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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; Mosquito control is implemented when arboviruses are detected in patients or in field-collected mosquitoes. However, mass screening of mosquitoes is usually laborious and expensive, requiring specialized expertise and equipment. Detection of virus in mosquito saliva using honey-impregnated filter papers seems to be a promising method as it is non-destructive and allows monitoring the viral excretion dynamics over time from the same mosquito. Here we test the use of filter papers to detect chikungunya virus in mosquito saliva in laboratory conditions, before proposing this method in large-scale mosquito surveillance programs. We found that 0.9 cm 2 cards impregnated with a 50% honey solution could replace the forced salivation technique as they offered a viral RNA detection until 7 days after oral infection of Aedes aegypti and Aedes albopictus mosquitoes with CHIKV.

International audience; The fermentation of dietary fibre (DF) leads to the production of bioactive metabolites, the most volatile ones being excreted in the breath. The aim of this study was to analyze the profile of exhaled breath volatile metabolites (BVM) and gastrointestinal symptoms in healthy volunteers after a single ingestion of maltodextrin (placebo) versus chitin-glucan (CG), an insoluble DF previously shown to be fermented into short-chain fatty acids (SCFA) by the human microbiota in vitro. Maltodextrin (4.5 g at day 0) or CG (4.5 g at day 2) were added to a standardized breakfast in fasting healthy volunteers (n = 15). BVM were measured using selected ion flow tube mass spectrometry (SIFT-MS) throughout the day. A single ingestion of 4.5 g CG did not induce significant gastrointestinal discomfort. Untargeted metabolomics analysis of breath highlighted that 13 MS-fragments (among 408 obtained from ionizations of breath) discriminated CG versus maltodextrin acute intake in the posprandial state. The targeted analysis revealed that CG increased exhaled butyrate and 5 other BVM - including the microbial metabolites 2,3-butanedione and 3-hydroxybutanone - with a peak observed 6 h after CG intake. Correlation analyses with fecal microbiota (Illumina 16S rRNA sequencing) spotlighted Mitsuokella as a potential genus responsible for the presence of butyric acid, triethylamine and 3-hydroxybutanone in the breath. In conclusion, measuring BMV in the breath reveals the microbial signature of the fermentation of DF after a single ingestion. This protocol allows to analyze the time-course of released bioactive metabolites that could be proposed as new biomarkers of DF fermentation, potentially linked to their biological properties. Trial registration: Clinical Trials NCT03494491. Registered 11 April 2018 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03494491.

A diversity of arthropods (myrmecophiles) thrives within ant nests, many of them unmolested though some, such as the specialized Eucharitidae parasitoids, may cause direct damage to their hosts. Ants are known to discriminate between nestmates and non-nestmates, but whether they recognize the strength of a threat and their capacity to adjust their behavior accordingly have not been fully explored. We aimed to determine whether Ectatomma tuberculatum ants exhibited specific behavioral responses to potential or actual intruders posing different threats to the host colony and to contribute to an understanding of complex ant-eucharitid interactions. Behavioral responses differed significantly according to intruder type. Ants evicted intruders that represented a threat to the colony's health (dead ants) or were not suitable as prey items (filter paper, eucharitid parasitoid wasps, non myrmecophilous adult weevils), but killed potential prey (weevil larvae, termites). The timing of detection was in accordance with the nature and size of the intruder: corpses (a potential source of contamination) were detected faster than any other intruder and transported to the refuse piles within 15 min. The structure and complexity of behavioral sequences differed among those intruders that were discarded. Workers not only recognized and discriminated between several distinct intruders but also adjusted their behavior to the type of intruder encountered. Our results confirm the previously documented recognition capabilities of E. tuberculatum workers and reveal a very fine-tuned intruder discrimination response. Colony-level prophylactic and hygienic behavioral responses through effective removal of inedible intruders appears to be the most general and flexible form of defense in ants against a diverse array of intruders. However, this generalized response to both potentially lethal and harmless intruders might have driven the evolution of ant-eucharitid interactions, opening a window for parasitoid attack and allowing adult parasitoid wasps to quickly leave the natal nest unharmed.

