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arXiv 2021年

作者： Gartner, Thomas E. Torquato, Salvatore Car, Roberto Debenedetti, Pablo G. Department of Chemistry Princeton University PrincetonNJ United States Department of Physics Princeton University PrincetonNJ United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ United States Department of Chemical and Biological Engineering Princeton University PrincetonNJ United States

Much attention has been devoted to water’s metastable phase behavior, including polyamorphism (multiple amorphous solid phases), and the hypothesized liquid-liquid transition and associated critical point. However, the possible relationship between these phenomena remains incompletely understood. Using molecular dynamics simulations of the realistic TIP4P/2005 model, we found a striking signature of the liquid-liquid critical point in the structure of water glasses, manifested as a pronounced increase in long-range density fluctuations at pressures proximate to the critical pressure. By contrast, these signatures were absent in glasses of two model systems that lack a critical point. We also characterized the departure from equilibrium upon vitrification via the non-equilibrium index;water-like systems exhibited a strong pressure dependence in this metric, whereas simple liquids did not. These results reflect a surprising relationship between the metastable equilibrium phenomenon of liquid-liquid criticality and the non-equilibrium structure of glassy water, with implications for our understanding of water phase behavior and glass physics. Our calculations suggest a possible experimental route to probing the existence of the liquid-liquid transition in water and other fluids. © 2021, CC BY.

关键词： Criticality (nuclear fission)

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BPS2025 - Structural basis of potassium selectivity in kalium channelrhodopsins

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Biophysical Journal 2025年第3期124卷 64a-65a页

作者： Seiya Tajima Yoon Seok Kim YoungJu Jo Peter Y. Wang Joseph M. Paggi Charu Ramakrishnan Shunki Takaramoto Takashi Nagata Masae Konno Kota Katayama Hideki Kandori Ron O. Dror Keiichi Inoue Karl Deisseroth Hideaki Kato Research Center for Advanced Science and Technology The University of Tokyo Tokyo Japan Department of Bioengineering Stanford University Stanford CA USA Department of Computer Science Stanford University Stanford CA USA Cracking the Neural Code Program Stanford University Stanford CA USA The Institute for Solid State Physics The University of Tokyo Kashiwa Japan PRESTO Japan Science and Technology Agency Kawaguchi Japan Department of Life Science and Applied Chemistry Nagoya Institute of Technology Nagoya Japan OptoBioTechnology Research Center Nagoya Institute of Technology Nagoya Japan Institute for Computational and Mathematical Engineering Stanford University Stanford CA USA Howard Hughes Medical Institute Stanford University Stanford CA USA Graduate School of Arts and Sciences The University of Tokyo Tokyo Japan

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Global genetic diversity, introgression, and evolutionary adaptation of indicine cattle revealed by whole genome sequencing

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NATURE COMMUNICATIONS 2023年第1期14卷 1-13页

