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arXiv 2020年

作者： Raju, Vidya Krishnaprasad, P.S. Department of Electrical and Computer Engineering University of Maryland College Park United States Institute for Systems Research University of Maryland College Park United States School of Engineering and Applied Sciences Harvard University

For over fifty years, the dynamical systems perspective has had a prominent role in evolutionary biology and economics, through the lens of game theory. In particular, the study of replicator differential equations on the standard (probability) simplex, specified by fitness maps or payoff functions, has yielded insights into the temporal behavior of such systems. However behavior is influenced by context and environmental factors with a game-changing quality (i.e., fitness maps are manipulated). This paper develops a principled geometric approach to model and understand such influences by incorporating replicator dynamics into a broader control-theoretic framework. Central to our approach is the construction of a Lie algebra structure on the space of fitness maps, mapping homomorphically to the Lie algebra of replicator vector fields. This is akin to classical mechanics, where the Poisson bracket Lie algebra of functions maps to associated Hamiltonian vector fields. We show, extending the work of Svirezhev in 1972, that a trajectory of a replicator vector field is the base integral curve of a solution to a Hamiltonian system defined on the cotangent bundle of the simplex. Further, we exploit the Lie algebraic structure of fitness maps to determine controllability properties of a class of replicator systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamical systems

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Improved analytical workflow towards machine learning supported N-glycomics-based biomarker discovery

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Talanta 2025年 295卷 128389页

作者： Agnes Vathy-Fogarassy Veronika Gombas Rebeka Torok Gabor Jarvas Andras Guttman Department of Computer Science and Systems Technology University of Pannonia Egyetem u 10. Veszprem H-8200 Hungary. Electronic address: vathy.agnes@mik.uni-pannon.hu. Department of Computer Science and Systems Technology University of Pannonia Egyetem u 10. Veszprem H-8200 Hungary. Research Institute of Biomolecular and Chemical Engineering University of Pannonia Veszprem H-8200 Hungary. Research Institute of Biomolecular and Chemical Engineering University of Pannonia Veszprem H-8200 Hungary Horvath Csaba Memorial Laboratory of Bioseparation Sciences Research Center for Molecular Medicine Doctoral School of Molecular Medicine Faculty of Medicine University of Debrecen Debrecen H-4032 Hungary.

The composition and function of glycans are very complex thus manual data interpretation of their structural elucidation is difficult. Capillary electrophoresis is one of the liquid phase separation techniques, which is most frequently used to address these challenging tasks. Combining high-resolution capillary electrophoresis with machine learning-supported data interpretation holds the promise to gain as much chemical and clinical information from the analyzed samples as possible. However, this combination requires significant technological improvements both in the analytical and the data processing aspects. In this study we report on the development of an automated, liquid-handling robot-based sample preparation method to obtain reproducible and N-glycome profiles by capillary electrophoresis for the subsequent machine learning-supported data interpretation, which was optimized for the special needs of the analysis. The resulting new glycoanalytical workflow was then tested for a demanding problem to predict the effectiveness of chemotherapy treatments of lung cancer patients ensuring the effective management of the disease. Our findings revealed that the achieved N-glycan data contained important clinical information to accurately predict patient response to chemotherapy with AUC values ranged from 0.8290 to 0.8410.

关键词： Capillary electrophoresis Feature selection Lung cancer Machine learning N-glycome

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and systems 2024年 1076 LNNS卷 v-vii页

作者： Biele, Cezary Kacprzyk, Janusz Kopeć, Wieslaw Możaryn, Jakub Owsiński, Jan W. Romanowski, Andrzej Sikorski, Marcin National Information Processing Institute Warszaw Poland Polish Academy of Sciences Systems Research Institute Warsaw Poland Polish-Japanese Academy of Information Technology Warsaw Poland Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Institute of Applied Computer Science Faculty of Electrical Electronic Computer and Control Engineering Lódż University of Technology LODZ Poland Department of Applied Informatics in Management Faculty of Management and Economics Gdańsk University of Technology Gdańsk Poland

