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Percutaneous Coronary Intervention With a Drug-Eluting Stent Versus Coronary Artery Bypass Grafting in Patients Receiving Dialysis: A National Study From Taiwan

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Kidney medicine 2024年第2期6卷 100768页

作者： Szu-Yu Pan Ju-Yeh Yang Nai-Chi Teng Yun-Yi Chen Shi-Heng Wang Chien-Lin Lee Kang-Lung Chen Yen-Ling Chiu Shih-Ping Hsu Yu-Sen Peng Yung-Ming Chen Shuei-Liong Lin Likwang Chen Department of Integrated Diagnostics and Therapeutics National Taiwan University Hospital Taipei Taiwan. Division of Nephrology Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan. Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Institute of Health Policy and Management College of Public Health National Taiwan University Taipei Taiwan. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Department of Research Shin-Kong Wu Ho-Su Memorial Hospital Taipei Taiwan. Institute of Hospital and Health Care Administration National Yang Ming Chiao Tung University Taipei Taiwan. National Center for Geriatrics and Welfare Research National Health Research Institutes Yunlin Taiwan. Department of Cardiology Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Cardiovascular Surgery Far Eastern Memorial Hospital New Taipei City Taiwan. Graduate Program in Biomedical Informatics Department of Computer Science and Engineering College of Informatics Yuan Ze University Taoyuan Taiwan. Graduate Institute of Clinical Medicine National Taiwan University College of Medicine Taipei Taiwan. Graduate Institute of Physiology College of Medicine National Taiwan University Taipei Taiwan.

Rationale & Objective:We aimed to study the comparative effectiveness of percutaneous coronary intervention with drug-eluting stent and coronary artery bypass grafting in patients receiving dialysis. Study Design:This was a retrospective observational cohort study. Setting & Participants:This population-based study identified patients receiving dialysis hospitalized for coronary revascularization between January 1, 2009 and December 31, 2015, in the Taiwan National Health Insurance Research Database. Exposures:Patients received percutaneous coronary intervention with drug-eluting stent versus coronary artery bypass grafting. Outcomes:The study outcomes were all-cause mortality, in-hospital mortality, and repeat revascularization. Analytical Approach:Propensity scores were used to match patients. Cox proportional hazards models and logistic regression models were constructed to examine associations between revascularization strategies and mortality. Interval Cox models were fitted to estimate time-varying hazards during different periods. Results:A total of 1,840 propensity score-matched patients receiving dialysis were analyzed. Coronary artery bypass grafting was associated with higher in-hospital mortality (coronary artery bypass grafting vs percutaneous coronary intervention with drug-eluting stent; crude mortality rate 12.5% vs 3.3%; adjusted OR, 5.22; 95% CI, 3.42-7.97; < 0.001) and longer hospitalization duration (median [IQR], 20 [14-30] days vs 3 [2-8] days; < 0.001). After discharge, repeat revascularization, acute coronary syndrome, and repeat hospitalization all occurred more frequently in the percutaneous coronary intervention with drug-eluting stent group. Importantly, with a median follow-up of 2.8 years, coronary artery bypass grafting was significantly associated with a higher risk of all-cause overall mortality (adjusted HR, 1.19; 95% CI, 1.05-1.35; = 0.006) in the multivariable Cox proportional hazard model. Sensitivity and subgroup analyses yie

关键词： Coronary artery bypass grafting coronary revascularization dialysis drug-eluting stent end-stage kidney disease mortality percutaneous coronary intervention survival

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

arXiv

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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

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Publisher Correction: PU.1 and BCL11B sequentially cooperate with RUNX1 to anchor mSWI/SNF to poise the T cell effector landscape

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Nature immunology 2024年第6期25卷 1123页

作者： Noah Gamble Alexandra Bradu Jason A Caldwell Joshua McKeever Olubusayo Bolonduro Ebru Ermis Caroline Kaiser YeEun Kim Benjamin Parks Sandy Klemm William J Greenleaf Gerald R Crabtree Andrew S Koh Department of Pathology University of Chicago Chicago IL USA. Graduate Program in Biophysical Sciences University of Chicago Chicago IL USA. Committee on Molecular Metabolism and Nutrition University of Chicago Chicago IL USA. Committee on Genetics Genomics Systems Biology University of Chicago Chicago IL USA. Department of Human Genetics University of Chicago Chicago IL USA. Immunology Program Stanford University Stanford CA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computer Science Stanford University Stanford CA USA. Department of Applied Physics Stanford University Stanford CA USA. Chan Zuckerberg Biohub San Francisco CA USA. Department of Pathology Stanford University Stanford CA USA. Department of Developmental Biology Stanford University Stanford CA USA. Department of Pathology University of Chicago Chicago IL USA. akoh@uchicago.edu. Institute for Biophysical Dynamics University of Chicago Chicago IL USA. akoh@uchicago.edu.

