检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Kang, Jingyang Liu, Chenhui Meng, Sikun Zhang, Mengge Han, Yibing Qi, Cheng Li, Mei Wang, Huachun Zhang, Tianzhen Cheeloo College of Medicine Shandong University 758 Hefei Road Shandong Qingdao266035 China Key Laboratory for Liquid Solid Structural Evolution and Processing of Materials Ministry of Education School of Materials Science and Engineering Shandong University 17923 Jingshi Road Shandong Jinan250061 China Department of Medical Data Science Center of Medical Innovation and Translational Research Osaka University Graduate School of Medicine Suita Yamadaoka 2-2 Osaka565-0871 Japan Cheeloo College of Medicine Shandong University 758 Hefei Road Shandong Qingdao266035 China Department of Oral and Maxillofacial Surgery Qi Lu Hospital of Shandong University Shandong Jinan250012 China Department of Stomatology Shandong Provincial Third Hospital Shandong University 11 Wuyingshan Middle Road Shandong Jinan250031 China

Titanium (Ti) is extensively utilized in dental implants due to its exceptional biocompatibility, corrosion resistance, and mechanical properties. Nonetheless, its biological inertness impedes its osteogenic and antimicrobial capabilities, which are essential for implant stability and infection prevention. This study explores the improvement of Ti's surface properties by coating it with polydopamine (PDA) and subsequently doping it with Cu²⁺ and Zn²⁺ ions. We first prepared Ti-PDA, followed by the fabrication of Ti-PDA-Cu and Ti-PDA-Cu/Zn samples. Material characterization revealed that Ti-PDA-Cu/Zn exhibits greater wettability compared to Ti and can consistently and steadily release Cu²⁺ and Zn²⁺. In vitro experiments demonstrated that Ti-PDA-Cu/Zn significantly enhanced the adhesion, proliferation, and mineralization of osteoblasts, increased the expression of osteogenic genes, and demonstrated exceptional antibacterial activity against Escherichia coli and Staphylococcus aureus. This dual-functional surface modification presents a promising approach to augment the osteogenic and antimicrobial properties of Ti implants. © 2024, The Authors. All rights reserved.

关键词： Escherichia coli

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Measurement of the Pr141(n,γ) cross section up to stellar s-process temperatures at the China Spallation Neutron Source Back-n facility

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Physical Review C 2023年第3期108卷 035802-035802页

作者： Xiankai Li Zhendong An Wei Jiang Jingyi Zhang Ruoran Bai Shaokun Liu Xinxiang Li Zhichao Zhu Gaole Yang Weiwei Qiu Zhengfa Liao Ziyue Zhuang Xiaoping Zhang Shengli Chen Chenchen Guo Erxi Xiao Xiao Fang Hongwei Wang Xinrong Hu Bing Jiang Wenqing Shen Jincheng Wang Jie Ren Xichao Ruan Wen Luo Haoyang Lan Zongwei Cao Xu Ma Yingdu Liu Pusen Wang Yi Yang Ping Su Xiangai Deng Wanbing He Yugang Ma Chunwang Ma Yuting Wang Zhitao Dai Ertao Li Pengqin He RenGuang Tang Tao Zhou Jing Wang Han Yi Yue Zhang Yonghao Chen Ruirui Fan Keqing Gao Qiang Li Kang Sun Zhixin Tan Minhao Gu Hantao Jing Jingyu Tang School of Nuclear Science and Technology University of South China Hengyang 421001 China School of Physics and Astronomy Sun Yat-sen University Zhuhai 519082 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China State Key Laboratory of Lunar and Planetary Sciences Macau University of Science and Technology Macau 999078 China CNSA Macau Center for Space Exploration and Science Macau 999078 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Spallation Neutron Source Science Center Dongguan 523803 China University of Chinese Academy of Sciences Beijing 100049 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai 519082 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing 102413 China State Key Laboratory of Nuclear Physics and Technology and CAPT Peking University Beijing 100871 China Beijing Laser Acceleration Innovation Center Huairou Beijing 101400 China Shool of Materials Science and Engineering Xiangtan University Xiangtan 411100 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Department of Nuclear Science and Technology Fudan University Shanghai 200433 China Institute of Particle and Nuclear Physics Henan Normal University Xinxiang 453007 China School of Physics Henan Normal University Xinxiang 453007 China Shenzhen Center Cancer hospital Chinese Academy of Medical Sciences Shenzhen 518100 China Institute for Advanced Study in Nuclear Energy and Safety College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China

