Portail HAL U-Bordeaux
Not a member yet
125366 research outputs found
Sort by
Working hours, a mirror of the specificities of home care in France
No full textInternational audienceWorking time in the French healthcare sector is a patchwork of derogations. All care and support workers, whether they work in institutions or at home, are subject to extensive and irregular working hours, with rest periods that are often inadequate, which increases the risk of accidents at work and exhaustion (tiredness). Home care and support workers are subject to these time constraints tenfold, partly because of the characteristics of the location, which corresponds to a number of private homes located in a more or less extensive geographical area, and partly because of the dual role of beneficiary/employer. The working hours of homecare workers are one of the main reasons why this professional activity is considered to be one of the most back-breaking, least qualified and least remunerated, because it remains discredited despite its ephemeral link with essential workers during the Covid pandemic, and because it is indelibly associated with gender prejudice. With one exception, adjustments to working hours are never the result of the wishes of the worker, but of imposed constraints, which create tensions with the rights of non-working time workers, whose effective legal and conventional protection comes up against two structural problems: the funding of care and the shortage of labour
The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering *
No full textInternational audienceObjective. Machine learning’s (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the validity and reproducibility of ML methods is critical. Unfortunately, this has not always been the case in practice, as there have been recent retractions across various scientific fields due to the misuse of ML methods and validation procedures. To address these concerns, we propose the first version of the neural engineering reproducibility and validity essentials for ML (NERVE-ML) checklist, a framework designed to promote the transparent, reproducible, and valid application of ML in neural engineering. Approach. We highlight some of the unique challenges of model validation in neural engineering, including the difficulties from limited subject numbers, repeated or non-independent samples, and high subject heterogeneity. Through detailed case studies, we demonstrate how different validation approaches can lead to divergent scientific conclusions, highlighting the importance of selecting appropriate procedures guided by the NERVE-ML checklist. Effectively addressing these challenges and properly scoping scientific conclusions will ensure that ML contributes to, rather than hinders, progress in neural engineering. Main results. Our case studies demonstrate that improper validation approaches can result in flawed studies or overclaimed scientific conclusions, complicating the scientific discourse. The NERVE-ML checklist effectively addresses these concerns by providing guidelines to ensure that ML approaches in neural engineering are reproducible and lead to valid scientific conclusions. Significance. By effectively addressing these challenges and properly scoping scientific conclusions guided by the NERVE-ML checklist, we aim to help pave the way for a future where ML reliably enhances the quality and impact of neural engineering research
Physiological Single trial decoding of art interest
No full textInternational audienceBoth well-being and aesthetic experience are correlated with physiology. Thus, optimizing art presentation to evoke desirable mental states in virtual environments based on physiological states could have beneficial effects on user experience and well-being. However, single trial aesthetic experience decoding from physiological signals has not been well studied. We tested a Support Vector Machine classifier with cardiac and electrodermal features to decode art interest. Although, average performance was poor (54%), the model yielded high accuracy beyond chance level for a few participants. This shows that art interest can, at least for some individuals, be decoded from single trial physiological data
Experimental evidence of reduced Ips typographus damage in mixed spruce plantations
No full textInternational audienceDue to ongoing climate change, outbreaks of the spruce bark beetle, Ips typographus, have caused massive tree mortality across Europe. As the forests most often damaged are pure spruce plantations, the question has arisen as to whether increasing tree species diversity could improve the resistance of spruce forests to the damage caused by these bark beetles. We took advantage of a spruce bark beetle infestation in a tree diversity experiment in Germany established in 2003, where spruce trees were planted in pure or mixed plots, with one to three other tree species in a substitutive design, to test this hypothesis of associational resistance. Using aerial images, we retrospectively counted the number of spruces killed in all pure and mixed spruce plots from 2019 to 2023 and monitored new colonization by I. typographus in 2024 using pheromone traps. Bark beetle damage decreased significantly when the proportion of spruce trees in mixed plots was lower, a consequence of greater tree species richness. The damage and colonisation by bark beetles decreased even more when taller heterospecific neighbours, in particular Douglas firs, overshadowed the spruces, which probably reduced their visual and chemical apparency. This associational resistance likely stems from a combination of reduced host tree availability and release of non-host volatiles, causing disruption to the localization of the hosts by the beetles. These results confirm that mixing tree species can help prevent forest insect damage and give an insight into the species composition of more resistant mixed spruce plantations, particularly with the association of other fast-growing species
ClimLoco1.0: CLimate variable confidence Interval of Multivariate Linear Observational COnstraint
No full textInternational audienceProjections of future climate are key to society's adaptation and mitigation plans in response to climate change. Numerical climate models provide projections, but the large dispersion between them makes future climate very uncertain. To refine them, approaches called observational constraints (OCs) have been developed. They constrain an ensemble of climate projections using some real-world observations. However, there are many difficulties in dealing with the large literature on OC: the methods are diverse, the mathematical formulation and underlying assumptions are not always clear, and the methods are often limited to the use of the observations of only one variable. To address these challenges, this article proposes a new statistical model called ClimLoco1.0, which stands for “CLimate variable confidence Interval of Multivariate Linear Observational COnstraint”. It describes, in a rigorous way, the confidence interval of a projected variable (its best guess associated with an uncertainty at a confidence level) obtained using a multivariate linear OC. The article is built up in increasing complexity by expressing three different cases – the last one being ClimLoco1.0, the confidence interval of a projected variable: unconstrained, constrained by multiple real-world observations assumed to be noiseless, and constrained by multiple real-world observations assumed to be noisy. ClimLoco1.0 thus accounts for observational noise (instrumental error and climate-internal variability), which is sometimes neglected in the literature but is important as it reduces the impact of the OC. Furthermore, ClimLoco1.0 accounts for uncertainty rigorously by taking into account the quality of the estimators, which depends, for example, on the number of climate models considered. In addition to providing an interpretation of the mathematical results, this article proposes graphical interpretations based on synthetic data. ClimLoco1.0 is compared to some methods from the literature at the end of the article and is used in a real case study in the appendix