Recent works revealed that bark is able to produce mechanical stress to control the orientation of young tilted stems. Here we report how the potential performance of this function changes with stem size in six Amazonian species with contrasted bark anatomy. The potential performance of the mechanism depends both on the magnitude of bark stress and the relative thickness of the bark. We measured bark longitudinal residual strain and density, and the allometric relationship between bark thickness and stem radius over a gradient of tree sizes. Constant tensile stress was found in species that rely on bark for the control of stem orientation in young stages. Other species had increasing compressive stress, associated with increasing density attributed to the development of sclereids. Compressive stress was also associated with low relative bark thickness. The relative thickness of bark decreased with size in all species, suggesting that a reorientation mechanism based on bark progressively performs less well as the tree grows. However, greater relative thickness was observed in species with more tensile stress, thereby evidencing that this reduction in performance is mitigated in species that rely on bark for reorientation., During ontogeny, species that rely on bark for posture control maintain thick bark and constant tensile stress, while other species have relatively thinner bark and increasing compressive stress.

Motivation One of the most widespread methods used in taxonomy studies to distinguish between strains or taxa is the calculation of average nucleotide identity. It requires a computationally expensive alignment step and is therefore not suitable for large-scale comparisons. Short oligonucleotide-based methods do offer a faster alternative but at the expense of accuracy. Here, we aim to address this shortcoming by providing a software that implements a novel method based on short-oligonucleotide frequencies to compute inter-genomic distances. Results Our tetranucleotide and hexanucleotide implementations, which were optimized based on a taxonomically well-defined set of over 200 newly sequenced bacterial genomes, are as accurate as the short oligonucleotide-based method TETRA and average nucleotide identity, for identifying bacterial species and strains, respectively. Moreover, the lightweight nature of this method makes it applicable for large-scale analyses. Availability and implementation The method introduced here was implemented, together with other existing methods, in a dependency-free software written in C, GenDisCal, available as source code from https://github.com/LM-UGent/GenDisCal. The software supports multithreading and has been tested on Windows and Linux (CentOS). In addition, a Java-based graphical user interface that acts as a wrapper for the software is also available. Supplementary information Supplementary data are available at Bioinformatics online.

Numerous genes have been identified that regulate leaf growth, which can be grouped into regulatory modules. Here, we review six important gene modules that affect cell proliferation during leaf development., Leaves are the primary organs for photosynthesis, and as such have a pivotal role for plant growth and development. Leaf development is a multifactorial and dynamic process involving many genes that regulate size, shape, and differentiation. The processes that mainly drive leaf development are cell proliferation and cell expansion, and numerous genes have been identified that, when ectopically expressed or down-regulated, increase cell number and/or cell size during leaf growth. Many of the genes regulating cell proliferation are functionally interconnected and can be grouped into regulatory modules. Here, we review our current understanding of six important gene regulatory modules affecting cell proliferation during Arabidopsis leaf growth: ubiquitin receptor DA1–ENHANCER OF DA1 (EOD1), GROWTH REGULATING FACTOR (GRF)–GRF-INTERACTING FACTOR (GIF), SWITCH/SUCROSE NON-FERMENTING (SWI/SNF), gibberellin (GA)–DELLA, KLU, and PEAPOD (PPD). Furthermore, we discuss how post-mitotic cell expansion and these six modules regulating cell proliferation make up the final leaf size.

Highlight Strigolactones monitor lateral root development in a spatiotemporal manner by an interplay with cytokinin., Strigolactones are important rhizosphere signals that act as phytohormones and have multiple functions, including modulation of lateral root (LR) development. Here, we show that treatment with the strigolactone analog GR24 did not affect LR initiation, but negatively influenced LR priming and emergence, the latter especially near the root–shoot junction. The cytokinin module ARABIDOPSIS HISTIDINE KINASE3 (AHK3)/ARABIDOPSIS RESPONSE REGULATOR1 (ARR1)/ARR12 was found to interact with the GR24-dependent reduction in LR development, because mutants in this pathway rendered LR development insensitive to GR24. Additionally, pharmacological analyses, mutant analyses, and gene expression analyses indicated that the affected polar auxin transport stream in mutants of the AHK3/ARR1/ARR12 module could be the underlying cause. Altogether, the data reveal that the GR24 effect on LR development depends on the hormonal landscape that results from the intimate connection with auxins and cytokinins, two main players in LR development.

