Portail HAL de l’Université Claude Bernard Lyon
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Functional positioning in robotic lateral unicompartmental knee arthroplasty: a step-by-step technique
No full textInternational audienceLateral unicompartmental knee arthroplasty (UKA) represents 1–2% of knee replacement procedures, yet offers distinct advantages including reduced surgical burden, bone stock preservation, and faster functional recovery. However, lateral UKA presents unique technical difficulties due to the surgical complexity of the lateral compartment. Recent advances in image-based robotic systems have demonstrated improved accuracy in implant positioning and promoted more individualized surgical strategies. This article presents a step-by-step surgical technique for lateral UKA using Functional Positioning (FP) principles in combination with an image-based robotic system. The technique ensures precise preoperative planning based on CT imaging, real-time intraoperative kinematic evaluation, and accurate component placement tailored to individual patient anatomy. The key steps of this surgical technique include comprehensive preoperative planning with 3D anatomical modeling, intraoperative kinematic evaluation following osteophyte removal, achieving centered femorotibial contact points throughout the full range of motion with precise lateral laxity gap boundaries, and cartilage mapping to ensure optimal component positioning and avoid overstuffing. FP addresses the characteristic posterior cartilage wear pattern of valgus knees while preserving pre-arthritic coronal alignment and avoiding varus overcorrection. This systematic approach demonstrates reproducible surgical steps that may translate into improved long-term outcomes and implant survivorship for lateral UKA procedures
De la co-construction à la co-évolution entre acteurs humains et IA au sein du cycle de personnalisation des EIAH
No full textThis manuscript presents my main contributions in the field of Technology-Enhanced Learning (TEL), and more specifically in the use of Artificial Intelligence (AI) for personalized learning, closely aligned with learners’ needs and teachers’ constraints. Within this context, I address two major themes.First, I propose a Knowledge Engineering-based approach to support teachers in the co-construction of personalized learning environments. This includes the development of meta-models for modeling pedagogical knowledge and designing authoring tools, as well as the integration of these models into adapted environ-ments. The aim is not to create automated systems that replace teachers, but rather adaptable tools aligned with their practices, enabling the generation and recommendation of learning activities tailored to learner profiles and specific pedagogical constraints. In recent years, these models and tools have been furtherdeveloped to support the implementation of Competency-Based Education and to fully leverage its potential within the personalization cycle of TEL.Second, I explore approaches based on the collection and analysis of learning traces, drawing on Knowledge Engineering, as well as Data Mining and Machine Learning techniques. This has led to the development of platforms that, on the one hand, allow learning data analysis without requiring technical expertise, and, on the other, facilitate the capitalization and sharing of such analyses.This work has also resulted in the design of recommender systems leveraging competency models, integrating both top-down approaches (expert knowledge) and bottom-up approaches (data-driven discovery). Furthermore, analyzing the operational traces of AI engines has enabled the proposal of initial mechanismsfor explainable AI in these systems.Building on these contributions, developed within national and international projects, a research agenda is also presented. It focuses on the following directions : the design of rich and adapted explainable AI mechanisms within the personalization cycle, the development of hybrid AI to detect and leverage learners’ sense of competence, the co-evolution of the Human-AI relationship within TEL, and the enrichment of our models through the paradigm of active learning in AI. These perspectives aim to better integrate AI capabilities into TEL while ensuring their alignment with the pedagogical and human realities of educational stakeholders.Ce manuscrit présente nos principales contributions dans le domaine des Environnements Informatiques pour l’Apprentissage Humain (EIAH), et plus particulièrement dans l’exploitation de l'Intelligence Artificielle (IA) pour la personnalisation de l’apprentissage, en lien étroit avec les besoins des apprenants et les contraintes des enseignants. Dans ce cadre, nous avons abordé deux grandes thématiques.Premièrement, nous avons proposé une approche fondée sur l’Ingénierie des Connaissances pour accompagner les enseignants dans la co-construction d'environnements personnalisés d’apprentissage. Cela comprend l’élaboration de méta-modèles exploités dans la modélisation des connaissances pédagogiques et dans la conception d’outils auteurs, ainsi que l’intégration de ces modèles dans des environnements adaptés. L’objectif est de proposer non pas des systèmes automatiques remplaçant l’enseignant, mais des outils adaptables à leurs pratiques, permettant de générer et recommander des activités pédagogiques en fonction des profils apprenants et des contraintes pédagogiques spécifiques. Ces modèles et outils ont été enrichis ces dernières