Éditeur(s) : HAL CCSD
Résumé : 6 pages
In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of large scale neural networks can take advantage of distributing the neurons on a set of processors (either workstation cluster or parallel computer). This article presents DAMNED, a large scale SNN simulation framework able to gather the benefits of EDS and parallel computing. Two levels of parallelism are combined: Distributed mapping of the neural topology, at the network level, and local multithreaded allocation of resources for simultaneous processing of events, at the neuron level. Based on the causality of events, a distributed solution is proposed for solving the complex problem of scheduling without synchronization barrier.
https://hal.archives-ouvertes.fr/hal-00015137

hal-00015137
https://hal.archives-ouvertes.fr/hal-00015137
https://hal.archives-ouvertes.fr/hal-00015137v2/document
https://hal.archives-ouvertes.fr/hal-00015137/file/hal_damned.pdf
ARXIV : cs.NE/0512018

Éditeur(s) : HAL CCSD IEEE
Résumé : International audience
In this paper, we present an evolutionary approach for extracting a model of flood prediction from hydrological data observed timely on water heights in a river watershed. Since this kind of data recorded by sensors on river basins is highly scarce and hopefully much unbalanced between cases of floods and non-floods, we have adopted the notion of aggregate variables which values are computed as aggregates on raw data. An evolutionary algorithm is involved to allow selecting the best sets - juries of classifiers- of such variables as predictive variables. Two real hydrological data sets are trained and they both show the efficiency of the method compared to traditional solutions for prediction.
IEEE Congress on Evolutionary Computation 2012
Brisbane, Australia

hal-00840739
https://hal.archives-ouvertes.fr/hal-00840739
DOI : 10.1109/CEC.2012.6252897

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Background: Ameloblastin (AMBN) is a phosphorylated, proline/glutamine-rich protein secreted during enamel formation. Previous studies have revealed that this enamel matrix protein was present early in vertebrate evolution and certainly plays important roles during enamel formation although its precise functions remain unclear. We performed evolutionary analyses of AMBN in order to (i) identify residues and motifs important for the protein function, (ii) predict mutations responsible for genetic diseases, and (iii) understand its molecular evolution in mammals. Results: In silico searches retrieved 56 complete sequences in public databases that were aligned and analyzed computationally. We showed that AMBN is globally evolving under moderate purifying selection in mammals and contains a strong phylogenetic signal. In addition, our analyses revealed codons evolving under significant positive selection. Evidence for positive selection acting on AMBN was observed in catarrhine primates and the aye-aye. We also found that (i) an additional translation initiation site was recruited in the ancestral placental AMBN, (ii) a short exon was duplicated several times in various species including catarrhine primates, and (iii) several polyadenylation sites are present. Conclusions: AMBN possesses many positions, which have been subjected to strong selective pressure for 200 million years. These positions correspond to several cleavage sites and hydroxylated, O-glycosylated, and phosphorylated residues. We predict that these conserved positions would be potentially responsible for enamel disorder if substituted. Some motifs that were previously identified as potentially important functionally were confirmed, and we found two, highly conserved, new motifs, the function of which should be tested in the near future. This study illustrates the power of evolutionary analyses for characterizing the functional constraints acting on proteins with yet uncharacterized structure.
ISSN: 1471-2148

Droits : http://creativecommons.org/licenses/by/
hal-01212816
http://hal.upmc.fr/hal-01212816
http://hal.upmc.fr/hal-01212816/document
http://hal.upmc.fr/hal-01212816/file/s12862-015-0431-0.pdf
DOI : 10.1186/s12862-015-0431-0

