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Characterization of Conus textile predatory venom by capillary electrophoresis hyphenated to mass spectrometry
No full textInternational audienc
Inondations par remontée de nappe dans les territoires agricoles côtiers : contribution d'un dispositif de suivi multi-sources en Normandie occidentale (Le Havre de Lessay et la Côte des Isles)
No full textInternational audienceComme sur plusieurs territoires côtiers français, les inondations par remontée de nappe sont un phénomène récurrent sur la côte ouest de la Normandie. Cet aléa hydrogéologique, encore peu étudié, devrait s’intensifier sous l’effet du changement climatique, en raison, d’une part, de l’élévation du niveau moyen de la mer, et d’autre part, de la modification du régime des précipitations, avec une accentuation des pluies hivernales. Il constitue un enjeu majeur pour le développement et la gestion durable du littoral normand, notamment pour les exploitations agricoles, qui subissent déjà annuellement des dommages coûteux. Ce travail vise à améliorer la compréhension de cet aléa notamment sur deux territoires littoraux à forte valeur ajoutée du département de la Manche : le Havre de Lessay et la Côte des Isles. L’étude poursuit quatre objectifs principaux :1) Réaliser un état des lieux des inondations par débordement de nappe (localisation, fréquence, évolution)2) Identifier les principaux facteurs de forçage (pluviométrie, niveaux piézométriques, effets de site liés à la topographie et à la géomorphologie)3) Évaluer les évolutions possibles de ces aléas dans un contexte de changement climatique4) Repérer les exploitations agricoles les plus exposées afin d’accompagner la mise en œuvre de stratégies d’adaptation.Pour situer et caractériser ces phénomènes, notamment déterminer les dynamiques spatio-temporelles des inondations et les interactions de leur déclenchement, un réseau de suivi multi-source a été déployé, associant capteurs d’humidité du sol, piézomètres, pièges photographiques, et imageries d’altitude (drone, données LiDAR et satellite). Les inondations par débordement de nappe se produisent principalement entre décembre et avril, dans des zones dont l’altitude est inférieure à 10 mètres NGF principalement localisées, dans les formations dunaires récentes, sableuses et perméables. L’année 2023-2024, marquée par des précipitations exceptionnelles, a permis d’identifier 92 épisodes d’inondation, révélant une grande sensibilité des nappes superficielles à la pluviométrie locale et à la micro-topographie. Les données LiDAR ont en incidence très locale clé des dépressions à fine échelle, tandis que les images drone et les pièges photos ont permis de suivre précisément les dynamiques d’apparition et de récession des eaux
Sulfate adjustment and early reactivity in cements containing kaolinitic calcined clays: investigation and assessment via a sulfate-limited model system
No full textInternational audienceUnderstanding the early reaction kinetics of calcined clays (CCs) in calcined clay-limestone cements (CCLC) is required to optimize the formulations for optimal early performance. In this study, a clinker-free sulfate-limited model system (SLiM) is utilized to compare the early reactivity of 9 diverse natural CCs. The SLiM consists of an excess of CC and portlandite, and limited gypsum It is demonstrated that the sulfate-carrier depletion time provides a rate of reaction for each clay which is characteristic of its reactivity in a blended cement. As such, it is shown that the SLiM test can be used to probe physico-chemical properties, including the standard enthalpy of formation of metakaolinite, and to understand the mechanical performance and hydration of blended cement with these clays up to 3 days. In conclusion, a foundation for a standard test allowing to adjust gypsum content in CCLC from a single calorimetry measurement is developed
Optimal excitation of single mode resonators: demonstration with a 3 T MRI metasolenoid
No full textInternational audienceWireless passive resonators have been developed to inductively couple to the birdcage body coil. Such systems have been explored in the form of ceramic resonators with high permittivity but also with metamaterial or metasurface devices that can exhibit resonant behaviour at a given Larmor frequency. The resonant focusing of the radiofrequency field is used to lower the input power during transmission and improve the sensitivity of the body coil during reception. The gain is only obtained in a limited volume located within or close to the resonant structure. Typically, such passive devices do not support parallel imaging and demonstrated limited SNR enhancement compared to dense multichannel receive arrays. Nonetheless, these resonators have seen recent development with applications to wrist or breast MRI mostly in 1.5 T MRI scanners. Here we propose to design, build, and study a metasolenoid resonator operating at 3 T. The metasolenoid was characterized on phantom to validate the B 1 efficiency increase with respect to the birdcage polarization excitation. We reported a high B 1 efficiency gain for circularly (3.2-fold) and linearly (5.8-fold) polarized excitation. Consequently, and according to analytical calculations, we demonstrated that when excited with linearly polarized excitation, the metasolenoid had a B 1 efficiency 26 % higher when excited by the default circularly polarized excitation. Numerical simulations on voxel model showed that in presence of the resonator the B 1 efficiency gain normalized by the maximum local SAR was significantly improved when introducing the metasolenoid but the influence of the excitation polarization was reduced to a few percent.</div
DIVA: An Ontology-based Approach to Model User Activity within Visualization Systems
