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Dense sampling of bird diversity increases power of comparative genomics (vol 587, pg 252, 2020)

NATURE 2021年第7856期592卷 E24-E24页

作者： Feng, Shaohong Stiller, Josefin Deng, Yuan Armstrong, Joel Fang, Qi Reeve, Andrew Hart Xie, Duo Chen, Guangji Guo, Chunxue Faircloth, Brant C. Petersen, Bent Wang, Zongji Zhou, Qi Diekhans, Mark Chen, Wanjun Andreu-Sanchez, Sergio Margaryan, Ashot Howard, Jason Travis Parent, Carole Pacheco, George Sinding, Mikkel-Holger S. Puetz, Lara Cavill, Emily Ribeiro, Angela M. Eckhart, Leopold Fjeldsa, Jon Hosner, Peter A. Brumfield, Robb T. Christidis, Les Bertelsen, Mads F. Sicheritz-Ponten, Thomas Tietze, Dieter Thomas Robertson, Bruce C. Song, Gang Borgia, Gerald Claramunt, Santiago Lovette, Irby J. Cowen, Saul J. Njoroge, Peter Dumbacher, John Philip Ryder, Oliver A. Fuchs, Jerome Bunce, Michael Burt, David W. Cracraft, Joel Meng, Guanliang Hackett, Shannon J. Ryan, Peter G. Jonsson, Knud Andreas Jamieson, Ian G. da Fonseca, Rute R. Braun, Edward L. Houde, Peter Mirarab, Siavash Suh, Alexander Hansson, Bengt Ponnikas, Suvi Sigeman, Hanna Stervander, Martin Frandsen, Paul B. van der Zwan, Henriette van der Sluis, Rencia Visser, Carina Balakrishnan, Christopher N. Clark, Andrew G. Fitzpatrick, John W. Bowman, Reed Chen, Nancy Cloutier, Alison Sackton, Timothy B. Edwards, Scott V. Foote, Dustin J. Shakya, Subir B. Sheldon, Frederick H. Vignal, Alain Soares, Andre E. R. Shapiro, Beth Gonzalez-Solis, Jacob Ferrer-Obiol, Joan Rozas, Julio Riutort, Marta Tigano, Anna Friesen, Vicki Dalen, Love Urrutia, Araxi O. Szekely, Tamas Liu, Yang Campana, Michael G. Corvelo, Andre Fleischer, Robert C. Rutherford, Kim M. Gemmell, Neil J. Dussex, Nicolas Mouritsen, Henrik Thiele, Nadine Delmore, Kira Liedvogel, Miriam Franke, Andre Hoeppner, Marc P. Krone, Oliver Fudickar, Adam M. Mila, Borja Ketterson, Ellen D. Fidler, Andrew Eric Friis, Guillermo Parody-Merino, Angela M. Battley, Phil F. Cox, Murray P. Lima, Nicholas Costa Barroso Prosdocimi, Francisco Parchman, Thomas Lee Schlinger, Barney A. Loiselle, Bette A. Blake, John G. Lim, Haw Chuan Day, Lainy B. Fuxjager, Matthew J. Baldwin, Maude W. Braun, Michael J. Wirthlin, Morgan Dikow, China National GeneBank BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China BGI-Shenzhen Shenzhen China Villum Centre for Biodiversity Genomics Section for Ecology and Evolution Department of Biology University of Copenhagen Copenhagen Denmark BGI Education Center University of Chinese Academy of Sciences Shenzhen China MOE Laboratory of Biosystems Homeostasis and Protection Life Sciences Institute Zhejiang University Hangzhou China Department of Neuroscience and Developmental Biology University of Vienna Vienna Austria Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming China James D. Watson Institute of Genome Sciences Hangzhou China UC Santa Cruz Genomics Institute UC Santa Cruz Santa Cruz CA USA Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Center for Macroecology Evolution and Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Biological Sciences Louisiana State University Baton Rouge LA USA Museum of Natural Science Louisiana State University Baton Rouge LA USA Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) Faculty of Applied Sciences AIMST University Kedah Malaysia Section for Evolutionary Genomics The GLOBE Institute Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Molecular Biology National Academy of Sciences Yerevan Armenia University Museum Norwegian University of Science and Technology Trondheim Norway Center for Reproductive Medicine The 2nd Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Novogene Durham NC USA Duke University Medical Center Durham NC USA The Rockefeller University New York NY USA Howard Hughes Medical Institute Chevy Chase MD USA Department of Dermatology Medical University of Vienna Vienna Austria

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Aggregating and visualizing public opinions and sentiment trends on the US higher education 15

Aggregating and visualizing public opinions and sentiment tr...

