检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Korzekwa, Kamil Puchala, Zbigniew Tomamichel, Marco Zyczkowski, Karol International Centre for Theory of Quantum Technologies University of Gdansk Gdańsk80-308 Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice44-100 Poland Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Centre for Quantum Software and Information School of Software University of Technology Sydney SydneyNSW2007 Australia Center for Theoretical Physics Polish Academy of Sciences Warszawa02-668 Poland

We introduce and analyse the problem of encoding classical information into different resources of a quantum state. More precisely, we consider a general class of communication scenarios characterised by encoding operations that commute with a unique resource destroying map and leave free states invariant. Our motivating example is given by encoding information into coherences of a quantum system with respect to a fixed basis (with unitaries diagonal in that basis as encodings and the decoherence channel as a resource destroying map), but the generality of the framework allows us to explore applications ranging from super-dense coding to thermodynamics. For any state, we find that the number of messages that can be encoded into it using such operations in a one-shot scenario is upper bounded in terms of the information spectrum relative entropy between the given state and its version with erased resources. Furthermore, if the resource destroying map is the twirling channel over some unitary group, we find matching one-shot lower bounds as well. In the asymptotic setting where we encode into many copies of the resource state, our bounds yield an operational interpretation of resource monotones such as the relative entropy of coherence and its corresponding relative entropy variance. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum optics

来源：评论

学校读者我要写书评

暂无评论

First Results from the REAL-time Transient Acquisition backend (REALTA) at the Irish LOFAR station

arXiv

引用

arXiv 2021年

作者： Murphy, Pearse C. Callanan, P. McCauley, J. McKenna, D.J. Fionnagáin, D.Ó. Louis, C.K. Redman, M.P. Cañizares, L.A. Carley, E.P. Maloney, S.A. Coghlan, B. Daly, M. Scully, J. Dooley, J. Gajjar, V. Giese, C. Brennan, A. Keane, E.F. Maguire, C.A. Quinn, J. Mooney, S. Ryan, A.M. Walsh, J. Jackman, C.M. Golden, A. Ray, T.P. Doyle, J.G. Rigney, J. Burton, M. Gallagher, P.T. School of Physics Trinity College Dublin Dublin 2 Ireland Astronomy & Astrophysics Section Dublin Institute for Advanced Studies D02 XF86 Ireland Department of Physics University College Cork Cork Ireland Astrophysics Research Group School of Mathematics Statistics and Applied Mathematics National University of Ireland Galway University Road GalwayH91 TK33 Ireland Centre for Astronomy School of Physics National University of Ireland Galway University Road GalwayH91 TK33 Ireland School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Deptartment of Computer and Software Engineering Athlone Institute of Technology Athlone Ireland Department of Astronomy University of California BerkeleyCA94720 United States School of Physics University College Dublin Belfield Dublin 4 Ireland Armagh Observatory and Planetarium College Hill ArmaghBT61 9DG United Kingdom Astrophysics Research Centre School of Mathematics and Physics Queen's University Belfast BelfastBT7 1NN United Kingdom

Modern radio interferometers such as the LOw Frequency ARray (LOFAR) are capable of producing data at hundreds of gigabits to terabits per second. This high data rate makes the analysis of radio data cumbersome and computationally expensive. While high performance computing facilities exist for large national and international facilities, that may not be the case for instruments operated by a single institution or a small consortium. Data rates for next generation radio telescopes are set to eclipse those currently in operation, hence local processing of data will become all the more important. Here, we introduce the REAL-time Transient Acquisition backend (REALTA), a computing backend at the Irish LOFAR station (I-LOFAR) which facilitates the recording of data in near real-time and post-processing. We also present first searches and scientific results of a number of radio phenomena observed by I-LOFAR and REALTA, including pulsars, fast radio bursts (FRBs), rotating radio transients (RRATs), the search for extraterrestrial intelligence (SETI), Jupiter, and the Sun. © 2021, CC BY.

