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Toward a unified information framework for cell atlas assembly

National Science Review 2022年第3期9卷 7-10页

作者： Sijie Chen Yanting Luo Haoxiang Gao Fanhong Li Jiaqi Li Yixin Chen Renke You Hairong Lv Kui Hua Rui Jiang Xuegong Zhang Bioinformatics Division of BNRIST and Department of Automation MOE Key Lab of BioinformaticsTsinghua University Fuzhou Institute of Data Technology School of Medicine Tsinghua University School of Life Sciences Center for Synthetic and Systems Biology Tsinghua University

Cells are the basic structural and functional unit of the human body. Virtually all cells of a human body possess the same genome, but they exhibit colorful heterogeneities in phenotypes and *** heterogeneities includ...

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Time series comparisons in deep space network

arXiv

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arXiv 2021年

作者： Yun, Kyongsik Verma, Rishi Rebbapragada, Umaa NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Technologist Autonomous Systems Division Software Systems Engineer Mission Systems and Operations Division Data Scientist Mission Systems and Operations Division

The Deep Space Network (DSN) is NASA’s international array of antennas that support interplanetary spacecraft missions. DSN provides radar and radio astronomy observations that enhance our understanding of the solar system and the larger universe. A track is a block of multi-dimensional time series from the beginning to end of DSN communication with the target spacecraft, containing thousands of monitor data items lasting several hours at a frequency of 0.2-1Hz. Monitor data on each track reports on the performance of specific spacecraft operations and the DSN itself. DSN is receiving signals from 32 spacecraft across the solar system. DSN has pressure to reduce costs while maintaining the quality of support for DSN mission users. DSN Link Control Operators (LCOs) need to simultaneously monitor multiple tracks and identify anomalies in real time. DSN has seen that as the number of missions increases, the data that needs to be processed increases over time. In this project, we look at the last 8 years of data for analysis. Any anomaly in the track indicates a problem with either the spacecraft, DSN equipment, or weather conditions. DSN operators typically write Discrepancy Reports (DR) for further analysis. It is recognized that it would be quite helpful to identify 10 similar historical tracks out of the huge database to quickly find/match anomalies. This tool has three functions: (1) identification of the top 10 similar historical tracks, (2) detection of anomalies compared to the reference normal track, and (3) comparison of statistical differences between two given tracks. The requirements for these features were confirmed by survey responses from 21 DSN operators and engineers. The preliminary machine learning model has shown promising performance (AUC=0.92). We plan to increase the number of data sets and perform additional testing to improve performance further before its planned integration into the track visualizer interface to assist DSN field operators and

关键词： Solar system

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NFDI4Energy Task Area 4: FAIR data for Energy System Research

NFDI4Energy Task Area 4: FAIR Data for Energy System Researc...

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2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Wein, Amanda Reinkensmeier, Jan Weidlich, Anke Lilliestam, Johan Hagenmeyer, Veit Richter, Mascha Auer, Sören Nieße, Astrid Lehnhoff, Sebastian OFFIS E.V. RandD Division Energy Escherweg 2 Oldenburg26121 Germany Friedrichstr. 39 Freiburg79110 Germany Friedrich-Alexander-Universität Erlangen-Nörnberg Lehrstuhl for Sustainability Transition Policy Lange Gasse 20 Nörnberg90403 Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Kaiserstr. 89 Karlsruhe76131 Germany Reiner Lemoine Institut GGmbH Research Unit Transformation of Energy Systems Rudower Chaussee 12 Berlin12489 Germany Technische Informationsbibliothek Data Science and Digital Libraries Research Group Welfengarten 1 B Hannover30167 Germany Carl von Ossietzky Universität Oldenburg Department for Informatik Ammerländer Heerstr. 114-118 Oldenburg26129 Germany

ISBN: (纸本)9783885797319

The NFDI4Energy consortium will create a research data infrastructure for energy system research, emphasizing the openness and FAIRness of data and models in this research domain. Within the consortium, Task Area 4 focuses on the development of resources and services that will provide a semantic layer for the overall platform built by NFDI4Energy. The team of this Task Area will produce artifacts including a domain ontology, metadata standards, a knowledge graph, a Persistent Identifier service, and integration infrastructure to join these artifacts to the NFDI4Energy platform. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Metadata

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Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs

arXiv

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arXiv 2024年

作者： Zhang, Tianyu Hou, Chengbin Jiang, Rui Zhang, Xuegong Zhou, Chenghu Tang, Ke Lv, Hairong The Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China The School of Computer Science and Engineering Fuyao Institute of Technology Fuzhou350109 China The State Key Laboratory of Resources and Environmental Information Systems Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Fuzhou Institute of Data Technology Fuzhou350200 China

