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The provision and utility of science and uncertainty to decision-makers: earth science case studies

Environment Systems and Decisions 2019年第3期39卷 307-348页

作者： Quigley, Mark C. Bennetts, Luke G. Durance, Patricia Kuhnert, Petra M. Lindsay, Mark D. Pembleton, Keith G. Roberts, Melanie E. White, Christopher J. School of Earth Sciences University of Melbourne Parkville 3010 VIC Australia School of Mathematical Sciences University of Adelaide Adelaide 5005 SA Australia GNS Science 1 Fairway Drive Lower Hutt 5010 New Zealand CSIRO Data61 GPO Box 1700 Canberra ACT Australia Centre for Exploration Targeting School of Earth Sciences University of Western Australia Crawley 6009 WA Australia School of Agricultural Computational and Environmental Sciences and Centre for Sustainable Agricultural Systems University of Southern Queensland Toowoomba 4350 QLD Australia School of Mathematics and Statistics University of Melbourne Parkville 3010 VIC Australia Australian Rivers Institute Griffith University Nathan 4111 QLD Australia School of Engineering University of Tasmania Hobart 7001 TAS Australia Department of Civil and Environmental Engineering University of Strathclyde Glasgow United Kingdom

This paper investigates how scientific information and expertise was provided to decision-makers for consideration in situations involving risk and uncertainty. Seven case studies from the earth sciences were used as a medium for this exposition: (1) the 2010–2011 Canterbury earthquake sequence in New Zealand, (2) agricultural farming-system development in North West Queensland, (3) operational flood models, (4) natural disaster risk assessment for Tasmania, (5) deep sea mining in New Zealand, (6) 3-D modelling of geological resource deposits, and (7) land-based pollutant loads to Australia’s Great Barrier Reef. Case studies are lead-authored by a diverse range of scientists, based either in universities, industry, or government science agencies, with diverse roles, experiences, and perspectives on the events discussed. The context and mechanisms by which scientific information was obtained, presented to decision-makers, and utilised in decision-making is presented. Sources of scientific uncertainties and how they were communicated to and considered in decision-making processes are discussed. Decisions enacted in each case study are considered in terms of whether they were scientifically informed, aligned with prevailing scientific evidence, considered scientific uncertainty, were informed by models, and were (or were not) precautionary in nature. The roles of other relevant inputs (e.g. political, socioeconomic considerations) in decision-making are also described. Here we demonstrate that scientific evidence may enter decision-making processes through diverse pathways, ranging from direct solicitations by decision-makers to independent requests from stakeholders following media coverage of relevant research. If immediately relevant scientific data cannot be provided with sufficient expediency to meet the demands of decision-makers, decision-makers may (i) seek expert scientific advice and judgement (to assist with decision-making under conditions of high epistemic

关键词： Decision-making Earth science Environmental science Natural disasters Policy Risk Uncertainty

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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Research Square

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Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

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Author Correction: Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats

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Nature communications 2025年第1期16卷 1507页

作者： Jakob Steinfeldt Benjamin Wild Thore Buergel Maik Pietzner Julius Upmeier Zu Belzen Andre Vauvelle Stefan Hegselmann Spiros Denaxas Harry Hemingway Claudia Langenberg Ulf Landmesser John Deanfield Roland Eils Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany. Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany. Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany. Institute of Cardiovascular Sciences University College London London UK. Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK. Institute of Health Informatics University College London London UK. Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA. Pattern Recognition and Image Analysis Lab University of Münster Münster Germany. British Heart Foundation Data Science Centre London UK. Health Data Research UK London UK. National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK. Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany. Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. roland.eils@bih-charite.de. Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany. roland.eils@bih-charite.de.

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High-Throughput computational Studies in Catalysis and Materials Research, and their Impact on Rational Design

arXiv

引用

arXiv 2019年

作者： Afzal, Mohammad Atif Faiz Hachmann, Johannes Schrödinger Inc. PortlandOR97204 United States Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo The State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

