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2018 TrEC Video retrieval Evaluation, TrECVId 2018

作者： Awad, George Soboroff, Ian Butt, Asad Curtis, Keith Ellis, Angela dimmick, darrin Fiscus, Jonathan Godil, Afzal delgado, Andrew Smeaton, Alan Graham, Yvette Kraaij, Wessel Quénot, Georges Magalhaes, Joao Semedo, david Blasi, Saverio Strassel, Stephanie Lee, Yooyoung Joy, david Dublin City University Ireland TNO Leiden University Netherlands Laboratoire d'Informatique de Grenoble France Universidade NOVA de Lisboa Portugal British Broadcasting Corporation BBC R&D United Kingdom Retrieval Group Multimodal Information Group Information Access Division Information Technology Laboratory NIST United States Linguistic Data Consortium Dakota Consulting Inc United States

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INTrOdUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. dunn, r.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–december) having each been record warm. Over land, the globally averaged temperature was also record high. dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. due to rapid volume loss beginning in 2021, St. A

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Sketch-based Medical Image retrieval

arXiv

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arXiv 2023年

作者： Kobayashi, Kazuma Gu, Lin Hataya, ryuichiro Mizuno, Takaaki Miyake, Mototaka Watanabe, Hirokazu Takahashi, Masamichi Takamizawa, Yasuyuki Yoshida, Yukihiro Nakamura, Satoshi Kouno, Nobuji Bolatkan, Amina Kurose, Yusuke Harada, Tatsuya Hamamoto, ryuji Division of Medical AI Research and Development National Cancer Center Research Institute 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Cancer Translational Research Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Machine Intelligence for Medical Engineering Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan Medical Data Deep Learning Team Advanced Data Science Project RIKEN Information R&D and Strategy Headquarters 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Experimental Therapeutics National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Diagnostic Radiology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Neurosurgery and Neuro-Oncology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Colorectal Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Thoracic Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Radiation Safety and Quality Assurance Division National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Division of Research and Development for Boron Neutron Capture Therapy National Cancer Center Exploratory Oncology Research & Clinical Trial Center 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Medical Physics Laboratory Division of Health Science Graduate School of Medicine Osaka University Yamadaoka 1-7 Osaka Suita-shi565-0871 Japan Department of Surgery Kyoto University Graduate School of Medicine 54 Shogoin Kawahara-cho Sakyo-ku Kyoto606-8507 Japan

The amount of medical images stored in hospitals is increasing faster than ever;however, utilizing the accumulated medical images has been limited. This is because existing content-based medical image retrieval (CBMIr) systems usually require example images to construct query vectors;nevertheless, example images cannot always be prepared. Besides, there can be images with rare characteristics that make it difficult to find similar example images, which we call isolated samples. Here, we introduce a novel sketch-based medical image retrieval (SBMIr) system that enables users to find images of interest without example images. The key idea lies in feature decomposition of medical images, whereby the entire feature of a medical image can be decomposed into and reconstructed from normal and abnormal features. By extending this idea, our SBMIr system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. Subsequently, it integrates the two kinds of input to construct a query vector and retrieves reference images with the closest reference vectors. Using two datasets, ten healthcare professionals with various clinical backgrounds participated in the user test for evaluation. As a result, our SBMIr system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for isolated samples. Our SBMIr system achieves flexible medical image retrieval on demand, thereby expanding the utility of medical image databases. Copyright © 2023, The Authors. All rights reserved.

关键词： Search engines

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Neuroprotective effects of Ginkgo biloba extract and GinkgoUde B against oxygen-glucose deprivation/reoxygenation and glucose injury in a new in vitro multicellular network model

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Frontiers of Medicine 2018年第3期12卷 307-318页

作者： Xiaohan Yang Tiezheng Zheng Hao Hong Nan Cai Xiaofeng Zhou Changkai Sun Liying Wu Shuhong Liu Yongqi Zhao Lingling Zhu Ming Fan Xuezhong Zhou Fengxie Jin Department of Biomedical Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian 116024 China Research Center for the Control Engineering of Translational Precision Medicine Dalian University of Technology Dalian 116024 China State Key Laboratory of Fine Chemicals Dalian R&D Center for Stem Cell and Tissue Engineering Dalian University of Technology Dalian 116024 China Liaoning Provincial Key Laboratory of Cerebral Diseases Institute for Brain Disorders Dalian Medical University Dalian 116044 China Institute of Basic Medical Sciences Academy of Military Medical Sciences Beijing 100850 China School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing 100044 China College of Biotecknology Dalian Polytechnic University Dalian 116034 China

