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.

Extended truncated Tweedie-Poisson model

It has been argued that by truncating the sample space of the negative binomial and of the inverse Gaussian-Poisson mixture models at zero, one is allowed to extend the parameter space of the model. Here that is proved to be the case for the more general three parameter Tweedie-Poisson mixture model. It is also proved that the distributions in the extended part of the parameter space are not the zero truncation of mixed poisson distributions and that, other than for the negative binomial, they are not mixtures of zero truncated Poisson distributions either. By extending the parameter space one can improve the fit when the frequency of one is larger and the right tail is heavier than is allowed by the unextended model. Considering the extended model also allows one to use the basic maximum likelihood based inference tools when parameter estimates fall in the extended part of the parameter space, and hence when the m.l.e. does not exist under the unextended model. This extended truncated Tweedie-Poisson model is proved to be useful in the analysis of words and species frequency count data.

On the use of IRMS in forensic science: proposals for a methodological approach.

The flourishing number of publications on the use of isotope ratio mass spectrometry (IRMS) in forensicscience denotes the enthusiasm and the attraction generated by this technology. IRMS has demonstratedits potential to distinguish chemically identical compounds coming from different sources. Despite thenumerous applications of IRMS to a wide range of forensic materials, its implementation in a forensicframework is less straightforward than it appears. In addition, each laboratory has developed its ownstrategy of analysis on calibration, sequence design, standards utilisation and data treatment without aclear consensus.Through the experience acquired from research undertaken in different forensic fields, we propose amethodological framework of the whole process using IRMS methods. We emphasize the importance ofconsidering isotopic results as part of a whole approach, when applying this technology to a particularforensic issue. The process is divided into six different steps, which should be considered for a thoughtfuland relevant application. The dissection of this process into fundamental steps, further detailed, enablesa better understanding of the essential, though not exhaustive, factors that have to be considered in orderto obtain results of quality and sufficiently robust to proceed to retrospective analyses or interlaboratorycomparisons.

What do we gain from simplicity versus complexity in species distribution models?

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building 'under fit' models, having insufficient flexibility to describe observed occurrence-environment relationships, we risk misunderstanding the factors shaping species distributions. By building 'over fit' models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

The link between public support and private R&D effort: What is the optimal subsidy?

The effectiveness of R&D subsidies can vary substantially depending on their characteristics. Specifically, the amount and intensity of such subsidies are crucial issues in the design of public schemes supporting private R&D. Public agencies determine the intensities of R&D subsidies for firms in line with their eligibility criteria, although assessing the effects of R&D projects accurately is far from straightforward. The main aim of this paper is to examine whether there is an optimal intensity for R&D subsidies through an analysis of their impact on private R&D effort. We examine the decisions of a public agency to grant subsidies taking into account not only the characteristics of the firms but also, as few previous studies have done to date, those of the R&D projects. In determining the optimal subsidy we use both parametric and nonparametric techniques. The results show a non-linear relationship between the percentage of subsidy received and the firms’ R&D effort. These results have implications for technology policy, particularly for the design of R&D subsidies that ensure enhanced effectiveness.

Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene¬fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor¬tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn¬ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo¬lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ¬ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se¬lection shapes animals' cognitive abilities. - La capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.

Effect of antiretroviral therapy on apoptosis markers and morphology in peripheral lymph nodes of HIV-infected individuals.

