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Atomic Force Microscopy Reveals Differences In Mechanical Properties Linked To Cortical Structure In Mouse And Human Oocytes
No full textInternational audienceCell mechanical properties regulate biological processes such as oocyte development. Cortical tension is regulated via actomyosin cortex remodeling to ensure optimal oocyte quality. However, the evolution of other mechanical parameters and their relationship with cortex structure remain poorly understood in mammalian oocytes. In this work, a methodology combining multiple mechanical parameters measured through Atomic Force Microscopy is proposed to investigate the relationship between oocyte mechanical properties and cortex organization. By studying mouse oocytes at various stages of development, along with engineered ones with specific cortex organization, it is demonstrated that a thin actin cortex corresponds to stiff oocytes while a thick one is associated with softer oocytes. It is further revealed that maternal age, a critical factor for fertility, affects mouse oocytes mechanics, correlating with alterations in their cortex structure. Finally, it is shown that the evolution of mechanical properties differs between human and mouse oocyte development, highlighting species-specific differences in cortex organization
Developmental Imprint and Later-Life Stability of the Central Sulcus
No full textInternational audienceThe central sulcus (CS) is a reliable anatomical landmark whose morphology and functional organization are sensitive to both developmental influences and age-related change. Studies of congenital one-handers and amputees provide complementary models to disentangle the effects of early deprivation from those of later-life adaptation. In this study, we found that in the hemisphere contralateral to the missing hand, congenital one-handers showed a flatter CS relative to both amputees and controls. This alteration was not observed in amputees. In addition, a distinct CS configuration was selectively preserved in the right hemisphere of congenital one-handers. Exploratory analyses further revealed associations between CS shape in congenital one-handers and functional activation of the intact hand, arm, lips, and feet contralateral to the missing hand. Flatter central sulcus folding in congenital one-handers likely reflects early sensory–motor deprivation, whereas preserved morphology in a subset of congenital one-handers suggests that compensatory plasticity can maintain sulcal structure, particularly in the right hemisphere. Extending this to aging, central sulcus shape, combined with measures like sulcal thickness and opening, may serve as a structural marker to distinguish degenerative from adaptive changes and to stratify individuals based on resilience. In addition, this study shows that sulcal form is sensitive to early developmental deprivation but stable during later-life input loss, highlighting its value as a marker of early cortical wiring. By extension, atypical sulcal morphology in genetic neurodegenerative diseases may signal subtle developmental alterations long before clinical onset, offering a potential tool for early detection and stratification
Archives et information des citoyens : un enjeu pour la démocratie (table-ronde), Festival Allez-Savoir (EHESS et Archives municipales de Marseille)
No full textTable-ronde "Archives et information des citoyens : un enjeu pour la démocratie" lors du Festival Allez-Savoir (EHESS et Archives municipales de Marseille)https://allez-savoir.fr/jcms/1886_JEvent/fr/archives-et-information-des-citoyens-un-enjeu-pour-la-democratieLes archives ne sont pas seulement le témoin d’une époque, elles représentent aussi un véritable enjeu de transparence démocratique. Sur quels critères se fonde le processus de sélection des documents ? Quels sont les usages ? Quelles conséquences pour les centres de ressources ?Faisant dialoguer sociologie de la culture et institutions patrimoniales, cette table ronde proposer d’interroger les logiques sociales à l’œuvre derrière la façon dont est conservée, invisibilisée ou mise en lumière l’information archivée.Organisation et modération : Marine Coquet (EHESS) et le service Archives de l'EHESSTable-ronde : Soizic Grasset (Bibliothèque de l'Alcazar), Noëlle Colombié (Archives et bibliothèque de la ville de Marseille), Agathe Meridjen-Manoukian (Archives départementales du Val-de-Marne)
ECGrecover : A Deep Learning Approach for Electrocardiogram Signal Completion