作者： Chen, Ningbo Xia, Xiaoting Hanif, Quratulain Zhang, Fengwei Dang, Ruihua Huang, Bizhi Lyu, Yang Luo, Xiaoyu Zhang, Hucai Yan, Huixuan Wang, Shikang Wang, Fuwen Chen, Jialei Guan, Xiwen Liu, Yangkai Li, Shuang Jin, Liangliang Wang, Pengfei Sun, Luyang Zhang, Jicai Liu, Jianyong Qu, Kaixing Cao, Yanhong Sun, Junli Liao, Yuying Xiao, Zhengzhong Cai, Ming Mu, Lan Siddiki, Amam Zonaed Asif, Muhammad Mansoor, Shahid Babar, Masroor Ellahi Hussain, Tanveer Silva, Gamamada Liyanage Lalanie Pradeepa Gorkhali, Neena Amatya Terefe, Endashaw Belay, Gurja Tijjani, Abdulfatai Zegeye, Tsadkan Gebre, Mebrate Genet Ma, Yun Wang, Yu Huang, Yongzhen Lan, Xianyong Chen, Hong Migliore, Nicola Rambaldi Colombo, Giulia Semino, Ornella Achilli, Alessandro Sinding, Mikkel-Holger S. Lenstra, Johannes A. Cheng, Haijian Lu, Wenfa Hanotte, Olivier Han, Jianlin Jiang, Yu Lei, Chuzhao Key Laboratory of Animal Genetics Breeding and Reproduction of Shaanxi Province College of Animal Science and Technology Northwest A&F University Yangling 712100 China Institute of Animal Science and Veterinary Medicine Shandong Academy of Agricultural Sciences Shandong Key Lab of Animal Disease Control and Breeding Jinan 250100 China Key Laboratory of Livestock Biology Northwest A&F University Yangling 712100 China National Institute for Biotechnology and Genetic Engineering Faisalabad 38000 Pakistan CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources Institute of Animal Science Chinese Academy of Agricultural Sciences (CAAS) 100193 Beijing China Livestock Genetics Program International Livestock Research Institute (ILRI) 00100 Nairobi Kenya Yazhouwan National Laboratory Sanya 572024 China Yunnan Academy of Grassland and Animal Science Kunming 650212 China Institute for Ecological Research and Pollution Control of Plateau Lakes School of Ecology and Environment Science Yunnan University Kunming 650500 China Academy of Science and Technology Chuxiong Normal University Chuxiong 675000 China Guangxi Vocational University of Agriculture Nanning 530007 China Guangxi Veterinary Research Institute Guangxi Key Laboratory of Veterinary Biotechnology Nanning 530001 China College of Landscape and Horticulture Southwest Forestry University Kunming 650224 China Genomics Research Group Department of Pathology and Parasitology Faculty of Veterinary Medicine Chattogram Veterinary and Animal Sciences University (CVASU) Chattogram 4225 Bangladesh The University of Agriculture Dera Ismail Khan Khyber Pakhtunkhwa 29050 Pakistan Department of Molecular Biology Virtual University of Pakistan Islamabad 44100 Pakistan Department of Animal Science University of Peradeniya Peradeniya 20400 Sri Lanka National Animal Breeding and Genetics Centre National Animal Science Research Institute Nepal Agriculture Research Council Khumaltar Lalitpur 45200

Indicine cattle, also referred to as zebu (Bos taurus indicus), play a central role in pastoral communities across a wide range of agro-ecosystems, from extremely hot semiarid regions to hot humid tropical regions. However, their adaptive genetic changes following their dispersal into East Asia from the Indian subcontinent have remained poorly documented. Here, we characterize their global genetic diversity using high-quality whole-genome sequencing data from 354 indicine cattle of 57 breeds/populations, including major indicine phylogeographic groups worldwide. We reveal their probable migration into East Asia was along a coastal route rather than inland routes and we detected introgression from other bovine species. Genomic regions carrying morphology-, immune-, and heat-tolerance-related genes underwent divergent selection according to Asian agro-ecologies. We identify distinct sets of loci that contain promising candidate variants for adaptation to hot semi-arid and hot humid tropical ecosystems. Our results indicate that the rapid and successful adaptation of East Asian indicine cattle to hot humid environments was promoted by localized introgression from banteng and/or gaur. Our findings provide insights into the history and environmental adaptation of indicine cattle.

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PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery

arXiv

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arXiv 2024年

作者： Das, Adrito Khan, Danyal Z. Psychogyios, Dimitrios Zhang, Yitong Hanrahan, John G. Vasconcelos, Francisco Pang, You Chen, Zhen Wu, Jinlin Zou, Xiaoyang Zheng, Guoyan Qayyum, Abdul Mazher, Moona Razzak, Imran Li, Tianbin Ye, Jin He, Junjun Plotka, Szymon Kaleta, Joanna Yamlahi, Amine Jund, Antoine Godau, Patrick Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Rivoir, Dominik Pérez, Alejandra Rodriguez, Santiago Arbeláez, Pablo Stoyanov, Danail Marcus, Hani J. Bano, Sophia Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department of Neurosurgery National Hospital for Neurology and Neurosurgery London United Kingdom HKISI CAS China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China National Heart and Lung Institute Faculty of Medicine Imperial College London United Kingdom Centre for Medical Image Computing University College London London United Kingdom University of New South Wales Sydney Australia Shanghai AI Lab Shanghai China Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center University of Amsterdam Amsterdam Netherlands Sano Center for Computational Medicine Krakow Poland Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg a partnership between DKFZ University Hospital Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Muroran Institute of Technology Hokkaido Japan Niigata University of Health and Welfare Niigata Japan Konica Minolta Inc. Osaka Japan National Center for Tumor Diseases Dresden Germany Centre for Tactile Internet TUD Dresden Germany Universidad de los Andes Bogota Colombia DKFZ UKDD TUD Germany

