The 'At-risk mental state' for psychosis in adolescents : clinical presentation, transition and remission.

Despite increased efforts over the last decade to prospectively identify individuals at ultra-high risk of developing a psychotic illness, limited attention has been specifically directed towards adolescent populations (<18 years). In order to evaluate how those under 18 fulfilling the operationalised criteria for an At-Risk Mental State (ARMS) present and fare over time, we conducted an observational study. Participants (N = 30) generally reported a high degree of functional disability and frequent and distressing perceptual disturbance, mainly in the form of auditory hallucinations. Seventy percent (21/30) were found to fulfil the criteria for a co-morbid ICD-10 listed mental health disorder, with mood (affective; 13/30) disorders being most prevalent. Overall transition rates to psychosis were low at 24 months follow-up (2/28; 7.1 %) whilst many participants demonstrated a significant reduction in psychotic-like symptoms. The generalisation of these findings may be limited due to the small sample size and require replication in a larger sample

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

The Coevolution of Virulence: Tolerance in Perspective

Coevolutionary interactions, such as those between host and parasite, predator and prey, or plant and pollinator, evolve subject to the genes of both interactors. It is clear, for example, that the evolution of pollination strategies can only be understood with knowledge of both the pollinator and the pollinated. Studies of the evolution of virulence, the reduction in host fitness due to infection, have nonetheless tended to focus on parasite evolution. Host-centric approaches have also been proposed—for example, under the rubric of “tolerance”, the ability of hosts to minimize virulence without necessarily minimizing parasite density. Within the tolerance framework, however, there is room for more comprehensive measures of host fitness traits, and for fuller consideration of the consequences of coevolution. For example, the evolution of tolerance can result in changed selection on parasite populations, which should provoke parasite evolution despite the fact that tolerance is not directly antagonistic to parasite fitness. As a result, consideration of the potential for parasite counter-adaptation to host tolerance—whether evolved or medially manipulated—is essential to the emergence of a cohesive theory of biotic partnerships and robust disease control strategies

ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples

<p>Abstract</p> <p>Background</p> <p>Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases.</p> <p>Results</p> <p>We propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases.</p> <p>Conclusions</p> <p>ProDiGe implements a new machine learning paradigm for gene prioritization, which could help the identification of new disease genes. It is freely available at <url>http://cbio.ensmp.fr/prodige</url>.</p

Is social stress in the first half of life detrimental to later physical and mental health in both men and women?

This study examined gender differences in the associations between affection- and status-related stressors encountered in the first half of life and physical and mental health problems later on. Based on the theory of Social Production Functions (SPF) two hypotheses have been formulated, which were tested in a representative sample of 446 men and 514 women (aged 40–79). Main outcome measures were number of chronic somatic diseases and level of psychological distress. As expected, regression analyses showed no gender differences in the associations between affection-related stressors and physical and mental health problems later on. In contrast, but as also expected, status-related stressors encountered in the first half of life were associated with later physical and mental health for men only. It is concluded that the gender differences in the associations between earlier social stressors and later health problems may be more complex than the common assumption that men are only affected by status stress and women only by affection stress. This study contributes to the knowledge on gender differences concerning the link between social stress and health, and it indicates that social experiences encountered earlier in life are of importance for being healthy and happy in later life

Multiple Molecular Mechanisms Cause Reproductive Isolation between Three Yeast Species

Incompatibility between nuclear and mitochondrial genomes in yeast species may represent a general mechanism of reproductive isolation during yeast evolution

Multi-model seascape genomics identifies distinct environmental drivers of selection among sympatric marine species

Background As global change and anthropogenic pressures continue to increase, conservation and management increasingly needs to consider species’ potential to adapt to novel environmental conditions. Therefore, it is imperative to characterise the main selective forces acting on ecosystems, and how these may influence the evolutionary potential of populations and species. Using a multi-model seascape genomics approach, we compare putative environmental drivers of selection in three sympatric southern African marine invertebrates with contrasting ecology and life histories: Cape urchin (Parechinus angulosus), Common shore crab (Cyclograpsus punctatus), and Granular limpet (Scutellastra granularis). Results Using pooled (Pool-seq), restriction-site associated DNA sequencing (RAD-seq), and seven outlier detection methods, we characterise genomic variation between populations along a strong biogeographical gradient. Of the three species, only S. granularis showed significant isolation-by-distance, and isolation-by-environment driven by sea surface temperatures (SST). In contrast, sea surface salinity (SSS) and range in air temperature correlated more strongly with genomic variation in C. punctatus and P. angulosus. Differences were also found in genomic structuring between the three species, with outlier loci contributing to two clusters in the East and West Coasts for S. granularis and P. angulosus, but not for C. punctatus. Conclusion The findings illustrate distinct evolutionary potential across species, suggesting that species-specific habitat requirements and responses to environmental stresses may be better predictors of evolutionary patterns than the strong environmental gradients within the region. We also found large discrepancies between outlier detection methodologies, and thus offer a novel multi-model approach to identifying the principal environmental selection forces acting on species. Overall, this work highlights how adding a comparative approach to seascape genomics (both with multiple models and species) can elucidate the intricate evolutionary responses of ecosystems to global change