161 research outputs found

    Etude scientifiques d’objets rĂ©alisĂ©s par Jean ProuvĂ©

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    Au cours de ce projet, nous avons rĂ©alisĂ© l’étude mĂ©canique de 2 objets caractĂ©ristiques de Jean ProuvĂ© : la chaise Jean ProuvĂ© et le portique de la Maison Tropicale, le but Ă©tant de savoir si ces objets Ă©taient correctement dimensionnĂ©s ou non. Nous avons commencĂ© par Ă©tudier les diffĂ©rents matĂ©riaux qui les composaient avec leurs propriĂ©tĂ©s qui nous semblaient importantes et utiles pour la suite, puis nous avons dessinĂ©s les piĂšces sous CATIA, qui est un logiciel dĂ©diĂ© Ă  la rĂ©alisation de piĂšces complexes. Nous obtenons alors un modĂšle purement gĂ©omĂ©trique que nous importons sous ABAQUS, qui est un logiciel de calcul par Ă©lĂ©ments finis qui va nous permettre de rĂ©aliser des simulations de chargement mĂ©canique. Il faut donc Ă©valuer les conditions limites pour chacun des objets : la chaise doit supporter le poids d’une personne sans subir de dĂ©formations plastiques et encore moins casser, c’est pourquoi pour simuler cette force, nous appliquons une contrainte calculĂ©e avec une personne de 100kg, nous fixons un en encastrement au sol et nous prenons en compte le poids de la chaise. le portique doit rĂ©sister au plafond de la Maison Tropicale sans cĂ©der, c’est pourquoi nous simulons l’action de ce dernier en appliquant une contrainte calculĂ©e avec un plafond de 60 tonnes, nous fixons un encastrement au sol pour qu’il ne bouge pas, et nous prenons Ă©galement en compte l’action de son poids. Ensuite, la piĂšce doit ĂȘtre maillĂ©e, c’est-Ă -dire dĂ©coupĂ©e en petits volumes sur lesquels seront faits les diffĂ©rents calculs. Maintenant, nous pouvons lancer la simulation et interprĂ©ter les calculs. Sous ABAQUS, il est possible de visualiser de nombreux rĂ©sultats diffĂ©rents, c’est pourquoi il a fallu choisir les plus cohĂ©rents avec notre dĂ©marche de savoir si le matĂ©riau Ă©tait correctement proportionnĂ©. Pour chacun des 2 objets, nous avons donc dĂ©cidĂ© d’étudier le dĂ©placement global, puis dans chacune des directions. Nous constatons alors un lĂ©ger dĂ©placement (de l’ordre du mm), mais qui est minime par rapport aux dimensions des piĂšces. Nous avons ensuite choisi le critĂšre de plasticitĂ© de Von Mises pour dĂ©terminer si les piĂšces se dĂ©forment Ă©lastiquement ou plastiquement. En effet, si la contrainte Ă©quivalente Von Mises est supĂ©rieure Ă  la limite d’élasticitĂ©, alors le matĂ©riau prĂ©sentera des dĂ©formations plastiques. Nous constatons pour la chaise et pour le portique que ce n’est pas le cas, donc leur dĂ©formation est seulement Ă©lastique et donc rĂ©versible. Pour finir, nous avons calculĂ© les coefficients de sĂ©curitĂ© qui nous permettent de savoir Ă  partir de quel poids l’objet commencera Ă  prĂ©senter des dĂ©formations plastiques. Cependant, nous avons fait des hypothĂšses telles que la limite d’élasticitĂ© qui vont influencer cette valeur. A l’aide des diffĂ©rents rĂ©sultats, nous pouvons conclure que les objets Ă©tudiĂ©s sont correctement proportionnĂ©s, car ils ne prĂ©sentent pas de dĂ©formations plastiques et possĂšdent un coefficient de sĂ©curitĂ© compris entre 1 et 2, permettant de faire face aux imprĂ©vus.Outgoin

    Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction

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    Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E

    Genetic diversity, linkage disequilibrium and power of a large grapevine (Vitis vinifera L) diversity panel newly designed for association studies

