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Heart-Brain Interactions: Mechanisms and Pathways
No full textNational audienceOur research focuses on understanding brain function, specifically investigating how neural communication pathways from the heart to the brain influence cerebral structure and function. Using our murine model for cardiac afferents, we demonstrated that desensitization of cardiac sensory fibres with resiniferatoxin (RTX) during myocardial infarction (MI) significantly alleviated anxiety- and depressive-like behaviours. Proteomic analyses revealed distinct molecular signatures in the frontal cortices, particularly in Wnt signaling pathways and circadian entrainment. Additionally, our studies using a hindlimb unloading (HU) murine model showed that beyond cardiovascular deconditioning, microgravity conditions disrupt day/night rhythmicity of locomotor activity, temperature, and blood pressure regulation. These findings suggest that the heart communicates with the brain through multiple pathways, including classical neuronal routes, blood-borne factors, and vascular mechanical pulsation. We are now developing an organ-on-a-chip system to further investigate these complex brain-heart communication mechanisms.Nos recherches portent sur la compréhension du fonctionnement cérébral, en particulier sur l'influence des voies de communication neuronales du coeur vers le cerveau sur la structure et la fonction cérébrale. Utilisant notre modèle murin pour les afférences cardiaques, nous avons démontré que la désensibilisation des fibres sensorielles cardiaques par la résinifératoxine (RTX) pendant l'infarctus du myocarde (IM) atténue significativement les comportements anxieux et dépressifs. Les analyses protéomiques ont révélé des signatures moléculaires distinctes dans les cortex frontaux, particulièrement dans les voies de signalisation Wnt et l'entraînement circadien. En outre, nos études utilisant un modèle murin de suspension par la queue ont montré qu'au-delà du déconditionnement cardiovasculaire, les conditions de microgravité perturbent la rythmicité jour/nuit de l'activité locomotrice, de la température et de la régulation de la pression artérielle. Ces résultats suggèrent que le coeur communique avec le cerveau par de multiples voies, incluant les routes neuronales classiques, les facteurs sanguins et la pulsation mécanique vasculaire. Nous développons actuellement un système organe-sur-puce pour approfondir l'étude de ces mécanismes complexes de communication cerveau-coeur
Solving moment and polynomial optimization problems on Sobolev spaces
No full textUsing standard tools of harmonic analysis, we state and solve the problem of moments for non-negative measures supported on the unit ball of a Sobolev space of multivariate periodic trigonometric functions. We describe outer and inner semidefinite approximations of the cone of Sobolev moments. They are the basic components of an infinite-dimensional moment-sums of squares hierarchy, allowing to numerically solve non-convex polynomial optimization problems on infinite-dimensional Sobolev spaces with global convergence guarantee
Visually Guided Model Predictive Robot Control via 6D Object Pose Localization and Tracking
No full textThe objective of this work is to enable manipulation tasks with respect to the 6D pose of a dynamically moving object using a camera mounted on a robot. Examples include maintaining a constant relative 6D pose of the robot arm with respect to the object, grasping the dynamically moving object, or co-manipulating the object together with a human. Fast and accurate 6D pose estimation is crucial to achieve smooth and stable robot control in such situations. The contributions of this work are three fold. First, we propose a new visual perception module that asynchronously combines accurate learning-based 6D object pose localizer and a high-rate model-based 6D pose tracker. The outcome is a low-latency accurate and temporally consistent 6D object pose estimation from the input video stream at up to 120 Hz. Second, we develop a visually guided robot arm controller that combines the new visual perception module with a torque-based model predictive control algorithm. Asynchronous combination of the visual and robot proprioception signals at their corresponding frequencies results in stable and robust 6D object pose guided robot arm control. Third, we experimentally validate the proposed approach on a challenging 6D pose estimation benchmark and demonstrate 6D object pose-guided control with dynamically moving objects on a real 7 DoF Franka Emika Panda robot
Evaluation de la précision de mesure du centre de pression d'un tapis instrumenté double-bandes : effet de la vitesse de marche
No full textNational audienceRévolutionner l'analyse du mouvement : le parasport ouvre de nouvelles perspectives.Cette thématique s'inscrit dans la continuité des Jeux Olympiques et Paralympiques de Paris 2024. En effet, les dynamiques territoriales liées à cet événement se manifestent, entre autres, par la promotion de l'activité physique pour la santé, le bien-être et le vivre ensemble. Le thème proposé fait écho à celui du congrès 2024, en poursuivant la réflexion autour de l'indépendance fonctionnelle pour une participation sociale accrue, notamment à travers la pratique d'activités physiques.Pour le congrès SOFAMEA 2025, nous souhaitons que notre communauté explore plus particulièrement les questions suivantes : Le post-paralympisme : quel sera l’héritage des Jeux, et comment pourra-t-il être utile et utilisable pour nos patients ? Comment lever les freins à la pratique d'activités physiques et, par conséquent, quelle est la place des laboratoires d'analyse du mouvement pour encourager les pratiques d'activités physiques et le développement du parasport ?Le programme du congrès SOFAMEA 2025 comprendra: Deux ateliers pratiques, Cinq symposiums, Des sessions composées de communications courtes, Des sessions composées de communications longues, Mais aussi un prélude convivial (inclu dans les frais d'inscriptions), Et le dîner des congressistes (également inclu dans les frais d'inscription).Contrairement aux éditions précédentes où chaque journée était dédiée à un seul type de session, ces différentes sessions seront réparties sur les trois jours du congrès SOFAMEA 2025, offrant ainsi une diversité d'approches tout au long de l'événement.De plus, le congrès SOFAMEA 2025 se tiendra sur trois journées pleines, permettant aux participants de profiter pleinement de toutes les sessions proposées
Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery
No full textPublisher: Nature Publishing GroupInternational audienceCitizen Science initiatives have a worldwide impact on environmental research by providing data at a global scale and high resolution. Mapping marine biodiversity remains a key challenge to which citizen initiatives can contribute. Here we describe a dataset made of both underwater and aerial imagery collected in shallow tropical coastal areas by using various low cost platforms operated either by citizens or researchers. This dataset is regularly updated and contains \textgreater1.6 M images from the Southwest Indian Ocean. Most of images are geolocated, and some are annotated with 51 distinct classes (e.g. fauna, and habitats) to train AI models. The quality of these photos taken by action cameras along the trajectories of different platforms, is highly heterogeneous (due to varying speed, depth, turbidity, and perspectives) and well reflects the challenges of underwater image recognition. Data discovery and access rely on DOI assignment while data interoperability and reuse is ensured by complying with widely used community standards. The open-source data workflow is provided to ease contributions from anyone collecting pictures
Learning Geometric Reasoning Networks for Robot Task and Motion Planning
No full textInternational audienceTask and Motion Planning (TAMP) is a computationally challenging roboticsproblem due to the tight coupling of discrete symbolic planning and continuousgeometric planning of robot motions. In particular, planning manipulation tasksin complex 3D environments leads to a large number of costly geometric plannerqueries to verify the feasibility of considered actions and plan their motions. Toaddress this issue, we propose Geometric Reasoning Networks (GRN), a graphneural network (GNN)-based model for action and grasp feasibility prediction,designed to significantly reduce the dependency on the geometric planner. More-over, we introduce two key interpretability mechanisms: inverse kinematics (IK)feasibility prediction and grasp obstruction (GO) estimation. These modules notonly improve feasibility predictions accuracy, but also explain why certain actionsor grasps are infeasible, thus allowing a more efficient search for a feasible solu-tion. Through extensive experimental results, we show that our model outperformsstate-of-the-art methods, while maintaining generalizability to more complex en-vironments, diverse object shapes, multi-robot settings, and real-world robots
Lipschitz Stability of an Inverse Problem of Transmission Waves with Variable Jumps
No full textInternational audienceThis article studies an inverse problem for a transmission wave equation, a system where the main coefficient has a variable jump across an internal interface given by the boundary between two subdomains. The main result obtains Lipschitz stability in recovering a zeroth-order coefficient in the equation. The proof is based on the Bukhgeim-Klibanov method and uses a new one-parameter global Carleman inequality, specifically constructed for the case of a variable main coefficient which is discontinuous across a strictly convex interface. In particular, our hypothesis allows the main coefficient to vary smoothly within each subdomain up to the interface, thereby extending the preceding literature on the subject
Mechanical compressive forces increase PI3K output signaling in breast and pancreatic cancer cells
No full textInternational audienceMechanical stresses, including compression, arise during cancer progression. In solid cancer, especially breast and pancreatic cancers, the rapid tumor growth and the environment remodeling explain their high intensity of compressive forces. However, the sensitivity of compressed cells to targeted therapies remains poorly known. In breast and pancreatic cancer cells, pharmacological PI3K inactivation decreased cell number and induced apoptosis. These effects were accentuated when we applied 2D compression forces in mechanically responsive cells. Compression selectively induced the overexpression of PI3K isoforms and PI3K/AKT pathway activation. Furthermore, transcriptional effects of PI3K inhibition and compression converged to control the expression of an autophagy regulator, GABARAP, whose level was inversely associated with PI3K inhibitor sensitivity under compression. Compression alone blocked autophagy flux in all tested cells, whereas inactivation of basal PI3K activity restored autophagy flux only in mechanically non-responsive compressed cells. This study provides direct evidence for the role of the PI3K/AKT pathway in compression-induced mechanotransduction. PI3K inhibition promotes apoptosis or autophagy, explaining PI3K importance to control cancer cell survival under compression
Experimental Validation of Sensitivity-Aware Trajectory Planning for a Redundant Robotic Manipulator Under Payload Uncertainty
No full textInternational audienceIn this paper, we experimentally validate the recent concepts of closed-loop state and input sensitivity in the context of robust manipulation control for a robot manipulator. Our objective is to assess how optimizing trajectories with respect to sensitivity metrics can enhance the closed-loop system's performance w.r.t. model uncertainties, such as those arising from payload variations during precise manipulation tasks. We conduct a series of experiments to validate our optimization approach across different trajectories, focusing primarily on evaluating the precision of the manipulator's end-effector at critical moments where high accuracy is essential. Our findings offer valuable insights into improving the closed-loop robustness of the robot's state and inputs against physical parametric uncertainties that could otherwise degrade the system's performance