Distress, Suicidality, and Affective Disorders at the Time of Social Networks.

PURPOSE OF REVIEW: We reviewed how scholars recently addressed the complex relationship that binds distress, affective disorders, and suicidal behaviors on the one hand and social networking on the other. We considered the latest machine learning performances in detecting affective-related outcomes from social media data, and reviewed understandings of how, why, and with what consequences distressed individuals use social network sites. Finally, we examined how these insights may concretely instantiate on the individual level with a qualitative case series. RECENT FINDINGS: Machine learning classifiers are progressively stabilizing with moderate to high performances in detecting affective-related diagnosis, symptoms, and risks from social media linguistic markers. Qualitatively, such markers appear to translate ambivalent and socially constrained motivations such as self-disclosure, passive support seeking, and connectedness reinforcement. Binding data science and psychosocial research appears as the unique condition to ground a translational web-clinic for treating and preventing affective-related issues on social media.

A new protein-protein docking scoring function based on interface residue properties.

MOTIVATION: Protein-protein complexes are known to play key roles in many cellular processes. However, they are often not accessible to experimental study because of their low stability and difficulty to produce the proteins and assemble them in native conformation. Thus, docking algorithms have been developed to provide an in silico approach of the problem. A protein-protein docking procedure traditionally consists of two successive tasks: a search algorithm generates a large number of candidate solutions, and then a scoring function is used to rank them. RESULTS: To address the second step, we developed a scoring function based on a Voronoï tessellation of the protein three-dimensional structure. We showed that the Voronoï representation may be used to describe in a simplified but useful manner, the geometric and physico-chemical complementarities of two molecular surfaces. We measured a set of parameters on native protein-protein complexes and on decoys, and used them as attributes in several statistical learning procedures: a logistic function, Support Vector Machines (SVM), and a genetic algorithm. For the later, we used ROGER, a genetic algorithm designed to optimize the area under the receiver operating characteristics curve. To further test the scores derived with ROGER, we ranked models generated by two different docking algorithms on targets of a blind prediction experiment, improving in almost all cases the rank of native-like solutions. AVAILABILITY: http://genomics.eu.org/spip/-Bioinformatics-tools-

Aspects histopathologiques de tumeurs vasculaires cervico faciales en orl au CNHU de Cotonou (1985-2005) / Histopathological aspects of cervico-facial vascular tumors in ENT at the CNHU of Cotonou (1985-2005)

Dans une étude rétrospective entre le 1er janvier 1985 et le 30 juin 2005, vingt six (26) cas de tumeurs vasculaires avec confirmation histopathologique ont été recensées en ORL au CNHU de Cotonou. Les sujets de 0 à 20 ans ont constitué 57,6% de la série. La prédominance a été masculine 57,6%. Les motifs de consultation les plus enregistrés ont été la tuméfaction de la face 53,9% et celle du cou 30,9%. L'histopathologie a révélé 88,5% de tumeurs bénignes et 11,6% de tumeurs malignes. Les principales étiologies retrouvées ont été : l'hémangiome 65,3%, le lymphangiome 19,2% et l'hémangio-endothéliome malin 7,6%.