A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation

Data assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as additive Gaussian noise, specified by their covariance matrices Q and R, respectively. These error covariances, and specifically their respective amplitudes, determine the weights given to the background (i.e., the model forecasts) and to the observations in the solution of data assimilation algorithms (i.e., the analysis). Consequently, Q and R matrices significantly impact the accuracy of the analysis. This review aims to present and to discuss, with a unified framework, different methods to jointly estimate the Q and R matrices using ensemble-based data assimilation techniques. Most of the methodologies developed to date use the innovations, defined as differences between the observations and the projection of the forecasts onto the observation space. These methodologies are based on two main statistical criteria: (i) the method of moments, in which the theoretical and empirical moments of the innovations are assumed to be equal, and (ii) methods that use the likelihood of the observations, themselves contained in the innovations. The reviewed methods assume that innovations are Gaussian random variables, although extension to other distributions is possible for likelihood-based methods. The methods also show some differences in terms of levels of complexity and applicability to high-dimensional systems. The conclusion of the review discusses the key challenges to further develop estimation methods for Q and R. These challenges include taking into account time-varying error covariances, using limited observational coverage, estimating additional deterministic error terms, or accounting for correlated noise

An adiponectin-like molecule with antidiabetic properties

Adiponectin increases glucose transport, reduces inflammation, and controls vascular functions. Hence, we propose that treatment with a recombinant globular domain of adiponectin (rgAd110-244) has significant therapeutic potential to treat insulin resistance. Mice were fed for 3 months on a high-fat diet (HFD) to induce insulin resistance, diabetes, and moderate weight gain. The mice were first infused iv with different doses of rgAd110-244 (0.12, 0.4, and 1.2 microg/kg x min) for 5 h. Basal and insulin-sensitive glucose use rates were assessed by the use of a submaximal rate of insulin in the awake free-moving mouse. rgAd110-244 reduced, with dose dependence, epinephrine-induced hyperglycemia and HFD-induced insulin resistance by increasing whole-body glucose use (35% at the highest dose) and glycolysis rates. Similarly, the reduction of plasma free fatty acid concentrations by insulin was dramatically improved. Basal hepatic glucose production was unchanged by rgAd110-244 infusion. This acute rgAd110-244 treatment improved glucose homeostasis and was associated with an increased content of muscle phospho-Akt, glycogen synthase kinase-3beta, and AMP-activated kinase. Second, HFD mice were chronically treated with sc rgAd110-244 injections (10, 30, and 100 microg/kg). Fasting glycemia and insulin-sensitive glucose use were improved by rgAd110-244 at the highest dose at completion of the treatment, with concomitant reduction in body weight gain. We here show for the first time that a recombinant adiponectin fragment (110-244 amino acids called rgAd110-244) is able to treat insulin-resistant diabetes. Our results strongly suggest further pharmacological investigation of rgAd110-244 with the objective of developing a new treatment of insulin-resistant diabetes

An in vivo reporter assay for sRNA-directed gene control in Gram-positive bacteria: identifying a novel sRNA target in Staphylococcus aureus

International audienceBacterial small regulatory RNAs (sRNAs) play a major role in the regulation of various cellular functions. Most sRNAs interact with mRNA targets via an antisense mechanism, modifying their translation and/or degradation. Despite considerable progresses in discovering sRNAs in Gram-positive bacteria , their functions, for the most part, are unknown. This is mainly due to difficulties in identifying their targets. To aid in the identification of sRNA targets in Gram-positive bacteria, we set up an in vivo method for fast analysis of sRNA-mediated post-transcriptional control at the 5 regions of target mR-NAs. The technology is based on the co-expression of an sRNA and a 5 sequence of an mRNA target fused to a green fluorescent protein (GFP) reporter. The system was challenged on Staphylococcus au-reus, an opportunistic Gram-positive pathogen. We analyzed several established sRNA–mRNA interactions , and in addition, we identified the ecb mRNA as a novel target for SprX2 sRNA. Using our in vivo system in combination with in vitro experiments, we demonstrated that SprX2 uses an antisense mechanism to prevent ecb mRNA translation initiation. Furthermore, we used our reporter assay to validate sRNA regulations in other Gram-positive organisms , Bacillus subtilis and Listeria monocyto-genes. Overall, our method is broadly applicable to challenge the predicted sRNA–mRNA interactions in Gram-positive bacteria

Saisie et analyse de signaux electrophysiologiques Interet clinique de l'enregistrement ambulatoire des etats de vigilance et du sommeil chez les sujets normaux et pathologiques ; possibilite eventuelle de traiter automatiquement les signaux recueillis

En annexe deux rapports de stages de DEA : Analyse automatique de signaux de sommeil, evaluation d'appareillage par Abdellatif Ziani ; et Analyse automatique d'electroencephalogrammes par Hocine BendoukhaAvailable at INIST (FR), Document Supply Service, under shelf-number : AR 14584 (1); AR 14584 (2); AR 14584 (3) / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

The Composite Genome Of The Legume Symbiont Sinorhizobium Meliloti

The scarcity of usable nitrogen frequently limits plant growth. A tight metabolic association with rhizobial bacteria allows legumes to obtain nitrogen compounds by bacterial reduction of dinitrogen (N2) to ammonium (NH4+). We present here the annotated DNA sequence of the alpha-proteobacterium Sinorhizobium meliloti, the symbiont of alfalfa. The tripartite 6.7-megabase (Mb) genome comprises a 3.65-Mb chromosome, and 1.35-Mb pSymA and 1.68-Mb pSymB megaplasmids. Genome sequence analysis indicates that all three elements contribute, in varying degrees, to symbiosis and reveals how this genome may have emerged during evolution. The genome sequence will be useful in understanding the dynamics of interkingdom associations and of life in soil environments

Psychiatric symptoms and mortality in older adults with major psychiatric disorders: results from a multicenter study

International audienc