Strong robustness in multi-phase adaptive control:the basic scheme

This paper introduces the general structure of adaptive control systems based on strong robustness. This adaptive approach splits into two phases. In the first phase, effort is put on identification until enough information is obtained in order to design a controller stabilizing the actual system. This is achieved if the input sequence is computed in such a way that the uncertainty on the system parameters to be controlled decreases sufficiently fast. Then, in the second phase, emphasis is shifted to control

Recherches de facteurs génétiques impliqués dans l'élaboration du rendement sous contrainte hydrique chez le tournesol Helianthus annuus par génétique d'association et analyse de liaison dans une population recombinante

La disponibilité en eau est un facteur essentiel pour le rendement des principales espèces cultivées. Le tournesol figurant parmi les espèces d'importance économique majeure est concerné par cet enjeu. L'objectif de cette thèse est d'identifier les régions génomiques permettant d'expliquer la variabilité du rendement sous contraintes hydriques. Une approche de génétique d'association a été menée sur un panel de 384 lignées cultivées et élites évalué en combinaison hybride sur un réseau expérimental de 17 environnements. L'analyse séparée de chaque environnement a permis d'identifier 157 marqueurs associés avec au moins un caractère grâce à l'utilisation d'un modèle prenant en compte la structuration du panel en deux groupes : restauratrices de fertilité et mainteneuses de stérilité mâle. Parmi ces marqueurs significatifs, 34 étaient associés avec un caractère lié à la productivité, mais ceci, le plus souvent, de manière spécifique à un environnement, soulignant ainsi l'importance de l'interaction génotype-environnement. Un modèle éco-physiologique simulant la performance de génotypes en fonction des conditions environnementales, a permis de caractériser le stress hydrique au sein du réseau. En utilisant des variétés témoins définies dans le modèle, un index de stress a été estimé pour chaque environnement. La réponse du panel à cet index a ensuite été testée en génétique d'association permettant d'identifier de nouveaux loci impliqués dans la tolérance au stress hydrique et la stabilité du rendement. Enfin, La détection de QTL à partir d'une population bi-parentale a permis de confirmer certaines zones détectées en génétique d'association et d'en découvrir de nouvelles.For most crops, water availability is a major component of yield. We must improve crop plants tolerance to water deficit in order to guarantee food security. Sunflower, a species of economic importance is also concerned by this challenge. The aim of this thesis is to identify genomic regions involved in the variability of yield and its components under drought. To this purpose, an association mapping approach has been led on a core collection of 384 cultivated and elite lines evaluated as hybrids combinations in a multi environment trial of 17 environments. In a first step, the separated analysis per environment led to the identification of 157 markers associated with at least one trait by apply a model taking into account the structuration in two groups: male restorer lines and female lines. Among these significant markers, 34 were associated with productivity but only on a specific environment, underlying the importance of genotype by environment interaction. In a second step, a crop model simulating sunflower genotypes yield depending on environmental conditions, led to the characterization of drought stress inside the multi environment trial. By using varieties defined before in the model, a drought index has been estimated for each environment. Thus, the panel response to this index has been tested in association mapping leading to the identification of several new loci involved in drought stress response and yield stability. QTL mapping in a bi parental population confirmed some regions and led to the detection of new ones

Construction de turbo-codes courts à treillis à 4 états ayant de bonnes propriétés de distance minimale

Cet article s'intéresse aux turbo-codes courts (longueur inférieure à 256) de rendement 1/3 et 1/2 dont les treillis composant ont 4 états. Ces turbo-codes sont recherchés de façon à présenter de bonnes propriétés de distance minimale ainsi que des distributions de poids les plus centrées possibles. Prendre ces différents critères de recherche a pour but de tenter de diminuer, voire de repousser, l'"error floor". Certains codes auto-duaux optimaux, dont le code de Golay [24,12,8], sont ainsi représentés sous forme de turbo-codes série ou parallèle

Genetic control of plasticity of oil yield for combined abiotic stresses using a joint approach of crop modeling and genome-wide association

