Impact of regulatory capital on bank interest margins: Moderating role of default risk.

The recent pandemic and aftermath debate regarding bank interest margins deserve special attention and have become policy dialogue in emerging economies. However, the previous literature's findings were largely inconclusive and ignored influential variables such as the impact of default risk on bank interest margins. Using a two-step system GMM estimation considering 32 Bangladeshi commercial banks from 2000 to 2022, we produce robust evidence that higher regulatory capital restrictions reduce the bank interest margin, while increased default risk induces the bank interest margin. The impact intensity during the COVID pandemic is higher than in the pre-COVID period. Moreover, we find the synergy effect of regulatory capital and default risk assists in reducing the bank interest margin. Bank margin persistently fell during the capital market crash period, whereas it rose in the financial crisis period. We cast several robustness tests to confirm our main findings. These findings could generate important implications for bank stakeholders and policymakers.

Ordinal Statistical Models of Physical Activity Levels from Accelerometer Data.

Improvements in accelerometer technology has led to new types of data on which more powerful predictive models can be built to assess physical activity. This paper explains and implements ordinal random forest and partial proportional odds models which both take into account the ordinality of responses given explanatory accelerometer data. The data analyzed comes from 28 adults performing activities of daily living in two visits while wearing accelerometers on the ankle, hip, right and left wrist. The first visit provided training data and the second testing data so that an independent sample, cross-validation approach could be used. We found that ordinal random forest produces similar accuracy rates and better linearly weighted kappa values than random forest. On the testing set, the ankle produced the best accuracy rates (33.3%), followed by the left wrist (34.7%), hip (36.9%) and then the right wrist (37.3%) using the best performing decision model for a four-activity level response. Linearly weighted kappa values indicated substantial agreement. For a two-activity level response, the error rates on the ankle, hip, left wrist and right wrist were 15.5%, 15.9%, 16.5% and 18.8%, respectively. The partial proportional odds model had significant goodness of fit (p < 0.0001) and provided interpretable coefficients (at p = 0.05), but there was significant variability in accuracy. These models can be used on accelerometer data collected during exercise studies and levels of activity can be assessed without direct observation. This work also can lead to theoretical improvements of current modeling techniques that are used for this purpose.

Pseudouridine synthase 7 impacts Candida albicans rRNA processing and morphological plasticity.

RNA can be modified in over 100 distinct ways, and these modifications are critical for function. Pseudouridine synthases catalyse pseudouridylation, one of the most prevalent RNA modifications. Pseudouridine synthase 7 modifies a variety of substrates in Saccharomyces cerevisiae including tRNA, rRNA, snRNA, and mRNA, but the substrates for other budding yeast Pus7 homologues are not known. We used CRISPR-mediated genome editing to disrupt Candida albicans PUS7 and find absence leads to defects in rRNA processing and a decrease in cell surface hydrophobicity. Furthermore, C. albicans Pus7 absence causes temperature sensitivity, defects in filamentation, altered sensitivity to antifungal drugs, and decreased virulence in a wax moth model. In addition, we find C. albicans Pus7 modifies tRNA residues, but does not modify a number of other S. cerevisiae Pus7 substrates. Our data suggests C. albicans Pus7 is important for fungal vigour and may play distinct biological roles than those ascribed to S. cerevisiae Pus7.

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r = 0.71-0.88, RMSE: 1.11-1.61 METs; p > 0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r = 0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r = 0.88, RMSE: 1.10-1.11 METs; p > 0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r = 0.88, RMSE: 1.12 METs. Linear models-correlations: r = 0.86, RMSE: 1.18-1.19 METs; p < 0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r = 0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r = 0.71-0.73, RMSE: 1.55-1.61 METs; p < 0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

A two-step integrated approach to detect differentially expressed genes in RNA-Seq data.

