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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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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

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Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Gradient Boosted Tree Based Feature Selection and Parkinson’s Disease Classification

Gradient Boosted Tree Based Feature Selection and Parkinson...

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International Conference on Science and Technology-computer (ICST)

作者： Adi Wijaya Kharis Wini Prastuti Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Department of Public Policy Management Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9781728123691

ISBN: (纸本)9781728123707

Parkinson's Disease (PD) is one of the most common neurodegenerative disorder with increasing prevalence in the world. Many studies have been conducted to detect or diagnose PD. Recently, voice signal based PD detection has been conducted rapidly. However, the performance of the detection still not excellent, hence the need for excellent detection still an open issue in PD detection. In this study, PD detection based on voice signal was conducted using public dataset. Since the dataset consist of many features, the proposed method combines feature selection mechanism and classification based on Gradient Boosted Tree (GBT) algorithm. Feature selection was conducted by using feature weighting from GBT. The experimental results show that the proposed method outperforms prior research with the accuracy about 0.92 with the minimum number of features. This result proves that GBT able to perform feature selection and good classifier. This concludes that the proposed method is promising as PD detection based on voice signal analysis.

关键词： Feature extraction Support vector machines Parkinson's disease Boosting Classification algorithms Signal analysis

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Improving immunoassay detection accuracy of anti-SARS-CoV-2 antibodies through dual modality validation

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Biosensors and Bioelectronics: X 2022年 11卷 100176页

作者： Ma, Yuhao To, Daniel Zeng, Jie Shoute, Lian C.T. Wu, Meng Babiuk, Shawn Zhuo, Ran Charlton, Carmen Kanji, Jamil N. Babiuk, Lorne Chen, Jie Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2V4 Canada Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2R3 Canada National Centre for Foreign Animal Disease Canadian Food Inspection Agency WinnipegMB Canada Department of Immunology University of Manitoba WinnipegMB Canada Department of Laboratory Medicine and Pathology University of Alberta EdmontonABT6G 2B7 Canada Public Health Laboratory Alberta Precision Laboratories CalgaryAB Canada Li Ka Shing Institute for Virology University of Alberta EdmontonAB Canada Division of Infectious Diseases Department of Medicine Cumming School of Medicine University of Calgary CalgaryAB Canada Department of Pathology & Laboratory Medicine Cumming School of Medicine University of Calgary CalgaryAB Canada Vaccine and Infectious Disease Organization University of Alberta EdmontonAB Canada Department of Biomedical Engineering University of Alberta EdmontonABT6G 2V2 Canada

A novel test strategy is proposed with dual-modality detection techniques for COVID-19 antibody detection. The full-length S protein of SARS-CoV-2 was chemically immobilized on a glass surface to capture anti-SARS-CoV-2 IgG in patient serum and was detected through either Electrochemical Impedance Spectroscopy (EIS) or fluorescence imaging with labeled secondary antibodies. Gold nanoparticles conjugated with protein G were used as the probe and the bound GNP-G was detected through EIS measurements. Anti-human-IgG conjugated with the fluorescent tag Alexa Fluor 488 was used as the probe for fluorescence imaging. Clinical SARS-CoV-2 IgG positive serum and negative controls were used to validate both modalities. For fluorescence-based detection, a high sensitivity was noticed with a quantification range of 0.01–0.1 A.U.C. and a LOD of 0.004 A.U.C. This study demonstrates the possibility of utilizing different measurement techniques in conjunction for improved COVID-19 serology testing. © 2022 The Authors

关键词： SARS

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Aberration detection of pertussis from the Mazandaran province, Iran, from 2012 to 2018: Application of discrete wavelet transform

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Journal of Acute Disease 2020年第3期9卷 114-120页

作者： Yousef Alimohamadi Seyed Mohsen Zahraei Manoochehr Karami Mehdi Yaseri MojtabaLotfizad Kourosh Holakouie-Naieni Department of Epidemiology and Biostatistics School of Public HealthTehran University of Medical SciencesTehranIran Center for Communicable Diseases Control Ministry of Health and Medical EducationTehranIran Research Center for Health Sciences Hamadan University of Medical SciencesHamadanIran School of Electrical&Computer Engineering Tarbiat Modares UniversityTehranIran

