Zeynep Kalaylioglu

Impacts of salinity and fish-exuded kairomone on the survival and macromolecular profile of Daphnia pulex

Global warming is already causing salinization of freshwater ecosystems located in semi-arid regions, including Turkey. Daphnids, which are important grazers on phytoplankton and a major food source for fish and invertebrates, are sensitive to not only changes in salinity levels, but also presence of predators. In this study, the interactive effect of salinity toxicity (abiotic factor) with predation pressure mimicked by the fish-exuded kairomone (biotic factor) and the effect of salt acclimation on daphnids were investigated. Impacts of these stressors on daphnid survival, life history and molecular profile were observed. The presence of the kairomone antagonistically alters the effect of salinity, as observed from the 24- and 48-h LC50 values and survival results. Molecular findings provided solid evidence to this antagonism at even lower salt concentrations, for which antagonism was not evident with organismal data. Fish predation counterbalances the negative effect of salinity in terms of reserve energy density. Therefore, it is important to investigate multiple stressor effects in ecotoxicological bioassays complemented with molecular techniques. The single effect of increasing salinity resulted in increased mortality, decreased fecundity, and slower somatic growth in Daphnia, despite their acclimation to salinity. This insignificance of acclimation indicates that Daphnia do not have any physiological mechanisms to buffer the adverse effects of salinity, making it a very crucial factor. Salinity-induced reduction in population growth rate of freshwater keystone species Daphnia—despite acclimation—indicates that global warming-induced salinity may cascade through the food web and lead to dramatic environmental consequences in the structure of lake ecosystems.

One-hour versus two-hour postprandial blood glucose measurement in women with gestational diabetes mellitus: which is more predictive?

The purpose of this study is to investigate postprandial 1-h (PP1) and 2-h (PP2) blood glucose measurements’ correlation with adverse perinatal outcomes. This prospective cohort study consisted of 259 women with gestational diabetes mellitus. During each antenatal visit, HbA1c and fasting plasma glucose (FPG) as well as plasma glucose at PP1 and PP2 were analyzed. There were 144 patients on insulin therapy and 115 patients on diet therapy. A total of 531 blood glucose measurements were obtained at different gestational ages between 24 and 41 gestational weeks. PP2 plasma glucose measurements (but not PP1) were positively correlated with fetal macrosomia. But on adjusted analysis, neither PP1 nor PP2 measurements predicted perinatal complications. In addition to PP1 and PP2, neither FPG nor HbA1c were able to predict perinatal complications or fetal macrosomia when controlled for confounding factors except for a positive correlation between fetal macrosomia and HbA1c in patients on diet therapy. Postprandial 1-h and postprandial 2-h plasma glucose measurements were not superior to each other in predicting fetal macrosomia or perinatal complications. Based on our findings, it can be concluded that both methods may be suitable for follow-up as there are no clear advantages of one measurement over the other.

Interleukin-28 gene polymorphisms may contribute to HBsAg persistence and the development of HBeAg-negative chronic hepatitis B.

Aim of this study was to investigate whether a potential association exists between several single nucleotide polymorphisms (SNPs) of the IL‐28B gene (rs12979860, rs1188122, rs8099917, rs8105790, rs12980275) and HBsAg persistence. Further, a potential effect on the development of HBeAg‐negative CHB vs. inactive HBsAg carrier state was assessed in a genotype D HBV cohort. A cohort of chronic HDV patients was also used to see if they behave differently compared to chronic HBV patients.

Cancer-testis gene expression is associated with the methylenetetrahydrofolate reductase 677 C>T polymorphism in non-small cell lung carcinoma

BackgroundTumor-specific, coordinate expression of cancer-testis (CT) genes, mapping to the X chromosome, is observed in more than 60% of non-small cell lung cancer (NSCLC) patients. Although CT gene expression has been unequivocally related to DNA demethylation of promoter regions, the underlying mechanism leading to loss of promoter methylation remains elusive. Polymorphisms of enzymes within the 1-carbon pathway have been shown to affect S-adenosyl methionine (SAM) production, which is the sole methyl donor in the cell. Allelic variants of several enzymes within this pathway have been associated with altered SAM levels either directly, or indirectly as reflected by altered levels of SAH and Homocysteine levels, and altered levels of DNA methylation. We, therefore, asked whether the five most commonly occurring polymorphisms in four of the enzymes in the 1-carbon pathway associated with CT gene expression status in patients with NSCLC.MethodsFifty patients among a cohort of 763 with NSCLC were selected based on CT gene expression status and typed for five polymorphisms in four genes known to affect SAM generation by allele specific q-PCR and RFLP.ResultsWe identified a significant association between CT gene expression and the MTHFR 677 CC genotype, as well as the C allele of the SNP, in this cohort of patients. Multivariate analysis revealed that the genotype and allele strongly associate with CT gene expression, independent of potential confounders.ConclusionsAlthough CT gene expression is associated with DNA demethylation, in NSCLC, our data suggests this is unlikely to be the result of decreased MTHFR function.

