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Production of K*(892)0 and ϕ(1020) in pp and Pb−Pb collisions at sNN=5.02 TeV

Physical Review C 2022年第3期106卷 034907-034907页

作者： S. Acharya D. Adamová A. Adler J. Adolfsson G. Aglieri Rinella M. Agnello N. Agrawal Z. Ahammed S. Ahmad S. U. Ahn I. Ahuja Z. Akbar A. Akindinov M. Al-Turany S. N. Alam D. Aleksandrov B. Alessandro H. M. Alfanda R. Alfaro Molina B. Ali Y. Ali A. Alici N. Alizadehvandchali A. Alkin J. Alme T. Alt L. Altenkamper I. Altsybeev M. N. Anaam C. Andrei D. Andreou A. Andronic M. Angeletti V. Anguelov F. Antinori P. Antonioli C. Anuj N. Apadula L. Aphecetche H. Appelshäuser S. Arcelli R. Arnaldi I. C. Arsene M. Arslandok A. Augustinus R. Averbeck S. Aziz M. D. Azmi A. Badalà Y. W. Baek X. Bai R. Bailhache Y. Bailung R. Bala A. Balbino A. Baldisseri B. Balis M. Ball D. Banerjee R. Barbera L. Barioglio M. Barlou G. G. Barnaföldi L. S. Barnby V. Barret C. Bartels K. Barth E. Bartsch F. Baruffaldi N. Bastid S. Basu G. Batigne B. Batyunya D. Bauri J. L. Bazo Alba I. G. Bearden C. Beattie I. Belikov A. D. C. Bell Hechavarria F. Bellini R. Bellwied S. Belokurova V. Belyaev G. Bencedi S. Beole A. Bercuci Y. Berdnikov A. Berdnikova D. Berenyi L. Bergmann M. G. Besoiu L. Betev P. P. Bhaduri A. Bhasin I. R. Bhat M. A. Bhat B. Bhattacharjee P. Bhattacharya L. Bianchi N. Bianchi J. Bielčík J. Bielčíková J. Biernat A. Bilandzic G. Biro S. Biswas J. T. Blair D. Blau M. B. Blidaru C. Blume G. Boca F. Bock A. Bogdanov S. Boi J. Bok L. Boldizsár A. Bolozdynya M. Bombara P. M. Bond G. Bonomi H. Borel A. Borissov H. Bossi E. Botta L. Bratrud P. Braun-Munzinger M. Bregant M. Broz G. E. Bruno M. D. Buckland D. Budnikov H. Buesching S. Bufalino O. Bugnon P. Buhler Z. Buthelezi J.B. Butt S.A. Bysiak D. Caffarri M. Cai H. Caines A. Caliva E. Calvo Villar J. M. M. Camacho R.S. Camacho P. Camerini F. D. M. Canedo F. Carnesecchi R. Caron J. Castillo Castellanos E. A. R. Casula F. Catalano C. Ceballos Sanchez P. Chakraborty S. Chandra S. Chapeland M. Chartier S. Chattopadhyay A. Chauvin T. G. Chavez C. Cheshkov B. Cheynis V. Chibante Barroso D. D. Chinellato S. Cho P. Chochula P. Christakoglou C. H. Christensen P. Christiansen T. Chujo C. Cicalo L. C Variable Energy Cyclotron Centre Homi Bhabha National Institute Kolkata India Nuclear Physics Institute of the Czech Academy of Sciences Řež u Prahy Czech Republic Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik Fachbereich Informatik und Mathematik Frankfurt Germany Lund University Department of Physics Division of Particle Physics Lund Sweden European Organization for Nuclear Research (CERN) Geneva Switzerland Dipartimento DISAT del Politecnico and Sezione INFN Turin Italy INFN Sezione di Bologna Bologna Italy Department of Physics Aligarh Muslim University Aligarh India Korea Institute of Science and Technology Information Daejeon Republic of Korea Faculty of Science P.J. Šafárik University Košice Slovakia Indonesian Institute of Sciences Jakarta Indonesia NRC «Kurchatov»Institute - ITEP Moscow Russia Research Division and ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt Germany Fudan University Shanghai China National Research Centre Kurchatov Institute Moscow Russia INFN Sezione di Torino Turin Italy Central China Normal University Wuhan China Instituto de Física Universidad Nacional Autónoma de México Mexico City Mexico COMSATS University Islamabad Islamabad Pakistan Dipartimento di Fisica e Astronomia dell'Università and Sezione INFN Bologna Italy University of Houston Houston Texas USA Department of Physics and Technology University of Bergen Bergen Norway Institut für Kernphysik Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany St. Petersburg State University St. Petersburg Russia Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest Romania Nikhef National institute for subatomic physics Amsterdam Netherlands Westfälische Wilhelms-Universität Münster Institut für Kernphysik Münster Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Padova Padova Italy Lawrence Berkeley National L