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The Prime Focus Spectrograph Galaxy Evolution Survey

arXiv

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arXiv 2022年

作者： Greene, Jenny E. Bezanson, Rachel Ouchi, Masami Silverman, John Arnouts, Stéphane Goulding, Andy D. Gu, Meng Gunn, James E. Harikane, Yuichi Heckman, Timothy Horowitz, Benjamin Johnson, Sean D. Kashino, Daichi Lee, Khee-Gan Leja, Joel Lin, Yen-Ting Marchesini, Danilo Matsuoka, Yoshiki Nagamine, Kentaro Ono, Yoshiaki Pearl, Alan Shibuya, Takatoshi Strauss, Michael A. Strom, Allison L. Sugahara, Yuma Tresse, Laurence Yabe, Kiyoto Yamashita, Takuji Akiyama, Masayuki Ata, Metin Azevedo, Gabriel M. Chen, Chian-Chou Chies-Santos, Ana L. Ellis, Richard S. Hahn, Changhoon Roier, Gabriel Roberto Hauschild Ichikawa, Kohei Ito, Kei Jiang, Linhua Jing, Y.P. Kawamuro, Taiki Kobayashi, Chiaki Kraljic, Katarina Le Brun, Vincent Liu, Xin Melchior, Peter Mendes De Oliveira, Claudia L. Monteiro-Oliveira, Rogério Nagao, Tohru Onodera, Masato Overzier, Roderik A. Schiminovich, David Schramm, Malte Shimakawa, Rhythm Sodré, Laerte Storchi-Bergmann, Thaisa Suzuki, Tomoko L. Tanaka, Masayuki Toba, Yoshiki Umehata, Hideki Wang, Jiaqi Welker, Charlotte Whitaker, Katherine Xu, Kun Xue, Yongquan Yang, Xiaohu Zu, Ying Department of Astrophysical Sciences Princeton University Princeton NJ08544 United States Department of Physics and Astronomy University of Pittsburgh PittsburghPA15260 United States Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8583 Japan Department of Astronomy School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Aix Marseille University CNRS CNES LAM Marseille France Johns Hopkins University United States University of Michigan United States Institute for Advanced Research Nagoya University Nagoya464-8601 Japan Division of Particle and Astrophysical Science Graduate School of Science Nagoya University Nagoya464-8602 Japan Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Institute of Astronomy and Astrophysics Academia Sinica Taiwan Tufts University United States Research Center for Space and Cosmic Evolution Ehime University Japan Theoretical Astrophysics Department of Earth & Space Science Graduate School of Science Osaka University 1-1 Machikaneyama Osaka Toyonaka560-0043 Japan Department of Physics & Astronomy University of Nevada Las Vegas 4505 S. Maryland Pkwy Las VegasNV89154-4002 United States Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Astronomical Institute Tohoku University Japan Av. Bento Gonçalves RS Porto Alegre9500

We present the Prime Focus Spectrograph (PFS) Galaxy Evolution pillar of the 360-night PFS Subaru Strategic Program. This 130-night program will capitalize on the wide wavelength coverage and massive multiplexing capabilities of PFS to study the evolution of typical galaxies from cosmic dawn to the present. From Lyman α emitters at z ∼ 7 to probe reionization, drop-outs at z ∼ 3 to map the inter-galactic medium in absorption, and a continuum-selected sample at z ∼ 1.5, we will chart the physics of galaxy evolution within the evolving cosmic web. This article is dedicated to the memory of Olivier Le Fevre, who was an early advocate for the construction of PFS, and a key early member of the Galaxy Evolution Working Group. © 2022, CC BY.

关键词： Spectrographs

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

引用

arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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Ab Initio Calculation on Spectroscopic Properties and Radiative Lifetimes of Low-Lying Excited States of NaK

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Chinese Journal of Chemical Physics 2019年第6期32卷 667-673,I0002页

作者： Shi-yang Zhang Feng Xie Feng-dong Jia Xiao-kang Li Ru-quan Wang Rui Li Yong Wu Zhi-ping Zhong School of Physics University of Chinese Academy of SciencesBeijing 100049China Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor Engineering and Safety of Ministry of EducationTsinghua UniversityBeijing 100084China Institute of Physics Chinese Academy of SciencesBeijing 100190China Data Center for High Energy Density Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Department of Physics College of ScienceQiqihar UniversityQiqihar 161006China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of SciencesBeijing 100190China

We performed high-level ab initio calculations on electronic structure of Na K. The potential energy curves (PECs) of 10 Λ-S states correlated with the three lowest dissociation limits have been calculated. On the basis of the calculated PECs, the spectroscopic constants of the boundΛ-S states are obtained, which are in good agreement with experimental results. The maximum vibrational quantum numbers of the singlet ground state X^1Σ^+ and the triplet ground state a^3Σ^+ have been analyzed with the semiclassical scattering theory. Transition properties including transition dipole moments, Franck-Condon factors, and radiative lifetimes have been investigated. The research results indicate that such calculations can provide fairly reliable estimation of parameters for the ultracold alkali diatomic molecular experiment.

