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现代生物医学进展 2017年第15期17卷 I0003-I0003页

作者： Huipeng Li, Elise T Courtois, Debarka Sengupta, Yuliana Tan Shyam Prabhakar Elise T Courtois, Yuliana Tan Paul Robson Debarka Sengupta Kok Hao Chen, Jolene Jie Lin Goh Paul Jongjoon Choi Say Li Kong, Axel M Hillmer Iain Beehuat Tan Clarinda Chua Iain Beehuat Tan Lim Kiat Hon Wah Siew Tan Mark Wong Lawrence J K Wee Iain Beehuat Tan Paul Robson Paul Robson Paul Robson Computational and Systems Biology Genome Institute of Singapore Singapore Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India Synthetic Biology Genome Institute of Singapore Singapore Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Department of Medical Oncology National Cancer Centre Singapore Singapore Department of Pathology Singapore General Hospital Singapore Department of Colorectal Surgery Singapore General Hospital Singapore Data Analytics Department Institute for Infocomm Research Singapore Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA Department of Biological Sciences National University of Singapore Singapore.

结合单细胞基因组学和计算机技术，一个研究团队（包括来自杰克逊实验室（JAx）单细胞生物学主任Paul Robson博士在内）鉴定出了11种结直肠癌肿瘤的癌细胞组成及邻近的非癌细胞。这对于更好的肿瘤靶向诊断和治疗很重要。”使用单细胞测... 详细信息

结合单细胞基因组学和计算机技术，一个研究团队（包括来自杰克逊实验室（JAx）单细胞生物学主任Paul Robson博士在内）鉴定出了11种结直肠癌肿瘤的癌细胞组成及邻近的非癌细胞。这对于更好的肿瘤靶向诊断和治疗很重要。”使用单细胞测序。”JAX科学家、这篇发表在Nature Genetics上的文章共同第一作者Elise Courtois说道。”我们可以根据肿瘤中的细胞组成将结直肠癌进一步分类。因为每种亚型的肿瘤病人生存率存在差别，我们的方法将为肿瘤医生提供更多的关于肿瘤预后和治疗的信息。”

关键词：单细胞分析结直肠癌癌细胞 Nat 细胞组成肿瘤病计算机技术细胞生物学

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The application of an extracellular vesicle-based biosensor in early diagnosis and prediction of chemoresponsiveness in ovarian cancer

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Cancer Letters 2024年 581卷 216533-216533页

作者： Tsang, Benjamin K. Asare-Werehene, Meshach Hunter, Rob Gerber, Emma Reunov, Arkadiy Brine, Isaiah Chang, Chia-Yu Chang, Chia-Ching Shieh, Dar-Bin Burger, Dylan Anis, Hanan Departments of Obstetrics & Gynecology and Cellular & Molecular Medicine Centre for Infection Immunity and Inflammation Interdisciplinary School of Health Sciences University of Ottawa Ottawa ON K1N 6N5 Canada Chronic Disease Program Ottawa Hospital Research Institute The Ottawa Hospital Ottawa ON K1Y 4E9 Canada Department of Biology St. Francis Xavier University 2320 Notre Dame Avenue Antigonish NS B2G 2W5 Canada School of Electrical Engineering and Computer Science Ottawa-Carleton Institute for Biomedical Engineering University of Ottawa Ottawa ON K1N 6N5 Canada Department of Biological Science and Department of Electrophysics Technology Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 30068 Taiwan Institute of Basic Medical Science Institute of Oral Medicine and Department of Stomatology Advanced Optoelectronic Technology Center and Center for Micro/Nano Science and Technology National Cheng Kung University Hospital National Cheng Kung University Tainan 704 Taiwan Institute of Physics Academia Sinica Taipei 10529 Taiwan