The production of K*(892)0 and ϕ(1020) mesons in proton–proton (pp) and lead–lead (Pb–Pb) collisions at sNN = 5.02 TeV has been measured using the ALICE detector at the Large Hadron Collider (LHC). The transverse momentum (pT) distributions of K*(892)0 and ϕ(1020) mesons have been measured at midrapidity (|y|<0.5) up to pT= 20 GeV/c in inelastic pp collisions and for several Pb–Pb collision centralities. The collision centrality and collision energy dependence of the average transverse momenta agree with the radial flow scenario observed with stable hadrons, showing that the effect is stronger for more central collisions and higher collision energies. The K*0/K ratio is found to be suppressed in Pb–Pb collisions relative to pp collisions: this indicates a loss of the measured K*(892)0 signal due to rescattering of its decay products in the hadronic phase. In contrast, for the longer-lived ϕ(1020) mesons, no such suppression is observed. The nuclear modification factors (RAA) of K*(892)0 and ϕ(1020) mesons are calculated using pp reference spectra at the same collision energy. In central Pb–Pb collisions for pT > 8 GeV/c, the RAA values of K*(892)0 and ϕ(1020) are below unity and observed to be similar to those of pions, kaons, and (anti)protons. The RAA values at high pT (>8 GeV/c) for K*(892)0 and ϕ(1020) mesons are in agreement within uncertainties for sNN= 5.02 and 2.76 TeV.

关键词： Hadron-hadron interactions Nucleon induced nuclear reactions Particle & resonance production Relativistic heavy-ion collisions

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Stochastic training of residual networks: A differential equation viewpoint

arXiv

引用

arXiv 2018年

作者： Sun, Qi Tao, Yunzhe Du, Qiang School of Mathematical Science University of Science and Technology of China Hefei230026 China Division of Applied and Computational Mathematics Beijing Computational Science Research Center Beijing100193 China Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States Department of Applied Physics and Applied Mathematics and Data Science Institute Columbia University New YorkNY10027 United States

During the last few years, significant attention has been paid to the stochastic training of artificial neural networks, which is known as an effective regularization approach that helps improve the generalization capability of trained models. In this work, the method of modified equations is applied to show that the residual network and its variants with noise injection can be regarded as weak approximations of stochastic differential equations. Such observations enable us to bridge the stochastic training processes with the optimal control of backward Kolmogorov’s equations. This not only offers a novel perspective on the effects of regularization from the loss landscape viewpoint but also sheds light on the design of more reliable and efficient stochastic training strategies. As an example, we propose a new way to utilize Bernoulli dropout within the plain residual network architecture and conduct experiments on a real-world image classification task to substantiate our theoretical *** Codes 49J20, 65C30, 62M45 Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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AtlFast3: The Next Generation of Fast Simulation in ATLAS

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Computing and Software for Big Science 2022年第1期6卷 1-54页

作者： Aad, G. Abbott, B. Abbott, D.C. Abud, A. Abed Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Hoffman, A. C. Abusleme Acharya, B.S. Achkar, B. Adam, L. Bourdarios, C. Adam Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Khoury, K. Al Alberghi, G.L. Albert, J. Albicocco, P. Verzini, M. J. Alconada Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Camelia, E. Alunno Estevez, M. Alvarez Alviggi, M.G. Coutinho, Y. Amaral Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Santos, S. P. Amor Dos Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Pozo, J. A. Aparisi Aparo, M.A. Bella, L. Aperio Aranzabal, N. Ferraz, V. Araujo Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Tame, Z. P. Arrubarrena Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Gupta, D. Bakshi Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Bal Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Univ. Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory and University of California Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Facultad de Ciencias y Cen

The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes. © 2022, Springer Nature Switzerland AG.

