Radiation-induced grafting for flame-retardant Miscanthus × giganteus stem fragments: Role of methodology and radiation source

International audienceIn this study, poly(dimethyl(methacryloyloxy)methyl phosphonate)-grafted Miscanthus × giganteus stem fragments were prepared using three radiation-induced grafting methods: pre-irradiation (PIG), simultaneous irradiation grafting (SIG), and simultaneous irradiation after pre-impregnation (SIGPI). In all cases, the influence of irradiation sources (e-Beam, γ-rays and X-rays), as well as the dose, were evaluated. X-ray fluorescence spectroscopy measurements and SEM/EDX cartographies evidenced that the grafting method both influences the grafted amounts and the localization of the grafted polymer chains within the substrate. Among all the methods used, SIG yielded the highest grafted amounts. In that case, the use of e-Beam and X-rays irradiation yielded respectively a phosphorus content of 2.6 wt% and 3 wt% at 10 kGy, whereas γ-rays yielded lower results. The dose rate, and more specifically the peak dose rate, seem to play a decisive role in the results. Pyrolysis Combustion Flow Calorimetry (PCFC) measurements highlighted variations in fire performance, which were linked to differences in the distribution of the grafted polymer within the substrate. All functionalization methods employed in this study successfully imparted flame-retardant properties to Miscanthus × giganteus stem fragments

Dynamical 2D-DFA for movement analysis in obstetrics

International audienceWhile perineal tears continues to occur in 90% of births worldwide, the PELVITRACK project aims at characterising perineum damages in order to predict them and thus to adapt obstetrical process and to improve perineal rehabilitation. With images and videos, that can be easily collected, predictive analysis based on texture descriptors could help perineal tears prevention. Fractal-based method have proved to be efficient at highlighting time series differences in terms of rugosity or complexity at different time scales. Among fractal methods, the Detrended Fluctuation Analysis (DFA) has mainly been applied to time series but some works have proposed extensions to images and videos. In this paper, 2D-DFA is performed on experimental perineal image sequence with the objective of perineal damage characterisation. At the global and local levels the preliminary results are encouraging and illustrate the suitability of 2D-DFA for movement analysis on images

Exploring multimodal neurophysiological synchrony and behaviour in choir performance: a preliminary study

International audienceChoir singing is a widely practiced activity that offers multiple benefits, as demonstrated by psychophysiological studies in both healthy and pathological populations. However, the processes underlying these outcomes are still not fully understood. Yet, advances in portable multimodal systems enable a more thorough investigation of these effects. The present study thus aimed to examine neurophysiological and behavioural responses of choirmasters and singers under various conditions of visual coupling. Preliminary results highlight the importance of combining different methods of analysis and metrics to better understand the intricate phenomena at play in this beneficial practice

The Sense of Touch in Healthcare HAPTIMED : A Digital Twin of Haptic Perception for Educational Purposes

International audienceThe recent reform of medical education in France, including the introduction of Objective Structured Clinical Examinations (OSCEs), underscores the need to standardize the learning and evaluation of clinical skills. Among these, palpation—a core component of the physical examination—relies on haptic perception, where practitioners assess the biomechanical properties of tissues through tactile and proprioceptive cues. Yet, palpation remains largely taught through traditional apprenticeship models, lacking standardized protocols or objective pedagogical tools. Modernizing the learning of touch in healthcare therefore represents a major educational challenge. The HAPTIMED project investigates the sense of touch as an active movement that structures diagnostic and therapeutic interaction. Anchored at the intersection of human movement sciences, electronics, and artificial intelligence, it aims to model and enhance the acquisition of haptic competence in healthcare education through the development of a digital twin of haptic perception. The project has just begun and brings together two mirror doctoral theses within a unified interdisciplinary framework: Topic A (Human Movement Sciences) focuses on the analysis and standardization of palpatory gestures. It seeks to identify objective markers of haptic expertise and to define pedagogical feedback mechanisms for learners. The study will characterize expert and novice gestures in terms of force, displacement, and temporal dynamics, providing a reference framework for training and assessment. Topic B (Flexible Electronics, Structures and Embedded Systems) aims to design and validate a wearable haptic glove capable of monitoring pressure and movement during palpatory examinations. Based on Hooke’s law, tissue stiffness (k), the ratio between applied force and resulting deformation, is treated as the key measurable parameter linking force and displacement. By quantifying stiffness, the glove will provide an objective measure of tissue resistance to deformation, allowing learners to visualize and compare their applied pressure with expert benchmarks. A transversal methodological axis combines machine learning and belief function theory to fuse multimodal data (force, displacement, motion trajectories) and to manage uncertainty in haptic signal interpretation. Ultimately, HAPTIMED aims to modernize and objectify the teaching of clinical touch, providing evidence-based recommendations for OSCE implementation. By rendering touch measurable, comparable, and computable, the project exemplifies how movement computing can bridge human expertise and digital intelligence—transforming the embodied act of palpation into a quantifiable and pedagogically actionable dimension of healthcare education

