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Research Square 2021年

作者： Kuchroo, Manik Huang, Jessie Wong, Patrick Grenier, Jean-Christophe Shung, Dennis Tong, Alexander Lucas, Carolina Klein, Jon Burkhardt, Daniel B. Gigante, Scott Godavarthi, Abhinav Rieck, Bastian Israelow, Benjamin Simonov, Michael Mao, Tianyang Oh, Ji Eun Silva, Julio Takahashi, Takehiro Odio, Camila D. Casanovas-Massana, Arnau Fournier, John Farhadian, Shelli Dela Cruz, Charles S. Ko, Albert I. Hirn, Matthew J. Wilson, F. Perry Hussin, Julie Wolf, Guy Iwasaki, Akiko Krishnaswamy, Smita Department of Neuroscience Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Immunobiology Yale University New HavenCT United States Montreal Heart Institute MontréalQC Canada Department of Medicine Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Computational Biology Bioinformatics Program Yale University New HavenCT United States Department of Applied Mathematics Yale University New HavenCT United States Department of Biosystems Science and Engineering ETH Zurich Switzerland Department of Epidemiology of Microbial Diseases Yale School of Public Health New HavenCT United States Department of Medicine Section of Infectious Diseases Yale University School of Medicine New HavenCT United States Department of Medicine Section of Pulmonary and Critical Care Medicine Yale University School of Medicine New HavenCT United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Department of Mathematics Michigan State University East LansingMI United States Clinical and Translational Research Accelerator Department of Medicine Yale University New HavenCT United States Faculty of Medicine Université de Montréal Québec Canada Mila – Quebec AI institute MontréalQC Canada Department of Mathematics and Statistics Université de Montréal MontréalQC Canada Howard Hughes Medical Institute Chevy ChaseMD United States

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hiCD66blo neutrophil and IFNγ+GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome. © 2021, CC BY.

关键词： Coronavirus

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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HER2 amplification in colorectal cancer with brain metastasis: A propensity score matching study

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European journal of cancer (Oxford, England : 1990) 2023年 181卷 62-69页

作者： Po-Chuan Chen Yu-Min Yeh Chun-Ting Chu Pei-Fang Su Pin-Hsuan Chiu Bo-Wen Lin Shang-Hung Chen Peng-Chan Lin Chung-Ta Lee Helen H W Chen Chien-Chin Chen Division of Colorectal Surgery Department of Surgery National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan. Department of Oncology National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan. Division of Colorectal Surgery Department of Surgery Ditmanson Medical Foundation Chia-Yi Christian Hospital Chiayi 600 Taiwan. Department of Statistics National Cheng Kung University Tainan 701 Taiwan. The Center of Quantitative Sciences Clinical Medicine Research Center National Cheng Kung University Hospital Tainan 704 Taiwan. Department of Oncology National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan National Institute of Cancer Research National Health Research Institute Tainan 704 Taiwan. Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science National Cheng Kung University Tainan 704 Taiwan. Department of Pathology National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan. Department of Oncology National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan. Electronic address: helen@mail.ncku.edu.tw. Department of Pathology Ditmanson Medical Foundation Chia-Yi Christian Hospital Chiayi 600 Taiwan Department of Cosmetic Science Chia Nan University of Pharmacy and Science Tainan 717 Taiwan Department of Biotechnology and Bioindustry Sciences College of Bioscience and Biotechnology National Cheng Kung University Tainan 701 Taiwan Ph.D. Program in Translational Medicine Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung 40227 Taiwan. Electronic address: hlmarkc@gmail.c