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Global, regional, and national burden of neck pain, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

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The Lancet Rheumatology 2024年第3期6卷 e142-e155页

作者： Wu, Ai-Min Cross, Marita Elliott, James M. Culbreth, Garland T. Cousin, Ewerton Haile, Lydia M. Steinmetz, Jaimie D. Hagins, Hailey Kopec, Jacek A. Brooks, Peter M. Woolf, Anthony D. Kopansky-Giles, Deborah R. Walton, David M. Treleaven, Julia M. Dreinhoefer, Karsten E. Betteridge, Neil Abbasifard, Mitra Abbasi-Kangevari, Zeinab Addo, Isaac Yeboah Adesina, Miracle Ayomikun Adnani, Qorinah Estiningtyas Sakilah Aithala, Janardhana P. Alhalaiqa, Fadwa Alhalaiqa Naji Alimohamadi, Yousef Amiri, Sohrab Amu, Hubert Antony, Benny Arabloo, Jalal Aravkin, Aleksandr Y. Asghari-Jafarabadi, Mohammad Atomsa, Gamechu Hunde Azadnajafabad, Sina Azzam, Ahmed Y. Baghdadi, Soroush Balogun, Saliu A. Balta, Asaminew Birhanu Banach, Maciej Banakar, Morteza Barrow, Amadou Bashiri, Azadeh Bekele, Alehegn Bensenor, Isabela M. Bhardwaj, Pankaj Bhat, Ajay Nagesh Bilchut, Awraris Hailu Briggs, Andrew M. Buchbinder, Rachelle Cao, Chao Chaurasia, Akhilanand Chirinos-Caceres, Jesus Lorenzo Christensen, Steffan Wittrup McPhee Coberly, Kaleb Dadras, Omid Dai, Xiaochen de Luca, Katie Dehghan, Azizallah Dong, Huan-Ji Ekholuenetale, Michael Elhadi, Muhammed Eshetu, Habitu Birhan Eskandarieh, Sharareh Etaee, Farshid Fagbamigbe, Adeniyi Francis Fares, Jawad Fatehizadeh, Ali Feizkhah, Alireza Ferreira, Manuela L. Ferreira, Nuno Fischer, Florian Franklin, Richard Charles Ganesan, Balasankar Gebremichael, Mathewos Alemu Gerema, Urge Gholami, Ali Ghozy, Sherief Gill, Tiffany K. Golechha, Mahaveer Goleij, Pouya Golinelli, Davide Graham, Simon Matthew Haj-Mirzaian, Arvin Harlianto, Netanja I. Hartvigsen, Jan Hasanian, Mohammad Hassen, Mohammed Bheser Hay, Simon I. Hebert, Jeffrey J. Heidari, Golnaz Hoveidaei, Amir Human Hsiao, Alexander Kevin Ibitoye, Segun Emmanuel Iwu, Chidozie C.D. Jacob, Louis Janodia, Manthan Dilipkumar Jin, Yingzhao Jonas, Jost B. Joshua, Charity Ehimwenma Kandel, Himal Khader, Yousef Saleh Khajuria, Himanshu Khan, Ejaz Ahmad Khan, Moien A.B. Khatatbeh, Moawiah Mohammad Khateri, Sorour Khayat Kashani, Hamid Reza Khonji, Mohammad Saeid Department of Orthopaedics The Second Affiliated Hospital of Wenzhou Medical University Wenzhou China Faculty of Medicine and Health University of Sydney Sydney NSW Australia Global Alliance for Musculoskeletal Health Sydney NSW Australia Kolling Institute of Medical Research University of Sydney Northern Sydney Local Health District St Leonards NSW Australia Department of Physical Therapy and Human Movement Sciences Northwestern University Chicago IL United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States School of Population and Public Health University of British Columbia Vancouver BC Canada Arthritis Research Canada Richmond BC Canada US Department of Veterans Affairs (VA) Birmingham AL United States Centre for Health Policy University of Melbourne Melbourne VIC Australia Bone and Joint Research Group Royal Cornwall Hospital Truro United Kingdom Department of Family and Community Medicine University of Toronto Toronto ON Canada Department of Research and Innovation Canadian Memorial Chiropractic College Toronto ON Canada Department of Physical Therapy University of Sydney Sydney NSW Australia School of Physical Therapy Western University London ON Canada School of Health and Rehabilitation Sciences The University of Queensland Brisbane Australia Center of Musculoskeletal Surgery Charité Universitätsmedizin Berlin (Charité Medical University Berlin) Berlin Germany London United Kingdom Department of Internal Medicine Rafsanjan University of Medical Sciences Rafsanjan Iran Clinical Research Development Unit Rafsanjan University of Medical Sciences Rafsanjan Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Tehran University of Medical Sciences Tehran Iran Centre for Social Research in Health University of New South Wales Sydney NSW