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    UMR-AGAP Equipe DAVV (DiversitĂ©, adaptation et amĂ©lioration de la vigne) ; Ă©quipe ID (IntĂ©gration de DonnĂ©es)International audienceAbstractBackgroundAs for many crops, new high-quality grapevine varieties requiring less pesticide and adapted to climate change are needed. In perennial species, breeding is a long process which can be speeded up by gaining knowledge about quantitative trait loci linked to agronomic traits variation. However, due to the long juvenile period of these species, establishing numerous highly recombinant populations for high resolution mapping is both costly and time-consuming. Genome wide association studies in germplasm panels is an alternative method of choice, since it allows identifying the main quantitative trait loci with high resolution by exploiting past recombination events between cultivars. Such studies require adequate panel design to represent most of the available genetic and phenotypic diversity. Assessing linkage disequilibrium extent and panel power is also needed to determine the marker density required for association studies.ResultsStarting from the largest grapevine collection worldwide maintained in Vassal (France), we designed a diversity panel of 279 cultivars with limited relatedness, reflecting the low structuration in three genetic pools resulting from different uses (table vs wine) and geographical origin (East vs West), and including the major founders of modern cultivars. With 20 simple sequence repeat markers and five quantitative traits, we showed that our panel adequately captured most of the genetic and phenotypic diversity existing within the entire Vassal collection. To assess linkage disequilibrium extent and panel power, we genotyped single nucleotide polymorphisms: 372 over four genomic regions and 129 distributed over the whole genome. Linkage disequilibrium, measured by correlation corrected for kinship, reached 0.2 for a physical distance between 9 and 458 Kb depending on genetic pool and genomic region, with varying size of linkage disequilibrium blocks. This panel achieved reasonable power to detect associations between traits with high broad-sense heritability (> 0.7) and causal loci with intermediate allelic frequency and strong effect (explaining > 10 % of total variance).ConclusionsOur association panel constitutes a new, highly valuable resource for genetic association studies in grapevine, and deserves dissemination to diverse field and greenhouse trials to gain more insight into the genetic control of many agronomic traits and their interaction with the environment

    Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

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    To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes

    Current status and potential of genomic selection to improve selective breeding in the main aquaculture species of International Council for the Exploration of the Sea (ICES) member countries

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    Selective breeding has been successfully applied to improve profitability and sustainability in numerous aquatic species. Recent developments of high throughput genotyping technology now enable the implementation of genomic selection, a method aiming to predict the breeding value of candidates based on their genotype at genome-wide markers. In this review article, we review the state of the arts, challenges and prospects for the application of genomic selection in aquaculture species. The particular focus is on the status of genomic selection in several major aquaculture species of International Council for the Exploration of the Sea (ICES) member countries: Atlantic salmon, rainbow trout, Atlantic cod, American catfish, Pacific oyster, European sea bass and gilthead sea bream. While the potential of genomic selection is clear, tailored species-specific applications will be needed to maximise its benefit for the aquaculture sector

    Searching for a Fundamental Understanding of the Melting Process of Aluminium and its Alloys in Classical Molecular Dynamics Simulations

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    RÉSUMÉ: Le travail prĂ©sentĂ© se focalise sur deux points principaux. Le premier consiste en la mise en place d’un protocole de simulation de dynamique molĂ©culaire classique (MD en anglais) en utilisant la tempĂ©rature de fusion (Tm) rĂ©sultante comme point de rĂ©fĂ©rence sur la prĂ©cision des simulations en raison de sa nature, qui est impactĂ©e par la simulation adĂ©quate des propriĂ©tĂ©s thermodynamiques et des comportements dynamiques. Le second point d’intĂ©rĂȘt de ces simulations de MD repose sur l’application de ces connaissances sur l’aluminium en raison de son applicabilitĂ© moderne dans la conception d’alliages et de son intĂ©rĂȘt parmi notre groupe de recherche. Pour ce faire, le protocole proposĂ© par ces objectifs visait Ă  dĂ©finir les valeurs des paramĂštres optimaux qui rentrent en jeu dans les simulations de LAMMPS (le logiciel libre de MD classique utilisĂ© dans ces simulations). Un outil complet de prĂ©traitement et post-traitement a Ă©tĂ© crĂ©Ă© pour automatiser la majoritĂ© des tĂąches requises pour suivre les protocoles de simulations proposĂ©s. Ceci comprend la crĂ©ation de fichiers de soumissions, le lancement des simulations sur une machine locale ou sur un superordinateur utilisant un gestionnaire de charge de travail SLURM, ainsi que le posttraitement des rĂ©sultats. Ce dernier inclut le triage et la lecture automatique des rĂ©sultats de simulation, l’obtention de rĂ©sultats secondaires Ă  partir des donnĂ©es thermodynamiques des simulations et la crĂ©ation de figures riches pour analyser rapidement les rĂ©sultats obtenus. Le protocole proposĂ©, disponible Ă  l’annexe A et rĂ©sumĂ© dans la figure 0.1, utilise trois mĂ©thodes de simulations qui visent Ă  se rapprocher autant que possible d’un Ă©chantillon rĂ©el. Un des objectifs de ces mĂ©thodes de simulation Ă©tait la dĂ©termination de l’impact de la maniĂšre de crĂ©ation d’un rĂ©seau atomique sur les rĂ©sultats tirĂ©s, expliquant leur nature trĂšs distincte. Les essais ont Ă©tĂ© effectuĂ©s sur plusieurs mĂ©taux et alliages avec une attention particuliĂšre sur l’alliage Al-10Zn en raison de son importance dans le domaine aĂ©rospatial dans la sĂ©rie 7xxx d’alliages d’aluminium. Tous les rĂ©sultats prĂ©sentĂ©s sont hautement dĂ©pendants sur le potentiel interatomique 2NN-MEAM utilisĂ©, dĂ©crit dans ce travail. ABSTRACT: The presented work focused on two main points. The first point of interest was the development of appropriate simulation protocols in classical Molecular Dynamics (MD) with a focus on the melting temperature (Tm) as a benchmark on accuracy of simulations due to its nature, being impacted by the appropriate simulation of thermodynamic properties and dynamic behaviors. The second main point of these MD simulations were to apply the obtained knowledge to aluminum and its alloys due to its applicability in current alloy designs and our group’s interests in this metal. To do so, the entire workflow required by these objectives was studied to define the optimal parameters for every setting involved in LAMMPS simulations (the open source classical MD software package used in this work). A comprehensive pre-processing and post-processing tool was created to automate most tasks that need to be taken out to do any part of the proposed protocol in this work. This includes the creation of input files, the launching of simulations either from local computing ressources or on remote computing ressources using SLURM workload managers such as supercomputers, and the post-processing of results. This included the sorting and automated reading of simulation results, to complex post-processing to derive data from the thermodynamic data of simulations, to the automated generation of intricate figures to analyse results at a glance. The proposed protocol, available in annex A and summed up in figure 0.2, used three simulation methods that aim to have the simulated lattice behave closely to a bulk sample. One of the underlying goals in these methods was to quantify the impact of the creation of the lattice on the yielded results and were henceforth vastly different in nature. The tests were ran on multiple metals and alloys with a special attention to the Al-10Zn alloy thanks to its importance in the aerospace field in 7xxx series of aluminum alloys. All results are highly dependent on the 2NN-MEAM interatomic potential used, which is described in this work

