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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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Development of Heterocyclic-C61-butyric Acid Methyl Ester/ Pyrazine (HCBM-Pyrazine) acceptor material for organic solar cells applications

Development of Heterocyclic-C61-butyric Acid Methyl Ester/ P...

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IEEE Conference on Nanotechnology

作者： Muath Bani Salim Reza Nekovei Tamer Elwaie Jeyakumar Ramanujam Sustainable Energy Systems Engineering Program Texas A&M University–Kingsville Kingsville TX Electrical Engineering and Computer Science Texas A&M University–Kingsville Kingsville TX Pharmaceutical Chemistry Department Cairo University Cairo Egypt Physics of Energy Harvesting Division CSIR-National Physical Laboratory Delhi India

This paper presents a novel modification on the Phenyl-C61-butyric acid methyl ester (PCBM) for better performance as an acceptor material by introducing a Heterocyclic ring (Pyrazine) in the place of the Phenyl group of the PCBM to form a new acceptor material, Heterocyclic-C61-butyric Acid Methyl Ester/ Pyrazine (HCBM-Pyrazine). The electronic properties for the new material are obtained theoretically through the use of the Density Functional Theory (DFT) method with the help of the Gaussian 16 program. The results show that the HCBM-Pyrazine can create more voltage if used with any donor material. Besides that, it has a considerably higher electron affinity, making it a more efficient electron acceptor than the original PCBM. Furthermore, the results show that both molecules have closer absorption spectrums and reorganization energies.

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Left ventricular hypertrophy detection using electrocardiographic signal

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Scientific reports 2023年第1期13卷 2556页

作者： Cheng-Wei Liu Fu-Hsing Wu Yu-Lun Hu Ren-Hao Pan Chuen-Horng Lin Yung-Fu Chen Guo-Shiang Tseng Yung-Kuan Chan Ching-Lin Wang Division of Cardiology Department of Internal Medicine Tri-Service General Hospital Songshan Branch National Defense Medical Center Taipei Taiwan. Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan. La Vida Tec. Co. Ltd. Taichung Taiwan. Preventive Medicine Center National Yang Ming Chiao Tung University Taipei Taiwan. Department of Information Management Tunghai University Taichung Taiwan. Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan. Department of Dental Technology and Materials Science Central Taiwan University of Science and Technology Taichung Taiwan. Division of Cardiology Department of Internal Medicine Taoyuan Armed Force General Hospital Hsinchu Branch Hsinchu Taiwan. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan. ykchan@nchu.edu.tw. Department of Information Management National Chin-Yi University of Technology Taichung Taiwan. clwang@ncut.edu.tw.

Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

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IoT-Based Architecture for Data Analytics of Arboviruses in Smart Cities

IoT-Based Architecture for Data Analytics of Arboviruses in ...

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IEEE Symposium on computers and Communications

作者： Priscylla da S. Tavares Emanuel B. Rodrigues Group of Computer Networks Software Engineering and Systems (GREat) Graduate Program in Computer Science (MDCC) Federal University of Ceará (UFC) Fortaleza Brazil

With the progress of sensor technologies, there has been an increase in the number of connected computing devices capable of collecting information and interacting with the environment in which they are inserted, forming the basis of the Internet of Things (IoT). Such devices enable the development of new applications capable of making cities more intelligent, providing improvements to society and urban infrastructure. As a result of the interaction between different applications, there is a large set of data, from which useful knowledge can be extracted. An important problem to be faced is the recurrent occurrence of arbovirus outbreaks in big cities with tropical climate. This research proposes an architecture capable of aggregating data from different types of IoT devices, and manipulating them using data analytics techniques to assist the surveillance of arbovirus outbreaks. As an example of case study, we present an exploratory analysis of climatology data and cases of arbovirus diseases in the city of Fortaleza, Ceara, Brazil, between the years of 2011 and 2017.

关键词： Internet of Things Data analytics Health care Smart city

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The application of an extracellular vesicle-based biosensor in early diagnosis and prediction of chemoresponsiveness in ovarian cancer

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Cancer Letters 2024年 581卷 216533-216533页

作者： Tsang, Benjamin K. Asare-Werehene, Meshach Hunter, Rob Gerber, Emma Reunov, Arkadiy Brine, Isaiah Chang, Chia-Yu Chang, Chia-Ching Shieh, Dar-Bin Burger, Dylan Anis, Hanan Departments of Obstetrics & Gynecology and Cellular & Molecular Medicine Centre for Infection Immunity and Inflammation Interdisciplinary School of Health Sciences University of Ottawa Ottawa ON K1N 6N5 Canada Chronic Disease Program Ottawa Hospital Research Institute The Ottawa Hospital Ottawa ON K1Y 4E9 Canada Department of Biology St. Francis Xavier University 2320 Notre Dame Avenue Antigonish NS B2G 2W5 Canada School of Electrical Engineering and Computer Science Ottawa-Carleton Institute for Biomedical Engineering University of Ottawa Ottawa ON K1N 6N5 Canada Department of Biological Science and Department of Electrophysics Technology Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 30068 Taiwan Institute of Basic Medical Science Institute of Oral Medicine and Department of Stomatology Advanced Optoelectronic Technology Center and Center for Micro/Nano Science and Technology National Cheng Kung University Hospital National Cheng Kung University Tainan 704 Taiwan Institute of Physics Academia Sinica Taipei 10529 Taiwan

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Data quality measures and data cleansing for research information systems

arXiv

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arXiv 2019年

作者： Azeroual, Otmane Saake, Gunter Abuosba, Mohammad Berlin10117 Germany Otto-Von-Guericke University Magdeburg Department of Computer Science Institute for Technical and Business Information Systems Database Research Group P.O. Box 4120 Magdeburg39106 Germany University of Applied Sciences Htw Berlin Study Program Computational Science and Engineering Wilhelminenhofstrasse 75a Berlin12459 Germany

The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions. Thereby an adequate and reliable basis for decision-making using an RIS is provided, and confidence in a given dataset increased. In this paper, possible measures and the new techniques of data cleansing for improving and increasing the data quality in research information systems will be presented and how these are to be applied to the research information. Copyright © 2019, The Authors. All rights reserved.