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Human brain mapping with multi-thousand channel PtNRGrids resolves novel spatiotemporal dynamics

arXiv

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arXiv 2021年

作者： Tchoe, Youngbin Bourhis, Andrew M. Cleary, Daniel R. Stedelin, Brittany Lee, Jihwan Tonsfeldt, Karen J. Brown, Erik C. Siler, Dominic Paulk, Angelique C. Yang, Jimmy C. Oh, Hongseok Ro, Yun Goo Choi, Woojin Lee, Keundong Russman, Samantha Ganji, Mehran Galton, Ian Ben-Haim, Sharona Raslan, Ahmed M. Dayeh, Shadi A. Integrated Electronics and Biointerfaces Laboratory Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093 United States Department of Neurological Surgery University of California San Diego San DiegoCA92093 United States Department of Neurological Surgery Oregon Health & Science University Mail Code CH8N 3303 SW Bond Avenue PortlandOR97239-3098 United States Department of Obstetrics Gynecology and Reproductive Sciences Center for Reproductive Science and Medicine University of California San Diego San DiegoCA92093 United States Department of Neurology Massachusetts General Hospital BostonMA02114 United States Department of Neurosurgery Massachusetts General Hospital BostonMA02114 United States Graduate Program of Materials Science and Engineering University of California San Diego San DiegoCA92093 United States

Electrophysiological devices are critical for mapping eloquent and diseased brain regions and for therapeutic neuromodulation in clinical settings and are extensively utilized for research in brain-machine interfaces. However, the existing devices are often limited in either spatial resolution or cortical coverage, even including those with thousands of channels used in animal experiments. Here, we developed scalable manufacturing processes and dense connectorization to achieve reconfigurable thin-film, multi-thousand channel neurophysiological recording grids using platinum-nanorods (PtNRGrids). With PtNRGrids, we have achieved a multi-thousand channel array of small (30 μm) contacts with low impedance, providing unparalleled spatial and temporal resolution over a large cortical area. We demonstrate that PtNRGrids can resolve sub-millimeter functional organization of the barrel cortex in anesthetized rats that captured the histochemically-demonstrated structure. In the clinical setting, PtNRGrids resolved fine, complex temporal dynamics from the cortical surface in an awake human patient performing grasping tasks. Additionally, the PtNRGrids identified the spatial spread and dynamics of epileptic discharges in a patient undergoing epilepsy surgery at 1 mm spatial resolution, including activity induced by direct electrical stimulation. Collectively, these findings demonstrate the power of the PtNRGrids to transform clinical mapping and research with brain-machine interfaces and highlights a path toward novel therapeutics. Copyright © 2021, The Authors. All rights reserved.

关键词： Mapping

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Anharmonicity and Universal Response of Linear Carbon Chain Mechanical Properties under Hydrostatic Pressure

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Physical Review Letters 2020年第10期125卷 105501-105501页

作者： Keshav Sharma Nathalia L. Costa Yoong Ahm Kim Hiroyuki Muramatsu Newton M. Barbosa Neto Luiz G. P. Martins Jing Kong Alexandre Rocha Paschoal Paulo T. Araujo Department of Physics and Astronomy University of Alabama Tuscaloosa Alabama 35487 USA Department of Physics Federal University of Ceara 60455-760 Fortaleza Ceara Brazil Department of Polymer Engineering School of Polymer Science and Engineering and Alan G. MacDiarmid Energy Research Institute Chonnam National University 77 Yongbong-ro Buk-gu Gwangju 61186 Republic of Korea Research Institute for Supra-Materials Shinshu University 4-17-1 Wakasato Nagano Japan Institute of Natural Sciences Graduate Program in Physics Federal University of Para 66075-110 Belem PA Brazil Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Isolated linear carbon chains (LCCs) encapsulated by multiwalled carbon nanotubes are studied under hydrostatic pressure (P) via resonance Raman scattering. The LCCs’ spectroscopic signature C band around 1850 cm−1 softens linearly with increasing P. A simple anharmonic force-constant model not only describes such softening but also shows that the LCCs’ Young’s modulus (E), Grüneisen parameter (γ), and strain (ϵ) follow universal P−1 and P2 laws, respectively. In particular, γ also presents a unified behavior for all LCCs. To the best of our knowledge, these are the first results reported on such isolated systems and the first work to explore universal P-dependent responses for LCCs’ E, ϵ, and γ.