In nuclear astrophysics, Pr141 is mainly produced by an s process, which has a neutron magic number (N=82) leading to its low capture cross section and strongly affecting the s process. Its neutron capture cross section is an important input parameter for nuclear astrophysical network calculations. According to the EXFOR database, the measurement precision for the Pr141(n,γ) cross section should be improved in the resolved and unresolved resonance region. The (n,γ) cross section of Pr141 was measured between 1 eV and 500 keV at the Back-n facility of the Chinese Spallation Neutron Source using the time-of-flight (TOF) method. The prompt γ rays were detected by four C6D6 liquid scintillator detectors, and the data was analyzed using the pulse-height weighting technique (PHWT). The majority of results are in agreement with the evaluated data libraries ENDF/B-VIII.0, JENDL-5.0, and TENDL-2021, with some significant exceptions for small resonances. The multilevel R-matrix Bayesian code sammy was used to extract the resonance parameters of Pr141 in the resolved resonance region. And the Maxwell average cross section (MACS) from kT=5 to 100 keV is calculated in the temperature range of the s-process nucleosynthesis model. In particular, at kT=30 keV the MACS value of Pr141 is 145.0±13.7 mb, which shows an obvious discrepancy with the Karlsruhe Astrophysical database of Nucleosynthesis in Stars (KADoNiS) recommended value 111.4±1.4 mb.

关键词： Neutron physics Nuclear astrophysics Nuclear reactions Nucleosynthesis in explosive environments Radiative capture Resonance reactions 90 ≤ A ≤ 149 Nuclear data analysis & compilation Radiation detectors

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The HuaBiao project:whole-exome sequencing of 5000 Han Chinese individuals

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Journal of Genetics and Genomics 2021年第11期48卷 1032-1035页

作者： Meng Hao Weilin Pu Yi Li Shaoqing Wen Chang Sun Yanyun Ma Hongxiang Zheng Xingdong Chen Jingze Tan Guoqing Zhang Menghan Zhang Shuhua Xu Yi Wang Hui Li Jiucun Wang Li Jin State Key Laboratory of Genetic Engineering Collaborative Innovation Center for Genetics and DevelopmentHuman Phenome InstituteFudan UniversityShanghai 200433China State Key Laboratory of Genetic Engineering Collaborative Innovation Center for Genetics and DevelopmentHuman Phenome InstituteInstitute for Six-sector EconomyFudan UniversityShanghai 200433China Ministry of Education Key Laboratory of Contemporary Anthropology Department of Anthropology and Human GeneticsSchool of Life SciencesInstitute of Archaeological ScienceFudan UniversityShanghai 200433China Ministry of Education Key Laboratory of Contemporary Anthropology Department of Anthropology and Human GeneticsSchool of Life SciencesFudan UniversityShanghai 200433China Ministry of Education Key Laboratory of Contemporary Anthropology Department of Anthropology and Human GeneticsSchool of Life SciencesInstitute for Six-sector EconomyFudan UniversityShanghai 200433China Taizhou Institute of Health and Sciences Fudan UniversityTaizhouJiangsu 225300China Key Laboratory of Computational Biology Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Ministry of Education Key Laboratory of Contemporary Anthropology Department of Anthropology and Human GeneticsSchool of Life SciencesHuman Phenome InstituteFudan UniversityShanghai 200433China

Next-generation sequencing technologies have significantly accelerated the identification of disease-causing mutations and facilitated the emergence of personalized medicine(Genomes Project Consortium et al.,2015;Goodwin et al.,2016;Sirugo et al.,2019).In comparison with whole-genome sequencing,whole-exome sequencing(WES),which covers the coding regions of the genome,offers a cost-efficacy *** provides deeper sequencing depth(>100)and allows the more accurate detection of rare variants that are tailored for clinical applications(Lek et al.,2016).

