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data annotation system for intelligent energy conservator in smart building

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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Correction: Spatial organization of B lymphocytes and prognosis prediction in patients with gastric cancer

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Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 2025年 2025 May 16页

作者： Ryan Yong Kiat Tay Manavi Sachdeva Haoran Ma Young-Woo Kim Myeong-Cherl Kook Hyunki Kim Jae-Ho Cheong Lindsay C Hewitt Katharina Nekolla Günter Schmidt Takaki Yoshikawa Takashi Oshima Tomio Arai Supriya Srivastava Ming Teh Xuewen Ong Su Ting Tay Taotao Sheng Joseph J Zhao Patrick Tan Heike I Grabsch Raghav Sundar Yong Loo Lin School of Medicine National University of Singapore 1E Kent Ridge Road Singapore 119228 Singapore. Department of Haematology-Oncology National University Cancer Institute National University Hospital Singapore Singapore. Cancer and Stem Cell Biology Program Duke-NUS Medical School Singapore Singapore. Department of Cancer Policy and Population Health National Cancer Center Graduate School of Cancer Science and Policy and Center for Gastric Cancer and Department of Surgery National Cancer Center Goyang Republic of Korea. Center for Gastric Cancer Department of Pathology National Cancer Center Goyang Republic of Korea. Department of Pathology Yonsei University College of Medicine Seoul Republic of Korea. Department of Surgery Yonsei University College of Medicine Seoul Republic of Korea. Department of Pathology GROW Research Institute for Oncology and Reproduction Maastricht University Medical Center+ Maastricht The Netherlands. Department of Precision Medicine GROW School for Oncology and Reproduction Maastricht University Center+ Maastricht The Netherlands. Computational Pathology Oncology R&D AstraZeneca Munich Germany. Department of Surgery Kanagawa Cancer Center Yokohama Japan. Department of Surgery Yokohama City University Yokohama Japan. Department of Surgery Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital Tokyo Japan. Department of Pathology Tokyo Metropolitan Institute for Geriatrics and Gerontology Tokyo Japan. Department of Medicine National University of Singapore Singapore Singapore. Department of Pathology National University Hospital Singapore Singapore. Genome Institute of Singapore Agency for Science Technology and Research Singapore Singapore. Cancer Science Institute of Singapore National University of Singapore Singapore Singapore. Cellular and Molecular Research National Cancer Centre Singapore Singapore. Singhealth/Duke-NUS Institute of Precision Medicine National Heart Centre Singapore

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Publisher Correction: Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance

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Nature genetics 2024年第10期56卷 2281页

作者： Qingbo S Wang Takanori Hasegawa Ho Namkoong Ryunosuke Saiki Ryuya Edahiro Kyuto Sonehara Hiromu Tanaka Shuhei Azekawa Shotaro Chubachi Yugo Takahashi Saori Sakaue Shinichi Namba Kenichi Yamamoto Yuichi Shiraishi Kenichi Chiba Hiroko Tanaka Hideki Makishima Yasuhito Nannya Zicong Zhang Rika Tsujikawa Ryuji Koike Tomomi Takano Makoto Ishii Akinori Kimura Fumitaka Inoue Takanori Kanai Koichi Fukunaga Seishi Ogawa Seiya Imoto Satoru Miyano Yukinori Okada Department of Genome Informatics Graduate School of Medicine University of Tokyo Tokyo Japan. qingbow@m.u-tokyo.ac.jp. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. qingbow@m.u-tokyo.ac.jp. M&D Data Science Center Tokyo Medical and Dental University Tokyo Japan. Department of Infectious Diseases Keio University School of Medicine Tokyo Japan. hounamugun@keio.jp. Department of Pathology and Tumor Biology Kyoto University Kyoto Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Department of Respiratory Medicine and Clinical Immunology Osaka University Graduate School of Medicine Suita Japan. Department of Genome Informatics Graduate School of Medicine University of Tokyo Tokyo Japan. Division of Pulmonary Medicine Department of Medicine Keio University School of Medicine Tokyo Japan. Faculty of Medicine Osaka University Suita Japan. Center for Data Sciences Brigham and Women's Hospital Harvard Medical School Boston MA USA. Divisions of Genetics and Rheumatology Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. Laboratory of Children's Health and Genetics Division of Health Science Osaka University Graduate School of Medicine Suita Japan. Department of Pediatrics Osaka University Graduate School of Medicine Suita Japan. Division of Genome Analysis Platform Development National Cancer Center Research Institute Tokyo Japan. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Health Science Research and Development Center (HeRD) Tokyo Medical and Dental University Tokyo Japan. Laboratory of Veterinary Infectious Disease Department of Veterinary Medicine Kitasato University Tokyo Japan. Department of Respiratory Medicine Nagoya University Graduate School of Medicine Nagoya Japan. In

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm's Arthropod Community Modelling 3

Ecological Show Cave and Wild Cave: Negative Binomial Gllvm'...

