Caractérisation expérimentale en traction/compression/torsion d'un matériau biosourcé type PHA

National audienceDe nouveaux matériaux polymères biosourcés et biodégradables ont fait leur apparition depuis une dizaine d'années. Ces nouveaux matériaux sont une réponse intéressante aux problèmes de ressource et de recyclage-posés par les polymères classiques provenant de la pétrochimie. Ils présentent le double avantage d'être issus de la biomasse, mais également d'être compostables, c'est-à-dire qu'il ne génère aucun toxique en se dégradant, sous certaines conditions spécifiques d'humidité et de température. Nous nous intéressons dans ce travail à une classe particulière de ces nouveaux matériaux biopolymères produits par des micro-organismes : les PolyHydroxyAlkanoates (ou PHA). Les PHA sont des polymères biosourcés, produits par une grande variété de bactéries (Ralstonia, Pseudomonas,…) en tant que réserve énergétique intracellulaire. Ces matériaux présentent malgré tout un défaut important : leur élaboration reste encore souvent difficile à contrôler conduisant à un coût de production souvent prohibitif et limitant leur dissémination dans des secteurs plus conventionnels comme par exemple celui de l'emballage. Pour que ces matériaux aient une diffusion plus importante dans ce secteur, il s'avère nécessaire d'optimiser la forme et la tenue mécanique de ces produits d'emballage. Cela nécessite une bien meilleure connaissance de leur comportement mécanique encore peu connue pour l'instant. Dans ce contexte, cette étude a pour objectif de caractériser expérimentalement puis numériquement le comportement de nuances de PHA [1]. Le but est ensuite d'aboutir à un outil numérique de calcul, capable de dimensionner et simuler le comportement thermo-mécanique de pièces d'emballages en PHA telles que ceux produits par la société EUROPLASTIQUES, partenaire industriel de ce projet. Parallèlement à ce matériau biosourcé, nous étudions également un polymère plus classique, le polypropylène, avec deux objectifs. Tout d'abord l'idée est de valider la méthodologie d'essai, compte tenu du fait que l'on dispose déjà d'une identification partielle d'une nuance de polypropylène, le PPC7712 [2]. D'autre part, ce polypropylène étant également utilisé en emballage, il permettra des comparaisons finales sur les comportements de structures. Pour la caractérisation mécanique de ces matériaux, un dispositif original a été conçu permettant la réalisation d'essais de cycles multiaxiaux simultanés comprenant des phases de traction, torsion et de compression. Ce dispositif comprend une cellule de force à six axes et d'un montage spécifique pour le serrage et le maintien d'une éprouvette cylindrique (Figure 1). Cette éprouvette est obtenue par injection, elle se compose d'une partie cylindrique et de deux têtes hexagonales (Figure 2). Contrairement, aux essais classiques, où les éprouvettes sont maintenues et entraînées par serrage, l'éprouvette est ici liée par obstacle dans les deux sens des trois directions, sans serrage afin d'éviter, autant que possible, l'apparition de contraintes mécaniques initiales. Un système de mors comprenant des plateaux, des vis et des empreintes hexagonales permettent le blocage total de l'éprouvette, quel que soit l'essai envisagé. Le dispositif prend aussi en compte la dispersion prévisible des dimensions des têtes d'éprouvette par l'intermédiaire de lamelles flexibles entre l'accouplement au vérin et le blocage des têtes. Les déformations sont mesurées directement sur l'échantillon grâce à un dispositif de corrélation d'images en 3D (Aramis 4M, GOM), permettant également de vérifier l'homogénéité de la cinématique. Figure 2: Éprouvette de chargement multiaxial Figure 1: Dispositif d'essais multiaxiaux Ce montage original autorise des cycles de sollicitations successives ou combinées de traction, compression et de torsion (Figure 3), à partir d'une seule géométrie d'éprouvette. Dans la littérature, les essais de traction et de cisaillement sont réalisés habituellement avec des éprouvettes de géométrie spécifique à chaque essai. Dans ce cas, il est difficile d'être sûr d'étudier la même structure de matériau, celle-ci étant fortement dépendante du type d'élaboration et des cinétiques de refroidissement, elles-mêmes directement liées aux dimensions géométriques. Le dispositif expérimental développé ici permet d'effectuer des chemins complexes avec changements de direction et de cycles au cours d'un même essai et sur la même éprouvette, autorisant ainsi l'exploration de tout le plan déviatoire de déformation avec prise en compte de l'histoire du chargement. Pour la simulation du comportement de structures, nous utilisons un modèle de comportement 3D d'Hyper-Visco-Hystérésis (HVH) [2], implanté dans le code de calcul Herezh++ [3]. Il tient sa singularité au fait que le comportement du matériau est décomposé en une contribution additive. Tout en incluant un potentiel hyperélastique, cette loi permet de décrire le phénomène d'hystérésis non-visqueux ainsi qu'une dépendance au temps du matériau. Le protocole d'identification, permettant l'obtention des paramètres utiles à ce modèle, est simple et rapide car il ne nécessite qu'un unique type d'essais de traction/compression relaxation [4]

