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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Ahmad El Shoghri Jessica Liebig Raja Jurdak Lauren Gardner Salil S. Kanhere University of New South Wales Sydney NSW Australia Data61 Commonwealth Scientific and Industrial Research Organization Brisbane QLD AUSTRALIA School of Computer Science Queensland University of Technology Brisbane QLD AUSTRALIA Department of Civil and Systems Engineering Johns Hopkins University Baltimore MD USA Research Center for Integrated Transport Innovation (rCITI) UNSW Sydney Sydney NSW AUSTRALIA School of Computer Science and Engineering University of New South Wales Sydney NSW AUSTRALIA

ISBN: (数字)9781728173740

ISBN: (纸本)9781728173757

The recent outbreak of a novel coronavirus and its rapid spread underlines the importance of understanding human mobility. Enclosed spaces, such as public transport vehicles (e.g. buses and trains), offer a suitable environment for infections to spread widely and quickly. Investigating the movement patterns and the physical encounters of individuals on public transit systems is thus critical to understand the drivers of infectious disease outbreaks. For instance, previous work has explored the impact of recurring patterns inherent in human mobility on disease spread, but has not considered other dimensions such as the distance travelled or the number of encounters. Here, we consider multiple mobility dimensions simultaneously to uncover critical information for the design of effective intervention strategies. We use one month of citywide smart card travel data collected in Sydney, Australia to classify bus passengers along three dimensions, namely the degree of exploration, the distance travelled and the number of encounters. Additionally, wes imulate disease spread on the transport network and trace the infection paths. We investigate in detail the transmissions between the classified groups while varying the infection probability and the suspension time of pathogens. Our results show that characterizing individuals along multiple dimensions simultaneously uncovers a complex infection interplay between the different groups of passengers, that would remain hidden when considering only a single dimension. We also identify groups that are more influential than others given specific disease characteristics, which can guide containment and vaccination efforts.

关键词： Pathogens Australia Suspensions Global Positioning System Infectious diseases Sociology

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Retraction notice to "A strategic review on sustainable approaches in municipal solid waste management and energy recovery: Role of artificial intelligence, economic stability and life cycle assessment" [Bioresour. Technol. 379 (2023) 129044]

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Bioresource technology 2025年 421卷 132185页

作者： Rajendiran Naveenkumar Jayaraj Iyyappan Ravichandran Pravin Seifedine Kadry Jeehoon Han Raveendran Sindhu Mukesh Kumar Awasthi Samuel Lalthazuala Rokhum Gurunathan Baskar Biological Systems Engineering University of Wisconsin-Madison Madison WI 53706 United States Forest Products Laboratory USDA Forest Service Madison WI 53726 United States. Department of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai 602107 India. Department of Biotechnology St. Joseph's College of Engineering Chennai 600119 India. Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon. Department of Chemical Engineering Pohang University of Science and Technology Pohang 37673 Republic of Korea. Department of Food Technology TKM Institute of Technology Kollam Kerala India. College of Natural Resources and Environment Northwest A&F University Yangling Shaanxi Province 712100 China. Department of Chemistry National Institute of Technology Silchar 788010 Assam India. Department of Biotechnology St. Joseph's College of Engineering Chennai 600119 India Department of Applied Data Science Noroff University College Kristiansand Norway. Electronic address: basg2002@***.

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Large language models surpass human experts in predicting neuroscience results

