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Author Correction: Characterization of the genetic architecture of infant and early childhood body mass index

Nature metabolism 2024年第2期6卷 378页

作者： Øyvind Helgeland Marc Vaudel Pol Sole-Navais Christopher Flatley Julius Juodakis Jonas Bacelis Ingvild L Koløen Gun Peggy Knudsen Bente B Johansson Per Magnus Ted Reichborn Kjennerud Petur B Juliusson Camilla Stoltenberg Oddgeir L Holmen Ole A Andreassen Bo Jacobsson Pål R Njølstad Stefan Johansson Center for Diabetes Research Department of Clinical Science University of Bergen Bergen Norway. Department of Genetics and Bioinformatics Health Data and Digitalization Norwegian Institute of Public Health Oslo Norway. Department of Obstetrics and Gynecology Institute of Clinical Sciences Sahlgrenska Academy University of Gothenburg Gothenburg Sweden. Department of Medical Genetics Haukeland University Hospital Bergen Norway. Norwegian Institute of Public Health Oslo Norway. Centre for Fertility and Health Norwegian Institute of Public Health Oslo Norway. Department of Mental Disorders Norwegian Institute of Public Health Oslo Norway. Institute of Clinical Medicine University of Oslo Oslo Norway. Department of Health Registry Research and Development National Institute of Public Health Bergen Norway. Department of Clinical Science University of Bergen Bergen Norway. Children and Youth Clinic Haukeland University Hospital Bergen Norway. HUNT Research Centre Department of Public Health and Nursing Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway. NORMENT Centre Institute of Clinical Medicine University of Oslo Oslo Norway. Division of Mental Health and Addiction Oslo University Hospital Oslo Norway. Center for Diabetes Research Department of Clinical Science University of Bergen Bergen Norway. pal.njolstad@uib.no. Children and Youth Clinic Haukeland University Hospital Bergen Norway. pal.njolstad@uib.no. Center for Diabetes Research Department of Clinical Science University of Bergen Bergen Norway. stefan.johansson@uib.no. Department of Medical Genetics Haukeland University Hospital Bergen Norway. stefan.johansson@uib.no.

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Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction

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Nature Communications 2025年第1期16卷 1-12页

作者： Xiang, Ruidong Ben-Eghan, Chief Liu, Yang Roberts, David Ritchie, Scott Lambert, Samuel A. Xu, Yu Takeuchi, Fumihiko Inouye, Michael Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne VIC Australia Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Agriculture Victoria AgriBio Centre for AgriBioscience Bundoora 3083 VIC Australia The School of Applied Systems Biology La Trobe University Melbourne 3086 VIC Australia Baker Department of Cardiometabolic Health The University of Melbourne Melbourne 3010 VIC Australia British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Victor Phillip Dahdaleh Heart and Lung Research Institute University of Cambridge Cambridge United Kingdom Health Data Research UK Cambridge Wellcome Genome Campus and University of Cambridge Cambridge United Kingdom British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge United Kingdom National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics University of Cambridge Cambridge United Kingdom NHS Blood and Transplant–Oxford Centre John Radcliffe Hospital and Radcliffe Department of Medicine University of Oxford John Radcliffe Hospital Oxford United Kingdom Department of Bioinformatics National Center for Global Health and Medicine Tokyo Japan

Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction;however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N ~ 408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N ~ 40,466), where the genetically most variable individuals had increased conventional PGS accuracy (by ~19%) relative to the genetically least variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction. © The Author(s) 2025.

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Adolescents with long-duration type 1 diabetes have low bone mass and reduced levels of bone indices reflecting altered bone resorption

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Bone 2025年 199卷 117560页

作者： Diana Swolin-Eide Auste Pundziute Lyckå Daniel Novak Björn Andersson Gun Forsander Per Magnusson Department of Paediatrics Institute for Clinical Sciences Sahlgrenska Academy University of Gothenburg Gothenburg Sweden Region Västra Götaland Sahlgrenska University Hospital Queen Silvia Children's Hospital Department of Paediatrics Gothenburg Sweden. Electronic address: diana.swolin-eide@vgregion.se. Region Västra Götaland Sahlgrenska University Hospital Queen Silvia Children's Hospital Department of Paediatrics Gothenburg Sweden. Electronic address: auste.pundziute-lycka@vgregion.se. Region Västra Götaland Sahlgrenska University Hospital Queen Silvia Children's Hospital Department of Paediatrics Gothenburg Sweden. Electronic address: daniel.novak@vgregion.se. Bioinformatics and Data Center Sahlgrenska Academy University of Gothenburg Gothenburg Sweden. Electronic address: bjorn.andersson@medic.gu.se. Department of Paediatrics Institute for Clinical Sciences Sahlgrenska Academy University of Gothenburg Gothenburg Sweden. Electronic address: gun.forsander@pediat.gu.se. Department of Clinical Chemistry and Department of Biomedical and Clinical Sciences Linköping University Linköping Sweden. Electronic address: per.magnusson@regionostergotland.se.

