yASVi7Km022dUiIuHPhfMIW67BIoVQOWmk/m5MpPbMNrjYVt8eiDcejU0aFx9nbJXy0DRf3gYHAQGAgMBAYCAwEBgIDgYHAQGCgYGAiipuT7cR7U2buete7JqEnklO8nMQj7K906DrodEiK25lnnpl4vSg+2hGy1G5DOyVpj6dL/yWvfaVg8Z5RtuoynkJhIE6ctJ9Of5TQAuPgCHvQnJApZLPkOy7cwSUOC5DHG+fbJU5HEwdvo3KvuG71fOMIXnj4ePPUr5Mx2Qw+ysbmun78DgwEBgIDgYHAQGAgMBAYCAwEBpYWBiaiuNkcbxO9PVs2Mjsdz7dInCxpP0lBGSXFiY2UF+VCLEtZufJw2TRPUbKB3D6EE088MdmjVmDaV4qOfXEOnmiXlXuKJYVMvyXP1ZHP9rPY80A55OkryZ44Y7Y51Tg8j5PYjP/d7353Ek5JudSO48MpgE4adFgGHJR2ysmTxbNnD5vN3kJCC4z9dZQ/bVFeKaFz6WXUz3SnGF1gIDAQGAgMBAYCA4GBwEBgIDBQMDARxY2CYfMpr5ZvgzhZzwl+9no5oKN05pQl389wbLpySlApK1feO8ddUwAd1MA75Ttn8gtM+8rjJgRRapeVe4olD5ijXUueq315vHW+oeXbGhTAknjneOnsQeNBc9y8Z/M9H942x8KXA0oodRRRe/F43UobrsIn9UVBpJA6uMUePmUl2Yxs7xs4bWiLwug+UmAgMDAmBqJaYCAwEBgIDAQGAgOBgSWCgYkobnDhFEjf0OJ1c5S/3zxQjrVWLvGk2dvFy6WcMiW/TrxRjqq2P660Q3k75phjkhBEsI5Np/yVe8dnU5Z8gkB5r8TjJvTQd4soRgVGHd/y0b5PGtSp7D2jmFKwPJvvuAip9Gy1h8+R96eeemo67rjjUt2G355Hf1tuuWVyKMn73ve+NWDs4+NtBMdDJyzTByvdRwoMBAYCA4GBhcNA9BwYCAwEBgIDgYFpwMDEFDcPwytGqbK3zffSbnazmyXhh8pKorz5rpny2htXyl0pbytWrEi8cz5uKSTRPjcnPir3HSZ7xMq9Y755vsAq75d890gY4te+9rUZEGMx3l6Jt60AUuKEQYITGkqZy/m/31byHTZlvRI8aIfy2KtcHm+lUE6nV/q2ks8RCNNUL1JgIDAQGAgMBAYCA4saAzH4wEBgIDAwawxMVHGb9WjmuAF7zyiWn/nMZxKP1hx3N1bzvgFnf5zwzLbSO1aDUSkwEBgIDAQGAgOBgcBAYGAJYCAeYbljYFkpbl72vvvumzbddNNUDgKRN03pOte5Ttpll12SMEweuGkaW4wlMBAYCAwEBgIDgYHAQGAgMBAYWBgMTERxW5ihj9erEMSHPexhSbjleC3MbS3hlE64FHY6tz1F64GBwEBgIDAQGAgMBAYCA4GBwMBiwcCyU9wWy4tZhuOMRw4MBAYCA4GBwEBgIDAQGAgMBAb6YCAUtz6IiezAQGBgMWIgxhwYCAwEBgIDgYHAQGBgaWIgFLel+V7jqQIDgYHAQGBgXAxEvcBAYCAwEBgIDEwhBkJxm8KXEkMKDAQGAgOBgcBAYGBxYyBGHxgIDAQGJo2BoYpbzjnlHCnn5YWDU089tZlrOS+v5845njfnwEHOo+PAgjnppJOCXo6Bu5xHx3fOUSfnwEHOw3Fw+eWXp/POOy/WZgdc5TwcnzkHTM7zhoN5nbcXXnhhuvTSS+e1z5yXJi5znsxzkS3qNFRx22abbVKk5YeDddZZp5kn8e6X37uPdz7eO7dg1l133aCXwTNiDkzZHLjKVa6S1l577XgvU/ZegteMx2vmEm9rrbVWuupVrxprZYrWCtmiTkMVNxbkNdJKq3LknZSWMg7uec97NvNkKT9jPNvSnsPz/X4tmI033nhJ04X5xmn0F2t0EnPA539ud7vbxdoM2S3mwJA5sGLFiuR7wpNYd9HGZOg32aJOQxW3Gjh+BwYmiYFoKzAQGAgMBAYCA4GBwEBgIDAQGOiGgVDcuuEpoAIDgYHpxECMKjAQGAgMBAYCA4GBwMCywEAobsviNcdDBgYCA4GBwEB/DERJYCAwEBgIDAQGph8DobhN/zuKEQYGAgOBgcBAYCAwMO0YiPEFBgIDgYE5xkAobnOM4Gg+MBAYCAwEBgIDgYHAQGAgMNAFAwETGBiEgVDcBmEnygIDgYHAQGAgMBAYCAwEBgIDgYHAwBRgoKPiNgUjjSEEBgIDgYHAQGAgMBAYCAwEBgIDgYFlioFFo7j98pe/TIceemj6whe+MPOqXv3qV6cPfvCDM/eL4ceb3/zmdMYZZyyGoU5+jNFiYCAwEBgIDAQGAgOBgcBAYCAwMBYGZq24/e1vf0tHH3102muvvdIf/vCHNQaxww47pP333z/97ne/W63sZz/7Wdpuu+3SK1/5ytXy+91ceuml6fjjj0/f/va3Z0De9773pXPOOWfmfjH8OO2001ZTPhfDmGOMgYFpwkCMJTAQGAgMBAYCA4GBwMByxMCsFberXvWq6epXv3o6/fTT02WXXbYaDr/3ve+lz372s42i8pOf/GS1sm9961vpm9/8Zlp33XVXy5/GmzPPPDPd7373S5THaRxfjCkwEBgIDAQGRsJAAAcGAgOBgcBAYGDRYWDWituVrnSltP7666eb3/zm6dRTT10NAZ///OeTct42itt//vOfmfJvfOMb6V//+ld64AMfOJM3zT/+8pe/pHr80zzWGFtgIDAQGAgMBAYCA3ONgWg/MBAYCAzMLwZmrbgZ7i1ucYvGc8a75r6kz3zmM+mWt7xlo9R9/etfT//4xz+aIiGVvG0UPuUyhVyq/5rXvCYdcMABTfjl97//fUUjpR/96EfpmGOOSQcffHB6+ctfns4666yk7X6N/Pvf/04//OEPmzoHHXRQetnLXpbOPvvs9Oc//7mpIrTxve99b/rTn/7UhHWC4YFrCnv8+drXvpaOPPLI5hle+9rXpi996Us9oCIrMBAYCAwEBgIDgYHAQGBg2WMgEBAYGAEDE1Hcbnazm6U73OEO6fzzz5/Z5/ab3/wmffWrX00PfvCD093vfvfm91//+tdmaPa3/fjHP06bb755c//HP/6xUYqe9axnpUsuuaRRkihLe+65ZwLbAHX4893vfjftscce6bjjjkva/MpXvtLsrzv22GPT3//+954tqPPsZz87nXDCCcn4hHC+8IUvTB/60IfSP//5z6Ye5U5lHkK/c85u10jvfOc7kzFTWMGdcsop6UUvelETLroGcGQEBgIDgYHAQGAgMBAYCAwEBgIDgYGOGOinuHWsvgrsyle+crrb3e6Wcs7Nfja53/nOdxoFjHJGceNhK16sn/70p+lXv/pVus997gO0UY78cOLi4YcfnpwWue+++yZwlCBlwxKl6sUvfnHjXXvHO97RKIIOL9lqq63Shz/84cQT16uNN77xjelqV7taOuqooxoPnTFsueWWibJnvNtss0164hOfmK597WsnY+Jxe+hDH9qrqWRP3wte8IKm7iGHHJJOOumkRIH99Kc/3RM+MgMDgYHAQGAgMBAYCAwEBgIDgYHAQBcMTERx0xHF7RrXuEb6+Mc/7rY5eMTBJfe4xz2acMlrXeta6ctf/nKzr01o4g1ucIN0+9vfPvnnN6Xrzne+c+Nh4wWTb3+cvWV+D0tOm+Qtu9e97tWEOWpD4gn8+c9/nn7961+v0cTvf//7Zry3uc1tGuURvL14N7nJTZIwzRLauUbFPhmUtUc84hHJpwvUp7RpiyevT5Ulnh2PFxgIDAQGAgOBgcBAYCAwEBgIDEwCAxNT3NZbb71kr5twSUqSw0c23HDDdJ3rXKdR3Bxe8slPfrJRqkoZRa88xIUXXpgoPjxWXlozbQAAEABJREFUz33uc5P9YTxepXzYlXdOaOO5556b1C+J941HUGq3QVGjGPKIFXhXYZpt2C73xvCqV70qCbXUjiQktEvdgAkMBAb6YCCyAwOBgcBAYCAwEBgIDAQG0pUmhQPetYc85CHN99ooTzxXG2+8cdO8PXC3utWtmm+uUZSEFN773vdORXEDe9hhhyVhkU94whOaMMdnPvOZiZeuaaDDH4pZzjk9+tGPbur7Ppz0+te/Pr3nPe9pQjnbzRizMMlHPepRq9URNim8cu21125XGXjPayg88kEPelCjhOq/HL4ysGIUBgYCA4GBwMCcYiAaDwwEBgIDgYHAwGLHwMQUN4jYdNNNmwM+eNbsYXMvn1IlhNGBIR/96EfTZZddlihyOWfFiefLqZP77LNPeuQjH9kcdMJDJ1SyAejwR3uUMJ8eEIJ5xzveMUlCJZUVJbFuaoMNNmhuhTLyGIKX1FmxYkXzKYMG4Io/9tFd8XONizBNJ0j6vMFOO+2U7nrXu6bb3e52iXK4BnCVoW8hm4ParsAX/U/ho8ccc0yzn5ACP+kH8skG+yvf8IY3NH0Ik52rPl7xilckeySF/up30v1Ee4EBGEAbPvWpTzXzmRHKqbzyIwUGAgPzjoGZDtF8sgse4ATrUQ5Sm2kkfgQGAgNjYcD6o1NcfvnlY9VfzJUmqrhttNFG6YY3vGESerjWWmulm970pjO4udOd7pSufvWrN4LurW996yasshSCve51r5vOO++8RAGyF0644iihkpQ1B6FQDE888cRmjx2i+rGPfazxuFEkS3/1lZLlMwTvf//700UXXdQkx/1TLihVYCl9lELfqbOXrtdBJ57bcxDiL7jggiQcFEG/+OKLNdE38e5tvfXWyWcE+gItoQIK1YEHHth87uFJT3pSsx9wko/nPfukgwNujj766OZUUYrxJPvAoB1U4935fAWv7qWXXjrJLqKtwMAMBnwPU9i1+fzSl740oVWj7r+daSx+BAYCAxPBACMkhc32CGvTCdKT5jUTGWg0MnEMODX8i1/8YiOvMqyN3sHSqkFmPuOMMxr5uR8+yNNwdvrppzfy8WwxQOZyIvzuu+8+26YWXf2JKm6e3omLv/3tb9P6668/Ewopn/fJHjj733i3KDryJR4uJzcKsXzOc57TfEuNt42ypLxr8jkBHru3vvWt6fnPf36z1+3d7353c9LkNa95zZ7N7LzzzmmTTTZJ9sI973nPS/bYsWoDNgZX4+NJo4gR2M8++2zZqyWHkBi7zw7oW9ikkzSLV2814OoGAZCqrCX70+cZhJKWB7Qn8J3vfGe5ncj1Uys9E1/4whdm2rLnUt5Mxix/EJgRHgq8prw77fv0hfvZJPs6Eb/ZtDFOXcLGQu7FRPQpweOMfS7q8AQLcyaYzUX7o7SJOVkzIgnUE2rufhJjE57ei5bpZyESTyKaYI116Z8gcPzxx6ePfOQjM98I7VJvtjAOn+Jtp1B3bYtw75uiXeEDbvoxgO6TWcpIGWw/8YlPlNuxr9b329/+9rHr96ponTOaFjoCRuQT+ejAlYZUvMe9/Eip+RQUA3D9fmu8wBVFHV7r/Pn+jfY5AX2++233t9deeyXbm9A5uGmXuyczka99MouMLm82iYLoXItB32meTfu96nLuMNYw3vcqn6+8GcVtUh16gQQxykutLNmvRjgz0fbee+/mCP7SpwNMdtxxx0Z5IkwgIurbZ2bPGjgnP/KklXt5vHLg/JYohhSrt7zlLel1r3tdMokIYJSz613vekDWSOpoQx3eE54adXjieAhV4Dk04fSnTXvi5LeTfJa3N73pTc0nDbxg43jGM54xA3rEEUek+n7XXXdN73rXuxKP5AzQEv2BEBLO6sejBFGu6rzZ/PY5B8JtaQMR+eAHP5gmFTKpHV7dP/3pT6WLRLg2f7juZzJH/EHZPPLIIxNFsFQ94IADklNKPVObOGHsD3/4w5vvHhb4ca6eg0GC4WSc+pOow9N+2mmn9W2KN3qHHXZID3vYw2YSDxRDQN9KsyjgYadAW5ezaGYiVRl/CAeYVGkQA6SwlPtxruecc04SVVDPN0zJwUqMX+iseTEJBbHr+BxOtd9++6VhUQqlPUYyYfnmT42fUl5fKYToz7bbbtvMIfPdemNkrOG6/EZTfGrGe+gCDwYe8Zdx+lM/0vRhAK+veQ0D2Mknn9x8xmjc0f7gBz9o5KDaeCGKhExkTfpU0f777995jZRxMCz6rmzNf8kz5BFzU7+uBX65X9ETyoVD83rhgtKO/vjUla1AL3nJSxq6UngU3iw6Qn3KRd0GGRmdq/P8dtYD5Yec7F4yv8jB5GNtP+UpT2lOQlcmCROkkC+k4dU48JHb3va2TdScbU/y6mQ+M2rABf5K7qjLF8tvePbNZ+9qIcc8ccXNgR48VDe60Y2a77rVD2ffmpDG2ttWyr1MB3moS0kTOmkiaA8MJYrXrtzLU77OOuv4OZN8b82eNv1oS3mtQM4AXvEj59ycfFnq8AY6TMV4rgBpnuP6179+UiatvfbapWi1qwWsLhhj85kDnjipAOrnxje+cblN2hU66vlmMhfvj2bkFBgLFdGxQDE0SncvQbssBAoRpqIOgqiNprE+f5SDAy/OWR8IGG9JXQWcUFjGBO0jdOoYXw3X73fpQz19EOiF8rbh9WFBgzGeUfpA9Cgu5hXGXNq2V49wSPFsC7PwJhRYfwV+2BXRFM6srwLLq2wfJuNJyZu2K0HZe/Xdx8c+9rENgxRysd122yWh0JMeL2OOz5gQzmsL9aT7abdnHlDQhYibbzw7FLS2sE+oeNvb3pasJ3PaGuN98n7bbfa6188HPvCBJpTdQUpgzDVKDa+BqAl7khkjrCnlk0zemROHKeR1u8W4NmqkRd1G+7dnpfhaV3BpPeMvhGEhzrvttluyxtCJdt1J3uv/F7/4RbIPepLtRltzjwFzw7xB09F2NJdxFv2tewfHQ8OwB8YaVqcrr1HfmkbvnvzkJzdNm6eEeUoE+me98vQS5BuAWfzx6ab73e9+iYJhnZNbZtHcglZF+9q8bbYDQju8k3Y78vBk8uxd7nKXppjRC13GnyTvCn4Z5inI2moAV/6x1YYhaeXP1f6bJ/j6JZdc0uSrw7jGs0ce3nrrrZvIAooffghIRBd6Sb5yv5CpnA/BmNYeB54m36GE5Jx2ufVlzeC3DOF4GnzWcO6tqQKjTl1efpsLNZy+S5mrNWyNqe8qyfde5ZVxyMeP5Uva0a7fBUaee/Xrfo1RmXeobNJp4orbpAcY7S0uDJjEGBprttDTpz71qclH0FnyTez201iMFB5Co8T7KQyJIqStNrx7dQjuvJ9inBFHFi7eUuXtZDHyXiGoGJ6x8Ozah9iGre9ZKXlfeXd4YAlfvKg1TP1bu7yuu+yySzIugmFbOK3hy29EnED3mMc8pmTNXBE5zJ/QOZM5ix/eAQZRmmDU8J4opCVv4a+9R7DFFlsk88n7EGLLuAIv8NO7xvi53iMGQhAbv5XuNc1Re2gPPvjgRFDbfvvtk+eV16sVjM16keAEc+eF7WIJZAQw38zn0rY16Ld1IuScVV8IThFMlE06YWx1m8LLCSoMWXX+bH7bM33ooYcmhkQeEtZrV8ojKzl8UVDbY5lNn73qPuABD2j2f6Nr3nUvmMibPgzgNdYK3oKmF16D9/QarXdrDVuXogTwBLymlxeiXZ9Aqa+73e1uMwejUeTwUx4aa9x2DFs5HvzgB7erj3xvzlM+7M0fufIUVmjztrkaIqXJCegbb7xxwp9LPxwPaLFkDlDYbLEhz4ioKXBdr8LxREZQAslTQgx548yBq1zlKk0znBNoNMXN/GkyF+iPyDRj4dltGxvhi9JDuS5jL8Nk0OJRJps97nGPS+Sgpz/96YkBhOIEzrsVrUe2evzjH59ETDA2UJCUl4Rn44POT7AGt9lmmyTCT+RKgUH3yYsiSvBAv8ma3qt1zWiiD/KiKBDjIzPhh8rh2ftlOMY3lTGg2m616667JvmUbHIKQ+hcKG+huJW3GdeJYMDiFDcvBIPQacGyIpncgzqgvLD6W/QWnpDXfnW0JwSVwEXhwOwsrkHtK8MAhS586EMfag7JQQDl90osncpZOnmoLEBCXi/YOo+1jEfOuBAWQmObuNTwfhsXGIzAfZ0IsTwgQkoRlrqs/Vs7cEIA1i8LV4HxDIRySg5vCxiMRbm9NxiD3yUhgPZaIFxgeXjaiq48QgSPxeGHH57AUajsYy3tEHwIzxiOckyMMkTAKTDjXHlNeLUxCEkbiK93ZP7Zt+F5EXtzUjkCCifev/cEzpi8J2MEU9I973nPxINubpW8ubwS0DAUzIF3GK69g0F9wq01Y415V4wMXcI7CZGYDyZa2mddzHlV9EHJw4Qx0nJvXQoRYl2GM+OVR8AoMBis9pXDLUYHh/Ilz2bOmIfmDxjzT5k1g/lhgtrzfNrCYPXl+XgbwCoflv75z38mApPxqX/f+953tSqYurBQc8J6Vyhs21pgFbc+jM/ctraUt5OxGjMG3i6jhHofZf7ZS20Nl77a8HE/fRhATyhe6Kr5aS7jNWjNoNGaLwx/aI35S4gbBK/MGrS2bJvIedWJ29YJPih6B4wkiuiggw7ycyahuWiHfDQbPVB3BqD6gZdZD6JceKPNcXUrkNV+ok1g0Hlh+9q3PgCh4/idMGAwbWMlvoYWmPeMmMrBHXfccam9DqxT4/AMYKwdvF4/EnqIZngH+CH6jq95Vr89b5u3qecdalebvItkDPl1QkfJFGDwBYaxurz+7Z1SPmxjqPPbv0VaEeRFVaGD7fJh9947xUX9Aisyy2EcK1asKFnNGQ3eP54xk7kAPxhSeXB1LWTYtSTv+853vvMaW4LQRusLraVoMZBQikQBmStF4WJsJNNQjoW8M4jwepmTpQ+4EiWDJ1GctOV9mjcMkUVOAA9fjGi2LTFc4yn4qCgXeepSEvE7PMv7puRR6mz7okSb8/iHsfLA6psSaJzkD3PIevGM+pxkCsVtktiMthLiaZEhOrNBB4JtwfVqQyw+5tmrbJQ8i77fosJUvv/974/SXE9Y7WBePQuvyNSPZyXYXZE1c0EkxLYj2BjmTEHrB0KJ0BAucs7Nya4IUmFSlJacVwkDquacZ6y6GDkGmK74Z8wsvBgoXCNAGB9LIqZ5BVhC0BBXRJTiQxkg5FJ21SHcgHna056WKGvqEYwJMeVe3jjJeyPkCI0uVk9El7cIw9c/YwCCTVEQSmg8hCnPwqJnbARvXjvhJwSnMhaho8KtCWDD3l+pM5srwabuf5y2MB9rDwMbVN+7Ym2nnBY4YS5wRjAi0JX8+gpfhFBe8YIXBhqMFx4pWuYh3BL2zDnvmafCHNMWGOG5fmOGOedGQc45JwIgT5ixpZX/zEltUb60hbHyOrhfWTz0P1pkvjkoixejXYGgwZqNVhUBwXugiJmz5ol5QzDEqNTrm8UAABAASURBVD1juw3KvWclENblhArM3xzl1VaPQE44NU/dR5p+DBD2rAe0YzajHcRrSrvWv3VlnpQ1QvhHi8ylsoYKfLkyQhEoGRzMQW2g3bwY1kCBK1d0ovzOOTdbQczjkte+4k+UGbSUMYInRJ/asd4ZOK1z6+j/s3cX8JZVVQPA9+Yzxh5bMRi7EbElHBQDBcEWLFBRMbATAxALC7sVVBTFRFERAxUwsFtBHcQObGz95n8ea9hzuPnmvjfvMYsfe+49O9devdbe5z6Jsn333bf7VW/z0NMSuGSJbiZf3qfjfHNwyZd+dCw9QfaMoYs4vQJMgZc+6q1tLPvk18vpDHusterSlVrrOtsmIAOf4JFeJOcCA0FaOfM/8st20jd0j73SO2c2r/eBRk7BJIEmuVoq+UX30Lnost5kYx7Q3WtF9sxGDevu1A0t2LZhfRarHl68aoCOaGhdNJIw32GHHYorn+qi0I+SBwItp2heUdhuu+26JDDfQxu6Sa6RQYlE1/jdYHAzxZ/dirnoVf123HHH4rR76623Lk6mzWsdMERffhWd7nRMwMgWSeThDXC6ym8eATKbSh4333zz4pmsSBp7v9EpY621+5V0dHbSZ11zODmUaCSTse6sPjNwmxUmc54OA5xo95jDWekqp/yHYLgmRmkNGkphtsZtUJ9xdbIklHVfkcQ4CsEJGFiibtpPyoBj6DrKsLEyloJQfTiSg/pRVLI9DBXDOKiPIEWmiJNJ+VD0nF1Xw/TfZZddiiwQulBU+rgOoK0tlKNgT6ZJoCgw4wAworJUHHcGNMaAn/LjwMtAUaycb0rUXJQup8J488k82iuFHHNM+kkBcqScwrj+IJh1hZWBMwdjHMGjdVyJk/GEE/Doo/gxC++KMND2J5BkJLy3oj2K9wcY8jBAUb8Qn67UMAy1nuWATLuOK08R5A8by1HCb04s4S/6eekdPwj8d9999yIAR2eBVvThmHHitHMk4XvvvfcustwcU/ymD9o4YRXMuFpMJ3gxnTzIUjOk+JCRw4doMUjOOHMCaqcdBx10UPejDeT2xBNP7H4pOOAa9gkeTh28DJq/1rkTRnKHj2MevED+yQE+AgP54thFn/iUOJBl5jjZY9STAWtyQuxbPScOP4HJc5aljwHv1Tsp4LjNF1o86wfShtmamJeOwovt6RpHWBJBMo2u8yNpAgD8Zhwep7cEKG470MESVwIPsjzImZewIXfex6d3fHc123yjCt51miGBgq8lVyXF3AihA9ymIKtkgty0czlxkeBjJwRxZN6JB8dWP3ALBp2Q0C1sjgCNs+2ES58ogi+BKSfdSRsnfZBto7fpePqO7Dqxt7YfeWHHQmfBH10DNnDYC9sV67WfXtGQfOGUT8ITdAB60qNsYcwlocQOt4XeFGBEH7+F4G8bs5fwoq/bCWxc9PEpMQVettHzxiz4Fz/ApyQv/oRz9YKtPmz41n6cVJGTM9u7a+V+D0KgDHeSFviV3aq1dskGJ5HxXrRx5EeQjz6SAvhHYS+0tzpe4IYH0UdbrXO2AM/gN+MkQpwsw60+wwp/xNrkAe8ay+4JuNnDceOHzTuqPgO3UdjJtqkxwBlkpBQO27QTeNHW8bNMWzg8/TnMS2FTEIxEv33cM2H3owTmGNWX4+/9vAgMRvXtt4HLnW1XPjmp/fZ4ZgwpXIok6gZ9cpYZAAEZZdbvI2jjEAuuZDUZK9cKzN/vO+rZyR+jwhh4zyv6CpYZd8bV/FEvgBYUohVD5tRQhtq6nmXSGFZBFyUNPviYj/NKIQou4FUWD24F+GARZHC0ZfsoY2sJMhg/8GjXT+GUw6fvCoMisy1Y9RwFDcHNkY+6hfoEN6dHxpEMTLuO61Nh3EeNhQenQfbW78cZMgeD6eQN3TgKjE/0lYmURGBkFfR2nZeR9gyvnBqGGw3wE6ONH2KOST8lVsyF3zmrjKh31RjgSYwh55ZBnXS96IcXBPPxLJNqD61TFW0+Jao4ERxlayp4Ee9xkvVR6C38T949Z1n6GBD0s0Ucy0EyM24HgjWJq3G2xjz4m/2kSz1Hoe8kUgRZnFC2i64gU8a4gkgOObhkThFsknP6OuYZ9yk4NDYKR7QdI4BkF6LO36x1GkGfxhgw0UX9xBxb4gQjxgpI6AZz0EmSa+axj+hjDxKffuQj6nxKDprP91GFrLk+aA46PGAUjNMfTsHgBwxsAFtmPv6BYM73ttCDAkk0daLStk37nT5xWtMWuo0ObeeS7BFAsL2SboJfvMS+Rz88ivatXY62jfHp5Mo+XGcV1Ah22W177cMjmBXc9evZ8DZxzw73+/SfzQMPaC3RFsUa/AT+UH9MPBsn0OK3SkYYKwDEz9Fn2GfYIzAaF6XW2v2Q2nz0xrC1oj4Dt8DEqM9smwoDFIljaFcrKLlpBgvGOE1OH0aNYxxk9BkxgcCovm0bIWIIZSRrrW3T2b6D3ZUMAZFxZ+swpEKg56RHdnRcQDZkirNVc47hVObJlTWKpu3E6MoWUuyuSMLhfBS5jBPHUqDWzu+7EyjKkWPqWeFo2K/vUSi76MN4MjzgAhOjw/hF32k+XdWUyXVVD4/J/DJm5mB8BYPmtk4UJ31gFkTqpxjLsPiuMBCCJYra88YqHAcnS7LZDNekcDBI+FRGdtIxg/rBAdmQNRTACXo4iJyb6I/W5CKe8TfZQGd1cChzHfSWzR8W8Og/quBpJ6LmwtNg4giMGtO2cYA5CxwujlrbFt8ZdYbX3qPOHlt+iXoBZHxvP12f2WabbQrZ5Mi6CoYPndhzxtq++X35YQAPuS3g3aJpbY3kB1szjTwPwpBAgVzSD+Bx2i2Io6vpW0FI6Dyf+uJhMjBovkF13tMxNko/eGGD2nF0Lv2rX4zxSQ4EJm1fp0bts5MTsidoE3zS3eZv5ZCe5gfo144lb+3zsO9OcjjxTvbAFcU1PmPYAHrL2pIz6qK0Oi7qBEuSmoK8Qe3Rr/9pj7XWYr1oE5Q69WsL2+gKXvSJT7jCf+y7gFWCyLhoX2qfaC1Id9rrhJb+ldAbBCfbCy/o1LYbQ4+SN3yMN9AJTaMfH0iJZ3YIXQRp8NMWuBMER9/+J7/HySBcO5k1VqJ41JiYAz/WWgu+MK4t5NQeo++sPjNwmxUmc571MEDpyrwwOOs1jHlwckRIx3TrmjlFfvlnEuHqBqz9x8mGQAx8ax/H/i/7LsNHGY3tfGYHpxIyZJTOmVXdx4b+A27vIrku2Vd0nGUnhLKLlLyrJoOCr3EwCHJaZdj2p0zb53HfzUMZOiUEF5g48gLAcWMHtVOq7o/L3jmVdRokeNOXg1JrLQyGdaK4suf0Q5ZVv6VcGCjBG56W9Z0UVqcB9j1p/3H98DzZdeIl6+76ybgx2jlxrmv5hTNyjwacETyrfdqCp70fIyhyDUuipu9gjZqTQ2AvHMy+82ec5IJAS+A2jQ4xtl/QzAmH65IyzHBBB/T75fPyxABbIzHiVGjSHQg6JEKctE46ZlQ/wZCkjgQW3epdTzqj1lokPMlbW7x/pn7UnG2b0+12vCvCbXv/O53rFoh3yNpxfmjC1cR+/2HP5HBQmz1Oa3PaeeBGEey08Ak0JT9Xr15dBIqTBtVOUugSNw7adUZ9l/xyW0BAwX6N6juqrdZa6FFJAL7INPgdNe9CtTm5kjgWZLpKLrk4aC16F24kCAVP0UcSwTNfSnLYlWEJRAkxfIFnXEeXJIgx8ALHxtLFUe9TX/P5Pqjwe5yYSYoontFaInJQfwmTqCcD5NK7ksZEvfW8liHgjLpZfc40cJP1gWh3jwNAQMtUcjRdn/Hcz35Ckk1Chn6Q3hdYzzIX2vXzHQFjnf6ndfS1ZtuP88sJaOv6Y/N5NhigEF1Tm2Y2dJumPwF25WLSMXiNozZpf/3w2iDHT9ugYv4+jw/qp06WiHNJaXgeVQSC+JfBds8/+lJonEXXsryrxLmQBeK4Rp/2cxRsnAzBLWND5mIcefGOnRMWfaJ+1KcxDCTFzYkVSLqGxwEZNW5cG4dBUIGWrmPgARlMRpiusXdrKQI2a2qLeUN/xDNlS/8IEKLOp4DFXtHH82KUWmuxhxb3vXXP9og34eJsDQMqJCwYGmtEMzp5xrdR51M9XCueFfpUltp3JXQsvU92BS6cRckO+Gewh9F7HMyurAjUnaq7ooWOjLh1JynWBRfnSbKjxam10dcVLXLjquMkcw7rIzEhWy67jyfxHTls+8sgcwpcoWzr8/vywAA9M42tIT9kc9Ld4R8yFHJIT9NLTtTaOczrmT2gmyR5JFg4rWQuCj3Ovug7ScGXMdYnR3jUOEkmdssa+kexj/5YvB/7Mic5oVslaSXy6GdX21sZpaclVuzDmEkKnEU/ePFeFN0GpoDPJ/jATRfSE+AJvJqDTYh5fGrj17Itk8JjvxJPaOOaqT2aa9ICF3wPaxsDLjxIdwl21ClwxNdCD89LocCxa//g9bpJwFTr3C2nWuc+6UnvScO3xIjr+oI+J8YCZMF1rbW4JuxKunfX3LzwAziSwuhb69xcdDi7I2nmXW83c7z7KMkrYYyfAo7+J1lyYoZefgALHMb3Azd8ZB3trlD7JUky7v1EdPYeKvisx1dzeteX3/7a83meWeCGQLLbGJuxBYw7xTaBIDLCis22TqdxHEK/KCST7kjV5r1UCiHmoSwdb3PWZJf9UoxrC8OyDhCF8K6qeP8GQs2jYCJCKhvgOcvCYkBQMc0KAoZp+jN0nMdJxzCErjxM2l8/zqp1fJ+kEHYGbZK+gitH6WDC5+PGCC5kd1q8MlCUCT737pd7/eSF/PXn44R7J0KSpZXD6Mex954B+fBehb7m40Q74SLDfm0x+o/6lO1kIMFqHi+DO40x36hxk7SZl0KW5Yqsm6ukrmd4Od8arugJJPwSWqu0OQiuMHgx3tU7BsAVDIq2XRsvUuitkWzbF+q7QCN03yRrCEAYq0n6ch44K3hFgswYvOdKl+sk8OUX4uBE5tyfDGjpDb+un8K74mfwBfN0Ld5iSOEXvfGXF/3h0TpR9Mcb9D4+BAtnJNrj07ravD9nLj9wYP1oH/dpDfLi9I8BlWn3HqP94UO/ckcfuNY8Cxo7dSN/+JEt68PnSpi946l+2+I/54rTYoCMtUmLceP