Éditeur(s) : HAL CCSD Oxford University Press (OUP)
Résumé : International audience
Motivation: Gene fusion is an important evolutionary process. It can yield valuable information to infer the interactions and functions of proteins. Fused genes have been identified as non-transitive patterns of similarity in triplets of genes. To be computationally tractable, this approach usually imposes an a priori distinction between a dataset in which fused genes are searched for, and a dataset that may have provided genetic material for fusion. This reduces the `genetic space' in which fusion can be discovered, as only a subset of triplets of genes is investigated. Moreover, this approach may have a high-false-positive rate, and it does not identify gene families descending from a common fusion event. Results: We represent similarities between sequences as a network. This leads to an efficient formulation of previous methods of fused gene identification, which we implemented in the Python program FusedTriplets. Furthermore, we propose a new characterization of families of fused genes, as clique minimal separators of the sequence similarity network. This well-studied graph topology provides a robust and fast method of detection, well suited for automatic analyses of big datasets. We implemented this method in the C++ program MosaicFinder, which additionally uses local alignments to discard false-positive candidates and indicates potential fusion points. The grouping into families will help distinguish sequencing or prediction errors from real biological fusions, and it will yield additional insight into the function and history of fused genes.
ISSN: 1367-4803

hal-01544795
https://hal.archives-ouvertes.fr/hal-01544795
DOI : 10.1093/bioinformatics/btt049

Éditeur(s) : HAL CCSD
Résumé : International audience
Symbiosis is a widespread phenomenon in the biosphere. In marine benthic environments breakdown of symbiosis is responsible for coral bleaching and dramatically affects coral reef ecosystems functioning [1]. In the water column, planktonic organisms are key component of pelagic ecosystems and a number of species form mutualistic association with microalgae forming a photosymbiosis [2]. Here we intended to investigate the genetic basis of photosymbiosis, through a transcriptomic approach on marine plankton organisms. We focused more specifically on associations occurring between radiolarian host (protist, zooplankton) and dinoflagellates symbiont (protist, phytoplankton) living inside the host cell. It has recently been highlighted that these holobionts are widespread in oligotrophic open oceans and there are also evidences of their fundamental implication in biogeochemical carbon, silica and strontium cycles [3, 4, 5, 6]. RNA-seq technologies allow obtaining an unprecedented amount of data for unicellular organisms isolated from the environment [7]. The study of such non-model holobionts datasets requires de novo assembly, which implies considerable computational resources and the potential production of chimeric sequences [8, 9].We therefore developed an original strategy aiming at accelerating and improving de novo assembly for holobiont datasets. We chose SIMKA [10] a fast kmer-based method initially developed to estimate the similarity between numerous metagenomic datasets, and which has been recently adapted to extract their common sequences. As our symbionts are identified, we used SIMKA to compare our holobionts transcriptomes to publicly available dinoflagellates transcriptomes [11] generating two datasets, one composed of reads from the symbionts and another with reads from the host (for which no reference data are currently available). Independent assemblies were then performed in parallel, accelerating the study process, and minimizing the proportion of resulting chimeric sequences. Our strategy produced a unique and comprehensive genomic dataset for Radiolaria [12, 13], and offers a pragmatic, large scale, comparison strategy to assemble and study holobionts [9]. Our new sequences obtained from holobionts study will be used for phylogenomics investigation, as reference for environmental metagenomic studies and ultimately to understand and characterize the molecular basis of symbiotic relationships in the plankton.Bibliografic references :[1] Simon K. Davy, Denis Allemand, and Virginia M. Weis. “Cell Biology of Cnidarian-Dinoflagellate Symbiosis”. In: Microbiology and Molecular Biology Reviews 76.2 (June 1, 2012), pp. 229–261. issn: 1092-2172, 1098- 5557. doi: 10.1128/MMBR.05014-11.[2] Janouškovec, J. et al. “Major transitions in dinoflagellate evolution unveiled by phylotranscriptomics”. in: PNAS 201614842 (2016). doi:10.1073/pnas.1614842114[3] Tristan Biard et al. “In situ imaging reveals the biomass of giant protists in the global ocean”. In: Nature advance online publication (Apr. 20, 2016). issn: 0028-0836. doi: 10.1038/nature17652.[4] Lionel Guidi et al. “Plankton networks driving carbon export in the olig- otrophic ocean”. In: Nature 532.7600 (Apr. 28, 2016), pp. 465–470. issn: 0028-0836. doi: 10.1038/nature16942.[5] Diane K. Stoecker et al. “Acquired phototrophy in aquatic protists”. In: Aquatic Microbial Ecology 57.3 (Nov. 24, 2009), pp. 279–310. doi: 10.3354/ame01340.[6] Johan Decelle et al. “An original mode of symbiosis in open ocean plankton”. In: Proceedings of the National Academy of Sciences 109.44 (Oct. 30, 2012), pp. 18000–18005. issn: 0027-8424, 1091-6490. doi: 10. 1073/pnas.1212303109.[7] Sergio Balzano et al. “Transcriptome analyses to investigate symbiotic relationships between marine protists”. In: Frontiers in Microbiology 6 (Mar. 17, 2015). issn: 1664-302X. doi: 10.3389/fmicb.2015.00098.[8] Li, B. et al. “Evaluation of de novo transcriptome assemblies from RNA-Seq data”. In: Genome Biology 15, 553 (2014).[9] Sangwan, N., Xia, F. & Gilbert, J. A. “Recovering complete and draft population genomes from metagenome datasets”. In: Microbiome 4, 8 (2016).[10] Gaëtan Benoit et al. “Multiple comparative metagenomics using mul- tiset k -mer counting”. In: PeerJ Computer Science 2 (Nov. 14, 2016). doi: 10.7717/peerj-cs.94.[11] Patrick J. Keeling et al. “The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the Functional Diversity of Eukaryotic Life in the Oceans through Transcriptome Sequencing”. In: PLOS Biol 12.6 (June 2014), e1001889. issn: 1545-7885. doi: 10.1371/journal.pbio.1001889.[12] Burki, F. et al. “Evolution of Rhizaria: new insights from phylogenomic analysis of uncultured protists”. In BMC Evolutionary Biology 10, 377 (2010).
International Conference on Holobionts
Paris, France