arXiv

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arXiv 2024年

作者： Luo, Xiaoliang Rechardt, Akilles Sun, Guangzhi Nejad, Kevin K. Yáñez, Felipe Yilmaz, Bati Lee, Kangjoo Cohen, Alexandra O. Borghesani, Valentina Pashkov, Anton Marinazzo, Daniele Nicholas, Jonathan Salatiello, Alessandro Sucholutsky, Ilia Minervini, Pasquale Razavi, Sepehr Rocca, Roberta Yusifov, Elkhan Okalova, Tereza Gu, Nianlong Ferianc, Martin Khona, Mikail Patil, Kaustubh R. Lee, Pui-Shee Mata, Rui Myers, Nicholas E. Bizley, Jennifer K. Musslick, Sebastian Bilgin, Isil Poyraz Niso, Guiomar Ales, Justin M. Gaebler, Michael Murty, N. Apurva Ratan Loued-Khenissi, Leyla Behler, Anna Hall, Chloe M. Dafflon, Jessica Bao, Sherry Dongqi Love, Bradley C. Department of Experimental Psychology University College London London United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Physiology Anatomy & Genetics University of Oxford Oxford United Kingdom Max Planck Institute for Neurobiology of Behavior – caesar Bonn Germany Bilkent University Ankara Turkey Department of Computer Science University of Bristol Bristol United Kingdom The Alan Turing Institute London United Kingdom Department of Psychiatry Yale University New Haven United States Psychology Emory University Atlanta United States Faculty of Psychology and Educational Sciences Université de Genève Genève Switzerland Neurosurgery Novosibirsk State Medical University Novosibirsk Russia Federal Center of Neurosurgery FSBI Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia Department of Data Analysis Ghent University Ghent Belgium Psychology New York University New York United States Department of Cognitive Neurology University of Tübingen Tübingen Germany Computer Science Princeton University Princeton United States ILCC University of Edinburgh Edinburgh United Kingdom Philosophy Psychology and Language Sciences The University of Edinburgh Edinburgh United Kingdom Department of Culture Cognition and Computation Aarhus University Aarhus Denmark Department of Molecular Life Sciences University of Zurich Zurich Switzerland Bioengineering University of Pennsylvania Philadelphia United States Linguistic Research Infrastructure University of Zurich Zurich Switzerland Electronic and Electrical Engineering University College London London United Kingdom Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States Institute of Neuroscience and Medicine INM-7: Brain and Behaviour Research Centre Jülich Jülich Germany Medical Faculty Institute of Systems Neuroscience Heinrich Hein

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors. © 2024, CC BY.

关键词： Prediction models

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TCIM: Triangle Counting Acceleration With Processing-In-MRAM Architecture

TCIM: Triangle Counting Acceleration With Processing-In-MRAM...

引用

Design Automation Conference

作者： Xueyan Wang Jianlei Yang Yinglin Zhao Yingjie Qi Meichen Liu Xingzhou Cheng Xiaotao Jia Xiaoming Chen Gang Qu Weisheng Zhao Fert Beijing Research Institute Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China Department of Electrical and Computer Engineering University of Maryland College Park MD USA

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and FPGA. However, these approaches suffer from the bandwidth bottleneck because TC calculation involves a large amount of data transfers. In this paper, we propose to overcome this challenge by designing a TC accelerator utilizing the emerging processing-in-MRAM (PIM) architecture. The true innovation behind our approach is a novel method to perform TC with bitwise logic operations (such as AND), instead of the traditional approaches such as matrix computations. This enables the efficient in-memory implementations of TC computation, which we demonstrate in this paper with computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) arrays. Furthermore, we develop customized graph slicing and mapping techniques to speed up the computation and reduce the energy consumption. We use a device-to-architecture co-simulation framework to validate our proposed TC accelerator. The results show that our data mapping strategy could reduce 99.99% of the computation and 72% of the memory WRITE operations. Compared with the existing GPU or FPGA accelerators, our in-memory accelerator achieves speedups of 9× and 23.4×, respectively, and a 20.6× energy efficiency improvement over the FPGA accelerator.

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Information Needs for Opioid Use Disorder Treatment Using Buprenorphine Product: Qualitative Analysis of Suboxone-Focused Reddit data

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Journal of medical Internet research 2025年 27卷 e68886页

