Martian dust storm impact on atmospheric H₂O and D/H observed by ExoMars Trace Gas Orbiter

Global dust storms on Mars are rare but can affect the Martian atmosphere for several months. They can cause changes in atmospheric dynamics and inflation of the atmosphere, primarily owing to solar heating of the dust. In turn, changes in atmospheric dynamics can affect the distribution of atmospheric water vapour, with potential implications for the atmospheric photochemistry and climate on Mars. Recent observations of the water vapour abundance in the Martian atmosphere during dust storm conditions revealed a high-altitude increase in atmospheric water vapour that was more pronounced at high northern latitudes, as well as a decrease in the water column at low latitudes. Here we present concurrent, high-resolution measurements of dust, water and semiheavy water (HDO) at the onset of a global dust storm, obtained by the NOMAD and ACS instruments onboard the ExoMars Trace Gas Orbiter. We report the vertical distribution of the HDO/H O ratio (D/H) from the planetary boundary layer up to an altitude of 80 kilometres. Our findings suggest that before the onset of the dust storm, HDO abundances were reduced to levels below detectability at altitudes above 40 kilometres. This decrease in HDO coincided with the presence of water-ice clouds. During the storm, an increase in the abundance of H2O and HDO was observed at altitudes between 40 and 80 kilometres. We propose that these increased abundances may be the result of warmer temperatures during the dust storm causing stronger atmospheric circulation and preventing ice cloud formation, which may confine water vapour to lower altitudes through gravitational fall and subsequent sublimation of ice crystals. The observed changes in H2O and HDO abundance occurred within a few days during the development of the dust storm, suggesting a fast impact of dust storms on the Martian atmosphere

No detection of methane on Mars from early ExoMars Trace Gas Orbiter observations

The detection of methane on Mars has been interpreted as indicating that geochemical or biotic activities could persist on Mars today. A number of different measurements of methane show evidence of transient, locally elevated methane concentrations and seasonal variations in background methane concentrations. These measurements, however, are difficult to reconcile with our current understanding of the chemistry and physics of the Martian atmosphere, which-given methane's lifetime of several centuries-predicts an even, well mixed distribution of methane. Here we report highly sensitive measurements of the atmosphere of Mars in an attempt to detect methane, using the ACS and NOMAD instruments onboard the ESA-Roscosmos ExoMars Trace Gas Orbiter from April to August 2018. We did not detect any methane over a range of latitudes in both hemispheres, obtaining an upper limit for methane of about 0.05 parts per billion by volume, which is 10 to 100 times lower than previously reported positive detections. We suggest that reconciliation between the present findings and the background methane concentrations found in the Gale crater would require an unknown process that can rapidly remove or sequester methane from the lower atmosphere before it spreads globally

Méthodes à noyaux scalaires pour l'inférence de réseaux de régulations géniques

New technologies in molecular biology, in particular dna microarrays, have greatly increased the quantity of available data. in this context, methods from mathematics and computer science have been actively developed to extract information from large datasets. in particular, the problem of gene regulatory network inference has been tackled using many different mathematical and statistical models, from the most basic ones (correlation, boolean or linear models) to the most elaborate (regression trees, bayesian models with latent variables). despite their qualities when applied to similar problems, kernel methods have scarcely been used for gene network inference, because of their lack of interpretability. in this thesis, two approaches are developed to obtain interpretable kernel methods. firstly, from a theoretical point of view, some kernel methods are shown to consistently estimate a transition function and its partial derivatives from a learning dataset. these estimations of partial derivatives allow to better infer the gene regulatory network than previous methods on realistic gene regulatory networks. secondly, an interpretable kernel methods through multiple kernel learning is presented. this method, called lockni, provides state-of-the-art results on real and realistically simulated datasets.De nouvelles technologies, notamment les puces à adn, multiplient la quantité de données disponibles pour la biologie moléculaire. dans ce contexte, des méthodes informatiques et mathématiques sont activement développées pour extraire le plus d'information d'un grand nombre de données. en particulier, le problème d'inférence de réseaux de régulation génique a été abordé au moyen de multiples modèles mathématiques et statistiques, des plus basiques (corrélation, modèle booléen ou linéaire) aux plus sophistiqués (arbre de régression, modèles bayésiens avec variables cachées). malgré leurs qualités pour des problèmes similaires, les modèles à noyaux ont été peu utilisés pour l'inférence de réseaux de régulation génique. en effet, ces méthodes fournissent en général des modèles difficiles a interpréter. dans cette thèse, nous avons développé deux façons d'obtenir des méthodes à noyaux interprétables. dans un premier temps, d'un point de vue théorique, nous montrons que les méthodes à noyaux permettent d'estimer, a partir d'un ensemble d'apprentissage, une fonction de transition et ses dérivées partielles de façon consistante. ces estimations de dérivées partielles permettent, sur des exemples réalistes, de mieux identifier le réseau de régulation génique que des méthodes standards. dans un deuxième temps, nous développons une méthode à noyau interprétable grâce à l'apprentissage à noyaux multiples. ce modèle fournit des résultats du niveau de l'état de l'art sur des réseaux réels et des réseaux simulés réalistes

Croissance et caractérisations de films minces de ZnO et ZnO dopé cobalt préparés par ablation laser pulsé

CAEN-BU Sciences et STAPS (141182103) / SudocSudocFranceF

The cultural mega event as dismantled system: challenges, stakes and pitfalls of governance. Comparative analysis of European capitalism of culture

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The cultural mega event as dismantled system: challenges, stakes and pitfalls of governance. Comparative analysis of European capitalism of culture

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Gene Regulatory Network Inference using ensembles of Local Multiple Kernel Models

International audienceReconstructing gene regulatory network from high-throughput data has many potential applications, from understanding a biological organism to identifying potential drug targets. It is also a notoriously difficult problem, tackled by many scientists with various methods. In this paper, we formulate GRN inference as a sparse regression problem. We decompose the prediction of a p-genes system in p different regression problems. For each gene (target gene), we train a kernel-based regression with feature selection, predicting the expression pattern of the target gene using all the other genes (input genes). The regression will give the importance of each input gene in the prediction of the target gene. We take this importance as an indication of a putative regulatory link. Putative links are then aggregated over all genes to provide a ranking of interactions, from which we infer the GRN. Furthermore, biological data are heterogeneous. The method we propose can learn from both steady-state and time-series data, using an ensemble method that can be applied to other regression model. Finally, we compare our method, called LocKING, to state-of-the-art methods on real and realistic datasets, which are widely spread in the GRN inference community. We show that our method is competitive against individual methods. Nevertheless, best results are obtained by integrating multiple methods. We show that using LocKING among other methods significantly enhances the accuracy of the network inferred

Full spin polarization of complex ferrimagnetic bismuth iron garnet probed by magneto-optical Faraday spectroscopy

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SrVO3 films grown by PLD : A fine physico-chemical study

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SrVO3 films grown by PLD : A fine physico-chemical study

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