关键词： exo cost rare

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A pulsed lock-in method for ensemble nitrogen-vacancy center magnetometry

arXiv

引用

arXiv 2021年

作者： Zhang, Jixing Yuan, Heng Liu, Tianzheng Xu, Lixia Bian, Guodong Fan, Pengcheng Li, Mingxin School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100191 China Research Institute of Frontier Science Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China Beijing Academy of Quantum Information Sciences Beijing100193 China

This article proposes a scheme for nitrogen-vacancy (NV) center magnetometry that combines the advantages of lock-in detection and pulse-type scheme. The optimal conditions, optimal sensitivity, and noise-suppression capability of the proposed method are compared with those of the conventional methods from both theoretical and simulation points of view. Through experimental measurements, a four-time improvement in sensitivity and 60-times improvement in minimum resolvable magnetic field (MRMF) was obtained. By using a confocal experiment setup, proposed scheme achieves a sensitivity of 3 nT·Hz-1/2 and a MRMF of 100 pT. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetometers

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Differentially private federated knowledge graphs embedding

arXiv

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arXiv 2021年

作者： Peng, Hao Li, Haoran Song, Yangqiu Zheng, Vincent Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Computer Science and Engineering HKUST Hong Kong AI Group Webank Co. Ltd School of Cyber Science and Technology Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518066 China SKLSDE Beihang University Beijing100191 China

Knowledge graph embedding plays an important role in knowledge representation, reasoning, and data mining applications. However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot make full use of the data from different knowledge domains while preserving the privacy of exchanged data. In addition, the centralized embedding model may not scale to the extensive real-world knowledge graphs. Therefore, we propose a novel decentralized scalable learning framework, Federated Knowledge Graphs Embedding (FKGE), where embeddings from different knowledge graphs can be learnt in an asynchronous and peer-to-peer manner while being privacy-preserving. FKGE exploits adversarial generation between pairs of knowledge graphs to translate identical entities and relations of different domains into near embedding spaces. In order to protect the privacy of the training data, FKGE further implements a privacy-preserving neural network structure to guarantee no raw data leakage. We conduct extensive experiments to evaluate FKGE on 11 knowledge graphs, demonstrating a significant and consistent improvement in model quality with at most 17.85% and 7.90% increases in performance on triple classification and link prediction tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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Shape-aware contrastive deep supervision for esophageal tumor segmentation from CT scans

Shape-aware contrastive deep supervision for esophageal tumo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiangguo Jin Hui Cui Changming Sun Jiapeng Huang Ping Xuan Yiyue Xu Linlin Wang Leilei Cao Leyi Wei Ran Su School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China Department of Computer Science and Information Technology La Trobe University Melbourne Australia CSIRO Data61 Sydney Australia Department of Computer Science School of Engineering Shantou University Guangdong China Department of Radiation Oncology Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Shandong China Innovation Center of Yangtze River Delta Zhejiang University Zhejiang China School of Software Shandong University Shandong China School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

Accurate tumor segmentation is crucial for esophageal cancer radiotherapy treatment planning. The low contrast among the esophagus, tumors, and surrounding tissues, and irregular tumor shapes limit the performance of automatic segmentation methods. In this paper, we aim to exploit the irregular shapes of tumors to facilitate accurate segmentation. We propose a simple and pluggable shape-aware contrastive deep supervision network (SCDSNet) with shape-aware regularization and voxel-to-voxel contrastive deep supervision. Specifically, the shape-aware regularization with an uncertainty minimization strategy encourages the precise predictions of an additional shape-aware head. The voxel-to-voxel contrastive deep supervision enhances the multi-scale shape-tumor contrast for better voxel-to-voxel prediction of shapes. The proposed method is simple and highly pluggable, which can easily be extended to other frameworks. Further, we establish a large in-house dataset on esophageal cancer to validate the effectiveness of our proposed method. The quantitative and qualitative experimental results demonstrate the effectiveness of SCDSNet on the esophageal cancer dataset.

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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Distributed cooperative guidance for multi-missile system with time-varying delays and switching topologies

Distributed cooperative guidance for multi-missile system wi...

