A consensus protocol for the recovery of mercury methylation genes from metagenomes

Mercury (Hg) methylation genes (hgcAB) mediate the formation of the toxic methylmercury and have been identified from diverse environments, including freshwater and marine ecosystems, Arctic permafrost, forest and paddy soils, coal-ash amended sediments, chlor-alkali plants discharges and geothermal springs. Here we present the first attempt at a standardized protocol for the detection, identification and quantification of hgc genes from metagenomes. Our Hg-cycling microorganisms in aquatic and terrestrial ecosystems (Hg-MATE) database, a catalogue of hgc genes, provides the most accurate information to date on the taxonomic identity and functional/metabolic attributes of microorganisms responsible for Hg methylation in the environment. Furthermore, we introduce "marky-coco", a ready-to-use bioinformatic pipeline based on de novo single-metagenome assembly, for easy and accurate characterization of hgc genes from environmental samples. We compared the recovery of hgc genes from environmental metagenomes using the marky-coco pipeline with an approach based on coassembly of multiple metagenomes. Our data show similar efficiency in both approaches for most environments except those with high diversity (i.e., paddy soils) for which a coassembly approach was preferred. Finally, we discuss the definition of true hgc genes and methods to normalize hgc gene counts from metagenomes

Quand les expériences physiques rencontrent les expériences d'apprentissage machine pour la compréhension et la prédiction du vieillissement des véhicules de transport frigorifique

The thermal insulation of the vehicles’enclosures used in refrigerated transport is a critical element both for the quality of the cold chain and for the energy consumption of these vehicles. Its efficiency is characterized by the overall insulation coefficient ("K coefficient"), which tends to increase over time due to the ageing of the enclosure. This thesis presents the results of a work carried out according to three complementary approaches to understand the ageing phenomenon. The results from experimental test campaigns have been confronted with results produced by methods from the world of data science to support physical modeling efforts. The experiments consisted of measurements of the K coefficient of prototype vehicles after their manufacturing and in service-vehicles after 12 years of use. This made it possible to evaluate the impact of the refrigerating unit and to quantify the difference between the new vehicles and their prototypes. Besides, the thermal and energy performances of a new type of vehicle built with sandwich panels with vacuum inserts in the walls were determined. The availability of a large amount of data stored in Cemafroid's Datafrig® databases makes it possible to study the ageing phenomenon using a data-centric approach. The data were statistically analysed using simple probability density concepts and artificial intelligence techniques. A numerical model of ageing was developed using a random forest algorithm: it allows the prediction of the ageing with an error of less than 6%. Finally, a 1D physical model was developed in order to understand the ageing phenomenon from a thermal point of view. This model reproduces the initial insulation performances (K_p and K_0) of a refrigerated vehicle and allows to simulate the ageing of the vehicle after 12 years of lifeL'isolation thermique de la caisse des véhicules utilisés dans le transport sous température dirigée est un élément critique tant pour la qualité de la chaîne du froid que pour la consommation énergétique de ces véhicules. Son efficacité est caractérisée par le coefficient d'isolation global ("coefficient K"), qui tend à augmenter au cours du temps en raison du vieillissement de la caisse. Cette thèse présente les résultats de travaux réalisés selon trois approches complémentaires pour comprendre le vieillissement. Des résultats de campagnes d'essais expérimentaux ont été confrontés à des résultats produits par des méthodes du monde de la science des données pour conforter des efforts de modélisation physique. Les expériences ont consisté à réaliser des mesures du coefficient K d'engins prototypes après leur fabrication et d'engins en service depuis 12 ans. Ceci a permis d'évaluer l'impact du groupe frigorifique et de quantifier la différence entre les nouveaux engins et leurs prototypes. Par ailleurs, les performances thermiques et énergétiques d'un nouveau type d’engin construit avec des panneaux sandwich ayant des inserts sous vide dans les parois ont été déterminées. La disponibilité d'un grand nombre de données stockées dans les bases de données Datafrig® du Cemafroid permet d'étudier le phénomène de vieillissement par une approche centrée sur les données. Les données ont été analysées statistiquement en utilisant des concepts simples de densité de probabilité et des techniques d'intelligence artificielle. Un modèle numérique de vieillissement a été développé à partir d’un algorithme de forêt aléatoire : il permet de prédire le vieillissement avec une erreur inférieure à 6%. Enfin, un modèle physique 1D a été développé afin de comprendre le phénomène de vieillissement d'un point de vue thermique. Ce modèle reproduit les performances d'isolation initiales (K_p et K_0) d'un véhicule frigorifique et permet de simuler le vieillissement du véhicule après 12 ans de vie

Quand les expériences physiques rencontrent les expériences d'apprentissage machine pour la compréhension et la prédiction du vieillissement des véhicules de transport frigorifique

The thermal insulation of the vehicles’enclosures used in refrigerated transport is a critical element both for the quality of the cold chain and for the energy consumption of these vehicles. Its efficiency is characterized by the overall insulation coefficient ("K coefficient"), which tends to increase over time due to the ageing of the enclosure. This thesis presents the results of a work carried out according to three complementary approaches to understand the ageing phenomenon. The results from experimental test campaigns have been confronted with results produced by methods from the world of data science to support physical modeling efforts. The experiments consisted of measurements of the K coefficient of prototype vehicles after their manufacturing and in service-vehicles after 12 years of use. This made it possible to evaluate the impact of the refrigerating unit and to quantify the difference between the new vehicles and their prototypes. Besides, the thermal and energy performances of a new type of vehicle built with sandwich panels with vacuum inserts in the walls were determined. The availability of a large amount of data stored in Cemafroid's Datafrig® databases makes it possible to study the ageing phenomenon using a data-centric approach. The data were statistically analysed using simple probability density concepts and artificial intelligence techniques. A numerical model of ageing was developed using a random forest algorithm: it allows the prediction of the ageing with an error of less than 6%. Finally, a 1D physical model was developed in order to understand the ageing phenomenon from a thermal point of view. This model reproduces the initial insulation performances (K_p and K_0) of a refrigerated vehicle and allows to simulate the ageing of the vehicle after 12 years of lifeL'isolation thermique de la caisse des véhicules utilisés dans le transport sous température dirigée est un élément critique tant pour la qualité de la chaîne du froid que pour la consommation énergétique de ces véhicules. Son efficacité est caractérisée par le coefficient d'isolation global ("coefficient K"), qui tend à augmenter au cours du temps en raison du vieillissement de la caisse. Cette thèse présente les résultats de travaux réalisés selon trois approches complémentaires pour comprendre le vieillissement. Des résultats de campagnes d'essais expérimentaux ont été confrontés à des résultats produits par des méthodes du monde de la science des données pour conforter des efforts de modélisation physique. Les expériences ont consisté à réaliser des mesures du coefficient K d'engins prototypes après leur fabrication et d'engins en service depuis 12 ans. Ceci a permis d'évaluer l'impact du groupe frigorifique et de quantifier la différence entre les nouveaux engins et leurs prototypes. Par ailleurs, les performances thermiques et énergétiques d'un nouveau type d’engin construit avec des panneaux sandwich ayant des inserts sous vide dans les parois ont été déterminées. La disponibilité d'un grand nombre de données stockées dans les bases de données Datafrig® du Cemafroid permet d'étudier le phénomène de vieillissement par une approche centrée sur les données. Les données ont été analysées statistiquement en utilisant des concepts simples de densité de probabilité et des techniques d'intelligence artificielle. Un modèle numérique de vieillissement a été développé à partir d’un algorithme de forêt aléatoire : il permet de prédire le vieillissement avec une erreur inférieure à 6%. Enfin, un modèle physique 1D a été développé afin de comprendre le phénomène de vieillissement d'un point de vue thermique. Ce modèle reproduit les performances d'isolation initiales (K_p et K_0) d'un véhicule frigorifique et permet de simuler le vieillissement du véhicule après 12 ans de vie

Ageing of in-service refrigerated transport vehicles: a statistical analysis

International audienc

Prediction of symmetry during intermittent and annular horizontal two-phase flows

An optical method is developed for a 2.95 mm inner diameter circular mini-channel to estimate liquid film thicknesses. The greyscale pictures obtained with a high-speed camera are processed to determine the liquid-vapour interface positions for annular and intermittent flows. The experiments are thus performed for a large range of flow conditions. The saturation temperatures tested ranged from 20 °C to 100 °C in steps of 10 °C and the mass velocities are 50, 100, 200, 300 and 400 kg m−2 s−1. A new parameter ranging from 0 to 1, the symmetry, is defined to account for the level of non uniformity of liquid distribution around the tube perimeter. New experimental data are presented (270 data points), that cover a range of symmetry parameter from 0.35 to 1.00. These data are merged with those available in the literature (406 data points with symmetry parameter from 0.71 to 1.00). A sensitivity analysis of the dimensionless numbers of major influence is run and a new correlation is proposed, that enables to predict over 90% of the data points in an error bandwidth of 10%. This correlation is proposed as criterion for asymmetry

Factors affecting the ageing of refrigerated transport equipment

International audienc

Experimental assessment of the difference between new refrigerated trucks and their corresponding prototypes

International audienc

Factors affecting the ageing of refrigerated transport equipment

International audienc

Attempt to better trust classification models: Application to the Ageing of Refrigerated Transport Vehicles

International audienceCEMAFROID is a company with a french delegated public service, delivering conformity attestations of refrigerated transport vehicles. It studies the ageing of those vehicles, depending on several physiochemical and mechanical factors, for which physical models have been proposed by researchers in thermal engineering. The DATAFRIG R database records more than 300 000 attestations of vehicles over 80 attributes, opening the opportunity to predict the ageing by building a numerical model using machine learning methods. During the development of such a model, several classical questions appeared, regarding the data quality, the field reality and the mistrust of domain experts. In this paper, we propose to use the notion of functional dependencies to address the aforementioned model's limitations. In particular, we investigate how FDs could help, especially using their counterexamples, that turn out to provide meaningful examples of such limitations, easily interpretable by domain experts. Interestingly, the existence of such counterexamples in the dataset is a way of demystifying the numerical model with the experts, by giving them back the control over their own data. This approach has been tested with domain experts from CE-MAFROID, with many positive feedbacks. It is worth noting that this attempt to better trust classification models is not limited to a particular application, and could be generalized to others