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The Impact of Exercise Interventions on the Network Structure of Psychotic Symptoms: Analysis From Two Clinical Trials
No full textInternational audienceBACKGROUND AND HYPOTHESIS: In people with psychotic disorders, exercise is known to improve psychotic symptoms; however, the mechanisms underlying these effects are unclear. In the network approach, mental disorders are conceptualised as complex systems of interacting symptoms. In this context, exercise interventions could modify the dynamic of psychotic symptoms within the network. Using data from two independent clinical trials using exercise, the aim was to investigate the impact of exercise interventions on network connectivity, then compare the network structure pre and post intervention. STUDY DESIGN: Combined data from two clinical trials on exercise with a total of 106 participants with a diagnosis of psychotic disorder were included. The Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity using semi-structured interviews. Networks analyses were performed to compare before and after exercise. STUDY RESULTS: At baseline, the PANSS network was densely connected with several strong positive connections. Symptoms being most central were negative symptoms. After exercise, the network was less dense and less connected, and the connections were different. When the networks before and after exercise were compared, they were significantly different in terms of structure, but not global strength. CONCLUSION: This study is the first to show that exercise seems to favour a disconnection between psychotic symptoms and could modify the network structure, providing a first mechanism of action which would require more investigation
Modèles précurseurs : Un cadre théorique pour enseigner et apprendre les sciences à l’école primaire
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Pyrroline-5-carboxylate dehydrogenase is a key actor of nitrogen metabolism in maturing seeds of Arabidopsis thaliana: P5CDH and seed development
No full textInternational audienceProline is a multifaceted amino acid in plants involved in both stress responses and development. Recent studies have shown that knock-out mutants lacking Pyrroline-5-carboxylate dehydrogenase (P5CDH), the enzyme responsible for the second step of proline catabolism, impaired nitrogen remobilisation and carbon allocation to seeds. Here, we demonstrate that seed development is also significantly impaired in p5cdh mutants, particularly from the transition between embryogenesis and maturation. Specifically, the p5cdh mutation leads to an arrest in embryo elongation and a reprogramming of seed metabolism during maturation, resulting in reduced accumulation of storage compounds and compromised acquisition of dehydration tolerance. These effects are further exacerbated under high nitrate conditions. Together, our findings highlight a crucial role for proline catabolism in supporting the ability of maturing embryos to utilize glutamine as a nitrogen source, particularly in response to nitrogen availability
Collaborative Action on Timing InterferenCes: Summary and Perspectives at Mid-term
No full textTo appear in Embedded Real Time Systems (ERTS) 2026International audienceCAOTIC is an ambitious initiative aimed at pooling and coordinating the efforts of major French research teams working on timing analysis of multicore real-time systems, with a focus on interference due to shared resources. The objective is to enable the efficient use of multicores in critical systems. Based on a better understanding of timing anomalies and interference, considering the specificities of applications (structural properties and execution model), and revisiting the links between timing analysis and synthesis processes (code generation, mapping, scheduling), we target significant progresses in timing analysis models and techniques for critical systems, as well as in methodologies for their application in industry. In this paper, at project mid-term, we show the progress of the project. We also present some original work, about the use of a Tricore plaform and its timing model, and discuss open questions and future work
Synthèse et perspectives : approches transformatives, changement d'échelle et implications pour l'Enseignement Supérieur et la Recherche
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Attitudes des Français vis-à-vis des facteurs de risque de cancer liés au milieu professionnel : résultats du Baromètre Cancer 2021
No full textNational audienceIntroduction. - Cancer is a major public health issue, with a rising incidence in France and a persistent underreporting of occupational cancers. In this study, we describe the French population's awareness of occupational cancer risk factors and their attitudes toward these factors in 2021, with a view to improving preventive actions. Methods. - This study is based on data from the 2021 Cancer Barometer, a national telephone survey conducted with a structured questionnaire. The survey targeted adults residing in mainland France with no history of cancer and at least one experience in the workplace. The questionnaire collected sociodemographic data, information about attitudes toward occupational risk factors, the degree to which participants considered themselves informed and the sources of information the participants considered reliable. Results. - The sample included 3,974 respondents with a mean age of 49.7 years (SD = 16.8 years), 42.0% of whom reported having been exposed to occupational risk factors, principally chemical agents (69.5%). Exposures to physical (6.5%) and biological (1.8%) agents were much less frequently identified. More than one third of the respondents (33.4%) considered night work to be a risk factor for cancer. Over half the participants (51.5%) reported feeling \"rather poorly\" or \"very poorly\" informed about these risks. Healthcare professionals (24.3%) and television (23.6%) were cited as the most reliable sources of information. Conclusions. - These findings highlight the need to improve public awareness of occupational cancer risk factors in France, particularly for lesser-known agents and night work. Targeted communication campaigns involving all primary prevention stakeholders appear essential to strengthen such campaigns.IntroductionLe cancer est un enjeu majeur de santé publique, avec une incidence en augmentation en France, et une sous-déclaration persistante des cancers d’origine professionnelle. Cette étude vise à décrire, en 2021, les attitudes et le niveau d’information de la population française concernant les facteurs de risque professionnels de cancer, afin de mieux orienter les actions de prévention.MéthodeL’étude repose sur les données du Baromètre Cancer 2021, une enquête nationale téléphonique réalisée à l’aide d’un