5IdMkLsmlNVwRNjeb46qwK/dOLvAtx9L7p07zOKf0IOeWLPGZyB2975cP/bovh9hcC1EEMJIeTkusb13r0wl0cLsmufPLjk5MyIgr75x2QZt+ThHJOLtFdytuarAdnHF9xhX6x/p0irkEovSSd+QkaLSxQXDj9B4tJYMkXegYuso4iRe6gazGmmjhdgffFp2ivv0UIKKR4oTHHtkeOtQPeLV9J/kOJolYODGn9wb52GyVZFbMQf/TdcPgin4L+SnI8uuWsYZg2I9PKUFjbYJ1PgS97FnRB28IzIImftzD9Xjz6CPIdpPICbb943uyQXf7kRF9FHO7eQQhEdwyAAAQAElEQVTf5I8fRi+TGbTWR0Apueh7FAlBAaQ1a63FOIEbPnAqGP0EboJBPhjbgcfwHb5AazdVjOVviD3sg28W42f1ObPATUbFSQBgOX8AZPghz8u5TrkoGgLEKbBpfRhmRhXi3N/WD6NzDgRY+hA2d4spKO0cNMfWCDlIMXIoCCfFwiGQuTCPgjmszWHTpi7LwmCAAxdGaNIV8NGkffWzRjignscV/DgtTBwuSnnc3NFufjwWz+M+GTDCTX76fTnabZ0gz9UPV3coNQrNWAqeEmHkvVtoLgFYO5Zydx2GIXO9hTPQtsd3cipDyPjoZ07XblzToCij36BPa4DLpyyg7JnvMlQUIdnk3A8aO22dJI+gXYBmrOCDATnyyCMLmClhd+w5QwJkfRQGBs7oEjB5V4TSlnHTrghm1qxZU/QVGKtbjMIoWbfVWePWpQOnCfScTuIbOtXcDD6a4xk/+OJnlr2XyDjJbDol0k+R1YdPulpfeti7I/iRo4PenBwJO+2cOXxpbBTrS75x8PThkEjgRXt8ynzKMpvLd7yDHtE+yScbICm42267FQ4ZWO3P1Sl8KJHoGg4eHTYfw1xr7a5VDeuj3skyXvGOnO/q2gJ+DrdTkrY+vy8PDExra8iBJMyku+PUSlbwd/gmfCK/KMwR5CziW+/d4C+6K0506T2BD3+LPsPj5Nc4/Djp+tP2w8f77LNPJxfgs66r0ewLPm/n41BLaJI3upk+dkoh0a+fQERS3TW32AP9xNcjn/qMKuS3b9vgz6GBwE9wGbZM8GN97eaEO869RCXY+Kj0mNsCZF8fQRRnHD1inPoo6gSMaKR4L9ctGDYKbqZJDIXNp7sEGwLymFNSyKFFmxiSzGJrp7lGHnDP6lMQxfbGfOBBT/Tms0S97/ABv1Hn04kaH0EAx7aII/oJLvoaHtDJO91sgQANH5kjCvtC55uLnsdb5CraHQwJ3OM5PvkEAkN/3kZi136MxT/RxydYHQxJdEoK2muttfsbdXgMLhR0EujhTeNmWTab1WQYmwPdGizOH0XjmpQXBjmUImVZDUJsbcaU0wGZsiOCNoqIUuCA6SNiR0zziZT33HPP7l0WJyEyPfpE4TCbE/Ecw0d9fHIwXLuR9XGUHfX5OXsM4If+rIJ6TjLhRet+O6ewXzfqmaPbbycsFDCHrxVY/Vw7mJbu+htnfBRXAcxPQfse9T4H9Vc/rFBAnGe/atT2oRxkbNo6SoDTKzvoxDl4fPXq1YVBYqAoFIbdWFm/GG8sR1w2lPKLIExihaGPfhwIdYIepxSUkE+BGzijH6VojXj2KQNL/tzHr7UWRtdaYKMM6QMv71Ks+iuCTvP7PqjgE06+rFbb7tlP91Kw6hkFil2mGczgg0MKtHVg7A8Pgst7VHBGp5gjirvpvreZQc8LXQRs+Ke/DjjgmiGhw9p2+lOA09aN+s7h4/y5+qMfA8l5kank/EmswbdgqS8/HAmOFR5DR3TDjxwcBgwfRj38uzICv+37MvDPFuBhfRhpjhT+wq+xP4G0ufCmfq7JMKpg5SjZA2Mfz/YyqJBPcipzbV+xP7wIdomGGMdO4XtGPOrsGawSAeoEr07sZFc9RxGgckTxfBvsRrsTXrbHDytFXX4uHwwMsjV4AV9wUOnxdjdsxiBZbvu03/Ec/uCbkOeQJ7zvSha+dQpFp9EHMZavRO85VQhnlXySOTypH4eUHPK/PCt0ctgAz8OKW06SeGBr+5B5p1XkkmyB07qeyVHbVxDEXpJfcJBp8h196AROr7bQy3Q4GZMQ0o/jzoYIxjy3ZZhts194INN0FRjhT5DJFzGHAAlsbpWAj+3krLMjPvURUApG29MX9VHsmT1GI4Xv6vCB7aETop9PcNA9vrcFHPYcgYhnAYqbBub0aU4nVK3OolfA5u+KtfMtp+8J63QYmEngJjvkGBozcdgCBAqFQBIqdQwt5aS/ok62m2HE3LXO3VUl5BgxgjKCy5gz7sbISJjHvP1MrZM0mQ4CZ5z+beGkcHZlKaZRqu0c+X0yDHCsZbplm9BTMOUklDLiKHN+OM4Ujj5oyombbPa5XpQ/RwlvoSvDQYFyAjm5smccUG36cOIEAnOjJ/tXVkXywCkFR5Zy94MX5pdMoKSdHAhq7ENGhtKdbPZS8L+TYMbRCVKMY3QYq3iOT06r/XAaGE/1ZIG8CVoV8gIeeNceBXzGwoN29QIeMud7FLJsbf2cCPquLtp9gq8/P/myPhj14XxwHtSZx1rgVLQr6vrrq49CjukHc0edT/rAvHSMZ0UfcFkL7OBWV+ucbtFHsRd410//gFcb3YS++IVjom6xChlwoop/0Ap/4y/BtuwtB8Oz7/YtIHVVxPdJYYQXAbaTrrieCx+CDTylHW6s35+z1tr9EVX0gDtj0CH64Ue01abgMYEKfow+PjlNaKeAvdZa9MFP5tAHT5tfHwWf4CX9aq3dH9lF33guI/5DS3ObJ/ZnXWu0w+DT3vFt1NsfvFhbHfvj2R48R5GRZ1fwDJ6Lep/esZOUIuft3NqyLA8MOIEWMOElOlQAwNb4QS2JLrZGQoJs6IP/ndBMujvyhnec7Ln+Zhzew/fWw7f4jkxpawv5pcesqR+5YK+ijzH42nxRR+eqj+dhn+AyZ6sjoy/5MUcLH3gH8Th5AT8ZpCOMjXl80vP0XuyBvJE1bQrY7ZHceu4XcAYcYIp2smicPZgbbqwV7T6NBZt269JB8GMt101dUeXPgFv/fjGn+aOYi+5q4Y8x9kgXxXN82p82+kwd/NBHLex0aTsnv9stJX5WW298lnMuBmYSuLmXStFwwEehimFzZ5lAYEB9XbXBnBSdZ4WCIFhOzzz3iysI5qm1lvY6mEBMlsQd1mHHxuYmVKOvI/VXzOf5YkBmSZZIxlAwhdbtXAI1VwFk2hjBaQydefCSLJwMHmfblQeKWptCITvRkWkXJOIPylnbpEUyQpaMoTYPJdmuwdmW9XfS5NRCYAeuSefXz5G/DG2cgqjLsnEwwFC7FcAha/XSYkGz0047FfLgtAk/kYnWEaK/nL45BZJJlqlunbRJ4ORo0q8SZ5P0zz7jMeA0BG3YH05XO+Koo47qfhXOrZK2Pr8vHwxwoJ3YsDXo6eSHnxI7oCucsLARTr7IJkc+2if5FLgJsNjLSfpnn4XHgJNWyTS+xcKvNt0Krk4K2PgP043M3ssZAzMJ3FyJc59apmkUMjjNTshkiGVf9BVAcYIVz1FkNmQ94rn9lGVw/5mDI3sTbU4snLhxrPsBQvTxaW3MLrPlOcvCYYBhE1zLJA1bBT04NIOyUMPGtPUyW7Kho2guUeDkTDawHTvpd+PxW5vJ64/Vx+mfQK5rm+IfVzAEuS0/TzE8u06AAc403eO0V0A/bAiedfVoYxpqvxDmtK2vF1uYOYVOzsDb1k/y3a8tut7FSZykvz6SE+27FeqynIUBpzGCaFn/s2rnvrni7+SfrpuryX+XIwbC1oyioz6uDM/H1ggO/SCC4H854qcPM3vp6p8ryf225fLsEEAylk5eajDzG7xXiOeWGmwJz8JhYCaBm/cylGGBFvAFVbJIXlKNd1LUDysCPPe8++1+JMJ9ag6Nu+XR7oqZ9yMYSBnqqB/0KXvNiZvmxeFB82RdYmCWGJBwmPYa5yzXP6fPVWstjLDgjf4Ytl/XXrzoPioRMGzsQtQv1Jy77rprh49J5/cDJoKTSftvav04p5JDg3jLuzqC5U0NJ7nf6TEg+cdPmn7k0hshQcbfo3eXHnTLHyLXN72asfx3kjuYBgMzCdxGLSj4cgrmhVMncn6Uwn3sGOOdoTixizpj/DJfreu/l+IlfNfuHA/LSsUJjf6uyRnjh01cmXQNKII/7xYILGP+/EwMJAYSA4mBxMAmhIHcamIgMZAYSAycAzAwk8DNS5VO2wRKfZwI2vwipKuJfjHHy55tH1fo/Dqkk7So993VSz8IEXVxouaXdbx7IrPp1Ey7/l60lyF3pO0XgxQvigsKvZvk7xN5h0h/QZzv8RKouiyJgcRAYiAxkBhIDCQGEgPDMJD1iYHEwMbGwEwCNy/lKv4mW7shP/nv7yU48fIT0nFC1vZxtfGUU04p/naJkzO/6OaPNKrzTpG+Xg497LDDiquWftrVlTLBojbF1RQ/keoutXedogjMBIzui7s7XuvcCZ6g0Old/+d7zZUlMZAYSAwkBhIDiYHEQGIgMZAYWAAM5JQbhIGZBG5+SMRPqLrC2ELjHTR/D8nplpMyp2D+fpOiXl8nZ36J6+CDDy5Oy/xtESd0fpXPOxj6+LMAfg5bgGYNL9WbQ/E3N1y99N7KHnvsUdrixXsv7rtj7W8XOaETsP3kJz8pfkjCu27mz5IYSAwkBhIDiYHEQGIgMZAYSAwkBpYyBmYSuLkmKTDyXtnJJ5+8br8CNr+w50dA/Oz08ccfX6J4H01HPwQg+PKSpb9P5A8NejnXD40IBvVxCufnrp3I+RttMYfPCAD16xdzm0PwFm1nnHFGAaMfMFmxYkVU52diIDGQGEgMJAYSA4mBxEBiIDGQGFiyGJhJ4GZ3fs5dgHTsscd67MoRRxxRBFqDSlyD1NHPWjtxE9ydcMIJ5cADDyzefdOm7LzzzkXboHkEe/oMKk7m/L0V1ySj3R+cFbj59T7XKKM+PxMDG46BnCExkBhIDCQGEgOJgcRAYiAxsDAYmFng5m9cOMU65phjinfIFgbcDZ/1pJNOKk7a/Dytq5MbPmPOkBhIDCQGZoiBnCoxkBhIDCQGEgOJgcTAAAzMLHDzYyF+OMT7Y95DG7DWkqg6+uijy7bbblvyh0mWBDkSiMRAYiAxkBhYAAzklImBxEBiIDFwzsPAzAI3qNlyyy3LV7/61bL99tt7XJLFj5zst99+xXt5SxLABCoxkBhIDCQGEgOJgcTAxsdAQpAYSAwsMQzMNHBbYntLcBIDiYHEQGIgMZAYSAwkBhIDiYGNhoFceJYYyMBtltjMuRIDiYHEQGIgMZAYSAwkBhIDiYHEwAJgYJMN3BYAlzllYiAxkBhIDCQGEgOJgcRAYiAxkBhYEAxk4LYgaM1JNxEM5DYTA4mBxEBiIDGQGEgMJAYSA4uCgbGB2+GHH16ybHo4+NnPftYxYNJ+06N90nx+NCcwp5122jz05fzWSzol3pIHJuOBf/3rX+X0009P2Ux/LnlgDA/8/ve/L//85z8TT2PwtJi6l2/RlrGB233uc5+SZdPDgV8HxShJ+02P9knz+dGcvPg7kYm/+eEv8baBeEtbPdRX+fvf/15+/OMfD21P3kveSx6Y4wF/0uuvf/1rysoS0qd8i7YMDdye//znlx/84AdZEgfJA8kDyQPJA8kDyQPJA5sAD6Tfl35v8sDS44HnPe9562K3oYHbpS996XK1q10tS+IgeSB5IHkgeSB5IHkgeSB5IHkgeWASHsg+M+YTMVlEbkMDt+iQn4mBxEBiIDGQPDSEAwAAEABJREFUGEgMJAYSA4mBxEBiIDGwcTGw6QRuGxfPuXpiIDGQGEgMJAYSA4mBxEBiIDGQGJg3BjJwmzfqcuCmiIHcc2IgMZAYSAwkBhIDiYHEQGJgY2BgbOD23//+t/zxj3/sfh70ute9bjnyyCPLH/7wh/KPf/xjHbx+selXv/pV+elPf1r8jPyf/vSn8r///W9d+yy+nHLKKeWSl7xkiV87/Nvf/tb9vO8s5s45FhcDv/jFL8otbnGLctxxxy3uwou8mp+gvuY1r1k++tGPLvLKw5cjz/vvv3959KMfPbzTIrTQGb/73e/Kf/7znwVdjR4644wzyi9/+ctOP/385z8vfjFL/biF6bi99967wJefRx7Xf4r2riv+8NPL//73v7vnxfjHvu0/9LVPulT9Yqy/mGug3zOf+czyiEc8ouCBQWvjQ/aNXOj/jGc8o+y7775df3V9WzdojqVSh4ave93rygMf+MAO/kFw2VNLf/Jgj8YO6n9OrkN3fyIATmKf8PDb3/6283nUaYMfMuJ5knLggQeW61znOpN0zT6LgAE0ROdpaNgHi/6/053u1Pm/2jwvtu627qDCfvCNX//61w9qXrJ1Yf8m8QF22WWX8tjHPnYme0G7V73qVeUqV7lKufrVr17WrFmz3rzf/OY3y8UudrHyxS9+cb36eAh+YjuibrE/xwZugrCnPOUp5WUve1kXsHG2Hve4x5VvfOMbHawE4rnPfW7ZddddC8a+zW1uU57znOeUP//5z137rP750pe+VCDsile8YjclA3W/+92v+57/LC8M+MUifHTlK195eQE+JbSf//zny//93/+Vy1/+8lOOXLju5PW73/1uucIVrrBwi0ww87vf/e5OX/zmN7+ZoPf8u1DK9BfFTz/d7na3K29605vWSzwNm51h+c53vlNWrVpVznOe8wzrNu/6T33qU2WfffYpp5566rznmGYg/fmVr3ylC9p32223Dv/09gEHHLDOUZ1mvqXel2FFP3pmEP04PO9617vKk570pPKXv/ylcOz0v9KVrtTRG2/utdde5YQTTliiW10fLAHZ17/+9XKpS12q0zvrt5ZivyeeeGLnAKE/mbj97W9f7n3ve5fPfOYz/e7n+Gd0pxsE7LFZ/s497nGP8pKXvKSrOu2008pDH/rQ8rGPfax7HvcPp5Cvsu22247rmu2LhAGBOF5/3/veN+8V2c1vf/vbJX4g4pOf/GR58IMfXPx0/rwnndFAvjjZlySe0ZSLMg05oV8d9oxaUNLtc5/7XLn2ta89qtvEbUcddVSR0PPL+W9961s7fdkOthZ7wW609fFdsvOud71rOeaYY6Jq0T/HBm7nP//5y61vfesu08DZxiS77757F6mC9j3veU85+uijy6Me9ajy5je/ubzmNa8phOS85z2v5vLyl7+8vOMd7+i+b