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Background Global estimates for cholera annually approximate 4 million cases worldwide with 95,000 deaths. Recent outbreaks, including Haiti and Yemen, are reminders that cholera is still a global health concern. Cholera outbreaks can rapidly induce high death tolls by overwhelming the capacity of health facilities, especially in remote areas or areas of civil unrest. Recent studies demonstrated that stool specimens preserved on filter paper facilitate molecular analysis of Vibrio cholerae in resource limited settings. Specimens preserved in a rapid, low-cost, safe and sustainable manner for sequencing provides previously unavailable data about circulating cholera strains. This may ultimately contribute new information to shape public policy response on cholera control and elimination. Methodology/Principal findings Whole genome sequencing (WGS) recovered close to a complete sequence of the V. cholerae O1 genome with satisfactory genome coverage from stool specimens enriched in alkaline peptone water (APW) and V. cholerae culture isolates, both spotted on filter paper. The minimum concentration of V. cholerae DNA sufficient to produce quality genomic information was 0.02 ng/μL. The genomic data confirmed the presence or absence of genes of epidemiological interest, including cholera toxin and pilus loci. WGS identified a variety of diarrheal pathogens from APW-enriched specimen spotted filter paper, highlighting the potential for this technique to explore the gut microbiome, potentially identifying co-infections, which may impact the severity of disease. WGS demonstrated that these specimens fit within the current global cholera phylogenetic tree, identifying the strains as the 7th pandemic El Tor. Conclusions WGS results allowed for mapping of short reads from APW-enriched specimen and culture isolate spotted filter papers. This provided valuable molecular epidemiological sequence information on V. cholerae strains from remote, low-resource settings. These results identified the presence of co-infecting pathogens while providing rare insight into the specific V. cholerae strains causing outbreaks in cholera-endemic areas., Author summary Cholera affects more than 4 million people globally every year; people predominantly living in poverty or in resource-constrained conditions including political crises or natural disasters. Cholera’s typical presentation is characterized by rapid onset of acute watery diarrhea and vomiting which can progress from watery stool to shock in as little as four hours. Laboratory conditions needed for culture confirmation and strain preservation are rarely to never present in these affected areas. In fact, many cholera endemic areas in Sub-Saharan African are so remote that even treatment response alone is often challenging. Here we present the genomic analysis of DNA extracted from dried filter paper, which is a low-cost, low-tech and sustainable method. Previously this method has facilitated cholera confirmation by PCR, but we demonstrate that this method is also suitable for whole genome sequencing and subsequent strain characterization by presenting the analysis of samples from an outbreak in a remote area of Cameroon. This method will facilitate the understanding of the molecular epidemiology in cholera-prone areas, which were previously too challenging to attempt. It also introduces a method that can be used on a broader scale for diarrheal disease surveillance, including providing a window into co-infection and microbiome analyses.

International audience; Background: Identification of the source of mosquito blood meals is an important component for disease control and surveillance. Recently, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) profiling has emerged as an effective tool for mosquito blood meal identification, using the abdomens of freshly engorged mosquitoes. In the field, mosquito abdomens are crushed on Whatman filter papers to determine the host feeding patterns by identifying the origin of their blood meals. The aim of this study was to test whether crushing engorged mosquito abdomens on Whatman filter papers was compatible with MALDI-TOF MS for mosquito blood meal identification. Both laboratory reared and field collected mosquitoes were tested.Material and methods: Sixty Anopheles gambiae Giles were experimentally engorged on the blood of six distinct vertebrate hosts (human, sheep, rabbit, dog, chicken and rat). The engorged mosquito abdomens were crushed on Whatman filter papers for MALDI-TOF MS analysis. 150 Whatman filter papers, with mosquitoes engorged on cow and goat blood, were preserved. A total of 77 engorged mosquito abdomens collected in the Comoros Islands and crushed on Whatman filter papers were tested with MALDI-TOF MS. Results The MS profiles generated from mosquito engorged abdomens crushed on Whatman filter papers exhibited high reproducibility according to the original host blood. The blood meal host was correctly identified from mosquito abdomens crushed on Whatman filter papers by MALDI-TOF MS. The MS spectra obtained after storage were stable regardless of the room temperature and whether or not they were frozen. The MS profiles were reproducible for up to three months. For the Comoros samples, 70/77 quality MS spectra were obtained and matched with human blood spectra. This was confirmed by molecular tools.Conclusion: The results demonstrated that MALDI-TOF MS could identify mosquito blood meals from Whatman filter papers collected in the field during entomological surveys. The application of MALDI-TOF MS has proved to be rapid and successful, making it a new and efficient tool for mosquito-borne disease surveillance.

International audience; Oxford Nanopore Technologies' (ONT) long read sequencers offer access to longer DNA fragments than previous sequencer generations, at the cost of a higher error rate. While many papers have studied read correction methods, few have addressed the detailed characterization of observed errors, a task complicated by frequent changes in chemistry and software in ONT technology. The MinION sequencer is now more stable and this paper proposes an up-to-date view of its error landscape, using the most mature flowcell and basecaller. We studied Nanopore sequencing error biases on both bacterial and human DNA reads. We found that, although Nanopore sequencing is expected not to suffer from GC bias, it is a crucial parameter with respect to errors. In particular, low-GC reads have fewer errors than high-GC reads (about 6% and 8% respectively). The error profile for homopolymeric regions or regions with short repeats, the source of about half of all sequencing errors, also depends on the GC rate and mainly shows deletions, although there are some reads with long insertions. Another interesting finding is that the quality measure, although overestimated , offers valuable information to predict the error rate as well as the abundance of reads. We supplemented this study with an analysis of a rapeseed RNA read set and shown a higher level of errors with a higher level of deletion in these data. Finally, we have implemented an open source pipeline for long-term monitoring of the error profile, which enables users to easily compute various analysis presented in this work, including for future developments of the sequencing device. Overall, we hope this work will provide a basis for the design of better error-correction methods.

International audience; This paper aims to further increase the reliability of optimal results by setting the simulation conditions to be as close as possible to the real or actual operation to create a Cyber-Physical System (CPS) view for the installation of the Fractional-Order PID (FOPID) controller. For this purpose, we consider two different sources of variability in such a CPS control model. The first source refers to the changeability of a target of the control model (multiple setpoints) because of environmental noise factors and the second source refers to an anomaly in sensors that is raised in a feedback loop. We develop a new approach to optimize two objective functions under uncertainty including signal energy control and response error control while obtaining the robustness among the source of variability with the lowest computational cost. A new hybrid surrogate-metaheuristic approach is developed using Particle Swarm Optimization (PSO) to update the Gaussian Process (GP) surrogate for a sequential improvement of the robust optimal result. The application of efficient global optimization is extended to estimate surrogate prediction error with less computational cost using a jackknife leave-one-out estimator. This paper examines the challenges of such a robust multi-objective optimization for FOPID control of a five-bar linkage robot manipulator. The results show the applicability and effectiveness of our proposed method in obtaining robustness and reliability in a CPS control system by tackling required computational efforts.

Even if shared mobility services are encouraged by transportation policies, they remain underused and inefficient transportation modes because they struggle to find their customer base. This paper aims to estimate the potential demand for such services by focusing on individual trips and determining the number of passengers who perform similar trips. Contrary to existing papers, this study focuses on the demand without assuming any specific shared mobility system. The experiment performed on data coming from New York City conducts to cluster more than 85% of the trips. Consequently, shared mobility services such as ride-sharing can find their customer base and, at a long time, to a significantly reduce the number of cars flowing in the city. After a detailed analysis, commonalities in the clusters are identified: regular patterns from one day to the next exist in shared mobility demand. This regularity makes it possible to anticipate the potential shared mobility demand to help transportation suppliers to optimize their operations.

This paper focuses on determining the structural similarity of two molecules, i.e., the similarity of the interconnection of all the elementary cycles in the corresponding molecular graphs. In this paper, we propose and analyze an algorithmic approach based on the resolution of the Maximum Common Edge Subgraph (MCES) problem with graphs representing the interaction of cycles molecules. Using the ChEBI database, we compare the effectiveness of this approach in terms of structural similarity and computation time with two calculations of similarity of molecular graphs, one based on the MCES, the other on the use of different fingerprints (Daylight, ECFP4, ECFP6, FCFP4, FCFP6) to measure Tanimoto coefficient. We also analyze the obtained structural similarity results for a selected subset of molecules.