关键词： Anharmonic lattice dynamics Compressive strength Elasticity Mechanical & acoustical properties Pressure effects Stress 1-dimensional systems Lattice models in condensed matter Pressure techniques Raman spectroscopy

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TOI-2015 b: A sub-Neptune in strong gravitational interaction with an outer non-transiting planet

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Astronomy and Astrophysics 2025年 695卷

作者： Barkaoui, K. Korth, J. Gaidos, E. Agol, E. Parviainen, H. Pozuelos, F.J. Palle, E. Narita, N. Grimm, S. Brady, M. Bean, J.L. Morello, G. Rackham, B.V. Burgasser, A.J. Van Grootel, V. Rojas-Ayala, B. Seifahrt, A. Marfil, E. Passegger, V.M. Stalport, M. Gillon, M. Collins, K.A. Shporer, A. Giacalone, S. Yalçinkaya, S. Ducrot, E. Timmermans, M. Triaud, A.H.M.J. De Wit, J. Soubkiou, A. Watkins, C.N. Aganze, C. Alonso, R. Amado, P.J. Basant, R. Baştürk, Ö. Benkhaldoun, Z. Burdanov, A. Calatayud-Borras, Y. Chouqar, J. Conti, D.M. Collins, K.I. Davoudi, F. Delrez, L. Dressing, C.D. De Leon, J. Dévora-Pajares, M. Demory, B.O. Dransfield, G. Esparza-Borges, E. Fernández-Rodriguez, G. Fukuda, I. Fukui, A. Gallardo, P.P.M. Garcia, L. Garcia, N.A. Ghachoui, M. Geraldía-González, S. Gómez Maqueo Chew, Y. González-Rodríguez, J. Günther, M.N. Hayashi, Y. Horne, K. Hooton, M.J. Hsu, C.C. Ikuta, K. Isogai, K. Jehin, E. Jenkins, J.M. Kawauchi, K. Kagetani, T. Kawai, Y. Kasper, D. Kielkopf, J.F. Klagyivik, P. Lacedelli, G. Latham, D.W. Libotte, F. Luque, R. Livingston, J.H. Mancini, L. Massey, B. Mori, M. Muñoz Torres, S. Murgas, F. Niraula, P. Orell-Miquel, J. Rapetti, David Rebolo-López, R. Ricker, G. Papini, R. Pedersen, P.P. Peláez-Torres, A. Pérez-Prieto, J.A. Poultourtzidis, E. Rodriguez, P.M. Queloz, D. Savel, A.B. Schanche, N. Sánchez-Benavente, M. Sibbald, L. Sefako, R. Sohy, S. Sota, A. Schwarz, R.P. Seager, S. Sebastian, D. Southworth, J. Stangret, M. Stefánsson, G. Stürmer, J. Srdoc, G. Thompson, S.J. Terada, Y. Vanderspek, R. Wang, G. Watanabe, N. Wilkin, F.P. Winn, J. Wells, R.D. Ziegler, C. Zúñiga-Fernández, S. Astrobiology Research Unit Université de Liège Allée du 6 Août 19C Liège4000 Belgium Department of Earth Atmospheric and Planetary Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Calle Vía Láctea s/n Tenerife La Laguna38200 Spain Lund Observatory Division of Astrophysics Department of Physics Lund University Box 118 Lund22100 Sweden Department of Earth Sciences University of Hawaii at Manoa 1680 East-West Rd HonoluluHI96822 United States Institute for Astrophysics University of Vienna Türkenschanzstrasse 17 Vienna1180 Austria Department of Astronomy and Astrobiology Program University of Washington Box 351580 SeattleWA98195 United States NExSS Virtual Planetary Laboratory University of Washington Box 351580 SeattleWA98195 United States Tenerife La LagunaE-38206 Spain Glorieta de la Astronomía s/n Granada18008 Spain INAF-Palermo Astronomical Observatory Piazza del Parlamento 1 Palermo90134 Italy Komaba Institute for Science University of Tokyo 3-8-1 Komaba Tokyo Meguro153-8902 Japan Astrobiology Center 2-21-1 Osawa Tokyo Mitaka181-8588 Japan Research Institute for Advanced Computer Science Universities Space Research Association WashingtonDC20024 United States Institute for Particle Physics and Astrophysics ETH Zürich OttoStern-Weg 5 Zürich8093 Switzerland Department of Astrophysics University of Zürich Winterthurerstrasse 190 Zürich8057 Switzerland Department of Astronomy & Astrophysics University of Chicago ChicagoIL United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Institute Université de Liège Allée du 6 Août 19C LiègeB-4000 Belgium Instituto de Alta Investigación Universidad de Tarapacá Casilla 7D Arica Chile Gemini Observatory/NSF NOIRLab 670 N. A'ohoku Place HiloHI96720 United States Departamento de Ingeniería Topográfica y Cartografía E.