Background: Neck pain is a highly prevalent condition that leads to considerable pain, disability, and economic cost. We present the most current estimates of neck pain prevalence and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) by age, sex, and location, with forecasted prevalence to 2050. Methods: Systematic reviews identified population-representative surveys used to estimate the prevalence of and YLDs from neck pain in 204 countries and territories, spanning from 1990 to 2020, with additional data from opportunistic review. Medical claims data from Taiwan (province of China) were also included. Input data were pooled using DisMod-MR 2.1, a Bayesian meta-regression tool. Prevalence was forecast to 2050 using a mixed-effects model using Socio-demographic Index as a predictor and multiplying by projected population estimates. We present 95% UIs for every metric based on the 2·5th and 97·5th percentiles of 100 draws of the posterior distribution. Findings: Globally, in 2020, neck pain affected 203 million (95% uncertainty interval [UI] 163–253) people. The global age-standardised prevalence rate of neck pain was estimated to be 2450 (1960–3040) per 100 000 population and global age-standardised YLD rate was estimated to be 244 (165–346) per 100 000. The age-standardised prevalence rate remained stable between 1990 and 2020 (percentage change 0·2% [–1·3 to 1·7]). Globally, females had a higher age-standardised prevalence rate (2890 [2330–3620] per 100 000) than males (2000 [1600–2480] per 100 000), with the prevalence peaking between 45 years and 74 years in male and female sexes. By 2050, the estimated global number of neck pain cases is projected to be 269 million (219–322), with an increase of 32·5% (23·9–42·3) from 2020 to 2050. Decomposition analysis of the projections showed population growth was the primary contributing factor, followed by population ageing. Interpretation: Although age-standardi

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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POS0253 PERSONALIZED RISK EVALUATION FOR OUTCOME PREDICTION IN ANCA ASSOCIATED VASCULITIS (AAV) USING LATENT CLASS ANALYSIS AND MACHINE LEARNING.

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Annals of the Rheumatic Diseases 2022年 81卷 367-367页