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net

eTRIKS Analytical Environment: A Modular High Performance Framework for Medical Data Analysis

Translational research is quickly becoming a science driven by big data. Improving patient care, developing personalized therapies and new drugs depend increasingly on an organization's ability to rapidly and intelligently leverage complex molecular and clinical data from a variety of large-scale partner and public sources. As analysing these large-scale datasets becomes computationally increasingly expensive, traditional analytical engines are struggling to provide a timely answer to the questions that biomedical scientists are asking. Designing such a framework is developing for a moving target as the very nature of biomedical research based on big data requires an environment capable of adapting quickly and efficiently in response to evolving questions. The resulting framework consequently must be scalable in face of large amounts of data, flexible, efficient and resilient to failure. In this paper we design the eTRIKS Analytical Environment (eAE), a scalable and modular framework for the efficient management and analysis of large scale medical data, in particular the massive amounts of data produced by high-throughput technologies. We particularly discuss how we design the eAE as a modular and efficient framework enabling us to add new components or replace old ones easily. We further elaborate on its use for a set of challenging big data use cases in medicine and drug discovery

High dimensional biological data retrieval optimization with NoSQL technology.

Background High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data. Results In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB. Conclusions The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data

Single-Step Extraction Coupled with Targeted HILIC-MS/MS Approach for Comprehensive Analysis of Human Plasma Lipidome and Polar Metabolome.

Expanding metabolome coverage to include complex lipids and polar metabolites is essential in the generation of well-founded hypotheses in biological assays. Traditionally, lipid extraction is performed by liquid-liquid extraction using either methyl-tert-butyl ether (MTBE) or chloroform, and polar metabolite extraction using methanol. Here, we evaluated the performance of single-step sample preparation methods for simultaneous extraction of the complex lipidome and polar metabolome from human plasma. The method performance was evaluated using high-coverage Hydrophilic Interaction Liquid Chromatography-ESI coupled to tandem mass spectrometry (HILIC-ESI-MS/MS) methodology targeting a panel of 1159 lipids and 374 polar metabolites. The criteria used for method evaluation comprised protein precipitation efficiency, and relative MS signal abundance and repeatability of detectable lipid and polar metabolites in human plasma. Among the tested methods, the isopropanol (IPA) and 1-butanol:methanol (BUME) mixtures were selected as the best compromises for the simultaneous extraction of complex lipids and polar metabolites, allowing for the detection of 584 lipid species and 116 polar metabolites. The extraction with IPA showed the greatest reproducibility with the highest number of lipid species detected with the coefficient of variation (CV) < 30%. Besides this difference, both IPA and BUME allowed for the high-throughput extraction and reproducible measurement of a large panel of complex lipids and polar metabolites, thus warranting their application in large-scale human population studies

An overview of the mid-infrared spectro-interferometer MATISSE: science, concept, and current status