Bayesian estimation of crop yield function: drought based wheat prediction model for tigem farms

Drought is one of the biggest threats that affects agriculture. Based on recent climatic observations and future projections, drought continues to increase its harmful impact on agricultural productivity especially in the arid areas of Turkey. Wheat farming in these arid and semi-arid areas such as Central Anatolia depends heavily on precipitation, thus monitoring for drought is needed. The timing of precipitation is also as important as its quantity. This study makes use of a crop and location specific model developed by FAO to simulate water related variables such as evapotranspiration, water deficiency and water satisfaction index to estimate the crop yield function for rain-fed wheat production in the arid regions of Turkey. A spatio-temporal yield model is estimated by Bayesian method known as Markov Chain Monte Carlo. By standardizing the simulated variables over normalized difference vegetation index (NDVI), impact of drought related variables on wheat yield is studied and two variables are found. Use of NDVI, as a numeraire, comes in handy in many ways. For actual evapotranspiration estimate, it strengthens separation between evaporation and transpiration and, for water deficiency, it better represents the drought properties of farms for the terrain chosen.

A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements

Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate variance prior in general lead to reduced bias (smaller relative bias) and improved efficiency (smaller interquartile range) in the posterior inference compared with the analysis with independent variance priors.

Performances of Bayesian model selection criteria for generalized linear models with non-ignorably missing covariates

This article deals with model comparison as an essential part of generalized linear modelling in the presence of covariates missing not at random (MNAR). We provide an evaluation of the performances of some of the popular model selection criteria, particularly of deviance information criterion (DIC) and weighted L (WL) measure, for comparison among a set of candidate MNAR models. In addition, we seek to provide deviance and quadratic loss-based model selection criteria with alternative penalty terms targeting directly the MNAR models. This work is motivated by the need in the literature to understand the performances of these important model selection criteria for comparison among a set of MNAR models. A Monte Carlo simulation experiment is designed to assess the finite sample performances of these model selection criteria in the context of interest under different scenarios for missingness amounts. Some naturally driven DIC and WL extensions are also discussed and evaluated.

Bayesian semiparametric models for nonignorable missing mechanisms in generalized linear models

Semiparametric models provide a more flexible form for modeling the relationship between the response and the explanatory variables. On the other hand in the literature of modeling for the missing variables, canonical form of the probability of the variable being missing (p) is modeled taking a fully parametric approach. Here we consider a regression spline based semiparametric approach to model the missingness mechanism of nonignorably missing covariates. In this model the relationship between the suitable canonical form of p (e.g. probit p) and the missing covariate is modeled through several splines. A Bayesian procedure is developed to efficiently estimate the parameters. A computationally advantageous prior construction is proposed for the parameters of the semiparametric part. A WinBUGS code is constructed to apply Gibbs sampling to obtain the posterior distributions. We show through an extensive Monte Carlo simulation experiment that response model coefficent estimators maintain better (when the true missingness mechanism is nonlinear) or equivalent (when the true missingness mechanism is linear) bias and efficiency properties with the use of proposed semiparametric missingness model compared to the conventional model.

A cluster tree based model selection approach for logistic regression classifier

ABSTRACT Model selection methods are important to identify the best approximating model. To identify the best meaningful model, purpose of the model should be clearly pre-stated. The focus of this paper is model selection when the modelling purpose is classification. We propose a new model selection approach designed for logistic regression model selection where main modelling purpose is classification. The method is based on the distance between the two clustering trees. We also question and evaluate the performances of conventional model selection methods based on information theory concepts in determining best logistic regression classifier. An extensive simulation study is used to assess the finite sample performances of the cluster tree based and the information theoretic model selection methods. Simulations are adjusted for whether the true model is in the candidate set or not. Results show that the new approach is highly promising. Finally, they are applied to a real data set to select a binary model as a means of classifying the subjects with respect to their risk of breast cancer.

Joint prior distributions for variance parameters in Bayesian analysis of normal hierarchical models

In random effect models, error variance (stage 1 variance) and scalar random effect variance components (stage 2 variances) are a priori modeled independently. Considering the intrinsic link between the stages 1 and 2 variance components and their interactive effect on the parameter draws in Gibbs sampling, we propose modeling the variances of the two stages a priori jointly in a multivariate fashion. We use random effects linear growth model for illustration and consider multivariate distributions to model the variance components jointly including the recently developed generalized multivariate log gamma (G-MVLG) distribution. We discuss these variance priors as well as the independent variance priors exercised in the literature in different aspects including noninformativeness and propriety of the associated posterior density. We show through an extensive simulation experiment that modeling the variance components of different stages multivariately results in better estimation properties for the response and random effect model parameters compared to independent modeling. We scrutinize the sensitivity of response model coefficient estimates to the parameters of considered noninformative variance priors and find that their full conditional expectations are insensitive to noninformative G-MVLG prior parameters. We apply independent and joint models for analysis of a real dataset and find that multivariate priors for variance components lead to better fitted hierarchical model than the univariate variance priors.