AGAP: équipe GE2pop; International audience; Genetic maps order genetic markers along chromosomes. They are, for instance, extensively used in marker-assisted selection to accelerate breeding programs. Even for the same species, people often have to deal with several alternative maps obtained using different ordering methods or different datasets, e.g. resulting from different segregating populations. Having efficient tools to identify the consistency and discrepancy of alternative maps is thus essential to facilitate genetic map comparisons. We propose to encode genetic maps by bucket order, a kind of order, which takes into account the blurred parts of the marker order while being an efficient data structure to achieve low complexity algorithms. The main result of this paper is an O(n log(n)) procedure to identify the largest agreements between two bucket orders of n elements, their Longest Common Subsequence (LCS), providing an efficient solution to highlight discrepancies between two genetic maps. The LCS of two maps, being the largest set of their collinear markers, is used as a building block to compute pairwise map congruence, to visually emphasize maker collinearity and in some scaffolding methods relying on genetic maps to improve genome assembly. As the LCS computation is a key subroutine of all these genetic map related tools, replacing the current LCS subroutine of those methods by ours - to do the exact same work but faster-could significantly speed up those methods without changing their accuracy. To ease such transition we provide all required algorithmic details in this self contained paper as well as an R package implementing them, named LCSLCIS, which is freely available at: https://github.com/holtzy/LCSLCIS.

International audience; The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL), and intermittent hypoxic exposure (IHE)) on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a ‘magnitude-based inference’ approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.

The formation of a diploid zygote is a highly complex cellular process that is entirely controlled by maternal gene products stored in the egg cytoplasm. This highly specialized transcriptional program is tightly controlled at the chromatin level in the female germline. As an extreme case in point, the massive and specific ovarian expression of the essential thioredoxin Deadhead (DHD) is critically regulated in Drosophila by the histone demethylase Lid and its partner, the histone deacetylase complex Sin3A/Rpd3, via yet unknown mechanisms. Here, we identified Snr1 and Mod(mdg4) as essential for dhd expression and investigated how these epigenomic effectors act with Lid and Sin3A to hyperactivate dhd. Using Cut&Run chromatin profiling with a dedicated data analysis procedure, we found that dhd is intriguingly embedded in an H3K27me3/H3K9me3-enriched mini-domain flanked by DNA regulatory elements, including a dhd promoter-proximal element essential for its expression. Surprisingly, Lid, Sin3a, Snr1 and Mod(mdg4) impact H3K27me3 and this regulatory element in distinct manners. However, we show that these effectors activate dhd independently of H3K27me3/H3K9me3, and that dhd remains silent in the absence of these marks. Together, our study demonstrates an atypical and critical role for chromatin regulators Lid, Sin3A, Snr1 and Mod(mdg4) to trigger tissue-specific hyperactivation within a unique heterochromatin mini-domain., Author summary Multicellular development depends on a tight control of gene expression in each cell type. This relies on the coordinated activities of nuclear proteins that interact with DNA or its histone scaffold to promote or restrict gene transcription. For example, we previously showed that the histone modifying enzymes Lid and Sin3A/Rpd3 are required in Drosophila ovaries for the massive expression of deadhead (dhd), a gene encoding for a thioredoxin that is essential for fertility. In this paper, we have further identified two additional dhd regulators, Snr1 and Mod(mdg4) and dissected the mechanism behind hyperactivation of this gene. Using the epigenomic profiling method Cut&Run with a dedicated data analysis approach, we unexpectedly found that dhd is embedded in an unusual chromatin mini-domain featuring repressive histone modifications H3K27me3 and H3K9me3 and flanked by two regulatory elements. However, we further showed that Lid, Sin3A, Snr1 and Mod(mdg4) behave like obligatory activators of dhd independently of this mini-domain. Our study unveils how multiple broad-acting epigenomic effectors operate in non-canonical manners to ensure a critical and specialized gene activation event. These findings challenge our knowledge on these regulatory mechanisms and their roles in development and pathology.

International audience; The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis-Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders.

Disease mapping aims to determine the underlying disease risk from scattered epidemiological data and to represent it on a smoothed colored map. This methodology is based on Bayesian inference and is classically dedicated to non-infectious diseases whose incidence is low and whose cases distribution is spatially (and eventually temporally) structured. Over the last decades, disease mapping has received many major improvements to extend its scope of application: integrating the temporal dimension, dealing with missing data, taking into account various a prioris (environmental and population covariates, assumptions concerning the repartition and the evolution of the risk), dealing with overdispersion, etc. We aim to adapt this approach to rare infectious diseases. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood; this can result in local overdispersion and in higher spatial and temporal dependencies due to direct and/or indirect transmission. We have proposed and tested 60 Bayesian hierarchical models on 400 simulated datasets and bovine tuberculosis real data. This analysis shows the relevance of the CAR (Conditional AutoRegressive) processes to deal with the structure of the risk. We can also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. In addition our study provided relevant maps which are congruent with the real risk (simulated data) and with the knowledge concerning bovine tuberculosis (real data).Author summaryDisease mapping is dedicated to non-infectious diseases whose incidence is low and whose distribution is spatially (and eventually temporally) structured. In this paper, we aim to adapt this approach to rare infectious pathologies. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood, resulting in local overdispersion and in high spatial and temporal dependencies. We thus explored different adapted spatial, temporal and spatiotemporal links and highlight the most adapted to likely risk structures for infectious diseases. We also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. Our study also provided relevant maps which are congruent with the real risk (in case of simulated data) and with the knowledge concerning bovine tuberculosis (when applying to real data). Thus disease mapping appears as a promising way to investigate rare infectious diseases.

Déposé dans medRxiv le 4 août 2020 : https://doi.org/10.1101/2020.08.01.20166538; International audience; To respond to the urgent need for COVID-19 testing, countries perform nucleic acid amplification tests (NAAT) for the detection of SARS-CoV-2 in centralized laboratories. Real-time RT-PCR (Reverse transcription-Polymerase Chain Reaction), used to amplify and detect the viral RNA., is considered, as the current gold standard for diagnostics. It is an efficient process, but the complex engineering required for automated RNA extraction and temperature cycling makes it incompatible for use in point of care settings [1]. In the present work, by harnessing progress made in the past two decades in isothermal amplification and paper microfluidics, we created a portable test, in which SARS-CoV-2 RNA is extracted, amplified isothermally by RT-LAMP (Loop-mediated Isothermal Amplification), and detected using intercalating dyes or fluorescent probes. Depending on the viral load in the tested samples, the detection takes between twenty minutes and one hour. Using a set of 16 pools of naso-pharyngal swab eluates, we estimated a limit of detection comparable to real-time RT-PCR (i.e. 1 genome copies per microliter of clinical sample) and no cross-reaction with eight major respiratory viruses currently circulating in Europe. We designed and fabricated an easy-to-use portable device called "COVIDISC" to carry out the test at the point of care. The low cost of the materials along with the absence of complex equipment will expedite the widespread dissemination of this device. What is proposed here is a new efficient tool to help managing the pandemics.

Gene regulatory networks exhibiting Boolean behaviour, e.g. AND, OR or XOR, have been routinely designed for years. However, achieving more sophisticated functions, such as control or computation, usually requires sequential circuits or so-called state machines. For such a circuit, outputs depend both on inputs and the current state of the system. Although it is still possible to design such circuits by analogy with digital electronics, some particularities of biology make the task trickier. The impact of two of them, namely the stochasticity of biological processes and the inhomogeneity in the response of regulation mechanisms, are assessed in this paper. Numerical simulations performed in two use cases point out high risks of malfunctions even for designed GRNs functional from a theoretical point of view. Several solutions to improve reliability of such systems are also discussed.