The prevalence of type 1 diabetes (T1D) is increasing globally and is associated with severe complications, including an increased risk of fractures. This case-control study investigated whether young individuals with well-controlled, long-duration T1D have differences in bone mass and bone biomarkers in comparison with healthy matched controls. Fifty individuals, aged 15.0-17.9 years, with a T1D duration of at least 8 years and (mean ± SD) 10.6 ± 2.1 years were included, hence the participants had diabetes throughout most part of their puberty and growth spurt. The mean HbA1c since diabetes diagnosis was 56 ± 6 mmol/mol (7.3 ± 0.6 %). Bone mass was assessed by dual-energy X-ray absorptiometry and peripheral quantitative computed tomography (pQCT). Clinical follow-up data were retrieved from the Swedish National Diabetes Registry. The control group comprised 50 healthy matched adolescents, aged 15.1-17.9 years. The groups were well-matched with no significant differences in age, sex, weight, height, body mass index and the self-reported physical activity. Total body less head aBMD and Z-scores were significantly lower in T1D individuals, p < 0.05. Total tibia density and trabecular density, by pQCT, were also lower in the T1D group, p < 0.05. There were no differences between the groups for parathyroid hormone, 25-hydroxyvitamin D, bone-specific alkaline phosphatase (BALP), intact procollagen type I N-propeptide (PINP), sclerostin, bioactive sclerostin and osteoprotegerin. However, individuals with T1D had reduced levels of C-terminal telopeptide of type I collagen (CTX) (p < 0.001) and nuclear factor κB ligand (a.k.a. RANKL) (p = 0.01), indicating altered regulation of osteoclasts. In conclusion, young individuals with well-controlled, long-duration T1D have subnormal bone mass accrual, impaired microstructure at several sites and suppressed RANKL-mediated osteoclastogenesis resulting in reduced bone resorption. Based on these findings, we suggest that bone health

关键词： Bone biomarkers Bone mineral density DXA Forearm Pediatric

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FLNA ROD1 and ROD2 domains are implicated in congenital lower urinary tract obstruction

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Kidney International Reports 2025年

作者： Clara Vidic Jil Stegmann Charlotte Bendixen Sophia Schneider Daniel Hahn Tim Felger Aybike Hofmann Wolfgang Rösch Tabea Schröder Marcin Zaniew Marcin Polok Przemysław Sikora Katarzyna Zachwieja Monika Miklaszewska Grazyna Krzemien Marcin Tkaczyk Hannah Hofmann Katarzyna Kilis-Pstrusinska Wolfgang Krebs Melanie Chan Alina C. Hilger Institute of Human Genetics University of Bonn Bonn Germany Institute of Anatomy and Cell Biology Medical Faculty University of Bonn Bonn Germany Department of General Visceral Vascular and Thoracic Surgery Unit of Pediatric Surgery University Hospital Bonn Bonn Germany Department of Pediatrics Charité University Hospital Berlin Berlin Germany Department of Pediatrics and Adolescent Medicine University Hospital Erlangen Erlangen Germany Department of Pediatric Urology Clinic St. Hedwig University Medical Center Regensburg Regensburg Germany Department of Pediatric Surgery Charité University Hospital Berlin corporate member of Freie Universität Berlin Humboldt-Universität Berlin and Berlin Institute of Health Berlin Germany Department of Pediatrics University of Zielona Góra Zielona Góra Poland Department of Pediatric Surgery and Urology University of Zielona Góra Zielona Góra Poland Department of Pediatric Nephrology Medical University of Lublin Lublin Poland Department of Pediatric Nephrology and Hypertension Faculty of Medicine Jagiellonian University Medical College Kraków Poland Department of Pediatrics and Nephrology Medical University of Warsaw Warsaw Poland Department of Pediatrics Immunology and Nephrology Polish Mother's Memorial Hospital Research Institute Lodz Poland Department and Clinic of Pediatric Nephrology Wroclaw Medical University Wroclaw Poland Core Unit Bioinformatics Data integration and analysis (CUBiDA) University Hospital Erlangen Erlangen Germany Centre for Kidney and Bladder Health University College London London UK Medical Research Council Laboratory of Medical Sciences Imperial College London London UK Research Center On Rare Kidney Diseases (RECORD) University Hospital Erlangen Erlangen Germany

