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Genome-Wide Association Analyses in Family Triads and Dyads Following Assisted Reproductive Technology

Genetic Epidemiology 2025年第5期49卷 e70011页

作者： Skodvin, Siri N. Gjessing, Håkon K. Jugessur, Astanand Romanowska, Julia Havdahl, Alexandra Håberg, Siri E. Hanevik, Hans Ivar Lyle, Robert Lie, Rolv Terje Gjerdevik, Miriam Centre for Fertility and Health Norwegian Institute of Public Health Oslo Norway Department of Global Public Health and Primary Care University of Bergen Bergen Norway PsychGen Centre for Genetic Epidemiology and Mental Health Norwegian Institute of Public Health Oslo Norway Nic Waals Institute Lovisenberg Diaconal Hospital Oslo Norway Department of Fertility Telemark Hospital Trust Porsgrunn Norway Department of Medical Genetics Oslo University Hospital and University of Oslo Oslo Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

Genetic selection occurs at different stages before a successful birth. The genetic makeup of a couple may influence the likelihood of needing assisted reproductive technology (ART) to achieve conception. However, frequent early fetal losses may also be perceived as reduced couple fertility and may thus be a contributing factor to the need for ART treatment. As ART procedures may enhance early fetal survival, genes that impact fetal viability may have a different allele distribution in ART offspring than expected under Mendelian transmission, as well as compared with the general population. With genetic data available from the Norwegian Mother, Father, and Child Cohort Study, we defined fetal survival as the study outcome and analyzed 1336 case-parent triads and dyads where the offspring were conceived by ART. Using log-linear models implemented in the R package Haplin, we conducted genome-wide scans to estimate fetal, maternal, and parent-of-origin effects and provided a detailed discussion on how these effects are estimated and interpreted. We detected fetal effects for single-nucleotide polymorphisms (SNPs) located in CXXC4-AS1, OPCML, and DYNLRB2-AS1. Since these effects were not observed in a limited follow-up analysis of non-ART triads, the identified effects are unlikely caused by genetic selection before fertilization. © 2025 The Author(s). Genetic Epidemiology published by Wiley Periodicals LLC.

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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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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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Towards Cleaner Air: Pm2.5 Exposure and Disparities Around Childcare Providers in England

SSRN

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SSRN 2025年

作者： Cruz, Joana Li, Guangquan Rammah, Amal Zhong, Jian Shoari, Niloofar Akaraci, Selin Hajna, Samantha Hart, Caroline Chamberlain, Rosemary Mitsakou, Christina Exley, Karen Bloss, William Fry, Richard Cummins, Steven Hardelid, Pia Population Policy and Practice Research and Teaching Department Great Ormond Street Institute of Child Health UCL 30 Guilford Street LondonWC1N 1EH United Kingdom Applied Statistics Research Group Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom School of Geography Earth & Environmental Sciences University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Computational Science and Engineering Group University of Greenwich Old Royal Naval College Park Row LondonSE10 9LS United Kingdom Department of Health Sciences Faculty of Applied Health Sciences Brock University St Catharines ON Canada Air Quality and Public Health Environmental Hazards and Emergencies UK Health Security Agency ChiltonOX11 0RQ United Kingdom Swansea University Singleton Park Swansea WalesSA28PP United Kingdom Population Health Innovation Lab Department of Public Health Environments & Society London School of Hygiene & Tropical Medicine 15-17 Tavistock Place LondonWC1H 9SH United Kingdom GOSH Biomedical Research Centre 30 Guilford St LondonWC1N 1EH United Kingdom

Air pollution poses a significant health risk for young children, particularly in urbanareas. This study examined changes in PM2.5 concentrations around Early YearsProviders (EYPs)—including nurseries—across England from 2018 to 2022 andidentified neighbourhood characteristics associated with EYPs located in areasexceeding WHO air quality guidelines (>5 μg/m3). Geographic Information Systems(GIS) methodology integrated data on EYP locations from Ordnance Survey with airpollution data from *** analysed temporal changes in PM2.5 concentrations around EYPs, identifiedclusters exceeding WHO interim target levels of 10 μg/m3 using Local Moran’s I, andexplored associations between PM2.5 exposure and socio-demographic factors. Dataon deprivation, urbanicity, and ethnic group distribution were sourced from the 2021Census of Population for England and Wales. A Bayesian spatial regression modelincorporating random effects assessed associations between PM2.5 exposure andsmall-area socio-demographic *** 2018 and 2022, the number of EYPs ranged from 15,780 to 18,427. AveragePM2.5 levels around EYPs declined by 17.8% (from 9.4 μg/m3, SD=1.8 to 7.8 μg/m3,SD=1.5), yet over 96% of EYPs consistently recorded concentrations above the WHOannual guideline of 5 μg/m3. Temporary reductions during COVID-19 lockdowns in2020 (mean=7.5 μg/m3, SD=1.3) rebounded by ***-High clusters (EYPs with PM2.5 >10 μg/m3 surrounded by similar EYPs) wereobserved in urban centres like Greater London, Birmingham, and Southeast England,while Low-Low clusters occurred in rural regions such as Devon, Cornwall, and northern England. EYPs in deprived, urban areas with predominantly non-whitepopulations had higher PM2.5 *** findings highlight the urgent need for targeted interventions to reduce airpollution in deprived urban areas, prioritizing young children’s health. © 2025, The Authors. All rights reserved.