BACKGROUND: CD4+ T cell depletion and destruction and the involution of the lymphoid tissue are hallmarks of HIV infection. Although the underlying mechanisms are still unclear, apoptosis appears to play a central role. The objective of this study was to investigate the effect of antiretroviral therapy on the lymph node tissue, particularly with respect to morphology and apoptosis. PATIENTS AND METHODS: Between 1997 and 1999, two inguinal lymph nodes were excised from 31 previously untreated individuals who were in an early stage of HIV infection, the first one prior to treatment and the second after 16 to 20 months of treatment. Paraffin sections were investigated for lymph node architecture, distribution of cellular and viral markers, apoptosis, and expression of apoptotic key molecules which indirectly reflect apoptotic processes. RESULTS: After 16-20 months of antiretroviral therapy, a significant decrease in highly activated HIV-driven immune response was observed in the lymph node tissue as a marked reduction in follicular hyperplasia, a normalization of the follicular dendritic cell network, a significant increase in the number of CD4+ T cells, and a significant decrease in the number of CD8+ T cells. The expression of several proapoptotic (Fas, TRAIL, and active caspase 3) and antiapoptotic (Bcl-2 and IL-7Ralpha) molecules that were reconstituted in the tissues during therapy resembled their expression in lymph nodes of HIV-negative individuals. Limitations of the study are (a) the lack of untreated patients in the late stages, (b) for ethical reasons, the lack of a control group with untreated patients, and (c) for methodological reasons, the restriction of sequential measurements of apotpotic markers to one-third of the patients. CONCLUSION: Antiretroviral therapy initiated in the early stages in HIV infection may halt the irreversible destruction of the lymph node tissue and may partially normalize apoptotic processes.

'Next generation' biogeography: towards understanding the drivers of species diversification and persistence

The drivers of species diversification and persistence are of great interest to current biogeography, especially in those global biodiversity hotspots' harbouring most of Earth's animal and plant life. Classical multispecies biogeographical work has yielded fascinating insights into broad-scale patterns of diversification, and DNA-based intraspecific phylogeographical studies have started to complement this picture at much finer temporal and spatial scales. The advent of novel next-generation sequencing (NGS) technologies provides the opportunity to greatly scale up the numbers of individuals, populations and species sampled, potentially merging intraspecific and interspecific approaches to biogeographical inference. Here, we outline these prospects and issues by using the example of an undisputed hotspot, the Cape of southern Africa. We outline the current state of knowledge on the biogeography of species diversification within the Cape, review the literature for phylogeographical evidence of its likely drivers and mechanisms, and suggest possible ways forward based on NGS approaches. We demonstrate the potential of these methods and current bioinformatic issues with the help of restriction-site-associated DNA (RAD) sequencing data for three highly divergent species of the Restionaceae, an important plant radiation in the Cape. A thorough understanding of the mechanisms that facilitate species diversification and persistence in spatially structured, species-rich environments will require the adoption of novel genomic and bioinformatic tools in biogeographical studies.

Association of CYP3A5 genotypes with blood pressure and renal function in African families.

OBJECTIVE: Renal cytochrome P450 3A5 (CYP3A5) activity has been associated with blood pressure and salt sensitivity in humans. We determined whether CYP3A5 polymorphisms are associated with ambulatory blood pressure (ABP) and with glomerular filtration rate (GFR) in African families. METHODS: Using a cross-sectional design, 375 individuals from 72 families, each with at least two hypertensive siblings, were recruited through a hypertension register in the Seychelles (Indian Ocean). We analyzed the association between the CYP3A5 alleles (*1, *3, *6 and *7) and ABP, GFR and renal sodium handling (fractional excretion of lithium), from pedigree data, allowing for other covariates and familial correlations. RESULTS: CYP3A5*1 carriers increased their daytime systolic and diastolic ABP with age (0.55 and 0.23 mmHg/year) more than non-carriers (0.21 and 0.04 mmHg/year). CYP3A5*1 had a significant main effect on daytime systolic/diastolic ABP [regression coefficient (SE): -29.6 (10.0)/-8.2 (4.1) mmHg, P = 0.003/0.045, respectively] and this effect was modified by age (CYP3A5*1 x age interactions, P = 0.017/0.018). For night-time ABP, the effect of CYP3A5*1 was modified by urinary sodium excretion, not by age. For renal function, CYP3A5*1 carriers had a 7.6(3.8) ml/min lower GFR (P = 0.045) than non-carriers. Proximal sodium reabsorption decreased with age in non-carriers, but not in CYP3A5*1 carriers (P for interaction = 0.02). CONCLUSIONS: These data demonstrate that CYP3A5 polymorphisms are associated with ambulatory BP, CYP3A5*1 carriers showing a higher age- and sodium- related increase in ABP than non-carriers. The age effect may be due, in part, to the action of CYP3A5 on renal sodium handling.

