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Recent advances in freeze-thaw starch: Mechanisms, property changes, influencing factors and modification strategies
No full textInternational audiencePapaya seed oil ( Carica papaya ) is gaining interest for its potential applications as a functional food ingredient. Papaya seeds, usually discarded as agro-industrial waste, represent a valuable source of oil rich in unsaturated fatty acids. This study evaluated several extraction methods, including Soxhlet extraction, maceration, microwave-assisted extraction (MAE), hydraulic pressing, and ultrasound-assisted extraction (UAE), using pure ethanol and pure isopropanol as green solvents. Soxhlet extraction with hexane for 24 h at 121 °C achieved the highest yield (30.91 ± 0.49%) and was used as the reference. Among the greener techniques, UAE performed at 400 W for 30 min with isopropanol achieved the best compromise between yield (22.50 ± 0.27%) and oil quality, showing the lowest acidity index (9.00 ± 2.16 mg KOH/g). Therefore, UAE was selected for further optimization by varying ultrasound power (0–400 W), extraction time (5–30 min), and solvent composition (isopropanol, 50:50 ethanol–isopropanol mixture, and ethanol). The optimal extraction conditions were 200 W for 30 min using a 1:1 ethanol-isopropanol solvent mixture, which resulted in an extraction yield of 26.34%, corresponding to an oil recovery of approximately 85% relative to the Soxhlet reference. The mixed solvent enhanced extraction efficiency by improving mass transfer through the combined effects of solvent polarity and ultrasonic cavitation, confirming that ultrasound-assisted extraction with green solvent mixtures is an effective method for papaya seed oil recovery
EcoBioCap querying : A Decision Support System to design modified atmosphere packaging for fresh produce based on a bipolar flexible querying approach
No full textMRGEN: A CONCEPTUAL FRAMEWORK FOR LLM-POWERED MIXED REALITY AUTHORING TOOLS FOR EDUCATION
No full textInternational audienceMixed Reality (MR) offers immersive and multimodal opportunities for education but remains difficult for teachers to author without technical expertise. We propose MRGEN, a conceptual framework for LLM-powered authoring tools to support teachers in creating MR learning activities that work on mobile devices (tablets and smartphones). MRGEN articulates three axes: Learning Objectives, MR Modality, and GAI Assistance. To validate our framework, we implemented a prototype based on the open-source MIXAP authoring platform and conducted a user study with 24 participants. Results show that LLM-powered authoring reduced task duration by 36% on average, and that over 90% of participants found the AI support helpful for brainstorming, structuring, and aligning content with their learning goals. These findings yielded very promising results for future AI-assisted MR authoring tools.</div
Mechanisms of blood-brain barrier penetration: A molecular dynamics study on R9 and MPG peptide translocation
No full textInternational audienceOne of the major obstacles in treating diseases that affect the central nervous system is delivering drugs across the blood-brain-barrier (BBB). Cell-penetrating peptides (CPPs) can be used as delivery vectors, but their translocation mechanism is still poorly understood, in part due to the simplistic membrane models applied to their interpretation. Here we investigate the translocation mechanism of two CPPs, R9 and MPG, using molecular dynamics and enhanced sampling techniques on a realistic membrane model of human brain microvascular endothelial cells. The results suggest that R9 induces greater membrane disruption compared to MPG, yet that both face a significant free energy barrier to translocation. In both peptides the first interactions were initiated by the N-terminus and prominently involved arginine residues even for MPG. The crucial role of the plasticity of both partners (BBB bending, partial CPP unfolding) on the translocation energetics was also explored by sampling ad hoc collective variables, revealing the important role of long polyunsaturated acyl chain lipids. Together, these findings provide mechanistic insight into CPP-mediated transport and offer guidelines for rational design
Optimisation de la gestion des risques professionnels du mélange équimolaire d'oxygène et de protoxyde d'azote (MEOPA) au CHU d'Amiens
