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Corrigendum to "FaMYB9 is involved in the regulation of C6 volatile biosynthesis in strawberry" [Plant Sci. 293 (2020) 110422]

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Plant science : an international journal of experimental plant biology 2021年 313卷 110496页

作者： Hongyan Lu Zisheng Luo Lei Wang Wusheng Liu Dong Li Tarun Belwal Yanqun Xu Li Li Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs College of Biosystems Engineering and Food Science Zhejiang University Hangzhou 310058 China Department of Horticultural Science North Carolina State University Raleigh NC 27607 USA. Electronic address: luhongyan@***. Ningbo Research Institute Zhejiang University Ningbo 315100 China. Electronic address: luozisheng@***. Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs College of Biosystems Engineering and Food Science Zhejiang University Hangzhou 310058 China. Electronic address: wangley@***. Department of Horticultural Science North Carolina State University Raleigh NC 27607 USA. Electronic address: wliu25@ncsu.edu. Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs College of Biosystems Engineering and Food Science Zhejiang University Hangzhou 310058 China. Electronic address: dong_li@***. Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs College of Biosystems Engineering and Food Science Zhejiang University Hangzhou 310058 China. Electronic address: tarungbpihed@***. Ningbo Research Institute Zhejiang University Ningbo 315100 China. Electronic address: xuyanqun@***. Ningbo Research Institute Zhejiang University Ningbo 315100 China National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang Key Laboratory for Agro-Food Processing Zhejiang Engineering Laboratory of Food Technology and Equipment Zhejiang University Hangzhou 310058 China. Electronic address: lili1984@***.

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Alternative metrics correlations: Do academic reviews correlate with library holdings? 16

Alternative metrics correlations: Do academic reviews correl...

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16th International Conference on Scientometrics and Informetrics, ISSI 2017

作者： Qingqing, Zhou Shenghui, Wang Chengzhi, Zhang Nanjing University of Science and Technology Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China OCLC Research Leiden Netherlands

Existing researches have proved that both academic reviews and library holdings can be alternative sources to assess impacts of academic books. This paper endeavors to identify correlations between the two sources, which may be useful for book ordering of libraries. Specifically, 69,263 academic reviews in Choice {Choice: Current Reviews for Academic Libraries) are collected with four metrics: recommendation levels, readership levels, numbers of interdisciplinary subjects and review contents. Then, a topic model is used to extract topics from review contents. Meanwhile, library holdings of each book are identified, including the total number of library holdings, holding regions and holding distributions based on an entropy method. Finally, correlation analysis between Choice reviews and library holdings are conducted. Experimental results reveal that books with higher recommendation levels or extensive readerships tend to be ordered more by academic libraries. Meanwhile, books with extensive readerships will be collected more uniformly by academic libraries. In conclusion, metrics derived from Choice academic book reviews can be used as indicators to recommend book ordering of academic libraries.

关键词： Libraries

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Prediction of splice site using support vector machine with feature selection 2017

Prediction of splice site using support vector machine with ...

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2017 International Conference on Bioinformatics and Computational Intelligence, ICBCI 2017

作者： Li, Wen Li, Jianzhuo Huo, Lijuan Li, Wenjun Du, Xiuquan School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9781450353113

Identification of splice site plays a key role for alternative splicing analysis. Many effective methods have been proposed over the past decades. However, there have still some limitations and need further improvement. In this paper, we collect splice site sequences from Homo Sapiens Splice Sites Dataset (HS3D), to transform these sequences, we use two kinds of methods to code them and then use support vector machines (SVM) as predictor. In order to reduce computational time, maximum relevance minimum redundancy (mRMR) is adopted to rank the features for finding optimal feature combination. On the donor splice site sequence data, our method achieves 92.85% accuracy, area under ROC (Receiver Operating Characteristic) curve (ROC-AUC) of 97.62%. On the acceptor splice site data, our method achieves 92.29% accuracy and 97.37% ROC-AUC. These results show that our method is effective and reliable for splice sites prediction. © 2017 ACM.

关键词： Support vector machines

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CitationAS: A summary generation tool based on clustering of retrieved citation content 2

CitationAS: A summary generation tool based on clustering of...

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2nd Workshop on Mining Scientific Papers: Computational Linguistics and Bibliometrics, CLBib 2017

作者： Wang, Jie Ma, Shutian Zhang, Chengzhi Department of Information Management Nanjing University of Science and Technology Nanjing210094 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control -Acty Fuzhou 350108 China

Usually, if researchers want to understand research status of any field, they need to browse a great number of related academic literatures. Luckily, in order to work more efficiently, automatic documents summarization can be applied for taking a glance at specific scientific topics. In this paper, we focus on summary generation of citation content. An automatic tool named CitationAS is built, whose three core components are clustering algorithms, label generation and important sentences extraction methods. In experiments, we use bisecting K-means, Lingo and STC to cluster retrieved citation content. Then Word2Vec, WordNet and combination of them are applied to generate cluster label. Next, we employ two methods, TF-IDF and MMR, to extract important sentences, which are used to generate summaries. Finally, we adopt gold standard to evaluate summaries obtained from CitationAS. According to evaluations, we find the best label generation method for each clustering algorithm. We also discover that combination of Word2Vec and WordNet doesn't have good performance compared with using them separately on three clustering algorithms. Combination of Ling algorithm, Word2Vec label generation method and TF-IDF sentences extraction approach will acquire the highest summary quality.