Acute ischemic stroke （AIS）, as the third leading cause of death worldwide, is characterized by its high incidence, mortality rate, high incurred disability rate, and frequent reoccurrence. The neuroprotective effects of Ginkgo biloba extract （GBE） against several cerebral diseases have been reported in previous studies, but the underlying mechanisms of action are still unclear. Using a novel in vitro rat cortical capillary endothelial cell- astrocyte-neuron network model, we investigated the neuroprotective effects of GBE and one of its important constituents, Ginkgolide B （GB）, against oxygenglucose deprivation/reoxygenation and glucose （OGd/r） injury. In this model, rat cortical capillary endothelial cells, astrocytes, and neurons were cocultured so that they could be synchronously observed in the same system. Pretreatment with GBE or GB increased the neuron cell viability, ameliorated cell injury, and inhibited the cell apoptotic rate through Bax and Bcl-2 expression regulation after OGd/r injury. Furthermore, GBE or GB pretreatment enhanced the transendothelial electrical resistance of capillary endothelial monolayers, reduced the endothelial permeability coefficients for sodium fluorescein （Na-F）, and increased the expression levels of tight junction proteins, namely, ZO-1 and occludin, in endothelial cells. results demonstrated the preventive effects of GBE on neuronal cell death and enhancement of the function of brain capillary endothelial monolayers after OGd/r injury in vitro; thus, GBE could be used as an effective neuroprotective agent for AIS/reperfusion, with GB as one of its significant constituents.

关键词： acute ischemic stroke Ginkgo biloba extract Ginkgolide B network model neuroprotection

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Correction: Spatial organization of B lymphocytes and prognosis prediction in patients with gastric cancer

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Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 2025年 2025 May 16页

作者： ryan Yong Kiat Tay Manavi Sachdeva Haoran Ma Young-Woo Kim Myeong-Cherl Kook Hyunki Kim Jae-Ho Cheong Lindsay C Hewitt Katharina Nekolla Günter Schmidt Takaki Yoshikawa Takashi Oshima Tomio Arai Supriya Srivastava Ming Teh Xuewen Ong Su Ting Tay Taotao Sheng Joseph J Zhao Patrick Tan Heike I Grabsch raghav Sundar Yong Loo Lin School of Medicine National University of Singapore 1E Kent Ridge Road Singapore 119228 Singapore. Department of Haematology-Oncology National University Cancer Institute National University Hospital Singapore Singapore. Cancer and Stem Cell Biology Program Duke-NUS Medical School Singapore Singapore. Department of Cancer Policy and Population Health National Cancer Center Graduate School of Cancer Science and Policy and Center for Gastric Cancer and Department of Surgery National Cancer Center Goyang Republic of Korea. Center for Gastric Cancer Department of Pathology National Cancer Center Goyang Republic of Korea. Department of Pathology Yonsei University College of Medicine Seoul Republic of Korea. Department of Surgery Yonsei University College of Medicine Seoul Republic of Korea. Department of Pathology GROW Research Institute for Oncology and Reproduction Maastricht University Medical Center+ Maastricht The Netherlands. Department of Precision Medicine GROW School for Oncology and Reproduction Maastricht University Center+ Maastricht The Netherlands. Computational Pathology Oncology R&D AstraZeneca Munich Germany. Department of Surgery Kanagawa Cancer Center Yokohama Japan. Department of Surgery Yokohama City University Yokohama Japan. Department of Surgery Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital Tokyo Japan. Department of Pathology Tokyo Metropolitan Institute for Geriatrics and Gerontology Tokyo Japan. Department of Medicine National University of Singapore Singapore Singapore. Department of Pathology National University Hospital Singapore Singapore. Genome Institute of Singapore Agency for Science Technology and Research Singapore Singapore. Cancer Science Institute of Singapore National University of Singapore Singapore Singapore. Cellular and Molecular Research National Cancer Centre Singapore Singapore. Singhealth/Duke-NUS Institute of Precision Medicine National Heart Centre Singapore

