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A Geoelectric Approach for Karst Groundwater Analysis

IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Amallia Ferhat Taufik Seizarsyah Mohammad Prasanto Bimantio Nanda Satya Nugraha Dian Pratama Putra Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agribusiness Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Geophysical Engineering Department Universitas Pembangunan Nasional 'Veteran' Yogyakarta Indonesia Agricultural Crop Technology Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Forestry Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Artesian well is one of the major efforts to obtain water source for fulfilling water demands in Eroniti Conservation park, Ponjong, Yogyakarta by drilling deep into several soil layers. Vertical Electrical Sounding (VES) method is the most common approach to determine the existence of groundwater. The method probes layers of rock below the ground surface based on their electrical properties, since the electrical resistivity of each layer is theoretically different. This study aims to utilize the VES approach for investigating the properties of the rock layers in the area to explore groundwater sources. We install six different VES points in the observation area using Schlumberger electrode configuration. The methodology of the work consists of three steps: the planning of the placement of the sounding points, the measurement step performed by a unit of IRIS SYSCAL Resistivity meter, and the data analysis and interpretation. Employing the resistivity analysis, we conclude that the observed region consists of Mediterranean soil as a result of weathering of limestone as well as lithology of the limestone. Furthermore, there are two types of aquifer found in the area: a perched aquifer found 27 meters below the ground surface of VES point 1 and a confined aquifer 166 meters below the ground surface of VES point 2 and 6.

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Association of vaccine-specific regulatory T cells with reduced antibody response to repeated influenza vaccination

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European journal of immunology 2023年第12期53卷 e2350525页

作者： Pin-Hung Lin Po-Ju Hsiao Ching-Fu Pan Ming-Tsan Liu Jann-Tay Wang Chi Ching Fang-Yi Wu Yi-Hsuan Lin Yu-Chan Yang Le-Yin Hsu Hung-Chih Yang Un-In Wu Graduate Institute of Microbiology College of Medicine National Taiwan University Taipei Taiwan. Department of Medical Research National Taiwan University Hospital Taipei Taiwan. Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan. Department of Internal Medicine College of Medicine National Taiwan University Taipei Taiwan. Center for Diagnostics and Vaccine Development Centers for Disease Control Taipei Taiwan. Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan. Graduate Program of Data Science National Taiwan University and Academia Sinica Taipei Taiwan. Department of Medicine National Taiwan University Cancer Center Taipei Taiwan.

Repeated annual influenza vaccinations have been associated with reduced vaccine-induced antibody responses. This prospective study aimed to explore the role of vaccine antigen-specific regulatory T (Treg) cells in antibody response to repeated annual influenza vaccination. We analyzed pre- and postvaccination hemagglutination inhibition (HI) titers, seroconversion rates, seroprotection rates, vaccine antigen hemagglutinin (HA)-specific Treg cells, and conventional T (Tconv) cells. We compared these parameters between vaccinees with or without vaccine-induced seroconversion. Our multivariate logistic regression revealed that prior vaccination was significantly associated with a decreased likelihood of achieving seroconversion for both H1N1(adjusted OR, 0.03; 95% CI, 0.01-0.13) and H3N2 (adjusted OR, 0.09; 95% CI, 0.03-0.30). Furthermore, individuals who received repeated vaccinations had significantly higher levels of pre-existing HA-specific Treg cells than those who did not. We also found that vaccine-induced fold-increases in HI titers and seroconversion were negatively correlated with pre-existing HA-specific Treg cells and positively correlated with the ratio of Tconv to Treg cells. Overall, our findings suggest that repeated annual influenza vaccination is associated with a lower vaccine-induced antibody response and a higher frequency of vaccine-specific Treg cells. However, a lower frequency of pre-existing Treg cells correlates with a higher postvaccination antibody response.

