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ACTB deletions or single-nucleotide loss-of-function variants: expansion and further delineation of the phenotype and review of the literature
No full textInternational audienceBackground Pathogenic gain-of-function or dominant-negative effect missense variations in ACTB are associated with a neurodevelopmental disorder characterised by intellectual disability (ID), seizures, sensorineural hearing loss, cerebral, renal and ocular abnormalities and dysmorphic features (Baraitser-Winter cerebrofrontofacial syndrome). ACTB encodes beta-actin, a highly conserved protein involved in cell motility, structure and integrity. Deletions including ACTB, and, more rarely, single-nucleotide loss-of-function variants in ACTB have been described in patients with a distinct phenotype including developmental delay, ID, microcephaly, growth restriction, cardiac and renal abnormalities and dysmorphic features. Methods We collected 14 individuals and 1 fetus carrying a heterozygous deletion including ACTB , and 4 individuals with a heterozygous truncating variant. Genotypic and phenotypic data were analysed. Furthermore, a comprehensive review of all cases reported to date was also undertaken. Results Twelve out of 17 individuals presented with ID, and 3 out of 17 with learning disabilities. Speech delay and behavioural abnormalities were observed in 15 out of 17 and 12 out of 17 individuals, respectively, motor delay in 9 out of 17 and growth restriction in 9 out of 18. Most of the individuals (13/18) had recognisable dysmorphic features. 11 anomalies were de novo, except for 1 deletion inherited from the mother. The size of the deletion varied from 125 kb to 1.6 Mb and could result from a fork stalling and template switching. Conclusion This study allowed us to better characterise the phenotype associated with the haploinsufficiency of ACTB, underlying the high prevalence of neurodevelopmental disorders (ID, speech and motor delay, behavioural abnormalities) and growth restriction in this recognisable syndrome
Path-conditioned training: a principled way to rescale ReLU neural networks
No full textDespite recent algorithmic advances, we still lack principled ways to leverage the well-documented rescaling symmetries in ReLU neural network parameters. While two properly rescaled weights implement the same function, the training dynamics can be dramatically different. To offer a fresh perspective on exploiting this phenomenon, we build on the recent path-lifting framework, which provides a compact factorization of ReLU networks. We introduce a geometrically motivated criterion to rescale neural network parameters which minimization leads to a conditioning strategy that aligns a kernel in the path-lifting space with a chosen reference. We derive an efficient algorithm to perform this alignment. In the context of random network initialization, we analyze how the architecture and the initialization scale jointly impact the output of the proposed method. Numerical experiments illustrate its potential to speed up training
Discrétisation de milieux multicouches à forts contrastes : qu'en est-il des interfaces ?
No full textWave propagation in multilayered media with high material contrasts poses significant numerical challenges, as large variations in wavenumbers lead to strong reflections and complex transmission of the incoming wave field. To address these difficulties, we employ a boundary integral formulation thereby avoiding volumetric discretization. In this framework, the accuracy of the numerical solution depends strongly on how the material interfaces are discretized. In this work, we demonstrate that standard meshing strategies based on resolving the maximum wavenumber across the domain become computationally inefficient in multilayered configurations, where high wavenumbers are confined to localized subdomains. Through a systematic study of multilayer transmission problems, we show that no simple discretization rule based on the maximum wavenumber or material contrasts emerges. Instead, the wavenumber of the background (exterior) medium plays a dominant role in determining the optimal boundary resolution. Building on these insights, we propose an adaptive approach that achieves uniform accuracy and efficient computation across multiple layers. Numerical experiments for a range of multilayer configurations demonstrate the scalability and robustness of the proposed approach
DBS-related infections in Parkinson's disease: Incidence, risk factors, management and outcome
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Risques sanitaires liés à l’épandage de digestats issus de matières organiques émergentes sur le sol et la phyllosphère en systèmes prairiaux
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ON THE FILTERED SPECTRAL ABSCISSA FOR DELAY-DIFFERENCE EQUATIONS AND ITS ROLE IN THE BOUNDARY CONTROL OF HYPERBOLIC PDES
No full textFeedback control systems governed by delay-differential equations of neutral type may be fragile, in the sense that arbitrarily small parametric perturbations and implementation errors of the control may destroy the exponential stability of the target closed-loop system. Such instability problems can be resolved by including a low-pass filter in the control loop, on the condition that the filter itself is not destabilizing. The analysis of this condition for delay-difference equations has recently led to the notion of filtered spectral abscissa. In this paper, we first analyze the filtered spectral abscissa, where we remove the stringent condition of commensurate delays previously made, and we derive novel mathematical and computationally tractable characterizations. Second, we highlight the role of the filtered spectral abscissa in the context of boundary control of first-order hyperbolic partial differential equations, grounded in integral transformations that result in delaydifference equation models with both discrete and distributed delays. In particular, we show that in the situation where reflection terms cannot be robustly canceled out by the control -that is, their direct cancellation would lead to a fragile closed-loop system -a negative filtered spectral abscissa of the delay-difference equation, obtained by removing the distributed delay terms, is the necessary and sufficient condition -in addition to the exponential stability of the target closed-loop system -for the safe inclusion of a filter with sufficiently high cut-off
Des réseaux de neurones sur graphes auto-explicatifs basés sur la logique
No full textInternational audienceLes graphes sont des structures complexes et non euclidiennes qui nécessitent des modèles spécialisés comme les réseaux de neurones sur graphes (Graph Neural Networks, GNNs) pour capturer efficacement les motifs relationnels associés à la variable de classe. Cette complexité intrinsèque rend particulièrement difficile l’explication des décisions prises par les GNNs. La plupart des méthodes actuelles d’intelligence artificielle explicable (XAI) appliquées aux GNNs se concentrent sur l’identification de nœuds influents ou l’extraction de sous-graphes pertinents, sans toutefois clarifier comment ces éléments contribuent réellement à la prédiction finale. Pour dépasser cette limite, les approches à base logique visent à dériver des règles explicites reflétant le raisonnement du modèle. Cependant, les méthodes logiques existantes demeurent majoritairement post-hoc et se limitent à la classification de graphes, laissant un manque important en matière d’architectures intrinsèquement explicables. Dans cet article, nous intégrons le raisonnement logique directement au sein du modèle d’apprentissage sur graphes. Nous introduisons LogiX-GIN, une nouvelle architecture de GNN auto- explicable qui incorpore des couches logiques afin de produire des règles logiques interprétables au cœur même du processus d’apprentissage. Contrairement aux approches post-hoc, LogiX-GIN fournit des explications transparentes, fidèles et cohérentes avec les calculs internes du modèle. Évalué sur plusieurs tâches basées sur des graphes, LogiX-GIN atteint des performances prédictives compétitives tout en explicitant son processus décisionnel. Ces travaux ont été acceptés à NeurIPS 202
A complete answer to the strong density problem in Sobolev spaces with values into compact manifolds
No full textAnalysis of Corona and Surface Discharge Signals from Different Non-Intrusive Sensors under HVDC
No full textInternational audienceThe use of high voltage direct current (HVDC) transmission technology has increased significantly in recent years due to its numerous advantages over traditional alternating current (AC) transmission. The aim of the present work is to investigate the efficiency of different non-intrusive sensors for partial discharge measurement in medium or high voltage direct current equipment such as air or gas insulated switchgear. In HVAC systems, partial discharge detection and quantification are well understood. However, under HVDC, it is a necessary to evaluate the efficiency of classic techniques and commercial non-intrusive sensors. This work presents a study of three different sensors for partial discharge measurement under HVDC: high frequency current transformer (HFCT), ultra-high frequency antenna (UHF), and transient earth voltage (TEV). The signals of the different sensors for corona and surface discharge detection are presented and compared in both temporal and frequency domains. The Pulse Sequence Analysis (PSA) technique is used for apparent charge and repetition rate study and the effect of the discharge type on the PSA patterns is investigated. The results show that the PSA patterns are sensitive to the discharge type and conditions. The increase of the applied voltage of corona leads to the increase of the apparent charge differences and the time duration between successive pulses. The spectrums of the discharge pulses of the three sensors are different and the maxima of the TEV and UHF PD signals are dependent on the apparent charge