No full textInternational audienceThe study of user activity supports evaluation of visualization systems, recommendation of suitable views or tasks, guidance of interaction, and validation of analytical results. It enables researchers to understand how these visualization systems are used and to gain insight into users' reasoning processes during data exploration. However, there is a lack of structured frameworks for systematically collecting and reasoning over such data. In this paper, we build upon Semantic Web standards to model and represent user activity as knowledge graphs. We introduce an OWL ontology specifically designed for this purpose and demonstrate its application by transforming system log data-collected during user studies with a multiview visualization tool for urban mobility data exploration-into an RDF knowledge graph. Finally, we illustrate the utility and expressiveness of our model by enabling intuitive exploration and interpretation of user activity through the implementation of competency questions as SPARQL queries and the visualization of these queries' results on the RDF graph representing user activity
SpeckSeq enables high-throughput functional stratification of MEFV variants in autoinflammatory diseases
No full textInternational audienceVariants of uncertain significance (VUS) are a major obstacle in genetic diagnosis, particularly when involving gain-of-function (GoF) mutations that are poorly predicted in silico. MEFV, which encodes the inflammasome sensor pyrin, is mutated in two autoinflammatory diseases, familial Mediterranean fever (FMF) and pyrin-associated autoinflammation with neutrophilic dermatosis (PAAND). Here, we developed SpeckSeq, a method that combines DNA bar-coding, ASC speck–based single-cell sorting and next-generation sequencing to systematically identify hypermorphic MEFV variants in response to different stimuli. SpeckSeq identified 49 GoF mutations separated into two distinct groups containing either PAAND variants or FMF variants. SpeckSeq was validated using patients’ cells and supported a reclassification of MEFV variant pathogenicity, leading to novel diagnoses. As a large-scale mutagenesis approach, using human genetics as a guide, SpeckSeq revealed structural and functional pyrin features, including a putative ligand-accommodating cavity in the B30.2 domain. Altogether, SpeckSeq classifies VUS to refine molecular diagnostics and improve our knowledge on the pyrin inflammasome
Towards Domain-Robust Activity Recognition using Textual Representations of Binary Sensor Events
No full textInternational audienceLanguage-based representations have recently emerged as a promising approach for cross-domain Human Activity Recognition (HAR) in smart homes, where binary sensor streams are verbalized into natural-language descriptions and processed by pretrained encoders. However, prior work has typically fixed both the textualization scheme and the text embedding model, leaving open how linguistic design choices influence transferability. This paper presents a comprehensive factorial analysis of textualization and embedding strategies for language-based HAR. We systematically vary (i) how sensor event windows are expressed-across seven existing and novel sequential and summarized textualizations-and (ii) how they are embedded using lexical (TF-IDF), static (Word2Vec), and contextual (SBERT) encoders. Experiments on four public smart-home datasets under consistent in-domain and cross-domain transfer conditions reveal that textualization design, not encoder complexity, governs performance. Sequential, event-ordered sentences maximize in-domain accuracy, while single-sentence, schema-based summaries-such as the proposed Compound Sensor Summary (CSS)-generalize best across homes. Clause-level ablations further show that event descriptions drive recognition, whereas explicit timing information can reduce robustness by overfitting to home-specific schedules. Overall, our findings establish a reproducible framework for analyzing and designing language-based representations in HAR, demonstrating that linguistic structure-rather than deep contextualization-is the primary determinant of domain robustness in smart-home activity recognition
TRACT : A Transformer and Statistical Framework for Anomaly Detection in Multivariate Non-stationary Time Series
No full textInternational audiencePhysiological and inertial signals analysis supports numerous healthcare applications, including disease detection, rehabilitation, and treatment. Advancements in signal processing enable the representation of most stationary and non-stationary phenomena using mathematical expressions. These representations provide valuable insights and help in identifying distinctive patterns of interest. In this paper, we propose TRACT, a deep learning and statistical framework designed to detect anomalies in non-stationary environments. It comprises two main components, a transformer-based reconstruction model that captures signal patterns through multi-resolution attention, extending the standard attention mechanism in transformer architecture. During inference, reconstruction errors are computed by comparing observed signals with their reconstructed versions. Statistical modeling is applied to these errors, with parameters estimated directly from the data. TRACT adapts to varying data rates across datasets without imposing strict distribution assumptions, resulting in enhanced robustness and accuracy in anomaly detection for multivariate non-stationary time series. We evaluate TRACT on 12 real-world multivariate time series datasets from diverse domains, demonstrating its performance in anomaly detection tasks with various constraints and its ability to provide early warnings for anomalous events