关键词： Health risks

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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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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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An India-specific compartmental model for Covid-19: Projections and intervention strategies by incorporating geographical, infrastructural and response heterogeneity

arXiv

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arXiv 2020年

作者： Gupta, Sanit Shah, Sahil Chaturvedi, Sumit Thakkar, Pranav Solanki, Parvinder Dibyachintan, Soham Roy, Sandeepan Sushma, M.B. Godbole, Adwait Jaseem, Noufal Kumar, Pradumn Ravikanti, Sucheta Das, Aritra Babu, Giridhara R. Bhatnagar, Tarun Maji, Avijit Mitra, Mithun K. Vinjanampathy, Sai Department of Mechanical Engineering IIT Bombay India Department of Computer Science and Engineering IIT Bombay India Department of Aerospace Engineering IIT Bombay India Department of Physics IIT Bombay India Department of Chemical Engineering IIT Bombay India Department of Civil Engineering IIT Bombay India Department of Electrical Engineering IIT Bombay India Epidemiology and Outcome Research Real World Solutions IQVIA United States Indian Institute of Public Health-Bengaluru Public Health Foundation of India India ICMR - National Institute of Epidemiology Chennai India Centre for Quantum Technologies National University of Singapore Singapore

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2024年 1057 LNNS卷 xiii-xiv页

作者： Bhateja, Vikrant Tang, Jinshan Sharma, Dilip Kumar Polkowski, Zdzislaw Ahmad, Afaq Department of Electronics Engineering Veer Bahadur Singh Purvanchal University Uttar Pradesh Jaunpur India Department of Health Administration and Policy College of Public Health George Mason University FairfaxVA United States GLA University Uttar Pradesh Mathura India Department of Humanities and Social Sciences The Karkonosze University of Applied Sciences Jelenia Góra Poland Department of Mathematics and Computer Science Modern College of Business and Science Muscat Oman

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Research into the moisture influence on the physical-chemical tuff-stone characteristics in basalt quarries of the Rivne-Volyn region 14

Research into the moisture influence on the physical-chemica...

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14th International Research and Practice Conference on Ukrainian School of Mining engineering, USME 2020

作者： Malanchuk, Yevhenii Korniienko, Valerii Malanchuk, Larisa Zaiets, Vitalii National University of Water Management and Nature Resources Use Department of Automation Electrical Engineering and Computer-Integrated Technologies 11 Soborna St. Rivne33028 Ukraine National University of Water Management and Nature Resources Use Department of Development of Deposits and Mining 11 Soborna St. Rivne33028 Ukraine National University of Water Management and Nature Resources Use Department of Public Administration Documenting and Information Activities 11 Soborna St. Rivne33028 Ukraine

This paper studies the rock mass stress state of deposits under highland conditions, depending on the geological structure of a particular rock mass area, the tectonic field of stresses and the region relief. This study is aimed to develop an experimental method for assessing and monitoring the properties and state of the rock mass adjacent to mine workings. Experimental studies are performed through stresses measurements in-situ. Based on research results, it has been revealed that the geological structures, tectonic fields of stresses and the earth's surface relief of the deposit normally reflect the values and direction of the main stresses acting in the rock mass. These patterns can be used to predict and assess the stress state of the rock mass. During the rock mass stress state assessment, quantitative dependences have been obtained for determining the stress tensors conditioned by the overlying rocks weight, tectonics and fracturing, and the deposit surface relief The research results make possible to assess the nature of the stresses distribution, to identify the areas of reduced, equal, increased and maximum stresses concentration of the virgin rock mass, as well as to increase the efficiency of the geoacoustic control developed by the authors for the state of the rock mass adjacent to mine working. © The Authors, published by EDP Sciences 2020.

关键词： Tectonics

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