作者： Madhusudan Basak Omar Sharif Sarah E Lord Jacob T Borodovsky Lisa A Marsch Sandra A Springer Edward V Nunes Charles D Brackett Luke J Archibald Sarah M Preum Department of Computer Science Dartmouth College Hanover NH United States. Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh. Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College Lebanon NH United States. Department of Biomedical Data Science Geisel School of Medicine Dartmouth College Lebanon NH United States. Department of Psychiatry Dartmouth Health Lebanon NH United States. Department of Psychiatry Geisel School of Medicine Dartmouth College Lebanon NH United States. Section of Infectious Disease Department of Internal Medicine Yale School of Medicine New Haven CT United States. Center for Interdisciplinary Research on AIDS Yale University School of Public Health New Haven CT United States. Section of Infectious Disease Department of Internal Medicine Veterans Administration Connecticut Healthcare System West Haven CT United States. Department of Psychiatry Division on Substance Use Columbia University Irving Medical Center and New York State Psychiatric Institute New York NH United States. Department of Medicine Geisel School of Medicine Dartmouth College Hanover NH United States. Department of Internal Medicine Dartmouth Hitchcock Medical Center Lebanon NH United States.

BACKGROUND:Buprenorphine is a Food and Drug Administration-approved medication for opioid use disorder. However, individuals with opioid use disorder often report information needs regarding buprenorphine treatment on social media platforms such as Reddit. The field lacks a systematic approach to organizing these data and characterizing treatment information needs that may be unique and unavailable elsewhere. OBJECTIVE:In this study, we curated and analyzed large-scale data from social media to characterize self-reported buprenorphine treatment information needs using a qualitative analysis. METHODS:We collected 15,253 Reddit posts from the subreddit r/Suboxone. Following a standard protocol and guidance from clinical experts, we first identified 5 main themes from the data and then manually coded 6000 posts based on these themes. Finally, we determined the most frequently appearing topics within each theme by analyzing samples from each group. RESULTS:Among the 6000 posts, 2417 (40.3%) contained a single theme, 2160 (36%) contained two themes, and 834 (13.9%) contained three themes. The most frequent topics found across these themes included reports of psychological and physical effects during recovery, complexities in accessing buprenorphine, and significant information gaps regarding medication administration, tapering, and substance use at different stages of recovery. Moreover, self-treatment strategies and peer-driven advice revealed potential rumors and misinformation. CONCLUSIONS:Our findings can guide the design of interventions to improve patient education and communication. They also help address knowledge gaps and misinformation related to treatment. Additionally, the results can support hypothesis generation for future clinical research on medication for opioid use disorder treatment.

关键词： MOUD Reddit access buprenorphine medication for opioid use disorder opioid use disorder patient experience social media substance use tapering treatment information needs

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Practical Implications and Challenges of Multispectral Image Analysis

Practical Implications and Challenges of Multispectral Image...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Syed Muslim Jameel Abdul Rehman Gilal Syed Sajjad Hussain Rizvi Mobashar Rehman Manzoor Ahmed Hashmani Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Malaysia High-Performance Cloud Computing Center (HPC3) Center for Research in Data Science (CERDAS) Universiti Teknologi PETRONAS Seri Iskandar Malaysia Faculty of Information and Communication Technology (FICT) Universiti Tunku Abdul Rahman Kampar Malaysia Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Department of computer science Sukkur IBA University Sukkur Pakistan

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

Multispectral Image Analysis (MIA) is an exciting area of research and famous due to its utilization in geo monitoring, such as environmental change detection and region classification. Currently, many other areas are also emerging and are utilizing the multispectral imagery for improving the existing business processes and ensuring the reliability in the existing business methods. However, the process of generating and analyzing multispectral data needs more sophisticated techniques and methods. Also, the multispectral data possesses some temporal and structural inconsistencies, which make the existing multispectral analysis a more challenging task. Therefore, this study surveys the practical implication and possible issues in multispectral image analysis. For that reason, this study aims to investigate the two simple research questions and follow a structured analysis approach to achieve the research objectives. The rest of the paper is structured as; Section II briefly explains the systematic methodology adopted for this survey. Section III is relevant to research finding in response to the RQ1 and RQ2. Finally, Section IV presents the conclusion of this study. In future work, we will sharpen this study towards Skin Disease Analysis using Multispectral Imagery.