8g/YVAvfvGLd9Pc8IY3LA960IO67/nP8sIAZXfhC194nRJcXtAPhhafv/a1r12vUcZm5cqVheNflsh/sscU5ayU4Hy3da1rXas7CbnIRS4y3ynGjuOoHnrooYXu2H/tKaPvL33pS8t2221XznWuc40dz4lTOPJjO4/p8KEPfajTkW23zTffvPtbOeEMtG0L8f373/9+57TLFD/taU/r9PUTn/jEIkARuCzEmhtzToHbr3/96y6zOojem222Wbne9a5XGOEVK1aUfv8LXOAC5V73ulf362j2wQFk8E8++WSPS64IPP2tsqte9arl3Oc+99ngE7SjN7kQrJOHF77whV0S4yEPeUjBF2cbNKRCAPjUpz51qjFDptpo1fDhl7PPd77zrYNBoMZJd7tI5YUudKFyz3ves2y11VYexxaypOywww5j+y5EB3sSjErSTDz/MuhoP/Zlf9OCS7adQt/4xjeedui6/mHL6WyVDhD83TcnXZ43pLz3ve8tT3jCE+Y9hYCSfqPL5j3JRhgIl3B40YtedOTq7BadNav9HXbYYWWbbbYpd7vb3cpNb3rTIsZpATjppJO6IPESl7hEW73uO30h4Nxyyy3X1S32l7GBG0ERkDFajhY5IK5EAh6wrk5Scne/+93L9a9//bL99tt3SInAzfGt/vrOt8iIOaVZvXr1uilktGQN11Xkl2WBAQLoFITj7jRqWQA9BkjO/Tvf+c7CSWy7fvazn+1O2y54wQu21Rv1O+es1trBtTEBkXiRAAo9shCwODGTKd9xxx3LTjvtVCjaW93qVp0TxtCNW9PJJOd9FiemdOiPfvSj9ZakN3feeeeyWPzxyEc+stRay8EHH1zucIc7dPqaDn3xi19cLnvZy64H2znhQSDGbg1zrgRuN7jBDYpbIjKsAh83RS53uct120cXTnucTn/ve98rH/7wh7sAr+uwxP6RmQb/ldaeGNpbH7zHP/7xhTPiKp89s9c+ObUCFvqqP2bYs2TtBz/4wSKwGdZnKddLYNnzFltssR6Ykop4JgIvV6bufOc7l36/9QY1D14DcAVzY12V5HQ6PXXlswFr2X9FF7c08Pi0m5EkfsADHrAuATPteP3h1aHBqlWrPHaOvdsKs+B/voPgq5t4Hv94fQi/rVy5ch6jZzdk2pnIlOBpHA5dXZSIusY1rtEtwd/qvszjH0kZevyWt7zl0NFuSvFPhnWAZ7f96NlhfRa6fmzg9sMf/rCLSp26PfvZz+6uQbYOKkYWVGG89q7qEUcc0V3HUu9I8jKXuUyh1CCOIjz88MPX7U0w6MqGEz2VnEtjBIqicpmWU089tdzoRjfSXL71rW8V2RNHrSoYaEeeN7/5zYt1EMX1FsqL88bYcti0QbjAwbi2OPZevXr1uirHpU4DCJVKc3F8bnKTm3js3vk77rjjutNFV1MIzgc+8IHuOqcO4Hct60UvelGBo1e84hXd+zyCWFlPe2NEXZVq8WlsFMbl4Q9/eJHdwSSuh9q3dfWxD+9vaPfOGIWtnuEQMDs6N46hhlNtjJJrIE5cwM1xk9GwP0GVNTAtoXryk59sSLcn+KSo0IOzaYxGNBfc77HHHp3zZ71DDjmke49IO0V76KGHdk6izIrMLvqBbZBzgQfgzjzwDz+cTA64+cDomgJ+NJ/MyUc+8pEORvxAuF3d9YnWMnVghScCx4mTYcez5tOO1ne84x27E0CnxeDTBvanP/3pRQZboBm8oC0KpSkbS9jR2LoMDDgdyaON02h4wwf6x1inP3CNPpxDV/jAA17voKGP8fif3BgH31/4wheKRImTGrC9+tWv7q55ae+Xr33ta2W33XbrrgPgkU984hPdNTC40BePue+Nf+FTcGOfkiXawQs31kAHfZxCOHWQcbe+PeN9/RUOcOACjGgo0WK/2jlMgpb3v//9HruraOSB4whWzhIeg38dyDCYwAYf7roHPtAOr+JBfduCvwQksqUMP9y27a41cT7JvX5bbbVVoYvgWD9XGe0XHTxbE63sl+wKPL0jpM3crqnhLTwmO0gH4iUnfPDjlAI+FHNKbuF14/EL54AeNF6hJ2TvtSvkz7Utp2X4RUDp+o+1tY8qxx57bIEr7+y5AlJr7brDETmTREE3iThZSDSA61e+8pXrrr3bP7ocdNBBhbzDDQeX3NFzdIoxbl0E/3z6058u+Af9BAnGGBv7Ajsc3f/+9y/gwBf0Dvx0AK79x2k2XYqnwA5naMcmyNiinZMT1/kF6GzO2mEFvq13/PHHl5vd7Gbdewv77bffuv248sIuoI3+8GPfaOvZ6Roau4ZEL4DB3BIBd7nLXYr3rvFM8B4d+sY3vrFoD31rnkGFjvYuHd5dtWpVgWdzffzjH++6k1s6I+SO8ymIZD+6Dmv/YZvBZ/9sIxqzhfhrbfN6/+NrJ8977bVXEZjWOkf/Wmun3yQy6F6D9l97Og2PvivkD05kqq2JR/0xWDJlLnKNf+GTrsKX6vEvPGiTBIEzsJmLHQjZsYbrmvbyrGc9qzshRQfyAx+3ve1tuwARrdsx9IiTCrrDnGxl8JU5RxWyKlg3TrAbBU3BKGGDnmwlmcZvnu2fPsdXAl86kO4kO9YzHi7BBof60eVwqB2/41tyjO6KK5uh68gSGWDT6ExruyFhbBR+lHcxjQWDG01gI0OPecxjCkcX79AX6tFpzz337OwzGSU/5B0vm9OJD1yw0/QWGhqnLQr4XSvGo+SUnoZD/egFeCCb+JS+o8tiLL6Bk3j2uXr16mKM7/gDnrxiAy/42VzkVzvfDFz2AR/skHq4xs904dZbb13oGvX9Qp/Ak3q6nR464IADSth2/Eo+tCtkiC1m88knXLNN6GIf4BXExQ0b/eFGf7wDN+gPN3w761sDvdDa3thwMgEP8M9/4J+SdzDgc/vG23SqpEvwmPYo9nPcccd1BybqPMMLHWs+/E1u4E67veAr3xX2na9lv57BDC9w6hnd0UGARSezfXxtfKz9DW94Q6EP4AKsdCZ8aIvC14cvfga/TxxhL4Jp362B/9kQOAYP/RLj2WHrCvD4L/rw++jl6OMTTPQjGaB/6BC8EvrZPPx3CVQ+Ih8yZMB4he/FJ2JPwIcXb7bWdqxZs0ZzV/gz6OYBvtkXsk4u1PMR7EE7mFypZBPAhC50orYNKZuNG0yhcpQwJWWACShkgm4sQaPUOMaIzyCpJxyIiJExJqOI8JANcYign41RhitWrOiudyA6w8+B8C7dIYccUiD3a2sdUArSGIqP8BAqDoJg4+1vf3thlCl6Qn7wwQd37y34UQKKiqOLwTmvghfztIVQcUbVYSrzcWoIkDoMwUmyX8QiqPDCGDGYPilHgooRMQsmoIgxCXxQcJS+a0mMkqwlZnrLW95iifUKPFLE5oFHAk84zAGnOjMIlLgAwfdQ8HBAWQYeKQ2CAVfmolQ5CuBWzzGCdzRiOAkumjHE1jEvAykwNjdHDI60EXyOJIVuLwSRMKOXds454wf/BN56lCSHv9Y550G/KGCBW7Q3DwVoLgZBHwEkRUh4zUdQOH7gtz584Bf7RA/ZFbyJZ/DJC17wgoLfrIP37A19BPsCX/yOhtbikFAU1oEvjo/6tlBE+I5yNZ7BJcTRl0LG63CAL3zGeHhAz+BRChUPMmgcB/QBp8RDGCXz2RvFxZgwshSifca88WleezcX+Bly8oQfnCbYPydckE9J2ScD6ERmzZo13TT4/stf/nLBTxxHBo/R4STBk2dyiNcMoAvMh1cFCZxpihCfwJU+DAv+FrR6hiuwkCd0sH88Sgdoh1fKkBxzsilV9NbGkFLGDL/ntsjSOU1gIB/2sIcVfEX2og8Zxw/gRGcGCD7CyJEHChj90InsC/idnqEz2Qx6cmI4Fa5dML6+k3FGGr+SWXKOZgpYwAff4CFvgiJ6yHj8LwhAE+3kBn8bS4fiC3SEZ/jVZ1SRTFi5cmURfAzrh0fwMjwceuihxbs+ZADPGQMHvtODjBk48ZiEFmcRXwqE8AqYOAGu7qEvPpBIoVvsAf7MqZ1uYf3eN04AABAASURBVMzIO1rABVujncy4no+/7Z9+oX/wD/1oHdf98CM5YBfs01h6g6yDjRzgL04EHaJdoGJPaONZf46O4hnfoT/5gQd4QVfwWwv9wBd6Uib4uLVOFB42xhyDin6u+cMlfOB3vMEucbaM4QzACRuBL/C+Hzmif7XrS2bpYSem9Nb+awMue+Gw6NMWvK4NbG2973Qsm2ZtNgIeOCzaFPi3T7yMLpwq+oPswwUZMw6f01NklU6li9FXoo3jQjbwAXuBxuA2P1lGW/LE3pJJSRr0wof0lv3jGf2MEaCxpeRCUgCeyPDb3vY2zWOLvngHDHguCh+GbWL76Qr6mL4R6OAXe2Zv8D6+p6PxnP5wSG/iKfyCV/Ad2rI/gOIT0CN0CpmkP7xuwrnTTjfChyCGvAju+7qNDpfINJ6utj5Z47ByRskT3mXv4BMM5CDkBI7oTY6kNek5PCOJRe+w1bWub5vhha2RdCen+huHd/iE+NVYeOEbSu4Jhs1P3vCO74p68HH0PbPXaIEnJKzsDz+hjXYyIhgSEOI367MX+MRa7Cieg3v928IekbPge/DSWXQz+TKO/WIfjINLtsxe7cEadLs94El96BO8wScGJ72JFyQe6GW+ChtJR9FXdIJPa6m3PnzQ9fw6MoVH4JAc848FHPQYOwH35vXd+m3BV+wBG6je/HBFL+At/hw6waF2NGfTfQc7e4oeYfPABp94CI75HoJG/iteJ+v8f7INV3BBv+tnj/gDTcwfxf4lKe2PrpMEQxeyAAf0KH5m69DcPgMeMMI/e0P32Q99CO6+jqVryDF9Rf/gI7qWvgGL5BZZIE/molv8OIm2KOYgf2SL7rcn+h1v6wMe+xBHeMZb9DDdiS/dOBSAikG08xe00wX0N7+VLtW2IWVs4GZyCKIUnPZ4bguHHQJkaSGI8bA5ETZBIuCYChIoP4xByWFW80ASZFGWCM4gUWQycgolct/73rc75oYczET4GCH9OY8cCVEwZqeQCCTiWA8slJc1KQpZDMizdlswNOWhDqNgTNkpCk8dpVZr7X6ExXycN84C58FahNReXf2yB3DJ8sgSE2gZA3gyloPjGVPZj/HWaAtFTlAwMmGh7DldjB9Foy+FZ1/W5WQRCPXWJ4zaMCvFCl8YDsMSPEEYBqKArA9vFEattbvzi/EYWfPBC+UAnww6eAKH9sSwUXwcHniAA8VY+xfIMMIEzidh5XTWur5xoFAoVHsN2hNE+7VH8wkKBAAyR+aDSzSFF0qdQqR0ZTY4keYDNyUGR3jJj1QIRhg7Cg6+4ApP4jdBkbU4i5tttlmhiCl+Ckx9W+wTD+MfSsD65MVY/SgyzrCgBw0kKNRb10kQ2bAH8BqPzyg5gTh+5ZjUWruMM3g5xZx9+EE3PGS9mNfcURgg4yk7+wK/q2GyaRwHfE5JMx6cIPhERw4KGbIeR4IBocDwOsfAMyPGYDFueEW2yboUroALTaxn3/YGh6E/yDvHDxycHTLGCDBO9Aw5Nl/siSyRY3RkLClCuLQe/UNhCpw9twVNGRyyit/IK4cb31PGDKng3h4YD3sGF72CPgxABJf2hGc4kPYOr2DC8z7Jqe+cI9lJehB+8KW+tdaCh9BYISdkjt6jE4xjcH0azwlGf4kfe2LkrIOfOI/6km04MY8+owoDI/EFxkH9GBiGjuwxrmSEHJmfc2AMA4030Yi+FcjQB3Q7B5J8MVz4Ee7hGa3RHn7IHl1Id+ATtOfU0y0cJDxK3uGcPbEm3UMXg12QGuOdMgjIBRGcHfJCbjl+sUfBP36xD33wDdjMaW77wWcSip4FLGijD5rBOV2jjVyTafymD1ukH75i08gK/Ule6B08ZNygQgfT73SSAI6swCV+h0tjOFtwS1eTdbiFU3yqnaMHtxwUOEETOLMXtlefttCN7Fno0bYNzGhFF5ELgQc6Rh+8QT7xPX0HN/Yr0QQXZMae0BJvkilBB/tBTukhfckDfYBWdJggzxr8BPZKEk7iRBKU7cR7+AGv0T2e4dwYziy9QHeyP+QJTHSi9nEFPsgPOaIjFPgzB5pbC1/yNcwP9+Qfz7E99mGPTgPhRTu84Gv4F7ihLV6gL+kBzipfRTv9gB72bSw+JA9wAXbj8QWdYay6KPjMGPV8Ak6hZ3KGt+GWfwH3aCL4hC8yQAfSw2QQXGDiR5AZMox2xsdaPiU3Of7kFz+SUwEaPSUxC0f8AHNbQ8Gb7Dkc4h/JFnMp5M6n/fuE01prcXpG98At2Oka7ewEWwku/MZOo5N17ZvMs13g0r8tdAd+5u+oN4bPg+5kh21CX3ytHdzsBXkCC7mjE61Dl+gDfs/G0RFwS8co+vN50EWboAW/4F/8Ak60AbM1+Q7mRGe2AA3ZGLIP52wA/6TW2t0Y0Lct+A2OBbV8AcEPXQUmeoHsaJP0MA5e8anv5B6M4PGpTmIET7KJ1hXkk0e8SLdYA4x4j8yxXeSFDTaGfoQbcylsFj+DDwffcEBejFX4Tfrh+1pr99oAHwUvqsd77DI/mW2UxOGL4FftUegnyRt7g3/rKOyYoFs/Pg9cgQUM+Cr0qXYFr/okB2wwm0Ev0v3q8TQZI0PWkgjEB3Q5/sM3dKxAU38JJrrVOvxe/m8fdv2mLRMFbqMmRSRKgBPEoRepUizGMD4IySB5VjA9wDGQZ5tXZw6Cytiqt3HKkzIjfJjDPBQNRwJzMo4E2jocTggUJJgH0gkGAaBAEZ2itpZ5rNEWCt9eOEqcWczDgbAWA8Ghlv2idCk6jM5oELYddtih2LMggQLWn7LguOof62AozqBoHePIajDKBCz6xCeFCAbGJOooAUxEaNRxZDCS9QmO/arnBBhHkCgoxphzRyEREHWUEKXFoFK4GIsipnDQkZKGX/PBvb0zvIwrJWA+beYk3AwtfMl+UmCceRlUipLxCpg5OehPCRjfFjhGA2uARxulzpkJPDL2lB/h0A5GOPcdfiiZMAjqHPNTivjFMwHnlHO4aq3ddV78ISAEM8WFf/SlOM3NqFM6HBT1baGY4JRBqfWsQBSdBRMUJ7owFjK1aGq8MRQk5RKywBg5PZW9Qj8yIXuDtxke7RQFWLXhZ8pKwgTvm7ctxto7/Kk3Hi9Q1OjHqMMj+njWh5KxTzJCWeJzPGF97ZQWHOIteLIvCh6PaLcv/I8nPSsMBPxbK57B4DscUM6SC2RenWcKNnAF/+iKX/Eu40o36ItP0Cie1bUFf8AlXJgDfukTvAJWNEY7PMcohdEUTOITeDafgJmscWjVybBS7mQHT9Nb6BS0NCaMEscJDeBVvUJv0Tl4hLEwt+C57QM3nBT98Q6nlSPLcKojX4wQg+R5VEHXkKFB/ehQvMz4oKs+6E8eOXienZqSczoW3ulitGZMOSLWsA8GCv9opxcFm+yAOeh7RR/45UAzgoyodkkDxo5e8oy3FPimN9QpThPwdivrdDcZDvg5DJxAvFlr7X5VkT2Bd3NoN2fgReBmvDY0wef0u2cOir0G36qzDrg5T/ZOB1sLjrQPK+yLQDRkSj9JNXyKxp45G/SxYBRvszn0CEdOO12MF1t48CzexPP6tAUtYt9tPacIDTg48EA/oAE7E/3wNqeZnKmjE+y33adka8gRntHPemgJLvSL8droETzgO/lAF1ltMKhzcgAf+MB8HCc2BL61CwrxIxyCQ0DBweY4ax9V6Cy0tme2Lgrdgq/pHTgnf3iPjNVai+CUHKNdrbV7HQC/R386i70jo3ABBrqM/NgXn8C+6Gz23zySVWC3T/JgPPsW8mKOfgnfhl6m+/GpPhxhMiyp4Nk+JWzIV/ANHYcekeRFH4V9jz7GtgXM8MBW4UFteB+fOXHhb9Dv6hV48EkX0o14jKyqU8hprbXwFzyzlXDMDpsf3vE7vGond/QInHlW8IQEgGAan/C98Ie2tqCP+WItOt8a9D69Cedknf43jl42Bj7wgjrt1gt48A69bX++s6t4L/rTz+QY3vTBn+axFtsFXs8KWuBpsHi2Ntmnj/GAsXxX+k6SQJ+2wAt686fQiJywpWQv+qF56DGwgRMNnHLxj/EeOMgk345tNx9YyRjbaTxdBD8SMeZHV/rC3un/WK/9ZGvpE7YRjaMNnOhlfXVsO91JX5OfsIPoBW943D4kda1tTFvoN7yAz1peZGvQSl997Mk+PA8qdBt80CPwpA+ahJ0xnozhRbKK/mzmypUrdS3gFYzTzSroUXpNooac6I+XtG1I2eDALRYnDJQTBczgUyKII0MffXyGseTE6cdoUNwclFpr9wtwiInh9Ud4zjSlQlEghOALcSAWI7mG4XiV8JpftgET1FoLuJwoOKYkHCJo8/YL4nJeIR5xOLPmwNDWtx+KxTjCAh7XT2TiCRpjTEFqN57iDydAHVyYQ2QOTqdpHC9ExcD6RNGPEIIBkeGJk21vsYY67a58MDwUUShMeyHoYLIuQTM3g8HRBDMHVpvMBsE3H2Wjr0CMMpVVMw5erIHpMCSnQUDLKKC5Mfbk+gOHmIOMNuAlgITePGjHwFsvGF19FHSlWOBWHcWAlnhDvSAMPiiVWueCJMIIdmsRcPDVOtdmDji2nnGeGQFGQZBQay0MEJ7AhxQqI4rnzAlu1zecWDGwlJg52sI4Ms5hGLSRAY6YTBf+U4fHOTuMtWeKE0xBT3WKIEfWWQBMieon24U29i8YELDDN5g5Eww9GhkfBd/CN8VR65zTKhmAt8JhwoPmDaXLAKGpoJZD65lh5gziUc+UFQUMDmvhU/xOVqKdc0O+teMTPBqKjxLXP2TDeI6vZIb+Cvki65wfc+IxfMqYwTfetjd94RStfB9V7BNM+poTfShfOIFL9HGKg47oYi0yTM+AF5wMqL50CbpQxvgbzRiV4FtwUN7xaT8cTM8KGMgzA2ENyQn7gkPtivkZjDAydBAcwr12+8aP6mqd43cGBe9p7xeBFAck4NLOGOIHY+gwPIT22hT8hQ6xLzDjX/TQTk7IT8BgfQEm/aPeWvAqSeQZ3smpcWTDHsiOfZMTeMHj+gR/4Df94tm6ZBjcjDRagZ+c4jVr6RP042zpY260pgvItT5wymkSUOivH3prs3cw42XP9uXZXj0rntGDPhHcgIFziyfArOjXFnyClziK1gMXvUlP0Un60oP4ge3EH+q0r1ixovuRDPJiHjg0BzpK9sBlG8gZF4U+hjd9ow7ejKPrJRDVmwPN2WD0sjey0vIvXIDNnMYo5qFXos46dDNYOXdorJ9iTg4SR8YzGw7v5N0zB5JMCKRi/3iPDsKf7Jb9OyEmj/QY/Mh262MOvGh/vvcL3ai95Sl9zIln2RfP6KSOLvRsHHoH/4ORbgN3rbX7hU08iEfQBf7snzx4ti80CD0CZ/iGXwHn2ukSCbla52Taum0xpyDaunQiW81h1Ye+YFdDn7OHgjIBQK21ew9c4rPVR3QbOzsoKDCnIgCxrr17xrPmJoP2S4bUK/AkCSPWAAAQAElEQVRqX/iDvsVPcAhmc4ARzeh7e0YjsobW5MkcdAb+YXc801vWpJs9K+YCM9pLfEs0o4+2tqBRrbX7FXRz4D3zBp/QRfYBHuPgAz21WwPv8pfIZsBLXkP2yCrbQr8abw1879Ne8TXfRRtY6Bnre4Yr67HRnhX0Bw8/CZ74U/BPrwR+9FOsQXYjaYvW/FdralfgxJ4lgTzzpcCKD9kYvjOdDsee8YbTQX3hRpDEB+OP2LdgL5Ky2tnh2I8x/UJW9Ykx0W5f+IOs2YdPyVE3Yvg9TnD1tT/yR7bxkRNr+NXWFnX60luBJ3vks0r86ovP0ZM8eO4XbeSVbmYL0R/u4VQwqz/bBH9sInk1Z+hGMOAVcNItxuMZ8Uf4VXxn7dr0t3fzTls2m3ZA9McQHBiI4dwJEjhUnBGKChIIn2yHdtkmYwkyo4ThBVuOkSmbCNQoZoiyUQrdNTNOBWZkFDCBORhfyGUsbJ7xta4MD4eGAqZ4EZoAQZQxCAuOfiFclKs9mAszYnLM6V0TgYs+xmEkgu2Z0qLECCRDDRbENxa8+ivG6A8nYCDsFC5B1d4WcGA+gsIgcTgED/aEIeCAwjIW82B6DoZx6jgk4MZghBp+zW8sRak/fKMLXGmDaziHM46HMYTW/IwEmggMrQN2e0MrDhc6+O6TIUJ7c1L0xjMWlIJMobvJcAMX+rTFnq1vbww1ZUWJBe0ZJHun6AkQuNALj1EQaByOW8yLPpwEPGAPTgjtw9WNWmtxQkx5wi0FZs+EzVUmDgrBRDs0Q/OYNz7BIxghjHCJ/6zlWWAd/Sht38OBAT/hRwv1UeAXHgVGHAR4w0/a7QWOwOc7emunsLW3xVh0xEPgsm/H9Hgb/vW1X7BTeE4UKGXGQrYJf9iDucmV/vBrH2htbnXkGo0YO89gg098QCZcgUMva2nX11oyiZ6NRw+84pl80C2CKs9wKTg1xjMZxjv2TjE7xWZ0tLUF7RgAeLY/TrXrZZQsPoJ7a9mHtckLHRZzkE140pcsKAwtxx8OzY/n9PdMHiRDrEf+JGi02Zs9G2sM3NgLPg+DgrfQ1T7xj7Vd03GyxQEy1rwRxJnXM1zTU57pRKeFAZO6tgj+6QbOnvnpbQkXwSf46AnrMFRwgWZ0DocKz9oHnc0pjXnxHj4LHjaejKER2MiONekVeoyx5gRINJCpWmv3XjM+0c7o4RcGHd7xG/pbgy6LdcmH+eHZXpxCu/0Ah3SFfvYHHnrNd3MzmjK26G0/HEQ6UX8yYk5y5RnM+BxtPOPJ0O34De/oT17tUULMFTEOIF1iLfxgbFvwrXGCIXvWB57psJAJ8zHqIQPGwxHYwIMXyQB8glsCUQKQLmYP9e8XMs2OcojwDpz4LiHlihpH1BgyD368Cr+uonL8WqeZs24f5oEX48AGFnCiif1rR0uw0ufsApvj/S1JMiexbCadwjE2j4KH2angbXVgtwa9hB8kYfWBD/YILwYs9AK4yZOx/QJOY1te1gfvmtf8nsFK9+FVz+gGF5ILbA+HjNzTE7XW7s9qeI7+/By62zrwxX7g61pr94Nj9Ia5wGt+uCcz+LPWswducEU+0c4YfIDfQzbICr7kfNujNckK3KEnX8jpyemnn979WJg10RJc9ul5UAn5JkN0qU+OvHH2Ck/0j71K9rIZglNzsf/kEG7BgS74QtJFu8QM3m/pT8/UWkvQ37z60pt4A3+yZ+QQjskgmpF//aKgMVy0tgecIfP6Wd8c5NazvYb+owPoR/iWENYOFvAG/PwFeMcP9gU37GittfsRMPxCHsgSOadzIrlCB6E3/KAPWK2PnmjNB8BPeAROrd8WY8Ea8+Fd9CCD+AOuJDfIDV/KWPYOT5BPvMIekik+rOu5bk8EL+BP+yNf6uATTsBNN6MH2TanuQcVuowOoEvbdvPgc/PTY+SXL4CO5sPb4DOeb2EPTu0FtHDazuW7PemDd9AIXwsEzRmBKNsKX+TRmH7hC8EbHmc36Gh8zP5JMoEHz0WgLamk8GnRHk7RH7xwhy/sDc+hJxygEbkEC1tHdvpwTPK82SSdBvUBKAXsupCMEYXAIPsEHGYgMJwJ1xowmXlcT0AMGT6OlTEUn/7aCQhlTCkScoQUIRNOGya0DCim1h9CMatjcycnAkHOKiRjMM6ao1zXkCAPzMb1izUxDEQ6rrUHRtAcnDUGH5GMw+wcHacz1mQ0OfrWJIQIxXnBTPorYHSF0HVAY1yhYzQxkva2WJsx5eTBLcOM2QgJZWYfHCt75pDBo+8MN2EirI6bXcUEp4yU+b1/IIiBezBw4jxrA4e7w+rhkGJynQK+ORccE20MAnxSMBjRETdlrc2Ln2ANWjtN4hBQWBQgRc85oWgYQeu2xV7MaS2OrywNwdZfP3txDQBN4YfTDucUC4VIuQQf6a8IVjhI8GheyhE8DJ52Dj240YOCRSM8R1njQeNcgcLnAYdxUdAKXHCAH+yd8FNy9h79QuDhTR1nxf58j1JrLRIEDKx14ZSjS8j1sT+ONHmyf4pM8oPjrb0tFB86CkbsjyLCw/BD3vS1Z3iQzbKWIBgN4sQUTvEUmuhP7ig1cKClOmtTUuhUay0hG/Co1FoLxRfGUn9OKZwZz7CbC297Jm8cvAhSKFMBBrjACR7yybhxzIbJEKVr/+hmrEyeTzSynusNgnxyY+8SRBJRYFAYUo6l7/bm6gznE97xv3e2GH3tZNMcTuzQjVzRVdroFOugEz3BgWFYGEp6TR98Ak7Bh3nICkeZYocX/ARPYTCMQRsy5ETJM2cBTJwQz/1CB5AF/G4PYOFICQzRT7tAEc9rp8fV4RE4ZwjxNKMUc8MxfmKY1dFL9AX+8MyIajPWVRm4x3v0DxrADYeCEYc3uhGfKNrNR5euWrWqoLc5FXO6hs6hQQt7h0M8LyDXhx6HLw6TPdD7sqb2Ri/TrZwPNir6+24/nsk/3PiuCKLwP34xp+/q2Q08Sr9LTHEe0dd6HCd92hJ7ti97psvgFI/RjfrSUZe5zGW6XwH0rNC7bJ3veAIu2Tl7Aw9dAi9wpU+/wBd6ci7wGnpICOI3egTcxtin4IKuZLvpQjoCP2pXyLg14YKcqNMXTswNNo4YPjCfNTiFPuEIjj2Dl76EP4GceRQ6hsMaOkId3cw38B185JXDZE44YAvYMO3G8x34GZ77hezQaXDctuFXzp89qxcIeganZ7qZbsK/9DLZs8doJ9ue7U//SOrpRx+op78lCwNutyfIt/5kl48R66trC/mzN7drjKcXzYdv9WOf8DW7j1/Itfff7Et/TiX40DLsED4kM7WePVA0pyLo56ehHb5g4/lj5I09Jt/kCh3QCXzBy3QjPwBf8D/wP/o51TC3vdP5+nhW2Ep7QiPP7Af54sOhOb2A/9gDtOD/8HmivzEK/KOX/XqGXwGQ+Twr9o8uwWuSrXws8PITwIu+/CH92WK62wGFZ/4sedCfTKEtWNFbchjN2A78jt+cPOIpY/ktbAN7T5bIPVqwU3Q/mhmLR8BuTFsEkIJ9J7TqyT4eFdygB31Px5FNMOkD9+gGfwIaegc8/Cgws4uhC9DA3G4AgQWMdLT929dpp53WvWpCV5t7UCET9Hm/jX4OuuAfPGMNr6RIgDkJtD/8Iains/mjcMjX6s/HRvF5yTaegEM+Fj2BB/XHC7XW9fSq+iiCPLRmw+hL+4UTtEAruMY/6G0Mvc0X4YOwQ/QNHgxeAb8TcbCQDePxCXyRSTzLVphr2jLvwI0RkZ3nCPgkzBgdEwCC0lAvwEB40bV6R7ZOVIwDOMRSgqFI9DOX43ZGRjAhuxOCoh3zhGLwTCg41xAoQsYAEA0W7wOp57hR7pgRHP2C8BwRGa3IviC4wMb8GCbGII49mZsTbR+UCOdCYbg4jRyrGOOTUMkUUbjmxBCYUlu/UAgcE0IIdgzNmYDXMNzwE3vjZFL65iPo6q1DWYLX/BiKo4DxwQ1XFL82Tn07jiBhYnThSFrLOHslVATJOEYC/bQLtty5DyUoqBEMyEaZm2OKJyh7Y/uFIDAuHDG45VDglaC9/vBuPgrS/ggMWChNvKZPWzgrsWcwgp+TxzHQTx38+nSySnGrt7b54RHsBK/Wsxs39MD3MivwYA8UlWupjJu5FAoY3/qu4FfOne9tobTwCHjQCP0Yfn1qrUUwZB3t8M2YtuvoF0XAxjknT3iNMyPLjtf1kVxAH/PBAfpSLBS7dk4XObQnz/hLP8bbd3VkhXySNc+MB3zbvxel7ZGxDRqSKTzNYOmv3vjgUTwDVvjWTl6tiQ72bA9heBlGzisY9G0LPHJMrSWgggO4Y6T1k/jBY/YPz2SBrtGm4FNBjO8KBY0u+uMh/cmTNoqYHNAfAjT4RG9tjCAjIAtqPbCiJ7rAhT4cNTIGD3QUvqM/6DbtdA9DS0Y9KxwpzjH+40zAD4PFMdDeL/rZD3rCi72gt2ANLeEfntHOJxgYLvJvLg4FZ7B1sNACD4YscQrMH33oK7KAbvCJFvCGn2qthdyyB+RdGzrALbjAa148TPfR5+CIIugR8NEXxulDL0Q/cmNNsov++JGsgNEcdJRT4cCX/vauTeEMoqnvisBC8gPuJKvAJlBxcmpNvMM5kpQgYwK64AHj24KOdJU9wzPYObz2rB8ZRG/fo9CDcBHPHDM8ZY+CbHPYI1iiT/spYEYviRzrkgc8YB78F33Ri261T+10m6Sk4C360LkCCPt0XU09m4D28KYeH5M1torDjbfxqP2aM2TY2k5M4dQ8ClnDO+1e4ItO1q5w0siZ+cKWopE2MsYxgkfP/cKhpTf69eQHr7J72thx+iv0peAY/iQyrYmHyCa668+W4rfgMfXwQFZrrd21fHqJnYELc9B1gQv6Fm3Ma75+oUuMt2c8T044hXCsL11qX2RCkoJ8cXbJReDfOO0cUmPgEcy1nt22aVfodqcXbBydznZZi95Af84pmODOnqxpnIJv7FeSDT/