International audience; The Psychometric Hepatic Encephalopathy Score (PHES) has previously been standardized in thirteen countries on three continents, confirming its status of gold standard test to detect minimal hepatic encephalopathy (MHE). In the meantime, performance has also been shown to vary with variables such as age, education, and barely sex. The present study aimed at standardizing the PHES in a French population. One hundred and ninety-six French healthy participants completed a French version of the paper-and-pencil PHES, involving five tests and six measures. Importantly, the balance was perfect between all levels of the three controlled factors, which were sex, age (seven decade-levels from 20-29 to 80-89 years), and education (two levels below or above 12 years of education). Raw measures were transformed to fit the normal distribution. ANOVAs on transformed variables showed no effect of sex, but an effect of age on all measures, and of education on five measures. Multiple or simple regressions were completed to build up normograms. Thorough analysis of variability within each test failed to find outliers that may bias the results. Comparison between French and seminal German data showed that they highly fitted though cultural and cognitive style specificities could be observed. This is the first study to standardize the PHES in a French population and to extensively explore the effects of sex, age and education using perfectly balanced samples. Subtle differences between countries of the same continent emphasize the need to build up normative data in each country to get accurate PHES in patients.

Based on a meta-analysis, this paper highlights the strength and relevance of several social incentive factors concerning pro-environmental behaviors, including social influence, network factors (like network size, network connection and leadership), trust in others, and trust in institutions. Firstly, our results suggest that social influence is necessary for the emergence of pro-environmental behaviors. More specifically, an internal social influence (i.e., motivating people to change their perceptions and attitudes) is essential to promote pro-environmental behaviors. Secondly, network connection encourages pro-environmental behaviors, meaning that the effectiveness of a conservation policy can be improved if connections among individuals are increased. Finally, trust in institutions can dictate individual behaviors to shape policy design and generate desired policy outcomes.

International audience; Sulfur isotope composition of organic tissues is a commonly used tool for gathering information about provenance and diet in archaeology and paleoecology. However, the lack of maps predicting sulfur isotope variations on the landscape limits the possibility to use this isotopic system in quantitative geographic assignments. We compiled a database of 2,680 sulfur isotope analyses in the collagen of archaeological human and animal teeth from 221 individual locations across Western Europe. We used this isotopic compilation and remote sensing data to apply a multivariate machine-learning regression, and to predict sulfur isotope variations across Western Europe. The resulting model shows that sulfur isotope patterns are highly predictable, with 65% of sulfur isotope variations explained using only 4 variables representing marine sulfate deposition and local geological conditions. We used this novel sulfur isoscape and existing strontium and oxygen isoscapes of Western Europe to apply triple isotopes continuous-surface probabilistic geographic assignments to assess the origin of a series of teeth from local animals and humans from Brittany. We accurately and precisely constrained the origin of these individuals to limited regions of Brittany. This approach is broadly transferable to studies in archaeology and paleoecology as illustrated in a companion paper (Colleter et al. 2021).

International audience; Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R 2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.

Fermentation by microorganisms is a key step in the production of traditional food products such as bread, cheese, beer and wine. In these fermentative ecosystems, microorganisms interact in various ways, namely competition, predation, commensalism and mutualism. Traditional wine fermentation is a complex microbial process performed bySaccharomycesand non-Saccharomyces(NS) yeast species. To better understand the different interactions occurring within wine fermentation, isolated yeast cultures were compared with mixed co-cultures of one reference strain ofS. cerevisiaewith one strain of four NS yeast species (Metschnikowia pulcherrima, M. fructicola, Hanseniaspora opuntiae and H. uvarum). In each case, we studied population dynamics, resource consumed and metabolites produced from central carbon metabolism. This phenotyping of competition kinetics allowed us to confirm the main mechanisms of interaction between strains of four NS species.S. cerevisiaecompeted withH. uvarumandH. opuntiaefor resources although bothHanseniasporaspecies were characterized by a strong mortality either in isolated or mixed fermentations.M. pulcherrimaandM. fructicoladisplayed a negative interaction with theS. cerevisiaestrain tested, with a decrease in viability in co-culture, probably due to iron depletion via the production of pulcherriminic acid. Overall, this work highlights the importance of measuring specific cell populations in mixed cultures and their metabolite kinetics to understand yeast-yeast interactions. These results are a first step towards ecological engineering and the rational design of optimal multi-species starter consortia using modeling tools. In particular the originality of this paper is for the first times to highlight the joint-effect of different species population dynamics on glycerol production and also to discuss on the putative role of lipid uptake on the limitation of some non-conventional species growth although interaction processes.

The population genetic structure of crop pest populations gives information about their spatial ecology, which helps in designing management strategies. In this paper, we investigated the genetic structure of the Mediterranean Corn Borer (MCB), Sesamia nonagrioides Lefèbvre (Lepidoptera: Noctuidae), one of the most important maize pests in the Mediterranean countries, using microsatellite markers for the first time in this species. Insects were collected in twenty-five locations in southwest and southeast France from cultivated and wild host plants (Zea mays, Sorghum halepense and Typha domingensis). Contrary to what has been reported so far in France, we found that MCB populations could be locally abundant on wild poales plants. Analysis was carried out at 11 polymorphic microsatellite markers. Molecular variance was significantly determined by geography, then by host plant, with 17% and 4%, respectively, when considered as a major effect, and with 14% and 1%, respectively, when considered as a marginal effect in permutational analysis. Multidimensional scaling (MDS) and GENELAND Bayesian clustering suggested that populations infecting wild plants (T. domingensis and S. halepense) were more structured locally than those affecting cultivated maize. In S. halepense, significant Isolation By Distance (IBD) indicated that this factor could explain genetic differentiation of the moth populations. In T. domingensis, local population differentiation was strong but did not depend on distance. The implication of this absence of population structure in maize and the heterogeneity of population genetics patterns in wild plants are discussed in the context of the population dynamics hypothesis and population management strategies.

Following fertilization of a mature oocyte, the formation of a diploid zygote involves a series of coordinated cellular events that ends with the first embryonic mitosis. In animals, this complex developmental transition is almost entirely controlled by maternal gene products. How such a crucial transcriptional program is established during oogenesis remains poorly understood. Here, we have performed an shRNA-based genetic screen in Drosophila to identify genes required to form a diploid zygote. We found that the Lid/KDM5 histone demethylase and its partner, the Sin3A-HDAC1 deacetylase complex, are necessary for sperm nuclear decompaction and karyogamy. Surprisingly, transcriptomic analyses revealed that these histone modifiers are required for the massive transcriptional activation of deadhead (dhd), which encodes a maternal thioredoxin involved in sperm chromatin remodeling. Unexpectedly, while lid knock-down tends to slightly favor the accumulation of its target, H3K4me3, on the genome, this mark was lost at the dhd locus. We propose that Lid/KDM5 and Sin3A cooperate to establish a local chromatin environment facilitating the unusually high expression of dhd, a key effector of the oocyte-to-zygote transition., Author summary Nuclear enzymes that add or remove epigenetic marks on histone tails potentially control gene expression by affecting chromatin structure and DNA accessibility. For instance, members of the KDM5 family of histone demethylases specifically remove methyl groups on the lysine 4 of histone H3, a mark generally correlated with gene expression. Lid (Little imaginal discs), the Drosophila KDM5, is essential for viability but is also required for female fertility. In this paper, we have found that the specific removal of Lid in developing oocytes perturbs the decompaction of the sperm nucleus at fertilization and the integration of paternal chromosomes in the zygote. Sperm nuclear decompaction normally requires the presence of a small redox protein called Deadhead (Dhd), which is massively expressed at the end of oogenesis. Strikingly, our analyses of ovarian transcriptomes revealed that the absence of Lid completely abolishes the expression of dhd. This direct functional link between a general histone modifier and the expression of an essential terminal effector gene represents a rare finding. We hope that our work will help understanding how histone demethylases function in controlling complex developmental transitions as well as cancer progression.