关键词： Influenza Regulatory T cells Repeated vaccination Seroconversion Seroprotection

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Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy

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Procedia Computer science 2019年 157卷 31-37页

作者： Puspita Anggraini Kaban Robert Kurniawan Rezzy Eko Caraka Bens Pardamean Budi Yuniarto Sukim Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia College of Informatics Chaoyang University of Technology Taichung City 41349 Taiwan (R.O.C.) Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Geographically, Indonesia is a meeting point of three continental plates. Scilicet, the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. Therefore, Indonesia is part of the infamous volcanic zone called the ”Ring of Fire” and one of the areas prone to natural disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. This study aims to capture the spatial pattern and identify the causes of social vulnerability in the districts/cities in Indonesia using the biclustering method. The data is extracted from the Indonesian National Socio-Economic Survey (SUSENAS) by BPS-Statistics in 2014. The biclustering result indicates that each district/city has its own social vulnerability characteristics and shows that the vulnerable aspects of each district/city are different. The adjacent observations tend to have social vulenrability characteristics. The results of this study can be used as a reference for national disaster mitigation policy in Indonesia.

关键词： biclustering disaster mitigation social vulnerability SoVI spatial pattern

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Correction: Identifcation and functional analyses of drought stress resistance genes by transcriptomics of the Mongolian grassland plant Chloris virgata

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BMC plant biology 2025年第1期25卷 257页

作者： Ganbayar Namuunaa Baldorj Bujin Ayumi Yamagami Byambajav Bolortuya Shintaro Kawabata Hirotaka Ogawa Asaka Kanatani Minami Shimizu Anzu Minami Keiichi Mochida Takuya Miyakawa Bekh-Ochir Davaapurev Tadao Asami Javzan Batkhuu Takeshi Nakano Laboratory of Plant Chemical Biology Graduate School of Biostudies Kyoto University Sakyo-ku Kyoto 606-8502 Japan. Laboratory of Plant Chemical Biology Graduate School of Biostudies Kyoto University Sakyo-ku Kyoto 606-8502 Japan. yamagami.ayumi.6s@kyoto-u.ac.jp. RIKEN Center for Sustainable Resource Science Tsurumi-ku Yokohama 230 - 0045 Kanagawa Japan. Kihara Institute for Biological Research Yokohama City University Totsuka-ku Yokohama Kanagawa 244-0813 Japan. Baton Zone Program RIKEN Tsurumi-ku Yokohama 230 - 0045 Kanagawa Japan. School of Information and Data Sciences Nagasaki University Bunkyo-machi Nagasaki 852-8521 Japan. School of Engineering and Technology National University of Mongolia Ulaanbaatar 14201 Mongolia. Graduate School of Agricultural and Life Sciences The University of Tokyo Bunkyo-ku Tokyo 113-8657 Japan. Laboratory of Plant Chemical Biology Graduate School of Biostudies Kyoto University Sakyo-ku Kyoto 606-8502 Japan. nakano.takeshi.6x@kyoto-u.ac.jp.

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In silico bacteria evolve robust cooperation via complex quorum-sensing strategies (vol 10, 8628, 2020)yy

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SCIENTIFIC REPORTS 2020年第1期10卷 1-10页

作者： Wang, Yifei Rattray, Jennifer B. Thomas, Stephen A. Gurney, James Brown, Sam P. School of Biological Sciences Georgia Institute of Technology Atlanta 30332 GA USA. yifei.wang@gatech.edu. The Institute for Data Engineering and Science (IDEaS) Georgia Institute of Technology Atlanta 30332 GA USA. yifei.wang@gatech.edu. Center for Microbial Dynamics and Infection Georgia Institute of Technology Atlanta 30332 GA USA. yifei.wang@gatech.edu. School of Biological Sciences Georgia Institute of Technology Atlanta 30332 GA USA. Center for Microbial Dynamics and Infection Georgia Institute of Technology Atlanta 30332 GA USA. Graduate Program in Quantitative Biosciences (QBioS) Georgia Institute of Technology Atlanta 30332 GA USA. School of Biological Sciences Georgia Institute of Technology Atlanta 30332 GA USA. sam.brown@biology.gatech.edu. Center for Microbial Dynamics and Infection Georgia Institute of Technology Atlanta 30332 GA USA. sam.brown@biology.gatech.edu.