作者： K. Wójcik A. Ćmiel T. Satława S. Lichołai K. Wawrzycka-Adamczyk G. Biedroń A. Masiak Z. Zdrojewski H. Storoniak B. Bułło-Piontecka A. Dębska-Ślizień R. Jeleniewicz M. Majdan K. Jakuszko H. Augustyniak-Bartosik M. Krajewska I. Brzosko M. Brzosko J. Kur-Zalewska W. Tłustochowicz J. Musiał Jagiellonian University Medical College 2nd Chair of Internal Medicine Kraków Poland AGH University of Science and Technology Faculty of Applied Mathematics Kraków Poland Sano Centre for Computational Medicine Sano Centre for Computational Medicine Kraków Poland Medical University of Gdańsk Department of Internal Medicine Connective Tissue Diseases and Geriatrics Gdańsk Poland Medical University of Gdańsk Department of Nephrology Transplantology and Internal Diseases Gdańsk Poland Medical University of Lublin Department of Rheumatology and Connective Tissue Diseases Lublin Poland Wroclaw Medical University Department of Nephrology and Transplantation Medicine Wrocław Poland Pomeranian Medical University Individual Laboratory for Rheumatologic Diagnostics Szczecin Poland Pomeranian Medical University Department of Rheumatology Internal Medicine Geriatrics and Clinical Immunology Szczecin Poland Military Medical Institute Department of Internal Medicine and Rheumatology Warszawa Poland Military Medical Institute Clinical Research Support Center Military Institute of Medicine Warszawa Poland

Background ANCA associated vasculitides (AAV) are a heterogeneous group of rare diseases with unknown etiology. In the most severe cases AAV can lead to end stage kidney disease or death. Since etiology and detailed pathogenesis of AAV is not known, the prediction of disease outcome at the time of diagnosis is challenging. Thus, there is an unmet need for tools to identify patients with the highest risk of organ dysfunction and death and apply effective personalized therapy. Objectives The aim of this work was to search for tools allowing outcome prediction at the time of AAV diagnosis. Early identification of patients, who are likely to develop severe organ dysfunction and death is crucial for appropriate disease management. Induction therapy in AAV relays on immunosuppressive drugs characterized by a high risk of severe side effects. Thus, their administration in high doses should be limited only to individual patients with an especially high risk of poor outcome. Methods We applied here two methods of identification of AAV patients at risk to develop severe organ dysfunction and death. First method (latent class analysis [LCA] followed by logistic regression) was meant to subcategorize patients and identify a subgroup at subjects at risk to develop chronic renal replacement therapy (CRRT) and death [1]. Second, served to assess individual poor outcome risk and was based on two machine learning (ML) classifiers, which by analyzing clinical information allow assigning computed risk for CRRT and death in an individual patient allowing to identify subjects with high risk of chronic replacement therapy (CRRT) and death. We have evaluated a number of different approaches to build the ML models (including logistic regression, support vector machines, random forests), and obtained the best results for the gradient boosting algorithm implementation called LightGBM [2]. It works as a sequential ensemble of so-called weak learners (decision trees) finally combined in a one

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Publisher Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

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Nature genetics 2023年第5期55卷 893页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Cambridge University Hospitals NHS Foundation Trust Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@***. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. hliang1@***. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson

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Azimuthal correlations of heavy-flavor hadron decay electrons with charged particles in pp and p–Pb collisions at √sNN NN = 5.02 TeV