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3rd International Conference on Computer science and Computational Intelligence, ICCSCI 2018

作者： Caraka, Rezzy Eko Shohaimi, Shamarina Kurniawan, Isma Dwi Herliansyah, Riki Budiarto, Arif Sari, Shinta Purnama Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Indonesia Kalimantan Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Ecology is a branch of biology that studies the interaction and relationship between organisms and their environment. Abundance, distribution of organisms and patterns of biodiversity are great interests for many ecologists. One of interesting ecosystems to be studied is a cave. A cave has a typical environment character with a vulnerable ecosystem. Many caves in Indonesia, particularly in Gunungsewu karst area have been developed into tourist objects (show caves) and managed imprudently. Such cave management has potential to harm the environment and leads to ecosystem destruction. Arthropods are the most abundance fauna in cave that play critical roles in maintaining cave ecosystems equilibrium. In the heart of statistical ecology, we need to analyze the differences on Arthropods community and abiotic (climatic-edaphic) parameters among show caves and wild caves. Statistical techniques are needed for the extraction of such information. GLLVM is one method that is able to explain spatial-based information and is particularly suitable for ecology. In this paper, we use negative binomial models to see the differences on spatial patterns of predator and decomposer Arthropods, also characteristic of edaphic and climatic in each cave. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Caves

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Maternal human papillomavirus infection and the risk of congenital malformations: A nationwide population-based cohort study

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Journal of Medical Virology 2024年第4期96卷 e29549-e29549页

作者： Hsieh, Tina Yi Jin Chen, Thomas Yen Ting Liao, Pei-Lun Huang, Jing-Yang Ma, Kevin Sheng-Kai Hung, Yao-Min Chang, Renin Wei, James Cheng-Chung Department of Obstetrics and Gynecology Beth Israel Deaconess Medical Center Boston MA United States Department of Bioinformatics Harvard Medical School Boston MA United States Department of Otolaryngology-Head and Neck Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Department of Dermatology Massachusetts General Hospital Boston MA United States Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA United States Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taipei Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Recreation and Sports Management Tajen University Pingtung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan

Previous research has explored theories regarding the vertical transmission of human papillomavirus (HPV) infection and its association with adverse pregnancy and perinatal outcomes. However, the impact of maternal HPV infection on congenital anomalies (CAs) in offspring remains relatively understudied. We conducted a population-based cohort study linking the Taiwan Birth Registry, Taiwan Death Registry, and National Health Insurance Research database, in which newborns born in Taiwan between 2009 and 2015 were included. We established a maternal HPV infection cohort comprising 37 807 newborns and matched them with a comparison group of 151 228 newborns at a 1:4 ratio based on index year, age, and sex. The study examined a composite outcome and subgroups of different types of congenital malformations. Differences in cumulative incidence of CAs were assessed using Kaplan–Meier curves and log-rank tests. Adjusted hazard ratios (aHRs) were estimated using Cox proportional hazard regressions. No significant association was found between HPV infection and the broad spectrum of CAs (aHR: 1.04, 95% confidence interval [CI]: 0.98–1.10;log-rank test p = 0.14). However, we observed a 19% increased risk of musculoskeletal CAs in the maternal HPV infection group (aHR: 1.19;95% CI: 1.05–1.34) compared to those without maternal HPV exposure. Other factors, including the type of HPV (aHR: 0.65;95% CI: 0.16–2.63), the timing of exposure (during or before pregnancy), and maternal age (aHR for <30 years: 1.02, 95% CI: 0.94–1.1;aHR for 30–39 years: 1.05, 95% CI: 0.99–1.11;aHR for ≥40 years: 0.88, 95% CI: 0.67–1.17), did not significantly affect the risk for any CA. In conclusion, gestation detection of HPV infection was associated with musculoskeletal CAs but not other major CAs. Prospective studies are warranted to elucidate the necessity of prenatal screening in populations at risk. © 2024 Wiley Periodicals LLC.

关键词： adverse pregnancy congenital malformations human papillomavirus infection outcomes

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Correction to: CpG‑creating mutations are costly in many human viruses

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Evolutionary Ecology 2020年第3期34卷 361-362页