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A Model of Visual Intelligent System for Genus Identification of Fish in the Siluriformes Order

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Taufik Budhi Pramono R Ardharyan Islamy Saprudin Joni Johanda Putra Teddy Suparyanto Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Aquaculture Study Program Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Aquatic Resources Management Brawijaya University Malang East Java Indonesia Graduate Program Aquatic Resources Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Ambiguity in the fish naming is present in several fish species database, especially for fish in Siluformes order. To fix the ambiguity, a visual intelligent system is needed to automate the fish naming correction in the database. In this study, we developed a deep-learning-based model as the core of the intelligent system. The proposed model achieved 89% accuracy for the classification of three genera in Siluformes order: Mystus, Hemibagrus, and Glyptothorax.

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Health Assistant Wearable-Based data Science System Model: A Pilot Study 3

Health Assistant Wearable-Based Data Science System Model: A...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Budiarto, Arif Febriana, Trisna Suparyanto, Teddy Caraka, Rezzy Eko Pardamean, Bens Computer Science Department Bina Nusantara University School of Computer Science Jakarta11480 Indonesia Information Systems Department Bina Nusantara University School of Information Systems Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Indonesia Faculty of Science National University of Malaysia School of Mathematical Science Bangi43000 Malaysia Computer Science Department Bina Nusantara University BINUS Graduate Program-Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

The rapid development of Information and Communication Technology (ICT) has an impact on the innovation of new technology that gives benefits for health. One of its latest development is wearable device technology. Nowadays, the growth of this technology market is very positive. But unfortunately the growth of this technology is not followed by the availability of supporting applications that provide maximum benefits for users by utilizing personal health and activity data. This paper is intended to provide a report of our initial research in developing a health assistant solution that utilizes wearable device generated data. The whole research process is executed using Design Thinking approach that put the users as the main consideration. This initial research is started by designing a field research that can potentially gathered users' data generated from wearable device. A series of statistical and machine learning techniques are presented to analyze time series data to create a model. Finally, to implement the model, a mobile application prototype is also proposed. The outcomes from this initial research will be used to develop a fully working solution. © 2018 IEEE.

关键词： Learning systems

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Stroke genetics informs drug discovery and risk prediction across ancestries (vol 611, pg 115, 2022)

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NATURE 2022年第7938期612卷 E7-E7页