Highlight Plant auxin efflux and influx proteins redirect the plant hormone auxin towards the feeding site upon root-knot nematode infection in Arabidopsis thaliana roots., Plant-parasitic root-knot nematodes induce the formation of giant cells within the plant root, and it has been recognized that auxin accumulates in these feeding sites. Here, we studied the role of the auxin transport system governed by AUX1/LAX3 influx proteins and different PIN efflux proteins during feeding site development in Arabidopsis thaliana roots. Data generated via promoter–reporter line and protein localization analyses evoke a model in which auxin is being imported at the basipetal side of the feeding site by the concerted action of the influx proteins AUX1 and LAX3, and the efflux protein PIN3. Mutants in auxin influx proteins AUX1 and LAX3 bear significantly fewer and smaller galls, revealing that auxin import into the feeding sites is needed for their development and expansion. The feeding site development in auxin export (PIN) mutants was only slightly hampered. Expression of some PINs appears to be suppressed in galls, probably to prevent auxin drainage. Nevertheless, a functional PIN4 gene seems to be a prerequisite for proper nematode development and gall expansion, most likely by removing excessive auxin to stabilize the hormone level in the feeding site. Our data also indicate a role of local auxin peaks in nematode attraction towards the root.

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.

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.

International audience; Food safety crises involving persistent organic pollutants (POPs) lead to systematic slaughter of livestock to prevent contaminants from entering the food chain. Therefore, there is a need to develop strategies to depurate livestock moderately contaminated with POPs to reduce economic and social damage. This study aimed to test undernutrition (37% of energy requirements) combined with mineral oil (10% in total dry matter intake) in nine non-lactating ewes contaminated with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and polychlorinated biphenyls (PCBs) 126 and 153 as a strategy to enhance the depuration of POPs through faecal excretion. To better understand the underlying mechanisms of the depuration process, lipophilic POPs and lipid fluxes were co-monitored in various body and excretion compartments. Body compartments (adipose tissues, muscle, liver and blood) and the total empty body were analyzed for lipids and POPs concentrations and burdens at slaughter, as well as excretion compartments (faeces and wool) collected during the depuration period. Decreases in empty body total and lipid weights were 6-fold higher in underfed and supplemented ewes compared to control ewes. In addition, over the depuration period undernutrition and supplementation treatment increased faecal TCDD, PCBs 126 and 153 excretions by 1.4- to 2.1-fold but tended to decrease wool PCB 153 excretion by 1.4-fold. This induced 2- to 3-fold higher decreases in the empty body POPs burdens for underfed and supplemented ewes. Nonetheless, when expressed relative to the calculated initial empty body burdens, burdens at slaughter decreased only slightly from 97%, 103% and 98% for control ewes to 92%, 97% and 94% for underfed and supplemented ones, for TCDD, PCBs 126 and 153, respectively. Fine descriptions at once of POPs kinetic (companion paper 1) and mass balance (companion paper 2), and of body lipid dynamics were very useful in improving our understanding of the fate of POPs in the ruminants.

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; Background: A review of literature published a decade ago noted a significant increase in empirical papers across nine bioethics journals. This study provides an update on the presence of empirical papers in the same nine journals. It first evaluates whether the empirical trend is continuing as noted in the previous study, and second, how it is changing, that is, what are the characteristics of the empirical works published in these nine bioethics journals.Method: A review of the same nine journals (Bioethics; Journal of Medical Ethics; Journal of Clinical Ethics; Nursing Ethics; Cambridge Quarterly of Healthcare Ethics; Hastings Center Report; Theoretical Medicine and Bioethics; Christian Bioethics; and Kennedy Institute of Ethics Journal) was conducted for a 12-year period from 2004 to 2015. Data obtained was analysed descriptively and using a non-parametric Chi-square test.Results: Of the total number of original papers (N = 5567) published in the nine bioethics journals, 18.1% (n = 1007) collected and analysed empirical data. Journal of Medical Ethics and Nursing Ethics led the empirical publications, accounting for 89.4% of all empirical papers. The former published significantly more quantitative papers than qualitative, whereas the latter published more qualitative papers. Our analysis reveals no significant difference (χ2 = 2.857; p = 0.091) between the proportion of empirical papers published in 2004–2009 and 2010–2015. However, the increasing empirical trend has continued in these journals with the proportion of empirical papers increasing from 14.9% in 2004 to 17.8% in 2015.Conclusions: This study presents the current state of affairs regarding empirical research published nine bioethics journals. In the quarter century of data that is available about the nine bioethics journals studied in two reviews, the proportion of empirical publications continues to increase, signifying a trend towards empirical research in bioethics. The growing volume is mainly attributable to two journals: Journal of Medical Ethics and Nursing Ethics. This descriptive study further maps the still developing field of empirical research in bioethics. Additional studies are needed to completely map the nature and extent of empirical research in bioethics to inform the ongoing debate about the value of empirical research for bioethics.