No full textMEOPA is a mixture of oxygen and nitrous oxide used for its analgesic and anxiolytic properties during medical procedures. Its clinical effectiveness relies on strict conditions of use, ensuring both patient comfort and safe clinical practice ; however, its use exposes healthcare professionals to health risks associated with chronic nitrous oxide exposure, requiring appropriate preventive measures and strict compliance with the regulatory framework in force. At Amiens-Picardie University Hospital, technical and organizational solutions have been implemented to limit this exposure, but they remain only partially effective or difficult to implement in older buildings due to structural, architectural, and technical constraints. More recent infrastructures allow better integration of preventive systems, without fully addressing all identified issues, particularly regarding the exposure of healthcare professionals. Various technical solutions for MEOPA gas capture, based on active or passive scavenging principles, as well as pharmacological and non-pharmacological alternatives, are presented in order to reduce occupational risks and adapt clinical practices to the specific constraints of healthcare facilities.Le MEOPA est un mélange d’oxygène et de protoxyde d’azote utilisé pour ses propriétés antalgiques et anxiolytiques lors de soins médicaux. utilisé pour ses propriétés antalgiques et anxiolytiques lors de soins médicaux. Son efficacité clinique repose sur des modalités d’utilisation rigoureuses, garantissant à la fois le confort du patient et la sécurité des pratiques. Toutefois, son usage expose les professionnels de santé à des risques sanitaires liés à une exposition chronique au protoxyde d’azote, ce qui impose la mise en place de mesures de prévention adaptées et le respect strict du cadre réglementaire en vigueur. Au Centre Hospitalier Universitaire (CHU) d’Amiens-Picardie, des solutions techniques et organisationnelles ont été déployées afin de limiter cette exposition. Néanmoins, celles-ci demeurent partiellement efficaces ou difficilement implantables dans les anciens bâtiments, principalement en raison de contraintes structurelles, architecturales et techniques. Les infrastructures plus récentes permettent une meilleure intégration des dispositifs de prévention, sans pour autant répondre pleinement à l’ensemble des problématiques identifiées, notamment en ce qui concerne l’exposition des professionnels soignants. Différentes solutions techniques de captation du MEOPA, reposant sur des principes de poussée active ou de poussée passive, sont présentées. Par ailleurs, des alternatives médicamenteuses et non médicamenteuses sont également abordées afin de réduire les risques professionnels et d’adapter les pratiques aux contraintes spécifiques des établissements de santé
Analyse du règlement (UE) 2021/2282 : guide d’appropriation pour les fabricants de dispositifs médicaux
No full textThis work examines the new European framework for health technology assessment defined by Regulation (EU) 2021/2282, and its interaction with Regulation (EU) 2017/745 on medical devices. While Regulation (EU) 2017/745 governs market access and clinical evaluation, Regulation (EU) 2021/2282 introduces a Joint Clinical Assessment (JCA) designed to harmonize, centralize, and structure the clinical evidence provided by manufacturers at the European level.The study highlights the eligibility criteria for selected devices, the key stakeholders involved, and the two main scientific outputs of the regulation : Joint Scientific Consultations (JSC) and Joint Clinical Assessments (JCA). Process analysis, complemented by field feedback, shows that manufacturers face difficulties in interpreting the regulation and understanding the additional clinical evidence required.To address these challenges, an interactive guide was developed. This guide offers simplified visual representations of regulatory steps, explanatory flowcharts, and a comparative analysis of requirements between the two regulations. Its purpose is to support medical device manufacturers in better understanding, anticipating, and implementing Regulation (EU) 2021/2282. This tool serves as a practical resource to facilitate adoption of the regulation and to support future regulatory developments in the medical device sector.Ce travail analyse le nouveau cadre européen d’évaluation des technologies de santé, défini par le Règlement (UE) 2021/2282, et son articulation avec le Règlement (UE) 2017/745 relatif aux dispositifs médicaux. Alors que le Règlement (UE) 2017/745 encadre la mise sur le marché et l’évaluation clinique, le Règlement (UE) 2021/2282 introduit une évaluation clinique commune (ECC) harmonisée au niveau européen afin de centraliser et structurer les données cliniques fournies par les fabricants. L’étude met en évidence les critères de sélection des dispositifs éligibles, les acteurs impliqués, ainsi que les deux productions scientifiques clés du règlement : les Consultations Scientifiques Communes (CSC) et les Évaluations Cliniques Communes (ECC). Une analyse des processus, complétée par des retours de terrain, montre que les fabricants rencontrent des difficultés d’interprétation du texte et un manque de clarté sur les preuves supplémentaires à fournir. Pour répondre à ces besoins, un guide interactif a été conçu. Ce guide propose une visualisation simplifiée des étapes réglementaires, des logigrammes explicatifs et une comparaison des exigences entre les deux règlements. Il vise à aider les fabricants de dispositifs médicaux à mieux comprendre, anticiper et appliquer le Règlement (UE) 2021/2282. L’outil constitue une base pratique pour faciliter l’appropriation du règlement et accompagner les futurs développements réglementaires dans le domaine des dispositifs médicaux
Polarity-Driven Selective Adsorption of Quercetin on Kaolinite: An Integrated DFT and Monte Carlo Study