hal-01575069
https://hal.inria.fr/hal-01575069

Éditeur(s) : HAL CCSD Wiley-Blackwell
Résumé : International audience
Understanding the distribution of genetic diversity in the light of past demographic events linked with climatic shifts will help to forecast evolutionary trajectories of ecosystems within the current context of climate change. In this study, mitochondrial sequences and microsatellite loci were analysed using traditional population genetic approaches together with Bayesian dating and the more recent approximate Bayesian computation scenario testing. The genetic structure and demographic history of a commercial fish, the black scorpionfish, Scorpaena porcus, was investigated throughout the Mediterranean and Black Seas. The results suggest that the species recently underwent population expansions, in both seas, likely concomitant with the warming period following the Last Glacial Maximum, 20 000 years ago. A weak contemporaneous genetic differentiation was identified between the Black Sea and the Mediterranean Sea. However, the genetic diversity was similar for populations of the two seas, suggesting a high number of colonizers entered the Black Sea during the interglacial period and/or the presence of a refugial population in the Black Sea during the glacial period. Finally, within seas, an east/west genetic differentiation in the Adriatic seems to prevail, whereas the Black Sea does not show any structured spatial genetic pattern of its population. Overall, these results suggest that the Black Sea is not that isolated from the Mediterranean, and both seas revealed similar evolutionary patterns related to climate change and changes in sea level.
ISSN: 1471-8278

hal-01330323
https://hal-univ-perp.archives-ouvertes.fr/hal-01330323
DOI : 10.1111/mec.13616

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
ISSN: 2072-4292

hal-01429052
https://hal.archives-ouvertes.fr/hal-01429052
DOI : 10.3390/rs4103244