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Spectroscopic Confirmation of a Population of Isolated, Intermediate-Mass YSOs

arXiv

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arXiv 2022年

作者： Kuhn, Michael A. Saber, Ramzi Povich, Matthew S. de Souza, Rafael S. Krone-Martins, Alberto Ishida, Emille E.O. Zucker, Catherine Benjamin, Robert A. Hillenbrand, Lynne A. Castro-Ginard, Alfred Zhou, Xingyu Department of Astronomy California Institute of Technology 1216 East California Boulevard PasadenaCA91125 United States Department of Physics and Astronomy California State Polytechnic University 3801 West Temple Ave PomonaCA91768 United States Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences 80 Nandan Rd. Shanghai200030 China Donald Bren School of Information and Computer Sciences University of California IrvineCA92697 United States CENTRA/SIM Faculdade de Ciências Universidade de Lisboa Ed. C8 Campo Grande Lisboa1749-016 Portugal Université Clermont Auvergne CNRS IN2P3 LPC Clermont-FerrandF-63000 France Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States NASA Hubble Fellowship Program United States Department of Physics University of Wisconsin-Whitewater 800 W Main St WhitewaterWI53190 United States Leiden Observatory Leiden University Niels Bohrweg 2 Leiden2333 CA Netherlands Kavli Institute for Astronomy and Astrophysics Peking University Yiheyuan 5 Haidian Qu Beijing100871 China Department of Astronomy Peking University Yiheyuan 5 Haidian Qu Beijing100871 China

Wide-field searches for young stellar objects (YSOs) can place useful constraints on the prevalence of clustered versus distributed star formation. The Spitzer/IRAC Candidate YSO (SPICY) catalog is one of the largest compilations of such objects (∼120,000 candidates in the Galactic midplane). Many SPICY candidates are spatially clustered, but, perhaps surprisingly, approximately half the candidates appear spatially distributed. To better characterize this unexpected population and confirm its nature, we obtained Palomar/DBSP spectroscopy for 26 of the optically-bright (G ☉, and the estimated accretion rates, ranging from 3 × 10−8 to 3 × 10−7 M☉ yr−1, are typical for YSOs in this mass range. The 3D spatial distribution of these stars, based on Gaia astrometry, reveals that the "isolated" YSOs are not evenly distributed in the Solar neighborhood but are concentrated in kpc-scale dusty Galactic structures that also contain the majority of the SPICY YSO clusters. Thus, the processes that produce large Galactic star-forming structures may yield nearly as many distributed as clustered YSOs. Copyright © 2022, The Authors. All rights reserved.

关键词： Spatial distribution

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Magnetized and quadratic convection based thermal transport in ternary radiative bio-nanofluid via intelligent neural networks: Two hidden layers mechanism

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Results in Physics 2024年 65卷

作者： Ayub, Assad Darvesh, Adil Shah, Syed Zahir Hussain Junjua, Moin-ud-Din Santos, Elizaldo Domingues dos Ahmad, Shabbir Hendy, Ahmed S. Ragab, Adham E. Department of Mathematics & Statistics Hazara University Manshera Pakistan School of Mathematical Sciences Zhejiang Normal University Zhejiang Jinhua 321004 China Programa de Pós-Graduação em Modelagem Computacional/LMCE Escola de Engenharia Universidade Federal do Rio Grande – FURG RS Rio Grande 96203900 Brazil FURG Escola de Engenharia RS Rio Grande Brazil Department of Basic Sciences and Humanities Muhammad Nawaz Sharif University of Engineering and Technology Multan 60000 Pakistan Department of Computational Mathematics and Computer Science Institute of Natural Sciences and Mathematics Ural Federal University 19 Mira St. Yekaterinburg 620002 Russian Federation Department of Industrial Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Mathematics Ghazi University Dera Ghazi Khan 32200 Pakistan Graduate Program of Ocean Engineering School of Engineering Universidade Federal do Rio Grande Italia Avenue km 8 96201-900 Rio Grande Brazil Department of Mechanics and Mathematics Western Caspian University Baku 1001 Azerbaijan

Significance: The thermal analysis of nanofluid in a vertical cylinder (artery) in a magnetized environment holds significant implications in physiological and thermal regulation networks. This research is significant in biomedical engineering with applications like medical diagnostics and treatment strategies. Motive: This study investigates the thermal behavior of a magnetized blood-based ternary Carreau nanofluid flowing through a vertically bounded artery with quadratic convection. Velocity and heat transfer analysis is conducted within the artery, utilizing thermal radiation and quadratic convection in a magnetized setting. The base fluid consists of blood, augmented with three nanoparticles: CuO, Al₂O₃, and TiO₂. The investigation centers on examining the properties of blood nanofluid, arterial geometry, and thermal dynamics. Methodology: The physical model generates a set of partial differential equations (PDEs) and similarities mechanism is utilized to fetch its non dimensional form in terms of ordinary differential equations (ODEs). Furthermore, numerical outcomes are obtained with Matlab function bvp4c and obtained data set is trained through robust scheme Levenberg-Marquardt neural network (LMNN) procedure to predict the solution. Findings: Velocity of blood is reduced with increased values of wiessenberg number, magnetic parameter and mixed convection parameter and velocity increases for Second-order convection parameter. Temperature profile decreases with curvature parameter, mixed convection parameter and permeability parameter. © 2024

