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Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII

Database 2023年第2023期2023卷 baad005-baad005页

作者： Leaman, Robert Islamaj, Rezarta Adams, Virginia Alliheedi, Mohammed A. Almeida, João Rafael Antunes, Rui Bevan, Robert Chang, Yung-Chun Erdengasileng, Arslan Hodgskiss, Matthew Ida, Ryuki Kim, Hyunjae Li, Keqiao Mercer, Robert E. Mertová, Lukrécia Mobasher, Ghadeer Shin, Hoo-Chang Sung, Mujeen Tsujimura, Tomoki Yeh, Wen-Chao Lu, Zhiyong National Center for Biotechnology Information (NCBI) National Library of Medicine National Institutes of Health 8600 Rockville Pike Bethesda 20894 MD United States Nvidia 2788 San Tomas Expressway Santa Clara 95051 CA United States Department of Computer Science Al Baha University 4781 King Fahd Rd Al Aqiq 65779 Saudi Arabia Department of Electronics Telecommunications and Informatics (DETI) Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Department of Information and Communications Technologies University of A Coruña Camiño Do Lagar de Castro A Coruña 15008 Spain Informatics Department Medicines Discovery Catapult Alderley Park Block 35 Mereside Macclesfield SK10 4ZF United Kingdom Graduate Institute of Data Science Taipei Medical University No. 172-1 Section 2 Keelung Rd Da’an District Taipei City 106 Taiwan Department of Statistics Florida State University 117 N. Woodward Ave Tallahassee 32306 FL United States Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata Tempaku-ku Nagoya Aichi 468-8511 Japan Department of Computer Science and Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 South Korea Department of Computer Science The University of Western Ontario Middlesex College Room 355 London N6A 5B7 ON Canada Scientific Databases and Visualization Group Heidelberg Institute for Theoretical Studies (HITS GGmbH) Schloss-Wolfsbrunnenweg 35 Heidelberg 69118 Germany Institute of Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg 69120 Germany Institute of Information Systems and Applications National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and - as highlighted during the coronavirus disease 2019 pandemic - their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substanti

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Feasibility study of Information Technology Investment (a case study of ODOO ERP: Project Management Module Implementation in Indonesia Based Company) 10

Feasibility study of Information Technology Investment (a ca...

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10th International Conference on ICT for Smart Society, ICISS 2019

作者： Jingga, Fredy Kosala, Raymond Supangkat, Suhono Harso Ranti, Benny School of Information Systems Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781728148809

IT Governance are one of the needs in managing Enterprise Level IT. This study shows part of the decision domain of IT Governance Help, which are IT Investment and Prioritization. The purpose of this study is to determine feasibility of implementation open source ERP system Odoo project management module that replaces current system based on operational, economic, and technical aspects. The method used are Fit/Gap Analysis and Cost Benefit model using economic impact worksheet. The analysis is done by comparing the requirement with features owned by current system and Odoo ERP system. This research resulted that Odoo ERP System is feasible in the 3 aspects, the operational feasibility Odoo reaching 72% Fit, the economic feasibility with ROI of 84%, and technical feasibility indicating the technical requirement can be fulfilled by leasing AWS server. The conclusion is the open source ERP system Odoo project management module is feasible to be applied in terms of 3 aspects that have been studied, those are operational, economic, and technical. © 2019 IEEE.

关键词： Cost benefit analysis

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Assessing Engraftment Following Fecal Microbiota Transplant

arXiv

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arXiv 2024年

作者： Herman, Chloe Barker, Bridget M. Bartelli, Thais F. Chandra, Vidhi Krajmalnik-Brown, Rosa Jewell, Mary Li, Le Liao, Chen McAllister, Florencia Nirmalkar, Khemlal Xavier, Joao B. Caporaso, J. Gregory Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Department of Clinical Cancer Prevention University of Texas MD Anderson Cancer Center HoustonTX United States Biodesign Center for Health Through Microbiomes Arizona State University TempeAZ United States School of Sustainable Engineering and the Built Environment Arizona State University TempeAZ United States Independent author Salt Lake CityUT United States Program for Computational and Systems Biology Memorial Sloan-Kettering Cancer Center New YorkNY United States Department of Gastrointestinal Medical Oncology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Immunology The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Biological Sciences Northern Arizona University FlagstaffAZ United States

Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for cancer treatment, and restoring microbiomes impacted by cancer treatment. Quantifying the extent of engraftment following an FMT is important in determining if a recipient didn’t respond because the engrafted microbiome didn’t produce the desired outcomes (a successful FMT, but negative treatment outcome), or the microbiome didn’t engraft (an unsuccessful FMT and negative treatment outcome). The lack of a consistent methodology for quantifying FMT engraftment extent hinders the assessment of FMT success and its relation to clinical outcomes, and presents challenges for comparing FMT results and protocols across studies. Here we review 46 studies of FMT in humans and model organisms and group their approaches for assessing the extent to which an FMT engrafts into three criteria: 1) Chimeric Asymmetric Community Coalescence investigates microbiome shifts following FMT engraftment using methods such as alpha diversity comparisons, beta diversity comparisons, and microbiome source tracking. 2) Donated Microbiome Indicator Features tracks donated microbiome features (e.g., amplicon sequence variants or species of interest) as a signal of engraftment with methods such as differential abundance testing based on the current sample collection, or tracking changes in feature abundances that have been previously identified (e.g., from FMT or disease-relevant literature). 3) Temporal Stability examines how resistant post-FMT recipient’s microbiomes are to reverting back to their baseline microbiome. Individually, these criteria each highlight a critical aspect of microbiome engraftment;investigated together, however, they provide a clearer assessment of microbiome engraftment. We discuss the pros and cons of each of these criteria,

关键词： Diseases

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Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity

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GEROscience 2023年第3期45卷 1817-1835页

作者： Dec, Eric Clement, James Cheng, Kaiyang Church., George M. Fossel., Michael B. Rehkopf, David. H. Rosero-Bixby, Luis Kobor, Michael S. Lin, David TS. Lu, Ake T. Fei, Zhe Guo, Wei Chew, Yap Ching Yang, Xiaojing Putra, Sulistyo E. Dwi Reiner, Alex P. Correa, Adolfo Vilalta, Adrian Pirazzini, Chiara Passarino, Giuseppe Monti, Daniela Arosio, Beatrice Garagnani, Paolo Franceschi, Claudio Horvath, Steve Department of Pediatrics Division of Genetics and Genomic Medicine University of California Irvine USA Betterhumans Inc. Gainesville USA Department of Chemical Pharmaceutical and Agricultural Sciences University of Ferrara Ferrara Italy Medical Informatics David Geffen School of Medicine University of California Los Angeles Los Angeles USA Department of Genetics Harvard Medical School Boston USA Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge USA Telocyte LLC Grand Rapids USA Epidemiology & Population Health and Medicine School of Medicine Stanford University Stanford USA Centro Centroamericano de Población Universidad de Costa Rica San Pedro Costa Rica Edwin S.H. Leong Healthy Aging Program Centre for Molecular Medicine and Therapeutics University of British Columbia Vancouver Canada Dept. of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles USA Altos Labs San Diego USA Dept. of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles USA Zymo Research Corp Irvine USA Department of Biology Faculty of Biotechnology University of Surabaya Surabaya Indonesia Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle USA Medicine and Population Health Science University of Mississippi Medical Center Jackson USA Outcomes Metrics LLC San Diego USA IRCCS Istituto Delle Scienze Neurologiche Di Bologna Bologna Italy Department of Biology Ecology and Earth Science University of Calabria Rende Italy Department of Experimental and Clinical Biomedical Sciences “Mario Serio” University of Florence Florence Italy Department of Clinical Sciences and Community Health University of Milan Milan Italy Department of Medical and Surgical Sciences University of Bologna Bologna Italy Alma Mater Research Institute On Global Challenges and Climate Change (Alma Climate) University of Bologna Bologna Italy Department of Laboratory

Claims surrounding exceptional longevity are sometimes disputed or dismissed for lack of credible evidence. Here, we present three DNA methylation-based age estimators (epigenetic clocks) for verifying age claims of centenarians. The three centenarian clocks were developed based on n = 7039 blood and saliva samples from individuals older than 40, including n = 184 samples from centenarians, 122 samples from semi-supercentenarians (aged 105 +), and 25 samples from supercentenarians (aged 110 +). The oldest individual was 115 years old. Our most accurate centenarian clock resulted from applying a neural network model to a training set composed of individuals older than 40. An epigenome-wide association study of age in different age groups revealed that age effects in young individuals (age < 40) are correlated (r = 0.55) with age effects in old individuals (age > 90). We present a chromatin state analysis of age effects in centenarians. The centenarian clocks are expected to be useful for validating claims surrounding exceptional old age.

