Selectome update: quality control and computational improvements to a database of positive selection.

Selectome (http://selectome.unil.ch/) is a database of positive selection, based on a branch-site likelihood test. This model estimates the number of nonsynonymous substitutions (dN) and synonymous substitutions (dS) to evaluate the variation in selective pressure (dN/dS ratio) over branches and over sites. Since the original release of Selectome, we have benchmarked and implemented a thorough quality control procedure on multiple sequence alignments, aiming to provide minimum false-positive results. We have also improved the computational efficiency of the branch-site test implementation, allowing larger data sets and more frequent updates. Release 6 of Selectome includes all gene trees from Ensembl for Primates and Glires, as well as a large set of vertebrate gene trees. A total of 6810 gene trees have some evidence of positive selection. Finally, the web interface has been improved to be more responsive and to facilitate searches and browsing.

Evolution of self-organized division of labor in a response threshold model.

Division of labor in social insects is determinant to their ecological success. Recent models emphasize that division of labor is an emergent property of the interactions among nestmates obeying to simple behavioral rules. However, the role of evolution in shaping these rules has been largely neglected. Here, we investigate a model that integrates the perspectives of self-organization and evolution. Our point of departure is the response threshold model, where we allow thresholds to evolve. We ask whether the thresholds will evolve to a state where division of labor emerges in a form that fits the needs of the colony. We find that division of labor can indeed evolve through the evolutionary branching of thresholds, leading to workers that differ in their tendency to take on a given task. However, the conditions under which division of labor evolves depend on the strength of selection on the two fitness components considered: amount of work performed and on worker distribution over tasks. When selection is strongest on the amount of work performed, division of labor evolves if switching tasks is costly. When selection is strongest on worker distribution, division of labor is less likely to evolve. Furthermore, we show that a biased distribution (like 3:1) of workers over tasks is not easily achievable by a threshold mechanism, even under strong selection. Contrary to expectation, multiple matings of colony foundresses impede the evolution of specialization. Overall, our model sheds light on the importance of considering the interaction between specific mechanisms and ecological requirements to better understand the evolutionary scenarios that lead to division of labor in complex systems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00265-012-1343-2) contains supplementary material, which is available to authorized users.

A pilot study on subject-based comprehensive steroid profiling: novel biomarkers to detect testosterone misuse in sports.

Context:  Until now, the testosterone/epitestosterone (T/E) ratio is the main marker for detection of testosterone (T) misuse in athletes. As this marker can be influenced by a number of confounding factors, additional steroid profile parameters indicating T misuse can provide substantiating evidence of doping with endogenous steroids. The evaluation of a steroid profile is currently based upon population statistics. Since large inter-individual variations exist, a paradigm shift towards subject-based references is ongoing in doping analysis. Objective:  Proposition of new biomarkers for the detection of testosterone in sports using extensive steroid profiling and an adaptive model based upon Bayesian inference. Subjects:  6 healthy male volunteers were administered with testosterone undecanoate. Population statistics were performed upon steroid profiles from 2014 male Caucasian athletes participating in official sport competition. Design:  An extended search for new biomarkers in a comprehensive steroid profile combined with Bayesian inference techniques as used in the Athlete Biological Passport resulted in a selection of additional biomarkers that may improve detection of testosterone misuse in sports. Results:  Apart from T/E, 4 other steroid ratios (6α-OH-androstenedione/16α-OH-dehydroepiandrostenedione, 4-OH-androstenedione/16α-OH-androstenedione, 7α-OH-testosterone/7β-OH-dehydroepiandrostenedione and dihydrotestosterone/5β-androstane-3α,17β-diol) were identified as sensitive urinary biomarkers for T misuse. These new biomarkers were rated according to relative response, parameter stability, detection time and discriminative power. Conclusion:  Newly selected biomarkers were found suitable for individual referencing within the concept of the Athlete's Biological Passport. The parameters showed improved detection time and discriminative power compared to the T/E ratio. Such biomarkers can support the evidence of doping with small oral doses of testosterone.

Active Contour-Based Segmentation of Head and Neck with Adaptive Atlas Selection

This paper presents automated segmentation of structuresin the Head and Neck (H\&N) region, using an activecontour-based joint registration and segmentation model.A new atlas selection strategy is also used. Segmentationis performed based on the dense deformation fieldcomputed from the registration of selected structures inthe atlas image that have distinct boundaries, onto thepatient's image. This approach results in robustsegmentation of the structures of interest, even in thepresence of tumors, or anatomical differences between theatlas and the patient image. For each patient, an atlasimage is selected from the available atlas-database,based on the similarity metric value, computed afterperforming an affine registration between each image inthe atlas-database and the patient's image. Unlike manyof the previous approaches in the literature, thesimilarity metric is not computed over the entire imageregion; rather, it is computed only in the regions ofsoft tissue structures to be segmented. Qualitative andquantitative evaluation of the results is presented.