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第三十九届中国控制会议

作者： Siquan Zhou Jie Ren Yong Heng Xiwang Dong Qingdong Li School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing Aerospace System Engineering Research Institute Beijing Institute of Electronic System Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Distributed cooperative guidance problems for multi-missile system with time-varying delays and switching topologies are investigated. Firstly, considering the complex communication environment, a two-step cooperative guidance strategy is proposed to achieve the simultaneous attack under time-varying delays and switching topologies. In the first step, a distributed cooperative guidance law using neighboring information is designed to coordinate range-to-go and leading angle based on feedback linearization and consensus theory. The second step starts while multi missiles disconnect from each other and hit the target using PNG（Proportional Navigation Guidance）. Then, the algorithm with three steps is presented to select the parameters in the cooperative guidance law. And the stability of the multi-missile system is proven using Lyapunov-Krasovskii theory. Finally, a numerical simulation is given to demonstrate the effectiveness of the proposed cooperative guidance strategy.

关键词： Cooperative guidance Distributed strategy Consensus theory Time-varying delays Switching topologies

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BCDAS: Blockchain-assisted Classifiable data Auditing Scheme with Dynamic Operations

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Blockchain: Research and Applications 2025年

作者： Yuxin Cui Chen Wang Bing Yang Wei Tong Jian Shen School of Information Science and Engineering (School of Cyber Science and Technology) Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang China Zhejiang Provincial Key Laboratory of Digital Fashion and Data Governance Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang China Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing 312000 Zhejiang China School of Computer Science Nanjing University of Information Science Technology Nanjing 210044 Jiangsu China

As cloud computing gains widespread adoption, cloud storage services have become the primary means of data management for users. Authenticated data structures (ADS) are a novel computational model designed to address data authentication problems in distributed environments. With the growing demand for robust data security, vulnerability detection in storage systems has become a critical area of focus to ensure resilience against potential threats. However, traditional ADS, while ensuring consistency between cloud data and source data, have limitations in handling dynamic data operations on multiple types of files, storage space expansion, and single-point failure issues. To tackle these issues, this paper proposes a blockchain-assisted classifiable data auditing scheme with dynamic operations. First, trapdoor hash functions are used to construct a binary tree. During dynamic data operations, the impact of hash updates is confined to a subset of nodes, ensuring global stability and reducing computational resource consumption. Second, innovative data structures and verification mechanisms are introduced, reducing the risk of single-point failures by decentralizing the dependency on verification paths. Finally, data types are confirmed based on data identifiers, and corresponding path information is recorded, enabling efficient and rapid dynamic operations on specific types of files within multi-source data. Both security analysis and performance assessment demonstrate that BCDAS conducts data auditing for with reliability and efficiency.

关键词： data auditing Dynamic operations Blockchain Trapdoor hash function

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Ecology in the digital world of Wikipedia

arXiv

引用

arXiv 2021年

作者： Ogushi, Fumiko Kertész, János Kaski, Kimmo Shimada, Takashi Center for Mathematical Modeling and Data Science Osaka University Toyonaka Osaka560-8531 Japan Department of Network and Data Science Central European University Quellenstrasse 51 Vienna1100 Austria Department of Computer Science Aalto University School of Science P. O. Box 15500 Espoo Finland Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United States Mathematics and Infomatics Center University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Systems Innovation Graduate School of Engineering University of Tokyo

Wikipedia, a paradigmatic example of online knowledge space is organized in a collaborative, bottom-up way with voluntary contributions, yet it maintains a level of reliability comparable to that of traditional encyclopedias. The lack of selected professional writers and editors makes the judgement about quality and trustworthiness of the articles a real challenge. Here we show that a self-consistent metrics for the network defined by the edit records captures well the character of editors’ activity and the articles’ level of complexity. Using our metrics, one can better identify the human-labeled high-quality articles, e.g., "featured" ones, and differentiate them from the popular and controversial articles. Furthermore, the dynamics of the editor-article system is also well captured by the metrics, revealing the evolutionary pathways of articles and diverse roles of editors. We demonstrate that the collective effort of the editors indeed drives to the direction of article improvement. © 2021, CC BY.

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