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Advancing covid-19 diagnosis with privacy-preserving collaboration in artificial intelligence

arXiv

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arXiv 2021年

作者： Bai, Xiang Wang, Hanchen Ma, Liya Xu, Yongchao Gan, Jiefeng Fan, Ziwei Yang, Fan Ma, Ke Yang, Jiehua Bai, Song Shu, Chang Zou, Xinyu Huang, renhao Zhang, Changzheng Liu, Xiaowu Tu, dandan Xu, Chuou Zhang, Wenqing Wang, Xi Chen, Anguo Zeng, Yu Yang, dehua Wang, Ming-Wei Holalkere, Nagaraj Halin, Neil J. Kamel, Ihab r. Wu, Jia Peng, Xuehua Wang, Xiang Shao, Jianbo Mongkolwat, Pattanasak Zhang, Jianjun Liu, Weiyang roberts, Michael Teng, Zhongzhao Beer, Lucian Sanchez, Lorena Escudero Sala, Evis rubin, daniel Weller, Adrian Lasenby, Joan Zheng, Chuangsheng Wang, Jianming Li, Zhen Schönlieb, Carola-Bibiane Xia, Tian Department of Radiology Tongji Hospital and Medical College Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Engineering University of Cambridge Cambridge United Kingdom Department of Radiology Union Hospital of Tongji Medical College Huazhong University of Science and Technology Wuhan China HUST-HW Joint Innovation Lab Wuhan China CalmCar Inc Suzhou China Wuhan Blood Centre Wuhan China MSA Capital Beijing China The National Centre for Drug Screening Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai China CardioVascular and Interventional Radiology Radiology for Quality and Operations The Cardiovascular Centre at Tufts Medical Centre Radiology Tufts University School of Medicine Medford United States Russell H Morgan Department of Radiology & Radiologic Science Johns Hopkins Hospital & Medicine Institute Baltimore United States Department of Radiation Oncology School of Medicine Stanford University Palo Alto United States Department of Radiology Wuhan Central Hospital Wuhan China Department of Radiology Wuhan Children's Hospital Wuhan China Faculty of Information and Communication Technology Mahidol University Thailand Thoracic/Head and Neck Medical Oncology Translational Molecular Pathology University of Texas MD Anderson Cancer Centre Houston United States Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Oncology R&D at AstraZeneca Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Department of Biomedical Data Science Radiology and Medicine Stanford University Palo Alto United States Alan Turing Institute London United Kingdom Department of Hepatobiliary Pancreatic Surgery Affiliated Tianyou Hospital Wuhan University of Science and Technology Wuhan China

Artificial intelligence (AI) provides a promising substitution for streamlining COVId-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVId AI diagnostic Initiative (UCAdI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health. © 2021, CC BY-NC-SA.

关键词： diagnosis

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Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMrEP22 workshop report

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ImmunoInformatics 2023年 9卷

作者： Pieter Meysman Justin Barton Barbara Bravi Liel Cohen-Lavi Vadim Karnaukhov Elias Lilleskov Alessandro Montemurro Morten Nielsen Thierry Mora Paul Pereira Anna Postovskaya María rodríguez Martínez Jorge Fernandez-de-Cossio-diaz Alexandra Vujkovic Aleksandra M. Walczak Anna Weber rose Yin Anne Eugster Virag Sharma AUDACIS University of Antwerp Antwerp Belgium ADREM data lab Department of Computer Science University of Antwerp Antwerp Belgium Institute of Structural and Molecular Biology University of London London United Kingdom Department of Mathematics Imperial College London London United Kingdom National Institute for Biotechnology in the Negev Ben-Gurion University of the Negev Beer-Sheva Israel Department of Industrial Engineering and Management Ben-Gurion University of the Negev Beer-Sheva Israel INSERM U932 PSL University Institut Curie Paris 75005 France Laboratoire de physique de l'Ecole normale superieure CNRS PSL University Sorbonne Universite Universite Paris-Cité Paris 75005 France Department of Physics University of Washington Seattle WA USA Department of Health Technology Technical University of Denmark Lyngby DK-2800 Denmark Sanofi R&D Chilly-Mazarin 91380 France Clinical Virology Unit Institute of Tropical Medicine Antwerp Belgium IBM Research Europe Säumerstrasse 4 Rüschlikon 8803 Switzerland Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA Center for Regenerative Therapies Dresden Faculty of Medicine TU Dresden Dresden Germany Department of Chemical Sciences School of Natural Sciences University of Limerick Limerick V94 T9PX Ireland