A new method of genomic maps analysis based on formal logic is described. The purpose of the method is to 1) use mitochondrial genomic organisation of current taxa as datasets 2) calculate mutational steps between all mitochondrial gene arrangements and 3) reconstruct phylogenetic relationships according to these calculated mutational steps within a dendrogram under the assumption of maximum parsimony. Unlike existing methods mainly based on the probabilistic approach, the main strength of this new approach is that it calculates all the exact tree solutions with completeness and provides logical consequences as very robust results. Moreover, the method infers all possible hypothetical ancestors and reconstructs character states for all internal nodes (ancestors) of the trees. We started by testing the method using the deuterostomes as a study case. Then, with sponges as an outgroup, we investigated the mutational network of mitochondrial genomes of 47 bilaterian phyla and emphasised the peculiar case of chaetognaths. This pilot work showed that the use of formal logic in a hypothetico-deductive background such as phylogeny (where experimental testing of hypotheses is impossible) is very promising to explore mitochondrial gene rearrangements in deuterostomes and should be applied to many other bilaterian clades.Author SummaryInvestigating how recombination might modify gene arrangements during the evolution of metazoans has become a routine part of mitochondrial genome analysis. In this paper, we present a new approach based on formal logic that provides optimal solutions in the genome rearrangement field. In particular, we improve the sorting by including all rearrangement events,e.g., transposition, inversion and reverse transposition. The problem we face with is to find the most parsimonious tree(s) explaining all the rearrangement events from a common ancestor to all the descendants of a given clade (hereinafter PHYLO problem). So far, a complete approach to find all the correct solutions of PHYLO is not available. Formal logic provides an elegant way to represent and solve such an NP-hard problem. It has the benefit of correctness, completeness and allows the understanding of the logical consequences (results true for all solutions found). First, one must define PHYLO (axiomatisation) with a set of logic formulas or constraints. Second, a model generator calculates all the models, each model being a solution of PHYLO. Several complete model generators are available but a recurring difficulty is the computation time when the data set increases. When the search of a solution takes exponential time, two computing strategies are conceivable: an incomplete but fast algorithm that does not provide the optimal solution (for example, use local improvements from an initial random solution) or a complete – and thus not efficient – algorithm on a smaller tractable dataset. While the large amount of genes found in the nuclear genome strongly limits our possibility to use of formal logic with any conventional computer, we show in our paper that, for bilaterian mtDNAs, all the correct solutions can be found in a reasonable time due to the small number of genes.

International audience; The perception and production of biological movements is characterized by the 1/3 power law, a relation linking the curvature and the velocity of an intended action. In particular, motions are perceived and reproduced distorted when their kinematics deviate from this biological law. Whereas most studies dealing with this perceptual-motor relation focused on visual or kinaesthetic modalities in a unimodal context, in this paper we show that auditory dynamics strikingly biases visuomotor processes. Biologically consistent or inconsistent circular visual motions were used in combination with circular or elliptical auditory motions. Auditory motions were synthesized friction sounds mimicking those produced by the friction of the pen on a paper when someone is drawing. Sounds were presented diotically and the auditory motion velocity was evoked through the friction sound timbre variations without any spatial cues. Remarkably, when subjects were asked to reproduce circular visual motion while listening to sounds that evoked elliptical kinematics without seeing their hand, they drew elliptical shapes. Moreover, distortion induced by inconsistent elliptical kinematics in both visual and auditory modalities added up linearly. These results bring to light the substantial role of auditory dynamics in the visuo-motor coupling in a multisensory context.

International audience; Virtual Reality (VR) has emerged as a promising tool in many domains of therapy and rehabilitation , and has recently attracted the attention of researchers and clinicians working with elderly people with MCI, Alzheimer's disease and related disorders. Here we present a study testing the feasibility of using highly realistic image-based rendered VR with patients with MCI and dementia. We designed an attentional task to train selective and sustained attention, and we tested a VR and a paper version of this task in a single-session within-subjects design. Results showed that participants with MCI and dementia reported to be highly satisfied and interested in the task, and they reported high feelings of security, low discomfort , anxiety and fatigue. In addition, participants reported a preference for the VR condition compared to the paper condition, even if the task was more difficult. Interestingly, apathetic participants showed a preference for the VR condition stronger than that of non-apathetic participants. These findings suggest that VR-based training can be considered as an interesting tool to improve adherence to cognitive training in elderly people with cognitive impairment.

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.

International audience; The challenges of securing future food security will require deployment of innovative technologies to accelerate crop production. Plant phenotyping methods have advanced significantly, spanning low-cost hand-held devices to large-scale satellite imaging. Field-based phenotyping aims to capture plant response to the environment, generating data that can be used to inform breeding and selection requirements. This in turn requires access to multiple representative locations and capacities for collecting useful information. In this paper we identify the current challenges in access to field phenotyping in multiple locations in Europe based on stakeholder feedback. We present a map of current infrastructure and propose opportunities for greater integration of existing facilities for meeting different user requirements. We also review the currently available technology and data requirements for effective multi-location field phenotyping and provide recommendations for ensuring future access and coordination. Taken together we provide an overview of the current status of European field phenotyping capabilities and provides a roadmap for their future use to support crop improvement. This provides a wider framework for the analysis and planning of field phenotyping activities for crop improvement worldwide.

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.

Miocene deposits of South America have yielded several species-rich assemblages of caviomorph rodents. They are mostly situated at high and mid- latitudes of the continent, except for the exceptional Honda Group of La Venta, Colombia, the faunal composition of which allowed to describe the late middle Miocene Laventan South American Land Mammal Age (SALMA). In this paper, we describe a new caviomorph assemblage from TAR-31 locality, recently discovered near Tarapoto in Peruvian Amazonia (San Martín Department). Based on mammalian biostratigraphy, this single-phased locality is unambiguously considered to fall within the Laventan SALMA. TAR-31 yielded rodent species found in La Venta, such as the octodontoid Ricardomys longidens Walton, 1990 (nom. nud.), the chinchilloids Microscleromys paradoxalis Walton, 1990 (nom. nud.) and M. cribriphilus Walton, 1990 (nom. nud.), or closely-related taxa. Given these strong taxonomic affinities, we further seize the opportunity to review the rodent dental material from La Venta described in the Ph.D. volume of Walton in 1990 but referred to as nomina nuda. Here we validate the recognition of these former taxa and provide their formal description. TAR-31 documents nine distinct rodent species documenting the four extant superfamilies of Caviomorpha, including a new erethizontoid: Nuyuyomys chinqaska gen. et sp. nov. These fossils document the most diverse caviomorph fauna for the middle Miocene interval of Peruvian Amazonia to date. This rodent discovery from Peru extends the geographical ranges of Ricardomys longidens, Microscleromys paradoxalis, and M. cribriphilus, 1,100 km to the south. Only one postcranial element of rodent was unearthed in TAR-31 (astragalus). This tiny tarsal bone most likely documents one of the two species of Microscleromys and its morphology indicates terrestrial generalist adaptations for this minute chinchilloid.