Résumé : Les travaux de recherche présentés dans ce mémoire s'inscrivent dans la lignée des approches de modélisation hydrologiques prédictives dirigées par les données. Nous avons particulièrement développé leur application sur le contexte difficile des phénomènes de crue éclairs caractéristiques des bassins versants de la région Caraïbe qui pose un dé fi sé.curi taire. En envisageant le problème de la prévision de crues comme un problème d'optimisation combinatoire difficile nous proposons d'utiliser la notion de métaneuristiques, à travers les algorithmes évolutionnaire notamment pour leur capacité à parcourir efficacement de grands espaces de recherche et fi fournir des solutions de bOlIDe qualité en des temps d'exécution raisonnables. Nous avons présenté l'approche de prédiction AV2D : Aggregate Variable Data Driven dom le concept central est la notion de variable agrégée. L'idée sous-jacente à ce concept est de considérer le pouvoir prédictif de nouvelles variables définies comme le résultat de fonctions tatistiques, dites d'agrégation calculées sur de donnée' correspondant à des périodes de temps précédent uo événem nt à prédire. Ces variable sont caractérisées par des ensembles de paramètres correspondant a leur pJ:opriétés. Nous avons imroduitle variables agrégées hydrométéorologiques permettant de répondre au problème de la classification d événements hydrologiques. La complexité du parcours de l'espace de recherche engendré par les paramètres définissant ces variables a été prise en compte grâce à la njse en oeuvre d'un algorithme évolutionnaire particulier dont les composants ont été spécifiquement définis pour ce problème. Nous avons montré, à travers une étude comparative avec d'autres approches de modélisation dirigées par les données, menée sur deux cas d'études de bassins versant caribéens, que l'approche AV2D est particulièrement bien adaptée à leur contexte. Nous étudions par la suite les bénéfices offerts par les approches de modélisation hydrologiques modulaires dirigées par les données, en définissant un procédé de division en sous-processus prenant en compte les caractéristiques paniculières des bassins versants auxquels nous nous intéressons. Nou avons proposé une extension des travaux précédents à travers la définition d'une approche de modélisation modulaire M2D: Spatial Modular Data Driven, consistant à considérer des sous-processus en divisant l'ensemble des exemples à classifier en sous-ensembles correspondant à des comportements hydrologiques homogènes. Nous avons montré à travers une étude comparative avec d autres approches dU'igées par les données mises en oeuvre sur les mêmes sous-ensembles de données que celte approche permet d améliorer les résultats de prédiction particulièrement à coun Lenne. Nous avons enfin proposé la modélisation d un outil de pi
The work presented in this thesis is in the area of data-driven hydrological modeling approaches. We particularly investigared their application on the difficult problem of flash flood phenomena typically observed in Caribbean watersheds. By considering the problem of flood prediction as a combinatorial optimization problem, we propose to use the notion of Oleraheuristics, through evolutionary algorithms, especially for their capacity ta visit effjciently large search space and to provide good solutions in reasonable execution times. We proposed the hydrological prediction approach AV2D: Aggregate Variable Data Driven which central concept is the notion of aggregate variable. The underlying idea of this [concept is to consider the predictive power of new variables defined as the results of statistical functions, called aggregation functions, computed on data corresponding ta time periods before an event ta predict. These variables are characterized by sets of parameters corresponding ta their specifications. We introduced hydro-meteorological aggregate variables allowing ta address the classification problem of hydrological events. We showed through a comparative study on two typical caribbean watersheds, using several common data driven modelling techniques that the AV2D approach is panicul.rly weil fitted ta the studied context. We also study the benefits offered by modulaI' approaches through the definition of the SM2D: Spatial Modular DataDriven approach, consisting in considering sub-processes partly defined by spatial criteria. We showed that the results obtained by the AV2D on these sub-processes allows to increase the performances particularly for short term prediction. Finally we proposed the modelization of a generic control tool for hydro-meteorological prediction systems, H2FCT: Hydro-meteorological Flood Forecasting Control 1'001

http://www.theses.fr/2013AGUY0673/document

Éditeur(s) : HAL CCSD
Résumé : International audience
https://hal.archives-ouvertes.fr/hal-01436542

hal-01436542
https://hal.archives-ouvertes.fr/hal-01436542
DOI : 10.1109/IGARSS.2011.6049595

Éditeur(s) : HAL CCSD Springer International Publishing AG.
Résumé : International audience
Underwater Seascapes. From geographical to ecological perspectives

hal-01429034
https://hal.archives-ouvertes.fr/hal-01429034
DOI : 10.1007/978-3-319-03440-9_16

Éditeur(s) : HAL CCSD
Résumé : International audience
Modelling techniques for river hydrologic forecasting systems have taken advantage of machine learning methods especially for flood prediction. But current solutions, mostly based on artificial neural networks do not always meet end users requirements on the readability and the understandability of predictive models. In this paper, we present a new version of our original solution based on the concept of aggregate variables in order to predict flash flood events from observed water level and/or rain measurements, particularly in the context of caribbean watersheds in which flash flood are much uncertain. We combine aggregate variables in juries. Juries of aggregate variables are trained and tested using a typical 10-fold cross validation scheme. Best juries are searched through an evolutionary approach that is optimized. Different parameters are set up like aggregation periods and jury sizes to prove the efficiency of the proposed approach compared to classical solutions.
Congress on Evolutionary Computation (CEC) 2013
Cancun, Mexico

hal-00847582
https://hal.archives-ouvertes.fr/hal-00847582
DOI : 10.1109/CEC.2013.6557656

Éditeur(s) : HAL CCSD Coastal Education and Research Foundation
Résumé : Special Issue 65 - International Coastal Symposium Volume 1
International audience
ISSN: 0749-0208

hal-01429044
https://hal.archives-ouvertes.fr/hal-01429044
DOI : 10.2112/SI65-117.1

Éditeur(s) : HAL CCSD D-side publications, Evere, Belgium
Résumé : International audience
This article underlines the learning and discrimination capabilities of a model of associative memory based on artificial networks of spiking neurons. Inspired from neuropsychology and neurobiology, the model implements top-down modulations, as in neocortical layer V pyramidal neurons, with a learning rule based on synaptic plasticity (STDP), for performing a multimodal association learning task. A temporal correlation method of analysis proves the ability of the model to associate specific activity patterns to different samples of stimulation. Even in the absence of initial learning and with continuously varying weights, the activity patterns become stable enough for discrimination.
Proceedings of 14 European Symposium on Artificial Neural Networks (ESANN 2006)

hal-00115420
https://hal.archives-ouvertes.fr/hal-00115420
https://hal.archives-ouvertes.fr/hal-00115420/document
https://hal.archives-ouvertes.fr/hal-00115420/file/esann06_HAL.pdf
ARXIV : cs.NE/0611104

Éditeur(s) : HAL CCSD
Résumé : International audience
International Coral Reef Symposium 2012

hal-01429056
https://hal.archives-ouvertes.fr/hal-01429056

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : International audience
ISSN: 1932-6203

hal-01520195
https://hal.archives-ouvertes.fr/hal-01520195
DOI : 10.1371/journal.pone.0176862

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
ISSN: 2072-4292

hal-01429038
https://hal.archives-ouvertes.fr/hal-01429038
DOI : 10.3390/rs5073583

Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
Measuring functional or phylogenetic diversity is the object of an active literature. The main issues to address are relating measures to a clear conceptual framework, allowing unavoidable estimation-bias correction and decomposing diversity along spatial scales. We provide a general mathematical framework to decompose measures of species-neutral, phylogenetic or functional diversity into α and β components. We first unify the definitions of phylogenetic and functional entropy and diversity as a generalization of HCDT entropy and Hill numbers when an ultrametric tree is considered. We then derive the decomposition of diversity. We propose a bias correction of the estimates allowing meaningful computation from real, often undersampled communities. Entropy can be transformed into true diversity, that is an effective number of species or communities. Estimators of α- and β-entropy, phylogenetic and functional entropy are provided. Proper definition and estimation of diversity is the first step towards better understanding its underlying ecological and evolutionary mechanisms.
ISSN: 2041-210X

hal-01502645
https://hal-agroparistech.archives-ouvertes.fr/hal-01502645
DOI : 10.1111/2041-210X.12323