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Search for the radiative Xi(-)(b) -> Xi(-)gamma decay
No full textThe first search for the rare radiative decay Xi(-)(b) -> Xi(-)gamma is performed using data collected by the LHCb experiment in proton-proton collisions at a center-of-mass energy of 13TeV, corresponding to an integrated luminosity of 5.4 fb(-1). The Xi(-)(b) -> Xi(-)-J/ psi channel is used as normalization. No Xi(-)(b) -> Xi(-)gamma signal is found and an upper limit of B(Xi(-)(b) -> Xi(-)gamma) < 1.3 x 10(-4) at 95% confidence level is obtained.LPH
An artificial intelligence-powered learning health system to improve sepsis detection and quality of care: a before-and-after study
No full textSepsis is a major global health crisis where early recognition and effective management remain significant challenges for healthcare systems. As part of the Lausanne University Hospital sepsis quality of care program, we developed and validated an Artificial Intelligence (AI)-powered Sepsis Learning Health System (SLHS) to enhance sepsis care. The SLHS combines a standardized clinical pathway with HERACLES, an AI algorithm that retrospectively classifies patient data into confirmed, possible, or invalidated sepsis cases every 6 h. Predictions inform dynamic dashboards displaying quality-of-care indicators to guide clinical interventions. Analysis of 97,559 stays in wards using the SLHS and 25,851 stays in control wards showed that in-hospital and 90-day mortality decreased for HERACLES-flagged sepsis in SLHS wards, while control wards did not. Further, sepsis coding increased in SLHS wards but did not change in control wards. This real-world example demonstrates how clinician-integrated AI systems can improve sepsis detection and outcomes.SDSC-G
ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models
No full textThe recent development of highly accurate protein structure prediction tools has led to a rapid expansion in the scope of computational structural biology, enabling a much wider range of modelling studies than ever before. These new in silico opportunities help life science researchers understand how proteins interact with their environment and support design of new molecules with desired properties. Ultimately, they have broad applications, e.g. in medicine, drug discovery or engineering. To ensure reproducibility and to facilitate data exchange and reuse, predicted structures or computed structure models can be stored using ModelCIF, a rich data representation designed to include the atomic coordinates/metadata. The previously published version of ModelCIF (1.4.4; 2022-12-21) mainly covered protein structure predictions generated by homology and ab initio modelling. In this work, we present an extension of the ModelCIF (https://github.com/ihmwg/ModelCIF) data standard and its associated tools. This extension supports important new use cases, including modelling protein–ligand and protein–protein interactions, sampling multiple conformational states and designing proteins de novo. We define guidelines for storage and validation of modelling results for those use cases by applying new and existing ModelCIF categories to capture protocols, inputs and outputs. Additionally, we outline updates to the software tools and resources that implement these new standards and provide functionality for model generation, validation, archiving, and visualisation. By enabling consistent metadata capture across different modelling workflows, this framework aims to support the FAIR dissemination of computational models, thereby promoting reproducibility and reusability in downstream applications.UPDALP
Bundle-Specific Axon Diameter Index as a New Contrast to Differentiate White Matter Tracts
No full textIn the central nervous system of primates, several pathways are characterized by different spectra of axon diameters. In vivo methods, based on diffusion-weighted magnetic resonance imaging, can provide axon diameter index estimates non-invasively. However, such methods report voxel-wise estimates, which vary from voxel-to-voxel for the same white matter bundle due to partial volume contributions from other pathways having different microstructure properties. Here, we propose a novel microstructure-informed tractography approach, COMMITAxSize, to resolve axon diameter index estimates at the streamline level, thus making the estimates invariant along trajectories. Compared to previously proposed voxel-wise methods, our formulation allows the estimation of a distinct axon diameter index value for each streamline, directly, furnishing a complementary measure to the existing calculation of the mean value along the bundle. We demonstrate the favourable performance of our approach comparing our estimates with existing histologically-derived measurements performed in the corpus callosum and the posterior limb of the internal capsule. Overall, our method provides a more robust estimation of the axon diameter index of pathways by jointly estimating the microstructure properties of the tissue and the macroscopic organisation of the white matter connectivity.LTS5This is an Open Access article under the terms of the Creative Commons Attribution Licens