关键词： Interferometers

来源：评论

学校读者我要写书评

暂无评论

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

Improving Quality of Life Based on Electronic Patient Reported Outcomes in Patients with CLL and MDS: The Mypal Study

引用

Blood 2024年 144卷 2358-2358页

作者： Thomas Chatzikonstantinou Maria Vasilopoulou Dimitrios Kyrou Georgios Karakatsoulis Panagiotis Argyropaidas Sotiria Besikli-Dimou Panos Bonotis Maria Chatzimina Jana Didi Christos Demosthenous Tina Garani-Papadatos Stefan Hoffman Christine Kakalou Eleni Kazantzaki Stephan Kiefer Lefteris Koumakis Eva Hellstrom Lindberg Julie Ling Christos Maramis Marcel Meyerheim Lydia Scarfo Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece Institute of Computer Science Foundation for Research and Technology-Hellas Heraklion Greece Center for Molecular Medicine Central European Institute of Technology Masaryk University Brno Czech Republic Hematology Department - BMT Unit G Papanicolaou Hospital Thessaloniki Greece Department of Public Health Policy School of Public Health University of West Attica Athens Greece Promotion Software GmbH Berlin Germany Department of Hematology University of Crete School of Medicine Heraklion Greece Fraunhofer Institute For Biomedical Engineering (IBMT) Sulzbach Germany Center for Hematology and Regenerative Medicine (HERM) Department of Medicine Huddinge Karolinska Institutet Stockholm Sweden European Association for Palliative Care Vilvoorde Belgium Department of Pediatric Oncology and Hematology Faculty of Medicine Saarland University Homburg Germany Strategic Research Program on CLL IRCCS Ospedale San Raffaele Milano Italy Università Vita-Salute San Raffaele Milan Italy

Introduction: Palliative care is a critical component of care for patients with chronic hematological malignancies. Leveraging eHealth approaches can potentially facilitate the delivery of palliative care, leading to improved quality of life (QoL). Methods: The MyPal study (NCT04370457) is a randomized controlled clinical trial assessing an eHealth intervention on QoL of patients. The study was conducted according to the Declaration of Helsinki. The ethical aspects of the study, given the vulnerability of the patients, were addressed with diligence and approvals were obtained from the appropriate Research Ethics Committees. Patients who were receiving or had previously received treatment for CLL or MDS were randomly assigned (1:1) to access the MyPal platform versus standard of care. The MyPal platform included a smartphone application used to report QoL status (ePRO questionnaires) and symptoms. The primary endpoint was QoL at 12 months, assessed by the European Organization for Research and Treatment of Cancer (EORTC) quality of life QLQ-C30 General Questionnaire and the Euroqol 5-dimension (Euroqol EQ-5D-3L). Secondary endpoints included physical and emotional functioning, measured by the Integrated Palliative Care Outcome Scale (IPOS), satisfaction with care, measured by the EORTC Patient Satisfaction with Cancer Care questionnaire PATSAT-C33 (PATSAT-C33), and overall survival (OS). Additionally, the Edmonton Symptom Assessment System (ESAS), the Brief Pain Inventory (BPI), and the Emotion Thermometers (ET) QoL questionnaires were assessed only in the intervention group. Results: A total of 171 patients (97 and 74 in the control and intervention arms, respectively) who answered multiple questionnaires were included in the analysis. The intervention group reported a significant decrease in pain (β2=-0.48 (-0.77, -0.19), p<0.001) compared to the control group (β1=0.3 (0.09, 0.5), p=0.01) as measured by the EORTC QLQ-C30. Communication and pain as measured by IPOS

关键词：

来源：评论

学校读者我要写书评

暂无评论

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

引用

arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

What industry wants from Academia in sofware testing? Hearing practitioners' opinions 21

What industry wants from Academia in sofware testing? Hearin...

