Causal association between social support and Osteoarthritis: a two- sample Mendelian randomization study

Causal association between social support and Osteoarthritis: a two- sample Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Causal association between social support and Osteoarthritis: a two- sample Mendelian randomization study Ruiming Liang, Gang Liu, Lixia Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4308864/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 Objective: We aimed to study whether social support (SS), including the frequency of friend / family visits (FFV), leisure / social activities (LSA), and ability to talk (AC) are causally associated with Osteoarthritis (OA). Methods: Data of FFV, LSA, AC and OA in the European population was obtained from the UK Biobank public genome-wide association study (GWAS) summary statistics included in the Mrbase.Three Mendelian randomization (MR) methods, including Inverse variance weighted method(IVW), Weighted median method(WM) and MR-Egger regression were used to evaluate the potential causal effect of FFV, LSA, AC on OA ，and the potential causal effect of OA on FFV, LSA, AC. Heterogeneity was tested using the Cochran's Q test and the I 2 statistic. A leave-one-out analysis and test for directional level pleiotropy by MR-Egger regression test served as a sensitivity analysis. Results: When assessing the causal effect of SS on OA, the IVW method supports causal relationship between FFV and OA risk (beta = -0.02883, SE=0.01293, P=0.02574), and causal relationship between LSA and OA risk (beta =-0.2255, SE=0.067, P =0.0007641) ; The WM method supports a causal relationship between FFV and OA risk (beta = -0.02968, SE=0.01272, P =0.01965), No significant causal relationship between LSA and OA risk was supported by the WM method (beta = -0.1748, SE=0.09266, P=0.05929); The MR-Egger regression did not support a significant causal relationship between FFV, LSA and OA risk. However, the evaluation did not find a causal relationship between AC and OA, nor find a causal relationship between OA and FFV, LSA, AC.The Cochran's Q test and I 2 statistics of FFV showed some heterogeneity (MR Egger P=0.003158, I 2 =0.53452; IWW P = 0.00282, I 2 =0.53075), and the Cochran's Q test and I 2 statistics of LSA showed low heterogeneity (MR Egger P=0.2916, I 2 =0.100719; IWWP = 0.4652, I 2 =0.306336). MR-Egger regression analyses of FFV, LSA on OA showed horizontal pleiotropy unlikely to affect the assessment results (FFV intercept =0.0011, P =0.369; LSA intercept =0.0037, P = 0.65). Conclusion : The results of MR analysis support that FFV and LSA may be causally associated with an decreased risk of OA.FFV and LSA may reduce or delay the development of OA. social support frequency of friend / family visits leisure / social activities ability to talk Osteoarthritis Mendelian randomization genome-wide association study causal relationship. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Osteoarthritis (OA) is the most common form of arthritis, affecting one in three people over the age of 65, resulting in limited function, poor sleep, fatigue, low mood and loss of independence.[ 1 ] The OA pain experience is also influenced by biological (e. g. pain mechanisms), psychological (e. g. emotional and coping), and social factors (e. g. social support).[ 2 ] Studies have confirmed that socioeconomic status and psychosocial factors influence pain or physical function in OA patients.[ 3 ] Psychological interventions yielded some encouraging results in OA pain, pain coping, self-efficacy and fatigue.[ 4 ] These studies reflect the role of psychosocial factors in the intervention management of osteoarthritis. However, the effects of psychosocial factors on health is complex and may affect each other. Mendelian randomization (MR) is a technique that uses genetic variation as an instrumental variable (Instrumental variance, IV) to assess whether the observed association between risk factors and outcome is consistent with a causal effect.[ 5 ] Two-sample MR estimates the causal effect of data regarding exposure and outcome in different samples.[ 6 ] Studies have evaluated the causal relationship between social isolation and osteoarthritis, indicating that social isolation has a causal relationship with an increased risk of osteoarthritis, and no causal relationship with the degree of social isolation.[ 7 ] There are no reported studies using MR methods to test for causal effects of social support (SS) and OA risk. Therefore, this study used a two-sample bidirectional MR analysis, aiming to investigate whether social support is causally associated with the risk of developing OA. 2 Materials and Methods 2.1 Data sources and selection of genetic variation A Schematic diagram of the study design in this two-sample MR analysis is outlined in Figure 1.We used the single nucleotide polymorphism sites (SNP) published in the MRBase database as IV to determine the risk impact between SS and OA in the European population, a large collection of summary statistics from hundreds of GWAS. In the UK Biobank, The SS includes Frequency of friend / family visits（FFV)(ID:ukb-b-5379, n=459830, The MRC-IEU Union), Leisure / social activities: Other group activity（LSA)(ID:ukb-b-4077, n=461369, The MRC-IEU alliance); Able to confide（CA)(ID:ukb-b-4982, n=448858, The MRC-IEU alliance); This category contains data from the touchscreen questionnaire on frequency of visitors, type of social activities, and ability to confide in others, included "How often do you visit friends or family or have them visit you?" "Which of the following do you attend once a week or more often? (sports club or gym，pub or social club，religious group，adult educationg class，other group activity)" and "How often are you able to confide in someone close to you?"[8] OA from features described as Non-cancer illness code, Self-reported: GWAS data of Osteoarthritis (ID: ukb-b-14486, n=462933, case=38472, control=424461, The MRC-IEU consortium)[8] (Table 1). This study was based on publicly available data, and therefore no additional ethical approval or consent was required. 2.2 Selection of instrument variables MR analysis has three core assumptions, namely correlation, independence, and exclusion restriction.[9] It calls for genetic variation associated with exposure but is not a potential confounder for exposure.(1) First, genome-wide divergent SNPs (P <5E10-8) and their linkage disequilibrium (r 2 =0.001, kb = 10000) were selected as IVs and then eliminated in linkage disequilibrium to ensure that the IVs satisfied independence.(2) Genetic variation is not associated with confounding factors.(3) Genetic variation was not associated with the outcome variables. The F statistic was used to assess the statistical strength of the correlation between the SNPs and the exposure. F statistic is ( F = R 2 /（1- R 2 ） · （ N-K-1 ） / （ K- 1), where N is the sample size of the dried fruit intake database, K is the number of SNPs, R2 represents the proportion of dried fruit intake explained by SNPs, calculate formula R 2 = 2·(1− EAF )· EAF · β 2 . EAF is the effect allele frequency of each SNP (effect allene frequency, EAF), and β is the effect value of the allele. A threshold of [10,11] F <10 was used to define a weak IV and was considered as a weak association between the SNP and the exposure.[12] 2.3 Statistical analysis of Mendelian randomization In this study, a two-sample bidirectional MR analysis was performed using three methods. The inverse variance weighted analysis method ( IVW) [12-14] was used as the main analysis method, with the weighted median (WM) [15] and MR-Egger regression [16] as the auxiliary methods. IVW combined the Wald estimates for each SNPs to obtain the overall estimates.[15] We assessed the causality of FFV, LSA, AC and OA, and reverse assessed the causality of OA and FFV, LSA ，AC. Tests were considered statistically significant at P less than 0.05. All MR analyses were performed on the MR Base platform (application version: 1.4.3 8 a 77 eb [25 October 2020], R version: 4.0.3).[17] The analysis results were presented as odds ratios (OR) and 95% confidence intervals (95%CI). OR> 1 is a positive correlation, indicating that exposure is unfavorable for outcome; OR <1 is a negative correlation, indicating that exposure is favorable for outcome. 2.4 Sensitivity analysis The sensitivity analysis included the heterogeneity test, the horizontal pleiotropic test, and the Leave-one-out sensitivity test. Heterogeneity is the variability of the causal estimates obtained for each SNP (i. e. the consistency of the causal estimates across all SNP). We used the MR-Egger method and IVW statistical methods to record Q-values and degrees of freedom Q-Q-df and P-values, with P> 0.05 showed no heterogeneity in the Cochran's Q heterogeneity test.[18] The I 2 statistic is the proportion of the observed inter-study variation (due to true heterogeneity rather than that observed by chance). The formula was calculated as I 2 =100% *(Q-df) / Q. Q is Cochran's Q heterogeneity statistic and df is the degree of freedom. Since 0 is taken when I 2 is negative, the value of I 2 is between 0% and 100%. An I 2 of 0% indicates no heterogeneity, and larger values indicate more significant heterogeneity. I 2 > 56% reveals great heterogeneity among studies; if I 2 <31%, they can be considered for I 2 between 31% and 56%. Low heterogeneity suggests an increased reliability of MR analysis . [19, 20] Multiple instrumental variables were tested for horizontal pleiotropy by the MR-Egger intercept test, and the robustness of the results was assessed. The intercept represents the average pleiotropic effect of the genetic variant (the average direct effect and result of the variation). Non-zero intercept (MR-Egger test) suggests directional pleiotropy. Set P> 0.05, meaning no pleiotropy. If the IV affects the outcome through factors other than exposure factors, it indicates that the IV is pleiotropic, and the assumption of independence and exclusivity is not valid [21]. Sensitivity analysis was performed using a Leave-one-out sensitivity test. It mainly calculates the MR results of the remaining IV after eliminating the IV one by one. If the MR results and the total results estimated by the other IV after excluding one IV differ significantly, it indicates that the MR results are sensitive to the IV. 3 Results 3.1 Causal relationship of SS for OA 3.1.1 IVs 20,3, and 13 independent SNPs were selected from the GWAS data with phenotypes FFV, LSA, and AC as IV assessing the causal relationship for OA, respectively (Table 2). The distribution of the F statistic corresponding to calculating a single SNPs has a range of 29.87-78.27 with all F values> 10, therefore, the weak instrumental variable bias is negligible. 3.1.2 MR results MR results of FFV on OA: The IVW method showed evidence to support a causal relationship between FFV and OA (beta = -0.02883, SE=0.01293, P=0.02574; Table 2,Figure 2), Stable relationship with OA risk [OR = 0.971582, 95%CI(0.947268-0.996519)]; The WM method shows the causal relationship between FFV and OA (beta = -0.02968, SE=0.01272, P=0.01965; Table 2, Figure 2）， Stable relationship with OA risk [OR = 0.970756, 95%CI(0.946853-0.995262)]; MR-Egger, the analysis shows no causal relationship between FFV and OA (beta = -0.09252, SE=0.07034, P=0.2049; Table 2, Figure 2) Unstable relationship with OA risk [OR = 0.911631, 95%CI(0.794227-1.04639)]. MR results of LSA for OA: The IVW method showed evidence supporting a causal relationship with OA (beta = -0.2255, SE=0.067, P =0.0007641; Table 2, Figure 3), Stable relationship with OA risk [OR = 0.798117, 95%CI(0.699898-0.910119)]; the WM method shows no causal relationship between it and OA (beta = -0.1748, SE=0.09266, P=0.05929; Table 2,Figure 3), Unstable relationship with OA risk [OR = 0.839625, 95%CI(0.700183-1.006837)]; MR-Egger analysis showed no causal relationship between it and OA (beta = -0.923, SE=1.14, P=0.5667; Table 2, Figure 3) Unstable relationship with OA risk [OR = 0.397352, 95%CI(0.042536-3.711366)]. MR results of CA for OA: the IVW method showed no causal relationship between it and OA (beta = -0.001173, SE=0.01282, P =0.9271; Table 2, Figure 4), Unstable relationship with OA risk [OR = 0.998828, 95%CI(0.974043-1.024243)]; the WM method shows no causal relationship between it and OA (beta = -0.0149, SE=0.009535, P =0.2281; Table 2, Figure 4), Unstable relationship with OA risk [OR = 0.988576, 95%CI(0.974043-1.024243)]; MR-Egger analysis showed no causal relationship between it and OA (beta = -0.0496, SE =0.06516, P= 0.4626; Table 2,Figure 4) Unstable relationship with OA risk [OR = 0.95161, 95%CI(0.837517-1.081245)]. 3.2 Causal relationship of OA for SS 3.2.1 IVs 6 independent SNPs from the GWASs of OA were selected as IVs to assess the causality for FFV, LSA, AC (Table 3). The distribution of the F statistic corresponding to calculating a single SNPs has a range of 29.99-78.27 with all F values> 10, therefore, the weak instrumental variable bias is negligible. 3.2.2 MR results Results of OA for FFV: IVW, WM, and MR-Egger showed no causal relationship between OA (IVW beta =0.1382, SE =0.4696, P =0.7685; WM beta =0.1013, SE = 0.3505, P = 0.7726; MR-Egger beta = -0747, SE =1.145, P = 0.5496) (Table 3). Results of OA for LSA: IVW, WM and MR-Egger showed no causal relationship between OA (IVW beta =0.05363, SE =0.08854, P =0.5447; WM beta =0.04182, SE = 0.1075, P =0.6974; MR-Egger beta = -0.08393, SE =0.2048, P=0.7096) (Table 3). Results of OA for CA: IVW, WM and MR-Egger showed no causal relationship between OA and it (IVW beta = -0.2345, SE =0.4952, P =0.6357; WM beta =0.316, SE =0.5614, P =0.5736; MR-Egger beta =1.028, SE =1.112, P = 0.4078) (Table 3). 3.3 Sensitivity analysis In the Cochran's Q heterogeneity test of FFV on OA, there was some heterogeneity (MR Egger P=0.003158, I2=0.53452; IWWP = 0.00282, I2=0.53075) (Table 2). In the Cochran's Q heterogeneity test of LSA, there was low heterogeneity (MR Egger P=0.2916, I2=0.100719; IWWP = 0.4652, I2=0.306336) (Table 2). The MR-Egger regression test for the causal effect of FFV on OA showed horizontal pleiotropy unlikely to affect the assessment results (intercept =0.0011; P = 0.369). MR-Egger regression test in LSA showed that it was unlikely to affect the assessment results (intercept =0.0037; P = 0.65). This means that FFV, LSA instrumental variables do not significantly affect outcome through pathways other than exposure. The results of the one-by-one test analysis showed that no single SNP affected the IVW site assessment.(Figure 5) 4 Discussion J Thumboo et al examined the effects of socioeconomic status and psychosocial factors on pain or physical functioning in patients with OA, confirming that better physical functioning is associated with longer years of education, less learned helplessness, less physical pain, and less severe OA.