检索结果-南通市图书馆

China CDC weekly 2022年第4期4卷 71-73页

作者： Ying Shen Da Huo TONG Yu Quanyi Wang Beijing Center for Disease Prevention and Control BeijingChina

For a long time,vaccination and herd immunity were considered to be the magic solution for controlling coronavirus disease 2019(COVID-19).However,the emergence of new variants altered people’s expectations and prolonged the pandemic duration,especially the Omicron strain with substantially increased transmissibility and decreased vaccine efficacy.Therefore,we are in urgent need of a practical solution to resume regular life to some extent,while mitigating COVID-19 risks in the tight race between vaccine rollouts and virus variation.This commentary proposed that bubble strategy(or closed loop management),utilized in Tokyo 2020 Olympic Games and to be implemented in Beijing 2022 Winter Games,could serve as a novel technique of nonpharmaceutical interventions in coping with such a situation.

关键词： Regular resume utilized

The Non-Inclusive Diagnosability of Regular Graphs

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Journal of the Operations Research Society of China 2023年第4期11卷 891-910页

作者： yu-Long Wei tong-tong Ding Min Xu School of Mathematical Sciences Beijing Normal UniversityLaboratory of Mathematics and Complex SystemsMinistry of EducationBeijing100875China Department of Mathematics Taiyuan University of TechnologyTaiyuan030024China

Fault diagnosis is an important area of study with regard to the design and maintenance of multiprocessor systems.A new measure for fault diagnosis of systems,namely,non-inclusive diagnosability(denoted by MM^(*)),was proposed by Ding et al.In this paper,we establish the non-inclusive diagnosability of a class of regular graphs under the PMC model and the MM^(*)model.As applications,the non-inclusive diagnosabilities of hypercubes,hierarchical hypercubes,folded hypercubes,star graphs,bubble-sort graphs,pancake graphs and dual cubes are determined under the PMC model and the[Math Processing Error]model.

关键词： PMC model MM^(*)model Regular graph Fault diagnosability

Ovule initiation:the essential step controlling offspring number in Arabidopsis

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Journal of Integrative Plant Biology 2022年第8期64卷 1469-1486页

作者： Shi-Xia yu yu-tong Jiang Wen-Hui Lin The Joint International Research Laboratory of Metabolic and Developmental Sciences School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China Shanghai Collaborative Innovation Center of Agri‐Seeds/Joint Center for Single Cell Biology Shanghai Jiao Tong UniversityShanghai 200240China School of Agriculture and Biology Shanghai Jiao Tong UniversityShanghai 200240China

Seed is the offspring of angiosperms.Plants produce large numbers of seeds to ensure effective reproduction and survival in varying environments.Ovule is a fundamentally important organ and is the precursor of the seed.In Arabidopsis and other plants characterized by multi-ovulate ovaries,ovule initiation determines the maximal ovule number,thus greatly affecting seed number per fruit and seed yield.Investigating the regulatory mechanism of ovule initiation has both scientific and economic significance.However,the genetic and molecular basis underlying ovule initiation remains unclear due to technological limitations.Very recently,rules governing the multiple ovules initiation from one placenta have been identified,the individual functions and crosstalk of phytohormones in regulating ovule initiation have been further characterized,and new regulators of ovule boundary are reported,therefore expanding the understanding of this field.In this review,we present an overview of current knowledge in ovule initiation and summarize the significance of ovule initiation in regulating the number of plant offspring,as well as raise insights for the future study in this field that provide potential routes for the improvement of crop yield.

关键词： asynchronous initiation of ovule primordia early and late ovule initiation placenta elongation plant hormones seed number per fruit

Biotoxicity dynamic change and key toxic organics identification of coal chemical wastewater along a novel full-scale treatment process

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Journal of Environmental Sciences 2024年第4期138卷 277-287页

作者： Weiwei Ma Xiaoqi Zhang Hongjun Han Xueqing Shi Qiaoping Kong TONG Yu Fei Zhao School of Environmental and Municipal Engineering Qingdao University of TechnologyQingdao 266520China State Key Laboratory of Urban Water Resource and Environment Harbin Institute of TechnologyHarbin 150090China

It is particularly important to comprehensively assess the biotoxicity variation of industrial wastewater along the treatment process for ensuring the water environment security.However,intensive studies on the biotoxicity reduction of industrial wastewater are still limited.In this study,the toxic organics removal and biotoxicity reduction of coal chemical wastewater(CCW)along a novel full-scale treatment process based on the pretreatment process-anaerobic process-biological enhanced(BE)process-anoxic/oxic(A/O)process-advanced treatment process was evaluated.This process performed great removal efficiency of COD,total phenol,NH_(4)^(+)-N and total nitrogen.And the biotoxicity variation along the treatment units was analyzed from the perspective of acute biotoxicity,genotixicity and oxidative damage.The results indicated that the effluent of pretreatment process presented relatively high acute biotoxicity to Tetrahymena thermophila.But the acute biotoxicity was significantly reduced in BE-A/O process.And the genotoxicity and oxidative damage to Tetrahymena thermophila were significantly decreased after advanced treatment.The polar organics in CCW were identified as the main biotoxicity contributors.Phenols were positively correlated with acute biotoxicity,while the nitrogenous heterocyclic compounds and polycyclic aromatic hydrocarbons were positively correlated with genotoxicity.Although the biotoxicity was effectively reduced in the novel full-scale treatment process,the effluent still performed potential biotoxicity,which need to be further explored in order to reduce environmental risk.