arXiv

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arXiv 2023年

作者： Acharya, S. Adamová, D. Adler, A. Rinella, G. Aglieri Agnello, M. Agrawal, N. Ahammed, Z. Ahmad, S. Ahn, S.U. Ahuja, I. Akindinov, A. Al-Turany, M. Aleksandrov, D. Alessandro, B. Alfanda, H.M. Molina, R. Alfaro Ali, B. Alici, A. Alizadehvandchali, N. Alkin, A. Alme, J. Alocco, G. Alt, T. Altsybeev, I. Anaam, M.N. Andrei, C. Andronic, A. Anguelov, V. Antinori, F. Antonioli, P. Apadula, N. Aphecetche, L. Appelshäuser, H. Arata, C. Arcelli, S. Aresti, M. Arnaldi, R. Arneiro, J.G.M.C.A. Arsene, I.C. Arslandok, M. Augustinus, A. Averbeck, R. Azmi, M.D. Badalà, A. Bae, J. Baek, Y.W. Bai, X. Bailhache, R. Bailung, Y. Balbino, A. Baldisseri, A. Balis, B. Banerjee, D. Banoo, Z. Barbera, R. Barile, F. Barioglio, L. Barlou, M. Barnaföldi, G.G. Barnby, L.S. Barret, V. Barreto, L. Bartels, C. Barth, K. Bartsch, E. Bastid, N. Basu, S. Batigne, G. Battistini, D. Batyunya, B. Bauri, D. Alba, J.L. Bazo Bearden, I.G. Beattie, C. Becht, P. Behera, D. Belikov, I. Hechavarria, A.D.C. Bell Bellini, F. Bellwied, R. Belokurova, S. Belyaev, V. Bencedi, G. Beole, S. Bercuci, A. Berdnikov, Y. Berdnikova, A. Bergmann, L. Besoiu, M.G. Betev, L. Bhaduri, P.P. Bhasin, A. Bhat, M.A. Bhattacharjee, B. Bianchi, L. Bianchi, N. Bielčík, J. Bielčíková, J. Biernat, J. Bigot, A.P. Bilandzic, A. Biro, G. Biswas, S. Bize, N. Blair, J.T. Blau, D. Blidaru, M.B. Bluhme, N. Blume, C. Boca, G. Bock, F. Bodova, T. Bogdanov, A. Boi, S. Bok, J. Boldizsár, L. Bombara, M. Bond, P.M. Bonomi, G. Borel, H. Borissov, A. Carcamo, A.G. Borquez Bossi, H. Botta, E. Bouziani, Y.E.M. Bratrud, L. Braun-Munzinger, P. Bregant, M. Broz, M. Bruno, G.E. Buckland, M.D. Budnikov, D. Buesching, H. Bufalino, S. Buhler, P. Buthelezi, Z. Bylinkin, A. Bysiak, S.A. Cai, M. Caines, H. Caliva, A. Villar, E. Calvo Camacho, J.M.M. Camerini, P. Canedo, F.D.M. Carabas, M. Carballo, A.A. Carnesecchi, F. Caron, R. Carvalho, L.A.D. Castellanos, J. Castillo Catalano, F. Sanchez, C. Ceballos Max-Planck-Institut für Physik Munich Germany Bologna Italy Dipartimento DET del Politecnico di Torino Turin Italy Department of Applied Physics Aligarh Muslim University Aligarh India Institute of Theoretical Physics University of Wroclaw Poland Foundation Yerevan Armenia AGH University of Science and Technology Cracow Poland Bogolyubov Institute for Theoretical Physics National Academy of Sciences of Ukraine Kiev Ukraine Kolkata India California Polytechnic State University San Luis ObispoCA United States Central China Normal University Wuhan China Havana Cuba Mérida Mexico City Mexico Chicago State University ChicagoIL United States China Institute of Atomic Energy Beijing China Chungbuk National University Cheongju Korea Republic of Comenius University Bratislava Faculty of Mathematics Physics and Informatics Bratislava Slovakia COMSATS University Islamabad Islamabad Pakistan Creighton University OmahaNE United States Department of Physics Aligarh Muslim University Aligarh India Department of Physics Pusan National University Pusan Korea Republic of Department of Physics Sejong University Seoul Korea Republic of Department of Physics University of California BerkeleyCA United States Department of Physics University of Oslo Oslo Norway Department of Physics and Technology University of Bergen Bergen Norway Dipartimento di Fisica Università di Pavia Pavia Italy Dipartimento di Fisica dell’Università and Sezione INFN Cagliari Italy Dipartimento di Fisica dell’Università and Sezione INFN Trieste Italy Dipartimento di Fisica dell’Università and Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Bologna Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Catania Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Padova Italy Dipartimento di Fisica E.R. Caianiello’ dell’Università Gruppo Collegato INFN Salerno Italy Dipartimento DISAT del

The azimuthal (∆) correlation distributions between heavy-flavor decay electrons and associated charged particles are measured in pp and p–Pb collisions at √sNN = 5.02 TeV. Results are reported for electrons with tr... 详细信息