    Optimisation des stratégies de génétique d'association et de sélection génomique pour des populations de diversité variable : Application au maïs

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    Major progresses have been achieved in genotyping technologies, which makes it easier to decipher the relationship between genotype and phenotype. This contributed to the understanding of the genetic architecture of traits (Genome Wide Association Studies, GWAS), and to better predictions of genetic value to improve breeding efficiency (Genomic Selection, GS). The objective of this thesis was to define efficient ways of leading these approaches. We first derived analytically the power from classical GWAS mixed model and showed that it was lower for markers with a small minimum allele frequency, a strong differentiation among population subgroups and that are strongly correlated with markers used for estimating the kinship matrix K. We considered therefore two alternative estimators of K. Simulations showed that these were as efficient as classical estimators to control false positive and provided more power. We confirmed these results on true datasets collected on two maize panels, and could increase by up to 40% the number of detected associations. These panels, genotyped with a 50k SNP-array and phenotyped for flowering and biomass traits, were used to characterize the diversity of Dent and Flint groups and detect QTLs. In GS, studies highlighted the importance of relationship between the calibration set (CS) and the predicted set on the accuracy of predictions. Considering low present genotyping cost, we proposed a sampling algorithm of the CS based on the G-BLUP model, which resulted in higher accuracies than other sampling strategies for all the traits considered. It could reach the same accuracy than a randomly sampled CS with half of the phenotyping effort.D'importants progrĂšs ont Ă©tĂ© rĂ©alisĂ©s dans les domaines du gĂ©notypage et du sĂ©quençage, ce qui permet de mieux comprendre la relation gĂ©notype/phĂ©notype. Il est possible d'analyser l'architecture gĂ©nĂ©tique des caractĂšres (gĂ©nĂ©tique d'association, GA), ou de prĂ©dire la valeur gĂ©nĂ©tique des candidats Ă  la sĂ©lection (sĂ©lection gĂ©nomique, SG). L'objectif de cette thĂšse Ă©tait de dĂ©velopper des outils pour mener ces stratĂ©gies de maniĂšre optimale. Nous avons d'abord dĂ©rivĂ© analytiquement la puissance du modĂšle mixte de GA, et montrĂ© que la puissance Ă©tait plus faible pour les marqueurs prĂ©sentant une faible diversitĂ©, une forte diffĂ©rentiation entre sous groupes et une forte corrĂ©lation avec les marqueurs utilisĂ©s pour estimer l'apparentement (K). Nous avons donc considĂ©rĂ© deux estimateurs alternatifs de K. Des simulations ont montrĂ© qu'ils sont aussi efficaces que la mĂ©thode classique pour contrĂŽler les faux positifs et augmentent la puissance. Ces rĂ©sultats ont Ă©tĂ© confirmĂ©s sur les panels cornĂ© et dentĂ© du programme Cornfed, avec une augmentation de 40% du nombre de SNP dĂ©tectĂ©s. Ces panels, gĂ©notypĂ©s avec une puce 50k SNP et phĂ©notypĂ©s pour leur prĂ©cocitĂ© et leur biomasse ont permis de dĂ©crire la diversitĂ© de ces groupes et de dĂ©tecter des QTL. En SG, des Ă©tudes ont montrĂ© l'importance de la composition du jeu de calibration sur la fiabilitĂ© des prĂ©dictions. Nous avons proposĂ© un algorithme d'Ă©chantillonnage dĂ©rivĂ© de la thĂ©orie du G-BLUP permettant de maximiser la fiabilitĂ© des prĂ©dictions. Par rapport Ă  un Ă©chantillon alĂ©atoire, il permettrait de diminuer de moitiĂ© l'effort de phĂ©notypage pour atteindre une mĂȘme fiabilitĂ© de prĂ©diction sur les panels Cornfed
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