引用

21st International Conference on Evaluation and Assessment in software engineering, EASE 2017

作者： Garousi, Vahid Felderer, Michael Kuhrmann, Marco Herkiloǧlu, Kadir University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Belval Luxembourg Luxembourg University of Innsbruck Institute of Computer Science Innsbruck Austria Clausthal University of Technology Institute for Applied Software Systems Engineering Goslar Germany HAVELSAN A.Ş. Quality Test and Process Directrate Ankara Turkey

ISBN: (纸本)9781450348041

The level of industry-academia collaboration (IAC) in software engineering in general and in software testing in particular is quite low. Many researchers and practitioners are not collaborating with the "other side" to solve industrial problems. To shed light on the above issue and to characterize precisely what industry wants from academia in software testing, we solicited practitioners' opinions on their challenges in diferent testing activities and also the particularly relevant topics that they want the research community to work on. This short paper aims to draw the community's attention to the important issue of strengthening IAC with the hope of more IAC in software testing in the areas of most importance to the industry. © 2017 Copyright held by the owner/author(s).

关键词： software testing

来源：评论

学校读者我要写书评

暂无评论

The need to implement FAIR principles in biomolecular simulations

arXiv

引用

arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein Data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

来源：评论

学校读者我要写书评

暂无评论

Risk factors for the development of knee osteoarthritis across the lifespan: A systematic review and meta-analysis

引用

Osteoarthritis and Cartilage 2025年

作者： Duong, Vicky Abdel Shaheed, Christina Ferreira, Manuela L. Narayan, Sujita W. Venkatesha, Venkatesha Hunter, David J. Zhu, Jimmy Atukorala, Inoshi Kobayashi, Sarah Goh, Siew Li Briggs, Andrew M. Cross, Marita Espinosa-Morales, Rolando Fu, Kai Guillemin, Francis Keefe, Francis Stefan Lohmander, L. March, Lyn Milne, George J. Mei, Yifang Mobasheri, Ali Namane, Mosedi Peat, George Risberg, May Arna Sharma, Saurab Sit, Regina Telles, Rosa Weiss Zhang, Yuqing Cooper, Cyrus Sydney Musculoskeletal Health Kolling Institute Faculty of Medicine and Health University of Sydney Level 10 St Leonards Sydney 2065 NSW Australia Faculty of Medicine and Health School of Public Health University of Sydney Level 10N King George V Building Royal Prince Alfred Hospital Sydney 2050 NSW Australia The George Institute for Global Health Faculty of Medicine and Health The University of New South Wales Level 18 International Towers 3 300 Barangaroo Ave Barangaroo Sydney 2000 NSW Australia Northern Sydney Local Health District Executive Royal North Shore Hospital The Kolling Institute Level 10 St Leonards Sydney 2065 NSW Australia Department of Rheumatology Royal North Shore Hospital Clinical Administration 7C St Leonards Sydney 2065 NSW Australia Liverpool Hospital Sydney NSW Australia Department of Clinical Medicine University of Colombo Colombo Sri Lanka Centre for Epidemiology and Evidence-Based Practice Universiti Malaya Kuala Lumpur Malaysia Sports and Exercise Medicine Research and Education Group Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Curtin School of Allied Health Curtin University Perth 6845 WA Australia National Institute of Rehabilitation National University Autonomous of Mexico Mexico City Mexico Department of Orthopedic Surgery Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine No. 600 Yishan Road Xuhui District Shanghai 200233 China Université de Lorraine Inserm INSPIIRE Nancy 54500 France Duke Pain Prevention and Treatment Research Program Department of Psychiatry and Behavioral Sciences Duke University School of Medicine Durham NC United States Department of Clinical Sciences Lund Orthopedics Lund University PO Box 117 Lund 22100 Sweden Marshall Centre for Infectious Disease Research and Department of Computer Science and Software Engineering University of Western Australia 35 Stirling Highway Crawley 6009 WA Australia Department of