BACKGROUND:The association between human epidermal growth factor receptor-2 (HER2) amplification and brain metastasis (BM) in patients having colorectal cancer (CRC) has been suggested but not yet established. This study investigated the expression patterns of HER2, its association with BM, and its prognostic value in patients having CRC. METHODS:We retrospectively identified 99 patients having metastatic CRC (mCRC) and BM (the BM cohort) and compared them with a cohort of 249 patients having mCRC and without BM (the stage IV cohort) by propensity score matching. Immunohistochemical studies of HER2 on all available paraffin-embedded tumour samples, either from the primary tumour, the metastasis (brain and/or extracranial sites) or both, were performed and analysed. HER2 fluorescent in situ hybridisation was applied when necessary. The expression of HER2 was compared and correlated with survival. RESULTS:HER2 amplifications were detected in 16 (18.4%) of 87 and 9 (3.6%) of 249 patients who had specimens available in the BM and stage IV cohorts, respectively (P < .001). After propensity score matching, HER2 amplification was significantly associated with BM (odds ratio: 5.38, P = .003). HER2 heterogeneity was frequently observed not only at the single tumour level but also in paired tumour samples. A marginally significant longer survival since BM was found in patients having HER2-amplified mCRC than in those without (P = .07). CONCLUSIONS:HER2 amplification was significantly associated with BM in patients having mCRC and might have prognostic value for survival since BM. Given the heterogeneity of HER2 expression, the testing of HER2 status on available tissues from both primary and metastatic tumours should be encouraged.

关键词： Brain metastasis Colorectal cancer HER2 amplification Heterogeneity Propensity score matching

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The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

arXiv

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arXiv 2024年

作者： Miao, Brenda Y. Chen, Irene Y. Williams, Christopher Y.K. Davidson, Jaysón Garcia-Agundez, Augusto Sun, Shenghuan Zack, Travis Saria, Suchi Arnaout, Rima Quer, Giorgio Sadaei, Hossein J. Torkamani, Ali Beaulieu-Jones, Brett Yu, Bin Gianfrancesco, Milena Butte, Atul J. Norgeot, Beau Sushil, Madhumita Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Medicine Division of Rheumatology University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Bayesian Health New YorkNY10282 United States Department of Computer Science Johns Hopkins University Whiting School of Engineering BaltimoreMD United States Department of Health Policy & Management Johns Hopkins University Bloomberg School of Public Health BaltimoreMD United States Department of Medicine Johns Hopkins Medicine BaltimoreMD21205 United States Departments of Medicine Radiology and Pediatrics University of California San Francisco San FranciscoCA United States Scripps Research Translational Institute La Jolla CA United States Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States Department of Medicine University of Chicago ChicagoIL United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Qualified Health PBC Palo AltoCA United States

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research. © 2024, CC BY.

关键词： Generative adversarial networks

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

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Publications of the Astronomical Society of Japan 2023年第2期75卷 311-337页

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 2-2-1 Miyama 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 Department of Physics Hiroshima University 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 Chiba Kashiwa277-8583 Japan Nagoya University Furo-cho Chikusa-

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 to be a non-cool-core cluster. The SZE centroid position is offset by more than 140 h−170 kpc 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. © The Author(s) 2023. Published by Oxford University Press on behalf of the Astronomical Society of Japan.

关键词： Millimeter waves

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Miscentering of Optical Galaxy Clusters Based on Sunyaev-Zeldovich Counterparts

arXiv

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arXiv 2024年

作者： Ding, Jupiter Dalal, Roohi Sunayama, Tomomi Strauss, Michael A. Oguri, Masamune Okabe, Nobuhiro Hilton, Matt Monteiro-Oliveira, Rogério Sifón, Cristóbal Staggs, Suzanne T. Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States CIERA Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Physics and Astronomy University of British Columbia VancouverBC Canada Department of Political Science University of British Columbia VancouverBC Canada Department of Astronomy Steward Observatory University of Arizona TucsonAZ85721 United States Center for Frontier Science Chiba University 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Department of Physics Graduate School of Science Chiba University 1-33 Yayoi-Cho Inage-Ku Chiba263-8522 Japan Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Higashi Hiroshima Campus Hiroshima739-8526 Japan Wits Centre for Astrophysics School of Physics University of the Witwatersrand Private Bag 3 Johannesburg2050 South Africa Astrophysics Research Centre School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa Institute of Astronomy and Astrophysics Academia Sinica Taipei10617 Taiwan Instituto de Física Pontificia Universidad Católica de Valparaíso Valparaíso Casilla4059 Chile Joseph Henry Laboratories of Physics Princeton University Jadwin Hall PrincetonNJ08544 United States