No full textInternational audienceIn this work, we address the challenge of reconstructing the complete 12-lead ECG signal from its incomplete parts. We focus on two main scenarios: (i) reconstructing missing signal segments within an ECG lead and (ii) recovering entire leads from signal in another unique lead. Two emerging clinical applications emphasize the relevance of our work. The first is the increasing need to digitize paper-stored ECGs for utilization in AI-based applications, often limited to digital 12 lead 10s ECGs. The second is the widespread use of wearable devices that record ECGs but typically capture only one or a few leads. In both cases, a non-negligible amount of information is lost or not recorded. Our approach aims to recover this missing signal. We propose ECGrecover, a U-Net neural network model trained on a novel composite objective function to address the reconstruction problem. This function incorporates both spatial and temporal features of the ECG by combining the distance in amplitude and sycnhronization through time between the reconstructed and the real digital signals. We used real-life ECG datasets and through comprehensive assessments compared ECGrecover with three state-of-the-art methods based on generative adversarial networks (EKGAN, Pix2Pix) as well as the CopyPaste strategy. The results demonstrated that ECGrecover consistently outperformed state-of-the-art methods in standard distortion metrics as well as in preserving critical ECG characteristics, particularly the P, QRS, and T wave coordinates
Stability Analysis of Fractional-Order Systems
No full textInternational audienceThe stability of fractional-order linear systems is a fundamental topic in control theory, given their ability to model complex dynamical behaviors more accurately than integer-order systems. Fractional-order systems are characterized by differential equations with non-integer derivatives, which provide a more flexible framework to describe processes such as viscoelasticity, diffusion, and memory effects in various physical and engineering applications
Gradual modality dropout for segmenting ischemic stroke lesions in an unseen center with missing modalities
No full textInternational audienceIn clinical practice, imaging modalities may not always be available for every patient due to scheduling, cost, or patient-specific constraints. Additionally, multi-center imaging studies often face inconsistencies in protocols, machine settings, and artifacts, compromising data quality. We propose a 3D U-Net model for ischemic lesion segmentation using a novel training technique, gradual modality dropout, which progressively deactivates imaging modalities during training. This approach ensures robust performances when all modalities are present and improves segmentation accuracy in scenarios where one or more modalities are missing in unfamiliar contexts. The model demonstrates adaptability and reliability when trained on MRI scans of stroke patients across different phases (hyper-acute,sub-acute, acute, and post-treatment) and various hospital settings. Code available here: https://github.com/sofiavarib/Gradual-modality-dropou
BOMO-RNN: a novel neural network controller for industrial robots with experimental validation
No full textInternational audienceThis paper introduces the Beetle Olfactory-based Manipulability Optimizer Recurrent Neural Network (BOMO-RNN), an advanced RNN-based controller designed to enhance the manipulability of redundantly actuated industrial robotic arms. The manipulability index, which quantifies the maneuverability of the robotic arm, is crucial for avoiding kinematic singularities that restrict the mobility of robotic arm in the task space. The proposed approach formulates an optimisation problem using the penalty method to incorporate the manipulability index into the tracking control objective function. Unlike conventional approaches that rely on velocity-level control and require precise initialisation, BOMO-RNN operates at the position level, allowing direct trajectory tracking from arbitrary starting configurations, thereby increasing flexibility and ease of deployment. This function aims to maximise maneuverability while ensuring accurate tracking of the reference trajectory, effectively avoiding joint-space singularities. The BOMO-RNN framework leverages a metaheuristic optimisation strategy, enabling efficient exploration of high-dimensional search spaces without requiring explicit Jacobian pseudo-inversion, significantly reducing computational overhead and improving numerical stability. The BOMO-RNN algorithm efficiently addresses the time-varying optimisation problem at the position level, eliminating the need for computationally intensive Jacobian pseudo-inversion. This ensures robustness in real-world scenarios where high-speed control and adaptability to dynamic environments are critical. The algorithm's convergence is theoretically analysed, and its performance is validated through numerical simulations and experimental results using the LBR IIWA 7-DOF robot. Extensive experimental verification demonstrates the effectiveness of BOMO-RNN across diverse trajectory patterns, including circular, sinusoidal, and piecewise straight-line motions, confirming its generalizability and practical applicability. The results demonstrate BOMO-RNN's practical effectiveness in optimising manipulability and its potential for real-world robotic applications
Essais sur l'évaluation économique du déploiement de l'hydrogène vert pour la neutralité carbone industrielle