questionnaire, auprès d’adultes résidant en France hexagonale, sans antécédent de cancer et ayant eu au moins une expérience professionnelle. Les items ont permis de recueillir des informations sociodémographiques, les attitudes vis-à-vis des facteurs de risque professionnels, le sentiment d’être informé et les sources d’information jugées fiables.RésultatsL’échantillon comprend 3974 répondants d’âge moyen 49,7 ans (ET = 16,8). Parmi eux, 42,0 % déclarent avoir été exposés à des facteurs de risque en milieu professionnel, principalement à des agents chimiques (69,5 %). Les agents physiques (6,5 %) et biologiques (1,8 %) sont beaucoup moins fréquemment identifiés. Par ailleurs, plus d’un tiers des répondants (33,4 %) considère le travail de nuit comme un facteur de risque de cancer. Plus de la moitié (51,5 %) se déclare « plutôt mal » ou « très mal » informée sur ces risques. Les professionnels de santé (24,3 %) et la télévision (23,6 %) sont les sources d’information jugées les plus fiables.ConclusionsCes résultats mettent en évidence un besoin de renforcer la sensibilisation de la population française aux facteurs de risque de cancer en milieu professionnel, notamment les agents moins connus et le travail de nuit. Des campagnes de communication ciblées, mobilisant l’ensemble des acteurs de la prévention primaire, apparaissent essentielles pour la renforcer
Injury risk and workload analysis in elite adolescent female volleyball players using machine learning
No full textInternational audienceBACKGROUND: This study investigates the key variables influencing injury occurrence in elite-level female volleyball players. It aims to evaluate three hypotheses: (1) the quantification of workload using the "System Training Response" score provides a superior explanation and prediction of injury occurrence compared to traditional methods such as sum or mean; (2) both menses and external workload serve as primary variables that explain injury occurrence; and (3) non-linear models yield better explanatory and predictive capabilities for injury occurrence than linear models. METHODS: Nineteen elite female volleyball players were monitored throughout a 190-day competitive season, during which various training-related parameters were collected. These parameters included internal and external workload quantification and menses data. To analyze and predict injury occurrence, machine learning techniques were employed, with a particular emphasis on Random Forest models. RESULTS: The Random Forest model demonstrated superior performance in describing injury occurrence, achieving an area under the receiver operating characteristic curve of 0.87. Key variables identified as significant contributors to injury occurrence included the players’ age, menses status, and the percentage of intense jumps executed. Furthermore, the cross-validation procedure conducted on a reserved portion of the dataset yielded positive results, with an area under the receiver operating characteristic curve of 0.74, indicating a good generalization performance of the model. CONCLUSIONS: The findings of this study suggest that intense training prior to performance may increase the risk of injury, while older players appear to exhibit a lower risk of injury. These insights highlight the importance of tailored training strategies that consider both physiological factors and individual player profiles to mitigate injury risks in elite female volleyball athletes, including the presence or absence of menstruation with associated discomfort, which appears to be a relevant factor
StripesCounter: A new image software for increment measurement in paleoclimate archives
No full textInternational audienceMost natural paleoclimate archives are accretionary material presenting periodic structures that bear environmental and/or chronological information. Here we present StripesCounter, an open access Python software designed for automated banding detection and measurement. As a study case, 16-year long profiles of daily growth increment measurements were conducted on a modern shell of the giant clam Tridacna gigas. High resolution images of shell thin sections were obtained using a confocal laser scanning microscopy and processed using StripesCounter. We demonstrate that StripesCounter provides highly reproducible and accurate results. The long time series of daily increments indicate that Tridacna gigas growth is strongly modulated by seasonal oceanographic variations, reflecting changes in sea surface temperature, precipitation, and salinity. Notably, growth profiles reveal semi-annual variations related to semi-annual variations in environmental factors, potentially linked to ENSO events. This automated growth increment analysis can be extended to other archives with cyclic structures, including tree rings, corals, and other biogenic or abiotic laminated materials. StripesCounter offers a powerful and accessible tool for generating long high-resolution, temporally explicit datasets, opening new perspectives for investigating rapid environmental changes across diverse ecosystems and geological timescales
Towards sustainable water treatment: Green fabrication of core-shell photocatalysts supported on fiberglass textiles
No full textInternational audienceConfronted with the serious issue of pollution generated by discharges from the textile industry, laden with toxic organic compounds such as dyes, this study explores an innovative and ecological solution for sustainable water treatment. It focuses on the photocatalytic degradation of Safranin O, a particularly problematic dye, using copper oxide (CuO) nanoparticles immobilized on a glass fiber fabric. In a resource recovery approach, CuO nanoparticles were synthesized via a green method, using an extract from local plant leaves. To optimize the process, the study examines the influence of the calcination temperature of the nanoparticles and introduces a local clay as a support, forming a core-shell structure to improve the synergy between adsorption and photo-catalysis. These photocatalysts were rigorously characterized through various structural, microstructural, and optical techniques. Glass fibers were used as a support to immobilize the photocatalysts. Operational parameters, such as the photocatalyst loading (0.2 g/97.5 cm2 to 0.4 g/97.5 cm2), the initial pollutant concentration (14 to 30 mg/L), and the pH (5 to 10), were systematically investigated to determine the optimal degradation conditions. A non-linear model based on the Langmuir-Hinshelwood approach was developed to understand the photodegradation kinetics. Finally, the study explores the valorization of treated water for irrigation and evaluates the antibacterial activity of the nanoparticles, demonstrating the potential of this approach for sustainable and multifunctional polluted water treatment