No full textInternational audienceQuercetin’s therapeutic potential is limited by its poor water solubility and rapid degradation. Natural clay minerals such as kaolinite present sustainable platforms for drug delivery, yet the molecular mechanisms of drug encapsulation are not fully understood. Specifically, the role of kaolinite’s structural polarity, its hydrophilic aluminol (001) and hydrophobic siloxane (00-1) basal surfaces, in selective drug adsorption remains unexplored. This study combines Monte Carlo sampling and Density Functional Theory (DFT) to provide the first quantitative, atomistic comparison of quercetin adsorption on both kaolinite surfaces. The results demonstrate a pronounced polarity-driven selectivity. Strong, exothermic adsorption (−206.65 kJ mol−1) occurs on the hydrophilic (001) surface, stabilized by a network of five hydrogen bonds. In contrast, the hydrophobic (00-1) surface exhibits significantly weaker sorption (−147.16 kJ mol−1), dominated by van der Waals interactions. Charge-transfer analysis shows that the hydrophilic (001) surface exhibits a net charge transfer of −0.198 e, approximately 2.4 times greater than that of the hydrophobic (00-1) surface (−0.083 e), consistent with differential electron density maps and partial density of states. By linking hydrogen bonding and charge transfer to adsorption energy, these results elucidate how surface polarity dictates drug encapsulation. This work establishes a predictive framework for designing kaolinite-based nanocarriers with optimized stability, bioavailability, and controlled release, guiding the development of sustainable drug delivery systems. It is noted that this DFT study models adsorption at 0 K using periodic slab models in a vacuum
MARL-based Traffic Management in Communication Networks: Randomized vs. Traffic-Based Agent Deployment
No full textInternational audienceThe rapid growth of consumer applications such as video streaming, cloud gaming, Augmented Reality/Virtual Reality (AR/VR), and Internet of Things (IoT) deployments has intensified the demand for intelligent and adaptive congestion control in communication networks. Existing centralized congestion control solutions often struggle with scalability and responsiveness under highly dynamic traffic conditions. In this paper, we present a decentralized traffic engineering framework based on Multi-Agent Reinforcement Learning (MARL), tailored to optimize routing and traffic balancing in real time. The framework introduces two types of agents: (i) Routing Agents, which dynamically adjust link weights to steer flows across alternative paths, and (ii) Balancing Agents, which regulate traffic splitting ratios over precomputed multi-path routes. Beyond agent design, we address a key yet underexplored challenge: agent placement strategy. We compare random deployment versus traffic-aware deployment (based on node degree) and evaluate their effectiveness in mitigating congestion. Using an OMNeT++ simulation environment with realistic consumer traffic patterns on the Abilene topology (a well-known Internet2 research network), we test the framework across diverse congestion scenarios. Results show that traffic-aware placement consistently improves throughput, reduces latency and packet loss compared to both random placement and traditional Equal-Cost Multi-Path (ECMP) routing baselines. These findings highlight the potential of MARL-driven, placement-aware agents to support future consumer network-ing services requiring low-latency, high-reliability, and scalable resource management
Fast recovery of parametric eigenvalues depending on several parameters and location of high order exceptional points
No full textInternational audienceA numerical algorithm is proposed to deal with parametric eigenvalue problems involving non-Hermitian matrices and is exploited to find location of defective eigenvalues in the parameter space of non-Hermitian parametric eigenvalue problems.These non-Hermitian degeneracies also called exceptional points (EP) have raised considerable attention in the scientific community as these can have a great impact in a variety of physical problems. The method first requires the computation of high order derivatives of a few selected eigenvalues with respect to each parameter involved. The second step is to recombine these quantities to form new coefficients associated with a partial characteristic polynomial (PCP). By construction, these coefficients are regular functions in a large domain of the parameter space which means that the PCP allows one to recover the selected eigenvalues as well as the localization of high order EPs by simply using standard root-finding algorithms.The versatility of the proposed approach is tested on several applications, from mass-spring systems to guided acoustic waves with absorbing walls and room acoustics. The scalability of the method to large sparse matrices arising from conventional discretization techniques such as the finite element method is demonstrated.The proposed approach can be extended to a large number of applications where EPs play an important role in quantum mechanics, optics and photonics or in mechanical engineering