MATISSE is the second-generation mid-infrared spectrograph and imager for the Very Large Telescope Interferometer (VLTI) at Paranal. This new interferometric instrument will allow significant advances by opening new avenues in various fundamental research fields: studying the planet-forming region of disks around young stellar objects, understanding the surface structures and mass loss phenomena affecting evolved stars, and probing the environments of black holes in active galactic nuclei. As a first breakthrough, MATISSE will enlarge the spectral domain of current optical interferometers by offering the L and M bands in addition to the N band. This will open a wide wavelength domain, ranging from 2.8 to 13 um, exploring angular scales as small as 3 mas (L band) / 10 mas (N band). As a second breakthrough, MATISSE will allow mid-infrared imaging - closure-phase aperture-synthesis imaging - with up to four Unit Telescopes (UT) or Auxiliary Telescopes (AT) of the VLTI. Moreover, MATISSE will offer a spectral resolution range from R ~ 30 to R ~ 5000. Here, we present one of the main science objectives, the study of protoplanetary disks, that has driven the instrument design and motivated several VLTI upgrades (GRA4MAT and NAOMI). We introduce the physical concept of MATISSE including a description of the signal on the detectors and an evaluation of the expected performances. We also discuss the current status of the MATISSE instrument, which is entering its testing phase, and the foreseen schedule for the next two years that will lead to the first light at Paranal.Comment: SPIE Astronomical Telescopes and Instrumentation conference, June 2016, 11 pages, 6 Figure

The future of metabolomics in ELIXIR.

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases

Repression of FLOWERING LOCUS C and FLOWERING LOCUS T by the Arabidopsis Polycomb Repressive Complex 2 Components

Polycomb group (PcG) proteins are evolutionarily conserved in animals and plants, and play critical roles in the regulation of developmental gene expression. Here we show that the Arabidopsis Polycomb repressive complex 2 (PRC2) subunits CURLY LEAF (CLF), EMBRYONIC FLOWER 2 (EMF2) and FERTILIZATION INDEPENDENT ENDOSPERM (FIE) repress the expression of FLOWERING LOCUS C (FLC), a central repressor of the floral transition in Arabidopsis and FLC relatives. In addition, CLF directly interacts with and mediates the deposition of repressive histone H3 lysine 27 trimethylation (H3K27me3) into FLC and FLC relatives, which suppresses active histone H3 lysine 4 trimethylation (H3K4me3) in these loci. Furthermore, we show that during vegetative development CLF and FIE strongly repress the expression of FLOWERING LOCUS T (FT), a key flowering-time integrator, and that CLF also directly interacts with and mediates the deposition of H3K27me3 into FT chromatin. Our results suggest that PRC2-like complexes containing CLF, EMF2 and FIE, directly interact with and deposit into FT, FLC and FLC relatives repressive trimethyl H3K27 leading to the suppression of active H3K4me3 in these loci, and thus repress the expression of these flowering genes. Given the central roles of FLC and FT in flowering-time regulation in Arabidopsis, these findings suggest that the CLF-containing PRC2-like complexes play a significant role in control of flowering in Arabidopsis

Parental Genome Dosage Imbalance Deregulates Imprinting in Arabidopsis

In mammals and in plants, parental genome dosage imbalance deregulates embryo growth and might be involved in reproductive isolation between emerging new species. Increased dosage of maternal genomes represses growth while an increased dosage of paternal genomes has the opposite effect. These observations led to the discovery of imprinted genes, which are expressed by a single parental allele. It was further proposed in the frame of the parental conflict theory that parental genome imbalances are directly mirrored by antagonistic regulations of imprinted genes encoding maternal growth inhibitors and paternal growth enhancers. However these hypotheses were never tested directly. Here, we investigated the effect of parental genome imbalance on the expression of Arabidopsis imprinted genes FERTILIZATION INDEPENDENT SEED2 (FIS2) and FLOWERING WAGENINGEN (FWA) controlled by DNA methylation, and MEDEA (MEA) and PHERES1 (PHE1) controlled by histone methylation. Genome dosage imbalance deregulated the expression of FIS2 and PHE1 in an antagonistic manner. In addition increased dosage of inactive alleles caused a loss of imprinting of FIS2 and MEA. Although FIS2 controls histone methylation, which represses MEA and PHE1 expression, the changes of PHE1 and MEA expression could not be fully accounted for by the corresponding fluctuations of FIS2 expression. Our results show that parental genome dosage imbalance deregulates imprinting using mechanisms, which are independent from known regulators of imprinting. The complexity of the network of regulations between expressed and silenced alleles of imprinted genes activated in response to parental dosage imbalance does not support simple models derived from the parental conflict hypothesis