Objective: To identify and quantify risk factors for incident knee osteoarthritis (KOA) across the lifespan. Methods: This systematic review and meta-analysis identified eligible studies from seven electronic databases and three registries. Longitudinal cohort studies or randomised controlled trials evaluating participants who developed incident symptomatic and/or radiographic KOA were included. Two independent reviewers completed data screening and extraction. Estimates were pooled using a random effects model and reported as odds ratio (OR), hazard ratio, or risk ratio and corresponding 95% confidence intervals (95% CI). Grading of Recommendations, Assessment, Development and Evaluation was used to determine the certainty of evidence. Population attributable fractions were calculated, including risk factors significantly associated with radiographic KOA based on the pooled meta-analysis and where we could determine communality scores using existing clinical datasets. Results: We identified 131 studies evaluating > 150 risk factors. Previous knee injury, older age and high bone mineral density were associated with an increased risk of incident radiographic KOA based on the pooled analysis [OR (95% CI): 2.67 (1.41, 5.05), 1.15 (1.00, 1.33) and 1.82 (1.12, 2.94), respectively], with moderate-to-high certainty. Two risk factors (overweight/obesity and previous knee injury) accounted for 14% of incident radiographic KOA. Other modifiable risk factors, including occupational physical activity, also contribute to radiographic or symptomatic KOA. Conclusion: Novel strategies addressing known modifiable risk factors including overweight/obesity, knee injuries and occupational physical activity are needed to reduce overall burden of KOA. Systematic review registration: PROSPERO ID: CRD42023391187 © 2025 The Authors

关键词： Cohort studies Knee osteoarthritis Risk factors

来源：评论

学校读者我要写书评

暂无评论

CITPM: A cluster-based iterative topical phrase mining framework 21st

CITPM: A cluster-based iterative topical phrase mining frame...

引用

21st International Conference on Database Systems for Advanced Applications, DASFAA 2016

作者： Li, Bing Wang, Bin Zhou, Rui Yang, Xiaochun Liu, Chengfei School of Computer Science and Engineering Northeastern University Liaoning110819 China Centre for Applied Informatics College of Engineering and Science Victoria University MelbourneVIC3011 Australia Department of Computer Science and Software Engineering Swinburne University of Technology MelbourneVIC3122 Australia

ISBN: (纸本)9783319320243

A phrase is a natural, meaningful, essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. Unfortunately, existing approaches predominately rely on the general distributional features between topics and phrases on an entire corpus, while ignore the impact of domain-level topical distribution. This often leads to losing domain-specific terminologies, and as a consequence, weakens the coherence of topical phrases. In this paper, we present a novel framework CITPM for topical phrase mining. Our framework views a corpus as a mixture of clusters (domains), and each cluster is characterized by documents sharing similar topical distributions. The CITPM framework iteratively performs phrase mining, topical inferring and cluster updating until a satisfactory final result is obtained. The empirical verification demonstrates our framework outperforms state-of-the-art works in both aspects of interpretability and efficiency. © Springer International Publishing Switzerland 2016.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

What industry wants from academia in software testing?: Hearing practitioners' opinions 17

What industry wants from academia in software testing?: Hear...

引用

Proceedings of the 21st International Conference on Evaluation and Assessment in software engineering

作者： Vahid Garousi Michael Felderer Marco Kuhrmann Kadir Herkiloğlu University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Belval Luxembourg University of Innsbruck Institute of Computer Science Innsbruck Austria Clausthal University of Technology Institute for Applied Software Systems Engineering Goslar Germany HAVELSAN A.Ş. Quality Test and Process Directorate Ankara Turkey

ISBN: (纸本)9781450348041

The level of industry-academia collaboration (IAC) in software engineering in general and in software testing in particular is quite low. Many researchers and practitioners are not collaborating with the "other side" to solve industrial problems. To shed light on the above issue and to characterize precisely what industry wants from academia in software testing, we solicited practitioners' opinions on their challenges in different testing activities and also the particularly relevant topics that they want the research community to work on. This short paper aims to draw the community's attention to the important issue of strengthening IAC with the hope of more IAC in software testing in the areas of most importance to the industry.

关键词： opinion survey industry-academia collaborations industrial challenges industrial needs software testing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：