Many species of bacteria collectively sense and respond to their social and physical environment via 'quorum sensing' (QS), a communication system controlling extracellular cooperative traits. Despite detailed understanding of the mechanisms of signal production and response, there remains considerable debate over the functional role(s) of QS: in short, what is it for? Experimental studies have found support for diverse functional roles: density sensing, mass-transfer sensing, genotype sensing, etc. While consistent with theory, these results cannot separate whether these functions were drivers of QS adaption, or simply artifacts or 'spandrels' of systems shaped by distinct ecological pressures. The challenge of separating spandrels from drivers of adaptation is particularly hard to address using extant bacterial species with poorly understood current ecologies (let alone their ecological histories). To understand the relationship between defined ecological challenges and trajectories of QS evolution, we used an agent-based simulation modeling approach. Given genetic mixing, our simulations produce behaviors that recapitulate features of diverse microbial QS systems, including coercive (high signal/low response) and generalized reciprocity (signal auto-regulation) strategists - that separately and in combination contribute to QS-dependent resilience of QS-controlled cooperation in the face of diverse cheats. We contrast our in silico results given defined ecological challenges with bacterial QS architectures that have evolved under largely unknown ecological contexts, highlighting the critical role of genetic constraints in shaping the shorter term (experimental evolution) dynamics of QS. More broadly, we see experimental evolution of digital organisms as a complementary tool in the search to understand the emergence of complex QS architectures and functions.

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An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter 3

An Improved Version of Texture-based Foreground Segmentation...

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

作者： Muchtar, Kahlil Rahman, Faris Cenggoro, Tjeng Wawan Budiarto, Arif Pardamean, Bens Nodeflux Inc. Jakarta Indonesia Syiah Kuala University Aceh Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Foreground segmentation is one of moving object detection techniques of computer vision applications. To date, modern moving object detection methods require complex background modeling and thresholds tuning to confront illumination changes. This paper proposes an adaptive approach based on non-overlapping block texture representation. It aims to design a computationally light and efficient solution to improve the robustness of detection. We evaluate our proposed method on internal and public sequences and provide the quantitative and qualitative measurements. Experimental results show that the proposed method can improve the results of previous method and suitable for real-time challenges. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Textures

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Incidence trends of oral cavity, oropharyngeal, hypopharyngeal and laryngeal cancers among males in Taiwan, 1980-2019: a population-based cancer registry study

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BMC cancer 2023年第1期23卷 213页

作者： Yu-Shiun Tsai Yong-Chen Chen Tzu-I Chen Yu-Kwang Lee Chun-Ju Chiang San-Lin You Wan-Lun Hsu Li-Jen Liao School of Medicine College of Medicine Fu-Jen Catholic University New Taipei City 242 Taiwan. Master Program of Big Data Analysis in Biomedicine College of Medicine Fu Jen Catholic University New Taipei City 242 Taiwan. Data Science Center College of Medicine Fu Jen Catholic University New Taipei City 242 Taiwan. Graduate Institute of Business Administration College of Management Fu Jen Catholic University New Taipei City 242062 Taiwan. Division of General Surgery Department of Surgery National Taiwan University Hospital Taipei 100 Taiwan. Graduate Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei 10055 Taiwan. Master Program of Big Data Analysis in Biomedicine College of Medicine Fu Jen Catholic University New Taipei City 242 Taiwan. 156521@mail.fju.edu.tw. Data Science Center College of Medicine Fu Jen Catholic University New Taipei City 242 Taiwan. 156521@mail.fju.edu.tw. Otolaryngology Head and Neck Surgery Far Eastern Memorial Hospital No. 21 Sec. 2 Nanya S. Rd. Banciao Dist. New Taipei City 220 Taiwan. liaolj@ntu.edu.tw. Head and Neck Cancer Surveillance & Research Group Far Eastern Memorial Hospital New Taipei Taiwan. liaolj@ntu.edu.tw. Department of Electrical Engineering Yuan Ze University Taoyuan Taiwan. liaolj@ntu.edu.tw.