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Corrigendum to “Artificial intelligence-assisted characterization and optimization of red mud-based nanofluids for high-efficiency direct solar thermal absorption” [Case Stud. Therm. Eng. 54, (February 2024), 104087]

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Case Studies in Thermal engineering 2024年 55卷

作者： K. Praveen Kumar Rohit Khedkar Prabhakar Sharma Rajvikram Madurai Elavarasan Prabhu Paramasivam V. Vicki Wanatasanappan Sesathiri Dhanasekaran Institute of Power Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Selangor Malaysia Department of Chemical Engineering School of Engineering P P Savani University Surat 395009 Gujarat India Department of Mechanical Engineering Delhi Skill and Entrepreneurship University Delhi 110089 India Research and Development Division Nestlives Private Limited Chennai 600091 India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamilnadu 602105 India Department of Computer Science UiT The Arctic University of Norway Tromso Norway

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Deep Learning-enabled Detection and Classification of Bacterial Colonies using a Thin Film Transistor (TFT) Image Sensor

arXiv

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arXiv 2022年

作者： Li, Yuzhu Liu, Tairan Koydemir, Hatice Ceylan Wang, Hongda O'Riordan, Keelan Bai, Bijie Haga, Yuta Kobashi, Junji Tanaka, Hitoshi Tamaru, Takaya Yamaguchi, Kazunori Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los Angeles90095 United States University of California Los AngelesCA90095 United States Department of Biomedical Engineering Texas A&M University College StationTX77843 United States Center for Remote Health Technologies and Systems Texas A&M University College StationTX77843 United States Department of Physics and Astronomy University of California Los AngelesCA90095 United States Device Development Department Research & Development Division Japan Display Inc. Japan Department of Surgery University of California Los AngelesCA90095 United States

Early detection and identification of pathogenic bacteria such as Escherichia coli (E. coli) is an essential task for public health. The conventional culture-based methods for bacterial colony detection usually take ≥24 hours to get the final read-out. Here, we demonstrate a bacterial colony-forming-unit (CFU) detection system exploiting a thin-film-transistor (TFT)-based image sensor array that saves ~12 hours compared to the Environmental Protection Agency (EPA)-approved methods. To demonstrate the efficacy of this CFU detection system, a lensfree imaging modality was built using the TFT image sensor with a sample field-of-view of ~10 cm2. Time-lapse images of bacterial colonies cultured on chromogenic agar plates were automatically collected at 5-minute intervals. Two deep neural networks were used to detect and count the growing colonies and identify their species. When blindly tested with 265 colonies of E. coli and other coliform bacteria (i.e., Citrobacter and Klebsiella pneumoniae), our system reached an average CFU detection rate of 97.3% at 9 hours of incubation and an average recovery rate of 91.6% at ~12 hours. This TFT-based sensor can be applied to various microbiological detection methods. Due to the large scalability, ultra-large field-of-view, and low cost of the TFT-based image sensors, this platform can be integrated with each agar plate to be tested and disposed of after the automated CFU count. The imaging field-of-view of this platform can be cost-effectively increased to >100 cm2 to provide a massive throughput for CFU detection using, e.g., roll-to-roll manufacturing of TFTs as used in the flexible display industry. Copyright © 2022, The Authors. All rights reserved.

关键词： Thin film transistors

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Corrigendum: Moving Next-Generation Phase-Field Models to BMS Applications -A Case Study that Confirms Professor Uzi Landau's Foresight [J. Electrochem. Soc., 171, 063507 (2024)]

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Journal of The Electrochemical Society 2024年第8期171卷 089002-089002页

作者： Tushar K. Telmasre Anthony César Concepción Suryanarayana Kolluri Lubhani Mishra Raghav S. Thiagarajan Aditya Naveen Matam Akshay Subramaniam Taylor R. Garrick Venkat R. Subramanian Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 United States of America Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 United States of America Battery Materials and Systems Research General Motors Global Research and Development Center Warren MI 48090 United States of America Chandra Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX 78712 United States of America Energy Storage and Propulsion R&D and Manufacturing Engineering General Motors Technical Center Warren MI 48092 United States of America Affiliate Faculty Oden Institute for Computational Science and Engineering The University of Texas at Austin Austin TX 78712 United States of America