[3]Zhang Lijuan et al examined the effects of psychological interventions (e. g. cognitive restructuring, relaxation) on the physical and mental health of patients with osteoarthritis, indicating that psychological interventions have achieved some encouraging results in osteoarthritis pain, pain coping, self-efficacy and fatigue. However, the effects on physical function, anxiety, depression, and psychological disorders were not statistically significant.[4] Traditional observational study designs are prone to interference by reverse causality and confounding factors, which limits the understanding of the relationship between psychosocial factors and osteoarthritis. More importantly, unlike the general physical risk factors, the causal relationship between the psychosocial determinants and the physical condition is generally more complex, which may be bidirectional. The study by Zheng Cong et al used MR to assess the causal relationship between social isolation and osteoarthritis, showing that social isolation is causally associated with an increased risk of osteoarthritis but not with the degree of social isolation.[7]To explore whether there is also a bidirectional effect between social support and osteoarthritis, we used a two-sample MR approach to assess the potential causal role of SS in OA risk and the potential causal effect of osteoarthritis on SS. Our MR analysis suggests a potential causal role of FFV, LSA in OA risk, in contrast, AC is not causally associated with OA, and osteoarthritis was not causally associated with FFV, LSA, AC. This also provides the evidence support of psychosocial intervention for us to strengthen the prevention and management of OA. Several possible mechanisms could account for the causal effect of SS on OA. First, participation in social support activities requires a certain amount of physical activity and reduces the occurrence of sedentary behavior. Recent Mendelian studies showed that TV viewing increases the risk of OA, reducing leisure screen time is protective against OA[22-24] , and reducing sedentary behavior is thought to reduce functional restriction [25] and reduce the impairment of functional exercise in patients with knee OA.[26] In addition, mild DIY (e. g., weeding,watering) and walking are beneficial for preventing OA.[27, 28]Jia Shuangshuo et al. reviewed the results of recent studies that highlight the important role of tissue-specific exercise factors, including exercise-induced myokines (muscle), myocardial factor (cardiac) and adipokines (fat tissue)in OA, and explore the underlying cellular and molecular mechanisms involved through endocrine, paracrine and / or autocrine pathways.[29] Secondly, social support acts through the influence of inflammatory factors. Elevated levels of proinflammatory cytokines, including tumor necrosis factor-α(TNF-α) and interleukin-6 (IL-6),were observed in individuals living in social isolation.[30]Meanwhile, the level of IL-6 is associated with the degree of joint degeneration in osteoarthritis.[31] Third, the role of psychological comfort in social support reduces pain self-report in patients. Studies confirmed that psychosocial factors can explain some of the differences in pain reporting in knee OA patients, with important potential implications for management. Some interventions with cognitive-behavioral approaches can reduce knee pain without significantly stopping or reversing the structural damage. And these programs are more effective if the spouse is involved. Suggesting a possible placebo effect of the intervention.[32] There are inevitably some limitations to our study. First, both social support and osteoarthritis are based on self-reported data rather than objective measurements, which may lead to measurement error and also account for some heterogeneity in the results. Second, due to the limitations of GWAS summary data, such as missing data on disease severity and other comorbidities, we were unable to perform further subgroup analysis or to more prominently understand how social support affects the occurrence and development of osteoarthritis. Third, all included participants were from European origin, so further research is needed to explore whether this finding can be generalizable to other populations. 5 Conclusion In conclusion, our findings suggest that FFV and LSA may reduce or delay the development of OA, which also provides evidence support for psychosocial intervention to strengthen the prevention and management of OA. Declarations Acknowledgments 1. We extend our appreciation to theMRBase database（http://www.mrbase.org/）and IEU Open GWAS database (https://gwas.mrcieu.ac.uk/) for their invaluable contributions to our research. 2.This study was sponsored byNational Natural Science Foundation of China (82274400), National Natural Science Foundation of China (82247619)，Natural Science Foundation of Guangdong Province (2022A1515011681) Conflicts of Interest No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Hawker G A. Osteoarthritis is a serious disease[J]. Clin Exp Rheumatol,2019,37 Suppl 120(5):3-6. Hawker G A. Experiencing painful osteoarthritis: what have we learned from listening?[J]. Curr Opin Rheumatol,2009,21(5):507-512. Thumboo J, Chew L H, Lewin-Koh S C. 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Tables Table 1 Summary of genome-wide association study data in this MR study Trait Data source website（http://www.mrbase.org/） GWAS ID Sample size Snp size First author consortium year Population studied Frequency of friend/family visits 1031: Output from GWAS pipeline using Phesant derived variables from UKBiobank ukb-b-5379 459830 9851867 Ben Elsworth MRC-IEU 2018 European Leisure/social activities 6160#5: Output from GWAS pipeline using Phesant derived variables from UKBiobank ukb-b-4077 461369 9851867 Ben Elsworth MRC-IEU 2018 European Able to confide 2110: Output from GWAS pipeline using Phesant derived variables from UKBiobank ukb-b-4982 448858 9851867 Ben Elsworth MRC-IEU 2018 European Non-cancer illness code, self-reported: osteoarthritis 20002#1465: Output from GWAS pipeline using Phesant derived variables from UKBiobank ukb-b-14486 462933 9851867 Ben Elsworth MRC-IEU 2018 NA Additional Declarations No competing interests reported. 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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-4308864","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":296562966,"identity":"be85cf5e-6309-4267-9852-983cdfd333a7","order_by":0,"name":"Ruiming Liang","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruiming","middleName":"","lastName":"Liang","suffix":""},{"id":296562967,"identity":"080b0f0f-a961-4ea6-8275-df88ddad33ef","order_by":1,"name":"Gang Liu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Liu","suffix":""},{"id":296562968,"identity":"b1c6d0ff-af27-4cb0-9ae6-974653048ed0","order_by":2,"name":"Lixia Yuan","email":"data:image/png;base64,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","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lixia","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2024-04-23 02:54:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4308864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4308864/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56016560,"identity":"941c2704-291f-469c-b8b0-aeaebcda50f1","added_by":"auto","created_at":"2024-05-07 15:21:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60400,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the study design in this \u0026nbsp;two-sample bidirectional MR analysis.\u003c/p\u003e\n\u003cp\u003e20,3, and 13 IVs for social support (SS) including the frequency of friend / family visits (FFV), leisure / social activities (LSA), and the ability to talk (AC) and Osteoarthritis were selected and then explored for bidirectional causality. The 3 basic assumptions of MR analysis, correlation assumption, independence assumption, and exclusion restriction assumption, are illustrated in this schematic diagram.MR= mendelian randomization, IVs= instrumental variables，SS=social support ，Osteoarthritis=OA.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4308864/v1/8d8a7cdfb9a18c1131521e24.jpg"},{"id":56016558,"identity":"709b4330-7dab-477d-9e25-1cfa98036e52","added_by":"auto","created_at":"2024-05-07 15:21:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72380,"visible":true,"origin":"","legend":"\u003cp\u003eCausal effects of FFV on osteoarthritis.\u003c/p\u003e\n\u003cp\u003eA. Forest plot of the causal effects of single nucleotide polymorphisms associated with FFV on osteoarthritis.\u003c/p\u003e\n\u003cp\u003eRed line represents the MR results of the MR-Egger test and the IVW method\u003c/p\u003e\n\u003cp\u003eB.\u003cstrong\u003e \u003c/strong\u003eScatter plots of genetic associations with FFV against the genetic associations with osteoarthritis.\u003c/p\u003e\n\u003cp\u003eThe slopes of each line represent the causal association for each method.The blue line represents the inverse‐variance weighted estimate, the green line represents the weighted median estimate, and the dark blue line represents the Mendelian randomization‐Egger estimate.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4308864/v1/5769403ea73fccbad380933c.jpg"},{"id":56016563,"identity":"a4a422db-51cf-4b75-9d92-17aa2a9eec84","added_by":"auto","created_at":"2024-05-07 15:21:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61130,"visible":true,"origin":"","legend":"\u003cp\u003eCausal effects of LSA on osteoarthritis.\u003c/p\u003e\n\u003cp\u003eA. Forest plot of the causal effects of single nucleotide polymorphisms associated with LSA on osteoarthritis.\u003c/p\u003e\n\u003cp\u003eRed line represents the MR results of the MR-Egger test and the IVW method\u003c/p\u003e\n\u003cp\u003eB.\u003cstrong\u003e \u003c/strong\u003eScatter plots of genetic associations with LSA against the genetic associations with osteoarthritis.\u003c/p\u003e\n\u003cp\u003eThe slopes of each line represent the causal association for each method.The blue line represents the inverse‐variance weighted estimate, the green line represents the weighted median estimate, and the dark blue line represents the Mendelian randomization‐Egger estimate.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4308864/v1/a9a45e86bfcb2c670ac67155.jpg"},{"id":56016561,"identity":"19cb97df-12d8-47fa-b1ac-3623ec1e6630","added_by":"auto","created_at":"2024-05-07 15:21:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64777,"visible":true,"origin":"","legend":"\u003cp\u003eCausal effects of CA on osteoarthritis.\u003c/p\u003e\n\u003cp\u003eA. Forest plot of the causal effects of single nucleotide polymorphisms associated with CA on osteoarthritis.\u003c/p\u003e\n\u003cp\u003eRed line represents the MR results of the MR-Egger test and the IVW method\u003c/p\u003e\n\u003cp\u003eB.\u003cstrong\u003e \u003c/strong\u003eScatter plots of genetic associations with CA against the genetic associations with osteoarthritis.\u003c/p\u003e\n\u003cp\u003eThe slopes of each line represent the causal association for each method.The blue line represents the inverse‐variance weighted estimate, the green line represents the weighted median estimate, and the dark blue line represents the Mendelian randomization‐Egger estimate.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4308864/v1/6000df9f280084218c0fee57.jpg"},{"id":56016559,"identity":"6b4fbee3-c7e6-4d8a-8d54-ac8a894fe966","added_by":"auto","created_at":"2024-05-07 15:21:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":71962,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out analysis of the causal effect of FFV and LSA on osteoarthritis.\u003c/p\u003e\n\u003cp\u003eEach black point represents result of the inverse-variance weighted (IVW) method applied to estimate the causal effect of FFV(A) and LSA(B) on osteoarthritis excluding each particular SNP from the analysis. Each red point depicts the IVW estimate using all SNPs. No single SNP is strongly driving the overall effect of FFV on osteoarthritis in this leave-one-out sensitivity analysis.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4308864/v1/f92febacbf68e22c7d3fe546.jpg"},{"id":56448321,"identity":"4f34a41a-b669-4e45-b6aa-fcec96c91b45","added_by":"auto","created_at":"2024-05-14 10:08:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":613395,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4308864/v1/f56a8d27-dc68-4b74-a702-f8adf14decf3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal association between social support and Osteoarthritis: a two- sample Mendelian randomization study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eOsteoarthritis (OA) is the most common form of arthritis, affecting one in three people over the age of 65, resulting in limited function, poor sleep, fatigue, low mood and loss of independence.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] The OA pain experience is also influenced by biological (e. g. pain mechanisms), psychological (e. g. emotional and coping), and social factors (e. g. social support).[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Studies have confirmed that socioeconomic status and psychosocial factors influence pain or physical function in OA patients.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Psychological interventions yielded some encouraging results in OA pain, pain coping, self-efficacy and fatigue.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] These studies reflect the role of psychosocial factors in the intervention management of osteoarthritis. However, the effects of psychosocial factors on health is complex and may affect each other.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a technique that uses genetic variation as an instrumental variable (Instrumental variance, IV) to assess whether the observed association between risk factors and outcome is consistent with a causal effect.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Two-sample MR estimates the causal effect of data regarding exposure and outcome in different samples.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Studies have evaluated the causal relationship between social isolation and osteoarthritis, indicating that social isolation has a causal relationship with an increased risk of osteoarthritis, and no causal relationship with the degree of social isolation.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] There are no reported studies using MR methods to test for causal effects of social support (SS) and OA risk. Therefore, this study used a two-sample bidirectional MR analysis, aiming to investigate whether social support is causally associated with the risk of developing OA.