作者： Mishra, Aniket Malik, Rainer Hachiya, Tsuyoshi Jurgenson, Tuuli Namba, Shinichi Posner, Daniel C. Kamanu, Frederick K. Koido, Masaru Le Grand, Quentin Shi, Mingyang He, Yunye Georgakis, Marios K. Caro, Ilana Krebs, Kristi Liaw, Yi-Ching Vaura, Felix C. Lin, Kuang Winsvold, Bendik Slagsvold Srinivasasainagendra, Vinodh Parodi, Livia Bae, Hee-Joon Chauhan, Ganesh Chong, Michael R. Tomppo, Liisa Akinyemi, Rufus Roshchupkin, Gennady V. Habib, Naomi Jee, Yon Ho Thomassen, Jesper Qvist Abedi, Vida Carcel-Marquez, Jara Nygaard, Marianne Leonard, Hampton L. Yang, Chaojie Yonova-Doing, Ekaterina Knol, Maria J. Lewis, Adam J. Judy, Renae L. Ago, Tetsuro Amouyel, Philippe Armstrong, Nicole D. Bakker, Mark K. Bartz, Traci M. Bennett, David A. Bis, Joshua C. Bordes, Constance Borte, Sigrid Cain, Anael Ridker, Paul M. Cho, Kelly Chen, Zhengming Cruchaga, Carlos Cole, John W. de Jager, Phil L. de Cid, Rafael Endres, Matthias Ferreira, Leslie E. Geerlings, Mirjam I. Gasca, Natalie C. Gudnason, Vilmundur Hata, Jun He, Jing Heath, Alicia K. Ho, Yuk-Lam Havulinna, Aki S. Hopewell, Jemma C. Hyacinth, I. Hyacinth Inouye, Michael Jacob, Mina A. Jeon, Christina E. Jern, Christina Kamouchi, Masahiro Keene, Keith L. Kitazono, Takanari Kittner, Steven J. Konuma, Takahiro Kumar, Amit Lacaze, Paul Launer, Lenore J. Lee, Keon-Joo Lepik, Kaido Li, Jiang Li, Liming Manichaikul, Ani Markus, Hugh S. Marston, Nicholas A. Meitinger, Thomas Mitchell, Braxton D. Montellano, Felipe A. Morisaki, Takayuki Mosley, Thomas H. Nalls, Mike A. Nordestgaard, Borge G. O'Donnell, Martin J. Okada, Yukinori Onland-Moret, N. Charlotte Ovbiagele, Bruce Peters, Annette Psaty, Bruce M. Rich, Stephen S. Rosand, Jonathan Sabatine, Marc S. Sacco, Ralph L. Saleheen, Danish Sandset, Else Charlotte Salomaa, Veikko Sargurupremraj, Muralidharan Sasaki, Makoto Satizabal, Claudia L. Schmidt, Carsten O. Shimizu, Atsushi Smith, Nicholas L. Sloane, Kelly L. Sutoh, Yoichi Sun, Yan V. Tanno, Kozo Tiedt, Steffen Tatlisumak, Turgut Torres-Aguila, Nuria P. Tiwari, Hemant K. Tregouet, Bordeaux Population Health Research Center University of Bordeaux Inserm UMR 1219 Bordeaux France Department of Public Health Bordeaux University Hospital Bordeaux France Department of Neurology Memory Clinic Bordeaux University Hospital Bordeaux France Department of Neurology Institute for Neurodegenerative Diseases CHU de Bordeaux Bordeaux France Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and the Massachusetts Institute of Technology Cambridge MA USA Munich Cluster for Systems Neurology Munich Germany German Center for Neurodegenerative Diseases (DZNE) Munich Germany Iwate Tohoku Medical Megabank Organization Iwate Medical University Iwate Japan Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia Institute of Mathematics and Statistics University of Tartu Tartu Estonia Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland University Center for Primary Care and Public Health Lausanne Switzerland Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Department of Genome Informatics Graduate School of Medicine The University of Tokyo Tokyo Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Integrated Frontier Research for Medical Science Division Institute for Open and Transdisciplinary Research Initiatives Osaka University Suita Japan Center for Infectious Disease Education and Research (CiDER) Osaka University Suita Japan Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA TIMI S

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Multiple analyses indicate the specific association of NR1I3,C6 and TNN with low hip BMD risk

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Journal of Genetics and Genomics 2017年第6期44卷 327-330页

作者： Ying-Ying Han Lan-Juan Zhao Yong Lin Hao He Qing Tian Wei Zhu Hui Shen Xiang-Ding Chen Hong-Wen Deng Center of System Biomedical Sciences University of Shanghai for Science and Technology Center for Bioinformatics and Genomics Department of Global Biostatistics and Data ScienceSchool of Public Health and Tropical MedicineTulane University Laboratory of Molecular and Statistical Genetics and the Key Laboratory of Protein Chemistry and Developmental Biology of the Ministry of Education College of Life SciencesHunan Normal University

Low hip bone mineral density(BMD)is an important index for osteoporosis and is associated with hip fracture,which leads to more cases of disability and mortality than all other kinds of fractures(Kanis et al.,2007).BMD’s heritability is more than 60%(Arden et al.,1996).A number of candidate loci for BMD have been previously identified by Genome Wide Association Studies(GWAS)(Xiong et al.,2009;Karasik et al.,2010;Zhang et al.,2013).Nevertheless,many significant signals based on GWAS are

关键词： BMD ERE is in Multiple analyses indicate the specific association of NR1I3,C6 and TNN with low hip BMD risk of GWAS BRCA NR with

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Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

arXiv

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arXiv 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Roshanzamir, Mohamad Joloudari, Javad Hassannataj Shoeibi, Afshin Jafari, Mahboobeh Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Panahiazar, Maryam Zare, Assef Shariful Islam, Sheikh Mohammed Rajendra, U. Acharya Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid hospital Iran University of Medical Sciences Tehran Iran Institute for Computational Health Sciences University of California San Francisco United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel) achieved the highest classification accuracy of 99.02% in accurate detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： data mining

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data annotation system for intelligent energy conservator in smart building

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IOP Conference Series: Earth and Environmental Science 2020年第1期426卷

作者： Bens Pardamean Tjeng Wawan Cenggoro Bloomest Jansen Chandra Reza Rahutomo Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department College of Letters and Science University of Wisconsin 53706 Madison WI USA

The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However, massive annotated dataset is necessary to train the system. Therefore, we propose a system that enables rapid data annotation to collect the massive dataset. With the proposed system, an image can be annotated within 4.8 seconds in average.

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