Impact of sulfamethoxazole, trimethoprim, diclofenac, carbamazepine, and their mixture on the metabolism of Lemna minor: a targeted metabonomic study

International audienceMetabolomics is an analytical profiling technique that measures and compares large numbers of metabolites in biological samples, providing insight into metabolic mechanisms. There are few studies concerning the effects of xenobiotics and their transformation products on aquatic plant metabolites, which can uptake and detoxify them using untargeted metabolomics. Objectives This study investigates how pharmaceuticals, including diclofenac (DCF) and carbamazepine (CBZ), as well as sulfamethoxazole (SMX) and trimethoprim (TRIM), present in aquatic environments, can influence the biosynthetic pathways of Lemna minor .Based on previous research on the effects of DCF, SMX, and TRIM on Lemna pathways, specifically phenylalanine, tyrosine, and tryptophan biosynthesis, folate biosynthesis, and the phenylpropanoid pathway, including flavonoid and anthocyanin metabolism. Methods Lemna was incubated with DCF, CBZ, SMX, and TRIM alone and in a mixture (MIX) at 5 ppb (5 µg/L) for 5 days, at concentrations near environmental levels. The methanolic extract was analysed using a Q Exactive Focus Orbitrap to investigate changes in the aforementioned biosynthetic pathways, as reported in previous studies. Results Lemna can modulate its pathways to produce more phenolic compounds as a defence mechanism against various drugs. This modulation can be considered an indicator for each drug. Conclusions The presence of pharmaceuticals in the aquatic environment can affect the biosynthetic pathways of Lemna . Therefore, Lemna minor can be used as a model to study the stress-response of different pharmaceuticals on plant metabolites and their pathways

Temporally Consistent and Controllable Video Generation of 2D Cine CMR via Latent Space Motion Modeling

International audienceCine cardiac magnetic resonance is the gold standard for assessing cardiac function, but the scarcity of public datasets limits the development of advanced data-driven models. To address this limitation, we propose a generative method for synthesizing temporally coherent and anatomically consistent cardiac sequences. Our text-to-video framework decouples cardiac spatial structure from temporal motion. First, a fine-tuned diffusion model synthesizes an initial frame from a clinical text prompt, controlling anatomical features. Then, a latent flow model conditioned on a cardiac phase embedding generates the complete cardiac motion, ensuring spatial consistency and temporal control. Our model generates anatomically and pathologically diverse sequences with high temporal coherence and strong fidelity to input prompts, achieving a FID of 31.68 for image realism and a CLIP score of 31.04 for text-image alignment. These experimental results highlight its potential to produce high-fidelity, on-demand medical data, offering a scalable solution to data scarcity

Human Movement Intelligence

International audienceHuman Movement Intelligence (HMI) explores the evolving identity of kinesiology, advocating for a unified vision that reflects the field's multidisciplinary nature in a rapidly transforming global context. In this position paper, I trace the historical development of human movement studies, drawing lessons from past terminological debates. It examines the historical, philosophical and socio-cultural dimensions of movement, highlighting the interplay between reductionist and holistic perspectives, and emphasizing the impact of advancements currently reshaping the field, inclusive of Embodiment, Artificial Intelligence and Extended Reality. Educational perspectives aligning curricula with the current focus on intelligent systems and technologies, while preserving a cohesive field identity, are envisaged. Strategic recommendations for our field are proposed, underscoring the need for collaborative actions that enhance its academic and societal influence, while fostering unity across disciplines. HMI positions itself as a forward-looking framework, addressing the challenges and opportunities of the 21st century in human movement science