关键词： Clustering algorithms

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Inferring microRNA-disease association by hybrid recommendation algorithm and unbalanced bi-random walk on heterogeneous network

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Scientific reports 2019年第1期9卷 2474页

作者： Dong-Ling Yu Yuan-Lin Ma Zu-Guo Yu Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan 411105 P.R. China. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan 411105 P.R. China. yuzuguo@***. School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Q4001 Australia. yuzuguo@***.

More and more research works have indicated that microRNAs (miRNAs) play indispensable roles in exploring the pathogenesis of diseases. Detecting miRNA-disease associations by experimental techniques in biology is expensive and time-consuming. Hence, it is important to propose reliable and accurate computational methods to exploring potential miRNAs related diseases. In our work, we develop a novel method (BRWHNHA) to uncover potential miRNAs associated with diseases based on hybrid recommendation algorithm and unbalanced bi-random walk. We first integrate the Gaussian interaction profile kernel similarity into the miRNA functional similarity network and the disease semantic similarity network. Then we calculate the transition probability matrix of bipartite network by using hybrid recommendation algorithm. Finally, we adopt unbalanced bi-random walk on the heterogeneous network to infer undiscovered miRNA-disease relationships. We tested BRWHNHA on 22 diseases based on five-fold cross-validation and achieves reliable performance with average AUC of 0.857, which an area under the ROC curve ranging from 0.807 to 0.924. As a result, BRWHNHA significantly improves the performance of inferring potential miRNA-disease association compared with previous methods. Moreover, the case studies on lung neoplasms and prostate neoplasms also illustrate that BRWHNHA is superior to previous prediction methods and is more advantageous in exploring potential miRNAs related diseases. All source codes can be downloaded from https://***/myl446/BRWHNHA .

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ISIA at the ImageCLEF 2017 image caption task 18

ISIA at the ImageCLEF 2017 image caption task

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18th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2017

作者： Liang, Sisi Li, Xiangyang Zhu, Yongqing Li, Xue Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Haidian District Beijing100190 China

This paper describes the details of our methods for participation in the caption prediction task of ImageCLEF 2017. The dataset we use is all provided by the organizers and doesn't include any external resources. The key components of our framework include a deep model part, an SVM part and a caption retrieval part. In deep model part, we use an end to end architecture with Convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) to encode and decode images and captions. According to the statistics of training dataset, we train different models with different lengths of captions. Then in SVM part, we use Support Vector Machine (SVM) to determine which model to use when generating the description for a test image. In this way, we can combine these models from the previous deep model part. In caption retrieval part, we use the image feature extracted from CNN and apply Nearest Neighbor method to retrieve the most similar image with caption in the training dataset. The final description is the aggregation of the generated sentence and the caption retrieved from the training dataset. The best performance of our 10 submitted runs ranks the 3rd in group which doesn't use external resources.

关键词： Support vector machines

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Deep Multi-Metric Learning for Person Re-Identification

Deep Multi-Metric Learning for Person Re-Identification

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yongxin Ge Xinqian Gu Min Chen Hongxing Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education (Chongqing University) Chongqing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China

In this paper, to exploit more discriminative information of the global-body and body-parts features, we present a novel deep multi-metric learning (DMML) network for person re-identification under the triplet framework. The main novelty of our learning framework lies in two aspects: 1) Unlike most existing metric learning-based approaches, which learn only one distance metric for comparison, our DMM-L method aims to learn different metrics for the global-body and body-parts features respectively by using convolutional neural network (CNN); 2) A new multi-metric loss function is proposed to train the DMML network, under which the distance of each negative pair is greater than that of each positive pair by a predefined margin, and the correlations of different metrics are maximized. Compared with the previous person re-identification methods that have shown state-of-the-art performances, our DMML approach can achieve competitive results on the challenging CUHK03, CUHKOl, VIPeR and iLIDS datasets.

关键词： Measurement Image color analysis Feature extraction Training Machine learning Kernel Learning systems

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

Image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Yan-Ru Zhong Hua-Yi Liu Xi-Yan Sun Ru-Shi Lan Xiao-Nan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

In this paper, a new image encryption algorithm is proposed by integrating Sine and piecewise linear chaotic maps. In order to realize the effect of encrypted image, the security key and 2D Sine-piecewise linear chaotic map (SPLCM) are used to encrypt the image by using random sequence and random matrix, and then using the replacement operation and the diffusion operation of the original image. The proposed image encryption algorithm is simple and practical, and the simulation results show that this algorithm is able to encrypt different types of digital images into unidentifiable random images. The security analysis also shows that this algorithm has a higher security level.

关键词： Encryption Two dimensional displays Digital images Chaotic communication

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Robust Output Synchronization of Lower Triangular Multi-Agents Via Sampled-Data Control

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IFAC-PapersOnLine 2017年第1期50卷 2415-2420页

作者： Chen X. Zhu L. Chai L. Zhang H.-T. School of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China Department of Electric and Electronic Engineering University of Hong Kong Hong Kong Key Laboratory of Image Processing and Intelligent Control School of Automation and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan 430074 China

This paper establishes a general sampled-data framework for robust output synchronization of nonlinear heterogeneous multi-agent system in the lower-triangular form. Inspired by our previous work, this problem can be solved in a two-step manner: (1) sampled-data perturbed consensus, and (2) sampled-data perturbed output regulation. These two steps are coupled by a small gain condition which must be satisfied to ensure the robust output synchronization is achieved. We will first show that the sampling conditions on accomplishing sampled-data perturbed consensus and perturbed output regulation separately. In addition, we strengthen our previous small gain condition to incorporate the sampling effort. The sampled-data synchronization controller can be designed provided that the sampling condition satisfies some additional condition. © 2017

关键词： Multi-agent systems Nonlinear systems Output regulation Sampled-data Synchronization

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