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The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach

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arXiv 2022年

作者： Bhattacharyay, Shubhayu Milosevic, Ioan Wilson, Lindsay Menon, David K. Stevens, Robert D. Steyerberg, Ewout W. Nelson, David W. Ercole, Ari Åkerlund, Cecilia Amrein, Krisztina Andelic, Nada Andreassen, Lasse Anke, Audny Antoni, Anna Audibert, Gérard Azouvi, Philippe Azzolini, Maria Luisa Bartels, Ronald Barzó, Pál Beauvais, Romuald Beer, Ronny Bellander, Bo-Michael Belli, Antonio Benali, Habib Berardino, Maurizio Beretta, Luigi Blaabjerg, Morten Bragge, Peter Brazinova, Alexandra Brinck, Vibeke Brooker, Joanne Brorsson, Camilla Buki, Andras Bullinger, Monika Cabeleira, Manuel Caccioppola, Alessio Calappi, Emiliana Calvi, Maria Rosa Cameron, Peter Lozano, Guillermo Carbayo Carbonara, Marco Cavallo, Simona Chevallard, Giorgio Chieregato, Arturo Citerio, Giuseppe Clusmann, Hans Coburn, Mark Coles, Jonathan Cooper, Jamie D. Correia, Marta Čović, Amra Curry, Nicola Czeiter, Endre Czosnyka, Marek Dahyot-Fizelier, Claire Dark, Paul Dawes, Helen De Keyser, Véronique Degos, Vincent Della Corte, Francesco den Boogert, Hugo Depreitere, Bart Đilvesi, Đula Dixit, Abhishek Donoghue, Emma Dreier, Jens Dulière, Guy-Loup Esser, Patrick Ezer, Erzsébet Fabricius, Martin Feigin, Valery L. Foks, Kelly Frisvold, Shirin Furmanov, Alex Gagliardo, Pablo Galanaud, Damien Gantner, Dashiell Gao, Guoyi George, Pradeep Ghuysen, Alexandre Giga, Lelde Glocker, Ben Golubovic, Jagoš Gomez, Pedro A. Gratz, Johannes Gravesteijn, Benjamin Grossi, Francesca Gruen, Russell L. Gupta, Deepak Haagsma, Juanita A. Haitsma, Iain Helbok, Raimund Helseth, Eirik Horton, Lindsay Huijben, Jilske Hutchinson, Peter J. Jacobs, Bram Jankowski, Stefan Jarrett, Mike Jiang, Ji-Yao Johnson, Faye Jones, Kelly Karan, Mladen Kolias, Angelos G. Kompanje, Erwin Kondziella, Daniel Kornaropoulos, Evgenios Koskinen, Lars-Owe Kovács, Noémi Kowark, Ana Lagares, Alfonso Lanyon, Linda Laureys, Steven Lecky, Fiona Ledoux, Didier Lefering, Rolf Legrand, Valerie Lejeune, Aurelie Levi, Leon Lightfoot, Roger Lingsma, Hester Maas, Andrew I.R. Castaño-León, Ana M. Maegele, Marc Majdan, M Division of Anaesthesia University of Cambridge Addenbrooke's Hospital Cambridge United Kingdom Division of Neurosurgery Department of Clinical Neurosciences Addenbrooke's Hospital University of Cambridge Cambridge United Kingdom Laboratory of Computational Intensive Care Medicine Johns Hopkins University BaltimoreMD United States Division of Psychology University of Stirling Stirling United Kingdom Department of Anesthesiology and Critical Care Medicine Johns Hopkins University BaltimoreMD United States Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Department of Physiology and Pharmacology Section for Perioperative Medicine and Intensive Care Karolinska Institutet Stockholm Sweden Cambridge Centre for Artificial Intelligence in Medicine Cambridge United Kingdom Division of Surgery and Clinical Neuroscience Department of Physical Medicine and Rehabilitation Oslo University Hospital University of Oslo Oslo Norway Department of Neurosurgery University Hospital Northern Norway Tromso Norway Department of Physical Medicine and Rehabilitation University Hospital Northern Norway Tromso Norway Trauma Surgery Medical University Vienna Vienna Austria Department of Anesthesiology & Intensive Care University Hospital Nancy Nancy France Raymond Poincare Hospital Assistance Publique - Hopitaux de Paris Paris France Department of Anesthesiology & Intensive Care S Raffaele University Hospital Milan Italy Department of Neurosurgery Radboud University Medical Center Nijmegen Netherlands Department of Neurosurgery University of Szeged Szeged Hungary International Projects Management ARTTIC Munchen Germany Department of Neurology Neurological Intensive Care Unit Medical University of Innsbruck Innsbruck Austria Department of Neurosurgery & Anesthesia & Intensive Care Medicine Karolinska University Hospital Stockholm Sweden NIHR Surgical Reconstruction and Microbiology Research Centre Birmingham United Kingdom Anesthesi

