The evolution of inbred social systems in spiders and other organisms: from short-term gains to long-term evolutionary dead-ends?

The question of why some social systems have evolved close inbreeding is particularly intriguing given expected short- and long-term negative effects of this breeding system. Using social spiders as a case study, we quantitatively show that the potential costs of avoiding inbreeding through dispersal and solitary living could have outweighed the costs of inbreeding depression in the origin of inbred spider sociality. We further review the evidence that despite being favored in the short term, inbred spider sociality may constitute in the long run an evolutionary dead end. We also review other cases, such as the naked mole rats and some bark and ambrosia beetles, mites, psocids, thrips, parasitic ants, and termites, in which inbreeding and sociality are associated and the evidence for and against this breeding system being, in general, an evolutionary dead end.

Big data and other challenges in the quest for orthologs.

Given the rapid increase of species with a sequenced genome, the need to identify orthologous genes between them has emerged as a central bioinformatics task. Many different methods exist for orthology detection, which makes it difficult to decide which one to choose for a particular application. Here, we review the latest developments and issues in the orthology field, and summarize the most recent results reported at the third 'Quest for Orthologs' meeting. We focus on community efforts such as the adoption of reference proteomes, standard file formats and benchmarking. Progress in these areas is good, and they are already beneficial to both orthology consumers and providers. However, a major current issue is that the massive increase in complete proteomes poses computational challenges to many of the ortholog database providers, as most orthology inference algorithms scale at least quadratically with the number of proteomes. The Quest for Orthologs consortium is an open community with a number of working groups that join efforts to enhance various aspects of orthology analysis, such as defining standard formats and datasets, documenting community resources and benchmarking. AVAILABILITY AND IMPLEMENTATION: All such materials are available at http://questfororthologs.org. CONTACT: erik.sonnhammer@scilifelab.se or c.dessimoz@ucl.ac.uk.

Development and testing of novel chloroplast microsatellite markers for Lolium perenne and other grasses (Poaceae) from de novo sequencing and in silico sequences

Twelve primers to amplify microsatellite markers from the chloroplast genome of Lolium perenne were designed and optimized using de novo sequencing and in silico sequences. With one exception, each locus was polymorphic with a range from two to nine alleles in L. perenne. The newly developed primer pairs cross-amplified in different species of Lolium and in 50 other grass species representing nine grass subfamilies.

Artifacts in the analysis of thioridazine and other neuroleptics.

Although the sensitivity to light of thioridazine and its metabolites has been described, the problem does not seem to be widely acknowledged. Indeed, a survey of the literature shows that assays of these compounds under light-protected conditions have been performed only in a few of the numerous analytical studies on this drug. In the present study, thioridazine, its metabolites, and 18 other neuroleptics were tested for their sensitivity to light under conditions used for their analysis. The results show that light significantly affects the analysis of thioridazine and its metabolites. It readily causes the racemization of the isomeric pairs of thioridazine 5-sulphoxide and greatly decreases the concentration of thioridazine. This sensitivity to light varied with the medium used (most sensitive in acidic media) and also with the molecule (in order of decreasing sensitivity: thioridazine > mesoridazine > sulforidazine). Degradation in neutral or basic media was slow, with the exception of mesoridazine in a neutral medium. Twelve other phenothiazines tested, as well as chlorprotixene, a thioxanthene drug, were found to be sensitive to light in acidic media, whereas flupenthixol and zuclopenthixol (two thioxanthenes), clozapine, fluperlapine, and haloperidol (a butyrophenone) did not seem to be affected. In addition to being sensitive to light, some compounds may be readily oxidized by peroxide-containing solvents.

Basal insulin and cardiovascular and other outcomes in dysglycemia.

BACKGROUND: The provision of sufficient basal insulin to normalize fasting plasma glucose levels may reduce cardiovascular events, but such a possibility has not been formally tested. METHODS: We randomly assigned 12,537 people (mean age, 63.5 years) with cardiovascular risk factors plus impaired fasting glucose, impaired glucose tolerance, or type 2 diabetes to receive insulin glargine (with a target fasting blood glucose level of ≤95 mg per deciliter [5.3 mmol per liter]) or standard care and to receive n-3 fatty acids or placebo with the use of a 2-by-2 factorial design. The results of the comparison between insulin glargine and standard care are reported here. The coprimary outcomes were nonfatal myocardial infarction, nonfatal stroke, or death from cardiovascular causes and these events plus revascularization or hospitalization for heart failure. Microvascular outcomes, incident diabetes, hypoglycemia, weight, and cancers were also compared between groups. RESULTS: The median follow-up was 6.2 years (interquartile range, 5.8 to 6.7). Rates of incident cardiovascular outcomes were similar in the insulin-glargine and standard-care groups: 2.94 and 2.85 per 100 person-years, respectively, for the first coprimary outcome (hazard ratio, 1.02; 95% confidence interval [CI], 0.94 to 1.11; P=0.63) and 5.52 and 5.28 per 100 person-years, respectively, for the second coprimary outcome (hazard ratio, 1.04; 95% CI, 0.97 to 1.11; P=0.27). New diabetes was diagnosed approximately 3 months after therapy was stopped among 30% versus 35% of 1456 participants without baseline diabetes (odds ratio, 0.80; 95% CI, 0.64 to 1.00; P=0.05). Rates of severe hypoglycemia were 1.00 versus 0.31 per 100 person-years. Median weight increased by 1.6 kg in the insulin-glargine group and fell by 0.5 kg in the standard-care group. There was no significant difference in cancers (hazard ratio, 1.00; 95% CI, 0.88 to 1.13; P=0.97). CONCLUSIONS: When used to target normal fasting plasma glucose levels for more than 6 years, insulin glargine had a neutral effect on cardiovascular outcomes and cancers. Although it reduced new-onset diabetes, insulin glargine also increased hypoglycemia and modestly increased weight. (Funded by Sanofi; ORIGIN ClinicalTrials.gov number, NCT00069784.).

