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Inside Cover: A novel in vivo Cerenkov luminescence image-guided surgery on primary and metastatic colorectal cancer (J. Biophotonics 3/2020)

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Journal of Biophotonics 2020年第3期13卷

作者： Zeyu Zhang Yawei Qu Yu Cao Xiaojing Shi Hongbo Guo Xiaojun Zhang Sheng Zheng Haifeng Liu Zhenhua Hu Jie Tian Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Gastroenterology the Third Medical Centre Chinese PLA General Hospital Beijing China Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Anorectal the Third medical Centre Chinese PLA General Hospital Beijing China University of Chinese Academy of Sciences Beijing China School of Information Sciences and Technology Northwest University Xi'an China Department of Nuclear Medicine Chinese PLA General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang University Beijing China

This study presents a novel intraoperative in vivo imaging approach which harnessed Cerenkov luminescence (CL) to detect primary and metastatic colorectal cancer (CRC) using clinically approved radiopharmaceuticals. In the mice and swine experiments, the proposed approach effectively improved the effect of CRC surgery. The approach is believed to be promising for utilizing CL in open surgery. Further details can be found in the article by Zeyu Zhang, Yawei Qu, Yu Cao et al. ( e201960152 )

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Virus Detection: Nanopore Targeted Sequencing for the Accurate and Comprehensive Detection of SARS-CoV-2 and Other Respiratory Viruses (Small 32/2020)

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Small 2020年第32期16卷

作者： Ming Wang Aisi Fu Ben Hu Yongqing Tong Ran Liu Zhen Liu Jiashuang Gu Bin Xiang Jianghao Liu Wen Jiang Gaigai Shen Wanxu Zhao Dong Men Zixin Deng Lilei Yu Wu Wei Yan Li Tiangang Liu Department of Clinical Laboratory Renmin Hospital of Wuhan University Wuhan 430060 China Key Laboratory of Combinatorial Biosynthesis and Drug Discovery Ministry of Education and Wuhan University School of Pharmaceutical Sciences Wuhan 430071 China CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai 200031 China Wuhan Dgensee Clinical Laboratory Co. Ltd. Wuhan 430075 China Wuhan Institute of Virology Chinese Academy of Sciences Wuhan 430071 China Department of Internal Medicine Renmin Hospital of Wuhan University Wuhan 430060 China Center for Biomedical Informatics Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine Shanghai Children's Hospital Shanghai Jiao Tong University Shanghai 200040 China

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Accuracy estimation of link-based similarity measures and its application

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Frontiers of Computer Science 2016年第1期10卷 113-123页

作者： Yinglong ZHANG Cuiping LI Chengwang XIE Hong CHEN School of Software East China Jiaotong University Nanchang 330045 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and Department of Computer Science Renmin University of China Beijing 100872 China Intelligent Optimization and Information Processing Laboratory East China Jiaotong University Nanchang 330013 China

Link-based similarity measures play a significant role in many graph based applications. Consequently, mea- suring node similarity in a graph is a fundamental problem of graph data mining. Personalized PageRank （PPR） and Sim- Rank （SR） have emerged as the most popular and influen- tial link-based similarity measures. Recently, a novel link- based similarity measure, penetrating rank （P-Rank）, which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calcula- tion. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guaran- tee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition： the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similar- ity scores becomes. Furthermore, we demonstrate the effec- tiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of exper- iments on real world data sets to verify the effectiveness and efficiency of our upper bounds.

关键词： personalized PageRank SimRank P-Rank up-per bound

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Erratum to 'Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study'

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EBioMedicine 2020年 55卷 102773页

作者： Qingxia Wu Kuan Yao Zhenyu Liu Longfei Li Xin Zhao Shuo Wang Honglei Shang Yusong Lin Zejun Wen Xiaoan Zhang Jie Tian Meiyun Wang Department of Medical Imaging Henan Key Laboratory of Neurological Imaging Henan Provincial People's Hospital People's Hospital of Zhengzhou University People's Hospital of Henan University Zhengzhou Henan China. School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China. CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China. Collaborative Innovation Centre for Internet Healthcare Zhengzhou University Zhengzhou Henan China. Department of Radiology the Third affiliated Hospital of Zhengzhou University Zhengzhou Henan China. Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine School of Medicine Beihang University Beijing China. Department of Radiology the Third affiliated Hospital of Zhengzhou University Zhengzhou Henan China. Electronic address: zxa@***. University of Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine School of Medicine Beihang University Beijing China Engineering Research Centre of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an Shanxi China. Electronic address: jie.tian@***. Department of Medical Imaging Henan Key Laboratory of Neurological Imaging Henan Provincial People's Hospital People's Hospital of Zhengzhou University People's Hospital of Henan University Zhengzhou Henan China. Electronic address: mywang@***.