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed;and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery;during live surgery;and when writing operation notes. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision;and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://***/10.5522/04/26531686. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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The CBIT + TMS trial: study protocol for a two-phase randomized controlled trial testing neuromodulation to augment behavior therapy for youth with chronic tics

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Trials 2023年第1期24卷 439-439页

作者： Conelea, Christine Greene, Deanna J. Alexander, Jennifer Houlihan, Kerry Hodapp, Sarah Wellen, Brianna Francis, Sunday Mueller, Bryon Hendrickson, Tim Tseng, Angela Chen, Mo Fiecas, Mark Lim, Kelvin Opitz, Alexander Jacob, Suma Department of Psychiatry and Behavioral Sciences Masonic Institute for the Developing Brain University of Minnesota 2025 E. River Parkway Minneapolis 55414 MN United States Department of Cognitive Science University of California San Diego San Diego United States Department of Psychiatry and Behavioral Sciences University of Minnesota Minneapolis United States Masonic Institute for the Developing Brain University of Minnesota Informatics Institute Minneapolis United States Non-Invasive Neuromodulation Lab Brain Conditions MnDRIVE Initiative University of Minnesota Minneapolis United States Neuroscience Program Research Department Gillette Children’s Specialty Healthcare Saint Paul United States School of Public Health Division of Biostatistics University of Minnesota Minneapolis United States Department of Biomedical Engineering University of Minnesota Minneapolis United States

Background: Comprehensive Behavioral Intervention for Tics (CBIT) is a first-line treatment for tic disorders that aims to improve controllability over tics that an individual finds distressing or impairing. However, it is only effective for approximately half of patients. Supplementary motor area (SMA)-directed neurocircuitry plays a strong role in motor inhibition, and activity in this region is thought to contribute to tic expression. Targeted modulation of SMA using transcranial magnetic stimulation (TMS) may increase CBIT efficacy by improving patients' ability to implement tic controllability behaviors. Methods: The CBIT + TMS trial is a two-phase, milestone-driven early-stage randomized controlled trial. The trial will test whether augmenting CBIT with inhibitory, non-invasive stimulation of SMA with TMS modifies activity in SMA-mediated circuits and enhances tic controllability in youth ages 12–21 years with chronic tics. Phase 1 will directly compare two rTMS augmentation strategies (1 Hz rTMS vs. cTBS) vs. sham in N = 60 participants. Quantifiable, a priori “Go/No Go Criteria” guide the decision to proceed to phase 2 and the selection of the optimal TMS regimen. Phase 2 will compare the optimal regimen vs. sham and test the link between neural target engagement and clinical outcomes in a new sample of N = 60 participants. Discussion: This clinical trial is one of few to date testing TMS augmentation of therapy in a pediatric sample. The results will provide insight into whether TMS is a potentially viable strategy for enhancing CBIT efficacy and reveal potential neural and behavioral mechanisms of change. Trial registration: *** NCT04578912 . Registered on October 8, 2020. © 2023, The Author(s).

