IntroductionThis study aims to investigate patterns of antibiotic treatment-seeking, describe current levels of and drivers for antibiotic use for common infections (respiratory tract and urinary tract infections) and test the feasibility of determining the prevalence and epidemiology of antimicrobial resistance (AMR) in rural areas of Anhui province, in order to identify potential interventions to promote antibiotic stewardship and reduce the burden of AMR in China.Methods and analysisWe will conduct direct observations, structured and semistructured interviews in retail pharmacies, village clinics and township health centres to investigate treatment-seeking and antibiotic use. Clinical isolates from 1550 sputum, throat swab and urine samples taken from consenting patients at village and township health centres will be analysed to identify bacterial pathogens and ascertain antibiotic susceptibilities. Healthcare records will be surveyed for a subsample of those recruited to the study to assess their completeness and accuracy.Ethics and disseminationThe full research protocol has been reviewed and approved by the Biomedical Ethics Committee of Anhui Medical University (reference number: 20170271). Participation of patients and doctors is voluntary and written informed consent is sought from all participants. Findings from the study will be disseminated through academic routes including peer-reviewed publications and conference presentations, via tailored research summaries for health professionals, health service managers and policymakers and through an end of project impact workshop with local and regional stakeholders to identify key messages and priorities for action.

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Whole-genome and exome sequence data can be cost-effectively generated for the detection of rare-variant (RV) associations in families. Causal variants that aggregate in families usually have larger effect sizes than those found in sporadic cases, so family-based designs can be a more powerful approach than population-based designs. Moreover, some family-based designs are robust to confounding due to population admixture or substructure. We developed a RV extension of the generalized disequilibrium test (GDT) to analyze sequence data obtained from nuclear and extended families. The GDT utilizes genotype differences of all discordant relative pairs to assess associations within a family, and the RV extension combines the single-variant GDT statistic over a genomic region of interest. The RV-GDT has increased power by efficiently incorporating information beyond first-degree relatives and allows for the inclusion of covariates. Using simulated genetic data, we demonstrated that the RV-GDT method has well-controlled type I error rates, even when applied to admixed populations and populations with substructure. It is more powerful than existing family-based RV association methods, particularly for the analysis of extended pedigrees and pedigrees with missing data. We analyzed whole-genome sequence data from families affected by Alzheimer disease to illustrate the application of the RV-GDT. Given the capability of the RV-GDT to adequately control for population admixture or substructure and analyze pedigrees with missing genotype data and its superior power over other family-based methods, it is an effective tool for elucidating the involvement of RVs in the etiology of complex traits.

The research aims to prioritize the pandemic's impact on the financial markets of developed and developing economies using a multi-criteria decision-making approach. The results revealed that COVID-19's pandemic effects on financial markets differ between developed and developing nations. COVID-19 pandemic affects developed countries' financial markets more through supply reduction, demand reduction, and economic instability. Regarding developing nations, confidence and expectations, changes in consumption patterns, and the bandwagon effect are the three most significant impacts of COVID-19 pandemic on financial markets. The best decisions to lower the effect of COVID-19 pandemic on developed nations' financial markets are the declaration of the stimulus package and support of smalland-medium-sized enterprises. Contrastingly, in developing countries, support for vulnerable households and declaration of the stimulus package are the best decisions to combat COVID-19's negative impact on their financial markets. As practical policy implications for lowering COVID-19's negative impact on financial markets, the promotion of new financing instruments, reconstruction of the relationship between public and private sectors, and support of vulnerable households and enterprises are highly recommended.

To analyze family-based whole-genome sequence (WGS) data for complex traits, we developed a rare variant (RV) non-parametric linkage (NPL) analysis method, which has advantages over association methods. The RV-NPL differs from the NPL in that RVs are analyzed, and allele sharing among affected relative-pairs is estimated only for minor alleles. Analyzing families can increase power because causal variants with familial aggregation usually have larger effect sizes than those underlying sporadic diseases. Differing from association analysis, for NPL only affected individuals are analyzed, which can increase power, since unaffected family members can be susceptibility variant carriers. RV-NPL is robust to population substructure and admixture, inclusion of nonpathogenic variants, as well as allelic and locus heterogeneity and can readily be applied outside of coding regions. In contrast to analyzing common variants using NPL, where loci localize to large genomic regions (e.g., >50 Mb), mapped regions are well defined for RV-NPL. Using simulation studies, we demonstrate that RV-NPL is substantially more powerful than applying traditional NPL methods to analyze RVs. The RV-NPL was applied to analyze 107 late-onset Alzheimer disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with WGS data, and statistically significant linkage (LOD R 3.8) was found with RVs in PSMF1 and PTPN21 which have been shown to be involved in LOAD etiology. Additionally, nominally significant linkage was observed with RVs in ABCA7, ACE, EPHA1, and SORL1, genes that were previously reported to be associated with LOAD. RV-NPL is an ideal method to elucidate the genetic etiology of complex familial diseases.