Strategies for the Functionalization of Polyhydroxyalkanoate Chains. Toward New Polyhydroxyalkanoate-Based Graft Copolymers

International audienceIn recent years, biodegradable bio-based polymers have attracted significant attention as potential alternatives to petroleum-derived plastics. Among them, poly (lactic acid) (PLA) is the most well-known and widely utilized; however, its biodegradability, especially in marine environments, has been frequently questioned. In contrast, polyhydroxyalkanoates (PHAs) -notably polyhydroxybutyrate (PHB)- are bio-based and biodegradable polyesters that do not present this limitation. To expand the scope of PHAs applications, researchers have explored the strategy of grafting polymer chains onto the PHA backbone. Since the PHA backbone lacks inherent functional groups, it’s sometimes necessary to functionalize it to introduce reactive sites that allow for subsequent grafting of polymer chains. This review outlines the various synthetic approaches used to obtain functionalized PHAs and PHA-based graft copolymers, as well as their potential applications in the biomedical sector

AI-enhanced RUL prediction of PEMFCs under dynamic operating conditions using XGBoost-based HI extraction and hybrid transformer-GRU model

International audienceProton Exchange Membrane Fuel Cells (PEMFCs) are critical for zero-emission energy systems, particularly in electro-hydrogen generators (GEH2). Accurate Remaining Useful Life (RUL) prediction is crucial for ensuring operational reliability and enabling predictive maintenance. However, dynamic operating conditions present a significant challenge for existing prognostic approaches, particularly in extracting robust Health Indicators (HIs). Conventional HIs, often based on voltage or power, are highly sensitive to mission profiles and fail to generalize in real-world conditions. To address this limitation, we propose a novel data-driven approach based on XGBoost regression to extract a degradation-specific HI directly from raw voltage measurements. This method effectively filters out transient fluctuations caused by varying power demands, isolating the true degradation trend without requiring complex preprocessing or domain expertise. Leveraging the extracted HI, we introduce a hybrid deep learning model that combines Transformer networks and Gated Recurrent Units (GRUs) to capture temporal dependencies and provide accurate RUL predictions under dynamic conditions. Explainable AI techniques are integrated to interpret the model’s predictions and analyze the influence of operational variables on fuel cell degradation. The proposed framework is validated on a real-world industrial dataset from four PEMFC stacks operating in GEH2 systems. Experimental results demonstrate superior accuracy, robustness, and generalizability compared to state-of-the-art methods, highlighting the potential of this scalable and interpretable approach for predictive maintenance in complex industrial environments

Characterization of volatile and semi-volatile organic compounds in the air of sports facilities

International audienceIndoor air quality (IAQ) in sports facilities is not yet well known compared to other indoor environments such as homes, schools or offices. However, the intense activity associated with sport and the widespread use of plasticbased furniture and coverings can lead to an increase in exposure to organic contaminants. The aim of this study was to characterize the indoor air quality of 10 sports halls of different activities. Targeted and non-targeted analytical approaches were applied to provide the broadest possible screening with a focus on emerging pollutants and led to identify and quantify more than a hundred volatile organic compounds (VOCs) and semivolatile organic compounds (SVOCs). Two sampling campaigns were carried out in unoccupied and occupied rooms to assess the impact of physical activities on IAQ. For VOCs, composition and concentration levels are globally close to those of other indoor environments, with a predominance of carbonyls, especially hexanal. However, some specific and emerging compounds (like benzothiazole, decamethylcyclopentasiloxane and 1-(2methoxy-1-methyl ethoxy)-2-propanol)) were highlighted. Acetone and 6-methyl-5-hepten-2-one, emitted by human body, were identified as occupancy tracers. For SVOCs, phthalates (DiBP, DBP) are the most abundant in compositions, followed by organophosphate flame retardants (EHDPP, TCPP) and PAHs (fluorene, phenanthrene) with particularly high PAH concentrations in one weight room, probably released from a recycled rubber flooring. The impact of ventilation on VOC and SVOC concentrations in air was also assessed, with an overall positive effect