Ptychographic imaging with a fiber endoscope via wavelength scanning
No full textPtychography has become a popular computational imaging method for microscopy in recent years. In the present work we employ a wavelength scanning ptychography technique enhanced by neural networks for imaging with a fiber endoscope. Illumination of the object at various wavelengths is achieved using a single mode fiber, while a multicore fiber collects diffracted light from a distance. Using a U-Net multilayer convolutional neural network, the diffraction pattern is recovered at the far end of the multicore fiber from the recorded intensity pattern at the proximal end. With the recovered diffraction pattern in place, the phase object can be reconstructed using the ptychography algorithm. The quality of the object reconstruction improves with the number of wavelengths used. Comparison with an end-to-end neural network highlights the effectiveness and practicality of this two-step hybrid system. This alternative and simplified ptychographic endoscopy setup delivers noticeable improvements through neural networks and wavelength scanning.LAPDLHT
CCNoC: Specializing On-Chip Interconnects for Energy Efficiency in Cache-Coherent Servers
No full textManycore chips are emerging as the architecture of choice to provide power efficiency and improve performance, while riding Moore’s Law. In these architectures, on-chip interconnects play a pivotal role in ensuring power and performance scalability. As supply voltages begin to level off in future technologies, chip designs in general and interconnects in particular will require specialization to meet power and performance objectives. In this work, we make the observation that cache-coherent manycore server chips exhibit a duality in on-chip network traffic. Request traffic largely consists of simple control messages, while response traffic often carries cache-block-sized payloads. We present Cache-Coherence Network-on-Chip (CCNoC), a design that specializes the NoC to fit the demands of server workloads via a pair of asymmetric networks tuned to the type of traffic traversing them. The networks differ in their datapath width, router microarchitecture, flow control strategy, and delay. The resulting heterogeneous CCNoC architecture enables significant gains in power efficiency over conventional NoC designs at similar performance levels. Our evaluation reveals that a 4x4 mesh-based chip multiprocessor with the proposed CCNoC organization running commercial server workloads is 15-28% more energy efficient than various state-of-the-art single- and dual-network organizations.PARSALSI
(Re)Claim Space, (Re)Build Networks. A Constellation of Interventions to Reclaim Public Space as a Place of Care
No full textSwitzerland, one of the world’s highest producers of waste per capita, has institutionalized waste management as an industry—one that privileges incineration, efficiency, and disappearance over responsibility, repair, and care. Waste, in this framework, is not a collective concern but a commodified residue—removed, erased, and rendered invisible by market-driven infrastructures. This project departs from a recognition that the built environment, and the systems that sustain it, function through forms of erasure: erasing waste, erasing responsibility, and ultimately erasing possibilities for alternative, care-based futures. Within this context, we ask: How can design engage with what has been systematically made invisible? The conceptual grounding of this work is inspired by Kathrin Böhm’s call, in The Social (Re)production of Architecture, to re-engage with processes we have severed from lived experience. Böhm emphasizes visibility, accessibility, and participation as conditions for collective agency. Taking Lausanne as a testing ground, this project proposes a constellation of interventions in public space—situated acts that aim to parasit the “take-make-waste” logic. Through site-specific explorations, three distinct situations have been identified—each revealing opportunities to weave fragmented chains of responsibility and restoring conditions for collective agency.ALICEENAC-SARCote: 2025.023MEM.1/1 A5 verticalGroupe de suivi: Dietz, Dieter (dir. pédagogique) ; Graezer Bideau, Florence (prof.) ; Logoz, Claire (mentor·e) ; Marinov, Marina (expert·e)Professeur responsable de l'Enoncé: Graezer Bideau, Florence (EPFL CDH-DIR)Enoncé théorique de master: Inhabited Territories: On Mapping Crisana’s Borderscap