Spin-labeling coronary MR angiography with steady-state free precession and radial k-space sampling: initial results in healthy volunteers.

The purpose of this study was to prospectively compare free-breathing navigator-gated cardiac-triggered three-dimensional steady-state free precession (SSFP) spin-labeling coronary magnetic resonance (MR) angiography performed by using Cartesian k-space sampling with that performed by using radial k-space sampling. A new dedicated placement of the two-dimensional selective labeling pulse and an individually adjusted labeling delay time approved by the institutional review board were used. In 14 volunteers (eight men, six women; mean age, 28.8 years) who gave informed consent, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), vessel sharpness, vessel length, and subjective image quality were investigated. Differences between groups were analyzed with nonparametric tests (Wilcoxon, Pearson chi2). Radial imaging, as compared with Cartesian imaging, resulted in a significant reduction in the severity of motion artifacts, as well as an increase in SNR (26.9 vs 12.0, P < .05) in the coronary arteries and CNR (23.1 vs 8.8, P < .05) between the coronary arteries and the myocardium. A tendency toward improved vessel sharpness and vessel length was also found with radial imaging. Radial SSFP imaging is a promising technique for spin-labeling coronary MR angiography.

Usefulness of microsatellite typing in population genetic studies of Trypanosoma cruzi

Through microsatellite analysis of 53 monoclonal populations of Trypanosoma cruzi, we found a remarkable degree of genetic polymorphism with no single multilocus genotype being observed more than once. The microsatellite profile proved to be stable during 70 generations of the CL Brener clone in culture. The microsatellite profiling presented also high diagnostic sensitivity since DNA amplifications could be achieved with less than 100 fg DNA, corresponding to half parasite total DNA content. Based on these technical attributes the microsatellite assay turns out to be an important tool for direct typing T. cruzi in biological samples. By using this approach we were able to type T. cruzi in feces of artificially infected bugs and in single cells sorted by FACS. The microsatellites have shown to be excellent markers for T. cruzi phylogenetic reconstruction. We used maximum parsimony based on the minimum number of mutational steps to build an unrooted Wagner network, which confirms previous conclusions based on the analysis of the D7 domain of the LSU rDNA gene that T. cruzi is composed by two major groups. We also obtained evidence that strains belonging to rRNA group 2 are subdivided into two genetically distant clusters, and that one of these clusters is more related to rRNA group 1/2. These results suggest different origins for these strains.

Smad3 deficiency in mice protects against insulin resistance and obesity induced by a high-fat diet.

OBJECTIVE-Obesity and associated pathologies are major global health problems. Transforming growth factor-beta/Smad3 signaling has been implicated in various metabolic processes, including adipogenesis, insulin expression, and pancreatic beta-cell function. However, the systemic effects of Smad3 deficiency on adiposity and insulin resistance in vivo remain elusive. This study investigated the effects of Smad3 deficiency on whole-body glucose and lipid homeostasis and its contribution to the development of obesity and type 2 diabetes.RESEARCH DESIGN AND METHODS-We compared various metabolic profiles of Smad3-knockout and wild-type mice. We also determined the mechanism by which Smad3 deficiency affects the expression of genes involved in adipogenesis and metabolism. Mice were then challenged with a high-fat diet to study the impact of Smad3 deficiency on the development of obesity and insulin resistance.RESULTS-Smad3-knockout mice exhibited diminished adiposity with improved glucose tolerance and insulin sensitivity. Chromatin immunoprecipitation assay revealed that Smad3 deficiency increased CCAAT/enhancer-binding protein beta-C/EBP homologous protein 10 interaction and exerted a differential regulation on proliferator-activated receptor beta/delta and proliferator-activated receptor gamma expression in adipocytes. Focused gene expression profiling revealed an altered expression of genes involved in adipogenesis, lipid accumulation, and fatty acid beta-oxidation, indicative of altered adipose physiology. Despite reduced physical activity with no modification in food intake, these mutant mice were resistant to obesity and insulin resistance induced by a high-fat diet.CONCLUSIONS-Smad3 is a multifaceted regulator in adipose physiology and the pathogenesis of obesity and type 2 diabetes, suggesting that Smad3 may be a potential target for the treatment of obesity and its associated disorders.