TOI-2015 is a known exoplanetary system around an M4 dwarf star, consisting of a transiting sub-Neptune planet in a 3.35-day orbital period, TOI-2015 b, accompanied by a non-transiting companion, TOI-2015 c. High-precision radial-velocity measurements were taken with the MAROON-X spectrograph, and high-precision photometric data were collected, primarily using the SPECULOOS, MUSCAT, TRAPPIST and LCOGT networks. We collected 63 transit light curves and 49 different transit epochs for TOI-2015 b. We recharacterized the target star by combining optical spectra obtained by the MAROON-X, Shane/KAST and IRTF/SpeX spectrographs, Bayesian model averaging (BMA) and spectral energy distribution (SED) analysis. The TOI-2015 host star is a K = 10.3 mag M4-type dwarf with a subsolar metallicity of [Fe/H] =-0.31 ± 0.16, and an effective temperature of Teff ≈ 3200 K. Our photodynamical analysis of the system strongly favors the 5:3 mean-motion resonance and in this scenario the planet b (TOI-2015 b) has an orbital period of Pb = 3.34 days, a mass of Mp = 9.02-0.36+0.32M⊕, and a radius of Rp = 3.309-0.011+0.013R⊕, resulting in a density of ρp = 0.25 ± 0.01 ρ⊕ = 1.40 ± 0.06 g cm-3;this is indicative of a Neptune-like composition. Its transits exhibit large (> 1 hr) timing variations characteristic of an outer perturber in the system. We performed a global analysis of the high-resolution radial-velocity measurements, the photometric data, and the TTVs, and inferred that TOI-2015 hosts a second planet, TOI-2015 c, in a non-transiting configuration. Our analysis places it near a 5:3 resonance with an orbital period of Pc = 5.583 days and a mass of Mp = 8.91-0.40+0.38M⊕. The dynamical configuration of TOI-2015 b and TOI-2015 c can be used to constrain the system's planetary formation and migration history. Based on the mass-radius composition models, TOI-2015 b is a water-rich or rocky planet with a hydrogen-helium envelope. Moreover, TOI-2015 b has a high transmission-spectroscopic met

关键词： Velocity measurement

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Re-framing and reimagining the doctoral student narrative: Black women's experiences in engineering and computer science 125

Re-framing and reimagining the doctoral student narrative: B...

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125th ASEE Annual Conference and Exposition

作者： Artis, Sharnnia Subbian, Vignesh Shavers, Marjorie C. Le Sure, Stade Spencer, Breauna Marie Joshi, Aishwarya P. School of Information and Computer Sciences University of California Irvine United States Heidelberg University Department of Graduate Studies in Counseling Germany American Society for Engineering Education Utah State University United States University of California Irvine United States Heidelberg University Clinical Counseling Program Germany

Recent data reveal that a higher percentage of Black women (9.7%) are enrolled in college than any other group, topping Asian women (8.7%), White women (7.1%) and White men (6.1%). Despite these gains in college attendance, Black women are often underrepresented in the fields of engineering and computer science. This paper presents the findings from a qualitative study that investigated the identity and experiences of Black women who are pursuing doctoral degrees in engineering and computer science. This research is grounded on the tenet that one cannot effectively serve or impact a community until he/she genuinely understands the issues and challenges facing the people who are its members. This work explores how Black female doctoral students persist in environments where they are grossly underrepresented. Content analysis is used to examine interview data obtained from 13 Black women who are pursing doctoral degrees in engineering and computer science. This paper concludes with some of the key challenges these women face in their programs on a daily basis. The goal of this research is to bring awareness to not only the challenges, but also potential strategies to increase the retention and persistence of Black women in engineering and computer science across all academic levels. © American Society for Engineering Education, 2018.