Understanding the genetic basis of phenotypic plasticity is crucial for predicting and managing climate change effects on wild plants and crops. Here, we combined crop modeling and quantitative genetics to study the genetic control of oil yield plasticity for multiple abiotic stresses in sunflower. First we developed stress indicators to characterize 14 environments for three abiotic stresses (cold, drought and nitrogen) using the SUNFLO crop model and phenotypic variations of three commercial varieties. The computed plant stress indicators better explain yield variation than descriptors at the climatic or crop levels. In those environments, we observed oil yield of 317 sunflower hybrids and regressed it with three selected stress indicators. The slopes of cold stress norm reaction were used as plasticity phenotypes in the following genome-wide association study. Among the 65,534 tested SNP, we identified nine QTL controlling oil yield plasticity to cold stress. Associated SNP are localized in genes previously shown to be involved in cold stress responses: oligopeptide transporters, LTP, cystatin, alternative oxidase, or root development. This novel approach opens new perspectives to identify genomic regions involved in genotype-by-environment interaction of a complex traits to multiple stresses in realistic natural or agronomical conditions.Comment: 12 pages, 5 figures, Plant, Cell and Environmen

The emotional component of Infant Directed-Speech: A cross-cultural study using machine learning

Backgrounds: Infant-directed speech (IDS) is part of an interactive loop that plays an important role in infants’ cognitive and social development. The use of IDS is universal and is composed of linguistic and emotional components. However, whether the emotional component has similar acoustics characteristics has not been studied automatically. Methods: We performed a cross-cultural study using automatic social signal processing techniques (SSP) to compare IDS across languages. Our speech corpus consisted of audio-recorded vocalizations from parents during interactions with their infant between the ages of 4 and 18 months. It included 6 databases of five languages: English, French, Hebrew (two databases: mothers/fathers), Italian, and Brazilian Portuguese. We used an automatic classifier that exploits the acoustic characteristics of speech and machine learning methods (Support Vector Machines, SVM) to distinguish emotional IDS and non-emotional IDS. Results: Automated classification of emotional IDS was possible for all languages and speakers (father and mother). The uni-language condition (classifier trained and tested in the same language) produced moderate to excellent classification results, all of which were significantly different from chance (P < 1 × 10−10). More interestingly, the cross-over condition (IDS classifier trained in one language and tested in another language) produced classification results that were all significantly different from chance (P < 1 × 10−10). Conclusion: The automated classification of emotional and non-emotional components of IDS is possible based on the acoustic characteristics regardless of the language. The results found in the cross-over condition support the hypothesis that the emotional component shares similar acoustic characteristics across languages

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

UMR-AGAP Equipe DAVV (Diversité, adaptation et amélioration de la vigne) ; équipe ID (Intégration de Données)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

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acquired in a healthy population. This study focuses on multi-echo T2 relaxometry, a qMRI technique that probes the complex tissue microstructure by differentiating compartment-specific T2 relaxation times. However, estimation methods are still limited by their sensitivity to the underlying noise. Moreover, estimating the model's parameters is challenging because the resulting inverse problem is ill-posed, requiring advanced numerical regularization techniques. As a result, the estimates from distinct regularization strategies are different. In this work, we aimed to investigate the variability and reproducibility of different techniques for estimating the transverse relaxation time of the intra- and extra-cellular space (T2IE) in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run T2 relaxometry dataset. To this end, we evaluated three different techniques for estimating the T2 spectra (two regularized non-negative least squares methods and a machine learning approach). Two independent analyses were performed to study the effect of using raw and denoised data. For both the GM and WM regions, and the raw and denoised data, our results suggest that the principal source of variance is the inter-subject variability, showing a higher coefficient of variation (CoV) than those estimated for the inter-site, inter-session, and inter-run, respectively. For all reconstruction methods studied, the CoV ranged between 0.32 and 1.64%. Interestingly, the inter-session variability was close to the inter-scanner variability with no statistical differences, suggesting that T2IE is a robust parameter that could be employed in multi-site neuroimaging studies. Furthermore, the three tested methods showed consistent results and similar intra-class correlation (ICC), with values superior to 0.7 for most regions. Results from raw data were slightly more reproducible than those from denoised data. The regularized non-negative least squares method based on the L-curve technique produced the best results, with ICC values ranging from 0.72 to 0.92