关键词： Blood fluid Intelligent neural network Magnetized environment Ternary nanoparticles Thermal scrutiny

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Quantum Simulation for High-Energy Physics

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PRX Quantum 2023年第2期4卷 027001-027001页

作者： Christian W. Bauer Zohreh Davoudi A. Baha Balantekin Tanmoy Bhattacharya Marcela Carena Wibe A. de Jong Patrick Draper Aida El-Khadra Nate Gemelke Masanori Hanada Dmitri Kharzeev Henry Lamm Ying-Ying Li Junyu Liu Mikhail Lukin Yannick Meurice Christopher Monroe Benjamin Nachman Guido Pagano John Preskill Enrico Rinaldi Alessandro Roggero David I. Santiago Martin J. Savage Irfan Siddiqi George Siopsis David Van Zanten Nathan Wiebe Yukari Yamauchi Kübra Yeter-Aydeniz Silvia Zorzetti Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics Maryland Center for Fundamental Physics and the NSF Institute for Robust Quantum Simulation University of Maryland College Park Maryland 20742 USA Department of Physics University of Wisconsin Madison Wisconsin 53706 USA T-2 Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Fermi National Accelerator Laboratory Batavia Illinois 60510 USA Enrico Fermi Institute University of Chicago Chicago Illinois 60637 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Department of Physics University of Chicago Chicago Illinois 60637 USA Department of Physics and Illinois Center for the Advanced Study of the Universe and Illinois Quantum Information Science and Technology Center University of Illinois Urbana Illinois 61801 USA QuEra Computing Inc Boston Massachusetts 02135 USA Department of Mathematics University of Surrey Guildford Surrey GU2 7XH United Kingdom Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University New York 11794-3800 USA Department of Physics Brookhaven National Laboratory Upton New York 11973 USA Peng Huanwu Center for Fundamental Theory Hefei Anhui 230026 China Interdisciplinary Center for Theoretical Study University of Science and Technology of China Hefei Anhui 230026 China Pritzker School of Molecular Engineering Chicago Quantum Exchange and Kadanoff Center for Theoretical Physics University of Chicago Illinois 60637 USA qBraid Co. Harper Court 5235 Chicago Illinois 60615 USA Department of Physics Harvard University Cambridge Massachusetts 02138 USA Department of Physics and Astronomy University of Iowa Iowa City Iowa 52242 USA Duke Quantum Center Duke University Durham North Carolina 27701 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Department of Physics Duke University

It is for the first time that quantum simulation for high-energy physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in quantum information sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This Roadmap is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond.

关键词： Quantum simulation

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Back Cover: Robust, Ultrafast and Reversible Photoswitching in Bulk Polymers Enabled by Octupolar Molecule Design (Angew. Chem. Int. Ed. 16/2024)

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Angewandte Chemie International Edition 2024年第16期63卷

作者： Long Fang Ziwei Lin Yang Zhang Bin Ye Jing Li Qishan Ran Xiaotong Wang Meijia Yang Prof. Zhongke Yuan Prof. Xiaofeng Lin Prof. Dingshan Yu Prof. Xudong Chen Prof. Quan Li Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education Key Laboratory of High Performance Polymer-based Composites of Guangdong Province GBRCE for Functional Molecular Engineering School of Chemistry Sun Yat-sen University Guangzhou 510006 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510275 China Institute of Applied Physics and Materials Engineering University of Macau Taipa Macao SAR 999078 China School of Chemical Engineering and Light Industry Guangdong University of Technology Guangzhou 510006 China Guangdong Laboratory of Chemistry and Fine Chemical Industry Jieyang Center Jieyang 515200 China Institute of Advanced Materials and School of Chemistry and Chemical Engineering Southeast University Nanjing 211189 China Materials Science Graduate Program Kent State University Kent OH 44242 USA