Effect of epinephrine on oxygen consumption and delivery during progressive hemorrhage.

OBJECTIVE: To determine whether, during hemorrhagic shock, the effect of epinephrine on energy metabolism could be deleterious, by enhancing the oxygen requirement at a given level of oxygen delivery (DO2). DESIGN: Prospective, randomized, control trial. SETTING: Experimental laboratory. SUBJECTS: Two groups of seven mongrel dogs were studied. The epinephrine group received a continuous infusion of epinephrine (1 microgram/min/kg) while the control group received saline. INTERVENTION: Dogs were anesthetized with pentobarbital, and shock was produced by stepwise hemorrhage. MEASUREMENTS AND MAIN RESULTS: Oxygen consumption (VO2) was continuously measured by the gas exchange technique, while DO2 was independently calculated from cardiac output (measured by thermodilution) and blood oxygen content. A dual-lines regression fit was applied to the DO2 vs. VO2 plot. The intersection of the two regression lines defined the critical value of DO2. Values above critical DO2 belonged to phase 1, while phase 2 occurred below critical DO2. In the control group, VO2 was independent of DO2 during phase 1; VO2 was dependent on DO2 during phase 2. In the epinephrine group, the expected increase in VO2 (+19%) and DO2 (+50%) occurred under normovolemic conditions. During hemorrhage, VO2 immediately decreased, and the slope of phase 1 was significantly (p < .01) different from zero, and was significantly (p < .05) steeper than in the control group (0.025 +/- 0.005 vs. 0.005 +/- 0.010). However, the critical DO2 (8.7 +/- 1.7 vs. 9.7 +/- 2.4 mL/min/kg), the critical VO2 (5.6 +/- 0.5 vs. 5.5 +/- 0.9 mL/min/kg), and the slope of phase 2 (0.487 +/- 0.080 vs. 0.441 +/- 0.130) were not different from control values. CONCLUSIONS: The administration of pharmacologic doses of epinephrine significantly increased VO2 under normovolemic conditions due to the epinephrine-induced thermogenic effect. This effect progressively decreased during hemorrhage. The critical DO2 and the relationship between DO2 and VO2 in the supply-dependent phase of shock were unaffected by epinephrine infusion. These results suggest that during hemorrhagic shock, epinephrine administration did not exert a detrimental effect on the relationship between DO2 and VO2.

Hierarchical conditional random fields for parts based models matching

A parts based model is a parametrization of an object class using a collection of landmarks following the object structure. The matching of parts based models is one of the problems where pairwise Conditional Random Fields have been successfully applied. The main reason of their effectiveness is tractable inference and learning due to the simplicity of involved graphs, usually trees. However, these models do not consider possible patterns of statistics among sets of landmarks, and thus they sufffer from using too myopic information. To overcome this limitation, we propoese a novel structure based on a hierarchical Conditional Random Fields, which we explain in the first part of this memory. We build a hierarchy of combinations of landmarks, where matching is performed taking into account the whole hierarchy. To preserve tractable inference we effectively sample the label set. We test our method on facial feature selection and human pose estimation on two challenging datasets: Buffy and MultiPIE. In the second part of this memory, we present a novel approach to multiple kernel combination that relies on stacked classification. This method can be used to evaluate the landmarks of the parts-based model approach. Our method is based on combining responses of a set of independent classifiers for each individual kernel. Unlike earlier approaches that linearly combine kernel responses, our approach uses them as inputs to another set of classifiers. We will show that we outperform state-of-the-art methods on most of the standard benchmark datasets.