关键词： Image analysis Agriculture Diseases Machine learning Quality assessment Remote sensing

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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CONTRIBUTIONS OF THE PETABYTE SCALE SEQUENCE SEARCH CODEATHON TOWARD EFFORTS TO SCALE SEQUENCE-BASED SEARCHES ON SRA

arXiv

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arXiv 2025年

作者： Ghosh, Priyanka Fagnan, Kjiersten Connor, Ryan Pannu, Ravinder Wheeler, Travis J. Pop, Mihai Brown, C. Titus Pierce-Ward, Tessa Patro, Rob Michaelis, Jacquelyn S. Madden, Thomas L. Camacho, Christiam Awe, Olaitan I. Krinos, Arianna I. Xavier, René K.M. Polo, Rodrigo Ortega Roddy, Jack W. Rhodes, Adelaide Sweeten, Alexander Viehweger, Adrian Ekim, Barış Muralidharan, Harihara Subrahmaniam Rahman, Amatur Salazar, Vinícius W. Tritt, Andrew Colligan, Thomas Kalantar, Katrina Krause, Genevieve R. Reiter, Taylor Lesica, George Babaian, Artem Lin, Victor Madaminov, Sergey Zalunin, Vadim Kristensen, David M. Salsbury, Alexa Rice, Daniel P. Brister, J. Rodney National Center for Biotechnology Information National Library of Medicine National Institutes of Health BethesdaMD United States Joint Genome Institute Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Pharmacy Practice and Science R. Ken Coit College of Pharmacy University of Arizona TucsonAZ United States University of California Davis DavisCA United States Department of Computer Science University of Maryland at College Park College ParkMD United States Center for Bioinformatics and Computational Biology University of Maryland College ParkMD United States Center for Genomics and Data Science Research National Human Genome Research Institute National Institutes of Health BethesdaMD United States African Society for Bioinformatics and Computational Biology Cape Town South Africa Department of Earth Environmental and Planetary Sciences Brown University ProvidenceRI United States Ocean Genomics Inc. PittsburghPA United States Florida Atlantic University Harbor Branch Oceanographic Institute Fort Pierce Florida United States Lethbridge Research and Development Center Agriculture and Agri-Food Canada Lethbridge Canada Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States Melbourne Bioinformatics University of Melbourne ParkvilleVIC Australia Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory CA United States Department of Computer Science University of Montana MissoulaMT United States Chan Zuckerberg Initiative Redwood CityCA United States Department of Population Health and Reproduction School of Veterinary Medicine University of California Davis CA United States Department of Molecular Genetics University of Toronto ON Canada Department of Computer Science Stony Brook University NY United St

The volume of biological data being generated by the scientific community is growing exponentially, reflecting technological advances and research activities. The National Institutes of Health’s (NIH) Sequence Read Archive (SRA), which is maintained by the National center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM), is a rapidly growing public database that researchers use to drive scientific discovery across all domains of life. This increase in available data has great promise for pushing scientific discovery but also introduces new challenges that scientific communities need to address. As genomic datasets have grown in scale and diversity, a parade of new methods and associated software have been developed to address the challenges posed by this growth. These methodological advances are vital for maximally leveraging the power of next-generation sequencing (NGS) technologies, but it can be difficult to make sense of their performance trade-offs (especially speed and accuracy). With the goal of laying a foundation for evaluation of methods for petabyte-scale sequence search, the department of Energy (DOE) Office of Biological and Environmental research (BER), the NIH Office of data science Strategy (ODSS), and NCBI held a virtual codeathon ‘Petabyte Scale Sequence Search: Metagenomics Benchmarking Codeathon’ on September 27 - Oct 1 2021, to evaluate emerging solutions in petabyte scale sequence search. The codeathon attracted experts from national laboratories including the Lawrence Berkeley National Laboratory (LBNL), research institutions including the Joint Genome Institute (JGI), and universities across the world to (a) develop benchmarking approaches to address challenges in conducting large-scale analyses of metagenomic data (which comprises approximately 20% of SRA), (b) identify potential applications (i.e. use-cases) that benefit from SRA-wide searches and the tools required to execute the search, and (c) produce commun

