Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis

Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth “Dialogue for Reverse Engineering Assessments and Methods” (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on “Systems Genetics” proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics

Transport of Snow by the Wind: A Comparison Between Observations in Adélie Land, Antarctica, and Simulations Made with the Regional Climate Model MAR

For the first time a simulation of blowing snow events was validated in detail using one-month long observations (January 2010) made in Adélie Land, Antarctica. A regional climate model featuring a coupled atmosphere/blowing snow/snowpack model is forced laterally by meteorological re-analyses. The vertical grid spacing was 2 m from 2 to 20 m above the surface and the horizontal grid spacing was 5 km. The simulation was validated by comparing the occurrence of blowing snow events and other meteorological parameters at two automatic weather stations. The Nash test allowed us to compute effi- ciencies of the simulation. The regional climate model simulated the observed wind speed with a positive efficiency (0.69). Wind speeds higher than 12 m s−1 were underestimated. Positive efficiency of the simulated wind speed was a prerequisite for validating the blowing snow model. Temperatures were simulated with a slightly negative efficiency (−0.16) due to overestimation of the amplitude of the diurnal cycle during one week, probably because the cloud cover was underestimated at that location during the period concerned. Snowfall events were correctly simulated by our model, as confirmed by field reports. Because observations suggested that our instrument (an acoustic sounder) tends to overestimate the blowing snow flux, data were not sufficiently accurate to allow the complete validation of snow drift val- ues. However, the simulation of blowing snow occurrence was in good agreement with the observations made during the first 20 days of January 2010, despite the fact that the blowing snow flux may be underestimated by the regional climate model during pure blowing snow events. We found that blowing snow occurs in Adélie Land only when the 30-min wind speed value at 2 m a.g.l. is >10 m s−1. The validation for the last 10 days of January 2010 was less satisfactory because of complications introduced by surface melting and refreezing

Moving Forward on Tumor Pathology Research Reporting: A Guide for Pathologists From the World Health Organization Classification of Tumors Living Evidence Gap Map by Tumour Type Group

\ua9 2024 The AuthorsEvidence-based medicine (EBM) can be an unfamiliar territory for those working in tumor pathology research, and there is a great deal of uncertainty about how to undertake an EBM approach to planning and reporting histopathology-based studies. In this article, reviewed and endorsed by the Word Health Organization International Agency for Research on Cancer\u27s International Collaboration for Cancer Classification and Research, we aim to help pathologists and researchers understand the basics of planning an evidence-based tumor pathology research study, as well as our recommendations on how to report the findings from these. We introduce some basic EBM concepts, a framework for research questions, and thoughts on study design and emphasize the concept of reporting standards. There are many study-specific reporting guidelines available, and we provide an overview of these. However, existing reporting guidelines perhaps do not always fit tumor pathology research papers, and hence, here, we collate the key reporting data set together into one generic checklist that we think will simplify the task for pathologists. The article aims to complement our recent hierarchy of evidence for tumor pathology and glossary of evidence (study) types in tumor pathology. Together, these articles should help any researcher get to grips with the basics of EBM for planning and publishing research in tumor pathology, as well as encourage an improved standard of the reports available to us all in the literature