关键词： Centenarian clocks Epigenetic clocks Longevity

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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Accuracy Enhancement in Refractive Index Sensing via Full-Spectrum Machine Learning Modeling

arXiv

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arXiv 2025年

作者： Aalizadeh, Majid Raut, Chinmay Afshar, Morteza Azmoudeh Tabartehfarahani, Ali Fan, Xudong Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States University of Michigan Ann ArborMI48109 United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI48109 United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI48109 United States Informatics Institute Istanbul Technical University Istanbul34485 Turkey

We present a full-spectrum machine learning framework for refractive index sensing using simulated absorption spectra from meta-grating structures composed of titanium or silicon nanorods under TE and TM polarizations. Linear regression was applied to 80 principal components extracted from each spectrum, and model performance was assessed using five-fold cross-validation, simulating real-world biosensing scenarios where unknown patient samples are predicted based on standard calibration data. Titanium-based structures, dominated by broadband intensity changes, yielded the lowest mean squared errors and the highest accuracy improvements—up to a 6065-fold reduction compared to the best single-feature model. In contrast, silicon-based structures, governed by narrow resonances, showed more modest gains due to spectral nonlinearity that limits the effectiveness of global linear models. We also show that even the best single-wavelength predictor is identified through data-driven analysis, not visual selection, highlighting the value of automated feature preselection. These findings demonstrate that spectral shape plays a key role in modeling performance and that full-spectrum linear approaches are especially effective for intensity-modulated index sensors. Copyright © 2025, The Authors. All rights reserved.

关键词： Linear regression

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Determinants and Predictors of Intentionality and Perceived Reliability in Human-AI Interaction as a Means for Innovative Scientific Discovery

Determinants and Predictors of Intentionality and Perceived ...

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IEEE International Conference on Big Data

作者： António Correia Benjamim Fonseca Hugo Paredes Ramon Chaves Daniel Schneider Shoaib Jameel INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) Federal University of Rio de Janeiro Brazil School of Computer Science and Electronic Engineering Colchester Campus University of Essex UK

ISBN: (纸本)9781665445993

With the increasing development of human-AI teaming structures within and across geographies, the time is ripe for a continuous and objective look at the predictors, barriers, and facilitators of human-AI scientific collaboration from a multidisciplinary point of view. This paper aims at contributing to this end by exploiting a set of factors affecting attitudes towards the adoption of human-AI interaction into scientific work settings. In particular, we are interested in identifying the determinants of trust and acceptability when considering the combination of hybrid human-AI approaches for improving research practices. This includes the way as researchers assume human-centered artificial intelligence (AI) and crowdsourcing as valid mechanisms for aiding their tasks. Through the lens of a unified theory of acceptance and use of technology (UTAUT) combined with an extended technology acceptance model (TAM), we pursue insights on the perceived usefulness, potential blockers, and adoption drivers that may be representative of the intention to use hybrid intelligence systems as a way of unveiling unknown patterns from large amounts of data and thus enabling novel scientific discoveries.

关键词： Geography Crowdsourcing Technology acceptance model Big Data Predictive models Data models Reliability

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Hopper flows of deformable particles

arXiv

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arXiv 2022年

作者： Cheng, Yuxuan Treado, John D. Lonial, Ben Habdas, Piotr Weeks, Eric R. Shattuck, Mark D. O'Hern, Corey S. Department of Physics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Physics Emory University AtlantaGA30322 United States Department of Physics Saint Joseph's University PhiladelphiaPA19131 United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY10031 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Numerous experimental and computational studies show that continuous hopper flows of granular materials obey the Beverloo equation that relates the volume flow rate Q and the orifice width w: Q ∼ (w/σavg − k)β, where σavg is the average particle diameter, kσavg is an offset where Q ∼ 0, the power-law scaling exponent β = d − 1/2, and d is the spatial dimension. Recent studies of hopper flows of deformable particles in different background fluids suggest that the particle stiffness and dissipation mechanism can also strongly affect the power-law scaling exponent β. We carry out computational studies of hopper flows of deformable particles with both kinetic friction and background fluid dissipation in two and three dimensions. We show that the exponent β varies continuously with the ratio of the viscous drag to the kinetic friction coefficient, λ = ζ/µ. β = d−1/2 in the λ → 0 limit and d− 3/2 in the λ → ∞ limit, with a midpoint λc that depends on the hopper opening angle θw. We also characterize the spatial structure of the flows and associate changes in spatial structure of the hopper flows to changes in the exponent β. The offset k increases with particle stiffness until k ∼ kmax in the hard-particle limit, where kmax ∼ 3.5 is larger for λ → ∞ compared to that for λ → 0. Finally, we show that the simulations of hopper flows of deformable particles in the λ → ∞ limit recapitulate the experimental results for quasi-2D hopper flows of oil droplets in water. © 2022, CC BY.