关键词： Electrons

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Measurement of the angle between jet axes in Pb-Pb collisions at √sNN = 5.02 TeV

arXiv

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arXiv 2023年

作者： Acharya, S. Adamova, D. Adler, A. Aglieri Rinella, G. Agnello, M. Agrawal, N. Ahammed, Z. Ahmad, S. Ahn, S.U. Ahuja, I. Akindinov, A. Al-Turany, M. Aleksandrov, D. Alessandro, B. Alfanda, H.M. Alfaro Molina, R. Ali, B. Alici, A. Alizadehvandchali, N. Alkin, A. Alme, J. Alocco, G. Alt, T. Altsybeev, I. Anaam, M.N. Andrei, C. Andronic, A. Anguelov, V. Antinori, F. Antonioli, P. Apadula, N. Aphecetche, L. Appelshäuser, H. Arata, C. Arcelli, S. Aresti, M. Arnaldi, R. Arneiro, J.G.M.C.A. Arsene, I.C. Arslandok, M. Augustinus, A. Averbeck, R. Azmi, M.D. Badala, A. Bae, J. Baek, Y.W. Bai, X. Bailhache, R. Bailung, Y. Balbino, A. Baldisseri, A. Balis, B. Banerjee, D. Banoo, Z. Barbera, R. Barile, F. Barioglio, L. Barlou, M. Barnaföldi, G.G. Barnby, L.S. Barret, V. Barreto, L. Bartels, C. Barth, K. Bartsch, E. Bastid, N. Basu, S. Batigne, G. Battistini, D. Batyunya, B. Bauri, D. Bazo Alba, J.L. Bearden, I.G. Beattie, C. Becht, P. Behera, D. Belikov, I. Bell Hechavarria, A.D.C. Bellini, F. Bellwied, R. Belokurova, S. Bencedi, G. Beole, S. Bercuci, A. Berdnikov, Y. Berdnikova, A. Bergmann, L. Besoiu, M.G. Betev, L. Bhaduri, P.P. Bhasin, A. Bhat, M.A. Bhattacharjee, B. Bianchi, L. Bianchi, N. Bielčı´k, J. Bielčı´kova, J. Biernat, J. Bigot, A.P. Bilandzic, A. Biro, G. Biswas, S. Bize, N. Blair, J.T. Blau, D. Blidaru, M.B. Bluhme, N. Blume, C. Boca, G. Bock, F. Bodova, T. Bogdanov, A. Boi, S. Bok, J. Boldizsár, L. Bombara, M. Bond, P.M. Bonomi, G. Borel, H. Borissov, A. Bossi, H. Botta, E. Bouziani, Y.E.M. Bratrud, L. Braun-Munzinger, P. Bregant, M. Broz, M. Bruno, G.E. Buckland, M.D. Budnikov, D. Buesching, H. Bufalino, S. Buhler, P. Buthelezi, Z. Bylinkin, A. Bysiak, S.A. Cai, M. Caines, H. Caliva, A. Calvo Villar, E. Camacho, J.M.M. Camerini, P. Canedo, F.D.M. Carabas, M. Carballo, A.A. Carcamo, A.G.B. Carnesecchi, F. Caron, R. Carvalho, L.A.D. Castillo Castellanos, J. Catalano, F. Ceballos Sanchez, C. Chakaberia, I. Bologna Italy Department of Applied Physics Aligarh Muslim University Aligarh India Institute of Theoretical Physics University of Wroclaw Poland University of Kansas LawrenceKS United States CERN Foundation Yerevan Armenia AGH University of Science and Technology Cracow Poland Bogolyubov Institute for Theoretical Physics National Academy of Sciences of Ukraine Kiev Ukraine Kolkata India California Polytechnic State University San Luis ObispoCA United States Central China Normal University Wuhan China Havana Cuba Mérida Mexico City Mexico Chicago State University ChicagoIL United States China Institute of Atomic Energy Beijing China Chungbuk National University Cheongju Korea Republic of Comenius University Bratislava Faculty of Mathematics Physics and Informatics Bratislava Slovakia COMSATS University Islamabad Islamabad Pakistan Creighton University OmahaNE United States Department of Physics Aligarh Muslim University Aligarh India Department of Physics Pusan National University Pusan Korea Republic of Department of Physics Sejong University Seoul Korea Republic of Department of Physics University of California BerkeleyCA United States Department of Physics University of Oslo Oslo Norway Department of Physics and Technology University of Bergen Bergen Norway Dipartimento di Fisica Università di Pavia Pavia Italy Dipartimento di Fisica dell'Università Sezione INFN Cagliari Italy Dipartimento di Fisica dell'Università Sezione INFN Trieste Italy Dipartimento di Fisica dell'Università Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell'Università Sezione INFN Bologna Italy Dipartimento di Fisica e Astronomia dell'Università Sezione INFN Catania Italy Dipartimento di Fisica e Astronomia dell'Università Sezione INFN Padova Italy Dipartimento di Fisica E.R. Caianiello' dell'Università Gruppo Collegato INFN Salerno Italy Dipartimento DISAT del Politecnico Sezione INFN Turin Italy Dipartimento di Scienze MIFT