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Pharmacological profiling of major depressive disorder-related multimorbidity clusters

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European Neuropsychopharmacology 2025年 96卷 71-83页

作者： Tamas Nagy Xenia Gonda Andras Gezsi Nora Eszlari Gabor Hullam Rubèn González-Colom Hannu Mäkinen Teemu Paajanen Dora Torok Zsofia Gal Peter Petschner Isaac Cano Mikko Kuokkanen Carsten O. Schmidt Sandra Van der Auwera Josep Roca Peter Antal Gabriella Juhasz Department of Pharmacodynamics Faculty of Pharmaceutical Sciences Semmelweis University Nagyvárad tér 4. H-1089 Budapest Hungary Center of Pharmacology and Drug Research & Development Semmelweis University Budapest Hungary NAP3.0-SE Neuropsychopharmacology Research Group Hungarian Brain Research Program Semmelweis University Üllői út 26. H-1085 Budapest Hungary Department of Artificial Intelligence and Systems Engineering Budapest University of Technology and Economics Műegyetem rkp. 3. H-1111 Budapest Hungary Department of Psychiatry and Psychotherapy Semmelweis University Gyulai Pál utca 2. H-1085 Budapest Hungary Department of Clinical Psychology Semmelweis University Üllői út 25 H-1092 Budapest Hungary Fundació de Recerca Clínic Barcelona - Institut d’Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS) Barcelona Spain Department of Public Health and Welfare Finnish Institute for Health and Welfare Mannerheimintie 166 00300 Helsinki Finland Universitat de Barcelona Barcelona Spain Department of Human Genetics and South Texas Diabetes and Obesity Institute School of Medicine at University of Texas Rio Grande Valley Brownsville TX United States Research Program for Clinical and Molecular Metabolism Faculty of Medicine University of Helsinki Finland Institute for Community Medicine University Medicine Greifswald 17475 Greifswald Germany Department of Psychiatry and Psychotherapy University Medicine Greifswald 17475 Greifswald Germany German Centre for Neurodegenerative Diseases (DZNE) Site Rostock/Greifswald 17475 Greifswald Germany

We previously identified seven distinct multimorbidity clusters associated with major depressive disorder through a comprehensive analysis of 1.2 million individuals of multiple cohorts. These clusters, characterized by unique clinical, genetic, and psychiatric and somatic illness risk profiles, implicate divergent treatment pathways and disease management strategies. This study aims to deepen the understanding of these clusters by analyzing drug prescriptions, evaluating the effectiveness of antidepressant treatment strategies, and identifying potential markers for personalized medicine. Utilizing drug prescription data in the format of ATC codes, we performed epidemiological assessments, including multimorbidity (number of diseases), polypharmacy (number of chemical substances), and drug burden (number of prescriptions) analyses across the clusters. We applied linear regression models to assess strength and predictive capability of cluster membership on various metrics, and logistic regression to explore associations with treatment-resistant depression. We also quantified and visualized common antidepressant treatment sequences within each cluster. Our findings indicate significant variations in polypharmacy and drug burden across clusters, with distinct patterns emerging that correlate with the clusters’ profiles. Clusters liable to multimorbidity have higher drug burden, even after correction for number of diseases. Furthermore, the three clusters with higher risk for MDD showed different antidepressant treatment profiles; two required significantly more antidepressant prescriptions and had a higher risk for TRD. The detailed pharmacological profiling presented in this study not only corroborates the initial cluster definitions but also enhances our predictive capabilities for treatment outcomes in MDD. By linking pharmacological data with comorbidity profiles, we pave the way for targeted therapeutic interventions.

关键词： Multimorbidity Pharmacology Major depressive disorder Antidepressants

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Photonic convolutional accelerator and neural network in the Tera-OPs regime based on soliton crystal Kerr microcombs

Photonic convolutional accelerator and neural network in the...