关键词： Behavior therapy Neuromodulation Pediatric Tic Tourette Transcranial magnetic stimulation

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Two-dimensional Dirac dispersion in the layered compound BaCdSb2

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Physical Review B 2021年第7期103卷 075143-075143页

作者： Dong-Yun Chen Jin Cao Botao Fu Yongkai Li Xiaoxiong Wang JunXi Duan Junfeng Han Yun-Ze Long Bing Teng Dong Chen Zhonghao Liu Yugui Yao College of Physics Qingdao University Qingdao 266071 China Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology 100081 Beijing China Micronano Centre Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing 100081 China College of Physics and Electronic Engineering Center for Computational Sciences Sichuan Normal University Chengdu 610068 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing 100049 China

We report a comprehensive study of the electronic structure of layered compound BaCdSb2 by using electrical transport measurements, first-principles calculations, and angle-resolved photoemission spectroscopy (ARPES). The samples show semiconductorlike temperature dependence of resistivity and low carrier density. The Shubnikov-de Haas (SdH) oscillations with a single frequency reveal a small two-dimensional (2D) cylinderlike Fermi surface (FS), a light cyclotron effective mass, and trivial Berry's phase. Quantum limit can be achieved under a moderate magnetic field. The calculated bands without any renormalization are well consistent with the ARPES results, showing the Dirac band crossing with 2D character near the Fermi energy and a gap of about 34 meV at the Dirac point induced by spin-orbit interaction. The SdH oscillations can be identified to the Dirac band by the similar cross sectional areas of FSs. Our findings indicate BaCdSb2 is a promising material for researching the quantum phenomena of the 2D Dirac fermions.

关键词： Dirac fermions 2-dimensional systems Angle-resolved photoemission spectroscopy Density functional calculations

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Joint Observation of the Galactic Center with MAGIC and CTA-LST-1 38

Joint Observation of the Galactic Center with MAGIC and CTA-...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Abe, Shotaro Berti, Alessio Di Pierro, Federico López-Coto, Rubén Moralejo, Abelardo Ohtani, Yoshiki Sitarek, Julian Strzys, Marcel Suda, Yusuke Vovk, Ievgen Abe, H. Abe, K. Abe, S. Abhir, J. Acciari, V.A. Aguasca-Cabot, A. Agudo, I. Alvarez Crespo, N. Aniello, T. Ansoldi, S. Antonelli, L.A. Aramo, C. Arbet-Engels, A. Cornelia, A. Artero, M. Asano, K. Aubert, P. Baack, D. Babić, A. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernardos, M.I. Bernete Medrano, J. Berti, A. Besenrieder, J. Bhattacharjee, P. Biederbeck, N. Bigongiari, C. Biland, A. Bissaldi, E. Blanch, O. Bonnoli, G. Bordas, P. Bošnjak, Ž. Bulgarelli, A. Burelli, I. Burmistrov, L. Buscemi, M. Busetto, G. Campoy-Ordaz, A. Cardillo, M. Caroff, S. Carosi, A. Carosi, R. Carrasco, M.S. Carretero-Castrillo, M. Cassol, F. Castro-Tirado, A.J. Cauz, D. Cerasole, D. Ceribella, G. Chai, Y. Cheng, K. Chiavassa, A. Chikawa, M. Chilingarian, A. Chytka, L. Cifuentes, A. Cikota, S. Colombo, E. Contreras, J.L. Cortina, J. Costantini, H. Covino, S. D’Amico, G. Dalchenko, M. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. de Bony de Lavergne, M. De Lotto, B. De Lucia, M. de Menezes, R. Del Peral, L. Deleglise, G. Delfino, M. Delgado, C. Delgado Mengual, J. della Volpe, D. Dellaiera, M. Del Popolo, A. Depaoli, D. Di Piano, A. Di Pierro, F. Di Pilato, A. Di Tria, R. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Eisenberger, L. Elsässer, D. Emery, G. Escudero, J. Fallah Ramazani, V. Fariña, L. Fattorini, A. Ferrara, G. Ferrarotto, F. Fiasson, A. Foffano, L. Font, L. Freixas Coromina, L. Fröse, S. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Garczarczyk, M. Gasbarra, C. Gasparrini, D. Gasparyan, S. Gaug, M. Geyer, D. Giesbrecht Paiva, J. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Grau, R. Green, J. Green, D. Gunji, S. Günther, P. Hackfeld, J. Hadasch, D. Hahn, A. Institute for Cosmic Ray Research Kashiwa-no-ha 5-1-5 Kashiwa-shi Chiba Japan Max-Planck-Institut für Physik Föhringer Ring 6 München80805 Germany INFN Sezione di Torino Via P. Giuria 1 Torino10125 Italy Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Faculty of Physics and Applied Informatics University of Lodz ul. Pomorska 149-153 Lodz90-236 Poland Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Institute for Cosmic Ray Research University of Tokyo Japan Department of Physics Tokai University Japan ETH Zürich Switzerland Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain EMFTEL department IPARCOS Universidad Complutense de Madrid Spain Italy INFN Sezione di Trieste Università degli Studi di Udine Italy Italy INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy INFN Sezione di Padova Università degli Studi di Padova Italy LAPP Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 Annecy France Department of Physics TU Dortmund University Germany Croatia Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan Universidad del Azuay Ecuador INAF Osservatorio di Astrofisica e Scienza dello spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil CIEMAT Spain INFN Sezione di Bari Politecnico di Bari Italy INAF Osservatorio Astronomico di Brera Italy University of Geneva Département de physique nucléaire et corpusculaire Switzerland INFN Sezione di Catania Italy Departament de Física CERES-IEEC Universitat Autónoma de Barcelona Spain Italy Università di Pisa INFN Pisa Italy Aix Mars