Objective:To define the level of alarm threshold for pertussisaberrations and to detect the aberrations of the reported suspectedcases of pertussis from the Mazandaran province in the north ***:The included cases were composed of the suspectedpertussis patients who came from Mazandaran province andregistered in the Center for Disease Control and Prevention from20 March 2012 to 20 March 2018.A discrete wavelet transformbasedmethod was used to detect the *** analyseswere performed using MATLAB Software version 2018a andExcel ***:A total of 1162 cases were recruited in the study,including 545(46.90%)males and 617(53.10%)females,withmedian age of 1.47(0.22-9.56)*** median age of maleswas 1.18(0.21-8.24)years,while that of females was 1.82(0.21-10.75)*** the level of the alarm threshold,it was1.28 case/d when k=2,while it was 1.34 case/d when k=*** detected aberration days were 123 d and 57 d by consideringk=2 and 3,*** most defined alarm threshold wasrelated to spring(>2 cases/d)and summer(>1 case/d),***:The sensitivity of the surveillance system issubjected to a different ***,determining the level of alarmthreshold periodically using different methods is recommended.

关键词： Aberration detection Pertussis Mazandaran Iran Discrete wavelet transform

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Environment International 2023年 174卷 107825页

作者： Rai, Masna Stafoggia, Massimo de'Donato, Francesca Scortichini, Matteo Zafeiratou, Sofia Vazquez Fernandez, Liliana Zhang, Siqi Katsouyanni, Klea Samoli, Evangelia Rao, Shilpa Lavigne, Eric Guo, Yuming Kan, Haidong Osorio, Samuel Kyselý, Jan Urban, Aleš Orru, Hans Maasikmets, Marek Jaakkola, Jouni J.K. Ryti, Niilo Pascal, Mathilde Hashizume, Masahiro Fook Sheng Ng, Chris Alahmad, Barrak Hurtado Diaz, Magali De la Cruz Valencia, César Nunes, Baltazar Madureira, Joana Scovronick, Noah Garland, Rebecca M. Kim, Ho Lee, Whanhee Tobias, Aurelio Íñiguez, Carmen Forsberg, Bertil Åström, Christofer Maria Vicedo-Cabrera, Ana Ragettli, Martina S. Leon Guo, Yue-Liang Pan, Shih-Chun Li, Shanshan Gasparrini, Antonio Sera, Francesco Masselot, Pierre Schwartz, Joel Zanobetti, Antonella Bell, Michelle L. Schneider, Alexandra Breitner, Susanne Institute of Epidemiology Helmholtz Munich - German Research Center for Environmental Health Neuherberg Germany Institute for Medical Information Processing Biometry and Epidemiology – IBE Pettenkofer School of Public Health LMU Munich Munich Germany Department of Epidemiology Lazio Regional Health Service ASL Roma 1 Rome Italy epartment of Hygiene Epidemiology and Medical Statistics Medical School National and Kapodistrian University of Athens Greece Department of Air Pollution and Noise Norwegian Institute of Public Health Oslo Norway School of Epidemiology & Public Health Faculty of Medicine University of Ottawa Ottawa Canada and Environmental Health Science & Research Bureau Health Canada Ottawa Canada Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Environmental Health School of Public Health Fudan University Shanghai China Department of Environmental Health University of São Paulo São Paulo Brazil Institute of Atmospheric Physics Czech Academy of Sciences Prague Czech Republic Faculty of Environmental Sciences Czech University of Life Sciences Prague Czech Republic Department of Family Medicine and Public Health University of Tartu Tartu Estonia Estonian Environmental Research Centre Tallinn Estonia University of Oulu Oulu Finland Santé Publique France Department of Environmental Health French National Public Health Agency Saint Maurice France Department of Global Health Policy Graduate School of Medicine The University of Tokyo Tokyo Japan School of Tropical Medicine and Global Health Nagasaki University Nagasaki Japan Department of Environmental Health Harvard T.H. Chan School of Public Health BostonMA United States Department of Environmental Health National Institute of Public Health Morelos Cuernavaca Mexico Department of Environmental Health Instituto Nacional de Saúde Dr. Ricardo Jorge Porto Portugal Department of Environmen