Node Importance Estimation (NIE) is a task of inferring importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests of NIE have been dedicating to knowledge graphs for predicting future or missing node importance scores. Existing state-of-the-art NIE methods train the model by available labels, and they consider every interested node equally before training. However, the nodes with higher importance often require or receive more attention in real-world scenarios, e.g., people may care more about the movies or webpages with higher importance. To this end, we introduce Label Informed ContrAstive Pretraining (LICAP) to the NIE problem for being better aware of the nodes with high importance scores. Specifically, LICAP is a novel type of contrastive learning framework that aims to fully utilize the continuous labels to generate contrastive samples for pretraining embeddings. Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores. The contrastive samples are generated from those bins, and are then used to pretrain node embeddings of knowledge graphs via a newly proposed Predicate-aware Graph Attention Networks (PreGAT), so as to better separate the top nodes from non-top nodes, and distinguish the top nodes within top bin by keeping the relative order among finer bins. Extensive experiments demonstrate that the LICAP pretrained embeddings can further boost the performance of existing NIE methods and achieve the new state-of-the-art performance regarding both regression and ranking metrics. The source code for reproducibility is available at https://***/zhangtia16/LICAP Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Optimizing Carbon Emissions in Green Logistics for Time-Dependent Routing

SSRN

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SSRN 2024年

作者： Liu, Yiming Yu, Yang Roberto, Baldacci Tang, Jiafu Sun, Wei State Key Laboratory of Synthetic Automation for Process Industries Institute of Intelligent Data and Systems Engineering Northeastern University Shenyang110819 China Dalian University of Technology School of Economics and Management Liaoning Dalian116024 China Division of Engineering Management and Decision Sciences College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation P.O. Box 34110 Doha Qatar College of Management Science and Engineering Dongbei University of Finance and Economics Shahekou Dalian116026 China Business School Liaoning University Shenyang110316 China

This paper considers a green vehicle routing problem termed the time-dependent green vehicle routing problem with time windows (TDGVRPTW). The TDGVRPTW is an extension of the green vehicle routing problem with time windows in green logistics. It considers time-dependent vehicle speed and aims to minimize carbon emissions. Since the travel times and carbon emissions between locations depend on the departure time from the starting location, optimizing carbon emissions requires determining the optimal departure times from both the depot and customers. This paper presents a branch-price-and-cut (BPC) algorithm to solve the TDGVRPTW. The problem is formulated based on a set-partitioning model. To solve the pricing problem associated with the set-partitioning model, we first define backward non-dominated time-dependent arcs and optimal timed routes. We also introduce three types of routes, forward, backward, and patchwork optimal timed routes, and analyze their characteristics. Then, we introduce a method to adjust the times in backward and patchwork optimal timed routes from the latest to the earliest. We prove that this method guarantees correctness and effectively reduces complexity. We propose a bidirectional labeling algorithm to generate routes. The pricing problem employs state-of-the-art techniques, including limited memory subset row cuts (lm-SRCs), ng-route relaxation, and bucket graphs to enhance the algorithm’s efficiency. The BPC algorithm is tested on a set of instances derived from benchmark instances in the literature. The results demonstrate the effectiveness of the proposed algorithm in solving the TDGVRPTW. © 2024, The Authors. All rights reserved.

关键词： Travel time

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uniLIVER: a human liver cell atlas for data-driven cellular state mapping

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Journal of Genetics and Genomics 2025年

作者： Wu, Yanhong Fan, Yuhan Miao, Yuxin Li, Yuman Du, Guifang Chen, Zeyu Diao, Jinmei Chen, Yu-Ann Ye, Mingli You, Renke Chen, Amin Chen, Yixin Li, Wenrui Guo, Wenbo Dong, Jiahong Zhang, Xuegong Wang, Yunfang Gu, Jin MOE Key Lab of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing 100084 China Hepato-Pancreato-Biliary Center Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 102218 China Clinical Translational Science Center Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 102218 China Fuzhou Institute of Data Technology Fujian Fuzhou 350207 China Center for Synthetic and Systems Biology School of Life Sciences and School of Medicine Tsinghua University Beijing 100084 China

