Proteomic identification of heterogeneous nuclear ribonucleoprotein L as a novel component of SLM/Sam68 nuclear bodies

Background: Active pre-mRNA splicing occurs co-transcriptionally, and takes place throughout the nucleoplasm of eukaryotic cells. Splicing decisions are controlled by networks of nuclear RNA-binding proteins and their target sequences, sometimes in response to signalling pathways. Sam68 (Src-associated in mitosis 68 kDa) is the prototypic member of the STAR (Signal Transduction and Activation of RNA) family of RNA-binding proteins, which regulate splicing in response to signalling cascades. Nuclear Sam68 protein is concentrated within subnuclear organelles called SLM/Sam68 Nuclear Bodies (SNBs), which also contain some other splicing regulators, signalling components and nucleic acids. Results: We used proteomics to search for the major interacting protein partners of nuclear Sam68. In addition to Sam68 itself and known Sam68-associated proteins (heterogeneous nuclear ribonucleoproteins hnRNP A1, A2/B1 and G), we identified hnRNP L as a novel Sam68-interacting protein partner. hnRNP L protein was predominantly present within small nuclear protein complexes approximating to the expected size of monomers and dimers, and was quantitatively associated with nucleic acids. hnRNP L spatially co-localised with Sam68 as a novel component of SNBs and was also observed within the general nucleoplasm. Localisation within SNBs was highly specific to hnRNP L and was not shared by the closely-related hnRNP LL protein, nor any of the other Sam68-interacting proteins we identified by proteomics. The interaction between Sam68 and hnRNP L proteins was observed in a cell line which exhibits low frequency of SNBs suggesting that this association also takes place outside SNBs. Although ectopic expression of hnRNP L and Sam68 proteins independently affected splicing of CD44 variable exon v5 and TJP1 exon 20 minigenes, these proteins did not, however, co-operate with each other in splicing regulation of these target exons. Conclusion: Here we identify hnRNP L as a novel SNB component. We show that, compared with other identified Sam68-associated hnRNP proteins and hnRNP LL, this co-localisation within SNBs is specific to hnRNP L. Our data suggest that the novel Sam68-hnRNP L protein interaction may have a distinct role within SNBs

Etude d'une série d'infections nosocomiales par une souche hypervirulente de calicivirus félin

Le calicivirus félin (FCV) est généralement connu pour provoquer un rhume et une conjonctivite dans l'espèce féline. Cependant, depuis 1998, aux Etats-Unis et au Royaume-Uni, plusieurs cas d'épidémies impliquant des couches hypervirulentes de calicivirus félin ont été rapportés. Les symptômes les plus caractéristiques de cette atteinte sont un abattement, de la fièvre, une anorexie, des oedèmes de la face et des membres, des ulcères touchant la sphère oropharyngée et la peau et un ict're. Une épidémie du même genre a été identifiée à l Ecole Nationale Vétérinaire de Toulouse en mars 2005. Huit cas ont été recensés, parmi lesquels trois ont survécu. La PCR sur écouvillon pharyngé semble être la méthode de choix pour confirmer le diagnostic. Les souches hygiéniques strictes doivent être appliquées pour éviter la propagation de l'épidémie. La gestion de cette épidémie permet de rappeler qu'il est important que les vétérinaires restent vigilants sur les symptômes évocateurs, pour permettre de prendre les mesures adéquates pour enrayer ce genre d'épidémie si elle venait à se reproduire, ce qui est fort probable étant donné la haute mutabilité génétique des calicivirus

Implications of differing attitudes and experiences between providers and persons with obesity: results of the national ACTION study