When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale - Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (center-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74 - 0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54% - 60%) explanation of ordinal variation in 6-month GOSE (Somers' Dxy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE a

关键词： Neural network models

TOI-2005b: An Eccentric Warm Jupiter in Spin-Orbit Alignment

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arXiv 2025年

作者： Bieryla, Allyson Dong, Jiayin Zhou, George Eastman, Jason D. Mayorga, L.C. Latham, David W. Carter, Brad Huang, Chelsea X. Quinn, Samuel N. Collins, Karen A. Abe, Lyu Beletsky, Yuri Brahm, Rafael Colón, Knicole D. Essack, Zahra Guillot, Tristan Henning, Thomas Hobson, Melissa J. Horne, Keith Jenkins, Jon M. Jones, Matías I. Jordán, Andrés Osip, David Ricker, George R. Rodriguez, Joseph E. Schulte, Jack Schwarz, Richard P. Seager, Sara Shporer, Avi Suarez, Olga Tan, Thiam-Guan Ting, Eric B. Triaud, Amaury Vanderburg, Andrew Villaseñor, Jesus Noel Vowell, Noah Watkins, Cristilyn N. Winn, Joshua N. Ziegler, Carl Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States University of Southern Queensland Centre for Astrophysics West Street ToowoombaQLD4350 Australia Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States The Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd LaurelMD20723 United States Observatoire de la Côte d’Azur UniCA Laboratoire Lagrange CNRS UMR 7293 CS 34229 06304 Nice cedex 4 France Las Campanas Observatory Carnegie Institution for Science Colina El Pino Casilla La Serena601 Chile Facultad de Ingeniería y Ciencias Universidad Adolfo Ibáñez Av. Diagonal las Torres 2640 Santiago Peñalolén Chile Millennium Institute for Astrophysics Chile Data Observatory Foundation Chile GreenbeltMD20771 United States Department of Physics and Astronomy The University of New Mexico 210 Yale Blvd NE AlbuquerqueNM87106 United States Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany SUPA Physics and Astronomy University of St. Andrews Fife ScotlandKY16 9SS United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States European Southern Observatory Alonso de Córdova 3107 Vitacura Casilla Santiago19001 Chile Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Earth Atmospheric and Planetary Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics MIT 77 Massachusetts Avenue CambridgeMA02139 United States Observatoire de la Côte d’Azur UniCA CNRS CS 34229 Nice06304 cedex 4

We report the discovery and characterization of TOI-2005 b, a warm Jupiter on an eccentric (e ∼ 0.59), 17.3-day orbit around a Vmag = 9.867 rapidly rotating F-star. The object was detected as a candidate by TESS and the planetary nature of TOI-2005 b was then confirmed via a series of ground-based photometric, spectroscopic, and diffraction-limited imaging observations. The planet was found to reside in a low sky-projected stellar obliquity orbit (λ =4.8+2−235 degrees) via a transit spectroscopic observation using the Magellan MIKE spectrograph. TOI-2005 b is one of a few planets known to have a low-obliquity, high-eccentricity orbit, which may be the result of high-eccentricity coplanar migration. The planet has a periastron equilibrium temperature of ∼ 2100 K, similar to some highly irradiated hot Jupiters where atomic metal species have been detected in transmission spectroscopy, and varies by almost 1000 K during its orbit. Future observations of the atmosphere of TOI-2005b can inform us about its radiative timescales thanks to the rapid heating and cooling of the planet. © 2025, CC BY.