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OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

arXiv

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arXiv 2022年

作者： Babier, Aaron Mahmood, Rafid Zhang, Binghao Alves, Victor G.L. Barragán-Montero, Ana Maria Beaudry, Joel Cardenas, Carlos E. Chang, Yankui Chen, Zijie Chun, Jaehee Diaz, Kelly Eraso, Harold David Faustmann, Erik Gaj, Sibaji Gay, Skylar Gronberg, Mary Guo, Bingqi He, Junjun Heilemann, Gerd Hira, Sanchit Huang, Yuliang Ji, Fuxin Jiang, Dashan Giraldo, Jean Carlo Jimenez Lee, Hoyeon Lian, Jun Liu, Shuolin Liu, Keng-Chi Marrugo, José Miki, Kentaro Nakamura, Kunio Netherton, Tucker Nguyen, Dan Nourzadeh, Hamidreza Osman, Alexander F.I. Peng, Zhao Muñoz, José Darío Quinto Ramsl, Christian Rhee, Dong Joo Rodriguez, Juan David Shan, Hongming Siebers, Jeffrey V. Soomro, Mumtaz H. Sun, Kay Hoyos, Andrés Usuga Valderrama, Carlos Verbeek, Rob Wang, Enpei Willems, Siri Wu, Qi Xu, Xuanang Yang, Sen Yuan, Lulin Zhu, Simeng Zimmermann, Lukas Moore, Kevin L. Purdie, Thomas G. McNiven, Andrea L. Chan, Timothy C.Y. Department Of Mechanical And Industrial Engineering University Of Toronto TorontoON Canada Vector Institute TorontoON Canada Department Of Radiation Oncology University Of Virginia Health System CharlottesvilleVA United States Department Of Molecular Imaging Radiation Oncology UCLouvain Louvain-la-Neuve Belgium Department Of Radiation Oncology Memorial Sloan Kettering Cancer Center New YorkNY United States Department Of Radiation Oncology The University Of Alabama At Birmingham BirminghamAL United States Department Of Engineering And Applied Physics University Of Science And Technology Of China Hefei China Shenying Medical Technology Co. Ltd. Guangdong Shenzhen China Department Of Radiation Oncology Yonsei University Seoul Korea Republic of Department Of Physics National University Of Colombia Medellín Colombia Atominstitut Vienna University Of Technology Vienna Austria Department Of Biomedical Engineering Cleveland Clinic ClevelandOH United States Department Of Radiation Physics The University Of Texas MD Anderson Cancer Center HoustonTX United States Department Of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department Of Radiation Oncology Medical University Of Vienna Vienna Austria Department Of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Department Of Radiation Oncology Peking University Cancer Hospital And Institute Beijing China Department Of Electrical Engineering And Automation Anhui University Hefei China Department Of Radiation Oncology Massachusetts General Hospital BostonMA United States Department Of Radiation Oncology University Of North Carolina At Chapel Hill Chapel HillNC United States Department Of Medical Imaging Taiwan AI Labs Taipei Taiwan Department Of Biomedical And Health Sciences Hiroshima University Hiroshima Japan Laboratory Department Of Radiation Oncology The University Of Texas Southwestern Medical Center DallasTX United States Department Of Radiatio

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved;the deviation in dosevolume histogram (DVH) criterion;and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBPgenerated plans performed significantly better (P © 2022, CC BY.

关键词： Forecasting

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Genomic Surveillance of SARS-CoV-2 in Taiwan: A Perspective on Evolutionary Data Interpretation and Sequencing Issues

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Biomedical journal 2024年 100820页

作者： Yu-Nong Gong Nai-Yu Kuo Ting-Syuan Yeh Shin-Ru Shih Guang-Wu Chen Research Center for Emerging Viral Infections College of Medicine Chang Gung University Taoyuan Taiwan International Master Degree Program for Molecular Medicine in Emerging Viral Infections College of Medicine Chang Gung University Taoyuan Taiwan Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. Medical Education Department Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Taiwan. Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Department of Medical Biotechnology and Laboratory Science College of Medicine Chang Gung University Taoyuan Taiwan Research Center for Chinese Herbal Medicine Research Center for Food and Cosmetic Safety and Graduate Institute of Health Industry Technology College of Human Ecology Chang Gung University of Science and Technology Taoyuan Taiwan. Department of Computer Science and Information Engineering College of Engineering Chang Gung University Taoyuan Taiwan. Electronic address: gwchen@mail.cgu.edu.tw.