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003e2.1 Data sources and selection of genetic variation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA Schematic diagram of the study design in this two-sample MR analysis is outlined in Figure 1.We used the single nucleotide polymorphism sites (SNP) published in the MRBase database as IV to determine the risk impact between SS and OA in the European population, a large collection of summary statistics from hundreds of GWAS. In the UK Biobank, The SS includes Frequency of friend / family visits（FFV)(ID:ukb-b-5379, n=459830, The MRC-IEU Union), Leisure / social activities: Other group activity（LSA)(ID:ukb-b-4077, n=461369, The MRC-IEU alliance); Able to confide（CA)(ID:ukb-b-4982, n=448858, The MRC-IEU alliance);\u0026nbsp;This category contains data\u0026nbsp;from the touchscreen questionnaire on frequency of visitors, type of social activities, and ability to confide in others, included \u0026quot;How often do you visit friends or family or have them visit you?\u0026quot; \u0026quot;Which of the following do you attend once a week or more often? (sports club or gym，pub or social club，religious group，adult educationg class，other group activity)\u0026quot; and \u0026quot;How often are you able to confide in someone close to you?\u0026quot;[8] OA from features described as Non-cancer illness code, Self-reported: GWAS data of Osteoarthritis (ID: ukb-b-14486, n=462933, case=38472, control=424461, The MRC-IEU consortium)[8] (Table 1). This study was based on publicly available data, and therefore no additional ethical approval or consent was required.\u003c/p\u003e\n\u003cp\u003e2.2\u0026nbsp;Selection of instrument variables\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMR analysis has\u0026nbsp;three\u0026nbsp;core assumptions, namely correlation, independence, and exclusion restriction.[9]\u0026nbsp;It\u0026nbsp;calls for genetic variation associated with exposure but is not a potential confounder for exposure.(1) First, genome-wide divergent SNPs (P \u0026lt;5E10-8) and their linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e=0.001, kb = 10000) were selected as IVs and then eliminated in linkage disequilibrium to ensure that the IVs satisfied independence.(2) Genetic variation is not associated with confounding factors.(3) Genetic variation was not associated with the outcome variables. The F statistic was used to assess the statistical strength of the correlation between the SNPs and the exposure. F statistic is\u0026nbsp;(\u003cem\u003eF\u0026nbsp;\u003c/em\u003e= \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e /（1-\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e）\u0026nbsp;\u0026middot;\u0026nbsp;（\u003cem\u003eN-K-1\u003c/em\u003e）\u003cem\u003e\u0026nbsp;/\u003c/em\u003e\u003cem\u003e（\u003c/em\u003e\u003cem\u003eK-\u003c/em\u003e1), where N is the sample size of the dried fruit intake database, K is the number of SNPs, R2 represents the proportion of dried fruit intake explained by SNPs, calculate formula\u0026nbsp;\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e= 2\u0026middot;(1\u0026minus;\u003cem\u003eEAF\u003c/em\u003e)\u0026middot;\u003cem\u003eEAF\u0026nbsp;\u003c/em\u003e\u0026middot; \u0026beta;\u003csup\u003e2\u003c/sup\u003e. EAF is the effect allele frequency of each SNP (effect allene frequency, EAF), and \u0026beta; is the effect value of the allele. A threshold of [10,11] F \u0026lt;10 was used to define a weak IV and was considered as a weak association between the SNP and the exposure.[12]\u003c/p\u003e\n\u003cp\u003e2.3 Statistical analysis of Mendelian randomization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, a two-sample\u0026nbsp;bidirectional\u0026nbsp;MR analysis was performed using three methods. The inverse variance weighted analysis method ( IVW) [12-14] was used as the main analysis method, with the weighted median (WM) [15] and MR-Egger regression [16] as the auxiliary methods. IVW combined the Wald estimates for each SNPs to obtain the overall estimates.[15] We assessed the causality of FFV, LSA, AC and OA, and reverse assessed the causality of OA and FFV, LSA\u0026nbsp;，AC. Tests were considered statistically significant at P less than 0.05. All MR analyses were performed on the MR Base platform (application version: 1.4.3 8 a 77 eb [25 October 2020], R version: 4.0.3).[17]\u003c/p\u003e\n\u003cp\u003eThe analysis results were presented as odds ratios (OR) and 95% confidence intervals (95%CI). OR\u0026gt; 1 is a positive correlation, indicating that exposure is unfavorable for outcome; OR \u0026lt;1 is a negative correlation, indicating that exposure is favorable for outcome.\u003c/p\u003e\n\u003cp\u003e2.4 Sensitivity analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sensitivity analysis included the heterogeneity test, the horizontal pleiotropic test, and the Leave-one-out sensitivity test. Heterogeneity is the variability of the causal estimates obtained for each SNP (i. e. the consistency of the causal estimates across all SNP). We used the MR-Egger method and IVW statistical methods to record Q-values and degrees of freedom Q-Q-df and P-values, with P\u0026gt; 0.05 showed no heterogeneity in the Cochran\u0026apos;s Q heterogeneity test.[18]\u003c/p\u003e\n\u003cp\u003eThe I\u003csup\u003e2\u003c/sup\u003e statistic is the proportion of the observed inter-study variation (due to true heterogeneity rather than that observed by chance). The formula was calculated as I\u003csup\u003e2\u003c/sup\u003e=100% *(Q-df) / Q. Q is Cochran\u0026apos;s Q heterogeneity statistic and df is the degree of freedom. Since 0 is taken when I\u003csup\u003e2\u003c/sup\u003e is negative, the value of I\u003csup\u003e2\u003c/sup\u003e is between 0% and 100%. An I\u003csup\u003e2\u003c/sup\u003e of 0% indicates no heterogeneity, and larger values indicate more significant heterogeneity. I\u003csup\u003e2\u003c/sup\u003e\u0026gt; 56% reveals great heterogeneity among studies; if I\u003csup\u003e2\u003c/sup\u003e\u0026lt;31%, they can be considered for I\u003csup\u003e2\u003c/sup\u003e between 31% and 56%. Low heterogeneity suggests an increased reliability of MR analysis . [19, 20]\u003c/p\u003e\n\u003cp\u003eMultiple instrumental variables were tested for horizontal pleiotropy by the MR-Egger intercept test, and the robustness of the results was assessed. The intercept represents the average pleiotropic effect of the genetic variant (the average direct effect and result of the variation). Non-zero intercept (MR-Egger test) suggests directional pleiotropy. Set P\u0026gt; 0.05, meaning no pleiotropy. If the IV affects the outcome through factors other than exposure factors, it indicates that the IV is pleiotropic, and the assumption of independence and exclusivity is not valid [21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSensitivity analysis was performed using a Leave-one-out sensitivity test. It mainly calculates the MR results of the remaining IV after eliminating the IV one by one. If the MR results and the total results estimated by the other IV after excluding one IV differ significantly, it indicates that the MR results are sensitive to the IV.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e3.1 Causal relationship of SS for OA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.1.1 IVs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e20,3, and 13 independent SNPs were selected from the GWAS data with phenotypes FFV, LSA, and AC as IV assessing the causal relationship for OA, respectively (Table 2). The distribution of the F statistic corresponding to calculating a single SNPs has a range of 29.87-78.27 with all F values\u0026gt; 10, therefore, the weak instrumental variable bias is negligible.\u003c/p\u003e\n\u003cp\u003e3.1.2 MR results\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMR results of FFV on OA: The IVW method showed evidence to support a causal relationship between FFV and OA (beta = -0.02883, SE=0.01293, P=0.02574; Table 2,Figure\u0026nbsp;2), Stable relationship with OA risk [OR = 0.971582, 95%CI(0.947268-0.996519)]; The WM method shows the causal relationship between FFV and OA (beta = -0.02968, SE=0.01272, P=0.01965; Table 2,\u0026nbsp;Figure\u0026nbsp;2），\u0026nbsp;Stable relationship with OA risk [OR = 0.970756, 95%CI(0.946853-0.995262)]; MR-Egger, the analysis shows no causal relationship between FFV and OA (beta = -0.09252, SE=0.07034, P=0.2049; Table 2,\u0026nbsp;Figure\u0026nbsp;2) Unstable relationship with OA risk [OR = 0.911631, 95%CI(0.794227-1.04639)].\u003c/p\u003e\n\u003cp\u003eMR results of LSA for OA: The IVW method showed evidence supporting a causal relationship with OA (beta = -0.2255, SE=0.067, P =0.0007641; Table 2,\u0026nbsp;Figure\u0026nbsp;3), Stable relationship with OA risk [OR = 0.798117, 95%CI(0.699898-0.910119)]; the WM method shows no causal relationship between it and OA (beta = -0.1748, SE=0.09266, P=0.05929; Table 2,Figure\u0026nbsp;3), Unstable relationship with OA risk [OR = 0.839625, 95%CI(0.700183-1.006837)]; MR-Egger analysis showed no causal relationship between it and OA (beta = -0.923, SE=1.14, P=0.5667; Table 2,\u0026nbsp;Figure\u0026nbsp;3) Unstable relationship with OA risk [OR = 0.397352, 95%CI(0.042536-3.711366)].\u003c/p\u003e\n\u003cp\u003eMR results of CA for OA: the IVW method showed no causal relationship between it and OA (beta = -0.001173, SE=0.01282, P =0.9271; Table 2,\u0026nbsp;Figure\u0026nbsp;4), Unstable relationship with OA risk [OR = 0.998828, 95%CI(0.974043-1.024243)]; the WM method shows no causal relationship between it and OA (beta = -0.0149, SE=0.009535, P =0.2281; Table 2,\u0026nbsp;Figure\u0026nbsp;4), Unstable relationship with OA risk [OR = 0.988576, 95%CI(0.974043-1.024243)]; MR-Egger analysis showed no causal relationship between it and OA (beta = -0.0496, SE =0.06516, P= 0.4626; Table 2,Figure\u0026nbsp;4) Unstable relationship with OA risk [OR = 0.95161, 95%CI(0.837517-1.081245)].\u003c/p\u003e\n\u003cp\u003e3.2 Causal relationship of OA for SS\u003c/p\u003e\n\u003cp\u003e3.2.1 IVs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6 independent SNPs from the GWASs of OA were selected as IVs to assess the causality for FFV, LSA, AC (Table 3). The distribution of the F statistic corresponding to calculating a single SNPs has a range of 29.99-78.27 with all F values\u0026gt; 10, therefore, the weak instrumental variable bias is negligible.\u003c/p\u003e\n\u003cp\u003e3.2.2 MR results\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults of OA for FFV: IVW, WM, and MR-Egger showed no causal relationship between OA (IVW beta =0.1382, SE =0.4696, P =0.7685; WM beta =0.1013, SE = 0.3505, P = 0.7726; MR-Egger beta = -0747, SE =1.145, P = 0.5496) (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults of OA for LSA: IVW, WM and MR-Egger showed no causal relationship between OA (IVW beta =0.05363, SE =0.08854, P =0.5447; WM beta =0.04182, SE = 0.1075, P =0.6974; MR-Egger beta = -0.08393, SE =0.2048, P=0.7096) (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults of OA for CA: IVW, WM and MR-Egger showed no causal relationship between OA and it (IVW beta = -0.2345, SE =0.4952, P =0.6357; WM beta =0.316, SE =0.5614, P =0.5736; MR-Egger beta =1.028, SE =1.112, P = 0.4078) (Table 3).\u003c/p\u003e\n\u003cp\u003e3.3 Sensitivity analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the Cochran\u0026apos;s Q heterogeneity test of FFV on OA, there was some heterogeneity (MR Egger P=0.003158, I2=0.53452; IWWP = 0.00282, I2=0.53075) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the Cochran\u0026apos;s Q heterogeneity test of LSA, there was low heterogeneity (MR Egger P=0.2916, I2=0.100719; IWWP = 0.4652, I2=0.306336) (Table 2).\u003c/p\u003e\n\u003cp\u003eThe MR-Egger regression test for the causal effect of FFV on OA showed horizontal pleiotropy unlikely to affect the assessment results (intercept =0.0011; P = 0.369). MR-Egger regression test in LSA showed that it was unlikely to affect the assessment results (intercept =0.0037; P = 0.65). This means that FFV, LSA instrumental variables do not significantly affect outcome through pathways other than exposure. The results of the one-by-one test analysis showed that no single SNP affected the IVW site assessment.(Figure 5)\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eJ Thumboo et al examined the effects of socioeconomic status and psychosocial factors on pain or physical functioning in patients with OA, confirming that better physical functioning is associated with longer years of education, less learned helplessness, less physical pain, and less severe OA.[3]Zhang Lijuan et al examined the effects of psychological interventions (e. g. cognitive restructuring, relaxation) on the physical and mental health of patients with osteoarthritis, indicating that psychological interventions have achieved some encouraging results in osteoarthritis pain, pain coping, self-efficacy and fatigue. However, the effects on physical function, anxiety, depression, and psychological disorders were not statistically significant.[4] Traditional observational study designs are prone to interference by reverse causality and confounding factors, which limits the understanding of the relationship between psychosocial factors and osteoarthritis. More importantly, unlike the general physical risk factors, the causal relationship between the psychosocial determinants and the physical condition is generally more complex, which may be bidirectional. The study by Zheng Cong et al used MR to assess the causal relationship between social isolation and osteoarthritis, showing that social isolation is causally associated with an increased risk of osteoarthritis but not with the degree of social isolation.[7]To explore whether there is also a bidirectional effect between social support and osteoarthritis, we used a two-sample MR approach to assess the potential causal role of SS in OA risk and the potential causal effect of osteoarthritis on SS. Our MR analysis suggests a potential causal role of FFV, LSA in OA risk, in contrast, AC is not causally associated with OA, and osteoarthritis was not causally associated with FFV, LSA, AC. This also provides the evidence support of psychosocial intervention for us to strengthen the prevention and management of OA.\u003c/p\u003e\n\u003cp\u003eSeveral possible mechanisms could account for the causal effect of SS on OA. First, participation in social support activities requires a certain amount of physical activity and reduces the occurrence of sedentary behavior. Recent Mendelian studies showed that TV viewing increases the risk of OA, reducing leisure screen time is protective against OA[22-24] , and reducing sedentary behavior is thought to reduce functional restriction [25] and reduce the impairment of functional exercise in patients with knee OA.[26] In addition, mild DIY (e. g., weeding,watering) and walking are beneficial for preventing OA.[27, 28]Jia Shuangshuo et al. reviewed the results of recent studies that highlight the important role of tissue-specific exercise factors, including exercise-induced myokines (muscle), myocardial factor (cardiac) and adipokines (fat tissue)in OA, and explore the underlying cellular and molecular mechanisms involved through endocrine, paracrine and / or autocrine pathways.