HAL-Université de Bretagne Occidentale
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Conditions de réparation des conséquences médicales d'une vaccination obligatoire
No full textInternational audienceNote sous CE, 20 mars 2025, no 472778 (Lebon T.
Les termes utilisés dans une fiche de poste ne suffisent pas à justifier l'attribution d'un logement de fonction
No full textInternational audienceObservations sous CAA Versailles, 28 mars 2025, no 23VE01751, Cne de Viry-Châtillo
Déchets, pollutions, saletés : prendre un mot pour un autre ? Une réflexion croisée sur les apports et les limites du terme de “déchet”
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A Stochastic Ekman-Stokes Model for Coupled Ocean-Atmosphere-Wave Dynamics
No full textInternational audienceAccurate representation of atmosphere-ocean boundary layers, including the interplay of turbulence, surface waves, and air-sea fluxes, remains a challenge in geophysical fluid dynamics, particularly for climate simulations. This study introduces a stochastic coupled Ekman-Stokes model (SCESM) developed within the physically consistent Location Uncertainty framework, explicitly incorporating random turbulent fluctuations and surface wave effects. The SCESM integrates established parameterizations for air-sea fluxes, turbulent viscosity, and Stokes drift, and its performance is rigorously assessed through ensemble simulations against LOTUS observational data. A performance ranking analysis quantifies the impact of different model components, highlighting the critical role of explicit uncertainty representation in both oceanic and atmospheric dynamics for accurately capturing system variability. Wave-induced mixing terms improve model performance, while wave-dependent surface roughness enhances air-sea fluxes but reduces the relative influence of wave-driven mixing. This fully coupled stochastic framework provides a foundation for advancing boundary layer parameterizations in large-scale climate models
Internal Waves Observations from the Surface Water Ocean Topography Mission: Combined sea surface height and roughness measurements
No full textInternational audienceObservations of strong internal waves (IWs) off the Amazon Shelf by the Surface Water and Ocean Topography (SWOT) mission are analyzed. Distinct IWs signatures with wavelengths ranging from 3 to 50 km in coincident sea surface height anomalies (SSHA) and near-nadir normalized radar cross section (NRCS) are clearly identified. Using a three-layer approximation to describe the upper ocean stratification, SWOT SSHAs are converted to IW-induced thermocline displacements, reaching up to 80 m amplitude. This confirms SWOT’s unique ability to quantitatively inform about the state of the ocean interior, the energy and depth distribution of IWs. Moreover, joint SWOT measurements of SSHAs and NRCS further provide new means to precisely study the mechanisms leading to identify IWs from radar intensity measurements. SWOT data can indeed be analyzed in terms of a modulation transfer function (MTF), relating the SWOT NRCS contrasts to divergence of IW surface currents derived from SWOT SSHA measurements. Thanks to these new observations, SWOT-based MTF estimates are derived to quantify relationships between the NRCS contrasts, the amplitude and wavenumber of IWs, and the local wind conditions. In particular, it is shown that the maximum SWOT NRCS contrasts occur when IWs propagate in the wind direction, corresponding to resonant conditions between short wind waves and internal waves
Incidence of Significant Prostate Cancer in the Follow-Up of Patients With Suspicious Lesion on MRI and Negative Targeted and systematic Biopsies
No full textInternational audienceIntroduction: Currently, there are no established guidelines for follow-up (FU) after negative prostate biopsies (PBx) despite the presence of a target on MRI. We aimed to evaluate the risk of clinically significant prostate cancer (csPCa) within 2 years (2y) and at final FU in such cases.Patients and methods: We analyzed 105 patients with negative systematic and targeted PBx despite positive mpMRI (PI-RADS ≥ 3) (median FU: 66.5 months [IQR: 40-83]). All patients underwent FU with serial PSA measurements, digital rectal examinations and, when indicated, FU-MRI and/or FU-PBx. Outcomes assessed csPCa occurrence (GGG /ISUP ≥ 2) at 2y and at final FU, and predictive factors for csPCa.Results: At 2y, the csPCa detection rate (Det-R) was 7.6%, increasing to 15.2% at final FU. No significant differences were observed at 2y based on baseline PI-RADS status. The mean initial PSAD was significantly higher in patients with csPCa at 2y versus without: 0.20 ng/mL² (SD: 0.11) versus 0.13 ng/mL² (SD: 0.12) (P = .008). Patients with baseline PSAD > 0.15 had a significantly higher 2y csPCa Det-R versus with PSAD 0.15 and PI-RADS ≥ 4 (23%, 3/13). At final follow-up: 53% of patients with csPCa had an increasing PSAD (vs. 14% without, P = .003). A total of 41% (43/105) of patients underwent FU-MRI. Patients with csPCa were significantly more likely to have upgraded MRI findings (56% vs. 2.2%, P 0.15, and/or PI-RADS ≥ 4 at baseline. FU-PSAD and FU-MRI emerged as the most significant predictive factors, aiding to stratify the need for FU-PBx
Knowledge graph to dissect genotype phenotype associations
No full textInternational audienceDefining the mechanisms underlying complex traits and phenotypes requires understanding their genetic basis and how genes interact with environmental and lifestyle factors. Population-based prospective cohorts provide valuable resources for such research, collecting extensive phenotypic and omics data. However, the wide range of phenotypes in these cohorts makes it difficult to define homogeneous and/or clinically meaningful subgroups, limiting traditional genome-wide association studies (GWAS).We aim to develop a novel graph-based methodology to identify genotypephenotype associations. Our method represents data as a graph, where nodes correspond to variables (e.g., participants, phenotypes), and edges represent relationships between them. These edges can connect the same type of nodes (for example, participant-participant interactions) or different types (for example, bipartite interactions connecting participant to SNP). We applied our approach to the GOLD project, which includes comprehensive information on medical conditions, drug consumption, demographics, and genotypes of 10,000 participants. The graph contains four node types (participant, drug, SNP, phenotype) and 10 edge types, with attributes such as drug reimbursements, genotypes, and participant similarity.This knowledge graph representation allows us to use graph theory tools, including clustering algorithms, random walks, and deep learning-based graph representation methods. We expect to detect weak genotype-phenotype association signals that could not be detected by GWAS
Variability of a changing climate : interactions between mean state and fluctuations
No full textClimate variability is generally divided into two main components: external variability, driven by external forcings such as greenhouse gas emissions or solar variability, often associated to changes in the mean climate; and internal variability, which refers to fluctuations arising from the intrinsic dynamics of the climate system, typically expressed as anomalies relative to a mean state. While this distinction is widely accepted,it oversimplifies the complex reality as these two components are not independent but interact in significant ways. Internal variability is influenced by the magnitude and nature of external forcings, and in turn, the climate mean is shaped by the cumulative effect of internal fluctuations. The aim of this thesis is twofold. First, to highlight the strong coupling between internal and external variability by examining how anthropogenic global warming affects key climate mechanisms across multiple scales, including mean temperature and precipitation, ENSO and AMOC. Second, to explore in depth how these two components influence one another by analyzing their interaction terms. Ultimately, the goal is to move beyond the simplistic–and misleading–view of internal and external variability as separate, independent components, and instead develop a more integrated understanding that accounts for the dynamic interplay between them.La variabilité climatique est généralement divisée en deux composantes principales : la variabilité externe, induite par des forçages externes tels que les émissions de gaz à effet de serre ou la variabilité solaire, souvent associée à des changements de l’état moyen ; et la variabilité interne, qui regroupe les fluctuations associées à la dynamique intrinsèque du système, exprimées généralement sous forme d’anomalies par rapport à un état moyen. Bien que cette distinction soit largement admise, elle simplifie à l’excès une réalité complexe, car ces deux composantes ne sont pas indépendantes, mais interagissent de manière significative. La variabilité interne est influencée par l’ampleur et la nature des forçages externes, tandis que la moyenne climatique est façonnée par l’effet cumulatif des fluctuations internes. L’objectif de cette thèse est double. Premièrement, mettre en lumière le couplage fort entre variabilité interne et externe en étudiant comment le réchauffement climatique d’origine anthropique affecte les mécanismes clés du climat à différentes échelles, notamment la température moyenne, les précipitations, ENSO (El Nino-Oscillation australe) et l’AMOC (Circulation méridienne de retournement atlantique). Deuxièmement, approfondir l’analyse des interactions entre ces deux composantes en étudiant leurs termes d’interaction. En définitive, il s’agit de dépasser la vision simpliste – et trompeuse – de variabilités interne et externe séparées et indépendantes, pour développer une compréhension plus intégrée, rendant compte de la dynamique des échanges entre elles