This paper proposes a model-based estimation of left ventricular (LV) pressure for the evaluation of constructive and wasted myocardial work of patients with aortic stenosis (AS). A model of the cardiovascular system is proposed, including descriptions of i) cardiac electrical activity, ii) elastance-based cardiac cavities, iii) systemic and pulmonary circulations and iv) heart valves. After a sensitivity analysis of model parameters, an identification strategy was implemented using a Monte-Carlo cross-validation approach. Parameter identification procedure consists in two steps for the estimation of LV pressures: step 1) from invasive, intraventricular measurements and step 2) from non-invasive data. The proposed approach was validated on data obtained from 12 patients with AS. The total relative errors between estimated and measured pressures were on average 11.9% and 12.27% and mean R2 were equal to 0.96 and 0.91, respectively for steps 1 and 2 of parameter identification strategy. Using LV pressures obtained from non-invasive measurements (step 2) and patient-specific simulations, Global Constructive (GCW), Wasted (GWW) myocardial Work and Global Work Efficiency (GWE) parameters were calculated. Correlations between measures and model-based estimations were 0.88, 0.80, 0.91 respectively for GCW, GWW and GWE. The main contributions concern the proposal of the parameter identification procedure, applied on an integrated cardiovascular model, able to reproduce LV pressure specifically to each AS patient, by non-invasive procedures, as well as a new method for the non-invasive estimation of constructive, wasted myocardial work and work efficiency in AS.

Using a common experimental framework, this paper addresses both the question of the short-term and the long-lasting effects of temporary monetary and non-monetary incentive mechanisms on increasing individual contributions to the public good. The results show that both punishments and rewards significantly increase contributions compared to the baseline, but that monetary sanctions lead to the highest contributions, whereas non-monetary sanctions lead to the lowest contributions. The four types of incentives display long-lasting effects, i.e., contributions do not go back to baseline levels directly after the withdrawal of the incentives. However, rewards appear to have much stronger persistent effects than sanctions, revealing some sort of delayed reciprocity.

International audience; Classical stochastic models of protein production usually do not consider several phenomena within the cell such as cell volume growth, cell division or gene replication, furthermore concentrations of RNA-polymerases and ribosomes are in general assumed to be constant. In this paper we study the effects induced by all these different aspects on protein variability. Starting from the classical two-stage protein production model, we successively integrate the volume growth, the random partition of compounds at division, the gene replication and the sharing of RNA-polymerases and ribosomes for the production of all types of proteins. Experimental measures are used to determine the numerical values of the parameters of our models. We are then able to investigate the impact of these different phenomena on the variability of a large class of proteins. The main conclusions are that, among all different mechanisms studied, the random partitioning of macro-molecules at division is the only one which may have a significant impact on the variance of protein concentration. In particular, the variability induced by the sharing of RNA-polymerases and ribosomes seems to be of a smaller order of magnitude than what is generally suggested in the literature.

The movement of microswimmers is often described by active Brownian particle models. Here we introduce a variant of these models with several internal states of the swimmer to describe stochastic strategies for directional swimming such as run and tumble or run and reverse that are used by microorganisms for chemotaxis. The model includes a mechanism to generate a directional bias for chemotaxis and interactions with external fields (e.g., gravity, magnetic field, fluid flow) that impose forces or torques on the swimmer. We show how this modified model can be applied to various scenarios: First, the run and tumble motion of E. coli is used to establish a paradigm for chemotaxis and investigate how it is affected by external forces. Then, we study magneto-aerotaxis in magnetotactic bacteria, which is biased not only by an oxygen gradient towards a preferred concentration, but also by magnetic fields, which exert a torque on an intracellular chain of magnets. We study the competition of magnetic alignment with active reorientation and show that the magnetic orientation can improve chemotaxis and thereby provide an advantage to the bacteria, even at rather large inclination angles of the magnetic field relative to the oxygen gradient, a case reminiscent of what is expected for the bacteria at or close to the equator. The highest gain in chemotactic velocity is obtained for run and tumble with a magnetic field parallel to the gradient, but in general a mechanism for reverse motion is necessary to swim against the magnetic field and a run and reverse strategy is more advantageous in the presence of a magnetic torque. This finding is consistent with observations that the dominant mode of directional changes in magnetotactic bacteria is reversal rather than tumbles. Moreover, it provides guidance for the design of future magnetic biohybrid swimmers., Author summary In this paper, we propose a modified Active Brownian particle model to describe bacterial swimming behavior under the influence of external forces and torques, in particular of a magnetic torque. This type of interaction is particularly important for magnetic biohybrids (i.e. motile bacteria coupled to a synthetic magnetic component) and for magnetotactic bacteria (i.e. bacteria with a natural intracellular magnetic chain), which perform chemotaxis to swim along chemical gradients, but are also directed by an external magnetic field. The model allows us to investigate the benefits and disadvantages of such coupling between two different directionality mechanisms. In particular we show that the magnetic torque can speed chemotaxis up in some conditions, while it can hinder it in other cases. In addition to an understanding of the swimming strategies of naturally magnetotactic organisms, the results may guide the design of future biomedical devices.

Background: Medical-related professions are at high suicide risk. However, data are contradictory and comparisons were not made between gender, occupation and specialties, epochs of times. Methods: To conduct a systematic review and meta-analysis on suicide risk among health-care workers, were searched in PubMed, Cochrane Library, Science Direct and Embase databases without language restriction on April 2019. We stratified results by gender, countries, time, and specialties. Estimates were pooled using random-effect meta-analysis. Differences by study-level characteristics were estimated using stratified meta-analysis and meta-regression. Information on suicides, suicidal attempts, and suicidal ideation, and related-characteristics were retrieved from national or local specific registers or case records. In addition, suicide attempts and suicidal ideation were also retrieved from questionnaires (paper or internet). Findings: The overall SMR for suicide in physicians was 1.44 (95CI 1.16, 1.72) with an important heterogeneity (I2=93.9%, p

Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets or biomarkers. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types. In this paper, we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including information about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types. We empirically show that LOTUS outperforms five other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types., Author summary Cancer development is driven by mutations and dysfunction of important, so-called cancer driver genes, that could be targeted by specific therapies. While a number of such cancer genes have already been identified, it is believed that many more remain to be discovered. To help prioritize experimental investigations of candidate genes, several computational methods have been proposed to rank promising candidates based on their mutations in large cohorts of cancer cases, or on their interactions with known driver genes in biological networks. We propose LOTUS, a new computational approach to identify genes with high oncogenic potential. LOTUS implements a machine learning approach to learn an oncogenic potential score from known driver genes, and brings two novelties compared to existing methods. First, it allows to easily combine heterogeneous sources of information into the scoring function, which we illustrate by learning a scoring function from both known mutations in large cancer cohorts and interactions in biological networks. Second, using a multitask learning strategy, it can predict different driver genes for different cancer types, while sharing information between them to improve the prediction for every type. We provide experimental results showing that LOTUS significantly outperforms several state-of-the-art cancer gene prediction software.

The cell cycle is the fundamental process of cell populations, it is regulated by environmental cues and by intracellular checkpoints. Cell cycle variability in clonal cell population is caused by stochastic processes such as random partitioning of cellular components to progeny cells at division and random interactions among biomolecules in cells. One of the important biological questions is how the dynamics at the cell cycle scale, which is related to family dependencies between the cell and its descendants, affects cell population behavior in the long-run. We address this question using a “mechanistic” model, built based on observations of single cells over several cell generations, and then extrapolated in time. We used cell pedigree observations of NIH 3T3 cells including FUCCI markers, to determine patterns of inheritance of cell-cycle phase durations and single-cell protein dynamics. Based on that information we developed a hybrid mathematical model, involving bifurcating autoregression to describe stochasticity of partitioning and inheritance of cell-cycle-phase times, and an ordinary differential equation system to capture single-cell protein dynamics. Long-term simulations, concordant with in vitro experiments, demonstrated the model reproduced the main features of our data and had homeostatic properties. Moreover, heterogeneity of cell cycle may have important consequences during population development. We discovered an effect similar to genetic drift, amplified by family relationships among cells. In consequence, the progeny of a single cell with a short cell cycle time had a high probability of eventually dominating the population, due to the heritability of cell-cycle phases. Patterns of epigenetic heritability in proliferating cells are important for understanding long-term trends of cell populations which are either required to provide the influx of maturing cells (such as hematopoietic stem cells) or which started proliferating uncontrollably (such as cancer cells)., Author summary All cells in multicellular organisms obey orchestrated sequences of signals to ensure developmental and homeostatic fitness under a variety of external stimuli. However, there also exist self-perpetuating stem-cell populations, the function of which is to provide a steady supply of differentiated progenitors that in turn ensure persistence of organism functions. This “cell production engine” is an important element of biological homeostasis. A similar process, albeit distorted in many respects, plays a major role in cancer development; here the robustness of homeostasis contributes to difficulty in eradication of malignancy. An important role in homeostasis seems to be played by generation of heterogeneity among cell phenotypes, which then can be shaped by selection and other genetic forces. In the present paper, we present a model of a cultured cell population, which factors in relationships among related cells and the dynamics of cell growth and important proteins regulating cell division. We find that the model not only maintains homeostasis, but that it also responds to perturbations in a manner that is similar to that exhibited by the Wright-Fisher model of population genetics. The model-cell population can become dominated by the progeny of the fittest individuals, without invoking advantageous mutations. If confirmed, this may provide an alternative mode of evolution of cell populations.

International audience; Object handovers between humans are common in our daily life but the mechanisms underlying handovers are still largely unclear. A good understanding of these mechanisms is important not only for a better understanding of human social behaviors, but also for the prospect of an automatized society in which machines will need to perform similar objects exchanges with humans. In this paper, we analyzed how humans determine the location of object transfer during handovers-to determine whether they can predict the preferred handover location of a partner, the variation of this prediction in 3D space, and to examine how much of a role vision plays in the whole process. For this we developed a paradigm that allows us to compare handovers by humans with and without on-line visual feedback. Our results show that humans have the surprising ability to modulate their handover location according to partners they have just met such that the resulting handover errors are in the order of few centimeters, even in the absence of vision. The handover errors are least along the axis joining the two partners, suggesting a limited role for visual feedback in this direction. Finally, we show that the handover locations are explained very well by a linear model considering the heights, genders and social dominances of the two partners, and the distance between them. We developed separate models for the behavior of 'givers' and 'receivers' and discuss how the behavior of the same individual changes depending on his role in the handover.

Macroscopic oscillations of different brain regions show multiple phase relationships that are persistent across time and have been implicated in routing information. While multiple cellular mechanisms influence the network oscillatory dynamics and structure the macroscopic firing motifs, one of the key questions is to identify the biophysical neuronal and synaptic properties that permit such motifs to arise. A second important issue is how the different neural activity coherence states determine the communication between the neural circuits. Here we analyse the emergence of phase-locking within bidirectionally delayed-coupled spiking circuits in which global gamma band oscillations arise from synaptic coupling among largely excitable neurons. We consider both the interneuronal (ING) and the pyramidal-interneuronal (PING) population gamma rhythms and the inter coupling targeting the pyramidal or the inhibitory neurons. Using a mean-field approach together with an exact reduction method, we reduce each spiking network to a low dimensional nonlinear system and derive the macroscopic phase resetting-curves (mPRCs) that determine how the phase of the global oscillation responds to incoming perturbations. This is made possible by the use of the quadratic integrate-and-fire model together with a Lorentzian distribution of the bias current. Depending on the type of gamma (PING vs. ING), we show that incoming excitatory inputs can either speed up the macroscopic oscillation (phase advance; type I PRC) or induce both a phase advance and a delay (type II PRC). From there we determine the structure of macroscopic coherence states (phase-locking) of two weakly synaptically-coupled networks. To do so we derive a phase equation for the coupled system which links the synaptic mechanisms to the coherence states of the system. We show that a synaptic transmission delay is a necessary condition for symmetry breaking, i.e. a non-symmetric phase lag between the macroscopic oscillations. This potentially provides an explanation to the experimentally observed variety of gamma phase-locking modes. Our analysis further shows that symmetry-broken coherence states can lead to a preferred direction of signal transfer between the oscillatory networks where this directionality also depends on the timing of the signal. Hence we suggest a causal theory for oscillatory modulation of functional connectivity between cortical circuits., Author summary Large scale brain oscillations emerge from synaptic interactions within neuronal circuits. Over the past years, such macroscopic rhythms have been suggested to play a crucial role in routing the flow of information across cortical regions, resulting in a functional connectome. The underlying mechanism is cortical oscillations that bind together following a well-known motif called phase-locking. While there is significant experimental support for multiple phase-locking modes in the brain, it is still unclear what is the underlying mechanism that permits macroscopic rhythms to phase lock. In the present paper we take up with this issue, and to show that, one can study the emergent macroscopic phase-locking within the mathematical framework of weakly coupled oscillators. We find that under synaptic delays, fully symmetrically coupled networks can display symmetry-broken states of activity, where one network starts to lead in phase the second (also sometimes known as stuttering states). When we analyse how incoming transient signals affect the coupled system, we find that in the symmetry-broken state, the effect depends strongly on which network is targeted (the leader or the follower) as well as the timing of the input. Hence we show how the dynamics of the emergent phase-locked activity imposes a functional directionality on how signals are processed. We thus offer clarification on the synaptic and circuit properties responsible for the emergence of multiple phase-locking patterns and provide support for its functional implication in information transfer.

An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants. In other words, why do some odorants smell like fruits and others like flowers? While the so-called stimulus-percept issue was resolved in the field of color vision some time ago, the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day. Although a series of investigations have demonstrated that this relationship exists, the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication. One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality, whereby two very different molecules can evoke a similar odor. Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction. In addition, by automatically extracting new knowledge linking chemistry of odorants and psychology of smells, our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling., Author summary An important issue in olfaction sciences deals with the question of how a chemical information can be translated into percepts. This is known as the stimulus-percept problem. Here, we set out to better understand this issue by combining knowledge about the chemistry and cognition of smells with computational olfaction. We also assumed that not only one, but several physicochemical models may describe a given olfactory quality. To achieve this aim, a first challenge was to set up a database with ~1700 molecules characterized by chemical features and described by olfactory qualities (e.g. fruity, woody). A second challenge consisted in developing a computational model enabling the discrimination of olfactory qualities based on these chemical features. By meeting these 2 challenges, we provided for several olfactory qualities new chemical models describing why an odorant molecule smells fruity or woody (among others). For most qualities, multiple (rather than a single) chemical models were generated. These findings provide new elements of knowledge about the relationship between odorant chemistry and perception. They also make it possible to envisage concrete applications in the aroma and fragrance field where chemical characterization of smells is an important step in the design of new products.

International audience; Intravoxel incoherent motion (IVIM) imaging is a magnetic resonance imaging (MRI) technique widely used in clinical applications for various organs. However, IVIM imaging at low b-values is a persistent problem. This paper aims to investigate in a systematic and detailed manner how the number of low b-values influences the estimation of IVIM parameters. To this end, diffusion-weighted (DW) data with different low b-values were simulated to get insight into the distributions of subsequent IVIM parameters. Then, in vivo DW data with different numbers of low b-values and different number of excitations (NEX) were acquired. Finally, least-squares (LSQ) and Bayesian shrinkage prior (BSP) fitting methods were implemented to estimate IVIM parameters. The influence of the number of low b-values on IVIM parameters was analyzed in terms of relative error (RE) and structural similarity (SSIM). The results showed that the influence of the number of low b-values on IVIM parameters is variable. LSQ is more dependent on the number of low b-values than BSP, but the latter is more sensitive to noise.

Background Epilepsy and progressively worsening severe chronic headaches (WSCH) are the two most common clinical manifestations of neurocysticercosis, a form of cysticercosis. Most community-based studies in sub-Saharan Africa (SSA) use a two-step approach (questionnaire and confirmation) to estimate the prevalence of these neurological disorders and neurocysticercosis. Few validate the questionnaire in the field or account for the imperfect nature of the screening questionnaire and the fact that only those who screen positive have the opportunity to be confirmed. This study aims to obtain community-based validity estimates of a screening questionnaire, and to assess the impact of verification bias and misclassification error on prevalence estimates of epilepsy and WSCH. Methodology/Principal findings Baseline screening questionnaire followed by neurological examination data from a cluster randomized controlled trial collected between February 2011 and January 2012 were used. Bayesian latent-class models were applied to obtain verification bias adjusted validity estimates for the screening questionnaire. These models were also used to compare the adjusted prevalence estimates of epilepsy and WSCH to those directly obtained from the data (i.e. unadjusted prevalence estimates). Different priors were used and their corresponding posterior inference was compared for both WSCH and epilepsy. Screening data were available for 4768 individuals. For epilepsy, posterior estimates for the sensitivity varied with the priors used but remained robust for the specificity, with the highest estimates at 66.1% (95%BCI: 56.4%;75.3%) for sensitivity and 88.9% (88.0%;89.8%) for specificity. For WSCH, the sensitivity and specificity estimates remained robust, with the highest at 59.6% (49.7%;69.1%) and 88.6% (87.6%;89.6%), respectively. The unadjusted prevalence estimates were consistently lower than the adjusted prevalence estimates for both epilepsy and WSCH. Conclusions/Significance This study demonstrates that in some settings, the prevalence of epilepsy and WSCH can be considerably underestimated when using the two-step approach. We provide an analytic solution to obtain more valid prevalence estimates of these neurological disorders, although more community-based validity studies are needed to reduce the uncertainty of the estimates. Valid estimates of these two neurological disorders are essential to obtain accurate burden values for neglected tropical diseases such as neurocysticercosis that manifest as epilepsy or WSCH. Trial registration ClinicalTrials.gov NCT03095339., Author summary Epilepsy and progressively worsening severe chronic headaches are the two most common clinical manifestations of neurocysticercosis, a form of cysticercosis. To understand where the prevalence of these neurological disorders is highest for targeted infection control, valid prevalence estimates are needed. Most neuroepidemiological studies conducted in low-resource settings use a two-step approach to identify cases (screening questionnaire followed by physician examination among screened positives) to obtain prevalence and burden estimates. We found that this most commonly-used two-step approach in community-based studies leads to an underestimation of the prevalence in our study. Our paper provides an analytic solution to reduce errors in estimating the prevalence of neurological disorders in community-based studies when using the two-step approach. Our proposed approach provides more valid estimates of the prevalence of these neurological disorders and could be used to better reflect the consequences that neglected tropical diseases manifesting as epilepsy or headaches have on people’s health and disabilities.

International audience; The existence of a functional relationship between speech perception and production systems is now widely accepted, but the exact nature and role of this relationship remains quite unclear. The existence of idiosyncrasies in production and in perception sheds interesting light on the nature of the link. Indeed, a number of studies explore inter-individual variability in auditory and motor prototypes within a given language, and provide evidence for a link between both sets. In this paper, we attempt to simulate one study on coupled idiosyncrasies in the perception and production of French oral vowels, within COSMO, a Bayesian computational model of speech communication. First, we show that if the learning process in COSMO includes a communicative mechanism between a Learning Agent and a Master Agent, vowel production does display idiosyncrasies. Second, we implement within COSMO three models for speech perception that are, respectively, auditory, motor and per-ceptuo-motor. We show that no idiosyncrasy in perception can be obtained in the auditory model, since it is optimally tuned to the learning environment, which does not include the motor variability of the Learning Agent. On the contrary, motor and perceptuo-motor models provide perception idiosyncrasies correlated with idiosyncrasies in production. We draw conclusions about the role and importance of motor processes in speech perception, and propose a perceptuo-motor model in which auditory processing would enable optimal processing of learned sounds and motor processing would be helpful in unlearned adverse conditions.

International audience; We propose a new sparsification method for the singular value decomposition—called the constrained singular value decomposition (CSVD)—that can incorporate multiple constraints such as sparsification and orthogonality for the left and right singular vectors. The CSVD can combine different constraints because it implements each constraint as a projection onto a convex set, and because it integrates these constraints as projections onto the intersection of multiple convex sets. We show that, with appropriate sparsification constants, the algorithm is guaranteed to converge to a stable point. We also propose and analyze the convergence of an efficient algorithm for the specific case of the projection onto the balls defined by the norms L1 and L2. We illustrate the CSVD and compare it to the standard singular value decomposition and to a non-orthogonal related sparsification method with: 1) a simulated example, 2) a small set of face images (corresponding to a configuration with a number of variables much larger than the number of observations), and 3) a psychometric application with a large number of observations and a small number of variables. The companion R-package, csvd, that implements the algorithms described in this paper, along with reproducible examples, are available for download from https://github.com/vguillemot/csvd.

International audience; Plant cell walls development is an integrated process during which several components are deposited successively. In the cell walls in grass, the accessibility of structural polysaccharides is limited by the cell walls structure and composition mainly as a result of phenolic compounds. Here, we studied the patterns of cell walls establishment in the internode supporting the ear in three distinct maize genotypes. The developmental patterns observed in the internode cell walls in terms of its composition are reported with an emphasis on lignification, p-coumaroylation and feruloylation. We combined biochemical and histological approaches and revealed that internode cell walls development in maize before flowering is characterized by the rapid deposition of secondary cell walls components and robust lignification in both the pith and the rind. After flowering and until silage maturity, the slow deposition of secondary walls components occurs in the cortical region, and the deposited lignins are rich in beta-O-4 bonds and are highly p-coumaroylated. We conclude the paper by proposing a revised spatiotemporal model based on that proposed by Terashima et al. (1993) for cell walls development in grass.

International audience; Microcontact printing has become a versatile soft lithography technique used to produce molecular micro-and nano-patterns consisting of a large range of different biomolecules. Despite intensive research over the last decade and numerous applications in the fields of biosensors, microarrays and biomedical applications, the large-scale implementation of microcontact printing is still an issue. It is hindered by the stamp-inking step that is critical to ensure a reproducible and uniform transfer of inked molecules over large areas. This is particularly important when addressing application such as cell microarray manufacturing, which are currently used for a wide range of analytical and pharmaceutical applications. In this paper, we present a large-scale and multiplexed microcontact printing process of extra-cellular matrix proteins for the fabrication of cell microarrays. We have developed a micro-fluidic inking approach combined with a magnetic clamping technology that can be adapted to most standard substrates used in biology. We have demonstrated a significant improvement of homogeneity of printed protein patterns on surfaces larger than 1 cm 2 through the control of both the flow rate and the wetting mechanism of the stamp surface during micro-fluidic inking. Thanks to the reproducibility and integration capabilities provided by microflui-dics, we have achieved the printing of three different adhesion proteins in one-step transfer. Selective cell adhesion and cell shape adaptation on the produced patterns were observed, showing the suitability of this approach for producing on-demand large-scale cell microarrays.

International audience; The purpose of this paper was to adapt and examine the psychometric properties of a French-language Drive for Muscularity Scale (DMS). First, a preliminary version of the scale, based on both the English-language version of the DMS and the literature on the drive for muscularity, was developed following a committee validation process. Second, the factor structure of the DMS-FR was investigated with principal component analysis (PCA) in a sample of 114 male athletes (Mage = 23.35; SDage = 4.93), leading to a nine-item scale (Study 1). Third, in Study 2, the internal factor structure, temporal stability, and concurrent validity were examined through a series of structural hypothetical modelisation in a sample of 129 male athletes (Mage = 27.03; SDage = 7.81). The results indicated that the scale has good psychometric properties. Specifically, the PCA, and the series of structural hypothetical modelisation, suggested two theoretical factors (i.e., Muscularity Body Dissatisfaction and Muscularity Behaviors), and more, with a bi-factor model in a SEM. The results also indicated sufficient concurrent validity with the Male Body Dissatisfaction Scale (MBDS) and adequate internal consistency (Cronbach's alphas were .87 for the Muscularity Body Dissatisfaction subscale, and .88 for the Muscularity Behaviors subscale). The findings overall suggest good reliability and construct validity for this French version of the DMS (DMS-FR), which will be useful for future research and clinical practice in French-speaking countries.

International audience; Assessment of researchers is necessary for decisions of hiring, promotion, and tenure. A burgeoning number of scientific leaders believe the current system of faculty incentives and rewards is misaligned with the needs of society and disconnected from the evidence about the causes of the reproducibility crisis and suboptimal quality of the scientific publication record. To address this issue, particularly for the clinical and life sciences, we convened a 22-member expert panel workshop in Washington, DC, in January 2017. Twenty-two academic leaders, funders, and scientists participated in the meeting. As background for the meeting, we completed a selective literature review of 22 key documents critiquing the current incentive system. From each document, we extracted how the authors perceived the problems of assessing science and scientists, the unintended consequences of maintaining the status quo for assessing scientists, and details of their proposed solutions. The resulting table was used as a seed for participant discussion. This resulted in six principles for assessing scientists and associated research and policy implications. We hope the content of this paper will serve as a basis for establishing best practices and redesigning the current approaches to assessing scientists by the many players involved in that process.

This is a PLOS Computational Biology Education paper.; International audience; We present a new educational initiative, called Meet-U, that aims at training students to collaborativework in computational biology and at bridging the gap between education and research.Meet-U mimics the set up of collaborative research projects and takes advantage of the most populartools for collaborative work and of cloud computing. Students are grouped in teams of 4-5 peopleand have to realize a project from A to Z that answers a challenging question in Biology.Meet-U promotes "coopetition", as the students collaborate within and across the teams and are also incompetition with each other to develop the best final product.Meet-U fosters interactions between different actors of education and research through the organization of a meeting day, open to everyone, where the students present their work to a jury of researchers and jury members give researchseminars. This very unique combination of education and research is strongly motivating for thestudents and provides a formidable opportunity for a scientific community to unite and increase itsvisibility. We report on our experience with Meet-U in two French universities with master studentsin bioinformatics and modeling, with protein-protein docking as the subject of the course.Meet-U is easy to implement, virtually costs nothing and can be straightforwardly transferred to other fieldsand/or universities. All the information and data are available at http://www.meet-u.org.

Robust, real-time event detection from physiological signals acquired during long-term ambulatory monitoring still represents a major challenge. In this paper, we propose an original and generic multi-feature probabilistic detector (MFPD) and apply it to real-time QRS complex detection.Methods: The proposed method first derives relevant features from one or more physiological signals and estimates their probability distributions. Bayesian probabilities are then calculated for each feature before being merged through a data fusion node, with regard to the Kullback-Leibler divergence measure. To this end, we derived a KLD estimation method between two Generalized Normal Distributions (GND), in the general case. The efficiency of our method is validated on two noisy ECG databases : 1) a benchmark database by adding noise recordings to the entire MIT-BIH arrhythmia database with noise levels ranging from -6dB to 24dB, 2) an exercise stress test database composed of 54 real ECG recordings (17.8 hours).Results: In both cases significant improvements in detection performance are obtained compared with reference methods. For the benchmark noise stress database, performance gains are consistent for all noise type and SNR levels, and are increasing in low SNR levels. For the exercise stress database, detection error criterion is lowered to 20.91%, as compared with Pan-Tompkins-based (33.08%) and wavelet-based QRS detector (29.02%). Conclusion: Thanks to its multi-feature aspect and its KLD-based decision method (able to adaptively adjust the relative contribution of each feature to the final decision in real-time), the proposed method yields some interesting results, especially on highly-artifacted signals.Significance: Robust, easy to implement and with a reasonable computational cost, MFPD offers some promising perspectives for various challenging monitoring situations., La détection robuste et en temps réel d'événements à partir des signaux physiologiques acquis lors de la surveillance ambulatoire à long terme représente toujours un défi majeur. Dans cet article, nous proposons un détecteur probabiliste multicaractéristique (MFPD) original et générique et l'appliquons à la détection du complexe QRS en temps réel.Méthodes : La méthode proposée dérive d'abord les caractéristiques pertinentes d'un ou de plusieurs signaux physiologiques et estime leur distribution de probabilité. Les probabilités bayésiennes sont ensuite calculées pour chaque caractéristique avant d'être fusionnées à travers un nœud de fusion de données basé sur la pondération de la divergence de Kullback-Leibler. Pour ce faire, nous avons dérivé une méthode d'estimation du KLD entre deux distributions normales généralisées (GND), dans le cas général. L'efficacité de notre méthode est validée sur deux bases de données d'ECG bruités : 1) une base de données simulées, en ajoutant des segments bruités à l'ensemble de la base de données d'arythmie du MIT-BIH avec des niveaux de bruit allant de -6dB à 24dB, 2) une base de données d'exercices sous contraintes composée de 54 enregistrements d'ECG (17,8 heures).Résultats : Dans les deux cas, des améliorations significatives des performances de détection sont obtenues par rapport aux méthodes de référence. En ce qui concerne la base de données simulée, les gains de performance sont importants pour tous les types de bruit et tous les niveaux de SNR, et ils augmentent dans les faibles niveaux de SNR. Pour la base de données d’exercices, le critère d'erreur de détection est abaissé à 20,91 %, comparativement au détecteur QRS basé sur la méthode Pan Tompkins (33,08 %) et au détecteur QRS basé sur les ondelettes (29,02 %). Conclusion : Grâce à son aspect multicaractéristique et sa méthode de décision basée sur le KLD (capable d'ajuster de manière adaptative la contribution relative de chaque fonction à la décision finale en temps réel), la méthode proposée permet d'obtenir des résultats intéressants, en particulier sur les signaux hautement bruités.

International audience; In this paper, we describe SAFlex (Structural Alphabet Flexibility), an extension of an existing structural alphabet (HMM-SA), to better explore increasing protein three dimensional structure information by encoding conformations of proteins in case of missing residues or uncertainties. An SA aims to reduce three dimensional conformations of proteins as well as their analysis and comparison complexity by simplifying any conformation in a series of structural letters. Our methodology presents several novelties. Firstly, it can account for the encoding uncertainty by providing a wide range of encoding options: the maximum a posteri-ori, the marginal posterior distribution, and the effective number of letters at each given position. Secondly, our new algorithm deals with the missing data in the protein structure files (concerning more than 75% of the proteins from the Protein Data Bank) in a rigorous proba-bilistic framework. Thirdly, SAFlex is able to encode and to build a consensus encoding from different replicates of a single protein such as several homomer chains. This allows localizing structural differences between different chains and detecting structural variability, which is essential for protein flexibility identification. These improvements are illustrated on different proteins, such as the crystal structure of an eukaryotic small heat shock protein. They are promising to explore increasing protein redundancy data and obtain useful quantification of their flexibility.

Over the last three decades, evidence has emerged that low-intensity magnetic fields can influence biological systems. It is now well established that migratory birds have the capacity to detect the Earth's magnetic field; it has been reported that power lines are associated with childhood leukemia and that pulsed magnetic fields increase the production of reactive oxidative species (ROS) in cellular systems. Justifiably, studies in this field have been viewed with skepticism, as the underlying molecular mechanisms are unknown. In the accompanying paper, Sherrard and colleagues report that low-flux pulsed electromagnetic fields (PEMFs) result in aversive behavior in Drosophila larvae and ROS production in cell culture. They further report that these responses require the presence of cryptochrome, a putative magnetoreceptor. If correct, it is conceivable that carcinogenesis associated with power lines, PEMF-induced ROS generation, and animal magnetoreception share a common mechanistic basis., This Primer explores recent evidence as to how low-intensity magnetic fields might influence biological systems.

Spatial indicators are used to quantify the state of species and ecosystem status, that is the impacts of climate and anthropogenic changes, as well as to comprehend species ecology. These metrics are thus, determinant to the stakeholder's decisions on the conservation measures to be implemented. A detailed review of the literature (55 papers) showed that 18 spatial indicators were commonly used in marine ecology. Those indicators were than characterized and studied in detail, based on its application to empirical data (a time series of 35 marine species spatial distributions, sampled either with a random stratified survey or a regular transects surveys). The results suggest that the indicators can be grouped into three classes, that summarize the way the individuals occupy space: occupancy (the area occupied by a species), aggregation (spreading or concentration of species biomass) and quantity dependent (indicators correlated with biomass), whether these are spatially explicit (include the geographic coordinates, e.g. center of gravity) or not. Indicator's temporal variability was lower than between species variability and no clear effect was observed in relation to sampling design. Species were then classified accordingly to their indicators. One indicator was selected from each of the three categories of indicators, to represent the main axes of species spatial behavior and to interpret them in terms of occupancy-aggregation-quantity relationships. All species considered were then classified according to their relationships among those three axes, into species that under increasing abundancy, primarily increase occupancy or aggregation or both. We suggest to use these relationships along the three-axes as surveillance diagrams to follow the yearly evolution of species distributional patterns in the future. MSFD from France info:eu-repo/semantics/publishedVersion

This paper investigates age variations in foreign-born vs. native-born mortality ratios in an international comparative perspective, with the purpose of gaining insight into the mechanisms underlying the so-called migrant mortality advantage. We examine the four main explanations that have been proposed in the literature for the migrant mortality advantage (i.e., in-migration selection effects, out-migration selection effects, cultural effects, and data artifacts), and formulate expectations as to whether they should generate an increase, a decrease, or no change in relative mortality over the life course. Using data from France, the US and the UK for periods around 2010, we then examine typical age patterns of foreign-born vs. native-born mortality ratios in light of this theoretical framework. We find that these mortality ratios vary greatly by age, with important similarities across migrant groups and host countries. The most systematic age pattern we find is a U-shape pattern: at the aggregate level, migrants often experience excess mortality at young ages, then exhibit a large advantage at adult ages (with the largest advantage around age 45), and finally experience mortality convergence with natives at older ages. The explanation most consistent with this pattern is the "in-migration selection effects" explanation. By contrast, the "out-migration selection effects" explanation is poorly supported by the observed patterns. Our age disaggregation also shows that migrants at mid-adult ages experience mortality advantages that are often far greater than typically documented in this literature. Overall, these results reinforce the notion that migrants are a highly-selected population exhibiting mortality patterns that poorly reflect their living conditions in host countries.

International audience; Knowledge of the human visual system helps to develop better computational models of visual attention. State-of-the-art models have been developed to mimic the visual attention system of young adults that, however, largely ignore the variations that occur with age. In this paper, we investigated how visual scene processing changes with age and we propose an age-adapted framework that helps to develop a computational model that can predict saliency across different age groups. Our analysis uncovers how the explorativeness of an observer varies with age, how well saliency maps of an age group agree with fixation points of observers from the same or different age groups, and how age influences the center bias tendency. We analyzed the eye movement behavior of 82 observers belonging to four age groups while they explored visual scenes. Explorative-ness was quantified in terms of the entropy of a saliency map, and area under the curve (AUC) metrics was used to quantify the agreement analysis and the center bias tendency. Analysis results were used to develop age adapted saliency models. Our results suggest that the proposed age-adapted saliency model outperforms existing saliency models in predicting the regions of interest across age groups.

The well established phenomenon of ribosome drop-off plays crucial roles in translational accuracy and nutrient starvation responses during protein translation. When cells are under stress conditions, such as amino acid starvation or aminoacyl-tRNA depletion due to a high level of recombinant protein expression, ribosome drop-off can substantially affect the efficiency of protein expression. Here we introduce a mathematical model that describes the effects of ribosome drop-off on the ribosome density along the mRNA and on the concomitant protein synthesis rate. Our results show that ribosome premature termination may lead to non-intuitive ribosome density profiles, such as a ribosome density which increases from the 5’ to the 3’ end. Importantly, the model predicts that the effects of ribosome drop-off on the translation rate are mRNA-specific, and we quantify their resilience to drop-off, showing that the mRNAs which present ribosome queues are much less affected by ribosome drop-off than those which do not. Moreover, among those mRNAs that do not present ribosome queues, resilience to drop-off correlates positively with the elongation rate, so that sequences using fast codons are expected to be less affected by ribosome drop-off. This result is consistent with a genome-wide analysis of S. cerevisiae, which reveals that under favourable growth conditions mRNAs coding for proteins involved in the translation machinery, known to be highly codon biased and using preferentially fast codons, are highly resilient to ribosome drop-off. Moreover, in physiological conditions, the translation rate of mRNAs coding for regulatory, stress-related proteins, is less resilient to ribosome drop-off. This model therefore allows analysis of variations in the translational efficiency of individual mRNAs by accounting for the full range of known ribosome behaviours, as well as explaining mRNA-specific variations in ribosome density emerging from ribosome profiling studies., Author summary Ribosomes often drop off the messenger RNA (mRNA) molecules during the process of translation. This phenomenon occurs under normal conditions and it is especially aggravated if the cell is subject to stress. In this paper we show that protein production rates and polysome profiles along the mRNA can be strongly affected by ribosome drop-off. It can lead to a non-intuitive increasing ribosome density profile from the 5’ to 3’ end, as well as coexistence between low and high ribosomal density along the mRNA. Importantly, we show that the repercussions of ribosome drop-off are mRNA-dependent, with the specific codon configuration and composition determining their resilience to ribosome drop-off effects.

View at Publisher| Export | Download | Add to List| More... PLoS ONE Volume 12, Issue 1, January 2017, Article number e0169658 Open Access Vigi4Med scraper: A framework for web forum structured data extraction and semantic representation (Article) Audeh, B.a , Beigbeder, M.a, Zimmermann, A.a, Jaillon, P.b, Bousquet, C.c a University of Lyon, MINES Saint-Etienne, CNRS, Hubert Curien Laboratory, UMR 5516, Saint-Etienne, France b Ecole Nationale Superieure des Mines de Saint-Etienne, Saint-Etienne, France c INSERM, U1142, LIMICS, Paris, France View additional affiliations View references (36) Abstract The extraction of information from social media is an essential yet complicated step for data analysis in multiple domains. In this paper, we present Vigi4Med Scraper, a generic open source framework for extracting structured data from web forums. Our framework is highly configurable; using a configuration file, the user can freely choose the data to extract from any web forum. The extracted data are anonymized and represented in a semantic structure using Resource Description Framework (RDF) graphs. This representation enables efficient manipulation by data analysis algorithms and allows the collected data to be directly linked to any existing semantic resource. To avoid server overload, an integrated proxy with caching functionality imposes a minimal delay between sequential requests. Vigi4Med Scraper represents the first step of Vigi4Med, a project to detect adverse drug reactions (ADRs) from social networks founded by the French drug safety agency Agence Nationale de Securite du Medicament (ANSM). Vigi4Med Scraper has successfully extracted greater than 200 gigabytes of data from the web forums of over 20 different websites. © 2017 Audeh et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

International audience; In this paper we present the development and validation a comprehensive questionnaire to assess college students' knowledge about modern genetics and genomics, their belief in genetic determinism, and their attitudes towards applications of modern genetics and geno-mic-based technologies. Written in everyday language with minimal jargon, the Public Understanding and Attitudes towards Genetics and Genomics (PUGGS) questionnaire is intended for use in research on science education and public understanding of science, as a means to investigate relationships between knowledge, determinism and attitudes about modern genetics, which are to date little understood. We developed a set of core ideas and initial items from reviewing the scientific literature on genetics and previous studies on public and student knowledge and attitudes about genetics. Seventeen international experts from different fields (e.g., genetics, education, philosophy of science) reviewed the initial items and their feedback was used to revise the questionnaire. We validated the questionnaire in two pilot tests with samples of university freshmen students. The final questionnaire contains 45 items, including both multiple choice and Likert scale response formats. Cronbach alpha showed good reliability for each section of the questionnaire. In conclusion, the PUGGS questionnaire is a reliable tool for investigating public understanding and attitudes towards modern genetics and genomic-based technologies.

International audience; Grade II gliomas are slowly growing primary brain tumors that affect mostly young patients. Cytotoxic therapies (radiotherapy and/or chemotherapy) are used initially only for patients having a bad prognosis. These therapies are planned following the " maximum dose in minimum time " principle, i. e. the same schedule used for high-grade brain tumors in spite of their very different behavior. These tumors transform after a variable time into high-grade gliomas, which significantly decreases the patient's life expectancy. In this paper we study mathematical models describing the growth of grade II gliomas in response to radiotherapy. We find that protracted metronomic fractionations, i.e. therapeutical schedules enlarging the time interval between low-dose radiotherapy fractions, may lead to a better tumor control without an increase in toxicity. Other non-standard fractionations such as protracted or hypoprotracted schemes may also be beneficial. The potential survival improvement depends on the tumor's proliferation rate and can be even of the order of years. A conservative metronomic scheme, still being a suboptimal treatment, delays the time to malignant progression by at least one year when compared to the standard scheme.

International audience; In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.