Electronic correlations in vanadium chalcogenides: BaVSe3 versus BaVS3

Albeit structurally and electronically very similar, at low temperature the quasi-one-dimensional vanadium sulfide BaVS3 shows a metal-to-insulator transition via the appearance of a charge-density-wave state, while BaVSe3 apparently remains metallic down to zero temperature. This different behavior upon cooling is studied by means of density functional theory and its combination with the dynamical mean-field theory and the rotationally-invariant slave-boson method. We reveal several subtle differences between these chalcogenides that provide indications for the deviant behavior of BaVSe3 at low temperature. In this regard, a smaller Hubbard U in line with an increased relevance of the Hund's exchange J plays a vital role.Comment: 16 pages, 11 figures, published versio

EFBAT: exact family-based association tests

Background: Family-based association tests are important tools for investigating genetic risk factors of complex diseases. These tests are especially valuable for being robust to population structure. We introduce a tool, EFBAT, which performs exact family-based tests of association for X-chromosome and autosomal biallelic markers. Results: The program EFBAT extends a network algorithm previously applied to autosomal markers to include the X-chromosome and to perform tests of association under the null hypotheses "no association, no linkage" and "no association in the presence of linkage" under additive, dominant and recessive genetic models. These tests are valid regardless of patterns of missing familial data. Conclusion: The general framework for performing exact family-based association tests has been usefully extended to the X-chromosome, particularly for the hypothesis of "no association in the presence of linkage" and for different genetic models

A meeting report: OECD-GESIS Seminar on Translating and Adapting Instruments in Large-Scale Assessments (2018)

This report summarizes the main themes and conclusions from the OECD-GESIS Seminar on Translating and Adapting Instruments in Large-Scale Assessments, which took place at the Organization for Economic Co-operation and Development (OECD), Paris, in June 2018. The five sessions covered the topics (1) etic (universal) vs. emic (culture-specific) measurement instruments, (2) language- and culture-sensitive development of measurement instruments, (3) international guidelines vs. implementation in countries and by translators, (4) tools and technological developments, and (5) quality control of translations. Key players in the field presented on best practice, lessons learned, and innovations and also made suggestions for moving the field forward

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study