This review presents a comprehensive perspective on the genomic surveillance of SARS-CoV-2 in Taiwan, with a focus on next-generation sequencing and phylogenetic interpretation. This article aimed to explore how Taiwan has utilized genomic sequencing technologies and surveillance to monitor and mitigate the spread of COVID-19. We examined databases and sources of genomic sequences and highlighted the role of data science methodologies in the explanation and analyses of evolutionary data. This review addressed the challenges and limitations inherent in genomic surveillance, such as concerns regarding data quality and the necessity for interdisciplinary expertise for accurate data interpretation. Special attention was given to the unique challenges faced by Taiwan, including its high population density and major transit destination for international travelers. We underscored the far-reaching implications of genomic surveillance data for public health policy, particularly in influencing decisions regarding travel restrictions, vaccine administration, and public health decision-making. Studies were examined to demonstrate the effectiveness of using genomic data to implement public health measures. Future research should prioritize the integration of methodologies and technologies in evolutionary data science, particularly focusing on phylodynamic analytics. This integration is crucial to enhance the precision and applicability of genomic data. Overall, we have provided an overview of the significance of genomic surveillance in tracking SARS-CoV-2 variants globally and the pivotal role of data science methodologies in interpreting these data for effective public health interventions.

关键词： SARS-CoV-2 data science genomic surveillance next-generation sequencing phylodynamics

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Galaxy clusters at z∼1 imaged by ALMA with the Sunyaev-Zel’dovich effect

arXiv

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arXiv 2022年

作者： Kitayama, Tetsu Ueda, Shutaro Okabe, Nobuhiro Akahori, Takuya Hilton, Matt Hughes, John P. Ichinohe, Yuto Kohno, Kotaro Komatsu, Eiichiro Lin, Yen-Ting Miyatake, Hironao Oguri, Masamune Sifón, Cristóbal Takakuwa, Shigehisa Takizawa, Motokazu Tsutsumi, Takahiro van Marrewijk, Joshiwa Wollack, Edward J. Department of Physics Toho University Chiba Funabashi274-8510 Japan No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Department of Physics Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Core Research for Energetic Universe Hiroshima University Department of Physics Hiroshima University 1-3-1 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Physics Program Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Mizusawa VLBI observatory National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Operation Division SKA Observatory Jodrell Bank Lower Withington MacclesfieldSK11 9DL United Kingdom Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa Wits Centre for Astrophysics School of Physics University of the Witwatersrand Private Bag 3 Johannesburg2050 South Africa Department of Physics and Astronomy Rutgers The State University of New Jersey PiscatawayNJ08854-8019 United States Department of Physics Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo171-8501 Japan Institute of Astronomy School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Research Center for the Early Universe School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Max-Planck-Institut für Astrophysik Karl-Schwarzschild Str. 1 GarchingD-85741 Germany The University of Tokyo Institutes for Advanced Study The University of Tokyo 5-1-5 Kashiwanoha Chiba277-8583 Japan Nagoya University Nagoya464-8

We present high angular-resolution measurements of the thermal Sunyaev-Zel’dovich effect (SZE) toward two galaxy clusters, RCS J2319+0038 at z = 0.9 and HSC J0947−0119 at z = 1.1, by the Atacama Large Millimeter/submillimeter Array (ALMA) in Band 3. They are supplemented with available Chandra X-ray data, optical data taken by Hyper Suprime-Cam on Subaru, and millimeter-wave SZE data from the Atacama Cosmology Telescope. Taking into account departures from spherical symmetry, we have reconstructed non-parametrically the inner pressure profile of two clusters as well as electron temperature and density profiles for RCS J2319+0038. This is one of the first such measurements for an individual cluster at z >∼ 0.9. We find that the inner pressure profile of both clusters is much shallower than that of local cool-core clusters. Our results consistently suggest that RCS J2319+0038 hosts a weak cool core, where radiative cooling is less significant than in local cool cores. On the other hand, HSC J0947−0119 exhibits an even shallower pressure profile than RCS J2319+0038 and is more likely a non-cool-core cluster. The SZE centroid position is offset by more than 140 h−170kpc from the peaks of galaxy distribution in HSC J0947−0119, suggesting a stronger influence of mergers in this cluster. We conclude that these distant clusters are at a very early stage of developing the cool cores typically found in clusters at lower redshifts. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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