Gender-specific association between night-work exposure and type-2 diabetes: results from longitudinal study of adult health, ELSA-Brasil

Objectives Diabetes is a multifactorial disease of increasing prevalence. The literature suggests an impact of night work on metabolic components, though the relationship with diabetes is unclear. Our aim was to investigate gender-specific associations between night work and type-2 diabetes (DM2) or impaired glucose tolerance (IGT) using baseline data of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Methods The cohort comprised 15 105 civil servants, aged 35–74 years. Baseline assessments (2008–2010) included clinical and laboratory measurements and interviews on sociodemographic, occupational, and health characteristics. Results In the baseline sample (N=14 427), 19.6% were classified as having DM2 and 20.5% as having IGT. Mean age was 52.1 (SD 9.1) years. A total of 2041 participants worked at night for 1–20 years and 687 for >20 years. Among women exposed to night work for >20 years compared with no night work after adjustments for potential confounders, including obesity, the odds ratios (OR) derived from multinomial logistic regression for DM2 and IGT were 1.42 [95% confidence interval (95% CI) 1.39–1.45] and 0.96 (95% CI 0.94–0.99), respectively. Among men exposed to night work for >20 years compared with no night work, the OR for DM2 and IGT were 1.06 (95% CI 1.04–1.08) and 0.99 (95% CI 0.98–1.01), respectively. Conclusions The association between years of night work and diabetes is stronger among women than men. Longitudinal studies from ELSA-Brasil will be able to corroborate or refute these findings

Growth of a common planktonic diatom quantified using solid medium culturing

The ability to grow on solid culture medium is a pre-requisite for a successful microbial genetic model organism. Skeletonema marinoi, a bloom-forming, planktonic marine microalga, is widely used in ecological, evolutionary and population genetics studies. We have tested and confirmed the ability of this common organism to grow on solid culture medium (agar) under experimentally manipulated conditions. We established a protocol for quantifying growth characteristics - length of lag phase, growth rate, maximum biomass yield - on agar medium. The procedure was tested under experimental treatments and the resulting growth changes correlated with those observed in standard liquid culture. The ability to grow on solid medium broadens the use of S. marinoi as a molecular model, where agar is routinely used for various purposes (growth, selection, storage); and the possibility to quantify colony growth opens the way for high throughput, automated, or semi-automated phenotyping solutions

Copula Theory and Regression Analysis

Researchers are often interested to study in the relationships between one variable and several other variables. Regression analysis is the statistical method for investigating such relationship and it is one of the most commonly used statistical Methods in many scientific fields such as financial data analysis, medicine, biology, agriculture, economics, engineering, sociology, geology, etc. But basic form of the regression analysis, ordinary least squares is not suitable for actuarial applications because the relationships are often nonlinear and the probability distribution of the response variable may be non-Gaussian distribution. One of the method that has been successful in overcoming these challenges is the generalized linear model (GLM), which requires that the response variable have a distribution from the exponential family. In this research work, we study copula regression as an alternative method to OLS and GLM. The major advantage of a copula regression is that there are no restrictions on the probability distributions that can be used. First part of this study, we will briefly discuss about copula regression by using several variety of marginal copula functions and copula regression is the most appropriate method in non Gaussian variable(violated normality assumption) regression model fitting. Also we validated our results by using real world example data and random generated (50000 observations) data. Second part of this study, we discussed about multiple regression model based on copula theory, and also we derived multiple regression line equation for Multivariate Non-Exchangeable Generalized Farlie-Gumbel-Morgenstern (FGM) copula function