关键词： Standardization

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Rewriting regulatory DNA to dissect and reprogram gene expression

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Cell 2025年第12期188卷 3349-3366.e23,26页

作者： Martyn, Gabriella E. Montgomery, Michael T. Jones, Hank Guo, Katherine Doughty, Benjamin R. Linder, Johannes Bisht, Deepa Xia, Fan Cai, Xiangmeng S. Chen, Ziwei Cochran, Kelly Lawrence, Kathryn A. Munson, Glen Pampari, Anusri Fulco, Charles P. Sahni, Nidhi Kelley, David R. Lander, Eric S. Kundaje, Anshul Engreitz, Jesse M. Department of Genetics Stanford University School of Medicine Stanford 94305 CA United States Basic Science and Engineering Initiative Stanford Children's Health Betty Irene Moore Children's Heart Center Stanford 94305 CA United States Calico Life Sciences LLC South San Francisco 94080 CA United States Department of Genitourinary Medical Oncology Division of Cancer Medicine The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Genomic Medicine The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Epigenetics and Molecular Carcinogenesis The University of Texas MD Anderson Cancer Center Houston 77230 TX United States Department of Bioengineering Stanford University Stanford 94305 CA United States Department of Computer Science Stanford University Stanford 94305 CA United States Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard Cambridge 02142 MA United States Gene Regulation Observatory Broad Institute of MIT and Harvard Cambridge 02142 MA United States Broad Institute of MIT and Harvard Cambridge 02142 MA United States Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston 77230 TX United States Quantitative and Computational Biosciences Program Baylor College of Medicine Houston 77030 TX United States Department of Biology MIT Cambridge 02139 MA United States Department of Systems Biology Harvard Medical School Boston 02115 MA United States Stanford Cardiovascular Institute Stanford University Stanford 94305 CA United States

Regulatory DNA provides a platform for transcription factor binding to encode cell-type-specific patterns of gene expression. However, the effects and programmability of regulatory DNA sequences remain difficult to map or predict. Here, we develop variant effects from flow-sorting experiments with CRISPR targeting screens (Variant-EFFECTS) to introduce hundreds of designed edits to endogenous regulatory DNA and quantify their effects on gene expression. We systematically dissect and reprogram 3 regulatory elements for 2 genes in 2 cell types. These data reveal endogenous binding sites with effects specific to genomic context, transcription factor motifs with cell-type-specific activities, and limitations of computational models for predicting the effect sizes of variants. We identify small edits that can tune gene expression over a large dynamic range, suggesting new possibilities for prime-editing-based therapeutics targeting regulatory DNA. Variant-EFFECTS provides a generalizable tool to dissect regulatory DNA and to identify genome editing reagents that tune gene expression in an endogenous context. © 2025 Elsevier Inc.

关键词： CRISPR enhancers gene regulation high-throughput screening non-coding variants predictive models prime editing RNA FlowFISH sequence design transcription factors

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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

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LANCET 2022年第10365期400卷 1766-1766页

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

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

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Unifying femtosecond and picosecond single-pulse magnetic switching in GdFeCo

arXiv

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arXiv 2020年

作者： Jakobs, F. Ostler, T.A. Lambert, C.-H. Yang, Y. Salahuddin, S. Wilson, R.B. Gorchon, J. Bokor, J. Atxitia, U. Dahlem Center for Complex Quantum Systems and Fachbereich Physik Freie Universität Berlin Berlin14195 Germany LiégeB-4000 Belgium College of Business Technology and Engineering Sheffield Hallam University Howard Street SheffieldS1 1WB United Kingdom Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Mechanical Engineering and Materials Science and Engineering Program University of California RiversideCA92521 United States ETH Zurich Hönggerbergring 64 Zürich8093 Switzerland Portland Technology Development Department Intel Corp. HillsboroOR97006 United States Universite de Lorraine CNRS IJL NancyF-54000 France

Many questions are still open regarding the physical mechanisms behind the magnetic switching in GdFeCo alloys by single optical pulses. Phenomenological models suggest a femtosecond scale exchange relaxation between sublattice magnetization as the driving mechanism for switching. The recent observation of thermally induced switching in GdFeCo by using both several picosecond optical laser pulse as well as electric current pulses has questioned this previous understanding. This has raised the question of whether or not the same switching mechanics are acting at the femoand picosecond scales. In this work, we aim at filling this gap in the understanding of the switching mechanisms behind thermal single-pulse switching. To that end, we have studied experimentally thermal single-pulse switching in GdFeCo alloys, for a wide range of system parameters, such as composition, laser power and pulse duration. We provide a quantitative description of the switching dynamics using atomistic spin dynamics methods with excellent agreement between the model and our experiments across a wide range of parameters and timescales, ranging from femtoseconds to picoseconds. Furthermore, we find distinct element-specific damping parameters as a key ingredient for switching with long picosecond pulses and argue, that switching with pulse durations as long as 15 picoseconds is possible due to a low damping constant of Gd. Our findings can be easily extended to speed up dynamics in other contexts where ferrimagnetic GdFeCo alloys have been already demonstrated to show fast and energy-efficient processes, e.g. domain-wall motion in a track and spin-orbit torque switching in spintronics devices. Copyright © 2020, The Authors. All rights reserved.

关键词： Ternary alloys

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