Exploiter la diversité des enzymes natives pour la biocatalyse

International audienceThe use of enzymes for synthetic purposes typically relies on well-known or commercially available proteins, valued for their established properties. However, these enzymes may not always be ideal for specific reactions, prompting researchers to explore the vast diversity of enzymes within biodiversity. Genome and metagenome mining offers a rich reservoir of sequences, revealing novel enzymes with enhanced properties such as thermostability and substrate specificity, which are crucial for industrial applications. Advances in large-scale sequencing have exponentially increased available protein sequences, with over 2.4 billion reported in 2023 compared to 123 million in 2018. Despite its potential, enzyme discovery from metagenomic data remains challenging due to the immense volume of sequences. This necessitates innovative computational tools and bioinformatics workflows to streamline the identification of biocatalysts. Bioinformatics plays a pivotal role in predicting enzyme functions, analyzing protein superfamilies, and selecting key enzymes via biosynthetic gene clusters. Integration of artificial intelligence (AI) further enhances enzyme discovery and retrosynthetic pathway design, enabling the customization of enzymes for specific applications. Case studies from our laboratory illustrate the efficiency of genome mining and bioinformatics in discovering enzymes complementary to known ones, modifying metabolic reactions, and identifying novel scaffolds. These methods have expanded the diversity of enzymes available for synthesis, underscoring the importance of synergizing bioinformatics with biocatalysis to harness biodiversity and develop a versatile enzymatic toolbox.L’utilisation des enzymes en synthèse repose généralement sur des protéines connues ou commerciales, employées pour leurs propriétés bien établies. Cependant, ces enzymes ne sont pas toujours adaptées à des réactions spécifiques, incitant les chercheurs à rechercher des enzymes dans la biodiversité. Les (meta)génomes offrent un riche réservoir et leur exploration permet de révéler des enzymes avec de nouvelles propriétés, telles que la thermostabilité et la spécificité des substrats, essentielles pour les applications industrielles. Les progrès en séquençage à grande échelle ont multiplié de manière exponentielle les séquences disponibles, avec plus de 2,4 milliards répertoriées en 2023 contre 123 millions en 2018.Malgré son potentiel, la découverte d’enzymes à partir de données métagénomiques reste complexe en raison de l’immense volume de séquences. Cela nécessite des outils de calcul innovants et des séquences d’opérations bioinformatiques pour rationaliser l’identification des biocatalyseurs. La bioinformatique joue un rôle clé dans la prédiction des fonctions enzymatiques, l’analyse des superfamilles de protéines et la sélection d’enzymes via les clusters de gènes biosynthétiques. L’intégration de l’Intelligence Artificielle (IA) améliore encore la découverte d’enzymes et la conception de voies rétrosynthétiques, permettant d’adapter des enzymes pour des applications spécifiques.Des études de cas de notre laboratoire illustrent l’efficacité de l’exploration des génomes et de la bioinformatique pour découvrir de nouvelles enzymes, modifier des réactions métaboliques et identifier de nouvelles structures. Ces méthodes ont élargi la diversité des enzymes disponibles pour la synthèse, soulignant l’importance de la synergie entre bioinformatique et biocatalyse pour exploiter la biodiversité et développer une boîte à outils enzymatique polyvalente

Essais sur l'évaluation des politiques publiques de décarbonation du parc automobile

This thesis explores the major challenges of decarbonizing the transport sector, with a focus on the French automobile market. It examines the tensions between efficiency and equity, considering not only new vehicles but the entire vehicle fleet. Structured around three articles, this work evaluates existing environmental policies and proposes recommendations to accelerate the energy transition without exacerbating social inequalities The first article identifies households vulnerable to fuel price increases and shows that precisely targeted compensation can protect these households while reducing public expenditure. The second article addresses the decarbonization of the entire vehicle fleet, demonstrating that policies combining subsidies for new and used vehicles can achieve a significant share of the collective welfare gains offered by a pollution tax. The final article examines various ways to make electric vehicles affordable. It highlights the limitations of poorly targeted subsidies and recommends more effective policies, such as support for used electric vehicles and standards for the size of new electric vehicles.Cette thèse explore les défis majeurs de la décarbonation du secteur des transports, en mettant l'accent sur le marché automobile français. Elle examine les tensions entre efficacité et équité, en s'intéressant non seulement aux véhicules neufs mais aussi à l'ensemble du parc automobile. Ce travail, structuré autour de trois articles, évalue des politiques environnementales existantes et propose des recommandations pour accélérer la transition énergétique sans exacerber les inégalités sociales. Le premier article identifie les ménages vulnérables aux hausses des prix des carburants et montre qu'un ciblage précis des compensations peut protéger ces ménages tout en réduisant les dépenses publiques. Le deuxième article s'intéresse à la décarbonation du parc automobile dans son ensemble. Il démontre que des politiques combinant subventions sur les véhicules neufs et d'occasion peuvent générer une part significative des gains de bien-être collectif qu'une taxe sur la pollution offrirait. Le dernier article s'intéresse aux différents moyen de rendre les véhicules électriques abordables. Il met en évidence les limites des subventions mal ciblées, en recommandant des politiques adaptées comme les aides pour les véhicules électriques d'occasion ou des standards sur la taille des véhicules électriques neufs

Exploring digital responsibility enactment: an evaluation of the social implications of digitally responsive employee advocacy platform