[29] Secondly, social support acts through the influence of inflammatory factors. Elevated levels of proinflammatory cytokines, including tumor necrosis factor-\u0026alpha;(TNF-\u0026alpha;) and interleukin-6 (IL-6),were observed in individuals living in social isolation.[30]Meanwhile, the level of IL-6 is associated with the degree of joint degeneration in osteoarthritis.[31] Third, the role of psychological comfort in social support reduces pain self-report in patients. Studies confirmed that psychosocial factors can explain some of the differences in pain reporting in knee OA patients, with important potential implications for management. Some interventions with cognitive-behavioral approaches can reduce knee pain without significantly stopping or reversing the structural damage. And these programs are more effective if the spouse is involved. Suggesting a possible placebo effect of the intervention.[32]\u003c/p\u003e\n\u003cp\u003eThere are inevitably some limitations to our study. First, both social support and osteoarthritis are based on self-reported data rather than objective measurements, which may lead to measurement error and also account for some heterogeneity in the results. Second, due to the limitations of GWAS summary data, such as missing data on disease severity and other comorbidities, we were unable to perform further subgroup analysis or to more prominently understand how social support affects the occurrence and development of osteoarthritis. Third, all included participants were from European origin, so further research is needed to explore whether this finding can be generalizable to other populations.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, our findings suggest that FFV and LSA may reduce or delay the development of OA, which also provides evidence support for psychosocial intervention to strengthen the prevention and management of OA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. We extend our appreciation to theMRBase database（http://www.mrbase.org/）and\u0026nbsp; IEU Open GWAS database (https://gwas.mrcieu.ac.uk/) for their invaluable contributions to our research.\u003c/p\u003e\n\u003cp\u003e2.This study was sponsored byNational Natural Science Foundation of China (82274400), National Natural Science Foundation of China (82247619)，Natural Science Foundation of Guangdong Province (2022A1515011681)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHawker G A. Osteoarthritis is a serious disease[J]. Clin Exp Rheumatol,2019,37 Suppl 120(5):3-6.\u003c/li\u003e\n\u003cli\u003eHawker G A. Experiencing painful osteoarthritis: what have we learned from listening?[J]. Curr Opin Rheumatol,2009,21(5):507-512.\u003c/li\u003e\n\u003cli\u003eThumboo J, Chew L H, Lewin-Koh S C. Socioeconomic and psychosocial factors influence pain or physical function in Asian patients with knee or hip osteoarthritis[J]. Ann Rheum Dis,2002,61(11):1017-1020.\u003c/li\u003e\n\u003cli\u003eZhang L, Fu T, Zhang Q, et al. Effects of psychological interventions for patients with osteoarthritis: a systematic review and meta-analysis[J]. Psychol Health Med,2018,23(1):1-17.\u003c/li\u003e\n\u003cli\u003eBurgess S, Daniel R M, Butterworth A S, et al. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways[J]. Int J Epidemiol,2015,44(2):484-495.\u003c/li\u003e\n\u003cli\u003eLawlor D A. Commentary: Two-sample Mendelian randomization: opportunities and challenges[J]. Int J Epidemiol,2016,45(3):908-915.\u003c/li\u003e\n\u003cli\u003eZheng C, He M H, Huang J R, et al. Causal Relationships Between Social Isolation and Osteoarthritis: A Mendelian Randomization Study in European Population[J]. Int J Gen Med,2021,14:6777-6786.\u003c/li\u003e\n\u003cli\u003eBen Elsworth, Matthew Lyon, Tessa Alexander, Yi Liu, Peter Matthews, Jon Hallett, Phil Bates, Tom Palmer, Valeriia Haberland, George Davey Smith, Jie Zheng, Philip Haycock, Tom R Gaunt, Gibran Hemani. bioRxiv 2020.08.10.244293v1. doi: 10.1101/2020.08.10.244293.\u003c/li\u003e\n\u003cli\u003eEmdin C A, Khera A V, Kathiresan S. Mendelian Randomization[J]. JAMA,2017,318(19):1925-1926.\u003c/li\u003e\n\u003cli\u003eKamat M A, Blackshaw J A, Young R, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations[J]. Bioinformatics,2019,35(22):4851-4853.\u003c/li\u003e\n\u003cli\u003ePalmer T M, Lawlor D A, Harbord R M, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors[J]. Stat Methods Med Res,2012,21(3):223-242.\u003c/li\u003e\n\u003cli\u003eBurgess S, Butterworth A, Thompson S G. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol,2013,37(7):658-665.\u003c/li\u003e\n\u003cli\u003eBurgess S, Scott R A, Timpson N J, et al. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors[J]. Eur J Epidemiol,2015,30(7):543-552.\u003c/li\u003e\n\u003cli\u003eYavorska O O, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data[J]. Int J Epidemiol,2017,46(6):1734-1739.\u003c/li\u003e\n\u003cli\u003ePierce B L, Burgess S. 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Joint Association of Moderate-to-vigorous Intensity Physical Activity and Sedentary Behavior With Incident Functional Limitation: Data From the Osteoarthritis Initiative[J]. J Rheumatol,2021,48(9):1458-1464.\u003c/li\u003e\n\u003cli\u003eGay C, Guiguet-Auclair C, Mourgues C, et al. Physical activity level and association with behavioral factors in knee osteoarthritis[J]. Ann Phys Rehabil Med,2019,62(1):14-20.\u003c/li\u003e\n\u003cli\u003eLi X, Wang S, Liu W, et al. Causal effect of physical activity and sedentary behaviors on the risk of osteoarthritis: a univariate and multivariate Mendelian randomization study[J]. Sci Rep,2023,13(1):19410.\u003c/li\u003e\n\u003cli\u003eQiu P, Wu J, Kui L, et al. Causal effects of walking pace on osteoarthritis: a two-sample mendelian randomization study[J]. Front Genet,2023,14:1266158.\u003c/li\u003e\n\u003cli\u003eJia S, Yu Z, Bai L. Exerkines and osteoarthritis[J]. Front Physiol,2023,14:1302769.\u003c/li\u003e\n\u003cli\u003eBrown E G, Gallagher S, Creaven A M. Loneliness and acute stress reactivity: A systematic review of psychophysiological studies[J]. Psychophysiology,2018,55(5):e13031.\u003c/li\u003e\n\u003cli\u003eSantos M L, Gomes W F, Pereira D S, et al. Muscle strength, muscle balance, physical function and plasma interleukin-6 (IL-6) levels in elderly women with knee osteoarthritis (OA)[J]. Arch Gerontol Geriatr,2011,52(3):322-326.\u003c/li\u003e\n\u003cli\u003eCreamer P, Hochberg M C. The relationship between psychosocial variables and pain reporting in osteoarthritis of the knee[J]. Arthritis Care Res,1998,11(1):60-65.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Summary of genome-wide association study data in this MR study\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.07624633431085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.193548387096776%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eData source website（http://www.mrbase.org/）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.583577712609971%\"\u003e\n \u003cp\u003eGWAS \u0026nbsp;ID\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.064516129032258%\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003eSnp size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003eFirst author\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.211143695014663%\"\u003e\n \u003cp\u003econsortium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.865102639296188%\"\u003e\n \u003cp\u003eyear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.824046920821115%\"\u003e\n \u003cp\u003ePopulation studied\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.07624633431085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFrequency of friend/family visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.193548387096776%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1031: Output from GWAS pipeline using Phesant derived variables from UKBiobank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.583577712609971%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eukb-b-5379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.064516129032258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e459830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9851867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBen Elsworth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.211143695014663%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.865102639296188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.824046920821115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.07624633431085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLeisure/social activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.193548387096776%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6160#5: Output from GWAS pipeline using Phesant derived variables from UKBiobank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.583577712609971%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eukb-b-4077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.064516129032258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e461369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9851867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBen Elsworth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.211143695014663%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.865102639296188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.824046920821115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.07624633431085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAble to confide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.193548387096776%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2110: Output from GWAS pipeline using Phesant derived variables from UKBiobank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.583577712609971%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eukb-b-4982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.064516129032258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e448858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9851867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBen Elsworth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.211143695014663%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.865102639296188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.824046920821115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.07624633431085%\" valign=\"top\"\u003e\n \u003cp\u003eNon-cancer illness code, self-reported: osteoarthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.193548387096776%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20002#1465: Output from GWAS pipeline using Phesant derived variables from UKBiobank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.583577712609971%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eukb-b-14486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.064516129032258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e462933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9851867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBen Elsworth\u003c/p\u003e\n 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Az8w+vrXvz76+OOP66lmZmZmZmZm1mWqjjj+3J/7c6O/+Tf/ZtUZ/5W/8lfWU83MzMzMzMyszdQdcfzxP/7HR7/wC78w+lt/62/VU8zMzMzMzMyszY464viv/qv/avTv/t2/G/34j/94PcXMzMzMzMzMmuz4vy/7sR/7sdG/+lf/avTH/tgfq6eYmZmZmZmZWZOZ/D/iX/3qV0d//+///dG1a9fqKWZmZmZmZmZWMpOO+C//5b+86ozfvXt3dOvWrXqqmZmZmZmZmWU7/o149L/9b//b6NNPPx195StfGf2+3/f76qlmZmZmZmZmJjPtiOPJkydVZ/ynf/qnR9/0Td9UTzUzMzMzMzMzzOTV9Oj48ePVa+pf/vKXR//gH/yDeqqZmZmZmZmZYeYdcXzbt33b6Pu///urzvg/+Sf/pJ5qZmZmZmZmZjN/NT26fv366L//7//70de//vXRr/pVv6qeamZmZmZmZrZ3zbUjjj/xJ/7E6Od+7ueq34ybmZmZmZmZ7XVz74jjD/7BPzj6t//2345+4id+op5iZmZmZmZmtjfN5Tfi2Y/+6I+O/s2/+TejP/JH/kg9xczMzMzMzGxvGqQjjq997WvVK+rf+73fW08xMzMzMzMz23sG64j/0l/6S6v/1uz+/fujH/7hH66nmpmZmZmZme0tg/xGPPqFX/iF0Td90zdV/73Z+fPn66lmZmZmZmZme8PgHXH8zM/8zOjTTz8d/Y//4/84OnXqVD3VzMzMzMzMbPUN9mp69F/8F/9F9Zr6l7/85dHf//t/v55qZmZmZmZmtvp2pSOO3/27f/foK1/5yuhLX/rS6NWrV/VUMzMzMzMzs9W2K6+mRz/wAz8wWl9fH339618f/Zpf82vqqWZmZmZmZmaradc74vju7/7u0c/+7M+OHj58WE8xMzMzMzMzW00L0RHHd3zHd4z+1b/6V9XTcTMzMzMzM7NVtWu/Ec9+5Ed+ZPT//X//3+g7v/M76ylmZmZmZmZmq2dhOuL42te+Vv0/49/zPd9TTzEzMzMzMzNbLQvVEQf/rdnf+Bt/o/qL6mZmZmZmZmarZuE64r/21/7aqjN+48aN0U/8xE/UU83MzMzMzMxWw8L8sbbs7/7dvzv69NNPq9fVv+VbvqWeamZmZmZmZrbcFu6JuHzjN35j9WT8y1/+8ujv/b2/V081s74uX7482rdvXzU8ffq0nrrd6dOn3y3z6tWreuruYT+uX79ej2137969ar6wnPadeUMgpsTMbC+grFPmF8mQ9d3Mdo7rD+pt2zUG5/NDhw7VY6uvz/VZxrJN10e2vBa2I45v/dZvHd28eXP0pS99afR//p//Zz3VVpE6WXno20DZdsTt1q1bo5cvX4546eXo0aP1nPeIOf93P/MZDhw4UM/ZjpMnebHbJ4A7d+6MLl26VH1nn65evTra2Nio9v3s2bPV9FnTyXIIbKdPp0c3IBbhxoltt5N6Qqd3kS5EKV+0D+fPn6+nzEffcr9bJmkD1OHoulGwaHlttlO6hvON6m6l6zPaA8dub1rojji+/du/ffTf/Df/TfVk/F/8i39RT+2Pwu07SIvvzZs31ac6hRpKHUjr9uzZs9HBgwcbO9d48uTJ6NSpU/VYt08++aT+NrzcKXj+/Hn1OUT5II5D2b9/f/2tm/JWF//umC+/Reqc3b9/vyr7Q9SxScr9PDW9ATBpG/Dxxx/X35qvQdwRt1XCjXKuJzhP78a5iG1yHlyGhzdN12dt12u2uha+I44/+kf/6Oi//C//y6ozbmbD4uTATZF5PXXuY8hOQcQbOS9evKjH5osYX7lypR5rxjIsa6vlwYMH1bAobt++Pbp48WI9Nj99y/1umaQNoH3qcwN50fLabCd0o5y6At0ot/5oYxQ/21uWoiOO7/u+7xv9pt/0m3p3xvWUiFc/eIVVr8zotc6MabprrbtqLM93hnyXXHffNPiVkvni6QGvPvFJvJVXffIhzicNlQOU7qJqWny9UOVJQy4PSkP7x5BfT8z7GsubvgvjTG+i+Rri/rAPlHnKPvNKT16YzqtRnDz5rrg1xZnv+XiYpiFvQzHWa50MpSdNMY2cfhQ7BaR97ty56rvWlZhe3icdG+trGR2fjpchlgXm53SE48nzSmUHKj/Mj9iXGHvtD/iufdIyIG2mgX04duxY9Z0bFXHbTevb7onljCEiL0v5zBDLWaksaZrKLnlPWioDcX2mK92cjpAO7ceZM2fqKdv3PZfvWNYYSnU9bjfuD99ZX3I7OWnZjXFjiGkjp898TaM9pF3ku/aR+fqe8whal+3qO/FTnuRrEJTSifFhIK0ozlM+my0C3Sjnpj0/H+PpeJNJynFuV3K7Iyynt1Y4H7JsrPe5bjWlI6q7peXzvNiWgbrNEPc91nWWL12fsUxuN7U+Q9M+x2XyvtiS2Fwyv+/3/b7N7/iO76jHuo0r5+ba2lo9trk5Lvw8StpcX1+vp2xubmxsVNOYB77HZTRf46U0Tp06VQ02HfKImDYhH3PM++QD6+Vx1lGZUBrkseR0lf9xGdIZn3DqsfdlRsuwLuMqU6V9JQ2mkw7fI/Y5ph8xnbQixuPyHF9OM2P5XGYVn7ifyNMYj/UqHwPpxmWajr8tb0TxVyyh+EZ5GdKLMcnHpjQYlG85tjmOMWalclHaL2F6PjamaX/YjubnY2aZvN9Sik/b+jY/xDznseTyyHIxH2PZIt+Yp7LFZ1PZgKZpeaWd6zfjcZrKUkwLcV+Qx/M80ogYj8eat8v+6XhiuddxR4xrPhhvKstMj9vJcVH6cX22r+Nn3bjfYNtqA0rx0jQofW0P8fikFM84nreT9ysua7bbKI8q46U6onoYp7N8Hlc9g+aL6lasu5Hmx7oH0oz1RfvSlI7mx3S0Hzq2OI+0436rPYz1lfHYBuRjRa7jeb8Zz+nkcdbP6dri237GWxLf9E3ftPkn/+SfrMfaUShjQUUu8HyPBZ7CnSsp87UM6cXlocobGx/rj5gSvzwI+RjzDF350JQnTFOZKDXemqYywHbzttUgS1xechpNDWTez9I+RaVtaX+UBsfX1SDnco9SnBG3yWdOO+8z6eZ04rQ+eSOl/dTxtsnrsc857XhcyPuV49iVZum4Ja+rbUlMq+348rxSLPvEx2aPmOcyhpzXwjSVv1g+muoHSvM0jU+wD3l7LF9Ks0+9aCvXeVmo/LGtvG9ZaftR3nZpe23i8qQT63NWOk72La6Tt8/yWkcxjsdaOj6WV173yZeuGJntllKZZzyWV7UBuYzH5fiM9YzvuZ7HepOV9qNpu6QTtxWRftM2Su2DthvbmLx+npaPFTHtPvFiezmNUgxs8S3Nq+kR/7f4T//0T49+8Ad/sJ4ymQsXLlSvnwnfmdbmxIkT734n9vjx43ev9GrQ66G2M+MyuW2I8h/06cqHL774ovrc6R/AePTo0bvXFTXo1egu+iN0vGZ48uTJ6nvG7wnHDWr1ehfafg+tV7kOHz5cfYrG3759W33uRNcfTuKPvI0b/G3xYH/70OuWk+RNn/oJvRaqIdbxSfWNI6/L89o82D7lsemvTDOd+YrB3bt33/0V+Izf4xNTjiO/wtrHTte32eKP8yCWT4Ym1P3xhVmVh9O+bpjrpH63qXKhgboc6RXI+DchPv/883dtYH5NE23tkep61++mJb/6SZ2ZRH6dNWprh/uizsZXb4nLTv6yfJ98uXbt2rvX2xVzs0VQul6h7dJ5sQ3LvX79uh57j3pK+ec6K9aJSc/pTX8U7fjx4x+0e8J1Ptf7JbRFrBuRNtvQtV6TSf7WTJ/ro51ch9liWcqO+H/wH/wH1f8x/hf+wl8Y/fiP/3g9tT9dYHBBoZNavOjog5Nx7jQy7LTTZ5MZKh/W1taK25mV2KHjBsMQfyRpJziBluLR92K7r1KnoIQTNyehmE9NndxZ4je0nAxpR7igzhckEdOZrwtvbvC0XcBz4iZt3Wxq+o1Yk52ub7NF3se6oqGpbPPHvJivi634m8edyNtniH8sLf43gULZZTnqly6O54F6xM3U9fX1d/tGW9MXMdLvL7X+rOmGGtjftjo/Ce1vHJQvlBHGyRfi49+C2qLgukVtlAbqB9N2etNoo/7vSePgP3L43lDXYTZfS9kRByc/OuN/+A//4dHf/Jt/s57aHwWYCw6eSvU50dM50smPTh4X0ba7uvJB/4WMnkBOi3wn/3eia1+PHDlSnbjoLHESY7zko48+qj7zXyXV+BANME/MdSE6rb55U+oUlOj4h/7ry+QrbRHtCPva9bSNGywspwuUrvwifU6sxID1JrXT9W02+K//qN/TtEXkHx3gPk+Y2qjOtV0cs3/U7aYbRPEv9tNW9WmP+mxX9ARr0hvjQjtNWafcl3S1w31wTOwjx0+93+lN00niw19VphzNopNjtlOUQcpiqcMMvQnUhLam9Aae6m/X+l2a2t22/7q17XqPes+6EWmzjaZrtmn0uT6axXWYLYal7YiD19/ojPOX1JsqLJWq9OoLr7pSiJtee+Wuv54g0diwLK/n4bPPPqsqXnxCwTJ+BXRYXfmgTs6NGzeqT+QnCTT4NK68+ieMR+Q7+R+fKPI9/4XLNlzYsq8xDfZFDa0u7ih3fDZ10NhfLjTzq/GMc7E+BHV24/FzHDm2bfrkDWkS9z6vfeaLWeK8k1fTJ8GFONvqs688QWc52qu2TjuxjWWFzkNT50Kdofg6/STr2/zRsaRel/4qbgltWmzX4o3gXHeoJ31+GsV6XHzm811sh5p+FhP3U3WMOtenPSIt0oxtbD4+yRfOLEN96Yv9iR3tfE7uaodZX9/bUOe5IGdbXRfgpF+6BpGufGFQ3kM3OVTvzXYLN6KarldoF/LNQ5aNdRtNN89pP3i7JdbHfF6LdH7Ta90otbu0X5yvdT2fqW+gdg5q/2jDWDfOI23qbykG01JabddHs7gOswWxuQJ+4id+YvM/+U/+k83//X//3+sp7+mPHjCMG4Z66hZNz5imP4SgZfIfjRifzN/NY2BZm9640a3i2IT4skzWlQ95vspDTCsvo/GY57EcMYwb3nrOlrw8mBa3k9PIx6MYlI4zoyx3pdVVJkkjH0dTnNlG6fjiEJFurm9Mi9sr5Q3ztf22Y2Bf8jZZPqbHeNxe6dhYrpTPfCLvQylmOo6ueAvrx21I3j/GWY4hbrN07LE8KN2m9W1+Ysw1xHqQ51N2JJctlRMG1otUTmM6fDaV2yimG9cB6+Q6gry93BbE8sdQSiMee9y3vM2YFvMYjzFket5+FLfDeoyX6riGuO3YJikvSrFsqvM5HxC3p+PgM9fJtnxRnS/NM9stqp8lKrOUVdWBWL80LqV6xrS4fKyrJXH5WOdJN6bTVX9yfYtp5Xm5HpfqttohKR0r68RY5lixzyyTYxCXYbDls49/xpm39H7oh35o9KM/+qOjr3/966Nf9+t+XT21HXeOeCqV/xN97n6NK9vUr8fZYiN/x43Z4K8xd+FOL0+Sxg2wn16OUT958rRo+VSyqGXKrC+e8vBk3e2PmZnZMJb61fSI34r/3t/7e6vX1H/xF3+xntqMiw4uOPq89mo2BH7De+rUKV8Ej6l+8ir3otOrcsuwr2ZN9PdS3P6YmZkNY2U64vjTf/pPj37zb/7NVWe8S9tvW8yGxm97+F1S6e8V7EXUS17WWYZOgW+g2CrgzTD/RWIzM7PhrMyr6dHv//2/f/QN3/ANO/4rs2ZmZmZmZmaztlJPxOWv/bW/Vv3n+t/1Xd9VTzEzMzMzMzNbDCvZEcfXvva16g+3ff/3f389xczMzMzMzGz3rWxH/Ff/6l9d/R/jP/IjPzL6sR/7sXqqmZmZmZmZ2e5ayd+IRz/7sz87+vTTT0c//uM/Pvo9v+f31FPNzMzMzMzMdsfKd8TxP/1P/9PoW7/1W6tX1fl/Us3MzMzMzMx2y8q+mh598zd/8+iv/tW/Wv23Zv/oH/2jeqqZmZmZmZnZ8PbEE3H5i3/xL1b/VypPxn/9r//19VQzMzMzMzOz4eypjji+7/u+r3pVnc74L/tlv6yeamZmZmZmZjaMPdcRxx/9o3909E/+yT8Z/dRP/VQ9xczMzMzMzGwYe7Ijjm//9m+vnoj/5b/8l+sptm/fvvqbmdl0+pxS3NaYWea2w8xWWamN27MdcfCX1P+z/+w/G12/fr2eYmZmZmZmZjZfe+Kvpjf56le/Onry5MlobW2tnmJmZmZmZmY2X3u6I/4N3/ANo6997WvV6+k/+qM/Wk81MzMzMzMzm589/Wq6/MN/+A9Hn376adUZ/72/9/fWU83MzMzMzMxmzx3x2uPHj0ff/M3fXP23Zr/jd/yOeqqZmZmZmZnZbO3pV9OjEydOjO7duzf68pe/PPpf/9f/tZ5qZmZmZmZmNlt+Ip78yI/8yOgrX/lK9WT8o48+qqeamZmZmZmZzYY74gV/9s/+2dHf/tt/u+qM/4pf8SvqqWZmZmZmZmY751fTC777u7979I3f+I3Va+pmu+Hy5cujffv2VcPTp09Hp0+ffjeu//ee6ZrGzyqasG7bfDCf5WaBtA4dOlSPrZaYDwxmy8TtynDcVpg1c1s0DLdDW8gvytNC4om4lf2BP/AHqsFsN1y6dGlzfX29HtvcPHXq1Oba2lo9tmVjY2PbMtMgDZoC0i9hmy9fvqzH2rEcaR08eLCesnqIR1OszBad25XhuK0wa+a2aBhuhxabn4i3uHPnzuhf/It/Mfpv/9v/tp5iNpzz58+Pnjx5Uo+NRp9//vno9u3b9diWu3fvjs6ePVuPTefo0aOj8YmuHvvQ1atX62/dDhw40JqWme0utytmtgjcFpn51fROX/3qV0d/9+/+3ep342ZD4uTx6NGjemxU/fHAly9fjl69elVPGY32799ff9t6XUqvH+nVLsRXcuKrXhriK12sxzS9dsV3HDx48F2a8VWnuC+aFk+se4Fei1NciBNxUTwUN2LKOEp5Rd6wjNIhDcYZWF55qDxiiHln1ofbld3jtsLsPbdFu2OZ2qG4XdLTfrJNaJ7mg5896KcPcTnlZdwfzVe6MQ5aPk5TucFOjmub+sm4tfi//+//e/M//U//081bt27VU8yGwatbvFYFvV6kV7fiK1t8j69KUbWZxvL6DpbRd9JWWqTDchrnu17Vit/zOtpmTJdtxn1ZNcoHEA/ioxgTk/gKWIyD4sNnnM66P/mTP7ktHZAWA7Evpa/pyhuzvtyuDEOxhdsKsw+5LZo/xRXL2A6RDvst2h7Hpen6zifbiPvL97jd0nc+43rsM/uOuDzzdZw7PS7xE/EeuEvHk/Hv+Z7vGX3ta1+rp5rN3/Hjx6tXsyS+uvXs2bN3r2zxfdwIvLs7hy+++GL04MGD6k6vcDeP6fL69ev622g0blRGV65cqcdGo7dv39bf3uPuNa9xsY1z585V29RdSO5u48KFC9XnXnDz5s0qbuNG+d3xv3jxovoE8dZdVcW9lFe/+Iu/OBqfXKq8UjqHDx+uYnzjxg1umFbTuRP/8OHDaj3laymfzNq4XRme2wqzD7ktGtYytkOkE9+c0H6RlxwPT7X18wKmjTvko4sXL1bjiMcH9o190LGA40Nc9s2bN1XeEy9+kgDmUyZn2b66I97Tb/ktv6XqjJMBjx8/rqeazRfljQZIDSMNEg0G4/EEAxofGhgNOoFFNFo0lDQepMv4pGh843asGSfs+/fv12Pv9ckrGn7mcaFCfunVp7W1tW3r6iRo1pfblcXjtsL2IrdFi2VR26GTJ09WnWIGOvzQa+vcvGEbfXGz4dq1a9W+9JE78jKr9tUd8Qn8jt/xO6on4l/60pdG//Af/sN6qtl80Whwh/HMmTPVOA0i4zR2cuTIkdGtW7fe3bnls/SbFX43o0ajqXFpQ2MY7waTnn7Xpe1xp5Bx5u11nKy4U0tsdOLqm1dM4zdIrMfda+JKnpOeLlpYRumYTcLtymJxW2F7lduixbGo7RB/2I/OM0/D9XSaP6gdn+z3oW3zJkUfujEUj5djnGn7Oi6sNqHbt29v/sbf+Bs337x5U08xm5+1tbVtv3fZ2Nj6rziycYNUTWfQ8qdObf1+imHcmGwb1xB/6zM+Ab77PQ0D62iceTlN9g1x28yP+7tKcvyIib4TC31nOWGZOI6cV8Q5r8sycXssg6btmE3C7cp85Zi4rTArc1s0Pzkey9wOsV3lB/I+6bsG5iPOy+N5PcZjjEhD5VHDrI9rH/+ME7EJjTNg9FM/9VOjr3/966Nv+IZvqKeaLTbu5MXfSHE37/nz5+/ufJqZTcrtipktArdFtmz8avqUeCWBVxO+/OUv11PMFhuvzuS/b8BvgfR7GzOzSbldMbNF4LbIlpE74jvAnbdf/+t//ejbv/3b6ylmi0t3hPnjFhqg39uYmU3K7YqZLQK3RbaM/Gr6DHzbt33b6Df8ht8w+u/+u/+unmJmZmZmZmZW5ifiM8B/a/YP/sE/GH3f931fPcXMzMzMzMyszB3xGfhlv+yXVZ1xfp/yF//iX6ynmpmZmZmZmX3Ir6bP0D/6R/9o9Omnn47+/J//8/4LjWZmZmZmZlbkjviMbWxsVJ3xv/23//bom7/5m+upZmZmZmZmZlv8avqMHTt2bPS1r31t9KUvfWn0sz/7s/VUMzMzMzMzsy3uiM/B7/k9v2f0gz/4g9X/Mf769et6qpmZmZmZmZlfTZ+r7//+76+ejn/9618f/epf/avrqWZmZmZmZraXuSM+Z9/1Xd81+l/+l/9l9ODBg3qKmZmZmZmZ7WXuiA/g4sWLo3/9r//16K/9tb9WTzEzMzMzM7O9yr8RH8Dt27dH/+//+/+O/vAf/sP1FDMzMzMzM9ur3BEfyFe/+tXRz//8z4/+9J/+0/UUMzMzMzMz24vcER/IL/klv6TqjP/1v/7XRz/0Qz9UTzUzMzMzM7O9xh3xAf26X/frqr+ivra2trS/Fz906NBo37591WfJq1evqvltw7179+qltzt9+nRxeQbbGWL49OnT6vv169dHly9frr7b8FRHbDKxbWkqv7QtWqZpIJ2srd1yXWlGfNyuzI/biuH5Gmc5EUO3RbNHHIlnm1m2U0qLzz2DP9Zmw/qZn/mZzV/xK37F5k//9E/XU5bLpUuX+AN/mxsbG/WU9/I8Pg8ePFh9x9raWuO6YD5pRHncJnPq1KnWmJstE9qTplNXnpfbE9WFJqyf64nbnzK3K7aq8nVMlOfx6Wuc3eW2aHcRe4YS8mR9fb0e66Zz+MuXL+spq89PxHfBkSNHqtfUv/zlL4+eP39eT10u4xPH6Nq1a/VYf1euXKk+v/jii+qzj5s3b9bfbBr+r/NslZw8eXI0Pll/cJeepyHMa/P5559Xn5PcbXf7U+Z2xVaZr3GWh9ui3TXuNNffPjRpHXr06FH9be9wR3yX/K7f9buq34rTGW8rxIvqs88+Gz18+PDdq0Dgdazz58/XY2V6Xejs2bPVZ5f8ehHjepVLA9iPPF2vh8XXwXQBTjpKi/mLSPurfecYtc8xLvF13Ng5KS0LxrVcjpsQE9JV7LpeTdorFA+9tkhcGCdWMR/ia42MKy9YhpjH+XoVUvNBusonpscyqmkMMV+0bwyr/loXJ/erV6/WY1vu3r3b2f5cuHBhdOrUqdGBAwfqKe1y3Yl5xaB8iXmiQXmg8bYyMSTtm/bL7cp86BiV7yojHH+MbVu5cFuxe3yNM3/aX+2726LZU5vB8TEoDsRdceUz5kMsL4qTaBkNMfYx1qTHutShc+fOvUtTecYQ65by4caNG/WUPaR+Mm675Ad+4Ac2f9tv+22b//Jf/st6yuLTa1R8xleq9J1iFV/bYlwDr2W10WtdcRDSiq+AMU+vr8RXStkPbSd+5/UYltM2YlqLhn2Lx89x8J1jUExRignTOEblB+vE6XxXTHilS68N6bu2pXWYxjzbQnyIi+h7LIP6rnxUvCmvjCvPYmz1nU/Fn/W0TlwGpK/ppXK+qlSuOUaOFcSI448xAdMUSwblTxPllwZtC6SlceUtYv6A6dpO/M66DNqG8mtI8fjA/vCdOMbYxeMD05kWY6Byqul81zGpHMfv2pbWYZrK8qrimDlW0fdSGcnlwm3F7lEZ51Pfoe/EUvmn2GpQbJswPy7PICoHwjzyFConiHkYvys/tY1Fzlv2LR4/x8F3jiGW11JMmMYxKj9UDzSd74qJ6kn8rm1pHaaprqwi4qdypOMXxYZYaBliofgyXcswrrjGmKnd0bjinL/HPIt5HNPlM+7LXvC+BbBd8yf/5J/c/KZv+qZ6bPHlikSFUWMGpjEOPtWIsp7WbRIrKuJ3VXaJ24mVPW4nNvYawHxV/EXFvqoxiscU46BGKw7EgeNWbMB0jZeOPa6LGE+lZ1uIh+JHTOMJQ3kT4833uAzTczzJE5bTiSzWA6XJJ9OUL1FTOV9FigsxUhz5TnyIbTz2GEeWLcUuYhnlG7QuWFf5k7fDd+WxtqN8i4PWj8sPLW6b49Mxan9B3LTPGjimHB+ma5x0WC+K62KvtSsco2JCnGKex/KhGPI9LsP0HCPizHJuK+ZH8ST+iiffFU+mKc9iHrGe1m0S8wvxu/JP4nZi3YnbacpP5uf6uGjYV5X3eEwxDhxDPDYGtR2KDZiu8dKxx3Wxl9qiGA/FExy3yrbio0F5EePEupoeY5bLLetoe3F9vuftaF2Vgzy+F/jV9AXAa5a8PtL3VaZFcfTo0eo1T14lefbsWef+8zsofv8RX3PpEn87xeuk48r97rUWvrMPYDlef2E624jrjRsZWoh3w6oZN4zbjm+ScqTXhFiPvLRuxPf27dvVd34HqNecebWK3ygTy/EJqprWRa9yHT9+fDQ+WdVT271586b+tt2ql/OM32KOT9bv2pOu181pF2gj9OpmH7EdUb0iv44dO1bFW8g78px5lIFYB2OeLNNvGd2u7JzbiuXma5zF4LZoZ/g5xePHj+uxURUHyijtg9ok2qEY41i+hHMuZY94Uhb5PinarridrvP2XuCO+IK4devW6Bd/8RdHf+gP/aF6ynLgjx+x733pYjj+NqQLy9KYcgH9+vXrdxVYfxQFzNf0Fy9e1FO3/rATvwsVllsl/OE/4q948kkDy40d/ZEMzaPzkLEunRmbDOUqXmxRNvktVCx7ffC7Zi62+l5YcBEefxvNPrDtVS/nTYgd7Qn1oAsnfHWYJ0EsVa+IsdoZXSDjzp0776brAobtsa2YF8uSL25XZsdtxXLzNc7uclu0c5yraHP4jfiZM2eq8vLkyZN67tZ8YhR/J18qR8yP57pJO9EnTpyo6oaQHmWe86R+G64/YE253jPGwbQF8umnn26OG/56bPGMK0z12gjDRv0aENPGlbj4ekscWAbji4kPpuFU4bUVDSzHkKezTtO6SjvOY9tx++ML82qZRRPjHPeX14L0nWXAMeRp0DQdP/mTjz2nFz8Z4vKKtW1/ZVFiOYsx1IBYhlk+1pnSOjH+DIh5FvMkbp/1Vk2u/5qmMp9jFQctA8UpTstpx0ExjvVMg+Kcp8e043TyO+bz0OK2Y7zcrsyP24rlEmNLzDWN/Ih5UBqUxzEvmvI9DywX81yD8q20rtLO+Rm3T31cRE3tgdui+SAO8fg4XpVv5LLHeIxdjq2GnDdxnRh/5Vucz3fEbSsvVbb3guGvBKzVP//n/3zzP//P//PNP//n/3w9xYSKmU8qNAzxU0rLmplNi/YkXxw0tT+lZc3M2vgaxxZZLoMoTbPJ+NX0BfMf/of/YfV/jP/gD/7g6K/+1b9aTzWUXpXhNS5eU4q/f8H9+/dHhw8frsfMzHYmvlIHXqlDqV3itT//9s3MJuFrHFtUnO/yfxnKTwI++eSTesymtY/eeP3dFsjf+3t/b/Tpp5+OfvInf3L0rd/6rfXUvU2/JYlevnxZXfDq/yuUtbW1bb+vMjPbCS6GY2ectki/1eSP10QbGxvbfkNuZtbF1zi2yPhNd+yMnzp1aqn+AOmickd8gf2dv/N3Rl/60pdGX//610ff+I3fWE81MzMzMzOzZeZX0xfYt3zLt4z+0l/6S1Vn/P/4P/6PeqqZmZmZmZktMz8RXwJf+cpXRn/lr/yV6sn4r/21v7aeamZmZmZmZsvIHfEl8T3f8z2jn/mZn6k642ZmZmZmZra83BFfIt/5nd85+n/+n/9n9LWvfa2eYmZmZmZmZstmbr8R56/r8Zdk81+TjfjT91pG/xUMn5oWB83fy374h3949O/9e//e6Du+4zvqKWZmZmZmZrZs5tYR5/8+1H/DwH/7UnLt2rUP/quGt2/fVp/89y88rGe4dOnSB8vtVevr66N//I//8ei7v/u76ylmZmZmZma2TOb+V9PpRN+5c6cee48n3PyfiHTGu3z22WfVZ1OHfq/56le/Onr06NHoB37gB+opZmZmZmZmtizm3hE/f/581eHOr5bfv3+/+s/gP/7443qK9fVrfs2vqTrjvKpeuslhZmZmZmZmi2vuHfGjR49Wr5XfuHGjnrLl6tWrowsXLtRj7S5fvlylcfbs2XqKHThwoPqjbX/kj/yR0d/4G3+jnmpmZmZmZmaLbu4dcVy8eHF069ateuz9K+ZtHetjx469+0NtdDpfvHhRzzH57b/9t1dPxr/85S9X/7WZmZmZmZmZLb5BOuJnzpypPtUB53VqfjveRn+sjT9ORieev7BuH+L1/h/7sR+rOuO/8Au/UE81MzMzMzOzRTVIR5wn2nQY6YDrj7Tx2/E+eGrOun3+qNteRSy/67u+q+qM/9N/+k/rqWZmZmZmZraIBumIg9+D0wHn9950rPnteF9a1/+XeLPv/M7vrN48oDP+7/7dv6unmtkiof27fv16PVZGO8dPcmZBabntNFsubivMzFbfYB1x/R6cDnXfP9ImWjf/wTfb7k/9qT81+q2/9beOvvSlL9VTzGyR3Lx5c3TlypV6rIw/TNmEn+hM8t84njx5sv5mZsvEbYWZ2eobrCMO/S58mr9+zrrxD75Z2Ve+8pXRv//v//ujP/gH/2A9xcyWycuXL+tvH5r0JzqPHj2qv5nZqnFbYWa23ObWEedubv5L50zjD7BFvKLONH5HHsfzq+ulda3sJ37iJ0ZffPHF6E/8iT9RTzGzWTh06FD1+iavjDLwXa9z8vSJ73zqNU+G06dP12uPqu/xKZWW0cDrqMITLU0nPdbljaJz5869S5PltUz8g5bMZ5rfIjLbHW4rzMysy6BPxG04/B/j//P//D93/sbMzPrj5iKvg/L3GHhtlP/Vgbd1uJHImz6M88kyPK3SzUPqIRfCXBwL42tra9UyrMffzuCGo/BEi3lMf/78+ejBgwfVd5blu+o2y/C/TPBfPoJ0T5w4UU3fv39/Nc3MhuW2wszMurgjvqJ+1a/6VdX/MX779u3RX/7Lf7meamY7xW8p79+/X31/8+bNu5/M8PTq8OHD7542cYHNkyYuqF+/fl1dOHNxLFz4Ml3yG0RcQAvbyR4/flxtm23owpqnYUzTfxmpTzMbntsKMzNr4474CvsNv+E3VJ3xP/7H//jop37qp+qpZrYT/HeBXNgKF8xcWHMBrJ/YcGHNUyYN8emV8JSM32VyccwrpNP8RpMnXnE72r6Z7T63FWZm1sYd8RX3237bb6s64/y3Zk+ePKmnmtm0+PsVPLnidU+eIvG/QMS6xXxeNdXroOAV0Iz5d+7cmfrCmFdKuSgX0uMpFxf2+r0nr6nCfxHZbHhuK8zMrNW4Ubc94N69e5v/0X/0H23+/M//fD3FzKZ16dKlzVOnTtVj4yvjcVO6sbFRj21uji+uq2kaGGcdjbPs2tratmUY1tfX330fXyRvW4d5Gmce4ny+I26bfeSTaWY2POql2wozMyvZxz/jxtf2AH4P9kM/9EOjr3/966P/+D/+j+upZrYbeCqV/5/g0jQz29vcVpiZrSZ3xPcY/roqr8rRGf/lv/yX11PNbEh6LTQ2v/zhJv7bQf6SspkZ3FaYma0ud8T3oD/2x/7Y6B//4388+h/+h/+hnmJmQ+OJ1tWrV+uxrT/kFP/6sZkZ3FaYma0md8T3qD/wB/7A6Jf+0l86+rEf+7F6ipmZmZmZmQ3BfzV9j/rxH//x0T/9p/+0+q/NzMzMzMzMbDjuiO9hX/va10Y/8zM/M/pzf+7P1VPMzMzMzMxs3twR38N+5a/8ldX/Mc7/T3r79u16qpmZmZmZmc2TfyNuo5/7uZ8bffrpp6N/9s/+WT3FzMzMzMzM5mXijvi+ffvqb2ZmZmZmZmbWptTl9hNxMzMzMzMzswH5N+JmZmZmZmZmA3JH3MzMzMzMzGxA7oibmZmZmZmZDcgdcTMzMzMzM7MBuSNuZmZmZmZmNiB3xM3MzMzMzMwGtOc74k+fPq3+b3QNly9frueMRq9evXo3ne/R9evXq+ly7969d8tqOH36dD3XdiLmg4aSOP/QoUP1VNspx393OO7DI34xnrTrNnuO83DytYnbiOGpLS9dEzp/hqeYcx2f0QeI+RH7BDZ7rht7vCNOJ/zYsWOj9fX16j9ZZ6BQMB1v376tPnHjxo36W9mbN2+qT6XD8PDhQ3fGZ+DkyZOjjY2Nd3E9ePDgBxWSSrq2tvZuGeb7hDYbjv/ucNyHxUXZxYsX38WSuJ47d646J9jsOM7DIraKNcPLly/duRgQ5Z22u4nzZ1hckxPzEq79aYeUF5x/b9265RuFc+K6URsf3J41vgDYbAvBuBJW8y9dulR9jgtBPefDdUtpjTv4H6xnO5fjWoq98o5Pmy3Hf3c47sMizsSSuNv8OM7D4npmfPFbj9k8xfb41KlT1dDF+TM/8ZzJJ+NdyAvyxGbLdeO9lX4izpMh7mRxB0yvNsRXUT755JPqU0/Am5w/f766azPpnZiPP/64/mazlOP6+PHj0bgS12Nbjh49Wn0+e/as+rTZcfx3h+M+LL0R5XZ8vhznYfHEz2/NDIP2eHyd/a5d7sP5Mz9Xrlyp8mMSL1++HO3fv78es1lx3Xhv5V9N59WGCxcuVBm+sbExunr16ruO99mzZ6tPXk/v6mRfu3atetW8q9Me6WL4wIED1afNxt27d6sbI4rrixcvijFmGZs9x393OO7D4rxBLCe5ULDJOc7D4cEE1zHE3BaP82exqF9w5syZ6tN2zyrXjZXviK+trb3rcHOi54QfnxbRQb906VL1OxCemDd1yEmDdemQ98GdGzr9pG2zQ1zJK35jaMNz/HeH4z6M+PYUT0K42WGz5zgPh4cHijUPJuI1ke0+589i4a1Z5QfnXB7glW542/ztlbqxJ/9Y2+vXr+tvW27evLmtQx5fX4/u3LnT+VRchYZOO4WGtG12+ANWvI7LK0Y2PMd/dzjuw3jw4EF1LmBYX1+v2nL/oZ7Zc5yHo1dANdy+fduvPi8Q589i0evrDNwk7PPGrM3HXqkbe/qvpmd0mumMk9klFAouhttejYiFxhfNs8VTFBpGLuIiKiZPDDOW1d8BsJ1z/HeH4747uPPO+eDzzz+vp9g8OM7DevToUdVG+MbHYnL+LA6ehHOjkAd0pXOtDWtV64Y74hPiYoGCwB9KsuFwR5K3EbjBkZ04ceKDVxv11sLhw4erT9sZx393OO5mZmZmq2lPd8R50hTvrHARy52vtt+B81Scu/dcHNsw+KkA+cINkBL+kAbz4k8KeGuBtxf8256dc/x3h+M+LN4wiD876nM+sMk5zsPh+obrnIibe/x0zr9D3n3On8VC7OP5FPw2mWt+n1OHtafqxuYKG2fYB/9PINPGlaoeqx4zbRvi/2Ua/5+7aHzxW00nLWE7TLPZU97kYdzhqJd4n1elebYzMa5xcPznK8YzDo77fORYMvj/tp49x3lYtAkx1vG6xearVNY1iPNnOLpOz0M8b+Z5sb9gs+O68d4+/hkfoJmZmZmZmZkNwL8RNzMzMzMzMxuQO+JmZmZmZmZmA3JH3MzMzMzMzGxA7oibmZmZmZmZDcgdcTMzMzMzM7MBuSNuZmZmZmZmNiB3xM3MzMzMzMwG5I64mZmZmZmZ2YDcETczMzMzMzMbkDviZmZmZmZmZgNyR9zMzMzMzMxsQO6Im5mZmZmZmQ3IHXEzMzMzMzOzAe2JjvirV69G+/bt+2Cw4cS4Hzp0qJ7a7N69e9vWuX79ej1nS87T06dP13Pei/MZWCfK8xkuX75cz92S5+c0lkU8hj7xf/r06bZ1clxy/pTiT57FZVinJC7DEGOc0yhtZ1H1KaNZV7nP8cj5Aqa1LZP3i6GrblAellHfMih98izOZ8ixW+Yy2xfHFI8xx6AJy+b2J5f5OJTKd2ybMtKO63fl9zLoaouzUv3OchuR25k8X4OUtsGQ01lFk7Yp6FtfVBdK5+i4ftM5PO9bV1lZBZO2RbnsNrXPimVb/k7SFq2CeDxNZTDq03b1yT/lWVNeQW1WvlaJaedB8jmoz7HN1OYesLGxscmh8imnTp3aPHjwYD3Wz6VLl6r1bDLEfm1trR7rjv36+nq1zsuXL6txPmMaeRyMkz/CNkhHGGeZiPG4TNYnjWWQY9UV/1J9YVzxVfxF4zH+bI9pykPlaUxT22nKA62jNJC3s6gUk3hsxJzYN8nHqzSUd8Qr5pviF/NWcZecBvI48YzrMB73O89fFjkWXeWtKVaxvHW1CaQR86iU5rLjmGM5Vpw51iYq2wxtbU/EsqW8Uj1ifpT3S9uMbc6yUZmNx8B4WxtIfOLyjMeYE5dSnGKsST8uk6lcL3NspzFpm4JcLtvqC9NZNtcRpsc8Vx2ISuutukliC5XbmF+lWDKN5fKymdZluSinyfdlzxuOMZ7Huo6pT9tFGjFOpfzTNIa4bKT5DH3apNy+sV7EeFsbO2vbt76iSgVC05oqbEnXyck+pEoUlfIjonLnSsC4Kj1p5gZAFxNN+VnaJuNtjWzWtd+LaJr458YRpXSimD/gO+tEOV2+52WiUn1bljqY44GuMsryrBeV0olKMc1pxGnK+7gPpWlRV3lZVOxzVxmMWHbSPOsTm7ZtLpum4y3FWogd84llV3kW0mKdjOnEUvkSaRtRadoyKZWdptg06SrDyG0P39vKrPJ02dqEneKYczkv5ZFMUl+IOQPTYx0plfWcrsb3kkliK6X2J9cPLRPbrRK2Qb7n/NF+xfrWldai41jjMaIp/lKqFzGdPvkXl2mqZ5relF7WJy9K5WSe9uxvxL/44ov623ttr6wwfuvWrdHDhw/fzZf8Gld+NWIve/z48WhcSeqxLUePHq0+nz17Vn1G5MG4ooyOHz9eT9nCONOZ//r16w9eHfn444+rz7dv31af2UcffVR/m94s0hjapPEHZfzEiRP12JYjR45Un01le//+/fW3LeTVJ598Uo9tOXDgwOjFixfVd/KR7SjdEtJkmejRo0dVOouO/Tx58mQ9tuXw4cPV5/Pnz6vPqE+5L8mxYJxtR8RQ+aMyHPdB5aAprmorl6n8q5zm8kW5zmVK5tWuUOaXocz2obKiNkRoY2hrSjj28bXG6OzZs/WUbrdv3x6NL4bqsS3UgatXr44+//zzesp24wun0ZMnT+qx92VAebiMpmmLsz7HP/irmEtomjalb30hba4vP/vss3rKe2/evKnKdqR2R23z3bt3PzjPr7pp2qI+5+WbN2++u05p0tYWKU9im6/v5OUymsd1ZJ/8Yx7njrxM9ODBg2ro6/79+9Vn2/mI/B2yTdyzHfE7d+5UGa4KQuBp7NbX16uMZ4A644xzYcA6cT6dcC5yNW1tbW107Nixap41X4TmE4vogjdfPOTx3FDmE1Omypcr9Llz597dQOmqeE1pLLJJ468OX+5Ed3U4uHDOJ7h80qGe0KmE8pkLiHgTK/7G8MqVK9V+Mp2Gm09wolx0HGe+OVHKB+lb7jMu3uLJjtiw7Rgz2ixiCfaBNopyT9vFwAXFxsZGNb+EZWNbuQyabh7kcp3Nql0RyjP5cf78+XrKcuNmRantmGXZ4Pd6xCx3SiirnIObYs05nfpAO04anIcp68vUXkfTtsUZbSx51pRHbIeL5nwTkGm0HxrUEY2Isea3/X5zFUzTpvStLxcuXKjKalMe6bwpWk7nWPKQaTG/GFbZNG3RpOflJl1tEVR/hX1ln5fRPK4jhziXlHC9Q11rwrmDto86OZQ91RGPJ40caC6ouNiMd0m428VyuUJFXPjqIhe649O2ju2MnhLGP6KhC+ImVL78hEU3TzSQZltnvJSGvb9wjp0N6hLxiuisiy5q4k0sOoOsE/OVEwCNtW5ucbFtW3TT4syZM9WnEEsoZvnGBe0VJyI6LQylCwrqgdpK4j/JHedlNat2hfUVO+Zzc3dZO4O7Id8kB51AzsVtN+GIMW0IecjNI8ptPDfvRVyHUMcvXrxYT/nQjRs3qs947UOc1S4zUMZpT9QZJ2/ifGJO/qx6Z3we1N40lVVdU8ab1LGNAudJ8jnmCXVoyKd6e0VXW6Qn7KpXYB3qiO0u1Zt8zUT+6JzNuYPro0ne4NqpPdUR5yStRorvBFyNG69CULmUGQy6kO3CyWfSdVZNLMgM8zwBUEGoKOSftqdXWUpPEMkfLsraLuLABTONpS42or5p7BYuuGL8GYbAdtVwxc4GHTfiFfeHMsG0KDaIrM98vV6qMsVFJHWWbVC/uBu9KIYs9xHbVScvdljUCdSbPVxAE9N4Ecc+clOE+ZR3LuBYJ+LCTm0l8Wd+qV4sgtj+MuSL1L5m1a6QjmLHQHosu2xiTBmGuLnMNjgP56cRjFOm29Au0D5Qpok72O9FLbeR6q2GWZUX3lKiQ9bUyWO71P+2N2KgMs7T9RLaIOoOeTdEOZk3ylLMj9h+zhrtQ9sNZs6LlH3ae+2Plo9PHPMNQeoMdWEV8iPmBcNuHlNXW0RdoD7pvMrAOpwr8tP4RUNcY5wZVknpJi+oY/GczfXRoDexxhtdeeNKUf04n89o3HC9+0H+OHOq8TbMZ7mIcaUBbWvcANZT9jbik2MGYjRuzOqx94hbaR7jbcW1Ke7kWd/8aCsnfdNYNJPGH8wbX1TVY1ua4su0Uvolsf40pcd81UPqlb4L+1Vab9GU9p19Zt9z+YLm9Sn3WjanD6bnvGM5paH4RUovrxc1laNF1VSXOcbYXndpKqeKaZ5e0rQvy6hUfhDrbRuWaYt/ab7KZ9PAPjXFmLSWqdxmOr6oqUxmHDfLNVE6bfU+KrVpkdqqPnViGTWVsbY2pau+KM2moekcrTqhfSnVP6W9qvnRFduSUhnOsRRNj3mgaU1DW11iflN+LjpiypC1HVMpHrFMTpp/TfsgSjvno3TNj0p5P0979jfiGXdI8h856oM7wNeuXavHLOP3q/l3l3pCoVd4IvJh3Fh+8PtinpKOK2E99iH+8APz450u7mJzV3JcqT64A1ZS+g3YpGksmknjD+KYf8tEfMmXGAPuGLJs39eWqV960qUY5z9cRn3SXWNinn+3mP9QzqLiSVS+a69jjW8OyCTlnuXGJ6oPnsRqezlGMYbka05P214linH+bTdPuPPfMmgzi3ZllTT99Ip6m+vqNIhrfo2aGI+vVbYN4wukah7fedqrfM51a5K8XkR92+KMJ7rkCfEpIf94e2B8Mdz79X3Ke9sTPbVdq1onpmlTuupLfhLHQJ6Qv3xvej2WcwnLaJ+Ieb5+1R/DWtX8mKYtmvS8nPVpi0p0zTXk686zNI/ryHmfSzL9rYw++Ty4ceFZeaU7IZqmOzbjk8y2cbDMuDDVY+U7n4zHuzekwUB6Vo4rMYtx5TvLCMvGGCqvmu5OMZ35MX9L0yK2EfdJ+xnzsiuNZTBN/PNxl9JgnVwX2pSWz9NyvvdZZ1GVyizjsXxxHG3HX0qD5YlBk9L8OK1UpvO0uA8orbMMiDX7Lfk4FN9YrqM+scpIK+YxuvJs2eTjyXFWOS7FiGVZvySX/zbKBym1UaVpyyaXt9IxMR7zo08cmZ/LqbBeziPSZx1hXfZNuurSqshlPedPKQ5d9SVj3aY6Am2jFP84jfGmPF4Vk7ZFk8RJdS0uW6Iy0GQV2qHSMeTY5zZCcVHs+6TRVjdYLi6bKW+1vagtD9jPnC7jbXVw1ppLzwpRJuQhV7C8XCkj8ry8jio+022LKoiGUqFneqQKqSHmVU6PISN/8jIMTfnGkCtpVxrLYpr4qxxriLEpxV+Dlsv5VzrRIcc4y/OXKfY6EWnIMeBY8vG0lfucXhzicnlezu+ctwzx5FXaTpy/TFS2S8ehcqwyWyrXWS6PGmI+5mVy/FdBPD6GSOVLsW46/zLE8yRx6xsrldGolH+x3VpWub7mY2JajFtcNg5aJqcXB+VZrje5nSrFOrZBqyzHRjGD4lLKozi0Yd0Y71L9KV1f5nY778OqisfMEKmsxzzKccrn5XwOblpOSm1RXjduf1nlOp/batWLKLc1pTIZ5zNEeZtxkFwfNcRtaT9K9QZd7d287eOf8YbNzMzMzMzMbAD+jbiZmZmZmZnZgNwRNzMzMzMzMxuQO+JmZmZmZmZmA3JH3MzMzMzMzGxA7oibmZmZmZmZDcgdcTMzMzMzM7MBuSNuZmZmZmZmNiB3xM3MzMzMzMwG5I64mZmZmZmZ2YDcETczMzMzMzMbkDviZmZmZmZmZgNyR9zMzMzMzMxsQO6Im5mZmZmZmQ3IHXEzMzMzMzOzAbkjbnNz+fLl0b59+94NT58+rec0O3To0LZ15NWrV9umx4F1oqY0JM5jaNuvPsssqlnGPyId5pF+dv369W3rx2X65mFbGstg0eIuXfuV0zh9+nQ9ZzVwPBwX5bAvls/tS6Z0id8qmUc5nrQMlsoxcjo59qW2ZtnNIz/QFMs8PQ737t2rlllVHF883j51u0/+5HRjG6t2JA5NbU/bPOR871NWFkmfchvl+l46d3XlT1ee98mfvMw0aSy6edUNMK8UD5aP65eWyfuVl+mTxq7ZNJuDS5cubR48eLAe29xcX1/fpLhtbGzUUz7E8qwnfO8qoqdOndq2TlcajLMv0rYN5pF+134vIvZ9HvHXNIa4LEg7bpNxlltbW6unlMU8nDaNRcFxLGLcc5rMY/zly5fVOJ8xDcZzGstKx6JBx9xG+cYQ45KxHPMZViFWQnmJxz2LctxVBvuUY7AMbUaTPvu6bOaRH+iKZaY86VOHlpViq2NU+9FWv/vkTyn+Ub6WKVG6DHF70mdfF12fchvpmImN5HLdlT95XOU8ptmVPywb5+c00SePF5mOadZ1Q9MYSuU6bhOMx/xVfsU0cxnoSmM3NZdusymVGkZQMZoaoVzB0ZSO5Mo3izRE07V+nr/Imo55p/GPy7SlFdHQtTV2TfGPutJYFIsc90n3C8sS9y46zlKsS2L8WY/1m7AcZZdllvniN8rlT9rKSym2OZ1J00Qug6zflh9gft7OMptXfvSJZbYqbUKbUlzb2oE++aNlYn5kxLWtLsTtNO0P09vSWHR9ym1WikVMp2n9mD+l2Od0u/KnhO3G88I0aSySGDPJcYr6xD4u05ZWRExZRxjP65WmRTmN3eRX02u8pqBXFnjFIer7WoVtef78efV5+PDh6lNOnjw5evToUT223ZMnT0bjSjM6cOBAPWVUfWca80ru3r1bzT969Gg1Pk0aX3zxRfX50UcfVZ9y4cKF0bii1mPLZV7xP3v2LK3WtmW6dC2b87Bkku3tpkWNu17F/vjjj6tPoc1re037xYsXSxP7NhzHzZs367FuHDPxJu5tOC+ML6xay+4ymkc5nrYM5vJ3586daj+acG4eX9h9sO/LbF7tSlcsM/Lp4cOH1blxVXGMlJ/jx4/XU7YwzvRSWe2TP/fv3/8gPybFul3t0q1btz7Y92XSp9xmxDiXY+UFedMnfzhH7N+/v/oujJPn01JZ+eSTT6rPZTevutGnXGc5poznfXj8+HHr6+eLlC/uiI9xQUWGURgYOEEJ8ygwmrexsTE6duyYO+Mt3rx5U33mk05u6CIqUKnSNFUkluekc+3atXrK5Gng3Llz1cV03Ff95uXKlSvV57IZIv59kUcnTpyox7Yr5WFJWxqLZNHjrptOwn5yAVJCHeDEdv78+XqKRbT/xHiSDv6ymGc5nqQMIpdjLRtvjMdtKP0bN25sWybfXF8m88qPrlhmxJQO0SQXzMvm7du31We+YZTHoz758/r16yq2DDHe5FNEeW+b30bL0mGNaXANuyz6tiMR56lcF2Je9K0/5FGkjlrMg0nyR3HP9WUnebyb5lU3pkEfjet2IcaM0z7R1lNeuGn44MGDeokP5TR2kzviY1SEWFBi5lFpYsecpx9kHk/ybPdwhxnTXBTEkyEVN1fWq1evbstzm45uaJw5c6b6zPrkYVca9qEcM9o2yvnnn39ejQttWxT/2Al1YH19feWe9s4KN494YyZfYFhZ3zIYleo+F93xxjgD9MeZdOHHUxrNpxxzw9U3z7frimVGXl28eLEes0lwjUnHIMb70qVLVZ0QrkNiXqhj0bejpo4SlAZ5TL6pLlkZT2iJU4x1vgbskz90vnUOJb+Jf7TTPLatm+ClN3OILfGkrSfutPtNmtLYLe6Ij3FxQCWk8sQ75zpx8wRclUsVzHbX7du3qxPZNHgSoIaQCwvyVHlNQ0q67oDsDPFUZ66ps9KVh33SsO2aYkaZ5+QU2zFOWvFJAzdE4kUCJ7Smi/K9jHME8VzWN2Z2S58yKG11P3cGGc/n5HhzT9+fPXtWfdp7fWIJXRf5huj0KOuxLOttI117ZHrbRjes+4pvMbE9tstrutaMWBMnOsVqm6Tp2qOUP0zT+ZO3Z0mv7SbItHm8l9Ef47oxtvHcyCDPyCtiz3mj7fqllMZuckd8jE4XmccTDjIvVkJw8aDKpUEVyD5UeqUHej2rhAqkV+UipuXXWLgoIE8+++yzesqWSdIQLqZpgPV6NHesdVOGgYYUVNxl6ZTMO/5d2G5XQ9eUh9InjUWz6HHPbRjYfhMuJLgob7pQ3Kt49TN3KBmn48j3ZTfPctynDLaVY7XHkfYX8XtUWm9ZzCs/umIZ8bCC/GhrL1aB/lZM/gmFxkvH3yd/Suvlv0uTTRrrpvSWKc/Y1z7tSEQ5JtaR8oLXpvvWn/y0mp/EtLUbXXGlX0GfgvNCk2XKm3nVjUmwDnmS+188QOM6XtM5b9AZL12/NKWxm9wRD+iU6eKAjoIKnv7ggPWjP8wQX5UCndym3/rqDz5EVF6mHTlypJ6yRb/tyBV/kjSa0ODHxljp0SmhoV4G845/Fxo5LtraGrqmPJQ+aSyaZYh7xEnKvwGfHPGNbQQDseeii+/LbshyXCqDbeWYi6j8dE9/4An6vWK8+NJ+NHUyF9288qMrlkIsWW8vtBWcjzh+/cRBiAvnq5I++UMnMr9poOvKpg40+YW+5Vbn0vzmB/vRdJ5dNNO0I7xWrliJYktneJr6A+pGfmMkmjR/SmaRxlDmVTf60oOwphs1OT1tO2pLY1eNLxz2vBgG/isaxvnE+IJg23yMC13rf7dkWzEaV9p67MP/M3a9/u8l+BTGibfkNKD8ietFXWnEfYD2oyk/WbZt/qKaV/yF6XFZYTrrtenKwz5pLKpFjnuUl2c/c7qTprnoFPtY/8Fxl6aDeU15EbEMMVwV8y7HKJWv0rRI21V7zP4wHmOf01D+LrN55EefWCKvt+pybPP5SnFqi21OoxTbWE7Zhr4L85vi3tQu5bKe83gZdMVWx9iUP+hKI+dPluPYJ3+YH/ch5/mkebyI5lE3ItYrxSPnR1aan6d1pbGbFnOvBqbCpCFWJigDm+ZbGRUqxi1WPGKYY6lKrGGaCtmVhrYbB/K/idJrW2ZRzTr+pdjF5ZrmM8TttOVh3zQW2SLGnZNhnB4vCCXvd75oWFb5uDQoNiqPyqecH3GIeRmxjVJMl1mO207LcVcZ7FOOoQu50jyJ8/N+LKtZ5we6Yqk0Vq1sd2m75lNMWCZqyx/k68yu9XP7m/MzDnFbuZ7l/Vh0+Thzuc3tNVT+NeTYoi1/cj0onfumyZ9cb7rSWAazrhuluGlgXtv8uJ1c7hmkbxq7ZR//jHfGzMzMzMzMzAbg34ibmZmZmZmZDcgdcTMzMzMzM7MBuSNuZmZmZmZmNiB3xM3MzMzMzMwG5I64mZmZmZmZ2YDcETczMzMzMzMbkDviZmZmZmZmZgNyR9zMzMzMzMxsQO6Im5mZmZmZmQ3IHXEzMzMzMzOzAbkjbmZmZmZmZjYgd8TNzMzMzMzMBuSOuJmZmZmZmdmA3BEfO3To0Gjfvn3vBsazy5cvb1uGgWn2Xo5jl3v37m1b/vr16/Wc7ZjOfJZvwvxSvknet6dPn9ZzRqPTp09vm7eT/dhNs4x/nheHWO5fvXq1bR6xlD5pkA+l+aUYt+XhbtqNuAvTS+U+l+nScnlbeX7Om9J2hjBpfPvU57Y087w4UN5RKrdRn3K9KPGVSeM8aflhyNrqQpT3jbQl53eMMfrUhaH1PW7kZeMQ24Q+aeY45lgJ89piRNql9XP6cVDdWQaT5I/kY4/6tAfSFFvJ+xbPudKVxqJri2VJn/zqKvs5jVz+u9qRnH4cctnPy8b2bNHQxkyyr23XhNKVZk6DIccwp5Gvj1QHmuajK43BbO5hGxsbm4Tg0qVL9ZQtp06dqqa/fPmynrI1jUGYxzJra2v1lL3t4MGD2+LI97bitb6+Xs0nD6C8YHpEukwvzYPSYWDZrCufWD/ud94v6dqP3Tav+GdxGcU2rsN+xHpSEtfRdmNdyxa5ru1G3KF0GErlnjyI+5Vpu9oP5LxjfswXxrvydtamiW9cPscb+Tj5XophRNmLy/A9xianqfi2letFiK+w/5PEuU/56YpRzhulGcs56zOtqe4zPe4343m/2GZcZrfpuBWbrmNsEmPVJ5bEoS3+0DQG8quE6Vompt8k151FpxhMkj+5bPM9HrPyQ2k26YptV73EpPmzaNj/Sdoi5ddOyr7W0ThKeTppO5LL/rR1fbdwvHH/c9yy0vEx3pafLMt4rBs5jaZ1JG+X/Yv7rfyNaXalMaTm0r0H5AoflSphHEcupHuVKmesSCrUsTGMSo1ajqfGm9KK05vygul5O11yZezaj902r/hnueEqLV/alyinoQayaXmwnbyvi2C34h630bRuaTsRaeb1StOivB/zNk18S1iefUepvPVJM6ZRkmPTp1xnQ8dXpokz+7rT8tP3HNBWjkvYBtuSrrowNI6v67i7TBPLUn7GaTHPm/ZH0/vUGWG5mB+LbtL86dOm9D3PtcVW03eSxqJjf/Mxdh3LLMp+qe3K06ZpR9hGLPusP2kau6Up7sSk6RhKccx52pVmqa7kaaW86Mof5jPINGnMy559NZ1XIcaZOjp//nw9ZbuLFy+OHj58WI9ZmydPnozGFWl04MCBesqo+s405pW8ePFitH///npsC+Pkidy8ebNargnbGJfh0dmzZ+spH7p169bo+PHj9Vg3vf7yySefVJ/o2o/dNq/4Z7dv3x6NG6l6bDR69OjR6OTJk/XYlsOHD1efz58/rz6znEYfk+bhUHYr7n3KfRfKN9uMr3s9fvz4g9fxolgnhjBNfLNcn7/44ovqM6eJN2/eVJ+ZXmE8c+ZM9Vny+vXrar92Yuj4yjRxnqb85Bj1qQvT1P2d5sM8ES+OLx8T4zmebXKb0CeWOT/1OujHH39cfZLnXe3KpOfCPnVnkUyTP9O0KSVdsb1///4H9TRb9GuVLtO0RbMo+9O0Z11KZX9Rr2VKdA2nazrhmo9rvxLa+Bwzxfjt27fv4qtpwjqa99FHH1Wf8Rry2bNn1afKBZ95H+iv5XIQ5XozTRrzsmc74mo8lemZLopKDS+oyFSqa9eu1VP2LmJUarC6GjEqbdQV80kpHRrgvr8D0byddHKGNkT8Oalwovrss8/qKaNqPDdaubGLSmkIJ8pS/kyTh0PZrbj3RfsUYxbTp3yfOnWqijvbYJ85CT148KBe4kN37typ1hnKtPGNmupzjjVxyPkin3/+edXpaSrbOhdw8zZrKtclQ8dXponzpOWnKUZtdUF5NEndZzvUlyNHjtRTtrTVhSFxMYp8IZrH2zS1CV3tCuWLOJBXpHHs2LHR2tra6OjRo9X8eeiqO4tmJ/mTy1SpTZmkPchIi7xjWISyPA8cS6ndKU2Ldlr2+7Znk7Qjuexr2UW8linRTaRcd7s6qvlGkPpY6nMhfgfb0Hp8J2/OnTtXxYbh6tWro42NjWo+uOFEG0j8aPP5JP+uXLlSL/Eh8u7EiRP12HRpzMue/2NtTSeIUsNLxSSzGKjIZOIyddgWCXfVqBixIaOxnCWdVMGdfgbyjO3GP+ZBRVe+kscss+omjb86CU31pY9SGpwIlTcMNLbsl05OffNwWQwVdy4gYlxJg4uMuF2WYTonPGK6vr5ez/kQJyrqxoULF+opi6utPuvu/o0bN6pPqPNWonn5zSniqG1wLuCCK57Au8p1tkzxla7y0xWjrrowTd3nxjjlPHYu+9SFZVJqE/q0K8SEckgMyTNiMM+Lzqa6s2r6tCmTtgcl5C1tBE/xlA51inzcy2ZV9rvas0nakVLZX7VrmRK9OcLNDOFNDqHNImbcpIiIQUTe0BlnOgPlPLbpIH7g3AI61k0U3/xmziRpzNOe74g3nYzzHRtQ8VSBKEyTNKK2HQVeDZku1mQnnb2S2BiSNtvltSNhX5SvNNbs06o0jE0mib8uAHbSSeibBo0tjW5umLvycFkMHXfRCUYnRdJm22yTcs9FBxcgpb9wCnWkluHGY1t95nh1Eaz4E1+WKd3pv3v3bjUvXwQobhoUzyZN5VqWKb7oU366YtS3LvSt++SxOiptcl1YJk1tQp9Ycr1COeNCmfwAy9FpmIemurNqiO8kbQq62oMm5HGpbswrD5fBLMr+pOdDtLUjbWV/Va5lSjh/6Wm28kLHpoebPPkmHzSfgRjEtx74zs9vyAvdrGA5oaPPOPnEMtQl4l26biePeaLOsrHuTJLGvO3ZjrgKRbxLFTW9liHcceOEuJcbQCFGpd8llX67E+U7jLw2QkWYlaafHTTlKWg4aUiouMti3vHnTj/TcyeBafmVME5oyG+UNKVREvd5mjwcym7FfRo5XlyYcPLTxQTb4IRUatM4KbIfWnYo08Y3KtVnpsX4kx4ne73OKJRlLgD6/PyIPEXbSbxpn3crvjJNnCcpP1KKUVtdmKTuc1FVutgq6Zo/T6XXNKHxrn1raxPaYkmeUJbpMGob5C/z5/HzuknqziKZNn/6tilR3zZMSttuqiPLimOctC3CTsv+NO1ZU1loKvuLfC1Tkl/vF/1EoglPs2Ne6Ol3PP44nwGKA+cH6o7KAdMZh84ddPQ5r6sdJN/oSOfrdva96SZ33zSGsGc74rpTxZ2rkq4/KsX6VOSm9fcSvY4SUQGYln+r14Y7Z6XfWU5LFVt/6EF4YrKojd805h1/Tiql6bwSlhtp/YEN1S9pSqOEhl4n0UXOw92K+zSUTzq5cpLjYiXKf5QFeiJQujiat1nFt4sutPKJWk868vRpxXItuxlfmSbOfcvPpGJd6Fv3yT8uqrhw7pNXuS4Mif2mDOQ/4sXvRukIdJmkTYixVEcyt8u04fMw67ozlJ3mjzS1KVGpPWhDR5SOYaTz7ap0yOdxTu1T9qdpz5rakaay37c9WxQ6/vywkv3NsWrD8VJ32o6Rcq03BagXua6pXkJxz+WBspOxDv24fJN7kjQGsbmHjU/c3Ir54L/WGGdeNT0aF4xtf/oerMdy40ainrJ3EYdxga/HtuJFHIV5bbHS/BLWYR751YT14/Ykp6s839jY+n8Q2c+YrraVywT67MduYb/mEf+2Mk4MczzyfqAtDfZTeQHlT0yzKw93Uz7eIeIesX4u98SF/YhYprRfUZ7WtG9DYvvsh3TFl/mx7DCd+aX6jLb5edtCfHPMc6ymKde7KR9rV5xL+x6n9YlR1pUmct3vyt8+dWFouW6zj4yrbOiYOPaob5uAHLdSnNpix/ptMdK6sTxHzMv7vyymzR8pxbVPeyBNsS2lSx7l8o2mNJYB+x1j29UWZZovpbjlaXkdxGmTtCOsE/c/yttROYhlY5Hk2Oe60VaO0ef4chkurZOn5XWQp5WWifqkMZTtJW8PUoWMQ6kSkTk5g0oVfK/KcaRAR2qAVIFVoTWUCr/WyYPyp5R3GrQdkHbTvFIaOT+79mMR5OOYRfzR1TCpgdRQiklbGnl9hlKj3ZaHu2k34p63GQdth/Xj9FJaOaYM0raNIcv9pPEt7Xeuz3l+qbx1XUBou3GIusr1osRXJo0z2soPumLUty7k7cR9KG1Dg5brUxeGlveb8iLKi1wO2tqEPrGk/MVlGGLdyGUgDoplU7zjvnbVnWUwaf7EZRnysXe1B+gT25yHcR76pLHocjnsaotmUfaR2xmGqE870qfs5+3oOBZVPu64vzpe1Y9SnLN8/DkfkPOUIcc0z4/5UapvGrSvyPNKeTqEffwz3gEzMzMzMzMzG8Ce/6vpZmZmZmZmZkNyR9zMzMzMzMxsQO6Im5mZmZmZmQ3IHXEzMzMzMzOzAbkjbmZmZmZmZjYgd8TNzMzMzMzMBuSOuJmZmZmZmdmA3BE3MzMzMzMzG5A74mZmZmZmZmYDckfczMzMzMzMbEDuiJuZmZmZmZkNyB1xMzMzMzMzswG5I25mZmZmZmY2oIXviO/bt+/dcOjQoXrqlnv37m2br+Hp06f1EqPR6dOn301/9epVPXULy5Wmg21pPQa2Jcy7fPlyPba3Xb9+vTFOWV42DsyTSdJUPuVlcv7FMoFcdignUZyXh0WSjyPGsUmOTRPVj6Zl2mKc94tlo1gv+y7T59iGMkkZlbh8PtZIsYvL5O3FIcalLU+iuAxDbAPztnLdGMI08c3lpdSuoxRf5DKbjzvP1zBJuZd8fLt1PpkkznnZODBPYrvBUDq2PnnFNOY1lb+4PkMu67kutB3bUPI+9dFVLrvSzOvHvJKuWM0ijUWj8qWhqZxFlOW4Ti7buY7ENPP24kDspDS/VIdUz/K8vI9xWOQ8ybHrs69x+RhDxHl5iLraolyuYzuT9zkOsY60pbGIZlE34jFOWiYV19J2cyybNKWR86zPsc3d5gJj9y5dulSPbW4ePHhw89SpU/XY5ub6+nq1TBPma/n4XUiP6dHLly8/2C5YdmNj4933PH8vWltb2xZ/4sN4jmkbxVuxVZpMh/JY8yOWJU/zNskb5gnjMU1tU5ryPGN+LkO7SbHJxxWPPct1iO9MK9GyMVbo2o7KQcyz0nbb4s2xxflt5WBoKqOi441lMGN+PJ4cj4hl2/JFlA+KCem3lXt07WsuU2C8La9mbZr4Eq8YT6URj0OYnuOrWIrGS2WwTZ6f00CfvB3CNHHOFCeVQaWhceQY9MkrTWOIywrxi2nmNBhnkNI2hpb3me/sU5uuZUgzxieXLZXZnD8xj3N+5HVmkcaiUbmNx5BjmakMidJQOWM8xj7Pb8I2Y7nI+1WicsEQ122iPNvN8t8mx7ZUxrJ87F35B5aPy/A9jud2guVj/inubXFUvqvsT5PGbtL+T1I3dEyS41jSVCa1/Zw3YD/YluTtSlMaTJ+mjs5bcwu/y9SQR8o4FfDSMhGZpExTwCXOi0qZn+XCsFeVCnCf+EXEMVYMvvdJU/mpz7YGu88yeT+yPmkMrVQO246j1PA1HRd5QMxLdYxt5O1GrJv3IU8j7bY0StiPXDZ2Q2k/SmVUSjHMbZkotqUYZizXtkwpb9nHthiSZj6O0rR5mjS+TbEspbOT+JbysUtOQ/u6CCaNc0k+vtL6bEPH3Cev4jKl9FSuYxqlaVFTOzcUlR32Q7r2SfPjOpHi1JYmsSOPopxnpX2YdRqLJu8/2Ff2uSnepTiUpkXMZ2gSy7p0xS3uJ8fQtn3p2o/dxvFM0hYpBlEpllGuG03Ll/ZFcholpbIV9UljN01TN0rH01U2m/KXacSfdeP80j40xbIpjRLmdy0zbwv7avqbN29G44ysx7Z89NFH1ecXX3xRfU6LVyEePXo0unnzZj1lC69jPHz4cPT555/XU6yJXjs5cuRI9SknTpyoYtjXrVu3RhcvXqzHRqNxxRp98skn9diWAwcOjF68eFGPbeFVl3Elq+Z16bPM/v37629l9+/frz7Pnj1bfe42yiqxOn78eD1lC+NMz69XQfUmxkPfqW/CulevXm2sB+RZ3m5E/uV9ePz48Qevjk1CaeWyMbRpyn3ftoy0ie1nn31WT2mX606Wyz0xZB/zvkfUg3wctJV96tAsTBPfZ8+eVZ9Hjx6tPmV8cq3KnUwa3642oQ9iHsv93bt3q/3abfNqv1mXNCJtg232ySvmja9NPlhGVBaVFp4/f159ql5lb9++rT4//vjj6nNoT548qdqA3PYyjXklnHPyOlGf9pzzZi7HjNM+S94HlQ3FahZpLBratJMnT9ZjWw4fPlx9qixlxJb1Isp7WztB7JryD7QHxK6prJdwDUL9aEs3Urt/4cKFespimaYtmqZ/kK/h+p43oj4xn/S8vGgmrRu6Nst1nfOe5mVNZZK+GdOvXLlST3mvbxvalkZJVx0dwkL/Rjw29FCwYqcB8X3/+LsMGkgVBAoQFYzxc+fOje7cuVNNj7pO5vaeGrscq0k6SsqrXGFy/uaTvipavpHSRL9BUWNScvv27Q8an4iO6draWj22+5ouLPtc+OTGkYbs9evX9dj7mxyliwOtS8MX6x3rCCc66hrpEnsaZPLrwYMH9RJbOGHFNPJ+RUp/t2+ETFvu+7RlnJQoY31OCk11J8rlXmWGi78Y99hmkh75xnQukPhE37q2U9PEl7LLPmc5jpPEF01tQlPsMrVT8WKDMs72YxoMQ5tH+636m9OI2+ibV13W19erNpnf97EfnNOZ1pQOecB2J+nwzBKxKd2IbLs5SayYzxDLSm4n8zjHGdvz+B3KH63HtRBtMduhzB47dqyqJzFWs0hjkdAe5w50VxmkDWQ98kBtI+e5pjaYcsny58+fr6dsR+yI2bVr1+op71Geld9tZaSPGzduVGViUR4iZPM8p0b5Gm6atiifU7PcJpZ0pbHbpqkbUD4K6+QHaNJUJtWOl1BfSnUhT2tLI+uqo0NZ2I647o6pYEMFWMhE7gxq0Mk5VgYKAo0ZmUNDygU9lZFCo4YubgN9Cp3tHBe6dPgiTmzkYcRyEU9q+1Y0sDzpNuUr5aqtMqrcnTlzpvpcVmr4aQSFC4p4QmO87SaHOnNQvWN9LihiPaLTTcypd8zP+cX8WHfVcY8XldRV1VH2Ke7nMunTlmm87QQelepOlsu9TpScZBX3jY2Nqr7F/aHNJC+4mEbppuWymTS+LJ/bhK7zDXSBzkDZ51wTLzaILXUlpkMe7fRiezf0KYPzQkyJP+0CeUAM80UdnXTlBXnZdFG4qGgLOT6eUKmsEG91Hvq059xIorzFdjXXZzrLtAOsR5kl/VhPZpHGqiAPoLYxnydpN1TmKJeU0aabEU1v2SmvNRDTnbQP5F3bE9pl1OecGmneTq/huq4lpzkvLzuOg/rOcUWUuyalMkleltrxSfRJY5I6OpSF7YgTGF3oKGhq/JvulBF8MiF23Dj5qkFTw8cJQh0EprONeJKJ320+dMGQO7900KjUynMGTkK6+KCiMd63stKZYzv5aayQ17pgbqqMlLvccMaLPIa2k8CiYP+5WKIR1H7rSZHugDJOvesS8410iY9e5yKmpM106hfpEeO2v06pCxrVUTBNdZf9Zj914o2ddIZ4Ql40fdoy4pMvbps01Z2ordzHixH2jbjq1S51JDlJEnfqBRedpIdlLPeYJL592gSUzjcsrzLLwLx8EZ0v1Khz5BXbXZb49imD80ScyCPFmQ4r8Yrn7nizj/oX47kscc7nHcWb+Pdpz2lDSYNpWkaULnWbOk5+EiuwHNvATtPQ+SAOy4gywr5TljhG3RSJ5x7aBJU5hrbzXt8bWWyPuCo/JqFyHdt8HYeGpv1bZH3OqVHpGm5SbedU9GkTS2ksS1vUhn4WxxWPg3iXbiCVyiRtBHmZb2xNom8abXV01+rGeEeWxjijqx/mj08+9ZQPjRu24h9KYB0dbvyOcYGpfuyv6W3pg/TZzl7WFKvxxWvrH6oQYs7QB7HWsnyy3dKQt8u+ML0tP5nfth99y8TQVBcotxHjTJ+E0lGaTQPxbKqDOY9yTLVfXXnBNpooP3dTU3noW+4lxlFpNg05j0vxjZrKvbbDtiPSUntWatuUXl5vHrSPed/b4ttULnRck8aXaW3xjUi/Ld+Vz9pGjLU05cs8TRPniOMoxYg0SSOKx9eVV1lpO2pLcryYVkpDuvJqnpq2zbQcL2GdfOwqTznfIubnMh3FPG4qB8wv5a/MIo3dxL7lstIVW+blvCINpjdpik1TGS5pSgOl44i65i+CpuOLZayPpvxrS5/pGWU2x0zL5jQi1uuqM11pLIJp6kZJKY4opa/60DSwXdYplQemEds+aZQwvW3+EBb+/xGP+A33OOitTym4K1K6C8Pd2nGg67EyPR3iN5TWTnmQfxfCU9G231qDPOIJBnfv++DVPC0bn3JowLgSbnv1kDtb3B0jz5vKC+Vk3Fg03uHENH9MZQjc2WW/8u+heLLJMfWlu+zcJSTNHFviCr7zJonuKMc/lATySPPIh/wHm7p+D0WZQNfvwnbbTsp9FNsyhhz38Ymlms/3+PZHV91pK/f6DV7+gyukpydo4xPuB3+IL/8RnXmaJr7aP5Uh4bg4lkni26dNiJrON02oI/kPPqkuqf4MYZo4S1sZJHb598QcH7Hm+Lryqg/aPKUXTdLuDU1/RDMiBkxrql/USeISdf0dm9ieNyGP9Vqo8j+3FV1lYBZp7Cb2LZdBxTYfB7Rszqu+ZTab5Amt4tuU5036PKFdBDtpi6Km/kHTNVzftqjPtSRpNLWJ6JPGopi0bjQhHrnsNZXJ/JSaYdzxruoI39luVxvaJ42FNd7BpTAuwNVdC+56yLhybbuj2HTHaZwR2+7AsA7Lad34XdshrYg0lC7bzXd09iJiQKxEd6QUp6ZY5vXaEHfi3Ya0YrnQduO0rE+6Kid5/xeFynsuuzpu7X9TWe1zfMrTqCvfS/kbp7Ec8Y/Ii5gfzI/5t0h50XX8yoemfc35VMK6pfJZiq30STeX+1yGSvWiT12ZpWniy/7FMtUWJ5Ti23WczFOcoNhpv9jPuA/IaZbyiHH2d2jTluO22OY0SvV2krxiuRzTUgzzNLahfUBpnaGx/ZjPuWwoDipjfWIXlZbPcqxL63SlM4s0dlupPDAe84dYx/wpxT5O41jj+iit01YWSaMUx5yukH7TvFy+FlkuU8SGcdVhxaypPDXFtKsc5vzJ+9GWV1FeL+qbxqIo7S/jsZwRt7ayleMqk5RJtpfTyPvRlV5Oo28dHVq55CwAVaA4MC0iqF3LUJhKGRXXzZW0tO1YKEkvZ+ZeRQGOcaISiyp0ji/TmuLH9JhenzizXMyfvE9xgParNMR9VRnJZWqR5HjFOKgcxxjGZRlifpWQHstlOcY5RqU8iKhDcV5uCEt1MJej3TRJuS8dS1eZYt1Su8W6TXWiq9xLjn2W55f2Y94mia/E5Rna5PgqzdKg7fCZ53WV+1LsVKc05OMY0rRxbmuXc5xKxxfnM0RteSE5hgyx7SulEefvhtwO5LKhtjyWqXwcOe5xHkPMP+S8IL+zUqxins0ijUWUy1COLfmT8yguz5BjwfJt86F8LimdK3IcS2WfIe6r0ln0PIgmaYtKcYr1RlR2S/MkpxPlfYpDxHhTm9g3jUUyad3Ix1gqd5OWSbaZ60/O91w/s1Iafero0Pbxz3hnzMzMzMzMzGwAS/UbcTMzMzMzM7Nl5464mZmZmZmZ2YDcETczMzMzMzMbkDviZmZmZmZmZgNyR9zMzMzMzMxsQO6Im5mZmZmZmQ3IHXEzMzMzMzOzAbkjbmZmZmZmZjYgd8TNzMzMzMzMBuSOuJmZmZmZmdmA3BE3MzMzMzMzG5A74mZmZmZmZmYDckfczMzMzMzMbEDuiNtcXb9+fbRv377RvXv36intDh06VC2voYnSPX36dD3lvVevXm1LI6eT5z19+rSesyWvX9rGIuobOyFP4vLENGtLk7jFeRpiXudlSK8k70uMed80dsvQcZe2mPE9zmNoilvbvEmPbdZUzzV01cW8v3GgXkd92ibml2JTKvslbXkkeZ9zezQklZscq5I+7eTly5e3LVM6tq4Y5fjk/NI+a4j1KacdB/Ztt+T9KrUBWd+6yLxSmUWf/EBbGmirO9Mc2yLo0x5EXfmRY91U3trqXFy/aRnRMjFPdUx56FO/d9uQbVHOyzho+ywfp5fqR26LclnK8xna6tmimHXdiPMYJi3XiOszzCKNPH8wm2ZzcvDgwU2KGMP6+no9tRnLX7p0qR7brL6XiujLly+r6adOnaqGiO0wb2Njo56yHWnGfcnbUNpra2v1lM1qPO7XIuobO8lx4pPxGBvSjPHlO9NE6xCzJnk+4znPuva1Txq7ZTfijq7tsE7crxLtC0NOH332Y57I87i9Ut3sg+XzfjOuY4+xlz6xiWWS8RgrdOXRtMczD9oXDfHYSrR8W7nl+GPsFFOVffQpxzHNnAbjsZyXtlGS931I2kfFuE85yLHle4wtlC5Dnoc++dGVBpiuZXIMc5p8lpZbNG3HVMLysdzlckxexvFSHmuaBpUHIY/jvjAe04zYvubH/Cy1fYuuKy6Zlo+x4phjfelT9rMcu7wvjMdtsHwsEyoDcRssH5dZBsSA42CYRd0gBjst17NIg+kxjbyfQ9qdrdrKo1BTIUuNZAnzWS42dE3rUqnU6PE9Yptd24qomLGClk5cpX1bJJPETogb8YuUZ1Bc2tIsLdNFJydRmjtJY7fsVtw1HpfJStuJYppx+9JnP3YDx5XrfBf2mTIjOt6m4+mKTck05Zq02/JoSBwj+8Ixd+03SnGJ68YYRtoO+sSolEZpWsT8mN/ZbrcfMQZSiqdM0iYwXkorLy+l/GhKA5relF5X+7aIuo4pYz7LsbzkdUtxyNMU+1J6JSoHfEaxfOT5lPVFjn3JpHFR/kVx3Zw3ou00YZ2dtiM5jVK5WGSKbVMMs1Keda07TbnO5pnGEPxqus3FzZs3Ry9evKjHuj158mQ0rvCjAwcO1FNG1XemMU94Nebhw4ejK1eu1FPe47WScYUbHT58uJ7S7Ysvvqg+P/roo+rz9evXH7wq9PHHH1efb9++rT4XTd/YReTN/v3767EtjBM/KC45Tbx586b6nMYnn3xSf9ty//79D/a9S05jt+xW3KeJWca64/Z/dPbs2XrKdvPK/50ifpMct16lO3PmTPWJrrapKzYltBvkifTJo1u3bo2OHz9ej+0u4kFc+nr06NHo5MmT9dgWtbvPnz+vBuS2mHVYF31ilOuSXh1Um5zp9cS2NuL27duj8QVmPTYs9o+6nvOdcaaXXq/sUxe7ymyf/OhKA111p6t9W0TzuFZhXHEVrltibCatc7pGyS5cuDAad/bqseW3G21RVjpvZH2uQygTy2wedSObRbmeRRq5LzAkd8RtIXABUvqtTJ527ty50fr6ej22nSrSjRs3tv3uo+13LaR36tSpbQ1HbnhUMZX+oukbu4zOQ6QTS7wYzBeGNKh5PaYp1l2/u7xz504Vb9GND4aYZ6ULUslp7JbdinvfmNHRa5vfR9t+DI3fqXFBf/78+XpKt88//7zqdMX6PWt0Don1xYsX6yndeaRPLk7i/K76syjIh9zhijGOHcQortOnHFPXiS3L0I4fO3asurA6evRovcR2il9TZ5I02PfPPvusnjIs3czNNxKabixEO6mLffJjVvI+ldq3ZcZxUB6zOI0ODOWM8kz7wCfnrNIDhL64cYVY9vX7+7Z0tR8a2q6HltEs2qKsz3mj6zpED4aOHDlST9kyi/PyoupTN7Jpy3U0izRKfYGhuCNuS4OKRUVpushSg8vTBe7sM9Bpp4LpSQpoFNQIcjHz4MGDes77JxPxZKVKvkq4G8wJIZ4EOLGI7h5zU0N0YhEaPcWZYWNjo0qzqTPB+jwV4C6lsH2mcWda6XACJF9KSmksk1nEvU/MKNOax0C9YX7cbps++zEE6qHq6tWrV6v63NQJy7S/k3Tc+yKO2i86h8Q/nvC78ii+XaP57CtlQxcQq65POSavaVeIDe048/KFVfwjTKTXVkZ18bwbF1vTWpS62EdX+7aXUJ5B+4BJnvKW0P5RPyKmtcWXuqK6xcBNLOrRqnXGZ6nPeYNluq5Drl27VrVX8Xy10/PyKpqmXGfTptHWFxiSO+K2FGioqFh9Tmaxo67vz549qz7BE281hDzFohLSsILldbJSBX38+HE1r89Ti2VBHHUS0HEKF6kM6lhrPicdlm+6k8wJh8aQdUrUYck3UvKFsU6AypOoKY1lMau4TxIzqN70vak0Tf7PA/kcL1yol6W/iFty9+7dan/7dtwnQXziftE+xbxEnzyKF3ssyzpqb/aCrhjRyabOc2FMnEGcYwwp28oHyix5XrqZoY7/st3EW5S62EdX+7ZX6AYiNw4pl5yzmsplH7R5rK92HNQN0p2kfaNjTjp79eZIH33OG13XIeSzbjK2mfS8vGpmUa53kkZbX2BI7ojbQuAkXfotCtO42NDvfKhwOsFzYUJjp8rT9Jsd1mnCiYkLB+5eSr6LzGtK2I3fjvTRFbsm+e7siRMntsWKRizOJz0uiNt+G9W0Pe485sYSpYuzpjg3pbFbdivuk8RMSut0mSb/540OCXW+62RJp4v2IdbredKddF1od+VRU35Nk0+7gfKaX0HWUx1uWDa9jqzX0dEVI/KYPCTPtSxlkG035Stllhup3LTNeKLMurt5E0/Hl3/mVPodeLTTutgnP2alq31bduQR8c+YpnafG4aUQ5U1zll0DErlsgudilKnjvF4c0YxppPYdrNy1vm922bRFgnLdJ03uq5DuAlDPnMTpqs975q/bPrUDZlFuZ5l3Sj1BYbijrgtBL0SHtEoMo3f2OQnYwyc2Kg4fOdCRU+s40W60thJ54Gn6fnJzSLpil1fPImLv3PNFNe2C1lObmr0RA1fqYGmcaYhjXTTJV6Ut6WxW3Yr7n1jFrFf2Ek96JP/i0JPGHZrX7vySG1JfFMHXEAsw8UZryCrTImOj7ZYr1PnP3DJ8dExQ1eM1DklvYhtT4MLs7Z6NgTylvZRP6MS/lYA55i+Jq2LffJjXrrat2XT1e6rXuRzAOtNiht7lFvSzu0C58J4PaR94sZV2yu2rLcMbUxfs2iLpOu80XUdQr3kJgyd8D51cxbn5UXS95poFuV6HnVj14x30mxuxhWg+i8Bxg1TPWUL43k64+POdT229V89jC9a6rEPsSzLRCwfp7FMLOZ8Z59E+zGuoPWU7brmL4qu2CkO8dijHKdM+biW/iuOGJdSnk6TblceLhL2a+i4d8WMPInxA/PjfkXsQ9M8KW1z3thWjC3icaIpvkzL62Y6plhes1JsiG+epv2QrjxCXmcR2hrtQ45njjP7yHiMHeMx5rkuEIuYRleMSvPzNJaN+1BaB3nbuynvS46ljiHGMmo6RmG9XD7RlR9RUxqifYixL1G5WQZNx8R4ns54W1mP5VhK06D0cz5oet/2QPsfl2c8Un6U8nzRNMUlH8Ms2iLJ60XabhPFv6lesp+lMhH3a1Hp2GKMoTyaJPZaZyflehZpMB7zf9I0Z2k5WkhbOmq08qAKWqrAqiwauhoo0iqd2NrS0HbjECse3/P8ZdAVO+WHGh6diDR0xZEhN1Bdscz7FIfYUOeYx3l909gtuxF3tMUM7Eecn7fTFlfta55e2o956zqOHF90nVC1Th4Uwz6xKaWRdeUROJ64TDyOIeU4a1D73BZnDaXjy+nm4+uKUZ7PEC90S3lVuhBmP0p1bbfk8qM4Q8cUYxGXZchluxQHDTHmbfnRJ4283xq0r33at0XTdUwq56U80kBcszifIceiq841zS9tC9qnWDZKx7bouuKiY4plV3mkQXkX5XTj+lAauW4h53cctK1SrDVoW3kfFr1+NB2TjlkxU94gxyqX16b8zcuJ0ov5Mos0cpnJ84e0j3/GO2BmZmZmZmZmA/BvxM3MzMzMzMwG5I64mZmZmZmZ2YDcETczMzMzMzMbkDviZmZmZmZmZgNyR9zMzMzMzMxsQO6Im5mZmZmZmQ3IHXEzMzMzMzOzAbkjbmZmZmZmZjYgd8TNzMzMzMzMBuSOuJmZmZmZmdmA3BE3MzMzMzMzG5A74mZmZmZmZmYDckfczMzMzMzMbECDd8QvX7482rdv3+j69ev1lN2l/dmJp0+fVmncu3evnjIf2tc4LEoc+yA+k+y74qrh0KFD9Zz3Xr16tW2Z06dP13M+xDyWyduddL+WRS4vxLMLMY7rlChfSD/rmx9532LM43QNpW0tqnnEPc5jIM5Rns+gmOU8iUOpTkHz++z7bpu0/qodiEMpDjndUlnO+aZ45elxyHm3qGZdjsmXOE9DjEef9iPOZ8jx7JPG0ObRJvQp901p5BjFgXWi0rIlyt+meKve5fxaBjspU21x6YptnleKXd/8WSXzuJZU+dTANrJcn0rLgHmlbayiaepGV3uYzxW6lkGpvGtoy9dS3VkYmwNjk6dOndo8ePBgPWV3Xbp0aaJ9WV9fr44h2tjYqKbxOU/EjUG03bW1tXrK4lLcXr58WY3z2bXvcXkwHo9faZC2kJdxGWEZ5jHEbWq/lHeKaUxzGeVynY+zhOVZT/jOOpGmMcRlUcrT0nJNeSSss6zx51hnHXdiFePBeJyPaWJGOjlvwDRto22/F4HiO0m70nTcUc6DrM92MpaNZWORcfyzLsddx9+n/eiqC33boCHNI5Z9yn1XGiW5bvTZV2j7rM8QaZ4G7fOy0P7Hckds83GWtMWlK7YsH7fJOMtHffNnleiYVY4U41j2s7g8GI/5wbqx3DOe45rzsBR7TWNoa+tWxTR1I7dDirXyh3jG2DHO/Lb8BduMeah90xDzf9G0t8ozRmYRYAV2kQPTRBVtN+SGABS8Zajw7GOsJJh031VhpbS+8ieXLaapgscKnSsvliWmTdQAxcYRpTyQUtxyOnGZUlrEtSs/GO+KbWnfl8G84p6p/eRTJo1ZKQ1ouvYhz180pdh21d9SnY907DFPMtZvS6OENLsuJhZBU/nbaTkutQ9Rn/Yjy+V4mjTmaV6xLK3PuI69TxpZjiVIr2n5iDpF7NmHfI2ifS3t0zKIcZW+x9IVlz6xlZ3kzypReYpKedSGPCGWbZjPcsJ4jnWcFuvXpPuzrKapG6U4lvI0ou7k+hM11Q3S7NqfRTDoq+l37twZnTx5cnT06NHROEij+/fv13O25FcO4qsE8VWF/OpDn9dO8qsOSpvpefn8ioqWZfq5c+eq75rHtrXf8fWKfCwMEdslvfiKzTSvsuzfv7/+triIxbgSjI4fP15P2cI40xXfLp988kn9bcujR4+q8hQdPny4+nz+/Hn1CV5rGVfiqtxlL168+CCGjLNfy0rHrlgIsSJmJU+ePKnq5IEDB+opo+o705iHs2fPcubatkz0+vXrD8rwxx9/XH2+ffu2+lQbsIrmFffso48+qr9N7+7du9U2cp24cOHCaHzxUY8ttlm1KxnnpZwn2a1btz7Ybhu9wnjmzJnqc5ENVY6zPu1HluvCNGnM0zxi2afcT5MfuU3geob08r5nlO2HDx+Orly5Uk/ZjnPszZs367Hl0/c6I2uLS9/YRrmsT5PGsptVm5+vJUuoC1GuO7reV/tC/eL6iOukvWLSuqH8UcyENrst72I7VlK6nlmmdmfQjjiN0vnz56vvZN7t27er78I0LgIpzAz6XQAF/urVq1VFYzoXi7qw4fPYsWOjjY2Nd+uRqfFkTKeX9TWfZeNvDiI6x6SvZS9duvSuoD148GC0vr5efdf8UueO/aVQsGxMJ3fGiQcdEy2Dpv1q8vjx4w8uPBaNLoBy5cvjXYgVHWqhPOROdK6w5AUXzW0Vkou3SI1030Z90bx586b6zLFou2nDsZbK0aRli8Yv0sXDF198UX1qvm4+Nd2A4oZX2/xFNFTcdQMztz19Y8Y2qRPXrl2rp2yhnUTTBfWi2Um7wvErVgyxrqszx1BaRp9clMX5bW33559/Xp0Dui4oFsE8yzFtdoyZzuPS1X5kpbowaRrzNI9Y9in3ffNDWD63CYrXjRs3WvOMdkfXRauoz3VGSVtc+sY2ymV9mjSW3byuJTPd5Dhy5Eg9ZWsd6gh1SP0O+iulPsBeMW3dyG0x6+R2OyLuJ06cqMe2K7Vdy2awjrgaBxVaOuRkou4qgfF4p4qOL5RpymDuOOmuE5WDC5xYGeh0kZa2SSc8PuVhWaWdMT3e0dKdtklwd4ZKHtNRR1AXu6CzHvfj4sWLjXfJSzg+OvPcOFh1lJNpjpXKSd43NQ7cZKESU5mFMmWTU12JFwL5rRfmU8Z180k3oOJbLnEeA+s0XUDuRbRntHnRJDFTnuQ796S7F8o+bW6MFW01bbHaAD5pa2I5Jd4sg/hkVfOJN+1IbN9FF3W6Cb1XcYNH8WKgXaazovaiT/uR5bowTRpWbhN0E4GYKs/oWJJnum6jvOdrHeuOS5/YZrmsT5OG9buW5LoxP2HlOw/xaF+IMfOX5ab1ouA6nLhxYzri3NlE59Smt8marmeWyWAdcXWYRQWcTqvoxMxdvdgxIsBkHtPjBTuoUDREkTKbhkrpxDtbXbiA1d1F9mdSXMCV7t7QMOenrxmVvA3HG/eNhoH4UFg1nWHSJ+uLjruPlJ9JKhsXY9xla2ssuUGiC3HFTpo671ZG3sQ6zMAbG4h3rLnhFDFOuW7CxQX1whcXWzcsKKtdr1y1xYw3kWJbDNoLpsULj71CsYwdNtqEWP/ViY7xjB1rlmUdlfeo6WcAex3tMnHRzZ++7YeU6sKkadiWUpsg8Zyr78+ePauuregcdrVFe80kcWmKbdbW7vdNw7Z0XUtyLa0bsRHnSNblvMpND9C++LpkMlyTE0O1zwycO0sPDogtdYnrmabr8ba2a1kM0hHXEwbuesTgI94J0R1zdYxip1uZp45ovOM9SxQGhniHcZEQG+0bgy7u8tOGRTo5Nr0amN90aEJ+lE5CTMs3NnTjhYsuXh3NFZ5xKrbKH/ITMm6ikPayanq1vvT7SSEPSq8GMa3tNcosl0Pd+VQZKMW16/day3IBPe+4cyFQukAoaYoZ7SZ14LPPPqunbCHN2D4rn7jwyDc/F8VO2xXJy5XW07YQv0el9SgLxHWZXpsbsv3I6XW1H9JWF/qmMYR5xLJPuZ8kP5rahKZ2WW2DfgPKuNoNyrqu0Valg8LxtV1nZH3i0hXbqKmsT5LGquhT9ttQ54hP0/UxdaHU8SPPyEMefGk6dYm0lvmV6J3i+CepGxLbZwbkvCOdrpsmTW3XshmkI64nDTn4FGrkBpuOEfNofOIJjIxiPTJGd9EpCPmPj7AOmcNTcGVunzuEWi+/NjEpKnvpyQjHk5/e7wXkAfmkV6mEfOPGQht1AkoXFbxWHssHdBLkBgWNbS5z7AdPTPjehLzLT22Xif5YRnyFFpzIm35nQ7mk7EexHk2Lekceqx6W6gblgHxpopPsblxIT2KececuPRcCTM8nrJKmmNFuxvwQ6lesJ9on2uH485lFspN2JVIbogtbOiq01ZHaFeKp2OVzCvmc47qMr80N2X5Q7trKc24/MGldKKUxlHnEkuPoKveT5EdTm6AL6Xh9pjSoK5Tp2GYwcG1GWnxflTdAuq4zsj5x6YqttJX1vmmskj5lv0nbtSSII2/S0AnP7bXOpzm/9fej9qpJ60YTzrf5p1vkM/Wm7aFiU9u1dMYNw9yNA9r4p+k1b3zBt+3P0487S/SUqu/MH1eO6jtiekxnOdYX0olpsWw81LgttkN6wnJMw7hBq8bzuowzT7Sc9kHLxH1mH+J28nYRj7kkH9cy0bEpbjlGiqHyFTnfslKccxoZMVf+lnRtc1lQTnJ547gUf9WbttjlNCKmt8UZ2obqBfI05bvyhM+YP6VyscjmEfdSHKO+MSvVlyZKo2mbiyLHNx9jjgXzcxtKrGO8tU6MKfPjeqTHMtKUR3Hby2Qe5Zj5kWKoNLNSTJvi3GTS5edhHrHsKvfoSgOl9SKWZz1RnjVhflw+0nE25fei6hNb4pRjG5Xi0hVbxaut7E6aP6tg0jYfXXHROrHNj0rz29Zhe23lYVXMom7kMozStKyr7RLVI5WXRTT3GqvC2hSsWEFUwTTEwJExmp4zSIFumg9tR4PSZpuxkChzNWifItLXfNLRMcYGM6eTC2LeLkrbithuV+FcZDkPYplQDFWBNV4aYiXPeR/nlRBz4iyKuYZljm8W6wyDyjwUt1IeaMjlM8c6L5fLPENJjnnbPjDE/FoGs457Ti8v1zdmqn99KM3Ypi2qSdoV5HiW6nwuy6V2JZ4HGGI+Q3m9DDEsmXU5zvnEEPVpP7rqQt82aGizjiXayj0mSaNNVxoR6eX6lI9dQ97fRaY80pDbA46xLTaluCCmmddvilterm3eqmor+yr3yqNcD+KgZXJ6cVBdLbUt8Tzbth2lsYomrRv5vJmvVXJ6cYj5rDxr0lR/YhqLYh//jHfOzMzMzMzMzAYw6P8jbmZmZmZmZrbXuSNuZmZmZmZmNiB3xM3MzMzMzMwG5I64mZmZmZmZ2YDcETczMzMzMzMbkDviZmZmZmZmZgNyR9zMzMzMzMxsQO6Im5mZmZmZmQ3IHXEzMzMzMzOzAbkjbmZmZmZmZjYgd8TNzMzMzMzMBuSOuJmZmZmZmdmA3BFv8OrVq9G+fftGT58+raeYmZmZmZmZ7dxKdsQvX748OnToUD22HR1rd7DNzMzMzMxst6xkR/z48eOjly9fFjvbz549Gx08eHB09OjReoqZmZmZmZnZcFayI3727Nnqk053dvv27dHJkyfrMTMzMzMzM7NhrexvxC9dulR1uiN+982T8vPnz1fjek1dA6+0N9GypCGl19zv3bu3Lc3r16/Xc8zMzMzMzMxWuCNOZzu/nn7//v13r6Uz/cKFC6PNzc1qYNlbt25VHelpse65c+eqtJTu1atX3Rk3MzMzMzOzd1a2I67fgMfX03lCfvHixeo781+8eFF9x4EDB6pO+ps3b+opk7tz585obW2tSksYz0/mzczMzMzMbO9a2Y444uvpei39yJEj1Tjya+TM34mHDx9WT8BjmoybmZmZmZmZyUp3xOPr6fG1dOg18o2NjXevkTN/p9bX19+lpyE+eTczMzMzM7O9baU74nS66Vzzenp8LR1PnjwZnTp16l3HfBbYFumamZmZmZmZNVnpjjjofPN6OE/Gz5w5U08djfbv31+9Si78xfS2V9PVYb9x40b1yavux44dq77LtWvXqj/4Fv9AHH+ozX+szczMzMzMzGTlO+L6TThPq+MfUbty5Ur1RFy/5aZjzXgbXmOno83ypJc77vz/5byaTgdd6b5+/bralpmZmZmZmRn2bfIjZjMzMzMzMzMbxMo/ETczMzMzMzNbJO6Im5mZmZmZmQ3IHXEzMzMzMzOzAbkjbmZmZmZmZjYgd8TNzMzMzMzMBuSOuJmZmZmZmdmA3BE3MzMzMzMzG5A74mZmZmZmZmYDckfczMzMzMzMbEDuiJuZmZmZmZkNyB1xMzMzMzMzswHt2Y745cuXR6dPn67HzMzMzMzMzIax0B3xe/fujfbt2/duOHToUD1nNV2/fn3b8TI8ffq0nrt6uBESj5Xjz8jzuAxlosurV6+2rcPQNF3DqpctWzzUbZW/jBuFsXxO0g40rbOTNJcRxzhJvVZ+EKcmarNoSzLFVUNpGbOd0DVCn/NgLo8MuWzn+U1tQlu53+u6Yqc86/Pgp+16R+1THvqUBbPdoDKqurHTa/C29o9pMb1Sf2JRLWxHnBPGuXPnRi9fvhxtbm5Ww7Vr11b2KTaF8OrVq++OlWF9fX107NixpSpQfVFpDhw4sO1YOf54MiOviUtchjLRdMID6R48eHC0sbHxbj0GxO3F4dSpU6OTJ09Wy5gN5cKFC1XZy2j7Hj169K58qh1oK/fCuk1p3rp1612aa2trVZqreGGtE/IkiA/xaKILiIcPH9ZTtqOtIp8UX/KAdshsVnSNMIlYJhlu3rxZz9kq83H+pUuXPqgDXeV+r2tqb4X4kWdty0jf6514Tcxw9uzZeo7Z4ijVjZ1cg7e1f5zzqSu67ueTZZm+FMY7vXDGQaTnVH3Oy/iksznO/Hpsd40viqvjLWmbt2o4To5XGB+fjOqxLaVp0fjit3V+NkRZM8so57Q/lNVYv8cXWcUyTrmmzWqjsqw0YpmeNs1lE+PHsXGMXZQHrNsUE02Py7Zxu2KzpLLc1D6U9F1OSmV20nK/lyheypNSXaeNp60nhl3Xm6X8itPi9swWWZ+6IVq2bZmu9o+6xTKR1lkGC/lE/O7du9UdkqNHj9ZTyrhTqNcQGLhjknFXJi6T6ekJQ37azjhPo/lkPmkhrsMQn1hrn/IrGG1Pnm7fvl3djS45c+ZM9bk0d3ampPh88skn1SfGlWj05MmTemwrtvj444+rz4z544o6Onz4cD2lG2WN7XSVNbNZ0VOSzz//vJ7y3vPnz6vPXIa5W8xT8jY8YR9f9NVj76lu5XpDe9nWLi0j3XGf5CkRy7IO6zZ58eLFtqeJXT766KP6m9nOUfYog/P0xRdfVJ+x7E5a7veSpvZWuGbjTYIrV67UU9pNer1jtqi66kbU5xq8q/1j3v79++uxLYzTH1gGC9kR54Kz7aIINHK8RrURXkHmwjJ2xuk451c81ZkGjSQNn+Yznl8D54L5xIkT1XwKA9u9c+fOu3XYPsuo0RQKlpahk9322gWFJRciURzevHlTfa4q5Uu8gCbOvE5Lniq/qdxNFVYXEjdu3Nh2E6TpJgadENLnJw9mQ6Gs0yaUyrHqeW7/mtoHUbvVdtGn+iFsY94X93vV/fv3q0/f4LPdxOuaOg+WHlRELMsDkK5rL+vX3hJPrjn76nu9w7Wl8jRez5otgj51Q2Z5Df769ev62xY91FuGhw0L+xvxrgtPGq18MUtHmU4tjVgpg+nkxbu7nHTiOOk9fvy4HtvCMrFAkcaDBw/qsfcXWvkiN96JOX/+fDXeViDik+CMhncVcRLRCYWbIPnuFbHlRgfTOakRh7bKrU7M8ePH390E4UTIuvlGCXSxPMnTM7OdoBxS1mO7MwvcDKRNLOHCmrqTn8DTPtp8kB9NbzmZDUHnQA2cR3NnnHGdg2kj4rWNNWtrb0FnhGvHSa4tuq53mB/zk2Vpw90Zt0XSVTeiWV2D86CTuhD7WH33YREsbEc8393IuJilwxXpgpMO2du3b6tpk7ymjPyEqHR3mEZWJy+GvrRPJW1PvHMHdVXQGYknFfJOd9PACYa7whw/y4B4lzrVUazU+v7s2bPqM2r7SYC1y3XAFwP98MrWJE9J+iD2TU/YhXaNehTzjAvFrqdkNjl+ykRbNuubLask3oRliO2+zQftDm1APH/SLugcfPHixSovus6ve11Xe0tngM7IpPV/0usdts9++Ibqzrgtmp0+1yLRrK7BqWtcz3DeVT7KMrzhs5AdcS4OF/V1AiopjawaSzWYO0HhabrxoDi0PTFfBVRcXsMituDkwwmGDroqEhcNxKrpNZamGLFOxlsT5OFnn31WT7FJcKde5Z/BnY5u1GXKHE87dLLgO3QB0PQ6Fe1DU6eZn99QV5SmyjsXdfHvXsT8YsAynKSWCRci3CTu+j3/XhdvwjL0eY3Rdqbrt8bkARez/qlWu672Vn/ng+lahuVpF/he6lhPc72DrjdHrZvbotnpey2CWV+D8zZPzEd+UqztL7qF7Ijz1IhGq60zToDjH7aALnSPHDny7g+OqFGcFV5d5w7OLC9g9VpFyV59fVqv+uc7a22/tdeFRjzRqUzkTjqvrXDR4Y6IDYWyFk8UDHo6zncuAPQGT357hhMcJ5aS+FSLgfIOLuraXjWljeVnMzYb3EihHSf+blds0ZT+GJtNrqu95VotzmfgmpHrDb6XnhZOc70DbtAuS2fDVt8k1yLzvganr8ZbPstgITviNGR6zSCig6W7Ktwl5KIndrp4GsF6NGZkLt/j7yK5A8MyO0G68WlHvsszDT1NzE+89PR91q+yLgLiRn4IHWaOVX9pUR2S+JoQy5Dn6pCwPnfdlA75TpmJd5D5w22INzIoM3RCuOFjtkjUbsWySR3ghBb/B4VY7qdBW6O2cq/h2JveLpgWeUH7FZ9omQ0ltwm0Gfncyds38SECyzNdWJfzYul/c7DZIu7EX9ejfa53uGaK17vkF/P9BoMtm7Zr8Fw3pqE305bm7YbNBTY+aVT/Z5yGcSernrNl3EHdNn98YVnPeY9pcRkh7bw80+I2mM+0jGWUntZhXzC+EKumjy+cq3FoGp9t8r6SrqaNO6j1UquB+MRjLR2j4ta0jPJfsZe4fC4zULky220qw1lsYxhie9JU7kV1K7Y3uW1ZtfZESu2KBsWQ2MZ2QfHMQ1wm54cG5UHT/JiG2bR0zsqDrk9ym9Dn/Foq9/kapavcW7m9zcineL2pdZR/6Hu9E4e2bZrttqa60XYNXqobWj4PWoZ6EqfHurYM9vHPeMdtwXGHaHxSrF79MDMzMzMzs+XljriZmZmZmZnZgBb2vy8zMzMzMzMzW0XuiJuZmZmZmZkNyB1xMzMzMzMzswG5I25mZmZmZmY2IHfEzczMzMzMzAbkjriZmZmZmZnZgNwRNzMzMzMzMxuQO+JmZmZmZmZmA3JH3MzMzMzMzGxA7oibmZmZmZmZDcgdcTMzMzMzM7MBuSNuZmZmZmZmNiB3xHt69erVaN++faPr16/XU5pdvny5WtbMzMzMzMwsW/mOOB3nQ4cO1WP9PH36tOpI0/nOPvnkk/pbu4MHD9bfzMzMzMzMzN7zE/GeDhw4MNrc3BydPXu2ntLs5s2boxcvXtRjZmZmZmZmZu+tTEf89OnT1VPs+Po4T8KvXr06evny5bt5esqt18c18BRc048dO1Z956k28+7du1eNx++IafBdSk/h8/bMzMzMzMxsb1qJjjgdX55A88Sa4fXr11WHm2lra2tVh1rzeLJNp3j//v3vprGMOt88zd7Y2Ki+04FnfukpOB3yW7duvUuD9NSZz9jeo0eP3i27vr6+reNuZmZmZmZme8dKdMTpeMcn0HSm6XA3Yf6VK1fqsdHoyJEj1WfpN+FN3rx5s+134KR39OjReuw90qTDfu3atXrKqOrYsw9mZmZmZma296xER/yzzz4bPXz4sHrlu89fNUd8lV1PwydBx1uvvLc93X779m31efjw4erTzMzMzMzM9raV6IjrD6nxyje/Cadz3PZ0m054fJVdr6JPSuvyxJttNr2abmZmZmZmZiYr88fawCvfdI55Zfz+/fv11A/x9Dy+Kr4TvI7ONk+dOjW6e/duPfW9jz76qPp8/vx59WlmZmZmZmZ720p0xPl9uJ6A88kr4/r/vvlkPKKj/uTJk3ps9MGr6eo867XyEp6qxyfgdO75g20ZT+vppH/++ef1lK0/9OY/1mZmZmZmZrY3rURHnL9Irv9qjE/+Crr+0jmfmscAltfr5AwsH9F5vnTpUtVBZ37plfMHDx68m8/A8vEPwEUsy80CLXvu3Dn/sTYzMzMzM7M9at8m71WbmZmZmZmZ2SBW6jfiZmZmZmZmZovOHXEzMzMzMzOzAbkjbmZmZmZmZjYgd8TNzMzMzMzMBuSOuJmZmZmZmdmA3BE3MzMzMzMzG5A74mZmZmZmZmYDckfczMzMzMzMbEDuiJuZmZmZmZkNyB1xMzMzMzMzswG5I25mZmZmZmY2IHfEO1y+fHm0b9++anj69Gk91czMzMzMzGw67oi3ePXqVfW5ubk52tjYGF27dq0aNzMzMzMzM5uWO+LJ6dOnqwEHDhwY3bx5s/puk4tvE/R5oyAuq4E0ojy/Lc3SMtevX9+2vgbddEFeJu+DWZvdKveHDh3atoxQtuP0OLCOlOYvQtlvOq4uLBuPL+rKoxwznRNw7969bfPiUIoX05v2Y9pjs+WWyxDnnDaUz7g8Q9ZWZqNc5mLZZ504L+9Xn/1APr6mfVkUk7TZedk4cNzoc50Rab6227fNztspxTnvb6mNMmsy67oRqfzmcpvXZVC57Vs30HV+neTY5mrTtjl16lQ1ZJcuXdrc2Niox6wL8Tp48GA9trm5vr6+SXFriyHzWa4Jacb5jDcVYeaRj3mba2tr2/YrY9k4n3HSYD2zLrtV7tkm06W0TEb9iOt07cdumOa4FHOGUl0njbY8evny5QexYPnSeSHK63TtR06T76XlbLWoXFDOoPLWdo6hXGh55LLTp8x2bYd1Y13Tfsa2q2s/0KeOLpKu9qAPXScoNl3XGRHbJ4Z9tslyyiO2FbdRyl++x7zoKgNm0TzqhqgsUqZzG8L02Jb1EesG2O84znfSlTyuupL3cwjL01oOpFQoyCA3XP2pguWKlCtGNmnlUwXPjUKs+Hk++dj3BCmlMmGW7Va5Z12Vd2naF8lpYNL9mLdpjivOJ+a5rjetH/OotF5pXyKdxKVrPxT/mF7TvtlqKbUHpTLSJpe3PmWWZfJ2u7B+27VPU7mP5XqRNdW5Uh61