Group selection and kin selection: two concepts but one process.

In a recent paper, Traulsen and Nowak use a multilevel selection model to show that cooperation can be favored by group selection in finite populations [Traulsen A, Nowak M (2006) Proc Natl Acad Sci USA 103:10952-10955]. The authors challenge the view that kin selection may be an appropriate interpretation of their results and state that group selection is a distinctive process "that permeates evolutionary processes from the emergence of the first cells to eusociality and the economics of nations." In this paper, we start by addressing Traulsen and Nowak's challenge and demonstrate that all their results can be obtained by an application of kin selection theory. We then extend Traulsen and Nowak's model to life history conditions that have been previously studied. This allows us to highlight the differences and similarities between Traulsen and Nowak's model and typical kin selection models and also to broaden the scope of their results. Our retrospective analyses of Traulsen and Nowak's model illustrate that it is possible to convert group selection models to kin selection models without disturbing the mathematics describing the net effect of selection on cooperation.

ABCtoolbox: a versatile toolkit for approximate Bayesian computations.

BACKGROUND: The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. RESULTS: Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC). It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABCtoolbox can also interact with most simulation and summary statistics computation programs. The usability of the ABCtoolbox is demonstrated by inferring the evolutionary history of two evolutionary lineages of Microtus arvalis. Using nuclear microsatellites and mitochondrial sequence data in the same estimation procedure enabled us to infer sex-specific population sizes and migration rates and to find that males show smaller population sizes but much higher levels of migration than females. CONCLUSION: ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results.

An immune-competent model of spontaneous breast cancer metastasis to the brain

In breast cancer, brain metastases are often seen as late complications of recurrent disease and represent a particularly serious condition, since there are limited therapeutic options and patients have an unfavorable prognosis. The frequency of brain metastases in breast cancer is currently on the rise. This might be due to the fact that adjuvant chemotherapeutic and targeted anticancer drugs, while they effectively control disease progression in the periphery, they only poorly cross the blood-brain barrier and do not reach effectively cancer cells disseminated in the brain. It is therefore of fundamental clinical relevance to investigate mechanisms involved in breast cancer metastasis to the brain. To date experimental models of breast cancer metastasis to the brain described in literature are based on the direct intracarotid or intracardiac injection of breast cancer cells. We recently established a brain metastasis breast cancer model in immunocompetent mice based on the orthotopic injection of 4T1 murine breast carcinoma cells in the mammary gland of syngeneic BALB/c mice. 4T1-derived tumors recapitulate the main steps of human breast cancer progression, including epithelial-to-mesenchymal transition, local invasion and metastatic spreading to lung and lymph nodes. 4T1 cells were engineered to stably express firefly Luciferase allowing noninvasive in vivo and ex vivo monitoring of tumor progression and metastatic spreading to target organs. Bioluminescence imaging revealed the appearance of spontaneous lesions to the lung and lymph nodes and, at a much lower frequency, to the brain. Brain metastases were confirmed by macroscopic and microscopic evaluation of the brains at necropsy. We then isolated brain metastatic cells, re-injected them orthotopically in new mice and isolated again lines from brain metastases. After two rounds of selection we obtained lines metastasizing to the brain with 100% penetrance (named 4T1-BM2 for Brain Metastasis, 2nd generation) compared to lines derived after two rounds of in vivo growth from primary tumors (4T1-T2) or from lung metastases (4T1-LM2). We are currently performing experiments to unravel differences in cell proliferation, adhesion, migration, invasion and survival of the 4T1-BM2 line relative to the 4T1-T2 and 4T1-LM2 lines. Initial results indicate that 4T1-BM2 cells are not more invasive or more proliferative in vitro and do not show a more mesenchymal phenotype. Our syngeneic (BALB/c) model of spontaneous breast carcinoma metastasis to the brain is a unique and clinically relevant model to unravel the mechanisms of metastatic breast cancer colonization of the brain. Genes identified in this model represent potentially clinically relevant therapeutic targets for the prevention and the treatment of brain metastases in breast cancer patients.

