MAIA, un modèle de données support de la démarche d'adaptation

To face climate change, mid-mountain territorial communities set up sectoral adaptations. This article presents a model of capitalization and linking of these adaptation practices (already tested or to be considered). This model makes it possible to move from a sectoral to an integrated and intelligent adaptation, aware of implications and synergies between several sectors of activities over a given territory. The model's concepts are presented in UML (Unified Modeling Language), a graphical modeling language used to design computer systems. From this, objective would be to use these concepts to build a web application dedicated to climate change adaptation practices, which could support governance of territorial communities. Conducted within the framework of the GICC-ONERC AdaMont (2015-2017) project, this operational aim may justify the setting up of new scientific and technical partnerships.Face au changement climatique, les communautés territoriales de moyenne montagne mettent en place, de façon sectorielle, des adaptations. Cet article présente un modèle de capitalisation et de mise en relation de ces pratiques d'adaptation (éprouvées ou envisagées). Celui-ci permet de passer d'une adaptation métier, sectorielle, à une adaptation intégrée, intelligente, consciente des implications et synergies entre les secteurs d'activités présents sur un territoire. Les concepts qui structurent le modèle sont présentés en UML (Unified Modeling Language), un langage de modélisation informatique graphique servant de support à la conception de systèmes informatisés. L'ambition, à termes, est de développer ces concepts jusqu'à la réalisation d'une application web destinée à la gouvernance des communautés territoriales qui traite des pratiques d'adaptation au changement climatique. Mené dans le cadre du projet GICC-ONERC AdaMont (2015-2017), cette visée opérationnelle pourra justifier la mise en place de nouveaux partenariats scientifiques et techniques

Fabrication and electrical integration of robust carbon nanotube micropillars by self-directed elastocapillary densification

Vertically-aligned carbon nanotube (CNT) "forest" microstructures fabricated by chemical vapor deposition (CVD) using patterned catalyst films typically have a low CNT density per unit area. As a result, CNT forests have poor bulk properties and are too fragile for integration with microfabrication processing. We introduce a new self-directed capillary densification method where a liquid is controllably condensed onto and evaporated from CNT forests. Compared to prior approaches, where the substrate with CNTs is immersed in a liquid, our condensation approach gives significantly more uniform structures and enables precise control of the CNT packing density and pillar cross-sectional shape. We present a set of design rules and parametric studies of CNT micropillar densification by this method, and show that self-directed capillary densification enhances the Young's modulus and electrical conductivity of CNT micropillars by more than three orders of magnitude. Owing to the outstanding properties of CNTs, this scalable process will be useful for the integration of CNTs as functional material in microfabricated devices for mechanical, electrical, thermal, and biomedical applications

Natural history of NF1 c.2970_2972del p.(Met992del): confirmation of a low risk of complications in a longitudinal study.

Individuals with the three base pair deletion NM_000267.3(NF1):c.2970_2972del p.(Met992del) have been recognised to present with a milder neurofibromatosis type 1 (NF1) phenotype characterised by café-au-lait macules (CALs) and intertriginous freckling, as well as a lack of cutaneous, subcutaneous and plexiform neurofibromas and other NF1-associated complications. Examining large cohorts of patients over time with this specific genotype is important to confirm the presentation and associated risks of this variant across the lifespan. Forty-one individuals with the in-frame NF1 deletion p.Met992del were identified from 31 families. Clinicians completed a standardised clinical questionnaire for each patient and the resulting data were collated and compared to published cohorts. Thirteen patients have been previously reported, and updated clinical information has been obtained for these individuals. Both CALs and intertriginous freckling were present in the majority of individuals (26/41, 63%) and the only confirmed features in 11 (27%). 34/41 (83%) of the cohort met NIH diagnostic criteria. There was a notable absence of all NF1-associated tumour types (neurofibroma and glioma). Neurofibroma were observed in only one individual-a subcutaneous lesion (confirmed histologically). Nineteen individuals were described as having a learning disability (46%). This study confirms that individuals with p.Met992del display a mild tumoural phenotype compared to those with 'classical', clinically diagnosed NF1, and this appears to be the case longitudinally through time as well as at presentation. Learning difficulties, however, appear to affect a significant proportion of NF1 subjects with this phenotype. Knowledge of this genotype-phenotype association is fundamental to accurate prognostication for families and caregivers

Prognostic value of CCND1 gene status in sporadic breast tumours, as determined by real-time quantitative PCR assays

The CCND1 gene, a key cell-cycle regulator, is often altered in breast cancer, but the mechanisms underlying CCND1 dysregulation and the clinical significance of CCND1 status are unclear. We used real-time quantitative PCR and RT–PCR assays based on fluorescent TaqMan methodology to quantify CCND1 gene amplification and expression in a large series of breast tumours. CCND1 overexpression was observed in 44 (32.8%) of 134 breast tumour RNAs, ranging from 3.3 to 43.7 times the level in normal breast tissues, and correlated significantly with positive oestrogen receptor status (P=0.0003). CCND1 overexpression requires oestrogen receptor integrity and is exacerbated by amplification at 11q13 (the site of the CCND1 gene), owing to an additional gene dosage effect. Our results challenge CCND1 gene as the main 11q13 amplicon selector. The relapse-free survival time of patients with CCND1-amplified tumours was shorter than that of patients without CCND1 alterations, while that of patients with CCND1-unamplified-overexpressed tumours was longer (P=0.011). Only the good prognostic significance of CCND1-unamplified-overexpression status persisted in Cox multivariate regression analysis. This study confirms that CCND1 is an ER-responsive or ER-coactivator gene in breast cancer, and points to the CCND1 gene as a putative molecular marker predictive of hormone responsiveness in breast cancer. Moreover, CCND1 amplification status dichotomizes the CCND1-overexpressing tumors into two groups with opposite outcomes

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

Background: We previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15–20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in ~ 80% of cases. Methods: We report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded. Results: No gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5–528.7, P = 1.1 × 10−4) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR = 3.70[95%CI 1.3–8.2], P = 2.1 × 10−4). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR = 19.65[95%CI 2.1–2635.4], P = 3.4 × 10−3), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR = 4.40[9%CI 2.3–8.4], P = 7.7 × 10−8). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD] = 43.3 [20.3] years) than the other patients (56.0 [17.3] years; P = 1.68 × 10−5). Conclusions: Rare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old

Modéliser la traçabilité et qualité de l'information dans les processus d'expertise des risques naturels

International audienceMountain natural risks management and expertise (torrent floods, rock-falls, snow avalanches…) induce several decision problems corresponding to the different temporal risk management steps (prevention, crisis and recovery) and based on heterogeneous imperfect information, provided by several more or less reliable sources. A methodology, based on UML formalism (Unified Modeling Language) is proposed 1) to improve the traceability of data, reasoning processes and methodologies used in the decision processes 2) to characterize employed information quality and its influence on results of the whole expert assessment process.La gestion et l’expertise des risques naturels en montagne (crues torrentielles, chutes de blocs, avalanches) génèrent de nombreuses problématiques de décision associées aux différentes phases de gestion (prévention, crise et réparation) et basées sur des informations hétérogènes provenant de sources inégalement fiables. Une méthodologie basée notamment sur le formalisme UML (Unified Modeling Language) est proposée pour 1) améliorer la traçabilité des données, raisonnements et méthodes mises en oeuvre dans les processus de décisions 2) caractériser la qualité de l‘information utilisée et son influence sur les résultats de l’expertise