关键词： Acute biotoxicity Genotoxicity Oxidative damage Toxic organics Coal chemical wastewater Novel full-scale treatment process

Overview of enhancing biological treatment of coal chemical wastewater:New strategies and future directions

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Journal of Environmental Sciences 2024年第1期135卷 506-520页

Coal chemical wastewater(CCW)is a type of refractory industrial wastewater,and its treatment has become the main bottleneck restricting the sustainable development of novel coal chemical industry.Biological treatment is considered as an economical,effective and environmentally friendly technology for CCW treatment.However,conventional biological process is difficult to achieve the efficient removal of refractory organics because of CCW with the characteristics of composition complexity and high toxicity.Therefore,seeking the novel enhancement strategy appears to be a favorable solution for enhancing biological treatment efficiency of CCW.This review focuses on presenting a comprehensive picture about the exogenous enhancement strategies for CCW biological treatment.The performance and potential application of exogenous enhancement strategies,including co-metabolic substrate enhancement,biofilm filler enhancement,adsorption material enhancement and conductive mediator enhancement,were expounded.Meanwhile,the enhancing mechanisms of different strategies were comprehensively discussed from a biological perspective.Furthermore,the prospects of enhancement strategies based on the engineering performance,economic cost and environmental impact(3E)evaluation were introduced.And novel enhancement strategy based on“low carbon emissions”,“resource recycling”and“water environment security”in the context of carbon neutrality was proposed.Taken together,this review provides technical reference and new direction to facilitate the regulation and optimization of typical industrial wastewater biological treatment.

关键词： Coal chemical wastewater Biological treatment Exogenous enhancement Refractory organics 3E evaluation

Semantic segmentation-assisted instance feature fusion for multi-level 3D part instance segmentation

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Computational Visual Media 2023年第4期9卷 699-715页

作者： Chun-yu Sun Xin tong Yang Liu Institute for Advanced Study Tsinghua UniversityBeijing 100084China Microsoft Research Asia Beijing 100080China

Recognizing 3D part instances from a 3D point cloud is crucial for 3D structure and scene understanding.Several learning-based approaches use semantic segmentation and instance center prediction as training tasks and fail to further exploit the inherent relationship between shape semantics and part instances.In this paper,we present a new method for 3D part instance segmentation.Our method exploits semantic segmentation to fuse nonlocal instance features,such as center prediction,and further enhances the fusion scheme in a multi-and cross-level way.We also propose a semantic region center prediction task to train and leverage the prediction results to improve the clustering of instance points.Our method outperforms existing methods with a large-margin improvement in the PartNet benchmark.We also demonstrate that our feature fusion scheme can be applied to other existing methods to improve their performance in indoor scene instance segmentation tasks.

关键词： 3D part instance segmentation feature fusion 3D deep learning

Tumor and dendritic cell dual-targeting nanocarriers maximize the therapeutic potential of IDO1 inhibitor in vivo

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Nano Research 2022年第10期15卷 9204-9214页

作者： TONG Yu Xiangyu Jin Fangying yu Xiqin Yang Yingping Zeng Tingting Meng Hong yuan Fuqiang Hu Ocean College Zhejiang UniversityZhoushan 316021China College of Pharmaceutical Sciences Zhejiang UniversityHangzhou 310058China

Researches on indoleamine-2,3-dioxygenase-1(IDO1),a neoplastic pathogenesis-related protein,have provided a new angle of view to regulate malignancy-related immunosuppression.However,the therapeutic efficacy of IDO1 inhibitors is subject to key limitations as both cancer and dendritic cells tend to be trapped in the IDO1-mediated immune dysfunction,which poses challenges to the inhibitory potency of drug regimens in multiple targets.Here,we report on the fabrication technique of a biomimetic nanocarrier that is endowed with the whole array of cancer cell membrane proteins for encapsulating the most used IDO1 probe indoximod(IND).By fully utilizing the homologous adhesion proteins and antigenic motifs on cytomembrane,these nanoparticulate particles are capable of infiltrating tumors and actively accumulating in cancer and dendritic cells,as well as hitching a ride on dendritic cells to tumor-draining lymph nodes.Ultimately,by increasing the distribution of drugs in both tumor cells and dendritic cells in tumor-draining lymph nodes,these formulations greatly enhance the efficacy of IND without the aid of chemotherapeutic drugs,achieving substantial control of tumor growth.Overall,this leverage of bionanotechnology maximizes the therapeutic potential of IND and can provide a theoretical reference for the clinical application of IDO1 inhibitors.