années pour favoriser la mise en œuvre de l'Approche par Compétences dans l'enseignement et en exploiter toute la richesse lors du cycle de personnalisation des EIAH.Deuxièmement, nous avons exploré des approches fondées sur la collecte et l’analyse des traces d’apprentissage, en nous appuyant de même sur l'ingénierie des connaissances, mais également sur des techniques de fouille de données et d'apprentissage machine. Cela a permis de proposer des plateformes permettant, d'une part, l'analyse des données d'apprentissage sans expertise technique, et d'autre part, la capitalisation et le partage de ces analyses. Ces travaux ont également mené à la construction de systèmes de recommandations exploitant des modèles de compétences, en intégrant à la fois des approches top-down (connaissances expertes) et bottom-up (découverte à partir des données). Enfin, l'exploitation des traces de fonctionnement de nos moteurs d'IA nous a permis de proposer des premiers mécanismes d'IA explicables pour ces moteurs.À partir de ces contributions développées dans le cadre de projets nationaux et internationaux, un plan de recherche est également présenté. Il s’articule autour des axes suivants : la proposition de mécanismes d'IA explicables riches et adaptés au sein du cycle de personnalisation, la construction d'une IA hybride pour détecter et exploiter le sentiment de compétences chez les apprenants, la co-évolution du couple Humain-IA au sein des EIAH, et enfin l'enrichissement de nos modèles via le paradigme d'IA d'apprentissage actif. Ces perspectives visent à mieux intégrer les capacités de l’IA dans les EIAH tout en garantissant leur adéquation aux réalités pédagogiques et humaines des acteurs de l’éducation
Role of surface oxidation in enhancing heat transfer across graphene–water interface via Thermal Boundary Resistance modulation
No full textInternational audienceFunctionalization or surface oxidation is a fundamental requirement for carbon-based nanoparticles to prevent self-aggregation and thus be homogeneously dispersed in a fluid. However, the presence of functional or oxidation groups dramatically affects the thermal boundary resistance (TBR) and thus the overall thermal properties of the resulting colloidal suspension. In this work, we systematically investigate through molecular dynamics simulations the effect of oxidation degree on the TBR at the graphene–water interface. We find a linear correlation between the oxidation degree and the thermal boundary conductance (reciprocal of TBR) at low-to-moderate degrees, which can be interpreted through a parallel thermal resistance model, considering the contributions of pristine graphene and hydroxyl (-OH) groups, confirming our previous experimental findings. Results are interpreted in the light of wettability, roughness and phonon density of states, which highlight the higher affinity between water and graphene as the oxidation degree rises. More generally, beyond the specific case study discussed in this work, this systematic approach can be applied to other solid–liquid interfaces to further explore the general correlation between TBR and surface oxidation degree
Automatic Physically-Based Sim2Real for Tactile Images through Differentiable Path-Tracing Rendering
No full textInternational audienceHigh-fidelity simulation of vision-based tactile sensors is essential for developing data-driven robotic manipulation algorithms. However, a significant sim-to-real gap persists due to the difficulty in modeling complex optical effects, such as refraction through protective glass layers, and in accurately estimating physical parameters like sensor pose and lighting. To bridge this gap, we introduce a novel, fully differentiable pipeline for visual tactile simulation. Leveraging a differentiable path tracer, our method optimizes critical parameters—including camera pose, lighting conditions, and object texture—directly from just three real images. This approach achieves highly realistic simulations with physically accurate light transport and glass refraction. We validate our method through a comprehensive benchmark against real-world data, demonstrating state-of-the-art sim-to-real accuracy. We also enable novel applications, such as mesh reconstruction from a single tactile image via inverse rendering. To overcome the computational cost of path tracing, we further use a image-to-image translation model. This model uses high-fidelity simulated data alongside Normalized Object Coordinate Space (NOCS) maps as input, preserving crucial deformation information while enabling rapid inference
MITIGATION OF NON-SYNCHRONOUS VIBRATION WITH GEOMETRIC MISTUNING: EXPERIMENTAL INVESTIGATION OF LEADING-EDGE MODIFICATION ON THE OPEN TEST CASE ECL5
No full textThe mitigation of Non-Synchronous Vibration (NSV) is essential to extend the operational range of fans and compressors in aircraft engines. Near the stability limit at part-speed conditions, increased tip blockage enables circumferential convection of aerodynamic disturbances, leading to coupled blade vibrations known as lock-in. Previous studies have shown that local variations in blade geometry influence the propagation of these disturbances. In particular, asymmetric tip clearance affects their speed and intensity, potentially altering the dominant flow wave numbers and the corresponding structural nodal diameters involved in lock-in.In earlier work, non-uniform tip clearance was found to significantly influence disturbance formation, though it did not provide sufficient NSV suppression. To enhance the local aerodynamic effect, this study investigates geometric modifications of the blade leading edge.A numerical investigation using unsteady RANS simulations is conducted on the open test case geometry ECL5 to assess the influence of leading-edge modifications on disturbance propagation. The study also examines variations in the mistuning pattern, defined by the circumferential distribution of cut blades. Selected geometrically mistuned configurations were experimentally tested at Centrale Lyon. This paper presents both numerical and experimental results, providing a detailed characterization of the effects of geometric modifications on local aerodynamics and fluid-structure interactions relevant to NSV behavior
