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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.

Part 8: Data Mining/Word Counts; International audience; Word clouds are a very useful tool for summarizing textual information. They can be used to illustrate the most frequent and important words of text documents or a set of text documents. In that respect they can also be used for topic visualisation. In this paper we present an experiment investigating how the crowd understands topics visualised via word clouds. In the experiment we use the topics mined from Instagram hashtags of a set of Instagram images corresponding to 30 different subjects. By subject we mean the research hashtag we use to gather pairs of Instagram images and hashtags. With the aid of an innovative topic modelling method, developed in a previous work, we constructed word clouds for the visualisation of each topic. Then we used a popular crowdsourcing platform (Appen) to let users identify the topic they believe each word cloud represents. The results show some interesting variations across subjects which are analysed and discussed in detail throughout the paper. Given that the topics were mined from Instagram hashtags, the current study provides useful insights regarding the appropriateness of hashstags as image annotation tags.

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.

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; A major challenge for the European electronic industry is to enhance productivity by ensuring quality of development, integration and maintenance while reducing the associated costs. Model-Driven Engineering (MDE) principles and techniques have already shown promising capabilities, but they still need to scale up to support real-world scenarios implied by the full deployment and use of complex electronic components and systems. Moreover, maintaining efficient traceability, integration, and communication between two fundamental system life cycle phases (design time and runtime) is another challenge requiring the scalability of MDE. This paper presents an overview of the ECSEL1 project entitled “MegaModelling at runtime – Scalable model-based framework for continuous development and runtime validation of complex systems” (MegaM@Rt2), whose aim is to address the above mentioned challenges facing MDE. Driven by both large and small industrial enterprises, with the support of research partners and technology providers, MegaM@Rt2 aims to deliver a framework of tools and methods for: 1) system engineering/design and continuous development, 2) related runtime analysis and 3) global models and traceability management. Diverse industrial use cases (covering strategic domains such as aeronautics, railway, construction and telecommunications) will integrate and demonstrate the validity of the MegaM@Rt2 solution. This paper provides an overview of the MegaM@Rt2 project with respect to its approach, mission, objectives as well as to its implementation details. It further introduces the consortium as well as describes the work packages and few already produced deliverables.

Close-range photogrammetry as a technique to acquire reality-based 3D data has, in recent times, seen a renewed interest due to developments in sensor technologies. Furthermore, the strong democratization of UAVs (Unmanned Aerial Vehicles) or drones means that close-range photogrammetry can now be used as a viable low-cost method for 3D mapping. In terms of software development, this led to the creation of many commercial black-box solutions (PhotoScan, Pix4D, etc.). This paper aims to demonstrate how the open source toolbox DBAT (Damped Bundle Adjustment Toolbox) can be used to generate detailed photogrammetric network diagnostics to help assess the quality of surveys processed by the commercial software, PhotoScan. In addition, the Apero module from the MicMac software suite was also used to provide an independent assessment of the results. The assessment is performed by the careful examination of some of the bundle adjustment metrics generated by both open source solutions. A UAV project was conducted on a historical church in the city center of Strasbourg, France, in order to provide a dataset with a millimetric level of precision. Results showed that DBAT can be used to reprocess PhotoScan projects under similar conditions and derive useful metrics from them, while Apero provides a completely independent verification of the results of commercial solutions. Overall, this paper shows that an objective assessment of photogrammetric results is important. In cases where problems are encountered in the project, this assessment method can be useful to detect errors that may not be explicitly presented by PhotoScan.

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; 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.

Following previous research findings, this paper argues that the current method of evaluating scholar performance, publication counts in “quality” journals is flawed by subjectivity in generating the list of approved journals and the definition of quality. Truex, Cuellar and Takeda (2009) sought to improve on this method by substituting the measurement of “influence” using the Hirsch statistics to measure ideational influence. Since the h-family statistics are a measure of productivity and the uptake of a scholar’s ideas this methodology privileges the uptake of a scholar’s ideas over the venue of publication. But influence is built through other means than by having one’s papers read and cited. This paper argues that interaction between scholars resulting in co-authored papers is another way to build academic influence. This academic influence, which we term social influence, can be assessed by Social Network Analysis (SNA) metrics which examine the nature and strength of coauthoring networks among IS Scholars. The paper demonstrates the method of assessing social influence by analysis of the social network of AMCIS scholars and compares the results of this analysis with other co-authorship networks from the ECIS and ICIS community.

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.

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.

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.

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

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.

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.

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.

Part 2: Workshop and Tutorial Papers; International audience; In the context of big data analytics, the possibilities and demands of online data services may change rapidly, and with it change scenarios related to the processing of personal data. Such changes may pose challenges with respect to legal requirements such as a transparency and consent, and therefore call for novel methods to address the legal and conceptual issues that arise in its course. We define the concept of ‘dynamic consent’ as a means to meet the challenge of acquiring consent in a commercial use case that faces change with respect to re-purposing the processing of personal data with the goal to implement new data services. We present a prototypical implementation that facilitates incremental consent forms based on dynamic consent. We report the results gained via two focus groups which we used to evaluate our design, and derive from our findings implications for future directions.