关键词： Photometry

Memory footprint reduction for the FFT-based volume integral equation method via tensor decompositions

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arXiv 2018年

作者： Giannakopoulos, Ilias I. Litsarev, Mikhail S. Polimeridis, Athanasios G. Skoltech Center for Computational Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow143026 Russia Q Bio CA94063 United States

We present a method of memory footprint reduction for FFT-based, electromagnetic (EM) volume integral equation (VIE) formulations. The arising Green's function tensors have low multilinear rank, which allows Tucker decomposition to be employed for their compression, thereby greatly reducing the required memory storage for numerical simulations. Consequently, the compressed components are able to fit inside a graphical processing unit (GPU) on which highly parallelized computations can vastly accelerate the iterative solution of the arising linear system. In addition, the element-wise products throughout the iterative solver's process require additional flops, thus, we provide a variety of novel and efficient methods that maintain the linear complexity of the classic element-wise product with an additional multiplicative small constant. We demonstrate the utility of our approach via its application to VIE simulations for the Magnetic Resonance Imaging (MRI) of a human head. For these simulations we report an order of magnitude acceleration over standard techniques. Copyright © 2018, The Authors. All rights reserved.

关键词： Fast Fourier transforms

Scaling up the lattice dynamics of amorphous materials by orders of magnitude

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arXiv 2020年

作者： Kriuchevskyi, Ivan Palyulin, Vladimir V. Milkus, Rico Elder, Robert M. Sirk, Timothy W. Zaccone, Alessio Department of Physics "A. Pontremoli" University of Milan via Celoria 16 Milan20133 Italy Centre for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobelya Ulitsa 3 Moscow121205 Russia Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Polymers Branch U.S. Army Research Laboratory Aberdeen Proving Ground MD United States Bennett Aerospace Inc. CaryNC27518 United States Center for Devices and Radiological Health U.S. Food and Drug Administration Silver SpringMD20903 United States

We generalise the non-affine theory of viscoelasticity for use with large, well-sampled systems of arbitrary chemical complexity. Having in mind predictions of mechanical and vibrational properties of amorphous systems with atomistic resolution, we propose an extension of the Kernel Polynomial Method (KPM) for the computation of the vibrational density of states (VDOS) and the eigenmodes, including the Γ-correlator of the affine force-field, which is a key ingredient of lattice-dynamic calculations of viscoelasticity. We show that the results converge well to the solution obtained by direct diagonalization (DD) of the Hessian (dynamical) matrix. As is well known, the DD approach has prohibitively high computational requirements for systems with N = 104 atoms or larger. Instead, the KPM approach developed here allows one to scale up lattice dynamic calculations of real materials up to 106 atoms, with a hugely more favorable (linear) scaling of computation time and memory consumption with N. Copyright © 2020, The Authors. All rights reserved.

关键词： Viscoelasticity

Author Correction: Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats

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Nature communications 2025年第1期16卷 1507页

作者： Jakob Steinfeldt Benjamin Wild Thore Buergel Maik Pietzner Julius Upmeier Zu Belzen Andre Vauvelle Stefan Hegselmann Spiros Denaxas Harry Hemingway Claudia Langenberg Ulf Landmesser John Deanfield Roland Eils Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany. Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany. Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany. Institute of Cardiovascular Sciences University College London London UK. Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK. Institute of Health Informatics University College London London UK. Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA. Pattern Recognition and Image Analysis Lab University of Münster Münster Germany. British Heart Foundation Data Science Centre London UK. Health Data Research UK London UK. National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK. Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany. Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. roland.eils@bih-charite.de. Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany. roland.eils@bih-charite.de.

Where do stars explode in the ISM? - The distribution of dense gas around massive stars and supernova remnants in M33

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arXiv 2023年

作者： Sarbadhicary, Sumit K. Wagner, Jordan Koch, Eric W. Chen, Ness Mayker Leroy, Adam K. Lahén, Natalia Rosolowsky, Erik Neugent, Kathryn F. Kim, Chang-Goo Chomiuk, Laura Dalcanton, Julianne J. Lopez, Laura A. Pingel, Nickolas M. Indebetouw, Remy Williams, Thomas G. Tarantino, Elizabeth Meyer, Jennifer Donovan Skillman, Evan D. Smercina, Adam Kepley, Amanda A. Murphy, Eric J. Strader, Jay Wong, Tony Stanimirović, Snežana Villanueva, Vicente Walter, Fabian Ott, Juergen Darling, Jeremy Roman-Duval, Julia Murray, Claire E. Department of Physics The Ohio State University ColumbusOH43210 United States Center for Cosmology & Astro-Particle Physics The Ohio State University ColumbusOH43210 United States Department of Astronomy The Ohio State University ColumbusOH43210 United States Center for Astrophysics Harvard & Smithsonian 60 Garden St. CambridgeMA02138 United States Max-Planck-Institute für Astrophysik Karl-Schwarzschild-Straße 1 GarchingD-85740 Germany Department of Physics University of Alberta EdmontonABT6G 2E1 Canada Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Astronomy University of Washington Box 351580 SeattleWA98195-1580 United States Center for Computational Astrophysics Flatiron Institute 162 Fifth Ave New YorkNY10010 United States Department of Astronomy University of Wisconsin-Madison 475 North Charter St. MadisonWI53706-15821 United States University of Virginia Astronomy Dept. 530 McCormick Road CharlottesvilleVA22904-4325 United States National Radio Astronomy Observatory 520 Edgemont Road CharlottesvilleVA22903-2475 United States Sub-department of Astrophysics Department of Physics University of Oxford Keble Road OxfordOX1 3RH United Kingdom Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Astronomy University of Maryland College ParkMD20742 United States Minnesota Institute for Astrophysics University of Minnesota MinneapolisMN55455 United States Department of Astronomy University of Illinois UrbanaIL61801 United States Max Planck Institute for Astronomy Königstuhl 17 HeidelbergD-69117 Germany National Radio Astronomy Observatory Pete V. Domenici Array Science Center P.O. Box O SocorroNM87801 United States New Mexico Institute of Mining and Technology 801