FNEGE 3, ABS 3International audienceOrganizations are increasingly investing in employee advocacy (EA) programs and experimenting with digital workplace EA platforms that are designed to proactively offset negative wellbeing outcomes such as isolation and disengagement, by fostering proactive organizational citizenship behavior (OCB) like digital employee advocacy (DEA). Calls have been made to explore such digital responsibility enactments by evaluating the impact of the implied design intentions of such digitally responsive tools in promoting social and well-being implications on end-users. Using a bricolage approach, we evaluate user reviews of EA platforms to uncover overarching dimensions and theoretically construct a well-being-led grounded model for testing. The model links platforms-enabled social and psychological well-being to digital EA behavior through social and emotional attachment mechanisms. We generate lexical intensity values and use regression followed by expert interviews to validate the hypothesized model. Our findings advance the literature on digital responsibility enactment and conceptualize a crucial employee outcome, DEA

Leaky-Integrator Echo State Network Incremental ISS Stability Analysis

International audienceThis paper proposes a novel incremental input-to-state stability condition for a discrete-time leaky-integrator echo state network. The derived condition is further utilized for control design through Linear Matrix Inequalities (LMIs). The corresponding observer design LMI condition is also derived. A numerical simulation showcases the effectiveness of the proposed approach

Online multivariate changepoint detection: leveraging links with computational geometry

International audienceThe increasing volume of data streams poses significant computational challenges for detecting changepoints online. Likelihood-based methods are effective, but a naive sequential implementation becomes impractical online due to high computational costs. We develop an online algorithm that exactly calculates the likelihood ratio test for a single changepoint in p-dimensional data streams by leveraging a fascinating connection with computational geometry. This connection straightforwardly allows us to exactly recover sparse likelihood ratio statistics: that is assuming only a subset of the dimensions are changing. Our algorithm is straightforward, fast, and apparently quasi-linear. A dyadic variant of our algorithm is provably quasi-linear, being O(nlog(n)p+1) for n data points and p less than 3, but slower in practice. These algorithms are computationally impractical when p is larger than 5, and we provide an approximate algorithm suitable for such p which is O(nplog(n)p~+1), for some user-specified p~≤5. We derive statistical guarantees for the proposed procedures in the Gaussian case, and confirm the good computational and statistical performance, and usefulness, of the algorithms on both empirical data and NBA data

BOMO-RNN: a novel neural network controller for industrial robots with experimental validation

International audienceThis paper introduces the Beetle Olfactory-based Manipulability Optimizer Recurrent Neural Network (BOMO-RNN), an advanced RNN-based controller designed to enhance the manipulability of redundantly actuated industrial robotic arms. The manipulability index, which quantifies the maneuverability of the robotic arm, is crucial for avoiding kinematic singularities that restrict the mobility of robotic arm in the task space. The proposed approach formulates an optimisation problem using the penalty method to incorporate the manipulability index into the tracking control objective function. Unlike conventional approaches that rely on velocity-level control and require precise initialisation, BOMO-RNN operates at the position level, allowing direct trajectory tracking from arbitrary starting configurations, thereby increasing flexibility and ease of deployment. This function aims to maximise maneuverability while ensuring accurate tracking of the reference trajectory, effectively avoiding joint-space singularities. The BOMO-RNN framework leverages a metaheuristic optimisation strategy, enabling efficient exploration of high-dimensional search spaces without requiring explicit Jacobian pseudo-inversion, significantly reducing computational overhead and improving numerical stability. The BOMO-RNN algorithm efficiently addresses the time-varying optimisation problem at the position level, eliminating the need for computationally intensive Jacobian pseudo-inversion. This ensures robustness in real-world scenarios where high-speed control and adaptability to dynamic environments are critical. The algorithm's convergence is theoretically analysed, and its performance is validated through numerical simulations and experimental results using the LBR IIWA 7-DOF robot. Extensive experimental verification demonstrates the effectiveness of BOMO-RNN across diverse trajectory patterns, including circular, sinusoidal, and piecewise straight-line motions, confirming its generalizability and practical applicability. The results demonstrate BOMO-RNN's practical effectiveness in optimising manipulability and its potential for real-world robotic applications

AURA Blockchain : un consortium technologique dans le secteur du luxe

International audienceCommunication lors de la session 2.1. "GTAIM - Digitalisation de la supply chain" (jeudi 22, 14h00 - 16h00), présidée par Jennifer Lazzeri Gracia-Campo et Laurence Sagliett

How to differentiate truly stochastic gene expression from structured gene expression variability ?

International audienceGene expression is classically assessed using RNA-seq quantification. The aim of such experiments is often to compare gene expression levels across different biological conditions. Each condition is typically represented by multiple samples (replicates), which are often composed of pools of biological individuals to better estimate average gene expression levels.Pooling biological individuals can mask inter-individual variations that may nevertheless have biological significance such as observed in bethedging responses. In the case of bet-hedging, a diversified response can be observed between individuals at integrated phenotypic levels, underpinned by molecular regulations that also varies between individuals. However, molecular regulations should remain coherent and structured at the scale of the individual.</div

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Government decisions for relief materials reserve under supply uncertainty within government-enterprise agreement

International audiencePre-positioned inventory of relief materials is crucial for the rapid response to potential disasters. Storing relief materials near disaster-prone areas facilitates quick delivery after a disaster. Consequently, governments often collaborate with local enterprises and stores to store materials. However, the proximity to disaster sites can result in the damage of relief materials, exacerbating shortages, particularly during major disasters. Despite this, the literature has not thoroughly addressed how governments pre-position materials considering damage risk. Therefore, this paper constructs a model for the relief supply chains, including a local government, a local supplier and a non-local supplier to determine the government's optimal reserve strategy and quantity. The demand and damage to materials depend on disaster intensity. We derive the conditions under which a specific reserve strategy (no-external reserve, local reserve, non-local reserve, bi-site reserve) is optimal and corresponding reserve quantity. The impacts of supplier characteristics, government-owned reserves, emergency procurement price, disaster intensity distribution, demand-intensity and supply-intensity relationships on optimal prepositioned reserves are identified. Several management insights are drawn from extensive numerical experiments