With or without you? Interaction and immersion in a virtual reality experience

International audienceThis collaborative research between a team of digital technology developers and academic researchers investigates how social interaction affects visitors' experience during a virtual reality (VR) underwater seascape exploration. Prior research in immersive VR focused more on individual perceptions of immersion, interactive features and enjoyment. Analysis of focus-group discussions revealed three categories of immersion, interaction with the virtual environment (VE) and social interaction salient to satisfaction with the experience. Moderated mediation analysis of survey results from a full-scale trial (N = 234) show that the three variables had a significant role in experience satisfaction and loyalty intentions. Specifically, immersion mediates person-VE interaction effects on satisfaction and loyalty. The results contrast with previous findings from online gaming contexts, showing that social interactions decrease the impact of immersion on satisfaction and loyalty. We call for caution in the positioning and communication of VR experiences and for further research in other settings

Determination of alpha dose rate profile at the HLW nuclear glass/water interface h i g h l i g h t s

International audienceThe nuclear glass/water interface is studied. The way the energy of alpha particles is deposited is modeled using MCNPX code. A model giving dose rate profiles at the interface using intrinsic data is proposed. Bulk dose rate is a majoring estimation in alteration layer and in surrounding water. Dose rate is high in small cracks; in larger ones irradiated volume is negligible. a b s t r a c t Alpha irradiation and radiolysis can affect the alteration behavior of High Level Waste (HLW) nuclear glasses. In this study, the way the energy of alpha particles, emitted by a typical HLW glass, is deposited in water at the glass/water interface is investigated, with the aim of better characterizing the dose deposition at the glass/water interface during water-induced leaching mechanisms. A simplified chemical composition was considered for the nuclear glass under study, wherein the dose rate is about 140 Gy/h. The MCNPX calculation code was used to calculate alpha dose rate and alpha particle flux profiles at the glass/water interface in different systems: a single glass grain in water, a glass powder in water and a water-filled ideal crack in a glass package. Dose rate decreases within glass and in water as distance to the center of the grain increases. A general model has been proposed to fit a dose rate profile in water and in glass from values for dose rate in glass bulk, alpha range in water and linear energy transfer considerations. The glass powder simulation showed that there was systematic overlapping of radiation fields for neighboring glass grains, but the water dose rate always remained lower than the bulk value. Finally, for typical ideal cracks in a glass matrix, an overlapping of irradiation fields was observed while the crack aperture was lower than twice the alpha range in water. This led to significant values for the alpha dose rate within the crack volume, as long as the aperture remained lower than 60 lm

Heavy ion radiation ageing impact on long-term glass alteration behavior

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Immunophenotyping of Stage III Melanoma Reveals Parameters Associated with Patient Prognosis

International audienceStage III metastatic melanomas require adequate adjuvant immunotherapy to prevent relapses. Prognostic factors are awaited to optimize the clinical management of these patients. The magnitude of metastatic lymph node invasion and the BRAF V600 activating mutation have clinical significance. Based on a comprehensive immunophenotyping of 252 parameters per patient in paired blood and metastatic lymph nodes performed in 39 metastatic melanomas, we found that blood markers were as contributive as tumor-infiltrated lymphocyte immunotypes, and parameters associated with lymphocyte exhaustion/suppression showed higher clinical significance than those related to activation or lineage. High frequencies of CD45RA þ CD4 þ and CD3 e CD56 e tumor-infiltrated lymphocytes appear to be independent prognostic factors of short progression-free survival. High NKG2D expression on CD8 þ tumor-infiltrated lymphocytes, low level of regulatory T-cell tumor-infiltrated lymphocytes, and low PD-L1 expression on circulating T cells were retained in the multivariate Cox analysis model to predict prolonged overall survival. Prospective studies are needed to determine whether such immunological markers may guide adjuvant therapies in stage III metastatic melanomas

Impaired Aryl Hydrocarbon Receptor Ligand Production by the Gut Microbiota Is a Key Factor in Metabolic Syndrome

In pressInternational audienceThe extent to which microbiota alterations define or influence the outcome of metabolic diseases is still unclear, but the byproducts of microbiota metabolism are known to have an important role in mediating the host-microbiota interaction. Here, we identify that in both pre-clinical and clinical settings, metabolic syndrome is associated with the reduced capacity of the microbiota to metabolize tryptophan into derivatives that are able to activate the aryl hydrocarbon receptor. This alteration is not merely an effect of the disease as supplementation with AhR agonist or a Lactobacillus strain, with a high AhR ligand-production capacity, leads to improvement of both dietary- and genetic-induced metabolic impairments, particularly glucose dysmetabolism and liver steatosis, through improvement of intestinal barrier function and secretion of the incretin hormone GLP-1. These results highlight the role of gut microbiota-derived metabolites as a biomarker and as a basis for novel preventative or therapeutic interventions for metabolic disorders

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers