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Anti-guide de l'agent public pour échapper à l'obligation vaccinale, Note sous CAA Bordeaux, 28 mai 2025, no 23BX00370
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GATE 10 Monte Carlo particle transport simulation: I. Development and new features
No full textInternational audienceWe present GATE version 10, a major evolution of the open-source Monte Carlo simulation application for medical physics, built on Geant4. This release marks a transformative evolution, featuring a modern Python-based user interface, enhanced multithreading and multiprocessing capabilities, the ability to be embedded as a library within other software, and a streamlined framework for collaborative development. In this Part 1 paper, we outline GATE’s position among other Monte Carlo codes, the core principles driving this evolution, and the robust development cycle employed. We also detail the new features and improvements. Part 2 will focus on the architectural innovations and technical challenges. By combining an open, collaborative framework with cutting-edge features, such a Monte Carlo platform supports a wide range of academic and industrial research, solidifying its role as a critical tool for innovation in medical physics
First evidence of the decay
No full textInternational audienceThe first search for the decay in the range is performed using data from proton-proton collisions collected by the LHCb experiment at centre-of-mass energies of 7, 8, and 13 TeV, corresponding to an integrated luminosity of 9 fb. The photons are reconstructed through their conversion into an electron-positron pair, which significantly improves the mass resolution of the reconstructed decays with respect to decays with an unconverted photon. A signal excess with a significance of 3.5 standard deviations is measured, constituting the first experimental evidence for this decay. In the range , the ratio between the branching fractions of the signal decay and the favoured decay is measured to be where the first uncertainty is statistical and the second is systematic. This measurement is consistent with the value predicted in the Standard Model. In the range , the ratio is measured
A cooperative learning framework for the integration of metabolomic data from multiple cohorts and common phenotype identification
No full textInternational audienceIntegrating metabolomic data from multiple studies/cohorts could be an efficient strategy to enhance statistical power and identify robust biomarkers. However, challenges associated with batch effects, study-specific biases, and dataset heterogeneity, hinder results reproducibility and translation. Limitations have been reported in shared variable mode data integration approaches, both for early fusion that struggles with inter-study variability, and late fusion that may overlook inter-dataset relationships. Here, we propose a novel cooperative learning framework for metabolomics data integration from multiple studies, designed to improve candidate biomarker discovery by balancing advantages of early and late fusion, while mitigating study-specific confounders. The proposed approach consists in leveraging univariate and multivariate analysis and an optimized loss function. To implement the approach, early-stage integration was based on a multiblock method (MINT-PLS-DA), while separate PLS-DA was used in late fusion. Univariate analysis was performed via a mixed model. The approach was first evaluated in controlled conditions using synthetic data, and then applied to two existing untargeted metabolomics human datasets. Preliminary assessment focused on batch effect reduction across datasets, and agreement between early and late fusion outputs. Using real word data, the results showed that 10% of the initial features were stable across early and late fusion. This showed improved consistency compared to when they were published separately on the integrated dataset. All results demonstrate the ability of the proposed approach to capture the common part of phenotypes. The developed integration model based on cooperative learning leverages the complementary strengths of early and late fusion, offering an efficient solution for metabolomics data integration, enhancing the reliability of potential biomarker discovery
MetaNetMap : cartographie automatique des données métabolomiques sur les réseaux métaboliques
No full textInternational audienceMetabolic networks represent genome-derived information about the biochemical reactions that cells are capable of performing. Mapping omic data onto these networks is important to refine model simulations. However, metabolomic data mapping remains very challenging due to difficulties in identifier reconciliation between annotation profiles and metabolic networks. MetaNetMap is a Python package designed to automatise the process of mapping metabolomic data onto metabolic networks. It includes several layers of identifier matching, the use of customisable databases, and molecular ontology integration to suggest the most matches between experimentally-identified metabolites and molecules defined in the network.Les réseaux métaboliques représentent les informations issues du génome concernant les réactions biochimiques que les cellules sont capables d'effectuer. La cartographie des données omiques sur ces réseaux est importante pour affiner les simulations de modèles. Cependant, la cartographie des données métabolomiques reste très difficile en raison des difficultés de rapprochement des identifiants entre les profils d'annotation et les réseaux métaboliques. MetaNetMap est un package Python conçu pour automatiser le processus de cartographie des données métabolomiques sur les réseaux métaboliques. Il comprend plusieurs niveaux de correspondance des identifiants, l'utilisation de bases de données personnalisables et l'intégration de l'ontologie moléculaire afin de suggérer les correspondances les plus pertinentes entre les métabolites identifiés expérimentalement et les molécules définies dans le réseau
Kination and the Inert Doublet Model
No full textInternational audienceThe inert doublet model is a two-Higgs-doublet extension of the standard model that provides a minimal and versatile framework for frozen-out dark matter. Assuming standard cosmology, if the dark matter mass ranges between approximately 120 GeV and 500 GeV then it turns out to be underabundant, as gauge interactions render its annihilation too efficient. In this work, we show that this mass window becomes allowed in cosmological scenarios where dark matter freeze-out occurs during a period with a stiff equation of state, w > 1/3, such as kination. This predictive setup satisfies all current experimental constraints while remaining within the reach of upcoming detection efforts