International audience; This study developed, validated, and evaluated a framework of factors influencing dietary behaviours in urban African food environments, to inform research prioritisation and intervention development in Africa. A multi-component methodology, drawing on concept mapping, was employed to construct a framework of factors influencing dietary behaviours in urban Africa. The framework adapted a widely used socio-ecological model (developed in a high-income country context) and was developed using a mixed-methods research approach that comprised: i. Evidence synthesis consisting of a systematic review of 39 papers covering 14 African countries; ii. Qualitative interview data collected for adolescents and adults (n = 144) using photovoice in urban Ghana and Kenya; and iii. Consultation with interdisciplinary African experts (n = 71) from 27 countries, who contributed to at least one step of the framework (creation, validation/evaluation, finalisation). The final framework included 103 factors influencing dietary behaviours. Experts identified the factors influencing dietary behaviours across all the four levels of the food environment i.e. the individual, social, physical and macro levels. Nearly half (n = 48) were individual-level factors and just under a quarter (n = 26) were at the macro environmental level. Fewer factors associated with social (n = 15) and physical (14) environments were identified. At the macro level, the factors ranked as most important were food prices, cultural beliefs and seasonality. Factors ranked as important at the social level were household composition, family food habits and dietary practices. The type of food available in the neighbourhood and convenience were seen as important at the physical level, while individual food habits, food preferences and socioeconomic status were ranked highly at the individual level. About half of the factors (n = 54) overlap with those reported in an existing socio-ecological food environment framework developed in a high-income country context. A further 49 factors were identified that were not reported in the selected high-income country framework, underlining the importance of contextualisation. Our conceptual framework offers a useful tool for research to understand dietary transitions in urban African adolescents and adults, as well as identification of factors to intervene when promoting healthy nutritious diets to prevent multiple forms of malnutrition.

International audience; In most developing countries, formal and informal transportation schemes coexist without effective and smart integration. In this paper, the authors show how to leverage opportunities offered by formal and informal transportation schemes to build an integrated multi-modal network. Precisely, the authors consider integration of rickshaws to a bus-train network, by taking into account accessibility and societal constraints. By modelling the respective networks with weighted graphs, a graph augmentation problem is solved with respect to a composite cost taking into account constraints on the use of rickshaws. The solution, is based on finding a minimum cost spanning tree of a merged graph. The method is applied in the South African context, in the city of Johannesburg where rickshaws are not yet a significant part of the transportation system. The implications of the study reveal that using non-motorised transportation services is a viable option of improving mobility in the city. The composite cost introduced herein could be used for new routing algorithm including societal, environmental, architectural contexts and commuter experiences through rating.

Smallholder farmer and tribal communities are often characterised as marginalised and highly vulnerable to emerging zoonotic diseases due to their relatively poor access to healthcare, worse-off health outcomes, proximity to sources of disease risks, and their social and livelihood organisation. Yet, access to relevant and timely disease information that could strengthen their adaptive capacity remain challenging and poorly characterised in the empirical literature. This paper addresses this gap by exploring the role of disease information in shaping the adaptive capacity of smallholder farmer and tribal groups to Kyasanur Forest Disease (KFD), a tick-borne viral haemorrhagic fever. We carried out household surveys (n = 229) and in-depth interviews (n = 25) in two affected districts–Shimoga and Wayanad–in the Western Ghats region. Our findings suggest that, despite the generally limited awareness about KFD, access to disease information improved households’ propensity to implement adaptation strategies relative to households that had no access to it. Of the variety of adaptation strategies implemented, vaccination, avoiding forest visits, wearing of protective clothing and footwear, application of dimethyl phthalate (DMP) oil and income diversification were identified by respondents as important adaptive measures during the outbreak seasons. Even so, we identified significant differences between individuals in exposure to disease information and its contribution to substantive adaptive action. Households reported several barriers to implement adaptation strategies including, lack of disease information, low efficacy of existing vaccine, distrust, religio-cultural sentiments, and livelihood concerns. We also found that informal information sharing presented a promising avenue from a health extension perspective albeit with trade-offs with potential distortion of the messages through misinformation and/or reporting bias. Altogether, our findings stress the importance of contextualising disease information and implementing interventions in a participatory way that sufficiently addresses the social determinants of health in order to bolster households’ adaptive capacity to KFD and other neglected endemic zoonoses., Author summary There is widespread consensus that smallholder farmer and tribal communities are highly vulnerable to zoonotic diseases (illness passed between animals and people). This is largely due to their relatively poor access to healthcare and closeness to sources of disease risks. Although access to relevant and timely disease information could enhance their adaptive capacity, remain difficult and poorly understood. Drawing on survey data (n = 229) and in-depth interviews (n = 25) in two affected districts (Shimoga and Wayanad); we explored the role of disease information in shaping adaptation to Kyasanur Forest Disease (KFD), a tick-borne viral haemorrhagic fever. We found that despite the limited awareness about KFD, access to disease information improved households’ likelihood to implement adaptation strategies as compared to households that had no access to it. Households identified several barriers to implementation of adaptation strategies including, lack of disease information, low efficacy of existing vaccine, distrust, religio-cultural sentiments, and livelihood concerns. Our findings highlight the relevance of contextualising disease information and implementing interventions in a participatory manner in order to strengthen households’ adaptive capacity to KFD and other neglected endemic zoonoses.