The "miscentering effect," i.e., the offset between a galaxy cluster’s optically-defined center and the center of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalog from the Hyper Suprime-Cam (HSC) Survey S19A Data Release and the Sunyaev-Zeldovich cluster catalog from Data Release 5 of the Atacama Cosmology Telescope (ACT). We obtain a sample of 186 clusters in common in the redshift range 0.1 ≤ z ≤ 1.4 over an area of 469 deg2. By modeling the distribution of centering offsets in this fiducial sample, we find a miscentered fraction (corresponding to clusters offset by more than 330 kpc) of ∼ 25%, a value consistent with previous miscentering studies. We examine the image of each miscentered cluster in our sample and identify one of several reasons to explain the miscentering. Some clusters show significant miscentering for astrophysical reasons, i.e., ongoing cluster mergers. Others are miscentered due to non-astrophysical, systematic effects in the HSC data or the cluster-finding algorithm. After removing all clusters with clear, non-astrophysical causes of miscentering from the sample, we find a considerably smaller miscentered fraction, ∼ 10%. We show that the gravitational lensing signal within 1 Mpc of miscentered clusters is considerably smaller than that of well-centered clusters, and we suggest that the ACT SZ centers are a better estimate of the true cluster potential centroid. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

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Corrigendum to High Keratin 8/18 Ratio Predicts Aggressive Hepatocellular Cancer Phenotype12 [Translational Oncology Volume 12, Issue 2, February 2019, Pages 256-268]

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Translational oncology 2020年第2期13卷 490-492页

作者： N Golob-Schwarzl K Bettermann A K Mehta S M Kessler J Unterluggauer S Krassnig K Kojima X Chen Y Hoshida N M Bardeesy H Müller V Svendova M G Schimek C Diwoky A Lipfert V Mahajan C Stumptner A Thüringer L F Fröhlich T Stojakovic K P R Nilsson T Kolbe T Rülicke T M Magin P Strnad A K Kiemer R Moriggl Johannes Haybaeck Institute of Pathology Medical University of Graz Graz Austria Center for Biomarker Research in Medicine Graz Austria. Institute of Pathology Medical University of Graz Graz Austria. Department of Medical Oncology Dana-Farber Cancer Institute 450 Brookline Avenue Boston MA 02215 USA. Department of Pharmacy Pharmaceutical Biology Saarland University Saarbrücken Germany. Liver Tumor Translational Research Program University of Texas Southwestern Medical Center Dallas USA. Center for Cancer Research Massachusetts General Hospital Harvard Medical School Boston USA. Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria. Institute of Medical Engineering Graz University of Technology Graz Austria Institute of Molecular Biosciences University of Graz Graz Austria. Institute of Medical Engineering Graz University of Technology Graz Austria. AG VABOS Department of Cranio-Maxillofacial Surgery Univerity of Muenster Germany. Clinical Institute of Medical and Chemical Laboratory Diagnostics Medical University of Graz Graz Austria. Department of Chemistry Linköping University Linköping Sweden. Biomodels Austria (Biat) University of Veterinary Medicine Vienna Vienna Austria Department IFA-Tulln University for Natural Resources and Applied Life Sciences Vienna Vienna Austria. Institute of Laboratory Animal Science University of Veterinary Medicine Vienna Vienna Austria. Institute of Biology University of Leipzig Leipzig Germany. Department of Internal Medicine III University Hospital RWTH Aachen Aachen Germany. Center for Biomarker Research in Medicine Graz Austria. Institute of Animal Breeding and Genetics University of Veterinary Medicine Vienna Vienna Austria Ludwig Bolzmann Institute of Cancer Research Vienna Austria Medical University Vienna Vienna Austria. Department of Pathology Medical Faculty Otto-von-Guericke-University Magdeburg Magdeburg Germany. Electronic address: Johannes.haybaeck@med.ovgu.de.