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Correction to: CpG‑creating mutations are costly in many human viruses

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Evolutionary Ecology 2020年第3期34卷 361-362页

作者： Caudill, Victoria R. Qin, Sarina Winstead, Ryan Kaur, Jasmeen Tisthammer, Kaho Pineda, E. Geo Solis, Caroline Cobey, Sarah Bedford, Trevor Carja, Oana Eggo, Rosalind M. Koelle, Katia Lythgoe, Katrina Regoes, Roland Roy, Scott Allen, Nicole Aviles, Milo Baker, Brittany A. Bauer, William Bermudez, Shannel Carlson, Corey Castellanos, Edgar Catalan, Francisca L. Chemel, Angeline Katia Elliot, Jacob Evans, Dwayne Fiutek, Natalie Fryer, Emily Goodfellow, Samuel Melvin Hecht, Mordecai Hopp, Kellen Hopson, E. Deshawn Jaberi, Amirhossein Kim, Jennifer Kinney, Christen Lao, Derek Le, Adrienne Lo, Jacky Lopez, Alejandro G. López, Andrea Lorenzo, Fernando G. Luu, Gordon T. Mahoney, Andrew R. Melton, Rebecca L. Nascimento, Gabriela Do Pradhananga, Anjani Rodrigues, Nicole S. Shieh, Annie Sims, Jasmine Singh, Rima Sulaeman, Hasan Thu, Ricky Tran, Krystal Tran, Livia Winters, Elizabeth J. Wong, Albert Pennings, Pleuni S. Department of Biology San Francisco State University San Francisco USA Department of Biology University of Oregon Eugene USA Quantitative Systems Biology Univeristy of California Merced USA Department of Ecology and Evolution University of Chicago Chicago USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center Seattle USA Department of Computational Biology School of Computer Science Carnegie Mellon University Pittsburgh USA Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK Department of Biology Emory University Atlanta USA Big Data Institute University of Oxford Oxford UK Department of Environmental Systems Science ETH Zurich Zurich Switzerland Department of Neurological Surgery University of California San Francisco USA Department of Organismic and Evolutionary Biology Harvard University Cambridge USA Department of Plant Biology Carnegie Institution for Science Stanford USA Health Sciences Center University of New Mexico Albuquerque USA UCSD Biomed Sciences PhD Program University of California San Diego USA Biochemistry Molecular Cellular and Developmental Biology Graduate Group University of California Davis USA UCSF Tetrad Graduate Program University of California San Francisco USA

In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.

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Iterative Motion Compensation reconstruction ultra-short TE(iMoCo UTE) for high resolution free breathing pulmonary MRI

arXiv

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arXiv 2020年

作者： Zhu, Xucheng Chan, Marilynn Lustig, Michael Johnson, Kevin Larson, Peder E.Z. UCSF/UC Berkeley Graduate Program in Bioengineering University of California San Francisco San FranciscoCA United States Department of Radiology and Biomedical Imaging University of California San Francisco San FranciscoCA United States Department of Pediatrics Division of Pediatric Pulmonology University of California San Francisco San FranciscoCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Department of Medical Physics University of Wisconsin Madison MadisonWI United States Department of Radiology University of Wisconsin Madison MadisonWI United States

Purpose: To develop a high scanning efficiency, motion corrected imaging strategy for free-breathing pulmonary MRI by combining a motion compensation reconstruction with a UTE acquisition, called iMoCo UTE. Methods: An optimized golden angle ordering radial UTE sequence was used to continuously acquire data for 5 minutes. All readouts were grouped to different respiratory motion states based on self-navigator signals, then motion resolved data was reconstructed by XD Golden-angle RAdial Sparse Parallel reconstruction (XD-GRASP). One state from the motion resolved images was selected as a reference, and motion fields from the other states to the reference were derived via non-rigid registration. Finally, all motion resolved data and motion fields were reconstructed by using an iterative motion compensation reconstruction with a total generalized variation sparse constraint. Results: The iMoCo UTE strategy was evaluated in volunteers and non-sedated pediatric patient(4-6 y/o) studies. Images reconstructed with iMoCo UTE provided sharper anatomical lung structures, and higher apparent SNR and CNR, compared to using other motion correction strategies, such as soft-gating, motion resolved reconstruction, and non-rigid motion compensation(MoCo). iMoCo UTE also showed promising results in an infant study. Conclusions: The proposed iMoCo UTE combines self-navigation, motion modeling, and a compressed sensing reconstruction to increase scan efficiency, SNR, and reduce respiratory motion in lung MRI. This proposed strategy shows improvements in free breathing lung MRI scans, especially in very challenging application situations, such as pediatric MRI studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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