Chemical composition, antibacterial activity and antibacterial mechanism of Kesum (Persicaria minus Huds.) essential oil
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GWTC-4.0: Updating the Gravitational-Wave Transient Catalog with Observations from the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run
No full textInternational audienceVersion 4.0 of the Gravitational-Wave Transient Catalog (GWTC-4.0) adds new candidates detected by the LIGO, Virgo, and KAGRA observatories through the first part of the fourth observing run (O4a: 2023 May 24 15:00:00 to 2024 January 16 16:00:00 UTC) and a preceding engineering run. In this new data, we find 128 new compact binary coalescence candidates that are identified by at least one of our search algorithms with a probability of astrophysical origin and that are not vetoed during event validation. We also provide detailed source property measurements for 86 of these that have a false alarm rate < 1 \rm{yr}^{-1}. Based on the inferred component masses, these new candidates are consistent with signals from binary black holes and neutron star-black hole binaries (GW230518_125908 and GW230529_181500). Median inferred component masses of binary black holes in the catalog now range from (GW230627_015337) to (GW231123_135430), while GW231123_135430 was probably produced by the most massive binary observed in the catalog. For the first time we have discovered binary black hole signals with network signal-to-noise ratio exceeding 30, GW230814_230901 and GW231226_01520, enabling high-fidelity studies of the waveforms and astrophysical properties of these systems. Combined with the 90 candidates included in GWTC-3.0, the catalog now contains 218 candidates with and not otherwise vetoed, doubling the size of the catalog and further opening our view of the gravitational-wave Universe
Digestive and bariatric surgery, digestive endoscopy and interventional radiology
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First characterization of insertions in the HIV-1 integrase-coding region from people with HIV
No full textInternational audienceAbstract Objectives This study aimed to gain a better understanding of the role of insertional mutations in the integrase (IN)-coding sequence of 13 HIV-1-infected people. Results Here, we present the first documentation of amino acid insertion in the IN-coding sequence of HIV-1-infected people at positions 168, 253, 255 and 260. The consequences of these mutations in terms of viral replication and resistance to INSTIs were analysed using virological and biochemical assays and 3D modelling. Analysis of viral genomes by quantitative PCR demonstrated that insertional mutations reduce reverse transcription efficiency and had different impacts on viral integration. Taken together, we showed that mutants were delayed in their replication. Virological assays using two potent strand-transfer inhibitors (INSTIs), raltegravir and dolutegravir, demonstrated that no resistance to INSTIs was observed. Conclusions Our study has revealed that the mutations observed in people living with HIV-1 do not confer resistance to anti-integrase compounds but are detrimental to viral replication
Extended Fused Carbazole‐BODIPY, High Brightness NIR Organic Dyes
No full textInternational audienceFluorescence‐based bioimaging enables noninvasive visualization of molecular and cellular processes with high sensitivity and without ionizing radiation. However, conventional fluorophores emitting in the visible or near‐red infrared I (NIR‐I) (650–800 nm) regions suffer from limited tissue penetration and scattering. Extending fluorescence emission into the deeper NIR region represents a promising strategy to overcome these drawbacks, yet achieving high brightness and stability in organic dyes remains a major challenge. We report an original family of hetero‐substituted‐fused boron‐dipyrromethene (BODIPY) dyes bearing carbazole and thienyl donors that exhibit record brightness and emission maxima up to 852 nm in toluene. The synthetic route combines successive Stille couplings from a 2,6‐dibromo‐3,5‐diiodo‐BODIPY precursor and an unprecedented silver(I)‐mediated oxidative cyclization, affording high yields and suppressing undesired chlorination. The resulting dyes display intense absorption ( ε = 1.8–2.5 × 10 5 M − 1 cm − 1 ) and exceptional fluorescence quantum yields ( Φ up to 0.73). Encapsulation in silica nanoparticles (NPs) preserves their photophysical properties and enables efficient NIR‐II in vivo imaging in mice, allowing tumor detection at doses as low as 0.2 nmol with tumor‐to‐muscle ratios > 4. These fused BODIPY derivatives rank among the brightest NIR fluorophores reported to date and open new avenues for high‐contrast deep‐tissue imaging and image‐guided surgery