关键词： Ultracold dipolar molecule Transition dipole moment Spectroscopic constants Potential curves

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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Microstructure and Water Absorption of Ancient Concrete from Pompeii: An Integrated Synchrotron Microtomography and Neutron Radiography Characterization

arXiv

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arXiv 2020年

作者： Xu, Ke Tremsin, Anton S. Li, Jiaqi Ushizima, Daniela M. Davy, Catherine A. Bouterf, Amine Su, Ying Tsun Marroccoli, Milena Mauro, Anna Maria Osanna, Massimo Telesca, Antonio Monteiro, Paulo J.M. Department of Civil and Environmental Engineering University of California BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Space Sciences Laboratory University of California BerkeleyCA94720 United States Berkeley Institute of Data Science University of California BerkeleyCA94720 United States Univ. Lille CNRS Centrale Lille ENSCL Univ. Artois UMR 8181 - UCCS - Unité de Catalyse et de Chimie du Solide LilleF-59000 France ENS Paris-Saclay CNRSUniversité Paris-Saclay Cachan Cedex94235 France Department of Environmental Engineering and Physics School of Engineering University of Basilicata Potenza85100 Italy Head of research and innovation area of Archeological Park of Pompeii via Plinio 4 Pompeii NA80045 Italy General Director of Archeological Park of Pompeii via Plinio 4 Pompeii NA80045 Italy

There is renewed interest in using advanced techniques to characterize ancient Roman concrete. In the present work, samples were drilled from the "Hospitium" in Pompeii and were analyzed by synchrotron microtomography (μCT) and neutron radiography to study how the microstructure, including the presence of induced cracks, affects their water adsorption. The water distribution and absorptivity were quantified by neutron radiography. The 3D crack propagation, pore size distribution and orientation, tortuosity, and connectivity were analyzed from μCT results using advanced imaging methods. The concrete characterization also included classical methods (e.g., differential thermal–thermogravimetric, X-ray diffractometry, and scanning electron microscopy). Ductile fracture patterns were observed once cracks were introduced. When compared to Portland cement mortar/concrete, Pompeii samples had relatively high porosity, low connectivity, and similar coefficient of capillary penetration. In addition, the permeability was predicted from models based on percolation theory and the pore structure data to evaluate the fluid transport properties. Copyright © 2020, The Authors. All rights reserved.

关键词： Pore structure

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Association of structural MRI with Braak staging using neural network-based risk scoring

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Matthew I. Miller Shangran Qiu Prajakta S. Joshi Joyce Lee Rhoda Au Vijaya B. Kolachalama Boston University School of Medicine Boston MA USA College of Arts & Sciences Boston University Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Center Boston University Boston MA USA Boston University School of Public Health Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background The gold standard of Alzheimer’s disease (AD) diagnosis remains histopathologic examination of neuropathologic changes. However, the dependence on post-mortem microscopic analysis limits the role of pathology in clinical care. Magnetic resonance imaging (MRI) is routinely used to diagnose suspected dementia (DE) due to AD, but correlating neuroimaging to underlying pathologic changes remains challenging. Herein, we report the development of two risk scores derived from convolutional neural network (CNN) processing of MRI scans, which we then validate against Braak staging of neurofibrillary tangles from autopsy reports. Method Using T1-weighted MRIs from the National Alzheimer’s Coordinating Center (NACC), we trained two CNNs (Figure 1) to predict continuous scores for overall cognitive status (“COG” score) and AD probability (“ALZ” score). The COG score, ranging from 0-2, graded a subject’s cognitive status on a spectrum from normal cognition (NC, 0), to mild cognitive impairment (MCI, 1), and DE due to any cause (2). The ALZ score then graded each DE individual’s probability of AD from 0 to 1. These CNNs were then applied to a subset of participants with autopsy reports available within 6 months of MRI. CNN performance was evaluated by area under receiver operating characteristic curve (AUC). Differences in risk scores were assessed by Kruskal-Wallis H testing with post hoc Dunn testing. Result The CNN models for COG and ALZ score estimation were trained on 4,822 NACC participants. Thresholding of COG scores distinguished individuals with DE from those with NC and MCI (AUC = 0.87). ALZ scores distinguished individuals with AD from those with non-AD causes of DE (AUC = 0.77). Among 74 persons with available neuropathology data, COG scores differed significantly among Braak staging groups (p<0.001), increasing from a mean of 0.99±0.36 in Stage 1 to 1.47±0.41 in Stage 6 (p = 0.010). Significant differences in ALZ scores were also observed (p = 0.039) betwe

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