作者： Caudill, Victoria R. Qin, Sarina Winstead, Ryan Kaur, Jasmeen Tisthammer, Kaho Pineda, E. Geo Solis, Caroline Cobey, Sarah Bedford, Trevor Carja, Oana Eggo, Rosalind M. Koelle, Katia Lythgoe, Katrina Regoes, Roland Roy, Scott Allen, Nicole Aviles, Milo Baker, Brittany A. Bauer, William Bermudez, Shannel Carlson, Corey Castellanos, Edgar Catalan, Francisca L. Chemel, Angeline Katia Elliot, Jacob Evans, Dwayne Fiutek, Natalie Fryer, Emily Goodfellow, Samuel Melvin Hecht, Mordecai Hopp, Kellen Hopson, E. Deshawn Jaberi, Amirhossein Kim, Jennifer Kinney, Christen Lao, Derek Le, Adrienne Lo, Jacky Lopez, Alejandro G. López, Andrea Lorenzo, Fernando G. Luu, Gordon T. Mahoney, Andrew R. Melton, Rebecca L. Nascimento, Gabriela Do Pradhananga, Anjani Rodrigues, Nicole S. Shieh, Annie Sims, Jasmine Singh, Rima Sulaeman, Hasan Thu, Ricky Tran, Krystal Tran, Livia Winters, Elizabeth J. Wong, Albert Pennings, Pleuni S. Department of Biology San Francisco State University San Francisco USA Department of Biology University of Oregon Eugene USA Quantitative Systems Biology Univeristy of California Merced USA Department of Ecology and Evolution University of Chicago Chicago USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center Seattle USA Department of Computational Biology School of Computer Science Carnegie Mellon University Pittsburgh USA Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK Department of Biology Emory University Atlanta USA Big Data Institute University of Oxford Oxford UK Department of Environmental Systems Science ETH Zurich Zurich Switzerland Department of Neurological Surgery University of California San Francisco USA Department of Organismic and Evolutionary Biology Harvard University Cambridge USA Department of Plant Biology Carnegie Institution for Science Stanford USA Health Sciences Center University of New Mexico Albuquerque USA UCSD Biomed Sciences PhD Program University of California San Diego USA Biochemistry Molecular Cellular and Developmental Biology Graduate Group University of California Davis USA UCSF Tetrad Graduate Program University of California San Francisco USA

In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.

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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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IoT Solution for Intelligent Pond Monitoring

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

作者： Usin Darmalim Ferdinan Darmalim Sutristo Darmalim Alam Ahmad Hidayat Arif Budiarto Bharuno Mahesworo Bens Pardamean Mega Medica Pharmaceuticals Jakarta Indonesia 11840 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 Bina Nusantara University Jakarta Indonesia 11480

Channa striata or the striped snakehead fish is one of snakehead fish species which inhabits all types of freshwater bodies distributed across Asian countries. Because this fish is known to have higher albumin fraction (64.61%) of protein and other economic values, domestication, and cultivation of this fish has been done in many Asian countries such as Indonesia, China, Malaysia, Thailand, Bangladesh, and India. Environmental factors such as temperature, water pH, dissolved oxygen, total dissolved solids, and turbidity are important parameters must be considered inbreeding and growing this type of fish. The aim of this paper is to propose an IoT solution to automatically monitor these environmental factors. It is designed with affordable and open-source electrical components to provide a cost-efficient solution for farmers. Five sensors are used to measure each parameter. A web application prototype is also presented as a companion application for the users to get useful information from the IoT device. It is developed using a Pythonframework. By accessing this web application, the users can immediately detect any abnormal conditions of the pond.

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Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram data

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Procedia Computer science 2018年 135卷 400-407页

作者： Bens Pardamean Tjeng Wawan Cenggoro Reza Rahutomo Arif Budiarto Ettikan Kandasamy Karuppiah 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 NVIDIA Corporation

Breast cancer is one of the deadliest cancer for female nowadays. Despite of the rapid advancement in medical image analysis with the rise of deep learning, development of breast cancer detection system is limited due to relatively small size of the publicly available mammogram dataset. In this paper, we discover an effective configuration for transfer learning from Chest X-Ray pre-trained Convolutional Neural Network to overcome the small-size mammogram dataset problem. We found that the best configuration achieve 90.38% validation accuracy for modified.

关键词： Transfer learning Convolutional neural network Medical image analysis Chest X-ray Mammogram

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159P Spatial organization of B cells in the prediction and prognosis of gastric cancer

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Annals of Oncology 2024年 35卷 S1467-S1467页

作者： R. Tay M. Sachdeva Y.W. Kim M-C. Kook H. Kim J-H. Cheong G. Schmidt T. Yoshikawa T. Oshima H. Ma T. Sheng P. Tan H.I. Grabsch R. Sundar Yong Loo Lin School of Medicine NUS - National University of Singapore Singapore Medical Oncology Department NCIS - National University Cancer Institute Singapore Singapore Department of Cancer Policy and Population Health NCC - National Cancer Center Goyang Republic of Korea Department of Pathology NCC - National Cancer Center Goyang Republic of Korea Department of Pathology Severance Hospital - Yonsei University College of Medicine Seoul Republic of Korea Department of Surgery Yonsei University Seoul Republic of Korea Image Data Sciences AstraZeneca Computational Pathology GmbH Munich Germany Gastric Surgery National Cancer Center Hospital Tokyo Japan Department of Gastrointestinal Surgery Kanagawa Cancer Center Yokohama Japan Cancer and Stem Cell Biology Program Duke-NUS Graduate Medical School Singapore Genome Institute of Singapore A*STAR - Agency for Science Technology and Research Singapore Programme in Cancer & Stem Cell Biology Duke-NUS Graduate Medical School Singapore Pathology Maastricht University Medical Center (MUMC) Maastricht Netherlands Haematology-Oncology NUHS - National University Health System Singapore

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