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Rat model of X-linked myotubular myopathy with pathological signs in skeletal muscles and liver
No full textInternational audienc
Meeting Healthcare Security Standards: From Legal Requirements to Technical Implementation
No full textInternational audienceEnsuring the security of healthcare data continues to pose a significant challenge, particularly for professionals operating beyond the confines of institutional frameworks. Existing tools frequently prove inadequate in meeting the regulatory requirements and usability expectations of these practitioners. This paper undertakes a comprehensive analysis of the legal and normative framework governing the protection of sensitive medical information. The analysis culminates in the derivation of a set of essential criteria that any compliant and practical solution must fulfill. We then proceeded to evaluate several secure messaging and health-focused platforms, highlighting their limitations with respect to these criteria. To address these criteria, we introduce U-HealthSec, an architecture is designed to ensure strong data protection while remaining accessible to non-expert users. U-HealthSec integrates established cryptographic methodologies and security protocols with regulatory compliance, thereby addressing the operational and legal requirements of healthcare professionals in a comprehensive and scalable manner
The sTDIF signaling peptide modulates the root stele diameter and primary metabolism to accommodate symbiotic nodulation
No full textInternational audienceLegume plants form specific organs on their root system, the nitrogen-fixing nodules, thanks to a symbiotic interaction with soil bacteria collectively named rhizobia. Rhizobia, however, do not only induce the formation of these nodule organs but also modulate root system architecture. We identified in Medicago truncatula a previously unnoticed increase in the root stele diameter occurring upon rhizobium inoculation. This symbiotic root response, similarly observed in another crop legume, pea, occurs rapidly and locally after rhizobium inoculation, leading to an increased number of vascular cells. Interestingly, this root stele diameter symbiotic response requires tracheary element differentiation inhibitory factor (TDIF) signaling peptides and, notably, the MtCLE37TDIF-encoding gene whose expression is increased during nodulation, thus being referred to as symbiotic nodulation TDIF (sTDIF). Indeed, a cle37/stdif mutant is not responsive to rhizobium regarding its root stele diameter increase and has a reduced nodule number. Combined transcriptomic and metabolomic analyses revealed that stdif has a defective primary metabolism, notably affecting carbohydrate/sugar accumulation in both roots and nodules. Remarkably, a sucrose or a malate exogenous treatment is able to rescue the rhizobium-induced stele diameter symbiotic response in stdif. This metabolic deregulation is thus instrumental in explaining the altered symbiotic response of the mutant. Overall, this study highlights a novel function of TDIF signaling peptides in legumes plants, which, beyond regulating stele development, also modulates the root primary metabolism adaptations required for symbiotic nodule development
The money and Exchange market in Cádiz and Seville as seen from Antwerp archives (mid-17th century)
No full textInternational audienc
Unsupervised Parallel Physics Informed Neural Networks for Blood Flow Estimation and Cardiovascular Parameters Assessment ⋆
No full textThis study presents a comprehensive investigation into the estimation of blood flow and cardiovascular parameters using physics-informed neural networks (PINNs) coupled with ensemble learning techniques. Four distinct architectures were investigated and implemented, incorporating various Windkessel models. The Windkessel model is a cardiovascular model linking blood flow to blood pressure and is categorized as a lumped (0D) model. We explored various Windkessel models: the two-and three-element Windkessels, as well as their fractional versions, which include fractionalorder capacitors. A complete physics-informed deep-learning estimation framework is proposed, taking as input blood pressure signals and giving as output estimated blood flow and cardiovascular parameters. Through rigorous training and fine-tuning, robust convergence was achieved, enabling accurate estimation of blood flow and cardiovascular parameters. Using two datasets for validation with various data distributions and patients' clinical conditions, the results demonstrate consistent performance across various cardiac profiles. The transferability of the trained models for unseen data distribution was investigated. Particularly, the introduction of fractional order elements in the Windkessel models exhibited improved accuracy without increasing model complexity. The analysis of parameters' distributions and their physiological coherence further validated the reliability of the obtained estimations. Precisely, we conducted an in-depth study of the estimated peripheral vascular resistance and fractional order, comparing them to their expected values according to the literature. A comparative study between the proposed method and existing literature showed the efficiency of the proposed framework. Finally, the framework is validated using In-Vivo human data to demonstrate its effectiveness on real-world measurements.</div
Nicole Stadelmann, Mobile Ökonomien. Das Wirtschaften und Haushalten St. Galler Handwerkerfamilien in der Frühen Neuzeit, Göttingen (Wallstein) 2024
No full textInternational audienceNicole Stadelmann, Mobile Ökonomien. Das Wirtschaften und Haushalten St. Galler Handwerkerfamilien in der Frühen Neuzeit, Göttingen (Wallstein) 2024, 509 S., 58 z. T. farb. Abb. (Frühneuzeit-Forschungen, Peter Burschel, Renate Dürr, André Holenstein, Achim Landwehr, 25), ISBN 978-3-8353-5605-4, DOI 10.46500/83535605, EUR 56,00
The polytopal composite element method for finite strain hyperelastic problems
No full textInternational audiencePolygonal elements have emerged as a cutting-edge discretization paradigm in computational solid mechanics, demonstrating significant potential for linear elasticity analyses. This work pioneers a robust computational framework extending polytopal composite elements to finite-strain hyperelasticity. The key idea by constructing a polynomial projection using least squares approximation for linear-compatible strain fields, followed by extending the derived linear operator to large deformation cases involving nonlinear strain. The computational framework of this method is fundamentally consistent with finite elements, allowing it to adapt and extend to various nonlinear problems. Through several numerical investigation we show that this approach maintains the excellent accuracy, convergence and stability, and is potentially offering new insights and references for polygonal elements in future nonlinear problems
Infrastructures of democracy - CivicTech and AI in the reimagination of corporate democracy
No full textInternational audienceIn the context of a global democratic backslide, digital platforms have emerged as infrastructures for civic participation and deliberation. While tools like vTaiwan and Decidim have gained attention in the public sector, their adoption in organizational contexts remains largely unexplored. This paper examines why AI-supported deliberative platforms—designed initially for civic engagement—are being repurposed to promote democratic practices within firms. Drawing on a qualitative study of six French CivicTech companies, we analyze the political imaginaries, design choices, and discursive strategies of platform creators as they navigate the tensions between democratic aspirations and market demands.Grounded in pragmatist theories of democracy as a form of life (Dewey, Follett), we argue that these platforms are normatively charged infrastructures that embody competing visions of democracy. Based on rich qualitative data, we develop an emerging typology of postures among platform designers—ranging from explicit advocacy to strategic avoidance—and illustrate how algorithmic and interface choices reflect embedded normative commitments.While the empirical scope focuses on the supply side of these platforms, this study contributes to theorizing organizational democracy as possibly shaped through technical design. It enriches ongoing debates on organizational democracy, digital governance, and the politics of technological mediation in the workplace
Finite Time Robust Flocking of Second-Order Linear Agents
No full textInternational audienceIn this paper, we propose a novel control strategy inspired by sliding mode principles, designed to drive a set of second-order linear agents into a lattice configuration in finite time, thereby satisfying Reynolds' flocking rules. Moreover, we demonstrate that the control law is robust against a class of external disturbances, ensuring stable flocking behavior even in the presence of uncertainties. To validate our theoretical results, we present several simulation scenarios that confirm both the effectiveness and robustness of the proposed approach