MAGIC is a system of two Imaging Atmospheric Cherenkov Telescopes (IACTs), designed to detect very-high-energy gamma rays, and is operating in stereoscopic mode since 2009 at the Observatorio del Roque de Los Muchachos in La Palma, Spain. In 2018, the prototype IACT of the Large-Sized Telescope (LST-1) for the Cherenkov Telescope Array, a next-generation ground-based gamma-ray observatory, was inaugurated at the same site, at a distance of approximately 100 meters from the MAGIC telescopes. Using joint observations between MAGIC and LST-1, we developed a dedicated analysis pipeline and established the threefold telescope system via software, achieving the highest sensitivity in the northern hemisphere. Based on this enhanced performance, MAGIC and LST-1 have been jointly and regularly observing the Galactic Center, a region of paramount importance and complexity for IACTs. In particular, the gamma-ray emission from the dynamical center of the Milky Way is under debate. Although previous measurements suggested that a supermassive black hole Sagittarius A* plays a primary role, its radiation mechanism remains unclear, mainly due to limited angular resolution and sensitivity. The enhanced sensitivity in our novel approach is thus expected to provide new insights into the question. We here present the current status of the data analysis for the Galactic Center joint MAGIC and LST-1 observations. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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MedRep: Medical Concept Representation for General Electronic Health Record Foundation Models

arXiv

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arXiv 2025年

作者： Kim, Junmo Lee, Namkyeong Kim, Jiwon Kim, Kwangsoo Interdisciplinary Program in Bioengineering Seoul National University Korea Republic of Dept. of Industrial and Systems Engineering KAIST Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University Korea Republic of Dept. of Transdisciplinary Medicine ICMIT Seoul National University Hospital Korea Republic of Center for Data Science Healthcare AI Research Institute Seoul National University Hospital Korea Republic of Dept. of Medicine College of Medicine Seoul National University Korea Republic of

Electronic health record (EHR) foundation models have been an area ripe for exploration with their improved performance in various medical tasks. Despite the rapid advances, there exists a fundamental limitation: Processing unseen medical codes out of the vocabulary. This problem limits the generality of EHR foundation models and the integration of models trained with different vocabularies. To deal with this problem, we propose MedRep for EHR foundation models based on the observational medical outcome partnership (OMOP) common data model (CDM), providing the integrated medical concept representations and the basic data augmentation strategy for patient trajectories. For concept representation learning, we enrich the information of each concept with a minimal definition through large language model (LLM) prompts and enhance the text-based representations through graph ontology of OMOP vocabulary. Trajectory augmentation randomly replaces selected concepts with other similar concepts that have closely related representations to let the model practice with the concepts out-of-vocabulary. Finally, we demonstrate that EHR foundation models trained with MedRep better maintain the prediction performance in external datasets. Our code implementation is publicly available at https://***/kicarussays/MedRep. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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On Performance Prediction of Big Data Transfer in High-performance Networks

On Performance Prediction of Big Data Transfer in High-perfo...