Twenty-year trends in the prevalence of Down syndrome and other trisomies in Europe: impact of maternal age and prenatal screening.

This study examines trends and geographical differences in total and live birth prevalence of trisomies 21, 18 and 13 with regard to increasing maternal age and prenatal diagnosis in Europe. Twenty-one population-based EUROCAT registries covering 6.1 million births between 1990 and 2009 participated. Trisomy cases included live births, fetal deaths from 20 weeks gestational age and terminations of pregnancy for fetal anomaly. We present correction to 20 weeks gestational age (ie, correcting early terminations for the probability of fetal survival to 20 weeks) to allow for artefactual screening-related differences in total prevalence. Poisson regression was used. The proportion of births in the population to mothers aged 35+ years in the participating registries increased from 13% in 1990 to 19% in 2009. Total prevalence per 10 000 births was 22.0 (95% CI 21.7-22.4) for trisomy 21, 5.0 (95% CI 4.8-5.1) for trisomy 18 and 2.0 (95% CI 1.9-2.2) for trisomy 13; live birth prevalence was 11.2 (95% CI 10.9-11.5) for trisomy 21, 1.04 (95% CI 0.96-1.12) for trisomy 18 and 0.48 (95% CI 0.43-0.54) for trisomy 13. There was an increase in total and total corrected prevalence of all three trisomies over time, mainly explained by increasing maternal age. Live birth prevalence remained stable over time. For trisomy 21, there was a three-fold variation in live birth prevalence between countries. The rise in maternal age has led to an increase in the number of trisomy-affected pregnancies in Europe. Live birth prevalence has remained stable overall. Differences in prenatal screening and termination between countries lead to wide variation in live birth prevalence.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale: some case studies

Every year, debris flows cause huge damage in mountainous areas. Due to population pressure in hazardous zones, the socio-economic impact is much higher than in the past. Therefore, the development of indicative susceptibility hazard maps is of primary importance, particularly in developing countries. However, the complexity of the phenomenon and the variability of local controlling factors limit the use of processbased models for a first assessment. A debris flow model has been developed for regional susceptibility assessments using digital elevation model (DEM) with a GIS-based approach.. The automatic identification of source areas and the estimation of debris flow spreading, based on GIS tools, provide a substantial basis for a preliminary susceptibility assessment at a regional scale. One of the main advantages of this model is its workability. In fact, everything is open to the user, from the data choice to the selection of the algorithms and their parameters. The Flow-R model was tested in three different contexts: two in Switzerland and one in Pakistan, for indicative susceptibility hazard mapping. It was shown that the quality of the DEM is the most important parameter to obtain reliable results for propagation, but also to identify the potential debris flows sources.

Omics meets hypothesis-driven research. Partnership for innovative discoveries in vascular biology and angiogenesis.

The emergence of omics technologies allowing the global analysis of a given biological or molecular system, rather than the study of its individual components, has revolutionized biomedical research, including cardiovascular medicine research in the past decade. These developments raised the prospect that classical, hypothesis-driven, single gene-based approaches may soon become obsolete. The experience accumulated so far, however, indicates that omic technologies only represent tools similar to those classically used by scientists in the past and nowadays, to make hypothesis and build models, with the main difference that they generate large amounts of unbiased information. Thus, omics and classical hypothesis-driven research are rather complementary approaches with the potential to effectively synergize to boost research in many fields, including cardiovascular medicine. In this article we discuss some general aspects of omics approaches, and review contributions in three areas of vascular biology, thrombosis and haemostasis, atherosclerosis and angiogenesis, in which omics approaches have already been applied (vasculomics).

Patterns of Subject Mix in Higher Education Institutions : A First Empirical Analysis Using the AQUAMETH Database

Teaching and research are organised differently between subject domains: attempts to construct typologies of higher education institutions, however, often do not include quantitative indicators concerning subject mix which would allow systematic comparisons of large numbers of higher education institutions among different countries, as the availability of data for such indicators is limited. In this paper, we present an exploratory approach for the construction of such indicators. The database constructed in the AQUAMETH project, which includes also data disaggregated at the disciplinary level, is explored with the aim of understanding patterns of subject mix. For six European countries, an exploratory and descriptive analysis of staff composition divided in four large domains (medical sciences, engineering and technology, natural sciences and social sciences and humanities) is performed, which leads to a classification distinguishing between specialist and generalist institutions. Among the latter, a further distinction is made based on the presence or absence of a medical department. Preliminary exploration of this classification and its comparison with other indicators show the influence of long term dynamics on the subject mix of individual higher education institutions, but also underline disciplinary differences, for example regarding student to staff ratios, as well as national patterns, for example regarding the number of PhD degrees per 100 undergraduate students. Despite its many limitations, this exploratory approach allows defining a classification of higher education institutions that accounts for a large share of differences between the analysed higher education institutions.