Star formation in galaxies is regulated by turbulence, outflows, gas heating and cloud dispersal - processes which depend sensitively on the properties of the interstellar medium (ISM) into which supernovae (SNe) explode. Unfortunately, direct measurements of ISM environments around SNe remain scarce, as SNe are rare and often distant. Here we demonstrate a new approach: mapping the ISM around the massive stars that are soon to explode. This provides a much larger census of explosion sites than possible with only SNe, and allows comparison with sensitive, high-resolution maps of the atomic and molecular gas from the Jansky VLA and ALMA. In the well-resolved Local Group spiral M33, we specifically observe the environments of red supergiants (RSGs, progenitors of Type II SNe), Wolf-Rayet stars (WRs, tracing stars >30M☉, and possibly future stripped-envelope SNe), and supernova remnants (SNRs, locations where SNe have exploded). We find that massive stars evolve not only in dense, molecular-dominated gas (with younger stars in denser gas), but also a substantial fraction (∼45% of WRs;higher for RSGs) evolve in lower-density, atomic-gas-dominated, inter-cloud media. We show that these measurements are consistent with expectations from different stellar-age tracer maps, and can be useful for validating SN feedback models in numerical simulations of galaxies. Along with the discovery of a 20-pc diameter molecular gas cavity around a WR, these findings reemphasize the importance of pre-SN/correlated-SN feedback evacuating the dense gas around massive stars before explosion, and the need for high-resolution (down to pc-scale) surveys of the multi-phase ISM in nearby galaxies. © 2023, CC BY.

关键词： Supernovae

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

关键词：

Flexible x-ray source with tunable polarization and orbital angular momentum from Hermite-Gaussian laser modes driven plasma channel wakefield

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Physical Review Accelerators and Beams 2019年第7期22卷 071302-071302页

作者： B. Lei T. Teter J. W. Wang V. Yu. Kharin C. B. Schroeder M. Zepf S. G. Rykovanov Helmholtz Institute Jena Fröbelstieg 3 07743 Jena Germany Faculty of Physics and Astronomy Friedrich-Schiller-Universität Jena 07743 Jena Germany Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences 201800 Shanghai China Lawrence Berkeley National Laboratory Berkeley California 94720 USA Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow 121205 Russia

A plasma channel undulator/wiggler may be created through the plasma wakefield excited by the beating of several Hermite-Gaussian laser modes propagating in a parabolic plasma channel. Control over both the betatron and undulator forces is conveniently achieved by tuning the amplitude ratios, colors, and order numbers of the modes. A special structure of the undulator/wiggler field without the focusing force near the propagation axis is generated inside the plasma wakefield by matching the strengths of the fundamental and first-order Hermite-Gaussian modes. The electron beam only experiences forced undulator oscillations in such a field, which significantly improves the quality of the emitted radiation. Since the value of the undulator strength parameter could be in a wide range, less or larger than unity, it is capable of generating narrow bandwidth x-ray, as well as the synchrotronlike high-energy x/γ-ray, radiation by harmonics. Additionally, controlling the relative phases between the laser modes allows for polarization control of the plasma undulator. High-order harmonics produced from a circularly polarized plasma undulator clearly show the vortex nature and carry well-defined orbital angular momentum.