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; Communication policies employed by policymakers and non-governmental organizations (NGOs) often appeal to the emotions to persuade people to adopt virtuous behavior. The aim of this paper is to study the impact of induced emotions on pro-environmental behavior (PEB). We design a three-stage laboratory experiment. In the first stage, we determine the level of the subjects’ environmental awareness. In the second stage, subjects read scripts that place them in realistic hypothetical scenarios designed to induce specific emotions. We implement a 2 x 2 in-between design by varying both the valence and social dimension of the four emotional states induced: happiness, sadness, pride and shame. In the third stage, subjects play a modified dictator game in which the recipient is an environmental non-governmental organization (ENGO). We show that the emotional states of subjects can influence PEB. In particular, negative emotions significantly reduce the average individual amount of donations made to ENGOs. We also find that the precise impact of the emotional states is more complex and appears to be dependent on individuals’ characteristics and awareness for environmental issues. For instance, in positive emotional states, men donate significantly less than women. In addition, a high level of environmental awareness increases donations in subjects experiencing shame and decreases their likelihood to donate when feeling pride. Also, we observe behavioral consistency for negative emotions and rather compensatory behavior for positive emotions.

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.

International audience; Emerging infectious diseases represent a global and major health problem. The understanding of biological processes involved in the transmission and evolution of infectious agents in host reservoirs is critical. Disease ecology has, therefore, become an important area of research, aiming at investigating the interactions between infectious agents, their hosts, and environments. Interactions between infectious agents exploiting the same vertebrate host at the same time (coinfections) can affect disease outcomes and transmissibility. In this paper, we review this point, as this could have significant consequences on zoonoses emergence.

Disparate biases associated with datasets and trained classifiers in hateful and abusive content identification tasks have raised many concerns recently. Although the problem of biased datasets on abusive language detection has been addressed more frequently, biases arising from trained classifiers have not yet been a matter of concern. Here, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT and evaluate the proposed model on two publicly available datasets annotated for racism, sexism, hate or offensive content on Twitter. Next, we introduce a bias alleviation mechanism in hate speech detection task to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model. Toward that end, we use an existing regularization method to reweight input samples, thereby decreasing the effects of high correlated training set' s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples. To evaluate our bias alleviation mechanism, we employ a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE) respectively. The results show the existence of systematic racial bias in trained classifiers as they tend to assign tweets written in AAE from AAE-aligned group to negative classes such as racism, sexism, hate, and offensive more often than tweets written in SAE from White-aligned. However, the racial bias in our classifiers reduces significantly after our bias alleviation mechanism is incorporated. This work could institute the first step towards debiasing hate speech and abusive language detection systems., Comment: This paper has been accepted in the PLOS ONE journal in August 2020

International audience; This paper analyses the evolution of COVID-19 in Cameroon over the period March 6-April 2020 using SIR models. Specifically, we 1) evaluate the basic reproduction number of the virus, 2) determine the peak of the infection and the spread-out period of the disease, and 3) simulate the interventions of public health authorities. Data used in this study is obtained from the Cameroonian Public Health Ministry. The results suggest that over the identified period, the reproduction number of COVID-19 in Cameroon is about 1.5, and the peak of the infection should have occurred at the end of May 2020 with about 7.7% of the population infected. Furthermore, the implementation of efficient public health policies could help flatten the epidemic curve.

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.

This paper compares and categorizes historical ideas about trees showing relationships among biological entities. The hierarchical structure of a tree is used to test the global consistency of similarities among these ideas; in other words we assess the "treeness" of the tree of historical trees. The collected data are figures and ideas about trees showing relationships among biological entities published or drawn by naturalists from 1555 to 2012. They are coded into a matrix of 235 historical trees and 141 descriptive attributes. From the most parsimonious "tree" of historical trees, treeness is measured by consistency index, retention index and homoplasy excess ratio. This tree is used to create sets or categories of trees, or to study the circulation of ideas. From an unrooted network of historical trees, treeness is measured by the delta-score. This unrooted network is used to measure and visualize treeness. The two approaches show a rather good treeness of the data, with respectively a retention idex of 0.83 and homoplasy excess ratio of 0.74, on one hand, and a delta-score of 0.26 on the other hand. It is interpreted as due to vertical transmission, i.e. an inheritance of shared ideas about biological trees among authors. This tree of trees is then used to test categories previously made. For instance, cladists and gradists are « paraphyletic ». The branches of this tree of trees suggest new categories of tree-thinkers that could have been overlooked by historians or systematists.