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17th International Conference on information Integration and web-Based Applications and Services, iiWAS 2015

作者： Qiu, Robin G. Ravi, Ramya R. Qiu, Lawrence L. Big Data Lab Division of Engineering and Information Science Penn State MalvernPA19355 United States School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA16802 United States

ISBN: (纸本)9781450334914

Through leveraging big data technologies and a quantitative and model-driven approach, we previously built an education monitoring and ranking system (eduMRS) that allowed us to change ranking factors that are used to assess quality of higher education. Taking a systems perspective, this short paper introduces an enhanced eduMRS - II that can effectively aggregate and visualize public opinions and sentiment trends on higher education services. Public opinions can be selectively retrieved from online media including tweets. This enhanced service system demonstrates that a big data based, real time, and interactive computer application possesses promising potential of facilitating decision-making of addressing the needs of customers in the service industry. © 2015 ACM.

关键词： Sentiment analysis

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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The Fudan participation in the 2015 BioASQ Challenge: Large-scale biomedical semantic indexing and question answering 16

The Fudan participation in the 2015 BioASQ Challenge: Large-...

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16th Conference and labs of the Evaluation Forum, CLEF 2015

作者： Peng, Shengwen You, Ronghui Xie, Zhikai Wang, Beichen Zhang, Yanchun Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China Shanghai Key Lab of Data Science Fudan University Shanghai200433 China Centre for Applied Informatics College of Engineering and Science Victoria University Australia

This article describes the participation of Fudan team in the 2015 BioASQ challenge. The challenge consists of two tasks, large-scale biomedical semantic indexing (task 3a) and biomedical question answering (task 3b). In task 3a, our method, MeSHlabeler, achieved the first place in all 15 weeks of three batches. Based on 3215 annotated citations (June 6, 2015) out of all 4435 citations in the official test set (batch 1, week 2), our submission best achieved 0.6194 in at Micro F-measure. This is 0.0576(10.25%) higher than 0.5618, obtained by the official NLM solution Medical Text Indexer (MTI). Task 3b includes two phases. Given the questions raised by a team of biomedical experts from around Europe, the main task of phase A is to find relevant documents, snippets, concepts and RDF triples, while the main task of phase B is to provide exact and ideal answers. In the phase A of task 3b, our submission, fdu, achieved the first place in both document and snippet retrieval in batch 5 (June 6, 2015). Suggestions and insightful discussion.

关键词： information retrieval

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Improved MILP modeling for automatic security evaluation and application to fox

Improved MILP modeling for automatic security evaluation and...

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作者： Qiao, Kexin Hu, Lei Sun, Siwei Ma, Xiaoshuang Kan, Haibin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing100093 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China

Counting the number of differentially active S-boxes is of great importance in evaluating the security of a block cipher against differential attack. Mouha et al. proposed a technique based on Mixed-Integer Linear Programming (MILP) to automatically calculate a lower bound of the number of differentially active S-boxes for word-oriented block ciphers, and applied it to symmetric ciphers AES and Enocoro-128v2. Later Sun et al. extended the method by introducing bit-level representations for Sboxes and new constraints in the MILP problem, and applied the extended method to PRESENT-80 and LBlock. This kind of methods greatly depends on the constraints in the MILP problem describing the differential propagation of the block cipher. A more accurate description of the differential propagation leads to a tighter bound on the number of differentially active S-boxes. In this paper, we refine the constraints in the MILP problem describing XOR operations, and apply the refined MILP modeling to determine a lower bound of the number of active S-boxes for the Lai-Massey type block cipher FOX in the model of single-key differential attack, and obtain a tighter bound in FOX64 than existing results. Experimental results show that 6, instead of currently known 8, rounds of FOX64 is strong enough to resist against basic single-key differential attack since the differential characteristic probability is upper bounded by 2-64, and thus the maximum differential characteristic probability of 12-round FOX64 is upper bounded by 2-128, where 128 is the key-length of FOX64. We also get the lower bound of the number of differentially active S-boxes for 5-round FOX128, and proved the security of the full-round FOX128 with respect to single-key differential attack. Copyright © 2015 The Institute of Electronics, information and Communication Engineers.

关键词： Integer programming

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Social Community-Oriented Social Attribute Analysis: An Empirical Study on QQ Group data

Social Community-Oriented Social Attribute Analysis: An Empi...

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IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Lei Li Di Ma Guanfeng Liu School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Hefei University of Technology Hefei Anhui CN Soochow Advanced Data Analytics Lab Soochow University Suzhou Jiangsu China

ISBN: (纸本)9781509018949

Social networks have got increasing attention in recent years, and Social Networking Services (SNS), such as QQ, Facebook and Twitter, are among the most popular network applications on the internet. The popularity of these SNS attract more and more researchers to focus on the study of attributes of social network, especially the groups which are ubiquitous in social networks, both to improve current systems and to design new applications of social networks. In this paper, we study statistically 1.4 billion personal social information from QQ and 80 million group social information from QQ, and systemically analysis the distribution of attributes of all these social context data. And then we discover that both the frequency of age and the frequency of age for each gender conform to normal distributions. In addition, we analysis the QQ user registration time and the relationship between digital number of QQ and their age. Moreover, the distribution of the number of users in QQ group conforms to a power-law distribution. Furthermore, as the KL distance between the sample data and the whole data is within 0.1, i.e. the similarity of the sample data and the whole data is high, we can use the results based on the sample data to estimate the properties of the whole data in the integrated QQ network.