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IEEE International Conference on Communications (ICC)

作者： Wuji Liu Daqing Yun Chase Q. Wu Nageswara S.V. Rao Aiqin Hou Wei Shen Department of Computer Science New Jersey Institute of Technology Newark NJ USA Computer and Information Sciences Program Harrisburg University Harrisburg PA USA Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA School of Information Science and Technology Northwest University Xi’an Shaanxi China School of Informatics Science and Technology Zhejiang Sci-Tech University Hangzhou Zhejiang China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

关键词： Data transfer Big Data Throughput Bandwidth Measurement Predictive models Protocols

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A rigorous benchmarking of methods for SARS-CoV-2 lineage abundance estimation in wastewater

arXiv

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arXiv 2023年

作者： Munteanu, Viorel Gordeev, Victor Saldana, Michael Aßmann, Eva Su, Justin Maine Drabcinski, Nicolae Zlenko, Oksana Kit, Maryna Patel, Khooshbu Kantibhai Xu, Yidian Al Nahid, Abdullah Skums, Pavel Agrawal, Shelesh Hölzer, Martin Smith, Adam Zelikovsky, Alex Mangul, Serghei Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau2045 Moldova Astani Department of Civil and Environmental Engineering University of Southern California 3620 South Vermont Avenue Los AngelesCA90089 United States Method Development and Research Infrastructure Robert Koch Institute Berlin13353 Germany Robert Koch Institute Berlin13353 Germany Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los AngelesCA90089 United States National Scientific Center "Institute of Experimental and Clinical Veterinary Medicine" Kharkiv61023 Ukraine Department of Translational Genomics Keck School of Medicine University of Southern California 1450 Biggy Street Los AngelesCA United States Department of Biochemistry and Molecular Biology School of Life Sciences Shahjalal University of Science and Technology Sylhet3114 Bangladesh Department of Computer Science and Engineering University of Connecticut StorrsCT United States Water and Environmental Biotechnology Institute IWAR Department of Civil and Environmental Engineering Sciences Technical University of Darmstadt Darmstadt Germany Astani Department of Civil and Environmental Engineering University of Southern California 920 Downey WayBHE 221 Los AngelesCA90089 United States Department of Computer Science College of Art and Science Georgia State University AtlantaGA United States Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California 1540 Alcazar Street Los AngelesCA90033 United States Department of Quantitative and Computational Biology University of Southern California Dornsife College of Letters Arts and Sciences Los AngelesCA90089 United States

In light of the continuous transmission and evolution of SARS-CoV-2 coupled with a significant decline in clinical testing, there is a pressing need for scalable, cost-effective, long-term, passive surveillance tools to effectively monitor viral variants circulating in the population. Wastewater genomic surveillance of SARS-CoV-2 has arrived as an alternative to clinical genomic surveillance, allowing to continuously monitor the prevalence of viral lineages in communities of various size at a fraction of the time, cost, and logistic effort and serving as an early warning system for emerging variants, critical for developed communities and especially for underserved ones. Importantly, lineage prevalence estimates obtained with this approach aren't distorted by biases related to clinical testing accessibility and participation. However, the relative performance of bioinformatics methods used to measure relative lineage abundances from wastewater sequencing data is unknown, preventing both the research community and public health authorities from making informed decisions regarding computational tool selection. Here, we perform comprehensive benchmarking of 18 bioinformatics methods for estimating the relative abundance of SARS-CoV-2 (sub)lineages in wastewater by using data from 36 in vitro mixtures of synthetic lineage and sublineage genomes. In addition, we use simulated data from 78 mixtures of lineages and sublineages co-occurring in the clinical setting with proportions mirroring their prevalence ratios observed in real data. Importantly, we investigate how the accuracy of the evaluated methods is impacted by the sequencing technology used, the associated error rate, the read length, read depth, but also by the exposure of the synthetic RNA mixtures to wastewater, with the goal of capturing the effects induced by the wastewater matrix, including RNA fragmentation and degradation. © 2023, CC BY.

关键词： Coronavirus

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