The French case of “restitutions” : imaginaries and literature of a contested heritage
No full textInternational audienc
New insights into the viruses responsible for sugar beet yellows
No full textProtecting sugar beets without of neonicotinoidsIn 2023, FranceAgriMer launched a call for projects aimed at gathering proposals for combating beet yellows, submitted by consortia made up of research and transfer stakeholders, as part of the ongoing National Research and Innovation Plan (PNRI) to combat beet yellows. This Plan aims to find solutions to protect sugar beet crops following the withdrawal of neonicotinoids. Thanks to the joint work of fundamental and applied research, possibilities are emerging, both through prophylaxis and the identification of new curative levers. These levers, combined with each other, could limit the impact of yellowing on beets. From the identification of vector reservoirs to tested biocontrol solutions and the quantification of their benefits and limitations, this issue of the journal outlines the steps needed to ensure satisfactory and sustainable production following the withdrawal of neonicotinoids, thereby ensuring the long-term viability of this industrial sector.International audienceSugar beet leaf yellowing is caused by four viruses, all transmitted by aphids, but belonging to different families which display different biological traits. The modes of transmission vary according to the virus, but all are transmitted by the species Myzus persicae. The Provibe project was intended to acquire knowledge on the biology of the four viruses and their transmission by aphids. Within this project it was confirmed that no additional viruses are involved in the disease. Multi-infections, where plants carry more than one virus, are frequent although their frequency changes from year to year. These combined infections are do not reduce sugar beet yield any more than single infections. However, the coexistence of several viruses within the same plant can have repercussions on their transmissibility by M. persicae. Analysis of viral sequences in both symptomatic and asymptomatic plants did not reveal any candidates that could be used to develop a cross-protection strategy
Seed2LP : inférence de graines dans les réseaux métaboliques pour des applications d'écologie inverse
No full textNational audienceBackground: A challenging problem in microbiology is to determine nutritional requirements of microorganisms and culture them, especially for the microbial dark matter detected solely with culture-independent methods. The latter foster an increasing amount of genomic sequences that can be explored with reverse ecology approaches to raise hypotheses on the corresponding populations. Building upon genome scale metabolic networks (GSMNs) obtained from genome annotations, metabolic models predict contextualised phenotypes using nutrient information. Results: We developed the tool Seed2LP, addressing the inverse problem of predicting source nutrients, or seeds, from a GSMN and a metabolic objective. The originality of Seed2LP is its hybrid model, combining a scalable and discrete Boolean approximation of metabolic activity, with the numerically accurate flux balance analysis (FBA). Seed inference is highly customisable, with multiple search and solving modes, exploring the search space of external and internal metabolites combinations. Application to a benchmark of 107 curated GSMNs highlights the usefulness of a logic modelling method over a graph-based approach to predict seeds, and the relevance of hybrid solving to satisfy FBA constraints. An extension to communities of microorganisms is currently in development. Considering a community of networks increases the possibility of seed compounds, but also add a new component to the problem, defined by the transfers of nutrients between networks, increasing further the combinatorics of the problem. Conclusions : Focusing on the dependency between metabolism and environment, Seed2LP is a computational support contributing to address the multifactorial challenge of culturing possibly uncultured microorganisms. Seed2LP is available on https://github.com/bioasp/seed2lpContexte : L'un des défis majeurs en microbiologie consiste à déterminer les besoins nutritionnels des micro-organismes et à les cultiver, en particulier pour la matière noire microbienne détectée uniquement à l'aide de méthodes indépendantes de la culture. Ces dernières favorisent une augmentation du nombre de séquences génomiques pouvant être explorées à l'aide d'approches d'écologie inverse afin d'émettre des hypothèses sur les populations correspondantes. S'appuyant sur les réseaux métaboliques à l'échelle du génome (GSMN) obtenus à partir d'annotations génomiques, les modèles métaboliques prédisent des phénotypes contextualisés à l'aide d'informations sur les nutriments. Résultats : Nous avons développé l'outil Seed2LP, qui traite le problème inverse consistant à prédire les nutriments sources, ou graines, à partir d'un GSMN et d'un objectif métabolique. L'originalité de Seed2LP réside dans son modèle hybride, qui combine une approximation booléenne évolutive et discrète de l'activité métabolique avec l'analyse d'équilibre des flux (FBA) numériquement précise. L'inférence des graines est hautement personnalisable, avec plusieurs modes de recherche et de résolution, explorant l'espace de recherche des combinaisons de métabolites externes et internes. Son application à un benchmark de 107 GSMN sélectionnés met en évidence l'utilité d'une méthode de modélisation logique par rapport à une approche basée sur des graphes pour prédire les graines, ainsi que la pertinence de la résolution hybride pour satisfaire les contraintes FBA. Une extension aux communautés de micro-organismes est actuellement en cours de développement. La prise en compte d'une communauté de réseaux augmente le nombre de composés graines possibles, mais ajoute également une nouvelle composante au problème, définie par les transferts de nutriments entre les réseaux, ce qui augmente encore la combinatoire du problème. Conclusions : En se concentrant sur la dépendance entre le métabolisme et l'environnement, Seed2LP est un support informatique qui contribue à relever le défi multifactoriel de la culture de micro-organismes potentiellement non cultivables. Seed2LP est disponible sur https://github.com/bioasp/seed2l
