The new epidemic Middle East Respiratory Syndrome (MERS) is caused by a type of human coronavirus called MERS-CoV which has global fatality rate of about 30%. We are investigating potential antiviral therapeutics against MERS-CoV by using host microRNAs (miRNAs) which may downregulate viral gene expression to quell viral replication. We computationally predicted potential 13 cellular miRNAs from 11 potential hairpin sequences of MERS-CoV genome. Our study provided an interesting hypothesis that those miRNAs, that is, hsa-miR-628-5p, hsa-miR-6804-3p, hsa-miR-4289, hsa-miR-208a-3p, hsa-miR-510-3p, hsa-miR-18a-3p, hsa-miR-329-3p, hsa-miR-548ax, hsa-miR-3934-5p, hsa-miR-4474-5p, hsa-miR-7974, hsa-miR-6865-5p, and hsa-miR-342-3p, would be antiviral therapeutics against MERS-CoV infection.
Purpose Considering the existing evidence on the impact of female board members on the default risks of an organization, the purpose of this study is to investigate the effect of board gender diversity, alongside institutional characteristics and macroeconomic factors, on the financing costs of microfinance institutions (MFIs). Design methodology approach This study collected unbalanced panel data of 1,190 unique MFIs between 2010 and 2018 from the World Bank. The collected data, which covers a total of 95 developing and emerging countries, was thereafter analyzed using the pooled ordinary least squares and random effects model. To overcome endogeneity and omitted variable bias (e.g. time-invariant variables), the authors have also used the generalized method of moments and fixed effects model, respectively. Different proxies of board gender diversity and sub-sample analysis by regions were further undertaken to examine the robustness of the obtained results. Findings The findings of this study revealed that board gender diversity has a statistically significant negative effect on the financing costs of MFIs. This suggests that a gender-diverse board can generate cheaper funding for MFIs by minimizing their default risks through effective monitoring and strategic management. Furthermore, the negative impact of board gender diversity on financing costs appears to be more pronounced when there is a minimum of two female board members in the boardroom of MFIs. The results of this study remain consistent and valid regardless of alternate model specifications (e.g. sub-sample analysis, use of alternative proxies of board gender diversity and application of different estimators) and endogeneity issues. Ultimately, the findings in this study reiterate the importance of promoting and implementing gender diversity in the boardroom to minimize the financing costs of MFIs. Originality value This study investigated the relationship between board gender diversity and financing costs of MFIs by using relatively recent and global data. The minimum number of female board members required to significantly reduce the financing costs of MFIs was also identified.
The Banking sector of Bangladesh is trapped in a gridlock of non-performing loans (NPLs) so much so that NPL accounts for 11.60 percent of the total volume of classified loans. This problem has started to be widening with an evil trend of loan embezzlement among the industrial borrowers in our country. Frequent scam series in banking industry is surely a red light and unfortunately the commercial banks are highly surrounded by it. The goal of the study is to analyze the impact of non-performing loan (NPL) on profitability where in this study considered net interest margin (NIM). This paper attempts to find out the time series scenario of non-performing loans (NPLs), its growth, provisions and relation with banks profitability by using some ratios and a linear regression model of econometric technique. The empirical results represent that non-performing loan (NPL) as percentage of total loans on listed banks in Dhaka Stock Exchange (DSE) is very high and they holds more than 50 % of total non-performing loans (NPLs) of the listed 30 banks in Dhaka Stock Exchange (DSE) for year 2008 to 2013. Moreover it is one of the major factors of influencing banks profitability and it has statistically significant negative impact on net profit margin (NPM) of listed banks for the study periods.
Overdispersion in count data analysis is very common in many practical fields of health sciences. Ignorance of the presence of overdispersion in such data analysis may cause misleading inferences and thus lead to incorrect interpretations of the results. Researchers should account for the consequences of overdispersion and need to select the correct choice of models for the analysis of such data. In this paper, Generalized Linear Models (GLMs) are applied in modelling and analysis of antenatal care (ANC) count data extracted from the Bangladesh Demographic and Health Survey (BDHS) 2014. Pearson chi-square and different score tests are used to investigate the effect of overdispersion in the analysis. Overdispersion is found to be significant in the antenatal health care count data and so appropriate modelling is used to produce valid inferences for the regression parameters. The zero-truncated negative binomial regression (0-NBR) is found to be the best choice for analysing such data while excluding zero counts. Study findings reveal that place of residence, order of birth, exposure to mass media, wealth index and education of mother have significant impacts on the ANC status of women during pregnancy in Bangladesh.
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