Background: Evidence on the potential interactive effects of heat and ambient air pollution on cause-specific mortality is inconclusive and limited to selected locations. Objectives: We investigated the effects of heat on cardiovascular and respiratory mortality and its modification by air pollution during summer months (six consecutive hottest months) in 482 locations across 24 countries. Methods: Location-specific daily death counts and exposure data (e.g., particulate matter with diameters ≤ 2.5 µm [PM2.5]) were obtained from 2000 to 2018. We used location-specific confounder-adjusted Quasi-Poisson regression with a tensor product between air temperature and the air pollutant. We extracted heat effects at low, medium, and high levels of pollutants, defined as the 5th, 50th, and 95th percentile of the location-specific pollutant concentrations. Country-specific and overall estimates were derived using a random-effects multilevel meta-analytical model. Results: Heat was associated with increased cardiorespiratory mortality. Moreover, the heat effects were modified by elevated levels of all air pollutants in most locations, with stronger effects for respiratory than cardiovascular mortality. For example, the percent increase in respiratory mortality per increase in the 2-day average summer temperature from the 75th to the 99th percentile was 7.7% (95% Confidence Interval [CI] 7.6–7.7), 11.3% (95%CI 11.2–11.3), and 14.3% (95% CI 14.1–14.5) at low, medium, and high levels of PM2.5, respectively. Similarly, cardiovascular mortality increased by 1.6 (95%CI 1.5–1.6), 5.1 (95%CI 5.1–5.2), and 8.7 (95%CI 8.7–8.8) at low, medium, and high levels of O3, respectively. Discussion: We observed considerable modification of the heat effects on cardiovascular and respiratory mortality by elevated levels of air pollutants. Therefore, mitigation measures following the new WHO Air Quality Guidelines are crucial to enhance better health and promote sustainable development. © 2023

关键词： Location

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Parameter Modeling for Small-Scale Mobility in Indoor THz Communication

arXiv

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arXiv 2019年

作者： Singh, Rohit Sicker, Douglas Engineering & Public Policy Carnegie Mellon University Pittsburgh United States School of Computer Science Carnegie Mellon University Pittsburgh United States

Despite such challenges as high path loss and equipment cost, THz communication is becoming one of the potentially viable means through which ultra-high data rate can be achieved. To compensate for the high path loss, we present parameter modeling for indoor THz communication. To maximize efficient and opportunistic use of resources, we analyze the potential workarounds for a single access point to satisfy most of the mobile terminals by varying such parameters as humidity, distance, frequency windows, beamwidths, antenna placement, and user mobility type. One promising parameter is antenna beamwidth, where narrower beams results in higher antenna gain. However, this can lead to "beamwidth dilemma" scenario, where narrower beamwidth can result in significant outages due to device mobility and orientation. In this paper, we address this challenge by presenting a mobility model that performs an extensive analysis of different human mobility scenarios, where each scenario has different data rate demands and movement patterns. We observe that for mobile users, there are optimal beamwidths that are affected by the mobility type (high mobility, constrained mobility, and low mobility) and AP placement. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile telecommunication systems

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Ethical Machine Learning in Healthcare

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Biomedical Data Science 2021年第1期4卷 123-144页

作者： Irene Y. Chen Emma Pierson Sherri Rose Shalmali Joshi Kadija Ferryman Marzyeh Ghassemi 1Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA email: iychen@mit.edu 2Microsoft Research Cambridge Massachusetts 02143 USA 3Center for Health Policy and Center for Primary Care and Outcomes Research Stanford University Stanford California 94305 USA 4Vector Institute Toronto Ontario M5G 1M1 Canada 5Department of Technology Culture and Society Tandon School of Engineering New York University Brooklyn New York 11201 USA 6Institute for Medical and Evaluative Sciences Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.

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Reliable THz Communications for Outdoor based Applications- Use Cases and Methods

arXiv

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arXiv 2019年

作者： Singh, Rohit Sicker, Douglas Engineering & Public Policy Carnegie Mellon University Pittsburgh United States School of Computer Science Carnegie Mellon University Pittsburgh United States

Future (beyond 5G) wireless networks will demand high throughput and low latency and would benefit from greenfield, contiguous, and wider bandwidth, all of which THz spectrum can provide. Although THz has been envisioned to be deployed in an indoor setting, with proper enforcement and planning, we can draw a limited number of use cases for outdoor THz communication. THz can provide high capacity and ultra-high throughput but at the cost of high path loss and sensitivity to device orientation/mobility.. We identify scenarios where the use of the THz spectrum for an outdoor setting is justified and their critical operating parameters. We further categorize the applications based on the relative mobility between the access point (AP) and user equipment (UE). We present an approach for deploying THz on an outdoor framework by presenting preliminary technical parameter analysis for scenarios, like wireless backhaul, high-speed kiosks, and the aerial base station (ABS). Our preliminary analysis shows that the application for each of these scenarios is limited based on multiple parameters, such as distance, device mobility, device orientation, user geometry, antenna gain, and environment settings, which requires separate consideration and optimization. Copyright © 2019, The Authors. All rights reserved.

关键词： Antennas

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