This letter presents the first measurement of the angle between different jet axes (denoted as ∆Raxis) in Pb-Pb collisions. The measurement is carried out in the 0-10% most-central events at √sNN = 5.02 TeV. Jets are assembled by clustering charged particles at midrapidity using the anti-kT algorithm with resolution parameters R = 0.2 and 0.4 and transverse momenta in the intervals 40 Tch jet Tch jet © 2023, CC BY.

关键词： Energy dissipation

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Proceedings of the EuBIC-MS 2020 Developers’ Meeting

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EuPA Open Proteomics 2020年 24卷 1-6页

作者： Christopher Ashwood Wout Bittremieux Eric W. Deutsch Nadezhda T. Doncheva Viktoria Dorfer Ralf Gabriels Vladimir Gorshkov Surya Gupta Andrew R. Jones Lukas Käll Dominik Kopczynski Lydie Lane Ludwig Lautenbacher Marc Legeay Marie Locard-Paulet Bart Mesuere Yasset Perez-Riverol Eugen Netz Sander Willems European Bioinformatics Community for Mass Spectrometry (info@***) Denmark Department of Cellular and Integrative Physiology University of Nebraska Medical Center Omaha USA Department of Computer Science University of Antwerp Antwerp Belgium Institute for Systems Biology Seattle WA 98109 USA Novo Nordisk Foundation Center for Protein Research University of Copenhagen Denmark Bioinformatics Research Group University of Applied Sciences Upper Austria Hagenberg Austria VIB-UGent Center for Medical Biotechnology VIB Ghent Belgium Department of Biochemistry and Molecular Biology University of Southern Denmark Denmark Institute of Systems Molecular and Integrative Biology University of Liverpool UK Science for Life Laboratory KTH - Royal Institute of Technology Solna Stockholm Sweden Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V. Dortmund Germany SIB-Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine Faculty of Medicine Geneva University Geneva Switzerland Technical University of Munich Chair for Proteomics and Bioanalytics Freising Germany European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SD UK Applied Bioinformatics Dept. of Computer Science University of Tübingen Tübingen Germany Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA 92093 USA Center for non-coding RNA in Technology and Health Department of Veterinary and Animal Sciences University of Copenhagen Denmark Department of Biomolecular Medicine Ghent University Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Biomolecular Interactions Max Planck Institute for Developmental Biology Tübingen Germany

The 2020 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers’ meeting was held from January 13 th to January 17 th 2020 in Nyborg, Denmark. Among the participants were scientists as well as developers working in the field of computational mass spectrometry (MS) and proteomics. The 4-day program was split between introductory keynote lectures and parallel hackathon sessions. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts, and to actively contribute to highly relevant research projects. We successfully produced several new tools that will be useful to the proteomics community by improving data analysis as well as facilitating future research. All keynote recordings are available on https://***/10.5281/zenodo.3890181 .

关键词： computational mass spectrometry Mass spectrometry Proteomics Bioinformatics Spectrum clustering Phosphoproteomics XIC extraction Proteomics graph networks Predicted spectra Metaproteomics Functional annotations Benchmark development

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