Introduction\ud Antiretroviral therapy (ART) has revolutionised the care of HIV-positive individuals resulting in marked decreases in morbidity and mortality, and markedly reduced transmission to sexual partners. However, these benefits can only be realised if individuals are aware of their HIV-positive status, initiated and retained on suppressive lifelong ART. Framed using the socio-ecological model, the present study explores factors contributing to poor ART uptake among community members despite high acceptance of HIV-testing within a Treatment as Prevention (TasP) trial. In this paper we identify barriers and facilitators to treatment across different levels of the socio-ecological framework covering individual, community and health system components.\ud \ud Methods\ud This research was embedded within a cluster-randomised trial (ClinicalTrials.gov, number NCT01509508) of HIV treatment as Prevention in rural KwaZulu-Natal, South Africa. Data were collected between January 2013 and July 2014 from resident community members. Ten participants contributed to repeat in-depth interviews whilst 42 participants took part in repeat focus group discussions. Data from individual interviews and focus group discussions were triangulated using community walks to give insights into community members’ perception of the barriers and facilitators of ART uptake. We used thematic analysis guided by a socio-ecological framework to analyse participants’ narratives from both individual interviews and focus group discussions.\ud \ud Results\ud Barriers and facilitators operating at the individual, community and health system levels influence ART uptake. Stigma was an over-arching barrier, across all three levels and expressed variably as fear of HIV disclosure, concerns about segregated HIV clinical services and negative community religious perceptions. Other barriers were individual (substance misuse, fear of ART side effects), community (alternative health beliefs). Facilitators cited by participants included individual (expectations of improved health and longer life expectancy following ART, single tablet regimens), community (availability of ART in the community through mobile trial facilities) and health system factors (fast and efficient service provided by friendly staff).\ud \ud Discussion\ud We identified multiple barriers to achieving universal ART uptake. To enhance uptake in HIV care services, and achieve the full benefits of ART requires interventions that tackle persistent HIV stigma, and offer people with HIV respectful, convenient and efficient services. These interventions require evaluation in appropriately designed studies.

International audience; In this paper, a novel self-learning digital twin strategy is developed for fluid sloshing phenomena. This class of problems is of utmost importance for robotic manipulation of fluids, for instance, or, in general, in simulation-assisted decision making. The proposed method infers the (linear or non-linear) constitutive behavior of the fluid from video sequences of the sloshing phenomena. Real-time prediction of the fluid response is obtained from a reduced order model (ROM) constructed by means of thermodynamics-informed data-driven learning. From these data, we aim to predict the future response of a twin fluid reacting to the movement of the real container. The constructed system is able to perform accurate forecasts of its future reactions to the movements of the containers. The system is completed with augmented reality techniques, so as to enable comparisons among the predicted result with the actual response of the same liquid and to provide the user with insightful information about the physics taking place.

International audience; Objectives: Large studies are needed to update risk factors of bronchiolitis hospitalization. We performed a nationwide analysis of hospitalization rates for bronchiolitis over four consecutive bronchiolitis seasons to identify underlying medical disorders at risk of bronchiolitis hospitalization and assess their frequency.Methods: Data were retrieved from the French National Hospital Discharge database. Of all infants discharged alive from maternity wards from January 2008 to December 2013 in France (N = 3,884,791), we identified four consecutive cohorts at risk of bronchiolitis during the seasons of 2009–2010 to 2012–2013. The main outcome was bronchiolitis hospitalization during a season. Individual risk factors were collected.Results: Among infants, 6.0% were preterm and 2.0% had ≥1 chronic condition including 0.2% bronchopulmonary dysplasia (BPD) and 0.2% hemodynamically significant congenital heart disease (HS-CHD). Bronchiolitis hospitalization rates varied between seasons (min: 1.26% in 2010–2011; max: 1.48% in 2012–2013; p

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.

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.