No full textThis dissertation investigates the economic conditions under which green hydrogen can be effectively deployed to support the energy transition of hard-to-abate industrial sectors. These sectors are particularly challenging to decarbonize due to their dependence on fossil fuels and high-temperature processes. While green hydrogen presents a promising low-carbon alternative, its large-scale deployment is constrained by high upfront costs, limited renewable electricity, and policy frictions stemming from uncertain economy-wide impacts. This thesis combines sectoral cost-benefit analysis, cross-sectoral allocation modeling, and long-run economic growth simulations to identify efficient strategies and public policies for scaling up hydrogen deployment while maintaining economic competitiveness and advancing climate objectives.The first chapter focuses on early-stage deployment through demonstration projects. It proposes an extended cost-benefit analysis (CBA) framework that incorporates knowledge spillovers and technical progress induced by pilot investments. Applied to the French container glass sector, the analysis demonstrates that accounting for sector-wide learning significantly reduces the estimated abatement cost of adopting hybrid hydrogen-electric furnaces, thereby justifying targeted public support. The study shows that a well-designed subsidy, coupled with emissions taxation, can align private incentives with social welfare, encouraging early adoption of disruptive low-carbon technologies.The second chapter addresses the optimal allocation of a limited hydrogen supply across various end-use sectors. It develops a merit-order framework based on social welfare maximization, taking into account emission intensities, abatement costs, technology learning, and the availability of low-carbon alternatives. Results reveal that the priority ranking of hydrogen applications is dynamic: as the Social Cost of Carbon increases, sectors with higher emissions should be prioritized—even if their short-term abatement costs are higher. Additionally, sectors with few viable low-carbon alternatives warrant early support. Moreover, a two-period dynamic model highlights that investing early in certain high-cost sectors can yield greater long-term benefits by accelerating learning and cost reductions. The chapter also evaluates second-best policy instruments and finds that demand-side subsidies targeted at hydrogen users outperform supply-side subsidies in aligning hydrogen allocation with social welfare goals. A calibration to the Marseille-Fos industrial-port zone underscores the importance of adapting policies to local sectoral and infrastructure conditions.The third chapter broadens the scope to macroeconomic analysis by developing an economic growth model tailored to small open economies (SOEs) that rely on imported fossil fuels. Using Uruguay as a case study, the chapter first applies a sectoral CBA to evaluate industrial incentives for substituting fossil fuels with green hydrogen. It then explores, through a two-sector growth model, how green hydrogen and its derivatives can drive structural transformation, reduce external energy dependency, and lower emissions. Simulations show that combining carbon pricing with green investment yields the most favorable outcomes—sustaining output growth, raising income, and achieving deep decarbonization. Sensitivity analyses confirm the robustness of these results under varying policy and investment scenarios. Overall, this thesis provides actionable insights into investment decisions and policy design to support the cost-effective and welfare-enhancing deployment of green hydrogen, especially in industrial hubs and emerging economies seeking to achieve carbon neutrality.Cette thèse examine les conditions économiques dans lesquelles l'hydrogène vert peut être efficacement déployé pour accompagner la transition énergétique des secteurs industriels difficiles à décarboner. Ces secteurs sont complexes à verdir en raison de leur dépendance aux combustibles fossiles et aux procédés à haute température. Bien que l'hydrogène vert représente une alternative bas carbone prometteuse, son déploiement à grande échelle est freiné par des coûts initiaux élevés, une disponibilité limitée d'électricité renouvelable et des incertitudes sur ses effets macroéconomiques. Cette thèse combine une analyse coût-bénéfice sectorielle, un modèle d'allocation intersectorielle et des simulations de croissance économique à long terme afin d'identifier des stratégies efficaces et des politiques publiques adaptées pour étendre l'usage de l'hydrogène tout en maintenant la compétitivité économique et les objectifs climatiques.Le premier chapitre porte sur le déploiement précoce via des projets pilotes. Il propose un cadre élargi d'analyse coût-bénéfice (ACB) intégrant les effets d'apprentissage et le progrès technique induits par ces investissements. Appliquée au secteur du verre creux en France, l'analyse montre que prendre en compte les apprentissages sectoriels réduit significativement le coût d'abattement estimé des fours hybrides hydrogène-électricité, justifiant ainsi un soutien public ciblé. L'étude démontre qu'une subvention bien conçue, combinée à une taxe carbone, permet d'aligner les incitations privées sur l'optimum social, et favorise l'adoption précoce de technologies bas carbone de rupture.Le deuxième chapitre aborde l'allocation optimale d'un approvisionnement limité en hydrogène entre différents usages finaux. Il développe un cadre de méritorde fondé sur la maximisation du bien-être social, tenant compte de l'intensité carbone, des coûts d'abattement, du potentiel d'apprentissage et de la disponibilité d'alternatives bas carbone. Les résultats montrent que ce classement est dynamique : à mesure que le coût social du carbone augmente, les secteurs plus émetteurs ou à fort potentiel d'apprentissage doivent être priorisés, même avec des coûts initiaux plus élevés. Les secteurs sans alternatives crédibles doivent aussi être ciblés. Un modèle dynamique à deux périodes montre que des investissements précoces dans certains secteurs coûteux peuvent générer des bénéfices futurs via l'apprentissage. Ce chapitre évalue également des politiques de second rang et conclut que les subventions à la demande, ciblant les utilisateurs finaux, sont plus efficaces que les aides à la production. Une calibration sur la zone industrielle-portuaire de Marseille-Fos souligne l'importance d'adapter les politiques aux réalités locales.Le troisième chapitre élargit l'analyse au niveau macroéconomique via un modèle de croissance à deux secteurs, adapté aux petites économies ouvertes dépendantes des énergies fossiles. L'Uruguay sert d'étude de cas. L'analyse débute par une ACB sectorielle évaluant les incitations à substituer les combustibles fossiles par de l'hydrogène vert. Le modèle explore ensuite comment l'hydrogène vert et ses dérivés peuvent soutenir la transformation structurelle, réduire la dépendance énergétique et atteindre les objectifs climatiques. Les simulations montrent que combiner une taxe carbone et un investissement vert génère les meilleurs résultats : croissance durable, hausse des revenus et forte baisse des émissions. Des analyses de sensibilité confirment la robustesse des résultats face à différents scénarios. Dans l'ensemble, cette thèse fournit des éléments concrets pour orienter les décisions d'investissement et les politiques publiques en faveur d'un déploiement efficace de l'hydrogène vert, en particulier dans les pôles industriels et les économies émergentes en transition vers la neutralité carbone
Igtracker: task and instance information gaps in multiple object tracking
No full textInternational audiencePedestrian multiple object tracking targets to track multiple pedestrian instances in real-time. Recently, the methods based on joint detection and embedding have improved performance by sharing task features. However, it has two obvious shortcomings: inconsistent task information and ambiguous neighbor instance overlap. Hence, the branch tasks information gap and instances information gap need to be carefully addressed. In this paper, IGTracker is proposed as a novel online tracking framework, which bridges different branch task optimization requirements from the perspective of task-specific information gaps and nearest instance information gaps. Firstly, to alleviate the competitive conflict between subtasks, we propose a shuffle involution decoupling (SID) module, which constructs task-specific features by focusing on local interaction information and global long-range dependencies of key points. Secondly, the nearest neighbor information enhancement (NNIE) strategy is proposed to reduce the ambiguity between similar instances by leveraging the adjacency key point information gap. As a bonus, our proposed IGTracker achieves competitive performance compared to various existing methods on the MOTChallenge benchmarks
Optimizing the diffusion coefficient of overdamped Langevin dynamics
No full textInternational audienceOverdamped Langevin dynamics are reversible stochastic differential equations which are commonly used to sample probability measures in high-dimensional spaces, such as the ones appearing in computational statistical physics and Bayesian inference. By varying the diffusion coefficient, there are in fact infinitely many overdamped Langevin dynamics which are reversible with respect to the target probability measure at hand. This suggests to optimize the diffusion coefficient in order to increase the convergence rate of the dynamics, as measured by the spectral gap of the generator associated with the stochastic differential equation. We analytically study this problem here, obtaining in particular necessary conditions on the optimal diffusion coefficient. We also derive an explicit expression of the optimal diffusion in some appropriate homogenized limit. Numerical results, both relying on discretizations of the spectral gap problem and Monte Carlo simulations of the stochastic dynamics, demonstrate the increased quality of the sampling arising from an appropriate choice of the diffusion coefficient