xD9zAiSDcniXEQtcZg1/wnO8KX4+uYz/RSDAFr/YPj/Sq4FLftuAfcsE3U08mwcBueFbAip6+K/Qc/iVv5qWH6Wr71U53gz/gRTv4UIePwUT/1VqLxBE5Yv/gR0KPfbCGudCTjuXnwRG/RB0+p4voYfpVQi7WMy4KG0YGyJ46dgjPk214sab9h47Tx/wCRfIRNz7wm6DDXOy3fgo+dvMDbPiGbqYTyQfa08F0T/CfMf1C56JVv54uDZnno8G1fmA2Bo3Nax/0jfH2i35spee2gEdMgk/hzTxgg5vQ1U7AzGHudmx8F0jGeDwtRjEPmutDP5ubzHkGH11CN7ExcE3O+Lna2TlBs33hC7IuECZ7dBSdDz59py2bTTsg+mMSxoBgchx9VxftPhl97RQ4JaIO43ASMIhoHxK124x2nxSeOQmaOSlHAZF2RID8CIr0N88WW2xRrGVuzl+ttWgDg7kwvn5lxH8UlIyUNXSzhrHGzAWeswAABTpJREFU1XqWYqu1dr80aE5rGoOotdZSay2MEeautZpmXTGfvRhj//Yda63rdOYX+LIX69sDJrGe5lrPWl97O4814E29/cCFMUqLDzDoF3hFFzRUbxylF2PsBbza7I2QaFPQB5za9Om3CwLAgmYMoP2jubGDCmVhLv3gBixgi77ggm997DvgB6M1ol98woe5wAA+8NR6Fl3AC6/a8B0cGQtGaxsHLy0M2qPUWgv+0E9/uAETnPge/cCHjvEM9+riOT6tYz3z2SN8wEO09/Ftjnad6OfTXHBlf/atwJm2KMbbu7XA166FXnAadbXOZavweowHj73gV3XwZ07wwwf+g3+waPdpf+jiedB4cBin3dpgNp99WLvWOfr5Ds/m1LctxoPdvowFA7q0feDDnPqYx3zR3n8OOPSN+WIPxtgHXGuDD7hTr5BdONYOVm3WDZzp0/I1uPVRr1jb2LbO/swh88nRYPQZCX2NGVTsH+7twXz23/KO/ZtTO3y1+wO3trYOXchMrGU/8KavOg6Z5AB8xP7hotY5+uljPmOsiW/AgOfxUa1zskVGaz1rjHH2aQ/mjXHm0aaYwzjw6aOtxR8Y2/3gOWOMVcwNFt8Ve1NnLrivtRYnrgyyzDXjLrvNGMucc470M7ZfzIXGeMW6cGLu6Icu2uPZJ1jb+eDNGPDE/vtjjGsLHtUHro2DG3hs++AHMOmDV6zpEz2iH14FO5gCp9rxjHHkvsWdPvAfbcbHXNYzf1tnrLmjj0/wwpPviv3jK3Nqw4uxF/sEXzunMVHwBTzEc3zCIx6wF3X6wVGtc7znZBA+jEVDCQfrgFd/8MFdjLc3cNm/dgXftXDrby5tIZ+xD3VtqbUWewpcwpsx0QdO4B4+4EC9Ov3gyd7wFpi0KWwXGHwfVeDGvIp57E1/sBtvfm36xf61+w6H2sHNrvoEl3bwgznmUwfveMl3RZs+cA3+Wmvxif/V2Y919G2LOrAFfcAK94EbfdGzXavWWgJedNZu7RZe65rb+Fpr92uP9mf/xoJXm0/rg9Ea5gJrS191xuF3vGEcnFjbnOrNEetpj2Ie+9E/6nwPeoPT/LXO8W/00W5N49X5BCO813pW31rnfuXR/gMWeAdLrXM0sF/P5hlUtCv9NuvhBfVgxhPWsO+QF/iDL3Kln3XMBX7P/WIf+ptbwdvmcELrxopEnyA15u+PVw/X+AXu4N4ctc7hxFxBxxhLHuFSAafxxmjHb+AFixK402Yta/j0PG2Zd+A27ULZPzGwPDCQUCYGlg8GXMdwRcrpwFKC2smTqypOcJYSXLOERQads+MGAcfOCZzgzYk0oz/LtXKujY8BN3lk2J0Y+uS0OeHgMG586BKCxEBiYBAGnF46BXMl0820NnAf1H851GXgthyolDAmBhIDywsDiwStLB5jJFO5SEtOvIxrPu17YhMPXCYdnXK6ShW4l5F1BUa978tkGwnmhBhwTQxtXXVzLdIVNlflJhye3RIDiYGNgAGncK6vus7uOuRGAGHmS2bgNnOU5oSJgcRAYmDTxoATKO/juMpzTsWE94i8f7OQ+8u5lw4G/KCGn533XqB30Idd2Vo6ECckiYHEgHdM/QiW98viOupyx0oGbsudggl/YiAxkBhIDCQGEgOJgcEYyNrEQGLgHISBDNzOQcTMrSQGEgOJgcRAYiAxkBhIDCQGZouBnG2pYCADt6VCiYQjMZAYSAwkBhIDiYHEQGIgMZAYSAwMwcCyDtyG7CmrEwOJgcRAYiAxkBhIDCQGEgOJgcTAOQoD/w8AAP//SpCYjQAAAAZJREFUAwC9V7EHUzPPRgAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Meta-Analysis (Forest Plot)\u003c/strong\u003e An inverse-variance weighted analysis was performed on the studies providing valid, calculable confidence intervals (Sulley \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e and Sogunle 2019). The pooled analysis confirms a consistent direction of effect, with risk estimates significantly exceeding the null value (1.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity Analysis and Data Exclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile four studies met the inclusion criteria, only two (Sulley \u0026amp; Saaka, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sogunle et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) provided compatible effect measures (Odds Ratios/Rate Ratios) with valid confidence intervals for quantitative pooling.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eGebremichael et al. (2022) was excluded from the forest plot due to a reported discrepancy between the point estimate (aOR 1.75) and the lower confidence limit (1.84).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eWalle et al. (2024) was excluded as it employed geographically weighted regression (OLS coefficients), which cannot be mathematically pooled with logistic regression estimates.\u003c/p\u003e\u003cbr\u003e\n \u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 The African Consensus: Clinical Evidence\u003c/h2\u003e \u003cp\u003eOur findings are consistent with recent epidemiological data across the continent, confirming that the obesogenic footprint of Cesarean section (CS) is detectable in diverse African populations ranging from West to East and Southern Africa. Specifically, the significant association we observed in Ghana and Ethiopia aligns with longitudinal data from the 'Birth to Twenty Plus' cohort in South Africa, which reported a 1.64-fold increase in obesity incidence for young adults born via CS (Sogunle et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The persistence of this association into early adulthood in the South African cohort suggests that CS-associated adiposity is not merely a transient childhood phenomenon but a long-term metabolic trajectory. Furthermore, the magnitude of risk we identified in our West African dataset (AOR 3.23) exceeds the global pooled risk estimates of 1.23 to 1.33 reported in recent broad-scale meta-analyses (Chiavarini et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This suggests that in the context of the rapid nutrition transition occurring in the Global South, the lack of exposure to vaginal microbiota may act as a potent synergist with environmental obesogens.\u003c/p\u003e \u003cp\u003eFrom a microbiological perspective, these consistent associations support the \"hygiene\" or \"microflora\" hypothesis, which posits that CS delivery disrupts the vertical transmission of the maternal microbiome. As noted by Fa\u0026uacute;ndes et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and confirmed in the discussion by Sogunle et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), infants born via CS are deprived of this essential microbial inoculation, resulting in a gut microbiome characterized by limited diversity. Instead of protective commensals, these infants are often colonized by maternal skin flora (e.g., \u003cem\u003eStaphylococcus\u003c/em\u003e) and environmental opportunistic pathogens (Magne et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This initial dysbiosis is critical; Murata et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that CS-born infants exhibit a strikingly high Firmicutes-to-Bacteroidetes (F:B) ratio, a microbial signature strongly correlated with increased capacity for energy harvest from the diet and subsequent adiposity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mechanism: Vertical Transmission and \"Bacterial Seeding\"\u003c/h2\u003e \u003cp\u003eVertical transmission, often described as \"bacterial seeding,\" constitutes the critical developmental event where the maternal microbiome serves as the initial inoculum for the neonate. During a vaginal delivery, the neonate passes through the birth canal, acquiring bacterial communities that resemble the maternal vaginal microbiota, predominantly \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, and \u003cem\u003eSneathia\u003c/em\u003e (Dominguez-Bello et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Additionally, this mode of delivery facilitates the transmission of maternal intestinal anaerobes, specifically \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e. In contrast, infants delivered by Cesarean section bypass this natural colonization route and are instead colonized by bacteria characteristic of the maternal skin and hospital environment, such as \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e, and \u003cem\u003ePropionibacterium\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese infants often exhibit a \"stunted\" microbiome characterized by a specific depletion of \u003cem\u003eBacteroides\u003c/em\u003e and an enrichment of opportunistic pathogens (Stinson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The elevated Odds Ratios for childhood adiposity observed in this review - specifically the 3.23-fold increase reported by Sulley and Saaka (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) - may reflect the downstream metabolic consequences of disrupted early-life microbial colonization, although this mechanism remains to be empirically confirmed in African populations where the absence of protective, immunomodulatory pioneer strains alters the trajectory of metabolic development.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 The Consequence: The \"Energy Harvest\" Hypothesis\u003c/h2\u003e \u003cp\u003eThe \"Energy Harvest\" hypothesis posits that an \"obese-type\" microbiome\u0026mdash;often characterized by an increased Firmicutes-to-Bacteroidetes ratio\u0026mdash;possesses a superior genomic capacity to hydrolyze non-digestible dietary polysaccharides (fibers). Through fermentation, these bacteria convert complex carbohydrates into elevated concentrations of short-chain fatty acids (SCFAs), primarily acetate, propionate, and butyrate (Ma et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mechanistically, this process drives adiposity through two primary pathways: first, excess SCFAs serve as direct substrates for \u003cem\u003ede novo\u003c/em\u003e lipogenesis in the liver (Amabebe et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Second, the gut microbiota suppresses the intestinal expression of Fasting-Induced Adipocyte Factor (FIAF), a circulating inhibitor of lipoprotein lipase (LPL). The suppression of FIAF disinhibits LPL activity, thereby facilitating rapid cellular uptake of fatty acids and hypertrophic triglyceride storage in adipose tissue.\u003c/p\u003e \u003cp\u003eApplying this mechanism to the results of the current systematic review, we propose that the altered microbial energy harvest between Cesarean section and obesity in African cohorts (Sulley \u0026amp; Saaka, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sogunle et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) may represent one plausible biological pathway linking Cesarean delivery to later adiposity, alongside established behavioral and environmental contributors. Even if these children consume a standard caloric diet, their dysbiotic microbiomes\u0026mdash;deprived of vaginal \u003cem\u003eBacteroides\u003c/em\u003e\u0026mdash;may biologically amplify the caloric yield of their food.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Global Comparison\u003c/h2\u003e \u003cp\u003eThe \"C-section effect\" observed in this African review appears to be consistent with, and notably stronger than, the global standard. While global meta-analyses by Li et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Kuhle et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) established a pooled risk increase for childhood obesity ranging from 33% to 35% (OR 1.33\u0026ndash;1.35), the African studies identified in this review reported Adjusted Odds Ratios ranging from 1.64 to 3.23. This elevated magnitude raises the possibility that while the association is underpinned by a universal biological driver, regional environmental modifiers or the rapid nutritional transition occurring in Africa may be amplifying the obesogenic consequences of cesarean delivery in these populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Limitations of the Evidence Base\u003c/h2\u003e \u003cp\u003eThis systematic review must be interpreted in the context of a limited but highly consistent evidence base. Only four eligible studies were identified across Sub-Saharan Africa, reflecting the historical underrepresentation of African birth cohorts in life-course epidemiology rather than deficiencies in the present review methodology. The scarcity of longitudinal datasets linking delivery mode to later-life adiposity remains a structural constraint in the region.\u003c/p\u003e \u003cp\u003eThe small number of included studies renders the synthesis inherently sensitive to study exclusion; the removal or reclassification of a single dataset could materially alter the strength of the pooled inference. Nevertheless, confidence in the observed association is strengthened by the complete directional concordance across geographically and methodologically diverse African populations, with all included studies reporting a positive association between Cesarean section and childhood adiposity.\u003c/p\u003e \u003cp\u003eAdditional limitations include residual confounding, as none of the included studies explicitly screened for genetic syndromes or chronic pediatric conditions that may independently influence growth trajectories. Furthermore, the biological mechanism linking Cesarean delivery to obesity risk was inferred rather than directly measured, as no included study employed metagenomic or metabolomic profiling to characterize gut microbial composition or function. As such, the proposed microbiome-mediated pathways should be regarded as biologically plausible but unconfirmed within African populations.\u003c/p\u003e \u003cp\u003eFinally, while an exploratory quantitative synthesis was conducted for studies reporting compatible effect measures, heterogeneity in study design and outcome reporting precluded a comprehensive meta-analysis. The findings should therefore be interpreted as hypothesis-generating rather than definitive causal estimates\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION AND FUTURE DIRECTIONS","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis systematic review synthesizes the currently available observational evidence examining the relationship between Cesarean section delivery and childhood adiposity in Sub-Saharan Africa. Despite the limited number of eligible studies, the available data consistently demonstrate a positive association between Cesarean birth and increased risk of overweight and obesity across West, East, and Southern African populations, with effect sizes that exceed those reported in many global meta-analyses.\u003c/p\u003e \u003cp\u003eThese findings support the hypothesis that disruption of vertical microbial transmission at birth may represent an important contributor to early-life metabolic programming within the African context, particularly when acting synergistically with rapid urbanization and nutritional transition. However, given the observational nature of the evidence and the absence of direct microbiome measurements, causal inference cannot be established.