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.

Biological systems can share and collectively process information to yield emergent effects, despite inherent noise in communication. While man-made systems often employ intricate structural solutions to overcome noise, the structure of many biological systems is more amorphous. It is not well understood how communication noise may affect the computational repertoire of such groups. To approach this question we consider the basic collective task of rumor spreading, in which information from few knowledgeable sources must reliably flow into the rest of the population. We study the effect of communication noise on the ability of groups that lack stable structures to efficiently solve this task. We present an impossibility result which strongly restricts reliable rumor spreading in such groups. Namely, we prove that, in the presence of even moderate levels of noise that affect all facets of the communication, no scheme can significantly outperform the trivial one in which agents have to wait until directly interacting with the sources—a process which requires linear time in the population size. Our results imply that in order to achieve efficient rumor spread a system must exhibit either some degree of structural stability or, alternatively, some facet of the communication which is immune to noise. We then corroborate this claim by providing new analyses of experimental data regarding recruitment in Cataglyphis niger desert ants. Finally, in light of our theoretical results, we discuss strategies to overcome noise in other biological systems., Author summary Biological systems must function despite inherent noise in their communication. Systems that enjoy structural stability, such as biological neural networks, could potentially overcome noise using simple redundancy-based procedures. However, when individuals have little control over who they interact with, it is unclear what conditions would prevent runaway error accumulation. This paper takes a general stance to investigate this problem, concentrating on the basic information-dissemination task of rumor spreading. Drawing on a theoretical model, we prove that fast rumor spreading can only be achieved if some part of the communication setting is either stable or reliable. We then provide empirical support for this claim by conducting new analyses of data from experiments on recruitment in desert ants.

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.

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.

Microalgae are promising microorganisms for the production of numerous molecules of interest, such as pigments, proteins or triglycerides that can be turned into biofuels. Heterotrophic or mixotrophic growth on fermentative wastes represents an interesting approach to achieving higher biomass concentrations, while reducing cost and improving the environmental footprint. Fermentative wastes generally consist of a blend of diverse molecules and it is thus crucial to understand microalgal metabolism in such conditions, where switching between substrates might occur. Metabolic modeling has proven to be an efficient tool for understanding metabolism and guiding the optimization of biomass or target molecule production. Here, we focused on the metabolism of Chlorella sorokiniana growing heterotrophically and mixotrophically on acetate and butyrate. The metabolism was represented by 172 metabolic reactions. The DRUM modeling framework with a mildly relaxed quasi-steady-state assumption was used to account for the switching between substrates and the presence of light. Nine experiments were used to calibrate the model and nine experiments for the validation. The model efficiently predicted the experimental data, including the transient behavior during heterotrophic, autotrophic, mixotrophic and diauxic growth. It shows that an accurate model of metabolism can now be constructed, even in dynamic conditions, with the presence of several carbon substrates. It also opens new perspectives for the heterotrophic and mixotrophic use of microalgae, especially for biofuel production from wastes., Author summary Most existing metabolic modeling tools are not suitable for studying diauxic growth with dynamic substrate shifts. This paper describes a successful modeling of Chlorella sorokiniana metabolism, based on 172 reactions and validated by nine independent dynamic experiments (nine experiments were used for its calibration), in which microalgae were grown heterotrophically or mixotrophically on acetate and/or butyrate, in the light or dark. Such an extensive validation has not been performed before for microalgae. It was demonstrated that the model could be used to assess the flux dynamics in the cell. This innovative simulation tool was also used to derive original strategies to bypass the toxicity of some substrates using mixotrophic regimes.

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.

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information., 28 pages ; 9 figures ; 5 Supplementary Materials

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.

The aim of this paper is to investigate whether the Matlab-based Damped Bundle Adjustment Toolbox (DBAT) can be used to provide independent verification of the BA computation of two popular software—PhotoModeler (PM) and PhotoScan (PS). For frame camera data sets with lens distortion, DBAT is able to reprocess and replicate subsets of PM results with high accuracy. For lens-distortion-free data sets, DBAT can furthermore provide comparative results between PM and PS. Data sets for the discussed projects are available from the authors. The use of an external verification tool such as DBAT will enable users to get an independent verification of the computations of their software. In addition, DBAT can provide computation of quality parameters such as estimated standard deviations, correlation between parameters, etc., something that should be part of best practice for any photogrammetric software. Finally, as the code is free and open-source, users can add computations of their own.