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DPListCF: A Differentially Private Approach for Listwise Collaborative Filtering

DPListCF: A Differentially Private Approach for Listwise Col...

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IEEE Symposium on Computers and Communications

作者： Yuncheng Wu Juru Zeng Hong Chen Yao Wu Wenjuan Liang Hui Peng Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China

ISBN: (纸本)9781509006809

Recently, listwise ranking-oriented collaborative filtering (CF) algorithms have gained great success in recommender systems. However, the ranked preference list may compromise the privacy of individuals. A notable paradigm for offering strong privacy guarantee is differential privacy. In this paper, we propose DPListCF, a differentially private algorithm based on ListCF (a state-of-art listwise CF algorithm). The main idea of DPListCF is to make both of the similarity calculation phase and rank prediction phase of ListCF satisfy differential privacy, by using input perturbation method and output perturbation method in the two phases respectively. Extensive experiments using two real datasets evaluate the performance of DPListCF, and demonstrate that the proposed algorithm outperforms state-of-art approaches.

关键词： Open area test sites Privacy collaborative filtering Recommender systems Perturbation methods Arts CONTAINMENT FAILURE

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Balanced Self-Paced Learning with Feature Corruption

Balanced Self-Paced Learning with Feature Corruption

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International Joint Conference on Neural Networks

作者： Yazhou Ren Peng Zhao Zenglin Xu Dezhong Yao Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China

ISBN: (纸本)9781509061839

Self-paced learning (SPL), a recently proposed learning strategy, which progressively adds instances to train from simplicity to complexity, could typically reduce the risk of achieving local optima. SPL selects instances based on their losses among the entire data set in each iteration. This probably causes that the selected instances are highly imbalanced, e.g., very few (even on) instances of some classes are chosen, and further negatively affects the training process. To address this issue, we propose a balanced self-paced learning (BSPL) scenario, which iteratively selects training samples based on their loss values from each class, instead of from the entire data set. From another perspective, learning with marginalized corrupted features is an approach to control overfitting by artificially corrupting the training data. However, feature corruption techniques typically lead to that the classification problem is non-convex and easily traps in local optima. To alleviate this, we propose balanced self-paced learning with feature corruption (BSPL-FC), which considers the instance sampling and feature corruption simultaneously. BSPL-FC first treats the feature corruption as a regularizer and then applies BSPL to solve the regularized classification problem. BSPL-FC inherently has advantages in controlling overfitting and avoiding local optima. Experimental results show the effectiveness of the proposed model.

关键词： Training Optimization Training data Logistics Adaptation models data models Support vector machines

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Whole-Genome Sequencing and Analysis of the Chinese Herbal Plant Panax notoginseng

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Molecular Plant 2017年第6期10卷 899-902页

作者： Wei Chen Ling Kui Guanghui Zhang Shusheng Zhu Jing Zhang Xiao Wang Min Yang Huichuan Huang Yixiang Liu Yong Wang Yahe Li Lipin Zeng Wen Wang Xiahong He Yang Dong Shengchao Yang Biological Big Data College Yunnan Agricultural University yunnan Research Institute for Local Plateau Agriculture and Industry Kunming 650201 China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming 650223 China University of Chinese Academy of Sciences Beijing 100049 China National-Local Joint Engineering Research Center on Germplasm Utilization and Innovation of Chinese Medicinal Materials in Southwest China Yunnan Agricultural University Kunming 650201 China State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan Key Laboratory for Agro-biodiversity and Pest Control of Ministry of Education Yunnan Agricultural University Kunming 650201 China NOWBIO Technology Co. Ltd Kunming 650202 China Institute of Panax notoginseng Wenshan University Wenshan 663000 China Longjing Pharmaceutical Co. Ltd Kunming 650228 China Farlong Pharmaceutical Walnut CA 91789 USA College of Life Science Kunming University of Science and Technology Kunming 650504 China

Dear Editor Panax notoginseng （Burk.） F.H. Chen （2n = 2x = 24, common name sanqi or tianql）, belonging to the Araliaceae family, is a slow-growing plant species documented in the ancient Chinese medical literatures for its ability to ameliorate hemostasis and improve blood circulation （Wang et al., 2016）. After decades of pharmacological research, a variety of P. notoginseng-specific secondary metabolites （notably ginsenosides, notoginsenosides and gypenosides） were isolated, identified, and implicated in conferring medicinal properties （Wang et al., 2016）.