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Signal Processing in Photonic Communications, SPPCom 2021 - Part of OSA Advanced Photonics Congress 2021

作者： Tan, M. Xu, X. Wu, J. Boes, A. Corcoran, B. Nguyen, T. Chu, S.T. Little, B.E. Morandotti, R. Lowery, A.J. Mitchell, A. Hicks, D. Moss, D.J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering Rmit University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi'an Institute of Optics and Precision Mechanics Precision Mechanics of Cas Xi'an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada Walter and Eliza Hall Institute of Medical Research Bioinformatics Division ParkvilleVIC3052 Australia

ISBN: (纸本)9781557528209

We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds. We demonstrate a single neuron perceptron at 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We then demonstrate a convolutional accelerator operating beyond 11 TeraOPs/s. We test the perceptron on handwritten-digit recognition and cancer-cell detection - achieving over 90% and 85% accuracy, respectively. © 2021 OSA - The Optical Society. All rights reserved.

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Influence of environmental variables and anthropogenic activities on soda-saline lakes chemistry in northern Tanzania: A remote sensing and GIS approach

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Heliyon 2025年第2期11卷 e41691页

作者： Lameck, Azaria Stephano Rotich, Brian Ahmed, Abdalrahman Kipkulei, Harison K. Akos, Peto Boros, Emil Doctoral School of Environmental Science The Hungarian University of Agriculture and Life Sciences Páter Károly u. 1 Gödöllő 2100 Hungary Department of Earth Science Mbeya University of Science and Technology PO BOX 131 Mbeya Tanzania Institute of Geomatics and Civil Engineering Faculty of Forestry University of Sopron Bajcsy-Zsilinszky ut. 4 Sopron 9400 Hungary Department of Forest and Environment Faculty of Forest Science and Technology University of Gezira Wad Madani Sudan Institute of Aquatic Ecology Centre for Ecological Research Karolina str. 29. Budapest 1113 Hungary Faculty of Environmental Studies and Resources Development Chuka University P.O. Box 109 Chuka 60400 Kenya Humboldt Universität zu Berlin Faculty of Life Sciences Invalidenstraße 42 Berlin 10115 Germany Department of Geomatic Engineering and Geospatial Information Systems Jomo Kenyatta University of Agriculture and Technology P.O. Box 62000 Nairobi 00200 Kenya University of Augsburg Faculty of Applied Computer Sciences Institute of Geography Alter Postweg 118 Augsburg 86159 Germany

This study employed Remote Sensing and Geographical Information systems to explore the influence of environmental factors and human-induced land use/land cover changes on the chemistry of soda-saline lakes in Northern Tanzania. Satellite-based rainfall data were sourced from the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) datasets, and temperature data were obtained from MERRA-2. Monthly precipitation, temperature, and drought conditions in lake watersheds were analyzed from 1981 to 2022, while land use and land cover changes were assessed for 2000, 2014, and 2023. Soil types were acquired from the FAO Digital Soil Map of the World, while geological characteristics were sourced from the US Geological Survey database. The findings revealed that the region's climate is ideal for enhancing evapotranspiration, leading to mineral precipitation, and altering the chemistry of soda-saline lakes. The Standardized Precipitation Evapotranspiration Index revealed increased drought events in the lake basins since 1987, with prolonged drought occurrence between 2000 and 2017. The results also showed that the region is characterized by a variety of soil types, including ferric acrisols, chromic cambisols, calcic cambisols, entisols, inceptisols, eutric fluvisols, distric nitisols, humic nitisols, mollic andosols, ochric andosols, and pellic vertisols. Furthermore, the region is distinguished by diverse geological processes, from Precambrian-Cambrian to tertiary intrusive, triggered by volcanic and tectonic activity. Land use/land cover changes results indicated dynamics in the various classes with an overall decrease in areas under water bodies (−39.80 %), forests (−22.57 %) and bareland (−36.18) while agricultural land (111.01 %) built-up areas (434.72 %), shrubs and grasses (72.77 %) increased in area coverage over the 23 years study period (2000–2023). This study underscores the complex interplay between environmental variables and human activities in sha

关键词： Climate variability Geology Land use /land cover change Soda-saline lakes Soil type

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