关键词： Stiffness

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Large language models for disease diagnosis: a scoping review

npj Artificial Intelligence

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npj Artificial Intelligence 2025年第1期1卷 1-17页

作者： Shuang Zhou Jeremy Yeung Mingquan Lin Rui Zhang Zidu Xu Mian Zhang Chunpu Xu Jiashuo Wang Kaishuai Xu Yawen Guo Zaifu Zhan Yi Fang Sirui Ding Liqiao Xia Daochen Zha Dongming Cai Genevieve B. Melton Division of Computational Health Sciences Department of Surgery University of Minnesota Minneapolis MN USA School of Nursing Columbia University New York New York USA Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas Richardson TX USA Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong SAR Department of Informatics University of California Irvine Irvine CA USA Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA Department of Computer Science New York University (Shanghai) Shanghai China Bakar Computational Health Sciences Institute University of California San Francisco San Francisco CA USA Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Hong Kong SAR Independent Researcher San Francisco CA USA Department of Neurology University of Minnesota Minneapolis MN USA Institute for Health Informatics and Division of Colon and Rectal Surgery Department of Surgery University of Minnesota Minneapolis MN USA

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various dimensions, including disease types and associated clinical specialties, clinical data, LLM techniques, and evaluation methods. Additionally, we offer recommendations for applying and evaluating LLMs for diagnostic tasks. Furthermore, we assess the limitations of current research and discuss future directions. To our knowledge, this is the first comprehensive review for LLM-based disease diagnosis.

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Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications

ACS ES&T air

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ACS ES&T air 2024年第4期1卷 283-293页

作者： Xuan Liu Jay R Turner Christopher R Oxford Jacob McNeill Brenna Walsh Emmie Le Roy Crystal L Weagle Emily Stone Haihui Zhu Wenyu Liu Zilin Wei Nicole P Hyslop Jason Giacomo Ann M Dillner Abdus Salam Al-Amin Hossen Zubayer Islam Ihab Abboud Clement Akoshile Omar Amador-Muñoz Nguyen Xuan Anh Araya Asfaw Rajasekhar Balasubramanian Rachel Ying-Wen Chang Craig Coburn Sagnik Dey David J Diner Jinlu Dong Tareq Farrah Paterne Gahungu Rebecca M Garland Michel Grutter de la Mora Sina Hasheminassab Juanette John Jhoon Kim Jong Sung Kim Kristy Langerman Pei-Chen Lee Puji Lestari Yang Liu Tesfaye Mamo Mathieu Martins Olga L Mayol-Bracero Mogesh Naidoo Sang Seo Park Yoav Schechner Robyn Schofield Sachchida N Tripathi Eli Windwer Ming-Tsang Wu Qiang Zhang Michael Brauer Yinon Rudich Randall V Martin Department of Energy Environmental & Chemical Engineering Washington University in St. Louis St. Louis Missouri 63130 United States. Air Quality Research Center University of California Davis Davis California 95616 United States. Department of Chemistry University of Dhaka Dhaka 1000 Bangladesh. Air Quality Research Division Environment and Climate Change Canada Toronto Ontario M3H 5T4 Canada. Department of Physics University of Ilorin Ilorin 240003 Nigeria. Instituto de Ciencias de la Atmósfera y Cambio Climático Universidad Nacional Autónoma de México Mexico City 04510 Mexico. Institute of Geophysics Vietnam Academy of Science and Technology Hanoi 11307 Vietnam. Institute of Geophysics and Space Science Addis Ababa University Addis Ababa 1176 Ethiopia. Department of Civil and Environmental Engineering National University of Singapore Singapore 117576 Singapore. Department of Physics and Atmospheric Science Dalhousie University Halifax Nova Scotia B3H 4R2 Canada. Department of Geography and Environment University of Lethbridge Lethbridge Alberta T1K 3M4 Canada. Centre for Atmospheric Sciences Indian Institute of Technology Delhi New Delhi 110016 India. Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 United States. School of Environment Tsinghua University Beijing 100084 People's Republic of China. Research Laboratories Khalifa University Abu Dhabi 127788 United Arab Emirates. Institute of Applied Statistics University of Burundi Bujumbura BP1550 Burundi. Council for Scientific and Industrial Research Pretoria 0001 South Africa. Unit for Environmental Sciences and Management North-West University Potchefstroom 2531 South Africa. Department of Geography Geo-Informatics and Meteorology University of Pretoria Pretoria 0002 South Africa. Department of Atmospheric Sciences Yonsei University Seoul 03722 Republic of Korea. Department of Community Health and Epidemiology Dalhousie University Halifax Nova Scotia B3

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 μg/m) in PM is also found in Dhaka (Bangladesh) along with a high average TEO level (6 μg/m). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

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