关键词： Whole-Genome Sequencing Chinese Herbal Plant Panax notoginseng

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Strategy intervention for the evolution of fairness

arXiv

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arXiv 2017年

作者： Zhang, Yanling Fu, Feng Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Mathematics Dartmouth College HanoverNH United States Department of Biomedical Data Science Geisel School of Medicine Dartmouth College HanoverNH United States

Masses of experiments have shown individual preference for fairness which seems irrational. The reason behind it remains a focus for research. The effect of spite (individuals are only concerned with their own relative standing) on the evolution of fairness has attracted increasing attention from experiments, but only has been implicitly studied in one evolutionary model. The model did not involve high-offer rejections, which have been found in the form of non-monotonic rejections (rejecting offers that are too high or too low) in experiments. Here, we introduce a high offer and a non-monotonic rejection in structured populations of finite size, and use strategy intervention to explicitly study how spite influences the evolution of fairness: five strategies are in sequence added into the competition of a fair strategy and a selfish strategy. We find that spite promotes fairness, altruism inhibits fairness, and the non-monotonic rejection can cause fairness to overcome selfishness, which cannot happen without high-offer rejections. Particularly for the group-structured population with seven discrete strategies, we analytically study the effect of population size, mutation, and migration on fairness, selfishness, altruism, and spite. A larger population size cannot change the dominance of fairness, but it promotes altruism and inhibits selfishness and spite. Intermediate mutation maximizes selfishness and fairness, and minimizes spite;intermediate mutation maximizes altruism for intermediate migration and minimizes altruism otherwise. The existence of migration inhibits selfishness and fairness, and promotes altruism;sufficient migration promotes spite. Our study may provide important insights into the evolutionary origin of fairness. Copyright © 2017, The Authors. All rights reserved.

关键词： Population statistics

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Differentially private density estimation via Gaussian mixtures model

Differentially private density estimation via Gaussian mixtu...

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International Workshop on Quality of Service

作者： Yuncheng Wu Yao Wu Hui Peng Juru Zeng Hong Chen Cuiping Li Renmin University of China Beijing P.R. China Ministry of Education Key Laboratory of Data Engineering and Knowledge Engineering

ISBN: (纸本)9781509026357

Density estimation can construct an estimate of the probability density function from the observed data. However, such a function may compromise the privacy of individuals. A notable paradigm for offering strong privacy guarantees in data analysis is differential privacy. In this paper, we propose DPGMM, a parametric density estimation algorithm using Gaussian mixtures model (GMM) under differential privacy. GMM is a well-known model that could approximate any distribution and can be solved via Expectation-Maximization (EM) algorithm. The main idea of DPGMM is to add two extra steps after getting the estimated parameters in the M step of each iteration. The first step is the noise adding step, which injects calibrated noise to the estimated parameters according to their L 1 -sensitivities and privacy budgets. The second step is the post-processing step, which post-processes those noisy parameters that might break their intrinsic characteristics. Extensive experiments using both real and synthetic datasets evaluate the performance of DPGMM, and demonstrate that the proposed method outperforms a state-of-art approach.

关键词： Privacy Gaussian distribution Estimation data privacy Covariance matrices Noise measurement data analysis

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Biogeography-based optimization for cluster analysis

Biogeography-based optimization for cluster analysis

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International Conference on Computer, Communication and Computational Sciences, ICCCCS 2016

作者： Wu, Xueyan Wang, Hainan Chen, Zhimin Lu, Zhihai Phillips, Preetha Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Nanjing Normal University NanjingJiangsu210023 China Key Laboratory of Statistical Information Technology & Data Mining State Statistics Bureau ChengduSichuan610225 China School of Computer and Information Engineering Henan Normal University XinxiangHenan453000 China School of Electronic Information Shanghai Dianji University Shanghai200240 China Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology GuilinGuangxi541004 China School of Natural Sciences and Mathematics Shepherd University ShepherdstownWV25443 United States West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Department of Electrical Engineering The City College of New York CUNY New YorkNY10031 United States State Key Lab of CAD & CG Zhejiang University HangzhouZhejiang310027 China Manchester Metropolitan University ManchesterM156BH United Kingdom

ISBN: (纸本)9789811037696

With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Cluster analysis

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