ydcIfa8zYjvAZ2yPs1KbneX94Hs+jtI0s2xedUOYRj0hrTy/tN02uW7ktg/5eGZxbLPiV9M76HWIK1euVJ/W7fnz59Xn4cOHq085efLk6NGjR/XYzn3xxRfV50cffVR9yoULF0bjCl6P7Rw/UTDrslvl/smTJ6PxCWRbOeU705hXcvfu3Wr+0aNH6ymLZ5rjYv74vDY6e/ZsPWW7PnnEJ+ORltf62e3bt0fjE3g91r0fysN8bHjz5k31aauH1yrHF4Sj48eP11O2MM70pleXs9evX1f1QPqU2Vu3bn2w3Tbal08++aT6LMn7cf/+/Q/q7CKbRZtNnB4+fFhdd0xqkmuVPm32ixcvPmhT8nGwr/v376/HzMrmWTd4TZ3ps+pX5brRde2gtu3jjz+uPoVX2fu2wbPkjngLfi/Ayevq1avvfkOwG5m0bHQhmU/GfRr/c+fOvYt1/r1HxrKnTp3ath39pq2tgnPBo20wlH67ElEGTpw4UY+Zle1WuadNKq3TlA7LU6ZLf3xykv2Yt0mPq48+eUT7kPMsLx/RfrDOZ599Vk/pL59PuFCgc2Or6e3bt9VnvgDM4210XXLx4sV6SneZVTnjIlT1m0EPGko0r+lmUmk/KLvUT4a4nVzOF8VO2my5ceNGVW9znLquM/pcq0hbmy2kxzbPnz9fTxlVf+NI+0F+8cm5ww+WrMs86wbXGevr6/VYWd9rkVLdYFqfawfdEBeOlZtZQ3NHvAV3V3iqEYe2CzLbmRxrTiC54sQTPBX8wYMH9Zwt3DS5c+dOPfYhTkBxG9yNpsI3dcZ1sjxz5kz1aTZrsTwyTFPuJ8FTK+STY5/9sA/R3uQbgl30lIELFeFCmZibZVxYqv4fO3asevtiks6UbgAg1m8uYHWOQ/zjRTyxyuWxaz+Yz3o8MdN2WIY2a1XlmxHoc53Rda0SNbXZpKf8ID06N/mJOdsH+QX/AWIbSqlu0N5wvmy6wYdYdxjarkWa6kYbztW0SZ9//nk9ZQv7uxvcEbeFxUmFCsgFqnC3SpWTCs4JSPO5iOCkP8nrtpwwqZClEyLp6uTmGzDb0ZjqAoCh7cmKTWbScj+p/Bp1k9J+2HbqeEz6WirtycbGRnXiVx0iDdqiSZ44LKvY4WOInUH7EOVF9Z9BHeJJxaelpMkF8ePHj+spW500bYPySXmMedNnP/JNKW1zFdsRday7btTn64xJr1Wa2mw6HzE/6OzHv0CtjjptOfN1U8T17T23RfNRqhu0F1xTT3ozqO1apO/1TMY1FWnGvKft2o2HD+6I28zpN2VUukivrfXV9boeJzcqjl5J4S58vLDlhAPuBLf99ymlfWLfdcd/kjtte0W+4++77LtX7rnoLb1OxbTcqePkyMmnz2vUk7wuOw+THFdfffKIdiO/Hq7lc0yaXr3rI79xpQuDtt/krorY4WOgTO8F+rsO+ZXI0t8MaKM3YtRp6Cqz+e+oSNv2KJ88yeXCuUnej1J6TdteBDtts3mixjVCn3yL6U1yrTJJm83NE24MqsNCx5w8VPtEvWN/2/J0r9mrbVGXedQN/e6c8q6yTz2gzPK96WZd07VIU91gm32uHWK+M6BPXZ41d8Rt5vTaZXwdDpx8Jvmtdf6jVF2oZLFSUUHByantVV7Wy5WPhoJGxB1M62u3yr3+0FPEyZNpR44cqadsmeQ16kn3Y9YmOa6++uQRf4wmX3zoAiI/weIiIr96Ny1dhPjG3+qi3nFu0e8vhd9uUy+n1VVmVd+fPXtWfQrlfpYXnlzkclEdaT8WsUO+kzab+kpbFN8yaBOvMya5Vpnmpy9Qecht5SR/sM/2rnnUjfwGBwPX2ZRvvje9IdJ0LdJUN6a9dqDt6lufZ2p88BaMM7X4p/ZtMsRwfMFRj73/b07GFaEaX6//ewE+wXwGYTnmjytpPWVcS8P6UBrjk1c9ZTulEeczHpF+Tpf9dhmwaexWuc/r5P0Ay8dtR332Yzd0HVep/grzcgzQlUelOJVikddr0rQfkeId88BWU1d5U1lQeWN+Lj8q99KnzOZ1cjtCvYjr5zLZZz9K5Zh1SHtRdbUHilOMDfJ6UYwJFCelmSluygsp5auwnzF/kWNdiv2i54ctjnnUjYwyHMsj24jth+pGLuttdQN5na592s16sb21sCojdiszVg0Fm8qgIZ6EcgVWZYtDrIzQOnHIJ66odHLTCTEOUWkbGpoqvFm0G+U+p1M64ajsl/TZj93QdVw6JsW4dBwatAza8gg55vkiAKTRdK7osx95eltbZqsln4fiuUVlJ5a5vDxD1qfMUl7jMrHcl8psbgP67IcukDWU9mPRTNJmQ7FqaiP7xClSerkNUDpN8n6X2qM4v2kZsyazrhsZZTyWSa0fh1JaXXUjp8NxRLkt3M3rnX38M94JMzMzMzMzMxuAfyNuZmZmZmZmNiB3xM3MzMzMzMwG5I64mZmZmZmZ2YDcETczMzMzMzMbkDviZmZmZmZmZgNyR9zMzMzMzMxsQO6Im5mZmZmZmQ3IHXEzMzMzMzOzAbkjbmZmZmZmZjYgd8TNzMzMzMzMBuSOuJmZmZmZmdmA3BE3MzMzMzMzG5A74mZmZmZmZmYDmntH/N69e6N9+/aNXr16VU95j+nXr1+vx6yE+DTFb9EcOnSo2lcNXTimuPzp06frOe9dvnx52zJPnz6t52xhnTi/qTwpjpTHiPTi+hrycvnY8nwz6SqzJV11R+2ohlzOVb41sA9Z3q+8TJ80FlFXbLJc54l91jdNxaypPcjplNo4s5JJz6cSy3dJTlftU59zYZ+6s9smbQ/Q1WbP4lplp2n0yR/ZSbuUr6kYFjGfbXKrWjeiWE+iUv1pwrymMj9tu9xqc87W19c32czLly/rKe8xfW1trR7rxrIHDx6sx/YGjjlmE3FkfGNjo56yGMiXS5cu1WOb1fe24qXjoHwIaZw6daoe20oj5rfKko6d8bjNPF9Ig+l5e2BZppfKp7BPcb+atmPWVWZLuuqO0lAZVd1R20nacZsq07FtbWpHJkljEXXFpiQuD8bb6rdiwfSorV1BzkezvrrahDasS3nOy3fVDZXzWDeyPJ/xWHd22zTtAbGNbV+u/0oj1nHFWIZIo0/+gDRYLm9PusoS+8QytlpUnlaxbkRal/kR02PdyduA0mWI25O8Dt9Ly01q7lcJOrAYAGH6JBd6LDuLg15mKrSlArhbSnlcqlxRrliI6TStzzptJwnWiWVK22lKr8/JrbReaZrtbdOUWZbN5S+nU1q/VH8iThD5hJHTKE2LchqLaJrYZLQXxFtKcclparwpzzU95qtZH33ahCaUZcqv0ogos7lcR33OhVmuO7tt0vagKa4xndL6MY+GSqNP/mg7TelpelsaXecFW06xLEmpXEqfMllaf9JyPYs0pK39y3LbFbdT2qdS/Wvat0kt3G/E4yP/+GoA369evToaH/i7+bzOgPzKAa8wREyLy+h1nbxe6VWEOF9DTD+/xqN9Aq99MD++DlLaRtsrF1oXpDcuHNX3Y8eOVdO1jfwqh44tpjUvT548qfbrwIED9ZRR9Z1pzCt59OjR6OTJk/XYlsOHD1efz58/rwZomrAO65Yo9p988kn1iZs3b45evHhRj00nH4di+vHHH1efZpimzHbVHco0bd7x48fruVsYZ3psb6KYHhjP+/Dw4cPR/v3767EP5TQWzbSxyWJ7AdqLHBfGSVO62pX79+9/kK9mfUxzPgXlnWukzz//vJ6y3a1btz6oKzuV685umqY96NNmz+JaZRZp9OF2yUr2Qt3oav+y169fV3VBqBPjPvHo7Nmz9ZTtvvjii+ozt8t48+ZN9TmtheqI03FcW1urgsFAoNVxpXFhHoHTfIJAp4hO6cbGxrvpZE7ujMdlCDQd3Lwe24odZTq3ly5dejefbTNOY6f50Pz19fVqmVioudi9c+fOu2UQ943v7K/msz/sV6kDfeXKlXcXg9pvpl24cKHaTtzus2fPqn05evRoPWV+2G7pBkNpmnAc+WI3FnAV7DgNbR0HxbWpIrUhVpQ/hlx2yD8uYjgelRvK4hCxteUxTZntqjtv376tPvNNn66bQJTXEydO1GNbF2jUOd2c4/PUqVNV+9Ekp7Fopo1NRv0mFhEn6UgdjtjGtmF98pBB7QpD3/Vt75rmfArOW1yflM5LKnd05GN5zOc6tJ0Ls1Ld2S3TtAd92uxZXKvM8npnkvzJ+rZLtP1t82257IW60db+ZVwDUcYvXrxYT+kv1wXqY75emNRgHfHYeGiI6OCwTLww/Oyzz6pMKnVK5e7dux8E/9q1a1WQo9xx4gSS19PFKvsCOrfxDhKZpkzgk/nqlIMOIMfAXUdh/MGDB/XYVhrxLg77yb4I+8OJjePqSx1P3TnC7du3pypky4bKp/JEfpB/kyDeugmiGyHkSTzBsQzTSfvcuXMflFOzRaI/wHLmzJnqUyjf4EYSYtuVNaWxaji30G5wM1O4AUwbEE+4sY3uQ+eHeJOV8w1th9msqRw31WldiEPlkfMZ5Vx1vc+5MCrVHZufSfOnpE+7xPVq3A7Xo8yP7aHZIulq/0D5VV+BayDK/STX8Xoif+PGjeoTbHfSPkfJYB1xdjZWboaIO7Uso0Ax9LlooUGhMYrr0VnK8itUZFp+TYM7LmxTd2BogOKrYHRudVdGnV6Wj9vukylahkwEhSKmwb5NikKli0UVjlW/iAYVT+WJExP5oQuLaXCyI5aUKeFERx6pDIN8Uv7tRcQ4ltlJLgZsfiiTvJ7F2znxDrJ+4sJ0yrAuvkp1pSmNVaQTcnyLhjZFF58q3zJJPEgjLn/+/Pnqcy+3G0J7odgy7KTNtlHVGaa+dlEZBGWTMvr48eN6ynalc2FUqjs2nK78aTJpu6TOTXzAtErcFi2/Pu0fZV59BQZ1zPtifd38Ullhu1wn5Kfzk1qoV9NpIGKgNNDgtImvs8dhFmLQeZ0n33EpbXfSp6Xq4MWh7c5OCY0pHXgKF6+l58Z2nthO6XdJpd9aCoU3v86hO668LtP0KqherSqhnFAW6ETsRNxnTk6UASqg4slxsf+8ebFXUcZ3Ul5X0TRltqvufPTRR9W4fp8kpd8rsd2mi2NuTlI3NJ38YrlcV9rSWDSTxKaEPKEel8pufirEK/os21dp29pf234DlcFvGL3X1SZk1FmuIajjulbRwwi+07FoKntddaTp/N1Wd3bLNO1BnzZ7Ftcqs0ijpCl/mkzTLnWVkWW3F9qiVa4bTO9q/0r0pvIkN17oY8SyQpvMtrWP01qYjjgNyjRPgsmIpju6bcj8/IdPlKFHjhypvrM/sZMcXzGngGAnTzdUOeIr5dPSzQruWvLkfsjfduoPPkQxliW8/pkrluLAseg1kPhKHXgDYt7HRgWnfEANVb4ZlP+4hNk0Zbar7nCCpCzqLR2h7eJmW8RydKDzxbHqWa6L+Y0gNKWxiCaJTaa/71Hq8JRwjpnkpz6l85nat64LX9vbJj2fUg/ixSGDng7xnY6FLrS5SR/RNpUuwiWeC2XSujOUadqDPm32LK5V5nW9U8qfNtO0S9rvnXY2bPesct3o0/7Ni/p/O35oMd7RuRoHhEfT2/7kuzB9be39fzXF+PgisB7b+tPw48JTj71PK9qo/6Q884TvMZ08H0qL9WVcIKtBmJ+HmC7Lxv0D4zpWji3PZxrpCOnFcZCu9qt0zIzn44HSYijFe57YZltstG/ar1K+daWh2CkN5sf1mc78WKZE83LcYqyheGu5Uppt27G9ravM5vIFxicp96W6w/Ks16Q0P0/rSmMRdcVGdTXGV21RX23LK/2YF9D02EYsY3xtd+QyS7mh/IjKpMp9pnYmyuVYy+j8xzbazoWYtO4MbZr2IMe2T3u7G2n0yR/RceZ5mt7ULpG+vgvz437ZclrlupGRFvOFbeQyTPpxmYh5XWVe8Yp1aVpzb1EVkFLASgfBtDhkBCfPU0ZqyA0J02Imi/ataT3G43qlwLNMTIN9EZbLmakCFKlAaIjb1D5GSiMvqzjk4xiCYqOhqdDHcpDjHyuexPxmiOvnbTLk8pRjq0HbyvvAEPMQuXwx5O2YSVuZVXmL9bar7qBPG1Ea4nJ5Xmwn+qaxiNpio9iqvpfaDA1aJravDKX2NG9Tg9JAbjfiPLM2XW2Cyl9sWyLV5yxfr8T1S21APBf2qTuLINfNtvZA2tps5NiUjnfeaXTlD/Kxa4jb6mqX8j7sxvWkzUcuH6tSNzKlF+Vjz/N1/KVB28rTc/2b1j7+GSdoCX/ciN8Z5PDwKjyvKC7i70h4vWNcWKvXMvwHVMzMzMzMzBbTQv2xtkUUfwNO5/xly++ed5v+qqU74WZmZmZmZovLT8Rb8Nf08l8V3tjY+OAPdy0Kntbzhw+W4Q8tmZmZmZmZ7VXuiJuZmZmZmZkNyK+mm5mZmZmZmQ3IHXEzMzMzMzOzAbkjbmZmZmZmZjYgd8TNzMzMzMzMBuSOuJmZmZmZmdmA3BE3MzMzMzMzG5A74mZmZmZmZmYDckfczMzMzMzMbEDuiJuZmZmZmZkNyB3xKVy/fn106NCheszMzMzMzMysv6XuiL969Wq0b9++D4Y2zKcjXXL58mV3sM3MzMzMzGyulroj/vbt2+pzY2NjtLm5WQ2nTp1q7Ywz//bt2/XYdo8ePRpdvHixHjMzMzMzMzObvZV7Nf3mzZvV571796rP7MKFC6OXL19WT9Ojp0+fVtOPHDlSTzEzMzMzMzObvZXriB84cKD+Vnb27Nnq8/79+9WnPHv2bHTw4MHR0aNHq3FeU4+vu9NRb8Kyp0+frse2lF5znyRNMzMzMzMzW00r1xFX5/bjjz+uPksuXbr0wevpjOu1dDrM+/fvf/e6+9ra2ujYsWPVvGmRJq++K01epydNd8bNzMzMzMz2lpXriPPqeXyyXXL8+PFtr6frtfQzZ85U47zefuXKleo79Lp6fp19Erdu3RrduXOnHhtV+8fv1e/evVtPMTMzMzMzs71gJTriPFnW6968Dv7ixYtqenwNnEEd6fx6ul5Lj6+186q51tvp03A99Y77yfDw4cNqupmZmZmZme0dK9ERj381/cGDB/XU0btpGmJHO76eHl9LB51wOvNaj/RngafucX8Y9MflzMzMzMzMbG9YuVfT+zp//nzVMeavq8fX0sGT6mvXrtVjO/fRRx9Vn8+fP68+zczMzMzMbO/asx1xfqPN6+jnzp2rfqsdn5Yz/cmTJ/XY1ivlbfjNOZ13vYJ+/fr16jfhQto8gWdbEU/e/cfazMzMzMzM9pY92xHHyZMnq88TJ05Un8JfN6cjrd9y81fT2/Cbczra+g3469evP1iHV9BZRmky8Ifl2v6onJmZmZmZma2efZv8UNnMzMzMzMzMBrGnn4ibmZmZmZmZDc0dcTMzMzMzM7MBuSNuZmZmZmZmNiB3xM3MzMzMzMwG5I64mZmZmZmZ2YDcETczMzMzMzMb0MT/fRn//7WZmZmZmZmZdSt1uf1E3MzMzMzMzGxAEz8RNzMzMzMzM7Pp+Ym4mZmZmZmZ2YDcETczMzMzMzMbkDviZmZmZmZmZgNyR9zMzMzMzMxsQO6Im5mZmZmZmQ3IHXEzMzMzMzOzAbkjbmZmZh+4d+/e6OnTp/WYmZmZzdKOOuKvXr0a7du3rxpOnz5djV+/fr06eWs6gzBP077t275t2zJxIJ3dpn1hn4eS49mFZQ4dOlSPTS7mx27gAk/bn9XF3uXLl4vHo+kM8yxfsewvQjnOFIemcrPTMtUklu08sE/zxjENta1ZiuWWOhLLF3nFJ9PasMyQxx3bFfaNcfJf+1sahmxn26iczKIOxLxbxLYgyvWTff87f+fvjD7++OPRD//wD49+7ud+rl5yepTfGNfY/muYZZziMcXyH7fblM95X2OZblvPzMxsEjvqiJ88eXK0trY22tzcHH3++eejgwcPVtPPnj072tjYqL5DncorV65U09fX10c/9VM/Va0H1uO71lE6u4l9HBrxPHXq1OjSpUujFy9e1FPnh/zYzVgfPXq0Otad6nMRf/PmzSq280bZH2I70yIOu+HAgQNVHVd547vGb9261SsPd9JZu3PnTv1tcrvVSaQTS2zUFj179mx07ty5qs0lbkO0EcK+9Okk0YG5evXq6OXLl1X+PnnypBrHgwcPqn0H9V5lIJ4rpjHL/JllTIdqc2Yh1k/KG/v+Ld/yLVUb/e3f/u31UtOjXBw7dmz06NGjapw8YzyWA76z/VnlJ8dUKlvaLnlDOaVjnVHXLl68WH3n+oUyTFy0r2A9jsvMzGxaO+qIcxITTtj5pKcLyIcPH/Y6uZKGLPoThHlQPLkI6nNByIXtkBfji4gOwu3bt+uxrdjpQskmN3SZ0sXu69evq88mPNHqWmYedBNxN7x586b65KkkZfrIkSPVOMgjBqZz86cNy+zkBgxtMTcA+vjiiy/qb1vYbtfNNtp9bgpOg/PK48eP6zGbh5//+Z8f/dbf+lvrsenQ+eV6gM6xbtYglkt9Z968zv8xXdo66gYd8vzGCOeUM2fOVOWL6xeWifVMN/YuXLhQfZqZmU2jtSPOSUivYul7fCWLkxMnTd0ZLl1QqXPOcl13j+N8TthN2Ae2yclT39XR1+uPfNJJ4zsDJ2CW5zvr6DvLsd24XMRFnuaRnmh9Bu23xtkX7UdOj3EtF+crrpz0Sxf/cR32I+ZNnB+nx/3VtDiUaL8Z2vY9bqd0/DHWEmPG95KYZ6Qjcb80nU86CHqq8Rf+wl94t4z2vbQf0rQtUdnSZ16O75peyrP/6//6v97NZ2CfdBzf+73f+256TAf6HvOvtK2YHyyr/YzHrPml/dNympf3I8ZH0/Q9LstyOS1ofZZtogvy8+fPV5+xLqqMkCZPhhmUj9qehr5yGZZ8PGBb1Ef2UcfFp5bTvpRoGQbVj3hsDFHMK7APig2dGMb5BNPj/uo44rExxPIW80Xztf85rTiPfdZbDHyyHzoO5U90+PDh6pNllQYdrKb2vJRGSY4dx8a6xII8YhrisWi5nOf6rnyB4l/an5g3imPcH33X8apsarxJTEPbjevG+hP3T8uwfkyDQZqW1/GrjHdhnf/6v/6v67H3lGZpyGmzTagjy9NmlN4W0LT79+9Xn9OI+X337t166haVZdoSjgF0phlnPXDMxI8yq5s8J06cqD5FDw0497C8mZnZVDY7jE9cPF7cXFtbqwa+jzvX9dz38zUIy6yvr1fftR4D0zQdcV0NXUhby45PhNU+MIBxpo9P6NU4n1oOWo99uHTp0rtllSb7CuZrHmI6mqfv2ramM8RlIpaNaWpdsLzmRXEb7B/jYBrrIG4bMS0dJ/uS8zCmoXn6XtoXLcN6pMd30of2gWWg5aB5bFf7A31nutJjWX2P+8z3mI7yTNsA+5yXjelpnsbztiKlX4qj5sX1ddxxHzRP+8g460DLka72lQF8artt21JsNJ9PaBo0XdvVPMbjMSGWB7av7eR81DL6XkpLyyoNKP04aHnE5fnOsesYSR+KB9O1DT7baB0dh/aV6ZoXv7NP2q72J25L81g+I27aDstov+N35it/mRb3S8vkbWjf8v5oXGnGMqLv2haf8XtcV+sw8F3xYDnGlU/aD+1npvka4nJKuzSvTYwL+8O+6Ph1PDpWlsux0nY5zrgc+GQ8pqn8iLGIy0HjDKzHOlqfT20nrhMxXfvHd7ah7wykrzyKMdU2NE3HofxH0/I6HqaV6BjA53d/93dX6zQt3wf7pHhCcdN+RyzLPMVFNL00KG7CtrS9vK2c91CsdIxsW9snnTgvYjpD3r6ZmVlfvV9Nb3p1UK9Hjk9Y1Xh88iKsOz7xVd9LrziyLmlo6Is0256cN2F78TUzft8u+fVX3QnX59u3b9+9lkY6eiKru+kYn8Sr9DmWuH/cOWdZTeOT8a476qTFctzh53W5uO8Z227zySefVJ/5FVLoFW+2oydNTa5du1Z/60cx++ijj6onZKV81lMQYqry9Pz583f7Rbx4TRc86Yg/ZSjh96lgHdYdX8xV42jaVony6/jx49Uny+lJi9JGfEVemEc5VT6z3Zx/n332Wf1te/7pFfE+2xpfKL4rc6Lj0lNKxQO5DpTwpEhPg/Pr6nE/c1rUEWh/Sm0H0xnYb9bn6ZSepLFNyiB4ehZfyQb5zrqUZbUneo27C3+HAcpL6oHiyxNnPXXmGFRXRMfDtthH5HpEO0C90dNQlqe869j2799ffTKf5Viep3Gq33zX72gnEdsWBrab46590xNkPvNr3XmdUh1T/ONrxZHmjzso1TjHlJ+Sqk70xRsTpEPceKWYY1S5Fh03yyofc3tearcUb9bPdYJ4kCZlhO1D5Vs4TtajjrS1OVFTWRfqBPHlWGOeEDeVQ5W9UpmSuDz5ouPJx1nCMn/mz/yZap28/CRPxHPbof2N+9lFr5GXhlhGSZNypfqnN23acE6C2hC9lg6l07d9MTMzm8RUvxHXBYBe7QInW076+aQrXFSoYzAPk1zUzQrb1MUAF13zxDbo/BDDfKHTRHmh2DPQcdHFWRMdE0Mfk1xQ9UHnTNvvc8G4E0NsSx3tSW9eDCl3LkA8iAvliAvseVBnKnY+KefKk9wxFC6Q6fSQf7NCh0rbLXVAoTh03fCaFHVT225qQ2eFzqG2RQenSelmXRfaJt1YVIccpY4MbaY687Qhbe2I0iLfyYOmZblpRD3TTYA2fdotlmF73Lih3Zy1PmV9EZFv2u88dLWjurHGTYNM09QRHprKhM7nugGfb1ipjFNvm9oKMzOzLjv6Y208IdDvv8BJTE+cSiZ90sMF0E47AKWT/TQ4Eeukq5OznqpyQ6LPRR3rkoaW5bPPiZyLW7bBhRoXg/HJZh/KF10o6eI3U96po196u6Gv/JRfMbtx40b1SbnJMdMFmp6esx8M+oNeLK/OQZ8nHXrqqSdNsYPTtK0S7afiTudR22d/NF/7mXFRR75RFqe52J5kW5FuTulJv+LRF/lPPipuOU+76PeosY3IVB7oYKlTrqf9bK+0LvnEsXE8uS70bTO0Hp0GxUVPxtlm6Vi1L5SdfGEu5DV1mrxWGtRddU70hJaY0iFmeT45Hi0/Tb1TG0KbrDISb5Qi7puWmWZbxJ8Y5/SFp9FKX5/5rYaMtof9a+posy22y40DjqH09grx49hoa+LT5S5q91g/b1flk7LZ5/zV1uZEfct6m7Yy1YQ4Et+mtm4e9FRZKKu6qRHLkL5zc6LtGNrEMo4+N5N0E5I3YDinx7aV9lrpxZjp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style=\"width: 844px; height: 443.813px;\" width=\"844\" height=\"443.813\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"social support, frequency of friend / family visits, leisure / social activities, ability to talk, Osteoarthritis, Mendelian randomization, genome-wide association study, causal relationship.","lastPublishedDoi":"10.21203/rs.3.rs-4308864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4308864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e We aimed to study whether social support (SS), including the frequency of friend / family visits (FFV), leisure / social activities (LSA), and ability to talk (AC) are causally associated with Osteoarthritis (OA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data of FFV, LSA, AC and OA in the European population was obtained from the UK Biobank public genome-wide association study (GWAS) summary statistics included in the Mrbase.Three Mendelian randomization (MR) methods, including Inverse variance weighted method(IVW), Weighted median method(WM) and MR-Egger regression were used to evaluate the potential causal effect of FFV, LSA, AC on OA ，and the potential causal effect of OA on FFV, LSA, AC. Heterogeneity was tested using the Cochran's Q test and the I\u003csup\u003e2 \u003c/sup\u003estatistic. A leave-one-out analysis and test for directional level pleiotropy by MR-Egger regression test served as a sensitivity analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e When assessing the causal effect of SS on OA, the IVW method supports causal relationship between FFV and OA risk (beta = -0.02883, SE=0.01293, P=0.02574), and causal relationship between LSA and OA risk (beta =-0.2255, SE=0.067, P =0.0007641) ; The WM method supports a causal relationship between FFV and OA risk (beta = -0.02968, SE=0.01272, P =0.01965), No significant causal relationship between LSA and OA risk was supported by the WM method (beta = -0.1748, SE=0.09266, P=0.05929); The MR-Egger regression did not support a significant causal relationship between FFV, LSA and OA risk. However, the evaluation did not find a causal relationship between AC and OA, nor find a causal relationship between OA and FFV, LSA, AC.The Cochran's Q test and I\u003csup\u003e2\u003c/sup\u003e statistics of FFV showed some heterogeneity (MR Egger P=0.003158, I\u003csup\u003e2\u003c/sup\u003e=0.53452; IWW P = 0.00282, I\u003csup\u003e2\u003c/sup\u003e=0.53075), and the Cochran's Q test and I\u003csup\u003e2\u003c/sup\u003e statistics of LSA showed low heterogeneity (MR Egger P=0.2916, I\u003csup\u003e2\u003c/sup\u003e=0.100719; IWWP = 0.4652, I\u003csup\u003e2\u003c/sup\u003e=0.306336). MR-Egger regression analyses of FFV, LSA on OA showed horizontal pleiotropy unlikely to affect the assessment results (FFV intercept =0.0011, P =0.369; LSA intercept =0.0037, P = 0.65).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The results of MR analysis support that \u0026nbsp;FFV and LSA may be causally associated with an decreased risk of OA.FFV and LSA may reduce or delay the development of OA.\u003c/p\u003e","manuscriptTitle":"Causal association between social support and Osteoarthritis: a two- sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 15:21:30","doi":"10.21203/rs.3.rs-4308864/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":"22e072b7-9231-43fc-ad1d-7d2e5c8f5b71","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-14T09:59:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 15:21:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4308864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4308864","identity":"rs-4308864","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}