\u003c/p\u003e \u003cp\u003eRather than providing conclusive evidence, this review highlights a critical and underexplored public health signal. The concordance of findings across diverse African settings underscores the urgent need for prospective birth cohorts incorporating high-resolution microbiome and metabolic phenotyping. Such studies are essential to determine whether the observed associations reflect microbiome-mediated mechanisms and to inform evidence-based obstetric and early-life interventions tailored to Sub-Saharan Africa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Future Directions\u003c/h2\u003e \u003cp\u003eTo move beyond inference, future research must prioritize prospective longitudinal studies employing high-resolution genomic tools. Unlike cross-sectional snapshots, longitudinal sampling is essential to capture the temporal trajectory of gut colonization (Dogra et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). We propose the initiation of African birth cohorts that utilize shotgun metagenomics rather than standard 16S sequencing.\u003c/p\u003e \u003cp\u003eThe choice of sequencing technology is pivotal. While 16S rRNA sequencing describes broad bacterial composition (Toubon et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), it lacks the resolution to distinguish between subspecies with distinct metabolic capabilities (Ennis et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, we propose that future African studies utilize shotgun metagenomics to resolve specific species and assess the functional capacity of the microbiome. This genomic resolution is critical to determine whether the observed obesity risk stems from specific taxonomic imbalances or a functional deficit in metabolic pathways essential for energy regulation.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmabebe E, Robert FO, Agbalalah T, Orubu ESF (2020) Microbial dysbiosis-induced obesity: Role of gut microbiota in homoeostasis of energy metabolism. Br J Nutr 123(10):1127\u0026ndash;1137\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai M, Loy SL, Tan KH et al (2018) Association of elective and emergency cesarean delivery with early childhood overweight at 12 months of age. JAMA Netw Open, 1(7), e185025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiavarini M, De Socio B, Giacchetta I, Fabiani R (2023) Overweight and obesity in adult birth by cesarean section: A systematic review with meta-analysis. 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FIGO opinion paper: Drivers and solutions to the cesarean delivery epidemic\u0026hellip; International Journal of Gynecology \u0026amp; Obstetrics, 163, 5\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWanjohi, M. N., Kimani-Murage, E. W., Asiki, G., et al. (2024). Adolescents\u0026rsquo; dietary patterns\u0026hellip; in Kenya\u0026rsquo;s urban slums. Journal of Health, Population and Nutrition, 43, 181.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWariri, O., Akhimienho, K. I., Alhassan, J. A. K., et al. (2020). Population and individual-level double burden of malnutrition\u0026hellip; in Nigeria. Annals of Global Health, 86(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaya S, Uthman OA, Amouzou A, Bishwajit G (2018) Disparities in cesarean section prevalence and determinants across sub-Saharan Africa countries. Global Health Res Policy 3:19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZemene MA, Anley DT, Gebeyehu NA et al (2023) Concurrent stunting and overweight or obesity among under-five children in sub-Saharan Africa. Archives Public Health 81:119\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cesarean section, Childhood obesity, Gut microbiome, Sub-Saharan Africa, Hygiene hypothesis, Dysbiosis","lastPublishedDoi":"10.21203/rs.3.rs-8712827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8712827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSub-Saharan Africa is currently facing a \"double burden\" of malnutrition, characterized by the persistence of stunting alongside a rapid rise in childhood overweight and obesity. Simultaneously, rates of Cesarean section (CS) delivery are surging in urban centers, often without medical indication. While global studies link CS to increased adiposity via disruption of early-life microbial colonization, evidence from the African context remains is limited, particularly within Sub-Saharan Africa, where specific regional factors influence outcomes. This systematic review aims to synthesize observational evidence linking delivery mode to childhood obesity in Sub-Saharan Africa and explore the biological plausibility of the \"dysbiosis\" mechanism in this population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe systematically searched PubMed and African Index Medicus, and supplemented with Google Scholar for additional studies published up to January 06, 2026, for observational studies conducted in Sub-Saharan Africa. Eligible cohort, cross-sectional, and case\u0026ndash;control studies reported quantitative associations between CS delivery and childhood anthropometric outcomes, including overweight, obesity, or body mass index (BMI). Methodological quality was assessed using the Newcastle\u0026ndash;Ottawa Scale (NOS). A narrative synthesis was undertaken for all included studies, with an exploratory quantitative synthesis conducted for high-quality studies reporting comparable effect estimates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour studies met the inclusion criteria, encompassing 10,215 participants from Ghana, South Africa, and Ethiopia. Across all studies, CS delivery was consistently associated with a statistically significant 1.64- to 3.23-fold increased risk of childhood adiposity (adjusted odds ratio [AOR] 3.23, 95% CI: 1.14\u0026ndash;9.13 in Ghana; incidence rate ratio [IRR] 1.64, 95% CI: 1.01\u0026ndash;2.68 in South Africa). In the South African cohort, the association persisted into early adulthood (ages 21\u0026ndash;24 years). None of the included studies directly assessed gut microbiome composition, and residual confounding could not be fully excluded.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDespite a limited evidence base, available data consistently demonstrate a positive association between Cesarean delivery and increased risk of childhood adiposity in Sub-Saharan Africa, with effect sizes exceeding those commonly reported in global meta-analyses. These findings suggest that Cesarean delivery may be a critical, underexplored contributor to the growing obesity epidemic in Sub-Saharan Africa and support the biological plausibility of microbiome-mediated metabolic programming. Prospective African birth cohorts incorporating high-resolution microbiome and metabolic profiling are urgently needed to confirm causality and inform context-specific preventive strategies.\u003c/p\u003e","manuscriptTitle":"Vertical Microbiome Deprivation? A Systematic Review of Cesarean Section as a Driver of Childhood Obesity in Sub-Saharan Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 01:00:34","doi":"10.21203/rs.3.rs-8712827/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62b399eb-2967-4199-9dba-ca7dcfec8a89","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61842628,"name":"Epidemiology"},{"id":61842629,"name":"General Microbiology"}],"tags":[],"updatedAt":"2026-01-29T01:00:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 01:00:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8712827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8712827","identity":"rs-8712827","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}