关键词： tumor cells dendritic cells dual-targeting nanocarriers indoleamine-2 3-dioxygenase-1(IDO1)

A Fiber Optic Sensor for the Simultaneous Measurement of Dual-parameter Based on Hydrogel-immobilized Enzyme Complex

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Journal of Wuhan University of Technology(Materials Science) 2023年第6期38卷 1311-1318页

作者： tong Yilin ZHANG yu HAN Xuecai yu Kan BAO Jiaqi Faculty of Information Science and Technology Wenhua CollegeWuhan 430074China Hubei Institute of Measurement and Testing Technology Wuhan 430223China

A novel fiber optic sensor based on hydrogel-immobilized enzyme complex was developed for the simultaneous measurement of dual-parameter,the leap from a single parameter detecting fiber optic sensor to a fiber optic sensor that can continuously detect two kinds of parameters was achieved.By controlling the temperature from high to low,the function of fiber sulfide sensor and fiber DCP sensor can be realized,so as to realize the continuous detection of dual-parameter.The different variables affecting the sensor performance were evaluated and optimized.Under the optimal conditions,the response curves,linear detection ranges,detection limits and response times of the dual-parameter sensor for testing sulfide and DCP were obtained,respectively.The sensor displays high selectivity,good repeatability and stability,which have good potentials in analyzing sulfide and DCP concentration of practical water samples.

关键词： hydrogel-immobilized enzyme complex dual-parameter simultaneous measurement fiber optic sensor

Chord length sampling correction analysis for dispersion fuel in Monte Carlo simulation

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Nuclear Science and Techniques 2024年第4期35卷 57-64页

作者： Zhao-yu Liang Ding She yu-tong Wen Lei Shi Institute of Nuclear and New Energy Technology(INET) Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of EducationTsinghua UniversityBeijing 100084China

Dispersion fuels,knowned for their excellent safety performance,are widely used in advanced reactors,such as hightemperature gas-cooled reactors.Compared with deterministic methods,the Monte Carlo method has more advantages in the geometric modeling of stochastic media.The explicit modeling method has high computational accuracy and high computational cost.The chord length sampling(CLS)method can improve computational efficiency by sampling the chord length during neutron transport using the matrix chord length?s probability density function.This study shows that the excluded-volume effect in realistic stochastic media can introduce certain deviations into the CLS.A chord length correction approach is proposed to obtain the chord length correction factor by developing the Particle code based on equivalent transmission probability.Through numerical analysis against reference solutions from explicit modeling in the RMC code,it was demonstrated that CLS with the proposed correction method provides good accuracy for addressing the excludedvolume effect in realistic infinite stochastic media.

关键词： Stochastic media Monte Carlo Chord length sampling Excluded-volume effect Chord length correction

DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 928-938页

作者： Shao-Wu Zhang Jing-yu Xu tong Zhang MOE Key Laboratory of Information Fusion Technology School of AutomationNorthwestern Polytechnical UniversityXi’an 710072China

Identification of cancer driver genes plays an important role in precision oncology research,which is helpful to understand cancer initiation and progression.However,most existing computational methods mainly used the protein–protein interaction(PPI)networks,or treated the directed gene regulatory networks(GRNs)as the undirected gene–gene association networks to identify the cancer driver genes,which will lose the unique structure regulatory information in the directed GRNs,and then affect the outcome of the cancer driver gene identification.Here,based on the multi-omics pan-cancer data(i.e.,gene expression,mutation,copy number variation,and DNA methylation),we propose a novel method(called DGMP)to identify cancer driver genes by jointing directed graph convolutional network(DGCN)and multilayer perceptron(MLP).DGMP learns the multi-omics features of genes as well as the topological structure features in GRN with the DGCN model and uses MLP to weigh more on gene features for mitigating the bias toward the graph topological features in the DGCN learning process.The results on three GRNs show that DGMP outperforms other existing state-of-the-art methods.The ablation experimental results on the Dawn Net network indicate that introducing MLP into DGCN can offset the performance degradation of DGCN,and jointing MLP and DGCN can effectively improve the performance of identifying cancer driver genes.DGMP can identify not only the highly mutated cancer driver genes but also the driver genes harboring other kinds of alterations(e.g.,differential expression and aberrant DNA methylation)or genes involved in GRNs with other cancer genes.The source code of DGMP can be freely downloaded from https://***/NWPU-903PR/DGMP.

关键词： Driver gene Directed graph convolutional network Multilayer perceptron Gene regulatory network Multi-omics data