The liver performs several vital functions such as metabolism, toxin removal, and glucose storage through the coordination of various cell types. With the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER (http://***/database/uniliver) by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Moreover, we introduce LiverCT, a novel machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of “variant” cellular states analogy to the sequence variants in genomic analysis. Applying LiverCT on liver cancer datasets, we find that the “deviated” states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma, and the enrichments of tumor cells with the hepatocyte-cholangiocyte “intermediate” states significantly indicate poor prognosis. Besides, we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis. This reference atlas mapping framework can also be extended to any other tissues. © 2025 institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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A VisualSfM based Rapid 3-D Modeling Framework using Swarm of UAVs

A VisualSfM based Rapid 3-D Modeling Framework using Swarm o...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Cody Lee Lundberg Hakki Erhan Sevil Aditya Das Research Engineer I University of Texas at Arlington Research Institute (UTARI) Research Scientist III University of Texas at Arlington Research Institute (UTARI) Division Lead Automation and Intelligent Systems Division University of Texas at Arlington Research Institute (UTARI)

This paper presents an unmanned aerial vehicle (UAV) swarm based rapid 3D modeling framework for objects of interest in unknown environments using Visual Structure from Motion (VisualSfM). The presented technology framework is driven by the goal to provide rapid and accurate situational awareness for mission planning in applications such as disaster control/recovery, search and rescue, industrial infrastructure monitoring, large inventory accounting, military asset movement and so on. Our approach to deliver the necessary and sufficient amount of information using a hierarchical swarm of UAVs. Processed information from UAV onboard sensors is synthesized into easy-to-interpret three dimensional (3D) contents for particular areas of interest. This paper presents the preliminary proof of concept experiments for the presented 3D modeling framework, tested with a heterogeneous swarm of UAVs of varying capabilities. The goal of the presented research and development effort is to deliver a rapid and enhanced surveillance/reconnaissance capability with selective 3D detailing and classification of only the areas of interest in the global map and not the entire map, thereby limiting the computational cost and cognitive load on human agents in the team.

关键词： Three-dimensional displays Task analysis Sensors Robot kinematics Solid modeling Unmanned aerial vehicles

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Precision Medicine:What Challenges Are We Facing?

引用

Genomics, Proteomics & Bioinformatics 2016年第5期14卷 253-261页

作者： Yu Xue Eric-Wubbo Lameijer Kai Ye Kunlin Zhang Suhua Chang Xiaoyue Wang Jianmin Wu Ge Gao Fangqing Zhao Jian Li Chunsheng Han Shuhua Xu Jingfa Xiao Xuerui Yang Xiaomin Ying Xuegong Zhang Wei-Hua Chen Yun Liu Zhang Zhang Kun Huang Jun Yu MOE Key Lahoratory of Molecular Biophysics College of Life Science and Technology and the Collaborative Innovation Center for Brain ScienceHuazhong University of Science and TechnologyWuhan 430074China School of Electronic and Information Engineering.Xi'an Jiaotong University.Xi'an 710049 China CAS Key Laboratory of Mental Health Institute of PsychologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of'Medical Molecular Biology Institute of Basic Medical SciencesChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100005China MOE Beijing Key Laboratory of Carcinogenesis and Translatiooal ResearchCenter for Cancer BioinformaticsPeking University Cancer Hospital&InstituteBeijing 100142China Center for Bioinformatics State Key Laboratory of Protein and Plant Gene ResearchSchool of Life SciencesPeking UniversityBeijing 100871China Beijing Institutes of Life Science.Chinese Academy of Sciences Beijing 100101China MOE Key Lahoratory of Developnlental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing 210096China State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology Chinese Academy of SciencesBeijing 100101China Max Planck Independent Research Group on Population Genomics CAS-MPG Partner Institute for Computational Biology(PICB)Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200031China CAS Ker Laboratory of Genome Sciences and Information Chinese Academy of Sciences.Beijing 100101China MOE Key Laboratory of Bioinformatics School of Life Sciences Center for Synthetic and Systems BiologyTsinghua UniversityBeijing 100084China Computational Omics Lahoratory Center of Computational BiologyBeijing Institute of Basic Medical SciencesBeijing 100850China Bioinformatics Division TNLIST and MOE Key Laboratory for Bioinformatics Department of AutomationTsinghua UniversityBeijing 100084China Department of Bioinformatics and Systems Biology College of LifeScience and Technol

Following the publication of the US National Research Council （N RC） report ＂ Toward PrecMon Medicine.＂ Building a Knowledge Network for Biomedical Research and a New Taxonomy of Diseases＂ in 2011 [1], several n... 详细信息

关键词： PMI Precision Medicine data We

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