关键词： Biodiversity

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Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation

arXiv

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arXiv 2024年

作者： LaBella, Dominic Schumacher, Katherine Mix, Michael Leu, Kevin McBurney-Lin, Shan Nedelec, Pierre Villanueva-Meyer, Javier Shapey, Jonathan Vercauteren, Tom Chia, Kazumi Ivory, Marina Barfoot, Theodore Al-Salihi, Omar Leu, Justin Halasz, Lia Velichko, Yury Wang, Chunhao Kirkpatrick, John Floyd, Scott Reitman, Zachary J. Mullikin, Trey Bagci, Ulas Sachdev, Sean Hattangadi-Gluth, Jona A. Seibert, Tyler M. Farid, Nikdokht Puett, Connor Pease, Matthew W. Shiue, Kevin Anwar, Syed Muhammad Faghani, Shahriar Taylor, Peter Haider, Muhammad Ammar Warman, Pranav Albrecht, Jake Jakab, András Moassefi, Mana Chung, Verena Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Coley, Aaron Huang, Christina Ghanta, Siddharth Schneider, Alex Sharp, Conrad Saluja, Rachit Kofler, Florian Lohmann, Philipp Vollmuth, Phillipp Gagnon, Louis Adewole, Maruf Li, Hongwei Bran Kazerooni, Anahita Fathi Tahon, Nourel Hoda Anazodo, Udunna Moawad, Ahmed W. Menze, Bjoern Linguraru, Marius George Aboian, Mariam Wiestler, Benedikt Baid, Ujjwal Conte, Gian-Marco Rauschecker, Andreas M. Nada, Ayman Abayazeed, Aly H. Huang, Raymond de Verdier, Maria Correia Rudie, Jeffrey D. Bakas, Spyridon Calabrese, Evan Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiation Oncology SUNY Upstate Medical University SyracuseNY United States San FranciscoCA United States Department of Neurosurgery King’s College Hospital London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Guy’s and St Thomas’ NHS Foundation Trust United Kingdom Department of Radiation Oncology University of Washington SeattleWA United States Department of Radiology Northwestern University EvanstonIL United States Department of Radiation Oncology Northwestern University EvanstonIL United States Department of Radiation Medicine and Applied Sciences University of California San Diego La Jolla CA United States Department of Radiology University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Radiation Oncology Indiana University IndianapolisIN United States Children’s National Hospital WashingtonDC United States George Washington University WashingtonDC United States Mayo Clinic RochesterMN United States CMH Lahore Medical College Lahore Pakistan Duke University Medical Center School of Medicine DurhamNC United States Sage Bionetworks United States University of Zürich Zürich Switzerland Artificial Intelligence Lab Department of Radiology Mayo Clinic RochesterMN United States MLCommons United States Factored AI United States Center For Federated Learning in Medicine Indiana University IndianapolisIN United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Medical Working Group MLCommons San FranciscoCA United States Intel United States Duke Universi

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes. © 2024, CC BY.

关键词： Radiotherapy

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Cellular automaton modeling of pedestrian movement behavior on an escalator

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Chinese Physics B 2018年第12期27卷 328-333页

作者： Fu-Rong Yue Juan Chen Jian Ma Wei-Guo Song Siu-Ming Lo School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application TechnologySouthwest Jiaotong University Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention State Key Laboratory of Fire Science University of Science and Technology of China Department of Architectural and Civil Engineering City University of Hong Kong

As a convenient passenger transit facility between floors with different heights, escalators have been extensively used in shopping malls, metro stations, airport terminals, etc. Compared with other vertical transit facilities including stairs and elevators, escalators usually have large transit capacity. It is expected to reduce pedestrian traveling time and thus improve the quality of pedestrian’s experiences especially in jamming conditions. However, it is noticed that pedestrians may present different movement patterns, e.g., queuing on each step of the escalator, walking on the left-side and meanwhile standing on the right-side of the escalator. These different patterns affect the actual escalator traffic volume and finally the passenger spatiotemporal distribution in different built environments. Thus, in the present study, a microscopic cellular automaton(CA) simulation model considering pedestrian movement behavior on escalators is built. Simulations are performed considering different pedestrian movement speeds, queuing modes, and segregation on escalators with different escalator *** actual escalator capacities under different pedestrian movement patterns are investigated. It is found that walking on escalators will not always benefit escalator transit volume improvement, especially in jamming conditions.

关键词： cellular automaton pedestrian movement escalator capacity

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