Objective: Our aim was to explore how differing attitudes, expectations, and experiences among people with obesity (PwO) and healthcare providers (HCPs) might have an impact on effectively implementing current obesity treatment guidelines. Methods: Online surveys were conducted among 3,008 adult PwO (BMI≥30 by self-reported height and weight) and 606 HCPs. Results: PwO with weight loss ≥ 10% during the previous three years were more likely to have been diagnosed with obesity and to have discussed a weight loss plan with an HCP. However, only 21% believe HCPs have a responsibility to actively contribute to their obesity treatment. Further, HCPs tend not to effectively communicate the diagnosis of obesity, its nature as a serious and chronic disease, the full range of treatment options, and obesity’s implications for health and quality of life. Regarding treatment goals, HCPs more often focus on BMI reduction, while PwO’s goals focus on improved functioning, energy, and appearance. HCPs also tend to underestimate their patients’ motivation to address their obesity. Twenty-eight percent of HCPs ‘completely agreed’ that losing weight was a high priority for PwO, whereas more than half of PwO ‘completely agreed’ that losing weight was a high priority for them. When asked how their HCP could better support them, PwO most often expressed a desire for helpful resources, as well as assistance with specific and realistic goal-setting to improve health. Conclusions: HCPs can more effectively implement obesity treatment guidelines by more clearly and proactively communicating with PwO about their diagnosis, health implications of obesity, desired treatment goals, and the full range of treatment options. HCPs should understand that most PwO believe that managing their disease is solely their own responsibility. HCPs can also encourage more effective conversations by better appreciating their patients’ motivation and treatment goals.publishedVersio

A random forest approach to quality-checking automatic snow-depth sensor measurements

State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low – especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a random forest classifier to automatize the quality assurance and quality control (QA/QC) procedure of near-surface snow-depth measurements collected through ultrasonic sensors, with particular reference to the differentiation of snow cover from grass or bare-ground data and to the detection of random errors (e.g., spikes). The model was trained and validated using a split-sample approach of an already manually classified dataset of 18 years of data from 43 sensors in Aosta Valley (northwestern Italian Alps) and then further validated using 3 years of data from 27 stations across the rest of Italy (with no further training or tuning). The F1 score was used as scoring metric, it being the most suited to describe the performances of a model in the case of a multiclass imbalanced classification problem. The model proved to be both robust and reliable in the classification of snow cover vs. grass/bare ground in Aosta Valley (F1 values above 90 %) yet less reliable in rare random-error detection, mostly due to the dataset imbalance (samples distribution: 46.46 % snow, 49.21 % grass/bare ground, 4.34 % error). No clear correlation with snow-season climatology was found in the training dataset, which further suggests the robustness of our approach. The application across the rest of Italy yielded F1 scores on the order of 90 % for snow and grass/bare ground, thus confirming results from the testing region and corroborating model robustness and reliability, with again a less skillful classification of random errors (values below 5 %). This machine learning algorithm of data quality assessment will provide more reliable snow data, enhancing their use in snow models

An Enkf-Based Scheme for Snow Multivariable Data Assimilation at an Alpine Site

Abstract The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production, water resource management, flood prevention, hydropower generation. Since simulations are affected by model biases and forcing data uncertainty, an increasing interest focuses on the assimilation of snow-related observations with the purpose of enhancing predictions on snowpack state. The study aims at investigating the effectiveness of snow multivariable data assimilation (DA) at an Alpine site. The system consists of a snow energy-balance model strengthened by a multivariable DA system. An Ensemble Kalman Filter (EnKF) scheme allows assimilating ground-based and remotely sensed snow observations in order to improve the model simulations. This research aims to investigate and discuss: (1) the limitations and constraints in implementing a multivariate EnKF scheme in the framework of snow modelling, and (2) its performance in consistently updating the snowpack state. The performance of the multivariable DA is shown for the study case of Torgnon station (Aosta Valley, Italy) in the period June 2012 - December 2013. The results of several experiments are discussed with the aim of analyzing system sensitivity to the DA frequency, the ensemble size, and the impact of assimilating different observations

Grassland dynamics of soil moisture and temperature

Alpine ecosystems are vulnerable to climate and land use changes. Measurement sites at different altitude and aspect can provide precious information on them. Also, vadose rootzone plays a major role in partitioning fluxes. In this work field data of soil water content, matric potential and soil temperature in some mountain grassland sites are compared with simulations results of the CLM model (The Community Land Model, NCAR, US). These are key state variables regulating the physical processes that determine the flows of two main greenhouse gases, water vapour and carbon dioxide, to the atmosphere in the presence of vegetation. Some transients show significant differences between data and CLM simulation results and further analyses are performed using the HYDRUS model from the US Salinity Laboratory, in order to better explore the soil, grass, and atmosphere roles in the dynamics of those state variables. Some insight is finally provided about the effects on water vapour and carbon dioxide fluxes