Improvement of therapeutic drug monitoring of imatinib by Bayesian prediction of trough levels. Personalized drug dosage

Aims: Plasma concentrations of imatinib differ largely between patients despite same dosage, owing to large inter-individual variability in pharmacokinetic (PK) parameters. As the drug concentration at the end of the dosage interval (Cmin) correlates with treatment response and tolerability, monitoring of Cmin is suggested for therapeutic drug monitoring (TDM) of imatinib. Due to logistic difficulties, random sampling during the dosage interval is however often performed in clinical practice, thus rendering the respective results not informative regarding Cmin values.Objectives: (I) To extrapolate randomly measured imatinib concentrations to more informative Cmin using classical Bayesian forecasting. (II) To extend the classical Bayesian method to account for correlation between PK parameters. (III) To evaluate the predictive performance of both methods.Methods: 31 paired blood samples (random and trough levels) were obtained from 19 cancer patients under imatinib. Two Bayesian maximum a posteriori (MAP) methods were implemented: (A) a classical method ignoring correlation between PK parameters, and (B) an extended one accounting for correlation. Both methods were applied to estimate individual PK parameters, conditional on random observations and covariate-adjusted priors from a population PK model. The PK parameter estimates were used to calculate trough levels. Relative prediction errors (PE) were analyzed to evaluate accuracy (one-sample t-test) and to compare precision between the methods (F-test to compare variances).Results: Both Bayesian MAP methods allowed non-biased predictions of individual Cmin compared to observations: (A) - 7% mean PE (CI95% - 18 to 4 %, p = 0.15) and (B) - 4% mean PE (CI95% - 18 to 10 %, p = 0.69). Relative standard deviations of actual observations from predictions were 22% (A) and 30% (B), i.e. comparable to the intraindividual variability reported. Precision was not improved by taking into account correlation between PK parameters (p = 0.22).Conclusion: Clinical interpretation of randomly measured imatinib concentrations can be assisted by Bayesian extrapolation to maximum likelihood Cmin. Classical Bayesian estimation can be applied for TDM without the need to include correlation between PK parameters. Both methods could be adapted in the future to evaluate other individual pharmacokinetic measures correlated to clinical outcomes, such as area under the curve(AUC).

Bayesian-MCMC inversion of time-lapse geophysical data to characterize vadose zone hydraulic behaviour

Geophysical methods have the potential to provide valuable information on hydrological properties in the unsaturated zone. In particular, time-lapse geophysical data, when coupled with a hydrological model and inverted stochastically, may allow for the effective estimation of subsurface hydraulic parameters and their corresponding uncertainties. In this study, we use a Bayesian Markov-chain-Monte-Carlo (MCMC) inversion approach to investigate how much information regarding vadose zone hydraulic properties can be retrieved from time-lapse crosshole GPR data collected at the Arrenaes field site in Denmark during a forced infiltration experiment.

Comprehensive approach for the detection of antifungal compounds using a susceptible strain of Candida albicans and confirmation of in vivo activity with the Galleria mellonella model.

An efficient screening strategy for the identification of potentially interesting low-abundance antifungal natural products in crude extracts that combines both a sensitive bioautography assay and high performance liquid chromatography (HPLC) microfractionation was developed. This method relies on high performance thin layer chromatography (HPTLC) bioautography with a hypersusceptible engineered strain of Candida albicans (DSY2621) for bioactivity detection, followed by the evaluation of wild type strains in standard microdilution antifungal assays. Active extracts were microfractionated by HPLC in 96-well plates, and the fractions were subsequently submitted to the bioassay. This procedure enabled precise localisation of the antifungal compounds directly in the HPLC chromatograms of the crude extracts. HPLC-PDA-mass spectrometry (MS) data obtained in parallel to the HPLC antifungal profiles provided a first chemical screening about the bioactive constituents. Transposition of the HPLC analytical conditions to medium-pressure liquid chromatography (MPLC) allowed the efficient isolation of the active constituents in mg amounts for structure confirmation and more extensive characterisation of their biological activities. The antifungal properties of the isolated natural products were evaluated by their minimum inhibitory concentration (MIC) in a dilution assay against both wild type and engineered strains of C. albicans. The biological activity of the most promising agents was further evaluated in vitro by electron microscopy and in vivo in a Galleria mellonella model of C. albicans infection. The overall procedure represents a rational and comprehensive means of evaluating antifungal activity from various perspectives for the selection of initial hits that can be explored in more in-depth mode-of-action studies. This strategy is illustrated by the identification and bioactivity evaluation of a series of antifungal compounds from the methanolic extract of a Rubiaceae plant, Morinda tomentosa, which was used as a model in these studies.