In a country with a high prevalence of cigarette smoking, betel chewing, and alcohol drinking, cancers of the oral cavity, nasopharynx, and larynx were the fourth, twelfth and seventeenth leading causes of cancer death, respectively, for men in 2020. We analyzed patients with head and neck cancer from 1980 to 2019 from the Taiwan Cancer Registration database and discussed the annual average percent change, average percent change, age period, and birth cohort. Obvious period effects and birth effects are seen in oral, oropharyngeal, and hypopharyngeal cancer; however, the most significant period effect was seen between 1990 and 2009, which mainly reflects the consumption of betel nuts per capita. In addition, the period effect lessens after 2010 in oral cancer and hypopharyngeal cancers, while oropharyngeal cancers remain an obvious period effect, which results from the rising prevalence of HPV. Due to the high prevalence rate of betel quid chewing and cigarette smoking in the 1990s, the government executed several acts. As a result, the age-adjusted incidence rates of oral, oropharyngeal, and hypopharyngeal cancers have flattened since 2010, which can be explained by the declining cigarette smoking rate. The strict policy indeed shows an obvious effect on the head and neck cancer incidence rates, and we expect to see a further decline in the future.

关键词： Age-period-cohort effect Head and neck cancers Incidence Secular trend

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The K2 & TESS Synergy III: search and rescue of the lost ephemeris for K2’s first planet

arXiv

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arXiv 2024年

作者： Thygesen, Erica Rodriguez, Joseph E. Beurs, Zoë L.D.E. Vanderburg, Andrew Livingston, John H. Irwin, Jonathon Venner, Alexander Cretignier, Michael Collins, Karen A. Bieryla, Allyson Charbonneau, David Crossfield, Ian J.M. Dumusque, Xavier Kielkopf, John Latham, David W. Werner, Michael Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Earth Atmospheric and Planetary Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Astrobiology Center 2-21-1 Osawa Mitaka Tokyo181-8588 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Astronomical Science Program Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Centre for Astrophysics University of Southern Queensland West Street ToowoombaQLD4350 Australia Department of Physics University of Oxford OxfordOX13RH United Kingdom Center for Astrophysics Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States Geneva Observatory University of Geneva Chemin des Mailettes 51 Versoix1290 Switzerland Department of Physics and Astronomy University of Louisville LouisvilleKY40292 United States NASA Jet Propulsion Laboratory PasadenaCA United States

K2-2 b/HIP 116454 b, the first exoplanet discovery by K2 during its Two-Wheeled Concept Engineering Test, is a sub-Neptune (formula presented) orbiting a relatively bright (KS = 8.03) K-dwarf on a 9.1 day period. Unfortunately, due to a spurious follow-up transit detection and ephemeris degradation, the transit ephemeris for this planet was lost. In this work, we recover and refine the transit ephemeris for K2-2 b, showing a ∼40σ discrepancy from the discovery results. To accurately measure the transit ephemeris and update the parameters of the system, we jointly fit space-based photometric observations from NASA’s K2, TESS, and Spitzer missions with new photometric observations from the ground, as well as radial velocities from HARPS-N that are corrected for stellar activity using a new modeling technique. Ephemerides becoming lost or significantly degraded, as is the case for most transiting planets, highlights the importance of systematically updating transit ephemerides with upcoming large efforts expected to characterize hundreds of exoplanet atmospheres. K2-2 b sits at the high-mass peak of the known radius valley for sub-Neptunes, and is now well-suited for transmission spectroscopy with current and future facilities. Our updated transit ephemeris will ensure no more than a 13-minute uncertainty through 2030. Copyright © 2024, The Authors. All rights reserved.

关键词： NASA

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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