关键词： Mobile communication Facebook Twitter Context Collaboration Mobile computing

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Peer pressure: Enhancement of cooperation through mutual punishment

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Physical Review E 2015年第2期91卷 022121-022121页

作者： Han-Xin Yang Zhi-Xi Wu Zhihai Rong Ying-Cheng Lai Department of Physics Fuzhou University Fuzhou 350108 China Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou Gansu 730000 China CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu 610054 China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong School of Electrical Computer and Energy Engineering Arizona State University Tucson Arizona 85287 USA

An open problem in evolutionary game dynamics is to understand the effect of peer pressure on cooperation in a quantitative manner. Peer pressure can be modeled by punishment, which has been proved to be an effective mechanism to sustain cooperation among selfish individuals. We investigate a symmetric punishment strategy, in which an individual will punish each neighbor if their strategies are different, and vice versa. Because of the symmetry in imposing the punishment, one might intuitively expect the strategy to have little effect on cooperation. Utilizing the prisoner's dilemma game as a prototypical model of interactions at the individual level, we find, through simulation and theoretical analysis, that proper punishment, when even symmetrically imposed on individuals, can enhance cooperation. Also, we find that the initial density of cooperators plays an important role in the evolution of cooperation driven by mutual punishment.

关键词： PEER pressure PUNISHMENT GAME theory in biology SYMMETRY (Mathematics) COOPERATIVE games (Mathematics)

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Extremal optimization-based semi-supervised algorithm with conflict pairwise constraints for community detection

Extremal optimization-based semi-supervised algorithm with c...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Lei Li Mei Du Guanfeng Liu Xuegang Hu Gongqing Wu School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Soochow Advanced Data Analytics Lab Soochow University Suzhou Jiangsu China

The research on community structure is a key to analyze the network functionality and topology, and thus it is significant to detect and analysis the community structure. During the abstract process from an actual system to a network, especially for a large-scale network, it is inevitable to have mistaken connections between nodes or have connection missing. In addition, in real applications, from time to time we can obtain prior information in the form of pairwise constraints between nodes besides topology information, although they may be inaccurate or conflicted. These noises in the network-related information will dramatically reduce the accuracy of community detection. Hence, in this paper, we introduce a dissimilarity index to determine the trustworthiness of pairwise constraints and settle the conflict of pairwise constraints. Then, focusing on the community detection with false connections or conflicted connections, we propose a pairwise constrained structure-enhanced extremal optimization-based semi-supervised algorithm (PCSEO-SS algorithm). Compared with existing semi-supervised community detection approaches, the experimental results executed on real networks and synthetic networks, show that PCSEO-SS can solve the problem of false connections or conflicted connections to some extent and detect the community structure more precisely.

关键词： Communities Optimization Detection algorithms Indexes Accuracy Algorithm design and analysis Noise

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Clinical phenotype network： the underlying mechanism for perso- nalized diagnosis and treatment of traditional Chinese medicine

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Frontiers of Medicine 2014年第3期8卷 337-346页

作者： Xuezhong Zhou Yubing Li Yonghong Peng Jingqing HU Runshun Zhang Liyun He Yinghui Wang Lijie Jiang Shiyan Yan Peng Li Qi Xie Baoyan Liu School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing 100044 China China Academy of Chinese Medical Sciences Beijing 100700 China School of Engineering and lnformatics University of Bradford BD7 IDP UK Institute of Basic Theory of Traditional Chinese Medicine China Academy of Chinese Medical Sciences Beijing 100700 China Guang 'anmen Hospital China Academy of Chinese Medical Sciences Beijing 100053 China Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical Sciences Beijing 100700 China

Traditional Chinese medicine （TCM） investigates the clinical diagnosis and treatment regularities in a typical schema of personalized medicine, which means that individualized patients with same diseases would obtain distinct diagnosis and optimal treatment from different TCM physicians. This principle has been recognized and adhered by TCM clinical practitioners for thousands of years. However, the underlying mechanisms of TCM personalized medicine are not fully investigated so far and remained unknown. This paper discusses framework of TCM personalized medicine in classic literatures and in real-world clinical settings, and investigates the underlying mechanisms of TCM personalized medicine from the perspectives of network medicine. Based on 246 well-designed outpatient records on insomnia, by evaluating the personal biases of manifestation observation and preferences of herb prescriptions, we noted significant similarities between each herb prescriptions and symptom similarities between each encounters. To investigate the underlying mechanisms of TCM personalized medicine, we constructed a clinical phenotype network （CPN）, in which the clinical phenotype entities like symptoms and diagnoses are presented as nodes and the correlation between these entities as links. This CPN is used to investigate the promiscuous boundary of syndromes and the co-occurrence of symptoms. The small-world topological characteristics are noted in the CPN with high clustering structures, which provide insight on the rationality of TCM personalized diagnosis and treatment. The investigation on this network would help us to gain understanding on the underlying mechanism of TCM personalized medicine and would propose a new perspective for the refinement of the TCM individualized clinical skills.

关键词： personalized medicine complex network clinical phenotype network traditional Chinese medicine

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