Strategy selection: An introduction to the modeling challenge

Modeling the mechanisms that determine how humans and other agents choose among different behavioral and cognitive processes-be they strategies, routines, actions, or operators-represents a paramount theoretical stumbling block across disciplines, ranging from the cognitive and decision sciences to economics, biology, and machine learning. By using the cognitive and decision sciences as a case study, we provide an introduction to what is also known as the strategy selection problem. First, we explain why many researchers assume humans and other animals to come equipped with a repertoire of behavioral and cognitive processes. Second, we expose three descriptive, predictive, and prescriptive challenges that are common to all disciplines which aim to model the choice among these processes. Third, we give an overview of different approaches to strategy selection. These include cost‐benefit, ecological, learning, memory, unified, connectionist, sequential sampling, and maximization approaches. We conclude by pointing to opportunities for future research and by stressing that the selection problem is far from being resolved.

The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in GC

Positive selection is widely estimated from protein coding sequence alignments by the nonsynonymous-to-synonymous ratio omega. Increasingly elaborate codon models are used in a likelihood framework for this estimation. Although there is widespread concern about the robustness of the estimation of the omega ratio, more efforts are needed to estimate this robustness, especially in the context of complex models. Here, we focused on the branch-site codon model. We investigated its robustness on a large set of simulated data. First, we investigated the impact of sequence divergence. We found evidence of underestimation of the synonymous substitution rate for values as small as 0.5, with a slight increase in false positives for the branch-site test. When dS increases further, underestimation of dS is worse, but false positives decrease. Interestingly, the detection of true positives follows a similar distribution, with a maximum for intermediary values of dS. Thus, high dS is more of a concern for a loss of power (false negatives) than for false positives of the test. Second, we investigated the impact of GC content. We showed that there is no significant difference of false positives between high GC (up to similar to 80%) and low GC (similar to 30%) genes. Moreover, neither shifts of GC content on a specific branch nor major shifts in GC along the gene sequence generate many false positives. Our results confirm that the branch-site is a very conservative test.

Heritability and quantitative genetic divergence of serotiny, a fire-persistence plant trait.

BACKGROUND AND AIMS: Although it is well known that fire acts as a selective pressure shaping plant phenotypes, there are no quantitative estimates of the heritability of any trait related to plant persistence under recurrent fires, such as serotiny. In this study, the heritability of serotiny in Pinus halepensis is calculated, and an evaluation is made as to whether fire has left a selection signature on the level of serotiny among populations by comparing the genetic divergence of serotiny with the expected divergence of neutral molecular markers (QST-FST comparison). METHODS: A common garden of P. halepensis was used, located in inland Spain and composed of 145 open-pollinated families from 29 provenances covering the entire natural range of P. halepensis in the Iberian Peninsula and Balearic Islands. Narrow-sense heritability (h(2)) and quantitative genetic differentiation among populations for serotiny (QST) were estimated by means of an 'animal model' fitted by Bayesian inference. In order to determine whether genetic differentiation for serotiny is the result of differential natural selection, QST estimates for serotiny were compared with FST estimates obtained from allozyme data. Finally, a test was made of whether levels of serotiny in the different provenances were related to different fire regimes, using summer rainfall as a proxy for fire regime in each provenance. KEY RESULTS: Serotiny showed a significant narrow-sense heritability (h(2)) of 0·20 (credible interval 0·09-0·40). Quantitative genetic differentiation among provenances for serotiny (QST = 0·44) was significantly higher than expected under a neutral process (FST = 0·12), suggesting adaptive differentiation. A significant negative relationship was found between the serotiny level of trees in the common garden and summer rainfall of their provenance sites. CONCLUSIONS: Serotiny is a heritable trait in P. halepensis, and selection acts on it, giving rise to contrasting serotiny levels among populations depending on the fire regime, and supporting the role of fire in generating genetic divergence for adaptive traits.

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.

Bayesian Networks and the Value of the Evidence for the Forensic Two-Trace Transfer Problem

Forensic scientists face increasingly complex inference problems for evaluating likelihood ratios (LRs) for an appropriate pair of propositions. Up to now, scientists and statisticians have derived LR formulae using an algebraic approach. However, this approach reaches its limits when addressing cases with an increasing number of variables and dependence relationships between these variables. In this study, we suggest using a graphical approach, based on the construction of Bayesian networks (BNs). We first construct a BN that captures the problem, and then deduce the expression for calculating the LR from this model to compare it with existing LR formulae. We illustrate this idea by applying it to the evaluation of an activity level LR in the context of the two-trace transfer problem. Our approach allows us to relax assumptions made in previous LR developments, produce a new LR formula for the two-trace transfer problem and generalize this scenario to n traces.