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCr) have been proposed. However several questions on the advantages and disadvantages of these different approaches remain unresolved, as most methods have only been evaluated within the context of their initial publications and data sets. Here, we report the findings of the first public TCr-epitope prediction benchmark performed on 23 prediction models in the context of the Immrep 2022 TCr-epitope specificity workshop. This benchmark revealed that the use of paired-chain alpha-beta, as well as Cdr1/2 or V/J information, when available, improves classification obtained with Cdr3 data, independent of the underlying approach. In addition, we found that straight-forward distance-based approaches can achieve a respectable performance when compared to more complex machine-learning models. Finally, we highlight the need for a truly independent follow-up benchmark and provide recommendations for the design of such a next benchmark.

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Causal discovery from discrete data using hidden compact representation 18

Causal discovery from discrete data using hidden compact rep...

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： ruichu Cai Jie Qiao Kun Zhang Zhenjie Zhang Zhifeng Hao School of Computer Science Guangdong University of Technology China Department of philosophy Carnegie Mellon University Singapore R&D Yitu Technology Ltd. School of Computer Science Guangdong University of Technology China and School of Mathematics and Big Data Foshan University China

Causal discovery from a set of observations is one of the fundamental problems across several disciplines. For continuous variables, recently a number of causal discovery methods have demonstrated their effectiveness in distinguishing the cause from effect by exploring certain properties of the conditional distribution, but causal discovery on categorical data still remains to be a challenging problem, because it is generally not easy to find a compact description of the causal mechanism for the true causal direction. In this paper we make an attempt to find a way to solve this problem by assuming a two-stage causal process: the first stage maps the cause to a hidden variable of a lower cardinality, and the second stage generates the effect from the hidden representation. In this way, the causal mechanism admits a simple yet compact representation. We show that under this model, the causal direction is identifiable under some weak conditions on the true causal mechanism. We also provide an effective solution to recover the above hidden compact representation within the likelihood framework. Empirical studies verify the effectiveness of the proposed approach on both synthetic and real-world data.

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Abstractive Text Summarization by Incorporating reader Comments

arXiv

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arXiv 2018年

作者： Gao, Shen Chen, Xiuying Li, Piji ren, Zhaochun Bing, Lidong Zhao, dongyan Yan, rui Institute of Computer Science and Technology Peking University Beijing China Center for Data Science Peking University Beijing China Tencent AI Lab Shenzhen China *** Beijing China R&D Center Singapore Machine Intelligence Technology Alibaba DAMO Academy

In neural abstractive summarization field, conventional sequence-to-sequence based models often suffer from summarizing the wrong aspect of the document with respect to the main aspect. To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect. Unlike traditional abstractive summarization task, reader-aware summarization confronts two main challenges: (1) Comments are informal and noisy;(2) jointly modeling the news document and the reader comments is challenging. To tackle the above challenges, we design an adversarial learning model named reader-aware summary generator (rASG), which consists of four components: (1) a sequence-to-sequence based summary generator;(2) a reader attention module capturing the reader focused aspects;(3) a supervisor modeling the semantic gap between the generated summary and reader focused aspects;(4) a goal tracker producing the goal for each generation step. The supervisor and the goal tacker are used to guide the training of our framework in an adversarial manner. Extensive experiments are conducted on our large-scale real-world text summarization dataset, and the results show that rASG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. The experimental results also demonstrate the effectiveness of each module in our framework. We release our large-scale dataset for further research1. Copyright © 2018, The Authors. All rights reserved.

关键词： Text processing

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Cover Image, Volume 5, Issue 4

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WIrEs Water 2018年第4期5卷

作者： remko Uijlenhoet Aart Overeem Hidde Leijnse Hydrology and Quantitative Water Management Group Wageningen University & Research Wageningen The Netherlands R&D Observations and Data Technology Royal Netherlands Meteorological Institute (KNMI) De Bilt The Netherlands

The cover image, by remko Uijlenhoet et al, is based on Primer Opportunistic remote sensing of rainfall using microwave links from cellular communication networks , dOI: 10.1002/wat2.1289 . Image courtesy of identim/S...

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