Université catholique de Louvain

About: Université catholique de Louvain is a education organization based out in Louvain-la-Neuve, Belgium. It is known for research contribution in the topics: Population & Catalysis. The organization has 25319 authors who have published 57360 publications receiving 2172080 citations. The organization is also known as: University of Louvain & UCLouvain.

Diabetes, obesity and gut microbiota.

Abstract: The gut microbiota composition has been associated with several hallmarks of metabolic syndrome (e.g., obesity, type 2 diabetes, cardiovascular diseases, and non-alcoholic steatohepatitis). Growing evidence suggests that gut microbes contribute to the onset of the low-grade inflammation characterising these metabolic disorders via mechanisms associated with gut barrier dysfunctions. Recently, enteroendocrine cells and the endocannabinoid system have been shown to control gut permeability and metabolic endotoxaemia. Moreover, targeted nutritional interventions using non-digestible carbohydrates with prebiotic properties have shown promising results in pre-clinical studies in this context, although human intervention studies warrant further investigations. Thus, in this review, we discuss putative mechanisms linking gut microbiota and type 2 diabetes. These data underline the advantage of investigating and changing the gut microbiota as a therapeutic target in the context of obesity and type 2 diabetes.

Thermohaline circulation hysteresis: A model intercomparison

Abstract: We present results from an intercomparison of 11 different climate models of intermediate complexity, in which the North Atlantic Ocean was subjected to slowly varying changes in freshwater input. All models show a characteristic hysteresis response of the thermohaline circulation to the freshwater forcing; which can be explained by Stommel's salt advection feedback. The width of the hysteresis curves varies between 0.2 and 0.5 Sv in the models. Major differences are found in the location of present-day climate on the hysteresis diagram. In seven of the models, present-day climate for standard parameter choices is found in the bi-stable regime, in four models this climate is in the mono-stable regime. The proximity of the present-day climate to the Stommel bifurcation point, beyond which North Atlantic Deep Water formation cannot be sustained, varies from less than 0.1 Sv to over 0.5 Sv.

Robust biomarker identification for cancer diagnosis with ensemble feature selection methods

Abstract: Motivation: Biomarker discovery is an important topic in biomedical applications of computational biology, including applications such as gene and SNP selection from high-dimensional data. Surprisingly, the stability with respect to sampling variation or robustness of such selection processes has received attention only recently. However, robustness of biomarkers is an important issue, as it may greatly influence subsequent biological validations. In addition, a more robust set of markers may strengthen the confidence of an expert in the results of a selection method. Results: Our first contribution is a general framework for the analysis of the robustness of a biomarker selection algorithm. Secondly, we conducted a large-scale analysis of the recently introduced concept of ensemble feature selection, where multiple feature selections are combined in order to increase the robustness of the final set of selected features. We focus on selection methods that are embedded in the estimation of support vector machines (SVMs). SVMs are powerful classification models that have shown state-of-the-art performance on several diagnosis and prognosis tasks on biological data. Their feature selection extensions also offered good results for gene selection tasks. We show that the robustness of SVMs for biomarker discovery can be substantially increased by using ensemble feature selection techniques, while at the same time improving upon classification performances. The proposed methodology is evaluated on four microarray datasets showing increases of up to almost 30% in robustness of the selected biomarkers, along with an improvement of ~15% in classification performance. The stability improvement with ensemble methods is particularly noticeable for small signature sizes (a few tens of genes), which is most relevant for the design of a diagnosis or prognosis model from a gene signature. Contact: yvan.saeys@psb.ugent.be Supplementary information: Supplementary data are available at Bioinformatics online.