One of the primary objectives of ribonucleic acid (RNA) sequencing or RNA-Seq experiment is to identify differentially expressed (DE) genes in two or more treatment conditions. It is a common practice to assume that all read counts from RNA-Seq data follow overdispersed (OD) Poisson or negative binomial (NB) distribution, which is sometimes misleading because within each condition, some genes may have unvarying transcription levels with no overdispersion. In such a case, it is more appropriate and logical to consider two sets of genes: OD and non-overdispersed (NOD). We propose a new two-step integrated approach to distinguish DE genes in RNA-Seq data using standard Poisson and NB models for NOD and OD genes, respectively. This is an integrated approach because this method can be merged with any other NB-based methods for detecting DE genes. We design a simulation study and analyze two real RNA-Seq data to evaluate the proposed strategy. We compare the performance of this new method combined with the three [Formula: see text]-software packages namely edgeR, DESeq2, and DSS with their default settings. For both the simulated and real data sets, integrated approaches perform better or at least equally well compared to the regular methods embedded in these [Formula: see text]-packages.

Low-dose risk assessment for arsenic: a meta-analysis approach.

We conducted a meta-analysis to explore dose-response relationships for bladder and lung cancers when people are chronically exposed to low doses of arsenic. We searched electronic databases for articles published through 2010. Ten studies on bladder cancer and ingested arsenic exposure and five studies on lung cancer and ingested arsenic exposure fit our selection criteria. We also investigate the sensitivity of the absolute risk of lung and bladder cancer under different underlying prevalence measures. Males have a higher risk of bladder cancer than do females at all maximum contamination levels. The absolute risk of bladder cancer and lung cancer from ingested arsenic correlates highly with smoking rates. For a maximum contamination level of 10 µg/L, we estimate that there are about 2.91 additional bladder cancer cases per 100,000 people and, considering studies since 2000, we estimate that there are about 4.51 additional lung cancer cases per 100,000 people.

Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized poisson regression model.

BACKGROUND: Malnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable. METHODS: The data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of households. A total of 4,460 under-five children is analysed using various statistical techniques namely Chi-square test and GPR model. RESULTS: The GPR model (as compared to the standard Poisson regression and negative Binomial regression) is found to be justified to study the above-mentioned outcome variable because of its under-dispersion (variance < mean) property. Our study also identify several significant predictors of the outcome variable namely mother's education, father's education, wealth index, sanitation status, source of drinking water, and total number of children ever born to a woman. CONCLUSIONS: Consistencies of our findings in light of many other studies suggest that the GPR model is an ideal alternative of other statistical models to analyse the number of under-five malnourished children in a family. Strategies based on significant predictors may improve the nutritional status of children in Bangladesh.

Intimate partner violence among Latinas in eastern North Carolina.

This article explores the correlates of intimate partner violence (IPV) among rural, southern Latinas. A sample of 1,212 women in blue-collar work sites in rural North Carolina completed a questionnaire assessing IPV and other social, demographic, and health-related variables. Social and demographic correlates of IPV were examined. Adult lifetime prevalence of IPV in Latinas was 19.5%, similar to that of non-Latinas. As compared to Latinas who did not experience IPV and non-Latinas who experienced IPV, Latinas who experienced IPV were more likely to lack social support and to have children in the home. Agencies that provide services to victims of IPV in the rural South need to be prepared to meet the unique needs of Latina immigrants.

Positive affect, exercise and self-reported health in blue-collar women.

OBJECTIVES: To determine if positive affect is related to the self-reported health and health behaviors of blue-collar women. METHODS: Analysis of baseline survey data of 1093 women participating in a worksite health promotion intervention at 12 workplaces in 5 rural counties. RESULTS: Positive affect was related to women's self-reported health and exercise. Also, positive coping behaviors were related to exercise. CONCLUSIONS: These findings suggest that incorporating strategies to encourage positive emotional states and positive coping into health promotion interventions might be helpful for improving women's levels of physical activity and overall reported health.