The external globus pallidus (GPe) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection. A class of computational models assumes that, during action selection, the basal ganglia compute for all actions available in a given context the probabilities that they should be selected. These models suggest that a network of GPe and subthalamic nucleus (STN) neurons computes the normalization term in Bayes’ equation. In order to perform such computation, the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons. However, the complex form of this function makes it unlikely that individual GPe neurons, or even a single GPe cell type, could compute it. Here, we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron, so-called ‘prototypic’ and ‘arkypallidal’ neurons, that have different response properties in vivo and distinct connections. We compare our model predictions with the experimentally-reported connectivity and input-output functions (f-I curves) of the two populations of GPe neurons. We show that, together, these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection. We conclude that, by virtue of their distinct response properties and connectivities, a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions., Author Summary Choosing an appropriate action as quickly and accurately as possible in a given situation is critical for the survival of animals and humans. One of the brain regions involved in action selection is a set of subcortical nuclei known as the basal ganglia. The importance of understanding information processing in the basal ganglia is further emphasised by the fact that their disturbed interactions in Parkinson’s disease results in profound difficulties in movement. Computational models have suggested how the basal ganglia could select actions in the fastest possible way for the required accuracy level. These models further predict that a part of basal ganglia, called the external globus pallidus (GPe), needs to calculate a particular function of its inputs. This paper proposes how this function could be computed in a mathematical model of a network within GPe. Furthermore, it shows that the experimentally observed connectivity and response properties of GPe neurons fulfil the requirements necessary to support optimal action selection. This suggests the GPe neurons have properties that allow them to contribute to optimal action selection in the whole basal ganglia.

International audience; In this paper, we apply an original scientometric analyses to a corpus comprising synthetic biology (SynBio) publications in Thomson Reuters Web of Science to characterize the emergence of this new scientific field. Three results were drawn from this empirical investigation. First, despite the exponential growth of publications, the study of population level statistics (newcomers proportion, collaboration network structure) shows that SynBio has entered a stabilization process since 2010. Second, the mapping of textual and citational networks shows that SynBio is characterized by high heterogeneity and four different approaches: the central approach, where biobrick engineering is the most widespread; genome engineering; protocell creation; and metabolic engineering. We suggest that synthetic biology acts as an umbrella term allowing for the mobilization of resources, and also serves to relate scientific content and promises of applications. Third, we observed a strong intertwinement between epistemic and socio-economic dynamics. Measuring scientific production and impact and using structural analysis data, we identified a core set of mostly American scientists. Biographical analysis shows that these central and influential scientists act as “boundary spanners,” meaning that their importance to the field lies not only in their academic contributions, but also in their capacity to interact with other social spaces that are outside the academic sphere.

International audience; System restoration from cascading failures is an integral part of the overall defense against catastrophic breakdown in networked critical infrastructures. From the outbreak of cascading failures to the system complete breakdown, actions can be taken to prevent failure propagation through the entire network. While most analysis efforts have been carried out before or after cascading failures, restoration during cascading failures has been rarely studied. In this paper, we present a modeling framework to investigate the effects of in-process restoration, which depends strongly on the timing and strength of the restoration actions. Furthermore, in the model we also consider additional disturbances to the system due to restoration actions themselves. We demonstrate that the effect of restoration is also influenced by the combination of system loading level and restoration disturbance. Our modeling framework will help to provide insights on practical restoration from cascading failures and guide improvements of reliability and resilience of actual network systems.

International audience; Soil invertebrates are known to be much involved in soil behaviour and therefore in the provision of ecosystem services. Functional trait-based approaches are methodologies which can be used to understand soil invertebrates' responses to their environment. They (i) improve the predictions and (ii) are less dependent on space and time. The way traits have been used recently has led to misunderstandings in the integration and interpretation of data. Trait semantics are especially concerned. The aim of this paper is to propose a thesaurus for soil invertebrate trait-based approaches. T-SITA, an Internet platform, is the first initiative to deal with the semantics of traits and ecological preferences for soil invertebrates. It reflects the agreement of a scientific expert community to fix semantic properties (e.g. definition) of approximately 100 traits and ecological preferences. In addition, T-SITA has been successfully linked with a fully operational database of soil invertebrate traits. Such a link enhances data integration and improves the scientific integrity of data.

International audience; Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.

This paper introduces a geometrical constraint kernel for handling the location in space and time of polymorphic k-dimensional objects subject to various geometrical and time constraints. The constraint kernel is generic in the sense that one of its parameters is a set of constraints on subsets of the objects. These constraints are handled globally by the kernel. We first illustrate how tomodel several placement problems with the constraint kernel. We then explain how new constraints can be introduced and plugged into the kernel. Based on these interfaces, we develop a generic k-dimensional lexicographic sweep algorithm for filtering the attributes of an object (i.e., its shape and the coordinates of its origin as well as its start, duration and end in time) according to all constraints where the object occurs. Experiments involving up to hundreds of thousands of objects and 1 million integer variables are provided in 2, 3 and 4 dimensions, both for simple shapes (i.e., rectangles, parallelepipeds) and for more complex shapes.