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Cerebrospinal fluid proteomics reveals the innate immunity and blood-brain barrier dysregulation in a patient with multidrug-resistant Acinetobacter baumannii ventriculitis treated with intrathecal and intravenous polymyxin B

引用

Heliyon 2024年第24期10卷 e40893页

作者： Li, Mengyao Liu, Dongyu Bergen, Phillip J. Liang, Silin Chen, Juan Kho, Zhi Ying Lu, Jing Sun, Huiying Hong, Weiqing Liu, Xiaofen Hong, Chengying Chen, Youlian Li, Wei You, Hongxia Xu, Shunyao Wang, Yu Gao, Huaiji Lam, Chun Hin Li, Jian Chen, Xiaoyin Liu, Xueyan Department of Critical Care Medicine The Second Clinical Medical College Jinan University (Shenzhen People's Hospital) Shenzhen 518020 China Integrated Chinese and Western Medicine Postdoctoral Research Station Jinan University Guangzhou 510632 China Biomedicine Discovery Institute Infection Program and Department of Microbiology Monash University Melbourne 3800 VIC Australia Department of Pharmacy The Second Clinical Medical College Jinan University (Shenzhen People's Hospital) Shenzhen 518020 China Institute of Infectious Diseases The Second Hospital of Tianjin Medical University 23 Pingjiang Road Tianjin 300211 China Institute of Antibiotics Huashan Hospital Fudan University / Key Laboratory of Clinical Pharmacology of Antibiotics National Health Commission of the People's Republic of China Shanghai 200040 China National Clinical Research Centre for Aging and Medicine Huashan Hospital affiliated to Fudan University Shanghai 200040 China Department of Stomatology The Second Clinical Medical College Jinan University (Shenzhen People's Hospital) Shenzhen 518020 China Mathematics and Statistics School of Computing Engineering and Mathematical Sciences La Trobe University Melbourne 3085 VIC Australia Faculty of Medicine Macau University of Science and Technology Taipa Macau 999078 China College of Traditional Chinese Medicine Jinan University Guangzhou 510632 China

Acinetobacter baumannii is a major pathogen of nosocomial meningitis and ventriculitis. Due to very limited antibiotic treatment options, polymyxins are often used as a last-line therapy. To optimise polymyxin use in the intraventricular environment, cerebrospinal fluid (CSF) proteomics was employed to investigate host-pathogen-polymyxin interactions in a 69-year-old patient with multidrug-resistant A. baumannii ventriculitis treated with a combination of intrathecal (ITH;50,000 IU q24h/q48h), intraventricular (IVT;50,000 IU q48h), and intravenous (500,000 IU, q12h) polymyxin B. CSF was collected before the first ITH dose in the ICU (0 h) and at 24 h, Day 7 and Day 26. The proteome was quantified at each time point and proteins with Qvalue <0.05 and fold change >1.2 were considered differentially expressed. Within 24 h of ITH/IVT polymyxin B administration, the innate immune system and neuroimmunity were highly active, evidenced by up-regulation of various pathways related to pathogen invasion, endocytosis and neutrophil degranulation. Blood-brain barrier impairment had worsened at 24 h but signs of repair were evident on Day 7 and Day 26. This is the first CSF proteomic study with polymyxins. Our findings provide critical mechanistic insights into optimizing ITH/IVT polymyxin administration. © 2024 The Authors

关键词： Acinetobacter baumannii Cerebrospinal fluid Intrathecal and intravenous administration Polymyxin B Proteomics Ventriculitis

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