关键词： Laser wakefield acceleration Laser-plasma interactions Plasma acceleration & new acceleration techniques Plasma waves X-ray generation in plasmas

Going deeper into the dark with COSMOS-Web JWST unveils the total contribution of Radio-Selected NIRfaint galaxies to the cosmic Star Formation Rate Density

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arXiv 2025年

作者： Gentile, Fabrizio Talia, Margherita Enia, Andrea Pozzi, Francesca Traina, Alberto Zamorani, Giovanni Andika, Irham T. Behiri, Meriem Barrufet, Laia Casey, Caitlin M. Cimatti, Andrea Drakos, Nicole E. Faisst, Andreas L. Franco, Maximilien Gillman, Steven Giulietti, Marika Gottumukkala, Rashmi Hayward, Christopher C. Ilbert, Olivier Jin, Shuowen Lapi, Andrea McKinney, Jed Shuntov, Marko Vaccari, Mattia Vignali, Cristian Akins, Hollis B. Allen, Natalie Harish, Santosh McCracken, Henry Joy Kartaltepe, Jeyhan S. Koekemoer, Anton M. Liu, Daizhong Paquereau, Louise Rhodes, Jason Rich, Micheal R. Robertson, Brant E. Toft, Sune CEA IRFU DAp AIM Université Paris-Saclay Université Paris Cité Sorbonne Paris Cité CNRS Gif-sur-Yvette91191 France Via Gobetti 93/2 BolognaI-40129 Italy INAF — Osservatorio di Astrofisica e Scienza dello Spazio via Gobetti 93/3 Bologna40129 Italy Technical University of Munich TUM School of Natural Sciences Department of Physics James-Franck-Str. 1 Garching85748 Germany Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Str. 1 Garching85748 Germany SISSA Via Bonomea 265 TriesteI-34136 Italy INAF/IRA Istituto di Radioastronomia Via Piero Gobetti 101 Bologna40129 Italy Department of Astronomy University of Geneva Chemin Pegasi 51 VersoixCH-1290 Switzerland Institute for Astronomy School of Physics & Astronomy University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom The University of Texas at Austin 2515 Speedway Boulevard Stop C1400 AustinTX78712 United States Department of Physics and Astronomy University of Hawaii Hilo 200 W Kawili St HiloHI96720 United States Caltech/IPAC MS314-6 1200 E. California Blvd PasadenaCA91125 United States Denmark DTU-Space Technical University of Denmark Elektrovej 327 Kgs. LyngbyDK-2800 Denmark Niels Bohr Institute University of Copenhagen Jagtvej 128 CopenhagenDK-2200 Denmark Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Aix Marseille Univ CNRS CNES LAM Marseille France IFPU - Institute for fundamental physics of the Universe Via Beirut 2 Trieste34014 Italy INFN-Sezione di Trieste via Valerio 2 Trieste34127 Italy Inter-University Institute for Data Intensive Astronomy Department of Astronomy University of Cape Town Rondebosch Cape Town7701 South Africa Inter-University Institute for Data Intensive Astronomy Department of Physics and Astronomy University of the Western Cape Bellville Cape Town7535 South Africa Laboratory for Multiwavelength Astrophysics School of Physics and Astronomy Rochester I

We present the first follow-up with JWST of radio-selected NIRfaint galaxies as part of the COSMOS-Web survey. By selecting galaxies detected at radio frequencies (S3GHz > 11.5 µJy;i.e. S/N> 5) and with faint counterparts at NIR wavelengths (F150W> 26.1 mag), we collect a sample of 127 likely dusty star-forming galaxies (DSFGs). We estimate their physical properties through SED fitting, compute the first radio luminosity function for these types of sources, and their contribution to the total cosmic star formation rate density. Our analysis confirms that these sources represent a population of highly dust-obscured (〈Av〉 ∼ 3.5 mag), massive (〈M★〉 ∼ 1010.8 M☉) and star-forming galaxies (〈SFR〉 ∼ 300 M☉ yr−1) located at 〈z〉 ∼ 3.6, representing the high-redshift tail of the full distribution of radio sources. Our results also indicate that these galaxies could dominate the bright end of the radio luminosity function and reach a total contribution to the cosmic star formation rate density equal to that estimated only considering NIR-bright sources at z ∼ 4.5. Finally, our analysis further confirms that the radio selection can be employed to collect statistically significant samples of DSFGs, representing a complementary alternative to the other selections based on JWST colors or detection at FIR/(sub)mm wavelengths. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmology